PHYTOSOCIOLOGICAL ATTRIBUTES OF DIFFERENT VEGETATIONAL ZONES OF NANDIAR KHUWAR CATCHMENT AREA FAIZ UL HAQ DEPARTMENT OF BOTANY HAZARA UNIVERSITY MANSEHRA 2015
PHYTOSOCIOLOGICAL ATTRIBUTES OF DIFFERENT VEGETATIONAL ZONES OF NANDIAR KHUWAR
CATCHMENT AREA
FAIZ UL HAQ
DEPARTMENT OF BOTANY
HAZARA UNIVERSITY MANSEHRA 2015
ii
HAZARA UNIVERSITY MANSEHRA
Department of Botany
PHYTOSOCIOLOGICAL ATTRIBUTES OF DIFFERENT VEGETATIONAL ZONES OF NANDIAR KHUWAR
CATCHMENT AREA
By
Faiz ul Haq
This research study has been conducted and reported as partial fulfillment for
the requirement of PhD degree in Botany awarded by Hazara University
Mansehra, Pakistan.
The Thursday 23, April 2015
iii
PHYTOSOCIOLOGICAL ATTRIBUTES OF DIFFERENT VEGETATIONAL ZONES OF NANDIAR KHUWAR
CATCHMENT AREA
Submitted by FAIZ UL HAQ
Ph.D. Scholar Research Supervisor PROF. DR. HABIB AHMAD
Tamgha-e-Imtiaz
Dean Faculty of Science Hazara University, Mansehra
Co Supervisor DR. ZAFAR IQBAL
Assistant Professor Department of Botany Hazara University, Mansehra
DEPARTMENT OF BOTANY HAZARA UNIVERSITY MANSEHRA
2015
iv
v
vi
DEDICATION
TO MY PARENTS WHO SACRIFICED THEIR LIVES FOR
SAKE OF MY STUDY MY ALLAH ALMIGHTY LIVE LONG
BOTH OF THEM
vii
TABLE OF CONTENTS
Title Page No.
List of Tables ix
List of Figures xi
Acknowledgments xiii
ABSTRACT xiv
Chapter 1 INTRODUCTION 1
1.1 Study Area 1
1.2 Climate 6
1.3 Forest Types 6
1.4 Rock/Minerals 8
1.5 Biodiversity 9
1.6 Phytosociology 10
Chapter 2 REVIEW OF LITERATURE 14
Chapter 3 MATERIALS AND METHODS 30
3.1 General Survey 30
3.2 Plant Collection 30
3.3 Phenology 31
3.4 Life Form 31
3.5 Leaf Size Spectra 32
3.6 Methodology for Phytosociological Attributes 32
3.7 Multivariate Analysis of Ecological Data 34
3.8 Similarity and Dissimilarity Indices 35
3.9 Diversity Index 36
3.10 Species Richness 36
3.11 Environmental and Geographical Data 36
3.12 Edaphic Factors 36
Chapter 4 RESULTS 38
4.1.1 Diversity and Distribution of Plant Species 38
4.1.2 Biodiversity Index 40
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4.1.3 Species Richness 40
4.2 Phenology, Life Form and Leaf Spectra 44
4.3 Similarity and Dissimilarity Indices 55
4.4 Multivariate Analysis 57
4.4.1 TWINSPAN Classification 57
4.4.2 Ordination (Bray-Curtis, DCA and CCA) 68
4.5 Phytosociological Attributes of Different Vegetational Zones 79
4.5.1 Phytosociology in Subtropical Zones 79
4.5.2 Mixed Pinus roxburghii and Pinus wallichiana Zones 89
4.5.3 Moist temperate pure Pinus wallichiana Zones 97
4.5.4 Mixed Coniferous Forests 107
4.5.5 Pure Abies pindrow and Picea smithiana Forests 115
4.5.6 Phytosociology of Alpine Zones 123
4.6 Ordination of Samples on the bases of Microclimatic Data 130
4.7 Ordination of Samples on the bases of Edaphic Factors 139
4.8 Dominance Diversity Curves 147
4.9 Medicinal Flora of the Study Area 151
4.10 Exotic Flora of Nandiar Khuwar Catchment 163
4.11 Market Survey of Important Plant Species 163
4.12 Conservation Status of Plant Species 165
Chapter 5 DISCUSSION 172
RECOMMENDATIONS 185
REFERENCES 188
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LIST OF TABLES
Title Page No.
4.1.1 Share of various plant groups 39
4.1.2 Dominant families of the study area 39
4.1.3 Diversity index and species richness 41
4.2.1 Phenology, life form and leaf spectra 46
4.3.1 Similarity and dissimilarity indices among vegetation zones 54
4.3.2 Similarity and dissimilarity indices among different communities 56
4.4.1 Number of species and IVI contribution of biological spectra of
different plant communities
64
4.4.2 Number of species and IVI contribution of leaf size spectra of
different plant communities
65
4.4.3 Regression of stands in species space on 17 parameters 76
4.4.4 The correlations and biplot scores for 17 parameters. 77
4.5.5 The correlation among environmental variables 78
4.5.1 The IVI contribution of biological spectrum of subtropical forests 82
4.5.2 The IVI contribution of leaf size spectra of subtropical forests 82
4.5.3 The similarity and dissimilarity indices of subtropical forests 82
4.5.4 The IVI contribution of biological spectrum of mixed Pinus Pinus
forests
91
4.5.5 The IVI contribution of leaf size spectra of mixed Pinus Pinus
forests
91
4.5.6 The similarity and dissimilarity indices of mixed Pinus Pinus forests 91
4.5.7 The IVI contribution of biological spectrum of pure Pinus wallichiana
forests
101
4.5.8 The IVI contribution of leaf size spectra of pure Pinus wallichiana
forests
101
4.5.9 Similarity and dissimilarity indices of pure Pinus wallichiana forests 101
4.5.10 The IVI contribution of biological spectrum of mixed coniferous
forests
110
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4.5.11 The IVI contribution of leaf size spectra of mixed coniferous forests 110
4.5.12 Similarity and dissimilarity indices of mixed coniferous forests 110
4.5.13 The IVI contribution of biological spectrum of pure Abies and Picea
forests
118
4.5.14 The IVI contribution of leaf size spectra of pure Abies and Picea
forests
118
4.5.15 Similarity and dissimilarity indices of pure Abies and Picea forests 118
4.5.16 The IVI contribution of biological spectrum of alpine scrub zone 125
4.5.17 The IVI contribution of leaf size spectra of alpine scrub zone 125
4.9 Medicinal plants of Nandiar Khuwar catchment area 151
4.10 The exotic/alien flora of Nandiar Khuwar catchment area 163
4.11 Medicinal plants in the local drug market 163
4.12.1 Critically Endangered species of Nandiar Khuwar catchment 171
4.12.2 Endangered species of Nandiar Khuwar catchment 171
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LIST OF FIGURES
Title Page No.
1.1 Map of District Battagram 2
1.2 Map of Nandiar Khuwar catchment area 3
4.4.1 TWINSPAN classification 61
4.4.2 Bray-Curtis ordination 71
4.4.3 DCA ordination of species 72
4.4.4 DCA ordination of stands 72
4.4.5 CCA ordination of species and environmental variables 74
4.4.6 CCA ordination of stands and environmental variables 74
4.5.1 TWINSPAN classification of subtropical vegetation 80
4.5.2 Bray-Curtis ordination of the subtropical zone 85
4.5.3 DCA ordination of species of the subtropical zone 85
4.5.4 CCA ordination of stands of the subtropical zone 86
4.5.5 CCA ordination of species of subtropical zone 86
4.5.6 TWINSPAN Classification of mixed Pinus and Pinus forests 90
4.5.7 PCA ordination in Pinus Pinus forests 94
4.5.8 DCA ordination of species of Pinus Pinus forests 94
4.5.9 CCA ordination of stands of Pinus Pinus forests 96
4.5.10 CCA ordination of species of Pinus Pinus forests 96
4.5.11 TWINSPAN classification of pure Pinus wallichiana forests 98
4.5.12 Bray-Curtis ordination of pure Pinus wallichiana forests 103
4.5.13 DCA ordination of species of pure Pinus wallichiana zone 103
4.5.14 CCA ordination of stands of pure Pinus wallichiana zone 104
4.5.15 CCA ordination of species of pure Pinus wallichiana zone 104
4.5.16 TWINSPAN classification of mixed coniferous forests 108
4.5.17 Bray-Curtis ordination of mixed coniferous forests 113
4.5.18 DCA ordination of species of mixed coniferous forests 113
4.5.19 CCA ordination of stands of mixed coniferous forests 114
4.5.20 CCA ordination of species of mixed coniferous forests 114
4.5.21 TWINSPAN classification of pure Abies and Picea forests 117
4.5.22 Bray-Curtis ordination in pure Abies and Picea forests 121
4.5.23 DCA ordination of species of pure Abies and Picea forests 121
4.5.24 CCA ordination stands of pure Abies and Picea forests 122
4.5.25 CCA ordination species of pure Abies and Picea forests 122
4.5.26 TWINSPAN classification of alpine zone 124
4.5.27 Bray-Curtis ordination of alpine zone 127
4.5.28 DCA ordination of species of alpine zone 127
4.5.29 CCA ordination species of alpine zone 129
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4.5.30 CCA ordination of stands of alpine zone 129
4.6.1 The correlation of temperature with stands in species space 132
4.6.2 The correlation of wind speed with stands in species space 132
4.6.3 The correlation of humidity with stands in species space 134
4.6.4 The correlation of heat index with stands in species space 134
4.6.5 The correlation of dew point with stands in species space 136
4.6.6 The correlation of wet bulb with stands in species space 136
4.6.7 The correlation of barometric pressure with stands in species space 137
4.6.8 The correlation of altitude with stands in species space. 138
4.6.9 The correlation of density altitude with stands in species space 138
4.7.1 The correlation of soil saturation with stands in species space 143
4.7.2 The correlation of electrical conductivity with stands in species space 143
4.7.3 The correlation of soil pH with stands in species space 144
4.7.4 The correlation of organic matter with stands in species space 144
4.7.5 The correlation of Phosphorous with stands in species space 148
4.7.6 The correlation of Potassium with stands in species space 148
4.7.7 The correlation of slope aspect with stands in species space 149
4.7.8 The correlation of slope angle with stands in species space 149
4.8.1 Abundance diversity curves of species 150
4.8.2 Frequency and rank abundance of species 150
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ACKNOWLEDGEMENTS
I am extremely thankful to Almighty ALLAH, the Sovereign and Beneficent
Who enables me to accomplish excellence in my research endeavors. All
respect and love to Holy Prophet Muhammad (S.A.W.) Who enables us to
recognize our Creator, Who is the ultimate guide in every aspect of our life. I
offer my humble words of gratitude to my parents and brothers for their
guidance, financial support and moral inspiration throughout my academic
career.
I am greatly indebted to my supervisor Prof. Dr. Habib Ahmad TI, Dean
Faculty of Sciences, Hazara University, whose guidance and criticism enabled
me to complete this task. Thanks are due to my Co-supervisor Dr. Zafar Iqbal,
Assistant Professor Department of Botany, Hazara University, for providing
his valuable hours of assistance in conducting and evaluation of the research
findings.
I am also thankful to Prof. Dr. Manzoor Hussain, Chairman Department of
Botany, Hazara University, Mansehra, Dr. Ghulam Mujtaba Shah, Dr. Jan
Alam, Dr. Shujaul Mulk Khan, Dr. Azhar Hussain Shah, Dr. Muhammad Fiaz
and Mr. Abdul Majid for their cooperation, suggestions and identification of
plant species. I am also very thankful to all my family members and relatives,
especially for their cooperation in collection of plants and their preservation,
Phytosociological analysis, collection of information regarding plant uses,
their historical range of distribution and conservation status.
I am thankful to Higher Education Department Government of Khyber
Pakhtunkhwa for providing me opportunity to complete this task. I am also
thankful to my colleagues in Government Degree College Battagram for their
cooperation during research. I am also thankful to my bosom friends Mr.
Rahatullah and Mr. Sajid Haroon for their manual and moral support in every
part of my study.
xiv
ABSTRACT
This dissertation communicates an analytical exploration of the vegetational
profile of Nandiar Khuwar Catchment area, District Battagram, Pakistan. The
Nandiar Khuwar Catchment starting from the alpine pastures in the east and
stretches towards the famous Indus River in the west. The area provides a
variety of geo-climatic regimes within a sharp relief of 525-3817m with total
land area of 1301km2. Based upon physiognomy of the vegetation, the study
area was divided into 80 stands. Sum 324 vascular plants species belong to 97
families were recorded among which, 157 plant species medicinally
important. The most diverse stand was Rajmira followed by Jaro in term of
Shannon Diversity Index and Species Richness. The widely distributed
species in the study area were Fragaria nubicola and Adiantum capillus-veneris
recorded in 53 stands out of 80 stands. With respect to phenology, the
maximum plant species flowered in April-July (68.5%) and maximum plant
species showed fruiting in May-August. Among life form classes,
phanerophytes were dominant with 118 (36.4%) followed by therophytes
group with 82 (24.05%) species. The leaf size spectra were dominated by
microphyll with 137 (40.2%) followed by mesophyll having 103 species i.e.
30.2%. The TWINSPAN classification sorted out vegetation of the area into 13
plant communities. Six sub communities were identified in subtropical zone,
4 in mixed Pinus roxburghii and Pinus wallichiana forests, 5 in pure Pinus
wallichiana forests, 4 in western mixed coniferous forests, 3 in pure Abies
pindrow and Picea smithiana forests and 2 plant communities were identified in
alpine zone. The index of similarity was maximum (35.7%) for Wikstroemia,
Viburnum, Androsace and Juniperus, Sibbaldia, Primula communities.
Ordination analyses of the data provided a compositional response with a
gradient of 6.4 SD units long. The total variance (inertia) in the species data
was 7.07. Bray-Curtis ordination score was maximum for axis 1 (0.96) having
regression coefficient -54.1 and variance in distance were 2.5. Detrended
Correspondence Analysis (DCA ordination) produced a maximum gradient
length of 6.3 recorded for axis 1 with eigenvalue of 0.71. DCA clustered
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different species having similar habitats and different stands having common
species. The Canonical Correspondence Analysis (CCA ordination) showed
that plant species and stands were in linear combination with environmental
variables. Acacia modesta and Ficus carica was positively correlated with
temperature. Betula utilis, Juniperus communis, Ciminalis karelinii and Wulfenia
amherstiana species were negatively correlated with temperature.
Conservation status of the plants species recorded for the area showed that 10
species fall under critically endangered and 12 more species fall under
endangered categories. Major threats recorded for the flora were habitat
losses, excessive logging, selective and unscientific collection of herbs, over
grazing, erosion, environmental changes and introduction of exotic taxa. We
concluded that Nandiar Khuwar Catchment has great potential for
conservation of the native species of the Western Himalayan Ecoregion. The
conservation issues needs to be addressed through devising strategies for
protection, recovery and rehabilitation of the threatened species within their
respective stands.
1
Chapter 1
INTRODUCTION
1.1 Study Area
Nandiar Khuwar catchment, District Battagram is located in Khyber
Pakhtunkhwa Province of Pakistan 34° 33’ and 34° 47’ north latitude and 72°
55’ and 73° 14’ east longitude (Haq et al., 2010). It is bounded by the Allai
Valley in the north, Siran Valley in the east, Konsh and Agror Valleys in the
south and Black mountain and Indus River in the west (Ahmad et al., 2010).
The altitudinal range of Nandiar Khuwar catchment is from 525m at Thakot
to 3817m above mean sea level at Malkisar with a total area of 1,301km2 (Haq
et al., 2011).
Baleja Maidan, Birth Maidan, Machai Sar, Malkai Sar, Ganja, Chail Sar,
Ramosay Sar and Gabrai Kandao are the well known mountainous location of
Nandiar Khuwar catchment area (Haq et al., 2011). The important tribes are
Swatis, Khankhel, Akhunkhel, Syeds and Gujars. District Battagram also
inhabit a small population of Kashmiries, Kohistanies and few Sikh families
(Haq et al., 2010).
Agricultural land, wasteland, forest and alpine meadows can be differentiated
in Nandiar Khuwar catchment. The slopes are variable from gentle to
precipitous. Majority of the people are dependent on agriculture as a first
source of livelihood, followed by pasture animal husbandry. They cultivate
rice, maize, wheat, red beans and vegetables (Muhammad, 2003). Beside
agriculture, labor, remittances and livestock are also major sources of income.
The people belonging to this area are generally poor; nearly 51% income
comes from agriculture, 12% from livestock, 9% from forest, 9% from labor,
4% from services and 15% from remittances (Haq et al., 2011).
2
Fig. 1.1: Map of District Battagram showing stands location.
3
Fig. 1.2: Climatic zones recognized in Nandiar Khuwar Catchment area
(Muhammad, 2003).
4
Fig. 1.3: A view of Nandiar Khuwar near Battamori.
Fig. 1.4: A view of Nandiar Khuwar near Battagram.
5
Fig. 1.5: A view of Chailsar, Ramosay sar and Karganja.
Fig. 1.6: A view of Baleja Mountain.
6
1.2 Climate
The vast variation in altitude, slope aspect, disposition of mountain ranges
and prevailing wind currents in the catchment of Nandiar Khuwar reveal that
the climate vary from sub tropical at lower altitude to “alpine” conditions
prevailing in the higher reaches. In subtropical zones the summer is more
severe; however slight relief is provided by occasional shower. The winter is
more severe in higher altitude as compared to lower altitudes (Muhammad,
2003). The exact data of temperature, rainfall, snowfall and humidity of
Nandiar Khuwar catchment District Battagram is not available in Pakistan
Meteorological Department (National Agromet Centre) Islamabad, as per
their reply vide letter No: Agr – 2(2) III/2013/807 Dated: 06/12/2013. There
are marked seasons of rainfall, drought and snowfall. Rainfall caused by the
southwest monsoon, during July to mid of September constitutes almost half
of the annual average precipitation. May, June, October and November brings
drought spell. Snowfall occurs at lower altitudes during the months of
December, January and February. Nandiar Khuwar catchment receives
annual precipitation in the form of snowfall. Snowfall increases from lower
altitude to high altitude (fig. 1.5 and 1.6). In higher altitudinal zones, snowfall
starts during the month of November and stay up to June, however at lower
altitude it stays only for few days (Haq et al., 2011).
1.3 Forest Types
On the basis of available indicator plant species Haq et al. (2010) classified the
vegetational zones of District Battagram into six categories which fall under
Moist Temperate category of the internationally known Western Himalayan
Moist Temperate ecology (Champion et al., 1965). The vegetational zones
include:
1.3.1 Tropical Sub Humid Forest
It is a scrub forest, consisting of dry bushy shrubs and small trees found up to
an elevation of 700m from Thakot to Peshora. The associated species are
7
Acacia modesta, Mallotus philippensis, Albezia lebbeck, Bauhinia variegata,
Dalbergia sissoo, Dodonaea vescosa, Justicia adhatoda, Rubus fructicosus and Mytus
roylianus.
1.3.2 Sub Tropical Pinus roxburghii Forests
Pinus roxburghii forests occur at the altitudinal zone of 530-2050m. The Pinus
roxburghii forests are found both in pure form and mixed with Pinus
wallichiana. The other associated species are Quercus incana, Rhododendron
arboreum, Grewia optiva, Ficus racemosa, Woodfordia fruticosa, Indigofera
heterantha, Berberis lycium, Colebrookea oppositifolia, Zanthoxylum armatum,
Ziziphus oxyphylla, Rosa moschata and Rubus species.
1.3.3 The Moist Temperate Blue Pine Forest
Blue pine (Pinus wallichiana) occurs at the altitudinal zone of 1800 - 2400m.
The crop on the whole varies from pole to sub mature. Scattered matures and
over-mature trees are also there in some compartment of Hill territory. The
other associated species are Juglans regia, Quercus dilatata, Quercus incana,
Rhododendron arboreum, Viburnum cotinifolium, Cotoneaster microphylla,
Cotoneaster nummularia, Sarcococca saligna, Berberis lycium, Indigofera heterantha,
Rubus fructicosus and Rosa moschata.
1.3.4 Mixed Coniferous Forests
It is an important forest type, with Silver Fir (Abies pindrow) and Spruce (Picea
smithiana) as predominant species. Mixed coniferous forests occur between
elevations of 2000- 3050m. The composition of the forest is strongly influenced
by slope aspects. Hot southern slopes contain more of Blue Pine while on
northern aspect Silver Fir is predominant. The forest is generally
heterogeneous in nature having mixed age classes. The other associated
species are Quercus dilatata, Juglans regia, Aesculus indica, and Prunus padus,
Taxus wallichiana, Berberis lycium, Spiraea vaccinifolia, Lonicera quinquelocularis,
8
Rosa moschata, Viburnum species, Podophyllum emodi, Paeonia emodi, Geranium
wallichianum, Skimmia laureola, and Euphorbia species.
1.3.5 Pure Fir and Spruce Forests
Silver Fir (Abies pindrow) and Spruce (Picea smithiana) are usually found on the
elevation ranging from 2200 - 3000m. On cooler aspects they merge with Blue
Pine in the lower reaches. The crop consists of mature and over mature trees.
Old Fir trees are mostly top dry. The other associated species are Juglans regia,
Aesculus indica, Prunus padus, Quercus semicarpifolia, Betula utilis, Berberis
lycium, Indigofera heterantha, Desmodium elegans, Spiraea vaccinifolia, Ranunculus,
Aquilegia, Aconitum, Skimmia, Fragaria, and Geranium species.
1.3.6 Alpine Pastures
The alpine meadow locally known as “Mali” stretches above the tree limit
between 2850 – 3800m above mean sea level. These pastures have excellent
grasses and forbs available during summer season. Alpine pastures of
Nandiar Khuwar catchment include Chailsar, Ramosysar, Ganja, Baleja top,
Alishera and Malkaisar. These pastures support a large number of sheep,
goats and cattle during summer. Some Birch trees and Juniperus communis are
found on steep rocky places. Birch trees are badly loped for fodder by grazers.
The other species included Berberis species, Salix species, Corydalis species,
Potentilla species, Sibbaldia cuneata, Tanacetum dolicophyllum and Ranunculus
palmatifidus.
1.4 Rock/Minerals
Nandiar Khuwar catchment is underlined by metamorphic and plutonic
igneous rocks which are in turn intruded by pegmatite, aplites and quartz
veins. Quaternary alluvium and glacial deposits are common. Low grade
metamorphic rocks like Graphite schist, Re-crystalline lime stone, Amphibole
schist, Quartz-mica Schist and green schist are exposed in the area. Granite,
Ultra mafic and massive amphibolites cover large area. Shale’s occur
9
occasionally in Pinus roxburghii zones. The surface soil is formed by the
weathering of the parent rocks. The top soil is constantly being washed away
by run off from higher slopes. The soil under fir and spruce is deep and quite
rich in humus, whereas it is shallow and poor under pines and scrub zones
including wasteland (Muhammad, 2003).
1.5 Biodiversity
Biodiversity the measure of the health of ecosystems is referring to organisms
found within the living world (Murthy, 2007). It consists of species diversity,
ecosystem diversity and genetic diversity (Ahmad and Khan, 2004).
Biodiversity varies greatly across the globe as well as within regions.
Globally, there is a latitudinal gradient in species diversity. The tropical
regions are rich in biodiversity as compared to Polar Regions (Haq et al.,
2012). Biodiversity is greatly affected by different genetic and environmental
factors such as temperature, precipitation, altitude, soil, geography and the
presence of other species (Tandon, 2005). Every third plant species is hosted
by the mountainous regions of the world (Namgail et al., 2012).
One half of all plant species in the planet grow in hot spots, but not yet
destroyed vegetation of these territories occupies only 2.3 % of the Earth
(Motiekaityte, 2006). About 1% of the known species of the Earth are extinct
due to environmental changes (Sahney et al., 2010; Sahney and Benton, 2008;
Raup, 1994). The Holocene extinction due to habitat destruction by humans
caused loss of genetic diversity (Biodiversity, 2012). Worldwide land cover, is
altered principally by direct human use; through agriculture, pasture,
forestry, and development (Meyer and Turner, 1992).
Pakistan is under severe ecological stress due to its population explosion,
urbanization, deforestation and over exploitation of natural resources (Ali et
al., 2014; Haq, 2012). The natural forests of Pakistan are rapidly declining at a
rate of 4-6% per year, resulting in a decline in population size of both flora
10
and fauna (Haq et al., 2010). The forests of Pakistan require special attention
for the conservation of environment and sustainable utilization of natural
resources (Khan et al., 2014). The decrease in forest cover and associated major
changes in community composition have led to the decline in population size
of many important plant and animal species (Haq et al., 2010).
1.6 Phytosociology
Phytosociology is the study of biocoenosis from a botanical perspective and is
concerned with plant communities, their relationships, structure,
composition, distribution, development and the short-term processes
modifying them (Poore, 1955). Phytosociological surveys help in planning,
management and use of natural resources (Mashwani et al., 2011). The aim of
Phytosociology is to achieve a sufficient empirical model of vegetation using
plant species combinations that characterize univocally vegetation units
which may express largely abstract vegetation concepts or actual readily
recognizable vegetation types (Weber et al., 2000).
Phytosociology is based on associations and an association is the characteristic
combination of plant taxa, habitat features, physiognomy, biogeographical
area, role in ecological succession, historical and paleo-biogeographical
relationships (Khan et al., 2013). Associations with floristic and territorial
affinities can be grouped in larger ecological conceptual units called
"alliances". Similar alliances may be grouped in "orders" and orders in
vegetation "classes". The setting of syntaxa in such a hierarchy makes up the
syntaxonomical system, or the reference model of the given vegetation and
territory. The higher levels of complexity in describing the vegetation units
including vegetation complexes and multivariate statistics were used for
defining syntaxa and their environmental interpretation (Weber et al., 2000;
Poore, 1955).
11
Habitat of species describes the environment over which a species is known to
occur and the type of community that is formed as a result (Whittaker et al.,
1973). Ecological niche is the set of biotic and abiotic conditions in which a
species is able to persist and maintain stable population sizes (Wiens and
Graham, 2005; Whittaker et al., 1973).
It is useful to collect data to describe the population dynamics of each species
in different abiotic conditions (Haq et al., 2012). Present external factors and
historical plant geography are responsible for the determination of a plant
community (Poore, 1955). The availability of suitable habitat determines the
species distribution patterns in habitat structure where ecogenesis and
phylogenesis interact in a complex manner to shape current species
distributions (Thorpe et al., 1994).
The presence or absence of vegetation is controlled by environmental
variables where soil is of high importance in plant growth, and is a function
of climate, organisms, topography, parent material and time while
topography affects soil and climate, in addition to affecting temperature and
evapo-transpiration, makes deeper soil and higher content of organic matter
(Hoveizeh, 1997; Leonard et al., 1984). Certain plants species perform well in a
wide range of environmental conditions while it is impossible for individual
genotypes to perform well across the full range of conditions (Iqbal et al.,
2013). In a community on the basis of similarity in structure and function the
plant can be classified in different life form and leaf size classes which
indicate the adaptation of plants to certain ecological condition (Khan et al.,
2013).
Phenology and climate are related in terms of temperature, rainfall and day
length. Leaf growth, leaf fall, flowering and fruiting of species occur in
specific seasons of the year. The phenology of life form classes vary and are
associated with day length/ temperature. Precipitation and soil affect species
12
richness. Slope aspect, slope angle, altitude, latitude and longitude also
influence species richness. Diversity indices provide composition, rarity and
commonness of species in a community. It reflects how many different species
are in a dataset, and simultaneously takes into account how evenly the basic
entities are distributed among those types. The value of a diversity index is
directly related to number of species and evenness (Khan et al., 2012).
Life form is the indicator of climate (micro and macroclimate) and can be used
in comparing geographically widely distributed plant communities. Life form
is characterized by plant adaptation to certain ecological conditions and
traditionally being used to describe world vegetation type at community
level. The life form and leaf size spectra are the attributes that have been
widely used in vegetation description. Leaf size classes have been found to be
very useful for plant associations. The leaf size knowledge helps in
understanding physiological processes of plants and plant communities.
Climate of a region is characterized by life form while the biological spectrum
of the region exceeds the percentage of the same life form in the normal
biological spectrum. Biotic agencies are the chief causes for changing the
biological spectrum in a given floristic zone (Amjad, 2012).
Phytosociological surveys help in planning, management and exploitation of
natural resources. Phytosociology provides sufficient empirical model of
vegetation using plant species combinations. The spatial pattern of vegetation
composition and the relationship between vegetation composition and
environmental factors can be easily understood with phytosociological study
(Khan et al., 2011). For a sustainable management of Nandiar Khuwar
Catchment area it is necessary to study the effects of climate and edaphic
factors on vegetation, species diversity and distribution in different
vegetational zones of the study area. The information regarding the
phytosociological study of District Battagram is not available, therefore the
present study was proposed with the following objectives.
13
1.7 OBJECTIVES
i. To explore the plant species diversity in various vegetational zones of
Nandiar Khuwar catchment area
ii. To study the role of physiographic and edaphic factors in determining
the vegetation structure of the study area
iii. To assess the plant communities on the basis of altitudinal gradient,
slope aspect and slope angles
iv. To document the economic important plant species and conservation
status of the flora of selected area
14
Chapter 2
REVIEW OF LITERATURE
Biodiversity is referring to organisms found within the living world and
consists of species diversity, ecosystem diversity and genetic diversity
(Ahmad and Khan, 2004). It is the variation of life forms within a species,
ecosystem, biome, or an entire planet and is a measure of the health of
ecosystems (Haq et al., 2010). Biodiversity is greatly affected by different
genetic and environmental factors (Tandon, 2005). Biodiversity is influenced
by land use patterns (Turner et al., 1998). Land use patterns alter relative
abundances of natural habitats, species richness, response of species to habitat
loss and fragmentation (Pearson et al., 1996; Matlack, 1994; Terborgh, 1992;
Walker, 1992).
The planet earth is the homeland of more than 270,000 vascular plant species
which are surviving in various ecosystems (Alam and Ali, 2009). Out of these,
more than 10,000 plant species have been reported in Himalayas (Khuroo et
al., 2007). In the Himalayas about 50% of the potential forest area has been
decreased due to major structural changes resulting in loss of indigenous
plant resources and their traditional knowledge during last hundred years
(Ibrar, 2003).
Phytosociology is concerned with plant communities, their relationships,
structure, composition, distribution, development and the short-term
processes modifying them (Poore, 1955). Phytosociological surveys helps in
planning, management and use of natural resources (Mashwani et al., 2011).
Present external factors and historical plant geography are responsible for the
determination of a plant community (Poore, 1955).
The presence or absence of vegetation is controlled by environmental
variables, where soil is of high importance in plant growth, and is a function
15
of climate, organisms, topography, parent material and time while
topography affects soil and climate, in addition to affecting temperature and
evapo-transpiration, makes deeper soil and higher content of organic matter
(Hoveizeh, 1997; Leonard et al., 1984). Certain plants species perform well in a
wide range of environmental conditions while it is impossible for individual
genotypes to perform well across the full range of conditions. In a community
on the basis of similarity in structure and function the plant can be classified
in different life form and leaf size classes which indicate the adaptation of
plants to certain ecological condition. Phenology and climate are related in
term of temperature, rainfall and day length. Leaf growth, leaf fall, flowering
and fruiting of species occurs in specific season of the year. The phenology of
life form classes vary and are associated with day length/ temperature.
Precipitation and soil affect species richness. Slope aspect, slope angle,
altitude, latitude and longitude also influence species richness (Khan et al.,
2014).
Vujnovic et al. (2002) analyzed the cover of vascular plants, mosses, and
lichens across a range of disturbance levels in 11 remnant grasslands within
the Aspen Parkland Ecoregion of central Alberta, western Canada. Lower
species diversity was found in undisturbed and lightly grazed as well as in
highly disturbed plots. Intermediate levels of disturbance reduced dominance
of Festuca hallii and increased abundance of most other species resulting inthe
highest species diversity. The species richness and diversity of exotic plant
species showed a significant positive relationship with the magnitude of the
disturbance. Zuo et al. (2014) measured the plant species richness, soil
properties and altitude across four spatial scales at three different dune
stabilization stages in Horqin Sandy Land, Northern China. Plant species
richness increased with the increase of spatial scales in each dune stabilization
stage, as well as with the increase of dune stabilization degrees. CCA analysis
showed that plant species richness was significantly and positively correlated
to soil organic carbon and total nitrogen in mobile dune, and significantly and
16
positively correlated to soil organic carbon, total nitrogen, carbon/nitrogen,
very fine sand and silt and clay. No significant correlation between plant
species richness and environmental factors was observed in fixed dune.
Pearson (1979) studied the effects of temperature and moisture on phenology
and productivity of Indian ricegrass. He reported that the growth in Indian
ricegrass commenced in the spring when soil temperatures stayed at 4°C for
at least 3-4 days. Maximum plant size was attained when soils warmed up
early in the spring. Higher soil temperature late in the vegetative phase of
growth delayed anthesis approximately 3 days for each degree Celsius above
10°C. Sundriyal et al. (1987) selected four stations, representing variation in
elevation, slope, exposure, snow cover, vegetation composition and cover at
Tungnath, a high altitude zone in the Garhwal Himalaya. The initial growth
of plants was noticed in early May to mid of June. A considerable difference
in phenological phases of different species was observed including, bud 4 to 6
weeks; flower 3 to 5 weeks; fruit, 5 to 7 weeks. Pangtey et al. (1990) studied
various phenological stages of 184 species of high-altitude plants in the
Pindari glacial moraine area of Kumaun in the Central Himalaya. The
initiation of growth was synchronized with the beginning of spring/or
summer temperature rise and snowmelt. In this high-altitude zone, the peaks
of various phenophases succeeded one after another over about 4 months
from early June to October. It was suggested that the plants complete various
growth cycles within a very short period of favorable conditions to ensure the
survival of their progeny.
Angelova and Tashev (2005) proposed a model for doing a complex analysis
for the flora of Mount Chepan (Bulgaria). 456 species of MtChepan were
classified, according to their levels of distribution, with altitude into 21
groups. Among them 4 groups were wide spread. The distribution of the
remaining 17 group species ranges in spread in altitude, depending on their
life form classes. Huelber et al. (2006) studied the phenological response of
17
snow bed species to snow removal dates in the central Alps, with implication
for climate warming. They concluded that low temperature and the short
growing season in high altitude snow patches in temperate mountains
constrain life cycle and reproduction of snow bed species. Winter
precipitation and temperature are the main factors for determining the
growing season length and are predicted to change with global warming.
Suresh and Paulsamy (2010) recorded the phenological observation and
population density of six uncommon medicinal plant species (Anaphalis
elliptica, Ceropegia pusilla, Hedyotis articularis, Heracleum rigens, Leucas vestita
and Luzula campestris) in four grasslands in Nilgiri Biosphere Reserve,
Western Ghats. All six species exhibited peak bud formation between
February and May and bud break in June. Most of the leaves were produced
in a single flush. Bijalwan et al. (2013) studied the impact of microclimatic
variations on the developmental stages of common alpine plant species at
four primary phenology sites at Dayara meadow of Garhwal Himalayas. The
study revealed that variations in topographical features and environmental
conditions directly influenced phenology of alpine plant species. Site I and IV
showed great variation in the timing of phenological phases whereas, site III
and IV showed approximately similar phenological timing. Anemone obtusiloba
and Anaphalis contorta showed early flowering whereas Aconitum
heterophyllum, Bupleurum longicaule and Parnassia nubicola flowered in late
August and early September.
Cain and Castro (1959) and Shimwell (1971) reported that hemicryptophytes
are characteristic of temperate zones; therophytes of desert climate and
geophytes of the Mediterranean climate. The environmental implication of life
form spectra from India were described by Meher-Homji (1981). The
reassessment of 38 spectra described the therophytic phytoclimate for arid
zones; thero-Chamaephytic type characterizes semi-arid zones and grasslands
of secondary origin. Marshy grasslands showed thero-Hemicryptophytic
18
spectrum, thero-nanophanerophytic and thero-phanerophytic spectra
correspond to the areas of disturbed woodlands, phanerophytic to the better
preserved forests. Nano- chamaephytic plant-climate was recorded for the
thicket-dotted regions of Madras, Mysore. Geo-chamaephytic type prevails in
the Western Himalayas where the mean of the coldest month is below 5°C.
Ram and Arya (1991) analyzed the life form and vegetation of Rudranth an
alpine meadow of Central Himalaya, India. They reported that the
phanerophytes were dominant life form before degradation while the
degraded vegetation supported therophytic and hemicryptophytic types of
vegetation. El-Demerdash et al. (1994) reported the distribution of the plant
communities in Tihamah coastal plains of Jazan region; Saudi Arabia. They
reported eight major community types constitute the major part of the natural
vegetation of the study area. They have discussed the factors affecting the
species distribution and the correlations between the vegetational gradients
and the edaphic variables. The study area was declared as subtropical desert
where therophytes were the most frequent life-form in this region. Barik and
Misra (1998) studied the phenological pattern, life form, plant type and uses
of 80 plant species of grassland of South Orissa. They reported that due to
diverse geo-morphology of this region the climate and altitude have provided
different microhabitats for specific plant growth. The alpine plants were
recorded on exposed dry rocks crevices, ravines and on much fertile loamy
soils constituting the alpine meadows. Bhandari et al. (1999) explored the
floristic composition, biological spectra and diversity of burnt and unburnt
submontane grazing lands of Garhwal Himalaya. Pharswan et al. (2010)
studied the floristic composition and biological spectrum of vegetation in
alpine meadows of Kedarnath Garhwal Himalaya.
Tareen and Qadir (1993) reported the life form and leaf size spectra of 102
plant communities of diverse areas ranging from Harnai, Sinjawi to Duki
regions of Pakistan. Malik et al. (2007) described the life form and leaf size
19
spectra of plant communities harbouring at Ganga Chotti and Bedorii hills.
Sher and Khan (2007) explored the floristic composition, life form and leaf
spectra of 222 plant species Chagharzai valley, District Buner. Ajaib et al.
(2008) explored the biological spectra of Saney Baney Hills District Kotli,
Azad Jammu and Kashmir. They reported that severe deforestation,
overgrazing, soil erosion and human influence reduced the macrophylls and
so therophytes appeared to occupy the vacant niches. Life form and leaf size
spectra of vegetation in Kotli Hills, Azad Jammu and Kashmir were reported
by Amjad (2012). He reported that therophytes are indicators of subtropical
and disturbed vegetation while hemicryptophytes are indicators of humid
condition. The dominance of therophytes reflects that environmental
conditions or human influences are not well suited to the phanerophytes. The
leaf size spectrum of these communities showed that the overall vegetation of
Kotli is dominated by leptophylls and nanophylls. Microphyll is also reported
as a dominant life form in higher altitudes due to low temperature, high
rainfall and moist condition. Generally, lower altitudes support small leaves
in lower belt and large leaves in upper niches.
Yavari et al. (2010) studied the floristic-ecologic data of 11 disperse
populations of Astragalus glaucops as mean endogenous milieou by eco-
phytosociologic method based on similarity and dissimilarity of floristic
composition in Alvand mountain. Malik and Malik (2004) recorded life form
and index of similarity during Monsoon of Kotli Hills, Pakistan. Life form and
index of similarity of plant communities of Sarsawa Hills were reported by
Malik and Ahmed (2006). Nazir and Malik (2006) worked on life form and
index of similarity of plant communities of Sarsawa hills District Kotli.
Kassas and Imam (1954) described the relation of habitat and plant
communities in the Egyptian desert. Batanouny (1979) analyzed the
vegetation pattern and process affected by human impact along the Jeddah-
Mecca road, Saudi Arabia. Batanouny and Baeshin (1983) described the plant
20
communities along Medina-Badr road across the Hejaz Mountains, Saudi
Arabia and recognized twenty three plant communities by species dominance
and habitat features. Among them ten communities were dominated by trees
and shrubs; five were dominated by Acacia spp. and seven communities were
dominated by suffrutescent species and six are dominated by ephemeral
species. The latter communities appear only during the wet season and
disappear at the beginning of summer. The wide variations in topography,
rock types and soil characteristics, have a marked influence on the water
resources and consequently on the vegetation.
Backeus (1992) analyzed the distribution and vegetation dynamics of humid
savannas in Africa and Asia. He concluded that the extension of savannas
under humid climatic conditions and the relation to the distribution of forests
is a function of cultivation, grazing by domestic and wild animals, present
and previous climate, geomorphology and soil characteristics. The established
savannas were maintained by fires. Montane savannas were generally
brought about by man's clearing, cultivation and burning. In humid African
lowland climates forests expand into savannas if not maintained by man. In
montane areas forest expansion may be delayed on degraded soils and when
diaspores were lacking. Blažková (1993) studied the phytosociology in the
lowlands of North Korea and he recorded two new plant communities. The
association of Digitario ciliaris and Zoysietum japonicae with many species of
the C4 photosynthetic pathway, growing on open sunny habitats and the
association of Plantagini asiaticae and Poetum pratensis consisting mostly of
species with the C3 photosynthetic pathway and growing on half shaded or
moist habitats. They are analogous to the communities described from Japan.
Backeus (1993) used data from transects on the shores and draw down area
of a small reservoir in Tanzania with a strongly fluctuating water level to
illuminate a spatial and short term temporal variation in the vegetation of a
border zone. Few perennial species survived the rainy season in the zone
exposed to the fluctuating water level. Most plants were annuals that
21
colonized yearly. The vegetation under the full supply level was sparse and
related to Eriochloetum nubicae and Ecliption albae.
Suzuki and Saenger (1996) studied the phytosociology of mangrove
vegetation in Australia and they have identified 17 plant communities
compared it with East Asia mangrove vegetation. The Lai Chi Wo mangrove
swamp vegetation in Hong Kong was analyzed by Lu Chang-yi et al. (1998).
These swamps have 10 mangrove species comprising 76.9% of the total
number of mangrove species in Hong Kong and were dominated by Aegiceras
corniculatum, Heritiera littoralis, Excoecaria agallocha with importance values of
37.88, 28.19 and 14.33 respectively. The mangrove species diversity and
evenness, as based on the Shannon-Wiener Function, were 1.44 and 62.01%.
The correlation information of the mangrove community was 1.008.
Hargreaves (2008) compared literatures of the international journals from
1998 to 2007 on the subject of phytosociology in Brazil on three fundamental
themes i.e. coverage of various plant physiognomies, representative of
Brazilian biomes and coverage of environmental factors for composition and
structure of plant communities. Ige et al. (2008) reported the phytosociology
of weed flora from three abandoned farm lands within Owo Local
Government area of Ondo State, Nigeria. Korzeniak (2013) studied the
phytosociological database as a tool for synthetic and comprehensive study of
semi-natural meadows in the Polish part of Carpathians.
Champion et al. (1965) and Beg (1975) recognized various types of forests and
different vegetational zones in Pakistan on the basis of temperature and
altitude. Chaudhri (1960) analyzed the vegetation of Kaghan valley and the
vegetation types described were grassland, Pinus excelsa forest, mixed
coniferous forest, Abies pindrow and Abies webbiana forest, Betula utilis forest,
Cedrus deodara forest, Juniperus macropoda forest, and alpine vegetation.
Chaudhri (1961) divided the vegetation of Karachi into three edaphic types.
22
The coastal vegetation consists of three main associations. In the protected
creeks facing the mouth of the rivers in shallow water were found mangrove
vegetation consisting of mainly Avicennia alba. In the muddy coastal swamps
Arthroenemum indicum were the main species. On the coastal sand dunes the
main species were Suaeda monoica, Ipomaea pes-caprae, Aerua pseudo-tomentosa,
Calotropis procera and Tamarix troupii. The vegetation of the calcareous rocks
consists of mainly Commiphora mukul, Grewia villosa, Grewia tenax, Euphorbia
caudicifolia and Acacia senegal. In the valleys between the hills where alluvium
has been deposited over the basic rock by various rivers, the vegetation
consists of Capparis decidua, Prosopis spicigera and Salvadora oleoides.
Qadir et al. (1966) conducted a phytosociological survey of Karachi University
Campus by mean area method and recognized six plant communities in
which xerophytic and deciduous shrubs were predominant. Amin and
Ashfaque (1982) carried out the phytosociological studies of Ayub National
Park, Rawalpindi. A total of 5 community types were recognized: depression
community (Acacia modesta, Cannabis sativa), level ground community (A.
modesta, Cynodon dactylon), foot hill community (A. modesta, Themeda anathera),
hill slopes community (A. modesta, Dodonaea viscose), and hill tops community
(A. modesta, Lantana camara). The ubiquitous presence of A. modesta
regeneration indicates little topographic influence on this species.
Beg and Khan (1984) investigated the dry oak (Quercus baloot) forest zone in
the Swat valley, Pakistan. They reported 3 new plant communities which
were relatively poor in grasses as compare to herbs and shrubs. Ahmed (1986)
carried out a quantaitative survey at 17 locations near the road side on the
Great Silk Road from Gilgit to Passu in the Himalayan range in Pakistan. Six
plant communities were recognized on the basis of species dominance,
importance value and similarity coefficent. The communities were generally
homogenous in nature. The soil of all communities were fine textured and
basic in nature with low fertility levels.
23
Qadri (1986) explored the vegetation of Kotli Hill, Azad Kashmir in his
Phytosociological study. Rashid et al. (1987) reported the Phytosociology of
Attock-Nizampur Hills. Malik and Hussain (1987) explored the vegetation of
Muzaffarabad in their Phytosociological investigation. Ahmed (1988) finds
out plant communities of some northern temperate forests of Pakistan.
Hussain and Shah (1989) recognized eleven non-stratified plant communities
winter in Docut Hills. The original vegetation has been changed to open
grassland where 35 species were recorded. Deforestation and over-grazing
followed by erosion were the major ecological problems. The species diversity
was low due to dormant winter season.
Hussain and Shah (1991) have also studied the phytosociology of vanishing
sub-tropical vegetation of Swat Docut Hills in spring aspect. Hussain and
Illahi (1991) presented ecology and vegetation types for Lesser Himalayan of
Pakistan. Iqbal and Shafiq (1996) recognized six plant community (Suaeda,
Haloxylon, Lasiurus, Prosopis, Aerva and Senna) from halophytes to xerophytes
with disturbed nature. On the basis of Importance Value Index Suaeda
fruticosa was the leading dominant species. Out of thirty six species, only five
species attained highest constancy class III as compared to rest of the species.
All the communities were heterogeneous due to the absence of certain
frequencies classes. Species diversity and community maturity index were
low. It was concluded that certain edaphic and anthropogenic activity were
responsible for variation in the composition and structure of the vegetation.
Hussain et al. (1997) worked on plant communities of Girbanr Hills, District
Swat Pakistan. Chaudhry et al, (2001) recognized five distinct plant
communities on core area, the mountainous region with sand stone and
patches of red sandy clay and two plant communities in the peripheral area in
the phyto-ecological studies conducted in the Chhumbi Surla Wildlife
Sanctuary, Chakwal, Pakistan. They reported that Chrysopogon serrulatus were
the dominant species in all plant communities.
24
Khan and Shaukat (2005) reported the above ground standing phytomass of
some grass-dominated communities of Karachi. Ahmed et al. (2006)
conducted a quantitative phytosociological survey in different climatic zones
of Himalayan forests of Pakistan and reported 24 different communities and 4
monospecific forest vegetations. Siddiqui et al. (2009) conducted a
phytosociological study of Pinus roxburghii forests in Lesser Himalayan and
Hindu Kush range of Pakistan. Thirteen stands were sampled in Mansehra,
Rawalpindi, Islamabad, Swat and Lower Dir between elevations of 750 –
1700m. Out of 13 stands pure Pinus roxburghii forests were recorded in 12
stands. Abbas et al. (2009) analyzed eight vegetative communities in the grey
goral range of Pakistan and Azad Kashmir. The indicator species was Pinus
roxburghii and loss of habitat was mainly responsible for reduction in
population size.
Ahmad et al. (2010) presented the floristic composition and communities of 47
stands of Cedrus deodara forests in the Himalayan range of Pakistan and
identified 7 different plant communities. Quantitative description and
structure of some forsets of Skardu District of Northern areas of Pakistan
were presented by Akbar et al. (2010). The dominant species in these forests
Pinus wallichiana, Juniperus excelsa and Betula utilis. In six stands three plant
communities were recognized which were Pinus, Juniperus community, Betula
utilis, Pinus community and Juniperus, Betula utilis community. The size class
structure shows gaps in each tree species indicating unstable conditions of
these forests due to anthropogenic disturbance. Farooq et al. (2010) recognized
five plant communities of Push Ziarat area (Shawal) in the South Waziristan,
Pakistan. Hussain et al. (2010) reported six plant communities in Central
Karakoram National Park of Northern Areas of Pakistan.
Khan et al. (2010) explored the species composition, diversity, equitability,
richness and concentration of dominance of tree species along an altitudinal
gradient of District Dir Lower Hindukush range of Pakistan. A total of 15
25
stands in Monotheca buxifolia forests were analyzed. Monotheca buxifolia was
the dominant tree species at all locations. Olea ferruginea and Acacia modesta
were reported in four stands as a second dominant species. Species diversity
ranged from 0 to 0.36. Concentration of dominance and equitability values in
some stands of 1 to 1.70 were relatively high due to the presence of single
species in the forests. Saima et al. (2010) discussed the floristic diversity of
Ayubia National park, District Abbottabad, Pakistan.
Brendenkamp et al. (1983) studied the ecological interpretation of plant
communities by classification and ordination of quantitative soil
characteristics. Peter and Erik (1992) studied woody vegetation in 44 sites in
Senegal. Sixteen vegetation types were recognized by TWINSPAN and CCA
by using species composition and density data. They have also used a
supervised and multi-spectral and multi-temporal classification of day and
night, dry season NOAA-AVHRR imagery to identify their distribution with
a classification accuracy ranging from 60-100%.
Velazquez (1994) used multivariate analysis to describe the composition and
distribution of vegetation types on the slopes of the volcanoes Tláloc and
Pelado, Mexico. The multivariate analysis included TWINSPAN, Detrended
Correspondence Analysis and Canonical Correspondence Analysis. These
volcanoes have relatively high α and β diversities. Floristic and
environmental data from 138 relives and seven explanatory environmental
variables were included among which soil moisture and elevation were the
most relevant variables to explain the distribution of the vegetation under
study.
The phytosociology and gradient analyses of a subalpine treed fen in Rocky
Mountain National Park, Colorado were conducted by Johnson (1996). Four
types of vegetation were subjectively defined; these same types were
distinguished by the DCA. Species composition was related to environment
26
using canonical correspondence analysis (CCA). Water-table depth, hummock
height, shading, groundwater temperature, and conductivity were
significantly correlated with species distribution, accounting for 51% of the
total species variance. Univariate regression was used to examine how tree
density varied with environment.
Johnson and Steingraeber (2003) analyzed the vegetation, environment, and
ecological gradients present on three calcareous mires in the South
Parkvalley, Park County, Colorado. Vegetation was classified into four habitat
classes, nine subclasses, and twelve species associations using TWINSPAN.
DCA was used to ordinate vegetation samples along two axes and CCA was
used to directly relate local environmental conditions to vegetation.
Vogiatzakis et al. (2003) explored the environmental factors that determine the
spatial distribution of oro-mediterranean and alti-mediterranean plant
communities in Crete. Classification of the vegetation was based on
TWINSPAN, while detrended correspondence analysis (DCA) and canonical
correspondence analysis (CCA) were used to identify environmental
gradients linked to community distribution. Hemicryptophytes and
chamaephytes were the most frequent, suggesting a typical oro-
mediterranean life form spectrum. The samples were classified into five main
community types and one transitional. The main gradients, identified by
CCA, were altitude and surface cover type in the North-west site, while in the
Central site the gradients were soil formation-development and surface cover
type.
Ghani and Amer (2003) studied the soil-vegetation relationships in a coastal
desert plain of southern Sinai, Egypt. 203 plant species were recorded in 19
sites. Five vegetation groups were recognized by TWINSPAN. DCA and CCA
ordination techniques were used to examine the relationship between the
vegetation and soil parameters. Therophytes and chamaephytes were the
most frequent, denoting a typical desert life-form spectrum. Floristic
27
composition in the different geomorphologic landscape units showed
differences in species richness. Nezerkova and Hejcman (2006) analyzed the
vegetation-environment relationships in Sudanese savannah, Senegal by
using canonical correspondence analysis. Khafagai et al. (2013) conducted the
vegetational survey on Saint Catherine Mountain Egypt. They reported ten
vegetational groups in 45 stands by using TWINSPAN for classification and
CANOCO for ordination.
Han et al. (2014) studied the characteristics of the vegetation and soil on
Mount Sejila in Tibet. Eleven sampling areas were examined, and the
vegetation composition, species diversity, plant biomass and soil properties
were measured in each one. Nowak et al. (2014) explored the geobotanical
investigations conducted in rush vegetation from the Phragmito-Magno-
Caricetea class in the central Pamir-Alai Mts (Tajikistan, Middle Asia). The
analyses classified the vegetation into 28 plant communities, including 26
associations. The vegetation patches occur mainly along the shores of water
bodies and in ditches. Jurišić et al. (2014) analyzed floristic diversity of
Posavina’s floodplain forests in Serbia. TWINSPAN classification and
ordinary Correspondence Analysis were used to detect floristic divergence of
analyzed stands. Both analyses have shown an almost identical result of
floristic composition, where 114 studied samples were grouped into seven
association groups at the third TWINSPAN classification level. ANOSIM
analyses were used to determine the degree of floristic discontinuity, which
was largest between forests of Pedunculate Oak, Hornbeam and Turkey Oak
and forests of Pedunculate Oak and Ash (statistics R = 0.8824 (p<0.001)).
Ahmed (1976) explored the vegetation of Skardu by using multivariate
analysis. Jabeen and Ahmad (2009) analyzed the vegetation of Ayub National
Park Rawalpindi by using multivariate analysis. The research was conducted
to determine the soil-vegetation relationship and quantify the floristic
composition. For classification TWINSPAN and for ordination DCA and CCA
28
were used and as a result four plant communities were recognized in Ayub
National Park Rawalpindi. Saima et al. (2009) explored the vegetation pattern
along a continuous 18 km long transect that crossed a mixed coniferous forest
in Ayubia National Park District Abbottabad. Five different plant associations
were recognized by cluster analysis. DCA and Spearman’s Rank Correlation
Coefficient were used to detect relationship between environmental factors
and species distribution.
Khan et al. (2013) worked on phyto-climatic gradient of vegetation and habitat
specificity in the high elevation western Himalayas by using Detrended
Correspondence Analysis (DCA) and Canonical Correspondence Analysis
(CCA).They reported 198 plant species in different life form classes among
which hemicryptophytes (51%) dominated the area. Phyto-climatic
relationships show that tree species are widely distributed on northern aspect
slopes whilst shrubs are more dominant on southern aspect slopes.
Vegetation structure, composition and diversity of Hub-dam catchment area,
Pakistan was conducted by Shaukat et al. (2014). They had investigated
variance/mean ratio and Morisita’s index for spatial patterns within-
community of plant populations. CA and CCA ordination were used to
analyze the distribution pattern of vegetation composition and the underlying
environmental gradients. Four major plant community types were recognized
by Ward’s cluster analysis. Biological spectrum of Hub dam catchment area
showed dominance of therophytes and chamaephytes.
Leonard et al. (1988) studied the vegetation-soil relationships on arid and
semiarid rangelands. The rangeland plant community distribution and
species composition were related to specific soil properties such as soil
climate, texture, depth, structure, fertility, pH, salinity and toxic influences.
Jensen et al. (1990) analyzed the correlation between soils and sagebrush-
dominated plant communities of northeastern Nevada. The influence of
29
climate and edaphic factors on vegetation cover, species diversity and
distribution on three lava flows of Mount Cameroon were recorded by Fronge
et al. (2011). They reported that the edaphic factors and climate play very vital
roles in the colonization process on Mount Cameroon.
Malik et al. (2007) studied the phytosociological attributes based on
environmental factor such as climate, soil condition, temperature, humidity,
rain fall, wind and biotic factor of different plant communities of Pir Chinasi
hills of Azad Jammu and Kashmir. Khan et al. (2010) sampled eight stands of
Quercus baloot forests for phytosociology, structure and soil characteristics of
Chitral Hindukush range of Pakistan ranging from 1770 −2370m.asl. Shaheen
et al. (2011) studied the patterns of species composition and diversity in the
lesser Himalayan subtropical forests of Kashmir in relation to environmental
variables and underlying anthropogenic influence. Ilyas et al. (2012) reported
vegetation composition and threats to the montane temperate forest
ecosystem of Qalagai hills, Swat, Khyber Pakhtunkhwa, Pakistan and
reported eight stratified plant communities.
Ahmad et al. (2010) worked on the diversity and distribution of vascular
plants of Nandiar Khuwar catchment District Battagram and reported 380
plant species. Haq et al. (2010) explored the species diversity along with
ethnobotanical uses of 402 plant species of Nandiar valley. These included 273
herbs, 77 shrubs, 68 trees, 18 climbers and 3 epiphytes. Haq et al. (2011)
reported 156 medicinal plants including 22 ethno veterinary important plant
species from Nandiar Khuwar catchment District Battagram. The information
regarding the phytosociological study on the plants of District Battagram is
not available.
30
Chapter 3
MATERIALS AND METHODS
3.1 General Survey
The entire Nandiar Khuwar catchment area District Battagram, Pakistan was
surveyed during 2011 – 2014. The study includes mature and least disturbed
vegetation (Laurance, 2004). The selected 80 stands were identified on the
basis of physiognomy, climates, topography, altitudinal variation and floristic
composition (McMahon et al., 2011).
3.2 Plant Collection
The plant specimens were collected from Nandiar Khuwar catchment area.
Field numbers were allotted to the specimens and field data were recorded in
field notebook. Scientific names, vernacular names, family and other relevant
information were recorded properly. The plant material was pressed and
dried by using blotting papers. The specimens were poisoned using mercuric
chloride, copper sulphate and absolute alcohol in the ratio of 1:2gm/L of
alcohol. The poisoned specimens were mounted on standard size herbarium
sheets and preserved in Herbarium Hazara University, for future reference.
The plant species were identified with the help of available literature (Nasir
and Ali, 1970 to 1989; Ali and Nasir, 1990 to 1992; Ali and Qaiser, 1993 to
2009).
Each species was individually evaluated in the field for its use patterns, range
of distribution, present frequency and compared with the known extent and
its normal ecological niche. The number of the plants scored with reference to
its ecological amplitude and calculated historical distribution were compared
with IUCN criteria version 3.1 (IUCN, 2001) for elaborating the conservation
status of the species concerned. Information regarding ethnobotanical uses of
plant species was obtained through semi-structured questionnaires from 320
peoples in different localities of the study area.
31
3.3 Phenology
Different phenological stages of life-cycle were determined in spring, summer
and autumn (Hänninen and Tanino, 2011; Singh and Singh, 2010).
3.4 Life Form
The adaptation of plants to climate and its biological spectrum were classified
into different life form classes as follows after (Raunkiaer, 1934).
3.4.1 Phanerophytes
In this class those species were included which have perennating buds
emerging at least 25 cm above from aerial parts of the plants and mainly
included woody trees and shrubs. They were further subdivided in to
following sub-classes:
Megaphanerophytes are trees over 30m tall.
Mesophanerophytes are trees between 8-30m tall.
Microphanerophytes are trees and shrubs between 2-8m tall.
Nanophanerophytes are shrubs between 25cm-2m tall.
3.4.2 Chamaephytes
Those plants were included in this class which have perennating buds that
lies on the surface of the ground up to 25cm and are mainly woody or semi-
woody perennials under shrubs.
3.4.3 Hemi-cryptophytes
In this class those plants were included where soil and leaves protect
perennating buds and are located on the surface of the ground.
3.4.4 Geophytes
In this class those plants were included where the perennating buds lie below
the ground surface.
32
3.4.5 Therophytes
Those annual species were included in this class which completed their life
history from seed to seed during favorable season of the year. Their only
perennating buds are those of the embryo in seeds, all other organs of the
plant having died.
3.5 Leaf Size Spectra
The leaf size spectra were determined and further classified as described by
Raunkiaer (1934).
Leptophyll (L): The leaf size is 25sq.mm.
Nanophyll (N): The leaf size is 225 sq.mm.
Microphyll (Mi): The leaf size is 2025sq.mm.
Mesophyll (Me): The leaf size is 18225sq .mm.
Macrophyll (Ma): The leaf size is 164025sq.mm
3.6 Methodology for Phytosociological Attributes
A line transect method was used for quantitative sampling (Buckland et al.,
2007), however, in some circumstances, like small survey plots, or when
plants are not easily detected by line transect method other methods were
used (Brown et al., 2011; Singh and Singh 2010). The study sites were
subdivided into stands for phytosociological data and points were taken at
20-meter intervals along 400 meters transects, however, in some area shorter
transects were also applied. The vegetative characteristics (density, relative
density, cover, relative cover, frequency, relative frequency and important
value index) of each stand were recorded. Importance value (Brown and
Curtis, 1952), were used to rank each species and the plant species with the
highest importance value in the stand were considered the dominant species.
The plant community was named on the basis of three dominant species
(Song, 1992).
33
3.6.1 Density
Density refers to the number of individuals of a species counted on a
sampling area and represents the numerical strength of a species in plant
community. It can be calculated as follows:
Density = Number of individuals
Total number of Transects
3.6.2 Relative Density
Relative density is the proportion of a density of a species to that of a stand as
a whole. It can be calculated as:
Relative Density = Density of a species ×100
Total density of all species
3.6.3 Frequency
Frequency is the percentage of sampling stands in which a given species
occurs and is concerned with the uniformity of occurrence of individual of a
species within an area. It can be calculated as follows:
Frequency = Number of transects in which a species occur ×100
Total number of Transects
3.6.4 Relative Frequency
Relative frequency is the proportion of a species to the sum of the frequency
of all the species in the area. It is determined by the following formula:
Relative Frequency = Frequency of a species ×100
Total frequency of all species
3.6.5 Average Cover
Coverage values are the rough measures of the degree of dominance of a
species in its layer. Each layer of vegetation is considered separately. Average
cover of a species can be determined as:
Average Cover = Total cover of a species
Total number of a species
34
3.6.6 Relative Cover
Relative cover of a species is the proportion of the total of a species to the sum
of the cover of all the plants of all species in the area. It can be calculated as
follows:
Relative Cover = Total cover of a species ×100
Total cover of all species
3.6.7 Importance Value Index
In heterogeneous plant community, data of density, cover and frequency of a
species do not give a clear picture about the dominant species. It can be
obtained by adding the values of relative density, relative frequency and
relative cover and dividing it by three will give the Importance value of the
species.
IVI = Relative Density + Relative Cover + Relative Frequency
3
3.7 Multivariate Analysis
Using classification and ordination techniques, multivariate analysis of the
ecological data was used to analyze data resulting from field observations and
experiments. The aim of this method is to find out the complex ecological
problems, such as the variation of biotic communities with environmental
conditions or the response of biotic communities to experimental
manipulation. For multivariate ordination analyses, Canoco 5 version, and
PC-ORD 6 were used. Ordination is the ordering of objects along axes
according to there resemblances. The major objective is to achieve an effective
data reduction, expressing many-dimensional relationships in a small number
of dimensions. This technique amounts to extracting the strongest correlation
structure in the data (using correlation in the broad sense). The correlation
structure is used to position objects in the ordination space. Objects close in
the ordination space are generally more similar than objects distant in the
ordination space.
35
To classify species and samples cluster analysis by TWINSPAN (Hill, 1979b)
was used. TWINSPAN is based on dividing a reciprocal averaging ordination
space. One of the most useful features of TWINSPAN is the final ordered two-
way table. Species names are arrayed along the left side of the table, while
sample numbers are along the top. The pattern of zeros and ones on the right
and bottom sides define the dendrogram of the classifications of species and
samples, respectively. The interior of the table contains the abundance class of
each species in each sample. Abundance classes are defined by pseudospecies
cut levels.
Detrended correspondence analysis (DCA; Hill and Gauch, 1980) is an
eigenanalysis ordination technique based on reciprocal averaging (RA; Hill
1973). DCA ordinates species and samples simultaneously. The DCA analysis
was used to investigate the relationship among vegetation types. Canonical
correspondence analysis (CCA, ter Braak, 1986, 1994) were used in the
ordination of main matrix (by reciprocal averaging) constrained by multiple
regression on variables included in the second matrix. In CCA method the
ordination of samples and species was constrained by their relationships to
environmental variables. Bray-Curtis Ordination (Polar ordination, Beals,
1984; McCune and Beals, 1993; McCune and Grace, 2002) was used for
ordination scores, endpoints, regression coefficient and variance in distance of
different axis.
3.8 Similarity Index
The community similarity was used for the comparison of all communities
within the study area and was calculated by using following formula after
Sorenson (1948).
Similarity Index = 2C ×100
A + B
Dissimilarity Index was calculated by: 100 –SI.
36
3.9 Diversity Index
The diversity index was used for the comparison of plant diversity at various
altitudes in different plant communities. The Shannon diversity index was
calculated using following formula:
Shannon Diversity Index =∑(p sub i × (ln p sub i))
Where p sub i = ni/N
3.10 Species Richness
Species richness is the number of different species represented in an
ecological stand, region or community. The species richness of each plant
community was calculated as per following formula:
Species Richness = Number of species
SQRT of total number of individuals
3.11 Environmental and Geographical Data
The data of slope aspect and slope angle were determined by clinometer. The
data of altitude, latitude and longitude were taken by GPS. The major
environmental conditions such as wind speed, temperature, chill, humidity,
heat index, dew point, wet bulb, barometric pressure and density altitude
were measured with the help of weather station (Kestrel 4000 weather
tracker).
3.12 Edaphic Factors
3.12.1 Soil Collection
Three kilogram soil samples were randomly collected from each stand from a
depth of 0-30cm. The soil samples were stored in polythene bag and labeled.
The soil samples were analyzed for different physico-chemical characteristics.
37
3.12.2 Soil Texture
Soil texture was determined by hydrometer method. The texture class was
determined with the help of textural triangle (Ghani and Amir, 2003).
3.12.3 Electrical Conductivity
The electrical conductivity of each sample was measured with the help of an
Electrical Conductivity meter (Khan et al., 2010).
3.12.4 Soil pH
The pH of each soil sample was determined by the help of pH Meter (Khan et
al., 2010).
3.12.5 Organic Matter
The organic matter concentration of each sample was calculated by Walkley
and Black’s titration method (Fonge et al., 2011).
3.12.6 Potassium and Phosphorus
The Potassium and Phosphorus concentration in each sample was determined
by atomic absorption spectrophotometer (Fonge et al., 2011).
38
Chapter 4
RESULTS
4.1.1 Diversity and Distribution of Plant Species
The study was carriedout to explore the biodiversity and phytosociology in
Nandiar Khuwar catchment area District Battagram Pakistan. A total of 324
vascular plants species belonging to 97 families was recorded in 80 stands
(Table- 4.2.1). 13 plant communities were recognized from sub tropical to
alpine zone of Nandiar Khuwar catchment. In the selected stands
angiosperms were represented by 84 families and 292 (90.1%) species,
gymnosperms were represented by 3 families and 7 (2.1%) species and
pteridophytes were represented by 10 families and 25 (7.7%) species (Table-
4.1.1). The dominant family was Asteraceae contributing 27 species, followed
by Rosaceae having 22 species, Labiatae (Lamiaceae) contributing 20 species;
Papilionaceae contributing 11 species, the Cyperaceae, Poaceae and
Ranunculaceae each contributing 10 species while Apiaceae were represented
by 9 (2.6%) species (Table- 4.1.2).
The maximum diversity index value was 4.18 and species richness value was
0.94 recorded in the moist temperate zone of the study area on north-facing
steep slope. Among life form phanerophytes were dominant by 118 (36.4%)
species, followed by therophytes having 82 (25.3%). The leaf size spectra were
dominated by microphyll with 137 (40.2%) species followed by mesophyll 103
(31.8%) species. The maximum flowering stages were recorded from April to
July (68.51%) while maximum fruiting stages were recorded from May to
August. The maximum frequencies were recorded for Fragaria nubicola (53)
and Adiantum capillus-veneris (53), followed by Pinus wallichiana (50), Viola
canescens (49), Berberis lyceum, Cynodon dactylon, Dryopteris jaxtapostia,
Indigofera heterantha, and Viburnum cotinifolium.
39
Table- 4.1.1 Share of various plant groups of Nandiar Khuwar catchment.
S. No Group Families Species Percentage
1 Angiosperms 84 292 90.12
2 Gymnosperms 3 7 2.16
3 Pteridophytes 10 25 7.71
Table-4.1.2 Dominant vascular plant familiesof Nandiar Khuwar catchment.
S. No Families Species Percentage
1 Asteraceae 27 8.33
2 Rosaceae 22 6.79
3 Labiatae (Lamiaceae) 20 6.17
4 Papilionaceae 11 3.39
5 Cyperaceae 10 3.09
6 Poaceae 10 3.09
7 Ranunculaceae 10 3.09
8 Apiaceae 9 2.78
9 Dryopteridaceae 9 2.78
10 Polygonaceae 6 1.85
40
4.1.2 Biodiversity Index (Shannon Index)
A diversity index is a mathematical measure of species diversity in a
community. Diversity indices provide important information about rarity and
commonness of species in a community. The Shannon diversity index for 80
stands along with altitude, latitude and longitude are presented in table 4.1.3.
The diversity index was recorded in 80 stands of Nandiar Khuwar Catchment
District Battagram between an elevation of 530 – 3780m on different slope
aspects and slope angles. The maximum Shannon diversity index (4.18) was
recorded in Rajmira on north facing moderate slopes at an elevation of 1488m
followed by Jaro (4.13) on north east facing slope at an elevation of 2222m,
Machaisar (3.88) on north facing steep slope at an elevation of 2899m and
Lekoni (3.86) on north east steep slope at an elevation of 2912m. The
minimum diversity index were recorded for the sub locality Kar Ganja (2.64)
on west facing steep slope at an elevation of 3265m due to harsh
environmental conditions, erosion and over grazing. The diversity index was
also low in Basha Khan (2.75) and Kiari (2.86) on west facing moderate steep
slopes.
4.1.3 Species Richness
Species richness is the number of different species represented in ecological
stand, region or community. The species richness data were recorded for 80
stands of Nandiar Khuwar Catchment between an elevation of 530 – 3780m
on different slope aspects and slope angles. The species richness value was
maximum for Rajmira (0.94) at an elevation of 1488m on north facing
moderate slopes, followed by Belandkot (0.77) on north facing moderate steep
slopes at an elevation of 1575m, Jaro (0.75) on north east facing slope at an
elevation of 2222m and Ledai (0.73) on north east facing slope at an elevation
of 2282m. The minimum species richness value was recorded for Basha Khan
(0.28) at an elevation of 1939m and Kiari (0.28) at an elevation of 1918m on
west facing moderate steep slopes. The detail species richness values of 80
stands are presented in table 4.1.3.
41
Table-4.1.3 Diversity index and species richness at different altitudes of Nandiar Khuwar catchment.
S. No
Name of Stands
Altitude Latitude Longitude Shannon Index
Species richness
1. Thakot I 1788 34º46΄.240 72º55΄.881 3.68 0.46
2. Thakot II 1808 34º46΄.187 72º55΄.838 3.46 0.40
3. Chorlangay 2422 34º44΄.467 72º56΄.731 3.58 0.69
4. Peshora 2568 34º42΄.820 72º58΄.131 3.58 0.62
5. Gajikot 3259 34º40΄.783 72º59΄.547 3.50 0.64
6. Shagai 3614 34º42΄.114 72º57΄.704 3.50 0.66
7. Paimal IV 3645 34º44΄.232 72º58΄.252 3.05 0.40
8. Naraza 3724 34º40΄.258 73º02΄.662 3.60 0.61
9. Paimal III 3800 34º43΄.898 72º58΄.532 3.39 0.43
10. Paimal II 3950 34º44΄.717 72º58΄.472 2.83 0.39
11. Lamai 4000 34º39΄.403 73º07΄.210 2.97 0.42
12. Khairabad 4140 34º37΄.100 72º59΄.515 3.21 0.49
13. Gada 4174 34º40΄.934 73º04΄.841 3.78 0.68
14. Nowshera 4224 34º40΄.229 72º59΄.833 3.17 0.41
15. Nili Reen 4316 34º36΄.609 73º05΄.161 2.76 0.45
16. Paimal V 4550 34º44΄.902 72º59΄.555 2.89 0.46
17. Paimal I 4680 34º42΄.057 72º59΄.943 2.49 0.36
18. Deshara 4684 34º35΄.405 73º02΄.384 2.75 0.35
19. Rajmira 4709 34º42΄.250 73º04΄.200 4.18 0.94
20. Paimal Dabrai 4963 34º43΄.907 72º58΄.977 2.79 0.35
21. Shabora I 4963 34º38΄.505 73º01΄.158 3.54 0.67
22. Shabora II 5014 34º39΄.007 73º02΄.061 2.95 0.39
23. Lundai I 5083 34º41΄.304 72º57΄.081 3.17 0.43
24. Nilishung 5089 34º37΄.188 73º06΄.356 2.83 0.39
25. Belandkot 5300 34º35΄.350 72º58΄.899 3.64 0.77
26. Anora 3 5320 34º41΄.382 73º07΄.299 3.53 0.66
27. Batangi 5328 34º41΄.999 73º02΄.295 3.27 0.48
28. Nil Batangi 5500 34º36΄.662 73º06΄.080 2.79 0.40
42
29. Lundai II 5500 34º41΄.009 72º56΄.759 3.88 0.53
30. Kiari 5519 34º35΄.521 73º00΄.317 2.86 0.28
31. Basha Khan 5578 34º35΄.874 73º00΄.305 2.75 0.28
32. Gat 5580 34º43΄.717 73º05΄.428 3.59 0.70
33. Shinglai 5640 34º42΄.198 73º02΄.453 3.47 0.65
34. Anora II 5699 34º41΄.282 73º07΄.525 3.59 0.53
35. Anora I 5704 34º41΄.167 73º07΄.509 3.15 0.47
36. Chapra 5900 34º43΄.200 73º07΄.319 3.36 0.54
37. Jarotia 6012 34º43΄.912 73º05΄.634 3.42 0.55
38. Chapar 6036 34º43΄.820 73º04΄.312 3.60 0.70
39. Habib Banda II 6150 34º43΄.016 73º03΄.333 3.60 0.49
40. Habib Banda I 6200 34º43΄.598 73º03΄.668 3.39 0.52
41. Bach Maidan 6200 34º42΄.004 73º10΄.269 3.38 0.54
42. Hill 6239 34º41΄.173 73º08΄.593 3.10 0.48
43. Jatial 6267 34º39΄.258 73º06΄.562 3.33 0.54
44. Sandawali 6500 34º43΄.294 73º07΄.497 3.47 0.54
45. Sharkolay 6711 34º38΄.565 73º06΄.644 3.16 0.50
46. Riar 6812 34º43΄.385 73º03΄.815 3.62 0.59
47. Doda I 6888 34º43΄.374 73º07΄.756 3.25 0.47
48. Sarmast 6892 34º38΄.642 73º06΄.681 3.17 0.47
49. Mirani Kandao 7034 34º44΄.016 73º03΄.533 3.38 0.57
50. Sheed 7255 34º44΄.074 73º05΄.539 3.41 0.66
51. Jaro 7362 34º43΄.795 73º04΄.353 4.13 0.75
52. Guchai 7367 34º44΄.212 73º10΄.416 3.64 0.57
53. Ledai 7500 34º44΄.443 73º05΄.302 3.57 0.73
54. Terkana 7701 34º41΄.074 73º09΄.280 3.08 0.41
55. Charoona 7746 34º44΄.831 73º05΄.353 3.46 0.56
56. Manra 7772 34º34΄.204 72º57΄.052 3.22 0.50
57. Buch upper 7876 34º42΄.104 73º10΄.569 3.31 0.49
58. Trapa 7895 34º44΄.985 73º05΄.301 3.46 0.62
43
59. Lunda Matra 7940 34º42΄.138 73º10΄.610 3.23 0.49
60. Doba 8000 34º44΄.515 73º03΄.365 3.40 0.47
61. Chail Kambar 8800 34º45΄.063 73º05΄.274 3.71 0.70
62. Gabrai Kandao 8920 34º45΄.002 73º05΄.202 3.79 0.68
63. Mirani I 9060 34º44΄.715 73º04΄.265 3.47 0.68
64. Baleja 9082 34º42΄.238 73º11΄.018 3.05 0.40
65. Chaprai 9092 34º41΄.465 73º10΄.536 3.28 0.45
66. Birth Maidan 9230 34º44΄.150 73º08΄.725 3.26 0.44
67. Harpal 9571 34º44΄.655 73º03΄.122 3.77 0.64
68. Doda II 9735 34º44΄.410 73º08΄.679 3.35 0.43
69. Kachkol 9768 34º42΄.890 73º11΄.863 3.76 0.53
70. Mirani II 9771 34º44΄.805 73º04΄.604 3.72 0.68
71. Machai sar 9820 34º35΄.009 72º54΄.759 3.88 0.53
72. Belmaz 9891 34º45΄.126 73º10΄.165 3.62 0.54
73. Lekoni 9998 34º44΄.687 73º08΄.880 3.86 0.45
74. Karganja L 10022 34º44΄.587 73º08΄.820 2.89 0.32
75. Chail 10138 34º44΄.987 73º04΄.719 3.83 0.64
76. Magrai 10220 34º45΄.003 73º11΄.242 3.44 0.54
77. Shaheed Gali 10238 34º44΄.874 73º08΄.974 2.78 0.31
78. Kar Ganja H 11172 34º45΄.356 73º09΄.372 2.64 0.34
79. Alishera 11863 34º45΄.463 73º11΄.882 2.27 0.35
80. Malkaisar 12400 34º45΄.012 73º11΄.999 2.25 0.33
44
4.2 Phenology, Life form and Leaf Spectra
4.2.1 Phenology
Phenology refers to the appearance of various plants at different seasons of
the year. The timing of different phenological events (flowering) is related to
environmental variables such as temperature. The changes in environments
therefore lead to changes in life-cycle events. Different phenological stages of
life-cycle were determined in spring, summer and autumn in different plant
stands of Nandiar Khuwar catchment District Battagram. The flowering and
fruiting stages of life-cycle of 324 vascular plant species were recorded in
spring, summer and autumn. The maximum flowering stages were recorded
from April-July (68.51%) and fruiting stages were recorded from May-August
(77.53%). The minimum flowering and fruiting stages were recorded during
the months of November, December, January and February. In lower altitudes
and open slopes the blooming of flowers started first while at higher altitudes
and shady places the blooming were delayed. In winter all of the higher
altitudinal zones of Nandiar Khuwar catchment are covered with snow,
therefore the phenology in winter season was excluded however in the
subtropical and temperate zones only few plants were recognized in
flowering and fruiting conditions. The phenology of different plant species
also depends on temperature, sunlight, rainfall, soil moisture and
atmospheric humidity. The details of phenology, life form and leaf spectra of
different plant species of Nandiar Khuwar catchment are presented in table
4.2.1.
4.2.2 Life Form
Life form is the indicator of climate (micro and macroclimate) and can be used
in comparing geographically widely distributed plant communities. Life form
is the characteristic vegetative appearance of the plant body and its longevity.
The general appearance of a community is caused more by the life form of the
most dominant species, then by any other characteristic of the vegetation. Life
form reflects the adaptation of plant species to the climate and the relative
45
proportion of different life form for a given area is called its biological
spectrum. In Nandiar Khuwar catchment out of 324 species the biological
spectra were dominated by phanerophytes contributing 118 (36.41%) species,
indicating that environmental conditions are well suited for phanerophytes.
The phanerophytes are followed by therophytes contributing 82 (25.30%)
species, indicating that in most of the study area severe deforestation,
overgrazing, soil erosion and human influence has reduced the
phanerophytes population and as a result therophytes appeared to occupy the
vacant niches in Nandiar Khuwar catchment. These are followed by
geophytes with 47 (14.50%) species, chamaephytes by 42 (12.96%) species and
hemi-cryptophytes by 35 (10.82%) species. Among phanerophytes the
nanophanerophytes were represented by 55 (16.98%) species,
microphanerophytes with 33 (10.18%) species, mesophanerophytes with 23
(67.09%) species and megaphanerophytes were represented by 7 (2.16%)
species. The detail of life form of different species is presented in table 4.2.1.
4.2.3 Leaf Size Spectra
Leaf size classes have been found to be very useful for plant associations. The
leaf size knowledge helps in understanding physiological processes of plants
and plant communities. There is consistent variation of leaf, leaf size and
texture between individual plant communities and in various climatic
conditions. In Nandiar Khuwar Catchment a total of 324 plant species were
analyzed for leaf size spectra. The leaf size spectra were dominated by
microphyll contributing 137 (40.28%) species, followed by mesophyll
contributing 103 (31.79%) species, nanophyll by 69 (21.29%) species,
macrophyll by 12 (3.70%) species and leptophyll by 02 (0.61%) plant species.
The dominance microphyll and mesophyll indicates that a large part of
Nandiar Khuwar catchment receive a high amount of rain fall, having
moderate temperature and moist condition. The detail of leaf size spectra of
different species are shown in table 4.2.1.
46
Table- 4.2.1 Phenology, Life form and Leaf spectra of different taxa collected from Nandiar Khuwar catchment.
Botanical name Family Flowering /Fruiting
Life Form
Leaf Spectra
Abies pindrow Royle Pinaceae Apr – May MAP Na
Acacia modesta Wall. Mimosaceae Apr – June MIP Mi
Acer cappadocicum Gled. Aceraceae Mar – May MEP Me
Achillea millefolium L. Asteraceae July – Sept CH Mi
Achyranthes aspera L. Amaranthaceae July – Sept TH Mi
Achyranthes bidentata Blume Amaranthaceae July – Sept TH Mi
Adiantum capillus-veneris L. Adiantaceae Sept – Dec GE Mi
Adiantum incisum Forssk Adiantaceae Sept – Dec GE Mi
Adiantum venustum D. Don Adiantaceae Sept – Dec GE Mi
Aegopodium burttii E. Nasir Apiaceae July – Sept TH Mi
Aesculus indica (Wall.ex. Cambess) Hook.f.
Hippocastinaceae June – Sep MEP Mac
Agrimonia eupatoria L. Rosaceae June – Aug TH Me
Ailanthus altissima (Mill.) Swingle Simaroubaceae Apr – May MEP Mac
Ajuga bracteosa Wall.ex Benth Labiatae Mar – June TH Me
Albezia lebbeck (L.) Benth. Mimosaceae Mar – May MEP Me
Alliaria petiolata (M. Bieb.) Cavara. Brassicaceae Apr – June TH Mi
Allium filidens Regel. Alliaceae Apr – June GE Mi
Alnus nitida (Spach.) Endl. Betulaceae Aug – Oct MEP Me
Alotis stoliczkai Clarke Gentianaceae July – Sept TH Na
Anagalis arvensis L. Primulaceae Apr – June TH Na
Anaphalis busa (Buch.-Ham. ex D.Don) DC.
Asteraceae July – Sept TH Na
Andrachne cordifolia Hemsl. Euphorbiaceae Apr – July NAP Mi
Andropogon sp. Poaceae Aug – Sept HC Mi
Androsace hazarica Nasir Primulaceae Apr – June TH Mi
Androsace rotundifolia Hardw. Primulaceae May – July TH Mi
Apluda mutica L. Poaceae July – Oct HC Mi
Aquilegia pubiflora Wall. ex Royle Ranunculaceae May – Aug TH Me
Arabis bijuga Watt. Brassicaceae Mar – May TH Na
Arisaema flavum (Forssk.) Schott Araceae June – Sept GE Mac
Aristida sp. Poaceae July – Oct HC Mi
Artemisia japonica Schmidt Asteraceae July – Sept TH Na
Artemisia roxburghiana Wall. Asteraceae July – Sept GE Me
Artemisia vulgaris L. Asteraceae July – Sept GE Me
Asparagus filicinus Buch. –Ham. ex. D.Don
Asparagaceae May – June GE Mi
Asplenium adiantum-nigrum L. Aspleniaceae Sept – Nov GE Mi
Asplenium cordatum G. Forst. Aspleniaceae Sept – Nov GE Mi
47
Asplenium cunifolium Altunat. Aspleniaceae Sept – Nov GE Mi
Asplenium dalhousiae Hook. Aspleniaceae Sept – Nov HC Mi
Asplenium trichomonas L. Aspleniaceae Sept – Nov GE Mi
Aster himalaicus C.B.Clarke Asteraceae Aug – Oct TH Mi
Astragalus ammophilus Karelin. Papilionaceae Feb – Apr TH Me
Astragalus graveolens Buch. Papilionaceae Mar – Apr TH Me
Astragalus leucocephalus Grah.ex Benth.
Papilionaceae Apr – Aug TH Me
Astragalus sp. Papilionaceae Mar – Apr TH Mi
Bauhinia variegata L. Caesalpinaceae Mar – Apr MIP Me
Berberis lycium Royle Berberidaceae Mar – June NAP Na
Berberis sp. Berberidaceae July – Sept NAP Na
Bergenia ciliata Sternb. Saxifragaceae Apr – June GE Me
Betula utilis D. Don Betulaceae Apr – June MIP Mi
Bistorta amplexicaulis (D. Don) Greene.
Polygonaceae June – Aug HC Me
Bistorta emodi (Meisn) Hara. Polygonaceae July – Sept CH Mi
Bombax ceiba L. Bombaceae Mar – Apr MAP Mac
Bupleurum hazaricum Nasir Apiaceae June – Aug TH Na
Bupleurum longicaule Wall .ex DC. Apiaceae July – Sept TH Na
Caltha alba L. Ranunculaceae Apr – July HC Me
Carex cardiolepis Nees. Cyperaceae Mar – May GE Mi
Carex foliosa D. Don Cyperaceae Mar – June GE Mi
Carex sanguine Boott. Cyperaceae June – Aug GE Mi
Carex serotina Merat. Cyperaceae Apr – Sept GE Mi
Caryopteris grata Benth. Verbenaceae Mar – May NAP Mi
Carpesium abrotanoides L. Asteraceae July – Sept TH Mi
Carpesium nepalense Less. Asteraceae July – Sept TH Mi
Cassia tora L. Caesalpinaceae Aug – Sep TH Mi
Catharanthus roseus G. Don Apocynaceae May – June TH Mi
Cedrus deodara (Roxb. ex D. Don) G. Don
Pinaceae Sept – Oct MEP Na
Celtis australis L. Ulmaceae Apr – June MEP Mi
Cephalanthera longifolia (L.) Fritsch Orchidaceae May – Aug GE Mi
Chaerophyllum sp. Apiaceae Mar – May CH Me
Cheilanthes anceps Blanf. Adianthaceae Sept – Dec HC Mi
Cheilanthus dalhousiae Hook.f. Adianthaceae Sept – Dec HC Mi
Chelanthus sp. Adianthaceae Sept – Dec HC Mi
Chenopodium album L. Chenopodiaceae May – Aug TH Mi
Ciminalis karelinii (Griseb.) Omer Gentianaceae Aug – Sept HC Na
Circium falconeri (Hook. f.) Petrak. Asteraceae June – Sept TH Me
Cirsium vulgare (Savi) Ten. Asteraceae June – Sept TH Me
Clematis connata D.C. Ranunculaceae Aug – Oct MIP Me
48
Clematis grata Wall. Ranunculaceae July – Sept MIP Me
Clematis montana Buch. Ranunculaceae Apr – May MIP Me
Colebrookea oppositifolia Smith Labiatae Apr – May NAP Me
Conyza canadensis L. Cronquist. Asteraceae July – Aug TH Na
Cornus macrophylla Wall. Cornaceae Apr – June MEP Me
Corydalis sp. Fumariaceae May – June TH Me
Cotinus coggyria Scop. Anacardiaceae Apr – June NAP Mi
Cotoneaster integerrima Medic. Rosaceae Apr – May NAP Na
Cotoneaster microphylla Wall. ex Lindl.
Rosaceae Apr – May NAP Na
Cotoneaster nummularia Fish and Mey
Rosaceae Apr – May NAP Na
Cuscuta gigantea Griff. Cuscutaceae Apr – June NAP Aph
Cynodon dactylon (L.) Pers. Poaceae June – Sept CH Na
Cynoglossum lanceolatum Forssk. Boraginaceae June – Aug TH Na
Cyperus iria L. Cyperaceae July – Sept HC Mi
Cyperus longus L. Cyperaceae June – Sept CH Mi
Cyperus niveus Retz. Cyperaceae May – July HC Mi
Cyperus sp. Cyperaceae May – July CH Mi
Cystopteris fragilis (L.) Benth. Aspidaceae May – July GE Mi
Dalbergia sissoo Roxb. Papilionaceae Mar – May MEP Mi
Daphne mucronata Royle Thymelaeaceae Apr – May NAP Na
Daphne papyracea Wall .ex G.Don Thymelaeaceae Nov – Apr NAP Na
Datura innoxia Mill. Solanaceae Apr – Aug TH Me
Datura stramonium L. Solanaceae May – Sept TH Me
Debregessia salcifolia (D. Don) Rendle
Urticaceae Mar – May MIP Me
Delphinum vestitum Boiss. Ranunculaceae Aug – Sept TH Mi
Desmodium elegans DC. Papilionaceae June – Aug NAP Me
Deutzia staminea R. Br .ex Wall. Philadelphaceae Apr – June NAP Mi
Dicliptra bupleorides Nees. Acanthaceae Apr – June TH Mi
Dioscorea deltoidea Wall. ex Griseb. Dioscoreaceae Apr – July HC Mi
Dioscorea melanophyma Prain & Burkill.
Dioscoreaceae Apr – July HC Mi
Diospyros lotus L. Ebenaceae Aug – Sept MEP Me
Dodonaea vescosa (L.) Jacq. Sapindaceae Aug – Feb NAP Mi
Dryopteris blandfordi Hope. Dryopteridaceae Sept – Dec HC Me
Dryopteris jaxtapostia Chirst Dryopteridaceae Sept – Dec HC Me
Dryopteris macdonellii Fraser. Dryopteridaceae Sept – Dec HC Mac
Dryopteris serrate-dentata (Bedd.) Hay
Dryopteridaceae Sept – Dec HC Me
Dryopteris wallichiana (Spring.) Hyl.
Dryopteridaceae Sept – Dec HC Me
Duchesnea indica (Andr.) Teschem. Rosaceae Apr – May CH Me
49
Duhaldea cappa Anderb. Asteraceae June – Sep NAP Me
Ehretia serrata Roxb. Boraginaceae Mar - May MEP Me
Elaeagnus umbellata Thunb. Elagnaceae Mar – June NAP Mi
Eleocharis umiglumis (Link) Schult. Cyperaceae Apr – May HC Na
Elsholtzia fruticosa (D. Don) Rehd. Labiatae Aug – Oct TH Mi
Elsholtzia strobilifera Benth. Labiatae July – Oct HC Mi
Epilobium rhychospermum Hausskn. Onagraceae July – Aug CH Na
Equisetum arvense L. Equisetaceae Sept – Oct GE Mi
Equisetum hiemale L. Equisetaceae Sept – Oct GE Mi
Eucalyptus globulus Labill. Myrtaceae Mar – May MEP Mi
Euphorbia cognata Boiss. Euphorbiaceae May – June CH Na
Euphorbia indica Lam. Euphorbiaceae July – Sept CH Na
Euphorbia wallichii Hook.f. Euphorbiaceae Apr – June CH Na
Ficus benghalensis L. Moraceae Apr – Nov MIP Me
Ficus carica L. Moraceae June – Sept MIP Me
Ficus palmata Forssk. Moraceae June – Sept MIP Me
Ficus racemosa L. Moraceae Apr – July MIP Me
Ficus sarmentosa Buch. Moraceae Apr – Aug NAP Mi
Filipendula vestita Maxim. Rosaceae July – Aug TH Me
Fragaria nubicola (Hook.f.) Lindl. Rosaceae Apr – July TH Mi
Gagea setifolia Baker. Liliaceae Mar – May GE Na
Galium aparine L. Rubiaceae July – Sep TH Na
Gentiana sp. Gentianaceae Apr – July TH Na
Gentianodes pedicellata (D. Don) Omer.
Gentianaceae May – Aug TH Na
Geranium collinum Steph .ex Willd. Geraniaceae July – Sept TH Mi
Geranium lucidum L. Geraniaceae Apr – May TH Mi
Geranium rotundifolium L. Geraniaceae May – Sept GE Me
Geranium wallichianum D. Don Geraniaceae May – Sept GE Me
Geum roylei Bolle. Rosaceae June – Aug CH Mi
Girardinia palmata Blume Urticaceae July – Sept TH Me
Grewia optiva Drum.ex Burret. Tiliaceae Apr – June MIP Me
Gymnosporia royleana Wall.ex Lawson.
Celastraceae Mar – May NAP Mi
Hedera nepalensis K. Koch. Araliaceae May – Aug MIP Mi
Heliotropium cabulicum Bunge. Boraginaceae July – Oct TH Na
Heracleum cachemiricum C.B. Clarke
Apiaceae June – Sep TH Na
Heteropogon contortus (L.) Beauv. Poaceae Aug – Oct HC Mi
Himalrandia tetrasperma (Wall. ex Roxb.) Yamaz.
Rubiaceae Apr – July NAP Na
Hypericum oblongifolium Choisy Guttiferae Feb – Apr NAP Mi
Hypericum perforatum L. Guttiferae May – Aug TH Na
50
Impatiens bicolor Royle Balsaminaceae June – Sept TH Me
Impatiens brachycentra Kar. Balsaminaceae June – Aug TH Mi
Impatiens edgeworthii Hook.f. Balsaminaceae July – Oct TH Mi
Impatiens sulcata Wall. Balsaminaceae July – Sept TH Mi
Indigofera heterantha Well.ex Brandis
Papilionaceae Apr – June NAP Mi
Inula acuminata Royle .ex DC. Asteraceae July – Sept TH Mi
Inula royleana DC. Asteraceae July – Sept GE Me
Isodon coetsa (Buch. Ham .ex D. Don) Kudo
Labiatae Apr – June NAP Mi
Isodon rugosus (Wall.ex Benth.) Codd.
Labiatae Mar – Apr NAP Mi
Jasminum humile L. Oleaceae Apr – June NAP Mi
Jasminum sp. Oleaceae Apr – May NAP Mi
Juglans regia L. Juglandanceae Feb – Apr MEP Me
Juncus sp. Juncaceae Sept – Nov CH Mi
Juniperus communis L. Cupressaceae Apr – June NAP Le
Justicia adhatoda L. Acanthaceae Feb – Apr NAP Me
Lallemantia royleana Benth. Labiatae July – Sept CH Na
Lamium album L. Labiatae Apr – Aug TH Mi
Lathyrus aphaca L. Papilionaceae Apr – May CH Na
Launea procumbens Roxb. Asteraceae Apr – May CH Mi
Leontopodium brachyoctis Gand. Asteraceae July – Sept CH Na
Leucostegia pulchra D. Don Davalliaceae Sept – Dec GE Me
Lindelofia stylosa Brand. Boraginaceae June – Aug TH Na
Litsea sp. Lauraceae Apr – June MIP Me
Lonicera quinquelocularis Hard. Caprifoliaceae Apr – July NAP Mi
Lotus corniculatus L. Papilionaceae Apr – June TH Na
Lygodium hazaricum Haq. Schizaeaceae Sept – Dec NAP Me
Lyonia ovalifolia (Wall.) Drude Ericaceae Apr – May MIP Me
Mallotus philippensis (Lam.) Mull. Euphorbiaceae Feb – Apr MIP Me
Malva sp. Malvaceae Aug – Oct CH Mi
Marrubium vulgare L. Labiatae May – Aug TH Mi
Medicago denticulata Willd. Papilionaceae Apr – June TH Na
Melia azedarach L. Meliaceae Mar – June MIP Me
Michelia sp. Magnoliaceae May – Aug MIP Me
Micromeria biflora (Buch.ex D. Don) Benth.
Labiatae Mar – June TH Le
Myrsine africana L. Myrsinaceae Mar – Apr NAP Na
Nepeta cataria L. Labiatae June – Sept CH Mi
Nerium indicum Mill. Apocynaceae Apr – May NAP Mi
Notholirion thomsonianum (Royle) Stapf.
Liliaceae Mar – Apr GE Mi
51
Oenothera affinis Camb. Onagraceae Apr – Aug TH Mi
Oenothera rosea L. Onagraceae Apr – July TH Na
Olea ferruginea Royle Oleaceae Apr – June MIP Na
Onopordum acanthium Linn. Asteraceae Apr – July TH Mi
Ophiopogon intermedius D. Don Liliaceae June – July CH Mi
Origanum vulgare L. Labiatae June – Sept TH Na
Otostegia limbata (Benth.) Boiss. Labiatae Mar – Apr NAP Na
Oxalis corniculata L. Oxalidaceae Mar – June TH Na
Paeonia emodi Wall. ex Royle Paeoniaceae Apr – June GE Mac
Panicum maximum Jacq. Poaceae Aug – Oct HC Me
Panicum sp. Poaceae Aug – Oct HC Mi
Phlomis bracteosa Royle .ex Benth. Labiatae July – Aug GE Mi
Phlomis rotata Royle ex Benth. Labiatae June – Aug GE Mi
Phytolacca latbenia (Moq.) H. Walt. Phytolaccaceae July – Sept GE Me
Picea smithiana (Wall.) Boiss. Pinaceae Apr – May MAP Na
Picris hieraciodes L. Asteraceae Apr – June HC Mi
Pinus roxburghii Surg. Pinaceae Apr May MAP Na
Pinus wallichiana A.B. Jack. Pinaceae May – June MAP Na
Pistacea integerrima J. L. Stewart Anacardiaceae Mar – Apr MEP Mac
Plantago lanceolata L. Plantaginaceae Apr – Aug TH Mi
Pleurospermum brunonis (D.C.)C.B. Apiaceae July – Aug HC Mi
Poa sp. Poaceae July – Sept HC Na
Podophyllum emodi Wall .ex Hook.f Podophyllaceae Apr – June GE Me
Polygonatum verticillatum (L.) All. Liliaceae Apr – June GE Mi
Polystichum lonchitis (L.) Roth. Dryopteridaceae Sept – Dec GE Mi
Populus ciliata Wall. Salicaceae Mar – Apr MAP Me
Populus euro-americana L. Salicaceae Mar – May MAP Me
Potentilla gerardiana Lindl. Rosaceae May – July HC Me
Potentilla nepalensis Hook.f. Rosaceae July – Aug HC Me
Potentilla sericophylla Parker. Rosaceae Apr – May NAP Mi
Potentilla sp. Rosaceae June – Aug HC Me
Primula denticulata Wight. Primulaceae Apr – June HC Me
Prunella vulgaris L. Labiatae May – Aug CH Mi
Prunus padus Hook.f. Rosaceae Apr – June MEP Me
Pseudognaphalium hypolecum (DC.) O. M.
Asteraceae July – Sept TH Na
Pseudognaphalium luteo album (L.) Hill.
Asteraceae July – Sept TH Na
Pseudomertensia sp. Boraginaceae May – June TH Na
Pteracanthus urticifolius Bremek Acanthaceae July – Oct CH Mi
Pteridium equilinum (L.) Kuhn. Pteridaceae Sept – Dec GE Mac
Pteris cretica L. Pteridaceae Sept – Dec GE Me
Pteris longifolia L. Pteridaceae Sept – Dec GE Me
52
Pycreus flavidus (Retz) T. Koyama Cyperaceae July – Sept HC Mi
Pyrus pashia L. Rosaceae Mar – Apr MIP Mi
Quercus baloot Griff. Fagaceae Apr – May MIP Mi
Quercus dilatata Lindl. Fagaceae Apr – May MEP Mi
Quercus glauca Thunb. Fagaceae Apr – May MEP Me
Quercus incana W. Bartram. Fagaceae Apr – May MEP Me
Quercus semicarpifolia Smith. Fagaceae May – June MEP Me
Ranunculus hirtellus Royle Ranunculaceae May – Aug TH Mi
Ranunculus laetus Wall .ex Hook.f. Ranunculaceae May – Aug TH Mi
Ranunculus palmatifidus Riedl. Ranunculaceae Apr – Aug TH Mi
Rananculus sp. Ranunculaceae May – June TH Mi
Rhamnus virgata Roxb. Rhamnaceae Apr – July MIP Mi
Rheum australe D. Don Polygonaceae June – July HC Me
Rhododendron arboreum Smith. Ericaceae Mar – May MIP Me
Rhus himalica J. D. Hook. Anacardiaceae Aug – Sept MIP Mac
Rhus javanica L. Anacardiaceae Aug – Sept MIP Mac
Ribes sp. Grossulariaceae July – Sept NAP Me
Ricinus communis L. Euphorbiaceae Jan –Mar MIP Mac
Robinia pseudoacacia L. Papilionaceae Mar – May MEP Me
Rosa moschata J. Herm. Rosaceae Apr – June MIP Me
Rosa sp. Rosaceae May – July MIP Me
Roscoea alpina Royle Orchidaceae June – July GE Na
Rubia cordifolia L. Rubiaceae July – Oct TH Na
Rubus ellipticus Smith. Rosaceae Feb – May NAP Me
Rubus fructicosus Hook.f. Rosaceae Apr – June NAP Me
Rubus ulmifolius Schott. Rosaceae May – July NAP Me
Rumex dentatus L. Polygonaceae Apr – June GE Me
Rumex hastatus D. Don Polygonaceae May – July NAP Mi
Rumex nepalensis Spreng. Polygonaceae June – Aug GE Me
Sageretia thea (Osbeck) M.C.Johnst Rhamnaceae Apr – July NAP Na
Salix calyculata Hook.f. Salicaceae June – July NAP Mi
Salix tetrasperma Roxb. Salicaceae Apr – May MEP Mi
Salvia lanata Roxb. Labiatae Apr – June CH Mi
Salvia sp. Labiatae Apr – June HC Me
Sarcococca saligna (Don) Mull. Buxaceae Sept – May NAP Mi
Sassuria sp. Asteraceae Apr – May HC Me
Saxifraga engleriana Smith. Saxifragaceae July – Aug CH Na
Scutellaria chamaedrifolia Hedge. Labiatae May – July CH Na
Sedum ewersii Ledeb. Crassulaceae July – Oct CH Na
Sedum sp. Crassulaceae July – Oct CH Na
Selaginella sanguinolenta Spring. Selaginellaceae July – Aug HC Le
Selinum vaginatum (Edgew.) Apiaceae July – Sept HC Mi
53
Clarke
Senicio sp. Asteraceae July – Sept TH Mi
Sibbaldia cuneata Hornum. Rosaceae June – Aug CH Na
Sium latijugum C.B. Clarke Apiaceae May – July HC Mi
Skimmia laureola DC. Rutaceae Apr – May NAP Mi
Smilax glaucophylla Klotzch Smilicaceae July – Aug NAP Mi
Solanum surattense Burm.f. Solanaceae Apr – May CH Mi
Solena amplexicaulis (Lam.) Gandhi Cucurbitaceae July – Sept GE Mi
Sonchus asper (L.) Hill. Asteraceae Apr – July CH Mi
Sorbaria tomentosa Lindl. Rosaceae June – Aug MIP Me
Spiraea vaccinifolia D. Don Rosaceae Mar – July NAP Mi
Stellaria media (L.) Vill. Caryophyllaceae Mar – Apr CH Na
Swertia paniculata Wall. Gentianaceae Aug – Sept CH Na
Syzygium sp. Myrtaceae Apr – May NAP Mi
Tagetes minuta L. Asteraceae Aug – Sept TH Me
Tanacetum dolicophyllum Kitam Asteraceae July – Sept CH Mi
Taraxiacum officinale Weber Asteraceae Mar – Apr TH Me
Taxus wallichiana Zuce. Taxaceae Mar – May MIP Na
Themeda anathera (Nees.ex Steud.) Hack.
Poaceae Aug – Oct HC Mi
Thlaspi sp. Brassicaceae Apr – June TH Na
Thymus linearis Benth. Labiatae Apr – Sept CH Na
Torilis japonica (Houtt.) DC. Apiaceae June – Aug GE Mi
Trachelospermum lucidum (D. Don) Schum.
Apocynaceae Apr – July NAP Me
Trillium govanianum Wall. ex Royle Liliaceae May – June GE Me
Tulipa stellata Hook.f. Liliaceae Mar – Apr GE Mi
Ulmus villosa Brandes ex Gamble Ulmaceae Jan – Mar MEP Mi
Ulmus wallichiana Planch. Ulmaceae Mar – Apr MEP Mi
Urtica dioica L. Urticaceae Aug – Sept TH Mi
Valeriana himalayana Grub. Valerianaceae May – June CH Na
Valeriana jatamansi Jones. Valerianaceae Mar – June GE Me
Verbascum thapsus L. Scrophulariaceae May – Sept HC Me
Veronica laxa Benth. Scrophulariaceae June – Aug TH Mi
Veronica persica Poir. Scrophulariaceae Apr – May TH Mi
Viburnum cotinifolium D.Don Caprifoliaceae Apr – May MIP Me
Viburnum grandiflorum Wall.ex DC.
Caprifoliaceae Apr – May MIP Me
Viburnum mullaha Buch. - Ham. ex D.Don
Caprifoliaceae June – Aug NAP Me
Viola canescens Wall. Violaceae Mar – Apr TH Me
Viola odorata L. Violaceae Mar – Apr TH Me
Vitex negundo L. Verbenaceae Apr – Oct NAP Me
54
Wikstroemia canescens Wall.ex Meisn.
Thymelaeaceae May – Aug NAP Mi
Withania somnifera (L.) Dunal Solanaceae July – Oct NAP Me
Woodfordia fruticosa (L.) Kurz Lythraceae Feb – Apr NAP Me
Woodwordia sp. Blechnaceae Sept – Dec GE Mac
Wulfenia amherstiana Wall.ex Bth. Scrophulariaceae July – Aug GE Na
Xanthium stromarium L. Asteraceae Aug – Oct TH Me
Xylosma sp. Salicaceae Apr – June NAP Me
Zanthoxylum armatum DC. Rutaceae Apr – May MIP Me
Ziziphus oxyphylla Edgew. Rhamnaceae Apr – May NAP Na
MAP: Megaphanerophytes MEP: Mesophanerophytes MIP: Microphanerophytes NAP: Nanophanerophytes CH: Chamaephytes HC: Hemi-cryptophytes GE: Geophytes TH: Therophytes Ma: Macrophyll Me: Mesophyll Mi: Microphyll Na: Nanophyll Le: Leptophyll Table- 4.3.1 Similarity and dissimilarity indices of different vegetational zones.
STF PPF PF MCF APF AS
STF
29.97 23.81 11.5 8.58 1.03
PPF 70.03
18.79 17.83 12.86 3.59
PF 76.19 81.21
26.69 23.79 3.8
MCF 88.5 82.17 73.31
37.83 17.1
APF 91.42 87.14 76.21 62.17
16.89
AS 98.97 96.41 96.2 82.9 83.11
STF: subtropical forests; PPF: Pinus Pinus forests; PF: Pinus wallichiana forests; MCF: mixed coniferous forests; APF: Abies Picea forests; AS: Alpine scrub zone.
55
4.3 Similarity and Dissimilarity Indices
The community similarity and dissimilarity were used for the comparison of
all communities within the study area. The similarity and dissimilarity indices
were recorded for vegetational zones as well as for plant communities of
Nandiar Khuwar catchment area.
4.3.1 Similarity and Dissimilarity Indices of Vegetational Zones
The maximum similarity index (37.83%) was recorded between mixed
coniferous forests and pure Abies pindrow and Picea smithiana forests, followed
by subtropical forests and Pinus Pinus forests (29.97). The maximum
dissimilarity index (98.97%) was recorded between subtropical forests and
alpine scrub followed by Pinus Pinus forests and alpine scrub (96.41). The
similarity and dissimilarity indices between different vegetational zones are
presented in table 4.3.1.
4.3.2 Similarity and Dissimilarity Indices of Plant Communities
The similarity and dissimilarity indices were also recorded between different
plant communities. The maximum similarity index (35.7%) were recorded for
Wikstroemia, Viburnum, Androsace community and Juniperus, Sibbaldia, Primula
community, followed by Abies, Picea, Pinus community and Pinus, Abies,
Wikstroemia community (32.9%), and Pinus, Quercus, Pinus community and
Pinus, Quercus, Indigofera community, (30.7%). The dissimilarity index of
Juniperus, Sibbaldia, Primula community were 100% with three different plant
communities Acacia, Dodonaea, Dalbergia community, Pinus, Cynodon, Rubus
community, and Quercus, Dodonaea, Myrsine community. The dissimilarity
index were 99.2% between Quercus, Dodonaea, Myrsine community and
Wikstroemia, Viburnum, Androsace community, and between Pinus, Quercus,
Indigofera community and Juniperus, Sibbaldia, Primula community. The detail
of similarity and dissimilarity index of different plant communities are
presented in table 4.3.2.
56
Table- 4.3.2 The similarity and dissimilarity indices of different communities of Nandiar Khuwar catchment area.
ADD PCR QDM PQP PQI PSB PQB APV APQ APP PAW WVA JSP
ADD 26.8 19.6 13.5 13 7.4 5.1 5.4 2.6 2.6 4.5 1 0
PCR 73.2 24.4 20.7 24.2 14.5 10.3 9.8 5.8 8.3 8.1 1.8 0
QDM 80.4 75.6 26.5 27.1 16.9 15.9 9.5 6.7 6.3 9 0.8 0
PQP 86.5 79.3 73.5 30.7 22.8 25.7 14.5 10.9 10.2 12.1 2.3 1.1
PQI 87 75.8 72.9 69.3 25.3 25.7 18.5 14.1 13.9 14.9 4 0.8
PSB 92.6 85.5 83.1 77.2 74.7 29.1 26.3 20.7 17.5 24.8 5.7 2.8
PQB 94.9 89.7 84.1 74.3 74.3 70.9 25.6 18.4 26.4 25.4 4.6 3.2
APV 94.6 90.2 90.5 85.5 81.5 73.7 74.4 27.6 24.9 21.5 10.7 11.1
APQ 97.4 94.2 93.3 89.1 85.9 79.3 81.6 72.4 25.3 21.1 18.9 20.9
APP 97.4 91.7 93.7 89.8 86.1 82.5 73.6 75.1 74.7 32.9 9.3 10.8
PAW 95.5 91.9 91 87.9 85.1 75.2 74.6 78.5 78.9 67.1 9.1 6.5
WVA 99 98.2 99.2 97.7 96 94.3 95.4 89.3 81.1 90.7 90.9 35.7
JSP 100 100 100 98.9 99.2 97.2 96.8 88.9 79.1 89.2 93.5 64.3
ADD:Acacia, Dodonaea, Dalbergia community, PCR:Pinus, Cynodon, Rubus community, QDM: Quercus, Dodonaea, Myrsine community, PQP: Pinus, Quercus, Pinus community, PQI: Pinus, Quercus, Indigofera community, PSB: Pinus, Sarcococca, Berberis, community, PQB: Pinus, Quercus, Berberis community, APV: Abies, Picea, Viburnum community, APQ:Abies, Picea, Quercus community, APP: Abies, Picea, Pinus community, PAW: Pinus, Abies, Wikstroemia community, WVA: Wikstroemia, Viburnum, Androsace community, JSP: Juniperus, Sibbaldia, Primula community.
57
4.4 Multivariate Analysis of Ecological Data of Nandiar Khuwar
Catchment
In multivariate analysis of ecological data of Nandiar Khuwar catchment a
total of 80 stands were used. For the classification of species and samples
simultaneously TWINSPAN were used which is based on dividing reciprocal
averaging ordination space. For ordination Bray-Curtis Ordination (Polar
ordination) were used for ordination scores, endpoints, regression coefficient
and variance in distance of different axis. Detrended correspondence analysis
(DCA) is an Eigenanalysis ordination technique based on reciprocal
averaging. DCA ordinates species and samples simultaneously. Canonical
correspondence analysis (CCA) was used for the ordination of samples and
species constrained by their relationships to environmental variables. Beside
classification and ordination the graph multivariate analysis included Dominance
Curves, species area curves and scatter plot were also used. The details of the
multivariate analyses of ecological data of Nandiar Khuwar catchment are described
below.
4.4.1 TWINSPAN Classification of the Vegetation of Nandiar Khuwar
For the classification of species and samples simultaneously TWINSPAN
(Two-way Indicator Species Analysis) were used. In this classification a total
of 80 stands and 324 species were used. The data were first classified into two
major groups on the basis of presences or absence of indicator species which
is the distinctive feature of this classification. In division 1 the eigenvalue
were 0.6391. The primary indicator species of division 1 were Abies pindrow,
Viola canescens and Berberis lyceum. 53 stands were placed in negative group
(*0) while 27 stands were placed in positive group (*1). In division 2 (53) the
eigenvalue were 0.4590 and the indicator species were Sarcococca saligna. 15
stands were placed in negative group (*00) while 38 stands were placed in
positive group (*01). In division 3 (27) the Eigenvalue were 0.4834 and the
indicator species were Picea smithiana and Fragaria nubicola 1. In this division
22 stands were placed in negative group (*10) while 5 stands were placed in
58
positive group (*11). The data were further refined after successive division
and redivision on the basis of indicator species and a total of 13 major plant
communities were recognized from subtropical to alpine zones of Nandiar
Khuwar catchment District Battagram (figure 4.4.1). These communities are
described as follow.
4.4.1.1 Acacia, Dodonaea, Dalbergia Community
Acacia modesta, Dodonaea vescosa, Dalbergia sissoo community was recorded
between altitudes of 530-700min two stands Thakot I on south facing steep
slope and Thakot II North facing steep slope. In this community 70 species
were recorded. The indicator species of this community was Acacia modesta.
This community was obtained at level 3 and the eigenvalue were 0.44. The
Shannon-Wiener diversity index value was 3.86. The biological spectrum was
dominated by phanerophytes with 32 species followed by therophytes with
14 species, geophytes by 9 species, chamaephytes by 8 species and
hemicryptophytes by 7 species (table- 4.4.1). The leaf size spectra were
dominated by mesophyll with 26 species followed by microphyll with 24
species, nanophyll having 13 species, macrophyll with 4 species and
leptophyll with 3 species (table- 4.4.2). Gravels, rocks and boulders are
common in this area. The soil is shallow, sandy loam; light brown in colour
and slightly basic in nature. The soil saturation and organic matter
concentration are generally low. The micro climatic data show that the
average temperature of this vegetation zone is 33.8ºC. The average
atmospheric humidity was 25.6%.
4.4.1.2 Pinus, Cynodon, Rubus Community
Pinus roxburghii, Cynodon dactylon, Rubus fructicosus community was recorded
in 8 sub-localities i.e. Peshora, Gajikot, Khairabad, Nowshera, Naraza,
Batangi, Paimal I and Nili Sharkolai between elevations of 880 – 1650m. This
community was obtained at level 4 and the eigenvalue were 0.46. In this
community 87 species were recorded. The Shannon-Wiener diversity index
59
value was 3.87. The biological spectrum was dominated by phanerophytes
having 33 plant species, followed by therophytes with 19 species,
hemicryptophytes 13 species, chamaephytes and geophytes each contributing
11 species (table- 4.4.1). The leaf size spectra were dominated by microphyll
contributing 33 species, mesophyll by 31 species, nanophyll by 17 species,
leptophyll by 3 species and macrophyll by 2 species while one species were
aphyllus in nature (table- 4.4.2). On the basis of leaf size spectra the maximum
IVI value were contributed by nanophyll (35.12). Limestone, granite and
sandstones are common in this community. The soil is shallow, calcareous
and sandy. The soil saturation is generally low. The organic matter
concentration ranges from 0.80-1.75%. The soil is acidic in nature and pH
value ranges from 5.15 - 5.90. The average temperature of this community was
27.8ºC. Atmospheric humidity ranges from 18.1% to 48.5%.
4.4.1.3 Quercus, Dodonaea, Myrsine Community
Quercus incana, Dodonaea vescosa, Myrsine africana community was recorded at
Chorlangai, Shagai, Paimal II, Paimal III and Paimal IV between altitudes of
750-1300m. This community was obtained at level 4 with eigenvalue 0.46
contributing 93 species with 3.88 Shannon-Wiener diversity index values.
Biological spectrum was dominated by phanerophytes with 53 species,
followed by therophytes (13), geophytes (11), chamaephytes (9) and
hemicryptophytes by 7 species (table- 4.4.1). The leaf size spectra were
dominated by microphyll having 36 species, followed by mesophyll (32),
nanophyll (17), leptophyll (4) and macrophyll with 4 species (table- 4.4.2). The
rocks, stones and gravels are numerous in this zone. The soil is shallow,
sandy loam, and light brown in colour. The average soil saturation recorded
for this community was 47.8%, organic matter concentration was 1.43% and
pH value was 5.40. The average temperature of this community was 29.5ºC
and atmospheric humidity was 27.8%.
60
Fig. 4.4.1: TWINSPAN classification of the vegetation of Nandiar Khuwar catchment.
61
4.4.1.4 Pinus, Quercus, Pinus Community
Pinus wallichiana, Quercus incana, Pinus roxburghii community was recorded
from Gada, Rajmira, Lundai I, Lundai II, Belandkot, Anora I, Anora II, Anora
III and Shinglai between an altitudinal ranges of 1250–1900m. This
community was obtained at level 4 and the eigenvalue were 0.36. In this
community 145 species were recorded. The Shannon-Wiener diversity index
value calculated for this community was 4.21. The biological spectrum was
dominated by phanerophytes contributing 59 species, geophytes with 32
species, therophytes with 22 species, hemicryptophytes with 20 species and
chamaephytes with 12 species (table- 4.4.1). The leaf size spectra were
dominated by microphyll contributing 61 species, followed by mesophyll
having 46 species, nanophyll having 26 species, macrophyll having 5 species,
leptophyll with 3 species while 2 species were aphyllus in nature (table- 4.4.2).
The soil varies from shallow to deep. Lime stone, sandstone and granite are
found in these zones. The average soil saturation was 45%, organic matter
concentration was1.32, pH value 6.8, temperature 25.4ºC and atmospheric
humidity 42.21%.
4.4.1.5 Pinus, Quercus, Indigofera Community
Pinus wallichiana, Quercus incana, Indigofera heterantha community were
recorded from Deshara, Lamai, Shabora I, Shabora II, Paimal V, Dabrai,
Bashakhan, Kiari, Nil-Reen, Nili-Batangi, Sharkola and Sarmast between
altitudinal zones of 1240 – 2040m. This community was obtained at level 4
and the eigenvalue were 0.32. In this community 99 species were recorded.
The Shannon-Wiener diversity index value calculated for this community was
3.85. The biological spectrum was dominated by phanerophytes (29), followed
by geophytes (22), therophytes (21), hemi-cryptophytes (18) and
chamaephytes (9) (table- 4.4.1). The leaf size spectra were dominated by
microphyll contributing 42 species, followed by mesophyll (30), nanophyll
(20), leptophyll (3) and macrophyll (3) while one species were aphyllus (table-
4.4.2). Rocks and boulders are also common. The soil is deep, loamy and light
62
brown to light dark in colour. The average organic matter concentration was
1.10%, pH values 5. 88, temperature is 25.8ºC and atmospheric humidity
36.4%.
4.4.1.6 Pinus, Sarcococca, Berberis, Community
Pinus wallichiana, Sarcococca saligna, Berberis lyceum community was recorded
from Chapra, Jarotia, Bach Maidan, Hill, Sandawali, Doda I and Terkana
between altitudinal zones of 1750 – 2350m. This community was obtained at
level 4 and the eigenvalue were 0.30. In this community 79 species were
recorded. The Shannon-Wiener diversity index value calculated for this
community was 3.77. The biological spectrum (table- 4.4.1) was dominated by
phanerophytes having 29 plant species. It was followed by hemicryptophytes
and therophytes each contributing 15 species, geophytes by 12 species and
chamaephytes by 8 species. The leaf size spectra (table- 4.4.2) were dominated
by microphyll contributing 34 species contributing 38.73IVI value, followed
by mesophyll (28), nanophyll (12), macrophyll (2), and leptophyll (2) while 1
species was aphyllus. The parent materials consist of mica schist, shale, gneiss
and lime stone. The soil is deep, moist and well drained. Huge rocks and
stones are found in this zone. The soil is deep, moist and loamy in nature.
The average soil saturation value was 52.57%, organic matter concentration
was 1.29%, pH value 6.78, temperature 24ºC and atmospheric humidity
was47%.
4.4.1.7 Pinus, Quercus, Berberis Community
Pinus wallichiana, Quercus incana, Berberis lyceum community were recorded
from Gat, Habib banda I, Habib banda II, Jatial, Chapar, Riar, Mirani kandao,
Sheed, Jaro and Doba between altitudinal zones of 1960–2222m. This
community was obtained at level 4 and the eigenvalue were 0.30. In this
community 127 species were recorded. The Shannon-Wiener diversity index
value of this community was 4.06. The life form (table- 4.4.1) was dominated
by geophytes with 37 species contributing maximum IVI value (20.15),
63
followed by phanerophytes (29), therophytes (28), hemicryptophytes (20) and
chamaephytes (13). The leaf size spectra (table- 4.4.2) were dominated by
microphyll contributing 58 species, followed by mesophyll (37), nanophyll
(28), macrophyll (2) and leptophyll (2). On the basis of leaf size spectra the
maximum IVI value were contributed by micophyll (39.32). The soil is deep,
moist and loamy in nature. The average soil saturation value was 55.6%,
organic matter concentration was 1.48%, pH value 6.05, temperature 24.1ºC
and atmospheric humidity was 54.7%.
4.4.1.8 Abies, Picea, Viburnum Community
Abies pindrow, Picea smithiana, Viburnum cotinifolium community were
recorded from Manra, Bach upper, Lunda Matra, Baleja, Chaprai,
Birthmaidan and Machaisar between altitudinal zones of 2220- 2900m. This
community was obtained at level 4 and the eigenvalue were 0.33. In this
community 96 species were recorded. Shannon-Wiener diversity index value
calculated for this community was 4.07. The biological spectrum (table- 4.4.1)
was dominated by therophytes contributing 24 species, followed by
geophytes (23), phanerophytes (19), chamaephytes (16) and hemicryptophytes
(14). The leaf size spectra (table- 4.4.2) were dominated by microphyll
contributing 36 species, followed by mesophyll (32), nanophyll (22),
macrophyll (4) and leptophyll (2).On the basis of leaf size spectra the
maximum IVI value were contributed by mesophyll (36.49). The soil is deep,
moist and well drained. The parent materials consist of mica schist, shale,
gneiss and lime stone. The average soil saturation value was 58.6%, organic
matter concentration was 1.61%, pH value 6.23, temperature was 20.6ºC and
atmospheric humidity was 47.4%.
64
Table- 4.4.1 Number of species and IVI contribution of life form classes of the plant communities.
Name of community
Phanerophytes Chamaephytes Hemicryptophytes Geophytes Therophytes
No of Species
IVI No of Species
IVI No of Species
IVI No of Species
IVI No of Species
IVI
Acacia, Dodonaea, Dalbergia 32 50.6 8 12.16 7 6.54 9 8.63 14 22.09
Pinus, Cynodon, Rubus 33 39.83 11 10.19 13 17.20 11 7.36 19 25.41
Quercus, Dodonaea, Myrsine 53 70.85 9 6.18 7 8.00 11 4.51 13 10.47
Pinus, Quercus, Pinus 59 55.17 12 9.29 20 10.82 32 13.07 22 11.66
Pinus, Quercus, Indigofera 29 52.24 9 7.91 18 16.25 22 10.97 21 12.63
Pinus, Sarcococca, Berberis 29 47.68 8 9.11 15 15.14 12 15.12 15 12.94
Pinus, Quercus, Berberis 29 47.91 13 9.55 20 7.66 37 20.15 28 14.74
Abies, Picea, Viburnum 19 35.57 16 6.75 14 16.24 23 21.54 24 19.90
Abies, Picea, Quercus 26 40.07 9 5.11 11 11.63 18 20.50 21 22.70
Abies, Picea, Pinus 16 30.52 10 10.52 15 14.59 21 21.84 19 22.54
Pinus, Abies, Wikstroemia 20 35.37 7 10.65 11 12.77 15 27.24 9 13.97
Wikstroemia, Viburnum, Androsace 06 36.88 5 10.74 5 15.76 6 20.03 4 16.60
Juniperus, Sibbaldia, Primula 07 32.68 5 17.76 7 24.63 3 9.76 8 15.17
65
Table- 4.4.2 Number of species and IVI contribution of leaf size spectra of the plant communities.
Name of community
Macrophyll Mesophyll Microphyll Nanophyll Leptophyll
No of Species
IVI No of
Species IVI
No of Species
IVI No of
Species IVI
No of Species
IVI
Acacia, Dodonaea, Dalbergia 12 5.97 26 26.73 24 41.59 12 22.67 2 2.27
Pinus, Cynodon, Rubus 17 1.56 31 26.93 33 31.04 17 35.12 3 4.78
Quercus, Dodonaea, Myrsine 17 2.18 32 37.62 36 34.07 18 22.32 3 3.80
Pinus, Quercus, Pinus 22 1.68 46 35.55 61 34.35 26 23.29 3 3.93
Pinus, Quercus, Indigofera 19 1.00 30 28.65 42 34.38 20 32.36 3 3.20
Pinus, Sarcococca, Berberis 10 5.92 28 25.86 34 38.73 12 27.55 2 1.20
Pinus, Quercus, Berberis 22 2.76 37 29.77 58 39.32 28 25.58 2 1.29
Abies, Picea, Viburnum 18 9.19 32 36.49 36 30.35 22 23.04 2 0.58
Abies, Picea, Quercus 19 6.04 30 38.94 30 26.11 19 27.44 1 1.20
Abies, Picea, Pinus 16 3.73 25 30.19 35 34.52 16 30.52 1 0.73
Pinus, Abies, Wikstroemia 11 9.14 26 30.12 21 33.97 11 26.77 0 0.00
Wikstroemia, Viburnum, Androsace 7 2.14 8 36.57 10 38.68 7 22.63 0 0.00
Juniperus, Sibbaldia, Primula 10 0.00 10 37.13 9 25.34 10 28.64 1 8.90
66
4.4.1.9 Abies, Picea, Quercus Community
Abies pindrow, Picea smithiana, Quercus semicarpifolia community was recorded
from Guchai, Doda II, Kachkol, Belmaz, Lekoni and Magrai between an
altitudinal zone of 2250 – 3000m. This community was obtained at level 4 and
the eigenvalue were 0.33. In this community 85 species were recorded.
Shannon-Wiener diversity index value of this community was 4.02. Biological
spectrum was dominated by phanerophytes having 26 species (table- 4.4.1),
followed by therophytes (21), geophytes (18), hemicryptophytes (11) and
chamaephytes (9). The leaf size spectra were dominated by mesophyll and
microphyll each contributing 30 species, followed by nanophyll (19),
macrophyll (4) and leptophyll (1) while 1 species were aphyllus (table- 4.4.2).
Rocks, stones and gravels are common. The parent materials consist of mica
schist, shale, gneiss and lime stone. The average soil saturation value was
56.5%, soil organic matter concentration was 1.3%, pH value 6.3, temperature
was 19.3ºC and atmospheric humidity was 53.9%.
4.4.1.10 Abies, Picea, Pinus Community
Abies pindrow, Picea smithiana, Pinus wallichiana community was recorded from
Chailkambar, Gabrai kandao, Mirani I, Mirani II, Harpal and Chail between
an altitudinal zone of 2650 – 3000m. This community was obtained at level 4
and the eigenvalue were 0.32. In this community 81 species were recorded.
Shannon-Wiener diversity index value calculated for this community was
4.10. Biological spectra were dominated by geophytes (table- 4.4.1) with 21
species, followed by therophytes (19), phanerophytes (16), hemicryptophytes
(15) and chamaephytes (10). The leaf size spectra were dominated by
microphyll (table- 4.4.2) with 35 species contributing maximum (34.52) IVI
value, followed by mesophyll (25), nanophyll (16), macrophyll (3) and
leptophyll (1) while 1 species were aphyllus. Rocks, stones and gravels are
numerous in this zone. The parent materials consist of mica schist, shale,
gneiss and lime stone. The soil is deep, moist and well drained. The average
soil saturation value were 56.83%, soil organic matter concentration were
67
1.4%, pH value 6.28, temperature were 19.8ºC and atmospheric humidity
were 55.2%.
4.4.1.11 Pinus, Abies, Wikstroemia Community
Pinus wallichiana, Abies pindrow, Wikstroemia canescens community was
recorded from Ledai, Charoona and Trapa between altitudinal zones of 2270 –
2450m. This community was obtained at level 4 and the eigenvalue were 0.32.
In this community 62 species were recorded. Shannon-Wiener diversity index
value was 3.8. Biological spectrum were dominated by phanerophytes
contributing 20 plant species, followed by geophytes with 15 species,
hemicryptophytes with 11 species, therophytes with 9 species and
chamaephytes with 7 species (table- 4.4.1). The leaf size spectra (table- 4.4.2)
were dominated by mesophyll contributing 26 species, followed by
microphyll (21), nanophyll (11) and macrophyll (3). On the basis of leaf size
spectra maximum IVI value were contributed by microphyll (33.97). Rocks,
stones and gravels are numerous in this zone. The parent materials consist of
mica schist, shale, gneiss and lime stone. The soil is deep, moist and well
drained. The average soil saturation was 58%, organic matter concentration
was 1.5%, pH value 6.18, temperature was 22.5ºC and atmospheric humidity
was 48.4%.
4.4.1.12 Wikstroemia, Viburnum, Androsace Community
Wikstroemia canescens, Viburnum cotinifolium, Androsace hazarica community
was recorded from two stands Karganja and Shaheed Gali between altitudinal
zones of 2850 – 3100m. This community was obtained at level 3 and the
eigenvalue were 0.40. In this community 26 species were recorded. Shannon-
Wiener diversity index value was 3.06. Biological spectrum was represented
by phanerophytes and geophytes each having 6 species, hemicryptophytes
and chamaephytes by 5 species each and therophytes with 4 species (table-
4.4.1). The leaf size spectra were dominated by microphyll with 10 species
contributing 38.68 IVI value (table- 4.4.2), followed by mesophyll (8),
68
nanophyll (7) and macrophyll (1). Rocks, stones and gravels are numerous.
The soil is steep and loamy. The average soil saturation was 52.5%, organic
matter concentration was 0.85%, pH value was 6.3, temperature was 17.7ºC
and atmospheric humidity 47.4%.
4.4.1.13 Juniperus, Sibbaldia, Primula Community
Juniperus communis, Sibbaldia cuneata, Primula denticulata community was
recorded from Kar Ganja top, Alishera and Malkaisar between an altitudinal
ranges of 3250 – 3800m. In this community 30 species were recorded and was
obtained at level 3 with eigenvalue 0.40. Shannon-Wiener diversity index
value for this community was 3.20. Biological spectrum was represented by
therophytes with 8 species, phanerophytes and hemicryptophytes each has 7
species, chamaephytes having 5 species and geophytes with 3 species (table-
4.4.1). The leaf size spectra were dominated by mesophyll with 10 species
contributing maximum IVI value (37.13) followed by nanophyll with 10
species, microphyll (9) and leptophyll by single species (table- 4.4.2). Rocks,
stones and gravels are common. The soil is steep and loamy. The average soil
saturation value was 54.3%, organic matter concentration was 0.89%, pH
value was 6.57, temperature was 16.5ºC and atmospheric humidity was
53.9%.
4.4.2 Ordinations of Vegetation of Nandiar Khuwar Catchment
The vegetation data of Nandiar Khuwar catchment were further analyzed by
ordination. Ordination is the ordering of objects along axes according to there
resemblances. The correlation structure is used to positions objects in the
ordination space. Objects close in the ordination space are generally more
similar than objects distant in the ordination space. In present study three
major ordination techniques were used including Bray-Curtis ordination,
DCA and CCA. For classification and ordination the IVI value of 80 stands
were used. These are described below.
69
4.4.2.1 Bray-Curtis Ordination of the over all vegetation of study area
Bray-Curtis Ordination (Polar ordination) was used for ordination scores,
endpoints, regression coefficient and variance in distance of different axis (fig.
4.4.2). The ordination scores (Distances) were from Paimal V (0.000) to Magrai
(0.960) on axes 1. The regression coefficient for this axis were -54.11, variance
in distance from the first end point were 2.53. Axis 1 extracted 21.95% of
original distance matrix. The ordination scores for axis 2 were from Mirani
kandao (0.000) to Peshora (0.904). The regression coefficient for this axis were
-43.54, variance in distance from the first end point were 2.00. Axis 2 extracted
13% of original distance matrix. The ordination scores for axis 3 was from
Doda I (0.000) to Malkaisar (0.826). The regression coefficient for this axis
were -20.60, variance in distance from the first end point were 1.20. Axis 3
extracted 6.45% of original distance matrix.
4.4.2.2 DCA Ordination of the vegetation of study area
The response data are compositional having gradient length 6.4 SD units long
(fig. 4.5.3 and 4.5.4). In DCA ordination with supplementary variables the
maximum gradient length (6.36) were recorded for axis 1 with eigenvalue
0.71. The gradient length for axis 2 was 2.74 with eigenvalue 0.28. The
gradient length for axis 3 was 2.14 with eigenvalue 0.18. Total variance
("inertia") in the species data were 7.07, supplementary variables account for
37.1%. The Pseudo-canonical correlation (suppl.) for axis 1, 2 and 3 were 0.99,
0.51 and 0.70 respectively. The DCA ordination clearly indicates that the
whole data set were dominated by single dominant gradient.
In DCA ordination different species clustered in ordination space showing
correlation. The subtropical species clustered together and having positive
correlation included Plantago lanceolatum, Panicum spp., Heteropogon contortus,
Tulipa stellata, Oxalis corniculata, Micromeria biflora, Pinus roxburghii and Rubus
fructicosus. The species of moist temperate blue pine forests clustered together
and having positive correlation included Pinus wallichiana, Berberis lyceum,
70
Indigofera heterantha, Carex cardiolepis, Adiantum capillus-veneris, Adiantum
venustum, Bergenia ciliata, Dryopteris jaxtapostia, Potentilla gerardiana, Themeda
anathera and Viola canescens.
The species of silver fir and spruce forests clustered together and having
positive correlation included Abies pindrow, Picea smithiana, Pteridium
equilinum, Paeonia emodi, Rumex nepalensis, Bistorta amplexicaulis, Artemisia
roxburghiana, Potentilla nepalensis, Aquilegia pubiflora, Viburnum cotinifolium,
Ranunculus palmatifidus, Skimmia laureola, Androsace hazarica, Caltha alba and
Primula denticulata. Similarly Arisaema flavum, Ranunculus laetus,
Pseudognaphalium hypolecum, Torilis japonica and Viburnum grandiflorum
showed positive correlation. Anaphalis busa, Impatiens edgeworthii, Dryopteris
serrate dentata and Pteracanthus urticifolius clustered together. The species of
alpine and subalpine zone clustered includes Salix calyculata, Juniperus
communis, Tanacetum dolicophyllum and Wikstroemia canescens.
4.4.2.3 CCA Ordination
Canonical correspondence analysis (CCA) was used for the ordination of
samples and species constrained by their relationships to environmental
variables. In CCA ordination the maximum Eigenvalue were recorded for
axis 2 (0.948) followed by axis 1 (0.699) and axis 3 (0.226). The percentage
variance explained for axis 1, 2 and 3 were 9.89%, 16.19% and 19.39%
respectively. The total variance (inertia) in the species data were 7.07,
explanatory variables account for 37.1%. Pseudo-canonical correlation for axis
1, 2 and 3 were 0.99, 0.94 and 0.82 respectively. The permutation test results
for all axes were pseudo-F=2.2, P=0.002. The Pearson correlation for axis 1, 2
and 3 were 0.988, 0.953 and 0.824. The Kendall (Rank) correlation for axis 1, 2
and 3 were 0.887, 0.670 and 0.602. The correlation between sample score for an
axis derived from the species data and the sample scores that are linear
combination of the environmental variable. The CCA ordinations are
presented in fig. 4.4.5 and 4.4.6.
71
Fig. 4.4.2: The distribution of 80 stands on different axis on the basis of Bray-Curtis ordination.
72
Fig. 4.4.3: DCA ordination of species of Nandiar Khuwar catchment.
Fig. 4.4.4: DCA ordination of stands of Nandiar Khuwar catchment.
-1 7
-14
Abi pinAdi cap
Adi inc
Adi ven
Aes indAil alt
Aju bra
Ana bus
And spp
And haz
And rot Aqu pub
Ari fla
Art rox
Asp dal
Ber lyc
Ber cil
Bis ampCal alb
Car carCor spp
Cot cog
Cot num
Cyn dac
Dap muc
Des ele
Deu sta
Dio lot
Dod ves
Dry bla
Dry jax
Dry ser
Dry wal
Duc ind
Eup walFra nub
Fun spp
Gag sat
Ger col
Ger wal
Het con
Hyp cup
Imp edg
Ind het
Jug reg Jun comLon qui
Lyo ova
Mar spp
Mic bif
Myr afr
Ori vul Oxa cor
Pae emo
Pan spp
Pic smi
Pin rox
Pin wal
Pla lan
Poa spp
Pol ver
Pot ger
Pot nepPot spp
Pri den
Pse hyp
Pte urt
Pte equ
Pte cre
Que dil
Que inc
Que sem
Ran lae
Ran pal
Rho arb
Ros mosRub ell
Rub fru
Rum den
Rum has
Rum nep
Sal cal
Sar sal
Sib cun
Ski lau
Spi vac
Tan dol
Tar off
The ana
Thy lin
Tor jap
Tri gov
Tul ste
Val jat
Ver laxVib cot
Vib gra
Vio can
Vio spp
Wik can
73
Among the environmental variables the maximum strength was recorded for
altitude followed by barometric pressure, temperature and density altitude.
The intermediate strength was recorded for phosphorus, atmospheric
humidity, soil saturation, wind speed, heat index and electrical conductivity.
The less strength was recorded for slope angle, wet bulb temperature, pH and
slope aspect. The minimum strength was recorded for potassium, organic
matter and dew point. Wind speed, phosphorus, electrical conductivity and
slope angle are positively correlated with each other. pH and slope aspect are
positively correlated. Altitude, density altitude, soil saturation and
atmospheric humidity showed positive correlation. Organic matter and dew
point are positively correlated in ordination space. Similarly positive
correlation was also shown by wet bulb, temperature, barometric pressure,
heat index and Potassium. CCA ordination also indicates that maximum
stands clustered near average position in ordination space. However few
stands were away from average position. The stands of Juniperus, Sibbaldia,
Primula community and Wikstroemia, Viburnum, Androsace community were
clustered at high wind speed and Phosphorous concentration.
CCA ordination indicates that different species were clustered along different
environmental variables. The species clusters showing positive correlation
with wind speed included Ciminalis karelinii, Berberis sp., and Wulfenia
amherstiana. The species clusters having positive correlation with
phosphorous included Betula utilis, Juniperus communis, Sibbaldia cuneata,
Valeriana himalayana and Aster himalaicus. The species having positive
correlation with electrical conductivity included Salix calyculata, Trillium
govanianum, Buplerum longicaule and Corydalis sp. The species having positive
correlation with slope angle included Caltha alba, Tanacetum dolicophyllum,
Pseudomertensia perviflora, Ranunculus palmatifidus and Primula denticulata. The
species clustered along slope aspect and pH included Artemisia roxburghiana,
Potentilla nepalensis, Paeonia emodi and Skimmia laureola.
74
Fig. 4.4.5: CCA ordination of species and environmental variables.
Fig. 4.4.6: CCA ordination of stands and environmental variables.
-1.0 1.0
-0.4
1.0
Slope Angle
Slope Aspct
Altitude
Barometric Pressure
Density AltitudeTemperature
Wind Speed
Atmospheric Humidity
Heat Index
Dew Point
Wet Bulb
Electrical Conductivity
PH
Soil Organic Matter
Phosphorus
Potassium
Soil Saturation
Abi pin
Aca mod
Ail alt
Aju bra
Alb leb
Aln nit
Ana arv
And haz
Art jap
Art rox
Ast him
Bau var
Ber lyc
Ber spp
Bet uti
Bom cei
Bup lon Cal alb
Cas tor
Cim kar
Col opp
Cor spp
Cot mic
Cyn dac
Dal sisDat alb
Dat str
Dod ves
Euc glo
Eup ind
Fic ben
Fic car
Fic rac
Fra nub
Ger luc
Ger wal
Gre opt
Gym roy
Ind het
Jun spp
Jun com
Jus adh
Lau pro
Mal phi
Med den
Mel aze
Mic bif
Ner ind
Ole fer
Oxa cor
Pae emo
Pic smi
Pin rox
Pin wal
Pis int
Pot nep
Pot spp
Pri den Pse par
Pte equ
Que inc
Ran pal
Ric com
Rub fru
Rum nep
Sal cal
Sar sal
Sib cun
Ski lau
Sol sur
Ste med
Tan dol
Thl spp
Thy lin
Tri gov
Val him
Ver tha
Ver lax
Vib cot
Vio can
Wik can
Woo fru
Wul amh
75
The species having positive correlation with altitude, density altitude,
humidity and soil saturation included Abies pindrow, Geranium wallichianum,
Picea smithiana, Rumex nepalensis, Viburnum cotinifolium, Veronica laxa and
Pteridium equilinum. The species having positive correlation with organic
matter and dew point temperature include Fragaria nubicola, Cynodon dactylon,
Indigofera heterantha, Berberis lyceum, Pinus wallichiana, Quercus incana,
Sarcococca saligna and Viola canescens. The species clustered along wet bulb,
temperature, barometric pressure and heat index included Oxalis corniculata,
Pinus roxburghii, Ficus carica, Micromeria biflora, Rubus fructicosus, Geranium
lucidum and Ajuga bracteosa. The species having positive correlation with
potassium included Alnus nitida, Dodonaea vescosa, Melia azedarach, Grewia
optiva, Albezia lebbeck and Acacia modesta.
Among environmental variable barometric pressure (0.967), temperature
(0.960), heat index (0.499), wet bulb (0.329) and potassium (0.063) were
positively correlated with axis 1. The environmental variables positively
correlated with axis 2 include humidity (0.347), organic matter (0.291),
altitude (0.137), dew point (0.119) and density altitude (0.104). The
environmental variables positively correlated with axis 3 include pH (0.554),
slope angle (0.501), soil saturation (0.425), humidity (0.309), organic matter
(0.279), phosphorous (0.273), electrical conductivity (0.205), dew point (0.193),
wet bulb (0.111), wind speed (0.065) and temperature (0.062). From
correlation data it was concluded that altitude, density altitude, barometric
pressure, temperature, humidity and phosphorous concentration were more
significant as compared to other environmental variables. The regression of
stands in species space on 15 parameters is presented in table 4.4.3, correlation
and biplot score are presented in table 4.4.4 and correlation among
environmental variables are shown in table 4.4.5.
76
Table- 4.4.3 Regression of stands in species space on 17 parameters.
Canonical Coefficients
Standardized Original units
Variables Axis 1 Axis 2 Axis 3 Axis 1 Axis 2 Axis 3 S. Dev
Altitude -0.114 -6.444 -1.505 0.000 -0.009 -0.002 0.688E+03
Barometric pressure
0.537 -6.573 -2.464 0.008 -0.098 -0.037 0.671E+02
Density altitude
0.136 0.040 -1.194 0.000 0.000 -0.002 0.730E+03
Dew point -0.057 0.150 -0.211 -0.013 0.033 -0.047 0.453E+01
Electric Conductivity
0.001 -0.061 0.113 0.008 -0.612 1.130 0.100E+00
Heat Index -0.022 -0.283 0.143 -0.003 -0.043 0.022 0.656E+01
Humidity 0.141 -0.146 0.222 0.011 -0.012 0.018 0.125E+02
Organic matter
0.080 0.121 0.096 0.235 0.352 0.281 0.342E+00
pH 0.072 0.084 0.266 0.109 0.126 0.401 0.663E+00
Phosphorous -0.086 -0.287 0.100 -0.017 -0.058 0.020 0.495E+01
Potassium -0.047 -0.065 -0.025 -0.001 -0.001 0.000 0.788E+02
Saturation -0.079 0.013 0.192 -0.010 0.002 0.025 0.766E+01
Slope angle -0.126 -0.124 0.190 -0.014 -0.013 0.020 0.925E+01
Slope aspect -0.049 0.030 0.004 -0.019 0.012 0.001 0.251E+01
Temperature 0.276 -0.148 0.146 0.071 -0.038 0.038 0.388E+01
Wet bulb 0.029 0.080 0.264 0.007 0.020 0.067 0.396E+01
Wind speed -0.054 -0.097 0.053 -0.157 -0.282 0.155 0.344E+00
77
Table- 4.4.4 The correlations and biplot scores for 17 parameters.
Correlations* Biplot Scores
Variables Axis 1 Axis 2 Axis 3 Axis 1 Axis 2 Axis 3
Altitude -0.974 0.137 -0.009 -0.824 0.094 -0.004
Barometric pressure
0.967 -0.188 -0.003 0.818 -0.129 -0.001
Density altitude
-0.954 0.104 -0.006 -0.807 0.071 -0.003
Dew point -0.004 0.119 0.193 -0.003 0.082 0.095
Electric Conductivity
-0.453 -0.322 0.205 -0.383 -0.221 0.101
Heat Index 0.499 -0.227 -0.056 0.422 -0.156 -0.028
Humidity -0.605 0.347 0.309 -0.512 0.239 0.152
Organic matter -0.018 0.291 0.279 -0.015 0.200 0.137
pH -0.211 -0.054 0.554 -0.178 -0.037 0.272
Phosphorous -0.688 -0.476 0.273 -0.582 -0.327 0.134
Potassium 0.063 -0.174 -0.053 0.053 -0.119 -0.026
Saturation -0.550 0.139 0.425 -0.465 0.095 0.209
Slope angle -0.390 -0.198 0.504 -0.330 -0.136 0.248
Slope aspect -0.173 -0.045 -0.125 -0.146 -0.031 -0.062
Temperature 0.960 -0.198 0.062 0.812 -0.136 0.031
Wet bulb 0.329 -0.076 0.111 0.278 -0.052 0.055
Wind speed -0.513 -0.518 0.065 -0.434 -0.356 0.032
* Correlations are "intra-set correlations" of ter Braak (1986).
78
Table- 4.4.5 The correlation among environmental variables of Nandiar Khuwar catchment.
Altitude
Baro Density altitude
Dew point
E. C. Heat Index
Humidity
Org matt
pH P mg / kg
K mg /kg
Saturation
Slope angle
Slope aspect
Temp Wet bulb
Wind speed
Altitude 1 -0.99 0.98 0.06 0.38 -0.51 0.70 0.07 0.22 0.60 -0.11 0.53 0.31 0.17 -0.96 -0.30 0.44
Barometric pressure
-0.99 1 -0.98 -0.07 -0.36 0.52 -0.71 -0.08 -0.22 -0.58 0.12 -0.53 -0.31 -0.16 0.97 0.30 -0.42
Density altitude
0.98 -0.98 1 0.17 0.40 -0.38 0.68 0.11 0.26 0.61 -0.08 0.50 0.37 0.17 -0.94 -0.17 0.45
Dew point 0.06 -0.07 0.17 1 0.07 0.63 0.38 0.27 -0.03 -0.04 0.08 0.03 0.02 0.15 0.003 0.86 -0.03
E. C 0.38 -0.36 0.40 0.07 1 -0.07 0.17 0.20 0.004 0.44 0.38 0.46 0.16 0.17 -0.37 -0.01 0.30
Heat Index -0.51 0.52 -0.38 0.63 -0.07 1 -0.41 0.21 -0.29 -0.30 0.28 -0.28 -0.23 -0.01 0.56 0.89 -0.19
Humidity 0.70 -0.71 0.68 0.38 0.17 -0.41 1 0.13 0.24 0.30 -0.14 0.43 0.31 0.22 -0.67 -0.04 0.20
Organic matter
0.07 -0.08 0.11 0.27 0.20 0.21 0.13 1 -0.23 -0.08 0.39 0.49 0.02 0.05 -0.03 0.23 -0.28
pH 0.22 -0.22 0.26 -0.03 0.004 -0.29 0.24 -0.23 1 0.40 -0.34 0.07 0.49 -0.09 -0.20 -0.17 0.32
P mg/ kg 0.60 -0.58 0.61 -0.04 0.44 -0.30 0.30 -0.08 0.40 1 -0.18 0.38 0.44 0.11 -0.56 -0.20 0.59
K mg /kg -0.11 0.12 -0.08 0.08 0.38 0.28 -0.14 0.39 -0.34 -0.18 1 0.27 -0.13 0.09 0.11 0.17 -0.19
Saturation 0.53 -0.53 0.50 0.03 0.46 -0.28 0.43 0.49 0.07 0.38 0.27 1 0.12 0.10 -0.51 -0.18 0.09
Slope angle 0.31 -0.31 0.37 0.02 0.16 -0.23 0.31 0.02 0.49 0.44 -0.13 0.12 1 -0.21 -0.30 -0.11 0.29
Slope aspect 0.17 -0.16 0.17 0.15 0.17 -0.01 0.22 0.05 -0.09 0.11 0.09 0.10 -0.21 1 -0.15 0.09 0.07
Temperature -0.96 0.97 -0.94 0.003 -0.37 0.56 -0.67 -0.03 -0.20 -0.56 0.11 -0.51 -0.30 -0.15 1 0.36 -0.39
Wet bulb -0.30 0.30 -0.17 0.86 -0.01 0.89 -0.04 0.23 -0.17 -0.20 0.17 -0.18 -0.11 0.09 0.36 1 -0.15
Wind speed 0.44 -0.42 0.45 -0.03 0.30 -0.19 0.20 -0.28 0.32 0.59 -0.19 0.09 0.29 0.066 -0.39 -0.15 1
79
4.5 Phytosociological Attributes in Different Vegetational Zones
The phytosociological attributes were recorded in different vegetational zones
of Nandiar Khuwar catchment. The vegetational zones of Nandiar Khuwar
catchment were divided in six major vegetation zones from subtropical to
alpine zone on the basis of indicator species. From these six vegetational
zones 80 stands were selected for phytosocialogical analysis on the basis of
physiognomy. TWINSPAN classification was used to classify the entire
vegetation of Nandiar Khuwar catchment and to classify the vegetation of
each zone. In subtropical zones of Nandiar Khuwar catchment 157 species
were recorded from 16 stands. In subtropical vegetational zones six plant
communities were recognized through TWINSPAN. In mixed Pinus roxburghii
and Pinus wallichiana forests 130 species were recorded from 12 stands. In this
vegetational zone four plant communities were recognized. In the moist
temperate pure Pinus wallichiana forests of Nandiar Khuwar catchment 200
species were recorded from 23 stands. Five plant communities were
recognized in the moist temperate pure Pinus wallichiana forests. In western
mixed coniferous forests of Nandiar Khuwar catchment 156 species were
recorded from 15 stands and four plant communities were recognized by
TWINSPAN classification. In pure Abies pindrow and Picea smithiana forests of
Nandiar Khuwar catchment 111 species were recorded from 9 stands and
three plant communities were identified by TWINSPAN. The five stands of
alpine pasture were classified into two plant communities with 37 plant
species. The details of plant communities of different vegetation zones are as
follow:
4.5.1 Phytosociology of Subtropical zone
In the subtropical zone of Nandiar Khuwar catchment 16 stands were selected
between altitudinal zones of 530 – 1850m above mean sea level. From this
zone a total of 157 species were recorded. Six plant communities were
recognized by TWINSPAN. In subtropical vegetation of Nandiar Khuwar
catchment the biological spectrum were dominated by nanophanerophytes
80
with 36 (22.92%) species followed by therophytes contributing 27 (17.19%)
species. The leaf size spectra were dominated by microphyll with 63 (40.12%)
species followed by mesophyll contributing 54 (34.39%) species. The
maximum similarity index (33.61) was recorded between Pinus, Micromeria,
Rubus community and Pinus, Rubus, Cynodon community. The maximum
dissimilarity index (94.91) was recorded between Acacia, Dodonaea, Dalbergia
community and Pinus, Quercus, Eleagnus community (table.4.5.3).
4.5.1.1 TWINSPAN classification
The data obtained from 16 stands in the subtropical vegetational zone of
Nandiar Khuwar catchment were classified by TWINSPAN classification and
a total of six communities were recognized (fig.4.5.1). The subtropical
communities are described below:
Fig. 4.5.1: TWINSPAN classification of subtropical vegetation of Nandiar Khuwar catchment.
81
4.5.1.1 .1 Acacia, Dodonaea, Dalbergia community
Acacia modesta, Dodonaea vescosa, Dalbergia sissoo community was recorded in
two stands Thakot I on south facing steep slope and Thakot II North facing
steep slope between an altitudes of 530-700m. In this community 70 species
were recorded. The indicator species of this community was Acacia modesta.
Among biological spectrum therophytes were dominated by 14 species
contributing 22.09 IVI value (table-4.5.1). Leaf size spectra were dominated by
mesophyll with 26 species while maximum IVI value (41.59) was contributed
by microphyll (table-4.5.2). The dominance of therophytes indicates that the
vegetation of this community is subtropical and disturbed due to soil erosion
and overgrazing.
4.5.1.1 .2 Pinus, Micromeria, Rubus community
Pinus roxburghii, Micromeria biflora, Rubus fructicosus community was recorded
in Peshora, Gajikot and Naraza between elevations of 850– 1250m. In this
community 69 species were recorded. The biological spectrum was
dominated by therophytes with 14 species contributing maximum (26.38) IVI
value (table-4.5.1). Among leaf size spectra mesophyll were dominated with
27 species contributing 34.33 IVI value (table-4.5.2). The dominance of
therophytes indicates disturbed vegetation due to overgrazing and human
interference; however the dominance of mesophyll leaf size spectra indicates
that this community receives a good amount of precipitation and having
moist environmental conditions.
4.5.1.1 .3 Pinus, Rubus, Cynodon community
Pinus roxburghii, Rubus fructicosus, Cynodon dactylon community was recorded
in 4 stands i.e. Batangi, Khaiabad, Nowshera and Paimal I between elevations
of 1200–1650m. In this community a total of 53 species were recorded.
82
Table- 4.5.1 The IVI contribution of Biological spectrum of the subtropical plant communities.
ADD PMR PRC PQE QDQ QSI
Megaphanerophytes 1.31 13.33 13.8 12.37 4.70 3.23
Mesophanerophytes 13.39 8.91 5.74 10.66 24.97 18.20
Microphanerophytes 19.52 4.40 0.97 8.61 8.13 8.40
Nanophanerophytes 16.38 13.01 24.54 29.2 28.37 50.84
Chamaephytes 12.16 13.2 7.99 10.26 7.33 3.41
Hemicryptophytes 6.54 11.03 20.6 14.77 8.35 7.20
Geophytes 8.63 9.74 4.88 0.00 4.84 4.47
Therophytes 22.09 26.38 21.48 14.13 13.31 4.07
Table- 4.5.2 The IVI contribution of leaf size spectra of the subtropical plant communities.
ADD PMR PRC PQE QDQ QSI
Macrophyll 5.97 2.19 1.48 4.46 3.63 0.00
Mesophyll 26.73 34.33 25.91 31.23 36.97 35.17
Microphyll 41.59 28.25 33.02 34.96 33.72 38.96
Nanophyll 22.67 29.07 35.31 29.35 20.67 24.21
Leptophyll 2.27 4.82 4.14 0.00 5.00 1.48
Aphyllus 0.78 1.35 0.14 0.00 0.00 0.00
Table- 4.5.3 The similarity and dissimilarity indices of the subtropical plant communities.
ADD PMR PRC PQE QDQ QSI
ADD 27.34 18.70 5.49 21.48 12.90
PMR 72.66 33.61 10.00 26.35 16.26
PRC 81.30 66.39 18.92 21.21 17.76
PQE 94.51 90.00 81.08 10.00 17.33
QDQ 78.52 73.65 78.79 90.00 27.82
QSI 87.10 83.74 82.24 82.67 72.18 ADD: Acacia, Dodonaea, Dalbergia community; PMR: Pinus, Micromeria, Rubus community; PRC: Pinus, Rubus, Cynodon community; PQE: Pinus, Quercus, Eleagnus community; QDQ: Quercus, Dodonaea, Quercus community; QSI: Quercus, Spiraea, Indigofera community
83
Among biological spectrum therophytes were dominated with 12 species
followed by hemicryptophytes with 11 species while maximum IVI value
(24.54) was contributed by nanophanerophytes (table-4.5.1). The leaf size
spectra were dominated by microphyll with 21 species, followed by
mesophyll with 18 species, while nanophyll with 10 species contributed
maximum (35.31) IVI value (table-4.5.2).). The dominance of therophytes and
hemicryptophytes vegetation indicates disturbed vegetation while leaf size
spectra showed slightly dry environmental conditions.
4.5.1.1 .4 Pinus, Quercus, Eleagnus community
Pinus roxburghii, Quercus incana, Elaegnus umbellata community was recorded
between elevations of 1800– 1900m in Lamai. In this community 21 species
were recorded. Biological spectrum was dominated by nanophanerophytes
with 7 species contributing 29.2 IVI value (table-4.5.1). Leaf size spectra was
dominated by microphyll with 8 species contributing maximum (34.96) IVI
value followed by mesophyll with 7 species (table-4.5.2). The dominance of
nanophanerophytes and microphyll indicates moist environmental
conditions.
4.5.1.1.5 Quercus, Dodonaea, Quercus community
Quercus incana, Dodonaea vescosa, Quercus glauca community was recorded in
Chorlangay, Shagai and Paimal II between elevations of 730–1160m. In this
community a total of 79 species were recorded. Among biological spectrum
nanophanerophytes was dominated with 22 species contributing 28.37 IVI
value (table-4.5.1), followed by therophytes with 13 species. Among leaf size
spectra microphyll was dominated with 30 species, followed by mesophyll
with 27 species contributing 36.97 IVI value (table-4.5.2). The dominance of
nanophanerophytes life form, microphyll and mesophyll leaf size spectra
indicates moist environmental conditions.
84
4.5.1.1.6 Quercus, Spiraea, Indigofera Community
Quercus incana, Spiraea vaccinifolia, Indigofera heterantha community were
recorded between elevations of 1220–1320m in Nilishung Reen, Paimal III and
Paimal IV. In this community 54 species were recorded. Biological spectrum
was dominated by nanophanerophytes with 19 species contributing 50.84 IVI
value (table-4.5.1). Among leaf size spectra microphyll were dominated with
22 species contributing 38.96 IVI value followed by mesophyll with 19 species,
nanophyll with 10 species and leptophyll with 3 plant species (table-4.5.2).
The dominance of nanophanerophytes and microphyll leaf size spectra
indicates good environmental conditions.
4.5.1.2 Ordination of Subtropical Vegetation
The data of subtropical vegetation were further analyzed for Ordination. In
ordination Bray-Curtis ordination, DCA with supplementary variables and
CCA were used.
In Bray-Curtis ordination the ordination scores (Distances) were recorded for
three axis. The ordination score was maximum for axis 2 (0.921). The
ordination scores on axes 1 were from Naraza (0.000) to Paimal III (0.825). The
regression coefficient for axis 1 was -8.81, variance in distance from the first
end point were 0.37. Axis 1 extracted 24.69% of original distance matrix. The
ordination scores for axis 2 was from Paimal I (0.000) to Thakot II (0.921). The
regression coefficient for axis 2 were –11.32, variance in distance from the first
end point were 0.34. Axis 2 extracted 22.89% of original distance matrix. The
ordination scores for axis 3 was from Thakot I (0.000) to Lamai (0.707). The
regression coefficient for axis 3 were -5.92, variance in distance from the first
end point were 0.15. Axis 3 extracted 8.53% of original distance matrix
(fig.4.5.2).
85
Fig 4.5.2: Bray-Curtis ordination of the subtropical zone of the study area.
Fig. 4.5.3: DCA ordination of the subtropical zone of the area.
86
Fig. 4.5.4: CCA ordination of the subtropical zone of the study area.
Fig. 4.5.5: CCA ordination of the subtropical zone of the study area.
87
The response data were compositional and have a gradient 3.3 SD units long,
so the recommended unimodal method (DCA and CCA) were used. In DCA
ordination with supplementary variables the maximum gradient length (3.35)
were recorded for axis 1 with eigenvalue 0.50. The gradient length for axis 2
was 2.38 with eigenvalue 0.30. Total variance ("inertia") in the species data
were 2.92, supplementary variables account for 100%, while the adjusted
explained variation were 0%. The DCA clearly indicates that the whole data
set is dominated by a single dominant gradient. Different stands and species
were clustered in the ordination space as summarized by TWINSPAN
classification.
In CCA ordination the maximum Eigenvalue were recorded for axis 1 (0.50)
followed by axis 2 (0.42) and axis 3 (0.31). The percentage variance explained
for axis 1, 2 and 3 were 17.21%, 31.88% and 42.64% respectively. The total
variance (inertia) in the species data were 2.92, explanatory variables account
for 100% while adjusted explained variation were 0.0%. The pseudo-canonical
correlation for axis 1, 2 and 3 were 0.996, 0.967 and 0.999. The correlation
between sample score for an axis derived from the species data and the
sample scores that are linear combination of the environmental variable. The
permutation test results for all axes were pseudo-F<0.1, P=1.
CCA ordination showed that the different stands of Acacia, Dodonaea,
Dalbergia community clustered at high temperature, barometric pressure,
phosphorus, slope angle and wind speed. Pinus, Micromeria, Rubus
community were positively correlated with dew point, wet bulb, heat index
and pH value. Pinus, Rubus, Cynodon community was positively correlated
with humidity and organic matter. Pinus, Quercus, Eleagnus community was
more associated with slope aspect and high altitude. Quercus, Dodonaea,
Quercus community were recorded at high soil Potassium and soil saturation.
Quercus, Spiraea, Indigofera community were negatively correlated with most
of the environmental variables. These results showed that the particular
88
environmental variable has a great affect on the distribution of different plant
communities in Nandiar Khuwar catchment (fig. 4.5.4).
The results of DCA and CCA ordination of species and environmental
variables showed that different species clustered along different
environmental variables. The maximum strength was recorded for
temperature, barometric pressure, altitude, density altitude, phosphorous and
wind speed. The minimum environmental variable strength was recorded for
organic matter, potassium and soil saturation. Indigofera heterantha and
Berberis lycium were more correlated with high altitudes. Plantago lanceolatum,
Panicum species, Origanum vulgare, Taraxicum officinale were more correlated
with high atmospheric humidity and soil organic matter. Xanthium
stromarium, Ficus carica were more closely correlated with high pH values,
heat index and wet bulb. Similarly Artemisia japonica, Acacia modesta, Albezia
lebbeck were more closely correlated with high barometric pressure,
temperature, wind speed and P mg/kg. These results show that a specific
environmental variable has a great impact on species distribution in different
vegetational zones of the study area (fig. 4.5.5).
89
4.5.2 Mixed Pinus roxburghii and Pinus wallichiana Forests
In mixed Pinus roxburghii and Pinus wallichiana forests a total of 130 plant
species were recorded from 12 stands between an altitudinal zones of 1250 –
2050m above mean sea level. In this vegetational zone four plant communities
were recognized by TWINSPAN classification. In mixed Pinus roxburghii and
Pinus wallichiana forests of Nandiar Khuwar catchment the biological
spectrum were dominated by nanophanerophytes with 25 species followed by
geophytes contributing 24 species. The leaf size spectra were dominated by
microphyll with 58 species followed by mesophyll contributing 42 species.
The maximum similarity index value (32.9) were recorded between Pinus
roxburghii, Pinus wallichiana, Quercus incana Community and Quercus incana,
Pinus roxburghii, Pinus wallichiana Community. The index of dissimilarity was
maximum (82.80) between Pinus roxburghii, Pinus wallichiana Quercus incana
community and Pinus wallichiana, Quercus incana, Fragaria nubicola community
(table-4.5.6).
4.5.2.1 TWINSPAN Classification
The data obtained from 12 stands in the mixed Pinus roxburghii and Pinus
wallichiana forests were analyzed by TWINSPAN classification. Four distinct
plant communities were recognized in this vegetational zone on cut level 2
(fig. 4.5.6). These communities are Pinus, Pinus, Quercus community, Quercus,
Pinus, Pinus community, Quercus, Pinus, Myrsine community and Pinus,
Quercus, Fragaria community.
4.5.2.1.1 Pinus, Pinus, Quercus Community
Pinus roxburghii, Pinus wallichiana, Quercus incana community was recorded in
two stands Gada and Belandkot between an elevation of 1250-1650m. In this
community a total of 83 species were recorded. The biological spectrum was
dominated by nanophanerophytes with 20 species contributing maximum IVI
value (20.72), followed by geophytes with 16 species (table-4.5.4). Among leaf
90
size spectra microphyll was represented by 37 species contributing 37.3 IVI
value, followed by mesophyll with 27 species (table-4.5.5). The dominance of
nanophanerophytes and microphyll indicates that this community receives a
good amount of precipitation, having moist conditions and was less
disturbed.
4.5.2.1.2 Quercus, Pinus, Pinus Community
Quercus incana, Pinus roxburghii, Pinus wallichiana community was recorded
between an elevation of 1450-1800m in three stands Lundai I, Anora II and
Anora III. In this community a total of 72 species were recorded. Biological
spectrum was dominated by nanophanerophytes with 18 species contributing
maximum (26.83) IVI value (table-4.5.4). Among leaf size spectra 26 plant
species were contributed by each mesophyll and microphyll, while maximum
IVI value (37.45) was contributed by mesophyll (table-4.5.5). The dominance
of nanophanerophytes, mesophyll and microphyll indicates that this
community receives a good amount of precipitation, having moist conditions
and was less disturbed.
Fig. 4.5.6: TWINSPAN Classification of mixed Pinus and Pinus forests.
91
Table- 4.5.4 IVI contributed by biological spectrum in plant communities of mixed Pinus Pinus forests.
PPQ QPP QPM PQF
Megaphanerophytes 10.81 11.78 16.64 14.42
Mesophanerophytes 13.68 9.96 11.9 12.32
Microphanerophytes 5.06 5.82 0.86 9.55
Nanophanerophytes 20.72 26.83 25.58 18.91
Chamaephytes 11.74 7.1 7.79 5.98
Hemicryptophytes 7.95 13.98 16.79 10.73
Geophytes 16.56 10.31 9.68 12.97
Therophytes 13.48 14.22 10.7 15.14
Table- 4.5.5 IVI contributed by leaf size spectra in plant communities of mixed Pinus Pinus forests.
PPQ QPP QPM PQF
Macrophyll 1.46 1.41 0.47 3.46
Mesophyll 32.23 37.45 26.94 41.53
Microphyll 37.3 34.14 34.03 27.67
Nanophyll 23.8 23.99 34.86 24.37
Leptophyll 4.26 2.67 3.64 2.98
Aphyllus 0.94 0.34 0 0
Table- 4.5.6 Similarity and dissimilarity indices in plant communities of mixed Pinus Pinus forests.
PPQ QPP QPM PQF
PPQ
32.9 25.49 17.6
QPP 67.1
30.99 25.44
QPM 74.51 69.01
24.11
PQF 82.4 74.56 75.89
PPQ: Pinus, Pinus, Quercus Community; QPP: Quercus, Pinus, Pinus Community; QPM: Quercus, Pinus, Myrsine Community; PQF: Pinus, Quercus, Fragaria Community
92
4.5.2.1.3 Quercus, Pinus, Myrsine Community
Quercus incana, Pinus wallichiana, Myrsine africana community was recorded
from five stands Shabora I, Shabora II, Deshara, Paimal V and Dabrai
between an elevations of 1250-1550m. In this community a total of 70 species
were recorded. The biological spectrum was dominated by hemicryptophytes
with 16 species, geophytes with 15 species and therophytes with 13 species
while maximum IVI value (25.58) was contributed by nanophanerophytes
(table-4.5.4). The leaf size spectra were dominated by microphyll with 30
species, while maximum IVI value (34.86) was contributed by nanophyll
(table-4.5.5). The vegetation of this community was disturbed due to
overgrazing, soil erosion and human impact.
4.5.2.1.4 Pinus, Quercus, Fragaria Community
Pinus wallichiana, Quercus incana, Fragaria nubicola community was recorded
between an elevation of 1800-2050min two stands Lundai II and Sarmast
contributing 42 plant species. Biological spectrum was dominated by
nanophanerophytes with 10 species contributing 18.91 IVI value was
nanophanerophytes (table-4.5.4). Among leaf size spectra mesophyll was
dominated with 18 species contributing 41.53 IVI value. The vegetation of this
community receives good amount of precipitation, having moist conditions,
low temperature and are less disturbed.
4.5.2.2 Ordination of Vegetation of Mixed Pinus Pinus Forests
In Bray-Curtis ordination (ordination of stands in species space) 12 stands and
130 species were analyzed. The ordination scores (Distances) were from
Anora II (0.000) to Deshara (0.746) on axes 1. The regression coefficient for this
axis were -4.74, variance in distance from the first end point were 0.09. Axis 1
extracted 18.14% of original distance matrix. The ordination scores for axis 2
was from Gada (0.000) to Dabrai (0.725). The regression coefficient for this
axis were –6.66, variance in distance from the first end point were 0.08. Axis 2
93
extracted 17.84% of original distance matrix. The ordination scores for axis 3
was from Belandkot (0.000) to Sarmast (0.658). The regression coefficient for
this axis were -5.88, variance in distance from the first end point were 0.07.
Axis 3 extracted 13.68% of original distance matrix.
The response data are compositional and have a gradient 2.4 SD units long, so
the recommended linear method was used. In linear method PCA with
supplementary variables showed that the total variation were 294.10,
supplementary variables account for 100%. The maximum eigenvalue were
recorded for axis 1 with 0.17. The Eigenvalues for axis 2 and axis 3 were 0.15
and 0.14 respectively. The explained variation for Axis 1, 2 and 3 were 17.71,
32.33 and 46.41 respectively. The Pseudo-canonical correlation (suppl.) for all
axis were 0.00. The PCA analysis showed the correlation of different species
in ordination space. The variables in the data set have linear interrelationship.
The correlation is positive when angle is sharp and negative when the angle is
larger than 90. In this analysis it was observed that Pinus wallichiana, Quercus
dilatata, Indigofera heterantha, Pinus roxburghii and Sarcococca saligna were
positively correlated with each other while negatively correlated with
Heteropogon contortus. Similarly Fragaria nubicola, Rhododendron arboreum, Rhus
javanica, Cotinus coggyria and Bergenia ciliata were positively correlated with
each other while negatively correlated with Rubus fructicosus (fig.4.5.7).
In DCA ordination with supplementary variables the maximum gradient
length (2.43) were recorded for axis 1 with eigenvalue 0.35. The gradient
length for axis 2 was 2.07 with eigenvalue 0. 17. Total variance ("inertia") in
the species data were 2.15, supplementary variables account for 100%. The
Pseudo-canonical correlation (suppl.) for all axis were 0.00. In DCA
ordination different species were clustered in ordination space. Bergenia
ciliata, Rhododendron arboreum and Viburnum cotinifolium were more correlated.
Similarly Pinus wallichiana, Pinus roxburghii, Indigofera heterantha and
Sarcococca saligna clustered near each other (fig 4.5.8).
94
Fig. 4.5.7: DCA ordination of mixed Pinus Pinus forests.
Fig. 4.5.8: DCA ordination of mixed Pinus Pinus forests.
0.0 2.5
0.0
2.5
Gada
Shabora I
Paimal V Deshara
Dabrai
Shabora II
Lundai I
Belandkot Anora III
Lundai II
Anora II
Sarmast
95
In CCA ordination the maximum Eigenvalue were recorded for axis 1 (0.35)
followed by axis 2 (0.30) and axis 3 (0.25). The percentage variance explained
for axis 1, 2 and 3 were 16.45%, 30.78% and 42.52% respectively. The total
variance (inertia) in the species data were 2.15, explanatory variables account
for 100%. Pseudo-canonical correlation for all axis were 1.00. The permutation
test results for all axes were pseudo-F<0.1, P=1.
In CCA ordination the maximum strength were recorded for the
environmental variables temperature, barometric pressure, heat index, wet
bulb, dew point, humidity and altitude. The average values of the
environmental variables were recorded for wind speed, slope aspect,
phosphorus and pH. The intermediate strengths were recorded for soil
saturation, slope angle, density altitude, soil organic matter, electrical
conductivity and Potassium. The species of different stands of Pinus, Pinus,
Quercus community were positively correlated with dew point, wet bulb and
electrical conductivity. The species of different stands of Quercus, Pinus, Pinus
community were clustered at almost average position. The species of different
stands of Quercus, Pinus, Myrsine community were negatively correlated with
most of the environmental variables. The species of different stands of Pinus,
Quercus, Fragaria community were clustered at high altitude, soil saturation
and atmospheric humidity (fig.4.5.9 and 4.5.10).
96
Fig. 4.5.9: CCA ordinationof mixed Pinus Pinus forests.
Fig. 4.5.10: CCA ordination of mixed Pinus Pinus forests.
97
4.5.3 Moist temperate pure Pinus wallichiana forests
In the moist temperate pure Pinus wallichiana forests of Nandiar Khuwar
catchment a total of 200 species were recorded from 23 stands between
altitudinal zones of 1800-2400m. Five distinct plant communities were
recognized by TWINSPAN. In over all pure Pinus wallichiana forests of
Nandiar Khuwar catchment the biological spectrum were dominated by
therophytes with 46 species followed by geophytes contributing 44 species.
The leaf size spectra were dominated by microphyll with 89 species followed
by mesophyll contributing 58 species. In unconstrained DCA ordination with
supplementary variables the maximum gradient length (3.51) were recorded
for axis 1. In CCA ordination the maximum strength were recorded for the
environmental variables temperature, barometric pressure, wind speed and
altitude. The maximum index of similarity (28.5) was recorded between Pinus
wallichiana, Sarcococca saligna, Berberis lycium community and Pinus wallichiana,
Quercus incana, Berberis lycium community. The maximum index of
dissimilarity (91.33) were recorded between Pinus wallichiana, Cynodon
dactylon, Themeda anathera Community and Pinus wallichiana, Quercus incana,
Berberis lycium community (table 4.5.9). The IVI contribution of various classes
of biological spectrum in different plant communities of pure Pinus wallichiana
forests are presented in table 4.5.7. The IVI contribution of various classes of
leaf size spectra in different plant communities of pure Pinus wallichiana
forests are presented in table 4.5.8.
4.5.3.1 TWINSPAN Classification of Pure Pinus wallichiana Forests
The data obtained from 23 stands in the moist temperate pure Pinus
wallichiana forests of Nandiar Khuwar catchment were analyzed by
TWINSPAN classification and a total of five distinct plant communities were
recognized. These communities are described below:
98
Fig. 4.5.11: TWINSPAN classification of moist temperate pure Pinus
wallichiana zones
99
4.5.3.1 .1 Pinus, Cynodon, Themeda Community
Pinus wallichiana, Cynodon dactylon, Themeda anathera community was recorded
between an elevation of 1600-1800m in two stands Nili-Sharkolai and Nili-
Batangi. In this community 33 species were recorded. The biological spectrum
was dominated by therophytes with 12 species contributing maximum (26.85)
IVI value (table-4.5.7). Leaf size spectra were dominated by microphyll with
14 species, followed by nanophyll with 10 species contributing 49.75 IVI value
(table-4.5.8). The vegetation of this community is disturbed due to
deforestation and overgrazing. This community receives good amount of
precipitation and having moist environmental conditions.
4.5.3.1 .2 Pinus, Quercus, Spiraea Community
Pinus wallichiana, Quercus incana, Spiraea vaccinifolia community was recorded
from three stands Rajmira, Anora 1 and Shinglai between an elevation of
1450-1850m contributing 107 plant species. In this community biological
spectrum was dominated by nanophanerophytes with 26 species contributing
32.03 IVI value (table-4.5.7). Among leaf size spectra microphyll was
dominated with 45 species contributing 35.87 IVI value (table-4.5.8) followed
by mesophyll with 31 species. Biological spectra and leaf size spectra showed
that the stands of this community is preserved, having good environmental
conditions.
4.5.3.1 .3 Pinus, Dryopteris, Fragaria Community
Pinus wallichiana, Dryopteris jaxtaposita, Fragaria nubicola community was
recorded between an elevation of 1850-2050min four stands i.e. Kiari,
Bashakhan, Sharkolai and Jatial. In this community a total of 58 species were
recorded among which biological spectrum was dominated by geophytes and
therophytes each contributing 15 plant species. Maximum (18.71) IVI value
was contributed by geophytes (table-4.5.7). The dominance of geophytes and
100
therophytes indicates disturbed vegetation due to deforestation and
overgrazing. The leaf size spectra were dominated by microphyll with 23
species contributing maximum 32.81 IVI value followed by mesophyll with 18
species (table-4.5.8). The leaf size spectra showed that this community has
moist environmental conditions.
4.5.3.1 .4 Pinus, Sarcococca, Berberis Community
Pinus wallichiana, Sarcococca saligna, Berberis lycium community was recorded
in six stands Jarotia, Chapra, Sandawali, Hill, Bachmaidan and Terkana
between an elevations of 1750-2350m. In this community 76 species were
recorded. The biological spectrum was dominated by therophytes and
hemicryptophytes each contributing 15 plant species, followed by
nanophanerophytes and geophytes each contributing 11 plant species while
maximum (24.56) IVI value were contributed by nanophanerophytes (table-
4.5.7). Among leaf size spectra microphyll was dominated with 34 species
contributing 38.77 IVI value, followed by mesophyll with 28 species (table-
4.5.8). This community is disturbed due to deforestation, overgrazing and soil
erosion however the environmental conditions are moist and low
temperature.
4.5.3.1 .5 Pinus, Quercus, Berberis Community
Pinus wallichiana, Quercus incana, Berberis lycium community were recorded
between an elevation of 1800-2300m in eight stands Gat, Sheed, Habibbanda I,
Habibbanda II, Chapar, Riar, Mirani kandao and Jaro. In this community a
total of 117 species were recorded among which biological spectrum was
dominated by geophytes with 35 species while maximum (20.98) IVI value
was contributed by nanophanerophytes (table-4.5.7). The leaf size spectra
were dominated by microphyll with 54 species contributing 39.96 IVI value
(table-4.5.7). Overgrazing and deforestation are common in this community.
101
Table-4.5.7 The IVI contribution of Biological spectrum in plant communities of pure Pinus wallichiana forests.
PCT PQS PDF PSB PQB
Megaphanerophytes 16.8 9.75 14.07 13.66 10.16
Mesophanerophytes 1.60 10.52 4.97 4.92 9.70
Microphanerophytes 0.00 6.62 1.74 4.45 8.10
Nanophanerophytes 15.25 32.03 15.95 24.56 20.98
Chamaephytes 11.21 8.96 9.72 9.32 9.51
Hemicryptophytes 20.1 9.73 18.3 15.57 7.22
Geophytes 8.20 14.5 18.71 14.83 19.64
Therophytes 26.85 7.88 16.55 12.7 14.69
Table- 4.5.8 The IVI contribution of leaf size spectra in plant communities of pure Pinus wallichiana forests.
PCT PQS PDF PSB PQB
Macrophyll 0.00 1.99 0.38 5.50 2.98
Mesophyll 9.80 33.86 32.02 26.84 30.03
Microphyll 34.77 35.87 32.81 38.77 39.96
Nanophyll 49.75 21.36 28.55 27.04 24.62
Leptophyll 5.68 4.30 4.63 1.40 1.26
Aphyllus 0.00 2.62 1.61 0.47 1.15
Table- 4.5.9 Similarity and dissimilarity indices in the communities of pure Pinus wallichiana forests.
PCT PQS PDF PSB PQB
PCT
10.00 24.18 13.76 8.67
PQS 90.00
21.82 21.86 25.00
PDF 75.82 78.18
25.37 20.00
PSB 86.24 78.14 74.63
28.50
PQB 91.33 75.00 80.00 71.50
PCT: Pinus, Cynodon, Themeda community; PQS: Pinus, Quercus, Spiraea community; PDF: Pinus, Dryopteris, Fragaria community; PSB: Pinus, Sarcococca, Berberis community; PQB: Pinus, Quercus, Berberis community.
102
4.5.3.2 Ordination of Vegetation of Pure Pinus wallichiana Forests
In Bray-Curtis ordination the ordination scores (distances) were from Riar
(0.000) to Nilishung Sharkolai (0.865) on axes 1. The regression coefficient for
this axis was -12.32, variance in distance from the first end point were 0.53.
Axis 1 extracted 21.26% of original distance matrix. The ordination scores for
axis 2 were from Mirani Kandao (0.000) to Rajmira (0.678). The regression
coefficient for this axis was –6.68, variance in distance from the first end point
was0.26. Axis 2 extracted 8.71% of original distance matrix. The ordination
scores for axis 3 was from Chapar (0.000) to Bashakhan (0.685). The regression
coefficient for this axis was -8.67, variance in distance from the first end point
was0.28. Axis 3 extracted 11.35% of original distance matrix (fig.4.5.12).
The response data were compositional and have a gradient 3.5 SD units long,
so the recommended unimodal method (DCA and CCA) was used. In
unconstrained DCA ordination with supplementary variables the maximum
gradient length (3.51) was recorded for axis 1 with eigenvalue 0.47. The
gradient length for axis 2 was 2.45 with eigenvalue 0.33. Total variance
("inertia") in the species data was 346, supplementary variables account for
82.1%, while adjusted explained variation was 21.3%. The DCA clearly
indicates that the whole data set is dominated by a single dominant gradient.
The pseudo-canonical correlation for axis 1, 2 and 3 were 0.896, 0.972 and
0.869 respectively.
In DCA ordination different species were clustered in ordination space.
Pseudognaphalium hypolecum, Geranium wallichianum and Anaphalis busa
clustered at the top of ordination space and are positively correlated. These
species were negatively correlated with Oxalis corniculata, Heteropogon
contortus, Plantago lanceolatum, Taraxiacum officinale, Indigofera heterantha, Pinus
wallichiana and Fragaria nubicola. Similarly all these species were negatively
correlated with Rosa moschata and Rubus ellipticus (fig.4.5.13).
103
Fig. 4.5.12: Bray-Curtis ordination of moist temperate pure Pinus wallichiana zone.
Fig. 4.5.13: DCA ordination of moist temperate pure Pinus wallichiana
zone.
104
Fig. 4.5.14: CCA ordination of pure Pinus wallichiana zone.
Fig. 4.5.15: CCA ordination of pure Pinus wallichiana zone.
-0.6 1.0
-0.8
0.8
Slope Angle
Slope Aspct
Altitude
Baro
Den Alti
Temp
Wind Speed
Humidity
Heat Index
Dew Point
Wet BulbEC
PHOrg Matt
P mg/kg
K mg/kg
Saturati
Adi cap
Ail alt
Aju bra
All pet
Ana bus
And cor
Asp fil
Ast amoAst spp
Ber cil
Bol bar
Car gra
Cel aus
Cir falCle con
Cle gra
Cot cog
Cot mic
Cot num
Cus gig
Cyn dac
Dap mucDeb sal
Des ele
Deu sta
Dic bupDio lotDio melDry mac
Duh cap
Ela umbEqu arvEqu heiFic carFic palFic sar
Gag sat
Gal apa
Ger wal
Het con
Imp bic
Lon qui
Lot cor
Lyg haz
Mar vul
Mic bif
Mor sppMyr afr
Oen aff
Ole fer
Ooklo
Oxa corPan spp
Pic hie
Pla lan
Poa spp
Pol lon
Pse hyp
Pte cre
Pte urt
Pyr pas
Que dil
Ran lae
Rhu javRob pse
Rub ell
Rub ulm
Rum denRum has
Sag the
Sar sal
Sel san
Smi gla
Sol amp
Spi vac
Tar offTul ste
Ulm spp
Vib cot
Vib mul
Vio can
Woo uniZan arm
105
In CCA ordination in the moist temperate pure Pinus wallichiana forests the
maximum Eigenvalue was recorded for axis 1 (0.43) followed by axis 2 (0.36)
and axis 3 (0.25). The percentage variance explained for axis 1, 2 and 3
was12.56%, 22.98% and 30.28% respectively. The total variance (inertia) in the
species data was 3.46, explanatory variables account for 82.1%, while adjusted
explained variation was 21.3%. Pseudo-canonical correlation for all axes was
1.00. The permutation test results for all axes was pseudo-F=1.4, P=0.006.
In CCA ordination the maximum strength was recorded for the
environmental variables temperature, barometric pressure, wind speed and
altitude. The average values of the environmental variables were recorded for
pH. The intermediate strengths were recorded for all other environmental
variables. Most of the environmental variables were positively correlated with
each other while negatively correlated with barometric pressure, temperature
and wind speed. Most of the stands were found near the average value.
The species of different stands of Pinus, Cynodon, Themeda community were
positively correlated with wind speed. The species of different stands of
Pinus, Quercus, Spiraea community were positively correlated with slope angle
and barometric pressure. The species of different stands of Pinus, Dryopteris,
Fragaria community were clustered at almost average position. The species of
different stands of Pinus, Sarcococca, Berberis community and Pinus, Quercus,
Berberis community were positively correlated with most of the
environmental variables (fig.4.5.14).
The DCA ordination of species and environmental variables and the results
obtained from CCA ordination showed that different species were clustered
along different environmental variables. Different species like Cotoneaster
microphylla, Celtis australis, Cuscuta gigantean, Daphne mucronata, Diospyros
lotus, Elaegnus umbellata, Ficus carica and Ficus palmata etc were positively
correlated with temperature and barometric pressure. Similarly Rumex
106
hastatus, Ajuga bracteosa, Heteropogon contortus etc were positively correlated
with wind speed. Many species were clustered near other environmental
variable. These results showed that a specific environmental variable has a
great impact on species distribution in moist temperate pure Pinus wallichiana
forests (fig.4.5.15).
107
4.5.4 Mixed Coniferous Forests
In western mixed coniferous forests of Nandiar Khuwar catchment 156
species were recorded from 15 stands between altitudinal zones of 2000-
3050m above mean sea level. Four distinct plant communities were
recognized by TWINSPAN. In unimodal method DCA ordination with
supplementary variables the maximum gradient length (2.69) was recorded
for axis 1 with eigenvalue 0.46. In CCA ordination in the western mixed
coniferous forests the maximum Eigenvalue was recorded for axis 1 (0.46) and
maximum strength was recorded for the environmental variables soil
saturation and phosphorus. In over all western mixed coniferous forests of
Nandiar Khuwar catchment the biological spectrum was dominated by
therophytes with 42 species followed by geophytes contributing 35 species.
The leaf size spectra were dominated by microphyll with 61 species followed
by mesophyll contributing 49 species. The maximum index of similarity
(28.57) was recorded between Pinus wallichiana, Abies pindrow, Picea smithiana
community and Pinus wallichiana, Viburnum cotinifolium Abies pindrow
community. The maximum index of dissimilarity (82.44) was recorded
between Pinus wallichiana, Abies pindrow, Picea smithiana community and Abies
pindrow, Pinus wallichiana, Picea smithiana community (table- 4.5.12).
4.5.4.1 TWINSPAN Classification of Mixed Coniferous Forests
The data obtained from 15 stands in the western mixed coniferous forests of
Nandiar Khuwar catchment were analyzed by TWINSPAN classification and
a total of four distinct plant communities were recognized. These
communities are described below:
4.5.4.1.1 Pinus, Abies, Picea Community
Pinus wallichiana, Abies pindrow, Picea smithiana community was recorded in
four stands i.e. Charoona, Harpal, Mirani I, Mirani II and Ledai between an
elevation of 2250-2950m.
108
Fig. 4.5.16: TWINSPAN classification of mixed coniferous forests.
109
In this community 87 species were recorded. The biological spectrum was
dominated by geophytes with 21 species contributing 21.97 IVI value
followed by therophytes with 19 species (table-4.5.10). Leaf size spectra were
dominated by microphyll with 35 species contributing 33.08 IVI value
followed by mesophyll with 30 species (table-4.5.11). The vegetation of this
community is disturbed due to soil erosion, deforestation and overgrazing
however environmental conditions are moist, receiving a good amount of
precipitation.
4.5.4.1.2 Pinus, Viburnum, Abies Community
Pinus wallichiana, Viburnum cotinifolium, Abies pindrow community were
recorded between an elevation of 2000-2900m in five stands Doba, Doda I,
Bach upper, Manra and Machaisar. In this community a total of 95 species
were recorded among which the biological spectrum was dominated by
geophytes with 24 species contributing 30.33 IVI value followed by
therophytes with 22 species (table-4.5.10). Among leaf size spectra microphyll
was dominated with 36 species contributing 29.47 IVI value followed by
mesophyll with 29 species. Overgrazing and deforestation are common in this
zone.
4.5.4.1.3 Abies, Quercus, Picea Community
Abies pindrow, Quercus semicarpifolia, Picea smithiana community was recorded
from four stands Kachkol, Belmaz, Lekoni and Magrai between an elevation
of 2800-3050m. In this community 67 species were recorded. The biological
spectrum was dominated by therophytes with 16 species contributing 22.14
IVI value (table-4.5.10). The leaf size spectra were dominated by mesophyll
with 24 species contributing 39.64 IVI value (table-4.5.11). The vegetation of
this community is disturbed due to overgrazing, deforestation, soil erosion
and land sliding however it receives good amount of precipitation in the form
of rainfall and snowfall.
110
Table-4.5.10 The IVI contribution of Biological spectrum in plant communities of mixed coniferous forests.
PAP PVA AQP APP
Megaphanerophytes 17.25 17.36 15.65 16.73
Mesophanerophytes 5.69 7.55 8.45 7.30
Microphanerophytes 7.55 6.72 4.70 5.29
Nanophanerophytes 3.53 8.86 13.68 9.78
Chamaephytes 11.33 9.06 4.28 4.77
Hemicryptophytes 12.41 11.79 11.12 7.68
Geophytes 21.97 20.33 20.00 21.23
Therophytes 20.26 18.33 22.14 27.13
Table- 4.5.11 The IVI contribution of leaf size spectra in plant communities of mixed coniferous forests.
PAP PVA AQP APP
Macrophyll 4.79 7.32 6.52 5.59
Mesophyll 30.37 29.44 39.64 40.79
Microphyll 33.08 29.47 25.22 25.99
Nanophyll 31.36 31.77 26.83 27.45
Leptophyll 0.00 0.81 1.80 0.00
Aphyllus 0.39 1.19 0.00 0.00
Table- 4.5.12 Similarity and dissimilarity indices in the communities of mixed coniferous forests.
PAP PVA AQP APP
PAP
28.57 20.78 17.56
PVA 71.43
23.46 20.14
AQP 79.22 76.54
27.93
APP 82.44 79.86 72.07
PAP: Pinus, Abies, Picea community; PVA: Pinus, Viburnum, Abies community; AQP: Abies, Quercus, Picea community; APP: Abies, Pinus, Picea community.
111
4.5.4.1.4 Abies, Pinus, Picea Community
Abies pindrow, Pinus wallichiana, Picea smithiana community was recorded in
Guchai between an elevation of 2250-2600m. In this community 44 plant
species were recorded. Biological spectrum was dominated by therophytes
with 15 species having 27.13 IVI value (table-4.5.10). Leaf size spectra were
dominated by mesophyll with 19 species contributing 40.79 IVI value (table-
4.5.11). The environmental conditions of this community are moist and low
temperature however this community was affected overgrazing, deforestation
and land sliding.
4.5.4.2 Ordination of Vegetation of Mixed Coniferous Forests
In Bray-Curtis ordination (ordination of stands in species space) 15 stands and
156 species were analyzed. The ordination scores were from Lekoni (0.000) to
Mirani I (0.752) on axes 1. The regression coefficient for this axis was -8.06,
variance in distance from the first end point were 0.48. Axis 1 extracted
28.82% of original distance matrix. The ordination scores for axis 2 were from
Harpal (0.000) to Doda I (0.752). The regression coefficient for this axis was –
7.13, variance in distance from the first end point was 0.26. Axis 2 extracted
16.09% of original distance matrix. The ordination scores for axis 3 were from
Kachkol (0.000) to Bach upper (0.486). The regression coefficient for this axis
was -0.88, variance in distance from the first end point were 0.19. Axis 3
extracted 7.75% of original distance matrix (fig.4.5.17).
In unimodal method DCA ordination with supplementary variables the
maximum gradient length (2.69) was recorded for axis 1 with eigenvalue 0.46.
The gradient length for axis 2 was 2.39 with eigenvalue 0. 25. Total variance
("inertia") in the species data was2.43, supplementary variables account for
79.2%, while adjusted explained variation were 2.9%. The DCA clearly
indicates that the whole data set is dominated by a single dominant gradient.
112
In DCA ordination different species of western mixed coniferous forests of
Nandiar Khuwar catchment were clustered in ordination space. The species
clustered together having positive correlation included Abies pindrow,
Viburnum cotinifolium, Primula denticulata, Quercus semicarpifolia, Paeonia emodi,
Picea smithiana, Potentilla nepalensis, Wikstroemia canescens, Geranium
wallichianum, Fragaria nubicola, Juglans regia, Pinus wallichiana, Androsace
hazarica and Caltha alba. These species were negatively correlated with Quercus
incana and Themeda anathera. The species of similar stands were clustered in
the ordination space almost same as were classified by TWINSPAN
ordination (fig.4.5.18).
In CCA ordination in the western mixed coniferous forests the maximum
eigenvalue was recorded for axis 1 (0.46) followed by axis 2 (0.32) and axis 3
(0.30). The percentage variance explained for axis 1, 2 and 3 was 18.98%,
32.33% and 44.98% respectively. The total variance (inertia) in the species data
was 2.43, explanatory variables account for 100%. Pseudo-canonical
correlation for all axis was 1.00. The permutation test results for all axes was
pseudo-F<0.1, P=1.
In CCA ordination the maximum strength was recorded for the
environmental variables like soil saturation and phosphorus while minimum
strength was recorded for atmospheric humidity and electrical conductivity.
In CCA ordination the different stands of Pinus, Abies, Picea community were
positively correlated with heat index, wet bulb and dew point. The stands of
Pinus, Viburnum, Abies community were positively correlated with
temperature, potassium, barometric pressure, soil saturation, organic matter
and pH value. The stands of Abies, Quercus, Picea community were strongly
correlated with altitude, density altitude and phosphorus. Abies, Pinus, Picea
community was positively correlated with slope angle (fig.4.5.19).
113
Fig. 4.5.17: Bray-Curtis ordination of mixed coniferous forests.
Fig. 4.5.18: DCA ordination of mixed coniferous forests.
114
Fig. 4.5.19: CCA ordination of mixed coniferous forests.
Fig. 4.5.20: CCA ordination of mixed coniferous forests.
115
The DCA ordination of species and environmental variables and the results
obtained from CCA ordination showed that different species were clustered
along different environmental variables. Themeda anathera and Pteracanthus
urticifolius were positively correlated with high temperature, Dryopteris serrate
dentata was positively correlated with soil organic matter. Androsace
rotundifolia and Paeonia emodi were positively correlated with slope angle.
Ranunculus palmatifidus was positively correlated with phosphorus. Lindelofia
stylosa and Valeriana himalayana were positively correlated with high altitude
and density altitude. Rheum austral and Delphinum vestitum were positively
correlated with slope aspect. Geranium wallichianum was positively correlated
with atmospheric humidity. These results showed that a specific
environmental variable has a great impact on species distribution in western
mixed coniferous forests of Nandiar Khuwar catchment (fig.4.5.20).
4.5.5 Pure Abies pindrow and Picea smithiana Forests
In pure Abies pindrow and Picea smithiana forests of Nandiar Khuwar
catchment a total of 111 species were recorded from 9 stands between
altitudinal zones of 2200-3000m above mean sea level. Three distinct plant
communities were recognized by TWINSPAN. In unimodal method DCA
ordination with supplementary variables the maximum gradient length (2.53)
was recorded for axis 1 with eigenvalue 0.50. In CCA ordination the
maximum eigenvalue was recorded for axis 1 (0.50). In CCA ordination the
maximum strength was recorded for the environmental variables like pH, soil
saturation, organic matter, heat index, wet bulb, dew point, wind speed and
humidity. In over all Pure Abies pindrow and Picea smithiana forests of Nandiar
Khuwar catchment the biological spectrum was dominated by therophytes
with 34 species followed by geophytes contributing 23 species. The leaf size
spectra were dominated by microphyll with 51 species followed by mesophyll
contributing 37 species. The maximum index of similarity (25.27) was
recorded between Abies pindrow, Quercus dilatata, Picea smithiana community
116
and Abies pindrow, Picea smithiana, Paeonia emodi community. The maximum
index of dissimilarity (79.67) was recorded between Picea smithiana, Abies
pindrow, Wikstroemia canescens community and Abies pindrow, Picea smithiana,
Paeonia emodi community (table 4.5.15).
4.5.5.1 TWINSPAN Classification of Pure Abies and Picea Forests
The data obtained from 9 stands in the vegetational zones of pure Abies
pindrow and Picea smithiana forests of Nandiar Khuwar catchment were
classified by TWINSPAN. Three distinct plant communities were recognized
by TWINSPAN. These communities are described below.
4.5.5.1.1 Picea, Abies, Wikstroemia Community
Picea smithiana, Abies pindrow, Wikstroemia canescens community was recorded
in four stands Trapa, Chail Kambar, Gabrai kandao and Chailsar between an
elevation of 2200-3000m. In this community a total of 77 species were
recorded among which the biological spectrum was dominated by geophytes
and therophytes, each group contributing 19 species. On the basis of
biological spectrum the maximum IVI value was contributed by geophytes
(25.73) (table-4.5.13). Among leaf size spectra microphyll was dominated
with 33 species contributing 35.90 IVI value followed by mesophyll with 25
species (table-4.5.14). This community receives a good amount of rainfall and
snowfall. The vegetation is disturbed due to land sliding, overgrazing and
deforestation.
4.5.5.1.2 Abies, Quercus, Picea Community
Abies pindrow, Quercus dilatata, Picea smithiana community was recorded
between an elevation of 2300-2700m in three stands Lunda matra, Baleja and
Chaprai. In this community a total of 46 species were recorded. Biological
spectrum was dominated by hemicryptophytes with 11 species followed by
geophytes with 10 species contributing 21.52 IVI value (table-4.5.13). Leaf size
117
spectra were dominated by mesophyll and microphyll each contributing 20
species. On the basis of leaf size spectra the maximum IVI value were
contributed by mesophyll (40.46) (table-4.5.14). This community is affected by
overgrazing.
4.5.5.1.3 Abies, Picea, Paeonia Community
Abies pindrow, Picea smithiana, Paeonia emodi community was recorded in two
stands Birthmaidan and Doda II between an elevation of 2700-2900m. In this
community 45 species were recorded. The biological spectrum was
dominated by therophytes with 14 species contributing 24.45 IVI value (table-
4.5.13). The leaf size spectra were dominated by microphyll with 20 species
followed by mesophyll with 15 species contributing maximum (33.09) IVI
value (table-4.5.14). Overgrazing and deforestation are common in this
community.
Fig. 4.5.21: TWINSPAN classification of pure Abies and Picea forests.
118
Table- 4.5.13 The IVI of Biological spectrum of pure Abies and Picea forests.
Picea, Abies, Wikstroemia
Abies, Quercus, Picea
Abies, Picea, Paeonia
Megaphanerophytes 13.94 14.05 19.20
Mesophanerophytes 2.41 15.28 0.00
Microphanerophytes 4.73 6.62 5.72
Nanophanerophytes 8.69 1.73 3.61
Chamaephytes 9.61 4.46 8.86
Hemicryptophytes 15.95 20.6 14.55
Geophytes 25.73 21.52 23.63
Therophytes 18.96 15.73 24.45
Table- 4.5.14 The IVI of leaf size spectra of pure Abies and Picea forests.
Picea, Abies, Wikstroemia
Abies, Quercus, Picea
Abies, Picea, Paeonia
Macrophyll 6.45 9.61 7.07
Mesophyll 29.91 40.46 33.09
Microphyll 35.9 34.59 32.63
Nanophyll 26.65 15.34 25.84
Leptophyll 1.09 0 0
Aphyllus 0 0 1.38
Table- 4.5.15 Similarity and dissimilarity indices of Abies - Picea forests.
Picea, Abies, Wikstroemia
Abies, Quercus, Picea
Abies, Picea, Paeonia
Picea, Abies, Wikstroemia
20.33 20.49
Abies, Quercus, Picea 79.67
25.27
Abies, Picea, Paeonia 79.51 74.73
119
4.4.5.2 Ordination of Vegetation of Pure Abies and Picea Forests
In Bray-Curtis ordination three axes were selected. The ordination scores
(Distances) were from Chail kambar (0.000) to Lunda matra (0.721) on axes 1.
The regression coefficient for this axis were -5.02, variance in distance from
the first end point were 0.15. Axis 1 extracted 31.54% of original distance
matrix. The ordination scores for axis 2 were from Gabrai kandao (0.000) to
Doda II (0.585). The regression coefficient for this axis was –4.96, variance in
distance from the first end point was0.07. Axis 2 extracted 16.54% of original
distance matrix. The ordination scores for axis 3 were from Baleja (0.000) to
Birth maidan (0.532). The regression coefficient for this axis was -3.25,
variance in distance from the first end point was0.05. Axis 3 extracted 12.31%
of original distance matrix (fig.4.5.22).
In unimodal method DCA ordination with supplementary variables the
maximum gradient length (2.53) was recorded for axis 1 with eigenvalue 0.50.
The gradient length for axis 2 was 2.40 with eigenvalue 0. 30. Total variance
("inertia") in the species data were 1.93, supplementary variables account for
100%. The DCA clearly indicates that the whole data set is dominated by a
single dominant gradient.
In DCA ordination different species of pure Abies pindrow and Picea smithiana
forests of Nandiar Khuwar catchment were clustered in ordination space.
The maximum correlation was recorded for Skimmia laureola, Androsace
hazarica and Pseudomertensia sp. The strong association was also recorded for
Prunus padus, Primula denticulata, Aesculus indica, Aquilegia pubiflora and
Polygonatum verticillatum. Similarly positive correlation was recorded for Abies
pindrow, Picea smithiana, Paeonia emodi, Viburnum cotinifolium and Rumex
nepalensis (fig.4.5.23).
The above species were softly correlated with Bistorta emodi, Trillidium
govanianum, Quercus semicarpifolia, Quercus dilatata and Geranium wallichianum.
Similarly all the above species were negatively correlated with Alotis stoliczkai,
120
Delphinum vestitum, Geum roylei, Inula acuminata, Inula royleana, Juniperus
communis and Pleurospermum brunonis. The species of similar stands were
clustered in the ordination space almost same as were classified by
TWINSPAN ordination.
In CCA ordination in the pure Abies pindrow and Picea smithiana forests the
maximum eigenvalue was recorded for axis 1 (0.50) followed by axis 2 (0.33)
and axis 3 (0.30). The percentage variance explained for axis 1, 2 and 3
was26.04%, 43.42% and 59.14% respectively. The total variance (inertia) in the
species data was1.93, explanatory variables account for 100%. Pseudo-
canonical correlation for all axis was 1.00. The permutation test results for all
axes were pseudo-F<0.1, P=1.
In CCA ordination the maximum strength was recorded for the
environmental variables pH, soil saturation, organic matter, heat index, wet
bulb, dew point, wind speed and humidity. The minimum strength was
recorded for phosphorus. In CCA ordination the different stands of Picea,
Abies, Wikstroemia community were positively correlated density altitude,
wind speed and dew point. The stands of Abies, Quercus, Picea community
were positively correlated with organic matter, temperature and barometric
pressure. The stands of Abies, Picea, Paeonia community were strongly
correlated with soil saturation and pH value (fig.4.5.24).
The DCA ordination of species and environmental variables and the results
obtained from CCA ordination showed that different species were clustered
along different environmental variables. Alotis stoliczkai, Delphinum vestitum,
Geum roylei, Inula acuminata, Inula royleana, Juniperus communis and
Pleurospermum brunonis were positively correlated with high wind speed,
density altitude and electrical conductivity. Tanacetum dolicophyllum and
Veronica laxa were positively correlated with slope aspect and humidity.
121
Fig. 4.5.22: Bray-Curtis ordination of pure Abies and Picea forests.
Fig. 4.5.23: DCA ordination of pure Abies and Picea forests.
122
Fig. 4.5.24: CCA ordination of pure Abies and Picea forests.
Fig. 4.5.25: CCA ordination of pure Abies and Picea forests.
123
Senicio species and Artemisia roxburghiana were positively correlated with
altitude and pH. Skimmia laureola and Androsace hazarica were positively
correlated with soil saturation. Abies pindrow, Picea smithiana, Paeonia emodi
and Primula denticulata were positively correlated with Potassium and soil
organic matter. Quercus semicarpifolia and Quercus dilatata were positively
correlated with barometric pressure and temperature. These results showed
that a specific environmental variable has a great impact on species
distribution in pure Abies pindrow and Picea smithiana forests of Nandiar
Khuwar catchment (fig.4.5.25).
4.5.6 Phytosociology in Alpine Vegetational Zone
The alpine pasture stretches above the tree limits in the Nandiar Khuwar
catchment between elevations of 2850 – 3800m above mean sea level. In this
zone five stands were selected and were analyzed by TWINSPAN
classification and two plant communities were identified. In Bray-Curtis
ordination maximum ordination scores were recorded on axes 1 from
Malkaisar (0.000) to Karganja (0.574). In unimodal method DCA ordination
with supplementary variables the maximum gradient length (1.67) was
recorded for axis 1 with eigenvalue 0.31. In CCA ordination in alpine pasture
the maximum eigenvalue was recorded for axis 1 (0.31). The biological spectra
of alpine pasture of Nandiar Khuwar catchment were dominated by
hemicryptophytes and therophytes each contributing 8 species indicating
disturbed vegetation due to overgrazing. The leaf size spectra were
dominated by microphyll contributing 12 species indicating moist
environmental conditions. The index of similarity between Wikstroemia,
Viburnum, Androsace community and Juniperus, Sibbaldia, Primula community
was 35.7%.
124
4.5.6.1 TWINSPAN Classification of Alpine Vegetational Zone
The data obtained from 5 stands in the alpine pasture of Nandiar Khuwar
catchment were classified by TWINSPAN. Two distinct plant communities
were recognized by TWINSPAN. These communities are described below.
4.5.6.1.1 Wikstroemia, Viburnum, Androsace Community
Wikstroemia canescens, Viburnum cotinifolium, Androsace community was
recorded from two stands Karganja and Shaheed Gali between altitudinal
zones of 2850–3100m. In this community 26 species were recorded. Biological
spectrum was dominated by geophytes with 6 species contributing 20.03 IVI
value (table-4.5.16). Leaf size spectra were dominated by microphyll with 10
species contributing 38.7 IVI value followed by mesophyll with 8 species
(table-4.5.17). The vegetation of this community is disturbed due to
overgrazing, however environmental conditions are moist and having low
temperature.
Fig. 4.5.26: TWINSPAN classification of alpine zone.
125
Table- 4.5.16 The IVI contribution of various classes of biological spectrum in different plant communities
Wikstroemia, Viburnum, Androsace community
Juniperus, Sibbaldia, Primula community
Individuals IVI Individuals IVI
Megaphanerophytes 01 6.15 00 00
Microphanerophytes 02 10.77 02 7.75
Nanophanerophytes 03 19.96 05 24.93
Chamaephytes 05 10.74 05 17.76
Hemicryptophytes 05 15.76 07 24.63
Geophytes 06 20.03 03 9.76
Therophytes 04 16.60 08 15.17
Table- 4.5.17 The IVI contribution of various classes of leaf size spectra in different plant communities
Wikstroemia, Viburnum, Androsace community
Juniperus, Sibbaldia, Primula community
Individuals IVI Individuals IVI
Macrophyll 01 2.14 00 00
Mesophyll 08 36.57 10 37.13
Microphyll 10 36.68 09 25.34
Nanophyll 07 22.63 10 28.64
Leptophyll 00 00 01 8.90
126
4.5.6.1.2 Juniperus, Sibbaldia, Primula Community
Juniperus communis, Sibbaldia cuneata, Primula denticulata community was
recorded from Kar Ganja top, Alishera and Malkaisar between an altitudinal
zones of 3250–3800m. In this community a total of 30 plant species were
recorded among which biological spectrum was dominated by therophytes
with 8 species while maximum IVI value (24.93) were contributed by
nanophanerophytes (table-4.5.16). The leaf size spectra were dominated by
mesophyll and nanophyll each contributing 10 species followed by
microphyll (9) and leptophyll (1). On the basis of leaf size spectra the
maximum IVI value (37.12) was contributed by mesophyll (table-4.5.17). The
environmental conditions are harsh in this community.
4.5.6.2 Ordination of Alpine Vegetational Zone
In Bray-Curtis ordination 5 stands and 37 species were analyzed. The
ordination scores were from Malkaisar (0.000) to Karganja (0.574) on axes 1.
The regression coefficient for this axis was -3.39, variance in distance from the
first end point was 0.15. Axis 1 extracted 55.99% of original distance matrix.
The ordination scores for axis 2 were from Alishera (0.000) to Shaheed Gali
(0.399) (fig.4.5.27). The regression coefficient for this axis was –2.98, variance
in distance from the first end point was 0.07. Axis 2 extracted 25.81% of
original distance matrix. The ordination scores (Distances) for axis 3 were
from Malkaisar (0.000) to Kar Ganja top (0.328). The regression coefficient for
this axis was -5.10, variance in distance from the first end point was 0.04. Axis
3 extracted 16.22% of original distance matrix.
Response data are compositional and have a gradient 1.7 SD units long, so a
linear method was also used. In linear method PCA with supplementary
variables showed that the total variation was 90.51. The maximum eigenvalue
was recorded for axis 1 with 0.54. The Eigenvalues for axis 2 and axis 3 were
0.23 and 0.19, respectively. The explained variation for Axis 1, 2 and 3 were
54.86, 78.82 and 98.38 respectively.
127
Fig. 4.5.27: Bray-Curtis ordination of alpine zone.
Fig. 4.5.28: DCA ordination of alpine zone.
128
In unimodal method DCA ordination with supplementary variables the
maximum gradient length (1.67) was recorded for axis 1 with eigenvalue 0.31.
The gradient length for axis 2 was 0.44 with eigenvalue 0.008. Total variance
("inertia") in the species data was 0.63, supplementary variables account for
100%. The DCA clearly indicates that the whole data set is dominated by a
single dominant gradient.
In DCA ordination different species of alpine pasture of Nandiar Khuwar
catchment were clustered in ordination space. The maximum correlation was
recorded for Sibbaldia cuneata, Potentilla nepalensis, Juniperus communis,
Valeriana himalayana and Primula denticulata. The correlation was also positive
for Thymus linearis, Aster himalaicus and Betula utilis. All the above species
were weakly correlated with Pseudomertensia, Caltha alba, Trillium govanianum,
Dryopteris jaxtapostia and Pteridium equilinum, while negatively correlated with
Thlaspi spp. and Skimmia laureola.
In CCA ordination in alpine pasture the maximum eigenvalue was recorded
for axis 1 (0.31) followed by axis 2 (0.17) and axis 3 (0.13). The percentage
variance explained for axis 1, 2 and 3 was 49.19%, 76.84% and 98.76%
respectively. The total variance (inertia) in the species data was 0.63,
explanatory variables account for 100%. Pseudo-canonical correlation for all
axis was 1.00. The permutation test results for all axes was pseudo-F<0.1, P=1.
In CCA ordination the maximum strength was recorded for all environmental
variables except slope aspect and slope angle. The stands of Juniperus,
Sibbaldia, Primula community were positively correlated with all
environmental variables except electrical conductivity, slope angle,
temperature and barometric pressure. Similarly one stand of Wikstroemia,
Viburnum, Androsace community was positively correlated with electrical
conductivity, slope angle temperature and barometric pressure while other
stands were negatively correlated with all environmental variables (fig.
4.5.30).
129
Fig. 4.5.29: CCA ordination of alpine zone.
Fig. 4.5.30: CCA ordination of alpine zone.
130
The DCA ordination of species and environmental variables and the results
obtained from CCA ordination showed that different species were clustered
along different environmental variables. Juniperus communis, Sibbaldia cuneata,
Potentilla nepalensis, Buplerum longicaule, Thymus linearis, Aster himalaicus were
positively correlated with different environmental variables like wet bulb,
dew point, density altitude, heat index, altitude, wind speed, pH,
phosphorus, organic matter, soil saturation and potassium. Bistorta emodi and
Adiantum incisum were more or less negatively correlated with all
environmental variables. Abies pindrow, Dryopteris jaxtapostia and Pteridium
equilinum were positively correlated with slope angle and temperature. These
results showed that a specific environmental variable has a great impact on
species distribution in pure Abies pindrow and Picea smithiana forests of
Nandiar Khuwar catchment area (fig.4.5.29).
4.6 Ordination of Samples on the Basis of Microclimatic Data
Microclimate is a local atmospheric zone where climate differs from the
surrounding area and it may be small as a few square feet or as large as many
square miles. The contributing factors to microclimate are the slope or aspect
of an area. The altitude, latitude and longitude were responsible for change in
microclimate. The microclimate has a great impact on vegetation of the study
area. The microclimate of Nandiar Khuwar catchment varies from sub
tropical to alpine zone. The vegetation was denser on north-facing slopes as
compared to south-facing slopes. Different microclimatic parameters like
temperature, wind speed, humidity, heat index, dew point, wet bulb,
barometric pressure, altitude and density altitude have great impact on local
vegetation. The ordinations of stands on the basis of microclimatic parameters
are described below.
4.6.1 Temperature
The air temperature of Nandiar Khuwar catchment is directly correlated with
altitude. The maximum temperature value was 33.9ºC recorded at Thakot
131
while the minimum temperature value was 15.5ºC recorded at Malkaisar. The
maximum temperature value represents the scrub forests at lower altitudes
while the minimum temperature value represents the alpine scrub at higher
altitudes. The correlation of temperature with axis 1 was 0.960, the regression
value was 0.276 and the total standard deviation of the response data was
0.878. The correlation of temperature with axis 2 was -0.198, the regression
value was -0.148 and the total standard deviation of the response data was -
0.156. The correlation of temperature with stands in species space is presented
in figure 4.6.1.
4.6.2 Wind Speed
In Nandiar Khuwar Catchment the average wind speed in different stands
ranged from 0.4m/s in Batangi, Paimal III, Paimal V, Chapar and Doda I to
1.9m/s at Malkaisar. The correlation of wind speed with axis 1 was -0.515, the
regression value was -0.054 and the total standard deviation of the response
data was -0.328. The correlation of wind speed with axis 2 was -0.518, the
regression value was -0.097 and the total standard deviation of the response
data was-0.173. The wind speed has no such great effect on the distribution of
species in different stands. The correlation of wind speed with stands in
species space is presented in figure 4.6.2.
4.6.3 Humidity
The humidity in Nandiar Khuwar catchment ranges from 18.1% measured in
Peshora to 62% in Bach upper. The correlation of humidity with axis 1 was -
0.605, the regression value was 0.141 and the total standard deviation of the
response data was -0.474. The correlation of humidity with axis 2 was -0.347,
the regression value was -0.146 and the total standard deviation of the
response data was 0.255. The correlation of atmospheric humidity with stands
in species space is presented in figure 4.6.3.
132
Fig. 4.6.1: The correlation of temperature with stands in species space.
Fig. 4.6.2: The correlation of wind speed with stands in species space.
133
4.6.4 Heat Index
Heat index is a practical measure of how hot the current combination of
relative humidity and temperature feel to human body. Higher relative
humidity makes it seem hotter because the body’s ability to cool itself by
evaporating perspiration is reduced. The heat index values in the Nandiar
Khuwar catchment ranges from 10.2 in Birth maidan to 38.6 in Khairabad. The
correlation of heat index with axis 1 was 0.499, the regression value was -0.022
and the total standard deviation of the response data was 0.376. The
correlation of heat index with axis 2 was -0.277, the regression value was-
0.283 and the total standard deviation of the response data was -0.125. The
correlation of heat index with stands in species space is presented in figure
4.6.4.
4.6.5 Dew Point
The dew point values in the Nandiar Khuwar catchment ranges from 2.6 in
Manra to 20.5 in Naraza. The correlation of dew point with axis 1 was -0.004,
the regression value was-0.057 and the total standard deviation of the
response data was -0.033. The correlation of temperature with axis 2 was
0.119, the regression value was 0.150 and the total standard deviation of the
response data was 0.134. The dew point temperature of the study area is
comfortable for most of the stands. The adaptation of different plant species
with dew point temperature and relative humidity indicates its ecological
amplitudes. The correlation of dew point with stands in species space is
presented in figure 4.6.5.
4.6.6 Wet Bulb
In the Nandiar Khuwar catchment the wet bulb data ranges from 6.7°C in
Birth Maidan to 23.6°C in Naraza. The wet-bulb temperature was suitable for
all stands of the study area as it is below 35°C. The correlation of wet bulb
with axis 1 was 0.329, the regression value was 0.029 and the total standard
deviation of the response data was -0.206.
134
Fig. 4.6.3: The correlation of Humidity with stands in species space.
Fig. 4.6.4: The correlation of Heat Index with stands in species space.
135
The correlation of temperature with axis 2 was -0.076, the regression value
was 0.080 and the total standard deviation of the response data was -0.005.
The correlation of wet bulb with stands in species space is presented in figure
4.6.6.
4.6.7 Barometric Pressure
The speed at which plants grow is affected by atmospheric pressure
conditions and at 101 kPa pressure each plant will grow at its ideal rate. At
too low atmospheric pressure, a plant cannot survive due to the lack of gas
exchange that can take place. The barometric pressure in different stands of
Nandiar Khuwar catchment ranged from 640.8 at Malkaisar 950.3 at Thakot II.
The barometric pressure has direct effect on the distribution of species. At
high barometric pressure the subtropical scrub forests occurred while at low
barometric pressure the alpine scrub occurred. The correlation of barometric
pressure with axis 1 was 0.967, the regression value was 0.537 and the total
standard deviation of the response data was 0.902. The correlation of
temperature with axis 2 was -0.188, the regression value was -6.573 and the
total standard deviation of the response data was -0.173. The correlation of
barometric pressure with stands in species space is presented in figure 4.6.7.
4.6.8 Altitude
In Nandiar Khuwar Catchment the altitude of different stands ranges from
530m in Thakot 1 to 3780m at Malkaisar. At low altitude the subtropical scrub
forests were identified while at higher altitude the alpine scrub forests were
recognized. The altitude has direct effect on the distribution of different
species of the study area. The correlation of altitude with axis 1 was -0.974, the
regression value was -0.114 and the total standard deviation of the response
data was -0.901.
136
Fig. 4.6.5: The correlation of Dew Point with stands in species space.
Fig. 4.6.6: The correlation of Wet Bulb with stands in species space.
137
The correlation of altitude with axis 2 was 0.137, the regression value was -
6.444 and the total standard deviation of the response data was 0.173. The
correlation of altitude with stands in species space is presented in figure 4.6.8.
4.6.9 Density Altitude
In Nandiar Khuwar catchment density altitude ranged from 1301 in Thakot 1
to 4712 at Malkaisar. In both extremes the scrub forests were recognized. Al
low density altitude the subtropical scrub forests were recognized while at
high density altitude the alpine scrub was identified. The correlation of
density altitude with axis 1 was -0.954, the regression value was0.136 and the
total standard deviation of the response data was -0.825. The correlation of
density altitude with axis 2 was 0.104, the regression value was 0.040 and the
total standard deviation of the response data was 0.122. The correlation of
density altitude with stands in species space is presented in figure 4.6.9.
Fig. 4.6.7: The correlation of Barometric Pressure with stands in species
space.
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Fig. 4.6.8: The correlation of Altitude with stands in species space.
Fig. 4.6.9: The correlation of Density Altitude with stands in species space.
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4.7 Ordination of Samples on the Basis of Edaphic Factors
4.7.1 Soil
Soil is the superficial weathered layer of the earth crust with which are mixed
living organisms and products of their decay. Plants obtain nutrients from the
soil. The soil is formed from the parent material. Edaphic factors play an
important role in the local difference of plant communities in Nandiar
Khuwar catchment District Battagram. The top soil is constantly being
washed away by run off from higher slopes. Gravels, stones, rocks and
boulders are common in the study area. The soil analysis showed that the soil
of Nandiar Khuwar catchment is derived from the underlined metamorphic
and plutonic igneous rocks which are in turn intruded by pegmatite, aplites
and quartz veins. Quaternary alluvium and glacial deposits are common. Low
grade metamorphic rocks like graphite schist, re-crystalline lime stone,
amphibole schist, quartz-mica schist and green schist are exposed in the area.
Granite, ultra mafic and massive amphibolites, limestone and sandstones
cover large area. The soil under fir and spruce is deep and quite rich in
humus, where as it is shallow and poor under pines and scrub zones.
4.7.2 Soil Profile
The soil profile also affects the vegetation of the area along with climatic
conditions. The vertical section of the soil showed a definite zonation in
different plant communities. In general soil profile consists of three distinct
horizons i.e. A, B, C and an unaltered zone of parent material known as D
horizon. The size thickness and number of sub horizons are different in
different plant communities.
4.7.3 Soil Texture
The relative proportion of soil particles indirectly affects the plant
communities by bringing about variations in the soil water and soil air. In the
study area the majority of soil particles consist of silica and silicates beside
other particles. Gravels, coarse sand, fine sand silt and clay particles were
140
recognized in different plant communities. Different soil texture classes were
recognized in different communities.
4.7.4 Soil Parameters
In Nandiar Khuwar catchment soil is the mixture of minerals, organic matter,
gases, liquids and a myriad of micro- and macro- organisms that support
plant life. In the study area soil performs four important functions: as a
medium for plant growth, water storage, as a modifier of the atmosphere and
as a habitat for organisms that take part in decomposition and creation of a
habitat for other organisms. Soil is the end product of the influence of the
climate, relief (elevation, orientation, and slope of terrain), biotic activities
(organisms), and parent materials (original minerals) acting over periods of
time.
For optimum plant growth, the generalized content of soil components by
volume should be roughly 50% solids (45% mineral and 5% organic matter),
and 50% voids of which half is occupied by water and half by gas. The
greatest influence on plant nutrition is soil pH, which is a measure of the
hydrogen ion (acid-forming) soil reactivity, and is in turn a function of the soil
materials, precipitation level, and plant root behavior. Soil pH strongly affects
the availability of nutrients. The organic material of the soil has a powerful
effect on its development, fertility, and available moisture. Following water
and soil colloids, organic material is next in importance to soil's formation and
fertility. The topography is characterized by the inclination (slope), elevation,
and orientation of the terrain. Topography determines the rate of
precipitation or runoff and rate of formation or erosion of the surface soil
profile. The topographical setting may either hasten or retard the work of
climatic forces. Steep slopes generally encourage rapid soil loss by erosion
and allow less rainfall to enter the soil before running off. Different soil
parameters were analyzed for each stands.
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4.7.5 Soil Saturation
Soil saturation is influenced by soil texture and soil structure. The presence of
water, land slopes, impervious subsurface layers, and compacted soil surface
can also affect drainage. Generally soils have 10–30% of the volume composed
of air–filled spaces but the percentage decreases as water content increases.
Excess soil moisture can actually interfere with water uptake by oxygen-
deprived roots. The result ranges from increased stress and reduced growth
to injury, to death of trees or other plants. The soil saturation data recorded
from different stands of Nandiar Khuwar catchment range from 38% in Anora
II and Anora III to 69% in Riar. The correlation of soil saturation with axis 1
was -0.550, the regression value was -0.079 and the total standard deviation of
the response data was -0.369. The correlation of soil saturation with axis 2 was
0.139, the regression value was 0.013 and the total standard deviation of the
response data was 0.149. The correlation of soil saturation with stands in
species space is presented in figure 4.7.1.
4.7.6 Soil Electrical Conductivity
Soil electrical conductivity is an indirect measurement that correlates very
well with several soil physical and chemical properties. Electrical conductivity
is the ability of a material to conduct (transmit) an electrical current and it is
commonly expressed in units of Siemens per meter (S/m). Sands have low
conductivity and clays have high conductivity; soil electrical conductivity
correlates very strongly with particle size and soil texture. Soils prone to
drought or excessive water will show variations in soil texture that can be
delineated using soil electrical conductivity. The soil electrical conductivity
data recorded from different stands of Nandiar Khuwar catchment ranges
from 0.38dS/m in Deshara to 0.80 dS/m in Kar Ganja, Chail and Mirani 1. The
correlation of electrical conductivity with axis 1 was -0.453, the regression value was
0.001 and the total standard deviation of the response data was -0.319. The
correlation of electrical conductivity with axis 2 was -0.322, the regression value was
-0.061 and the total standard deviation of the response data was -0.161. The
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correlation of soil electrical conductivity with stands in species space is
presented in figure 4.7.2.
4.7.7 Soil pH
The soil pH is a measure of the acidity or basicity in soils and is defined as the
negative logarithm of the activity of hydronium ions in a solution. In water, it
normally ranges from 1 to 14, with 7 being neutral. A pH below 7 is acidic and
above 7 is basic. Soil pH is considered a master variable in soils as it controls
many chemical processes that take place. It specifically affects plant nutrient
availability by controlling the chemical forms of the nutrient. The optimum
pH range for most plants is between 5.5 and 7.0. The pH of soil data recorded
from different stands of Nandiar Khuwar catchment ranges from 5.15 in
Batangi to 7.72 in Chapra and Anora II. The correlation of pH with axis 1 was -
0.211, the regression value was 0.072 and the total standard deviation of the response
data was -0.265. The correlation of pH with axis 2 was -0.054, the regression value
was 0.084 and the total standard deviation of the response data was 0.123. The
correlation of soil pH with stands in species space is presented in figure 4.7.3.
4.7.8 Soil Organic Matter
The organic matter component of soil consists of plant and animal residues at
various stages of decomposition, cells and tissues of soil organisms and
substances synthesized by soil organisms. The presence of soil organic matter
exerts positive effects on soil physical and chemical properties, as well as the
soil’s capacity to provide regulatory ecosystem services. Soil organic matter
acts the major sink and source of soil carbon and generally ranges from 1 to
6% of the total topsoil mass for most upland soils. Soils whose upper horizons
consist of less than 1% organic matter are mostly limited to desert areas. Soils
containing 12-18% organic matter are generally classified as organic soils
(Senesi, et al., 2006).
143
Fig. 4.7.1: The correlation of soil saturation with stands in species space.
Fig. 4.7.2: The correlation of electrical conductivity with stands in species space.
144
Fig. 4.7.3: The correlation of soil pH with stands in species space.
Fig. 4.7.4: The correlation of Organic Matter with stands in species space.
In Nandiar Khuwar catchment the soil organic matter concentration ranges
from 0.73% in Hill and Jatial to 1.92% in Chapar. Out of 80 stands 23 stands
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has less than 1% of organic matter showing weak soil. 67 stands have the
organic matter concentration more than 1% showing suitable conditions for
most of the species. Due to the difference in the organic matter concentration
the species composition of different stands was different in the study area. The
correlation of organic matter with axis 1 was -0.018, the regression value was 0.080
and the total standard deviation of the response data was -0.045. The correlation of
organic matter with axis 2 was 0.291, the regression value was 0.121 and the total
standard deviation of the response data was 0.187. The correlation of soil organic
matter with stands in species space is presented in figure 4.7.4.
4.7.9 Phosphorous
The soil phosphorous (P) concentration ranges from 1.8mg/kg in Lunda
matra and Chorlangai to 19.50mg/kg in Thakot I. In 30 stands the
phosphorous concentrations were 7.1-14mg/kg indicating suitable soil for
most of the plant species. In 10 stands the soil phosphorous concentration was
more than 14mg/kg indicating that some plant species will grow actively
while others will not. Variation in the concentration of phosphorous in the
soil of various stands results in the difference of species composition. The
correlation of phosphorous with axis 1 was -0.688, the regression value was -
0.086 and the total standard deviation of the response data was -0.493. The
correlation of phosphorous with axis 2 was -0.476, the regression value was -
0.287 and the total standard deviation of the response data was -0.148. The
correlation of soil phosphorous with stands in species space is presented in
figure 4.7.5.
4.7.10 Potassium
The soil potassium (K) concentration ranges from 100mg/kg in Lundai 1 to
400mg/kg in Shagai. In 30 stands the potassium concentration was less than
200mg/kg showing that the soil was suitable for most of the species. In 50
stands the potassium concentration was more than 200mg/kg showing that
the specialized plant species will grow actively. The varied concentration of
146
potassium in the soil of various stands will result in the difference of species
composition of different stands of Nandiar Khuwar catchment. The
correlation of potassium with axis 1 was 0.063, the regression value was -0.047
and the total standard deviation of the response data was 0.006. The
correlation of potassium with axis 2 was -0.174, the regression value was -
0.065 and the total standard deviation of the response data was -0.082. The
correlation of soil potassium with stands in species space is presented in
figure 4.7.6.
4.7.11 Slope Aspect
Slope aspect has direct impact on the diversity and species richness. The
northern aspects were denser as compared to southern aspect. In the study
area 29 stands were on north-facing slopes, 11 stands were on south facing
slopes, 8 stands were on east facing slope, 7 on west facing slope, 2 on north-
east facing slope, 9 on north-west facing slopes, 6 on south-east facing slope
and 8 stands were on south-west facing slope. The correlation of slope aspect
with axis 1 was -0.173, the regression value was -0.049 and the total standard
deviation of the response data was -0.129. The correlation of slope aspect with
axis 2 was -0.045, the regression value was 0.030 and the total standard
deviation of the response data was 0.008. The correlation of slope aspect with
stands in species space is presented in figure 4.7.7.
4.7.12 Slope Angle
Slope angle has also a great effect on the diversity and species richness. The
slope angle of Nandiar Khuwar catchment varies in different stands as well as
in the same stand. However the average slope angles were from 20° to 55°. At
gradual slope the over grazing activity were more as compared to steep slope
of the study area.
The correlation of slope angle with axis 1 was -0.390, the regression value was
-0.126 and the total standard deviation of the response data was -0.314. The
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correlation of slope angle with axis 2 was -0.198, the regression value was -
0.124 and the total standard deviation of the response data was -0.131. The
correlation of slope angle with stands in species space is presented in figure
4.7.8.
4.8 Dominance Diversity Curves
Dominance diversity curves are used to study the distribution of abundance
among species in a sample. Species-abundance patterns within trophic levels,
taxonomic groups and whole communities provide clues to the nature of the
niche relationships in groups of species that are closely associated ecologically
in the same macro-habitat. In the dominance diversity curves of Nandiar
Khuwar catchment the rank abundance of species were dominated by Pinus
wallichiana, followed by Quercus incana, Indigofera heterantha, Berberis lyceum
and Pinus roxburghii while Sageretia thea and Populus ciliata were at the base of
dominance abundance curves (Fig. 4.8.1).
In dominance diversity curves the maximum frequency value was recorded
for Fragaria nubicola (53) and Adiantum capillus-veneris (53), followed by Pinus
wallichiana (50), Viola canescens (49), Berberis lyceum, Cynodon dactylon,
Dryopteris jaxtapostia, Indigofera heterantha, and Viburnum cotinifolium. The
minimum frequencies (1) were recorded for many species as shown in fig.
4.8.2.
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Fig. 4.7.5:The correlation of Phosphorous with stands in species space.
Fig. 4.7.6:The correlation of Potassium with stands in species space.
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Fig. 4.7.7: The correlation of slope aspect with stands in species space.
Fig. 4.7.8: The correlation of slope angle with stands in species space.
150
Fig. 4.8.1: Abundance diversity curves of species of Nandiar Khuwar
catchment.
Fig. 4.8.2: Frequency and Rank abundance of species of Nandiar Khuwar
catchment.
Ind het
Pin w al
Pop cil
Que inc
Sag the
Rank Abun
Lo
g S
um
Adi cap
Ber lyc
Ber cil
Fra nubPin w al
Pop cil
Pte urt
Que inc
Sag the
Vio can
Rank Abun
Fre
q
151
4.9 Medicinal Flora of the Study Area
An ethno medicinal survey was carried out to collect information regarding
the various traditional uses, especially the medicinal plant uses in Nandiar
Khuwar catchment. A total of 157 plant species were reported as locally used
for various medicinal purposes. Majority of the recipes are prepared in the
form of decoction from freshly collected plant parts. Mostly a single species
was used and mainly taken orally. All of these plants are collected from the
wild, 12 of which are reported as scarce locally. The people of study area use
medicinal plants for asthma, cough, tonic, abdominal pain, expectorant,
anthelmintic, carminative, on boils, snakebites, jaundice, diarrhea and
dysentery etc. Among 157 medicinal plants 22 were used for curing livestock.
The detail traditional uses of medicinal plants of Nandiar Khuwar catchment
are given below:
Table- 4.9 Medicinal Plants of Nandiar Khuwar Catchment area.
S. No
Botanical name Local name Local uses
1. Abies pindrow Royle Achal Decoction of the dried shoots and fresh leaves is used in cough, asthma and other chest infection.
2. Acacia modesta Wall. Palosa Gum is used for backache and weakness. It is also given to pregnant and lactating women as tonic.
3. Achillea millefolium L. Qarqara / Dambrai
The whole plant is used as stimulant, tonic, diaphoretic and in fever and cold.
4. Achyranthes aspera L. Geshay Roasted fruits are grinded and are used as expectorants. Juice of leaves and roots are used as anthelmintic.
5. Acorus calamus L. Skhawaja Rhizome is used in dysentery and chronic diarrhea.
6. Adiantum capillus-veneris L.
Babozea Whole plant is used in lowering blood pressure.
7. Adiantum incisum Forssk.
Babozea Fronds are used for skin diseases, for cough and cold.
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8. Adiantum venustum D. Don
Babozae
The plant is used in combination with other plant species as expectorant, hypothermic, diuretic and in stomachache.
9. Aesculus indica (Wall.ex. Cambess) Hook.f.
Banakor
Powered seeds are traditionally administered to livestock as anthelmintic. Powered seeds are also used for jaundice.
10. Ajuga bracteosa Wall.ex Benth.
Aseelaboti Its decoction is used for curing jaundice, hypertension and very effective in sore throat.
11. Albezia lebbeck (L.) Benth.
Srikh Powdered bark is used in diarrhea and dysentery.
12. Allium filidens Regel. Oogakay
Leaves are bitter in taste and are eaten raw or cooked along with other pot herbs for gastrointestinal disorders especially stomachache.
13. Amaranthus caudatus L.
Chaleray
The decoction of shoots and leaves are used in cough and asthma. The root is boiled with honey and is used as laxative.
14. Amaranthus viridus L. Ganhar It is used as a vegetable and the paste of leaves and roots are applied on boils and scorpion sting.
15. Arisaema flavum (Forssk.) Schott.
Marjarai Fruit is eaten without chewing in cough and cold.
16. Artemisia vulgaris L. Tarkha
Respiratory stimulant, anti mycotic and effective in urinary tract infections. The juice of leaves and inflorescence are used as anthelmintic.
17. Asparagus officinalis Linn.
Tindoray Young shoots are fed to livestock for promoting lactation.
18. Berberis lycium Royle Kwaray
Bark is used as tonic and is effective in nephrological complaints. It is also used as astringent, antiseptic and as bone tonic for healing bone fractures
19. Bergenia ciliata Sternb. Gut panra
The rhizome are crushed and used in stomach and duodenal ulcers. Also used as tonic and in muscular disorders.
20. Betula utilis D. Don. Broj
Bark is used in various recipes and for amulet. Birch bark soaked until moist in water, and then formed into a cast for a broken arm.
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21. Caesalpinia decapitala (Roth) Alston.
Jara Decoction of young shoots is used as analgesic and antipyretic.
22. Calotropis procera (Wild.) R. Br.
Spalamay Leaves are applied as poultice on dog bitten wounds. Latex is used against ringworm diseases.
23. Caltha alba L. Makan path Flowering shoots is also used as a laxative and diuretic. It is also used for cleaning skin lesions and sores.
24. Cannabis sativa Linn. Bhang Flowering tops are sedative, anodyne and narcotic
25. Cedrella serrata Royle Meem Whole plant is considered as poisonous. The leaf extract is used for curing roundworms.
26. Cedrus deodara (Roxb. ex D.Don) G. Don
Ranzrah The extract of the wood (Ranzrah) is administrated to the livestock as anthelmintic.
27. Cephalanthera longifolia (L.) Fritsch.
The rhizome is considered as promoting lactation in livestock.
28. Celtis australis L. Batkar The fruits are effective in colic and amenorrhea. Decoction from bark is administrated as anti-allergic.
29. Cephalanthera longifolia (L) Fritsch.
The rhizome is considered as promoting lactation in livestock, and is given along Maize flour.
30. Chenopodium album L. Batu It is uses in hepatic disorder and enlarges spleen. Whole plant is used in abdominal pains and as diuretic.
31. Cichorium intybus L. Kasni The roots are washed, boiled and filtrate is kept for whole night in open sky and then used for abdominal pain.
32. Cissampelos pareira L. Gorisum The leaves extract are administrated to livestock for diarrhea treatment.
33. Clematis grata Wall. Chinjanoly The shoots extracts is considered as antimycotic, applied to ring worm and baldness.
34. Clematis montana Buch.
Chinjanoly The decoction of flowers is used in cough.
35. Convolvulus arvensis L. Ellay The roots are dried, powdered and used as purgative.
36. Corydalis stewartii Fedde.
Mamera Floral drops are used for curing eye diseases.
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37. Cotinus coggyria Scop. Chamyar-lakhta
Leaves are given to livestock against liver fluke.
38. Cotoneaster microphylla Wall. ex Lindl.
Kharawa Fruit are used as expectorant and astringent, also effective in stomachache.
39. Cotoneaster nummalaria Fish.
Mamanra Fruit is edible and are used as astringent.
40. Crataegus songarica C. Koch.
Batsinga Fruits are edible and considered as cardio tonic.
41. Cuscuta gigantea Griff. Akasbail Juice of plant is used as anti poisonous agent.
42. Cynodon dactylon (L.) Pers.
Kabal Fresh leaves are applied on cuts and bleeding wounds, bleeding piles, diuretic, antipyretic and diarrhea.
43. Dalbergia sissoo Roxb. Shawa
Decoction of leaves is bitter, stimulant, used in gonorrhea. Root is astringent. Wood is used as alterative, useful in leprosy, boils and to stop vomiting.
44. Daphne mucronata Royle
Kutilal Seeds and roots are used as anthelmintic.
45. Daphne papyracea Wall.ex G. Don
Jangali Kutilal
The juice of the leaves is used to kill the ecto parasites of livestock.
46. Datura innoxia Mill. Baturai Juice of the leaves is applied to the cutaneous affection of the head. Seeds are employed in fever.
47. Datura stramonium L. Datura Leaves are applied for the softening of the boils. Juice of the flower is used in earache.
48. Debregessia salcifolia (D.Don) Rendle
Ajlai Leaves are antiseptic also used for boils and other swellings.
49. Descurainia sophia (L.) Webb. & Berth.
Khoob kalan
Shoots and seeds are powdered and used for gas trouble and intestinal disorders. The decoction is used as painkiller.
50. Desmodium elegans DC.
Jamkat
Root is carminative, tonic and diuretic. It is used in chronic fever, cough, vomiting, asthma, and in snakebite.
51. Deutzia staminea R. Br .ex Wall.
Boritus The whole plant is used to remove the fleas from houses.
155
52. Dioscorea deltoidea Wall. ex Griseb.
Kanis zela
The powder tuber is mixed with powdered root of Berberis lycium and is used for the treatment of jaundice. The juice is applied in hair to kill lice. Locally whole plant is crushed and used to kill fishes.
53. Diospyros lotus L. Tor amlok
Fruits are carminative, purgative, anti febrile and cause flatulence. Local people boil the fruit in milk and take it for curing of constipation and dysentery.
54. Dodonaea vescosa (L.) Jacq.
Ghwarasky It is used as astringent, anti rheumatic, aromatic, also used in swelling and burns.
55. Dryopteris jaxtapostia Chirst.
Kuanjay The fronds are used as potherb. It is commonly used as vegetable and believed to enhance digestion.
56. Elaeagnus umbellata Thunb.
Ghanamr-anga
Flowers and seeds are stimulant and astringent. Seed oil is used in pulmonary infections.
57. Equisetum arvense L. Bandakay The extract of the whole plant is used in jaundice.
58. Euphorbia indica Lam. Jangali Spalamai
The milky juice is used against ringworm disease.
59. Euphorbia wallichii Hook. f.
Hirvi It is poisonous, highly laxative causes sever diarrhea and dysentery. Used in skin diseases.
60. Ficus carica L. Inzar
Fruit is demulcent. The latex is placed on the spot in which prickle has hidden, the prickle is easily drawn out from the outer skin of the body.
61. Ficus palmata Forssk. Inzar Fruit is laxative and demulcent used in constipation and piles.
62. Ficus racemosa L. Oormal
Leaves infusion is astringent. Root is used in dysentery and in diabetics. Fruits are used as carminative and astringent.
63. Foeniculum vulgare Mill.
Saunf
It is carminative and purgative also used in stomach disorders. The decoction of fruits is given to livestock in fever.
156
64. Fragaria nubicola (Hook.f.) Lindl.
Budhi maiwa
Fruit is carminative. Leaves and fruits are mixed with leaves of Berberis lycium and used in cure of stomach ulcers, also used as antiseptic on the wound externally.
65. Fumaria indica (Husskin) H. N.
Papra
It is used as alterative, diuretic, anthelmintic and also used in diabetes. Decoction is used in constipation.
66. Galium aparine L. Cochna Leaves are used in jaundice.
67. Gentianodes pedicellata (D.Don) Omer.
Nilkant Decoction of root is used for urinary tract infections, also used for stomachic.
68. Geranium wallichianum D.Don
Rattanjot
Powdered root is mixed with sugar and milk and used in backache, gout and is also used in strengthening of the body muscles and bones.
69. Grewia optiva Drum.ex Burret.
Pastawonay
Infusion of the bark is used as astringent. The leaves are given to livestock for increase in milk production.
70. Gymnosporia royleana Wall.ex Lawson.
Sorazghay The fruit is placed in mouth to relive toothache.
71. Hedera nepalensis K. Koch.
Albomour Leaves are used in diabetes. Juice of the leaves is used for the removal of leeches from the nose of livestock.
72. Heliotropium cabulicum Bunge.
Geshay Whole plant is applied on boils and swellings.
73. Hypericum perforatum L.
Shen chai Decoction is used in cold and in cough. It is also used as carminative and stimulant.
74. Impatiens bicolor Royle Bantil It is diuretic, tonic and has cooling effect.
75. Indigofera heterantha Well.ex Brandis
Ghoreja Powdered roots are used as remedy for headache and chest pain.
76. Inula royleana D.C. Kut Plant is considered to be poisonous. Roots are used to control high blood pressure
77. Isodon rugosus (Wall.ex Benth.) Codd
Sperkay The dried leaves are considered useful for toothache.
78. Jasminum humile L. Konkoni Root decoction is used for curing ringworm disease. Flowers are used as astringent and tonic.
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79. Jasminum officinale L. Chamba
Decoction of leaves and flowers are given to infants during fever and as blood purifier. It is also given to livestock during cough and fever and also to increase milk production.
80. Juglans regia L. Ghuz
Decoction of leaves is used in eczema and intestinal worms. Fruit is alterative in rheumatism. Bark is used as detergent.
81. Juniperus communis L. Gogar It is believed that the smoke of dried leaves cures the effect evil eyes.
82. Justicia adhatoda L. Baiker
Roots and leaves are used in asthma, bronchitis, cough and rheumatism. Decoction of leaves is antispasmodic and expectorant.
83. Lathyrus aphaca L. Kokorbang
Ripe seeds are narcotic, also used for wound healing. Dried roots are mixed with wheat flour is administrated orally to livestock for various body infections.
84. Launea procumbens Roxb.
Shauda pai Powdered made from the leaves is mixed with sugar and used to enhance lactation.
85. Mallotus philippensis (Lam) Mull.
Kambela Glands and hairs on the fruits are used as anthelmintic. Bark is astringent and diuretic.
86. Malva neglecta Wall. Banerak The roots are boiled and mixed with the seeds of Lepidium sativum and used as purgative for young cattle.
87. Marrubium vulgare L. Kharbotay Decoction is made from the young leaves and is used against cough. Sugar is added for enhancing flavor.
88. Melia azedarach L. Bikyana
Bark is cathartic and emetic. Decoction of leaves is used in hysteria. Seeds are used in rheumatism and hypertension. Ripened fruits are used against diabetes.
89. Mentha longifolia (L.) Huds.
Villanay It is carminative and is used in diarrhea, dysentery and stomachache.
90. Mentha spicata L. Podina It is carminative and is used in diarrhea, dysentery and stomachache. Leaves are used for salad, spice etc.
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91.
Micromeria biflora (Buch.ex D. Don) Benth.
Yakha booti The stem and the leaves of the plant are plucked, chewed and the juice is swallowed to relive abdominal pain.
92. Mirabalis jalpa L. Gule badam
A hot poultice of leaves is used to mature and resolve boils. Leaf juice is used for cleaning and healing wounds.
93. Morchella sp. Gochai Whole plant is used as general body tonic.
94. Morus alba L. Spin toot Fruit is laxative and purgative. Leaves and bark is used as anthelmintic.
95. Morus nigra L. Tor toot
Fruit are laxative, cooling and aromatic. Leaves decoction is used for cleaning throat. Root is anthelmintic and astringent.
96. Myrtus communis L. Manoo Leaves are boiled in water with ghur, and its decoction is used for abdominal pain and diarrhea.
97. Narcissus tazetta L. Gul-e-Nargis
It is used in the treatment of boils and mastitis. The root is emetic and applied on boils and other skin complaints.
98. Nerium indicum Mill. Ganderay The plant is poisonous. Decoction of leaves is used to reduce swilling. Root is used against snakebites.
99. Olea ferruginea Royle Khona Leaves are astringent, antiseptic and diuretic. Locally the leaves are used in soar throat and toothache.
100. Origanum vulgare L. Ishpain Shoot is chewed for toothache. It is also used as flavoring agent.
101. Otostegia limbata (Benth.) Boiss.
Pishkand Dried powdered plant is used in jaundice.
102. Oxalis corniculata L. Zmakay tarookay
Leaves are anti ascorbic, cooling and used in stomach disorder. The plant is mixed with maize flour and used for diarrhea treatment in livestock.
103. Paeonia emodi Wall ex Royle
Mamekh
The infusion of dried flower is used in diarrhea. Rhizome is used to increase milk production in livestock, also used as tonic.
104. Persicaria stagnina Buch. - Ham. ex Meisn
Pulpulak Root is cooling and astringent. Seed is used in colic. The plant is also locally used for killing fishes.
159
105. Pinus roxburghii Surg. Nakhtar Resin of bark (jaula) is stimulant used in ulcer, skin diseases, snakebites and scorpion stings.
106. Pistacea integerrima J. L. Stewart
Shnai Fruits and galls extract are used as tonic and expectorant.
107. Plantago lanceolata L. Chamchi patar
Seeds are used in dysentery and diarrhea. Powdered leaves are used as antiseptic.
108. Plantago major L. Jabai
It is used as astringent, tonic, stimulant, antiseptic, also used in stomach disorders, in fever and dysentery.
109. Platanus orientalis L. Chinar
Bark is useful remedy in diarrhea and dysentery. Fresh leaves bruised and applied to the eye in ophthalmic diseases.
110. Podophyllum emodi Wall .ex Hook.f
Bankakri
Rhizome and root are hepatic stimulant, purgative and emetic. Flower is used for fever and body pain. Rhizome is given to cattle for fever and milk production.
111. Polygonatum verticillatum (L.) All.
Noorealam
Rhizome is mixed with sugar and used for treatment of joint pain, also used as aphrodisiac. The decoction of dried rhizome is administrated to livestock for removal of placenta.
112. Polygonum amplexicaule D. Don
Masloon Rhizome is crushed and mixed with milk to soften mammary gland of livestock and also given in diarrhea.
113. Populus alba L. Bensa / Aspai / Shafeda
The juice of fresh leaves is given to livestock for Mouth and Foot diseases. The branches are supposed to control diseases of rice crop.
114. Portulaca oleracea L. Warkharay
Its leaves are used for external inflammation in the form of poultice and seeds decoction is used as a cooling, demulcent and stomachache.
115. Primula denticulata Wight.
Asal Mamera
Flowers are used ophthalmic and hair tonic
116. Prunus padus Hook.f. Barith Fruits is used as narcotic. The bark infusion is used in the treatment of colds.
117. Pteris cretica L. Qinchi panra
The whole plant is given to livestock during cough.
160
118. Punica granatum L.
Anangoray Decoction of fruit pericarp is used in whooping cough.
119. Pyrus pashia L. Tangai Juice of fruits is used for eyes infections in livestock.
120. Quercus dilatata Lindl. Tor banj
Powdered fruit are used to treat gonorrhea and urinary diseases. It is also astringent and diuretic, used in diarrhea, indigestion and asthma.
121. Quercus incana Bartram.
Spin banj It is used as astringent, diuretic, diarrhea and asthma.
122. Rhododendron arboreum Smith.
Gulamair Flower petals are tonic and leaves are applied in headache.
123. Rhus javanica L. Tetray The fruits are carminative and are recommended in colic.
124. Ricinus communis L. Arind Leaves are emetic, narcotic and purgative. Leaves poultice is applied to swellings. Seed oil is purgative.
125. Rosa moschata Herm. Qurach Decoction of flowers is used in stomach disorder.
126. Rubus ellipticus Smith.
Goraj
The juice and decoction of the root is used in the treatment of fever, gastric troubles, diarrhea and dysentery. A paste of the roots is applied externally to wounds. Both the root and young leaves are used in colic. The juice of the fruit is used in the treatment of fever, colic, cough and sore throat.
127. Rubus fructicosus Hook .f.
Karwara Leaves are used for the treatment of diarrhea, cough and fever. Fruit are used as carminative.
128. Rubus ulmifolius Schott.
Goraj
Fruits are edible and carminative. Unripe fruit are used as tonic and aphrodisiac. Roots and leaves are used for the treatment f skin diseases.
129. Rumex dentatus L. Shalkhay Fresh leaves mixed with wheat are used for treatment of constipation in livestock.
130. Rumex hastatus D.Don Tarukay
It is used as carminative, purgative, astringent and diuretic. Root is used in jaundice. It is also used as antiseptic.
131. Rumex nepalensis Spreng.
Shalkhaey It is diuretic, astringent, purgative and demulcent
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132. Salix babylonica L. Asela ola Leaves are used in diabetics.
133. Sarcococca saligna (Don) Mull.
Bansatra
Bark of the root is antiseptic and also used as blood purifier. Leaves and shoots are boiled and applied on swollen joints in the form of poultice. The leaves are heated in mustered oil and applied to muscular pain.
134. Silene conoidea L. Mashroa
A paste is prepared by grinding seeds and young leaves which is applied on pimples. This paste is also used for backache.
135. Silene vulgaris (Moench) Garcke.
Mashroa Shoots are used as stomachic and emollient.
136. Silybum marianum Gaertn.
Rejakai Infusion of leaves is used in throat and chest infections. Seeds are expectorant and stimulant.
137. Skimmia laureola DC. Ner Smoke of burned leaves is used in cleaning the nasal tract also used in cough and cold.
138. Solanum nigrum L. Kachmachu Fruit is edible and are used in jaundice.
139. Solanum surattense Burm.f.
Maraghonay
The plant is expectorant, digestive, astringent and diuretic. It is used in asthma, cough, fever and chest pain. Fruits are used in jaundice.
140. Solena amplexicaulis (Lam.) Gandhi
Kakora
The rhizome is crushed and mixed with maize flour and given to livestock for promotion milk production and for fever.
141. Stellaria media (L.) Vill. Larolay It is used in rheumatism, joint diseases and constipation.
142. Swertia paniculata Wall.
Momera The ripe shoots have powder-like substance, which is used for curing eye diseases.
143. Taraxacum officinale Weber.
Hind The root is used in diabetics, jaundice and kidney disorders.
144. Taxus wallichiana Zuce.
Barmi
Bark is used in cancer and pneumonia. Leaves are used in bronchitis, whooping cough and asthma. Fruit is sedative.
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145. Thymus linearis Benth. Jaman
Decoction of leaves is used for fever, cough and cold. Seeds are used for abdominal pain. Juice from leaves is applied to toothache.
146. Urtica dioica L. Jalbang
Juice of the plant is external irritant. Leaves are mixed with fodder are fed to livestock to increase milk production.
147. Valeriana jatamansi Jones.
Mushkbala Whole plant is fed to livestock to promote milk production.
148. Verbascum thapsus L. Khardhag Leaves and flowers are used in cough, pulmonary diseases, bleeding of bowels and other skin diseases.
149. Verbena officinalis L. Shamakay
Herb is febrifuge and nerve tonic and is used in amenorrhea. It is used in rheumatism and joint diseases. Root is antidote to snakebite.
150. Viburnum cotinifolium D.Don
Bring Fruits are used as general body tonic.
151. Viola canescens Wall. Banafsha Flower and leaves are used in cough, cold and fever. Whole plant is used in jaundice.
152. Vitex negundo L. Marvanday
Fresh roots are used as bandage to relive pain of chest and back. Branches are used as miswak. Leaves are smoked to relive headache.
153. Withania somnifera (L.) Dunal.
Asghand Leaves and roots are used as poultice on swellings. Fruits and seeds are used as diuretic.
154. Xanthium stromarium L.
Desi Arind
Fruit is demulcent and cooling, used in small pox. Leaves decoction is recommended in long standing malarial fever.
155. Zanthoxylum armatum DC.
Dambara
Seed and bark are tonic and aromatic and are used in fever, cholera and dyspepsia. Fruit is used to cure stomachache and toothache.
156. Zizyphus oxyphylla Edgew.
Elanai Root is used to cure jaundice. Fruits are edible and used in gas trouble.
157. Ziziphus oxyphylla Edgew.
Markhanai All parts of the plant are used in diabetics.
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4.10 Exotic flora of Nandiar Khuwar catchment
Plant species that have been transported by humans from one region to
another are defined as alien or exotic. There were 13 exotic/alien plant species
identified in the study area. These plant species have been introduced in the
Nandiar Khuwar by deliberate attempts of human beings during the recent
past. The introduction have been made from different parts of the world
including; Africa, Australia, Europe, America and other parts of the Asia.
Population size of some of the exotic plant species is increasing at high rate
while other increasing at lower rate. Some exotic species are cultivated for
ornamental purposes restricted to the habitations and waste places. Some of
the exotic plant species reproduce at high rate but are constantly used and
hence seems to be stable. Some of the plant species are cultivated for edible
fruits while other for fuel wood and timber purposes. Tagetus minuta
increasing more rapidly and are considered as ornamental at waste localities.
The alien plant species are presented in table 4.10.
4.11 Market survey of important plant species
Market survey reveals that the inhabitants of the valley use 157 plants as
healing agents for the treatment of different diseases, but only 15 of them are
sold in the local market. A summary of the plants sold in the local market is
presented in Table-10. Mostly, freshly collected plants are sold in the market.
Morchella spp. is also sold in the local market. The highest purchase price
among medicinal plants is that of Morchella species @ Rs. 23500 /kg, Viola
canescens @ Rs. 1300 /kg. Zanthoxylum armatum is sold for Rs. 160 / kg,
Diospyros lotus for Rs. 120 / kg and Geranium wallichianum for Rs. 100 / kg.
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Table-4.10 The exotic/alien flora of Nandiar Khuwar catchment.
S. No
Botanical name Habit Altitudinal range
1 Acacia farnesiana (L.) Willd. Shrub 2490 – 3515ft
2 Ailanthus altissima (Mill.) Swingle Tree 2154 – 5782ft
3 Broussonetia papyrifera Vent. Tree 1934 – 3981ft
4 Cupressus sempervirens L. Tree 1977– 4892ft
5 Diospyros kaki L. Tree 3131 – 5143ft
6 Eucalyptus globules Labill. Tree 2710 – 4555ft
7 Phleum pratense L. Herb 3405 – 6510ft
8 Populus euro-americana L. Tree 2618– 5567ft
9 Ricinus communis L. Shrub 1920 – 3540ft
10 Robinia pseudoacacia L. Tree 2498 – 6551ft
11 Sapindus mukorossi Gaertn. Tree 3010- 5165ft
12 Tagetus minuta L. Herb 2040 – 5433ft
13 Xanthium stromarium L. Shrub 2034 – 5145ft
Table- 4.11 Medicinal plants in the local drug market.
S. No
Botanical Name Season of collection
Part used Formal material
Price /Kg
1 Adiantum species Mar-Apr Fronds Dried 90
2 Aesculus indica Sep-Oct Fruits Dried 25
3 Berberis lyceum Feb-Mar Root bark Dried 30
4 Bergenia ciliata June-July Rhizome Dried 50
5 Datura stramonium July-Aug Seeds Dried 40
6 Diospyros lotus Nov-Des Fruits Dried 120
7 Dryopteris jaxtapostia Apr –May Fronds Fresh 30
8 Geranium wallichianum May – June Roots Dried 100
9 Mentha longifolia Apr –May Leaves Dried 70
10 Morchella spp. Mar –May Whole Dried 23500
11 Paeonia emodi July – Sept Rhizome Dried 60
12 Skimmia laureola Apr – Oct Leaves Fresh 50
13 Valeriana jatamansi May – June Rhizome Dried 70
14 Viola canescens Mar – Apr Flowers Dried 1300
15 Zanthoxylum armatum July – Aug Fruits Dried 160
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4.12 Conservation Status of the Most Important Species
In the present study 324 vascular plant species were recorded from Nandiar
Khuwar catchment area. Due to small area size, the criteria published by
IUCN 2001; cannot be applied to determine the conservation status of the
flora of the study area (1,301km2) and there is no previous complete data
regarding deserved species. However the criteria D of IUCN Version 3.1
(IUCN, 2001) can be applied to some of the plant species having very small
population size or very restricted distribution. The reduction in population
size was observed by direct observation. These included a decline in area of
occupancy (AO), extent of occurrence (EO), loss of habitat, actual or potential
level of exploitation, effect of introduced taxa and attack of pathogens.
4.12.1 Critically Endangered Species
There are 10 plant species evaluated and falling in the Critically Endangered
category under criteria D having population size less then 50 mature
individuals and restricted distribution. These are described below:
1. Acer cappadocicum Gled.
Seventeen mature individuals of Acer cappadocicum were recorded in six
stands Guchai, Ledai, Charoona, Chailkambar, Gabrai kandao and Lekoni on
steep slopes between elevations of 2262-2912m. The reduction in its
population size was due to loss of habitat, sliding, fuel wood and leaf fodder
collection.
2. Betula utilis D. Don
Fifteen mature individuals of Betula utilis were recorded in six stands of
alpine and subalpine zones including Belmaz, Lekoni, Shaheedgali, Kar-
Ganja, Alishera and Malkaisar between elevations of 2908-3780m on steep
slopes. There was no regeneration and reduction in population size was due
to sliding, leaf fodder and bark collection for spiritual means (Plate 9).
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3. Cedrus deodara (Roxb. ex D. Don) G. Don
In the study area 13 mature individuals of Cedrus deodara in wild state were
recorded in Anora and Gada between elevations of 1254-1766m on moderate
steep slopes. The reduction in population size was due to its collection for fuel
wood, timber and medicinal uses.
4. Opuntia dilleni Haw.
It was recorded in the subtropical zone of Nandiar Khuwar catchment with 47
mature individuals restricted to graveyards and waste places. The reduction
in its population size was loss of habitat, attack of pathogen and effect of
exotic taxa.
5. Podophyllum emodi Wall .ex Hook.f
Forty-one mature individuals of Podophyllum emodi was recorded in Jaro,
Trapa, Chailkambar, Gabraikandao and Belmaz between elevations of 2222-
2908m. The reduction in its population size was due to its medicinal
collection, over-grazing and loss of habitat.
6. Populus ciliata Wall. ex Royle
Thirteen mature individuals of Populus ciliata were recorded in Chapra hill at
1781m above mean sea level. The reduction in its population size was due to
its fuel wood collection and timber.
7. Psilotum nudum L.
Psilotum nudum was recorded in single locality of subtropical zone of Nandiar
Khuwar catchment area restricted in rock crevices in very small patches with
a total of 38 individuals. The reduction in its population size was due to loss
of habitat, effect of exotic species and overgrazing (Plate 1).
167
8. Taxus contorta Griff.
Thirty-two mature individuals of Taxus contorta were recorded in nine stands
in the sub alpine zones between elevations of 2339-2997m. The regeneration
capacity was low and 13 plants were recorded in bushy form. The reduction
in population size was due its collection for fuel wood and furniture.
9. Ulmus wallichiana Planch.
A total of 20 mature individuals of Ulmus wallichiana were recorded singly
and in group of two restricted in graveyards, paddy fields and nullahs beds of
Nandiar Khuwar catchment. The reduction in its population size was due to
its collection for fuel wood, making wood utensils and loss of habitat (Plate 6).
10. Viscum album L.
Viscum album the parasitic epiphyte of Ulmus wallichiana in the study area is
also Critically Endangered and was recorded on 9 plants. The loss of the host
plants has also reduced the population size of Viscum album (Plate 6).
4.12.2 Endangered Species.
There are 12 plant species identified as endangered under criteria D of
endangered species having a population size less than 250 mature
individuals. The endangered species are described below.
1. Aesculus indica (Wall.ex. Cambess) Hook.f.
A total of 198 mature individuals of Aesculus indica were recorded in eleven
stands between altitudinal range of 1781-2899m in moist temperate and sub
alpine zones of Nandiar Khuwar catchment area. The reduction in its
population size was due to fuel wood collection, timber, house and
agricultural tools and medicinal uses.
168
2. Bauhinia variegata L.
Bauhinia variegata was recorded in three stands of subtropical zone of Nandiar
Khuwar catchment between elevations of 539-759m. A total of 178 mature
individuals were recorded. Its population size was reduced due to fuel wood
collection and effect of exotic species.
3. Cornus macrophylla Wall.
It was recorded in subtropical and temperate zone of Nandiar Khuwar
catchment. A total of 198 mature individuals were recorded along nullah
beds. It is used for fuel wood and timber. The bark is collected for medicinal
uses.
4. Crataegus songarica G. Koch.
A total of 245 mature individuals of Crataegus songarica were recorded in the
subtropical zone of Nandiar Khuwar catchment in wild state. The reduction
in its population size was due to fuel wood, loss of habitat and attack of exotic
species.
5. Dioscorea deltoidea Wall. ex Griseb.
Ninety-nine mature individuals of Dioscorea deltoidea were recorded in four
stands between altitudinal range of 1488-2032m along nullah beds and moist
shady places. The plant is endangered under criteria D of Endangered
species. The reduction in population size was due to loss of habitat, medicinal
uses, fish poison and effect of exotic species.
6. Dioscorea melanophyma Prain & Burkill.
It was recorded in two stands Gada and Rajmira with 112 individuals
between elevations of 1254-1488m. The reduction in its population size was
unknown.
169
7. Ehretia serrata Roxb.
A sum of 240 mature individuals of Ehretia serrata was recorded between
altitudinal zones of 986-1500m along nullah beds. Its population size was
reduced due to introduction of exotic species, collected for fuel wood and leaf
fodder.
8. Filipendula vestita Maxim.
Ninety-nine individuals of Filipendula vestita were recorded in single locality
of Machaisar at altitude of 2812m. The reduction in its population size was
unknown.
9. Grewia optiva Drum.ex Burret.
It was recorded in three stands of subtropical zone of the study area between
altitudinal zones of 550-1210m. Presently 151 mature individuals were
recorded. The reduction in its population size was due to loss of habitat,
exotic species, fuel wood and leaf fodder collection.
10. Populus alba L.
One hundred and forty-four mature individuals Populus alba in its wild state
were recorded in two stands Sarmast and Lamai between elevations of 1814-
2036m along nullah beds and paddy fields. The reduction in its population
size was due to its leaf collection, mouth and foot disease of cattle, fuel wood
and thatching purposes.
11. Salix babylonica L.
The plant was recorded in the subtropical zone of the study area. In the wild
state 190 mature individuals were recorded. The reduction in its population
size was due to loss of habitat, attack of exotic species and medicinal
collection.
170
12. Trachelospermum lucidum (D. Don) Schum.
It was recorded in the temperate zone of Nandiar Khuwar catchment along
nullah beds and moist places. A total of 123 individuals were recorded in
different localities. The population size was reduced due to loss of habitat and
attack of exotic species.
4.13 Major threats to the plant resources
Due to increase of human population and constant over use of vascular plants
for medicine, timber, firewood, leaf fodder and for thatching purposes has
resulted in ill or unplanned collection of wild vascular plants particularly
medicinal plant species. This over collection has damaged the flora and even
threatens the extinction of many plant species. In the study area habitat loss
was observed in many localities especially in the subtropical and temperate
zone where the forest land has been converted into agriculture land and the
native plant species were removed for construction purposes (Plate 14). The
loss of habitat results reduction in population size of both flora and fauna of
study area. Deforestation due to cutting of trees and shrubs in bulk has also
threatened the extinction of many plant species. The alpine plant diversity has
been reduced due to overgrazing and lumbering process. Fire factor in the
study area has also reduced the population of native plant species ((Plate 7).
Soil erosion and land sliding in subalpine steep slopes has threatened many
plant species. In the study area it was observed that the population size of
native plant species has been reduced due to increase in the population size of
exotic plant species particularly in the vacant localities.
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Table- 4.12.1 Critically Endangered Species of Nandiar Khuwar Catchment.
S. No.
Botanical Name Habit Mature individuals
Altitude (meter)
1 Acer cappadocicum Tree 17 2262 - 2912
2 Betula utilis Tree 15 2908 - 378
3 Cedrus deodara Tree 13 1254 - 1766
4 Opuntia dilleni Hedge plant 47 1092 - 1276
5 Podophyllum emodi Herb 41 2222 - 2908
6 Populus ciliata Tree 13 1780 - 1912
7 Psilotum nudum Herb 38 1205 - 1235
8 Taxus contorta Tree 32 2339 - 2997
9 Ulmus wallichiana Tree 20 1710 - 1943
10 Viscum album Epiphyte 33 1710 - 1943
Table- 4.12.2 Endangered Species of Nandiar Khuwar Catchment.
S. No.
Botanical Name Habit Mature individuals
Altitude (meter)
1 Aesculus indica Tree 198 1781-2899
2 Bauhinia variegata Tree 178 539-759
3 Cornus macrophylla Tree 198 1223 - 1587
4 Crataegus songarica Small tree 245 1212 - 1498
5 Dioscorea deltoidea Climber 99 1488-2032
6 Dioscorea melanophyma Climber 112 1254-1488
7 Ehretia serrata Tree 240 986-1500
8 Filipendula vestita Herb 99 2744 - 2812
9 Grewia optiva Tree 151 550-1210
10 Populus alba Tree 144 1814-2036
11 Salix babylonica Small tree 190 1197 - 1310
12 Trachelospermum lucidum Shrub 123 1254 - 1515
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Chapter 5
DISCUSSION
Biodiversity is referring to organisms found within the living world and
consists of ecosystem diversity, genetic diversity and species diversity
(Ahmad, 2009; Murthy, 2007). Biodiversity varies greatly across the globe as
well as within regions and there is a latitudinal gradient in species diversity.
The tropical regions are rich in biodiversity as compared to polar regions.
Biodiversity is greatly affected by different factors such as temperature,
precipitation, altitude, soil, geography and the presence of other species
(Tandon, 2005).
In Nandiar Khuwar catchment area a total of 324 vascular plant species were
recorded. Maximum diversity index value was recorded at Rajmira (4.18)
followed by Jaro (4.13) in moist temperate zone while minimum diversity
index value was recorded at Kar-Ganja (2.64) in the alpine zone of the study
area. The maximum species richness value was recorded at Rajmira (0.94)
followed by Belandkot (0.77) on north-facing slopes indicating undisturbed
vegetation. The minimum species richness value was recorded at Basha Khan
(2.75) and Kiari (2.86) showed the disturbed vegetation due to overgrazing.
Similar results were also presented by Vujnovic et al. (2012) from central
Alberta, western Canada who reported that species diversity was low in
disturbed and lightly grazed plots.
Life form is the indicator of climate (micro and macroclimate) and can be used
in comparing geographically widely distributed plant communities
(Angelova and Tashev, 2005). In Nandiar Khuwar catchment area biological
spectrum was dominated by phanerophytes having 118 (36.41%) plant species
followed by therophytes with 82 species (25.30%). The dominance of
phanerophytic life form indicates that most of the stands are better preserved
while therophytic life form indicates that some stands of the study area are
173
under severe deforestation, overgrazing, soil erosion and loss of habitat.
Shimwell (1971) reported similar results from India that therophytes are the
indicators of desert climate. Our results are in agreement with Meher-Homji
(1981) who reported that phanerophytic life form indicates the most
preserved forests.
Phenology refers to the appearance of various plants at different seasons of
the year and depends on temperature, sunlight, rainfall, soil moisture and
atmospheric humidity (Pearson, 1979; Angelova and Tashev, 2005). The
flowering and fruiting stages of life-cycle of 324 vascular plant species were
recorded in spring, summer and autumn. The maximum flowering stages
were recorded from April-July (68.51%). The maximum fruiting stages were
recorded from May-August (77.53%). The appearance of the flowers of
different plant species started first at lower altitude. Suresh and Paulsamy
(2010) recorded similar phenological observation from Western Ghats and
concluded that maximum flowering stages were found from May-July.
Bijalwan et al. (2013) also observed similar results from Garhwal Himalayas
and reported that appearance of flowers of different plant species started first
at lower altitude while delayed at alpine zone due to snow cover.
Leaf size classes have been found to be very useful for plant associations
(Tareen and Qadir, 1993). There is consistent variation of leaf, leaf size and
texture between individual plant communities and in various climatic
conditions (Malik et al., 2007). In Nandiar Khuwar catchment area microphyll
was dominant with 137 (40.28%) species followed by mesophyll having 103
(31.79%) species. The dominance of microphyll and mesophyll indicates that a
large part of Nandiar Khuwar catchment receive a high amount of rain fall,
having moderate temperature and moist condition. Our results lie close to the
observations of Amjad (2012) who reported that leptophyll and nanophyll are
the indicators of subtropical and disturbed vegetation while microphyll and
174
mesophyll are the indicators of temperate zone with low temperature and
moist conditions from Azad Jammu and Kashmir.
Out of 324 vascular plant species 157 plant species were used for medicinal
purposes including 22 ethno veterinary important plants. Majority of the
recipes are prepared in the form of decoction from freshly collected plant
parts. Mostly a single species was used and mainly taken orally for the
treatment of asthma, cough, tonic, abdominal pain, expectorant, anthelmintic,
carminative, jaundice, diarrhea and dysentery etc. Haq et al. (2011) reported
156 medicinal plants from the same area and concluded that majority of the
recipes are prepared from freshly collected plant parts.
Phytosociology is concerned with plant communities, their relationships,
structure, composition, distribution, development and the short-term
processes modifying them (Poore, 1955). Phytosociological surveys helps in
planning, management and exploitation of natural resources (Haq et al.,
2015a). The presence or absence of vegetation is controlled by environmental
variables where soil is of high importance in plant growth. Topography
affects soil and climate, in addition to affecting temperature and evapo-
transpiration, makes deeper soil and higher content of organic matter. Certain
plants species perform well in a wide range of environmental conditions
while it is impossible for individual genotypes to perform well across the full
range of conditions (Hoveizeh, 1997; Leonard et al., 1984).
The phytosociological attributes were recorded in six vegetational zones of
Nandiar Khuwar catchment area. These include: subtropical zones, mixed
Pinus roxburghii and Pinus wallichiana zone, moist temperate pure Pinus
wallichiana zone, mixed coniferous zone, pure Abies pindrow and Picea
smithiana zone and alpine zone (Haq et al., 2010). From these six vegetational
zones 80 stands were selected on the basis of physiognomy for
phytosociological attributes. The IVI data obtained from these stands were
175
further analyzed for classification and ordination. Ahmed et al. (2006) also
divided the Himalayan forests of Pakistan into different climatic zone for
phytosociological investigations on the basis of indicator plant species.
Six plant communities were recognized by TWINSPAN classification in
subtropical zone of Nandiar Khuwar catchment area. The biological spectrum
dominated by phanerophytes and leaf size spectra dominated by microphyll.
Our results resemble to certain extent with Siddiqui et al. (2009) who worked
on subtropical forests of Lesser Himalayan and Hindu Khush range of
Pakistan and reported 13 plant communities among which 12 communities
were of pure Pinus roxburghii forests.
In mixed Pinus roxburghii and Pinus wallichiana zone four plant communities
were recognized by TWINSPAN classification. Biological spectrum
dominated by phanerophytes and leaf size spectra dominated by microphyll.
Ahmed et al. (2006) examined such type of vegetation in Himalayan forests
and declared it as subtropical moist temperate ecotonal zone.
Five plant communities were recognized in moist temperate pure Pinus
wallichiana zone of Nandiar Khuwar catchment. Life form dominated by
phanerophytes and leaf size spectrum dominated by microphyll. Ilyas et al.
(2012) also described eight plant communities in the temperate zone of
Qalagai hills Swat.
In western mixed coniferous forests of Nandiar Khuwar catchment four plant
communities were recognized. Biological spectrum dominated by
phanerophytes and leaf size spectrum dominated by microphyll. Saima et al.
(2009) explored same type of vegetation of mixed coniferous forests of Ayubia
National Park Abbottabad and reported five plant communities by cluster
analysis. They reported that the environmental factors has direct role on
species distribution
176
Three plant communities were recorded in pure Abies pindrow and Picea
smithiana forests. Life form dominated by phanerophytes and leaf size spectra
dominated by microphyll. Our results are in line with Akber et al. (2010) who
analyzed similar vegetation of Skardu forests and reported three plant
communities from six stands.
Two plant communities were identified in alpine zone. The biological
spectrum was dominated by hemicryptophytes and therophytes each
contributing 8 species. The leaf size spectra dominated by microphyll having
12 species. Our results resemble to certain extent those of Pharswan et al.
(2010) who explored the floristic composition and biological spectrum of
vegetation in alpine meadows of Garhwal Himalaya and reported low plant
diversity due to harsh climatic conditions.
Among different vegetational zones the maximum Bray-Curtis ordination
scores (0.921) and maximum gradient length (3.35) was recorded in
subtropical zone. The permutation test result was more significant in the
moist temperate pure Pinus wallichiana zone as compared to other
vegetational zones. In all vegetational zones the plant species were sensitive
to environmental variables including altitude, density altitude, temperature
and barometric pressure. Other environmental variables are positively
correlated in one zone may be negatively correlated in other zone. Similarly
the effects of environmental variables were also varied in different vegetation
zones. Similar observations were also reported by Khan et al. (2013) during
their study on phyto-climatic gradient of vegetation and habitat specificity in
the high elevation western Himalayas and Shaukat et al. (2014) in their
phytosociological investigations of the vegetation of Hub dam catchment
area, Pakistan.
The data obtained from 80 stands were collectively analyzed for classification
and ordination beside separate classification and ordination in each
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vegetational zone (Brendenkamp et al., 1983). In division 1 of TWINSPAN
classification the eigenvalue were 0.63 and the primary indicator species were
Abies pindrow 1(+), Viola canescens 1(-) and Berberis lyceum 1(-). In this division
53 stands were placed in negative grouped (*0) while 27 stands were placed in
positive group (*1). In division 2 (53) indicator species was Sarcococca saligna
1(+) and in division 3 (27) the indicator species were Picea smithiana 1(-) and
Fragaria nubicola 1(-). At the end of TWINSPAN classification 13 major plant
communities were recognized from the vegetation of Nandiar Khuwar
catchment area. Peter and Erik (1992) obtained similar results from Senegal
and reported sixteen plant communities in 44 stands by TWINSPAN
classification. Jurisic et al. (2014) also reported similar results from Posavina’s
floodplain forests in Serbia. 114 samples were grouped into seven association
groups at the third TWINSPAN classification level.
The community 1 Acacia, Dodonaea, Dalbergia community was similar to the
first community of subtropical vegetational zone while community 13
Juniperus, Sibbaldia, Primula community was similar to the last community of
alpine zone. The remaining communities obtained from whole data set and
the communities of each vegetational zone are slightly different due to
similarity and borderline of some stands and species. Similar observations
were also recorded by Ahmed et al. (2006) who listed such type of
communities during exploring the vegetation of Himalayan forests.
Ordination is the ordering of objects along axes according to their
resemblances (Saima et al., 2009; Brendenkamp et al., 1983). Objects close in
the ordination space are generally more similar than objects distant in the
ordination space (Nezerkova and Hejcman, 2006). The response data are
compositional and have a gradient 6.4 SD units long so linear method is not
appropriate (Khan et al., 2013).
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In Bray-Curtis Ordination the score was maximum for axis 1 (0.960). The
regression coefficient for axis 1 was -54.11; variance in distance from the first
end point was 2.53. Axis 1 extracted 21.95% of original distance matrix. In
DCA ordination maximum gradient length (6.36) was recorded for axis 1 with
eigenvalue 0.71. Total variance in the species data was 7.07, supplementary
variables account for 37.1%. The DCA ordination clearly indicates that the
whole data set was dominated by single dominant gradient. Our results
coincide with the observations of Khan et al. (2013) who indicated that the
DCA of whole data set in the western Himalayas is dominated by single
gradient length.
In DCA ordination space clusters of different species show their positive and
negative correlation. The subtropical species showed positive correlation at
one side of ordination space while the species of alpine and subalpine zone
clusters on other side. Between these two ends the species of other zones
showed correlation. DCA ordination showed that the subtropical vegetation
is negatively correlated with alpine vegetation due to environmental
conditions. Similar results were also indicated by Khafagi et al., (2013) during
exploring the vegetation composition and ecological gradients in Saint
Katherine Mountain, Egypt.
Canonical correspondence analysis (CCA) was used for the ordination of
samples and species constrained by their relationships to environmental
variables. In CCA ordination the maximum eigenvalue was recorded for axis
1 (0.699). The permutation test results for all axes were pseudo-F=2.2, P=0.002.
The correlation between sample score for an axis derived from the species
data and the sample scores that are linear combination of the environmental
variable. Our results are in line with the results of Khafagai et al. (2013) and
Khan et al. (2013) that also showed the correlation of species with
environmental variables.
179
Among the environmental variables the maximum positive strength was
recorded for barometric pressure (0.967) and temperature (0.960) while
maximum negative strength was recorded for altitude (-0.974) and density
altitude (-0.954). Barometric pressure and temperature were negatively
correlated with altitude and density altitude. Wind speed, phosphorus,
electrical conductivity and slope angle are negatively correlated with wet
bulb, dew point and organic matter. CCA ordination also indicates that
maximum stands clusters near average position. In CCA ordination different
plant species were sensitive to environmental variables. Khan et al. (2013) also
indicated similar results of environmental factors on species distribution in
the high elevation western Himalayas. Shaukat et al. (2014) also estimated
similar effects of environmental gradients on species composition in Hub-
Dam catchment area.
The community similarity and dissimilarity were used for the comparison of
all communities within the study area. Among vegetational zones the
maximum similarity index (37.83%) was recorded between mixed coniferous
forests and pure Abies pindrow and Picea smithiana forests. The maximum
dissimilarity index value (98.97%) was recorded between subtropical forests
and alpine scrub. The maximum index of similarity (35.7%) was recorded for
Wikstroemia, Viburnum, Androsace community and Juniperus, Sibbaldia, Primula
community. The index of dissimilarity of Juniperus, Sibbaldia, Primula
community were 100% with three different plant communities Acacia,
Dodonaea, Dalbergia community, Pinus, Cynodon, Rubus community, and
Quercus, Dodonaea, Myrsine community. Our results resemble to the results of
Nazir and Malik (2006) who examined the vegetation of Sarsawa hills, district
Kotli.
Microclimate is a local atmospheric zone where climate differs from the
surrounding area and it may be small as a few square feet or as large as many
square miles (Haq et al., 2015a). The contributing factor to microclimate is the
180
slope or aspect of an area. South-facing slopes in the northern hemisphere and
north-facing slopes in the southern hemisphere are exposed to more direct
sunlight than opposite slopes and are warmer. The microclimate has a great
impact on vegetation of the study area. The microclimate of Nandiar Khuwar
catchment varies from sub tropical to alpine zone (Haq et al., 2015b). The
altitude, latitude and longitude were responsible for change in microclimate.
Vegetation was denser on north facing slopes as compared to south facing
slopes (Haq et al., 2015a).
The altitudinal, latitudinal and longitudinal spatial variation of temperature
affect climate and distribution of biodiversity. In the Nandiar Khuwar
catchment area maximum temperature value represents the scrub forests
while the minimum temperature value represents the alpine scrub. Our
results are in agreement with Daubenmire (1943) who discussed the effect of
temperature on mountain vegetation. The average wind speed in different
stands ranges from 0.4 to 1.9m/s. and are negatively correlated with axis 1
and axis 2. The atmospheric humidity ranges from 18.1% to 62% and
negatively correlated with axis 1 and axis 2. Similar results were also
presented by Malik et al. (2007) who reported that humidity of certain zones
varies with altitude and is a significant factor in determining altitudinal
zonation.
Dew point is the temperature at which air must be cooled in order for
condensation to occur at constant barometric pressure resulting in the
formation of dew on solid surface. The dew point values range from 2.6 to
20.5. The dew point has not much great effect on the distribution of plant
community in the study area and it is almost parallel to axis 1 and 2 and its
value lies below 21. The wet bulb data range from 6.7 to 23.6. Lawrence (2005)
reported that wet-bulb temperature below 35°C is suitable while above this
value is likely to be fatal even to fit and healthy life.
181
Heat index is a practical measure of how hot the current combination of
relative humidity and temperature feel to human body. The heat index values
ranges from 10.2 to 38.6. The correlation of heat index with axis 1 was 0.499
and with axis 2 was -0.277. Barometric pressure in different stands of Nandiar
Khuwar catchment ranges from 640.8 at Malkaisar 950.3 at Thakot II and is
positively correlated with axis 1 and 2 and its value are significant. The
barometric pressure has direct effect on the distribution of species. At high
barometric pressure the subtropical scrub forests were recognized while at
low barometric pressure the alpine scrub were identified. Altitude of different
stands ranges from 530m in Thakot 1 to 3780m at Malkaisar. Altitude and
density altitude are significantly negatively correlated with axis 1. At low
altitude and density altitude the subtropical scrub forests were identified
while at higher altitude and density altitude the alpine scrub forests were
recognized. Similar results were also presented by Daubenmire (1943), Peter
and Rob (1991), Shipley and Keddy (1987), Angelova and Tashev (2005). They
reported that altitudinal zonation in mountainous regions describes the
natural layering of ecosystems that occurs at distinct altitudes due to varying
environmental conditions and produces discrete plant communities along an
elevation gradient.
Edaphic factors play an important role in the local difference of plant
communities in Nandiar Khuwar catchment area (Leonard et al., 1984). The
top soil is constantly being washed away by run off from higher slopes (Malik
et al., 2007). Gravels, stones, rocks and boulders are common. Soil under fir
and spruce is deep and quite rich in humus, whereas it is shallow and poor
under pines and scrub zones (Haq et al., 2012). The relative proportion of soil
particles indirectly affects plant communities by bringing variations in the soil
water and soil air (Hoveizeh, 1997). Gravels, coarse sand, fine sand silt and
clay particles were recognized in different plant communities (Ajaib et al.,
2008).
182
In the study area slope aspect and slope angle have direct impact on diversity
and species richness. 29 stands were recorded on north-facing slopes which
are rich in biodiversity as compared to south-facing slopes. In the study area
it was also noted that at gradual slope the grazing activity were more as
compared to steep slope. Similar results of slope aspect on diversity and
species richness were also presented by Hoveizeh (1997) and Ahmed (1988).
The soil saturation data ranges from 38% in Anora II and Anora III to 69% in
Riar and are negatively correlated with axis 1 was -0.550. Soil saturation is
influenced by soil texture and soil structure. The effect of soil saturation on
plant community was also recorded by Ajaib et al. (2008).
In study area sandy soils have low conductivity and clays soil have high
conductivity and their values ranges from 0.38dS/m in Deshara to 0.80 dS/m
in Kar Ganja, Chail and Mirani 1. Due to variation in electrical conductivity
the species composition of different stands was different. Johnson (1996) in his
study on subalpine treed fen in Colorado also reported that electrical
conductivity was significantly correlated with species distribution. Malik et al.
(2007) and Zuo et al. (2014) also reported similar results. Soil pH ranges from
5.15 in Batangi to 7.72 in Chapra and Anora II. The pH values were optimal
for most of the species of the study area. The optimum pH range for most
plant species is between 5.5 and 7.0 (Hanet al., 2014).
In Nandiar Khuwar catchment area the soil organic matter concentration
ranges from 0.73% in Hill and Jatial to 1.92% in Chapar. Out of 80 stands 23
stands have less then 1% of organic matter showing weak soil and 67 stands
have the organic matter more than 1% showing suitable for most of the
species. Senesi, et al. (2006) declared that organic soils contain 12-18% organic
matter while desert soil contains less than 1% organic matter.
The soil phosphorous (P) concentration ranges from 1.8 -19.50mg/kg and it is
significantly correlated with both axis 1 and 2. The soil of 28 stands was too
183
weak, 11 stands were slightly weak, 30 stands were suitable for most of the
plant species and soil of 10 stands indicating that some plant species will
grow actively while others cannot. The soil potassium (K) concentration
ranges from 98 – 400mg/kg. In 30 stands the potassium concentration was
less than 200mg/kg showed that the soil was suitable for most of the species.
In 50 stands the potassium concentration was more than 200mg/kg showing
that the specialized plant species will grow actively. Our results are in line
with the observations of Nezerkova and Hejcman (2006), Khafagai et al.
(2013), Khan et al. (2013) and Han et al. (2014).
Present study reveals that the inhabitants of the valley use 157 plants as
healing agents for the treatment of different diseases, 15 of them are sold in
the local market. Mostly, freshly collected plants are sold in the market. The
highest purchase price is that of Morchella species. Haq et al. (2011) also
described similar results from the market survey of the same area.
In present study the mature individuals of 22 plant species were recorded in
few numbers. According to criteria D of IUCN criteria (version 3.1) 10 plant
species are critically endangered having population size less then 50 mature
individuals and 12 plant species are endangered having population size less
then 250 mature individuals. The major threats to the flora of the study area
include urbanization, deforestation, overgrazing, habitat loss, medicinal,
timber and fuel wood collection and effect of exotic species. Our results are in
agreement with the observation of Haq et al. (2010) who explored the species
diversity of vascular plants of Nandiar Khuwar catchment, district Battagram.
The people of the study area mainly depend on the plant diversity for various
purposes and thus leading many plants to the verge of extinction. These
include; increase the demand of timber for construction purposes, fuel wood,
torchwood, fodder and medicinal uses. Damage to the plants are careless and
illicit cutting and smuggling of trees and shrubs, fire factor that damages the
184
seedling of many plants, overgrazing and browsing along with roots, bark
extracted as medicine and extensive storage of fuel wood for winter. Beside
that habitat loss, urbanization, converting the plan slopes in the forests for
cultivation also exerts enormous stress on vegetation and result in
environmental degradation. Some other causes, which threaten species
diversity, include ignorance, poverty, joblessness and lack of scientific
knowledge. Extensive grazing and deforestation should be minimized
because these factors may lead to further fragmentation and degradation of
the habitat. We concluded that Nandiar Khuwar Catchment has great
potential for conservation of the native plant species of Western Himalayan
Ecoregion. The conservation issues can easily be addressed through devising
strategies for protection, recovery and rehabilitation of the threatened species
within their respective stands.
185
RECOMMENDATIONS
1. Biodiversity conservation
The study area is rich in plant biodiversity and we need it for its invaluable
ecosystem to survive. However over consumption of the plant resources is the
main cause of biodiversity loss. We should consume less and be more mindful
about what we consume.
2. Control of major threats
The major threats to the biodiversity of the study area, like deforestation,
overgrazing, habitat loss, soil erosion, overexploitation of natural resources
and invasive species should be controlled.
3. Establishment of plant communities
Habitats should be recognized to establish a new plant community having
similar climatic and edaphic factors.
4. Cultivation of native plants
To protect biodiversity it is necessary to cultivate the native plants along
roadsides and waste places.
5. Medicinal plants cultivation
Medicinal plants should be cultivated and it will reduce pressure on natural
medicinal flora.
6. Documentation and conservation of indigenous knowledge
The local communities of the area have the knowledge of traditional uses of
most of the medicinal plants. But the future generation will not inherit the
precious indigenous knowledge of medicinal plants if it is not properly
documented and conserved.
186
7. Proper training of the community
The forests are continuously being depleted due to high human pressure and
lack of proper management. The local people are unaware of the proper
collection of timber, fuel wood and medicinal plants. Community training
and induction of the scene of the conservation for floral diversity will lead to
sustainable use of plants in the area.
8. Social organization
Social organization and community training on the sustainable use of plant
resources is only effective, if carried out through an organized community.
With local organization, the community can fully and efficiently achieves its
goals and objectives.
9. Rangeland management
Due to lack of management the rangelands are degraded, because of livestock
pressure. The potential of these rangelands should be restored through
control grazing.
10. Agricultural development
Agricultural can play an important role in the development of the area due to
the availability of fertile land and water resources adopting modern
agricultural tools and techniques can increase the production of existing
crops. The promotion of horticultural activity, fruits trees plantation and
vegetables growing is strongly needed through extension services.
11. Development of cottage industry
The selected area provides ideal potential for poultry forming. To train the
community with the modern techniques of apiculture and poultry forming
will create extra job opportunities and dependence of people on the natural
resources will be minimized. Improved apiculture will also improve the crop
yield through effective pollination.
187
12. Mass awareness
Governmental and community level campaign should be launched for mass
awareness about conservation and importance the flora.
13. Provision of civic facilities
Provision of civic facilities in the area like road, health, education, water
supply, electricity, natural gas and telephone will improve not only the living
standard of the people but will also lead to the sustainable use of the
resources and ecological development of the area.
188
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APPENDIX I
Stands Altitude Barom Press
Density altitude
Temp Wind speed
Hum Heat Index
Dew point
Wet bulb
Thakot 1 539 949.4 1301 33.9 1.3 26.6 31.3 11.2 19
Thakot 2 530 950.3 1363 33.6 1.5 24.6 32.3 9.6 18.4
Chorlangay 759 924.7 1700 30 0.5 23.8 30.5 10.5 18
Peshora 881 915.2 1751 31.1 0.9 18.1 33.7 8.7 18.1
Gagikot 986 899.3 1840 30.6 0.5 23.9 30.4 9.8 17.7
Shagai 1092 889 2015 30.2 0.8 26.1 34.1 8.6 18.5
Paimal 2 1145 882.9 2102 31.1 0.5 25.9 33.1 8.7 18.3
Naraza 1197 876.8 2189 28.4 0.5 48.5 38 20.5 23.6
Paimal 3 1264 869.8 2027 27 0.4 23.5 23.5 3.8 13.3
Paimal 4 1284 867.7 2075 29 0.5 39.5 28.5 13.7 18.6
Lamai 1814 813.9 2210 27.9 0.7 36.7 22.6 8.6 14.4
Khairabad 1328 862.8 2426 26.7 0.7 22.8 38.6 12.7 20.7
Gada 1254 870.8 1946 25.8 0.6 37.7 22.7 8.7 14.5
Nowshera 1212 875.3 1892 26.9 0.7 36.5 22.8 8.5 14.6
Nili Reen 1243 872.1 1923 27.4 0.5 23.5 23.5 5.8 13.6
Shabora 1 1265 869.7 2089 26.4 0.9 23.5 23.5 3.8 13.4
Paimal 5 1463 848.5 2240 25.8 0.4 37.9 22.9 8.9 14.6
Paimal 1 1229 873.4 1894 26.1 0.6 20.2 22.4 2.9 12
Deshara 1493 845.5 2195 26.2 0.6 20.1 23.6 2.7 11.9
Dabrai 1449 850.1 2148 27 1 19.2 23.7 5.6 8.4
Rajmira 1488 846.9 2381 25.6 0.5 37.9 22.9 8.8 14.7
Shabora 2 1456 849.3 1939 26.3 0.5 20 15.2 5 7.3
Lundai 1 1480 846.7 2377 26.1 0.7 32.8 28.6 13.7 20.7
Nil Sharkolai 1626 832.2 2090 26 1.6 48 15 6.2 10.4
Belandkot 1575 837.3 2520 25.7 0.6 32.9 28.7 13.6 20.6
Anora 3 1600 834.8 2607 25.5 0.9 50.1 16.4 8.8 12.5
Batangi 1624 832.3 2693 27 0.4 40 35.5 16.6 21.2
Nil Batangi 1792 815.3 2872 24.2 0.6 49.1 33.1 18.7 21.9
Lundai 2 1876 806.8 2961 24.1 1 49 16.4 7.7 12.4
Kiari 1918 802.6 3006 25.9 0.6 49.1 33.1 18.6 21.9
Bashakhan 1939 800.4 3028 25.8 0.7 49.2 33.2 18.7 21.8
Gat 1960 798.3 3050 25.6 0.8 49.3 33.3 18.8 21.7
Shinglai 1720 822.5 2760 25.5 0.5 41.2 34.5 18.4 22.5
201
Anora 2 1751 819.4 2683 25.4 1.1 49.1 16.4 7.8 12.5
Anora 1 1766 817.9 2644 25.2 0.9 49.2 16.6 7.9 12.6
Chapra 1781 816.3 2605 25.3 0.8 43.9 22.9 11.1 15.9
Jarotia 1858 808.6 2653 25.1 0.9 49.4 33.4 18.9 21.6
Habib ban 2 1934 800.8 2701 23.7 0.5 60.4 22.3 15.3 17.5
Habib ban 1 1840 810.3 2584 24.3 0.6 54.4 22.2 15.2 17.6
Bach maidan 2047 789.9 2764 21.2 0.6 46.3 19 8.1 12.9
Hill 1873 807 2572 24.4 0.5 61.2 20.9 13 15.6
Jatial 1889 805.3 2636 25.1 0.8 49.3 16.7 7.6 12.7
Chapar 1897 804.4 2668 25 0.4 59.3 22.3 14 16.5
Sandawali 1905 803.6 2699 24.9 1.2 35.5 22 7.8 14
Sharkolai 2016 792.8 2673 24.7 0.9 49.4 16.8 7.5 12.8
Riar 2042 790.4 2813 24.8 0.6 58 23.1 14 16.6
Doda 1 2032 791.3 2778 24.6 0.4 46.1 21.6 10.1 14.3
Sarmast 2036 791 2441 24.5 0.6 54.5 12.3 4.2 8.1
Mirani kandao 2105 784.3 3053 24 0.9 61.5 25.7 17.1 19.5
Sheed 2164 778.7 3128 23.5 0.9 51.5 23.7 15.3 18.1
Jaro 2222 773 3202 23.3 0.8 46.6 27.6 15.3 18.5
Guchai 2262 769.1 3145 22.8 0.7 50.6 22.6 13.3 17.5
Ledai 2282 767.2 3116 23 0.6 51.7 23.9 15.1 18
Terkana 2302 765.3 3087 22.5 0.6 48.3 21.6 10.3 14.2
Charoona 2365 759.1 3314 22.3 0.9 46.8 26.7 16.2 17.4
Manra 2336 762.1 3194 22.1 0.8 24.5 21.5 2.6 11.3
Bach upper 2241 771.3 3062 20.1 0.7 62 21 13.6 15.9
Trapa 2299 765.7 3122 22.2 0.9 46.9 26.8 16.1 17.7
Lunda Matra 2357 760.1 3182 21.7 0.7 44.6 20.3 8.6 13.4
Doba 2513 745.6 3356 21.5 0.8 56.7 21.6 13.4 17.3
Chail kambr 2668 731.1 3529 20.9 1.5 58.3 20.1 11.5 13.9
Gabrai 2664 731.6 3548 20.6 1.6 58.4 20.2 11.6 13.8
Mirani 1 2659 732 3567 21 0.9 56.3 21.5 13.3 16.3
Baleja 2697 728.4 3679 20.8 0.9 41.8 22.8 10.5 14.9
Chaprai 2677 730.2 3583 20.7 0.7 44.1 22.1 9.8 14.2
Birthmaidan 2760 722.7 3500 20.4 0.8 57.5 10.2 3.1 6.7
Harpal 2801 719 3458 19.7 0.7 54.2 20.5 11.9 15.2
Doda 2 2842 715.2 3416 19.6 0.8 57.4 10.4 3.5 6.8
Kachkol 2862 713.3 3626 19.5 0.9 55.2 15.2 7.8 10.4
202
Mirani 2 2882 711.5 3836 19.1 0.9 50.7 21.6 12.2 14.7
Machaisar 2899 710 3830 18.5 0.7 57.3 10.6 3.9 6.9
Belmaz 2908 709.2 3827 18.4 0.7 53.1 13.2 6.1 9.9
Lekoni 2912 708.9 3825 18.3 0.7 53.3 13.3 6.3 9.8
Karganja L 2916 708.5 3823 17.9 1.4 50.2 15.8 7.5 11.9
Chail 2997 701.1 4086 17.8 1.3 53.8 26.6 16.1 18.6
Magrai 2991 701.6 3974 17.7 1.4 53.6 26.4 16.3 18.4
Shaheed Gali 2985 702.1 3861 17.6 1.5 44.7 17.6 6.8 11.2
Kar Ganja H 3265 677.8 4363 17.5 1.6 53.5 20.5 11.7 14.5
Alishera 3608 657.7 4505 16.5 1.7 53.8 20.6 11.8 14.5
Malkaisar 3780 640.8 4712 15.5 1.9 54.5 20.7 11.9 14.5
203
APPENDIX II
Stands Slope angle
Slope aspect
E. C. pH Organic matter
P mg/ kg
K mg /kg
Saturation
Thakot 1 50 S 0.72 7.2 0.88 19.5 220 41
Thakot 2 50 N 0.67 7.3 0.98 12.7 200 50
Chorlangay 45 N 0.66 5.55 1.03 1.8 397 52
Peshora 30 SE 0.7 5.2 1.65 2.9 300 47
Gagikot 30 S 0.52 5.4 1.7 2.9 350 52
Shagai 30 N 0.58 5.24 1.67 2.8 400 49
Paimal 2 35 SW 0.59 5.3 1.66 2.7 300 48
Naraza 25 NE 0.59 5.9 1.2 2.9 250 44
Paimal 3 30 SW 0.64 5.6 1.1 2.8 205 41
Paimal 4 30 SW 0.59 5.34 1.69 2.8 380 49
Lamai 25 N 0.64 5.3 0.89 3.1 142 42
Khairabad 25 N 0.6 5.9 1.75 2.7 302 48
Gada 35 N 0.65 5.54 1.3 1.9 398 53
Nowshera 20 NW 0.66 5.29 0.88 2.1 140 43
Nili Reen 25 NW 0.62 5.55 1.08 5.1 160 45
Shabora 1 30 N 0.6 5.59 1.08 5.1 160 51
Paimal 5 40 S 0.65 5.64 1.3 1.9 398 54
Paimal 1 20 S 0.39 5.6 0.8 2.6 198 40
Deshara 25 N 0.38 5.65 0.94 3.2 180 40
Dabrai 30 S 0.6 5.9 0.9 2.6 270 44
Rajmira 30 NE 0.7 5.91 1.85 2.1 322 46
Shabora 2 30 N 0.51 6.7 1 4.1 123 47
Lundai 1 40 N 0.68 7.21 1.1 7.4 100 45
Nil Sharkolai 25 S 0.56 5.44 0.94 3.5 118 42
Belandkot 35 N 0.61 6 1.24 4.5 125 46
Anora 3 50 NW 0.45 7.72 0.98 7.8 128 38
Batangi 30 SE 0.62 5.15 1.72 3.2 320 41
Nil Batangi 40 N 0.56 5.48 0.94 3.5 118 42
Lundai 2 55 N 0.72 7.31 1.7 8.4 120 54
Kiari 40 N 0.41 6.8 1.04 4.5 121 42
Bashakhan 40 N 0.42 6.7 1.03 4.6 119 43
204
Gat 45 S 0.65 6 1.8 2.4 308 44
Shinglai 40 N 0.65 5.8 1.8 2.4 310 44
Anora 2 55 N 0.45 7.72 0.98 7.8 128 38
Anora 1 55 N 0.45 7.71 0.97 7.9 117 41
Chapra 40 NW 0.45 7.72 0.98 7.8 129 40
Jarotia 30 E 0.6 6.1 1.7 2.6 318 54
Habib ban 2 45 N 0.44 5.48 1.5 2.9 149 46
Habib ban 1 40 SE 0.45 5.46 1.3 2.8 139 41
Bach maidan 25 W 0.68 6.2 1.5 8.3 206 57
Hill 30 W 0.66 6.19 0.73 1.8 342 45
Jatial 25 S 0.66 6.19 0.73 1.8 340 45
Chapar 30 SW 0.55 5.4 1.92 13.3 230 68
Sandawali 30 W 0.45 7.62 0.9 7.9 131 50
Sharkolai 25 S 0.56 5.44 0.94 3.5 122 42
Riar 30 S 0.65 6.2 1.85 11.2 215 69
Doda 1 45 N 0.68 7.1 1.5 8.3 204 57
Sarmast 40 N 0.39 5.26 1.1 2.9 145 55
Mirani kandao
25 SW
0.5 6.5 1.1 2.8 150 61
Sheed 30 SE 0.65 6.4 1.8 4.6 305 64
Jaro 30 E 0.7 6.6 1.3 9.3 250 60
Guchai 50 W 0.7 6.25 1.4 9.4 248 59
Ledai 30 E 0.7 6.25 1.6 9.3 260 59
Terkana 30 W 0.66 6.5 1.73 10.8 316 65
Charoona 45 E 0.68 6.2 1.5 8.3 203 57
Manra 45 N 0.6 6.25 1.4 9.3 253 59
Bach upper 45 NW 0.7 6.25 1.4 9.3 251 59
Trapa 45 E 0.66 6.1 1.4 8.2 197 58
Lunda Matra 50 N 0.66 6.19 1.73 1.8 341 58
Doba 40 E 0.7 6.3 1.5 8 240 58
Chail kambr 50 E 0.68 6.2 1.5 8.3 199 57
Gabrai 45 SE 0.67 6.1 1.6 8.2 201 56
Mirani 1 35 SW 0.8 6.4 1.3 7.3 230 55
Baleja 40 N 0.65 5.7 1.68 15.4 178 53
Chaprai 40 N 0.64 5.6 1.69 15.3 179 54
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Birthmaidan 35 W 0.68 6.9 1.6 8.7 235 67
Harpal 50 SE 0.6 6.5 1.1 7 210 56
Doda 2 45 N 0.6 6.7 1.5 8.5 219 60
Kachkol 40 E 0.68 6.2 1.5 8.3 202 57
Mirani 2 30 W 0.6 6.24 1.5 9.3 250 59
Machaisar 50 N 0.7 6.75 1.8 9.5 245 60
Belmaz 45 NW 0.7 6.5 0.98 10.3 208 55
Lekoni 45 NW 0.75 6.5 1 18.6 243 55
Karganja L 45 NW 0.8 6.2 0.8 17.3 216 50
Chail 35 S 0.8 6.26 1.4 9.4 255 58
Magrai 45 NW 0.65 5.7 1.68 15.4 182 53
Shaheed Gali 50 N 0.7 6.4 0.9 18.3 241 55
Kar Ganja H 55 N 0.75 6.5 0.9 18.6 239 55
Alishera 35 SW 0.73 6.6 0.88 18.6 239 53
Malkaisar 35 SW 0.74 6.6 0.89 18.6 238 55
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A FEW FIELD ACTIVITIES DURING RESEARCH STUDY
Plate-1: Psilotum nudum in rock crevices Plate-2: Quercus glauca population in
Chorlangay
Plate-3: Rhododendron arboreum in Rajmira Plate-4: Ficus racemosa in Chorlangay
Pate-5: Caltha alba population in Baleja Plate-6: Ulmus wallichiana the host of Vescum
album in Shamlai
207
Plate-7: Overgrazing and fire destroyed vegetation in Lamai
Plate-8: The gorgeous location in Baleja maidan
Plate-9: Betula utilis population in
Shaheedgali
Plate-10: Primula denticulata population in
Karganja
Plate-11: Paeonia emodi and Viburnum
cotinifolium in Birth maidan
Plate-12: Gerardiana palmata and Pteridium
equilinum in Ledai
208
Plate-13: Wikstroemia canescens population
in Charoona
Plate-14: Pinus wallichiana forests showing
loss of habitat
Plate-15: A view of Nandiar Khuwar near
Chorlanga
Plate-16: A view of Pinus wallichiana stem
in Magrai
Plate-17: A view of regeneration of Quercus glauca
Plate-18: A view of field study near Banser
209
Plate-19: A view of field study near Banser Plate-20: A view of field study in Shamlai
Plate-21: Field study in Ayeen Plate-22: Field farming in Hill
Plate-23: A view of field study in Sandasaray
Plate-24: Leaf fodder collection of Quercus
semicarpifolia in Baleja