Gilgit-Baltistan), Pakistan By

Home , Astore (city), Astore District, Sermik, Skardu Valley, Stak Valley

Forest Vegetation and Dendrochronology of Gilgit, Astore and Skardu Districts of Northern Areas (Gilgit-Baltistan), Pakistan By

MUHAMMAD AKBAR

Under the supervision of Prof. Dr. Moinuddin Ahmed Co-Supervisor Dr. Muhammad Faheem Siddiqui A thesis submitted to the Graduate Research and Management Council in partial fulfillment for the degree of doctor of Philosophy 2013 Laboratory of Dendrochronology and Plant Ecology, Department of Botany, Federal Urdu University of Arts Science and Technology, Karachi-75300, Pakistan

Federal Urdu University of Arts, Sciences & Technology

GulshanEIqbal Karachi

Department Of Botany

CERTIFICATE

Certified that Mr. Muhammad Akbar was enrolled for the program M.Phil leading to Ph.D. He has passed the required course work successfully and fulfilled all the criteria of Higher Education Commission (HEC) for the degree of doctorate. The dissertation titled “Forest vegetation and Dendrochronology of Gilgit, Astore and Skardu Districts of Northern Areas (Gilgit Baltistan), Pakistan” submitted by him is satisfactory and confide for the partial requirement of the award of Doctor of Philosophy degree.

------

Supervisor Co-Supervisor

Prof. Dr. Moinuddin Ahmed Dr. Muhammad Faheem Siddiqui (Foreign Professor) (Assistant Professor) Department of Botany University of Karachi

DEDICATION

DEDICATED TO

KACHO HABIBULLAH KHAN

(SHAHEED)

AND

MY STEP UNCLE MUHAMMAD

IBRAHIM

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ACKNOWLEDGMENTS

Special admiration and thanks goes to Prof. Dr Moinuddin Ahmad for his kind supervision, constant encouragement, continuous moral support, valuable comments, guidance, constructive criticism, encouragement and suggestions throughout the felid and laboratory work and thesis writing which made me able to complete my research on “Forest vegetation and Dendrochronology of Gilgit, Astore and Skardu Districts of Northern Areas (Gilgit-Baltistan), Pakistan”.

I would like to express my appreciation and gratitude to Prof. Dr. Syed Shahid Shaukat who guided me very kindly during the different phases of my research work, especially during statistical and multivariate analysis. I am also grateful to Chairperson, Dept of Botany and the other faculty members for their cooperation and support.

Sincere thanks are due to all my seniors especially Dr. Muhammad Faheem Siddiquii, Dr. Muhammad Wahab, Dr. Toqeer Ahmed Rao, Dr. Kanwal Nazim, Dr. Muhammad Uzair Khan, Dr. Nasrullah Khan, kindness, guidance and the doctoral students Alamdar Hussain, Faisal Hussain, Muhammad Usama Zafar, Hina Zafar, Shahina Arshad Khan, Farzana Usman, Jawaria Sultana and Tuba for their cooperation and moral support during this study.

My deepest gratitude goes to my beloved parents Haji Abdullah, and Bano for their endless love, prayers and my brothers Mushtaq Hussain, Zahid Hussain, Sajjad Hussain, Muhammad Hasan, for their continuous financial support and encouragement. Also I am much thankful to all my family members, particularly Muhammad Asgher, Zakwat Hussain, Tariq Aziz, Manzoor Hussain, Habibullah, Sakhawat Hussain and Kamal Hussain for their moral support throughout my academic period. I would like to pay many heartiest thanks and appreciation to my beloved wife Ms. Shazia Batool for her tolerance and patience to during a large span of time during the doctoral work and also for her love and care of the kids during the period when I could not devote time to my family.

I especially thank and pay gratitude to Prof Dr. Ghulam Mehdi (Life trustee and Chairman Education Fund), Afzal Ali Shigri (Managing Director), Fazil Ali (Regional Managing director) Muzafar Hussain (Managing trustee) and Muhammad Jaffar (Finance

officer) of Marafie Foundation Pakistan to facilitate my monetary grant for the period of my research.

I would like to pay heartiest gratitude to Muhammad Ismail Zafar (Conservator of Forest Gilgit Region) to encourage and appreciate each and every step during my filed work. It would have been almost impossible to collect data from the high altitudinal remote forested area without his kind cooperation and provision of field facilities, transport and equipment.

I am also grateful to Mr. Mayoor Khan Program Manager Wildlife Conservation Society (WCS) to provide financial grant in terms of internship for one year and provide logistic support during the field trip of Gilgit and Astore. My thanks also go to Muhammad Jamil, Chairman Mountain Conservation Developmental Program (MCDP) and Babar Khan Head (WWF) World Wide Fund for nature Gilgit-Baltistan to make available logistic support and facilitation during field works of Gilgit and Astore District.

I would like to thanks all Laboratory staff especially Mr. Syed Azhar Kazmi who helped me in the problems related to computer.

In this study identification of plant species was the initial step. Dr. Sher Wali Khan Assistant Professor of Biological Science Department, Karakorum International University (KIU), an excellent taxonomist, identified all the plant species recorded and collected during the sampling. To him I extend my special thanks.

I would like to appreciate and thank to Mr. Muhammad Askari lecturer at F.G Boys Degree College Skardu and Mr. Muhammad Ali (forester Kharmang Oliding), Muhammad Ayoub (Forester Basho) for their kind support and cooperation during field work in Skardu District.

The deepest thanks go to all my friends specially Syed Hasan Rizvi, Syed Najm-u- Hasan, Illyas Habib, Fida Ali, Anwer Hussain, Kamran Haider, Muhammad Ali Gasingi, Muhammad Raza, Muhammad Ali Shigri, Musa Ali, Mehdi Hasan, Muhammad Kazim, Muhammad Ibarhim, Ashraf Hussain, Khadim Hussain, Ajaz Haider, Engineer Shabbir Hussain, Nazir Hussain and Irfan Haider for their constant encouragement and worthy support.

Muhammad Akbar

TABLE OF CONTENTS Certificate………………………………………………………………….. II

Dedication…………………………………………………………………. III

Acknowledgments…………………………………………………………. IV

Table of contents…………………………………………………………... VI

List of tables……………………………………………………………….. XVI

List of figures……………………………………………………………… XIX

List of appendices…………………………………………………………. XXIV

Abstract in Urdu…………………………………………………………… XXVI

Abstract……………………………………………………………………. XXVIII

CHAPTER-1 GERNAL INTRODUCTION

1.1- Introduction…………………………………………………………… 1

1.1.1-Montane dry sub tropical scrub zone……………………………... 1

1.1.2-Dry alpine zones and permanent snowfields……………………... 1

1.1.3-Alpine meadows and alpine scrub zone………………………….. 1

1.1.4-Sub-alpine scrub zone…………………………………………….. 2

1.1.5-Dry temperate coniferous forest………………………………….. 2

1.1.6-Dry temperate evergreen Oak scrub……………………………… 2

1.2-Profile of study area…………………………………………………… 6

1.2.1-Geography………………………………………………………… 6

1.2.2-Geology…………………………………………………………… 8

1.2.4-Biodiversity……………………………………………………….. 9

1.2.5-Climate……………………………………………………………. 11

1.3-District Gilgit………………………………………………………….. 12

1.4-District Skardu………………………………………………………… 15

1.5-District Astore…………………………………………………………. 18

1.6-Problems and issues…………………………………………………… 20

PART-I PHYTOSOCIOLOGY

CHAPTER-2 REVIEW OF LITERATURE

2.1-Introduction…………………………………………………………… 23

2.1.1-Ecology…………………………………………………………… 23

2.1.2-Soil-Vegetation relation…………………………………………… 28

2.1.3-Multivariate Analysis……………………………………………... 31

CHAPTER-3 PHYTOSOCIOLOGICAL STUDIES

3.1- Introduction…………………………………………………………… 35

3.1.1-Objective………………………………………………………….. 35

3.2- Materials and Methods……………………………………………….. 36

3.3- Results………………………………………………………………… 38

3.3.1-Description of communities………………………………………. 45

3.3.1.1-Pinus-Juniperus community……………………………………. 45

3.3.1.2-Pinus-Betula community……………………………………... 45

3.3.1.3-Picea-Juniperus community………………………………….. 46

3.3.1.4-Picea-Pinus community………………………………………. 48

3.3.1.5-Pinus wallichiana-Pinus gerardiana community……………. 48

3.3.1.6-Picea smithiana pure stands………………………………….. 49

3.3.1.7-Pinus wallichiana pure stands………………………………... 49

3.3.1.8-Betula utilis pure stands………………………………………. 51

3.3.1.9-Juniperus macropoda pure stand……………………………... 52

3.3.1.10-Abies pindrow pure stands…………………………………... 52

3.4-Discussion and conclusion…………………………………………….. 52

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CHAPTER-4 STRUCTURE OF FOREST

4.1-Introduction……………………………………………………………. 60

4.2-Objectives……………………………………………………………… 61

4.3-Materials and Methods………………………………………………… 62

4.3.1-Sampling…………………………………………………………… 62

4.3.2-Statistical analysis…………………………………………………. 62

4.3.3-Size class structure………………………………………………… 62

4.3.4-Weibull distribution model……………………………………….. 62

4.3.5-Formula……………………………………………………………. 63

4.4-Results and discussion 63

4.4.1-Stand No 01 (Basho-A)…………………………………………… 63

4.4.2-Stand No 02 (Basho-B)…………………………………………… 64

4.4.3-Stand No 03 (Gasing-A)………………………………………….. 64

4.4.4-Stand No 04 (Gasing-B)………………………………………….. 65

4.4.5-Stand No 05 (Gasing-C)………………………………………….. 66

4.4.6-Stand No 06 (Hargosil-A)………………………………………... 66

4.4.7-Stand No 07 (Hargosil-B)………………………………………… 67

4.4.8-Stand No 08 (Memosh-A)………………………………………… 67

4.4.9-Stand No 09 (Memosh-B)………………………………………… 68

4.4.10-Stand No 10 (Memosh-C)……………………………………….. 68

4.4.11-Stand No 11 (Ganji-A)…………………………………………... 69

4.4.12-Stand No 12 (Ganji-B)…………………………………………... 69

4.4.13-Stand No 13 (Ganji-C)…………………………………………... 70

4.4.14-Stand No 14 (Ganji-D)…………………………………………... 70

4.4.15-Stand No 15 (Kargah-A)………………………………………… 71

4.4.16-Stand No 16 (Kargah-B)………………………………………… 71

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4.4.17-Stand No 17 (Kargah-C)………………………………………… 72

4.4.18-Stand No 18 (Jutial-A)…………………………………………... 72

4.4.19-Stand No 19 (Jutial-B)…………………………………………... 73

4.4.20-Stand No 20 (Naltar-A)…………………………………………. 73

4.4.21-Stand No 21 (Naltar-B)…………………………………………. 73

4.4.22-Stand No 22 (Naltar-C)…………………………………………. 74

4.4.23-Stand No 23 (Naltar-D)…………………………………………. 74

4.4.24-Stand No 24 (Danyore)………………………………………….. 75

4.4.25-Stand No 25 (Joglotgah-A)……………………………………… 75

4.4.26-Stand No 26 (Joglotgah-B)……………………………………… 75

4.4.27-Stand No 27 (Rama-A)………………………………………….. 76

4.4.28-Stand No 28 (Rama-B)………………………………………….. 76

4.4.29-Stand No 29 (Rama-C)………………………………………….. 77

4.4.30-Stand No 30 (Rama-D)………………………………………….. 77

4.4.31-Stand No 31 (Mushken-A)………………………………………. 78

4.4.32-Stand No 32 (Mushken-B)………………………………………. 78

4.4.33-Stand No 33 (Mushken-C)………………………………………. 78

4.4.34-Stand No 34 (Mushken-D)………………………………………. 79

4.4.35-Stand No 35 (Mushken-E)………………………………………. 79

4.4.36-Stand No 36 (Dashken)………………………………………….. 80

4.4.37-Stand No 37 (Gudaie)…………………………………………… 80

4.4.38-Stand No 38 (Chelim-A)………………………………………… 82

4.4.39-Stand No 39 (Chelim-B)………………………………………… 82

4.4.40-Stand No 40 (Chelim-C)………………………………………… 8

4.5-Overall distribution pattern of dominant tree species………………… 90

4.5.1-Overall Dbh size class distribution of dominant species………… 90

4.5.2-Wiebull distribution modeling 93

4.6-Correlation of overall Density ha-1 and Basal area m2 ha-1of tree 97 species with the Topographic variables…………………………………….

4.6.1-Correlation between total basal area m2 ha-1 of stands with 98 topographic factors…………………………………………………………..

4.7-Discussion……………………………………………………………... 104

MULTIVARIATE ANALYSIS

CHAPTER- 5 SOIL-VEGETATION RELATION

5.1-Introduction…………………………………………………………….. 108

5.1.1-Aims and objective………………………………………………… 109

5.2-Material and method…………………………………………………… 110

5.2.1-Sample preparation………………………………………………… 110

5.2.2-Edaphic variables of soil…………………………………………... 110

5.2.3-Soil Macro and Micro Nutrients…………………………………… 110

5.2.4-Statistical analysis…………………………………………………. 111

5.3-Results………………………………………………………………….. 112

5.3.1.1.1-(a) Ward’s Cluster analysis of Stands (Tree vegetation 112 data set)……………………………………………………………………...

5.3.1-Multivariate analysis of environmental variables…………………. 112

5.3.1.1-Classification……………………………………………….. 112

5.3.1.1.2-Group-I………………………………………………….. 112

5.3.1.1.3-Group-II…………………………………………………. 115

5.3.1.1.4-Group-III………………………………………………… 115

5.3.1.1.5-Group-IV………………………………………………... 116

5.3.1.1.6-Group-V-Isolated stand…………………………………. 117

5.3.1.1.7-Group-VI………………………………………………... 117

5.3.1.1.8-Group-VII……………………………………………….. 118

5.3.1.2-Univariate analysis of variance (ANOVA) of tree cluster 122 groups………………………………………………………………………..

5.3.2-(b) Ward’s Cluster analysis of Stands (Understory vegetation data) 125

5.3.2.1-Group-I………………………………………………………. 125

5.3.2.2-Group-II……………………………………………………… 126

5.3.2.3-Group-III…………………………………………………….. 126

5.3.2.4-Group-IV…………………………………………………….. 129

5.3.2.5-Group-V……………………………………………………… 129

5.3.2.6-Group-VI…………………………………………………….. 130

5.3.2.7-Univariate analysis of variance (ANOVA) of Understory 133 vegetation cluster groups……………………………………………………

5.4-ORDINATION…………………………………………………………. 135

5.4.1-PCA ordination of tree vegetation data……………………………. 135

5.4.2-Correlation of Axis………………………………………………… 139

5.4.2.1-Relationship (PCA) ordination axes with Topographic, 139 Edaphic and Soil nutrients of tree vegetation data…………………………. 5.4.2.2-PCA ordination of Understory vegetation data……………... 141

5.4.2.3-Relationship (PCA) ordination axes with Topographic, 145 Edaphic and Soil nutrients of Understory vegetation data………………….

5.5-Soil Physico-Chemical status…………………………………………... 147

5.5.1-Edaphic factors…………………………………………………….. 147

5.5.1.1-pH…………………………………………………………… 147

5.5.1.2-Water Holding Capacity……………………………………. 148

5.5.1.3-Organic Matter……………………………………………… 149

5.5.1.4-Electrical Conductivity……………………………………... 150

5.5.1.5-Salinity……………………………………………………… 151

5.5.1.6-Total Dissolve Salt………………………………………….. 152

5.5.2-Soil Nutrients………………………………………………………. 153

5.5.2.1-Nitrogen……………………………………………………... 153

5.5.2.2-Phosphorus………………………………………………….. 154

5.5.2.3-Potassium……………………………………………………. 155

5.5.2.4-Calcium…………………………………………………….... 156

5.5.2.5-Magnesium …………………………………………………. 157

5.5.2.6-Sulfur……………………………………………………….. 158

5.5.2.7-Cobalt……………………………………………………….. 159

5.5.2.8-Manganese…………………………………………………... 160

5.5.2.9-Zinc………………………………………………………….. 161

5.5.2.10-Iron………………………………………………………… 162

5.6-Discussion………………………………………………………………. 164

CHAPTER-6 CLASSIFICATION AND ORDINATION

6.1-Introduction…………………………………………………………….. 171

6.2-Materials and methods………………………………………………….. 173

6.3-Results………………………………………………………………….. 174

6.3.1-Classification……………………………………………………… 174

6.3.1.1-Ward’s Cluster analysis of Stands (Tree vegetation data)..... 174

6.3.1.1.1-Group I (a) Pinus wallichiana mix group…………... 174

6.3.1.1.2-Group I (b) pure Pinus wallichiana group…………. 177

6.3.1.1.3-Pinus wallichiana and Picea smithiana group…….. 177

6.3.1.1.4-Picea smithiana and Juniperus excelsa group……… 179

6.3.1.1.5-Pure Betula utilis group…………………………….. 180

6.3.1.1.6-Abies pindrow and Juniperus macropoda group…… 181

6.3.1.2-Univariate analysis of variance (ANOVA)…………………. 183

6.3.2-Ward’s Cluster analysis of Stands (Understory vegetation data)….. 185

6.3.2.1-Group I………………………………………………………. 186

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6.3.2.2-Group II……………………………………………………... 186

6.3.2.3-Group III…………………………………………………….. 187

6.3.2.4-Group IV…………………………………………………….. 191

6.3.2.5-Group V……………………………………………………... 191

6.3.3-Univariate analysis of ground vegetation data set (ANOVA) 193

6.3.4-ORDINATION…………………………………………………….. 196

6.3.4.1-DCA ordination of tree vegetation data…………………….. 196

6.3.4.2-Relationship (correlation coefficient) of three ordination 198 axes with Topogaraphic, Edaphic and Soil nutrients of tree vegetation data…………………………………………………………………………..

6.3.4.3-DCA ordination of understory vegetation data 200

6.3.4.4-Relationship (correlation coefficient) of three ordination 202 axes with Topogaraphic, Edaphic and Soil nutrients of understory vegetation data…………………………………………………………...... 6.5-Discussion………………………………………………………………. 204

PART-II DENDROCHRONOLOGY

CHAPTER-7 INTRODUCTIONTO DENDROCHRONOLOGY

7.1-Introduction……………………………………………………………. 207

7.2-Description of Study area (Ganji valley)……………………………… 208

CHAPTER-8 REVIEW OF LITERATURE

8.1-Introduction……………………………………………………………. 210

8.2-Review of Literature…………………………………………………... 210

CHAPTER-9 MATERIALS AND METHODS

9.1-Introduction…………………………………………………………….. 214

9.2-Material and methods…………………………………………………... 214

9.2.1-Field Methods……………………………………………………... 214

9.2.1.1-Site discription……………………………………………… 214

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9.2.1.2-Site selection……………………………………………….. 214

9.2.1.3-Extraction of core…………………………………………... 214

9.2.2-Laboratory Method………………………………………………... 215

9.2.2.1-Preparation of sample………………………………………. 215

9.2.2.2-Sanding……………………………………………………... 215

9.2.2.3-Crossdating…………………………………………………. 215

9.2.3-Measurement using Velmex………………………………………. 216

9.2.4-Age and Growth rate……………………………………………… 216

9.2.5-Computer Software used in the analysis………………………….. 216

9.2.6-COFECHA………………………………………………………... 217

9.2.7-ARSTAN………………………………………………………….. 218

9.2.7.1-Standard chronology………………………………………... 218

9.2.7.2-Residual chronology………………………………………... 218

9.2.7.3-ARSTAN chronology………………………………………. 219

9.2.8-Growth-climate response………………………………………….. 219

CHAPTER-10 AGE AND GROWTH RATES

10.1-Introduction…………………………………………………………… 222

10.2-Materials and Methods………………………………………………... 223

10.3-Results………………………………………………………………… 223

10.3.1-Age and Growth rate of seedlings……………………………….. 223

10.3.2-Age and growth rate of tree……………………………………… 227

10.3.3-Growth rates of trees in different periods………………………... 232

10.4-Discussion……………………………………………………………... 235

10.4.1-Age and growth rates of seedlings……………………………….. 235

10.4.2-Growth rate in different period…………………………………... 236

10.4.3-Age and growth rate of tree……………………………………… 237

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CHAPTER-11 CHRONOLOGY AND GROWTH CLIMATE RESPONSE

11.1-Introudction…………………………………………………………… 238

11.2-Metrials and Methods…………………………………………………. 240

11.3-Results ………………………………………………………………... 240

11.3.1-Development of chronology…………………………………….. 241

11.3.2-Raw chronology…………………………………………………. 241

11.3.3-Standard chronology…………………………………………….. 242

11.3.4-Residual chronology……………………………………………... 243

11.3.5-ARSTAN chronology……………………………………………. 244

11.3.6-Sample Depth……………………………………………………. 245

11.3.7-EPS and Rbar…………………………………………………….. 246

11.3.8-Correlation and response function analysis……………………… 250

11.3.8.1-Correlation coefficients of Residual Vs Skardu 250 meteorological climate………………………………………………………

11.3.8.2-Response coefficients of residual Vs Skardu 251 meteorological climate………………………………………………………

11.3.8.3-Correlation coefficients of Residual Vs grid climate……... 252

11.3.8.4 –Response coefficients of Residual Vs grid climate…….... 253

11.3.8.5-Correlation coefficients of standard Vs Skardu 254 meteorological climate………………………………………………………

11.3.8.6-Response coefficients of standard Vs Skardu 255 meteorological climate………………………………………………………

11.3.8.7-Correlation coefficients of standard Vs grid climate……… 256

11.3.8.8-Response coefficients of standard Vs grid climate……….. 257

11.4-Discussion …………………………………………………………….. 269

CHAPTER-12 GENERAL DISCUSSION………………………………. 262

LIST OF TABLES Table 1.1 Characteristics of sampling sites of Skardu, Gilgit and Astore 22 districts……………………………………………………………………...

Table 3.1 Phytosociological attributes, rank, and absolute values of 40 39 stands in District Skardu, Astore and Gilgit………………………………..

Table 3.2 List of plants and families associated with dominant tree 41 species of the study area……………………………………………………

Table 3.4 Phytosociological summary of sampled trees species…………. 44

Table 4.1 Showing the statistical description of Weibull functions………. 94

Table 4.2 Correlation between overall stand density /basal area and stand 98 density with topographic variables…………………………………………

Table 4.3 Correlation of individual species Density / Basal area with their 99 environmental variables……………………………………………………..

Table 5.1 Six groups derived from Ward’s cluster analysis of 40 stands and 119 their average tree species composition (average importance value for each group)…………………………………………………………………..

Table 5.2 Average frequency of understory species in the seven groups derived from Ward’s cluster analysis of the tree vegetation data…………... 120

Table 5.3 Mean value of topographic, edaphic and nutrients of soil of 121 seven cluster groups of tree vegetation data set……………………………..

Table 5.4 Analysis of variance of individual environmental variables 123 (topographic, edaphic and soil nutrients seven groups were extracted by Ward's cluster analysis using tree vegetation data of 40 stands……………..

Table 5.5 Mean value of topographic, edaphic and nutrients of soil of 131 seven cluster groups of tree vegetation data set……………………………..

Table 5.6 Average frequency of understory species in the six groups 132 derived from Ward’s cluster analysis of the understory vegetation data………………………………………………......

Table 5.7 Analysis of variance of individual environmental variables 133 (topographic, edaphic and soil nutrients of six groups were derived by Ward's cluster analysis using understory vegetation data of 40 stands………………………………………………………………......

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Table 5.8 Relationship (correlation coefficients) of environmental variables 140 (topographic and edaphic variables) and soil nutrients with PCA ordination axes obtained by tree vegetation data based on importance value of tree species and soil physico chemical properties………………………..

Table 5.9 Relationship (correlation coefficients) of environmental variables 146 (topographic and edaphic variables) and soil nutrients with PCA ordination axes obtained by understory vegetation data based on frequency of species and soil physico-Chemical properties……………………………

Table 5.10 Summery of statistics of Box plot……………………………… 163

Table 6.1 Five groups derived from Ward’s cluster analysis of 40 stands 179 and their average tree species composition (average importance value for each group)…………………………………………………………………..

Table 6.2 Average frequency of understorey species in the five groups derived from Ward’s cluster analysis of the tree vegetation data………….. 182

Table 6.3 Mean values ± SE of environmental variables (topographic, 183 edaphic and Soil nutrient) based on five groups derived from Ward’s cluster analysis using tree vegetation data of 40 stands form three districts of Gilgit-Baltistan. (Mean ± SE)…………………………………………….

Table 6.4 Analysis of variance of individual environmental variables 184 (topographic, edaphic five groups were derived by Ward's cluster analysis using tree vegetation data of 40 stands……………………………………...

Table 6.5 Average frequency of understorey species in the five groups 190 derived from Ward’s cluster analysis of the understory vegetation data…………………………………………………………………………...

Table 6.6 Mean values of the environmental variables based on the five 193 groups obtained from Ward’s method of cluster analysis using understorey vegetation data of 40 stands from forested areas of Gilgit-Baltistan. (Mean ± SE)…………………………………………………………………………

Table 6.7 Analysis of variance of individual environmental variables 194 (topographic, edaphic five groups were derived by Ward's cluster analysis using circular plot data of 40 stands…………………………………………

Table 6.8 Relationship (correlation coefficients) of environmental variables 199 (topographic and edaphic variables) with 3 DCA ordination axes obtained by tree vegetation data based on importance value of tree species……………………………………………………………………......

Table 6.9 Relationship (correlation coefficients) of environmental 203

xvii

(topographic and edaphic variables) with 3 DCA ordination axes obtained by understorey vegetation data based on frequency of understorey species………………………………………………………………………..

Table 10.1 Growth rates of Pinus wallichiana in different periods 234

Table 11.1 Dendrochronological characteristics of the ring-width 247 chronology from Pinups wallichiana Ganj (Skardu)………………………..

Table 11.2 Descriptive statistics of COFECHA Pinus wallichiana from 248 Ganji Skardu…………………………………………………………………

Table 11.3 Summary of statistics of Raw, Standard, Residual and 249 ARSTAN chronologies………………………………………………………

Table 11.4 Correlation and response coefficients significant relations of 258 different chronologies and climate and grid data……………………………

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LIST OF FIGURES Fig 1.1 Mean monthly amount of Precipitation in (mm) of Gilgit station 13 based on the data period (1972-2011)……………………………………...

Fig 1.2 Mean monthly maximum temperature in (Co) of Gilgit station 14 based on the data period (1972-2011)……………………………………...

Fig 1.3 Mean monthly minimum temperature in (Co) of Gilgit station 14 based on the data period (1972-2011)……………………………………...

Fig 1.4 Mean monthly amount of precipitation in (mm) of Skardu station 16 based on the data period (1972-2011)……………………………......

Fig 1.5 Mean monthly maximum temperature in (Co) of Skardu station 17 based on the data period (1972-2011)……………………………………...

Fig 1.6 Mean monthly minimum temperature in (Co) of Skardu station 17 based on the data period (1972-2011)……………………………………...

Fig 1.7 Mean monthly amount of precipitation in (mm) of Astore station 18 based on the data period from 1980 to 2011……………………………….

Fig 1.8 Mean monthly maximum temperature in (Co) of Astore station 18 based on the data period from 1980 to2011………………………………..

Fig 1.9 Mean monthly minimum temperature in (Co) of Astore station 19 based on the data period from 1980 to201…………………………………

Fig 1.10 Map of study area, Numbers are stand numbers, for detail refer to 21 the Table.1.1…………………………………………………………………

Fig 3.1 Shows mix forest of Pinus wallichiana, and Juniperus excelsa 47 from Basho valley district Skardu………………………………………….

Fig 3.2 Shows mix forest of Pinus wallichana, and Betula utilis from 47 Memosh valley district Skardu……………………………………………..

Fig 3.3 Shows Picea smithiana monospecific forest from Nalter valley of 49 District Gilgit……………………………………………………………......

Fig 3.4 Shows Pinus wallichiana monospecific forest from Gudiae Valley 50 of district Astore…………………………………………………..

Fig 3.5 Shows Betula utilis monospecific forest from Nalter valley of 51 district Gilgit………………………………………………………………..

Fig 3.6 Shows some dominant herbs of the study area……………………. 53

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Fig 3.7 Shows some dominant shrubs species of the study area………….. 54

Fig 4.1Shows some evidence of sliding, grazing and illegal cutting in the 81 study area…………………………………………………………………...

Fig 4.2 Shows the size class distribution of tree species from 40 stands of 83 study area……………………………………………………………………

Fig 4.3 Overall Dbh Size class Structure of dominant tree species of study 92 area on the basis of density ha-1…………………………………………….

Fig 4.4 Showing the Dbh Size classes of Dominant tee species using 95 Weibull distribution fitting model………………………………………….

Fig 4.5 Significant correlation of stand density/ stand basal area and 100 dominant trees density and basal area with topographic factors i.e. slope and elevation………………………………………………………………..

Fig. 5.1 Dendrogarm obtained from Ward’s cluster analysis using Soil 115 properties and IVI of tree species showing seven distinct groups…………..

Fig. 5.2 Dendrogarm obtained from Ward’s cluster analysis using Soil 128 properties and frequency of understory species showing distinct groups six distinct groups…………………………………………………………...

Fig. 5.3 PCA ordination Axis 1 and 2 of tree species using soil properties 136 and Importance Value Index of tree species showing seven distinct groups………………………………………………………………………..

Fig. 5.4 PCA ordination Axis 1 and 3 of tree species using soil properties 137 and Importance Value Index of tree species showing seven distinct groups………………………………………………………………………..

Fig. 5.5 PCA ordination Axis 2 and 3 of tree species using soil properties 138 and Importance Value Index of tree species showing seven distinct groups

Fig. 5.8 PCA ordination Axis 2 and 3 of understory species using soil 142 properties and mean frequency of understory species showing six distinct groups………………………………………………………………………..

Fig. 5.6 PCA ordination Axis 1 and 2 of understory species using soil 143 properties and mean frequency showing 4 distinct groups while two groups are overlapping and not distinguishable…………………………………….

Fig. 5.7 PCA ordination Axis 1 and 3 of understory species using soil 144 properties and mean frequency of understory species showing six distinct groups………………………………………………………………………..

Fig 5.7 Box plots show the status of pH in 40 stands of study area. The solid 147 line within the box plot expressed the mean values. The upper and lower ends of the box plot are represents maximum and minimum values respectively………………………………………………………………….

Fig 5.8 Box plots show the status of water holding capacity in 40 stands of 148 study area……………………………………………………………………

Fig 5.9 Box plots show the status of organic matter in 40 stands of study 149 area………………………………………………………………………….

Fig 5.10 Box plots show the status of electrical conductivity in 40 stands of 150 study area……………………………………………………………………

Fig 5.11 Box plots show the status of salinity in 40 stands of study area 151

Fig 5.12 Box plots show the status of total dissolve salt in 40 stands of study 152 area……………………………………………………………………

Fig 5.13 Box plots show the status of Nitrogen in 40 stands of study 153 area…………………………………………………………………………..

Fig 5.14 Box plots show the status of Phosphorus in 40 stands of study 154 area…………………………………………………………………………..

Fig 5.15 Box plots show the status of Potassium in 40 stands of study 155 area…………………………………………………………………………..

Fig 5.16 Box plots show the status of Calcium in 40 stands of study area. 156

Fig 5.17 Box plots show the status of Magnesium in 40 stands of study 157 area…………………………………………………………………………..

Fig 5.18 Box plots show the status of Sulfur in 40 stands of study area…… 158

Fig 5.19 Box plots show the status of Cobalt in 40 stands of study 159 area…………………………………………………………………………..

Fig 5.20 Box plots show the status of Manganese in 40 stands of study 160 area…………………………………………………………………………..

Fig 5.21 Box plots show the status of Zinc in 40 stands of study area…….. 161

Fig 5.22 Box plots show the status of Iron in 40 stands of study 162 area…………………………………………………………………………..

Fig 6.1Dendrogram derived from Ward’s Cluster analysis, using 176 Topographic variables and importance value of tree species, showing five distinct groups……………………………………………………………….

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Fig 6.2 Dendrogarm obtained from Ward’s Cluster analysis of understory 189 species on the basis of frequency, showing five distinct groups……………

Fig 6.3 DCA ordination of stands, using tree species data of 40 stands of 197 forested areas from three districts of Gilgit-Baltistan……………………….

Fig. 6.4 Showing DCA stands ordination on axis 1 and 2 of ground flora…. 201

Fig.7.1 Map showing the Ganji sampling area from district Skardu circle 209 showed the sampling site…………………………………………………...

Fig 10.1 Showing the Histogram of Dbh size classes in (cm) Vs mean age of 223 Pinus wallichiana seedlings……………………………………………...

Fig 10.2 Showing regression between Dbh size classes Vs actual age of 224 seedlings of Pinus wallichiana……………………......

Fig 10.3 Showing Histogram of growth rates and Dbh size classes of Pinus 225 wallichiana seedlings………………………………………………………..

Fig 10.4 Showing regression of actual growth rates Vs Dbh size classes of 226 Pinus wallichiana seedlings…………………………………………………

Fig 10.5 Shows Histogram of Dbh size classes Vs mean age of Pinus 128 wallichiana trees…………………………………………………………….

Fig 10.6 Shows regression analysis of Dbh size classes Vs actual age of 229 Pinus wallichiana tree……………………………………………………….

Fig 10.7 Shows Histogram of mean growth rates years/cm of Pinus 230 wallichiana tree……………………………………………………………...

Fig 10.8 Shows regression actual growth rates years/cm Vs Dbh size classes 231 of Pinus wallichiana trees…………………………………………...

Fig 10.9 Growth rate years/cm of Pinus wallichiana in 10 years interval 233 from 1720 to 2010…………………………………………………………...

Fig 11.1 shows Raw ring width chronology of Pinus wallichiana…………. 241

Fig 11.2 Shows standard ring with chronology of Pinus wallichiana………. 242

Fig 11.3 Shows residual ring width chronology of Pinus wallichiana……… 243

Fig 11.4 Shows ARSTAN ring width chronology of Pinus wallichiana…... 244

Fig 11.5 Shows sample depth of Pinus wallichiana species……………….. 245

Fig 11.6 Shows the running Rbar and EPS of Pinus 246

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wallichiana…………………………………………………………………………….

Fig 11.7 Correlation coefficients of residual Vs Skardu climate…………… 250

Fig 11.8 Response coefficients of residual chronology Vs Skardu 251 climate……………………………………………………………………….

Fig 11.9 Correlation coefficients of residual Vs grid climate……………… 252

Fig 11.10 Response coefficients of residual with grid climate…………….. 253

Fig 11.11 Correlation coefficients of standard chronology with Skardu 254 climate……………………………………………………………………….

Fig11.12 Response coefficients of standard chronology with Skardu 255 climate……………………………………………………………………….

Fig 11.13 Correlation coefficients of standard chronology with grid 256 climate…………………………………………………………………….....

Fig 11.14 Coefficients of standard chronology with grid climate of Skardu.. 257

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LIST OF APPENDICES Appendix 1.1 Mean monthly precipitation (mm) of district Gilgit………… 310

Appendix 1.2 Mean monthly precipitation (mm) of district Skardu……….. 311

Appendix 1.3 Mean monthly precipitation (mm) of district Astore……….. 312

Appendix 1.4 Mean monthly maximum Temperature (Cº) of district 313 Gilgit………………………………………………………………………...

Appendix 1.5 Mean monthly maximum Temperature (Cº) of district 314 Skardu……………………………………………………………………….

Appendix 1.6 Mean monthly maximum Temperature (Cº) of district 315 Astore………………………………………………………………………..

Appendix 1.7 Mean monthly minimum Temperature (Cº) of district 316 Gilgit………………………………………………………………………...

Appendix 1.8 Mean monthly minimum Temperature (Cº) of district 317 Skardu……………………………………………………………………….

Appendix 1.9 Mean monthly minimum Temperature (Cº) of district 318 Astore………………………………………………………………………..

Appendix 3.1 Ground flora, frequency, relative Frequency and presence in 319 number of Quadrate of 40 forested stands of study area…………………… Appendix 5.1 Topographic and Edaphic properties of sampling sites…….. 335

Appendix 5.1 Soil macro and micro chemicals properties of sampling 336 sites…………………………………………………………………………..

Appendix 5.2 Frequency of ground flora species in different stands……… 337

Appendix 5.3 IVI of tree species in different sampling stands……………. 345

Appendix 5.4 PCA ordination Axis of trees and understory vegetation 346 data set……………………………………………………………………….

Appendix 6.1 DCA ordination axis of trees and understorey vegetation of 347 sampling area……………………………………………………………….

Appendix 10.1 Age and growth rates of Pinus wallichiana seedlings from 348 Ganji valley of District Skardu……………………………………………...

Appendix 10.2 Growth rate of Pinus wallichiana trees species from Ganji 349 valley of District Skardu…………………………………………………….

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Appendix 10.2 Age of Pinus wallichiana trees species from Ganji valley 350 of District Skardu……………………………………………………………

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ABSTRACT

A quantitative forest vegetation study was conducted in 40 stands from Astore, Gilgit and Skardu three districts of Gilgit-Baltistan. The arboreal vegetation was analyzed using point centered quarter method while understory was sampled using circular quadrates. On the basis of phytosociological analysis following 5 communities of mixed tree species and 5 pure stands were recognized on the basis of importance value. 1) Pinus wallichiana-Juniperus community, 2) Pinus wallichiana- Betula community, 3) Picea-Juniperus community, 4) Picea-Pinus wallichiana, 5) Pinus wallichiana-Pinus gerardiana community, 1) Picea smithiana pure stands, 2) Pinus wallichiana pure stands, 3) Betula pure stands, 4) Juniperus macropoda pure stand and 5) Abies pindrow pure stand . Eighty three plants species of various herbs, shrubs and seedlings of trees were observed and identified on the forest floor. Among the understory vegetation Thymus serpyllum (2.8-18%), Fragaria nubicola (3.8- 17.5%), Leontopodium leontopodinum (3.7-17.1%), Bergenia stracheyi (1.5-16.1%), Artemisia brevifolia (2.7-15.3%), Bistorta affinis (2.2-15.1%), Tanacetum artemisioides (1.2-15.2%), Thymus linearis (6.2-15.1%), Geranium wallichianum (6-15%), Leontopodium himalayanum (3-14.9%) were abundantly distributed with the range of relative frequency respectively. Dbh size classes of each stand as well as overall size class of four dominant species i.e. Pinus wallichiana, Picea smithiana, Juniperus excelsa and Betula utilis were studied. Size class showed varied distribution patterns in different stands. Most of the deviation from an ideal distribution may be explained in terms of anthropogenic disturbances, i.e. grazing, cutting, sliding, burning and other human induced factors. Therefore, these forests do not appear to be in stable condition. It is concluded that if prompt action is not taken to stop current damaging practices, these valuable forests will vanish in a few decades. Ward’s cluster analysis and Principal Component Analysis (PCA) were applied to investigate the relationships between soil properties and vegetation. Sixteen soil characteristics and Importance value index were taken in case of tree vegetation while in case of understory vegetation frequency of species was used to investigate the distribution pattern of vegetation. Based on Ward’s cluster analysis seven groups were identified from tree vegetation data and six groups were recognized in case of understory flora. These groups clearly showed the distinct species composition and

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associated environmental (soil) characteristics. Relationship between vegetation and topographic factors was also studied. Ward’s cluster analysis and Detrended correspondence analysis (DCA) were applied to seek the vegetation distribution and composition. The groups of tree and understory vegetation could be readily superimposed on DCA ordination plane. Classification and ordination showed similar distribution pattern of tree species as well as understory vegetation thereby complementing the results of each other. Modern Dendrochronological methods were applied to analyze growth rates and age estimation of Pinus wallichiana A.B.Jackson (blue pine) from the forest of Ganji valley Himalayan range of Pakistan. Twenty eight cores from 14 trees were obtained to determine age and growth rates. Pinus wallichiana showed mean age of 173 years while highest (363years) age was recorded from 70 cm Dbh tree. The mean growth rate observed in this species ranged from 3 to 15 year/cm in case of trees while in case of seedlings showed 0.8 to 2 while correlation between Dbh and age was significant. Correlation between age and growth rate in case of seedling found significant while in case of tree it was not significant. Dbh and age were found significant in both cases. Growth rates in every ten years interval from 1720 to 2010 also estimated. It is concluded that growth rate is gradually not significantly on every 10 year basis, however each period is significantly differs 40 to 50 years. Seedling in the past period also show that the vegetation is deteriorating with the passage of time, therefore a special attention is required to save these forests. These important forests exist under anthropogenic threat and environmental disturbances .Some of them may easily be managed as indicated by the presence of large number of seedling. However stands with paucity of seedlings shall need more serious attention. In addition ring-width chronology of 211 years (AD 1730-2010) was developed. Chronology statistics showed that Pinus wallichiana have the past climate signal. Strong correlation has been observed between tree growth and previous November temperature and July indicating that winter warmth is the main factor responsible for tree growth. Correlation analysis between tree ring chronology and grid data also indicates that summer temperature is useful for tree growth. Greater numbers of samples are needed to provide better results in this regard.

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Chapter No 1 General Introduction

CHAPTER-1

GENERAL INTRODUCTION

1.1- Introduction

In Pakistan the vegetation types has been studied by various researchers including Champion et al. (1965), Rafi (1965) Beg (1975), Ahmed et al. (1988, 1990a, 1990b, 2005, 2006, 2009a, 2009b) However Roberts (1991, 1997) have been classified following the major ecological zones in Northern areas (Gilgit-Baltistan).

1.1.1-Montane Dry Sub Tropical Scrub Zone

This zone is mainly situated along the main Indus River up to Chelas Rikot to Bunji Partab. The elevation ranges from 750 to1219 meters above sea level. This zone consists of lesser reaches and southern slopes of mountains particularly along Indus River Gilgit and Rivers of Hunza in southern and central parts of Gilgit- Baltistan.

1.1.2-Dry alpine zones and permanent snowfields

It falls mainly in the upper Hunza and the northern parts of Baltistan. This zone consists of the high peaks of Karakorum Range. The landscape of this zone is characterized by the gigantic glaciers, an uninhabited devastate of boulders and absolute cliffs. Humid areas are found below glaciers, snowfields and alongside tributary banks. The vegetation is mostly xerophytic in this zone.

1.1.3-Alpine meadows and alpine scrub zone

Permanent snowfield and some high valleys are included in this zone. Lush green and watershed alpine meadows located between 3,500 to 3,800 meter above sea level on valley bottoms or high plateaus neighboring the major watercourses. These plateaus provide main habitat for endemic plants.

Chapter No 1 General Introduction

1.1.4-Sub-alpine scrub zone

This zone covers extensive high elevation throughout Karakoram, Himalaya and Hindu Kush, mountains including District Skardu, Gilgit Ghizer, and Astore regions, but regularly restrained to small ravines on upper slopes.

1.1.5-Dry temperate coniferous forest

This zone is most important for the survival of coniferous forest. These forests are usually present inside or northerly slopes of the Himalayas with little monsoon pressure. It is situated between 1500 to 3400 meter elevations above sea level. Most of the forest in this zone characterized ever green tree species. In Gilgit- Baltistan these forests exist in some parts of Gilgit, Diamer, and Skardu districts.

1.1.6-Dry temperate evergreen Oak scrub

It is located between 1500 to 2500 meters elevation above sea level. This is a midway zone fleeting following the moist temperate zone, which covers inferior valleys of District Diamer mainly areas bordering to Kohsitan District.

Forests are important to our ecological, economic and social welfare. They provide wood, non-wood goods, recreational opportunities, other non-market goods and services such as water and clean air. They also perform important environmental functions, such as protecting the health of our water catchments, providing habitats for plant and animal species, thereby playing an essential role in the conservation of biodiversity. It is fact that forests have considerable native and civilizing tradition values. They are used for education, and their artistic values are also highly appreciated. The provision of purification water, conversion of special carbon into wood (stored carbon) through the method of photosynthesis (carbon confiscation), and the upholding of fertile soils that support healthy and dynamic ecosystems are significant processes performed by our natural forests. Finding a balance between these multiple uses, while underneath and conserving forests for the future is the basis of sustainable Forest conservation and Management. In My studies /mostly dry temperate coniferous forests are included.

Chapter No 1 General Introduction

In Pakistan forest recourses are very limited. Forest is distributed only 4.8 percent of total land area according to the forest department, while FAO (2009) recorded 2.2% forested cover. These scarce, forests of Pakistan are very wealthy in terms of biodiversity and present a unique blend of tree, shrub, grass and animal species, living across various ecological (climatic) zones from sea level in the south, to high altitude alpine pastures of the north.

Forests in state constitutional rights in the Gilgit-Baltistan have been nominated as “protected forests” Under Forest Department Pakistan Act (1927). Whereas the other officially permitted type of forests here are “private forest” which is owned by the local people of the area. These forests are officially sheltered under the Gilgit Private Forests Regulations (1970) and the rules framed under the regulation.

Some protected forests are also the property of the government or the government has respectability rights or is permitted to the whole or part of the forest produces (Ali, 2004). The local people may be able to utilize these forests for grazing of livestock, collection of fuel wood to fulfill domestic needs and other non-timber products. These forests are located in Gilgit, Baltistan and Astore areas and are regulated under the Northern Areas Forest Rules 1983. The total area under protected forests in Northern Areas is 64,512 ha. The dominant tree species found here are Pinus wallichiana, Betula utilis, Juniperus excelsa, and Picea smithiana, (Ali, 2004). Total shrub cover is 381,200 ha but further classification into private or protected forests is not estimated. The distribution, forest type, significance, and right are given by district wise in following. In district Gilgit and Nagar forest covered area is approximately 17,028 ha these are mostly Montane dry temperate sub-alpine. In nature these forest are very important for timber, firewood, grazing of livestocks, eco- tourism, biodiversity and wildlife etc. Free grant of timber and fuel wood in Nagar area is allowed to the locals as per notification of (1974).

In Gilgit, the timber is supplied on concessional rates to the locals but fire wood from dead and dying trees is free.

In district Ghizer forest resources are limited. Forest are covering 7,740 ha are also Motane dry temperate and sub-alpine in nature. These forests provide timber, firewood, grazing, sustainable eco-touris, biodiversity, watershed shelter and food for wildlife.

Chapter No 1 General Introduction

In Diamer and Astore forest covered 30,960 ha of total area of land. These forests are also Montane dry Temperate in nature. Most of the forests of Chlas, Darel- Tangir are private. People of these area has free grant of timber and fuel wood in to the local people as per Ailan no.40 of 1940. These forests also provide timber, firewood, eco-tourism opportunities, nutrition and habitat for wildlife, watershed and rich biodiversity.

In district Skardu forest cover is 9,288 ha. These forests are also mountainous dry temperate and sub- alpine in nature. Forest in these areas are important for timber, firewood, watershed, eco-tourism, In Skardu, the timber is supplied on concessional rates to the locals but firewood from dead and dying trees is free. Rao and Marwat (2003). Private forests are owned by the people and regulated under the Gilgit Private Forests Regulations, 1970 and the rules notified in 1975. In agreement with the Accession deed of 1952 with the government of Pakistan the tribal communities of Chilas, Darel and Tangir in Diamer district own the private forests of the Northern Areas. But these are managed by the Gilgit-Baltistan Forest Department (GBFD) as agreed by the local communities in the deed. GBDF has been serving the communities in profitable harvesting of private forests by preparing working schemes, marking of trees for felling and issuing permits for transportation of timber to outside markets. These forests have commercial value and therefore a working scheme is prepared for every 10 years for commercial exploitation. Locals have a 100% ownership and the government gets 50% royalties from the revenue and Rs. 1 per cubic ft of timber sold through commercial harvesting (Ali, 2004).Mismanagement and illegal commercial harvesting is the main causes of deforestation in western Himalayan region of Pakistan (Ali et al. 2005).

Most of the natural forests are existed in the northern areas of Pakistan covering three great ranges of mountain, Himalaya, Hindukush and Karakoram, where more than 60% of the country’s natural forest resources are found. The northern mountains of Pakistan are well known for their rich biodiversity as they are located in 3 mountain ranges i.e.., Karakorum, Himalaya and Hindu Kush (Shinwari et al. 2000a). In these areas forests are main source of income for the local people. Hussain and Khaliq (1996) reported that local people in the mountainous regions of Pakistan mostly depend to fulfill their domestic need from forest .They use wood for construction of building as well as domestic fire fuel and to fulfill other needs in

Chapter No 1 General Introduction large scale Agri, orchids and grazing because there is no any alternate source of income. These forests also fulfill the needs of food for livestock and provide traditional medicinal plants. Kazmi & Siddiqui (1953) described the value and uses of vegetation around Astore district. Several surveys has been conducted and published by many researchers about these valuable forests in the passage of time. Ahmed and his team visited many forested and non forested areas of Northern areas of Pakistan to investigate vegetation population dynamics and to determine dedrochronological potential of these forests. (Ahmed et al 1976, 1986, 1988a, 1988b, 1990, 1991, 2006, 2009a, 2009b, 2010a, 2010b, 2010c, 2011) however recently Akbar et al. (2010, 2011) and Hussain et al. (2010, 2011) conducted study about some forest of Gilgit-Baltistan.

Unfortunately these valuable forests are highly under the pressure due to the illegal cutting and lack of conservational activities.

This thesis has two portions (1) Vegetation ecology of Gilgit, Astore and Skardu Districts (2) Dendrochronology of Pinus wallichiana from Ganji Valley of Skardu District.

First portion of this thesis included plant communities, size class structure multivariate analysis and soil nutrients and vegetation relationship (Plant Ecology). The main purposes of this study were.

¾ To carry out quantitative survey and describe the forest communities and associated species of study area. ¾ To present size class structure of the study area. ¾ To perform the multivariate analysis (classification and ordination) to described vegetation of the area. ¾ To explore the status of Physico-chemical properties of soil of the study area.

Chapter No 1 General Introduction

Second portion of my thesis is based on Dendrochoronology. Samples were collected from Pinus wallichiana species Ganji valley of District Skardu. Dendrochronological potential of other species belong to the different areas were presented by Ahmed et al (2005), Ahmed at al.(2009), Zafar et al.(2010) and Zafar (2013).However no work was carried out on Pinus wallichiana belong to Ganji valley.

The main objective of this part is following.

¾ To estimate age and growth rates of Pinus wallichiana species. ¾ To present a standardized chronology based on Pinus wallichiana species from Ganji (Skardu) valley. ¾ To explore growth climate response of Pinus wallichiana.

It is hoped that both portion, vegetation ecology of forested areas and Dendrochoronological study presented in this thesis will help to extent and explore the scientific information about vegetation, Dendrochoronological potential of Pinus wallichiana and also helpful to mange and conserve the forest for upcoming generation.

1.2-Profile of study area

1.2.1-Geography

Gilgit-Baltistan is situated in the North of Pakistan bordering China to north, India to the southwest, Afghanistan wakhan corridor to the northwest, and Khyber Pakhtunkwa to the west. Gilgit-Baltistan formerly known as the Northern Areas is the northern most political entity within Pakistan. It is situated between 34.050 to 37.090 north latitudes and 740 to 76.50 east longitudes covering 72971 km2 areas and having 970,347 populations according to the 1998 census.

Gilgit-Baltistan is now considered as a separate province. By the Pakistani national assembly there was a resolution of self-Governance passed on 29 August 2009, and later signed by the President of Pakistan. The order approved self-rule to the people of the previous Northern Areas of Pakistan, now renamed Gilgit- Baltistan, by creating, among other things, an elected legislative assembly.

Chapter No 1 General Introduction

Gilgit-Baltistan of Pakistan occupies an exclusive bio-geographic position. Nature has decorated this area with huge high snow covered mountains, substantial glaciers, glorious rivers, lakes, and attractive forested valleys. The area is such a spectacular attentiveness of high mountains, which grant an ecological environment for the diverse floral and faunal species, modified to rocky, high mountains. The importance of this area cannot be denied due to the presence of three great mountain ranges of the world the Karakoram, Himalaya, and Hindu Kush joining together of the Gilgit and Indus rivers near Partab bridge Bunji, while the Karakoram Range also joins by the Pamir and Kun Lun ranges in the north. The landscape is conquered by some of the world’s highest mountain peaks including 5 peaks over 8,000 m, which overshadows the biological diversity of this section.

The unique chart eristic of this region is that it contains some of the largest glaciers of the world outside the polar region. Almost 12 percent of the region is shaped by mightiest glaciers as., Siachen 62 km Hispar-hoper 61 (km) , Biafo chogo 62 (km), Baltoro 58 (km), Batura 58 (km) Gasherbrun 38 (km), Chogo Lungma 38 (km), Passu 32 (km), Nabandi 32 (km) , Baraldu 30 (km), Rupal 29 (km), Snow lake sim glacier20 km, along with hundreds of other glaciers (Stein, 1987). An A study denotes that only Karakorumis are 23 to 25 percent under ice. It will not be out of place to mention that Tirich Mir located in Chitral with an elevation of 7736 m, the highest peak in the Hindukush system, is also contiguous to Gilgit-Baltistan region (Watters, 1978). The mountain ranges of the area form the headwaters of major rivers, counting the impressive Indus. The Shyok River and the Indus River come through occupied Kashmir but inside Northern Areas, hundreds of their tributaries offer some of the finest spots for fishing, navigation and water sports. But except for rare places such as Skardu and Chilas, living along the Indus banks, has so far been hard for agricultural production and, therefore, people have immovable to smaller valleys and mountain slopes where glacial water is effortless at hand for drinking and irrigation. However, it is only the Indus River and its tributaries that overlook the landscape of Northern Areas by running through the chain of famous mountain systems. The physical surroundings of the Gilgit-Baltistan province can be understood from two different angles the first is linked to the passes and the routes that lead from outside into the region and open the land for outside intervention and the second is the inner break-up into smaller valleys, plateaus and hill girt sub-zones that have helped in the

Chapter No 1 General Introduction

sustenance of human communication or the expansion of economic tricks by breaking the substantial barriers. There are countless traditional passes opening routes from Gilgit-Baltistan to India, Afghanistan, and China on the one hand and to Azad Kashmir, Hazara and Khyber Pukhtunkhaw’s high altitude mountain systems on the other (Map of Northern Area, 2008: 02). These are Zoji la, Kamri la, Chorbat la, Gasherbrum la, Skyang la, Sovoia la, Muztagh la, Sarpolago la, Khunjerab pass, Mintika pass, Chulung la, Gyang la, Marpola, Burzil pass, Killik pass, Hapuchan pass, Irshad Uween, Khora Bhurt, Qalandar Uween, Kheli Gali, Shrilli Gali, Jumagh gali, Kuba gali, Karumbar An, Bashkaro An, Dadarilli An, Babusar pass, Shonthar pass and Fulway pass. These passes or routes are considered to be intentional points as far as national security is apprehensive. The average elevation of these passes is about 16000 feet and during wartimes, they serve as the first line of defense. Besides mountains, this region is also famous for its amazing plateaus such as Deosai plateau, a tract of land, almost isolated northwest from Skardu and neighboring to Kargil sector of India. The Deosai plateau which is of glacial derivation is the mainly frequented route between Kashmir and Gilgit Baltistan (Dani, 2001). As a whole, the defensive profile and geological setting of this area makes it a precinct of high value concern.

1.2.2-Geology

The geologists trust that these mountains are immature and are still increasing. Stony peaks, cliffs and steep slopes distinguish the unique topography of the area. The mountain valleys are typically constricted, profound and gentle in manifestation. The high mountains block the monsoon to arrive at in this area.

The geology of the rocky mountains of Gilgit-Baltistan is very primordial, with some of the world’s oldest rocks forming the highly stratified Precambrian peak groups i.e.. Mashbrum Gasherbrum, Biafo chogo, Hisper-Hoper, Baltoro, Rakaposhi, Ultar, Diran, Broadpeak, Muztagh towers, Trango Towers, Batura, Saltoro, Kangri and countless others (Trench, 1992).These areas are one of the most complex and difficult terrain in the world exhibiting a great verity of rock types and structures. The uncovered rocks range in age from pre-cambrian to current and consist of igneous and metamorphic rocks of different types (Hussain and Awan, 2009).

Chapter No 1 General Introduction

1.2.4-Biodiversity

Gilgit-Baltistan is also famous for its rich biodiversity. This is due to the excessive altitudinal zones and connected changes in environment and soil topography, creating outstanding upright zones in ordinary vegetation species. This is further improved by the diversity in slope angles exposure and aspects to solar emission. An extensive variety of fauna corresponds to the ordinary vegetation of the area

In Pakistan 5,700 species of flowering plants have been served. According to (Nasir and Ali 1970) among them 400 are endemic species and approximately 1,000 species of vascular plants are recognized to happen in mountainous region of Pakistan. Stewart (1972), Ali and Qaiser (1986) studied and declared these endemic plant species are found in northern and western mountains of Pakistan.

The whole figure of plant species present in Gilgit-Baltistan is not known accurately but, several researchers conducted study in different periods suggested the diversity of plants communities. The richness of plants is due to the great variations in climate and topography of the area which developed variety of micro- climate that support a marvelous diversity in plant species. Patterns of species richness show common fashion of improved diversity in plant species from north to south and from west to east. For example, during the vegetation sampling (WWF 1996) recorded total 134 plants belonging to 35 families and about 90 genera from Khunjerab National Park. From Deosai Natinoal Park in western Himalayas there are 342 plant species belonging to 36 families and 142 genera have been reported Woods et al. (1997).

Natural vegetation from valley floors as well as higher elevation above the settlements has been cleared. The natural limitations Gilgit-Baltistan regarding occurrence of natural forests in the form of patches and somewhere dense include arid and semiarid climate. Most of the forest is evergreen coniferous then deciduous.

The common coniferous trees are Pinus wallichiana, Picea smithiana, Pinus gerardiana, Juniperus excelsa, Abies pindrow .A angiospermic Betula utilis found abundant in these forests. Many bushes and herbs i.e.. Bergenia stracheyi, Rosa webbiana, Daphne oleoides ,Bergenia stracheyi Fragaria nubicola ,Geranium

Chapter No 1 General Introduction

pratense ,Geranium wallichianum, Hieracium lanceolatum ,Inula rhizocephala ,Juniperus communis , Leontopodium himalayanum, Leontopodium leontopodinum ,Lonicera caerulea, Myosotis asiatica, Oxyria digyna, Potentilla anserina ,Ribes orientale, Rubus irritans, Rumex dentatus, Solidago virgaurea, Tanacetum artemisioides, Taraxacum sp, Viola rupestris , Thymus serpyllum ,Trifolium repens ,Urtica dioica ,Thymus linearis also associated with these forests most of them locally and commercially used for medicinal purpose. Important of these medicinal plants are described by Rasool (1998), Sher (2002), Khan (2004), Shinwari and Gillani (2000a, 2002, 2003), Wali and Khatoon (2007). About 3000 plant species have been listed from the area, out of which at least 124 are valuable medicinal UNDP/IUCN, (1999, 2003). Moreover, together these contain about 25,000 species (about 10% of world plant species), out of which approximately 10,000 commercially and economically useful Pei (1992).

A number of worldwide important species of mammals are found in the area including some of the endangered species and also included other fauna and avifauna. Most of them directly and indirectly depend for food and shelter on the forest i.e. Capra falconeri (Markhor), Mochus cupreus (Mush deer), Phoenicurus ochruros (Black Redstart), Chaimorrornis leucocephalus (White Capped Redstart), Myophonus caeruleus (Blue Whistling Thrush Scopoli), Monticola solitarius (Blue Rock Thrush), Oenanthe deserti (Desert Wheatear) Phoenicurus erythrogaster (White Winged Redstart) , Carpodacus erythrinus (Common Rose Finch), Serinus pusillus (Fire Fronted Serine), Carpodacus rubicilla (Great Rose Finch), Leucosticte nemoricola (Plain Mountain Finch),Motacilla cinerea (Grey Wagtail), Motacilla alba personata (Masked Wagtail), Motacilla citreola (Citerine Wagtail), Motcilla alba (White Wagtail), Pyrrhocorax pyrrhocorax (Red Billed Chough), Pyrrhocorax graculus (Yellow Billed Chough), Corvus corax(Raven), Oriolus oriolus (Golden Oriole), Sylvia curruca (Lesser Whitethroat), Cinclus pallasii (Brown Dipper), Phylloscopus collybita (Eurasian Chiffchaff), Phylloscopus trochiloides (Greenish Warbler), Eremophila alpestris (Horned Lark), Passer domesticus (House Sparrow), Emberiza cia (Rock Bunting), Tichodroma muraria (Wall Creeper), Prunella fulvescens (Brown Accentor), Prunella ocularis (Radde’s Accentor), Lanius schach (Long Tail or Rufous Back Shrike), Pycnonotus leucogenys (White Cheeked Bulbul), Gypaetus barbatus (Lammergeier), Gyps himalayensis (Himalayan Griffon

Chapter No 1 General Introduction

Vulture), Accipter nisus melaschistos (Eurasian Sparrow Hawk), Falco tinnunculus (Eurasian Kestrel), Alectoris chukar (Chukor Partridge), Tetraogallus himalayansis (Himalayan Snow cock), Columba leuconota (Snow Pigeon) ,Streptopelia turtur (Eurasian or Western Turtle Dove), Aquila chrysaetos (Golden Eagle), Actitis hypoleucos (Common Sandpiper), Calidris minuta (Little Stint), Cuculus canorus (Eurasian Cuckoo), Cuculus varius (Oriental Hawk Cuckoo), Upupa epops (Hoopoe),Gallinula chloropus (Common Moorhen), Pipistrellus pipistrellus ( Common Pipistrelle), Plecotus austriacus (Grey Long-eared Bat) ,Canis lupus (Indian wolf) , Vulpes vulpes montana (Common Re d fox), Ursus arctos (Brown Bear), Uncia uncia (Snow leopard) Capra ibex sibrica (Himalayan Ibex), Pseudois nayaur( Blue sheep), Ovis ammon polii (Marco Polo Sheep), Lepus capensis (Cape Hare), Ochotona macrotis( Karakoram Pika), Marmota caudata aurea (Golden marmot), Apodemus rusiges (Field (Mouse), Cricetulus migratorius (Migratory Hamster), Streptopelia turtur (Eurasian Turtle Dove), Cuculus varius (Oriental Hawk Cuckoo), Carpodacus erythrinus (Common Rose Finch), Erithacus svecicus (Blue Throat), Phylloscopus collybita (Eurasian Chiffchaff), Prunella modularis (Radde’s Accentor), Monticola solitarius (Blue Rock Thrush ), Passer domesticus (House Sparrow), Anthus novaeseelandiae (Indian Pipit), Emberiza cia (Rock Bunting) Lanius schach (Rufous Backed Shrike), Pycnonotus leucogenys (White Cheeked Bulbul).

1.2.5-Climate

The climate of Gilgit-Baltistan is generally dry temperate. The major source of scanty precipitation is snow and rains during winter and spring. The local understanding of the climate is arid if rainfall is from 80-200mm, semi arid if rainfall is from 200-350mm and sub humid if it is from 350-500mm which is the upper limit in Gilgit Baltistan.

Due to this most of the valleys of Gilgit-Baltistan entertain minute rainfall and are mostly recognized, as a cold desert. Standard rainfall is under 200 mm. Snowfall chiefly happens above 4,000 mm and increases with elevation (Ali, IUCN 2003)

Chapter No 1 General Introduction

There are seven districts in Gilgit-Baltistan, Gilgit, Skardu, Ghanche, Astore, Diamer and Hunza-Nagar. Administratively, Gilgit-Baltistan is divided into two regions i.e.. Baltistan region and Gilgit region, Astore, Ghizer, Diamer and Hunza- Nagar included in Gilgit Region while Skardu and Gangche included into Baltistan Region. As for my study is concern I have selected Gigilt the capital of Gilgit- Baltistan, Skardu the capital of Baltistan region and District Astore. Brief information about the three districts is following.

1.3-District Gilgit

This is one of the most important district of Gilgit-Baltistan being administrative the Capital of Gilgit-Baltistan.

Gilgit district is culturally and religiously mixed population, reflecting its geographical and political importance as the regional focal point. The native language is Shina, while the invasion of outsiders means that such diverse languages as Khowar, Burushaski, Wakhi, Pashto, Gujrati, Khohistani, Urdu and English are all spoken here. Likewise, the various religious group ( Suni, Shia, Ismali and Noorbakhshi) who all have their own well defined turf across the rest of the Part of Gilgit-Baltistan, live in close proximity here, which sometime leads to sectarian strain.

Population of Gilgit city is approximately 216,760 according to 1998 census. Administratively it is divided into four Tehsil and Shina is the main language of this District.

Gilgit is located at the elevation of 1,454 meters (4770 ft) above sea level with latitude and longitude 35o 55’N and 74o 20’E correspondingly (PMD 1961-1990). To the north its jointed Afghanistan(Wakhan corridor),on the northerwest China (Xinjiang) , Astore and Diamer to the south, Ghizar District to the west and Skardu to the east.

Only a part of the basin of the Gilgit River,i.e.. Gilgit Valley is integrated inside the political limitations of Gilgit District. There is an dominant width of mountainous motherland, represented mainly by ice fields, glaciers and, and intersected by constricted hygienic valleys, measuring some 100 meters (330 ft) to

Chapter No 1 General Introduction

150 meters (490 ft) in width, to the north and north-east, which separates the province of Gilgit from the Chinese frontier beyond the Muztagh and Karakoram. Towering above Gilgit is Mount Rakaposhi at 7,788 meters (25,551 ft).

The average monthly precipitation in (mm), mean monthly maximum and minimum temperature data of 39 years collected nearby Gilgit station.

The summer season of Gilgit is concise and warm and mean maximum temperature occurs 36 and 35 Coin month of July and August while in winter temperature goes below zero mean minimum Temperature occurs -2Co in the month of December and January respectively (Fig 1.2, 1.3)

The station data shows that highest mean precipitation occurs in the month of (April-May) the late spring which is also famous as pre-monsoon period and minimum precipitation recorded in November and January. (Fig 1.1).High temperature show in summer (June-August) and lowest temperature occurs in December and January. (Fig 1.2, 1.3)

Mean monthly amount of Precipitation (mm) Gilgit

30.0

25.0

20.0

15.0

10.0 Precipetation (mm)

5.0

0.0 Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec Months

Fig 1.1 Mean monthly amount of Precipitation in (mm) of Gilgit station based on the data period (1972-2011).

Chapter No 1 General Introduction

Mean monthly Maximum Temperature(C) Gilgit

40.0

35.0

30.0

25.0

20.0

15.0 Temperature (C) Temperature 10.0

5.0

0.0 Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec Months

Fig 1.2 Mean monthly maximum temperature in (Co) of Gilgit station based on the data period (1972-2011).

Mean monthly minimum tempeature (Co) Gilgit

5 Temperature

0 Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec -5 Months

Fig 1.3 Mean monthly minimum temperature in (Co) of Gilgit station based on the data period (1972-2011)

Vegetation of Gilgit is covered with shrub/ herbs, grasses and patches of many forests on mountainous areas. Valleys included in this district i.e. Jutial, Kargah, Naltar, Haramosh, Bagrot, Joglot gah, Danyore, Napura-Basin, Oshkandas,

Chapter No 1 General Introduction

Chamugat, Sai Jagot, and Pahote. But the study was conducted in the forested areas of Nalter, Kargah, Danyore, and Joglotgah valleys.

1.4-District Skardu

District Skardu is the executive center and biggest town of Baltistan even if no great attention in itself, its setting is theatrical one and the town provides a starting position for some of the most fabulous trekking and mountaineering that Pakistan has to offer. The town is small, dusty place, growing quickly along its one main street, but still entirely dwarfed by the towering showground of magnificent rock Mountains all around and by the broad sweep of the Indus. Dominating the town on the north side is the huge rocky outcrop of Kharpochu. To the west and east are rolling sand dunes starching out into the distance. To the south, the wide stony course of the satpara Nullah, an irrigated oasis scattered with trees and field. Hidden from view but the surrounding mountains is the vast tangle of high Karakoram to the north, and the equally vast high plateau of the Deosai to the south.

Skardu is the main town and also capital of Baltistan region. It is situated between 34042 to 35056 on north latitude and 74059 to 76006 on east longitudes. Administratively the district divided in to three sub divisions Kharmang, Shigar and Rundo.

Skardu perched at 2,438 meters above sea-level in the backdrop of the great peaks of 'The Karakoram Mountain Range. Baltistan also known as "Little Tibet" for its resemblance in geographic features with Tibet nestles world's greatest concentration of lofty peaks. A 100 km thick wall of majestic mountains separates it from China in the north. To the south is the mysterious Deosai Plateau lying between Kashmir and Baltistan. In the East lies Laddakh and in the west is Gilgit and Hunza-Nagar. Within an area of 26,000 sq. km contain 60 mountain peaks of above 7000m. Five of these are above 8000m including K-2 (8611m), the second highest peak on earth. These mountains contain the greatest concentration of glaciers outside the Polar Regions i.e.. Siachan, Biafo, Saltoro, Mashabrum, Gashabrum.

Chapter No 1 General Introduction

The climate of Skardu during the summer is moderated by its mountain setting and the intense heat of lowland Pakistan does not reach here. The mountains also block out the summer monsoon and summer rainfall therefore it receives a little rainfall due to these mountains resulting in very severe winter weather.

The average monthly precipitation in (mm), mean maximum and minimum in (Co) of 39 years collected nearby Skardu meteorological station. The summer season of Skardu moderate mean maximum temperature occurs 32 and 31 Coin month of July and August respectively while in winter temperature goes below zero mean minimum Temperature occurs -8, -4 and -5 Coin the month of January February and December respectively (Fig 1.5, 1.6) The station data shows that highest mean precipitation occurs in the month of (Feb-April) the early spring while period and minimum precipitation recorded in October and November (Fig 1.4)

Mean monthly Amount of Precipitation (mm)Skardu

50 45 40 35 30 25 20 Precipitation 15 10 5 0 Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec Months

Fig 1.4 Mean monthly amount of precipitation in (mm) of Skardu station based on the data period (1972-2011).

Chapter No 1 General Introduction

Mean monthly Maximum Temperature(Co) Skardu

Temperature Temperature 15

0 Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec Months

Fig 1.5 Mean monthly maximum temperature in (Co) of Skardu station based on the data period (1972-2011).

Mean monthly Minimum Temperature(Co) Skardu

5 Temperature Temperature 0 Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec -5

-10 Months

Fig 1.6 Mean monthly minimum temperature in (Co) of Skardu station based on the data period (1972-2011).

There are many valleys present in this district i.e.. Ganji, Stak, Turmic, Thwar, Dambodas, Sheangus, Talu, chari, Basho, Kachora, Satpara, Kwardo, Brgardo, Qumra, Hussainabad, Thorgo, Gole, Sermik, Mehdiabad, Katisho, Gasing, Manthokha, Madopure, Gahori, Hilalabad, Tulti, Pari, Mayordw, Kharmang khar,

Chapter No 1 General Introduction

Bagicha, Tarkati, Hamzigound, Olding, Hargosil, Memosh, Belargo, Brlomu, Gerakh, Gangani, Alchori, Seldi, Gulabpore, Wazirpore, Braldo, Baltoro, Arandu, etc among these valleys Basho, Ganj, Hargosil, Gasing, and Mamosh forests are famous.

4.5-District Astore

Astore is one of the six districts of the Gilgit-Baltistan. It is located at 35° 2'20.30"N, 75° 6'36.91"E covered by 5,092 km² area with elevation from 2600 to 3500m. According to the 1998 census of Pakistan the population of Astore was 71,666 habitants. Astore existed to the west by Diamer, to the north by Gilgit to the east by Skardu and to the south by Khyber-Pakhtunkhwa and Neelum District of Azad Kashmir. The population was 71,666 according to the census (1998).

Climate of Astore is moderate during summer. In winter it may receive 6 inches to 3ft snow from main valleys to the mountains. The main languages spoken in the valley is mostly Shina then Urdu.

Climate of Astore is moderate during summer. In winter it may receive 6 inches to 3ft snow from main valleys to the mountains. Due to its unique climatic conditions, the valley provides excellent fauna and flora, especially economically important medicinal plants.

The average monthly precipitation in (mm), mean monthly maximum and minimum temperature data of 31 years collected nearby Astore station.

The summer season of Astore is moderate, maximum temperature occurs 24, 27 and 26 Coin month of June, July and August while in winter temperature goes below zero mean minimum Temperature occurs -7,-5, and -4 Co in the month of January, February and December respectively (Fig 1.8, 1.9)

The station data shows that highest mean precipitation occurs in the month of March, April and May respectively minimum precipitation recorded in October and November. (Fig 1.7)

Chapter No 1 General Introduction

Monthly amount of Precipitation (mm) Astore

90 80 70 60 50 40 30 Precipitation in (mm) 20 10 0 Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec Months

Fig 1.7 Mean monthly amount of precipitation in (mm) of Astore station based on the data period from 1980 to 2011.

Mean monthly maximum Temperature(Co) Astore

30.0

25.0 20.0

15.0

Temperature 10.0

5.0

0.0 Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec

Months

Fig 1.8 Mean monthly maximum temperature in (Co) of Astore station based on the data period from 1980 to2011.

Chapter No 1 General Introduction

Mean monthly minimum temperature(c) Astore

20.0

15.0

10.0

5.0

Temperature 0.0 Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec -5.0

-10.0 Months

Fig 1.9 Mean monthly minimum temperature in (Co) of Astore station based on the data period from 1980 to2011.

The district has many small valleys i.e. Rama, Mushkin, Dashken, Ratu, Loas, Louze, Bubin, Gorikot, Guhdae, Parisheng, Eid Ghah, Fina, Bulen, Chongra, Tari Shing, Kamri, Main, Chilem and Minimarag. The forest of Rama, Mushken- Dasken, Gudaie, are selected for this study.

1.6-Problems and issues

¾ Lack of health facilities ¾ Lack of higher educational facilities ¾ Unaware modern to agriculture techniques and equipments. ¾ Unaware about importance of medicinal plant. ¾ Deficiency of irrigation and drinking water due to the damage caused by sliding to the water channel. ¾ Lack of awareness about conserve natural resources and towards developmental activities

Chapter No 1 General Introduction

Fig 1.10 Map of study area, Numbers are stand numbers, for detail refer to the Table 1.1.

Chapter No 1 General Introduction

Table 1.1 Characteristics of sampling sites of Skardu, Gilgit and Astore Districts

Main Location and Lat Long Ele Slope Stn Canopy sites (N) (E) (M) Aspect (o) Skardu District 1 Basho-A 35.17 75.38 3700 NE 35 Mdr 2 Basho-B 35.17 75.38 3550 NE 30 Opn 3 Gasing-A 35.09 75.98 3500 E 25 Mdr 4 Gasing-B 35.09 75.98 3400 W 20 Cls 5 Gasing-C 35.09 75.98 3600 N 27 Opn 6 Hargosil-A 34.75 76.14 3586 E 20 Sct 7 Hargosil-B 34.68 76.15 3463 N 15 Opn 8 Memosh-A 34.71 76.18 3463 NE 35 Opn 9 Memosh-B 34.72 76.17 3414 E 30 Opn 10 Memosh-C 34.73 76.18 3477 E 23 Mdr 11 Ganji-A 35.56 74.98 3310 SE 15 Cls 12 Ganji-B 35.56 74.98 3472 SW 35 Cls 13 Ganji-C 35.56 74.98 3585 SE 37 Cls 14 Ganji-D 35.60 74.96 3374 SE 35 Cls Gilgit District 15 Kargah-A 35.76 74.17 3255 NE 43 Mdr 16 Kargah-B 35.74 74.19 3427 E 33 Opn 17 Kargah-C 35.72 74.18 3216 SE 25 Opn 18 Jutial-A 35.90 74.75 3250 N 40 Mdr 19 Jutial-B 35.90 74.74 3250 N 40 Mdr 20 Naltar-A 36.09 74.11 2930 S 36 Mdr 21 Naltar-B 36.08 74.11 3401 S 40 Mdr 22 Naltar-C 36.11 74.18 2893 Pln 5 Mdr 23 Naltar-D 36.11 74.18 2893 Pln 5 Mdr 24 Danyore 35.90 74.42 3736 NE 45 Opn 25 Joglotgah-A 36.07 74.24 3523 W 35 Mdr 26 Joglotgah-B 36.07 74.22 3055 Pln 5 Mdr Astore District 27 Rama-A 35.20 74.48 3508 NE 40 Opn 28 Rama-B 35.20 74.48 3464 NW 45 Mdr 29 Rama-C 35.20 74.48 3275 S 35 Opn 30 Rama-D 35.20 74.48 3016 S 15 Mdr 31 Mushken-A 35.49 74.72 2691 E 40 Mdr 32 Mushken-B 35.48 74.73 2719 SE 35 Cls 33 Mushken-C 35.48 74.74 2659 NE 25 Cls 34 Mushken-D 35.48 74.74 3078 NE 40 Mdr 35 Mushken-E 35.49 74.75 2639 NE 30 Opn 36 Dashken 35.46 74.77 2616 E 45 Mdr 37 Gudaie 35.17 74.97 3775 N 50 Cls 38 Chelim-A 35.03 75.10 3458 SE 45 Cls 39 Chelim-B 35.01 75.07 3559 E 40 Mdr 40 Chelim-C 35.00 75.06 3596 E 20 Sct Note: Stn= Stand number Lat=Latitude, Long=Longitude, Ele=Elevation, Opn=open, Mdr=Moderate, Sct=Scatted, Cls=Close, Pln=Plain.

PART-I

PHYTOSOCIOLOGY

Chapter No 2 Review of literature

CHAPTER-2

REVIEW OF LITERATURE

2.1-Introduction

This chapter comprises the review of literature of Part-I of my thesis including phytosociology, structure, Soil vegetation relationship and Multivariate analysis.

2.1.1-Ecology

A number of researchers have conducted studied about vegetation of different areas in Pakistan. Bharucha and Shanbhag (1956) described the vegetation type from India. Khan (1957) conducted study to explore the importance of Pinus roxburghii in ecology. According to him this species found in pure from on lower elevation while on higher elevation it may be associated with other species and formed community. Chaudhri (1957a) described the phytosoocioogy of District Chitral. He recognized five different types of forests from this District. Chaudhri (1957b) surveyed Gilgit Agensy, Hazara district, Kurram Agency, Swat, Kohistan, Hills of Muree ,Dir and Chitral to investigate the flora and described the phytosociology of these area. Zaman et al. (1968) described the medicinal importance and status of the Juniperus macropoda from Baluchistan forest. Hussain (1969) carried out phytosociology of Wah Garden, Cambellpur near Rawalpendi. Chagthaii (1976) studied four vegetations communities from Kohat. Ahmed et al. (1976) carried out multivariate analysis of vegetation from Skardu. Ahmed and Qadir (1976) conducted study of communities near road sides from Gilgit to Shandur. Chagthaii et al. (1978) studied phytosociology of the graveyards of Peshwar district. He checked the effect of soil variables on the species distribution. Chaghtai et al. (1983) described ecology of a dry stream bed in Peshawar. According to them the stream offers a variety of habitats depending on the availability of moisture and the extent of biotic disturbance. A great proportion of the inhabiting plants comprises of the annual weeds of winter crops. The moist sites are invaded in early winter. After the winter rains, only the deep-rooted species

Chapter No 2 Review of literature survive along the water coarse. On raised and gravel–excavated sites, only those species thrive which either resist or evade drought. All the sites do not seem to differ much floristically. Kayani et al. (1984) conducted a study to investigate the phytosociology of wasteland of Qutta Pashin districts. They identified 6 plants communities and also correlated the soil physical and chemical properties with the plant communities. Ahmed (1986) presented vegetation of some foothills of Himalayan range in Pakistan. He surveyed 17 locations along road side from Gilgit to Passu. During this expedition he recognized six plant communities. He also studied soil texture of each community. Tareen at al. (1987) presented eleven plant communities from Chiltan of Queta district. Ahmed et al. (1988a, 1988b) worked on 32 locations along road side of Gilgit to Chillas and investigated population structure of planted tree species in Quetta. A quantitative ecological survey was carried out at 17 locations near road side on the great Silk Road from Gilgit to Passu. Six communities have been recognized on the basis of species dominance. Chaghtai et al. (1988) presented ecology of an upland forest near Nowshera, NWFP. They observed that the lower valley slopes were dominated by arboreal vegetation, the middle by tall shrub and the top exposed by grasses. Ahmed et al. (1989) described natural regeneration of Juniperus excelsa M.BIEB from Balochistan. They studied size frequency distribution of seedling showing a wave of recruitment, in addition, future trend of the seedling population suggested that Juniper forest are not deteriorating. Chaghtai et al. (1989) presented temporal changes in vegetation of Miranjani top, Galis, Hazara, and NWFP. They observed the vegetation of Miranjani top has considerably changed in twelve years (1974-86). More changes have occurred in the vegetation on east-, west- and south–facing aspects Ahmed et al. (1990) described population structure and dynamics of Juniperus excelsa in Balouchistan. Ahmed et al. (1991) described Vegetation structure and dynamics of Pinups gerardiana Forests in Balouchistan Shinwari & Gilani (2003) conducted study district Astore and Gilgit to investigate and collect the information on the according to them these area are highly important for the commercial and economical valuable plant. They also give out line to conserve the diverse vegetation. They mentioned the plants are declining because people harvest the medicinal plant without any training so these should be legalized. Eberhardt (2004) conducted study to investigate floristic composition, diversity and vegetation ecology

Chapter No 2 Review of literature from upper Hunza catchment, Western Karakorum (Pakistan). The annotated checklist appended includes data on regional and altitudinal distribution of 528 vascular plant species, belonging to 244 genera and 62 families. Jawed and Benjaminsen (2004) presented fuel wood, timber and deforestation in the Basho valley of Himalayans. Jawed et al. (2005) described administrative reforms, State involvement, Local perceptions of deforestation and community struggle for control the deforestation in this valley Eberhardt et al. (2006) described vegetation ecology, diversity altitudinal distribution and human impact from upper Hunza valley.

Ahmed et al. (2006) also conducted studies in 184 sampling stands and identified 24 different communities and 4 monospeciefic forest vegetation from different climatic zones of Himalayan forests Pakistan. The Wali and Khatoon (2007) worked on medicinal Plants of Bagrote and Harmosh valleys of Gilgit. Ahmed et al. (2007) showed that indigenous vegetation of soan valley is at the risk of extinction. This paper report the eco-geographic factors for in-situ conservation and description of location various taxonomic and genetic diversity for those provision of critical assistance in the formation of effective conservation campaign for target plant species which one point of extinction Qureshi (2008) described vegetation of Nara desert. He studied different vegetation communities using Quadrat method and observed their frequency, density and covers and used these values to obtain IVI. Arshad et al. (2008) distribution of vegetation in Cholistan desert. They studied different aspect like salinity, organic matter, moisture content and ionic concentration of soils and measured plants density, frequency, cover and important value index. According to their research ecological characteristics, responsible for plant distribution in Cholistan desert seem to be salinity, organic matter and ionic concentration.

Many workers have also presented quantitative Phytosociological work from different forested and non forested areas of Pkatistan i.e.. Shaukat et al. (1976) presented Phytosociological study of Gadap area. Sotuern Sind. Twenty two stands in Gadap area of Southern Sind were sampled quantitatively and soil samples were analysis physically and chemically. They studied vegetation composition and structure, dominant group of vegetation, diversity relations of leading dominant groups. Chaghtai et al. (1978) described Phytosociological study of the graveyards

Chapter No 2 Review of literature

of Peshawar district. Chaghtai et al. (1983) described phytosociology of the Muslim graveyards of Kohat division. According to them the number of species in a stand was controlled by the sand and CaCo3 proportions of the soil. The developmental status of the vegetation was also determined. Qadir and Ahmed (1989) presented phytosociology of woodland communities of Hazarganji National Park Quetta. The soils of the communities were coarse-textured, calcareous and non-saline. Tareen and Qadir (1991) presented Phytosociology of the Hills of Quetta District. Fifty seven plant communities were recognized. Forty five communities were grouped into 8 steppe type’s viz., individual communities in each steppe type also segregated on the basis of edaphic factors. The total coverage as well as species diversity tended to be high in protected areas as compared on un-protected areas. Twenty eight species were found to be indicator species of specific soil condition. Hussain et al. (1993) described phytosociology of the vanishing tropical dry deciduous forests in district Swabi. Three main communities Dalbergia sissoo Melia azedarach, Ziziphus maritiana with 2 subtypes, and Acacia modesta with 5 subtypes, were recognized on the basis of similarity indices, important value and floristic composition of the stands. The variation in the dominant species was due to the edaphic and biotic disturbance. It is suggested that the existing vegetation might further change due to underground seepage of water from nearby Tarbela dam. A vegetation profile of all the communities is given. Diskore et al. (2000) presented the vascular plants from the valleys and slopes of Nanga Parbat, West Himalaya. They recorded 962 indigenous or naturalized plant species and 9 additional subspecies. Moreover 106 species are recorded as unsure. The incidence of 32 cultivated taxa is listed. Ecology, general distribution .life form and potential utilization, environmental changes, and human interference were discussed

Ahmed et al. (2006) presented Phytosociological and structure of Himalayan forest from different climatic zones of Pakistan. A quantitative Phytosociological survey was conducted in 184 sampling stands in various climatic zones of Himalayan forest of Pakistan. Based on floristic composition and importance value, 24 different communities and 4 nonspecific forests of vegetation were recognized and quantitative description and their population structure are presented. Many communities show similar floristic composition however different in quantitative values. Vegetation of forests ground flora also presented. Perveen et al. (2008)

Chapter No 2 Review of literature

described plant Biodiversity and Phytosociological attributes of Dureji (Khirthar range) An Inventory of plant species of Dureji game reserve was prepared on the basis of field trips conducted in different parts of the year particularly in winter, they studied phonological status of each species i.e.. flowering and fruiting condition, species diversity. Wahab et al. (2008) presented phytosociology and dynamics of some pine forests of Afghanistan. Vegetation structure of Olea ferruginea forests of Lower Dir was presented by Ahmed et al. (2009). Siddiqui et al. (2009) carried out phytosociology of Pinus Roxburghii Sergent. (Chir pine) from lesser Himalayan and Hindu Kush range of Pakistan. Ahmed et al. (2010) described the status of vegetation analysis in Pakistan. They presented researchers contribution about observational studies, quantitative studies without multivariate analysis, and studies on population and vegetation dynamics, studies on species diversity, functional studies and multivariate studies. Akber et al. (2010) conducted study to investigate floristic and phyto-sociology of vegetation from Kenjhar Lake and surrounding area of Sindh. They identified 262 plant species and seven plant communities. Yousifzai et al. (2010) studied vegetation of selected graveyard from upper swat. During this study they recognized 7 different plant communities from 7 different locations. Khan et al. (2010a) conducted phytosociology, structure and physiochemical analysis of soil in Quercus baloot, forest from District Chitral. Khan et al. (2010b) presented the structure, dynamics, and regeneration positional of Monotheca buxifolia dominated forest from 15 locations of Dir district. Wahab et al. (2010) presented 5 communities and 5 nonspecific forests from 25 sites of district Dir. Nawaz et al. (2010) described impact of fencing on vegetation structure in Lehari and Jindi sub-Mountainous open scrub forest. Hussain et al. (2010) presented 18 trees, 11 shrubs, 29 under shrub, 11 woody climbers, and 7 climbers and 59 Herbaceous, threatened and endangered native plant species from Karachi. Akbar et al. (2010.2011) also explored the Phytosociology, structure and community description and ground flora from some forested area of Gilgit, Astore and Skardu District. During this study they surveyed 15 valleys and identified 5 pure stands and 5 communities of tree species while 83 species from forest floor in this herbs, shrubs and seedling of trees included. Hussain et al. (2010, 2011) presented phytosociology, structure and community description of Central Karakorum National Park. They recognized five communities and 5 pure stands of herb shrubs

Chapter No 2 Review of literature and trees. Khan et al. (2011) conducted study on the Medicinal Plants of Khunjerab National Park. They identified an aromatic traditional medicinal 43 plants species belonging 40 genera and 28 families from Dhee, Barkhn, Shimashal, and pasture of Khunjerab. Shaheen and Qureshi (2011) recognized 114 plant species belonging to 28 families from the Sheosar Lake surrounding of Deosai National Park at the 4142 m elevetaion above sea level. According to them the distribution of vegetation is controlled by complex adaphic, climatic and anthropogenic factors Like exposure,Humidity, and grazing intensity. Nazim et al. (2011) described population structure of mangrove forest from six different sites i.e.. Sand spit, Korangi crossing, Ketti Bunder, Port Qasim, and Sonmani. Shaheen et al. (2011) studied the pattern of plant species, pattern, composition and diversity from Himalayan forest. They recorded total 72 species belonging 31 families. Qureshi et al. (2011) contributed the first report on the biodiversity of Khunjerab National Park. They visited 14 valleys and Nallahas of KNP and identified four type of vegetation zones i.e. Dry Alpine Scrub, Moist Alpine pasture, Dry Alpine Plateau Pasture and Sub Alpine Scrub and Brich Forests and recognized 62 plant species belonging to 45 genera and25 families. Siddiqui et al. (2011) studied the vegetation and current status of moist temperate forest from Himalayan and Hundukus region of Pakistan. Khan et al. (2012) described present status of moist temperate vegetation of Thandiani forest district Abbottabad. They recorded fifteen communities and 90 plant species including 44 herbs and 23 shrub and trees with their medicinal values. They also studied soil profile and major causes of deforestation. Khan et al. (2012) contributed value of 56 medicinal plant species belonging to 36 families from Poonch valley of Azad Kashmir. They described Marketing status, Major threat and local people conservation efforts of medicinal pant.

2.1.2-Soil-Vegetation relation

Most of the researchers were inattentive to evaluate the status and relationship of soil nutrients with the vegetation composition and distribution of Gilgit, Skardu and Astore forest in Pakistan during the last few decades but Ahmed (1976) studied the vegetation of Skardu, using multivariate analysis. Ahmed and Qadir (1976) conducted Phytosociological studies along the way of Gilgit to Gopis, Yasin and Shunder.

Chapter No 2 Review of literature

Some researchers investigated the relationship between environmental characteristics with the vegetation composition in different areas of Pakistan. Hussain (1969) find out relationship between vegetation and climatic and adaphic factors from Wah Garden Cambellpur District Rawalpendi. Hussain and Qadir (1970) conducted a study of Euphorbia caducifolia Haines from Karachi and its surrounding area, mainly at the banks of the Lyari and Malir rivers; they evaluated the relationship of species growth and abundance with the soil physical and chemical parameters. They also

observed correlation of plants density with soil texture and CaCo3. Malik et al. (1973) recognized the nutrients condition of representative soil profiles, minerals compositions, humus, semi-decomposed litter and levels of mineral elements in the needles of different aged trees from the forests of Malakand Division. They elaborated the role of organic matter in the enrichment of soil- site and towards maintenance of nutrients balance of the conifers. Shaukat et al. (1976) evaluated the relationship of vegetation types with the physiographic variables and found significant correlations with water holding capacity, soil texture and exchangeable potassium and sodium. Ahmed et al. (1978) applied multivariate technique to explain the relationship between vegetation and soil variables around the Gharo, Dhabiji and Manghopir industrial area Karachi. Chagthai et al. (1987) find out the relationship between soil factors i.e.. TDS, Sodium, Potassium and Calcium, NO3, PO4 and CaCo3 with

vegetation. According to CaCo3 is an important factor to control the dominance of species. Shaukat et al (1981) conducted a study to determine the effect of edaphic factors on the vegetation of calcareous hills around Karachi and observed excellent correlations with CaCo3 and organic matter content. Kayani et al. (1984) analyzed soil chemical and physical properties but did not find any relationship with the vegetation type. Ahmed et al. (1986) identified six plant communities on the basis of their importance value from some foothills of Himalayan range of Pakistan and observed significant correlation with the soil properties. Kayani et al. (1988) studied relationship between soil properties and vegetation types. Qadir and Ahmed (1989) carried out phytosociology and the soil chemical and physical properties. They found relationship of organic matter and bicarbonate with the vegetation communities. Tareen and Qadir (1990) described the phytosociology of the water courses of Quetta district. They recognized 23 plant communities and correlate them with the edaphic

factors. Tareen and Qadir (2000) analyzed soil organic matter, WHC, CaCo3,

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conductivity, bicarbonate, chloride ,calcium, magnesium, sodium and potassium in the plains of diverse areas ranging from Harnai, Sinjawi to Duki region of Pakistan. Mahmudi et al. (2003) stated the soil chemical and physical properties are most important to the diversity and growth of any trees in any region. Jaffari et al. (2003) investigated the soil chemical and physical relationship with the vegetation type. They determined soil factors included acidity (pH), texture, electrical conductivity, and - lime, calcium, magnesium, sodium, potassium, Cl , CO3 and HCO3- They used PCA and CCA to find out the relationship of vegetation type with the mentioned soil factors. Tesu et al. (2004) & Johnson et al. (2000) investigate the relationship of soil nutrients with the forest vegetation. They stated that soil nutrients of any forest are basic source of food for the vegetation that growing in a particular area. Enright (2005), Mirzaie et al. (2009), and Asadi (2009) demonstrated that soil chemical properties have overriding role in the distribution and diversity of different forest vegetation communities. They stated that these soil characteristics play an important role in vegetation structure and growth by contributing food and water, and helpful to continue the plant physiological activities. Mataji et al. (2008) conducted study to evaluate relation between soil physical and chemical condition in Kheirud Kenar forests. They reported that the soil physical and chemical properties showed strong relationship with the vegetation communities. Mataji et al. (2010) reported that knowledge about of the vegetation condition and soil properties of ecosystem is useful in judgment of dynamic of any forest. Nimatullah et al (2011) investigated soil properties of 87 various areas from Roh Kohi D.I. Khan Division. During this study they determined soil salinity, conductivity and NPK. Khan et al. (2011) studied soil and tree nutrient status influence on the quality of ‘Kinnow’ Mandarin. They correlated soil nutrients (NPK) with the leaf and fruit samples. Khalid et al. (2012) conducted a study to explore the importance of soil nutrients on the vegetation from the Chakwal district.

The previous results clearly indicated that the soil characteristics play a key role in the distribution of plant species. The main objective of this study is to determine the relationship between the vegetation type and soil chemical properties as well as to investigate the most significant factors, which may have overriding role in distribution of vegetation types. Another purpose of this research is to recognize that how soil influenced on particular species. By recognizing the interaction between soil

Chapter No 2 Review of literature

and flora of studied area, it is suggested that the appropriate guidance should be implemented for better management and rehabilitation of forest species in any other area of this region.

2.1.3-Multivariate Analysis

The multivariate approaches have been applied in different area of Pakistan to classify the vegetation types and groups, to investigate the relationship between environmental factors and soil nutrients with the vegetation communities during different expedition. First time this technique was used by Shaukat and Qadir, (1971) applying polar ordination of Bary-Curtis (1973) to explore the vegetation of calcareous hills around Karachi. Ahmed (1973) and Ahmed et al (1978) performed 3- dimentinoal polar (stand) species and environmental ordination first time to explore vegetation environmental complex of some industrial area of Karachi. Ahmed (1976) highlighted and explained the vegetation along the road site of Skardu using stand ordination. Shaukat et al. (1980) studied the vegetation of Gadap area using the ordination method. They construct the environmental and species ordinations for the vegetation description of Gadap area,they noted significant correlations in vegetation with soil nutrients and moisture. Khan et al. (1987) conducted study to describe the structure, composition and pattern of Achyranthes aspera dominated ruderal vegetation in the suburbs of Karachi. They also found the correlation between the vegetation and the environmental gradient pH and MWHC. Shaukat (1988) suggested a multivariate approach for vegetation sampling and pattern recognition at various scales. Qadir and Shabbir (1989) utilized Bray and Curtis 1957 index of similarities and they observed two Plant community. Shaukat and Uddin (1989a) defined various techniques of factor analysis using different kinds of factor rotation and found multiple group method with varimax rotation to be most effective. Again Shaukat and Uddin (1989b) described his relationships in a deserts landscape in southern Sindh using (CCA), they developed three new variants of CCA (models I, II and III).Hussain et al (1994) carried out the multivariate analysis during the study of the tropical dry deciduous forest near Swabi and Mardan District. They analyzed the soil nutrient and the communities of this area. During this study they recognized three major communities of plants and observed correlation the soil nutrients and vegetation. Shaukat (1994) used multivariate approaches to describe the niches and

Chapter No 2 Review of literature

guild structure of plant in a deserts landscape. The PCA was used to define the ecological niches overlaps were computed using principal components rather than the individual environmental variables. Hussain et al. (1994) presented phytosociology of the vanishing tropical deciduous forests in district Swabi. The study deals with the multivariate analysis of vegetation of Swabi District. Sociological relationship among the leading dominants in tree, shrub and herb layers are discussed separately .Chemical and physical analyses of soil were examined with the help of polar

ordination. Soil pH.CaCo3 and P2O5 were found to be the controlling factors in the distribution of vegetation. Awan et al (2001) applied the multivariate technique on the vegetation of Swat. They observed twenty one plant communities. Maria et al (2004) conducted study on vegetation of some industrial sites near the Punjab. They used TIWNSPAN two way indicator species analysis. They recognized two main groups of vegetation. Shaukat et al. (2005) discussed correspondence analysis (CA), detrended correspondence analysis (DCA) and canonical correspondence analysis (CCA) techniques and included that CCA provides better interpretation of ecological data as the techniques incorporates the underlying common covariance structure of species and environmental matrices.

Moreover Enright et al. (2005) studied the deserts vegetation using classification (TWINSPAN) and ordination (NMDS) technique of Kirthar National Park ,Sindh Pakistan. They also evaluated the vegetation- environment relationship. Malik and Hussain (2006) described Classification and ordination of vegetation communities of the Lohibehr reserve forest and its surrounding areas, Rawalpindi. They used two Way Species Analysis and Detrended Correspondence Analysis and studied floristic composition environmental and spatial data were collected. Four plant communities were recognized. Classification and ordination technique provided very similar results based on the floristic composition. The results formed the basis for the mapping spatial distribution of vegetation communities using image analysis technique. Again Malik and Hussain (2007) conducted study to explore the invasion of Broussoneta papyrifera on the native scrub forest at the Himalayan foothill, Islamabad. They used hierarchical agglomerative cluster analysis for the species grouping, pattern and ordination technique. Peer at al. (2007)studied Phytosociology, structure and diversity of vegetation in the mountains areas of Northern Pakistan .They utilized the modern multivariate method TWINSPAN for the

Chapter No 2 Review of literature

classification of community data and CCA for investigate the relationship between the vegetation and environmental gradient. Dasti et al. (2007) used cluster analysis and ordination to study the vegetation and understand the relationship between the vegetation and topography of Photohar Plateau. During this study they identified five plant communities. Wazir et al. (2008) described multivariate analysis of vegetation of Chapursan valley an Alpine meadow in Pakistan. Summarizing the main findings it is concluded that both classification and ordination are able to delimit the plant association according to their environment. Saima et al. (2009) described the floristic compositions along Ayubia National Park District Abbottabad, Pakistan applying the multivariate approaches i.e.. cluster analysis DCA ordination and correlation coefficient. They studied the relationship between environmental factors and species distribution. Sheikh et al. (2009) presented multivariate analysis of roadside vegetation. On the basis of DCA and CCA to analysis they identified the floristic composition of Havalian in five groups. They also checked they correlation between the vegetation and environmental factors. Jabeen and Ahmed (2009) conducted study to investigate the correlation between variable and vegetation of Ayubia National Park, flowing multivariate technique TWINSPAN, DCA ordination and Canonical correspondence analysis. Ahmed et al. (2009) evaluated the ecological aspects of roadside vegetation around Havalian City using multivariate techniques technique (DCA ordination and CANOCO). Ahmed (2009) investigates growth, distribution pattern, classification and correlation of herbaceous vegetation with edaphic factors of Margalla Hills National Park, Islamabad. They used multivariate analysis technique viz .TWINSPAN and DCA ordination. Rao et al. (2009) a group of average cluster analysis used to observe the airborne fungal flora of Karachi, Pakistan. Ali and Malik (2010) described the vegetation communities of the open urban space viz., green belts gardens and parks of Islamabad city, using TWINSPAN classification they observed vegetation communities types which showed some overlap in an ordination space, showing gradients to define vegetation distribution. Ahmed et al. (2010) explored the phytosociology of the vegetation from Motorway, Pakistan .They applied the multivariate analysis i.e.. TWINSPAN and DCA ordination to classify and ordinate the data. TWINSPAN results showed the vegetation into two major communities, which are further divided into 16 sub-communities. Siddiqui et al. (2010a) described quantitative vegetation of moist temperate areas of Pakistan. They used agglomerative

Chapter No 2 Review of literature cluster analysis and PCA ordination to investigate the relationship between the vegetation and environmental factors. Again Siddiqui et al. (2010b) employed different multivariate techniques like Ward, s cluster analysis, TWINSPAN ,DCA ordination, univariate analysis of variance and Hotelling,s T2 (multivariate comparison) test to investigate vegetation environmental complex of this area.

Recently Ahmed et al. (2011) used TWINSPAN and DCA ordination to categorize and ordinate the vegetation of Cedrus deodara dominating forests from mountainous areas of Pakistan. Khan et al. (2011) investigated the vegetation pattern of Chitral district, Pakistan by using TWINSPAN, Ward, s cluster analysis and DCA ordination while Wahab et al. (2011) evaluated the quantitative vegetation description of District Dir, Pakistan by using Ward’s cluster analysis and NMS ordination. Ahmed (2012) conducted study to investigate and explore the vegetation relationship using multivariate technique from Ayubia National Parak. Khan et al (2013) studied the relationship between environmental variables and forest vegetation of Chtiral Districts.

In the light of the above research work no quantitative work has been done on the vegetation of forested area of Gilgit, Astore and Skardu district so this study will provide quantitative information about the forest vegetation of these three districts.

Chapter No 3 Phytosociological studies

CHAPTER-3

PHYTOSOCIOLOGICAL STUDIES

3.1- Introduction

This chapter focuses and deals with Quantitative vegetation description of plant communities. Oosting (1872) used first time the terminology of Phytosociology the described that the word Phytosociology deals with detailed structure, description and classification of plant communities. According to the Shimwell, (1971) and Muller-Dombois and Ellenberg (1974) on the basis of vegetation, compositions, structure, relationship and association between species in diverse region of the world different system of vegetation investigation and categorization evolved. Pege (1990) described the phytosociology of the vegetation from British. According to the record of (Arthur, 2009 and IUCN.2010), Pakistan has more than 321212 plant species. Shinwari (2010) reported 6,000 plant species in Pakistan. (Stewart, 1972) described more than 6000 vascular from different area of Pakistan. Nasir and Ali (1972) reported 6000 medicinal plant species which has high economical and commercial importance. Champion et al. (1956) and Hussain (1984) described the ecology of coniferous and non coniferous forested areas of Kaghan, Swat, Baltisan, Gilgit, Chitral, and Dir. Many other researchers worked on forest vegetation with the passage of time i.e.. Alamgir (2004), Ahmed et al. (2006). Recently, Khan et al. (2010), Akbar et al. ((2010, 2011), Hussain et al. (2010, 2011) also worked on the different forested areas of Pakistan.

3.1.1- Objective

1. The purpose of this chapter is to quantitatively describe forest vegetation and different communities.

2. Describe associated understory vegetation of these communities.

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3.2- Materials and Methods

The data was collected during the summer season between June to August of 2009, 2010 and 2011 respectively. Most of areas are subjected to different kinds of disturbances i.e.. human induce or anthropogenic. Least disturbed areas and stands were preferred for Phytosociological studies.

Total 40 stands from 15 different locations were studied from Gilgit, Astore and Skardu Districts of Gilgit-Baltistan during this field sampling. Data was collected following the method mentioned below.

Point centered quarter (PCQ) method of Cottam & Curtis (1956) was used in various forests of above mentioned districts following Ahmed and Shaukat (2012). In each stand 20 points were taken at 20-meter intervals. For this a cross made by iron was used as PCQ to collect the information about tree species. Diameter tap was used to measure Dbh of each tree. Vegetation sampling was carried out forested areas which contained trees at least 60 cm Dbh with no sign of recent disturbances and covering at least two hectors in area. Phytosociological attributes (relative density, relative frequency & relative basal area) and absolute values (density ha-1 and basal area of species m2/ha-1) were calculated, according to the method described by Mueller-Dombois and Ellenberg (1974) and Ahmed and Shaukat (2012). Importance Value Index (Brown and Curtis, 1952) was used to rank each species and the plant species with the highest importance value in the stand was considered the dominant species. The plant community was named on the basis of first two dominant species. To investigate ground flora, a circular plot (1.5 m diameter) at each sampling point were applied and frequency and relative frequency of tree seedling, shrubs and herbs were recorded. Lower plants of ground flora i.e.. Algae, fungi, Lichens, Bryophytes and Pteridophytes were ignored. Plants samples were collected from the field and were transferred on Herbarium sheets in lab for identification followed by flora of Pakistan (Nasir and Ali, 1972-1994: Ali and Qaiser 1995-2009).

Global Positioning System (GPS) device was used to obtain Geographical co-ordinates, elevation, Longitude and Latitude. Slope angle was measured by the clinometers (Suunto Height & Normal slop meter PM-5/1520 PC).

Chapter No 3 Phytosociological studies

3.3- Results

This chapter describes the Phytosociology of 40 stands. Stand Numbers and sampling locations are given in Districts map Fig.1.10 Chapter-1. Geographical coordinates, main location and site characteristics of each stand of study area are given in Table 1.1. Phytosociological attributes (Frequency, Relative Frequency, Relative Basal area and Important value Index) and absolute values (Density/ha and Basal area m2/ ha) are presented in Table 3.1 while 83 Plant species are listed with their Relative frequency and family in Table 3.2. Among the tree species Pinus wallichiana, Picea smithiana, Juniperus excelsa were the most widely distributed species while Pinus gerardiana, Abies pindrow and Juniperus macropoda were found only one stand correspondingly. An angiospermic tree species Betula utilis also widely distributed in the study area.

On the basis of phytosociological analysis, importance value index and floristic compositions 5 pure stands and 5 communities were recognized. Communities were named on the basis of first two leading tree dominant species

1. Pinus wallichiana -Juniperus community 2. Pinus wallichiana -Betula community 3. Picea-Juniperus community 4. Picea-Pinus wallichiana community 5. Pinus wallichiana-Pinus gerardiana community 6. Picea smithiana pure stands. 7. Pinus wallichiana pure stands. 8. Betula utilis pure stands. 9. Juniperus macropoda pure stand. 10. Abies pindrow pure stand.

Chapter No 3 Phytosociological studies

Table 3.1 Phytosociological attributes, rank, and absolute values of 40 stands in District Skardu, Astore and Gilgit.

Phytosociological Attributes Absolute Values Main Location Name of Species Rank BAm2 and sites -1 R.F R.D R.B.A IVI D/ha ha-1

Skardu District Pinus wallichiana 75.3 91.7 96.66 87.8 1st 184.3 42.38 1. Basho-A Juniperus excelsa 24.2 8.93 3.34 12.2 2nd 18.06 14.63 Pinus wallichiana 77.8 92.5 91.32 87.3 1st 159.5 32.39 2. Basho- B Juniperus excelsa 22.2 7.14 8.67 12.7 2nd 12.26 3.09 Pinus wallichiana 49.2 67.6 73.73 63.2 1st 132 16.17 3. Gasing-A Juniperus excelsa 35.8 21.3 16.67 24.4 2nd 41.69 3.7 Betula utilis 15.7 10.1 10.59 12.4 3rd 20.83 2.35 Pinus wallichiana 39.3 35.1 39.12 38.1 2nd 141.7 10.38 4.Gasing-B Juniperus excelsa 30.8 24.1 11.41 22.2 3rd 95.63 3.02 Betula utilis 29.7 40.7 49.46 39.7 1st 159.4 13.12 Pinus wallichiana 19.4 11.1 11.32 14 2nd 18.88 1 5.Gasing-C Juniperus excelsa 59.7 79.6 77.95 72.3 1st 129.3 6.93 Betula utilis 21.7 8.92 10.73 13.6 3rd 14.51 0.95 Pinus wallichiana 73.8 88.9 91.94 84.7 1st 73.34 7.97 6.Hargosil- A Juniperus excelsa 26.1 11.1 8.05 15.3 2nd 9.65 0.7 Pinus wallichiana 87.5 94.64 96.94 93 1st 38.78 5.26 7. Hargosil- B Juniperus excelsa 12.9 5.36 3.05 6.76 2nd 3.2 0.17 Pinus wallichiana 75 88.9 80.83 81.4 1st 113.9 17.35 8.Memosh-A Juniperus excelsa 16.6 8.92 17.05 14.2 2nd 11.49 3.65 Betula utilis 8.33 2.67 2.1 4.36 3rd 3.44 0.45 Pinus wallichiana 75.8 91.6 91.96 86.5 1st 158.4 26.6 9. Memosh-B Juniperus excelsa 13.1 4.46 5.76 7.93 2nd 7.68 1.66 Betula utilis 10.2 3.57 2.28 5.36 3rd 6.15 0.22 Pinus wallichiana 68.9 85.1 85.3 79.8 1st 180 21.74 10. Memosh-C Juniperus excelsa 22 10.1 7.82 13.5 2nd 22.49 3.35 Betula utilis 9.75 3.57 6.78 6.7 3rd 7.49 1.43 11. Ganji -A Pinus wallichiana 100 100 100 100 Pure 308.92 36.02 Pinus wallichiana 48.57 46.25 56.27 50.4 1st 99.06 12.16 12.Ganji-B Betula utilis 31.43 36.25 35.52 34.4 2nd 77.64 7.67 Juniperus excelsa 20 17.5 8.213 15.3 3rd 37.48 1.77 Pinus wallichiana 70.83 75.5 83.76 75.1 1st 168.5 16.8 13.Ganji-C Betula utilis 29.17 27.5 16.24 24.9 2nd 63.91 3.83 14.Ganji-D Pinus wallichiana 65.51 67.5 81.02 71.4 1st 102.6 11.23

Chapter No 3 Phytosociological studies

Betula utilis 34.48 32.5 18.98 28.7 2nd 49.41 2.63 District Gilgit 15.Kargah-A Picea smithiana 100 100 100 100 Pure 91.58 34.48

16.Kargah-B Picea smithiana 100 100 100 100 Pure 106.3 13.84

17.Kargah-C Pinus wallichiana 100 100 100 100 Pure 99.2 10.15

Picea smithiana 62.5 68.75 79.51 70.3 1st 161.7 56.25 18.Jutial-A Juniperus excelsa 37.5 31.25 20.49 29.8 2nd 73.51 14.25

19.Jutial-B Picea smithiana 100 100 100 100 Pure 104.5 14.04

20.Naltar-A Picea smithiana 100 100 100 100 Pure 237.4 51 21.Naltar ,B Betula utilis 100 100 100 100 Pure 96.3 10.81 22.Naltar-C Pinus wallichiana 100 100 100 100 Pure 112.9 6.99

23.Naltar-D Betula utilis 100 100 100 100 Pure 73.81 6.33

24.Danyore-A Juniperus macropoda 100 100 100 100 Pure 125.7 10.08

25.Joglotgah-A Picea smithiana 100 100 100 100 Pure 216.3 17.33

26.Jogloygah-B Betula utilis 100 100 100 100 Pure 121.8 7.07 District Astore 27.Rama-A Betula utilis 100 100 100 100 Pure 105.7 4.99 28.Rama-B Abies pindrow 100 100 100 100 Pure 107.4 7.87 Picea smithiana 60.04 66.25 64.24 61 1st 45.24 3.18 29.Rama-C Pinus wallichiana 38.96 33.75 35.76 39 2nd 23.05 3.17 30.Rama-D Pinus wallichiana 100 100 100 100 Pure 115.3 11.14 31.Mushken-A Pinus wallichiana 100 100 100 100 Pure 98.43 8.39 Pinus wallichiana 54.29 68.75 62.5 61.8 1st 94.71 5.96 32.Mushken-B Picea smithiana 45.71 31.25 37.5 38.2 2nd 43.05 3.57 33.Mushken-C Pinus wallichiana 100 100 100 100 Pure 156.3 14.74 34.Mushken-D Pinus wallichiana 100 100 100 100 Pure 142.1 13.25 Pinus wallichiana 51.61 57.5 81.09 63.4 1st 56.04 6.02 35.Mushken-E Pinus gerardiana 48.39 42.5 18.89 36.6 2nd 41.41 1.9 Picea smithiana 58.82 72.5 81.8 71 1st 78.2 7.48 36.Dashken Juniperus excelsa 41.18 27.5 18.2 29 2nd 29.66 1.66 37.Gudaie Pinus wallichiana 100 100 100 100 Pure 146.6 10.36 38.Chelim-A Pinus wallichiana 100 100 100 100 Pure 180.1 8.73 Pinus wallichiana 59.38 67.5 84.65 82.6 1st 70.51 6.77 39.Chelim-B Betula utilis 40.63 32.5 15.35 39.8 2nd 29.49 1.23 40.Chelim-C Pinus wallichiana 100 100 100 100 Pure 92.28 5.37

Chapter No 3 Phytosociological studies

Key to abbreviations: R.F= Relative Frequency, R.D = Relative density, R.B.A = Relative Basal area, IVI= Importance value Index, D/ha-1=Density/hector of species, BAm2ha-1=Basal area of species m2/hectar, 1st = First dominant species, 2nd= Second dominant species, 3rd= Third dominant species, Stn = Stand number

Table 3.2 List of Plants and families associated with dominant tree species of the study area.

RF in S.No Name of Plants species PRST stands Family (Range) 1 Acantholimon lycopodioides (Girad) Boiss., 6 2.2-5.2 Plumbaginaceae 2 Aconitum heterophyllum Wall. ex. Royle, 1 0-3.8 Ranunculaceae 3 Anaphalis nepalensis (spreg.) Hand. 13 1.1-13 Compositae 4 Anaphalis virgata T.T.ex Clarke 8 1.5-11.3 Compositae 5 Aquilegia moorcroftiana Wall.ex 2 0.7-1.8 Ranunculaceae Artemisia brevifolia (Wall.ex DC) Ling 6 10 2.7-15.3 Compositae &Y.R.Ling 7 Artemisia obsinthium L. 1 0-5 Compositae 8 Aster sp. 1 0-3.8 Compositae 9 Astragalus gilgitensis Ali. 1 0-3.0 Fabaceae 10 Astragalus rhizanthus Royle exBth. 6 1.8-6.87 Fabaceae 11 Astragalus zanskarensis Bth. ex Bunge, 19 0.9-12 Fabaceae 12 Berberis lycium Royle 3 1.9-3.3 Barberidaceae 13 Berberis orthobotrys Bien ex Aitch., J.L.S 7 0.9-9.7 Barberidaceae 14 Bergenia stracheyi (H. &T.) Engl. 17 1.5-16.2 Barberidaceae 15 Betula utilis D.Don, 7 1.0-7.0 Betulaceae 16 Bistorta affinis(D.Don) Green 16 2.2-15.1 Polygonaceae 17 Cerastium alpinum 1 0-2.8 Celastraceae 18 Cicer songaricum Steph.ex DC. 12 2.0-7.8 Fabaceae 19 Colutea nepalensis Sims. 2 2.2-4.8 Fabaceae 20 Corydalis moorcroftiana Wall.ex H.&T. 2 2.4-4.4 Fumariaceae 21 Cotoneaster integerrima Medik. 3 1.5-6.5 Rosaceae 22 Daphne oleoides Scherb., 3 1.9-10 Thyeleaceae

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23 Delphinium brunonianum Royle, 2 1.8-2.8 Ranunculaceae 24 Dictyolimon macrorrhabdos (Boiss.) Rech.f. 3 1.5-3.2 Plumbaginaceae 25 Ephedra gerardiana Wall ex Stapf, 4 0.9-2.3 Caryophylaceae 26 Ephedra tibetica Stapf, 3 2.1-4.8 Caryophylaceae 27 Epilobium angustifolium L., 3 0.9-1.9 Ornagraceae 28 Erigeron multicaulis Wall.ex DC., 3 1-3 Compositae 29 Fragaria nubicola Lindl.ex Lacaita 25 3.8-17.5 Rosaceae 30 Geranium pratense L., 27 0.7-17.2 Geraniaceae 31 Geranium wallichianum.D.Don .ex Sweet, 4 6.0-15 Geraniaceae 32 Hieracium lanceolatum Hk.,f., 9 0.9-10.4 Compositae 33 Hippophae rhamnoides L., 6 0.9-3.6 Elaegnaceae 34 Impatiens balfourii Hook.f. 6 1.6-9.0 Balsaminaceae 35 Inula rhizocephala Wend, 9 1.5-12.2 Compositae 36 Juniperus communis L. 23 1.2-13.7 Cupressaceae 37 Juniperus excelsa M.B., 3 1.9-7.6 Cupressaceae 38 Juniperus macropoda H.k.f., 1 0-3 Cupressaceae 39 Leontopodium himalayanum D.C., 10 3-14.9 Compositae Leontopodium leontopodinum (DC) 40 19 3.7-17.1 Compositae Hand.Mazz., 41 Leonurus cardiaca L., 3 3.1-4.7 Labiatae 42 Lonicera caerulea L. 2 6.6-13.6 Caprifoliaceae 43 Mentha longifolia (L.) Huds., 4 3.9-6.6 Labiatae 44 Myosotis asiatica Schischk.&Serg., 7 1.0-11.2 Boraginaceae 45 Nepeta discolor Role ex Bth. 10 1.5-10 Labiatae 46 Oxyria digyna (L.) Hill, 13 2.2-13.2 Polygonaceae 47 Picea smithiana (Wall.) Boiss. 6 2.2-5 Pinaceae 48 Pinus wallichiana A.B. Jackson 12 1.0-13.9 Pinaceae Podophyllum hexandrum (Royle) Chatt. 49 2 1.1-2.9 Berberidaceae Mukh., 50 Polygonum alpinum All., 4 2.8-8.8 Polygonaceae 51 Potentilla anserina L., 23 4.9-13.2 Rosaceae 52 Pseudomertensia echioides Riedl 3 3.1-5.2 Boraginaceae 53 Rheum tibeticum Maxim.ex Hk.f., 8 1.1-5.4 Polygonaceae 54 Rheum webbianum Royle,Ill. 3 2.9-4.5 Polygonaceae

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55 Ribes alpestre Dcne.exJacq., 12 1.2-8.3 Grossulariaceae 56 Ribes himalensis Royle, 2 1.9-4.1 Grossulariaceae 57 Ribes orientale Desf., 13 1.0-11.3 Grossulariaceae 58 Rosa webbiana Wall.ex Royle, 31 0.9-10.6 Rosaceae 59 Rubus irritans Hk.f., 8 0.9-12.1 Rosaceae 60 Rumex dentatus L. 4 2.8-12 Polygonaceae 61 Rumex hastatus D.Don, 9 2-8.3 Polygonaceae 62 Saxifraga flagellaris Willd. 1 0-7.6 Saxifragaceae 63 Sedum quadrifidum Pall.,Reise 2 3.3-4 Carssulaceae 64 Silene moorcroftiana Wall.ex Bth. 6 0.9-7.5 Umbelliferae 65 Silene vulgaris (Moench) Garche, 6 0.9-7.2 Umbelliferae 66 Solidago virgaurea L.Sp.Pl. 3 8.8-10.7 Compositae 67 Spiraea canescens D.Don, 9 1-5.4 Rosaceae 68 Swertia petiolata D.Don, 2 2.7-2.8 Gentianaceae 69 Tamarix indica Willd., 3 0.9-1.9 Tamaricaceae 70 Tanacetum falconerii Hk . f., 2 3.6-5.8 Compositae 71 Tanacetum artemisioides Sch. Bip. ex Hk. f., 14 1.2-15.2 Compositae 72 Tanacetum fruticulosum Clarke, 1 0-3.7 Compositae 73 Taraxacum sp. 16 1.2-10.5 Compositae 74 Taraxacum baltistanicum v.Soet 10 2-10 Compositae 75 Thalictrum alpinum L. 3 8.2-9.8 Ranunculaceae 76 Thymus linearis Benth., 6 6.2-15.1 Labiatae 77 Thymus serpyllum L. 19 2.8-18 Labiatae 78 Tragopogon orientalis L. 2 6.5-7.7 Compositae

79 Trifolium pratense L. 8 1.1-7.3 Fabaceae

80 Trifolium repens L. 12 1.8-11.2 Fabaceae

81 Urtica dioica L. 17 1.0-15.2 Urticaceae

82 Verbascum thapsus L.Sp.Pl. 7 1.0-5.5 Verbenaceae 83 Viola rupestris F.W.Schm., 15 1.9-15 Violaceae

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Key to abbreviations: PRST =Number of stand in which a species occur, RF= Relative frequency

Table 3.4 Phytosociological summary of sampled trees species.

Spp. Mean Mean D Mean B.A m2 Dominant S.No PNST -1 -1 Code IVI ha ha 1st 2nd 3rd 1 P.W 27 79 ± 4.4 120.9± 11.5 13.6±1.96 24 3 0 2 P.S 9 83± 7.6 120.4± 23.5 22.3± 6.6 8 1 0 3 J.E 11 21± 4.4 37.8± 10.7 4.50± 1.3 1 10 0 4 B.U 9 44± 10 59.2± 13 4.57± 1 4 4 5 5 J.M 1 100±00 125±00 10±00 0 0 0 6 A.P 1 100±00 107±00 8±00 0 0 0 7 P.G 1 100±00 41.41±00 2±00 0 0 0

Key to Abbreviations: ± = Standard Error, PNST= Presence in Number of stands. D = Density, B.A Basal area, Sp=species, PW= Pinus wallichiana, P.S= Picea smithiana, J.E= Juniperus excelsa, B.U = Betula utilis, J.M= Juniperus macropoda, A.P= Abies pindrow, P.G= Pinus gerardiana.

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3.3.1- Description of communities

3.3.1.1- Pinus-Juniperus community

This community was distributed in Skardu district (Stands 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12) with the elevation from 3414 to 3700 m. The slope angle ranged between 15° to 35°. Due to illegal cutting the canopy was mostly open but in some areas it was closed. In few stands moderate canopy was also observed. Soil texture was silt and loamy in both Basho and Gasing. Ground surface was covered with dense vegetation but mostly the trees were tilted and disturbed due to higher elevation and snow fall for the larger part of the year. In Memosh and Hargosil, soil erosion was rampant. Boulders were scattered in all stands. Pinus wallichiana was the first dominant species with importance value ranging from 63.2 to93%, density from 73.34 to 180/ha and basal area 1 to 42.38 m2/ha. Second dominant species was Juniperus excelsa with importance value ranging between 12.2 to 24.4%, 7.68 to 129.3/ha density and 0.7 to 14.63m2/ha basal area. Betula utilis appeared in seven stands (3, 4, 5, 8, 9, 10, 12) with 4.36 to 34.39% importance value, 3.2 to159.4/ha density and 0.22 to2.35m2/ha basal area. Understorey flora comprised of 57 species including herbs, shrubs and seedlings of tree species among them Anaphalis nepalensis, Astragalus zanskarensis, Berberis orthobotrys, Hieracium lanceolatum, Oxyria digyna, Pinus wallichiana, Potentilla anserina, Rosa webbiana, Tanacetum artemisioides, Taraxacum baltistanicum, Thymus linearis, and Leontopodium himalayanum were distributed with the ranges 2-16.26% relative frequency in all the stands. Tanacetum fruticulosum was only found with 3.7% relative frequency from Basho-B (Stand-2).

3.3.1.2- Pinus-Betula community

This community was recorded from Ganji- C and D on South East facing slope in District Skardu with close canopy and Chelim-B on East facing in District Astore with moderate canopy. Pinus wallichiana appeared as dominant species attending from 71.35 to 82.6% IVI, 70.51 to 168.5/ha density and 6.77 to16.8m2/ha basal area while the co-dominant angiospermic tree Betula utilis showed IVI from 28.65 to 39.77%, density 29.49 to 63.91/ha and from2.63 to 3.68 m2/ha basal area. The slope angle ranged from 35° to 40° and elevation ranged from 3374 to 3585 m.

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As far as ground flora of these three locations is concerned Bergenia stracheyi, Leontopodium leontopodinum, Bistorta affinis, and Potentilla anserina commonly distributed in all stands with the ranges 4.8-12.19% relative frequency but in Chelim B (Stand-39) Solidago virgaurea, Tanacetum falconerii Sedum sp, and Polygonum alpinum recorded with 1.96-10.78% relative frequency and Berberis orthobotrys was found only in Ganji with 6.75% relative frequency .C(Stand-13).

3.3.1.3-Picea-Juniperus community

Sampling area Jutial-A on North facing with 40° slope at District Gilgit and Dashken district Astore on East facing with 45° slope angle were dominating by these two species . The ground surface was covered with dense vegetation in Jutial as compare to Dasken. Most of the tree was disturbed in Jutial while recent illegal cutting was observed in Dashken. The canopy was moderate in both stands. The elevation ranged from 2616-3150 m, Picea smithiana showed higher IVI ranged from 70.25 to82.6%, density 78.2-161.7/ha while co-dominant Juniperus excelsa contained from 29.75 39 to77% IVI, 29.66 to 73.51/ha density and 1.66 to14.05m2/ha basal area.

Thirty species including seedling of dominant tree species were observed from the ground floor surface of these sampling site in which few species like Picea smithiana seedlings, Leontopodium leontopodinum, Fragaria nubicola, Geranium pratense and Anaphalis virgata were found with 2-10% relative frequency in both stands. Jutial-A (stand-18) sampling site indicated quite different species composition in which Thymus serpyllum, Urtica dioica, Viola rupestris, Bistorta affinis, Bergenia stracheyi and grasses were found with 2.2- 12.22% relative frequency. There were no any seedlings of co-dominant tree species in circular plots.

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Fig 3.1 Shows mix forest of Pinus wallichana, and Juniperus excelsa from Basho valley district Skardu.

Fig 3.2 Shows mix forest of Pinus wallichana, and Betula utilis from Memosh valley district Skardu.

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3.3.1.4-Picea-Pinus community

Rama-C (Stand-29) on south facing with 45° slopes and Mushken (Stand-32) on South East facing with 35° slopes were dominated by this community. The canopy was closed in Mushken while in Rama it was open due to illegal cutting. The elevation ranged from 2719 to3275 m. Ground surfaces was covered with dense vegetation in Rama where as scattered boulders and dead fallen trees were observed in Mushken. Picea smithiana attained from 38.16 to 61.04% IVI, 43.05 to 45.24 density/ha and 3.57 to 3.18 basal area m2/ha while in this community Pinus wallichiana showed from 38.96 to 61.84% IVI, from 23.05-94.71 density /ha and 3.17 to 5.96 basal area m2/ha.

During the ground flora analysis, 16 pants species were recognized in which Fragaria nubicola, Geranium sp., Taraxacum sp., Trifolium pratense, Urtica dioica, and Viola rupestris was common with 5- 17% relative frequency in both stands. Potentilla anserina, Colutea nepalensis, Lonicera caerulea, Ribes alpestre, Rubus irritans, Rumex hastatus, and seedlings of trees was found only in Mushken with 2.81-12.67% while Inula rhizocephala, Juniperus communis, and Leontopodium leontopodinum with 8.67-13.79% relative frequency were recorded in Rama.

3.3.1.5-Pinus wallichiana-Pinus gerardiana community

Mushken E sampling site (Stand-35) situated on North East facing with 30° slopes. The canopy was open at 2639 m above sea level. In this community Pinus wallichiana attained 63.4% IVI, 56.04 density/ha and 6.02 basal area m2/ha while the co-dominant species Pinus gerardiana received 36.3% IVI, 41.41 density/ha with (1.9m2/ha) basal area. A total of 10 species of ground flora were recorded in this community but 80% forest floor was covered with Fragaria nubicola, Geranium wallichianum, Lonicera caerulea, Rosa webbiana, Tanacetum artemisioides, Thymus serpyllum and seedlings of Pinus wallichiana with 3-18% relative frequency.

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3.3.1.6-Picea smithiana pure stands

Picea smithiana pure forest was distributed in five sites i.e..Stand-15, 16, 19, 20, 25.The elevation ranged from 2993-3275 m while slope ranged between 5° to 43°.The canopy was open in (Stand-16) while moderate in others. In these locations Picea smithiana density ranged from 91.58 to 237.4 density/ha with 13.84 to 51 basal area m2/ha.

Under these stands composition of ground flora comprised of 42 species including seedlings the of Picea smithiana. In these sampling site floristic configurations was 20% similar. In all stands Fragaria nubicola, Rosa webbiana, Ribes alpestre, and Rumex hastatus found with ranged from 4 to 12% relative frequency.

Fig 3.3 Shows Picea smithiana monospecific forest from Nalter valley of District Gilgit.

3.3.1.7-Pinus wallichiana pure stands

These pure stands of Pinus wallichiana was distributed in 10 different locations (11, 17, 22, 30,31,33,34,37,38,40 stands).These stands were situated at the elevation ranging from 2691 to 3775 m and 5° to 50° slope. Pinus wallichiana pure

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stands were most prominent in Skardu and Astore District where density ranged from 92 to 180 density/ha with 8.72 to 36.02 basal area m2/ha.

During the analysis of ground flora 56 species were recorded. Among them Geranium pratense, Leontopodium leontopodinum, Rosa webbiana, Thymus serpyllum and Viola rupestris were recorded in all stands with 2-17.4% relative frequency while Ribes himalensis (4.14%) was recorded in Ganj-A (Stand- 11),Acantholimon lycopodioides (3.29%) in Kargah-C (Stand-17),Polygonum alpinum, Aconitum heterophyllum, Swertia petiolata, Saxifraga flagellaris and Delphinium brunonianum found in Chelim-A,C(Stand-38,40) with 2.75- 7.69% relative frequency. In Gudaie (Stand-37) Oxyria digyna and Hippophae rhamnoides were present with 2.66% relatively frequency respectively. Lonicera caerulea was recorded only in Mshken-D (Stand-34) with 10% relative frequency. Impatiens balfourii (9%) and Hieracium lanceolatum (4.45%) found only in Mshken-A (Stand-31) seedling of Pinus wallichiana was also present in these stands with 6% relative frequency.

Fig 3.4 Shows Pinus wallichiana monospecific forest from Gudiae Valley of district Astore.

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3.3.1.8-Betula utilis pure stands

Betula utilis, in a pure form is distributed at Naltar B (Stand-21) on South facing Naltar D (Stand-23) Plain, Joglotgah (Stand-26) Plain and Rama A (Stand- 27) on North East facing. The elevation ranged from 3055 to 3508 m while degree of slope ranged between 5° to 40° .The canopies were open in Rama while others showed moderate. In Joglotgah dead and fallen trees were observed, soil was sandy Huge cutting was observed therefore land sliding was common while in Naltar soil was loamy, and over grazing was recorded. In Rama (Stand-27) ground surface was covered with dense vegetation with loamy soil. The density of Betula utilis ranged from73.81 to 121.8/ha with 4.99 to 10.81 m2/ha basal area.

`Ground flora of these sampling site composed of 26 plant species including seedlings of Betula utilis. Eleven species Anaphalis nepalensis, Bergenia stracheyi, Bistorta affinis, Fragaria nubicola, Geranium pratense, Inula rhizocephala, Ribes sp, Thymus serpyllum, Urtica dioica, Viola rupestris and seedling of Betula utilis similar and occupied 1.8-14.5% relative frequency .Hippophae rhamnoides and Acantholimon lycopodioides were recorded only in Joglotgah B (Stand-26) with 1.14% and 2.29% relative frequency respectively. Oxyria digyna was found in Rama-A only (Stand-27) with 4% relative frequency.

Fig 3.5 Shows Betula utilis monospecific forest from Nalter valley of District Gilgit.

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3.3.1.9-Juniperus macropoda pure stand

This unique pure stand was recorded only one location of sampling area Danyore Stand-24) on the North East facing steep slope (45°) with open canopy at 3736m above sea level. This location is very important due to the population of endangered wild animal species Capra falconerii (Markhor) and Juniperus macropoda is one of the favorite food of Markhre .Juniperus macropoda attained 125.7 density /ha with 10.08 basal area m2/ha.

The ground surface of this stand was lash green covered with dense vegetation, cut stem, burning, soil erosion and bad shaped tree were also present in this site. Ground flora comprised of fourteen plant species among them Acantholimon lycopodioides, Anaphalis nepalensis, Artemisia brevifolium, Bistorta affinis, Leontopodium leontopodinum, Potentilla anserina and Rubus irritans were found with 1-13.8% relative frequency. The seedlings of Juniperus macropoda were also recorded in ground flora with 3% relative frequency.

3.3.1.10-Abies pindrow pure stand

This species formed pure stand only in District Astore on North West exposure with moderate canopy and 30° degree of slop at 3464 m above sea level. Abies pindrow showed density of 107.4/ha with 7.87 m2/ha basal area.

During the analysis of ground flora total fifteen pants species were identified among them Bergenia stracheyi, Fragaria nubicola ,Geranium pratense, Juniperus communis, Lonicera caerulea, Nepeta discolor, Polygonum alpinum, Rosa webbiana, Solidago virgaurea and Thalictrum alpinum were recorded with 2.22- 8.88% relative frequency .

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A B

C D

E F

G H

Fig 3.6 Shows some dominant herbs of the study area A= Potentilla anserina, B=Bergenia stracheyi, C= Artemisia sp, D= Bistorta affinis E= Anaphalis sp, F= Thymus serpyllum, G=Urtica dioica, H= Geranium sp

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1 2

3 4

Fig 3.7 Shows some dominant shrubs species of the study area, 1=Rosa webbiana, 2= Ribes orientale, 3= Spiraea canescens, 4=Berberis lycium

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3.4-Discussion and conclusion

Quantitative vegetation analysis of dry temperate forests areas from Gilgit, Astore and Skardu of Pakistan is described in this portion. This study concentrated on quantitative vegetation data of 7 tree species and 83 ground flora which are including herb, Shrub and seedlings of trees.

Like other forested areas of Pakistan these forests are also under the severe anthropogenic pressure i.e.. cutting, over grazing, urbanization and climatic change. Study areas are included under dry temperate area. According to Ahmed et al. (2006) Pinus wallichiana and Abies pindrow are characteristics of moist temperate area while Picea smithiana leading to dry temperate area but due to the wide ecological amplitude these species are distributed in both dry and moist temperate. Pinus gerardiana and Juniperus species are restricted to drier sites of dry temperate area Ahmed (1990). In this area both species occupied timber line area (elevation about 3700 m) where temperature is limiting factor.

During this study out of 40 stands 22 stands were monospecific like ten stands of Pinus wallichiana with a range from 92-180 density ha-1,6 Picea smithiana 92-237density ha-1 and 4 Betula utilis 74-122 density ha-1while one Juniperus macropoda and Abies pindrow 125 and 107 density ha-1 respectively. Monospeciefic forest also studied many other researchers in different locations of Northern Pakistan (Ahmed et al. 1988, 2006; Wahab et al. 2008; Ahmed et al 2009; Khan et al. 2010; and Siddiqui et al. 2010) .Moreover Pinus wallichiana were recorded in 14 stands as a dominant species and in 3 stands as a co-dominant. This showed widespread distribution with no any third rank in any stands indicates the dominance of Pinus wallichiana in these forested areas. The second highly distributed species was Betula utilis occupied as leading dominant species in 5 stands while it is attained as co-dominant in 4 stands and also showed third position in 5 stands. Pinus wallichiana and Betula utilis were not restricted in any specific District. Juniperus excelsa is wildly distributed in district Skardu as 1st dominant in 1 stand, as co-condiment in 10 stands while with third position in 2 stands. Picea smithiana attained the position of leading dominant in 9 stands while as co- dominant in 2 stands. This species never placed third rank in any stand. Picea

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smithiana dominates mostly in district Gilgit while in district Skardu this species never recorded in any stands. Abies pindrow and Pinus gerardiana were recorded only from one location correspondingly in District Gilgit and Astore as pure form while Juniperus macropoda found only from Gilgit in monospecific condition this specie was not seen in any sites of Skardu and Astore district.

Chaghtai et al. (1989) recognized Pinus wallichiana, Picea smithiana, Abies pindrow and Cedrus deodara community from Nathia Gali on North West exposure at 2133 m elevation. Ahmed et al. (2006) surveyed different climatic zones of Himalayan forest of Pakistan and recognized 4 monospecific and 24 different communities. They observed Pinus wallichiana as monospecific condition on south exposure at 2770 m elevation from Nalter Gilgit and higher elevation 3100 m from Tukht-e-Sulaiman. Chaudhri (1960) declared that Pinus wallichiana is a species which can survive and distributed on all aspects with extensive altitudinal zones. Naqvi (1976) documented this species as concerning link up other coniferous species in the area. Further Hussain and Illahi (1991) categorized this species on the basis of ability to survive in different climatic zones. They suggested Pinus wallichiana as mixed temperate forest species. According to Beg (1975) this species is dry zone Blue-Pine forests Where as Champion et al. (1965) claimed Pinus wallichiana needs additional humidity than other species of dry temperate zones. The reports of all previous researchers agreed that Pinus wallichiana has widespread environmental amplitude.

According to Ahmed et al. (2006) recognized Picea- Pinus wallichiana community. Dominant species Picea smithiana appeared with 66% and and 155 density/ha-1 74 while co-dominant Pinus wallichiana occupied 34% importance value and density/ha-1 respectively from Astore Rama. This community was located on south facing at 3300 m above sea level. In this study we also studied Picea-Pinus wallichiana community from Astore Rama on south facing where Picea smithiana attained 61% with 45.24 density/ha-1 and Pinus wallichiana shared 39% importance value with 23 density/ha-1. These differences may due to the different aspect or due to the illegal cutting and other anthropogenic disturbances. Chapion et al. (1965) and Ahmed et al. (2006) recognized Picea smithiana as dry zone spruce forests. Ahmed et al. (2006) studied more or less pure Picea smithiana forest showing

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higher density 333 ha-1 with 167 m2 ha-1 basal area on North exposure from Gilgit Nalter valley while during this study the same specie recorded as pure state from district Gilgit, Jutial with density 104 ha-1 and 14 m2 ha -1 basal area , Nalter density 237 ha-1 and 51 m2 ha-1 basal area , and Joglot gah with density 216 ha-1 and 17 m2 ha-1 basal area valleys of District Gilgit.Due to the different location present finding showed variation from the previous work.

Betula utilis species is widely distributed in the study area in pure form as well as with the association of other coniferous tree species .Champion et al. (1965) considered Betula utilis forests as a sub-alpine Brich forests. Ahmed et al. (2006) studied this type of community from Nalter near District Gilgit at 3350 to 3500 m on north exposure as dominant species with 90% importance value, 666 density ha-1 and 30 basal area m2 ha-1 while the co-dominant Picea smithiana found with 10 important value,32 density ha-1 and 4 m2 ha-1 basal area respectively. We observed Betula utilis from Nalter and Joglot gah of district Gilgit with 100 importance value,121.8 density ha-1 with 7.07 basal area m2 ha-1 , and from Rama of District Astore with 105 density ha-1 and 4.99 basal area m2 ha-1 at elevation 3055- 3508 m above sea level. This species was also observed with Pinus wallichiana and Juniperus excelsa as co-dominant species from Chelim of Astore with 29 importance value, 29.49 density ha-1 with 1.23 basal area m2 ha-1 respectively .In District Skardu this species was studied from Ganji, Gasing and Memosh forests with IVI ranged from 7 to 77.64% and 5.36 to 34.39 density ha- as co- dominant. species. The finding did not agree with the previous worker. This may due to the different location and different species composition or these areas were more disturbed as compare to the previous site.

According to Ahmed et al. (2006) due to the presence of Betula utilis of Sub- alpine and Juniperus communis from dry temperate zone this community was placed in the intermediate zone. They observed Betula utilis as co-dominant specie from Astore Rama near rest House at 3250 m elevation on south facing.

Champion et al. (1965) recognized Abies pindrow forests in as Western Himalayan sub-alpine birch forests. Hussain and Illahi. (1991) reported Abies pindrow community prefers cool and moist sites even in dry zones. Ahmed et al. (2006) sampled Abies pindrow community on North West facing slopes from Rama

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Astore at an elevation of 3451 meters. According to them Abies pindrow attained 90% importance as leading species while Pinus wallichiana showed 10% importance value. Wahab et al. (2010) described this species as pure state from Satto Khwa District Dir. In this study we recognized Abies pindrow as pure condition from Rama Astore District while this specie was not seen in any location from district Gilgit and Skardu.

Champion et al. (1965) studied Pinus gerardiana forests as Chilghoza Pine forest. Ahmed et al. (2006) described Pinus gerardiana community in Takht-e- Sulaiman range from 2000 m to 2700 meters. According to them Pinus gerardiana showed 46% importance value as leading species while the Pinus wallichiana appeared as with 40% importance value as co-dominate while is this study Pinus gerardiana as co-dominant with 37% importance value from Astore Mushken stand 35 on North- East facing at 2636 elevation.

Ground flora of the study area was composed of total 83 species including herb, shrubs and seedlings of tree species. Among the understory vegetation Thymus serpyllum (2.8-18%), Fragaria nubicola (3.8-17.5%), Leontopodium leontopodinum (3.7- 17.1%), Bergenia stracheyi (1.5-16.1%), Artemisia brevifolia (2.7-15.3%), Bistorta affinis (2.2-15.1%), Tanacetum artemisioides (1.2-15.2%), Thymus linearis (6.2-15.1%), Geranium wallichianum (6-15%), Leontopodium himalayanum (3- 14.9%) were dominantly distributed with the range of relative frequency respectively. Many workers were also studied these species with coniferous forest from different area of Pakistan i.e.. Ali et al. (2004,2005); Ahmed and Naqvi (2005); Ahmed at al.(2006); Eberhardt et al. (2007); Wazir et al. (2008). These species also have medicinal value which were described by Rasool (1998); Sher (2002); Khan (2004); Shinwari and Gillani (2000a, 2002, 2003); Wali and Khatoon (2007); Qureshi et al. (2011) and Khan et al. (2011).

Chapter No 3 Phytosociological studies

in forested areas. Recently Hussain et al. (2010, 2011): Akber et al. (2010, 2011) described these forests. Shaheen and Qureshi (2011) also reported that the distribution of vegetation is controlled by complex adaphic, climatic, and anthropogenic factors like exposure, Humidity, and grazing intensity. Another major factor of degradation of forest is the poor condition of socio-economic status of the valley around the forested area which has been briefly discussed in the chapter-1.

This is concluded that Pinus wallichiana is the dominant tree species among all the species of these forested areas. These forests are under the pressure due to illegal cutting, grazing and other anthropogenic disturbances so the valuable forest should be save by the concern department with the collaboration of local people. Due to the poor socioeconomic condition, the major parts of the local community depends on forest to fulfill their domestic need, by promoting the standard of live of the local people these forest may be conserved and mange for the upcoming generation.

Therefore, it is suggested that if present disturbance continued, these forests will vanish within a few decade. Serious and urgent action plan to save these forests is to be recommended. Besides tree species, shrub/herbs and grasses should also be saved due to their ecological and medicinal values.

More research work is needed to explore and understand the importance of forest and related vegetation in these areas.

Chapter No 4 Structure of Forest

CHAPTER-4

STRUCTURE OF FOREST

4.1-Introduction

Detail description of study area, review of literature and community of tree species given in Chapter-1, 2 and 3 respectively. This chapter deals with the density, basal area and size classes of each stand as well as overall size class of the dominant tree species of the 40 forested stands of study area.

Population structure is one of the important features of population dynamics or ecology and its perceptive is necessary for sustainable administration of forest resource. Hitimana et al. (2004) and Coomes and Allen (2007) reported that in any forest tree size classes and number of individuals, may change considerably. In the forest around the world many causal factors i.e. regeneration pattern, succession, disturbances, competition, nutrition requirement and climatic conditions etc influences tree size distribution (Denslow 1995, Coomes et al. 2003, Webster et al. 2005). Moreover, the size class distributions of trees are mostly used in assessing the possible outcome due to the disturbances within the forest (Hett and Loucks 1976, Denslow 1995, Baker et al. 2005, Coomes and Allen 2007). This may also be helpful in exploration of successional pathway and structural development of forest (Goff and West 1975, Poorter et al. 1996, Zenner 2005). On The basis of the present status of the forest the future trends may be predicted (Feeley et al. 2007). In addition, the tree size classes may vary among the natural forest but they also shows some similarities i.e. reverse J-shaped Dbh distribution (Hough 1932, Robertson et al. 1978, Kohyama 1986, Niklas et al. 2003).

During the last decades many studies have been conducted to analyze the current status and predict the future trends of forest vegetation from different localities of Pakistan. Ahmed (1988b) presented population structure of planted tree species of Quetta while population structure of Juniperus excelsa M.B. and Pinus gerardiana Wall.ex Lamb., from Balochistan was studied by Ahmed et al. (1990) and Ahmed et al. (1991) respectively. Ahmed et al. (2006) also presented structure of

Chapter No 4 Structure of Forest various Himalayan forests from different climatic zones of Pakistan. Wahab et al. (2008) presented dynamics of some pine forests of Afghanistan, close to the Pakistani border. Vegetation structure of Olea ferruginea forest of Lower Dir was examined by Ahmed et al. (2009). Phytosociology of Pinus roxburghii Sergeant was carried out by Siddiqui et al. (2009) in which special attention was paid to size structure of trees. Khan et al. (2010b, 2011) described the size structure of Quercus baloot, and Monotheca buxifolia forest from District Chitral and Dir respectively. Hussain et al. (2010, 2011) investigated the structure of communities from CKNP. Structural diversity, vegetation dynamics and anthropogenic impact on lesser Himalayan subtropical forests of Bagh district Kashmir has been studied by Shaheen et al. (2011).

The above studies contain some forested areas of Astore, Gilgit and Skardu from Gilgit-Baltistan Pakistan which is expected to extend the information about the forest structure of this area. It is hoped that this study would help to mange and conserve the valuable natural forest in future.

4.2-Objectives

The major purpose of the study as follows

1. To describe the population structure of tree species of each study site. 2. To present overall size classes distribution pattern of dominant tree species. 3. To describe present status and future trend of these forest.

Chapter No 4 Structure of Forest

4.3-Materials and Methods

4.3.1-Sampling

Sampling technique has been described in chapter-3

4.3.2-Statistical analysis

Density of each stand was calculated according to the method described by Mueller-Dombois and Ellenberg (1974) and Ahmed and Shaukat (2012).

4.3.3-Size class Structure

Diameter at breast height (Dbh) of each tree in a stand was divided into 10cm Dbh size classes. Total 11 size classes were made according to the range of Dbh which was lesser than 120 cm.Various size classes and size structure of individual stands were made using by MS Excel 2003 and 2007. Further more in each stand, size classes divided into four categories i-e small size classes (10 to 30 cm Dbh), middle size classes (40 to 60 Dbh cm), large size classes (70 to 90) and above (90 Dbh) extra large size classes. It should be borne in mind that this area was subjected to extensive logging and cutting and large sized trees were removed. The dominant tree species was select following the method described by Ahmed (1984), Siddiqui (2011), Wahab (2011) and Khan (2011).

4.3.4-Weibull distribution model

To evaluate the overall size class distribution of dominant tree species we arranged the Dbh of each class of individual tree species then used these values in CumFreq software (cumulative frequency analysis with probability distribution fitting) selecting the Weibull granulized distribution method which was introduced first time by Baiely and Dell (1973) the results were obtained. The formula of Weibull distribution is given below.

Chapter No 4 Structure of Forest

4.3.5-Formula

Freq = 1-exp {-(X^E/C) ^A)}

The exponent E = 0.440

C = exp (-B/A) = 3.47,

A = 0.2688, B = -0.334

4.4-Results and discussion

During this study total seven tree species were identified in the study area. Among them 6 species i.e. Pinus wallichiana, Picea smithiana, Pinus gerardiana, Abies pindrow, Juniperus excelsa and Juniperus macropoda were gymnospermic while one of them Betula utilis belongs to an angiosperm family. Study area map was given in chapter one. Diameter size class structure of each stand is presented in Fig.4.3. Each and every forest stands are briefly discussed with their sites, locations, environmental characteristics i.e. elevation, slop angles, exposures, and graphical information i.e. altitude and longitude. Physical and other observational characteristics of forests stands are also given. Overall size classes of dominant tree species of the study area also presented in (Fig 4.3).The size-class structure of each stands and overall size class structure of dominant tree i.e. Pinus wallichiana, Picea smithiana, Juniperus excelsa and Betula utilis species are described and discussed below in detail.

4.4.1-Stand No 01 (Basho-A)

Basho-A stand was located between 350.17 and 710.38 E with 3700 m a.s.l where Pinus wallichiana was found as leading tree species with 148 stems ha-1 and 42.38 basal area m2 ha-1with moderate canopy on North East exposure .The slope angle of this stand was 350. Basho is one of the remote valleys of Skardu District .Juniperus excelsa was observed as co dominant species attaining 18stem ha-1 with 14.63 basal area m2 ha-1. Size class structure of Pinus wallichiana was satisfactory because in small size classes it appeared with 28% individuals, in middle 51% and large classes showed 12% distribution while there were no extra large trees. Juniperus excelsa showed 7% trees in small size classes and it showed some gaps in

Chapter No 4 Structure of Forest

middle classes with 2% density (Fig 4.2, 1) no tree was seen in higher classes therefore the size class structure of Juniperus excelsa was not satisfactory. The gap in small size classes indicating no regenerations possibility because seedlings are destroyed due to the extensive grazing by livestock. This situation may be improved by preventing grazing and replanting seedlings. The structure of this stand showed more or less normal distribution with some positive skewness. Sliding, cutting and grazing were common phenomena while many boulders were exposed due to soil erosion in this forest. If the present activities do not stop these Juniper trees will disappear in near future.

4.4.2-Stand No 02 (Basho-B)

Basho- B also was situated on northeast exposure having same geographical coordinates closed to stand one with open canopy and 3350 meter above sea level. The slope was 300. Pinus wallichiana was as a dominant species having 159stems ha-1with 32.39 basal area m2 ha-1 .This species contributed 28 % in small size classes, 54% in middle classes, and 10% in large classes while no extra large trees were recorded. Juniperus excelsa appeared as a co-dominant species with 12 stems ha-1 and 3.09 basal area m2 ha-1.This species entertained 3% in small size classes, 3% in large middle classes and 2% in large size classes. The size classes of Pinus wallichiana showed ideal distribution pattern while size class of Juniperus excelsa showed some gaps in small size classes (Fig 4.2, 2). No trees of Juniperus excelsa were observed above (60 Dbh cm). Size class structures of this stand designated normal with slight positive skewness. Huge cutting, soil erosion and land sliding were observed in this site. Grazing was familiar in this forest. Due to the poor distribution in small size classes, Juniperus excelsa seems to be losing ground in this forest.

4.4.3-Stand No 03 (Gasing-A)

Gasing- A lies between 350.09 N and 750.98 E at 3500 m a.s.l on East facing slope. The slope angle of this stand was 250 with moderate canopy. Gasing forest is located on the upper site near Deosai plateau. Three tree species were recorded among them Pinus wallichiana was first leading dominant species showing highest density 132 stems ha-1 with 16.17 basal area m2 ha-1 while the 2nd leading species

Chapter No 4 Structure of Forest

Juniperus excelsa was observed with 42 stems ha-1 and 3.7 basal area m2 ha-1.The associated angiospermic tree Betula utilis appeared with 21 stems ha-1 with 2.35 basal area m2 ha-1. In small size classes Pinus wallichiana received higher density with 39% individuals, in middle size classes 27% individuals while large size classes received 2%. Juniperus excelsa was distributed with 16% in small size classes and gradually decreases when with increase in size class as the result middle size classes have 4% individuals. Juniperus excelsa showed some gaps in large size classes with 1% individuals’ theses gaps indicated that the large size trees of this species were removed in the past. The Dbh size classes showed roughly inverse J- Shaped structure (Fig 4.2, 3). In this forest Juniperus excelsa was highly under pressure due to the disturbances i.e. cutting, grazing etc. Pinus wallichiana is showing better distributional pattern while Juniperus excelsa showing gape in large classes, determined the old trees were removed from this forest. If special action will not apply to save this species it will be disappeared first from this forest.

4.4.4-Stand No 04 (Gasing-B)

This study site was also situated between 350.09 N and 750.98 facing but on west exposure with close canopy. The slope was 20o at 3400 m a.s.l. Betula utilis was appeared as first leading species with 159 trees/ha with 13.12 basal area m2 ha-1 while the second dominant species was Pinus wallichiana obtained 142 stems ha-1 with 10.38 basal area m2 ha-1. Juniperus excelsa also associated as 3rd dominant with 95.63 stems ha-1 and basal area m2 ha-1.In this stand trees above than (60 Dbh cm) were not found. In small size classes, Juniperus excelsa received high density with 22 % individual’s decreases in middle size classes with 1% individuals. No Juniperus excelsa trees were found above 40 Dbh cm. Pinus wallichiana and Betula utilis showed suitable distribution pattern in each class i.e. small and middle size classes with 27%, 9%, 29% and 12% respectively (Fig 4.2, 4). Due to the heavy cutting large sized trees are vanished. The size classes of Gasing-B showed normal distribution with positive skewness .Mostly trees were in bad shape and cutting grazing practices were observed. Future trend of this forest cannot be considered acceptable.

Chapter No 4 Structure of Forest

4.4.5-Stand No 05 (Gasing-C)

This forest was explored from Gasing valley of Sub Division Kharmang between 340.68 N and 750.98 E 3600 m a.s.l. The slope was 27o facing north while due to the heavy cutting the canopy was opened. Three tree species Juniperus excelsa, Pinus wallichiana, and Betula utilis were recorded having, 129 trees/ha, 19 trees/h, 14 stems ha-1 and basal area 6.93 m2 ha-1 ,1 m2 ha-1,0.95 m2 ha-1 respectively. In this forest trees in large size classes were not seen. Juniperus excelsa received 70% individuals in small and 10% in middle size classes. This species showed best distribution as compared to other two species .Pinus wallichiana and Betula utilis appeared in small size classes with 10 %, 7%and in middle 1%, 2% individuals respectively. These two species showed very poor distribution due to the excessive cutting. Entire stand size classes showed inverse J- shaped structure (Fig 4.2, 5). The distribution of Pinus wallichiana and Juniperus excelsa are not satisfactory. If prompt action is not taken and conservation plan is not imposed these species will vanish in near future.

4.4.6-Stand No 06 (Hargosil-A)

On the East exposure with scattered canopy and20o slope the Hargosil-A was located between 34.75 N to 76.14 E the elevation of 3586 m a.s.l. This is one of the nearest sites to Kargil Border. Pinus wallichiana attained 73 density/ha as leading species with 7.97 basal area m2 ha-1while the co-dominant Juniperus excelsa showed 10 stems ha-1 with 0.7 basal area m2 ha-1.Due to cutting and other anthropogenic disturbances the canopy was scattered. Small and middle size classes of Pinus wallichiana attained higher values with 59% and 23% while decreased in large size classes. Small size classes of Juniperus excelsa attained 10% and middle size classes 2% individuals. There were no Juniperus excelsa trees above (50 Dbh cm). Size classes showed incomplete normal distribution (right half) (Fig 4.2, 6). In this forest the future trend of Pinus wallichiana is better than that of Juniperus excelsa because the distribution pattern of Juniperus excelsa is unsatisfactory. Therefore proper management and conservation plan should be introduced by the stakeholders to save the future of this forest.

Chapter No 4 Structure of Forest

4.4.7-Stand No 07 (Hargosil-B)

This stand was recorded from Hargosil valley of Sub Division Kharmang between 340.68 N to 760.15 E and the elevation was 3464 m a.s.l. This was also nearest to Kargil border. The slope was 15o while the canopy was opened, facing on north exposure. Pinus wallichiana was recorded as dominant species with 39 density/ha with 5.26 basal area m2 ha-1while the Juniperus excelsa recorded as co- dominant species 3 stems ha-1 with 0.17 basal area m2 ha-1.Pinus wallichiana received 51% in small, 32% in middle and 5% individuals in large size classes while Juniperus excelsa showed 10% in small and 2% individuals in middle size classes. In this stand Juniperus excelsa received less density as compared to Pinus wallichiana and no tree of Juniperus excelsa was found above (40 Dbh cm). It is anticipated that Juniperus excelsa species will disappear soon. The size classes of this stand showed inverse-J-shaped structure on over all bases (Fig 4.2, 7).

4.4.8-Stand No 08 (Memosh-A)

Memosh is one of remote and small valley of Sub Division Kharmang. Memosh-A stand distributed between 340.71 N and 760.17 E on northeast facing with 35o slope and open canopy at 3463 meters elevation above sea level. Pinus wallichiana occupied 114 density/ha with 17.35 basal area m2 ha-1as first dominant species and the co-dominant species Juniperus excelsa showed 11 density/ha with 3.65 basal area m2 ha-1while the associated Betula utilis contributed 3 stems ha-1with 0.34 basal area m2 ha-1.The density of Pinus wallichiana was satisfactory in each class this species showed 46% individuals in small, 35% in middle and 8% in large size classes while Juniperus excelsa occupied 2% Individuals in small, 3% in middle and 4%in large size classes. The small size trees of Juniperus excelsa may be removed soon, showing gapes in small class. Betula utilis appeared just in size class number 2 and 4 with 2% individuals. Size classes of this stand represented part of normal distribution (right half) (Fig 4.2, 8). Illegal cutting, soil erosion disturbed trees were recorded in this site. The distribution pattern of Juniperus excelsa and Betula utilis showing these species may be vanished with the passage of time if suitable action is not taken immediately.

Chapter No 4 Structure of Forest

4.4.9-Stand No 09 (Memosh-B)

Memosh-B site is located between 340.72 N and 760.17 E facing slopes with 3414 m a.s.l. The canopy was opened while the slope angle was 300. Pinus wallichiana was the first leading species with 158.4 density/ha with 26.6 basal area m2 ha-1and the co-dominant species Juniperus excelsa occupied 22.49 density/ha with 1.66 basal area m2 ha-1while the associated angiospermic tree Betula utilis showed 6.15 density/ha with 0.22 basal area m2 ha-1.In small size classes Pinus wallichiana showed 44% trees in middle size classes 38%, and in large size classes 10% individuals. This species showed better distribution as compared to other two species. However, due to cutting no tree was seen above (80 Dbh cm). Juniperus excelsa attained 2% in small and 2% individuals in large sized classes showing gaps in middle classes (Fig 4.2, 9). Therefore in this forest Juniperus excelsa may disappear first, followed by Betula utilis. To save these two species, conservation plan and regeneration of seedlings must be applied by the forest departments and other concern stakeholders. Due to the illegal cutting and other anthropogenic disturbances this forest is highly under pressure .Cut stem and bad shaped trees were observed in whole forest. However, the size classes of this stand show roughly linear negative relationship.

4.4.10-Stand No 10 (Memosh-C)

This stand was situated between 340.73 N and 760.18 east longitude, facing on east exposure with 230 slopes. The elevation was 3477 m a.s.l while the canopy of the forest was moderate. Memosh-C is also near to Kargil border. Pinus wallichiana was leading dominant species having 180 stems ha-1 with 21.74 basal area m2 ha-1 and co-dominant Juniperus excelsa showed 23 stems ha-1 with 3.35 basal area m2 ha-1 while the associated angiospermic tree Betula utilis attained 8% stems ha-1 with1.43 basal area m2 ha-1.Small size classes of Pinus wallichiana received 52% individuals and it’s decreased in middle (31%) and large size classes showed 2% individuals. Betula utilis and Juniperus excelsa showed 5% and 1% in small size classes and 5% and 2% in middle respectively. In large size classes no individual of Juniperus excelsa were seen while 1% individuals of Betula utilis were present (Fig 4.2, 10). This shows that these species were under high stress. In this forest, distribution pattern of Pinus wallichiana is satisfactory while Juniperus

Chapter No 4 Structure of Forest

excelsa and Betula utilis indicated that these species may vanish soon from this forest. Conservation plan should be introduced immediately to save the future of these two species. Size classes of this stand showed roughly linear with negative trend due to illegal cutting, sliding, and grazing. If present practices will not stop these species will be disappear with the passage of time

4.4.11-Stand No 11 (Ganji-A)

Ganji is one of a small valley of Sub-Division Rundo. Ganji-A is located between 350.56 N and 780.98 E with 150 slope and South East facing exposure. The elevation was3310 m a.s.l while the canopy was closed. This was pure stand of Pinus wallichiana having 309 stems ha-1 with 36 basal area m2 ha-1.The size classes showed close to normal distribution but some gaps were found in lower classes and the density was 5% individuals, while in the medium size classes it was 56%. The stand showed satisfactory density that gradually decreased in the large size classes with 39% but no extra large trees were seen (Fig 4.2, 11). Low density in smaller size classes indicates that there is very poor regeneration of seedlings .This situation may be improved by promoting seedling growth.

4.4.12-Stand No 12 (Ganji-B)

This mix forest of Pinus wallichiana, Betula utilis and Juniperus excelsa was located at 3472 meters elevation above mean sea level between north latitude 350.56 and east longitude 740.98 facing South West with the slope of 350. The total tree density was 214 stem ha-1.The dominant tree Pinus wallichiana and co-dominant Betula utilis were determined 99 and 78 stems ha-1 with 12.16 and 7.67 basal area m2 ha-1espectively while the associated Juniperus excelsa attained 37 stems ha-1 with 1.77 basal area m2 ha-1.Small size classes of Pinus wallichiana shared 5% individuals but the number of individuals increases with increase in the Dbh size. In middle 16% and in large size classes 25 % individuals were recorded. Betula utilis contribute 3% in small size classes, 30 % in middle size classes and 4% individuals in large size classes. Juniperus excelsa attained 8% individuals in small size classes, 7% in middle and 2% in large size classes (Fig 4.2, 12). In this stand small size classes of Pinus wallichiana and Betula utilis show low density. It may be anticipated that lesser numbers of seedlings are reproducing or the seedlings are

Chapter No 4 Structure of Forest

under the pressure by the livestock’s grazing .This situation may be prevented by proper regenerations of seedlings and conservation plan. Size classes diagram showed roughly normal with some negative skeweness. No extra large tree was observed. This forest is also under the natural and human induced turbulence. If the proper conservation activities will not be imposed this forest may disappear in upcoming years.

4.4.13-Stand No 13 (Ganji-C)

Pinus wallichiana and Betula utilis forest was situated on Ganji-C at the elevation of 3585 meter a.s.l with the closed canopy between north latitude 350.56 and east longitude 740.98 on southeast exposure .The slope angle was 370 degrees. This stand was studied on the upper site of Ganji valley. The dominant tree species Pinus wallichiana occupied 168 stems densities with 16.8 basal area m2 ha-1while the Betula utilis covered only 64 stems of total stand density with 3.83 basal area m2 ha-1. Distribution of trees of Pinus wallichiana was13% in small size classes, 24%in middle size classes and 35%in large size class’s .Small size classes received less individuals, showing the regeneration process affected by disturbance. Betula utilis also showed 9% density in small size classes while middle classes attained 19% individuals. Low density in small size classes identifying that there is poor recruitments of seedlings or the seedlings are destroyed by the grazing of huge live stocks. This situation may be controlled by promoting of seedling growth and applying conservational activities .The size classes of this stand showed roughly normal with some negative skewness both species attained low density in small size classes between (10 to 30 Dbh cm) while showed large density between (50 to 60 Dbh cm) (Fig 4.2, 13). No individuals of Betula utilis were observed above (60 Dbh cm) while Pinus wallichiana was present in large size classes. The future of this stand does not seem to be secure.

4.4.14-Stand No 14 (Ganji-D)

This stand was surveyed from Ganji-D which is located between 35o60 north latitude and east longitude 740.96 on south-east exposure at 3374 meter elevation a.s.l with close canopy and 350 slope angle. Dominant tree Pinus wallichiana covered 103 stems ha-1 and 11.23 basal area m2 ha-1 while Betula utilis appeared 49

Chapter No 4 Structure of Forest

stems ha-1 with 2.63 basal area m2 ha-1.Pinus wallichiana received 16% individuals in small size classes, 22% in middle and 6% in large size class individuals. In small size classes Betula utilis distribution was very poor, small size classes and middle classes attained 13% and 18% respectively. The large trees were removed therefore no trees were seen in large size classes (Fig 4.2, 14). Both species showed almost flat structure. In class 6 Betula utilis attained low density as compared to Pinus wallichiana but no Betula utilis was observed above (60 Dbh cm) while Pinus wallichiana showed higher density in higher size classes (70 Dbh to 80 Dbh cm). Few individuals were also present in the class 9 but no extra large individual were found in this forest. Forest showed roughly normal size class structure. In this forest both species are showing poor density in small size classes. This pattern shows there is poor recruitment of seedlings while the gaps in small size class show harvesting. In this forest all the species are under risk if no conservational action is taken.

4.4.15-Stand No 15 (Kargah-A)

This monospecific pure stand (Kargah-A) of Picea smithiana was observed from Kargah Valley of District Gilgit which was located at 3255 a.s.l meters elevation between North latitude 350.76 and East longitude 740.17 on Northeast exposure with 350 slope angle and moderate canopy. The density of Picea smithiana was 92stems ha-1.With 34.48 basal area m2 ha-1 .In this stand middle and large size classes showed good distribution with 48% and 34 % individuals while small size classes received 14% individuals (Fig 4.2, 15). Density of small size class shows poor regeneration of seedlings while gaps in early class indicated no recruitments. This situation may be recovered by introducing regeneration of seedlings .The size class structure represented roughly normal with platykurtic trend. Gap was found in the begging but middle and large classes showed high density some extra large individuals were also found in this stand. Future of this forest is under threat due to gaps in small size classes however this forest may be saved by little effort.

4.4.16-Stand No 16 (Kargah-B)

Kargah-B Stand situated at 3427 meters elevation a.s.l on East exposure between North longitude 350.74 and East longitude 740.19 with 330 slope angle and open canopy. The density of this forest was 106 stems ha-1 with 13.84 basal area m2

Chapter No 4 Structure of Forest

ha-1 .In this forest the Picea smithiana received few trees (9%) while middle size classes and large size classes attained 42% and 41% correspondingly. In this stand some extra large size trees were recorded. Size class structure showed roughly normal distribution with negative skewness (Fig 4.2, 16). Less number of individuals in small size classes indicated that the there is no proper regeneration. This situation may be controlled by promoting regeneration of seedlings.

4.4.17-Stand No 17 (Kargah-C)

This forest was located between North latitude 350.72 and East longitude 740.18 at 3216 meters elevation a.s.l on Southeast exposure with 250 slope angle and open canopy. This was monospecific pure forest of Pinus wallichiana .The density of this species was 99 stems ha-1 with 10.15 basal area m2 ha-1.Small size classes’ attained 23% trees and gap was found. The middle size and large size classes attained 39% and 37% individuals while no extra large trees. This was also unsound Size class structure showed roughly normal but missed small size classes. The gap and low density of small size classes shows there is no recruitments of seedlings in this forest or it may be destroyed due to grazing by livestock by local people (Fig 4.2, 17). This situation may be easily controlled by reproducing seedlings and controlled grazing.

4.4.18-Stand No 18 (Jutial-A)

Picea smithiana and Juniperus excelsa forest (Jutial-A) was situated at 3250 meters elevation with moderate canopy between 350.90 north latitude and 740.75 east longitude facing on North exposure and having steep slope of 400 . The total density was 235 stems ha-1where the dominant Picea smithiana and co dominant Juniperus excelsa added 161 and 73 stems ha-1respectively with 56.25 and 14.25 basal area m2 ha-1respectievly. Picea smithiana was not present in first size class indicating a gap while the small size classes received 6% whereas middle and large size classes showed 38% and 21% trees distribution respectively. Juniperus excelsa shared 20% in small, 6% in middle, and 3% in large size classes. Size class structure showed roughly normal distribution with platykurtic trend (Fig 4.2, 18). This forest is also under threat due to cutting, and overgrazing. The distribution patterned of Juniperus excelsa seemed close to satisfactory whereas Picea smithiana showed

Chapter No 4 Structure of Forest

very low density with gaps in small size classes. This shows almost no regeneration of seedlings, therefore, to save this species prompt action should be taken.

4.4.19-Stand No 19 (Jutial-B)

This pure Picea smithiana forest (Jutial-B) was situated at 3250 meters elevation between North latitude 350.90 and east longitude 740.74 facing on north exposure. The canopy was moderate with steep (400) slope. In this stand single pure tree species density was recorded 105 stems ha-1with 14 basal area m2 ha-1 .The size classes of the stand were roughly normal with some negative skewness. Due to the minor (19%) trees in small size classes condition is not satisfactory condition is not. However it could be controlled easily by adding seedlings and restricted grazing. In middle size and large size classes’ tree distribution was better with 28% and 46% individuals uniformly while some extra large size classes were seen. The future of this forest was also not secure because small size trees showed poor distribution (Fig 4.2, 19).

4.4.20-Stand No 20 (Naltar-A)

Picea smithiana pure monospecific stand (Naltar-A) is distributed between North latitude 360.09 and East longitude 740.11 at 2930 m elevation asl. The canopy was moderate facing south exposure with 360 slope angle. In this forest density was 237 stems ha-1 with 51 basal area m2 ha-1.Size classes of Picea smithiana showed more or less normal distribution. The density was increasing from small size to middle but decreases towards the large size classes. In small size classes 14% individuals were present while the middle and large classes received 61% and 21% respectively (Fig 4.2, 20). Some extra large trees were found in this stand. Cut stems dead fallen trees and grazing was also recorded. Therefore it is anticipated that small size trees were destroyed by grazing by cattle or there is no any recruitments of seedlings in this forest. However this format may be controlled by reproducing seedlings and forest is still manageable.

4.4.21-Stand No 21 (Naltar-B)

Monospecific Betula utilis stand (Naltar-B) was recorded between North latitude 360.08 and East longitude 740.11 at 2401 meters elevation above sea level.

Chapter No 4 Structure of Forest

This forest was facing South with Moderate canopy on steep slope of 400 degree. The tree density was 96 stems ha-1 with 10.81 basal area m2 ha-1.Structure of forest showed roughly normal with some skewness. The density distribution in small size classes was poor with 36% individuals but its increase in meddle size classes (58%) and decrease towards large size classes 6% trees (Fig 4.2, 21). Low density in small size classes indicated poor recruitments of seedlings while low density of large size classes pointed to the extensive cutting. No tree was observed above 70 Dbh cm .Misshaped trees were recorded while soil erosion was seen. This forest also shows influence of disturbance. Large sized trees were removed small size trees also showed low representation here the future trend of this forest seems unstable.

4.4.22-Stand No 22 (Naltar-C)

This pure forest of Pinus wallichiana stand (Naltar-C) was situated at 2893 meters elevation a.s.l between north latitude 360.11and 740.18 east longitudes on slightly plain surface while slope angle was 50 degree. The canopy of this forest was moderate. Trees density was 113 stems ha-1 with 6.99 basal area m2 ha-1.Small size classes received less number 35% of trees individuals but in middle classes it increases and attained 62% trees while in large classes few (3%) individuals were present but no old tree was observed above (70 Dbh cm) (Fig 4.2, 22). The future of this forest may be saved. To secure the future of this forest cutting, grazing and other kinds of disturbances should be stopped. The size classes of this forest showed roughly normal distribution

4.4.23-Stand No 23 (Naltar-D)

Betula utilis pure forest stand (Naltar-D) was located at 2893 meters elevation a.s.l between north latitude 360.11 and 740.18 east longitude on slightly plain surface .The slope angle was 50 degree with moderate canopy. In this forest the density of stand was 74 stems ha-1 with 6.33 basal area m2 ha-1.The small size classes received 13% trees with gap in first class. The middle size classes showed stable distribution with 77% individuals while large size classes received less value 10% trees but no trees were found above (70 Dbh cm) (Fig 4.2, 23).This forest type of distribution pattern was considered unsatisfactory due to the gap in the first size class. It seems that small size class will show more gaps in future. This forest may

Chapter No 4 Structure of Forest

be saved by promoting seedlings in this stand. Size classes of this forest showed roughly normal distribution with some negative skewness and gaps.

4.4.24-Stand No 24 (Danyore)

This monospecific pure Juniperus macropoda stand was recorded only from Danyore between north latitude 350.90 and east longitude 740.42 at 3736 meters above sea level on steep (400) slope facing Northeast exposure with open canopy. This species was recorded 125 density/ha-1with 10 basal area m2 ha-1. Size class structure diagram showed more or less normal distribution with platykurtic trend (Fig 4.2, 24). Small size classes have lower density with 24% trees while the middle size classes showed higher density with 57% trees .Large size classes received 19% individuals. Middle class have stable distribution while small classes have unstable distribution pattern. This shows huge cutting of young and old trees or poor recruitment from seedling. From an ecological viewpoint this forest is unstable.

4.4.25-Stand No 25 (Joglotgah-A)

Joglotgah-A Picea smithiana forest lies between north latitude 360.07 and east longitude 740.24 at 3523 meters a.s.l facing West exposure. The canopy was moderate while slope angle was 350 degree. The density of pure forest was 216 stems ha-1with 17.33 basal area m2 ha-1. Size class structure is approaching normal distribution. Small size classes have low density with 31% trees where as middle size classes was showing higher density with 45% individuals and large size classes have low density with 24% trees (Fig 4.2, 25). This suggests that due to poor regeneration of seedlings or extensive grazing by livestock small size classes are receiving low number of individuals. This may be controlled by promoting regenerations of seedlings while the low number in older classes shows extensive cutting. This forest may be maintained with some efforts towards protection.

4.4.26-Stand No 26 (Joglotgah-B)

Betula utilis pure forest (Joglotgah-B) was studied between north latitude 360.07 and east longitude 740.22 at 3055 meters a.s.l on rough surface with moderate canopy on slightly plain surface. This forest showed 122 stems ha-1 with 7 basal area m2 ha-1.Size classes’ structure showed roughly normal with negative skewness.

Chapter No 4 Structure of Forest

In this forest the density of small size classes showed poor distribution with 38% trees but it increases from small to middle size classes showing 62% individuals. Large size class was without any individuals which show that the trees above (60 Dbh cm) were removed (Fig 4.2, 26). Low number in small size classes indicating poor recruitments in this forest while no larger trees means that old trees were removed from this forest. These may be replaced by promoting regeneration of seedlings. Future of this forest is threatened by extensive cutting and other kinds of disturbances. However, this forest can be saved by little efforts.

4.4.27-Stand No 27 (Rama-A)

This monospecific forest of Betula utilis (Rama-A) was located between north latitude 350.20 and east longitude 740.48 at 3508 meters above sea level facing Northeast exposure with open canopy and steep (400) slope. This pure forest attained total density of 106 stems ha-1 with 4.99 basal area m2 ha-1.Size class structure documented roughly normal distribution with positive skewness. In this forest the small size showing good regeneration pattern of seedling with 56% individuals. Middle size classes received 40% trees while large size classes attained less than 4% trees as compare to earlier classes (Fig 4.2, 27). Overall small size classes show good recruitments of seedlings therefore future of this forest can be assumed to be satisfactory.

4.4.28-Stand No 28 (Rama-B)

Abies pindrow pure forest (Rama-B) was situated at 3464 meters above sea level facing on Northwest exposure between north latitude 350.20 and east longitude740.48 with moderate canopy and on steep (450)slope angle. Density of this forest was 107 stems ha-1with 7.87 basal area m2 ha-1. This forest was only found in Rama Astore in whole study area. Size class structure was approximately normal with platykurtic trend. Small size classes show 37% individuals while middle size classes received 47% trees whereas large size classes attained 16% trees (Fig 4.2, 28). The future trend of this forest is somewhat satisfactory because the value of smaller classes and middle classes were showed good regeneration of seedlings.

Chapter No 4 Structure of Forest

4.4.29-Stand No 29 (Rama-C)

Mix forest of Picea smithiana and Pinus wallichiana (Rama-C) was observed at 3275 meters above sea level between north latitude 350.20 and 770.48 east longitude on South facing with open canopy and 350 slope angle. The total density was 68 stems ha-1 in which Picea smithiana and Pinus wallichiana contributed 45 stems ha-1, 23 stems ha-1 with 3.18, 3.17 basal area m2 ha-1 respectively. The size classes were showing some normal distribution with a positively skewed trend. Picea smithiana received high value 35% trees in small size classes and in middle 21% while in large classes 10% individuals. This feature shows satisfactory regenerations of seedling and distribution pattern (Fig 4.2, 29). Small size classes of Pinus wallichana showed 10% individuals with a gap in first few classes while it attained 15% trees in middle classes and 9% in large size classes. In this forest Pinus wallichana seems to be disappearing because the gap in first class indicated no regeneration of seedling here the future of Pinus wallichiana is not sheltered but Picea smithiana showed good regeneration pattern.

4.4.30-Stand No 30 (Rama-D)

Pinus wallichiana pure forest (Rama-D) was studied at 3016 meters a.s.l between north latitude 350.20 and East longitude 740.48 facing on South exposure with moderate canopy and 150 slope angles. The density of this forest was 115 stems ha-1 with 11.14 basal area m2 ha-1.Small size classes received low density with 37% trees while middles and large size classes attained 40% and 32% individuals respectively. The size classes of this forest showed roughly normal with negative skewness (Fig 4.2, 30). Due to poor recruitments of young seedling the earlier size classes have low number of individuals. This can be maintained by reproducing seedlings in this forest. Large size class structure showed sign of disturbances. Presence of low number of individuals in small and large size classes is indicating that this forest is unstable and disturbed. It is suggested that immediate consideration should be given to stop these activities otherwise this forest may be destroyed with the passage of time.

Chapter No 4 Structure of Forest

4.4.31-Stand No 31 (Mushken-A)

Another pure forest of Pinus wallichiana (Mushken-A) found at 2691 meters elevation above sea level between north latitude 340.49 and east longitude 740.72 facing on East exposure having steep (400)slope angle with moderate canopy. The density of this forest was 99stems ha-1 with 8.38 basal area m2 ha-1.Size classes structure of this forest showed roughly normal distribution with platykurtic trend. The small size classes received low density with 31% trees as compared to middle size classes. Middle size classes attained 46% individuals while large classes entertained 23% individuals. No extra large trees were seen in this forest (Fig 4.2, 31).The first small class received very low density which indicates that the seedling regeneration is poor while less number in the large size classes indicating that this forest is highly disturbed due to, cutting. Seedlings must be introduced for proper recruitment and special attention should be paid to save the future of this valuable forest.

4.4.32-Stand No 32 (Mushken-B)

Mix forest of Pinus wallichiana and Picea smithiana (Mushken-B) was distributed between North latitude 350.48 and East longitude 740.73 at 2719 meters above sea level facing Southeast exposure with close canopy and 350 slope angle. Total density of forest was 138 stems ha-1. Pinus wallichiana and Picea smithiana shared 95 stems ha-1, 43 stems ha-1 with 5.96, and 3.57 basal area m2 ha-1 respectively. In this forest Pinus wallichana showed somewhat better distribution pattern in small size classes with 30% and 32% individuals respectively while large size classes attained 6% trees. Picea smithiana also showed satisfactory distribution pattern in small and large size classes with 10% and 4% individuals while in middle size classes its received 18% trees (Fig 4.2, 32). The low value of small size classes and large size classes identified anthropogenic disturbances. This situation may be ameliorated by promoting regeneration in this stand.

4.4.33-Stand No 33 (Mushken-C)

Pinus wallichiana monospecific pure forest (Mushken-C) was located at 2659 meters above sea level between North latitude 350.48 and East longitude 740.74 facing on Northeast exposure with 250 slope angle and close canopy. Pinus

Chapter No 4 Structure of Forest

wallichiana attained 156 stems ha-1 density with14.74 basal area m2 ha-1. The size classes showed normal distribution with platykurtic trend. The younger size classes received low density with 31% trees but the distribution of trees increases with the Dbh size classes and middle size classes and large size classes attained 33% and 36% individuals respectively (Fig 4.2, 33). Earlier small size classes have low number of individuals indicating disturbance. This is suggested that the small sizes classes may show gaps in future. This distribution pattern may be recovered by promoting seedling regeneration in this forest.

4.4.34-Stand No 34 (Mushken-D)

Mushken- D pure forest of Pinus wallichiana was situated between North latitude 350.48 ha-1 and east longitude 740.74 at 3078 meter elevation above sea level facing Northeast exposures having steep (400) slop angles and moderate canopy. The density of this forest was 142 stems/ha with 13.25 basal area m2 ha-1. Size class showed somewhat bimodal distribution. Small and large size classes obtained high value 33% and 35% correspondingly while middle size classes received 31% trees (Fig 4.2, 34). The distribution patterns in small and large classes are satisfactory while the middle size classes showed disturbances. In near future this forest have no threat, however situation may be further improved by focusing attention on regeneration.

4.4.35-Stand No 35 (Mushken-E)

This forest of Pinus wallichiana and Pinus gerardiana (Mushken-E) was distributed at 2636 meters between north 350.49 latitude and seat longitude 740.75 on Northeast exposure with 300 slop angles and closes canopy. Here the total density was 99 stems ha-1 the leading species Pinus wallichiana attained 56 stems ha-1 with 6 basal area m2 ha-1 while the co-dominant Pinus gerardiana occupied 42 stems ha-1with 1.9 basal area m2 ha-1.In this forest Pinus wallichiana shows gap with 7% trees in smaller size classes while middle and large size classes received 30% and 35% individuals. Pinus gerardiana obtained high density (30% trees) and 12% individuals in middle size classes. Above than (50 Dbh cm) Pinus gerardiana trees were logged from this stand. Size classes of this forest showed somewhat bimodal structure. Distribution of Pinus wallichiana showed this species is

Chapter No 4 Structure of Forest

deteriorating fast because gap is present in first class and small size classes received very low density (Fig 4.2, 35). This gap may be controlled by promoting regeneration. Pinus gerardiana is showing satisfactory distribution pattern in small and middle classes though large sized trees have been cut down. The condition of the forest can be improved by paying attention to regeneration and seedling recruitment.

4.4.36-Stand No 36 (Dashken)

This mix forest of Picea smithiana and Juniperus excelsa of Dashken was located between 350.46 North latitude and East longitude 740.77 at 2616 meters above sea level facing East with the moderate canopy on steep (400) slope. Total density of this forest was 108ha-1. Pinus wallichiana and Juniperus excelsa contributed 78 stems ha-1, 30 stems ha-1 with 7.48, and 1.66basal area m2 ha- 1respectively. The stand size class structure showed something bimodal distribution (Fig 4.2, 36). Illegal cutting and soil erosion was also seen in this forest. Small size classes of Picea smithiana attained low density with 16% trees while middle and large size classes contained 33% and 23% individuals. Juniperus excelsa attained 15% trees with a gap in small classes while middle size classes received 12% individuals. In this stand Juniperus excelsa may disappear first because smaller classes have gaps and large trees have been logged. Diagram shows no recruitments in Juniper and poor recruitment in Pinus wallichiana.

4.4.37-Stand No 37 (Gudaie)

At Gudaie Pinus wallichiana pure forest was situated at 3775 meters a.s.l between North latitude 350.17 and East longitude 740.97 facing on East exposure. The canopy was closed having steep (500) slop. Pinus wallichiana attained 147 density m/ha with 10.36basal area m2 ha-1. Size classes showed roughly normal distribution but with some negative skewness (Fig 4.2, 37). The distribution pattern of this forest is not stable. Small and large size classes show low density with 37% and 17% individuals while the middle size classes show stable distribution with 46% individuals. This indicates disturbances in small size and large size classes. The future of this forest may be protected by regeneration of seedling and proper management to control the illegal cutting and other anthropogenic activities.

Chapter No 4 Structure of Forest

Fig 4.1 shows some evidence of Illegal cutting, grazing and sliding in the study area.

Chapter No 4 Structure of Forest

4.4.38-Stand No 38 (Chelim-A)

This monospecific pure forest (Chelim-A) was distributed at 3458 meters above sea level between 350.03 North latitude and 750.01 East longitude. Forest was facing on southeast exposure with close canopy having steep (450) slop angles. Here the density was 108 ha-1 with 8.73 basal area m2 ha-1.The distributions pattern seems to be satisfactory. In small size classes the density was high with 55% trees though early size class show low number of individuals indicating low recruitment. Middle and large size classes have 41% and 4% individuals respectively (Fig 4.2, 38). No sign of recent disturbances was recorded in this forest though large size trees were logged in the past. The future trend of this forest can be improved with some attention. Size class structure showed roughly normal but with positive skewness.

4.4.39-Stand No 39 (Chelim-B)

Pinus wallichiana and Betula utilis mix forest (Chelim-B) was investigated between north latitude 350.01 and east longitude 750.07 at 3559 meters above sea level facing on East on steep (400) slop with moderate canopy. Total density of this forest was 122 m2/ha. Pinus wallichiana and Betula utilis shared 92, 30 stems ha-1 with 6.77, 1.23 basal area m2 ha-1respectively. In this stand Pinus wallichiana shows low density 18% trees with gap also found in early class. Middle and large size classes attained 36% and 12% individuals. Betula utilis showed 23% trees in small size classes and 10% in middle size classes while no large size tree above (50 Dbh cm) was seen. Size classes of this forest showed normal distribution with some fluctuations (Fig 4.2, 39). Low density in smaller size classes shows overgrazing of livestock while gaps in early class showed lack of regeneration. The gap may be recovered by reproducing seedlings. The low number in large size classes shows extensive cutting. Therefore the future of this forest may be affected by these disturbances so immediate action should be taken to save this forest. In general the distribution patterns of both species were satisfactory though Pinus wallichiana has gap in early class. However, this situation can be improved by promoting young seedlings and controlled grazing.

Chapter No 4 Structure of Forest

4.4.40-Stand No 40 (Chelim-C)

Pure forest of Pinus wallichiana (Chelim-C) was studied at 3596 meters above sea level between North latitude 350.00 and East longitude 750.06, facing East with 200 slopes. Here the density of stand was 92 density/ha and 5.37 basal area m2 ha-1with open canopy. In this stand small size classes attained low (25%) density with a gap. Middle and large classes received 48% and 27% trees (Fig 4.2, 40). The gap shows that the forest is highly disturbed due to the, grazing and burning. This condition may be halted by regeneration of seedlings. If urgent action is not taken to protect this forest it may vanish gradually in future.

Stand No 01 Stand No 02 60 2 50 Density m /ha=202 2 45 Density m /ha=172 50 40 40 35 /ha /ha 2 Fig 4.2,1 2 30 30 25 Fig 4.2,2 20 20 15 Density m 10 Density m 10 5 0 0 1234567891011 1234567891011 Dbh Size Classes Dbh Siz e Cla sse s

Stand No 04 Stand No 03 80 Density m2/ha=396 Density m2/ha=194 70 40 60

/ha 30 2 50 /ha

2 Fig 4.2,4 Fig 4.2,3 40 20 30 20 10 Density m

Density m 10 0 0

1234567891011 1234567891011 Dbh Size Classes Dbh Size Classes

Fig 4.2 Shows the size class distribution of tree species from 40 stands of study area. On Y axis tree density /ha where as on X axis diameter classes of tree in Dbh >10 are presented.

Chapter No 4 Structure of Forest

Figs 4.2 continue…

Stand No 05 Stand No 06 70 Density m2/ha=162 Density m2/ha=83 60 25 50

/ha 20 2

40 /ha 2 Fig 4.2,6 Fig 4.2,5 15 30 10 20 Density m 5 10 Density m 0 0 1234567891011 1234567891011 Dbh Size Classes Dbh Size Classes

Stand No 07 Stand No 08 Density m2/ha=41 Density m2/ha=129 14 30 12 25

10 /ha Fig 4.2,8 Fig 4.2,7 2 20 8 15 6 4 10 Density m Density m2/ha Density 2 5 0 0 1234567891011 1234567891011

Dbh Size Classes Dbh Size Classes

Stand No 09 Stand No 10 2 Density m /ha=172 60 Density m2/ha=210 40 50

/ha Fig 4.2,10 30 Fig 4.2,9 2

/ha 40 2 20 30 20 10 Density m

Density m 10 0 0 1234567891011 1234567891011 Dbh Siz e Cla sse s Dbh Siz e Cla sse s

Chapter No 4 Structure of Forest

Figs 4.2 continue…

Stand No 12 2 Stand No 11 Density m /ha=214 35 Density m2/ha=309 100 30 Fig 4.2,12 80 25 /ha 2 /ha

2 Fig 4.2,11 20 60 15 40 10

20 Density m 5 Density m 0 0 1234567891011 1357911 Dbh Siz e Cla sse s Dbh Siz e Cla sse s

Stand No 14 Stand No 13 Density m2/ha=152 Density m2/ha=232 40 25 35 20 Fig 4.2,14 30 /ha /ha 2

2 25 Fig 4.2,13 15 20 15 10 10 5 Density m Density m 5 0 0 1234567891011 1234567891011 Dbh Siz e Cla sse s Dbh Size Classes

Stand No 16 Stand No 15 Density m2/ha=106 Density m2/ha=92 25 20 Fig 4.2,15 Fig 4.2,16 20

15 /ha /ha 2 2 15 10 10 5 5 Density m

Density m 0 0 1234567891011 1234567891011 Dbh Size Classes Dbh Siz e Cla sse s

Chapter No 4 Structure of Forest

Figs 4.2 continue…

Stand No 18 2 Stand No 17 Density m /ha=235 Density m2/ha=99 40 30 35

25 30 Fig 4.2,18 /ha 2 /ha 25 2 20 Fig 4.2,17 20 15 15 10 10 Density m 5 Density m 5 0 0 1234567891011 1234567891011 Dbh Siz e Cla sse s Dbh siz e cla sse s

Stand No 19 Stand No 20 2 Density m2/ha=237 25 Density m /ha=105 60 50 20 /ha /ha Fig 4.2,19 40 2 2 15 Fig 4.2,20 30 10 20

density m density 5 Density m 10 0 0 1234567891011 1234567891011

Dbh Siz e Cla sse s Dbh Siz e Cla sse s

Stand No 22 2 Stand No 21 30 Density m /ha=113 2 Density m /ha=96 25 25

/ha 20 20 2 /ha 2 Fig 4.2,21 15 15 Fig 4.2,22 10 10

5 m Density 5 Density m 0 0 1234567891011 1234567891011 Dbh Siz e Cla sse s Dbh Size Classes

Chapter No 4 Structure of Forest

Figs 4.2 continue…

Stand No 23 Stand No 24 2 25 Density m2/ha=74 Density m /ha=126 30

20 25 /ha 2 /ha 20 15 2 Fig 4.2,23 15 10 Fig 4.2,24 10 Density m

5 Density m 5 0 0 1234567891011 1234567891011

Dbh Siz e Cla sse s Dbh Size Classes

sta nd No 25 Stand No 26 50 Density m2/ha=216 35 Density m2/ha=122 30 40 Fig 4.2,25 25

/ha Fig 4.2,26 /ha 2

30 2 20 20 15

10 Density m

10 m Density 5 0 0 1234567891011 1357911 Dbh Siz e Cla sse s Dbh Size Classes

Stand No 27 Stand No 28 Density m2/ha=106 Density m2/ha=107 30 30 25 /ha

/ha 20 Fig 4.2,28

2 20 15 Fig 4.2,27

10 10 Density m Density Densitym 5 0 0 1234567891011 1234567891011 Dbh Size Classes Dbh Siz e Cla sse s

Chapter No 4 Structure of Forest

Figs 4.2 continue…

Stand No 30 Stand No 29 Density m2/ha=115 12 Density m2/ha=68 25

10 20 Fig 4.2,30

/ha 8 2 15 /ha 6 Fig 4.2,29 2 10 4

Density m 5

2 Density m

0 0 1234567891011 1234567891011 Dbh Size Classes Dbs Siz e Cla sse s

Stand No 31 Stand No 32 2 Density m /ha=99 30 Density m2/ha=138 25 25 20 20 Fig 4.2,33 /ha /ha 2 2 15 15 Fig 4.2,31 10 10

Density m Density Density m 5 5

0 0 1234567891011 1234567891011 Dbs Siz e Cla sse s Dbh Siz e Cla sse s

Stand No 33 Stand No 34 Density m2/ha=156 35 30 Density m2/ha=142

30 25 25

/ha 20 /ha

Fig 4.2,33 2 Fig 4.2,34 2 20 15 15 10 10 Density m Density Density m 5 5 0 0 1234567891011 1234567891011 Dbh Siz e Cla sse s Dbh Size Classes

Chapter No 4 Structure of Forest

Figs 4.2 continue…

Stand No 35 16 Density m2/ha=99 Stand No 36 Density m2/ha=108 14 18 12 16 14 /ha 10 2

/ha 12 Fig 4.2,35 2 Fig 4.2,36 8 10 6 8

Densitym 4 6

Density m Density 4 2 2 0 0 1234567891011 1234567891011 Dbh Siz e Cla sse s Dbh Size Classes

Stand No 37 Stand No 38 Density m2/ha=147 35 Density m2/ha=180

30 60 25 50 /ha 2 40 20 /ha 2 Fig 4.2,38 15 Fig 4.2,37 30 10 20 Densitym 5 Density m 10 0 0 1234567891011 1234567891011 Dbh Siz e Cla sse s Dbh Size Classes

Stand No 40 Stand No 39 30 2 Density m2/ha=122 Density m /ha=92 20

15 /ha 20 Fig 4.2,40 2

10 Fig 4.2,39 10

5 m Density Density m2/ha Density 0 0 1234567891011 1357911 Dbh Size Classes Dbh Size Classes

Chapter No 4 Structure of Forest

Pinus wallichiana Juniperus excelsa

Juniperus excelsa Picea smithiana

Picea smithiana Juniperus macropoda

Pinus wallichiana &Betula utilis Pinus wallichiana &Betula utilis

Abies pindrow Picea simthiana &Pinus wallichiana

Pinus wallichiana, Juniperus excelsa & Betula utilis

4.5-Overall distribution pattern of dominant tree species

4.5.1-Overall Dbh size class distribution of dominant species

Among 40 stands Pinus wallichiana, Picea smithiana, Betula utilis and Juniperus excelsa were found in 27,9,14, and 13 stands, respectively.

A total of 40 stands were sampled among which Pinus wallichiana, Picea smithiana, Betula utilis and Juniperus excelsa found to be the dominant species.

Overall diameter size class distributions of the dominant tree species are presented in (Fig 4.3). Results shows that Pinus wallichiana prevailed with highest mean 128±21.1 stems ha-1 among the four tree species showing platykurtic Gaussian distribution with slight positive skewness. The size class distribution of this species indicates low recruitment of seedling because the first class show low mean density while in size class number four the mean density increases while it decreases within the higher size classes while the last two higher size classes have no individuals due to the cutting of old trees. This pattern may inhibit promoting seedling regeneration therefore requires demanding immediate action about the illegal harvesting of trees. The second dominant tree was Picea smithiana which occurred with low mean

Chapter No 4 Structure of Forest

density 121±30.5 stems ha-1 as compared to Pinus wallichiana this species showed low mean density in earlier size classes. The density increases with respect to the middle size classes gradually then decreases towards the higher size classes. Size class model of Picea smithiana shows close to symmetrical distribution. This implies low recruitment of seedling is occurring and the old trees are cut down excessively. An angiospermic tree species Betula utilis showed mean density of of 60±18.8 stems ha-1.The first attained few individuals while the other lower and middle size classes showed better mean density showing roughly Gaussian distribution. Juniperus excelsa showed mean density of 38±14.3 stems ha-1.This species showed better recruitment of seedling with the maximum individuals in the lower size classes but the frequencies declines gradually in the meddle size classes and large size classes which reflects the influence of anthropogenic disturbance. The model of size class of Pinus wallichiana, Picea smithiana and Betula utilis do not show any ideal size classes distributions pattern with low number of individuals in younger as well as mature or old classes whereas the size classes of Juniperus excelsa presents an inverse J-shaped distribution which is promising pattern indicating higher individuals in lowers size classes decline gradually within the higher size classes.

However for further protection, conservation and management point of view each and every forested stand should be given special attention. Nearly all stands indicating low number of density in early size classes sharing poor recruitment. Therefore for this purpose regeneration should be improved. This may be accomplished by control grazing because a large number of young seedlings are destroyed or crushed by the trampling of the domestic animals. Illegal harvesting should be band hence during this operation large numbers of seedlings are also destroyed. Pakistan has only 2.5% forested area and we need to increase it by promoting seedlings in forested and non forested areas.

Chapter No 4 Structure of Forest

Mean density ha-1 PW=128

25 -1 20 Fig 4.3,1 15

5 Mean density ha 0 1234567891011

Dbh size classes

Mean density ha-1 of PS=121

-1 25 Fig 4.3,2 20

15 10

Mean density ha density Mean 5

0 1234567891011

Dbh size classes

Mean density of ha-1 of BU=61

20 -1 15 Fig 4.3,3

5 Mean density ha

0 1234567891011

Dbh size classes

Fig 4.3 Overall Dbh Size class Structure of dominant tree species of study area on the basis of density ha-1. Size classes 1= 10.1-20 cm, 2= 20.1-30, 3= 30.1-40, 4=40.1-50, 5=50.1-60, 6=60.1-70, 7=70.1-80, 9=80.1-90, 9=90.1-100, 10=100.1- 110, 11=110.1-120

Chapter No 4 Structure of Forest

Fig.4.3 continue…

Mean density ha-1 of JE=38

-1 20

15 Fig 4.3,4

Mean density ha 5

0 1234567891011

Dbh size classes

4.5.2-Wiebull distribution modeling

Overall Dbh size classes of Pinus wallichiana , Picea smithiana, Betula utilis and Juniperus excelsa derived applying the Cumulative frequency function of the three-parameter Weibull distribution are presented in (Fig 4.3) while the calculated parameters are given in (Table 4.1)The results show Pinus wallichiana distributed with the mean of 48, and standard deviation 12.3 and the observed data is fitted with the cumulative distribution with the Efficiency coefficient (0.99996) at (P < 0.001) Picea smithiana which has a low mean 60 with standard deviation 9.7. The Efficiency coefficient calculated and observed cumulative frequency was (0.8933) at (P < 0.001) and The overall size classes distribution of an angiospermic tree Betula utilis was recorded with mean 44 diameter at breast height with standard deviation 8.8. The value of Efficiency coefficient calculated and observed cumulative frequency was observed (0.8539) at (P < 0.001) .Juniperus excelsa recorded with a low mean 33 with 12 standard deviation compared to the other dominant species this species is distributed between the value of Efficiency coefficient calculated and observed cumulative frequency (0.9271) at (P < 0.001) . Thus the Weibull model gave well fit for all tree species tested because all showed significantly correlation at (P < 0.001). Using parameter estimation methods the Weibull model was fitted to Dbh of evergreen and Broad-leaf tree species in which Dbh served as an independent

Chapter No 4 Structure of Forest variable of the diameter distribution of the natural unevenaged forests. The parameters of Weibull distribution differed with species of these parameters the most important is the shape parameters. In case of Juniperus excelsa the shape parameters is much lower (1.65) that reflect invers-J distribution. According to Lieblein and Zelen (1956); Thoman et al. (1969) distribution of trees was consider good when the shape parameter showed lesser than 2. Moreover Corrado and Su (1996); Corrado and Su (1997) stated that Invers-J shaped structure is an ideal structure.

Thus the Weibull model gave good fit for all tree species tested. Using parameter estimation method the Weibull model was fitted to Dbh of evergreen and broad-leaf tree species in which Dbh served as an independent variable of the diameter distribution of natural uneven aged forest. These models can be handy for an effective management of forest resources (Denslow 1995, Coomes et al. 2003, Webster et al. 2005).

Table 4.1 Showing the statistical description of Weibull functions

Mean S.No Sp Name Density ha-1 S.E a b c 1 Pinus wallichiana 128 21.7 0.5 -0.4 2.25 2 Picea smithiana 121 30.5 0.5 -0.5 2.5 3 Betula utilis 60 18.8 0.07 -0.1 7.79 4 Juniperus excelsa 38 14.3 0.64 -0.28 1.65 Key to abbreviations: Sp= Species, a= location, b= scale, c= shape parameters

Chapter No 4 Structure of Forest

Fig 4.4,1 (P.W)

Fig 4.4 Showing the Dbh Size classes of Dominant tee species using Weibull distribution fitting model

Note: P.W= (Pinus wallichiana), P.S= (Picea smithiana), B.U= (Betula utilis), J.E= (Juniperus excelsa)

Chapter No 4 Structure of Forest

Fig.4.4 continue...

Fig 4.4,2 (P.S)

Fig 4.4,3 (B.U)

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Fig.4.4 continue...

Fig 4.4,4 (J.E)

4.6-Correlation of overall Density ha-1 and Basal area m2 ha-1of tree species with the Topographic variables

The overall densities ha-1 and basal area m2 ha-1 of 40 stands correlation and individuals trees results was presented in Table 4.3. The densities and basal area showed highly significance correlation (P < 0.001) where as correlation between overall densities and Topographic factors did not showed any significance correlation. The individual trees densities ha-1 and basal area also showed strong correlation to each other i.e. Pinus wallichiana, Picea smithiana, Betula utilis attained at P < 0.001 while Juniperus excelsa showed correlation at P < 0.01 (Table 4.2) .Correlation of density and environmental variables i.e. elevation and slope. Densities of Pinus wallichiana, Picea Smithiana, Juniperus excelsa were not showed any significance correlation while only the Betula utilis showed Significance correlation at P < 0.01. Juniperus macropoda, Abies pindrow and Pinus gerardiana were found only one stand therefore these species were excluded from this exercise.

Chapter No 4 Structure of Forest

4.6.1-Correlation between total basal area m2 ha-1 of stands with topographic factors

Correlation between overall basal area m2 ha-1 with the environmental variables i.e. elevation and slope did not show any significance relationship to each other. The correlation between individual basal area m2 ha-1 of each tree with elevation and slope also presented in Table 4.2 .This indicated that Pinus wallichiana and Picea smithiana did not show any significance correlation with topographic factors where as Betula utilis and Juniperus excelsa showed significance correlation with elevation and slope at P< 0.01 respectively. Juniperus macropoda, Abies pindrow and Pinus gerardiana were found only one stand therefore these species were excluded from this exercise.

Table 4.2 Correlation between overall stand density /basal area and stand density with Topographic variables

Species r-value Significance level

1.Density ha-1/Basal area m2 ha-1 0.516 P < 0.001 Topographic 2.Elevation/Density ha-1 0.183 ns -1 3.Slope/Density ha 0.014 ns

Chapter No 4 Structure of Forest

Table 4.3 Correlation of individual species Density / Basal area with their environmental variables.

Variables r- value Significance level

(1) Pinus wallichiana 1.Density ha-1/Basal area m2 ha-1 0.771 P < 0.001 2.Elevation/Density ha-1 0.113 ns 3.Slope/Density ha-1 0.054 ns (2) Picea smithiana 1.Density ha-1/Basal area m2 ha-1 0.683 P < 0.001 2.Elevation/Density ha-1 0.279 ns 3.Slope/Density ha-1 0.173 ns (3) Betula utilis 1.Density ha-1/Basal area m2 ha-1 0.911 P < 0.001 2.Elevation/Density ha-1 0.374 P < 0.01 3.Slope/Density ha-1 0.253 ns (4) Juniperus excelsa 1.Density ha-1/Basal area m2 ha-1 0.329 P < 0.01 2.Elevation/Density ha-1 0.014 ns 3.Slope/Density ha-1 0.001 ns

Chapter No 4 Structure of Forest

Fig 4.5 Significant correlation of stand density/ stand basal area and dominant trees density and basal area with topographic factors i.e. Slope and elevation.

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Fig 4.5 continue…

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Fig 4.5 continue…

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Fig 4.5 continue…

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4.7-Discussion

Present study shows that Pinus wallichiana is present in 14 stands as the leading dominant tree and as second dominant in 3 stands while this species is distributed as pure stands at 10 sites. Juniperus excelsa appeared as first dominant only in Gasing-C with 72 stems ha-1while in 10 stands this species occurred as second dominant. Betula utilis was present in 1 stand as first dominant and in 4 stands as second abundant species while it was also found in pure form in 4 stands. Picea smithiana was not recorded in any locations of District Skardu while from Giligit and Astore this species was recorded as leading dominant in 1 stand, second leading in 3 stands and as pure population in 4 stands. Juniperus macropoda was recorded only from Gilgit in 1 stand as a pure population. Pinus gerardiana was distributed only in Astore at one site as second dominant. Abies pindrow was only recorded from Astore in 1 stand as pure from. Most of the forests show gaps in earlier size classes with low density. This shows livestock overgrazing, cutting of young trees in which it is hard for young seedlings to survive. This situation may be overcome by promoting seedling regeneration in these areas but many stands also show gaps in large size classes, indicating extensive cutting. Due to the extensive cutting the soil erosion was a common phenomena in these forests and in many places rocks and boulder were exposed. The density of sampled forests ranged between 3 to 309 stems ha-1 with 1.6 to 51 basal area m2 ha-1. Juniperus excelsa occupied very low density with 3, 8, and 9 stems ha-1 from Hargosil-B, Memosh-B while Betula utilis recorded with 3,6 and 7 stems ha-1 from Memosh-A, Memosh-B and C respectively. The highest density was recorded from Pinus wallichiana at Ganji-B with 309 stems ha-1 as a pure stand while second and third highest density were recorded Picea smithiana with 237 and 216 at Nalter-A and Joglotgah-A respectively. Juniperus excelsa attained highest value from Gasing-B and C with 96 and 129 stems ha-1 where as Betula utilis occupied highest density 159 and 121 stems ha-1 from Jogltgah-B and Gasing-C. Pinus wallichiana attended low density from Gaing-C which was 19 stems ha-1 while the low density of Picea smithiana was recorded 42 stems ha-1 from Mushken-B. Two species Juniperus macropoda and Abies pindrow were appeared in only one location Danyore and Rama-B as pure form with 126 and 107 stems ha-1 respectively.

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Among the 40 stands the highest density was recorded from Pinus wallichiana at Ganji-B with 309 stems ha-1 as pure from while Pinus wallichiana attended low density from Gaing-C which was 19 stems ha-1. Ahmed and Naqvi (2005) recorded 96 stems ha-1 of Pinus wallichiana with 18% relative basal area from Miandam. Ahmed et al. (2006) recorded the densities of Pinus wallichiana 337 stem ha-1 and 232 stems ha-1from different climatic zones of Pakistan and Takht-e- Silaiman (Baluchistan) respectively. We also recorded the same species from Astore Rama with 387 stems ha-1. The present finding was supported above mentioned. Pinus wallichiana tree is naturally distributed from Afghanistan to all Himalayan region including Pakistan, India, Nepal and Bhuttan having altitude ranging from 1800-3900 meters (Singh and Yadav, 2007). Ahmed and Naqvi (2005) and Ahmed et al. (2006) described that Pinus wallichiana may grow in moist temperate as well as in dry temperate areas. This shows the wide ecological amplitude of this species. In this study Picea smithiana attained with 237 and 216 density at Nalter-A and Joglotgah-A respectively. The low density of Picea smithiana was recorded 42 stems ha-1 from Mushken-B. Many other workers also studied this species i.e. Ahmed et al. (2006) described a density of 333 stems ha-1 with 167 m2 ha basal area from Gilgit Nalter forest and Wahab et al. (2008) reported lowest density 35 stems ha-1 from Afghanistan.The deference of these may be due to the huge cutting and other disturbances in these areas. Human induced factors, overgrazing of livestock, cutting of timber for domestic needs, and other natural disturbances have been also reported by Champion et al. (1965) and Hussain and Illahi (1991). Betula utilis recorded with 3, 6 and 7 stems ha-1 from Memosh-A, Memosh-B and C respectively. Betula utilis occupied highest density 159 and 121 stems ha-1 from Jogltgah-B and Gasing-C. Hussain et al. (1984) recorded this species from timber line at Kagan, Swat, Baltistan, Gilgit, chital and Koh Safed region on elevation from 2350 to 3500. Ahmed et al. (2006) also described Betula utilis as co-dominant with Pinus wallichian from near Matiltan the same species also rcorded from Nalter Gilgit with 666 stems ha-1 and 30 m2 ha basal area. Champion et al. (1965) described this forest as a sub- alpine birch forest. In this study the results are within the range of previous researcher.

Juniperus excelsa occupied very low density with 3, 8, and 9 stems ha-1 from Hargosil-B, Memosh-B and respectively. Juniperus excelsa attained highest value

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from Gasing-B and C with 96 and 129 stems ha-1. The same species also studied Ahmed et al. (2006) with 175 stems ha-1and 42 m2 ha-1 basal area from Balochistan province. In this study the finding of Gasing-C near to these values. Abies pindrow were appeared in only one location d Rama-B as pure form with 126 and stems ha-1. Ahmed et al. (2006) also reported Abies pindrow community with 134 stems ha-1 and 16 m2ha basal areas near the lack Rama Astore. These results are more or less similar to each other. Juniperus macropoda is only recorded from Danyore of District Gilgit as pure condition with 107 stems ha-1 at 3700 elevation above sea level. The same species also reported by Rawat et al. (2010) with density 160 stems ha-1 and 15 m2 ha basal from the neighboring country from Lahaul valley north western Himalaya India at 3400 to 3600 elevation above sea level.

The pattern of Dbh size classes’ distribution of different species indicates the present status and the future trend of these forests. Among the 40 stands the distribution of species i.e. Betula utilis (Rama-A), Mix forest of Pinus wallichiana and Picea smithiana (Mushken-B), (Mushken- D) pure forest of Pinus wallichiana were satisfactory these can assume regular distribution pattern whereas most of the stands sowed unsatisfactory. The variation of distribution pattern of size classes, density and basal may be due to the overgrazing, illegal cutting, or various other disturbances. These were also discussed by the previous researcher during the investigations of different forested area i.e. Beg 1984, Ahmed (1984), Ahmed et al. (2009c), Wahab et al (2008), Siddiqui et al. (2009) and Khan et al. (2010a).

Overall size classes of dominant tree species i.e. Pinus wallichiana, Picea smithiana, Betula utilis and Juniperus excelsa was presented and performed Weibull model. Among the dominant tree species Juniperus excels was showed best value (1.65) of shape parameter as compare to the other tree species this represent the invers-J distributiont. According to Lieblein & Zelen (1956); Thoman et al. (1969) distribution of trees was consider good when the shape parameter showed lesser than 2. Moreover Corrado & Su (1996); Corrado & Su (1997) stated that Invers-J shaped structure is an ideal structure.

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(1990), Khan (2011), Siddiqui (2011) and Wahab (2011). In case of topographic variables only density of Betula utilis showed significant relationship with elevation.

In the light of this study it is concluded that each forest is disturbed, unstable showing varied size distribution. Most of the forests have low seedlings, young trees or they do not show signs of seedling recruitment. Anthropogenic disturbances i.e. illegal cutting, grazing, sliding and burning e.t.c. are most familiar in these areas. Present practices are threatening and alarming for the future of these forests. So proper regeneration activities, management skills and conservation plan should be introduced and applied immediately to rehabilitate and save these valuable forests.

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MULTIVARIATE

ANALYSIS

Chapter No 5 Soil-Vegetation relation

CHAPTER- 5

SOIL-VEGETATION RELATION

5.1-Introduction

The vibrant relationship between vegetation and soil is physically powerful. Soils protect and restore the forest and the forest vegetation. The forest covers protects the soil from extreme heat and cold while slowing the natural forces of erosion like water, wind, and gravity. Soil sustains the forest and provides raw materials in the form of humus for its life: leaves, woody debris, and dead animals recycle through the soil. Time, weather and soil-borne organisms break these decomposing plants and animals down into nutrients that restructure the soil, and again become accessible for plant development. The relationship between soil and vegetation in case of the forest is too strong. Therefore this chapter composes physical and chemical properties of soil and its relation with the vegetation composition and distribution.

The forest soil nutrients condition and composition manipulate the nature of vegetation, so this is the major factor that affect directly or indirectly to the forest vegetation (Bhatangar, 1965). Vegetation is the major source to formulate the soil organic matter, which influences the physico-chemical feature of soil such as, structure, WHC, pH and nutrients availability (Johnston, 1986). The various environmental features significantly influenced the properties of soil and human activities (Zhang, 1986). The soil structure influenced by the vegetation because the major part of the plant i.e.. Leaves, cones, needles, pollen and branches become a part of soil due to the decomposition (Champan and Reiss, 1992; Singh and Bhatnagar, 1997). Vegetation and soil are so closely linked (Ellis and Mellor 1995). The soil composition varies widely among ecosystems (Binkly and Vitousek, 1989), resulting in differences in plant community configuration and its creation (Ruess and Innis, 1977). Soil physical and chemical properties of the forest vary due to the variation in environmental condition i.e.. topography, weathering process, and climate change (Paudel and Sah, 2003). All micro and macro elements play a vital role in the growth

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and development of plant species (Donegan et al. 2001; Lal, 2005). In some cases, soil characteristics such as soil acidity and nutrient accessibility also control the vegetation types, growth setting and distribution of vegetation types at different slope and altitudes (Ouyang et al. 2003; Kong et al. 2004; Griffiths et al. 2009). The different type of decay part of the vegetation also refill the nutrient of the soil by the microbial activities and nutrient recycling in the forest soil (Tsui et al. 2004; Wang et al. 2004; Romanya et al. 2005). The importance of soil chemical and physical properties and relationship are also evaluated by Couteaux et al. (2002); Yang et al. (2005); Navarrete et al. (2009) etc.

5.1.1-Aims and objective

• The basic objective of this study is to evaluate the status of soil chemical characteristics, edaphic characteristics, soil nutrients, and topographic variables of the study area and correlate them with the vegetation distribution and composition.

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5.2-Materials and methods

5.2.1-Sample preparation

To investigate the soil nutrients and edaphic variables of the 40 stands of the study area, soil samples were collected during the felid sampling. In each stand soil samples were collected randomly from three different points at 5-30 cm depth from the main surface. About 500 g soils put in plastic bags then labeled. These samples were brought to Laboratory for further analysis. All the samples were air dried at 25 to 30 oC, crushed and sieved using 2mm (10 mesh) sieve.

5.2.3-Soil Macro and Micro Nutrients

To determine the soil nutrients 5 gm sieved soil of each stand were put in different crucibles then heat up in temperature of 400oC in Furnace so that the organic matter of soil may burn (Dean, 1974). After that 40 beakers were washed with the distal water then in each beaker 50 ml distal water added after that 2 gm of ash was put in a beaker then add 2 ml concentrated nitric acid in each beaker. Then the solution was heated on hot plate after that it was filtered with Whatman filter paper No .42, after that more distal water added so that the volume of solution may become 100 ml thus using the atomic absorption spectrophotometer PG 990 for the determination of macro and micro nutrients.

5.2.2-Edaphic variables of soil

Salinity, pH, total dissolved salts (TDS) and electrical conductivity (EC) were measured in the field with the portable instrument, Multiparameter [Model Sension TM 105, U.K.]. Maximum water holding capacity was determined following the method of Keen (1931) where as the soil organic matter (SOM) was measured according to Bremner (1965).

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5.2.4-Statistical analysis

To investigate the relationship between vegetation and soil properties Principal Component Analysis (PCA) described by Orloci, (1975) while hierarchical agglomerative cluster analysis of Ward’s (1963) method was chose. For the computation of multivariate analysis a computer program package PC-ORD Ver. 5.10 was used. We used eighteen environmental variables including topographic, edaphic and soil nutrients. In first matrix (main matrix) environmental variables while in second matrix importance value index in case of tree data and frequency in case of understory vegetation were used. The vegetation groups were separate out on the basis of these environmental variables. All the physico-chemical variables were correlated with PCA ordination axes 1, 2 and 3. Descriptive statistics of soil properties is presented in Box plot, using the package SPSS ver. 14.0.

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5.3-Results

5.3.1-Multivariate analysis of environmental variables

5.3.1.1-Classification

5.3.1.1.1-(a) Ward’s Cluster analysis of Stands (Tree Vegetation data set)

One way cluster analysis of environmental variables and tree vegetation discloses six main groups and one isolated stand (Fig. 5.1) at a squared Euclidean distance of 5.8×104. On the basis of Ward’s classification, the tree vegetation was divided into six groups and one isolated stand Table 5.1 and understorey vegetation Table 5.2. The values of all environmental parameters in different cluster groups were presented in Table 5.3.

5.3.1.1.2-Group-I

Cluster group-I is located on an average of 3421±102 m evaluation with moderate 28°±2.4 slope angles. This groups includes total of 9 stands, predominantly Pinus wallichiana with 80.77% average importance value while Juniperus excelsa attained 11.88% average IVI where as The Pinus gerardiana also showed average importance 4% (Table 5.1). The angiospermic tree species Betula utilis present with the very low (3.20%) average importance value.

Ground flora of this group was composed of thirty one plant species including herbs, shrubs and seedlings of trees. Among them four species found as occasional while 25 were observed at rare position where as only Thymus serpyllum was recognized as frequent species with 60% frequency (Table 5. 2). In this group no any species was seen as abundant and very abundant therefore this result showed that the ground flora of this group is under extreme pressure due to the anthropogenic disturbances.

Edaphic properties of this group-I of tree vegetation data was recorded with the mean value of total dissolved salts (TDS) 19.1±0.1 g/L, water holding capacity (WHC) 55±0.8%, salinity 00±00%, electrical conductivity 75±09 µS/cm and Organic matter (OM) 6.2±0.0%. The soil of this cluster group was strongly acidic in nature having the man value of pH 5.3±0.0. While in case of macro and micro soil nutrients

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this group was occupied mean value of Calcium 116±0.9, Magnesium 115±0.3, Phosphorus 86±0.5, Nitrogen 116± 0.4, Potassium 86±0.5, Sulfur 114± 40.4 and ,Cobalt 0.7±00, Manganese 12±0.3, Zinc 1.4±0.1 and Iron 87±0.3ppm respectively (Table5.3). This cluster groups showed low pH, Magnesium and Iron respectively as compare to the other cluster groups of trees.

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Fig. 5.1 Dendrogarm obtained from Ward’s cluster analysis using Soil properties and IVI of tree species showing seven distinct groups.

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5.3.1.1.3-Group-II

This is the smallest group among all the cluster groups in which only two tee species occurred with a monospecific condition. Both Abies pindrow and Juniperus macropoda received 100±00 importance value respectively (Table5.1). With respect to topographic variable this groups was located at highest elevation 3600±136 m with 45°±00 steep slopes as compare to the other cluster groups.

Understory vegetation of this group comprises of total sixteen species among them six are occasionally occurring species while five are recorded as rare species whereas five species attempt frequent position. Due to the natural and human induced disturbances no any species was recorded as abundant and very abundant position (Table 5. 2).

Cluster group-II of tree vegetation was noted with the edaphic properties i.e.. pH 7±0.0, Water Holding Capacity(WHC) 25±0.0%, Organic Matter (OM)3.9±0.0%, electrical conductivity 44±0.0 µS/cm, Salinity 0.2±0.0% and Total dissolve salt (TDS) 19±0.0 g/L respectively. In case of the soil nutrient this group showed mean value of Nitrogen (N) 198±0.0, Phosphorus (P) 86±0.1, Potassium (K+) 344± 0.5, Calcium (Ca++)237±0.0, Magnesium (Mg++)199±0.0, Sulfur (S) 99±2.9, Cobalt (Co++) 0.7±0.0, Manganese (Mn++)14±0.0, Zinc (Zn++)0.9±0.0, and Iron (Fe++ ) 103±0.0 respectively (Table5.3). Among all the cluster groups of tree this groups was contained high amount of Potassium, Magnesium, Calcium and Sulfur and also pH which is neutral in nature as compare to the other groups.

5.3.1.1.4-Group-III

This group was situated on an average elevation 3373±101 m and slope angle 330 ±2.3. This groups was distinguished leading species Pinus wallichiana with 52.54 ±7.5%, co-dominant Picea smithiana 49.6± 11%, and associated species Juniperus excelsa 36.59±17% and an angiospermic tree Betula utilis with 28.46±3% average importance values(Table5.1). Pinus wallichiana occurs as leading species in group-I too but there is difference in elevation and slope angle to group-III.

Ground flora of this group is composed of 35 species including herb, shrub and seedlings of tree species. Among these species 17 are occasionally occurring

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species where as 17 are found as rare species. No any species was recorded in frequent, abundant and very abundant position. The results seemed that due to anthropogenic disturbances most of the species are distributed rarely (Table 5. 2).

In case of Edaphic variables Group-III was found with the mean value of pH 5.5±0.1 which was strongly acidic in nature, Water Holding Capacity 45±1.0, Organic Matter 7.8±0.0, electrical conductivity 35±1.3 µS/cm, Salinity 0.0±0.0 and TDS 17±0.1 g/L correspondingly while on the other hand this groups showed mean value of macro and micro nutrients i.e.. of N 198±0.0, P 86±0.1, K+ 215± 0.8 Ca++ 175±1.1, Mg++126±0.9, S 85±1.1 ,Co++ 0.7±0.0, Mn++15±0.8, Zn++ 1.7±0.1, and Fe++ 124± 0.6ppm correspondingly. Among all the groups this group was observed low mean of Sulfur (Table 5.3).

5.3.1.1.5-Group-IV

Group-IV is isolated due to the nonspecific stands of Pinus wallichiana. This group includes of 9 stands in which Pinus wallichiana shared 100±00% average importance value in this group (Table5.1). This cluster group was located on low average elevation 3122±121 as compare to the other cluster groups and 29±5.2 slope angles.

Total 34 plant species were recorded in forest floor. Among the associated ground flora of this group 20 are occasionally occurring species,13 are showing rare position however only one is attempted frequent position with 45% mean frequency where as no any species attempted abundant and very abundant position. Understory vegetation of this group showed mostly occasional where as ground flora of Group-I mostly represent rare position (Table 5. 2).

Soil edaphic properties of 4th cluster groups of tree vegetation was observed with slightly acidic mean pH 6.3±0.1, Water Holding Capacity 65±0.9%, Organic Matter 7.4±0.0%, electrical conductivity 45±0.7µS/cm, Salinity 0.1±0.0% and Total dissolve salt 238.7 ±0.0 g/L correspondingly while in case of macro and micro nutrients this cluster group was distinguished with the mean value of Nitrogen 106±0.8, Phosphorus 68±0.5, Potassium 204± 0.6, Calcium 196±1.0, Magnesium 146±0.6,Sulfur 97±0.9 ,Co++ 0.8±0.0, Mn++ 15±0.1, Zn++ 0.6±0.0, and Fe++ 175±

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9.9ppm respectively (Table5.3). As compare to the other cluster groups this groups showed high concentration of water holding capacity, Cobalt and Iron respectively.

5.3.1.1.6-Group-V-Isolated Stand

The Dendrogarm exposed that one isolate stand which is consists only one stand of monospecific Pinus wallichiana tree species. The environment characteristics of this group showed 3596±0.0 mean elevation.1n above sea level with moderate 20o slope angle (Table 5.1). On forest floor total 17 plant species were observed. Among theses 8 and 8 species were found rare, occasional respectively while no any species occurs in frequent, abundant and very abundant position (Table 5.2).

Soil edaphic factors of this cluster (group-V) was recorded with the mean value of moderately acidic pH 5.7± 0.0,Water Holding Capacity 54±0.0%, Organic Matter 10±0.0%, electrical conductivity 21±0.0µS/cm, Salinity 0.0±0.0% and TDS 8.7±0.0 g/L correspondingly while in case of soil macro and micro nutrients this cluster group was observed with the mean value of Nitrogen 243±0.0, Phosphorus 148±0.0, Potassium 123±0.0, Calcium 130±0.0, Magnesium 171±0.0,Sulfur 111±0.0 ,Co++ 0.8±0.0, Mn++ 11±1.0, Zn++ 1.5±0.0, and Fe++ 148± 0.0ppm respectively (Table5.3). Among the other cluster groups this groups showed high amount of Nitrogen, Phosphorus and Zinc while showed low mean value of electrical conductivity, TDS, Potassium, and Manganese as compare to the other groups.

5.3.1.1.7-Group-VI

This group is located on an average of 3214±144 meter elevation on with moderate 230 slopes. This group composed of seven stands i.e.. 15, 16, 18, 19, 20, 25, and 36 Picea smithiana appeared as leading specie with 91.61±5.4% average importance value while associated Juniperus excelsa showed 29.4±04% average importance value (Table5.1).

In this group the ground flora comprises of twenty four species including seedlings of trees, herbs and shrubs among them 9 occasionally occurring species. Moreover 14 are showing rare position only one species Fragaria nubicola was recorded as frequent with 43% frequency while no any species attempt abundant, and very abundant position(Table 5.2).

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Cluster group-VI of tree vegetation was observed with mean value of neutral pH 6.9±0.2 while in case of other edaphic factor this group was recorded with mean value of Water Holding Capacity 45±3.1%, Organic Matter 6.5±0.1%, electrical conductivity 55±1.7µS/cm, Salinity 0.0±0.0% and Total dissolve salt 26±0.2 g/L respectively. Soil macro and micro nutrients of this group was found with the mean value of Nitrogen 77±4.5, Phosphorus 88±3.8, Potassium 207±0.5, Calcium 228±4.5, Magnesium 125±3.5,Sulfur 102±1.1 ,Co++ 0.6±0.0.0, Mn++ 17±0.3, Zn++1.4±0.2 and Fe++ 131± 5.3ppm respectively (Table5.3). This group showed high amount of Cobalt as compare to the other cluster groups of trees.

5.3.1.1.8-Group-VII

This group included four stands i.e.. 21, 23, 26, and 27 of an angiospermic monospecific tree species Betula utilis with 100±00% average importance value. On the basis of environmental properties this group was found on an average elevation 3183±101 with moderate slope 37o angle (Table5.1).

Understory vegetation of this group was composed of 17 species including herb shrub and Betula utilis seedlings. Among these 8 species are occasionally occurring while 10 rare and four were frequent. There was no any species attempted abundant and very position (Table 5.2).

Soil edaphic factors of cluster (group-VII) was seen with the mean value of slightly acidic pH 6.2± 0.1,Water Holding Capacity 53±0.9%, Organic Matter 12±0.4%, electrical conductivity 76±0.9µS/cm, Salinity 0.1±0.0% and TDS 36±1.1 g/L correspondingly while in case of soil macro and micro nutrients this cluster group was recorded with the mean value of Nitrogen 56±0.9, Phosphorus 75±0.7, Potassium 278±0.5, Calcium 214±0.8, Magnesium 133±0.8,Sulfur 113±1.5 ,Co++ 0.6±0.1, Mn++ 20±0.0, Zn++ 0.4±0.0, and Fe++ 134± 0.1ppm respectively(Table5.3). This cluster groups was found with high mean value of Organic Matter, Total dissolve salt, and Manganese while low mean value of Nitrogen and Zinc as compare to the other cluster group of trees.

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Table 5.1 Six groups derived from Ward’s cluster analysis of 40 stands and their average tree species composition (average importance value for each group).

S.N Species Group I Group Group Group Group Group Group Name II III IV V VI VII

1 P. w 80.77±2.8 * 52.54±7.5 100±0.0 100±0.0 * * 2 P. s * * 49.6±11 * * 91.61±5.4 * 3 B. u 2.20±0.2 * 28.46±3 * * * 100±0.0 4 J. e 11.88±4.5 * 36.59±17 * * 29.4±0.4 * 5 J. m * 100±0.0 * * * * * 6 A. p * 100±0.0 * * * * * 7 P. g 4±1.3 * * * * * *

Key to abbreviations :( *) Absent, Pinus wallichiana (P.w), Pinus gerardiana (P.g), Betula utilis (B.u), Juniperus excelsa (J.e), Juniperus macropoda (J.m), Abies pindrow (A.p), Picea smithiana(P.s)

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Table 5.2 Average frequency of understory species in the seven groups derived from Ward’s cluster analysis of the tree vegetation data.

S.No Name of species G-I G-II G-III G-IV G-V G-VI G-VII 1 Anaphalis nepalensis 18 45 20 25 30 25 27 2 Anaphalis virgata 22 * * 15 * 17 5 3 Artemisia brevifolium 20 50 * 20 * 15 * 4 Astragalus zanskarensis 17 * 20 31 20 30 * 5 Berberis orthobotrys 12 * 28 30 * * * 6 Bergenia stracheyi 14 30 39 30 40 5 43 7 Betula utilis 15 * 5 * * * 20 8 Bistorta affinis 15 45 36 38 20 32 47 9 Cicer songaricum 14 * 15 45 35 32 * 10 Fragaria nubicola 20 55 34 34 * 43 32 11 Geranium pratense 9 25 34 28 20 34 28 12 Hieracium lanceolatum 16 * 7 15 * * * 13 Inula rhizocephala 7 * 29 15 * * 18 14 Juniperus communis 7 20 17 20 15 14 7 15 Leontopodium himalayanum 30 * 12 * * * * Leontopodium 16 leontopodinum * 30 28 27 30 25 42 17 Myosotis asiatica 5 * 40 22 25 * * 18 Nepeta discolor 7 45 15 12 25 * * 19 Oxyria digyna 16 * 39 10 * 15 20 20 Pinus wallichiana 35 * 10 17 * * * 21 Potentilla anserina 40 25 28 25 35 22 35 22 Rheum tibeticum * * 15 15 * 13 12 23 Ribes alpestre * 10 17 17 * 18 20 24 Ribes orientale 10 * 11 17 * 5 10 25 Rosa webbiana 18 22 22 21 10 12 12 26 Rubus irritans 5 25 35 37 * 20 15 27 Rumex hastatus 8 * 15 13 * 21 30 28 Spiraea canescens 6 * 10 22 * 5 * 29 Tanacetum artemisioides 31 * 32 22 15 5 * 30 Taraxacum sp * 20 17 18 15 20 20 31 Taraxacum baltistanicum 16 * 8 35 * * * 32 Thymus serpyllum 60 * 26 40 40 33 37 33 Trifolium pratense 12 * 35 * * 14 45 34 Trifolium repens 9 10 35 23 * 15 * 35 Urtica dioica 12 10 35 37 * 40 27 36 Verbascum thapsus * * 20 17 10 13 * 37 Viola rupestris * * 26 37 * 37 33

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Table 5.3 Mean value of topographic, edaphic and nutrients of soil of seven cluster groups of tree vegetation data set.

Variables Group Group Group Group IV Group Group Group VII I II III V VI 1- Topographic variables 1- Elevation 3421±102 3600±136 3373±101 3122±121 3596±0.0 3183±101 3214±144 2-Slopeº 28±2.4 45±0.0 33±2.3 29±5.2 20±0.0 37±2.2 23±10 2- Edaphic variables 1-pH 5.3±0.0 7±0.0 5.5±0.1 6.3±0.1 5.7±0.0 6.9±0.2 6.2±0.1 2-WHC 55±0.8 25±0.0 45±1.0 65±0.9 54±0.0 45±3.1 53±0.9 3-OM 6.2±0.0 3.9±0.0 7.8±0.0 7.4±0.0 10±0.0 6.5±0.1 12±0.4 4-Cond 75±0.9 44±0.0 35±1.3 45±0.7 21±0.0 55±1.7 76±0.9 5-Sali 0.0±0.0 0.2±0.0 0.0±0.0 0.1±0.0 0.0±0.0 0.0±0.0 0.1±0.0 6-TDS 19±0.1 19±0.0 17±0.1 24±0.6 8.7±0.0 26±0.2 36±1.1 3-Soil Nutrients 1-N 116±0.4 198±0.0 119±0.1 106±0.8 243±0.0 77±4.5 56±0.9 2-P 86±0.5 86±0.1 91±0.1 68±0.5 148±0.0 88±2.8 75±0.7 3-K 257±4.4 344±0.5 215±0.8 204±0.6 123±0.0 207±0.5 278±0.5 4-Ca 176±0.8 237±0.0 175±1.1 196±1.0 130±0.0 228±4.5 214±0.8 5-Mg 115±0.3 199±0.0 126±0.9 146±0.6 171±0.0 125±3.5 133±0.8 6-S 114±0.6 132±0.0 85±1.1 97±0.9 111±0.0 102±1.1 113±1.5 7-Co 0.7±0.0 0.7±0.0 0.7±0.0 0.8±0.0 0.8±0.0 0.6±0.0 0.6±0.1 8-Mn 12.5±0.3 14±0.0 15±0.8 15±0.1 11±0.0 17±0.3 20±0.0 9-Zn 1.4±0.1 0.9±0.0 1.3±0.1 0.6±0.0 1.5±0.0 1.4±0.2 0.4±0.0 10-Fe 87±0.3 103±0.0 124±0.6 175±9.9 148±0.0 131±5.3 134±0.1

Key to abbreviations: WHC=Water Holding Capacity, OM=Organic Matter, TDS=Total Dissolve Salt, Cond=Conductivity, Sali=Salinity, N=Nitrogen, P=Phosphorus, K=Potassium, Ca=Calcium, Mg=Magnesium, S=Sulfur, Co=Cobalt, Mn= Manganese, Zn=Zinc, Fe=Iron

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5.3.1.2-Univariate analysis of variance (ANOVA) of tree cluster groups

Seven main groups of tree vegetation data were derived using by Ward’s cluster analysis where as using univariate analysis of variance (ANOVA) the environmental characteristics i.e.. topographic factors and edaphic factors (Table 5.4) of each groups were analyzed. Both of the topographic variables (elevation and slope) were found non-significant with 1.5 and 2.7 F ratio respectively while all edaphic variable i.e.. TDS, pH, water holding capacity, salinity, organic matter and conductivity all found significantly correlated at P< 0.001. While on the other hand in case of soil nutrients all showed significant at P< 0.001 while only Cobalt showed low significant at P < 0.05. (Table 5.4)

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Table 5.4 Analysis of variance of individual environmental variables (topographic, edaphic and soil nutrients seven groups were extracted by Ward's cluster analysis using tree vegetation data of 40 stands.

Source of Variation SS df MS F P-level 1- Topographic Variables 1-Elevation Between Groups 864522.063 6 144087.011 1.513 ns Within Groups 3236934.181 34 95203.946 2-Slope Total 4101456.244 40 Between Groups 1280.916 6 213.486 1.748 ns Within Groups 4152.986 34 122.147 Total 5433.902 40 2- Edaphic Variables 1-pH Between Groups 15.63 6 2.605 28.73 P < 0.001 Within Groups 3.08 34 0.091 Total 18.713 40 2-WHC Between Groups 3712.774 6 618.796 29.63 P < 0.001 Within Groups 710.134 34 20.886 Total 4422.91 40 3-OM Between Groups 146.32 6 24.387 277.63 P < 0.001 Within Groups 2.99 34 0.088 Total 149.31 40 4-Cond Between Groups 10954.68 6 1825.780 172.66 P < 0.001 Within Groups 359.53 34 10.574 Total 11314.20 40 5-Salinity Between Groups 0.16 6 0.026 36.87 P < 0.001 Within Groups 0.02 34 0.001 Total 0.18 40 6-TDS Between Groups 1387.54 6 231.256 162.00 P < 0.001 Within Groups 48.53 34 1.427 Total 1436.07 40 3-Soil Nutrients 1-N Between Groups 54258.56 6 9043.094 258.33 P < 0.001 Within Groups 1190.22 34 35.006 Total 55448.78 40 2-P Between Groups 7273.92 6 1212.320 83.01 P < 0.001 Within Groups 496.53 34 14.604 Total 7770.45 40 3-K Between Groups 65631.98 6 10938.663 255.12 P < 0.001 Within Groups 1457.78 34 42.876 Total 67089.76 40 4-Ca Between Groups 24693.80 6 4115.633 106.44 P < 0.001 Within Groups 1314.64 34 38.666

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Total 26008.44 40 5-Mg Between Groups 6794.24 6 1132.374 50.69 P < 0.001 Within Groups 759.51 34 22.339 Total 7553.76 40 6-S Between Groups 6050.00 6 1008.333 144.46 P < 0.001 Within Groups 237.32 34 6.980 Total 6287.32 40 7-Co Between Groups 0.37 6 0.062 5.85 P < 0.05 Within Groups 0.36 34 0.011 Total 0.73 40 8-Mn Between Groups 205.49 6 34.249 26.29 P < 0.001 Within Groups 44.30 34 1.303 Total 249.79 40 9-Zn Between Groups 5.75 6 0.959 14.85 P < 0.001 Within Groups 2.20 34 0.065 Total 7.95 40 10-Fe Between Groups 36899.20 6 6149.867 24.16 P < 0.001 Within Groups 8654.19 34 254.535 Total 45553.40 40

Key to abbreviations : SS = Sum of square, MS = Mean square, F = F ratio, df = Degree of freedom, P level = Probability level and ns = Non significant.WHC=Water Holding Capacity, OM=Organic Matter, TDS=Total Dissolve Salt, Cond=Conductivity, N=Nitrogen, P=Phosphorus, K=Potassium, Ca=Calcium, Mg=Magnesium, S=Sulfur, Co=Cobalt, Mn= Manganese, Zn=Zinc, Fe=Iron

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5.3.2-(b) Ward’s Cluster analysis of Stands (Understory vegetation data)

The Dendrogarm of cluster analysis of understory vegetation was derived based on frequency, Physico-chemical properties, elevation and slope applying ward’s method is given in Fig. 5.2 while the frequencies of groups are shown in Table 5.6. Environmental groups based on cluster analysis are presented in Table 5.5.

On the basis of frequency, Physico-chemical properties and two environmental characteristics i.e.. slope and elevation the ground flora divided in to six main groups these groups are described in following.

5.3.2.1-Group-I

This Cluster group includes 10 stands. As compare to the other groups this is the largest group. It was situated on highest 3515±48 mean elevation above sea leave with lowest 26±2 gentle slope angles as compare to the other cluster group’s stands of understory vegetation data set. Total thirty plant species including herb shrub and seedlings of Betula utilis and Pinus wallichiana were recorded in this group. Among them Potentilla anserine appeared as dominant species with 36% an average frequency while Tanacetum artemisioides attained 33% frequency as co-dominant most of the species of this group showed low frequency and attempt rare position only three species excluding dominant and co-dominant species were showed occasional occurrence whereas no any species was seen at frequent, abundant and very abundant position (Table 5.5).This shows the vegetation of this group is under the anthropogenic disturbance.

The Edaphic characteristics of this group-I showed mean value of total dissolved salts (TDS) 18.1±0.1 g/L, water holding capacity (WHC) 54.±1.4%, salinity 00±00%, conductivity 68±4.8 µS/cm and Organic matter 6.45±0.2%. The soil of this group was strongly acidic in nature having the man value of pH 5.3±0.0. While in case of the soil nutrients this group showed the high mean value of Ca++ 175±0.9, Mg++ 117±1.5, P 87±0.7 , N 116± 0.4,K+ 250±7.1, S108± 4.2 and low mean value of Co++ 0.7±00, Mn++ 13±0.3, Zn++ 1.3±0.1ppm respectively while this group showed high value of Fe++ 91.6±19.2ppm. This group showed high WHC and Conductivity as compare to the other cluster groups (Table 5.5).

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5.3.2.2-Group-II

This is the second largest cluster group among the cluster stands groups of understory vegetation data set. This group was located on low elevation 3067±102 with 29±4.7. The elevation of this group was low as compare to the other cluster groups while slightly high to cluster group-IV. In this group total 27 plant species were noted among them Fragaria nubicola and Urtica dioica were recorded as dominant and co-dominant with 44% and 41% frequency respectively. Thirteen species showed occasional occurrence where as ten species were found rare. No any species was occurring as very abundant (Table 5.5).

Edaphic properties of Group-II was recorded mean value of pH 6.55±0.2, Water Holding Capacity 50±3.3%, Organic Matter 7.57±0.5%, electrical conductivity 52±4.4 µS/cm, Salinity 0.05±0.02% and TDS 25±0.8 g/L respectively. Soil of this group was showed high pH which is slightly acidic in nature as compare to the other cluster group. In case of the soil nutrient this group showed mean value of N 117±0.4, P 84±3.3, K+ 215± 7.8Ca++ 209±8.2,Mg++ 129±3.7 S 99±2.9 Co++ 0.5±0.0, Mn++ 17±0.5,Zn++ 1.09±0.1, Fe++ 134± 5.4 respectively.

5.3.2.3-Group-III

Cluster group-III was the smallest as compare to the other groups of understory vegetation data set. This group includes only two stands 24 and 34. It was recorded on 3407±129 elevation with moderate mean 42 ± 2.5 slope angles. Vegetation of this groups composed of 14 species including only herbs and shrubs no any seedlings of tree was seen in this group. Among them Leontopodium himalayanum and Bistorta affinis were recorded with 52 % and 45 % an average frequency as leading and co-dominant species while 5 species found frequent and 7 appeared as rare species respectively. No any species occur in the rage of abundant and very abundant position (Table 5.5).

Group-III was recorded with the mean value of edaphic factors i.e.. pH 6.48±0.4 which was also slightly acidic in nature, Water Holding Capacity 43±18, Organic Matter 5.65±1.7, electrical conductivity 45±0.1 µS/cm, Salinity 0.1±0.0 and TDS 21±2.3 g/L correspondingly. In case of soil nutrient this cluster group was noted with the mean value of N 153±45, P 77±9, K+ 275± 7.8 Ca++ 216±20,Mg++ 132±13,S

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155±17 ,Co++ 0.7±0.0, Mn++ 14±0.7, Zn++ 0.7±0.1, Fe++ 135± 5.4ppm respectively. This cluster group occupied high amount of Nitrogen, Potassium, Calcium, and Sulfur as compare to the other cluster groups (Table 5.5).

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Fig. 5.2 Dendrogarm obtained from Ward’s cluster analysis using Soil properties and frequency of understory species showing Distinct groups six distinct groups.

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5.3.2.4-Group-IV

As compare to the other cluster group this group was recorded on lowest mean elevation 3028±200 with moderate slope 35± 5.5 angles. The flora of this group was consisted of 24 species including herbs, shrubs and seedling of an angiospermic tree species Betula utilis. Nepeta discolor and Thymus serpyllum were recorded as dominant species with 45% mean frequency respectively where as co-dominant Fragaria nubicola was attained with 42% mean frequency where as in the rest of the vegetation 13 attempts occasional and 8 were recorded as rare species correspondingly. No nay species occurred at abundant and very abundant category (Table 5.5).

Group-IV occupied slightly acidic mean pH 6.38±, Water Holding Capacity 53±6.6%, Organic Matter 6.47±0.5%, electrical conductivity 52±5.6µS/cm, Salinity 0.08±0.03% and TDS 23 ±1.3 g/L correspondingly. In case of soil nutrient this cluster group was observed with the mean value of Nitrogen 118±17, Phosphorus 78±4.6, Potassium 233± 22, Calcium 205±10, Magnesium 133±6.1,Sulfur 106±5.9 ,Co++ 0.7±0.0, Mn++ 15±0.5, Zn++ 1.07±0.2, Fe++ 150± 24ppm respectively. This Group showed high amount of Iron as compare to the other cluster groups.

5.3.2.5-Group-V

This group includes six stands i.e.. 11, 13, 14, 38, 39 and 40. It was situated on 3480±48 mean elevation with moderate 32±4.3 slope angles. On the basis of elevation this group was second highest group among all the groups. Total 25 species were included in the ground flora of this group. Bergenia stracheyi was appeared as leading species with 39% and Bistorta affinis attained 36% mean frequency as co-dominant species. Including these two species total 13 species were occurred occasionally while 12 species found rarely. No any species was recorded at frequent, abundant and very abundant level.

Cluster group-V was observed with the mean value of moderately acidic pH 5.76± 0.1 while in case of other edaphic variables this group showed Water Holding Capacity 52±4.1%, Organic Matter 8.06±0.4%, electrical conductivity 34±3.5µS/cm, Salinity 0.03±0.02% and TDS 17±1.9 g/L correspondingly. In case of soil macro and micro nutrients this cluster group was observed with the mean value of Nitrogen

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134±21, Phosphorus 93±12, Potassium 198±15, Calcium 175±10, Magnesium 140±7.4,Sulfur 95±3.8 ,Co++ 0.78±0.002, Mn++ 16±1.0, Zn++ 1.11±0.1, Fe++ 142± 7.8ppm respectively. This groups showed high amount of Phosphorus, Magnesium and Cobalt as compare to the other cluster groups.

5.3.2.6-Group-VI

This group was recorded with 7 stands on 3307±129 mean elevation and moderate 32± 4.9 slope angles. Ground flora comprises of 28 species including herbs, shrubs and seedlings of Pinus wallichiana and Betula utilis. Among them Bergenia stracheyi, Bistorta affinis, attained 45% and 44% mean frequency respectively and placed frequent category and dominant and co-dominant species while 9 species were recorded occasionally and 17 were found rarely. Bergenia stracheyi and Bistorta affinis were also recorded as dominant and co-dominant species in cluster group-V but both attained low mean frequency and placed in occasional category (Table 5.5).

Cluster group-VI was recorded with mean value of slightly acidic pH 6.29±0.2 while in case of other edaphic factor this group was noted with mean value of Water Holding Capacity 50±3.2%, Organic Matter 9.1±1.1%, electrical conductivity 59±6.4µS/cm, Salinity 0.05±0.02% and Total dissolve salt 29±2.8 g/L correspondingly. In case of soil macro and micro nutrients this cluster group was found with the mean value of Nitrogen 78±9.9, Phosphorus 81±3.6, Potassium 239±14, Calcium 211±7.4, Magnesium 130±3.7,Sulfur 103±4.5 ,Co++ 0.68±0.004, Mn++ 18±1.0, Zn++0.87±0.2, Fe++ 135± 5.7ppm respectively. This group showed high amount of TDS and Mn while low amount of Nitrogen as compare to the other cluster groups.

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Table 5.5 Mean value of topographic, edaphic and nutrients of soil of seven cluster groups of tree vegetation data set.

Variable Group Group Group Group Group Group I II III IV V VI 1- Topographic variables 1-Elevation (m) 3515±29.48 3067±102 3407±129 3028±200 3480±48 3307±60 2-Slopeº 26±2 29±4.7 42±2.5 35±5.5 32±4.3 32±4.9 2- Edaphic variables 1-pH 5.3±0.0 6.55±0.2 6.48±0.4 6.38±0.2 5.76±0.1 6.29±0.2 2-WHC 54±1.4 50±3.3 43±18 53±6.6 52±4.1 50±3.2 3-OM 6.45±0.2 7.57±0.5 5.65±1.7 6.47±0.5 8.06±0.4 9.1±1.1 4-Cond 68±4.8 52±4.4 45±0.1 52±5.6 34±3.5 59±6.4 5-Sali 0.0±0.0 0.05±0.02 0.1±0.0 0.08±0.03 0.03±0.02 0.05±0.02 6-TDS 18±0.1 25±1.8 21±2.3 23±1.3 17±1.9 29±2.8 3-Soil Nutrients 1-N 116±0.4 117±0.4 153±45 118±17 134±21 78±9.9 2-P 87±0.7 84±3.3 77±9 78±4.6 93±12 81±3.6 3-K 250±7.1 215±7.8 275±70 233±22 198±15 239±14 4-Ca 175±0.9 209±8.2 216±20 205±10 175±10 211±7.4 5-Mg 117±1.5 129±3.7 132±13 133±6.1 140±7.4 130±3.7 6-S 108±4.2 99±2.9 155±17 106±5.9 95±3.8 103±4.5 7-Co 0.7±0.0 0.5±0.0 0.7±0.0 0.7±0.0 0.78±0.02 0.68±0.04 8-Mn 13±0.3 17±0.5 14±0.7 15±0.5 16±1.0 18±1.0 9-Zn 1.3±0.0 1.09±0.1 0.7±0.1 1.07±0.2 1.11±0.1 0.87±0.2 10-Fe 94±5 134±5.4 135±32 150±24 142±7.8 135±5.7

Key to abbreviations: WHC=Water Holding Capacity, OM=Organic Matter, TDS=Total Dissolve Salt, Sali=Salinity, Cond=Conductivity, N=Nitrogen, P=Phosphorus, K=Potassium, Ca=Calcium, Mg=Magnesium, S=Sulfur, Co=Cobalt, Mn= Manganese, Zn=Zinc, Fe=Iron

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Table 5.6 Average frequency of understory species in the six groups derived from Ward’s cluster analysis of the understory vegetation data

S.No Name of species G-I G-II G-III G-IV G-V G-VI 1 Anaphalis nepalensis 14 * 40 20 23 25 2 Anaphalis virgata 23 * * 15 * 13 3 Artemisia brevifolium 11 18 38 21 15 * 4 Astragalus zanskarensis 16 30 * 20 27 * 5 Berberis orthobotrys 15 * * * 27 * 6 Bergenia stracheyi 15 * * 30 39 45 7 Betula utilis 5 20 * 15 * 20 8 Bistorta affinis 18 0 45 35 36 44 9 Cicer songaricum 14 33 * * 33 * 10 Fragaria nubicola 16 44 * 42 * 27 11 Geranium pratense 9 36 15 34 30 27 12 Hieracium lanceolatum 16 15 * * * * 13 Inula rhizocephala 6 32 * * * 12 14 Juniperus communis 11 24 5 25 17 17 15 Leontopodium himalayanum 27 * * * * * 16 Leontopodium leontopodinum * 20 52 23 33 37 17 Myosotis asiatica 6 * * 30 20 40 18 Nepeta discolor 13 * * 45 15 20 19 Oxyria digyna 20 23 * 10 * 20 20 Pinus wallichiana 31 15 * * * 20 21 Potentilla anserina 36 25 28 30 27 26 22 Rheum tibeticum * 15 * * 17 11 23 Ribes alpestre * 21 10 25 15 15 24 Ribes orientale 11 13 20 22 10 13 25 Rosa webbiana 18 16 20 24 16 10 26 Rubus irritans 5 29 30 * * 15 27 Rumex hastatus 9 21 * 10 * * 28 Spiraea canescens 12 8 * * 20 7 29 Tanacetum artemisioides 33 * 25 32 12 * 30 Taraxacum sp * 22 * 20 15 12 31 Taraxacum baltistanicum 18 * * * 22 5 32 Thymus serpyllum * 20 10 45 34 40 33 Trifolium pratense 12 30 * * 30 10 34 Trifolium repens 13 23 * 21 * * 35 Urtica dioica 13 41 10 17 * 22 36 Verbascum thapsus * 35 * * 25 5 37 Viola rupestris * 30 * * 17 35

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5.3.2.7-Univariate analysis of variance (ANOVA) of Understory vegetation cluster groups.

Six main groups of understory vegetation data groups were derived using by Ward’s cluster analysis where as using univariate analysis of variance (ANOVA) the environmental characteristics i.e.. topographic factors and edaphic factors (Table 5.7) of each groups were analyzed. Between the topographic variables elevation was recorded significant P< 0.05 while among the edaphic factors Conductivity and TDS were recorded significant P< 0.05 respectively where as soil pH showed strong significant P< 0.001. While on the other hand in case of soil nutrients Nitrogen, Calcium, Magnesium and Iron showed significant P< 0.05 respectively while Manganese was recorded with strongly significant P< 0.001.

Table 5.7 Analysis of variance of individual environmental variables (topographic, edaphic and soil nutrients of six groups were derived by Ward's cluster analysis using understory vegetation data of 40 stands.

Source of Variation SS df MS F P-level 1- Topographic Variables 1-Elevation Between Groups 1611255.62 5 322251.12 4.410 P < 0.05 Within Groups 2484203.98 34 73064.82 2-Slope Total 4095459.60 39 Between Groups 685.41 5 137.08 0.989 ns Within Groups 4712.19 34 138.59 Total 5397.60 39 2- Edaphic Variables 1-pH Between Groups 9.153 5 1.831 6.512 P < 0.001 Within Groups 9.558 34 0.281 Total 18.711 39 2-WHC Between Groups 237.397 5 47.479 0.409 ns Within Groups 3947.707 34 116.109 Total 4185.104 39 3-OM Between Groups 45.505 5 9.101 2.982 ns Within Groups 103.769 34 3.052 Total 149.273 39 4-Cond Between Groups 4577.434 5 915.487 4.691 P < 0.05 Within Groups 6634.870 34 195.143 Total 11212.304 39 5-Salinity Between Groups 0.054 5 0.011 2.898 ns Within Groups 0.126 34 0.004 Total 0.180 39 6-TDS Between Groups 666.388 5 133.278 5.999 P < 0.05 Within Groups 755.392 34 22.217 Total 1421.780 39 3-Soil Nutrients

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1-N Between Groups 19519.459 5 3903.892 3.694 P < 0.05 Within Groups 35929.221 34 1056.742 Total 55448.680 39 2-P Between Groups 953.648 5 190.730 0.987 ns Within Groups 6568.370 34 193.187 Total 7522.018 39 3-K Between Groups 16996.638 5 3399.328 2.338 ns Within Groups 49432.337 34 1453.892 Total 66428.975 39 4-Ca Between Groups 11635.591 5 2327.118 5.506 P < 0.05 Within Groups 14371.384 34 422.688 Total 26006.975 39 5-Mg Between Groups 2226.116 5 445.223 3.068 P < 0.05 Within Groups 4933.484 34 145.102 Total 7159.600 39 6-S Between Groups 1081.818 5 216.364 1.430 ns Within Groups 5143.802 34 151.288 Total 6225.620 39 7-Co Between Groups 0.168 5 0.034 2.105 ns Within Groups 0.542 34 0.016 Total 0.710 39 8-Mn Between Groups 132.851 5 26.570 7.726 P < 0.001 Within Groups 116.921 34 3.439 Total 249.772 39 9-Zn Between Groups 1.027 5 0.205 1.049 ns Within Groups 6.657 34 0.196 Total 7.685 39 10-Fe Between Groups 16334.847 5 3266.969 4.014 P < 0.05 Within Groups 27671.784 34 813.876 Total 44006.631 39

Key to abbreviations: SS = Sum of square, MS = Mean square, F = F ratio, df = Degree of freedom, P level = Probability level and ns = Non significant.WHC=Water Holding Capacity, OM=Organic Matter, TDS=Total Dissolve Salt, Cond=conductivity N=Nitrogen, P=Phosphorus, K=Potassium, Ca=Calcium, Mg=Magnesium, S=Sulfur, Co=Cobalt, Mn= Manganese, Zn=Zinc, Fe=Iron

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5.4-ORDINATION 5.4.1-PCA ordination of tree vegetation data

Principal component analysis of ordination was used to explore the relationship among different soil factors i.e.. edaphic, nutrients and topographic (elevation and slope) factors with Importance value index of tree species. Seven main groups were isolated by Ward’s cluster analysis which are clearly superimposed on PCA ordination with axes 1,2 ; 1,3 & 2,3.It was also observed that there was no overlapping among the groups in all three axis was seen. Group-I is clearly separate out on all Axis. This Group is present on Axis 1-2, 1-3 and 2-3 close to Axis-1, parallel with Axis-1, and parallel to Axis-2 on ordination plane respectively. Group-II was also well define on all Axis i.e.. on Axis 1-2,1-3 parallel to the Axis-1with downward position, parallel with Axis-1, and on Axis 2-3 it was placed left position between Axis 2 and 3 on ordination plane respectively. Group-III was clearly exposed on all ordination Axis. This group was distributed on Axis 1-2 close to the Axis-2 on left side, on Axis 1-3 upward in the center and on Axis 2-3 it was located approximately in the center of ordination. Group-IV was existed on Axis 1-2 upward which is close to the right side, on Axis-1-3 slightly left side with Axis-1 and on Axis 2-3 close to the left side of the ordination plane. Group-V was recorded as an isolated stand which is found on Axis 1-2 extreme left corner on Axis-2, on Axis1-3 extreme left and upward side with Axis-3 and on Axis 2-3 extreme right upward side of ordination plane respectively. Group-VI was noted on Axis 1-2 parallel with Axis-1 which is situated more or less in the center of downward, on Axis 1-3 this group is found slightly left side with Axis-1 and on Axis 2-3 it was recorded close to the left side of the ordination plane respectively. Group-VII is situated on Axis 1-2 extreme right corner parallel to Axis-1, on Axis 1-3 this group was found extreme left side opposite to the Axis-3 and on Axis 2-3 it was located left side with Axis- 3 on ordination plane respectively.

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Fig. 5.3 PCA ordination Axis 1 and 2 of tree species using Soil properties and Importance Value Index of tree species showing seven distinct groups

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Fig. 5.4 PCA ordination Axis 1 and 3 of tree species using Soil properties and Importance Value Index of tree species showing seven distinct groups

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Fig. 5.5 PCA ordination Axis 2 and 3 of tree species using Soil properties and Importance Value Index of tree species showing seven distinct groups

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5.4.2-Correlation of Axis

5.4.2.1-Relationship (PCA) ordination axes with Topographic, Edaphic and Soil nutrients of tree vegetation data.

The Results between three (PCA) Ordination axes with the different variables are disclosed in Table (5.8). Between the topographic variables only elevation was observed positively correlated with Axis-1 (P < 0.05) while among the edaphic factors pH showed significant relationship with Axis-1 and 3(P < 0.01) and (P < 0.05), conductivity with Axis-3 (P < 0.01), salinity and TDS (P < 0.01), (P < 0.05), with ordination Axis-1 and 2, and organic matter with Axis-1(P < 0.01) correspondingly.

In case of the Soil macro micro nutrients i.e.. Nitrogen (P < 0.001), (P < 0.05) with ordination Axis 1 and 2, Potassium (P < 0.01) with Axis 1 and 3, Calcium (P < 0.01) with Axis 2, Magnesium (P < 0.01), (P < 0.001) with Axis 1 and 2 , Sulfur (P < 0.01) with Axis 2 and 3, Cobalt (P < 0.001) and (P < 0.01) with Axis 2 and 3,Manganese (P < 0.001) and (P < 0.01) with ordination 1 and 3, Zinc (P < 0.001), (P < 0.05), (P < 0.01) with Axis1,2,3 and Iron (P < 0.001) with ordination Axis 1and 2 were showed respectively (Table 5.8).

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Table 5.8 Relationship (correlation coefficients) of environmental variables (topographic and edaphic variables) and soil nutrients with PCA ordination axes obtained by tree vegetation data based on importance value of tree species and soil physico chemical properties

Axis 1 Axis 2 Axis 3 S.No. Variables r Prob. Level r Prob. Level r Prob. Level 1- Topographic variables 1. Elevation -0.382 P < 0.05 -0.099 ns -0.154 ns 2. Slope -0.069 ns -0.196 ns 0.214 ns 2- Edaphic variables 1. PH 0.577 P < 0.01 -0.247 ns 0.369 P < 0.05 2. WHC 0.254 ns 0.602 P < 0.001 -0.394 P < 0.05 3. Conductivity 0.106 ns -0.665 P < 0.001 -0.439 P < 0.01 4. Salinity 0.605 P < 0.001 0.181 ns -0.492 P < 0.01 5. TDS 0.895 P < 0.001 -0.329 P < 0.05 0.003 ns 6. OM 0.506 P < 0.01 0.216 ns 0.111 ns 3- Soil Nutreint 1. N -0.631 P < 0.001 0.319 P < 0.05 -0.233 ns 2. K 0.040 ns -0.660 P < 0.001 -0.612 P < 0.001 3. P -0.657 P < 0.001 -0.066 ns 0.466 P < 0.01 4. Ca 0.613 P < 0.001 -0.583 P < 0.01 0.218 ns 5. Mg 0.416 P < 0.01 0.799 P < 0.001 0.030 ns 6. S -0.015 ns -0.597 P < 0.01 -0.550 P < 0.01 7. Co -0.185 ns 0.693 P < 0.001 -0.443 P < 0.01 8. Mn 0.774 P < 0.001 -0.222 ns 0.424 P < 0.01 9. Zn -0.712 P < 0.001 -0.324 P < 0.05 0.464 P < 0.01 10. Fe 0.620 P < 0.001 0.654 P < 0.001 0.197 ns

Key to abbreviations: WHC=Water Holding Capacity, OM=Organic Matter, TDS=Total Dissolve Salt, N=Nitrogen, P=Phosphorus, K=Potassium, Ca=Calcium, Mg=Magnesium, S=Sulfur, Co=Cobalt, Mn= Manganese, Zn=Zinc, Fe=Iron

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5.4.2.2-PCA ordination of Understory vegetation data

Principal component analysis of ordination was used to explore the relationship among different soil factors i.e.. edaphic, nutrients and topographic (elevation and slope) with frequency of ground flora species. Six main groups were extracted by Ward’s cluster analysis which are clearly superimposed on PCA ordination with axes 1,2 and 2,3 while on ordination Axis 1-3 only Group-I is exposed on left side nearest to the center of ordination pine whereas other groups cannot differentiate due to overlapping .

Group-I is started from the center of ordination plane to the left side on Axis 1-2 and Axis 1-3 while this group is located on Axis 2-3 in the center of ordination plane. Group-II is situated on Axis 1-2 between the Axis 1 and 2 on left side of the ordination plane while on Axis 1-3 this group cannot differentiate due to the overlapping with other groups where as on Axis 2-3 this group was distributed left side and parallel to the Axis-3. Group-III was recorded on Axis 1-2 approximately close to the Axis-2 while on Axis 1-3 was observed on Axis 1-3 downward nearest to Axis- where as on Axis 2-3 it was located downward and parallel with the Axis-2 of ordination plane. Group-IV was observed on Axis1-2 left side and parallel to Axis-2 while this group did not differentiate due to the overlapping with other groups where as on Axis 2-3 it was found approximately in the center of ordination plane which spread from downward to upward. Group-V was situated on Axis 1-2 and Axis 1-3 upward, near to left side and parallel with Axis-2 respectively while on Axis 2-3 this groups was recorded extreme right corner of the ordination plane. Group-VI was observed on Axis 1-2 upward side in the nearest to left side and parallel with Axis-2 while this group found on Axis 1-3 parallel and close to Axis-3 where as this group found on Axis 2-3close to the extreme left side on the ordination plane .

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Fig. 5.8 PCA ordination Axis 2 and 3 of understory species using Soil properties and mean frequency of Understory species showing six distinct groups.

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Fig. 5.6 PCA ordination Axis 1 and 2 of understory species using Soil properties and mean frequency showing 4 distinct groups while two groups are overlapping and not distinguishable

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Fig. 5.7 PCA ordination Axis 1 and 3 of understory species using Soil properties and mean frequency of Understory species showing six distinct groups

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5.4.2.3-Relationship (PCA) ordination axes with Topographic, Edaphic and Soil nutrients of Understory vegetation data.

The Results between three (PCA) Ordination axes with the different variables are shown in Table (5.9). Between the topographic variables only elevation was observed positively correlated with Axis-2 (P < 0.01) while among the edaphic factors pH was recorded significant relationship (P < 0.01),(P < 0.05) with Axis-1 and 2, conductivity with Axis-1 (P < 0.05), salinity (P < 0.05) with Axis 1, TDS(P < 0.01), (P < 0.05) with ordination Axis-1 and 3, and organic matter with Axis-1(P < 0.05) correspondingly.

In case of the Soil macro and micro nutrients Nitrogen, Potassium, Sulfur and Zinc did not show any significant relationship with any ordination Axis. Calcium, and Magnesium, and Iron showed positive significant at (P < 0.01) with ordination Axis. Phosphorus and Cobalt showed significant at (P < 0.05), (P < 0.01) with Axis 3 and Axis 2 respectively. Manganese was observed with strongly significant at (P < 0.001) with Axis1.

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Table 5.9 Relationship (correlation coefficients) of environmental variables (topographic and edaphic variables) and soil nutrients with PCA ordination axes obtained by understory vegetation data based on frequency of species and soil physico-chemical properties.

Axis 1 Axis 2 Axis 3 S.No. Variables r Prob. Level r Prob.Level r Prob.Level 1- Topographic variables 1. Elevation 0.286 ns 0.415 P < 0.01 0.278 ns 2. Slope -0.197 ns -0.020 ns -0.089 ns 2- Edaphic variables 1. PH -0.560 P < 0.01 -0.315 P < 0.05 -0.130 ns 2. WHC 0.245 ns 0.005 ns -0.084 ns 3. Conductivity 0.396 P < 0.05 -0.160 ns -0.241 ns 4. Salinity -0.310 P < 0.05 -0.048 ns -0.274 ns 5. TDS -0.460 P < 0.01 -0.131 ns -0.317 P < 0.05 6. OM -0.383 P < 0.05 0.207 ns -0.135 ns 3- Soil Nutrient 1. N 0.201 ns 0.200 ns 0.148 ns 2. K 0.204 ns 0.014 ns -0.247 ns 3. P 0.044 ns 0.093 ns 0.335 P < 0.05 4. Ca -0.491 P < 0.01 -0.264 ns -0.154 ns 5. Mg -0.400 P < 0.01 0.189 ns 0.005 ns 6. S 0.210 ns -0.025 ns -0.140 ns 7. Co 0.258 ns 0.429 P < 0.01 -0.085 ns 8. Mn -0.700 P < 0.001 -0.059 ns -0.119 ns 9. Zn 0.267 ns -0.121 ns 0.263 ns 10. Fe -0.480 P < 0.01 0.039 ns -0.147 ns

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5.5-Soil Physico-Chemical Status

The status of each individual variables of soil in the study area was analyzed using by Box plots which are presented in following. The solid line within the box represents the mean value while the upper and lower ends represents Maximum and minimum level of soil variables. Summary of statistics of all variables are shown in Table 5.10.

5.5.1-Edaphic Factors

5.5.1.1-pH

Box plot shows the overall status of pH in the study area which was presented in Fig 5.7. Maximum pH 7.77 which was slightly alkaline in nature was observed from stand-25 Joglotgah-A forest and minimum pH 5.19 which was slightly acidic was recorded from stand-12 Ganji-B forest while mean pH was found 6.05 with 0.69 standard deviation which showed moderately acidic (Table 5.10).

8.0

7.5

7.0

6.5

6.0

5.5

5.0 N = 40 PH

Fig 5.7 Box plots show the status of pH in 40 stands of study area. The solid line within the box plot expressed the mean values. The upper and lower ends of the box plot are represents Maximum and minimum values respectively.

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5.5.1.2-Water holding capacity

The box plot of maximum water holding capacity of study area was shown in Fig 5.8 which showed the maximum value 69% from the stand-37 Gudaie forest and minimum was 25% recorded from Stand-28 Rama-B forest while mean value was found 51.4% with 10.36 standard deviation(Table 5.10).

20 N = 40 WHC

Fig 5.8 Box plots show the status of water holding capacity in 40 stands of study area.

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5.5.1.3-Organic matter

The overall status of organic matter was presented in Fig 5.9 was extracted using by Box plot which showed maximum value 13.1% from the stand-21 Naltar-B forest and minimum value was recorded 3.9 % from stand-24 Danyore-A forest while the mean value was observed 7.4% with 1.96 standard devastation(Table 5.10). Some outlier values were recorded from the Stand-21, Stand-23, Stand-26, Stand-27 and Stand-40 with the value 13.1%, 12.1%, 12.3%, 11.2% and 10.3% respectively.

26 12 23

40 10

2 N = 40 OM

Fig 5.9 Box plots show the status of Organic matter in 40 stands of study area.

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5.5.1.4-Electrical Conductivity

The status of Electrical conductivity was presented in box plot Fig 5.10.The maximum value was recorded 79 µS/cm from stand-1 Basho-A forest and minimum value 20.8 µS/cm from stand-40 Chelim forest while the mean value was 22.3 µS/cm with 16.96 standard deviation (Table 5.10).

10 N = 40 COND

Fig 5.10 Box plots show the status of Electrical conductivity in 40 stands of study area.

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5.5.1.5-Salinity

The results box plot of salinity Fig 5.1 was not clearly extracted due to the low range of salinity values most of the stand were showed no salinity. However the maximum value was noted 0.2% from the forest stand-22 Nalter-A, stand-24 Danyore-A and stand-Mushken-A forests respectively and minimum value was 0.0% (Table 5.10)

0.0

-.1 N = 40 SALIN

Fig 5.11 Box plots show the status of salinity in 40 stands of study area.

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5.5.1.6-Total Dissolve Salt

Fig 5.12 showed the results of total dissolve salt of study area. According to the box plot the maximum value of TDS was recorded 38.2 g/L from stand-27 Rama- A forest and minimum value was observed 8.7 total dissolve salt from stand-40 Chelim forest. Tow outlier value were recorded from the stand-27 Rama-A and stand- 21Nalter-B with the value 37.4ppm and 38.2ppm respectively. The mean value was 22g/L with 6.04 standard deviation (Table 5.10)

40 27 21

0 N = 40 TDS

Fig 5.12 Box plots show the status of total dissolve salt in 40 stands of study area..

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5.5.2-Soil Nutrients

5.5.2.1-Nitrogen

The status of Nitrogen in the entire study area is presented in Box pot Fig 5.13. The maximum value was recorded 242.6ppm from stand-40 Chelim forest and minimum 52.8ppm was seen from stand-27 Rama-A forest while the mean value was observed 107.9ppm with 37.71 standard deviation (Table.5.10). Two outlier stands 28 and 40 were observed with the value 242.6ppm and 197.8ppm respectively.

300

200 2824

100

0 N = 40 N

Fig 5.13 Box plots show the status of Nitrogen in 40 stands of study area.

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5.5.2.2-Phosphorus

Result of overall status of Phosphorus is printed in Fig 5.14. The maximum value was 148ppm found from stand-40 Chelim forest and minimum value was 64.8ppm recorded from stand-37 Gudaie forest while mean value was recorded 84.3ppm with 13.89 standard deviation (Table 5.10) where as stand-40 was observed on outlier with the value of 148.4ppm.

160

140

120

100

40 N = 40 P

Fig 5.14 Box plots show the status of Phosphorus in 40 stands of study area.

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5.5.2.3-Potassium

Overall feature of Potassium of the study area is shown in Fig 5.15. The maximum value 345ppm was recorded from stand-24 Danyore-A forest and minimum value 123ppm was seen from stand-40 Chelim forest while the mean value was recorded 23ppm with 41.27 standard deviation (Table 5.10). There outlier stands i.e.. stand-22, stand-28 and stand-40 were seen with the value of 345ppm, 344ppm and 123ppm respectively.

400

2824

300

200

40 100 N = 40 K

Fig 5.15 Box plots show the status of Potassium in 40 stands of study area.

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5.5.2.4-Calcium

The Box plot shows status of Calcium in 40 stands of study area which was presented in Fig 5.16. The maximum value 237ppm was recorded from stand-24 Danyore-A forest and minimum value 130ppm was observed from stand-40 Chelim forest. Mean value was recorded 195.8ppm with 25.82 standard deviation (Table 5.10).

260

240

220

200

180

160

140

120 N = 40 CA

Fig 5.16 Box plots show the status of Calcium in 40 stands of study area.

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5.5.2.5-Magnesium

Box plot of Magnesium of 40 stands is shown in Fig 5.17.The results showed maximum value 171ppm which was recorded from stand-40 Chelim forest and minimum 114ppm was observed from stand-1 Basho-A and stand-7 Hargosil-B forest respectively. Mean value was found 128.9ppm with 13.55 standard deviation (Table 5.10). Stand-40 Chelim forest was seen outlier of the box plot with the value 171 ppm.

180

170 40

160

150

140

130

120

110 N = 40 MG

Fig 5.17 Box plots show the status of Magnesium in 40 stands of study area.

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5.5.2.6-Sulfur

The status of Sulfur concentration in the study area is shown in box plot Fig 5.18.Maximum value was seen 132ppm from stand-24 Danyore-A forest and minimum value 80.3ppm was recorded from stand-4 Gasing-B forest while mean value was observed 103.4ppm with 12.63 standard deviation (Table 5.10).

140

130

120

110

100

70 N = 40 S

Fig 5.18 Box plots show the status of Sulfur in 40 stands of study area.

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5.5.2.7-Cobalt

The status of cobalt of the study area is shown in box plot Fig 5.19. Maximum value 0.864ppm was seen from stand- 17 Kargah-C forest and minimum 0.13 was recorded from stand-23 Nalter-D forest while mean value was noted (0.7ppm) with 0.13 standard deviation (Table 5.10). Many stands i.e..Stand-15, 18, 19, 23 and 25 were observed outlier of the box plot with value 0.502ppm, 0.524ppm, 0.543ppm, 0.131ppm and 0.534ppm respectively.

1.0

19 1825 15

0.0 N = 40 CO

Fig 5.19 Box plots show the status of Cobalt in 40 stands of study area.

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5.5.2.8-Manganese

Manganese of soil from 40 stands of the study area is shown in box plot Fig 5.20.It showed maximum value 20.1ppm from stand-27 Rama-A forest and minimum value 11.23ppm from stand-10 Memosh-C forest while the mean value was recorded 15.3ppm with 2.53 standard deviation (Table 5.10).

10 N = 40 MN

Fig 5.20 Box plots show the status of Manganese in 40 stands of study area.

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5.5.2.9-Zinc

The box plot Fig 5.21 is showing the overall status of the Zinc of 40 stands. The maximum value 2.0ppm was recorded from stand-36 Dashken forest and minimum value 0.4ppm was seen from stand-27 Rama-A forest while mean was noted 1.1ppm with 0.4 standard deviation (Table 5.10).

2.5

2.0

1.5

1.0

0.0 N = 40 ZN

Fig 5.21 Box plots show the status of Zinc in 40 stands of study area.

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5.5.2.10-Iron

Overall status of Iron of the study area is printed in box plot Fig 5.22. Maximum value 254ppm was seen from stand-33 Mushken-C forest and minimum 85ppm was observed from stand-8 Memosh forest while mean value was recorded 128ppm with 33.59 standard deviation (Table 5.10). Stand-33 was found outlier of box plot with the value 254ppm.

300

200

100

0 N = 40 FE

Fig 5.22 Box plots show the status of Iron in 40 stands of study area.

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Table 5.10 Summery of statistics of Box plot.

Variables Minimum Maximum Mean Standard Deviation 1. Edaphic Variables 1. pH 5.19 7.8 6.1 0.69 2.WHC 25 69.0 51.4 10.36 3.OM 3.9 13.1 7.4 1.96 4.COND 20.8 79.0 54.2 16.96 5.SALINITY 0 0.2 0.0 0.07 6.TDS 8.7 38.2 22.3 6.04 2. Soil Nutrients 1.N 52.8 242.6 107.9 37.71 2.P 64.8 148.4 84.3 13.89 3.K 123 345.0 231.0 41.27 4.Ca 130 237.0 195.8 25.82 5.Mg 114 171.0 128.9 13.55 6.S 80.3 132.0 103.4 12.63 7.Co 0.13 0.9 0.7 0.13 8.Mn 11.23 20.1 15.3 2.53 9.Zn 0.41 2.0 1.1 0.44 10.Fe 85.91 254.0 128.2 33.59

Key to abbreviations: WHC=Water Holding Capacity, OM=Organic Matter, COND=Conductivity, TDS=Total Dissolve Salt, N=Nitrogen, P=Phosphorus, K=Potassium, Ca=Calcium, Mg=Magnesium, S=Sulfur, Co=Cobalt, Mn= Manganese, Zn=Zinc, Fe=Iron

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5.6-Discussion

The present study aims to investigate the relationship between the vegetation of and environmental characteristics. Ward’s Cluster Analysis (1963); Goodall, 1973) and Principal component analysis (PCA) (Grieg-Smith, 1983) were applied to observe the group structure and vegetation composition. Advanced multivariate techniques were mostly used to determine the relationship among the soil properties and forest vegetation composition Gajoti et al. (2010). This method is highly recommended and consider useful to investigate the relationship among the soil properties and vegetation distribution (Chatifield and Collin 1980; Mazlum et al. 1999; Grace & Mcune, 2002; Wuench, 2006; Li et al. 2008) .The importance of these methods also described by Okono (1996) and Lovtt et al. (2001).

In present study sixteen soil properties and Importance value index was taken in case of tree vegetation while in case of Understory vegetation frequency of species was used to investigate the distribution pattern of vegetation in 40 stands of the study area. Based on ward’s cluster analysis seven groups were identified from tree vegetation data. These groups were clearly showed the specific species composition and environmental gradients.

In Group-I Pinus wallichiana was the leading dominant species while Juniperus excelsa, Betula utilis and Pinus gerardiana were associated species that exhibit the typical dry temperate environment. Thymus serpyllum was found frequently with 60% frequency in this group while this species was found occasionally in other groups whereas it was absent in Group-II. Siddiqui et al., (2013) recorded Pinus wallichiana communities from moist temperate area, which shows it wide ecological amplitude. Ahmed et al. (2006) recorded Pinus wallichiana community from Rama Astore with an association of Betula utilis and Juniperus communis. The finding of present study based of 9 different stands from different location therefore the result shows difference in importance values. The other characteristic properties of this group was that it has low mean pH 5.3, Manganese 115ppm and Iron 87ppm as compare to the other cluster groups.

Group-II was smallest group found at highest mean elevation 3600 meter. In this group two monospecific stand Abies pindrow and Juniperus macropoda were

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recorded. Abies pindrow was reported from Batura Valley Hunza by Visser (1982) while Duthie (1993) observed this species from Nalter Gilgit. On the other hand this species was sampled by Ahmed at al. (2006) from Rama Astore with 87% mean Importance value while Pinus wallichiana and Betula utilis found as associated species. In this study Abies pindrow recorded as monospecific forest, showed that the associated species were widely cut down even those have been disappeared. Using Multivariate analysis Abies pindrow was studied by Siddiqui et al (2010); Siddiqui et al. (2011) and Ahmed at al. (2011) from Himalayan moist temperate range. They obtained the range of mean IVI 44.25% to 58.8% within the cluster groups. Ground flora showed poor floristic composition as compare to the other groups excluding group-V. Only sixteen species were found among them Fragaria nubicola was found with 55% frequency. The soil of this group showed neutral properties with 7.0 mean pH, low water holding capacity 25%, low organic matter 3.9%, high amount of calcium 237ppm, magnesium 199ppm and sulfur 132ppm as compare to the other groups.

Group-III was dominated by Pinus wallichiana with 52.54% mean IVI and Picea smithiana was found as co-dominant species with 49.6 while the associated Juniperus excelsa and Betula utilis was attempt 36.59% and 28.46% mean IVI respectively. The differentiating characteristics of this group is that this group has low mean 85ppm sulfur content as compare to the other cluster groups. Wahab et al. (2011) recorded Pinus wallichiana community with 51% mean IVI and the associated spices Cedrus deodara and Abies pindrow attained 25% and 10.3% mean IVI. They found high amount of calcium, magnesium, manganese and cobalt. Although IVI of dominant species was more or less similar while due to the different species and location the other findings did not match with finding. Siddiqui et al. (2011) and Ahmed at al. (2011) recognized Pinus wallichiana with 56-81% Importance value from Himalayan moist temperate and Hindukush ranges. The associated species were Cedrus deodara, Abies pindrow and Picea smithiana. They recorded high salinity and conductivity, low calcium and nitrogen but high potassium contents in the soil. The results did not agree with our finding due to the different climatic zone because our study area was placed in dry temperate region. Khan et al. (2013) Pinus wallichiana and Cedrus deodara community was studied from Chtrial with 57.82% and 29.82% respectively. They observed high potassium and magnesium and low nitrogen

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contents. Pinus wallichiana and Picea smithiana community also studied Schickhoff (1995, 2000) from Rakaposhi area of Karakorum Range but their finding was observational. Ground flora of this group composed of 35 species among them Oxyria digyna was found with highest average frequency (39%) while Leontopodium himalayanum was found only in this group and group-I only.

Group-IV was includes 9 monospecific stands of Pinus wallichiana. This group showed high mean water holding capacity (65%), low mean amount of cobalt 0.8ppm and high concentration of iron 175ppm as compare to the other cluster groups. Forest floor vegetation of this group composed of 34 species among them Cicer songaricum was found as a dominant species. One isolated stand of monospecific Pinus wallichiana was showed in the cluster group-5, it could be due different environmental condition. The differentiating characteristics of this groups was that it has low mean electrical conductivity 21µS/cm, TDS 8.7 g/L, potassium 123ppm, calcium 130ppm and manganese 11ppm while high amount of nitrogen 243ppm and phosphorus148ppm and zinc 1.5ppm as compare to the other cluster groups. Ground flora of this group showed very poor vegetation configuration with sixteen species. Among them Bergenia stracheyi was recorded with highest frequency (40%).

Champion et al. (1965); Beg (1975) and Hussain and Illahi (1991) recognized Pinus wallichiana as dry temperate blue Pine where snowfall is the alternate source of precipitation. Monospecific forest of Pinus wallichiana has been studied by Ahmed et al. (2006) from Nalter Gilgit. They suggested that except ground flora these pure forest is similar with the moist temperate forest of Pinus wallichiana. Siddiqui et al., (2011) reported this species with 95% mean importance value from Himalayan moist temperate range of Pakistan.

Cluster group-VI composed of six stands. This group represents Picea smithiana- Juniperus excelsa community in which Picea smithiana shared 91.61% and Juniperus excelsa attained 29.4% mean important value Index respectively as compare to the other groups. The characteristic of this group was low amount of cobalt 0.6ppm. Forest ground vegetation was includes 28 species. Among them Fragaria nubicola showed high frequency 43%. Wahab et al., (2011) studied Picea smithiana as dominant species with 93.7% mean important value while associated

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species Abies pindrow was noted with 5.5% important value. Soil properties showed acidic pH, high mean organic matter, and water holding capacity, manganese, low mean magnesium and calcium contents.

Group-VII was composed of stand 21, 23 26 and 27 monospecific forest of Betula utilis. This Group has high concentration of mean Organic matter 12%, low nitrogen contents 56ppm, high amount of manganese 20ppm and low amount of zinc 0.4ppm. Ground flora of this group was consists of twenty three species. Among them Triflolium pratense was found with 45% frequency. Khan et al. (2013) reported Juniperus excelsa-Betula utilis community where Juniperus excelsa attained 95%mean IVI and associated species Betula utilis was found with 5.19% mean IVI. Our study did not agree with this results because we have recorded Betula utilis in pure from.

Cluster of Understory vegetation data was showed total six groups .Group-I was differentiated by dominance of Potentilla anserine with 36% an average frequency with co-dominant Tanacetum artemisioides 33%. This group was attained high mean elevation 3515 meter, water holding capacity 54%, electrical conductivity 68µS/cm and Zinc 1.3ppm as compare to the other cluster groups. The Group-II was isolated by Fragaria nubicola and Urtica dioica with 44% and 41% an average frequency respectively. The other differentiating characteristics of this group were slightly acidic nature of soil with high 6.5 mean pH, and low concentration of Iron 134ppm as compare to the other cluster groups of ground flora. Group-III showed poor floristic composition. Leontopodium himalayanum and Bistorta affinis were recorded with 52% and 45% an average frequency in this group. The differentiating feature of this group was that it has high amount of mean nitrogen 153ppm, potassium 275ppm, calcium 216ppm and sulfur 155ppm as compare to the other cluster groups. In Group-IV Nepeta discolor and Thymus serpyllum were appeared as leading species with 45% an average frequency respectively. This group showed high amount of Iron content as compare to the other cluster groups. In group-V Bergenia stracheyi, Bistorta affinis attained 39% and 36% respectively. The differentiating properties of this group was that it has high concentration of mean phosphorous 93ppm, magnesium 140ppm and cobalt 0.78ppm as compare to the other groups. Group-VI was also showed Bergenia stracheyi, Bistorta affinis, as dominant and co-dominant

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species with 45% and 44% average frequency respectively. Although the dominant and co-dominant species of group-V and Group-IV were similar but other species were wildly different in both groups. As compare to the other cluster groups this group attained high amount of mean TDS 29 g/L and Manganese 18ppm while low mean concentration of Nitrogen 78ppm.

Most of the species are similar in the cluster groups of trees vegetation data set and cluster groups of understory vegetation data set.

On the basis of the above investigation the dominant species were recorded Potentilla anserine, Bistorta affinis, Thymus serpyllum, Urtica dioica Nepeta discolor, Leontopodium himalayanum, Tanacetum artemisioides, and Fragaria nubicola, in different forest of the study area. These species also reported Eberhardt et al. (2004, 2007) form Murkhon and Batura valley of Hunza.

Among the understory vegetation of (Stands of tree data set) six species i.e.. Anaphalis nepalensis, Geranium pratense, Bistorta affinis, Juniperus communis, Potentilla anserine and Rosa Webbiana were commonly found in all cluster groups. Among the understory vegetation of (stands of understory data set) Geranium pratense, Juniperus communis, Potentilla anserine, Ribes orientale and Rosa webbiana were commonly distributed in all cluster groups.

Analysis of variance (ANAVA) single factor of trees cluster groups were also employed, slope and elevation did not shows any significant difference while all edaphic variable i.e.. TDS, pH, water holding capacity, salinity, organic matter and conductivity showed significantly difference between the groups. In case of soil nutrients all showed significant difference while only cobalt showed weak significant difference. Univariate analysis of variance of understory stands cluster groups was also analyzed. Between the topographic variables elevation was recorded significant while among the edaphic factors conductivity and TDS were recorded weekly significant where as soil pH showed strong significant difference. On the other hand, in case of soil nutrients nitrogen, calcium, magnesium and Iron showed significant weakly respectively while manganese was recorded with strongly significant. Many other worker also performed ANOVA i.e.. Siddiqui et al. (2010) was recorded elevation strongly significant and soil pH found weakly significant from Himalayan

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moist temperate regions of Pakistan. Khan et al (2013) was found only elevation and TDS significant at (P< 0.01) respectively from forest of District Chitral. In the present study elevation and all edaphic variables found to be significantly correlated. The deference may due to the different location of samplings area.

Esbensen et al (1987) described that Principal component analysis is one of the most important analysis of Multivariate which helps to extract the information and highly useful to investigate similarities had difference from any confusing data set. This technique is also helpful to summarize the environmental data to estimate the affiliation between the variables and to extract the factors that may cause difference in dependent variable (Kosaki et al. 1989; Kyuma 1973; Yanai et al. 2001).

The cluster groups of trees and understory vegetation was clearly separated out on the PCA ordination planes except only ordination Axis 1, 3 of understory vegetation. This shows good correspondence between clusters results and PCA ordination results. Wazir et al (2008) from Chapurson Valley, Khan et al. (2012, 2013) from District Chitral, and Siddiqui et al (2010, 2011) from Himalyan moist temperate rang of Pakistan also observed clusters groups superimposed on ordination. Greig-Smith (1983) suggested both techniques to investigate better interpretation among environmental variables and distribution of vegetation. Correlation of individual environmental factors with PCA Axis of tree were checked. Among the Topographic factors elevation showed significantly correlated with axis1.Most of the soil nutrient were showed weekly correlation except Nitrogen, Magnesium, Zinc, Iron and Manganese which were showed strongly significant relationship with Axis 1. In case of the understory data set between the topographic variables only elevation was observed positively correlated with Axis-2 while among the edaphic factors pH was recorded significant relationship with Axis-1 and 2, conductivity with Axis-1, salinity with Axis 1, TDS with ordination Axis-1 and 3, and organic matter with Axis respectively.

On the other hand among the Soil macro and micro nutrients Nitrogen, Potassium, Sulfur and Zinc did not show any significant relationship with any ordination Axis. Calcium, and Magnesium, and Iron showed positive significant with ordination Axis 1. Phosphorus and Cobalt showed significant with Axis 3 and Axis 2 respectively. Only Manganese was observed with strongly significant with Axis1.

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The classification, PCA ordination and environmental variables were showed some important relationship with the distribution pattern of the vegetation composition in the study area. In both cases i.e.. overstorey (trees) and understory objective classification showed well-defined group structure and the resulting groups (clusters) were correlated to a considerable extend with the topographic and edaphic factors and as well as soil macro and micro nutrients.

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CHAPTER-6

CLASSIFICATION AND ORDINATION

6.1-Introduction

Multivariate technique are most popular among the ecologist, these methods has been utilized to summarized the huge data set, describe and detect the pattern variables and to investigate the interpretation among the species (Green, 1971, 1974, 1980). This method mostly used by different Ecologies to inquire about and summarize the ecological data set (Shaukat, S.S. 1994). The analysis of multivariate data in ecology is appropriate progressively more essential. This is imperative for foundation ecological research and in studies of biodiversity (Anderson, M. J. 2001). Multivariate analysis is important to better understanding the relationship between the species as well as communities so this method commonly used in a various fields (Orloci and Kenkel, 1985). Hill (1979) accurate some of the flaws of CA and in that way created DCA which is the most famous method and widely used meandering gradient analysis method today. Importance and description about detrended corresponding analysis has been discussed by Hill and Gauch (1982) and Jackson and Somers (1991). In ecology the ordination technique was introduced by McIntosh (1985). The software to employ Detrended Correspondence Analysis became the strength of character of many later software Gauch’s (1982) and Roberts (1986). They also described the importance of multivariate analysis in ecology. The terms ordination was introduced in ecology first time by Goodall (1954) while the Polar ordination was developed by Bray and Curtis (1957) which is mostly used in ecology. According to Ahmed (1984) ordination technique and classification both are important to understand and vegetation Ecology.

In Pakistan many researcher used different method of multivariate to evaluate the ecological data set and explore the importance of this method in vegetation analysis in different period of time i.e. Shaukat and Qadir, (1971) ,Ahmed (1973,1976), Ahmed et al. (1978), Shaukat et al. (1980), Khan et al. (1987) ,Shaukat (1988) ,Qadir and Shabbir (1989),Hussain et al (1994), Awan et al (2001), Shaukat et al. (2005), Malik and Hussain (2007), Peer et al. (2007), Dasti et al. (2007) ,Wazir et

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al. (2008), Saima et al. (2009) , Jabeen and Ahmed (2009), Ahmed (2009),Ahmed et al. (2010), Ahmed et al. (2011), Wahab et al. (2011), Khan et al. (2011), Siddiqui (2011), Khan(2011) and Wahab (2011).

In this chapter we also used the multivariate analysis to investigate and explore the underlying relationship between different forest vegetation of Gilgit, Astore and Skardu District of Gilgit-Baltistan. This study may help to understand the relationship of forest vegetation with the environmental factors.

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6.2-Material and method

Forty stands were sampled using Point centered Quarter (PCQ) Method (Cottam & Curtis, 1956) from the fifteen main forested valleys of study area. The details of sampling procedure have been described in (chapter-3).

To investigate the relationship between the vegetations and environmental factors Ward’s Hierarchical agglomerative clustering techniques (McCune and Grace, 2002) was used. The importance value index of trees were used as it provides the degree of dominance and abundance of given species in relation to other species in the area Kent and Coker (1992); Song et al. (2009). To categorize the vegetation into groups the importance value of trees and frequency of understory vegetation was taken. The understorey species that attained > 6% frequency are considered for multivariate analysis. Therefore the importance value index (IVI) of 7 tree species and frequency of 37 understory species were used to perform the ordination and classification analysis. From the 83 ground flora total 37 species including herb, shrub, and seedling of tree species were selected to perform multivariate analysis. The rare species (that found in less than 6 stands) were ignored when applying multivariate techniques. Similar method also applied by Shaukat (1989), McCune et al. (2000), Khan et al (2011a; 2012), Siddiqui et al. (2010a’b) and Ahmed et al. (2011b). The vegetation species on the forest floor were stated as rare, occasional, frequent, abundant, and most abundant following Tansely and Chipp (1926), Tansely (1946) and Khan et al. (2011). These classes based on actual frequency as follows; (1) 1-20% rare, (2) 21- 40 Occasional, (3) 41- 60 Frequent, (4) 61-80% Abundant, (5) 81- 100 most abundant.

The environmental characteristics including elevation and slope were used to check the response of vegetation groups with the environmental factor. Slope angle were categorized into 4 classes i.e. Plain, 15° gentle, 16°-30° moderate, 31°-45° steep, 46°-60° very steep.

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6.3-Results

6.3.1-Classification 6.3.1.1-Ward’s Cluster analysis of Stands (Tree vegetation data)

The Dendrogarm was constructed using Ward’s clustering method (Fig. 6.1) clearly separate out the five major groups of vegetation and on the basis of these groups environmental variables are also divided into five groups. Characteristics of vegetation groups (mean±SE) are presented in Table 6.1 while the environmental features (mean±SE) of each group are given in Table 6.2.

6.3.1.1.1-Group I (a) Pinus wallichiana mix group

The sub group of Group I consisting of a total of 9 stands was predominantly Pinus wallichiana with 80.77% average importance value while Juniperus excelsa showed as a second dominant species with 11.88% average IVI whereas the angiospermic associated tree species Betula utilis was present with the very low 3.20% average importance value. The Pinus gerardiana also attained very low i.e. 4% average importance.

Total thirty one plant species including herbs, shrubs and seedlings of trees were also associated with this tree species as understory vegetation. Among theses Anaphalis virgata, Potentilla anserina ,Pinus wallichiana seedling and Leontopodium himalayanum were found as occasional while Anaphalis nepalensis, Anaphalis virgata, Artemisia brevifolium, Hieracium lanceolatum, Berberis orthobotrys, Bergenia stracheyi, Betula utilis, Bistorta affinis, Cicer songaricum, Fragaria nubicola, Geranium pratense, Inula rhizocephala, Juniperus communis, Myosotis asiatica, Nepeta discolor, Oxyria digyna, Ribes orientale, Urtica dioica, Rubus irritans, Rumex hastatus, Taraxacum baltistanicum, Trifolium pratense, Trifolium repens, Rosa webbiana and Astragalus zanskarensis occurred as rare whereas only Taraxacum baltistanicum was found as frequent species with 60% frequency in this group no other species was recorded in the category of abundant and very abundant. These results showed that the understory vegetation of these stands is under the exposed to severe anthropogenic disturbances.

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This group of vegetation was recorded on high elevation 3519±117 m and moderate (27°) slope angle. The edaphic feature of this group showed mean value of total dissolved salts (TDS) 18.1±3.1, water holding capacity (WHC) 45.52±5, salinity 00±00, conductivity 42.46±7.1 and Organic matter 5.5±0.6. The soil of this group was strongly acidic in nature having the man value of pH 5.5±0.1 while in case of the soil nutrients this group showed the mean value of Ca++171±16.1, Mg++130±95, K+202±15.6, Co++0.7±00, Mn++ 8.7±1.5, Zn++1.2±0.1 and Fe++ 91.6±19.2ppm.(Table 6.3).

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Fig 6.1 Dendrogarm derived from Ward’s Cluster analysis, using Topographic variables and importance value of tree species, showing five distinct groups

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6.3.1.1.2-Group I (b) pure Pinus wallichiana group

This is a sub-group of group I .The cluster analysis agglomerate 10 pure stands of Pinus wallichiana that shared 100 ± 00% average importance value in this group. Among all groups this is the largest group.

A considerable ground flora was associated with this group in which Anaphalis nepalensis, Artemisia brevifolium, Astragalus zanskarensis, Berberis orthobotrys, Bergenia stracheyi, Bistorta affinis, Fragaria nubicola, Geranium pratense, Leontopodium leontopodinum, Myosotis asiatica, Potentilla anserine, Rosa webbiana, Rubus irritans, Viola rupestris, Thymus serpyllum, Trifolium pratense, Trifolium repens, Urtica dioica, Tanacetum artemisioides and Taraxacum baltistanicum are occasionally occurring species while some species i.e. Anaphalis virgata, Hieracium lanceolatum, Pinus wallichiana, Juniperus communis, Nepeta discolor, Oxyria digyna, Inula rhizocephala, Ribes alpestre, Rheum tibeticum, Ribes orientale, Rumex hastatus, Spiraea canescens, Verbascum Thapsus and Taraxacum sp. are rare in this group. However only Cicer songaricum occurred as a frequent species with 42% mean frequency whereas no any species attained the position of abundant and very abundant position. Ground flora is mostly present occasionally whereas in Group I (a) most of the understory vegetation represented rare position.

The topographic characteristics of this group reveled comparatively low elevation 3169±117 m and low slope angle (28o) as compare to the Group I (a).incase of soil edaphic properties this groups showed TDS of 18.4±3.5, high WHC 50.97±5, salinity 0.04±0.2, high conductivity 42.7±7.8 and low organic matter 8.3±1.5% are the characteristics of this group. The soil of this group was slightly acidic in nature having the mean value of pH 6.2±0.2 while in case of the soil nutrients this group showed the mean value of Ca++ 192±11.9, Mg++144.9±8.5, K+ 202±21.7, Co++ 0.8±00, Mn++ 13.6±1.6, Zn++ 1.2±0.2 and Fe++ 165.7±17ppm (Table 6.3).

6.3.1.1.3-Pinus wallichiana and Picea smithiana group

This group consists of eight stands having three coniferous tree species i.e. Pinus wallichiana with 52.54 ±7.5%, Picea smithiana 49.6± 11%, Juniperus excelsa 36.59±17% and an angiospermic tree Betula utilis with 28.46±3% average importance values.

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The understory vegetation of this group comprises of thirty fives species among these Berberis orthobotrys, Bergenia stracheyi, Bistorta affinis, Fragaria nubicola, Geranium pratense, Inula rhizocephala, Leontopodium leontopodinum, Myosotis asiatica, Oxyria digyna, Potentilla anserine, Rosa webbiana, Rubus irritans, Tanacetum artemisioides, Viola rupestris, Trifolium pratense, Trifolium repens, Urtica dioica and Thymus serpyllum are occasionally occurring species whereas Anaphalis nepalensis, Astragalus zanskarensis, Cicer songaricum, Betula utilis(Seedlings), Hieracium lanceolatum, Juniperus communis, Leontopodium himalayanum, Nepeta discolor, Pinus wallichiana (seedlings), Rheum tibeticum , Ribes alpestre, Ribes orientale, Verbascum thapsus, Spiraea canescens, Taraxacum sp., Taraxacum baltistanicum and Rumex hastatus occur as rare. In this group no species occurs as frequent, abundant or very abundant. The results indicated that most of the understory species are under pressure due to the natural and human induced disturbances. This seems to be the reason for their poor status of distribution. The elevation of this group’s member was surprisingly constant 3178±116 m while average slope was 290. The Edaphic feature of this group showed mean value of TDS 25.6±3.4, water holding capacity 41.14±5, salinity 0.01±0.1, conductivity 58.4±7.6 and Organic matter 7.7±1.3 percent respectively. The soil of this group was neutral in nature having the mean value of pH 6.9±0.2. While in case of the soil nutrients this group showed the mean value of Ca++ 177±14, Mg++127±8.4,K+ 206±15.5, Co++ 0.7±00,Mn++ 13.6±1.9, Zn++ 1.2±0.1 and Fe++ 125.7±14.6ppm respectively (Table 6.3).

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Table 6.1 Five groups derived from Ward’s cluster analysis of 40 stands and their average tree species composition (average importance value for each group)

Group I S.N Sp Name Group II Group III Group IV Group V (a) (b) 1 P. w 82.95± 3.16 100±00 52.54±7.5 - - - 2 P. s - - 49.6±11.4 91.61±5.4 - - 3 B. u 3.6± 1.59 - 28.46±3.6 - 100±00 - 4 J. e 13.37± 1.8 - 36.59±17.9 29.4± 0.4 - - 5 J. m - - - - - 100±00 6 A. p - - - - - 100±00 7 P. g 36.6±00 - - - - -

Note:( -) Absent, (P.w)= Pinus wallichiana, (P.g)=Pinus gerardiana, (B.u)=Betula utilis, (J.e)= Juniperus excelsa, (J.m)=Juniperus macropoda, (A.p)=Abies pindrow, (P.s)=Picea smithiana

6.3.1.1.4-Picea smithiana and Juniperus excelsa group

Having seven stands this cluster group has two gymnospermic tree species i.e. Picea smithiana as leading species with 91.61±5.4% average importance value while co-dominant Juniperus excelsa contributed 29.4±04% average importance value.

In this group the ground flora comprises of twenty eight species including seedlings of trees, herbs and shrubs among them Anaphalis nepalensis, Astragalus zanskarensis, Bistorta affinis, Cicer songaricum, Geranium pratense, Leontopodium leontopodinum, Potentilla anserine, Urtica dioica, Viola rupestris, Thymus serpyllum and Rumex hastatus are occasionally occurring species. Moreover some species i.e. Rosa webbiana, Anaphalis virgata, Artemisia brevifolium, Bergenia stracheyi, Juniperus communis, Oxyria digyna, Rheum tibeticum, Ribes alpestre, Ribes orientale, Rubus irritans, Verbascum thapsus, Tanacetum artemisioides, Taraxacum sp., Trifolium pratense, Trifolium repens and Spiraea canescens are rare. Whereas only one species Fragaria nubicola was recorded as frequent with 43% frequency. The frequency of any of the species did not reach at the level of abundant and very

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abundant. This indicates the degraded condition of forest understory vegetation in this area.

The magnitude of the environmental variables of this group is considerably similar to the group I (b) while at low elevation as compared to the other groups. This group is associated with an average of 3178±116 meter elevation with 390 steep slopes.

The Edaphic feature of this group showed mean value of TDS 32.9±10, water holding capacity 46.5±3.7, salinity 0.05±0.02, conductivity 75.8±22 and Organic matter 6.8±1.3 percent. The soil of this group III was slightly acidic in nature having the mean value of pH 6.4±0.6. Soil nutrients of this group showed the mean value of Ca++ 231±22, Mg++117±4.6, K+ 206±19, Co++ 0.8±00, Mn++ 14±1.9, Zn++1.4±0.2 and Fe++ 127±19.2ppm (Table 6.3).

6.3.1.1.5-Pure Betula utilis group

This is a smaller group as compared to the earlier described groups having a single angiospermic tree species Betula utilis with (100±00% average importance value) and 22±10 moderate slope.

The understory vegetation shared twenty three species including seedlings of Betula utilis among these Anaphalis nepalensis, Rumex hastatus, Geranium pratense, Potentilla anserine, Fragaria nubicola, Viola rupestris, Urtica dioica, Thymus serpyllum occurred occasionally while Betula utilis (seedlings), Anaphalis virgata, Inula rhizocephala, Juniperus communis, Oxyria digyna, Rheum tibeticum, Taraxacum sp., Ribes orientale, Rosa webbiana, Rubus irritans and Ribes alpestre were rare species. Some species were also found as frequent in this group i.e. Bistorta affinis 47%, Bergenia stracheyi 43%, Trifolium pratense 47% and Leontopodium leontopodinum with 45% mean frequency whereas none of the species was found abundant position.

With the respect to topographic features this group is associated with an average elevation of 3214±144 and 220 moderate slope.

The Edaphic feature of this group showed mean value of TDS 32.9±10, water holding capacity 46.5±3.7, salinity 0.05±0.2, conductivity 75.82±22 and Organic

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matter 13.4±5.8 percent. The soil of this group was slightly acidic in nature having the mean value of pH 6.4±0.6. While in case of the soil nutrients this group showed the mean value of Ca++ 213±20.7, Mg++125±3.9,K+ 277±37.9, Co++ 0.7±00,Mn++ 14.8±2.3, Zn++ 0.8±0.1 and Fe++ 132±6.3ppm (Table 6.3).

6.3.1.1.6-Abies pindrow and Juniperus macropoda group

Among all the clusters this is the smallest group having only two stands. The coniferous tree species Abies pindrow shared (100±00 importance value) while the Juniperus macropoda also contributed (100±00 importance value) for this group. It should be noted that even though the two stands are consisted by different species they fall within the same group.

The forest ground flora comprises of sixteen species among them Bergenia stracheyi, Geranium pratense, Leontopodium leontopodinum, Rubus irritans, Rosa webbiana and Potentilla anserina are occasionally occurring species while Urtica dioica, Trifolium repens, Taraxacum sp., Ribes alpestre and Juniperus communis are recorded as rare whereas some species were present frequently i.e. Anaphalis virgata 45%, Artemisia brevifolium 50%, Bistorta affinis 45%, Fragaria nubicola 55% and Nepeta discolor 45%. None of the species present attained the position of abundant in this group.

As compared to the other group this groups is entirely different because this group was located on the highest elevation of 3600± 136 m with 45° steep slopes.

The Edaphic feature of this group showed mean value of TDS 20.9±0.9, water holding capacity 32.5±7, salinity 0.1±0.1, conductivity 46.2±2.2 and Organic matter 4.2±0.3 percent. The soil of this group-V was also slightly acidic in nature having the mean value of pH 6.2±0.6. While in case of the soil nutrients this group showed the mean value of Ca++ 237±21.3, Mg++132±13.5,K+ 250±94.5, Co++ 0.7±00,Mn++ 12.4±1.5, Zn++ 0.9±0.1 and Fe++ 130±26ppm (Table 6.3).

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Table 6.2 Average frequency of understorey species in the five groups derived from Ward’s cluster analysis of the tree vegetation data

Group I Group I Group Group Group Group S.N Name of species (a) (b) II III IV V 1 Anaphalis nepalensis 18 26 20 25 27 45 2 Anaphalis virgata 22 18 - 17 5 - 3 Artemisia brevifolium 20 22 - 15 - 50 4 Astragalus zanskarensis 17 31 20 30 - - 5 Berberis orthobotrys 12 30 28 - - - 6 Bergenia stracheyi 14 33 39 5 43 30 7 Betula utilis 15 - 5 - 20 - 8 Bistorta affinis 15 36 36 32 47 45 9 Cicer songaricum 14 42 15 32 - - 10 Fragaria nubicola 20 34 34 43 32 55 11 Geranium pratense 9 27 34 34 28 25 12 Hieracium lanceolatum 16 15 7 - - - 13 Inula rhizocephala 7 15 29 - 18 - 14 Juniperus communis 7 19 17 14 7 20 15 Leontopodium himalayanum 30 - 12 - - - Leontopodium 16 - 36 28 25 42 30 leontopodinum 17 Myosotis asiatica 5 23 40 - - - 18 Nepeta discolor 7 17 15 - - 45 19 Oxyria digyna 16 10 39 15 20 - 20 Pinus wallichiana 35 17 10 - - - 21 Potentilla anserina 40 27 28 22 35 25 22 Rheum tibeticum - 15 15 13 12 - 23 Ribes alpestre - 17 17 18 20 10 24 Ribes orientale 10 17 11 5 10 - 25 Rosa webbiana 18 19 22 12 12 22 26 Rubus irritans 5 37 35 20 15 25 27 Rumex hastatus 8 15 15 21 30 - 28 Spiraea canescens 6 13 10 5 - - 29 Tanacetum artemisioides 31 20 32 5 - - 30 Taraxacum sp - 19 17 20 20 20 31 Taraxacum baltistanicum 16 35 8 - - - 32 Thymus serpyllum 60 40 26 33 37 - 33 Trifolium pratense 12 - 35 14 45 - 34 Trifolium repens 9 23 35 15 - 10 35 Urtica dioica 12 37 35 40 27 10 36 Verbascum thapsus - 20 20 13 - - 37 Viola rupestris - 32 26 37 33 - Note. - = Absent,

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Table 6.3 Mean values ± SE of environmental variables (topographic, edaphic and Soil nutrient) based on five groups derived from Ward’s cluster analysis using tree vegetation data of 40 stands form three districts of Gilgit-Baltistan. (Mean ± SE)

Group I Group I Group Group Group Group Variable (a) (b) II III IV V 1- Topographic variables 1-Elevation(m) 3421±101 3169±117 3373±101 3178±116 3214±144 3600±136 2-Slopeº 27±2.2 28±4.7 33±2.5 39±1.6 22±10 45±00 2- Edaphic variables 1-TDS 18.1±3.1 18.4±3.5 25.6±3.4 32.9±10 32.9±10 20±0.9 2-pH 5.5±0.1 6.2±0.2 6.9±0.2 6.4±0.6 6.4±0.6 6.3±0.6 3-WHC 45.52±5 50.97±5 41.14±5 46.5±3.7 46.5±3.7 32.5±7 4-Salinity 00±00 0.04±0.2 0.01±0.1 0.05±0.02 0.05±0.02 0.1±0.1 5-Conductivity 42.46±7.1 42.7±7.8 58.4±7.6 75.8±22 75.82±22 46.2±2.2 6-OM 5.5±0.6 8.3±1.5 7.7±1.3 6.8±1.3 13.4±5.8 4.2±0.3 3-Soil Nutrients 1-Ca 171±16.1 192±11.9 177±14 231±22 213±20.7 237±21.3 2-Mg 130±9.5 144.9±8.5 127±8.4 117±4.6 125±3.9 132±13.5 3-K 202±15.6 205±21.7 206±15.5 206±19 277±37.9 250±94.5 4-Co 0.7±00 0.8±00 0.7±00 0.8±00 0.7±00 0.7±00 5-Mn 8.7±1.5 13.6±1.6 13.6±1.9 14±1.9 14.8±2.3 12.4±1.5 6-Zn 1.2±0.1 1.2±0.2 1.2±0.1 1.4±0.2 0.8±0.1 0.9±0.1 7-Fe 91.6±19.2 165.7±17 125±14.6 127±19.2 132±6.3 130±26.3 SE = Standard error

6.3.1.2-Univariate analysis of variance (ANOVA)

Five main groups of tree vegetation data were derived using Ward’s cluster analysis whereas using univariate analysis of variance (ANOVA) the environmental characteristics i.e. topographic factors and edaphic factors (Table 6.4) of each groups were analyzed. Both of the topographic variables (elevation and slope) were found non-significant with 1.3 and 2.3 F ratio respectively. Among the five edaphic variable i.e. TDS, pH, water holding capacity, salinity and conductivity all were found non-

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significant except pH with 3.6 F ration (P < 0.05). While on the other hand in case of soil nutrients all showed non-significant (Table 6-4).

Table 6.4 Analysis of variance of individual environmental variables (topographic, edaphic five groups were derived by Ward's cluster analysis using tree vegetation data of 40 stands

Source of Variation SS df MS F P-level 1- Topographic Variables 1-Elevation Between Groups 657343.8 5 131468.8 1.300113 ns Within Groups 3438116 34 101121.1 Total 4095460 39 2-Slope Between Groups 1379.118 5 275.8236 2.342242 ns Within Groups 4003.857 34 117.7605 Total 5382.975 39 2- Edaphic Variables 1-TDS Between Groups 921.7948 5 184.359 1.50731 ns Within Groups 4036.228 33 122.3099 Total 4958.023 38 2-pH Between Groups 9.930164 5 1.986033 3.69951 P < 0.05 Within Groups 18.44973 34 0.542639 Total 28.3799 39 3-WHC Between Groups 831.7013 5 166.3403 0.813813 ns Within Groups 6949.473 34 204.3963 Total 7781.174 39 4-Salinity Between Groups 0.027179 5 0.005436 2.238235 ns Within Groups 0.082571 34 0.002429 Total 0.10975 39 5-Conductivity Between Groups 4834.794 5 966.9589 1.67244 ns Within Groups 19657.86 34 578.1725 Total 24492.66 39 6-OM Between Groups 205.16147 5 41.032294 1.8133551 ns Within Groups 769.34628 34 22.627832 Total 974.50775 39

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3-Soil Nutrients 1-Ca Between Groups 23133.273 4 5783.3182 2.5295187 ns Within Groups 75448.938 33 2286.3314 Total 98582.211 37 2-Mg Between Groups 3555.204 5 711.04079 1.3159702 ns Within Groups 18370.771 34 540.3168 Total 21925.975 39 3-K Between Groups 197.64424 5 39.528847 1.5963257 ns Within Groups 841.9214 34 24.762394 Total 1039.5656 39 4-Co Between Groups 0.0230831 5 0.0046166 0.6668908 ns Within Groups 0.2353688 34 0.0069226 Total 0.2584519 39 5-Mn Between Groups 197.64424 5 39.528847 1.5963257 ns Within Groups 841.9214 34 24.762394 Total 1039.5656 39 6-Zn Between Groups 1.1185376 5 0.2237075 0.9796535 ns Within Groups 7.7640268 34 0.2283537 7-Fe Total 8.8825644 39 Between Groups 26337.835 5 5267.5671 2.1895925 ns Within Groups 81794.8 34 2405.7294 Total 108132.64 39 Note: SS = Sum of square, MS = Mean square, F = F ratio, df = Degree of freedom, P level = Probability level and ns = Non significant.

6.3.2-Ward’s Cluster analysis of Stands (Understory vegetation data)

The dendrogram of cluster analysis of understory vegetation based on frequency used in ward’s method is presented in Fig. 6.2 while the frequencies of groups are given in Table 6.3. Environmental groups based on cluster analysis are presented in Table 6.4.

On the basis of frequency and two environmental characteristics i.e. slope and elevation the ground flora was divided in to five main groups. The groups are briefly described in the following paragraphs.

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6.3.2.1-Group I

This is the largest group as compared to the other groups which comprises of 10 stands. In this group a total thirty species were recorded among them Potentilla anserina showed 36%, Tanacetum artemisioides attained 33.48% and the seedlings of coniferous tree species Pinus wallichiana occupied 31%, Leontopodium himalayanum 27.14%, Bistorta affinis 27%, Anaphalis virgata 23.21% average frequency showing occasional representation. Most of the plants in this group i.e. Anaphalis nepalensis, Artemisia brevifolium, Astragalus zanskarensis, Berberis orthobotrys, Bergenia stracheyi, Betula utilis (seedlings), Cicer songaricum, Fragaria nubicola, Geranium pratense, Hieracium lanceolatum, Inula rhizocephala, Juniperus communis, Myosotis asiatica, Nepeta discolor, Oxyria digyna, Ribes orientale, Rosa webbiana, Rubus irritans, Rumex hastatus, Spiraea canescens, Urtica dioica, Trifolium pratense, Trifolium repens, Taraxacum baltistanicum are found as rare species.

The Topographic characteristics of this group recorded highest mean value 3515±29 m and with low mean slope 26±2.1 angle as compare to the other cluster groups of ground vegetation data.

Edaphic variables of this cluster group were recorded i.e. TDS 18.7±2.7, water holding capacity 46.14±4.5, salinity 0.0, conductivity 43.6±6.2 and Organic matter 6.2±0.5 percent recorded. The soil of this group was recorded moderately acidic in nature having the mean value of pH 5.6±6.0 while in case of the soil nutrients this group showed the mean value of Ca++ 174±14.2, Mg++ 125±7.4, K+ 207±16, Co++0.7±00, Mn++ 9.9±1.5, Zn++ 1.2±0.1 and Fe++ 79.8±14.1ppm (Table 6.6).

6.3.2.2-Group II

This group attains the second position among all groups having nine stands. The leading species Urtica dioica and, Fragaria nubicola were recorded with 44.44% and 41% average frequency respectively. Most of the plant species i.e. Astragalus zanskarensis, Cicer songaricum, Geranium pratense, Inula rhizocephala, Juniperus communis, Oxyria digyna, Potentilla anserina, Ribes alpestre, Viola rupestris, Rumex hastatus, Taraxacum sp., Trifolium pratense, Trifolium repens, and Rubus irritans are found occasionally with the frequency ranging from 21% to 36% while some are

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recorded as rare i.e. Artemisia brevifolium, Verbascum thapsus, Hieracium lanceolatum, Leontopodium leontopodinum, Pinus wallichiana (seedlings), Rheum tibeticum, Ribes orientale, Rosa webbiana, Spiraea canescens, Thymus serpyllum, and Betula utilis (seedlings) with 7% to 20% average frequency.

According to the environmental variables this group recorded at low mean value of elevation i.e. 3026±29 m and with low mean slope 29±4.7 angle as compare to the other cluster groups of ground vegetation data.

On the basis of soil physical characteristics this cluster group were recorded TDS 26.9±2.8, water holding capacity 37.33±3.4, high salinity level 0.04±0.02, conductivity 61.66±6 and Organic matter 7.2±1 percent respectively. The soil of this group was recorded neutral in nature having the mean value of pH 6.7±0.2. Soil nutrients of this group showed the mean value of Ca++ 198±15.9, Mg++ 123±7, K+ 236±18.6, Co++ 0.7±00, Mn++ 13.9±1.9, Zn++ 1±0.2 and Fe++ 117±11.8ppm (Table 6.6).

6.3.2.3-Group III

This group includes eight stands and was composed of twenty six species among these fourteen species are common in group I and group II. The dominant species were Nepeta discolor, Viola rupestris, and Fragaria nubicola having 42%, 45% and 45% average frequency respectively representing higher position. Other associated species found in this group are Anaphalis nepalensis, Artemisia brevifolium, Bergenia stracheyi, Geranium pratense, Leontopodium leontopodinum, Myosotis asiatica, Potentilla anserina, Ribes orientale, Rosa webbiana, Rubus irritans, Thymus serpyllum, Urtica dioica, Trifolium repens, and Tanacetum artemisioides showing 23 % to 39% average frequency representing the occasional category. Few species are rare i.e. Anaphalis virgata, Astragalus zanskarensis, Juniperus communis, Ribes alpestre, Oxyria digyna and Betula utilis (seedlings) having 10% to 20% average frequency.

On the basis of the topographic characteristics this group was recorded mean elevation 3122±170 m and with mean steep slope 36±4.3 angle. The slope of this group was high as compare to the other cluster groups.

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Soil edaphic variables of this cluster group were recorded i.e. TDS 16.06±2.08, water holding capacity 49.25±6.5, slightly high salinity 0.3±0.2, conductivity 37.57±4.7 and low organic matter 4.3±0.7 percent respectively. The soil of this group was recorded neutral in nature having the mean value of pH 6.6±0.2. Soil nutrients of this group showed the mean value of Ca++ 195±14.1, Mg++ 150±8.1, K+ 225±27, Co++ 0.7±00, Mn++ 10±1.6, Zn++ 1.3±0.3 and Fe++ 187±18.3ppm (Table 6.6).

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Fig 6.2 Dendrogarm obtained from Ward’s Cluster analysis of understory species on the basis of frequency, showing five distinct groups.

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Table 6.5 Average frequency of understorey species in the five groups derived from Ward’s cluster analysis of the understory vegetation data

Group Group Group Group Group S.No Name of Plant Species SP cod 1 2 3 4 5 1 Anaphalis nepalensis ANE 14.28 0 33.33 23.33 25 2 Anaphalis virgata AVI 23.21 0 15 0 20 3 Artemisia brevifolium ABR 11 17.5 27 15 0 4 Astragalus zanskarensis AZA 16.42 30 20 27 0 5 Berberis orthobotrys BOR 15 0 0 27 0 6 Bergenia stracheyi BST 15.24 0 30 39 45 7 Betula utilis BUT 5.35 20 15 0 20 8 Bistorta affinis BAF 27 0 40 36 43 9 Cicer songaricum CSO 14.28 32 0 41 0 10 Fragaria nubicola FNU 16 44.44 42 0 27 11 Geranium pratense GPA 9.27 36 27 30 27 12 Hieracium lanceolatum HLA 16.49 15 0 0 0 13 Inula rhizocephala IRH 6.24 32 0 0 12 14 Juniperus communis JCO 7.19 32 16 17 12 15 Leontopodium himalayanum LHI 27.14 0 0 0 0 Leontopodium 16 leontopodinum LLE 0 20 31.43 33.33 36.42 17 Myosotis asiatica MAS 6 0 30 20 40 18 Nepeta discolor NDI 12 0 45 15 20 19 Oxyria digyna ODI 20 22 10 0 20 20 Pinus wallichiana PWA 31 15 0 0 20 21 Potentilla anserina PAN 36 25 28.33 27 26 22 Rheum tibeticum RTI 0 15 0 17 11 23 Ribes alpestre RAL 0 21 17 15 15 24 Ribes orientale ROR 11 12 22 10 13.33 25 Rosa webbiana RWE 18 16 23 16.25 10 26 Rubus irritans RIR 5 29 30 0 7 27 Rumex hastatus RHA 9 21 10 0 0 28 Spiraea canescens SCA 9 7 0 20 7 29 Tanacetum artemisioides TAR 33.48 0 22 12 0 30 Taraxacum sp TSP 0 22 20 15 12 31 Taraxacum baltistanicum TBA 18.32 0 0 22 0 32 Thymus serpyllum TSE 0 20 38 34 40 33 Trifolium pratense TPA 12.49 30 0 30 10 34 Trifolium repens TRE 12 22 21 0 0 35 Urtica dioica UDI 13 41 22 0 22 36 Verbascum thapsus VTH 0 17 0 25 5 37 Viola rupestris VRU 0 30 45 17 35

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6.3.2.4-Group IV

Among the entire cluster this is the smallest group which includes six stands. In this group a total of twenty five species are present. The floristic composition is more or less similar to that of group I. Among these Cicer songaricum, Bergenia stracheyi, and Bistorta affinis are dominant species with 41%, 39% and 36% average frequency respectively. Other associated species i.e. Anaphalis nepalensis 23.33%, Astragalus zanskarensis 27%, Berberis orthobotrys 27%, Geranium partens30%, Leontopodium leontopodinum 33.33%, Potentilla anserina 27% were recorded with average frequency. The rare species i.e. Artemisia brevifolium, Juniperus communis, Myosotis asiatica, Nepeta discolor, Rheum tibeticum, Ribes alpestre, Ribes orientale, Rosa webbiana, Spiraea canescens, Viola rupestris, Taraxacum sp., Tanacetum artemisioides associated with low average frequency.

On the basis of the topographic characteristics this group was recorded at mean elevation of3480±48 m and with mean slope of 32±4.8 angle.

The Physical characteristics were recorded i.e. TDS 11.48±1.7, water holding capacity 48.95±6.1, salinity 0.0, conductivity 27.16±3.9 and organic matter 9.7±1.3 percent respectively. The soil of this group was showed strongly acidic in nature having the mean value of pH 5.5±0.09.While in case of the soil chemical properties this group showed the mean value of Ca++ 199±20.9, Mg++137±12.7, K+177±18.3, Co++0.8±00, Mn++16±1.5, Zn++1.5±0.1 and Fe++ 138±12.9ppm (Table 6.6).

6.3.2.5-Group V

This group consist of seven stands having twenty six species, predominantly Bergenia stracheyi, Bistorta affinis, Myosotis asiatica and Thymus serpyllum with 45%, 43%, 40% and 40% average frequency respectively. Anaphalis nepalensis, Fragaria nubicola, Geranium pratense, Potentilla anserina, and Leontopodium leontopodinum are recorded occasionally from range of 23% to 36% average frequency. Some species are dispersed with the range of average frequency from 10% to 20% i.e. Inula rhizocephala, Juniperus communis, Nepeta discolor, Rosa webbiana, Rheum tibeticum, Ribes alpestre, Oxyria digyna and Pinus wallichiana (seedlings).

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On the basis of the environmental properties this group was recorded at mean elevation of 3307±60 m and with mean slope of 32±4.9 angle.

Soil edaphic variables of this cluster group were recorded i.e. TDS 30.38±5.9, water holding capacity 44.85±40, salinity 0.02±0.01, conductivity 69.24±13 and Organic matter 12±3.5 percent. The soil of this group was recorded slightly acidic in nature having the mean value of pH 6.2±0.4.While in case of the soil nutrients this group showed the mean value of Ca++ 220±21.7, Mg++ 120±3.9, K+ 215±27.9, Co++ 0.8±00, Mn++ 16±1.6, Zn++ 1.1±0.2 and Fe++ 139±8.2ppm (Table 6.6).

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Table 6.6 Mean values of the environmental variables based on the five groups obtained from Ward’s method of cluster analysis using understorey vegetation data of 40 stands from forested areas of Gilgit-Baltistan. (Mean ± SE)

Variable Group I Group II Group III Group IV Group V 1- Topographic variables 1-Elevation (m) 3515±29 3026±102 3122±170 3480±48 3307±60 2-Slopeº 26±2.1 29±4.7 36±4.3 32±4.8 32±4.9 2- Edaphic variables 1-TDS 18.7±2.7 26.9±2.8 16.06±2.08 11.48±1.7 30.38±5.9 2-pH 5.4±0.05 6.7±0.2 6.6±02 5.5±0.09 6.28±0.4 3-WHC 46.14±4.5 37.33±3.4 49.25±6.5 48.95±6.1 44.85±40 4-Salinity 00±00 0.04±0.02 0.30±0.2 00±00 0.02±0.01 5-Conductivity 43.6±6.2 61.66±6 37.57±4.7 27.16±3.9 69.24±13 6-OM 6.2±0.5 7.2±1 4.3±0.7 9.7±1.3 12±3.5 3-Soil Nutrients 1-Ca 174±14.2 198±15.9 195±14.1 199±20.9 220±21.7 2-Mg 125±7 123±7 150±8.1 137±12.7 120±3.9 3-K 207±16 236±18.6 225±27 177±18.3 215±27.9 4-Co 0.7±0 0.7±00 0.7±00 0.8±00 0.8±00 5-Mn 9.9±1.5 13±1.9 10±1.6 16±1.5 16±1.6 6-Zn 1.2±0.1 1±0.2 1.3±0.3 1.5±0.1 1.1±0.2 7-Fe 79.8±14.1 117±11.8 187±18.3 138±12.9 139±8.2

6.3.3-Univariate analysis of Variance ground Vegetation Data (ANOVA)

Five main groups of ground flora vegetation data were derived using Ward’s cluster analysis and using univariate analysis of variance (ANOVA) the environmental characteristics i.e. topographic factors and Edaphic factors ( Table 7) of each groups were analyzed. Between the topographic variables i.e. Elevation and slope the elevation was found significant difference in group means with 4.5 F ratio while the slope was non-significant with 1.0 F ratio. Among the five Edaphic variable i.e. TDS, water holding capacity, salinity and all found non-significant except pH ,conductivity and organic matter were recorded significantly correlation with 7.79 ,

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4.64 and 3.617 F-ratio respectively.While in case of the soil chemical properties of cluster groups of tree vegetation data Mn++ and Fe++ were showed significantly correlation with F- ration 3.23 and 8.65 respectively (Table 6.7).

ANOVA: Single Factor

Table 6.7 Analysis of variance of individual environmental variables (topographic, edaphic five groups were derived by Ward's cluster analysis using circular plot data of 40 stands

Source of Variation SS df MS F P-level 1- Topographic Variables 1-Elevation Between Groups 1395984 4 348995.9 4.524899 P < 0.05 Within Groups 2699476 35 77127.89 Total 4095460 39 2-Slope Between Groups 557.2429 4 139.3107 1.010391 ns Within Groups 4825.732 35 137.8781 Total 5382.975 39 2- Edaphic Variables 1-TDS Between Groups 1184.096 4 296.024 2.949503 ns Within Groups 3512.742 35 100.364 Total 4696.838 39 2-pH Between Groups 13.37163 4 3.342908 7.795824 P< 0.001 Within Groups 15.00826 35 0.428808 Total 28.3799 39 3-WHC Between Groups 780.7475 4 195.1869 0.975875 ns Within Groups 7000.426 35 200.0122 Total 7781.174 39 4-Salinity Between Groups 0.014492 4 0.003623 1.331181 ns Within Groups 0.095258 35 0.002722 Total 0.10975 39 5-Conductivity Between Groups 8492.849 4 2123.212 4.644582 P < 0.05 Within Groups 15999.81 35 457.1374 Total 24492.66 39 6-OM Between Groups 285.0378 4 71.25944 3.617388 P < 0.05

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Within Groups 689.47 35 19.69914 Total 974.5078 39 3-Soil Nutrients 1-Ca Between Groups 9269.605 4 2317.401 1.012163 ns Within Groups 80134.37 35 2289.553 Total 89403.98 39 2-Mg Between Groups 4809.491 4 1202.373 2.458627 ns Within Groups 17116.48 35 489.0424 Total 21925.98 39 3-K Between Groups 13756.79 4 3439.196 0.915388 ns Within Groups 131498.2 35 3757.091 Total 145255 39 4-Co Between Groups 0.021054 4 0.005263 0.776 ns Within Groups 0.237398 35 0.006783 Total 0.258452 39 5-Mn Between Groups 280.8672 4 70.2168 3.239216 P < 0.05 Within Groups 758.6984 35 21.6771 Total 1039.566 39 6-Zn Between Groups 0.912663 4 0.228166 1.001995 ns Within Groups 7.969902 35 0.227711 Total 8.882564 39 7-Fe Between Groups 53777.8 4 13444.45 8.65711 P < 0.001 Within Groups 54354.83 35 1552.995 Total 108132.6 39 Note: SS = Sum of square, MS = Mean square, F = F ratio, df = Degree of freedom, P level = Probability level and ns = Non significant.

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6.3.4-ORDINATION

6.3.4.1-DCA ordination of tree vegetation data

DCA was performed on Importance value of tree species and topographic variables (elevation and slope) used in DCA ordination to find out the correlation between the vegetation composition and topographic variables. Five main groups are differentiated by Ward’s cluster analysis are clearly superimposed on DCA ordination (axes 1,2 ; 1,3 & 2,3), no overlapping was seen on ordination axis 1.2 and 1,3 where as on ordination axis 2,3 five groups sperate out clearly. Having 9 stands Group I is the largest group among all five groups. This group is further divided into two sub groups i.e. group I (a) and group I (b) due to the different species composition. In all the nineteen stands Pinus wallichiana is dominated.

Group I (a) consists of 9 stands dominated by Pinus wallichiana but some other species are also associated with this group i.e. Juniperus excelsa, Betula utilis and Pinus gerardiana while Group I (b) consists of 10 stands in which Pinus wallichiana found as monospecific order. Group I (a) was located on the elevation of 2639 to 3700 meters and 15 to 35° slope angle while the Group I (b) located on the elevation of 2669 to 3596 meters with plain to 45° slope angle.

Group II is composed of (stands, 4, 5, 12, 13, 14, 29, 32, 39) in which stand 4 is dominated by Betula utilis while Pinus wallichiana and Juniperus excelsa are also associated.

Stand 5 is dominated by Juniperus excelsa while Pinus wallichiana is found as co-dominant species whereas Betula utilis is also found in this stand as associated species. In Stand 12, 13, 14 and 39 are occupied Pinus wallichiana as first dominant species while Betula utilis found as co-dominant. Picea smithiana is first dominant species in stand 29 while in this stand Pinus wallichiana is recorded as second dominant species. In stand 32 Pinus wallichiana contributed highest importance value being first dominant and Picea smithiana occupied second leading species. This group was recorded on the elevation range between 2719 to3600 with 20 to 40 slope angle.

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Fig. 6.3 DCA ordination of stands, using tree species data of 40 stands of forested areas from three districts of Gilgit-Baltistan

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Seven stands of group III are dominated by Picea smithiana among these stands stand 15, 16, 19 and 25 are monospecific Picea smithiana stand while in stand 18, 20 and 36 Juniperus excelsa found as co-dominant species.

Group IV is separated on the basis of monospecific condition of an angiospermic species Betula utilis. In this group 4 stand i.e. stand 21, 23, 26 and 27 were recorded as pure Betula utilis stands. The elevation and slope ranged between 2616 to3523 and 33 to 45 respectively. The environmental variables varied in this group i.e. elevation and slope from 2893 to 3508 meters and plain to 40 angles. This species was also found in Group I (a) as third dominant and in Group II as second leading species

The last group is the smallest group (group-V) among all the groups which was composed of only two stands i.e. 24 and 28 Stand no 24 is monospecific stands of Juniperus macropoda while stand 28 is monospecific Abies pindrow species. These two species never found with any other species in any groups or stands as co- dominant as well as associated form. The elevation and slope ranges is high as compare to the other groups because this groups is lie on the elevation ranged from 3464 to 3736 meters and 45° slope angle.

6.3.4.2-Relationship (correlation coefficient) of three ordination axes with Topogaraphic, Edaphic and Soil nutrients of tree vegetation data

The Results between three Ordination axes with the different variables are presented in Table-8. Among the environmental factors only edaphic factor salinity (P < 0.05) and soil nutrient K+ (P < 0.05), (P < 0.05) showed positively correlated with ordination axes 1, and ordination axes 2,3 respectively (Table-8)

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Table 6.8 Relationship (correlation coefficients) of environmental variables (topographic and edaphic variables) with 3 DCA ordination axes obtained by tree vegetation data based on importance value of tree species

Axis 1 Axis 2 Axis 3 S.No. Variables r Prob. Level r Prob. Level r Prob. Level 1- Topographic variables 1 Elevation 0.218 ns 0.095 ns 0.0665 ns 2 Slope 0.277 ns 0.012 ns -0.078 ns 2- Edaphic variables 1 TDS -0.01 ns -0.01 ns 0.31 ns 2 PH 0.045 ns 0.16 ns 0.07 ns 3 WHC -0.2 ns -0.12 ns -0.06 ns 4 Salinity 0.345 P < 0.05 -0.42 ns 0.14 ns 5 Cond. -0.02 ns -0.02 ns 0.31 ns 6 OM -0.16 ns -0.016 ns 0.32 ns 3- Soil Nutrients 1 Ca 0.201 ns 0 ns 0.154 ns 2 Mg 0.021 ns -0.129 ns -0.230 ns 3 K 0.133 ns 0.351 P < 0.05 0.384 P < 0.05 4 Co -0.123 ns -0.081 ns -0.062 ns 5 Mn -0.010 ns -0.066 ns 0.227 ns 6 Zn -0.155 ns 0.028 ns -0.243 ns 7 Fe 0.002 ns 0.113 ns -0.062 ns r = Correlation coefficient, ns = Non significant, Prob. Level = Probability level.

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6.3.4.3-DCA ordination of understory vegetation data

The two dimensional DCA ordinations of all three axes i.e. 1and 2, 1 and 3 and 2 derived using the mean frequency of 37 understory vegetation species. The stands on ordination axis 1,3 and 2 and 3 are overlapping with each other therefore there are no any clear grouping is occurring while the stands clearly separate out five groups only on the ordination axis 1 and 2. These groups are matched to the Wards cluster analysis of understory vegetation.

Group-I is the largest group as compare to the other cluster groups which comprises of 10 stands. In this ordination group total thirty species were recorded among them Potentilla anserine, Tanacetum artemisioides and the seedling of coniferous tree species Pinus wallichiana were recorded as dominant species. Group- II attempts the second position among the all ordination groups having nine stands. This group was separate out due to the abundance of Urtica dioica and Fragaria nubicola. Group-III includes eight stands and was composed of twenty six species among these fourteen species are common in group I and group II. The dominant species were Nepeta discolor, Viola rupestris, and Fragaria nubicola. Among the entire ordination groups this is the smallest group consist of six stands. In this group total twenty five species are present the floristic composition more or less similar to the group I. Among these Cicer songaricum, Bergenia stracheyi, and Bistorta affinis were recorded as leading species. Group-V was consisted of seven stands having twenty six species predominantly Bergenia stracheyi, Bistorta affinis, Myosotis asiatica and Thymus serpyllum.

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Fig. 6.4 Showing DCA stands ordination on axis 1 and 2 of ground flora

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6.3.4.4-Relationship (correlation coefficient) of three ordination axes with Topogaraphic, Edaphic and Soil nutrients of understory vegetation data

Results of different environmental factors relationship with the three ordination axes was presented in table-9. Between the Topogaraphic variables Elevation (P < 0.05) was found positively correlated with axes 1. While among the edaphic factors only pH (P < 0.05), (P < 0.01) was showed positively correlated with axes 2 and 3 respectively. Whereas among the soil nutrients only Fe++ was (P < 0.05) positively correlated with ordination axes 3.

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Table 6.9 Relationship (correlation coefficients) of environmental (topographic and edaphic variables) with 3 DCA ordination axes obtained by understorey vegetation data based on frequency of understorey species

Axis 1 Axis 2 Axis 3 S.No. Variables r Prob. Level r Prob. Level r Prob. Level 1- Topographic variables 1 Elevation 0.38 P < 0.05 -0.41 ns -0.27 ns 2 Slope -0.21 ns -0.03 ns 0.16 ns 2- Edaphic variables 1 TDS -0.23 ns 0.236 ns -0.138 ns 2 PH -0.52 ns 0.399 P < 0.05 0.423 P < 0.01 3 WHC 0.046 ns -0.174 ns -0.074 ns 4 Salinity -0.26 ns 0.24 ns -0.27 ns 5 Cond -0.22 ns 0.23 ns -0.13 ns 6 OM -0.190 ns -0.254 ns -0.179 ns 3- Soil Nutrients 1 Ca -0.271 ns -0.058 ns 0.102 ns 2 Mg -0.128 ns -0.067 ns -0.189 ns 3 K -0.116 ns 0.241 ns 0.085 ns 4 Co -0.102 ns -0.177 ns -0.305 ns 5 Mn -0.284 ns -0.194 ns -0.259 ns 6 Zn 0.093 ns -0.174 ns 0.004 ns 7 Fe -0.444 ns -0.149 ns 0.375 P < 0.05 Key to abbreviations: r = Correlation coefficient, ns = Non significant, Prob. Level = Probability level.

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6.5-Discussion

The multivariate technique of Ward’s cluster analysis (1963),Goodall 1973) and DCA method (Hill and Gauch 1980) were used to analyze 40 forested stands. Five tree groups and five ground vegetation groups were classified. Greig-Smith (1983) has described the advantage of both approaches. Okono (1996) described cluster analysis as a quantitative method which is used for objective categorization. Lovtt et al. (2001) and Gajoti et al. (2010) advocated that the environmental variables play a very important role in recognizing the vegetation distribution pattern. They also suggested that the elevation is most important factor to investigate the vegetation distribution pattern. In the present study both elevation and slopes were taken into account to determine the vegetation pattern. By Ward’s cluster analysis a total of five groups were recognized using tree vegetation data. Group-I (a) is composed of 9 stands dominated by Pinus wallichiana with second co-dominant Juniperus excelsa and 3rd associated species Betula utilis. This community prefers to grow high elevation at 3421 m and low slope 27o angle. In this group Pinus gerardiana found as second leading species in one stand. Ahmed et al. (1990) described that Pinus gerardiana and Juniperus species are restricted to drier sites of dry temperate area. The study area falls in dry temperate area and Juniperus excelsa is distributed wildly whereas Pinus gerardiana found only from Mushkin valley.

Group-I (b) comprises of ten stands which is also the largest group. This is pure Pinus wallichiana group which is recoded on low average elevation (3169 m) as compared to the other and low average slope 28o angle. Ahmed et al. (2010) reported Pinus wallichiana from different climatic zones of Pakistan at the elevation of 1950 to 2700 m and 23o to 45o slope. Wahab et al. (2010) studied Pinus wallichiana community from district Dir on 1875 elevation. Kahn et al. (2013) reported Pinus wallichiana community at 2559 meter from Chitral district .This shows that this species is distributed from 1875 to 3700 meter ranges of elevation.

Group-II is also differentiated by the predominance of Pinus wallichiana but second leading species was Betula utilis. This group composed of 8 stands, in which two stands were dominated by Betula utilis and Juniperus excelsa respectively while one stand dominated by Picea smithiana. This group is recoded on average elevation at 3373m and slope 33o angle. Ahmed et al. (2006) came across different climatic

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zones of Himalayan forest of Pakistan and identified 4 monospecific and 24 different communities. They observed pure stand of Pinus wallichiana on south exposure at 2770 m elevation from Nalter Gilgit and higher elevation 3100 m from Tukht-e-Sulaiman. Group-III is composed of 7 stands characterized by the predominance of Picea smithiana on low average elevating 3178 m and steep slope 39o angle. Ahmed et al. (2006) also studied more or less pure Picea smithiana forest from Nalter Gilgit on 3100 to 3250 m. Wahab et al (2010) reported Picea smithiana- Pinus wallichiana community on 2527 to 2645 elevation ranges from Danair valley district Dir.

Group-IV is monospecific Betula utilis group which is found on medium average elevation on 3214 and low slope 22o angles. Betula utilis community also identified Ahmed et al. (2006) on the elevation of 3350 to 3500 with co-dmoniance species of Picea smithiana. The smallest group among all the groups is group-V composed of two stands stand-24 is pure Juniperus macropoda and stand-28 is pure Abies pindrow forest. This group is located on high average elevation 3600 m and high slope 45o angle. Ahmed at al. (2006) also described Abies pindrow community on 3450 elevation from Astore near Rama Lake. Siddiqui (2011) studied Abies pindrow on 3000 elevation from Lalazar, Naran Himalayan moist temperate range while Wahab et al. (2010), Wahab (2011) recorded Abies pindrow community from District Dir Satto Khwar valley on 2670 elevation. It means this species can grow and exist in both i.e. moist and dry temperate from 2670 to 3600 meter above sea level.

On the basis of stands of tree flora five main groups were recognized by Ward’s cluster analysis. These five groups were separate out due to the presences of different vegetation species. Group I is largest groups among the entire cluster which is composed of 10 stands dominated by Potentilla anserina located on high average elevation 3515 with low slop 26o angle. The second largest group consist of 9 stands is Group II which is recognized by abundance of Urtica dioica species with low mean elevation 3026 m and low slope 29o angle. Viola rupestris and Fragaria nubicola are found as dominant species in Group III which is situated on low medium elevation 3122 m and high slope 36 o angle. Group IV is differentiated due to the dominance of Cicer songaricum on high elevation3480 m and medium 32o slope

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angle whereas Bergenia stracheyi is dominated species in Group V located on medium elevation 3307 with medium slope 32o angle.

The vegetation groups of tree and ground flora also analyzed by detrended correspondence analysis (Hill 1979a; Hill and Gauch, 1980). The tree vegetations stands showed distinguished groups only on the axis 2 and 3 while on axis 1and 2 and 1 and 3 stands are overlapping therefore no any distinguishable groups were found. The groups which are separate out clearly on the ordination axis 2 and 3 are dominated by different tree species likewise the ground flora shows five groups only on the axis of 1and 2. The resulted group showed similar distribution pattern to the cluster groups of tree and understory vegetation stands. Classification and ordination showed similar distribution pattern of tree species as well as understory vegetation. Relationships between the ordination axes with topographic variables i.e. elevation and slope and edaphic variables i.e. pH, TDS, Salinity, conductivity and water holding capacity was also analyzed. Among the environmental variables elevation and pH were found significantly correlated (P < 0.05) and (P < 0.001) in groups mean with the ground flora data respectively whereas tree vegetation data set showed significant difference only with the ph of soil (P < 0.05) value. Similar results also recorded Siddiqui et al (2010),Siddiqui (2011),Khan (2011), Khan (2012), Khan et al. (2013). Relationship between environmental variables and DCA ordination axes also evaluated. The environmental variables were not found significant correlation with axis 1, 2 and 3 except salinity which is significant (P < 0.05) value to the axis 1 of tree data set while in case of understory vegetation data set Elevation (P < 0.05) with axis 1 and pH (P < 0.05), (P < 0.001),with axis 2 and 3 showed significantly correlation respectively. The groups resulting from tree vegetation data set and ground flora data set were associated with the topographical i.e. elevation and slope and Edaphic i.e. water holding capacity, TDS, pH, salinity and conductivity. The classification and ordination and environmental variables disclosed some important relationship and the distribution pattern of the vegetation. For both overstorey (trees) and understory objective classification showed well-defined group structure and the resulting groups (clusters) were correlated to a considerable extend with the topographic and edaphic factors.

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PART-2

DENDROCHRONOLOGY

Chapter No 7 Introduction to Dendrochronology

CHAPTER 7

INTRODUCTION TO DENDROCHRONOLOGY

7.1-Introduction

According to Fritts (1976) Dendrochronology can be defined as all tree-rings studies where annual growth layers have been assigned or assumed to be associated with specific calendar year. Dendrochronology is defined by Cook and Kariukstis, (1992) the systematic study of tree rings pattern with the passage of years. According to Heizer (1956), the early Greeks were considered first to note the annual tree layers and anticipated that widths of these layers were dependent on environmental conditions. Duhemel and Buffon, two French naturalists in 1737 examined 20 frost damaged rings occurred in the bark of several felled trees. Other investigators confirmed their observations. A. C. Twining in 1827 and Charles Babbage of England in 1838 recognized crossdating based on relative ring widths. In 1892, a Russian worker F. N. Shevedov crossdated the annual rings but and identified that these ring structure was due to the past climatic variations.

The founder of Dendrochronology was considered A.E. Douglass who established the first laboratory entirely devoted to tree-ring research. Douglass' preliminary interest was to observe the impact of solar cycles on the Earth’s climate. At Flagstaff, Arizona in 1904 A.D he noticed a distinct annual ring pattern in the stems of many Ponderosa pine trees, a repeated signature of narrow and wide rings. This was the pattern, which Douglass subsequently found in trees throughout the region, became known as the Flagstaff signature. Ring-width patterns enable dendrochronologists to precisely and accurately date every individual tree ring in a area. When all trees on a site are limited by a common factor, such as variability in climate, the size of their annual growth rings is affected in a similar manner, and a common ring-width pattern emerges across the site or region. Crossdating, i.e. matching the ring patterns in tree-ring samples across a site, can provide an accurate chronological record of the natural history of the stand.

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In early period dendrochronological techniques were recognized to date archeological structure but now a days this modern technique have been used in various disciplines i.e. plant ecology, geomorphology, hydrology, glaciology, seismology and entomology and prominently climatology. To keep a record of tree ring data an International Tree-Ring Data Bank (ITRDB) was established in 1974, which enables the global scientific community to contribute and without restraint access to tree ring data (Grissino-Mayer and Fritts, 1997)

7.1-Description of Study area (Ganji valley)

Ganji is one of a small valley of Sub-Division Rundo of District Skardu in northern areas of Pakistan. Ganji is located between 350.56 N and 740 98 E with 150 slope and South East facing exposure about 75 km from Skardu District on the upper bank of Indus river. The elevation was3310 m a.s.l while the canopy was closed. Map of sampling site are given in (Fig 7.1) This valley comprises of many sub valleys i.e. Sisingpo, Bultifachlchachi, Khamidos, Terila, Chiskam, Ganji Pine, Poydas and Ganji bala. The history of this valley genesis back to centuries it is said that the people of Ganji came from Astore, Chilas and Gilgit and settled here. There are approximately 600 households having population of 3600. The main tribes are Sheen but some Balti also lives in this Valley. The main dialects spoken are Shina then Balti but people can speak and understand urdu too.

This Valley is also rich in forest and wildlife. Willow (Salix spps), juniper (Juniperus excelsa), Kail (Pinups wallichiana), and birch (Betula utilis) species are found in the natural forest. People are not aware about the importance of the forest and other natural resources therefore forest species are being used for domestic purpose by the local people since many generations. Similarly snow leopard (Uncia uncia), markhor(Capra falconeri falconeri), ibex (Himalayan Ibex sibirica), fox(Vulpes vulpes), wolf(Canis lupes), chakor (Alectoris chukar),migratory birds, vultures and eagle species found in wilderness of the village There are 12 traditional hunters in the village which normally hunt for body parts and for domestic consumption of wild meat.

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Local people claim to have the grazing rights of these pastures. June to end of September is the suitable season for grazing these pastures, which are full of different medicinal plants like Thymus linearis, Mentha longifolia, Delphinium sp, Acantholimon lycopodioides, Curm sp, Artemisia sp, Ribes himalensis, Rosa webbiana etc. Due to aridity, scarcity of water and grazing these medicinal plants are rapidly degrading.

Fig.7.1 Map showing the Ganji sampling area from district Skardu circle showed the sampling site.

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Chapter No 8 Review of Literature

CHAPTER-8

REVIEW OF LITERATURE

8.1-Introduction

This chapter described of brief review of literature.

8.2-Review of Literature

A preliminary research on tree-ring and their climatic response was conducted in India by Chwdhury et al. (1939, 1940a) the pioneer tree ring researcher of subcontinent. From the western Himalayan, Pant (1979) estimated the sample correlation between climate and tree ring sequences. Wu and Lin (1987) presented a preliminarily research on modern climatic change from the Mountainous area of Hengduan.

Tree ring data was used by Hughes and Davies (1987) to investigate the climatic situation of India. Bhattacharyya et al. (1988) described the dendroclimatic potential of some coniferous species from Jammu and Kashmir. According to Ahmed (1987) Abies pindrow, Pinus gerardiana, Pinus wallichiana, Picea smithiana, Pinus roxburgii and Cedrus deodara has dendroclimatic potential so these species can be used to investigate the climatic information. Ahmed (1989) described tree ring chronologies of Abies pindrow (Royle spach) from Himalayan region of Pakistan and dated chronologies with maximum period of 237 years were obtained. Sample and chronology statistics were discussed. According to him, these chronologies showed similar climatic signals. However, it is suggested that west facing steep slope are the most suitable sites for tree-ring studies. It is concluded that Abies pindrow may be used in Dendroclimatologcal investigation. Ahmed and Sarangzai (1991) presented dendrochronological approach to estimate age and growth rates of various species from Himalayan region of Pakistan. According to them, Age and growth rate vary among closely growing trees of the same species. Diameter is a poor predictor of age in the absence of ring count. Using the tree rings of Abies pindrow Hughes (1992) described the dendroclimatic response. They also

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reconstructed the mean temperature for spring and late summer using the data. Bhattacharyya and Yadav (1992) conducted a study from Harshil and Kinnaur site to investigate the climatic response of Pinus gerardiana and Cedrus deodara. From south Mustang of India, Schmidt et al. (1993) cross dated some living pine trees and archeological wood samples and extended chronologies further in time. Esper et al. (1995) studied 1000 years tree ring of Juniperus excelsa from the upper timber line of Karakorum Range of Pakistan. They concluded that in this area the tree ring can be use as climatic indicator. Tree ring chronologies of Picea smithiana, Pinus wallichiana, and Cedrus deodara was studied from Garhwal of Himalyan region by Yadav et al (1997a). They correlated tree ring data with temperature and found positive relationship. On the basis of this relationship they reconstructed April-May temperature. Yadav et al. (1997b) conducted a study to investigate tree ring growth effect in the light of climatic variables from western Himalyan region. Regmi (1998) conducted study using rings of Pinus wallichiana and Pinus roxburghii from Kulekhani site. According to him Pinus roxburghii displayed indistinguishable boundaries while the tree ring of Pinus wallichiana showed very clear rings.. Living tree species of Pinus wallichiana, Abies spectabilus and Picea smithiana was studied by Schmidt (1999) from the Mustang region. He cross dated the chronologies of these living species with some archeological wood sample and suggested that these species were suitable for the past climate reconstruction. Seven coniferous tree species was studied by Chaudhary et al. (1999) from eastern India. They observed the relationship between temperature and growth rate of trees. Sha and Fan. (1999) was used Dargon spruce ring-width to reconstruct the past climate from West Sichuan Plateau. Esper (2000) conducted study to investigate the climatic response of Juniperus excelsa from the upper timber line of Karakorum Range. Brauning (2001) studied the tree ring of Junipers from Tibetan Plateau and developed the climatic information. From the Kalinchok of Nepal, Douglas (2002) conducted study to investigate Abies spectabilis tree ring response against the climate. He reconstructed pre-monsoon extending back to 1702 A.D and concluded that temperature reconstruction did not demonstrate any significant indication of latest global warming. Khanal and Rijal (2002) worked on tree ring of Abies spectabilis from the Ganesh Himalaya area of Centeral Nepal. They observed negative correlation between tree ring growth and temperature in the month of May

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while positive correlation between tree growth and precipitation in April. Form the Humala district western Nepal, Furuta et al. (2002) produced ring width and minimum density chronologies of Picea smithiana. They observed tree width is the best indicator of pre-monsoon of this area. Cook et al. (2003) carried out study of 46 tree ring-width chronologies network in Nepal that is suitable for reconstructing temperature over the past years. Using the dendrochoronological technique Yang et al. (2003) described the Late Holocene Temperature fluctuations on the Tibetan Plateau. Studying the tree ring of the northeastern Qinghai-Tibetan Plateau, Zhang et al. (2003, 2004) developed a past climate record of 2,326- year. Using tree rings Brauning and Mantwill (2004) produced summer temperature and summer monsoon history of 400 years on the Tibetan plateau. Ahmed and Naqvi (2005) described tree-ring chronologies of Picea smithiana (wall.) Boiss. and its quantitative vegetational description from Himalayan range of Pakistan. They gave dendrochronological description, ring-width characteristics, and sample characteristics, inter chronology characteristics and also concluded that Picea smithiana could be used for dendrochronological investigation. It is also suggested that detailed sampling is required to present strong database. Shoa et al. (2005) studied tree rings from Delnga, Qinghai. They produced 1000 years past precipitation data. A study was carried out by Brauning (2006) to investigate the little ice age using tree rings from southern Tibet. From northeastern Tibetan Plateau, Liu et al. (2006), 1156 years tree ring data was used to reconstruct the precipitation variations. Huang and Zhang (2007) developed 680 year chronologies from tree rings of Walan area of northeastern Tibet. Zhang (2007) studied tree rings of Sabina przewalskii from the northeastern Qinghai-Tibetan Plateau. They developed 1017 years chronologies. They also found the positive response between tree rings growth with May and June precipitation. Khan et al. (2008) presented dendroclimatical potential of Picea smithiana (Wall.) Boiss., from Afghanistan. Modern dendrochronological techniques were applied on Picea smithiana from district Dangam of Afghanistan. A first dated chronology (1663-2006) from Afghanistan was presented by Wahab et al. (2008) in which he developed first time 343 years tree ring chronologies from Afghanistan Adjacent to Pakistan border. Various statistics were described. It was indicated that all cores were highly correlated showing similar climatic signals. Ahmed et al. (2009a) used

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Dendrohoronological techniques on 49 different forested stands to investigate age and growth rate. Ahmed (2009b) described some preliminary results for dendroclimatic investigation using Picea smithiana of Chera and Nalter and presented more than 600 years chronology. From Astore and Ayubia Abies pindrow was studied for growth- climatic response function by Ahmed et al. (2010a). Ahmed et al. (2010b) et al. described choronologies form upper Indus Basin of Karakorum Range. Zafar et al. (2010) carried out standardized tree ring choronologies of Picea smithiana from Bagrot and Haramosh valleys of Gilgit. Khan (2011) and Wahab (2011) studied chronologies of coniferous tree species from Chital and Dir district respectively. Ahmed at al.(2011a) explained the dendrochronological potential of coniferous forests from Northern area of Pakistan Zafar et al. (2012) studied growth climate response of Picea smithina from Afghanistan. Ahmed and Shaukat (2012) discussed the scenario of climte change. Zafar (2013) carried out growth climate response of some coniferous tree species from Gilgit and Hunza districts of Gilgit- Baltistan. Ahmed et al. (2013) investigated dendroclimatical and dendrohydrological potential of two coniferous tree species from Gilgit district. Cook et al. (2013) provide five centuries database of Indus rives using tree ring chronologies from the forest of Northern Areas of Pakistan. Since no dendrochronological studies were carried out from Skardu. The present study focused on the Growth climate response of Pinus wallichiana from Ganji valley a new site from District Skardu.

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Chapter No 9 Materials and Methods

CHAPTER-9

MATERIALS AND METHODS

9.1-Introduction

This chapter describe of materials and methods of age and growth rate and growth climate response.

9.2-Material and methods

9.2.1-Field Methods

9.2.1.1-Site discription

Sampling site is located in Ganji which is one of a small valley of Sub- Division Rundo of District Skardu in northern areas of Pakistan at the latitude and longitude 350.56 N and 740 98 E. Trees were on South East facing exposure with 15o slope about 75 km from Skardu District on the upper bank of Indus river. The elevation was 3310 m a.s.l while the canopy was closed. Map of sampling site are given in Chapter-7 (Fig 7.1)

9.2.1.2-Site selection

First step was selection of site which is one of the important principles of dendrochronology. In this regard high elevation site was targeted , anticipating that rings of trees were expected to be quite sensitive there as compared to the low elevation.

9.2.1.3-Extraction of core

To collect the wood sample (cores) from the living Pinus wallichiana trees the Swedish Increment Borer was used. The criteria for the sampling from the trees were (1) Trees should not infect due to any diseases or fire. (2) Undisturbed and mature trees were selected. (3) Trees growing on steep site were selected. These criteria were recommended by Ahmed (1984) and Ahmed et al. (2009).

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Two cores from each tree were collected on breast height and Dbh of the trees were measured using Dbh tape. All cores were persevered in plastic straws in order to prevent damage during the field. Each cores were labeled with tree Dbh tree number, date of sampling and name of the site.

9.2.2-Laboratory Method

9.2.2.1-Preparation of sample

Second step is laboratory work: in this step the cores were air dried for two to three days for further processing. Then the dried cores were mounted on grooved wooden strips with the help of water soluble glue and were fixed with masking tape and again left for drying for two days. Each core at the time of mounting was given an ID, sample collection date, species name, trees number, site name , core number and Dbh of trees.

9.2.2.2-Sanding

Using different grade of sand papers each core was sanded until suitable polished surface was obtained.

9.2.2.3-Crossdating

Cross dating is one of the basic principal and most essential methods in dendrocronological study (Fritts, 1976).

After sanding, the next important step was to compare rings of one tree to another tree from bark to pith and to assign calendar years for each rings under powerful microscope using skeleton plot method followed by Stokes and Smiley (1968). First those cores were selected whose outside ring was known means that year of collection were dated from outside to pith. Narrow and wide rings were marked on the skeleton plot and most narrow rings in the whole stand were circled as pointer years. One point was marked after every ten years, two points after every fifty years and three points after every century using lead pencil. Pointer rings memorizing method (Speer, 2010) was used during the crossdating.

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9.2.3-Measurement using Velmex

Velmex J2X machine was used to bring the tree ring width in numeric form. The ring’s widths were measured in millimeter. The machine was connected with central processing unit. The identity was given using the criteria SSSSTTC. The first two SS is used for Species name, the next two SS stands for site location, TT stands for tree number and C represents the core number like in case of Pinus wallichiana from Ganji (PWGJ151) PWGJ describes species name: Pinus wallichiana, GJ shows site Ganji and 15 represents that this is the 15th tree of the stand and 1 quantifies that this is core number 1 of the two or three. Black mark on the monitor screen was used to establish the values by measuring the distance travelled between two successive rings. The program started counting from bark to pith one by one. Ring widths were measured to 0.001 accuracy selected from the program menu. Each rings width was measured using print option of Tab and numeric data was stored automatically in computer.

9.2.4-Age and Growth rate

Age and growth rates of trees and seedling were calculated following Ahmed (1984), and Ahmed et al. (1990a, 1990b, 1991 and 2009) to determine the relationship between Dbh/age and age/growth rates of seedling as well as trees Linear Regression was used whereas Dbh size classes Histogram were plotted using MS excel 2003-2007. Dbh size classes interval of each seedling is based on two cm Dbh and each trees seedling were divided into four classes where as Dbh size classes of tree were divided into 9 classes each class based on 10 Dbh cm. Moreover to investigate growth rates in different period from 1720 to 2010 ten years interval i.e 1720-1730, 1731-1740……… 2001-2010 were made and studied growth rates. 9.2.5-Computer Software used in the analysis

Following software were used in overall analysis

Velmex Measure J2X, COFECHA, DPL (Dendrochronological Program Library) are the packaged program containing 36 different programs written by Richard Holmes (1934-2003).ARSTAN and CORRELATION AND RESPONSE FUNCTION

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Chapter No 9 Materials and Methods

9.2.6-COFECHA

The wood samples were visually cross-dated under binocular microscope following the methods described by Stokes & Smiley (1968), Fritts (1976) and Grissino-Mayer (2001). The cross dating of each core were verified measuring to nearest 0.00l mm under the microscope using Volmex apparatus and also checked the quality of rings and growth rate using COFECHA program designed by Holems et al. (1986).

COFECHA is a computer program to checked and confirm the visual crossdating. The raw ring width measurement taken in millimeter was subjected to COFECHA (Holmes et al. 1986; Grissino-Mayer 2001) to check the quality of visual crossdating. Default commands were followed with 32 year cubic spline 50% wavelength cutoff for filtering; 50 year segment length with 25 year lagged and 99% confidence interval with 0.3281 critical level of correlation value to incorporate the results. COFECHA embodies seven parts; part one explain title page, options selected, summary and absent rings; part two inform graphical representation in the form of Histogram; part three demonstrate master series with samples depth and missing rings by year; part four express Bar plots of master dating series; part five exemplify correlation of each series by master series; part six represents potential problems including low correlation, divergent year to year changes, absent rings and outliers. The second part of COFECHA is of much connotation and denotes the following results;

1. Number of dated series which demonstrates how many samples in a stand is crossdated. 2. Master series which inform the longest crossdated core in the whole series. 3. Total rings and total dated ring in the whole stand. 4. Series intercorrelation which explains how much pattern of rings is similar or dissimilar to one another. 5. Average mean sensitivity is the measure of relative differences in widths between two neighboring rings. 6. Flags which are the source of problems in crossdating.

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Chapter No 9 Materials and Methods

9.2.7-ARSTAN

In 1983, Cook provided the source code for Program ARSTAN to the Laboratory of Tree-Ring Research at the University of Arizona. Program ARSTAN produces chronologies from tree-ring measurement series by detrending and indexing (standardizing) the series, then applying a robust estimation of the mean value function to remove effects of endogenous stand disturbances. Autoregressive modeling of index series often enhances the common signal. Extensive statistical analysis of a common time interval provides characterization of the data set. Three versions of the chronology are produced, intended to contain a maximum common signal and a minimum amount of noise. This program is used to standardize tree rings series in Dendrochronological study (Fritts, 1976) and (Cook, 1985). Software ARSTAN was used to transfer crossdated raw data to develop standardized chronology. Master chronologies were developed through first deterending method include Standard, Residual and Arstan chronologies.

9.2.7.1-Standard chronology

This chronology computed of series of tree-ring data which have been used to remove the large variance due to the cause of non-climatic. ARSTAN provide many choices to compute for this chronology; sign or two stages detrending of measurement series. Tree ring indices for a series may be computed either as ratios or as residuals (by subtraction) and variance may be stabilized and then the mean value function may be computed either as arithmetic means or as biweight robust mean to remove the effects of internal disturbances and to improve the common signal. If there is no autoregressive model then this chronology produce STNDRD version which removes the lag years effect (Cook and Holmes, 1999).

9.2.7.2-Residual chronology

The version of residual chronology is similar with standard chronology but the series averaged are residuals from autoregressive modeling of the detrended measurement series. In this chronology robust estimation of the mean value function produces a chronology with strong common signal. Modeling of this chronology containing four or more series, applying the model to the entire residual chronology.

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The version RESID produces if the initial residual chronology is not an autoregressive process. The earliest date of RESID version may be one or more years later than the STNDRD version which depends on the order of AR model and rewhitening process. In this chronology lag years effect are remained (Cook and Holmes, 1999).

9.2.7.3-ARSTAN chronology

The pooled model of autoregression is reincorporated into RESID version which produces the ARSTAN chronology. The pooled autoregression includes persistence common and synchronous among the large portion of the series (Cook, 1985). This chronology intended to contain the strongest climatic signal. The earliest date of ARSTAN chronology is usually the same year as STNDRD. If the RESID version required whitening then this version act as intermediate between STNDRD and RESID version. This version of chronology used to enhance the common signal of the lag years influence (Cook and Holmes, 1999).

9.2.8-Growth-climate response

There are many methods adopted in the past to check the relationship between climate and tree growth. Fritts (1971) introduced the method of response function to explore the growth-climate relation which was the modification of multiple regressions. According to Hughes and Milson (1982) this method did not investigate the reaction of climate and growth but explore the nature of climate which affect the growth. Although Fritts (1976) found some mistakes in this method, instead of these errors this method is widely and successfully used in different countries of the world. The method is connected with the climatic record of the investigated area. It is must that the climatic station should be near to the investigated area or geographically and climatically resemble with each other. Gray (1982) stated that it is difficult to conclude that the significant of response function result. If we apply this method in different location of the area and check the correlation among these locations then may obtain good results. Ahmed (1984) investigated the response function analysis without 3 lag years and compare with other lag response function. Further this method is used in Pakistan by many researchers (Ahmed, 2009; Ahmed, 2011; Khan, 2011; Wahab, 2011 and Zafar, 2013). I used correlation analysis and response function

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Chapter No 9 Materials and Methods

analysis for the development of growth-climate relationship from the packaged software DPL (Holmes, 1992).

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Chapter No 10 Age and Growth rates

CHAPTER-10

AGE AND GROWTH RATES

10.1-Introduction

To understand the dynamics, sustainable conservation and management of any forest it is necessary to get better knowledge of age and growth rates of associated tree species (Jacoby, 1989, Peet and Christensen, 1980).According to Stewart (1986) forest tree species have different ecophysiological properties, one of the most important character is growth rate. Knowledge about the age and growth rate of tree species is very importance for better understanding of the forest dynamics, structure and sustainable management (Ma 2008). Most of dendrochronologists described that tree-ring analysis is an effective device to recognize the age of trees for evaluating forest dynamics and reconstructing the stand development patterns (Baker et al. 2005; Dang et al. 2010). Tree rings has been used to obtain growth rates of trees and age (Landis and Peart 2005; Lusk and Smith 1998).

In forestry, there is a general need to estimate the age of trees from their diameter. Such estimation is usually based on empirical relationship between the tree’s age and diameter. The age of trees is determined by counting the number of rings in the stem, if the rings are formed annually and clearly visible (Burley et al. 2007; Liu and Hong 1999).

Although tree rings are being used in feretory to estimate the age of trees by the Pakistan forest Institute, Peshawar. Khan (1968) counting tree ring of Pinus wallichiana from Tarkhal forest of Azad Kashimir to estimate age of trees. Champion et al. (1965) evaluated the age of Pinus gerardiana using the tree rings from the forest of Zhob District. Shiekh (1985) determined the age of Juniperus exclesa from the Ziarat forest of Baluchistan. These observations were chiefly based only counting tree rings, no missing, double and missing radius were considered. In addition no allowance was made to the years when tree reached the height of the breast height. Dendrochronological techniques were used first time in Pakistan by Ahmed (1988a) who estimated ages and growth rates of a some planted tree species of Quetta, again

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Chapter No 10 Age and Growth rates

Ahmed (1988b) also presented the problem encountered to determine the age of trees. Ahmed et al. (1990a, 1990b) and Ahmed et al. (1991) determined age and growth rates of Juniperus excelsa and Pinus gerardiana trees from Balochistan. Recently Siddqui (2011) conducted study to estimate the growth rate and age of different coniferous tree species from the moist temperate area of Himalayan region of Pakistan. Khan (2011) also described age and growth rate of some tree from the forest of Chitral district. Wahab (2011) presented age and growth rates of tree species from District Dir. Hussain et al. (2012) evaluated age and growth rates of Picea smithiana from Stak valley, Central Karakorum National Park. Siddiqui et al (2013) estimated age and growth rates of dominant coniferous from moist temperate areas of Himalayan and Hindukhush range of Pakistan.

Beside the above mentioned research work no work has been conducted in Ganji valley. Therefore aim and objective of present study to estimate the age and growth rates of Pinus wallichiana from Ganji valley of Gilgit-Baaltistan.

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10.2-Materials and Methods

Materials and methods have been discussed in previous chapter-9.

10.3-Results

10.3.1-Age and Growth rate of seedlings

Map (Fig 7.1) and detailed description of the sampling area have been presented in chapter-7 while histogram of Dbh/age of seedlings is presented in Fig 10.1. Each size class based on 2 cm interval which showoed that mean age is increasing with respect to the Dbh increment. Relationship between actual age and Dbh size classes is shown in Fig 10.2. Dbh showed significantly correlation (r=0.544, p<0.01) with actual age of the seedlings. Histogram of mean growth rates and Dbh size classes of Pinus wallichiana seedlings is presented in Fig 10.3 which indicated that growth rate of seedlings in 2 cm classes attained slow growth while 4cm, 6cm and 8cm classes appeared with more or less similar growth. The regression analysis between Dbh and growth rates showed significantly correlated (r=0.571 p<0.01) (Fig 10.4).

Histogram of Mean age years of seedling/ Dbh size classes

25.0

20.0

15.0

10.0

Mean ages years 5.0

0.0 1234 Size classes

Note: 1=2cm, 2=4cm, 3=6cm and 4=8 cm Dbh

Fig 10.1 Showing the Histogram of Dbh size classes in (cm) Vs mean age of Pinus wallichiana seedlings.

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Fig 10.2 Showing regression between Dbh size classes Vs actual age of seedlings of Pinus wallichiana.

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Histogram of Mean grwoth rate years/cm and Dbh size classes of seedling 2.5

2.0

1.5

1.0

0.5 Mean growth rates years/cm growth Mean 0.0 1234 Dbh Size classes in (cm)

Note: 1=2cm, 2=4cm, 3 =6cm and 4=8 cm

Fig 10.3 Showing Histogram of growth rates and Dbh size classes of Pinus wallichiana seedlings.

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Fig 10.4 Showing regression of actual growth rates Vs Dbh size classes of Pinus wallichiana seedlings.

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10.3.2-Age and growth rate of tree

Size classes of mean age of trees are presented in Fig.10.5. On the basis of Dbh total 9 size classes were made with 10 cm interval. Fig 10.5 shows that mean age is increasing with the increasing of Dbh. Relationship between age and Dbh size classes are shown in Fig 10.6 which shows a significant correlation (r =0.53, p<0.01). Fig 10.7 shows histogram of mean growth rates years/cm of Pinus wallichiana tree while regression between actual growth rates and Dbh size classes are presented in Fig 10.8. Mean growth rates and Dbh histogram shows that growth rates decreased with the increasing of age. This phenomenon occurred due to the old age trees, natural and human induced disturbances. Growth rate did not showed significant relationship (r =0.285).

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Histogram of mean age years of trees

200 180 160 140 120 100 80

Mean age years 60 40 20 0 123456789 Dbh size classes

Note: 1= 10.1-20, 2=20.1-30, 3= 30.1-40, 4= 40.1-50, 6= 50.1-60, 7=60.1-70, 8=70.1- 80 and 9= 80.1-90 Dbh cm Fig 10.5 Shows Histogram of Dbh size classes Vs mean age of Pinus wallichiana trees

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Note: 1= 10.1-20, 2=20.1-30, 3= 30.1-40, 4= 40.1-50, 6= 50.1-60, 7=60.1-70, 8=70.1- 80 and 9= 80.1-90 Dbh cm

Fig 10.6 Shows regression analysis of Dbh size classes Vs actual age of Pinus wallichiana tree.

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Histogram of mean grwoth rates years /cm of trees

16.0

14.0

12.0

10.0

8.0

6.0

4.0 Mean grwoth rates years/cm grwoth Mean 2.0

0.0 123456789 Dbh size classes

Note: 1= 10.1-20, 2=20.1-30, 3= 30.1-40, 4= 40.1-50, 6= 50.1-60, 7=60.1-70, 8=70.1- 80 and 9= 80.1-90 Dbh cm Fig 10.7 Shows Histogram of mean growth rates years/cm of Pinus wallichiana tree.

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Note: 1= 10.1-20, 2=20.1-30, 3= 30.1-40, 4= 40.1-50, 6= 50.1-60, 7=60.1-70, 8=70.1- 80 and 9= 80.1-90 Dbh cm

Fig 10.8 Shows regression actual growth rates years/cm Vs Dbh size classes of Pinus wallichiana trees.

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10.3.3-Growth rates of trees in different periods

Growth rates of every ten year interval from 1720 to 2010 are shown in Fig 10.9 while statistical summary is presented in Table 10.1. Growth rate varied considerably in different periods. Extremely slow mean growth rates were observed (22.9, 22.5 and 22.4 years/cm) during the period 1981-1990, 1991-2000 and 1971- 1980 AD. Growth rates of every ten years interval from 1720 to 2010 showed that it was different in different periods. Growth rate in early stages was fast as compared to the middle and old-tree intervals. Among the different intervals fast growth rates (3.4 to 4.9 years/cm) was recorded in the period 1720-1760 which gradually declined with the passage of time. The growth rate was almost similar in the period 1751-1810 with 5.7-6 years/cm. Similar trend was found in the upcoming periods intervals from 1811- 1910 where growth rate was 6.1 to 10 years/cm. Each interval period from 1811 to1960 was recorded with markedly slow growth rates even though period from 1971- 2000 showed slowest growth rate among all the time periods considered.

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Grwoth rate years/cm of Pinus wallichiana (10 years interval from 30 1720 to 2010)

10 Growth rate Growth year/cm 5

0 1720-1730 1731-1740 1741-1750 1751-1760 1761-1770 1771-1780 1781-1790 1791-1800 1801-1810 1811-1820 1821-1830 1831-1840 1841-1850 1851-1860 1861-1870 1871-1880 1881-1890 1891-1900 1901-1910 1911-1920 1921-1930 1931-1940 1941-1950 1951-1960 1961-1970 1971-1980 1981-1990 1991-2000 2001-2010 Years

Fig 10.9 Growth rate years/cm of Pinus wallichiana in 10 years interval from 1720 to 2010.

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Table 10.1 Growth rates of Pinus wallichiana in different periods.

Periods M.G.R.Y/CM S.E± 1720-1730 4.0 0.2 1731-1740 3.4 0.1 1741-1750 3.4 0.1

1751-1760 4.9 0.3 1761-1770 5.7 0.2 1771-1780 6.0 0.2 1781-1790 6.0 0.5 1791-1800 6.1 0.6

1801-1810 6.0 0.5 1811-1820 7.1 0.7 1821-1830 6.8 0.8 1831-1840 7.8 0.8 1841-1850 8.4 1.0 1851-1860 8.9 1.0 1861-1870 8.8 1.0 1871-1880 9.3 1.0 1881-1890 9.9 1.1 1891-1900 9.8 0.9 1901-1910 10.0 0.9 1911-1920 11.7 1.2

1921-1930 12.7 1.0 1931-1940 14.1 1.4 1841-1850 15.2 1.3 1951-1960 16.1 1.4 1861-1870 17.7 2.0

1971-1980 21.4 2.1 1981-1990 22.9 2.2 1891-2000 22.5 1.8 2001-2010 17.7 1.1

Note: M.G.R.Y/CM= Mean growth rate year per centimeter, S.E= Standard error

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10.4-Discussion

10.4.1-Age and growth rates of seedlings

Four size classes of mean age of various seedlings, based on two cm interval, showed that the relationship between Dbh classes and actual age was significantly correlated (r =0.544, p<0.01). Dbh size classes showed that mean age increases with respect to Dbh, though like seedlings due to wide variance it is not prudent to forecast age from Dbh. In normal circumstance, seedlings show variation in regeneration pattern in different periods, this condition may be due to the different ecological, biotic and biological situation. It indicates that the growth rate of seedlings was more or less similar to different size classes. Relationship between growth rates year/cm and actual age of seeding attained a significant relation (r=0.571, p<0.01). It is shown that the growth rates of seedlings varies in different size classes. This situation may be due to the natural and human induced disturbance i.e. illegal cutting, grazing, burning, sliding and other environmental stress. It is recorded that the average age of Pinus wallichiana seedlings ranged from 10-37 years in 8 Dbh cm seedlings with 2.8 to 8.6 years/cm growth rates. Ahmed et al, (1991) in case of Pinus gerardiana, reported 43 years/cm growth rates in seedlings of Pinus gerardiana and also found strong correlation between age and growth rate with the value of r=0.64,p<0.001.Furthermore Syampungani et al.(2010) and Ahmed el al. (1988) observed strong relationship between age and growth rates of different tree species. According to Ahmed and Ogden (1987) the oldest seedling may attain a maximum of 100 years/cm while they also found significant relationship between age and growth rates. In case of Juniperus excelsa. Ahmed et al, (1989) described age of seedlings 66 years from 6 Dbh class and they also observed that age varies from seedling to seedling. While Hussain et al. (2012) studied seedling of Picea smithiana from Stak valley according to them maximum age was 126 years with the average growth rate of 2- to 15.7 year/cm. They also found strong correlation between Dbh and age of seedlings. In the present study seedling age of Pinus wallichiana ranged from 10 to 37. This estimation of age is within the range of the findings of above mentioned researchers. This study shows that though there is a significant relationship between Dbh and age and age and growth rate of seedlings of Pinus wallichiana, however due to wide variance, diameter is not good indicator of age.

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10.4.2-Growth rate in different period

Similarity between two elements assessed by the element matching test of Gray et al. (1981).If the confident limit of one element do not overlap with the confident limit of second element, the both are considered significantly different at the 0.5 level . Period from 1720 to 1750 shows significantly fast growth rate than all other classes. It means in these periods there were least population, cutting and grazing occurred. Period from 1761 to 1830 AD were not significant growth rate.Period of almost similar not significant growth rate from 1931 to 1960 but significantly different from other classes. Growth rate increased significantly from 1971-2000AD but similar to 1951-1970AD. In general growth rate is gradually not significantly on every 10 year basis, however each period is significantly differs 40 to 50 years.

According to Ahmed et al. (1989) it is not necessary that growth rate should be the same within a same size class. It may change from seedling to seedling and even in the same size classes. Ahmed et al. (1990, 2009) also found age and growth rates of similar Dbh of trees and seedlings were different in various periods while Hussain et al.(2012) reported that due to competition for nutrition, influence of natural disturbance, human induced disturbances, cutting, forest fire, overgrazing and unfavorable climatic conditions in the past, seedlings showed variation in growth rates.

It can be asserted that growth rates and age of seedlings and trees are better parameters to describe past and current conditions and predict the future trend of any forest. Fritts (1776) and Priya and Butt (1998) stated that growth rate is helpful in describing and understanding the dynamics, conservation and better management of the forest. Furthermore, according to Syampungnai et al. (2010) stated that the study of age and growth rate is useful in size determination of any trees and also helpful to understand the different kinds of disturbances in different periods of time.

Therefore, the present study will help to better understand the regeneration status of the forest and the present and future trends of the forest community. This study is expected to be helpful in conservation and management of this forest for the future generations.

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10.4.3-Age and growth rate of tree

It is recorded that 70 cm Dbh tree of Pinus wallichiana may attain 363 years in the study area while growth rates ranged from 4 to 19 years/cm. Ahmed and Sarangzai (1991) in Pinus wallichana trees from Zhob Baluchistan forest recorded growth rates of 3.13 to 14.28 years/cm while they obtained 230 years age in 60 cm Dbh trees. Ahmed et al. (2009) recorded oldest tree (177years) of Picea smithiana from 177cm Dbh while oldest tree (347years) attained small Dbh (91cm) from the same location Naltar Gilgit. Ahmed and Ogden reported oldest tree having 600 years with Dbh ranging from 130 to 150 cm from Agathis australis trees. They also observed significant relationship (r =0.58, p<0.05) between age and Dbh with average growth rates of 7.5 to 12.5 years/cm. Siddiqui (2011) found significant relationship (r =0.549, p<0.001) between Dbh and age from Pinus wallichiana tree species. He also observed significant relationship between age and growth rates. Ahmed et al. (1990) found average age (221years) of 16 Juniperus excelsa trees from 20 to 30 cm Dbh. They did not find any significant relationship between diameter and growth rates of Anthis species from Baluchistan. They observed variation in age and growth rate even in similar sized classes. Ahmed et al. (1991) obtained an average growth rates ranges from 5.7 to 15.3 years from Pinus gerardiana. They also found strong significant relationship between age and Dbh with the value of (r-0.64, p<0.001). Wahab et al. (2008) studied Picea smithiana and recorded largest tree 154 cm Dbh with 133 years old with 4.0 to 7.1 year/cm growth rates. They did not find any significant relationship between Dbh and age . In current study, Pinus wallichiana growth rates ranges from 4 to 19 year/ cm. This value is within the range of previous researchers finding while Dbh did not show any significant relation with growth rates. This may be due to wide variance and anthropogenic disturbances. This forest is under stress, therefore, immediate consideration of necessary ameliorative actions can save and mange these forest.

However, it is concluded that due to wide variance, Dbh could not be consider as a good predictor of age.

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Chapter No 11 Chronology and Growth climate response

CHAPTER-11

CHRONOLOGY AND GROWTH CLIMATE RESPONSE

11.1-Introudction

This chapter deals with the chronology development and growth-climate response of Pinus wallichiana. Detials about the sampling sites is given in chapter-7.

Growth of tree rings and responses to climate change gives essential information to evaluate future forest productivity, vegetation dynamics and distribution of tree-species (e.g. Peterson, 2001; Saxe et al. 2001; Frenzel et al. 2003; Tardif et al. 2006). According to Briffa et al. (1998) and Tessier et al. (1997), on the basis of growth climate response of any tree species climate data may be reconstructed. Furthermore (Fritts, 1976) stated that the physiological means by which climatic constraints are converted into radial growth distinctions are complex, because radial growth in any given year integrates the effects of climate conditions during and prior growth, local site conditions and physiological characteristics of tree species. Growth of trees at high elevations usually shows temperature variations, whereas growth rates of trees at lower elevations generally mirror precipitation changes (Schweingruber, 1996) and (Fritts, 1976).

A preliminary the importance tree rings growth and climatic response was studied in India Chwdhury et al., (1939, 1940a), the pioneer tree ring researcher of subcontinent. Correlation between climate and tree rings was presented by Pant (1979) and Wu and Lin (1987) from Western Himalyan and Mountainous area of Hengduan. Furthermore the importance and relationship between of tree rings and climate also discussed by Davies (1987) and Bhattacharyya et al. (1988) they explored the dendroclimatological potential of different coniferous tree species From Jammu and Kashmir. Moreover, Ahmed (1989) described tree ring chronologies of Abies pindrow from northern Pakistan, and dated chronologies with maximum period of 237 years. Sample and chronology statistics were discussed. According to him, these chronologies showed similar climatic signals. However, it is suggested that west facing steep slope are the most suitable sites for tree-ring

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studies. It is concluded that Abies pindrow may be used in Dendroclimatology investigation. Ahmed and Sarangzai (1991) presented dendrochronological approach to estimate age and growth rates of various species from Himalayan region of Pakistan. According to them, Age and growth rate may vary among closely growing trees of the same species. Furthermore to describe Dendrochronological potential of different tree species explored i.e. Ahmed and Naqvi (2005); Khan et al. (2008) ;Wahab et al. (2008); Ahmed et al. (2009a 2009b)Ahmed et al. (2010a), Ahmed et al. (2010b); Zafar et al. (2010); Khan (2011); Wahab (2011);Ahmed at al. (2011a); Ahmed et al (2011, 2012); Zafar et al. (2012); Zafar (2013) and Ahmed et al. (2013) from different forested areas of Pakistan.

Up to now Pinus wallichiana growing at Ganji valley was not studied therefore this research has been designed to explore the response of Pinus wallichiana species from Ganji valley. This study may help full to understand growth-climate response of Pinus wallichiana species.

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11.2-Metrials and Methods

Metrial methods have been disscussed in chapter- 9

11.3-Results

The raw ring-width, standard, residual, ARSTAN chronologies are presented in Fig 11.1, 11.2, 11.3 and 11.4 respectively while sample depth Rbar and EPS shown in Fig 11.5 and 11.6 respectively. Dendrochronological characteristics of the ring-width chronology COFECHA and ARSTAN statistics based on 50-years segments lagged 25 years are shown in Table 11.1. Critical level of correlation at 99% confidence level is shown in Table 11.1.Descriptive statistics of COFECHA is given in Table 11.2 while Summary chronologies are given in Table 11.3. Correlation and response coefficients significant relations of different chronologies and climate and grid data is presented in (Table 11.4) while coefficients correlation and response function of Residual, standard chronologies with local climate data and grid data are shown in (figure 11.7, 11.8, 11.9, 11.10, 11.11, 11.12, 11.13 and 11.14) respectively.

Fifteen trees were used to extract the cores and nearly 70% of cores (22 from 30 cores) were cross dated showing no associated problem. Portion with two or more series were found only 281 years (1730-2010) which means age of tree were not longer. Age and growth rates of trees and seedlings have been discussed in previous chapter-9. Series inter correlation was 57% with average mean sensitivity of 0.16. Mean length of series was more than two hundred years i.e. 211 years with individual series correlation ranging from 0.31 to 0.67. Individual mean sensitivity ranged 0.12- 0.19. The highest correlation of 50 years dated segment wit 25 years lagged was observed 0.66 in 1825-1874 and lowest correlation of 50 years dated segment was 0.43 in 1725-1774.

All cores showed positive autocorrelation ranging from 0.31-0.87. All series common period principal component analysis exhibited that first six PCs obtained having the eigenvalue greater than one with total variance of 80.2 percent satisfying the eigenvalue criterion. First PC eigenvalue is 6.055 with a cumulative variance of 28.8 percent.

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11.3.1-Development of Chronology

11.3.2-Raw Chronology

Raw chronology showed 0.12mean sensitivity. This value is similar to residual while higher as compared to the rest of chronology. Raw chronology attained mean index of 1.28 which was highest among the chronologies. The serial correlation was appeared 0.94 with 0.66 standard deviation these values also high as compared to other chronologies. The skweness coefficient and kurtosis coefficient were observed 1.98 and 7.09 respectively (Table 11.3 ).Period of above average growth was seen up to 1800 AD and declined period from average growth was observed in the last century i.e. 1900-2000 AD while 1800-1850AD, period of below average growth occurred whereas from 1850 to 1890 AD, period of above average growth occurred.

Fig 11.1 shows Raw ring width chronology of Pinus wallichiana.

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Chapter No 11 Chronology and Growth climate response

11.3.3-Standard Chronology

Standard chronology showed mean index 0.99 with 0.15 standard deviation while the skweness was 0.49 with 3.98 kurtosis coefficient. Mean sensitivity and serial correlation was observed on 0.1 and 0.6 respectively (Table 11.3). Period of average growth was observed from 1850-1870, 1925-1960AD, above average growth was seen from 1765 to 1810, 1870AD to 1895 around 1950, 1970 to onward. A declined period was seen 1810- 1850AD, 1895 to1925 and around 1960 to1970AD.

Fig 11.2 Shows standard ring with chronology of Pinus wallichiana.

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Chapter No 11 Chronology and Growth climate response

11.3.4-Residual Chronology

Mean index of this chronology was recorded 0.99 with 0.11 standard deviation. The Skweness of coefficient was observed -0.29 with 4.3 kurtosis coefficient. Means sensitivity and serial correlation were found 0.12 and -0.01 respectively (Table 11.3). The graph showed above average growth in the period from 1790-1800 while it shows below average growth from 1800-1840 and1900-1915 AD while the rest of period showed within the average growth.

Fig 11.3 Shows residual ring width chronology of Pinus wallichiana.

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Chapter No 11 Chronology and Growth climate response

11.3.5-ARSTAN Chronology

This chronology showed 0.99 mean index with 0.14 standard deviation. The skweness of coefficient was 0.47 with 3.73 kurtosis coefficient. Mean sensitivity and serial correlation were recorded 0.1 and 0.57 respectively (Table 11.3).From 1745- 1760,1810-1850,1900-1925 and 1960-1970AD showed below the average growth period while 1760- 1810,1880-1900,1925-1960 around 1970 and1990 AD to onward showed above the average growth whereas the rest of period appeared within the ranges of average growth.

Fig 11.4 Shows ARSTAN ring width chronology of Pinus wallichiana.

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11.3.6-Sample Depth

Fig 11.5 Shows sample depth of Pinus wallichiana species.

Figure 11.5 shows the Sample depth of Pinus wallichiana tree species ample depth increases gradually from 1800 AD. Nearly twenty cores attained the age of 150 years (1850-2000 AD).

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11.3.7-EPS and Rbar

Figure 11.6 shows the running Rbar and EPS of Pinus wallichiana Skardu valley. Rbar and EPS expressed reliability of chronology with respect to time. The graph of EPS for Pinus wallichiana indicates that chronology is suitable to 1790.Pinus wallichiana from Ganj got the EPS value of 0.89 by using common period analysis of 100 years from 1900-2000 as shown in the Five cores failed to negative exponential curve fit and changed to linear regression (any slope). SNR and Rbar values are 8.29 and 0.298 respectively. Rbar values within trees and between trees varied from 0.65 and 0.27 respectively.

Fig 11.6 Shows the running Rbar and EPS of Pinus wallichiana.

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Table 11.1 Dendrochronological characteristics of the ring-width chronology from PinuswallichianaGanj (Skardu). COFECHA and ARSTAN statistics based on 50- years segments lagged 25 years. Critical level of correlation at 99% confidence level.

Mean sensitivity 0.16 Series intercorrelation 0.57 Total absent rings in all series 0 Common years interval 1900-2000 (100 years) Expressed population signal 0.89 Signal to noise ratio 8.92 Rbar 0.298 Rbar within the trees 0.65 Rbar between the trees 0.27

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Table 11.2 Descriptive statistics of COFECHA Pinus wallichiana from Ganji Skardu.

Corr No. with Mean Max Std Auto Mean Max Std Auto S.No Series Years Master1 msmt2 msmt3 dev4 corr5 sens6 value7 dev8 corr9 1 PWGJ011 181 0.314 1.25 4.11 0.774 0.917 0.188 2.86 0.558 0.04 2 PWGJ012 171 0.364 1.45 5.02 1.011 0.942 0.173 2.67 0.477 -0.011 3 PWGJ021 191 0.659 1.08 2.33 0.419 0.842 0.165 2.49 0.336 -0.039 4 PWGJ022 181 0.61 1.09 2.14 0.419 0.869 0.152 2.85 0.43 -0.045 5 PWGJ061 281 0.54 0.92 2.47 0.467 0.907 0.168 2.69 0.421 -0.029 6 PWGJ062 261 0.6 0.94 2.49 0.447 0.882 0.176 2.67 0.418 -0.043 7 PWGJ071 271 0.586 0.81 3.22 0.553 0.965 0.128 2.6 0.354 -0.033 8 PWGJ042 161 0.616 1.67 4.3 0.66 0.865 0.148 2.49 0.313 -0.044 9 PWGJ041 169 0.536 1.65 4.14 0.84 0.947 0.132 2.79 0.431 -0.025 10 PWGJ042 161 0.616 1.67 4.3 0.66 0.865 0.148 2.49 0.313 -0.044 11 PWGJ081 271 0.549 0.85 4.28 0.719 0.967 0.166 2.52 0.354 -0.025 12 PWGJ082 261 0.61 0.73 2.45 0.511 0.955 0.155 2.46 0.303 0.006 13 PWGJ092 261 0.55 0.83 2.87 0.585 0.953 0.158 2.59 0.324 -0.04 14 PWGJ091 290 0.465 1.05 4.22 0.974 0.963 0.176 2.59 0.302 -0.024 15 PWGJ122 271 0.649 0.87 3.21 0.557 0.952 0.149 2.45 0.266 -0.024 16 PWGJ121 261 0.673 0.8 2.43 0.455 0.937 0.158 2.41 0.284 -0.022 17 PWGJ131 221 0.661 1.25 2.47 0.386 0.731 0.182 2.59 0.364 -0.017 18 PWGJ132 211 0.678 1.24 2.54 0.384 0.756 0.164 2.53 0.343 -0.027 19 PWGJ141 111 0.543 2.36 3.72 0.623 0.753 0.143 2.63 0.441 0.008 20 PWGJ142 101 0.612 2.28 4.03 0.636 0.67 0.164 2.53 0.453 -0.033 21 PWGJ151 177 0.61 1.47 3.26 0.631 0.837 0.19 2.54 0.311 -0.039 22 PWGJ152 180 0.565 1.47 5.03 0.82 0.874 0.193 2.6 0.401 0.017 Total 4644 0.575 1.15 5.03 0.606 0.894 0.163 2.86 0.364 -0.023

Note: 1=Correlation with mater choronology, 2=Mean ring width, 3=Maximun rings width, 4=Standard correlation in filtered, 5= Auto correlation in feltered, 6=Mean sensitivity, 7=Maximum value, 8=Standard devation in unfiltered , 9=Auto correlation in unfeltered.

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Table 11.3 Summary of statistics of Raw, Standard, Residual and ARSTAN chronologies.

Parameters Raw StandardResidual ARSTAN Mean index 1.28 0.99 0.99 0.99 Standard Deviation 0.66 0.15 0.11 0.14 Skweness of Coefficient 1.98 0.49 -0.29 0.47 Kurtosis coefficient 7.09 3.98 4.3 3.73 Mean Sensitivity 0.12 0.1 0.12 0.1 Serial Correlation 0.94 0.6 -0.01 0.57

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11.3.8-Correlation and response function analysis

11.3.8.1-Correlation coefficients of Residual Vs Skardu meteorological climate

Correlation coefficients of residual vs Skardu climate are presented in (Fig 11.7). The variance obtained in correlation analysis was 57.12%. Among the whole month previous November, June and July were showed positively significant relationship while January attains negative correlation in case of temperature. Precipitation shows positively significant in January while negatively significant in previous November and May respectively.

0.40 Residual vs Skardu climate

0.30

0.20

0.10 coefficients 0.00 pO pN pD J F M A M J J A S O ‐0.10

correlation ‐0.20

‐0.30

‐0.40

Fig 11.7 Correlation coefficients of residual Vs Skardu climate.

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Chapter No 11 Chronology and Growth climate response

11.3.8.2-Response coefficients of residual Vs Skardu meteorological climate

Response coefficients of residual chronology with Skardu climate is shown in Fig 11.8. Temperature showed positively significant relationship in previous November and current July while negatively significant in September and October. In case of precipitation January attains positively significant and October shows negatively significant relationships.

0.4 Residual vs Skardu climate

0.3

0.2

0.1

0 coefficients pO pN pD J F M A M J J A S O ‐0.1

‐0.2 response

‐0.3

‐0.4

‐0.5

Fig 11.8 Response coefficients of residual chronology Vs Skardu climate.

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Chapter No 11 Chronology and Growth climate response

11.3.8.3-Correlation coefficients of Residual Vs grid climate

Correlation coefficients of residual with grid climate data was preformed which is presented in Fig 11.9. The chronology expressed 43.12% of total variance with gridded climate. The results of this analysis showed that previous November, June and July were positively significantly correlated with the growth of tees in case of temperature. While January and August showed positively significant relationship respectively with the growth of trees in case of precipitation.

Residual vs grid climate 0.4

0.3

0.2 coefficients

0.1

0 pO pN pD J F M A M J J A S O correlation ‐0.1

‐0.2

Fig 11.9 Correlation coefficients of residual Vs grid climate.

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Chapter No 11 Chronology and Growth climate response

11.3.8.4 -Response coefficients of Residual Vs grid climate

The results of response coefficients of residual with grid climate are shown in Fig 11.10. Temperature showed positively significant relationship in previous November current June and July respectively while negatively correlated in February. In case of precipitation only January attains positively significant relationship with tree growth.

0.3 Residual vs grid climate

0.2

0.1

coefficients 0

pO pN pD J F M A M J J A S O ‐0.1 response

‐0.2

‐0.3

Fig 11.10 Response coefficients of residual with grid climate.

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11.3.8.5-Correlation coefficients of standard Vs Skardu meteorological climate

Results of correlation coefficients of standard chronology with Skardu climate is presented in Fig 11.11. The variance explained in correlation analysis was 69.12% with 43% by climate and 26% by prior growth. In case of temperature lag years 1, 2 and 3 were showed positively relationship with tree indices while negatively significant in September. Precipitation found to be negatively significant relationship in May with the growth of trees.

0.6 Standard vs Skardu climate

0.4

0.2 coefficients 0 pO pN pD J F M A M J J A S O Crn‐1Crn‐2Crn‐3 ‐0.2 correlation

‐0.4

‐0.6

Fig 11.11 Correlation coefficients of standard chronology with Skardu climate.

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11.3.8.6-Response coefficients of standard Vs Skardu meteorological climate

Response coefficients of standard chronology with Skardu metrological data is given in the Fig 11.12. Results showed that temperature of previous November, current July and 1st lag year were positively significantly correlated with the growth of trees while found to be negatively correlated to August and September. In case of precipitation January attained positively significant relationship with growth of trees.

0.4 Standard vs Skardu climate

0.3

0.2 coefficients

0.1

response 0 pO pN pD J F M A M J J A S O Crn‐1Crn‐2Crn‐3 ‐0.1

‐0.2

‐0.3

‐0.4

Fig11.12 Response coefficients of standard chronology with Skardu climate.

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11.3.8.7-Correlation coefficients of standard Vs grid climate

Correlation coefficients of standard chronology with grid climate are shown in Fig 11.13. Chronology showed 57.13% of total variance with gridded climate having 33% by climate and 24% by prior growth. In case of temperature previous October, previous November, April, July, and lag years 1, 2, 3 were found to be positively significant correlation with tee indices.

0.5 Standard vs grid climate

0.4

0.3

0.2 coefficients

0.1

0 correlation pO pN pD J F M A M J J A S O Crn‐1Crn‐2Crn‐3 ‐0.1

‐0.2

Fig 11.13 Correlation coefficients of standard chronology with grid climate.

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11.3.8.8-Response coefficients of standard Vs grid climate

Results of coefficients of standard chronology with grid climate of Skardu are presented in Fig. 11.14. In case of temperature previous October, July and first lag year were observed to be significantly correlated with growth of trees while February found to be negatively significant. Precipitation shows no significant response.

Standard vs grid climate 0.4

0.3

0.2

0.1 coefficients

0 pO pN pD J F M A M J J A S O Crn‐1Crn‐2Crn‐3

response ‐0.1

‐0.2

‐0.3

Fig 11.14 Coefficients of standard chronology with grid climate of Skardu.

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Table 11.4 Correlation and response coefficients significant relations of different chronologies and climate and grid data.

Months CRS RRS CRG RRG CSS RSS CSG RSG P-Oct + + P-Nov + + + + + + P-Dec Jan - Feb - - Mar Apr + Temperature Temperature May Jun + + + Jul + + + + + + + Aug - Sep - - - Oct - + P-Oct P-Nov - P-Dec Jan + + + + Feb + Mar Apr

Precipitation Precipitation May - - Jun Jul + Aug Sep Oct - L1 + + + + L2 + + Lags L3 + +

Note: CRS=Correlation residual vs Skardu climate, RRS= Response function residual vs Skardu climate, CRG= Correlation residual vs grid, RRG= Response function residual vs grid, CSS= Correlation standard vs Skardu climate, CSG= Correlation Standard vs grid, RSG= Response function Standard vs grid

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11.4-Discussion

As already discussed that 15 trees were used to extract the cores and nearly 70% of cores (22 from 30 cores) were cross dated showing no associated problem. Portion with two or more series were found only 281 years (1730-2010) which means age of tree were not longer. Series inter correlation was 57% with average mean sensitivity of 0.16.In another study based on Pinus wallichiana from Astore and Mushkin (Ahmed et al. 2011), quite identical results from chronology statistics were observed. Pinus wallichiana from Astore was found to be older than the current study. Similar series inter correlation and mean sensitivity was observed by Singh (2007) in Indian region. Higher values of mean sensitivity in current study show that tree rings were towards complacent side. Mean length of series was more than two hundred years i.e. 211 years with individual series correlation ranging from 0.31 to 0.67. Individual mean sensitivity ranged 0.12-0.19. The highest correlation of 50 years dated segment wit 25 years lagged was observed 0.66 in 1825-1874 and lowest correlation of 50 years dated segment was 0.43 in 1725-1774.

Here we have presented various statistics of Pinus wallichiana from Ganj got the EPS value of 0.89 by using common period analysis of 100 years from 1900-2000. Five cores failed to negative exponential curve fit and changed to linear regression (any slope). SNR and Rbar values are 8.29 and 0.298 respectively. Rbar values within trees and between trees varied from 0.65 and 0.27 respectively. Moderate values of mean sensitivity represented good dendroclimatic potential of the species (Fritts, 1976) whereas high values of SNR, EPS greater than 0.85 (Wigley et al. 1984) and Rbar show that chronology is useful for the determination of past climatic signals.

All cores showed positive autocorrelation ranging from 0.31-0.87. All series common period principal component analysis exhibited that first six PCs obtained having the eigen value greater than one with total variance of 80.2 percent satisfying the eigen value criterion. First PC eigenvalue is 6.055 with a cumulative variance of 28.8 percent.

Correlation and response function analysis were performed among residual and standard chronologies with Skardu station and corresponding grid data .The

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parallel use of correlation and response analysis was performed to pick the similarities in the results.

Analysis showed that temperature of previous November was significantly positively correlated with tree growth. In the same way, July temperature was found to be positive significant correlated in the analysis. In case of precipitation January was observed positively significantly correlated with tree-ring chronology.

In Himalayan region, availability of long term climatic data is problem and same is the case with the present study. Another problem is that meteorological stations are situated on low altitude and far from tree ring sites. We used Skardu station data having the length of just 39 years which is located on 2400 meters elevation while the study side was situated on 3400 meters above sea level. Chronology was also compared with grid climate that covers the duration of 100 years. The results obtained in comparison of tree ring chronology and grid data are quite useful as compared to local climate data.

The mean monthly temperature showed direct relationship with tree growth having the significance in previous October and November in case of grid comparison. It means that species continue photosynthesis during warm winters and reserves are used in growing season. Correlation analysis between tree rings chronology and grid data showed significant positive relationship in July temperature and January precipitation. As our sampling site is situated at high elevation where spring season is short, therefore for the growth of plants, July temperature will be better. Due to high elevation temperature is the dominating and most important factor for the better growth, as may be seen from response summery.

We compare our study site with that of Pinus wallichiana of Astore (Rama) and Mushkin Ahmed et al. (2011). Our results match with the conclusion of Astore (Rama) in case of temperature, where chronologies showed significant positive relationship in June-July temperature but the similar agreement was not seen in current study and results of Pinus wallichiana from Mushken.

The reason might be the elevation. Our study site elevation (3310m) is more or less similar with that of Astore (Rama) (3450 m), therefore expressed similar results while Pinus wallichiana from Mushkin was from lower elevation (2750 m) therefore

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expressed different results. The same results were also highlighted by Treydte et al. (2006) who worked over the elevations of different species situated at Bagrot site and concluded that sites situated at different elevations showed different response.

In case of Precipitation Ahmed et al. (2011) recoreded Pinus wallichiana from Mushkin with positive relationship in January, February and May. Pinus wallichiana from present study also showed positive relationship in the month of January. The study area fall in dry temperate area where the precipitation of winter season is stored for the spring because in this area sow fall is occurs mostly in January and February which is used in spring season.

Similar observations have been noticed in neighboring India with the Himalayan pine trees (Yadav et al. 1997). Also other studies showed that several pine species continues its growth during warm winter (Hepting, 1945; Kramer, 1958).Most recently Hussain (2013) presented growth climate response using Picea smithiana from Astak about 60km away from the site which is strongly supported to this study.

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CHAPTER-12

GENERAL DISCUSSION

In Pakistan forest recourse are very limited. Forest is distributed only 4.8 percent of total land area according to the forest department while FAO (2009) recorded 2.2% forest cover for a country. However scarce, forests of Pakistan are very wealthy in terms of biodiversity and present a unique blend of tree, shrub, grass and animal species, living across various ecological (climatic) zones from sea level in the south, to high altitude alpine pastures of the north. Amjad et al. (1996); Sardar (2001) and Biag et al. (2008) stated that the current forest resources of Pakistan are insufficient to fulfill the ideal level.

Most of the natural forests are existed in the northern areas of Pakistan covering three great ranges of mountain, Himalaya, Hindukush and Karakoram, where more than 60% of the country’s natural forest resources are found. The northern mountains of Pakistan are well known for their rich biodiversity as they are located in mountain ranges i.e., Karakorum, Himalaya and Hindu Kush (Shinwari et al.2000a). In these areas forests are main source of income for the local people. Local people in the mountainous regions of Pakistan mostly depend to fulfill their

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domestic need from forest .They use wood for construction of building as well as domestic fire fuel and to fulfill other needs in large scale Agri, orchids and grazing because there is no any alternate source of icom. These forests also fulfill the needs of food for livestock and provide edible traditional medicinal plants (Hussain and Khaliq 1996); Eberhardt et al. (2007); Ali et al.(2004, 2005). Kazmi & Siddiqui (1953) described the value and uses of vegetation around Astore district. Several surveys has been conducted and published by many researchers about these valuable forests in the passage of time. Ahmed and his team visited many forested and non forested areas of Northern areas of Pakistan to investigate population dynamics and to determine dedrochronological potential of these forests. (Ahmed et al. 1976, 1986, 1988a, 1988b, 1990, 1991, 2006, 2009a, 2009b, 2010a, 2010b, 2010c, 2011) however recently Akbar et al. (2010, 2011, 2012) and Hussain et al. (2010, 2011) conducted study about some forest of Gilgit-Baltistan.

The present study focuses on forest of Gilgit, Astore and Skardu districts of Gilgit-Baltsitan. This study comprises of quantitative survey and describe the forest community and associated herb and shrub of the forest, size class structure, multivariate analysis (classification and ordination) ,status of Physico-chemical properties of soil , age and growth rates of Pinus wallichiana species, chronology based on Pinus wallichiana species from Ganj (Skardu) valley, growth climate response of Pinus wallichiana.

Using Point centered quarter (PCQ) method of Cottam & Curtis 1956 and Ahmed and Shaukat (2012) various forests of above mentioned districts were studied. During this study out of 40 stands 22 stands were monospecific ten of them were Pinus wallichiana stands with a range from 92-180 density ha-1,6 were Picea smithiana with 92-237 density ha-1 and 4 Betula utilis 74-122 density ha-1while one Juniprus macropoda and Abies pindrow 125 and 107 density ha-1 respectively. Monospeciefic forest also studied many other researchers in different locations of Northern Pakistan (Ahmed et al. 1988, 2006; Wahab et al. 2008; Ahmed et al 2009; Khan et al. 2010; Siddiqui et al. 2010; Khan (2011),Wahab (2011) and Siddiqui (2011).

Moreover Piuns wallichiana were recorded in 14 stands as a dominant species and in 3 stands as a co-dominant. This showed widespread distribution with

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no any third rank in any stands indicates the dominance of Pinus wallichiana in these forested areas. The second highly distributed species was Betula utilis occupied as leading dominant species in 5 stands while it is attained as co-dominant in 4 stands and also showed third position in 5 stands. Pinus wallichiana and Betula utilis were not restricted in any specific District. Juniperus excelsa is wildly distributed in district Skardu as 1st dominant in 1 stand, as co-condiment in 10 stands while with third position in 2 stands. Picea smithiana attained the position of leading dominant in 9 stands while as co-dominant in 2 stands. This species never placed third rank in any stand. Picea smithiana dominates mostly in district Gilgit while in district Skardu this species never recorded in any stands. Abies pindrow and Pinus gerardiana were recorded only from one location correspondingly in District Gilgit and Astore as pure form while Juniperus macropoda found only from Gilgit in monospecific condition this specie was not seen in any sites of Skardu and Astore district. Chaghtai et al. (1989) recognized Pinus wallichiana, Picea smithiana, Abies pindrow and Cedrus deodara community from Nathia Gali on North West exposure at 2133 m elevation. Ahmed et al. (2006) surveyed different climatic zones of Himalyan forest of Pakistan and recognized 4 monospecific and 24 different communities. They observed Pinus wallichiana as monospecific condition on south exposure at 2770 m elevation from Nalter Gilgit and higher elevation 3100 m from Tukht-e-Sulaiman. Chaudhri (1960) declared that Pinus wallichiana is a species which can survive and distributed on all aspects with extensive altitudinal zones. Naqvi (1976) documented this species as concerning link up other coniferous species in the area. Further Hussain and Illahi (1991) categorized this species on the basis of ability to survive in different climatic zones. They suggested Pinus wallichiana as mixed temperate forest species. According to Beg (1975) this species is dry zone Blue-Pine forests whereas Champion et al. (1965) claimed Pinus wallichiana needs additional humidity than other species of dry temperate zones. The reports of all previous researchers agreed that Pinus wallichiana has widespread environmental amplitude. According to Ahmed et al. (2006) recognized Picea- Pinus wallichiana community from Astore Rama. This community was located on south facing at 3300 m above sea level. Champion et al. (1965) and Ahmed et al. (2006) recognized Picea smithiana as dry zone spruce forests. Ahmed and Naqvi (2005) and studied more or less pure Picea smithiana

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from Gilgit Nalter valley. Wahab et al (2008) reported this species from Sheshan, Afghanistan. This shows the wild distribution ranges of Picea smithiana in different climatic areas.

Champion et al. (1965) recognized Abies pindrow forests in as Western Himalayan sub-alpine forests. Hussain and Illahi. (1991) reported Abies pindrow community prefers cool and moist sites even in dry zones. Ahmed et al. (2006) sampled Abies pindrow community on North West facing slopes from Rama Astore at an elevation of 3451 meters. Wahab et al. (2010) described this species as pure state from Satto Khwa District Dir. Siddiqui (2011) reported Abies pindrow at 2500 meter above sea level from Ghora Dhaka Himalayan moist temperate ranges. In present study we recognized Abies pindrow as pure condition from Rama Astore District at 3600 meter above sea level. This mean this species prefer to grow from 2500 to 3600 meter above sea level.

Champion et al. (1965) studied Pinus gerardiana forests as Chilghoza Pine forest from Mushken Astoer. Ahmed et al. (2006) described Pinus gerardiana community in Takht-e-Sulaiman range from 2000 m to 2700 meters while is this

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study Pinus gerardiana as co-dominant from Astore Mushken stand 35 on North- East facing at 2636 elevation.

The understory vegetation composed of a total 83 species including herb, shrubs and seedlings of tree species. Among the understory vegetation Thymus serpyllum, Fragaria nubicola, Leontopodium leontopodinum, Bergenia stracheyi, Artemisia brevifolia, Bistorta affinis, Tanacetum artemisioides, Thymus linearis, Geranium wallichianum, and Leontopodium himalayanum were dominantly distributed with the range of relative frequency respectively. These species also reported with coniferous forest from different area of Pakistan i.e. Ali et al.(2004, 2005); Ahmed and Naqvi (2005); Ahmed at al. (2006); Eberhardt et al.(2007); Wazir at al. (2008). These species also have medicinal value which were discussed by Rasool (1998); Sher (2002); Shinwari and Gillani (2000a, 2002, 2003); Khan (2004); Wali and Khatoon (2007); Qureshi et al. (2011) and Khan et al (2011).

Furthermore size classes of each stands was studied following the method by Ahmed (1984), Siddiqui (2011), Wahab (2011) and Khan (2011) while the overall size class distribution of dominant tree species was investigated by the Weibull granulized distribution method which was introduced first time by Baiely and Dell (1973).

Population structure is a significant characteristic of population ecology and its perceptive is necessary for sustainable management of forest resource to utilize in proper way. Hitimana et al. (2004) and Coomes and Allen (2007) reported that in any forest the tree size classes and number of individuals, may change considerably. In the forest around the world many causal factors i.e. regeneration pattern, succession disturbances, competition for nutrition, climatic conditions influences tree size distribution (Denslow 1995, Coomes et al. 2003, Webster et al. 2005).In addition, the size class distribution of trees are mostly used in assessing the possible outcome due to the disturbances within the forest (Hett and Loucks 1976, Denslow 1995, Baker et al. 2005, Coomes and Allen 2007), this may also be helpful in exploration of successional pathway and structural development of forest (Goff and West 1975, Poorter et al. 1996, Zenner 2005). On The basis of the present status of the forest the future trends may be predicted (Feeley et al. 2007). In addition, the tree size classes may vary among the natural forest but they also shows some similarities i.e. reverse J-

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shaped DBH distribution (Hough 1932, Robertson et al. 1978, Kohyama 1986, Niklas et al. 2003).

In the present study pattern of Dbh size classes’ distribution of different species indicates the present status and the future trend of these forests. Among the 40 stands the distribution of individuals i.e. Betula utilis (Rama-A), Mix forest of Pinus wallichiana and Picea smithiana (Mushken-B), (Mushken- D) pure forest of Pinus wallichiana were satisfactory these can assume regular distribution pattern whereas most of the stands sowed unsatisfactory. The variation of distribution pattern of size classes, density and basal may be due to the overgrazing, illegal cutting, or various other disturbances. These kinds of reason also discussed by the previous researcher during the survey of different forested area i.e. Baig (1984), Ahmed (1984), Ahmed et al. (2009c), Wahab et al (2008),Siddiqui et al.(2009) and Khan et al. (2010a).

Overall size classes of dominant tree species i.e. Pinus wallichiana, Picea smithiana, Betula utilis and Juniperus excelsa was presented and performed Weibull model. Among the dominant tree species Juniperus excels was showed best value of shape parameter with (1.65) as compare to the other tree species this represent the invers-J distribution. According to Lieblein & Zelen (1956); Thoman et al. (1969) distribution of trees was consider good when the shape parameter showed lesser than 2. Moreover Corrado & Su (1996); Corrado & Su (1997) stated that Invers-J shaped structure is an ideal structure.

Correlation between absolute values of dominant tree species and topographic variables were analyzed. Basal area m2 ha-1 and density of each tree showed strongly significant correlation with each other. Similar results were found Khan (2011), Siddiqui (2011) and Wahab (2011).In case of topographic variables only density of Betula utilis showed significant relationship with elevation.

The different environmental factors was significantly influenced the properties of soil of forest and also affect the human activities. (Zhang 1986).The soil structure also on vegetation because the major part of the plant i.e. Leaves, cones, needles, pollen and branches become a part of soil due to the decomposition (Champan and Reiss, 1992; Singh and Bhatnagar, 1997). Vegetation and soil are so closely linked

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(Ellis and Mellor 1995).The soil composition varies widely among ecosystems (Binkly and Vitousek, 1989), resulting in differences in plant community configuration and its creation (Ruess and Innis, 1977). Soil physical and chemical properties of the forest vary due to the variation environmental condition i.e. topography, weathering process, and climate change (Paudel and Sah 2003). All micro and macro elements which plays a vital rule in the growth and development are an important source of nutrients for vegetation (Donegan et al. 2001; Lal 2005) where as in some cases, soil characteristics such as soil acidity and nutrient accessibility also control the vegetation types, and the growth setting and distribution of vegetation types at different slope locations and altitudes are controlled by the bioavailability of soil nutrients (Ouyang et al. 2003; Kong et al. 2004; Griffiths et al. 2009). The different type of death part of the vegetation also refill the nutrient of the soil by the microbial activities and nutrient recycling in the forest soil (Tsui et al. 2004; Wang et al. 2004; Romanya et al. 2005). The importance of soil chemical and physical properties and relationship are also evaluated (Couteaux et al. 2002; Yang et al. 2005; Navarrete et al. 2009).

To study the 40 forested stand’s vegetation distribution pattern and relationship among the environmental characteristics was carried out using Ward’s Cluster Analysis (1963; Goodall 1973) and Principal component analysis (PCA).This advanced multivariate method was mostly used to determine the relationship among the soil properties and forest vegetation composition. This method is highly recommended and consider useful to investigate the relationship among the soil properties and vegetation distribution Li et al. (2008); Mazlum et al. (1999); Chatifield and Collin (1980) and (Wuench,2006) .The importance of these methods also described by Okono (1996); Lovtt et al. (2001) and Gajoti et al. (2010).

In the present study sixteen soil properties of stand’s and Importance value index was taken in case of tree vegetation while in case of Understory vegetation stands species frequency was used to investigate the distribution pattern of vegetation in 40 stands of the study area. On the basis of ward’s cluster analysis seven groups were identified from tree vegetation data. These groups were differentiated by their different characteristics features.

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The multivariate technique of Ward’s cluster analysis (1963),Goodall (1973) and DCA method (Hill and Gauch 1980) were used to analyze of 40 forested stands. Five tree groups and five ground vegetation groups were classified. Greig-Smith (1983) has a described the advantage both approaches concurrently on the grounds that of using they yield corresponding results are as such useful for better explanation of ecological results. Okono (1996) described cluster analysis as a quantitative method which is used for objective categorization. Lovtt et al. (2001) and Gajoti et al. (2010) advocated that the environmental variables play a very important role in recognizing the vegetation distribution pattern. They also suggested that the elevation is most important factor to investigate the vegetation distribution pattern. In the present both the elevation and slopes were taken into account to determine the vegetation pattern. By Ward’s cluster analysis a total of five groups were recognized using tree vegetation data. Group-I (a) is composed of 9 stands dominated by Pinus wallichiana with second co-dominant Juniperus excelsa and 3rd associated species Betula utilis this community prefer to grow high elevation at 3421 m and low slope 27o angle. In this group Pinus gerardiana found as second leading species in stand- 35. Ahmed et al. (1990) described that Pinus gerardiana and Juniperus species are restricted to drier sites of dry temperate area. Although the study area falls in dry temperate in this study Juniperus excelsa is distributed wildly whereas Pinus gerardiana found only from Mushkin valley.

Group-I (b) comprises of ten stands which is also the largest group. This is pure Pinus wallichiana group which is recoded on low average elevation 3169 m and low average slope 28o angle. Ahmed et al. (2010) reported Pinus wallichiana from different climatic zones of Pakistan at the elevation of 1950 to 2700 m and 23o to 45o slope. Wahab et al. (2010) studied Pinus wallichiana community from district Dir on 1875 elevation. Kahn et al. (2013) reported Pinus wallichiana community at 2559 meter from Chitral district .This shows that this species in distributed from 1875 to 3700 meter. Group-II is also differentiated by the predominance of with Pinus wallichiana but second leading species was Betula utilis. This group composed of 8 stands, stand-4 and stand-5 dominated by Betula utilis and Juniperus excelsa respectively while stand- 29 dominated by Picea smithiana. This group is recoded on average elevation at 3373m and slope 33o angle. Ahmed et al. (2006) came across different climatic zones of Himalyan forest of Pakistan and identified 4

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monospecific and 24 different communities. They observed pure stand of Pinus wallichiana on south exposure at 2770 m elevation from Nalter Gilgit and higher elevation 3100 m from Tukht-e-Sulaiman. Group-III is composed of 7 stands characterized by the predominance of Picea smithiana on low average elevating 3178 m and steep slope 39o angle. Ahmed et al. (2006) also studied more or less pure Picea smithiana forest from Nalter Gilgit on 3100 to 3250 m. Wahab et al (2010) reported Picea smithiana-Pinus wallichiana community on 2527 to 2645 elevation ranges from Danair valley district Dir.

Group-IV is monospecific Betula utilis group which is found on medium average elevation on 3214 and low slope 22o angles. Betula utilis community also identified Ahmed et al. (2006) on the elevation of 3350 to 3500 with co-dmoniance species of Picea smithiana. The smallest group among all the groups is group-V composed of two stands stand-24 is pure Juniperus macropoda and stand-28 is pure Abies pindrow forest. This group is located on high average elevation 3600 m and high slope 45o angle. Ahmed at al. (2006) also described Abies pindrow community on 3450 elevation from Astore near Rama Lake. Siddiqui (2011) studied Abies pindrow on 3000 elevation from Lalazar, Naran Himalayan moist temperate range while Wahab et al. (2010), Wahab (2011) recorded Abies pindrow community from District Dir Satto Khwar valley on 2670 meter elevation. It means this species can grow and exist in both i.e moist and dry temperate from 2670 to 3600 meter above sea level.

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angle whereas Bergenia stracheyi is dominated species in Group V located on medium elevation 3307 with medium slope 32o angle.

Classification and ordination showed similar distribution pattern of tree species as well as understory vegetation. Relationships between the ordination axes with topographic variables i.e. elevation and slope and edaphic variables i.e. pH, TDS, Salinity, conductivity and water holding capacity was also analyzed. Among the environmental variables elevation and pH were found significantly correlated (P < 0.05) and (P < 0.001) in groups mean with the ground flora data respectively whereas tree vegetation data set showed significant difference only with the ph of soil (P < 0.05) value. Similar results also recorded Siddiqui et al (2010),Siddiqui (2011),Khan (2011), Khan (2012), Khan et al. (2013).

Relationship between environmental variables and DCA ordination axes also evaluated. The environmental variables were not found significant correlation with axis 1, 2 and 3 except salinity which is significant (P < 0.05) value to the axis 1 of tree data set while in case of understory vegetation data set Elevation (P < 0.05) with axis 1 and pH (P < 0.05), (P < 0.001),with axis 2 and 3 showed significantly correlation respectively.

The groups resulting from tree vegetation data set and ground flora data set were associated with the topographical i.e. elevation and slope and Edaphic i.e. water holding capacity, TDS, pH, salinity and conductivity.

The classification and ordination and environmental variables disclosed some important relationship and the distribution pattern of the vegetation. For both overstorey (trees) and understory objective classification showed well-defined group

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structure and the resulting groups (clusters) were correlated to a considerable extend with the topographic and edaphic factors. In addition the communities were mostly similar which were extracted using the method described by Cottam & Curtis 1956 and Ahmed and Shaukat (2012) from the study area.

Many sampling sites showed seedlings of tree species indicating regeneration potential despite the illegal cutting and over grazing. These stands or forests could easily be saved by better planning and management however stands without regenerating seedlings indicating the presence of disturbance. Many other researchers i.e. Zarif (2004); Rheman (2004); Alamgir (2004) and Khan et al. (2010) also reported that anthropogenic factor is one of the most disturbance causes in forested areas. Recently Hussain et al. (2010, 2011): described these forests. Shaheen and Qureshi (2011) also reported that the distribution of vegetation is controlled by complex edaphic, climatic, and anthropogenic factors like exposure, Humidity, and grazing intensity.

To understand sustainable conservation and management of any forest it is necessary to get better knowledge of age and growth rates (Jacoby, 1989); (Peet & Christensen, 1980). The importance of estimation age and growth rates also described by several researchers i.e. Khan (1968); Champion et al. (1965); Shiekh (1985) Ahmed et al. (1988a, 1988b, 1990a, 1990b, 1991).

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years/cm growth rates. Ahmed et al, (1991) in case of Pinus gerardiana, reported 43 years/cm growth rates in seedlings of Pinus gerardiana and also found strong correlation between age and growth rate with the value of r=0.64,p<0.001.Furthermore Syampungani et al.(2010) and Ahmed el al. (1988) observed strong relationship between age and growth rates of different tree species. According to Ahmed and Ogden (1987) the oldest seedling may attain a maximum of 100 years/cm while they also found significant relationship between age and growth rates. In case of Juniperus excelsa. Ahmed et al, (1989) described age of seedlings 66 years from 6 dbh class and they also observed that age varies from seedling to seedling. While Hussain et al. (2012) studied seedling of Picea smithiana from Stak valley according to them maximum age was 126 years with the average growth rate of 2- to 15.7 year/cm. They also found strong correlation between Dbh and age of seedlings. In the present study seedling age of Pinus wallichiana ranged from 10 to 37. This estimation of age is within the range of the findings of above mentioned researchers. This study shows that though there is a significant relationship between Dbh and age and age and growth rate of seedlings of Pinus wallichiana, however due to wide variance, diameter is not good indicator of age.

Similarity between two elements assessed by the element matching test of Gray et al. (1981).If the confident limit of one element do not overlap with the confident limit of second element, the both are considered significantly different at the 0.5 level . Period from 1720 to 1750 shows significantly fast growth rate than all other classes. It means in these periods there were least population, cutting and grazing occurred. Period from 1761 to 1830 AD were not significant growth rate. Period of almost similar not significant growth rate from 1931 to 1960 but significantly different from other classes. Growth rate increased significantly from 1971-2000AD but similar to 1951-1970AD. In general growth rate is gradually not significantly on every 10 year basis, however each period is significantly differs 40 to 50 years.

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Hussain et al.(2012) reported that due to competition for nutrition, influence of natural disturbance, human induced disturbances, cutting, forest fire, overgrazing and unfavorable climatic conditions in the past, seedlings showed variation in growth rates.

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relationship between age and Dbh with the value of (r-0.64, p<0.001). Wahab et al. (2008) studied Picea smithiana and recorded largest tree 154 cm Dbh with 133 years old with 4.0 to 7.1 year/cm growth rates. They did not find any significant relationship between Dbh and age. In current study, Pinus wallichiana growth rates ranges from 4 to 19 year/ cm. This value is within the range of previous researchers finding while Dbh did not show any significant relation with growth rates. This may be due to wide variance and anthropogenic disturbances. This forest is under stress, therefore, immediate consideration of necessary ameliorative actions can save and mange these forest. However, it is concluded that due to wide variance, Dbh could not be consider as a good predictor of age.

Similarly in this study tree ring of Pinus wallichana also used to study growth climate response. Chwdhury et al. (1939, 1940a) was consider the pioneer tree ring researcher of subcontinent. After that many other researchers was presented climate response of different tree species i.e. Pant (1979) and Wu and Lin (1987) from Western Himalyan and Mountainous area of Hengduan. Furthermore the importance and relationship between of tree rings and climate also discussed by Davies (1987); Bhattacharyya et al. (1988); Ahmed (1989); Ahmed and Sarangzai (1991);Ahmed and Naqvi (2005);Khan et al. (2008) ;Wahab et al. (2008);Ahmed et al. (2009a);Ahmed (2009b) described some preliminary results for dendroclimatic investigation using Picea smithiana of Chera and Nalter . Ahmed et al. (2010a). Ahmed et al. (2010b) et al. described choronologies form upper Indus Basin of Karakorum Range. Zafar et al. (2010) carried out standardized tree ring choronologies of Picea smithiana from Bagrot and Haramosh valleys of Gilgit. Khan (2011) and Wahab (2011) studied chronologies of coniferous tree species from Chital and Dir district respectively. Recently Ahmed at al. (2011a) explained the dendrochronological potential of coniferous forests from Northern area of Pakistan. Ahmed et al (2011, 2012) investigated dendroclimatical potential of coniferous tree species from Northern areas Karakorum ranges of Pakistan. Zafar et al. (2012) studied growth climate response of Picea smithina from Afghanistan. Ahmed and Shaukat (2012) discussed the scenario of climate change. Zafar (2013) carried out growth climate response of some coniferous tree species from Gilgit and Hunza districts of Gilgit-Baltistan. Ahmed et al. (2013) investigated dendroclimatical potential of two coniferous tree species from Gilgit district.

275

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Correlation and response function analysis were performed among residual and standard chronologies with Skardu station and corresponding grid data .The parallel use of correlation and response analysis was performed to pick the similarities in the results.

276

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277

Chapter No 12 General discussion

(2006) who worked over the elevations of different species situated at Bagrot site and concluded that sites situated at different elevations showed different response.

In case of Precipitation Ahmed et al. (2011) recorded Pinus wallichiana from Mushkin with positive relationship in January, February and May. Pinus wallichiana from present study also showed positive relationship in the month of January. The study area fall in dry temperate area where the precipitation of winter season is stored for the spring because in this area sow fall is occurs mostly in January and February which is used in spring season.

More research work is needed to explore and understand the Phytosociology, size classes structure, age and growth rates, and Dendrochronological potential of these forest and related vegetation in these areas.

278

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309

APPENDICES

Appendices

Appendix 1.1 Mean monthly precipitation (mm) of District Gilgit.

Year Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec 1972 6.6 0.5 20.6 10.7 53.6 5.3 17.5 25.9 7.6 0.8 0 1.3 1973 29 15.7 73.7 28.2 17 0.3 21.8 16.5 9.9 2.5 0 0 1974 9.4 2.8 0.8 1.1 7 37.3 12.3 7.9 10.6 0 1.8 6.7 1975 7.7 7.3 8.9 12.5 51.3 1.6 24.9 30.9 0.6 2.8 0.8 0 1976 11.5 13.2 4.1 1 2.8 6.3 0.5 55.8 5 6.3 0 0 1977 0 0 0 7.4 0 0 14.5 3.8 3.5 3.8 1.3 6.4 1978 1.8 0 8 1.8 58.8 0.8 45.3 21.8 15.8 0 5.1 0 1979 1.8 0 12.1 127 40.3 6.1 2.5 39 1.8 0 0 4.5 1980 0.8 13.5 5.1 10.6 43.8 7.6 27.9 8.3 15.1 1.5 0 0 1981 3.3 5.2 47.5 95.6 21.3 13.4 2.2 15.7 7.8 9.6 0.8 0 1982 0.7 4 10.9 19 7.1 0.6 10.3 5.3 7.4 22.6 10.5 4 1983 0 1 28.3 2.1 11.4 9.1 4 7.5 0 0 1 0 1984 0 0 8.1 3.5 57.6 6 4.3 7 14.4 0 3.2 0.6 1985 2.3 1.2 1.8 9.8 37.8 0 11.3 11.3 0 0.2 0.2 25.1 1986 0 9.9 14 24.2 6.8 5.8 12.3 26.8 4.8 0 14.7 14.3 1987 0 0 5.7 45.3 14.8 16 13 0 2.2 102.4 0 0 1988 0 8.2 26.7 10 0.5 22.6 38.1 15.6 5 4.7 0 4.8 1989 0.8 2.3 2 3.3 68.8 2 35.7 40.5 2 0 2.2 0 1990 6 5.5 6.6 17.9 0 4.2 10.9 12.4 0 3.7 0 22.1 1991 0.7 10.8 21.3 10.6 30.1 8.2 17.1 5.7 13.4 0.5 0 0 1992 6.7 1.5 10.8 9.1 0.5 0 0.3 2.4 61.3 1.7 0 0 1993 0 0.2 0 0 16.1 3 43.5 1.9 2.1 0.4 27.4 0 1994 3.2 8.8 18.6 4.5 32.5 8.8 13.6 2 11.4 3.2 0.6 12 1995 0.1 3.6 1 25.7 18.6 9.8 22.5 5.6 7.1 4.8 2 7.5 1996 20.1 6 40.1 41.1 72.8 48 13.2 4.2 0 5.7 0 0.5 1997 0 0 12.1 2.1 2 3.9 12.5 88.1 0.1 3.4 0.8 3.7 1998 6 8.6 6.8 58.9 44.8 17.6 3.1 7.3 14.3 0.5 0 0 1999 1.6 34.5 6.8 89.7 15.8 3.5 12 18.5 11.6 4.1 8.7 0 2000 4.9 0 1.7 13.3 0.7 18.7 22.4 16.6 12.9 2 0 4 2001 0 0.5 12.9 6.2 1.8 20.6 12.7 14 1.3 0.6 15 2.4 2002 0.5 9.3 2.9 31.2 9.4 18 16.4 20.8 3.7 0 0 0.2 2003 0 33.4 18.5 23.8 87.2 6.9 15.4 9.4 17.6 8.4 0.3 4.7 2004 0.4 6.9 6 45.2 13.3 14.8 4.8 10.4 2.3 6.5 0 36.5 2005 11.2 14.1 12.4 58.9 32.6 2.7 9.3 2.1 2.1 0 4 0.4 2006 15.4 6 2.1 23 1.5 11.1 8.1 39 11.9 4.7 4 5.8 2007 0 1.3 11.9 15.2 12 18.8 12.5 6.3 6.3 0 0 0 2008 2.9 0 0 8.3 75.8 15.6 3.5 10.9 8.3 12.9 1 31.5 2009 32.2 4.7 6.1 42.9 3.1 21.6 2.5 1.4 16.8 1.2 0 8.6 2010 0 13.3 20.7 24.6 60.7 23.2 52.9 60.1 10.4 1 0 0.6 2011 4.7 35.5 10.6 5.8 16.6 19.8 14.5 11.1 32.7 4.9 0.2 2.3

Source: Muhammad Ali (Meteorological Assistant) Pak. Met. Deptt Headquarter Campus office University road Karachi

310

Appendices

Appendix 1.2 Mean monthly precipitation (mm) of District Skardu

Year Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec 1972 1.5 27.9 161.3 12.4 137.2 2.5 16 6.9 5.6 0 1.3 9.1 1973 66.8 35.8 79.8 78.2 24.4 0 4.3 30.5 1.8 0 0 1.5 1974 112.2 92.5 91 6.2 9.9 30.7 8 0 11.4 0 1 42.3 1975 31.7 49.3 124.6 8.9 40.9 0 25.7 36.2 4.5 0 0 8 1976 16 50.9 15.4 0 0 5 0.9 59.6 5.3 11.4 0 4.9 1977 32.6 4.3 6.3 29 1.6 0.8 2.8 2.9 1.3 28.6 3.3 30.2 1978 26.9 6.3 27.7 4.6 36.2 24.1 23.5 16.2 5.1 0 23.6 7.7 1979 7 0 41.4 9.1 28.4 0 1.1 5.3 6.9 1.4 19.8 20.6 1980 7 40.3 20 6.9 10.7 5.3 20.5 4.5 17.8 4.8 2 5.6 1981 14.3 16.2 26.9 78.8 24.8 13.3 12 7.4 0 0 16.4 0.4 1982 3.7 20 3.5 0 9.6 1 0.8 6.8 1.5 42.8 31.9 21.7 1983 27.4 13.8 111 7.8 8.7 25.9 12.6 4.7 6.1 0.5 0 0.3 1984 3.6 4.7 17.8 27 74.8 1.3 12.7 5.2 4.2 0 2.1 12.4 1985 27.6 4.8 7.9 0.8 28.9 0.8 3.1 18.1 0 5.9 0 52.5 1986 0 25.9 66.4 25.8 4.1 18 8.9 9.7 0.8 0 66.2 12.4 1987 10.1 26.2 14.8 66.7 30.1 33.3 0 0 0 114.5 0 0.8 1988 60.7 32.6 96 14.7 2.6 24.5 13.6 8.4 35.4 2.5 0 27.6 1989 5.7 16.8 19.6 15.2 46.5 3 12.7 15.6 0 11.5 9.4 2.9 1990 59.7 45.2 59.1 31.2 7.6 0 25.9 6.8 8.3 3 1 44.4 1991 40.3 21.1 43.8 14.5 31.1 0.8 1 3.8 1.1 0 1.3 6.5 1992 73.2 36.2 57 10 0.5 0 3.3 8.1 83.2 4.6 2.2 8.3 1993 35.4 13.9 57.9 0 29.9 11.7 21.9 0 6.2 0 43.7 0 1994 61.9 38.4 72.6 34.8 7.9 1.2 3.6 13.1 11.2 0 0 86.1 1995 16 39.8 2.8 17.8 14.7 7.5 49 12.8 5 0 0 12.4 1996 61.1 67 80.8 64.3 74.1 32.3 5.9 38 0 8.8 0.3 17.6 1997 4.2 10.4 27.5 1.1 7.7 16.8 7.7 61.5 9.4 0 6 9.8 1998 77.9 43.9 30.8 48.7 61.8 5.6 1 2.3 18.9 4.9 0 17.9 1999 58.1 41 12.1 87.3 35.2 1.9 9.9 22.9 0.7 0.3 9.6 0 2000 58.5 6.9 8.5 11.1 6.7 11.3 21 19.6 8 0 0 32.5 2001 0 4.2 6.4 3.3 3.8 13.1 12 9 10.5 8.3 12.3 32.7 2002 4 20.7 24.6 36.2 8.4 15.8 5.1 18.3 5 0 0 2.3 2003 0 22.6 38.4 46.3 101 2.8 6.4 12.1 22.7 2 0 8.3 2004 19.1 18.6 17.5 28.9 5.4 9.8 3.4 15.6 4 3.5 1.9 22.6 2005 30.9 46 13.5 59 43.5 3.5 22.7 1.4 2.9 0.3 0.6 8.1 2006 63.1 55.6 16.7 145 2.1 4.5 8.2 14.6 17.7 2.6 1 19.4 2007 0 4.8 55.6 3 0 21.4 12.1 4.7 4.4 0 0 0 2008 34.7 21.4 7.4 39.8 3.9 13.4 7.3 24.8 31.1 16.1 0 89.5 2009 117.6 65 21.7 51.2 0.8 5.1 6.9 16.3 1.8 4.5 9.1 16.1 2010 11.1 124.3 76.4 104 115.3 5.1 24.8 29.6 0.6 2 0 1.8 2011 24.6 60.3 79.5 14 14.9 6 8.3 15.6 19.5 1 11.4 0

Source: Muhammad Ali (Meteorological Assistant) Pak. Met. Deptt Headquarter Campus office University road Karachi

311

Appendices

Appendix 1.3 Mean monthly precipitation (mm) of District Astore

Year Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec 1971 4.1 51.6 12.4 121 21.3 7.1 24.6 11.4 0 2.5 1.3 2.8 1972 46.5 44.2 221.2 60.2 202.9 4.8 13.5 8.9 30.5 25.4 1.3 24.6 1973 129.8 53.1 140 159 44.7 0 26.2 37.3 0.8 15.7 0 1.3 1974 71.6 50.1 60.2 50.9 105.4 55.6 22 17 27.2 3 0.5 27 1975 40.8 34.6 67.9 59.6 152.3 3 26.4 47.3 11.3 0.8 6.3 13.5 1976 50.5 76.8 64.7 3 21.1 6.1 5.9 102.5 69.1 38.2 0 15 1977 57.1 18.2 24.1 27.7 12.8 4.6 3.3 18.8 16.2 73.1 20.9 35 1978 26.1 39.4 82.6 39.5 80.3 13 89.3 12.3 4.4 0 69.7 2.5 1979 17.9 11.5 96.8 136 119.6 16.7 1 37.2 31.3 4.5 19.1 22.8 1980 25.9 144.4 50.4 47.3 79.4 24.9 17.3 19.1 48.6 16.3 1.8 7.1 1981 63.1 63 152 192 44.6 37.1 30.4 5.8 15.7 23.7 19 0.8 1982 15.8 41.7 50.3 10.8 15.6 3 6.9 2.2 9 77.6 59.4 60.6 1983 62.5 12.9 139.4 21.8 14.9 51.6 5.8 15.2 0 0 2.2 12.2 1984 6.9 18.3 101.7 95.5 154.7 13.7 22.6 62.8 31 2.5 11 21.2 1985 13.5 15.4 25.5 43 100.2 2.7 28.7 30.8 4.4 19.6 9.1 79 1986 3.8 69.4 94.7 104 18.6 18.7 7.6 35 3.5 3.2 84.8 72.6 1987 7.5 22.7 38.1 137 62.3 44.2 26.7 15.2 11 170.6 0 4.2 1988 31.4 82.3 108.7 34.1 4.5 45.6 21.7 37.7 11.4 17.5 0 43.6 1989 47.6 32.3 31 78.6 170 15.1 56.8 24.9 12.7 24.9 8.7 7.7 1990 58.6 54.4 91.4 66.4 30.5 29.3 8.6 40.7 4.3 25 4.8 98.4 1991 32.1 47 156.2 51.4 146.3 2.7 19.3 17.5 12.5 0 6 16.5 1992 143.1 32.7 145.9 68.3 57.5 2 17 12.3 172.3 13.3 9.8 12.4 1993 38.2 44.3 96.3 1.3 68.4 33.9 63.5 6.8 16.4 13.7 110 1.2 1994 54.9 53 94.1 98.4 45.1 31.1 33.7 34.3 25.8 9.9 1.5 168 1995 20.6 59.5 43.2 91 36.8 45.7 63.6 11.2 4.6 25.8 16.9 30.2 1996 35.3 52.5 211.9 171 174.1 94.1 18.4 29.4 5.6 52.2 3.6 9.8 1997 9.4 4.2 90 37.1 44.9 12.6 42.5 106.6 10.9 7.7 31.4 16.5 1998 33.8 87.2 23.8 110 85.5 20.7 4.6 3.3 14.8 2.7 0 0 1999 111.1 32.5 88 221 58 17.4 29.7 53 30.5 5.2 51.9 0 2000 72.8 9.8 29.7 41.6 17.3 23.1 26.1 11.3 10.9 13.8 9.9 19.6 2001 0.2 3.4 56.4 52 7.8 14.3 26 17.4 36.4 45.5 68.9 53.5 2002 29.3 63.6 48.7 162 16.8 38.5 19.9 15.4 15.5 3.2 0 3 2003 2 63.7 35.3 112 121.2 14.7 23.3 28.7 80.6 17.6 12.5 78.5 2004 95.4 48.5 33.1 136 39.2 49.5 20.4 34.9 6.9 26.3 11.3 12.6 2005 52.2 111.2 27.6 86.5 105.7 5.1 17.4 2.2 3.6 0 14.7 32 2006 118.4 58.4 10 36.6 0.2 18 10 21.3 13.6 13.1 28.3 18 2007 0.3 0 92.2 53.3 19.1 25.6 23 31.5 17.1 0.6 0 0 2008 17 29.5 16.8 47.1 10.9 30.7 8.8 28.3 26.8 13.3 5.8 157 2009 145 94.1 58.7 102 26.9 38 22.1 9.2 9.4 5.8 7.9 15.9 2010 1.4 93.8 68.2 103 177.6 70.8 79.3 26.2 11.2 2.5 0 0

Source: Muhammad Ali (Meteorological Assistant) Pak. Met. Deptt Headquarter Campus office University road Karachi

312

Appendices

Appendix 1.4 Mean monthly maximum Temperature (Cº) of District Gilgit.

Year Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec 1972 9.7 9.7 17.8 23.1 26 33.4 33.5 32.6 30.9 23.8 18.2 10.7 1973 6.5 11.4 15.5 23.1 28.3 38 36.5 37.1 33.2 25.8 19.7 11.3 1974 8.4 10.7 19.9 26.3 27.5 32.1 34.8 35.4 30 26.3 17.8 8.7 1975 8 11 17.1 23 27.7 33.7 34.4 34.3 31.4 27 16.9 11.2 1976 11.6 10.7 16.6 24.7 30.5 34.3 38.9 32.8 31.2 24.1 19.4 10.7 1977 8 12.6 21.2 25.7 28.7 35.6 39 36.2 32 26.1 19.1 11.2 1978 8 12 15.9 25.6 30.7 36.8 34.4 35.8 32 27.1 15.7 12.3 1979 10.1 13.5 16.9 24.6 23.4 34.1 38 32.4 31 27 18.5 11.7 1980 9.3 11.8 16.9 26.3 29.1 34.9 35.5 34.9 29.5 26.4 18.8 13.1 1981 9.5 13.1 18 23.1 31 32.3 35.1 34.1 30.6 24 17.6 10.9 1982 10.6 10.7 16.5 25.5 29.8 33.2 36.1 37 29.4 24.8 15.6 8.7 1983 8.4 11.6 14.5 23.9 30.7 32.8 35.7 36.4 34.2 25.9 19.7 11.7 1984 9.3 10.5 19.9 23.9 26.6 36.8 35.4 38.4 29.3 25.1 15.9 10.5 1985 8.5 14.5 21.2 25.8 27.6 33.6 38.4 34.9 32.1 25 17.9 10.5 1986 9 11.7 15.6 22.4 27.1 33 35.9 32.2 31.2 27.3 17.4 9 1987 9.6 13.6 18.8 23.9 26.6 31.7 34.2 36.2 33.4 21.3 18.3 12.4 1988 10.3 12.5 16.5 26 32.4 34.6 37.6 34.7 33.4 24.2 20.5 12.8 1989 9.1 11 17.5 23.1 25.1 33 32.8 31.3 31.9 26.7 17 12.3 1990 11.5 10.5 17.5 23.2 34.2 34.7 38.6 37.6 34 25.5 19.8 12.3 1991 8.2 12 17.2 23.4 25.3 34.1 34.5 36.1 32.6 24.6 18.6 12.6 1992 9.3 11.1 15 23 28.5 34.6 36 35.5 29.9 24.7 19.2 13.1 1993 9.3 15.2 16.8 27 30.1 33.4 33.7 34.6 33.6 25.8 17.7 13.5 1994 9.8 10.9 18.8 22.1 29.8 34.1 38.2 38.2 30.7 25.2 19.7 10.8 1995 7.7 12.5 18.3 22.4 29.3 33.6 36.9 36.5 32.5 25 20.1 9.9 1996 8.9 13.5 17.9 24.3 23.2 32.3 35.5 35.8 35.5 24.6 19 12 1997 12 14.7 17.7 26.4 28.6 34.3 39.7 35.5 32.9 25.2 17.9 12.3 1998 9.8 13 18.7 26 28.9 31 38.2 36.5 33 28.5 21.9 14.9 1999 11.1 13 18.4 23.4 30.7 35.1 37.8 34.3 34.1 26.6 17.9 14.6 2000 10.6 13.3 19.3 26.5 34.9 35.2 34.6 35.3 32.9 27.8 19.9 12.9 2001 12.6 16.4 21.6 26.7 34.8 35.9 37.2 35.5 31.1 28.2 18 13 2002 11.2 14.1 21.3 24 30.7 34.5 35 36.1 30.3 28.5 20.9 13.3 2003 13.4 13.5 17.6 25.2 26.3 35.3 38.2 35 30.9 26 18.4 12 2004 11.3 15.6 23.4 24.9 30.1 33.2 34.7 34 33 24.1 20.6 13 2005 9 10.8 20 22.9 27.4 35.3 35.8 35.9 33.5 27.1 18.3 11.5 2006 8.5 16.9 20.3 24.9 34 32.3 36.8 32.5 30.1 27.2 19 11.2 2007 12.4 16 17.8 29.1 32 35.2 33.7 33.9 31 25.5 20.9 12.6 2008 7.8 12.5 23 25.6 32.5 37.6 36.5 35.5 30.3 27.4 19.2 11.5 2009 9.5 12.7 18.5 22.7 31.1 32.1 36.1 36.8 31 25.1 18.7 12.5 2010 13.9 12.5 22.1 24.4 26.5 30.9 33.2 31.2 29.3 28.1 22.8 14.6 2011 11.9 11.6 20 25.6 33.2 36.6 34.2 35.3 29.5 25.9 19.6 13.9

Source: Muhammad Ali (Meteorological Assistant) Pak. Met. Deptt Headquarter Campus office University road Karachi

313

Appendices

Appendix 1.5 Mean monthly maximum Temperature (Cº) of District Skardu.

Year Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec 1972 6 3.8 11.5 15.8 20.6 26.8 27.9 27.9 26.1 0 12.4 6.2 1973 0.3 3.6 8.9 17.3 21.4 30.7 32.3 31.2 28.7 20.2 14.3 6.8 1974 2.3 1.6 11.5 21 22.5 26.3 30.2 31.5 25.3 20.9 12.4 1.8 1975 -0.5 1.8 7.5 17.1 20.5 27 28.7 30.4 25.4 21.4 11.1 5.1 1976 2.7 4.8 10.3 19.1 23.8 27.3 33.3 27.1 25.2 18.6 13.7 5.8 1977 2.8 3.7 15 19.5 22.5 29.5 33.5 30.6 27.8 19 12.7 5.2 1978 -1 3.6 10.5 18.9 25.1 30.7 32.4 31.5 26.8 20.9 9.1 5.6 1979 4.3 8.5 9.7 19.9 19.7 28.5 32.5 30.5 25 20.2 13.2 6 1980 2.4 4.9 10.5 19.9 23.8 29.9 31.1 29.9 25.2 20.9 13.1 7.6 1981 4.2 7.8 13.5 17.8 25.3 26.2 30.3 30.3 26.2 19.9 12 6 1982 6.4 6 12.4 20.6 25.1 28.8 32 32.6 26.2 19.6 9.7 2.8 1983 0.5 1.9 6.8 18 24.7 27.9 29.6 33.1 29.6 20.6 15.2 7.6 1984 5.7 7.1 15.5 18.9 22.8 31.8 32.3 35.9 26 21.6 12 6.3 1985 2.7 8.2 15.8 21.8 23.5 28.4 35.4 32.9 28.9 20.9 14.2 6.5 1986 -0.7 4.9 10.8 18.2 22.6 27.9 32.9 31.1 27.8 22.6 12.3 5 1987 2.5 8.1 13.5 19.2 21.3 26.5 30.1 32.8 29.5 17.9 14.2 9.5 1988 4.8 6.7 11.2 20.5 25.4 28.1 31.3 30.3 28.5 19.3 15.7 8.1 1989 5.2 7.2 13 17.7 22.1 27.2 29 28.6 29.5 22.5 12.4 7.5 1990 6.8 5.4 11.7 18.4 28.2 31.5 34.2 34.4 31 21.9 15.7 9 1991 2.2 7 11.9 19.3 21.9 30.7 32 32.8 30 20.4 14 7.5 1992 3.4 5.5 10.9 19.6 24.5 30.1 32.9 32.9 25.8 21.5 15.1 9 1993 1.9 7.9 11.4 20.7 23.9 27.9 29.2 30.3 28.4 21.8 12.8 9.8 1994 4.9 5.8 13.2 17 24.9 29.8 34.5 33.1 27.4 21.2 16.2 5.1 1995 -2.7 2.8 11 17.6 25 30.2 31.6 31.1 27.2 20.9 16 5.2 1996 1.1 4.5 11.9 18.2 18.5 27.2 30.5 29.6 29.4 19.3 15.1 7.8 1997 5.3 8.9 13.2 22 23.9 29.7 35.8 31.6 28.6 20.7 13 7 1998 2.4 5.1 11.9 20.6 23.6 27.3 33.8 32.8 29.2 23.2 17.5 10.4 1999 5.2 8.9 12.2 17.2 24.4 28.4 31.3 28.4 27.7 18.5 12.8 8.8 2000 1.8 3.1 11.9 20.1 27.5 29.3 30.3 29.6 26.9 21 13.3 4.5 2001 3.2 11.4 16.6 21.5 29.1 31.4 34.3 33 26.7 23.1 13.5 5.9 2002 5.2 8.4 15.2 19.8 25.2 29.7 31.4 32.7 25.5 23 16.5 9.5 2003 8.7 9.3 12.9 19.6 21.6 29.5 33.3 29.8 26.4 22 14.7 8.6 2004 6.3 9.1 17.8 21.3 25.7 28.7 31.5 30.4 30.1 20.1 15.9 10.4 2005 2.8 5.9 12.5 16.9 21.4 28.6 29.9 31 28.5 20.9 13.1 6.5 2006 2.1 9.2 13.9 18.1 27.2 27.8 33.1 30.6 26.8 21.8 13.8 5.6 2007 6.4 10.3 12.6 23.8 26.9 29.4 31.1 30.6 27.1 20.2 15.2 7.9 2008 2.8 5.5 16.6 20 26.1 32.9 32.2 31 24.5 20.3 14.1 6 2009 3.1 5.7 12.3 18.1 24.2 27.2 30.5 32.3 27.1 19.9 11.7 6.4 2010 7.2 6.6 15.1 18.9 21.5 26.3 29 28.8 26.1 21.8 15.6 8.2 2011 4.4 6 12.7 19.6 26.7 30.5 30.2 31.2 26.1 20.8 14.3 0

Source: Muhammad Ali (Meteorological Assistant) Pak. Met. Deptt Headquarter Campus office University road Karachi

314

Appendices

Appendix 1.6 Mean monthly maximum Temperature (Cº) of District Astore

Year Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec 1980 3 4.6 7.7 17 21.1 25.8 27.7 27.1 21.7 18.3 12.2 7.3 1981 3.6 4.4 8.5 14.2 21.9 23 26.8 26 21.5 17.3 10.3 5.3 1982 5.6 2.2 7.9 17.1 21.5 24.4 27.5 29.1 22.3 15.7 8.2 1.9 1983 1.3 1.8 5.3 14.3 21.5 23.3 26.2 28.5 25.8 18.4 12.5 5.6 1984 1.7 2 10.8 14.2 18.4 26.6 26.1 29.7 20.9 17.7 8.6 4.1 1985 1.6 6 12.1 16.9 18.6 23.5 29.1 27.3 23.7 16.8 10.9 3.4 1986 1 2.9 5.6 12.6 18.2 21.7 27 24.9 22.5 18.6 9.6 2.2 1987 1.9 4.5 9.5 14.7 17.7 22.2 24.7 27.2 24.7 13.4 12.5 7.3 1988 3.4 4.3 7.2 16.6 21.2 23.6 27.4 25.6 24 16.7 13.7 7 1989 2.3 3 8.9 13.4 16.7 22.5 23.9 23.4 23.9 18.6 9.4 6.1 1990 4.6 3.7 7.3 14.1 23.5 25.4 28.5 28.5 25.2 18.2 13 6.6 1991 0.9 3.7 7.5 13.9 16.9 25.1 25.4 27.1 24.6 17.1 11.6 6.1 1992 2.7 2.2 6.1 13.4 18.8 23.4 26.8 27.1 21 16.4 12.5 7.1 1993 2.2 3.8 7 16.9 20.9 23.4 24.8 25.6 23.7 17.5 10 7 1994 2.2 2.8 9 12.2 20.7 24.5 29.6 28.9 22.9 16.7 12.5 3.1 1995 -0.8 3.5 8.5 13.6 20.5 24.4 26.9 28.1 24 17.4 13.6 3.2 1996 1.4 4.6 8.1 13.7 15 23.5 25.6 26.7 26.1 16.7 12 6.5 1997 5.3 6.9 8.9 16.7 18.8 24.5 29.4 26.5 24 17.2 10 4.2 1998 2.3 3.9 8.4 16.7 19.6 22.3 28.3 27.6 23.7 20 14.7 9.7 1999 3.7 5.5 8 14.6 20.9 24.2 27.6 25 24.9 18.4 10.2 8.4 2000 3.6 2.9 9.5 17.5 24.9 25.3 26.4 26.5 23.9 19.8 12.1 5.7 2001 5.5 8.5 12.6 17.4 24.7 26.1 27.7 27.5 21.7 19.9 10 4.9 2002 3 4.5 10.6 14.3 21.2 24.7 25.6 27.7 21.5 19.3 13.3 6.7 2003 5.9 4.9 7.5 15.3 16.7 25 28.2 25.7 22.1 17.5 11.1 4.5 2004 2.8 5 13.3 16.7 21.2 23.7 26.1 25.3 24.5 15.8 12.4 7.2 2005 1.7 2.7 10.4 13.8 18 25 26.4 27.1 25 17.6 10.9 3.8 2006 1.7 8.5 10.7 14.8 24.5 24.4 29.3 26.1 23.3 18.8 11.2 4.1 2007 4.4 8.3 10.1 20.6 22.5 26 27 26.8 23.6 18.5 14.3 6.5 2008 1.1 3.7 14.2 16.6 22.7 28.4 28 27.3 21.9 19.3 12.6 5 2009 2.4 4.3 9.3 14.1 20.9 22.9 26.7 28.4 23.1 16.9 11 5.6 2010 6.9 4.4 13.2 16.1 18.8 22.9 25.7 24.9 22.2 19.3 14.9 8 2011 4.7 4.4 9.4 15.2 17 20 22 23 23 17 13 9

Source: Muhammad Ali (Meteorological Assistant) Pak. Met. Deptt Headquarter Campus office University road Karachi

315

Appendices

Appendix 1.7 Mean monthly minimum Temperature (Cº) of District Gilgit

Year Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec 1972 -2.1 -1.1 6.2 8.4 10.8 14.3 17.3 17.4 11.2 5.5 1.6 -0.8 1973 -2.3 1.4 4.6 10.2 12.7 16.3 20.3 18.9 14.3 5.1 -1.4 -4.1 1974 -3.7 -0.4 6.4 10.3 12.1 14.5 17.9 16.7 11.7 4.6 -0.6 -2.3 1975 -3.5 -0.8 3.2 9.8 11.7 15 17.3 18.4 13.4 6.6 -1.8 -3.5 1976 -0.6 0.3 4.7 9.7 12.4 15.4 21.2 17.3 12.7 7 0.3 -3.3 1977 -1.9 -0.8 5.7 10.7 11.4 19.8 22.3 19.2 13.9 8.9 3.1 -1.2 1978 -2.6 0.6 4.6 9.3 13 16.9 20.3 19.2 12.7 7.1 1.7 -2.2 1979 -2.3 0.9 4 10.1 10.7 14 18.4 17.4 11.3 6.8 2.3 -0.9 1980 -1.9 0.8 5.7 10 12.7 15.3 19 17.4 12.3 6.8 0.4 -1.7 1981 -1.8 1.7 5.8 8.9 12.9 13.9 20 17.3 11.5 6.8 0.3 -4.5 1982 -3 -0.9 5.5 9.9 12.5 14 17.9 19.9 12.8 7.2 2.4 -0.8 1983 -2.6 -2.3 4 9.9 12.7 13.8 17.6 18.6 14.3 6.5 -0.2 -2.5 1984 -3.9 -0.9 6.4 9.2 12.5 16.1 19.2 20.4 11.9 5.8 1.5 -3.1 1985 -1.4 -1.3 5.7 9.9 12 14.4 20 19 12.6 6.9 1.3 -0.2 1986 -3.3 1.3 4.6 9 11.2 14.4 18.7 17.2 12.1 4.9 1.9 -0.7 1987 -4.6 0.6 6.2 8.8 9.9 13.5 16 17.3 12.7 7.5 -0.2 -2.8 1988 0.2 0.6 5.2 8.6 11.6 13.4 19.1 17.1 12.6 6.3 -0.5 -1.8 1989 -3.7 -1.4 6.1 6.9 9.9 13.3 16.4 14.5 11.3 4.8 1.3 0.4 1990 -1.1 2 3.8 7.6 11.9 15.8 18.5 16.3 12.4 4.8 -0.9 -2.1 1991 -3.1 1.9 6.1 8.1 11.2 13.5 16 14.7 11.7 4.8 -0.3 1.8 1992 -0.5 1.6 4.5 9.2 11.5 13.5 17.5 16.6 12.3 5.8 0.2 -2 1993 -3.6 1.1 4 8.8 12.3 13.8 16.5 15.9 12.2 5.3 1.3 -2 1994 -1.1 0.8 7.3 7.5 12.5 14.3 19.1 19.6 12.3 5.9 0.6 -1.8 1995 -5.5 -0.8 5 9.6 12.3 14.7 17.6 17.7 12.5 7.3 -1.1 -2.4 1996 -3.6 1.4 6.1 8.4 9.3 13.3 13.9 18.7 12.3 5.6 -1.5 -5.7 1997 -4.3 -2.4 4.5 9.2 11.4 15.5 18.2 15.5 12.9 7.4 1.5 -0.9 1998 -2.3 1.8 5.1 9.3 13 15.1 17.9 16.3 12.4 7.7 -0.8 -4.5 1999 -0.9 2.4 6.7 9.8 12.1 13.3 17.3 16.8 12.8 4.4 0.9 -6.8 2000 -4 -2.2 2.6 7.8 11.9 14.5 16.7 14 9.4 4.6 -0.2 -1.3 2001 -5.6 0 3.6 9.4 12.8 15.2 18.6 14.7 9.3 3.9 -0.7 -1.1 2002 -4.6 0.3 5.1 9 11.2 14.6 15.7 15.8 8.7 6.1 1.1 -1.5 2003 -3.2 0.2 5.3 8.9 9.5 13.6 17.3 18.5 13.1 4.5 0.5 -1.6 2004 0 1.1 6.9 9.8 11.3 14.8 16.4 16.3 11.2 6.7 1.3 0.3 2005 0 0.9 7.5 8 10.9 14 17.5 16.1 11.8 4.9 -1.1 -6 2006 -2.3 4.1 5.9 7.7 12.8 14.4 18.8 18.3 12.6 7.4 2.1 -2.5 2007 -4.8 2.5 5.1 10.7 13.5 15.9 16.6 17.3 13.6 5.2 -0.8 -3 2008 -3 -1.7 6 10.1 12.8 18.1 17.9 17.5 11.4 7.1 1 -1.5 2009 0.1 2.5 6.1 9.3 11.5 14.3 16 17 12.2 6.5 -0.4 0 2010 -1.9 1.9 7.3 10.3 12.4 13.9 16.7 17.8 13.3 5.9 -0.8 -5.9 2011 -4.3 1 5.8 8.5 12.8 15.8 18.1 18 14.1 8 3.3 -3.1

Source: Muhammad Ali (Meteorological Assistant) Pak. Met. Deptt Headquarter Campus office University road Karachi

316

Appendices

Appendix 1.8 Mean monthly minimum Temperature (Cº) of District Skardu

Year Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec - 1972 -4.3 -4.3 2 6.2 8.8 13.6 15.8 16.2 0 -1.8 -3.4 11.2 1973 -8.2 -5.4 0.8 6.6 9.9 15.3 17.7 17.2 13.7 4.3 -3.8 -8.2 1974 -9.8 -10.6 0.4 8.6 10.1 13.2 17 17.1 11 4.9 -2.6 -5.9 1975 -10.5 -9.7 -1.5 6.1 9.1 13.9 15.2 16.5 12.2 6.1 -3.1 -6.1 1976 -5.2 -2.1 0.9 7.8 11.8 15.8 18.5 15.7 11.3 5.1 -0.8 -4.8 1977 -7.5 -7.7 2.2 7.6 9.9 14.2 19.2 14.1 14 5.6 -0.1 -5.7 1978 -12.1 -5.5 0.9 5.7 10.7 14.7 18 18.2 11.9 4.2 -1 -6.2 1979 -5.4 -2.3 0.3 8.2 9 13.7 17.4 16.4 10.6 5.5 0 -4 1980 -5.8 -3.8 0.9 7.3 11.8 15.6 17.5 15.6 12.8 9.6 -1.4 -6.6 1981 -6.6 -2.6 1.5 5.9 12.3 12.9 17.6 15.6 10.8 3.7 -2.6 -8.4 1982 -6.9 -4.3 2 7.5 9.8 12.2 16.7 17.9 11.4 4 -0.6 -4.7 1983 -10.6 -10.2 -2.5 5.9 9.9 10.9 13.8 16.1 12.5 3.7 -3.8 -6 1984 -7.3 -4.5 3.2 6.5 8.8 13.6 16 18.4 10.3 3.3 -2.6 -5.7 1985 -7.6 -5.4 3.6 7.7 9.3 12.3 18.2 17.2 11.8 3.7 -1.7 -5.7 1986 -14.6 -5.6 0.8 6 8.2 11.8 16.5 14.8 10.9 3.2 -0.8 -4.7 1987 -10.9 -2.5 2.2 6.1 8.4 11.3 14.2 16.1 11.4 4.8 -2.3 -5.3 1988 -3.3 -2.7 1.4 6.5 9.6 11.6 15.6 14.7 11.2 3.5 -3 -4.9 1989 -7.1 -3.7 1.7 5.3 8.9 11.9 14.7 13.9 11.1 3.5 -1.1 -3.9 1990 -3.1 -3.7 0.8 5.6 11 14.8 16.8 16.9 13 3.8 -2.4 -4.2 1991 -9.4 -3.1 1.6 6.5 9.2 12.9 16 14.6 12.2 2.9 -3.5 -2.6 1992 -6.8 -5.3 1.2 6.7 9.7 13.5 16.5 16 11 4 -2.1 -4.3 1993 -9.8 -2.1 0.9 7.1 9.8 12 14.6 13.4 10.1 2.1 -1.6 -5.3 1994 -4.8 -3 2.2 4.8 10.8 13.5 17.8 17.3 10.4 3.4 -2.4 -5.5 1995 -17.9 -9 0.3 6.2 10.1 12.9 16.1 15.2 10.3 4.6 -3 -4.7 1996 -10 -5.9 2.5 5.7 7.5 13 14.5 15.6 11.3 3.1 -2.3 -8.2 1997 -7.9 -4 2.2 8.5 10.3 14.1 16.3 14.2 10.7 5 -1 -2.7 1998 -8.9 -4 1.4 7.4 9.8 12.9 17.1 16 12.6 5.6 -2.9 -6.4 1999 -4 -1 2.1 6 10.1 11.5 15.1 14.4 11.1 2.2 -1.1 -8.6 2000 -8.9 -7.2 0.5 6.9 11.6 14.5 16.9 15.1 11.3 4.7 -0.5 -5.7 2001 -9.4 -2.6 1.1 7.7 11.8 14.9 19 16.7 9.8 4.4 -2.1 -5 2002 -9.3 -3.9 1.3 6.5 9.4 13.5 16.6 17.8 9.6 4.4 -1.7 -2.8 2003 -6.6 -2.6 1.6 6.3 7.6 13 17.5 16.4 11.4 2.4 -1.9 -3.9 2004 -4.2 -3.5 3.1 7.1 9.7 13.8 15.4 14.4 11 3.6 -1.8 -3.5 2005 -8.2 -4.9 2.4 4.3 8.1 11.9 14.6 14.7 10.4 2.4 -3.6 -8.9 2006 -8.7 -0.6 1.5 4.8 10.8 11.3 15.5 15.7 9.7 3.9 -2 -4.4 2007 -9.1 -2.1 -0.3 6.9 10.3 12.5 15.2 15.2 10.8 1.6 -4.7 -7.9 2008 -7.3 -5.9 1.7 6 9.4 15.3 15.3 14.8 8.5 3.8 -3.2 -7 2009 -8.5 -5.7 0.2 6.1 7.8 10.9 13.7 13.5 8.3 2.6 -3.8 -4.5 2010 -6.9 -2.3 3.3 7.3 9.1 11.2 13.6 15.1 10.3 2.6 -4.2 -9.4 2011 -7.4 -3.6 1 5.7 9.8 13.9 15 14.8 11.3 4.1 -1.2 0

Source: Muhammad Ali (Meteorological Assistant) Pak. Met. Deptt Headquarter Campus office University road Karachi

317

Appendices

Appendix 1.9 Mean monthly minimum Temperature (Cº) of District Astore

Year Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec 1980 -6.8 -3.8 -1 4.5 8.1 12.3 15.5 14.7 10 5.7 0.7 -2.9 1981 -6.1 -4 -1.1 3 9.2 10.7 15.5 15 9.4 3.9 -1.2 -6 1982 -5.1 -6.3 -0.7 4.4 7.6 11.3 14.7 16.5 9.4 4.8 -1.9 -6.9 1983 -9.3 -9.2 -3.8 3.2 8.5 9.6 13.5 16.2 12.7 4.4 -0.8 -4.6 1984 -7.7 -5.9 0.9 3.7 7 12.1 13.8 16.1 8.8 4.2 -0.4 -4.2 1985 -8 -6.1 1.8 5.5 7.2 10.1 15.2 14.9 10.5 4.7 -0.4 -5.4 1986 -9.7 -6.6 -2.5 2.9 6.5 10.1 14.1 13.1 9.2 4.6 -0.4 -7.1 1987 -10.4 -4 0.2 3.7 6.2 8.6 11.1 13.8 10.3 2.9 -0.9 -3.1 1988 -4.9 -5.1 -1.4 4.1 8 10.2 14.8 13 9.9 3.4 -0.4 -3.4 1989 -9.5 -6.4 -0.8 1.4 5 9.2 11.6 11.6 8.7 3.1 -2 -3.6 1990 -4.1 -6.8 -4 1.9 8.3 10.8 13.6 14.3 10.8 3.2 -1 -3.4 1991 -11.2 -5.6 -0.9 3.3 6.6 10.5 13.2 13.7 11.5 3.9 -0.6 -2 1992 -5.3 -9 -1.7 3 6.7 10.4 14 14.2 9.5 4.5 0.3 -2.5 1993 -8.2 -2 -2.1 4.7 8.7 10.6 12.8 12.8 11.2 4 -0.4 -3 1994 -5.8 -4.9 0 2.2 8.1 11.2 16.6 16.5 9.6 4.3 0.1 -6.7 1995 -12.1 -6.8 -1.2 3.4 8 10.8 14.4 14.6 10.1 4.9 -0.4 -5.9 1996 -9.5 -4.5 0.3 3.1 5.8 11 12.8 14.5 11.4 3.7 -0.9 -5.9 1997 -5.9 -5.4 -0.4 4.9 7.2 11.5 15.4 13.1 10.7 5 -0.3 -3.8 1998 -6.7 -5.1 -1 5.2 8.3 10.4 14.8 14.9 11.5 6.5 0.9 -2.7 1999 -4.8 -2.3 -0.4 4.2 8.4 11.1 14.6 13.9 12 4.8 0.6 -4.2 2000 -7 -7.5 -1.1 5.2 7.5 12.4 15.2 13.8 10.7 5.8 1.1 -4.4 2001 -6.3 -2.7 -0.3 5.9 10.5 13.3 16 14.6 8.9 5.6 -1.7 -4.3 2002 -10.5 -5.2 -1 3.9 8.7 11.5 13.1 14.9 8.8 5.5 0.8 -3.6 2003 -5 -4.1 -1.5 4.5 5.8 11.6 15 13.6 10 3.9 -0.5 -5.9 2004 -5.8 -5.7 1.9 4.8 7.8 11.2 12.9 13.1 9.7 4.1 0 -1.5 2005 -7.8 -4.2 1 3.1 7.2 11.2 14.2 13.8 11.4 4.7 -1.1 -7 2006 -6.4 -0.4 0.4 3.5 10.7 11.3 15.9 15 10 6 1.1 -4.6 2007 -6.9 -1 -0.3 6.8 10.3 13.1 14.1 14.6 11.1 4.1 0.9 -4.1 2008 -7.6 -7.2 1.8 5.6 9.7 15.2 15.3 14.3 9.2 6.1 1.2 -4.8 2009 -6.3 -4.1 -0.4 3.8 7.8 10 13.3 14.4 9.4 4.1 -1.1 -4.2 2010 -4.3 -3.7 1.5 5.2 7.6 10.2 13.2 14.6 10.2 5.2 0.3 -5 2011 -8.9 -5.4 -1.8 2.6 0 9 12 13 9 4.3 -1.2 -4

Source: Muhammad Ali (Meteorological Assistant) Pak. Met. Deptt Headquarter Campus office University road Karachi

318

Appendices

Appendix 3.1 Ground flora, frequency, relative frequency and presenc in number of Quadrate of 40 forested stands of stduy area.

Std.1 Basho-A F R.F PNQ Astragalus zanskarensis 4 0.97 1 Berberis orthobotrys 11 2.91 3 Bergenia stracheyi 22 5.82 6 Cicer songaricum 11 2.91 3 Ephedra gerardiana 4 0.97 1 Epilobium angustifolium 7 1.94 2 Fragaria nubicola 14 3.88 4 Geranium partens 14 3.88 4 Hippophae rhamnoides 4 0.97 1 Juniperus excelsa 7 1.94 2 Juniperus communis 11 2.91 3 Leontopodium himalayanum 36 9.7 10 Oxyria digyna 11 2.91 3 Pinus wallichiana 21 5.82 6 Ribes orientale 25 6.79 7 Rosa webbiana 36 9.7 10 Rubus irritans 4 0.97 1 Tamarix indica 7 1.94 2 Taraxacum baltistanicum 21 5.82 6 Thymus linearis 32 8.73 9 Tragopogon orientalis 29 7.76 8 Trifolium partense 14 3.88 4 Trifolium repens 11 2.91 3 Urtica dioica 14 3.88 4 Std.2 Basho-B Aquilegia moorcroftiana 7 1.87 2 Astragalus rhizanthus 11 2.8 3 Astragalus zanskarensis 7 1.87 2 Berberis lycium 11 2.8 3 Berberis orthobotrys 4 0.93 1 Bergenia stracheyi 14 3.74 4 Cerastium alpinum 11 2.8 3 Cicer songaricum 21 5.6 6 Cotoneaster integerrima 25 6.54 7 Ephedra gerardiana 4 0.93 1 Epilobium angustifolium 21 5.6 6 Fragaria nubicola 11 2.8 3 Geranium wallichianum 7 1.87 2 Geranium partens 4 0.93 1 Hieracium lanceolatum 4 0.93 1 Juniperus excelsa 4 0.93 1 Juniperus communis 29 7.47 8 Leontopodium himalayanum 57 14.95 16 7 Pinus wallichiana 25 6.54 Rumex dentatus 11 2.8 3

319

Appendices

Appendix 3.1 Cont.

Tanactum fruticulosum 14 3.7 4 Thymus linearis 46 12.15 13 Tragopogon orientalis 25 6.54 7 Trifolium partense 11 2.8 3 Std.3 Gasing-A Aquilegia moorcroftiana 4 0.75 1 Astragalus rhizanthus 29 6.06 8 Astragalus zanskarensis 11 2.27 3 Berberis orthobotrys 7 1.51 2 Bergenia stracheyi 7 1.51 2 Bistorta affinis 11 2.27 3 Cicer songaricum 21 4.54 6 Cotoneaster integerrima 7 1.51 2 Dictyolimon macrorrhabdos 7 1.51 2 Ephedra gerardiana 7 1.51 2 Erigeron multicaulis 14 3.03 4 Fragaria nubicola 18 3.78 5 Geranium partens 4 0.75 1 Hieracium lanceolatum 21 4.45 6 Hippophae rhamnoides 7 1.51 2 Impatiens balfourii 7 1.51 2 Inula rhizocephala 7 1.51 2 Juniperus communis 7 1.51 2 Leontopodium himalayanum 14 3.03 4 Lapidium lantifolium 11 2.27 3 Nepeta discolor 7 1.51 2 Oxyria digyna 11 2.27 3 Pinus wallichiana 39 7.57 10 Potentilla anserina 61 12.87 17 Ribes orientale 11 2.27 3 Rosa webbiana 18 3.79 5 Rubus irritans 7 1.51 2 Spiraea canescens 11 2.27 3 Tamarix indica 7 1.51 2 Tanactum artemisioides 18 3.79 5 Taraxacum baltistanicum 14 3.03 4 Thymus linearis 50 10.6 14 Std.4 Gasing-B Artemisia brevifolium 14 4.35 4 Astragalus zanskarensis 29 8.7 8 Berberis orthobotrys 32 9.78 9 Betula utilis 7 2.17 2 Bistorta affinis 21 6.52 6 Fragaria nubicola 21 6.52 6 Hieracium lanceolatum 7 2.17 2 Juniperus communis 7 2.17 2

Leontopodium himalayanum 18 5.43 5

320

Appendices

Appendix 3.1 Cont. Oxyria digyna 39 11.96 11 Pinus wallichiana 18 5.43 5 Potentilla anserina 25 7.6 7 Ribes orientale 4 1.08 1 Rosa webbiana 25 7.6 7 Spiraea canescens 4 1.08 1 Tanactum artemisioides 36 10.87 10 Thymus linearis 21 6.52 6 Std.5 Gasing-C Artemisia obsinthium 18 5.43 5 Astragalus zanskarensis 7 2.17 2 Betula utilis 4 1.08 1 Bergenia stracheyi 18 5.43 5 Cotoneaster integerrima 7 2.17 2 Daphne oleoides 11 3.26 3 Dictyolimon macrorrhabdos 11 3.26 3 Juniperus excelsa 25 7.6 7 Juniperus communis 4 1.08 1 Leontopodium himalayanum 7 2.17 2 Nepeta discolor 18 5.43 5 Pinus wallichiana 4 1.08 1 Potentilla anserina 39 11.95 11 Rosa webbiana 25 7.6 7 Spiraea canescens 18 5.43 5 Tanactum artemisioides 50 15.22 14 Taraxacum baltistanicum 11 3.26 3 Thymus linearis 54 16.26 15 Std.6 Hargosil-A Acantholimon lycopodioides 18 5.2 5 Anaphalis virgata 11 3.12 3 Anaphalis nepalensis 4 1.04 1 Astragalus zanskarensis 18 5.2 5 Cicer songaricum 7 2.08 2 Erigeron multicaulis 4 1.04 1 Geranium partens 14 4.16 4 Hieracium lanceolatum 25 7.29 7 Inula rhizocephala 7 2.08 2 Juniperus excelsa 7 2.08 2 Leontopodium himalayanum 25 7.29 7 Leonurus cardiaca 11 3.12 3 Myostis asiatica 7 2.08 2 Oxyria digyna 11 3.12 3 Pinus wallichiana 39 11.45 11 Potentilla anserina 43 12.5 12 Pseudomertensia echioides 11 3.12 3 Ribes orientale 4 1.04 1 Rosa webbiana 11 3.12 3 Rumex hastatus 7 2.08 2 7 2.08 2

321

Appendices

Appendix 3.1 Cont. Silene moorcroftiana Silene vulgaris 7 2.08 2 Tanactum artemisioides 39 11.45 11 Taraxacum baltistanicum 7 2.08 2 Std.7 Hargosil-B Acantholimon lycopodioides 18 4.72 5 Anaphalis virgata 43 11.32 12 Artemisia brevifolium 11 2.83 3 Astragalus rhizanthus 7 1.88 2 Astragalus zanskarensis 7 1.88 2 Cicer songaricum 11 2.83 3 Epilobium angustifolium 4 0.94 1 Geranium partens 11 2.83 3 Hieracium lanceolatum 11 2.83 3 Inula rhizocephala 7 1.88 2 Leontopodium himalayanum 25 6.6 7 Leonurus cardiaca 18 4.71 5 Oxyria digyna 14 3.77 4 Pinus wallichiana 39 10.37 11 Potentilla anserina 50 13.2 14 Pseudomertensia echioides 14 3.77 4 Rosa webbiana 4 0.94 1 Rumex hastatus 11 2.83 3 Silene moorcroftiana 4 0.94 1 Silene vulgaris 7 1.88 2 Tamarix indica 4 0.94 1 Tanactum artemisioides 36 9.4 10 Taraxacum baltistanicum 18 4.71 5 Trifolium repens 7 1.88 2 Std.8 Memosh-A Anaphalis virgata 14 4.65 4 Astragalus zanskarensis 32 10.46 9 Hieracium lanceolatum 32 10.46 9 Impatiens balfourii 4 1.16 1 Leontopodium himalayanum 36 11.62 10 Myostis asiatica 7 2.32 2 Oxyria digyna 21 6.98 6 Pinus wallichiana 43 13.95 12 Potentilla anserina 21 6.98 6 Ribes orientale 11 3.49 3 Rosa webbiana 4 1.16 1 Silene moorcroftiana 21 6.98 6 Silene vulgaris 7 2.32 2 Tanactum artemisioides 18 5.81 5 Taraxacum baltistanicum 18 5.81 5 Trifolium repens 11 3.49 3 Urtica dioica 7 2.32 2 Std.9 Memosh-B Anaphalis nepalensis 11 2.75 3

322

Appendices

Appendix 3.1 Cont. Anaphalis virgata 25 6.42 7 Astragalus zanskarensis 21 5.5 6 Berberis orthobotrys 7 1.83 2 Dictyolimon macrorrhabdos 11 2.75 3 Erigeron multicaulis 4 0.91 1 Hieracium lanceolatum 25 6.42 7 Impatiens balfourii 14 3.66 4 Inula rhizocephala 4 0.91 1 Leontopodium himalayanum 36 9.17 10 Oxyria digyna 11 2.75 3 Pinus wallichiana 39 10.09 11 Potentilla anserine 25 6.42 7 Ribes orientale 18 4.58 5 Rosa webbiana 14 3.66 4 Silene vulgaris 4 0.91 1 Silene moorcroftiana 25 6.42 7 Tanactum artemisioides 36 9.17 10 Taraxacum baltistanicum 39 10.09 11 Trifolium repens 21 5.5 6 Std.10 Memosh-C Astragalus zanskarensis 29 8.33 8 Berberis orthobotrys 29 8.33 8 Bistorta affinis 21 6.25 6 Hieracium lanceolatum 7 2.08 2 Juniperus communis 7 2.08 2 Leontopodium himalayanum 18 5.2 5 Myostis asiatica 4 1.04 1 Oxyria digyna 39 11.45 11 Pinus wallichiana 39 11.45 11 Potentilla anserina 25 7.29 7 Pseudomertensia echioides 18 5.2 5 Ribes orientale 4 1.04 1 Rosa webbiana 25 7.29 7 Spiraea canescens 4 1.04 1 Tanactum artemisioides 36 10.41 10 Thymus linearis 21 6.25 6 Urtica dioica 18 5.2 5 Std.11 Ganj-A Astragalus zanskarensis 35 5.79 7 Bergenia stracheyi 20 3.31 4 Berberis orthobotrys 30 4.97 6 Bistorta affinis 35 5.79 7 Cecer songricum 40 6.62 8 Ephedra gerardiana 14 2.32 3 Geranium partens L., 50 8.28 10 Juniperus communis 15 2.48 3 Leontopodium leontopodinum 40 6.62 8 Nepeta discolor 10 1.66 2 Potentilla anserina 30 4.97 6

323

Appendices

Appendix 3.1 Cont.

Rheum tebaticum 15 2.48 3 Ribes aplester 20 3.31 4 Ribes himalyansis 25 4.14 5 Taraxacum baltistanicum 35 5.79 7 Thymus serpyllum 60 9.93 12 Viola pilosa 70 11.6 14 Spiraea canescens 20 3.31 4 Rosa Webbiana 25 4.14 5 Colutea nepalensis 15 2.48 3 Std.12 Ganj-B Bergenia stracheyi 45 12.67606 9 Bistorta affinis 35 9.859155 7

Colutea nepalensis 15 4.225352 3 Fragaria nobicola 30 8.450704 6 Juniperus communis 25 7.042254 5 Leontopodium leontopodinum 30 8.450704 6 Myostis asiatica 40 11.26761 8 Nepeta discolor Role ex Bth. 20 5.633803 4 Rehium tebaticum 10 2.816901 2 Ribes aplester 15 4.225352 3 Ribes orntale 20 5.633803 4 Rosa webbiana 10 2.816901 2 Taraxacum baltistanicum 5 1.408451 1 Thymus serpyllum 25 7.042254 5 Viola ruperstirs 30 8.450704 6 Std.13 Ganj-C Astragalus zanskarensis 20 5.405405 4 Berberis orthobotrys 25 6.756757 5 Bergenia stracheyi 60 16.21622 12 Bistorta affinis 45 12.16216 9 Cecer songricum 10 2.702703 2 Ephedra intermedia 10 2.702703 2 Geranium partens 25 6.756757 5 Juniperus communis 15 4.054054 3 Leontopodium leontopodinum 35 9.459459 7 Nepeta discolor. 10 2.702703 2 Potentilla anserina 25 6.756757 5 Rheum tebaticum 20 5.405405 4 Ribes aplester 10 2.702703 2 Taraxacum baltistanicum 10 2.702703 2 Thymus serpyllum 30 8.108108 6 Viola ruperstirs 20 5.405405 4

324

Appendices

Std.14 Ganj-D Appendix 3.1 Cont. Anaphlis neplensis 20 4.878049 4 Astragalus zanskarensis 30 7.317073 6 Bergenia stracheyi 50 12.19512 10 Bistorta affinis 45 10.97561 9 Colutea nepalensis 20 4.878049 4 Geranium partens L., 35 8.536585 7 Leontopodium leontopodinum 40 9.756098 8 Mentha royleana 20 4.878049 4 Nepeta discolor 15 3.658537 3 Potentilla anserina 25 6.097561 5 Rheum tebaticum 15 3.658537 3 Rosa webbiana 20 4.878049 4 Taraxacum affcianle 20 4.878049 4 Thymus serpyllum 25 6.097561 5 Trifolium partense 30 7.317073 6 Std.15 Kargah-A Berberis lyceum 10 1.960784 2 Cicer songricum 40 7.843137 8 Daphne oleoides 10 1.960784 2 Fragaria nubicola 50 9.803922 10 Geranium partens 15 2.941176 3 Hippophae rhamnoides 5 0.980392 1 Impatiens balfourii 15 2.941176 3 Juniperus communis 20 3.921569 4 Mentha longifolia 20 3.921569 4 Oxyria digyna 15 2.941176 3 Picea smithiana 20 3.921569 4 Podophyllum hexendrum 15 2.941176 3 Potentilla anserina 30 5.882353 6 Rehium tebaticum 15 2.941176 3 Ribes alpester 20 3.921569 4 Ribes himalyansis 10 1.960784 2 Rosa webbiana 15 2.941176 3 Rubus irritans 25 4.901961 5 Rumex hastatus 35 6.862745 7 Silene vulgaris 35 6.862745 7 Taraxacum ssp 25 4.901961 5 Urtica dioica 45 8.823529 9 Verbscum thapsus 20 3.921569 4 Std.16 Kargah-B Astragalus zanskarensis 30 8 6 Fragaria nubicola 55 14.66667 11 Geranium partens 35 9.333333 7 Juniperus communis 20 5.333333 4 Mentha longifolia 25 6.666667 5 Oxyria digyna 30 8 6 Picea smithiana 15 4 3 Rosa webbiana 15 4 3

325

Appendices

Appendix 3.1 Cont.

Rubus irritans 15 4 3 Rumex dentatus 30 8 6 Rumex hastatus 15 4 3 Suadum quadrifidum 15 4 3 Taraxacum ssp 15 4 3 Trifolium partense 25 6.666667 5 Urtica dioica 35 9.333333 7 Std.17 Kargah-C Acantholimon lycopodioides 15 3.296703 3 Anaphalis virgata 15 3.296703 3 Anaphlis neplensis 20 4.395604 4 Astragalus zanskarensis 25 5.494505 5 Bergenia stracheyi 40 8.791209 8 Bistorta affinis 45 9.89011 9 Ephedra tebatica 10 2.197802 2 Fragaria nubicola 20 4.395604 4 Geranium partens 25 5.494505 5 Inula rhizocephala 15 3.296703 3 Leontopodium leontopodinum 50 10.98901 10 Pinus wallchiana 20 4.395604 4 Ribes alpster 15 3.296703 3 Ribes orientale 10 2.197802 2 Spiraea canescens 10 2.197802 2 Taraxacum ssp 10 2.197802 2 Thymus serpyllum 65 14.28571 13 Verbscum thapsus 5 1.098901 1 Viola ruperstirs 40 8.791209 8 Std.18 Jutial-A Anaphlis verigata 20 4.444444 4 Astragals gilgitensis 15 3.333333 3 Astragals rizanthus 20 4.444444 4 Berginia strachye 55 12.22222 11 Bistorta affines 30 6.666667 6 Corydalis moorcroftiana 15 3.333333 3 Ephedra tebatica 20 4.444444 4 Fragaria nubicola 20 4.444444 4 Geranium partens 20 4.444444 4 Leontopodium leontopodinum 25 5.555556 5 Picea smithiana 10 2.222222 2 Podophyllum hexendrum 5 1.111111 1 Potentilla anserina 10 2.222222 2 Rehium tebaticum 5 1.111111 1 Ribes alpester 15 3.333333 3 Rosa webbiana 5 1.111111 1 Spiraea canescens 5 1.111111 1 Suadum quadrifidum 15 3.333333 3 Taraxacum ssp 15 3.333333 3

326

Appendices

Appendix 3.1 Cont.

Thymus serpyllum 50 11.11111 10 Trifolium partence 5 1.111111 1 Urtica dioica 30 6.666667 6 Verbscum thapsus 5 1.111111 1 Viola ruperstirs 35 7.777778 7 Std.19 Jutial-B Anaphlis neplensis 25 6.097561 5 Astragals rizanthus 5 1.219512 1 Berginia strachye 45 10.97561 9 Bistorta affines 35 8.536585 7 Corydalis moocroftina 10 2.439024 2 Ephedra tebatica 20 4.878049 4 Fragaria nubicola 35 8.536585 7 Geranium partens 40 9.756098 8 Juniperus communis 10 2.439024 2 Potentilla anserina 20 4.878049 4 Rehium tebaticum 15 3.658537 3 Ribes alpester 5 1.219512 1 Rosa webbiana 10 2.439024 2 Spiraea canescens 5 1.219512 1 Taraxacum ssp 5 1.219512 1 Thymus serpyllum 30 7.317073 6 Trifolium partence 15 3.658537 3 Viola ruperstirs 40 9.756098 8 Std.20 Naltar-A Acantholimon lycopodioides 10 2.702703 2 Astragals rizanthus 25 6.756757 5 Cicer songricon 25 6.756757 5 Fragaria nubicola 65 17.56757 13 Geranium partens 45 12.16216 9 Juniperus communis 15 4.054054 3 Picea smithiana 25 6.756757 5 Picea smithiana 10 2.702703 2 Ribes alpester 20 5.405405 4 Rosa webbiana 10 2.702703 2 Rumex dentatus 10 2.702703 2 Taraxacum ssp 15 4.054054 3 Thymus serpyllum 20 5.405405 4 Trifolium partence 10 2.702703 2 Urtica dioca 50 13.51351 10 Verbscum thapsus 15 4.054054 3 Std.21 Naltar-B Anaphlis neplensis 20 6.060606 4 Anaphlis vergata 5 1.515152 1 Berginia strachye 30 9.090909 6 Betula utilis 15 4.545455 3 Bistorta affines 50 15.15152 10

327

Appendices

Appendix 3.1 Cont. Fragaria nubicola 30 9.090909 6 Geranium partens 20 6.060606 4 Leontopodium leontopodinum 30 9.090909 6 Potentilla anserina 20 6.060606 4 Rehium tebaticum 10 3.030303 2 Taraxacum ssp 15 4.545455 3 Thymus serpyllum 25 7.575758 5 Urtica dioica 30 9.090909 6 Viola ruperstirs 30 9.090909 6 Std.22 Naltar-C Astragalus zanskarnsis 30 12 6 Fragaria nubicola 40 16 8 Geranium partens 35 14 7 Pinus wallichiana 15 6 3 Rumex dentatus 30 12 6 Rumex histatus 15 6 3 Taraxacum ssp 25 10 5 Trifolium rupenes 20 8 4 Urtica dioica 40 16 8 Std.23 Naltar-D Betula utilis 20 7.017544 4 Fragaria nubicola 40 14.03509 8 Geranium partens 35 12.2807 7 Impetance bulforii 15 5.263158 3 Inula rizocephla 35 12.2807 7 Ribes alpester 15 5.263158 3 Rosa webbiana 10 3.508772 2 Rumex dentatus 10 3.508772 2 Rumex histatus 20 7.017544 4 Taraxacum ssp 30 10.52632 6 Trifolium partence 10 3.508772 2 Urtica dioca 45 15.78947 9 Std.24 Danyore Acantholimon lycopodioides 15 4.615385 3 Anaphlis neplensis 45 13.84615 9 Artimsia bervifolia 50 15.38462 10 Bistorta affines 45 13.84615 9 Geranium partens 20 6.153846 4 Juniperus communis 5 1.538462 1 Juniperus macropoda 10 3.076923 2 Leontopodium leontopodinum 45 13.84615 9 Leonurus cardiaca 10 3.076923 2 Potentilla anserina 25 7.692308 5 Ribes alpester 10 3.076923 2 Rosa webbiana 10 3.076923 2 Rubus irritance 25 7.692308 5 Urtica dioca 10 3.076923 2 Std.25 Joglotgah-A 15 5 3

328

Appendices

Appendix 3.1 Cont. Artimsia bervifolia Artimsia obsinthiam 15 5 3 Berberis lycium Royle 10 3.333333 2 Daphne oleoides 30 10 6 Fragraria nubicola 35 11.66667 7 Picea smithiana 15 5 3 Ribes alpester 25 8.333333 5 Ribes orentale 5 1.666667 1 Rosa webbiana 15 5 3 Rumex dentatus 15 5 3 Rumex histatus 25 8.333333 5 Spiraea canescens 5 1.666667 1 Traxcum ssp 30 10 6 Trifolium rpense 20 6.666667 4 Urtica dioica 40 13.33333 8 Std.26 Joglotgah-B Acantholimon lycopodioides 10 2.298851 2 Anaphlis neplensis 30 6.896552 6 Artimisia brvifolia 45 10.34483 9 Berginia stracheyi 45 10.34483 9 Betula Utilis 20 4.597701 4 Bistorta affines 45 10.34483 9 Hippophae rhamnoides 5 1.149425 1 Inula rhizocephala 10 2.298851 2 Juniperus communis 5 1.149425 1 Leontopodium leontopodinum 40 9.195402 8 Potentilla anserina 30 6.896552 6 Rehium tebaticum 15 3.448276 3 Ribes alpester 10 2.298851 2 Ribes orentale 10 2.298851 2 Rosa webbiana 15 3.448276 3 Thymus serpyllum 40 9.195402 8 Urtica dioica 20 4.597701 4 Viola ruperstirs 40 9.195402 8 Std.27 Rana-A Anaphalis neplensis 30 6.122449 6 Bergenia stracheyi 55 11.22449 11 Betula utilis 25 5.102041 5 Bistirta affinis 65 13.26531 13 Fragaria nubicola 25 5.102041 5 Geranium partens 30 6.122449 6 Inula rhizocephla 10 2.040816 2 Juniperus communis 10 2.040816 2 Leontopodium leontopodinum 55 11.22449 11 Oxyria digyna 20 4.081633 4 Potentilla anserine 50 10.20408 10 Rubus irritans 15 3.061224 3

Silene vulgaris 5 1.020408 1

329

Appendices

Appendix 3.1 Cont. Taraxacum ssp 15 3.061224 3 Thymus serpyllum 45 9.183673 9 Urtica dioica 5 1.020408 1 Viola ruperstirs 30 6.122449 6 Std.28 Rama-B Bergenia stracheyi 30 6.66667 6 Colutea neplensis 10 2.22222 2 Corydalis ssp 20 4.44444 4 Fragaria nubicola 55 12.2222 11 Geranium partens 25 5.55556 5 Juniperus communis 35 7.77778 7 Leontopodium leontopodinum 15 3.33333 3 Lonicera coerulea 30 6.66667 6 Nepeta disclar 45 10 9 Polygonum alpinum 40 8.88889 8 Rosa webbiana 35 7.77778 7 Solidago virgaure 40 8.88889 8 Taraxacum ssp 20 4.44444 4 Thalictrum alpinum 40 8.88889 8 Trifolium repense 10 2.22222 2 Std.29 Rama-C Fragaria nubicola 40 13.7931 8 Geranium partens 50 17.2414 10 Inula rhizocephla 25 8.62069 5 Juniperus communis 40 13.7931 8 Leontopodium leontopodinnum 20 6.89655 4 Taraxacum ssp 25 8.62069 5 Tifolium partanse 40 13.7931 8 Urtica dioica 35 12.069 7 Viola ruperstirs 15 5.17241 3 Std.30 Rama-D Fragaria nubicola 40 11.594 8 Geranium partens 45 13.043 9 Juniperus communis 25 7.2464 5 Leontopodium leontopodinum 30 8.6957 6 Myostis arvenses 30 8.6957 6 Silene vulgaris 25 7.2464 5 Taraxacum ssp 20 5.7971 4 Thymus serpyllum 40 11.594 8 Trifolium repense 40 11.594 8 Viola ruperstirs 50 14.493 10 Std.31 Mushken-A Artimsia bravifolia 20 6.060606 4 Fragaria nubicola 30 9.090909 6 Geranium wallchianum 20 6.060606 4 Hieracium lanceolatum 15 4.545455 3 Impatiens balfourii 30 9.090909 6 Rebis orentale 20 6.060606 4 Rosa webbiana 20 6.060606 4

330

Appendices

Appendix 3.1 Cont.

Rubus irritanse 40 12.12121 8 Rumex histatus 20 6.060606 4 Silene moorcroftiana 25 7.575758 5 Spiraea canescens 10 3.030303 2 Taraxacum ssp 20 6.060606 4 Trifolium repens 15 4.545455 3 Urtica dioica 45 13.63636 9 Std.32 Mushken-B Colutea neplensis 10 2.8169 2 Fragaria nubicola 45 12.6761 9 Geranium wallchianum 45 12.6761 9 Lonicera coerulea 10 2.8169 2 Potentilla anserine 20 5.6338 4 Rebis alpester 25 7.04225 5 Rosa webbiana 25 7.04225 5 Rubus irritanse 35 9.85915 7 Rumex histatus 15 4.22535 3 Taraxacum ssp 10 2.8169 2 Trifolium repens 35 9.85915 7 Urtica dioica 35 9.85915 7 Viola ruperstirs 45 12.6761 9 Std.33 Mushken-C Artimsia bravifolia 25 9.434 5 Fragaria nubicola 35 13.208 7 Geranium wallchianum 40 15.094 8 Leontopodium leontopodinum 10 3.7736 2 Rebis orentale 30 11.321 6 Rosa webbiana 25 9.434 5 Thymus serpyllum 40 15.094 8 Trifolium repens 20 7.5472 4 Viola ruperstirs 40 15.094 8 Std.34 Mushken-D Artimsia brvifolia 25 7.1429 5 Anaphlis neplensis 35 10 7 Astragalus rhizanthus 35 10 7 Geranium partens 10 2.8571 2 Leontopodium leontopodinum 60 17.143 12 Potentilla anserine 30 8.5714 6 Ribes orientale 20 5.7143 4 Rosa webbiana 30 8.5714 6 Rubus irritans 35 10 7 Lonicera coerula 35 10 7 Tanactum artemisioides 25 7.1429 5 Thymus serpyllum 10 2.8571 2 Std.35 Mushken-E Artimsia brvifolia 30 9.0909 6 Aster sp 15 4.5455 3 Betula utilis 15 4.5455 3

331

Appendices

Appendix 3.1 Cont.

Fragaria nubicola 40 12.121 8 Geranium wallchianum 40 12.121 8 Lonicera coerulea 45 13.636 9 Rosa webbiana 35 10.606 7 Tanactum artemisioides 40 12.121 8 Thymus serpyllum 60 18.182 12 Urtica dioica 10 3.0303 2 Std.36 Dashken Artimsia bervofolia 15 3.61446 3 Anaphalis virgata 15 3.61446 3 Dalphnium brononium 10 2.40964 2 Frgaria nubicola 40 9.63855 8 Geranium partanse 50 12.0482 10 Hapophae rhmnodeis 15 3.61446 3 Juniperus communis 5 1.20482 1 Leontopodium leontopodinum 25 6.0241 5 Mentha longifolia 20 4.81928 4 Oxyria digyna 55 13.253 11 Pinus wallichiana 30 7.22892 6 Potentilla anserine 30 7.22892 6 Ribes aplester 25 6.0241 5 Rosa webbiana 15 3.61446 3 Silene moorcroftiana 10 2.40964 2 Silene vulgaris 10 2.40964 2 Tanactum artemisioides 5 1.20482 1 Taraxacum ssp 25 6.0241 5 Trifolium repens 15 3.61446 3 Std.37 Gudaie Anaphalis nepalensis 20 5.333333 4 Artimisa bervifolia 15 4 3 Astragalus zanskarensis 20 5.333333 4 Bistorta affinis 35 9.333333 7 Frgaria nubicola 40 10.66667 8 Geranium partens 10 2.666667 2 Hapophae rhmnodies 10 2.666667 2 Juniperus communis 25 6.666667 5 Leontopodium leontopodinum 35 9.333333 7 Oxyria digyna 10 2.666667 2 Ribes orientale 15 4 3 Rosa webbiana 10 2.666667 2 Rumex hastatus 10 2.666667 2 Tanactum artemisioides 20 5.333333 4 Taraxacum ssp 15 4 3

332

Appendices

Appendix 3.1 Cont. Trifolium repnse 20 5.333333 4

Thymus serpyllum 40 10.66667 8 Urtica dioica 25 6.666667 5 Std.38 Chelim-A Artimisa bervifolia 15 2.752294 3 Astragalus zanskarensis 40 7.33945 8 Cecer songricum 50 9.174312 10 Dalphonium brononium 10 1.834862 2 Geranium partens 25 4.587156 5 Juniperus communis 30 5.504587 6 Leontopodium himalayanum 35 6.422018 7 Myostis ssp 15 2.752294 3 Nepeta discolor 15 2.752294 3 Polygnum alpinum 35 6.422018 7 Potentilla anserina 15 2.752294 3 Rheum webbianum 25 4.587156 5 Ribes orentale 10 1.834862 2 Rosa Webbiana 20 3.669725 4 Sewercua petiolata 15 2.752294 3 Solidago virgaurea 50 9.174312 10 Tanactum falconerii 20 3.669725 4 Thalictrum alpinum 45 8.256881 9 Thymus serpyllum 25 4.587156 5 Verbscum Thapsus 30 5.504587 6 Viola ruperstirs 20 3.669725 4 Std.39 Chelim-B Anaphlis neplensis 20 3.921569 4 Astragalus zanskarensis 15 2.941176 3 Bergenia srachyie 25 4.901961 5 Cecer songricum 30 5.882353 6 Geranium partens 25 4.901961 5 Juniperus communis 10 1.960784 2 Leontopodium leontopodinum 20 3.921569 4 Nepeta discolor 15 2.941176 3 Pinus wallchiana 30 5.882353 6 Polygonum alpinum 35 6.862745 7 Potentilla anserina 35 6.862745 7 Rheum webianum 15 2.941176 3 Sedum multicepes 10 1.960784 2 Solidago virgaurea 55 10.78431 11 Tanacetum artimisiodes 10 1.960784 2 Tanacetum falconerii 30 5.882353 6 Tarxacum ssp 15 2.941176 3 Thalictrum alpinum 50 9.803922 10 Thymus serpyllum 25 4.901961 5 Verbscum Thapsus 20 3.921569 4 Viola ruperstirs 20 3.921569 4 Std.40 Chelim-C

333

Appendices

Appendix 3.1 Cont. Acnotium heteroscphllum 20 3.84615 4 Anaphlis neplensis 30 5.76923 6 Astragalus zanskarensis 20 3.84615 4 Bergenia strachye 40 7.69231 8 Bistorta affinis 20 3.84615 4 Cicer songricum 35 6.73077 7 Delphonium bronnium 15 2.88462 3 Geranium partens 20 3.84615 4 Juniperus communis 15 2.88462 3 Leontopodium leontopodinum 30 5.76923 6 Myostis ssp 25 4.80769 5 Nepeta discolor 25 4.80769 5 Polygonum alpinum 15 2.88462 3 Potentilla anserina 35 6.73077 7 Rheum webianum 25 4.80769 5 Rosa webbiana 10 1.92308 2 Saxifrae fagellaris 40 7.69231 8 Sewertia petiolata 15 2.88462 3 Tanacetum artimisiodes 15 2.88462 3 Tarxacum ssp 15 2.88462 3 Thymus serpyllum 20 3.84615 4 Verbuscum Thapsus 25 4.80769 5 Viola ruperstirs 10 1.92308 2

Key to abbreviations: Std=Stand Number, F=Frequency, RF= Relative Frequency, PNQ=Presence in number of quadrate, Cont=Continue

334

Appendices

Appendix 5.1 Topographic and Edaphic properties of sampling sites Std Elev Slope pH WHC OM Cond Sali TDS St.1 3700 35 5.31 54 6.2 79 0 18.8 St.2 3550 30 5.31 51 6.2 77 0 18.6 St.3 3500 25 5.36 57 6.1 70 0 18.5 St.4 3400 20 5.35 48.2 7.8 40 0 17.6 St.5 3600 27 5.48 45.2 7.7 38 0 17.4 St.6 3586 20 5.33 58 6.274 74 0 19.1 St.7 3463 15 5.34 56 6.3 77 0 18.3 St.8 3463 35 5.33 55 6 74 0 18.5 St.9 3414 30 5.32 54 6 75 0 18.3 St.10 3477 23 5.35 59 6 73 0 18.4 St.11 3310 15 6.12 62 7.3 42 0.1 21.6 St.12 3472 35 5.19 44.5 7.8 33 0 17.4 St.13 3585 37 5.44 40.5 7.9 39 0 17.1 St.14 3374 35 5.43 42.8 7.8 31 0 17.8 St.15 3255 43 6.61 41 6.7 56 0 25.1 St.16 3427 33 6.96 42 6.4 57 0 26.3 St.17 3216 25 6.11 67 7.2 44 0.1 26.1 St.18 3250 40 7.08 42 6.2 55 0 25.2 St.19 3250 40 7.03 42 6.5 58 0 25.4 St.20 2930 36 7.02 43 6.3 57 0 25.2 St.21 3401 40 6.11 52 13.1 75 0.1 37.4 St.22 2893 5 6.11 66 7.4 45 0.2 26.6 St.23 2893 5 6.12 55 12.1 77 0.1 35.7 St.24 3736 45 6.95 25 3.9 44 0.2 19.1 St.25 3523 35 7.77 44 6.4 59 0 25.4 St.26 3055 5 6.23 51 12.3 74 0.1 33.1 St.27 3508 40 6.33 52 11.2 78 0.1 38.2 St.28 3464 45 6.95 25 3.9 44 0.2 19.1 St.29 3275 35 5.96 47.2 7.7 33 0 17.9 St.30 3016 15 6.01 63 7.5 44 0.1 24.9 St.31 2691 40 6.97 67 7.3 47 0.2 22.3 St.32 2719 35 5.43 46.2 7.9 36 0 17.3 St.33 2659 25 6.91 62 7.6 41 0.1 24.5 St.34 3078 40 6.02 61 7.4 46 0.1 23.7 St.35 2639 35 5.32 54 6.3 78 0 18.5 St.36 2616 45 6.89 43 6.1 55 0 26.7 St.37 3775 50 6.23 69 7.4 48 0.1 22.2 St.38 3458 45 6.11 66 7.2 44 0.1 21.5 St.39 3559 40 5.73 48.9 7.9 31 0 17.4 St.40 3596 20 5.74 54 10.3 20.8 0 8.7

Key to abbreviations: Elev=Elevation, St= Stand, CD=Conductivity, SL=SalinityMWH Maximum water holding capacity, OM=organic matter

335

Appendices

Appendix 5.1 Soil macro and micro chemicals properties of sampaling sites Std N P K Ca Mg S Co Mn Zn Fe St.1 117 85 242 175 114 111.2 0.70614.1 1.32 87.34 St.2 116.2 86 267 174 115 112.3 0.734 11.68 1.54 88.14 St.3 115.6 87 256 172 116 113.1 0.765 13.21 1.28 86.12 St.4 118.3 90.2 213 170 123 80.3 0.767 14.14 1.02 125 St.5 118.3 90.3 214 172 129 85.3 0.725 12.4 1.43 124 St.6 117.1 87.3 251 179 116 116.5 0.711 12.12 1.21 87.45 St.7 113.8 84.2 257 178 114 115.5 0.764 13.17 1.1 85.91 St.8 114.1 88 285 174 115 113.2 0.721 11.42 1.42 86.73 St.9 115.2 84.2 256 177 116 114.7 0.708 12.41 1.6 86.81 St.10 116.3 88.4 258 178 117 116.2 0.722 11.23 1.14 87.41 St.11 101.6 67.6 204 198 144 99.5 0.861 15.1 0.62 167 St.12 118.5 90.5 217 177 124 81.2 0.712 13.23 1.37 123 St.13 118.9 90.6 218 173 127 87.5 0.711 17.44 1.43 126 St.14 118.5 90.5 214 179 126 88.9 0.732 15.57 1.18 125 St.15 71.2 90.4 206 233 120 102 0.502 17.53 1.73 126.21 St.16 70.4 90.4 207 231 121 102 0.551 17.86 1.31 126.32 St.17 108.2 68.3 205 197 149 95.4 0.864 15.2 0.57 168 St.18 72.3 90.3 208 234 120 103 0.524 17.23 1.8 126.22 St.19 74.1 91.1 206 232 123 105 0.543 17.53 1.13 125.34 St.20 72.5 92.3 207 235 122 104 0.551 17.22 1.11 123.29 St.21 55.9 74.1 278 212 134 111.4 0.712 20.06 0.44 134.11 St.22 107.2 69.6 203 191 147 98.9 0.821 15.31 0.67 161 St.23 56.8 77.2 277 214 132 112.5 0.131 20.01 0.44 134.13 St.24 197.8 86.3 345 237 119 132 0.677 13.83 0.93 103.44 St.25 77.2 90.4 205 232 121 102 0.534 17.23 1.64 127.21 St.26 56.6 74.5 277 216 131 110.3 0.712 20.01 0.43 134.65 St.27 52.8 76.2 279 214 134 117.1 0.711 20.01 0.41 134.56 St.28 197.8 86.4 344 237 119 132 0.677 13.92 0.92 103.45 St.29 118.8 90.5 215 177 129 85.7 0.744 15.77 1.42 123 St.30 102.8 68.4 202 191 146 98.7 0.841 15.4 0.42 165 St.31 104.6 66.6 206 197 148 99.5 0.822 15.1 0.52 164 St.32 118.7 90.2 211 175 122 84.5 0.701 15.1 1.01 122 St.33 108.9 69.6 201 197 148 91.5 0.825 15.4 0.81 254 St.34 107.8 68.3 205 196 146 98.5 0.813 15.31 0.55 168 St.35 117.5 85.3 242 176 115 113.1 0.761 13.12 1.74 88.24 St.36 73.2 91.6 209 233 124 105 0.555 17.31 1.95 125.22 St.37 106.3 64.8 204 195 145 97.8 0.832 15.51 0.63 167 St.38 104.6 68.4 207 199 149 98.5 0.811 15.1 0.72 162 St.39 118.9 90.9 217 174 125 87.3 0.778 18.9 1.18 127 St.40 242.6 148.4 123 130 171 111 0.788 11.52 1.53 148

Key to abbreviations: N=Nitrogen, P=Phosphorus, K=Potassium, Ca=Calcium, Mg=Magnesium, S=Sulfur, Co=Cobalt, Mn= Manganese, Zn=Zinc, Fe=Iron

336

Appendices

Appendix 5.2 Frequency of ground flora species in different stands

Sp Species Name Cod St 1 St 2 St 3 St 4 St 5 St 6 St 7 St 8 St 9 St 10 Acantholimon lycopodioides ALY 0 0 0 0 0 17.86 17.86 0 0 0 Acnotium heterophyllum AHE 0 0 0 0 0 0 0 0 0 0 Anaphalis nepalensis ANE 0 0 0 0 0 17.85 0 0 10.71 0 Anaphalis virgata AVI 0 0 0 0 0 10.71 42.86 14.28 25 0 Aquilegia moorcroftian AMO 0 7.14 3.57 0 0 0 0 0 0 0 Artemisia brevifolium ABR 0 0 0 0 0 0 10.71 0 0 0 Artemisia obsinthium AOB 0 0 0 0 17.86 0 0 0 0 0 Aster sp ASP 0 0 0 0 0 0 0 0 0 0 Astragalus gilgitensis AGI 0 0 0 0 0 0 0 0 0 0 Astragalus rhizanthus ARH 0 10.71 28.57 0 0 0 7.14 0 0 0 Astragalus zanskarensis AZA 3.57 7.14 10.71 28.57 7.14 17.86 7.14 32.14 21.42 28.57 Berberis lycium BLY 0 10.71 0 0 0 0 0 0 0 10 Berberis orthobotrys BOR 10.71 3.57 7.14 32.14 0 0 0 0 7.14 28.57 Bergenia stracheyi BST 21.71 14.28 7.14 0 17.86 0 0 0 0 0 Betula utilis BUT 0 0 0 7.14 3.57 0 0 0 0 0 Bistorta affinis BAF 0 0 10.71 21.42 0 0 0 0 0 21.42 Cerastium alpinum CAL 0 10.71 0 0 0 0 0 0 0 0 Cicer songaricum CSO 10.71 21.42 21.42 0 0 7.14 10.71 0 0 0 Colutea nepalensis CNE 0 0 0 0 0 0 0 0 0 0 Corydalis moorcroftiana CMO 0 0 0 0 0 0 0 0 0 0 Cotoneaster integerrima CIN 0 25 7.14 0 7.14 0 0 0 0 0 Daphne oleoides DOL 0 0 0 0 10.71 0 0 0 0 0 Delphinium brunonianum DBR 0 0 0 0 0 0 0 0 0 0 Dictyolimon macrorrhabdos DMA 0 0 7.14 0 10.71 0 0 0 10.71 0 Ephedra gerardiana EGE 3.57 3.57 7.14 0 0 0 0 0 0 0 Ephedra tibetica ETI 0 0 0 0 0 0 0 0 0 0 Epilobium angustifolium EAN 7.14 21.42 0 0 0 0 3.57 0 0 0 Erigeron multicaulis EMU 0 0 14.48 0 0 3.57 0 0 3.57 0 Fragaria nubicola FNU 14.28 10.71 17.85 21.42 0 0 0 0 0 0 Geranium partens GPA 14.28 3.57 3.57 0 0 14.25 10.71 0 0 0 Geranium wallichianum GWA 0 0 0 0 0 0 0 0 0 0 Hieracium lanceolatum HLA 0 3.57 21.43 7.14 0 25 10.57 32.14 25 7.14 Hippophae rhamnoides HRH 3.57 0 7.14 0 0 0 0 0 0 0 Impatiens balfourii IBL 0 0 7.14 0 0 0 0 3.57 14.28 0 Inula rhizocephala IRH 0 0 7.14 0 0 7.14 7.14 0 3.57 0 Juniperus communis JCO 10.71 7.47 7.14 7.14 3.57 0 0 0 0 7.14 Juniperus excelsa JEX 7.14 3.57 0 0 25 7.14 0 0 0 0 Juniperus macropoda JMA 0 0 0 0 0 0 0 0 0 0 Leontopodium himalayanum LHI 35.71 57.14 14.28 17.86 7.14 25 25 35.71 35.71 17.85 Leontopodium leontopodium LLE 0 0 0 0 0 0 0 0 0 0

LCA 0 0 0 0 0 10.71 17.86 0 0 0

337

Appendices

Appendix 5.2 Cont.

Leonurus cardiaca Lonicera coerula LCO 0 0 0 0 0 0 0 0 0 0

Mentha longifolia MLO 0 0 0 0 0 0 0 0 0 0 Myostis asiatica MAS 0 0 0 0 0 7.14 0 7.14 0 3.57 Nepeta discolor NDI 0 0 7.14 0 17.86 0 0 0 0 0 Oxyria digyna ODI 10.71 0 10.71 39.28 0 10.71 14.28 21.42 10.71 39.28 Picea smithiana PSM 0 0 0 0 0 0 0 0 0 0 Pinus wallichiana PWA 21.42 25 38.71 17.86 3.57 39.28 39.28 42.85 39.28 39.28 Podophyllum hexandrum PHE 0 0 0 0 0 0 0 0 0 0 Polygonum alpinum PAL 0 0 0 0 0 0 0 0 0 0 Potentilla anserina PAN 0 0 60.71 25 39.28 42.86 50 21.42 25 25 Pseudomertensia echioides PEC 0 0 0 0 0 10.71 14.28 0 0 17.86 Rheum tibeticum RTI 0 0 0 0 0 0 0 0 0 0 Rheum webbianum RWE 0 0 0 0 0 0 0 0 0 0 Ribes alpestre RAL 0 0 0 0 0 0 0 0 0 0 Ribes himalensis RHI 0 0 0 0 0 0 0 0 0 0 Ribes orientale ROR 25 0 10.71 3.57 0 3.57 10.71 0 17.85 3.57 Rosa webbiana RWE 35.71 0 17.86 25 25 10.71 3.57 3.57 14.28 25 Rubus irritans RIR 3.57 0 7.14 0 0 0 0 0 0 0 Rumex dentatus RDE 0 10.71 0 0 0 0 0 0 0 0 Rumex hastatus RHA 0 0 0 0 0 7.14 10.71 0 0 0 Saxifraga flagellaris SFL 0 0 0 0 0 0 0 0 0 0 Sedum quadrifidum SQU 0 0 0 0 0 0 0 0 0 0 Silene moorcroftiana SMO 0 0 0 0 0 7.14 3.57 21.42 25 0 Silene vulgaris SVU 0 0 0 0 0 7.14 7.14 7.14 3.57 0 Soldigo virgaurea SVI 0 0 0 0 0 0 0 0 0 0 Spiraea canescens SCA 0 0 10.71 3.57 17.86 0 0 0 0 3.57 Swertia petiolata SPE 0 0 0 0 0 0 0 0 0 0 Tamarix indica TIN 7.14 0 7.14 0 0 0 3.57 0 0 0 Tanacetum falconerii TFA 0 0 0 0 0 0 0 0 0 0 Tanacetum artemisioides TAR 0 0 17.86 35.71 50 39.28 35.71 17.86 35.71 35.71 Tanacetum fruticulosum TFR 0 14.28 0 0 0 0 0 0 0 0 Taraxacum sp TSP 0 0 0 0 0 0 0 0 0 0 Taraxacum baltistanicum TBA 21.14 0 14.28 0 10.71 7.14 17.86 17.85 39.28 0 Thalictrum alpinum TAP 0 0 0 0 0 0 0 0 0 Thymus linearis TLI 32.14 46.42 50 21.42 53.57 0 0 0 0 21.42 Thymus serpyllum TSE 0 0 0 0 0 0 0 0 0 0 Tragopogon orientalis TOR 28.57 25 0 0 0 0 0 0 0 0 Trifolium partense TPA 14.28 10.71 0 0 0 0 0 0 0 0 Trifolium repens TRE 10.74 0 0 0 0 0 7.14 10.71 21.42 0 Urtica dioica UDI 14.28 0 0 0 0 0 0 7.14 0 17.86 Verbascum thapsus VTH 0 0 0 0 0 0 0 0 0 0 Viola rupestris VRU 0 0 0 0 0 0 0 0 0 0

338

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Appendix 5.2 Cont.

Sp Cod St 11 St 12 St 13 St 14 St 15 St 16 St 17 St 18 St 19 St 20 ALY 0 0 0 0 0 0 15 0 0 10 AHE 0 0 0 0 0 0 0 0 0 0 ANE 0 0 0 20 0 0 20 0 25 0 AVI 0 0 0 0 0 0 15 20 0 0 AMO 0 0 0 0 0 0 0 0 0 0 ABR 0 0 0 0 0 0 0 0 0 0 AOB 0 0 0 0 0 0 0 0 0 0 ASP 0 0 0 0 15 0 0 0 0 0 AGI 0 0 0 0 0 0 0 15 0 0 ARH 0 0 0 0 0 0 0 20 5 25 AZA 35 0 20 30 0 30 0 0 0 0 BLY 0 0 0 0 0 0 0 0 0 0 BOR 30 0 25 0 0 0 0 0 0 0 BST 20 45 60 50 0 0 40 55 45 0 BUT 0 0 0 0 0 0 0 0 0 0 BAF 35 35 45 45 0 0 45 30 35 0 CAL 0 0 0 0 0 0 0 0 0 0 CSO 40 0 10 0 40 0 0 0 0 25 CNE 0 0 0 20 0 0 0 0 0 0 CMO 0 0 0 0 0 0 15 10 0 CIN 0 0 0 0 0 0 0 0 0 0 DOL 0 0 0 0 10 0 0 0 0 0 DBR 0 0 0 0 0 0 0 0 0 0 DMA 0 0 0 0 0 0 0 0 0 0 EGE 14 0 0 0 0 0 0 0 0 0 ETI 0 0 10 0 0 0 10 20 20 0 EAN 0 0 0 0 0 0 0 0 0 0 EMU 0 0 0 0 0 0 0 0 0 0 FNU 0 30 0 0 50 55 20 20 35 65 GPA 50 0 25 35 15 35 25 20 40 45 GWA 0 0 0 0 0 0 0 0 0 0 HLA 0 0 0 0 0 0 0 0 0 0 HRH 0 0 0 0 5 0 0 0 0 0 IBL 0 0 0 0 15 0 0 0 0 0 IRH 0 0 0 0 0 0 15 0 0 0 JCO 15 25 15 0 20 20 0 0 10 15 JEX 0 0 0 0 0 0 0 0 0 0 JMA 0 0 0 0 0 0 0 0 0 0 LHI 0 0 0 0 0 0 0 0 0 0 LLE 40 30 35 40 0 0 50 25 25 0 LCA 0 0 0 0 0 0 0 0 0 0 LCO 0 0 0 0 0 0 0 0 0 0 MLO 0 0 0 20 20 25 0 0 0 0

339

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Appendix5.2Cont.

MAS 0 40 0 0 0 0 0 0 0 0 NDI 10 20 10 15 0 0 0 0 0 0 ODI 0 0 0 0 15 30 0 0 0 0 PSM 0 0 0 0 20 15 0 10 15 10 PWA 0 0 0 0 0 0 20 0 0 0 PHE 0 0 0 0 15 0 0 5 0 0 PAL 0 0 0 0 0 0 0 0 0 0 PAN 30 0 25 25 30 0 0 10 20 0 PEC 0 0 0 0 0 0 0 0 0 0 RTI 15 10 20 15 15 0 0 5 15 0 RWE 0 0 0 0 0 0 0 0 0 0 RAL 20 15 10 0 20 0 15 15 5 20 RHI 25 0 0 0 10 0 0 0 0 0 ROR 0 20 0 0 0 0 10 0 0 0 RWE 0 10 0 20 15 15 0 5 10 10 RIR 0 0 0 0 25 15 0 0 0 0 RDE 0 0 0 0 0 30 0 0 0 10 RHA 0 0 0 0 35 15 0 0 0 10 SFL 0 0 0 0 0 0 0 0 0 0 SQU 0 0 0 0 0 15 0 15 0 0 SMO 0 0 0 0 0 0 0 0 0 0 SVU 0 0 0 0 35 0 0 0 0 0 SVI 0 0 0 0 0 0 0 0 0 0 SCA 20 0 0 0 0 0 10 5 5 0 SPE 0 0 0 0 0 0 0 0 0 0 TIN 0 0 0 0 0 0 0 0 0 0 TFA 0 0 0 0 0 0 0 0 0 0 TAR 0 0 0 0 0 0 0 0 0 0 TFR 0 0 0 0 0 0 0 0 0 0 TSP 0 0 0 0 25 15 10 15 5 15 TBA 35 5 10 0 0 0 0 0 0 0 TAP 0 0 0 0 0 0 0 0 0 0 TLI 0 0 0 0 0 0 0 0 0 0 TSE 60 25 30 25 0 0 65 50 30 20 TOR 0 0 0 0 0 0 0 0 0 0 TPA 0 0 0 30 0 25 0 5 15 10 TRE 0 0 0 0 0 0 0 0 0 0 UDI 0 0 0 0 45 35 0 30 0 50 VTH 0 0 0 0 20 0 5 5 0 15 VRU 0 30 20 0 0 0 40 35 40 0

340

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Appendix 5.2 Cont.

Sp Cod St 21 St 22 St 23 St 24 St 25 St 26 St 27 St 28 St 29 St 30 ALY 0 0 0 15 0 10 0 0 0 0 AHE 0 0 0 0 0 0 0 0 0 0 ANE 20 0 0 45 0 30 30 0 0 0 AVI 5 0 0 0 0 0 0 0 0 0 AMO 0 0 0 0 0 0 0 0 0 0 ABR 0 0 0 50 15 0 0 0 0 0 AOB 0 0 0 0 0 0 0 0 0 0 ASP 0 0 0 0 0 0 0 0 0 0 AGI 0 0 0 0 0 0 0 0 0 0 ARH 0 0 0 0 0 0 0 0 0 0 AZA 0 30 0 0 0 0 0 0 0 0 BLY 0 0 0 0 10 0 0 0 0 0 BOR 0 0 0 0 0 0 0 0 0 0 BST 30 0 0 0 0 45 55 30 0 0 BUT 15 0 20 0 0 20 25 0 0 0 BAF 50 0 0 45 0 45 65 0 0 0 CAL 0 0 0 0 0 0 0 0 0 0 CSO 0 0 0 0 0 0 0 0 0 0 CNE 0 0 0 0 0 0 0 10 0 0 CMO 0 0 0 0 0 0 0 0 0 0 CIN 0 0 0 0 0 0 0 0 0 0 DOL 0 0 0 0 30 0 0 0 0 0 DBR 0 0 0 0 0 0 0 0 0 0 DMA 0 0 0 0 0 0 0 0 0 0 EGE 0 0 0 0 0 0 0 0 0 0 ETI 0 0 0 0 0 0 0 0 0 0 EAN 0 0 0 0 0 0 0 0 0 0 EMU 0 0 0 0 0 0 0 0 0 0 FNU 30 40 40 0 35 0 25 55 40 40 GPA 20 35 35 20 0 0 30 30 50 45 GWA 0 0 0 0 0 0 0 0 0 0 HLA 0 0 0 0 0 0 0 0 0 0 HRH 0 0 0 0 0 5 0 0 0 0 IBL 0 0 15 0 0 0 0 0 0 0 IRH 0 0 35 0 0 10 10 0 29 0 JCO 0 0 0 5 0 5 10 35 40 25 JEX 0 0 0 0 0 0 0 0 0 0 JMA 0 0 0 10 0 0 0 0 0 0 LHI 0 0 0 0 0 0 0 0 0 0 LLE 30 0 0 45 0 40 55 15 20 30 LCA 0 0 0 10 0 0 0 0 0 0 LCO 0 0 0 0 0 0 0 0 0 0 MLO 0 0 0 0 0 0 0 0 0 0

341

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Appendix5.2Cont.

MAS 0 0 0 0 0 0 0 0 0 30 NDI 0 0 0 0 0 0 0 45 0 0 ODI 0 0 0 0 0 0 20 0 0 0 PSM 0 0 0 0 15 0 0 0 0 0 PWA 0 15 0 0 0 0 0 0 0 0 PHE 0 0 0 0 0 0 0 0 0 0 PAL 0 0 0 0 0 0 0 40 0 0 PAN 20 0 0 25 0 30 50 0 0 0 PEC 0 0 0 0 0 0 0 0 0 0 RTI 10 0 0 0 0 15 0 0 0 0 RWE 0 0 0 0 0 0 0 0 0 0 RAL 0 0 15 10 25 26 0 0 0 0 RHI 0 0 0 0 0 0 0 0 0 0 ROR 0 0 0 0 5 10 0 0 0 0 RWE 0 0 10 10 15 15 0 35 0 0 RIR 0 0 0 25 0 0 15 0 0 0 RDE 0 30 101 0 15 0 0 0 0 0 RHA 0 15 30 0 25 0 0 0 0 0 SFL 0 0 0 0 0 0 0 0 0 0 SQU 0 0 0 0 0 0 0 0 0 0 SMO 0 0 0 0 0 0 0 0 0 0 SVU 0 0 0 0 0 0 5 0 0 25 SVI 0 0 0 0 0 0 0 40 0 0 SCA 0 0 0 0 5 0 0 0 0 0 SPE 0 0 0 0 0 0 0 0 0 0 TIN 0 0 0 0 0 0 0 0 0 0 TFA 0 0 0 0 0 0 0 0 0 0 TAR 0 0 0 0 0 0 0 0 0 0 TFR 0 0 0 0 0 0 0 0 0 0 TSP 15 25 30 0 30 0 15 20 25 20 TBA 0 0 0 0 0 0 0 0 0 0 TAP 0 0 0 0 0 0 0 40 0 0 TLI 0 0 0 0 0 0 0 0 0 0 TSE 25 0 0 0 0 40 45 0 0 40 TOR 0 0 0 0 0 0 0 0 0 0 TPA 0 0 45 0 0 0 0 0 40 0 TRE 0 20 0 0 20 0 0 10 0 40 UDI 30 40 45 10 40 0 5 0 35 0 VTH 0 0 0 0 0 0 0 0 0 0 VRU 30 0 0 0 0 40 30 0 15 50

342

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Appendix 5.2 Cont.

St Sp Cod 31 St 32 St 33 St 34 St 35 St 36 St 37 St 38 St 39 St 40 ALY 0 0 0 0 0 0 0 0 0 0 AHE 0 0 0 0 0 0 0 0 0 20 ANE 0 0 0 35 0 0 20 0 20 30 AVI 0 0 0 0 0 15 0 0 0 0 AMO 0 0 0 0 0 0 0 0 0 0 ABR 20 0 25 25 30 15 15 15 0 0 AOB 0 0 0 0 0 0 0 0 0 0 ASP 0 0 0 0 0 0 0 0 0 0 AGI 0 0 0 0 0 0 0 0 0 0 ARH 0 0 0 0 0 0 0 0 0 0 AZA 0 0 0 0 0 0 20 40 15 20 BLY 0 0 0 0 0 0 0 0 0 0 BOR 0 0 0 0 0 0 0 0 0 0 BST 0 0 0 0 0 0 0 0 25 40 BUT 0 0 0 0 15 0 0 0 0 0 BAF 0 0 0 0 0 0 35 0 0 20 CAL 0 0 0 0 0 0 0 0 0 0 CSO 0 0 0 0 0 0 0 50 30 35 CNE 0 0 0 0 0 0 0 0 0 0 CMO 0 0 0 0 0 0 0 0 0 0 CIN 0 0 0 0 0 0 0 0 0 0 DOL 0 0 0 0 0 0 0 0 0 0 DBR 0 0 0 0 0 10 0 0 0 15 DMA 0 0 0 0 0 0 0 0 0 0 EGE 0 0 0 0 0 0 0 0 0 0 ETI 0 0 0 0 0 0 0 0 0 0 EAN 0 0 0 0 0 0 0 0 0 0 EMU 0 0 0 0 0 0 0 0 0 0 FNU 30 45 35 0 40 40 40 0 0 0 GPA 0 0 0 10 0 50 10 25 25 20 GWA 20 45 40 0 40 0 0 0 0 0 HLA 15 0 0 0 0 0 0 0 0 0 HRH 0 0 0 0 0 15 10 0 0 0 IBL 30 0 0 0 0 0 0 0 0 0 IRH 0 0 0 0 0 0 0 0 0 0 JCO 0 0 0 0 0 5 10 30 10 15 JEX 0 0 0 0 0 0 0 0 0 0 JMA 0 0 0 0 0 0 0 0 0 0 LHI 0 0 0 0 0 0 0 0 0 0 LLE 0 0 10 60 0 25 35 35 20 30 LCA 0 0 0 0 0 0 0 0 0 0

343

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Appendix5.2Cont LCO 0 0 0 35 45 0 0 0 0 0 MLO 0 0 0 0 0 20 0 0 0 0 MAS 0 0 0 0 0 0 0 15 0 25 NDI 0 0 0 0 0 0 0 15 15 25 ODI 0 0 0 0 0 0 10 0 0 0 PSM 0 0 0 0 0 0 0 0 0 0 PWA 0 0 0 0 0 0 0 0 0 0 PHE 0 0 0 0 0 0 0 0 0 0 PAL 0 0 0 0 0 0 0 35 35 15 PAN 0 20 0 30 0 30 0 15 35 35 PEC 0 0 0 0 0 0 0 0 0 0 RTI 0 0 0 0 0 0 0 0 0 0 RWE 0 0 0 0 0 0 0 25 15 10 RAL 0 25 0 0 0 25 0 0 0 0 RHI 0 0 0 0 0 0 0 0 0 0 ROR 20 0 30 20 0 0 15 10 0 0 RWE 20 25 25 30 35 15 10 20 15 10 RIR 40 35 0 35 0 0 0 0 0 0 RDE 0 0 0 0 0 0 0 0 0 0 RHA 20 15 0 0 0 0 10 0 0 0 SFL 0 0 0 0 0 0 0 0 0 40 SQU 0 0 0 0 0 0 0 0 0 0 SMO 25 0 0 0 0 10 0 0 0 0 SVU 0 0 0 0 0 10 0 0 0 0 SVI 0 0 0 0 0 0 0 40 55 0 SCA 10 0 0 0 0 0 0 0 0 0 SPE 0 0 0 0 0 0 0 15 0 15 TIN 0 0 0 0 0 0 0 0 0 0 TFA 0 0 0 0 0 0 0 20 30 0 TAR 0 0 0 25 40 5 20 0 10 15 TFR 0 0 0 0 0 0 0 0 0 0 TSP 20 10 0 0 0 25 15 0 15 15 TBA 0 0 0 0 0 0 0 0 0 TAP 0 0 0 0 0 0 0 45 50 0 TLI 0 0 0 0 0 0 0 0 0 0 TSE 0 0 40 10 60 0 40 25 25 40 TOR 0 0 0 0 0 0 0 0 0 0 TPA 0 0 0 0 0 0 0 0 0 0 TRE 15 35 20 0 0 15 20 0 0 0 UDI 45 35 0 0 10 0 25 0 0 0 VTH 0 0 0 0 0 0 0 30 20 25 VRU 0 45 40 0 0 0 0 20 20 10

344

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Appendix 5.3 IVI of tree species in different samping stands

StNO Pw Ps Ap Je Bu Jm Pg St.1 87.8 0 0 12.2 0 0 0 St.2 87.2 0 0 12.7 0 0 0 St.3 63.2 0 0 24.4 12.4 0 0 St.4 38.11 0 0 22.19 39.7 0 0 St.5 14 0 0 72.3 13.6 0 0 St.6 84.7 0 0 15.3 0 0 0 St.7 93 0 0 6.76 0 0 0 St.8 81.4 0 0 14.2 4.36 0 0 St.9 86.5 0 0 7.93 5.36 0 0 St.10 79.8 0 0 13.5 6.7 0 0 St.11 100 0 0 0 0 0 0 St.12 50.4 0 0 15.3 34.4 0 0 St.13 75.1 0 0 0 24.9 0 0 St.14 71.4 0 0 0 28.7 0 0 St.15 0 100 0 0 0 0 0 St.16 0 100 0 0 0 0 0 St.17 100 0 0 0 0 0 0 St.18 0 70.3 0 29.8 0 0 0 St.19 0 100 0 0 0 0 0 St.20 0 100 0 0 0 0 0 St.21 0 0 0 0 100 0 0 St.22 100 0 0 0 0 0 0 St.23 0 0 0 0 100 0 0 St.24 0 0 0 0 0 100 0 St.25 0 100 0 0 0 0 0 St.26 0 0 0 0 100 0 0 St.27 0 0 0 0 100 0 0 St.28 0 0 100 0 0 0 0 St.29 39 61 0 0 0 0 0 St.30 100 0 0 0 0 0 0 St.31 100 0 0 0 0 0 0 St.32 61.8 38.2 0 0 0 0 0 St.33 100 0 0 0 0 0 0 St.34 100 0 0 0 0 0 0 St.35 63.4 0 0 0 0 0 36.6 St.36 0 71 0 29 0 0 0 St.37 100 0 0 0 0 0 0 St.38 100 0 0 0 0 0 0 St.39 70.51 0 0 0 29.49 0 0 St.40 100 0 0 0 0 0 0

Key to abbreviations: (P.w)= Pinus wallichiana, (P.g)=Pinus gerardiana, (B.u)=Betula utilis, (J.e)= Juneperus excelsa, (J.m)=Juniperus macropoda, (A.p)=Abies pindrow, (P.s)=Picea smithiana

345

Appendices

Appendix 5.4 PCA ordination Axis of trees and understory vegetation data set.

Axis of trees Axis of understory vegetation St.NO AXIS1 AXIS2 AXIS3 AXIS1 AXIS2 AXIS3 St.1 -1.9978 -1.1874 -1.2038 2.4614 -0.9394 0.2129 St.2 -2.5737 -1.3168 -1.5227 2.1487 -0.3862 1.043 St.3 -2.1828 -0.8071 -1.523 3.9897 0.644 0.6438 St.4 -1.5872 1.4669 0.7556 3.856 0.3497 0.5894 St.5 -2.0294 1.2313 0.9496 2.4307 0.9216 0.1088 St.6 -2.1041 -1.0238 -1.6816 4.4946 0.2257 -0.1038 St.7 -1.9298 -1.0854 -1.8492 4.9623 -0.1641 -0.6415 St.8 -2.5329 -1.3251 -1.8085 5.4912 -0.1945 0.4984 St.9 -2.4301 -1.3008 -1.375 6.3103 0.1149 -0.6287 St.10 -2.2629 -0.8951 -1.8988 4.4617 0.2628 0.808 St.11 1.4654 2.3589 -0.7655 -0.1538 4.3429 3.4468 St.12 -2.0096 1.1772 1.1413 -1.9582 2.2521 0.381 St.13 -1.3786 0.7032 1.5446 -1.2108 3.9586 1.3968 St.14 -1.3919 1.0257 1.2201 -1.334 2.958 0.4606 St.15 0.3873 -2.1265 2.4928 -1.838 -3.1979 2.2369 St.16 0.9151 -1.9181 2.1862 -0.5935 -3.2648 1.7892 St.17 1.877 2.648 -0.8586 -2.0074 2.4726 -1.6117 St.18 0.4217 -2.1943 2.5488 -2.1291 1.1124 -0.6406 St.19 0.97 -1.87161.985 -2.5852 2.1629 -0.4266 St.20 0.8861 -1.8646 1.9638 -2.3577 -2.8957 2.1619 St.21 4.1343 -1.1237 -0.9128 -2.3428 1.3901 -1.5346 St.22 2.1336 2.4429 -1.2593 -0.5078 -3.0873 0.9487 St.23 4.2215 -2.7007 0.2456 -2.7493 -4.6262 0.4159 St.24 -0.6754 -2.8171 -2.0708 -0.967 1.0978 -3.5243 St.25 0.8303 -2.1189 2.5864 -1.1412 -3.9698 -0.9053 St.26 3.7657 -1.1688 -0.8051 -2.3273 3.453 -2.7657 St.27 4.0716 -1.5227 -1.0819 -2.6716 3.0709 -2.2042 St.28 -0.6584 -2.8098 -2.055 -1.646 -0.8823 2.138 St.29 -1.2917 1.1067 1.4874 -2.884 -2.673 2.2149 St.30 1.8042 2.4569 -0.906 -2.5861 -0.6054 0.5951 St.31 2.4475 2.4044 -1.2032 0.2795 -3.835 -2.2071 St.32 -1.4049 1.0758 1.0617 -1.1743 -3.3425 -1.9643 St.33 2.6237 3.2274 0.392 -0.1818 -0.7564 -2.7893 St.34 1.6487 2.2406 -0.7558 0.5264 0.7517 -3.7443 St.35 -2.47 -1.1403-1.158 0.3342 -0.7798 -2.2665 St.36 0.3278 -2.0917 2.4563 -1.0783 -1.4827 -0.4024 St.37 1.7349 2.4495 -0.9332 -0.6732 -0.7289 -1.3822 St.38 1.4515 2.3681 -0.7318 -0.5965 1.6426 3.5164 St.39 -0.8345 1.2037 1.4422 -0.7478 1.557 1.6609 St.40 -4.3726 4.8231 1.9003 -1.304 3.0706 2.4758

346

Appendices

Appendix 6.1 DCA ordination axis of trees and understorey vegetation of sampling area

Axis of trees Axis of ground flora St.NO AXIS1 AXIS2 AXIS3 AXIS1 AXIS2 AXIS3 St.1 -1 0 16 94 28 -25 St.2 -1 0 16 180 5 -94 St.3 -1 0 50 130 -6 0 St.4 -1 0 102 139 -3 0 St.5 -1 0 89 109 -40 4 St.6 -1 0 18 173 -3 10 St.7 -1 0 11 178 10 24 St.8 -1 0 26 231 11 -6 St.9 -1 0 23 207 1 20 St.10 -1 0 30 158 1 -1 St.11 -1 0 6 4 -66 -44 St.12 -1 0 86 -64 -80 -26 St.13 -1 0 56 -28 -99 -31 St.14 -1 0 63 -43 -75 -32 St.15 -1 0 52 -40 98 -8 St.16 -1 0 52 -23 117 -37 St.17 -1 0 6 -49 -67 12 St.18 -1 0 61 -64 -40 -1 St.19 -1 0 52 -72 -59 -6 St.20 -1 0 52 -73 96 -54 St.21 -1 0 206 -72 -44 19 St.22 -1 0 6 -22 130 -30 St.23 -1 0 206 -79 147 -59 St.24 97 100 88 -54 -25 129 St.25 -1 0 52 -66 163 42 St.26 -1 0 206 -66 -92 36 St.27 -1 0 206 -53 -62 25 St.28 100 -100 88 -59 9 -74 St.29 -1 0 34 -83 88 -89 St.30 -1 0 6 -81 -2 -25 St.31 -1 0 6 -23 152 110 St.32 -1 0 24 -61 107 76 St.33 -1 0 6 -54 9 80 St.34 -1 0 6 -9 -15 134 St.35 -1 0 0 -11 19 67 St.36 -1 0 61 -35 45 14 St.37 -1 0 6 -30 17 37 St.38 -1 0 6 -22 -39 -62 St.39 -1 0 65 -25 -54 -39 St.40 -1 0 6 -28 -76 -44

347

Appendices

Appendix 10.1 Age and growth rates of Pinus wallichiana seedlings from Ganji valley of District Skardu.

S.NO ID 2cm G.R 4cm G.R 6 cm G.R 8cm G.R T.Age TGR Dbh 1 P.W 1.1 4.0 2.0 4.0 1.0 5.0 0.8 6.0 0.8 19.0 4.6 80 2 P.W 1.2 4.0 2.0 4.0 1.0 6.0 1.0 7.0 0.9 21.0 4.9 80 3 P.W 2.1 4.0 2.0 5.0 1.3 9.0 1.5 10.0 1.3 28.0 6.0 70 4 P.W 2.2 5.0 2.5 4.0 1.0 10.0 1.7 11.0 1.4 30.0 6.5 70 5 P.W 4.1 4.0 2.0 3.0 0.8 3.0 0.5 5.0 0.6 15.0 3.9 90 6 P.W 4.2 5.0 2.5 4.0 1.0 6.0 1.0 7.0 0.9 22.0 5.4 90 7 P.W 5.1 4.0 2.0 3.0 0.8 3.0 0.5 4.0 0.5 14.0 3.8 70 8 P.W 5.2 3.0 1.5 4.0 1.0 3.0 0.5 5.0 0.6 15.0 3.6 70 9 P.W 6.1 3.0 1.5 5.0 1.3 5.0 0.8 7.0 0.9 20.0 4.5 60 10 P.W 6.2 3.0 1.5 4.0 1.0 5.0 0.8 6.0 0.8 18.0 4.1 60 11 P.W 7.1 4.0 2.0 5.0 1.3 4.0 0.7 6.0 0.8 19.0 4.7 50 12 P.W 7.2 4.0 2.0 4.0 1.0 5.0 0.8 7.0 0.9 20.0 4.7 50 13 P.W 8.1 3.0 1.5 4.0 1.0 3.0 0.5 3.0 0.4 13.0 3.4 40 14 P.W 8.2 2.0 1.0 3.0 0.8 3.0 0.5 4.0 0.5 12.0 2.8 40 15 P.W 9.1 4.0 2.0 3.0 0.8 3.0 0.5 4.0 0.5 14.0 3.8 60 16 P.W 9.2 5.0 2.5 4.0 1.0 5.0 0.8 7.0 0.9 21.0 5.2 60 17 P.W 10.1 5.0 2.5 5.0 1.3 5.0 0.8 6.0 0.8 21.0 5.3 50 18 P.W 10.2 4.0 2.0 5.0 1.3 5.0 0.8 6.0 0.8 20.0 4.8 50 19 P.W 11.1 3.0 1.5 3.0 0.8 4.0 0.7 4.0 0.5 14.0 3.4 60 20 P.W 11.2 3.0 1.5 4.0 1.0 4.0 0.7 5.0 0.6 16.0 3.8 60 21 P.W 12.1 2.0 1.0 2.0 0.5 3.0 0.5 3.0 0.4 10.0 2.4 30 22 P.W 12.2 3.0 1.5 3.0 0.8 2.0 0.3 4.0 0.5 12.0 3.1 30 23 P.W 13.1 8.0 4.0 6.0 1.5 10.0 1.7 11.0 1.4 35.0 8.5 90 24 P.W 13.2 7.0 3.5 7.0 1.8 11.0 1.8 12.0 1.5 37.0 8.6 90 25 P.W 14.1 4.0 2.0 4.0 1.0 4.0 0.7 5.0 0.6 17.0 4.3 80 26 P.W 14.2 3.0 1.5 4.0 1.0 3.0 0.5 5.0 0.6 15.0 3.6 80 27 P.W 15.1 5.0 2.5 5.0 1.3 6.0 1.0 7.0 0.9 23.0 5.6 90 28 P.W 15.2 4.0 2.0 4.0 1.0 6.0 1.0 6.0 0.8 20.0 4.8 90

Key to abbreviations: GR=Grwoth rate, TGR=Total growth rate, T.Age= Total age Dbh=Diameter at breast hight, P.W=Pinus wallichiana

348

Appendices

Appendix 10.2 Growth rate of Pinus wallichiana trees species from Ganji valley of District Skardu.

S.NO Core ID 5cm 10cm 15cm 20cm 25cm 30cm 35cm 40cm 45cm T.GR Dbh 1 P.W 1.1 2 1 1 1 1 1 1 1 0 10 80 2 P.W 1.2 1 1 1 1 1 1 1 1 0 10 80 3 P.W 2.1 3 3 3 3 3 2 2 0 0 19 70 4 P.W 2.2 3 3 3 3 2 3 2 0 0 19 70 5 P.W 4.1 3 2 1 1 1 1 1 1 1 12 90 6 P.W 4.2 3 2 2 1 1 1 1 1 1 12 90 7 P.W 5.1 2 1 1 1 1 1 1 0 0 7 70 8 P.W 5.2 2 1 1 1 1 1 1 0 0 7 70 9 P.W 6.1 5 3 2 2 2 2 0 0 0 15 60 10 P.W 6.2 5 3 2 2 2 2 0 0 0 16 60 11 P.W 7.1 3 2 2 3 4 0 0 0 0 14 50 12 P.W 7.2 4 2 2 3 4 0 0 0 0 14 50 13 P.W 8.1 3 2 2 2 0 0 0 0 0 8 40 14 P.W 8.2 3 2 1 2 0 0 0 0 0 9 40 15 P.W 9.1 3 2 2 2 3 3 0 0 0 14 60 16 P.W 9.2 3 2 2 2 3 3 0 0 0 15 60 17 P.W 10.1 3 2 2 4 5 0 0 0 0 15 50 18 P.W 10.2 3 2 3 4 5 0 0 0 0 17 50 19 P.W 11.1 2 1 2 3 3 2 0 0 0 14 60 20 P.W 11.2 3 1 2 2 3 2 0 0 0 14 60 21 P.W 12.1 2 1 1 0 0 0 0 0 0 5 30 22 P.W 12.2 2 1 1 0 0 0 0 0 0 4 30 23 P.W 13.1 5 3 2 2 2 2 1 1 1 19 90 24 P.W 13.2 5 3 2 2 2 2 1 1 1 19 90 25 P.W 14.1 3 2 1 1 1 1 1 1 0 11 80 26 P.W 14.2 3 2 1 1 1 1 1 1 0 11 80 27 P.W 15.1 2 1 1 1 1 1 1 1 1 10 90 28 P.W 15.2 2 1 1 1 1 1 1 1 0 10 90

Key to abbreviations: GR=Grwoth rate, TGR=Total growth rate, T.Age= Total age Dbh=Diameter at breast hight, P.W=Pinus wallichiana

349

Appendices

Appendix 10.2 Age of Pinus wallichiana trees species from Ganji valley of District Skardu.

S.NO Core ID 5cm 10cm 15cm 20cm 25cm 30cm 35cm 40cm 45cm T.Age Dbh 1 P.W 1.1 8.0 11.0 17.0 22.0 33 34 40 52 0 217.0 80 2 P.W 1.2 7.0 12.0 15.0 24.0 30 37 38 54 0 217.0 80 3 P.W 2.1 17.0 31.0 44.0 55.0 67 72 77 0 0 363.0 70 4 P.W 2.2 16.0 32.0 43.0 56.0 60 78 77 0 0 362.0 70 5 P.W 4.1 16.0 16.0 22.0 24.0 23 30 32 30 33 226.0 90 6 P.W 4.2 14.0 18.0 23.0 25.0 22 31 33 29 34 229.0 90 7 P.W 5.1 10.0 12.0 14.0 17.0 22 24 24 0 0 123.0 70 8 P.W 5.2 9.0 13.0 13.0 18.0 23 23 24 0 0 123.0 70 9 P.W 6.1 25.0 26.0 30.0 34.0 43 68 0 0 0 226.0 60 10 P.W 6.2 26.0 27.0 30.0 35.0 44 68 0 0 0 230.0 60 11 P.W 7.1 17.0 24.0 33.0 51.0 90 0 0 0 0 215.0 50 12 P.W 7.2 18.0 23.0 34.0 50.0 90 0 0 0 0 215.0 50 13 P.W 8.1 13.0 15.0 26.0 40.0 0 0 0 0 0 94.0 40 14 P.W 8.2 15.0 18.0 22.0 45.0 0 0 0 0 0 100.0 40 15 P.W 9.1 14.0 15.0 30.0 44.0 70 93 0 0 0 266.0 60 16 P.W 9.2 13.0 16.0 32.0 45.0 72 95 0 0 0 273.0 60 17 P.W 10.1 13.0 23.0 36.0 72.0 114 0 0 0 0 258.0 50 18 P.W 10.2 14.0 24.0 40.0 77.0 120 0 0 0 0 275.0 50 19 P.W 11.1 12.0 14.0 36.0 55.0 67 70 0 0 0 254.0 60 20 P.W 11.2 14.0 13.0 32.0 48.0 70 72 0 0 0 249.0 60 21 P.W 12.1 10.0 12.0 20.0 0.0 0 0 0 0 0 42.0 30 22 P.W 12.2 9.0 13.0 20.0 0.0 0 0 0 0 0 42.0 30 23 P.W 13.1 24.0 30.0 35.0 38.0 40 45 42 51 52 357.0 90 24 P.W 13.2 25.0 33.0 36.0 38.0 40 45 42 51 53 363.0 90 25 P.W 14.1 15.0 18.0 20.0 25.0 26 28 32 33 0 197.0 80 26 P.W 14.2 14.0 19.0 21.0 24.0 27 27 33 34 0 199.0 80 27 P.W 15.1 12.0 12.0 13.0 15.0 22 34 40 42 44 234.0 90 28 P.W 15.2 11.0 14.0 13.0 18.0 23 35 42 44 0 200.0 90 Key to abbreviations: T.Age= Total age Dbh=Diameter at breast hight, P.W=Pinus wallichiana

350

PUBLICATIONS

Phytosociology and Structure of Central Karakoram National Park (CKNP) of Northern Areas of Pakistan

1Alamdar Hussain, 21 M. Afzal Farooq Moinuddin Ahmad, 12Muhammad Akbar and Muhammad Usama Zafar

1Laboratory of Dendrochronology and Plant Ecology, Department of Botany, 2Department of Environmental Sciences, Federal Urdu University of Arts, Science and Technology, Gulshan-e-Iqbal, Karachi, 75300, Pakistan

Abstract: A study was carried out to asses the phytosocology and structures of National Park. For tree species, point center quarter method (PCQ) and understorey vegetation, 1.5m circular plot at each PCQ point, while for bushes 20 quadrats 3x5 m were used. Five stands dominated by trees and eight stands of bushes were recorded. Picea smithiana and Pinus wallichiana form a community in two sites, associated with Juniperus excelsa. These pine tree species were also distributed as a pure stands in different sites with higher density and basal area. In pure stands, Juniperus excelsa attained lowest density ha121 with highest basal area m ha . Stands of mixed species stands show considerable low basal area. Diameter size class structure of tree species and bushes gives the current status and future trend of these forests. These forests show uneven and disbalanced size class distribution, therefore need special attention to save and protect these forests and vegetation.

Key words: CKNP Phytosociological Trees Bushes Understory vegetation

INTRODUCTION [2] studied the vegetation of some foothills of Himalayan range in Pakistan along the great silk rout from Gilgit to Central Karakoram National Park (CKNP) is located in Passu. First multivariate analysis of Skardu was presented Northern Areas of Pakistan. It is one of the 24 national by Ahmed [4]. Hussain and Mustafa [5] reported parks of Pakistan. Because of its unique and diverse ecological studies of plants in relation to animal, found in habitat of flora and fauna, it was declared as National Park Nasirabad valley Hunza Pakistan. Rasool [6] had provided in the year 1993. The CKNP extends from 35°N to 36.5°N a detailed account of the northern areas plants of Latitude and from 74°E to 77°E Longitude. Elevation economically important. Alpine deserts have little values ranges from 2000m-6000m.Climate of the park is cold arid as grazing lands due to the absence of forage and difficult and dry temperate in the lower elevation. Various topography. Alpine pastures were subjected to heavy researchers have studied the vegetation from different grazing during summer. No planned grazing system is still sites of Northern areas of Pakistan. Stewart [1] worked on followed in this area. According to a study by WWF the flora of Deosai plains. (2000) in northern areas, the pattern of species richness Ahmed et al. [2] described phytosociology and showed a general trend of increase richness in plant structure of Himalayan forest of different climatic zones of species from north to south and from west to east. Pakistan.According to Ahmed and Qadir [3] a Shinwari and Gilani [7] surveyed the Astore area to phytosociological study along Gilgit to Gupis revealed provide information on the conservation of plant that Juniperus macropda, Pinus geradiana, Pinus diversity. Ahmed et al. [8] described Community of wallichiana, Cedrus deodara, Astragalus spp, Thumus deodar forests from Himalayan range of Pakistan. Over all serphyllum, Nepeta spp, Taraxacum affinale were the vegetation of CKNP was presented by WWF (2009) using dominant species. Ahmad and Qadir [3] described many remote sensing and satellite images techniques. Besides communities near road site from Gilgit to shandur. Ahmed these studies there is no comprehensive quantitative

Corresponding Author: Alamdar Hussain, Laboratory of Dendrochronology and Plant Ecology, Department of Botany Federal Urdu University of Arts, Science and Technology, Gulshan-e-Iqbal, Karachi, Pakistan. E-mail: [email protected]. 1443 World Applied Sciences Journal 11 (12): 1531-1536, 2010 ISSN 1818-4952 © IDOSI Publications, 2010

Standardized Tree Ring Chronologies of Picea smithiana from Two New Sites of Northern Area Pakistan

12Muhammad Usama Zafar, Moinuddin Ahmed, 12M. Afzal Farooq, Muhammad Akbar and 2 Alamdar Hussain

1Department of Environmental Science, FUUAST, Karachi, Pakistan 2Department of Botany, Laboratory of Dendrochronology and Plant Ecology, FUUAST, Karachi 75300, Pakistan

Abstract: From two sites of Northern Area of Pakistan, 60 cores were taken from Picea smithiana and after cross dating annual ring widths were measured. Climate sensitivity of tree rings of Picea smithiana from Haramosh and Bagrot were investigated. Approximately 550 years were obtained and quality of cross dating was checked by Computer software COFECHA and the success of crossdating was quite satisfactory within and between these two stands. Mean correlation among samples was high (0.74 to 0.85). ARSTAN program was used to remove non climatic trends and difference between raw and chronology statistics are discussed. Signal strength in Haramosh chronology was found to be higher. The chronology values in both stands were showing similar trends. The results showing are encouraging for growth and climatic response. It is suggested that sample size should be increased to improve the results.

Key words: Picea smithiana Chronology Cofecha Arstan

INTRODUCTION need to know about the past variations. We have no long past records of climate and river flow. Meteorological Many people are unaware of coming effects of global department’s data provides us only 40 to 50 years of past environmental change Hester, R.E. and Harrison, R.M. [1]. data which is insufficient for searching the trend of the This environmental change is not autonomous but caused future climate and flow. One of the best ways of getting by human activities. Pakistan is also underthreat with this long term past climatic data has been recognized as tree change and its glaciers are continuously melting due to ring (the Science of dendrochronology) Ahmed et al. [3]. rise in temperature. Muhammad. N [2] described that In Pakistan, dendrochronological studies started in recent flood is an example of glacier melting. The flood the late 80s but it was used for climatic research after 2005. began in late July 2010 and affected Khyber Pakhtunkha, Standardized chronologies of Abies pindrow was Sindh, Punjab and Balochistan regions of Pakistan and presented by Ahmed [4]. Dendrochronological potential also affected Indus River basin. Approximately one-fifth of different species Ahmed et al. [5] and Picea smithiana of Pakistan's total land area was under water. According was pointed out by Ahmed and Naqvi [6] and Khan et al. to government of Pakistan data, the floods directly [7]. However we need a network of maximum number of affected about 20 million people, mostly by destruction of species from maximum sites for better understanding of property, livelihood and infrastructure and with a death ring width sensitivity of desired information. Therefore toll of close to 2,000 people. this study would add more information in the field of Millions of people lost their food and shelter due to dendrochronology and it’s potential. This paper will this unpredictable disaster. Question arises how we will present some preliminary results obtained from tree rings manage ourselves for future if we are unaware of future of Picea smithiana from two different sites of northern fluctuations of climate? For better understand of future we area of Pakistan.

Corresponding Author: Muhammad Usama Zafar, Department of Environmental Science, FUUAST, Pakistan. E- mail: [email protected]. 1531 AKBAR ET AL (2011), FUUAST J. BIOL., 1(2): 149-160

QUANTITATIVE FORESTS DESCRIPTION FROM SKARDU, GILGIT AND ASTORE DISTRICTS OF GILGIT-BALTISTAN, PAKISTAN

MUHAMMAD AKBAR1, MOINUDDIN AHMED1, ALAMDAR HUSSAIN1 MUHAMMAD USAMA ZAFAR1 AND MAYOOR KHAN2

Laboratory of Dendrochronology and Plant Ecology, Department of Botany, Federal Urdu University of Arts, Science and Technology, Gulshan-e-Iqbal, Karachi, Pakistan 2Wildlife Conservation Society, near Serena Hotel, Jutial, Gilgit, Baltistan, Pakistan

Abstract

A quantitative forest study of vegetation was conducted in 40 stands from three District of Gilgit-Baltistan. On the basis of phytosociological analysis and maximum important value index, following 5 pure stands and 5 communities of mixed tree species were recognized and quantitatively analyzed. Pinus wallichiana -Juniperus community, Pinus wallichiana-Betula community, Picea-Juniperus community, Picea-Pinus wallichiana, Pinus wallichiana-Pinus gerardiana community, Picea smithiana pure stands, Pinus wallichiana pure stands, Betula pure stands, Juniperus macropoda pure stand and Abies pindrow pure stand . Eighty three plants species of various herbs, shurbs and tree seedlings were observed and identified on the forest floor. Numbers of seedlings were also counted in each stand. These important forests are existing under anthropogenic threat and environmental disturbances .Some of them may easily be managed as indicated by the presence of large number of seedling, however stands with low paucity of seedlings shall need more serious attention.

Introduction

The importance, locations and climate of District Skardu is briefly described by Akbar et al. (2010). Gilgit is the capital city of Gilgit-Baltistan. The city extends from 35° 55' 0" North, 74° 17' 49" East. The elevation ranges 1600 to 3000 m above sea level and area covered 3800 Km2. It is bounded by Afghanistan in the north, China in the northeast and east Skardu, Astore and Diamer in the south and Ghizar District to the west. Gilgit city is covered with snow mountains. The combination of three great mountains range is also situated in this District. Maximum temperature ranges from -10 to above 40 °C. In summer temperature is hot and cold in winter. The rainfall ranges from 120 to 240 mm. Population of Gilgit city is approximately 216,760 (1998 report). Administratively it is divided into four Tehsil and Shina is the main language of this District. Vegetation of Gilgit is covered with shrub/ herbs, grasses and patches of many forests on mountainous areas. The most forested areas are Jutial, Karghah, Naltar, Haramosh, Bagrot, Joglotgah, Danyore and Pahote. Astore is one of the six districts of the Gilgit Baltistan. It is located at 35° 2'20.30"N, 75° 6'36.91"E covered by 5,092 km² area with elevation from 2600 to 3500m. Astore existed to the west by Diamer, to the north by Gilgit to the east by Skardu and to the south by Khyber-Pakhtunkhwa and Neelum District of Azad Kashmir.The population was 71,666 (1998). Climate of Astore is moderate during summer. In winter it may receive 6 inches to 3ft snow from main valleys to the mountains. The main language spoken in the valley is mostly Shina then Urdu. Due to its unique climatic conditions the valley provides excellent fauna and flora, especially economically important medicinal plants. Main forested areas of this District are Rama, Muhken, Dashken, Guhdae , Chilem and Minimarag. First quantitative and multivariate analysis of the vegetation around Skardu was presented by Ahmed (1976), during a scientific expedition of Northern Areas of Pakistan. This was funded by Planning Commission of Pakistan, Pakistan Science Foundation and National Development and Volunteer Program of Government of Pakistan in (1973). Ahmed and Qadir (1976), Ahmed (1986, 1988) also presented phytosociological investigation from Gilgit to Shandur and Gilgit to Astor respectively, during the same expedition. Ahmed (1988), Ahmed et al. (1989, 1990, and 1991) carried out quantitative vegetational work at Quetta plantation, regenerating juniper, Juniperus execlsa and Pinus gerardiana forests of Baluchistan. Hussain et al.(1991) studied vegetation of Lesser Himalayan Pakistan. Ahmed and Naqvi (2005) and Ahmed et al (2006) presented results from Picea smithiana forest and structure and description of various forests belonging to various climatic zones of Pakistan.Siddiqui et al.(2009, 2010) described Pinus ruxburghii and moist temperate forest of Pakistan.Wahab et al.(2008, 2010) and Khan et al (2010) analyzed pine forests and Monotheca buxifolia forests of Dir District while Khan et al (2010) and Ahmed et al.(2009) presented structure and quantitative description of Quercus baloot and Olia ferruginea forests of Chitral .Ahmed et al (2010) summarized the status of vegetation analysis in Pakistan Hussain and Mustafa (1995) investigated the ecological study of plant and animal relation from Nasirabad Hunza Pakistan.

HUSSAIN ET AL (2011), FUUAST J. BIOL., 1(2): 135-143

QUANTITATIVE COMMUNITY DESCRIPTION FROM CENTRAL KARAKORAM NATIONAL PARK (CKNP), GILGIT-BALTISTAN, PAKISTAN ALAMDAR HUSSAIN1*, MOINUDDIN AHMED1, MUHAMMAD AKBAR1, MUHAMMAD USAMA ZAFAR2, KANWAL NAZIM3 AND MAYOOR KHAN4

1Laboratory of Dendrochronology and Plant Ecology, Department of Botany, 2Department of Environmental Science, Federal Urdu University of Arts, Science and Technology, Gulshan-e-Iqbal Karachi, Pakistan. 3Department of Marine recourses and reference collection, University of Karachi, Karachi, Pakistan 4Wildlife Conservation Society, near Serena Hotel, Jutial, Gilgit, Baltistan, Pakistan

Abstract

A study was carried out to asses the communities and floristic composition of 32 stands of forest, shrubs and herbs from CKNP. On the basis of phytosociological analysis and maximum important value index, following 1 forest community, 3 pure stands and 6 shrubs and herbs communities are identified and quantitatively described. In forested areas Picea-Pinus wallichiana community, Juniperus excelsa pure stand. Picea smithiana pure stand, Pinus wallichiana pure stand, while at non forested places Rosa-Hippophae community, Hippophae- Ribes community, Rosa-Ribes community, Rosa-Berberis community, Hippophae-Tamarix community and Berberis-Tamarix community, were dominated. Poor floristic similarities between and within the communities at different elevations and slopes were seen however Rosa-Hippophae and Picea-Pinus wallichiana community showed higher floristic similarities within the community. Pine tree species were also distributed as a pure stand in different areas with higher density and basal area. It is shown that vegetation was deteriorating under anthropogenic disturbance therefore needs special attention to protect these forests and vegetation.

Introduction

Central Karakoram National Park is one of the 24 national parks of Pakistan. It is located in Northern areas (now Gilgit-Baltistan) of Pakistan. Many organizations are involved to protect this National park by various means. Many researchers quantitatively investigated the vegetation of Northern Areas. First multivariate analysis of Skardu was conducted by Ahmed (1976). Ahmed and Qadir (1976) recorded many communities near road sites from Gilgit to Shandur. Ahmed (1986) also studied the vegetation of some foothills of Himalayan range of Pakistan. Ahmed et al. (1990 a, b) described the status and population structure of Juniperus excelsa in Baluchistan. Ahmed et al.(1991) also worked vegetation structure and dynamics of Pinus geradiana forest of Baluchistan. Hussain and Mustafa (1995) investigated the ecological study of plant and animal relation from Nasirabad Hunza Pakistan. Rasool (1998) worked on the protection of medicinal plants of Northern Areas of Pakistan. Shinwari and Gillani (2003) also reported the sustainable harvest of medicinal plants from Astor. Malik (2005) comparatively studied with special reference to range conditions on the vegetation of Ganga Chotti and Bedori Hills District Bagh of Azad Jammu Kashmir. Ahmed et al., (2006) described the plant communities and forest structure of different climatic zones of Pakistan. Nafeesa (2007) described the phytosociological attributes and different plant communities of Pie Chinasi Hills of Azad Jammu Kashmir. Wali and Khatoon (2007) listed the detail of economically important species of Bagrot Gilgit. Wahab et al., described the phytosociology and dynamics of some forests of Afghanistan. Ahmed et al., (2010) studied the floristic composition and communities of deodar forest from Himalayan range of Pakistan. Akbar et al., (2010) also studied the phytosociology and structure of skardu district.Hussain et al., (2010) described the phytosociology and structure of few sites from Central Karakoram National Park. Siddiqui et al, (2011) described communities of moist temperate areas of Pakistan. Beside these studies no inclusive quantitative investigation were carried out in the National Park .Therefore present study was conducted to describe the communities description and floristic composition of one of the most important National Park of Pakistan.

Materials and Methods

For quantitative sampling mature and least disturbed sites were selected. Point Centered Quarter Method of Cottam & Curtis (1956) was applied for tree species. In each stands 20 points were taken at every 20 meter interval. Quadrat method size (3 x 5 m) of Cox, (1990) was used for shurbs and herb species .GPS was used to record the elevation and quardinates while degree of slope was recorded by slope meter. Sci., Tech. and Dev., 31 (4): 301-304, 2012

GROWTH-CLIMATE RESPONSE OF PICEASMITHIANA FROM AFGHANISTAN

MUHAMMAD USAMA ZAFAR1*, MOINUDDIN AHMED2, M. AFZAL FAROOQ1, MUHAMMAD AKBAR2 AND ALAMDAR HUSSAIN2

1Department of Environmental Science, Federal Urdu University of Arts, Science and Technology, Gulshan-e-Iqbal Campus, Karachi, Pakistan. 2Department of Botany, Federal Urdu University of Arts, Science and Technology, Gulshan-e-Iqbal Campus, Karachi, Pakistan.

Abstract Twenty eight cores were taken from fifteen Piceasmithiana trees and only twenty four cores were cross-dated. Various chronology statistics like EPS, SNR and Rbar were described having the values 0.89, 8.11 and 0.28, respectively. The standardised chronology was compared with temperature and precipitation of Dir meteorological and gridded data. Residual chronology was used to find out the correlation coefficients. No correlation was found between chronology and station data but previous October temperature was found to be negatively correlated and previous October and current January precipitation was found to be positively correlated with gridded data. Total variance explained was 17.19 percent with significant at 0.05 levels. It is suggested that a higher sample size would produce better results. Keywords: Piceasmithiana, Residual chronology, ARSTAN.

Introduction Material and methods Piceasmithiana, also known as Morinda or Twenty one cores from fifteen trees were Western Himalayan Spruce, is native to the cross-dated independently showing no flags in Western Himalayan and adjacent mountains COFECHA statistics followed by Holmes et al. starting from northeast Afghanistan to the central (1986); Grissino-Mayer (2001). Non climatic Nepal. It usually grows at the elevation of 2400- signals were removed using negative exponential 3600m in forests with Cedrusdeodara. curve method by software ARSTAN developed Pinuswallichiana and Abiespindrow in the middle by Cook (1985) and important chronology limit whilst associated with Betullautilis in the statistics were further discussed like EPS, SNR upper limit (Ahmed et al., 2011). It is an and Rbar. We acquired mean monthly evergreen tree that grows up to 40-55 m tall with temperature and precipitation from the trunk diameter of 1-2 m. It has conical crown meteorological data in Dir and gridded data from with usually pendulous branchlets. CRU TS 2.1 (http//www.cru.uea.uk/). Data of Piceasmithiana has been widely used for temperature and precipitation from Dir station dendroclimatic potential from North Pakistan, spanned 42 years (1967-2009) and gridded data India and Nepal (Ahmed et al., 2011; Borgaonkar spanned 101 years (1901-2002). The climate- et al., 2009; Cook et al., 2003). Some dated growth relationship was estimated using chronology using Piceasmithiana from PeDUVRQ¶V SURGXFW PRPHQW FRUUHODWLRQ DQDO\VLV Afghanistan without describing EPS, SNR and between chronologies and the mean monthly Rbar were evaluated by Khan et al. (2008). temperature and total monthly precipitation of Dir Therefore, the aim of the present study is to station and gridded data in the program explore growth-climate response of this species Correlation and Response Function (DPL) by comparing correlation and response introduced by Fritts (1976). A set of thirteen coefficients using different chronology versions months window from previous October to current with Dir climate and gridded data. October was used.

*Author for correspondence E-mail:[email protected] Pak. J. Bot., 45(3): 987-992, 2013.

DENDROCLIMATIC AND DENDROHYDROLOGICAL RESPONSE OF TWO TREE SPECIES FROM GILGIT VALLEYS

MOINUDDIN AHMED1, MUHAMMAD USAMA ZAFAR2, ALAMDAR HUSSAIN1, MUHAMMAD AKBAR1, MUHAMMAD WAHAB3 AND NASRULLAH KHAN4

1Laboratory of Dendrochronology and Plant Ecology of Pakistan, Department of Botany Federal Urdu University of Arts, Science and Technology, Gulshan-e- Iqbal, Karachi Pakistan 2Department of Environmental Science Federal Urdu University of Arts, Science and Technology, Karachi, Pakistan 3Centre of Botany and Biodiversity Conservation, University of Swat Khyber Pukhtoonkhwa, Pakistan 4Department of Botany, University of Malakand Khyber Pukhtoonkhwa, Pakistan

Abstract

Picea smithiana and Juniperus excelsa are two tree species growing in Gilgit valleys are used to explore growth climate and growth river flow response. About 100 wood samples in the form of cores from three sites were collected. Picea smithiana from Bagrot and Haramosh (only chronologies published) and Juniperus excelsa from Nalter were sampled. A large number of Juniperus excelsa samples were rejected due to various associated problems. Crossmatched and standardized chronologies of three sites were compared with temperature, precipitation (meteorological and gridded data) and instrumental Indus river flow data. Juniperus excelsa showed strong lag year response. These species showed significant negative relationship of tree ring index with May- June temperature and positive response with March-April precipitation using instrumental and gridded data. Tree ring of these species indicate significant positive response with May-June river flow. It is shown that these species have potential to evaluate past climatic variations of the area and past water flow response of Indus River.

Introduction growth-climate correlations by using four species including Picea smithiana, Juniperus excelsa, Pinus Gilgit valley in northern areas of Pakistan is famous gerardiana, and Cedrus deodara from seven sites. Ahmed for its tourism and has economic, social and environmental et al., (2011a) also developed growth climate reponse importance. The valley is situated at 1500 meters (4921 using 28 tree ring chronologies from six species extended feet) while the surrounding mountains are 1830 to 2286 back 700 years. Recently Cook et al., (2013) used tree meters above sea level. Area falls under dry temperate area ring chronologies to reconstruct 500 years of flow of with scanty of rainfall averaging 120-240 mm annually Indus River. (4.7-9.4 inches). The summer is hot and short and the In this paper, we are presenting dendroclimate and dominant season is winter. It is the junction of three great dendrohydrological potential of Juniperus excelsa and mountainous ranges i.e., the Hindukush, the Himalayas and Picea smithiana from three new sites of Gilgit valleys of the Karakoram. The climate ranges of Himalayas varies northern areas of Pakistan. from dry cold desert and wet temperate to subtropical (Borgaonkar et al., 2008). Below the snow covered peaks, Materials and Methods green belt of Picea smithiana and Pinus wallichiana are distributed while in the dry areas scattered Juniperus For the selection of sampling sites, we targeted high excelsa trees are distributed with various shrubs and herbs. elevations because rings of trees were supposed to be Melting snow, springs, streams and Gilgit River play an quite sensitive there. Trees with higher dbh (diameter at important role for its agriculture orchids and domestic use. the breast height) were selected and wood samples in the How changing climate (global warming) will affect natural form of cores were obtained using Swedish increment resources and the daily life of the people of this area is an borer. These cores were kept in plastic straws and were immediate interest and concern. air dried in laboratory for further processing. Sand papers In addition, floods and droughts affect and future variation of temperature, precipitation and river flow of of different grades were used for surfacing then rings this area will not only affect this valley but also other were crossdated under powerful microscope followed by parts of the country. However, for reliable modeling for Stokes & Smiley (1968). The ring’s widths were future prediction, a long term meteorological record is measured in millimeter using measure J2X and then needed (Xiang et al., 2000) therefore tree rings of suitable subjected to COFECHA (Holmes et al., 1986 and tree species are being used to obtain long term past Grissino-Mayer, 2001) to check the quality of visual climatic or hydrological record (Stockton, 1971; 1990). crossdating. For this purpose, default commands were Dendrochronological work started in Pakistan when followed in which 32 year cubic spline, 50 years segment Ahmed (1987) presented Dendrochronological potential with 25 years overlap and critical level of correlation of gymnospermic tree species of northern areas of were maintained. Trends (systematic changes) in the trees Pakistan. He also described (1988 a,b) age and growth were removed from the software ARSTAN (Cook, 1985) rate of forest tree species and problems encountered in and three types of standardized chronologies were their age estimation. Treydte et al., (2006) reconstructed obtained i.e. raw chronology, standard chronology and July precipitation for the millennium, based on Oxygen residual chronology. To determine which chronology best isotope concentration using Juniperus excelsa from suited our study; we developed a preliminary correlation northern Pakistan. Ahmed et al., (2010; 2011b) created between climatic data of Gilgit and three chronologies Sci., Tech. and Dev., 32 (1): 56-73, 2013

SIZE CLASS STRUCURE OF SOME FORESTS FROM HIMALAYAN RANGE OF GILGIT-BALTISTAN

MUHAMMAD AKBAR1*, MOINUDDIN AHMED1, S. SHAHID SHAUKAT3, ALAMDAR HUSSAIN1, MUHAMMAD USAMA ZAFAR2, ATTA MUHAMMAD SARANGZAI4 AND FAISAL HUSSAIN1

1Laboratory of Dendrochronology and Plant Ecology, Department of Botany, Federal Urdu University of Arts, Science and Technology, Gulshan-e-Iqbal, Karachi 75307, Pakistan. 2Institute of Environmental sciences, University of Karachi, Karachi, Pakistan. 3Department of Environmental Science Federal Urdu University of Arts, Science and Technology, Karachi, Pakistan. 4Department of botany, University of Baluchistan, Quetta, Pakistan.

Abstract The investigation of forest size class structure was carried out in 40 stands from 15 different locations of Astore, Gilgit and Skardu, districts of Gilgit-Baltistan, Pakistan, ranging from 2616-3735 meters above sea level (a.s.l.). This study attempts to expose the present status and future trends of arboreal vegetation in these areas. Size class showed varied distribution patterns in different stands. Most of the deviation from an ideal distribution may be explained in terms of anthropogenic disturbances, i.e., grazing, cutting, sliding, burning and other human induced factors, therefore, these forests are not in the stable condition. It is concluded that if prompt action not taken to stop current damaging practices, these valuable forests will vanish in a few decades. Keywords: Stands, Structure, Trends, Anthropogenic disturbances.

Introduction of some pine forest of Afghanistan, close to the Vegetation of the Districts Skardu, Giglit and Pakistani border. Vegetation structure of Olea Astore was described in detail by Akbar et al. ferruginea forest of Lower Dir was presented by (2010, 2011) Ahmed et al. (2009). Siddiqui et al. (2009) carried out Phytosociology of Pinus roxburghii Sergeant Forest vegetation and structure ha been (Chir pine) in Lesser Himalayan and Hindu Kush studied in Pakistan by many researchers from range of Pakistan. different locations. Ahmed (1976) carried out multivariate analysis of vegetation of Skardu. Khan et al. (2010) conducted phytosociology, Ahmed and Qadir (1976) conducted a study of structure and physiochemical analysis of soil in communities near road sides from Gilgit to Quercus baloot, forest from District Chitral. Shandur. Ahmed (1986) investigated the Akbar et al. (2010, 2011) also explored the vegetation of some foothills of Himalayan range phytosociology, structure and community of Pakistan. Ahmed (1988) presented population description of Gilgit, Astore and Skardu District. structure of planted tree species of Quetta, while Hussain et al. (2010, 2011) presented population structure of Juniperus excelsa M.B. phytosociology, structure and community and Pinus gerardiana Wall.ex Lamb. from description of Central Karakorum National Park. Baluchistan was studied by Ahmed et al. (1990) Shaheen et al. (2011) studied structural diversity, and Ahmed et al. (1991), respectively. Ahmed et vegetation dynamics and anthropogenic impact al. (2006) also presented phytosociology and on lesser Himalayan subtropical forests of Bagh structure of various Himalayan forests from district Kashmir. different climatic zones of Pakistan. Wahab et al. The above studies include some forested (2008) carried out Phytosociology and dynamics areas of Gilgit-Baltistan, Pakistan, and these are

*Author for correspondence E-mail: [email protected]