ANALYSIS OF GENETIC DIVERSITY IN GREY LANGUR (Semnopithecus spp.) POPULATIONS OF

By

RIAZ AZIZ MINHAS Reg. No. 98-GMDB-1840

Session: 2010-2013

Department of Zoology Faculty of Sciences UNIVERSITY OF AZAD JAMMU AND KASHMIR MUZAFFARABAD

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ANALYSIS OF GENETIC DIVERSITY IN GREY LANGUR (Semnopithecus spp.) POPULATIONS OF PAKISTAN

By

RIAZ AZIZ MINHAS (Reg. #: 98-GMDB-1840)

A Thesis submitted in partial fulfillment of the requirement for the degree of

Doctor of Philosophy

in

Zoology

Session: 2010 – 2013

Department of Zoology Faculty of Sciences UNIVERSITY OF AZAD JAMMU AND KASHMIR MUZAFFARABAD

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Author’s Declaration

I, Riaz Aziz Minhas hereby state that my PhD thesis titled “Analysis of Genetic

Diversity in Grey Langur (Semnopithecus spp.) Populations of Pakistan” is my own work and has not been submitted previously by me for taking any degree from this

University, the University of Azad Jammu and Kashmir, Muzaffarabad, or anywhere else in the country/world.

At any time if my statement is found to be incorrect even after my Graduation, the university has the right to withdraw my PhD degree.

Riaz Aziz Minhas

Date: 04-10-2017

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Plagiarism Undertaking

I solemnly declare that research work presented in the thesis titled “Analysis of

Genetic Diversity in Grey Langur (Semnopithecus spp.) Populations of Pakistan” is solely my research work with no significant contribution from any other person.

Small contribution/help wherever taken has been duly acknowledged and that complete thesis has been written by me.

I understand the zero-tolerance policy of the HEC and University of Azad

Jammu and Kashmir, Muzaffarabad, Pakistan towards plagiarism. Therefore, I as an

Author of the above titled thesis declare that, no portion of my thesis has been plagiarized and any material used as reference is properly referred/cited.

I undertake that, if I am found guilty of any formal plagiarism in the above titled thesis even after award of PhD degree, the University reserves the rights to withdraw/revoke my PhD degree and that HEC and the University has the right to publish my name on the HEC/University Website on which names of students are placed who submitted plagiarized thesis.

Student/ Author Signature: ______Name: Riaz Aziz Minhas

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Dedicated To my Parents and Family

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CONTENTS

LIST OF TABLES ...... xi LIST OF FIGURES ...... xiv LIST OF ABBREVIATIONS/ SYMBOLS ...... xvii ACKNOWLEDGEMENTS ...... xix ABSTRACT ...... xx LIST OF PUBLICATIONS ...... xxiii 1. INTRODUCTION ...... 1 1.1. BACKGROUND OF THE PROBLEM ...... 1 1.2. AIM(S) AND OBJECTIVES...... 6 1.3. JUSTIFICATION OF STUDY ...... 6 1.4. GENETIC DIVERSITY ...... 8 1.4.1. Assessment of Genetic Diversity ...... 8 2. REVIEW OF LITERATURE ...... 11 2.1. GREY LANGUR ...... 11 2.2. PHYLOGENY AND OF GREY LANGUR ...... 13 2.2.1. Generic Level Classification ...... 15 2.2.2. Species Level Classification ...... 17 2.3. GENETIC SAMPLING ...... 22 2.3.1. Blood and Tissue Samples ...... 23 2.3.2. Non–Invasive Sampling ...... 24 2.4. MOLECULAR TECHNIQUES USED IN CONSERVATION ...... 25 2.4.1. Nuclear DNA markers ...... 26 2.4.1.1. Randomly amplified polymorphic DNA markers ...... 27 2.4.1.2. Microsatellite markers ...... 28 2.4.2. Mitochondrial DNA Markers ...... 33 2.4.2.1. Mitochondrial protein–coding genes markers ...... 34 2.4.2.2. Mitochondrial non-protein-coding markers ...... 40 2.4.3. Numts ...... 41 3. MATERIALS AND METHODS ...... 43 3.1. STUDY AREA ...... 43 3.2. SAMPLING ...... 48 3.2.1. Storage of Samples ...... 49 3.2.2. Ethics Statement ...... 50 3.3. DNA EXTRACTION ...... 50 3.3.1. Extraction by Manual Protocols ...... 50 3.3.1.1. Blood ...... 50 3.3.1.2. Tissues ...... 52 3.3.1.3. Hair ...... 52 3.3.1.4. Feces ...... 52

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3.3.2. Extraction by Commercial Kits ...... 53 3.3.3. Assessment of DNA Quality and Quantity ...... 54 3.3.3.1. Agarose gel electrophoresis ...... 54 3.3.3.2. Spectrophotometry ...... 55 3.4. PRIMERS AND AMPLIFICATION OF DNA ...... 55 3.4.1. RAPD Markers ...... 55 3.4.2. Microsatellite Markers ...... 56 3.4.3. PCR Optimization and Amplification ...... 56 3.5. GENETIC DATA ANALYSIS ...... 59 3.5.1. Nuclear DNA Markers ...... 59 3.5.1.1. Scoring of bands ...... 59 3.5.1.2. Discriminatory powers of markers ...... 60 3.5.1.3. Genetic diversity, genetic differentiation and gene flow ...... 61 3.5.1.4. Genetic similarity and genetic distance ...... 61 3.5.1.5. Cluster analysis ...... 62 3.5.1.6. AMOVA and PCoA ...... 62 3.5.1.7. Spatial genetic analysis ...... 62 3.5.2. Mitochondrial DNA Markers ...... 63 3.5.2.1. Sequence editing and alignment ...... 64 3.5.2.2. Phylogenetic analyses ...... 64 3.5.2.3. Species delimitation ...... 66 3.6. STATISTICAL ANALYSIS ...... 68 4. RESULTS AND DISCUSSION ...... 69 4.1. DISTRIBUTION ...... 69 4.1.1. Geographic Barriers and Movement Routs ...... 71 4.2. SAMPLE COLLECTION...... 74 4.3. DNA EXTRACTION ...... 77 4.4. PCR OPTIMIZATION ...... 80 4.4.1. RAPD Markers ...... 80 4.4.2. Microsatellite Markers ...... 81 4.4.3. Mitochondrial DNA Markers ...... 84 4.4.4. PCR Amplification Program ...... 84 4.5. GENETIC ANALYSIS ...... 85 4.5.1. RAPD Markers ...... 85 4.5.1.1. Banding pattern of amplified fragments ...... 86 4.5.1.2. Discriminatory power of markers ...... 90 4.5.1.3. Reproducibility ...... 92 4.5.1.4. Genetic diversity ...... 93 4.5.1.5. Genetic differentiation and gene flow ...... 105 4.5.1.6. Genetic similarity and genetic distance ...... 108 4.5.1.7. Multivariate analysis ...... 112

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4.5.1.8. Spatial genetic analysis ...... 115 4.5.1.9. Analysis of molecular variance (AMOVA) ...... 116 4.5.2. Microsatellite Markers ...... 117 4.5.2.1. Primer selection ...... 117 4.5.2.2. Genotyping error and allelic dropout ...... 118 4.5.2.3. Number and pattern of amplified fragments ...... 118 4.5.2.4. Discriminatory power ...... 120 4.5.2.5. Genetic diversity ...... 122 4.5.2.6. Genetic differentiation and gene flow ...... 128 4.5.2.7. Genetic distance and similarity ...... 130 4.5.2.8. Multivariate analysis ...... 134 4.5.2.9. Spatial genetic analysis ...... 137 4.5.3. Mitochondrial DNA Markers ...... 137 4.5.3.1. Cytochrome c oxidase subunit I...... 138 4.5.3.2. Cytochrome b ...... 152 4.5.3.3. Mitochondrial 16S ribosomal DNA ...... 164 4.6. GENERAL DISCUSSION ...... 171 4.6.1. Comparative Assessment of Results by Nuclear DNA Markers ...... 173 4.6.1.1. Selection of markers ...... 173 4.6.1.2. Banding patterns and polymorphism ...... 174 4.6.1.3. Genetic diversity, genetic differentiation and gene flow ...... 175 4.6.1.4. Genetic distance and similarity ...... 177 4.6.2. Taxonomy and phylogeny of grey langurs of Pakistan ...... 180 4.6.2.1. Morphological analyses ...... 182 4.6.2.2. Species confirmation...... 184 4.6.2.3. Comments of foreign primate taxonomists ...... 186 4.7. CONCLUSIONS AND RECOMMENDATIONS ...... 188 LITERATURE CITED ...... 192 APPENDICES ...... 228 Appendix I. Different classifications schemes of extant grey langurs. Modified after Takai et al. (2016) ...... 228 Appendix II: Distribution/Sighting record of grey langur in Pakistan during 2014-2016 ...... 230 Appendix III: Photographs ...... 235 Appendix IV: Multiple Sequence Alignment ...... 240

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LIST OF TABLES

Table 3.1: RAPD markers used in genotyping of Himalayan langurs (after Ding et al., 2000). 55 Table 3.2: Microsatellite primers used in genotyping of Himalayan grey langurs. 57 Table 3.3: Newly designed primers used for amplification of partial sequences of mitochondrial genes (Cyt b, COI and rRNA) from Himalayan langurs of Pakistan. 63 Table 4.1: Populations of grey langurs used for genetic analysis during present study. 72 Table 4.2: Movement routs used by grey langurs during 2012-2015 in Pakistan. 73 Table: 4.3: Basic data on different field samples of grey langur with successful DNA extraction and amplification. 75 Table 4.4: Success rate of DNA extraction and amplification from different field samples of grey langur. 77 Table 4.5: Optimized conditions for amplification of RAPD primers in grey langur genome. 81 Table 4.6: Optimized conditions for amplification of microsatellite primers in grey langur genome. 82 Table 4.7: PCR program used for amplification of RAPD markers in grey langur genome 84 Table 4.8: PCR program used for amplification of microsatellite markers in grey langur genome. 85 Table 4.9: Number of bands produced by RAPD primers in grey langurs genotypes from Pakistan. 87 Table 4.10: Banding patterns of RAPD markers in grey langur populations of Pakistan. 88 Table 4.11: Discriminatory powers analysis for different RAPD markers used in genetic analysis of grey langurs of Pakistan. 91 Table 4.12: Spearmen correlation matrix between different discriminatory power indices of different RAPD markers used in genetic analysis of grey langurs of Pakistan. 91 Table 4.13: Summary of allelic diversity exhibited by RAPD markers in grey langur populations in Pakistan. 95 Table 4.14: Summary of different genetic diversity constants exhibited by RAPD markers in grey langur populations in Pakistan. 99 Table 4.15: Total heterozygosity, genetic diversity, genetic differentiation and gene flow between different populations of grey langur as exhibited by RAPD markers. 106 Table 4.16: Correlation matrix between total heterozygosity (Ht), genetic diversity within (Hs) and between (Dst) populations, genetic differentiation within (Rst) and between (Gst) populations as exhibited by RAPD markers in grey langur. 107 Table 4.17: Pairwise Population Matrix of Nei Genetic Identity exhibited by 108

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RAPD markers in grey langur populations of Pakistan. Table 4.18: Pairwise Population Matrix of Nei Genetic Distance exhibited by RAPD markers in grey langur populations of Pakistan. 109 Table 4.19: Nei's unbiased RAPD based genetic identity (above diagonal) and genetic distance (below diagonal) in different populations of grey langurs from Pakistan. 110 Table 4.20: Percentage of variation under PCoA in different grey langur populations of Pakistan. 112 Table 4.21: Percentage of variation under PCoA in different genotypes of grey langur of Pakistan. 113 Table 4.22: Percentage of variation under geographic distance based PCoA for different genotypes of grey langur. 114 Table 4.23: Summary of AMOVA results showing genetic variation within and among populations of grey langur from Pakistan. 116 Table 4.24: Banding patterns of microsatellite markers in grey langur populations of Pakistan. 119 Table 4.25: Discriminatory powers analysis for different microsatellite markers used in genetic analysis of grey langurs of Pakistan. 120 Table 4.26: Spearmen correlation matrix between different discriminatory power indices of microsatellite markers used in genetic analysis of grey langurs of Pakistan. 121 Table 4.27: Description of alleles produced by microsatellite markers in grey langur populations of Pakistan. 122 Table 4.28: Numbers of private alleles amplified by different microsatellite marker in grey langur populations of Pakistan. 124 Table 4.29: Summary of Chi-Square Tests for Hardy-Weinberg Equilibrium 126 Table 4.30: Mean values (±SE) of genetic diversity indices and fixation index exhibited by 16 microsatellite markers in different populations of grey langurs. 127 Table 4.31: F-Statistics and estimates of gene flow between populations for different microsatellite loci in grey langurs from Pakistan. 129 Table 4.32: Correlation matrix between different F-Statistics and gene flow over all populations for different microsatellite loci in grey langur. 130 Table 4.33: Pairwise population matrix of Nei's genetic identity (above diagonal) and genetic distance (below diagonal) exhibited by microsatellite markers in grey langur populations of Pakistan. 131 Table 4.34: Pairwise population matrix of Nei's unbiased genetic identity (above diagonal) and genetic distance (below diagonal) exhibited by microsatellite markers in grey langur populations of Pakistan. 131 Table 4.35: Percentage of variation explained under PCoA in different grey langur populations of Pakistan. 134 Table 4.36: Microsatellite based percentage of variation explained by PCoA in different genotypes of grey langur of Pakistan. 136 Table 4.37: Percentage of variation explained under geographic distance based PCoA for different genotypes of grey langur. 136

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Table 4.38: Grey langur samples with successful amplification of COI partial gene fragments from AJK/ Pakistan. 139 Table 4.39: Estimates of Evolutionary Divergence between COI partial gene sequences of grey langur from Pakistan. 141 Table 4.40: Top ten species sequences producing significant alignments with COI of grey langurs from Pakistan. 144 Table 4.41: COI partial gene sequence based estimates of evolutionary divergence between different species of colobines using the Maximum Composite Likelihood model. 148 Table 4.42: COI partial gene sequence based estimates of Evolutionary Divergence between different closely related species of colobines using the p-distance method. 149 Table 4.43: Species delimitation of grey langurs by D/ϴ method using COI sequences. 151 Table 4.44: Grey langur samples with successful amplification of Cyt b partial gene fragments from AJK/ Pakistan. 153 Table. 4.45: Estimates of Evolutionary Divergence between Cyt b partial gene sequences of grey langur from Pakistan. 154 Table 4.46: Top 15 species sequences producing significant alignments with Cyt b of grey langurs from Pakistan. 157 Table 4.47: Cyt b partial gene sequence based estimates of Evolutionary Divergence between different closely related species of colobines 159 Table 4.48: Species delimitation of grey langurs by D/ϴ method using Cyt b sequences. 164 Table 4.49: Grey langur samples with successful amplification of rRNA partial gene fragments from AJK/ Pakistan. 165 Table 4.50: Top ten species sequences producing significant alignments with partial sequence of rRNA gene of grey langurs from Pakistan 166 Table 4.51: 16S-rRNA partial gene sequence based estimates of Evolutionary Divergence between different closely related species of colobines using the Maximum Composite Likelihood model. 167 Table 4.52: Estimates of Evolutionary Divergence between Sequences of 16S- rRNA partial gene fragments from different most related langur taxa. 167 Table 4.53. Comparative account of banding patterns of two groups of markers in grey langur. 174 Table 4.54. Comparative account of genetic diversity and genetic differentiation and gene flow exhibited by two groups of markers in grey langur 176 Table 4.55. Comparisons of pair-wise genetic distances among different populations using two classes of markers in grey langur 178

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LIST OF FIGURES

Figure 1.1: Distribution of different species of grey langur in the Indian Subcontinent. 2 Figure 3.1: Location map of grey langur populations with sampling localities in Pakistan. 44 Figure 4.1: Distribution of Grey langur in different areas of Pakistan/AJK during 2014-2016. 69 Figure 4.2: Movement routs used by grey langurs across different geographic barriers (rivers/mountains) in Pakistan. 73 Figure 4.3: Photo plates of agarose gel (1%) of purified DNA extracted from different tissues and stained with ethidium bromide. 78 Figure 4.4: Numbers of bands produced by different RAPD primers in different populations of grey langurs from Pakistan. 89 Figure 4.5: Overall bands produced by RAPD primers in different populations of grey langurs from Pakistan. 89 Figure 4.6: Total Band Patterns for RAPD markers across different populations of grey langur in Pakistan. 94 Figure 4.7: Numbers of observed alleles (Na) exhibited by RAPD markers in different populations of grey langurs in Pakistan. 96 Figure 4.8: Numbers of effective alleles (Ne) exhibited by RAPD markers in different populations of grey langurs in Pakistan. 96 Figure 4.9: Overall, mean (±SE) numbers of observed and effective alleles revealed by RAPD markers in all populations of grey langur in Pakistan. 97 Figure 4.10: Frequency of polymorphic loci (%) in different populations of grey langur in Pakistan. 98 Figure 4.11: Genetic diversity indices for different RAPD markers in Poonch population of grey langur. 100 Figure 4.12: Genetic diversity indices for different RAPD markers in Muzaffarabad population of grey langur. 101 Figure 4.13: Genetic diversity indices for different RAPD markers in Neelum population of grey langur. 102 Figure 4.14: Genetic diversity indices for different RAPD markers in Mansehra population of grey langur from Pakistan. 102 Figure 4.15: Genetic diversity indices for different RAPD markers in Kohistan population of grey langur from Pakistan. 103 Figure 4.16: Overall mean (±SE) values of different genetic diversity indices exhibited by RAPD markers in different populations of grey langur from Pakistan. 103 Figure 4.17: Linear correlation between Shannon and Nei’s diversity indices across 8 RAPD markers for grey langurs of Pakistan. 104 Figure 4.18: UPMGA dendrogram based on genetic distance. 110

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Figure 4.19: Clustering of different genotypes from grey langur populations (Euclidean similarity index) based on Paired group cluster analysis of binary data produced by RAPD markers. 111 Figure 4.20: Graphical presentation of PCoA results for different populations of grey langur. 113 Figure 4.21: Graphical presentation of PCoA results for different genotypes of grey langur. 114 Figure 4.22: Graphical presentation of PCoA results based on geographic distance for different genotypes of grey langur. 115 Figure 4.23: Association between RAPD based genetic and geographic distances as depicted by Mantel Test 116 Figure 4.24: Mean (with SE bars) allelic patterns for microsatellite markers across populations of grey langur from Pakistan. 123 Figure 4.25: Percentage of polymorphic loci in different population of grey langur in Pakistan. 125 Figure 4.26: Dendrogram based on Genetic distance (Nei's, 1972, 1978) as exhibited by microsatellite markers using UPGMA method modified from neighbor procedure of phylip version 3.5. 132 Figure 4.27: Clustering of different genotypes from grey langur populations (Euclidean similarity index) based on Paired group cluster analysis of binary data produced by microsatellite markers. 133 Figure 4.28: Graphical presentation of PCoA results based on genetic distance and similarities by microsatellite markers for different populations of grey langur. 135 Figure 4.29: Graphical presentation of PCoA results based on genetic distance by microsatellite markers for different genotypes of grey langur. 136 Figure 4.30: Graphical presentation of PCoA results based on geographic distance for different genotypes of grey langur. 136 Figure 4.31: Association between microsatellite based genetic and geographic distances as depicted by Mantel Test. 137 Figure 4.32: Gel picture of COI gene amplified products of grey langurs of Pakistan. 139 Figure 4.33: Cladogram based on COI partial gene sequences of grey langur from Pakistan 140 Figure 4.34: COI gene based molecular phylogenetic analysis of grey langurs from Pakistan by Maximum Likelihood method. 142 Figure 4.35: COI gene based evolutionary relationships of taxa inferred using the Neighbor-Joining method. 143 Figure 4.36: COI gene based Maximum Parsimony analysis of different grey langurs from Pakistan. 143 Figure 4.37: COI gene based molecular phylogenetic analysis of different langur taxa by Maximum Likelihood method. 145 Figure 4.38: COI gene based evolutionary relationships of different langur taxa inferred using the Neighbor-Joining method. 146 Figure 4.39: COI gene based Evolutionary relationships of different langur taxa

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inferred using the Minimum Evolution method 147 Figure 4.40: COI gene based Maximum Parsimony analysis of taxa inferred using the Maximum Parsimony method. 147 Figure 4.41: COI partial gene sequence based phylogram displaying branch support values (%) using phylogeny.fr. 148 Figure 4.42: Cyt b gene based Molecular Phylogenetic analysis of grey langurs from Pakistan by Maximum Likelihood method 155 Figure 4.43: Cyt b gene based Evolutionary relationships of taxa inferred using the Neighbor-Joining method (Saitou and Nei, 1987). 156 Figure 4.44: Cyt b gene based Maximum Parsimony analysis of different grey langur from Pakistan. 156 Figure 4.45: Cyt b gene based molecular phylogenetic analysis of different langur taxa by Maximum Likelihood method. 160 Figure 4.46: Cyt b gene based Evolutionary relationships of different langur taxa inferred using the Neighbor-Joining method. 160 Figure 4.47: Cyt b gene based Evolutionary relationships of different langur taxa inferred using the Minimum Evolution method. 161 Figure 4.48: Cyt b gene based Maximum Parsimony analysis of taxa inferred using the Maximum Parsimony method. 161 Figure 4.49: Gel picture of amplified products of rRNA gene of grey langur from Pakistan (L=100bp ladder). 165 Figure 4.50: rRNA gene based Molecular Phylogenetic analysis of different langur taxa by Maximum Likelihood method. 168 Figure 4.51: rRNA gene based Evolutionary relationships of different langur taxa inferred using the Neighbor-Joining method. 169 Figure 4.52: rRNA gene based Evolutionary relationships of different langur taxa inferred using the Minimum Evolution method. 169 Figure 4.53 rRNA gene based Maximum Parsimony analysis of taxa inferred using the Maximum Parsimony method 170 Figure 4.54: UPMGA dendrogram based on genetic distance (Nei, 1972, 1978) exhibited by; a) RAPD markers, b) microsatellite markers in different populations of grey langurs. 178 Figure 4.55: Graphical presentation of PCoA, a) RAPD markers; b) by microsatellite for different populations of grey langur. 179 Figure 4.56: Possible geographic linkage of S. ajax from Pakistan to its other Populations (Outside Pakistan). 185

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LIST OF ABBREVIATIONS/ SYMBOLS

~ Approximately °C Degrees Celsius µL Microliter A Adenine AFLP Amplified Fragment Length Polymorphism AJK Azad Jammu and Kashmir AMOVA Analyses of Molecular Variance BLAST Basic Local Alignment Search Tool bp Base Pair C Cytosine CITES Convention on International Trade in Endangered Species cm Centimeter COI Cytochrome Oxidase I CTAB Cetyltrimethylammonium Bromide Cyt b Cytochrome b ddH2O Double Distilled Water DI Diversity Index DNA Deoxyribonucleic Acid dNTP Deoxynucleotide Triphosphate Dst Genetic Diversity Between Population E East e.g. Latin: Exempli Gratia: “for example” EDTA Ethylene Diamine Tetra Acetic Acid EMR Effective Multiplex Ratio et al. et alia: and others Fis Fixation indices (inter-individuals) Fit Fixation indices (total population) Fst Fixation indices (subpopulations) g Gram G Guanine GoAJK Government of Azad Jammu and Kashmir GoKP Government of GPS Global Positioning System Gst Genetic differentiation between populations He Nei's Gene Diversity Index or expected heterozygosity hrs Hours Hs Genetic diversity within population Ht Total Heterozygosity I Shannon’ Information Index i.e. id est: in other words, that is IUCN International Union for Conservation of Nature and Natural Resources kb Kilobase kg Kilogram km Kilometer KP Khyber Pakhtunkhwa Province LoC Line of Control m Meter M Mole (Molarity) m asl. Meter above mean sea line (elevation) Ma Millions of years ago MEGA Molecular Evolutionary Genetics Analysis mg Milligram

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MgCl2 Magnesium Chloride MI Marker Index min Minute ML Maximum Likelihood mL Milliliter mm Millimeters mM Millimolar mtDNA Mitochondrial deoxyribonucleic acid MUSCLE Multiple Sequence Comparison by Log-Expectation N North Na Numbers of observed alleles NADH Nicotinamide Adenine Dinucleotide + Hydrogen NCBI National Center for Biotechnology Information nDNA nulear DNA Ne Numbers of effective alleles NJ Neighbor Joining nm Nanometer Nm Numbers of migrants (Gene flow) NT Northern types Numts Nuclear Mitochondrial-like DNA sequences OD Optical Density PCoA Principle Coordinate Analysis PCR Polymerase Chain Reaction PIC Polymorphism Information Content pmol Picomole Prm1 Protamine 1 RAPD Randomly Amplified Polymorphic DNA rDNA Ribosomal RNA Genes RFLP Restriction Fragment Length Polymorphism RNA Ribonucleic Acid RP Resolving Power rpm Revolutions per Minute rRNA Ribosomal Ribonucleic Acid Rst Genetic differentiation within populations SD Standard deviation SDS Sodium Dodecyl Sulphate sec Second SEM Standard Error of Means spp. Species (More than one) SSR Simple Sequence Repeats (Microsatellites) ST Southern types STR Short Tandem Repeats T Thymine Ta Annealing Temperature Taq Thermus aquaticus TBE Tris-borate – EDTA TE Tris-EDTA Tm Melting Temperature tRNA Transfer Ribonucleic Acid uHe Unbiased Expected Heterozygosity UNEP United Nations Environment Program UPGMA Unweighted Pair Group Method using Arithmetic Averages UV Ultraviolet viz Latin: Videlicet: "namely", "that is to say", "which is", VNTR Variable Number Tandem Repeats

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ACKNOWLEDGEMENTS

All the praise be to Almighty Allah, the Lord of the Day of Judgment, Who blessed me the courage, and enticed me to learn about the living creatures. All tributes and respects are for His best creature, the Holy Prophet Muhammad (peace be upon him), who showed us the right path and enabled us to differentiate between the truth and evil. It is the opportunity to pay my heartful gratitude and sincere thanks to my estimable Supervisor, Prof. Dr. Muhammad Nasim Khan, and Co-supervisor Prof. Dr. Afsar Mian, for their skillful guidance, affectionate behavior and special interest in my studies and research project. I am grateful to Dr. Basharat Ahmad, Chairman Department of Zoology, Dr. Nuzhat Shafi and Prof. Dr. Muhammad Siddique Awan (Ex-chairpersons) for their encouraging behavior and providing all possible lab facilities to carry out my research work. I am also thankful to my respected teachers (Prof. Dr. Khurshid Anwar, Prof. Dr. Abdul Qayyum Nayyer) and colleagues (Dr. Shaukat Ali, Dr. Saiqa Andleeb, Dr. Abdul Rauf, Dr. Ghazanfar Ali, Dr. Nasra Ashraf, Saba Khalid) for their affectionate behavior, encouragements and guidance during my studies. I would like to thank my class-fellows, lab-fellows and friends Dr. Ansar Ahmad Abbasi, Dr. Zahid Latif, Dr. Tasleem Akhtar, Mr. Mohsin Hanif, Mr. Usman Ali, Mr. Siraj-ud-Din, Mr. Imran Tahir, and Ms. Sadia Idrees for their helps and memorable company during my studies. My special thanks and cordial gratitude to Dr. Christian Roos (Senior Scientist, German Primate Center, Germany) for his kind help and skillful guidance in sequence analysis, Prof. Colin Groves (Australian National University) and Doug Brandon-Jones (UK) for their input in identification of the langur specimens. I would also like to express my gratitude for Mr. Naeem Iftikhar Dar (Director Wildlife and Fisheries) and Ms. Safia Janjua (BRC, Islamabad) for their guidance and cooperation during my research. I am really indebted to my parents, wife and kids, and all family members including sisters, brothers (especially Dr. Fiaz Aziz Minhas), uncles and cousins for their everlasting loves, prayers, and financial supports, during every step of my life. Riaz Aziz Minhas

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ABSTRACT

Grey langurs (Semnopithecus spp., , Cercopithecidae, Primata,

Mammalia) is a group of the old-world leaf eating monkeys widely distributed in the

Indian subcontinent, with two species viz. Semnopithecus ajax and S. schistaceus reported from higher altitudes of the Himalayan hills extending into northern Pakistan.

Species status of these populations is still debated, though S. ajax is regarded as

Endangered globally. In Pakistan, small declining population of these grey langurs is distributed in pockets, but the level of isolation is still unknown. To resolve such uncertainties, the present study was undertaken to assess intrapopulation genetic diversity, and to settle taxonomic status of different populations, using modern molecular biology tools. We collected 86 noninvasive (feces 64, hair 13, blood 5, tissues 4) samples from 5 geographic langur populations of Pakistan and Azad Jammu and Kashmir (AJK) and succeeded in extraction of DNA from 23 samples, which were used for further genetic analysis. We used nuclear (Random Amplified Polymorphic

DNA-RAPD and microsatellites) and mitochondrial DNA (Cytochrome oxidase-I,

Cytochrome b and 16S rRNA) markers.

RAPD makers (n=8) produced 245 bands (30.62±2.87 mean±SE / primer) of different molecular weights (126-3342 bp), of which, 96 were population specific.

Polymorphism was (37.71±5.29%; mean ± SE), with the highest in Muzaffarabad population (54.29%), followed by Poonch (43.67%) and Neelum (36.73%). Values of

Shannon’s (I: 0.129-0.200) and Nei's genetic diversity (He: 0.082-0.117) indices were low. Total heterozygosity (Ht: 0.144±0.007), genetic diversity within population (Hs:

0.096±0.005), between populations (Dst: 0.018±0.003), genetic differentiation constants among populations (Gst: 0.153±0.025) and within populations (Rst:

0.847±0.025) were calculated. Gene flow (Nm: 3.246 0.448) and genetic similarity

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(97-98%) between populations was high. UPGMA (unweighted pair group method with arithmetic mean) based dendrogram identified five distinct geographic groups, and

Mantel tests (Rxy=-0.008, P0.05) suggested a non-significant relationship between genetic distance and geographic distance. Phist (PT) value suggested a significance difference within population and between populations (PT=0.042; p=0.006) variances, suggesting that within populations variation was higher (96%) than variation between populations (4%).

Microsatellite analysis, using 16 primers, exhibited successful cross-species amplification suggesting high discriminatory powers (PIC = 0.94±0.01). A total of 256 polymorphic bands comprising on 97 different sized (88-383 bp) alleles (2-10 alleles/marker) were amplified in different genotypes sampled. Mean level of polymorphism in different populations was 45±6.06%. Tests for linkage disequilibrium between different loci exhibited no significant deviations from expected values

(p>0.05). Mean values of Shannon’s (0.357±0.05), Nei’s genetic diversity

(0.241±0.03), fixation indices (-0.894±0.03), genetic differentiation coefficient (Fst: range = 0.223 - 0.898, mean = 0.438±0.097) and mean gene flow (1.185±0.374) were calculated. The largest Nei’s genetic distance (0.752) was between Mansehra and

Neelum populations, while the least (0.255) between Mansehra and Kohistan populations. UPGMA based dendrogram identified two main clusters, Cluster one subdivided into Poonch population (as outgroup) and a monophyletic clade of

Muzaffarabad and Neelum populations. Second cluster included Mansehra and

Kohistan populations. Principal Coordinates Analysis (PCoA) also indicated three clusters: (1) AJK (Muzaffarabad-Neelum-Poonch), (2) Mansehra, and (3) Kohistan.

The value of Rxy=0.302 (P<0.01) revealed a significant association between genetic distance and geographic distance.

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Phylogenetic analysis, based on different mitochondrial genes partial sequences

(COI, Cyt b and rDNA), using Maximum likelihood, Neighbor-Joining, and Minimum

Evolution methods, suggested close relationship of grey langurs of Pakistan with S. schistaceus and S. entellus populations from different regions of the Indian subcontinent. Estimated evolutionary divergence values showed a low genetic distance

(<0.01) indicating that different populations belong to a single species. Analysis for species delimitation using 4×-rule or K/ϴ (D/ϴ) method also indicated status of a single species.

Present study suggested a low level of isolation and inbreeding between grey langur populations of Pakistan and Azad Jammu and Kashmir. This study confirmed that Semnopithecus ajax is the only species found in different areas of Pakistan and

AJK. Further molecular, as well as morphological, studies using larger sample size and analysis of complete mitochondrial genome sequences are suggested.

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LIST OF PUBLICATIONS

By Author On

Himalayan Grey Langurs (Semnopithecus ajax)

Published Articles

1. Minhas, R. A.*, Khan, M.N., Awan, M. S., Ahmad, B., Bibi, S. S., Hanif, M. and Mian, A. 2018. RAPD based Genetic Diversity of Endangered Himalayan (Semnopithecus ajax) Populations of Pakistan. Pakistan Journal of Zoology, 50(6): 2059-2071. (IF=0.790; Citations = 02)

2. Minhas, R. A.*, Ali, U., Awan, M.S., Ahmed, K.B., Khan, M.N., Dar, N.I., Qamar, Q.Z., Ali, H., Cyril C. Grueter and Yamato Tsuji. 2013. Ranging and Foraging of Himalayan Grey Langur (Semnopithecus ajax) in Machiara National Park, Pakistan. , 54: 147-152 (IF=1.363; Citations = 07)

3. Minhas, R. A.*, Ahmed, K.B., Awan, M.S., Qamar Z.Q., Dar, N.I. and Ali, H. 2012. Distribution Patterns and Population Status of the Himalayan Grey Langur (Semnopithecus ajax) in Machiara National Park, Azad Jammu and Kashmir, Pakistan. Pakistan Journal of Zoology, 44(3): 869-877. (IF= 0.547; Citations = 09)

4. Minhas, R. A.*, Ahmed, K.B., Awan, M.S. and Dar, N.I. 2010. Habitat Utilization and Feeding Biology of Himalayan Grey Langur (Semnopithecus entellus ajax) in Machiara National Park Azad Kashmir (Pakistan). Zoological Research, 31(2):177- 188. (Citations = 16)

5. Minhas, R.A.*, Ahmed, K. B., Awan, M. S. and Dar, N. I. 2010. Social Organization and Reproductive Biology of Himalayan Grey Langur (Semnopithecus entellus ajax) in Machiara National Park Azad Kashmir (Pakistan). Pakistan Journal of Zoology, 42(2): 143-156. (IF= 0.547; Citations = 16)

Manuscripts Under Preparation

1. Taxonomic Status of Himalayan grey langurs (Semnopithecus sp.) in Pakistan.

2. Analysis of Genetic diversity inferred by microsatellite markers in Himalayan grey langurs (Semnopithecus ajax) populations of Pakistan.

3. Conservation Status of Himalayan grey langurs (Semnopithecus ajax) in Pakistan.

4. Evaluation of DNA Extraction and PCR Optimization methods for genetic analysis in grey langurs (Semnopithecus spp.).

xxiii

Chapter 1

INTRODUCTION

1.1. BACKGROUND OF THE PROBLEM

Grey langur or Hanuman langur (Semnopithecus spp., subfamily: Colobinae,

Family: Cercopithecidae) is a nonhuman primate widely distributed in Indian

Subcontinent. Different species of grey langurs are distributed in Bhutan, Bangladesh,

Sri Lanka, India, Nepal, northern Pakistan and possibly extending into Afghanistan and southwestern Tibet-China (Fig. 1.1; Robert, 1997; Brandon-Jones, 2004; Sheikh and

Molur, 2004; Groves, 2005; Kumar et al., 2008; Karanth, 2010; Nag et al., 2011).

Grey langurs are large sized morphological variable monkeys. The coloration varies between species/ subspecies from grey to brown with a black face (Pocock,

1928). The ears are naked or concealed by the facial ruff. Hands and feet are black in the northern populations but pale in the southern ones (Tritsch, 2001). Different species are usually distinguished by the darkness of the outer sides of lower fore and hind legs.

The size significantly varies in different species (weight: male 9.0-20.9 kg; female 7.5-

18.0 kg).

Grey langurs are found in a wide variety of habitats, including temperate forests, arid areas and tropical evergreen rainforests, inhabiting from the snow-covered peaks of the Himalayas (up to 4,270 m above mean sea line, asl) in the north, with temperature of -7oC, to tropical areas and even in the Great Indian Desert, where temperature touches 46oC, in the south west (Roonwal and Mohnot, 1977; Chhangani,

2002; Chhangani and Mohnot, 2004; Sayers and Norconk, 2008; Minhas et al., 2012;

1

Chetan et al., 2014). In India, they have also adopted a communal way of life and are commonly sighted in villages, towns and tourist places (Chetan et al., 2014).

N

Semnopithecus schistaceus Semnopithecus ajax Semnopithecus hector Semnopithecus entellus Semnopithecus hypoleucos Semnopithecus dussumieri Semnopithecus priam Semnopithecus vetulus

Figure 1.1: Distribution of different species of grey langur in the Indian Subcontinent (developed using information from http://www.iucnredlist.org).

Very little information is available on the evolutionary history of grey langurs

(Newton, 1988; Osterholz et al., 2008). Subspecies or species level taxonomy of these langurs have extensively been debated as there has been much disagreement on the status of its various populations (Nag et al., 2011). Formerly, all types of langurs were combined in a single genus (Presbytis or Semnopithecus). They have been divided into

2 three genera, viz. Semnopithecus, Trachypithecus and Presbytis (sometimes a fourth genus viz. Kasi also) (Hill, 1934; Pocock, 1935; Osterholz et al., 2008).

All taxa of grey langurs were once regarded as subspecies within a single species (Semnopithecus entellus). Pocock (1939) recognized 14 subspecies and Hill

(1934) classified grey langur populations into four polytypic species. Different alternative classification schemes have proposed different number of species, ranging from 2 (northern Indian and southern Indian; Brandon-Jones, 2004), 3 (northern Indian, southern Indian and Sri Lankan) to 16 (Ellerman and Morrison-Scott, 1966; Napier and

Napier, 1967, Roonwal and Mohnot, 1977; Roonwal, 1984; Appendix-I). Groves

(2001) recognized 7 species, viz., Kashmir grey langur (S. ajax), Nepal grey langur (S. schistaceus), northern plains grey langur (S. entellus), Tarai grey langur (S. hector), southern plains grey langur (S. dussumieri), black-footed grey langur (S. hypoleucos) and tufted grey langur (S. priam) (Groves, 2005). Though majority of modern taxonomists follow Groves (2001) classification, yet phylogenetic evidences suggested presence of three distinct species (from north Indian, south Indian and Sri Lankan) in the Indian subcontinent (Osterholz et al., 2008; Karanth, 2010). Further molecular studies added two more species, viz., Purple-faced langur (S. vetulus) and Nilgiri langur

(S. johnii) (Osterholz et al., 2008; Mittermeier et al., 2013; Wang et al., 2015a; Takai et al., 2016). The above discussion indicates that the taxonomy of Hanuman langurs has always been in debate, and even now no unanimously agreed classification scheme exists.

In the Himalayas, two species of grey langur, viz., Kashmir grey langur

(Himalayan grey langur/ Chamba sacred langur/ dark-armed Himalayan grey langur; S. ajax) and Nepal grey langur (pale-armed central Himalayan langur, central Himalayan

3 grey langur; S. schistaceus) have been reported (Sayers and Norconk, 2008; Minhas et al., 2012). The Himalayan grey langurs (S. ajax, S. schistaceus) can be differentiated from other species of Semnopithecus by their larger body size and silvery-dark or pale colored outer sides of fore and hind limbs (Wilson and Reeder, 1992; Roberts, 1997).

Long tail of Himalayan langurs always forms a broad arc over the back when on the ground and is curved towards the head as against the southern populations, where the tail is curved away from the hind quarter (Jay, 1965). The color ranges from darkish grey to slightly yellowish grey (Pocock, 1928). Hands and feet are black, and ears are usually concealed by the facial ruff (Tritsch, 2001). The long tail is covered by brownish-grey fur with a tip having longer creamy white hair, forming a terminal tuft.

Naked skin around the eyes, cheeks and muzzle is purplish-black and is framed by a ruff of radiating creamy white hair. In Himalayan populations, during winter, the hair on the shoulders and back of adult individuals become much longer, forming a mane

(Roberts, 1997). The S. ajax has a characteristic shaggy outer mane and stark dark forearms and a dusky brownish-grey upper side (Pocock, 1928). A greyish-brown mane in adult males is a unique characteristic to this species (Brandon-Jones, 2004).

Kashmir grey langur has been reported from Nepal (Brandon-Jones, 2004) to northwestern India (Himachal Pradesh) and Jammu and Kashmir, including the Azad

Jammu and Kashmir (Groves 2001; Minhas et al., 2010, 2012, 2013; Mir et al., 2015,

Sharma and Ahmed, 2017). Though, Roberts (1997) and Groves (2005) reported this species from Pakistan, yet Groves and Molur (2008) restricted the range of S. ajax upto

Chamba valley in Himachal Pradesh, India (Anandam et al., 2015). Distribution of

Nepal grey langur (S. schistaceus) has been reported from the higher Himalayan elevations (1,500-4,000 m asl) from Afghanistan to northern Pakistan, northern India,

Tibet (southwestern China in Bo Qu, Ji Long Zang Bu and the Chumbi valleys), Nepal

4 and up to the Sankosh River in Bhutan (Molur et al., 2003; Brandon-Jones, 2004;

Kumar et al., 2008; Groves and Molur, 2008).

In Pakistan, grey langurs are mainly distributed in Azad Jammu and Kashmir

(AJK: District Neelum/Neelum valley: Salkhala Game Reserve, Muzaffarabad:

Machiara National Park and Jhelum valley including Moji Game Reserve Bagh: Hillan and Phalla) (Roberts, 1997; Ahmed et al., 1999; Ifikhar, 2006; Minhas et al., 2010b,

2012) and Khyber Pukhtunkhwa (KP) province of Pakistan (: Manshi

Wildlife Sanctuary; Nagan-Nadi-Musala; Kalam Ban-Kaghan; Bhunja Kalah Beari-

Chor; Siran Forest Reserve; Kunhar Valley around Shogran, extending up to Khadir

Gali above Mahandri; Kohistan Pallas, Bankad Dubair and Pattan) (Mirza, 1965;

Roberts, 1997; Minhas et al., 2012). The species status of these populations is not confirmed; S. ajax (Roberts, 1997; Minhas et al., 2012) or S. schistaceus (Sheikh and

Molur, 2004; Groves and Molur, 2008).

The present research has been designed to know the hither to undecided taxonomic status of different populations of grey langur distributed in Pakistan and

Azad Jammu and Kashmir (AJK). For the purpose, we planned to identify individual populations and characterize them using molecular genetic tools to identify the species

(by comparing with database available for other species), and work out the interaspecific or interspecfic variability using molecular genetic analysis. Grey langur, being endangered / vulnerable in Pakistan, we tried to use non-invasive sampling of populations through field collected hair or fecal samples, and exploiting molecular genetic markers and relevant computer based statistical analysis tools.

5

1.2. AIM(S) AND OBJECTIVES

The study was based upon the hypothesis that different inbreeding populations of the grey langur distributed in Pakistan and Azad Jammu and Kashmir are isolated to different degrees from one another and represent 1-2 separate species. Under this hypothesis, the study was carried out with the following specific objectives:

1. to settle species status of different populations of grey langurs, found in

Pakistan, using genetic molecular markers.

2. to investigate possible distribution of each sub-species existing in the area

3. to map the distribution range of each species.

4. to investigate level of isolations existing between different populations

5. to study levels of genetic fixation in different populations using homozygosity

test.

The research finding will have a value for the conservation biologists and managers in settling the present status of langurs in Pakistan and can be used in development of future langur population management strategy.

1.3. JUSTIFICATION OF STUDY

Grey langur is important as key ecological services provider as pollinators, seed dispersers, primary consumers and as food for top predators e.g., leopards. Therefore, they are considered helpful in ecosystem conservation/ management, especially in areas, where leopard claims high livestock depredation affecting local economy due to the reduction in wild natural prey. Due to their close genetic and physiological

6 resemblance with human, they are also being used as model organism in the biomedicines, behavior and evolutionary research (Nag et al. 2011).

These investigations are demanding, not only to resolve taxonomic problems, but also to explore the intraspecific and interspecific phylogenetic relationship. S. ajax is considered highly Endangered, because of its restricted range of distribution and continuous decline in population, while S. schistaceus has Least Concerned status.

Wildlife managers are confused on which population of grey langur is population deserves more serious conservation due to ambiguity in distributions ranges of the two species. Population decline has been suggested for langur in Pakistan and AJK (Minhas et al., 2012) demanding serious protective measures for continued survival of the species in the area.

There are geographical and ecological reasons to believe that different populations of langur were isolated from one another, but the level of isolation is not known. Threatened species with isolated populations of few breeding individuals often have lower genetic diversity (Lacy, 1997). Knowledge of the extent of isolations existing between different populations and sizes of individual populations has a conservation value. Smaller isolated populations having higher chances of being genetically fixed through inbreeding and dwindle to extinction due to accumulation of unflavored alleles and genetic homogeneity (Lacy, 1997). Use of molecular genetic markers in assessment of the conservation status of a population/ species is a new trend.

Genetic variability in the population has a predictive value for the potentials of the species/ population to cope with future environmental fluctuations and its continued survival under environmental odds (Frankham et al., 2002).

7

1.4. GENETIC DIVERSITY

Genetic diversity refers to the variety of alleles found in the genome of a species. Threatened species have often reduced genetic diversity because the lower numbers of individuals promote inbreeding which reduces the genetic diversity among populations (O’Brien, 1994; Lacy, 1997). Reduction in genetic diversity decreases the health of the population as effect of genetic uniformity is injurious under adverse environmental fluctuations (Schaffer, 1987; Lande, 1988). Use of genetic studies in conservation is a new trend to preserve species enabling it to cope with environmental fluctuations and hence, minimizing the risk of extinction (Frankham et al., 2002).

Information of genetic variation within and among populations and applications of genetic principles are considered as crucial scientific tools for wildlife managers in making proper decisions for conserving populations (Bellemain, 2004; Oyler-McCance and Leberg, 2005).

1.4.1. Assessment of Genetic Diversity

The assessment of genetic diversity has been made easier with the development of molecular genetic markers. Molecular genetic markers are heritable characters with multiple states ranging between one, two or multiple states (alleles) per character

(locus). All genetic markers reflect variations in DNA sequences, usually with a trade- off between precision and convenience (Sunnucks, 2000). Genetic diversity can also be analyzed by examining polymorphic characters appearing at morphological, biochemical and protein system, but DNA markers are considered very powerful tools for studying genetic diversity (Bruford and Wayne, 1993; Karp et al., 1997).

Several molecular markers have been used in genetic diversity studies, including, informative or co-dominant markers, e.g., microsatellite (SSRs) and

8 restriction fragment length polymorphism (RFLP), and non-informative or dominant markers, such as, random amplified polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP; Nassiry et al., 2009). RAPD markers are easily developed and analyze using PCR amplification techniques. During RAPD analysis the banding patterns of two closely related species/ populations are more similar than the evolutionary unrelated species/ populations (Miesfeld, 1999).

Microsatellite or SSR markers are also good tools used for genetic studies having a co-dominant mode of inheritance. High degree of reproducibility and polymorphism increase their discriminative power (Nassiry et al., 2009). Due to high mutation rate, these microsatellites are very polymorphic with different number of repeat units which can be easily examined on high resolution gels (Levinson and

Gutman, 1987). Microsatellite markers have also been used for several genetic relationships studies of primates, including some related genera of grey langurs, e.g.,

Rhinopithecus (Chambers et al., 2004; Liu et al., 2008; Roeder et al., 2009).

Beside nuclear DNA, mitochondrial DNA (mtDNA) markers are also being extensively used in genetic diversity studies. The haploid mtDNA, having a maternal inheritance mode and a high mutation rate, does not recombine. Thus, by assessing the mutation patterns in mtDNA, intra/interspecific phylogenetic relationships are extensively being reconstructed (Geissmann et al., 2004).

Numerous mitochondrial DNA markers from protein–coding and ribosomal

RNA genes have been used in genetic diversity and phylogenetic analysis (Grechko,

2002; Arif and Khan, 2009). However, the extensively used mtDNA markers are cytochrome b (Cyt b), cytochrome c oxidase I (COI), cytochrome c oxidase II (COII), rRNA (12S and 16S) and D loop or mitochondrial control region (Balitzki-Korte et al.,

9

2005; Kartavtsev and Lee 2006; Zhang and Jiang, 2006; Roe and Sperling, 2007;

Melton and Holland, 2007; Mitani et al., 2009; Dubey et al. 2009; Arif and Khan,

2009; Wang et al., 2015a). Among these, 12S rDNA > 16S rDNA are relatively conserved regions, cytochrome b is moderately conserved, while control region is highly variable (Arif and Khan, 2009). The most frequently used mitochondrial genetic loci for species identification and systematic studies are the cytochrome b gene (Cyt b) and cytochrome oxidase I gene (COI) (Tobe et al., 2010; Patwardhan et al., 2014).

10

Chapter 2

REVIEW OF LITERATURE

2.1. GREY LANGUR

Grey langur in Pakistan and AJK is limited to moist temperate coniferous forests and are human shy living away from human settlements (Nowak, 1999; Roberts, 1997;

Molur et al., 2003; Sayers and Norconk, 2008; Minhas et al., 2012). Most of the grey langur species are facing population decline in their distribution ranges. The major and common threats to grey langurs throughout the region are the and degradation by the anthropogenic activities such as deforestation, overgrazing, lopping, mining, fire, encroachment for agriculture and development practices e.g., roads through forests. These threats are also accompanied by attack of several viral and bacterial diseases and shortage of food (Nowak, 1999; Bagchi et al., 2003; Nandi et al.,

2003; Sommerfelt et al., 2003). Grey langur parts have a value in traditional Chinese medicine (Kumar et al., 2008; Nag et al., 2011). As Himalayan langurs do not raid or damage crops, therefore, they do not invite the anger of local villagers, but naturally the living conditions for such a leaf eating species are always harsh during long winter season at higher altitudes (Roberts, 1997).

Himalayan langurs are exclusively herbivorous. They are generally folivorous, but can consume young shoots, flowers, fruits, cultivated crops, and even distasteful vegetation and seeds with high levels of toxins, e.g., strychnine (Strychnos non-vomica;

Roberts, 1997). Their diet includes leaf buds, evergreen mature leaves, deciduous young and mature leaves, young shoots and ripe fruits, etc. Some 126 food plants have been recorded as the food of the grey langurs (Punekar, 2002; Minhas et al., 2010b).

Supplemental resources including bark, and herbaceous vegetation and underground

11 storage organs are also seasonally consumed (Sayers and Norconk, 2008; Minhas et al.,

2012). Although, langurs are herbivores, but insects may also constitute a regular part of their diet particularly during the summer monsoons (July-September; Srivastava,

1991). They can also use insect pupae on leaves and eggs of nesting birds as food

(Moore, 1985; Punekar, 2002; Minhas et al., 2012).

Langurs live in social groups comprising of 40-184 individuals (Roberts, 1997;

Minhas et al., 2012). The social structure of these monkeys varies as one-male bisexual troops, multi-male bisexual troops and all-male bands at different locations (Rajpurohit,

1992; Newton, 1994; Minhas et al., 2012). Multimale-bisexual troops have higher densities, especially of females for defensive coalitions (Treves and Chapman, 1996;

Minhas et al., 2012). The densities of bisexual troops are generally restrained by feeding competition and many other factors such as infanticide etc. (Koenig et al.,

1997).

Bisexual troops are generally matrilineal or philopatric. Females remain in their natal troops for life, while males (usually as juveniles) are expelled from the troops to join all-male bands (Rajpurohit, 1992; Newton, 1994). Resident males of troops are generally replaced after an average period of 2.5 years (Rajpurohit et al., 2003; Meena et al., 2015); however, an adult male may also remain in a group as long as 45 months

(Newton, 1987). Two types of resident replacement processes, i.e., abrupt changes and gradual changes, have been reported (Rajpurohit et al., 2003; Sharma et al., 2010).

During the process of an adult-male replacement, invading males of an all-male band first drive out the resident and subsequently the leading male of the band installs himself as the new resident male. This process may also be followed by the expulsion

12 of weaned male juveniles and/or killing of the un-weaned infants (Rajpurohit et al.,

1995; Rajpurohit et al., 2008; Sharma et al., 2010; Meena et al., 2015).

Breeding season varies in different locations. Generally, the births take place between January and March until June after a gestation period of 190-210 days

(Roonwal and Mohnot, 1977; Sommer et al., 1992). Langurs usually give a single birth, but twins and three births have also been reported (Agoramoorthy, 1992). Langur can survive for 18-30 years in the wild (Semke, 2011).

Kashmir grey langur (S. ajax) is considered the rarest grey langur species, and has been enlisted as 'Endangered' due to its very low population and restricted range

(<5,000 km2), and area of occupancy (<500 km2) (IUCN, 2017; Groves and Molur,

2008). These monkeys are also included in the Appendix-I of the CITES which include threatened species (UNEP-WCMC, 2013). Roberts (1997) reported that these monkeys were very rare in Pakistan (n=<200). Recent studies indicate that, the state of AJK supports a population of 783 individuals in Machiara National Park besides many other known areas with its distribution in AJK (Minhas et al., 2012).

2.2. PHYLOGENY AND TAXONOMY OF GREY LANGUR

Grey langurs (Semnopithecus spp.) are the leaf eating monkeys, belong to the group of old-world monkeys of subfamily Colobinae in family Cercopithecidae. The ancestral history of colobines traced back to 20-15 Ma (millions of years ago) in the

Miocene with the fossils of Victoriapithecus found from Kenya (Delson, 1994).

Traditionally, based on various morphological, behavioral and ecological characteristics, the old-world monkeys (Cercopithecidae) are subdivided into Colobinae and Cercopithecinae (Groves, 2001) and the Victoriapithecus is the common ancestor

13 shared by these two subfamilies. Based on the fossil record, the separation between

Colobinae and Cercopithecinae is estimated at 13 to 15 Ma (Harrison, 1989). Both subfamilies have probably originated in Africa during the middle Miocene and penetrated Eurasia during the late Miocene in separate dispersal events (Delson, 1994,

2000; Chang et al., 2012a). The colobines or leaf eating monkeys, emerged in Africa during the middle Miocene, and diverged into African and Asian clades, during 10-13

Ma (Delson, 1994; Stewart and Disotell, 1998; Raaum et al., 2005). The Asian clade probably entered Eurasia during early to middle late Miocene. After these penetrations around 8.8 Ma, some groups dispersed eastward diverging into leaf eating monkeys or langurs (Presbytis, Trachypithecus, Semnopithecus) and odd-nosed monkeys

(Rhinopithecus, Nasalis, Pygathrix, Simias; Schmitz et al., 2005a; Sterner et al., 2006;

Osterholz et al., 2008).

Fossils of Semnopithecus (also considered as Presbytis), have been recorded from the Plio/Pleistocene and Late Pleistocene sediments of northern India and southern India, respectively (Szalay and Delson, 1979; Barry, 1987; Delson, 1994;

Jablonski, 2002; Roberts et al., 2014). However, the correct identification and classification of these fossils remained provocative, especially in the case of extant species (Delson, 1994; Jablonski, 2002; Roberts et al., 2014; Takai et al., 2016).

Although some recent molecular studies have been carried out, however, the evolutionary history of the Asian colobines is still poorly understood (Rosenblum et al.,

1997; Sterner et al., 2006; Osterholz et al., 2008).

Recently, Takai et al. (2016) described a new fossil species (Semnopithecus gwebinensis) of an Asian colobine from the Late Pliocene Irrawaddy sediments in central Myanmar. However, the present day Semnopithecus (grey langur) has been replaced by a closely related Trachypithecus in Myanmar (Takai et al., 2016). The

14 existing species Trachypithecus pileatus is assumed to be a hybrid of Semnopithecus and Trachypithecus appeared in the Early Pleistocene (Osterholz et al., 2008; Karanth,

2010; Roos et al., 2011).

2.2.1. Generic Level Classification

Karanth (2010), referring to classifications of Groves (2001) and Brandon-Jones et al. (2004), divided the Asian colobines into five species groups, viz. snub-nosed monkeys (Rhinopithecus and Pygathrix), pig-tailed monkey (Simias), (Nasalis), (Presbytis), leaf monkeys (Trachypithecus) and grey langurs

(Semnopithecus).

Based upon morphological characters, grey langurs were initially combined in a single genus Presbytis or Semnopithecus (Pocock, 1928, 1939; Napier and Napier,

1967; Groves, 1970), or three to four different genera Presbytis, Trachypithecus and

Semnopithecus (Pocock, 1935; Groves, 1989; Oates et al., 1994), and Kasi (Hill, 1934).

However, most of the recent classifications usually agrees on three distinct genera

Presbytis, Trachypithecus and Semnopithecus with several species and subspecies

(Groves, 2001; Brandon-Jones, 2004; Osterholz et al., 2008).

The genus Trachypithecus is the most diverse with its members distributed from

Southern India and Sri Lanka to the Sundaland (Osterholz et al., 2008). The genus

Presbytis occur solely in the Sundaland (Groves, 2001). Genus Semnopithecus (Grey langurs) is confined to the Indian subcontinent and its members have been recorded in different habitats from China (Tibet), Bhutan, Bangladesh, India, Sri Lanka, Nepal,

Pakistan, (Ellerman and Morrison-Scott, 1966; Roonwal, 1984; Oates et al., 1994;

Choudhury, 2007; Walker and Molur, 2004, Kumar et al., 2008, Minhas et al., 2012;

Fig. 1.1).

15

Based on the nulear DNA (nDNA) marker (lysozyme gene), Messier and

Stewart (1997) recorded two major phylogenetic groups and placed grey langur

(Semnopithecus) and purple-faced langur (Trachypithecus) in one group while

Southeast Asian species (i.e., dusky and Francois' leaf monkeys) into another group.

Similar findings have been reported by Zhang and Ryder (1998) in a phylogenetic study analyzing mitochondrial tRNAThr and cytochrome b gene fragments. The study supported monophyly of Nilgiri langur, purple-faced langur and grey langur, coinciding with the morphology-based classification scheme of Brandon-Jones et al. (2004).

Osterholz et al. (2008) used mitochondrial and Y chromosomal DNA sequences and presence/absence of retroposon integrations data to analyze the phylogenetic relationship among these genera. They also reported that retroposon integrations confirmed a common origin of Trachypithecus and Semnopithecus. Further, based on Y chromosomal and mitochondrial sequence (573 bp) data, they suggested the purple- faced langur (Trachypithecus vetulus) clusters within Semnopithecus.

Ting et al. (2008) analyzed the mitochondrial dataset and fragment of the X- chromosome for comparing the mitochondrial and morphological phylogenetic hypotheses about the different langur genera. They recorded incongruence between mitochondrial and nuclear markers results, because, mitochondrial results supported a

Presbytis-Trachypithecus close relationship, while, the X-chromosomal dataset suggested a Semnopithecus-Trachypithecus group. They suggested that this strong discordance may be due to the differential lineage sorting or ancient hybridization between the mitochondrial and X-chromosomal markers in these taxa (Ting et al.,

2008).

Karanth et al. (2008) also attempted to resolve the phylogenetic positions of several grey langur species and other leaf monkeys of South Asia. Based on

16 mitochondrial DNA, they recommended that the grey langurs (Semnopithecus) are polyphyletic with respect to purple-faced langurs and Nilgiri langurs (Karanth et al.,

2008; Karanth et al., 2010). They also supported the results of Zhang and Ryder (1998) suggesting the placement of purple-faced langurs and Nilgiri in the genus

Semnopithecus along with the grey langurs. However, these studies did not use nDNA markers in along with mtDNA markers from all species of langurs of South Asia.

Furthermore, these studies were also lacking sampling of the widely distributed and variable species of grey langur and most of these investigations focused on resolving the phylogenetic positions of other Asian colobines but failed to resolve the grey langur polyphyly issue (Karanth et al., 2008; Osterholz et al., 2008; Karanth et al., 2010).

Wang et al. (2012) investigated the phylogenetic relationships among colobine genera using nuclear genes and the mitochondrial genomes. They suggested a sister- grouping between Trachypithecus-Semnopithecus, and between Presbytis and the odd- nosed monkey group. They also analyzed that the divergence of Semnopithecus was the earliest among the Asian colobines.

2.2.2. Species Level Classification

Majority of the authors have considered the grey langurs or Hanuman langurs as a single species, Semnopithecus entellus (or Presbytis entellus or Pithecus entellus)

(Pocock, 1928; Karanth et al., 2010; Takai et al., 2016). However, these langurs have been subdivided into several distinct species and subspecies ranging between 1 to 16

(Appendix-I).

Pocock (1928, 1939) declared all populations of grey langur in a single species

Semnopithecus entellus, with 14 sub-species (Pocock 1939). Hill (1939) classified grey langur into 4 species (Semnopithecus entellus, S. schistaceus, S. hypoleucos and S.

17 priam), giving Semnopithecus entellus entellus as full species status with no subspecies

(Appendix I). He placed the Himalayan subspecies (ajax, schistaceus, hector, achilles and lanius) in a single species S. schistaceus (Appendix-I)

These classification schemes were followed by several other classification schemes (e.g., Ellerman and Morrison-Scott, 1966; Napier and Napier, 1967; Roonwal and Mohnot, 1977; Roonwal, 1984). However, with few amendments (e.g.,

Semnopithecus was considered into Presbytis), most of them followed the classification scheme of Pocock (1928, 1939) (Appendix-I). Based on pattern of tail carriage,

Roonwal (1979, 1984) classified various subspecies of grey langur into 2 broader groups i.e., the Northern (NT) and Southern (ST) types. The tail of NT forms the loops forward toward the head, while that of the ST it loops backward i.e. away from the head (Hill, 1939; Roonwal, 1979, 1984).

Groves (2001) recommended several distinctive former subspecies to elevate as full species status, and the Semnopithecus was divided into seven species including

Kashmir grey langur (S. ajax), Nepal grey langur (S. schistaceus), Northern plains grey langur (S. entellus), Southern plains grey langur (S. dussumieri), Black-footed grey langur (S. hypoleucos), Tufted grey langur (S. priam) and Tarai grey langur (S. hector).

Brandon-Jones (2004) placed all grey langurs into 2 species viz. a northern S. entellus and a southern S. priam, with 7 (S. e. schistaceus, S. e. ajax, S. e. entellus, S. e. hector,

S. e. hypoleucos, S. e. achates, and S. e. anchises) and 2 subspecies (S. p. thersites and

S. p. priam) respectively. However, the classification scheme of Groves (2001) was followed by Grove (2005).

Osterholz et al. (2008) tried to analyze the phylogenetic position of grey langurs by using mitochondrial and Y chromosomal sequence and presence/ absence of retroposon integrations. They proposed the splitting of genus into three species groups

18

(S. entellus - North India, S. entellus - South India + T. johnii, S. entellus – Sri Lanka +

T. vetulus).

Karanth et al. (2010) tried to further investigate these ambiguities of grey langur phylogeny through the analysis of the nuclear protamine 1 (Prm1) and the mitochondrial cytochrome b (Cyt-b) genes. Based on these investigations, they also suggested splitting of Hanuman (grey) langurs into three divergent clades or species namely; Northern-typed Hanuman (northern India), Southern-type Hanuman from southern India, and Southern-type Hanuman from Sri Lanka supporting the suggestions of Osterholz et al. (2008). However, they did not assign the names to the species because these investigations were based on limited sampling and did not cover all species (Nag et al., 2011). These studies suggested further investigation about the phylogenetic relationship of Hanuman langurs using some other marker (e.g., X/Y- chromosome markers and microsatellites) on the samples collected from all over its distribution range (Karanth et al., 2010). Based on these molecular studies, Karanth

(2010) suggested that Semnopithecus lineage has at least five species, Northern Type-

Hanuman, Southern Type-Hanuman (from south India), Southern Type-Hanuman

(from Sri Lanka), Nilgiri langur and purple-faced langur.

Nag et al. (2011) suggested that taxonomic status of different grey langur species has remained unresolved. They studied external morphological variations among several southern grey langur species to identify different morphologically recognizable units (morphotypes) of these langurs in peninsular India. They compared their field observations with already existing classification schemes. By comparing the five color-independent characters, they reported six distinct morphotypes (1 NT and 5

ST). However, they only focused on ST grey langurs because their morphological variations are higher than NTs (Nag et al., 2011). Recently, based on

19 external morphological characters and ecological niche modelling, Chetan et al. (2014) also supported these six main types of these langurs from Peninsular India. But again, they did not focus on Himalayan populations.

All the above discussion reveals that in this context, recent molecular studies on grey langurs of the Indian subcontinent have gained a prime importance. Recent advances in molecular biology and phylogenetic analysis enabled scientists to revisit the traditional taxonomic classifications with respect to the evolutionary histories

(Packer et al., 2009). Currently the classification scheme of Groves (2001, 2005) has generally been accepted widely and the same is also adopted by the International Union of Conservation (IUCN). However, based on resent molecular phylogenetic studies

(Messier and Stewart, 1997; Karanth et al., 2008 and Osterholz et al., 2008), two further species T. vetulus and T. johnii have also been included now in the species list of Semnopithecus elevating the Groves (2001) species list to 9 (Osterholz et al., 2008;

Mittermeier et al., 2013; Nag et al., 2011; Wang et al., 2015a).

Two species of grey langur have been reported inhabiting high altitudes in

Himalaya, viz. dark-armed Himalayan langurs (S. ajax) and pale-armed (S. schistaceus)

(Napier, 1985; Groves, 2001; Brandon-Jones, 2004; Brandon-Jones et al., 2004; Sayers et al., 2010). Himalayan langurs (S. ajax and S. schistaceus) are distributed in the

Himalayas of India, Nepal and Pakistan (including Kashmir), and possibly may also be penetrating to Afghanistan from west of Pakistan’s Indus Valley (Roonwal and

Mohnot, 1977; Brandon-Jones, 2004; Sayers et al., 2010; Karanth et al., 2010; Minhas et al., 2012). Among these, pale-armed Himalayan langurs are widely distributed ranging from possibly Afghanistan to Bhutan, while dark-armed Himalayan langurs are reported from Himachal Pradesh, Jammu and Kashmir and Pakistan (Brandon-Jones,

2004; Sayers et al., 2010; Minhas et al., 2012). According to Sayers et al. (2010), with

20 reference to Napier (1985), these two species are mainly distinguished by darkness of the forelimbs. S. schistaceus have forearms with similar coloration of upper arms and back or a little darker, while the S. ajax have dark brown/black forearms (Brandon-

Jones, 2004; Sayers et al., 2010).

Like other areas, the taxonomic uncertainty in langur populations of Pakistan has been remained problematic and hence needs clarification (Minhas et al., 2012).

The Himalayan or Kashmir grey langur (S. ajax) (Groves, 2001) is native to Jammu

Kashmir and Himachal Pradesh (India), with very restricted distribution range (i.e.,

India-Jammu Kashmir and Himachal Pradesh, Pakistani Kashmir (AJK) and Nepal-

Lang Tang National Park) (Walker and Molur, 2004; Brandon-Jones, 2004; Groves,

2005; Groves and Molur, 2008). Mirza (1965) and Roberts (1997) have reported both the species from Pakistani territory but Groves and Molur (2008) restricted S. ajax only to Himachal Pradesh (India) and suggested that other populations need to be validated.

Nepal grey langur (S. schistaceus), is reported to be distributed in Pakistan (Khyber

Pukhtunkhawa, AJK) and other states in the Himalayan belt including Afghanistan,

India (Shimla, Himachal Pradesh, Bihar, and Kargil district of Jammu Kashmir), China,

Tibet, Nepal (Central, Eastern and Mid-Western Provinces) and Bhutan (Walker and

Molur, 2004, Kumar et al., 2008). Groves (2001) reported S. schistaceus in the

Himalayas ranging from central Nepal, Tibet, and areas of China near northwestern

Bhutan. Recently, after Mirza (1965), Roberts (1997) and Groves (2005), Minhas et al.

(2010a,b, 2012) have also reported the Kashmir grey langur (S. ajax) from Pakistani

Kashmir i.e., AJK. The distribution and boundaries between distribution of two langur species in Pakistan still needs studied.

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2.3. GENETIC SAMPLING

Molecular techniques are extensively being used for the conservation management of threatened wildlife species (Larbi et al., 2012). Acquisition of appropriate samples is the basic requirement for obtaining genetic materials for molecular studies. In the past, the genetic analysis of wild populations required relatively larger amounts of fresh tissue, which generally required destructive sampling technique (Lewontin, 1991; Murphy et al., 1996; Larbi et al., 2012). However, with discovery of the Polymerase Chain Reaction, the scientists have enabled to amplify the targeted template of nucleic acid from a very minute quantity (Larbi et al., 2012).

These novel techniques have widely been used by the conservation biologists for the population genetic studies of wild . Because such low quantity of DNA for PCR can be obtained from nondestructive samples such as blood samples, small biopsies, plucked hairs and feathers. Blood, hair, biopsy samples and intestinal epithelial cells recovered from feces are the preferred biomaterial for genetic studies (Larbi et al.

2012). Now the further advancement in this area of research allowed biologists to extract DNA from genetic samples acquired through completely non-destructive and non-invasive methods from shed skin, hairs, and feathers, and even feces or saliva

(Valsecchi et al., 1998; Zhang et al., 2006; Frantz et al., 2003; Caryl et al., 2012, Ebert et al., 2012; Larbi et al., 2012; Ramón-Laca et al., 2015).

Both invasive (tissues, blood) and noninvasive (hair, feces without handling or observing animals) genetic samplings methods are being used in the genetic sampling of nonhuman primates including grey langurs. However, many nonhuman primate species are threatened, and there are often additional hurdles in acquiring the appropriate samples. Thus, the sampling strategy used for these animals are of critical importance as these sample once obtained may be very precious and limited (Kirmaier

22 et al., 2009). Consequently, it is critical to handle these samples appropriately, or the use of renewable or semi-renewable sources of genetic material (e.g., fibroblasts or immortalized cell lines) or of non-invasive sources of material (e.g. feces and hair), should be encouraged (Kirmaier et al., 2009). A brief review account of genetic sampling in primates is given below:

2.3.1. Blood and Tissue Samples

Blood is perhaps the best source of cells, DNA and RNA. Although, considered an invasive procedure, and despite of potential threats in handling of these animals and presence of blood-borne pathogens, blood sampling from nonhuman primates is still under practice from wild animals as well as from animals living under captivity (Ding et al., 2000; Sterner et al., 2006; Roos et al., 2007; Karanth et al., 2008; Karanth et al.,

2010; Smith et al., 2014; Ogata and Seino, 2015; Jiang et al., 2016). However, blood sampling from nonhuman primates is very difficult task especially from the wild animals (Kirmaier et al., 2009). Tissue sampling is another way of obtaining the genetic materials extensively carried out in nonhuman primates including grey langur (Lawler et al., 2003; Erler et al., 2004; Sterner et al., 2006; Andriaholinirina et al., 2006; Roos et al., 2007; Karanth et al., 2008; Karanth et al., 2010; Coetzer, 2012; He et al., 2012;

Smith et al., 2014; Ogata and Seino, 2015). Although, blood and tissues samples are the easiest way of extracting high quality genetic material, but due to different practical and legal limitations, their acquisition is difficult especially from free ranging animals

(Kirmaier et al., 2009). Therefore, despite of several disadvantages, the noninvasive way of sampling is usually preferred.

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2.3.2. Non–Invasive Sampling

Noninvasive genetic sampling provides great potential for research and management applications in wildlife biology. Noninvasive genetic analysis has become a powerful tool, especially for molecular studies of the stress-sensitive or rare and threatened animals (Kohn et al., 1997; Archie et al., 2008). Conservation biologist and ethologist were particularly attracted by the non-invasive sampling techniques. Because using these methods, the genetic sampling is considered much easier, as animal does not have to be disturbed, captured or even observed (Blair and Marsh, 1996; Reed et al., 1997; Taberlet and Luikart, 1999). Non-invasive samples, particularly faeces, provide potential sources of genetic information for genetic and ecological research

(Zhang et al., 2006). This information includes the studies about presence, abundance, genetic diversity, relatedness, phylogeography, sex and dispersal of the targeted species

(Beja-Pereira et al., 2009; Ramón-Laca et al., 2015).

Different kinds of noninvasive samples, including cheek cells shed in saliva, hair, feces and urine can be used as sources of genetic material for molecular level studies of wild primates. However, the fecal samples are the easiest noninvasive samples to collect for most of the species (Di Fiore, 2003). Hair samples can also be easy to acquire, at least for some species as the shed hair in night nests (e.g., chimpanzee). These samples can provide DNA for genetic analyses using microsatellites or mitochondrial DNA (Gagneux et al., 1997; Di Fiore, 2003; Roos et al., 2007; Karanth et al., 2008; Karanth et al., 2010; Ang et al., 2016).

Fecal material as a DNA source has many advantages, because it can be available and easily detected without handling or even locating the animals (MacKay et al. 2008). However, the collection of genetic materials from free-ranging animals always accompanies several snags (Larbi et al., 2012). The extraction or further

24 analysis of DNA extraction from noninvasive samples is often very challenging because, the DNA is usually degraded and present in very minute quantity which can result in errors producing incorrect genotypes or even complete failure of PCR amplification (Taberlet et al., 1996; Taberlet and Luikart, 1999; Bellemain and

Taberlet, 2004).

Both samples (hair and feces) are extensively being used in the molecular studies of nonhuman primates including grey langur (Lawler et al., 2003; Roos et al.,

2007; Karanth et al., 2008; Karanth et al., 2010; Chang et al., 2012b; Liedigk et al.,

2012, Ogata and Seino, 2015; Jiang et al., 2016; Ang et al., 2016). However, due to different disadvantages (scarcity and degradation of the DNA and the co-purification of

PCR inhibitors) of this sampling technique, many researches still used mixed methods of sampling i.e., noninvasive and invasive (Ding et al., 2000; Sterner et al., 2006; Roos et al., 2007; Karanth et al., 2008; Karanth et al., 2010; Smith et al., 2014: Ogata and

Seino, 2015; Ang et al., 2016; Jiang et al., 2016). Because several factors (e.g., low quantity and quality of DNA, presence of PCR inhibitors etc.) dramatically reduce the genotyping success of fecal samples and impose the need for pilot optimization analyses and multiple genotyping replicates, which overall increase the cost of such studies (Fernando et al., 2003). These limitations have historically made fecal DNA either impracticable or unaffordable. Despite an increase has been recorded in non- invasive DNA usage in general and fecal DNA since the 1990s due to continuously improving mythologies and technologies (Taberlet et al., 1999; Frantz et al., 2003;

Caryl et al., 2012; Ebert et al., 2012).

2.4. MOLECULAR TECHNIQUES USED IN PRIMATE CONSERVATION

The measurement of genetic diversity has made easy by the development of modern technologies using molecular genetic markers. Genetic diversity can also be

25 measured by examining polymorphic characters happening at morphological, biochemical and protein system, but DNA markers are considered very powerful tools for studying genetic diversity (Karp et al., 1997). In most of the eukaryotes, the DNA is found in two separate genomic organizations, i.e., nuclear DNA (nDNA) and mitochondrial DNA (mDNA). Both of these genomes provide excellent molecular makers for analysis of the phylogenies or genetic diversity in populations.

2.4.1. Nuclear DNA markers

There are two copies of each locus on homologous chromosomes in diploid organisms. These two copies of a locus are called alleles. Coding regions or exons are often intermingled with comparatively more variable noncoding regions (introns). The noncoding DNA is often consisting of duplicated or repetitive regions having a core of sequences repeated in variable degrees. These variations in number of repeated units include some of the most variable molecular markers used in the genetic analysis of eukaryotes (Parker et al., 1998). These variable number tandem repeated sequences or

VNTRs are called minisatellites, microsatellites or satellite DNA (Jeffreys et al., 1985).

Several nDNA markers have been used in genetic diversity studies, including informative or co-dominant markers, e.g., microsatellite (SSRs) and restriction fragment length polymorphism (RFLP), and non-informative or dominant markers, such as, random amplified polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP; Nassiry et al., 2009). These markers have been used for analyzing the genetic relationship of langurs and their related species (Ding et al.,

2000; Chaveerach et al., 2007; Kolleck et al., 2013).

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2.4.1.1. Randomly amplified polymorphic DNA markers

Randomly Amplified Polymorphic DNA, generally known as the RAPD markers can be easily developed and amplified, as they consist of a single short length primer (usually 10-bp) and produces dominant, multi-locus DNA profiles which can be readily detected and observed through agarose gel electrophoresis (Di Fiore, 2003).

During RAPD analysis of the banding patterns of two closely related species/ populations are more similar than the unrelated species/ populations (Miesfeld, 1999).

These markers are considered as dominant markers, because of their inability to differentiate between homozygotes and heterozygotes, as they can only display the presence or absence of an allele (Williams et al., 1990; Arif and Khan, 2009). Further, the low repeatability is another disadvantage of this technique, however, it is still utilized for population genetic studies due to its simplicity, the requirement of small quantities of DNA, and being least expensive as well as requires no prior knowledge of a species genome (Di Fiore, 2003; Kumar and Gurusubramanian, 2011). RAPD technique has become popular for genetic comparison of organisms where relatively small-scale DNA sequences are compared using many genetic markers without cloning and sequencing of the genome of the species in question (Kumar and

Gurusubramanian, 2011). Therefore, despite its several disadvantages, these markers are still being used for genetic diversity analysis of different vertebrates probably be important until better techniques are developed in terms of cost, time and labour

(Bardakci, 2001; Rodrigues et al., 2007; Beja‐Pereira et al., 2009, Kumar and

Gurusubramanian, 2011; Vasave et al., 2014; Shafi et al., 2016; Mudasir et al., 2016).

These markers have also been used for genetic studies in different taxa of primates including langurs (Neveu et al., 1998; Ding et al., 2000; Vernesi et al., 2000;

Ravaoarimanana et al., 2001; Arif and Khan, 2009).

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Ravaoarimanana et al. (2001) genetically compare the wild isolated populations of Lepilemur dorsalis and Lepilemur septentrionalis using RAPD markers. They found that despite of separating barriers, the two populations of L. septentrionalis, belong to one population, though, the level of genetic differentiation was higher between the separated population.

Vernesi et al. (2000) analyzed the RAPD profiles of 61 individuals from 10 macaque species. They used 23 different primers on each sample, which an average yielded 17 bands/primer. The RAPD profiles appeared to be highly reproducible without any differences in the amplification patterns by the DNA extracted from hair or blood. Each species had a unique band pattern homogeneously shared by all individuals.

Ding et al. (2000) analyzed allozyme and RAPD to infer the taxonomic status of white-head langurs (Trachypithecus francoisi leucocephalus). They found only one polymorphic locus from a total of 44 loci surveyed. Out of thirty 10-mer primers used,

22 generated clear bands for RAPD analysis. Phylogenetics based on genetic distances exhibited that T. leucocephalus and T. francoisi were not monophyletic (Ding et al.,

2000).

2.4.1.2. Microsatellite markers

Microsatellite markers also known as SSR (Simple Sequence Repeats) or STR

(Short Tandem Repeats) are the most frequently used markers for genetic diversity studies because of their high mutation rate and codominant mode of heritance (Selkoe and Toonen, 2006; Arif and Khan, 2009). These markers were initially developed for genetic mapping in humans (Litt and Luty, 1989; Weber and May, 1989). High degree of reproducibility and polymorphism of these markers increase their high

28 discriminative power (Nassiry et al., 2009). Microsatellites markers are repeats of short nucleotide sequences (1-6 bases in length), that vary in number between 5 and 40 repeats (Selkoe and Toonen, 2006; Arif and Khan, 2009).

Due to their relatively small size, microsatellites can easily be amplified using

PCR from DNA extracted from several sources including blood, skin, hair or even feces (Jarne and Lagoda, 1996; Goldstein and Schlötterer, 1999). Microsatellites are hypervariable; they often show tens of alleles at a locus that differ from each other in the numbers of the repeats units which can be easily examined on high resolution gels

(Levinson and Gutman, 1987). Due to these reasons, microsatellites have extensively used as genetic markers for population genetic analysis of various organisms including primates (Grobler et al., 2006; Raveendran et al., 2006; Liu et al., 2008; Roeder et al.,

2009; Kolleck et al., 2013). In primates, 1-6 repeat units of microsatellite have been recorded (Tóth et al., 2000).

The studies on polymorphism of primates was extensively increased with the discovery of microsatellite variations in human genome (Litt and Luty, 1989; Weber and May, 1989). As microsatellites are highly polymorphic and informative, hence they have been widely used to study the genetic structures of various nonhuman primate species. However, the use of microsatellite markers or analysis of genetic diversity in grey langur (Semnopithecus spp.) is scarce or unavailable. A brief account of microsatellites based genetic analysis studies in primates is as under.

Morin and Woodruff (1992) described a set of human microsatellite loci that were polymorphic in chimpanzees. Inoue and Takenaka (1993) cloned and characterized polymorphic microsatellites from Japanese macaques (Macaca fuscata), and showed that those markers were polymorphic in baboons. Other investigators also described useful DNA polymorphisms in primates, with most these analyses involving

29 either chimpanzees (Deka et al., 1994) or macaques (Kayser et al., 1995). The screening of human microsatellites from specific regions of the chromosomes allowed scientists to target the loci which were likely to be linked closely in the old-world monkeys. Because several earlier studies had compared the karyotypes of these primates to that of humans and suggested to which chromosomal regions were likely to show similar order among microsatellites across species (Dutrillaux et al., 1979).

Rogers et al. (1995) analyzed microsatellite polymorphisms in baboons. They performed polymerase chain reaction (PCR) to amplify homologous loci in individual baboons using primers designed to assess human microsatellites.

Erler et al. (2004) analyzed newly identified male specific microsatellites from human Y-chromosomes in different species of chimpanzees, gorillas, orangutans and mandrills. They detected polymorphism at different loci of these nonhuman primates.

They found a significantly negative correlation between amplification success of these markers with divergence time of nonhuman primates from the human lineage.

Chambers et al. (2004) tried 47 human-derived microsatellite markers to assess their efficacy in genetic characterizations of white-handed gibbon. Only eight makers exhibited adequate levels of polymorphism to be used for individual identification.

Based on this surprising low success rate of these markers, they emphasized on conducting the pilot studies before starting large-scale genotyping projects using cross- species genetic markers.

Pan et al. (2005) scanned 14 microsatellite loci of Rhinopithecus roxellana in a sample of 32 individuals from its three major habitats in China. They found a considerable polymorphisms and significant differentiations among the local populations.

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Raveendran et al. (2006) used a whole-genome sequence strategy for microsatellite markers development in rhesus macaques. They used 54 rhesus-derived newly developed microsatellites for genotyping of rhesus monkeys. They found that these newly identified microsatellites also showed polymorphism in other Old-World monkeys e.g., baboons.

Hao et al. (2007) isolated and characterized 11 different microsatellite loci in

Rhinopithecus roxellana. They screened 20 microsatellite loci from the libraries and found 11 polymorphic loci. The number of observed alleles among 32 samples of these monkey ranged from three to nine with observed and expected heterozygosity of 0.071–0.815 and 0.201–0.819, respectively.

Liu et al. (2008) used 72 human-derived markers to assess their utility in

Yunnan snub-nosed monkeys Rhinopithecus bieti. Only 13 of them showed reliable results and exhibited moderate level of polymorphism.

Roeder et al. (2009) screened a large numbers of microsatellite loci for genetic studies in the Infra-Order Catarrhini. They found 76 microsatellite loci to be polymorphic across a range of catarrhine species comprising 40 species of Old World monkeys, which also included the langur genera Semnopithecus, Presbytis and

Trachypithecus.

Chang et al. (2012b) investigated the genetic consequences of anthropogenic effects on Rhinopithecus roxellana in China. They observed a high level of genetic variation among 202 individuals of these monkeys collected from three populations using 16 microsatellite loci.

Kolleck et al. (2013) investigated the genetic diversity in Rhinopithecus brelichi using microsatellite and mitochondrial DNA markers. However, they obtained

31 contrasting results from microsatellite and mitochondrial DNA data. The genetic diversity of nuclear DNA was high and the examined microsatellite loci were highly polymorphic as in other species of the genus. The reason about the difference as they explained in mitochondrial and nuclear genetic diversity was the biased male dispersal.

Because the females most likely stay within their natal groups and males migrate between different bands, thus mitochondrial DNA will not be exchanged between bands but nuclear DNA via males.

Smith et al. (2014) genetically compared the two subspecies of Philippine cynomolgus macaques. They used short tandem repeat loci along with 835 bp fragment of the mtDNA to compare the different populations of cynomolgus macaques originating from southern Sumatra, Indonesia, Mauritius, Singapore, Cambodia and

Malaysia. They found that Philippine population exhibited a lower level of genetic diversity and higher genetic divergence than all other populations.

Ogata and Seino (2015) investigated DNA polymorphisms of four microsatellite loci of five proboscis monkeys (Nasalis larvatus) found in a captivity in Japan to clarify their genetic relationship. From these five monkeys, two monkeys were seemed to be genetically related.

Wang et al. (2015b) studied the paternity of free-ranging Taihangshan macaques (Macaca mulatta tcheliensis) from China. They evaluated different microsatellite loci for their efficiency in paternity testing in these macaques. The found that 10 out of 18 tested microsatellite loci were successfully amplified from the fecal- derived DNA of Taihangshan macaques. They recorded very low genotyping error probability as indicated by the values of polymorphic information content, and discrimination powers of markers.

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Zhou et al. (2015) assessed the genetic status of a provisioned group of the

Rhinopithecus roxellana in Shennongjia National Natural Reserve. They used 12 microsatellite markers to analyze the genetic diversity and relatedness of the group.

These microsatellite loci were successfully amplified and assayed in 51 individuals by a total of 50 alleles detected. Mean values of observed heterozygosity, expected heterozygosity and polymorphic information content (PIC) were 0.668, 0.630 and

0.567, respectively.

Duan et al. (2017) isolated 29 polymorphic genomic microsatellite loci in golden snub-nosed monkey (Rhinopithecus roxellana). They found that the seventeen loci showed high polymorphism and per locus number of alleles was 5.034 ± 1.349

(mean ± SD), the observed and expected heterozygosity was 0.624 ± 0.116 and 0.614 ±

0.098, respectively.

2.4.2. Mitochondrial DNA Markers

Beside nuclear DNA, mitochondrial DNA (mtDNA) also provides most popular population genetic markers extensively being used in genetic diversity studies

(Geissmann et al., 2004; Nabholz et al., 2008; Kolleck et al., 2013; Ram et al., 2016).

Mitochondrial genome size varies among different organisms ranging between 15700 to 19500 kb (Grechko, 2002). In animals, mtDNA encodes 13 proteins, 2 ribosomal

RNAs, and 22 transfer RNAs (Maqsood and Ahmad, 2017). The haploid mtDNA, have a maternal inheritance mode and a high mutation rate, as it does not recombine or proofread. Thus, by assessing the mutation patterns in mtDNA, intra/interspecific phylogenetic relationships are extensively being reconstructed (Roman and Palumbi,

2003; Nabholz et al., 2008).

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Mitochondrial DNA has advantages over the nDNA, making it more useful tool for species identification and phylogenetic analysis. Each cell has 100s of copies of mtDNA. This DNA is less rapidly degraded due to its own rigid and highly proteinous membrane. Thus, it can be even extracted from highly degraded samples (e.g. feces, hair, bone etc.) which do not contain much nuclear DNA (Satoh and Kuroiwa, 1991;

Panday et al., 2014).

Several loci of the mitochondrial DNA have been used as genetic markers.

Mitochondrial DNA markers can be categorized into Mitochondrial ribosomal RNA markers and Mitochondrial protein–coding genes markers (Grechko, 2002; Arif and

Khan, 2009). The most frequently used mDNA markers in genetic diversity and phylogenetic analysis are cytochrome c oxidase I (COI), cytochrome c oxidase II

(COII) (Kartavtsev and Lee, 2006; Roe and Sperling, 2007), cytochrome b (Cyt b;

Zhang and Jiang, 2006), 12S rRNA (Balitzki-Korte et al., 2005; Melton and Holland,

2007), 16S rRNA (Mitani et al., 2009; Dubey et al., 2009), ATPase 6 gene (Fu et al.,

1999), mitochondrial NADH-3 and NADH-4 genes with associated tRNAs genes

(Rosenblum et al., 1997; Md-Zain et al., 2008); and D loop or Control region (Norman et al., 1994; Arif and Khan, 2009).

2.4.2.1. Mitochondrial protein–coding genes markers

Due to their faster evolutionary rates compared to ribosomal RNA genes, the mitochondrial protein–coding genes are regarded as more powerful markers for genetic diversity analysis at lower categorical levels, i.e. families, genera and species (Arif and

Khan, 2009). Animal mitochondria contain 13 protein–coding genes; however, the most extensively used protein coding genes of the mitochondrial genome for molecular analysis are cytochrome oxidase I gene (COI) and cytochrome b (Cyt b) (Blancher et al., 2008; Liu et al., 2008; Tobe et al., 2010; Karanth et al., 2010; Roos et al., 2011;

34

Meyer et al., 2011; Liu et al., 2013; Patwardhan et al., 2014; Badhan et al., 2015;

Wang et al., 2015a; Ang et al., 2016).

2.4.2.1.1. Cytochrome c oxidase I (COI)

The Cytochrome C Oxidase Subunit I (COI) is an important mitochondrial protein–coding gene used in genetic analysis and identification of various animal species. A fragment of ~658 bp of the COI mitochondrial gene was proposed as a barcoding region for members of the animal kingdom (Hebert et al., 2003). Most of the available studies suggested the 650-bp fragment from the 5′-terminus of COI gene is the best barcode sequence in different animal taxa, because the most species investigated possess distinct COI barcode arrays, with low intra-specific variation and high degrees of divergence from closely allied taxa (Hebert et al., 2004; Hajibabaei et al., 2006; Santamaria et al., 2007; Ward et al., 2009; Clare et al., 2011; Khaliq et al.,

2015; Pradhan et al., 2015).

DNA barcoding has attained the status of a universal molecular identification system of living beings for which efficacy and universality have been largely demonstrated in almost all animal taxa including (Francis et al., 2010; Cai et al., 2011; Tobe et al., 2010; Galimberti et al., 2015; Tahir and Akhtar, 2016). DNA barcoding has extensively been used in primate taxonomy and forensic studies (Lorenz et al., 2005; Hajibabaei et al., 2006; Eaton et al., 2010; Nijman and Aliabadian, 2010;

Minhós et al., 2013; Ruiz-García et al., 2014; Pradhan et al., 2015).

Nijman and Aliabadian (2010) compared the performance of COI in species identification by analyzing 166 sequences from 50 species. They concluded that COI allowed accurate species delimitation and suggested that the DNA barcoding can play an important role in comparative primatology.

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Hajibabaei et al. (2006) tested the potentials and limitations of DNA barcoding in primates. They recorded that DNA barcodes provided enough information for efficient identification and species delineation in primates. However, they suggested that these barcodes do not contain reliable information to define a new species or resolve the deeper phylogenetic relationships, but they can be used for assigning the unknown specimens to an already known species.

Eaton et al. (2010) analyzed sequences of COI genes from 23 species of five vertebrate families including Cercopithecidae. Using polymorphism in the COI fragment, they reliably identified unambiguously identified unknown and misidentified samples. They concluded that DNA barcoding can be an effective tool for monitoring and control of poaching and commercial trade of endangered species along with semi- processed or morphologically unidentifiable wildlife products.

Minhós et al. (2013) identified species of 50 collected samples from carcasses of primate species traded in various urban markets of west Africa for which species identifications have already confirmed by the traders. They used DNA barcodes to identify and find the correct frequency estimates for different traded species. They recorded that six of ten extant primate species were traded. They highlighted the importance of using molecular tools for reliable identification of bushmeat species as misidentification bias can hurdle the conservation management policies.

Ruiz-García et al. (2014) sequenced COI and COII mitochondrial genes of 141

Neotropical woolly monkeys to analyzed the genetic diversity phylogenetic relationships. Based on these results they suggested that all woolly monkeys could be included in one unique genus, Lagotrix, with two species L. lagotricha and L. flavicauda.

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2.4.2.1.2. Cytochrome b

Cytochrome b (Cyt b) is a protein-coding gene of the mtDNA genome which encodes Cytochrome b protein which functions as part of the electron transport chain in the process of energy production in the mitochondria of eukaryotic cells (Esposti et al.,

1993). Cyt b gene provides the great phylogenetic information for species–species information and species identification in vertebrates (Hsieh et al., 2001; Wan and Fang,

2003; Li et al., 2013; Yu et al., 2014; Abdul-Latiff et al., 2014; Gupta et al., 2014;

Gupta et al., 2015; Muangkram et al., 2016). The evolution speed varying in different regions makes it suitable for evolutionary research on different levels. The Cyt b gene can be targeted by universal primers and has been widely used in genetic diversity and phylogenetic analysis in nonhuman primates including grey langurs and their closely relative genera (Geissmann et al., 2004; Kyes et al., 2006; Roos et al., 2007; Karanth et al., 2008; Nabholz et al., 2008; Osterholz et al., 2008; Karanth et al., 2010; Kolleck et al., 2013; Guo et al., 2014a).

Li et al. (2004) analyzed the sequences of mitochondrial DNA Cyt b and 12S rRNA genes of Rhinopithecus. They found that the four species of Rhinopithecus are quite different from other colobines and hence, these species justify to be in an independent genus. However, authors were unable to solve the subgeneric level subdivision between the Vietnamese and Chinese species, and suggested to collect more evidence from a large range of Asian colobine species.

Wangchuk et al. (2008) used Cyt b region of the mtDNA to analyze the phylogeny of Golden langurs (Trachypithecus geei) and to assess the genetic relationship between two geographically isolated population of capped langurs (T. pileatus) and T. geei. Based on constructed evolutionary history, they predicted that, the golden and capped langurs are closely related to each other and grey langurs

37

(Semnopithecus) are only distantly related to them. They also suggested that, divergence between capped and golden langurs is more recent, than between Trachypithecus and Semnopithecus.

Roos et al. (2008) sequenced a 573 bp long fragment of the mitochondrial cytochrome b gene of silvered langurs species group (Trachypithecus) to settle the systematic classification and the phylogenetic relationships among its different members. They detected five monophyletic clades referring to the five taxa cristatus, auratus, margarita, mauritius and germaini.

Osterholz et al. (2008) investigated evolutionary history of the Asian colobines using mitochondrial sequence data by presence/absence analyses of retroposon integrations. They explored the relationships among langur genera

(Presbytis, Trachypithecus and Semnopithecus) and affiliations of different species under Trachypithecus. They recorded that analysis of 573 bp fragment of Cyt b gene, suggested a common origin of T. cristatus, T. obscurus and T. francoisi, while T. vetulus clustered within Semnopithecus.

Karanth et al. (2008) evaluated the mitochondrial Cyt b gene sequences along with two nuclear DNA genes (lysozyme and protamine P1), to understand the evolution and taxonomy of Nilgiri and purple-faced langurs. They concluded that all these markers suggested the grouping of both these langurs species with Semnopithecus clade. However, they separated the leaf monkeys of Southeast Asian Trachypithecus as a distinct clade. They proposed that hybridization events between Semnopithecus and

Trachypithecus clade might have played a role in the evolution of this species group.

Karanth et al. (2010) also used Cyt b gene sequences to investigate the phylogenetic relationship of various grey langur populations (Semnopithecus) and their

38 relationships with Nilgiri langurs and purple-faced langurs. Based on mitochondrial

DNA analysis they suggested that the grey langurs are not monophyletic. Instead, the mtDNA of the Southern Type from Sri Lanka clusters with purple-faced langur of Sri

Lanka, and the Southern Type from south India clusters with Nilgiri langur. Based on these findings, they suggested at least three divergent clades in genus Semnopithecus from north Indian and South India and Sri Lanka.

Vun et al. (2011) analyzed the mtDNA Cyt b and 12S rRNA gene sequences to investigate the phylogenetic relationship of different species/subspecies of the genus

Presbytis. They sequenced gene fragments of 388 (Cyt b) and 371 bp (12S rRNA), from various subspecies of P. melalophos, P. rubicunda and P. hosei and proposed that

P. m. chrysomelas was the most primitive, followed by P. hosei. They recommended that Cyt b and 12S rRNA can be used as gene candidates for phylogenetic analysis at the species level.

Meyer et al. (2011) analyzed fragments of the Cyt b gene, the hypervariable region I of the D-loop and the intermediate tRNAs, from nine different species of the genus Presbytis. Based on this data, they obtained well-supported different terminal clades, however, interrelationships among these clades could not be resolved properly.

He et al. (2012) investigated the phylogeny of the genus Trachypithecus using the mitochondrial Cyt b and Prm1 genes sequences. Their study included 16 recognized species of Trachypithecus and three subspecies of T. phayrei. The mitochondrial Cyt b gene suggested T. p. crepuscula as a distinct species, while, nuclear Prm1 gene showed that T. p. crepuscula and T. p. phayrei were closely related. They suggested that this incongruence between nuclear and mitochondrial genes might be due to hybridization.

39

Abdul-Latiff et al. (2014) analyzed the phylogenetic relationships among

Macaca fascicularis. They used 383 bp of Cyt b sequences to test the morphology based classification of different subspecies of these monkeys. They detected only 23% parsimony informative character among ingroups revealing that Cyt b locus was a highly-conserved region.

2.4.2.2. Mitochondrial non-protein-coding markers

2.4.2.2.1. D-loop or control region

Mitochondrial genome contains a non–coding region known as D–loop or control region due to its role in regulation of replication and transcription of mitochondrial DNA (Patwardhan et al., 2014). D-loop or control region of mtDNA is the highly polymorphic region making it more useful for analysis of population genetics (Norman et al., 1994; Arif and Khan, 2009). The polymorphisms in the sequence of the hypervariable region of the D-loop have contributed greatly to the constructing phylogeny and studying geographic patterns of genetic diversity of different organisms including grey langurs (Geissmann et al., 2004; Liu et al., 2007;

Liu et al., 2008; Blancher et al., 2008; Osterholz et al., 2008; Karanth et al., 2008,

2010; Roos et al., 2011; Meyer et al., 2011; Liu et al., 2013; Badhan et al., 2015; Wang et al., 2015a; Ang et al., 2016).

2.4.2.2.2. Mitochondrial ribosomal RNA markers

Animal mitochondria contain two ribosomal RNA genes (rDNA), 12S rDNA and 16S rDNA. Mitochondrial 12S rDNA is highly conserved and has been applied for assessing the genetic diversity of higher taxonomical categories (i.e. phyla), while the

16S rDNA is generally used for middle categorical levels e.g. families or genera

(Gerber et al., 2001). Both genetic markers are used in wildlife conservation genetics

40 including nonhuman primates (Alvarez et al., 2000; Lei et al., 2003; Van der Kuyl et al., 2005; Turan, 2008; Vun et al., 2011). However, these markers are somewhat less variable than the D–loop and Cyt b gene fragments, due to the inherently slower evolutionary rate of rRNA genes (Pesole et al., 1999; Guha et al., 2007; Katsares et al.,

2008).

2.4.3. Numts

Numts are the nuclear mitochondrial-like DNA sequences transferred from the mtDNA to the nuclear genome (Lopez et al., 1994). They exhibit variation in size and degrees of homology to their mitochondrial counterparts (Bensasson et al., 2001). They are thought as ‘molecular fossils’ and after inserting into the nuclear genomes, they can confuse the population genetic and phylogenetic analyses due to their biparental mode of inheritance and slow evolutionary rate as compared to the mitochondrial DNA

(Smith et al., 1992; Zhang and Hewitt, 1996).

Karanth (2008) analyzed the nuclear copies of mitochondrial Cyt b gene

(numts) from different leaf monkeys to investigate the reticulate evolution of capped and golden leaf monkeys through ancient hybridization events between Trachypithecus and Semnopithecus. They aligned these numts with mitochondrial Cyt b sequences of various species and suggested that numts in different clades were sequences derived from species not represented in their relevant sister mitochondrial groups. They concluded that, this unusual placement of some numts provides evidence about the hybridization occurred between these species around 7.1 to 3.4 Ma.

Wang et al. (2015c) tried to investigate the reasons for existence of

Semnopithecus mitochondrial like numt in the nuclear genome of T. pileatus. They investigated to clarify their characteristics in T. pileatus, through the coalescence and

41 phylogenetic analyses by comparing Trachypithecus and other colobine genera (Wang et al., 2015c).

The above literature revealed that various types of molecular markers, including mDNA and nDNA markers, are available but none of them can be regarded as optimal for all applications (Sunnucks, 2000). Many studies have utilized mtDNA markers; however, with few exceptions, mtDNA only represents the geneology of genes inherited maternally (Arif and Khan, 2009). Both nDNA and mtDNA markers have been used to analyze the genetic diversity and phylogenetic relationships of langurs of the Indian subcontinent and other Asian colobines (Ting et al., 2008; Osterholz et al.,

2008; Karanth et al., 2008, 2010; Roos et al., 2011; Kolleck et al., 2013). However, the molecular studies on Himalayan grey langurs are not available. Hence, there is a huge gap in research on these grey langurs especially in this part of the globe.

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Chapter 3

MATERIALS AND METHODS

3.1. STUDY AREA

Grey langur areas in Pakistan fall in eastern parts of the Khyber Pakhtunkhwa province (KP, 34.00°N; 71.32°E) and the northern and central parts of Azad Jammu and Kashmir (AJK, 34.22°N; 73.28°E). The area extends over the mountain ranges falling in the western reaches of the Himalayan mountain Range. Langur samples were collected from different sites of the following localities (Fig. 3.1; Appendix-II).

Palas valley (34°52'-35°16'N and 72°52'-73°35'E) with a watershed area of

1,413.23 km2 falls in districts Kohistan of KP on the eastern side of the River Indus

(Raja et al., 1999; Arshad, 2003; Perveen and Abid, 2013; Fig. 3.1). The valley borders with Jalkot, Kaghan and Allai to the north, east and south, respectively. The water from southwestern watershed-Kuz-Palas is drained into Sharakot and Sharial Nalahs

(rivulets) and from the larger northern watershed-Bar Palas/ Daro Palas into the Musha-

Ga Nalah which after travelling 75 km enters the River Indus at Pattan (Saqib et al.,

2011; Perveen and Abid, 2013). Topographically, the valley is mostly arduous and precipitous with altitude ranges between 500 m mean above sea line (asl) at Chakot

(near the river Indus) to 5200 m asl at Mukchaki Peak (Saqib et al., 2011). Summers are usually warm at lower altitudes and winters are harsh at higher altitudes with mean annual precipitation of 900-1350 mm, mostly received as winter snow (Raja et al.,

1999).

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Figure 3.1: Location map of grey langur populations with sampling localities in Pakistan (Courtesy: Google maps).

In , lower altitudes hold a dry sub-tropical climate, while at higher altitudes moist temperate conditions dominates comprising a mix of evergreen conifers with deciduous broadleaf vegetations. At lower elevations, Quercus baloot, Olea europaea and Ficus sycomorus are present, while at higher elevations Quercus floribunda, Cedrus deodara, Picea abies, Abies pindrow and Pinus wallichiana are important tree species (Perveen and Abid, 2013). Deciduous broadleaf species include

Acer, Juglans regia and the rare Ulmus wallichiana (Raja et al., 1999).

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District (Allai valley: 34°33΄- 34°47΄ N and 72°54΄ - 73°15΄ E; KP) with 1301 km2 area, (Haq 2012a) is bounded by district Kohistan in the north, districts

Tor Ghar/Shangla in the west and district Mansehra in the east and south. The area is comprised of high mountains with elevations ranging between 525 m asl at to

4690 m asl at Sukaisar. Climatic conditions also vary from sub-tropical Chir-pine forests, Himalayan moist temperate mixed coniferous forests and alpine pastures in the higher reaches (Haq, 2012b; Haq et al., 2010). The area is drained by several small gullies and nulahs from different sub-valleys, which merge into two main streams

Nandiar Khuwar and Allai Khuwar to join River Indus (Haq, 2012b).

Kaghan valley (34°14′-35 °11′ N and 72° 49′-74° 08′ E; District Mansehra; KP) is 161 km long beautiful valley (Awan et al., 2011) surrounded by Kohistan and

Battagram districts on the west, Muzaffarabad and Neelum districts on the east,

Abbottabad and Haripur districts in the south and Diamer district on the north. Some

1627 km2 area of Kaghan valley is drained into the Kunhar river (Kayani et al., 2017).

The elevation ranges between 1343 m asl to 5291 m asl. The maximum average rainfall is usually received in summer (2500 mm/year) with heavy snowfall in winter

(November-February). The average mean minimum and maximum temperatures are -6 and 40°C, respectively (Sultana et al., 2011; Awan et al., 2011; Qasim et al., 2013).

Based on climatology, the Kaghan valley has different ecological zones, including sub- tropical chir-pine (Balakot, Kewai, Mahandari, Bhunja Sharif), moist/dry temperate

(Sharhan, Shogran, Sari hut, Bella, Kaghan), sub-alpine birch forest (Naran, Saif-ul-

Maluk, Lalazar, Batakundi, Burawai and Besal), alpine and snow-covered peaks

(Lulusar lake, Gittidas, Babusar) (Sultana et al., 2011). The important tree species include Pinus wallichiana, Cedrus deodara, Picea smithiana, Abies pindrow, Aesculus indica, Accer caesium and Betula utilis.

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Machiara National Park (34º-31’ N and 73º-37’ E; AJK) is surrounded by the

Kaghan Valley (KP Province) and the Neelum Valley on its western and eastern borders respectively. It covers 135.32 km2 area with an elevation of 1300-4733 m asl

(Minhas et al., 2010a). The climatic conditions of the area vary with respect to the varying aspects and elevations. The area is influenced by the southwest monsoon and experiences an extra tropical mountainous climate with moderate monsoon conditions

(Minhas et al., 2010b). From biological point of view, the area harbors a variety of ecozones, including mixed Himalayan moist temperate and dry temperate forests with sub-alpine scrubs and alpine-rangeland ecosystems, which provide habitats to thousands of wild faunal and floral species. The important floral species are Taxus wallichiana, Pinus wallichiana, Cedrus deodara, Pinus roxburghii, Abies pindrow,

Picea smithiana, Quercus incana, Aesculus indica, Juglans regia, Betula utilis,

Juniperus communis and Rhododendron spp. (Awan et al., 2004; Qamar et al., 2008;

Minhas et al., 2010b).

Neelum valley (34°28′–34°48′N; 73°91–74°58′E; AJK) is a 200-km long bow- shaped valley along the Neelum river in parallel to the Kaghan Valley with altitudes of

900-6325 m asl. It is surrounded by Chilas and Astor districts in the north and northeast; Kaghan valley in the northwest, Muzaffarabad district in the south and the

Line of Control (LoC) along the Indian held Kashmir in the east. Valley cover an area of 3621 km2 with high mountains deep valleys, dissected small terraces, gentle to steep slopes and inclined spurs. The area is drained by the river Neelum with several streams including, Jagran, Lawat, Dawarian, Changan, Kharigam, Surgun, Neriel, Shonthar,

Janawai, Pholawai and Ghaghi. The major part of upper valley remains under snow from December to April, however, from mid-June to September some parts of the valley received rain under the summer monsoon. At higher elevations winter

46 temperature drop to -20ºC and rise to 30ºC in summer. Based on climate, the natural vegetation is divided into sub-tropical pine forests, moist and dry temperate coniferous forests, sub alpine scrub forests, Alpine pastures and cold deserts. Important vegetation is represented by Pinus wallichiana, Taxus wallichiana, Picea smithiana, Cedrus deodara, Abies pindrow, Pinus roxburghii, Aesculus indica, Juglans regia, Quercus incana, Betula utilis, Juniperus communis, and Rhododendron spp.

Jhelum valley (34°23'44.4"N-73°31'44.2"E to 34°08'29.7"N-73°56'12.6"E;

AJK) is a 72 kilometers long valley (854 km2) running along the Jhelum river in District Jhelum valley (Hattian Bala). Valley is surrounded by the LoC (east),

Neelum district (northwest), Muzaffarabad (west) and Bagh (south). The area is mainly hilly with mountainous stretches of plains along the river Jhelum, which enters into the valley at Chakothi and passes through the entire valley till Muzaffarabad. The main valley has several sub valleys like Leepa, Khalana Chham, Ghail, and Chikar. Through

Chikar (Jhelum valley) is linked with Bagh and Poonch districts via Sudhan Gali.

Leepa valley (34º 24 N and 73º 48 E), falls at altitude of 1600-4000 m asl. Topography of the area is characterized by rugged mountain terrains with glaciated peaks, waterfalls, pastures, river, streams and lush green forests. The climatology of the area has diverse features comprising on Himalayan moist and dry temperate forests, sub- alpine scrubs, high-alpine pastures, along with elevated peaks and cold deserts.

Las Danna (33.9212°N; 73.9550° E; AJK) is situated at the junction of Bagh and Haveli districts at 2625 m asl. The area has lush green with temperate coniferous forests and meadows. The same range of mountains continues towards Mehmood Gali

Range and Shero Dhara areas of District Haveli.

Haveli Kahuta (33.8841°N; 74.1083°E; AJK) is a mountainous district located in the extreme east, along the LoC with an area of 598 km2. It is surrounded by the

47

Bagh and Poonch districts in the west, while other three sides are surrounded by the

Indian held Jammu and Kashmir. Meteorologically, most part of the year temperature remains very low, below freezing during winter receiving heavy snowfall. Several game reserves (Mohri Said Ali, Phala, Hillan) have been established to protect the biodiversity. The human population (0.157 million; GoAJK, 2015) almost entirely depends on the natural resources of the area for their livelihood and subsistence.

3.2. SAMPLING

Based on information available on langur population (available with literature and wildlife staff), a reconnaissance survey of potential grey langur areas was conducted in 2012-2013. Hunters, shepherds, forest and wildlife officials and wildlife field staff in different areas were contacted with photographs of grey langur to extract the information on presence/absence of langurs in each area. A tentative langur distribution map was developed, identifying individual populations and the potential localities, keeping to possible geographic/ habitat barriers limiting langur movement.

More intense field surveys were conducted in each of the potential valley during winter

(November- May; when langurs were easily accessible) to physically ascertain langur population in each locality. Population size of encountered langur troops was estimated and different habitat features (vegetation type, land use, altitude and habitat quality), nearby streams and rivers and topographic features of the area were recorded.

Geographic coordinates of the locations of langur troop were recorded and used to develop maps. Still photographs of langurs was also taken, wherever possible and used for taxonomic identification. A total of 86 samples (feces 64, hair 13, blood 5, tissues

4) were collected from 5 different geographic langur populations of Pakistan and Azad

Jammu and Kashmir (Appendix-II).

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3.2.1. Storage of Samples

Grey langur fecal materials (n=64) and hair (n=13) samples were collected from different localities during winter, when preservation of samples was easier. For the purpose, langur troops were followed, and fresh fecal samples collected whenever available. About 20-50 g of out layer of fecal samples containing epithelial layer of cells were placed in sterilized polypropylene tubes having 95% ethanol (Nsubuga et al.,

2004; Zhang et al., 2006). After transportation to the laboratory, ethanol was poured off carefully and remaining solid material was transferred to silica tubes (Nsubuga et al.,

2004; Ang et al., 2016). These samples were stored at room temperature for 1-15 days before extraction of DNA. Hair samples, collected from the roosting sites/ resting rocks/ bushes were removed with forceps and transferred to a clean paper envelope. All samples were packed separately in zipped polyethylene bags and transferred to

Conservation Genetics Laboratory in Department of Zoology, University of AJK,

Muzaffarabad. Field collected fecal/ hair samples were assigned field number, related information (locality, geographic coordinates and date) recorded, and transferred to laboratory, where these were maintained at 4oC till DNA was extracted and analyzed.

Tissues samples (skin, muscle, liver) were collected from the carcasses of leopard/ human killed (n=4) langurs and preserved in sterilized polyethylene bags polypropylene tubes filled with 95% ethanol or simply collected in sterile polyethylene bags. Dried muscle samples (n=3) were also collected from preserved material, available locally, which were stored in clean sterile paper envelopes, and stored at -

20°C. Most of the samples were collected during winter or early spring with temperature ranging between ~10-15 oC.

Blood (n=5) samples were collected from langurs freshly killed (n = 2; within

24 hours) or maintained in illegal custody (n=3), using 10 mL sterile syringes (BD,

49

USA), transferred to 10 mL potassium EDTA vacutainer tubes (BD, USA), labeled, and stored at 4°C, in refrigerator in the laboratory.

All samples were collected using sterilized equipment, wearing latex gloves and packed in sterile tubes and plastic bags. DNA extractions from different samples were done within 10 days to 6 months of the storage (Alda, 2007).

3.2.2. Ethics Statement

This study did not require approval by any ethics committee as all samples were collected through noninvasive methods without incision of live wild animals. However, for field samplings a permit was acquired by the Department of Wildlife vide letter

Number. WL&F/713/2015, dated November 16, 2015. Three blood samples were collected through the sterile syringes from individuals illegally held by the locals.

Information about these illegally maintained animals in human custody was communicated to the concerned Wildlife Departments for taking actions against culprits according to the law.

3.3. DNA EXTRACTION

DNA extraction was carried out from different tissues using different protocols.

Phenol-chloroform method/ organic isolation method was used (Sambrook et al., 1989;

Sambrook and Russell, 2001) for DNA extraction, with slight adjustments for different tissues. Mean room temperature during DNA extraction was ~10oC.

3.3.1. Extraction by Manual Protocols

3.3.1.1. Blood

About 750 µL blood was taken in a sterilized Eppendorf tube (autoclaved at 121oC for

20 minutes) to which an equal volume (750 µL) of Solution A (0.32 M Sucrose; 10 mM

Tris-pH 7.5; 5 mM MgCl2; 1% (v/v) Triton X-100) was added and mixed thoroughly 50 by inverting the tubes for several minutes. The mixture was kept at room temperature for 30 minutes, and then centrifuged (Eppendorf, Germany) at 13,000 rpm for 1 minute.

After discarding the supernatant, the nuclear pellet was again mixed with 400 µL of

Solution A. After inverting and mixing several times, the mixture was again centrifuged at 13,000 rpm for 1 minute. The supernatant was discarded and the nuclear pellet was mixed with 400 µL of Solution B (10 mM Tris-pH 7.5; 400 mM NaCl, 2 mM EDTA - pH 8.0) along with 20 µL of 20% SDS (sodium dodecyl sulphate) and proteinase-K (20 mg/mL in distilled water) each. After thorough mixing, this suspension was incubated

(Memmert, Germany) at 37˚C overnight. After incubation, the samples were mixed with 500 µL of freshly prepared mixture of equal volume of Solution D (24:1 mixture of chloroform and isoamyl alcohol) and Solution C (Phenol: Invitrogen, USA). After thorough mixing, the contents were centrifuged at 13000 rpm for 10 minutes. The upper layer (aqueous phase) was carefully collected and transferred into a new tube.

Equal volume (500 µL) of Solution D was added and centrifuged at 13,000 rpm for 10 minutes. Upper aqueous phase was transferred into a new tube and precipitated the

DNA by adding 55-60 µL of sodium acetate (3M, pH 6) and 1000 µL of chilled ethanol

(100%). The contents were mixed by inverting the tubes several times and kept at –

20°C for one hour to precipitate the DNA. Tubes were centrifuged at 13000 rpm for 10 minutes. The supernatant was discarded, and to the DNA pallet 200 µL of 70% ethanol was added and centrifuged at 13000 rpm for 7 minutes. After careful discarding of ethanol, tubes were kept allowing DNA to dry and to evaporate the remaining ethanol at 37˚C for 30-45 minutes. The dried DNA was dissolved in 70-100 µL of TE buffer

(10 mM Tris -pH 8.0; 0.1 mM EDTA) or ultrapure double distilled water (Invitrogen

Cat. # 10977023). The dissolved DNA samples were stored at -20˚C in refrigerator till the further use.

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3.3.1.2. Tissues

With few modifications, the method adopted for blood was also used for DNA extraction from fresh and dried tissues. The tissues were washed twice with 100% ethanol (by dipping for 15 min) and distilled water (10 min) each. Tissues were grinded using liquid nitrogen. To the powdered tissues, 750 µL of Solution A was added and incubated overnight at 37°C. On the next day after centrifugation for 1 minute at 13000 rpm, the supernatant was discarded, and the nuclear pellet mixed with 500 µL of

Solution B along with 20 µL of 20% SDS and proteinase-K each. This mixture was incubated at 37°C for 24-36 hours. For rest the DNA extraction technique describe for blood sample was followed.

3.3.1.3. Hair

Using a sterile razor blade, hairs were cut to about one cm from the root

(follicle) (Karanth et al., 2008). Parts of hair containing root follicles were first washed with distilled water, and then in 95% ethanol. Subsequent steps were the same as described above for blood. However, the extracted DNA was dissolved in 50-70 µL of

TE buffer.

3.3.1.4. Feces

Methods of Zhang et al. (2006) was adopted for extraction of DNA from fecal samples. Approximately 1-1.5 g of fecal samples were placed in 15 mL centrifuge tube.

After vortexing and washing with ethanol (5 mL), the samples were centrifuged at

4000×g for 2 minutes to pellet the fecal particle. The supernatant was discarded and washing process repeated using 5 mL TE (10 mM Tris, 1 mM EDTA, pH 8). A volume of 3 mL TNE (10 m mol/l Tris-Cl, 1 m mol/L CaCl2, 0.5% SDS) and 50 µL Proteinase-

K (20 mg/mL) was added to the samples and after well mixing the contents, tubes were

52 incubated at 55°C for 2–3 hrs in water-bath. The lysate was centrifuged again at

4000×g, for 1 min. The supernatants were transferred to new 15 mL centrifuge tubes containing 3 g potato starch and mixed thoroughly by vortex mixer for 1 minute to suspend the starch completely. The suspensions were incubated at room temperature for

1-2 min. The contents were centrifuged at 8000×g for 3 min and the 600 µL of supernatants were transferred to the new 2 mL centrifuge tubes containing 150 µL of

NaCl solution (3.5 mol/L) and 250 µL of CTAB solution (0.7 M NaCl, 10% cetyl trimethyl-ammonium bromide). After incubation at 70°C for 10 min, the contents were mixed twice with freshly prepared mixture of equal volume of phenol : chloroform : isoamyl alcohol (25:24:1) and the supernatants were transferred to the new tubes. After adding equal volumes of binding buffer (4 M guanidine hydrochloride, 1 M potassium acetate, pH 5.5) and mixing gently, the contents of tubes were transferred to the spin columns loaded in 2 mL microcentrifuge tubes (from Norgen Stool DNA Extraction

Kit), and centrifuged at 8000×g for 1 min. The filter membranes were washed twice by centrifugation (8000× g, 1 min) using 750 µL 75% ethanol. The DNA was eluted with

50 µL TE. While extraction DNA from fecal materials, negative extraction controls were also processed following Liu et al. (2008).

3.3.2. Extraction by Commercial Kits

Besides the chemical extraction methods, the total genomic DNA was also extracted using kits from blood (Thermo Scientific GeneJET Whole Blood Genomic

DNA Purification Mini Kit, Cat#K0781) and feces (Norgen Stool DNA Kit,

Cat#27600; Çilingir et al., 2016) by following protocols given therein with minor modification in digestion/ lysis times i.e., increasing the lysis timings.

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3.3.3. Assessment of DNA Quality and Quantity

3.3.3.1. Agarose gel electrophoresis

For confirmation of DNA presence, the extracted DNA was run on 1% agarose gel. Agarose gel was prepared by dissolving 0.5 g of agarose (Vivantis) in 50 mL of 1X

TBE (Tris borate 10 mM, EDTA 1 mM, pH 8.0). The solution was kept in a microwave oven for 1-2 minutes until the agarose is completely dissolved. After cooling down of the solution to ~50-60ºC, 5-6 µL of ethidium bromide (1g/95 mL distilled H2O) was added and mixed thoroughly. The mixture was poured into a gel electrophoresis tray and combs placed into the gel. Tray was kept at room temperature to solidify the gel.

The solidified gel along with the tray was transferred to the horizontal gel electrophoresis apparatus (Wealtec Mini-GES Horizontal Electrophoresis Systems with

ELITE 300 power supply) filled with 1X TBE buffer. Combs were removed leaving wells in the gel.

Extracted DNA (4 µL) was mixed with 2 µL of loading dye (25 mg of bromophenol blue + xylene cyanol and 3 mL glycerin/ 10 mL distilled H2O) was loaded in the wells. For estimating the molecular size of the extracted DNA, 1 kb DNA ladders

(Thermo Fisher Scientific) was also loaded in one or two lanes on both sides of the

DNA loaded lanes. After loading samples, system was connected to the power supply set at 100 volts for 30-45 minutes. Gel was placed in gel documentation system (Vilber

Lourmat Bio-Print-ST4, France) for visualization of the quality of the extracted DNA bands. After observations and taking pictures gels were properly disposed-off in the specified containers.

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3.3.3.2. Spectrophotometry

The quantity and purity of extracted DNA was determined by calculating the ratio of absorbance at 260-280 nm wave length by using spectrophotometer (UV3000,

Germany). DNA concentration was determined using following formula (Linacero et al., 1998):

Where: A (260) = absorbance at 260 nm; DF = dilution factor.

DNA samples with 1.7-2.0 absorbance ratios were subjected to PCR amplification.

3.4. PRIMERS AND AMPLIFICATION OF DNA

3.4.1. RAPD Markers

Initially, 13 randomly amplified polymorphic DNA (RAPD) marker primers with 10 mers (Operon series, synthesized by Invitrogen) were randomly selected from published papers (Ding et al., 2000, Shafi et al., 2016) and screened for amplification with 23 DNA samples. However, based on maximum amplification, clarity of bands and polymorphism, only 8 of these primers were used in further studies (Table 3.1).

Table 3.1: RAPD markers used in genotyping of Himalayan langurs (after Ding et al., 2000). Sr. # Primer Sequence G+C Content (%) 1 OPC-2 5’-GTGAGGCGTC-3’ 70 2 OPC-5 5’-GATGACCGCC-3’ 70 3 OPC-7 5’-GTCCCGACGA-3’ 70 4 OPC-9 5’-CTCACCGTCC-3’ 70 5 OPD-02 5’-GGACCCAACC-3’ 70 6 OPD-03 5’-GTCGCCGTCA-3’ 70 7 OPD-04 5’-TCTGGTGAGG-3’ 60 8 OPA-04 5’-AATCGGGCTG-3’ 60

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3.4.2. Microsatellite Markers

Human based microsatellite or simple sequence repeat (SSR) primers were obtained from published papers on primates including grey langurs and their relative genera (Table 3.2). Nineteen (19) microsatellite primers (synthesized by Invitrogen) were initially screened in all 23 samples however, only 16 primers amplified successfully in 15 individuals, which were used for genetic diversity analysis (Table

3.2).

3.4.3. PCR Optimization and Amplification

Polymerase chain reaction (PCR) technique was used to amplify RAPD and microsatellite markers with template DNA. The PCR reaction mixture was prepared in

0.2 mL PCR tubes (Axygen USA) by taking 3-4 μL of genomic DNA (20-100 ng/μL) and adding 3 μL of 10X PCR Buffer (in WizPure Taq DNA Polymerase Kit by wizbiosolution; 100 mM Tris HCl, 500 mM KCl, 15 mM MgCl2, pH 8.5), primers (1

μL; 0.5 pmol), 10 mM dNTPs mixture (0.5 μL, Thermo Scientific), 0.3 μL DNA polymerase (5 U/μL; WizPure Taq DNA Polymerase), and ~17.2 μL Ultrapure ddH2O

(Invitrogen) to make the total volume of reaction as 25 μL. This reaction mixture was centrifuged for 30 seconds at 8,000 rmp. The short spinned sample tubes were transferred to the gradient thermal cycler machine (MultiGene OptiMax, USA) for amplification of primers.

The PCR reaction consisted of initial denaturation at 94ºC for 5 minutes followed by 39 cycles of 1 minute denaturation at 94ºC, annealing at different temperatures (specific to each primer) for 30 second to 1 minute and extension of strand at 72ºC for 1 minute. To ensure the completion of extension process, a further extension at 72ºC was carried out for 10 minutes.

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Table 3.2: Microsatellite primers used in genotyping of Himalayan grey langurs.

Sr Locus Primer sequences (5′–3′) Allele size Melting Repeat References No Forward range temp. motif Reverse (bp) Tm [°C] 1 D1S207 CACTTCTCCTTGAATCGCTT 131–170 59 (CA)n Clisson et al., GCAAGTCCTGTTCCAAGTCT 2000

2 D1S518 TGCAGATCTTGGGACTTCTC 192–208 59 Tetra Newman et al., AAAAAGAGTGTGGGCAACTG 2002 3 D1S533 CATCCCCCCCAAAAAATATA 200–216 57 Tetra Goosens et al., TTGCTAATCAAATAACAATGGG 2000 4 D1S548 GAACTCATTGGCAAAAGGAA 160–188 57 Tetra Goosens et al., GCCTCTTTGTTGCAGTGATT 2000 5 D1S550 CCTGTTGCCACCTACAAAAG 170–194 63 Tetra Bradley et al., TAAGTTAGTTCAAATTCATCAGT 2000 GC 6 D2S169 TTCTAAGACTTGGCAGAACAT 58 Roeder et al., AGCTCTTTCAGGTGACTTCA 2009 7 D2S1326 AGACAGTCAAGAATAACTGCCC 198–206 60 Tetra Kolleck et al., CTGTGGCTCAAAAGCTGAAT 2013; Morin et al., 1998; Bradley et al., 2000 8 D2S1329 TTGTGGAACCGTTCTCAAAT 171–223 58 Tetra Kolleck et al., GAAACTTCCACCTGGGTTCT 2013 9 D2S1333 CTTTGTCTCCCCAGTTGCTA 269-341 57 Tetra Kyes et al., TCTGTCATAAACCGTCTGCA 2006 10 D2S141 ACTAATTACTACCCNCACTCCC 152–178 64 Di Coote and TCCAAACAGATACAGTGAACTT Bruford, 1996 11 D8S505 CAAAAGTGAACCCAAACCTA 140–172 58 Di Kolleck et al. AGTGCTAAGTCCCAGACCAA 2013 12 D6S264 AGCTGACTTTATGCTGTTCCT 113–123 58 Di Kolleck et al. TTTTCCATGCCCTTCTATCA 2013 13 D6S501 GCTGGAAACTGATAAGGGCT 157–171 61 Tetra Chu et al., GCCACCCTGGCTAAGTTACT 2006 Kolleck et al., 2013 14 D10S1432 CAGTGGACACTAAACACAATCC 145–161 65 Tetra Bradley et al. TAGATTATCTAAATGGTGGATTT 2000 CC Kolleck et al. 2013 15 D17S1290 GCCAACAGAGCAAGACTGTC 216–258 59 Tetra Kolleck et al. GGAAACAGTTAAATGGCCAA 2013 16 D20S206 TCCATTATTCCCCTCAAACA 130-164 57 Tetra Chambers et GGTTTGCCATTCAGTTGAGA al., 2004

57

For optimization of the annealing temperature of primers, all other PCR reaction conditions, e.g., the concentrations of PCR buffer, and dNTP mixture, Taq polymerase etc., were kept constant. PCR was performed using the suggested concentration together with optimized genomic DNA. Annealing temperatures of primers were adjusted by lowering the 2-5 degrees from the calculated melting temperatures by using the equation Tm = 4(G+C) + 2(A+T), where ‘G’, ‘C’, ‘A’, and ‘T’ represent the number of respective nucleotides in the primer. The optimized annealing temperature was selected using gradient thermocycler. The best annealing temperature was then used for PCR amplification of all other samples. Other PCR conditions were also optimized by the same method.

Nuclear DNA extracted from hair or feces was usually of low concentrations which when amplified often resulted in allelic dropout or the stochastic amplification of only one allele. This may result in a “false homozygote” (Gagneux et al., 1997; Morin et al., 2001). Fernando et al. (2003) recommended at least two extractions and two replications are necessary for reliable genotyping. Hence, to ensure the reproducibility of banding patterns and confirmation of actual homozygote and heterozygous genotypes, the process of extraction and amplification of DNA was repeated 5-7 times

(Taberlet et al., 1996; Liu et al., 2008). For further genetic analysis, only those primers were selected which amplified the clear and reproducible bands (Ng and Tan, 2015).

The amplification success and quality of the PCR products of both microsatellite and RAPD was assessed by performing 2% agarose gel electrophoresis.

For preparation of 2% gel, instead for 0.5 g (as used for genomic DNA), 1g agarose was dissolved in 50 mL of 1X TBE buffer. About 4-5 μL of PCR products were loaded on the gel and electrophoresed at 100 volts for 1 hour. Gel documentation system was used for visualization of the quality of the amplified products and picturizing of the

58 band patterns. The gel photographs of the amplified products were used for further analysis by scoring the absence or presence of different bands (Appendix-III; Plate 2,

3).

3.5. GENETIC DATA ANALYSIS

3.5.1. Nuclear DNA Markers

3.5.1.1. Scoring of bands

Beside visual observations, the software (GelAnalyzer-2010a) was used to record presence or absence of bands (Arruda et al., 2012; Abeykoon et al., 2015).

Binary matrix of each genotype was recognized as presence “1” and absence “0” of bands. There is perhaps no standard criteria of scoring DNA bands generated by most dominant molecular markers so far and the scoring results may differ from person to person (Pompanon et al. 2005; Ng and Tan, 2015). Therefore, guidelines of Ng and Tan

(2015) were used to minimize the human and stochastic errors. Clear and distinctive bands with strong intensities were scored only. Bands between 100-3000 bp were selected as electrophoresed through a 2.0% agarose gel at 100 V, band sizes <100 bp are usually products of primer-dimer amplification.

Molecular size of each fragment was estimated by comparing it with the bands of DNA ladders (100 bp Plus DNA Ladder, GeneON, UK) using software

(GelAnalyzer-2010a). From the binary matrix, total number of RAPD/ microsatellite fragments, the unique/ private and the polymorphic bands were estimated for each marker. Total number of bands frequency of bands of different RAPD/ microsatellite band observed in all samples. The number of “polymorphic bands” was the number of bands showing variations, i.e. the bands are present for some samples and absent for the others. The percentage of polymorphic bands was calculated using MS Excel 2016 (Ng

59 and Tan, 2015): A ‘band’ scored was also termed as a ‘locus’ and each band was separately considered as one locus or one data point in any analysis.

Bivariate (1-0) data matrix for each primer was analyzed using the software

(MS Excel-2016, GenAlEx-6.5; Popgene-1.31). Using presence or absence data, the calculations of genetic distances (Excoffier et al., 1992; Fitzpatrick, 2009) and the degrees of similarities or dissimilarities of SSR/RAPD fragments between individuals and populations were carried out using Popgene and GenAlEx-6.5 (Yeh et al., 1999;

Peakall and Smouse, 2012).

3.5.1.2. Discriminatory powers of markers

To assess the different discriminatory powers of each primer/marker, different informative indices including Polymorphism Information Content (PIC), Resolving

Power (RP), Marker Index (MI) and Diversity Index (DI) were calculated in MS Excel

2016 using the following relationships (Mandal et al., 2016; Pecina-Quintero et al.,

2012; Kayis et al., 2010):

PIC for RAPD markers

PIC = 2fi (1-fi); where, fi is the frequency of marker band present and 1-fi is the frequency of marker absent bands (Roldàn-Ruiz et al., 2000)

PIC for microsatellite markers

2 PICi=1-∑(Pij) where, Pij is the frequency of jth allele for ith marker and the summation extends up to the total number of alleles for the given marker (Anderson et al., 1993;

Fu et al., 2016).

MI = PIC × number of polymorphic loci, or, MI = PIC × EMR, where, EMR = nβ is the effective multiplex ratio, measured as the product of the total number of loci per

60 fragments per primer (n) and the fraction of polymorphic loci fragments (β)

(Pecina-Quintero et al., 2012).

RP = Σ Ib, were “Ib” is the informativeness of band, calculated as Ib=1-[2 × (0.5-p)], being “p” the proportion of each genotype containing the band.

The diversity index (DI), indicating the genetic diversity or expected heterozygosity at the locus, = 1 - 1/L ΣPi2, where, Pi is the allele frequency in (each individual allele is considered a unique fragment amplification) and L is the number of loci (Mandal et al., 2016).

To determine the interrelationships between the findings of the above- mentioned discriminatory power indices, Spearman rank correlation were also analyzed.

3.5.1.3. Genetic diversity, genetic differentiation and gene flow

For calculation of different genetic diversity attributes including allele frequencies, total numbers of observed (Na) and effective alleles (Ne), genetic diversities, Shannon’ Information Index (I), Nei's Gene Diversity Index (He) or expected heterozygosity and unbiased expected heterozygosity (uHe), total heterozygosity (Ht), genetic diversity within (Hs) and between (Dst) populations, genetic differentiation within (Rst) and between (Gst) populations and gene flow (Nm) were calculated and analyzed by using the Popgene (1.32), GenAlEx (6.5) and MS

Excel (2016) software programs (Nei, 1987; Yeh et al., 1999; Gaudeul et al., 2004).

3.5.1.4. Genetic similarity and genetic distance

Nei’s genetic similarities and distances were calculated based on pairwise population matrix of different populations using GenAlEx (6.5).

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3.5.1.5. Cluster analysis

Cluster analysis was carried out by constructing dendrograms in Popgene (1.32) program using un-weighed pair group mathematical averages (UPGMA). These dendrograms were based on Nei’s genetic distances/similarities data for different populations as well as different genotypes (Nei, 1972, 1978). Clustering of different genotypes from grey langur populations was performed using Euclidean similarity index based on Paired group cluster analysis of binary data (present/absent) produced by different markers in Past (3.16) software (Hammer et al., 2001).

3.5.1.6. AMOVA and PCoA

The Analysis of Molecular Variance (AMOVA) was carried out to calculate the

F-statistics and/or their analogues for estimation of hierarchical partitioning of genetic variation, variance components and their significance level within populations and among populations or regions (Excoffier et al., 1992; Fitzpatrick, 2009). The AMOVA analysis was done using GenAlEx 6.5 software program (Gaudeul et al., 2004).

Principal Coordinate Analysis (PCoA) or Classical Multidimensional Scaling was carried out using GenAlEx 6.5. PCoA is a spatial way of ordinating the data to better visualize the patterns of genetic relationship. PCoA was calculated on genetic and geographical distances and similarities for different populations as well as for different individuals. PCoA calculated from geographic distance of different genotypes was calculated based on Global Position System coordinates.

3.5.1.7. Spatial genetic analysis

To analyze the distribution patterns of genetic variations across different spatial scales, Spatial Genetic Analysis was also done (Diniz-Filho et al., 2013). Global

Position System technology was used to collect latitude-longitude coordinates used for

62 regression analyses in conjunction with genetic distances by using Mantel tests

(Mantel, 1967) in GenAlEx 6.5 software.

3.5.2. Mitochondrial DNA Markers

Four mtDNA fragments from cytochrome b (Cyt b), Cytochrome c oxidase subunit I (COI), Ribosomal RNA (rDNA) and D-loop region were tried to amplify using newly designed primers using Primer 3 and BLAST at NCBI based on the sequences of different langur species already present in NCBI (Table 3.3).

Table 3.3: Newly designed primers used for amplification of partial sequences of mitochondrial genes (Cyt b, COI and rRNA) from Himalayan langurs of Pakistan

Name Melting Estimated Sr. of Primer Sequence temperature Product # Primer (Tm) size (bp)

Forward ACCATAGCAACAGCCTTCAT 1 Cyt b 55 536 Reverse AGAATGAGAATAGATAGTAG

Forward TCGCATCAGCCATAGTAGA 2 COI 57 667 Reverse TATTGCGGGGGATCATGTG

Forward TAGCCCAAACCCCAATCAGC 3 rRNA 64 329 Reverse CTCGATAGGCGTGTCACCTC

Forward TCAAGCATCCAAGGCCTACC 4 D loop 62 427 Reverse ATGGAAAGCTCCCGTGACTG

The PCR reaction mixture consisted of 3 μL of 10X reaction buffer (MgCl2 included), 0.5 μL dNTP mixture, 1 μL (0.5 pmol) of each forward and reverse primers,

0.3 μL of Taq polymerase, and 3.0-4.0 μL of template DNA and Ultrapure ddH2O added to a total volume of 25 μL. PCR reaction was composed of initial denaturation at

94°C for 5 minutes; followed by the 39 cycles of denaturing for 30 seconds at 94°C; annealing for 30 seconds (at 53°C for Cyt b, 55°C for COI and 62°C for rRNA) and

63 extension/elongation at 72°C for 1 minute. Finally, the reaction continued to complete the amplification process for further 10 minutes at 72°C. The amplified products were electrophorized on 2% agarose gel and the Gel Documentation System was used for visualization of the quality of the amplified products. The above said process was repeated several times for optimization of the PCR conditions for obtaining the best quality amplified products.

The amplified products were got sequenced obtaining Sanger sequences of gene amplicons using capillary-based Applied Biosystems Genetic DNA Analyzer (ABI-

3730) from First BASE Laboratories, Malaysia. Both strands were sequenced separately in different reactions using forward and reverse primers.

3.5.2.1. Sequence editing and alignment

Raw sequences were read using the Chromas (version 2.6.4) software program to visual check the automatic base calls. The sequences were aligned by MUSCLE and manually edited using Unipro Ugene 1.26 software as per the given guidelines given in the user’s manual (Thompson et al., 1997; Okonechnikov et al., 2012). Useful sequences were obtained from 13 samples for Cyt b, each of 490 base pairs (bp) length and 15 samples for each COI and rRNA with 661 bp and 292 bp length respectively.

The cleaned and corrected sequences were used in further analysis and are being submitted to GenBank (Appendix-IV).

3.5.2.2. Phylogenetic analyses

The partial gene sequences of mitochondrial Cyt b, COI and rRNA of grey langurs from study area were compared against orthologous sequences present in

GenBank by using the BLASTn at NCBI (National Center for Biotechnology

Information; https://www.ncbi.nlm.nih.gov).

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The corrected sequences of the all three genes were aligned with multiple sequence alignment using MUSCLE in Unipro Ugene 1.26 software and compared with each other and with other related langur species retrieved from GenBank nucleotide sequence database at NCBI using BLASTn. Phylogenetic analyses were conducted using MEGA version 7 software (Kumar et al., 2016). The phylogenetic analyses were carried out using four different methods viz., Maximum Likelihood (Tamura and Nei,

1993), Neighbor-Joining method (Saitou and Nei, 1987), Minimum Evolution

(Rzhetsky and Nei, 1992) and Maximum Parsimony (Nei and Kumar, 2000). Maximum likelihood methods arranged the possible trees based on evolutionary changes that calculates the probabilities of specific nucleotide substitutions and then picks the tree with highest likelihood or probability (Kimura, 1980; Felsenstein, 1984). Neighbor- joining is a distance based method that successively groups the least different pairs of taxa based on a distance matrix (Saitou and Nei, 1987). This method finds the evolution tree by evaluating the overall sequence differences, rather than individual nucleotide substitutions (Roos and Geissmann, 2001). The maximum parsimony is a phylogenetic tree building method that minimize the number of evolutionary steps and total tree length. This method relies on the concept that the simplest explanation is the most likely near the truth. It uses nucleotide substitutions to explain the data (Swofford et al.,

1996).

The evolutionary divergence between sequences were calculated based on the number of base substitutions/difference per site from between sequences using MEGA

7 software (Kumar et al., 2016). Analyses were conducted using the p-distance method and Maximum Composite Likelihood model (Tamura et al., 2004). P-distance is the measure of base differences per site from between sequences. It is the proportion (p) of nucleotide sites, where two sequences being compared are dissimilar. It is calculated by

65 dividing the number of nucleotide differences by the total number of nucleotides under comparison (Kumar et al., 2016).

While constructing different phylogenetic trees, and calculating evolutionary divergence values, Bootstrapping Tests were used to provide a measure of statistical confidence for internal branches of the trees through an estimate of sampling variance

(Swofford et al., 1996). Bootstrap is a measure of repeatability, the probability that the branch would be recovered if the taxa were sampled again. The bootstrap consensus tree inferred from 1000 replicates were also used to represent the evolutionary relationships of different taxa analyzed (Felsenstein, 1985). All other parameters were left in default settings in MEGA 7. Branches corresponding to partitions reproduced in

<50% bootstrap replicates were collapsed. The percentage of replicate trees are shown next to the branches in which the associated taxa clustered together in the bootstrap test

(1000 replicates). The Minimum Evolution tree was searched using the Close-

Neighbor-Interchange (CNI) algorithm (Nei and Kumar, 2000) at a search level of 1.

The Neighbor-joining algorithm (Saitou and Nei, 1987) was used to generate the initial tree and the Maximum Parsimony trees were constructed using the Subtree-Pruning-

Regrafting (SPR) algorithm (Nei and Kumar, 2000) with search level of 0 in which the initial trees were obtained by the random addition of sequences (10 replicates).

3.5.2.3. Species delimitation

Phylogenetic trees and pairwise evolutionary divergence values were used to analyze whether the observed intraspecific (populations of the present study) or interspecific (populations of other related species/taxa) clades can be considered separate species. For this purpose, the ‘4×-rule or K/ϴ Method’ was applied (Birky et al., 2005; Birky and Barraclough, 2009; Birky et al., 2010; Birky, 2013).

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The 4×-rule method is based on the calculation of the ratio of “K/ϴ” to determine the sequence differences within and between species, where, “K” is the average difference of sequence between individuals of different species, and “ϴ” is the average difference of sequence between individuals of the same species. However, when the uncorrected difference of sequence between individuals of different species is

<1 (as in present study), the probability of multiple changes at a site is negligible and K can be replaced by D (Birky, 2013).

The 4×-rule states that when the values of D/ϴ≥4 or the values of D≥4ϴ, the two groups are assumed to be belonging to two separate species with a probability ≥

95%, or in other words, a monophyletic lineage reaches the status of a separate species, when the mean sequence divergence between specimens belonging to these lineages

(D) is greater than the depth of clades formed by random drift with a 95% confidence or 4ϴ (Birky et al., 2005; Birky, 2013). The values of D and ϴ were calculated by the following ways using MS Excel (2016) following Marie-Stephane et al. (2012) and

Birky (2013):

ϴ (sequence differences in a same clade) =µ ⁄ (1-4µ ⁄ 3),

Where, µ = n ⁄ (n -1) x d

d = mean intraclade genetic distance,

n = number of sequences in the clade,

D = sequence divergence between individuals in two candidate clades.

In case of different values of ϴ for two sister clades, the largest value was used

(Birky, 2013). The values/ratio of ϴ and D were calculated for all three mitochondrial

DNA markers used in the study. The probability values were calculated based on

Figure 6 of Rosenberg (2003) acquired by personal communication through email.

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3.6. STATISTICAL ANALYSIS

The statistical analysis of different data sets was carried out using different software programs including, Popgene (1.32), GenAlEx (6.5), Past (3.16) and MS

Excel (2016) software programs. Calculation of different genetic diversity attributes were carried out using Popgene (1.32), GenAlEx (6.5). Scoring of bands, calculation of discriminatory powers of markers attributes, percentage polymorphism, correlations and regression analysis were carried out using MS Excel (2016).

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Chapter 4

RESULTS AND DISCUSSION

4.1. DISTRIBUTION

Langur populations were confined to nine northern hilly and Azad Jammu and Kashmir (AJK), having Himalayan moist temperate coniferous forests mixed with deciduous plant species. Three of these districts (Mansehra,

Battagram and Kohistan) are fall in Khyber Pukhtunkhwa (KP) province (Pakistan) and six districts (Neelum, Muzaffarabad, Jhelum valley, Bagh, Poonch, Haveli) in AJK.

The distribution of langur in Pakistan and AJK is given in Figure 4.1 and Appendix-II.

Figure 4.1: Distribution of Grey langur in different areas of Pakistan/AJK during 2014- 2016. (Courtesy: Google Maps)

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Presence of grey langurs in Kohistan, has previously been reported from Palas,

Bankad Dubair and Pattan valleys (GoKP, 1997; Roberts, 1997; Minhas et al., 2012).

Presence of langur was confirmed from different areas of Palas valley falling on the eastern side of the Indus river (Nasroo, Nasroo Gail Khas, Gabair, Alli Jan, Dadder

(Salkhanabad), Machh Bela, Gadar Nalah, Bada Coat, Kaharat, Achhro Banda, Kot

Banda, and Kolah), but no population was sighted/ validated from areas falling in the west of the Indus river (Dubair and Pattan).

In , the langur was sighted at Mamdinsar (Pazang Bela),

Korela, Mada Khel, Buda () and Mangri Pashta areas of Allai Valley (Appendix-

II). The reports of their presence were also received from Chorr Valley, Kopra, Pokal,

Banian , , Batkul, , , Kuza, Banda

Pashto and catchment areas of Nandiar Khuwar and Allai Khuwar, which could not be confirmed.

In District Mansehra, the distribution of grey langurs has been reported from

Manshi Wildlife Sanctuary, Nagan-Nadi-Musala, Kalam Ban-Kaghan, Bhunja Kalah

Beari Chor, Siran Forest Reserve and on the south-east face of the Kunhar Valley around Shogran, extending up to Khadir Gali above Mahandri (GoKP, 1997; Mirza,

1965; Roberts, 1997; Minhas et al., 2012). Presence of langur were confirmed at catchment areas/ sub valleys of Shorgan and Kaghan valley. Around Shogran, these monkeys were sighted at Upper Manna, Makhir, Malkandi Reserved Forest, Pathra,

Kankali, Dregan up to Jalai and Anni Shungi areas along the borders of the Machiara

National Park (AJK). While, in Kaghan valley, these monkeys were sighted in Chitta

Par Reserved Forest, Nuri Ichla Reserved Forest, Kothara, Sboharan Pain (Doga),

Bagnuan, Kamal Ban Reserved Forest (Mandri), Biari Char (Mandri), Manshi Reserved

Forest, Chhapri Katha, Jabri Nakka (Naga Reserved Forest) and Naga Reserved Forest.

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In Neelum valley (AJK), langur populations have sighted in Jagran valley

(Barnat, Doga Ziarat, Kamu, Bangas Forest, Rahwali Mohri, Lari Forest), Ashkot

(Saila Gali-Ashkot Nalah), Gehl Nalah, Salkhala Game Reserve, Doga Baihk (Lawat),

Dawarian (Parli, Trekanna, Barthal, Lari), Changan, Dudhnial, Tehjian, Dosut, Sinjlli

Nalah, Surgum valley (Surgun, Samgam, Kundi, Kamakhdori Narh), Arangkel,

Shonther valley (Bagnuwali Sari), Yamgar Nalah, Sao Narh, Halmat and Taobutt areas along the eastern side of the Neelum river.

In district Muzaffarabad, langurs were sighted in Machiara National Park (from forest compartments 6-15) and Lachrat Forest Range (Arlian, Panjgran, Panjkot, Pir

Chinasi areas up to Pir Hasimar). In Jhelum valley, these monkeys were found in Leepa valley (Pathra, Panjal, Kuth Nari), Moji Game Reserve (Menda Gali, Pakhi Ban Forest) and Qazi Nag Game Reserve (Nanga Par, Dagi Chham, Burzi Forest, Drag) and surrounding areas.

In Bagh, langurs are sighted in Sudhan Gali and Las Danna areas. In district

Poonch, langurs were sighted in Toli Pir National Park in areas adjacent to the Las

Danna. District Haveli, probably, harbors the largest langur population of Pakistan. In this district langurs were sighted at Mehmood Gali, Dangwali (Haji Pir), Motanwala

Forest, Bhedi Doba Forest, Khurshidabad (Sherpur Gali-Hillan) and Mohri Said Ali

Khan.

4.1.1. Geographic Barriers and Movement Routs

The distribution of grey langur populations is apparently confined between different above discussed valleys due to several possible geographic barriers (i.e., high mountains and rivers) between these populations. These barriers may include river

Jhelum (between Kahuta, Poonch, Bagh and Muzaffarabad, Neelum), river Neelum

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(between Neelum and Muzaffarabad and Mansehra), high mountain ranges (above

4500 m elevations above sea level) separating Neelum-Muzaffarabad; Neelum-

Mansehra; Mansehra-Battagram and Kohistan districts. Therefore, based on these possible geographic barriers, the entire langur population found in Pakistan/AJK was divided into following natural groups (populations) during present study (Table 4.1;

Fig. 3.1).

Table 4.1: Populations of grey langurs used for genetic analysis during present study.

Sr. #. Populations Areas (valleys/districts) of distribution 1 Poonch Haveli, Poonch, Bagh, Jhelum valley (east of the river Jhelum) 2 Muzaffarabad Jhelum valley (west of the river Jhelum) and district Muzaffarabad 3 Neelum Neelum valley (District Neelum) 4 Mansehra District Mansehra 5 Kohistan District Kohistan and District Battagram

Although, geographic barriers can possibly isolate different populations, but effectiveness of these barriers or level of this isolation has never been determined.

Preliminary information collected indicate that many of these physical barriers were not very effective for langurs. There are several routs (galies) along the high elevations mountain boundaries which were being used by langurs for their movements between such valleys/districts. Langurs have already been recorded at snow-covered peaks up to

4270 m asl of the Himalayas (Sayers and Norconk, 2008; Minhas et al., 2012; 2013).

However, these movements were not very frequent as without any stress (e.g., food availability and human disturbance etc.) langurs did not prefer to penetrate the open

(e.g., alpine) areas, because it always accompanies several threats. Movement routs possibly used by these langurs across different valleys as identified during the present study have been presented in Table 4.2 and Figure 4.2.

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Table 4.2: Movement routs used by grey langurs during 2012-2015 in Pakistan. GPS coordinates Elevation Used for movements Name of rout (Gali) above sea Latitude Longitude between (N) (E) level (m) Mehmood Gali- Las 33.9069° 73.9385° 2438 Haveli-Bagh Danna Haji Pir- Las Danna 33.9744° 74.0631° 2385 Haveli-Bagh Las Danna-Ghori Mar 33.9001° 73.8862° 2393 Bagh-Haveli-Poonch Sudhan Gali-Khalabat 34.0774° 73.7265° 2396 Bagh-Jhelum Valley Leepa- Qazi Nag- Pir 34.3433° 73.6309° 2839 Jhelum valley - Hasimar Muzaffarabad Leepa- Qazi Nag- 34.3629° 73.6672° 2417 Jhelum valley - Neelum Panjkot/Noseri/Titwal valley Jagran-Sar Sangar Gali Neelum- Muzaffarabad (MNP) Musa Gali (MNP)- 34.583° 73.5902° 3453 MNP (Muzaffarabad)- Dregan Kaghan Valley (Mansehra) Shah Khori (MNP)- 34.5883° 73.5622° 3163 MNP (Muzaffarabad)- Badridi Gali Kaghan Valley (Mansehra) Kala Jabra (MNP)- 34.5879° 73.537° 2470 MNP (Muzaffarabad)- Pathra Kaghan Valley (Mansehra) Sangar Gali 34.57° 73.4543° 2904 MNP (Muzaffarabad)- (Co.6/7 MNP)- Makhir Shogran (Mansehra) (KP) Upper Manna 34.5916° 73.4717° 2779 MNP (Muzaffarabad)- Shogran (Mansehra) Allie Valley-Buda- 34.9097° 73.0784° 2547 Battagram-Kohistan Palas Mangri Pashta-Palas 34.9576° 73.0554° 2779 Battagram-Kohistan

Figure 4.2: Movement routs used by grey langurs across different geographic barriers (rivers/mountains) in Pakistan. (Courtesy: Google Maps)

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Rivers and large streams were comparatively more effective barriers. However, it has been observed and reported by several locals in Neelum valley, that these langurs can cross the river Neelum either by leaping on the rocks from restricted width areas during winter or by using different bridges made by the armed forces. Again, such crossings are very rare and could only be possible where areas along both sides of the river have suitable habitats. Such crossing was observed at Singli Nalah in Neelum valley, where a langur troop crossed the Neelum river via a bridge.

4.2. SAMPLE COLLECTION

Acquisition of workable genetic samples for DNA extraction is the major problem in molecular studies, especially for grey langur, a human shy species, inhabiting remote hilly areas in mountain with thicker forested vegetation. We could collect 86 field samples for DNA during 24 months in 21 field visits (Appendix-II), however, 23 of these could be used for successful extraction of DNA (Table 4.3), probably due to poor preservation of samples and longer duration required for their transportation to laboratory. This is even though the sampling was carried out in winter, when low temperatures helps better preservation of DNA.

Several previous studies reported the difficulties faced in acquisition of langur samples for genetic analysis (Karanth et al., 2008; Karanth et al., 2010; Nag et al.,

2011). All possible efforts were made to collect samples (n=86) from all the different localities of the study area following Karanth et al. (2010); yet only 23 could be used in

DNA analysis. DNA from samples collected from live individuals could had better served the purpose, yet keeping in view Endangered conservation status and doubtful taxonomic status of langur in the area, we preferred to resort to non-invasive sampling, i.e. without invading, capturing or even disturbing the live wild animals.

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Table: 4.3: Basic data on different field samples of grey langur with successful DNA extraction and amplification.

Sampling Location Collection GPS Location Markers amplified Sample type (source) Date No. Latitude Longitude

District Locality levation RAPD SSR mDNA

Sample Sample (D/M/Y) o o (meters)

(N ) (E ) E 1 Haveli Bhedi Doba Blood (killed by locals) 21/02/2016 33.9789° 74.0973° 2577 + + + 2 Motanwala Forest Feces 21/02/2016 33.9264° 74.0614° 2539 + + + 3 Kiran (Mohri Said Ali Khan) Feces 22/02/2016 33.9264° 74.0614° 2539 + + + 4 Bagh Las Danna Feces 23/02/2016 33.9215° 73.9535° 2412 + + + 5 Hattian/ Jhelum Chikar Blood (adult male killed 01/04/2012 34.1489° 73.6755° 1678 + + valley by locals) 6 Pir Hisimar Fecesl 17/01/2016 33.9215° 73.9535° 2412 + 7 Muzaffarabad Arlian Lachrat Tissue (killed by leopard) 20/11/2015 34.5367° 73.6309° 2224 + + + 8 Chakolni Fecal 21/11/2015 34.5335° 73.6444° 3172 + + + 9 Lone Gali Blood (male infant, 25/11/2016 34.5719° 73.586° 2726 + + + captured by locals) 10 Domailan MNP Fecal 16/01/2016 34.5367° 73.6309° 2224 + 11 Neelum Arangkel Dried Tissue 06/02/2016 34.8042° 74.3472° 2399 + + + 12 Sinjli Nalah Fecal 20/12/2015 34.8056° 74.2583° 2784 + + + 13 Salkhala GR Hairs 07/04/2016 34.5434° 73.8731° 2293 + 14 Salkhala GR Fecal 07/04/2016 34.5434° 73.8731° 2293 + 15 Mansehra Kankoli Fecal 13/05/2015 34.5981° 73.5725° 2518 + + 16 Bhoonja Dried Tissue 23/06/2016 34.6313° 73.4545° 2267 + + + 17 Mandri Kothara Blood (female juvenile 13/08/2015 34.6721° 73.6212° 2650 + + + captured by locals) 18 Anni Shungi Hairs 13/05/2015 34.6067° 73.5804° 2931 + 19 Kohistan Seri Machh Bela-Palas valley Dried Tissue 09/12/2014 35.2877° 73.1436° 2281 + + + 20 Nasro Palas valley Blood (infant captured by 17/03/2016 35.0575° 73.0661° 2194 + + + locals) 21 Dadder (Salkhanabad) Fecal 17/03/2016 35.09° 73.041° 2469 + 22 Battagram Pazang Bela Alai Fecal 12/04/2015 34.891° 72.9231° 1711 + + + 23 Mangri Pashta Fecal 13/04/2015 34.9576° 73.0554° 2779 +

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The blood samples were collected from animals in possession of some local peoples, while, tissue sample were obtained from leopard/ human killed animals

(Appendix-III; Plate 1). Both invasive (tissues, blood) and noninvasive (hair, feces) genetic samplings methods are being used in the genetic sampling of nonhuman primates including grey langurs (Ding et al., 2000; Lawler et al., 2003; Erler et al.,

2004; Sterner et al., 2006; Andriaholinirina et al., 2006; Roos et al., 2007; Karanth et al., 2008; Karanth et al., 2010; He et al., 2012; Smith et al., 2014; Ogata and Seino,

2015; Jiang et al., 2016). Ding et al. (2000) used 10 (tissues and blood) samples for

RAPD based genotyping of white-headed langurs.

Acquiring tissue samples of an Endangered species is always laborious and costly because wild animal populations are generally scattered over large areas, where sampling is a major problem. Therefore, noninvasive samples, including feces and hair have been used as sources of genetic material for molecular studies in wild primates.

Among these, fecal and hair samples are considered as the easiest way for microsatellite genotyping or mitochondrial DNA sequencing (Gagneux et al., 1997; Di

Fiore, 2003; Roos et al., 2007; Karanth et al., 2008; Karanth et al., 2010; Ang et al.,

2016). However, due to several disadvantages (scarcity and degradation of the DNA and the co-purification of PCR inhibitors) of this sampling technique, many researches still used mixed methods of sampling i.e., noninvasive and invasive (Ding et al., 2000;

Sterner et al., 2006; Roos et al., 2007; Karanth et al., 2008; Karanth et al., 2010; Smith et al., 2014; Ogata and Seino, 2015; Ang et al., 2016; Jiang et al., 2016). Because the low quantity and quality of DNA and presence of PCR inhibitors in fecal samples reduce the genotyping success and increase the overall cost of research studies

(Fernando et al., 2003). These limitations have historically made fecal DNA either impracticable or unaffordable.

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4.3. DNA EXTRACTION

Extraction of DNA was tried from 86 different field samples using standardized methods. However, the successful extractions and amplification was possible for 23 samples with the overall success rate of 24.41% (Table 4.4). Low extraction success has though been indicated in many previous studies.

Table 4.4: Success rate of DNA extraction and amplification from different field

samples of grey langur

DNA

n (#) n (%) Sample Methods Type Quantity Quality

(ng/µL) (A260/A280)

Rate (%) Rate

Successful Successful Successful

Success

PCR Success PCR

Extractio

TotalSamples (#) Amplification Zhang et al. 64 7 11 4 57.14 207.11± 13.09 1.71 ± 0.09 Fecal (2006) Commercial Kit 43 18 41.86 8 44.44 189.34± 10.09 1.66 ± 0.12 Phenol- 13 4 30.76 3 75 201± 7.14 1.74± 0.1 chloroform Hair extraction (Sambrook et al., 1989 Phenol- 3 3 100 3 100 312.2± 16.37 1.99±0.23 chloroform extraction Blood (Sambrook et al., 1989 Commercial Kit 3 3 100 3 100 223.6± 10.44 1.81±0.06 Phenol- 6 6 100 6 100 243.9± 14.33 1.85±0.12 Tissue chloroform (Fresh extraction and (Sambrook et al., dry) 1989

The extracted DNA was visualized on the agarose gel (1%) stained with ethidium bromide: Fig. 4.3). The quantity of the extracted DNA was determined by taking the optical density (OD) at wavelength 260 nm and 280 nm using

77 spectrophotometer (Table 4.4). Samples with OD ratio (260 nm to 280 nm) falling between 1.66 and 1.99 were successfully amplified.

Figure 4.3: Photo plates of agarose gel (1%) of purified DNA extracted from different tissues and stained with ethidium bromide; A= blood using kit; B= blood using phenol– chloroform method; C= fecal material using kit; D= fecal material using Zhang et al. (2006) method; E= hairs; F= tissues.

Though, DNA extraction from various noninvasively samples was low; yet at least two samples were successfully amplified from each geographic pocket holding

78 langur population. Ding et al. (2000) used 10 samples of white headed and grey langurs one from different populations/localities for RAPD based studies. Vernesi et al. (2000) also used 2-6 samples of 10 macaque species from different sampling populations for

RAPD profiling. Almeida et al. (2000) also collected at least one sample from each locality for genotyping study of Nectomys squamipes populations. Liu et al. (2013) used only two samples for species identification of Endangered Francois’ Leaf-Monkey using nuclear and mitochondrial ribosomal DNA.

Low DNA extraction from noninvasive samples can be largely ascribed to poor field preservation, especially in fecal samples. Samples took 5-10 days to reach laboratory. Fecal samples were preserved in ethanol for 5-10 days, which is considered as the preferred methods when expensive field sampling kit with preservatives are not available (Zhang et al., 2006; Panasci et al., 2011; Tende et al., 2014). Nsubuga et al.

(2004) recommended transfer the samples to silica gel within 24 hrs. Nsubuga et al.

(2004) indicated that, the different storage methods also produced effected DNA extraction success in gorillas and chimpanzees. Panasci et al. (2011) recorded that fecal sample age, storage buffer and even dietary remains in fecal samples had a significant effect on genotyping success and reliability in coyotes. Beside preservation methods fecal/ hair samples have very low quantity of degraded DNA, which is accompanied by different PCR inhibitors (Fernando et al., 2003). Temperature at the time of collection had also negative effects on quantity of DNA extracted from fecal samples of wild gorillas and chimpanzees (Nsubuga et al., 2004) and winter collected fresh samples also produce a high DNA yield (Lucchini et al., 2002). Dried and frozen samples have been reported to give a higher DNA yield (Piggott, 2004; Prugh et al., 2005). Under influence of such factors, DNA extraction from noninvasive samples is often challenging which can result in errors and low genotyping success or even complete

79 failure of PCR amplification (Taberlet et al., 1996; Taberlet and Luikart, 1999; Smith et al., 2000a,b; Fernando et al., 2003; Bellemain and Taberlet, 2004). Taberlet et al.

(1999) proposed that, the major limitation of noninvasive samples is the low yield quantities of host DNA, which is often highly degraded. To overcome these problems, the pilot optimization analyses and multiple genotyping replicates are generally required (Fernando et al., 2003). Although, the use of noninvasive samples as the source of genetic material is a recent and very promising technique, yet are still unreliable and costly to completely replace the invasive techniques (Larbi et al., 2012).

4.4. PCR OPTIMIZATION

The genetic analyses using PCR techniques require optimal conditions for successful amplification of the target template DNA. Without optimization of PCR conditions, the amplifications may result into non-specific amplification producing multiple bands, or reduces the PCR product or even result in complete failure of PCR reaction. The optimization of PCR conditions is essential for different primers and species because they may vary from marker to marker and species of template genome.

The most important conditions to be optimized during a PCR reaction is annealing temperature of the primer and concentration of quantity of different chemicals (e.g., primers, MgCl2, dNTPs, Taq polymerase) used for amplification of the targeted template of DNA.

4.4.1. RAPD Markers

Of the total 13 RAPD markers screened, 8 (61.53%) could be amplified and were selected for scoring with well readable bands. The markers amplifying successfully in the present study also amplified with white-headed langurs genome

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(Ding et al., 2000). The optimized conditions for PCR reactions for each RAPD markers are presented in Table 4.5.

Table 4.5: Optimized conditions for amplification of RAPD primers in grey langur genome. Annealing 10X PCR DNA Primer dNTPs Taq Primer Buffer template temperature polymerase (µL) (µL) (µL) (µL) (ºC) (µL) OPC-2 3.0 3 1 31 0.5 0.3 OPC-5 3.0 3 1 32 0.5 0.3 OPC-7 3.0 4 1 33 0.5 0.3 OPC-9 2.5 3 1 32 0.5 0.3 OPD-02 2.5 3 1 32 0.5 0.3 OPD-03 2.5 4 1 32 0.5 0.3 OPD-04 3.0 3 1 30 0.5 0.3 OPA-04 3.0 4 1 33 0.5 0.3

4.4.2. Microsatellite Markers

Of 16 microsatellites or SSR (short sequence repeats) markers grey langur genome successfully amplified 15 genotypes, which were used for genetic diversity analysis (Table 4.3). The primers were originally designed from human genome and have been successfully used in different primate taxa. Effective amplification of grey langur DNA by these primers specifically indicated homologies in the genome of different primate species, including the human being. The successful amplifications of microsatellite markers were made possible from 15 samples including tissue (n=4), fecal material (n=6) and blood (n=5). Details of samples is already presented in Table

4.3.

The success rate of DNA amplification from fecal samples is recorded very low during present study. Several other studies have also reported that, fecal samples

81 always deliver a low amount of degraded DNA often accompanied by the inhibitive factors, which generally result into low amplification success (Deuter et al., 1995;

Kohn and Wayne 1997; Monteiro et al., 1997; Liu, et al., 2008).

Like RAPD markers, the PCR conditions were also optimized for the amplification of microsatellite markers (Table 4.6). First attempt was made to optimize the annealing temperature, because for obtaining bands of high intensity, and to reduce non-specific amplification, annealing temperature optimization is necessary (Rychlik et al., 1990). The optimized annealing temperatures of microsatellite markers ranged between 54-62ºC in the present study (Table 4.6).

Table 4.6: Optimized conditions for amplification of microsatellite primers in grey langur genome. 10X PCR DNA Annealing Taq Primer dNTPs Sr.# Primer Buffer template temperature polymerase (µL) (µL) (µL) (µL) (ºC) (µL) 1 D1S207 2.5 3 0.8 56 0.5 0.3 2 D1S518 2.5 3 0.7 55 0.5 0.3 3 D1S533 3.0 3 0.7 55 0.5 0.3 4 D1S548 2.5 4 0.7 54 0.5 0.3 5 D1S550 3.0 3 0.7 62 0.5 0.3 6 D2S169 2.5 4 0.7 55 0.5 0.3 7 D2S1326 3.0 3 0.8 57 0.5 0.3 8 D2S1329 3.0 3 1.0 55 0.5 0.3 9 D2S1333 3.0 4 0.8 55 0.5 0.3 10 D2S141 3.0 3 1.0 61 0.5 0.3 11 D8S505 3.0 3 1 57 0.5 0.3 12 D6S264 2.5 5 1 56 0.5 0.3 13 D6S501 3.0 3 0.8 59 0.5 0.3 14 D10s1432 3.0 3 0.7 62 0.5 0.3 15 D17S1290 2.5 5 0.8 58 0.5 0.3 16 D20s206 2.5 3 1 55 0.5 0.3

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Quantity of 10X PCR buffer (100 mM Tris HCl, 500 mM KCl, 15 mM MgCl2, pH 8.5), DNA template and annealing temperature are the determinants responsible for the amplification of targeted parts of the genome. Markoulatos et al. (2002) also recorded that optimized concentration of magnesium ions (Mg++) are necessary for proper functioning of Taq polymerase and thereby have a critical role in successful

PCR amplification of the template DNA. The higher concentration of Mg++, although increases the activity of enzyme but the nonspecific amplifications is enhanced, while the lesser concentration of these ions lowers its activity but increases its specificity.

Volume of template DNA was lowered from 1 to 0.7 µL, while template volume was increased from 3 to 5 µL. The importance of using an optimized concentration and amount of template DNA has also been highlighted by Lorenz

(2012). The excessive concentration of DNA can bind magnesium ions to stabilize its own structure, thereby reducing the Taq polymerase effectivity. Similarly, too much template DNA will engage the primers and dNTPs at multiple sites before enough copies of the target region is amplified. This can cause the exhaustion of primers and dNTPs before producing the enough copies of the target region.

Different primers used under present study amplified at different volume however, the concentration was kept constant at 0.5 pmol. The microsatellite primers were optimally amplified at 0.7-1.0 µL of primer concentration, while RAPD primers amplified at 1 µL (Table 4.5, 4.6). Higher concentration of primers increases probability of non-specific PCR products. Optimization stages for primers are essential because the efficacy and sensitivity of PCR depends largely on efficiency of primers.

The excess of primer concentration can also cause the self-annealing of primers between themselves (Lorenz, 2012).

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The volume and concentration of dNTP and Taq polymerase were kept constant for all primers, because all 24 primers were successfully amplified with 0.5 µL of dNTP and 0.3 µL of Taq polymerase (Table 4.5, 4.6).

4.4.3. Mitochondrial DNA Markers

Amplification of mitochondrial DNA Cytochrome C Oxidase subunit I (COI),

Cytochrome b (Cyt b), Ribosomal RNA (rDNA) partial gene sequences and D loop of mitochondrial control region was tried (Table 3.3). All these primers were successfully amplified using the PCR conditions except for D loop of mitochondrial genome. This primer could not be amplified even by changing different conditions of reactions in multiple efforts. This part of the mitochondrial genome is hypervariable and is used for species and subspecies identification (Norman et al., 2004; Arif and Khan, 2009). This is even though the primer was designed using available sequences of Semnopithecus entellus. This hints a possible difference in the nucleotide sequences between two species.

4.4.4. PCR Amplification Program

The optimized PCR reaction programs for amplification of different markers used in present study are presented in Table 4.7 and Table 4.8.

Table 4.7: PCR program used for amplification of RAPD markers in grey langur genome. Stages 1 2 3 Denaturation Denaturation Primer Primer Final Annealing Extension Extension Temperature (ºC) 94 94 30-33 72 72 Time 5 min 30 sec 30 sec 30 sec 10 min Cycles 1 35-39 1

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Table 4.8: PCR program used for amplification of microsatellite markers in grey langur genome. Stages 1 2 3 Denaturation Denaturation Primer Primer Final Annealing Extension Extension Temperature 94 94 54-62 72 72 (ºC) Time 5 min 1 min 1 min 1 min 10 min Cycles 1 35-39 1

Optimization of thermocycler condition is necessary for reliable genotyping results. Amplification of RAPD primers by PCR technique can often produce non- reproducible amplification products due to change in thermocycler amplification profile

(Bakht et al., 2013). Therefore, different PCR profiles were tested by repeating the reactions for 5-7 times following a strict protocol with standardized conditions to ensure the consistency and repeatability of fingerprints generated by these primers.

Fernando et al. (2003) also recommended at least two extractions and two replications necessary for reliable genotyping.

4.5. GENETIC ANALYSIS

4.5.1. RAPD Markers

RAPD markers are considered as dominant markers and are still used for genome analysis of different species where no prior genetic information is available.

These markers have widely been used for population genetic characterization and within and between population genetic variation analysis in different animal taxa including primates (Neveu et al., 1998; Ding et al., 2000; Vernesi et al., 2000;

Ravaoarimanana et al., 2001; Arif and Khan, 2009; Wolf et al., 2010). Different

85 pattern of DNA bands in terms of numbers and molecular size provide these markers the discriminatory power, which is ultimately used for genetic diversity analysis.

4.5.1.1. Banding pattern of amplified fragments

Total numbers of amplicons/ bands, polymorphic bands and unique bands for different primers in each individual/ genotype and population was calculated (Table

4.9, 4.11). A total of 585 scoreable randomly amplified DNA fragments were recognized in 21-23 genotypes of grey langurs belonging to six different populations.

Based on molecular weights 245 score-able bands were detected with a mean of

30.62±2.87 (SE) bands per primer (Table 4.9, Table 4.10, Appendix-III-Plate 2).

Number of bands for different primers ranged between 14 (OPD-4) and 39

(OPC-2). Band sizes varied between 126 and 3342 bp. All bands were polymorphic and no monomorphic band was detected (100% polymorphism). However, 96 unique bands

(39.18%) were detected in different individuals with a mean of 12±1.54 per primer.

Number of unique loci ranged between 7 (OPA-04) and 17 (OPC-02, OPD-02) (Table

4.10).

The maximum number of bands (n=105, mean=5.00±0.34, 27 polymorphic) were produced by OPA-04, and the minimum numbers by the OPD-04 (n=32, mean=1.52±0.15, 14 polymorphic; Table 4.9).

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Table 4.9: Number of bands produced by RAPD primers in grey langurs genotypes from Pakistan. Bands Genotypes/markers OPC-2 OPC-5 OPC-7 OPC-9 OPD-02 OPD-03 OPD-04 OPA-04 Total Mean SD SE Bhedi Doba 1 2 2 2 1 4 1 5 18 2 1.49 0.53 Motanwala Forest 0 3 5 1 1 4 1 5 20 3 2.00 0.71 Kiran (Mohri Said Ali Khan) 4 1 3 2 5 2 1 5 23 3 1.64 0.58 Las Danna 8 4 4 1 6 4 2 2 31 4 2.30 0.81 Chikar 2 3 3 7 7 6 1 6 35 4 2.39 0.84 Pir Hisimar 6 2 1 9 8 3 1 6 36 5 3.16 1.12 Arlian Lachrat 0 4 1 2 0 7 1 6 21 3 2.72 0.96 Thora MNP 9 3 7 1 3 5 1 5 34 4 2.82 1.00 Lone Gali MNP 6 3 3 2 9 5 2 5 35 4 2.39 0.84 Domailan MNP 7 4 4 8 4 4 2 6 39 5 1.96 0.69 Arangkel 2 2 5 1 9 3 2 7 31 4 2.85 1.01 Sinjli Nalah 6 4 5 7 3 5 2 2 34 4 1.83 0.65 Salkhala GR 0 6 3 8 7 6 1 5 36 5 2.88 1.02 Salkhala GR 4 0 3 1 0 0 2 5 15 2 1.96 0.69 Kankoli 0 1 2 3 4 4 1 6 21 3 2.00 0.71 Bhoonja 7 3 4 3 0 1 1 5 24 3 2.33 0.82 Mandri Kothara 3 4 1 3 4 4 1 5 25 3 1.46 0.52 Anni Shungi 6 4 4 3 2 1 1 5 26 3 1.83 0.65 Seri Mach Bela 3 5 1 0 3 2 3 1 18 2 1.58 0.56 Nasro Palas 2 2 4 2 5 5 2 6 28 4 1.69 0.60 Pazang Bela Alai 4 5 5 3 4 4 3 7 35 4 1.30 0.46 Total 80 65 70 69 85 79 32 105 585 73 20.78 7.35 Mean 3.81 3.10 3.33 3.29 4.05 3.76 1.52 5.00 27.86 3.0 0.99 0.35 SD 2.84 1.48 1.62 2.74 2.85 1.79 0.68 1.55 7.32 0.91 0.53 0.19 SE 0.62 0.32 0.35 0.60 0.62 0.39 0.15 0.34 1.60 0.20 0.12 0.04

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Table 4.10: Banding patterns of RAPD markers in grey langur populations of Pakistan. Bands Band size Marker Unique Polymorphic Total No. of (bp) amplicons No. (%) No. (%) OPC-2 39 17 43.59 39 100 182-1393 OPC-5 28 9 32.14 28 100 292-3342 OPC-7 37 15 40.54 37 100 189-2842 OPC-9 30 11 36.67 30 100 164-1386 OPD-02 38 17 44.74 38 100 126-2350 OPD-03 32 14 43.75 32 100 147-2220 OPD-04 14 6 42.86 14 100 306-2489 OPA-04 27 7 25.93 27 100 185-1410 Total 245 96 39.18 245 100 Mean 30.62 12 38.77 30.62 100 SD 8.14 4.37 6.72 8.14 0 SE 2.87 1.54 2.37 2.87 0

The maximum RAPD bands (n=28) were produced by OPC-2 and OPA-04 in

Muzaffarabad population, while minimum bands (n=4) by OPD-4 primers in Mansehra population. Similarly, OPC-5, OPC-9, OPD-02 and OPD-03 also produced maximum bands in Muzaffarabad population. Primers OPC-7 produced maximum bands in

Poonch population, while OPD-04 produced maximum bands in Kohistan population, while, OPD-04 produced maximum (n=8) in Kohistan population (Fig. 4.4).

Muzaffarabad population produced the maximum number of RAPD bands (n=165) followed by Poonch (n=127) and Neelum (n=116) populations (Fig. 4.5).

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Figure 4.4: Numbers of bands produced by different RAPD primers in different populations of grey langurs from Pakistan.

Figure 4.5: Overall bands produced by RAPD primers in different populations of grey langurs from Pakistan.

Amplification of a large number of RAPD markers (8/13) producing many polymorphic bands in different grey langur populations suggested a higher level of polymorphism at various loci. The remarkable differences in band frequencies/ patterns between different langurs populations also indicated the high discriminatory potentials and usefulness of RAPD markers as also reported earlier for different vertebrate

89 populations including White-headed langurs and black langurs (Ding et al., 2000; Arif and Khan, 2009; Wolf et al., 2010; Sanches et al., 2012). High level of polymorphism and patterns of unique bands also indicate highly variability in different grey langur populations. Ding et al. (2000) also recorded a high level of polymorphism in Whit- headed langurs and black langurs using the similar markers. They also reported no monomorphism in 22 RAPD primers and 13 of 22 primers showing 100% polymorphism.

4.5.1.2. Discriminatory power of markers

To assess the discriminatory powers of primers, each RAPD primer was analyzed using different informative indices, including polymorphism information content (PIC), resolving power (RP), marker index (MI), and effective multiplex ratio

(EMR).

The PIC values of different RAPD primers ranged between 0.17 (OPC-2) to

0.35 (OPD-04; Table 4.11). The PIC value of a marker represents the probability of finding a marker in present or absent states randomly in two individuals from the population. PIC values range from zero (monomorphic markers) to 0.5 for markers which is present in 50% of the individuals and absent in others 50% (Roldàn-Ruiz et al., 2000). In present study, the mean PIC value (±SE) was was 0.31±0.02, which indicated that all markers used were highly polymorphic and efficient (Table 4.11).

Mean value of MI was 9.19±0.85 with the highest for OPD-02 (12.16) and the lowest for OPD-04 (4.90). Similarly, the mean EMR value was 30.63±2.87 with the highest for OPC-2 (39.00) and the lowest for OPD-04 (14.00). Velasco-Ramírez et al.

(2014), reported similar values of MI (21.6) and EMR (50.33) for different RAPD markers in Dioscorea spp.

90

Table 4.11: Discriminatory powers analysis for different RAPD markers used in genetic analysis of grey langurs of Pakistan.

Markers MI EMR RP PIC OPC-2 6.63 39.00 7.62 0.17 OPC-5 8.68 28.00 6.19 0.31 OPC-7 10.91 37.00 7.24 0.29 OPC-9 10.20 30.00 6.57 0.34 OPD-02 12.16 38.00 8.10 0.32 OPD-03 10.88 32.00 7.52 0.34 OPD-04 4.90 14.00 3.05 0.35 OPA-04 9.17 27.00 10.00 0.34 Total 73.54 245.00 56.29 2.46 Mean 9.19 30.63 7.04 0.31 SE 2.417 2.878 0.699 0.021

MI=Marker Index, EMR=Effective Multiplex Ratio, RP=Resolving Power, PIC=Polymorphism Information Content.

OPA-04 (10.00) and OPD-02 (8.10) primers exhibited higher values of RP, while OPD-04 has the lowest values of RP (3.05). The primers with higher RP values are considered more informative in distinguishing the genotypes (Heikrujam et al.,

2015).

There was no significant correlation between PIC and other parameters (Table

4.12). Guo et al. (2014b) also could not find correlation of PIC with other parameters.

Table 4.12: Spearmen correlation matrix between different discriminatory power indices of different RAPD markers used in genetic analysis of grey langurs of Pakistan.

PIC MI EMR

MI 0.263

EMR -0.588 0.624

RP -0.178 0.593 0.646

PIC=Polymorphism information content, MI= Marker index, EMR=Effective Multiplex ratio, RP=Resolving power.

91

Discriminatory power analysis of RAPD markers in terms of PIC, EMR, MI,

RP and DI has not been yet carried out in any langur species. However, several studies are available in plants which used these parameters for assessing the efficiency of

RAPD primers (Tonk et al., 2014; Velasco-Ramírez et al., 2014; Drine et al., 2016).

Values of PIC, EMR, MI for a primer are used to determining its effectiveness in analysis of genetic diversity (Heikrujam et al., 2015). The present study indicated that all RAPD markers used have good discriminatory powers regarding different parameters. Therefore, the results of these markers may have reliability in genotypic analysis of different populations of grey langurs from Pakistan. These markers have already been used by Ding et al. (2000) for genotyping of the white-headed langurs and black langurs.

4.5.1.3. Reproducibility

Reproducibility is the major concern associated with RAPD markers, because the number and size of bands for the same marker and individual may differ in different laboratory conditions (Kumar and Gurusubramanian, 2011; Bakht et al., 2013). Present study tried to establish repeatability of RAPD patterns at various stages of DNA extraction and amplification. Five to seven extractions and PCR reactions were carried out to confirm the repeatability of the banding patterns in each genotype under the same standardized laboratory conditions by the same individual. However, 100% reproducibility could not be achieved for all primers tried. Only those primers were selected which amplified the clear and reproducible bands (Taberlet et al., 1996; Ng and Tan, 2015). The amplification results were repeatable even after storage of DNA at

-20°C for more 4-6 months. Therefore, the findings of the present study may have significant predictive values for the genetic analysis of different grey langur genotypes from Pakistan.

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4.5.1.4. Genetic diversity

The binary data of band amplification was used to statistically analyze different genetic diversity attributes, including differential banding patterns, number of observed and effective alleles and estimation of genetic variations by different diversity indices.

4.5.1.4.1. Allelic diversity

Amplification patterns of RAPD markers were compared between and within different populations. The highest numbers of total bands and number of different bands with a frequency of >5% was recorded in Muzaffarabad population (n=134) followed by Poonch (n=107) and Neelum (n=91) populations. Numbers of unique bands were the highest in Poonch population (n=41) followed by Muzaffarabad (n=40) and Neelum (n=13). Numbers of locally common bands (Frequency >= 5%) in 50% or fewer populations were the highest in Muzaffarabad population (n=54) and the lowest in Mansehra population (n=16). However, the numbers of locally common bands

(Frequency >= 5%) in 25% or fewer populations were zero in all populations (Fig. 4.6).

4.5.1.4.2. Observed and effective alleles

In Poonch population, the mean number of observed alleles (Na) was

0.877±0.040 (±SE), with a range of 0.800 (OPC-9) - 1.063 (OPD-03) for different

RAPD markers (Table 4.13; Fig. 4.7). The highest (1.216) effective number of alleles

(Ne) in Poonch population were for OPA-04 and the lowest for OPC-2 (1.090; Fig.

4.8).

In Muzaffarabad population, the mean numbers of observed and effective alleles at different loci were 1.077± 0.063 and 1.158±0.013, respectively (Table 4.13).

The numbers of observed alleles ranged between 1.267 (OPC-9) and 0.857 (OPD-04),

93 while the numbers of effective alleles ranged between 1.214 (OPA-04) to 1.105 (OPC-

7; Fig. 4.8).

160 0.140 140 0.120 120 0.100 100 0.080 80 0.060 Number 60

40 0.040 Heterozygosity 20 0.020 0 0.000 Poonch Muzaffarabad Neelum Mansehra Kohistan Populations

No. Bands No. Bands Freq. >= 5% No. Private Bands No. LComm Bands (<=25%) No. LComm Bands (<=50%) Mean He

No. Bands = No. of Different Bands No. Private Bands = No. of Bands Unique to a Single Population He = Expected Heterozygosity uHe = Unbiased Expected Heterozygosity

Figure 4.6: Total Band Patterns for RAPD markers across different populations of grey langur in Pakistan.

In Neelum population, the variation in the mean numbers of observed alleles were between 1.000 (OPC-9) and 0.564 (OPC-2; Fig. 4.7), while effective numbers of allele fell between 1.179 (OPA-04) and 1.096 (OPC-2; Fig. 4.8). The mean numbers of observed and effective alleles at different loci were 0.738±0.056 (SE) and 1.149±0.010

(SE), respectively (Table 4.13).

Mean numbers of observed alleles and effective alleles in Mansehra population were 0.591±0.047 (SE) and 1.121±0.014 (SE), respectively (Table 4.13). The highest numbers of observed alleles were recorded for OPC-2 (0.769) and the lowest for OPD-

02 (0.421; Fig. 4.7). Similarly, the highest numbers of effective alleles in this

94 population were produced by OPA-04 (1.204), while the lowest by OPD-02 (1.085;

Fig. 4.8).

Table 4.13: Summary of allelic diversity exhibited by RAPD markers in grey langur

populations in Pakistan.

2 5 7 9

02 03 04 04

- - - -

- - -

-

SE

Mean

OPC OPC OPC OPC

OPD OPD OPD OPA Populations B.F. 0.077 0.093 0.092 0.087 0.105 0.125 0.086 0.170 0.104 0.011

p 0.041 0.050 0.049 0.046 0.057 0.067 0.045 0.098 0.057 0.007 q 0.959 0.950 0.951 0.954 0.943 0.933 0.955 0.902 0.943 0.007

Poonch Na 0.769 0.786 0.811 0.800 0.895 1.063 0.857 1.037 0.877 0.040 Ne 1.090 1.113 1.111 1.103 1.128 1.152 1.100 1.216 1.127 0.014

B.F. 0.144 0.114 0.086 0.147 0.126 0.150 0.100 0.207 0.134 0.013 p 0.077 0.061 0.046 0.080 0.068 0.085 0.054 0.135 0.076 0.010

q 0.923 0.939 0.954 0.920 0.932 0.915 0.946 0.865 0.924 0.010 ffarabad

za Na 1.231 1.071 0.757 1.267 1.158 1.125 0.857 1.148 1.077 0.063 Mu Ne 1.174 1.136 1.105 1.179 1.152 1.183 1.121 1.214 1.158 0.013 B.F. 0.077 0.107 0.108 0.142 0.125 0.109 0.125 0.176 0.121 0.010

p 0.042 0.059 0.062 0.078 0.071 0.061 0.076 0.114 0.070 0.007 q 0.958 0.941 0.938 0.922 0.929 0.939 0.924 0.886 0.930 0.007

Neelum Na 0.564 0.714 0.649 1.000 0.789 0.688 0.571 0.926 0.738 0.056 Ne 1.096 1.137 1.139 1.178 1.159 1.142 1.165 1.179 1.149 0.010

B.F. 0.103 0.107 0.074 0.100 0.066 0.078 0.071 0.194 0.099 0.015

p 0.056 0.059 0.040 0.056 0.037 0.043 0.040 0.136 0.058 0.011 q 0.944 0.941 0.960 0.944 0.963 0.957 0.960 0.864 0.942 0.011

Mansehra Na 0.769 0.714 0.595 0.600 0.421 0.500 0.429 0.704 0.591 0.047 Ne 1.127 1.137 1.090 1.131 1.085 1.101 1.094 1.204 1.121 0.014 B.F. 0.077 0.143 0.090 0.056 0.105 0.115 0.190 0.173 0.119 0.017

p 0.044 0.097 0.054 0.031 0.061 0.067 0.141 0.118 0.076 0.014 q 0.956 0.903 0.946 0.969 0.939 0.933 0.859 0.882 0.924 0.014

Kohistan Na 0.410 0.607 0.378 0.333 0.526 0.563 0.643 0.630 0.511 0.043 Ne 1.101 1.141 1.124 1.071 1.140 1.153 1.160 1.185 1.134 0.013

B.F= Band Frequency; Na = No. of Alleles; Ne = No. of Effective Alleles

95

1.400

1.200

1.000

0.800

0.600 Numbers 0.400

0.200

0.000 OPC-2 OPC-5 OPC-7 OPC-9 OPD-02 OPD-03 OPD-04 OPA-04 RAPD markers

Poonch Muzaffarabad Neelum Mansehra Kohistan

Figure 4.7: Numbers of observed alleles (Na) exhibited by RAPD markers in different populations of grey langurs in Pakistan.

Figure 4.8: Numbers of effective alleles (Ne) exhibited by RAPD markers in different populations of grey langurs in Pakistan.

The number of observed alleles in Kohistan population ranged between 0.333

(OPC-9) and 0.643 (OPD-04), while the number of effective alleles were recorded between 1.071 (OPC-9) and 1.185 (OPA-04; Fig. 4.7, 4.8). The mean numbers of the

96 observed alleles and effective alleles in this population were 0.511±0.043 and

1.134±0.013, respectively (Table 4.13).

On the average, the primer OPA-04 revealed the maximum numbers of observed (0.889±0.098) and effective (1.200) alleles, while OPC-7 exhibited the lowest numbers of observed (0.638±0.075, SE) and effective alleles (1.113±0.008 SE; Fig.

4.9).

Figure 4.9: Overall, mean (±SE) numbers of observed and effective alleles revealed by RAPD markers in all populations of grey langur in Pakistan.

4.5.1.4.3. Polymorphism

Amplification of RAPD loci exhibited different patterns based on relative frequency (%) of polymorphism in different grey langur populations (Fig. 4.10). The highest frequency of polymorphic loci was recorded in Muzaffarabad population

(54.29%) followed by Poonch (43.67%) and Neelum (36.73%), while the mean polymorphism was 37.71±5.29 (%±SE).

97

60.00%

50.00%

40.00%

30.00%

Percentage 20.00%

10.00%

0.00% Poonch Muzaffarabad Neelum Mansehra Kohistan Populations

Figure 4.10: Frequency of polymorphic loci (%) in different populations of grey langur in Pakistan.

The number and size of fragments/ alleles produced correspond to the nucleotide sequence of the primer and DNA template, which is represented in the genome specific fingerprints of RAPD fragments (Welsh et al., 1991). Deferential amplification, as depicted in distinct band sizes, indicated a high genetic variability in different grey langur populations of Pakistan. The average values of observed (Na), and effective number of alleles (Ne) and percentage of polymorphic loci are considered as indicators of the actual level of genetic variability in the species under question. The percentage of polymorphic loci exhibited for different RAPD markers in different populations of grey langur indicates a good level of genetic diversity among different populations. The results also indicated that RAPD–PCR technique has potentials of revealing genetic variations among different populations. These techniques have been used in several studies in different vertebrate species, including primates (Neveu et al.,

1998; Ding et al., 2000; Vernesi et al., 2000; Ravaoarimanana et al., 2001; Bardakci,

2001; Rodrigues et al., 2007; Beja‐Pereira et al., 2009, Kumar and Gurusubramanian,

2011; Vasave et al., 2014; Shafi et al., 2016; Mudasir et al., 2016).

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4.5.1.4.4. Shannon’s information and Nei's heterozygosity indices

Shannon’ information index (I), expected heterozygosity or Nei's (1972) gene diversity index (He) and unbiased expected heterozygosity (uHe) exhibited by different primers for each population are presented in Table 4.14.

Table 4.14: Summary of different genetic diversity constants exhibited by RAPD

markers in grey langur populations in Pakistan.

2 5 7 9

02 03 04 04

- - - -

- - -

- Mean SE

OPC OPC OPC OPC

OPD OPD OPD OPA Populations

I 0.130 0.146 0.147 0.141 0.166 0.198 0.144 0.231 0.163 0.012 He 0.073 0.086 0.085 0.081 0.097 0.115 0.081 0.145 0.095 0.008

Poonch uHe 0.081 0.095 0.095 0.090 0.108 0.128 0.09 0.161 0.106 0.009

I 0.228 0.188 0.138 0.231 0.204 0.219 0.159 0.234 0.200 0.013

He 0.133 0.107 0.08 0.134 0.117 0.131 0.092 0.144 0.117 0.008

uHe 0.147 0.119 0.089 0.149 0.13 0.145 0.103 0.16 0.130 0.009 Muzaffarabad

I 0.116 0.156 0.147 0.211 0.174 0.155 0.149 0.198 0.163 0.011 He 0.070 0.096 0.092 0.128 0.108 0.097 0.098 0.123 0.102 0.006

Neelum uHe 0.080 0.11 0.106 0.146 0.124 0.111 0.113 0.141 0.116 0.007

I 0.157 0.156 0.117 0.139 0.094 0.112 0.099 0.176 0.131 0.011 He 0.094 0.096 0.069 0.088 0.058 0.069 0.063 0.117 0.082 0.007

Mansehra uHe 0.107 0.110 0.079 0.100 0.067 0.079 0.072 0.134 0.093 0.008

I 0.103 0.143 0.107 0.079 0.136 0.147 0.151 0.164 0.129 0.010 He 0.066 0.092 0.072 0.05 0.089 0.096 0.099 0.110 0.084 0.007

Kohistan uHe 0.08 0.111 0.086 0.06 0.107 0.115 0.119 0.132 0.101 0.008 Mean I 0.147 0.158 0.131 0.160 0.155 0.166 0.140 0.201 0.157 0.007 He 0.087 0.095 0.080 0.096 0.094 0.102 0.087 0.128 0.096 0.007 uHe 0.099 0.109 0.091 0.109 0.107 0.116 0.099 0.146 0.110 0.007

SE I 0.022 0.008 0.008 0.027 0.019 0.019 0.011 0.014 He 0.012 0.003 0.004 0.016 0.010 0.010 0.007 0.007 uHe 0.013 0.004 0.005 0.017 0.011 0.011 0.008 0.006

I = Shannon's Information Index = -1* (p * Ln (p) + q * Ln(q)); He = Expected Heterozygosity = 2 * p * q; uHe = Unbiased Expected Heterozygosity = (2N / (2N-1)) * He; Where for Diploid Binary data and assuming Hardy-Weinberg Equilibrium, q = (1 - Band Freq.) ^0.5 and p = 1 - q.

99

In Poonch population, the mean (±SE) values Shannon’s index (I), expected heterozygosity or Nei's genetic diversity index (He) and unbiased expected heterozygosity (uHe) exhibited by different primers were 0.163±0.012, 0.095±0.008 and 0.106±0.009, respectively (Table 4.14). The Shannon’s index values varied between 0.231 (OPA-04) and 0.130 (OPC-2, Fig. 4.11), while the Nei’s genetic diversity index values were also the highest for OPA-04 (0.145) and lowest for OPC-2

(0.073). Similarly, the values of unbiased expected heterozygosity (uHe) were the highest in OPA-04 (0.161) and lowest for OPC-2 (0.081; Fig. 4.11).

Pop1 0.250

0.200

0.150 I 0.100 He

Valuesof indices uHe 0.050

0.000 OPC-2 OPC-5 OPC-7 OPC-9 OPD-02 OPD-03 OPD-04 OPA-04 Mean Primers/markers

Figure 4.11: Genetic diversity indices for different RAPD markers in Poonch population of grey langur.

Mean (±SE) values of Shannon’s index (I), expected heterozygosity or Nei's genetic diversity index (He) and unbiased expected heterozygosity (uHe) as showed by different primers in Muzaffarabad population were 0.200±0.013, 0.117±0.008 and

0.130±0.009, respectively (Table 4.14). Shannon’s index values were the highest for

OPA-04 (0.234) and lowest for OPC-7 (0.138, Fig. 4.12). Similarly, the Nei’s genetic

100 diversity index and unbiased expected heterozygosity values were also the highest for

OPA-04 (He=0.144; uHe=0.160) and the lowest for OPC-7 (He=0.080; uHe=0.089;

Fig. 4.12).

Figure 4.12: Genetic diversity indices for different RAPD markers in Muzaffarabad population of grey langur.

In Neelum population, the mean (±SE) values of Shannon’s index (I), expected heterozygosity or Nei's genetic diversity index (He) and unbiased expected heterozygosity (uHe) revealed by different primers were 0.163±0.011, 0.102±0.006 and

0.116±0.007, respectively (Table 4.14). The Shannon’s index values varied between

0.211 (OPC-9) and 0.116 (OPC-2, Fig. 4.13). The Nei’s genetic diversity index and unbiased expected heterozygosity values were also the highest for OPC-9 (He=0.128, uHe=0.146) and the lowest for OPC-2 (He=0.070, uHe=0.080; Fig. 4.13).

The mean (±SE) values of Shannon’s index, Nei's genetic diversity index and unbiased expected heterozygosity for different RAPD primers in Mansehra population, were 0.131±0.011, 0.082±0.007 and 0.093±0.008, respectively (Table 4.14). The values

101 of Shannon’s index, Nei’s genetic diversity index and unbiased expected heterozygosity were the highest for OPA-04 (I=0.176, He=0.117, uHe=0.134) and lowest for OPD-02 (I=0.094, He=0.058, uHe=0.067, Fig. 4.14).

Figure 4.13: Genetic diversity indices for different RAPD markers in Neelum population of grey langur.

Pop4 0.200 0.150 0.100 I 0.050 0.000 He

Values Values indices of uHe

Primers/markers

Figure 4.14: Genetic diversity indices for different RAPD markers in Mansehra population of grey langur from Pakistan.

In Kohistan population, the mean (±SE) values Shannon’s index, Nei's genetic diversity index and unbiased expected heterozygosity exhibited by different RAPD primers were 0.129±0.010, 0.084±0.007 and 0.101±0.008, respectively (Table 4.14).

102

These values were the highest for OPA-04 (I=0.164, He=0.110, uHe=0.132) and the lowest for OPC-9 (I=0.079, He=0.050, uHe=0.060, Fig. 4.15).

Pop 5 0.180 0.160 0.140 0.120 0.100 I 0.080 He 0.060

Valuesof indices uHe 0.040 0.020 0.000 OPC-2 OPC-5 OPC-7 OPC-9 OPD-02 OPD-03 OPD-04 OPA-04 Mean Primers/markers

Figure 4.15: Genetic diversity indices for different RAPD markers in Kohistan population of grey langur from Pakistan.

Overall, the mean (±SE) values of different genetic diversity indices were the highest in Muzaffarabad (I=0.200, He=0.117) population followed by Neelum

(I=0.163, He=0.102) and Poonch (I=0.163, He=0.095) populations (Fig. 4.16).

Figure 4.16: Overall mean (±SE) values of different genetic diversity indices exhibited by RAPD markers in different populations of grey langur from Pakistan.

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The results also indicated a decreasing trend in genetic diversity attributes from eastern populations (Poonch, Muzaffarabad) towards western population (Mansehra,

Kohistan). This might be due to the higher chances of interpopulation movement of individuals in the eastern parts as these populations are linked to the other populations of langurs found in different areas of Indian held Jammu and Kashmir (Mir et al., 2015;

Sharma and Ahmed, 2017).

During current study, the mean values of the Shannon’s index and Nei's genetic diversity index in all populations was in the ranges of 0.129-0.200 and 0.082-0.117, respectively (Table 4.14). A strong significant positive correlation (r=0.991, p<0.0001) was recorded between Shannon’s index and Nei's genetic diversity index (Fig. 4.17).

Figure 4.17: Linear correlation between Shannon and Nei’s diversity indices across 8 RAPD markers for grey langurs of Pakistan.

The present findings about different genetic diversity constants suggest high dissimilarities in genome compositions of different populations of grey langur found in

Pakistan and AJK. These values of genetic diversity (I=0.129-0.200 and He=0.082-

104

0.117) were lower as compared to those suggested by Ding et al. (2000). Using RAPD markers, they calculated the mean values of Shannon’s index as 0.55 and 0.85 with the ranges between 0.00-0.162 and 0.27-1.51 in white headed langurs and black langurs, respectively. Similar values of genetic diversity indices (I=0.13 and He=0.20) have been recorded by Shafi et al. (2016) in a RAPD based study on Tor putitora. Rodrigues et al. (2007) calculated the mean values of Nei’s diversity index as 0.26 in a RAPD based study on Ozotoceros bezoarticus. Based on the calculated values of the gene diversity indices and as compared with the above studies, it can be assessed that, a moderate level of genetic diversity is present in different langur populations found in

Pakistan.

4.5.1.5. Genetic differentiation and gene flow

Based on Hardy-Weinberg equilibrium, various genetic diversity criteria including total heterozygosity (Ht), genetic diversity within (Hs) and between (Dst) populations, genetic differentiation within (Rst) and between (Gst) populations were calculated. These calculations were used to estimate the genetic flow (Nm) between different populations of grey langurs.

4.5.1.5.1. Total heterozygosity or heterogeneity (Ht)

The mean (±SE) value of total heterozygosity or heterogeneity (Ht) as exhibited by different RAPD primers among all populations of grey langurs was 0.144±0.007

(Table 4.15). The highest value of Ht was expressed by the OPA-04 (0.152) followed by OPD-04 (0.125) and OPC-5 (0.118). The lowest value of Ht was showed by the

OPC-7 (0.093).

105

Table 4.15: Total heterozygosity, genetic diversity, genetic differentiation and gene flow between different populations of grey langur as exhibited by RAPD markers.

Total Genetic Genetic diversity differentiation Gene flow Markers Heterozygosity (Nm) (Ht) Hs Dst Gst Rst

OPC-2 0.096 0.087 0.009 0.094 0.906 4.809

OPC-5 0.118 0.096 0.021 0.182 0.818 2.253

OPC-7 0.093 0.080 0.013 0.145 0.855 2.958

OPC-9 0.106 0.096 0.010 0.095 0.905 4.748

OPD-02 0.106 0.094 0.012 0.111 0.889 3.989

OPD-03 0.116 0.102 0.015 0.126 0.874 3.460

OPD-04 0.125 0.087 0.038 0.307 0.693 1.127

OPA-04 0.152 0.128 0.024 0.160 0.840 2.621

Mean 0.114 0.096 0.018 0.153 0.847 3.246

SE 0.007 0.005 0.003 0.025 0.025 0.448

Hs = genetic diversity within population, Dst =genetic diversity between population, Gst =genetic differentiation among population, Rst = genetic differentiation within population

4.5.1.5.2. Genetic diversity within and between populations

Genetic diversity within population (Hs) and between populations (Dst) were calculated by using software Popgene (1.32). The mean value of genetic diversity (Hs) within population as exhibited by all RAPD markers for all populations was

0.096±0.005, while the mean genetic diversity between populations (Dst) is calculated as 0.018±0.003 (Table 4.16). The highest value of Hs was recorded as 0.128 (OPA-04) and the lowest was 0.080 (OPC-7). Similarly, the highest value of Dst was 0.038 (OPD-

04) and the lowest was 0.009 (OPC-2). This indicated very low genetic variations within populations than the variation between different populations.

106

4.5.1.5.3. Gene flow

The mean values (±SE) of genetic differentiation constants among populations (Gst) and within populations (Rst) were 0.153±0.025 and 0.847±0.025. These low values of

Gst and higher values of Rst are indicative of a higher gene flow between populations, and thus a lower level of genetic isolation between populations. Therefore, the mean value of gene flow/numbers of migrants (Nm) between populations was higher

(3.246±0.448) ranging between 4.809 (OPC-2) and 1.127 (OPD-04; Table 4.15). The values of Nm > 1 suggest a higher level of genetic flow existing between different populations (Mallet et al., 1990).

The gene flow (Nm) was significantly correlated with genetic diversity between population (Dst; r= -0.919, p<0.01), differentiation among populations (Gst; r= -0.926, p<0.001) and within populations (Rst; r=0.926, p<0.001; Table 4.16). Genetic diversity between population (Dst) was also strongly correlated with genetic differentiation within populations (Rst; r= -0.968, p<0.0001) and among populations (Gst; r=0.968, p<0.0001). Similarly, and total heterozygosity between population (Ht) was strongly correlated with heterozygosity within population (Hs; r= 0.856, p<0.01).

Table 4.16: Correlation matrix between total heterozygosity (Ht), genetic diversity within (Hs) and between (Dst) populations, genetic differentiation within (Rst) and between (Gst) populations as exhibited by RAPD markers in grey langur.

Ht Hs Dst Gst Rst Nm

Ht 1.000

Hs 0.856 1.000

Dst 0.648 0.160 1.000

Gst 0.442 -0.085 0.968 1.000

Rst -0.442 0.085 -0.968 -1.000 1.000

Nm -0.542 -0.077 -0.919 -0.926 0.926 1

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The mean value of gene flow (3.246±0.448) between all populations (>1) indicated that, more than one individuals migrate between different population to ensure the genetic flow. Similar findings have (Nm=3.22) been reported by Shafi et al.

(2016) for Tor putitora using RAPD markers. The finding of the present study also suggests that, the inter-population migration of individuals still exists. Therefore, the probability of inbreeding and genetic drift is low in these populations, indicating that, the genetic fixation is not a serious problem for the existing langur populations. Slatkin

(1987) suggested that values of Nm >1 (i.e, above the minimum number of migrants per generation) needed to avoid differentiation by genetic drift.

4.5.1.6. Genetic similarity and genetic distance

Nei’s genetic similarities and distances were calculated using pairwise population matrix of different populations. As expected, the values of genetic similarities were very high (97-98%) among populations of grey langurs found in

Pakistan/AJK (Table 4.17). Inversely, values of genetic distances were very low (< 3%) between different populations (Table 4.18). Higher values of genetic similarity and lower values of genetic distance between different populations indicate a higher rate of gene flow and lower heterogeneity (Table 4.16).

Table 4.17: Pairwise Population Matrix of Nei Genetic Identity exhibited by RAPD markers in grey langur populations of Pakistan.

Populations Poonch Muzaffarabad Neelum Mansehra Kohistan

Poonch 1.000

Muzaffarabad 0.984 1.000

Neelum 0.981 0.983 1.000

Mansehra 0.982 0.980 0.981 1.000

Kohistan 0.973 0.971 0.972 0.976 1.000

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Table 4.18: Pairwise Population Matrix of Nei Genetic Distance exhibited by RAPD markers in grey langur populations of Pakistan.

Populations Poonch Muzaffarabad Neelum Mansehra Kohistan

Poonch 0.000

Muzaffarabad 0.016 0.000

Neelum 0.019 0.018 0.000

Mansehra 0.018 0.020 0.019 0.000

Kohistan 0.028 0.030 0.029 0.024 0.000

Seasonal movements of troops and individuals have been recorded in grey langur (Minhas et al., 2012). During these movements, inter-population gene flow possibly occurs. This allows us to propose that physical/ habitat barriers expected for the area are not effective in genetically separating different langur populations, and all grey langur populations of Pakistan and AJK act as a single genetic unit. Shafi et al.

(2016) also recorded a high values genetic similarity (94 - 99%) between different populations of Tor putitora distributed in Pakistan.

Based on genetic distance, dendrogram constructed using UPGMA in Popgene

(1.32) divided the whole population of grey langurs into five groups (Fig. 4.18).

Poonch and Muzaffarabad populations very closely related to each other forming a clade, while all others are comparatively distinctly related to each other. Nei's genetic identity and genetic distance and dendrogram developed therefrom (Fig. 4.18) suggest a closer association of Neelum and Muzaffarabad populations. Poonch population is out- group of the Muzaffarabad-Neelum clade. Mansehra and Kohistan populations form separate clades. Muzaffarabad and Kohistan populations were the least related (genetic distance 0.0133; Table 4.19).

109

A. B. Figure 4.18: UPMGA dendrogram based on genetic distance (A=Nei’s genetic distance; B= Nei's unbiased genetic distance) exhibited by RAPD markers in different populations of grey langurs.

Table 4.19: Nei's unbiased RAPD based genetic identity (above diagonal) and genetic distance (below diagonal) in different populations of grey langurs from Pakistan.

Populations Poonch Muzaffarabad Neelum Mansehra Kohistan

Poonch **** 0.9972 0.9946 0.9940 0.9872

Muzaffarabad 0.0028 **** 0.9979 0.9943 0.9868

Neelum 0.0054 0.0021 **** 0.9956 0.9886

Mansehra 0.0060 0.0057 0.0044 **** 0.9914

Kohistan 0.0129 0.0133 0.0115 0.0086 ****

Paired group cluster analysis with Euclidean similarity index using presence absence data of each locus resulted into three main clusters (Fig. 4.19). These divisions of genotypes follow geographical logic. The individuals from Poonch (Haveli, Bagh,

Poonch) form a separate clade. Genotypes of other populations also showed separate clusters, however, some of these individuals clustered with other closely related populations.

110

Figure 4.19: Clustering of different genotypes from grey langur populations (Euclidean similarity index) based on Paired group cluster analysis of binary data produced by RAPD markers.

Population grouping developed by different dendrogram clusters based on genetic distances and similarities seem natural, as expected under the geographic conditions. It appears that despite the presence of possible physical/ habitat barriers

111 different geographic populations of grey langur are not completely isolated from one another and gene flow persists between these populations as indicated by the higher values of gene flow exhibited in present study. The lowest similarity between Poonch and Kohistan populations can be expected on distance logic, being located on two extremes of the range Kohistan population is geographically placed at the wider distances from other population, hence, emerged as a separate cluster. However, it is also linked to other population via the adjacent Mansehra population.

4.5.1.7. Multivariate analysis

4.5.1.7.1. Principal coordinate analysis

Principal coordinate analysis (PCoA) or classical multidimensional scaling is a spatial way of ordinating the data to visualize the patterns of genetic relationship contained in larger matrices which are impossible to read and interpret. PCoA is a multivariate analysis to locate and plot the main patterns within a multivariate data set with multiple loci and multiple samples. It exhibits the location of the major axes (i.e., first 2-3) of variation within a multidimensional data set. Each successive axis explains proportionately less of the total variation, such that when there are distinct groups, the first 2 or 3 axes will typically reveal most of the separation among groups (Peakall and

Smouse, 2012). Three-dimensional principal coordinate analysis accounted for 38.99%,

22.29% and 21.21% of the variability as explained by the first three axes with a total

82.50% variability (Table 4.20).

Table 4.20: Percentage of variation under PCoA in different grey langur populations of Pakistan

Axis 1 2 3

% 38.99 22.29 21.21

Cum % 38.99 61.28 82.50

112

Based on the maximum variability among first two axes of the Eigen values, the

PCoA output has produced three or four distinct clusters. Two populations of AJK

(Muzaffarabad-Poonch) form one cluster, while, Neelum, Mansehra and Kohistan populations formed three separate clusters (Fig. 4.20). PCoA analysis also provided similar clustering pattern as already suggested by dendrograms.

Principal Coordinates (PCoA)

Neelum

Mansehra

Kohistan Coord. 2 Muzaffarabad

Poonch

Coord. 1

Figure 4.20: Graphical presentation of PCoA results for different populations of grey langur.

PCoA calculated from genetic distance and similarity matrices of genotypes exhibited a lower (25.56%) percentage of variability than the different populations

(82.50%) (Table 4.21). Based on first two axes of Eigen values, the PCoA results showed different patterns of genotypes (Fig. 4.21).

Table 4.21: Percentage of variation under PCoA in different genotypes of grey langur of Pakistan Axis 1 2 3 % 9.12 8.47 7.97 Cum % 9.12 17.59 25.56

113

Figure 4.21: Graphical presentation of PCoA results for different genotypes of grey langur.

PCoA calculated from geographic distance of different genotypes obtained by using the global position system coordinates in GenAlEX 6.5 exhibited a higher

(81.91%) percentage of variability as also exhibited by the genetic distance (82.50%)

(Table 4.22). Based on first two axes of Eigen values, the PCoA results showed three main clusters of genotypes (Fig. 4.22). These clusters are coinciding with the PCoA results based on genetic distance.

Table 4.22: Percentage of variation under geographic distance based PCoA for different genotypes of grey langur.

Axis 1 2 3

% 45.73 19.21 16.97

Cum % 45.73 64.94 81.91

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Figure 4.22: Graphical presentation of PCoA results based on geographic distance for different genotypes of grey langur.

4.5.1.8. Spatial genetic analysis

To detect the distribution patterns of genetic variations across different spatial scales, spatial genetic analysis was also done (Diniz-Filho et al., 2013). This analysis is used to assess the process of isolation by distance i.e., genetic similarities generally decrease with increasing geographic distance. Migration barriers can hinder the gene flow, which result into increasing genetic distance. Global Positional System technology was used to collect latitude-longitude coordinates used for regression analyses in conjunction with genetic distances by using Mantel tests (Mantel, 1967).

The calculated value of Rxy=-0.008, with p0.05 revealed a nonsignificant relationship between genetic distance and geographic distance (Fig. 4.23). Hence, the genetic distances between different genotypes are not related to the geographic isolation considerably. Absence of any significant correlation between genetic and geographical distance suggests that geographic distance is not significantly working as effective restricting factor in gene flow between populations as already confirmed by the values of gene flow (Nm).

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Figure 4.23: Association between RAPD based genetic and geographic distances as depicted by Mantel Test.

4.5.1.9. Analysis of molecular variance (AMOVA)

The Analysis of Molecular Variance (AMOVA) was carried out to calculate the

F-statistics and/or their analogues for estimation of hierarchical partitioning of genetic variation within populations and among populations or regions (Table 4.23).

Table 4.23: Summary of AMOVA results showing genetic variation within and among populations of grey langur from Pakistan.

Est. Phist Source df SS MS % P Var. (PT) Between Populations 4 106.800 26.700 1.000 4 0.042 0.006 Within Populations 15 340.500 22.700 22.700 96 Total 20 447.300 23.700 100

Analysis of molecular variance exhibited only 4% variance between populations and 96% variance within populations. The Phist (PT) value suggested a significance difference in the variance within population and between populations (PT =0.042;

116 p=0.006) (Table 4.23). The results suggested that the variance contributed by individual variation within populations was higher (96%) than variation between population (4%).

4.5.2. Microsatellite Markers

4.5.2.1. Primer selection

From 19 different microsatellite or simple sequence repeat (SSR) primers screened for all 23 samples, 16 microsatellite markers were successfully amplified in

15 samples/ genotypes. These markers could not amplify the template DNA in other genotypes (as RAPDs did) probably due to poor quality and quantity of the extracted

DNA, especially from fecal materials. Three primers (D6S291, D6S2741, D6S2876), though amplified in rhesus monkeys yetcould not be amplified in grey langurs (Roeder et al., 2009). Therefore, 16 microsatellite primers were used for genetic diversity analysis in 15 different genotypes from various populations of grey langur across

Pakistan and AJK (Table 4.3). The maximum numbers of primers (16/19; 84.21%) tried in the present study amplified template DNA, because these primers have already amplified successfully in several studies carried out on different primate taxa, including rhesus monkeys, gibbons, baboons and chimpanzee etc. (Table 3.2 for reference detail).

Based on molecular size, different patterns of DNA bands were produces by different microsatellite markers in grey langur populations of Pakistan (Appendix-III-Plate 3).

All primers used in current study have been previously used in different primate species (Table 3.2). Because, the identification and development of new microsatellite markers is time consuming and requires high technical expertise (Zane et al., 2002;

Chambers et al., 2004), therefore, the cross-species amplification strategy (i.e., using loci characterized in other closely-related species) was adopted as already been widely used in different primate species (Coote and Bruford, 1996; Kayser et al., 1996;

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Perelygin et al., 1996; Rogers et al., 2000; Smith et al., 2000a,b, Chambers et al., 2004;

Liu et al., 2008).

4.5.2.2. Genotyping error and allelic dropout

The use of noninvasive samples for microsatellite genotyping is sometimes associated with certain limitations due to very low quantity and quality of extracted

DNA. This may result in incorrect genotypes due to different types of scoring errors including, allelic dropout and false alleles (Taberlet et al., 1996; Taberlet et al., 1999;

Smith et al., 2000a,b). Out of total 15, only 6 DNA samples were extracted from fecal materials (Table 4.4). In these samples, the above-mentioned genotyping issues were addressed in the present study by DNA extraction using different methods and optimizing the PCR reaction for 5-7 repeats by using positive control DNA as well (Liu et al., 2008). Loci can be considered as heterozygous for an individual, if each allele was observed at least twice from a minimum of two separate amplifications (Morin et al., 2001). No evidence of PCR inhibition was detected in PCR amplifications in which fecal extracted DNA was used to positive control DNA. Only these primers were selected that gave the reproducible results in several repeats. Furthermore, most of the primers selected for analysis have a tetranucleotide repeat motif, which is thought to reduce the probability of genotyping error often associated with dinucleotide markers

(Chambers et al., 2004).

4.5.2.3. Number and pattern of amplified fragments

A total of 256 alleles were amplified with the average (±SE) of 16.00±1.910 alleles/ marker (Table 4.24). The highest number of alleles were recorded for D1S207

(n=28) followed by D2S141 (n=27) and D17S1290 (n=20), while, the lowest for

D1S550 (n=8). All microsatellites markers (100%) and bands produced by them under

118 the present study were polymorphic. None of the band was shared by all genotypes.

Segesser et al. (1999) also recorded that 6/7 microsatellite loci were polymorphic in social groups of Macaca sylvanus. The size of different amplicons differed from marker to marker. The maximum and minimum band size was 88 bp (D1S518) and 383 bp (D6S264) (Table 4.24).

Table 4.24: Banding patterns of microsatellite markers in grey langur populations of Pakistan.

Numbers of bands Band size Marker Total No. of Polymorphic (bp) amplicons No. (%) D1S207 28 28 100.00 91-291 D1S518 17 17 100.00 88-159 D1S533 12 12 100.00 212-348 D1S548 12 12 100.00 175-257 D1S550 8 8 100.00 212-235 D2S169 14 14 100.00 225-366 D2S1326 15 15 100.00 194-292 D2S1329 14 14 100.00 221-276 D2S1333 10 10 100.00 289-323 D2S141 27 27 100.00 143-336 D8S505 13 13 100.00 123-171 D6S264 15 15 100.00 147-383 D6S501 16 16 100.00 166-356 D10s1432 17 17 100.00 117-275 D17S1290 20 20 100.00 147-206 D20s206 18 18 100.00 108-181 Total 256 256 1600 Mean 16.00 16.00 100.00 SE 1.91 1.91 0.00

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4.5.2.4. Discriminatory power

The discriminatory powers of microsatellite primers were assessed through calculation of their polymorphism information content (PIC), marker index (MI), effective multiplex ratio (EMR), resolving power (RP) and diversity index (DI) (Kayis et al., 2010; Pecina-Quintero et al., 2012; Mandal et al., 2016). The mean PIC value

(±SE) for all markers was 0.94±0.01 ranging between 0.862 (D2S169) and 0.987

(D2S1326; Table 4.25).

Table 4.25: Discriminatory powers analysis for different microsatellite markers used in genetic analysis of grey langurs of Pakistan.

Marker PIC MI EMR RP DI D1S207 0.944 26.424 28.000 3.733 0.733 D1S518 0.944 16.048 17.000 2.267 0.838 D1S533 0.978 11.733 12.000 1.600 0.867 D1S548 0.936 11.227 12.000 1.600 0.800 D1S550 0.924 7.396 8.000 1.067 0.733 D2S169 0.862 12.071 14.000 1.867 0.767 D2S1326 0.987 14.807 15.000 2.000 0.900 D2S1329 0.942 13.191 14.000 1.867 0.767 D2S1333 0.944 9.437 10.000 1.333 0.778 D2S141 0.885 23.897 27.000 3.600 0.743 D8S505 0.986 12.819 13.000 1.733 0.892 D6S264 0.968 14.522 15.000 2.000 0.833 D6S501 0.974 15.589 16.000 2.133 0.881 D10s1432 0.963 16.367 17.000 2.267 0.858 D17S1290 0.952 19.035 20.000 2.667 0.810 D20s206 0.927 16.688 18.000 2.400 0.760 Total 15.12 241.25 256.00 34.13 12.96 Mean 0.94 15.08 16.00 2.13 0.81 SE 0.01 1.74 1.91 0.25 0.02

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The mean value of MI was 29.91±2.9 (SE) with the highest for D1S207

(26.424) and the lowest for D1S550 (7.396). Value of EMR and RP were the highest for D1S207 (EMR=28.00, RP=3.733) and the lowest for D1S550 (EMR=8.00,

RP=1.067). The highest DI was the calculated for D2S1326 (0.900) and the lowest for

D1S207 and D1S550 (0.733; Table 4.25). There was strong positive correlation between PIC and DI (r=0.769, p< 0.001), MI and EMR (r=0.993, p< 0.0001) and MI and RP (r=0.993, p< 0.0001) (Table 4.26).

Table 4.26: Spearmen correlation matrix between different discriminatory power indices of microsatellite markers used in genetic analysis of grey langurs of Pakistan PIC MI EMR RP DI

PIC 1 MI -0.11065 1 EMR -0.21813 0.993591 1 RP -0.21813 0.993591 1 1 DI 0.769361 -0.18906 -0.2658 -0.2658 1

PIC values are derived from the number and frequency of allele per locus, which provides index of diversity i.e., DI (Zhou et al., 2007). The higher values of PIC and its strong association with DI suggested that all markers used had the maximum efficiency and capability for genetic analysis. PIC values (> 0.5) of different markers indicated that all primers were highly effective DNA markers for detecting the genetic diversity among different genotypes (Botstein et al., 1980). Zhou et al. (2007) calculated mean PIC values of different microsatellite makers (0.818 ±0.0158 SE) in

Tibetan antelope, while Xu et al. (2013) recorded mean PIC value of different microsatellite markers as 0.7638 (range 0.4613 - 0.8989) in rhesus macaques.

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4.5.2.5. Genetic diversity

4.5.2.5.1. Allelic frequencies

A total of 97 different alleles were amplified by 16 different microsatellite markers in 15 genotypes of grey langurs from Pakistan. The highest numbers and frequency of alleles were produced by D2S1326 (n=10, 10.309%, 0.103), while the lowest by D1S550 (n=2, 2.062%, 0.021). The number of amplified allele by different markers ranged between 2 and 10 (Table 4.27). Similar range (3-12) of amplified alleles hasve been recorded in other studies on different animal taxa including primates using different microsatellite markers, e.g. Segesser et al. (1999) recorded 4-12 alleles per locus in Macaca sylvanus, Liu et al. (2008) recorded 3-9 alleles in Rhinopithecus bieti, Newman et al. (2002) recorded 5-7 in Chlorocebus aethiops sabaeus, and Zhou et al. (2007) recorded 7–12 alleles per locus in Pantholops hodgsonii.

Table 4.27: Description of alleles produced by microsatellite markers in grey langur populations of Pakistan. Marker Amplified Alleles (No.) Proportion of total alleles (%) Allele Frequency D1S207 9 9.278 0.093 D1S518 5 5.155 0.052 D1S533 6 6.186 0.062 D1S548 4 4.124 0.041 D1S550 2 2.062 0.021 D2S169 4 4.124 0.041 D2S1326 10 10.309 0.103 D2S1329 4 4.124 0.041 D2S1333 3 3.093 0.031 D2S141 7 7.216 0.072 D8S505 8 8.247 0.082 D6S264 6 6.186 0.062 D6S501 9 9.278 0.093 D10s1432 8 8.247 0.082 D17S1290 7 7.216 0.072 D20s206 5 5.155 0.052 Total 97 100.00 1.000

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4.5.2.5.2. Observed and effective alleles

The highest numbers of observed alleles (Na) and effective alleles (Ne) were recorded in Poonch population (Na=1.438±0.316, Ne=1.391±0.296), followed by

Neelum (Na=1.250±0.348, Ne=1.183±0.321) and Muzaffarabad (Na=1.062±0.281,

Ne=1.035±0.270) populations (Fig. 4.24). The lowest numbers of these alleles were recorded in Mansehra population (Na=0.625±0.239, Ne=0.625±0.239).

Legend Na = No. of Different Alleles Ne = No. of Effective Alleles = 1 / (Sum pi^2) No. Private Alleles = No. of Alleles Unique to a Single Population Figure 4.24: Mean (with SE bars) allelic patterns for microsatellite markers across populations of grey langur from Pakistan.

The observed number is the actual number of alleles found in the study population, while the effective number of alleles is the number of equally frequent alleles that would take to achieve the same expected heterozygosity in population.

Alleles with low frequencies or rare alleles contribute very little to the effective number of alleles so little difference between 'number of alleles' and 'effective number of alleles' suggested a higher number of rare alleles. Thus, the effective number of alleles is a reciprocal of expected homozygosity or it absolutely correlates with genetic diversity expressed as the expected heterozygosity.

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4.5.2.5.3. Private alleles

Private alleles are unique alleles for a given locus present in only one population (Rico et al., 2017). A total of 32 private alleles were detected by amplification of 16 primers in different populations. The highest numbers of private alleles were recorded in Neelum (n=11, 2.200±0.112) population followed by Poonch

(n=9, 1.5±0.137) and Muzaffarabad (n=5, 1.667±0.144). Out of 16, twelve markers amplified private alleles in different populations (Table 4.28). The highest numbers of private alleles were produced by D1S207 (n=6) followed by D2S1326 (n=5).

Table 4.28: Numbers of private alleles amplified by different microsatellite marker in grey langur populations of Pakistan. Populations Markers Poonch Muzaffarabad Neelum Mansehra Kohistan Total D1S207 1 2 1 2 6 D1S518 2 2 D1S533 2 2 D2S1326 1 3 1 5 D2S1329 2 2 D2S1333 1 1 D2S141 1 1 D8S505 2 2 D6S264 2 2 D6S501 2 2 4 D10s1432 2 2 4 D20s206 1 1 Total 9 5 11 3 4 32 Mean 1.500 1.667 2.200 1.000 2.000 2.667 SE 0.137 0.144 0.112 0.000 0.000 0.417

Presence of private alleles in different populations shows its uniqueness among populations. Segesser et al. (1999) recorded 1-8 private alleles in different subpopulations of Macaca sylvanus using microsatellite markers. Bastos et al. (2010) recorded 74 private alleles in a study of genotyping the red-handed howler monkey populations using 15 microsatellite loci.

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4.5.2.5.4. Polymorphism

Mean (±SE) level of polymorphism in different populations of grey langur was

45±6.06%. Amplification of different microsatellite loci exhibited different patterns based on relative frequency of polymorphism (Fig. 4.25). The highest frequency of polymorphic loci was recorded in Poonch population (62.50%) followed by

Muzaffarabad (50.00%) and Neelum (50.00%).

Figure 4.25: Percentage of polymorphic loci in different population of grey langur in Pakistan.

The percentage of polymorphic loci as exhibited for different microsatellite markers indicates a good level of genetic variability among different populations of grey langur in Pakistan.

4.5.2.5.5. Linkage disequilibrium

A total of 36 possible pair-wise comparisons for five populations suggested that there was no significant association between any pair (p>0.05) of microsatellite loci for any population (p>0.05). Overall, tests for linkage disequilibrium among all loci exhibited no significant deviations from expected values. This indicated linkage disequilibrium and suggested an independent allelic variation at all microsatellite loci

(Table 4.29). 125

Table 4.29: Summary of Chi-Square Tests for Hardy-Weinberg Equilibrium

Populations Locus DF ChiSq Prob Significance Poonch D1S207 1 2.000 0.157 ns Poonch D1S518 1 1.000 0.317 ns Poonch D1S533 6 9.000 0.174 ns Poonch D2S1326 1 1.000 0.317 ns Poonch D2S1333 1 1.000 0.317 ns Poonch D2S141 3 2.000 0.572 ns Poonch D8S505 1 1.000 0.317 ns Poonch D10s1432 1 1.000 0.317 ns Poonch D17S1290 1 1.000 0.317 ns Poonch D20s206 1 1.000 0.317 ns Muzaffarabad D1S207 3 3.000 0.392 ns Muzaffarabad D1S518 1 1.000 0.317 ns Muzaffarabad D1S533 1 1.000 0.317 ns Muzaffarabad D2S1329 1 1.000 0.317 ns Muzaffarabad D2S1333 1 1.000 0.317 ns Muzaffarabad D2S141 1 3.000 0.083 ns Muzaffarabad D6S501 1 1.000 0.317 ns Muzaffarabad D17S1290 1 1.000 0.317 ns Neelum D1S207 6 9.000 0.174 ns Neelum D1S518 3 2.000 0.572 ns Neelum D2S1326 3 2.000 0.572 ns Neelum D2S141 1 2.000 0.157 ns Neelum D6S264 1 2.000 0.157 ns Neelum D6S501 1 1.000 0.317 ns Neelum D17S1290 1 1.000 0.317 ns Neelum D20s206 1 1.000 0.317 ns Mansehra D1S207 1 3.000 0.083 ns Mansehra D2S1326 1 1.000 0.317 ns Mansehra D2S1333 1 1.000 0.317 ns Mansehra D2S141 1 2.000 0.157 ns Mansehra D17S1290 1 2.000 0.157 ns Kohistan D1S207 3 3.000 0.392 ns Kohistan D2S141 6 6.000 0.423 ns Kohistan D10s1432 1 1.000 0.317 ns Kohistan D17S1290 1 1.000 0.317 ns Kohistan D20s206 1 1.000 0.317 ns Key: ns=not significant, * P<0.05, ** P<0.01, *** P<0.001

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4.5.2.5.6. Shannon’s information index and heterozygosity

Shannon’s Information Index (I), observed (Ho) and expected (He) heterozygosity or Nei's (1972) gene diversity index and unbiased expected heterozygosity (uHe) exhibited by different microsatellite markers for each population have been presented in Table 4.30.

Table 4.30: Mean values (±SE) of genetic diversity indices and fixation index exhibited by 16 microsatellite markers in different populations of grey langurs.

Populations I Ho He uHe F Poonch 0.495±0.10 0.625±0.12 0.334±0.06 0.585±0.11 -0.898±0.05 Muzaffarabad 0.366±0.09 0.500±0.13 0.257±.06 0.458±0.12 -0.955±0.03 Neelum 0.430±0.11 0.500±0.13 0.280±0.07 0.429±0.11 -0.823±0.06 Mansehra 0.217±0.08 0.313±0.12 0.156±0.06 0.246±0.09 -1.000±0.00 Kohistan 0.280±0.11 0.313±0.12 0.179±0.07 0.296±0.11 -0.794±0.07 Overall Mean±SE (all loci) 0.357±0.05 0.450±0.06 0.241±0.03 0.403±0.06 -0.894±0.03 Overall Mean±SE (variable loci) 0.794±0.035 1.000±0.000 0.536±0.012 0.895±0.025 Legend I = Shannon's Information Index = -1* Sum (pi * Ln (pi)) Ho = Observed Heterozygosity = No. of Hets / N He = Expected Heterozygosity = 1 - Sum pi^2 uHe = Unbiased Expected Heterozygosity = (2N / (2N-1)) * He F = Fixation Index = (He - Ho) / He = 1 - (Ho / He)

Mean (±SE) values of Shannon’s index, observed and expected heterozygosity or Nei's genetic diversity index and unbiased expected heterozygosity at all loci were the highest in Poonch population (I=0.495±0.10, Ho=0.625±0.12, He=0.334±0.06, uHe=0.585±0.11) and the lowest in Mansehra population (I=0.217±0.08,

Ho=0.313±0.12, He=0.156±0.06, uHe=0.246±0.09). The mean values of fixation index

127 ranged between -0.794±0.07 and 1.000±0.00 for Kohistan and Mansehra populations respectively (Table 4.30).

Overall mean values of Shannon’s index, observed and expected heterozygosity, unbiased expected heterozygosity and fixation index at all loci were

0.357±0.05, 0.450±0.06, 0.241±0.03, 0.403±0.06 and -0.894±0.03 respectively (Table

4.30). Similar values of observed (Ho=0.00 to 0.84) and expected heterozygosity

(He=0.41 to 0.87) were recorded by Liu et al. (2008) using microsatellite markers in

Rhinopithecus bieti. The range of mean observed heterozygosity (Ho=0.313 to 0.625) is also close to such values reported in various previous studies on different animal species, such as, Macaca sylvanus (H=0.67; Segesser et al., 1999), Gorilla gorilla

(H=0.554; Zhang et al., 2001), Ailuropoda melanoleuca (H = 0.44; Lu et al., 2001),

Lynx lynx (H = 0.53; Hellborg et al., 2002), Macaca mulatta (H = 0.7; Morin et al.,

1998), R. roxellana (H=0.5; Pan et al., 2005), Alouatta palliate (H= 0.35; Cortés-Ortiz et al., 2010 and Macaca mulatta (H= 0.6918; Xu et al., 2013).

Population genetic diversity indices, viz. Shannon diversity index, Nei’s genetic diversity index and polymorphism analysis revealed inter-population variability.

Comparing levels of heterozygosity with other organisms indicated that the present values ranging between 0.313 and 0.625 are rather low. Such a difference can be an artifact caused by unequal sample sizes used in different studies (Ellsworth and

Hoelzer, 1998).

4.5.2.6. Genetic differentiation and gene flow

Three fixation indices are generally used to evaluate the population subdivision.

These include Fis (inter-individuals), Fit (total population), and Fst (subpopulations).

The mean values of genetic differentiation coefficient (Fst) ranged between 0.223 for

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D2S141 to 0.898 for D2S1329, D8S505 and D6S264 with a mean of 0.438±0.097

(Table 4.31). The values of Fst are inversely related to the gene flow or migration coefficient (Nm). The values of Fst are always positive, ranging between 0 (indicating no subdivision or occurring of random mating population) and 1 indicating extreme subdivision or complete isolation. The higher values of Fst are indicative of a lower gene flow between populations. Therefore, the mean value of gene flow/ numbers of migrants (Nm) between populations was low (1.185±0.374). The values of gene flow ranged between 3.589 (D20s206) and 0.028 (D2S1329, D8S505 and D6S264; Table

4.31). The values of Nm above 1 (Nm > 1) suggested that more than one individuals migrate between different population to ensure the genetic flow (Mallet et al., 1990).

The gene flow was significantly negative correlated with Fst between population (r= -

0.846, p<0.0001) (Table 4.32).

Table 4.31: F-Statistics and estimates of gene flow between populations for different microsatellite loci in grey langurs from Pakistan. Locus Fis Fit Fst Nm D1S207 -0.698 -0.181 0.304 0.571 D1S518 -0.846 0.306 0.624 0.150 D1S533 -0.636 0.581 0.744 0.086 D2S1326 -0.846 0.351 0.111 2.002 D2S1329 -1.000 0.796 0.898 0.028 D2S1333 -1.000 0.302 0.651 0.134 D2S141 -0.739 -0.351 0.223 0.871 D8S505 -1.000 0.796 0.898 0.028 D6S264 -1.000 0.796 0.898 0.028 D6S501 -1.000 0.583 0.075 3.074 D10s1432 -1.000 0.583 0.075 3.074 D17S1290 -1.000 -0.351 0.124 1.766 D20s206 -1.000 0.302 0.065 3.589 Mean -0.905 0.347 0.438 1.185 SE 0.038 0.114 0.097 0.374

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Table 4.32: Correlation matrix between different F-Statistics and gene flow over all populations for different microsatellite loci in grey langur. Fis Fit Fst Nm

Fis 1 Fit -0.39092 1 Fst -0.01147 0.5525 1 Nm -0.23018 -0.0955 -0.846 1

The negative values of inbreeding coefficient (Fis) in all populations suggested that currently there is no significant inbreeding effect in any population and all populations exhibited heterozygosity excess (negative Fis value). The value of Fis ranges between -1 and +1. The negative Fis values indicate the excess of heterozygote or out-breeding, while, positive values indicate heterozygote deficiency or inbreeding.

Zhou et al. (2007) also recorded the negative values of Fis ranging between −0.269 to

−0.097 (mean= −0.163 ± −0.0197) among nine microsatellite loci, showing excess in heterozygote in Pantholops hodgsonii (Zhou et al., 2007).

4.5.2.7. Genetic distance and similarity

Results of pairwise population Nei’s genetic distance and similarity analysis

(Nei, 1972) indicated that the largest genetic distance (0.752) was found between

Mansehra and Neelum populations, while the least distance (0.255) was found between

Mansehra and Kohistan populations (Table 4.33). Likewise, the maximum genetic similarity (0.774) was recorded between Kohistan and Mansehra populations, and the least (0.471) between Mansehra and Neelum (Table 4.33). Similar findings have also been detected under Nei’s unbiased genetic distance and genetic similarity indices

(Table 4.34).

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Table 4.33: Pairwise population matrix of Nei's genetic identity (above diagonal) and genetic distance (below diagonal) exhibited by microsatellite markers in grey langur populations of Pakistan

Poonch Muzaffarabad Neelum Mansehra Kohistan *** 0.6396 0.6155 0.5222 0.6742 Poonch 0.4469 *** 0.7217 0.6124 0.4743 Muzaffarabad 0.4854 0.3262 *** 0.4714 0.5477 Neelum 0.6496 0.4904 0.7520 *** 0.7746 Mansehra 0.3942 0.7458 0.6020 0.2554 *** Kohistan

Table 4.34: Pairwise population matrix of Nei's unbiased genetic identity (above diagonal) and genetic distance (below diagonal) exhibited by microsatellite markers in grey langur populations of Pakistan

Poonch Muzaffarabad Neelum Mansehra Kohistan *** 0.6396 0.6155 0.5222 0.6742 Poonch 0.4469 *** 0.7217 0.6124 0.4743 Muzaffarabad 0.4854 0.3262 *** 0.4714 0.5477 Neelum 0.6496 0.4904 0.7520 *** 0.7746 Mansehra 0.3942 0.7458 0.6020 0.2554 *** Kohistan

The higher genetic distance and lower genetic similarities between Neelum and

Mansehra population was contradictory to the results depicted by RAPD markers in the present study. However, these results can be explained under present geographic distribution as both populations are geographically separated from each other by river

Neelum and mountains as well.

Dendrogram generated by Popgene based on genetic similarities/differences between different populations identified two main clusters (Fig. 4.26). First cluster can be subdivided into two sub-clusters comprising on, i) Poonch population (as outgroup) and ii) a monophyletic clade of Muzaffarabad and Neelum Population. Second cluster comprises on a monophyletic clade of Mansehra and Kohistan populations (Fig. 4.26).

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The pattern of cluster generated through the genetic data using microsatellite markers can be well explained under the model expected in the current geographic distribution.

Similar results were expected to have closer affinity within populations distributed in the adjoining areas of each other. Unlike RAPD markers analysis (where Mansehra population was clustered in group one as out group of first three population), Mansehra population was clustered with Kohistan population. However, both results can fit under the expected model of current geographic distribution of different langur populations.

Figure 4.26: Dendrogram based on Genetic distance (Nei's, 1972, 1978) as exhibited by microsatellite markers using UPGMA method modified from neighbor procedure of phylip version 3.5.

Paired group cluster analysis with Euclidean similarity index using presence absence data of each microsatellite locus produced three main clusters of all genotypes

(Fig. 4.27). These findings are absolutely like the results of RAPD markers.

Furthermore, these divisions of genotypes are undeniably based on geographically separated population groups of grey langurs found in different parts of the study area.

Individuals from Poonch region (Haveli, Bagh, Poonch) form a separate clade, while,

132 individuals from Neelum and Muzaffarabad regions formed another main group with two subgroups comprising on each population. Similarly, individuals from Mansehra and Kohistan regions constituted a separate main cluster with two sub-clusters comprising on each population (Fig. 4.27).

Figure 4.27: Clustering of different genotypes from grey langur populations (Euclidean similarity index) based on Paired group cluster analysis of binary data produced by microsatellite markers.

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The appearing of population groups into different dendrogram clusters based on genetic distances and similarities were expectation under the geographic conditions.

Similar results of cluster analysis were also produced by RAPD markers. It seems that all populations are linked to each other and genetic gene flow may persist between the adjacent populations. As depicted by RAPD markers, Poonch and Kohistan populations have the least similarity due to a larger geographic distance and having no direct geographic linkage between the two populations. Similar findings by both types of molecular markers also affirm the reliability of the current study.

4.5.2.8. Multivariate analysis

4.5.2.8.1. Principal coordinate analysis

Three-dimensional principal coordinate analysis accounted for 60.30%, 19.41% and 15.50% of the variability as explained by the first three axes with a total 95.22% variability (Table 4.35).

Table 4.35: Percentage of variation explained under PCoA in different grey langur populations of Pakistan Axis 1 2 3 % 60.30 19.41 15.50 Cum % 60.30 79.72 95.22

Based on the maximum variability among first two axes of the Eigen values, the

PCoA output has produced three distinct clusters. Three populations of AJK

(Muzaffarabad-Neelum-Poonch) form one cluster, while, Mansehra and Kohistan populations formed two separate clusters (Fig. 4.28). PCoA analysis also provided almost similar clustering pattern as already suggested by dendrograms.

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Figure 4.28: Graphical presentation of PCoA results based on genetic distance and similarities by microsatellite markers for different populations of grey langur.

PCoA calculated using genetic distance and similarity matrices of genotypes showed an overall 43.18% of variability lower than the different populations (95.22%;

Table 4.36). Based on first two axes of Eigen values, the PCoA results showed different patterns of genotypes. Genotypes 1, 2, 4, 5 and 6 constitute one cluster, genotypes 3, 7,

8 and 10 form another cluster, while, all remaining genotypes form the third cluster

(Fig. 4.29).

PCoA calculated from geographic distance exhibited a higher (87.07%) percentage of variability as exhibited by the genetic distance (43.18%; Table 4.37).

Based on first two axes of Eigen values, the PCoA results showed three main clusters of genotypes (Fig. 4.30). Genotypes under Poonch area form a separate cluster, genotypes from Muzaffarabad, Neelum and Mansehra form another cluster, while, the genotypes from Kohistan and Battagram clumped into a separate cluster (Fig. 4.30.

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Table 4.36: Microsatellite based percentage of variation explained by PCoA in different genotypes of grey langur of Pakistan Axis 1 2 3 % 21.31 12.18 9.69 Cum % 21.31 33.49 43.18

Figure 4.29: Graphical presentation of PCoA results based on genetic distance by microsatellite markers for different genotypes of grey langur.

Table 4.37: Percentage of variation explained under geographic distance based PCoA for different genotypes of grey langur. Axis 1 2 3 % 49.83 20.18 17.06 Cum % 49.83 70.01 87.07

Figure 4.30: Graphical presentation of PCoA results based on geographic distance for different genotypes of grey langur.

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4.5.2.9. Spatial genetic analysis

Based on latitude-longitude coordinates, Mantel tests were applied. The calculated value of Rxy=0.302, with P<0.01 revealed that, there was a significant association between genetic distance and geographic distance (Fig. 4.31). The genetic distances between different genotypes were associated with the geographic distance.

Unlike RAPD based results, the presence of a significant correlation between genetic and geographical distance in populations suggested that geographic distance might be affecting the gene flow between different populations. This phenomenon is also supported by the lower values of migration coefficient by microsatellite markers

(Nm=1.185) than the RAPD markers (Nm=3.246).

GD vs GGD 2.000

1.500 y = 0.0341x + 0.1219 R² = 0.0913 1.000

0.500 Genetic Distance

0.000 0.000 5.000 10.000 15.000 20.000 25.000 30.000 Geographic Distance

Figure 4.31: Association between microsatellite based genetic and geographic distances as depicted by Mantel Test.

4.5.3. Mitochondrial DNA Markers

Mitochondrial DNA (mtDNA) being smaller in size, maternally inherited with high evolutionary rate than the nuclear DNA, is considered as a preferable tool for investigation of phylogenetic relationships among closely related taxa (Melnick and

Hoelzer, 1992). Due to its unique characteristics, mtDNA is widely being used to study

137 the phylogenetic relationships of closely related species of both invertebrates and vertebrates (Vun et al., 2011). Furthermore, different databases have large numbers of mitochondrial sequences which can easily be accessed and used of comparative purposes (Sterner et al., 2006; Karanth, 2008; Md-Zain et al., 2010a, b). Keeping in view, along with nuclear DNA markers (i.e., RAPD and microsatellites), different mitochondrial gene markers, including, cytochrome c oxidase subunit I (COI), cytochrome b (Cyt b), D loop and 16S rRNA, were also tried to analyze in the present study. These mtDNA gene markers were sequenced and analyzed to understand and clarify the controversies associated with grey langur classification and phylogeny in

Pakistan.

4.5.3.1. Cytochrome c oxidase subunit I

Cytochrome oxidase I gene (COI), also known as the Barcoding Gene is one of the most commonly used mitochondrial genes in species identification and systematic studies (Hebert et al., 2003; Santamaria et al., 2007; Clare et al., 2011; Khaliq et al.,

2015; Pradhan et al., 2015).

4.5.3.1.1. PCR Products

From cytochrome c oxidase subunit I (COI) gene, fragments of 248-684 bp size was successfully amplified by forward and reverse separate reactions from the mitochondrial genomes of 15 different samples (Table 4.38; Fig. 4.32). After careful examining the both forward and reverse reaction products, trimming and cleaning was carried out and the final product size of 661 bp obtained for all 15 samples (Appendix-

IV).

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Figure 4.32: Gel picture of COI gene amplified products of grey langurs of Pakistan.

Table 4.38: Grey langur samples with successful amplification of COI partial gene fragments from AJK/ Pakistan.

Raw PCR product size Sr.#. Sample name Locality (bp) Forward Reverse 1 Poonch1-AJK-Pakistan Bhedi Doba-Haveli 654 675 2 Poonch2-AJK-Pakistan Motanwala- Haveli 675 635 3 Poonch3-AJK-Pakistan Mohri Said Ali Khan- 661 679 Haveli 4 Poonch4-AJK-Pakistan Las Danna- Bagh/Haveli 656 681 5 Muzaffarabad-L1-AJK- Lachrat-Muzaffarabad 679 661 Pakistan 6 Muzaffarabad-MNP1- Machiara National Park, 657 567 AJK-Pakistan Muzaffarabad 7 Muzaffarabad-MNP2- Machiara National Park, 681 677 AJK-Pakistan Muzaffarabad 8 Neelum-SN-AJK- Arangkel-Neelum Valley 653 681 Pakistan 9 Neelum-AK-AJK- Sinjli Nalah-Neelum 661 Pakistan Valley-AJK 10 Mansehra-1-Pakistan Kankoli- Shogran 656 675 11 Mansehra-2-Pakistan Mandri Kothara- Kaghan 667 658 Valley 12 Mansehra-3-Pakistan Bhoonja- Kaghan Valley 667 658 13 Kohistan-1-Pakistan Palas Valley-Kohistan 659 248 14 Kohistan-2-Pakistan Palas Valley-Kohistan 668 658 15 Kohistan-3-Pakistan Alai Valley-Battagram 658 649

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4.5.3.1.2. Intraspecific phylogenetic analysis

The phylogenetic analysis showed that all 15 samples taken from different localities from Pakistan belong to one species. The alignment and comparison of different sequences of the standard portion of the barcoding gene confirmed that there is only a single difference of nucleotide substitution at the position of 143, where, “G” is replaced by “A” in all four samples of Poonch population (Appendix-IV). The estimated evolutionary divergence using maximum composite likelihood model

(Tamura et al., 2004) based on COI gene sequences of different grey langur from across Pakistan showed that there was low genetic distance (0.002) between individuals of Poonch populations and all other populations (Table 4.39).

The cladogram constructed using Clustal Omega (1.2.4) showed two main clades of populations with the 98% confident level as indicated by bootstrap values

(Fig. 4.33). The phylogenetic analysis based on evolutionary history inferred by using the maximum likelihood, neighbor-joining, and minimum evolution methods also suggested two main clades of grey langurs from Pakistan separating Poonch population from all others (Fig. 4.34-4.36).

Figure 4.33: Cladogram based on COI partial gene sequences of grey langur from Pakistan.

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Table 4.39: Estimates of Evolutionary Divergence between COI partial gene sequences of grey langur from Pakistan. The number of base differences per site from between sequences are shown (p-distance).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 Poonch-1 2 Poonch-2 0.000 3 Poonch-3 0.000 0.000 4 Poonch-4 0.000 0.000 0.000 Muzaffarabad- 5 MNP1 0.002 0.002 0.002 0.002 Muzaffarabad- 6 Lachrat 0.002 0.002 0.002 0.002 0.000 Muzaffarabad- 7 MNP2 0.002 0.002 0.002 0.002 0.000 0.000 8 Neelum-SN 0.002 0.002 0.002 0.002 0.000 0.000 0.000 9 Neelum-AK 0.002 0.002 0.002 0.002 0.000 0.000 0.000 0.000 10 Mansehra-1 0.002 0.002 0.002 0.002 0.000 0.000 0.000 0.000 0.000 11 Mansehra-2 0.002 0.002 0.002 0.002 0.000 0.000 0.000 0.000 0.000 0.000 12 Kohistan-1 0.002 0.002 0.002 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.000 13 Kohistan-2 0.002 0.002 0.002 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 14 Kohistan-3 0.002 0.002 0.002 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 15 Mansehra-3 0.002 0.002 0.002 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

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Figure 4.34 explained the evolutionary history inferred by maximum likelihood method (Tamura and Nei, 1993). The percentages of replicate in the bootstrap test

(1000 replicates) are shown next to the branches (Felsenstein, 1985).

4 Kohistan-3 0.0000 0 0.0000 Mansehra-3 0.0000 0.0000 0 Kohistan-2 0.0000 0 0.0000 Kohistan-1 0 0.0000 0.0000 Mansehra-2 0 0.0000 0.0000 Mansehra-1 0 0.0000 0.0000 Neelum-AK 0 0.0000 0.0000 Neelum-SN 5 0.0000 0.0000 Muzaffarabad-MNP2 0.0000 0.0000 Muzaffarabad-MNP1 0.0008 0.0000 Muzaffarabad-Lachrat 0.0000 Poonch-1 63 0.0000 Poonch-2 0.0008 10 0.0000 0.0000 14 Poonch-3 0.0000 0.0000 Poonch-4 0.0000

Figure 4.34: COI gene based molecular phylogenetic analysis of grey langurs from Pakistan by Maximum Likelihood method.

Figure 4.35 shows the evolutionary relationships of taxa acquired using the

Neighbor-Joining method (Saitou and Nei, 1987). Branch length is the number of changes that have occurred. The percentage of replicate trees bootstrap test (1000 replicates) is shown next to the branches (Felsenstein, 1985).

Figure 4.36 represents the evolutionary history acquired using the Maximum

Parsimony method. The bootstrap values are shown next to the branches (Felsenstein,

1985).

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4 Kohistan-3 0.0000 0 0.0000 Mansehra-3 0.0000 0.0000 0 Kohistan-2 0.0000 0 0.0000 Kohistan-1 0 0.0000 0.0000 Mansehra-2 0 0.0000 0.0000 Mansehra-1 0 0.0000 0.0000 Neelum-AK 0 0.0000 0.0000 Neelum-SN 0.0000 0.0000 Muzaffarabad-MNP2 0.0000 0.0008 5 Muzaffarabad-Lachrat 0.0000 0.0000 Muzaffarabad-MNP1 0.0000 Poonch-1 65 0.0000 Poonch-2 0.0008 10 0.0000 0.0000 16 Poonch-3 0.0000 0.0000 Poonch-4 0.0000

Figure 4.35: COI gene based evolutionary relationships of taxa inferred using the Neighbor-Joining method.

1 Muzaffarabad-Lachrat 0.0000 0 0.0000 Mansehra-3 0.0000 0.0000 0 Kohistan-3 0.0000 0 0.0000 Kohistan-2 0 0.0000 0.0000 Kohistan-1 0 0.0000 0.0000 Mansehra-2 0 0.0000 0.0000 Mansehra-1 2 0.0000 0.0000 Neelum-AK 5 0.0000 0.0000 Neelum-SN 0.0000 0.0000 Muzaffarabad-MNP2 0.5000 0.0000 Muzaffarabad-MNP1 0.0000 Poonch-2 99 0.0000 Poonch-4 0.5000 27 0.0000 0.0000 20 Poonch-1 0.0000 0.0000 Poonch-3 0.0000

Figure 4.36: COI gene based Maximum Parsimony analysis of different grey langurs from Pakistan.

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4.5.3.1.3. Interspecific phylogenetic analysis

Results of the BLASTn after comparison to the closely related species of other related genera of colobines indicated that grey langurs from Pakistan have 98% similarity with Semnopithecus entellus from northern India (DQ355297.1; Sterner et al., 2006) and 96% similarity with Trachypithecus johnii from Sri Lanka (HQ149050.1;

Wang et al., 2012; Table 4.40). However, this comparison did not include S. schistaceus, as no sequence for this species is available on web. The sequences of this species used in this study were acquired from Christian Roos (Senior Scientist, German

Primate Center, Göttingen, Germany). The complete aligned sequences of all species appear in Appendix-IV.

Table 4.40: Top ten species sequences producing significant alignments with COI of grey langurs from Pakistan (retrieved from NCBI on web as on August 15, 2017, 11:00 PM, PST, GMT+05:00).

Description Max Total Query E Identity Accession

score score cover value (%) (%)

Semnopithecus entellus 1155 1155 100 0.0 98 DQ355297.1

Trachypithecus johnii 1077 1077 99 0.0 96 HQ149050.1

Trachypithecus vetulus 998 998 99 0.0 94 HQ149049.1

Rhinopithecus avunculus 808 808 99 0.0 89 JF293093.1

Rhinopithecus avunculus 797 797 99 0.0 89 HM125578.1

Trachypithecus germaini 778 778 99 0.0 88 HQ149047.1

Presbytis femoralis 761 761 99 0.0 88 KU899140.1 femoralis

Nasalis concolor 761 761 99 0.0 88 JF293095.1

Trachypithecus 756 756 99 0.0 87 KY024473.1 poliocephalus

Rhinopithecus bieti 756 756 99 0.0 87 JQ821837.1

Phylogenetic analysis based on evolutionary history inferred by using maximum likelihood, neighbor-joining, and minimum evolution methods suggested that grey langurs from Pakistan were closely related to S. entellus and S. schistaceus (Fig. 4.37-

144

4.39). However, maximum parsimony and estimated evolutionary divergence analysis indicated that langurs from Pakistan were more closely related to the S. schistaceus than the S. entellus (Fig. 4.40). The lowest estimates of evolutionary divergence values

(0.007 and 0.008) were recorded between Semnopithecus of the current study and S. schistaceus, while these values were 0.012 and 0.011 for S. entellus (Table 4.39).

However, the phylogenetic analysis of different langur taxa by maximum likelihood method indicated that Semnopithecus from Pakistan were comparatively more closely related to S. entellus than the S. schistaceus (Fig. 4.37a). Though, the bootstrap consensus tree (1000 replicates) placed these langurs in a distinct clade separating from S. entellus and S. schistaceus, where the later form a separate clade

(Fig. 4.37b).

Semnopithecus-Pakistan-1 0.0015 85 0.0043 Semopithecus-Pakistan2 36 0.0000 0.0024

100 Semnopithecus-entellus DQ355297.1 0.0126 0.0219

Semnopithecus-schistaceus 0.0032 0.0040

Trachypithecus-johnii HQ149050.1 0.0177

Trachypithecus-vetulus HQ149049.1 a. 0.0401

40 Semnopithecus-schistaceus Semnopithecus-entellus DQ355297.1 Trachypithecus-johnii HQ149050.1 100 Trachypithecus-vetulus HQ149049.1 Semnopithecus-Pakistan-1 85 Semopithecus-Pakistan2 b. Figure 4.37: COI gene based molecular phylogenetic analysis of different langur taxa by Maximum Likelihood method (Tamura and Nei, 1993). a) Original tree. b) The bootstrap consensus tree (1000 replicates). Bootstrap values are given next to the branches.

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The evolutionary history inferred using Neighbor-Joining method is represented by an optimal tree with the sum of branch length = 0.056 (Fig. 4.38a). Both trees

(original and bootstrap consensus) showed that Pakistani langurs have a distinct clade from the clade of S. entellus and S. schistaceus (Fig. 4.38 a, b).

Semnopithecus-schistaceus 0.0029 41 0.0003 Semnopithecus-entellus DQ355297.1 0.0060 100 0.0103 Semnopithecus-Pakistan-1 0.0005 87 0.0025 0.0031 Semopithecus-Pakistan2 0.0003

Trachypithecus-johnii HQ149050.1 0.0102

Trachypithecus-vetulus HQ149049.1 a. 0.0198

41 Semnopithecus-schistaceus Semnopithecus-entellus DQ355297.1 Trachypithecus-johnii HQ149050.1 100 Trachypithecus-vetulus HQ149049.1 Semnopithecus-Pakistan-1 87 Semopithecus-Pakistan2 b

Figure 4.38: COI gene based evolutionary relationships of different langur taxa inferred using the Neighbor-Joining method. a) Original tree. b. The bootstrap consensus (1000 replicates).

Minimum evolution and maximum parsimony analyses, as represented by original and bootstrap consensus trees, also confirmed results of first two methods (Fig.

4.39, 4.40). Another independent analysis as represented in phylogram displaying branch support values (%) using ‘phylogeny.fr’ (online software; Dereeper et al., 2008) also showed the same results indicating that Semnopithecus of the present study represent a separate cluster closely related to S. entellus and S. schistaceus (Fig. 4.41).

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0.0029 Semnopithecus-schistaceus

0.0003

0.0060 Semnopithecus-entellus DQ355297.1

0.0103

0.0004 Semnopithecus-Pakistan-1

0.0031 0.0025 0.0004 Semopithecus-Pakistan2

0.0102 Trachypithecus-johnii HQ149050.1

Trachypithecus-vetulus HQ149049.1 0.0198 Figure 4.39: COI gene based Evolutionary relationships of different langur taxa inferred using the Minimum Evolution method (Rzhetsky and Nei, 1992).

Semnopithecus-schistaceus 39

Semnopithecus-entellus DQ355297.1

Trachypithecus-johnii HQ149050.1

99 Trachypithecus-vetulus HQ149049.1

Semnopithecus-Pakistan-1

81 Semopithecus-Pakistan2

Figure 4.40: COI gene based Maximum Parsimony analysis of taxa inferred using the Maximum Parsimony method.

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Figure 4.41: COI partial gene sequence based phylogram displaying branch support values (%) using phylogeny.fr. Branches with less than 70% support values were not included.

Table 4.41: COI partial gene sequence based estimates of evolutionary divergence between different species of colobines using the Maximum Composite Likelihood model. Above the diagonal are values of standard error inferred by a bootstrap procedure (1000 replicates). Name of species with NCBI Accession #. 1 2 3 4 5 6 7 8 9 10 S. schistaceus 1 (From C. Roos) 0.005 0.009 0.013 0.025 0.023 0.023 0.024 0.004 0.004 S. entellus 2 DQ355297.1 0.011 0.010 0.013 0.025 0.023 0.024 0.026 0.005 0.005 T. johnii 3 HQ149050.1 0.029 0.035 0.012 0.025 0.023 0.023 0.025 0.010 0.009 T. vetulus 4 HQ149049.1 0.047 0.047 0.041 0.026 0.027 0.024 0.027 0.013 0.013 R. avunculus 5 JF293093.1 0.092 0.097 0.095 0.103 0.023 0.024 0.024 0.024 0.024 T. germaini 6 HQ149047.1 0.087 0.092 0.090 0.108 0.085 0.021 0.012 0.024 0.024 P. femoralis femoralis 7 KU899140.1 0.093 0.097 0.094 0.094 0.094 0.079 0.023 0.025 0.025 T. poliocephalus 8 KY024473.1 0.096 0.100 0.101 0.109 0.092 0.041 0.092 0.025 0.026 Semnopithecus 9 Pakistan-1 0.008 0.012 0.029 0.045 0.090 0.095 0.099 0.101 0.001 Semnopithecus 10 Pakistan-2 0.007 0.011 0.030 0.046 0.088 0.094 0.099 0.100 0.001

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Table 4.42: COI partial gene sequence based estimates of Evolutionary Divergence between different closely related species of colobines using the p-distance method. Standard error values are given above the diagonal.

1 2 3 4 5 6 7 8 9 10 S. schistaceus 0.005 0.008 0.009 0.012 0.012 0.012 0.012 0.004 0.004 1 (From C. Roos) S. entellus 0.017 0.009 0.009 0.012 0.012 0.012 0.012 0.005 0.005 2 DQ355297.1 T. johnii 0.041 0.050 0.009 0.012 0.012 0.012 0.012 0.008 0.008 3 HQ149050.1 T. vetulus 0.065 0.065 0.057 0.013 0.013 0.012 0.013 0.009 0.009 4 HQ149049.1 R. avunculus 0.118 0.123 0.121 0.130 0.012 0.012 0.012 0.012 0.012 5 JF293093.1 T. germaini 0.113 0.118 0.116 0.136 0.110 0.011 0.008 0.012 0.012 6 HQ149047.1 P. femoralis femoralis 0.120 0.124 0.121 0.121 0.120 0.103 0.012 0.012 0.012 7 KU899140.1 T. poliocephalus 0.123 0.127 0.129 0.138 0.118 0.057 0.118 0.012 0.012 8 KY024473.1 Semnopithecus 0.012 0.018 0.041 0.062 0.115 0.123 0.127 0.129 0.001 9 Pakistan-1 Semnopithecus 0.011 0.017 0.042 0.064 0.113 0.121 0.127 0.127 0.002 10 Pakistan-2

The results showed that there were very low genetic differences between the langur samples from various areas of Pakistan. There was just one variable position among all samples at position 143 in the alignment. It seems that all the Poonch samples have an A at that position and all others have G. This confirmed that all the samples belong to one species/ subspecies; thus, inter-population variation analysis makes no sense. Very low genetic difference and high genetic similarities as exhibited by the COI is not unexpected. COI is considered as the barcoding gene used in the identification of the different species. Thus, intra-species variations are generally not expected. Several other studies have also suggested that COI genes have very low intra- specific variation and high degrees of divergence from closely allied taxa (Hebert et al.,

2004; Hajibabaei et al., 2006; Santamaria et al., 2007; Ward et al., 2009; Clare et al.,

2011; Khaliq et al., 2015; Pradhan et al., 2015).

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For phylogenetic analysis, i.e., comparison with other related taxa, some sequenced from other related Semnopithecus species were acquired from the NCBI.

Sequence of S. schistaceus was shared by Dr. Christian Roos of German Primate

Center Gottingen. Unfortunately, no sequence from S. ajax was available.

The phylogenetic analysis and the evolutionary history inferred by using the different standard statistical methods suggested that Semnopithecus from Pakistan are distinct species and thus formed a separate clade. However, surprisingly and unexpectedly, by some methods, these langurs were more closely related to S. entellus rather than S. schistaceus, as also depicted by evolutionary divergence values estimated by maximum composite likelihood model and p-distance method, where Pakistani

Semnopithecus are closer to the S. schistaceus than S. entellus (Table 4.41, 4.43).

4.5.3.1.4. Species delimitation

It can be inferred from all above phylogenetic analysis adopting several different methods, that langur populations from Pakistan can be divided into two closely related clades. Because of very low values of genetic distance, they all belong to a single species/subspecies. This inference is also confirmed by the analysis for species delimitation based on COI gene partial sequences using so-called 4×-rule or K/ϴ (D/ϴ) method, which indicates that, the two clades consisting on 15 samples of the present study (consisting on the populations of Poonch Vs rest of the samples from all other populations) belong to the same species as exhibited by D<4ϴ and D/ϴ<4 with a >95% probability (Table 4.43). It is further confirmed that, all 15 samples (i.e., Semnopithecus from Pakistan) used in the present study have a distinct evolutionary lineage, possibly representing an independently evolving species. Because, all ratios of D/ϴ with other related species are >4 and D>4ϴ (Table 4.43).

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Table 4.43: Species delimitation of grey langurs by D/ϴ method using COI sequences.

Method used Clades d µ ϴ 4ϴ D D/ϴ

Rest of the p-distance Poonch samples of the 0.001 0.001 0.001 0.004 0.002 1.864 (Intraspecific) present study

Semnopithecus- S. schistaceus 0.002 0.002 0.002 0.009 0.012 5.584 Pakistan p-distance (Interspecific) Semnopithecus- S. entellus 0.002 0.002 0.002 0.009 0.018 8.376 Pakistan

Semnopithecus- S. schistaceus 0.001 0.001 0.001 0.004 0.008 7.456 Pakistan ML (Interspecific) Semnopithecus- S. entellus 0.001 0.001 0.001 0.004 0.012 11.184 Pakistan

Although initially designed for asexual organisms, the “4×rule” (Birky et al.,

2005) or the D/ϴ (K/ϴ) method (Birky, 2013) is now widely being applied to mitochondrial genes in sexual organisms and has become a promising tool for species delimitation in both invertebrates and vertebrates (Birky et al., 2009; Birky et al., 2010;

Marrone et al., 2010; Marie-Stephane et al., 2012; Tang et al., 2012; Birky, 2013). The

4×rule or K/ϴ method use DNA sequences to discriminate between transient gaps in genotypes due to stochastic effects and longer-lasting gaps because of natural selection or physical isolation that separate species. Thus, using gene sequences in phylogenetic tree, it distinguishes between clades within species and clades that are species (Birky,

2013). It states that monophyletic lineages represent independent evolutionary units

(species), when the mean sequence difference between lineages is more than four times greater than the average variation within the lineage (Birky et al., 2005; Birky, 2013).

Thus, concludingly based on COI analysis, it can be inferred from the results that, all langur populations found in Pakistan belong to single species/subspecies. This species of langurs closely relates with S. schistaceus and S. entellus, but it distinguishes

151 enough to be a separate species as confirmed by the phylogenetic analysis and K/ϴ method (K/ϴ>4). Therefore, most probably they are S. ajax as only remaining species found at high altitudes in Himalaya (Sayers and Norconk, 2008; Minhas et al., 2012).

But, unfortunately, there is no sequence available from S. ajax to compare with. Similar interpretation was done by the C. Roos after initial examining and analyzing the COI sequences of the current study (personal communication on email). He wrote “It seems that all your samples cluster with S. entellus and not with S. schistaceus, unfortunately, we have no sequences from ajax. Thus, your animals are probably ajax or entellus and not schistaceus”.

4.5.3.2. Cytochrome b

Cytochrome b (Cyt b) is a protein-coding gene found in mtDNA genome of eukaryotes. It is extensively being used for the analysis of inter and intra specific phylogenetic relationships in vertebrates (Yu et al., 2014; Muangkram et al., 2016).

Cytochrome b genes were also targeted in the current study to investigate the phylogenetic position of the grey langurs found in Pakistan.

4.5.3.2.1. PCR Products

Partial gene fragments of Cyt b were amplified in 15 different grey langur samples acquired from different localities of the study area. The size of different amplified products ranged between of 210-517 bp (Table 4.44). After careful examining the chromatograms of the reaction products, trimming and cleaning was carried out and the final product size of 490 bp was obtained for 12 samples

(Appendix-IV). Three samples with 209, 211 and 213 bp length were excluded from further analysis.

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Table 4.44: Grey langur samples with successful amplification of Cyt b partial gene fragments from AJK/ Pakistan.

Sr.#. Sample name Locality Raw product size (bp) 1 31_Poonch_1 Bhedi Doba-Haveli 512 2 32_Poonch_2 Motanwala- Haveli 514 3 33_Poonch_3 Mohri Said Ali Khan- Haveli 515 4 34_Poonch_4 Las Danna- Bagh/Haveli 515 5 35_Muzaffarabad-L Lachrat-Muzaffarabad 211 6 36_Muzaffarabad_MNP1 Machiara National Park, 517 Muzaffarabad 7 37_Muzaffarabad_MNP2 Machiara National Park, 483 Muzaffarabad 8 38_Neelum_SN Arangkel-Neelum Valley 517 9 39_Neelum_AK Sinjli Nalah-Neelum Valley- 515 AJK 10 40_Mansehra_1 Kankoli- Shogran 510 11 41_Mansehra_2 Mandri Kothara- Kaghan 510 Valley 12 42_Kohistan_1 Palas Valley-Kohistan 209 13 43_Kohistan_2 Palas Valley-Kohistan 213 14 44_Kohistan_3 Alai Valley-Battagram 484 15 45_Mansehra_1 Bhoonja- Kaghan Valley 513

4.5.3.2.2. Intraspecific phylogenetic analysis

Intra-specific phylogenetic analysis based on Cyt b also suggested that with exceptions of two samples, all samples taken from different localities from

AJK/Pakistan belong to one species. However, in contrast to the COI, the alignment and comparison of different sequences exhibited several differences of nucleotide substitutions at the different positions (Appendix-IV). The estimated evolutionary divergence using Maximum Composite Likelihood model (Tamura et al., 2004) based on Cyt b gene sequences of different grey langur from study area showed that there was low evolutionary divergence (GD=0.002) between most of the individuals of different

153 populations (Table 4.45). However, interestingly, two sequences, each from Poonch

(32_Poonch_2) and Mansehra (41_Mansehra_2) populations were closely related with each other (GD=0.002) and distinctly related with all other samples (GD=0.025-0.029).

Table. 4.45: Estimates of Evolutionary Divergence between Cyt b partial gene sequences of grey langur from Pakistan. The number of base substitutions per site from between sequences are shown as inferred by Maximum Composite Likelihood model (Tamura et al., 2004).

1 2 3 4 5 6 7 8 9 10 11

1 31_Poonch_1

2 32_Poonch_2 0.025

3 33_Poonch_3 0.001 0.025

4 34_Poonch_4 0.001 0.025 0.000

5 36_Muzaffarabad_MNP1 0.002 0.027 0.002 0.002

6 37_Muzaffarabad_MNP2 0.002 0.027 0.002 0.002 0.000

7 38_Neelum_SN 0.002 0.027 0.002 0.002 0.004 0.004

8 39_Neelum_AK 0.002 0.027 0.002 0.002 0.004 0.004 0.000

9 40_Mansehra_1 0.002 0.027 0.002 0.002 0.000 0.000 0.004 0.004

10 41_Mansehra_2 0.027 0.002 0.027 0.027 0.029 0.029 0.029 0.029 0.029

11 42_Kohistan_1 0.002 0.027 0.002 0.002 0.004 0.004 0.004 0.004 0.004 0.029

12 45_Mansehra_3 0.004 0.030 0.004 0.004 0.006 0.006 0.006 0.006 0.006 0.032 0.006

The Cyt b based phylogenetic analysis inferred by Maximum Likelihood,

Neighbor-Joining and Maximum Parsimony methods also suggested that two individuals namely 32_Poonch_2 and 41_Mansehra_2 form a separate clade. All other samples are included in a separate main clade with several subclades (Fig. 4.42).

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0.0000 37_ Muzaffarabad_MNP2 0.0000 0.0000 0.0020 40_Mansehra_1

0.0000 36_Muzaffarabad_MNP1 0.0000 0.0041 45_Mansehra_3

0.0000 0.0000 38_Neelum_SN 0.0000 0.0000 0.0020 39_Neelum_AK

0.0000 31_Poonch_1 0.0115 0.0000 0.0000 33_Poonch_3 0.0000 0.0000 34_Poonch_4

0.0020 42_Kohistan_1

0.0000 32_Poonch_2

0.0020 0.0136 41_Mansehra_2

Figure 4.42: Cyt b gene based Molecular Phylogenetic analysis of grey langurs from Pakistan by Maximum Likelihood method (Tamura and Nei, 1993).

The cladogram (Fig. 4.42) indicated that individuals from Muzaffarabad,

Mansehra and Neelum are closely related to each other with the evolutionary divergence of only 0.002-0.004 (Table 4.45). Three samples of the Poonch region also have a separate clade with 0.000 evolutionary divergence, while one sample from

Kohistan has a separate branch (Fig. 4.42). Except unusual behavior of ‘32_Poonch_2’ and ‘41_Mansehra_2’, all other samples have exhibited their relationship which can be explained naturally as per the geographical distribution.

The phylogenetic tree using the Neighbor-Joining method indicated that individuals from populations of Kohistan, Mansehra and Neelum are much closer than the individual from Neelum and Muzaffarabad. Again, Poonch population form separate cluster lines from all other populations. This explanation is also apparently natural (Fig. 4.43).

Maximum Parsimony analysis also indicated the naturally explainable cladogram, except for the unusual pair of individuals. All individuals from naturally

155 separable populations are forming the closely related clade including monophyletic clades (Fig. 4.44). Individuals form Mansehra populations showed their associations to the populations on their both sides i.e., sample collected from area closer to

Muzaffarabad district are clustered with Muzaffarabad population and those from other side are related to Kohistan (Fig. 4.44).

0.0000 38_Neelum_SN 0.0020 0.0000 39_Neelum_AK 0.0000 0.0020 42_Kohistan_1

0.0042 0.0000 0.0000 45_Mansehra_3

0.0000 36_Muzaffarabad_MNP1

0.0000 0.0000 0.0020 37_ Muzaffarabad_MNP2 0.0000 0.0000 40_Mansehra_1 0.0000 0.0000 31_Poonch_1 0.0116 0.0000 33_Poonch_3

0.0000 34_Poonch_4

0.0000 32_Poonch_2

0.0021 0.0137 41_Mansehra_2

Figure 4.43: Cyt b gene based Evolutionary relationships of taxa inferred using the Neighbor-Joining method (Saitou and Nei, 1987).

0.0000 0.0000 36_Muzaffarabad_MNP1 0.0000 1.0000 37_ Muzaffarabad_MNP2 0.0000 0.0000 40_Mansehra_1 0.0000 0.0000 33_Poonch_3 0.0000 0.0000 31_Poonch_1 0.0000 0.0000 34_Poonch_4 0.0000 38_Neelum_SN 5.5000 0.0000 1.0000 39_Neelum_AK 1.0000 42_Kohistan_1 2.0000 0.0000 45_Mansehra_3 0.0000 32_Poonch_2 1.0000 6.5000 41_Mansehra_2

Figure 4.44: Cyt b gene based Maximum Parsimony analysis of different grey langur from Pakistan.

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4.5.3.2.3. Interspecific phylogenetic analysis

Inter-specific comparison of all sequences with other related species of colobines using BLASTn at NCBI indicated that grey langurs from Pakistan showed

95-98% identities with different isolates of Semnopithecus entellus belonging to different areas of Indian subcontinent (Table 4.46). However, unfortunately, again this comparison did not include S. schistaceus, as no sequence of this species is available on web. The complete aligned sequences of all species can be viewed in Appendix-IV.

Table 4.46: Top 15 species sequences producing significant alignments with Cyt b of grey langurs from Pakistan (retrieved from NCBI on web as on August 15, 2017, 11:30 PM, PST, GMT+05:00).

Max Total Query E Ident. Description Accession score score cover (%) value (%)

Semnopithecus entellus 839 839 99 0.0 98 DQ355297.1

S. entellus 833 833 99 0.0 98 EU004478.1

Presbytis entellus 833 833 99 0.0 98 AF012470.1

S. entellus 828 828 99 0.0 97 EU004471.1

S. entellus 828 828 100 0.0 97 AF293959.1

S. entellus hector 828 828 100 0.0 97 AY519451.1

S. entellus 802 802 97 0.0 97 EU519221.1

S. entellus 784 784 100 0.0 96 JQ734733.1

S. entellus 784 784 100 0.0 96 JQ734702.1

S. entellus 784 784 100 0.0 96 JQ734701.1

S. entellus 784 784 100 0.0 96 AF293958.1

S. entellus 778 778 100 0.0 95 JQ734760.1

S. entellus 773 773 100 0.0 95 JQ734761.1

S. entellus 773 773 100 0.0 95 JQ734726.1

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The phylogenetic analysis based on evolutionary history inferred by using the

Maximum Likelihood, Neighbor-Joining, Minimum Evolution and Maximum

Parsimony methods also suggested that grey langurs from Pakistan were phylogenetically related to S. entellus (Fig. 4.45-4.48). The low estimates of

Evolutionary Divergence values (<0.1) were recorded between Semnopithecus of the current study and other different species of genus Semnopithecus and Trachypithecus

(Table 4.47).

Phylogenetic analysis and evolutionary history inferred by using all possible methods as given above indicated that two samples (32_Poonch_2 and

41_Mansehra_2) are significantly distinct from other members of the current study

(GD=0.025-0.027). These individuals are also distinct from other known species of

Semnopithecus with >0.032 genetic distance. Based on Maximum Likelihood,

Neighbor-Joining and Maximum Parsimony analysis with bootstrap consensus (99-

100%), these two individuals (32_Poonch_2 and 41_Mansehra_2) seam more closely related to S. hector and T. vetulus, while all other are related to S. entellus (Fig. 4.45,

4.46, 4.48). By Minimum Evolution method analysis these individuals are placed in between the S. hector and S. hypoleucos (Fig. 4.47). However, both of these are closely related to each other forming a monophyletic group with 0.002 genetic distance (Table

4.47).

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Table 4.47: Cyt b partial gene sequence based estimates of Evolutionary Divergence between different closely related species of colobines.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 1 31_Poonch_1 2 32_Poonch_2 0.025 3 33_Poonch_3 0.000 0.025 4 34_Poonch_4 0.000 0.025 0.000 5 36_Muzaffarabad_MNP1 0.002 0.027 0.002 0.002 6 37_Muzaffarabad_MNP2 0.002 0.027 0.002 0.002 0.000 7 38_Neelum_SN 0.002 0.027 0.002 0.002 0.004 0.004 8 39_Neelum_AK 0.002 0.027 0.002 0.002 0.004 0.004 0.000 9 40_Mansehra_1 0.002 0.027 0.002 0.002 0.000 0.000 0.004 0.004 10 41_Mansehra_2 0.027 0.002 0.027 0.027 0.029 0.029 0.029 0.029 0.029 11 42_Kohistan_1 0.002 0.027 0.002 0.002 0.004 0.004 0.004 0.004 0.004 0.029 12 45_Mansehra_3 0.004 0.030 0.004 0.004 0.006 0.006 0.006 0.006 0.006 0.032 0.006 13 S. entellus DQ355297.1 0.023 0.036 0.023 0.023 0.025 0.025 0.025 0.025 0.025 0.038 0.025 0.027 14 P. entellus_AF012470.1 0.025 0.034 0.025 0.025 0.027 0.027 0.027 0.027 0.027 0.036 0.027 0.030 0.002 15 S. entellus EU004471.1 0.027 0.036 0.027 0.027 0.029 0.029 0.029 0.029 0.029 0.038 0.029 0.032 0.004 0.002 S. entellus hector 16 AY519451.1 0.027 0.032 0.027 0.027 0.029 0.029 0.029 0.029 0.029 0.034 0.029 0.032 0.038 0.036 0.038 S. hypoleucos 17 JQ734711.1 0.070 0.070 0.070 0.070 0.072 0.072 0.072 0.072 0.072 0.072 0.072 0.075 0.082 0.084 0.087 0.082 18 T. johnii JQ734755.1 0.074 0.065 0.074 0.074 0.077 0.077 0.077 0.077 0.077 0.067 0.077 0.079 0.086 0.089 0.091 0.081 0.049 19 S. priam JQ734748.1 0.074 0.060 0.074 0.074 0.077 0.077 0.077 0.077 0.077 0.063 0.077 0.079 0.086 0.089 0.091 0.077 0.056 0.021 T. shortridgei 20 KP834335.1 0.077 0.077 0.077 0.077 0.079 0.079 0.079 0.079 0.079 0.079 0.079 0.082 0.084 0.087 0.084 0.084 0.068 0.072 0.072 21 T. pileatus KP834333.1 0.077 0.077 0.077 0.077 0.079 0.079 0.079 0.079 0.079 0.079 0.079 0.082 0.084 0.087 0.084 0.084 0.068 0.072 0.072 0.000 22 T. geei AF294618.1 0.082 0.082 0.082 0.082 0.084 0.084 0.084 0.084 0.084 0.084 0.084 0.087 0.084 0.087 0.084 0.089 0.073 0.077 0.077 0.004 0.004 23 T. vetulus HQ149049.1 0.107 0.092 0.107 0.107 0.109 0.109 0.109 0.109 0.109 0.094 0.109 0.112 0.105 0.102 0.100 0.102 0.095 0.102 0.107 0.066 0.066 0.066 24 N. larvatus U62664.1 0.169 0.175 0.169 0.169 0.169 0.169 0.172 0.172 0.169 0.178 0.172 0.176 0.170 0.167 0.164 0.155 0.170 0.187 0.164 0.131 0.131 0.131 0.165

159

97 Trachypithecus_johnii_JQ734755.1 85 Semnopithecus_priam_JQ734748.1 99 Semnopithecus_hypoleucos_JQ734711.1 87 Trachypithecus_vetulus_HQ149049.1 32_Poonch_2 62 99 41_Mansehra_2 99 Semnopithecus_entellus_hector_AY519451.1 Semnopithecus_entellus_DQ355297.1 19 99 Presbytis_entellus_AF012470.1

8 67 Semnopithecus_entellus_EU004471.1 45_Mansehra_3 42_Kohistan_1

65 38_Neelum_SN 39_Neelum_AK

6 34_Poonch_4

9 31_Poonch_1 12 33_Poonch_3 36_Muzaffarabad_MNP1

66 37_ Muzaffarabad_MNP2 25 40_Mansehra_1 Figure 4.45: Cyt b gene based molecular phylogenetic analysis of different langur taxa by Maximum Likelihood method.

98 Trachypithecus_johnii_JQ734755.1 86 Semnopithecus_priam_JQ734748.1 98 Semnopithecus_hypoleucos_JQ734711.1 80 Trachypithecus_vetulus_HQ149049.1 32_Poonch_2 75 100 41_Mansehra_2 99 Semnopithecus_entellus_hector_AY519451.1 Semnopithecus_entellus_DQ355297.1 22 99 Presbytis_entellus_AF012470.1

14 74 Semnopithecus_entellus_EU004471.1 33_Poonch_3 21 31_Poonch_1 34_Poonch_4

61 38_Neelum_SN 39_Neelum_AK

7 40_Mansehra_1

62 36_Muzaffarabad_MNP1 22 37_ Muzaffarabad_MNP2 42_Kohistan_1 10 45_Mansehra_3

Figure 4.46: Cyt b gene based Evolutionary relationships of different langur taxa inferred using the Neighbor-Joining method.

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0.0000 0.0020 38_Neelum_SN 0.0000 0.0000 39_Neelum_AK 0.0019 42_Kohistan_1 0.0042 0.0001 0.0001 45_Mansehra_3 0.0000 40_Mansehra_1 0.0000 0.0000 0.0020 36_Muzaffarabad_MNP1 0.0000 0.0000 0.0000 37_ Muzaffarabad_MNP2 0.0000 33_Poonch_3 0.0077 0.0000 31_Poonch_1 0.0000 0.0036 34_Poonch_4 0.0005 Semnopithecus_entellus_DQ355297.1 0.0061 0.0001 0.0156 Presbytis_entellus_AF012470.1 0.0020 0.0016 Semnopithecus_entellus_EU004471.1 0.0207 0.0160 Semnopithecus_entellus_hector_AY519451.1 0.0000 32_Poonch_2 0.0077 0.0021 0.0079 41_Mansehra_2 0.0250 Semnopithecus_hypoleucos_JQ734711.1 0.0105 0.0093 Trachypithecus_johnii_JQ734755.1 0.0103 0.0174 Semnopithecus_priam_JQ734748.1 Trachypithecus_vetulus_HQ149049.1 0.0573

Figure 4.47: Cyt b gene based Evolutionary relationships of different langur taxa inferred using the Minimum Evolution method.

99 Trachypithecus_johnii_JQ734755.1 95 Semnopithecus_priam_JQ734748.1 99 Semnopithecus_hypoleucos_JQ734711.1 90 Trachypithecus_vetulus_HQ149049.1 32_Poonch_2 59 99 41_Mansehra_2 97 Semnopithecus_entellus_hector_AY519451.1 Presbytis_entellus_AF012470.1

36 99 Semnopithecus_entellus_DQ355297.1 53 Semnopithecus_entellus_EU004471.1

37 38_Neelum_SN 74 39_Neelum_AK 37_ Muzaffarabad_MNP2 34 73 36_Muzaffarabad_MNP1 45 40_Mansehra_1 34_Poonch_4 31_Poonch_1 42_Kohistan_1

33 33_Poonch_3 35 45_Mansehra_3 Figure 4.48: Cyt b gene based Maximum Parsimony analysis of taxa inferred using the Maximum Parsimony method.

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The results of all phylogenetic analyses indicated that, langur from Pakistani territory formed a distinct clade. Regardless of all phylogenetic methods or model employed, all analyses supported the relationships of Pakistani grey langurs as sister taxa of S. entellus. However, all bootstrap consensus trees inferred from 1000 replicates, also confirmed that they were distinct from S. entellus. Unfortunately, again, the sequence from S. ajax and S. schistaceus were not available to compare.

The unusual behavior of two individuals (32_Poonch_2 and 41_Mansehra_2) is unexpecting and surprising. It needs further investigation to clarify the phylogenetic positions of these two individuals. However, most probably they might be the nuclear copies or numts of mitochondrial Cyt b gene. Numts are the nuclear mitochondrial-like sequences transferred from the mitochondrial DNA to the nuclear genome (Lopez et al., 1994). They exhibit homology to their mitochondrial counterparts and can confuse the population genetic and phylogenetic analyses due to their biparental mode of inheritance and slow evolutionary rate as compared with mtDNA (Smith et al., 1992;

Zhang and Hewitt, 1996; Bensasson et al., 2001). Schmitz et al. (2005b), Karanth

(2008) and Wang et al. (2015c) also reported numts in different species of grey langurs and their related taxa. Karanth (2008) analyzed nuclear copies of the mitochondrial

Cyt b gene (numts) for investigation of the ancient hybridization between

Trachypithecus and Semnopithecus by comparing them with mitochondrial

Cyt b sequences. He also recorded that numts sequences did not cluster in their respective mitochondrial based sister groups. Wang et al. (2015c) also found that a

Semnopithecus mitochondrial like numt exists in the T. pileatus nuclear genome. Thus, unusual placement of numts can be used for investigation of hybridization. Schmitz et al. (2005b), also reported the independent evolution of mitochondrial genes and their corresponding nuclear numts or pseudogene in primates. Brown et al. (1982) and

162

Schmitz et al. (2005b) proposed that the rate of evolution of mitochondrial protein- coding genes in primates is 5 to 10 times higher than their counter parts nuclear genes.

Further investigations are required to explain any occurrence of hybridization between the S. hector, T. vetulus and S. hypoleucos because the sequences closely resembled to these species (Fig. 4.45, 4.46, 4.48).

Several studies (Ting et al., 2008; Osterholz et al., 2008; Karanth et al., 2008,

2010; Roos et al., 2011) have been conducted based on Cyt b sequences of the different langur species (Semnopithecus spp.), however, the Himalayan langurs have never been studied at molecular level, or at least none of these studies has been published, probably due to the unavailability of the samples as indicated by the Karanth et al. (2008),

Osterholz et al. (2008); Karanth (2010) and Nag et al. (2011).

4.5.3.2.4. Species delimitation

Phylogenetic analysis using several different approaches, indicated that, langur populations from Pakistan formed closely related clades. Because of very low values of genetic distance (<0.007), they all belong to a single species/subspecies (with few exceptions as already discussed above). This inference was further confirmed by the species delimitation analysis based on Cyt b gene partial sequences using K/ϴ (D/ϴ) method, which showed that D<4ϴ and D/ϴ<4 (Table 4.48). It is further confirmed that, all 12 samples (i.e., Semnopithecus from Pakistan) used in the present study have a distinct evolutionary lineage, possibly representing an independently evolving species.

Because all ratios of D/ϴ with other closely related taxa are >4 and D>4ϴ (Table 4.48).

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Table 4.48: Species delimitation of grey langurs by D/ϴ method using Cyt b sequences.

Clades d µ ϴ 4ϴ D D/ϴ

Poonch- Intraspecific Muzaffarabad, 0.001 0.001 0.001 0.005 0.002 1.597 Poonch-Neelum

Poonch-Kohistan 0.001 0.001 0.001 0.005 0.004 2.995

Poonch-Mansehra 0.001 0.001 0.001 0.005 0.004 2.995

Neelum-Kohistan, Muzaffarabad- 0.002 0.003 0.003 0.010 0.006 2.392 Mansehra

Semnopithecus- Interspecific 0.004 0.004 0.004 0.018 0.027 6.039 Pakistan Vs S. entellus

4.5.3.3. Mitochondrial 16S ribosomal DNA

Mitochondrial 16S Ribosomal RNA gene (16S rDNA) is also being used as genetic marker for phylogenetic analysis in various animal taxa including nonhuman primates (Gerber et al., 2001; Alvarez et al., 2000; Lei et al., 2003; Van der Kuyl et al.,

2005; Turan, 2008; Vun et al., 2011).

4.5.3.3.1. PCR Products

Various sized fragments of rDNA gene ranging between 299-306 bp were successfully amplified by forward primer reactions from the mitochondrial genomes of

15 different samples (Table 4.49; Fig. 4.49). However, after careful examining the chromatograms, trimming and cleaning was carried out and the final product size of

292 bp was obtained for all 15 samples (Appendix-IV).

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Figure 4.49: Gel picture of amplified products of rRNA gene of grey langur from Pakistan (L=100 bp ladder).

Table 4.49: Grey langur samples with successful amplification of rRNA partial gene fragments from AJK/ Pakistan.

Raw PCR Sr.#. Sample name Locality product size (bp)

1 Poonch1-AJK-Pakistan Bhedi Doba-Haveli 300 2 Poonch2-AJK-Pakistan Motanwala- Haveli 300 3 Poonch3-AJK-Pakistan Mohri Said Ali Khan- Haveli 301 4 Poonch4-AJK-Pakistan Las Danna- Bagh/Haveli 304 5 Muzaffarabad-L1-AJK- Lachrat-Muzaffarabad 300 Pakistan 6 Muzaffarabad-MNP1-AJK- Machiara National Park, 299 Pakistan Muzaffarabad 7 Muzaffarabad-MNP2-AJK- Machiara National Park, 303 Pakistan Muzaffarabad 8 Neelum-SN-AJK-Pakistan Arangkel-Neelum Valley 300 9 Neelum-AK-AJK-Pakistan Sinjli Nalah-Neelum Valley- 302 AJK 10 Mansehra-1-Pakistan Kankoli- Shogran 306 11 Mansehra-2-Pakistan Mandri Kothara- Kaghan 301 Valley 12 Mansehra-3-Pakistan Bhoonja- Kaghan Valley 299 13 Kohistan-1-Pakistan Palas Valley-Kohistan 299 14 Kohistan-2-Pakistan Palas Valley-Kohistan 303 15 Kohistan-3-Pakistan Alai Valley-Battagram 301

165

4.5.3.3.2. Intraspecific phylogenetic analysis

The trimmed sequence alignment showed that there was no nucleotide variation in all 15 samples taken from different localities from Pakistan. All sequences have same set of nucleotides (Appendix-IV). This indicated that all samples belong to same taxon. Hence, between individual further intra-specific genetic analysis have not been carried out.

4.5.3.3.3. Interspecific phylogenetic analysis

Results of the BLASTn after comparison to the closely related species of other related genera of colobines indicated that Grey langurs from Pakistan have 99% similarity with Semnopithecus entellus from northern India (DQ355297.1; Sterner et al., 2006) and 97% similarity with Trachypithecus johnii from Sri Lanka (HQ149050.1;

Wang et al., 2012; Table 4.50). However, this comparison did not include S. schistaceus and S. ajax, as no sequence of these species are available on web.

Table 4.50: Top ten species sequences producing significant alignments with partial sequence of rRNA gene of grey langurs from Pakistan (retrieved from NCBI on web as on August 15, 2017, 11:00 PM, PST, GMT+05:00). Description Max Total Query E Identity Accession

score score cover (%) value (%) S. entellus 516 516 94 2e-142 99 EU004478.1 S. entellus 516 516 94 2e-142 99 DQ355297.1 S. entellus 499 499 94 2e-137 99 AF420043.1 T. johnii 477 477 94 7e-131 97 HQ149050.1 T. vetulus 468 468 95 4e-128 96 HQ149049.1 T. shortridgei 460 460 94 7e-126 96 KP834335.1 T. pileatus 460 460 94 7e-126 96 KP834333.1 P. femoralis 388 388 94 3e-104 92 KU899140.1 femoralis T. obscurus 383 383 95 2e-102 91 EU004477.1 P. melalophos 383 383 94 2e-102 91 DQ355299.1

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The phylogenetic analysis based on evolutionary history inferred by using the

Maximum Likelihood, Neighbor-Joining, and Minimum Evolution methods also suggested that grey langurs from Pakistan were closely related to S. entellus (Fig. 4.50-

4.53). However, the estimated Evolutionary Divergence values were 0.053-0.064 between S. entellus and Semnopithecus from Pakistan (Table 4.51; 4.52). It indicated that Semnopithecus from Pakistan have distinct identity.

Table 4.51: 16S-rRNA partial gene sequence based estimates of Evolutionary Divergence between different closely related species of colobines using the Maximum Composite Likelihood model.

1 2 3 4 5

1 Semnopithecus-Pakistan

2 AF420043.1_Semnopithecus_entellus 0.064

3 DQ355297.1_Semnopithecus_entellus 0.053 0.020

4 EU004478.1_Semnopithecus_entellus 0.053 0.020 0.000

5 HQ149049.1_Trachypithecus_vetulus 0.091 0.059 0.052 0.052

6 HQ149050.1_Trachypithecus_johnii 0.079 0.045 0.024 0.024 0.048

Table 4.52: Estimates of Evolutionary Divergence between Sequences of 16S-rRNA partial gene fragments from different most related langur taxa. The number of base differences per site from between sequences are shown (p-distance). Standard error values are shown above the diagonal.

1 2 3 4 5 6

1 Semnopithecus-Pakistan 0.014 0.012 0.012 0.016 0.015

2 AF420043.1_Semnopithecus_entellus 0.060 0.008 0.008 0.013 0.011

3 DQ355297.1_Semnopithecus_entellus 0.050 0.020 0.000 0.012 0.009

4 EU004478.1_Semnopithecus_entellus 0.050 0.020 0.000 0.012 0.009

5 HQ149049.1_Trachypithecus_vetulus 0.084 0.057 0.050 0.050 0.012

6 HQ149050.1_Trachypithecus_johnii 0.074 0.043 0.023 0.023 0.047

167

Phylogenetic analysis of different langur taxa using Maximum Likelihood method exhibited in the tree with the highest log likelihood (-1248.46) and the bootstrap values from 1000 replicates indicated that Semnopithecus from Pakistan were closely related to S. entellus. Both of these taxa were placed in a single monophyletic clade (Fig. 4.50).

DQ355297.1_Semnopithecus_entellus 0.0000 95 0.0000 EU004478.1_Semnopithecus_entellus 0.0000

0.0007 HQ149049.1_Trachypithecus_vetulus 0.0382 80 0.0138 HQ149050.1_Trachypithecus_johnii 0.0100

Semnopithecus-Pakistan 0.0495 59 0.0032 AF420043.1_Semnopithecus_entellus 0.0165 Figure 4.50: rRNA gene based Molecular Phylogenetic analysis of different langur taxa by Maximum Likelihood method (Tamura and Nei, 1993).

The evolutionary history inferred using the Neighbor-Joining method is represent by an optimal tree with the sum of branch length=0.1275 showed

Semnopithecus from Pakistan as a distinct taxon from S. entellus. However, the bootstrap consensus tree inferred from 1000 replicates again showed them with S. entellus in a single monophyletic clade (Fig. 4.51).

168

DQ355297.1_Semnopithecus_entellus 0.0000 89 0.0031 EU004478.1_Semnopithecus_entellus 0.0000 42 0.0016 HQ149049.1_Trachypithecus_vetulus 0.0344 77 0.0106 0.0009 HQ149050.1_Trachypithecus_johnii 0.0138

AF420043.1_Semnopithecus_entellus 0.0157

Semnopithecus-Pakistan a. 0.0475

42 Semnopithecus-Pakistan AF420043.1_Semnopithecus_entellus DQ355297.1_Semnopithecus_entellus 89 EU004478.1_Semnopithecus_entellus HQ149049.1_Trachypithecus_vetulus 77 HQ149050.1_Trachypithecus_johnii b. Figure 4.51: rRNA gene based Evolutionary relationships of different langur taxa inferred using the Neighbor-Joining method. a. Original tree. b. The bootstrap consensus tree inferred from 1000 replicates.

Similarly, Minimum Evolution and Maximum Parsimony analyses, as represented by original and bootstrap consensus trees, also confirming the results of first two methods (Fig. 4.52, 4.53).

0.0000 DQ355297.1_Semnopithecus_entellus

0.0031

0.0000 EU004478.1_Semnopithecus_entellus

0.0016

0.0344 HQ149049.1_Trachypithecus_vetulus

0.0009 0.0106 0.0138 HQ149050.1_Trachypithecus_johnii

0.0157 AF420043.1_Semnopithecus_entellus

Semnopithecus-Pakistan 0.0475 Figure 4.52: rRNA gene based Evolutionary relationships of different langur taxa inferred using the Minimum Evolution method.

169

Semnopithecus-Pakistan 60

AF420043.1_Semnopithecus_entellus

DQ355297.1_Semnopithecus_entellus

47 EU004478.1_Semnopithecus_entellus

HQ149049.1_Trachypithecus_vetulus

79 a. HQ149050.1_Trachypithecus_johnii

60 Semnopithecus-Pakistan

79 AF420043.1_Semnopithecus_entellus DQ355297.1_Semnopithecus_entellus 47 EU004478.1_Semnopithecus_entellus HQ149050.1_Trachypithecus_johnii b. HQ149049.1_Trachypithecus_vetulus Figure 4.53 rRNA gene based Maximum Parsimony analysis of taxa inferred using the Maximum Parsimony method. a) Tree #1 out of 2 most parsimonious trees. b. The bootstrap consensus tree (1000 replicates).

The results showed that there were no genetic differences between the langur samples from various areas across Pakistan. For phylogenetic analysis by comparing with other sequences of related taxa acquired from the NCBI, also suggested the close relationship of these langurs with S. entellus (99% identity). The phylogenetic analysis and the evolutionary history inferred by using the different standard statistical methods also suggested that Semnopithecus from Pakistan are closely related to S. entellus forming a monophyletic clade. These results are not surprising as the ribosomal RNA gene sequences are generally considered as the relatively conserved region of the mitochondrial genome. Many other authors have recorded that rRNA genes are less variable than the cytochrome b gene and the D–loop fragments, due to the inherently

170 slower evolutionary rate of rRNA genes (Pesole et al., 1999; Guha et al., 2007;

Katsares et al., 2008).

However, the Evolutionary Divergence values (0.053-0.064) obtained by a bootstrap procedure based on the number of base substitution/differences per site from between sequences, showed higher distance between S. entellus and Semnopithecus from Pakistan (Table 4.52). It indicated that Semnopithecus from Pakistan have distinct identity. Again, unfortunately, there are no sequence available from S. ajax and S. schistaceus to compare with.

4.5.3.3.4. Species delimitation

The sequences of these all 15 specimens are identical, and the values µ and ϴ are estimated as zero, and thus, D/ϴ cannot be calculated for rDNA sequences (Birky,

2013). However, as the mean values of ‘D’ is 0.053, hence, it can be inferred that, the values of D/ϴ must be >4, confirming an independence evolutionary group (species).

4.6. GENERAL DISCUSSION

Grey langurs are colobine monkeys and usually live away from human settlements, in the forests, where they play very important ecological services as primary consumers, pollinators, seed dispersers, and food for top predators (Rajpurohit,

2005; Minhas et al., 2010a). They also ensure food for ground frugivorous mammals

(deer, porcupines and small carnivores), which consume fruits dropped during langur foraging episodes. Therefore, they are considered as the biological indicators of the general health of forests (Rajpurohit, 2005). They are highly important organism for supporting ecotourism and providing their services in biomedicines and evolutionary and behavioral studies (Karanth et al., 2010; Nag et al., 2011).

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However, most of the grey langur species are facing population decline in their distribution ranges. The major and common threats to grey langurs throughout the region are the habitat destruction by anthropogenic activities often accompanied by attack of infectious diseases (Nowak, 1999; Bagchi et al., 2003; Nandi et al., 2003;

Sommerfelt et al., 2003). Although hunting of grey langur is uncommon, but it still exists in different areas (Nag et al., 2011). However, the living conditions for such a leaf eating species are very harsh in the long winter season at these altitudes (Roberts,

1997). From Himalayan species, S. ajax is considered Endangered because of its restricted range distribution and continuous population decline (Groves and Molur,

2008).

Different authors have reported two species of grey langur, viz. Kashmir grey langur and Nepal grey langur (S. schistaceus), from the northern hills of Pakistan and

Azad Jammu and Kashmir (AJK). There were geographical and ecological reasons to believe that different populations of langur are isolated from one another, but the level of isolation was unknown. The study was based upon the hypothesis that different inbreeding populations of the grey langur distributed in Pakistan and Azad Jammu and

Kashmir are isolated to different degrees from one another and represent 2-3 separate species. The study collected some baseline data on distribution of the grey langur populations in different parts of Pakistan and AJK; and tried to explain the species status of different populations, and to explore the extent of isolation and genetic fixation existing between different populations by manipulating the modern molecular biology techniques and statistical tools. Knowledge of genetic structures of populations is very important for conservation point of view. The importance of such studies becomes more critical when the targeted species have already threatened status of conservation. Because, the small isolated populations having higher chances of being

172 genetically fixed through inbreeding and hence are more critically prone to be extinct due to accumulation of unflavored alleles and genetic homogeneity (Lacy, 1997). Most of research findings have already been discussed in the relevant section, however, a comparative deliberation along with conclusion and recommendation is provided in this section.

The information about the genetic diversity or genetic differentiation among any grey langur species is scarce or even unavailable throughout its distribution range. In present study, attempts were made to investigate the genetic characteristics of

Himalayan grey langurs distributed in different areas of northern Pakistan and Azad

Jammu and Kashmir for discriminating the natural populations by 1) analysis of patterns of random amplified polymorphic DNA (RAPD), 2) microsatellites (or simple sequence repeats-SSR) using by cross species amplification of the primers already developed from other related species, and 3) mitochondrial DNA makers including

COI, Cyt b and rDNA gene partial sequences.

4.6.1. Comparative Assessment of Results by Nuclear DNA Markers

The RAPD and microsatellite markers have different respective genetic diversity measures. The comparative assessment of two nuclear DNA markers viz,

RAPD and microsatellites, in grey langurs are as follows:

4.6.1.1. Selection of markers

The average values of PIC and other parameters of genetic markers assessment showed that, all genetic markers of both classes were highly effective and efficient markers. The higher values of PIC suggest that, all markers used had the higher levels of efficiency and capability for genetic analysis in the current study. Eight RAPD and sixteen microsatellite loci were used for genetic analysis during present study.

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Although, these numbers of loci would have covered a small fraction of genome-level microsatellites, but this number of markers is equal or even higher than several available studies. Zhao et al. (2011) used 15 microsatellites for genetic diversity assessment of Moschus berezovskii. Xu et al. (2013) used 10 loci in rhesus monkeys.

Guschanski et al. (2009) used 16 microsatellite loci in gorillas, Bastos et al. (2010) genotyped 15 microsatellite loci in red-handed howler monkey and Zhou et al. (2007) used nine microsatellite loci in Pantholops hodgsonii.

4.6.1.2. Banding patterns and polymorphism

Total number of amplicons obtained was 245 in RAPD and 256 in microsatellites. All bands produced by both classes of markers were polymorphic i.e., none of the band was commonly shared by all genotypes used under the study. RAPD markers amplified a total of 96 unique bands, while, there were 32 private alleles amplified by microsatellite (SSR) markers in different populations. The mean percentage of polymorphic loci was higher by SSR markers (45.0±6.06%) than the

RAPD markers (37.71±5.29%). The overall band size range was 126-2489 in RAPD and 88-383 in SSR markers (Table 4.53).

Table 4.53. Comparative account of banding patterns of two groups of markers in grey langur.

Bands Band Percentage of size Unique/ polymorphic PIC Marker range Total No. Private Polymorphic loci (numbers) of alleles (bp) amplicons No (%) No (%)

RAPD (8) 245 96 39.18 245 100 37.71±5.29% 126-2489 0.31/0.5

SSR (16) 256 32 0.125 256 100 45.0±6.06% 88-383 0.94/1.0

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A remarkable difference in the banding patterns, percentages of polymorphic loci and higher PIC values in different langurs populations indicated a high discriminatory potentials and usefulness of RAPD and microsatellite markers in genetic analysis. They high level of polymorphism and unique patterns of bands also indicated the highly variability of different grey langur populations found in different areas, as also reported in different earlier studies (Ding et al., 2000; Sanches et al., 2012).

A relatively larger number of unique bands (RAPD) and private alleles (SSR) have been recorded in different populations. The presences of private alleles in different populations requires further investigation that, why do they exist? Is their existence a normal phenomenon? Did they arise due to population isolation or inbreeding? (Schroeder et al., 2009), is this a consequence of the Wahlund effect resulting from population admixture? (Bastos et al. (2010). However, the negative values of inbreeding coefficient (Fis) in all populations suggested that, currently there is no significant inbreeding effect to any population and all populations exhibited heterozygosity excess. In these conditions, the presence of private alleles may also be due to a process of expansion rather than the elimination of rare alleles through genetic drift, because sometimes the rapid growth of populations encourages the retention of the new mutations (Rogers and Harpending, 1992). This is also in accordance with the evolution theories, because the genetic variability is a necessary process for adaptation of a species into new environments (Frankham, 2003). Therefore, as per this point of view, presence of private alleles can be an important indicator of the relatively good genetic viability of populations, at least for a shorter duration (Bastos et al., 2010).

4.6.1.3. Genetic diversity, genetic differentiation and gene flow

In RAPDs, the values of Shannon’s index (I), expected heterozygosity (He) and genetic differentiation constants (Gst/Fst) were lower than that of the microsatellites.

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However, the values of migration coefficient or gene flow (Nm) were higher in RAPD than the microsatellite or SSR markers (Table 4.54).

Table 4.54. Comparative account of genetic diversity and genetic differentiation and gene flow exhibited by two groups of markers in grey langur.

Markers Shannon’s Heterozygosity Genetic Gene flow index (I) differentiation (Nm) Observed Expected (Fst/Gst) (Ho) (He)

RAPD 0.157 --- 0.096 0.153 3.246

SSR 0.357 0.450 0.241 0.438 1.185

Values of different diversity indices indicate that the genetic diversity in different langur populations found in Pakistan was low to moderate, (I=0.157 and

0.357; Ho=0.450; He=0.096 and 0.241). The microsatellites based observed heterozygosity in present study (0.450) is comparatively lower than many other studies on different non-human primates such as, gorillas 0.83, Bradley et al., 2004; Baboons,

0.81, Nguyen et al., 2009; Rhesus macaques, 0.73, Charpentier et al., 2008. However, such comparison cannot be much meaningful, as the average number of alleles and heterozygosity are generally affected by sample size (Pan et al., 2005). Another critical reason for low genetic diversity estimates would also be due to the presence of female philopatry in grey langurs (Minhas et al., 2010a). Because, this phenomenon can affect the uniform migration events, hence, affecting the genetic characterizations of populations (Melnick and Hoelzer, 1992; Shimada, 2000). Role of females philopatry in lowering the levels of genetic diversity at the nuclear DNA levels has also been recorded in various African Cercopithecine primates (Grobler and Matlala, 2002;

Grobler et al., 2006; Coetzer, 2012).

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The comparatively higher values of genetic differentiation (Fst =0.438) between populations along with relatively low values of heterozygosity (Ho=0.450), low values of migration coefficient values (Nm=1.185) and higher frequency of private alleles apparently seems to be a little bit indication of genetic drift operating in shaping the population genetic structure of langurs in Pakistan. The inconsistency in the microsatellites generated results might be due to smaller sample size. However, the effect of smaller samples size was tried to overcome by the simultaneous dependence on a larger number of loci.

4.6.1.4. Genetic distance and similarity

The values of genetic distances exhibited by microsatellites were higher than that of RAPD markers. The higher genetic distance and lower genetic similarities have been exhibited by microsatellite makers between Neelum/Poonch and Mansehra populations. However, the RAPD markers depicted the highest genetic distance between Neelum/Poonch and Kohistan populations (Table 4.55). Cluster analysis with

RAPD markers showed that Poonch and Muzaffarabad populations formed a monophyletic clade, while, all other populations formed almost separate groups.

Microsatellites analysis showed that, the individuals from Poonch region (Haveli,

Bagh, Poonch) form a separate branch, while, populations of Neelum and

Muzaffarabad formed a separate clade and populations of Mansehra and Kohistan constituted a separate (Fig. 4.54). Although some inconsistency is present in genetic associations of some population, however, both results of cluster analysis can be explained under present geographic distribution as populations of Neelum and Poonch are geographically linked with the Muzaffarabad population. Hence, the affinities of both populations with Muzaffarabad population is acceptable under the current geographic scenario.

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Table 4.55. Comparisons of pair-wise genetic distances among different populations using two classes of markers in grey langur.

Populations Poonch Muzaffarabad Neelum Mansehra Kohistan

RAPD SSR RAPD SSR RAPD SSR RAPD SSR RAPD SSR

Poonch 0.000 ***

Muzaffarabad 0.016 0.446 0.000 ***

Neelum 0.019 0.485 0.018 0.326 0.000 ***

Mansehra 0.018 0.649 0.020 0.490 0.019 0.752 0.000 ***

Kohistan 0.028 0.394 0.030 0.745 0.029 0.602 0.024 0.2554 0.000 ***

a. b. Figure 4.54: UPMGA dendrogram based on genetic distance (Nei, 1972, 1978) exhibited by; a) RAPD markers, b) microsatellite markers in different populations of grey langurs.

Three-dimensional principal coordinate analysis Based on the maximum variability among first two axes of the Eigen values, the PCoA output has produced three distinct clusters. Two or three populations of AJK (Muzaffarabad-Poonch-

Neelum) form one cluster, while, Mansehra population is separate but close to the AJK and Kohistan populations formed a distinct cluster (Fig. 4.55). PCoA analysis also provided similar clustering pattern as already suggested by dendrograms.

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The Mantel test (Mantel, 1967) with spatial autocorrelation analyses also showed a pattern of isolation. The calculated value of genetic relatedness by exhibited by microsatellite genetic markers Rxy=0.302, with P<0.01) revealed a significant association between genetic distance and geographic distance. However, RAPD maker showed no significant relationship between these distance (Rxy=-0.008, P0.05).

a.

b. Figure 4.55: Graphical presentation of PCoA, a) RAPD markers; b) by microsatellite for different populations of grey langur.

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Results indicated that all haplotypes group together as per their geographical area of origin. The exhibited level of genetic structuring in different populations can be ascribed to various factors affecting the migration events. The significant correlation between genetic and geographical distance especially by microsatellites suggests that geographic distance might be working as effective restricting factor in gene flow between populations as already confirmed by the lower values of gene flow (Nm) and higher numbers of private alleles. These features can also be explained under the presence of the philopatry in female langurs, which has a definite effect on the genetic characterizations of populations as already recorded by various authors in other related species of primates (Melnick and Hoelzer, 1992; Shimada, 2000). As discussed earlier in this chapter that, the geographic barriers (rivers and mountains) could not be appeared as the absolute barriers for grey langurs. Because, these monkeys could find ways to pass through some of the mountain ranges (especially those having suitable corridors at lower elevations) and to cross the rivers. Therefore, mountains and rivers are not absolute barriers to langur migration between different populations.

Nevertheless, mountains with very high elevations (e.g., above 4000m) will be more difficult to cross because of different risks such as lower temperatures, lack of shelters and other resources etc.

4.6.2. Taxonomy and phylogeny of grey langurs of Pakistan

For phylogenetic analysis and systematic clarification, a total of 1443 base pairs

(bp) from mtDNA COI (661 bp), Cyt b (490) and 16S rRNA (292) were aligned, compared and analyzed along with the nDNA markers (RAPD and microsatellites).

With some minor inconsistencies, all markers suggested that the grey langurs from all areas of Pakistan and AJK belong to a single species/subspecies. A BLAST search in the Genbank database showed that, the sequences of all three mitochondrial genes

180 aligned closely with those of S. entellus. Alignments of COI gene sequences showed that, these langurs were closely related to S. schistaceus and S. entellus. However, phylogenetic analysis and the evolutionary history inferred by using the different standard statistical methods suggested that, Semnopithecus from Pakistan formed an independently evolving clade, and thus belong to a distinct species. The COI analysis based on Evolutionary Divergence estimated by Maximum Composite Likelihood model and p-distance model inferred that, the Pakistani Semnopithecus are comparatively closer to the S. schistaceus than S. entellus. Further, by applying 4×-rule or D/ϴ method to all three mtDNA molecular markers, it was confirmed that, all langur populations found in Pakistan belong to single species/subspecies. Most probably, they are S. ajax, because they are neither S. entellus nor S. schistaceus. But, unfortunately, there is no sequence available from S. ajax to compare with. Similar interpretation was done by the C. Roos after initial examining and analyzing the sequences of the current study (personal communication). These results of molecular systematics are also coinciding the morphology based taxonomy as already reported by Mirza (1965),

Roberts (1997) and Minhas et al. (2012). However, merely based on molecular evidences, the species confirmation of the Pakistani langurs would not be possible as no sequence of this species are present to compare with. Hence, the role of morphological analysis or traditional taxonomy was considered critical for species confirmation.

Although not directly related to the objectives of the present study, the results of phylogenetic analysis, using all three gene sequences, showed that, the grey langurs

(either from Himalaya or from northern and southern plains of India (Semnopithecus spp.) are closely related to the Nilgiri langur (Trachypithecus johnii), and purple-faced langur (Trachypithecus vetulus) (Fig. 4.36; 4.45-4.48; 4.50-4.53). Similar, findings have been reported by several studies, using nuclear DNA and mDNA molecular

181 markers, that both groups of these langurs are closely related to Hanuman langurs

(Semnopithecus spp.; Messier and Stewart, 1997; Zhang and Ryder, 1998). Osterholz et al. (2008) using Y chromosomal and 573 bp mitochondrial sequence data, suggested the purple-faced langur (Trachypithecus vetulus) clusters within Semnopithecus.

Karanth et al. (2008) and Karanth et al. (2010) based on mitochondrial DNA also supported the results of Zhang and Ryder (1998) suggesting the placement of Nilgiri and purple-faced langurs in the genus Semnopithecus along with the Hanuman langurs.

Wang et al. (2012) also suggested a sister-grouping between Semnopithecus and

Trachypithecus, using nuclear genes and the mitochondrial genomes. All these findings of the different authors are confirming the reliability and credibility of the findings under the present study.

4.6.2.1. Morphological analyses

Morphological analysis also supports the molecular taxonomy. Traditional taxonomy based on external morphology has been the primary method for species delineation for centuries. The importance of classical taxonomy is made further obvious due to the unavailability of the DNA sequences to compare with. Like molecular analysis, the morphological differentiation between S. ajax and S. schistaceus is very difficult, because both species are inhabitants of high altitudes in Himalaya. Both species are highly identical morphometrically and have larger body size than other grey langur species. Further, the distribution of S. schistaceus is reported to be all the way from Bhutan to Pakistan and possibly into Afghanistan (Brandon-Jones, 2004; Sayers et al., 2010). Similarly, the S. ajax has also been reported from areas laying between the range of S. schistaceus (Brandon-Jones, 2004; Groves, 2005; Sayers et al., 2010;

Minhas et al., 2012). Thus, as per these findings, both species may have sympatric distribution in some areas, or at least have close merging boundaries. But at the same

182 time, when observed carefully, there are some definite distinguishing characters between the two as well. They are certainly not S. entellus, because S. entellus is morphologically more different from both aforesaid species and is distributed in lower altitudes (up to 400 m), usually beyond the foothills of Himalaya (Mitra and Molur,

2008).

There is a characteristic mane on the shoulder (especially in male) and a grove between the hairs on the head in S. ajax, which is absent in S. schistaceus and all other species (Appendix-III-Plate 1). The cheek hairs of S. ajax are comparatively longer and the loose long body whitish grey hairs of S. ajax hang down on the both sides of the body between fore and hind legs (especially in males), while in S. schistaceus, these hairs are not so whitish and long, and usually do not hang along the sides. The whitish part of the tail-tip is longer in S. ajax than in S. schistaceus. The S. ajax has a much blackish-brown forearm (especially the front facing parts up to the shoulder), while in S. schistaceus, it is merely distinguishable from the rest of the body (Brandon-Jones,

2004). These findings were further confirmed by Colin Groves (Emeritus Professor,

School of Archaeology and Anthropology, Australian National University,

Australia/Member IUCN Primate Specialist Group) based on his photograph of the type specimens of these high-altitude grey langur species viz., schistaceus and ajax (Groves,

2017-Personal communication). He confirmed that ajax has long, loose pelage with a very light, brindled tone and the cheek hairs of ajax are longer than the general hairs around the head.

The above-mentioned morphological chrematistics of S. ajax were observed during field visits of all five populations of the present study ranging from Poonch

(east-AJK) to Kohistan (Pakistan). However, some minor variations (in color and length of hairs) have been observed in different populations or even in the same

183 population between different seasons and sexes. Langurs from the present study area also showed some differences with those of Chamba valley of Himachal Pradesh

(India). Semnopithecus of present study seems to be slightly different from the

Semnopithecus of Himachal Pradesh and parts of southern Jammu and Kashmir (e.g.,

Sharma and Ahmed, 2017) in having blackish-brown on much of the shoulder and thigh as well as on the forearm. The length of white tail-tip of the present study

Semnopithecus seems to be slightly reduced than Semnopithecus of Himachal Pradesh, although not as much as in S. schistaceus. Further, the langurs from Pakistan are slightly darker than the langur found in Himachal Pradesh.

4.6.2.2. Species confirmation

Based on the molecular and morphological analysis (an integrative taxonomic approach), it is confirmed that Semnopithecus ajax is the only species found in different areas of Azad Jammu and Kashmir (district Haveli, Poonch, Bagh, Jhelum valley,

Muzaffarabad and Neelum) and Pakistan (district Mansehra, Battagram and Kohistan).

These findings coincide and confirm the previous results of different authors reporting grey langurs from Pakistan (Mirza, 1965; Roberts, 1997; Minhas et al., 2012). Further, these findings are also supported by the presence of a continuous geographic link between the S. ajax populations of Himachal Pradesh, Jammu and Kashmir (India),

Azad Jammu and Kashmir (Pakistan) and northern Pakistan (Fig. 4.56). Therefore, it is strongly suggested that further morphological as well as molecular analysis should be carried out using complete sequences of the entire mitochondrial genome to confirm species status of Pakistan/ AJK langur populations.

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Figure 4.56: Possible geographic linkage of S. ajax from Pakistan to its other populations outside Pakistan (Courtesy: Google maps).

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4.6.2.3. Comments of foreign primate taxonomists

For further confirmation of taxonomic findings of present study, the following opinions of foreign renown primate taxonomists were also obtained:

1. Colin Groves Emeritus Professor School of Archaeology & Anthropology, Australian National University, Australia Member IUCN Primate Specialist Group Author of Groves (2001); Groves, (2005); Groves and Molur (2008). Email on Thursday, 1 June 2017, 7:07, Colin Groves

From the photos in the second paper, it would seem that the full development of the ajax distinctive characters must be age/sex dependent – the photo is of the female and of the juvenile could be mistaken for schistaceus. Perhaps that is what happened when I looked at your photos, because the two of your photos which I commented on to show langurs with the ajax characteristics. And even when I re-examine the first photo you sent me (attached), the langur on the left, much larger than the one on the right

(male and female?) does have ajax features.

All right, apologies: you have Semnopithecus ajax!

All the best,

Colin

------

2. Roos, Christian [email protected] Senior Scientist, German Primate Center, Göttingen, Germany. Email on 23 Apr at 7:13 PM

Here are the corrected results from CO1.

There is just one variable position among your samples: position 143 in the alignment.

Thus, inter-population variation analysis make no sense. It seems that all the Haveli

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Kahota samples have an A at that position and all others a G; if numbering is correct

(16-19: Haveli, etc)

I added also some sequenced from other Semnopithecus species. It seems that all your samples cluster with entellus and not with schistaceus, unfortunately, we have no sequences from ajax.

Thus, your animals are probably ajax or entellus and not schistaceus.

Best wishes

Christian

3. Doug Brandon-Jones

Author of Brandon-Jones (2004), Brandon-Jones et al., 2004.

Email on 26 May, 2017 at 7:01 AM

Dear Riaz, Your photos are very informative. The langur in both photos is distinct from S. e. schistaceus and more referable to S. e. ajax, but even seems to differ from the latter in having blackish-brown on much of the shoulder and thigh as well as on the forearm. Parts of the tail seem darker than usual. Are these individuals typical of the local population, or do you see considerable individual variation? While I would be reluctant to describe them as a new (sub)species, they strengthen my impression that langurs along the northwestern margin of S. e. schistaceus distribution are progressively becoming darker towards the west, but it would be good to confirm my suspicion that S. e. ajax has a relatively extensive distribution in northwest Pakistan. Are you likely to visit the Shogran vicinity and, if not, do you have colleagues who might be able to shed some light on this issue? Many thanks for sharing your photos with me. Unless you do find much individual variation, they substantiate the reorganizability of S. e. ajax. Cheers, Doug

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4.7. CONCLUSIONS AND RECOMMENDATIONS

The present study is the pioneer step towards the molecular characterizations of grey langur genomes using different nuclear and mitochondrial DNA markers for genetic diversity analysis and molecular systematics. The present study has successfully completed with the following conclusions:

➢ The study provided insights that despite of the enormous applications of

noninvasive sampling in defining the population structure of threatened animals,

one must look before leaping. Because, it is usually accompanied by many

expected and unexpected difficulties including but not limited to low success rate

and genotyping errors, especially in studies having financial constraints.

➢ The results argue that both types of molecular markers are suitable for genetic

characterization of primates especially the colobines, and can provide an

important input into conservation initiatives of the focal species. However, our

analysis showed that microsatellites are the better choice for detecting genetic

characterization at limited spatial scales. Despite of different limitations, the

RAPD-PCR technique can be used effectively for initial assessment of genetic

variations among different animal species, particularly in species for which very

little genetic information is available. However, such analysis should also be

carried out with special care for reproducibility.

➢ Despite of some inconsistencies between the two markers, a moderate level of

genetic diversity and genetic differentiation were exhibited in different langur

populations found in Pakistan/AJK. All individuals from various populations

generally grouped together as per their respective geographical area of origin.

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➢ Although, both markers using several loci examined under present study

apparently could not provide persuasive evidences of significantly reduced genetic

diversity or inbreeding/genetic sub-structuring within the grey langur populations

of Pakistan, but different indications, such as comparatively lower values of

heterozygosity, higher numbers of private alleles, higher values of genetic

differentiations and lower values of gene flow, significant correlation between

genetic and geographical distance showed that the genetic isolation and genetic

drift are acting or can operate in near future. The possible reasons for this case can

be the female philopatry, geographic barriers (mountains and rivers) and

geographic distance. However, none of these barriers could be found as absolute

barrier to migration, as these monkeys could find ways to traverse across

mountains and rivers.

➢ The Himalayan grey langurs have limited tolerance of habitat disturbance and

fragmentation. They cannot be exempted from the long-term deleterious effects of

population isolation or the loss of genetic diversity due to female philopatry and

geographic barriers such as rivers and high-altitude mountains.

➢ Mitochondrial DNA based phylogenetic analysis showed that grey langurs from

Pakistan are evolutionary related to S. schistaceus and S. entellus. However, the

evolutionary history inferred by using the different standard statistical methods

suggested that, these langurs have an independently evolving clade, and thus

belong to a distinct species.

➢ The study suggests that, all populations of grey langurs found in Pakistan and AJK

are representing a single species i.e., Semnopithecus ajax, the Himalayan grey

langur or Kashmir grey langur. Thus, confirming the findings of Mirza (1965),

Roberts (1997) and Minhas et al. (2010a, b, 2012). However, further studies are

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required/ suggested in support of these findings to completely elucidate the

taxonomy using complete mitochondrial genome sequences and other molecular

markers.

4.7.1. General Recommendations

The present study provides a preliminary baseline data on the genetic insights of a threatened grey langur found in different northern localities of AJK and Pakistan.

Based on research findings, following conservation measures are recommended to be considered for proper conservation managements of these monkeys in Pakistan:

➢ Present level of genetic diversity of different langur populations, although not

reached the inbreeding thresholds, is maintained at the lower levels to save it from

bottleneck effects and population fixation. However, the results clearly indicate that

if nothing is done, this may lead to the severe consequences leading to the more

genetically distinctness and inbreeding. Therefore, to harmonize the gene flow

integrated conservation efforts must be initiated at this stage when nothing is loosed

yet. It is, therefore, strongly suggested that these populations must be kept under the

keen observation by relevant conservation organizations. These conservation

measure must consider various ways for bridging between the different populations.

➢ Small, almost isolated populations of grey langurs is present at different localities

of AJK and Pakistan. However, the scientific information about their population

status and ecology is very scarce. Comprehensive investigations of their

demographic features along with their ecology is highly required to be carried out

for proper conservation management of these langurs in the area.

➢ Limited sampling from different populations probably resulted in certain

inconsistencies in the present analysis. Therefore, further studies are required using

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adequate sample size to analyze the population structure for further confirming the

results of the present study.

➢ For proper conservation management and long-term survival of the endangered

species, a continuous genetic and ecological monitoring of the populations is also

required. An increase in the sample size and further coverage of distribution range

will also help to give a better picture of the genetic status of these monkeys.

➢ A wide range of taxonomic controversies are still existing for different langur

species throughout their distribution ranges. One of the main reasons for such

controversies is the lack of integrative research approaches by different authors.

Such taxonomic inconsistencies in langur taxonomy can be resolved by the

collaborative research approach across the borders.

➢ Himalayan grey langurs are generally specialized in terms of habitat use. With few

exceptions, they are exclusively found in moist temperate coniferous forests mixed

with deciduous trees in Pakistan. They have very limited tolerance towards the

human disturbances and habitat fragmentations. That is why their distribution is

usually limited to deep forest patches in Pakistan. Therefore, for continuous

survival of these monkeys, these forests (habitats) must be conserved. Though,

community-based forest management has been initiated in some areas of

Pakistan/AJK, however, the application of this process should be increased

gradually at least to all protected areas.

➢ Connectivity of different core habitats is necessary to enhance the genetic flow

between different populations. This can be made possible by developing the links or

bridges between different naturally occurring migration corridors, particularly along

the rivers which can be more effective barriers for these langurs.

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APPENDICES

Appendix I. Different classifications schemes of extant grey langurs. Modified after Takai et al. (2016)

Pocock Pocock (1939) Hill (1939) Ellerman and Roonwal Roonwal Groves (2001) Brandon-Jones Groves, 2005 Osterholz et Karanth et al., Takai et al., (1928) Morrison- and (1984) et al., 2004 al., 2008 2010 2016 Scott 1966 Mohnot (1977) Pithecus Semnopithecus Semnopithecus Presbytis Presbytis Presbytis Semnopithecus Semnopithecus Semnopithecus Semnopithecus Semnopithecus Semnopithecus entellus entellus schistaceus entellus entellus entellus schistaceus entellus schistaceus entellus -Northern schistaceus schistaceus schistaceus schistaceus schistaceus schistaceus schistaceus (North India) Type-Hanuman P. e. S. e. entellus S. entellus P. e. entellus P. e. P. e. S. entellus S. e. entellus S. entellus S. entellus Semnopithecus S. entellus entellus entellus entellus (South India) -Southern Type-Hanuman (South India), P. e. ajax S. e. ajax S. schistaceus P. e. ajax P. e. ajax P. e. ajax S. ajax S. e. ajax S. ajax S. johnii Semnopithecus- S. ajax ajax Southern Type- Hanuman (Sri Lanka) P. e. S. e. achilles S. s. achilles P. e. achilles P. e. P. e. S. hector S. e. hector S. hector S. entellus Semnopithecus S. hector achilles achilles achilles (Sri Lanka) johnii P. e. lanius S. e. achates S. s. lanius P. e. achates P. e. lania P. e. lania S. hypoleucos S. e. anchises S. hypoleucos S. vetulus (Sri Semnopithecus S. Lanka) vetulus (Sri hyploleucos Lanka) P. e. S. e. iulus S. s. hector P. e. P. e. P. e. S. dussumieri S. e. achates S. dussumieri S. dussumieri hector hypoleucos anchises anchises P. e. S. e. S. hypoleucos P. anchises P. e. S. priam S. e. S. priam S. priam achates hypoleucos Achates shanicus hypoleucos P. e. iulus S. e. aeneas S. h. iulus P. e. P. e. P. e. Trachypithecus S. priam T. johnii S. johnii dussumieri achates achates johnii priam P. e. S. e. S. h. P. e. priam P. e. iulus P. e. iulus Trachypithecus S. e. thersites T. v. vetulus S. vetulus hypoleucos dussumieri hypoleucos vetulus vetulus P. e. S. e. priam S. h. aeneas P. e. P. e. P. e. T. v. S. johnii T. v. aeneas thersites hypoleucos hypoleucos monticola monticola P. e. S. e. thersites S. h. P. e. P. e. P. e. T. v. nestor S. vetulus T. v. nestor

228

Pocock Pocock (1939) Hill (1939) Ellerman and Roonwal Roonwal Groves (2001) Brandon-Jones Groves, 2005 Osterholz et Karanth et al., Takai et al., (1928) Morrison- and (1984) et al., 2004 al., 2008 2010 2016 Scott 1966 Mohnot (1977) dussumieri dussumieri anchises aeneas aeneas vetulus P. e. S. e. anchises S. priam P. e. lania P. e. P. e. T. v. philbricki S. vetulus T. v. pallipes priam dussumieri dussumieri monticola philbricki P. e. S. e. S. priam P. e. iiilus P. e. P. e. S. vetulus priamellus priamellus thersites priam priam nestor P. e. elissa S. e. elissa S. priam P. e. aene. P. e. P. e. S. vetulus anchises thersites thersites philbricki – S. h. elissa P. e. P. e. P. e. riamellus riamellus riamellus Kai johni P. e. P. e. elissa elissa – K. v. vetulus – K. v. monticola K. v. nestor K. v. philbricki

229

Appendix II: Distribution/Sighting record of grey langur in Pakistan during 2014-2016

GPS Location Numbers Number of Sample type Date of Elevation Zone District Localities Sub localities of troop individuals (source) collections Latitude (m) Longitude (E) seen (Approx.) (N) KP Kohistan Palas Nasroo Fecal 17/03/2016 35.1658° 73.1600° 2476 1 27 Blood (infant 17/03/2016 Nasroo Gail Khas captured by 35.0575° 73.0661° 2194 - - locals) Gabair Fecal 17/03/2016 35.2541° 73.2163° 2170 1 17 Alli Jan 35.3030° 73.1536° 1174 1 Dadder Fecal 17/03/2016 35.09° 73.041° 2469 1 31 (Salkhanabad) Seri Machh Bela 35.09° 73.1125° 2025 1 15 Machh Bela-Palas Dried Tissue 09/12/2014 35.2877° 73.1436° 2281 - Valley Gadar Nala Fecal 19/03/2016 350674° 73.232° 2358 1 36 Bada coat 35.1333° 73.0530° 2431 1 Kaharat Fecal 24/03/2016 35.2761° 73.1691° 2862 2 Achhro Banda Fecal 24/03/2016 34.9524° 73.1138° 2692 1 19 Kot Banda Fecal 25/03/2016 34.9691° 73.071° 2575 1 29 Pattan/Palas Nazro 35.0041° 72.9549° 2016 2 Kolah 35.0009° 73.0165° 2126 1 13 Battagram Allai Valley Pazang Bela Fecal 12/04/2015 34.891° 72.9231° 1711 1 42 Korela 34.7884° 73.1461° 2654 1 Mada Khel 34.8097° 73.1636° 2322 1 17 Buda (Pashto) 34.9097° 73.0784° 2547 1 23 Mangri Pashta Fecal 13/04/2015 34.9576° 73.0554° 2779 3 Mansehra Shogran Upper Manna 34.5916° 73.4717° 2779 1 38 Makhir 34.5744° 73.4327° 3040 1 Malkandi Reserved 34.6123° 73.511° 2637 1 44

230

Forest Pathra 34.5879° 73.537° 2470 1 30 Kankoli Fecal 13/05/2015 34.5981° 73.5725° 2518 1 15 Bhoonja Dried Tissue 23/06/2016 34.6313° 73.4545° 2267 - - Dregan 34.5981° 73.5866° 2757 2 Jalai 34.5956° 73.6021° 2859 1 21 Kaghan Hairs 13/05/2015 Anni Shungi 34.6032° 73.5923° 3247 1 34 valley Chitta Par Reserved 34.6143° 73.5727° 2654 1 53 Forest Nuri Ichla Reserved 34.6448° 73.5682° 2674 1 32 Forest Kothara 34.6721° 73.6212° 2650 1 29 Mandri Kothara Blood (female 13/08/2015 34.6726° 73.6214° 2332 juvenile - - captured by locals) Sboharan Pain 34.6866° 73.6237° 2454 1 (Doga) Bagnuan 34.7772° 73.6565° 3010 1 18 Kamalban Reserved 34.7097° 73.5333° 2447 1 46 Forest (Mandri) Biari Char (Mandri) 34.6959° 73.5317° 2587 1 Manshi Reserved 34.7032° 73.4223° 2 438 1 38 Forest Chhapri Katha 34.7137° 73.4163° 2322 1 16 Jabri Nakka (Naga 34.671° 73.3868° 2145 2 Reserved Forest) Naga Reserved 34.6753° 73.381° 2432 1 47 Forest Jagran AJK Neelum Barnat 34.6053° 73.7985° 2600 1 valley Ziarat Doga 34.5971° 73.6983° 2701 1 16 Kamu 34.5667° 73.7506° 2867 2 Bangas Forest 34.5895° 73.7178° 2463 1 59

231

Rahwali Mohri 34.5915° 73.6985° 2891 1 23 Lari Forest 34.5462° 73.7558° 2632 1 Saila Gali (Ashkot Ashkot 34.4578° 73.8551° 2482 1 25 Nala) Gehl Gehl Nallah 34.5047° 73.8674° 2178 3 Nallah Salkhala Salkhala GR Hairs/ Fecal 07/04/2016 34.5434° 73.8731° 2293 1 41 Parli Nallah Fecal 08/04/2016 34.5629° 73.9086° 2262 1 24 Lawat Doga Baihk (Lawat) 34.7507° 73.7887° 3166 1 18 Dawarian Parli Forest Fecal 10/4/2016 34.7169° 74.0254° 2761 1 Trekanna 34.7593° 73.9956° 2229 1 26 Barthal 34.5404° 73.8069° 2377 1 16 Lari Forest 34.5462° 73.7558° 2632 1 Changan Kandri Forest 34.7458° 74.0562° 2759 1 33 Dudhnial Pathrewali Baihk 34.6794° 74.1151° 2141 3 Banwali Baihk 34.6756° 74.1362° 2816 1 27 Tehjian Domel Forest 34.7202° 74.1656° 2191 1 23 Dosut Bantil/ Seri Forest 34.7325° 74.191° 2825 3 Sinjlli Nala Sinjlli Forest Fecal 20/12/2015 34.7931° 74.2694° 3009 1 44 Sinjli Nala Fecal 20/12/2015 34.8056° 74.2583° 2784 3 Surgum Surgun 34.8684° 74.2029° 2316 1 Valley Samgam Fecal 28/12/2015 34.8974° 74.207° 2315 1 22 Kundi (Surgan 34.9549° 74.2287° 2526 1 44 Nala) Kamakhdori Nar 34.9831° 74.2394° 2781 1 Arangkel Arangkel Dried Tissue 06/02/2016 34.8042° 74.3472° 2399 1 49 Arangkel Hair/ fecal 21/12/2015 34.8083° 74.3514° 2378 2 Shonther Bagnuwali Sari 34.8597° 74.3944° 2166 1 Valley Yamgar Khariwala Nar 34.8083° 74.4242° 2216 1 41 Yamgar 34.8025° 74.4567° 2310 1 Sao Nar 34.7569° 74.6028° 2470 1 35 Halmat Nilsar Forest 34.7361° 74.6611° 2805 1 22 Chharrinar Forest 34.7431° 74.7667° 2677 1

232

Muzaffara MNP Chitti Pash 34.5042° 73.6616° 2492 2 bad Banrh Forest 34.4773° 73.7112° 2462 1 46

Sokarh 34.5119° 73.7492° 2876 1 Thora (Chathian Fecal 14/01/2016 34.5044° 73.6378° 2870 1 55 Mohri) Domailan Fecal 16/01/2016 34.5367° 73.6309° 2224 1 38 (Machiara) Chokolni Fecal 21/11/2015 34.5335° 73.6444° 3172 1 26 Kothiali 34.5474° 73.6163° 3118 2 Thora Duliarh Fecal 21/11/2015 34.5288° 73.6174° 2931 1 31 Kundi 34.5401° 73.6088° 2612 1 16 Thora (Gahatian) 34.5441° 73.602° 2946 1 Khori Fecal/ hair 23/11/2015 34.5566° 73.5965° 2695 1 60 Kuledabar 34.5675° 73.6019° 3144 2 Blood (male 25/11/2016 infant, Lone Gali 34.5719° 73.586° 2726 1 37 captured by locals) Musa Gali 34.583° 73.5902° 3453 2 Bedri di Gali 34.5883° 73.5622° 3163 1 29 Shah Khori Forest 34.5727° 73.5585° 2510 1 Dirigan Jungle Fecal --- 34.5243° 73.5205° 2887 1 22 Domel (Co. 6/7) 34.5518° 73.4891° 2630 3 Lachrat Pir Chinasi Forest 34.3961° 73.5622° 2231 45 Range Tissue (killed 20/11/2015 Arlian (Lachhrat) 34.4108° 73.5987° 2388 1 11 by leopard) Bagnuan Forest 34.3629° 73.6672° 2417 1 24 (Panjkot) Bagnun Bhaik 34.3661° 73.6681° 2235 2 Pir Hasimar Fecal 17/01/2016 34.3433° 73.6309° 2839 1 12 Kiran Baik 34.3669° 73.6395° 2890 1 Jhelum Jhelum Chark Gali 34.1031° 73.6756° 2368 1

233

Valley valley Chikar Blood (adult 01/04/2012 34.1489° 73.6755° 1678 male killed by 1 locals) Leepa Pathra (Leepa) 34.2812° 73.8388° 2486 1 58 valley Panjal (Leepa) 34.2729° 73.8302° 2754 1 Kuth Nari 34.2701° 73.8783° 2498 1 34 Kuth Nar Baihk 34.2668° 73.8965° 2503 2 (Leepa) Dheri 34.262° 73.8885° 2994 1 Sunai 34.2688° 73.9188° 2437 1 20 Qazi Nag Nanga Par 34.1944° 73.9444° 2636 2 Dagi Chham 34.2002° 73.9432° 2419 1 47 Burzi Forest 34.1905° 73.9582° 2510 1 30 Drag 34.1975° 73.9594° 2674 1 Moji Moji Village 34.32° 73.8122° 1685 Menda Gali 34.2953° 73.8247° 2486 1 35 Pakhi Ban Forest 34.2895° 73.8037° 2538 1 23 Bagh Las Danna Danna Bhaik Fecal 23/02/2016 33.9069° 73.9385° 2438 1 46 Sudhan Khalabat 34.0774° 73.7265° 2396 1 14 Gali) Poonch Toli Pir Ghori Mar Dhok 33.9001° 73.8862° 2393 1 22 Haveli Mehmood Gali Fecal 21/02/2016 33.8648° 74.002° 2209 1 26 Dangwali (Haji Pir) Fecal 22/02/2016 33.9744° 74.0631° 2385 1 65 Motanwala Forest Fecal 33.9978° 74.0942° 2436 1 52 Blood (killed 21/02/2016 Bhedi Doba Forest 33.9789° 74.0973° 2577 1 45 by locals) Khurshid- Sherpur Gali 33.9087° 74.1943° 2236 1 58 Abad (Hillan) Mohri Said Kiran 2539 33.9264° 74.0614° 1 39 Ali Khan

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Appendix III: Photographs

Plate 1

a. b.

c. d.

e. f. Plate 1: Photographs of Grey langurs from different populations found in Pakistan/AJK. a, b, c = from Machiara National Park, Muzaffarabad; d, e = Neelum Valley, AJK; f = Kaghan valley, district Mansehra, Pakistan. Plate 1. Cont………

235 g. h.

i. j.

k. l. Plate 1 Cont.: Photographs of Grey langurs from different populations found in Pakistan/AJK. g, h, = adult male killed by locals at Chikar, Muzaffarabad; i = adult female killed by Common leopard at Machiara National Park, AJK; j = Infant captured by locals from Machiara National Park, AJK; k, l = Fresh Fecal Material of grey langurs collected for DNA extraction. 236

Plate 2

Plate 2: Gel pictures (2%) of PCR products of RAPD markers (1-8) for grey langur populations of Pakistan (M=100bp/1kb Ladder).

237

Plate 3

Plate 3: Gel photographs (2%) of PCR amplification of microsatellite markers (1-8) in grey langur from Pakistan. (M=100bp ladder).

Plate 3 Cont…..

238

Plate 3: Gel photographs (2%) of PCR amplification of microsatellite markers (9-16) in grey langur from Pakistan. (M=100bp ladder).

239

Appendix IV: Multiple Sequence Alignment

COI multiple sequence alignment by CLUSTAL O (1.2.4)

Presbytis-melalophos TTCCCCCGTCTAAATAACATAAGTTTCTGGCTTCTTCCACCATCATTCCTACTTCTTCTC Presbytis-femoralis TTTCCCCGTCTAAATAATATAAGTTTCTGGCTTCTTCCACCATCATTCCTACTTCTTCTC Trachypithecus-vetulus TTCCCCCGTCTAAACAACATAAGCTTCTGACTTCTCCCACCATCATTTCTACTTCTTCTC Trachypithecus-johnii TTTCCCCGTCTAAACAATATAAGCTTCTGACTTCTCCCACCATCATTTCTACTTCTTCTT Semnopithecus-entellus TTTCCCCGTCTAAACAATATAAGCTTCTGACTTCTCCCACCATCATTTCTACTTCTTCTC Semnopithecus-schistaceus ------TC Poonch-1 ------TC Poonch-2 ------TC Poonch-3 ------TC Poonch-4 ------TC Muzaffarabad-Lachrat ------TC Muzaffarabad-MNP1 ------TC Muzaffarabad-MNP2 ------TC Neelum-SN ------TC Neelum-AK ------TC Mansehra-1 ------TC Mansehra-2 ------TC Kohistan-1 ------TC Kohistan-2 ------TC Kohistan-3 ------TC Mansehra-3 ------TC Trachypithecus-germaini TTTCCCCGCCTAAATAATATAAGCTTCTGACTTCTTCCACCATCCTTCCTACTTCTTCTC Trachypithecus-poliocephalus TTCCCCCGCCTAAATAATATAAGCTTCTGACTTCTTCCACCATCCTTCCTACTTCTTCTC Rhinopithecus-avunculus TTTCCTCGTCTAAATAATATAAGCTTCTGACTTCTTCCACCATCTTTTCTACTTCTTCTT Rhinopithecus-bieti TTTCCTCGTCTAAATAATATAAGCTTCTGACTTCTTCCACCATCTTTCCTACTTCTTATT Nasalis-concolor TTCCCCCGTCTAAATAATATAAGCTTTTGACTTCTTCCACCATCTTTCCTGCTCCTTCTT Nasalis-larvatus TTCCCCCGCCTAAATAATATAAGCTTTTGACTTCTTCCACCGTCTTTCCTGCTCCTTCTT *

Presbytis-melalophos GCTTCGGCCATAGTAGAAGCCGGTGCCGGAACAGGTTGAACAGTCTACCCCCCTTTAGCA Presbytis-femoralis GCTTCAGCCATAGTAGAAGCCGGTGCCGGAACAGGTTGAACAGTCTATCCCCCTTTAGCA Trachypithecus-vetulus GCATCAGCCATAGTAGAGGCCGGTGCGGGAACAGGTTGAACAGTTTATCCCCCTTTAGCA Trachypithecus-johnii GCATCAGCCATAGTAGAAGCCGGTGCAGGGACAGGTTGAACAGTTTATCCTCCTTTAGCA Semnopithecus-entellus GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTATCCTCCTTTAGCA Semnopithecus-schistaceus GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTATCCTCCTTTAGCA Poonch-1 GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Poonch-2 GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Poonch-3 GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Poonch-4 GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Muzaffarabad-Lachrat GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Muzaffarabad-MNP1 GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Muzaffarabad-MNP2 GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Neelum-SN GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Neelum-AK GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Mansehra-1 GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Mansehra-2 GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Kohistan-1 GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Kohistan-2 GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Kohistan-3 GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Mansehra-3 GCATCAGCCATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTTTACCCTCCTTTAGCA Trachypithecus-germaini GCATCAGCCATAGTAGAAGCTGGTGCAGGAACAGGTTGAACAGTATATCCACCTTTAGCA Trachypithecus-poliocephalus GCATCAGCCATAGTAGAAGCTGGTGCCGGAACAGGTTGAACAGTATATCCACCCTTAGCA Rhinopithecus-avunculus GCATCAGCCATAGTAGAGGCTGGTGCCGGGACAGGCTGAACAGTTTACCCGCCTCTAGCG Rhinopithecus-bieti GCATCAGCCATAGTAGAGGCTGGCGCCGGGACAGGCTGAACAGTCTACCCGCCTCTAGCA Nasalis-concolor GCATCAGCTATAGTAGAAGCCGGTGCAGGAACAGGCTGAACAGTATACCCGCCTCTAGCA Nasalis-larvatus GCATCAGCTATAGTAGAAGCTGGTGCAGGGACAGGCTGAACAGTATACCCGCCTCTAGCA ** ** ** ******** ** ** ** ** ***** ******** ** ** ** ****

Presbytis-melalophos GGGAATTTATCTCACCCAGGAGCCTCCGTAGACTTAACTATCTTTTCACTTCATCTAGCA Presbytis-femoralis GGGAATCTATCTCACCCAGGAGCTTCCGTAGACTTAACTATCTTTTCACTTCACCTAGCA Trachypithecus-vetulus GGAAACTTATCTCATCCAGGAGCTTCTGTAGACTTAACTATTTTTTCACTTCATCTAGCA Trachypithecus-johnii GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATCTTCTCACTTCATCTAGCA Semnopithecus-entellus GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Semnopithecus-schistaceus GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Poonch-1 GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Poonch-2 GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Poonch-3 GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Poonch-4 GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA

240

Muzaffarabad-Lachrat GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Muzaffarabad-MNP1 GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Muzaffarabad-MNP2 GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Neelum-SN GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Neelum-AK GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Mansehra-1 GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Mansehra-2 GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Kohistan-1 GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Kohistan-2 GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Kohistan-3 GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Mansehra-3 GGAAACCTATCTCACCCAGGAGCTTCTGTAGACTTGACTATTTTCTCACTTCATCTAGCA Trachypithecus-germaini GGAAACCTCTCTCACCCAGGAGCCTCCGTAGACCTAACTATCTTCTCACTTCATCTAGCA Trachypithecus-poliocephalus GGAAACCTCTCTCATCCGGGAGCCTCCGTAGACCTGACTATTTTCTCGCTTCACCTAGCA Rhinopithecus-avunculus GGAAACCTCTCTCACCCAGGGGCCTCTGTAGATTTAACTATTTTCTCACTACACCTAGCA Rhinopithecus-bieti GGGAACTTCTCTCACCCAGGAGCCTCTGTAGATTTAACTATTTTCTCACTACACCTAGCA Nasalis-concolor GGAAATTTCTCCCATCCAGGAGCCTCTGTAGACCTAACTATTTTTTCACTTCACCTAGCA Nasalis-larvatus GGAAATTTCTCCCACCCAGGAGCTTCTGTAGACCTAACTATCTTCTCACTTCATCTAGCA ** ** * ** ** ** ** ** ** ***** * ***** ** ** ** ** ******

Presbytis-melalophos GGTATTTCTTCTATCTTGGGTGCTATTAATTTTATCACAACCATTATCAATATAAAACCC Presbytis-femoralis GGCATTTCTTCTATTCTAGGTGCTATTAATTTCATCACAACTATTATTAATATAAAACCT Trachypithecus-vetulus GGCATCTCCTCTATTCTAGGAGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Trachypithecus-johnii GGCATCTCCTCTATTCTAGGAGCTATTAACTTTATTACAACTATTATTAACATAAAACCC Semnopithecus-entellus GGCATTTCCTCTATTCTAGGGGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Semnopithecus-schistaceus GGCATTTCCTCTATTCTAGGGGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Poonch-1 GGCATTTCCTCTATTCTAGGAGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Poonch-2 GGCATTTCCTCTATTCTAGGAGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Poonch-3 GGCATTTCCTCTATTCTAGGAGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Poonch-4 GGCATTTCCTCTATTCTAGGAGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Muzaffarabad-Lachrat GGCATTTCCTCTATTCTAGGGGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Muzaffarabad-MNP1 GGCATTTCCTCTATTCTAGGGGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Muzaffarabad-MNP2 GGCATTTCCTCTATTCTAGGGGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Neelum-SN GGCATTTCCTCTATTCTAGGGGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Neelum-AK GGCATTTCCTCTATTCTAGGGGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Mansehra-1 GGCATTTCCTCTATTCTAGGGGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Mansehra-2 GGCATTTCCTCTATTCTAGGGGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Kohistan-1 GGCATTTCCTCTATTCTAGGGGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Kohistan-2 GGCATTTCCTCTATTCTAGGGGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Kohistan-3 GGCATTTCCTCTATTCTAGGGGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Mansehra-3 GGCATTTCCTCTATTCTAGGGGCTATTAACTTTATTACAACTATTATTAATATAAAACCC Trachypithecus-germaini GGTATTTCCTCTATTCTAGGGGCTATTAACTTCATTACAACTATCATTAATATAAAACCC Trachypithecus-poliocephalus GGTATTTCTTCTATTCTAGGGGCTATTAATTTTATTACAACTATTATTAACATAAAACCC Rhinopithecus-avunculus GGTATTTCTTCTATTCTAGGGGCTATTAATTTCATCACAACTATTATTAATATAAAACCT Rhinopithecus-bieti GGTATTTCTTCTATCTTAGGGGCTATTAATTTTATCACAACTATTATTAATATAAAACCC Nasalis-concolor GGCATTTCTTCTATCCTAGGAGCTATCAACTTTATCACGACTATCATTAATATAAAACCC Nasalis-larvatus GGCATTTCTTCTATCTTAGGGGCTATCAATTTTATCACAACTATCATTAACATAAAACCC ** ** ** ***** * ** ***** ** ** ** ** ** ** ** ** ********

Presbytis-melalophos CCTGCAATATCTCAATACCAAACCCCCTTATTTGTATGATCGGTCCTAATCACAGCAGTC Presbytis-femoralis CCTGCAGTATCTCAATACCAAACCCCCTTGTTTGTGTGATCAGTCCTAATTACAGCAGTC Trachypithecus-vetulus CCTGCAATATCCCAATATCAAACTCCCCTGTTTGTTTGATCAGTTCTAATCACAGCAGTC Trachypithecus-johnii CCTGCAATATCCCAATATCAAACCCCCTTATTTGTTTGATCAGTCCTAATCACAGCAGTC Semnopithecus-entellus CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATT Semnopithecus-schistaceus CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCAGTCCTAATCACAGCAATC Poonch-1 CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Poonch-2 CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Poonch-3 CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Poonch-4 CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Muzaffarabad-Lachrat CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Muzaffarabad-MNP1 CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Muzaffarabad-MNP2 CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Neelum-SN CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Neelum-AK CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Mansehra-1 CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Mansehra-2 CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Kohistan-1 CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Kohistan-2 CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Kohistan-3 CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Mansehra-3 CCTGCAATATCCCAATATCAAACCCCCCTATTTGTTTGATCGGTTCTAATCACAGCAATC Trachypithecus-germaini CCTGCAATATCTCAATACCAAACCCCTCTATTTGTTTGATCAGTCCTGATTACAGCAGTC Trachypithecus-poliocephalus CCTGCAATATCTCAATACCAAACTCCTCTATTTGTTTGATCAGTCCTAATTACAGCAGTA Rhinopithecus-avunculus CCTGCTATGTCTCAATATCAAACCCCCCTATTTGTTTGATCAGTTCTAATTACAGCAGTC Rhinopithecus-bieti CCTGCTATATCTCAATATCAAACCCCCCTCTTTGTTTGATCAGTTCTAATTACAGCAGTC Nasalis-concolor CCCGCTATATCCCAATATCAAACTCCTTTGTTTGTTTGATCTGTCCTAATTACAGCAGTC Nasalis-larvatus CCCGCTATATCCCAATATCAAACTCCTTTGTTTGTCTGATCTGTTCTAATTACAGCAGTC ** ** * ** ***** ***** ** * ***** ***** ** ** ** ****** *

241

Presbytis-melalophos TTACTGCTCCTATCTCTACCCGTACTAGCCGCAGGCATTACAATGCTATTAACAGACCGC Presbytis-femoralis TTACTACTCCTATCCCTACCCGTACTAGCCGCAGGCATTACAATGCTACTAACAGACCGC Trachypithecus-vetulus CTACTTCTCCTCTCCCTCCCTGTATTAGCCGCCGGCATTACAATACTACTAACGGATCGT Trachypithecus-johnii CTACTTCTCCTCTCTCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGATCGT Semnopithecus-entellus CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Semnopithecus-schistaceus CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Poonch-1 CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Poonch-2 CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Poonch-3 CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Poonch-4 CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Muzaffarabad-Lachrat CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Muzaffarabad-MNP1 CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Muzaffarabad-MNP2 CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Neelum-SN CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Neelum-AK CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Mansehra-1 CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Mansehra-2 CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Kohistan-1 CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Kohistan-2 CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Kohistan-3 CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Mansehra-3 CTACTTCTCCTTTCCCTCCCTGTACTAGCCGCCGGCATTACAATACTATTGACGGACCGT Trachypithecus-germaini CTACTACTCCTATCTCTGCCCGTACTAGCCGCTGGTATTACAATATTATTAACAGACCGC Trachypithecus-poliocephalus TTGCTCCTCCTATCCTTGCCCGTACTAGCCGCTGGTATTACAATATTATTAACAGACCGC Rhinopithecus-avunculus CTATTACTCCTATCCCTGCCTGTATTAGCTGCAGGTATTACAATACTATTAACAGACCGT Rhinopithecus-bieti CTATTACTCTTATCCCTACCTGTATTAGCTGCTGGTATTACAATACTATTAACAGACCGT Nasalis-concolor CTGCTACTTCTATCCCTGCCCGTACTGGCTGCCGGCATTACAATGCTATTAACAGACCGT Nasalis-larvatus CTGCTACTTCTATCCCTACCCGTACTGGCTGCCGGCATTACAATGCTACTAACAGACCGC * * ** * ** * ** *** * ** ** ** ******** ** * ** ** **

Presbytis-melalophos AATCTCAACACTACCTTCTTTGATCCTGCCGGAGGGGGAGACCCTATCCTATATCAACAC Presbytis-femoralis AATCTCAATACTACTTTCTTTGACCCTGCAGGAGGAGGAGACCCTATCCTATACCAACAC Trachypithecus-vetulus AACCTCAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTGTATCAACAC Trachypithecus-johnii AACCTCAACACCACTTTCTTCGATCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Semnopithecus-entellus AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTTTATATCAGCAC Semnopithecus-schistaceus AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAGCAC Poonch-1 AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Poonch-2 AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Poonch-3 AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Poonch-4 AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Muzaffarabad-Lachrat AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Muzaffarabad-MNP1 AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Muzaffarabad-MNP2 AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Neelum-SN AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Neelum-AK AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Mansehra-1 AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Mansehra-2 AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Kohistan-1 AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Kohistan-2 AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Kohistan-3 AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Mansehra-3 AACCTTAACACCACTTTCTTCGACCCTGCCGGAGGAGGAGATCCTATTCTATATCAACAC Trachypithecus-germaini AATCTTAACACTACCTTCTTTGACCCTGCGGGAGGGGGAGACCCTATCTTATATCAACAC Trachypithecus-poliocephalus AACCTTAACACCACCTTCTTTGACCCTGCAGGAGGGGGAGATCCTATCTTATATCAACAC Rhinopithecus-avunculus AACCTCAACACTACATTCTTTGACCCTGCCGGAGGAGGAGATCCGATCTTATATCAACAC Rhinopithecus-bieti AACCTCAACACTACTTTCTTTGACCCTGCTGGAGGGGGAGACCCGATCTTATATCAACAC Nasalis-concolor AATCTCAATACTACTTTCTTTGATCCCGCCGGAGGAGGGGATCCAATCCTATATCAACAT Nasalis-larvatus AATCTCAATACTACTTTCTTTGACCCCGCCGGAGGAGGGGATCCAATCCTATATCAACAT ** ** ** ** ** ***** ** ** ** ***** ** ** ** ** * ** ** **

Presbytis-melalophos TTATTCTGATTCTTTGGACATCCAGAAGTGTATATTCTTATTTTACCTGGCTTTGGAATA Presbytis-femoralis CTATTTTGATTCTTTGGTCACCCTGAAGTCTACATTCTTATTCTTCCCGGCTTTGGAATA Trachypithecus-vetulus CTGTTCTGATTCTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGAATA Trachypithecus-johnii CTATTCTGGTTCTTTGGTCATCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGAATA Semnopithecus-entellus CTATTTTGATTCTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Semnopithecus-schistaceus CTATTCTGATTCTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Poonch-1 CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Poonch-2 CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Poonch-3 CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Poonch-4 CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Muzaffarabad-Lachrat CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Muzaffarabad-MNP1 CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Muzaffarabad-MNP2 CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Neelum-SN CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Neelum-AK CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Mansehra-1 CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Mansehra-2 CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA 242

Kohistan-1 CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Kohistan-2 CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Kohistan-3 CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Mansehra-3 CTATTCTGATTTTTCGGTCACCCCGAAGTCTATATTCTTATTCTCCCTGGTTTTGGGATA Trachypithecus-germaini TTATTCTGATTCTTTGGACACCCTGAAGTCTATATTCTTATTCTACCTGGCTTCGGAATA Trachypithecus-poliocephalus TTATTCTGATTCTTCGGACACCCTGAAGTCTATATTCTTATTTTACCTGGCTTCGGAATA Rhinopithecus-avunculus CTATTCTGATTTTTCGGTCACCCTGAAGTCTACATTCTTATTTTACCTGGCTTTGGAATA Rhinopithecus-bieti CTATTCTGATTTTTTGGCCACCCTGAAGTCTATATTCTTATTTTACCTGGCTTTGGAATA Nasalis-concolor CTATTCTGATTCTTCGGTCACCCTGAAGTCTATATTCTTATCTTACCTGGGTTTGGAATA Nasalis-larvatus TTATTCTGATTTTTCGGTCACCCTGAAGTCTACATTCTTATCTTGCCTGGGTTTGGAATA * ** ** ** ** ** ** ** ***** ** ******** * ** ** ** ** ***

Presbytis-melalophos ATCTCCCACATCGTAACATATTATTCTGGAAAAAAAGAACCATTCGGGTATATAGGTATA Presbytis-femoralis ATCTCCCACATCGTAACATACTATTCTGGAAAAAAAGAACCATTCGGGTACATAGGAATA Trachypithecus-vetulus ATCTCTCATATCGTGACATACTACTCCGGAAAAAAAGAACCATTCGGTTACATAGGAATA Trachypithecus-johnii ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Semnopithecus-entellus ATCTCTCACATCGTAACATACTACTCTGGAAAAAAAGAGCCATTCGGGTATATGGGAATA Semnopithecus-schistaceus ATCTCTCACATCGTAACATATTATTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Poonch-1 ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Poonch-2 ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Poonch-3 ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Poonch-4 ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Muzaffarabad-Lachrat ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Muzaffarabad-MNP1 ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Muzaffarabad-MNP2 ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Neelum-SN ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Neelum-AK ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Mansehra-1 ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Mansehra-2 ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Kohistan-1 ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Kohistan-2 ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Kohistan-3 ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Mansehra-3 ATCTCTCACATCGTAACATATTACTCTGGAAAAAAAGAACCATTCGGGTATATGGGAATA Trachypithecus-germaini ATTTCCCACATTGTAACATACTATTCTGGGAAAAAAGAGCCATTCGGGTATATAGGCATA Trachypithecus-poliocephalus ATTTCCCACATTGTAACACACTATTCTGGAAAAAAAGAACCATTCGGGTATATAGGCATA Rhinopithecus-avunculus ATTTCACACATCGTAACATATTACTCTGGAAAAAAAGAACCTTTCGGGTACATGGGCATA Rhinopithecus-bieti ATTTCACACATTGTAACATATTATTCTGGAAAAAAAGAACCTTTCGGGTATATAGGCATA Nasalis-concolor ATTTCCCACATCGTGACATATTATTCTGGAAAAAAAGAACCATTCGGATACATAGGCATG Nasalis-larvatus ATTTCCCACATCGTAACATATTATTCTGGAAAAAAGGAACCATTCGGATATATAGGTATG ** ** ** ** ** *** * ** ** ** ***** ** ** ***** ** ** ** **

Presbytis-melalophos GTCTGAGCTATAATATCAATTGGGTTTCTAGGCTTTATTGTGTGAGCTCACCATATATTT Presbytis-femoralis GTCTGAGCTATGGTATCAATTGGGTTTCTAGGCTTTATTGTGTGAGCTCACCATATATTT Trachypithecus-vetulus GTCTGAGCCATAATATCAATTGGATTCCTAGGCTTTATTGTGTGAGCTCACCACATGTTT Trachypithecus-johnii GTCTGAGCCATAATATCAATTGGATTCCTAGGCTTTATTGTATGAGCTCACCACATGTTT Semnopithecus-entellus GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTGTGAGCTCACCACATGTTT Semnopithecus-schistaceus GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Poonch-1 GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Poonch-2 GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Poonch-3 GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Poonch-4 GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Muzaffarabad-Lachrat GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Muzaffarabad-MNP1 GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Muzaffarabad-MNP2 GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Neelum-SN GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Neelum-AK GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Mansehra-1 GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Mansehra-2 GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Kohistan-1 GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Kohistan-2 GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Kohistan-3 GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Mansehra-3 GTCTGGGCCATAATATCAATTGGATTCCTAGGCTTCATTGTATGAGCTCACCACATGTTT Trachypithecus-germaini GTCTGAGCTATAGTATCAATTGGGTTCCTAGGCTTTATTGTATGAGCTCACCATATATTC Trachypithecus-poliocephalus GTCTGAGCCATAGTATCAATTGGGTTCCTAGGCTTTATTGTATGAGCTCATCATATATTT Rhinopithecus-avunculus GTCTGAGCTATAATATCAATTGGGTTTTTAGGTTTTATCGTATGAGCCCACCATATATTT Rhinopithecus-bieti GTCTGAGCTATAGTATCAATTGGATTTTTAGGTTTTATCGTATGAGCCCACCACATATTT Nasalis-concolor GTCTGAGCTATAGTATCAATTGGATTCCTAGGCTTTATCGTATGAGCTCATCACATATTT Nasalis-larvatus GTCTGAGCTATAGTATCAATTGGATTCCTAGGCTTTATCGTATGAGCTCACCATATATTT ***** ** ** ********** ** **** ** ** ** ***** ** ** ** **

Presbytis-melalophos ACAGTTGGCATAGACGTAGATACACGGGCCTATTTTACCTCTGCCACTATAATTATTGCA Presbytis-femoralis ACAGTTGGCATAGACGTAGATACACGGGCCTATTTTACCTCTGCCACTATAATTATTGCA Trachypithecus-vetulus ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCCGCCACTATAATTATTGCA Trachypithecus-johnii ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCA Semnopithecus-entellus ACAGTTGGTATAGACGTTGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCA Semnopithecus-schistaceus ACAGTTGGTATAGACGTAGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG 243

Poonch-1 ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Poonch-2 ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Poonch-3 ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Poonch-4 ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Muzaffarabad-Lachrat ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Muzaffarabad-MNP1 ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Muzaffarabad-MNP2 ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Neelum-SN ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Neelum-AK ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Mansehra-1 ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Mansehra-2 ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Kohistan-1 ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Kohistan-2 ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Kohistan-3 ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Mansehra-3 ACAGTTGGTATAGACGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCG Trachypithecus-germaini ACAGTTGGAATAGACGTAGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCA Trachypithecus-poliocephalus ACAGTCGGAATAGATGTGGATACACGAGCCTATTTTACCTCTGCCACTATAATTATTGCA Rhinopithecus-avunculus ACCGTTGGTATAGACGTAGATACACGAGCCTATTTTACCTCTGCCACTATGATTATTGCA Rhinopithecus-bieti ACTGTTGGAATAGACGTAGATACACGAGCCTATTTTACCTCTGCTACTATAATTATTGCA Nasalis-concolor ACTGTTGGTATAGACGTGGATACACGAGCCTACTTTACCTCTGCCACTATAATTATTGCA Nasalis-larvatus ACTGTTGGTATAGACGTGGATACACGAGCCTACTTTACCTCTGCCACTATAATTATTGCA ** ** ** ***** ** ******** ***** ******** ** ***** ********

Presbytis-melalophos ATTCCAACTGGCGTTAAAGTCTTTAGCTGATTAGCCACATTACACGGAGGAAATATTAAA Presbytis-femoralis ATTCCAACTGGCGTTAAAGTCTTCAGCTGATTAGCTACATTACACGGAGGCAATATTAAG Trachypithecus-vetulus ATTCCAACTGGCGTCAAAGTTTTCAGCTGGCTCGCTACACTACATGGAGGTAATATCAAG Trachypithecus-johnii ATTCCAACTGGCGTCAAAGTTTTTAGCTGGCTCGCTACATTACACGGAGGAAATATCAAG Semnopithecus-entellus ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCACA Semnopithecus-schistaceus ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Poonch-1 ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Poonch-2 ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Poonch-3 ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Poonch-4 ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Muzaffarabad-Lachrat ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Muzaffarabad-MNP1 ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Muzaffarabad-MNP2 ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Neelum-SN ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Neelum-AK ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Mansehra-1 ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Mansehra-2 ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Kohistan-1 ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Kohistan-2 ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Kohistan-3 ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Mansehra-3 ATTCCAACTGGCGTCAAAGTTTTCAGCTGACTCGCTACATTACACGGAGGAAATATCAC- Trachypithecus-germaini ATCCCAACTGGCGTCAAAGTCTTTAGCTGACTTGCCACGCTACACGGAGGAAATATTAAA Trachypithecus-poliocephalus ATCCCAACTGGCGTTAAAGTCTTTAGCTGACTTGCTACGCTACACGGAGGAAATATTAAG Rhinopithecus-avunculus ATTCCAACTGGCGTCAAAGTCTTTAGCTGACTCGCTACACTACACGGAGGAAATATTAAA Rhinopithecus-bieti ATTCCAACTGGCGTTAAAGTATTTAGCTGACTCGCTACGCTACACGGAGGAAATATCAAG Nasalis-concolor ATTCCAACTGGTGTTAAAGTCTTTAGCTGACTCGCTACATTACACGGAGGAAATATCAAG Nasalis-larvatus ATTCCAACTGGCGTTAAAGTCTTTAGCTGACTCGCTACATTACACGGAGGAAATATCAAG ** ******** ** ***** ** ***** * ** ** **** ***** ***** *

Cytochrome b multiple sequence alignment by CLUSTAL O (1.2.4)

Nasalis_larvatus_U62664.1 TATCATTTTGAGGCGCCACAGTAATTACAAATTTATTATCTGCAATTCCATACATTGGGA Semnopithecus_entellus_hector_AY519451.1 TATCATTCTGAGGCGCCACAGTAATCACAAACCTATTATCCGCAATTCCATATATTGGGA Semnopithecus_entellus_EU004471.1 TATCATTCTGAGGCGCCACAGTAATCACAAATCTATTATCCGCAATCCCATATATTGGGA Presbytis_entellus_AF012470.1 TATCATTCTGAGGCGCCACAGTAATCACAAATCTATTATCCGCAATCCCATATATTGGGA Semnopithecus_entellus_EU004478.1 TATCATTCTGAGGCGCCACAGTAATCACAAATCTATTATCCGCAATCCCATATATTGGGA Semnopithecus_entellus_DQ355297.1 TATCATTCTGAGGCGCCACAGTAATCACAAATCTATTATCCGCAATCCCATATATTGGGA 32_Poonch_2 TATCATTCTGAGGCGCCACAGTAATCACAAACCTACTATCCGCAATTCCATATATTGGGA 41_Mansehra_2 TATCATTCTGAGGCGCCACAGTAATCACAAACCTACTATCCGCAATTCCATATATTGGGA 45_Mansehra_3 TATCATTCTGAGGCGCCACAGTAATCACAAATCTATTATCCGCAATTCCATATATTGGGA 42_Kohistan_1 TATCATTCTGAGGCGCCACAGTAATCACAAATCTATTATCCGCAATTCCATATATTGGGA 38_Neelum_SN TATCATTCTGAGGCGCCACAGTAATCACAAATCTATTATCCGCAATTCCATATATTGGGA 39_Neelum_AK TATCATTCTGAGGCGCCACAGTAATCACAAATCTATTATCCGCAATTCCATATATTGGGA 31_Poonch_1 TATCATTCTGAGGCGCCACAGTAATCACAAATCTATTATCCGCAATTCCATATATTGGGA 33_Poonch_3 TATCATTCTGAGGCGCCACAGTAATCACAAATCTATTATCCGCAATTCCATATATTGGGA 34_Poonch_4 TATCATTCTGAGGCGCCACAGTAATCACAAATCTATTATCCGCAATTCCATATATTGGGA 36_Muzaffarabad_MNP1 TATCATTCTGAGGCGCCACAGTAATCACAAATCTATTATCCGCAATTCCATATATTGGGA 37_Muzaffarabad_MNP2 TATCATTCTGAGGCGCCACAGTAATCACAAATCTATTATCCGCAATTCCATATATTGGGA 40_Mansehra_1 TATCATTCTGAGGCGCCACAGTAATCACAAATCTATTATCCGCAATTCCATATATTGGGA Trachypithecus_vetulus_AY519454.1 TGTCATTCTGAGGCGCCACAGTAATCACAAACCTACTATCCGCAATTCCATATATTGGAA Trachypithecus_vetulus_HQ149049.1 TGTCATTCTGAGGCGCCACAGTAATCACAAATCTACTATCCGCAGTTCCATATATTGGGT Trachypithecus_pileatus_EU004472.1 TATCATTCTGAGGCGCCACAGTAATCACAAATTTACTATCCGCAGTTCCATATATTGGGA

244

Trachypithecus_geei_AF294618.1 TATCATTCTGAGGCGCCACAGTAATCACAAATTTACTATCCGCAGTTCCATATATTGGGA Trachypithecus_pileatus_KP834333.1 TATCATTCTGAGGCGCCACAGTAATCACAAATTTACTATCCGCAGTTCCATATATTGGGA Trachypithecus_shortridgei_KP834335.1 TATCATTCTGAGGCGCCACAGTAATCACAAATTTACTATCCGCAGTTCCATATATTGGGA Trachypithecus_johnii_AY519453.1 TATCATTCTGAGGTGCCACAGTAATCACAAATCTACTATCCGCAGTTCCATATATTGGAA Semnopithecus_priam_JQ734745.1 TATCATTCTGAGGTGCCACAGTAATTACAAACCTACTATCCGCAGTTCCATATATTGGAA Trachypithecus_johnii_JQ734755.1 TATCATTCTGAGGTGCCACAGTAATTACAAACCTACTATCCGCAGTTCCATATATTGGAA Semnopithecus_priam_JQ734747.1 TATCATTCTGAGGTGCCACAGTAATTACAAACCTACTATCCGCAGTTCCATATATTGGAA Semnopithecus_priam_JQ734748.1 TATCATTCTGAGGCGCCACAGTAATTACAAACCTACTATCCGCAGTTCCATATATTGGAA Semnopithecus_hypoleucos_JQ734711.1 TATCATTCTGAGGAGCCACAGTAATCACAAACCTACTATCCGCAGTTCCATATATCGGAG Semnopithecus_hypoleucos_JQ734692.1 TATCATTCTGAGGAGCCACAGTAATCACAAACCTACTATCCGCAGTTCCATATATTGGAG Semnopithecus_hypoleucos_JQ734705.1 TATCATTCTGAGGGGCCACAGTAATCACAAACCTACTATCCGCAGTTCCATATATTGGAG * ***** ***** *********** ***** ** **** *** * ***** ** **

Nasalis_larvatus_U62664.1 CCAACCTTGTCCAATGGGTTTGAGGCGGGTACTCCATCGACAACCCAACCCTTACGCGAT Semnopithecus_entellus_hector_AY519451.1 CCGACCTTGTCCAATGACTTTGAGGGGGGTACTCCATCGATAATCCAACCCTTACACGAT Semnopithecus_entellus_EU004471.1 CCGACCTTGTCCAATGACTTTGAGGAGGATACTCCATCGATAATCCAACCCTTACACGAT Presbytis_entellus_AF012470.1 CCGACCTTGTCCAATGACTTTGAGGAGGATACTCCATCGATAATCCAACCCTTACACGAT Semnopithecus_entellus_EU004478.1 CCGACCTTGTCCAATGACTTTGAGGAGGATACTCCATCGATAATCCAACCCTTACACGAT Semnopithecus_entellus_DQ355297.1 CCGACCTTGTCCAATGACTTTGAGGAGGATATTCCATCGATAATCCAACCCTTACACGAT 32_Poonch_2 CCGACCTTGTCCAGTGACTTTGAGGGGGATACTCCATCGATAATCCAACCCTTACACGAT 41_Mansehra_2 CCGACCTTGTCCAGTGACTTTGAGGGGGATACTCCATCGATAATCCAACCCTTACACGAT 45_Mansehra_3 CCGACCTTGTCCAATGACTTTGAGGGGGATATTCCATCGATAATCCAACCCTTACACGAT 42_Kohistan_1 CCGACCTTGTCCAATGACTTTGAGGGGGATATTCCATCGATAATCCAACCCTTACACGAT 38_Neelum_SN CCGACCTTGTCCAATGACTTTGAGGGGGATATTCCATCGATAATCCAACCCTTACACGAT 39_Neelum_AK CCGACCTTGTCCAATGACTTTGAGGGGGATATTCCATCGATAATCCAACCCTTACACGAT 31_Poonch_1 CCGACCTTGTCCAATGACTTTGAGGGGGATATTCCATCGATAATCCAACCCTTACACGAT 33_Poonch_3 CCGACCTTGTCCAATGACTTTGAGGGGGATATTCCATCGATAATCCAACCCTTACACGAT 34_Poonch_4 CCGACCTTGTCCAATGACTTTGAGGGGGATATTCCATCGATAATCCAACCCTTACACGAT 36_Muzaffarabad_MNP1 CCGACCTTGTCCAATGACTTTGAGGGGGATATTCCATCGATAATCCAACCCTTACACGAT 37_Muzaffarabad_MNP2 CCGACCTTGTCCAATGACTTTGAGGGGGATATTCCATCGATAATCCAACCCTTACACGAT 40_Mansehra_1 CCGACCTTGTCCAATGACTTTGAGGGGGATATTCCATCGATAATCCAACCCTTACACGAT Trachypithecus_vetulus_AY519454.1 CGAACCTTGTCCAATGACTTTGAGGAGGATACTCCATCGACAATCCAACCCTAACACGAT Trachypithecus_vetulus_HQ149049.1 CCAACCTTGTCCAATGACTTTGAGGAGGATACTCCATCGATAATCCAACCCTAACACGAT Trachypithecus_pileatus_EU004472.1 CCAACCTTGTCCAATGACTTTGAGGAGGATATTCCATCGACAATCCAACCCTTACACGAT Trachypithecus_geei_AF294618.1 CCAACCTTGTCCAATGACTTTGAGGAGGATATTCCATCGACAATCCAACCCTTACACTAT Trachypithecus_pileatus_KP834333.1 CCAACCTTGTCCAATGACTTTGAGGAGGATATTCCATCGACAATCCAACCCTTACACGAT Trachypithecus_shortridgei_KP834335.1 CCAACCTTGTCCAATGACTTTGAGGAGGATATTCCATCGACAATCCAACCCTTACACGAT Trachypithecus_johnii_AY519453.1 CCAACCTTGTTCAGTGACTTTGAGGGGGGTATTCCATCGACAATCCAACCCTTACACGAT Semnopithecus_priam_JQ734745.1 CCAACCTTGTTCAGTGACTTTGAGGGGGGTATTCCATCGACAATCCAACCCTTACACGAT Trachypithecus_johnii_JQ734755.1 CCAACCTTGTTCAGTGACTTTGAGGGGGGTATTCCATCGACAATCCAACCCTTACACGAT Semnopithecus_priam_JQ734747.1 CCAACCTTGTTCAGTGACTTTGAGGGGGGTATTCCATCGACAATCCAACCCTTACACGAT Semnopithecus_priam_JQ734748.1 CCAACCTTGTTCAGTGACTTTGAGGGGGGTATTCCATCGACAATCCAACCCTTACACGAT Semnopithecus_hypoleucos_JQ734711.1 CCAACCTTGTTCAATGACTTTGGGGGGGATATTCCATCGACAATCCTACCCTTACACGAT Semnopithecus_hypoleucos_JQ734692.1 CCAACCTTGTTCAATGACTTTGAGGGGGGTATTCCATCGACAATCCAACCCTTACACGAT Semnopithecus_hypoleucos_JQ734705.1 CTAACCTTGTTCAATGACTTTGAGGGGGATATTCCATCGACAATCCAACCCTTACACGAT * ******* ** ** **** ** ** ** ******** ** ** ***** ** * **

Nasalis_larvatus_U62664.1 TCTTTACCCTCCACTTTACCCTACCCTTCATTATCGCAACGTTCACAATTTTACATCTGC Semnopithecus_entellus_hector_AY519451.1 TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACATCTAC Semnopithecus_entellus_EU004471.1 TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC Presbytis_entellus_AF012470.1 TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC Semnopithecus_entellus_EU004478.1 TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC Semnopithecus_entellus_DQ355297.1 TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC 32_Poonch_2 TTTTTACCCTTCACTTTATCCTACCCTTTATTATTGCAACCTTCACAATCTTACACCTAT 41_Mansehra_2 TTTTTACCCTTCACTTTATCCTACCCTTTATTATTGCAACCTTCACAATCTTACACCTAT 45_Mansehra_3 TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC 42_Kohistan_1 TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC 38_Neelum_SN TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC 39_Neelum_AK TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC 31_Poonch_1 TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC 33_Poonch_3 TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC 34_Poonch_4 TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC 36_Muzaffarabad_MNP1 TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC 37_Muzaffarabad_MNP2 TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC 40_Mansehra_1 TTTTTACCCTTCACTTTATCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC Trachypithecus_vetulus_AY519454.1 TTTTTACTCTTCACTTCACCCTACCCTTTATTATCGCAGCCTTCACAATCTTACATCTAC Trachypithecus_vetulus_HQ149049.1 TTTTCACTCTTCACTTCACCCTACCCTTTATTATTGCAACCTTCACAATCTTACATCTAC Trachypithecus_pileatus_EU004472.1 TCTTTACCCTTCACTTTACCCTACCCTTTATTATCGCAACCTTCACAATCTTACATCTAC Trachypithecus_geei_AF294618.1 TCTTTACCCTTCACTTTACCCTACCCTTTATTATCGCAACCTTCACAATCTTACATCTAC Trachypithecus_pileatus_KP834333.1 TCTTTACCCTTCACTTTACCCTACCCTTTATTATCGCAACCTTCACAATCTTACATCTAC Trachypithecus_shortridgei_KP834335.1 TCTTTACCCTTCACTTTACCCTACCCTTTATTATCGCAACCTTCACAATCTTACATCTAC Trachypithecus_johnii_AY519453.1 TTTTTACCCTTCACTTTACCCTGCCCTTTATTATCGCAACCTTCACAATCTTACACCTAC Semnopithecus_priam_JQ734745.1 TTTTTACCCTTCACTTCACCCTGCCCTTTATTATCACAACCTTCACAATCTTACACCTAC Trachypithecus_johnii_JQ734755.1 TTTTTACCCTTCACTTCACCCTGCCCTTTATTATCACAACCTTCACAATCTTACACCTAC Semnopithecus_priam_JQ734747.1 TTTTTACCCTTCACTTTACCCTCCCCTTTATTATCGCAACCTTCACAATCTTACACCTAC Semnopithecus_priam_JQ734748.1 TTTTTACCCTTCACTTTACCCTGCCCTTTATTATCGCAACCTTCACAATCTTACACCTAC Semnopithecus_hypoleucos_JQ734711.1 TCTTTACCCTTCACTTTACCCTACCCTTTATTATCGCAACCTTCACAATCTTACACCTAC Semnopithecus_hypoleucos_JQ734692.1 TCTTTACCCTTCACTTTACCCTGCCCTTTATTATCGCAACCTTCACAATCTTACACCTAC Semnopithecus_hypoleucos_JQ734705.1 TCTTTACCCTTCACTTTACCCTGCCCTTTATTATCGCAACCTTCACAATCTTACACCTAC * ** ** ** ***** * *** ***** ***** ** * ******** ***** **

Nasalis_larvatus_U62664.1 TTTTCCTACACGAAACAGGATCAAACAACCCCTGTGGAATCCCCTCCGACTCCGACAAAA Semnopithecus_entellus_hector_AY519451.1 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGTGGAATCCCCTCCGACTCCGACAAAA Semnopithecus_entellus_EU004471.1 TTTTCCTACACGAAACAGGGTCAAATAATCCCTGCGGGATCCCCTCCGATTCCGACAAAA Presbytis_entellus_AF012470.1 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGCGGGATCCCCTCCGATTCCGACAAAA Semnopithecus_entellus_EU004478.1 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGCGGGATCCCCTCCGATTCCGACAAAA Semnopithecus_entellus_DQ355297.1 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGCGGGATCCCCTCCGATTCCGACAAAA 32_Poonch_2 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGTGGGATCCCCTCCGATTCCGACAAAA 245

41_Mansehra_2 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGTGGGATCCCCTCCGATTCCGACAAAA 45_Mansehra_3 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGCGGGATTCCCTCCGATTCCGACAAAA 42_Kohistan_1 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGCGGGATTCCCTCCGATTCCGACAAAA 38_Neelum_SN TTTTCCTACATGAAACAGGGTCAAATAATCCCTGCGGGATTCCCTCCGATTCCGACAAAA 39_Neelum_AK TTTTCCTACATGAAACAGGGTCAAATAATCCCTGCGGGATTCCCTCCGATTCCGACAAAA 31_Poonch_1 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGCGGGATTCCCTCCGATTCCGACAAAA 33_Poonch_3 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGCGGGATTCCCTCCGATTCCGACAAAA 34_Poonch_4 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGCGGGATTCCCTCCGATTCCGACAAAA 36_Muzaffarabad_MNP1 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGCGGGATTCCCTCCGATTCCGACAAAA 37_Muzaffarabad_MNP2 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGCGGGATTCCCTCCGATTCCGACAAAA 40_Mansehra_1 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGCGGGATTCCCTCCGATTCCGACAAAA Trachypithecus_vetulus_AY519454.1 TTTTCCTACATGAAACAGGATCAAATAATCCCTGTGGAATCCCCTCCGAATCTGACAAAA Trachypithecus_vetulus_HQ149049.1 TTTTCCTACACGAAACAGGATCAAATAATCCCTGTGGAATCCCCTCCGAATCTGACAAAA Trachypithecus_pileatus_EU004472.1 TTTTCCTACACGAAACAGGATCAAATAACCCCTGTGGAATTCCCTCCGATTCCGATAAAA Trachypithecus_geei_AF294618.1 TTTTCCTACACGAAACAGGATCAAATAACCCCTGTGGAATTCCCTCCGATTCCGATAAAA Trachypithecus_pileatus_KP834333.1 TTTTCCTACACGAAACAGGATCAAATAACCCCTGTGGAATTCCCTCCGATTCCGATAAAA Trachypithecus_shortridgei_KP834335.1 TTTTCCTACACGAAACAGGATCAAATAACCCCTGTGGAATTCCCTCCGATTCCGATAAAA Trachypithecus_johnii_AY519453.1 TTTTCCTACATGAAACAGGATCAAATAATCCCTGCGGGATCCCCTCCAATTCCGACAAAA Semnopithecus_priam_JQ734745.1 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGTGGAATCCCCTCCAATTCCGACAAAA Trachypithecus_johnii_JQ734755.1 TTTTCCTACATGAAACAGGGTCAAATAATCCCTGCGGAATCCCCTCCAATTCCGACAAAA Semnopithecus_priam_JQ734747.1 TTTTCCTACATGAAACAGGATCAAATAATCCCTGTGGAATCCCCTCCAATTCCGACAAAA Semnopithecus_priam_JQ734748.1 TTTTCCTACATGAAACAGGATCAAACAATCCCTGTGGAATCCCCTCCAATTCCGACAAAA Semnopithecus_hypoleucos_JQ734711.1 TTTTCCTACATGAAACAGGATCAAATAATCCCTGCGGAATCCCCTCCGATTCCGACAAAA Semnopithecus_hypoleucos_JQ734692.1 TTTTCCTACATGAAACAGGATCAAATAACCCCTGTGGGATCCCCTCCGATTCCGACAAAA Semnopithecus_hypoleucos_JQ734705.1 TTTTCCTACATGAAACAGGATCAAATAACCCCTGTGGGATCCCCTCCGATTCCGACAAAA ********** ******** ***** ** ***** ** ** ****** * ** ** ****

Nasalis_larvatus_U62664.1 TCCCCTTCCACCCCTACTACACAATCAAAGACATCTTAGGCCTAACCCTCCTCCTCCTTA Semnopithecus_entellus_hector_AY519451.1 TCCCCTTCCATCCCTATTATACAACTAAAGATATCCTAGGCATAGCCCTTCTCCTCCTTA Semnopithecus_entellus_EU004471.1 TCCCCTTCCACCCCTATTATACAACTAAGGATATCCTGGGCATTGCCCTTCTCCTCCTTA Presbytis_entellus_AF012470.1 TCCCCTTCCACCCCTATTATACAACTAAGGATATCCTGGGCATTGCCCTTCTCCTCCTTA Semnopithecus_entellus_EU004478.1 TCCCCTTCCACCCCTATTATACAACTAAGGATATCCTGGGCATTGCCCTTCTCCTCCTTA Semnopithecus_entellus_DQ355297.1 TCCCCTTCCACCCCTATTATACAACTAAGGATATCCTGGGCATTGCCCTTCTCCTCCTTA 32_Poonch_2 TCCCCTTCCACCCCTATTATACAACTAAAGATATCCTAGGCATAGCCCTTCTCCTCCTTA 41_Mansehra_2 TCCCCTTCCACCCCTATTATACAACTAAAGATATCCTAGGCATAGCCCTTCTCCTCCTTA 45_Mansehra_3 TCCCCTTCCACCCCTATTATACAACTAAAGATATCCTAGGCATAGCCCTTCTCCTCCTTA 42_Kohistan_1 TCCCCTTCCACCCCTATTATACAACTAAAGATATCCTAGGCATAGCCCTTCTCCTCCTTA 38_Neelum_SN TCCCCTTCCACCCCTATTATACAACTAAAGATATCCTAGGCATAGCCCTTCTCCTCCTTA 39_Neelum_AK TCCCCTTCCACCCCTATTATACAACTAAAGATATCCTAGGCATAGCCCTTCTCCTCCTTA 31_Poonch_1 TCCCCTTCCACCCCTATTATACAACTAAAGATATCCTAGGCATAGCCCTTCTCCTCCTTA 33_Poonch_3 TCCCCTTCCACCCCTATTATACAACTAAAGATATCCTAGGCATAGCCCTTCTCCTCCTTA 34_Poonch_4 TCCCCTTCCACCCCTATTATACAACTAAAGATATCCTAGGCATAGCCCTTCTCCTCCTTA 36_Muzaffarabad_MNP1 TCCCCTTCCACCCCTATTATACAACTAAAGATATCCTAGGCATAGCCCTTCTCCTCCTTA 37_Muzaffarabad_MNP2 TCCCCTTCCACCCCTATTATACAACTAAAGATATCCTAGGCATAGCCCTTCTCCTCCTTA 40_Mansehra_1 TCCCCTTCCACCCCTATTATACAACTAAAGATATCCTAGGCATAGCCCTTCTCCTCCTTA Trachypithecus_vetulus_AY519454.1 TCCCCTTCCACCCCTATTACACAATTAAAGATATCTTAGGCATATCTCTTCTCCTCCTCA Trachypithecus_vetulus_HQ149049.1 TCCCCTTCCACCCCTATTATACAATCAAAGATATCTTAGGCATATCTCTTCTCCTCCTCA Trachypithecus_pileatus_EU004472.1 TCCCCTTCCACCCCTATTACACAATTAAAGATATCTTAGGCATAGCCCTCCTCCTCCTCA Trachypithecus_geei_AF294618.1 TCCCCTTCCACCCCTATTACACAATTAAAGATATCTTAGGCATAGCCCTTCTCCTCCTCA Trachypithecus_pileatus_KP834333.1 TCCCCTTCCACCCCTATTACACAATTAAAGATATCTTAGGCATAGCCCTTCTCCTCCTCA Trachypithecus_shortridgei_KP834335.1 TCCCCTTCCACCCCTATTACACAATTAAAGATATCTTAGGCATAGCCCTTCTCCTCCTCA Trachypithecus_johnii_AY519453.1 TCCCCTTCCACCCCTATTACACAATTAAAGATATCTTAGGTATAGCCCTTCTTCTCCTCA Semnopithecus_priam_JQ734745.1 TCCCCTTCCACCCCTATTACACAATTAAAGATATCTTAGGTATAGCCCTTCTTCTCCTCA Trachypithecus_johnii_JQ734755.1 TCCCCTTCCACCCCTATTACACAATTAAAGATATCTTAGGTATAGCCCTTCTTCTCCTCA Semnopithecus_priam_JQ734747.1 TCCCCTTCCACCCCTATTACACAATTAAAGATATCCTAGGTATAGCCCTTCTTCTCCTCA Semnopithecus_priam_JQ734748.1 TCCCCTTCCACCCCTATTACACAATTAAAGATATCCTAGGTATAGCCCTTCTTCTCCTTA Semnopithecus_hypoleucos_JQ734711.1 TCCCCTTCCACCCCTATTATACAATTAAAGATATCTTAGGCATAGCCCTCCTTCTCCTCA Semnopithecus_hypoleucos_JQ734692.1 TCCCCTTCCACCCCTATTATACAATTAAAGATATTTTAGGCATGGCCCTTCTTCTCCTTA Semnopithecus_hypoleucos_JQ734705.1 TCCCCTTCCACCCCTATTATACAATTAAAGATATCTTAGGTATGGCTCTTCTTCTCCTTA ********** ***** ** **** ** ** ** * ** * * ** ** ***** *

Nasalis_larvatus_U62664.1 TCCTAATAGTGCTAGTATTATTTTCACCTGGCCTTTTAAGCGATCCAGACAACTACACCC Semnopithecus_entellus_hector_AY519451.1 TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAAGCGACCCAGATAACTACATAC Semnopithecus_entellus_EU004471.1 TCCTAATAACATTAGTGTTATTTTCACCCGACCTTTTAAGCGACCCAGATAACTACATAC Presbytis_entellus_AF012470.1 TCCTAATAACATTAGTGTTATTTTCACCCGACCTTTTAAGCGACCCAGATAACTACATAC Semnopithecus_entellus_EU004478.1 TCCTAATAACATTAGTGTTATTTTCACCCGACCTTTTAAGCGACCCAGATAACTACATAC Semnopithecus_entellus_DQ355297.1 TCCTAATAACATTAGTGTTATTTTCACCCGACCTTTTAAGCGACCCAGATAACTACATAC 32_Poonch_2 TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAAGCGACCCAGATAACTACACAC 41_Mansehra_2 TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAAGCGACCCAGATAACTACACAC 45_Mansehra_3 TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAAGCGACCCAGATAACTACATAC 42_Kohistan_1 TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAAGCGACCCAGGTAACTACATAC 38_Neelum_SN TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAAGCGACCCAGATAACTACATAC 39_Neelum_AK TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAAGCGACCCAGATAACTACATAC 31_Poonch_1 TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAAGCGACCCAGATAACTACATAC 33_Poonch_3 TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAAGCGACCCAGATAACTACATAC 34_Poonch_4 TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAAGCGACCCAGATAACTACATAC 36_Muzaffarabad_MNP1 TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAAGCGACCCAGATAACTACATAC 37_Muzaffarabad_MNP2 TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAAGCGACCCAGATAACTACATAC 40_Mansehra_1 TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAAGCGACCCAGATAACTACATAC Trachypithecus_vetulus_AY519454.1 TCCTAATAATACTAGTGTTATTTTCACCCGATCTTTTAGGTGATCCAGACAACTACACAC Trachypithecus_vetulus_HQ149049.1 TCCTAATAATACTAGTGTTATTTTCACCCGATCTTTTAGGTGACCCAGATAACTACACAC Trachypithecus_pileatus_EU004472.1 TCCTAATAATATTAGTGTTATTTTCACCCGATCTTTTAGGCGATCCAGATAACTACACAC Trachypithecus_geei_AF294618.1 TCCTAATAATATTAGTGTTATTTTCACCCGATCTTTTAGGCGATCCAGATAACTACACAC Trachypithecus_pileatus_KP834333.1 TCCTAATAATATTAGTGTTATTTTCACCCGATCTTTTAGGCGATCCAGATAACTACACAC Trachypithecus_shortridgei_KP834335.1 TCCTAATAATATTAGTGTTATTTTCACCCGATCTTTTAGGCGATCCAGATAACTACACAC Trachypithecus_johnii_AY519453.1 TCCTAATAATATTAGTGTTATTTTCACCCGATCTTTTAGGCGACCCAGATAACTACACAC Semnopithecus_priam_JQ734745.1 TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAGGCGACCCAGATAACTACACAC 246

Trachypithecus_johnii_JQ734755.1 TCCTAATAACATTAGTGTTATTTTCACCCGATCTTTTAGGCGACCCAGATAACTACACAC Semnopithecus_priam_JQ734747.1 TCCTAATAACATTAGTATTATTTTCACCCGATCTTTTAGGCGACCCAGATAACTACACAC Semnopithecus_priam_JQ734748.1 TCCTAATAACATTAGTATTATTTTCACCCGATCTTTTAGGCGACCCAGATAACTACACAC Semnopithecus_hypoleucos_JQ734711.1 TCCTAATAACATTAGTGTTATTTTCACCCGACCTTTTAGGCGACCCAGATAACTACACAC Semnopithecus_hypoleucos_JQ734692.1 TCCTAATAACATTAGTGTTATTTTCACCCGACCTTTTAGGCGACCCAGATAACTACACAC Semnopithecus_hypoleucos_JQ734705.1 TCCTAATAACATTAGTGTTATTTTCACCCGACCTTTTAGGTGACCCAGATAACTACACAC ******** **** *********** * ****** * ** **** ******* *

Nasalis_larvatus_U62664.1 CAGCCAACCCACTAAGCACCCCACCACACATCAAACCAGAATGATATTTCCTGTTCGCAT Semnopithecus_entellus_hector_AY519451.1 CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT Semnopithecus_entellus_EU004471.1 CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT Presbytis_entellus_AF012470.1 CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT Semnopithecus_entellus_EU004478.1 CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT Semnopithecus_entellus_DQ355297.1 CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT 32_Poonch_2 CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT 41_Mansehra_2 CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT 45_Mansehra_3 CAGCCAACCCGCTGAGCACCCCACAACATATTAAACCAGAATGATATTTCCTGTTCGCAT 42_Kohistan_1 CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT 38_Neelum_SN CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT 39_Neelum_AK CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT 31_Poonch_1 CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT 33_Poonch_3 CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT 34_Poonch_4 CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT 36_Muzaffarabad_MNP1 CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT 37_Muzaffarabad_MNP2 CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT 40_Mansehra_1 CAGCCAACCCGCTGAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT Trachypithecus_vetulus_AY519454.1 CAGCTAATCCACTAAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCGT Trachypithecus_vetulus_HQ149049.1 CAGCTAACCCACTAAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCGT Trachypithecus_pileatus_EU004472.1 CAGCCAACCCGCTAAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCGT Trachypithecus_geei_AF294618.1 CAGCCAACCCGCTAAGCACCCCACCACATATTAAACCAGAATGATACTTCCTGTTCGCGT Trachypithecus_pileatus_KP834333.1 CAGCCAACCCGCTAAGCACCCCACCACATATTAAACCAGAATGATACTTCCTGTTCGCGT Trachypithecus_shortridgei_KP834335.1 CAGCCAACCCGCTAAGCACCCCACCACATATTAAACCAGAATGATACTTCCTGTTCGCGT Trachypithecus_johnii_AY519453.1 CAGCCAACCCGCTAAGCACCCCACCACATATTAAACCAGAATGGTACTTCCTGTTCGCAT Semnopithecus_priam_JQ734745.1 CAGCCAACCCGCTAAGCACCCCACCACATATTAAACCAGAATGGTATTTCCTGTTCGCAT Trachypithecus_johnii_JQ734755.1 CAGCCAACCCGCTAAGCACCCCACCACATATTAAACCAGAATGGTATTTCCTGTTCGCAT Semnopithecus_priam_JQ734747.1 CAGCCAACCCGCTAAGCACCCCACCACATATTAAACCAGAATGGTATTTCCTGTTCGCAT Semnopithecus_priam_JQ734748.1 CAGCCAACCCGCTAAGCACCCCACCACATATTAAACCAGAATGGTATTTCCTGTTCGCAT Semnopithecus_hypoleucos_JQ734711.1 CAGCCAACCCGCTAAGCACCCCACCACATATCAAACCAGAATGATATTTCCTGTTCGCAT Semnopithecus_hypoleucos_JQ734692.1 CAGCCAACCCACTAAGCACCCCACCACATATTAAACCAGAATGATATTTCCTGTTCGCAT Semnopithecus_hypoleucos_JQ734705.1 CAGCCAACCCACTAAGCACCCCACCACATATTAAACCAGAATGATACTTCCTGTTCGCAT **** ** ** ** ********** *** ** *********** ** *********** *

Nasalis_larvatus_U62664.1 ATGCAATCCTATGATCTGTTCCTAATAAGTTAGGGGGAGTATTAGCGCTCCTCCTATCCA Semnopithecus_entellus_hector_AY519451.1 ACGCAATCCTACGGTCCGTTCTCAACAAATTAGGGGGGGTCTTGGCACTTCTACTATCCA Semnopithecus_entellus_EU004471.1 ACGCGATCCTACGGTCCGTTCCCAATAAATTAGGGGGGGTCTTGGCACTTCTACTATCCA Presbytis_entellus_AF012470.1 ACGCGATCCTACGGTCCGTTCCCAATAAATTAGGGGGGGTCTTGGCACTTCTACTATCCA Semnopithecus_entellus_EU004478.1 ACGCGATCCTACGGTCCGTTCCCAATAAATTAGGGGGGGTCTTGGCACTTCTACTATCCA Semnopithecus_entellus_DQ355297.1 ACGCGATCCTACGGTCCGTTCCCAATAAATTAGGGGGGGTCTTGGCACTTCTACTATCCA 32_Poonch_2 ACGCGATCCTACGGTCCGTTCCCAATAAATTAGGGGGGGTCCTGGCACTTCTACTATCTA 41_Mansehra_2 ACGCGATCCTACGGTCCGTTCCCAATAAATTAGGGGGGGTCCTGGCACTTCTACTATCTA 45_Mansehra_3 ACGCAATCCTACGGTCCGTCCCCAATAAATTAGGGGGGGGCTTGGCACTTCTACTATCTA 42_Kohistan_1 ACGCAATCCTACGGTCCGTCCCCAATAAATTAGGGGGGGTCTTGGCACTTCTACTATCTA 38_Neelum_SN ACGCAATCCTACGGTCCGTCCCCAATAAACTAGGGGGGGTCTTGGCACTTCTACTATCTA 39_Neelum_AK ACGCAATCCTACGGTCCGTCCCCAATAAACTAGGGGGGGTCTTGGCACTTCTACTATCTA 31_Poonch_1 ACGCAATCCTACGGTCCGTCCCCAATAAATTAGGGGGGGTCTTGGCACTTCTACTATCTA 33_Poonch_3 ACGCAATCCTACGGTCCGTCCCCAATAAATTAGGGGGGGTCTTGGCACTTCTACTATCTA 34_Poonch_4 ACGCAATCCTACGGTCCGTCCCCAATAAATTAGGGGGGGTCTTGGCACTTCTACTATCTA 36_Muzaffarabad_MNP1 ACGCAATCCTACGGTCCGTCCCCAATAAATTAGGGGGGGTTTTGGCACTTCTACTATCTA 37_Muzaffarabad_MNP2 ACGCAATCCTACGGTCCGTCCCCAATAAATTAGGGGGGGTTTTGGCACTTCTACTATCTA 40_Mansehra_1 ACGCAATCCTACGGTCCGTCCCCAATAAATTAGGGGGGGTTTTGGCACTTCTACTATCTA Trachypithecus_vetulus_AY519454.1 ACGCGATCTTACGATCCGTTCCTAATAAATTAGGAGGAGTCCTAGCACTTCTACTATCCA Trachypithecus_vetulus_HQ149049.1 ACGCGATCTTACGATCCGTTCCTAATAAATTAGGAGGGGTCCTAGCACTTCTACTATCCA Trachypithecus_pileatus_EU004472.1 ACGCGATCCTACGATCCGTTCCTAATAAATTAGGAGGGGTCCTAGCACTTCTCCTATCCA Trachypithecus_geei_AF294618.1 ACGCGATCCTACGATCCGTTCCTAATAAATTAGGAGGGGTCCTAGCACTTCTCCTATCCA Trachypithecus_pileatus_KP834333.1 ACGCGATCCTACGATCCGTTCCTAATAAATTAGGAGGGGTCCTAGCACTTCTCCTATCCA Trachypithecus_shortridgei_KP834335.1 ACGCGATCCTACGATCCGTTCCTAATAAATTAGGAGGGGTCCTAGCACTTCTCCTATCCA Trachypithecus_johnii_AY519453.1 ACGCGATCCTACGATCCATTCCCAATAAATTAGGAGGAGTCCTGGCACTTCTATTATCTA Semnopithecus_priam_JQ734745.1 ACGCGATCCTACGATCCATTCCCAATAAATTAGGAGGGGTCCTGGCACTTCTACTATCTA Trachypithecus_johnii_JQ734755.1 ACGCGATCCTACGATCCATTCCCAATAAATTAGGAGGGGTCCTGGCACTTCTACTATCTA Semnopithecus_priam_JQ734747.1 ACGCGATCCTACGATCCATTCCCAATAAATTAGGAGGAGTCCTGGCACTTCTACTATCTA Semnopithecus_priam_JQ734748.1 ACGCGATCCTACGATCCATTCCCAATAAATTAGGAGGAGTCCTGGCACTTCTACTATCTA Semnopithecus_hypoleucos_JQ734711.1 ACGCAATCCTACGATCCGTTCCCAATAAATTAGGAGGGGTCCTGGCACTTCTACTATCTA Semnopithecus_hypoleucos_JQ734692.1 ACGCAATCCTACGATCCGTTCCCAATAAATTAGGAGGGGTCCTGGCGCTTCTACTATCTA Semnopithecus_hypoleucos_JQ734705.1 ACGCAATCCTACGATCCGTTCCCAATAAATTAGGGGGGGTCCTGGCACTTCTACTATCTA * ** *** ** * ** * * ** ** **** ** * * ** ** ** **** *

Nasalis_larvatus_U62664.1 TTCTCATCCT Semnopithecus_entellus_hector_AY519451.1 TTCTCATTCT Semnopithecus_entellus_EU004471.1 TTCTCATCCT Presbytis_entellus_AF012470.1 TTCTCATCCT Semnopithecus_entellus_EU004478.1 TTCTCATCCT Semnopithecus_entellus_DQ355297.1 TTCTCATCCT 32_Poonch_2 TTCTCATTCT 41_Mansehra_2 TTCCCATTCT 45_Mansehra_3 TTCTCATTCT 42_Kohistan_1 TTCTCATTCT 38_Neelum_SN TTCTCATTCT 39_Neelum_AK TTCTCATTCT 247

31_Poonch_1 TTCTCATTCT 33_Poonch_3 TTCTCATTCT 34_Poonch_4 TTCTCATTCT 36_Muzaffarabad_MNP1 TTCTCATTCT 37_Muzaffarabad_MNP2 TTCTCATTCT 40_Mansehra_1 TTCTCATTCT Trachypithecus_vetulus_AY519454.1 TTCTCATCCT Trachypithecus_vetulus_HQ149049.1 TTCTTATCCT Trachypithecus_pileatus_EU004472.1 TTCTCATTCT Trachypithecus_geei_AF294618.1 TTCTCATCCT Trachypithecus_pileatus_KP834333.1 TTCTCATTCT Trachypithecus_shortridgei_KP834335.1 TTCTCATTCT Trachypithecus_johnii_AY519453.1 TTCTCATTCT Semnopithecus_priam_JQ734745.1 TTCTCATTCT Trachypithecus_johnii_JQ734755.1 TTCTCATTCT Semnopithecus_priam_JQ734747.1 TTCTCATTCT Semnopithecus_priam_JQ734748.1 TTCTCATTCT Semnopithecus_hypoleucos_JQ734711.1 TTCTTATTCT Semnopithecus_hypoleucos_JQ734692.1 TTCTCATTCT Semnopithecus_hypoleucos_JQ734705.1 TTCTCATTCT *** ** ** rRNA gene multiple sequence alignment by CLUSTAL O (1.2.4)

EU004477.1_Trachypithecus_obscurus AACACTAGCCCAAAAACAAT-CAATACTACTACCAAACAAATATATA-ATACTAAATCAT AF420042.1_Pygathrix_nemaeus AATACTAGCTCAACAAACCT-ACCAATACTACTATTAAATTAATATA-TTACTAAACCAT JQ821842.1_Pygathrix_cinerea AATACTAGCTCAACAAACCT-ACCAATACTACTATTAAATTAATATA-TTATTAAACCAT KP834334.1_Trachypithecus_shortridgei AACACTAGCCCAAAAACCAATCAATACTACTATTAAACAAGCATATAAATACTAAACCAT KF680163.1_Trachypithecus_pileatus AACACTAGCCCAAAAACCAATCAATACTACTATTAAACAAGCATATAAATACTAAACCAT HQ149047.1_Trachypithecus_germaini AACACTAGCCCAAAAACCGATCAATACTACTACCAAACAAATATAT-AATACTAAATCAT KY425609.1_Trachypithecus_phayrei_crepuscula AACACTAGCCCAAAAACCAATCAATACTACTACCAAACAAATATAT-AATACTAAACCAT KU899140.1_Presbytis_femoralis_femoralis AATACTAGCCCAAAAACCCCTAACCAATATTAAT-ACCACGCACGTATAAATTAAATCAT DQ355299.1_Presbytis_melalophos GATACTAGCCCAAA---CCCCAACCAACATTAGT-ACCACACATATATAAATTAAATCAT JF293092.1_Procolobus_verus AACACTAGCCCAAAAATCAA-AGATATCAATATTAAAACAATATACATAGACTAAATCAT JQ821839.1_Rhinopithecus_bieti AATGCTAGCCCTAAACTAAC-CAATACTACTACCAAACTAACATTTTCATATTAAACCAT HQ149049.1_Trachypithecus_vetulus AATACTAGCCC-AAATCCTC-AATCAACACTAATCTTAACTCATAAATAAACTAAATCAT Semnopithecus-Pakistan ------AGGGGTTTGCTGCACTAATCA HQ149050.1_Trachypithecus_johnii AATACTAGCCC-AAATCC-C-AACCAACACTAATATCAACTTACAGGCAAGCTAAATCAT DQ355297.1_Semnopithecus_entellus AATACTAGCCC-AAACCC-C-AATCAGCACTAATATCAACTTACAGGCAAGCTAAATCAT EU004478.1_Semnopithecus_entellus AATACTAGCCC-AAACCC-C-AATCAGCACTAATATCAACTTACAGGCAAGCTAAATCAT AF420043.1_Semnopithecus_entellus AATACTAGCCC-AAACCC-C-AATCAGCACTAATATCAACTTGCAAACAAGCTAAATCAT *

EU004477.1_Trachypithecus_obscurus TCACAAATATAAAAGTATAGGTGATAGAAAGTTTA--TTCTGGCGCTATAGACACAGTAC AF420042.1_Pygathrix_nemaeus TCACAAGTATAAAAGTATAGGAGATAGA-AATTTA--CTCTGACGCTATAGATGCAGTAC JQ821842.1_Pygathrix_cinerea TCACAAGTATAAAAGTATAGGAGATAGA-AATTTA--CTCTGACGCTATAGATGCAGTAC KP834334.1_Trachypithecus_shortridgei TCACAAATATAAAAGTATAGGTGATAGAAAATTTA--CTCTGGCGCTATAGACACAGTAC KF680163.1_Trachypithecus_pileatus TCACAAATATAAAAGTATAGGTGATAGAAAATTTA--CTCTGGCGCTATAGACACAGTAC HQ149047.1_Trachypithecus_germaini TTACAAATATAAAAGTATAGGTGATAGAAAATTTA--TTCTGGCGCTATAGACACAGTAC KY425609.1_Trachypithecus_phayrei_crepuscula TTACAAATATAAAAGTATAGGTGATAGAAAATTTA--TTCTGGCGCTATAGACACAGTAC KU899140.1_Presbytis_femoralis_femoralis TCACAAATATAAAAGTATAGGTGATAGAAAATTTA--CTCTGGCGCCATAGAGATAGTAC DQ355299.1_Presbytis_melalophos TCACAAATATAAAAGTATGGGTGATAGAAAGTCTA--CTCTGGCGCCATAGAGACAGTAC JF293092.1_Procolobus_verus TTATACGTATAAAAGTATAGGCGATAGAAAGTTTACTTTTTGGCGCTATAGAGATAGTAC JQ821839.1_Rhinopithecus_bieti TTACAAATATAAAAGTATAGGTGATAG-AAATTTA--TTCTGGCGCCATAGATATAGTAC HQ149049.1_Trachypithecus_vetulus TTACAAATATAAAAGTATAGGTGATAGAAAATTTA--TTCTGGCGCTATAGATAAAGTAC Semnopithecus-Pakistan TTTACAATATAGAAGTATAGGTGATAGAAAATTTA--TTCTGGCGCTATAGATAAAGTAC HQ149050.1_Trachypithecus_johnii TTACAAATATAAAAGTATAGGTGATAGAAAATTTA--TTCTGGCGCTATAGATAAAGTAC DQ355297.1_Semnopithecus_entellus TTACAAATATAGAAGTATAGGTGATAGAAAATTTA--TTCTGGCGCTATAGATAAAGTAC EU004478.1_Semnopithecus_entellus TTACAAATATAGAAGTATAGGTGATAGAAAATTTA--TTCTGGCGCTATAGATAAAGTAC AF420043.1_Semnopithecus_entellus TTACAAATATAGAAGTATAGGTGATAGAAAATTTA--TTCTGGCGCTATAGATAGAGTAC * **** ****** ** ***** * * ** * ** *** ***** *****

EU004477.1_Trachypithecus_obscurus CGTAAGGGAAAGATGAAAAAAAAG--ACTAAGCACAAAATAGCAAGGATCTACCCCTGTA AF420042.1_Pygathrix_nemaeus CGCAAGGGAAAGATGAAAAAAGCA--ACCAAGCATAAAACAGCAAGGACTCACCCCTATA JQ821842.1_Pygathrix_cinerea CGCAAGGGAAAGATGAAAAAAGCA--ACCAAGCATAAAACAGCAAGGACTCACCCCTATA KP834334.1_Trachypithecus_shortridgei CGCAAGGGAAAGATGAAAAAAAAA-GACCAAGCACAAAACAGCAAGGACTTACCCCTGTA KF680163.1_Trachypithecus_pileatus CGCAAGGGAAAGATGAAAAAAAAA-GACCAAGCACAAAACAGCAAGGACTTACCCCTGTA HQ149047.1_Trachypithecus_germaini CGCAAGGGAAAGATGAAAAAAAAA-GATCAAGCATAAAATAGCAAGGATTTACTCCTATA KY425609.1_Trachypithecus_phayrei_crepuscula CGCAAGGGAAAGATGAAAAAAAAA-GACTAAGCATAAAATAGCAAGGATTTACTCCTATA KU899140.1_Presbytis_femoralis_femoralis CGTAAGGGAAAGATGAAAAAAAAT-AACTAAGCACAAAATAGCAAGGACTCACCCCTGTA DQ355299.1_Presbytis_melalophos CGTAAGGGAAAGATGAAAAAAAAA-AACTGAGCACAAAATAGCAAGGACTCACCCCTGTA JF293092.1_Procolobus_verus CGCAAGGGAAAGATGAAAAAAAGA-AATAAAGCACAAAATAGCAAGGATTCACCCCTGTA JQ821839.1_Rhinopithecus_bieti CGCAAGGGAAAGATGAAAAAAAAATAATTAAGTACAAAATAGCAAGGACTCACCCCTATA HQ149049.1_Trachypithecus_vetulus CGTAAGGGAAAGATGAAAAAAGAA-ATACAAGCACAAAAAAGCAAGGACTTACCCCTGTA Semnopithecus-Pakistan CGTAAGGGAAAGATGAAAAAAGAA-ACATAAGCACAAAAAAGCAAGGACTCACCCCTGTA HQ149050.1_Trachypithecus_johnii CGTAAGGGAAAGATGAAAAAAGAA-ATATAAGCACAAAAAAGCAAGGACTCACCCCTGTA DQ355297.1_Semnopithecus_entellus CGTAAGGGAAAGATGAAAAAAGAA-ACATAAGCACAAAAAAGCAAGGACTCACCCCTGTA EU004478.1_Semnopithecus_entellus CGTAAGGGAAAGATGAAAAAAGAA-ACATAAGCACAAAAAAGCAAGGACTCACCCCTGTA AF420043.1_Semnopithecus_entellus CGTAAGGGAAAGATGAAAAAAGAA-ACATAAGCACAAAAAAGCAAGGACTCACCCCTGTA ** ****************** ** * **** ******** ** *** **

EU004477.1_Trachypithecus_obscurus CCTTCTGCATAATGAGCTAGCTAGAGAATATTTCGCAAAGAGAACTAAAGCCAAATACCC AF420042.1_Pygathrix_nemaeus CCTTTTGCATAATGAGCTAACTAGAGACCACTTCGCAAAGAGAACTAAAGCCAAGTACCC JQ821842.1_Pygathrix_cinerea CCTTTTGCATAATGAACTAGCTAGAGACCACTTCGCAAAGAGAACTAAAGCCAAGTACCC KP834334.1_Trachypithecus_shortridgei CCTTCTGCATAATGAGCCAGCTAGAAATTATTTCACAAAGAGAACTAAAGCCAAATACCC KF680163.1_Trachypithecus_pileatus CCTTCTGCATAATGAGCCAGCTAGAAATTATTTCACAAAGAGAACTAAAGCCAAATACCC

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HQ149047.1_Trachypithecus_germaini CCTTCTGCATAATGAGCCAGCTAGAGATTATTTCGCAAAGAGAACTAAAGCCAAATACCC KY425609.1_Trachypithecus_phayrei_crepuscula CCTTCTGCATAATGAGCCAGCTAGAGATTATTTCGCAAAGAGAACTAAAGCCAAATACCC KU899140.1_Presbytis_femoralis_femoralis CCTTCTGCATCATGAACTAGCCAGAAACTATTTCGCAAAGAGAACTAAAGTCAACTACCC DQ355299.1_Presbytis_melalophos CCTTCTGCATTATGAACTAACTAGAAACTATTTCGCAAAGAGAACTAAAGCCAACTACCC JF293092.1_Procolobus_verus CCTTCTGCATAATGAACTAATTAGAAATTACTTGGCAAAGAGAACTAAAGCCAAAAACCC JQ821839.1_Rhinopithecus_bieti CCTTCTGCATAATGAATTAGCTAGAGATCACTTCGCAAAGAGAACTAAAGTCAAGTACCC HQ149049.1_Trachypithecus_vetulus CCTTCTGCATAATGAACTAACTAGAGATTATTTCGCAAAGAGAACTATAGCCAAACACCC Semnopithecus-Pakistan CCTTCTGCATAATGAACTAACTAGAAATAATTTCGCAAAGAGAACTAAAGCTAAACACCC HQ149050.1_Trachypithecus_johnii CCTTCTGCATAATGAGTTAACTAGAAATTATTTCGCAAAGAGAACTAAAGCCAAATACCC DQ355297.1_Semnopithecus_entellus CCTTCTGCATAATGAACTAACTAGAAATAATTTCGCAAAGAGAACTAAAGCTAAACACCC EU004478.1_Semnopithecus_entellus CCTTCTGCATAATGAACTAACTAGAAATAATTTCGCAAAGAGAACTAAAGCTAAACACCC AF420043.1_Semnopithecus_entellus CCTTCTGCATAAAGAACTAACTAGAAATAATTTCGCAAAGAGAACTAAAGCTAAACACCC **** ***** * ** * *** * * ** ************ ** ** ****

EU004477.1_Trachypithecus_obscurus CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCGCACCCGTCTATGTGGCAAAAT AF420042.1_Pygathrix_nemaeus CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCACACCCGTCTATGTAGCAAAAT JQ821842.1_Pygathrix_cinerea CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCACACCCGTCTATGTAGCAAAAT KP834334.1_Trachypithecus_shortridgei CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCACACCCGTCTATGTGGCAAAAT KF680163.1_Trachypithecus_pileatus CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCACACCCGTCTATGTGGCAAAAT HQ149047.1_Trachypithecus_germaini CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCGCACCCGTCTATGTAGCAAAAT KY425609.1_Trachypithecus_phayrei_crepuscula CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCGCACCCGTCTATGTAGCAAAAT KU899140.1_Presbytis_femoralis_femoralis CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCACACCCGTCTATGTAGCAAAAT DQ355299.1_Presbytis_melalophos CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCACACCCGTCTATGTGGCAAAAT JF293092.1_Procolobus_verus CGAAACCAGACGAGCTACCCAAAAACAGCCAAAAGAGCACACCCGTCTATGTAGCAAAAT JQ821839.1_Rhinopithecus_bieti CGAAACCAGACGAGCTACCCAAAAACAGCTGAAAGAGCGCACCCGTCTATGTAGCAAAAT HQ149049.1_Trachypithecus_vetulus CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCGCACCCGTCTATGTAGCAAAAT Semnopithecus-Pakistan CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCACACCCGTCTATGTAGCAAAAT HQ149050.1_Trachypithecus_johnii CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCACACCCGTCTATGTAGCAAAAT DQ355297.1_Semnopithecus_entellus CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCACACCCGTCTATGTAGCAAAAT EU004478.1_Semnopithecus_entellus CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCACACCCGTCTATGTAGCAAAAT AF420043.1_Semnopithecus_entellus CGAAACCAGACGAGCTACCCAAAAACAGCTAAAAGAGCACACCCGTCTATGTAGCAAAAT ***************************** ******* ************* *******

EU004477.1_Trachypithecus_obscurus AGTGGGAAGATTTTTGGGTAGAGGCGATACGCCCACCGAGCCTGGCGATAGCTGGTTATC AF420042.1_Pygathrix_nemaeus AGTGGGAAGATTTCTGGGTAGAGGTGATACGCCTACCGAGCCTGGAGATAGCTGGTTATC JQ821842.1_Pygathrix_cinerea AGTGGGAAGATTTCTGGGTAGAGGTGATACGCCTACCGAGCCTGGAGATAGCTGGTTATC KP834334.1_Trachypithecus_shortridgei AGTGGGAGGATTTTTGGGTAGAGGTGATACACCAACCGAGCCTGGCGATAGCTGGTTATC KF680163.1_Trachypithecus_pileatus AGTGGGAGGATTTTTGGGTAGAGGTGATACACCAACCGAGCCTGGCGATAGCTGGTTATC HQ149047.1_Trachypithecus_germaini AGTGGGAAGATTTTTGGGTAGAGGCGATACACCCACCGAGCCTGGCGATAGCTGGTTATC KY425609.1_Trachypithecus_phayrei_crepuscula AGTGGGAAGATTTTTGGGTAGAGGCGATACACCCACCGAGCCTGGCGATAGCTGGTTATC KU899140.1_Presbytis_femoralis_femoralis AGTGGGAAGATTTTTAGGTAGAGGTGATACACCTAACGAGCCTGGTGATAGCTGGTTATC DQ355299.1_Presbytis_melalophos AGTGGGAAGATTTCTGGGTAGAGGTGATACACCTAACGAGCCTGGTGATAGCTGGTTATC JF293092.1_Procolobus_verus AGTGGGAAGATTTCTGGGTAGAGGTGATAGGCCTACCGAGCCTGGTGATAGCTGGTTATC JQ821839.1_Rhinopithecus_bieti AGTGGGAAGATTTCTGGGTAGAGGTGATACACCTACCGAGTCTGGAGATAGCTGGTTATC HQ149049.1_Trachypithecus_vetulus AGTGGGAAGATTTCTGGGTAGAGGTGACACGCCTATCGAGCCTGGTGATAGCTGGTTATC Semnopithecus-Pakistan AGTGGGAAGATTTCTGGGTAGAGGTGACACGCCTATCGAGAA------HQ149050.1_Trachypithecus_johnii AGTGGGAAGATTTCTGGGTAGAGGTGACACGCCTATCGAGCCTGGTGATAGCTGGTTATC DQ355297.1_Semnopithecus_entellus AGTGGGAAGATTTCTGGGTAGAGGTGACACGCCTATCGAGCCTGGTGATAGCTGGTTATC EU004478.1_Semnopithecus_entellus AGTGGGAAGATTTCTGGGTAGAGGTGACACGCCTATCGAGCCTGGTGATAGCTGGTTATC AF420043.1_Semnopithecus_entellus AGTGGGAAGATTTCTGGGTAGAGGTGATACGCCTATCGAGCCTGGTGATAGCTGGTTATC ******* ***** * ******** ** * ** * ****

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