WAQAS KHAN - prr.hec.gov.pk

EVALUATION OF PLANT DIVERSITY OF THANDIANI

SUB FOREST DIVISION, DISTRICT ABBOTTABAD; A

STEP TOWARDS CONSERVATION MANAGEMENT

WAQAS KHAN

DEPARTMENT OF BOTANY

HAZARA UNIVERSITY MANSEHRA

2016

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WAQAS KHAN

A dissertation submitted to the Department of Botany, Hazara University,

Mansehra in partial fulfillment of the requirements for the degree of the

Doctor of Philosophy (PhD) in Botany



2016

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The thesis of Mr. Waqas Khan is approved in its present shape for the award of PhD degree in

Botany

Supervisor: DR. SHUJAUL MULK KHAN

Department of Plant Sciences,

Quaid-i-Azam University, Islamabad

Co Supervisor: (PROF. DR. HABIB AHMAD)

Vice Chancellor,

Islamia College University, Peshawar, Pakistan



2016

institution/Quaid-i-Azam_University

https://www.researchgate.net/institution/Islamia_College_Peshawar

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Declaration

I hereby declare that no part of this thesis has been previously

submitted to this or any other university as part of the requirements for a

higher degree. The content of this thesis is the result of my own work unless

otherwise acknowledged in the text or by reference. The work was conducted

in the field at the Thandiani Sub Forests Division and Department of

Botany Hazara University, Mansehra during the period September 2011 to

June 2016.

Waqas Khan

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Dedicated

To

My loving

parents, grandmother, wife

and those who pray for my success

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Abstract

Evaluation of plant diversity of Thandiani sub forests division, District

Abbottabad; A step towards conservation management

By

Waqas Khan

The Thandiani sub forests division in the lower western Himalayas lies at an important

geographic location. Variations in its aspect and elevation further enhance its high floristic

importance. This study was designed to follow the objectives i.e., measurement of

phytosociological attributes, Estimating vegetation dynamics, identification of the environmental

variables responsible for the vegetation variation and identification of indicator species for future

conservation and management. The species attributes were measured along latitudinal gradients

using quadrat and transect methods on slopes with different aspects (elevation range 1290-2626

m). Two hundred and fifty two plant species from 79 families were quantified along 08 elevation

transects with 50 station and five different plants associations. The elevation of the study area

was determined via GPS. Personal Geo-database (pertaining 3D analysis of surface data) was

created in ArcGIS 10.2.1 to save all Geo-datasets. It was hypothesized that aspect, altitude and

soil composition were the main driving forces of vegetation composition. The low p value (p ≤

0.002) showed that the variation in the vegetation composition in the study area was highly

significant in terms of test statistics. Classification and ordination techniques (PCORD &

CANOCO) identified 5 major plant communities. Indicator Species Analysis (ISA) and an

assortment of fidelity classes identified indicator/characteristic species. Detrended

Correspondence Analysis (DCA) and Canonical Correspondence Analysis (CCA) showed

altitude and aspect to be the strongest drivers of community classification. The vegetation

changed from a subtropical to moist-cool temperate community characterized by woody species.

Plant species diversity reached an optimum at mid-altitude (1700masl to 2200masl) as compared

to lower (1200masl to 1700masl) and higher elevations (2200masl to 2600masl). Variations in

species richness and composition among sites ultimately led to varied vegetation types. The

family Pinaceae was the most abundant family with 1892.4 Family Importance Value (F.I.V),

followed by Rosaceae with 14.78.2 and Ranunculaceae with 762.1 Value respectively. Out of 79

plant families the most abundant plant family in term of species, was Asteraceae with 20 species

and followed by Rosaceae and Lamiaceae with 19 and 13 plant species each. It is concluded that

altitude, aspects, soil composition were the main factors affecting vegetation composition of

Thandiani sub forests division.

This study contributes to an enhanced understanding of (i) plant diversity in the

Western Himalayas; (ii) ecosystem service values of mountain vegetation within the context of

anthropogenic impacts; (iii) local and regional plant conservation strategies and priorities.

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ACKNOWLEDGEMENT

All praises to Almighty Allah, the most merciful and beneficent, who is

Omnipotent, Omnipresent and the source of all knowledge and wisdom. I also pay my

respect to Hazrat Muhammad (P.B.U.H.) and his faithful companions, who are forever a

true torch of guidance for humanity as a whole.

I am very grateful and wish to express my deep sense of gratitude to my research

supervisor Dr. Shujaul Mulk Khan, Department of Botany, Hazara University,

Mansehra for his kind, loving, immense help, valuable guidance, keen interest,

constructive criticism and encouragement throughout the period of my research work. I

also express my cordial thanks to my Co SupervisorProf. Dr. Habib Ahmad, Chairman,

Department of Genetics, Hazara University, Mansehra for his kind, loving,

accommodating behavior, keen personal interest and dynamic co-supervision.

I also offer my special thanks to Prof. Dr. Manzoor Hussain, Chairman,

Department of Botany, Hazara University, Mansehra and Prof. Dr. Ghulam Mujtaba

Shah, Vice Chairman, Department of Botany, Hazara University, Mansehra for their

sincere cooperation and proper guidance throughout my research work.

I am also thankful to Dr. Abdul Majid, Dr. Zafar Iqbal, Dr. Muhammad Fiaz,

Dr. Azhar Hussain Shah, Dr. Muhammad Afzal, Dr. Aftab Ahmad and Dr. Faisal

Naurooz Department of Botany, Hazara University, Mansehrafor their cooperation

during the whole span of these entire studies.

I feel great pleasure and honor to express sincere appreciation to Dr. Zafar Jamal,

Chairman, Department of Botany Govt Post Graduate College, Abbottabad, Aaftab

Ahmad, Mustajab Ahmad and Muhammad Sajid Department of Botany Govt Post

Graduate College, Abbottabad Aamir Shakeel GIS/GPS Specialist, Department of

Geography, Govt Post Graduate College Abbottabad for their help and moral support.

Last but not the least I am very much thankful to my unforgettable, affectionate

and sympathetic parents, grandmother, and wife for their endless cooperation, prayers

and passions for my brilliant future,I am deeply thankful to all those who have always

wished to see me glittering high in the skies of success. May Allah the Almighty bless

them with great help and prosperous long lives and be a source of prayers. Amin

Waqas Khan

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List of publications of research articales and abstract that arise during PhD

session

The following papers have been published based on some results presented in chapter 3

1. Khan, W., S.M. Khan and H. Ahmad, Z. Ahmad and S. Page. 2016. Vegetation mapping and multivariate approach to indicator species of a forest ecosystem: A case study from the Thandiani sub Forests Division (TsFD) in the Western Himalayas. Ecological Indicators, 71 .336-351.

2. Khan, W., S.M. Khan and H. Ahmad, A.A. Alqarawi, G.M. Shah, M. Hussain and E.F. Abd_Allah. 2016. Life forms, leaf size spectra, regeneration capacity and diversity of plant species grown in the Thandiani forests, district Abbottabad, Khyber Pakhtunkhwa, Pakistan. Saudi Journal of Biological Sciences. http://dx.doi.org/10.1016/j.sjbs.2016.11.009

3. Khan.W, S. M. Khan and H. Ahmad, 2016. Floral Biodiversity and Conservation status of the Himalayan Foothills Region, Thandiani Sub Forests Division, Abbottabad, KPK. Journal of Conservation Biology Pakistan. (JCBP), 1(1): 1-9.

4. Khan.W, S. M. Khan and H. Ahmad, 2015. Altitudinal variation in plant species richness and diversity at Thandiani sub forests division, Abbottabad, Pakistan. , Journal of Biodiversity and Environmental Sciences (JBES), 7(1): 46-53.

5. Khan, W., S.M. Khan., H. Ahmad., Shakeel. A and S. Page. 2017. Ecological gradient analyses of plant associations in the Thandiani forests of the Western Himalayas, Pakistan. Turkish Journal of Botany, DOI: 10.3906/bot-1602-22.

The following abstracts have been published in the abstract books/proceedings international conference based.

1. Khan. W, Khan. S, and Ahmad. H, 2014. Life form and Leaf size spectra of plant communities of Thandiani Sub Forests Division, District Abbottabd, KPK. 97-OP-ISHP-2014 (Pharmacologyonline Supplementary Issue - ISSN: 1827-8620: page -118)

http://dx.doi.org/10.1016/j.sjbs.2016.11.009

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2. Khan. W, Khan. S, and Ahmad. H, 2014. Floral Biodiversity and Conservation status of the Himalayan Foothills Region, Thandiani Sub Forests Division, Abbottabad, KPK. 98-OP-ISHP-20142014 (Pharmacologyonline Supplementary Issue - ISSN: 1827-8620: page -119)

3. Khan. W, Khan. S, and Ahmad. H, 2014”Altitudinal Variation in Plant Species Diversity and Its Components at Thandiani Sub Forests Division, Abbottabad, KPK” “99-OP-ISHP-2014 2014 (Pharmacologyonline Supplementary Issue - ISSN: 1827-8620: page -120)

The following research articals are under review in different international research journals

1. Khan. W, S. M. Khan, H. Ahmad, Hussain.M and Shah.G.M. 2016.Conservational status and community services of Thandiani Forests, The Western Himalayas region,under review in Pakistan Journal of Botany. Under Review

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Glossary of abbreviations

CA Cluster Analysis

CANOCO Canonical Community Ordination

CCA Canonical Correspondence Analysis

CITES Convention on International Trade of Endangered Species

Com. Community

Cons. Constancy

DCA Detrended Correspondence Analysis

GIS Geographic Information System

GNP Gross National Product

GPS Global Positioning System

ISA Indicator Species Analysis

IUCN International Union for Conservation

IV Importance Value

IVI Important Value Index

MASL Meters above sea level

MS Microsoft

S.D Species diversity

S.M Species maturity

S.R Species richness

SAC Species Area Curve

Spp Species

TWCA Two Way Cluster Analysis

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TABLE OF CONTENTS

S.No Page No

Chapter 1. Introduction 01

1.1 Vegetation quantification 01

1.2 Ecological factors and plant diversity 02

1.3 Use of multivariate statistical tools in vegetation quantification 04

1.4 Floristic importance of mountain’s vegetation of Pakistan 06

1.4.1 Phytogeographic regions and mountains of Pakistan 07

1.4.2 Forest types in the Himalayas of Pakistan 08

1.5 Threats to Biodiversity special emphasis on Pakistan 10

1.6 Botanical Conservat ion 12

1.6.1 Why to conserve forests 12

1.6.2 Conservation effortsin Pakistan 13

1.7 Research area - the Thandiani Sub Forests Division (TsFD) 14

1.7,1 Endangered flora and fauna and threats to habitat 16

1.7.2 Sampling sites in the study area 17

1.8 Objectives of the Study 20 Chapter 2. Materials and Methods 21

2.1 Materials 21

2.2 Methods 21

2.2.1 Field data collection 21

2.2.2 Ecological characteristics 22

2.2.2.1 Leaf spectra 22

2.2.2.2 Biological spectrum 25

2.2.3 The phytosociological parameters 26

2.2.4 Species richness 30

2.2.5 Diversity index 30

2.2.6 Degree of aggregation 31

2.2.7 Degree of maturity 31

2.2.8 Regeneration capacity 32

2.2.9 Index of homogeneity 32

2.2.10 Family Importance Value 34

2.2.11 Similarity and dissimilarity index 35

2.2.12 Evenness or equitability 35

2.2.13 Ethnomedicinal uses 36

2.2.14 Soil analysis 36

2.2.15 Data analysis 40

2.2.16 The qualitative & quantitative characteristics by using MS-EXCEL 2007 41

2.2.17 Establishing data for CANOCO and PC-ORD 41

2.2.18 Index of Indicator Species Analysis 42

2.2.19 Cluster Analysis 42

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2.2.20 The naming of plant communities 43

2.2.21 Ordination investigates to classify environmental gradients by using CANOCO 44

2.2.22 Detrended Correspondence Analysis 45

2.2.23 Canonical Correspondence Analysis 46

2.2.24 Vegetation Mapping 46 Chapter 3. Results 48

3.1 Floristic diversity 48

3.2 Life form 49

3.3 Leaf spectra 50

3.4 Species richness 54

3.5 Diversity index 54

3.6 Degree of maturity 54

3.7 Equetibility or evenness 55

3.8 Degree of aggregation 58

3.9 Regeneration capacity 61

3.10 Degree of Homogeneity 63

3.11 Similarity (SI) and Dissimilarity (DI) Indices 66

3.12 Ethnomedicinal applications 68

3.13 Physio-chemical analysis of soil samples 77

3.14 Classification of vegetation of TsFD 80

3.14.1 Data organized by Cluster Analyses 80

3.14.2 Two way cluster analysis 83

3.14.3 Indicator Species Analysis 85

3.14.4 Mental test 86

3.14.5 Species area curve 87

3.14.6 Data attribute plots and Diversity Indices 88

3.15 Plants community organized by Cluster analysis, Two Way Cluster Analysis, Indicator species analysis and confirmed through ordination analysis (DCA & CCA

89

3.15.1 Melia azedarach, Punica florida & Euphorbia helioscopia Community 89

3.15.2 Ziziphus jujuba, Zanthoxylum armatum & Rumex nepalensis Community 95

3.15.3 Quercus incana, Cornus macrophylla & Viola biflora Community 101

3.15.4 Cedrus deodara, Viburnum grandiflorum & Achillea millefolium Community 106

3.15.5 Abies pindrow, Daphne mucronata and Potentilla fruticosa Community 111

3.16 Ordination of vegetation 117

3.16.1 Indirect gradient analysis 117

3.16.2 Direct gradient analysis 121

3.17 Vegetation analysis and plant communities 128

3.17.1 Integrated GIS/GPS and Data Loggers 129 Chapter 4. Discussions 132

4.1 Floristic composition 132

4.2 Species diversity and richness 132

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4.3 Species maturity and Regeneration capacity 134

4.4 Degree of aggregation 136

4.5 Index of similarity & dissimilarity 137

4.6 Life form and Leaf Spectra 139

4.7 Ecological Gradient of Vegetation with special emphasis on Indicator Species 143

4.8 Vegetation mapping and biodiversity 148

4.9 Use of Database Technology in vegetation monitoring 151

4.10 Conservation management 151

4.11 Conclusions 152

References 189

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LIST OF TABLES

Tab.No Page No

2.1 Daubenmire coverage classes of herbs and shrubs 28

2.2 Frequency classes according to Raunkiaer’s classification for determining degree of homogeneity

33

2.3 Age classes of woody species as defined on the basis of available data 34

2.4 Criteria for determining Fidelity (Faithfulness) classes 44

3.1 The life form spectra of plants of Thandiani Sub Forests Division, Abbottabad 50

3.2 Leaf spectra of plants of Thandiani Sub Forests Division 51

3.3 Percentage of life forms recorded in the study area 52

3.4 Percentage of leaf spectra recorded from Thandiani Forests 53

3.5 Shows station’s name with their GPS reading and elevation, D.I, Equetibility, T.F, S.R and S.M of different plant communities of TsFD

56

3.6 Degree of aggregation of Thandiani Sub Forests Division 59

3.7 Regeneration capacity of important tree species of Thandiani sub forests division

62

3.8 Degree of Homogeneity among the plants in the study area 64

3.9 Similarity and dissimiler Index of different plant communities 67

3.10 Sources of information about the use of medicinal plants in the Thandiani forests 69

3.11 Traditional uses/provisioning ecosystem services of medicinal plants by local communities in the Thandiani forests

69

3.12 The physio-chemical analysis of soil samples 77

3.13 The indicator species of the Melia azedarach, Punica florida and Euphorbia helioscopia Community With their indicator values

92

3.14 The indicator species of the Ziziphus jujuba, Zanthoxylum armatum and Rumex nepalensis Community With their indicator values

97

3.15 The indicator species of the Quercus incana, Cornusma crophylla and Viola biflora Community With their indicator values

103

3.16 The indicator species of the Cedrus deodara, Viburnum grandiflorum and Achillea millefolium Community With their indicator values

109

3.17 The indicator species of the Abies pindrow, Daphne mucronata and Potentilla fruticosa Community With their indicator values

114

3.18 Summary of the first four axes of DCA plot using Deterended Correspondance Analyses a multivariate ordination technique

119

3.19 Summary of the first four axes of the CCA for the vegetation data {using abundance/Importance Value data}

124

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LIST OF FIGURES

Fig. No Page No

1.1 The map showing the 3D-DEM View (SRTM) of the project area Thandiani sub forests division with sampling localities (GIS based, station distribution), with graph and elevation profile for the stations of all altitudinal transacts

19

3.1 Plant families with highest Family Importance Value (FIVs) in the TsFD 48

3.2 Graph showing most represented plant families based on number of species in the region.

49

3.3 Cluster Dendrogram of 50-stations created on Sorenson procedures showing 5-plant assemblages/habitat types

82

3.4 Two Way Cluster Analysis Dendrogram of 50 stations grounded on Dufrêne & Legendre procedures showing 5 plant communities/environment types

84

3.5 Species Area Curve (S.A.C) and compositional curves discribed on I.V.I data for entirely 252 plant species and 50-stations

88

3.6 Map of elevation profile, point profile in the scatter Plot Matrix of 1st plant community comparing all phytosociological parameters (D.I, Z, T.F, T.S, S.M, Ev)

91

3.7 Data Attribute plots of Melia azedarach, Punica florida and Euphorbia helioscopia the indicator species of community 1st at lower elevation (1290m to 1591m)

93

3.8 Top 20 species of 1st community 95

3.9 The 20 rare species of 1st community 95

3.10 Map of elevation profile, point profile in the scatter Plot Matrix of 2nd plant community comparing all phytosociological parameters (D.I, Z, T.F, T.S, S.M, Ev)

96

3.11 Data Attribute plot of Zanthoxylum alatum and Rumex nepalensis the indicator species of community 2nd at lower elevation (1600m to 1900m)

99



3.14 Map of elevation profile, point profile in the scatter Plot Matrix of 3rd plant community comparing all phytosociological parameters (D.I, Z, T.F, T.S, S.M, Ev)

102

3.15 Data Attribute plot of Quercus incana, Cornus macrophylla and Viola biflora the indicator species of community 3rd at middle elevation (1900m to 2150m)

105



3.18 Map of elevation profile, point profile in the scatter Plot Matrix of 4th plant community comparing all phytosociological parameters (D.I, Z, T.F, T.S, S.M, Ev)

108

3.19 Data Attribute plot of Cedrus deodara, Viburnum grandiflorum and Achillea millefolium the indicator species of community 4th at higher elevation (2150m to

110

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2400m)



3.22 Map of elevation profile, point profile in the scatter Plot Matrix of 5th plant community comparing all phytosociological parameters (D.I, Z, T.F, T.S, S.M, Ev)

113

3.23 Data Attribute plots of Abies pindrow, Daphne mucronata and Potentilla fruticosa the indicator species of community 5th at the highest elevation (2400m to 2626m)

115



3.26 Detrended Correspondence Analysis (DCA) plot shows the distribution of 252 plant species

120

3.27 DCA plot showing the distribution of 50 stations among five plant communities /habitat types

121

3.28 Canonical Correspondence Analysis (CCA) bi-plot of 252 plant species in relation to environmental variables in the 5 plant communities of study area

125

3.29 CCA bi-plot showing the distribution of 50 stations among five plant associations in relation to various ecological factors

126

3.30 Vegetation map according to different environmental attributes and communities distribution

131

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LIST OF APPENDIX

App. No Page No

4.1 Pre-prepared sheet-I for recording the qualitative and quantitative attributes of

plant species (Quadrates sampling)

154

4.2 Pre-prepared sheet-II for recording the qualitative and quantitative attributes

of plant species (C.C Herbs & Shrubs)

155

4.3 Pre-prepared sheet-III for recording the qualitative and quantitative attributes

of plant species (C.C Trees)

156

4.4 Pre-prepared sheet-III for recording the data of an individual of a species 157

4.5 General Ethnobotanical survey 158

4.6 Alphabetical list of plant species, reported from Thandiani sub forests division,

Abbottabad, during quadrate sampling. List also show life form, leaf spectra

and habit forms of the reported species

159

4.7 Showing Indicator Species Analysis (ISA) with all environmental variables 171

4.8 Showing community wise environmental variables influence 175

4.9 Results of Indicator Species Analysis (I.S.A) through PC-ORD, Showing

Indicator (Variables) plant species (with bold font) for each of the five plant

communities (1-5) at a threshold level of Indicator 30% and Monte Carlo tests

of significance for the observed maximum indicator value of species (P value ≤

0.05)

179

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LIST OF PLATES

Plate. No Page No

1-1 Aesculus indica (Comb) Hook, 72

1-2 Aquilegia pubiflora Wall ex Royle 72

1-3 Arisaema flavum Forrsk 72

1-4 Berberis lyceum Royle 72

2-1 Clematis Montana Buch 73

2-2 Convolvulus arvinces Forssk 73

2-3 Cotoneaster minuta Klotz 73

2-4 Foeniculum vulgare Mill 73

3-1 Geranium wallichianum D. Don ex Sweet 74

3-2 Poeneia emodi Wall 74

3-3 Punica florida L 74

3-4 Morus nigra L 74

4-1 Podophyllum amodi Wall. Ex Royle 75

4-2 Sorbaria tomentosa (Lindl.) 75

4-3 Rubus fruticosis Hk.f 75

4-4 Senecio chrysenthemoides DC 75

5-1 Solanum nigrum L 76

5-2 Verbescum thapsis L 76

5-3 Vitex negundo Linn 76

5-4 Zanthoxylum armatum Roxb 76

3.6 Pictorial view of indicator species of first plant community 94

3.7 Pictorial view of indicator species of second plant community 100

3.8 Pictorial view of indicator species of third plant community 106

3.9 Pictorial view of indicator species of fourth plant community 111

3.10 Pictorial view of indicator species of fifth plant community 116

6-1 Pictorial view of some plants in TsFD 127

6-2 Pictorial view of some herbaceous flora in TsFD 128

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Map of Pakistan Showing the location of Study area (TsFD)

Source: GIS/GPS section Watershed project Abbottabad

xxi

Map showing the study area –TsFD.

Source: GIS/GPS section Watershed project Abbottabad

1

CHAPTER 1

INTRODUCTION

1.1 Vegetation quantification

Plant species are facing various environmental changes throughout their

ecological and evolutionary life histories (Bryson et al., 1970). Proper documentation

quantification of vegetation under the influence of abiotic factors is therefore, crucial

not only to understand variation in it but also for proper management and conservation

purposes. Over the last decades study of environmental related subjects are emerging

more rapidly as compared to some other areas of ecological research. Many of such

ecological changes are driven by anthropogenic activities (Alig et al., 2002; Davis and

Zabinski, 1992).Vegetation is the totality of plants occurring in a specific time and space

under the influence of environment and hence can properly be studied in relation to

nonliving surrounding both at species as well as community levels (Khan et al.,2012a).

The most important aspects for future of plant species are degree and rate of change in

the environmental factors. Such changes may be serious for the plant species to adjust

itself with the modified condition especially those which have relatively less genetic

diversity (Critchfield, 1984; Davis and Zabinski, 1992). Moreover, the rate of

environmental changes is so rapid that the plant species having long generation time

are unable to adapt rapidly to keep pace with predicted changes (Davis and Shaw,

2001). In such circumstances tree species may contain sufficient genetic diversity to

2

react to altered conditions but may not be able to cope the situation in their changing

habitats (Clark, 1998). Floristic attributes such as species diversity, richness, maturity,

density etc and ecological parameters for example aspect, elevation and humidity of the

sites having a reciprocal relationship with each other (Schuster and Diekmann, 2005)

and hence imperative to take into consideration during documentation of plant

biodiversity of a region. Plant community classification is the best way to understand

vegetation of a region. A community has seasonal variations and its composition and

structure are highly affected by the degree variables to which the species are

periodically exposed to (Körner, 2003). The properties of plant communities are

biodiversity variables such as species richness and species turnover; these are resulting

from interaction between individual plant species and their environment (Begon, 2006).

1.2 Ecological factors and plant diversity

Understanding ecological factors is as important as plant species attributes for

conservation and management purposes of an ecosystem. Biological variation at species

and associations levels occurs under the influence of different environmental variables

(Shrestha and Jha, 2009). These interactions in nature results usually a gradient slope

such that they lead to transitions in a gradual gradual way (Austin, 1999). Many studies

show the relation among plant species abundance, climate and environmental

parameters in plant communities studies around the globe (Kala and Mathur, 2002;

Panthi et al., 2007). The composition of plant species is varied according to altitude and

latitude (Kitayama, 1992) in relatively smaller areas (Lieberman et al., 1985; Shaheen et

3

al., 2012). In mountains elevation is one of the main ecological factor that determine

species types and plant communities (Chawla et al., 2008). Aspect is another important

determinant ecological variable in the hydrological and solar regime of mountainous

topography, that ultimately cause differentiation in composition and distribution of

vegetation (Ganuza and Almendros, 2003), organic matter decomposition and Soil

formation (Hicks and Frank, 1984). The aspect causes local variation in temperature and

rainfall, which along with chemical and physical composition of the substrate become

influencing regulators of soil the organic matter and ultimately vegetation of a region

(Mendoza, 2002). Topographic aspect causes local and regional climate by varying the

pattern of rainfall and temperature (Tsui et al., 2004), relative humidity and solar

radiation (Franzmeier et al., 1969). Thus topographic aspect sometimes become the most

significant factor in generating differences in ecosystem attribute (Khan et al., 2013;

Takyu et al., 2002).

In addition to elevation and aspect soil is the natural reservoir containing varied

mineral components, structures and chemical, physical and geological features. Plant

macro and micro nutrients are also important determinant of vegetation of a region. The

association between soil characteristics and species composition is helpful for

understanding restoration because the success of maintaining or restoring a specific

community depends on how to manage impacts of such soil characteristics (Critchley et

al., 2002; Binkley and Vitousek, 1989), on structure and growth of plants (Lamber et al.,

2008). Several studies are found that a gradient of nutrient availability show

4

relationships with changes in species richness (Tilman, 1982). At low nutrient levels

Species richness is low; this richness rises to a peak at middle levels and again decreases

further gradually at high nutrients levels (Lichter, 1998). Dahlgren et al., 1997 State that

microclimatic variations with elevation considerably affect the nature and composition

of plant species, erosion rate and leaching intensity, resulting in feedback on soil

properties such as quantity and quality of organic matter, mineralogy and clay, cation

exchange capacity and base saturation. Physicochemical soil characteristics are

associated to natural soil properties and influence both evenness and species richness of

higher plants (Marini et al., 2007). Evaluating the relations of various plants species with

different environmental factors can sucessfully be used in management of specific

habitats such as range land, forest and desert ecosystems. For example, calcareous soils

can be identified by the assembleges of bryophytes (Downing and Selkirk, 1993) and

what should be grown in the calcareous habitat. Moreover, in recent times permanent

plots have been used to address changes that may happen in vegetation of an ecosystem

due to climatic variations (Phillips et al., 1994).

1.3 Use of multivariate statistical tools in vegetation quantification

The statistical analyses are usually used to know association among distribution

and measured ecological factors (TerBraak, 1986). It increases the accuracy of data and

establishment of its relations with the environmental components via unbiased

approach (McCune and Mefford, 1999). Multivariate analyses are also used to examine

the indicator species of specific environment and microhabitats (Anderson et al., 2006).

5

The collective term used for species composition in multivariate techniques on the basis

of which the data arrange along axes is called ordination. Ordination techniques in

plant ecology reduce community data to low dimensional spaces in which similar

species come close together in the graph and vice versa (Digby & Kempton, 2010).

Ordination is a conceptual model in which a species distribution can be seen along the

environmental gradient. The purpose of this is to unite the points that are close together

in species composition, and points that are far apart agree to sites that are dissimilar in

species composition (TerBraak, 1987). These advance statistical approaches changed the

conventional methods used of phytosociological classification. Detrended

Correspondence Analysis (DCA) is an indirect gradient analysis to investigate the

association among vegetation types based on gradient length and early correspondence

analysis technique in which environmental gradients are not considered directly

(Palmer, 1993, Hill & Gauch, 1980, Jongman et al., 1995). DCA estimates the

heterogeneity in the species and community composition. The Canonical

correspondence analysis (CCA) for direct ordination gradient for treating floristic and

environmental data matrices brought together in CANOCO (Khan et al., 2013). The

pattern of variation in a species data with that of the observed environmental data can

be identified through the canonical ordination techniques. It gives a perfect graphic

representation to maximize the dispersion among the species with that of the linear

combination of environmental variables (Green, 1979). The Deterended correspondence

analysis (DCA) and canonical correspondence analysis (CCA) also help to understand

6

the phyto-climatic gradient of a region. Multivariate analysis covers a set of techniques

dedicated to the analysis of data sets with more than one variable.

There is a very rare use of phytosociological classifications of vegetation in the

developing world which is based on these statistical tools. The present study is one of

such studies having the use of these modern tools and techniques.

1.4 Floristic importance of mountain’s vegetation of Pakistan

Pakistan is one of few countries in the world hosting large forest reserves in the

biosphere. Pamir-knot is the most projecting spot on the map of Asia that contains 3662

m high flat terrain on which several foothill ranges of over 5000 m elevation are present

for example the Karakorum and Hindu Kush ranges. Moreover south-east ward of

Palmir, the Himalayan chain forms a huge hilly system in Pakistan including high

peaks of the world, such as K2 (8611m), Nanga Parbat (8126m), Rakaposhi (7788m) and

Tirich Mir (7690m). In general about 2/3 of Pakistan‟s surface area i.e. 47600000 hectares

out of the total area79609600 hectares of the country is mountainous (Rasul and

Hussain, 2015). These mountains host mostly coniferous forests with some broad

leaved species (Ilyas et al., 2012) that account for 5.01 % of the total land cover. The

natural forest cover is decreasing at the rate 0.75% annually (Anonymous, 2009). The

composition and structure of forest ecosystem varies with topographic, climatic,

edaphic and anthropogenic activities (Namgail et al., 2012). Due to the unique

geography of Pakistan having the Hindu Kush, Himalayas and Karakorum regions

7

having altitudes starts from 0m - 8611m and for that reason has a mixture of climatic

zones and rich vegetation diversity (Shinwari and Qaisar, 2011). Many areas of

Himalaya due to harsh climate, rough topography and inaccessible location have been

poorly understood for their vegetation ecosystem services (Khan et al., 2012a). The

Pakistan has diverse flora comprising about 6000 plant species. There are 130

ferns, 25 Gymnosperms, 1142 Mono-cots counting 577 Grasses and 4490 species

of Dicots (Jan et al., 2015). There is a great diversity of species because of the

physiographic and climatic deviation of the region. Its vegetation is of many

kinds such as;-

a. Marsh forests.

b. Tropical deciduous forests.

c. Tropical thorn forests.

d. Subtropical broad leaved evergreen forests.

e. Subtropical pine forests.

f. Himalayan moist temperate forests.

g. Himalayan dry temperate forests.

h. Subalpine forests.

i. Alpine forests.

1.4.1 Phytogeographic regions and mountains of Pakistan

There are four recognized phytogeographic regions in Pakistan, which host

vegetation differ from each other in terms of diversity as well as richness. The 70

8

percent species are uni-regional and 30% species types are bi-pluri regional. Among the

uni-regions, the Irano-Turanian element is the most common (45%), followed by the

Sino-Japanese (11%), Saharo-sindians (10%) and Indians elements of about 4% (Ali &

Qaiser, 1986). Habitats of Pakistan can be classified as Cold desert and dry alpine zone,

Himalayan dry coniferous forests, Himalayan moist temperate forests, Steppe forest in

higher ranges, Steppe forest in Baluchistan hills, Tropical dry mixed deciduous forests,

Subtropical pine-forests, Dry temperate semi perennial scrubland, Dry subtropical semi

perennial scrubland, Sand dune dessert, Tropical thorn vegetation, Swamps, seasonal

innovation, Seepage and jheels, Riverian and Indus plains regions (Afzal et al.,2001).

1.4.2 Forest types in the Himalayas of Pakistan

The Himalayas stretches approximately 3000 km in length and 220 to 300 km in

width in the courtiers of Nepal, Bhutan, India, Pakistan and China. Geogaphially, the

Himalayas are subdivided into the following four segments (Le-Fort and Patrick, 1975).

• The Outer Himalayas or foot hill belt is a series of low peaks slowly growing

from the prairies about 1200 m asl.

• The Lesser Himalayas lying north of the Outer Himalayas, where elevation

varies from 1200 m to 4000 m asl.

• The Greater Himalayas or the central belt mainly consists of the Higher

Himalayas at elevation of up to 5000 m asl.

• The Trans Himalayas bordering with the Hindu Kush and Karakorum, located in

the north of the central-Himalayas that covered with dense masses of snow.

9

Concurrent with changing topography, altitude, precipitation, temperature,

geological formation, soil condition and resultant variation in climates, this region is

rich in Horticulture, Forestry, Agriculture and Wildlife. The Himalayans comprise one

of the most diverse bio-diversity of forest types. The lesser Himalaya at the 900 ‐1500 m

elevation range host sub-tropical broad leaved forests of Olea cuspidata, Acacia nilotica,

Dodonea viscose and Punica florida species (Shinwari, 2003). Higher elevation of 1500‐3000

m elevation include moist temperate forests (monsoon) dominated with Pinus

wallichiana, Abies pindrow, Quercus dilatata, Cedrus deodara and Asculus indica (Beg and

Khan, 1984). The-higher limits between 3000 ‐4000 m were are dominating by cold-

temperate and sub‐alpine vegetation of Abies pendrow, Viburnum cotonifolium and Betula

spp (Kharkwal et al., 2005). The highest peaks above the timber line (>4000 m asl) host

alpine vegetation comprised of ephemerals. The high elevation ranges of Himalaya are

relatively protected due to far long and low human population density. The middle

altitudinal sub-tropical and moist-temperate forests are exposed to most serious threats

of human population pressure with huge vegetation losses. It is estimated that three in

the last 100 years a 60% forest decrease in Indian Himalaya (Cronin & Pandya, 2009;

Ferraro et al., 2011; Bolch et al., 2012). Pakistan lost 24.7% of its forest canopy in just 15-

16 years from 1990-2005 (Abbasi and abbasi, 2012). A 27.1% (821 x 104.1 hectare)

damage of woodland canopy was noted in Jammu Kashmir estimated via satellite

imaginary from 1980-2000 (Krishnaswami, et al., 1992). A total vegetation loss of 8.1% in

Eastern and 23.1% in western Himalayas has been approximated by applying GIS and

remote sensing techniques in the last three decades (Ravindranath et al., 2011).

10

1.5 Threats to Biodiversity special emphasis on Pakistan

All sort of ecosystems such as forests, fresh-water, marine and grass lands

are rapidly changing all over the world. In Pakistan in particular a continuous

decline in many native plants and animal species make the situation more

vulnerable and more alarming, for example,in the recent decades 4-5 mammal

species are known to have been perished including tiger, swamp deer, lionand

one-horned rhinoceros. In addition to, the IUCN (1998-99) registered 39 species

and 15 sub-species occur in Pakistan internationally vulnerable or

nearbyendangered. As a whole, 26globallyvulnerable bird species and 10-reptile

species also exist in Pakistan (Jan et al, 2015). All these animal species are

noteworthy to mention as all of them depends on plant diversity for their

shelter, food and other life processes.

Pakistan host variety of forest lands i.e, mangrove forests, swamp forests

of the Indus delta, semi-arid rangeforests, scrub forests and upland forests of

subtropical and temperate natures. The woody bio-masses of all these forests

are declining at the rate of 4-6% per year (Khan and Naqvi, 2000) due to mainly

anthropogenic disturbances. Most common reasons of deforestation include the

issue of ownership, grazing and multi purpose collection of timber and non timber

products. The fuel wood has the supreme important energy source for heating and

cooking in developing countries and thus the main cause of deforestation (Patel et al,

2001; Vine, 2005). About 54.1% of world vegetation harvest occurs to get fuel wood

11

(Bhatt et al., 2004; Sharma et al., 1997; Eckholm et al, 1984). In Himalayan mountain

region, fuel-wood is the only main source of energy, supplying almost all the cooking

energy requirement (Shantha and Decker, 1994). The regular fuel-wood use is high

(5023g/day/capita) for Himalayan region (Bhatt & Sachan, 2004). The overgrazing in

developing countries is also becoming an uncontrolled activity. The livestock such as

goats, sheep and cows affect all sort of palatable grasses, shrubs and woody vegetation

(Illius & Gordon, 1992). Goats consume the needles of conifer trees up to 230 cm by

winding the trees down or ascending up and especially damage the seedlings. During

the forage shortage in long rainy monsoon season, live-stock has to nourish upon lower

value forage grasses as well (Hyder, 1975). In some tests the plant canopy was

deterioration to just 1.1%, mark up to 90.1% basal canopy destruction with only few

annual and perennial grasses left (Fuls, 1992). The non cultivated lands around villages

in Pakistani part of Kashmir present a pitiable condition due to severe grazing (Polunin

& Stainton, 1984). Particularly in summer seasons, lives stock migration from the

villages to the forests of temperate and alpine meadows of Northern Pakistan cause

removal of very important plants of the region. The overgrazing, uncontrolled

utilization of plant species and all other causal agents of biodiversity loss lead to the

disappearance of many important taxa which are of global importance. Similarly,

another reason for plant diversity decline is an unmaintainable assembly of dry season

forage by the indigenous people through substantial and non selective vegetation

cutting (Shrestha and Paudel, 1996; Singh, 1987). Moreover, indirect causes of

deforestation are over population, urbanization, dams and road construction and other

12

catastrophic disturbances (Pringle and Scott, 2001 & Bhat et al., 2012). In these

situations documentation and quantification of vegetation of such places should be

done prior to the extinction of our precious plant wealth.

1.6 Botanical Conservation

Over the last thirty years administrators, ecologists and conservationists

have started to perceive the maintenance of biodiversity. Still this part of the

worl is well behind from rest of the words in terms of preservation and

conservation of natural resources. One of the main reasons is lake of the base

line data of natural resouces including plants and their micro environments.

Such local documentations are more crucial in the scenarioof global struggles to

establish and sustain biosphere reserves, wildlife sanctuaries, national parks,

reserves forests and other secure areas (Jan et al., 2014).

1.6.1 Why to conserve forests

The conventional diverse uses of forest resources inhigh land areas of the

western Himalayas; Khyber Pakhtunkhwa, Pakistan reveals a varied timbre and non-

timber products. This botanical diversity supports other types of biodiversity in these

high land habitat and play vital roles in the ecosystem functions and services.

Vicinities of the forests are usually inhabited bycultural people who use their

indigenous knowledge to utilize the forest resources. Such cultural knowledge of

those people passes on from generation to generation as well. On the other hand, there

13

remains a huge stress of socio-economic pressureon such ecosystems. The

conservation of biodiversity calls for both global consciousness and action at the local

levels as well. Many local civilizations have established their own conventional

conservation approaches, together with the standard one on flora, fauna and habitats

based on the society's cultural heritage and native knowledge (Hamayun, 2006).

Therefore, forests are of preliminary interests in terms of botanical conservation.

1.6.2 Conservation efforts in Pakistan

Pakistan is situated at that part of the globe where geopolitical conflicts have a

long history of over one thousand years. Rulers used to invade through its northern-

passes and utilize its botanical resources regularly over this long history.

Consequently, they established small scales agriculture in the peripheries of perennial

stream up to mid-elevations of Himalayan Mountains (Kothari, 2008). After

independence in 1947 various legislations were done for natural resource

conservation. For example Sericulture act was implemented in the Himalayas region.

Similarly, through 1970-1995, Pakistan has also participated actively in numbers of

international bio-diversity and conservation conventions, for example;

1. The Convention of Biodiversity (1992)

2. International trade Convention of Endangered species (1985)

3. RAMSAR Convention (1987-1990)

4. World heritage convention (1993)

5. Conservation of Migratory Species Convention (2004)

14

6. International Waterfowl and Wetland Bureaus Convention (1995)

In spite of signatory of all these bodies due to internal law and order situation,

poverty and other reasons Pakistan could not do much better for its biodiversity. One

of the main reasons was baseline studies. The present project is a step of such

initiatives to document the plant diversity of an important part of western Himalayas

for long term planning.

1.7 Research area - the Thandiani Sub Forests Division (TsFD)

The TsFD is part of the moist temperate forests with a rich biodiversity. The

study area is situated in the Galis forest division of Abbottabad. Geographically, it is

bounded bythe Siran forests division from the west, Muzaffarabad and Garhihabe

bullah from north, Abbottabad sub forests division from south and Berangali forests

range from east, between 3329° to 3421° North latitude and 7255° to 7329º East

longitudes. Forests have an area of 24987 hectare. Out of which 2484 hectare possesses

reserve forests and 947 hectare possess guzara forests. The whole area under reserve

forests division KPK in order to preserve the valuable plants and animal species of the

area. According to Champion, Seth and Khattak (1965) the forests of Galis forests can

mainly be classified into the following three types of forests.

1- Dry subtropical broad leaved-forest

2- Subtropical pine-forests

3- Himalayan moist temperate-forest

15

The Thandiani lies in the third category that is the Himalayan moist temperate

forest. This can furtherbroadly be divided into the following sub types.

1. Low level blue pine forest

The range of low lying blue-pine wood lands spreads over 8,682 acres which is up to

44% of the region. A separate feature of blue pine forests is that they have improved

stocking as compared to any other coniferous forest types. Blue pines forestsas

awhole contain adequate regeneration and younger age classes with less number of

mature trees.

2. Mixed Coniferous Forests

The entire remaining region occupied by mixed coniferous forests vegetation.

Composition of species varies at different places, but the main growing stock consists of

blue pine and silver fir, in which fir is predominating on cooler aspects. Deodar occurs

sporadically in Birangali, Inderseri, and Pichbhanna and Riala guzaras but on the whole

it constitutes a significant proportion of the total growing stock. Regeneration of silver

fir and its younger age classes is either absent or inadequate. The mature blue pine trees

occur occasionally and younger age classes occur frequently in this type as well.

The highest point of the area is Thandiani top with the elevation of 2626 m. Most

of this area is covered with pine forests and may be divided into three elevation ranges

namely top range (2200 to 2600 m), medium range (1700 to 2200m) and lower range

16

(1200 to 1700m). The agriculture is main source of socio economy, contributing about

30% to the GNP and employing 55% of labor force in the region. The main crops of the

area are Maize, wheat and Potatoes etc. The top range does not have human population.

The human population starts from the medium range and the density increases toward

lower regions. Some of the adjoining villages include, Gurlania, Tarheri, Riyala,

Tarnawai, Balolia, Neelor, Kalapani, Mandroch, Bhoji, Larri, and Pahge. This is a

difficult area in term of communication and road services.The main tribes in the

surrounding villages are Sardar Gujjars, Jadoons, Abbasis, Karrlals, Awans, Khokhars

and Rajputs. Local languages Hindko andGujri are spoken in the region. Inhabitants of

the region have traditional knowledge of plants and local environment.

Study region has subtropical and temperate sort of climate. During winter

(December to March), heavy snowfalls take place, ranging from a few inches to 4 feet.

The monsoon rains tend to start from July and continue up to the end of August. The

moisture, precipitation and temperature condition of the regions are extremely

favorable for rich vegetation. Topographically the Thandiani sub Forests Division

consists of hilly ridges interrupted by lateral water streams/Nallahs, creating numbers

of side valleys.

1.7.1 Endangered flora and fauna and threats to habitat

In the Thandiani sub forests division and its environment due to deforestation,

agriculture land and settlement expansion, poor implementation of forest laws numbers

17

of important plant and animal species are disappearing at an alarming rate. Due to

these changes natural habitats are also deteriorated. Pure populations of some native

tree species have been destroyed several of which cannotbe renewed. Trees of high

quality woods are cut by the villagers as a source of fuel and timber, such as Quercus

dilatata, Pinus wallichiana, Taxus wallichiana and Quercus incana. Taxus walloichiana

Valeriana jackmontti, Podophyllum emodi and Paeonia emodi are rare locally due to

multipurpose collection by locals. Due to loss of flora numbers of animalssuch as

common leopard and numerous other bird species are going to extinct locally. The

population of the Kalij Pheasant (Lophuraleu comelana) and Kaklass pheasant (Pucrasiama

crolopha) are the highest known in Pakistan but decreasing quite rapidly due to habitat

loss. Only thirty mature individuals of the kalij pheasant are acknowledged to occur in

the Thandiani sub forests division in the las five years.

1.7.2 Sampling sites in the study area

Thandiani was selected for Phytosociological study and it was sampled in the

summers of 2012 and 2013. E following fifty stations was selected for quantification of

various phytosociological attributes and associated ecological factors. Mandroch,

Battnga, Neelor, Baribak, Mand Dar, PkhrBnd, Lowr Dna, BandiTC, Qalndrbd, Riala,

Malch Lower, Malch Up, Danna, Uper Dna, Pejjo, Lowr Bal, Upr Balo, Mera Bun, Lonr

Pat, GaliBan, Riala, Resrv FC, Upper GB, Chatrri, Terarri, UprRial, Terari C, Mathrika,

Mthrka T, Jabbra, Darral, Makali, Ladrri, Upper KP, Kakl RFC, Parringa, Satu Top,

18

Lower KP, Larri, Pallu Zr, Lari Tra, Lari Top, Sawan Gl, Lower Th, Upper TC, Mera

RKC, Mera RKT, Lower Nmal, Upper Nmal and Sikhr. Fig. 1.1.

Thandiani sub Forests Division has not been evaluated for vegetation

quantification and proper classification under the influence of existing ecological factors

since the creation of Pakistan. This study was conducted therefore to assess vegetation

variation associated to environmental gradients. Various ecological indicators and

anthropogenic influences were also taken into consideration.The study was initiated

with questions/hypotheses if species composition of Thandiani sub Forests Division

(TsFD) varies with variation in edaphic, topographic and climatic variables.Also if

different indicator species of plants for each sort of habitat could be identified for

future conservation planning and management.

19

Fig 1.1 The map showing the 3D-DEM view (SRTM) of the study area Thandiani sub forests division with sampling localities (GIS based, station distribution), with graph and elevation profile for the stations of all altitudinal transacts.

20

1.8. Objectives of the Study

Specific objectives of the study were;

1. Quantification of vegetation by measurement of phytosociological attributes.

2. Identification of the environmental variables responsible for the vegetation

variation.

3. Plant community classification of the area for the first time.

4. Identification of indicator species of each community/association/habitat type

throughrobust statistical approaches for future conservation and management

planning.

21

5.

CHAPTER 2

MATERIALS AND METHODS

2.1. Materials

The following tools were used during field survey.

Altimeter, compass, cutter, hygrometer, field notebook and pencils, paper,

polythene bags, GPS (Global Positioning System), mercuric chloride, digital camera,

binocular, ropes, steel rods, plant presser, measuring tape and herbarium sheets etc.

2.2. Methods

2.2.1. Field data collection

Field trips were arranged regularly to collect the ecological and taxonomical data

of TsFD. With the help of opographic chart of the region repeated visits were made in

the spring 2012, the entire area was divided into 8 altitudinal transact and 50 stations.

The station location was based on the altitude, physiogamy, aspect, degradation stage

and floristic arrangement of the area. This resulted in the identification of the different

plant relations. Each station was approximately located at a distance of 100m. The data

were collected both in the spring and monsoon seasons.

The quadrat method was applied for sampling vegetation. The Size of quadrat

for each layer of vegetation was determined experimentally by species area curve. The

Trees, shrubs and herbs were sampled in 10m x 5m, 5m x 2m and 0.5m x 0.5m

22

correspondingly. Likewise, the satisfactory numbers of quadrats in each stand remained

5, 10 & 15. The Quadrats were placed systematically with the help of GPS where the

vegetation was uniform and sometimes systematically where it was necessary. The

Quadrat method was used following Cox, 1985; Malik, 1990; Khan et al., 2011-16). Soil

will be sampled up to the depth of 15cm and mix to get accumulation sample for each

station.

The whole specimen of each species was composed in triplicate, dehydrated,

preserved and display on herbarium sheets. The plants were identified with the help of

the flora of Pakistan (Nasir & Ali, 1972-2009: Ali and Qaiser 1992-2007) and established

at H.U Herbarium. A whole floristic list along with their families was compiled. The

explanations of Leaf spectra; Life form and phenological behavior were studied during

field survey. The voucher specimens were placed in the Herbarium Hazara University,

Mansehra, KP, Pakistan.

2.2.2. Ecological characteristics

2.2.2.1. Leaf spectra

Leaf spectra were determined by categorizing the leaves into the following

classes by Raunkier (1934). The Oosting, (1956) introduced that leaf size information

much assistance in understanding the bodily process of plants species and their

societies. Moreover the leaf size classes have confirmed to be valuable in categorizing

there lationship of leaf size and aspect.

23

a. Leptophylls

The size of leaf 25 sq. mm or (0.000025 sq m)

b. Nanophylls

The size of leaf is 9 25 = 225 sq. mm (o.ooo25sq. m)

c. Microphylls

The size of leaf is 92 25 = 2025 sq. mm (0.002025 sq. m)

d. Mesophylls

The size of leaf is 9325 sq. mm (0.018225 sq. m)

e. Macrophylls

The size of leaf is 9425 = 164025 sq. mm (0.16405 sq. m)

f. Megaphylls

Than Macrophyll is called as Megaphyll

24

Fig.3. Leaf size classes

1. Low than A = Leptophylls (L)

2. Among A&B = Nanophylls (N)

3. Among B &C = Microphylls (Mi)

4. Among C & 2 times D = Mesophylls (Me)

5. Among 2 time D & 8 times = Macrophylls (Ma)

The size of the figure as

Restricted by the black-line

(Raunkier 1934)

25

2.2.2.2. Biological Spectrum

The biological range is defined as "the percentage distribution of species among

the life form of a flora". It was categorized following the Raunkier (1934).

a. Phenarophytes

The resting buds born more than 25cm above the ground, the aerial shoots can be

divided into.

b. Megaphanerophytes

This group comprises all the tall trees.

c. Nanophanerophytes

It comprised all the shrubs.

d. Chamaephytes:

The buds on persistent shoots that no more than 25cm above the ground surface.

e. Hemicryptophytes

The perennial buds at or near the soil surface.

f. Therophytes

26

The annual plant species those completes their life cycle rapidly under

favourable conditions. They have very short life span.

g. Geophytes

Resting in dry ground, plants with bulbs, corm and rhizome are incorporate

under this group.

h. Lianas

The woody climbers (epiphytes) were treated as a species group.

2.2.3. The phytosociological parameters

The following phytosociological parameters were described in each stand and

their comparative values were planned to measure status of vegetation. The frequency,

Density and canopy canopy were recorded after followed by (Mueller & Ellenberg,

1974).

a. Density

Density is defined as thewhole number of entities of each species per component area

experimented.

Density = whole individuals of a species Whole quadrats

27

b. Relative Density:

Relative density was determined by species as afraction of the quantity of the entire

density of all existing species.

Relative density = Density of a species X 100 Density of all species

c. Frequency

Frequency is a grade of consistency of the incidence of the entities of a species in

the region.

Frequency = No of quadrats in which a species happen X 100 Total no of quadrats

d. Relative Frequency

Relative frequency was dogged by the fraction/percentage associated of the total

frequency of altogether species.

Relative frequency = Frequency of one species X 100 Frequency of all species

e. Canopy cover or dominance:

The canopy cover or domination is the capacity of a space full or the quantity of

earth sheltered by the crown.

Canopy cover = Total cover of a species Total No. of quadrats

28

The coverage of herbs and shrubs was calculated using the Daubenmire coverage

classes (Daubenmire, 1959)

Table 2.1 Daubenmire coverage classes of herbs and shrubs

Classes Range of cover Midpoint (%)

1. up to 5% of the earth surface 2.5

2. 5% to 25% of the earth surface 15.0





(Daubenmire, 1959)

The shrubs and herbs canopy were transformed into the middle points and

whole canopy cover was intendedtotaling of all the middle points for the species in the

entire sampled regions by means of the above methods.

For trees, the conversion of circumference into basal area method will be used which is

fallows.

29

For circumference more than 200 inches following formula should be applied for direct

conversion of circumference in basal area in ft.

Basal area (B.A) = (Circumference)² 1808.64

f. Relative Canopy Cover

The relative canopy cover was intended from the whole canopy standards of

species as a share the whole canopy values for altogether the species.

Relative canopy cover = Canopy of a species X 100 Canopy of all the species

g. Importance Values Index

In vegetal communities the data coverage and frequency of a species does not

provide a clear lay out near the leading species. It can be acquire by the sum of

comparative density, relative frequency and relative cover than dividing the sum value

by three give the importance value index (IVI) of the species (Sadruddin, 1992)

Importance Value Index = Relative density + Relative cover + Relative frequency 3 I.V.I = RD+RC+RF 3

After receiving the importance value index of all the plant species, the values

were organized in anarising ordr according to their I.V.I. The names of the societies

were assign description to first prevailing plant in every i.e., herbs, shrubs and trees.

30

2.2.4. Species richness

Species richness was the appearance of simple proportion between the number

of species and the square root of the whole no of characters (Menhinick, 1964).

Index of Menhinick:-

d = S √N

Where;

d = Species richness

S = Whole no of species in a sample area

N = Whole no of individual in sample area

2.2.5. Diversity index:

The diversity index was calculated by Shannon & Wiener diversity index (1949).

Shannon diversity index was determined by;

D = ∑ [∑ n (n – 1)] N (N – 1)

Where as;

n = Real no of individuals of the species in a stand

N = Whole no of individuals of all the species existing in that stand

31

2.2.6. Degree Of Aggregation

The degree of aggregation was calculated after Curtis & McIntosh (1950)

According to Curtis and McIntosh there are four classes of aggregated plant species.

Aggregated species = If the value two or more than two

Regular species = If the value less than one

Intermediate species = If the value in between one and two

United species = If the value is equal to one

Degree of aggregation was calculated by following formula

{Deg of Agr = D} d

Where;

D = Observed density

d = Expected density

2.2.7. Degree of maturity

The degree of maturity was calculated after Pichli and Sermolli (1948)

32

According to Pichli and Sermolli if the community contains 60% or more than 60% than

the community was mature if the value was lower then 60% the community become

immature.

D. M = Whole frequency of all the spp in a station Whole no spp present in a station

2.2.8. Regeneration capacity

The distribution of plants in different age classes indicates age and population

status of a community, i.e weather regenerating or declining. The self generation

capacities of forests were determined by classifying woody plants into different classes.

The class interval was kept 30cm (Table 2.3)

2.2.9. Index of homogeneity

The index of homogeneity in flora was calculated by categorizing plants in

various classes and relating Raunkier (1934), rule of frequency as follows (Table 2.2)

The natural distribution of the frequency proportion attained from such

association is described as

A>B>CD<E

And it has been named as Raunkier law of frequency.

33

Table 2.2 Frequency classes according to Raunkier classification for determining degree

of homogeneity

Frequency classes Range

1. 01-20 %

2. 21-40 %

3. 41-60 %

4. 61-80 %

5. 81-100 %

34

Table 2.3 Age classes of woody species as defined on the basis of available data

Class Range of Diameter

1 01-30

2 31-60

3 61-90

4 91-120

5 121-150

6 151-180

7 181-210

8 211-240

9 241-270

10 271-300

11 301-330

12 331-360

13 361-390

14 391-420

15 421-450

16 451-480

17 481-510

2.2.10. Family Importance Value

There were 252 plants species belong to 97 families; each species has its own

importance value. The family importance value was determined by adding importance

35

value of all the species of a family. Each family has its own specific family impoprtance

value that shows the abunce within study area.

2.2.11. Similarity and dissimilarity index

It is used for the comparison of all communities within study area. It is defined

as the number of species shared to the two societies and is communicated as

proportions of the whole number of together the communities. It was calculated by

Sorenson (1948).

I.S = 2C_ X 100 (A+B)

Where as;

C = Amount of species shared in two stations (A & B)

A = Whole no of plant species in stations-A

B = Whole no of plant species in station-B

I = Index of similarity

S = Sorenson Index

Index of Dissimilarity = 100 – I X S

2.2.12. Evenness or equitability

36

It is a component of variety and shows the proportion of the perceived variety to

the highest diversity possible for the similar number of species. The evenness

(consistency) components are connected to allocated individuals among the species

(Peet, 1974). The evenness was calculated by following formula Pielou (1969).

Evenness = Natural log of expected value from index of Shanon – Wiener Natural log of whole species in a station

2.2.13. Ethnomedicinal uses

Traditional methods of drug therapy were also documented. The frequent field

trips were organized in different regions of Thandiani forests. The information

regarding conventional uses of medicinal plants was collected through questionnaires,

interviews, semi-structured interviews and group discussions with local inhabitants of

different occupations, gender and age (Table 2.10).

2.2.14. Soil Analysis

One kilogram soil was collected from each site up to a deepness of 15cm and

thoroughly diverse to make a complex sample. The soil was reserved in polythene

stacks and consideredcorrectly. The soils were analyzed substantially and chemically

representative.

Lab Work

Soil samples will be analyzed for their physical and chemical characteristics. The

major characteristics of particle size investigation are the distribution of soil sums into

37

distinct units by mechanical or biochemical incomes and then the separation of the soil

particles by sedimentation approaches (Gee et al., 1986). The biochemical distribution is

capable by first removing strengthening materials, such as organic substance and iron

oxides and formerly category outsoil particles binding ions i.e., calcium and magnesium

ions, with sodium ions, which surround each soil particle with a film of hydrated ions.

The calcium and magnesium ions are removed from explanation by appearance with

oxalate or hexa-metaphosphate anions (Sheldrick & Wang 1993; Baver et al., 1972 and

Gee et al., 1986).

1. Soil texture

The soil texture classes were determined after followed by (Moodi et al., 1959;

Boellstorff, 2009 and Brady, 1996).

2. pH

The pH of soil samples were calculated by pH meter using 1: 5 soil saturation

excerpts. The conductors of the pH meter were dipped into the soil suspension and

interpretation was directly noted (Rayment & Lyons, 2011).

3. Electrical Conductivity

It was measured by using electrical conductivity meter and 1:5 soil

saturation pastes. Prepare the saturation extract and read out the temperature of

the extract. Solution and fill the conductivity cell. Set the temperature handle to

38

that of the test solution. Close the interaction switch on the cell temporarily

while correspondings the bond with the chief dial, than read and record the

electrical conductivity (Hussain, 1989 and Rayment & Lyons, 2011).

4. Organic matter

The detritus was separated from each sample and mentioned in

percentage. It was determined by Walkley and Black's titration method

(Hussain, 1989 and Rayment & Lyons, 2011).

Organic Matter percentage = (S-T) x (6.7) (S)

Where as;

S= Total read

T - Volume used of FeSo4

5. Calcium carbonate

The amount of CaCO3 was determined by the acid neutralization method.

I-ICI was used for neutralization (Hussain, 1989).

CaCO3 % = Blank read inn - Sample read inn X5 2

6. Potassium

39

It was determined by atomic absorption spectrophotometer in the flame

omission mode after diluting the sample With 2.50 ml of 1% lithium sulfate solution, a

normal clarification was prepared to plot the normal curve and potassium of each

sample was considered from the respected curve (Rayment & Lyons, 2011).

7. Calcium and Magnesium

The soulable Ca & Mg was obtained by extracting the soil by water and

measurement of their concentration in the extract by titration with ethylene

diaminetetra acetic acid (Richards, 1954).

8. Phosphorus

The phosphorus was extracted with hydrochloric ammonium-fluoride method

and determined calorimetrically (Kitayama et al., 2002)

9. Sodium

The amount of sodium can be extracted with ammonium acetate solution. The soulable

sodium obtained from a saturated paste and sodium in extract determined by flame

photometry. Sodium has the property that, when their salts are introduced into a flame,

they emit light with specific wavelength (color). This is especially true for Na emiting a

sparkling yellowish-red color (Richards, 1954).

10. Nitrates and Nitrogen

40

It was determined by using chromotropic acid spectrophotometric method. It was used

originally for water and later for soil (Sims and Jackson, 1971). It is an alternate for NO3-

N determination by the distillation method. A close relationship exists between NO3-N

determined by chromotropic acid.

11. Chloride

Soulable chloride was obtained in the saturated extract form and its concentration was

determined by silver nitrate titration method (Richards, 1954).

2.2.15. Data Analysis

Data sets will be analyzed in the available standard statistical software to see the

quantify plant species and communities of the Thandiani sub forests division, to explore

environmental gradient responsible for vegetation variation and to identify rare and

indicator species for future conservation. The Data for 252 plant species, 6 ecological

gradients and 50 locations (1750-releves) were analyzed in four computer software

programs, i.e., MS-EXCEL 2007, PC-ORD version-5, ArcGIS 10.2.1 (Geo-sensing) and

CANOCO version 4.5. Stepwise particulars for analyzing the data are providing

underneath. As phytosociology is distribution of flora, the ecological variables

associated to those locations necessity to be observed in a statistical outline (Lambert &

Dale, 1964 and Kent & Coker, 1995). The Phytosociologists frequently need to test

suggestions concerning the belongings of tentative variables on whole set of species

(Anderson et al., 2006). The growth of the exceeding designated computer softwares are

41

the latest progressions in the field of synecology which provides ainnovative method to

vegetation classification pattern (Gilliam and Elizabeth, 2003; Hettrich and Rosenzweig,

2003). These examines have been clarified according to the approaches and techniques

recommended by Rieley and Page, (1990), Mueller and Ellenberg, (1974) and Greig,

(1983).

2.2.16. The qualitative & quantitative characteristics by using MS-EXCEL 2007

MS-EXCEL 2007 was used for a variety of elementarycalculations.

2.2.17. Establishing data for CANOCO and PC-ORD

After calculation of qualitative and quantitative character of the 50 stations by

means of the methods understood in the table, two assemblies of data medium were

produced in line with the requirements of the CANOCO and PC-ORD packages. The

assembly comprised 252 plant species medium, for both species qualitative features

(occurrence/missing data) and quantitative features (cover; density; frequency; relative

cover; relative frequency; relative density and Importance Value Index) at 50 stations.

The second assembly comprised the ecological inclines data for every location. All the

ecological inclines were arranged as independent variables while quantitative qualities

of plant species were measured as dependent variables. The Graph production MS-

EXCEL was used to clearly show the edophic, floristic and climatic data, comprising

plant pattern form, Raunkier life form, 20 most descriptive families and modification in

species variety and productivity with elevation.

42

2.2.18. Index of Indicator Species Analysis

The Indicator Species Analysis (ISA) used as a technique to identify the

characteristic indicators for TsFD. These collective informations are helpful in species

richness of a specific assembly and existence of a species in those specific communities.

It is hypothesized that indicator values for every species in every assembly which test

for statistical implication by means of a Monte-Carlo method. After applying ISA, the

species medium for all positions (252 x 50) was defined six times for each ecological

inclines, i.e., soil pH, aspect, soil phosphorus, soil texture, electrical conductivity and

soil organic matter, from the ecological medium (50 stations x 6 environmental

variables). Each species were assessed for its capacity to classify amongst all the actions

of ecological variables. The indicator species analysis had given explanation on how

well the occurrence of a species was presenting a position/model category (Dufrene &

Legendre, 1997).

2.2.19. Cluster Analysis

The plant community investigation was to classify and clarify the flora

ordination in the TsFD. The first technique used was ranked to collecting the 50 stations

for all of the 252 plant species constructed on qualitative data, i.e. existence/missing

(Greig, 2010). This technique continued from the individual substances, i.e., species or

models and progressively collective them into assemblies, in relations of their

resemblance. It was hierarchical in the way that each of the small groups belongs to a

43

better and more varied cluster. The Cluster Analysis (CA) recognized important

environment and plant community kinds grounded on Sorenson (Bray Curtis) space.

The Sørensen's similarity space is applied to occurrence/missing data and measured by

the formula;

Q.S = (2C)/(A+B)

Where as;

A and B are the number of species in stations A and B and C is the number of

species common by the two positions (Dalirsefat et al., 2009 and Sorenson, 1948). The

main associations distinct inside the dendrogram were qualified as environment and

community categories; their development was established more by using ordination

methods.

2.2.20. The naming of plant communities

The naming of plant communities was established on Indicator Species features.

A threshold level indicator value of 20 to 25% (p value ≤ 0.05) for the index was selected

as the cut off for classifying significant indicator species, recognized by Indicator

Species Analysis as followed by Dufrene and Legendre (1997). A lesser number of

indicator species were recognized for naming the communities; these were the species

that extremely connected with those particular communities. An Indicator species of a

specific community were reconsidered as having faithfulness of 30% of the

44

stations/places of that community (Intended in MX-EXCEL) (Bergmeier, 2002; TerBraak

, 1986; Dai et al., 2006; Dufrene & Legendre, 1997).

Table 2.4 Criteria for determining fidelity (Faithfulness) classes

Fidelity

Class.

Descriptions

5.

Limited species: Totally or nearly limited to one

community/environment

4.

Discriminating species: Originate maximum often in sure communities,

but also infrequently in other societies/environments

3.

Preferred species: Existing in numerous communities more or less

abundant, but mainly in one sure community and there with excessive

contract of potency

2.

In different species: Lacking a certain attraction for any specific

community/Environment

1.

Unintentional species: Species which are infrequent/insufficient and

unintentional invader from another community/environment

2.2.21. Ordination investigates to classify environmental gradients by using

CANOCO

The ordination is a multivariate statistical technique that identifies community

data by building a low dimensional space in which comparable models and species

come nearer composed whilst different ones goes more apart. CANOCO yields actual,

legal and low dimensional summaries from ground data by appropriate and desire

means. The Ordination actions provide two varieties of evidences, i.e., on community

45

pattern through indirect gradient investigation and structure through straight gradient

analysis. The literature shows that the former ordination method is usually used by

European ecologists, while the latter is more usually used in North America (Whittaker,

1973; Greig, 1983; Ter Braak, 1987, Jongman, 1995; Gauch et al., 2003, Khan et al., 2012;

Digby & Kempton, 2010).

Keeping the aims of the current study in attention, together of the ordination

methods, i.e. indirect and direct ecological gradient investigation were accomplished by

means of CANOCO version-4.5 (Khan et al., 2012; Ter Braak and Smilauer, 2002). All

data were used in the gradient investigation. By means of indirect approaches, i.e., the

Detrended Correspondence Analysis (DCA) and Correspondence Analyses (CA) the

forest vegetation data were preserveddeprived of the input of ecological data in order

to measure the truthfulness of the numerous accumulations of models (stations) and

species. The DCA providing more strong results than CA and therefore was improved

as a tool for more indirect gradient analysis.

2.2.22. Detrended Correspondence Analysis

Amongst the indirect methods, DCA is the greatest applied method in the field

of vegetation analysis. It is greatly evaluate by ecologists as it is approved out deprived

of the input of ecological data and therefore outcomes are allowed from organization

(Ter Braak & Schaffers, 2004 and Hill & Gauch, 1980). Species richness data mediums

i.e., frequency; density; cover and importance values index were used for DCA

46

processes. The outcomes describe the environmental variables for the

community/environment types recognized by a Cluster Dendrogram.

2.2.23. Canonical Correspondence Analysis

Composed on assessment of the literature on ordination methods, it was clear

that Canonical Correspondence Analysis is the most common and approximately used

straight ordination incline investigation method and hence this was used for the current

research. Direct gradient analyses were complete that treated floristic and ecological

data mediums organized in CANOCO-V-4.5. The CCA was originated to be the greatest

expressive as a direct gradient analysis method. Its submission to the data more

validates the outcomes of the DCA (McCune, 1986; Kent and Coker, 1995; Dufrene and

Legendre, 1997; Greig, 1983 and Khan et al., 2012).

2.2.24. Vegetation Mapping

Personal Geodatabase was created in ArcGIS 10.2.1 to save all Geo-datasets. A

study area boundary which was demarcated through GARMIN eTrex Vista GPS

receiver was added in ArcMap 10.2.1. Forest area boundary was marked through

google earth and field data collection and observation. Digital Elevation Model (DEM)

SRTM SRTM_u03_p150r036 (three arcsecond, i.e., x,y = 90 x 90m) was used to mask the

study area through Spatial Analyst (Extraction> mask) tool (Faruque et al., 2006). The

masked DEM layer was filled to remove small imperfections in the data through

hydrology>fill tool for best results. Elevation classes from symbology were increased to

47

30 on the bases of natural breaks. Natural breaks method is considered to be more

appropriate for representing the values of spatial characteristics. In this method, the,

variance between classes is maximized and minimized within classes. In this way the

actual clusters of values are naturally grouped together. ArcGIS 10.2.1 automatically

calculates the Natural Breaks. The Same data was utilized in ArcScene 10.2.1 to create

3D scenes of plant communities (Scott et al., 2004). Streams were delineated through

hydrology>stream order tool which were later converted to feature class through

“stream to feature” tool of hydrology toolset. Excel sheets comprising different plant

communities analysis results gained from CANOCO (version 4.5) were added in

ArcMap through “excel to table” of Conversion toolbox (Krivoruchko, 2012). The

parameters were classified according to the type of a community.

The point map was created through overlaying of plant community attribute

data on study area DEM layer. Point Profile Graphs were created from 3D points based

on Z value of each point of plant communities. The scatterplot matrix graphs for all

plant communities were created to compare different values/results gained from

CANOCO software. Similarly, 3D line (elevation cross section) profile was created

through interpolating heights from the study area cross section on DEM surface. A 3D

view of the study area was generated in ArcScene 10.2.1 that showing all the plant

communities. Each point profile, scatter plot and 3D line profile graph was added to the

final layout of plant community maps (Waters, 1998).

48

CHAPTER 3

RESULTS

3.1. Floristic diversity

Field Preliminary surveys confirmed the family Pinaceae as the most abundant

family with 1892.4 Family Importance Value (abbreviated as FIV), followed by Rosaceae

with 1478.2 and Ranunculaceae with 762.1. The other major families like Piplionaceae,

Polygonaceae, Poaceae, Asteraceae, Plantaginaceae and Euphorbiaceae were

represented by 742.6, 689.1, 539.4, 494.1, 405.2 and 397.1 FIV respectively (Figure 3.1).

Based upon plant habits vegetation of the region can be classified in to 51 trees (20.24%),

48 shrubs (19%) and 153 herbs (60.76%).

Figure 3.1 Plant families with highest Family Importance Value (FIVs) in the TsFD

49

Figure 3.2 Graph showing most represented plant families based on number of species

in the region.

The most abundant plant family was Rosaceae with 20 species and a share of

20.6% followedby Asteraceae with 14 species and 14.43% share. Ranunculaceae,

Papilionaceae, Apiaceae, Caprifoliacea, Labiateae, Solanaceae and Araceae were

represented by 12, 9, 6, 6, 6, 6, and 5 plant species each respectively. The remaining

families were represented by less than 5 species each (Figure 3.2).

3.2. Life form

The life form spectra have been recorded from TsFD during 2012 to 2014. There

were 31.74% Hemicryptophytes (80-spp), 20.24% Megaphanerophytes (51-spp), 19.44

Therophytes (49-spp), 17.86% Nanophanerophytes (45-spp), 07.93% geophytes (20-spp),

01.98% lianas (05-spp) and 00.79% Chamaephytes (02-spp) (table-3.1, 3.3 & 3.4).

50

3.3. Leaf spectra

The Leaf size spectra of TsFD showed 34.92% Microphylls (88-spp) as a

dominant followed by 29.36% Leptophylls (74-spp), 23.80% Nanophylls (60-spp) and

11.90% Mesophylls (30-spp), (Table 3.2, 3.3 & 3.4).

Table 3.1 The life form spectra of plants of Thandiani Sub Forests Division, Abbottabad

S. No Life Form No of species %

1 Megaphanerophytes 51 20.24

2 Nanophanerophytes 45 17.86

3 Chamaephytes 02 00.79

4 Hemicryptophytes 80 31.74

5 Geophytes 20 07.93

6 Therophytes 49 19.44

7 Lianas 05 01.98

Total 252 100

51

Table 3.2 Leaf spectra of plants of Thandiani Sub Forests Division

S.

No

Leaf Spectra No of species %

1 Leptophylls 74 29.36

2 Nanophylls 60 23.80

3 Microphylls 88 34.92

4 Mesophylls 30 11.90

Total 252 100

52

Table 3.3 Percentage of life forms recorded in the study area

Key

T.S= Total species Ch= Chamaephytes G= Geophytes H= Hemicryptophytes

Mp= Megaphanerophytes Np= Nanophanerophytes Th= Therophytes Li= Lianas

S. No Plant associations Elevation (m) T.S Li Ch G H Mp Np Th

No % No % No % No % No % No % No %

1 Melia-Punica-Euphorbia 1200-1500

masl

94 03 3.2 02 2.1 02 2.1 26 27.7 29 30.8 19 20.2 16 17

2 Zizyphus-Zanthoxylum-

Rumex

1501-1800

masl

174 03 1.7 02 1.1 11 6.3 51 29.3 41 23.6 34 19.5 33 18.9

3 Quercus-Cornus-Viola 1801-2100

masl

170 04 2.4 01 0.6 16 9.4 53 31.2 34 20 33 19.4 29 17

4 Cedrus-Viburnum-Achillea 2101-2350

masl

142 02 1.4 01 0.7 15 10.6 47 33.1 22 15.5 32 22.5 23 16.2

5 Abies-Daphne-Potentilla 2351-2626

masl

81 00 00 01 01.2 07 8.6 26 32 20 24.7 18 22.2 09 11.1

53

Table 3.4 Percentage of leaf spectra recorded from Thandiani Forests

Key

T.S= Total species Le= Leptophylls Me= Megaphylls

Mi= Microphylls N= Nanophyll

S. No Plant associations Elevation (m) T.S Le Me Mi N

No % No % No % No %

1 Melia-Punica-Euphorbia 1200-1500 masl 94 24 25.5 13 13.8 40 42.5 17 18

2 Zizyphus-Zanthoxylum-

Rumex

1501-1800 masl 174 46 26.4 24 13.8 68 39.1 36 20.7

3 Quercus-Cornus-Viola 1801-2100 masl 170 51 30 22 12.9 57 33.5 40 23.5

4 Cedrus-Viburnum-Achillea 2101-2350 masl 142 44 31 16 11.3 48 33.8 34 23.9

5 Abies-Daphne-Potentilla 2351-2626 masl 81 25 30.8 19 23.5 28 34.6 09 11

54

3.4. Species Richness

The species richness values increase from bottom to top. The lowest Species richness

value was observed in station no 03 Baribak having 1.10158211 while the highest

Species richness value were observed at Station no 12 Upper Balolia 2.477767101 (Table

3.5).

3.5. Diversity Index

The analysis was carried out in Thandiani Sub Forest Division during 2012 -2014.

Fifty stations were reported in the study area. The Species diversity, the value increases

from bottom to top; the lowest species diversity was contained Station No 03 Baribak

11.01 species diversity value. This station was recorded at an elevation of 1523m. The

highest species diversity was contained station no 11Lower balolia which has 39.441

species diversity value. This station was recorded at an elevation of 1785m. The species

diversity is provided in (Table 3.5).

3.6. Degree of Maturity

There were 50 stations in the study area in which 47 shows maturity while only

three stations that is station no 16, station no 48 and stations no 49 were found

immature (Table 3.5).

55

3.7. Evenness or equetibility

There were 50 stations in study area the whole data show incomplete evenness in

between the individuals of different species. The high equetibility values were observed

at station no 14 Malach upper 0.928266087 followed by station no 11 lower Balolia

0.921238551, station no 33 Larri 0.894990591 and station no 13 Malach lower

0.894396496. The least most equetibility value was observed in station no 50 Sikher

0.754937264 followed by station no 46 Mera RKC 0.763056485, station no 48 Lower

Namal 0.767745186 and stations no 04 Lower danna 0.769663526 (Table 3.5).

56

Table 3.5 Shows station‟s name with their GPS reading and elevation, D.I, Equetibility, T.F, S.R

and S.M of different plant communities of TsFD.

S.No Stations GPS Readings Elevation T.F T.S S.M E D.I S.R

Melia-Punica-Euphorbia Community

1 Mandrch N34 11 03.0 E73 16 35.4. 4261 ft 1620 19 85.26 0.82 11.31 1.14

2 Batanga N34 11 25.0 E73 16 44.1. 4685 ft 1600 20 80.00 0.88 14.12 1.17

3 Neelor N34 11 10.0 E73 16 30.8. 4333 ft 1660 20 83.00 0.8 11.1 1.1

4 Bari Bak N34 11 36.9 E73 17 53.3. 4996 ft 1520 18 84.44 0.83 11.01 1.1

5 Mand Dar N34 11 29.5 E73 17 36.3. 4580 ft 2420 33 73.33 0.86 20.2 1.51

6 Pkhr Bnd N34 11 19.7 E73 16 57.3. 5088 ft 3160 41 77.07 0.88 26.29 2.03

7 Lowr Dna N34 11 30.7 E73 17 52.6. 5190 ft 2580 38 67.89 0.77 16.44 1.88

8 Bandi TC N34 15 53.6 E73 15 49.1. 4648 ft 2820 43 65.58 0.87 26.36 1.77

9 Qalndrbd N34 15 53.5 E73 14 15.7. 4232 ft 2300 36 63.89 0.89 24.22 1.59

10 Riala N34 16 14.6 E73 18 15.7. 5006 ft 2480 36 68.89 0.83 19.61 1.48

11 Malch Lw N34 12 29.0 E73 17 16.1. 4908 ft 2720 39 69.74 0.89 26.49 1.54

12 Malch Up N34 12 50.7 E73 17 18.4. 5219 ft 2720 43 63.26 0.93 32.83 1.87

Zizyphus-Zanthoxylum-Rumex Community

1 Dana N34 11 41.9 E73 18 24.8. 5567 ft 3340 48 69.58 0.83 25.28 1.95

2 Uper Dna N34 11 13.0 E73 18 18.5. 5823 ft 4100 51 80.39 0.87 30.33 1.87

3 Pejjo N34 12 44.0 E73 17 41.4. 5449 ft 3900 55 70.91 0.88 34.22 2.18

4 Lowr Bal N34 12 56.3 E73 17 35.2. 5856 ft 3600 54 66.67 0.92 39.44 2.2

5 Upr Balo N34 12 53.8 E73 17 42.0. 6066 ft 3660 59 62.03 0.86 32.89 2.48

6 Mera Bun N34 15 50.6 E73 16 45.0. 5026 ft 3280 44 74.55 0.89 29.47 1.58

7 Lonr Pat N34 15 38.4 E73 17 30.3. 5472 ft 3380 47 71.91 0.88 29.46 1.6

8 Gali Ban N34 10 19.7 E73 17 32.9. 5036 ft 3080 43 71.63 0.85 24.14 1.71

9 Riala Ca N34 15 50.2 E73 17 33.6. 5305 ft 2960 42 70.48 0.87 26.2 1.67

10 Resrv FC N34 13 16.3 E73 20 19.0. 7142 ft 3740 52 71.92 0.85 28.51 1.74

11 Upper GB N34 11 35.6 E73 19 02.7. 5370 ft 3200 43 74.42 0.86 25.06 1.54

12 Chatrri N34 12 15.1 E73 19 41.2. 5626 ft 3160 45 70.22 0.85 25.05 1.59

13 Terarri N34 16 30.3 E73 19 09.3. 5954 ft 3500 48 72.92 0.85 27.35 1.75

14 Upr Rial N34 16 27.7 E73 19 01.7. 5600 ft 2840 43 66.05 0.84 23.49 1.68

15 Terari C N34 16 30.0 E73 19 33.6. 6292 ft 3360 49 68.57 0.87 29.28 1.74

16 Mathrika N34 13 09.0 E73 17 32.4. 6000 ft 2800 56 50.00 0.86 31.89 2.09

Quercus-Cornus-Viola Community

57

1 Mthrka T N34 13 14.3 E73 17 34.1. 6295 ft 3640 52 70.00 0.87 31 1.87

2 Jabbra N34 13 22.6 E73 17 43.8. 6650 ft 3520 53 66.42 0.87 31.27 1.84

3 Darral N34 16 27.3 E73 19 32.3. 6564 ft 3700 49 75.51 0.83 25.6 1.71

4 Makali N34 16 18.1 E73 19 41.3. 6991 ft 3540 48 73.75 0.83 25.28 1.66

5 Ladrri N34 16 30.5 E73 19 54.2. 7280 ft 3640 48 75.83 0.82 23.46 1.64

6 Upper KP N34 13 43.5 E73 20 13.8. 6617 ft 3720 49 75.92 0.86 28.54 1.66

7 Kakl RFC N34 12 31.7 E73 17 40.2. 5649 ft 3140 46 68.26 0.87 28.18 1.73

8 Parringa N34 16 42.4 E73 20 02.0. 7519 ft 3740 51 73.33 0.83 26.09 1.71

9 Satu Top N34 16 46.2 E73 20 06.3. 7814 ft 3720 51 72.94 0.87 30.26 1.68

10 Lower KP N34 13 02.0 E73 19 59.2. 6102 ft 3080 43 71.63 0.87 26.31 1.5

11 Larri N34 13 48.0 E73 20 29.8. 6968 ft 3580 48 74.58 0.89 31.97 1.75

Cedrus-Viburnum-Achillea Community

1 Pallu Zr N34 13 58.2 E73 18 02.3. 7122 ft 3620 55 65.82 0.86 30.91 1.86

2 Lari Tra N34 13 23.3 E73 20 08.4. 6601 ft 2920 41 71.22 0.87 25.2 1.59

3 Lari Top N34 14 21.5 E73 20 50.7. 7844 ft 3980 58 68.62 0.88 35.5 1.96

4 Sawan Gl N34 14 53.1 E73 20 51.4. 7644 ft 4000 53 75.47 0.89 34.76 1.83

5 Lower Th N34 14 20.7 E73 20 45.8. 7483 ft 4040 53 76.23 0.85 29.57 1.69

6 Upper TC N34 13 16.4 E73 20 42.5. 7874 ft 4080 62 65.81 0.86 34.21 1.94

Abies-Daphne-Potentilla Community

1 Mera RKC N34 1401.7 E73 2025.7. 7198 ft 1920 30 64.00 0.76 13.4 1.67

2 Mera RKT N34 14 45.8 E73 20 04.4. 7575 ft 2940 44 66.82 0.86 25.75 2.35

3 Lwr Nmal N34 15 04.5 E73 20 22.4. 7896 ft 1580 30 52.67 0.77 13.62 1.51

4 Upr Nmal N34 14 47.5 E73 20 25.5. 8330 ft 1720 30 57.33 0.86 18.52 1.41

5 Sikher N34 14 54.1 E73 20 35.0. 8615 ft 2480 39 63.59 0.75 15.89 1.6

Key

GPS= Global Positioning System D.I= Diversity Index T.F= Total Frequency

S.R= Species Richness S.M= Species Maturity E= Eveness

58

3.8. Degree of Aggregation

The overall situation of the area is shown in Table 3.6 It is seen that with the increase

in altitude the regular pattern of different plants species fluctuates. The percentage of

aggregated and intermediate pattern with few exceptions enhances as one move from

lower to higher altitude. The woodland temperate associations extending from 2000 m

to 2600 m, it was seen that the majority of plant stations exhibited aggregated and

regular pattern of occurrence. The regular pattern dominated throughout the study area

followed with intermediate and aggregated pattern.

59

Table 3.6 Degree of aggregation of Thandiani Sub Forests Division.

S.No Stations T.S A I R U Status

Melia-Punicaa-Euphorbia Community

1 Mandroch 19 1 3 15 0 R

2 Battanga 20 0 0 20 0 R

3 Neelor 20 0 0 20 0 R

4 Bari Bak 18 0 6 12 0 R

5 Mand Dar 33 1 8 23 1 R

6 Pkhr Bnd 41 0 2 38 1 R

7 Lowr Dna 38 0 3 34 1 R

8 Bandi TC 43 1 7 34 1 R

9 Qalndrbd 36 1 9 25 1 R

10 Riala 36 1 13 22 0 R

11 Malch Lw 39 0 7 32 0 R

12 Malch Up 43 1 11 31 0 R

Zizyphus-Zanthoxylum-Rumex Community

1 Danna 48 1 7 40 0 R

2 Uper Dna 51 2 6 40 3 R

3 Pejjo 55 1 7 46 1 R

4 Lowr Bal 54 0 7 45 2 R

5 Upr Balo 59 1 4 53 1 R

6 Mera Bun 44 1 13 29 1 R

7 Lonr Pat 47 5 14 28 0 R

8 Gali Ban 43 1 9 32 1 R

9 Riala Ca 42 1 12 29 0 R

10 Resrv FC 52 2 17 32 1 R

11 Upper GB 43 2 16 24 1 R

12 Chatrri 45 5 11 28 1 R

13 Terarri 48 2 12 33 1 R

14 Upr Rial 43 2 10 31 0 R

15 Terari C 49 2 17 29 1 R

16 Mathrika 56 1 11 40 4 R


1 Mthrka T 52 1 11 38 2 R

60

2 Jabbra 53 1 17 34 1 R

3 Darral 49 3 14 31 1 R

4 Makali 48 3 15 30 0 R

5 Ladrri 48 3 14 30 1 R

6 Upper KP 49 2 18 29 0 R

7 Kakl RFC 46 0 5 41 0 R

8 Parringa 51 6 12 30 3 R

9 Satu Top 51 2 20 28 1 R

10 Lower KP 43 7 11 25 0 R

11 Larri 48 1 18 28 1 R


1 Pallu Zr 55 2 14 39 0 R

2 Lari Tra 41 1 14 24 2 R

3 Lari Top 58 1 16 41 0 R

4 Sawan Gl 53 2 14 33 4 R

5 Lower Th 53 3 17 32 1 R

6 Upper TC 62 5 14 43 0 R


1 Mera RKC 30 1 4 25 0 R

2 Mera RKT 44 0 1 43 0 R

3 Lwr Nmal 30 1 3 26 0 R

4 Upr Nmal 30 1 6 23 0 R

5 Sikher 39 2 4 33 0 R

61

3.9. Regeneration capacity

The number of trees of each of the species in different girth classes is depicted in

Table 3.7. It was perceived that Abies pindrow and Pinus wallichiana had a maximum

diameter of 241 cm and 270 cm respectively. The plants were distributed among

different diameter classes, which show that plants of different age group were present.

The presence of young plants from seedling to mature or over mature shows a chance

of regeneration of both tree species. The Diospyrus lotus, Juglens regia, Pinus roxburghii,

Pyrus pashia and Quercus incana were regenerated, while Acacia modista, Aesculus indica,

Ficus carica, Morus nigra and Pistacia antegrrimma were not regenerating as they were

sporadic individuals in some of the girth classes they were dying.

62

Table 3.7 Regeneration capacity of important tree species of Thandiani sub forests division

S. No Age Classes 0-30 31-60 61-90 91-120 121-150 151-180 181-210 211-240 241-270 Status

1 Abies pindrow 0 0 0 0 6 9 97 6 5 R

2 Acacia Arabica 0 0 0 2 8 1 0 0 0 D

3 Aesculus indica 0 0 2 4 4 8 3 0 0 D

4 Diospyrus lotus 0 0 1 12 9 11 5 2 0 R

5 Ficus carica 0 0 11 20 35 13 0 0 0 D

6 Juglens regia 0 0 0 1 6 8 11 3 0 R

7 Morus nigra 0 0 4 10 15 10 1 0 0 D

8 Pinus roxburghii 0 0 0 13 14 13 13 6 0 R

9 Pinus wallichiana 0 0 11 34 63 64 48 21 26 R

10 Pistacia antegrrimma 0 0 2 1 3 7 6 0 0 D

11 Pyrus pashia 0 0 5 15 19 9 3 0 0 R

12 Quercus incana 0 0 0 7 14 12 6 2 0 R

Key

R= Regenerative

D=Dying

63

3.10. Degree of Homogeneity

The degree of homogeneity determined on the basis of Species richness value. So,

any reduction in species richness, especially unique/endemic species, could be reason

as advocating the production of a homogenous environment. There were 50 stations all

fallen in heterogeneity due to the diversity among the plants of different species (Table

3.8).

http://en.wikipedia.org/wiki/Species_richness

http://en.wikipedia.org/wiki/Endemic_species

64

Table 3.8 Degree of Homogeneity among the plants in the study area

S.No Stations A.(1-20) B.(21-40) C.(41-60) D.(61-80) E.(81-100) T.S Remarks


1 Mandroch 0 2 3 2 12 19 He

2 Battanga 1 2 2 6 9 20 He

3 Neelor 0 3 1 6 10 20 He

4 Bari Bak 1 2 1 2 12 18 He

5 Mand Dar 1 1 16 8 7 33 He

6 Pkhr Bnd 2 1 12 13 13 41 He

7 Lowr Dna 0 4 21 7 6 38 He

8 Bandi TC 1 9 18 7 8 43 He

9 Qalndrbd 0 8 18 5 5 36 He

10 Riala 0 8 12 8 8 36 He

11 Malch Lw 0 8 14 8 9 39 He

12 Malch Up 3 10 15 7 8 43 He

Ziziphus-Zanthoxylum-Rumex Community

1 Danna 0 5 23 12 8 48 He

2 Uper Dna 0 2 14 16 19 51 He

3 Pejjo 0 14 12 17 12 55 He

4 Lowr Bal 1 11 21 13 8 54 He

5 Upr Balo 3 21 14 7 14 59 He

6 Mera Bun 0 5 15 11 13 44 He

7 Lonr Pat 3 7 14 9 14 47 He

8 Gali Ban 2 8 10 9 14 43 He

9 Riala Ca 1 5 16 11 9 42 He

10 Resrv FC 1 9 17 8 17 52 He

11 Upper GB 2 5 13 6 17 43 He

12 Chatrri 0 8 17 9 11 45 He

13 Terarri 0 6 19 9 14 48 He

14 Upr Rial 3 9 16 2 13 43 He

15 Terari C 1 9 18 10 11 49 He

16 Mathrika 5 25 19 3 4 56 He


1 Mthrka T 0 8 20 13 11 52 He

65

2 Jabbra 2 7 25 9 10 53 He

3 Darral 0 7 13 13 16 49 He

4 Makali 1 4 20 7 16 48 He

5 Ladrri 0 7 11 15 15 48 He

6 Upper KP 0 6 12 15 16 49 He

7 Kakl RFC 2 10 14 9 11 46 He

8 Parringa 0 6 17 16 12 51 He

9 Satu Top 0 9 15 12 15 51 He

10 Lower KP 2 6 14 11 10 43 He

11 Larri 1 7 11 14 15 48 He


1 Pallu Zr 3 13 17 9 13 55 He

2 Lari Tra 0 8 13 9 11 41 He

3 Lari Top 2 10 22 10 14 58 He

4 Sawan Gl 0 4 22 9 18 53 He

5 Lower Th 1 9 11 10 22 53 He

6 Upper TC 0 6 26 17 13 62 He


1 Mera RKC 3 6 8 8 5 30 He

2 Mera RKT 2 8 16 10 8 44 He

3 Lwr Nmal 8 9 5 4 4 30 He

4 Upr Nmal 6 7 7 4 6 30 He

5 Sikher 3 13 10 5 8 39 He

66

3.11. Similarity (SI) and Dissimilarity (DI) Indices

There were 5 plant communities, each having different SI and DI. Data

shows that the highest similarity (IS) (92.1348%) was reported between Kakl RFC

and Mathrika in Transect 3, Kukule, while the majority of the stations shared 45 to

60% similarity among them. The Mueller and Ellenberg (1974) stated that

communities consuming less than sixty pecent resemblances are called as

dissimilar (ID) and hence we followed him. The remaining stations had less than

30 % similarity among themselves (Tables 3.9).

67

Table 3.9 Similarity and dissimiler Index of different plant communities

Melia-Punica-

Euphorbia

Zizyphus-

Zanthoxylum-Rumex

Quercus-

Cornus-Viola

Cedrus-Viburnum-

Achillea

Abies-Daphne-

Potentilla

Melia-Punicaa-

Euphorbia

X

33.58

58.94

69.49

73.72

Zizyphus-

Zanthoxylum-Rumex

66.42

X

26.53

45.57

58.43

Quercus-Cornus-

Viola

41.06

73.47

X

31.83

54.4

Cedrus-Viburnum-

Achillea

30.51

54.43

68.17

X

57.85

Abies-Daphne-

Potentilla

26.28

41.57

45.6

42.15

X

68

3.12. Ethnomedicinal applications

Humans have long used some species of plants as food sources and others for

curing diseases and injuries. During the current study the 252 plant species were

explore out of which, 47 species of high medicinal value were presenting rarest I.V.I

values; all were used by local people for curing different diseases (Table 3.10 & 3.11,

Plate # 1, 2, 3, 4 & 5). There were 97 different plant families were explored, out of which

36 having all medicinal medicinally important plants. The dominant families were

Rosaceae with 5 species followed by Ranunculaceae with 3 species and Moraceae,

Buxaceae and Apiaceae having 2 species each, whereas remaining families have only

species each.

69

Table 3.10 Sources of information about the use of medicinal plants in the Thandiani forests

Source of information

Local elders Farmers Group discussions

Questionnaires Interviews/ semi structured Interviews

Conventional healers

Numbers 13 31 04 34 18 02-women & 01-man

Table 3.11 Traditional uses/provisioning ecosystem services of medicinal plants by local communities in the Thandiani

forests

S. No Botanical name Local name I.V.I Family Disorder treated The medicinal importance of rarest species of 1st community

1 Hedera nepalensis Belrri 2.44 Araliaceae Skin disorders

2 Jacaranda mimosifolia Nelagul 4.33 Bignoniaceae Syphilis & vulnerary

3 Clematis amplexicaulis

Churanhar 4.47 Ranunculaceae Anti-inflammatory, cytotoxic & antimicrobial effects

4 Cuscuta reflexa Akashbail 4.5 Cuscutaceae Eczema & scabies

5 Lonisera bicolor Foota 5.4 Caprifoliaceae Emiticocathartic, tonic & diuretic

6 Vitex negundo Marwand 5.8 Verbenaceae Hairs colour

7 Cyperus rutundus Deela 5.8 Cyperaceae stomachic, emmenagogue, deobstruent & emollient

8 Celtus australis Batkarar 5.9 Celasteraceae Amenorrhea, lenitive, colic, diarrhea, dysentery & peptic ulcers

9 Polygonum amplexicaule

Masloonrr 6.3 Polygonaceae Infectious diseases, inflammation, gastrointestinal disorders &

cancer

10 Buxus papilosa Angaroo 6.8 Buxaceae Joints pain, skin disorder & baldness

11 Senecio chrysenthemoides

Ragwort 8.05 Asteraceae Antiseptic & rheumatic pain

12 Foeniculum vulgare Sonf 9.41 Apiaceae Constipation The medicinal importance of rarest species 2nd of community

1 Aesculus indica Bankhorr 2.38 Hippocastenaceae Rheumatism & colic pain

https://en.wikipedia.org/wiki/Stomachic

https://en.wikipedia.org/wiki/Emmenagogue

https://en.wikipedia.org/w/index.php?title=Deobstruent&action=edit&redlink=1

https://en.wikipedia.org/wiki/Emollient

70

2 Platanus oriantalis Chinar 2.71 Platanaceae Astringent, ophthalmic & vulnerary

3 Rubus spp Chal 2.85 Rosaceae Diarrhea & dysentery

4 Pistacia antegrrimma Kangarr 3.34 Anacardiaceae Anti-microbial, antioxidant & analgesic,

5 Jasminum officinale Chambeli

3.75 Oliaceae Aphrodisiac, sedative, antidepressant, antispasmodic &

analgesic

6 Sarcococca saligna Ladan 3.87 Buxaceae Laxative, blood purifier & muscular pains

7 Convolvulus prostrates Ilrra

3.87 Convolvulaceae Purgative, diuretic & laxative.

8 Solanum nigrum Kachmach 4.04 Solanaceae Diuretic, diaphoretic, anodyne & expectorant alternative

9 Buplorum spp Beichaihu 4.06 Apiaceae common cold, bronchitis & pneumonia

10 Rhus punjabensis Sumac

4.25 Rosaceae Diarrhea, hemorrhoids, leucorrhea, ophthalmia, conjunctivitis &

dieresis

11 Budleja asiatica Booi 4.6 Berberideceae Abortifacient

The medicinal importance of rarest species of 3rd community

1 Rubus fruticosis Chal 3.23 Rosaceae Menstruation disorders

2 Malva neglecta Sonchal 4.06 Malvaceae Diarrhea & piles

3 Ailanthus altissima Darawa 4.08 Simarubaceae Astringent, demulcent, aphrodisiac & expectorant

4 Morus nigra Kala Toot 4.16 Moraceae Diuretic & expectorant

5 Poeneia emodi Mamekh 4.23 Paeoniaceae Joint pain

6 Papaver somniferum Poppy 4.32 Papavaraceae Sedative, analgesic & antitussive

7 Thalictrum cultratum Momyrun 4.49 Ranunculaceae Opthalmia & gastritis

8 Hedera nepalensis Belrri 4.51 Araliaceae Cathartic, diaphoretic, skin & stimulant

9 Rosa moshcata Jungli gulab 4.55 Rosaceae Astringent, tonic & piles

10 Punica granatum Darunna 4.74 Punicaceae Cooling, refrigerant & breast development

11 Morus alba Safeed toot 4.98 Moraceae Antirheumatic, antispasmodic, diuretic, alterative & diaphoretic

The medicinal importance of rarest species of 4th community

1 Geranium wallichianum Rattanjot

1.10 Geraniaceae Vision problem, blood purification, jaundice, kidney & spleen

problems

2 Podophyllum amodi Bankhakhrri 1.81 Podophylaceae Jaundice, liver ailment, fever, syphilis, hearing loss & cancer

http://www.pfaf.org/user/Search_Use.aspx?glossary=Astringent

http://www.pfaf.org/user/Search_Use.aspx?glossary=Ophthalmic

http://www.pfaf.org/user/Search_Use.aspx?glossary=Vulnerary

http://www.webmd.com/cold-and-flu/ss/slideshow-cold-or-flu

http://www.webmd.com/lung/understanding-bronchitis-basics

http://www.webmd.com/lung/tc/pneumonia-topic-overview

http://www.pfaf.org/user/Search_Use.aspx?glossary=Cathartic

http://www.pfaf.org/user/Search_Use.aspx?glossary=Diaphoretic

http://www.pfaf.org/user/Search_Use.aspx?glossary=Skin

http://www.pfaf.org/user/Search_Use.aspx?glossary=Stimulant

71

3 Jasminum officinale Chambeli

1.82 Oliaceae Aphrodisiac, sedative, antidepressant, antispasmodic &

analgesic

4 Aesculus indica Bankhorr 3.29 Hippocastenaceae skin diseases, rheumatism, astringent, and narcotic & headaches

5 Budleja crispa Booi 3.33 Berberideceae Abortifacients

6 Zanthoxylum alatum Timber 3.33 Rutaceae Antiseptic, disinfectant & deodorant properties

7 Rhus punjabensis Sumac

3.46 Rosaceae Diarrhea, ulcer, hemorrhoids, hemoptysis, conjunctivitis &

dieresis

8 Clematis amplexicaulis Churanhar

3.63 Ranunculaceae Anti-inflammatory, cytotoxic & antimicrobial effects

9 Berberis spp Sumblo 3.73 Berberidaceae Stomach ache

10 Ailanthus altissima Darawa 3.84 Simarubaceae Antidiarrhoeal, antispasmodic, astringent & diuretic

11 Rosa moshcata Chal 4.44 Rosaceae Antispasmodic & antidiarrhoeal

12 Robinia pseudoacacia Kekar 8.47 Pipleonaceae Diuretic, emetic, emollient laxative, purgative & tonic

The medicinal importance of rarest species of 5th community

1 Acacia nilotica Kikar 1.91 Mimosoideae Searing, sweltering & torrid

2 Cotoneaster minuta Bansathra 2.42 Rosaceae Antipyretic & calmative

3 Populus ciliate Safeeda 2.95 Salicaceae Anti-inflammatory & febrifuge

4 Sorbaria tomentosa Kaanhaji 3.39 Sonneratiaceae Burns & wounds

5 Verbescum thapsis Kuttey kan 3.42 Scrophuleriaceae Emollient

6 Thalictrum cultratum Momyrun 3.56 Ranunculaceae Stomach pain & gastric trouble

7 Buplorum spp Beichaihu 4.15 Apiaceae common cold, bronchitis & pneumonia

8 Cuscuta reflexa Akashbel 4.32 Cuscutaceae Urine problems & constipation

9 Capsella bursapastoris Shufrt purse 4.46 Brassicaceae Hemorrhages

10 Arisaema flavum Adbis

4.53 Araceae Expectorant, chronic tracheitis, bronchiectasis, tetanus &

epilepsy

11 Aquilegia pubiflora Koo-kuk 4.91 Ranunculaceae Skin burns & wound healing

http://www.pfaf.org/user/Search_Use.aspx?glossary=Antidiarrhoeal

http://www.pfaf.org/user/Search_Use.aspx?glossary=Antispasmodic

http://www.pfaf.org/user/Search_Use.aspx?glossary=Astringent

http://www.pfaf.org/user/Search_Use.aspx?glossary=Diuretic

http://www.pfaf.org/user/Search_Use.aspx?glossary=Diuretic

http://www.pfaf.org/user/Search_Use.aspx?glossary=Emetic

http://www.pfaf.org/user/Search_Use.aspx?glossary=Emollient

http://www.pfaf.org/user/Search_Use.aspx?glossary=Laxative

http://www.pfaf.org/user/Search_Use.aspx?glossary=Purgative

http://www.pfaf.org/user/Search_Use.aspx?glossary=Tonic

https://www.google.com.pk/search?biw=1280&bih=699&q=define+searing&sa=X&ved=0ahUKEwj32biAhrnLAhUCS44KHadmADoQ_SoILDAA

https://www.google.com.pk/search?biw=1280&bih=699&q=define+sweltering&sa=X&ved=0ahUKEwj32biAhrnLAhUCS44KHadmADoQ_SoILTAA

https://www.google.com.pk/search?biw=1280&bih=699&q=define+torrid&sa=X&ved=0ahUKEwj32biAhrnLAhUCS44KHadmADoQ_SoILjAA

http://www.pfaf.org/user/Search_Use.aspx?glossary=Antiinflammatory

http://www.pfaf.org/user/Search_Use.aspx?glossary=Febrifuge

http://www.webmd.com/cold-and-flu/ss/slideshow-cold-or-flu

http://www.webmd.com/lung/understanding-bronchitis-basics

http://www.webmd.com/lung/tc/pneumonia-topic-overview

72

Plate-1 the important medicinal plants of 1st community

73

Plate-2 the important medicinal plants of 2nd community

74

Plate-3 the important medicinal plants of 3rd community

75

Plate-4 the important medicinal plants of 4th community

76

Plate-5 the important medicinal plants of 5th community

77

3.13. The physio-chemical analysis of soil samples

Table 3.12 Soil analyses values of all the sampling sites (stations) of Thandiani Sub Forests Division.

S.No

Stations PH EC (dsm-1)

% O.M

% CaCO3

% Sand

% Silt % Clay

T.Classes P (ppm) K (ppm)


1 Mandroch 5.2 0.63 0.55 11 25.8 52 22.2 1 8 155

2 Battanga 5.3 0.29 1.04 6.5 49.8 36 14.2 4 6 125

3 Neelor 5 0.31 1.24 9.7 37.8 46 16.2 4 6 145

4 Bari Bak 5.3 0.28 0.85 12 39.4 42 16.2 4 7 140

5 Mand Dar 5.2 1.02 1.06 6.3 47.8 36 16.2 4 5 130

6 Pkhr Bnd 5.4 0.52 0.57 8.6 26.3 49.5 24.1 4 5 110

7 Lowr Dna 5.4 0.26 1.32 8 51.8 36 12.2 4 6 130

8 Bandi TC 4.9 0.92 0.5 12.5 15.8 64 20.2 1 6 135

9 Qalndrbd 4.8 0.54 0.65 13.7 17.8 64 18.2 1 7 145

10 Riala 4.8 0.54 0.55 8.5 26.4 49.4 24.2 4 5 110

11 Malch Lw 5.5 1.03 1.08 8.7 45.8 30 24.2 4 5 125

12 Malch Up 5.5 0.41 1.1 7.5 35.2 34 30.2 2 6 135

Ziziphus-Zanthoxylum-Rumex Community

1 Danna 5.5 0.62 1.15 8.3 33.8 48 18.2 4 6 135

2 Uper Dna 5.7 0.35 0.72 1.3 29.2 60 10.2 1 6 120

3 Pejjo 5.5 0.48 1.05 8.2 27.8 52 20.2 1 5 115

4 Lowr Bal 5.9 0.4 1.07 7.7 45.8 28 26.2 2 7 145

5 Upr Balo 6.4 0.36 1.1 6.6 29.9 40 32.1 2 6 120

78

6 Mera Bun 4.9 0.28 0.75 13 39.8 44 16.2 4 7 140

7 Lonr Pat 5.2 0.34 0.65 8 35.8 52 12.2 1 8 150

8 Gali Ban 5.8 0.27 1.2 8 41.8 44 14.2 4 6 105

9 Riala Ca 4.9 0.61 0.5 12.7 21.8 54 24.2 1 6 105

10 Resrv FC 6.6 0.41 1.15 6.8 37.8 44 18.2 4 6 110

11 Upper GB 6.2 0.24 1.06 8.4 35.8 40 24.2 4 6 120

12 Chatrri 6.4 0.43 1.1 9.2 21.8 58 20.2 1 6 115

13 Terarri 5.1 0.37 0.7 9.5 40.4 45.4 14.2 1 7 135

14 Upr Rial 4.9 0.31 0.72 1.1 29.8 60 10.2 1 6 125

15 Terari C 5.4 0.6 0.56 11 33.8 46 20.2 4 7 130

16 Mathrika 5.9 0.45 1.24 6.7 29.8 56 14.2 1 6 135


1 Mthrka T 6.2 0.22 1.2 7.4 55.8 34 10.2 3 7 145

2 Jabbra 6.3 0.49 1.07 7.3 69.6 20.1 10.1 3 5 120

3 Darral 5.5 0.2 0.6 13 41.8 42 16.2 4 5 110

4 Makali 6.5 0.25 0.55 10.5 35.8 50 14.2 4 6 120

5 Ladrri 6.1 0.62 0.6 9.5 16.8 58 26.2 1 7 130

6 Upper KP 6.5 0.53 1.05 7.2 69.2 20.6 10.2 3 5 90

7 Kakl RFC 5.8 0.51 1.18 5.8 29.2 44.6 26.2 4 5 140

8 Parringa 6.6 0.44 0.8 7.8 21.8 56 22.2 1 6 120

9 Satu Top 6.8 0.45 0.55 12 31.8 58 10.2 1 5 115

10 Lower KP 6.4 0.33 1.08 6.5 29.8 38 32.2 2 6 115

11 Larri 6.5 0.55 1.15 8 35.8 50 14.2 4 5 110


1 Pallu Zr 6.7 0.41 1.2 6.9 37.1 43 18.1 4 6 115

2 Lari Tra 6.3 0.26 1.25 7 57.2 26.2 16.2 3 5 110

3 Lari Top 6.8 0.73 1.07 7.4 45.8 42 12.2 4 6 125

79

4 Sawan Gl 6.7 0.38 1.2 6 49.2 28.6 22.2 2 6 115

5 Lower Th 6.6 0.39 1.1 9.2 53.2 32 14.8 4 5 110

6 Upper TC 6.7 0.57 1.15 9 45.2 44.2 10.2 4 5 105


1 Mera RKC 6.6 0.22 0.7 10 29.8 44 26.2 4 8 140

2 Mera RKT 6.8 0.34 0.65 8 21.8 54 24.2 1 8 145

3 Lwr Nmal 7.1 0.2 0.6 8 35.8 44 20.2 4 6 120

4 Upr Nmal 7.2 0.36 0.55 6 33.8 60.6 15.6 1 6 125

5 Sikher 7.2 0.39 0.75 9 46.8 35.2 18.2 1 7 135

80

3.14. Classification of vegetation of TsFD

The mathematical organization as an explorative technique was used to

recognize designs in the data, leading to data deterioration. Further facts of each of the

classification approaches used are given below.

3.14.1. Data organized by Cluster Analyses

Clustering carried out to collect the positions /samples to make five clusters of

communities, which can be clearly understood in two main divisions of the

dendrogram;

a. The lesser elevation (1290m to 1900m) dominated by Sub-Tropical and

Temperate vegetation.

The lower altitudinal region of Thandiani sub forests division comprised the

following stations, Mandroach Kalan, Batanga, Neelor, Barriback, Mandroach Darra,

Pakheer Bandi, Lower Danna, Bandi Toond Cathment, Qalanderabad, Riala, Malach

Lower, Malach Upper, Danna, Upper Danna, Pejjo, Lower Balolia, Upper Balolia, Mera

Bun, Loon Patian, Galibanian, Riala Catchment, Reserve Forests Catchment, Galibanian

Upper, Chatrri, Terarri, Upper Riala Terarri Cathment and Mathrikka. The elevation

ranges of the lower region of the study area were 1299m to 1900m. The area was

represented sub-tropical and temperate vegetation.

81

b. The higher altitude (1900m to 2626m) dominated by moist temperate flora. Three

clusters (community-habitats) weregathered in the 1st and major half of the

dendrogram and two in the 2nd and additional one (hereafter each cluster will be

called a plant-community) correspondingly.

The Upper Thandiani Sub Forests Division region was contained the following

plant stations. Mathrikka Top, Jabra, Darral, Makali, Ladrri, Kala Pani Upper, Reserve

Forests Cathment, Parranga, Sattu Top, Kala Pani Lower, Larri Catchment, Pallu Ziarat,

Larri Forests Catchment, Larri Top, Sawan Gali, Lower Thandiani, Upper Thandiani

Cathment, Mera Rehmat Khan Cathment, Mera Rehmat Khan Top, Lower Namal,

Upper Namal and Sikher. The elevation ranges of the upper region of the study area

were 1900m to 2626m. The area was represented temperate vegetation (Fig-3.3).

82

Fig 3.3 Cluster Dendrogram of 50-stations created on Sorenson procedures showing 5-plant assemblages/habitat types

83

3.14.2. Two way cluster analysis (TWCA)

The TWCA was approved out for arrangement of plant societies using PC-ORD-

V-5 which caused in a two way cluster dendrogram. The dendrogram was explained

the two cut planes, which further confirmed the result of TWCA. A dendrogram is a

ranked image of species and station in graphical layout. The purpose of two way

clustering was to graphically show the relationship between stations and individual

species. Dendrogram classification broadly divided the plant species in to 5

communities which could be clearly understood in two chief subdivisions of the

dendrogram, i.e., lesser and higher elavational ranges;

I. The lower altitude (1290-1900 masl) including 3 communities/habitat prevailed

by sub-tropical and temperate vegetation.

II. The higher altitude (1900-2626 masl) including 2 communities dominated by

moist temperate species (Fig-3.4).

The Indicator Species Analysis (ISA) identified indicator species and the main

variables responsible for those communities. It showed that aspect, altitude and soil

chemical and physical composition are the stronger ones among variables. It also

showed the strength of the environment species relationship using Monte Carlo

procedures (Table-4.8).

84

Fig 3.4 Two Way Cluster Analysis Dendrogram of 50 stations grounded on Dufrêne & Legendre procedures showing 5

plant communities/environment types

85

3.14.3. Indicator species analysis (ISA)

After conclusion the overall forest vegetation ordination and community

composition, Indicator Species Analysis (ISA) was agreed out to classify the species

conditions gathered on the number and richness of species in a specific habitat and the

consistency of existence of species of a particular environment (defined by an ecological

incline). It also gave the indicator analysis for each species in each group. These values

were verified for arithmetical proposition by means of a Monte-Carlo technique.

Through out indicator species analysis, major species standard was defined by number

by amount, by the standards of ecological variables, i.e., soil phosphorus, soil organic

matter, aspect, soil electrical conductivity, soil pH and by soil texture classe. Running

ISA (1000 runs) every species were assessed for its aptitude to distinguish amongst all

the provided groups of the ecological variables. The Indicator Species Analysis

available asign of how well the occurrence of a species presents a habitat type. The

outcomes of ISA were later on accepted through CCA, representative that amongst the

five groups superior by the cluster analysis, one community happened at lesser heights

(low Electrical conductivity, least Organic matter and low soil pH), two of the

communities were limited to Northern-west and south-east facing grades and two of

them were limited and signifying rather acidity and basicity in soil (i.e., ranges between

6-8 pH values) (Appendix 4.9). In shared with the ISA, the Canonical Correspondence

Analysis (CCA), (fig. 3.28 & 3.29) presented the power of every habitat variable by

trying the implication of that specific variable by means of a Monte-Carlo technique,

86

both of these analysis shows that soil pH and aspect were the major ecological

variables. Only data quality plots formed through CCA are comprised in the account of

each community/environment type while complete facts are given in section 3.16 a

brief instant of the Indicator Species Analyses (ISA) is given in the fig. 3.7, 3.11, 3.15,

3.19 & 3.23 with complete information providing in Appendix 4.9.

3.14.4. Mantel test

Before start the overall field visit informations, it was assumed that deviation in

aspect (north & south ) and elevation would have an influence on the biodiversity of

both plant species and plant societies type qualitatively as well as quantitatively. To

evaluate the impact of this proposal the mantel test was applied. This is value in PC-

ORD-V-5 that approximates the power of the association between two mediums. It was

run to measure the associations between the floristic and ecological distance matrices.

The value of the Mantel statistic can differ from (−1) to (+l). The Mantel statistical test is

out of range to total spatial auto correlation between the quantitative and qualitative

values of diverse vegetation in study area, (Mantel, 1967 and Legendre & Fortin, 1989).

However, two variables may be associated due to a third, collective variable, thus

before the final conclusion is made on whether the real two variables are significantly

associated, the third variable is isolated. In the first step the species data matrix was

patterned together with the environmental variable data matrix. There was aimportant

association between both the matrices. To further confirmed the test the implication free

87

of other inclines effects, the species data matrix was examined in relation with one

ecological variable at one time, i.e. soil pH, aspect, soil phosphorus, soil organic matter,

electrical conductivity and soil texture.

3.14.5. Species Area Curves (SAC)

The Species Area Curves are used to vague the adequate model size, maximum

number of plant species and the normal moving powers on forest vegetation in a area.

The SACs are also significant to understand the biodiversity and for manufacturing

calculations for its preservation (He & Legendre, 1996 and Legendre et al., 2005). This

manner was applied to measures the normal number of species at every of the plant

stations. The Species area and compositional curves were used in order to assess

whether the sample size was adequate to realize sufficient appearance of species

arrangement in relative to the full sample of the TsFD. To evaluate such reliable and

apprehension it with fundamental variables, Species Area Curve 60, as a PC-ORD

utility was run for all the sampled species and stations. Using the importance value

(I.V.I) and Sorenson distance, Species Area Curves were improved following the

technique of McCune and Mefford (1999). SAC was calculated for all 50 stations, which

indicated standard deviances as avaluation of the discrepancy of the data (Grandin,

2006 and Turner & Tjørve, 2005).

88

Fig 3.5 Species Area Curve (S.A.C) and compositional curves discribed on I.V.I data for

entirely 252 plant species and 50-stations

3.14.6. Data attribute designs:

The environmilists use a number of indices of diversity for determining plant

species diversity in diverse environment type communities. The CANODRAW a value

of CANOCO was applied to produce data quality plots (in graphic forms) of features

species. It also given the chance to apply the index number of species; Shannon &

Wiener index; species richnes sindex; sample variance and evenness, etc. the indices of

diversity were measured at the community level through data quality plots beneath the

DCA purpose of CANODRAW.

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3.15. Plants community organized by Cluster analysis, Two Way Cluster Analysis,

Indicator species analysis and confirmed through ordination analysis (DCA &

CCA):

The community types recognized by indicator species analysis, cluster analysis

and confirmed through ordination analyses (DCA & CCA). All the

communities/habitat names were agreed on the source of the most revealing species of

the precise community. Standards for choosing indicator/realistic species were

grounded on three different tests, i.e., p value less than 0.05, indicator value more than

30% and constancy more than 30% (based on Indicator Species Analysis; ISA) (Dufrêne

and Legendre, 1997)

The whole plants of the area were divided into the five Communities. Details of

each of the communities/habitat are as follows.

3.15.1. Melia azedarach, Punica florida and Euphorbia helioscopia Community:

This community composed of Mandroach Kalan, Batanga, Neelor, Barriback,

Mandroach Darra, Pakheer Bandi, Lower Danna, Bandi Toond Cathment,

Qalanderabad, Riala, Malach Lower and Malach Upper stations. This community

occurred at 12 stations (360 quadrats/relevs) at the lowest elevations (1299 to 1591masl).

The tree, shrub and herb layers were characterized by Melia azedarach, Punica florida and

Euphorbia helioscopia respectively, which are the top indicator species (Table-3.13). Other

indicator species of this community are Ziziphus jujube, Euclaptus globolus, Rosa moshcata,

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Zanthoxylum armatum, Cnicus argyracanthus, Medicago denticulate, Poa annua, Themeda

anathera, Rumex hastatus, Taraxacum officinale and Cynodon dactylon (Fig-3.3, 3.4, 3.6, 3.26,

3.27, 3.28, 3.29 & Appendix-4.9).

The further most significant ecological variables responsible the gradient of this

community were low electrical conductivity {0.26-1.03 (dsm-1)}, low soil organic matter

(0.5%-1.24%) and low soil pH (4.8-5.5), coupled with associated the co-variables of

aspect (W-S), phosphorus (5-8 ppm) contents and Soil texture was Silt loamy

(Appendix-4.6 & 4.7).

91

Fig 3.6 Map of elevation profile, point profile in the scatter Plot Matrix of 1st plant

community comparing all phytosociological parameters (D.I, Z, T.F, T.S, S.M, Ev)

Key: D.I= Diversity Index Z= Elevation T.F= Total Frequency T.S= Total Species S.M= Species Maturity Ev= Eveness

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Table 3.13 The indicator species of the Melia azedarach, Punica florida and Euphorbia

helioscopia Community With their indicator values

Top indicator of the community IV P* IVI TIVI

Melia azedarach L. 85.7 0.0406 36.53 61.11

Punica florida Salisb. 58.9 0.0008 80.1 69.5

Euphorbia helioscopia L. 40.3 0.0222 103.98 72.14

IV= Indicator Value, P= Portability, IVI= Important Value Index in the community

TIVI= Total Important Value Index (Average Importance Value)

Being located at lower elevations this community occurs in the vicinity of human

settlement and is therefore under burden from a variety of anthropogenic actions, i.e.,

deforestation for fire wood and adaptable plant collection, wooden increase of

agricultural land and grazing. These lesser altitude communities are easily available by

indigenous people who utilize the native plant resources. The IVI values of top 20 plant

species of community Melia, Punica and Euphorbia are mentioned in (fig. 3.8, while least

20 plant species mentioned in (fig. 3.9).

http://www.theplantlist.org/tpl1.1/record/kew-2536845

93

Fig 3.7 Data Attribute plots of Melia azedarach, Punica florida and Euphorbia helioscopia the indicator species of community

1st at lower elevation (1290 to 1591m)

94

Plate-3.6 Pictorial view of indicator species of the first plant community

95

Fig 3.8 Top 20 species of 1st community

Fig 3.9 The 20 rare species of 1st community

3.15.2. Ziziphus jujuba, Zanthoxylum armatum and Rumex nepalensis Community

This community was founded at the altitudinal range of 1600 to 1900 masl and was

represented by 16 different stations (480 quadrats).

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Fig 3.10 Map of elevation profile, point profile in the scatter Plot Matrix of 2nd plant community comparing all Phytosociological parameters (D.I, Z, T.F, T.S, S.M, Ev) Key: D.I= Diversity Index Z= Elevation T.F= Total Frequency

T.S= Total Species S.M= Species Maturity Ev= Eveness

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The following plant stations were reported during field work in the study area that

is Danna, Upper Danna, Pejjo, Lower Baloliua, Upper Balolia, Mera Bun, Loon Patian,

Galibanian, Riala Catchment, Reserve Forests Catchment, Galibanian Upper, Chatrri,

Terarri, Upper Riala Terarri Cathment and Mathrikka. The tree, shrub and herb layers

are characterized by the indicator species Ziziphus jujube, vulgaris, Zanthoxylum armatum

and Rumex nepalensis (Table-3.14).

Table 3.14 The indicator species of the Ziziphus jujuba, Zanthoxylum armatum and Rumex

nepalensis Community With their indicator values

Top Indicator of the community IV P* IVI TIVI

Ziziphus jujuba Mill. 40.4 0.0126 43.4 41.9

Zanthoxylum armatum DC. 60.9 0.0006 63.26 62.08

Rumex nepalensis Spreng. 52.2 0.0188 97.52 74.86

IV= Indicator value, P= Probability, IVI= Important value Index, TIVI= Total Important Value Index (Average Importance Value)

Other indicator species of this community are Abies pindrow, Punica granatum,

Rosa moshcata, Rubus fruticoses, Achillea millefolium, Cnicus argyracanthus, Poa annua,

Rumex hastatus, Nepeta erecta, Taraxacum officinale, Medicago denticulate, Senecio

chrysenthemoides, Cynodon dactylon, Chenopodium album and Capsella bursapastoris (fig-3.3,

3.4, 3.10, 3.11, 3.26, 3.27, 3.28, 3.29 & Appendix-4.9). North-West aspect was one of the

main environmental determinants of this community indicating that this community

receives comparatively less direct sunlight. Other strong environmental variables were

low soil pH (4.9-6.6) and only trace amounts of organic matter (0.5%-1.24%) coupled

with other variables such as low soil electrical conductivity {0.24-0.62 (dsm-1)}, and



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sandy loam and clay loam soil texture. The main anthropogenic impacts for this

community were identified as timber and medicinal plant collection (Appendix-4.7 &

4.8). The IVI values of top 20 plant species of community Ziziphus, Zanthoxylum and

Rumex, are mentioned in (fig. 3.12), while least 20 plant species mentioned in (fig. 3.13).

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Fig 3.11 Data Attribute plot of Zanthoxylum armatum and Rumex nepalensis the indicator species of community 2nd at lower

elevation (1600 to 1900m)

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Plate-3.7 Pictorial view of indicator species of second plant community

101

Fig 3.12 The top 20 species of 2nd community

Fig 3.13 Twenty rare species of 2nd community

3.15.3. Quercus incana, Cornus macrophylla and Viola biflora Community:

This community occurs at mid- altitude elevations (1900 to 2150masl) and was

present at 11 stations and 330 quadrats.

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Fig 3.14 Map of elevation profile, point profile in the scatter Plot Matrix of 3rd plant

community comparing all Phytosociological parameters (D.I, Z, T.F, T.S, S.M, Ev)

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The Mathrikka Top, Jabra, Darral, Makali, Ladrri, Kala Pani Upper, Reserve Forests

Cathment, Parranga, Sattu Top, Kala Pani Lower, and Larri Catchment were the

representative stations of Quercus incana, Cornus macrophylla and Viola biflora

Community (Table 3.15).

Table 3.15 The indicator species of the Quercus incana, Cornus macrophylla and Viola

biflora Community With their indicator values


Quercus incana Bartram 42.7 0.018 36.78 39.74

Cornus macrophylla Wall. 48.6 0.021 41.38 44.99

Viola biflora L. 54.4 0.008 47.17 50.79

IV= Indicator value, P= Probability, IVI= Important Value Index TIVI= Total Important Value Index (Average Importance Value)

In addition to the three main indicator species, the additional characteristic

species of this community are Abies pindrow, Viburnum grandiflorum, Chrysanthemums

cenarifolium, Euphorbia wallichii, Plantago lanceolata, Actaea spicata, Nepeta erecta, Rumex

nepalensis, Viola biflora and Achillea millefolium (Fig-3.3, 3.4, 3.14, 3.15, 3.26, 3.27, 3.28, 3.29

& Appendix-4.9). This community shows its best development in south-east facing

slopes where it is exposed to direct solar radiation. Other strong factors were higher soil

phosphorus content (5-7 ppm), moderate soil, Organic Matter (0.6%-1.18%), a weakly

acidic soil pH (4.0), lower soil electrical conductivity (0.2-0.62 dsm-1) and a sandy loam

soil texture (Appendix-4.7 & 4.8). The IVI values of top 20 plant species of community


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Quercus, Cornus and Viola, are mentioned in (fig. 3.16), while least 20 plant species

mentioned in (fig. 3.17).

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Fig 3.15 Data attribute plot of Quercus incana, Cornus macrophylla andViola biflora the indicator species of community 3rd at

middle elevation (1900 to 2150m)

106

Plate-3.8 Pictorial view of indicator species of third plant community

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Fig 3.16 The top 20 species of 3rd community

Fig 3.17 Twenty rare species of 3rd community

3.15.4. Cedrus deodara, Viburnum grandiflorum and Achillea millefolium Community:

This community can be found at relatively high elevations (2150–2400 masl)

occurring at six stations (180 quadrats).

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Fig 3.18 Map of elevation profile, point profile in the scatter Plot Matrix of 4th plant



109

This community comprised the following plant stations, i.e., Pallu Ziarat, Larri

Forests Catchment, Larri Top, Sawan Gali, Lower Thandiani and Upper Thandiani

Cathment. This is a tree dominated community of moist temperate vegetation,

including the indicator species Cedrus deodara, Viburnum grandiflorum and Achillea

millefolium from the tree, shrub and herb layers, respectively (Table-4.16). Abies pindrow

was the other prominent indicator tree found in this community. The most important

environmental variables responsible for the formation of this community are mildly

acidic soil pH (6.3 – 6.8), high soil organic matter (1.07%-1.25%), low soil electrical

conductivity (0.26-0.73 dsm-1), moderate soil phosphorus contents (5-6 ppm) and a

sandy loam soil texture (Fig-3.3, 3.4, 3.18, 3.19, 3.26, 3.27, 3.28, 3.29 & Appendix-4.7, 4.8

& 4.9).

Table 4.16 The indicator species of the Cedrus deodara, Viburnum grandiflorum and Achillea

millefolium Community With their indicator values


Cedrus deodara (Roxb. ex D.Don) G.Don 34.5 0.0574 88.2 61.35

Viburnum grandiflorum Wall. ex DC. 49.9 0.0016 31.44 40.67

Achillea millefolium L. 47.2 0.019 47.43 47.315

IV= Indicator Value, P= Probability, IVI= Important Value Index, TIVI= Total Important Value Index (Average Importance Value)

The IVI values of top 20 plant species of community Cedrus, Viburnum and

Achillea, are mentioned in (fig. 3.20), while least 20 plant species mentioned in (Fig.

3.21). The main anthropogenic pressures on this community was the medicinal plant

collection, fodder collection and grazing.


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Fig 3.19 Data attribute plot of Cedrus deodara, Viburnum grandiflorum and Achillea millefolium the indicator species of

community 4th at higher elevation (2150 to 2400m)

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Plate-3.9 Pictorial view of indicator species of fourth plant community

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Fig 3.20 Top 20 species of 4th community.

Fig 3.21 Twenty rare species of 4th community

3.15.5. Abies pindrow, Daphne mucronata and Potentilla nepalensis Community

It was the highest elevation community in TsFD at altitudes of 2400 to 2626 masl. It

was contained five stations (150 quadrats), including Mera Rehmat Khan Cathment,

Mera Rehmat Khan Top, Lower Namal, Upper Namal and Sikher stations (Table-3.17).

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Fig3.22 Map of elevation profile, point profile in the scatter Plot Matrix of 5th plant


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Table 3.17 The indicator species of the Abies pindrow, Daphne mucronata and Potentilla

nepalensis Community With their indicator values


Abies pindrow (Royle ex D.Don) Royle 40.5 0.007 179.18 109.84

Daphne mucronata Royle 75 0.0002 31.43 53.215

Potentilla nepalensis Hook. 44.4 0.0102 89.27 66.835

IV= Indicator Value, P= Probability, IVI= Important Value Index, TIVI= Total Important Value Index (Average Importance Value)

Abies pindrow, Daphne mucronata and Potentilla fruticosa, were the characteristic indicator

species of this community. Other diagnostic indicator species are Berberis orthobotyrus,

Viburnum grandiflorum, Rumex nepalensis, Drypteris spp. Euphorbia wallichii, Plantago major

and Pteris vittata (Fig 3.3, 3.4, 3.22, 3.23, 3.26, 3.27, 3.28, 3.29 & Appendix-4.9). Due to the

high altitude, low temperatures prevail throughout the growing season. The important

environmental variables were soil pH (6.6-7.2), soil phosphorus content (6-8 ppm), soil

electrical conductivity {0.2-0.39 (dsm-1)} and soil organic matter (0.55%-0.75%)

(Appendix-4.7 & 4.8). The IVI values of top 20 plant species of community Abies, Daphne

and Potentilla, were mentioned in (fig. 3.24), while least 20 plant species mentioned in

(fig. 3.25).


http://www.theplantlist.org/tpl1.1/record/rjp-2004

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Fig 3.23 Data attribute plots of Abies pindrow, Daphne mucronata and Potentilla fruticosa the indicator species of community

5th at the highest elevation (2400 to 2626m).

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Plate-3.10 Pictorial view of indicator species of fifth plant community

117

Fig 3.24 Twenty 20 species of 5th community.

Fig 3.25 Twenty rare species of 5th community

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3.16. Ordination of vegetation

Cossespondance analyses through CANOCO, offers ecologists the choices to

explore ecological gradients of vegetation directly throght Canonical Correspondance

Analysis (CCA) or or indirectly through Deterended Correspondance Analysis (DCA).

Results obtained through both sort of techniques are presented as follow.

3.16.1. Indirect gradient analysis:

Plant species matrix was used for DCA procedures that showed long gradient

length of the 1st axis that was equal to 4.205 SD (Standard Deviation) (Table 3.18).

Primary two axes of DCA described 16.29% of the variance in the species data. The

scatter plot of the data was generated as ordination graph for better visual elucidation

(Figure 3.26). The DCA diagrams indicate a continuous gradient in composition and

diversity of species as well as association. The 1st axis demarkates the association

between lower elevation to the higher one. Communities 1 and 2 occupy the left hand,

communities 3 and 4 can be located on the right hand side while communities 5 is

located at the top side of the DCA diagram. In ecological termes subtropical plant

species are present at lower elevation in communities 1. Plant species of slightly dry

habitats of forests (at lower middle elevations and North-West aspect) and of more

likely moist temperate habitats (at lower middle elevations with South-East aspect) are

clustered in the middle of the graph (communities 2 & 3), while species of moist

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temperate of a mesic nature (occurring at high elevations (communities 4 & 5) can be

seen on the right hand side of the Figure 3.26.

Table 3.18 Summary of the first four axes of DCA plot using Deterended

Correspondance Analyses a multivariate ordination technique

All the species (252) and all the stations/samples (50) were included

Central lines 1 2 3 4 Total inertia

Eigen values 0.606 0.189 0.143 0.118 4.745

Extension of gradient 4.205 2.285 2.129 2.018

Cumulative percentage (CP) variance of species data

12.8 16.8 19.8 22.2

Species richness and diversity increases from left to right up to middle and then

decreases upto the end at right hand side. The 2nd axis of DCA plot showd the aspect

variation of vegetation i.e., by grouping the communities of north-west aspect slopes to

the upper and south-east aspect to the lower side of the graph. As a whole the 2nd axis

exhibit a geomorphological and physiographic gradient complex of relatively thinner

soils on south-eastern slopes to the shady surfaces of relatively deeper soils of north

eastern slopes (Figure 3.27).

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Figure 3.26 Detrended Correspondence Analysis (DCA) plot shows the distribution of 252 plant species

121

Figure 3.27 DCA plot showing the distribution of 50 stations among five plant communities /habitat types

122

3.16.2. Direct gradient analysis

It was hypothesized that various environmental factors i.e., aspect/slope, elevation

and soil composition would be the key determinent factors for vegetation‟s variation

and associations formation in the region. To test this hypothesis, both the species and

environmental data matrices were treated through CCA method together in CANOCO

to examine whether the formation of plant communities was associated with the

calculated environmental variables or not. Our findings showed high significance of

these variables in the formation of plant communities in terms of test statistics (p ≤

0.002). The input of environmental data through CCA identified the ecological gradients

for the constitution of a specific habitat type. The CCA graph (bi-plot), exhibited that

both the composition and abundance of plant species were a reflection of the variations

in the ecological gradients like elevation, aspect and soil composition, i.e., community -

1 was marked under the significant effect of electrical conductivity and least Organic

matter of the soil, 2nd and 3rd communities were unique under the influence of aspect

and communities 4 and 5 were prominenet due to the impact of pH value (p value ≤

0.002). Determination of ecological gradient procedures through CCA both for stations

and species adovocate that the first axes was primarily associated with soil electrical

conductivity and soil, organic matter; the second axes were correlated mainly with

aspect and partially with soil pH, texture and phosphorus contents. The strongest

ecological variable of the 1st axes can be clearly showed from the stations and species

CCA diagrams (Figures 3.28 & 3.29). The stations + environmental bi-plots and species

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+ environmental bi-plots confirm each other by making habitat & species association

with the environmental data respectively. Pearson„s correlations with ordination axes

for the CCA plot pointed out a significant correlation of the axis with the geo-climatic

variables (i.e. aspect, elevation and soil composition). The Pearson„s correlations with

CCA ordination axis indicate that the first axis (e = 0.543) were principally correlated

with the soil E.C (r = -0.2136). The second axis (e = 0.176) were correlated mainly with

soil pH (r = 0.8867), while the 3rd axis (e = 0.128), was associated partially with aspect

i.e., (r = -0.514). The overall stations and species ordination diagrams utilized the first

two axes, (Table 3.19 and Figures 3.28 & 3.29).

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Table 3.19 Summary of the first four axes of the CCA for the vegetation data {using abundance/Importance Value data}

252 plant species, 50 stations/samples and environmental variables were included in the analysis

Axes

2

2

3

4

Total inertia

Eigen values.

0.543

0.176

0.128

0.110

4.745

Species-Environment associations

0.957

0.866

0.856

0.927

Cumulative percentage variance of species data.

11.4

15.2

17.9

20.2

Species-environment relation.

37.2

49.3

58.1

65.6

Summary of Monte Carlo test, (499 permutations under reduced model).

Test of significance of first canonical central lines (axes)

Test of significance of all canonical central lines (axes)

Eigen value

0.543

Trace

1.458

F-ratio

4.909

F-ratio

1.532

P-value

0.0020

P-value

0.0020

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Figure 3.28 Canonical Correspondence Analysis (CCA) bi-plot of 252 plant species in relation to environmental variables in the 5 plant communities of study area

126

Figure 3.29 CCA bi-plot showing the distribution of 50 stations among five plant associations in relation to various ecological factors

127

Plate-6 Pictorial view of some herbaceous flora in TsFD

128

Plate-3.7 Pictorial view of some plants in TsFD

129

3.17. Vegetation mapping and plant communities

According to elevation variation the area was divided into the following two

broad regions, i.e., the lower TsFD ranging from 1299 to 1900 m a.s.l. and the Upper

TsFD region ranging from 1900 to 2626 m a.s.l.. Lower region represented sub-tropical

to temperate sort of vegetation while the upper one represented moist temperate to cool

temperate type of floristic elements. The association types identified through Personal

GEO database and Coanoco software were given names based on respective indicator

of the specific association. The whole vascular flora of the region was divided into five

plant associations /habitat types (fig- 3.30).

3.17.1. Integration of GIS/GPS and Data Loggers

One of the most encouraging new tools is the mechanization of field data

assembly using association of GIS/GPS. The divergence of GPS, GIS and document

organizations kills and the entrance of uneven and moveable computers provide

original means of assembling field data. Moveable GIS with GPS will allow the quick

release of information about elevational and aspects, and the capacity to map them in

the field. The assimilation of programmed forms packages and data classification

software can speed data capture by means of menus and pick lists with pre defined

features, qualities, and space for numeric or character value entry (i.e., edaphic, climatic

or topographic). These computerized forms packages can be modified with

130

mathematical term to achieve some functions that would need a computer, such as

diversity index, species richness, species maturity etc. calculated spontaneously by

entering the species existence along a quadrat. These data can be mechanically labeled

with a GPS organize via connections to GPS receivers. Data assembly patterns can also

be designed for rapid transfer to relational databases or any other arrangement. It is

suggested that a data classification organization constructed from off the shelf

hardware and software to be tested during the model stage (fig-3.30).

131

Fig 3.30 Vegetation mapping according to different environmental attributes, (Aspect/elevation)

4. Scene 1 Legend Scene 2

Aspect/Slope direction model (with no elevation values from surface) of study area marked with

proposed plant communities indicated as a bar graph arranged according to their elevation range.

Masked forest boundary from DEM marked with proposed plant communities

View1

Legend View2

Digital Elevation Model with no elevation values from surface, showing proposed plant communities

indicated as bar graphs arranged according to their elevation range

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CHAPTER 4

DISCUSSION

4.1. Floristic composition

We studied how the plant communities differ and develop under the influence of

its respective ecological or environmental gradients. We took evidences from

relationships between species and environment as well as changes in the communities‟

structure among fifty stations. Family Pinaceae was the most abundant family followed

by Rosaceae. Rare families were Acanthaceae, Asclepiadaceae and Dioscoreaceae.The

present studies is in harmony with Ahmad,(2012) & Bano et al.,(2014) who

reported that Rosaceae, Pinaceae & Polygonaceae were the top representative

families of the Himalayas Forests. FIVs show the overall dominance and

biomass level of an area at ecological levels. Rosaceae representated by 20 species

was the most dominant family followed by Asteraceae and Ranunculaceae with 14 and

12 species each respectively.

4.2. Species diversity and richness

The species richness was more or less even in all the habitat types of Thandiani

Sub Forest Division. The lowest Species richness value was observed in station no 03,

Baribak that having 1.10158211 while the highest Species richness value were observed

in Station no 12, Upper Balolia 2.477767101. Our findings agree with El-Wahab, (2015)

133

and Durrani (2000) who documented great species richness in their conclusions. Tareen

& Qadir (1987, 1990 and 1991) perceived small species variety of plant societies of

Quetta. Shimada & Wilson (1985) stated that species production in a region depends

upon the collective properties of 213 habitat variety and environmental variables.

Species diversity showes to the variability and diversity amongst the plant

species and ecosystem developments in which they happen. It is a significant attraction

of any forest vegetation type which not only reveals the strength of forest but also its

efficiency (Hussain, 1989). The Ardakani (2004) quantified that; species diversity is the

most significant index used to assess the ecosystem biodiversity. Many studies identify

the association between species richness, species diversity, weather and other ecological

factors (Vetaas, 2000; Nautiyal et al., 2001; Kala & Mathur, 2002, Ali, 2006; Panthi et al.,

2007; Peer et al., 2001). The Species diversity values increase with altitudinal variations.

In the current study the highest diversity index (39.441) was detected for station no-11

Lower balolia, while the lowest value (11.01) was recorded for station No 03 Bari bak.

The Ahmad, (2012) detected the maximum species diversity (2.72) in Garhi Dopatta

Hills at low altitude which reduced with cumulative altitude. Our results differ with

them. This might be the reason in our study that the documented data showed

inconsistent actions concerning the association between species diversity and altitude.

Highest species index of diversity was recorded in high elevational communities. Ram

et al., (2004) and Kumar & Bhutt, (2006) connected lower plant diversity with collection

of medicinal plants, deforestation, human interaction and quick disappearance of

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annual plants because of different conditions. The species diversity in Thandiani Sub

Forest Division was high in favorable conditions like summer weather and high altitude

because summer favors many plant seedlings and high altitude make difficulty for

anthropogenic activities while diversity, value decreased in adverse conditions like

winter season and lower elevation because numerous annuals and geophytes disappear

during the winter. Similarly conclusions have been described in various studies (Peer et

al., 2001) that support the current development.

4.3. Species maturity and regeneration capacity

The index of maturity varies from 50 at station No 16 - Mathrikka to 85.2632 at

station No 01 - Mandroach. The index of maturity of dissimilar stations differs with the

variation of altitude. The maximum maturity index in the study region was due to the

occurrence of shrubs and stable grasses. Likewise, the apparent index of maturity in the

plant communities of Harboi-rangelands was high (Durrani, 2000) that agreed with the

current results.

An age class is the ratio of various age groups of a population at a given

time. Age class has been used as a guide to timber execution. The diameter of

the trunk at breast height or dbh has been used as an indicator of the age on the

estimated that diameter increases with age. The greater the diameter the older

would be the tree. It is suitable for dominant canopy trees, but with their

growth extinguish due to lack of moisture or nutrients, under story trees.

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Sometimes small trees often are the same age as large individuals in the canopy

(Smith & Smith, 1998). Age pyramids help to consider the age structure of a

population. As the population changes with time, the numbers of young

individuals, which expand the base of the pyramid, characterize a growing

population. This large class of young‟s finally moves up into the older age

classes characterizes a declining population with fewer individuals whom enter

to the reproductive age classes and hence further distress the population. In this

way age structure change over time. In the present study it was observed that

Pinus wallichiana and Abies pindrow were the tree species which shows regenerating

capacity. The girth of both these trees ranged up to 241cm to 270cm respectively.

Chaghtai & Ghawas (1976) observed that Pinus roxburghii having 2 to 33 cm girth

in various classes in Malakand showed regeneration. In the present study

cutting of the few mature individuals of Pinus roxburghii was present without

showing any regeneration because Pinus roxburghii grows at lower elevations

whereas there is maximum human interference. Pinus roxburghii have been

removed from natural habitat for accommodation and for agricultural purposes.

In our case Pinus wallichiana varies from 5 to 210cm in diameter, Majority of the

individuals of Abies pindrow and Pinus wallichiana population in Thandiani Sub

Forests Division had a medium size tree, which shows their regenerating capacity

in existing climate. However abiotic factor influences their regenerating capacity

(Shaheen et al., 2011). Cierjacks & Hensen (2003) reported that grazing activity

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has an important impact on community structure and regenerating capacity.

Grazing livestock exerts high pressure on the regeneration of Quercus ilex and

other palatable trees. Age and growth rate of plants greatly differ among

different species, sites and even with two similar sized trees of the same species.

The size of trees either increased or decreased with increase in elevation,

depending upon the species altitudinal requirements. However, Ahmed et al.,

(1990) found materially negative correlation between altitude and growth of

trees in Ziarat forests. Pinus wallichiana forest at Thandiani Sub Forests Division is

adversely affected by the fuel wood collection. The entire requirements for

heating, fodder, and timber wood extraction are fulfi lled by these forests. The

over exploitations of these resource leads to their consumption, which

ultimately causes habitat destruction of plant and animals. The distribution of

plants in different age classes indicates age and population status of plant species i.e.,

regeneration capacity, declining population or facing other environmental changes.

4.4. Degree of aggregation

Clark and Evans (1954) specified that peak woodland trees that have

reached to enough height to proceed. The forest tree peak may form a constant

sharing due to the competition for sun light etc. So the constant distribution

may occur where struggle between individuals is safer for their properties. An

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accidental spreading is, however, comparatively uncommon in environment as

it happens where the conditions are very constant with no tendency of growth.

4.5. Index of similarity anddissimilarity

A similarity index does not show the relative abundance of a species but

these become more useful when major interest lies in the presence or absence of

a species. Degree of similarity between two plant stations allows combining

them into an association or vegetation types. It reduces the higher number of

plant species into few similar vegetation types. The higher similarities between

the communities might be due to the less difference in altitude, similarity in

aspects, proximity of stands to each other, which almost had similar habitat

conditions in terms of nutrients, etc. The least similarity among the

communities/habitat types was due to change in altitude, climatic, edaphic and

biotic conditions, such as erosion, soil differences such as silty loam to sandy

loam, pH, EC, nitrates, overgrazing, trampling and deforestation as discussed in

Khan et al.,2 016. Such differences cause changes in nutrient status and low

relationship of habitat and thus species. The indices of similarities between

plant communities showed high differences among themselves owing to the

variation in altitude and biotic factors (Malik et al., 1990, Malik, 2007 and Malik,

2005). The communities in which dominant vegetation comprised of therophytes

showed least similarities. The present study is in agreement with those of Shah

138

et al., (1991), Durrani (2000). Malik and Malik (2004), Nazir and Malik (2006)

who reported that plant communities differ from one another due to

topography, exposure, erosion and biotic factors.

The majority of plant species were found to occur in mid altitude of TsFD. The

altitudinal gradients were complex and involve many environmental gradients such as

topography, soil and climate (Pausas et al., 2001). In the mountain areas like the

Himalayas, the extreme numbers of unique species were predictable to arise at high

altitude due to separation phenomena (Shrestha and Joshi, 1996). The various

therapeutic plants species flourish in the Thandiani forests region, reflecting great ranks

of floral variety. The study in this region necessity practice modern tradition of

identifying species; which will lead to information of new taxa (Palumbi, 2001). The

majority of the vegetation in this region has significant uses in relations of medications,

inexpensive value and other usages. Much care had been rewarded to records of the

Himalayan region‟s medicinal plants, but no elongated term maintenance approaches

have been out lined. The plant resources were declining rapidly due to bigger old style

usage by the foothill societies for therapeutic purposes (Shinwari, 2010). The major

factors declining floral bio-diversity in the region include changing climatic situations,

over harvesting and overgrazing. The individuals existing in the buffer regions near by

the core park region mainly depend on normal resources and community facilities. The

anthropogenic factors and pressure on natural resources make it necessary to develop a

comprehensive and maintainable forests management strategy (Hagler, 1999). The

139

growing tourist traffic and activities in Abbottabad region at high altitude pastures may

cause threats to indigenous flora and would be another alarming factor for plants

prevailing in area.

4.6. Life form and Leaf Spectra

A biological spectrum is established when all the species of higher plants of a

region are classified into life forms. Biological spectrum is useful in comparing

geographically widely separated plant habitats and is also regarded as an indicator of

climatic conditions. According to life form of various species recorded from Thandiani

Sub Forest Division were classified into major life form classes. The similar biological

spectra in different regions show similar climatic conditions. In Thandiani Sub Forest

Division, life form of various plant species was recorded. Raunkier, (1934) prepared

anormal spectrum based on sampling of world flora using one thousand

structures. Climate of a region, is characterized by life form, however, biotic

components are the potential causes for variation in the biological spectrum in a

given floristic zone i.e. agricultural practices, grazing and deforestation etc. In

the study area life form spectra of different plant communities showed that

hemicryptophytic, megaphanerophytic (20.24%) and therophytic elements were

dominating the vegetation. Cain & Castro (1959) and Shimwell & Laurie (1972)

reported that hemicryptophytes indicates the temperate zone, whereas

therophytes and geophytes are the indicator of the desert and Mediterranean

140

sort of climates respectively. The climate of a study area differs from

subtropical; moist temperate to sub-alpine meadow type vegetation at different

altitudes (Khan et al., 2015 & 2016). Floristically, Thandiani Sub Forest Division

comes under cool moist temperate zone. Close to our findings, Malik et al., (1994;

1996) observed that in the moist temperate part of Dhirkot and Neelum valleys

of Kashmir region, hemicryptophytes and therophytes were the the dominant

life form classes. Malik (2005) reported hemicryptophytic and therophytic

species were dominating in Ganga Chotti and Bedori hills at an elevation of

1700-3700m as well. Malik & Malik (2004) reported that qualitatively

nanophanerophytes and hemicryptophytes were dominant in Kotli hills. Malik

(2007) reported that hemicryptophytes and therophytes were dominant in Pir -

chinasi hills. Due to severe pressure of deforestation and other human activities

growth of trees and shrubs regeneration capacity is badly affected in these

regions (Rahman et al., 2016). Tareen & Qadir (1993) reported that from Harnai

to Duki moist and cold conditions, low temperature and winds characterize

specific life form classes. Habitat destruction was also common in the region

due to overgrazing, crushing, deforestation and over exploitation. Therophytes

thus dominated in such disturbed zones. Similar domination of therophytes was

observed by Hussain et al., (1997) in Girbanr and Dabargai hills, due to

destruction of natural habitat. In open physiognomies, hemicryptophytes

prevail, whereas in dense ones megaphanerophytes is the best representation

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class (Hussain, 2009). Degraded vegetation supports hemicryptophytes and

disturbed vegetation supports therophytes. Nazir & Malik, (2006) reported

similar results from Sarsawa hills that vegetation degradation occurs due to

severe biotic pressure. Barik & Misra (1998) reported that the biological

spectrum of grassland ecosystem of the South Orissa consisted of therophytes,

chamaephytes, hemicryptophytes and cryptophytes. The supremacy of

therophytes is reflected similar to the present study. Chamaephytes become

more prominent in sub-alpine zones due to soil and climatic conditions. In the

study area high proportion of therophytes presence is indication of enormous

anthropogenic disturbances. Ram & Arya (1991) reported 36% short herbaceous

flowering plants, 27% cushion & spreading reported hemicryptophytes as

dominant vegetation in temperate and sub-alpine meadow zone. Studies of

Thandiani Sub Forest Division also in close agree with them. The finding of Tareen

& Qadir (1993) showed dominance of hemicryptophytes in temperate regions of

Balochistan. Hence the Raunkier life form spectra fail to fulfill the numerical

status of plants in the field, therefore, quantitative characters such as density,

frequency and canopy cover explains vegetation structure with dominating

environmental conditions has been discussed by Khan et al., 2016 for this region.

Even then the life spectra classification is preferable when researcher work at

smaller scales and need a quick description via physiognomy (Batalha &

Martins, 2002).

142

The Present study reveals that microphylls and nanophylls were present

at high elevations while leptophylls present in the lower elevation. Malik et al.,

(1990) reported that a high percentage of leptophylls and nanophylls occurred in

dry subtropical semi evergreen forests of Kotli hills Azad Jammu and Kashmir.

Malik (2005) also reported microphyllous & nanophyllous were dominant at

Ganga Chotti and Bedori hills (Azad Jammu and Kashmir). His findings are in

resemblance with our results. The leaf structure, usually determines habitat

condition such as smaller leaves generally are characteristics of dry and adverse

environmental condition. The observed relationship between small leaves and

cold or hot desert climates are adaptive feature in retaining soil moisture.

Moisture retaining is critical when the root is sensitive to low temperature result

a decrease of water absorption from the soil (Greller, 1988). In our case, high

percentage of microphylls represents the cool climate of temperate and sub-

alpine meadow under cold conditions. The roots absorb low moisture and

nutrients. Our findings are comparable with with those of Qadir & Tareen (1987)

who reported high percentage of microphylls and nanophylls in a temperate

sort of climate in thedistrict Quetta of Baluchistan. Malik (2007) reported similar

findings in Pir-chinasi hills. Saxena et al., (1982) observed that the percentage of

microphylls was positively correlated with the increasing elevation. In the

present case the percentage of leaf form classes differ with increasing altitude.

Dolph & Dilcher (1980) reported that the large leaf species were dominant in

143

tropical wet forest of Costarica. These findings are contradictory with ours due

to climatic variations such as temperature and wet tropical sort of conditions

overthere. The size of the leaf alone could not explain the specific leaf zone or

climate. Plant habit and its root system also play an important role. The leaf size

knowledge also helps in understanding physiological processes of plants and plant

communities and is useful while classifying plants into various associations.

4.7. Ecological gradient of vegetation with special emphasis on indicator Species:

The indicator species analysis is used at the similar time to deliver facts on the

specific species and the specific site association. While rare species are generally not

similar in relations to their environmental need (Grime 1998; Mouillot et al., 2013), some

of them could be well modified to the ecological conditions of a given group of releve,

without being analytic of that environment type, founded on species existence. That is,

the species might be actualy close functionally to an ideal habitat, but cannot be used to

identify the environment. However, we trust that to reflect a species as „functionally

diagnostic‟ of a specific environment, the species should havea reasonable possibility of

being finding in the field. Consequently, its indicator value should be greatest

investigated in relations of both the useful relationship with the ideal habitat of releve

and its ecological variables inside the stands. The use of indicator species to display or

measure ecological conditions, or to identify communities‟ type, is a certainly

recognized habit of both hypothetical and practical determinations in forest vegetation

144

ecosystem. The ecological indicator species are used as indicator of habitat types or

environmental variations. The usage of a set of multi species ecological or

environmental indicator some what than single indicators has been suggested to

increase the regularity of bio indication measures (Carignan and Villard 2002;

McGEOCH, 1998; Niemi and McDonald 2004; Butler et al., 2012).

The multivariate analyses carried out as part of this study established five

distinct plant communities in the TsFD study area. Being located in the Western

Himalayan Province, the vegetation was mainly Sino Japanese in nature and the

communities were classified on the basis of environmental factors/gradients i.e., soil

pH, soil organic matter, soil phosphorus contents, soil texture, aspect, altitude and soil

electrical conductivity. This allows our results to be compared with the studies already

undertaken in other adjacent locations in the Sino Japanese Region (Takhtadzhian and

Cronquist, 1986; Ali & Qaiser 1986; Champion et el, 1965; Khan et al, 2011 & 2014;

Mehmood et al., 2015; Shaheen et al., 2011). At lower elevation ranges the vegetation was

of a sub tropical nature with indicator species including Dodonea viscosa, Punica florida,

Berberis lyceum and Pinus roxburghii. A similar community was described by Siddiqui et

al. (2009) during a phytosociological survey of the lesser Himalayan and Hindu Kush

ranges of Pakistan.At upper altitudinal ranges, the vegetation contains characteristics

species of moist temperate types of forests, e.g., Pinus wallichiana, Abies pindrow,

Aesculus indica, Prunus padus, Indigofira heterantha, Viburnum grandiflorum, Paeoniaemodi,

Bistortaamplexicaule, Euphorbia wallichii and Trifolium repens; which could be compared

145

with the communities reported in the moist temperate Himalaya by Saima et al., (2009).

Species diversity reached an optimum at middle elevations (1700m-2200m), as

compared to the lower locations where there was greater impact of anthropogenic

activities, while at high elevations (2200m-2626m) diversity was lowest mainly due to

extreme conditions. Such kinds of species distributional phenomena have also been

observed in other mountainous ecosystems (Anderson et al, 1992; Jan et al, 2015).

Moreover an increase in herbaceous vegetation is positively correlated to increase in

elevation which seems to be a function of eco-physiological processes associated with

these higher elevations. The findings of this study clearly indicate that the lower

elevational ranges exhibit sub tropical floristic elements which gradually change on the

one hand to moist temperate types in the upper ranges, i.e. along the latitudinal

gradient, and to subalpine types near the peaks of the mountains in response to the

altitudinal gradient.

The methods applied in this study allow users to compare multiple classification

procedures of the same sites, for authentication of the information resulting from the

analysis. However, in mountainous regions, which are difficult to access, vegetation

surveys need to be conducted rapidly and with limited resources, such as for vegetation

mapping. In such situations, it may be desirable to survey the largest possible number

of localities, but simplify the field work help by focusing on a small subset of species

that have high predictive value. The use of indicator species to monitor environmental

conditions or to determine habitat or community types is a firmly established technique

146

for both theoretical and applied purposes in vegetation ecology in the recent past. Such

indicators are used as indicative of a specific micro climatic condition or environmental

change. The use of a suite of multi species ecological or environmental indicators rather

than single indicators has been recommended to increase the reliability of bio-indication

systems (Niemi and McDonald 2004; Khan et al, 2016).In order to determine indicator

species, the characteristic to be predicted is represented in the form of a classification of

the sites, which is compared to the patterns of distribution of the species found at the

sites. For this purpose, Indicator Species Analysis (ISA) takes into account the fact that

species have different niche breadths.

Another important application, of this project is illustration of vegetation

classification schemes according to the modern rules. Vegetation types are often defined

using the complete composition of vascular plants (De Caceres & Wiser 2012). When

complete composition is available, there are several alternatives for assigning

vegetation plot records to predefined vegetation types (Van Tongeren 2008;Tongren &

Hennekens 2008; De Caceres et al, 2010), which are preferable to the approach presented

here. When an indicator value index is used, the method provides the set of site groups

that best matches the observed distribution pattern of the species. When applied to

community types, it allows one to distinguish those species that characterize individual

types from those that characterize the relationships between them. This distinction is

useful to determine the number of types that maximizes the number of indicator

species. Consideration of combinations of groups of sites provides an extra flexibility to

147

qualitatively model the habitat preferences of the species of interest. If at a given site,

one finds a species combination with high predictive value, the site can be assigned

with confidence to the indicated type. If none of the valid indicators is found, then a full

vegetation plot may need to be established. Users of the method should bear in mind

that when site groups have been defined using species composition data, they are by

definition non independent from species. In these cases, the indicator value statistic will

be larger than the value expected under the null hypothesis of independence, leading to

a high rate of rejection in inferential tests (Caceres & Legendre 2009). When confidence

intervals are being used to assess the uncertainty of the estimation, however, they are

still valid. A variety of environmental gradients determines the boundaries of

altitudinal zones found on mountains, ranging from direct effects of temperature and

precipitation to indirect characteristics of the mountain itself, as well as biological

interactions of the species. Zonation produces distinct communities along an elevation

gradient (Haq et al., 2015; khan et al., 2015). In addition to environmental factors, other

factors related to historical plant geography may also be responsible for the

determination of a plant community (Poore, 1955).

The Western Himalayan TsFD in Pakistan is a highly diverse region, particularly

in terms of the wide range of natural forest types that occur there. These forests are,

however, under considerable conversion pressure as land use intensifies with

expanding human population and economic development. Conservation strategies

based on the geographic patterns of botanical species richness and diversity, including

148

the identification of significant floristic regions and priority areas for conservation,

could improve the effectiveness of forest policy and management. These strategies

should also include current threats of loss due to forest conversion to address the more

urgent challenges for sustainable development. Here, we produce distribution models

for 252 plant speciesusing multivariate analysis, collecting geo-referenced herbarium

specimens. Our findings provide clear priorities for the development of a sustainable

and feasible biodiversity conservation strategy for TsFD through indicator species

approach.

4.8. Vegetation mapping and biodiversity

The forest ecosystems all over the world usually have diverse biological associations

due to their quickly changing micro climate, landscape and geo-morphological history

(Herben et al., 2003; Fosaa, 2004; Khan et al., 2011). The distribution of individuals of the

same and different plant species in a particular association is the application of micro

environmental impacts, time and biotic relationships. The plant species grouped in an

association in a definite fashion and hence can assist in vegetation quantification and

evaluation. The classification of natural ecosystems into potential plant communities or

associations and habitat types is important for the long-term management of natural

resources (Ewald, 2003; Abbasi & abbasi, 2012; Khan et al., 2016; Rahman et al., 2016b).

The vegetation classification and ordination also overcome problems of comprehension

149

upto some extent by summarizing field data in a low dimensional space with similar

samples and species near together and dissimilar ones far apart (Khan et al., 2013c).

Uneven topography, rough terrain and far long location make it difficult to sample

vegetation easily in the Western Himalayan region. Previous studies of plant

communities in the developing world have often adapted conventional ways of

phytosociological classifications, where the communities‟ names were given on the

basis of dominant species having high importance values. In this paper we have

adopted statistical approaches for classification and ordination of plant communities.

The DCA and CCA explained that aspect, altitude and soil chemical composition as the

strongest explanatory variables. The results further revealed sub-tropical characteristic

species in communities 1and 2 at the lower elevations for example Pinus roxburghii,

Punica florida, Dodonaea viscosa, Zanthoxylum armatum and Zizyphus jujuba. Onthe other

hand moist temperate zone can be classified into 3rd, 4th and 5th communities with

characteristics species of Pinus wallichiana , Aesculus indica, Prunus padus ,

Indigofera heterantha, Viburnum grandiflorum, Viburnum cotinifolium, Paeonia emodi,

Persicaria amplexicaul and Trifolium repens etc., Similar sort of plant communities have

also been observed in adjacent moist temperate locations by number of researchers and

can be studied in the literature (Wazir et al., 2008; Saima et al., 2009; Shaheen et al., 2011;

Khan et al., 2015). Vegetation of the study area lies between the sub-tropical and moist-

temperate zones having Sino-Japanese and Irano-Turanian floristic elements. On the

basis of the definition of Ecotone provided by Peters et al., (2002), as a zone where a




http://www.theplantlist.org/tpl1.1/record/ild-33239



150

directional spatial change in vegetation is more faster than on either side of the zone we

believes that the TsFD occupies a sort of transitional floristic position between these two

regions. Studies of the adjacent mountain ranges, e.g., the Karakorum and the Hindu

Kush (Nusser & Clemens, 1996; Chawla et al., 2008; Chevallier et al., 2011), also

confirmed many of the Himalayan regions as a transitional location on the edge of the

moist and dry temperate zones of the Western Himalayan Province on one side and

subtropical on the other. Our findings showed the moist temperate floral elements

within the vegetation, with the overall dominance of herbaceous species (61%) which

can be seen in the publication raised from studies on the Indian parts of Himalayas

(Kharkwal et al., 2005). Zobel & Singh,(1997) wrote that such vegetation features can be

expected in the Himalayas and western parts of the Himalayas (like our study area)

become more identical to the Hindu Kush and the Karakorum mountain ranges rather

than to the eastern Himalayas itself. Similar to our 1st & 2nd communities, Siddiqui et al.,

(2009) reported phytosociological communities of Pinus roxburghii (Chir pine) in the

Lesser Himalayan and Hindu Kush range of Pakistan. Saima et al., (2009), also reported

Pinus wallichiana communities but with different dominant species from the Ayubia

National Park, Abbottabad. Effects of soil pH, slope and aspect in species zonations

were also observed by a number of authors in other mountain systems around the globe

like ours and can be seen in the published literature (Hegazy et al., 2011; Wang et al.,

2003; Davies et al., 2008; Khan et al., 2012). The above mentioned studies differ from ours

as they lack the use of any sort of statistical analysis.


151

4.9. Use of database technology in vegetation monitoring

Management and monitoring of plant habitats and restoration of the disturbed one

requires multi-scale techniques. GEO database is one of a suitable database for

managing ecological data for monitoring purposes. It eases the data processing for

meta-analysis via producing convenient structures and consistency in data sources

(Safford et al., 2005). Such database combines ArcMap data with Microsoft Access for a

single entry, processing, storage, organization, visualization and analysis. This sort of

data sets then not only allow use of topology and rules application for data quality

control and cover re-classification, but also serve as beneficial organizational holder

within a GEO database. The execution of this GIS framework also aids spatial modeling

which ultimately helps in the recognition of indicator species of specific habitat or

communities.

4.10. Conservation management

This study has several important suggestions for the modal & plan of guzara and

reserve forests area. The study reveals that TsFD within upland forests are certainly a

valuable resource for the conservation of plant biodiversity as these has high species

richness and a number of unique species. The plant communities in these habitats

represent distinctive combination of different plant species. Clearly, moist temperate

and subtropical habitats in Thandiani forests establish an ideal ground for conservation.

Ecologists and conservationalists have often neglect small forest regions in part due to a

152

lack of clear sense of habitat types. By identifying indicators for a representative zone

(Khan et al., 2016) and the finding presented here should facilitate the recognition and

conservation of these valuable plant habitat types. Forest and communities type

identified here will also help to all the stake holders to understand the relative

conservation value of different sites and plant biodiversity. It is concluded from this

study that ecological gradient of the region has vital role in the determination of various

plant associations of the area. Individual plant species and communities changed with

the change in topographic, edaphic and climatic gradients both qualitatively and

quantitatively. Plant ecologists have commonly been aware that vegetation shows

variations over a broad variety of specific variables in an ecosystem. Therefore, it was

necessary to apply such multifold approaches and methods through CANACO and

GEO database to document the present day status and make a way to its future

conservation management of Plant biodiversity in the studied as well as adjacent

regions.

4.11. Conclusions

The vegetation of TsFD shows a divergence over broad variety of particular factors

and areas. Therefore, Multivariate techniques and Geo database (created in ArcGIS

10.2.1) were used as a perfect way to assess and mapthe vegetation and community

typesof the region for the first time. It was concluded that the environmental factors

have significant influence on vegetation pattern and diversity.The association of plant

153

species composition, abundance and distribution pattern fluctuate in response to

edaphic, topographic and climatic factors in general. More specifically the vegetation

varies with variation insoil pH, electrical conductivity, aspect, phosphorous, potassium

and elevation range as compared to other supplementary factors. Three main

implications of the current study included: (1) How to document plant species

abundance, composition and distribution pattern at peak growing season and classify it

in potential plant communities in an unbiased way using modern statistical tools such

as Cluster Analyses (CA) and Two Way Cluster Analysis through PCORD. (2) How to

correlate plant species and communities with complex set of environmental variables

using laboratory and computer based approaches such as various chemical tests use of

PCORD software for Indicator Species Analyses (ISA) and CANOCO for CCA etc. (3)

How to show the field and software data in GIS mapping system to generate an

understandable map for common readers natural resource planners. These techniques

give a perfect way to identify indicator species of specific habitats and hence directly or

indirectly contribute to biodiversity, habitat conservation and management plans not

only in the study area but in the adjacent regions exhibit to similar sort of

environmental conditions.

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Appendix 4.1 Pre-prepared sheet-I for recording the qualitative and quantitative attributes of plant species (Quadrat sampling)

5 - Quadrats for Trees

10 - Quadrats for Shrubs

15 – Quadrats for Herbs

Field visit number

Date

Examiner

Sample No

Aspect

Study location

Gps Reading

Altitude

Baseline Beginning point stake

Baseline End point stake

Soil Texture

Latitude

Altitude

S. No

Speci

es

Name

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

155

Appendix 4.2 Pre-prepared sheet-II for recording the qualitative and quantitative attributes of plant species (C.C Herbs & Shrubs)

Canopy cover according to Daubenmire Cover Classes

Field visit number

Date

Examiner

Altitude

Aspect

Study location Gps Reading Number of Quadrats

Baseline Beginning point stake Baseline End point stake

Soil Texture Latitude Altitude

Cover

class

Mid-point

Species

Species

Species

species

species

species

Species

species

species

species

Species

Nn

um

ber

Pro

du

ct

Nu

mb

er

Pro

du

ct

Nu

mb

er

Pro

du

ct

Nu

mb

er

Pro

du

ct

Nu

mb

er

Pro

du

ct

Nu

mb

er

Pro

du

ct

Nu

mb

er

Pro

du

ct

Nu

mb

er

Pro

du

ct

Nu

mb

er

Pro

du

ct

Nu

mb

er

Pro

du

ct

Nu

mb

er

Pro

du

ct

1 1-5%

2.5

2 6-25

15

3 26-50

37.5

4 51-75

62.5

5 76-95

85

6 96-100

97.5

Total canopy

Number of samples

% canopy cover

Species composition

Frequency

156

Appendix 4.3 Pre-prepared sheet-III for recording the qualitative and quantitative attributes of plant species (C.C Trees)

Canopy Cover of different plant species (Trees)

Field visit number

Date

Examiner

Number of Quadrats

Baseline Beginning point stake

Baseline End point stake

I

Sampling

Number

1

2

3

4

5

6

7

8

9

10

11

II

Altitude

III

Latitude

IV

Longitude

V

Aspect

VI

GPS Reading

VII

Study Location

VIII

Soil Texture

S. No

Species Name

Total

Indivi

duals

Nature

(S & T)

C.C

C.C

C.C

C.C

C.C

C.C

C.C

C.C

C.C

C.C

C.C

1

157

HERBARIUM HAZARA UNIVERSITY

Department of Botany, Hazara University Mansehra

Botanical Name ____________________________________________________________

Family ____________________________________________________________________

Local Name ________________________________________________________________

Date_______________Field No_______________Photo No_________________________

District__________________ Locality___________________________________________

Flower color _____________ Habit ____________________________________________

Habitat____________________________________________________________________

Soil type__________________ Aspect:______________________________ ___________

Life form: phan, chamae, h. cryto, crypto, thero, geo, hydro , etc.

Altitude _______________Latitude ___________Langitude_________________________

Abundant ( ), Common ( ), Frequent ( ), Rare ( ), Very Rare ( )

Remarks:__________________________________________________________________

_________________________________________________________________________

Collector (s):_______________________________________________________________

Appendix 4.4 Pre-prepared sheet-III for recording the data of a n individual af a species

158

Appendix 4.5 General Ethno botanical Survey

(a) Information about the informants

1. Name ________________

2. Locality ________________

3. Age ________________

4. Sex _________________

5. Occupation _________________

6. Qualification (if any) _________________

7. Date _________________

(b) Information about the plant species used

1. Local name of plant ___________________

2. Source area ___________________

3. Part used ___________________

4. Uses (Medicinal/Fuel/Fodder/Others) ___________________

5. Method of use __________________

6. Season of collection ___________________

7. Who collects (Child/Women/Man) ___________________

8. Which plants are grown/cultivated ___________________

159

Appendix 4.6 Alphabetical list of plant species, reported from Thandiani sub forests division, Abbottabad, during quadrat

sampling. List also show life form, leaf spectra and habit forms of the reported species.

S.

NO

Accepted Botanical Names Synonyms Names Family Name L.F L.S Ha

1 Abies pindrow (Royle ex D.Don) Royle Abies pindrow Royle. Pinaceae MP L T

2 Acacia modesta Wall. Mimosa obovata Roxb. Legumoniseae MP L T

3 Acacia nilotica (L.) Delile Acacia arabica (Lam.) Willd., Legumoniseae MP Mi T

4 Acer caesium Wall. Ex Brandis Acer caesium Wall. Ex Brandis Sapindaceae MP L T

5 Aesculus indica (Wall. ex Cambess.) Hook Aesculus indica (Comb) Hook. Sapindaceae MP Me T

6 Ailanthus altissima (Mill.) Swingle Ailanthus altissima (Mill.) Swingle Simaroubaceae MP Me T

7 Broussonetia papyrifera (L.) L'Hér. ex Vent. Broussonetia papyrifera Vent., Moraceae MP Me T

8 Cedrela serrata Royle Meliaceae MP L T

9 Cedrela toona Roxb. Ex Rottler & Willd. Meliaceae MP L T

10 Cedrus deodara (Roxb. ex D.Don) G.Don Cedrus deodara Rox ex Lamb. Pinaceae MP L T

11 Celtis australis subsp. caucasica (Willd.) C.C.Towns.

Celtis australis L. Cannabaceae MP Mi T

12 Cornus macrophylla Wall. Cornus macrophylla Wall. Ex Roxb. Cornaceae MP Me T

13 Cotoneaster minuta Saporta Cotoneaster minuta Klotz. Rosaceae MP Mi T

14 Dalbergia sissoo DC. Dalbergia sissoo Roxb., Legumoniseae MP L T

15 Diospyros lotus L. Diospyros lotus Blanco Ebenaceae MP Mi T

16 Diospyros kaki L.f. Diospyros kaki L. Ebenaceae MP Mi T

17 Eucalyptus abdita Brooker & Hopper Eucalyptus globolus L. Myrtaceae MP L T

18 Ficus carica L. Ficus carica var. afghanica Popov Moraceae MP Mi T





http://www.theplantlist.org/1.1/browse/A/Simaroubaceae/









160

19 Ficus palmata Forssk. Ficus palmata Roxb. [Illegitimate] Moraceae MP Mi T

20 Grewia optiva J.R.Drumm. ex Burret Grewia optiva Drum. ex. Burret Malvaceae MP L T

21 Ilex dipyrena Wall. Ilex dipyrena Walld. Aquifoliaceae MP L T

22 Jacaranda mimosifolia D.Don Jacaranda chelonia Griseb. Bignoniaceae MP Mi T

23 Juglans regia L. Juglans regia subsp. fallax Popov Juglandaceae MP Mi T

24 Melia azedarach L. Melia azedarach var. glabrior C.DC. Meliaceae MP L T

25 Morus alba L. Morus alba f. alba Moraceae MP L T

26 Morus nigra L. Moraceae MP L T

27 Olea ferruginea Wall. ex Aitch. Olea ferrugenea Royle. Oleaceae MP Mi T

28 Pinus roxburghii Sarg. Pinus roxburghii Sargent. Pinaceae MP L T

29 Pinus wallichiana A.B.Jacks. Pinus wallichiana A.B.Jackson. Pinaceae MP L T

30 Pistacia khinjuk Stocks Pistacia antegrrimma J.L. Anacardiaceae MP Me T

31 Platanus oriantalis L. Platanus orientalis var. acerifolia Aiton Platanaceae MP Me T

32 Populus ciliata Wall. Ex Royle Populus ciliata var. ciliata Salicaceae MP L T

33 Populus nigra L. Populus nigra var. betulifolia (Pursh) Torr. Salicaceae MP L T

34 Prunus armeniaca L. Prunus armeniaca Thunb. Rosaceae MP Mi T

35 Prunus domestica subsp. insititia (L.) Bonnier & Layens

Prunus domestica L. Rosaceae MP Mi T

36 Prunus padus L. Prunus padus (Hk) f. Rosaceae MP Mi T

37 Prunus persica var. nucipersica (L.) C.K.Schneid.

Prunus persica (Linn.) Batsch Rosaceae MP Mi T

38 Pyrus pashia Buch.-Ham. ex D.Don Pyrus pashia D.Don. Rosaceae MP Mi T

39 Quercus incana Bartram Quercus incana Roxb. Fagaceae MP L T

40 Quercus robur L. Quercus dilatata Lindl. Ex Royle, Fagaceae MP L T

41 Robinia pseudoacacia f. monophylla- Robinia pseudoacacia L. Paplionaceae MP L T




http://www.theplantlist.org/tpl1.1/record/tro-16700008




















161

pendula (Dieck) Voss

42 Salix alba L. Salix alba var. australior Poljakov Salicaceae MP Mi T

43 Salix denticulata Andersson Salix denticulata N.J. Anderss. Salicaceae MP Mi T

44 Salix eleagnos Scop. Salix angustifolia Willd. Salicaceae MP Mi T

45 Staphylea emodi Wall. Staphylea emodi Wall. Ex Brandis. Staphyleaceae MP Mi T

46 Taxus wallichiana var. mairei (Lemée & H.Lév.) L.K.Fu & Nan Li

Taxus walliciana (Zucc.) Taxaceae MP L T

47 Ulmus wallichiana Planch. Ulmaceae MP L T

48 Ziziphus jujuba Mill. Ziziphus vulgaris Lam. Rhamnaceae MP Mi T

49 Abelia triflora R.Br. ex Wall. Abelia triflora R. Br., Caprifoliaceae NP Mi S

50 Arundo donax L. Arundo donax var. angustifolia Döll Poaceae G Me S

51 Astragalus aaronii (Eig) Zohary Astragalus flaccidum (Royle) Leguminoceae NP L S

52 Berberis lycium Royle Berberis lycium var. simlensis Ahrendt Berberidaceae NP Mi S

53 Berberis orthobotyrus Bien. ex Aitch. Berberis orthobotrys var. canescens Ahrendt Berberidaceae NP Mi S

54 Berberis pachyacantha Bien. ex Koehne Berberis pachyacantha Koehne, Deutsche Dender. Berberidaceae NP Mi S

55 Berberis parkeriana C.K.Schneid Berberis parkeriana C.K.Schn., Berberidaceae NP Mi S

56 Buddleja asiatica var. brevicuspe Koord. Buddleja asiatica Lour., Scrophulariaceae NP L S

57 Buddleja crispa Benth. Buddleja crispa Bth., Scrophulariaceae NP L S

58 Buxus papillosa C.K.Schneid. Buxus papilosa C.K. Schn. Buxaceae NP L S

59 Clematis connata DC. Clematis amplexicaulis Edgew. Ranunculaceae L N S

60 Clematis montana Buch.-Ham. ex DC. Clematis montana Buch.- Ranunculaceae L N S

61 Cuscuta reflexa var. anguina (Edgew.) C.B. Clarke

Cuscuta reflexa Roxb Amar. Convolvulaceae L N S

62 Daphne mucronata Royle Daphne mucronata var. affghanica Meisn. Thymeleaceae NP Mi S

63 Daphne papyracea Wall. ex G. Don Thymeleaceae NP Mi S





















162

64 Debregeasia saeneb (Forssk.) Hepper &J.R.I.Wood

Debregeasia salicifolia (D. Don) Rendle, Urticaceae NP Mi S

65 Desmodium gangeticum (L.) DC. Desmodium gangeticum (Linn) DC. Leguminoceae NP Mi S

66 Desmodium hookerianum D.Dietr. Desmodium podocarpum DC. Leguminoceae NP Mi S

67 Dodonaea viscosa (L.) Jacq. Dodonaea viscosa Jack Sapindaceae NP Mi S

68 Elaeagnus parvifolia Wall. ex Royle Elaeagnus parvifolia Wall Elaeagnaceae Th N S

69 Euonymus hamiltonianus Wall Euonymus hamiltonianus f. hamiltonianus Celastraceae NP Mi S

70 Hedera nepalensis K. Koch Hedera nepalensis var. sinensis (Tobler) Rehder Araliaceae L N S

71 Indigofera angustifolia L. Indigofera angustifolia var. brachystachya DC. Leguminoceae NP N S

72 Indigofera heterantha Brandis Indigofera gerardiana Wall. Leguminoceae NP N S

73 Isodon coetsa (Buch.-Ham. ex D.Don) Kudô Isodon coetsa (Spr.) Lamiaceae NP N S

74 Leptopus chinensis (Bunge) Pojark. Andrachne cordifolia (Don) Muell Phyllanthaceae NP Mi S

75 Lonicera bicolor Klotzsch Lonicera bicolor KI. &Garcke., Caprifoliaceae NP Mi S

76 Lonicera hispida Pall. ex Schult. Lonicera hispida Pall. Loony Caprifoliaceae NP Mi S

77 Lonicera quinquelocularis Hard. Lonicera quinquelocularis Hardw. Caprifoliaceae NP Mi S

78 Paeonia emodi Royle Paeonia emodi Wall. Paeoniaceae H Mi S

79 Parrotiopsis jacquemontiana (Decne.) Rehder Parrotiopsis jacquemontiana (Dcne.) Hamamelidaceae NP L S

80 Punica florida Salisb. Punica granatum L. Lythraceae NP Mi S

81 Rhamnus purpurea Edgew Rhamnus purpurea Edgew. Rhamnaceae NP Mi S

82 Rhus punjabensis var. sinica (Diels) Rehder & E.H. Wilson

Rhus punjabensis Stewart ex Brandis., Anacardiaceae NP Mi S

83 Rosa webiana Wall. ex Royle Rosa webbiana var. microphylla Cr‚p. Rosaceae NP Mi S

84 Rosa moschata Herrm. Rosa moshcata non J. Herrm. Rosaceae NP Mi S

85 Rubus ellipticus Sm. Rubus ellipticus Smith in Rees., Rosaceae NP Mi S

86 Rubus macilentus Jacquem. ex Cambess. Rubus macilentus Camb. In Jacq. Rosaceae NP Me S























163

87 Rubus ulmifolius f. variegatus (G.Nicholson) Rehder

Rubus ulmifolius Schott in Oken., Rosaceae NP Me S

88 Rubus vulgaris Weihe & Nees Rubus fruticosis Hk.f. Rosaceae NP Me S

89 Sageretia brandrethiana Aitch Sageretia brandrethiana Aitch., J.L.S. Rhamnaceae NP Mi S

90 Sarcococca pruniformis Lindl. Sarcococca saligna (Don) Muell. Buxaceae NP L S

91 Skimmia laureola Franch. Skimmia lareuli D.C. Rutaceae NP Mi S

92 Solanum pseudocapsicum L. Solanum pseudocapsicum var. ambiguum Hassl. Solanaceae NP L S

93 Sorbaria tomentosa (Lindl.) Rehder Sorbaria tomentosa (Lindl.) Rosaceae NP L S

94 Spiraea gracilis Maxim. Spiraea parvifolia Bertol. Rosaceae NP Mi S

95 Syringa emodi Wall. ex Royle Syringa emodi Wall. Ex G. Don., Oleaceae NP Mi S

96 Viburnum cotinifolium D. Don. Viburnum multratum K. Koch Adoxaceae NP L S

97 Viburnum grandiflorum Wall. ex DC. Viburnum grandiflorum Wallich. Adoxaceae NP L S

98 Vitex negundo L. Vitex negundo Linn. Lamiaceae NP Mi S

99 Zanthoxylum armatum DC. Zanthoxylum armatum Roxb. Rutaceae NP Mi S

100 Ziziphus mairei Dode Ziziphus mauritiana Lam. Rhamnaceae NP Mi S

101 Achillea millefolium L. Achillea millefolium f. albiflora Dabrowska Compositeae H L Hr

102 Achyranthes aspera L. Achyranthes aspera var. albissima Suess. Amaranthaceae H N Hr

103 Aconitum violaceum Jacquem. ex Stapf Aconitum violaceum Jacq. ex Stapf Ranunculaceae H Mi Hr

104 Actaea spicata var. acuminata (Wall. ex Royle) H.Hara

Actaea spicata L. Ranunculaceae G L Hr

105 Adiantum venustum D. Don Adiantum venustum Linn. Sraj, Pteridaceae G N Hr

106 Aegopodium burttii Nasir Aegopodium burttii E. Apiaceae H Mi Hr

107 Ainsliaea aptera DC Ainsliaea aptera DC., Compositae H N Hr

108 Ajuga integrifolia Buch.-Ham. Ajuga bracteosa L. Lamiaceae Ch Mi Hr

109 Anemone tetrasepala Royle Anemone thalictroides L. Ranunculaceae Th L Hr















http://www.theplantlist.org/tpl1.1/record/gcc-73252









164

110 Anemone falconeri Thomson Anemone falconeri T. T. in Hk., Ranunculaceae Th L Hr

111 Anemone vitifolia Buch.-Ham. ex DC. Anemone vitifolia Ham. DC. Ranunculaceae Th L Hr

112 Aquilegia moorcroftiana var. afghanica (Brühl) Riedl

Aquilegia mussooriensis Royle. Ranunculaceae H Mi Hr

113 Aquilegia pubiflora Wall. ex Royle Aquilegia pubiflora var. hazarica Qureshi &

Chaudhri

Ranunculaceae H Mi Hr

114 Argemone mexicana L. Argemone mexicana var. aculeatissima Moric. ex

Prain

Papaveraceae H N Hr

115 Arisaema flavum (Forssk.) Schott Arisaema flavum Forrsk. Araceae G Mi Hr

116 Arisaema jacquemontii Blume Arisaema cornutum Schott Araceae G Mi Hr

117 Arisaema utile Hook.f. ex Schott Arisaema utile Hk.f., Araceae G Mi Hr

118 Artemisia absinthium L. Artemisia absinthium var. absinthium Compositae H N Hr

119 Aster molliusculus (Lindl. ex DC.) C.B.Clarke Aster molliusculus (DC.) Compositae H N Hr

120 Atropa acuminata Royle ex Lindl. Atropa acuminata Royle., Solanaceae H Mi Hr

121 Bergenia ciliata (Haw.) Sternb. Bergenia ligulata var. cliata (Royle) Engl. Saxifragaceae H Me Hr

122 Bupleurum falcatum L. Bupleurum falcatum var. africanum P.J.Bergius Apiaceae Th N Hr

123 Bupleurum jacundum Kurz Bupleurum jucundum var. cashemiricum C.B.Clarke Apiaceae Th N Hr

124 Bupleurum lanceolatum Wall. Ex DC., Apiaceae Th N Hr

125 Bupleurum candollei Wall. ex DC. Bupleurum candollei f. acutifolium H.Wolff Apiaceae Th N Hr

126 Cannabis sativa L. Cannabis sativa f. afghanica Vavilov Cannabaceae Th N Hr

127 Capsella × gracilis Gren. Capsella bursapastoris Moench. Brassicaceae Th L Hr

128 Capsicum annuum L. Capsicum annuum var. abbreviata Fingerh. Solanaceae Th N Hr

129 Chenopodium album L. Chenopodium album var. acuminatum (Willd.)

Kuntze

Amaranthaceae H N Hr

130 Chrysanthemum chalchingolicum Grubov Chrysanthemum cinerariifolium (Trevir.) Vis. Compositae Th Me Hr




























165

131 Cichorium intybus L. Cichorium intybus Linn. Compositae G L Hr

132 Cirsium argyracanthum DC. Cirsium stenobracteatum Gilli Compositae H N Hr

133 Cirsium luzoniense Merr. Cnicus argyracanthus (DC) Hk.f. Compositae H Me Hr

134 Clinopodium vulgare L. Calamintha valgaris (L.) Lamiaceae Th N Hr

135 Colchicum luteum Baker Colchicum luteum var. alberti (Regel) Priszter Colchicaceae Th Mi Hr

136 Convolvulus prostratus Forssk. Convolvulus pruinosus Steud. Convolvulaceae G N Hr

137 Coriandrum sativum L. Coriandrum sativum Linn. Apiaceae G L Hr

138 Corydalis diphylla Wall Corydalis diphylla var. cyrtocentra (Prain) Jafri Papaveraceae H N Hr

139 Corydalis cornuta Royle Corydalis stewartii Fedde, Papaveraceae H N Hr

140 Cynodon dactylon (L.) Pers. Cynodon dactylon L. Poaceae H L Hr

141 Cyperus rotundus L. Cyperus rotundus var. acutus Boeckeler Cyperaceae G N Hr

142 Dasiphora fruticosa (L.) Rydb. Potentilla fruticosa L. Rosaceae H L Hr

143 Datura stramonium L. Datura stramonium var. canescens Roxb. Solanaceae H Me Hr

144 Dicliptera chinensis (L.) Juss. Dicliptera roxburghiana Nees in Wall., Acanthaceae Th Mi Hr

145 Dioscorea bulbifera L. Dioscorea bulbifera Linn. Dioscoreaceae H N Hr

146 Dipsacus sativus (L.) Honck. Dipsacus fullonum var. sativus L. Caprifoliaceae G Me Hr

147 Dipsacus inermis Wall. Dipsacus strictus D.Don., Caprifoliaceae H L Hr

148 Dryopteris abbreviata Newman Dryopteris abbreviata (C. Presl) Kuntze Dryopteridaceaea G Me Hr

149 Duchesnea indica (Jacks.) Focke Duchesnea indica (Andr.) Focke. Rosaceae Th Me Hr

150 Echinops niveus Wall. ex Wall. Echinops niveus Wall. Ex DC., Compositae H L Hr

151 Epilobium royleanum Hausskn Epilobium royleanum f. glabrum P.H.Raven Onagraceae H N Hr

152 Epipactis helleborine (L.) Crantz Epipactis helleborine (L.) Orchidaceae Th L Hr

153 Erigeron canadensis L. Conyza canadensis (L.) Compositae Th Mi Hr

154 Erigeron karvinskianus DC. Erigeron karvinskianus var. Compositae H Me Hr






http://www.theplantlist.org/tpl1.1/record/ifn-61102




















166

155 Eulophia dabia (D.Don) Hochr. Eulophia hormusjii Du. Orchidaceae H L Hr

156 Euphorbia helioscopia L. Euphorbia helioscopia subsp. dominii (Rohlena)

K.Malý

Euphorbiaceae Th N Hr

157 Euphorbia hirta L. Euphorbia hirta var. destituta L.C.Wheeler Euphorbiaceae Th N Hr

158 Euphorbia wallichii Hook.f. Euphorbia wallichii Hk.f., Euphorbiaceae Th N Hr

159 Foeniculum vulgare Mill Foeniculum vulgare var. capillaceum Burnat Apiaceae Th N Hr

160 Fragaria nubicola (Lindl. ex Hook.f.) Lacaita Fragaria nubicoli L. Rosaceae H Mi Hr

161 Galium aparine L. Galium aparine var. agreste P.D.Sell Rubiaceae Th N Hr

162 Galium asperifolium Wall. Galium asperifolium var. asperifolium Rubiaceae Th N Hr

163 Galium elegans Wall. ex Roxb. Galium elegans Wall. In Roxb., Rubiaceae Th N Hr

164 Galium hirtiflorum Req. ex DC. Galium hirtiflorum DC., Rubiaceae Th N Hr

165 Gamochaeta malvinensis (H.Koyama) T.R.Dudley

Gnaphalium affine D. Don., Compositae G N Hr

166 Geranium wallichianum D.Don ex Sweet Geranium wallichianum D. Don ex Sweet, Geraniaceae Th Mi Hr

167 Gerbera gossypina (Royle) Beauverd Gerbera gossypina (Royle.) Compositae H L Hr

168 Girardinia diversifolia (Link) Friis Girardinia diversifolia subsp. Urticaceae H L Hr

169 Heliotropium zeylanicum subsp. paniculatum (R. Br.) Kazmi

Heliotropium paniculatum (R.Br.) Boraginaceae H L Hr

170 Heracleum candicans Wall. ex. DC. Heracleum candicans var. candicans Apiaceae H N Hr

171 Hypericum oblongifolium Choisy. Hypericum cernuum Roxb. ex D.Don Hypericaceae H N Hr

172 Hypericum perforatum L. Hypericum perforatum var. albiflorum Choisy Hypericaceae H N Hr

173 Impatiens balsamina L. Impatiens balsamina var. brevicalcarata T.Cooke Balsaminaceae G L Hr

174 Impatiens bicolor Royle. Impatiens bicolor var. brevifolia Warburg ex Engler Balsaminaceae G L Hr

175 Impatiens abbatis Hook. f. Impatiens edgworthii Hk.f., Balsaminaceae G L Hr

176 Impatiens flemingii Hook. f. Impatiens flemingii Hk.f., Balsaminaceae G L Hr

























167

177 Isodon rugosus (Wall. ex Benth.) Codd Plectranthus rugosus Wall. Lamiaceae NP N Hr

178 Jasminum officinale L. Jasminum officinale Linn Oliaceae NP N Hr

179 Lactuca brunoniana (DC.) Wall. ex C.B.Clarke Lactuca brunoniana (Wall. ex DC.) Compositae G Mi Hr

180 Lavatera flava Desf. Lavatera kashmeriana Camb., Malvaceae H L Hr

181 Lecanthus peduncularis (Wall. ex Royle) Wedd lecanthus peduncularis (Royle.) Urticaceae Th Mi Hr

182 Lepidium sativum L. Lepidium sativum Cambess. Brassicaceae H N Hr

183 Lyonia ovalifolia (Wall.) Drude Lyonia ovalifolia (Wall.) Ericaceae Th L Hr

184 Malcolmia africana (L.) R.Br. Malcolmia africana (L.) Brassicaceae H L Hr

185 Malva neglecta Wallr. Malva neglecta Wallr. Malvaceae Th N Hr

186 Malva sylvestris L. Malva sylvestris var. mauritiana (L.) Boiss. Malvaceae Th N Hr

187 Medicago polymorpha L. Medicago denticulata Willd. Leguminosae Th N Hr

188 Mentha longifolia (L.) L. Mentha longifolia (Linn.), Huds Lamiaceae H L Hr

189 Micromeria biflora (Buch.-Ham. ex D.Don) Benth.

Micromeria biflora Benth. Lamiaceae Th L Hr

190 Myosotis asiatica (Vestergr.) Schischk. & Serg Myosotis asiatica Schischk. Boraginaceae H N Hr

191 Myrsine africana L. Myrsine africana var. acuminata C.Y. Wu & C. Chen

Primulaceae H L Hr

192 Nepeta erecta (Royle ex Benth.) Benth. Nepeta erecta Bh Bth. Lamiaceae Ch Mi Hr

193 Nerium oleander L. Nerium oleander subsp. kurdicum Rech.f. Apocynaceae H L Hr

194 Oenothera rosea L'Hér. ex Aiton Oenothera rosea Soland., Onagraceae H N Hr

195 Onychium contiguum C.Hope Onychium contiguum Wall. ex Hope., Pteridaceae G L Hr

196 Otostegia hildebrandtii (Vatke & Kurtz) Sebald Otostegia limbata( Benth.) Boiss. Lamiaceae NP L Hr

197 Oxalis cardenasiana Lourteig Oxalis carniculata L. Oxalidaceae H N Hr

298 Papaver somniferum subsp. setigerum (DC.) Arcang.

Papaver somniferum.Linn Papaveraceae H L Hr




















168

199 Pecteilis radiata (Thunb.) Raf. Orchis susannae L. Orchidaceae H Mi Hr

200 Persicaria amplexicaulis (D.Don) Ronse Decr. Bistorta amplexicaule (D.Don) Greene. Polygonaceae H L Hr

201 Persicaria amplexicaulis (D.Don) Ronse Decr. Polygonum amplexicaule D. Don Polygonaceae Th Mi Hr

202 Phalaris minor Retz. Phalaris minor f. bracteata Jansen & Wacht. Poaceae Th L Hr

203 Phytolacca latbenia (Moq.) H. Walter phytolacca latbenia (Moq.) Phytolaccaceae H Mi Hr

204 Pimpinella acuminata (Edgew.) C.B. Clarke Pimpinella acuminata (Edgew.) Apiaceae Th N Hr

205 Plantago lanceolata L. Plantago lanceolata var. lanceolata Plantaginaceae H Me Hr

206 Plantago major L. Plantago major var. asiatica (L.) Decne. Plantaginaceae H Me Hr

207 Poa annua L. Poa annua Cham. & Schltdl. Poaceae H N Hr

208 Podophyllum peltatum L. Anapodophyllum peltatum Moench Berberidaceae H Me Hr

209 Polygonatum verticillatum (L.) All. Polygonatum verticillatum L., Asparagaceae Th Mi Hr

210 Potentilla nepalensis Hook. Potentilla nepalensis Hk. f. Rosaceae H L Hr

211 Primula veris L. Primula veris var. acaulis L. Primulaceae H Mi Hr

212 Prunella vulgaris L. Prunella vulgaris f. alba J.C.Nelson Lamiaceae H N Hr

113 Pseudocaryopteris bicolor (Roxb. ex Hardw.) P.D.Cantino

Caryopteris odorata (Ham) B. L Lamiaceae H Mi Hr

214 Pseudomertensia parviflorum (Decne.) Riedl Pseudomertensia parviflorum (Decne.) Boraginaceae H L Hr

215 Pteris vittata L. Pteris vittata f. cristata Ching Pteridaceae G Mi Hr

216 Ranunculus muricatus L. Ranunculus muricatus var. brasilianus DC. Ranunculaceae H Mi Hr

217 Ranunculus laetus Wall. ex Hook. f. & J.W. Thomson

Ranunculus laetus Wall. ex H. Ranunculaceae H Mi Hr

218 Reinwardtia indica Dumort. Kittelocharis trigyna (Rchb.) Alef. Linaceae Th L Hr

219 Rochelia stylaris Bioss. Boraginaceae H N Hr

220 Rumex dentatus L. Rumex dentatus subsp. klotzschianus (Meisn.)

Rech. f.

Polygonaceae H L Hr























169

221 Rumex hastatus D. Don. Rumex hastulatus Sm. Polygonaceae H L Hr

222 Rumex nepalensis Spreng. Rumex nepalensis var. nepalensis Polygonaceae H Me Hr

223 Salvia Moorcroftiana Wall.ex Benth Lamiaceae Th Mi Hr

224 Sauromatum venosum (Dryand. ex Aiton) Kunth

Sauromatum venosum (Ait.) Schott., Araceae H N Hr

225 Scrophularia robusta Pennell. Scrophulariaceae Th L Hr

226 Scutellaria linearis Benth. Lamiaceae H Me Hr

227 Senecio chrysanthemoides DC. Senecio chrysanthemoides Phil. Compositae H Me Hr

228 Sibbaldia cuneata Schouw ex Kunze Sibbaldia cuneata Kunze., Rosaceae H L Hr

229 Silene vulgaris (Moench) Garcke Silene vulgaris (Moench.) Caryophyllaceae Th N Hr

230 Silybum marianum (L.) Gaertn. Silybum marianum Gaertn., Compositae H N Hr

231 Sinopodophyllum hexandrum (Royle) T.S.Ying Podophyllum amodi Wall. Ex Royle. Berberidaceae H Me Hr

232 Solanum americanum Mill. Solanum nigrum L. Solanaceae Th Mi Hr

233 Sonchus arvensis L. Sonchus arvensis subsp. aquatilis (Pourr.) P.Fourn. Compositae Th Mi Hr

234 Strobilanthes affinis (Griff.) Terao ex J.R.I. Wood & J.R. Benn.

Strobilanthes alata Nees non Blume Acanthaceae Th L Hr

235 Swertia alata C.B. Clarke Ophelia alata (D. Don) Griseb. Gentianaceae Th Mi Hr

236 Swertia angustifolia Buch.-Ham. ex D. Don Swertia angustifolia Ham. Ex. D.Don., Gentianaceae Th Mi Hr

237 Swertia ciliata (D. Don ex G. Don) B.L. Burtt Swertia ciliata (G. Don) B. L. Burtt Gentianaceae Th Mi Hr

238 Tagetes minuta L. Tagetes bonariensis Pers. Compositae Th N Hr

239 Taraxacum campylodes G.E.Haglund Taraxacum officinale Weber. Compositae H Mi Hr

240 Thalictrum cultratum Wall.

Thalictrum cultratum subsp. platycarpum (Hook. f. & Thomson) Brühl

Ranunculaceae H Me Hr

241 Themeda anathera (Nees ex Steud.) Hack. Themeda anathera (Ness) Hack. Poaceae H Mi Hr

242 Trichodesma indicum (L.) Lehm. Trichodesma indicum var. amplexicaule (Roth) T. Boraginaceae L Mi Hr





















170

Cooke

243 Trifolium repens L. Trifolium repens var. giganteum Lagr.-Foss. Leguminosae H N Hr

244 Tussilago farfara L. Cineraria farfara Bernh. Compositae H N Hr

245 Urtica dioica L. Urtica dioca Link, Hort. Urticaceae Th Me Hr

246 Valeriana jatamansi Jones. Valeriana jatamansi D.Don Caprifoliaceae H Me Hr

247 Valeriana officinalis L. Valeriana officinalis (non L.) Hk. F. Caprifoliaceae H Me Hr

248 Verbascum thapsis L. Verbascum thracicum Velen. Schrophulariaceae H Me Hr

249 Verbena bonariensis L. Verbena bonariensis Rendle Verbenaceae H L Hr

250 Vincetoxicum arnottianum (Wight) Wight Apocynaceae MP Mi Hr

251 Viola biflora L. Viola biflora var. acuminata Maxim. Violaceae H Mi Hr

252 Viola canescens Wall. Viola canescens f. glabrescens W. Becker Violaceae H Mi Hr

Key:

Ch= Chamaephytes G= Geophytes H= Hemicryptophytes

Ha= Habit Hr= Herb Le= Leptophylls

L.F= Life form Li= Lianas L.S= Leaf spectra

Me= Megaphylls Mi= Microphylls Mp= Megaphanerophytes

N= Nanophyll Np= Nanophanerophytes S= Shrub

T= Tree Th= Therophytes











171

Appendix 4.7 Showing Indicator Species Analysis (ISA) with all environmental variables

Aspect

SNO BN Maxgrp

value

(IV) Mean S.Dev P* I.V.I-1 I.V.I-2 I.V.I-

3

I.V.I-4 I.V.I-

15

1 Pistacia antegrrimma 2 40.4 13.8 7.20 0.0086 26.89 3.34 8.06 0.0 0.0

2 Quercus incana 4 35 18.2 7.14 0.0306 0.00 11.43 36.79 15.1 0.0

3 Ziziphus jujube 2 40.4 14 7.39 0.0126 40.66 43.40 0.00 0.0 0.0

4 Chenopodium album 1 37.9 22.7 6.47 0.0328 29.26 82.08 26.66 6.0 10.0

5 Cnicus argyracanthus 2 35.5 20.3 6.83 0.0488 71.49 62.66 0.00 0.0 12.5

6 Euphorbia helioscopia 2 40.3 18.7 7.23 0.0222 103.98 27.97 0.00 0.0 0.0

7 Galium asperifolium 1 27.5 12.5 7.16 0.0236 0.00 25.93 14.68 0.0 0.0

8 Poa annua 2 43.3 17.7 7.12 0.014 91.00 47.63 0.00 0.0 0.0

9 Solanum nigrum 2 50.6 11.9 6.92 0.003 21.74 4.04 0.00 0.0 0.0

Electrical Conductivity

1 Diospyrus kaki 1 87.3 26.7 14.5 0.0364 27.1 21.3 9.2 0.0 4.1

2 Melia azedarach 1 85.7 28.5 13.8 0.0406 36.5 22.5 0.0 0.0 0.0

3 Themeda anathera 1 85.7 28.2 13.2 0.0358 114.7 36.5 0.0 0.0 0.0

Organic Matter

1 Celtus australis 1 33.1 16.5 4.8 0.0058 5.9 3.9 18.3 19.0 6.3

2 Ilex dipyrena 1 22.2 10.7 3.9 0.0258 0.0 0.0 5.8 17.6 0.0

3 Daphne mucronata 0 28.7 15.5 4.4 0.033 0.0 0.0 16.9 9.8 31.4

4 Lonisera quinquelocularis 1 26.4 16.4 4.8 0.045 0.0 23.1 15.4 5.7 0.0

5 Punica granatum 0 38.7 26.1 5.0 0.039 80.1 54.0 4.7 0.0 0.0

6 Ainsliaea astera 1 29.6 13.2 4.2 0.0056 0.0 6.6 7.2 41.8 0.0

172

7 Corydalis stewartii 1 29.6 13.2 4.2 0.0052 0.0 4.7 17.3 19.0 0.0

8 Rumex nepalensis 0 52.2 37.9 4.7 0.0188 125.7 97.5 40.4 0.0 61.9

Phosphorus

1 Quercus incana 8 37.3 18.9 7.5 0.0392 0.0 11.4 36.8 15.1 0.0

2 Cnicus argyracanthus 8 34.6 20.8 7.5 0.0324 71.5 62.7 0.0 0.0 12.5

3 Colchicum luteum 7 32.8 14 7.7 0.0298 0.0 19.4 12.9 0.0 0.0

4 Dipsacus strictus 5 28.6 12 6.9 0.041 9.7 4.2 7.0 2.5 0.0

5 Drypteris spp 8 43.5 17.2 7.9 0.0184 0.0 25.6 16.6 0.0 40.7

6 Euphorbia hirta 7 40.5 12.6 7.4 0.005 0.0 27.9 0.0 0.0 0.0

7 Foeniculum vulgare 7 29.5 11.8 6.9 0.0348 9.4 17.4 0.0 0.0 0.0

8 Taraxacum officinale 8 39 24.7 6.1 0.047 127.3 83.3 5.1 0.0 24.0

Soil pH

1 Abies pindrow 7 40.5 26.5 7.1 0.007 0.0 98.9 133.8 66.6 179.2

2 Acacia Arabica 4 29 12.6 8.1 0.0352 23.4 0.0 0.0 0.0 89.1

3 Aesculus indica 7 44.3 17.1 9.2 0.0082 6.1 2.4 11.1 3.3 19.0

4 Cornus macrophylla 6 48.6 19.5 10.2 0.021 0.0 9.1 41.4 13.9 5.5

5 Euclaptus globolus 4 51.3 16.5 9.3 0.006 44.4 21.5 0.0 0.0 2.9

6 Populus nigra 6 47.6 17.7 9.5 0.0056 0.0 0.0 17.9 13.1 0.0

7 Prunus padus 7 60 21.8 9.3 0.0008 0.0 0.0 23.9 29.7 0.0

8 Salix angustifolia 6 40.7 19.8 10.3 0.0266 6.9 10.2 15.9 28.4 0.0

9 Berberis orthobotyrus 7 69.5 18.5 9.8 0.0016 17.4 15.8 0.0 8.3 33.8

10 Daphne mucronata 7 75 17.7 9.8 0.0002 0.0 0.0 16.9 9.8 31.4

11 Punica granatum 4 58.9 23.1 8.5 0.0008 80.1 54.0 4.7 0.0 0.0

12 Rosa moshcata 4 53 24.7 8.0 0.001 51.9 56.4 4.6 4.4 0.0

173

13 Rubus fruticosis 4 41.9 21.1 9.5 0.0364 24.7 42.5 3.2 0.0 15.3

14 Skimmia lareuli 7 91.3 14.7 8.9 0.0004 0.0 0.0 0.0 8.1 25.8

15 Solanum pseudocapsicum 4 38.6 15.2 9.1 0.0488 8.7 15.0 0.0 0.0 0.0

16 Viburnum grandiflorum 7 49.9 24.7 8.1 0.0016 0.0 6.6 52.6 31.4 30.6

17 Vitex negundo 7 44.1 17.2 9.6 0.0242 5.8 9.9 0.0 2.1 29.8

18 Zanthoxylum alatum 4 60.9 22.5 8.5 0.0006 33.3 63.3 0.0 3.3 0.0

19 Achillea millefolium 6 47.2 24.3 8.1 0.019 0.0 58.5 92.8 47.4 0.0

20 Actaea spicata 6 42.9 17.3 9.6 0.0362 0.0 0.0 46.9 13.7 0.0

21 Capsella bursapastoris 4 54.7 21.6 9.3 0.017 48.6 57.0 0.0 0.0 4.5

22 Chrysanthimum cenarifolium 7 60.9 19.4 10.0 0.0044 0.0 15.9 35.0 24.9 10.3

23 Cynodon dactylon 4 60.8 22.8 8.7 0.0008 115.8 55.9 8.1 0.0 0.0

24 Drypteris spp 7 69.7 18.6 9.9 0.0012 0.0 25.6 16.6 0.0 40.7

25 Euphorbia helioscopia 4 50.2 20.9 9.6 0.0316 104.0 28.0 0.0 0.0 0.0

26 Euphorbia wallichii 7 58.3 22.4 9.2 0.0006 0.0 0.0 89.3 22.8 36.1

27 Medicago denticulate 4 55.8 23.9 8.2 0.0006 92.9 83.4 0.0 0.0 0.0

28 Plantago lanceolata 7 44.2 17.2 9.4 0.0186 0.0 5.8 36.7 0.0 23.3

29 Plantago major 7 38.7 18.4 10.1 0.0308 0.0 23.2 27.3 7.1 42.1

30 Poa annua 4 54.1 19.5 9.8 0.0158 91.0 47.6 0.0 0.0 0.0

31 Polygonum amplexicaule 7 74.3 17.6 9.4 0.0012 6.3 29.5 0.0 5.2 24.4

32 Potentilla fruticosa 7 44.4 17.3 9.6 0.0102 0.0 0.0 24.7 7.4 89.3

33 Pteris vittata 7 72 18.1 9.9 0.0006 0.0 10.5 16.3 0.0 37.9

34 Rumex hastatus 4 48.6 21.4 9.1 0.0348 73.7 81.3 0.0 0.0 5.7

35 Solanum nigrum 4 41.7 14.6 8.8 0.0322 21.7 4.0 0.0 0.0 0.0

36 Viola biflora 6 42.9 17.3 9.7 0.0384 0.0 0.0 47.2 24.4 0.0

174

37 Cedrus deodara 6 34.5 28 5.22 0.0574 0 139.3 122.6 88.02 6.4407

Soil texture

1 Quercus incana 3 42.7 20 8.3 0.018 0.0 11.4 36.8 15.1 0.0

2 Berberis pachyacantha 3 61.9 14.5 8.1 0.0026 0.0 0.0 19.8 3.7 0.0

3 Clematis amplexicaulis 2 34.3 15.3 8.8 0.033 4.5 16.4 13.2 3.6 0.0

4 Clematis Montana 3 52.3 16.9 9.2 0.0072 0.0 15.7 16.1 8.1 0.0

5 Rubus macilentus 3 55.3 16.3 9.4 0.005 0.0 6.7 23.2 0.0 3.6

6 Viburnum grandiflorum 3 44.1 24 7.5 0.0288 0.0 6.6 52.6 31.4 30.6

7 Achillea millefolium 3 44.9 23.7 7.7 0.0276 0.0 58.5 92.8 47.4 0.0

8 Actaea spicata 3 54.4 16.4 9.3 0.0078 0.0 0.0 46.9 13.7 0.0

9 Argemone Mexicana 2 39.6 14 7.8 0.0116 0.0 18.6 12.3 0.0 0.0

10 Capsicum annuum 2 51.4 13.9 7.8 0.0052 21.2 5.2 0.0 0.0 0.0

11 Corydalis stewartii 2 34.8 15.4 8.9 0.0284 0.0 4.7 17.3 19.0 0.0

12 Eulophia hormusjii 3 66.1 14 7.8 0.0012 4.7 3.2 16.5 0.0 0.0

13 Impetiens flemingii 3 36.6 14.3 8.0 0.0266 0.0 0.0 16.0 14.9 0.0

14 Nepeta erecta 3 45.4 21.9 8.1 0.0196 13.2 68.2 38.4 0.0 0.0

15 Otostegia limbata 3 50 10.8 7.3 0.0052 0.0 0.0 14.8 0.0 0.0

16 Plantago lanceolata 3 54.4 16.3 9.4 0.0082 0.0 5.8 36.7 0.0 23.3

17 Senecio chrysenthemoides 2 36.8 16.8 9.0 0.0428 8.0 50.3 5.4 0.0 11.3

18 Silene vulgaris 3 31.2 13.8 8.0 0.0462 0.0 6.3 11.8 13.2 0.0

19 Sonchus arvensis 3 42.9 13.3 7.4 0.0116 4.4 0.0 12.6 7.0 0.0

20 Valeriana jatamansi 3 63 14.6 8.5 0.003 0.0 12.7 22.7 0.0 0.0

21 Verbina bonariensis 3 38.9 13.8 7.7 0.0242 0.0 12.7 15.6 0.0 8.3

22 Viola biflora 3 54.4 16.3 9.4 0.008 0.0 0.0 47.2 24.4 0.0

175

Appendix 4.8 Showing community wise environmental variables influences

1st Community

SNO BN (IV) P* I.V.I-1 T.I.V.I

Aspect

1 Ziziphus jujuba Lam. 40.4 0.0126 40.66 40.53

2 Cnicus argyracanthus (DC) Hk.f. 35.5 0.0488 71.49 53.50

3 Euphorbia helioscopia L., 40.3 0.0222 103.98 72.14

4 Poa annua L. 43.3 0.014 91.00 67.15


1 Melia azedarach L. 85.7 0.0406 36.53 61.11

2 Themeda anathera (Ness) Hack. 85.7 0.0358 114.72 100.21

Organic Matter

1 Punica granatum L. 38.7 0.039 80.11 59.40

2 Rumex nepalensis Spreng. 52.2 0.0188 125.71 88.96

Phosphorus


2 Taraxacum officinale Weber. 39 0.047 127.33 83.16

Soil pH

1 Euclaptus globolus L. 51.3 0.006 44.41 47.85


3 Rosa moshcata non J. Herrm. 53 0.001 51.86 52.43

4 Zanthoxylum alatum Roxb. 60.9 0.0006 33.29 47.09

5 Capsella bursapastoris Moench. 54.7 0.017 48.63 51.66

6 Cynodon dactylon L. 60.8 0.0008 115.76 88.28

7 Euphorbia helioscopia L., 50.2 0.0316 103.98 77.09

8 Medicago denticulata Willd. 55.8 0.0006 92.86 74.33

9 Poa annua L. 54.1 0.0158 91.00 72.55

10 Rumex hastatusD.Don., 48.6 0.0348 73.75 61.17

2nd Community

Aspect

1 Zizyphus vulgaris Lam. 40.4 0.0126 43.4 41.9

2 Chenopodium album L. 37.9 0.0328 82.08 59.99

176


4 Poa annua L. 43.3 0.014 47.63 45.465


1 Themeda anathera (Ness) Hack. 85.7 0.0358 36.5 61.1

Organic Matter



Phosphorus


2 Taraxacum officinale Weber. 39 0.047 83.28 61.14

Soil pH

1 Abies pindrow Royle. 40.5 0.007 98.94 69.72


3 Rosa moshcata non J. Herrm. 53 0.001 56.38 54.69

4 Rubus fruticosis Hk.f. 41.9 0.0364 42.54 42.22

5 Zanthoxylum armatum Roxb. 60.9 0.0006 63.26 62.08

6 Achillea millefolium L. 47.2 0.019 58.45 52.825

7 Capsella bursapastoris Moench. 54.7 0.017 56.95 55.825

8 Cynodon dactylon L. 60.8 0.0008 55.92 58.36

9 Medicago denticulata Willd. 55.8 0.0006 83.36 69.58

10 Poa annua L. 54.1 0.0158 47.63 50.865

11 Rumex hastatusD. Don. 48.6 0.0348 81.3 64.95

Soil texture


2 Nepeta erecta Bh Bth. 45.4 0.0196 68.16 56.78

3 Senecio chrysenthemoides DC. 36.8 0.0428 50.25 43.525

3rd Community

Aspect

1 Quercus incana Roxb. 35 0.0306 36.79 35.89

Organic Matter


Phosphorus

1 Quercus incana Roxb. 37.3 0.0392 36.79 37.04

177

Soil pH


2 Cornus macrophylla Wall. Ex Roxb. 48.6 0.021 41.38 44.99

3 Viburnum grandiflorum Wallich. 49.9 0.0016 52.64 51.27


5 Actaea spicata L. 42.9 0.0362 46.94 44.92

6 Chrysanthimum cenarifolium Trey 60.9 0.0044 35.00 47.95

7 Euphorbia wallichii Hk.f., 58.3 0.0006 89.31 73.80

8 Plantago lanceolata Linn. 44.2 0.0186 36.74 40.47

9 Viola biflora L. 42.9 0.0384 47.17 45.04

Soil texture

1 Quercus incana Roxb. 42.7 0.018 36.79 39.74



4 Actaea spicata L. 54.4 0.0078 46.94 50.67

5 Nepeta erecta Bh Bth. 45.4 0.0196 38.40 41.90

6 Plantago lanceolata Linn. 54.4 0.0082 36.74 45.57

7 Viola biflora L. 54.4 0.008 47.17 50.79

4th Community

Soil pH




4 Cedrus deodara Rox ex Lamb. 34.5 0.0574 88.20 61.35

Soil texture



5th Community

Organic Matter


Phosphorus

1 Drypteris spp 43.5 0.0184 40.70 42.10

Soil pH

178


2 Acacia arabica (Lam.) Willd., 29 0.0352 89.07 59.03

3 Berberis orthobotyrus Bien. Ex Aitch., 69.5 0.0016 33.83 51.67

4 Daphne mucronata Royle. 75 0.0002 31.44 53.22


6 Drypteris spp 69.7 0.0012 40.70 55.20

7 Euphorbia wallichii Hk.f., 58.3 0.0006 36.05 47.18

8 Plantago major L. 38.7 0.0308 42.10 40.40

9 Potentilla fruticosa L. 44.4 0.0102 89.28 66.84

10 Pteris vittata L. 72 0.0006 37.93 54.97

Soil texture


179

Appendix 4.9 Results of Indicator Species Analysis (I.S.A) through PC-ORD, Showing Indicator (Variables) plant species

(with bold font) for each of the five plant communities (1-5) at a threshold level of Indicator 30% and Monte Carlo tests of

significance for te observed maximum indicator value of species (P value ≤ 0.05).

S NO

BOTANICAL NAME Melia, Punica and Euphorbia Community

Ziziphus, Zanthoxylum and Rumex Community

Quercus, Cornus and Viola Community

Cedrus, Viburnum and Achillea Community

Abies, Daphne and Potentilla Community

Group was defined by value of Electrical Conductivity

Group was defined by value of Aspect

Group was defined by value of Phosphorus

Group was defined by value of Soil pH

Group was defined by value of Soil pH

Max grp

(IV) P* Max grp

(IV) P* Max grp

(IV) P* Max grp

(IV) P* Max grp

(IV) P*

1 5Abies pindrow (Royle ex D.Don) Royle 0 64.6 0.1394 3 21.4 0.8276 3 38 0.0614 7 40.5 0.007 7 40.5 0.007

2 Acacia modesta Wall. 1 46.2 0.1212 2 12 0.3653 1 8.7 0.7578 4 29 0.0352 4 29 0.0352

3 Acacia nilotica (L.) Delile 1 38.7 0.2861 2 19.6 0.2094 4 17 0.3651 4 17.6 0.2747 4 17.6 0.2747

4 Acer caesium Wall. Ex Brandis 0 29.2 1 3 19.5 0.3041 2 23.6 0.2364 6 33.5 0.0696 6 33.5 0.0696

5 Aesculus indica (Wall. ex Cambess.) Hook 0 18.7 1 4 9.8 0.7197 3 25.4 0.1576 7 44.3 0.0082 7 44.3 0.0082

6 Ailanthus altissima (Mill.) Swingle 1 75 0.1292 2 23 0.2805 1 14.3 0.8458 4 32.7 0.0912 4 32.7 0.0912

7 Broussonetia papyrifera (L.) L'Hér. exVent. 1 38.7 0.2907 1 8.7 0.7584 4 8.3 0.9144 5 17.5 0.2833 5 17.5 0.2833

8 Cedrela serrata Royle 0 14.6 1 4 7.5 0.8522 2 8 1 6 33.3 0.0728 6 33.3 0.0728

9 Cedrela toona Roxb. Ex Rottl. &Willd., 0 8.3 1 4 16.3 0.2222 4 8.5 0.8486 6 10.6 0.5415 6 10.6 0.5415

10 4Cedrus deodara (Roxb. ex D.Don) G.Don 0 72.9 0.0828 1 30.4 0.2208 3 33.4 0.1732 6 34.5 0.0574 6 34.5 0.0574

11 Celtus australis subsp. caucasica (Willd.) C.C.Towns. 0 22.9 1 3 9.3 0.8506 3 23.8 0.173 6 34.7 0.0954 6 34.7 0.0954

12 3Cornus macrophylla Wall. 0 29.2 1 4 17.4 0.3559 6 48.6 0.021 6 48.6 0.021 6 48.6 0.021

13 Cotoneaster minuta Saporta 0 27.1 1 4 11.9 0.8758 3 36.2 0.0592 6 20.1 0.3229 6 20.1 0.3229

14 Dalbergia sissoo DC. 0 6.2 1 1 6.6 0.8262 4 4.9 1 6 6.2 1 6 6.2 1

180

15 1Diospyrus kaki L.f. 1 87.3 0.0364 3 7.5 0.9474 2 21.5 0.2539 4 16.3 0.3527 4 16.3 0.3527

16 Diospyrus lotus L. 0 29.2 1 2 13.5 0.6741 2 31 0.1158 4 23.9 0.3055 4 23.9 0.3055

17 Eucalyptus abdita Brooker & Hopper 0 16.7 1 2 20.1 0.2034 1 28.6 0.1006 4 51.3 0.006 4 51.3 0.006

18 Ficus carica L. 1 64 0.3373 2 28.5 0.1582 3 22.8 0.6981 5 38.1 0.1156 5 38.1 0.1156

19 Ficus palmata Forssk. 1 51 0.2373 2 28.5 0.1482 5 4.9 1 3 21.9 0.3052 3 21.9 0.3052

20 Grewia optiva J.R.Drumm. ex Burret 0 14.6 1 2 6.7 0.957 1 10.7 0.6019 6 10.7 0.776 6 10.7 0.776

21 Ilex dipyrena Wall. 0 12.5 1 3 10.5 0.4767 3 13.6 0.2869 6 28.6 0.0556 6 28.6 0.0556

22 Jacaranda mimosifolia D.Don., 0 16.7 1 3 8.3 0.808 2 17.6 0.3257 5 7.9 0.9198 5 7.9 0.9198

23 Juglens regia L. 0 25 1 1 13.7 0.6839 1 20.5 0.3115 6 10.1 0.8922 6 10.1 0.8922

24 1Melia azedarach L. 1 85.7 0.0406 1 9.2 0.796 1 8.6 0.8574 5 32.8 0.1062 5 32.8 0.1062

25 Morus alba L. 1 81.4 0.0686 3 14.9 0.4901 4 14.5 0.6681 4 25.1 0.2753 4 25.1 0.2753

26 Morus nigra L. 0 33.3 0.5541 2 23.8 0.225 2 28.5 0.1678 5 21.9 0.4245 5 21.9 0.4245

27 Olea ferruginea Wall. ex Aitch. 0 18.7 1 2 21 0.1094 2 21 0.2803 5 21.7 0.15 5 21.7 0.15

28 Pinus roxburghii Sarg. 1 37.5 0.3279 2 18.6 0.2729 4 14.7 0.4597 5 17.9 0.211 5 17.9 0.211

29 Pinus wallichiana A.B.Jacks. 0 52.1 1 1 24.4 0.7209 2 28 0.6961 6 30.2 0.1942 6 30.2 0.1942

30 Pistacia khinjuk Stocks 1 38.7 0.2917 2 40.4 0.0086 1 13.8 0.4455 4 17.7 0.2623 4 17.7 0.2623

31 Platanus oriantalis L. 0 6.2 1 1 19.7 0.102 4 4.9 1 4 10.5 0.5263 4 10.5 0.5263

32 Populus ciliata Wall. Ex Royle 1 38.7 0.2971 3 10.6 0.6185 1 13.8 0.4461 7 13.9 0.6587 7 13.9 0.6587

33 Populus nigra L. 0 20.8 1 3 6.9 1 3 24 0.207 6 47.6 0.0056 6 47.6 0.0056

34 Prunus armenica L. 1 36.4 0.3627 3 10.2 0.7532 1 18.4 0.3643 5 21.8 0.2156 5 21.8 0.2156

35 Prunus domestica subsp. insititia (L.) Bonnier & Layens 1 33.3 0.4585 2 13.5 0.6791 2 16.9 0.5319 5 18.3 0.4079 5 18.3 0.4079

36 Prunus padus L. 0 35.4 0.5445 4 21.3 0.3533 4 11.9 1 7 60 0.0008 7 60 0.0008

37 Prunus persica var. nucipersica (L.) C.K.Schneid. 0 10.4 1 2 8.3 0.6843 4 6.4 1 7 17.1 0.3649 7 17.1 0.3649

38 Pyrus pashia Buch.-Ham. ex D.Don 0 43.7 0.4931 2 20.9 0.3993 2 21.2 0.4907 5 21.9 0.5643 5 21.9 0.5643

39 Quercus incana Bartram 0 10.4 1 1 15.3 0.1896 2 10.4 0.6963 6 10.2 0.5075 6 10.2 0.5075

40 3Quercus robur L. 0 31.2 1 4 35 0.0306 3 42.7 0.018 6 27.2 0.2402 6 27.2 0.2402

41 Robinia pseudoacacia f. monophylla-pendula (Dieck) Voss 1 65.8 0.2861 1 26.3 0.3315 2 16.7 0.933 5 18.4 0.9662 5 18.4 0.9662

181

42 Salix alba L. 0 4.2 1 1 9.2 0.4423 1 3.4 1 4 12.8 0.3253 4 12.8 0.3253

43 Salix eleagnos Scop. 0 29.2 1 3 13.8 0.6169 2 27.7 0.1248 6 40.7 0.0266 6 40.7 0.0266

44 Salix denticulata Andersson 1 38.7 0.2901 2 21.7 0.1264 3 8.3 0.8554 7 14 0.6089 7 14 0.6089

45 Staphylea emodi Wall. Ex Brandis., 0 4.2 1 2 12.3 0.3013 2 16.6 0.2394 5 2.6 1 5 2.6 1

46 Taxus walliciana (Zucc.) 0 2.1 1 1 12.5 0.2565 1 5.9 0.5203 7 33.3 0.0608 7 33.3 0.0608

47 Ulmus wallichiana Planch. 0 14.6 1 4 8.7 0.7199 3 11.7 0.4947 7 17.9 0.2627 7 17.9 0.2627

48 2Ziziphus jujuba Mill. 1 38.7 0.2951 2 40.4 0.0126 2 7.4 1 4 33.3 0.0962 4 33.3 0.0962

49 Abelia triflora R.Br. ex Wall. 0 22.9 1 4 25.5 0.1018 4 9.9 0.7802 6 27.3 0.2022 6 27.3 0.2022

50 Arundo donax L. 0 16.7 1 3 13.8 0.3895 2 22.8 0.2224 4 17.7 0.2621 4 17.7 0.2621

51 Astragalus aaronii (Eig) Zohary 0 29.2 1 4 10.5 0.9616 3 23 0.2623 6 23.1 0.3347 6 23.1 0.3347

52 Berberis lycium Royle 1 63.2 0.3557 2 25.1 0.4101 2 25.9 0.3625 5 28.7 0.3465 5 28.7 0.3465

53 Berberis orthobotyrus Bien. ex Aitch. 0 25 1 1 16.2 0.3685 2 18.1 0.4527 7 69.5 0.0016 7 69.5 0.0016

54 Berberis pachyacantha Bien. ex Koehne 0 12.5 1 2 8.9 0.6181 3 61.9 0.0026 6 28.6 0.058 6 28.6 0.058

55 Berberis parkeriana C.K.Schneid 0 10.4 1 4 5.1 1 2 10.4 0.6967 6 23.8 0.0546 6 23.8 0.0546

56 Buddleja asiatica var. brevicuspe Koord. 0 10.4 1 4 9 0.5985 3 10.3 0.7341 7 21 0.239 7 21 0.239

57 Buddleja crispa Benth. 0 25 1 3 11.2 0.8686 3 22.2 0.243 6 20.8 0.2753 6 20.8 0.2753

58 Buxus papillosa C.K.Schneid. 1 38.7 0.3043 3 18.3 0.2611 2 18.3 0.2869 6 14.6 0.4987 6 14.6 0.4987

59 Clematis connata DC. 0 16.7 1 3 9.2 0.6717 2 34.3 0.033 5 10.2 0.8562 5 10.2 0.8562

60 Clematis montana Buch.-Ham. ex DC. 0 20.8 1 3 15.9 0.4223 3 52.3 0.0072 6 30.2 0.1184 6 30.2 0.1184

61 Cuscuta reflexa var. anguina (Edgew.) C.B. Clarke 0 14.6 1 2 6.7 0.9576 2 24.2 0.18 4 19.2 0.1854 4 19.2 0.1854

62 5Daphne mucronata Royle 0 20.8 1 4 17.9 0.2631 1 24 0.1772 7 75 0.0002 7 75 0.0002

63 Debregeasia saeneb (Forssk.) Hepper & J.R.I.Wood 0 8.3 1 4 7.9 0.7516 1 6.9 1 5 5.1 1 5 5.1 1

64 Desmodium gangeticum (L.) DC. 0 16.7 1 4 6.9 0.8824 3 23.1 0.1912 6 11.2 0.8064 6 11.2 0.8064

65 Desmodium hookerianum D.Dietr. 1 35.3 0.4151 3 10.3 0.7688 2 30.6 0.0726 6 20.8 0.2753 6 20.8 0.2753

66 Dodonaea viscosa (L.) Jacq. 1 36.4 0.3637 2 17.2 0.3503 4 10 0.7027 5 21.8 0.2128 5 21.8 0.2128

67 Elaeagnus parvifolia Wall. ex Royle 0 6.2 1 3 5.2 1 3 17.8 0.1696 6 14.3 0.3287 6 14.3 0.3287

68 Euonymus hamiltonianus Wall 0 8.3 1 3 9.2 0.5683 2 12.3 0.4141 6 19 0.2555 6 19 0.2555

182

69 Hedera nepalensis K. Koch 0 22.9 1 3 19.4 0.1806 2 35.9 0.0594 4 28 0.1786 4 28 0.1786

70 Indigofera angustifolia L. 1 67.6 0.2498 2 29.1 0.1618 2 17.4 0.8532 5 28.8 0.23 5 28.8 0.23

71 Indigofera heterantha Brandis 0 35.4 0.5485 4 17.6 0.5315 3 17.6 0.5917 7 32 0.0844 7 32 0.0844

72 Isodon coetsa (Buch.-Ham. ex D.Don) Kudô 0 10.4 1 1 16.2 0.1512 1 20 0.1654 4 7.6 1 4 7.6 1

73 Leptopus chinensis (Bunge) Pojark. 0 45.8 0.5013 3 19.6 0.5621 3 30.1 0.189 6 37.6 0.098 6 37.6 0.098

74 Lonicera bicolor Klotzsch 1 35.3 0.3987 4 7.9 0.9562 3 7 1 5 13 0.6315 5 13 0.6315

75 Lonicera hispida Pall. ex Schult. 0 33.3 0.5533 4 14.7 0.6105 2 24.8 0.229 4 22.1 0.3799 4 22.1 0.3799

76 Lonicera quinquelocularis Hard. 0 22.9 1 3 17.5 0.2609 4 7.1 0.9468 6 17.1 0.3257 6 17.1 0.3257

77 Paeonia emodi Royle 0 4.2 1 4 4 1 4 8.3 0.6773 6 9.5 0.6573 6 9.5 0.6573

78 Parrotiopsis jacquemontiana (Decne.) Rehder 0 12.5 1 3 4.7 1 2 9.4 0.8308 6 28.6 0.0584 6 28.6 0.0584

79 1Punica florida Salisb. 0 38.7 0.039 1 20.8 0.4205 1 29.7 0.1894 4 58.9 0.0008 4 58.9 0.0008

80 Rhamnus purpurea Edgew 0 12.5 1 4 8.1 0.7493 3 12.7 0.3991 6 28.6 0.0576 6 28.6 0.0576

81 Rhus punjabensis var. sinica (Diels) Rehder & E.H. Wilson

0 10.4 1 3 13.3 0.3341 3 26.1 0.1036 6 23.8 0.0516 6 23.8 0.0516

82 Rosa webiana Wall. ex Royle 1 68.6 0.2296 2 28.9 0.183 4 17.7 0.8222 4 53 0.001 4 53 0.001

83 Rosa moschata Herrm. 0 25 1 3 12.9 0.7676 3 19 0.3845 6 25 0.2412 6 25 0.2412

84 Rubus ellipticus Sm. 0 29.2 1 4 13.9 0.6003 2 13.4 0.8316 6 18.2 0.4655 6 18.2 0.4655

85 Rubus macilentus Jacquem. ex Cambess. 1 30.8 1 2 22.9 0.2585 1 16.7 0.6253 4 41.9 0.0364 4 41.9 0.0364

86 Rubus ulmifolius f. variegatus (G.Nicholson) Rehder 0 18.7 1 4 14.4 0.4645 3 55.3 0.005 7 15.5 0.3913 7 15.5 0.3913

87 Rubus vulgaris Weihe & Nees 0 8.3 1 3 9.2 0.5689 2 11.7 0.5573 6 10.6 0.5459 6 10.6 0.5459

88 Sageretia brandrethiana Aitch 0 12.5 1 3 10.5 0.4841 2 9.4 0.8312 5 7.7 0.8068 5 7.7 0.8068

89 Sarcococca pruniformis Lindl. 0 10.4 1 3 6.9 0.829 2 10 0.8286 6 15.1 0.4537 6 15.1 0.4537

90 Skimmia laureola Franch. 0 10.4 1 1 14.6 0.2252 2 10 0.8306 7 91.3 0.0004 7 91.3 0.0004

91 Solanum pseudocapsicum L. 0 12.5 1 2 7.3 0.8886 2 9.4 0.8406 4 38.6 0.0488 4 38.6 0.0488

92 Sorbaria tomentosa (Lindl.) Rehder 0 31.2 1 4 20.5 0.2977 3 37.6 0.056 6 32.5 0.0796 6 32.5 0.0796

93 Spiraea gracilis Maxim. 0 16.7 1 1 11.1 0.5403 4 11.2 0.5235 6 16.7 0.2883 6 16.7 0.2883

94 Syringa emodi Wall. ex Royle 0 12.5 1 2 8.5 0.7175 4 9.8 0.7273 6 28.6 0.059 6 28.6 0.059

95 Viburnum cotinifolium D. Don. 0 22.9 1 1 7.1 1 3 20.1 0.3045 6 11.9 0.6743 6 11.9 0.6743

183

96 4Viburnum grandiflorum Wall. ex DC. 0 50 0.4963 4 20.5 0.4685 3 44.1 0.0288 7 49.9 0.0016 7 49.9 0.0016

97 Vitex negundo L. 0 18.7 1 1 16.2 0.4049 2 21 0.2781 7 44.1 0.0242 7 44.1 0.0242

98 2Zanthoxylum armatum DC. 1 28.6 1 4 60.9 0.0006 1 22.2 0.3861 4 60.9 0.0006 4 60.9 0.0006

99 Ziziphus mairei Dode 1 40 0.2561 2 24.7 0.055 1 9.1 0.7568 5 19.3 0.1232 5 19.3 0.1232

100 4Achillea millefolium L. 0 47.9 0.4871 3 22.3 0.3395 3 44.9 0.0276 6 47.2 0.019 6 47.2 0.019

101 Achyranthes aspera L. 0 20.8 1 1 7.7 0.911 2 15.6 0.5213 7 12.4 0.7686 7 12.4 0.7686

102 Aconitum violaceum Jacquem. ex Stapf 1 31.6 0.5137 3 12.9 0.7417 2 25.5 0.1904 5 14.4 0.6675 5 14.4 0.6675

103 Actaea spicata var. acuminata (Wall. ex Royle) H.Hara 0 18.7 1 3 8.2 0.826 3 54.4 0.0078 6 42.9 0.0362 6 42.9 0.0362

104 Adiantum venustum D. Don 0 14.6 1 1 12.5 0.3813 2 8 1 5 8.7 0.841 5 8.7 0.841

105 Aegopodium burttii Nasir 0 2.1 1 3 4.5 1 4 4.2 1 6 4.8 1 6 4.8 1

106 Ainsliaea aptera DC 0 16.7 1 4 7.9 0.8616 3 8.7 0.7536 6 29 0.1094 6 29 0.1094

107 Ajuga integrifolia Buch.-Ham. 0 39.6 0.5121 2 20.3 0.4591 1 17.8 0.5785 4 31.6 0.109 4 31.6 0.109

108 Anemone tetrasepala Royle 1 34.3 0.4315 3 12 0.8062 2 34.5 0.071 5 20.8 0.2643 5 20.8 0.2643

109 Anemone falconeri Thomson 0 27.1 1 3 14.9 0.4967 3 19.1 0.3817 6 23 0.3027 6 23 0.3027

110 Anemone vitifolia Buch.-Ham. ex DC. 0 18.7 1 4 12.4 0.5475 3 29.1 0.1042 6 18.7 0.2132 6 18.7 0.2132

111 Aquilegia moorcroftiana var. afghanica (Brühl) Riedl 0 10.4 1 3 6.9 0.8228 3 13.9 0.3295 6 15.1 0.4635 6 15.1 0.4635

112 Aquilegia pubiflora Wall. ex Royle 0 20.8 1 4 14 0.5655 3 7.3 1 6 30.2 0.1156 6 30.2 0.1156

113 Argemone mexicana L. 0 10.4 1 3 22.7 0.1182 2 39.6 0.0116 4 7.7 0.8108 4 7.7 0.8108

114 Arisaema flavum (Forssk.) Schott 0 18.7 1 1 9.9 0.6905 3 30.3 0.0714 7 15.5 0.3897 7 15.5 0.3897

115 Arisaema jacquemontii Blume 0 16.7 1 1 8.4 0.7846 3 8.3 0.8468 6 16.7 0.2875 6 16.7 0.2875

116 Arisaema utile Hook.f. ex Schott 0 4.2 1 3 9.1 0.6235 4 8.3 0.6669 6 9.5 0.6489 6 9.5 0.6489

117 Artemisia absinthium L. 1 80 0.0772 2 14.4 0.5495 4 13.4 0.8054 5 32.5 0.0922 5 32.5 0.0922

118 Aster molliusculus (Lindl. ex DC.) C.B.Clarke 0 16.7 1 3 9.2 0.6837 3 8.5 0.803 6 29 0.1038 6 29 0.1038

119 Atropa acuminata Royle ex Lindl. 0 4.2 1 4 13.3 0.2693 4 8.3 0.6591 6 9.5 0.6601 6 9.5 0.6601

120 Bergenia ciliata (Haw.) Sternb. 0 18.7 1 4 16.5 0.3785 4 7.4 1 6 20.3 0.1814 6 20.3 0.1814

121 Bupleurum falcatum L. 1 38.7 0.3021 1 8.4 0.7776 3 8.3 0.8526 5 15.9 0.4691 5 15.9 0.4691

122 Bupleurum jacundum Kurz 0 14.6 1 3 9.4 0.6151 3 28.3 0.0924 6 33.3 0.0764 6 33.3 0.0764

184

123 Bupleurum lanceolatum Wall. Ex DC., 0 18.7 1 1 9.9 0.6775 2 16.5 0.4129 6 14.8 0.5059 6 14.8 0.5059

124 Bupleurum candollei Wall. ex DC. 0 12.5 1 3 8.9 0.6757 3 9.9 0.6347 6 19.7 0.2068 6 19.7 0.2068

125 Cannabis sativa L. 1 80 0.0742 2 26.8 0.1288 4 17.4 0.5403 5 28.8 0.1992 5 28.8 0.1992

126 Capsella × gracilis Gren. 1 30 1 1 23 0.2625 1 32.5 0.1162 4 54.7 0.017 4 54.7 0.017

127 Capsicum annuum L. 0 10.4 1 2 28.7 0.0528 2 51.4 0.0052 4 23.1 0.1236 4 23.1 0.1236

128 Chenopodium album L. 0 50 0.4993 1 37.9 0.0328 2 35.8 0.086 5 23.3 0.3339 5 23.3 0.3339

129 Chrysanthemum chalchingolicum Grubov 0 29.2 1 1 20.6 0.2851 3 23.4 0.2216 7 60.9 0.0044 7 60.9 0.0044

130 Cichorium intybus L. 0 8.3 1 1 5.5 1 2 12.3 0.4313 6 10.6 0.5469 6 10.6 0.5469

131 Cirsium argyracanthum DC. 0 8.3 1 3 9.2 0.5719 3 11.7 0.4487 6 19 0.2639 6 19 0.2639

132 Cirsium luzoniense Merr. 1 73.8 0.135 2 35.5 0.0488 4 17.3 0.6019 4 31.6 0.1054 4 31.6 0.1054

133 Clinopodium vulgare L. 0 8.3 1 3 9.2 0.5653 3 15.3 0.3045 4 8.8 0.7936 4 8.8 0.7936

134 Colchicum luteum Baker 0 14.6 1 3 7.5 0.8028 3 8.8 0.85 5 11.7 0.7119 5 11.7 0.7119

135 Convolvulus prostratus Forssk. 0 4.2 1 4 13.3 0.2767 1 3.4 1 5 2.6 1 5 2.6 1

136 Coriandrum sativum L. 0 6.2 1 2 13.7 0.1622 4 4.9 1 6 6.2 1 6 6.2 1

137 Corydalis diphylla Wall 0 4.2 1 4 4 1 4 8.3 0.6663 6 9.5 0.6507 6 9.5 0.6507

138 Corydalis cornuta Royle 0 16.7 1 3 18.3 0.2691 2 34.8 0.0284 6 29 0.1076 6 29 0.1076

139 Cynodon dactylon (L.) Pers. 1 73.8 0.1414 2 34 0.0756 2 22.8 0.3449 4 60.8 0.0008 4 60.8 0.0008

140 Cyperus rotundus L. 0 20.8 1 2 17.6 0.2937 4 9.2 0.7634 7 14.4 0.4945 7 14.4 0.4945

141 Dasiphora fruticosa (L.) Rydb. 1 36.4 0.3615 1 29.9 0.0718 4 12.9 0.6283 7 74.3 0.0012 7 74.3 0.0012

142 Datura stramonium L. 0 10.4 1 4 18.2 0.1282 4 6.4 1 6 15.1 0.4707 6 15.1 0.4707

143 Dicliptera chinensis (L.) Juss. 0 11.4 1 4 17.2 0.1182 5 9.2 0.8634 6 14.4 0.4945 6 14.4 0.4945

144 Dioscorea bulbifera L. 0 4.2 1 4 4 1 1 3.4 1 5 2.6 1 5 2.6 1

145 Dipsacus sativus (L.) Honck. 0 6.2 1 4 9.9 0.5557 2 14.1 0.3019 6 14.3 0.3277 6 14.3 0.3277

146 Dipsacus inermis Wall. 1 44.4 0.1632 4 7.9 0.7455 4 16.7 0.1658 5 11.4 0.4325 5 11.4 0.4325

147 Dryopteris abbreviata Newman 0 25 1 1 16.6 0.3279 3 20.9 0.3021 7 69.7 0.0012 7 69.7 0.0012

148 Duchesnea indica (Jacks.) Focke 1 66.7 0.2735 2 27.5 0.2392 2 33.6 0.1124 4 39.1 0.0944 4 39.1 0.0944

149 Echinops niveus Wall. ex Wall. 0 10.4 1 4 5.1 1 2 10.9 0.5645 6 23.8 0.0562 6 23.8 0.0562

185

150 Epilobium royleanum Hausskn 0 2.1 1 3 4.5 1 4 4.2 1 6 4.8 1 6 4.8 1

151 Epipactis helleborine (L.) Crantz 0 4.2 1 4 13.3 0.2693 4 8.3 0.6591 6 9.5 0.6601 6 9.5 0.6601

152 Erigeron canadensis L. 0 6.2 1 1 6.6 0.8248 4 12.5 0.5849 6 14.3 0.3333 6 14.3 0.3333

153 Erigeron karvinskianus DC. 1 42.9 0.198 3 6.9 0.8234 3 66.1 0.0012 6 8.4 0.7469 6 8.4 0.7469

154 Eulophia dabia (D.Don) Hochr. 0 14.6 1 3 12.3 0.4241 2 24.9 0.1574 6 7.9 0.9102 6 7.9 0.9102

155 1Euphorbia helioscopia L. 2 40.3 0.0222 2 40.3 0.0222 4 15.2 0.7281 4 50.2 0.0316 4 50.2 0.0316

156 Euphorbia hirta L. 0 10.4 1 1 16.2 0.1448 2 10.4 0.7003 4 7.6 1 4 7.6 1

157 Euphorbia wallichii Hook.f. 0 37.5 0.5433 4 21.9 0.3387 3 31.3 0.1468 7 58.3 0.0006 7 58.3 0.0006

158 Foeniculum vulgare Mill 0 8.3 1 1 5.5 1 4 8.5 0.8264 4 25.8 0.0612 4 25.8 0.0612

159 Fragaria nubicola (Lindl. ex Hook.f.) Lacaita 0 27.1 1 1 15 0.4825 3 21.1 0.3017 6 20.1 0.3107 6 20.1 0.3107

160 Galium aparine L. 1 31.6 0.5245 2 27.5 0.0882 2 15.3 0.6819 5 32.7 0.0804 5 32.7 0.0804

161 Galium asperifolium Wall. 0 12.5 1 1 27.5 0.0236 3 9.4 0.8522 6 8.9 0.7117 6 8.9 0.7117

162 Galium elegans Wall. ex Roxb. 0 8.3 1 4 5.9 0.8762 1 6.9 1 6 10.6 0.5383 6 10.6 0.5383

163 Galium hirtiflorum Req. ex DC. 0 2.1 1 3 4.5 1 3 25 0.0856 6 4.8 1 6 4.8 1

164 Gamochaeta malvinensis (H.Koyama) T.R.Dudley 0 8.3 1 4 16.3 0.2104 2 11.7 0.5537 6 19 0.2643 6 19 0.2643

165 Geranium wallichianum D.Don ex Sweet 0 16.7 1 3 22.8 0.077 2 17.9 0.3011 5 15.9 0.4655 5 15.9 0.4655

166 Gerbera gossypina (Royle) Beauverd 0 6.2 1 3 5.2 1 4 4.9 1 4 10.4 0.5833 4 10.4 0.5833

167 Girardinia diversifolia (Link) Friis 0 4.2 1 4 4 1 4 8.3 0.6773 6 9.5 0.6573 6 9.5 0.6573

168 Heliotropium zeylanicum subsp. paniculatum (R. Br.) Kazmi

0 4.2 1 4 4 1 4 8.3 0.6613 6 9.5 0.6503 6 9.5 0.6503

169 Heracleum candicans Wall. ex. DC. 0 6.2 1 3 13.6 0.2442 2 14.1 0.3179 6 14.3 0.3277 6 14.3 0.3277

170 Hyoscymus niger L. 0 4.2 1 4 4 1 2 15.5 0.3165 5 10 0.4787 5 10 0.4787

171 Hypericum oblongifolium Choisy. 0 2.1 1 4 6.7 0.5607 4 4.2 1 6 4.8 1 6 4.8 1

172 Hypericum perforatum L. 0 2.1 1 1 12.5 0.2669 4 4.2 1 6 4.8 1 6 4.8 1

173 Impatiens balsamina L. 0 10.4 1 2 9.9 0.5267 3 13.9 0.3377 6 23.8 0.0538 6 23.8 0.0538

174 Impatiens bicolor Royle. 0 8.3 1 4 7.9 0.7518 2 11.7 0.5535 6 19 0.2563 6 19 0.2563

175 Impatiens abbatis Hook. f. 0 4.2 1 1 9.2 0.4437 4 8.3 0.6679 6 9.5 0.6635 6 9.5 0.6635

176 Impatiens flemingii Hook. f. 0 12.5 1 4 11.9 0.4019 3 36.6 0.0266 6 28.6 0.0522 6 28.6 0.0522

186

177 Isodon rugosus (Wall. ex Benth.) Codd 0 8.3 1 1 17.3 0.1608 3 16.7 0.162 4 9 0.5709 4 9 0.5709

178 Jasminum officinale L. 0 16.7 1 2 6.2 0.9486 4 11.3 0.5151 4 17.5 0.2795 4 17.5 0.2795

179 Lactuca brunoniana (DC.) Wall. ex C.B.Clarke 0 2.1 1 3 4.5 1 2 20 0.1836 6 4.8 1 6 4.8 1

180 Lavatera flava Desf. 0 4.2 1 3 9.1 0.6281 3 13.9 0.3299 6 9.5 0.6633 6 9.5 0.6633

181 Lecanthus peduncularis (Wall. ex Royle) Wedd 0 3.2 1 3 8.1 0.5281 3 21 0.1936 5 9.2 0.5709 5 9.2 0.5709

182 Lepidium sativum L. 0 16.7 1 2 6.2 0.9432 3 10.2 0.6793 6 7.2 1 6 7.2 1

183 Lyonia ovalifolia (Wall.) Drude 0 4.2 1 1 8.2 0.7357 1 3.4 1 6 9.5 0.6603 6 9.5 0.6603

184 Malcolmia africana (L.) R.Br. 0 6.2 1 4 9.9 0.5499 3 18.7 0.0976 7 25.9 0.0778 7 25.9 0.0778

185 Malva neglecta Wallr. 0 25 1 1 17 0.2963 2 14.4 0.6871 4 13.5 0.5689 4 13.5 0.5689

186 Malva sylvestris L. 0 12.5 1 3 10.5 0.4733 3 9.4 0.8428 6 12.5 0.6399 6 12.5 0.6399

187 Medicago polymorpha L. 1 70.6 0.188 2 30.5 0.136 2 23.8 0.3863 4 55.8 0.0006 4 55.8 0.0006

188 Mentha longifolia (L.) L. 1 42.9 0.1886 2 9.4 0.5883 4 6.4 1 4 23.1 0.116 4 23.1 0.116

189 Micromeria biflora (Buch.-Ham. ex D.Don) Benth. 0 2.1 1 4 6.7 0.5475 4 4.2 1 5 5 0.5753 5 5 0.5753

190 Myosotis asiatica (Vestergr.) Schischk. & Serg 0 10.4 1 4 5.1 1 2 10.9 0.5533 6 15.1 0.4595 6 15.1 0.4595

191 Myrsine africana L. 0 2.1 1 3 4.5 1 4 4.2 1 6 4.8 1 6 4.8 1

192 Nepeta erecta (Royle ex Benth.) Benth. 1 28.6 1 3 16.9 0.6419 3 45.4 0.0196 5 19.4 0.6119 5 19.4 0.6119

193 Nerium oleander L. 0 2.1 1 4 6.7 0.5607 4 4.2 1 6 4.8 1 6 4.8 1

194 Oenothera rosea L'Hér. ex Aiton 0 47.9 0.4927 1 23.2 0.2975 3 25.2 0.4055 6 18.6 0.7864 6 18.6 0.7864

195 Onychium contiguum C.Hope 0 8.3 1 4 16.3 0.2204 4 8.5 0.8464 6 19 0.2549 6 19 0.2549

196 Otostegia hildebrandtii (Vatke & Kurtz) Sebald 0 4.2 1 3 9.1 0.6299 3 50 0.0052 6 9.5 0.6529 6 9.5 0.6529

197 Oxalis cardenasiana Lourteig 1 27.9 1 2 21 0.4087 3 14.7 0.9402 5 34.6 0.0896 5 34.6 0.0896

198 Papaver somniferum subsp. setigerum (DC.) Arcang. 0 2.1 1 3 4.5 1 2 20 0.1836 6 4.8 1 6 4.8 1

199 Pecteilis radiata (Thunb.) Raf. 0 25 1 1 17 0.2999 3 19.5 0.3529 7 35.1 0.0624 7 35.1 0.0624

200 Persicaria amplexicaulis (D.Don) Ronse Decr. 0 10.4 1 3 13.3 0.3453 3 10.3 0.7329 6 8.4 0.7457 6 8.4 0.7457

201 Persicaria amplexicaulis (D.Don) Ronse Decr. 0 12.5 1 4 11.9 0.4107 2 9.4 0.8358 6 12.5 0.6353 6 12.5 0.6353

202 Phalaris minor Retz. 0 6.2 1 3 5.2 1 2 14.1 0.3103 6 14.3 0.3355 6 14.3 0.3355

203 Phytolacca latbenia (Moq.) H. Walter 0 6.2 1 1 7.2 0.6429 4 12.5 0.5735 6 14.3 0.3259 6 14.3 0.3259

187

204 Pimpinella acuminata (Edgew.) C.B. Clarke 0 2.1 1 1 6.2 0.5429 4 4.5 1 7 4.8 1 7 4.8 1

205 Plantago lanceolata L. 0 18.7 1 3 7.5 0.9476 3 54.4 0.0082 7 44.2 0.0186 7 44.2 0.0186

206 Plantago major L. 0 25 1 4 25.5 0.1282 1 9.1 0.946 7 38.7 0.0308 7 38.7 0.0308

207 Poa annua L. 1 80 0.0778 2 43.3 0.014 2 16.2 0.6035 4 54.1 0.0158 4 54.1 0.0158

208 Podophyllum peltatum L. 0 6.2 1 1 7.2 0.6343 3 12.7 0.4313 6 14.3 0.3287 6 14.3 0.3287

209 Polygonatum verticillatum (L.) All. 0 18.7 1 1 7.9 0.9032 1 9.2 0.784 7 13.3 0.5573 7 13.3 0.5573

210 5Potentilla nepalensis Hook. 0 18.7 1 4 12.4 0.5305 3 29.7 0.0908 7 44.4 0.0102 7 44.4 0.0102

211 Primula veris L. 0 16.7 1 1 8.7 0.7441 2 17.6 0.3191 7 14.1 0.5227 7 14.1 0.5227

212 Prunella vulgaris L. 0 4.2 1 4 4 1 4 8.3 0.6663 6 9.5 0.6507 6 9.5 0.6507

213 Pseudocaryopteris bicolor (Roxb. ex Hardw.) P.D.Cantino 0 10.4 1 1 14.6 0.2318 1 20 0.1768 7 17.2 0.3399 7 17.2 0.3399

214 Pseudomertensia parviflorum (Decne.) Riedl 0 4.2 1 3 9.1 0.6423 2 16.6 0.2476 6 9.5 0.6557 6 9.5 0.6557

215 Pteris vittata L. 0 22.9 1 4 14.5 0.6075 2 18.2 0.4131 7 72 0.0006 7 72 0.0006

216 Ranunculus muricatus L. 1 35.3 0.3951 3 15.7 0.4507 2 19.4 0.3667 7 40 0.0514 7 40 0.0514

217 Ranunculus laetus Wall. ex Hook. f. & J.W. Thomson 0 22.9 1 2 29.4 0.0724 3 22.3 0.2484 4 28 0.188 4 28 0.188

218 Reinwardtia indica Dumort. 0 6.2 1 3 13.6 0.2547 3 18.7 0.087 6 6.2 1 6 6.2 1

219 Rochelia stylaris Bioss. 0 6.2 1 4 9.9 0.5593 1 8.7 0.7558 4 10.6 0.4023 4 10.6 0.4023

220 Rumex dentalus L. 1 42.9 0.1928 2 25.1 0.082 2 28.6 0.1042 7 20.9 0.2581 7 20.9 0.2581

221 Rumex hastatusD.Don., 1 30 1 2 22.5 0.3023 1 32.5 0.1152 4 48.6 0.0348 4 48.6 0.0348

222 2Rumex nepalensis Spreng. 0 36.4 1 0 52.2 0.0188 1 23.4 0.5779 4 32 0.3243 4 32 0.3243

223 Salvia Moorcroftiana Wall.ex Benth 0 6.2 1 4 9.9 0.5509 3 18.7 0.0936 6 6.2 1 6 6.2 1

224 Sauromatum venosum (Dryand. ex Aiton) Kunth 0 2.1 1 4 6.7 0.5607 4 4.2 1 6 4.8 1 6 4.8 1

225 Scrophularia robusta Pennell. 0 2.1 1 4 6.7 0.5649 1 5.9 0.5239 6 4.8 1 6 4.8 1

226 Scutellaria linearis Benth. 0 2.1 1 4 6.7 0.5645 4 4.2 1 6 4.8 1 6 4.8 1

227 Senecio chrysenthemoides DC. 0 20.8 1 3 15.5 0.4733 2 36.8 0.0428 5 24.9 0.1696 5 24.9 0.1696

228 Sibbaldia cuneata Schouw ex Kunze 0 2.1 1 3 4.5 1 4 4.2 1 6 4.8 1 6 4.8 1

229 Silene vulgaris (Moench) Garcke 0 10.4 1 3 10.8 0.4425 3 31.2 0.0462 6 15.1 0.4685 6 15.1 0.4685

230 Silybum marianum (L.) Gaertn. 0 7.3 1 1 11.8 0.5425 3 4.2 1 5 21.9 0.1491 5 21.9 0.1491

188

231 Sinopodophyllum hexandrum (Royle) T.S.Ying 0 2.1 1 4 6.7 0.5553 1 5.9 0.5141 6 4.8 1 6 4.8 1

232 Solanum americanum Mill. 0 10.4 1 2 50.6 0.003 4 6.4 1 4 41.7 0.0322 4 41.7 0.0322

233 Sonchus arvensis L. 0 8.3 1 3 18.2 0.1228 3 42.9 0.0116 6 10.6 0.5551 6 10.6 0.5551

234 Strobilanthes affinis (Griff.) Terao ex J.R.I. Wood & J.R. Benn.

0 6.2 1 4 9.9 0.5527 2 12.6 0.4659 6 14.3 0.3253 6 14.3 0.3253

235 Swertia alata C.B. Clarke 0 6.2 1 1 6.6 0.8248 3 17.8 0.1764 7 25.8 0.1372 7 25.8 0.1372

236 Swertia angustifolia Buch.-Ham. ex D. Don 0 8.3 1 3 9.2 0.5645 3 11.7 0.4435 6 19 0.2591 6 19 0.2591

237 Swertia ciliata (D. Don ex G. Don) B.L. Burtt 0 12.5 1 2 8.9 0.6135 2 9 0.9432 5 7.7 0.817 5 7.7 0.817

238 Tagetes minuta L. 1 38.7 0.2985 2 21.7 0.1328 1 9.9 0.7149 4 17.5 0.2807 4 17.5 0.2807

239 Taraxacum campylodes G.E.Haglund 1 64.9 0.2995 2 25.2 0.4297 1 22.3 0.7087 4 37.7 0.1164 4 37.7 0.1164

240 Thalictrum cultratum Wall. 0 8.3 1 1 17.3 0.1592 2 11.1 0.6589 7 23.1 0.1944 7 23.1 0.1944

241 1Themeda anathera (Nees ex Steud.) Hack. 1 85.7 0.0358 2 18.1 0.2639 2 20.4 0.3031 4 29.5 0.1566 4 29.5 0.1566

242 Trichodesma indicum (L.) Lehm. 0 7.3 1 4 15.3 0.2104 3 4.5 1 6 4.8 1 6 4.8 1

243 Trifolium repens L. 0 6.2 1 3 5.2 1 3 12.7 0.4303 6 14.3 0.3449 6 14.3 0.3449

244 Tussilago farfara L. 0 8.3 1 3 9.2 0.5575 3 15.9 0.2523 5 11.4 0.4227 5 11.4 0.4227

245 Urtica dioica L. 0 6.2 1 3 5.2 1 4 12.5 0.5765 6 14.3 0.3241 6 14.3 0.3241

246 Valeriana jatamansi Jones. 0 12.5 1 3 17.6 0.2034 3 63 0.003 6 8.9 0.7185 6 8.9 0.7185

247 Valeriana officinalis L. 0 35.4 0.5497 1 25 0.1576 1 23.8 0.2891 7 27.1 0.2851 7 27.1 0.2851

248 Verbascum thapsis L. 1 36.4 0.3611 2 32.2 0.0528 4 12.9 0.6231 4 35.6 0.0854 4 35.6 0.0854

249 Verbena bonariensis L. 0 10.4 1 3 6.9 0.821 3 38.9 0.0242 6 15.1 0.4663 6 15.1 0.4663

250 Vincetoxicum arnottianum (Wight) Wight 0 2.1 1 3 4.5 1 1 5.9 0.5067 5 5 0.5763 5 5 0.5763

251 3Viola biflora L. 0 18.7 1 3 11.2 0.5637 3 54.4 0.008 6 42.9 0.0384 6 42.9 0.0384

252 Viola canescens Wall. 0 20.8 1 4 12.7 0.5969 2 21.3 0.2272 6 16.8 0.2841 6 16.8 0.2841

189

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