The conservation issues of medicinal plants of the Swat Valley, with special reference to the tree flora


Doctoral Thesis / Dissertation, 2011

496 Pages, Grade: A


Excerpt

Contents

The University of Reading

DECLARATION

DEDICATION

ACKNOWLEDGEMENTS

ABSTRACT

Table of Figures

Table of Plates

Table of Maps

Table of Tables

Chapter 1 General Introduction
1.1. Project justification
1.2. Study area profile
1.2.1. History: The story of Swat state
1.2.2. Ethnology and Social structure of the area
1.2.3. Topographic description of the study area
1.2.4. Climatology and Phytogeography
1.3. Agro-ecological zonation of the Swat district
1.3.1. Sub-humid tropical zones
1.3.2. Sub-tropical zone
1.3.3. Humid temperate Zone
1.3.4. Cool temperate zone
1.3.5. Cold temperate zone
1.3.6. Subalpine zone
1.3.7. Alpine zone
1.3.8. Cold desert zone
1.4. Hydrology and irrigation of Swat Valley
1.5. Economy and Agriculture
1.6. Common Crops and orchards
1.7. Geology and mineral resources of Swat Valley
1.7.1. China clay or kaolin
1.7.2. Marble
1.7.3. Emerald mines
1.7.4. Dolomite/limestone
1.7.5. Granite
1.8. Wild flora and fauna of Swat
1.9. Medicinally and other economically important plants
1.10. Scope and future prospects of MAPs
1.11. Studies of MAPs in Swat
1.12. Project objectives:
1.13. Thesis overview and flow chart of the project

Chapter 2 GIS, Climate Change and Species Distribution
2.1. Introduction
2.2. What is GIS?
2.3. GIS and Remote Sensing
2.4. GIS applications
2.5. GIS use for habitat mapping of individual species
2.6. Mapping of different plant communities
2.7. Modelling of species distribution based on environmental and topographical variables
2.8. Predictive models using past records
2.9. GIS studies in Pakistan
2.10. Climate change and GIS in Pakistan

Chapter 3 Medicinal and Aromatic Plants (A Literature Review)
3.1. Introduction
3.2. Current global trends in the MAP trade
3.3. What Plants are in the Trade?
3.4. Plants and their active ingredients (leads)
3.5. Threat to MAPs
3.6. Conservation measures for MAPs
3.7. Phyto-chemical and ethnomedicinal research in Pakistan
3.8. Ethnomedicinal Study in the Swat Valley
3.9. Threats and future prospects of the Swat MAPs and NTFPs

Chapter 4 Climate Change and Species Modelling
4.1. Introduction
4.2. Material and Methods
4.2.1. Species presence data
4.2.2. Bioclimatic layers: extraction and formatting
4.2.3. Software requirements for modelling
4.2.4. Species data
4.3. Results
4.3.1. Abies Pindrow
4.3.2. Acacia modesta
4.3.3. Alnus nitida
4.3.4. Aesculus Indica
4.3.5. Cedrella serrata
4.3.6. Cedrus deodara
4.3.7. Celtis caucasica
4.3.8. Diospyrus lotus
4.3.9. Eucalyptus species
4.3.10. Ficus species
4.3.11. Juglans regia
4.3.12. Melia azedarach
4.3.13. Morus species
4.3.14. Olea ferruginea
4.3.15. Picea smithiana
4.3.16. Pinus roxburghii
4.3.17. Pinus wallichiana
4.3.18. Platanus orientalis
4.3.19. Quercus dilatata
4.3.20. Quercus incana
4.3.21. Quercus baloot
4.3.22. Salix species
4.3.23. Taxus baccata
4.4. Review of the results
4.5. Discussion and Conclusions
4.5.1. Species walking effect
4.5.2. Species association and bioclimatic variables
4.5.3. Scarcity of the NTFPs and MAPs
4.5.4. Socio-cultural effects of the floristic changes on the study area
4.5.5. Accuracy of the predictive modelling

Chapter 5 Ethnobotany of the Swat Valley
5.1. Introduction
5.2. Materials and Methods
5.3. Results and Discussions
5.3.1. Results for Questionnaire
5.3.2. Data analysis and results from Questionnaire
5.3.3. Relative Frequency of Citation (RFC)
5.3.4. Relative Importance Index (RII)
5.3.5. Salience Index (SI)
5.3.6. Informant Agreement Ratio (IAR)
5.3.7. Cultural Value Index (CVI)
5.3.8. Cultural Importance Index (CII)
5.3.9. Ali’s Conservation Priority Index (CPI)
5.4. Discussion and Conclusions
5.4.1. Objectives achieved
5.5. Recommendations

Chapter 6 Vegetation Analysis of the District Swat
6.1. Introduction
6.2. Materials and Method
6.2.1. Data collection
6.2.2. Analysis of the data
6.3. Results
6.3.1. Cluster analysis for locations
6.3.2. Cluster analysis of species interaction
6.3.3. Biodiversity indices
6.4. Locations ranking
6.5. Linear regression model
6.6. Discussion and Conclusions

Chapter 7 GIS Mapping of the Plant Communities
7.1. Introduction
7.2. Materials and Method
7.2.1. Digital elevation model (DEM) and species elevation maps
7.2.2. Ordination analysis
7.2.3. Hotspot analysis
7.2.4. Extinction risk estimate (Future directions)
7.2.5. Next step (Red listing species)
7.3. Results
7.3.1. Digital Elevation Maps
7.3.2. Ordination results
7.4. Conclusions and Discussion

Chapter 8 Discussion and Conclusions
8.1. Introduction
8.2. Loss of forest is the loss of MAPs and NTFPs
8.3. Climate change and the consequences for the Swat Valley
8.4. Species-walking effect
8.5. Species association and bioclimatic variables
8.6. Lack of the NTFPs and MAPs
8.7. Socio-cultural effects
8.8. Reliability of the predictive models
8.9. Conclusions regarding biodiversity analysis
8.10. The use of GIS and ordination analysis
8.11. Research objectives accomplished
8.11.1. Objective
8.11.2. Objective
8.11.3. Objective
8.11.4. Objective
8.12. Limitations and obstacles in the project
8.13. Recommendations

Bibliography

Appendix 1

Appendix 2

Appendix 3

Appendix 4

Appendix 5

Appendix 6

Appendix 7

Appendix 8

DECLARATION

I confirm that this is my own work and the uses of all material from other sources have been properly and fully acknowledged.

Signature:

Kishwar Ali

DEDICATION

This thesis is dedicated to my beloved parents, who at every step of my life supported and encouraged me and blessed me with bundles of prayers.

ACKNOWLEDGEMENTS

“With the name of Allah (SWT), the entirely merciful, the especially merciful”

I am very thankful to Allah, who gave me all the courage, patience, strength, guidance and ability to fulfil this painstaking job of research and writing-up in a very difficult time of crises at my study area. I have no words to pay respect to my sincere and very dear supervisor, Dr Stephen Jury, who at every step of my study remained beside me, gave me valuable guidance and directions, with immense forbearance and sincerity. There were waves of ups and down during the project, but his professional approach in a friendly manner has always helped me overcome all the hurdles in the way. I would also like to pay gratitude to my external supervisor, Prof. Dr. Habib Ahmad, whose affection, not just in the PhD project, but since I was his MSc research student, stayed with me. He has a unique way of supervision, and has taught me more in action than words. I would forward special thanks to Dr. Geoffrey Griffith of the Environmental Sciences, The University of Reading, for his support and guidance in tackling the tricky bits of GIS data processing, Dr. Jonathan Mitchley, for his valuable tips in the ordination analysis, and Dr. Alastair Culham, for his facilitation in access to Canoco software package. I would also like to extend my thanks to my colleague Hypolite Bayor for his valuable advice and all the time he gave me during the predictive modelling phase. He is really a gentleman of great moral character and I wish him best for the future. I would also like to pay thanks to all my colleagues in the Postgraduate study room, but especially to Miss Kulchana Ketsuwan, Miss Anushka Wickramasuriya (Srilankan lady) and Dr. Olajire Gbaye for their continuous help and support in encouraging me for the achievement of the goal on time.

I am extremely grateful to my cousin, Mr. Shah Wazir Khan alias Khaki/Swat Sing Khan, who is the man of his word; without him this project would have never been completed. During the data collection phase and field trips, he completely put aside all his family commitments and his job and joined me to take the challenge of hiking for days and nights in the tricky mountains of the Swat Valley. Mr Inayat ur Rehman is another of the great contributor to the project, provided his precious time during data collection and sorting phases. Mr Inayat’s expertise in the field of botany and his attachment to plants has helped enormously in the robust identification of the plant specimens. I would like to express my thanks to my best friend Dr. Fazal Subhan for his timely guidance and Mr Fazal Shah for his moral support in the accomplishment of the project. I would forward my great graduate to my colleague Mr Asim Pir, for his brotherly advice at every step of the project. Mr Asim has always been there to push me for the achievement of the goal and acted like my real elder brother.

I have no words to submit my humble gratitude to my loving parents, (Sayed Muhammad and S. begum) for all their sacrifices, prayers and dedication of immeasurable significance, without that this task would have never been accomplished. I wish my success in PhD will give them the joy of their lifetime. I would pay special thanks to all my sisters, who have great contribution in the project, from pressing plants, to searching their local names and finding their medicinal uses. During the course of the project, they have unknowingly turned into amateur botanists. Sincere thanks would go to all my brothers, but especially to Mr Irfan Ali, who has practically been involved in assisting me in the project by playing a variety of roles including my personal driver and assistant.

ABSTRACT

The Swat district has very varied vegetation structure due to the great geophysical variation. There is a great potential for the Valley to act as an economic hub for Pakistan in the Medicinal and Aromatic Plants (MAPs) industry. However, the forest ecosystem services of the Valley are under continuous threat from the direct and indirect anthropogenic stresses. This study has revealed that climate change will significantly affect adversely the distribution of some of the most important medicinal, ecological and economically important tree species of the Valley. This change will not only modify the landscape but the whole socio-ecological system of the area. Plants like Abies pindrow, Pinus wallichiana, Cedrus deodara and Pinus roxburghii will be directly affected by the a2a climate change scenario for the year 2080. This study also predicts the trend in altitudinal movement of species as a consequence of climate change; as the northern parts of the Valley provide high altitudes of significantly colder environment than the southern parts. The ethno- cultural study revealed that the people of the area have a well established ethnomedicinal culture in place and some plants could be at high risk of extinction from the unsustainable practices of plant collection. The core plants of the ethno-culture were Berberis lyceum in five calculated indices: Salience Index (SI), Relative Importance Index (RII), Cultural Value Index (CVI), Cultural Importance Index (CII), and Ali’s Conservation Priority Index (CPI); Skimmia laureola ranked second in the SI, fifth in the RII, seventh in CVI, third in CII, but ninth in CPI, and Mentha longifolia ranked third in the SI, and RII. Detailed biodiversity analysis has revealed that different parts of the Valley have different biodiversity index values. Some locations like location 1(Landakay, Kota, Aboha and Barikot), Location 4 (Aqba and Saidu Sharif) and Location 7(Fateh Pur), have high rankings in various biodiversity indices and further GIS analysis has identified present and future biodiversity hotspots. Digital Elevation Models (DEMs), extrapolated in the GIS, have revealed species preferences for certain altitudinal zones and the Ordination Analyses confirmed plant-plant and plant- location interaction responses in the area. It is concluded that a real threat to the biodiversity, forests and MAPs exists and only careful policy planning can rescue the area from permanent biodiversity loss in the Swat district.

Table of Figures

Figure 1.1. Marketing chain for medicinal plants collected in Swat within Pakistan (adopted from Shinwari et al., 2003b)

Figure 1.2. Flow chart of the project

Figure 3.1. Schematic diagram of typical medicinal plant drug discovery and development (adopted from Balunas and Kinghorn, 2005)

Figure 3.2. A schematic diagram; representation of conservation strategy (adopted from Hamilton, 2004)

Figure 3.3. A graph representing total papers published in all 3 categories, i.e. plant uses, conservation and mixture of both categories since

Figure 4.1. Jackknife of AUC for Abies pindrow

Figure 4.2. Omission and Predicted Area for Abies pindrow

Figure 4.3. Jackknife of AUC for Abies pindrow, future projection

Figure 4.4. Graphical representation of Sensitivity vs. 1- specificity for Acacia modesta

Figure 4.5. Jackknife of AUC for Acacia modesta

Figure 4.6. Jackknife of AUC for Alnus nitida (present distribution)

Figure 4.7. Jackknife of regularized training gain for Alnus nitida

Figure 4.8. Omission and predicted area of Aesculus indica

Figure 4.9. Jackknife of regularized training gain for Aesculus indica

Figure 4.10. Jackknife of the regularized training gain for Cedrella serrata (present distribution model)

Figure 4.11. Response of Cedrella serrata to bio-7 (present distribution model)

Figure 4.12. Response of Cedrella serrata to bio-8 (present distribution model)

Figure 4.13. Response of Cedrella serrata to bio-19 (future projection)

Figure 4.14. Jackknife of regularized training gain for Cedrella serrata (future projection)

Figure 4.15. Sensitivity vs. 1- specificity for Cedrus deodara (present distribution model)

Figure 4.16. Jackknife of regularized training for Cedrus deodara (present distribution). ..142 Figure 4.17. Jackknife of regularized training gain for Cedrus deodara (future projection)

Figure 4.18. Response of Cedrus deodara to bio-6 (future projection)

Figure 4.19. Jackknife of regularized training gain for Celtis caucasica

Figure 4.20. The most important bioclimatic variable for Celtis caucasica in present distribution modelling

Figure 4.21. Jackknife of the training gain for Celtis caucasica

Figure 4.22. Jackknife of regularized training gain for Diospyrus lotus

Figure 4.23. The most important variable for present distribution modelling of Diospyrus lotus

Figure 4.24. Test of the model fitness (omission and prediction curve)

Figure 4.25. Jackknife for regularized training gain for Diospyrus lotus (future projection)

Figure 4.26. Sensitivity vs. 1- specificity for Eucalyptus spp

Figure 4.27. Omission and Predicted area for Eucalyptus spp

Figure 4.28. Jackknife of regularized test gain for Eucalyptus spp

Figure 4.29. The most important bioclimatic variable for the present distribution model of Eucalyptus spp

Figure 4.30. Jackknife of test gain for Eucalyptus spp

Figure 4.31. Jackknife of test gain for Ficus spp. for present predicted distribution

Figure 4.32. Sensitivity vs. 1- specificity for Ficus spp present distribution model

Figure 4.33. Future projection; Jackknife of AUC for Ficus spp

Figure 4.34. Future projected model; Sensitivity vs. 1- specificity for Ficus spp

Figure 4.35. Future predictive model; Omission and Predicted area for Ficus spp

Figure 4.36. Jackknife of AUC for Juglans regia

Figure 4.37. Response of the species to the most important variable for Juglans regia distribution

Figure 4.38. Jackknife of the AUC for Juglans regia, future prediction model

Figure 4.39. Response curve of the most important variable for Juglans regia

Figure 4.40. Omission and predicted area for Juglans regia

Figure 4.41. Jackknife of AUC for Melia azedarach (present predictive distribution). 164 Figure 4.42. The most important bioclimatic variable for the prediction of Melia azedarach

Figure 4.43. Omission and prediction area for Melia azedarach

Figure 4.44. Jackknife of AUC for Morus spp., present distribution model

Figure 4.45. Jackknife of AUC for Morus spp; future distribution model

Figure 4.46. Sensitivity vs. 1- specificity for Morus spp

Figure 4.47. Omission and predicted area for Morus spp. (future projection)

Figure 4.48. Jackknife of AUC for Olea ferruginea

Figure 4.49. Omission and predictive area for Olea ferruginea (present distribution model)

Figure 4.50. Omission and predicted area for Olea ferruginea

Figure 4.51. Jackknife of AUC for Olea ferruginea; future distribution model

Figure 4.52. The most important climatic variable in the future distribution of Olea ferruginea

Figure 4.53. Jackknife of AUC for Picea smithiana -- present distribution

Figure 4.54. Omission and Predicted area for Picea smithiana; Present distribution.176 Figure 4.55. Sensitivity vs. 1- specificity for Picea smithiana; future predicted distribution

Figure 4.56. Jackknife of AUC for Picea smithiana for future projected distribution...177 XVII

Figure 4.57. Response of the most important variable (bio-3) in predicting the future distribution of Picea smithiana

Figure 4.58. Sensitivity and 1- specificity for Pinus roxburghii for present distribution model

Figure 4.59. Omission and Predicted area for Pinus roxburghii; present predicted distribution

Figure 4.60. Jackknife of AUC for Pinus roxburghii, present distribution model

Figure 4.61. Jackknife of AUC for Pinus roxburghii, future prediction model

Figure 4.62. The most important environmental variable in the future and present prediction models of Pinus roxburghii

Figure 4.63. Jackknife of AUC for Pinus wallichiana, present predictive model

Figure 4.64. Sensitivity vs. 1- specificity for Pinus wallichiana, present prediction distribution model

Figure 4.65. Omission and predicted area for Pinus wallichiana, present predictive model

Figure 4.66. Sensitivity vs. 1- specificity for Pinus wallichiana for the future prediction model

Figure 4.67. Jackknife of AUC for Pinus wallichiana, future predictive model

Figure 4.68. The most important variable for the future prediction model of Pinus wallichiana

Figure 4.69. Jackknife of AUC for Platanus orientalis, present prediction model

Figure 4.70. Sensitivity vs. 1- specificity for Platanus orientalis, Present distribution model

Figure 4.71. Omission and predicted area for Platanus orientalis, present prediction model

Figure 4.72. Omission and predicted area of Platanus orientalis, future distribution model

Figure 4.73. Jackknife of AUC for Platanus orientalis, future distribution model

Figure 4.74. Sensitivity vs. 1-specificity for Platanus orientalis, future prediction model ..192 Figure 4.75. Omission and predicted area for Quercus dilatata; present prediction model

Figure 4.76. Jackknife of AUC for Quercus dilatata; present prediction model

Figure 4.77. Jackknife of AUC for Q dilatata; future prediction model

Figure 4.78. Omission and prediction of Q. dilatata, future prediction model

Figure 4.79. Response of Quercus dilatata to the most important variable, future prediction model

Figure 4.80. Sensitivity vs. 1- specificity of Quercus incana; present distribution model. ..198 Figure 4.81. Omission and predicted area of Q incana; present distribution model

Figure 4.82. Jackknife of AUC for Q. incana

Figure 4.83. Sensitivity vs. 1-specificity for Quercus incana; future model

Figure 4.84. Omission and Predicted area for Q. incana; future model

Figure 4.85. Jackknife of AUC for Q. incana, future model

Figure 4.86. Response of the most important variable

Figure 4.87. Omission and predicted area of Q. baloot; present distribution model

Figure 4.88. Jackknife of AUC for Q baloot; present distribution model

Figure 4.89. Sensitivity and 1- specificity for Q. baloot; future distribution model

Figure 4.90. Jackknife of AUC for Q. baloot; future distribution model

Figure 4.91. Response to the most important variable (bio-4)

Figure 4.92. Omission and predicted area of Salix spp., present distribution model

Figure 4.93. Jackknife of AUC for Salix spp.; present distribution model

Figure 4.94. Sensitivity vs. 1- specificity for Salix spp.; present distribution model

Figure 4.95. Jackknife of AUC for Salix spp.; future distribution model

Figure 4.96. Omission and predicted area of Salix spp,; future distribution model

Figure 4.97. The most important variable in the future prediction model of Salix spp.

Figure 4.98. Sensitivity vs. 1- specificity for Taxus baccata

Figure 4.99. Jackknife of AUC for Taxus baccata

Figure 4.100. Sensitivity vs. 1- specificity for Taxus baccata; future prediction model

Figure 4.101. Jackknife of AUC for Taxus baccata for future prediction model

Figure 5.1. A. Respondents sex ratio; B. employment status

Figure 5.2. A. Use of MAPs; B. Knowledge acquired from

Figure 5.3. A. Main drug market role; B. Medicinal plant use in the family

Figure 5.4. A. Plant extraction; B. Importance of the trees

Figure 5.5. A. View about the Government role; B. Trends in the use of MAPs

Figure 5.6. A. Future perception of forest conservation; B. Self-role in conservation

Figure 5.7. Habit of the plants reported

Figure 5.8. Common families of the ethno-botanical culture of the area contributing more than one plant species

Figure 6.1. Locations and the tree species

Figure 6.2. A dendrogram of location using average linkages between groups

Figure 6.3. A dendrogram of tree species using Chi-square between the sets of frequencies used

Figure 6.4. Clusters formed as a result of the species similarities

Figure 6.5. Clusters formed as a result of the location similarities

Figure 6.6. Dominant and sub-dominant species of the location

Figure 6.7. Dominant and sub-dominant species of the location

Figure 6.8. Rarefaction curve (after Sanders, 1968) for location

Figure 6.9. Dominant and sub-dominant species of the location

Figure 6.10. Dominant and sub-dominant species of the location

Figure 6.11. Dominant and sub-dominant species of the location

Figure 6.12. Dominant and sub-dominant species of the location

Figure 6.13. Dominant and sub-dominant species of the location

Figure 6.14. Dominant and sub-dominant species of the location

Figure 6.15. Dominant and sub-dominant species of the location

Figure 6.16. Dominant and sub-dominant species of the location

Figure 6.17. Dominant and sub-dominant species of the location

Figure 6.18. Dominant and sub-dominant species of the location

Figure 6.19. Dominant and sub-dominant species of the location

Figure 6.20. Dominant and sub-dominant species of the location

Figure 6.21. Dominant and sub-dominant species of the location

Figure 6.22. Dominant and sub-dominant species of the location

Figure 6.23. Dominant and sub-dominant species of the location

Figure 6.24. Dominant and sub-dominant species of the location

Figure 6.25. Dominant and sub-dominant species of the location

Figure 6.26. Dominant and sub-dominant species of the location

Figure 6.27. Dominant and sub-dominant species of the location

Figure 6.28. Dominant and sub-dominant species of the location

Figure 6.29. Dominant and sub-dominant species of the location

Figure 6.30. A histogram of regression model for Equitability index and average population size and number of organisms

Figure 6.31. A P-P plot for Equitability index, average population size and number of organisms

Figure 7.1. Altitudinal variations between the tree species in the Swat District

Figure 7.2 DCA ordination plot of trees only (for species names see Appendix 7 and 8)

Figure 7.3. DCA ordination plot for location

Figure 7.4. DCA ordination plot for locations and tree species

Figure 7.5. DCA ordination graph for herbs (MAPS) only data

Figure 7.6. DCA ordination plot of locations only

Figure 7.7. CCA ordination plot of the locations using disturbance factors

Figure 7.8. CCA ordination triplot for tree species, environmental variables and Location data

Figure 7.9. CCA ordination plot for herbaceous species, tree data used as environmental variables (for species names see Appendix 7 and 8)

Figure 7.10. CCA ordination triplot of herbaceous species; tree data used as environmental variables (for species names see Appendix 7 and 8)

Figure 7.11. CCA ordination biplot for altitude and tree species (for species names see Appendix 7 and 8)

Figure 7.12. CCA for herbs using elevation data as environmental variable

Figure 7.13. CCA ordination plot for altitude

Figure 7.14. CCA ordination plot for herbaceous species using altitude as environmental variable (for species names see Appendix 7 and 8)

Figure 7.15. CCA ordination diagram of Locations, using altitude as environmental variable

Figure 8.1. Change in the reproductive cycle of organisms. (source: adopted from Root et al., 2003)

Table of Plates

Plate 1.1. A. Reforestation in Marghazar, Swat; B. Accelerated erosion in Marghazar forests

Plate 1.2. A. The author and his team mate in the Lalkoh Valley; B. Author and his team mate interviewing the local; C. Author in the cold temperate zone of Swat Valley; D. Hiking team observing the plants collected by a local

Plate 6.1. Overgrazing and fragmentation in Location

Plate 6.2. Farming overtaking the forests at Location

Plate 6.3. The unprecedented urbanization at Mingora town (close to Location 3)

Plate 6.4. A reforestation effort in location

Plate 6.5. Severely disturbed vegetation of Pinus wallichiana in Marghuzar (L9)

Plate 6.6. Land conversion from a Quercus dilatata stand into small farms at L

Plate 6.7. A new stand of Eucalyptus spp. taking over the native plants

Plate 6.8. Landslides and floods in the upper parts of location

Table of Maps

Map 1.1. A. Swat River map, developed for this project; B. Map of the Swat River and the network of small streams, developed for this project using ArcInfo

Map 1.2. Study area map

Map 4.1. A. Tree species map of district Swat

Map 4.2. The 19 bioclimatic layers cut to the study location

Map 4.2. The 19 bioclimatic layers cut to the study location

Map 4.3. A. Present distribution of Abies pindrow; B. Future Projected distribution of A. pindrow

Map 4.4. A. Present distribution pattern; B. Future projection of 2080 for Acacia modesta

Map 4.5. A. Present distribution of Alnus nitida; B. Future projection of Alnus nitida. 132 Map 4.6. A. Present distribution of Aesculus indica; B. Future distribution of Aesculus indica

Map 4.7. A. Present distribution of Cedrella serrata; B. Future distribution of Cedrella serrata

Map 4.8. A. Present distribution of Cedrus deodara; B. Future projection of the Cedrus deodara

Map 4.9. A. Current predicted distribution; B. Future projected distribution of Celtis caucasica

Map 4.10. A. Present predicted distribution; B. Future projection for 2080 of Diospyrus lotus

Map 4.11. A. Present distribution of Eucalyptus spp.; B. Future projected distribution of Eucalyptus spp

Map 4.12. A. Present predicted distribution of Ficus spp.; B. Future projection of Ficus spp

Map 4.13. A. Present distribution map of Juglans regia; B. Future distribution map of Juglans regia

Map 4.14. A. Present predictive map of Melia azedarach; B. Future predictive map of Melia azedarach

Map 4.15. A Present distribution map of Morus spp.; B. future distribution map of Morus spp

Map 4.16. A. Present distribution map of Olea ferruginea; B. Future distribution map of Olea ferruginea

Map 4.17. A. Present predicted distribution of Picea smithiana; B. Future predicted distribution of Picea smithiana

Map 4.18. A. Present predicted distribution of Pinus roxburghii; B. Future projected distribution of Pinus roxburghii

Map 4.19. A. Present predicted distribution of Pinus wallichiana; B. Future predicted distribution of Pinus wallichiana

Map 4.20. A. Present predicted distribution of Platanus orientalis; B. Future predicted distribution of P. orientalis

Map 4.21. A. Present prediction model of Quercus dilatata; B. Future prediction model of Q. dilatata

Map 4.22. A. Present predicted distribution of Quercus incana; B. Future predicted distribution of Q. incana

Map 4.23. A. Present distribution of model of Q. baloot B. Future distribution map of Q. baloot

Map 4.24. A. Present distribution of Salix spp.; B. Future distribution of Salix spp

Map 4.25. A. Present distribution of Taxus baccata; B. Future projection of T. baccata...211 Map 4.26. A. Present potential distribution of Olea ferruginea with the use of a bias file;

Map 6.1. NDVI map of the Swat District: investigated locations (L1L23)

Map 7.1. DEM map of Abies pindrow

Map 7.2. DEM map of Cedrus deodara

Map 7.3. DEM map of Picea smithiana

Map 7.4. Hotspots of Abies pindrow for present distribution

Map 7.5. Hotspots of Salix spp. for present distribution

Map 7.6. Hotspots of Salix spp. for future distribution

Table of Tables

Table 1.1. Pakistan’s forest cover (source: Rainforests, 2011)

Table 1.2. Land dynamics in Pakistan (adopted from: GOP, 2000)

Table 1.3. Endangered plant species in Pakistan (adopted from Khan, 1997). Plants highlighted (bold) were observed in the study area

Table 1.4. Trends in natural forest cover (deforestation) between1990-2010 in Pakistan (source: FAO, 2011)

Table 1.5. Policies and institutions for environment protection and protected area development (source: GOP, 2009)

Table 1.6. Monthly 30 year mean temperatures, precipitation and relative humidity recorded at Dir station (Source: adopted from Shinwari et al., 2003b)

Table 1.7. Agro-ecological Zones of the Swat River catchment (source: adopted from Ahmad and Ahmad, 2004)

Table 1.8. Land Utilization Statistics of District Swat, 2007 / 2008 (Source: GOP, 2008; Director of Agriculture Statistics, NWFP, and Peshawar)

Table 1.9. District area, production, yield per hectare, production per capita and percentage share of fruits with NWFP, 2007-08 (source: GOP, 2008)

Table 1.10. Community Game reserves of District Swat (Source: Khyber Pakhtunkhwah, 2008)

Table 1.11. Distribution and conservation status of wildlife (source Ahmad and Ahmad, 2004)

Table 1.12. The endangered plant species of the Swat River watershed (source: Ahmad and Ahmad, 2004)

Table 1.13. Top 12 leading countries exporting medicinal plants (Shinwari et al., 2003b)

Table 3.1. China’s Exports of Plants / herbal and pharmaceutical products (SITC Rev .3 code 2924) in 2010 (Source: UN Comtrade, 2011)

Table 3.2. Flowering plants as useful sources of drugs in the US during 1980 (adopted from Farnsworth and Soejarto, 1985)

Table 3.3. Major plant drugs for which no synthetic alternatives were available for the year 1997 (adopted from Kumar et al., 1997)

Table 3.4. Therapeutic indications of plant-derived drugs (adopted from Farnsworth et al., 1985)

Table 4.1. Different species distribution models and their key references

Table 4.2. Bioclimatic variables and their description (source: WorldClim, 2011)

Table 4.3. Softwares required for the Maxent species distribution modelling

Table 4.4. List of the species sampled in District Swat

Table 4.5. Training AUC values, important variables and percentage contribution of the 23 modelled species

Table 5.1. The calculated indices and their descriptions

Table 5.2. Plant part used values for the calculation of CPI

Table 5.3. Informant Agreement Ratio for different use categories

Table 5.4. Plants reported in the freelisting survey, their scientific, English, and local (Pashto) names and their uses

Table 5.5. Top 30 plants and comparison of their ranks based on their indices: SI=Salience Index, RII= Relative Importance Index, CVI= Cultural Value Index, CII= Cultural Importance Index, CPI=Conservation Priority Index

Table 5.6. Top 15 reported plants based on the Salience Index, SI=Salience Index, RII= Relative Importance Index, CVI= Cultural Value Index, CII= Cultural Importance Index, CPI=Conservation Priority Index

Table 6.1. The selected locations and their codes

Table 6.2. Commonly used biodiversity indices in ecology and conservation investigations

Table 6.3. Plants indices calculated for all the locations

Table 6.4. Hierarchical clustering, location, places and their dominant species

Table 6.5. Important biodiversity indices of location

Table 6.6. Important vegetation indices of Location

Table 6.7. Important biodiversity indices for location

Table 6.8. Important biodiversity indices for location

Table 6.9. Important biodiversity indices for location

Table 6.10. Important biodiversity indices for location

Table 6.11. Important biodiversity indices for location

Table 6.12. Important biodiversity indices for location

Table 6.13. Important biodiversity indices for location

Table 6.14. Important biodiversity indices for location

Table 6.15. Important biodiversity indices for location

Table 6.16. Important biodiversity indices for location

Table 6.17. Important biodiversity indices for location

Table 6.18. Important biodiversity indices for location

Table 6.19. Important biodiversity indices for location

Table 6.20. Important biodiversity indices for location

Table 6.21. Important biodiversity indices for location

Table 6.22. Important biodiversity indices for location

Table 6.23. Important biodiversity indices for location

Table 6.24. Important biodiversity indices for location

Table 6.25. Important biodiversity indices for location

Table 6.26. Important biodiversity indices for location

Table 6.27. Important biodiversity indices for location

Table 6.28. Ranks of all the locations based on the different indices. (for location code see Table 6.1)

Table 6.29. Coefficients table of linear regression model: Equitability as dependent variable

Table 7.1. Tree species, lowest and highest points of altitudes and GPS data

Table 7.2. Summary of DCA Ordination of Trees only

Table 7.3. Summary table of DCA ordination for herbs only

Table 7.4. Summary table of CCA ordination of all species and the environmental variables

Table 7.5. Summary of CCA ordination using trees and disturbance variables

Table 7.6. Summary table using trees as environmental variable and herbs plotted..

Chapter 1 General Introduction

1.1. Project justification

Biodiversity incorporates the range and abundance of plant and animal species, the interactions between them and the natural systems that support them (Armsworth et al., 2004). “Biodiversity” the term first used by the wildlife scientist and conservationist Dasmann in 1968 (Dasmann, 1968). Since then the term is defined, used and very much exploited in a variety of ways. There are estimated 5-50 million living organisms (Wilson, 1988) of which over 2 million are formally classified and named (May, 1988) contributing to the biodiversity of the earth’s complex ecosystem. Currently, over 1.7 million of the world's species of animals, plants and algae have been described (IUCN, 2010). Among these millions of species, different estimates have been given, according to the one by IUCN (2010), the total numbers of plants are 321,212 of which 281,821 are Angiosperms and 1,021 are Gymnosperms (IUCN, 2010).

For hundreds of millions of people, biodiversity is about eating, staying healthy, and finding shelter (Kaimowitz and Sheil, 2007). People use natural resources like forest for the extraction of more and more in order to fulfil their growing demands of a modern life style. Human beings from time immemorial are dependent on plants and plant resources. There are numerous examples of plants that are used by humans in day-to-day life. Plants are used for food, construction and shelter, clothing, furniture, fibres, resins and medicine. Plants have great industrial uses as well; they are used for making rubbers, dyes and colours, oils, etc. Not to mention the black market of the plant products like heroine, cannabis and cocaine, worth billions of dollars and which have an enormous socioeconomic and socio-cultural effects on a huge part of the human population. Of course, one of the most obvious uses of plants is for food. There are more than 20,000 estimated known species of edible plants in the world growing in different regions and climates (Schultes, 1976). Some of the important uses of plants are aromatic fragrances and for medicinal recipes. For centuries, locals have used plants for treating all sorts of diseases and disorders. Many plants are also known to have played role in the religious rituals of humans. Some of these plants of a poisonous nature were used in human aggression and warfare. In some cultures, some plants were even given the value of god or sacred entities. People used them for inducing hallucinogenic effects. No matter whether we believe that man's intake of hallucinogens in primitive or sophisticated societies constitutes use, misuse, or abuse, hallucinogenic plants have undeniably played an extensive role in human culture and probably will continue to do so. It follows that a “clear understanding of these physically and socially potent agents should be a part of man's general education” (Schultes, 1976).

The earliest known scientific works were “Enquiry into Plants and Growth of plants” by Theophrastus (c. 300 BC); seminal texts of the Middle Ages were Avicenna's “Canon Medicinae” (c. 1020), and the Anglo-Saxon “Leech Book” of Bald (c. 950). With the passage of time and with the expansion of the human population, demands have grown for more and more natural resources and so a negative effect has started to become more and more significant. People clearing the forests for wood and to satisfy their need of cultivatable land for staple food crops, they have over harvested the pastures and raised more and more livestock to transform the green into meat.

All over the world, forests, especially tropical forests, are depleting at a very fast pace. Today, rainforest cover which is less than about 6% of the Earth's land area, contains more than 50% of all species (Arora, 2010). Depletion in forest area threatens the sustainability of global agricultural production systems and en-dangers the economy of the world. A forest is an ecosystem, thus deforestation means not only the loss of trees but also the loss of a life support ecosystem (Siddiqui et. al., 2004b). There is far more demand than ever for natural resources and a continuous degradation of the global ecosystem is underway. Wild and semi-wild plants and animals contribute significantly to nutrition, health care, income, and culture in the developing world, and the poorest and most vulnerable nations often the most reliant on such resources (Kaimowitz and Sheil, 2007). Depleting those resources or making them inaccessible and can impoverishes these people even further. 'Pro-poor conservation' - that is, conservation that aims to support poor people - explicitly seeks to address basic human needs (Kaimowitz and Sheil, 2007). Human- driven land-use and climatic changes are perhaps the greatest threats to terrestrial biodiversity (Millennium Ecosystem Assessment, 2005; IPCC, 2007) given the mounting empirical evidence that these anthropogenic forces substantially exacerbate species’ endangerment (Brook et al., 2003; Sodhi et al., 2008).

We often hear the term “ecosystem services” which means the total output of the global ecosystem for the benefit of the human beings. Forests provide a good deal of ecosystem services to humanity.

Nasi et al., (2002) highlight these in the following way:

(a) Ecosystem services are essential to the survival of human beings.
(b) Forest ecosystems operate and provide services on such a grand scale and in such intricate and little-explored ways that most cannot effectively be replaced by technology.
(c) Human activities are already impairing the flow of ecosystem services from the forests on a large scale.
(d) If current trends continue, humanity will dramatically alter a large share of the Earth's remaining natural forest ecosystems within a few decades, especially in the tropics.

The forest ecosystem services provide human beings with an enormous amount of output. The World's annual industrial round wood production is estimated at 1.52 billion m3 (FAO, 2001). No accurate estimates of the total financial value of world timber output appears to be available but the annual value of world trade in industrial wood products is around $140 billion, (FAO, 2001). Statistics suggest that in 1999 some 1.75 billion m3 of wood was taken for fuel-wood and converted into charcoal, about 90% of which has been produced and consumed in the developing world (FAO, 2001). There are currently about 2.7 billion people in developing countries who rely for cooking primarily on biomass, including wood, charcoal, tree leaves, crop residues and animal dung, using inefficient devices (IEA et al., 2010). All sources agree that fuel-wood is of a great importance for poorer countries, and for the poor within those countries. In the non-timber products of the forests, in the developed world, in 1996 the estimated value of the global markets for all herbal medicines was exceeding US$14 billion (Genetic Engineering News, 1997). In 1998, the total retail market for medicinal herbs in the United States alone was estimated at $3.97 billion, more than double the estimate for North America in 1996 (Brevoort, 1998; Genetic Engineering News, 1997). During 1992, an estimated 3.9 million pounds of mushrooms were harvested from the forests of Idaho, Oregon, and Washington (Schlosser and Blatner, 1994, 1995). The total estimated economic contribution to these states from the sale and processing of these mushrooms was more than $40 million (Nasi et al., 2002).

Nasi et al. (2002) recommended strengthening biophysical research on forest services, the loss of which would seem to have the highest economic value potential (e.g. climatic/hydrological changes), encouraging the use of valuation studies as a tool for revealing current incentives, i.e. the existing distribution of net forest benefits/ opportunity costs across stakeholders - rather than claiming valuation to be an instrument to determine "optimal" land use. Nasi et al. (2002) emphasise that in spatial terms, we should try to identify those critical forest areas where, on the one hand, “forest ecosystem services are substantial” and, on the other, “changed financial incentives could tip the balance”. This means that for communities the degradation and deforestation currently are marginally more profitable options than conserving forests (Nasi et al., 2002).

Pakistan in general and the Swat Valley in particular, are currently under severe anthropogenic stress and the already depleted forest strata are rapidly vanishing forever. Every year extensive areas of arable agricultural and forestlands are degraded and turned into wastelands, due to natural causes or human interventions (Siddiqui et al., 2004a).

Plate 1.1. A. Reforestation in Marghazar, Swat; B. Accelerated erosion in Marghazar forests.

Table 1.1. Pakistan’s forest cover (source: Rainforests, 2011).

Total Land Area (1000 square kilometres) 77088

Total forest area (1000 ha) 1687

Percent forest cover 2

Primary forest cover (1000 ha) 0

Primary forest, % total forest 0

Other wooded land (1000 ha) 1455

According to FAO (2011), 2.2% or about 1,687,000 ha of Pakistan is forested. Of this, 20.2% (340,000 ha) is classified as primary forest, the most biodiverse and carbon-dense form of forest. Between 1990 and 2010, Pakistan lost an average of 42,000 ha or 1.66% per year. In total, between 1990 and 2010, Pakistan lost 33.2% of its forest cover or around 840,000 ha. Pakistan's forests contain 213 million metric tons of carbon in living forest biomass (FAO, 2011). It is reported (Rainforests, 2011) that Pakistan has around 1027 known species of amphibians, birds, mammals and reptiles. Of these, 3.5% animals are endemic, meaning they exist in no other country, while 5.5% are threatened. Pakistan also has at least 4950 species of vascular plants, of which 7.5% are endemic. Only 4.0% of Pakistan flora and fauna is protected under IUCN categories I-V (Rainforests, 2011).

Table 1.2. Land dynamics in Pakistan (adopted from: GOP, 2000)

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This whole scenario is very gloomy (Table 1.1, 1.2 and 1.3) and therefore sound investment in conservation research is needed in order to get reliable data and make prompt decisions at governmental level.

Table 1.3. Endangered plant species in Pakistan (adopted from Khan, 1997). Plants highlighted (bold) were observed in the study area.

(names are given at no particular order)

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As a result of this severe deforestation, many eco-regions and small biological niches are under serious threat. The government can only act if they have reliable and consistent data available (Table 1.4); then they can persuade the politicians and step forward towards conservation and sustainable ecosystem development. As Swat comes under the ecosystem of moist and dry temperate Himalayan forest and Trans-Himalayan Alps and plateaus, which are under the threatened global hotspots and home for thousands of valuable flora and fauna (GOP, 2000).

Table 1.4. Trends in natural forest cover (deforestation) between1990-2010 in Pakistan (source: FAO, 2011).

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Table 1.5. Policies and institutions for environment protection and protected area development (source: GOP, 2009).

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Plate 1.2. A. Indiscriminate use of the forest for fuel wood; B. Natural and anthropogenic tree falling and clearing of accessible slopes in the study area; C. Tree cutting. The devastating effect of modern machinery on the already threatened forests of the study area; D. A 50 year old tree chopped down in the Marghazar area.

The situation is alarming in Pakistan with regards to natural resources conservation, but there is a hope as with the recent technological advances, it is now possible to view the earth’s surface, generating synaptic views and understanding and accumulating extensive spatial data on species distribution with the combination of field data. We clearly need more and more information about the occurrence and distribution of species and occurrences of ecological processes in a way useful to their management. The current study is designed to know the present situation and predict the future distribution of some selected tree species of high ecological and economical importance.

1.2. Study area profile

The Swat Valley, known as the Switzerland of the East, is situated in the Khyber Pukhtun Khwa (KPK) (former North West Frontier) Province of Pakistan and can be traced on the globe at 34° 34’ to 35° 55’ N and 72° 08’ to 72° 50’ E (Shinwari et al., 2003b). Swat is bounded by Chitral and Ghizer on the north, Indus Kohistan and Shangla to the east, Bunir and FATA Malakand Agency on the south and district Dir on the west (Anon, 1998). Swat remained as an independent State but was absorbed into Pakistan in 1969. The total area is 5337 square kilometres from the village of Landaakay in the south to the Valley of Gabral in the north (Shinwari et al., 2003b). In the centre of the valley is the main river, the River Swat (“d’ Swat Sind” in the Pashto language). Swat is the most picturesque holiday hotspot for the whole of Pakistan and people come here all year long for the different attractions: in summer to escape the burning heat of the centre and south of Pakistan, and in winter to enjoy Pakistan’s only ski resort. Swat used to get foreign tourists in the last decade but because of the negligence of the government towards the development of ecotourism industry and other unfortunate events in the area, it is now considered un-safe for the foreigners.

1.2.1. History: The story of Swat state

According to Lindholm (1979), the formation of Swat state was the result of the direct British intervention in the area. The British first intervened directly in Swat during the Ambela Campaign of 1863, which ended in victory for the Swati armies, led by the great Sufi Pir, Akhund Abdul Ghafur alias Saidu Baba (Mishra 1972; Lindholm, 1979). From this conflict the local Khans (Tribal leaders) fearful of another British attack, asked Saidu Baba to become the ruler of Swat but, mindful of his ecclesiastical role, he refused. He worked out a different strategy and accumulated large tracts of land, and having acquired numerous dependents and followers, became the most powerful in terms of resources in all Lower Swat. With the passage of time, over the space of two generations, the Saidu Baba family secularized itself and began active participation in politics. Their position as outsiders in the segmentary system, combined with a charismatic history and a permanent base, gave Saidu Baba's descendants a great advantage in the political game (Lindholm, 1979).

In 1895, the British occupied the adjoining Malakand region, just south of Swat, and were able to resist the Pashtun attack in 1897. The Dir Nawab, as formal rival of Swat, was given support by the British to restrain the Swaties. When the Dir Nawab was invited to invade Swat in 1908, the Khans suddenly found that the Dir army could no longer be expelled with relative ease. It took a supreme effort for the Swat Pakhtuns to finally defeat the Nawab, who promised to return soon, with or without the traditional invitation. Faced with this threat, a council (jirga) of Khans met in 1915 and, after much dispute, gave the title of Badshah (ruler) to Miangul Abdul Wadud, the sole descendant of Saidu Baba left alive.

He himself had killed his two patrilineal first cousins (tarbur), while his only brother had died in the Dir war (Lindholm, 1979) .

The new ruler (Badshah) was well schooled in intrigue and trained in warfare, and with the support of the Swaties, managed to defeat decisively the next Nawab invasion. And this created a new scenario for Khan as the new internally evolved ruler was more difficult to depose than one imposed from without. The Badshah, was aware of the historical weakness of his position, made it his business to establish an alliance with the British, who were happy to have a friendly figure with whom to negotiate in the troubled Valley of Swat. With British backing, the Badshah was in an impregnable position. Using his role as judge and arbitrator, he followed a consistent policy of favouring the weaker party in any dispute and of confiscating the lands of dissident Khans and fragmenting their followers. The Badshah also made a point of stopping the distribution system (wesh), thus depriving others of their mediating role in redistribution. Further- more, he banished all mendicant Sufis from his realm, thereby eliminating the very class from which he himself arose. British aid allowed him to build roads and control weaponry etc. and thus a new stronger Swat State emerged which eventually submerged into Pakistan on July 28th, 1969 (Lindholm, 1979; Sultan-I-Rome, 1999)

1.2.2. Ethnology and Social structure of the area

The majority of the people of the Swat proper belong to the Akhozai branch of the Yusufzai Afghans (Pukhtuns/Pashtuns) (Sultan-i-Rome, 2005). The surroundings of Swat, i.e. Buner, Ghwarband, Kanra, Puran and Chakesar, were also parts of the Swat State, and the people here are also mainly the Yusufzai tribe (Sultan-i-Rome, 2005). Swat Kohistan is mainly inhabited by Gawris in the north and Torwalis in the south. All of these groups, inhabiting Swat and Indus Kohistan, have been called Dardic, i.e. old Indo-Aryan speaking people (Sultan-i- Rome, 2005). A large number of the Gujars reside throughout area. Some minorities like Hindus and Sikhs have also settled here for several generations. The overwhelming majority of the people are Muslims and the further distribution of religious classes is Sayyads (Saidan), Mians (Miagan), Sahibzadas (Sahibzadgan) and Mulas (Mulan).

Currently, with the spread of education and media, the people are getting more modernized or westernised, but it is a reality that Swat is part of the greater Pashtun society with a unique lifestyle. According to Taizai (2007), all Pashtuns are organized into more than 50 tribes residing in Afghanistan and Pakistan, each divided into sub-tribes, clans and sub-clans. Pashtuns have always resisted efforts to impose government control on their society.

Traditionally, a social code known as Pashtunwali regulates the behaviour of Pashtun men. In history, when there was no government and no state, the sword made the ruler and the ruler used the sword - the people had to develop some sort of ethics to regulate their lives. This code is practiced throughout the Pashtun residing areas, Swat being one of them. The salient features of the system are: hujra (public guesthouse), juma’at (mosque), godar (water point from where women fetch water) and warsho (meadow). Some traditional practices of Pukhtunwali are: jirgah, waak (authority/delegation of power), tiga or kanray (stone; truce), nagha (restriction, fine), melmastia (hospitality), chegha (communal turn out for defence), ashar or balandra (voluntary participation in works on communal projects or help for an individual of the community in the construction of his house or in the collection of harvest), badal (retaliation), machalga (surety), swara (the girl married to an enemy in order to compromise a feud), walwar (money fixed for the bride), khoon-baha (blood money), tor (any type of accusation, particularly an act of rape or adultery, which are considered unforgivable crimes), matiza (a woman who elopes with her lover or is enticed away - in both cases, she is liable to be killed by the nearest relative), nanawati (a formality through which an individual begs forgiveness for a harmful act or speech), badraga (local escort), baramta (recovery of claimed money or property by force), bonga (abduction for ransom), Gag or zhagh (when a man wants to get married to a lady of his choice and is denied permission by the girl’s family he manage to fire rifle shots in front of the house of the girl to warn the community that he had a claim on her and no one else should think of marring her (Taizai, 2007).

1.2.3. Topographic description of the study area

Swat district can mainly be vaguely classified into two topographical regions, i.e. the valley basin, the plain region and the mountainous region. Although there is no actual flat area in the valley, the lowlands of the valley are considered uniform low terrain. The plain area is very small and the average approximate width of the valley is 6 km, while the total length of the valley from Landakay to Gabral is 145 km (Shinwari et al., 2003b). The plain area can be further subdivided into lower (kuz) and upper (bar) Swat. The widest open portion of the valley is between Barikot and Khwazakheila. There are some subsidiary valleys, which help to increase the width of the main valley. These subsidiary valleys are called "darey" which are narrow passages between mountains and they are basically the branch outs/off-shoots of the main valley. Swat is the area where the world’s great mountain ranges, namely the Karakorum, Hindu Kush, and Himalaya meet and give this area an immense strategic and geographic importance (Shinwari et al., 2003b). Some of these mountains have their peaks covered in snow all year long. These mountains provide a natural barrier to the monsoon and help with precipitation in the Valley. There are a number of high mountains such as: Falkser 5917m, Chokial 6174m and Mankial 5589m. People from time immemorial have travelled through these rugged mountains which have witnessed primitive civilizations.

1.2.4. Climatology and Phytogeography

The weather of the valley is not very harsh, but there is a considerable variation in the mean temperature of the lower and the upper parts of the valley. The hottest month is normally June and the maximum temperature reaches 33 Cº while the minimum 16 Cº (Shinwari et al., 2003b). January is the coldest month and the mean maximum and minimum temperatures are 11C° and 2C°, respectively (Table 1.6).

Phytogeographically, most of the area comes under the Sino-Japanese region (Ali and Qaiser, 1986), where monsoon rain mostly occurs in the summer. The climate supports bi-crop culture in the lower areas while only one crop is grown in the upper, mountainous areas (Ahmad and Ahmad, 2004).

Table 1.6. Monthly 30 year mean temperatures, precipitation and relative humidity recorded at Dir station (Source: adopted from Shinwari et al., 2003b).

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1.3. Agro-ecological zonation of the Swat district

Depending on various factors; climatic variability, floral diversity and agricultural land use patterns, eight agro-ecological zones were identified by Ahmad and Ahmad (2004). These zones are listed in Table 1.7 and elaborated here:

1.3.1. Sub-humid tropical zones

This is the lowest basin of the Valley and is characterised by shorter winters and longer summers. Two crops are harvested here mainly rice in rotation with wheat. Citrus is the most commonly grown fruit of the zone (Ahmad and Ahmad, 2004). This zone contains the graveyard of Pando Baba (Jola Gram). Here the indicator species are: Phoenix sylvestris, Bauhinia variegata, Reptonia buxifolia and Nannorrhops ritchiana.

1.3.2. Sub-tropical zone

Most of the Swat comes under this category of classification (Map 1.1). The altitudinal variation is from 600m to 1000m. This is the lower extreme and the summer is hot and humid (Ahmad and Ahmad, 2004). Bananas, guava and oranges are the tropical indicator fruits of this region, whilst rice and wheat are the commonly grown cereals. Acacia modesta and Olea ferruginea are the indicator species.

1.3.3. Humid temperate Zone

This zone starts from the 1000m altitude and reaches 1500m. The summer is hot and humid especially in the months of June and July. The apple is the commonest fruit, but maize rice, onion and lentils are also commonly grown. Pinus roxburghii and Quercus incana are the native indicator trees (Ahmad and Ahmad, 2004).

1.3.4. Cool temperate zone

The altitude for this zone is from 1500m to 2000m and the typical examples are the sub-valleys e.g. Miandam, Malam Jaba, Chail, Mankial and Lalkoh (Ahmad and Ahmad, 2004). The weather is very cold in winter and a considerable snowfall is a common occurrence although the summer is very short, a double cropping system still prevails. The common crops are: potato, maize and wheat. Keeping livestock is a common profession here, while the people are dependent on forest products to a great extent. The indicator species are Pinus wallichiana and Quercus dilatata (Ahmad and Ahmad, 2004) .

1.3.5. Cold temperate zone

This is very densely forested zone in the Swat Valley, with an altitude range of 2000-2500m. The summer here is short and the snowfall is heavier in winter, remaining for up to four months. Potato and beans are grown mixed with maize. Livestock is the major part of the economy. This zone includes areas like Sulatanr, Mankial Jaba and Ladoo. Abies pindrow and Picea smithiana are the indicator species (Ahmad and Ahmad, 2004).

1.3.6. Subalpine zone

These are the areas covered with snow for more than five months and demarcate the treeline from 2500-3500m. These are the stomachs of the livestock of Swat districts and are grazed by herds of sheep and goats in the summer months. Normally, agricultural is not a common practice in these areas and the products are usually the medicinal herbs growing here. Betula utilis and Q. semecarpifolia are the indicator tree species of the zone (Ahmad and Ahmad, 2004).

1.3.7. Alpine zone

The highest points of the Swat Valley consist of glacial lakes and streams. The altitude is 3500 - 4500m with extremely harsh weather, intense UV radiation and strong winds, all characteristics of the zone. Chukial, Basaro Sar in Chail and Soor Karr in Mankial share the same zonal conditions. Medicinal plants are collected from the region in summer, from the limited resources available (Ahmad and Ahmad, 2004).

1.3.8. Cold desert zone

These are the extreme high peaks of the Swat Valley and are covered by snow and glaciers all year long. The mountains range from 4500 to 6000m; the Falakser and Mingo Pass are the representative areas (Ahmad and Ahmad, 2004). Some threatened endemic fauna of the region is present, including snow leopards and snow cocks, but no obvious macro flora is present. It is very obvious that the presence of these different ecological zones provide micro-climates and ecological niches to a wide variety of flora and fauna, contributing to the complexity of biodiversity of the Swat Valley.

1.4. Hydrology and irrigation of Swat Valley

Swat has an immense network of natural water channels flowing down from glaciers and water absorbed in the mountains crusts. All these channels drain into one big central river, the Swat River (d’ Swat Sind [Pashto] or Daryaye Swat [Urdu]). The actual river starts from the high glaciers in the north from Kalam, Matilthan, Ushu and the borders of Chitral (Sher, 2002). Because of the drastic altitudinal variation in a short distance, it gains a lot of momentum and speed. The amount of water increases by the addition of more rivulets (Khwarr, Pashto) along the course to the south at different points, e.g. Mankial, Bahrain, Madayan, Khwazakhela, Matta Aronai, Manglawar, Mingora, Dewlai, Shamozai and Barikot.

Table 1.7. Agro-ecological Zones of the Swat River catchment (source: adopted from Ahmad and Ahmad, 2004)

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The main Swat River and the rivulets joining it have a great impact on the irrigation system of the Valley. A considerable amount of land is cultivated in the south of the Valley because of the easy access to the water from river Swat, although there is no explicit support for an irrigation system from the Government. Some NGOs have taken the initiative and have made small irrigation channels in some parts of the Valley but these are far less than needed to fulfil the demands of the whole Valley. Some local community projects have also been successful in drawing water from the river for irrigation and drinking purposes. Drinking water as a whole is in short supply in the valley and usually tube wells are installed to fulfil the demand. The drilling of more and more tube wells is having an adverse effect on the normal wells of the Valley, but no one will acknowledge the fact until the problem gets severe.

The melting ice and snow in the upper reaches of the mountains have created some fascinating and beautiful waterfalls and lakes. The very well known lakes are Mahodand Lake, Jabagai Lake, Kundal Lake, Sur Khwareh Lake, Shangri Dand, Mankial Dand, Beshigram Lake, Daral and Sedgai Lakes. These lakes provide a sustainable flow of water to the streams and are part of the underground network of channels of springs.

Most of these lakes exist in the remote parts of the Valley, and very few people have access to them but those who do have no idea of ecotourism and spread all sorts of pollutants. A serious threat of pollution and contamination persists in these lakes and the Government is absolutely ignorant of exploiting them for ecotourism which can not only promote the local economy but can have a very positive impact on the national economy as well.

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Map 1.1. A. Swat River map, developed for this project; B. Map of the Swat River and the network of small streams, developed for this project using ArcInfo.

1.5. Economy and Agriculture

Swat has always remained an economically active region of the country until very recently. It was the second most economically active region after Peshawar, in the KPK province. It had a booming tourism industry until 2007 and has a great deal of mineral resources. It has also acted as a hub for Chinese goods imported through the Silk Route and many families are actively involved in the business of imports and exports with China.

Table 1.8. Land Utilization Statistics of District Swat, 2007 / 2008 (Source: GOP, 2008; Director of Agriculture Statistics, NWFP, and Peshawar).

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Agriculture is the main profession of the people and 99 % of the residents are linked to it (Balala, 2000). The agricultural practices are currently evolving from primitive hand tools to machinery, but it is still a long way away from fully mechanised farming. The use of machinery is not feasible because some people are not able to afford it and also most of the land is mountainous to which accessibility of heavy machinery is a problem. Usually, the soil is under a continuous cropping and is deprived of yearly rest (Shinwari et al., 2003b). Normally two crops are grown in most of the area but in some high altitudinal zones a monocropping (Table 1.7) practice is the only natural way (Ahmad and Ahmad, 2004). Although Swat is an agricultural region, the total output per acre is very low and, therefore, some family members of the Upper Swat migrate to the big cities to earn extra family support.

1.6. Common Crops and orchards

In the majority of Swat, a two crop practice is common, i.e. both a spring and autumn harvest. Common crops are wheat, barley, rice, pulses and vegetables. Wheat is the commonly used staple food of the people and is a common need of every household in the area. The area has also a unique variety of rice grown called Begamai or Botey Wreje (Wreje = rice) which in some areas is an essential part of daily meals. This is grown in the lowlands of the valley in areas like Barikot, Shamozo, Parrai, etc. Maize is another common crop but is not normally grown on any wider commercial scale and people normally grow it for their family use -- boiling cobs, making pop corn, etc.

Swat has a variety of fruits grown in different ecological zones (Table 1.9). In the extreme lowlands in the south, the adjoining areas of Dir and Bunir: oranges, banana and guavas are commonly grown while in the central part of the River Swat catchment, apple, peaches, plums, persimmons, apricot and pears are grown. There is a huge transformation of the fruit market in Swat from traditional prunes/apple orchards to the newly introduced peaches. There are many cultivars of peaches which have different ripening times and, therefore, are favoured by the farmers as this provides them with a good time difference for transportation into the southern parts of Pakistan. These are commonly known as ‘Sohani’, ‘Chinese’, ‘Number2’, ‘Number6’, and ‘Number4’. Peaches are also favoured by the farmers because they are very fast growing and trees mature to fruit in 2-4 years, but on the other hand they are considered very susceptible to aphids and other fungal diseases which are treated by the application of not very environmentally friendly pesticides.

Map 1.2. Study area map

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Plate 1.2. A. The author and his team mate in the Lalkoh Valley; B. Author and his team mate interviewing the local; C. Author in the cold temperate zone of Swat Valley; D. Hiking team observing the plants collected by a local.

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Table 1.9. District area, production, yield per hectare, production per capita and percentage share of fruits with NWFP, 2007-08 (source: GOP, 2008).

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1.7. Geology and mineral resources of Swat Valley

The Swat Valley is rich in mineral resources of and valuable gem stones of international recognition. These include: sulphides, oxides and hydroxides, nitrates, carbonates, borates, silicates and other unclassified minerals. The following minerals outlined below are found in the Swat Valley are already in the commercial market.

1.7.1. China clay or kaolin

China clay or kaolin is used for white china table ware, as a special type of cement and as a paper and rubber filler. The village of Shahdehrai has the largest mine of china clay--the finest quality in Pakistan (Khan, 2005). The total reserves were estimated 2.8 million tons (Khan, 2005). The similar type of china clay has is reported from Shahdin and Doshagram near Matta.

1.7.2. Marble

A commonly found natural stone (CaCO3) re-crystallized limestone, which is quite durable and beautiful. The uses are diverse, commonly used for fireplace mantels, counter tops, flooring, and is also used as decorating stone on the exterior of a building. In many part of the Swat Valley a high quality of white and green marble are found supporting the local industry and the surrounding districts of Dir, Shangla and Bunir. The reserves are wide spread in Saidu Sharif, , Ghalegay, Spal Bandai, Khadag, Parrai, Ghakhay Kandaw and Malam Jabba (online: Khan, 2005).

1.7.3. Emerald mines

A mineral beryl, green in colour by the presence of a trace amount of chromium or iron, a very expensive gemstone having the hardness of eight on the ten- point Moh scale of hardness. The mine is located just outside Mingora city in the suburbs of Fizagat. This is of a very high quality, deep green in colour and exceptional clarity. The potential for export is very high if excavated and cut up to the international standards (Khan, 2005).

1.7.4. Dolomite/limestone

Dolomite and limestone are sedimentary rocks that occur in regular beds, limestone is calcium carbonate (CaCO3) mixed with impurities and dolomite has clay, sand and other impurities. Both of them occur in various colours and shades. They are used for manufacturing cement and lime, used in bleaching powder, glass industry, soap, paint and as an essential ingredient in many other chemical processes. There are a number of deposits in the valley: in Nawagai, Kot, Maanyaar, Panr, Serai, Ghaligay, Shagai, Salam Pur, Marghuzaar, Mingora and Saidu Sharif (Khan, 2005).

1.7.5. Granite

Another hard, crystalline, plutonic rock found in the study area is granite found in gray to pink in colour and often used as floorings in offices or as kitchen worktops and in bathrooms. This is extracted from various locations such as, Cham, Khwar Mandaona, Nawe Kalay, Fateh Pur, Malakand and Barikot (Khan, 2005).

Apart from these, the other commonly found minerals are feldspars, mica, quartz, and barite. These mineral are found in great quantities widespread in the Swat district.

The presence of such a wide variety of mineral resources promotes a diverse soils formation and in turn provides greater diversity to the vegetation structure.

[...]

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Details

Title
The conservation issues of medicinal plants of the Swat Valley, with special reference to the tree flora
College
University of Reading
Course
Doctorate
Grade
A
Author
Year
2011
Pages
496
Catalog Number
V373729
ISBN (eBook)
9783668524071
ISBN (Book)
9783668524088
File size
12941 KB
Language
English
Tags
Conservation, Climate Change, Swat Valley, Ethnobotany, Hindukush
Quote paper
Kishwar Ali (Author), 2011, The conservation issues of medicinal plants of the Swat Valley, with special reference to the tree flora, Munich, GRIN Verlag, https://www.grin.com/document/373729

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