Gilgit-Baltistan), Pakistan by ALAMDAR HUSSAIN

Phytosociology and Dendrochronological Study of Central Karakoram National Park, Northern Areas (Gilgit-Baltistan), Pakistan By ALAMDAR HUSSAIN

Under the supervision of Professor Dr. Moinuddin Ahmed

A thesis submitted to the Graduate Research 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

In the best name of Allah, Almighty, the most Beneficent, and the most Merciful

“And use split the earth in fragments and produce there corn, and grapes and nutrition plants, and olives and dates and enclosed gardens dense with lofty trees, and fruits and fodder, for use and convenience to and your cattle”

(Al-Quran, Part 30, Surah Abas 80, Ayat 25-32)

Federal Urdu University of Arts, Sciences & Technology GulshanEIqbal Karachi Department Of Botany

CERTIFICATE

Certified that Mr. Alamdar Hussain 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 “Phytosociology and Dendrochronological Study of Central Karakoram National Park, Northern Areas (Gilgit Baltistan), Pakistan” submitted by him is satisfactory and confide for the partial requirement of the award of Doctor of Philosophy degree.

‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ Chairperson Supervisor Department of Botany Prof. Dr. Moinuddin Ahmed (Foreign Professor) II

III

DEDICATION

This thesis is lovingly dedicated to my respective parents, brothers,

sisters, relatives and honorable teachers who have been my constant

source of inspiration. They have given me the drive and discipline to

tackle any task with enthusiasm and determination. Without their love

and support this thesis would not have been made possible. IV

ACKNOWLEDGEMENTS

First of all my greatest thanks to Almighty ALLAH for bestowing upon me the courage to face the complexities of life and complete this thesis successfully. I would like to gratefully acknowledge the enthusiastic supervision of Professor Dr. Moinuddin Ahmed (Foreign Professor) for the unforgettable supervision, useful comments, remarks and encouragement through out the learning process of this thesis. I have no word to explain his generosity and I proudly accept him as an ideal person for my whole life. Furthermore I would like to thank Professor Dr. S. Shahid Shaukat (Eminent Scholar) for introducing me to the statistical analysis as well for the support on multivariate analysis and valuable comments and suggestions on the manuscript. I am grateful to my best colleague and Research associate Mr. Muhammad Akbar, who has supported me throughout this work, both on the field and laboratory. I would be remiss if I did not mention Dr. Muhammad Faheem Siddiqui, Dr. Muhammad Uzair Khan, Dr. Kanwal Nazim, Dr. Muhammad Wahab and Dr. Nasrullah Khan, for their constant moral support as well as for useful information for the completion of the thesis. I wish to avail myself this opportunity to express a sense of gratitude and love to my friend and senior Dr. Toqeer Ahmed Rao for his generosity, support, valuable suggestion and encouragement during the completion of the thesis. I take this opportunity to express my gratitude to my father Gulam Jan and elder brother Dildar Hussain, who have been instrumental in the successful completion of this thesis. I can’t say thank them enough for their tremendous financial and moral support. I feel motivated and encouraged every time and without their encouragement and guidance this thesis would not have materialized and the reason why I am here. The guidance and support received from all the teaching and non-teaching members of Botany Department who contributed to this thesis in various ways. I am grateful for their constant support and help. I cannot forget to pay my thank to Laboratory staff members Mr. Azhar Ali Kazmi, Mr. Mirza Waqar Ali Baig, Mr. Abdul Basit and Mr. Muhammad Fahad for their support and love. I am very thankful to Mr. Ahsan (GIS laboratory supervisor) for his support and cooperation in mapping and other facilities. V

My thanks and appreciations also go to my class fellows Mr. Faisal Hussain, Hina Zafar, Fariha Naz, Farzana Usman, Muhammad Usama Zafar, Jawaria Sultana, Shaheena Arshad, Tuba Tahir and people who have willingly helped me out with their abilities for their support and love during the completion of this thesis. It is my pleasure to acknowledge my elder sister Miss. Shaheen Abbas (Assistant Professor) and brother Muhammad Ilyas for their encouragement support and love. I wish to express my sincere gratitude to Dr. Sher Wali (Assistant Professor KIU) for his help in identification of plant specimens. I take an immense pleasure in thanking Mr. Muhammad Ismail (Forest Conservator Gilgit-region), Mr. Syed Yasir Abbas Rizvi (Ecologist, CKNP), Mr. Babar Khan (WWF Head, Gilgit) for their guidance and support during field work. I am deeply indebted to Seerat Hussain, my cousin Ashiq Hussain, Arif Hussain, Sajid Ali, Kamran Haider, Muzahir Hussain with family, for their love hospitality and support in the field. Last but not the least I cannot express my gratitude to my mother in words, whose unconditional love and prayers have been my greatest strength. Without any doubt the most astonishingly patience women I have ever seen. The constant love and support of my younger sister Sajida Batool is also sincerely acknowledged. I cannot forget my brothers Imdad Hussain, Sakhawat Hussain and Sajid Hussain for their unconditional support and love throughout my life.

Alamdar Hussain VI

TABLE OF CONTENTS

Certificate……………………………………………………………………………… I Thesis similarity index result………………………………………………………….. II Dedication……………………………………………………………………………... III Acknowledgments…………………………………………………………………….. IV Table of Contents……………………………………………………………………… VI List of tables…………………………………………………………………………… XV List of figures………………………………………………………………………….. XVII Abstract in Urdu……………………………………………………………………….. 1 Abstract………………………………………………………………………………... 3 General introduction…………………………………………………………………... 5 Aims and objectives…………………………………………………………………… 6 CHAPTER 1 DESCRIPTION OF THE STUDY AREA 1.1-Geography of Gilgit-Baltistan…………………………………………………….. 7 1.2-Karakorum Mountainous Range…………………………………………………... 8 1.3-Geology of Gilgit- Baltistan………………………………………………………. 9 1.4-Economic structure of Gilgit-Baltistan……………………………………………. 10 1.5-Central Karakoram National Park (CKNP)……………………………………….. 11 1.6-Management of Central Karakoram National Park……………………………….. 11 1.7-Climate of Gilgit-Baltistan………………………………………………………... 12 1.8-Ecological Zonation……………………………………………………………….. 15 1.8.2-Montane Dry Temperate Coniferous forests……………………………………. 15 1.8.3- Montane Dry Temperate Broadleaved forests………………………………….. 15 1.8.4- Sub-Alpine forests……………………………………………………………… 15 1.8.5-Northern Dry Scrub……………………………………………………………... 16 1.9-Land used in Gilgit-Baltistan……………………………………………………… 17 1.10-Medicinal Plants…………………………………………………………………. 17 1.11-Wildlife…………………………………………………………………………... 20 1.12-Water Resources…………………………………………………………………. 20 1.13-Forest Cover in Gilgit-Baltistan…………………………………………………. 21 1.13.1-Protected forests………………………………………………………… 21 VII 1.13.2-Private Forests…………………………………………………………... 22 1.14-Polices and legislation…………………………………………………………… 23 1.15-Problems and issues……………………………………………………………… 23 PART-1 ECOLOGY CHAPTER 2 REVIWE OF LITERATURE 2.1-Introduction……………………………………………………………………….. 26 2.2-Literature review …………………………………………………………………. 26 CHAPTER 3 STRUCTURE AND FUTURE TREND OF THE VEGETATION OF CKNP 3.1- Introduction………………………………………………………………………. 36 3.2-Materials and Methods……………………………………………………………. 38 3.2.1-Size class structure……………………………………………………………… 38 3.2.2-Weibull distribution…………………………………………………………….. 38 3.3-Results…………………………………………………………………………….. 40 3.3.1-Vegetation description and size class structure of each stand……………….. 40 A.FORESTED AREA 3.3.1.1-Stand No 1-Bagrot……………………………………………………… 40 3.3.1.2-Stand No 2-Haramosh…………………………………………………... 40 3.3.1.3-Stand No 3-Hopar………………………………………………………. 41 3.3.1.4-Stand No 4-Stak -1……………………………………………………… 41 3.3.1.5-Stand No 5-Stak -2……………………………………………………… 41 3.3.1.6-Stand No 6-Rakaposhi-1………………………………………………... 42 3.3.1.7-Stand No 6-Rakaposhi-1………………………………………………... 42 3.3.1.8-Stand No 8-Rakaposhi-3……………………………………………….. 42 3.3.1.9-Stand No 9 Rakaposhi- 4……………………………………………….. 43 B.NON-FORESTED AREA

3.3.1.10-Stand No 10-Bagrot…………………………………………………… 43 3.3.1.11-Stand No 11-Hopar……………………………………………………. 44 3.3.1.12-Stand No 12-Stak 1……………………………………………………. 44 3.3.1.13-Stand No 13-Stak-2……………………………………………………. 44 3.3.1.14-Stand No 14-Stak 3……………………………………………………. 45 VIII 3.3.1.15-Stand No 15-Thally 1………………………………………………….. 45 3.3.1.16-Stand No 16-Thally 2………………………………………………….. 46 3.3.1.17-Stand No 17-Kowardo………………………………………………… 46

3.3.1.18-Stand No 18-Arandu-1………………………………………………… 46 3.3.1.19-Stand No 19-Arandu- 2……………………………………………… 47 3.3.1.20-Stand No 20-Arandu- 3……………………………………………… 47 3.3.1.21-Stand No 21 Shigar 1………………………………………………….. 48 3.3.1.22-Stand No 22-Shigar 2………………………………………………….. 48 3.3.1.23-Stand No 23-Shimshal- 1-1…………………………………………… 48 3.3.1.24-Stand No 24-Shimshal- 1-2…………………………………………… 49 3.3.1.25-Stand No 25-Shimshal- 2-1…………………………………………… 49 3.3.1.26-Stand No 26-Shimshal- 2-2…………………………………………… 50 3.3.1.27-Stand No 27-Braldu -1-1……………………………………………… 50 3.3.1.28-Stand No 28-Braldu- 1-2……………………………………………… 51 3.3.1.29-Stand No 29-Braldu- 2-1……………………………………………… 51 3.3.1.30-Stand No 30-Braldu- 2-2……………………………………………… 52 3.3.1.31-Stand No 31-Chungo- 1……………………………………………….. 52 3.3.1.32-Stand No 32-Chungo- 2……………………………………………….. 53 3.3.2-Overall diameter distribution of the tree species…………………………….. 65 3.3.3-The Weibull function………………………………………………………… 67 3.3.4-Density ha-1 of trees and shrubs……………………………………………… 69 3.3.5-Basal area m2ha-1 of trees and shrubs………………………………………... 70 3.3.6-Correlation of density ha-1 with basal area m2 ha-1 and topographic factors with density ha-1 72 3.3.7-Correlation of topographic factors with basal area (m2 ha-1)………………… 80 3.4-Discussion and conclusion………………………………………………………… 85 3.4.1-Size class structure…………………………………………………………… 85 3.4.2-Overall diameter distribution of dominant species…………………………... 88 3.4.3-The Weibull function………………………………………………………… 89

IX CHAPTER 4 VEGETATION COMMUNITY ANALYSIS 4.1-Introduction ...... 93 4.2-Materials and methods…………………………………………………………….. 94 4.3-Results……………………………………………………………………………... 96 4.3.1-Forest Community and Pure Stands…………………………………………. 96 4.3.2-Shrubs and Herbs Communities……………………………………………... 96 4.3.1.1-Picea-Pinus wallichiana community………………………………………. 96 4.3.1.2-Juniperus excelsa Pure Stand……………………………………………… 97 4.3.1.3-Picea smithiana Pure Stand……………………………………………….. 98 4.3.1.4-Pinus wallichiana pure stand………………………………………………. 98 4.3.2.1-Rosa-Hippophae Community……………………………………………… 100 4.3.2.2-Hippophae-Ribes alpestre Community……………………………………. 100 4.3.2.3-Rosa-Ribes orientale Community………………………………………….. 101 4.3.2.4-Rosa-Berberis lycium Community………………………………………… 101 4.3.2.5-Hippophae-Tamarix indica Community…………………………………… 101 4.3.2.6-Berberis lycium-Tamarix indica Community……………………………… 102

MULTIVARIATE ANALYSIS CHAPTER 5 ORDINATION AND CLASSIDFICATION OF VEGETATION 5.1-Introduction………………………………………………………………………... 111 5.2-Purpose of the study ...... 112 5.3.-Materials and methods……………………………………………………………. 112 5.3.1-Data analysis…………………………………………………………………. 112 5.3.2-Soil analysis………………………………………………………………….. 113 5.3.3-Environmental variables……………………………………………………... 113 5.3.4-Ward’s clustering Method…………………………………………………… 113 5.3.5-DCA Ordination……………………………………………………………… 114 5.4-Results…………………………………………………………………………….. 114 5.4.1-Classification………………………………………………………………… 114 5.4.2-Ward’s cluster Analysis (Forested and non forested vegetation)……………. 114 5.4.2.1-Group I………………………………………………………………….. 114 X 5.4.2.2-Group II………………………………………………………………… 115 5.4.2.3-Group III………………………………………………………………... 116 5.4.2.4-Group IV………………………………………………………………... 116 5.4.3-Ward’s clustering Analysis (Understorey vegetation)……………………….. 123 5.4.3.1-Group I………………………………………………………………...... 123 5.4.3.2-Group II………………………………………………………………… 123 5.4.3.3-Group III………………………………………………………………... 124 5.4.4.4-Group IV………………………………………………………………... 124 5.4.4.5-Group V………………………………………………………………… 125 5.4.4.6Group VI………………………………………………………………… 126 5.4.5-DCA Ordination……………………………………………………………… 130 5.4.6-Ordination of forested and non-forested vegetation…………………………. 130 5.4.7-Correlation of ordination axes with environmental variables and soil nutrients (Forested and non forested vegetation)……………………………………… 134 5.4.8- Ordination of Understorey vegetation………………………………………. 136 5.4.9-Correlation of ordination axes with environmental variables and soil nutrients (Understorey vegetation)……………………………………………………. 140 5.4.10-Univariate analysis of variance (Forested and non-forested vegetation)…… 142 5.4.11-Univariate analysis of variance (Understorey vegetation)………………….. 145 5.4.12– Discussion and conclusion………………………………………………… 148 CHAPTER 6 VEGETATION-ENVIRONMENTS RELATIONSHIP 6.1-Introduction ...... 153 6.2-Objectives of the study……………………………………………………………. 154 6.3-Materials and methods……………………………………………………………. 154 6.3.1-Classification………………………………………………………………… 154 6.3.2-PCA Ordination……………………………………………………………… 155 6.4-Results……………………………………………………………………………... 155 6.4.1-Classification of forested and non-forested vegetation on the basis of 155 environmental variables……………………………………………………………….. 6.4.1.1-Group-I…………………………………………………………………. 155 6.4.1.2-Group-II………………………………………………………………… 156 6.4.1.3-Group-III………………………………………………………………... 156 6.4.1.4-Group-IV………………………………………………………………... 157 XI 6.4.2-Classification of Understorey vegetation on the basis of environmental 163 variables……………………………………………………………………………….. 6.4.2.1-Group-I…………………………………………………………………. 163 6.4.2.2-Group II A………………………………………………………………. 163 6.4.2.3-Group II B………………………………………………………………. 164 6.4.2.4-Group III………………………………………………………………... 165 6.4.2.5-Group IV A……………………………………………………………... 165 6.4.2.6-Group IV B……………………………………………………………... 166 6.4.3-PCA Ordination……………………………………………………………… 170 6.4.3.1-Ordination of forested and non-forested vegetation…………………..... 170 6.4.3.2-Ordination of Understorey vegetation………………………………….. 174 6.4.4-Correlation of ordination axes with environmental variables, edaphic factors and soil nutrients (Forested and non-forested vegetation)…………………………….. 178 6.4.5-Correlation of ordination axes with environmental variables, edaphic factors and soil nutrients (Understorey vegetation)…………………………………………… 180 6.4.6-Univariate analysis of variance (Forested and non-forested vegetation)…….. 182 6.4.7-Univariate analysis of variance (Understorey vegetation)…………………… 185 6.4.8-Physico-chemecal status of CKNP…………………………………………... 188

6.4.8.1-Elevation………………………………………………………………... 188 6.4.8.2-Slope……………………………………………………………………. 188 6.4.8.3-Conductivity……………………………………………………………. 188 6.4.8.4-Salanity…………………………………………………………………. 188 6.4.8.5-pH………………………………………………………………………. 189 6.4.8.6-MWHC…………………………………………………………………. 189 6.4.8.7-TDS……………………………………………………………………... 189 6.4.8.8-Organic matter………………………………………………………….. 189 6.4.8.9-Nitrogen………………………………………………………………… 189 6.4.8.10-Phosphorus…………………………………………………………….. 190 6.4.8.11-Potassium……………………………………………………………… 190 6.4.8.12-Calcium………………………………………………………………... 190 6.4.8.13-Magnesium……………………………………………………………. 190 6.4.8.14-Cobalt………………………………………………………………….. 190 6.4.8.15-Manganese…………………………………………………………….. 191 XII 6.4.8.16-Zinc……………………………………………………………………. 191 6.4.8.17-Iron…………………………………………………………………….. 191 6.4.8.18-Sulphur………………………………………………………………… 191 6.5-Discussion and conclusion………………………………………………….. 196 PART II DENDROCHRONOLOGY CHAPTER 7 AN INTRODUCTION TO DENDROCHRONOLOGY 7.1-Introduction ...... 201 7.2-Dendroclimatology………………………………………………………………... 201 7.3-Brief history of dendrochronology ...... 202 7.4-Principles of dendrochronology ...... 204 7.4.1-Uniformitarian ………………………………………………………………. 204 7.4.2-Limiting Factors……………………………………………………………… 205 7.4.3-Modeling growth-environmental relationship……………………………….. 205 7.4.4-Ecological Amplitude………………………………………………………... 205 7.4.5-Site Selection………………………………………………………………… 205 7.4.6-Crossdating…………………………………………………………………... 206 7.4.7-Replication…………………………………………………………………… 206 7.4.8-Sensitivity……………………………………………………………………. 206 7.4.9-Standardization………………………………………………………………. 206 7.4.10-Calibration and verification………………………………………………… 206 7.4.11-The domain of climate……………………………………………………… 207 7.5-Chracteristic features of Picea smithina tree rings…………………………….. 209 CHAPTER 8 LITERATURE REVIEW 8.1-Literature review………………………………………………………………….. 210 CHAPTER 9 MATERIALS AND METHODS 9.1-Site description……………………………………………………………………. 214 9.2-Field methods……………………………………………………………………… 214 9.3-Mouting and crossdating…………………………………………………………... 215 9.4-Measurement using Velmex ...... 216 9.5-Age and growth rates……………………………………………………………… 216 9.6-Chronology preparation…………………………………………………………… 216 XIII 9.6.1-COFECHA………………………………………………………………………. 216 9.6.2-ARSTAN ...... 217 9.6.3-Standard chronology…………………………………………………………….. 218 9.6.4-Residual chronology……………………………………………………………. 218 9.6.5-ARSTAN chronology…………………………………………………………… 218 9.7-Growth-climate response………………………………………………………….. 219 CHAPTER 10 AGE AND GROWTH RATES 10.1-Introduction………………………………………………………………………. 221 10.2-Materials and Methods…………………………………………………………... 222 10.3-Results……………………………………………………………………………. 222 10.3.1-Age and growth rates of seedlings………………………………………….. 222 10.3.2-Growth rate of past seedling………………………………………………... 225 10.3.3-Age and growth rate of tress………………………………………………... 227 10.4-Discussion and conclusion……………………………………………………. 230 10.4.1-Age and growth rates of seedlings………………………………………….. 230 10.4.2-Growth rate of past seedling……………………………………………….. 231 10.4.3-Age and growth rate of tress………………………………………………... 232 CHAPTER 11 CHRONOLOGY DEVELOPMENT 11.1-Introduction………………………………………………………………………. 234 11.2-Materials and Methods…………………………………………………………... 235 11.3-Results…………………………………………………………………………… 235 11.3.1-COFECHA statistics………………………………………………………... 235 11.3.2-Raw chronology……………………………………………………………. 237 11.3.3-Residual chronology………………………………………………………... 238 11.3.4-Standard chronology……………………………………………………….. 239 11.3.5-ARSTAN chronology………………………………………………………. 240 11.3.6-RBAR, EPS and sample depth……………………………………………… 242

XIV CHAPTER 12 GROWTH-CLIMATE RESPONSE 12.1-Introduction……………………………………………………………………… 246 12.2-Materials and Methods………………………………………………………….. 248 12.3-Results……………………………………………………………………………. 249 12.3.1-Correlation coefficients of residual chronology vs local climate ………….. 249 12.3.2-Response coefficients of residual chronology vs local climate…………….. 250 12.3.3-Correlation coefficients of residual chronology vs grid……………………. 251 12.3.4-Response coefficients of residual chronology vs grid……………………… 252 12.3.5-Correlation coefficients of standard chronology vs local climate………….. 253 12.3.6-Response coefficients of standard chronology vs local climate……………. 254 12.3.7-Correlation coefficients of standard chronology vs grid…………………… 255 12.3.8-Response coefficients of standard chronology vs grid……………………... 256 CHAPTER 13 GENERAL DISCUSSION AND CONCLUSION……………….. 263 REFRENCES………………………………………………………………………… 268 APPENDICES………………………………………………………………………... 298 PUBLICATIONS……………………………………………………………………. 320

XV LIST OF TABLES

Table 1.1 Land distribution in Gilgit Baltistan………………………………………... 17 Table 1.2 Some important medicinal gymnospermic plant species from CKNP……... 18 Table 1.3 Protected Forests cover in Gilgit-Baltistan………………………………… 21 Table 1.4 Trees volume in private forests in Chilas, Darel and Tangir………………. 22 Table 1.5 Overall status of private forests (ha)………………………………………. 22 Table 1. 6 Environmental characteristics of study sites of CKNP……………………. 25 Table 3.1 The Weibull function parameters of three dominant conifer species of 67 CKNP…………………………………………………………………………………. Table 3.2 Mean values of density ha-1, basal area m2 ha-1 and IVI…………………… 71 Table 3.3 Correlation of stand density ha-1 with stand basal area m2 ha-1, slope and 73 elevation with density ha-1…………………………………………………………….. Table 3.4 Correlation of species density ha-1 with species basal area m2 ha-1, slope 73 and elevation with density ha-1………………………………………………………… Table 3.5 Correlation of topographic (slope and elevation) factors with basal area m2 80 ha-1……………………………………………………………………………………... Table 4. 1 Communities of CKNP with IVI, absolute values and topographic range... 103 Table 4.2 Phytosociological attributes and absolute values of Forest, Bushes and Herbs from CKNP……………………………………………………………………... 104 Table 5.1 Four groups and one isolated stand obtained from Ward’s cluster analysis of forested and non forested species from 32 stands based on density ha-1 and environmental variable (elevation, slope)……………………………………………... 120 Table 5.2 showing understorey mean frequency of forested and non-forested vegetation …………………………………………………………………………….. 121 Table 5.3 Mean values of environmental variables (topographic and edaphic) and soil nutrients based on forested and non forested groups derived from Ward’s cluster analysis using 32stands of CKNP. (Mean ± SE)……………………………………… 122 Table 5.4 Showing means of groups of circular plot species (understorey vegetation) on the basis of frequency and environmental variables using Ward’s cluster analysis.. 128 Table 5.5 Mean values of environmental variables (topographic and edaphic) and soil nutrients based on circular plot groups derived from Ward’s cluster analysis using 32stands of CKNP. (Mean ± SE)……………………………………………….. 129 Table 5.6 Relationship (correlation coefficients) of environmental variables (topographic variables and edaphic variables) and soil nutrients with 3 DCA ordination axes obtained by forested and non forested vegetation data based on density ha-1…………………………………………………………………………….. 135 Table 5.7 Relationship (correlation coefficients) of environmental variables (topographic variables and edaphic variables) and soil nutrients with 3 DCA ordination axes obtained by understorey vegetation data based on frequency………... 141 Table 5.8 Analysis of variance of individual environmental variables (topographic and edaphic) and soil nutrients. Four groups were derived by Ward's cluster analysis using forested and non forested vegetation data of 32 stands of CKNP Gilgit- Baltistan, Pakistan……………………………………………………………………... 143 Table 5.9 Analysis of variance of individual environmental variables (topographic and edaphic and) and soil nutrients. Six groups were derived by Ward's cluster XVI analysis using understorey vegetation data of 32 stands of CKNP Gilgit-Baltistan, Pakistan………………………………………………………………………………... 146 Table 6.1 Four groups isolated from Ward’s cluster analysis of forested and non forested species from 32 stands based on environmental data and density ha-1 (elevation, slope), edaphic factors and soil nutrients………………………………….. 160 Table 6.2 Showing means of groups of forested and non forested species frequency on the basis of environmental variables (topographic and edaphic) and soil nutrients using Ward’s cluster analysis………………………………………………………….. 161 Table 6.3 Mean values ± SE of environmental variables (topographic, and edaphic) and soil nutrients based on forested and non forested groups derived from Ward’s cluster analysis using 32stands of CKNP. (Mean ± SE)………………………………. 162 Table 6.4 Showing means of groups of circular plot species (understorey vegetation) frequency on the basis of environmental variables and soil nutrients using Ward’s cluster analysis………………………………………………………………………… 168 Table 6.5 Mean values ± SE of environmental variables (topographic, and edaphic) and soil nutrients based on circular plot groups derived from Ward’s cluster analysis using 32stands of CKNP. (Mean ± SE)……………………………………………….. 169 Table 6.6 Relationship (correlation coefficients) of environmental variables (topographic variables and edaphic variables) and soil nutrients with 3 DCA ordination axes obtained by forested and non forested vegetation data based on density ha-1…………………………………………………………………………….. 179 Table 6.7 Relationship (correlation coefficients) of environmental variables (topographic variables and edaphic variables) and soil nutrients with 3 DCA ordination axes obtained by understorey vegetation data based on frequency………... 181 Table 6.8 Analysis of variance of individual environmental variables (topographic and edaphic) and soil nutrients of 32 stands from CKNP Gilgit-Baltistan, Pakistan…. 183 Table 6.9 Analysis of variance of individual environmental variables (topographic and edaphic and soil nutrients. Six groups were derived by Ward's cluster analysis using understorey vegetation data of 32 stands of CKNP Gilgit-Baltistan, Pakistan………………………………………………………………………………... 186 Table 6.10 Physical and chemical properties of the vegetation of CKNP…………... 195 Table 11.1 COFECHA Statistics of Picea smithiana from Stak Valley……………… 236 Table 11.2 Descriptive statistics of Raw, Standard, ARSTAN and Residual chorology of Picea smithiana…………………………………………………………. 241 Table 11.3 EPS, SNR and Rbar values……………………………………………….. 242 Table 12.1 Summary of various correlation and response function analysis using different chronologies with local climate and grid data. Only significant elements are shown………………………………………………………………………………….. 257

XVII LIST OF FIGURES

Fig. 1.1 Mean monthly temperature (Co) and precipitation (mm) of Gilgit and Skardu (1972-2011)…………………………………………………………………………… 14 Fig. 1.2 Map of the study area with sampling Locations……………………………… 24 Fig. 3.1 dbh size classes of forested and non forested vegetation of CKNP………….. 64 Fig.3.2 Overall dbh size class structure of dominant conifer tree species on the basis of mean density ha-1……………………………………………………………. 66 Fig 3.3 Generalized Weibull distribution models of dominant coniferous species…… 68 Fig. 3.4 Correlation of stand density ha-1 with stand basal area m2 ha-1 , slope and elevation with density ha-1…………………………………………………………….. 75 Fig. 3.6 Correlation of forested and non-forested species density ha-1 with species basal area m2 ha-1, slope and elevation with species density ha-1……………………... 79 Fig. 3.7 Correlation of forested and non-forested topographic factors (slope and elevation) with stand and species basal area m2 ha-1…………………………………... 84 Fig.5.2 Dendrogram resulting from cluster analysis based on frequency of understorey vegetation………………………………………………………………… 127 Fig.5.3 DCA ordination axes 1 and 2 of forested and non forested vegetation data based on density ha-1. The groups derived from Ward’s cluster analysis are not superimposed on 2-D ordination axes...... 132 Fig.5.4 DCA ordination axes 1 and 3 of forested and non forested vegetation data based on density ha-1. The groups derived from Ward’s cluster analysis are not superimposed on 2-D ordination axes…………………………………………………. 132 Fig.5.5 DCA ordination axes 2 and 3 of forested and non forested vegetation data based on density ha-1. The groups derived from Ward’s cluster analysis are superimposed on 2-D ordination axes…………………………………………………. 133 Fig.5.6 DCA among axes 1 and 2 of understorey vegetation data based on frequency. 137 Fig.5.7 DCA among axes 1 and 3 of understorey vegetation data based on frequency. 138 Fig.5.8 DCA among axes 2 and 3 of understorey vegetation data based on frequency (Not superimposed)...... 139 Fig.6.1 Dendrogram, based on Information level and Euclidean distance of the 32 stands of forested and non-forested environmental and vegetation data are presenting four groups…………………………………………………………………………….. 159 Fig.6.2 Dendrogram, based on Information level and Euclidean distance of the 32 stands of understorey vegetation………………………………………………………. 167 Fig.6.5 DCA ordination axes 2 and 3 of forested and non forested vegetation data based on density ha-1………………………………………………………………….. 173 Fig.6.6 DCA among axes 1 and 2 of understorey vegetation data based on frequency. 175 Fig.6.7 DCA among axes 1 and 3 of understorey vegetation data based on frequency. 176 Fig.6.8 DCA among axes 2 and 3 understorey vegetation data based on frequency….. 177 Fig.6.9 Whisker box plots of Physico-chemical factors of CKNP……………………. 194 Fig.9.1 Map of study area, circle shows sampling site (Stak valley of CKNP)………. 220 Fig.10.1 dbh vs age histograms analysis of Picea smithiana seedlings……………….. 223 XVIII Fig.10.2 dbh vs age regression analysis of Picea smithiana seedlings………………... 223 Fig.10.3 Age vs growth rates histogram analysis of Picea smithiana seedlings……… 224 Fig. 10.4 Age vs growth rates regression analysis of Picea smithiana seedlings……... 224 Fig.10.5 Growth rates of seedlings in various time periods…………………………... 226 Fig.10.6 dbh vs age histograms analysis of Picea smithiana…………………………. 227 Fig. 10.7 dbh vs age regression analysis of Picea smithiana...... 227 Fig. 10.8 Age vs growth rates histograms analysis of Picea smithiana……………… 228 Fig.10.9 Age vs growth rates regression analysis of Picea smithiana...... 228 Fig.11.1 Raw chronology plot of Picea smithiana……………………………………. 237 Fig.11. 2 Residual chronology plot of Picea smithiana………………………………. 238 Fig.11.3 Standard chronology plot of Picea smithiana……………………………….. 239 Fig.11.4 ARSTAN chronology plot of Picea smithiana……………………………… 240 Fig. 11.5 Graphs of rbar , EPS (Expressed population signal) and sample depth of Picea smithiana from Stak valley……………………………………………………... 243 Fig.12.1 Correlation coefficients of residual chronology vs local climate……………. 249 Fig.12.2 Response coefficients of residual chronology vs local climate……………… 250 Fig.12.3 Correlation coefficients of residual chronology vs grid……………………... 251 Fig.12.4 Response coefficients of residual chronology vs grid……………………….. 252 Fig.12.5 Correlation coefficients of standard chronology vs local climate…………… 253 Fig.12.6 Response coefficients of standard chronology vs local climate……………... 254 Fig.12.7 Correlation coefficients of standard chronology vs grid…………………….. 255 Fig.12.8 Response coefficients of standard chronology vs grid………………………. 256

Abstract

ABSTRACT This study focuses on quantitative community description, structure, multivariate analyses, vegetation-environment relationships and dendrochronological potential of Central Karakoram National Park Gilgit-Baltistan, Pakistan. Thirty two stands of forested and non-forested vegetation were sampled using point centered quarter method (PCQ) for trees while 3×5m quadrat were employed for non-forested vegetation sampling respectively. On the basis of phytosociological analysis, one mixed community, two pure conifer stands, a juniper forest and 6 non-forested communities were recognized. Advanced multivariate techniques including Ward’s agglomerative clustering method were used to seek group structure. For the forest vegetation groups were based on density ha-1 of tree species while the typification of understorey vegetation was performed on the basis of species frequency (%). DCA ordination was used to examine the distribution pattern of vegetation, to seek trends and gradients in vegetation. The groups derived from cluster analysis were superimposed on the ordination plane. Vegetation-environment relationship was investigated employing environmental variables including topographic factors, edaphic factors and soil nutrients using PCA ordination. It is observed that elevation and slope are significantly correlated with vegetation. Among the edaphic factors conductivity and soil pH were significantly correlated with vegetation while among the soil nutrients Nitrogen, Potassium, Calcium, Cobalt and Iron attains significant relation with vegetation. An attempt was also made to investigate structure and future trends of the vegetation of CKNP. In the forested vegetation Picea smithiana, Pinus wallichiana and Juniperus excelsa while in non-forested vegetation Rosa webbiana, Hippophae rhamnoides, Berberis lycium, Ribes orientale, Ribes alpestre and Tamarix indica exhibited high densities ha-1. Some gaps were observed in different size classes of these species which indicates that the size structures of these species were deteriorating with the passage of time. It may be due to overgrazing, illegal cutting, logging, soil erosion and competition. Most of the stands did not show the ideal situation and no inverse J- shaped curve was formed. Seven stands showed the positive skweness distribution, 5 stands attained flat distribution, 4 stands normal distribution,3 stands distributed in rectangular manner, 3 stands gave bimodal shape, 3 stands attained unimodal while the

Abstract

remaining stands distributed with U-shaped and leptokurtic shape. It is also noticed that these species have lesser recruitment which is alarming as the vegetation may completely disappear with the passage of time. Size class distribution of Picea smithiana, Pinus wallichiana and Juniperus excelsa was performed using Weibull probability function. It was observed that Weibull model gave a good fit for all three tree species examined. Age and growth rates of Picea smithiana trees and seedlings from Stak valley were investigated. It was observed that 90 cm dbh tree may attain 400 years age while growth rate ranges 3.9 to 17.4 year/cm in trees. In the seedling, the maximum age of seedling was 126 years while growth rates ranged 2 to 15.8 year/cm. Dendrochronological potential of Picea smithiana was also investigated using 22 corssdated wood samples out of 32 samples whereas remaining cores were rejected due to complacent ring. The results of master series showed that Picea smithiana from Stak valley covered highest age of 330 (1680-2009) years whereas average length of this forest was 156 years. The maximum length of two or more portions attained 230 years (1780-2009). The EPS, SNR and Rbar values were observed as 0.94, 18.06 and 0.68 respectively. An attempt was also made to check the relationship between growth and climate of Picea smithiana form Stak valley of CKNP. The results show that, in case of temperature, previous November and previous December were observed to be significantly positively correlated with tree growth. June, July temperature was also seen to be significantly positively correlated with tree growth. However, April temperature was negatively correlated with tree growth in both correlation and response function analysis. While in case of precipitation, tree ring indices showed significant positive relationship with April precipitation. The results show the suitability of Picea smithiana with respect to dendroclimatic potential. With extended sampling chronology can be extended to series more than five hundred years.

General introduction

GENERAL INTRODUCTION

My research objective covers two aspects of the study, first relates with the phytosociological studies while the second part is about the dendrochronological potential of tree species from CKNP. I discussed the work of these two studies separately in my review of literature. Central Karakorum National Park is located in Gilgit-Baltistan area which lies in the dry temperate zone of Pakistan. The altitude ranges from 2000m to 8000m with the coordinates at North 35° to 36.5 while at East 74°to 77 °. Due to the unique variety of flora and fauna, it is decaled as national park in 1993. The park is rich of beneficial plants s including medicinal and economically important species. In the study area three gymnospermic tree species included Picea smithiana, Pinus wallichiana and Juniperus excelsa while in non-forested species Rosa webbiana, Hippophae rhamnoides, Berberis lycium, Tamarix indica, Juniperus communis, Ribes orientale and Ribes alpestre were the common and dominant shrub species in the park. These species are great economic, environmental and medicinal importance. In this thesis an attempt was made to investigate the ecology of this forested and non-forested vegetation and also present the dendrochronological potential of conifer tree species of the national park. The first part of my thesis covers quantitative description of vegetation, soil analysis, multivariate analysis, future trend and structure of the vegetation while in the second part an endeavor was made to investigate age and growth rates, develop the chronology and growthclimate response of gymnospermic trees of the Stak valley of CKNP. Due to the importance and commercial value of above mentioned forests different NGOs conducted the qualitative study in this region. Therefore current studies present the first detailed quantitative data of this national park. This study may be useful for the management of forests, ecology and to understand vegetation environment relationship of this area. In addition dendrochronological investigation may provide growth climate response of the species.

Aims and objectives

AIMS AND OBJECTIVES The main purpose of my work is as under:

¾ To perform quantitative sampling in gymnospermic forests as well as herbs and shrubs species of the National Park. ¾ Quantitative description of the vegetation. ¾ To present the population structure and its future trend. ¾ To present classification ordination in order to expose the underlying group structure and measure gradient that controls the vegetation dynamics. ¾ Physico-chemical analysis of soil from the forested and non-forested vegetation of CKNP ¾ To estimate age and growth rates of conifer species, using Dendrochronological technique. ¾ To develop the standardized chronology of conifer species from (CKNP). ¾ To explore the tree growth-climate response.

Chapter 1: Description of the study area

CHAPTER-1 DESCRIPTION OF THE STUDY AREA 1.1-Geography of Gilgit-Baltistan Gilgit–Baltistan formerly known as the Northern Areas was renamed by national assembly on 29 August, 2009 as “Gilgit-Baltistan Empowerment and Self-Governance Order 2009”. Geographically it borders with Afghanistan in west, Xinjiang area of China in north, India in the east and Khyber Pakhtunkhwa province of Pakistan in the west , Afghanistan's Wakhan corridor to the north, China to the east and northeast, Azad Kashmir to the south-west and in the south-east Jammu and Kashmir . The territory of Gilgit-Baltistan consists of seven districts namely Gilgit, Ghizir, Diamar, Hunza Nagar, Astore, Skardu and Ghanche. Gilgit–Baltistan covers an area of 72,971 km² and is highly mountainous. It has an estimated population approximately 2 millions (Census 1998). Its administrative center is the Gilgit city. There are different languages are spoken in different regions like Shina, Balti, Brushushki, Khowar and Wakhi but the dominant language is Shina which is spoken in all seven districts of Gilgit-Baltistan while Urdu and English are also spoken. This area is unique in its culture, tourism, natural resources and topography that undeniably make the area glorious. The area is rich of flora and fauna and its alpine pastures are full of economical, medicinal and aromatic plants. Mountains of Gilgit-Baltistan are covered with snow which is the prime watershed of Indus River, providing the fresh water to the whole country. The culture and costumes of the Gilgit-Baltistan is more or less similar in all seven districts which includes food, music, dressing, folk dance, and traditional sports. The region of Gilgit-Baltistan is famous for hospitality which attracts the tourists. The tourists, nature lovers, and adventurers also attracted due to the presence of K-2 world’s 2nd highest mountain peak. After north and south Pole, the area holds the largest amount of snow. The glaciers are the main source of fresh water for drinking, agriculture and electric power generation. The region is also attractive due to the junction of world's three famed mountain ranges Karakorum, Hindukush and Himalaya. The unique diversity of flora, fauna, watersheds, tourism, pastures, glaciers, lakes, culture, minerals

Chapter 1: Description of the study area and mines attracts the researchers, visitors, tourists, trekkers, adventurers from all over the world. The areas is accessible both by bus and airplane from Islamabad to Gilgit- Baltistan 1.2-Karakoram Mountain Range The word “Karakoram” originated from the Turkish word “Kara-koram” which means “black rock”. The Karakoram mountain range begins from the Chilnji, south east of Wakhjir Pass which extends to the Ladakh beyond the Siachen glaciers to the extreme ,it touch the Tibet in China. The Karakoram Range is 300 miles long and 60 miles wide which originated from the Tethys Sea 50 million years ago due to the collision between Indian and Asian tectonic plates. The results of this collision, Indian plate penetrated the edge under the Asian plates. The evidence of that tectonic moment still felt in Nanga Parbat with 7mm Richter magnitude scale of frequent earthquakes and landslide. The positive effect of this collision is that hot springs in this range erupting which provides the dynamic geology to the Karakoram Range. There are seven famous glaciers are located in Karakoram Range namely, Batura, Hisper, Panamah, Baltoro, Siachen, Rimo and Saser which are collectively called Mustagh. The altitude of the mostly mountains are above 6000m while K-2 (Karakorum II,8611m) is the highest peak of this region along with 133 peaks above 7000m and hundreds more above 6000m which include Rakapohi, Hidden peak, Broad peak, Gasherbrum 1 to IV, Kiangshish, Masherbrum, Spatnik,. The slope of Karakoram Range is very steep with gigantic glaciers. Forty glaciers are found in the Karakorum range including famous glaciers which are Siachen, Hisper-Hopar, Batura, Baltoro, Biafo, Balafond, Godwin Austin Gondgoro, Chogholunggma, Chogolunsa, Chomaig, Gasheburum, Sapolago, Bozgil, Broad peak Glacier, Weyin, Wirjerab, and Khurdopin.

Chapter 1: Description of the study area

1.3-Geology of Gilgit- Baltistan

The geological history of the massive mountains of Gilgit-Baltistan is very ancient, some worlds highest peak group including Rakaposhi, Ultar, Diran, Broadpeak, Muztagh towers, Gasherbrum, Mashabrum, Baltoro, Trango Towers, Batura, Saltoro Kangri and many others peaks are found in Gilgit-baltistan (Trench, 1992). According to Stein (1987) these mountains covered with snow and glaciers and approximately 12 % of the regions formed by the glaciers which including Hispar 62 km, Siachen 62 km, Biafo – 61 km , Baltoro – 58 km , Batura – 58 km ,Gasherbrun – 38 km , Chungo Lungma – 38 km , Passu – 32 km , Nabandi – 32 km , Baraldu – 30 km ,Rupal 29 km and Snow lake sim glacier – 20 km , along with hundreds of other glaciers. The region contains mostly metamorphic rocks, due to the tectonic activity or magmatism in past, resulting in the creation of precious gemstones. The area is also a centre of tectonic activity where earthquakes and land sliding are common. Atabad Lake is the recent disaster of these activities which occurred in 4 January 2010. Other stories of geological activities i.e disasters of the earthquake, lightening flesh, floods, land sliding are frequently reported from the different regions of Pakistan in the past. The mountain ranges are rich for gem stones which including aquamarine, tourmaline, topaz, quartz etc. There are many types of gemstone mines in Gilgit-Baltistan around of 30 types are reported from this region. The famous sites for mining are Haramosh, Nagar, Shigar, Khaplu and Rundu. These valleys are the boundaries of CKNP where thousand of miners worked in above 2000 mines. These

Chapter 1: Description of the study area mines are the public property however, in some places localities formed a society which decided to work in mining. The gem stone of the region is famous nationally and internationally due to the commence of divers tourist. Although these gemstones benefit for the localities and the economy of the country but there are some health, environmental and safety issues are also associated. It is scientific fact that silicosis and carbon monoxide affect the lungs which are the lethal agents for human beings. On the other hand wildlife of the area is directly or indirectly affected by mining activates. There are several problems are safety cannot be denied because the miners are not well trained and many causalities are reported from the region. 1.4-Economic structure of Gilgit-Baltistan The economy of Gilgit Baltistan is endurance of extensive and diverse. The area depends upon agriculture, forestry, tourism, gem stones and mineral resources. The unique beauty of this area attracts the thousand of national and international tourists. The region is rich for precious gem stones and minerals which including Nickel, Cobalt, Copper, Lead, Tin, Bismuth, Mica, Quartz, Zircon, Coal and Actinolite that are famous for their good quality and economic importance directly or indirectly polished and converted into jewelry form. These gemstones are introduced and supplied to the national and international markets. Some mineral resources of Iron, Silver, Gold, Zinc, Marble, Granite, Sulpher, Calcite, Fluorite, Lime Stone, Arsenic, Spinal, Garnet, Epidote, Topaz, Moon Stone, Pargasite, Tourmaline, Aquamarine, Pyrite and feldspar are also reported from the region by Hussainabadi, (2003). Some cultural goods, fruits, vegetables are also the source of income. The other source of the high income is hotel industry which rapidly increases due to the scenic beauty of Gilgit-Baltistan convinced the national and foreign visitors, researchers and adventurers. Although deforestation is a bad practice but still going on, in various localities. The wood of Gilgit-Baltistan forest is very important both commercially and economically (Hussain, 2010).The Local people cut the forests for the purpose of fuel, shelter and to earn money from the market. It is concluded that the major sources of income in this region are tourism, mines and minerals, agriculture, vegetables, fruit farms, forestry and hotel industry.

Chapter 1: Description of the study area

1.5-Central Karakoram National Park (CKNP) Central Karakoram National Park (CKNP) is located in Gilgit-Baltistan of Pakistan. It falls into the administrative boundaries of district Gilgit, Skardu and Ghanche. It is one of the 24 national parks of Pakistan. Because of its unique and diverse habitat of flora and fauna, it was declared as National Park in the year 1993.It is the biggest National park in Pakistan, covering an area of 10,000 km2 .The CKNP extends from 35°N to 36.5°N latitude and from 74°E to 77°E longitude. The elevation ranges from 2000m-8000m. Sixty peaks over 7,000 m, and ten of the world highest and most famous mountains, including Gasherbrum, Broad Peak, and Masherbrum are located within the park' boundary and the world’s 2nd highest peak K-2 is also situated in this park. According to the 1998 census there are 350 settlements (permanent and non- permanent) and total population of these settlements is approximately 211,000. The fresh water source is mainly glaciers. The largest glaciers i.e Siachen (75km long), Baltoro (57km), and Hispur-Biafo (122km) are originating within the park. The main valleys included in the park are Bagrot, Haramosh, Hopar-Hisper, Shimshal I, Shimshal II, Braldu I, Braldu II, Hushe, Thalley, Stak-Tormik, Shigar, Nar, Kowardo, Chungo, and Basha (Arandu). 1.6-Management of Central Karakoram National Park National Parks in Gilgit-Baltistan are the responsibility of the Forest Department falling under the Directorate of Parks and Wildlife. The Park is governed by the 1975 Northern Areas Wildlife Preservation Act. Though declared National Park in 1993 there is neither physical indication nor is public awareness on the ground to suggest the existence of CKNP. Currently CKNP Directorate is developed and director of CKNP is the responsible for all matters collaboration with different institution involved in and around the park includes the Aga Khan Rural Support Program (AKRSP), IUCN, and the World Wildlife Fund. However there are a number of smaller NGOs working in or adjacent to the park with a range of conservation projects.

Chapter 1: Description of the study area

1.7-Climate of Gilgit-Baltistan Due to the influence of higher mountains, rain shadows in some laces and high precipitation in other regions present. The Karakorum and Hindukush create barriers between monsoon lands of South-Asia to south and west deserts of the Central-Asia to their north. Therefore in the summer season this area is influence by the residue of the monsoon system coming from south and during the winter and spring it is influenced by westerly depressions originating in the Mediterranean and Caspian Sea. The climate is varies between lowlands and valleys. The valleys are dry with the annual precipitation around 200 mm but totals can go up as high as 600 mm at elevations of 13,000 ft (Kreutzmann, 2006, 2000a and 2000b). In the month of July and August the temperature of Gilgit-Baltistan is maximum around 20-35 Co in the valleys while in cold arid regions in January with an average of -10 to 00 Co. Archer (2010) found that both Gilgit and Skardu annual rainfall has increases for all seasons. The annual one day maximum rainfall has risen from 12 to 30 mm at Skardu and from 9 to 28mm at Gilgit. Archer (2003 and 2004) suggested that the changes in precipitation at high elevation can be interfered by increment of ice cores and snowfall.

Chapter 1: Description of the study area

Monthly Mean Pricipitation (mm) of Gilgit (1972-2011) 30.0 25.0 20.0 (mm) 15.0 10.0 5.0 0.0 Precipitation Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec Months

Monthly Mean Maximum Temperature of Gilgit (1972-2011) 40.0 )

30.0 C

(

20.0

10.0

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

Mean Monthly Minimum Temperature of Gilgit (1972-2011)

15 )

(c 10 5 0 Temperature ‐5 JanFebMar Apr May Jun Jul Agu Sep Oct Nov Dec Months

Chapter 1: Description of the study area

Monthly Mean Pricipitation (mm) of Skardu (1972-2011)

50 40 30 (mm)

20 10 0 Pricipitation Jan Feb Mar Apr May Jun Jul Agu Sep Oct Nov Dec Months

Mean Monthly Maximum Temperature of Skardu (1972-2011) 40

30 )

(

Temperature JanFebMar Apr May Jun Jul Agu Sep Oct Nov Dec Months

Mean Monthly Minimum Temperature of Skardu (1972-2011) 20 ) 10 (c

‐10

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

Fig. 1.1 Mean monthly temperature (Co) and precipitation (mm) of Gilgit and Skardu (1972-2011)

Data source: Pakistan Metrological Department

Chapter 1: Description of the study area

1.8-Ecological Zonation According to Rao and Marwat (2003) on the basis of ecological Zonation five main types of forests are existed in Northern Areas i.e Montane Sub-Tropical Scrub, Montane Dry Temperate Coniferous, Montane Dry Temperate Broadleaved, Sub-Alpine and Northern Dry Scrub. 1.8.1- Montane Sub-Tropical Scrub forests These forests are found between the elevation 750 to 3,900m a.s.l. These forests divided into Montane sub-tropical scrub comprises of Capparis spinosa, Pisticia, Artemisia, Saccharum, Dodonaea, Berberis, Rosa moschata and Daphne oloides. This area covered along the Indus River up to Raikot and Bunji. 1.8.2-Montane Dry Temperate Coniferous forests This Zonation is consisted by dry deodar (Cedrus deodara), blue pine (Pinus wallichiana), fir (Abies spectabilis), spruce (Picea smithiana), chilghoza (Pinus gerardiana) and juniper (Juniperus spp.). These important species are found in district Diamer, some parts of districts Gilgit, Skardu and two villages in Ghizer district. 1.8.3- Montane Dry Temperate Broadleaved forests These forests contain the species oak (Quercus ilex), ash (Fraxinus spp.), poplar (Populus), willow (Salix) and Artemisia. 1.8.4- Sub-Alpine forests This zone consists of snowfall up to 3 m/year with slight rainfall. In this zone plant species included birch, willow, and juniper, Ephedra, Vibernum, Andropogon, Berberis, Lonicera and Ribes. 1.8.5-Northern Dry Scrub This is the zonation of scattered scrub vegetation. In this zone, important and common species are included sea buckthorn (Hippophae rhamnoides) Willow (Salix) and stunted Juniper trees also grow on the hill sides.

Chapter 1: Description of the study area

Mix forest at Bagrot valley Picea smithiana forest at Haramosh Valley

Shrub communities at Thally Valley A mix-forest view of Stak valley

1.9-Land used in Gilgit-Baltistan As we discussed that Gilgit-Baltistan is covered of high mountains which are rich of glaciers therefore melting of glaciers would affect the land use patterns around the glacier, e.g., agriculture, forestry and cultivated areas and also affect the habitat of plants and animals. The classification of land use in Gilgit-Baltistan is as under:

Chapter 1: Description of the study area

Table 1.1 Land distributions in Gilgit Baltistan

Type Area (ha) Percentage (%) Mountain/ Lakes/ Rivers/ 4,810 66 Glaciers Forest 646 09 Rangeland 1,646 23 Cultivated Area 58 1 Cultivable waste 90 1 Total 7,250 100 Source: Forest department of Gilgit-Baltistan

Chapter 1: Description of the study area

1.10-Medicinal Plants Gilgit-Baltistan is rich of a wide variety of plants which have high medicinal and economic importance. Wali and Khatoon (2007) reported some important gymnospermic tree species are medicinally used by the local communities (Table 1.2).

Table 1.2 Some important medicinal gymnospermic plant species from CKNP

S.No Name of species Local Name Purpose of usage 1 Juniperus communis L. Mitthary Kidney stone, Urine problem, Leucorrhea and Tuberculosis 2 Juniperus excelsa. M.Bieb Cheleh Kidney stone, Urine problem, Weakness of urinary bladder 3 Pinus wallichiana A.B.Jackson Cheenh Resin is used against wound 4 Pinus gerardiana Wall.ex. Lamb Cheenh Resin is used against wound 5 Picea smithiana (Wall). Bois. Kachul Heart problems 6 J.turkestanica Komarov Cheenh Kidney stone, Urine problem,Weakness of urinary bladder 7 Ephedra gerardiana Wall.ex Stapf Soom Asthma, Cough and other respiratory problems 8 E.intermedia Schrenk & Meyer Shaay soom Wound, Asthma, Rheumatism and Gouts Source: Wali and Khatoon (2007).

Some other plant species are also practiced medicinally such as Betula utilis, Berberis lycium, Hippophae rhamnoides, Ribes alpester, Ribes orientale, Rosa webbiana, Rubus irritans, Spiraea canescens etc.

Chapter 1: Description of the study area

Some dominant shrubs and herbs of CKNP

Ribes orientale Rosa webbiana

Hippophae rhamnoides Spiraea canescens

Thymus linearis Taraxacum sp.

Artemisia brevifolium Anaphalis virgata

Chapter 1: Description of the study area

1.11-Wildlife According to Virk et al., (2003) the Gilgit-Baltistan has one of the most diverse avi fauna of the mountain region of the world. But little information is available on the distribution, status, diversity and ecology of many of these bird species. The most comprehensive account of the avifauna of Pakistan comes from Robert (1991). In spite of ruthless hunting wildlife in Gilgit-Baltistan supports a wide variety of endangered species of mammals and birds like Marco Polo sheep, blue sheep, markhor, black bear, brown bear, chakor and ram chakor. Fifty-four mammal species are reported from Gilgit-Baltistan. These species consist of one shrew, 10 bats, 18 carnivores, 6 artiodactyls, 3 lagomorphs, and 16 rodents. Upper Hunza and the triangle between Indus and Astore rivers are considered the “Hot Spot” for large animal diversity including snow leopard, Marco Polo sheep, Himalayan brown bear, black bear, musk deer, flare horned markhor, Laddakh urial, blue sheep, and Himalayan lynx. Other species like wolf (Canis lupis), Urial (Ovis orientalis) and Marmot (Marmota caudata and Marmota bobock) also reported in different areas of Gilgit-Baltistan. 1.12-Water Resources As we know that glaciers are asset of the country and the mountains of the Gilgit- Baltistan are covered by glaciers and snow. These glaciers and the snow are the main source of water for Gilgit-Baltistan and Pakistan. Irrigation is also essential in this region because of meager precipitation in the valleys. The peoples of Gilgit-Baltistan used water from the water nallahs which fed by glaciers and snow melt water for their drinking and irrigation purposes. The water is contaminated due to the interruption of human and animal feces. Different NGOs and GBPWD are working on to purify the water. Inspite of these precautions there is still an immense need to improve drinking water quality in Gilgit-Baltistan.

Chapter 1: Description of the study area

1.13-Forest Cover in Gilgit-Baltistan Forest in Gilgit-Baltistan is divided into two categories i.e protected and private forest. 1.13.1-Protected forests Those forests which are ownership to the Gilgit-Baltistan Government have been declared as “protected forests” under the Pakistan forest Act (1927). The other category of forests is “private forests” which are owned by the local communities. These forests are also legally enclosed under the Gilgit Private Forests Regulations (1970). According to the Act (1976) protected forests are the property of the government. However local communities may have used these forests for grazing and fuel wood. These protected forests are found in Gilgit, Astore and Baltistan and regularized under the Northern Areas Forest Rules 1983. According to Ali (2004) the total area of protected forests in Gilgit-Baltistan is 64,512 ha with the dominant species including spruce (Picea smithiana), silver fir (Abies pindrow), blue pine (Pinus wallichiana), Juniper (Juniperus excels). The total shrubs cover is 381,200 ha.

Table 1.3 Protected Forests cover in Gilgit-Baltistan.

District Area (ha) Forest Types Gilgit and Nagar 17,028 Montane Dry Temperate and Sub- alpine Ghizer (Punial) 7,740 Montane Dry Temperate and Sub- alpine Astore 30,960 Montane Dry Temperate and Sub- alpine Baltistan 9,288 Montane Dry Temperate and Sub- alpine Total 65016

Source: Rao (2003).

1.13.2-Private Forests Private forests are the property of local people which are regularized under the Gilgit Private Forests Regulations, 1970 and the rules notified in 1975. In an 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 Gilgit-Baltistan. The local communities agreed that the management of these forest

Chapter 1: Description of the study area by the Northern Areas Forest Department (NAFD). This forest department has been helping in harvesting of private forest, working schemes and to resolve the problems. The Local communities have a 100% ownership and the government gets 50% royalties from the revenue (Ali, 2004).

Table 1.4 Trees volume in private forests in Chilas, Darel and Tangir

Period Total trees (million) Total volume (m3) Volume to be harvested (m3) 2002-2013 5.51 9,169,924 1,281,507

Table 1.5 Overall status of private forests (ha)

Govt. protected forests in Gilgit, Skardu, Ghizer & Astore 65,000 Private forests (govt. managed) in Chilas, Darel and Tangir 2,17,000 Private owned farmland forestry in seven districts Gilgit-Baltistan 3,25,000 Total 6,06,000

Source of Tables 2 and 3: Rao (2003).

Chapter 1: Description of the study area

1.14-Polices and legislation The first forest policy of Pakistan was declared in 1955 and updated in 1992, 1975, 1980 and 1988 as part of the National Agricultural Policy. In 1991 National forest policy revised and integrated formal policy on the federal level (Ahmed and Mahmood, 1998). The purposes of these polices was to protect and the productivity of the forest. In 2001 a draft has been prepared by the National Forest Policy which is also applicable for Northern Areas. In this report it is stated that use of natural resources with community participation was allowed and recommended that timber harvesting can be used for poverty alleviation.

1.15-Problems and Issues Although there are the legislation and policies to protect the forests, however some of the following problems and issues are common in the forest areas: ¾ Lack of community collaboration with government departments. ¾ Insufficient funds for the management and protection of forests. ¾ Illegal cutting and overgrazing. ¾ Lack of awareness in local communities. ¾ Inadequate research approach. ¾ Lack of skilled staff or professional in the forest department. ¾ No alternative jobs for forest people. ¾ No alternative fuel except wood. ¾ Poverty. ¾ Lack of education.

Chapter 1: Description of the study area

Fig. 1.2 Map of the study area with sampling Locations

Chapter 1: Description of the study area

Table 1. 6 Environmental characteristics of study sites of CKNP

Stands Main Location Lat.(N) Long.(E) Ele.(m) Asp. Slo. CN Forest areas 1 Bagrot 36.02918 74.60156 3130 E 45° Mod. 2 Haramosh 35.88388 74.88433 3296 E/S 53° Ope 3 Hoper 36.14278 74.94410 3486 E 49° Clo 4 Stak 1 35.75901 75.05340 3344 E 35° Mod 5 Stak 2 35.77398 75.04300 3600 E 20° Mod 6 Rakaposhi 1 36.12485 74.94081 3444 N 70° Mod 7 Rakaposhi 2 36.18000 74.66000 3263 N 59° Mod 8 Rakaposhi 3 36.04021 74.54186 3188 N 64° Mod 9 Rakaposhi 4 36.15703 74.92910 3512 N/E 70° Mod Shrubs/Herbs area 10 Bagrot 36.03400 74.57735 27774 N Pla. Ope 11 Hopar 36.16258 74.84320 3353 N/E 30° Ope 12 Stak 01 35.73915 75.10943 2949 E/N 35° Ope 13 Stak 02 35.74459 75.05935 2782 E/S 20° Ope 14 Stak 03 35.74053 75.05651 2742 E Pla Ope 15 Thallay 01 35.17268 76.33680 3300 E 20° Ope 16 Thallay 02 35.17575 76.33440 3500 E/N 25° Ope 17 Kowardo 35.40611 75.60833 3559 E 50° Ope 18 Arandu 1 35.93333 75.70375 2790 S/W 30° Ope 19 Arandu 2 35.83333 75.73858 2815 S 25° Ope 20 Arandu 3 35.79565 75.73868 2875 S/W 35° Ope 21 Shigar 1 35.68970 75.88908 2527 N/E 40° Ope 22 Shigar 2 35.72278 75.79650 2444 E 35° Ope 23 Shimshal 1-1 36.73260 75.53743 3047 E/S Pla Ope 24 Shimshal 1-2 36.73423 75.54813 3065 E/S Pla Ope 25 Shimshal 2-1 36.73228 75.55151 3076 E/S Pla Ope 26 Shimshal 2-2 36.72853 75.55553 3097 E/S Pla Ope 27 Braldu 1-1 35.67020 75.76706 2895 E 25° Ope 28 Braldu 1-2 35.70150 75.75516 2910 E 20° Ope 29 Braldu 2-1 35.70516 75.75353 2948 E/S 35° Ope 30 Braldu 2-2 35.71848 75.85377 3055 E 30° Ope 31 Chungo 1 35.81715 75.68707 3010 N 40° Ope 32 Chungo 2 35.81808 75.92186 3109 N/E 35° Ope Lat = Latitude, Long = Longitude, Ele = Elevation, Asp = Aspect, Slo = Degree of slope, CN = Canopy, Mod = Moderate, Ope = Open, Clo = Closed, CS = Covered surface, Pla = Plain.

PARTI ECOLOGY

Chapter 2: Review of literature

CHAPTER- 2 REVIEW OF LITERATURE 2.1-Introduction This chapter focuses on the brief literature review from the different parts of Pakistan regarding observational studies, quantitative studies, multivariate analysis, and vegetation relationship with environmental variables and physico-chemical analysis. 2.2-Literature review Many researchers conducted the observational studied from different sites of Pakistan (Chaudhri, 1952, 1953, 1960; Khan, 1955 & 1960). Hussain (1960) carried out the vegetation survey of Ayub National park. Stewart (1961) worked on the flora of Deosai plains. Jafri (1962) classify the vegetation of Bolan Pass. Khan and Repp (1961) described the Riverian forest and irrigated plantation of Pakistan while Nasir and Webster (1965) worked on the vegetation of Hushe valley, Baltistan. Similarly Rafi (1965 and 1973) presented the vegetation of Baluchistan. Champion et al., (1965) conducted a detail survey of different parts of Pakistan, they also described the vegetation of different climatic zones (i.e subtropical, moist temperate, dry temperate and sub-alpine) of Northern Pakistan. Rep and Beg (1966) studied the Juniper forest of Ziarat Balochistan. Shaukat and Qadir (1969) quantitatively describe the vegetation of calcareous hills around Karachi and they described the lithosere succession. Hussain (1969) studied the relationship between vegetation and edaphic factors from Wah Garden Cambellpur District Rawalpendi. Hussain and Qadri (1970) investigated the relationship between the plants species growth and quality of the physical and chemical properties from the surrounding areas of Karachi. They found correlation of plants density with soil texture and CaCo3.Shaukat and Qadir (1971) presented Multivariate analysis of the vegetation of calcareous hill around Karachi, and stated that potential continuity in vegetation with the aid of an indirect gradient analysis. Shaukat and Hussain (1972) analyzed the vegetation of Khadeji-fall area and described the hydrosere and lithosere succession and also studied quantitatively four stands representing different aspects of the hills. Their results revealed that the vegetation of the hills is principally constituted by perennial xerophytic shrubs like Commiphora

Chapter 2: Review of literature

wightii, Grewia tenax, Euphorbia coducifolia and Acacia senegal. The distribution of vegetation types was correlated with obvious physiographic factors. Malik et al., (1973) studied in the forests of Malakand Division and observed the nutrients condition of representative soil profiles, minerals compositions Khan and Ahmed (1976) quantitatively studied the medicinal plants in Rawalpindi north, Rawalpindi south and Muree forest division and described the ethnobotanical importance of these plants. Ahmed and Qadir (1976) carried out quantitative phytosociological survey along the way of Gilgit to Gopis, Yasin and Phunder. They sampled 46 different locations and recognized ten communities on the basis on physiognomy, floristic composition and importance value index. The attributes, maturity and homogeneity of every stand were also studied. First stand ordination of Skardu Northern area was presented by Ahmed (1976). Shaukat et al., (1976) presented Phytosociological study of Gadap area, Southern Sindh. Twenty two stands in Gadap area of Southern Sindh were sampled quantitatively and soil samples were analyzed physico-chemically. They studied vegetation composition and structure, dominant group of vegetation, diversity relations of leading dominant groups, objective classification and correlation of edaphic variables with the vegetation types. Ahmed (1973) and Ahmed et al., (1978) presented multivariate approaches to the analysis of the vegetation-environmental complex of Gharo, Dhabeji and Manghopir industrial areas. They studied the vegetation characteristics, vegetation ordination, relationships between environmental variables and stand composition, species ordination, the behavior of species along environmental gradients. Chaghtai et al., (1983a, b) described the ecology of a dry stream bed in Peshawar and phytosociology of the Muslim graveyards of Kohat division respectively. According to them the variety of habitats depends on the availability of moisture and the extent of biotic disturbance. The phytosociology of the Muslim graveyards of Kohat explained that the numbers of species

in a stand were controlled by the sand and CaCo3 proportions of the soil. Ahmed (1986) presented vegetation of some foothills of Himalayan range in Pakistan. Six communities have been recognized on the basis of species dominance at 17 locations near road side on the great Silk Road from Gilgit to Passu. Chagthai et al., (1987) gave a detail description

of soil factors i.e TDS, Sodium, Potassium and Calcium, NO3, PO4 and CaCo3 with

Chapter 2: Review of literature

vegetation. According to them CaCo3 is an important factor to control the supremacy of species. Tareen and Qadir (1987) reported sixteen plant communities on the basis of important index value in the plains of Quetta district and also they correlate soil with type of vegetation and concluded that total coverage as well as species diversity tended to be relatively high in protected areas and graveyards. Ahmed (1988a, b) studied plant communities of some northern temperate forests of Pakistan. Chaghtai et al., (1988) presented ecology of an upland forest near Noshehra, NWFP. They observed that the lower valley slope was dominated by arboreal vegetation, the middle by tall shrubs and the top exposed by grasses. Kayani et al., (1988) also studied the relationship between soil properties and vegetation types. Ahmed et al., (1989) described the natural regeneration potential of Juniperus excelsa Balochistan. They also studied the regenerating seedlings from 60 mature stands on Juniper track ranged from zero to 219 ha-1 with a mean of 52 ha-1. Seedling density and basal area were significantly correlated (P<0.001) while tree basal area and seedling density were also significantly correlated (P<0.05), indicating that seedlings are sciophytic and are found under the shade of trees. Height average seedling density and basal area were recorded from west facing slope, in addition, future trend of the seedling population suggesting that Juniper forest are not deteriorating. Shaukat & Uddin (1989) investigated the vegetation-environmental data set of Gadap area using Canonical Correlation Analysis (CCA). They also provided three new alternative models that could be used when singular value decomposition algorithm of CCA failed to provide any worthwhile results. Qadir et al., (1989) presented phytosociology of woodland communities of Hazarganji National Park Quetta. According to them the soil of the communities was coarse-textured, calcareous and non –saline. Ahmed et al., (1990) described population structure and dynamics of Juniperus excelsa in Balouchistan. Ahmed (1991) et al., described vegetation structure and dynamics of Pinus gerardiana forests in Balochistan and also noticed that diameter distributions within the stands were mostly skewed and unimodal with gaps appearing in large size classes. Cross sections of 16 trees were also used to determine age and growth rate. Moreover, adequate recruitment of Pinus seedlings was observed. The average growth rate was estimated 0.08 cm/year radial growth. However, trees on high elevations

Chapter 2: Review of literature

and cooler slopes grow faster. They analyzed soil variables which showed no correlation with density, basal area or importance values. They also suggested that the present degraded stage of the forests in the study area is of anthropogenic origin. Tareen and Qadir (1991) presented Phytosociology of the Hills of Quetta District. Fifty seven plant communities were recognized. Out of forty five communities were grouped into 8 steppe type viz. Twenty eight species were found as indicator species of specific soil condition. Hussain et al., (1993) described phytosociology of the vanishing tropical dry deciduous forests in district Swabi. According to him the variation in the dominant species was due to the edaphic and biotic disturbance. He suggested that the existing vegetation might further change due to underground seepage of water from nearby Tarbela dam. Rasool (1994) described the status and management polices of the protected areas included forested vegetation in the Northern Areas of Pakistan. 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. He examined the physico-chemical analysis of soil with the help of polar ordination. Soil pH, CaCo3 and P2O5 were found as the controlling factors in the distribution of vegetation. Hussain and Mustafa (1995) reported ecological studies of plants in relation to animal, found in Nasirabad valley Hunza Pakistan. Malik et al., (1994) investigated the vegetation of Samani and Dhirkot Hills and stated that heavy illegal logging, soil erosion and overgrazing has led to degradation of forests into scrubs. Ashraf, (1995) focused on the phytosociology of the vegetation in Pir- Chinasi hills and he recognized ten different communities. Among the tree species Pinus wallichiana and Abies pindrow were found to be the dominant species, while understorey species consisted of Viburnum grandiflorum, Indigofera, Elsholtzia, Sorbaria and Sibbaldia. Pinus wallichiana appeared to be the only regenerating species while Abies pindrow, Picea smithiana and Juglans regia did not regenerate. Malik and Zandiyal, (1996) also reported ten plant communities in Machyara Hills and declared that Cedrus deodara, Pinus wallichiana, Abies pindrow and Bistorta were the most common species of the vegetation. Rasool (1998) had provided a detail account of the economically important plants of northern areas. Alpine deserts have little values as grazing lands due to the absence of forage and difficult topography. Alpine pastures were subjected to heavy

Chapter 2: Review of literature

grazing during summer. Siddiqui et al., (1999) investigated the climate change impact and adaptation approaches of forest ecosystem in Pakistan. Tareen and Qadir (2000) carried out the soil organic matter, MWHC, CaCo3, conductivity, Bicarbonate, Chloride, Calcium, Magnesium, Sodium and Potassium from the plains Harnai, Sinjawi to Duki region of Pakistan. Ahmed and Khattak (2001) studied the vegetation of Islamabad and they recognized 17 plant communities on the basis of highest importance value. Acacia modesta was the dominant tree followed by Broussonetia papyrifera and Dalbergia sissoo respectively. Jaffari et al., (2003) studied the relationship of chemical and physical properties of soil with vegetation type. They used PCA and CCA methods of ordination to determine the soil factors included acidity (pH), texture, electrical conductivity, and - lime, Calcium, Magnesium, Sodium, Potassium, Cl , CO3 and HCO3Gilani (2003) surveyed the Astore area to provide information on the conservation of plant diversity. Malik and Malik, (2004) recognized seven plant communities and fifty eight plant species from Kotli hills and also reported the deforestation and overgrazing were common in the study area. Ali et al., (2005) described in detailed about deforestation and its causes from the forest of Basho valley Skardu. Shaukat et al., (2005) compared the techniques CA, DCA and CCA (canonical correspondence analysis) using a field data set from Lower Sindh and found that of the three techniques CCA was most useful in exposing the underlying correlation structure between vegetation and environment. Hussain et al., (2006) also recognized 28 different plant communities of trees, shrubs and herbs vegetation. Ahmed et al., (2006) presented phytosociology 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 index, 24 different communities and 4 monospecific forests were recognized. The quantitative description and their population structure were also presented. Most of the communities showed similar floristic composition however they were different in quantitative values. Parveen and Hussain (2007) conducted a study for the plant biodiversity and phytosociological attributes of the Gorakh hill and they provide inclusive inventory of the area. Shahbaz et al., (2007) gave a detailed description about

Chapter 2: Review of literature

the forest policies and implementation on forests of Pakistan. Malik (2007) described the phytosociological attributes of different plant communities of Pir Chinasi Hills Azad Jammu Kashmir. However, Dasti et al., (2007) also investigated vegetation composition and multivariate analysis on the Pothwar Plateau and recognized five plant communities the basis of cluster analysis. He also noticed that the application of the classification result may be used to ordination in terms of topography, redistribution of rain water, the nature of bedrock and soil depth. The relationship between vegetation and soil characteristics were evaluated in the Cholistan desert by Arshad et al., (2008). Noureen et al., (2008) used the Calligonum polygonoides species to improve the fertility of soil from the Cholistan desert, Pakistan. They observed that association between these two parameters are common and also concluded that ecological characteristics are the responsible for the distribution of plants seem to be salinity, organic matter and ionic concentration. Wazir et al., (2008) studied the multivariate analysis of vegetation of Chapursan valley an alpine Meadow of Pakistan .They performed classification and ordination of the species on the basis of cluster analysis and recognized 5 vegetation types and were discussed in terms of topography and edaphic heterogeneity. Perveen et al., (2008) 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 filed 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, phytosociological attributes, and some ecological parameters such as density, relative density, cover, relative cover, frequency and relative frequency were investigated. Wahab et al., (2008) described the Phytosociology and dynamics and age and growth rates relation of some forests from Afghanistan. Abbas et al., (2009) also investigated the vegetation of Gery Goral Range in Azad Kahmir with respect to the phytosociology. Karim et al., (2009) studied the effect of canopy cover on the organic and inorganic content of soil from Cholistan desert Ahmed et al., (2009) described the vegetation structure of Olea ferruginea Royle forests of lower Dir district of Pakistan and recognized ten different communities with similar floristic composition but different quantitative values. Correlation was checked among density/ basal area, elevation/

Chapter 2: Review of literature

density, and elevation/ basal area. They concluded that as the forest showed no recruitment since last 10-15 years; therefore no future trends could be predicted for these forests. The evaluation of ecological aspect of roadside vegetation around Havalian city using multivariate techniques (DCA and CCA) was given by Sheikh et al., (2009) . Jabeen and Ahmed (2009) used multivariate techniques to explore the relationship between vegetation and environment from Ayubia National Park Rawalpindi and they observed that Pb, Cr and Cd were not correlated with the vegetation whereas Zn showed a signification relation with the vegetation. They recognized 5 major communities and 63 plant species and also observed that copper, Zinc and Lead concentrations are the most important factors influencing the vegetation of the study area. Ajaib et al., (2009) analyzed the soil and community description from Goharabad valley District Diamer, Gilgit-Baltistan, Pakistan and they demonstrated that the changing of community composition is due to the deforestation, overgrazing, human influence, soil erosion. Ahmed et al., (2010) studied the floristic composition and communities of deodar forest from Himalayan range of Pakistan. Saima et al.,(2010) described the floristic composition during moon soon in Ayubia National Park, Abbotabad. Khan et al., (2010) described the phytosociology, structure and chemical analysis of soils in Quercus baloot Griff, forest from district Chitral and concluded that Monotheca buxifolia and Quercus baloot showed good regeneration potential but the associated broad leaved species were at the risk of elimination. Ahmed et al., (2010) described the status of vegetation analysis in Pakistan. They divided into five categories i.e observational, quantitative phytosociology, multivariate analysis of ordination, population structure and advanced multivariate techniques. They also gave a description about the trend of this analysis. Siddiqui et al., (2010a, b) described the vegetation of moist temperate forests of Northern areas of Pakistan using Ward’s cluster analysis, TWINSPAN and ordination (DCA and PCA).Ahmed et al., (2011) described the multivariate approach to evaluate the structure and dynamics of Cedrus deodara in Hindu Kush and Himalayan range of Pakistan and they used 13 tree species in the cluster analysis and found six groups while 46 understorey species were reported. They also studied the environmental characteristics using DCA ordination such

Chapter 2: Review of literature

as elevation, slope, aspect and canopy and other edaphic factors such as soil compaction, MWHC, salinity, pH and conductivity. Mashwani et al., (2011) classified the vegetation of Saif-ul-Mulook lake Western Himalaya and also described the floristic composition of vegetation using multivariate techniques of TWINSPAN and DCA. Ahmed and Ann (2011) explored the vegetation dynamics and community description of Aubia National Park, Rawalpindi using CCA ordination. They found a significant relation among species abundance, soil moisture and pH. Shaheen et al.,(2011) described in detail, structural diversity, vegetation dynamics and anthropogenic impacts of district Bagh ,Kashmir forests. They observed 0.58 to 1.96 Simpson’s diversity, 1.49 to 1.37 Menhinick‘s diversity while evenness was recorded 0.23 to 0.66. They also stated that this range of diversity was similar with the forests of Himalayan Range of Pakistan but the density, basal area and seedling count were very low. Nimatullah et al., (2011) studied the edaphic factors of soil from 87 different sites of D.I.Khan Division. Khan (2011) gave a detail note about vegetation relationship with soil from forest of Chitral using multivariate analysis and also studied the community structure. On the other hand Wahab (2011) studied the forest of Dir District regarding the status of soil nutrients, physical and chemical properties of forest soil, forests description. Siddiqui (2011) investigated the moist temperate forest of the Pakistan and provide a database for the better management of forests. Ahmed et al., (2011) investigated the soil characters and soil nutrients availability of an open scrub rangeland in the sub-mountainous Himalayan tract of Pakistan. Siddiqui et al., (2011) described communities of moist temperate areas of Pakistan. Khan et al., (2011) described the regeneration potential of Monotheca buxifolia and also investigated the structure and diversity of lower Dir districts forests using multivariate analysis. Akbar et al., (2010, 2011) studied the phytosociology and structure of Skardu district. Hussain et al., (2010, 2011) described the phytosociology and structure of a few sites from Central Karakoram National Park. Ali et al.,(2012) analyzed the ecological ranking in the districts of Pakistan. Khalid et al., also (2012) conducted a study to evaluate the importance of soil nutrients, physical and chemical properties from the Chakwal district. Khan (2012) analyzed the community of Quercus baloot forest from district dir forests at the mean elevation of 1524m to 1753m.He also analyzed the relationship

Chapter 2: Review of literature

among edaphic factors, topographic factors and soil nutrients including calcium, magnesium, sodium, potassium and nitrogen. He observed a significant relation among vegetation, potassium, conductivity, TDS, MWHC, salinity and pH. Sikandar and Pandit (2012) phytosociology studied the forest trees and seedling of Kashmir and gave a brief description about the status of forests. Irshad et al., (2012) described the protection impact of the Himalyan and moist temperate forest of Galayat, Pakistan. On the other side Fahad and Bano (2012) quantitatively described the endangered species from the two site of Gilgit and also explore the medicinal important of species. Khan et al., (2012) described the vegetation dynamics, diversity and change of climate from the vegetation of Western Himalaya. Ilyas et al., (2012) described the vegetation composition and pressure to the montane temperate forest from Swat valley. The common growth of plants species recorded 43% perennial, 23% herbs, 16% shrubs and 15% trees. They also concluded that the vegetation of Swat valley is deteriorating with the passage of time due to the deforestation. Khan and Hussain (2013) presented ordination and classification of Karak district Khyber Pakhtunkhwa using multivariate techniques of HCA and DCA. Khan et al., (2013) used the multivariate techniques of cluster analysis and DCA ordination to analyze the vegetation the vegetation and environment relationship from the forests of dir district Hindukush range of Pakistan. They found a significant relation among vegetation, elevation and slope at the probability level P<0.05. They also observed weak correlation among the vegetation, many soil variables and edaphic factors and concluded that the weak correlation due to the anthropogenic disturbance. Siddiqui et al.,(2013) found 8 conifer tree communities with 3 monospecific stands from 41 locations of moist temperate areas of Himalayan and Hindukush range of Pakistan. They also observed the relationship between vegetation and environment and gave a detail description. Central Karakorum National Park is an important national park due to its unique flora and fauna. Initially some researchers and NGO conducted observational studies but with the passage of time quantitative parameters were introduce. In 2009 WWF studied over all vegetation of CKNP, using remote sensing and satellite images techniques; however no quantitative phytosociological work, advance multivariate analysis and population dynamics of vegetation from this park. Therefore present study carried out the

Chapter 2: Review of literature

vegetation analysis of study area using quantitative techniques. Also make an attempt to evaluate the population structure of the vegetation. However no quantitative phytosociological work, advance multivariate analysis and dynamics of gymnosperm trees were presented, so far from CKN Park.

Chapter 3: Structure and future trend of the vegetation of CKNP

CHAPTER-3 STRUCTURE AND FUTURE TREND OF THE VEGETATION OF CKNP

3.1- Introduction This chapter reveals the structure, present status and future trend of the vegetation of Central Karakoram National Park. The distributions of vegetation, density, basal area, canopy, cover are often used in bio-mass distribution in forest communities (Goff and Zedler, 1968). Spatial forest structure is an important parameter to determine the habitat and diversity of the species (Pommerning, 2002). Structure, composition and the function are the important factors of the forest (Gairola et al., 2008). Different researchers stated that using of diameter distribution transversely a range of diameter class is an aspect of stand structure (Koop et al., 1994). The structure and future trend of forests would be better by selected cutting. (Uuttera et al., 2000). According to FAO (2009) the forest cover of Pakistan is 2 % while 4 % mentioned by forest service which include all trees planted in gardens ,cities, along rivers, canals and agricultural. Ahmed (2008) reported that most of the forest cover found in Northern Areas of Pakistan. These forests are deteriorating with the passage of time due to poor management, less research and anthropogenic disturbance. Forest management can be characterized by silviculture practices which maintain the age class of forests (Matthews, 1999; Schütz, 2001). The cutting of these fortes should be selective with extensive practice of silviculture which could be changed in different times, depending on the need of forest owners and market situation. Additionally apply the system of proportion among small, medium and large dbh size tress. Secondly the possibility to cut trees from different species, aspect of stands (Brang, 2001). Single tree selection silviculture maintains the stand growth and maintains the different size classes and a reverse J-shaped diameter distribution (Matthews 1991). The structure of the forest has been studied at different levels through different communities (Ogden and Ahmed, 1987; Kimmins, 1987). The structure of vegetation determined by the presence of species, quantitative

Chapter 3: Structure and future trend of the vegetation of CKNP

relation between the species, species distribution and interaction between them (Boncina, 2000), or even succession (Begon et al., 1990; Cook, 1996).Ahmed (1986) used this method for the vegetation of some foothills of Himalayan range of Pakistan. Ahmed et al. (1990 a, b) also described the status and population structure of Juniperus excelsa in Baluchistan. Ahmed et al., (1991) worked on the vegetation structure and dynamics of Pinus gerardiana forest of Baluchistan. Malik (2005) conducted a comparative study with special reference to range conditions on the vegetation of Ganga Chotti and Bedori Hills District Bagh of Azad Jammu Kashmir. Wahab (2008) et al., described the phytosociology and dynamics of some forests of Afghanistan. Ahmed et al., (2009) described the vegetation structure of Olea ferruginea Royle forests of lower Dir district of Pakistan. Khan et al., (2010) described the phytosociology, structure and chemical analysis of soils in Quercus baloot Griff, forest from district Chitral .Akbar et al., (2010, 2011) also studied the phytosociology and structure of Skardu district while Hussain et al., (2010,2011) studied the quantitative study from the valleys of Central Karakoram National Park, Gilgit. Other researchers Siddique (2011, Khan (2011) and Wahab (2011) studied the structure, present status and future trend of different species from regions of the country. Beside the above studies no such type of study has been conducted to explore the present status, future trend and diameter distribution in terms of probability distribution of vegetation from the Central Karakoram National Park .This study is anticipated to play an important role in modeling diameter distribution and elsewhere in Pakistan. This type of modeling is widely used in forestry. Diameter distribution models are used to obtain an estimate of tree size distribution. This predicted distribution is needed for the further computation of forest volume characteristics and effective management of forests and also expected to be helpful in preserving and conserving the flora of the Park. Objectives of the study ¾ To determined the present status and future trend of the vegetation of Central Karakoram National Park. ¾ To explore the diameter distribution of the forests from CKNP

Chapter 3: Structure and future trend of the vegetation of CKNP

3.2-Materials and Methods 3.2.1-Size class structure For size class structure, interval of classes in forested areas was 10cm while in non-forested areas, class interval was 50cm. Ten classes of dbh in forested while 13 classes in non-forested vegetation were exhibited. Diameter at breast height of trees and cover of the shrubs were arranged and counted in each class and covert into density ha-1. Class density of each stand was plotted through MS Excel bar chart. Dbh size classes showed in x- axis while density ha -1 showed in vertical bars. Structure of forested vegetation is divided into three classes i.e. young classes (10-30cm), middle classes (40-, 70cm) large classes (80-100cm) following Ahmed, 1984; Khan, 2011; Wahab, 2011 and Siddique, 2011. Overall diameter of size class is also taken following by Ahmed, 1984; Khan, 2011; Wahab, 2011 and Siddiqui, 2011. 3.2.2-Weibull distribution Many techniques used to explore the modeling diameter distribution. Many distribution functions, such as normal, gamma, Johnson’s SB, Gram-Charlier, beta and Weibull, have been used in describing diameter distributions for forest stands (Von Gadow, 1984; Borders et al. , 1987 ) .Several researchers have reported Weibull function as the most suitable one to portray the diameter distributions (Bailey and Dell 1973, Von Gadow, 1984; Borders et al. , 1987 ). Weibull function is introduced by Bailey and Dell (1973) to describe the distribution of forest. The reputation of the Weibull function is based on its relative simplicity and flexibility Bailey and Dell (1973). Kinerson et al., (1974) fitted a nonlinear least squares model to cumulative crown class frequency data. The model gave a satisfactory fit to their data. The diameter distribution model can be used to obtain the distribution of trees into diameter classes. ( Hyink and Moser, 1983 ). Diameter distribution is also used in the possible outcome of disturbances in the forests. (Hett and Louks, 1976: Denslow, 1995; Baker et al., 2005; Cooms and Allen, 2007). This function is also helpful to explore the structure development of forest (Goff and West, 1975; Poorter et al., 1996 and Zenner,2005).

Chapter 3: Structure and future trend of the vegetation of CKNP

For diameter distribution and histograms CumFreq program was used with option of Weibull function (Bailey and Dell 1973).

Three parameter Weibull probability distribution function is given by

; a, b, c

Where a, b and c are the location, scale and shape parameters of the Weibull distribution respectively and is the tree DBH.

The cumulative distribution function of the Weibull model is

F 1‐exp ‐ ‐ a/b

0 ∞

a, b 0

Chapter 3: Structure and future trend of the vegetation of CKNP

3.3-Results 3.3.1-Vegetation description and size class structure of each stand Vegetation of National Park is divided into two types i.e forested and non- forested which is separately described below. Size class structure plots are shown in Fig. 3.1. A. Forested area 3.3.1.1-Stand No 1-Bagrot This is located in Bagrot valley at an elevation of 3130m while degree of slope was 45° on East exposure. The total density of this area was 96 individuals ha-1. Picea smithiana is leading dominant species with 67 density ha-1 while Pinus wallichiana and Juniperus excelsa attains 17 and 12 density ha-1 respectively. This structure shows close to normal distribution but positively skewed. 24% individuals of Picea smithiana and Juniperus excelsa were found in small classes, 71 % individuals of all species in middle classes and 5% of Picea smithiana and Pinus wallichiana individuals were found in large classes. The structure diagram shows that in the small classes Pinus wallichiana is absent which indicates that this species may be vanishing before some of the other species. Some gaps have been seen in large classes. These gaps are created due to illegal cutting that required a prompt action in this regard. 3.3.1.2-Stand No 2-Haramosh Haramosh is situated in District Gilgit with the elevation of 3296m while degree of slope was 53°. Exposure of this area was South-East facing. Total density was 122 density ha-1. Picea smithiana was the leading dominant species have density of 75 ha- 1and the associated species Pinus wallichiana occupied 29 density ha-1while Juniperus excelsa attains 18 individuals ha-1. The size class structure of this stand was close to symmetrical normal distribution. The coordinates of Bagrot and Haramosh are close together therefore vegetation type and distribution was also similar. In this area small classes occupied 18% individual of all species, middle classes have 73% of all species and only 9% Picea smithiana individuals were found in the large classes. In this stand Juniperus excelsa was absent in the beginning two classes while Pinus wallichiana and Picea smithiana were absent in first class. It is indicated that Juniperus excelsa show dangerous sign which may vanished first. This forest is also not reproducing well and if

Chapter 3: Structure and future trend of the vegetation of CKNP

care is not taken and new seedlings are not planted or regenerated the important tree vegetation is expected to vanish in future. 3.3.1.3-Stand No 3-Hopar Hopar is located in District Hunza-Nagar at an elevation of 3486m while degree of slope was 49° on East exposure. Juniperus excelsa exists as a pure forest which occupied 123 individuals ha-1. Boulders and cut poles are seen in the forest. Soil erosion can also be seen in the forested and non-forested areas. This distribution was some what negatively skewed. In the small classes 37% individuals were found, 63% individuals in middle classes while no tree in large classes shows some gaps. These gaps are produced as a result of illegal cutting. This structure is the sign of unstable forest due to the lesser amount of individuals in small classes in this. If no seedlings are recruited, this forest may disappear with the passage of time. 3.3.1.4-Stand No 4-Stak 1 Stak 1 is situated in Baltistan region with the elevation of 3344m and East exposure while degree of slope was 35°. Picea smithiana existed as a pure species occupied 109 density ha-1 .This distribution was a unimodal with somewhat platykurtic distribution. In the forest ground, cut stems and dead stems were found. Soil erosion is seen due to the anthropogenic disturbance and the melting of glaciers. In this area small classes received 28% individuals and 66% in middle classes while in large classes 6% individuals were found. This structure shows anthropogenic disturbance but the future of this forest may be secured if illegal cutting is ceased and seedling development is promoted. 3.3.1.5-Stand No 5-Stak 2 Stak 2 is also located in Baltistan region with the elevation of 3600m above sea level on East facing slope while degree of slope was 20°. Juniperus excelsa was distributed as a pure population in this forest with the density of 106 density ha-1. The distribution was a negatively skewed and normal. Small classes occupied 18% tree and in middle classes 75% individuals while in large classes 8% trees were found. This forest was also unstable and it is suggested that this forest can be maintained by increasing the number of seedlings and reducing the degree of disturbance. It seems that if no

Chapter 3: Structure and future trend of the vegetation of CKNP

recruitment was taken place, this plant would eventually vanish from the site by with time. 3.3.1.6-Stand No 6-Rakaposhi-1 Rakaposhi 1 is situated in District Hunza-Nagar of Gilgit-Baltistan with the elevation of 3444m on North exposure while degree of slope was 70°. Juniperus excelsa was the dominant and forms pure population with the high density of 135 density ha-1. This was a unimodal distribution with some positive skweness. This diagram of structure shows that small classes have 51% individuals, 49% trees in middle classes while no large classes exist. Large number of individuals in small classes shows better recruitment therefore it is hoped that though large sized trees are absent, in future this species may prevail. 3.3.1.7-Stand No 7-Rakaposhi 2 Rakaposhi 2 also located in District Hunza-Nagar with the elevation of 3263m above sea level on North exposure while degree of slope was 59°. Picea smithiana was existed as pure forest which attains 143 density ha-1. This seems to be a flat distribution with some normal gaps. In this stand small size classes received 29% individuals, middle classes have 65% and large classes have 6% individuals. Middle to old classes have stable distribution but earlier small classes have low number of individuals, indicating disturbances in the young population. It seems that small size classes show gaps in future. This forest may be saved by promoting seedling growth in this stand. 3.3.1.8-Stand No 8-Rakaposhi 3 Rakaposhi 3 was situated in District Hunza-Nagar. Elevation of this forest above sea level was 3188m on North facing slope while degree of slope was 64 °. Pinus wallichiana was distributed as pure species have 94 density ha-1. This was a platykurtic normal distribution with marked positive skweness. This diagram shows that small classes occupied 19% of the individuals; middle classes have 73% individuals while 8% trees were found in large classes. Gap in small classes may be due to cutting or no recruitment .Low density shows extensive cutting. This unstable forest may disappear with time if no action in taken for its conservation.

Chapter 3: Structure and future trend of the vegetation of CKNP

3.3.1.9-Stand No 9-Rakaposhi 4 Rakaposhi 4 was also located in District Hunza-Nagar. Elevation of this forest above sea level was 3512m on North-East facing while degree of slope was 70°. Picea smithiana was the leading dominant species with a density of 62 ha-1while Pinus wallichiana and Juniperus excelsa attained 21 and 16 density ha-1respectively. This was a unimodal distribution with certain irregularities. In this area 29% of the individuals of all tree species were found in small classes, 67% of all three species in the middle classes while 4% individuals of only Picea smithiana were found in large classes which gradually decrease with large classes. There is no evidence of Juniperus excelsa individuals in young classes. It shows that there is no evidence of regeneration and forest is unstable and disturbed. Other species also have irregular size-distribution without seedlings in small size-classes. It is indicated that this forest need special attention, otherwise all species may disappear with time.

B. Non forested area 3.3.1.10-Stand No 10-Bagrot This is located in Bagrot valley at the elevation of 2774 m and on lightly North facing surface. In this stand total density was 1733 ha-1. Rosa webbiana was leading dominant species have higher density 667 density ha-1 and associated species Hippophae rhamnoides and Berberis lycium have 533 density ha-1density. The size-distribution diagram shows a distribution close to normal with some positive skweness and irregularities. In this area small classes have 11% of Rosa webbiana and Hippophae rhamnoides with some gaps, middle classes have 82% bushes of all species while in the large classes 7% individuals of Rosa webbiana and Hippophae rhamnoides were found. It shows that this is an irregular shape structure of non-forested area which is the sign of disturbance. The gaps in small classes shows that there is anthropogenic activity and indicate that this vegetation may be vanished in future if illegal cutting and overgrazing is not checked.

Chapter 3: Structure and future trend of the vegetation of CKNP

3.3.1.11-Stand No 11-Hopar This stand situated in Hopar valley. Elevation of this area above sea level was 3353m on North-East facing while degree of slope was 30°.Total density was 1600 ha-1. Rosa webbiana was leading dominant species which attains higher density 667 ha-1 and associated species Hippophae rhamnoides have 533 individuals ha-1and Berberis lycium occupied 400 density ha-1density. This was also some what normal distribution but with irregularities. In this non forested area there is no individuals found in beginning five classes and small classes have only 8% individuals of Rosa webbiana and Berberis lycium, middle classes 75% individuals of all species were found while in large classes 17% trees of Rosa webbiana and Hippophae rhamnoides were recorded. This is similar to previous stand which shows gaps and irregularities. It is indicated that these important bushy area may disappear in future if special attention is not given for its restoration. 3.3.1.12-Stand No 12-Stak 1 This area is located in Stak 1 with the elevation of 2949m above sea level on East-North facing while degree of slope was 35°.Total density was 1199 ha-1. Hippophae rhamnoides was leading dominant species obtains higher density 466 density ha-1 and associated species Ribes alpestre have 400 density ha-1and Rosa webbiana have 333 density ha-1. This diagram appears to be a bimodal distribution. In this stand small classes have no individuals, in the middle classes 76% individuals of all species were found while in large classes 24% individuals of all species were recorded. Gaps in small classes were observed, it may be due to human influence and overgrazing. This structure is an irregular and unstable structure of non- forested area. Future trend of this forest is that it may be vanished in future if overgrazing is not restricted. 3.3.1.13-Stand No 13-Stak-2 This stand situated in Stak 2. Elevation of this area was 2782 m on East-South facing while degree of slope was 59°. Total density of this stand was 1000 ha-1. Hippophae rhamnoides was leading dominant species attains 400 density ha-1and associated species Ribes alpestre occupied 333 density ha-1and Rosa webbiana have 267 density ha-1. This shows a bimodal pattern with some gaps. In this location small classes occupied 13% individual of Hippophae rhamnoides, middle classes have 73% individuals of all species while 14% individuals of Hippophae rhamnoides and Ribes alpestre were

Chapter 3: Structure and future trend of the vegetation of CKNP

recorded in large classes. Some gaps were also observed. These gaps are due to illegal cutting and overgrazing. It is indicated that these bushy areas may vanished in future if no proper action are taken. 3.3.1.14-Stand No 14-Stak 3 This stand is located Stak 3. Elevation of this area was 2742m on East facing while on plain surface. Rosa webbiana, Hippophae rhamnoides and Ribes alpestre were the top three dominant species of this stand. Total density of these species was 866 ha-1. Rosa webbiana was the leading dominant species obtained 333 density ha-1and associated species were Hippophae rhamnoides shared 333 density ha-1 and Ribes alpestre have 200 density ha-1. In the small classes there is no evidence of the presence of any species, 85% individuals of all species were recorded in middle classes while in the large classes only 15% bushes of Rosa webbiana were found with some gaps. It shows that there is an irregular and disturbed structure which may vanish in future if vegetation growth is not promoted. 3.3.1.15-Stand No 15-Thally 1 This stand is situated in Thally 1. Elevation of this location above sea level is 3300m on East facing while degree of slope was 20°.Rosa webbiana, Hippophae rhamnoides and Berberis lycium are the top three dominant species of this stand. Total density is 1733 ha-1Among these species the leading dominant species was Hippophae rhamnoides with the higher density 800 density ha-1while associated species Rosa webbiana with 600 density ha-1and Berberis lycium with 333 density ha-1. This appears to be normal but contains some gaps or irregularities. In this stand small classes received only 4% individuals of Berberis lycium with some gaps which are due to overgrazing while in middle classes 76% individuals of all species were recorded with some gaps and 20% individuals of all species were found in large classes. These gaps and irregular structure shows that the vegetation is unstable which indicates that these important species need special attention. If no rules and regulations are imposed then this vegetation may gradually disappear in future.

Chapter 3: Structure and future trend of the vegetation of CKNP

3.3.1.16-Stand No 16-Thally 2 This is located in Thally 2 with the elevation of 3500m above sea level on East- South facing while degree of slope was 25°. Rosa webbiana, Hippophae rhamnoides and Ribes alpestre were the top three dominant species of this stand. Total density of the species was 1333 ha-1. Rosa webbiana was leading dominant species contains 733 density ha-1and associated species Hippophae rhamnoides obtains 333 density ha-1 and Ribes alpestre have 267density ha-1. This appears to be a U-shaped distribution. Small classes have 10% individuals of Hippophae rhamnoides with some gaps, in the middle classes 62% individuals of all species were recorded while in large classes 28% individuals of Ribes alpestre and Rosa webbiana were found. Some gaps were seen in small and middle classes which are due to overgrazing and cutting. It is an irregular shaped structure which shows disturbance. Future of this vegetation is insecure unless steps are taken to cease anthropogenic disturbance. 3.3.1.17-Stand No 17-Kowardo This stand is located in Kowardo with the elevation of 3559m above sea level on East-North exposure while degree of slope was 50°. Rosa webbiana, Berberis lycium and Ribes orientale are the three dominant species of this stand. Total density of these species was 1867 ha-1. Rosa webbiana was leading dominant species which contributes very high density 1066 density ha-1 while associated species Ribes orientale attains 467 density ha-1 and Berberis lycium shared 333 density ha-1. This was somewhat flat distribution. In this stand small classes have gaps, in the middle classes 79% individuals of all three species while in the large classes 21% individuals of Rosa webbiana and Ribes orientale were found. These gaps show disturbance and unstability which indicates that this vegetation may vanish in future. 3.3.1.18-Stand No 18-Arandu-1 This stand is situated in Arandu 1 with the elevation of 2790m with South-West facing while degree of slope was 30°. Rosa webbiana, Hippophae rhamnoides and Berberis lycium were the top three dominant species of this location. Total density was 733 ha-1. Hippophae rhamnoides was the leading dominant species which attains 333 individuals ha-1and associated species were Rosa webbiana have 267 density ha-1and Berberis lycium shared 133 density ha-1. This stand shows leptokurtic distribution .In this

Chapter 3: Structure and future trend of the vegetation of CKNP

stand small class received 40% individuals of all three species, in the middle classes 60% individuals of all three species while in large classes there is no evidence of any species but shows some gaps that indicate extreme disturbance in this area. This vegetation may disappear in future by time. It can be saved by promoting growth of the vegetation 3.3.1.19-Stand No 19-Arandu 2 This is located in Arandu 2. Elevation of this area was 2815 on South exposure while degree of slope was 20°. Rosa webbiana, Hippophae rhamnoides and Berberis lycium was the top three dominant species of this location. Total density was 1600 ha-1. Berberis lycium was the leading dominant species contains 600 density ha-1 and associated species were Rosa webbiana contributes 533 density ha-1and Hippophae rhamnoides have 467 individuals ha-1. This was somewhat normal distribution with some irregularities. In this stand young classes have 18% individuals of Rosa webbiana and Berberis lycium, in the middle classes 73% individuals of all three species while in large classes 9% individuals of Rosa webbiana and Hippophae rhamnoides were recorded. Some gaps were also seen in small and large classes which show that these species are deteriorating under anthropogenic disturbance. 3.3.1.20-Stand No 20-Arandu 3 This is situated in Arandu 3 at the elevation of 2875m with South-West facing while degree of slope was 35°. Rosa webbiana, Hippophae rhamnoides and Berberis lycium were the top three dominant species of this location. Total density was 1267 ha-1. Rosa webbiana was the leading dominant species which attains 467 density ha-1 and associated species were Hippophae rhamnoides and Berberis lycium occupied 400 individuals ha-1. This was close to normal distribution with some gaps. In this stand small classes received 28% of all three species, in the middle classes 68% individuals of all species while in the large classes only Hippophae rhamnoides was found with 10% of individuals. Some gaps were reported in the small and large classes which show disturbance. It is indicated that these species may vanish in future if proper ameliorative actions are not taken.

Chapter 3: Structure and future trend of the vegetation of CKNP

3.3.1.21-Stand No 21 Shigar 1 This is located in Shigar 1 at the elevation of 2527m on North-East exposure while the degree of slope was 40°.Rosa webbiana, Hippophae rhamnoides and Berberis lycium were the top three dominant species of this location. Total density was 1199 ha-1. Rosa webbiana was the leading dominant species attains 533 density ha-1 and associated species were Hippophae rhamnoides and Berberis lycium covered 333 density ha-1. This can be regarded as a flat distribution with two modes. In this stand small classes occupy 28% individuals of Hippophae rhamnoides and Berberis lycium while Rosa webbiana is absent in this class. In the middle classes all three species occupied 72% individuals while in large classes there is no evidence of any species but gaps were observed which shows that this is unstable vegetation. It is indicated that these important species may be disappeared with the time. These species need more promotion of vegetation for future existents. 3.3.1.22-Stand No 22-Shigar 2 This stand is situated in Shigar 2. The elevation above sea level was 2444m on East facing while degree of slope was 35°. Rosa webbiana, Hippophae rhamnoides and Tamarix indica were the top three dominant species of this location. Total density was 1266 ha-1. Hippophae rhamnoides was the leading dominant species contains 533 density ha-1 and associated species were Rosa webbiana obtains 400 density ha-1 and Tamarix indica shared 333 density ha-1. This was also more or less flat but with two modes. In this location small classes occupies 53% individuals of all three species, in the middle classes 45% individuals of all three species were found while in the large classes only Rosa webbiana was found which contain only 2% individuals. Some gaps were also observed in the large classes. These gaps due to illegal cutting, shows extreme disturbance and unstable vegetation. Proper actions should be taking to maintain the vegetation and the anthropogenic disturbance should be halted. 3.3.1.23-Stand No 23-Shimshal 1-1 This site is located in Shimshal 1-1 at the elevation of 3047m on East-South facing while on plain surface. Rosa webbiana, Hippophae rhamnoides and Tamarix indica were the top three dominant species of this location. Total density was 867 ha-1. Tamarix indica was the leading dominant species attains 333 density ha-1 and associated

Chapter 3: Structure and future trend of the vegetation of CKNP

species were Hippophae rhamnoides and Rosa webbiana contains 267 density ha-1. This was a rectangular distribution with a gap. In this area small classes have all three spices which contain 39% individuals. In the middle classes all species were found with the density of 54% while in the large classes only Rosa webbiana was found with the density of 7%. Some gaps were also seen in the large classes which show extreme disturbance. It is indicated that these important species may disappear in future with time if no serious action is taken. 3.3.1.24-Stand No 24-Shimshal 1-2 This stand is situated in Shimshal 1-2. The elevation was 3065m on East-South facing while on plain surface. Ribes orientale, Hippophae rhamnoides and Tamarix indica were the top three dominant species of this location. Total density among these species was 1266 ha-1. Hippophae rhamnoides was the leading dominant species with 600 density ha-1 and associated species were Rosa webbiana and Tamarix indica recorded 333 density ha-1. This was a flat distribution but has a number of modes. In this area small classes have all three species which occupied 53% density. In the middle classes all three species were recorded with the density of 42% while in the large classes only Hippophae rhamnoides was found with the low density of 5%.Structure of this area showed that all three species were found in the young and middle classes, gradually decrease to the large classes. In the large classes Hippophae rhamnoides was found in only one class and some large classes were absent which show extreme disturbance and unstable ecosystem. It appears that the vegetation is in serious threat and it may be vanished in future. 3.3.1.25-Stand No 25-Shimshal 2-1 This stand is located in Shimshal 2-1 at the elevation of 3076m on East-South facing while on plain surface. Hippophae rhamnoides, Tamarix indica and Juniperus communis were the top three dominant species of this location. Total densities among these species were 1266 ha-1. Hippophae rhamnoides was the leading dominant species attains 533 density ha-1 and associated species were Tamarix indica observed 333 density ha-1 and Juniperus communis have 400 individuals ha -1. This was a positively skewed distribution. This diagram shows that all three species were in good association and found in small classes with the density of 68% which gradually decreases to the large classes. In

Chapter 3: Structure and future trend of the vegetation of CKNP

the small classes Tamarix indica and Hippophae rhamnoides were occupied of 27% density while Juniperus communis was absent in this class. In the large classes only Hippophae rhamnoides was found which have 5% density with wide gaps. Future of this vegetation is also at stake which may disappear in future. These important species need special attention from the concerned authorities. 3.3.1.26-Stand No 26-Shimshal 2-2 This stand is located in Shimshal 2-2 at the elevation of 3097m on East-South facing while on plain surface. Hippophae rhamnoides, Tamarix indica and Rosa webbiana were the top three dominant species of this location. Total density among these species was 1399 ha-1. Hippophae rhamnoides was the leading dominant species contains 533 individuals ha-1 and associated species were Tamarix indica shared 533 density ha-1 and Rosa webbiana attains 333 density ha-1. This was an irregular distribution with gaps and fluctuations. In this area the structure shows in the small classes all three species were appeared with the density of 62% while in the middle classes Rosa webbiana and Hippophae rhamnoides were found with the density of 28%. In the large classes Rosa webbiana and Hippophae rhamnoides were found with low density of 10%. In the middle and large classes some gaps were observed. It shows that this vegetation is deteriorating and under the threat of anthropogenic disturbance. It seems that these species are gradually vanishing in this stand. 3.3.1.27-Stand No 27-Braldu 1-1 This area is situated in Braldu 1-1 at the elevation of 2895m above sea level on East facing while degree of slope was 25°. Rosa webbiana, Hippophae rhamnoides and Berberis lycium were the top three dominant species of this location. Total density was 1266 ha-1. Rosa webbiana was the leading dominant species attains 400 density ha-1 and associated species were Hippophae rhamnoides contributes 533 density /ha and Berberis lycium attains 333 density ha-1. This was a flat to positively skewed distribution. In this stand Berberis lycium were found in the beginning first class while two classes Berberis lycium and Hippophae rhamnoides were found with similar density. In the middle class all three species were found with similar density with different species associations while in the large classes there is no evidence of any species which indicates unstable situation of the ecosystem mainly due to anthropogenic disturbance.

Chapter 3: Structure and future trend of the vegetation of CKNP

3.3.1.28-Stand No 28-Braldu 1-2 This area is situated in Braldu 1-2 at the elevation of 2910m above sea level and East facing while degree of slope was 20°. Berberis lycium, Hippophae rhamnoides and Tamarix indica was the top three dominant species of this location. Total density is 1267 ha-1. Berberis lycium was the leading dominant species observed 467 density ha-1 and associated species were Hippophae rhamnoides attains 467 density ha-1 and Tamarix indica shared 333 individuals ha-1. This distribution was some what positively skewed with some gaps. In this stand the diagram shows that in the small classes all three species were recorded with the density of 68% while in middle classes all three species were found with the density of 21%. In the large classes 11% individuals of Hippophae rhamnoides were found .In the beginning first class Berberis lycium and Tamarix indica were found while in second and third class Berberis lycium and Hippophae rhamnoides were found with similar density. Some gaps were also seen in the middle and large classes which may be due to overgrazing and illegal cutting. If this activity is not discouraged these species may disappear in the future. 3.3.1.29-Stand No 29-Braldu 2-1 This area is situated in Braldu 2-1 at the elevation of 3076m above sea level on East-South facing while degree of slope was 35°. Rosa webbiana, Berberis lycium and Hippophae rhamnoides was the top three dominant species of this location. Total density is 1133 ha-1. Rosa webbiana was the leading dominant species obtains 400 density ha-1 and associated species were Hippophae rhamnoides 333 density ha-1 and Berberis lycium shared 400 density ha-1. This was also more or less positively skewed with some gaps. In this location the diagram of population structure shows that the first small class was absent. However, in the next small classes all three species were occupied 47%. In the middle classes association of Rosa webbiana and Hippophae rhamnoides and Berberis lycium were found with some gaps while in the large classes only Rosa webbiana appeared with very low density of 6%. Some gaps were also seen in the large classes which shows that this vegetation in unstable and need special attention for future existence.

Chapter 3: Structure and future trend of the vegetation of CKNP

3.3.1.30-Stand No 30-Braldu 2-2 This area is located in Braldu 2-2 at the elevation of 3055m above sea level on East facing while degree of slope was 30°. Rosa webbiana, Berberis lycium and Hippophae rhamnoides were the top three dominant species of this location. Total density was 1133 ha-1. Rosa webbiana was the leading dominant species attains 467 density ha-1 and associated species were Hippophae rhamnoides and Berberis lycium shared 333 density ha-1. This was some what rectangular distribution with mode at the extreme left. In this area Berberis lycium was found in the first small class while in next classes Berberis lycium was associated with Rosa webbiana, Hippophae rhamnoides and in this class all three species were recorded with the density of 35% while in the s middle classes association of Rosa webbiana and Hippophae rhamnoides and Berberis lycium were observed 65% density with some gaps. In the large class there is no evidence of any individual which shows that this area is extremely disturbed and unstable. It is indicated that these important species may disappear if no counter measures are taken. 3.3.1.31-Stand No 31-Chungo 1 This stand is situated in Chungo 1. The elevation was 3010m above sea level on North exposure while degree of slope was 40°. Rosa webbiana, Ribes orientale and Hippophae rhamnoides were the top three dominant species of this location. Total density was 1000 ha-1. Hippophae rhamnoides was the leading dominant species contains 400 density ha-1 and associated species were Rosa webbiana attains 333 density ha-1 and Ribes orientale have 267 density ha-1. This was also rectangular but with a modal value .In this structure some individuals of Rosa webbiana were found in first small class while in the next class all three species were found with almost similar density .In the next few classes association of Hippophae rhamnoides and Ribes orientale was found. The whole density of this class was 53%. While in middle classes some individuals of Rosa webbiana and Hippophae rhamnoides and Ribes orientale were found with the density of 33% but some gaps were also seen. In the large classes some individuals of Rosa webbiana, Hippophae rhamnoides were found with low density 14% and also observed some gaps which suggests disturbance. It is indicated that these species need special attention for future existence.

Chapter 3: Structure and future trend of the vegetation of CKNP

3.3.1.32-Stand No 32-Chungo 2 This stand is located in Chungo 2. The elevation was 3109 m above sea level on North-East facing while degree of slope was 35°. Rosa webbiana, Berberis lycium and Hippophae rhamnoides were the top three dominant species of this location. Total density is 1200 ha-1. Hippophae rhamnoides was the leading dominant species with 400 density ha-1 and associated species was Rosa webbiana attains 333 density ha-1 and Berberis lycium shared 467 density ha-1. This stand seems to be a bimodal distribution but shows tendency towards rectangular. In this diagram small classes have all three species with the density of 55% while in the middle classes all three species were recorded with the density of 45% which gradually decrease up to large classes and there is no individual recorded in the large classes. It shows that this vegetation is deterioration under anthropogenic disturbance .It is indicated that this bushy area may vanished in future with time therefore it is suggested that legal actions must be taken to save these important species.

Chapter 3: Structure and future trend of the vegetation of CKNP

Stand 1 96 Density ha-1

18 16 14

-1 12 10 8 6

density ha 4 2 0 12345678910 dbh size classes

Stand 2 122 Density ha-1

20 18 16 14 -1 12 10 8 6 density ha 4 2 0 12345678910 dbh size classes

Stand 3 123 Density ha-1

40 35 30

-1 25 20 15 10 density ha 5 0 12345678910 dbh size classes

Chapter 3: Structure and future trend of the vegetation of CKNP

Stand 4 109 Density ha-1

20 18 16 14 -1 12 10 8 6 density ha 4 2 0 12345678910 dbh size classes

Stand 5 106 Density ha-1

30 25

-1 20 15 10

density ha 5 0 12345678910 dbh size classes

Stand 6 135 Density ha-1

45 40 35

-1 30 25 20 15

density ha 10 5 0 12345678910 dbh szie classes

Chapter 3: Structure and future trend of the vegetation of CKNP

Stand 7 143 Density ha-1

25 20

-1 15 10

density ha 5 0 12345678910 dbh size classes

Stand 8 94 Density ha-1

30 25

-1 20 15 10

density ha 5 0 12345678910 dbh size classes

Stand 9 146 Density ha-1

25 20

-1 15 10

density ha 5 0 12345678910 dbh size classes

Chapter 3: Structure and future trend of the vegetation of CKNP

Stand 10 1733 Density ha-1

300 250

-1 200 150 100 density ha 50 0 12345678910111213 dbh size classes

Stand 11 1600 Density ha-1

300 250

-1 200 150 sity ha 100 den 50 0 12345678910111213 dbh size classes

Stand 12 1200 Density ha-1

300 250

-1 200 150 100 density ha 50 0 12345678910111213 dbh size classes

Chapter 3: Structure and future trend of the vegetation of CKNP

Stand 13 1000 Density ha-1

250

200

-1 150

100

density ha 50

0 12345678910111213 dbh size classes

Stand 14 866 Density ha-1

250 200

-1 150 100

density ha 50 0 12345678910111213 dbh size classes

Stand 15 1733 Density ha-1

300 250

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density ha 50 0 12345678910111213 dbh size classes

Chapter 3: Structure and future trend of the vegetation of CKNP

Stand 16 1333 Density ha-1

200

-1 150

100

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0 12345678910111213 dbh size classes

Stand 17 1867 Density ha-1

300 250

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density ha 50 0 12345678910111213 dbh size classes

Stand 18 733 Density ha-1

150 -1 100

50 density ha

0 12345678910111213 dbh size classes

Chapter 3: Structure and future trend of the vegetation of CKNP

Stand 19 1600 Density ha-1

200

150 -1 100

50 density ha

0 12345678910111213 dbh size classes

Stand 20 1267 Density ha-1

220 170

-1 120 70 ensity ha

d 20

-30 12345678910111213

dbh size classes

Stand 21 1199 Density ha-1

300 250

-1 200 150 100 density ha 50 0 12345678910111213 dbh size classes

Chapter 3: Structure and future trend of the vegetation of CKNP

Stand 22 1266 Density ha-1

200

150 -1 100

50 density ha

0 12345678910111213 dbh size classes

Stand 23 867 Density ha-1

140 120 100 -1 80 60 40 density ha 20 0 12345678910111213 dbh size classes

Stand 24 1266 Density ha-1

220 200 180 160 -1 140 120 100 80 60 density ha 40 20 0 12345678910111213 dbh size classes

Chapter 3: Structure and future trend of the vegetation of CKNP

Stand 25 1266 Density ha-1

250

200

-1 150

100 density ha 50

0 12345678910111213 dbh size classes

Stand 26 1399 Density ha-1

250

200

-1 150

100

density ha 50

0 12345678910111213 dbh size classes

Stand 27 1266 Density ha-1

180 150 -1 120 90 60 density ha 30 0 12345678910111213 dbh size classes

Chapter 3: Structure and future trend of the vegetation of CKNP

Stand 28 1267 Density ha-1

240 210 180 -1 150 120 90

density ha 60 30 0 12345678910111213 dbh size classes

Stand 29 1133 Density ha-1

240 210 180 -1 150 120 90

density ha 60 30 0 12345678910111213 dbh size classes

Stand 30 1133 Density ha-1

180 150 -1 120 90 60 density ha 30 0 12345678910111213 dbh size classes

Chapter 3: Structure and future trend of the vegetation of CKNP

Stand 31 1000 Density ha-1

180 150 -1 120 90 60 density ha 30 0 12345678910111213 dbh size classes

Stand 32 1200 Density ha-1

210 180 150 -1 120 90 60 density ha 30 0 12345678910111213 dbh size classes

Picea smithiana Pinus wallichiana

Juniperus excelsa Rosa webbiana

Hippophae rhamnoides Berberis lycium

Ribes alpestre Tamarix indica Ribes orientale Juniperus cummunis

Fig. 3.1 dbh size classes of forested and non forested vegetation of CKNP

Chapter 3: Structure and future trend of the vegetation of CKNP

3.3.2-Overall diameter distribution of the tree species Overall diameter distribution of Picea smithiana, Pinus wallichiana and Juniperus excelsa is shown in the Fig. 3.2. The dominant species Picea smithiana attained 92±20.67 mean density ha-1 while associated species Juniperus excelsa and Pinus wallichiana have 69±27.18 and 43±10.7 mean density ha-1 respectively. The plot of Picea smithiana was negatively skewed unimodal which shows that small classes have low frequency of recruitments while middle classes have abundant frequency which declined gradually. The diagram of Pinus wallichiana was unimodal platykurtic which shows that some small classes are totally absent while in the medium classes number of trees increasing, with the passage of time these trees are deteriorating in large classes. More or less similar situation was observed in Juniperus excelsa. This is not an ideal situation because this is the all over diameter distribution of these species, therefore it is concluded that the individual distribution of these species may have gaps in different classes. These species are in dangerous situation and may be vanished with the passage of time. Therefore a special attention is need to save these forests of important National Park.

Chapter 3: Structure and future trend of the vegetation of CKNP

Fig.3.2 Overall dbh size class structure of dominant conifer tree species on the basis of mean density ha-1

Note: PS= Picea smithiana, PW= Pinus wallichiana, JE=Juniperus excelsa

Chapter 3: Structure and future trend of the vegetation of CKNP

3.3.3-The Weibull function Cumulative distribution function (CDF) of Picea smithiana, Pinus wallichiana and Juniperus excelsa is shown in Fig.3.3. In Picea smithiana average median was 15 while standard deviation was observed 21.01. The cumulative frequency was 11.1 % with 0.8765 efficiency coefficient. Location (a), scale (b) and shape parameter ( c ) was recorded 0.3183,-0.3183 and 3.28 respectively. The cumulative function of Pinus wallichiana shows that the average median was 3 with 9.95 standard deviation while cumulative frequency was recorded 10.17 % with 0.5982 efficiency coefficients. The location (a), scale (b) and shape parameter (c ) was 0.1505, 0.171 and 3.12 respectively. The CDF of Juniperus excelsa shows that the average median was 10 with the standard 20.10 while the cumulative frequency was 10.82 % with 0.8837 efficiency coefficient (Table 3.1).

Table 3.1 The Weibull function parameters of three dominant conifer species of CKNP.

Species M.D/ha Median S.D E.C Location Scale S.P C.F (a) (b) (c ) PS 98±20.67 15 21.01 0.8765 0.3183 -0.378 3.28 11.1 PW 43±10.7 3 9.95 0.5982 0.1505 -0.171 3.12 10.17 JE 69±27.18 10 20.1 0.8837 0.2723 -0.357 3.71 10.82

Note: PS=Picea smithiana, PW= Pinus wallichiana, JE=Juniperus excelsa, M.D= Mean density/ha, S.D= Standard deviation, E.C=Efficiency coefficient, S.P= Shape parameter, C.F=Cumulative frequency

Chapter 3: Structure and future trend of the vegetation of CKNP

Fig 3.3 Generalized Weibull distribution models of dominant coniferous species

Note: PS= Picea smithiana, PW= Pinus wallichiana, JE= Juniperus excelsa

Chapter 3: Structure and future trend of the vegetation of CKNP

3.3.4-Density ha-1 of trees and shrubs Mean density ha-1 of dominant species are presented in Table 3.2. A total number of ten dominated species were found in the study area out of which three species were forested while sevens species belong to the non-forested vegetation. Among forested species Picea smithiana occupied highest mean density ha-1 which was 97 trees ha-1 while Juniperus excelsa and Pinus wallichiana attained 70 and 43 individuals ha-1 respectively. These species were also observed as monospecific. Picea smithiana was recorded as pure species from Stak-1 and Rakaposhi-2 while Pinus wallichiana was found from the Rakaposhi-2 and Juniperus excelsa was recorded as pure from the valleys Stak-2, Rakaposhi-1 and Hopar. Topographically, Picea smithiana and Pinus wallichiana were recorded from the elevation of 3130 m to 3512 m above sea level and with a very steep slope (35◦-70◦). Juniperus excelsa was recorded from the elevation of 31130 m to 3600 m above sea level with slope ranges from 20◦ to 70◦. The highest density (143 ha-1) of Picea smithiana recorded from the valley of Rakaposhi-2 at the elevation of 3260 m, steep slop 59◦ which was distributed on North facing while low density (67 ha-1) observed from Bagrot with an elevation of 3130 m, slope 45◦ and exposure of this species was found on East facing. The highest density (135 ha-1) of Juniperus excelsa was reported from the valley of Rakaposhi-1 at the elevation of 3444 m, steep slope 70◦ which distributed on North facing while low density (12 ha-1) from Bagrot valley with a elevation of 3130 m, 45◦ slope and North exposure. Pinus wallichiana was occupied highest density 94 ha-1 was observed from Rakaposhi-2 valley at the elevation of 3263 m, 59◦ slope and this species was situated on North facing while low density (17 ha-1) of Pinus wallichiana was found in Bagrot valley. This species was distributed at the elevation of 3130 m above sea level with a slope of 45◦ on East facing. Among these species Picea smithiana was appeared in five stands, Pinus wallichiana in four stands and Juniperus excelsa in six stands. Among non forested vegetation Rosa webbiana was the dominant species attains 470 mean density ha-1 .The highest density was 1068 ha-1 recorded from Kowardo valley while the low density 267 ha-1 recorded from Arandu and Shimshal. Hippophae rhamnoides was appeared in 22 stands with a mean density of 439 while highest density 800 ha-1 from the valley of Thally-1 and low density (267 ha-1) was recorded from

Chapter 3: Structure and future trend of the vegetation of CKNP

Shimshal-1. Other species Berberis lycium, Tamarix indica and Ribes orientale were appeared in 12, 6 and 4 stands respectively. Among these species Ribes orientale was attained 533 mean density ha-1, Berberis lycium have 405 ha-1 bushes while Tamarix indica was observed 400 individuals ha-1.The highest density (467 ha-1) of Ribes orientale was recorded from Kowardo valley while lowest density ha-1 of this species from the valley of Chungo-1 which was 267 ha-1.The highest density (600 ha-1) of Berberis lycium was recorded from Arandu-2 while lowest (133 ha-1) value of density ha- 1 from the valley of Arandu-1. The highest value of Tamarix indica was observed 533 density ha-1 from the valleys of Shimshal-2 and Braldu-1. Remaining two species Ribes alpestre was appeared in two stands (Stak-2 and Stak-3) while Juniperus communis was reported from Shimshal-2. Topographically the non-forested vegetation was distributed from the elevation of 2444 m to 3539 m above sea level while the exposure of this vegetation was mostly on North and East facing. The slope of non-forested vegetation was varies from location to location which was ranged from 20◦-50◦. Some vegetation from the valleys of Bagrot, Stak-3, Shimshal-1 and Shimshal-2 were prominently distributed in plain surface. The non-forested vegetation was found in open surface. 3.3.5-Basal area m2ha-1 of trees and shrubs

Mean basal area m2 ha-1 of dominant species from CKNP presented in Table 3.2. Among the forested vegetation the mean basal area m2 ha-1 of Picea smithiana was recorded 54 and the highest value of basal area m2 ha-1 was found from Rakaposhi-4 which was 76 m2 ha-1 with an elevation of 3512 m, slope 70◦ and exposure was found on N/E facing while the low basal area (17 m2 ha-1 ) was observed from Bagrot valley with an elevation of 3130 m, East exposure and 45◦ slope. Other forested species Pinus wallichiana and Juniperus excelsa were occupied 43 and 70 basal area m2 ha-1 respectively. The highest basal area (40 m2 ha-1 ) of Pinus wallichiana was recorded from the valley of Rakaposhi-2 with an elevation of 3263 m. The slope of this species in this basal area was 59◦ while the vegetation distribution was observed on North facing. The low basal area (5 m2 ha-1) of this species was reported from Bagrot valley at the elevation of 3130 m and slope 45◦ while the distribution was reported on East facing. The highest basal area of Juniperus excelsa was recorded from the valley of Stak-2 which was 47 m2 ha-1 while low basal area (1 m2 ha-1) was found in Bagrot valley. Topographically, this

Chapter 3: Structure and future trend of the vegetation of CKNP

species was occurred at the elevation of 3444m, North facing and very steep slope of 70◦ with high basal area m2 ha-1 while low basal area m2 ha-1 of this species was found at the elevation of 3130 m with 45◦ slope while distribution of this species was observed on East facing. Among non-forest vegetation the mean basal area m2 ha-1of Rosa webbiana was observed 470, Hippophae rhamnoides, Berberis lycium, Ribes orientale, Ribes alpestre, Juniperus communis and Tamarix indica attained 439, 405, 533, 267, 400 and 400 mean basal m2 ha-1 respectively. The highest basal area among all species was observed in Rosa webbiana which was 2198 m2 ha-1. This species was observed from Kowardo valley at the elevation of 3559 m above sea level, 50◦ slope and exposure of this species on East facing. The low basal area (116 m2 ha-1) was reported in Berberis lycium which was located in Braldu-2 at the elevation of 3055m and slope of this species was 30◦ while exposure was found on East facing.

Table 3.2 Mean values of density ha-1, basal area m2ha-1

S.No Name of species PNS Mean Density ha-1 Mean B.A m2ha-1 Mean IVI 1 Picea smithiana 5 97±14 54±16 77±9 2 Pinus wallichiana 4 43±1 17±8 41±19 3 Juniperus excelsa 6 70±23 18±7 58±20 4 Rosa webbiana 20 470±45 761±107 21±1 5 Hippophae rhamnoides 22 439±26 619±70 19±1 6 Berberis lycium 12 405±36 344±51 14±1 7 Ribes orientale 4 533±183 836±477 10±1 8 Ribes alpestre 2 267±67 540±136 12±1 9 Juniperus communis 1 400±00 119±00 9±00 10 Tamarix indica 6 400±42 245±41 13±1

*PNS= Presence in number of stands

Chapter 3: Structure and future trend of the vegetation of CKNP

3.3.6-Correlation of density ha-1 with basal area m2 ha-1 and topographic factors with density ha-1 Correlation between stand density ha-1 and basal area m2 ha-1 of nine forested stands showed highly significant relation (P < 0.001) while topographic factors slope attained a significant relation (P < 0.01) with density ha-1 and elevation showed significant relation (P<0.01) with density (Table 3.3). Among the species, Picea smithiana, Pinus wallichiana, Juniperus excelsa, Rosa webbiana, Hippophae rhamnoides and Ribes orientale showed significant correlation between density ha-1 and basal area m2ha-1 at the probability level p<0.001 whereas Berberis lycium showed significant relation at the probability level p<0.01. Correlation of topographic factors (elevation and slope) with density ha-1 revealed that Picea smithiana did not show any significant relation while Pinus wallichiana showed a significant correlation ( P < 0.01) with slope and Juniperus excelsa showed a strong significant relation ( P < 0.001) with elevation. A total number of ten dominant species were recognized out of which two species including Juniperus communis and Ribes alpestre which were found in one and two stands respectively. Therefore correlation of these species did not perform. Among the remaining species including Rosa webbiana and Tamarix indica showed significant relation between edaphic factors (elevation and slope) and density ha-1 whereas Berberis lycium attained significant relation between slope and density ha-1 and Ribes orientale appeared significant relation with elevation (Table 3.4).

Chapter 3: Structure and future trend of the vegetation of CKNP

Table 3.3 Correlation of stand density ha-1 with stand basal area m2 ha-1, slope and elevation with density ha-1

Forested Vegetation r- value Significant level Stand density -1/ Stand basal area m2 ha-1 0.5 p<0.001 Slope/Stand density 0.52 p<0.001 Elevation /Stand density 0.41 p<0.01 Non-forested vegetation Stand density -1/ Stand basal area m2 ha-1 0.76 p<0.001 Slope/Density ha-1 0.098 ns Elevation/Density ha-1 0.43 p<0.01

*ns= non significant, p= probability level

Table 3.4 Correlation of species density ha-1 with species basal area m2 ha-1, slope and elevation with density ha-1

Forested vegetation r-value Significant level (1)Picea smithiana Density ha-1 / Basal area m2 ha-1 0.5 p<0.001 Slope/Density ha-1 0.12 ns Elevation/Density ha-1 0.16 ns (2) Pinus wallichiana Density ha-1 / Basal area m2 ha-1 0.98 p<0.001 Slope/Density ha-1 0.41 p<0.01 Elevation/Density ha-1 0.21 ns (3) Juniperus excelsa Density ha-1 / Basal area m2 ha-1 0.77 p<0.001 Slope/Density ha-1 0.11 ns Elevation/Density ha-1 0.62 p<0.001 Non-forested vegetation (4) Rosa webbiana Density ha-1 / Basal area m2 ha-1 0.95 p<0.001. Slope/Density ha-1 0.3 p<0.05

Chapter 3: Structure and future trend of the vegetation of CKNP

Elevation/Density ha-1 0.56 p<0.001 (5) Hippophae rhamnoides Density ha-1 / Basal area m2 ha-1 0.8 p<0.001 Slope/Density ha-1 0.24 ns Elevation/Density ha-1 0.22 ns (6) Berberis lycium Density ha-1 / Basal area m2 ha-1 0.47 p<0.01 Slope/Density ha-1 0.42 p<0.01 Elevation/Density ha-1 0.13 ns (7) Ribes orientale Density ha-1 / Basal area m2 ha-1 0.99 p<0.001 Slope/Density ha-1 0.026 ns Elevation/Density ha-1 0.67 p<0.001 (8) Tamarix indica Density ha-1 / Basal area m2 ha-1 0.19 ns Slope/Density ha-1 0.46 p<0.01 Elevation/Density ha-1 0.44 p<0.01

*ns= non significant, p= probability level

Chapter 3: Structure and future trend of the vegetation of CKNP

Stand density ha-1/Stand basal area m2ha-1 of forested vegetation Stand density ha-1/Basal area m2ha-1 of non-forested vegetation

120 4000 y = 0.6244x - 31.962 y = 2.0193x - 907.56 100 r=0.50 3500

-1 -1 r=0.76 3000

ha p<0.001 ha 2 2 p<0.001 80 2500 60 2000 40 1500 1000 Basal area m area Basal m area Basal 20 500 0 0 0 20 40 60 80 100 120 140 160 0 500 1000 1500 2000

Density ha-1 Density ha-1

-1 Stand slope/stand density ha of forested vegetation Stand elevation/Stand density ha-1 of forested vegetation y = 0.0503x - 49.858 160 y = 0.6243x + 87.077 160 r=0.41 140 r=0.52 140 p<0.01 p<0.001 120 120 -1 100 -1 100 80 80 60 60 Density ha Density 40 ha Density 40 20 20 0 0 0 1020304050607080 3100 3200 3300 3400 3500 3600 3700 Slope Elevation

Slope/density ha-1 of non-forested vegetation Elevation/density ha-1 of non-forsted vegetation

y = 1.8273x + 1227.2 2000 2000 y = 0.4638x - 114.59 r=0.098 r=0.43 ns 1500 1500 p<0.01 -1 -1

1000 1000

Density ha Density 500 ha Density 500

0 0 0 102030405060 0 500 1000 1500 2000 2500 3000 3500 4000 Slope Elevation

Fig. 3.4 Correlation of stand density ha-1 with stand basal area m2 ha-1 , slope and elevation with density ha-1

Chapter 3: Structure and future trend of the vegetation of CKNP

Density ha-1/basal area m2 ha-1 of Ps Slope /density ha-1 of Ps y = 0.2784x + 82.412 y = 0.6076x - 4.538 120 160 r=0.12 r=0.50 100 140 ns -1 p<0.001 120 ha -1 2 80 100 60 80 40 60 Density ha Density 40 Basal area m area Basal 20 20 0 0 0 20406080100120140160 0 1020304050607080 density ha-1 Slope

Elevation/density ha-1 of Ps Density ha-1/basal area m2 ha-1 of Pw y = 0.0357x - 21.203 y = 0.4591x - 3.1271 160 r=0.16 50 r=0.98 140 ns

-1 40 p<0.001 120 ha -1 100 2 30 80 60 20 Density ha Density 40 Basal area m area Basal 10 20 0 0 3100 3200 3300 3400 3500 3600 020406080100 Elevation Density ha-1

Slope/density ha-1 of Rw Elevation/density ha-1 of Pw

100 y = 1.5241x - 45.646 100 y = -0.0447x + 189.33 r=0.49 80 80 r=0.21 p<0.001 -1 -1 ns 60 60

40 40 Density ha Density ha Density 20 20

0 0 0 1020304050607080 3100 3200 3300 3400 3500 3600 Slope Elevation

Chapter 3: Structure and future trend of the vegetation of CKNP

Density ha-1/basal area m2 ha-1 of Je Slope/density ha-1of Je

50 160 y = -0.3571x + 87.939 y = 0.2328x + 1.7787 140 r=0.11

-1 40 r=0.77 ns 120 ha

p<0.001 -1 2 30 100 80 20 60 Density ha Density 40 Basal area m area Basal 10 20 0 0 0 20 40 60 80 100 120 140 160 0 1020304050607080 Density ha-1 Slope

Elevation/density ha-1 of Je density ha-1/basal area m2 ha-1of Rw

160 2500 y = 2.2999x - 319.95 y = 0.211x - 650.11 140 r=0.95

r=0.62 -1 2000 120 p<0.001 ha -1

p<0.001 2 100 1500 80 60 1000 density ha density 40 Basal area m area Basal 500 20 0 0 3100 3200 3300 3400 3500 3600 3700 0 200 400 600 800 1000 1200 Elevation Density ha-1

Slope/density ha-1 of Rw Elevation/density ha-1 of Rw y = 4.0454x + 366.79 1200 r=0.30 1200 y = 0.3919x - 697.42 1000 p<0.05 1000 r=0.56 -1 800 -1 800 p<0.001 600 600 400 Density ha Density 400 Density ha Density 200 200 0 0 0 102030405060 0 500 1000 1500 2000 2500 3000 3500 4000 Slope Elevation

Chapter 3: Structure and future trend of the vegetation of CKNP

Density ha-1/basal area m2 ha-1 of Hr Slope/density ha-1 of Hr

2000 1000 y = -1.9999x + 482.86 y = 2.1244x - 314.43 r=0.24 -1 1500 r=0.80 800 ns ha -1 2 p<0.001 600 1000 400 500 ha Density Basal area m area Basal 200

0 0 0 200 400 600 800 1000 0 1020304050 Density ha-1 Slope

-1 Elevation/density ha of Hr Density ha-1/basal area m2 ha-1 of Bl

1000 y = 0.6668x + 73.411 y = 0.1127x + 105.82 700 r=0.47 r=0.22 600 800 -1 500 p<0.001 ha -1 ns 2 600 400 400 300

Density ha Density 200

200 m area basal 100 0 0 0 500 1000 1500 2000 2500 3000 3500 4000 0 100 200 300 400 500 600 700 Elevation Density ha-1

Slope/density ha-1 of Bl Elevation/density ha-1 of Bl

700 y = -4.333x + 528.19 700 y = -0.0583x + 580.37 r=0.42 600 600 r=0.13 p<0.001 ns 500 500 -1 -1 400 400 300 300

Density ha Density 200 ha Density 200 100 100 0 0 0 102030405060 0 500 1000 1500 2000 2500 3000 3500 4000 Slope Elevation

Chapter 3: Structure and future trend of the vegetation of CKNP

-1 2 -1 Density ha /basal area m ha of Ro Slope/density ha-1 of Ro

2500 y = 2.5968x - 549.39 1200 r=0.99 y = -0.4564x + 546.62

-1 2000 1000 p<0.001 r=0.026 ha 2 1500 -1 800 ns 600 1000

density ha density 400

Basal area m area Basal 500 200 0 0 0 200 400 600 800 1000 1200 0 102030405060 -1 Density ha Slope

Elevation/density ha-1 of Ro Density ha-1 /basal area m2 ha-1 of Ti

1200 y = 0.8718x - 2329.2 500 y = -0.1913x + 321.44 1000 r=0.67 -1 400 r=0.19

p<0.001 ha -1 800 2 300 ns 600 200 400 Density ha Density

Basal area m area Basal 100 200 0 0 2900 3000 3100 3200 3300 3400 3500 3600 0 100 200 300 400 500 600 Elevation Density ha-1

Slope/density ha-1 of Ti Elevation/density ha-1 of Ti y = -3.2714x + 429.65 600 r=0.46 600 y = 0.185x - 144.17 p<0.01 500 500 r=0.44

-1 400 -1 400 p<0.01 300 300 200 200 Density ha Density ha Density 100 100 0 0 0 5 10 15 20 25 30 35 40 0 500 1000 1500 2000 2500 3000 3500 Slope Elevation

Fig. 3.6 Correlation of forested and non-forested species density ha-1 with species basal area m2 ha-1, slope and elevation with species density ha-1

Note: Ps = Picea smithiana, Pw = Pinus wallichiana, Je = Juniperus excelsa, Rw= Rosa webbiana, Hr = Hippophae rhamnoides, Bl = Berberis lycium, Ro = Ribes orientale and Ti = Tamarix indica.

Chapter 3: Structure and future trend of the vegetation of CKNP

3.3.7-Correlation topographic factors with basal area (m2 ha-1)

Correlation between stand elevation and basal area (m2 ha-1) of all nine forested stand showed highly significant relation with slope while in non forested vegetation both edaphic factors did not show any significant correlation (Table 3.5). The forested species Juniperus excelsa and Pinus wallichiana showed highly significant (P< 0.001) correlation between basal area (m2 ha-1) and topographic factors (elevation and slope) Picea smithiana showed significant relation with elevation. In the non forested species Rosa webbiana, Hippophae rhamnoides and Ribes orientale showed a high significant relation ( P <0.001) between elevation and basal area (m2 ha-1) while Berberis lycium attained a significant relation (P < 0.01) with slope. Tamarix indica did not show any significant relation between topographic factors and basal area m2 ha-1 (Table 3.6).

Table 3.5 Correlation of topographic (slope and elevation) factors with basal area m2 ha-1

Forested vegetation r-value Significant level Slope/Stand Basal area m2 ha-1 0.25 ns Elevation/Stand Basal area m2 ha-1 0.33 p<0.05 Non forested vegetation Slope/Stand Basal area m2 ha-1 0.13 ns Elevation/Stand Basal area m2 ha-1 0.5 ns Forested vegetation (1)Picea smithiana Slope/Stand Basal area m2 ha-1 0.12 ns Elevation/Stand Basal area m2 ha-1 0.67 p<0.001 (2) Pinus wallichiana Slope/Stand Basal area m2 ha-1 0.74 p<0.001 Elevation/Stand Basal area m2 ha-1 0.45 p<0.01 (3) Juniperus excelsa Slope/Stand Basal area m2 ha-1 0.53 p<0.001 Elevation/Stand Basal area m2 ha-1 0.79 p<0.001 Non-forested vegetation (4) Rosa webbiana

Chapter 3: Structure and future trend of the vegetation of CKNP

Slope/Stand Basal area m2 ha-1 0.19 ns Elevation/Stand Basal area m2 ha-1 0.61 p<0.001 (5) Hippophae rhamnoides Slope/Stand Basal area m2 ha-1 0.16 ns Elevation/Stand Basal area m2 ha-1 0.38 p<0.05 (6) Berberis lycium Slope/Stand Basal area m2 ha-1 0.53 p<0.001 Elevation/Stand Basal area m2 ha-1 0.1 ns (7) Ribes orientale Slope/Stand Basal area m2 ha-1 0.06 ns Elevation/Stand Basal area m2 ha-1 0.74 p<0.001 (8) Tamarix indica Slope/Stand Basal area m2 ha-1 0.026 ns Elevation/Stand Basal area m2 ha-1 0.26 ns

*ns= non significant, p= probability level

Chapter 3: Structure and future trend of the vegetation of CKNP

Stand slope/stand basal area m2 ha-1 of forested vegetation Stand elevation and basal area m2 ha-1 of forested vegetation

120 120 y = 0.3764x + 23.107 y = 0.0518x - 131.75 100 100 -1 r=0.25 -1 r=0.33 ha ha ns 2 80 2 80 p<0.05 60 60 40 40

Basal area m area Basal 20 m area Basal 20 0 0 0 1020304050607080 3100 3200 3300 3400 3500 3600 3700 Slope Sope

Stand slope and basal area m2 ha-1 of non-forested vegetation Stand elevation/basal area m2 ha-1 of non-forested vegetation y = 6.4039x + 1508 4000 r=0.13 4000 y = 1.4286x - 2606.7 3500 ns 3500 r=0.50 -1 -1 3000 3000 p<0.001 ha ha 2 2500 2 2500 2000 2000 1500 1500 1000 1000 Basal area m area Basal m area Basal 500 500 0 0 0 102030405060 0 500 1000 1500 2000 2500 3000 3500 4000 Slope Elevation

Slope /basal area m2 ha-1 of Ps Elevation/basal area m2 ha-1 of Ps

120 120 y = 0.1733x - 518.98 y = -0.3301x + 71.7 r=0.67 100 r=0.12 100 -1 -1 p<0.001

ha ns ha

2 80 2 80 60 60 40 40

Basal area m area Basal 20 m area Basal 20 0 0 0 1020304050607080 3100 3200 3300 3400 3500 3600 Slope Elevation

Slope/basal area m2ha-1of Pw Elevation/basal area m2 ha-1of Pw

y = -0.0447x + 189.33 50 100 r=0.45 y = 0.8235x - 31.265 p<0.01 -1 40 -1 80 r=0.74 ha ha 2 30 p<0.001 2 60

20 40

Basal area m area Basal 10 m area Basal 20

0 0 0 1020304050607080 3100 3200 3300 3400 3500 3600 Slope Elevation

Chapter 3: Structure and future trend of the vegetation of CKNP

Slope/basal area m2 ha-1of Je Elevation/basal area m2 ha -1 of Je

50 y = -0.4945x + 43.304 50 y = 0.0808x - 257.74 r=0.53 r=0.79

-1 40 40

p<0.001 -1 p<0.001 ha 2

ha 30

30 2

20 20 10 Basal area m area Basal 10 Basal area m area Basal 0 0 0 1020304050607080 -103100 3200 3300 3400 3500 3600 3700 Slope Elevation

Slope/basal area m2 ha-1 of Rw Elevation/basal area m2 ha-1 of Rw

2500 2500 y = 6.2613x + 601.24 y = 1.0299x - 2306.7 -1 2000 r=0.19 -1 2000

ha ns ha r=0.61 2 2 1500 1500 p<0.001 1000 1000

Basal area m area Basal 500 m area Basal 500

0 0 0 102030405060 0 500 1000 1500 2000 2500 3000 3500 4000 Slope Elevation

Slope/basal area m2 ha-1 of Hr Elevation/basal area m2 ha-1 of Hr

y = -3.7197x + 699.84 2000 2000 r=0.16 y = 0.5041x - 856.29

-1 ns -1 1500 1500 r=0.38 ha ha 2 2 p<0.05 1000 1000

500 500 Basal area m area Basal m area Basal

0 0 0 1020304050 0 500 1000 1500 2000 2500 3000 3500 4000 Slope Elevation

Chapter 3: Structure and future trend of the vegetation of CKNP

Slope/basal area m2 ha-1 of Bl Elevation/basal area m2 ha-1 of Bl

y = -7.8x + 564.75 y = 0.063x + 154.57 700 r=0.53 700 r=0.10 600 p<0.001 600 -1 -1 ns

ha 500 ha 500 2 2 400 400 300 300 200 200 Basal area m area Basal 100 m area Basal 100 0 0 0 102030405060 0 500 1000 1500 2000 2500 3000 3500 4000 Slope Elevation

Slope/basal area m2 ha-1 of Ti Elevation/basal area m2 ha-1of Ti

500 500 y = 0.174x + 243.41 y = 0.1041x - 61.002 -1 -1 400 r=0.026 400 r=0.26 ha ha 2 2 ns 300 300 ns

200 200 Basal area m area Basal Basal area m area Basal 100 100

0 0 0 5 10 15 20 25 30 35 40 0 500 1000 1500 2000 2500 3000 3500 Slope Elevation

Slope/basal area m2 ha-1 of Ro Elevation/basal area m2 ha-1 of Ro

2500 2500 y = 2.4783x - 7301.5 -1 -1 2000 y = 2.7841x + 755.96 2000 r=0.74 ha ha p<0.001 2 2 r=0.06 1500 1500 ns 1000 1000 basal area m area basal 500 m area Basal 500

0 0 0 102030405060 2900 3000 3100 3200 3300 3400 3500 3600 Slope Elevation

Fig. 3.7 Correlation of forested and non-forested topographic factors (slope and elevation) with stand and species basal area m2 ha-1

Note: Ps = Picea smithiana, Pw = Pinus wallichiana, Je = Juniperus excelsa, Rw= Rosa webbiana, Hr = Hippophae rhamnoides, Bl = Berberis lycium, Ro = Ribes orientale and Ti = Tamarix indica Pinus wallichiana, JE=Juniperus excelsa

Chapter 3: Structure and future trend of the vegetation of CKNP

3.4-Discussion and conclusion

3.4.1-Size class structure Size class structure was prepared for each tree and bushy stand. In the forested area Picea smithiana, Pinus wallichiana and Juniperus excelsa species existed while in non forested areas Rosa webbiana, Hippophae rhamnoides, Berberis lycium, Ribes orientale, Ribes alpestre, Juniperus communis and Tamarix indica were the major species. Among the forest stands the highest density was recorded from Rakaposhi 2 which is 143 individuals ha-1. Picea smithiana was also existed as pure species and dominant in 4 stands out of 9 stands. Other associated species Pinus wallichiana occupied 17 to 94 individuals ha-1 while Juniperus excelsa attained 12 to 123 individuals ha-1. In the small classes number of individuals was less which indicates that the recruitments of these forests are absent. Ahmed and Naqvi (2005) also found less individuals (12%) of Picea smithiana in 10 to 30 cm classes, 24% individuals in 50 to 70 cm classes and 46% individuals in 30-60cm classes. The present study also ranges with this study. Ahmed (1984) found low number of individuals in small classes of Kauri with dense understorey vegetation. On the other hand Ahmed (2006) observed high individuals (170 ha-1) of Pinus wallichiana in the small classes (10-40cm) which gradually decreases in the large classes. Siddiqui (2011) also reported gaps in the different dbh classes from the moist temperate forest of Pakistan. These gaps does not mean that the particular size class species are absent, it is due to the poor recruitment (Ahmed, 1984 and 2011). Khan (2011) also reported some gaps in small size classes of Picea smithiana and Abies pindrow from the forests of Chitral Gol National Park. Wahab (2011) also found low individuals of Pinus wallichiana from Batharae valley and suggested that the seedling of the plant species need shelter and protection for the better growth and survival. However Juniperus excelsa attains in pure stand high individuals (135ha-1) and small classes also obtain high individuals as compare to other species. Khan (2011) found J-shaped uneven structure of forest Pinus wallichiana from Chitral Gol National Park while abundant individuals found in the small classes (<30sm dbh) in Juniperus excelsa .Wahab (2011) also found high density of recruitment from the valleys of Shahoor, Pennakot and

Chapter 3: Structure and future trend of the vegetation of CKNP

Markhno forest of Dir district. The abundant amount in the small classes indicate that there is a balanced structure of forest and in the future these forests attains good outcome to control the human disturbance, overgrazing ,logging and soil erosion. In the present study, it is noted that the forest are deteriorating with the passage of time may vanished. It may be due to human induced disturbance, overgrazing, soil erosion and logging. In non forested area highest 1866 density ha-1 was recorded from Kowardo. In this stand Rosa webbiana, Ribes orientale and Berberis lycium were found while Rosa webbiana was the leading dominant species among non- forested stands which appeared in 16 stands out of 23. The structure of these vegetation types show that there is a major influence of human being, overgrazing by domesticated animals, soil erosion, storm and floods. Therefore, vegetation constituted by these important species is deteriorating and rapidly dwindling down with time. It is indicated that these economically and medicinally important species may be included in the red list in future. On the basis of this recent investigation, results and discussion it is concluded that extreme anthropogenic disturbance prevail in this important National Park. In forested area gaps in small size classes indicate illegal cutting of young trees or no natural recruitment of the seedlings, therefore, seedling should be planted and grazing should be restricted. Gilgit, Baltistan and KPK have the highest annual rates of deforestation about 34,000 hectares in Gilgit, Baltistan and 8000 hectares in KPK (Ahmed et al., 2012). In non-forested areas gaps in young classes show the sign of overgrazing. It seems that species distributed in one or low number of classes may disappear with time. However, species existing in greater number of size classes certainly have greater chances to prevail for greater duration. On the other hand Cameron (1954) reported that due to the less individuals of mature trees, their seedlings do not approached until the dense mature canopy begins to thin out by mortality, this situation creates regeneration gaps in their size structure. Competition is also a factor which affect the dbh size class structure of the forest (Robbins,1962). Ahmed (1984) added that the gaps in size class structure do not mean that the particular size class is absent from the stand, it is due the poor recruitment potential in the past. On the basis of above studies it may be concluded that the species that are not reproducing or have lesser recruitment have greater chances of disappearing completely

Chapter 3: Structure and future trend of the vegetation of CKNP

from the vegetation. The size class structure data is useful to understand the present status and future trend of the vegetation. The concept of climax species should be well represented in all size classes which is showing the regeneration and replacing itself from a region. However the species which are found in the largest size class may gradually lost from the population (McCune and Grace 2002). It is desirable to resolve the relative importance of the undetermined factors associated with the present status, future trend and the interaction of the species to understand the structure of the forest (White,1979). In the study area most of the stands did not show the ideal situation and no inverse J-curve is formed. Seven stands showed the positive skweness distribution, 5 stands attained flat distribution, 4 stands normal distribution,3 stands distributed in rectangular shape, 3 stands have bimodal shaped, 3 stands attained unimodal while the remaining stand distributes with U-shaped and leptokurtic shaped. This type of vegetation distribution indicates the disturbance (Spies, 1998). The old growth forests are approaching an equilibrium condition and exhibited an irregular size structure, but anthropogenic disturbance allowing new individuals to emerge and creating unimodal, bimodal and multimodal distributions such as those observed in the present study (Leak, 1996).In the study area the small classes are absent or less individuals found. This may be the anthropogenic disturbance because people used poles (small size trees) for making huts and construction of houses. This is a non ideal situation in which small classes have less individuals therefore the regeneration of these forests is poor and these frosts are in critical situation. Some gaps also noticed in the middle and large size classes .This may be illegal cutting and soil erosion. In the study area flat distribution is reported from some stands. This type of structure unable to suggest any future trend which was primarily due to the extensive cutting, soil erosion and logging. However less individuals in small classes indicated lack of recruitments in the area. Wahab et al., (2008) also reported this type of distribution in Picea smithiana from Afghanistan. Ahmed et al., (2010) also found the similar situation in Cedrus deodara from the Hindukush and Himalayan regions of Pakistan. It is suggested that due to the different disturbances, present status and situation of recruitments, each forest should be treated on individual basis. These forests could be managed after full protection of

Chapter 3: Structure and future trend of the vegetation of CKNP

seediness. The size class structure not only indicated the present status but also show the future trend of the forest .If prompt and essential steps are not taken to protect these fortes, this vegetation disappeared with the passage of time. Some other researchers also suggested these situations from the different regions of the Pakistan.i.e Ahmed (1988a), Ahmed et al., (1990, 1991, 2006, 2009, and 2010), Siddique et al., (2009). Therefore it needs special attention for the sake of future generation and biodiversity. It is suggested that legal action should be to protect this important Park. 3.4.2-Overall diameter distribution of dominant species The Overall diameter distribution of Picea smithiana, Pinus wallichiana and Juniperus excelsa was presented to explore the present status and future rend of these species. Among these species Picea smithiana attained 92±20.67 mean densityha-1 while associated species Juniperus excelsa and Pinus wallichiana have 69±27.18 and 43±10.7 mean density ha-1 respectively. The distribution pattern of these species shows that Picea smithiana distributed negatively skew while Pinus wallichiana and Juniperus excelsa showed a unimodal platykurtic distribution. The overall diameter distribution of Picea smithiana shows that less individuals are found in small classes which gradually increase to middle classes and then decline to the large classes. Pinus wallichiana shows that the individuals in the small class are absent, it is not in a particular area but the overall situation therefore it is concluded that this species is in critical situation. If prompt action not taken to save this forest, it will be disappeared with passage of time. Similar situation was also observed in Juniperus excelsa, this species also need more recruitment for the survival in future. Similar results reported by (Ahmed, 2006, Ahmed, 2010, Wahab, 2008, Wahab, 2011 and Khan, 2011) from the different areas of Pakistan. Siddiqui (2011) observed over all diameter size class of Pinus wallichiana, Cedrus deodara, Picea smithiana and Abies pindrow. He recoded the wide range of diameter size class in Pinus wallichiana, Cedrus deodara and Abies pindrow while low generation was observed in Picea smithiana. Ahmed and Naqvi (2005) observed 96 ha-1 individuals of Pinus wallichiana and 153 ha-1 individuals of Picea smithiana from Naltar. This result is similar with the present study. These forests need more attention and improve the situation of law and order to protect these important forests from the Central Karakoram National Park.

Chapter 3: Structure and future trend of the vegetation of CKNP

3.4.3-The Weibull function On the basis of the above results it is concluded that Weibull model gave a good fit for all three tree species examined. The Weibull model was found to be promising to diameter at breast height (DBH) of gymnosperm tree species in which DBH is an independent variable of the diameter distribution of the natural unevenaged forests. Kilkki et al., (1989) demonstrated that very few stands variables in addition to those directly describing the diameter distribution have significance in the estimation of parameters of Weibull distribution. When applying any distribution it should be borne in mind that every measurement variable existing in the stand variable will accentuate in the estimates of distribution parameters and that Weibull parameters estimates are no exception. This type of models can be very useful for the better management of the forests under study. 3.4.4-Density ha-1 and Basal area m2ha-1 of forested and non forested vegetation The tree vegetation generally grow and survive in a specific range of environmental gradients i.e. temperature, precipitation, slope and altitude (Block and Treter, 2001).In the forested vegetation some monospecific species also recognized with high density and basal area. Picea smithiana, Juniperus excelsa and Pinus wallichiana attained highest density of 143 with 76 basal area m2 ha-1, 135 with 47 basal area m2ha-1 and 94 ha-1 with 40 basal area m2ha-1 respectively. Siddiqui (2011) observed 77 individuals ha-1 of Picea smithiana with 102 m2ha-1 basal area while 108 individuals ha-1 of Pinus wallichiana with 35.3 m2ha-1 average basal area from moist temperate forests of Pakistan. Ahmed and Naqvi (2005) found 96 individuals ha-1 of Pinus wallichiana with 18% basal area from Miandam, 332 individuals ha-1 from Rama, 387 individuals ha-1 from Naltar and 337 individuals -1 from Takht-e-Sulamani while 337 trees ha-1 with 167 basal area from Takht-e-Sulaimani. Picea smithiana attains 333 individuals ha-1 with 167 m2ha-1 basal area from Naltar valley of Pakistan. Wahab et al., (2008) also found Picea smithiana from Sheshan Afghanistan which attains 35 trees ha-1 with 15.9m2ha-1 basal area. Siddiqui et al., (2009) observed the density and basal area of lesser Himalayan and Hindukush range of Pakistan which ranges 20 to 667 individials ha-1 and 0.2 to 118 m2ha-

Chapter 3: Structure and future trend of the vegetation of CKNP

1 basal areas. On the other hand Khan et al., (2010) studied the density and basal area of Quercus baloot which ranges 166 to 351 density ha-1 and 12-30 m2ha-1 basal area. The density and basal area of present study ranges with above mentioned results which were taken from the different sites of the Pakistan. However, Khan (2011) investigated Juniperus excelsa from Sarooj Goline which attains 109 density ha-1 and 9.13 m2ha-1 basal areas. This result did not resemble with the present study and it may be due to anthropogenic disturbance, environmental historical, elevation and slop difference. According to Ahmed (1984), the density and basal varies from site to site and it is unspecified that density of trees depend upon the environmental factors. Altitude and aspect play a role in the pattern of plant communities and their composition and structure. The mean density of Picea smithiana recorded from the study area was 97 individuals ha- 1 while basal area was 54.Wahab et al., (2008) also recorded 35 density ha-1 and 15.9 m2ha-1 basal area of Picea smithiana from Afghanistan. In the non-forested vegetation Rosa webbiana attained highest density (1068 ha-1) and basal area (2198 m2 ha-1) from Kowardo valley. This valley is located at high altitude (3559 m) as compare to other sites and the slope was 50 ◦ on East facing. It is noted in the present study, vegetation which was exhibited at high elevation and east slope have high density and basal area. Hippophae rhamnoides was associated species with Rosa webbiana having approximately similar density and basal area which was 800 density ha-1 and 1600 basal area m2 ha-1 from the valley of Thally. It is also noticed that the species i.e Ribes alpestre, Ribes orientale and Juniperus communis were found in few stands and these species exhibited high mean density and basal area. The density and basal area of species was different from site to site due to different environmental and topographic factors. An attempt also made to observe the correlation of density ha-1 / basal area m2 ha-1 and topographic factors. The results revealed that stand density ha-1 shows highly significant relation with basal area m2 ha-1. The topographic factors (elevation and slope) also exhibited a significant relation with density and basal area. The species which were distributes at high elevation and steep slpoe like Juniperus excelsa showed highly significant relation with topographic factors while density of Pinus wallichiana and Picea smithiana showed a significant relation with slope and basal area showed significant

Chapter 3: Structure and future trend of the vegetation of CKNP

relation with elevation. Siddiqui (2011) also found highly significant correlation (P<0.001) between the density and basal area of Pinus wallichiana, Picea smithiana and Abies pindrow from the moist temperate forests of Pakistan. He also found a weak significant correlation with slope. Ahmed et al., (1990b) found a significant correlation between density and basal area of Juniperus excelsa from the forest of Baluchistan. The present study also agrees with these observations and correlation. However, Ahmed (1984) found a weak correlation between density and slope in Agathis australis from the forest of New Zealand. This observation was varying with the current study and it may be due to the different topography. It is supported by the statement of Grubb et al., (1963), density is strongly related to slope. Topographic factors are the important in leading the density and basal area of the species. In the non-forested vegetation two species including Juniperus communis and Ribes alpestre which were found in one and two stands respectively. Therefore correlation of these species did not perform. Among the remaining species i.e Rosa webbiana, Hippophae rhamnoides, Berberis lycium, Ribes alpestre and Ribes orientale showed a significant relation between density ha-1 and basal area m2 ha-1 except Tamarix indica which did not sow any significant relation. The correlation of topographic factors with density ha-1 revealed that Rosa webbiana and Tamarix indica showed a significant correlation with both elevation and slope while Ribes orientale showed highly significant relation (P < 0.001 ) with elevation and Berberis lycium showed a significant relation (P < 0.01) with slope. Hippophae rhamnoides did not show significant relation between basal area/ slope and topographic factors. The significant relation provides evidence of the influence of that variable on the growth, abundance and distribution of the species. The distribution of the vegetation was closely related with altitudinal climate change. Therefore the distribution of the vegetation was varying in different topographic factors. The reason of weak correlations is the disturbance factors which alter the density. The disturbance factor includes anthropogenic causes such as fire, logging, illegal cutting and overgrazing. Another important factor is competition that influences the survival capacity of species, resulting in decreased density. Diseases are also the cause of change in density. However in these forests the leading factor is human disturbance which has a long history. The vegetation

Chapter 3: Structure and future trend of the vegetation of CKNP

is deteriorating with the passage of time. Therefore need a proper management to save these important forests.

Chapter 4: Vegetation community analysis

CHAPTER-4 VEGETATION COMMUNITY ANALYSIS 4.1-Introduction This chapter focuses on the community analysis of Central Karakoram National Park, Gilgit-Baltistan. Community analysis deals with the types of vegetation, their structure, floristic composition, distribution and association between species (Raunkair, 1928). Many researchers defined the Phytosociology which deals with the study of vegetation with its floristic composition, structure, development and distribution (Tansley, 1920). Due to the cutting plant communities losing their species richness (Johnson et al., 1993). The deforestation of vegetation due to the salanity, soil erosion and acid rain in the different regions of the world had strained the phytosociologist for conservation of the endangers plant communities (Hussain, 2003). There are over 321,212 plant species found in the world (Arthur,2009) .The forest cover in Gilgit-Baltistan is about 6,06,000 ha-1 .Pakistan is rich of plant species and approximately 6000 plants species are found (Nasir and Ali ,1972). The total forest cover in Pakistan is about 2.2 % area of Pakistan (FAO, 2009).However these plant species are deteriorating due to anthropogenic disturbance (Perveen and Hussain, 2007). Pakistan has 24 National parks and CKNP is one of the important national park which is rich of flora and fauna. (Detail description is presented in chapter 1). This National Park is situated in the Karakoram Range which is included in the dry temperate zone. Dry temperate area has species with high diversity (Champion et al, 1965).According to Ali and Qaiser (1986) flora of Gilgit-Baltistan included in the Irano-Turanian region and sub Sino- Japanese which has great diversity of medicinal and economical plant species. As mentioned before that no extensive quantitative work has carried out in this park. Therefore it was necessary to conduct detailed quantitative study in CKNP. Purpose of the study ¾ Provide information about the current status of vegetation communities in quantitative term from CKNP.

Chapter 4: Vegetation community analysis

4.2-Materials and methods This study was conducted during 2010-2012 in the different regions of Central Karakoram National Park, Gilgit-Baltistan. 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 shrubs and herb species .GPS was used to record the elevation and coordinates while degree of slope was recorded by slope meter. Phytosociological attributes and absolute values were calculated according to the method described by Mueller –Dombois & Ellenberg (1974) and Ahmed and Shaukat (2012). Species with highest important value in the stand was considered as its dominant species (Brown & Curtis, 1952).Plant community of a particular area was named on the basis of first two dominant species following Curtis & McIntosh (1950) and Hussain (1989). Important value gives extensive information about the species as compare to any single phytosociological attribute (Brown and Curtis, 1952). Plant species identified with the help of flora of Pakistan (Nasir and Ali, 1972) and University of Karachi Herbarium. Following formulae (Ahmed and Shaukat, 2012) were used to calculate the phytosociological attributes and absolute values.

Mean density d1 Mean area of an individual (M)= (d1)2 Stand density ha-1 (D9) = Frequency F1

Relative Frequency F3 X 100

Relative Basal area B3 X 100

Relative Density D3 X 100

Chapter 4: Vegetation community analysis

F3 B3 D3 Important value index IVI 3

. Density ha-1 of a species (D2) =

Basal area m2ha-1 of a species = . Basal area = r2 Radius ( r ) = ½ diameter at breast height (dbh) Circumference = 2 3.14159

However in Quadrat sampling density of a species calculated by following formulae

Total number of a individuals of a species in all plots Density of a species D1 Total area of the sample plot

Density ha-1 of species = Density in unit area x 10000 In Quadrat sampling circumference is used therefore basal area convert into circumference by following formulae In case of metric system Circumfernce m Basal area BA 125700

In case of inches or feet Circumference Basal area BA 1808.64

Chapter 4: Vegetation community analysis

4.3-Results Study sites and environmental characteristics are shown in Table 1.6 (Chapter 1) while communities and their associated physiographic conditions are shown in Table 4.1.Phytosociological attributes and absolute values are presented in Table 4.2. On the basis of phytosociological analysis and the maximum IVI, one tree community, three pure tree stands and six shrubs/ herb communities were recognized. 4.3.1-Forest Community and Pure Stands ¾ Picea-Pinus wallichiana community ¾ Juniperus excelsa pure stand ¾ Picea smithiana pure stand ¾ Pinus wallichiana pure stand 4.3.2-Shrubs and Herbs Communities ¾ Rosa-Hippophae community ¾ Hippophae-Ribes alpestre community ¾ Rosa-Ribes orientale community ¾ Rosa-Berberis lycium community ¾ Hippophae-Tamarix indica community ¾ Berberis lycium-Tamarix indica community 4.3.1.1-Picea-Pinus wallichiana community This community is distributed at Bagrot, Haramosh and Rakaposhi-4 (1, 2, 9 stands). The elevation ranged from 3110 to3512 m while degree of slope was between 45 to 70° (Table 4.1). The canopy of Bagrot was moderate while open canopy existed in Haramosh and Rakaposhi-4. Ground surface of Haramosh and Rakaposhi-4 was rich in grasses with scattered boulders in the community. Muddy type of soil, cut stems, burning stems and soil erosion was observed in Haramosh and Bagrot while in Rkaposhi-4 no cut stem and burnt stem were seen; however soil erosion seen in loamy soil. Being close to the village the forest of Bagrot was accessible to local people, therefore overgrazing, illegal harvesting, burning of stems and soil erosion was comparatively greater than that of remaining two stands. Picea smithiana was the leading dominant species having varied values of IVI (57-64 %), density (67-91 ha-1) and basal area (17-76 m2 ha-1). Co- dominant species Pinus wallichiana was distributed with 20 to 25 % IVI, density was 17

Chapter 4: Vegetation community analysis

to 31 ha-1 and 5 to 7 m2 ha-1 basal area. The associated species Juniperus excelsa was recorded with low density and basal area (Table 4.2). A total of 42 species of ground flora were recorded under this community. Seven species, Astragallus gilgitensis, Impatiens balfourii, Lentopodium himalayanum, Rubus ulmifolius, Spiraea canescens, Taraxacum officinale, and Taraxacum karakorium were frequently distributed in all three stands. Forty % similarity was found in the floristic composition of Bagrot and Haramosh and 17% similar floral distribution were recorded from Bagrot and Rakaposhi- 4 while 20% similar flora were recorded from Haramosh and Rakaposhi- 4. Erigeron multicepes and Rosa webbiana were found at Haramosh while Acontholimon lycopodiodes, Artemisia roxburgiana, Bergenia stracheyii, Bistorta affinis, Epilobium angustifolium, Juniperus communis, Lentopodium nanum, Sedum quadifidum and Taraxacum nigrum were found only in Rakaposhi-4. 4.3.1.2-Juniperus excelsa Pure Stand Pure stand of Juniperus excelsa is located at Hopar, Stak-2 and Rakaposhi-1(3, 5, 6 stands). The elevation of these sampling sites ranged from 3486 to 3600 m while degree of steep slope ranged 20 to 70° (Table 4.1) .The canopy of these pure forests was open and surface of ground was rich in vegetation and boulders with muddy soil .Cut stems and dead stems were found in Stak-2 and Rakaposhi-1 while many dead standing trees were found in Hopar but no cut stem was recorded from rest of two sites. At above mentioned locations Juniperus excelsa pure stand occupied 106 to 135 individuals per hectare with 22 to 47 m2 ha-1 basal area (Table 4.2). Ground flora was composed of 36 species. Eleven species Astragalus gilgitensis, Berberis vulgaris, Cicer songaricum, Fragaria nubicola, Lentopodium himalayanum, Rubus ulmifolius, Sedum multicepes, Sedum quadifidum, Tanacetum artemisiodes, Taraxacum officinale and Trifolium repnes were recorded in all three stands. Floristic composition of Hopar and Stak-2 occupied 42 % similarity and between Stak-2 and Rakaposhi-1, 32 % similarity was recorded while 61 % similar floristic composition was found in Hopar and Rakaposhi-1. Acontholimon lycopodiodes recorded only in Hopar and all other species of Rakaposhi-1 found in Hopar while Lentopodium linearifolium, Artemisia maritima, Potentilla baltistana, Ribes orientale, Rosa webbiana, Rubus irritans, Taraxacum baltistanicum and Thymus linearis were recorded only in Stak-2.

Chapter 4: Vegetation community analysis

4.3.1.3-Picea smithiana Pure Stand Pure forest of Picea smithiana is distributed in Stak-1 and Rakaposhi-2 (4, 7 stands). The elevation ranged from 3263 to 3344 m while degree of slope ranged between 35 to 59° (Table 4.1). The canopy of these forests was open and ground surface was rich in grasses. Thick liter layer was found in Stak- 2 with muddy soil while loamy soil and boulders were found in Rakaposhi-2. Cut stems, standing dead trees and burnt stems were recorded in Stak-2 while wood cutting was seen in Rakaposhi-2. Picea smithiana was distributed with 43 to 143 density per hectare with 42 to 53 m2 ha-1 basal area (Table 4.2). Ground flora was composed of 34 species. Only 8 species Astragalus gilgitensis, Bergenia stracheyi, Geranium pratense, Juniperus communis, Sedum quadifidum, Taraxacum officinale, Trifolium repnes and Urtica dioca distributed in both stands with 24 % similar floristic. Artemisia maritima, Astragallus zanskrensis, Cicer songaricum, Hippophae rhamnoides, Lentopodium linearifolium, Potentilla baltistana, Ribes orientale, Rosa webbiana, Rubus irritans, Taraxacum baltistanicum and Thymus linearis were recorded in Stak-1 while Anaphalis virgata, Artemisia roxburgiana, Bistorta affinis, Fragaria nubicola, Impatiens balfourii, Lentopodium himalayanum, Lentopodium nanum, Potentilla anserina, Rubus ulmifolius, Sedum multicepes, Silene vulgaris, Tanacetum artemisiodes, Taraxacum karakorium and Taraxacum nigrum were recorded in Rakaposhi-2. 4.3.1.4-Pinus wallichiana pure stand This community is recorded from Rakaposhi-3 (stand 8), at the elevation of 3188 m where degree of slope was 64° (Table 4.1) .The canopy of this pure forest was open and ground surface was rich with various bushes, grasses and boulders. Cut stems and loamy soil were found in this forest. Pinus wallichiana attained 94 individuals ha-1 with 40 m2 ha-1 basal area (Table 4.2). Ground flora was rich and composed of 22 species in which Tarxacum karakorium occupied 10 % of relative frequency. Astragallus gilgitensis, Rubus ulmifolius and Taraxacum nigrum were distributed with 8 % and Artemisia roxburgiana and Lentopodium nanum were recorded with 6 % relative frequency.

Chapter 4: Vegetation community analysis

Some natural and human disturbances in CKNP areas

Human influence Glacier degrading vegetation

Illegal cutting Natural disturbance

Soil erosion Forest fire

Chapter 4: Vegetation community analysis

4.3.2.1-Rosa-Hippophae Community This shrubby community is widely distributed at 16 locations i-e Bagrot, Hopar, Stak-1, Stak-3, Thally-1, Thally-2, Arandu-2, Shigar-1, Shigar-2, Shimshal 1- 1, Shimshal 2-2, Braldu 2-1,Braldu 2-2, Chungo-1 and Chungo-2 (10,11,12,14,15,16,19,20,21,22,23,26,29,30,31,32 stands). The elevation ranged from 2444 to 3500 m while degree of slope was plain to 40° (Table 4.1). The leading dominant species Rosa webbiana was found in 11 stands having 13-31% IVI, 333-800 density ha-1 and 362-1600 m2 ha-1 cover (Table 4.2) while Hippophae rhamnoides was also dominant species in 5 stands occupied 10-24 % IVI, 267-600 density ha-1 and 294-1050 m2 ha-1 cover (Table 4.2). The associated species Thymus linearis distributed in 15 stands, Berberis lycium in 13 stands and Artemisia maritima in 12 stands. Other associated species contributed varied IVI values i-e Nepeta discolor 4% and Lentopodium himalyanum 2% IVI in Arandu-3(stand 8), Ephedra gerardiana 3% and Sedum roseum 2% in Shigar-1(stand 21), Mentha longifolia and Lectuca decipiens 5% Rheum sp 2% in Shigar-2(stand 22), and Astragallus gilgitensis 1% in Bagrot (stand 10). Artemisia roxburgiana and Taraxacum nigrum 5%, Lentopodium nanum and Rubus ulmifolius 3%, Geranium collinum 2% in Hopar (stand 11). Festuca communsii 1% in Thally-1(stand15). 4.3.2.2-Hippophae-Ribes alpestre Community This community is located at Stak-2 (Stand 13) at the elevation of 2782 m where degree of slope was 20°(Table 4.1).The vegetation was existed along with glaciers, indicating soil erosion .Village is also close to these bushy areas therefore anthropogenic disturbance also present. The dominant species Hippophae rhamnoides was distributed with 15% IVI, 400 Density ha-1 and 623 m2 ha-1 cover. The co-dominant species was Ribes alpestre associated with 13% IVI, 333 density ha-1 and 675 m2 ha-1 cover (Table 4.2). Eighteen species contributed to this stand in which Rosa webbiana and Ribes orientale, 9 % IVI, Berberis lycium 8 % IVI and Bistorta affinis associated with 6% IVI. 4.3.2.3-Rosa-Ribes orientale Community Like previous community this community is also distributed at one location (Kowardo, stand 17). Its elevation was 3559m while degree of slope was 50° (Table 4.1). Anthropogenic disturbance was less than previous stand due to the high elevation and

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steep slope. Ground flora was rich in grasses with loamy soil. The dominant species was Rosa webbiana occupied 33% IVI, 1067 density ha-1 and 2197 m2 ha-1 cover (Table 4.2). The co-dominant species Ribes orientale was distributed with 13 % IVI, 467 density ha-1 and 790 m2 ha-1 cover. Eighteen species contributed to this stand in which Berberis lycium attains 9% IVI, Sedum quadifidum 8 % IVI, Taraxacum officinale and Thymus linearis 5% IVI and Ribes alpestre were contributed 4% IVI. 4.3.2.4-Rosa-Berberis lycium Community This community is located at Arandu-1 and Braldu 1-1 (Stand 18, 27). The ground surface was rich in vegetation and close to village therefore anthropogenic disturbance were prominent resulting soil erosion in loamy soil. The elevation ranges from 2790 to 2858 m while degree of slope was between 20 to 25°(Table 4.1).The leading dominant species Rosa webbiana occupied 21 to 26 % IVI, 267 to 400 density ha-1 and 452 to 513 m2 ha-1 cover. The co-dominant species Berberis lycium attains 12 to 16% IVI, 133-467 density ha-1 and 188-434 m2 ha-1 cover (Table 4.2). This community composed of 15 species in which Hippophae rhamnoides (IVI 27-28%) Astragallus zanskrensis (5-9 %), Anaphalis virgata (4-9%) and Spiraea canescens (2-3% IVI) were recorded in both stands. Artemisia maritima shared10% IVI and Ephedra gerardiana (6% IVI) were found only in Arandu-1 while Thymus linearis (8% IVI) Taraxacum officinale (7% IVI) Bistorta affinis (6% IVI), Carum carvi (5% IVI), Lentopodium lentopodinum (4 % IVI) and Potentilla biflora (3 % IVI) were found only in Braldu 1-1. 4.3.2.5-Hippophae-Tamarix indica Community Shimshal 1-2 and Shimshal 2-1 (stand 24, 25) were dominated by this community where elevation ranged from 3065 to 3076m on nearly flat surface (Table 4.1).Vegetation was rich and existed along with river. Soil was sandy in Shimshal 1-2 and muddy type in Shimshal 2-1. Village is surrounding with high mountains along with glacier. The dominant species Hippophae rhamnoides showed 22-24 % IVI, 533-600 density ha-1 and 649-686 m2 ha-1 cover. The co-dominant species Tamarix indica have 10 to 14 IVI %, 333-533 density ha-1 and 215-244 m2 ha-1 cover (Table 4.2). This community composed of 17 species with 8 similar species in both stands i-e Rosa webbiana (IVI 10-12%), Juniperus communis (7-9% IVI) , Tarxacum officinale

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(7% IVI), Thymus linearis (5-7% IVI), Silene vulgaris (5-7% IVI) , Ribes alpestre (5-6 % IVI), Bistorta affinis (4-5% IVI), and Anaphalis virgata 4% IVI . Cicer songaricum 5% IVI and Geranium pratense shared 1% IVI recorded from Shimshal 2-1 while Ribes orientale attains 9% IVI, Artemisia maritima 4% IVI, Lentopodium lentopodinum 3% IVI, Sedum roseum and Potentilla biflora 2% IVI found only in Shimshal 1-2. 4.3.2.6-Berberis lycium-Tamarix indica Community This community is located at Braldu 1-2 (stand 28) at the elevation of 2910m while degree of slope was 20 °. Vegetation was near to river where anthropogenic disturbances were less as compare to other stands. Soil erosion was present in sandy soil. The dominant species was Berberis lycium attains 13% IVI, 467 density ha-1 and 434 m2 ha-1 cover. The co-dominant species Tamarix indica occupied 17 % IVI, 333 density ha-1 and 435 m2 ha-1 cover. Twelve species were associated with this community. Hippophae rhamnoides contains 13 % IVI, Rosa webbiana 8% IVI , Thymus linearis and Taraxacum officinale shared 7% IVI , Bistorta affinis and Artemesia brevifolium contributed 5 %, IVI and Geranium pratense attains with 4% IVI.

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Table 4. 1 Communities of CKNP with IVI, absolute values and topographic range.

S.No Community Name Stands Nos. IVI Density ha-1 Basal m2ha-1 Elevation Slope

A Forested Area 1 Picea-Pinus wallichiana 1,2,9 57-64 67-91 17-76 3110-3512 45-53° 14-25 17-31 5-14

2 Juniperus excelsa* 3,5,6 100 106-135 22-47 3486-3600 Plain

3 Picea smithiana* 4,7 100 109-143 41-53 3263-3344 20°

4 Pinus wallichiana* 8 100 94 40 3188 64° B Bushes/Herbs Area

5 Rosa-Hippophae 10,11,12,14, 13-31 333-800 362-1600 2444-3500 Plain-40°

15,16,19,20, 10-14 267-600 294-1050

21,22,23,26, 29,30,31,32

6 Hippophae-Ribes Alpestre 13 15 400 623 2782 20° 13 333 675 7 Rosa-Ribes orientale 13 33 1067 2197 3559 50° 13 467 790 8 Rosa-Berberis lycium 18,27 21-26 267-467 452-513 2790-2895 20° 12-16 133-533 188-260 9 Hippophae-Tamarix indica 24,25 22-24 533-600 649-686 3065-3076 Plain 10-14 333-533 215-244 10 Berberis –Tamarix indica 28 13 467 434 2910 20° 17 333 435

*Pure stands

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Table 4.2 Phytosociological attributes and absolute values of Forest, Bushes and Herbs from CKNP.

Stand No. Location Dominant Species R.F R.D R.B.A IVI Rank D. ha-1 B.A.m2ha-1

(A) Forested Area

1 Bagrot Picea smithiana 52 61 73 64 1st 67 17 Pinus wallichiana 28 28 24 20 2nd 17 5 Juniperus excelsa 21 15 6 13 3rd 12 2 2 Haramosh Picea smithiana 43 61 57 57 1st 75 22 Pinus wallichiana 30 23 22 25 2nd 29 7 Juniperus excelsa 26 15 11 17 3rd 18 3 3 Hopar Juniperus excelsa 100 100 100 100 Pure 123 22 4 Stak 1 Picea smithiana 100 100 100 100 Pure 109 41 5 Stak 2 Juniperus excelsa 100 100 100 100 Pure 106 47 6 Rakaposhi 1 Juniperus excelsa 100 100 100 100 Pure 135 25 7 Rakaposhi 2 Picea smithiana 100 100 100 100 Pure 143 53 8 Rakaposhi 2 Pinus wallichiana 100 100 100 100 Pure 94 40 9 Rakaposhi 4 Picea smithiana 54 62 76 64 1st 91 76 Pinus wallichiana 24 21 14 20 2nd 31 14 Juniperus excelsa 22 16 10 16 3rd 24 10 (B) Bushes and Herbs Area 10 Bagrot Rosa webbiana 18 24 39 25 1st 667 1240 Hippophae rhamnoides 13 14 27 18 2nd 533 853 Berberis lycium 16 14 20 17 3rd 533 656 11 Hoper Rosa webbiana 15 16 36 22 1st 667 1239 Hippophae rhamnoides 13 13 30 19 2nd 533 1050 Berberis lycium 10 10 36 21 3rd 400 297 12 Stak 1 Rosa webbiana 11 14 36 21 1st 333 570 Hippophae rhamnoides 9 11 25 15 2nd 466 947 Ribes alpestre 11 10 13 7 3rd 400 431 13 Stak 2 Hippophae rhamnoides 11 11 21 15 1st 400 623 Ribes alpestre 8 9 23 13 2nd 333 675 Rosa webbiana 6 7 14 10 3rd 266 429 14 Stak 3 Rosa webbiana 6 8 24 13 1st 333 563 Hippophae rhamnoides 8 9 18 12 2nd 333 431 Ribes alpestre 6 5 17 10 3rd 200 404 15 Thallay 1 Hippophae rhamnoides 17 21 47 29 1st 800 1688 Rosa webbiana 15 15 27 19 2nd 600 960 Berberis lycium 9 9 15 11 3rd 333 523 16 Thallay 2 Rosa webbiana 12 19 50 27 1st 733 1616

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Hippophae rhamnoides 8 9 12 10 2nd 333 400 Ribes orientale 6 7 13 9 3rd 267 418 17 Kowardo Rosa webbiana 24 26 49 33 1st 1067 2198 Ribes orientale 10 11 18 13 2nd 467 790 Berberis lycium 10 8 10 10 3rd 333 459 18 Arandu 1 Rosa webbiana 22 17 43 26 1st 267 452 Berberis lycium 9 8 18 12 2nd 133 188 Hippophae rhamnoides 13 17 41 10 3rd 333 418 19 Arandu 2 Rosa webbiana 14 14 35 21 1st 533 824 Hippophae rhamnoides 14 13 36 21 2nd 467 845 Berberis lycium 11 16 24 17 3rd 600 562 20 Arandu 3 Hippophae rhamnoides 13 13 45 24 1st 400 633 Rosa webbiana 13 15 38 22 2nd 467 633 Berberis lycium 10 13 42 8 3rd 400 462 21 Shigar 1 Rosa webbiana 16 17 59 31 1st 533 759 Hippophae rhamnoides 10 10 23 15 2nd 333 294 Berberis lycium 10 10 18 13 3rd 333 231 22 Shigar 2 Rosa webbiana 11 12 34 19 1st 400 462 Hippophae rhamnoides 16 16 41 24 2nd 533 547 Tamarix indica 11 10 11 10 3rd 333 142 23 Shimshal 1-1 Rosa webbiana 7 7 44 19 1st 267 547 Hippophae rhamnoides 7 7 16 10 2nd 267 547 Tamarix indica 12 9 19 13 3rd 333 239 24 Shimshal 1-2 Hippophae rhamnoides 13 12 40 22 1st 600 686 Tamarix indica 9 7 13 10 2nd 333 215 Ribes orientale 7 7 13 9 3rd 333 215 25 Shimshal 2-1 Hippophae rhamnoides 12 13 48 24 1st 533 649 Tamarix indica 12 13 18 14 2nd 533 244 Juniperus communis 10 10 9 9 3rd 400 119 26 Shimshal 2-2 Hippophae rhamnoides 13 13 49 25 1st 533 774 Rosa webbiana 9 8 23 13 2nd 333 361 Tamarix indica 11 13 12 12 3rd 533 195 27 Braldu 1-1 Rosa webbiana 10 9 45 21 1st 400 513 Berberis lycium 14 12 23 16 2nd 533 260 Hippophae rhamnoides 10 7 28 15 3rd 333 321 28 Braldu 1-2 Berberis lycium 11 11 17 13 1st 467 434 Tamarix indica 7 8 36 17 2nd 333 435 Hippophae rhamnoides 11 11 17 13 3rd 467 203 29 Braldu 2-1 Rosa webbiana 10 10 47 22 1st 400 580 Hippophae rhamnoides 7 8 33 16 2nd 333 408 Berberis lycium 10 10 15 12 3rd 400 193 30 Braldu 2-2 Rosa webbiana 11 10 50 24 1st 467 586 Hippophae rhamnoides 9 7 35 17 2nd 333 415

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Berberis lycium 6 7 10 8 3rd 333 116 31 Chungo 1 Hippophae rhamnoides 9 9 46 21 1st 400 517 Rosa webbiana 9 7 28 15 2nd 333 313 Ribes orientale 7 6 13 8 3rd 267 141 32 Chungo 2 Hippophae rhamnoides 8 8 30 15 1st 400 362 Rosa webbiana 6 6 31 14 2nd 333 373 Berberis lycium 8 9 17 11 3rd 467 206

R.F=Relative frequency, R.D=Relative density, B.A=Basal area, R.B.A=Relative basal area, IVI=Important value indexed, D.ha-1=Density per hectare

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4.4-Discussion National Park spans an area of 10,000 sq. km. It is one of the largest and famous national parks of Pakistan. It is important for its unique topography, landscape, snow covered peaks, harsh weather, wildlife and flora. However due to long history of human interference the flora is rapidly degrading. Few areas under the snow covered peak are dominating with pine species like Picea smithiana, Pinus wallichiana while comparatively some dry valleys are occupied by scattered, stunted and disturbed Juniperus excelsa trees. These forests tree species are under high pressure and threatened due to unavailability of cheap alternate fuel. In this important National Park 4 forested and 6 non frosted communities were recorded which were distributed from 24444m to 3600m elevation. Due to difficult terrain, extreme slope and lack of trampling facilities, it was not possible to conduct vegetation sampling above this range. Therefore alpine vegetation is not included in this study. Among the forested vegetation one mix community of Picea-Pinus wallichiana community and two monospecific stands including Pinus wallichiana and Juniperus excels were recorded. The underground vegetation i.e Astragallus gilgitensis, Impatiens balfourii, Lentopodium himalayanum, Rubus ulmifolius, Spiraea canescens, Taraxacum officinale, and Taraxacum karakorium were recorded in Picea-Pinus wallichiana community while Astragallus gilgitensis, Berberis vulgaris, Cicer songaricum, Fragaria nubicola, Lentopodium himalayanum, Rubus ulmifolius, Sedum multicepes, Sedum quadifidum, Tanacetum artemisiodes, Taraxacum officinale and Trifolium repnes were distributed in Juniperus excelsa pure stand. On the other hand Astragallus gilgitensis, Bergenia stracheyii, Geranium pratens, Juniperus communis, Sedum quadifidum, Taraxacum officinale, Trifolium repnes and Urtica dioca were the dominating understory species in Picea smithiana pure stand. Pinus wallichiana pure stand was occupied with Taraxacum karakorium, Astragallus gilgitensis, Rubus ulmifolius, Taraxacum nigrum, Artemisia roxburgiana and Lentopodium nanum. Picea-Pinus wallichiana community shared 67 to 91 density ha-1, 17-37 basal area m2 ha-1and 57-76% IVI. Among the monospecific stands Picea smithiana attains highest density and basal area 109 to 143 individuals ha-1 and 41 to 53 m2ha-1 respectively.

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Juniperus excelsa pure stands range was 106 to 135 density ha-1 and 22-47 basal area m2 ha-1 while Pinus wallichiana attained 94 individuals ha-1 and 40 basal area m2ha -1. Siddiqui (2011) found the pure forest of Pinus wallichiana form moist temperate forest of Pakistan which attains 135 to 429 indivdulas ha-1 with 69 to 78 m2ha-1 basal area. These values are within the range of present study. Berberis lycium and Ribes alpestre both species distributed in above mentioned forest and also found in pure forest of Juniperus excelsa in the present study. He also observed the Pinus wallichiana attains Picea smithiana contains 120 to 125 density ha-1 and 55 to 79 basal area m2ha-1 and Picea smithiana occupied with 20 to 60 individuals ha-1 and 6 to 28 basal area m2ha-1. Picea - Pinus wallichiana community also recorded from Astore at the elevation of 3300m.This community also recorded from the present study but the density and basal area are high as compare to the moist temperate forests of Pakistan. Ahmed et al.,(2006) also observed pure stand of Pinus wallichiana from Naltar valley and Takht-e-Sulaimani Baluchistan in which mean density was 63 ha-1 with 88 m2ha-1 basal area while Picea-Pinus wallichiana community also recorded from Astore at the elevation of 3300m. This community occupied 66% individuals of Picea smithiana and 34% individuals of Pinus wallichiana. On the other hand Ahmed et al., (2010) found Cedrus- Pinus wallichiana community from the moist temperate forest of Pakistan which attains 40 to 276 individuals ha-1. Wahab (2011) found Picea-Pinus wallichiana community from Dandair Usherie district Dir forests in which Picea smithiana was the dominating species with 137 individuals and 37m2ha-1 basal aream2ha-1 while he also found pure forests of Picea smithiana from Batkhalae, Gojar Kali , Benshabi and Sundhrae. The density of these forests ranges 72 to 112 ha-1 while basal area recorded from 21.52 to 84.52 m2ha-1. The understorey vegetation was Carum carvi, Artemisia brevifolium, Geranium pratense, Taraxacum indicum, Silene vulgaris, Rosa webbiana and Urtica dioca which were also found in the current study. The low density and basal area of Pinus wallichiana pure stand was also reported from the location of Batharae which attains 43 individuals ha-1 with 39.63 m2ha-1 basal area. The pure forest of Juniperus excelsa from Sarooj Goline was found by Khan (2011) which attains 109 individuals ha-1 with 9.13 m2ha-1. The similar communities of the present study also within the range of the other workers

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of Pakistan. These type of communities were also reported by other workers from different regions of the country (Ahmed et al., 1988; Wahab et al., 2008; Ahmed et al., 2009; Khan et al., 2010 and Siddiqui et al., 2010). Ahmed et al., (2005) also observed Pinus wallichiana pure forest from Naltar Gilgit. Pinus wallichiana is a pioneer species which is exhibited in all aspects with different elevation (Chaudhri 1960) and adoptability of Pinus wallichiana is phenomenal (Huusain and Illahi 1991). In the current study Pinus wallichiana formed a community with Picea smithina and these species also found as pure form in different locations. Ahmed et al., (2006) reported Picea-Pinus wallichiana community from Astore on south facing aspect at 3300 elevation. Ashraf (1995) focused on the phytosociology of the vegetation in Pir- Chinasi hills and he recognized ten different communities. Among the tree species Pinus wallichiana and Abies pindrow were found to be the dominant species, while understorey species consisted of Viburnum grandiflorum, Indigofera, Elsholtzia, Sorbaria and Sibbaldia. This result is more or less same with the current study. However other researchers ( Khattak ,2002 ,Gilani ,2003, Malik, 2004, Nafeesa ,2007) found different communities from different parts of country. It is concluded that, in the communities of current study the ranking of species and the ground flora of the vegetation was vary from the other locations of Pakistan. The variety of these communities Showed the area exhibited diversity of flora pine trees, coniferous trees, shrubs and herbs. This type of communities and vegetation is found in dry temperate region (Ahmed et al., 2006; Ahmed et al., 2011). Non forested vegetation was distributed from the elevation 2790m to 3500m elevation. This vegetation was widely dominated by Rosa-Hippophae community. This community occupied in sixteen sites. This community attains 333 to 800 individuals ha-1 with 362 to 1600 basal area m2 ha-1.The IVI of this community ranges from 13 to 31%. Rosa-Ribes orientale community was found only one stand having 1067 individuals ha-1 with highest basal area of 2197 m2 ha-1 while Rosa-Berberis lycium community was occupied in 2 stands which shared 267 to 400 individuals ha-1 with 452 to 513 basal area m2ha-1 . Hippophae-Tamarix indica community was composed of 2 stands which attains 533 to 600 individuals ha-1 with 649 to 686 basal area m2ha-1 whereas Berberis-Tamarix indica community obtained 467 individuals with 434 basal area m2ha-1 which was found

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only in one stand. Khan (2011) also found some shrub communities from Chitral Gol National Park which were quite different from the current study. However some ground vegetation was common with the current study. Ahmed and Qadir (1976) recorded many communities near road sites from Gilgit to Shandur. They sampled 46 different locations and recognized ten communities .The community and distribution pattern of the vegetation resemble with the current study. Tareen and Qadir (1987) reported sixteen plant communities from Quetta district and he also found the pure and mix communities with similar ratio of shrub and herb vegetation. All above communities preferred higher amount of moisture therefore distributed near glaciers, dry streams near the springs or rivers. These areas are highly affected by overgrazing .several medicinal plants going in the park are also in high risk due to plant pickers. Therefore prompt action, conservational measure and scientific management plan is required to save flora of the unique national Park.

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MULTIVARIATE ANALYSIS

Chapter 5: Ordination and classification of vegetation

CHAPTER-5 ORDINATION AND CLASSIFICATION OF VEGETATION 5.1-Introduction Quantitative description of vegetation is discussed in the chapter 3 and 4. This chapter focuses the description of vegetation using multivariate techniques (cluster analysis and ordination). These techniques are frequently employed for the purpose of vegetation description and to summarize the data set and the both cluster analysis and ordinations can be used to support each other (Greig-Smith, 1983). Quantitative description of vegetation among the ecological parameters has become an important tool to signify in vegetation ecology (Zhang et al., 2006).This method is widely used in ecology. Some researchers (Gauch, 1982, Greig-Smith, 1983, McCune and Grace, 2002) have discussed in detail of both cluster analysis and ordination methods. In the present study agglomerative cluster analysis is used as the clustering strategy. The agglomerative methods grip the computation of similarities and dissimilarities of the medium. Agglomerative method has various types such as nearest neighbor, farthest neighbor median, group average, centroid flexible beta McQuitty’s method and Ward’s method (1963). This is also known as the “within sum of group square” and used recently by McCune et al., 2000, Paal and Trei, 2004 , Ahmed et al., 2010, Siddique et al., 2010, Khan, 2011 and 2012, Wahab, 2011 and Siddiqui, 2011). Ordination deals with the rational arrangement of species or stands (Greigh- Smith, 1983) on different axes. A number of ordination methods are used to explore the relationship between species and stands. Each ordination method has its own intrinsic worth and demerits and the choice of method are depending on the data set (Orloci, 1978). In the present study detrended correspondence analysis (DCA) was chosen. DCA is geared to the ecological data set and it is referred as DECORANA in computer program. This program is discussed in detail by Gauch (1982).Different workers such as Ahmed (1984, 1988), Kutnar and Martincic (2003), Hokkanen (2004) and Dulamsuren et al., (2005) used DCA ordination in the vegetation ecology. Many quantitative descriptions and distributions of plant species and communities have been presented using multivariate techniques in Pakistan i.e. Shaukat and Qadir, 1971;Shaukat et al., 1976, 1980;Ahmed and Qadir, 1976; Ahmed et al., (1978); Ahmed, 1986, 1988a, b; Khan et al., 1987; Hussain, 1994; Shaukat, 1994; Malik and Hussain, 2006; Wazir et al., 2008; Jabeen and Ahmed, 2009;Ahmed

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Chapter 5: Ordination and classification of vegetation et al., 2010; Siddiqui et al., 2010a; Khan, 2012; Ahmed et al., 2011; Khan,2011; Wahab;2011; Siddique,2011; Siddiqui et al.,(2013); Khan et al., (2013). However, no one has applied these techniques to describe the vegetation of CKNP. 5.2-Purpose of the study

¾ To expose the underlining group structure and trends of vegetation. ¾ To present a quantitative description of the forested and non forested vegetation. ¾ To examine the relationship between vegetation communities and environmental attribute. ¾ To understand the factor causing deterioration of the vegetation.

5.3.-Materials and methods 5.3.1-Data analysis Among phytosociological attributes and absolute values of vegetation, density ha-1 of trees, bushes and herbs while species frequency of understorey vegetation was used for multivariate analysis. Twelve most occurring species (top three dominant species of trees, herbs and shrubs) were selected for multivariate investigation while those understorey species that occurred in at least five stands (35 species out of 65 species) were selected for multivariate analysis (Shaukat,1985; McCune et al., 2000 and Siddiqui,2011). In current study the understorey vegetation is divided into 5 classes on the basis of actual frequency i.e. (1) 1-20% Rare, (2) 21-40% Occasional, (3) 41-60% Frequent, (4) 61-80% Abundant and (5) 81-100% Very abundant following Tansley & Chipp (1926); Tansley (1946). Cluster analysis and ordination (Jabeen, 2009; Khan, 2011 and Khan et al., 2013) were applied on both mix (trees, herbs and shrubs) and understorey vegetation that were correlated them with environmental variables.

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5.3.2-Soil analysis For soil analysis, four to five samples were collected from each stand and pooled together. The litter from the surface was removed and soil was dug out from 20cm using soil auger. About 500g of each sample was placed in a polythene bag labeled and brought to laboratory. Samples were air dried at 25 to 30 ºC and then crushed lightly with a rubber mallet and passed through a 2 mm sieve and analyzed for different physical and chemical characteristics. Soil pH, Total dissolved salts, salinity and conductivity were measured using Multi-parameter meter (Model sension, TM 105, England) in the field during sampling. Maximum water holding capacity (MWHC) was determined following the method of Keen (1931). The organic matter was calculated using loss of weight through ignition (400º C) method. The Soil nutrients Ca++, Mg++, Co++, Mn+, Zn+ ,N, P, K+, S and Fe++ was estimated by PG Atomic Absorption Spectrophotometer (PG 990) following the method of Bilings and Harris (1965). 5.3.3-Environmental variables Environmental variables i.e elevation and slope of each forest (stand) were determined with the help of Global Positioning System (GPS) during the field work. These two parameters were used in Ward’s cluster analysis and DCA ordination. Slope is categorized into 4 classes i.e. gentle (0-15°), moderate (16-30°), steep (31- 45°) and very steep (> 46°) following by Siddiqui (2011), Khan (2011). 5.3.4-Ward’s clustering Method The purpose of cluster analysis is to define groups of items based on their similarities (McCune, & Grace, 2002). Program CLUSTR performs eight variants on the general class of cluster analyses that are hierarchical, agglomerative, and polythetic. In this context, "hierarchical" means that large clusters are composed of smaller clusters. "Agglomerative" means that the analysis proceeds by joining clusters rather than by dividing them. "Polythetic" means that many attributes are used simultaneously to decide the optimum way to combine the clusters. In the present study, agglomerative method of cluster analysis (Ward, 1963) was used to classify the mix (trees, herbs and shrubs) and understorey vegetation. In the current study first matrix was vegetation data while second matrix was environmental data. Ward’s cluster method classify the groups on the basis of first matrix therefore current study is on the basis of vegetation data.

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5.3.5-DCA Ordination Detrended correspondence analysis (Hill and Gauch, 1980) is an eigenanalysis ordination technique based on reciprocal averaging (Hill, 1973). DCA is geared to ecological data sets and the terminology is based on samples and species. Detrended correspondence analysis (DCA) was used to ordinate the vegetation and to seek the relationships within and between the vegetation types and correlate them with the environmental variables. This method is chosen because it provides better understanding to describe the vegetation and ecological parameters (McCune and Grace, 2002). DCA was performed using the package PC-ORD for windows, version 5.10 (McCune and Mefford, 2005).

5.4-Results 5.4.1-Classification 5.4.2-Ward’s cluster Analysis (Forested and non forested vegetation) Four distinct groups in addition to an isolated stand were extracted when data were subjected to cluster analysis using Ward’s agglomerative method (Fig. 5.1). On the basic of these four groups vegetation and environmental variables are grouped. The groups were extracted at the 75 percent information level equal to 1.9 x 106 Euclidean distances. 5.4.2.1-Group I This group consists of 9 stands and three species. The dominant species was Picea smithiana having 97±13 mean density ha-1 while associated species were Juniperus excelsa and Pinus wallichiana have 70±23 and 43±17 density ha-1 respectively (Table 5.1). Understorey vegetation comprised of 33 species, out of which Artemisia brevifolium and Taraxacum nigrum were frequent while Anaphalis virgata, Astragallus zanskrensis, Astragallus gilgitensis, Bistorta affinis, Caram carvi, Geranium pratense , Hippophae rhamnoides, Impatiens balfouriii, Juniperus communis, Lentopodium lentopodinum, Potentilla anserina, Rubus irritans, Rubus ulmifolius, Spiraea canescens, Tanacetum artemisiodes, Taraxacum indicum, Taraxacum xanthophyllum, Thymus linearis and Urtica dioca, were occasional while remaining species Trifolium repnes, Taraxacum baltistanicum, Sedum quadifidum, Sedum pacycloides, Rosa webbiana, Ribes orientale, Potentilla biflora, Geranium

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Chapter 5: Ordination and classification of vegetation neplensis and Berberis orthoborty were recorded as rare species in this group (Table 5.2). This group was situated at the high mean elevation (3362±53) and very steep slope (52°± 6). Soil pH was moderately acidic (5.6± 0.14), salinity level was zero, total dissolved salts (TDS) 26.41±0.48 and the conductivity (55.14±6.87) was high. The maximum water holding capacity (MWHC) of soil for this group was high 37.44%±4.98 and mean organic matter was moderate 5.36±0.63 %. Soil nutrients Mn+ attained highest concentration (10.14±1.3 ppm) while Zn+ showed low concentration (0.09±0.02) among the groups. Other soil nutrients K+, N, Ca++, P, Mg++, Fe++, Co++ and S were found in moderate concentration with the values of 224.4±21.7, 189±8.4, 188.2±13.5, 185±6.5, 142.8±5.98, 136±3.54, 127±1.9 and 0.58±0.01 respectively (Table 5.3). 5.4.2.2-Group II This group also comprises of 9 stands and three species. The dominant species of this group are Rosa webbiana occupied 511±40 mean density ha-1. Hippophae rhamnoides and Berberis lycium was also distributed contains 452±52 and 437±33 density ha-1 respectively (Table 5.1). The understorey vegetation composed of 29 species, in which Rosa webbiana (52%), Hippophae rhamnoides (47%), Thymus linearis (43%) and Berberis lycium (41%) are abundant species. However, Artemisia brevifolium, Astragallus zanskrensis , Bistorta affinis, Lentopodium lentopodinum, Ribes orientale, Sedum pacycloides, Taraxacum baltistanicum, Taraxacum indicum, Urtica dioca and Ribes alpestre were reported as occasional species while Anaphalis virgata, Astragallus gilgitensis, Caram carvi, Geranium neplensis, Geranium pratense, Potentilla anserina, Rubus irritans, Rubus ulmifolius, Sedum quadifidum, Spiraea canescens, Tarxacum nigrum, Trifolium repnes are recorded as rare species (Table 5.2). This group mainly occupied at lower elevation (2975±87m) than first group and moderate slope angle (27°±3°). Edaphic condition of this group showed variable results i.e. mean conductivity (44.58± 4.48), salinity (0.03±0.03), pH (5.77±0.23), TDS (21.83±2.57), MWHC (33.66±3.67) and average organic matter content for this group was 7.02±1.87. Soil nutrient concentrations (mean±SE) for this group showed following in decreasing order K+ > N> P > Ca++ > S > Mg++ (250.88±27.3, 224±0.89, 193.55±6.55, 186.44±19.4, 138.7±7 and 132.33±5.5 ppm respectively) while among

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Chapter 5: Ordination and classification of vegetation the micro-nutrients the order is as follows: Fe++ > Mn+ > Co++ > Zn+ (130.06±5.23, 8.82±0.96, 1.26±0.05 and 0.35±0.14 ppm) respectively (Table 5.3). 5.4.2.3-Group III This group is comprised of 7 stands and 8 species in which Rosa webbiana was leading dominant species contains 390±62 density ha-1 while co-dominant species Hippophae rhamnoides attained 389±20 trees ha-1. Artemisia brevifolium and Ribes alpestre occupied 334±67 and 267±67 density ha-1 respectively. Other species i.e Berberis lycium, Ribes orientale, Urtica dioca and Artemisia brevifolium were recorded as associated species which were restricted only to one stand (Table 5.1). The mean elevation for this group was slightly lower (2950±99m) than previous groups while mean slope was moderate slope (27±4) was found .The average conductivity for this group recorded (36.32±5.33) with slightly acidic pH (6.06±0.21) while salinity observed (0.01±0.01). Mean MWHC was found (34.71±2.48) with a low total dissolved salt (16.95±2.76). The moderate organic matter (3.17±0.56) was found for this group. Soil nutrient concentrations attained Ca++ (237.42±35.13), K+, (225.7±18.44), N (218.85±2.97), P (189.42±8.05), and Fe++ (148.92±9.64). S (139.28±2.29), Mg++ (140.85±6.78), Co++, (1.47±0.16), Mn+ (6.06±1.10) and Zn+ (0.09±0.004). Understorey vegetation comprised of 24 species, out of these Hippophae rhamnoides (46%) and Rosa webbiana (43%) were frequent species while some occasional species included Anaphalis virgata, Artemisia brevifolium, Bistorta affinis, Potentilla biflora, Ribes orientale, Sedum quadifidum, Taraxacum baltistanicum, Thymus linearis, Urtica dioca, Berberis lycium Trifolium repnes and Ribes alpestre. However some rare species were also reported in which Astragallus zanskrensis, Berberis orthoborty, Geranium neplensis, Geranium pratense, Lentopodium lentopodinum, Lentopodium linearifolium, Rubus irritans, Sedum sp, Spiraea canescens, Taraxacum indicum and Tarxacum xanthophyllum (Table 5.2).

5.4.2.4-Group IV This group includes 6 stands and 6 species. The leading dominant of this group was Hippophae rhamnoides with 444±47 trees ha-1 while Rosa webbiana and Tamarix indica were also distributed with 338±38 and 378±33 density ha-1 respectively. In this group Berberis lycium, Ribes orientale and Juniperus communis were restricted only in one stand. Tamarix indica and Juniperus communis were the

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Chapter 5: Ordination and classification of vegetation restricted species to this group with high density ha-1 because of less abundance in the stands (Table 5.1). Understorey vegetation composed by 25 species, in which the highest frequency was recorded in Hippophae rhamnoides (53%), Astragallus gilgitensis (50%), Silene vulgaris (50%) and Berberis lycium (50%). Occasional species included Anaphalis virgata, Artemeisa brevefolium, Bistorta affinis, Juniperus communis, Ribes orientale, Taraxacum baltistanicum, Taraxacum nigrum, Thymus linearis and Ribes alpestre. Carum carvi, Geranium pratense, Lentopodium lentopodinum, Lentopodium nanum, Potentilla anserina, Potentilla biflora, Rubus irritans, Sedum quadifidum, Spiraea canescens and Trifolium repnes were found rare in this group (Table 5.2). The slightly lower mean elevation (2940±103m) than previous two groups and gentle slope (13°±5) was the characteristic feature of this group. Among the edaphic variables conductivity, strong acidic pH and salinity were recorded in medium range (49.66±3.37, 5.48±0.06 and 0.08±0.03 respectively). Total dissolved salt (22.78±2.54) and MWHC (29.16±4.11) were also in a medium range while low organic matter (4.21±0.90) content was observed. Soil nutrients K+ was found with highest concentration (265.83±13.97) followed by Ca++, N, P, S, Mg++ and + Fe++ which attained the concentrations of 205.5±12.94, 205.33±16.40, 192±16, 141±2.06, 139.3±7.07 and 128.3±5.93. Mn+ > Co++ > Zn showed decreasing order concentration with the value of 7.79±0.87, 1.20±0.03 and 0.39±0.25 respectively (Table 5.3). The isolated stand (17) was composed of 15 species in which three species were dominant (Rosa webbiana occupied 1067 density ha-1 , Ribes orientale 467 density ha- 1 and Berberis lycium occupied 333 shrubs per hectare). Understorey vegetation of this isolated stand consists of 15 species, out of these species the highest frequency was reported for Rosa webbiana (100%) while some occasional species were also distributed like Ribes orientale (40%) and Berberis lycium (40%). However, Anaphalis virgata, Artemeisa brevifolium, Geranium neplensis, Geranium pratense, Sedum quadifidum, Taraxacum baltistanicum, Taraxacum indicum, Trifolium repnes, Urtica dioca and Ribes alpestre were rarely present in this stand (Table 5.2). This stand is located at high elevation (3559m) with very steep slope 50°. The conductivity and MWHC were recorded 39 and 29% respectively. Low salanity (0.1) and Low organic matter (1.3 %) was also recorded with moderate mean TDS (15.3).

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Chapter 5: Ordination and classification of vegetation strong acidic soil pH (5.4) was found in this isolated stand. The soil nutrient concentrations recorded as in descending order K+ (244), P (204), N (202), Fe++ (179), Mg++ (163), Ca++ (156), S (134) Mn+ (5.91), Co++ (1.26) and Zn+ (0.42) .

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Fig.5.1 Dendrogram, based on Information level and Euclidean distance of the 32 stands of forested and non forested vegetation data representing four groups

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Table 5.1 Four groups and one isolated stand obtained from Ward’s cluster analysis of forested and non forested species from 32 stands based on density ha-1 and environmental variable (elevation, slope).

S.No Name of Species Group 1 Group II Group III Group IV Isolated stand (17) 1 Picea smithiana 97±13 * * * * 2 Pinus wallichiana 43±17 * * * * 3 Juniperus excelsa 70±23 * * * * 4 Rosa webbiana * 511±40 390±62 333±38 1067±00 5 Hippophae rhamnoides * 452±52 389±20 444±47 * 6 Berberis lycium * 437±33 133±00 467±00 333±00 7 Ribes alpestre * * 267±67 * * 8 Urtica dioca * * 400±00 * * 9 Ribes orientale * * 267±00 600±00 467±00 10 Tamarix indica * * 0 378±33 * 11 Artemisia brevefolium * * 334±67 * * 12 Junipers communis * * * 533±00 * *Absent

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Table 5.2 showing understorey mean frequency of forested and non-forested vegetation .

S.No Name of species Group I Group II Group III Group IV Isolate stand (17) 1 Anaphalis virgata 27 18 23 30 20 2 Artemisia brevifolium 43 24 23 27 10 3 Astragallus zanskrensis 27 32 10 20 * 4 Astragallus gilgitensis 28 10 * 50 * 5 Berberis orthoborty 19 * 20 * * 6 Bistorta affinis 29 33 33 30 * 7 Carum carvi 22 15 * 17 * 8 Geranium neplensis 15 17 20 * 20 9 Geranium pratense 23 18 20 20 20 10 Hippophae rhamnoides 30 47 46 53 * 11 Impatiens balfouriii 26 * * * * 12 Juniperus communis 24 * * 40 * Lentopodium 13 lentopodinum 26 25 20 20 * 14 Lentopodium linefolium 22 * 20 * * 15 Lentopodium nanum 27 30 * 20 * 16 Potentilla anserina 26 20 * 20 * 17 Potentilla biflora 10 20 23 20 * 18 Ribes orientale 17 27 24 40 40 19 Rosa webbiana 18 52 43 33 100 20 Rubus irritans 27 20 18 20 * 21 Rubus ulmifolius 21 20 * * * 22 Sedum pacycloides 19 30 10 * * 23 Sedum quadifidum 19 13 23 15 20 24 Silen vulgaris 20 20 * 50 * 25 Spiraea canescens 35 15 10 20 * 26 Tanacetum artemisiodes 24 * * * * 27 Taraxacum baltistanicum 15 37 30 35 10 28 Taraxacum indicum 34 23 20 * 10 29 Taraxacum nigrum 44 20 * 40 30 30 Taraxacum xanthophyllum 26 * 20 * * 31 Thymus linearis 23 43 30 32 30 32 Trifolium repnes 19 18 22 20 10 33 Urtica dioca 21 25 35 * 20 34 Berberis Lycium * 41 29 50 40 35 Ribes alpestre * 27 33 30 10

*Absent

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Table 5.3 Mean values of environmental variables (topographic and edaphic) and soil nutrients based on forested and non forested groups derived from Ward’s cluster analysis using 32stands of CKNP. (Mean ± SE).

Mean ± SE Mean ± SE Mean ± SE Mean ±SE Mean ± SE Mean ± SE Variable Group I Group II Group III Group IV Isolated (17) 1-Topographic Variables of Soil Elevation (m) 3362±53 2975±87 2950±99 2940±103 3559±00 Slope º 52±6 27±3 27±4 13±5 50±00 2- Edaphic Variables of Soil Conductivity (µs/cm) 55.14±6.87 44.58±4.48 36.32±5.33 49.66±3.37 39±00 Salinity (%) 0±0 0.03±0.03 0.01±0.01 0.08±0.03 0.1±00 pH 5.66±0.14 5.77±0.23 6.06±0.21 5.48±0.06 5.4±00 MWHC (%) 37.44±4.98 33.66±3.67 34.71±2.48 29.16±4.11 29±00 TDS (g/L) 26.41±1.48 21.83±2.57 16.95±2.76 22.78±2.54 15.3±00 Organic matter % 5.36±0.63 7.02±1.87 3.17±0.56 22.78±2.54 15.3±00 3- Soil Nutrients Calcium(ppm) 188.22±13.48 186.44±19.41 237.42±35.13 205.5±12.94 156±00 Magnesium (ppm) 142.8±5.98 132.33±5.48 140.85±6.78 139.3±7.07 163±00 Nitrogen (ppm) 189±8.4 224.4±0.89 218.85±2.97 205.33±16.40 202±00 Phosphorus (ppm) 185±6.5 193.55±6.55 189.42±8.05 192.16 204±00 Potassium (ppm) 224.4±21.7 250.88±27.33 225.7±18.44 265.83±13.97 244±00 Sulphur (ppm) 127±1.9 138.7±1.69 139.28±2.29 141.33±2.06 134±00 Cobalt (ppm) 0.58±0.01 1.26±0.05 1.47±0.16 1.20±0.03 1.26±00 Manganese (ppm) 10.14±1.3 8.82±0.96 6.06±1.10 7.79±0.87 5.91±00 Zinc (ppm) 0.09±0.02 0.35±0.14 0.09±0.004 0.39±0.25 0.42±00 Iron (ppm) 5.36±0.63 130.06±5.23 148.92±9.64 128.3±5.93 179±00 SE = Standard error TDS= Total dissolve salt, MWHC= Maximum water holding capacity, OM= Organic matter.

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5.4.3-Ward’s cluster Analysis (Understorey vegetation) The dendrogram (Fig.5.2) obtained by cluster analysis using Ward’s method, discloses six groups at 53 % information level and Euclidean distance near to 1.2x105. 5.4.3.1-Group I This group consists of 5 stands and 34 species, characterized by the predominance of Artemisia brevefolium (mean frequency = 38%) with Impatiens balfouriii and Silene vulgaris (mean frequency=33%). Other associated species found in this group were Taraxacum indicum (32%) and Rubus irritans (31%). Some species including Astragallus zanskrensis, Astragallus gilgitensis, and Geranium pratense and Sedum pacycloides were distributed with the frequency of 27%. Three species Juniperus communis, Taraxacum nigrum and Trifolium repnes were also situated with similar mean frequency. Bistorta orthoborty, Carum carvi, Lentopodium linearifolium, Potentilla biflora, Ribes orientale, Tarxacum baltistanicum and Urtica dioca were the rare species in this group (Table 5.4). This group was situated at high elevation 3283±92m and steep slope 32±9°.The conductivity was very high (70.9±5.75) with low salinity (0.02±0.02) while soil was strong acidic (5.34±0.09 pH). High mean amount of Total dissolved salt (28.6±2.34), maximum water holding capacity (46.4±5.90) and organic matter (6.52±0.84). Soil nutrient concentrations N, Ca++,P, Mg++, K+, Co++, Mn+, Zn+ and Fe++ were found with the values of 204±11.4, 201.8±22.61, 194±3.05, 142.4±8.52, 216.8±26.12, 126.8±3.26, 0.95±0.10, 11.13±2.15, 0.12±0.02 and 131.6±8.23 respectively (Table 5.5). 5.4.3.2-Group II It comprises of 5 stands and 24 species, out of these Taraxacum nigrum was dominant species having mean frequency of 49% and co-dominant species Spiraea canescens attained 38% average frequency while Lentopodium linearifolium and Taraxacum indicum attains similar average frequency of 35 % and other two species Astragallus gilgitensis and Rubus ulmifolius have also similar frequency of 34%. Anaphalis virgata, Bistorta affinis and Juniperus communis were distributed with similar frequency of (29%). Some rare species were also present in this group which including Berberis orthoborty, Sedum pacycloides and Trifolium repnes (Table 5.4). This group was located at higher elevation 3379±65m (mean±SE) with very steep slope 62±4° than group I. The mean conductivity for this group was observed

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37.96±1.91 with moderate acidic pH (5.96±0.14). TDS (22.8±0.73) and water holding capacity (25.6±3.12) were moderately found while salinity was recoded in this group. The low amount of organic matter (0.422±0.30%) was observed in this group with soil micro nutrients concentration Fe++ Mn+, Co++ and Zn+ attained 135.6±4.50, 8.25±1.18, 0.84±0.01 and 0.07±0.03 respectively while macro nutrient concentrations recorded as follow: K+ (247.4±34.15) P (194±3.05) Ca++ (188.6±18.44), N (181.6±11.44), Mg++ (139.4±8.53), and S (129.2±2.37). 5.4.3.3-Group III This group was composed of 6 stands and 23 species. Among these species Rosa webbiana was dominant having highest frequency (58%) while associated species were Hippophae rhamnoides and Thymus linearis attains 48% and 37 % respectively while Bistorta affinis, Ribes orientale and Berberis lycium have similar average frequency of 30%. Berberis orthobortoy, Geranium pratense, Lentopodium linearifolium, Taraxacum nigrum and Trifolium repnes were rarely present with low average frequency i.e. 20%. Some species also reported as rare in this group which were including Anaphalis virgata, Artemisia brevefolium, Astragallus gilgitensis, Geranium neplensis, Geranium pratense, Rubus irritans, Taraxacum indicum and Trifolium repnes (Table 5.4). This group was distributed on lower elevation (3051±154m) than previous two groups with moderate mean slope 23±7°. The moderate amount of conductivity (29.45±4.23), MWHC (31.66±3.92) and TDS (13.56±2.63) were found to this group with strong acidic soil pH (5.52±0.11).Salinity (0.03±0.02) and the organic matter (2.16±0.53) were both of in low order. Soil nutrient concentrations were found in descending order as K+ (243.5±19.26) > N (219.83±3.34) > Ca++ (219.5±45.54) > P (201.5±6.78) > Mg++ (154.3±8.48) > Fe+ (145.5±13.34) > S (136.16±1.81) > Mn+ (5.81±0.72) > Co++ (1.54±0.18) > Zn+ (0.15±0.06). 5.4.4.4-Group IV This was a largest group as compare to other groups and composed of 7 stands and 24 species. In which Rosa webbiana attains highest average frequency of 55% while Hippophae rhamnoides and Berberis lycium were distributed with the average frequency of 53% and 37% respectively. Lentopodium nanum, Taraxacum indicum and Taraxacum nigrum have similar average frequency of 30 %. Other species, Artemisia brevefolium and Taraxacum baltistanicum with 26%, Thymus linearis 25% and Anaphalis virgata 24% were also present. Some rare species were also reported in

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Chapter 5: Ordination and classification of vegetation this group i.e Ribes alpestre, Urtica dioca, Trifolium repnes, Spiraea canescens, Sedum quadifidum, Sedum pacycloides, Rubus ulmifolius, Rubus irritans, Ribes orientale, Lentopodium lentopodinum, Geranium pratense, Geranium neplensis and Astragallus zanskrensis (Table 5.4). This group was located at lowest elevation (2872±13m) and steep slope (31±3°). The conductivity, maximum water holding capacity and total dissolved salt observed 47.41±5.46 36±4.56 and 23.64±3.49 respectively. The soil pH was found slight acidic (6.17±0.29) with high salanity (0.04) while organic matter content was observed with a high amount of 6.88±1.69. The soil nutrients for this group recorded as follow: K+, (278.71±15.54), N (222.66±1.90) , P (191.66±7.65), Ca++ (190±19.16), S (138.33±2.47), Mg++ (137.85±8.09), Fe++ (137.77±7.78), Co++, (1.29±0.06), Mn+ (8.61±1.30) and Zn+ (0.25±0.06) . 5.4.4.5-Group V This group was very small than other groups, composed of only three stands and 17 species. The dominant species was Hippophae rhamnoides with the average frequency of 60% and co-dominant species were Silen vulgaris with 50% frequency while Taraxacum nigrum distributed as associated species with 47% frequency. Three species i.e Juniperus communis, Ribes orientale and Thymus linearis were recorded with similar average frequency of 40 % while other four species including Anaphalis virgata, Artemisia brevefolium, Rubus ulmifolius and Ribes alpestre attained 30% average frequency. Geranium pratense, Potentilla anserina, Sedum quadifidum and Trifolium repnes are rare species in this group (Table 5.4). This group was distributed at almost same mean elevation (3079±9m) of group III with a gentle mean slope (5°±00). The conductivity (48.33±0.88) and salinity (0.13±0.03) was moderately present in this group with acidic strong acidic soil pH (5.46±0.08). Maximum water holding capacity and TDS also showed a moderate amount of 23.66±1.20 and 19.76±0.32 respectively. The organic matter for this group was observed 4.7±1.51% while highest soil nutrients concentration was recorded in K+, (247.66±0.66), N (218±2.33), Ca++ (181.66±7.79), P (166.33±0.88), S (145.66±1.20), Mg++ (135.66±4.05) and Fe++ (125.33±10.13). Concentration of other soil nutriments were observed as Mn+ (9.34±1.12), Co++, (1.18±0.03) and Zn+ (0.038±0.01) in decreasing order (Table 5.5).

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5.4.4.6Group VI This group is comprised of 6 stands and 19 species; out of these species Taraxacum baltistanicum and Berberis lycium have highest average frequency of 47% while other co-dominant species Thymus linearis and Hippophae rhamnoides lies with the average frequency of 43 % and 40% respectively. Other species Rosa webbiana 38%, Ribes orientale and Bistorta affinis 35%, Astragallus zanskrensis 30 % and Artemisia brevefolium attains 28 % average frequency. Some rare species were also reported including Anaphalis virgata, Carum carvi, Potentilla anserina, Potentilla biflora, and Sedum quadifidum and Spiraea canescens (Table 5.4). This group occupied higher (2988±35m) elevation than group IV while lower elevation than group with a steep slope 31±3°. The average conductivity was high 48.5±3.03 with moderate MWHC (35.66±1.25) and TDS (23.61±0.72). The soil observed to be moderate acidic (5.73±0.19 pH) with no salinity. The moderate organic matter (5.21±2.25) was also found .The soil nutrients concentration showed a similar situation among the groups which presented in descending order as Ca++ (213±16) > N (207±16.6) > K+ (203.5±34.98) > P (197±10.74) > S (140±2) > Fe++ (135.66±2.25) > Mg++ (134.83±5.60) > Co++(1.15±0.02) > Mn+ (7.62±1.16) > Zn+ (0.61±0.30) .

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Fig.5.2 Dendrogram resulting from cluster analysis based on frequency of understorey vegetation.

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Table 5.4 Showing means of groups of circular plot species (understorey vegetation) on the basis of frequency and environmental variables using Ward’s cluster analysis.

S. No Name of species Group 1 Group II Group III Group IV Group V Group VI 1 Anaphalis virgata 20 29 18 24 30 20 2 Artemisia brevifolium 38 * 17 26 30 28 3 Astragallus zanskrensis 27 * * 20 * 30 4 Astragallus gilgitensis 27 34 10 * * * 5 Berberis orthoborty 20 19 20 * * * 6 Bistorta affinis 30 29 30 * 27 35 7 Carum carvi 16 25 * * 20 15 8 Geranium neplensis 15 * 18 20 * * 9 Geranium pratense 27 21 20 20 15 20 10 Hippophae rhamnoides 30 * 48 53 60 40 11 Impatiens balfouriii 33 24 * * * * 12 Juniperus communis 23 29 * * 40 * 13 Lentopodium lentopodinum 20 30 * 10 * 25 14 Lentopodium linefolium 13 35 20 * * * 15 Lentopodium nanum 20 27 * 30 * * 16 Potentilla anserina 21 33 * * 10 20 17 Potentilla biflora 10 * 25 * * 20 18 Ribes orientale 18 * 30 20 40 35 19 Rosa webbiana 21 * 58 55 33 38 20 Rubus irritans 31 20 14 20 30 30 21 Rubus ulmifolius 12 34 * 20 * * 22 Sedum pacycloides 27 14 25 20 * * 23 Sedum quadifidum 10 22 23 15 20 13 24 Silen vulgaris 10 23 * 20 50 * 25 Spiraea canescens 33 38 * 10 * 16 26 Tanacetum artemisiodes 18 26 * * * * 27 Taraxacum baltistanicum 15 * 22 26 * 47 28 Taraxacum indicum 32 35 15 30 * * 29 Taraxacum nigrum 23 49 20 30 47 * 30 Taraxacum xanthophyllum 25 20 20 * 0 * 31 Thymus linearis 22 * 37 25 40 43 32 Trifolium repnes 23 17 20 18 10 20 33 Urtica dioca 20 21 33 10 * * 34 Berberis lycium * * 30 37 * 47 35 Ribes alpestre 30 * 33 10 30 * *Absent

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Table 5.5 Mean values of environmental variables (topographic, and edaphic) and soil nutrients based on circular plot groups derived from Ward’s cluster analysis using 32stands of CKNP. (Mean ± SE).

Mean ± SE Mean ± SE Mean ± SE Mean ±SE Mean ± SE Mean ± SE Mean ± SE

Variable Group I Group II Group III Group IV Group V Group VI

1-Topographic Variables of Soil

Elevation (m) 3283±95.78 3378±64.53 3051±154.34 2872±131.54 3079.33±9.38 2987±34.61

Slope º 31.6±8.63 62.4±3.93 23.3±7.14 30.71±2.54 5.00±0.00 30.83±3.00

2- Edaphic Variables of Soil

Conductivity µs/cm 70.9±5.75 37.96±1.91 29.45±4.23 47.41±5.46 48.33±0.88 48.5±3.03

Salinity (%) 0.02±0.02 0.0±0.0 0.03±0.02 0.04±0.04 0.13±0.03 0.00±0.00

pH 5.34±0.09 5.96±0.014 5.52±0.11 6.17±0.29 5.46±0.08 5.73±0.19

MWHC (%) 46.4±5.90 25.6±3.12 31.66±3.92 36±4.56 23.33±1.20 35.66±1.25

TDS (%) 28.6±2.34 22.8±0.73 13.56±2.63 23.64±3.49 19.76±0.32 23.61±0.72

Organic matter % 6.52±0.84 2.16±0.30 2.16±0.53 6.88±1.69 4.7±1.51 5.21±2.25

3-Soil Nutrients

Calcium (ppm 201.8±22.61 188.6±18.44 219.5±45.54 190±19.16 181.66±7.79 213.16±19.30

Magnesium (ppm) 142.4±8.52 139.4±8.53 154.3±8.48 137.85±8.09 135.66±4.05 134.83±5.60

Nitrogen (ppm) 204±11.41 181.6±11.44 219.83±3.34 222.66±1.90 218±2.33 207±16.6

Phosphorus (ppm) 194±3.05 197.2±11.2 201.5±6.78 191.66±7.65 166.33±0.88 197±10.74

Potassium (ppm) 216.8±26.12 247.4±34.15 243.5±19.26 278.71±15.54 247.66±0.66 203.5±34.98

Sulphur (ppm) 126.8±3.26 129.2±2.37 136.16±1.81 138.33±2.47 145.66±1.20 140±2

Cobalt (ppm) 0.95±0.10 0.84±0.01 1.54±0.18 1.29±0.06 1.18±0.03 1.15±0.02

Manganese (ppm) 11.13±2.15 8.25±1.18 5.81±0.72 8.61±1.30 9.34±1.12 7.62±1.16

Zinc (ppm) 0.12±0.02 0.07±0.03 0.15±0.06 0.25±0.06 0.038±0.01 0.61±0.30

Iron (ppm) 131.6±8.23 135.6±4.50 145.5±13.34 137.77±7.78 125.33±10.13 135.66±2.25 SE = Standard error

TDS= Total dissolve salt, MWHC= Maximum water holding capacity, OM= Organic matter.

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5.4.5-DCA Ordination The correlation between environmental variables (elevation and slope) and different forested and non forested vegetation stands was analyzed by DCA ordinations.DCA ordination was based on the density ha-1 of species from 32 stands of the study area. 5.4.6-Ordination of forested and non-forested vegetation DCA ordination plotted axes 1 and 2 are shown in Fig.5.3 while axes 1 and 3 shown in Fig.5.4 which shows continuity and vegetation tend to separate out in a plane therefore cannot obtain groups from these axes and these plots are not imposed on ordination. The ordination of axes 2-3 is shown in Fig.5.5 which shows more or less continuous pattern. However, four groups and one isolated stand were obtained from this plot. In this plot distribution pattern of stands in group I is extreme left and lower side of the ordination plane. This group attains 9 forest stands which are quite separated from the non forested stands. In group I Picea smithiana was dominant over the remaining two species Pinus wallichiana and Juniperus excelsa. Group II was the largest group which exhibited at upper middle side of the ordination plane and overlapping with group III and Group IV. Rosa webbiana was dominant species while Hippophae rhamnoides and Berberis lycium also found with frequently in this group. Group III was the second largest group among the non forested vegetation, in which 7 stands and 7 species were obtained. This group was situated near group II, IV and isolated stand, shows some overlapping with group II. Rosa webbiana was again dominant in this group while Hippophae rhamnoides was associated with this species. However Berberis lycium, Urtica dioca, Ribes alpestre, Ribes orientale and Artemisia brevefolium were scattered with these species but not abundantly. The result of ordination also reveals that the group IV was overlapping with group II and distribution pattern of stands was discontinuous pattern. In this group Hippophae rhamnoides was dominating species over Rosa webbiana and Tamarix indica while Ribes orientale, Juniperus communis and Berberis lycium were found with low density. An isolated stand (17) was also reported which is placed between group II and group III. This stand was composed of 3 species, out of these Rosa webbiana was the

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Chapter 5: Ordination and classification of vegetation dominant species while Berberis lycium and Ribes orientale were the associated species with low density.

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Fig.5.3. DCA ordination axes 1 and 2 of forested and non forested vegetation data based on density ha-1. The groups derived from Ward’s cluster analysis are not superimposed on 2-D ordination axes.

Fig.5.4 DCA ordination axes 1 and 3 of forested and non forested vegetation data based on density ha-1. The groups derived from Ward’s cluster analysis are not superimposed on 2-D ordination axes.

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Fig.5.5 DCA ordination axes 2 and 3 of forested and non forested vegetation data based on density ha-1. The groups derived from Ward’s cluster analysis are superimposed on 2-D ordination axes.

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5.4.7-Correlation of ordination axes with environmental variables and soil nutrients (Forested and non forested vegetation) The relationship between DCA ordination axes with environmental variables showed some significant correlations (Table 5.6).Ordination axes 1 was positively correlated with slope (P <0.05), TDS (P <0.05), conductivity (P<0.05) , N (P<0.05) , P (P<0.05) and Mn+ while other environmental variables did not show any significant correlation with axes I. The ordination axes 2 and 3 did not show significant correlation with any environmental variables.

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Table 5.6 Relationship (correlation coefficients) of environmental variables (topographic variables and edaphic variables) and soil nutrients with 3 DCA ordination axes obtained by forested and non forested vegetation data based on density ha-1

Axes 1 Axes 2 Axes 3 S. No. Variables r Prob. Level r Prob. Level r Prob. Level 1- Topographic variables 1 Elevation -0.074 ns 0.01 ns -0.008 ns 2 Slope 0.65 P < 0.05 -0.10 ns 0.17 ns

2- Edaphic variables 1 TDS 0.39 P < 0.05 -0.03 ns -0.09 ns 2 PH -0.08 ns 0.17 ns 0.10 ns 3 MWHC 0.19 ns 0.009 ns -0.20 ns 4 Salinity -0.28 ns 0.004 ns -0.01 ns 5 Conductivity 0.34 P < 0.05 -0.04 ns -0.18 ns 6 Organic matter 0.06 ns -0.004 ns -0.09 ns 3-Soil nutrients 1 Calcium -0.13 ns 0.004 ns -0.28 ns 2 Magnesium 0.12 ns 0.11 ns 0.13 ns 3 Nitrogen -0.50 P < 0.01 -0.34 P < 0.05 -0.31 P < 0.05 4 Phosphorus -0.19 ns 0.06 ns -0.45 P < 0.05 5 Potassium -0.16 ns -0.16 ns 0.09 ns 6 Sulphur -0.71 P < 0.01 0.18 ns 0.22 ns 7 Cobalt -0.66 ns -0.00068 ns -0.02 ns 8 Manganese 0.35 P < 0.05 -0.07 ns -0.11 ns 9 Zinc -0.24 ns -0.04 ns -0.05 ns 10 Iron -0.09 ns -0.26 ns -0.03 ns Key to abbreviations: r = Correlation coefficient, ns = Non significant and Prob. Level = Probability level, TDS= Total dissolved salt, MWHC= Maximum water holding capacity, OM= Organic matter.

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5.4.8- Ordination of Understorey vegetation DCA ordination of understorey vegetation on axes 1 -2 is shown in Fig.5.6 while axes 1-3 are shown in Fig. 5.7. The groups extracted by Ward’s cluster analysis can readily be superimposed on DCA ordination axes 1-2 & 1-3. The distribution pattern of stands in the axes 1-2 and 1-3 shows more or less continuous while on axes 2-3 the distribution pattern of stands was discontinuous (Fig.5.8) therefore this plot cannot imposed to the ordination of understory vegetation. In the axes 1-2, group 1 was located in lower side of the centre which consists 5 stands while group II was placed in the right side of group I which contains 5 stands. Group III was situated in the centre of all four groups which overlapped with the group IV and composed of 6 stands. Group IV was the largest group which has 7 stands and overlapped with group VI. Group V lies at the top of all three groups which was a small group and comprised of only three stands while group VI was overlapping with group III and IV attains 6 stands. In the axes 1-3 the vegetation distribution was good as compare to other axes, in which group I and II situated in left side of ordination space while group III and IV overlapping each other which are exhibited in the left side of ordination space. Group V was placed on the base of group I and II which lies in the centre while group VI situated in the left of the ordination space.

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Fig.5.6 DCA among axes 1 and 2 of understorey vegetation data based on frequency.

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Fig.5.7 DCA among axes 1 and 3 of understorey vegetation data based on frequency.

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Fig.5.8 DCA among axes 2 and 3 of understorey vegetation data based on frequency. (Not super imposed)

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5.4.9-Correlation of ordination axes with environmental variables and soil nutrients (understorey vegetation) The correlation of DCA-ordination axes with environmental variables is presented in Table 5.7. As far as the topographic and edaphic variables are concerned, they showed some correlation with ordination axes. Ordination axes 1 was positively correlated (P< 0.01) with slop while other variables like elevation, TDS, MWHC, salinity, conductivity, organic matter and pH were not significant. Axes 2 of DCA ordination did not exhibit significant correlation with all topographic and edaphic variables except salinity which was positively correlated (P<0.05). Axes 3 were not significantly correlated with all topographic and edaphic variables. Similarly soil nutrient did not show more significant correlation with DCA axes. Ca++ showed significant relationship (P<0.02) with axes 2 while Mg++ exhibited significant (P< 0.05) relation with axes 1. Nitrogen and Phosphorus have also significant relation with the ordination axes 3. However other nutrients like K+, Co++, Mn+, Zn+, S and Fe+ are not significantly correlated with all the three axes.

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Table 5.7 Relationship (correlation coefficients) of environmental variables (topographic variables and edaphic variables) and soil nutrients with 3 DCA ordination axes obtained by understorey vegetation data based on frequency.

Axes 1 Axes 2 Axes 3 S. No. Variables r Prob. Level r Prob. Level r Prob. Level 1- Topographic variables 1 Elevation -0.01 ns 0.16 ns 0.29 ns 2 Slope 0.49 P < 0.01 -0.30 ns -0.01 ns

2- Edaphic variables 1 TDS 0.25 ns -0.41 ns -0.13 ns 2 PH -0.14 ns 0.14 ns 0.15 ns ns 3 WHC -0.10 -0.59 ns 0.17 ns ns 4 Salinity -0.13 0.41 P < 0.01 -0.02 ns ns 5 Conductivity 0.13 -0.42 ns -0.11 ns 6 Organic matter -0.12 ns -0.13 ns -0.10 ns 3-Soil nutrients 1 Calcium 0.20 ns 0.31 P < 0.05 -0.07 ns 2 Magnesium 0.30 P < 0.05 0.16 ns 0.05 ns 3 Nitrogen -0.50 P < 0.01 -0.34 P < 0.05 -0.31 P < 0.05 4 Phosphorus -0.19 ns 0.06 ns -0.45 P < 0.05 5 Potassium -0.20 ns -0.21 ns -0.36 ns 6 Sulphur -0.71 P < 0.01 0.18 ns 0.22 ns 7 Cobalt 0.11 ns 0.06 ns -0.15 ns 8 Manganese 0.19 ns 0.09 ns 0.25 ns 9 Zinc -0.29 ns -0.14 ns -0.06 ns 10 Iron 0.09 ns 0.15 ns -0.10 ns

r = Correlation coefficient, ns = Non significant, Prob. Level = Probability level

TDS= Total dissolved salt, MWHC= Maximum water holding capacity, OM= Organic matter.

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5.4.10-Univariate analysis of variance (Forested and non-forested vegetation) Using Ward’s cluster analysis for forested and non forested vegetation resulted in four groups and one isolated stand. The individual environmental variables corresponding to the four groups were analyzed using univariate analysis of variance (ANOVA). Among the topographic variables elevation and slope were found to be significant (F=5.86; P<0.01) and (F=9.27; P<0.001) respectively (Table 5.8). However, total dissolved salt gave a significant (P<0.05) relation while remaining edaphic factors (MWHC, conductivity, salinity and organic matter and pH) did not significantly correlated. The soil nutrients Cobalt (Co), N, S, and Iron (Fe) showed significant relation (P<0.001 and P<0.05 respectively) between the groups.

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Table 5.8 Analysis of variance of individual environmental variables (topographic and edaphic) and soil nutrients. Four groups were derived by Ward's cluster analysis using forested and non forested vegetation data of 32 stands of CKNP Gilgit-Baltistan, Pakistan.

ANOVA: Single Factor

Source of Variation SS df MS F P-level 1- Topographic Variables 1 Elevation Between Groups 1280395 4 320098.7 5.869504 P <0.01 Within Groups 1472469 27 54535.9 Total 2752864 31 2 Slope Between Groups 6457.056 4 1614.264 9.277552 P <0.001 Within Groups 4697.913 27 173.9968 Total 11154.97 31 2- Edaphic Variables 1 TDS Between Groups 401.9303 4 100.4826 2.35307 P <0.05 Within Groups 1152.974 27 42.70275 Total 1554.905 31 2 PH Between Groups 1.290568 4 0.322642 1.166236 ns Within Groups 7.469619 27 0.276653 Total 8.760188 31 3 MWHC Between Groups 276.4846 4 69.12116 0.529216 ns Within Groups 3526.484 27 130.6105 Total 3802.969 31 4 Salinity Between Groups 0.031845 4 0.007961 1.838722 ns Within Groups 0.116905 27 0.00433 Total 0.14875 31 5 Conductivity Between Groups 1546.006 4 386.5015 1.633361 ns Within Groups 6388.999 27 236.6296 Total 7935.005 31

6 Organic matter Between Groups 78.78 4 19.69 1.65321 ns Within Groups 321.07 27 11.89 Total 399.86 31 3- Soil nutrients 1 Calcium Between Groups 14809.48 4 3702.36 1.02 ns Within Groups 97094.66 27 3596.11 Total 111904.5 31 2 Magnesium Between Groups 1507.55 4 376.88 1.47 ns Within Groups 6644.19 27 255.54 Total 8151.74 31

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3 Nitrogen Between Groups 39661.23 4 9915.30 17.71 P <0.001 Within Groups 13429.53 24 559.56 Total 53090.76 31 4 Phosphorus Between Groups 1174.79 4 293.69 0.62 ns Within Groups 11269.76 24 469.59 Total 12444.54 31 5 Potassium Between Groups 8684.59 4 2171.14 0.54 ns Within Groups 107985.4 27 3999.45 Total 116670 31 6 Sulphur Between Groups 803.99 4 200.99 6.78 P <0.001 Within Groups 652.00 27 29.63 Total 1456.00 31 7 Cobalt Between Groups 1.67 4 0.419 7.85 P <0.001 Within Groups 1.44 27 0.053 Total 3.12 31 8 Manganese Between Groups 75.30 4 18.82 1.91 ns Within Groups 265.52 27 9.83 Total 340.82 31 9 Zinc Between Groups 0.62 4 0.15 1.16 ns Within Groups 3.63 27 0.13 Total 4.25 31 10 Iron Between Groups 3742.58 4 935.64 2.89 P <0.05 Within Groups 8727.43 27 323.23 Total 12470.02 31 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 TDS= Total dissolved salt, MWHC= Maximum water holding capacity, OM= Organic matter.

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5.4.11-Univariate analysis of variance (Understorey vegetation) Using Ward’s clustering strategy for understorey vegetation; six groups of species were recognized. Environmental variables corresponding to the six groups were analyzed using univariate analysis of variance (ANOVA). Among topographic factors elevation and slope showed significant correlation (F=3.005; P<0.05) and (F=9.76; P<0.001) respectively (Table 5.9). Edaphic factors like TDS (F=4.22; P<0.01), pH (F=2.54 P<0.05); MWHC (F=3.45; P<0.01), conductivity (F=9.48; P<0.001) and salinity (F=2.15, P<0.05) exhibited significant relation. However, organic matter did not significantly correlated. The soil nutrients Cobalt (Co), Zinc (Zn) , Nitrogen (N) , Phosphorus (P) and Sulphur (S) attained significant relation at P<0.01 and P<0.05 respectively (Table 5.9).

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Table 5.9 Analysis of variance of individual environmental variables (topographic and edaphic and) and soil nutrients. Six groups were derived by Ward's cluster analysis using understorey vegetation data of 32 stands of CKNP Gilgit-Baltistan, Pakistan.

ANOVA: Single Factor

Source of Variation SS df MS F P-level 1- Topographic variables 1- Elevation Between Groups 1008230 5 201646.1 3.005099 P <0.05 Within Groups 1744634 26 67101.3 Total 2752864 31 2- Slope Between Groups 7278.974 5 1455.795 9.765405 P <0.001 Within Groups 3875.995 26 149.0767 Total 11154.97 31 2- Edaphic variables 1- TDS Between Groups 697.2392 5 139.4478 4.22734 P <0.01 Within Groups 857.6655 26 32.98713 Total 1554.905 31 2- pH Between Groups 2.881333 5 0.576267 2.548614 P <0.05 Within Groups 5.878855 26 0.22611 Total 8.760188 31 3- Water holding capacity Between Groups 1519.235 5 303.8471 3.459259 P <0.05 Within Groups 2283.733 26 87.8359 Total 3802.969 31 4- Salinity Between Groups 0.043607 5 0.008721 2.156658 P <0.05 Within Groups 0.105143 26 0.004044 Total 0.14875 31 5- Conductivity Between Groups 5125.582 5 1025.116 9.487014 P <0.001 Within Groups 2809.422 26 108.0547 Total 7935.005 31 6-Organic matter Between Groups 88.20 5 17.64 1.47 ns Within Groups 311.66 26 11.98 Total 399.86 31 3-Soil nutrients 1- Calcium Between Groups 56.79 5 1135.89 0.27 ns Within Groups 106225 26 4085.57 Total 111904.5 31 2- Magnesium Between Groups 439.78 5 87.95 0.25 ns Within Groups 8866.09 26 341 Total 9305.87 31 3-Nitrogen Between Groups 5980.80 5 1196.16 2.13 P <0.05 Within Groups 13992.17 26 559.68

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Total 19972.97 31 4-Phosphorus Between Groups 4010.1 5 802.02 2.27 P <0.05 Within Groups 8451.1 26 352.12 Total 12461.2 31 5- Potassium Between Groups 21690.87 5 4338.17 1.18 ns Within Groups 94979.1 26 3653.04 Total 116670 31 6-Sulphur Between Groups 1051.86 5 210.37 6.97 P <0.001 Within Groups 761.23 26 30.44 Total 1813.09 31 7- Cobalt Between Groups 1.69 5 0.33 6.15 P <0.001 Within Groups 1.42 26 0.054 Total 3.12 31 8- Manganese Between Groups 83.91 5 16.88 1.6 ns Within Groups 256.91 26 9.88 Total 340.82 31 9-Zinc Between Groups 1.19 5 0.23 2.03 P <0.05 Within Groups 3.06 26 0.11 Total 4.25 31 10-Iron Between Groups 997.90 5 199.58 0.45 ns Within Groups 11472.12 26 441.23 Total 12470.02 31

Note: SS = Sum of square, MS = Mean square, F = F ratio, df = Degree of freedom, P level = Probability level and ns = Non significant. TDS= Total dissolved salt, MWHC= Maximum water holding capacity, OM= Organic matter.

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5.4.12-Discussion and conclusion Nine forested and 23 non-forested stands of vegetation and understorey vegetation from Central Karakoram National Park, Gilgit-Baltistan were classified using Ward’s (1963) agglomerative clustering method. In the forested and non- forested vegetation type 4 groups were obtained and one isolated stand while in understory vegetation type 6 groups were obtained. In the absence of any traditional classification, it is imperative to employ the objective classification method such as Wards’ agglomerative clustering to avoid any personal bias towards classified entities. This method is used to expose the underlying group structure and the associated environmental variables. The groups derived from the forest and non forested vegetation and understorey vegetation were grouped that were allied with different topographic and edaphic levels. The classification and ordination is also combined with the environmental gradients (elevation and slope) which show a relationship between the vegetation and the local environmental conditions. In the cluster analysis of forested and non forested vegetation group I was composed of 9 forested stands in which Picea smithiana was dominant species and attained 3 mixe and 1 pure stand while Pinus wallichiana and Juniperus excelsa were associated species. In this group Pinus wallichiana exhibited as pure in only one stand while Juniperus excelsa was existed as pure form in two stands. The mean density ha- 1 of the species Picea smithiana (97±13), Pinus wallichiana (43±17) and Juniperus excelsa (70±23) while Ahmed (2011) also conducted multivariate analysis for Cedrus deodara forest in Hundukush and Himalayan range of Pakistan in which they found diffrent mean values i.e Picea smithiana (21±12), Pinus wallichiana (92±31) and Juniperus excelsa (24±14). These values differ from the present study due to the differences in sites and disturbances. Both studies were conducted in a dry temperate region and the climate and elevation level is mostly similar therefore topographic factors are significant in both forested and understorey vegetation while edaphic factors did not significantly correlated with forested and non- forested vegetation. In the understorey vegetation all edaphic factors are significantly correlated except of salinity which did not significantly correlate. All these topographic and edaphic factors are very important to the type and prevalence of vegetation. Titshall et al., (2000) confirmed that the elevation and slope are the main topographic factors that control the distribution and patterns of vegetation in mountain areas.

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The same mean values of Picea smithiana (77 density ha-1) and Pinus wallichiana (41 density ha-1) were also recorded by Wahab et al., (2008) from neighbor country Afghanistan. Khan(2011) investigated multivariate study in Naran valley and the results resembles with the present study, in which he found Pinus wallichiana and Juniperus excelsa community while from on the ground flora Artemisia brevefolium, Trifolium repnes, Urtica dioca and Juniperus communis were the similar species with the present study. While Siddiqui et al., (2010) found Picea smithiana and Pinus wallichiana community from the moist temperate areas of Pakistan and ground flora resembles with the study area such as species like Thymus linearis, Rosa webbiana, and Berberis lycium. Siama (2009) found the community of Pinus wallichiana with Taxus wallichiana and Abies pindrow from Ayubia National Park . Ahmed et al., (2010) recorded Picea smithiana from 4 locations, Pinus wallichiana from 25 sites and Juniperus excelsa from 3 locations of different climatic zones, present values are in the range of previous result. Mean elevation of this group was differing from the other group. Ilyas et al., (2012) investigated from montane temperate forests from Sawat and they reported Pinus wallichiana community while Berberis lycium, Urtica dioca and Trifolium repnes are associated species with the present study. Non forested vegetation was observed in the group II, III and IV. The elevation of these group was mostly similar therefore related vegetation among these groups are observed. Rosa webbiana was dominating in group II and III while associated species were Hippophae rhamnoides and Berberis lycium. In group III Hippophae rhamnoides was dominant species while associated species were Tamarix indica and Rosa webbiana. Wazir et al., (2008) found an association of the species Rosa webbiana and Hippophae rhamnoides .Some other species like Ribes orientale was present in the group III and IV with a very low density while Urtica dioca, Ribes alpestre and Artemisia brevefolium were restricted to only group IV .However, Tamarix indica and Juniperus communis is restricted to group V. The isolated stand (17) was situated at high elevation, in which Rosa webbiana was dominant species with the combination of Ribes orientale. It is also noticed that among the non-forested stands, many places, vegetation composition was same where Rosa webbiana was dominant species with the association of Hippophae rhamnoides. The understorey vegetation of forested and non-forested vegetation was common in different groups. Anaphalis virgata, Rosa webbiana, Ribes orientale,

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Artemisia brevefolium, Geranium pratense, Sedum quadifidum, Taraxacum baltistanicum and Thymus linearis are present in all four groups while Impatiens balfouriii and Tanacetum artemisiodes were restricted to only group I. The highest average frequency (100%) is recorded in Rosa webbiana while other species i.e Hippophae rhamnoides have 53%, Berberis lycium, Silene vulgaris and Astragallus gilgitensis occupied 50% of average frequency. Although communities density and formation of the communities in forested stands are within the range of above workers but the non-forested communities are quite different from the other locations of Pakistan while understorey vegetation of the trees are more or less similar with the present study. The cluster analysis of understorey vegetation resulted into 6 groups. The vegetation of these groups was common within the groups. Ahmed et al., (2010) recorded the species Rosa webbiana, Berberis lycium and Urtica dioca on ground flora from the Himalayan range of Pakistan while Khan (2012) noticed Berberis lycium on the ground flora of Querus baloot forest from District Dir. However other species were not common due to the low elevation (1503-1753m). The cluster of understorey species formation and association was not resembles with the above mentioned researchers. Ordination technique is used to understand the vegetation pattern in particular trends and gradients in vegetation ecology (McCune and Grace, 2002).The result of DCA ordination is also helpful to the results of cluster analysis. It has been noticed by Greig-Smith (1983) that the two basic techniques viz. classification and ordination provide corresponding results for different purposes. Plant communities change gradually along environmental gradients (Jin et al., 2008; El-Bana and Al-Mathnani, 2009). Hill and Gauch (1980) and McCune and Grace (2002) also stated that DCA ordination is capable of yielding at least one basic gradient associated with the vegetation. Therefore, to understand the distribution pattern of vegetation along with associated factors in a particular area is important (Weiser et al., 1986; Stephenson 1990; Endress and Chinea 2001; Bai et al., 2004). In the present study of ordination, the vegetation distribution pattern was more ore less continuous. Anthropogenic disturbance leads to continuity therefore, the present study revealed that the anthropogenic disturbance is responsible for the distribution pattern of vegetation communities. It is noticed that vegetation type is different at different elevation levels. Forested vegetation was existed at high elevation and it is

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Chapter 5: Ordination and classification of vegetation also observed that some non-forested vegetation was found at the elevation of forested vegetation. The vegetation of the study area was disturbed due to different factors. Connell & Slatyer (1977) described that some species are specialized to exploit disturbances that are particularly well adopted to exploit large gaps in forest canopies. Canham and Loucks (1984) added that the forests can be affected by fires and wind. However these disturbances are not reported by any worker from the study area. In the present study Picea smithiana, Rosa webbiana, Hippophae rhamnoides are the climax communities which tolerate the environmental conditions, having distinct spatial structure and diversity. Wazir et al., (2008) noticed Hippophae rhamnoides as climax community from Chupursan valley. The understorey vegetation was associated on the ground flora of forested vegetation and non forested vegetation which are mostly common. This vegetation is important because it’s potential to reflect site quality and forest productivity, which is related to the forest management, decisions (Stephen, 2006). Most of the understorey vegetation dominated by perennial herbs and shrubs like Rosa webbiana, Hippophae rhamnoides, Berberis lycium, Astragallus gilgitensis, Artemisia brevefolium, Urtica dioca, Potentilla biflora and Anaphalis virgata. Although these species are fast growing but faced many internal and external problems which include competition, overgrazing, soil erosion, overcutting and sever wind. Connell and Slatyer (1977) confirmed that such type of vegetation communities in early stages are dominated by fast growing but with the passage of time, these species will tend to be replaced by more competitive species. Among the vegetation and edaphic factors including elevation, TDS, conductivity and salinity slope showed a significant correlation with ordination axes while other edaphic factors are not significant. Similarly, the soil nutrients Cobalt, Calcium, Phosphorus, Nitrogen and magnesium attained a significant correlation with the ordination axes. The remaining soil nutrients did not show significant relation with ordination axes while in the univariate analysis Cobalt, Zinc and Iron are significantly related between the groups and within the groups. This weak correlation may be the anthropogenic disturbance and similar results are reported by Siddiqui et al., (2010), Khan et al., (2011) and Wahab (2011). The 60% forest of Pakistan is deteriorating due to illegal cutting and overgrazing (Bai et al., 2008) while Shaltout et al., (2008) stated that the illegal cutting and overgrazing play a vital role in the deterioration of vegetation. Therefore, a special attention is required to maintain these forests. It is stated that this

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CHAPTER-6 VEGETATION-ENVIRONMENT RELATIONSHIP 6.1-Introduction Vegetation is influenced by many environmental factors which varies from location to location (Williams et al., 2002). These environmental variables play an important role for the formation and association of vegetation communities (Weiher & Howe 2003). This chapter focuses on relationship among vegetation and environmental variables including topographic factors, edaphic factors and soil nutrients. Soil is the biological active of the earth and many scientists considered that soil is the skin of our planet (Bridges, 1997).As we discussed in previous chapters (3 and 4) that Gilgit- Baltistan is rich for forested and non-forested vegetation. Soil nutrients are important for the growth vegetation. The soil of forest manipulates the composition, growth and vigor of the forest and non-forested vegetation (Bhatnagar, 1965). Vegetation also plays an important role to fertile the soil (Chapman and Reiss, 1992). This statement is supported by Singh and Bhatnagar (1997), leaves, cones, branches gradually decomposes and convert into fertile soil. The deficiency or efficiency of the soil nutrients resulted that the growth is restricted (Bates, 1971). This situation may be controlled by the controlled experiments in which nutrition requirement and nutrients ratio relate each other (Gerloff and Krombholz, 1966). These experimental information may useful in forest management and silviculture. However soil properties cannot estimate properly but the sensitivity of the soil properties changed the vegetation which can be used as the indicator (Andrews and Cambardella, 2004). Mahmudi et al., (2003) stated that the soil chemical and physical properties are most important to the diversity and growth of any trees in any region. Soil fertility is a important attribute for the growth of plant which can be determine to evaluate the relationship between vegetation and soil nutrients (Chaudhari, 2012).The quality of soil is maintained by the relationship among physical, chemical and biological factors of the soil (Papendick and Parr, 1992).The physical and chemical properties of the soil vary due to the different environmental and topographic factors (Paudel and Shah 2003). Different plant species can vary their influence on soil properties as well as soil fertility (Augusto et al. 2002). Soil acidity and nutrient convenience also control the

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vegetation natures, and the growth (Kong et al. 2004; Griffiths et al. 2009). Soil is being degraded worldwide due to the erosion, organic matter reduction and nutrient imbalance (Papendick and Parr, 1992). Therefore an attempt also made to evaluate the quality and the status of the soil. Such type of studies conducted in Pakistan from the different location. Tareen et al., (1987) and Tareen and Qadir (1987) investigated in Chiltan and plains of Quetta and stated that the vegetation was interconnected with edaphic factors. Soil erosion and deforestation is due to the reduction of soil nutrients (Hussain et al., 1995; Hussain and Badshah ,1998 ). Some other researchers Jaffari et al.,2003; Karim et al., 2009; Ahmed et al., 2011 ;Khan,2011; Wahab, 2011 and Siddiqui,2011 also investigated the relationship between vegetation and soil from different areas of Pakistan. However no extensive work has carried out from the forest of Gilgit-baltistan. Therefore this study may be helpful as database for the forest management and CKNP management to improve the better growth of the forests.

6.2-Objectives of the study

¾ Physico-chemical status from CKNP. ¾ Vegetation-environment relation from CKNP. ¾ Provide a database regarding to the health of forests ¾ Describe the characteristic and availability of the soil nutrients.

6.3-Materials and methods 6.3.1-Classification Materials and method for soil edaphic factors, soil nutrients concentration and clustering of groups is presented in previous chapter (5). Ward’s cluster method (1963) used to explore the relationship between vegetation. In this study first matrix was environmental data while second matrix was vegetation data therefore the present study is on the basic of environmental data.

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6.3.2-PCA Ordination Principal component analysis (Orloci, 1978) was used to summarize the environmental and vegetation data. PCA was performed using the package PC-ORD for windows, version 5.10 (McCune and Mefford, 2005) following Jafferi et al., (2003) and Siddiqui et al., (2010).

6.4-Results 6.4.1-Classification of forested and non-forested vegetation on the basis of environmental variables: The relationship between vegetation and soil was investigated by one way cluster analysis which extracted into four groups at 65 percent level equal to 2.7 ×104 (Fig.6.1). 6.4.1.1-Group-I This group was located at high elevation of 3313±56 m with steep slope (45±27°) angle. This was the second largest group among the all cluster group which composed of 9 stands. Eight stands were forested while one stand (14) was non-forested. In the forested stands Picea smithiana (97±14 density ha-1) was the dominant species followed by Juniperus excelsa (70±23 density ha-1) and Pinus wallichiana (26±4 density ha-1) while in non-forested stand, Rosa webbiana attains (300±00 density ha-1) Hippophae rhamnoides (300±00 density ha-1) and Ribes alpestre. (200±00 density ha-1) (Table 6.1). Ground flora composed of 35 species in which Taraxacum nigrum attained highest frequency (43%) followed by Artemesia brevifolium (36%) and Hippophae rhamnoides (35%). All ground species frequently found in this group. Lentopodium linearifolium was found only in this group and group-IV while Tanacetum artemisiodes was restricted to this group and group- IV (Table 6.2). Among the edaphic factors conductivity (61±8) organic matter (7±0.7) and maximum water holding capacity (34±2) was high as compare to other groups. Highly Acidic pH (4.9±0.1) soil was found with a moderate amount of TDS while salanity was not found in this group. The soil nutrients, Manganese (Mn) attained the highest value (10±0.57) while Zinc occupied low amount (0.12±0.03) among the groups. The moderate amount of other

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nutrient concentrations were recorded in descending order K (203±9), Ca (194.8±1.75) ,N (194±8), P (192±2), Mg (138±2), Fe (133±3) and Co (0.78±0.07) (Table 6.3)

6.4.1.2-Group-II This group was distributed at low mean elevation (3039±56m) as compare to group I with a moderate slope angle (21±5°). This group was smaller than group I which consists 8 stands. Hippophae rhamnoides was the dominant species of this group occupies 438±29 individuals ha-1 while the second dominant species was Rosa webbiana which attained 390±37 density ha-1. Some pure species i.e Juniperus communis, Ribes alpestre and Pinus wallichiana were recorded from this group contains highest densities of 533, 400 and 94 respectively (Table 6.1). Understorey vegetation comprised of 28 species in which Hippophae rhamnoides attained the highest frequency (55%) followed by Silene vulgaris (50%) and Rosa webbiana (39%).Potentilla anserina and Spiraea canescens were the rare species of this group. Geranium pratense and Trifolium repens were occasionally found in this group (Table 6.2). The moderate amount of conductivity (36±1), MWHC (22±0.21) and TDS (27±0.46) was found in this group. Slightly acidic soil pH (6.2 ±0.009) with a slight amount of mean salanity (0.087±0.039).The mean amount of organic matter was recoded for this group was 4.5±0.09 %. Among the soil nutrients Nitrogen attained the highest mean amount of 221±2.07 followed by the other soil nutrient concentration as follows: Potassium (214±0.63), Calcium (174±0.88), Phosphorus (166±0.51), Sulphur (146±0.49), Iron (131±0.29) , Magnesium (127±0.29), Manganese (5±0.05), Cobalt (2±0.03) and Zinc (0.24±0.005) (Table 6.3).

6.4.1.3-Group-III This group was declared at low mean elevation of 3049±56m as compare to group I while high mean elevation than group II with a steep slope angle of 42±22 °.This was the smallest group among all the cluster groups which composed of only two stands (8 and 28).In this group Pinus wallichiana was found as pure form in the Rakaposhi-2 occupied 94 density ha-1 while in the stand 28 Hippophae rhamnoides and Berberis

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lycium occupied 467 plants ha-1 while associated species Tamarix indica attained 333 individuals ha-1 in this group (Table 6.1). Understory vegetation of this group was recorded 26 species. Out of these species Taraxacum indicum was found abundantly 60% frequency while Hippophae rhamnoides, Rubus ulmifolius and Tarxacum nigrum occupied 50% of frequency. Impatiens balfourii was a rare species in this group which was also rare in group-I. However Berberis orthobortoy and Taraxacum xanthophyllum were the occasional species of this group (Table 6.2). In the edaphic factors moderate amount of conductivity (38±2), MWHC (29±4.5, TDS (30±10) and organic matter (3±1.3) were found in this group. Slightly acidic soil of pH (6.3±0.31) was found with no salanity. Among the soil nutrient concentrations including Potassium attained the highest amount (300±18) while Zinc was found with low amount (0.84±0.77). Remaining soil nutrients recorded with moderate amounts which were presented in descending order i.e Phosphorus (184±49), Calcium (170±47), Nitrogen (141±18), Magnesium (141±10), Sulphur (137±0.5), Iron (129±13.5), Manganese (8±1.93) and Cobalt (0.98±0.13) (Table 6.3). 6.4.1.4-Group-IV This group was located at lowest elevation (2974±93m) among the all groups with a moderate slope angle (28±3°). On the other hand this was the largest group among the all forested and non forested group which distributed in 13 stands. In this group Rosa webbiana was the dominant species attains 513 individuals ha-1 while the second and third species were Hippophae rhamnoides and Berberis lycium which occupied 444 and 385 individuals ha-1. Some monospecific species were associated with this group i.e Urtica dioca (400 individuals ha-1) Tamarix indica (333 individuals ha-1), Ribes orientale (200 individuals ha-1) and Artemisia brevifolium (200 individuals ha-1) (Table 6.1). Ground vegetation composed of 32 species in which Rosa webbiana occupied highest frequency (59%) followed by Hippophae rhamnoides (44%) and Juniperus communis (40%).Spiraea canescens was found as rare species while Trifolium repens , Sedum quadifidum, Geranium pratense, Carum carvi and Geranium neplensis were the occasionally found in this group (Table 6.2).

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The physical factors including conductivity, maximum water holding capacity and total dissolved salt was high as compare to other groups which attains 47 ±1.16, 32±0.71 and 46±0.38 respectively while slightly acidic soil pH (6.5±0.007) was found with low salanity soil (0.02±0.01) . The moderate amount of organic matter (5±0.02) was found in this group. Among the soil nutrient concentration including Potassium attains the highest amount (264±0.51) followed by Nitrogen (224±0.65), Calcium (211±2), Phosphorus (207±1.07), Iron (142±2), Magnesium (137±0.71), Sulphur (135±0.48), Manganese (8±0.07), Cobalt (1.32±0.002) and Zinc (0.63±0.07) (Table 6.3).

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Fig.6.1 Dendrogram, based on Information level and Euclidean distance of the 32 stands of forested and non-forested environmental and vegetation data are presenting four groups.

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Table 6.1 Four groups isolated from Ward’s cluster analysis of forested and non forested species from 32 stands based on environmental data and density ha-1 (elevation, slope), edaphic factors and soil nutrients.

S.No Name of Species Group I Group II Group III Group IV 1 Picea smithiana 97±14 * * * 2 Pinus wallichiana 26±4 94±00 94±00 * 3 Juniperus excelsa 70±23 * * * 4 Rosa webbiana 333±00 390±37 * 513±64 5 Hippophae rhamnoides 333±00 438±29 467±00 444±42 6 Berberis lycium * * 467±00 385±41 7 Ribes alpestre 200±00 * * * 8 Urtica dioca * * * 400±00 9 Ribes orientale * 400±00 * 260±00 10 Tamarix indica * 422±59 333±00 333±00 11 Artemesia brevefolium * * * 260±00 12 Juniper communis * 533±00 * *

*Absent

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Table 6.2 Showing means of groups of forested and non forested species frequency on the basis of environmental variables (topographic and edaphic) and soil nutrients using Ward’s cluster analysis.

S.No Name of species Group 1Group II Group III Group IV 1 Anaphalis virgata 28 25 25 20 2 Artemesia brevifolium 36 23 30 24 3 Astragallus zanskrensis 27 20 * 30 4 Astragallus gilgitensis 29 * 45 30 5 Berberis orthoborty 19 30 15 * 6 Bistorta affinis 30 32 25 32 7 Carum carvi 20 20 25 13 8 Geranium neplensis 17 25 * 16 9 Geranium pratense 22 18 23 18 10 Hippophae rhamnoides 35 55 50 44 11 Impatiens balfourii 32 * 10 * 12 Juniperus communis 25 40 25 40 13 Lentopodium lentopodinum 25 20 25 30 14 Lentopodium linearifolium 22 * * 20 15 Lentopodium nanum 27 * 40 25 16 Potentilla anserina 19 10 * 25 17 Potentilla biflora 17 20 20 20 18 Ribes orientale 18 33 * 26 19 Rosa webbiana 21 39 30 59 20 Rubus irritans 24 26 * 13 21 Rubus ulmifolius 15 * 50 20 22 Sedum pacycloides 22 20 * 25 23 Sedum quadifidum 18 20 25 15 24 Silene vulgaris 28 50 * 20 25 Spiraea canescens 28 15 20 13 26 Tanacetum artemisiodes 24 * 20 * 27 Taraxacum baltistanicum 17 34 40 32 28 Taraxacum indicum 30 30 60 20 29 Taraxacum nigrum 43 47 50 23 30 Taraxacum xanthophyllum 24 * 15 20 31 Thymus linearis 26 35 40 35 32 Trifolium repens 20 17 23 19 33 Urtica dioca 21 20 30 32 34 Berberis lycium 20 36 50 37 35 Ribes alpestre 30 28 * 22

*Absent

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Table 6.3 Mean values ± SE of environmental variables (topographic, and edaphic) and soil nutrients based on forested and non forested groups derived from Ward’s cluster analysis using 32stands of CKNP. (Mean ± SE).

S.No Variables Group I Group II Group III Group IV ( A ) Topographic Factors 1 Elevation (m) 3313±56 3039±56 3049±139 2974±93 2 Slope º 45±7 21±5 42±22 28±3 ( B ) Edaphic Factors 3 Conductivity (µs/cm) 61±8 36±1 38±2 47±1.16 4 Salanity % 00±00 0.087±0.039 00±00 0.02±0.01 5 pH 4.9±0.1 6.2±0.009 6.3±0.31 6.5±0.007 6 MWHC (%) 34±2 22±0.21 29±4.5 32±0.71 7 TDS (g/L) 35±2 27±0.46 30±10 46±0.38 8 OM (%) 7±0.7 4.5±0.09 3±1.3 5±0.02 ( C ) Soil Nutrients 9 Nitrogen (ppm) 194±8 221±2.07 141±18 224±0.65 10 Potassium (ppm) 203±9 214±0.63 300±18 264±0.51 11 Phosphorus (ppm) 192±2 166±0.51 184±49 207±1.07 12 Calcium (ppm) 194.8±1.75 174±0.88 170±47 211±2 13 Magnesium (ppm) 138±2 127±0.49 144±10 137±0.71 14 Sulphur (ppm) 126±2 146±0.49 137±0.5 135±0.48 15 Cobalt (ppm) 0.78±0.07 2±0.03 0.98±0.13 1.32±0.002 16 Manganese (ppm) 10±0.57 5±0.05 8±1.93 8±0.07 17 Zinc (ppm) 0.12±0.03 0.24±0.005 0.84±0.77 0.63±0.07 18 Iron (ppm) 133±3 131±0.29 129±13.5 142±2

TDS= Total dissolved salt, MWHC= Maximum water holding capacity, OM= Organic matter

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6.4.2-Classification of Understorey vegetation on the basis of environmental variables: Fig.6.2 shows dendrogram resulting from cluster analysis based on environmental data and frequency of the understorey vegetation data using Ward’s method. 6.4.2.1-Group-I This group was located at the mean elevation (3162±49m) with a steep slope angle (33±8°) .Among the understorey vegetation groups this was the largest group, which composed of 10 stands. Out of these 4 stands (1, 2, 3, and 8) were forested while reaming 6 stands (24, 25, 26, 30, 31, and 32) were non-forested. Undestorey vegetation comprised of 35 species which was shared by the predominated species Hippophae rhamnoides (52%) followed by Taraxacum nigrum (48%) and Taraxacum baltistanicum attains 45% frequency. Berberis lycium and Artemisia brevifolium occupied with similar frequency of 35 % while low frequency of 15 % was recorded in Geranium neplensis, Trifolium repens and Ribes alpestre. The species Sedum quadifidum was found rarely in this group (Table 6.4). The moderate amount of conductivity, organic matter and salanity was found 47±5, 5±0.7 and 0.04±0.02 respectively. The maximum water holding capacity (26±3), and TDS (31±2) was less than other groups. The slightly acidic soil pH (6.1 ±0.09) was shared by this group. The soil nutrient concentrations including Potassium and Nitrogen occupied with almost similar highest values of 209±12 and 208±8 respectively while Cobalt and Zinc shared the lowest values of 1.2±0.6 and 0.17±0.02 respectively. Similar trend followed by Calcium and Phosphorus which attains 174±7 and 171±6 respectively while Sulphur , Magnesium and Iron also occupied with almost similar range of 137±2, 135±3 and 133±1.86 respectively and Manganese shared 8±0.90 (Table 6.5). 6.4.2.2-Group II A This group was distributed at low elevation (2781±12) than group I with a gentle slope angle of 17±5°.This group consists 5 stands (10, 13, 14, 18, and 19). The dominant vegetation consist of 24 species which were less than group-I. Hippophae rhamnoides was the predominated species having 50% frequency which was also seen as a dominant species in group-I. The second dominant species of this group was

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Rosa webbiana which attains 44 % frequency. The associated species Rubus irritans and Ribes alpestre were found occasionally in this group while Thymus linearis and Sedum pacycloides were the rare species (Table 6.4). The edaphic factors moderately found in this group in which conductivity, MWHC, TDS and organic matter attains 42±3, 30±2, 38±5 and 5±0.12 respectively. The slightly acidic soil pH (6.2±0.027) was found to this group with no salanity. Among the soil nutrient concentration, Potassium attains the highest amount (246±10) in soil while the lowest amount shared by Zinc which was 0.48±0.06. The soil nutrients Sulphur, Iron and Magnesium occupied with almost similar values of 137±2, 136±4 and 135±3 respectively. The remaining soil nutrients Nitrogen , Calcium Phosphorus, Manganese, Cobalt and Zinc attains 219±4, 208±9, 198±8, 7±0.44 and 1.44±0.11 respectively (Table 6.5). 6.4.2.3-Group II B This group was also located at low mean elevation (2915±15m) than group I while at high elevation than group II A with steep slope angle 30±3°. This group also comprises 5 stands in which 23 dominant shrubs and herbs species were distributed. Urtica dioca was the predominant species having the highest frequency of 60 % followed by Berberis lycium, Hippophae rhamnoides and Rosa webbiana which attains 44 %, 42% and 42 % respectively. Bistorta affinis and Astragallus zanskrensis occupied with a similar frequency of 35%.Other associated species Spiraea canescens and Trifolium repens were found occasionally while Carum carvi was observed as the rare species of this group (Table 6.4). The physical factors including conductivity (41±3), MWHC (31±2), organic matter (4.46±0.79) and TDS (37±6) were found moderately in this group. The highly acidic soil pH (5.5±0.07) was found with no salanity to this group. Among the soil nutrients Potassium attains the highest value of 264±16 while Zinc occupied with a low amount of 0.90±0.28. The soil nutrient concentrations, Nitrogen and Calcium shared a similar amount of 202±20 and 202±7 respectively while Magnesium and Iron also attains a similar amount of 135±2 and 135±5 respectively. The moderate amount of Phosphorus (205±11), Sulphur (139±2) and Manganese (7±0.44) was found in this (Table 6.5)

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6.4.2.4-Group III This group was distributed at lowest mean elevation of 2485±42m with a steep slope (37±3°). This was the smallest group among the all cluster groups of understorey vegetation which was composed of only two stands (21 and 22). The understorey vegetation comprised of 13 species. Rosa webbiana and Hippophae rhamnoides were the predominant species which attains 50% frequency followed by Berberis lycium which shared 40% frequency in this group. Trifoilum repens was observed as occasional species while Spiraea canescens was considered as rare species of this group (Table 6.4). Among the edaphic factors conductivity and organic matter was slightly high as compare to other groups which attains 50±00 and 5.3±0.05. The soil was found to highly acidic pH (5.5±0.01) with no salanity. The soil nutrient concentrations values in descending order as follows: Potassium (265±2) > Nitrogen (226±2) > Calcium (210±5) > Phosphorus (206±4) > Iron (143±0.5) > Magnesium (138±3) > Sulphur (134±0.5) > Manganese (7.3±0.2) > Cobalt (1.32±0.01) > Zinc (0.56±0.01) (Table 6.5). 6.4.2.5-Group IV A This group was located at high mean elevation (3311±17m) than previous four groups (group I, group IIA, group IIB and group III) with a steep slope angle of 39±7°). This group consists five stands (2, 4, 7, 11 and 15). The ground vegetation consist of 35 species which were equal to the group I. Bistorta affinis was the leading dominant species 60% frequency followed by Hippophae rhamnoides and Impatiens balfourii which attains 53% and 42 % respectively. Further associated species Rosa webbiana and Berberis lycium recorded with a similar frequency of 40%.Astragallus zanskrensis, Carum carvi, Rubus irritans, Sedum quadifidum, Silene vulgaris and Urtica dioca were the occasionally found to this group while Berberis orthoborty, Potentilla biflora, Taraxacum xanthophyllum and Ribes alpestre were the rare species of this group (Table 6.4). Among the edaphic factors conductivity and organic matter was high as compare to other groups with the amount of 56±6 and 7±1.07 while maximum water holding capacity (32±3) and total dissolved salt (35±3) were moderately found to this

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Chapter 6: Vegetationenvironment relationship group. The highly acidic soil pH (5.3±0.12) was found with a low amount salanity (0.06±0.06). The soil nutrient concentrations including Potassium occupied highest amount (220±15) while Zinc attains low amount (0.24±0.07). The amount of Nitrogen (216±4) was almost close to the Potassium. Similarly Calcium and Phosphorus recorded with almost similar amount which was 195±6 and 191±6 respectively while Iron, Magnesium and Sulphur shared almost similar values of 136±1.81, 134±2 and 130±5 respectively. Furthermore soil nutrients Manganese occupied with a moderate value of 9±1.34 and Cobalt contributed with a low amount of 1.03±0.18 (Table 6.5). 6.4.2.6-Group IV B This group was distributed at highest mean elevation (3517±22m) with a steep slope angle (47±9°). This group was the second largest group which attains six stands (3, 5, 6, 9, 16 and 17). Ground vegetation of this group was comprised of 34 species in which Rosa webbiana was the predominated species having 60% frequency while Taraxacum nigrum and Hippophae rhamnoides occupied with 43% and 40% respectively. Tarxacum nigrum was dramatically abundant in this group while this species was rarely found in group II A. Geranium neplensis, Potentilla biflora, Rubus irritans, Sedum quadifidum, and Trifolium repens were occasionally found in this group while Sedum pacycloides Lentopodium linearifolium and Taraxacum baltistanicum were recorded as rare species (Table 6.4). Among the physical factors conductivity (54±3), MWHC (31±1.3), TDS (42±2) and organic matter (6±0.67) were slightly high as compare to other groups. Moderate acidic soil of pH (5.6±0.11) was found with a low amount of salanity (0.03±0.02). The soil nutrient concentrations were found descending order as follow: K (225±13) > Ca (198±5) > P (195±4) > N (191±12) > Mg (140±3) > Fe (132±5) > S (131±1.4) > Mn (9±0.63) > Co (0.87±0.13) > Z (0.22±0.11) (Table 6.5).

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Fig.6.2 Dendrogram, based on Information level and Euclidean distance of the 32 stands of understorey vegetation.

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Table 6.4 Showing means of groups of circular plot species (understorey vegetation) frequency on the basis of environmental variables and soil nutrients using Ward’s cluster analysis. S.No Name of species Group I Group IIA Group IIB Group III Group IVA Group IVB 1 Anaphalis virgata 28 20 23 20 30 24 2 Artemisia brevifolium 35 20 27 30 28 23 3 Astragallus zanskrensis 25 * 35 20 33 * 4 Astragallus gilgitensis 27 * * * 17 33 5 Berberis orthoborty 20 20 * * 5 26 6 Bistorta affinis 28 30 35 * 60 25 7 Carum carvi 21 * 10 * 15 25 8 Geranium neplensis 15 20 20 20 20 15 9 Geranium pratens 21 20 25 20 28 28 10 Hippophae rhamnoides 52 50 42 50 53 40 11 Impatiens balfourii 23 * * * 42 27 12 Juniperus communis 33 * * * 23 25 13 Lentopodium lentopodinum 28 * 20 * 30 23 14 Lentopodium linearifolium 23 20 20 * 30 13 15 Lendopodium nanum 28 * * * 25 25 16 Potentilla anserina 27 * 20 * 23 32 17 Potentilla biflora 20 30 20 * 5 15 18 Ribes orientale 37 20 20 * 23 27 19 Rosa webbiana 31 44 42 50 40 62 20 Rubus irritans 26 13 20 * 28 17 21 Rubus ulmifolius 30 * * * 18 22 22 Sedum pacycloides 27 40 * * 21 13 23 Sedum quadifidum 18 25 20 20 13 19 24 Silene vulgaris 25 20 * * 15 23 25 Spiraea canescens 30 10 15 10 20 38 26 Tanacetum artemisiodes 27 * * * 23 25 27 Taraxacum baltistanicum 45 23 40 30 20 13 28 Taraxacum indicum 31 10 * * 32 25 29 Taraxacum nigrum 48 10 * * 30 43 30 Taraxacum xanthophyllum 21 * * * 10 21 31 Thymus linearis 28 40 42 20 30 30 32 Trifolium repens 15 23 18 15 23 17 33 Urtica dioca 23 30 60 * 19 20 34 Berberis lycium 35 28 44 40 40 35 35 Ribes alpestre 15 15 * * 10 20

*Absent

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Table 6.5 Mean values ± SE of environmental variables (topographic, and edaphic) and soil nutrients based on circular plot groups derived from Ward’s cluster analysis using 32stands of CKNP. (Mean ± SE).

S.No Variables Group I Group II A Group II B Group III Group IV A Group IV B ( A ) Topographic Factors 1 Elevation (m) 3162±49 2781±12 2915±15 2485±42 3311±17 3517±22 2 Slope º 33±8 17±5 30±3 37±3 39±7 47±9 ( B ) Edaphic Factors Conductivity 3 (µs/cm) 47±5 42±3 41±3 50±00 56±6 54±3 4 Salanity (%) 0.04±0.02 00±00 00±00 00±00 0.06±0.06 0.03±0.02 5 pH 6.1±0.09 6.2±0.027 5.5±0.07 5.5±0.01 5.3±0.12 5.6±0.11 6 MWHC (%) 26±3 30±2 31±2 34±1 32±3 31±1.3 7 TDS (g/L) 31±2 38±5 37±6 45±0.5 35±3 42±2 8 OM (%) 5±0.7 5±0.12 4.46±0.79 5.3±0.05 7±1.07 6±0.67 ( C ) Soil Nutrients 9 Nitrogen (ppm) 205±8 219±4 202±20 226±2 216±4 191±12 10 Potassium (ppm) 209±12 246±10 264±16 265±2 220±15 225±13 Phosphorus 11 (ppm) 171±6 198±8 205±11 206±4 191±6 195±4 12 Calcium (ppm) 174±7 208±9 202±7 210±5 195±6 198±5 Magnesium 13 (ppm) 135±4 135±3 135±2 138±3 134±2 140±3 14 Sulphur (ppm) 138±4 137±2 139±2 134±0.5 130±5 131±1.4 15 Cobalt (ppm) 1.2±0.16 1.44±0.11 1.35±0.10 1.32±0.01 1.03±0.18 0.87±0.13 Manganese 16 (ppm) 8±0.90 7±±0.49 7±0.44 7.3±0.2 9±1.34 9±0.63 17 Zinc (ppm) 0.17±0.02 0.48±0.06 0.90±0.28 0.56±0.01 0.24±0.07 0.22±0.11 18 Iron (ppm) 133±1.86 136±4 135±5 143±0.5 136±1.81 132±5

TDS= Total dissolved salt, MWHC= Maximum water holding capacity, OM= Organic matter

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6.4.3-PCA Ordination The correlation between environmental variables (elevation and slope), edaphic factors, soil nutrients and different forested and non forested vegetation stands was analyzed by PCA ordinations.PCA ordination was based on the density ha- 1 of species from 32 stands of the study area. 6.4.3.1-Ordination of forested and non-forested vegetation Fig. 6.3, 6.4 and 6.5 shows PCA ordination plots between axis 1-2, axis 1-3 and 2-3 axis. All three plots superimposed on PCA ordination which shows discontinuous pattern of vegetation. Group I was exhibited at the left upper side of ordination plane which attained the 6 forested and one non forested stand (14) in axis 1-2 while in axis 1-3 this group separated in two poles which differentiated the forested and non forested stands. Similarly in the axis 2-3 forested and non forested stands of this group separated which was exhibited at extreme left lower side. Group I mainly dominated by Picea smithiana and associated with Pinus wallichiana and Juniperus excelsa. Group II was mainly dominated by Hippophae rhamnoides and Rosa webbiana which was distributed in axis 1-2 at right side of ordination space which was close to the group II while in axis 1-3, it was distributed at extreme upper side and in the axis 2-3 this group overlapped with group I at upper side of ordination space. Group III was the smallest group which composed of a monospecific stand of Pinus wallichiana and Berberis lycium-Tamarix indica community therefore this group quite separate from other groups in all axis. In the axis 1-2, this group was stared from the origin point of x-axis and Y-axis which parallel to the axis-2.This group was distributed in axis 1-2 at the centre of extreme lower side of ordination plane while in axis 2-3, it was touched the extreme lower left side. Group IV was the largest group in this ordination which shows a continuous pattern among the stands. This group was exhibited at the left side of ordination space in axis 1-2 while in the axis 1-3 it was situated at the centre of the ordination space and upper side of centre position in axis 2-3. The dominated species in this group was Rosa webbiana while associated species were Hippophae rhamnoides and Berberis lycium.

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Fig.6.3 DCA ordination axes 1 and 2 of forested and non forested vegetation data based on density ha-1. The groups derived from Ward’s cluster analysis are superimposed on 2-D ordination axes.

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Fig.6.4 DCA ordination axes 1 and 3 of forested and non forested vegetation data based on density ha-1. The groups derived from Ward’s cluster analysis are super imposed on 2-D ordination axis.

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Fig.6.5 DCA ordination axes 2 and 3 of forested and non forested vegetation data based on density ha-1.

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6.4.3.2-Ordination of Understorey vegetation Fig. 6.6, 6.7, and 6.8 shows the PCA ordination plots of understorey vegetation in the axis 1-2, axis 1-3 and axis 2-3. More or less discontinuous pattern of vegetation was observed in axis 1-2 and axis 1-3 which was imposed to the ordination while in the axis 2-3 shows continuity vegetation distribution therefore this axis plot cannot used for the ordination of understorey vegetation. Group I was isolated at extreme left side of the ordination space in both axis 1-2 and 1-3. The leading dominant species of this group was Hippophae rhamnoides followed by Taraxacum nigrum and Taraxacum baltistanicum. The dominant species of group IIA were distributed in this stand was Hippophae rhamnoides and Rosa webbiana which found at the upper side of ordination plane and slightly overlapped with group III in axis 1-2 and 1-3. Group IIB mainly dominated by Urtica dioca which followed by Berberis lycium, Hippophae rhamnoides and Rosa webbiana which was exhibited at extreme upper side and close to the group II A and group III in axis 1-2 whereas in axis 1-3 this group was quite separated at the lower surface of ordination plane. However group IV A was situated at left side and parallel to Y-axis in both axis 1-2 and 1-3 which was dominated by Bistorta affinis while associated species were Hippophae rhamnoides and Impatiens balfourii. Group IV B was exhibited at the lower side of ordination plane which was parallel to X-axis and the distribution of the stands in continuous pattern. Rosa webbiana was the leading dominant which was associated with Taraxacum nigrum and Hippophae rhamnoides.

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Fig.6.6 DCA among axes 1 and 2 of understorey vegetation data based on frequency.

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Fig.6.7 DCA among axes 1 and 3 of understorey vegetation data based on frequency.

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Fig.6.8 DCA among axes 2 and 3 understorey vegetation data based on frequency (Not super imposed)

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6.4.4-Correlation of ordination axes with environmental variables, edaphic factors and soil nutrients (Forested and non-forested vegetation) The relationship among the PCA ordination axis, environmental variables, edaphic factors and topographic were presented in Table 6.6. In the forested and non forested vegetation elevation and slope did not correlated with all three axis while all edaphic factors including conductivity, salanity, organic matter, TDS, pH and MWHC was strongly correlated with the ordination axis 1 at the probability level P < 0.01.Among the soil nutrients Cobalt showed significant relation in all three axis while Potassium, Calcium and Zinc found highly significant relation in axis 1 and 2 with probability level of P < 0.01. The soil nutrient Nitrogen attains significant relation in axis 2 at probability level P < 0.05 while in axis 2 this nutrient attained the significant relation at P<0.01 probability level . However Sulphur showed a strong correlation in axis 1 at probability level P<0.001 whereas this nutrients did not significantly correlated with remaining axis 2 and 3 (Table 6.6).

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Table 6.6 Relationship (correlation coefficients) of environmental variables (topographic variables and edaphic variables) and soil nutrients with 3 DCA ordination axes obtained by forested and non forested vegetation data based on density ha-1.

Axis 1 Axis 2 Axis 3 S.No Variables r Prob.level r Prob.level r Prob.level (A) Topographic Factors 1 Elevation -0.087 ns 0.15 ns -0.038 ns 2 Slope -0.5 ns -0.35 ns -0.15 ns (B) Edaphic Factors 3 Conductivity -0.88 P <0.01 -0.3 ns 0.083 ns 4 Salanity 0.49 P <0.01 -0.05 ns 0.29 ns 5 pH 0.72 P <0.01 -0.1 ns -0.44 ns 6 MWHC -0.91 P <0.001 0.24 ns 0.1 ns 7 TDS -0.43 P <0.01 0.48 P <0.01 -0.2 ns 8 OM -0.74 P <0.01 -0.33 P <0.05 0.55 P <0.01 (C) Soil Nutrients 9 Nitrogen 0.15 ns 0.33 P <0.05 0.82 P <0.01 10 Potassium 0.037 ns 0.79 P <0.01 -0.37 P <0.05 11 Phosphorus -0.56 P <0.01 0.72 P <0.01 -0.01 ns 12 Calcium -0.51 P <0.01 0.7 P <0.01 0.1 ns 13 Magnesium -0.54 P <0.01 -0.08 P <0.01 -0.67 P <0.01 14 Sulphur 0.96 P <0.001 0.16 P <0.01 -0.02 ns 15 Cobalt 0.83 P <0.01 0.35 P <0.05 0.36 P <0.05 16 Manganese -0.89 P <0.01 -0.34 P <0.05 -0.05 ns 17 Zinc 0.001 ns 0.83 P <0.01 -0.19 ns 18 Iron -0.23 ns 0.42 P <0.01 0.42 P <0.01

r = Correlation coefficient, ns = Non significant, Prob. Level = Probability level

TDS= Total dissolved salt, MWHC= Maximum water holding capacity, OM= Organic matter

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6.4.5-Correlation of ordination axes with environmental variables, edaphic factors and soil nutrients (Understorey vegetation) The relationship among the PCA ordination axis, environmental variables, edaphic factors and topographic were presented in Table 6.7. In the understorey vegetation elevation and slope was significantly correlated with axis 1 and 2 whereas with axis 2 the both topographic factors did not show any significant relation. Among the edaphic conductivity, salanity TDS, MWHC, organic matter and pH shows highly significant relation at the probability level P<0.01 with axis 1 while MWHC and TDS was found significant relation with axis 2 whereas pH and organic matter showed a significant relation with axis 3 at probability level P<0.05. Among the soil nutrients Sulphur and manganese showed strong significant relation in axis 1 at the probability level P< 0.001. The soil nutrients Calcium, Magnesium, Cobalt and Manganese showed a significant relation with axis 1 at the probability level P<0.01. Nitrogen and Iron also attained a significant relation with axis 2 and 3 at the probability level P<0.05 (Table 6.7).

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Table 6.7 Relationship (correlation coefficients) of environmental variables (topographic variables and edaphic variables) and soil nutrients with 3 DCA ordination axes obtained by understorey vegetation data based on frequency.

Axis 1 Axis 2 Axis 3 S.No Variables r Prob.level r Prob.level r Prob.level A Topographic Factors 1 Elevation -0.33 P <0.05 -0.57 P <0.01 -0.06 ns 2 Slope -0.59 P <0.01 -0.36 P <0.05 -0.15 ns B Edaphic Factors 3 Conductivity -0.9 P <0.001 -0.18 ns 0.13 ns 4 Salanity 0.48 P <0.01 -0.14 ns 0.28 ns 5 pH 0.69 P <0.01 -0.22 ns -0.45 P <0.01 6 MWHC -0.87 P <0.01 0.36 P <0.05 0.11 ns 7 TDS -0.4 P <0.01 0.47 P <0.01 -0.26 ns 8 OM -0.76 P <0.01 -0.19 ns -0.59 P <0.01 C Soil Nutrients 9 Nitrogen 0.2 P <0.01 0.35 P <0.01 0.77 P <0.01 10 Potassium 0.1 P <0.01 0.73 P <0.01 -0.45 P <0.01 11 Phosphorus -0.47 P <0.05 0.78 P <0.01 -0.04 ns 12 Calcium -41 P <0.01 0.76 P <0.01 0.07 ns 13 Magnesium -0.56 P <0.01 -0.06 ns -0.66 ns 14 Sulphur 0.97 P <0.001 0.04 ns -0.05 ns 15 Cobalt 0.87 P <0.01 0.29 ns 0.31 P <0.01 16 Manganese -0.93 P <0.01 -0.24 ns -0.016 ns 17 Zinc 0.086 ns 0.8 ns -0.25 ns 18 Iron -0.18 ns 0.45 P <0.01 0.37 P <0.01 r = Correlation coefficient, ns = Non significant, Prob. Level = Probability level

TDS= Total dissolved salt, MWHC= Maximum water holding capacity, OM= Organic matter

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6.4.6-Univariate analysis of variance (Forested and non-forested vegetation) One way Ward’s cluster method was used to explore the relationship among the environmental variable, edaphic factors and vegetation which resulted four groups and these groups further analyzed using univariate analysis of variance which was presented in the table 8. The topographic factors (slope and elevation) did not show any significant relation while in the physical factors conductivity showed a strong significant correlation at the probability level P<0.001 (F = 26.35). However maximum water holding capacity (F=12.82), total dissolved salt (F= 17.59) and organic matter (12.97) also showed a significant relation with the probability level ( P<0.01). Among the soil nutrients Potassium (F=29.85) Cobalt (F=52.79) and Sulphur (F=44.08) exhibited highly significant correlation at probability level P<0.001. Other soil nutrients Nitrogen (F=27.12) and Manganese (26.10) showed significant relation with the probability level P<0.01. However Phosphorus (F=9.05), Calcium (F=10.52), Magnesium (F=8.77), Zinc (F=7.61) and Iron (F=4.03) contributed significant relation with the probability level P<0.05 (Table 6.8).

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Table 6.8 Analysis of variance of individual environmental variables (topographic and edaphic) and soil nutrients of 32 stands from CKNP Gilgit-Baltistan, Pakistan.

S.No Source of Variation SS df MS F P-level ( A) Topographic Factors 1 Elevation Between Groups 669348.89 3 223116.3 2.946 ns Within Groups 2120377.11 28 75727.75 Total 2789726.00 31 2 Slope Between Groups 2521.50 3 840.50 2.861 Within Groups 7932.43 27 293.79 Total 10453.94 30 ( B ) Edaphic Factors 3 Conductivity Between Groups 2649.75 3 883.25 26.358 P <0.001 Within Groups 938.27 28 33.51 Total 3588.02 31 4 Salanity Between Groups 0.03 3 0.01 2.103 ns Within Groups 0.12 28 0.00 Total 0.14 31 5 pH Between Groups 0.36 3 0.12 2.922 ns Within Groups 1.14 28 0.04 Total 1.49 31 6 MWHC Between Groups 557.93 3 185.98 12.824 P <0.01 Within Groups 406.07 28 14.50 Total 964.00 31 7 TDS Between Groups 1603.97 3 534.66 17.599 P <0.01 Within Groups 850.65 28 30.38 Total 2454.62 31 8 OM Between Groups 57.18 3 19.06 12.973 P <0.01 Within Groups 41.14 28 1.47 Total 98.31 31 ( C ) Soil Nutrients 9 Nitrogen Between Groups 15231.79 3 5077.26 27.127 P <0.01 Within Groups 5240.68 28 187.17 Total 20472.47 31 10 Potassium Between Groups 28699.89 3 9566.63 29.853 P <0.01

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Within Groups 8652.31 27 320.46 Total 37352.19 30 11 Phosphorus Between Groups 6368.07 3 2122.69 9.056 P <0.05 Within Groups 6563.15 28 234.40 Total 12931.22 31 12 Calcium Between Groups 6960.00 3 2320.00 10.524 P <0.05 Within Groups 6172.47 28 220.45 Total 13132.47 31 13 Magnesium Between Groups 720.51 3 240.17 8.775 P <0.05 Within Groups 766.37 28 27.37 Total 1486.88 31 14 Sulphur Between Groups 1432.61 3 477.54 44.089 P <0.001 Within Groups 303.27 28 10.83 Total 1735.88 31 15 Cobalt Between Groups 3.59 3 1.20 52.790 P <0.001 Within Groups 0.63 28 0.02 Total 4.22 31 16 Manganese Between Groups 99.52 3 33.17 26.101 Within Groups 35.59 28 1.27 Total 135.10 31 17 Zinc Between Groups 1.88 3 0.63 7.616 P <0.05 Within Groups 2.31 28 0.08 Total 4.19 31 18 Iron Between Groups 671.59 3 223.86 4.032 P <0.05 Within Groups 1554.63 28 55.52 Total 2226.22 31

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 TDS= Total dissolved salt, MWHC= Maximum water holding capacity, OM= Organic matter

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6.4.7-Univariate analysis of variance (Understorey vegetation) Six groups of cluster analysis in understorey vegetation were used for further univariate analysis of variance in which elevation showed highly significant relation whereas all edaphic factors did not show any significant relation. Among the soil nutrients Phosphorus (F=3.53), Calcium (F=3.69) and Zinc (F=4.74) showed a significant relation at the probability level P<0.05 (Table 6.9).

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Table 6.9 Analysis of variance of individual environmental variables (topographic and edaphic and)and soil nutrients. Six groups were derived by Ward's cluster analysis using understorey vegetation data of 32 stands of CKNP Gilgit-Baltistan, Pakistan.

S.No Source of Variation SS df MS F P-level ( A) Topographic Factors 1 Elevation Between Groups 2731524.30 5 546304.86 70.02 P <0.001 Within Groups 202855.42 26 7802.13 Total 2934379.72 31 2 Slope Between Groups 2764.24 5 552.85 1.74 ns Within Groups 8243.26 26 317.05 Total 11007.50 31 ( B ) Edaphic Factors 3 Conductivity Between Groups 1030.56 5 206.11 1.75 ns Within Groups 3062.67 26 117.79 Total 4093.23 31 4 Salanity Between Groups 0.02 5 0.00 0.70 ns Within Groups 0.13 26 0.00 Total 0.14 31 5 pH Between Groups 0.28 5 0.06 1.00 ns Within Groups 1.44 26 0.06 Total 1.72 31 6 MWHC Between Groups 219.34 5 43.87 1.27 ns Within Groups 898.53 26 34.56 Total 1117.88 31 7 TDS Between Groups 632.35 5 126.47 1.86 ns Within Groups 1770.46 26 68.09 Total 2402.80 31 8 OM Between Groups 25.22 5 5.04 1.48 ns Within Groups 88.38 26 3.40 Total 113.60 31 ( C ) Soil Nutrients 9 Nitrogen Between Groups 3689.05 5 737.81 1.07 ns Within Groups 17889.42 26 688.05 Total 21578.47 31 10 Potassium

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Between Groups 13952.77 5 2790.55 2.62 ns Within Groups 27677.23 26 1064.51 Total 41630.00 31 11 Phosphorus Between Groups 5236.05 5 1047.21 3.53 P <0.05 Within Groups 7705.92 26 296.38 Total 12941.97 31 12 Calcium Between Groups 5510.23 5 1102.05 3.69 P <0.05 Within Groups 7761.99 26 298.54 Total 13272.22 31 13 Magnesium Between Groups 188.50 5 37.70 0.66 ns Within Groups 1493.72 26 57.45 Total 1682.22 31 14 Sulphur Between Groups 415.05 5 83.01 1.32 ns Within Groups 1635.92 26 62.92 Total 2050.97 31 15 Cobalt Between Groups 1.29 5 0.26 1.80 ns Within Groups 3.74 26 0.14 Total 5.03 31 16 Manganese Between Groups 32.24 5 6.45 1.45 ns Within Groups 115.95 26 4.46 Total 148.19 31 17 Zinc Between Groups 2.07 5 0.41 4.74 P <0.05 Within Groups 2.27 26 0.09 Total 4.34 31 18 Iron Between Groups 196.80 5 39.36 0.51 ns Within Groups 1870.00 24 77.92 Total 2066.80 29

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6.4.8-Physico-chemecal status of CKNP

6.4.8.1-Elevation The Whisker box plot of elevation is presented in Fig.6.91. Mostly vegetation sampling was performed between the elevation of 2900 m and 3300 m.The maximum elevation was observed 3600 m above sea level while the minimum elevation was 2444 m. The saplmes were collected at the mean elevations of 3090 m. The standard deviation among the elavation of different sampling sites attained 297.99. The elevation was found high in most of stand while some stands received the slightly high elevation. The elevetion consistency was same therefore vegetation pattern of the study area is more or less common (Table 6.10). 6.4.8.2-Slope The box plot of slope is presented in Fig.6.9.2 which shows that the vegetation was distributed between gentle and very steep slope. Maximum slope was 70° and the minimum slope was 5°. The mean slope was 32.03° with a standard deviation of 18.96 (Table 6.10). 6.4.8.3-Conductivity

The Whisker box plot of maximum conductivity is presented in Fig.6.9.3 which shows that the maximum conductivity in the study area was 78.30 µs/cm and the minimum conductivity was 22.30 µs/cm while the mean conductivity was 46.52 µs/cm with 15.99 standard deviation. The conductivity of the soil in all stands almost similar due the similar topographic factors (Table 6.10). 6.4.8.4-Salanity The box-plot of salanity is shown in Fig.6.9.4.The salanity in the study area was very low and majority of stands have no salanity. The maximum salanity was 0.3% with a mean of 3.12 x 10-2 with of 6.9 x 10-2.standard deviation. The soil has no salanity or low salanity but two sites i.e Thallay-1 and Shimshal-1 attained highest salanity of 0.3% and 0.2% respectively. The box plot of salanity did not show the clear status, due to the low and absence of salanity in mostly stands (Table 6.10). 6.4.8.5-pH The box plot of pH is shown in Fig.6.9.5. The maximum value of pH was 7.30 (neutral) while the minimum value was 5 (highly acidic). The mean value of pH was 5.73 (moderate acidic) with a standard deviation of 0.53. The soil of sampling sites was moderate acidic except of two locations i.e Arandu-1 and Arandu-2 attained the values of 7.1 and 7.3 (neutral) respectively. (Table 6.10).

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6.4.8.6-MWHC

The Whisker box plot of maximum water holding capacity (MWHC) is shown in Fig.6.9.6. Maximum value of MWHC was 55% and the minimum values was 15% while the mean MWHC attained 33.96% with 11.07 standard deviation. Most of the soil was observed as loamy while some stands have silt and clay soil structure. The structure of the loam soil was good for the best growth of vegetation (Table 6.10). 6.4.8.7-TDS The Whisker box-plot of Total dissolved salt (TDS) is presented in Fig.6.9.7 which shows that the maximum value of TDS was 35.40 g/L while the minimum dissolved salt observed was 9.60 g/L. The mean value of TDS attained 22.02 g/L with a least standard deviation of 7.08. Majority of the TDS values occurred between 18 and 25 g/L (Table 6.10). 6.4.8.8-Organic matter The box plot of organic matter is shown in the Fig.6.9.8 which shows that the stands numbers 11 (Hoper) and 27 (Braldu-1) have the highest amount of organic matter. The maximum organic matter recorded 16.50% while the minimum organic matter was 0.70%. The mean organic matter was 4.96% with a standard deviation of 3.70. The occurrence of the sampling data represents between the values of 0.1% to approximately 5% except of two location i.e Hoper and Braldu-1 attained the highest amount of organic matter was 16.5% in both stands (Table 6.10). 6.4.8.9-Nitrogen

The Whisker box plot of Nitrogen is shown in Fig.6.9.9. The maximum concentration of Nitrogen was recoded 124 ppm and the minimum value was observed 229ppm. The mean value of Nitrogen attained 209.56ppm with a standard deviation of 25.60. The location Stak-3 attained the highest (192ppm) value among the stand while the whole data occupied between 120ppm and 130ppm. Most of the data occurred between 200 to 220 ppm. However, some stands deviate from other stands i.e stands number 4, 7, 6, 9 and 29 (Table 6.10).

6.4.8.10-Phosphorus The Whisker box plot of Phosphorus is shown in Fig.6.9.10. Maximum value of phosphorus was recorded 134 ppm and the minimum value was 233ppm. The mean value was 191.43ppm with a standard deviation of 20.82. There was no deviation

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6.4.8.15-Manganese The Whisker box-plot of Manganese is shown in Fig.6.9.15. The maximum concentration of manganese was 17.94ppm and the minimum value was 2.62ppm. The mean value was 8.30ppm with 3.31 standard deviation. The majority of the data occurred between 6ppm to 10ppm. The stand number 2 (Haramosh valley) attained the highest value (17.943) of manganese (Table 6.10). 6.4.8.16-Zinc Fig.6.9.16 shows the Whisker box plot of Zinc. The amount of Zinc was low attained the maximum value of 1.62ppm and the minimum value was 0.02ppm while the mean value of 0.23ppm. The standard deviation for this nutrients attained 0.37 and the locations Braldu-1(Stand 28) and Braldu-2(Stand 29) deviated from the other sites attained the highest values of 1.62ppm and 1.28ppm respectively. Most of the values occurred between 0.09 ppm and 0.25 ppm (Table 6.10). 6.4.8.17-Iron The Whisker box plot of the Iron is shown in Fig.6.9.17 which demonstrates that the maximum value of Iron was 192ppm and the minimum value was 106ppm. The mean value of Iron was 136.34ppm with a standard deviation of 20.05. The location Stak-3 attained the highest (192ppm) value among the stand while the whole data occupied between 120ppm and 130ppm (Table 6.10). 6.4.8.18-Sulphur The Whisker box plot of Sulphur is shown in Fig.6.9.18 which illustrate that the maximum concentration of Sulphur was 120 ppm and the minimum value was 148ppm. The mean value attained 135.62ppm with a standard deviation of 7.64. The concentration of Sulphur was more or less similar in all stands while in many stands the data occupied between 135 to 145ppm (Table 6.10).

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3800 80

3600 70

3400 60

3200 50

3000 40 Slope Elevation 2800 30

2600 20 Fig.6.9.1 Fig.6.9.2 2400 10

2200 0

90 .4

.3 15 70

60 .2 24

50 Salanity

Conducivity .1 40 2316261725

30 Fig.6.9.3 0.0 Fig.6.9.4

10 -.1

7.5 60 19 18 7.0 50

6.5

6.0 pH MWHC 30

5.5

Fig.6.9.5 20 5.0 Fig.6.9.6

4.5 10

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40 20

30 15 27

20 10 TDS Organic matter Organic

10 5 Fig.6.9.7 Fig.6.9.8

0 0

Fig.6.9.9 Fig.6.9.10

400 500

450 14 350 400

350 300 300

K 250 250 Ca

200

200 150

Fig.6.9.1 100 Fig.6.9.12 150

100 0

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180 2.5 13

160 2.0

140 1.5 Co Mg

120 1.0 Fig.6.9.13 Fig.6.9.14

100 .5

20 2.0 1.9 18 2 1.8 1.7 16 1.6 28 1.5 29 14 1.4 1.3 12 1.2 1.1 10 1.0 Zn Mn .9 8 .8 .7 6 .6 .5 Fig.6.9.16 4 Fig.6.9.15 .4 .3 2 .2 .1 0 0.0

200

180

160 Fe

140

120 Fig.6.9.17 Fig.6.9.18

100

Fig.6.9 Whisker box plots of Physico-chemical factors of CKNP.

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Table 6.10 Physical and chemical properties of the vegetation of CKNP

S.No Variables Minimum Maximum Mean Std. Deviation Topographic factors 1 Elevation (m) 2444.00 3600.00 3090.1563 297.9968 2 Slope° 5.00 70.00 32.0313 18.9694 Edaphic factors 3 Conductivity(µs/cm) 22.30 78.30 46.5281 15.9990 4 Salanity (%) 0.00 0.30 3.125E-02 6.927E-02 5 pH 5.00 7.30 5.7394 0.5316 6 MWHC (%) 18.00 55.00 33.9688 11.0759 7 TDS (g/L) 9.60 35.40 22.0281 7.0822 8 Organic matter (%) 0.70 16.50 4.9406 3.3855 Soil nutrients 9 Ca (ppm) 117.00 435.00 200.7188 60.0818 10 Mg (ppm) 106.00 172.00 139.4375 17.3260 11 N (ppm) 124 229 209.56 25.60 12 P (ppm) 134 233 191.43 20.82 13 K (ppm) 118.00 345.00 240.5313 61.3478 14 S (ppm) 120 148 135.62 7.64 15 Co (ppm) 0.79 2.38 1.1829 0.3173 16 Mn (ppm) 2.62 17.94 8.3098 3.3158 17 Zn (ppm) 0.02 1.62 0.2337 0.3707 18 Fe (ppm) 106.00 192.00 136.3434 20.0564

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6.5-Discussion and conclusion Relationship between vegetation and environmental variables of Central Karakoram National Park, Gilgit-Baltistan, Pakistan was investigated. Thirty two stands of mix vegetation (nine gymnospermic forest and twenty three angiospermic (bushes, forests) were used to check the relationship among the vegetation, topographic factors, edaphic factors and soil nutrients. Multivariate techniques including cluster analysis (Ward’s agglomerative method) and PCA ordination were used. Four groups extracted in forested and non forested vegetation while six groups were isolated in undestorey vegetation by Ward’s cluster method. A total number of 18 factors are considered including topographic factors (elevation and slope), edaphic factors (conductivity, salanity, organic matter, maximum water holding capacity, total dissolved salt and pH whereas soil nutrients including Nitrogen ,Potassium ,Calcium Phosphorus , Sulphur ,Iron, Magnesium ,Manganese, Cobalt and Zinc . These nutrients play an important role in plant nutrition and work as the productivity of the vegetation (Bell, 1982). The fertility of the soil depends upon the environmental conditions, edaphic factors, and micro and macro elements of the area (Scholes, 1991). The macronutrients consumed in large quantity because plants use large amount for their growth and survival while micronutrients ranges 5 to 200 ppm in plants (Taiz and Zeiger, 2002). In the present study the values of micronutrients including Coblat, Manganese and Zinc ranges from 0.12 to 10ppm in forested vegetation while 0.17 to 8 ppm in understorey vegetation. However the quantity of Iron observed high which ranges from 129 to 142ppm in forested while in understory vegetation it ranges 132 to 143ppm. The amount of Iron was high as compare to other micronutrients because of high elevation. Vegetation need more Iron for the growth and survival. Malik et al., (1973) reported that the accurate requirement of forested vegetation cannot be measure and they need comparatively minor level of nutrients. Among the macronutrients Potassium attains the highest quantitative which ranges from 203 to 300ppm while the lowest amount was recorded in Magnesium which ranges from 127 to 144 ppm. Nitrogen is also an important nutrient which affects the growth of plant (Bergman, 1985). It is used in the form of nitrate and ammonium in plants. According to Salisbury and Ross (1969) the normal amount of nitrogen in forested species is

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1.5%. In the study area nitrogen contents concentration ranges from 141 to 221 ppm which is equal to 0.014% to 0.022.Malik et al., (1973) found the low amount of nitrogen in soil which was 0.13% to 0.16% form the forests of Dir . Ahmed (1990b) also recorded the low amount of nitrogen which was 0.8 from Juniperus excelsa forest of Baluchistan. Hussain and Badshah (1998) attained the low amount of nitrogen from the South Waziristan forest of Pakistan. They found the amount of nitrogen from different altitudinal zones which ranges from 0.112% to 0.28%. Siddiqui (2011) found the range of nitrogen from the moist temperate forest which was 0.15% to 1.25%.Our values of nitrogen are less than the above mentioned researchers because of different topography and sites. Malik et al., (1973) observed the amount of Phosphorus from Dir forests which was ranges 0.27% to 0.49% while Sheikh and Kumar (2010) also reported the amount of Phosphorus in the soil of oak forest was higher (17.99 kg ha-1) than in pine forest (16.88 kg ha-1). Siddiqui (2004) observed 0.15 to 0.98% of phosphorus from moist temperate areas of Pakistan. However in the study area the amount of Phosphorus ranges 76.3 to 92.41 kg ha-1 which is higher than the above mentioned researchers. Plants need as much Sulphur as Phosphorus which is also important for the plant nutrition and survival. In the present study this variable range from 126ppm to 146ppm in forested vegetation while in the understoery vegetation it ranges from 130ppm to 139ppm Potassium nutrients concentration ranges 203 to 300pmm in the study area while this nutrient is reported by Hussain and Badshsh (1998) from different elevation of Waziristan forests and they observed the values of Potassium which ranges 136 to 173 mg/kg while Malik et al., (2007) recorded the highest range (100-500ppm) from Pir- Chinasi hills of Azad Kashmir forests. Tareen and Qadir (2000) also found a high range (76 to 925) form Quetta. Siddiqui (2011) recorded the range of potassium which was 0.34% to 2.85%In the study area the quantity of Phosphorus is ranges from 171 to 207ppm (0.017% to 0.020%) which was more or less similar range from other researcher of Pakistan and which was considered as a good range for the growth and survival. Calcium is also an important content which balanced the membrane integrity which support to the cell wall (Resh, 1983). Siddiqui (2011) investigated the range of

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Chapter 6: Vegetationenvironment relationship calcium 0.27 to 2.4%. In the study area Calcium content ranges 174 to 208 ppm which was differ from the mentioned researchers. The important of NPK quantity is well predictable (Mandre, 2003). The NKP ratio of the study area was low as compare to the other researcher except of Potassium which attains more or less similar range with the other researchers from the different areas of Pakistan. Din et al., (2007) investigated the NKP ratio in angiospemic belt soil from Juglote Gilgit which was 4:3:2. This ratio also differs from the current study it may be the elevation factor and the need of angiospermic and gymnospermic vegetation. For the plants nutrition and the transfer of energy, Magnesium is a key nutrient in plants (Bergman, 1985). In the current study area Magnesium content ranges 127 to 144 ppm which was equal to 0.0127% to 0.0144%. Qadir and Ahmed (1989) found the high amount of magnesium from Hazaragangi National Park Quetta and they observed 1.46% which was higher than the current study due to the different topographic factors. Siddiqui (2011) also found 0.16% to 0.95% range of magnesium from the moist temperate areas of Pakistan. Karim et al., (2009) stated that Nitrogen, Potassium, Magnesium, Calcium etc is almost scarce in the soil of all the areas of Pakistan. In the current study the quantity of all organic minerals especially macronutrients were very low. The topographic factors elevation was high in this study which ranges from 2485m to 3517m while slope angle was recorded from gentle to steep. The slope and elevations was significantly correlated with the vegetation. Siddiqui (2011) and Wahab (2011) also found a significant relation between elevation and vegetation from moist temperate forests and district Dir forests respectively. Among the edaphic factors the soil was found to acidic both in forested and non-forested vegetation which ranges from 4.9 to 6.5. The ideal pH is ranges from 6 to 6.5. Malik et al., (1973) found pH range from the forest of Dir which was 5.8 to 6.5 and they suggested that the this range of pH is favorable for the growth of plant species. Siddiqui (2011) ranges the pH 5.2 to 6.98 from the moist temperate forest soil. The current study range of pH also similar with them whereas Qadir and Ahmed (1989) found a high range of pH (7.56 to 8) from Hazaragangi National Park Quetta. More or less similar results also found Tareen and Qadir (1990) from water courses of Quetta and they observed 7.5 to 8.4 while Ahmed et al., (1991) reported the approximately neutral soil pH (7.1 to 7.8) from Baluchistan forests. Malik et al., (2007) found the pH soil form

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Pir Chinasi forest and they observed 5.4 to 7.5. This range of soil pH also more or less resemble with the current study. The salanity was very low in the current study which found in few sites ranges 0.01 to 0.087 %. Total dissolved salt (TDS) is ranges 27 to 45 mg/ lit (0.0027 to 0.0045%). Hussain and Badshah (1998) found a range of 0.032 to 0.16% from different elevations while Siddique (2011) recoded TDS 0.00168 to 0.0377% from the moist temperate areas forest of Pakistan. The TDS range current study similar with the moist temperate forests. Conductivity was high which ranges from 36 to 61µs/cm in forested vegetation while in understorey vegetation it ranges from 41 to 56. Siddiqui (2011) found 1.8 to 3.5 mmhos/cm from the moist temperate forest which was within the range of current study. Qadir and Ahmed (1989) observed amount of conductivity from Hazaragangi National Park which ranges 0 to 0.22 mmhos/cm whereas Malik et al., (2007) ranged 0.02 to 1.18 Ds m-1 from Pir Chinasi forest. The range of organic matter of soil in Pakistan was 0.69% to 2.45% (Anon,2008). The organic matter is an important content of soil which improve the water holding capacity of soil (Karim et al., 2009).Malik et al., (1973) observed 0.5% to 1.6% organic matter from the forest of Dir while Qadir and Ahmed (1989) obtained 1.6% to 2.29% from Hazaragangi National Park. Hussain and Badshah (1998) ranged 2.72% to 5.6% whereas Malik et al., (2007) observed 2.62% to 10.52% and Khan (2012) attains 5.3% to 7.8 organic matter. In the current study range of organic matter was 3% to 7% .The present study range of organic matter was within the range of above finding. Maximum water holding capacity (MWHC) is also an important physical factor of soil, in the current study the range of MWHC 22 to 34 %. Siddique (2011) found the range of MWHC from moist temperate forest soil which was 32.5% to 65.4%. Qadir and Ahmed (1989) found the range of MWHC 36.3 to 59.3% from Hazaragangi National Park while Tareen and Qadir (2000) observed 24.1 to 56.01% from Quetta District. Similar results also obtain Khan (2012) form Chitral Gol National Park. The current investigation is within the range of these studies. Siddiqui (2011) suggested that these variables have some controlling affects over the distribution pattern of vegetation and the abundance of the vegetation. The growth and development of plants depends upon the availability of soil nutrients, topographic and edaphic factor. The status of nutrition is linked with the relationship bwteen growth and nutrients concentration (Luyssaert et al., (2004). Soil nutrients play an important role in growth and development (Siddiqui, 2011 ;

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Malik,2007). Hussain and Qadir (1970) found a significant relation among the topographic factors, edaphic factors, soil nutrients and the distribution of vegetation. Kayani et al., (1988) found significant relation (P<0.05) the vegetation Cynodon of dactylon with calcium and magnesium. Ahmed (1990 a, b) did not found any significant relation the soil variables from the forest of Baluchistan. However in the study area some weak correlations were recorded. In the forest vegetation conductivity showed a high significant relation with the probability level (P<0.001) while MWHC, TDS and organic matter showed a significant relation at the probability level P<0.01). Jabeen and Ahmed (2009) found strong correlation among the vegetation, conductivity and pH. Among the soil nutrients potassium, phosphorus, calcium, magnesium, cobalt, sulphur, manganese, zinc and iron showed a significant relation within the groups which were extracted by Ward’s cluster method while in understorey vegetation elevation attains a high significant relation among the group All physical factors did not show any correlation. In this vegetation Phosphorus, calcium and zinc showed significant relation within the group. Some significant relations also recorded between the ordination axis and different variable (topographic, edaphic and soil nutrients) of the forested and non-forested soil .Shaukat et al., (1976) found significant correlation of MWHC, organic matter and potassium with the vegetation communities of Gadap area Karachi .However Ahmed (1986) stated that potassium and organic matter influence and affect the vegetation of foothills of Himalaya while pH , MWHC and phosphorus nutrient did not show any influence in the vegetation. On the other hand Ahmed et al., (1990) found a significant relation of conductivity with the density of Juniperus excelsa. Khan (2011) found the values of Mg, K and N 0.054%, 0.31% and 0.18% respectively. He also observed a significant relation of these variables with ordination axis while using DCA ordination from the forest of Chitral. Wahab also found a significant relation among vegetation and soil variables (Magnesium and Nitrogen). It is concluded that the weak correlation due to the cutting, logging and grazing. Our results of correlations vary from these mentioned workers it may be the profile of soil, different topography and altitudinal zonation of the area which is differ from other study areas.

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PARTII DENDROCHRONOLOGY

Chapter 7: An introduction to dendrochronology

CHAPTER-7 AN INTRODUCTION TO DENDROCHRONOLOGY 7.1-Introduction According to Frits (1971) Dendrochronology deals with the dating of annual growth layers of forested plants species. However, some authors have restricted the term dendrochronology to use of tree rings to date events only despite the fact that the techniques are applied to a verity of problems. Robinson (1990) defines as “Dendrochronology is a “young” science, and the pioneer of this growing science is an astronomer, Andrew Ellicott Douglass, in western North America in the early 20th century”. Dendrochronology is the scientific method of dating based, on the analysis of patterns of tree rings, also known as growth rings. Dendrochronology can date the time at which tree rings were formed, in many types of wood, to the exact calendar year. The main branches of the Dendrochronology are as under: ¾ Dendroecology ¾ Dendroclimatology ¾ Dendroglaciology ¾ Dendroarcheology ¾ Dendrosesimology ¾ Dendrohydrology The present study mainly focuses on dendroclimatology therefore brief description of dendroclimatology is presented below. 7.2-Dendroclimatology The development of annual layers of wood by trees in response to the conditions within their growing season provides an alternative measure of climate on an exactly dated timescale which is called dendroclimatology. This branch of science used to reconstruct the temperature and precipitation (Mann et al., 1998). It is must to care the site and species selection where tree growth is highly sensitive to available soil moisture which can be used to reconstruct of past climate beyond the historical records. Ahmed et al., (2011) stated that the potential age of trees is one important consideration for dendroclimatic study. These studies may help to increase the metrological records (Yadav and Bhattacharyya 1992, 1994). Climate always affects the life style of human

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being. The unfavorable climatic conditions always force to migrate the people from one place to another favorable climatic area. The impact of climate in Pakistan also influences economy, largely through restrictions in hydroelectric power generation and accessibility of water for human population, industry and agriculture (Ahmed, 2009). The solution of these problems solved to use different modern techniques in different countries of the world. Different climatic stations are established in the world to record the climatic variation. This science plays an important role in Pakistan. The mountainous range of Pakistan which is located in Gilgit-Baltistan, explore the climatic variations in Pakistan and also affect the climate of India, China, Afghanistan, Nepal and Russian. Gilgit- Baltistan is rich for forest and the glaciers of that region are the main source of water. These assets are fading with the passage of time due to our own mistakes i.e illegal cutting, logging, overgrazing and pollution. Pakistan have 3% forested land (FAO, 2009) and it is estimated that for the better economic rate 22% of land should be forested (Ahmed,2009).Forests play an important role in the natural vegetation, climate, water shed management, soil protection and biodiversity conservation (Kotru et al., 2003). Therefore the climate and growth relation is an important factor to save the biodiversity and climatic variation. 7.3-Brief history of dendrochronology

An Italian scientist Leonardo Da Vira (1452-1519) observed the relationship between tree-rings and climate before 500 years (http://www.nationalgallery.org.uk/artists/leonardo-da- vinci). According to Heizer (1956), the early Greeks were considered first to note the annual tree layers and widths which were dependent on environmental conditions. Duhamel and Buffon in 1737, two French naturalists examined the frost damaged layers of 20 rings occurred in the bark of several felled trees. Other investigators confirmed their observations. A. C. Twining in 1827 and Charles Babbage from England in 1838 recognized crossdating based on relative ring widths. In 1892, a Russian worker F. N. Shevedov was first to crossdate the annual rings but identify that these ring structure was due to the past climatic variations.

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Andrew Ellicot Douglas was acknowledged father of dendrochronology. In 1937, Douglass established Laboratory of Tree Ring Research at University of Arizona, Tucson which became the first institution specialized only for tree ring studies. Originally, dendrochronological techniques were established to date archeological structure; but recently, tree ring analyses have been used in various disciplines. These include plant ecology, geomorphology, hydrology, glaciology, seismology and entomology and importantly climatology (http://web.utk.edu/~grissino/ltrs/lectures.htm).One important establishment in the field of dendrochronology is the establishment of the International Tree-Ring Data Bank (ITRDB) in 1974, which enables the global scientific community to contribute and freely access to tree ring data (Grissino-Mayer and Fritts, 1997). Although in Pakistan dendrochronology is started in terms of without modern techniques in the late of 80s. However, modern techniques were applied by Ahmed (1988) to point out the missing and false rings. Furthermore Ahmed et al., (1990a, 1990b, and 1991) used these techniques to explore the forests of Baluchistan while Ahmed and Sarangzai (1991 and 1992) used these techniques to investigate the gymnosperm species from Himalayan region of Pakistan. After a long gap Ahmed (2005) established first Laboratory of Dendrochronology and Plant Ecology at Department of Botany Federal Urdu University of Arts Science and Technology, Karachi. Ahmed introduced the modern dendrochronogical techniques in different branches i.e dendroecology, dendroclimatology, dendrosesimology and dendrohydrology. He is also collaborating with other worldwide Universities and within the country. According to the forest Department 9% land of Gilgit-Baltistan is forested. These forests are very important for climate, biodiversity, economy and tourism. Global warming, climate change, earthquakes, floods and water shortage are the main problem of Pakistan. These problems are increasing day by say due to the lack of proper management, insufficient database, poverty and lack of knowledge. The high altitudinal forests and glaciers are the assets of our country and wee need a complete database to understand this problem. Dendrochronological techniques are being used worldwide to understand and resolve these problems. A Ahmed and Naqvi (2005) focused the forest of Northern Areas of Pakistan to understand the dendrochronological potential of gymnosperm species while Khan et al.,

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(2008) inspected dendrochronological potential of Picea smithiana from Afghanistan. Ahmed et al., (2009) examined the relation between diameter and age from the different forests of Pakistan. Ahmed et al., 2011; Zafar et al., 2012 and Ahmed et al., 2013 also focused the forests of Gilgit-Baltistan to provide database about dendrohydrology and dendroclimatology. 7.4-Principles of dendrochronology The followings are the principles of Dendrochronology described in detail by Fritts (1974) ¾ Uniformitarian ¾ Limiting factors ¾ Modeling growth-Environmental relationship ¾ Ecological amplitude ¾ Site selection ¾ Crossdating ¾ Replication ¾ Sensitivity ¾ Standardization ¾ Calibration and verification ¾ The domain of climate

7.4.1-Uniformitarian This principle stated that the biological processes which connect with environmental conditions and the pattern of tree growth. It is well said by Hutton (1785) "the present is the key to the future (http://en.wikipedia.org/wiki/James_Hutton). However, dendrochronology modifies this statement that "the past is the key to the future." In short we can say that, by knowing environmental conditions of past and present, can better predict for future environmental conditions.

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7.4.2-Limiting Factors This is an important principle of dendrochronology which declared the rates of plant developments, are restricted by the environmental factors. For example, precipitation is often the most limiting factor to plant growth in arid and semiarid areas. In the forested areas (high elevation) temperature is mostly because forest trees are growing in closed canopies. Whereas precipitation may be limiting factor in arid and semiarid regions due to the low precipitation amounts. Liebig (1843) stated that minimum environmental factors reduce the growth of tree in a specific time which is named “Law of minimum” (http://books.google.com.pk/books. For example, if the quantity of water excess than the process of different chemical and biological process stopped. The maximum temperature does not affect directly but the different enzymes stopped the system and the growth of trees. 7.4.3-Modeling growth-environmental relationship This principle also play an important role which stated that tree-growth of a tree may be affected by natural disturbance, environmental and anthropogenic disturbance. Therefore the maximum preferred environmental conditions being studies to minimized the other factors i.e ecological conditions affecting the growth of trees. 7.4.4-Ecological Amplitude This principle indicates that plant species have different habitats which vary from location to location and a certain range of habitats. (Fritts, 1976). For example, Picea smithiana is the most widely distributed of all pine species in Pakistan, growing in a diverse range of habitats. Therefore, Picea smithiana has wide ecological amplitude. 7.4.5-Site Selection This principle of site selection is very important in dendrochronology which refers that the how much site useful for dendrochronological techniques?. It should be noted that tree-ring series sensitive to the environmental variable which examined. For example, gymnosperm plants which are especially receptive to drought conditions can usually be found where rainfall is limiting. The low altitudinal sites did not response and not sensitive for rainfall, therefore a dendrochronologist prefer to the high elevation, no disturbance that will maximize the environmental signals.

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7.4.6-Crossdating This is also an important principal of dendrochronology which stats that the ring pattern matched among several tree ring series to check the exact year in which each tree ring found. This principal also investigates the missing or double rings in the tree ring series. Binocular microscope is used to crossdating the wood samples. 7.4.7-Replication This principal states that obtaining more than one increment core per tree which reduced the intra-trees variability .Therefore many trees from one sites and many sample from a tree. Usually the dendrochronologist obtains 2 core samples per tree (Ahmed, 2009). 7.4.8-Sensitivity Tree rings are very important and the indicators of the climatic variations. The potential and severity of the trees investigation regarding to climatic variation is called sensitivity. Due to the climatic variation the width of a tree ring vary from other rings. If there is no variation observed in the tree rings it is termed as complacent tree rings. Thefore complacent wood samples are not used for further dendrochronological investigation. The sensitivity of the trees rings are checked by crossdating method then measures the width of these rings. The variation of these tree rings are important and the good indicators for further investigation. 7.4.9-Standardization Standardization is an important principal to obtain the accurate result. The growth of tree is depending upon different factors (biological, chemical and environmental). The study of dendrochronology focuses on the environmental factors therefore the remaining factors are removed that is called standardization. After the standardization the numerical value of tree ring is termed ring width indices. 7.4.10-Calibration and verification For the verification of past climatic variation it is must to calibrate chronology and climatic station record of the investigated area. The climatic data of the investigated area verify the indices record which is called verification. Further climatic history or information also calibrate with the indices result if any information or climatic history available.

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7.4.11-The domain of climate In some regions the weather consistency is maintain for many years. The similar trend of whether is called macroclimate which can be demonstrated to use minimum 30 years climatic record from a climatic station. If the area has divers geographical position then collects the data from the different station to check the trend of the climatic variation

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Some snaps about dendrochronologyy

Swedish increment borer wood sample taken from tree

Mounting method Crossdating method

Binocular microscope Measure J2X (Ring width measurement machine)

Prof.Dr. Moinuddin Ahmed briefing Taking core from tree about dendrochronology

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7.5-Chracteristic features of Picea smithina tree rings Picea smithiana is an important species among which is widely distributed in Gilgit-Baltistan forests .It is belong to the family Pinaceae and the cones of this species bloom from April to May and fruit is female cone. The annual rings of this species are clear which is good for dendrochronological potential. The missing and double rings are found minor in this species (Ahmed, 2009). Picea smithina forests play an important role in the natural vegetation, climate, water shed management, soil protection and biodiversity conservation (Kotru et al., 2003). The spruce (Picea smithiana) has commercial and medicinal value. Ahmed and Naqvi, 2005 reported that tree ring research of Picea smithina is helpful to understand the climatic potential 7.6-Objevtives of the study

¾ Age and growth rates of gymnosperm trees. ¾ To investigate the growth rate of various past periods. ¾ To develop the complete chronology of gymnosperm trees. ¾ To explore the tree growth-climate- response.

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Chapter 8: Literature review

CHAPTER-8 LITERATURE REVIEW This chapter focuses on the brief literature review of age and growth rates, chronology development and growth climate relation, conducted on different forest of Pakistan. 8.1-Literature review Champion et al., (1965) calculated the age of Picea smithiana trees from Zhob District while Khan (1968) calculated age of Pinus wallichiana, from Trarkhal forest of Azad Kashmir and Sheikh (1985) calculated age of Juniperus excelsa from Ziarat Balochistan. All these estimates of age were generally based on simple ring count without any extrapolation for any missing rings (absent ring), double rings (false rings) or the time required for the tree to reach the height at which wood samples were taken for investigation, it is therefore considered that these age are over or under estimates. Ahmed (1987) described the dendroclimatology and its scope in Pakistan. Ahmed (1988) to calculate age and growth rates, pointing missing and false rings. Ahmed (1989) described tree ring chronologies of Abies pindrow from Himalayan region of Pakistan. He applied dendrochronological method on three Abies pindrow stand in northern Pakistan, and dated chronologies with maximum period from 1750 to 1987 AD were obtained and sample chronology statistics were discussed. According to him the sample and chronology 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 could be used in Dendroclimatology investigation. Furthermore they investigated natural regeneration potential of Juniperus excelsa Balochistan from 60 mature stands on Juniper track ranged from zero to 219 ha-1 with a mean of 52 ha-1. Seedling density and basal area were significantly correlated (P<0.001) and tree basal area and seedling density were also significantly correlated (P<0.05) indicating that seedlings are sciophytic and are found under the shade of trees. Highst average of seedling density and basal area were recorded from west facing slope, in addition, future trend of the seedling population suggesting that Juniper forest are not deteriorating. Ahmed et al., (1990a, 1990b, and 1991) calculated age and growth rates of Juniper and chilghoza pine from Balochistan. Ahmed and Sarangezi (1991) presented

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dendrochronological approach to estimate age and growth rate 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. Ahmed and Sarangzai (1992) studied Dendrochronological approach to estimate age and growth rate of various species from Himalayan region of Pakistan. Esper (2000) reconstruct the gymnospermic trees data which are growing close to the upper timber line (4000m asl) and found that from the mid nineteen century to the present these trees growing with favorable (AD 1579 and 1603)and unfavorable (1825 and 1850) growth periods of different amplitude and duration . Esper et al., (2001) presented a tree ring reconstruction of extreme climate years since 1427 AD from western central Asia. They sampled from twelve Juniperus sites and three mixed sites. They analyzed 429 tress for this purpose and classified the extreme growth reaction into event years-reflection extreme years, point years, common extreme years within the site and the regional pointer year. Ahmed and Naqvi (2005) described Tree-ring chronologies of Picea smithiana and its quantitative vegetational description from Himalayan range of Pakistan. They briefly discussed using modern dendrochronological techniques on five stands of Picea smithiana and provided dendrochronological description, ring-width characteristic, and chronology and sample characteristics, inter chronology characteristics and concluded that Picea smithiana could be used for dendrochronological investigation. It is suggested that more sampling is required to present strong database. Ahmed et al., (2006) gave a detail description about dendrochronological potential of conifer tree species from Himalayan range of Pakistan. Tredyte et al., (2006) used two juniper species i.e J.excelsa and J.turkestanica from Gilgit-Baltistan to explore the millennial precipitation reconstruction based on tree-ring oxygen isotopes concentration. Wahab (2008) et al., described the phytosociological and dynamics of some forests of Afghanistan and age and growth rates were also obtained with no significant correlation. Tree seedlings indicate poor regeneration status of the forests. Khan et al., (2008) presented dendroclimatic potential of Picea smithiana (Wall.) Boiss., from Afghanistan, modern dendrochronological technique were applied on Picea smithiana from district Dangam of Afghanistan twenty eight wood samples in the from of

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cores were obtained from 15 Picea trees and cross-dating was obtained among 24 cores of 12 trees .A first dated chronology (1663-2006) from this country was presented with various statistic. They indicated that all cores are highly correlated showing similar climatic signals and suggested that this species has high dendroclimatic value and more information could be obtained if this chronology is correlated with other regional chronologies. Ahmed et al., (2009) described age and growth rate of some gymnosperms forests and also presented preliminary results of Dendroclimatic investigation in Pakistan using Picea smithiana and examined the relation between diameter and age from the different forests of Pakistan. Correlation was checked among density/ basal area, elevation/ density, and elevation/ basal area. They concluded that forest showed no recruitment since last 10-15 years; therefore no future trends could be predicted for these forests. Ahmed et al (2010a) used response function analysis on Abies pindrow species. Ahmed et al., (2010b) investigated four tree species from seven different areas in the upper Indus Basin of Himalayan region of Pakistan to explore the dendrohydrological potential and found that annual ring width have significant relationship between tree samples of the same tree species and with other tree species. Zafar et al., (2010) also investigated standard tree ring chronologies of 60 cores of Picea smithiana from two new sites of Northern areas of Pakistan. They obtained 500 years of age with high mean correlation (0.74 and 0.85) among the samples they suggested that the given result is encouraging for growth and climate. Ahmed et al., (2011) sampled 28 tree-rings of six different species from Northern areas of Pakistan, They found several tree species attaining age of 700 years and also observed that the stronger correlation occurred between different species growing adjacent to each other than the same species which are growing 0.5 km separation. The response function results shows that all species negatively relationship to temperature in the previous October and then towards positive during winter while positive precipitation correlation from the late winter to the spring. Khan (2011) investigated dendrochronological potential and population structure of the forest of Chital Goal National Park using dendrochronological techniques while Wahab (2011) studied the dendroclimatology and population structure of forest from District Dir. Siddiqui (2011) provide a database about the population structure of moist temperate forests of Pakistan.

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Zafar et al., (2012) investigated the growth-climate relationship using Picea smithiana from Afghanistan. They found 101 years chronology with 0.18 sensitivity and 0.54 inter correlations. Ahmed et al., (2012) studied the climate-growth correlations of tree species from Indus Basin of Karakoram range, north Pakistan. Zafar (2013) used the dendroclimatic techniques and reconstruct climatic history of Gilgit and Hunza Districts. Ahmed et al., (2013) studied the dendroclimatic and dendrohydrological response of two tree species from Gilgit valley. Recently, Cook et al., (2013) reconstructed five centuries record of Indus river flow using tree rings.

So far no dendrochronological investigation was presented from CKN Park. Therefore bearing these points in mind present work is carried out from this park.

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CHAPTER 9 MATERIALS AND METHODS This chapter focuses on materials and methods of age and growth rates, chronology development and growth-climate response. 9.1-Site description Stak is located in District Skardu, Gilgit-Baltistan with the coordinates of 35.77◦ North and 75.04◦ East and an altitude of 2600m while the forest belt is distributed on the elevation of 3600m above sea level with the slop angle of 20◦. This valley (Fig.9.1) is included in the Central Karakoram National Park. The geographical location of this valley is fascinating, surrounded by Nagar valley in the North, Skardu valley in the South, Shigar valley in the East while Haramosh valley in the West. The valley is accessible by jeep .According to CKNP management 1205 household living in this valley which is mixed with balti and shina speakers. The valley has unique diversity of flora and fauna. In the forest vegetation mix species of spruce (Picea smithina), blue pine (Pinus wallichiana) and juniper (Juniperus excelsa) are distributed. In this area scrub vegetation is dominated by Rosa webbiana, Hippophae rhamnoides, Berberis lycium, Artemisia brevefolium, Thymus linearis, Bistorta, Anaphalis virgata etc (Hussain et al.,2011). The type of soil was both clay and silt with erosion. Sign of illegal cutting and overgrazing was also observed. 9.2-Field methods During the sampling, standard dendrochronological techniques were applied following Stokes and Smiley, 1968; Ahmed, 2009. Least disturbed site and tress were selected to obtain core. Thirty two samples from 16 tress were sampled. Two samples were taken from each tree at breast height. Swedish increment borer was used to take the samples from trees while drinking straws were used to preserve the samples during the field trip. GPS was used to record the elevation and location coordinates while dbh measuring tape was used to measure the diameter at breast height (dbh) of each tree.

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Picea smithiana forest at Stak Valley of CKNP

9.3-Mounting and Crossdating In the laboratory, cores were air dried and mounted on wooden strips. These cores were sanded by a sanding machine and using different sand papers until the surface of core sample was clear for visual cross dating. After that visual cross dating under Binocular microscope following the method described by Stokes and Smiley (1968), Fritts (1976) Grissino-Mayer (2001) and Ahmed (2009). 9.4-Measurement using Velmex

The ring’s widths were measured in millimeter using measure J2X. The machine was connected with central processing unit. The identity was given using the criteria SSSSTTC. The first two SS stands for species, the next two SS stands for species, TT stands for tree number and C represents the core number like in case of Picea smithiana from Stak (PSST011) PS describes species name: Picea smithiana, ST shows location: Stak, 011 represents the sample number. Black mark on the monitor screen was used to calculate the values by measuring the distance travelled between two successive rings. The values were stored numerically by the program itself. The program started counting

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from bark to pith one by one. Ring widths were measured to 0.001 accuracy selected from the program menu. 9.5-Age and growth rates Age is calculated on the base of crossdating between cores of the same tree following Ogden (1980) and Ahmed et al., (2009) and Ahmed (2009) tracing the missing and double ring. The length of the cores was divided by the number of rings present in the core and the growth rate in year per centimeter was calculated following Ahmed (1984), and Ahmed et al, (1990a, 1990b and 1991). For the growth rate of seeding in past periods cores were selected which have complete pith. From the pith to the bark side of wood sample 8cm was considered to the various past seedlings which were further divided into 2cm interval of 4 classes. The growth rate year/cm of each class was calculated by the total rings found in the class divided by the length of class. Histogram and regression was plotted by MS Excel to establish the relationship between age, growth and diameter at breast height. 9.6-Chronology preparation Chronology development is prepared using program COFECHA for removing lags ARSTAN used for standardization. 9.6.1-COFECHA The raw ring width measurement taken in millimeter was subjected to COFECHA (Holmes et al. 1986; Grissino-Mayer 2001) to check the quality of 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 describes title page, options selected, summary and absent rings; part two tells graphical representation in the form of Histogram; part three shows master series with samples depth and absent rings by year; part four demonstrates Bar plots of master dating series; part five illustrates 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 importance and signifies the following results; ¾ Number of dated series which tells how many samples in a stand is crossdated.

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¾ Master series which tells the longest crossdated core in the whole series. ¾ Total rings and total dated ring in the whole stand. ¾ Series intercorrelation which shows how much pattern of rings is similar or dissimilar to one another. ¾ Average mean sensitivity is the measure of relative differences in widths between two adjacent rings. ¾ Flags which are the source of problems in crossdating.

9.6.2-ARSTAN Dendrochronologists don’t use ring width measurement to find past climatic variations as climatic signals in tree ring widths are small so these signals must be enhanced by indexing procedure. Mean chronology of a given locale can be obtained by averaging the indices of many trees. The random non climatic noise caused by any measurement errors cancel one another and signal to noise ratio is enhanced. If there is greater climatic variations among the sample ring width we require small number of cores to extract signal-to-noise ratio. The raw ring-widths of trees were standardized to remove long term non climatic signals (biological growth trend) (Cook, 1985) and to enhance the climatic signals. Standardization was carried out using negative exponential curve or straight line fitted to each individual core. ARSTAN program was used for standardization process which is composed of residual, standard and arstan chronologies and different chronology statistics are discussed. Software ARSTAN was used to transfer cross-dated raw data to develop standardized chronology. Master chronology was developed through first deterending method include Standard, Residual and Arstan chronologies.

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9.6.3-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 this chronology; singe 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 b e 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 generates then this chronology produce STNDRD version which removes the lag years effect (Cook and Holmes, 1999). 9.6.4-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 which applying the model to the entire residual chronology. 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). 9.6.5-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).

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9.7-Growth-climate response There are many methods adopted in the past to check the relationship between climate and tree growth. Frits (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. In Pakistan this methods is used by Ahmed (1984). This 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 correlatiosn 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 methods is used in Pakistan by many researchers (Ahmed,2005; Ahmed; 2009; Ahmed ;2011; Khan,2011; Wahab,2011 and Zafar,2013). I used correlation and response function analysis for the development of growth-climate relationship (Fritts, 1976) from the packaged software DPL (Holmes, 1992).

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Fig.9.1 Map of study area, circle shows sampling site (Stak valley of CKNP)

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Chapter 10: Age and growth rates CHAPTER-10 AGE AND GROWTH RATES 10.1-Introduction Growth is an important factor to understand the management of forest and forest cover (Worrell and Malcolm, 1990a, b). The maximum age of plant species can be strongly affected by the growth stipulation (Castagneri et al., 2013). In the radial growth of trees, annual rings are the sensitive indicator for age and growth (Warming and Pitman, 1985). The vegetation is dynamic because constantly degraded by anthropogenic and natural disturbance, which dramatically altered the structure and survival of the forest (Boisvenue and Running 2006, Abrams and Nowacki 2008). The age structure of trees is also play a vital role on population dynamics, the tree age distribution would be helpful for the management of forest and increasing of recruitments (Ågren and Zackrisson 1990). It is supported by Fricker et al., (2006) age structure provide information to understand the ecological processes, estimated the age of archeological places. Additionally Lanner (2002) stated that fast growth rates and large diameter size of plant species seem clearly to benefit the vigor of trees. Age and growth rates are widely used in silviculture, ecology and forestry. Some researchers (Swathi, 1953; Champion et al., 1965; Khan, 1968; Sheikh, 1985) investigated age of trees based on simple ring count without using standard techniques. Therefore studies were coupled with errors due to the missing and double rings. First time in Pakistan dendrochronological techniques were applied by Ahmed (1988) to calculate age and growth rates pointing missing and false rings. Ahmed et al., (1990a, 1990b, and 1991) calculated age and growth rates of Juniper and chilghoza pine from Balochistan. Ahmed and Sarangzai (1991 and 1992) investigated age and growth rates of many gymnospermic trees belonging to Himalayan region of Pakistan. Ahmed and Naqvi (2005) studied the tree ring chronologies describing age and growth rates of Picea smithiana while Khan et al., (2008) inspected dendrochronological potential of Picea smithiana from Afghanistan. Ahmed et al., (2009) examined the relation between diameter and age from the different forests of Pakistan.

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Chapter 10: Age and growth rates Hence age and growth rate are the important attribute of the forest structure which are very useful to understand the forest dynamics and improvement of forest management, but there is no reported investigation of age and growth rates carried out from Central Karakoram National Park region so far, therefore this study investigates the age and growth rates of Picea smithiana forest from Stak valley of CKNP which may be helpful in forest management, ecology and silviculture. 10.2-Materials and Methods Materials and methods are discussed in previous chapter 9. 10.3-Results 10.3.1-Age and growth rates of seedlings The histogram of dbh and mean age are shown in Fig.10.1. .Each class is based on two cm dbh intervals showing that mean age is increasing with increasing dbh. The mean age of Picea smithiana seedling was 43 year while the mean growth rate was 11 year/cm. The maximum age of seedling was 126 years. Relation between dbh classes and mean age was highly significant (r =0.88, p<0.001) with wide variance in few points. (Fig.10.2). The histogram of mean age and growth rates is shown in Fig.10.3 which shows that growth rates of 2 to 8cm dbh classes are almost similar while the regression analysis between these two variables shows highly significant (r=0.96, p<0.001) relation without any variance ( Fig.10.4).

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Fig. 10.1 dbh vs age histograms analysis of Picea smithiana seedlings.

Fig. 10.2 dbh vs age regression analysis of Picea smithiana seedlings

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Fig. 10.3 Age vs growth rates histogram analysis of Picea smithiana seedlings

Fig10.4 Age vs growth rates regression analysis of Picea smithiana seedlings

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10.3.2-Growth rate of past seedling An attempt was also made to calculate the growth rates of different periods of past seedlings .Fig.10.5 shows growth rates of seedling in different periods of past. It was anticipated that past growth rates may reflect the over all situation of the forest in which seedlings were survived. It is shown that around the year 1576 AD the seedlings were growing in most stressed situation (13years/cm). This situation improved around year 1632 AD after 56 years when some of the seedling may be thinned out and rate of growth significantly increased. In the period around 1760 AD and 1851 AD growth rate was almost the same. Similarly, period 1767 to 1789, 1809 to 1812 and 1855 to 1870 showed same growth rates but significantly different to previously descended periods. Periods of fastest growth were 1820 to 1827,1848,1852,1872 while 1902 with significant reduction of growth rates around occurred 1851 AD.

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Fig. 10.5 Growth rates of seedlings in various time periods. Note: Each year represents the mean growth rate year/cm of seedlings arround 8cm dbh

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10.3.3-Age and growth rate of tress The histogram of dbh and mean age of Picea smithiana trees is presented in Fig.10.6 which shows that age increases with the increasing of dbh while the regression analysis shows significant relation (r= 0.54, P <0.01) with wide variance (Fig.10.7). The histogram plot of mean age and growth rates of 9 tree classes are shown in Fig.10.8 which shows that growth rate almost similar from the small classes to middle classes and decreases in large classes. The growth rates of 10 to 50cm dbh are decreasing while up to 90 cm dbh reverse was the case. Growth rate decreased with the increased age. It may be due to the old age and natural disturbances. It is observed that 90 cm dbh tree may attain 400 years. The maximum growth rate was 17 year/cm while the mean growth rate was 6 year/cm. The mean age of Picea smithiana trees was 222 years while the actual age was 434 years. The regression analysis of these variables show highly significant (r =0.76, p<0.001) relation with wide variance in few points (Fig.10.9)

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Fig.10.6 dbh vs age histograms analysis of Picea smithiana.

Fig.10.7 dbh vs age regression analysis of Picea smithiana.

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Fig. 10.8 Age vs growth rates histograms analysis of Picea smithiana.

Fig.10.9 Age vs growth rates regression analysis of Picea smithiana.

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10.4-Discussion and conclusion 10.4.1-Age and growth rates of seedlings Growth rate is decreasing with the increasing age of seedling. It may be due to anthropogenic disturbances i.e cutting of trees or overgrazing. It is observed that 8cm dbh young tree may attain the age of 126 years. Similar significant results were obtained in correlation analysis between age and growth rates (Syampungani et al, 2010). Ahmed et al, (1991) reported 43 years/cm radial growth in seedlings of Pinus gerardiana forest while high significant(r=0.64, p<0.001) correlation between age and dbh observed. Ahmed (1988) found significant relation between dbh and age from planted tree species in Quetta. It was anticipated that in the even age planted species size was similar therefore among these trees age did not vary while in natural and uneven aged population may varies among individuals. Ogden et al, (1987) reported that small seedling may have been up to 100 years. Ahmed et al, (1989) obtained maximum age (66 years) with 6 dbh classes in Juniperus excelsa and also observed that age varies from seedling to seedling even in a same size class. In the present seedling study, maximum age was 126 years with 2 to 15 year/cm growth rate. Wahab (2011) stated that the young stage of trees growth rate is fast which gradually decreased with the increasing of age and also suggested that the recruitments of trees need special attention of shelter and protection. The study shows that there is highly significant relationship between dbh and age of seedlings with wide variances. It was also found that seedling classes did not show significant correlation in growth rate. Mode of generation may be an important factor in influencing the growth rate. Therefore, dbh does not prove to be a good indicator of age. It is also concluded that a large diameter stem does not give reliable estimation of age. Due to wide variance, it is not advisable to predict age from dbh in trees. In natural condition, seedlings belong to different regeneration periods and may be subjected to different microclimatic, biological situation or stress. Therefore, growth rates and age varied among the seedlings of the same size.

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10.4.2-Growth rate of past seedling Age varies from seedling to seedling even in same size classes (Ahmed et al, 1989). According to Ahmed et al, (1990) growth rate also varies in tress of similar size. According to Ahmed et al, (2009) age and growth rate varied from species to species, site to site and even trees of same species in a same site. Growth rate of seedling in various periods of time was different in different years. In the past decades growth rate was slow and varied in different years. It is anticipated that in the old period there may be high competition due to no grazing and no deforestation, therefore seedlings growth inhibits but with the passage of time these factors changed which released the rate of growth. Growth rate in the old tress are reduced due to the old age associated with diseases (Ahmed, 2009) and anthropogenic disturbance (Di Filippo et al., 2012).Due to deforestation and overgrazing competition may be diffused and provide a suitable chance for seedling to grow fast. The natural and ecological factors also effect the growth of seedlings which is quite different in the different periods therefore the rate of growth is also change in different periods. Growth rates in the past period of trees can be possibly affected to reach the high age (Castagneri et al., 2013).It is suggested that form 18th century human impact may have increased when logging and cutting of trees increased resulting increased space for better growth of seedlings. Present analysis indicated that even in one stand or natural forest seedling show different rates of growth due to the competition, release of competition, natural disturbance, human disturbance, cutting, fires, overgrazing and harsh or favorable environmental condition. According to Fritts (1976); Priya and Butt (1998) growth rate helps to clarify the forest dynamics and forest management. Additionally, the growth rate will help in size predictions of the trees and indication of disturbance (Syampungani et al., (2010).Therefore, this study may be helpful to save this important forest and improve better forest management techniques

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10.4.3-Age and growth rate of tress Ahmed and Sarangzai (1991) obtained highly significant(r= 0.93, p<0.001) relationship between age and dbh in Pinus wallichiana having 3.13 to 14.28 year/cm growth rates from Zhob and maximum age (230 years) was reported in 60 cm dbh. Ahmed et al, (2009) found largest tress (148cm dbh) of Picea smithiana with 177 years while oldest tree (347 years) with small sized (91cm dbh) tree from the same site of Naltar. Ahmed and Ogden (1987) found oldest tree (600 years) which had also the highest dhb (130-150 cm) from Agathis australis and found significant relation (r =0.58, p<0.05) between age and dbh with growth rates ranged 7.5 ±2.9 to 21.3±12.5 year/cm. Ahmed (1988) found 12 years age from the small dbh (12 to 14cmdbh) trees of Pinus gerardiana from the forest of Tkhta-e-Sulaimani .Ahmed et al, (1990b) found average age of 16 tress (160 years) while growth rate ranged 5.09 to 16.05 year/cm.He also noticed that age and growth rate varied among individuals of similar size. Ahmed et al.,(1990a) did not found significant relation between dbh and growth rate of Juniperus excelsa from the forest of Baluchistan. On the other hand Siddiqui (2011) found highly significant relation between age and growth rate of Pinus wallichiana, Abies pindrow and Cedrus deodara. However, Picea smithina did not show correlation between these two variables. According to Larson (2001) that slow growing species have good vigor to face the pathogens and environmental pressure and these species attains high ages. Ahmed et al, (1991) estimated the average growth rates 5.7 to 15.3 year/cm and found highly significant (0.64, p<0.001) relation between age and dbh from Pinus geraradiana forest in Balouchistan while Ahmed et al., (1991) observed the highest age (112 years) from small dbh tree in Takht-e-sulaimani forest while same age was also recoded in large dbh (65cm) from Ayubia. Ahmed and Saranzai (1992) found fast growth of Picea smithiana forest from Murree valley which was 2.5 year/cm. Wahab (2008) reported 4.0 to 7.1 year/cm growth rates and found no significant relationship between dbh and age of Picea smithiana and largest (154cm) tree was younger (133 years). In the present study growth rate ranges from 3.9 to 17.3 year/cm while oldest tress (434 years) belonged to 90 cm dbh tree. Although growth rate was slow

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Chapter 10: Age and growth rates as compare to other findings in Pakistan but the relationship of age/growth rate and dbh/age is within the range of other workers. The current study showed highly significant relationship between actual age and growth rates in Picea smithiana trees and seedling while the histogram of mean age and growth rate of these trees indicates that growth rate reduces after 50cm dbh class which is not an ideal situation. This is the stage of fast growing but due to anthropogenic disturbance the growth decreased due to the some reasons and trees was deteriorating in the growing stage. Therefore, we also agree with other workers that diameter is not a good indicator of age due to wide variance. In addition, it may also be concluded that this forest is under anthropogenic disturbances and a special attention should be paid to save this important forest. 10.5-Recommendations ¾ Prepare long term polices and plans for the implement of regeneration, recruitments and silviculture practice in the forest. ¾ Take legal action against timber mafia and improve law and order situation. ¾ Further research is needed to explore the regeneration potential of these forests. ¾ It is well said that “Forests are the lungs and heart of the world” .Therefore give awareness to the localities and owners of these forests to save these assets. ¾ Proper management and research activities encouraged in important National Park.

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CHAPTER-11 CHRONOLOGY DEVELOPMENT 11.1-Introduction This chapter focuses on the development of chronology. The review of literature confirmed that only a small work of chronology development has been published especially from the forest of Central Karakoram National Park. The chronology development is further used to investigate the growth-climate relation. The proxy data may be helpful in climatology, hydrology, glaciology, seismology, archeology, ecology and forestry in Pakistan. In the trees annual rings developed with different widths depending upon the temperature, precipitation, environmental variables etc. The pattern of these trees rings can compare and crossmatched with the other tree rings, growing in similar geographical location under the similar climatic conditions. Many researchers developed about 5000 chronologies of different species from the different locations of world and successfully presented results. In Pakistan Ahmed (1987) and Ahmed and Naqvi (2005) developed Abies pindrow and Picea smithiana chronology from Ayubia and Naltar valley, Gilgit respectively and described the ring width and inter chronology characteristics. Khan et al., (2008) developed 343 years chronology from Dangam district Afghanistan while Ahmed et al., (2009) presented Picea smithiana chronology from Cherah and Naltar valley. Ahmed et al., (2010a) developed Picea smithiana chronology (1680 AD to 2010 AD) from Ayubia while Zafar et al., (2010) developed 500 years chronology of Picea smithina species with high mean correlation (0.74 and 0.85) from Bagrot and Haramosh valley, Gilgit. Ahmed et al., (2011) produced a network of 28 tree ring chronologies from six locations of Northern areas of Pakistan. The purpose of this study was to develop standardized tree-ring width chronology of Picea smithiana to investigate the relationship between tree growth and climatic factors including temperature and precipitation.

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11.2-Materials and Methods Materials and methods are discussed in previous chapter 9. 11.3-Results

11.3.1-COFECHA statistics A total number of 32 wood samples were obtained from the Stak valley of CKNP. The details of COFECHA statistics is presented in Table 11.1. It is apparent from the table that out of 32 wood samples 22 were crossdated (68%). The mean ring width was 1.6±0.16 cm/year. Master series correlation was 68% while auto-correlation 67%. The results of master series showed that Picea smithiana from Stak valley attained highest age of 330 (1680-2009) years whereas average age of these trees was 156 years. In many trees, samples attained 230 years (1780-2009). Average mean sensitivity indicates the change in ring width from one year to next year which was 0.19. Maximum ring width was 8.19±0.31 cm/year. Total rings in the series were 3442 while the standard deviation was 0.49. Following are the narrow and wide rings appeared in maximum number of individual tree sample. Narrow rings: 1997, 1962, 1940, 1927, 1917, 1903, 1877, 1868, 1820, 1812, 1743, 1721, 1717, 1705 and 1664.

Wide rings: 1684, 1689, 1700, 1710, 1739, 1764, 1800, 1828, 1900, 1924, 1958 and 2005.

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Table 11.1 COFECHA Statistics of Picea smithiana from Stak Valley

Corr. //…………….Filtered…………….\\ //…..Unfiltered…..\\ No. with Mean Max Std Auto Mean Max Std Auto S.No Series Interval Years Master1 msmt2 msmt3 dev.4 corr.5 sens.6 value.7 dev.8 corr.9 1 PSSK011 1890 2009 120 0.628 1.62 4.77 0.651 0.698 0.233 2.51 0.382 0.045 2 PSSK012 1900 2009 110 0.711 2.25 3.45 0.489 0.35 0.197 2.62 0.479 0.029 3 PSSK022 1870 2009 140 0.81 1.85 2.71 0.401 0.408 0.195 2.4 0.388 -0.004 4 PSSK031 1870 2009 140 0.528 0.88 1.95 0.324 0.725 0.212 2.64 0.537 -0.008 5 PSSK041 1810 2009 200 0.711 0.89 1.9 0.354 0.704 0.247 2.52 0.4 -0.029 6 PSSK051 1830 2009 180 0.634 1.44 2.76 0.449 0.875 0.136 2.58 0.46 0.015 7 PSSK052 1900 2009 110 0.742 1.86 3.32 0.423 0.704 0.133 2.69 0.535 -0.051 8 PSSK062 1888 2009 122 0.783 1.5 2.7 0.393 0.568 0.192 2.55 0.437 -0.016 9 PSSK071 1870 2009 140 0.81 1.85 2.71 0.401 0.408 0.195 2.4 0.388 -0.004 10 PSSK072 1880 2009 130 0.714 1.87 3.26 0.566 0.778 0.16 2.64 0.379 -0.048 11 PSSK081 1680 2009 330 0.578 1.22 3.37 0.526 0.792 0.229 2.4 0.277 -0.036 12 PSSK101 1800 2009 210 0.776 1.01 2.09 0.324 0.699 0.19 2.84 0.474 -0.008 13 PSSK102 1780 2009 230 0.719 1.02 2.09 0.326 0.711 0.186 2.85 0.471 -0.008 14 PSSK111 1940 2009 70 0.32 4.59 8.04 1.147 0.645 0.17 2.58 0.536 -0.088 15 PSSK112 1860 2009 150 0.638 1.91 5.36 0.966 0.898 0.162 2.56 0.42 -0.046 16 PSSK122 1800 2009 210 0.776 1.01 2.09 0.324 0.699 0.19 2.84 0.474 -0.008 17 PSSK141 1810 2009 200 0.639 1.32 3.37 0.532 0.755 0.23 2.34 0.284 -0.03 18 PSSK142 1890 2009 120 0.786 1.51 2.7 0.392 0.569 0.191 2.55 0.434 -0.009 19 PSSK152 1830 2009 180 0.582 1.55 8.19 0.931 0.542 0.192 2.53 0.44 0.004 20 PSSK161 1890 2009 120 0.634 1.86 3.32 0.411 0.697 0.131 2.71 0.547 -0.084 21 PSSK162 1900 2009 110 0.742 1.86 3.32 0.423 0.704 0.133 2.69 0.535 -0.051 22 PSSK202 1890 2009 120 0.786 1.51 2.7 0.392 0.569 0.191 2.55 0.434 -0.009 Total 3442 0.689 1.6 8.19 0.49 0.677 0.191 2.85 0.427 -0.019

Note: 1= Correlation with master chronology, 2= Mean ring width, 3=Maximum ring width, 4=Standard correlation in filtered, 5= Auto correlation in filtered, 6= Mean sensitivity, 7= Maximum value, 8= Standard deviation in unfiltered, 9= Auto correlation in unfiltered

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11.3.2-Raw chronology The average mean correlation gained 0.43 while average correlation within trees and between the trees was 0.68 and 0.46 respectively. The sample depth was observed from 1680 to 2009. The mean sensitivity of this chronology attained 0.17. Mean index was 1.31 while auto correlation appeared 0.70 with 0.36 standard deviation. The negatively skweness coefficient and kurtosis coefficient was -0.15 and 2.69 respectively (Table 11.2). The Fig.11.1 indicated that the growth rate was decreased and below the average in the year 1720-1800, 1810-1830, and 1960-2000.

Fig.11.1 Raw chronology plot of Picea smithiana

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11.3.3-Residual chronology The mean index (0.98) of this chronology was equal to the ARSTAN. The mean sensitivity (0.20) was greater than both standard and ARSTAN chronology. The negative auto correlation was -0.06 while negative skweness coefficient and kurtosis coefficient was -0.20 and 3.07 respectively with 0.16 standard deviation (Table 11.2). Fig.11.2 shows that residual chronology in which growth trends were almost similar with ARSTAN chronology. Growth rate was below the average in the year 1720-1795, 1810- 1850 and 1950-190.

Fig.11. 2 Residual chronology plot of Picea smithiana

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11.3.4-Standard chronology This chronology helps to eliminate the variance which was appeared other than climate. The mean index of this chronology was 0.95 with 0.24 standard deviation .The skweness coefficient was 0.32 with 3.60 kurtosis coefficient. The mean sensitivity of this chronology was 0.17 whereas auto correlation was 0.67 (Table 11.2). The Fig.11.3 shows that the growth was decreased below the average in the year A.D 1720-1790, 1810-1840, 1890-1930 and 1950-190.

Fig.11.3 Standard chronology plot of Picea smithiana

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11.3.5-ARSTAN chronology The mean index of this chronology was 0.98 while mean sensitivity was 0.17. The auto correlation was 0.46 with 0.19 standard deviation. However, skweness coefficient and kurtosis were 0.06 and 3.13 respectively (Table 11.2). The Fig.11.4 shows that the growth trend was almost in average pattern. However in the year 1715-1740, 1750-1790, 1810-1840, 1900-1920 and 1955-1980 shows decline growth below the average.

Fig.11.4 ARSTAN chronology plot of Picea smithiana

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Table 11.2 Descriptive statistics of Raw, Standard, ARSTAN and Residual chorology of Picea smithiana

Parameters Raw Standard ARSTAN Residual Mean index 1.31 0.95 0.98 0.98 St.Dev.1 0.36 0.24 0.19 0.16 Skew.coeff.2 -0.15 0.32 0.06 -0.20 Kurtosis coeff.3 2.69 3.60 3.13 3.07 Mean sens.4 0.1 0.17 0.16 0.20 Auto corr.5 0.70 0.67 0.46 -0.06

Note: 1= Standard deviation, 2= Skweness of coefficient, 3= Kurtosis coefficient, 4= Mean sensitivity, 5= Auto correlation.

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11.3.6-Rar, EPS and sample depth Running Rbar and EPS graphs are shown in the Fig. 11.5. Graphs expressed that the sample depth of 20 cores up to 1900, 10 cores up to 1870 while only a few cores sample depth attained up to 1780. Sample depth was not long but chronology span can be improved by collection of more samples from the present study site. Table 11.3 shows the values of expressed population signal (EPS), signal to noise-ratio (SNR) and average mean correlation between series and within the series (Rbar). These values expressed the reliability of chronology for climatic studies. The expressed population signal value was 0.94 while signal to noise ratio was 18.06. The running Rbar value was 0.47 while Rbar within the trees was 0.68 and between the trees was 0.46.

Table 11.3 EPS, SNR and Rbar values

Parameters Statistics Express population signal 0.94 Signal to noise ratio 18.06 Rbar 0.47 Rbar within trees 0.68 Rbar between trees 0.46

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Fig. 11.5 Graphs of Rbar, EPS (Expressed population signal) and sample depth of Picea smithiana from Stak valley

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11.4-Discussion and conclusion A total number of 32 wood samples were taken from Picea smithiana forest from Stak valley of CKNP. Out of 32 wood samples 22 samples (69%) were crossdated. 1997, 1962, 1940, 1927, 1917, 1903, 1877, 1868, 1820, 1812, 1743, 1721, 1717, 1705 and 1694 rings were narrow while 1917, 1877 and 1717 narrow rings were observed in another study conducted by Zafar (2013) and also in the study of Esper et al., (2001). The highest age of this species was 330 years (1680-2009) while the mean sensitivity was 0.17 in standard chronology and raw chronology while residual and ARSTAN chronology attained 0.20 and 0.16 respectively which is good enough to explore growth-climate response (Fritts, 1976; Speer, 2010). Khan et al., (2008) recorded 0.18 mean sensitivity in Picea smithiana from neighboring country Afghanistan which was quite similar with the current study while Zafar et al., (2010) observed high mean sensitivity (0.3) in Picea smithiana from Haramosh valley and 0.4 from Bagrot valley. Similar results were reported Ahmed et al., (2009) from Karghah valley of Gilgit. The expressed population signals (EPS) value was 0.94.Wigley (1984) suggested > 0.85 value of EPS and the value of EPS in current study is within this range. Signal to noise ratio was 18.06 while Rbar within the trees was 0.68 and between the trees was 0.46. The sample depth was 1780-2009 and the chronology depth can be improved by collecting more samples from older trees. Zafar et al., (2012) observed 8.9 SNR from Afghanistan forest which was lesser than the current study. Frits (1976) suggested that the higher value of signal to noise ratio indicates the higher climatic signal therefore it is anticipated that Picea smithiana from Stak valley was highly potential for the climatic studies. The average correlation was 0.47 whereas average correlation within trees and between the trees was 0.68 and 0.46 respectively. Zafar et al., (2012) reported 0.28 average correlation, 0.42 correlation within the trees and 0.27 correlation between the trees which was lesser than the current study. In the present study the mean index among the different version of chronologies range 0.95 to 1.31 while standard deviation was observed from 0.16 to 0.36. The serial correlation range from -0.06 to 0.70. However, skweness of coefficient shows negative trend in residual and raw chronology while standard and ARSTAN chronology gained 0.32 and 0.06 respectively. Whereas kurtosis

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coefficient was high in standard chronology which was 3.60 while lowest (2.69) observed in raw chronology. The ARSTAN and residual chronology attained almost similar kurtosis coefficient which was 3.13 and 3.07 respectively while correlation with master series was 0.689. Ahmed et al., (2009), Ahmed et al., (2010a, 2010b) and Ahmed et al., (2011) presented correlation with master series from 0.46 to 0.69. Similar results were also reported by Zafar et al., (2010) form Bagrot and Haramosh valley of Gilgit. Present correlation with master series was within the range of above mentioned workers. Wahab (2011) investigated Picea smithiana from district Dir forest and obtained 0.19 to 0.23 mean sensitivity , 1.23 to 1.33 mean ring width, 0.430-0.58 standard deviation, mean index 1.14 to 1.189, skweness -0.22-2.50, kurtosis 2.7-11.4 and serial correlation 0.41-0.678. These values were within the range of present study. It is concluded that the chronology of Picea smithiana from Stak valley enclosed climatic signals and suitable for further dendroclimatic investigation. It is also suggested that, to increase the sample size of this species, large sized trees should be acquired. According to Frits (1976), to extend the chronologies older trees should be targeted for obtaining wood samples.

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CHAPTER-12 GROWTH-CLIMATE RESPONSE 12.1-Introduction Dendroclimatology is one of the most important branches of Dendrochronology (Fritts 1976; Grissino-Mayer 1995; Cook, 2003). According to Ahmed et al., (2009 and 2011) glaciers are the good indicator of the climatic history and also stated that potential age of trees is good source for the exploration of dendroclimatic history. Ahmed et al., (2011) stated that the potential age of trees is one important consideration for dendroclimatic study. These studies may help to extend the metrological records (Yadav and Bhattacharyya 1992, 1994). According to Hughes and Milson (1982) response function is not able to demonstrate the climate growth response; it is limited to the nature of the climatic factors which effect the tree growth. These limitations are also described by other researchers (Fritts (1976), Guiot et al., (1982). Besides these limitations response function is widely used to explore the ring-width growth response to the monthly climatic condition (Cook, 1990). In sub-continent many researchers investigated the climatic history using pine and conifer species including Pinus wallichiana, Abies pindrow, Cedrus deodara, Pinus gerardiana and Picea smithiana (Bhattacharya and Yadav 1999, Yadav and Park 2000, Sing and Yadav 2007, Wahab et al., 2008, Ahmed et al., (2012), Ahmed et al., 2009, Ahmed et al., 2011, Wahab, 2011, Zafar et al., 2012). Picea smithiana forests play an important role in country economics, climate, environment, wildlife, watershed management, soil protection, wildlife and biodiversity conservation (Kotru et al., 2003). Ahmed and Naqvi (2005) reported that tree ring research of Picea smithiana is helpful to understand the climatic potential of an area. Growth is an important factor for the management of forest and forest cover, to understand the conditions and climatic change of the site (Worrell and Malcolm, 1990). Picea smithiana is commonly known as Himalayan spruce which grows at the elevation of 2400m-3600m with other species Pinus wallichiana, Cedrus deodara and Juniperus excelsa (Ahmed et al., 2011). This species widely used in North Pakistan to explore the dendroclimatic potential (Cook et al., 2003; Ahmed et al., 2012). Himalayan spruce (Picea smithiana) have good dendrochronological potential (Ahmed and Naqvi, 2005; Zafar et al., 2012).

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Although in Pakistan dendrochronology is started in late 80s, modern dendrochronological techniques including growth climate response is used after 2005. Esper et al., (1995, 2000, and 2001) described tree rings of Juniperus excelsa from Karakorum Range of Pakistan. Ahmed et al., (2010a, b) described the growth climatic response of Picea smithiana and Abies pindrow. Furthermore Ahmed et al. (2010) investigated climate response of Abies pindrow from Astor and Ayubia. Ahmed et al., (2011 and 2012) studied the dendroclimatic potential and climate-growth response from Northern areas and Karakoram Range of Pakistan. Zafar et al., (2012) analyzed the growth climate response of Picea smithiana form neighboring country Afghanistan. Zafar (2013) used response function analysis to check the growth climate response of Gilgit and Hunza Districts. Recently, Ahmed et al., (2013) investigated the dendroclimatic and dendrohydrological response of two conifer species from Gilgit Valley. However no dendrochronological work was carried out in the CKNP area therefore current study may helpful to understand the relationship between tree growth and climate. In addition, to increase tree-ring network of other areas.

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12.2-Materials and Methods Materials and methods are discussed in previous chapter 9. However, an outline of tree-ring chronologies is presented below.

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12.3-results Correlation coefficient and response function of residual and standard chronology presented using 40 years of (1972-2011) local climate data and 101 years (1901-2001) of grid data. 12.3.1-Correlation coefficients of residual chronology vs local climate (1972-2011) The correlation coefficients of residual chronology and Skardu climate relationship is shown in Fig.12.1 which shows that previous December, current January, February and June were significantly positively correlated with tree growth in case of temperature. However April was negatively significant with tree growth. In case of precipitation current April and September were positively correlated with tree indices while previous December, current June, August and October were negatively correlated with tree growth. Total variance explained was 66.25%. Temperature showed 4 positive significant elements and 1 negative significant while precipitation attained 2 positive significant and 4 negative elements.

Fig.12.1 Correlation coefficients of residual chronology vs local climate

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12.3.2-Response coefficients of residual chronology vs local climate Response function of residual chronology was performed in which response coefficients of residual chronology was correlated with Skardu metrological climate data. Fig.12.2 shows that, in case of temperature previous October, previous December, current June and July were positive significantly correlated while April showed negative significant response. In case of precipitation similar response was found as correlation coefficient analysis which showed April as positive while previous December, current August and October were negatively significant. Temperature and precipitation have equal significant element in this analysis (Fig.12.2).

Fig.12.2 Response coefficients of residual chronology vs local climate

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12.3.3-Correlation coefficients of residual chronology vs grid The correlation between coefficients of residual chronology and grid data showed that previous November, current June and July were positive and significantly correlated with tree indices. In case of precipitation current February was positive significantly correlated. Total variance explained was 29.36%. In this analysis temperature showed 3 positive significant elements while precipitation showed only one positive significant.

Fig.12.3 Correlation coefficients of residual chronology vs grid

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12.3.4-Response coefficients of residual chronology vs grid Fig.12.4 shows relationship between response coefficient of residual chronology and grid data. In case of temperature only July showed positive significant response with the growth of trees while in case of precipitation no element show positive or negative significant relation with tree indices.

Fig.12.4 Response coefficients of residual chronology vs grid

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12.3.5-Correlation coefficients of standard chronology vs local climate The correlation coefficients of standard tree indices and Skardu metrological climate data are shown in fig.12.5. In case of temperature previous November, previous December, current January, February and March were positive and significantly correlated with tree growth. However, current April and September showed negative significant correlation with tree growth. All three lag years positive significantly correlated while in case of precipitation April, August and October were negatively significant relation with tree indices. The total variance explained was 83.62% for this analysis. This analysis has strong relation with temperature, having 5 positive and 1 negative significant elements (Fig.12.5)

Fig.12.5 Correlation coefficients of standard chronology vs local climate

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12.3.6-Response coefficients of standard chronology vs local climate Relationship between response function coefficients of standard chronology also performed with Skardu climate which is shown in Fig.12.6. In case of temperature April and September were negative significantly while first two lag years were positive significantly correlated and 3rd lag year was not significantly correlated with tree indices. In the case of precipitation, only April was positive significantly correlated. In this analysis precipitation have significant response with tree growth (Fig.12.6).

Fig.12.6 Response coefficients of standard chronology vs local climate

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12.3.7-Correlation coefficients of standard chronology vs grid Correlation coefficients of standard chronology and grid data are shown in Fig.12.7. In case of temperature previous November and current July were positive and significantly correlated with tree indices with first two lag years. In case of precipitation only January was observed to be positive and significantly correlated with tree indices. Total variance explained was 43.83%. In this analysis temperature showed 2 positive significant elements while precipitation showed only one significant (Fig.12.7).

Fig.12.7 Correlation coefficients of standard chronology vs grid

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12.3.8-Response coefficients of standard chronology vs grid Relationship between coefficients of standard chronology and grid data are shown in Fig.12.8 which showed that in case of temperature only current July was positive and significantly correlated with tree growth while first two lag years also positive and significantly response. In case of precipitation any month no element was attained any significant response.

Fig.12.8 Response coefficients of standard chronology vs grid

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Table 12.1 Summary of various correlation and response function analysis using different chronologies with local climate and grid data. Only significant elements are shown.

Months CRS RRS CRG RRG CSS RSS CSG RSG Total P-Oct + +1 P-Nov + + + +3 P-Dec + + + +3 Jan + + +2 Feb + + +2 Mar + +1 Apr ‐ ‐ ‐ ‐ - 4 Temperature Temperature May Jun + + +2 Jul + + + + + +5 Aug Sep ‐ ‐ -2 Oct P-Oct P-Nov P-Dec ‐ ‐ -2 Jan + +1 Feb + +1 Mar Apr + + - + +3,-1

Precipitation Precipitation May Jun ‐ -1 Jul Aug ‐ ‐ ‐ -3 Sep + +1 Oct ‐ ‐ ‐ ‐3 L1 + + + + +4 L2 + + + + +4 Lags L3 + ‐ +1,‐1

Total +Ve significant in temperature=19, -Ve significant in temperature= 6, Total +Ve significant elements in precipitation= 15, -Ve significant elements in precipitation=11 Note: CRS=Correlation coefficients of residual vs Skardu climate, RRS=Response coefficients of residual vs Skardu climate, CRG=Correlation coefficients of residual vs grid, RRG= Response coefficients of residual vs grid, CSS=Correlation coefficients of standard vs Skardu climate, RSS= Response coefficients of standard vs Skardu climate, CSG= Correlation coefficients of standard vs Skardu grid, RSG= Response coefficients of standard vs Skardu grid, + Positively significant, - Negatively significant

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12.4-Discussion and conclusion The study areas lies in dry temperate zone in which, mean annual precipitation was around 200mm and maximum temperature was around 35ºC while minimum temperature was around -10 ºC. In this region summer is short while winter is harsh and long with snow fall. The distance between climatic station (Skardu) and study area (Stak valley) was 60km. The elevation of climatic station is nearly 2500m while the elevation of the study area was 3600m above sea level. According to Stiell (1976) Picea smithiana required higher moisture and fertility on moderately well-drained soil to achieve best growth. However, it can also occupy extremely harsh sites. Under the shade it is classified as intermediate to tolerant. Farrar (1995) stated that the seeds of this species ripe from October to November. Spring is considered to the season of regeneration. Another feature of spring is the appearance of male and female cone while the summer season is the peak of flowering in conifer species. Many abiotic factors effect the growth of Picea smithiana in forest ecosystem i.e forest fire, floods, soil erosion can eliminate the supply of seed while logging, cutting and overgrazing destroy the young seedling. Growth-climate response of this pine species from Stak valley was evaluated following Fritts (1976). Correlation and response function analysis were performed between different tree ring indices with Skardu local climate data from 972 to 2011 and gridded data from 1901 to 2001(http://www.cru.uea.ac.uk/).Response function and correlation coefficient showed that in case of temperature overall 19 positive and 6 negative significant relations while 15 positive and 11 negative significant correlations were occupied by precipitation. However total variance explained was 66.25% in residual ring width data and local climate data while 29.36% attained in residual chronology and grid data. Standard chronology and Skardu local climate showed strong correlation with 83.62% variance while low variance (R= 43.83%) was observed in standard chronology and grid data. These analyses were used to find out the similar response among different types of correlations and response function analysis. In case of temperature, July was significantly positively correlated with tree growth. Previous November and previous December were also positive and significantly correlated in 3 different response and correlation analysis. It is also observed that April was negative significantly correlated

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with tree growth in both correlation and response function analysis. In case of precipitation, tree ring indices showed significant positive relationship with April and negative response in October. This indicates that the more than average rainfall in April and low rainfall in the month of October dropped the temperature and reduced sunlight which decreased the photosynthesis and harmonic activity hence plant growth was affected. Ahmed et al., (2009) obtained significant positive rainfall and negative temperature in the month of October from Cherah and Naltar valley, with Picea smithiana. First two lag years significantly positive correlated with the growth of tree which indicates that the growth of trees was biennial (Ahmed and Ogden, 1985). In the preset study reveres was the case in which October precipitation was negatively significant while in the case of temperature, October does not show any significant response. Ahmed et al., (2010) investigated response function analysis of Abies pindrow from Ayubia and Astore valley. They observed 10 positive and 7 negative significant elements in precipitation while 11 positive and 6 negative significant responses in temperature with 15% and 25% variance from Ayubia and Astore valley respectively. Almost similar responses of significant elements were obtained in the present study. However present variance of the study area was high. Wahab (2011) investigated two species (Picea smithiana and Cedrus deodara) from District Dir. He explained about 25 correlation function significant elements and 43 response function significant elements with high values of variance (70% and 56%) from Salam Baiky forest. Furthermore he observed that temperature of previous October declined the growth of Cedrus deodara while precipitation of previous October and current April to May positively significant with tree growth. On the other hand Picea smithina showed positive correlation with spring, summer and autumn temperature while Cedrus deodara showed positive response with late winter (February-May). In the present study current April precipitation was also support to the better growth while June and July temperature was within the range of District Dir. However, variance values in correlation function of the current study were higher while in response analysis reveres was the case. Khan (2011) obtained negative correlation with temperature from current January to May with Cedrus deodara while April exhibited a positive correlation. In the case of precipitation he observed strong

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correlation with tree growth from February to June of current year. In response function analysis he observed similar trend of positive and negative response from March to May temperature while December exhibited a negative response with the growth of Cedrus deodara from Chitral Gol National Park. He also found a positive response in current August precipitation for Cedrus deodara and Pinus gerardiana. In the present study January and February attained positive response with temperature while the response of April (negative response) was similar with this study despite different species. However in case of precipitation positive response of April was similar with the current study. Ahmed et al., (2012) investigated conifer tree species from the Indus Basin of Karakoram region. They obtained the range of variance from 42.5% to 70.9%. They observed that Cedrus deodara from Tangir and Pinus gerardiana from Chaprot showed positive response in June temperature while Picea smithiana showed non significant response in both standard and residual chronology with June temperature and the same species form Kargah valley showed negative significant response with climatic data. They also analyzed overall response of tree growth to precipitation which shows positive correlation during the entire winter season (September–February). These observations are more or less similar with the present study which may be due to the regional control. In another independent study, the results of correlation and response function analysis showed same response and related to Himalayan and Karakorum regions (Borganonkar et al., 1999, Ahmed et al., 2012, Zafar, 2013). Current results showed that chronology of Picea smithiana exhibited significant negative relationship with temperature in April and significant positive relationship with precipitation in the same period. Ahmed at al., (2013) reported positive response of temperature with tree indices in the previous November and December and negative response in the current April, May and June while positive response of precipitation in current Month of February-April. Similar results were obtained by Ahmed et al., (2011a) and Singh et al. (2006). The current study was within the range of above mentioned significant responses. Therefore the similar response may be due to the similar topography and environmental conditions. Zafar et al., (2012) observed negative correlation with previous October with temperature while positive correlation with previous October and current January in correlation function. In case of response function they observed negative response to previous and

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current temperature in October and positive response with previous October and current January with precipitation. The present study does not agree with this study, may be due to the different limiting factors in the present study. On the basis of above discussion it is evident that June and July temperature are strongly significantly correlated and suitable for the growth of trees. Although June and July are the warm months but the conifer tree species situated at high elevation where temperature favored growth due to the higher elevation and snow fall. In addition, present study agreed that April precipitation support the growth of trees. There is no sign of moon-soon found in this location as reported by Ahmed et al., (2012) and Cook et al., (2013). As mentioned before that the purpose of running various correlation and response function analysis was to obtain common response in different months. It is showed that in the case of temperature, previous November and previous December attained common elements (3 positive significant) while January, February and June attained common elements (2 positive significant). However, current Septembers attained 2 negative significant elements. In the case of precipitation current August and current October attained similar elements (3 negative significant) while current January, February and September attained common elements (1 positive significant). First two lag years also obtained common element (4 positive significant). According to Fritts (1976) atleast 3 elements of the response function should be significant for reliable response function. In the currents study highest elements of 13 significant elements observed in correlation coefficient of standard chronology with Skardu local climate analysis while lowest significant elements (1) were found in response function of residual chronology with grid data. Although in this analysis, it seems unreliable but the only significant element (July) also appeared in other four analyses which indicate the similar strong trend of climate. The second highest significant elements (11) were found in correlation coefficient of residual chronology with local climate while the second lowest significant elements (3) were found in response function of standard chronology with grid data. According to Speer (2010), residual chronologies are more suitable for regression analysis but not necessarily the most suitable for the signal of interest, therefore standard chronologies were also included in the response function analyses for comparison, and to see a common trend. Low numbers of significant elements in some individual response

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functions may suggest that only climatically extreme years will be significantly correlated with growth. However, it was found by (Ahmed et al., (2012) that a low number of significant elements in response functions do not necessarily mean a low total variance accounted for. The present study does not suggest a uniform rule to obtain the best response function or correlation function from these chronologies. However, reliable climate/growth responses may be evaluated by running various types of response functions and correlation functions exploring common trends and strengths. The higher temperature tends to increase the evapotranspiration which results in moisture stress for tree growth thereby creating a negative response of temperature. At the same time, higher precipitation more than average will eliminate the moisture stress and is conductive for tree growth, therefore showed positive relationship of tree growth. The growth of trees also affected by slope, exposure, altitude, edaphic factors and structure while the seed of Picea smithiana normally germinate in spring season (Nienstaedt and Zasada 1990) and dormancy may be broken by exposure to low temperature under moist conditions, i.e. Cold stratification (Wang, 1974). Our study gives additional information about growth-climate response of Picea smithiana from Stak valley of Skardu region. In addition it will include a new site in a tree-ring network of Northern areas of Pakistan. This tree ring chronology may be extended in time if larger and older trees are targeted in future.

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CHAPTER-13 GENERAL DISCUSSION AND CONCLUSION Gilgit-Baltistan is dominated by the forest vegetation which covers 9 % of the land of Gilgit-Baltistan according to forest department. Forests play a vital role in biodiversity, ecosystem, commercial and medicinal sector. The present study focuses the forest community structure and dynamic of the forested and non-forested vegetation of the Central Karakoram National Park of Gilgit-Baltistan. The forest communities are rapidly deteriorated with the passage of time which is qualitatively reported by different NGOs including WWF, forest department and CKNP management. Some researchers (Ahmed, 1973, Ahmed and Qadir, (1976), Ahmed et al., (2009) Hussain et al., (2010 and 2011), Akbar et al., (2010 and 2011) and Zafar et al., (2010) investigated quantitatively some forest of Gilgit-Baltistan but the current study covers first quantitative vegetation description of the important National Park. The study area ranges the elevation from 2444m to 3600m above sea level. This study covers both ecology and dendrochronology. Due to difficult terrain above this elevation range of vegetation is not included in this study. First part covers the phytosociology and community description while second part covers the dendrochronological potential and growth-climate response of the Picea smithiana. Thirty two stands of forested and non-forested vegetation were sampled using Brown and Curtis (1952) methods to recognize the community on the basic of important value index and floristic composition. One mixed conifer community two pure conifer stands a juniper forest and 6 non-forested communities were recognized. Among the forested vegetation Picea-Pinus wallichiana community were recorded with pure forest of Pinus wallichiana, Picea smithiana and Juniperus excelsa. The understorey vegetation consists of Astragallus gilgitensis, Impatiens balfourii, Lentopodium himalayanum, Rubus ulmifolius, Spiraea canescens, Taraxacum officinale, Taraxacum karakorium Astragallus gilgitensis, Berberis vulgaris, Cicer songaricum, Fragaria nubicola, Lentopodium himalayanum, Rubus ulmifolius, Sedum multicepes, Sedum quadifidum, Tanacetum artemisiodes, Taraxacum officinale, Trifolium repnes Juniperus excelsa, Astragallus gilgitensis, Bergenia stracheyii, Geranium pratens, Juniperus communis, Sedum quadifidum, Taraxacum officinale, Trifolium repnes, Urtica dioca Picea

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smithiana, Taraxacum karakorium, Astragallus gilgitensis, Rubus ulmifolius, Taraxacum nigrum, Artemisia roxburgiana, Lentopodium nanum, Rosa webbiana, Hippophae rhamnoides, Berberis lycium, Ribes orientale, Ribes alpestre and Tamarix indica. In the non-forested vegetation 6 communities were recognized i.e Rosa- Hippophae, Hippophae-Ribes alpestre, Rosa-Ribes orientale, Hippophae-Tamarix indica and Berberis lycium-Tamarix indica. These species usually found near glaciers, dry streams, spring and revivers these species existed in high amount of moisture. The vegetation areas are highly affected by human disturbance, overgrazing, logging and soil erosion. The important vegetation including several medicinal plants growing in the park is in high risk. Therefore prompt conservational and scientific management plan is required to save the flora of the unique national park. Further this study is updated by the advanced multivariate techniques. In the advance multivariate techniques 4 groups and one isolated stand was derived from the forested and non-forested vegetation while an attempt was prepared to explore the understorey vegetation which resulted into six groups. A check list and frequency of understorey species is also made to check the status of the ground vegetation in terms of frequently, rare and occasionally. This method is used to expose the underlying group structure and the environmental gradients. The groups derived from the forest and non forested vegetation was allied with different topographic and edaphic levels. The classification and ordination is also combined with the environmental gradients (elevation and slope) which show a relationship between the vegetation and the place of vegetation. The cluster’s extracted groups were super imposed on ordination axes. Vegetation-environment relationship was also investigated using cluster analysis (Ward’s agglomerative method) and PCA ordination. A total number of 18 factors are considered including topographic factors (elevation and slope), edaphic factors (conductivity, salanity, organic matter, maximum water holding capacity, total dissolved salt and pH whereas soil nutrients including Nitrogen ,Potassium ,Calcium Phosphorus , Sulphur ,Iron, Magnesium ,Manganese, Cobalt and Zinc . These soil nutrients are very important for plant nutrition and work as the productivity of the vegetation (Bell, 1982). Topographic factors, edaphic factors and soil nutrients are the responsible for the fertility of soil (Scholes, 1991).

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Among micronutrients Coblat, Manganese and Zinc ranged from 0.12 to 10ppm in forested vegetation while 0.17 to 8 ppm in understorey vegetation. However the quantity of Iron observed high which ranges from 129 to 142ppm in forested while in understory vegetation it ranges 132 to 143ppm. The amount of Iron was high as compare to other micronutrients because of high elevation vegetation need more Iron for the growth and survival. It is also observed that Nitrogen, Potassium, Calcium, Cobalt, soil pH and electric conductivity attains significant relation with vegetation The size class structure, present status and future trend of showed that in the non- forested vegetation Rosa webbiana attained highest density (1068 ha-1) and basal area (2198 m2 ha-1) from Kowardo valley. This valley is located at high altitude (3559 m) as compare to other sites and the slope 50 ◦ to East facing. It is noted in the present study the vegetation was exhibited at high elevation and east slope have high density and basal area. Hippophae rhamnoides was associated species with Rosa webbiana having approximately similar density and basal area which was 800 density ha-1 and 1600 basal area m2 ha-1 from the valley of Thally. It is also observed that the species i.e Ribes orientale, Ribes orientale and Juniperus communis were found in few stands and these species exhibited high mean density and basal area. The density and basal area of species was different site to site due to different environmental and topographic factors. All these vegetation are in high risk due to the influence of human being, overgrazing by domesticated animals, soil erosion, storm and floods. Therefore, vegetation is deteriorating and rapidly dwindling down with the passage of time. Some gaps also found in the diameter distribution of the vegetation which indicates illegal cutting of young trees or no natural recruitment of the seedlings, therefore, seedling should be planted and grazing should be restricted. This is confirmed by Ahmed et al., (2012), Gilgit-Baltistan and KPK have the highest annual rates of deforestation (about 34,000 hectares in Gilgit, Baltistan and 8000 hectares in KPK).Competition is also a factor which affects the dbh size class structure of the forest (Robbins, 1962). Ahmed (1984) added that the gaps in size class structure do not mean that the particular size class is absent from the stand, it is due to the poor recruitment potential in the past. It is indicated that the species which have lesser number of recruitments have greater chances to disappear.

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The part of dendrochronology described by the age and growth rates of Picea smithiana from Stak valley. Growth rate is decreasing with the increasing age of seedling. It may be due to anthropogenic disturbances i.e cutting of trees or overgrazing. It is observed that 8cm dbh young tree may attain the age of 126 years while 90cm dbh tree attained 400 years. It is suggested that form 18th century human impact may have increased when logging and cutting of trees increased resulting increased space for the seedlings better growth. Present analysis indicated that even in one stand or natural forest seedling show different rates of growth due to the competition, release of competition, natural disturbance, human disturbance, cutting, fires, overgrazing and harsh or favorable environmental condition. An attempt was also made to investigate the growth-climate response of Picea smithiana from Stak valley. It was observed that 1997, 1962, 1940, 1927, 1917, 1903, 1877, 1868, 1820, 1812, 1743, 1721, 1717, 1705 and 1694 rings were narrow while 1917, 1877 and 1717 narrow rings were observed in another study conducted by Zafar (2013) and also in the study of Esper et al., (2001). The highest age of this species was 330 years (1680-2009) while the mean sensitivity was 0.17 in standard chronology and raw chronology while residual and ARSTAN chronology attained 0.20 and 0.16 respectively which is good enough to explore growth-climate response (Fritts, 1976; Speer, 2010). The expressed population signals (EPS) value was 0.94.Wigley (1984) suggested > 0.85 value of EPS and the value of EPS in current study is within this range. Signal to noise ratio was 18.06 while Rbar within the trees was 0.68 and between the trees was 0.46. The sample depth was 1780-2009 and the chronology depth can be improved by collecting more samples from older trees. Growth-climate response of this pine species from Stak valley was evaluated following Fritts (1976). Correlation and response function analysis were performed between different tree ring indices with Skardu local climate data (1972-2011) and gridded data (1901-2001). Response function and correlation coefficient showed that in case of temperature overall 19 positive and 6 negative significant relations while 15 positive and 11 negative significant correlations were occupied by precipitation. However total variance explained was 66.25% in residual ring width data and local climate data

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while 29.36% attained in residual chronology and grid data. Standard chronology and Skardu local climate showed strong correlation with 83.62% variance while low variance (R= 43.83%) was observed in standard chronology and grid data. These analyses were used to find out the similar response among different types of correlations and response function analysis. In case of temperature, July was significantly positively correlated with tree growth. Previous November and previous December were also positive and significantly correlated in 3 different response and correlation analysis. It is also observed that April was negative significantly correlated with tree growth in both correlation and response function analysis. In case of precipitation, tree ring indices showed significant positive relationship with April and negative response in October. This indicates that the more than average rainfall in April and low rainfall in the month of October dropped the temperature and reduced sunlight which decreased the photosynthesis and harmonic activity hence plant growth was affected. It is concluded that the temperature in June and July temperature are strongly significantly correlated and suitable for the growth of trees. Although June and July are the warm months but the conifer tree species situated at high elevation where temperature favored growth due to the higher elevation and snow fall. In addition, present study agreed that April precipitation support the growth of trees. There is no sign of moon-soon found in this location as reported by Ahmed et al., (2012) and Cook et al., (2013). This study provided additional information to understand the growth-climate response of Stak valley of CKNP. In addition it is the first and new site of tree-ring network of Central Karakoram National Park, Gilgit-Baltistan. If larger and older trees are targeted in future, this chronology may be extended for 500 years.

267

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297

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: Pakistan Metrological Department

298

Appendices

Appendix 1.2 Mean monthly precipitation (mm) of DistrictSkardu

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: Pakistan Metrological Department

299

Appendices

Appendix 1.3 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: Pakistan Metrological Department

300

Appendices

Appendix 1.4 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: Pakistan Metrological Department

301

Appendices

Appendix 1.5 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: Pakistan Metrological Department

302

Appendices

Appendix 1.6 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 -11.2 0 -1.8 -3.4 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: Pakistan Metrological Department

303

Appendices

Appendix 4.1 Understorey vegetation list of forested and non-forested from CKNP.

S.No Name of species Family PRS RFR Acantholimon lycopodiodes (Girad) 1 Boiss., Plumbaginaceae 1 6.66 2 Anaphalis virgata T.T.ex Clarke Compositae 15 3.33--5.17 3 Artemisia brevifolium (Wall.ex DC) Compositae 20 2.5---8.27 4 Artemisia roxburgiana Wall.ex Besser, Compositae 2 099---3.79 5 Astragalus brevicapus B.Fedtsch Fabaceae 3 0.83---3.44 6 Astragalus gilgitensis Ali, Fabaceae 6 2.53---4.95 7 Astragalus rhizanthus Royle exBth. Fabaceae 2 2.97---6.32 8 Astragalus zanskarensis Bth.ex Bunge, Fabaceae 10 1.72---10.89 9 Berberis lycium Royle Berberidaceae 19 0.86---6.20 10 Berberis orthobotrys Bien ex Aitch.,J.L.S Berberidaceae 2 0.68---7.75 11 Bergenia stracheyi (H.&T.) Engl. Berberidaceae 3 1.26---2.97 12 Bistorta affinis (D.Don) Green Polygonaceae 14 0.86---9.8 13 Carum carvi L.forma gracile (Lindl.) Umbelliferae 5 1.26---3.44 14 Cicer songaricum Steph.ex DC., Fabaceae 5 4.31---7.5 15 Ephedra tibetica Stapf, Caryophylaceae 1 3.44 16 Epilobium angustifolium L., Ornagraceae 1 7.92 17 Erigeron multicaulis Wall.ex FC., Compositae 1 5.94 18 Festuca communsii Stapf, Gramineae 1 3.33 19 Fragaria nubicola Lindl.ex Willd. Rosaceae 3 2.53---3.96 20 Geranium collinum Steph.ex Willd. Geraniaceae 2 2.53---5.17 21 Geranium neplensis Sweet, Geraniaceae 8 1.26--1.98 22 Geranium pratense.L Geraniaceae 11 2.06---8.6 23 Geranium wallichianum.D.Don.ex.Sweet Geraniaceae 2 2.17---6.66 24 Hippophae rhamnoides.L Elaegnaceae 22 3.33--7.17 25 Hippophae sp Elaegnaceae 1 4.27 26 Impatiens balfouri hook .f. Balsaminaceae 3 099---3.79 27 Inula obtusifolia kerner Compositae 2 0.83---3.44 28 Inula rhizocephala wend Compositae 2 2.53---4.95 29 Juniperus communis L. Cupressaceae 6 2.97---6.32 30 Juniperus excelsa M,B Cupressaceae 5 1.72---10.89 31 Lactuca decipiens(H&T) Clarke Compositae 1 6.2 32 Lentopodium cardiaca L, Labiatae 2 0.68---4.75 33 Lentopodium lentopodinum (DC) Compositae 8 1.26---7.97

304

Appendices

34 Lentopodium linearifolium Hanz.Mazz., Compositae 2 2.31---5.33 35 Lentopodium nanum (H.&T.) Compositae 2 1.06---3.72 36 Mentha Longifolia (l) huda, Labiatae 1 2.53 37 Nepeta discolor Role ex Bth. Labiatae 1 3.44 38 Oxyria digyna (L) Hill, Polygonaceae 3 2.75---7.92 39 Picea smithiana (Wall) Boiss Pinaceae 6 2.53--3.96 40 Pinus wallichiana A.B.Jackson Pinaceae 5 1.37---4.16 41 Potentilla baltistana Th Wolf Rosaceae 6 1.23---4.13 42 Potentilla anserina L, Rosaceae 5 1.37--2.58 43 Potentilla biflora Wild.ex Schlecht, Rosaceae 3 1.66--7.92 44 Rheum webbianum Royle, lll polygonaceae 2 2.5---6.93 45 Ribes orientale Desf., Grossulariaceae 8 2.75--5.96 46 Ribes alpestre Dcne.ex Jacq., Grossulariaceae 11 1.37--8.58 47 Ribes himalensis Royle, Grossulariaceae 2 1.66--7.92 48 Rosa webbiana Wall.ex Royle. Rosaceae 25 2.5---6.93 49 Rubus irritans Hk.f., Rosaceae 12 1.37--2.58 50 Rubus ulmifolius Schott ssp. Rosaceae 5 1.66--7.92 51 Sedum pachyclados Aitch.& Hemsl., Carssulaceae 3 2.5---6.93 52 Sedum quadifidum Pall.,Reise Carssulaceae 11 1.37--9.58 53 Sedum roseum (L.) Scp., Carssulaceae 2 1.66--7.92 54 Silene moorcroftiana Wall.ex Bth. Umbelliferae 3 2.5---6.93 55 Silene vulgaris (Mponch) Garche, Umbelliferae 4 1.72---2.06 56 Spiraea canescens D.Don, Rosaceae 11 2.53--7.58 57 Spiraea lycloides Prker, Rosaceae 4 3.44--6.79 58 Tamarix indica Willd., Tamaricaceae 10 2.75---7.92 59 Tanacetum artemisiodes Sch.Bip.exHk.f., Compositae 3 2.53--3.96 60 Taraxacum baltistanicum V.Soet Compositae 16 1.37---4.16 61 Taraxacum indicum Hand.Mazz., Compositae 5 0.68---7.75 62 Taraxacum xanthophylum Haglund, Compositae 1 2.97 63 Tarxacum nigrum V.S., Compositae 8 4.31---8.33 64 Thymus linearis Benth., Labiatae 21 2.06---6.72 65 Thymus serphyllum L. Labiatae 4 2.53--7.58 66 Trifolium pratense L. Fabaceae 5 3.44--6.79 67 Trifolium repens L. Fabaceae 19 2.75---7.92 68 Urtica dioca L. Urticaceae 10 2.53--7.96

Note: PRS= Presence in stands, RFR= Relative frequency range

305

Appendices

Appendix 5.1 Summary of circular plot sampling. Percent frequencies of species 32 stands from CKNP

Name of species 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Acanthlolimon lycopodiodes * * 50 * * * * 20 25 * * * * * * * Anaphalis virgata 20 * 30 * * 20 40 25 30 10 * 20 10 30 20 20 Artimisia brevifolium 65 30 * 45 30 * * * * 10 * 20 20 10 10 30 Artmesia roxburgiana * * 25 * * 30 15 40 25 * 30 * * * * * Astragallus zanskrensis 15 45 * 20 * * * * * * * * * * * * Astragallus gilgitensis 25 10 * 30 20 35 10 45 45 10 * * * * * * Berberis orthoborty 10 * 35 * 30 20 5 15 20 * * * 30 10 * * Bergenia stracheyii * * 10 20 40 * 20 * 15 * * * * * * * Bistorta affinis * * 35 * * 15 60 10 25 * * 30 40 20 * * Cicer songricum * 10 30 20 25 25 * 25 20 * * * * * * * Epilobium angustifolium * * 40 * * 20 * * 25 * * * * * * * Erigeron multicaulis * 5 * * * * * 20 * * * * * * * Fragaria nubicola 15 20 20 * 5 60 55 * 40 * * * * * * * Geranium wallichianum 45 * * * * * * * * * * * * * * * Geranium neplensis 10 20 * * * * * * * 10 * 20 30 20 20 10 Geranium pratense 15 25 40 40 * 20 20 15 10 20 * * * * * 20 Hippophae rhamnoides * * * 30 * * * * * 50 50 50 60 40 80 40 Impatiens balfourii 10 55 15 * * 45 30 10 20 * * * * * * * Inula rhizocephala 20 * 10 * * * * * 25 * * * * * * * Juniper communis * * 20 20 10 * 25 25 45 * * * * * * * Juniperus excelsa 20 30 45 * 30 25 * * 20 * * * * * * * Lentopodium lentopodinum * 30 * * 10 40 * 30 20 * * * * * * * Lentopodium linearifolium 25 * 20 10 5 * 50 * * * * 20 * 20 * * Lentopodium nanum * * 15 * * 35 20 40 25 * 30 * * * * * Leonurus cardiaca 40 30 * * * * * * * * * * * * * * Oxyria digyna 20 * * * * * * * * * * * * * * * Picea smithiana 35 25 * 35 * * 30 * 25 * * * * * * * Pinus wallichiana 25 20 * * * * * 40 40 * * * * * * * Potentilla anserina 15 20 60 * 20 * 25 * 15 * * * * * * * Potentilla baltistana * * * 5 15 * * * * * * 20 * 30 * * Potentilla biflora 5 * * * * * * * * 10 * * * * 20 20 Ribes orientale * * * 25 10 * * * * 20 20 20 20 20 * 30 Rosa webbiana * 15 * 15 25 * * * * 70 60 60 30 30 70 60 Rubus irritans 30 10 * 55 20 * * * 20 10 * 20 20 10 20 10

*Absent

306

Appendices

Rubus ulmifolius 20 5 45 * 10 10 30 50 * * 20 * * * * * Sedum roseum * 45 * * * * * * * * * * * * * * Sedum pacycloides 50 25 5 * 5 20 20 * 10 40 * * * * 20 10 Sedum quadifidum * * 15 15 5 25 15 25 30 * 10 20 30 20 * * Silene vulgaris * 10 15 * * 30 20 * 25 * * * 0 * * * Spiraea canescens 45 20 55 * * * * * 20 * * * 0 * * * Tanacetum artemisiodes * 20 25 * 15 20 25 20 40 * * * 0 * * * Taraxacum baltistanicum * * * 20 10 * * * * * * 30 30 20 20 20 Taraxacum indicum 25 30 10 40 0 45 30 60 30 10 30 * * 10 30 30 Taraxacum nigrum * * * * 25 55 30 50 60 10 30 * * * * * Taraxacum officinale * * * * * * * * * * * * * * * * Taraxacum xanthophyllum 40 10 20 * * 25 * 15 20 * * * * * * 20 Thymus linearis 10 10 * 40 30 * * * * * * 40 * 40 40 * Trifolium repense 15 15 10 35 20 20 20 25 10 * * 10 20 30 * 30 Urtica dioca * 20 20 20 * 15 25 30 15 40 10 60 20 30 * 30 Astragallus brevicapus * 20 * * * * * * * * 20 * * 10 * 10 Ephdera geradiana * * 10 * * * * * * * * * * * * * Spiraea lyclodes * * * * 10 * * * * * * 10 * * * * Trifolium pratense * * * * * 20 * * * * * * * * * * Berberis lycium * * * * * * * * * * 40 30 30 20 40 30 Ribes alpestre * * * * * * * * * 60 10 30 40 30 10 30 Thymus serphylum * * * * * * * * * * 20 * 30 * * 30 Ribes himalensis * * * * * * * * * * 10 30 40 30 * * Geranium collinum * * * * * * * * * * 10 * * * * * Festuca communsii * * * * * * * * * * * * * * 10 * Taraxacum repens * * * * * * * * * * * * * * 10 * Astragalus rhizanthus * * * * * * * * * * * * * * * * Caram carvi * * * * * * * * * * * * * * * * Tamarix indica * * * * * * * * * * * * * * * * Nepeta discolor * * * * * * * * * * * * * * * * Ephedra tibetica * * * * * * * * * * * * * * * * Mentha longifolia * * * * * * * * * * * * * * * * Lactuca decipiens * * * * * * * * * * * * * * * * Rheum webbianum * * * * * * * * * * * * * * * * Silene moorcroftina * * * * * * * * * * * * * * * * Inula obtusifolia * * * * * * * * * * * * * * * *

*Absent

307

Appendices

Name of species 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 Acanthlolimon lycopodiodes * * * * * * * * * * * * * * * * Anaphalis virgata 20 30 20 30 20 * * 40 30 20 20 * * * * * Artimisia brevifolium 10 30 30 30 30 * 20 30 * * * 30 30 30 20 30 Artmesia roxburgiana * * * * * * * * * * * * * * * * Astragallus zanskrensis * * * * 20 20 * * * * 30 * 40 40 10 30 Astragallus gilgitensis * * * * * * 50 * * * * * * * * * Berberis orthoborty * * * * * * * * * * * * * * * * Bergenia stracheyi * * * * * * * * * * * * * * * * Bistorta affinis * * * * * * 30 30 20 30 30 40 40 30 40 30 Cicer songricum * * * * * * 10 * 30 10 * * 10 20 * * Epilobium angustifolium * * * * * * * * * * * * * * * * Erigeron multicaulis * * * * * * * * * * * * * * * * Fragaria nubicola * * * * * * * * * * * * * * * * Geranium wallichianum * * * 10 * 20 * * * * * * * * * * Geranium neplensis 20 * * * 20 * * * * * * * * * * * Geranium pratense 20 * * * 20 * * * 10 20 * 30 20 10 20 20 Hippophae rhamnoides * 50 50 40 40 60 30 70 50 60 40 50 30 40 40 40 Impatiens balfourii * * * * * * * * * * * * * * * * Inula rhizocephala * * * * * * * * * * * 10 * * * * Juniper communis * * * * * * 40 30 40 50 * * * * * * Juniperus excelsa * * * * * * * * * * * * * * * * Lentopodium lentopodinum * * * 10 * * * * * * 30 20 * * 30 20 Lentopodium linearifolium * * * * * * * * * * * * * * * * Lentopodium nanum * * * * * * 20 * * * * * * * * * Leonurus cardiaca * * * * * * * * * * * * * * * * Oxyria digyna * * * * * * * * * * * * * * * * Picea smithiana * * * * * * * * * * * * * * * * Pinus wallichiana * * * * * * * * * * * * * * * * Potentilla anserina * * * * * * 30 10 * * 20 * * * * * Potentilla baltistana * * * * * * * * * * * 20 * * 20 20 Potentilla biflora * * * * * * * * * * * * * * * * Ribes orientale 40 * * * * * * 40 * * * * * * 30 40 Rosa webbiana 100 40 50 40 60 40 30 40 20 40 40 30 40 50 40 30 Rubus irritans * * * * * * 10 * * 30 * * * * 30 30

*Absent

308

Appendices

Rubus ulmifolius * * * * * * * * * * * * * * * * Sedum roseum 10 * * * 10 * * * * * * * * * * * Sedum pacycloides * * * * * * * * * * * * * * * * Sedum quadifidum 20 * * * 20 * 10 20 * * * * * 10 20 10 Silene vulgaris * * 20 * * * * * 50 * * * * * * * Spiraea canescens * 10 * * 10 * * * * * 10 20 * 20 10 20 Tanacetum artemisiodes * * * * * * * * * * * * * * * * Taraxacum baltistanicum 10 * 20 30 30 30 * * * * 40 40 60 50 50 40 Taraxacum indicum 10 * * * * * * * * * * * * * * * Taraxacum nigrum 30 * * * * * 20 50 40 50 * * * * * * Taraxacum officinale * * * * * * 0 * * * * * * * * * Taraxacum xanthophyllum * * * * * * 0 * * * * * * * * * Thymus linearis 30 * * 20 20 20 20 40 * 40 60 40 50 40 20 50 Trifolium repense 10 * 20 20 10 20 30 * * 10 0 20 20 20 * * Urtica dioca 20 * * * * * * * * * * * * * * * Astragallus brevicapus * * * * * * * * * * * * * * * * Ephdera geradiana * 20 * * * * * * * * * * * * * * Spiraea lyclodes * * * * * * * * * * * * * * * * Trifolium pratense * * * * * * * * * * * * * * * * Berberis lycium 40 20 40 40 40 * * * * * 60 50 40 30 30 40 Ribes alpestre 10 * * * * * 30 30 30 30 * * * * * * Thymus serphylum * * 40 * * * * * 40 * * * * * * * Ribes himalensis 10 * * * * * * * * * * * * * * * Geranium collinum * * * * * * * * * * * * * * * * Festuca communsii * * * * * * * * * * * * * * * * Taraxacum repens * * * * * * * * * * * * * * * * Astragalus rhizanthus * 30 30 * * * * * * * * * * * * * Caram carvi * * 30 20 * 20 * * * * 20 * * 40 * * Tamarix indica * * 20 * * 40 50 50 50 50 20 30 30 30 30 30 Nepeta discolor * * * 20 * * * * * * * * * * * * Ephedra tibetica * * * * 20 * * * * * * * * * * * Mentha longifolia * * * * * 30 * * * * * * * * * * Lactuca decipiens * * * * * 20 * * * * * * * * * * Rheum webbianum * * * * * 10 * * * * * * * * * * Silene moorcroftina * * * * * * * 40 * 20 * 10 * * * * Inula obtusifolia * * * * * * * * * * * * * * 20 10

*Absent

309

Appendices

Appendix 5.2 DCA ordination axes of forested and understorey vegetation

Forested vegetation Understorey vegetation Axis 1 Axis 2 Axis 3 Axis 1 Axis 2 Axis 3 99 -678 -117 77 -66 83 99 -622 35 78 -42 -9 99 1571 0 135 -48 62 100 -992 -599 -3 -49 -18 99 1571 0 50 -54 -19 99 1571 0 150 24 -37 100 -992 -599 128 3 16 99 -1026 1697 146 7 -11 99 -578 -12 139 18 -43 -3 0 0 -26 48 58 -3 0 0 -2 65 22 -3 0 0 -64 -6 16 -3 0 0 -52 4 16 -3 0 0 -49 -15 11 -3 0 0 -79 36 37 -3 0 0 -39 18 45 -3 0 0 -59 31 5 -3 0 0 -86 60 57 -3 0 0 -82 68 31 -3 0 0 -97 21 14 -3 0 0 -89 3 20 -3 0 0 117 3 -6 -3 0 0 47 9 -27 -3 0 0 -27 50 -32 -3 0 0 51 135 -33 -3 0 0 -6 49 -42 -3 0 0 -81 -25 -28 -3 0 0 -80 -38 -15 -3 0 0 -96 -40 -25 -3 0 0 -80 -36 -11 -3 0 0 -75 -48 -47 -3 0 0 -83 -59 -42

310

Appendices

Appendix 6.1 Edaphic properties of 32 stands from CKNP.

St.No. Conductivity Salanity pH MWHC TDS OM St.1 70.5 0 5.1 38 30.5 9.4 St.2 66.3 0 5.2 39 32 9.3 St.3 59.6 0 5.81 29 41 5.1 St.4 68.3 0 5.14 37 34 9.2 St.5 60.4 0 5.83 30 42 5.2 St.6 58.4 0 5.82 28 41 5.1 St.7 64.2 0 5.15 38 35 9.1 St.8 36.4 0 5.92 24 40 3.9 St.9 61.2 0 5.13 37 33 9.3 St.10 44.4 0 5.55 32 45 5.4 St.11 46.9 0 5.56 25 47 5.5 St.12 42.3 0 5.51 31 44 5.3 St.13 32.4 0 5.71 24 26 4.8 St.14 42.6 0 5.6 30 26 5.5 St.15 37 0.3 5.73 23 27 4.3 St.16 46 0.1 5.56 31 46 5.5 St.17 41 0.1 5.51 33 47 5.3 St.18 43 0 5.59 34 48 5.4 St.19 48 0 5.58 32 47 5.3 St.20 32 0 5.79 24 28 4.9 St.21 50 0 5.57 33 45 5.4 St.22 50 0 5.55 35 46 5.3 St.23 48 0.1 5.53 31 44 5.5 St.24 40 0.2 5.72 22 25 4.5 St.25 37 0.1 5.74 20 26 4.3 St.26 38 0.1 5.74 18 28 4.1 St.27 48 0 5.54 32 48 5.5 St.28 40 0 5.3 33 20 1.3 St.29 44 0 5.59 34 47 5.3 St.30 57 0 5.57 35 45 5.3 St.31 40 0 5.71 24 29 4.4 St.32 34 0 5.72 20 27 4.7

311

Appendices

Appendix 6.2 Soil nutrient concentrations of 32 stands from CKNP.

N K P Ca Mg S Co Mn Zn Fe 212 156 196 195 138 123 0.87 11.4 0.17 139 211 167 193 194 137 120 0.72 10.5 0.13 138 166 199 189 190 148 132 0.64 9.19 0.02 125 210 226 194 197 135 121 0.77 12.21 0.15 135 160 198 186 189 146 131 0.64 9.22 0.02 121 164 199 185 188 145 130 0.63 9.34 0.02 120 209 227 193 199 133 125 0.72 11.81 0.13 136 159 282 134 123 155 137 0.85 10.24 0.07 143 210 226 195 198 135 124 0.72 11.73 0.15 137 221 260 214 221 141 136 1.32 7.58 0.53 140 222 263 205 210 138 135 1.32 7.45 0.54 141 221 261 210 205 139 135 1.31 7.32 0.52 142 228 214 169 176 127 145 1.9 5.1 0.23 131 204 230 201 204 130 134 1.33 6.92 0.36 144 229 215 168 175 125 147 1.62 5.2 0.26 130 223 264 211 211 135 133 1.31 7.24 0.59 147 222 265 204 215 134 134 1.31 7.81 0.56 145 224 262 203 216 136 136 1.32 7.477 0.55 140 221 264 205 224 140 133 1.33 7.844 0.57 124 215 213 167 174 129 145 1.73 5.3 0.25 132 225 267 202 215 141 134 1.34 7.52 0.55 143 228 263 211 205 135 135 1.32 7.108 0.59 144 226 265 210 200 138 138 1.32 7.89 0.57 145 214 214 168 172 127 148 1.63 5.4 0.23 130 222 218 166 177 128 145 1.72 5.6 0.23 131 218 217 165 170 127 144 1.62 5.1 0.26 132 227 263 205 203 139 139 1.32 7.12 0.57 146 124 317 233 218 134 138 1.11 6.37 1.62 116 225 264 209 209 134 136 1.32 7.91 1.56 141 224 265 203 204 135 135 1.32 7.45 0.57 143 217 213 165 175 126 147 1.73 5.2 0.25 130 225 215 167 171 125 145 1.82 5.3 0.25 131

312

Appendices

Appendix 6.3 PCA ordination of forested and non-forested vegetation in Vegetation- environment relationship.

Forested vegetation Understorey vegetation Stands AXIS 1 AXIS 2 AXIS 3 AXIS1 AXIS 2 AXIS 3 1 -4.3697 -1.5935 2.3511 -4.2115 -2.3995 2.0299 2 -4.3937 -1.8163 1.9483 -4.0583 -2.3241 1.714 3 -1.9035 -2.6587 -2.4836 -1.364 -2.3472 -2.6514 4 -4.3099 -1.0921 1.5724 -4.2168 -1.6678 1.3064 5 -1.6384 -2.6861 -2.4465 -1.3865 -2.5638 -2.7888 6 -1.9827 -3.1283 -2.6233 -1.1759 -2.6399 -2.7105 7 -4.2177 -1.1012 1.4737 -3.8703 -1.4502 1.3665 8 0.3874 -3.4118 -3.9465 1.1739 -2.7479 -3.912 9 -4.4397 -1.4644 1.3717 -3.85 -1.4563 1.3653 10 -0.1801 2.5151 -0.2351 -0.8224 1.9659 -0.2678 11 -0.2236 1.1893 -0.3597 -0.252 1.53 -0.1684 12 -0.3231 1.7178 -0.2597 -0.4871 1.6663 -0.1035 13 3.677 -0.6922 1.0391 3.5631 -0.7175 0.9892 14 0.8531 0.917 0.8554 0.3422 0.3082 0.6806 15 4.1948 -1.6658 1.5769 4.3575 -0.992 1.6253 16 -0.3332 1.5657 0.3602 -0.4088 2.006 0.6004 17 -0.6363 1.261 0.203 -0.3795 2.0151 0.5945 18 -0.2474 2.1701 -0.122 -0.5629 1.9166 0.0066 19 -0.4948 1.7822 -0.8481 -0.834 1.4556 -0.8434 20 3.2461 -1.1436 0.3523 3.3772 -0.929 0.3735 21 -0.6371 2.2506 -0.2346 -0.9463 1.769 -0.1194 22 -0.3993 2.5712 0.2471 -0.8229 1.9654 0.3474 23 0.2071 1.8417 0.4024 -0.2712 1.6329 0.4289 24 4.0958 -1.5242 1.1196 3.9892 -1.3212 0.9854 25 3.9318 -1.2432 0.8717 3.8052 -1.056 0.7618 26 4.0296 -1.4006 0.7107 3.9303 -1.1643 0.6018 27 -0.1947 2.0564 -0.0198 -0.5177 1.8606 0.1098 28 0.7217 2.7842 -3.815 0.3071 2.8699 -3.7999 29 -0.3006 3.0987 -0.5103 -0.5782 3.1539 -0.2223 30 -0.7595 1.6693 0.1225 -0.955 1.5741 0.2435 31 3.0881 -1.3333 0.5213 3.3326 -0.9594 0.5789 32 3.5525 -1.435 0.8051 3.7934 -0.9534 0.8778

313

Appendices

Appendix 10.1 Age and growth rates of Picea smithiana trees from Stak valley of CKNP

Core ID 5cm 10cm 15cm 20cm 25cm 30cm 35cm 40cm 45cm Age G.R Dbh PS 1.1 19 17 20 31 33 37 0 0 0 157 5.2 60 PS 1.2 13 14 23 19 20 26 23 0 0 138 3.9 70 PS 2.1 15 15 22 20 18 26 22 20 0 158 4.0 80 PS 2.2 13 16 16 17 31 31 29 30 0 183 4.6 80 PS3.1 36 49 66 0 0 0 0 0 0 151 10.1 30 PS3.2 28 40 51 43 0 0 0 0 0 162 8.1 40 PS4.1 11 26 32 40 66 75 0 0 0 250 8.3 60 PS4.2 26 41 50 73 0 0 0 0 0 190 9.5 40 PS5.1 13 16 22 29 40 37 33 0 0 190 5.4 70 PS5.2 26 24 33 25 0 0 0 0 0 108 5.4 40 PS6.1 18 21 20 30 26 0 0 0 0 115 4.6 50 PS6.2 19 18 26 28 33 39 0 0 0 163 5.4 60 PS7.1 30 25 26 26 31 24 0 0 0 162 6.8 60 PS7.2 25 23 25 21 35 30 0 0 0 159 5.3 60 PS8.1 14 19 29 39 64 80 62 63 0 370 9.3 80 PS8.2 15 22 36 68 80 50 74 0 0 345 9.9 70 PS9.1 51 53 67 50 55 63 0 0 0 339 11.3 60 PS9.2 42 44 61 58 54 75 0 0 0 334 11.1 60 PS10.1 14 22 30 29 25 26 34 46 48 274 6.1 90 PS10.2 20 19 34 30 29 29 35 43 44 283 6.3 90 PS11.1 21 20 34 39 61 0 0 0 0 175 7.0 50 PS11.2 16 20 33 29 42 0 0 0 0 140 5.6 50 PS12.1 36 39 34 46 66 0 0 0 0 221 3.3 50 PS12.2 24 26 52 42 51 60 0 0 0 255 8.5 60 PS13.1 35 43 62 128 166 0 0 0 0 434 17.4 50 PS13.2 21 34 38 39 113 133 0 0 0 378 12.6 60 PS14.1 38 32 25 26 53 32 37 0 0 243 6.9 70 PS14.2 26 33 37 39 64 0 0 0 0 199 8.0 50 PS15.1 27 25 26 33 42 48 0 0 0 201 6.7 60 PS15.2 28 27 23 23 34 48 53 0 0 236 6.7 70 PS16.1 30 23 29 31 35 50 0 0 0 198 6.6 60 PS16.2 22 25 28 31 32 40 0 0 0 178 5.9 60

*G.R= Growth rate. Dbh= Diameter at breast height

314

Appendices

Appendix 10.2 Age and growth rates of Picea smithiana seedlings from Stak valley of CKNP.

S.NO Core ID 2cm 4cm 6cm 8cm Age G.R 1 PS 1.1 8 8 8 7 31 3.9 2 PS 1.2 4 5 5 4 18 2.3 3 PS 2.1 5 4 3 4 16 2 4 PS 2.2 6 6 5 7 24 3 5 PS3.1 9 10 15 19 53 6.6 6 PS3.2 6 7 9 9 31 3.9 7 PS4.1 12 17 19 14 62 7.8 8 PS4.2 12 17 18 13 60 7.5 9 PS5.1 6 7 6 7 26 3.3 10 PS5.2 4 4 5 6 19 2.4 11 PS6.1 6 5 5 5 21 2.6 12 PS6.2 9 7 7 7 30 3.8 13 PS7.1 4 5 6 5 20 2.5 14 PS7.2 5 5 6 7 23 2.9 15 PS8.1 11 12 14 14 51 6.4 16 PS8.2 16 15 16 14 61 7.6 17 PS9.1 14 11 14 12 51 6.4 18 PS9.2 17 18 16 11 62 7.8 19 PS10.1 11 10 11 8 40 5 20 PS10.2 9 17 8 10 44 5.5 21 PS11.1 16 10 16 12 54 6.8 22 PS11.2 6 9 9 10 34 4.3 23 PS12.1 11 12 12 14 49 6.1 24 PS12.2 7 10 12 15 44 5.5 25 PS13.1 22 27 34 43 126 15.8 26 PS13.2 15 16 22 45 98 12.3 27 PS14.1 7 8 9 8 32 4 28 PS14.2 12 13 19 12 56 7 29 PS15.1 10 9 9 9 37 4.6 30 PS15.2 11 12 11 10 44 5.5 31 PS16.1 10 11 9 10 40 5 32 PS16.2 8 7 7 9 31 3.9

*G.R= Growth rate

315

Appendices

Appendix 12.1 Correlation coefficients of residual vs Skardu climate

Months Temperature Precipitation 13 pO 0.1851 0.1092 12 pN 0.1912 -0.0604 11 pD **0.5251 **-0.2287 10 J **0.2224 0.0616 9 F **0.2301 -0.0316 8 M 0.1253 0.0592 7 A **-0.3355 **0.3149 6 M 0.114 0.166 5 J **0.2285 **-0.2179 4 J 0.188 0.1044 3 A 0.0862 **-0.3033 2 S -0.0459 **0.213 1 O -0.0432 **-0.3012

Appendix 12.2 Response function coefficients of residual vs Skardu climate

Months Temperature Precipitation 13 pO **0.215 0.0718 12 pN 0.1461 0.0657 11 pD **0.2247 **-0.2524 10 J -0.0097 -0.0223 9 F -0.09 -0.0765 8 M -0.0457 0.0845 7 A **-0.306 **0.2224 6 M 0.0758 -0.0316 5 J 0.1892 0.0139 4 J **0.2048 -0.0229 3 A -0.0713 **-0.2669 2 S -0.1655 0.0663 1 O -0.0286 **-0.2115

316

Appendices

Appendix 12.3 Correlation coefficients of residual vs grid

Months Temperature Precipitation 13 pO 0.0096 0.018 12 pN **0.2409 -0.04 11 pD 0.0718 0.0586 10 J 0.0086 0.1986 9 F 0.0747 **0.2184 8 M 0.0111 0.141 7 A 0.0143 0.1224 6 M 0.0163 0.0748 5 J **0.2411 -0.134 4 J **0.3087 0.0067 3 A 0.0692 -0.1471 2 S -0.079 0.0873 1 O -0.0643 -0.1511

Appendix 12.4 Response function coefficients of residual vs grid

Months Temperature Precipitation 13 pO 0.0847 0.0083 12 pN 0.1652 0.1078 11 pD 0.0822 -0.0055 10 J -0.0348 0.1228 9 F -0.0195 -0.0086 8 M -0.0106 0.1357 7 A -0.1094 0.1043 6 M 0.0779 0.1316 5 J 0.0276 -0.1209 4 J **0.2201 0.0358 3 A 0.092 -0.0151 2 S -0.1429 0.049 1 O -0.1223 -0.1339

317

Appendices

Appendix 12.5 Correlation coefficients of Standard vs Skardu climate

Months Temperature Precipitation 13 pO 0.0445 -0.0004 12 pN **0.2469 -0.1175 11 pD **0.3767 -0.015 10 J **0.2035 0.0828 9 F **0.3308 0.0345 8 M **0.2844 -0.1679 7 A **-0.2779 **0.4116 6 M 0.1706 0.0602 5 J 0.1244 -0.1123 4 J -0.0087 0.1293 3 A -0.0797 **-0.2038 2 S **-0.2841 0.1821 1 O -0.1978 **-0.2739 L1 **0.4638 L2 **0.6066 L3 **0.3255

Appendix 12.6 Response function coefficients of Standard vs Skardu climate

Months Temperature Precipitation 13 pO 0.0921 -0.0093 12 pN 0.0788 -0.0517 11 pD 0.0738 -0.0905 10 J 0.08 -0.0543 9 F 0.0219 -0.0075 8 M 0.1033 0.0076 7 A **-0.2112 **0.2433 6 M 0.0228 0.0663 5 J 0.0456 0.0438 4 J 0.1588 0.0846 3 A 0.0037 -0.121 2 S **-0.2336 0.1096 1 O -0.1297 -0.1879 L1 **0.2015 L2 **0.5137 L3 **-0.2055

318

Appendices

Appendix 12.7 Correlation coefficients of Standard vs grid

Months Temperature Precipitation 13 pO -0.0114 0.0029 12 pN **0.249 -0.0085 11 pD 0.1696 0.0482 10 J 0.0407 **0.2245 9 F 0.0787 0.1883 8 M -0.0139 0.0319 7 A 0.0035 0.1003 6 M 0.0172 0.0703 5 J 0.0574 -0.0421 4 J **0.3476 -0.0203 3 A 0.0345 -0.0656 2 S -0.0411 0.0166 1 O -0.0473 -0.1236 L1 **0.3566 L2 **0.3443 L3 0.0554

Appendix 12.8 Response function coefficients of Standard vs grid.

Months Temperature Precipitation 13 pO -0.0059 -0.0278 12 pN 0.1362 0.121 11 pD 0.0732 0.037 10 J -0.0143 0.1565 9 F 0.0363 0.0869 8 M -0.0937 0.0431 7 A -0.0681 0.0776 6 M 0.1514 0.0915 5 J -0.0068 -0.0793 4 J **0.2869 0.0341 3 A 0.1423 0.027 2 S -0.0895 -0.0569 1 O -0.0031 -0.1205 L1 **0.2352 L2 **0.2466 L3 -0.0861

319

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

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 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. 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.

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]