Study of Genetic Diversity in the Threatened Ornamental Fishes, Badis Badis (Hamilton- Buchanan, 1822) and Amblyceps Mangois (H

Study of Genetic Diversity in the Threatened Ornamental Fishes, Badis badis (Hamilton- Buchanan, 1822) and Amblyceps mangois (Hamilton-Buchanan, 1822) from Terai and the Dooars region of North Bengal, India

A THESIS SUBMITTED TO THE UNIVERSITY OF NORTH BENGAL FOR THE AWARD OF DOCTOR OF PHILOSOPHY (Ph.D.) IN ZOOLOGY

BY TANMAY MUKHOPADHYAY

SUPERVISOR

PROF. SOUMEN BHATTACHARJEE

DEPARTMENT OF ZOOLOGY UNIVERSITY OF NORTH BENGAL

November, 2018

DEDICATED TO MY PARENTS

-: PREFACE :-

The research work has been started in 2013 and documented in the dissertation entitled “Study of Genetic Diversity in the Threatened Ornamental Fishes, Badis badis (Hamilton-Buchanan, 1822) and Amblyceps mangois (Hamilton-Buchanan, 1822) from Terai and the Dooars region of North Bengal, India.” under the supervision of Prof. (Dr.) Soumen Bhattacharjee, Cell and Molecular Biology Laboratory, Department of Zoology, University of North Bengal.

The studied region is located in the sub-Himalayan Terai and Dooars region of North Bengal, India. The important feature of this region is rich diversity in the faunal and floran resource. The snow capped mountain peaks, the dense foothill, the luxuriant forests, the frothing rivers, the lush paddy fields, vibrant green tea gardens makes this region of North Bengal a hotbed of bio-diversity or biodiversity hotspot. This region is intersected by numerous streams, rivers and rivulets. All their sources are the Darjeeling Himalaya in the north and are mainly snow fed and rain fed. Most of the rivers drain considerable amount of water during rainy season due to their large catchment area in the hills. Over last few years, wild populations two ornamental fishes viz., Badis badis and Amblyceps mangois have declined steadily mainly due of habitat loss, overexploitation, pollution, and destructive fishing procedures; the information is confirmed by the NBFGR (National Bureau of Fish Genetic Resources), India country’s apex body for the purpose.

There exists scanty information on the status of genetic diversity of Badis badis and Amblyceps mangois in India. Absolutely no information exists from Terai-Dooars region of West Bengal, India under the Eastern Himalayan biodiversity hotspot. Therefore, to the research work has been targeted to ascertain the present status of genetic diversity of this fishes that have potential ornamental and export values in a region of India. Moreover, proper exploitation and management of such ichthyofauna with regard to sustainable aquaculture can substantially improve human livelihood and also to conserve the germplasms in the wild.

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Genetic variability of an organism is of great importance for its sustenance and loss of this curtails its adaptability and increases the risk of its extinction. However, maintaining the genetic variation in a threatened species is essential for survivability and its adaptation. Molecular markers are realistic and useful tool to investigate the genetic structure of populations of different species. Three major river systems have been studied from Terai-Dooars region of West Bengal, India and total seventeen populations (for Badis badis) and fourteen (for Amblyceps mangois) were selected from different geographic locations for collection of fish samples. The catch of the fishes from each location was very low these indicate the threatened population status of this species in this region. Therefore, a non-invasive DNA isolation technique was developed and used for extraction of genomic DNA from the fishes. The genomic DNA was extracted from the minute quantities of fins without sacrificing the organisms. In the present study RAPD and ISSR techniques are used for genetic diversity study because they are easy, cost effective and fast methods, especially when other sophisticated methods are not yet developed. Moreover, mitochondrial cytochrome oxidase sub-unit 1 (mtCOI) gene is also analyzed to observe the genetic diversity and the phylogenetic relationship with other populations from geographically isolated regions; and to substantiate the findings based on RAPD and ISSR markers

A correlative study based on several anthropogenic factors causing the dwindling population genetic structure with that of the available genetic diversity in the studied species can add a newer dimension to find out the cause of this low level of genetic variability as well as a possible conservational strategy. Information on intra and inter-specific genetic variation from the present study should form a baseline on which further studies can be undertaken. As the species is commercially important and has an ornamental value; and the region being located in the sub- Himalayan hotspot region, the management and proper rehabilitation of this ichthyofauna in the wild is essential from the standpoint of livelihood of rural fishermen and their economic restoration.

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ABSTRACT

Badis badis or Chameleon Fish (Hamilton-Buchanan, 1822) (Actinopterygii, Perciformes, Badidae) and Amblyceps mangois or Indian Torrent Catfish (Hamilton- Buchanan, 1822) (Actinopterygii, Siluriformes, Amblycipidae) are two a tropical, freshwater species that has ornamental importance and thus possesses commercial value. The species has been categorized as vulnerably susceptible in the list of threatened freshwater fishes of India by NBFGR, Lucknow, India- country’s apex body for the purpose. However, studies with regard to genetic diversity of fish species especially in the eastern sub-Himalayan region of West Bengal, India have been found to be very inadequate. On the standpoint of validating its threatened status, assessment of its genetic diversity from this particular region with the efforts to conserve its wild population seem to be of great significance. Genetic variability of an organism is of paramount importance from the standpoint of survival and reproductive fitness as well as for sustenance of the whole population; therefore, the erosion of genetic diversity of a population curtails its capability for adaptation and increases the risk of its extinction. The vulnerable organisms having small/minute population size may experience a perpetual reduction in the genetic variation. However, maintaining the genetic variation in a threatened imperilled species is essential to ascertain its adaptation, expansion and opportune reestablishment in natural populations. The study of intra and inter - population genetic variability within and between local populations is profoundly utilizable to gain information on individual identity, breeding patterns, genetic degree of relatedness and genetic diversity/variability within as well as between them. Genetic variations can be assessed by means of DNA polymorphisms. Molecular markers are realistic and useful tools to for the investigation and monitoring the genetic structure of populations both in native and captive lots condition. RAPD and ISSR techniques are of prime importance for widely used for genetic diversity study because they are easy, cost effective and fast methods, especially when other sophisticated methods are not yet developed. Moreover, the RAPD and ISSR amplifications do not require prior knowledge of flanking sequence of the genome of the concerned species. RAPD-PCR technique has been extensively used to characterize genetic structure as well as to study genetic diversity of different fishes. ISSR primers usually amplify DNA segments present at an amplifiable distance between two oppositely oriented identical microsatellite repeat regions. ISSR markers are generated via PCR reactions amplification with a single

Page | III microsatellite primer designed from repetitions of two or three nucleotides anchored (either 5´ or 3´) with in a sequence of one to three nucleotides that aim to eliminate slippage-related artefacts. Some advantages related to these markers are rapidness, requirement of small quantities of template DNA, a fewer/less number of PCR reactions, high PCR reactions annealing temperatures that reduce the quantity of artifacts and multi-locus highly polymorphic marker in nature. Thus, ISSR-PCR technique was preferred many times over to other dominant markers by several groups of researchers in studying genetic diversity at the interspecific and intraspecific levels of species. Therefore, due to the unavailability of microsatellite or SNP markers in Badis badis and Amblyceps mangois till date, we resorted to the time-tested RAPD and ISSR primers to ascertain and compare the available genetic variations in this ichthyofauna. Moreover, I have analyzed mtCOI gene to observe the genetic and phylogenetic relationship with other geographically isolated population across worldwide; to corroborate the findings based on the RAPD and ISSR markers with that of mtCOI gene Three major river systems (Mahananda, Teesta and Jaldhaka) have been studied and total seventeen populations (for Badis badis) and fourteen (for Amblyceps mangois) were selected from different geographic locations for the collection of the fish samples. The catch of the fishes from each location was very low and as the species are belong to the threatened category a non-invasive DNA isolation technique was developed and used for extraction of genomic DNA from the fishes. The genomic DNA was extracted from the minute quantities of fins without sacrificing the organisms. Different diversity indices like total number of polymorphic loci, percentage of polymorphic loci, Nei’s genetic diversity, Shannon’s information index, and measure of evenness are used to study the genetic architecture of two studied fishes. After compiling all the data and comparing with some other related studies it was found that the genetic diversity of the studied fishes were reasonably positioned within the range of low to moderate level. The mtCOI based analyses also showed that the number of haplotypes and haplotype diversity also low in the studied fishes compared to other related studies. Thus the mtCOI gene based findings also corroborate with that of the RAPD and ISSR based findings. Different level of anthropogenic pressures and several natural processes are lead to the decline of the genetic diversity of the studied ichthyofauna the three river system of the studied region. Although, the Teesta river system populations showed the comparatively greater level of genetic diversity than the other two river system populations viz., Mahananda-Balasan river system and Jaldhaka river system. But the individual population of both the fishes of the Jaldhaka river system showed greater level of diversity Page | IV than other river system populations but the Teesta river system showed overall higher level of genetic diversity after considering all the Teesta river populations together. This is happed due to joining of different tributaries to the Teesta river which causes the overall genetic diversity of the Teesta river system population to increase. This situation occurred in the both the studied fishes. The genetic relationship between different populations of Badis badis and Amblyceps mangois separately revealed that the three river populations form three distinct groups but the some populations from Mahananda river system showed close relationship with that of the Teesta river system. This is happen due to the connection of Mahananda river system with that of Teesta river system via a narrow channel which leads to the admixture of Mahananda population with that of Teesta river population. Moreover, excessive rain and flood also leads to mixing of river water with nearby tributaries and genetic exchange occurred. This ultimately led to the close genetic and geographic association of the Mahananda and Teesta river system. Whereas the Jaldhaka river system is free from any connection other two river systems so the Jaldhaka river system forms a separate group and the populations showed very close association with each other. It was also found that the pattern of diversity (H´), richness (S) and evenness (E) have varied across all seventeen populations of Badis badis and fourteen populations of Amblyceps mangois in a substantial manner as the river flows from higher to lower altitude. Several anthropogenic, climatic and geographic factors are responsible for this observed change and alternation of the diversity pattern in the studies fish populations. In my present study a significant levels of genetic divergence and population differentiation occurred in the Badis badis and Amblyceps mangois populations; and this indicates populations are highly structured as evident by the hierarchical gene diversity analyses. That is, a population having local breeding units in each stream have high genetic divergence from similar breeding units in other streams, and need to be managed as evolutionary significant units (ESU) for conservation purpose. Therefore, in Mahananda river system there is a need to manage the whole river system as evident from the hierarchical analyses to conserve the Badis badis and Amblyceps mangois population; and in Teesta and Jaldhaka river system it is necessary to manage the first and second order streams to conserve the gene pool of Badis badis and Amblyceps mangois of the whole river system.

This study is the first attempt to characterize and compare the genetic architecture of Badis badis from the three major river system of sub-Himalayan biodiversity hotpost region of West Bengal, India. Low levels of genetic diversity/variation were found in the present

Page | V study among the seventeen populations as an indicative of the recent threatened status of this species. In addition to over-fishing, presence of barrage/dam at the upper reaches of the Teesta, pesticide run offs from the nearby tea gardens, and urban effluents could be the possible reasons behind the lower catch frequency and low level of genetic diversity of the studied fish in the Mahananda and Teesta river population. Whereas the Jaldhaka population experiences less anthropogenic pressure leads to comparatively high level of diversity than other two river systems. Therefore, the Jaldhaka population should be managed and conserved to preserve the available gene pool of this threatened species. Information on the available genetic variation and phylogenetic relationship based on the mtCOI gene from the present study should form a baseline on which further studies can be undertaken. As the species is commercially expensive and has an potential ornamental value; and the region being located in the sub-Himalayan biodiversity hotspot region, the management and proper rehabilitation of this threatened ichthyofauna in the river systems is essential from the standpoint of livelihood of rural inhabitants and their socio-economic upliftment.

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ACKNOWLEDGMENTS

I will remain ever grateful to my supervisor Prof. (Dr.) Soumen Bhattacharjee, Professor, Department of Zoology, University of North Bengal, Siliguri. Throughout my tenure, in different phases I always got the spontaneous help from him. I express my gratitude and sincere thankfulness to him for successful completing my dissertation work with his intellectual guidance and constant inspiration.

I would be very thankful to other teachers of the Department, Prof. Ananda Mukhopadhyay, Prof. Tapas Kumar Chaudhuri, Prof. Sudip Barat, Prof. Min Bahadur, Dr. Dhiraj Saha, Mr. Tilak Saha, Dr. Sourav Mukherjee and Dr. Ritwik Mondal of Zoology Department, University of North Bengal, Siliguri for their valuable suggestions during my work.

I would be also thankfull to Prof. Arnab Sen of the Botany Department, University of North Bengal, Siliguri for his support and valuable suggestions during my work.

I would be cordially thankful to the Hon'ble Vice Chancellor, Head of the Zoology department, Finance Officer and Registrar for their constant support and co-operation.

I would be also thankful to the nonteaching staffs for their kind cooperation and support during my research work.

I feel very lucky to work with my laboratory collegues Sutanuka Chattaraj, Uttara Dey Bhowmick, Tanmoy Dutta, Subhashis Paul, Ajoy Paul, Najnin Islam, Debabrata Modak and thankful for their constant support and co-operation. We, the lab mates, also share some beautiful moments and unforgettable sweet and sour memories of our life together.

I am very much thankful to Bappaditya Ghosh, Dr. Kumar Basnet, Swati Singh, Trishita Majumdar, Minu Bharti, Susmita Dutta and Dr. Somit Dutta for their friendly support and necessary help.

I would be thankful to my seniors Dr. Pokhraj Guha, Dr. Priyankar Dey, Dr. Subhrojyoti Roy, Dr. Anjali Prasad, Sangita Khewa, Ritesh Biswa, Binoy Rai, Dawa Bhutia, Dr.

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Rudraprasad Roy and Biplab Das whose constant guidance and day to day suggestions help me to complete my work. I also thankful to whom, I really miss a lot in my neighbour laboratory. Not only they share his research experiences but we discuss a lot on different topics day to day.

I would be heartily and especially thankful to Ms. Paromeeta Roy for her help, support and inspiration in every step of my life and work.

I would be especially thankful to my old friend Mr. Avishek Das for his help and support in every step of my work.

I exceptionally thankful to Mr. Tinku Roy for his help and support during my research; and sample collection. Without his help it will be impossible to complete my research work.

I am wholeheartedly thankful to Mr. Biswajit Roy, who was a dedicated non-teaching staff of our department. His accidental demise was a great shock for all of us. I pray May his soul rest in peace.

I like to sincerely acknowledge the UGC-Major Research Project [File No. - 40-289/2011 (SR) dated 29th June, 2011] executed by Prof. Soumen Bhattacharjee as a principal investigator for providing the financial support to carry the research work smoothly.

I would be also thankful to UGC-BSR Meritorious Fellowship for providing me the financial support at the end of my research work to carry out my study smoothly.

Dated: Tanmay Mukhopadhyay

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TABLE OF CONTENTS

PAGE NO.

Preface ...... I-II Abstract ...... III-VI Acknowledgements ...... VII-VIII List of Tables ...... XVII-XIX List of Figures ...... XXI-XXVII List of Abbreviations ...... XXIX-XXXII

Chapter 1. INTRODUCTION 1.1 What is genetic diversity? ...... 3 1.2 Importance of genetic diversity ...... 5 1.2.1 Effects on the individuals...... 5 1.2.2 Effects on the population ...... 6 1.2.3 Phenotypes and functions of organisms are depends on genetic diversity ...... 7 1.2.4 Genetic diversity helps to adapt with the environmental variability ...... 8 1.3 Genetic diversity and Ecology ...... 9 1.4 Genetic diversity and Evolutionary processes ...... 13 1.4.1 Mutation ...... 14 1.4.2 Mating system ...... 14 1.4.3 Inbreeding ...... 15 1.4.4 Out- breeding ...... 16 1.4.5 Hybridization ...... 17 1.4.6 Gene flow ...... 17 1.4.7 Genetic drift ...... 20 1.4.8 Population bottleneck...... 20 1.4.9 Genetic erosion ...... 21 1.4.10 Natural selection ...... 21 1.5 Neutral vs. Adaptive genetic diversity ...... 22 1.5.1. Neutral genetic diversity ...... 22

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1.5.2. Adaptive genetic diversity ...... 23 1.5.2.1 Measurement of Adaptive genetic diversity ...... 24 1.6 Habitat fragmentation and genetic diversity ...... 25 1.6.1 Fragmentation process ...... 25 1.6.2 Effects of habitat loss on genetic diversity ...... 28 1.7 Meta-population dynamics and genetic diversity ...... 29 1.8 Background and origin of research problem ...... 35 1.9 Review of literature...... 37 1.9.1 Origin of ichthyofaunal conservation ...... 37 1.9.2 Molecular techniques to study genetic diversity ...... 38 1.9.2.1 Molecular genetics method in conservation biology ...... 38 1.9.2.2 Genomic approaches in conservation biology ...... 39 1.9.2.3 Types of molecular markers...... 40 1.9.2.4 Criteria for choosing molecular markers ...... 41 1.9.2.4.1 Sensitivity and reliability ...... 41 1.9.2.4.2 Multi-locus or Single-Locus ...... 41 1.9.2.4.3 Gene genealogies and Frequencies ...... 42 1.9.2.4.4 Organelle and Nuclear DNA ...... 42 1.9.2.5 Applications of different molecular markers/tools in ichthyofaunal conservation ...... 42 1.9.2.5.1 Allozyme ...... 43 1.9.2.5.2 RFLP ...... 45 1.9.2.5.3 AFLP ...... 47 1.9.2.5.4 RAPD ...... 48 1.9.2.5.5 ISSR ...... 50 1.9.2.5.6 VNTRs ...... 51 1.9.2.5.7 Microsatellite or SSRs ...... 52 1.9.2.5.8 SNPs ...... 54 1.9.2.5.9 Other advanced DNA Markers ...... 55 1.9.3 Mitochondrial DNA (MtDNA) and Genetic Diversity ...... 56 1.9.3.1 Origin and Evolution of Mitochondria ...... 56 1.9.3.2 Nuclear integration of Mitochondrial genome ...... 58

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1.9.3.3 Biology and genetics of Mitochondrial genome ...... 59 1.9.3.3.1 mtDNA is clonally inherited ...... 60 1.9.3.3.2 mtDNA is neutral in nature ...... 60 1.9.3.3.3 Mutation rate of mtDNA is constant ...... 61 1.9.3.4 mtCOI gene and Barcoding ...... 61 1.9.3.5 mtCOI in fish genetic diversity study ...... 63 1.10 Objectives of the study...... 66

Chapter 2. STUDY AREA 2.1 Study area (sub-himalayan hotspot region) ...... 69 2.1.1 Terai ...... 69 2.1.2 Dooars ...... 70 2.2 River systems ...... 71 2.2.1 Mahananda-Balasan River System ...... 72 2.2.2 Teesta River System and important tributaries ...... 73 2.2.3 Jaldhaka River System and important tributaries ...... 74 2.3 Detailed Map of the sample collection sites ...... 77 2.4 Local Names of sample collection Site ...... 78

Chapter 3. MATERIALS & METHODS 3.1 Survey of Major River System of Terai and Dooars Region ...... 81 3.2 Habit and Habitat Identification of the Fishes ...... 92 3.3 Sample collection and Preservation ...... 92 3.3.1 Collection Procedure ...... 92 3.3.2. Preservation...... 93 3.4 Identification of Collected Fish Samples ...... 94 3.4.1 Badis badis ...... 94 3.4.1.1 Morphometric Measurement ...... 94 3.4.1.2 Taxonomic Categorization ...... 94 3.4.1.3 Photographs...... 95 3.4.2 Amblyceps mangois ...... 95 3.4.2.1 Morphometric Measurement ...... 95

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3.4.2.2 Taxonomic Categorization ...... 96 3.4.2.3 Photographs...... 96 3.5 Genomic DNA Extraction...... 97 3.5.1 Materials ...... 97 3.5.1.1. Phenol-Chloroform Procedure ...... 97 3.5.1.2. DNA extraction Kit (DNAse Easy Blood and Tissue DNA extraction kit, Qiagen) ...... 99 3.5.2 Methods...... 99 3.5.2.1 Phenol-Chloroform Procedure ...... 99 3.5.2.2 DNA extraction Kit (DNAse Easy Blood and Tissue DNA extraction kit, Qiagen) ...... 100 3.6 Characterization of Genomic DNA ...... 100 3.6.1 Integrity Checking by Electrophoresis...... 100 3.6.1.1 Materials ...... 100 3.6.1.2 Methods...... 101 3.6.2 Purity Checking ...... 102 3.6.3 Quantification for PCR Amplification ...... 102 3.7 RAPD-PCR Amplification...... 104 3.7.1 Selection of Primers ...... 104 3.7.2 PCR Reaction Mixture ...... 105 3.7.3 PCR Programming ...... 106 3.8 ISSR-PCR Amplification ...... 106 3.8.1 Selection of Primers ...... 106 3.8.2 PCR Reaction Mixture ...... 107 3.8.3 PCR Programming ...... 107 3.9 Electrophoresis and Checking of Amplified Products ...... 108 3.9.1 Materials ...... 108 3.9.2 Methods...... 109 3.9. 3 Photographic Records ...... 109 3.10 Statistical and Analytical Procedures to Study Genetic Diversity ...... 109 3.10.1 Band-based approaches ...... 110 3.10.2 Polymorphism or polymorphic loci ...... 110

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3.10.3 Hardy-Weiberg equilibrium test (HWT) ...... 110 3.10.4 Average number of Alleles per locus ...... 111 3.10.5 Effective Number of Alleles ...... 111

3.10.6 Average Expected Heterozygosity (He) ...... 111 3.10.7 Shannon’s Information Index (I) ...... 111 3.10.8 Allelic Diversity (A) ...... 112 3.10.9 Heterozygosity (H) ...... 112 3.10.10 F-statistics ...... 113 3.10.11 Gene Flow (Nm) ...... 113 3.10.12 Shannon’s Evenness measure ...... 114 3.10.13 Heip’s Index of Evenness ...... 114 3.10.14 SHE Analysis ...... 114 3.10.15 Phylogenetic Diversity or phylogenetic tree ...... 115 3.10.16 Hierarchical Gene Diversity Analyses ...... 116 3.11 mtCOI amplification and analyses ...... 118 3.11.1 Primer Selection ...... 118 3.11.2 PCR reaction mixture ...... 118 3.11.3 PCR Programme ...... 118 3.11.4 Checking and documentation of amplified product ...... 119 3.11.5 Purification of PCR product, sequencing of Badis badis and Amblyceps mangois cytochome oxidase I (COI) partial coding sequences (CDS) and submitted to public database ...... 119 3.11.6 Retrieval of Publicly available Badis badis and Amblyceps mangois COI partial CDS ...... 120 3.11.7 Genetic diversity and population structure of two species ...... 122 3.11.8 Preparation of sequence dataset and phylogenetic analyses of mtDNA COI sequences ...... 122 3.11.9 Phylogenetic analyses based on a mtCOI partial CDSs of Badis badis and Amblyceps mangois ...... 123 3.12 Software Used for Statistical and Genetic Diversity Analyses ...... 124

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Chapter 4. RESULTS AND DISCUSSION 4.1 Standardization of genomic DNA extraction from fish tissue ...... 131 4.2 Genetic diversity study of Badis badis ...... 134 4.2.1 Findings based on RAPD based analyses ...... 135 4.2.1.1 Intra-Population Genetic Diversity Study ...... 135 4.2.1.1.1 Mahananda River System ...... 135 4.2.1.1.2 Teesta River System ...... 136 4.2.1.1.3 Jaldhaka River System ...... 137 4.2.2 Findings based on ISSR based analyses ...... 138 4.2.2.1 Intra-Population Genetic Diversity Study ...... 138 4.2.2.1.1 Mahananda River System ...... 138 4.2.2.1.2 Teesta River System ...... 139 4.2.2.1.3 Jaldhaka River System ...... 140 4.2.3 Findings based on RAPD + ISSR based analyses ...... 141 4.2.3.1 Intra-Population Genetic Diversity Study ...... 141 4.2.3.1.1 Mahananda River System ...... 141 4.2.3.1.2 Teesta River System ...... 142 4.2.3.1.3 Jaldhaka River System ...... 143 4.2.4 Comparative discussion based on RAPD, ISSR and RAPD+ISSR based analyses ...... 147 4.2.5 Comparison of genetic diversity between three river system populations of Badis badis ...... 149 4.2.6 Genetic relationship between populations ...... 153 4.2.7 SHE Analyses...... 157 4.2.8 Genetic Hierarchical Structure ...... 162 4.2.9 mtCOI based findins of Badis badis ...... 168 4.2.9.1 Amplification and Checking of amplified fragments ...... 168 4.2.9.2 Ten Cytochrome oxidase subunit 1 (COI) gene sequences of Badis badis and their NCBI Accession No...... 169 4.2.9.3 Genetic diversity and population structure of Badis badis- ...... 173 4.2.9.4 Phylogenetic relationship of different populations of Badis badis based on mtCOI gene ...... 175

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4.3 Genetic diversity study of Amblyceps mangois ...... 177 4.3.1 RAPD based analyses ...... 178 4.3.1.1 Intra-Population Genetic Diversity Study ...... 178 4.3.1.1.1 Mahananda River System ...... 178 4.3.1.1.2 Teesta River System ...... 179 4.3.1.1.3 Jaldhaka River System ...... 180 4.3.2 ISSR based analyses ...... 181 4.3.2.1 Intra-Population Genetic Diversity Study ...... 181 4.3.2.1.1 Mahananda River System ...... 181 4.3.2.1.2 Teesta River System ...... 182 4.3.2.1.3 Jaldhaka River System ...... 183 4.3.3 RAPD + ISSR based analyses ...... 184 4.3.3.1 Intra-Population Genetic Diversity Study ...... 184 4.3.3.1.1 Mahananda River System ...... 184 4.3.3.1.2 Teesta River System ...... 185 4.3.3.1.3 Jaldhaka River System ...... 186 4.3.4 Comparative discussion based on RAPD, ISSR and RAPD+ISSR based analyses ...... 190 4.3.5 Comparison of genetic diversity between three river system populations of Amblyceps mangois ...... 192 4.3.6 Genetic relationship between populations ...... 195 4.3.7 SHE Analyses...... 199 4.3.8 Genetic Hierarchical Structure ...... 203 4.2.9 mtCOI based findins of Amblyceps mangois ...... 208 4.3.9.1 Amplification and Checking of amplified fragments ...... 208 4.3.9.2 Ten Cytochrome oxidase subunit 1 (COI) gene sequences of Amblyceps mangois and their NCBI Accession No ...... 209 4.3.9.3 Genetic diversity and population structure of Amblyceps mangois ...... 214 4.3.9.4 Phylogenetic relationship of different populations of Amblyceps mangois based on mtCOI gene ...... 216

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Chapter 5. COMPREHENSIVE DISCUSSION 5.1 Extraction of Genomic DNA ...... 221 5.2 Genetic Architecture of Badis badis and Amblyceps mangois ...... 223 Chapter 6. SUMMARY AND CONCLUSION 6.1 Genomic DNA extraction based study ...... 233 6.2 Study based on the genetic Architecture of Badis badis and Amblyceps mangois ...... 234

BIBLIOGRAPHY ...... 237

INDEX ...... 285

APPENDICES Appendix: A [List of publications] ...... 295 Appendix: B [Seminar & Conferences] ...... 296

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List of Tables

Study Area

Table 1. Local Names of sample collection Sites 78

Materials and Methods

Table 2. Geographical coordinates recorded with the help of hand-held GPS device 91

Table 3. RAPD Primers for Badis badis 104

Table 4. RAPD Primers for Amblyceps mangois 105

Table 5. ISSR Primers for Badis badis 106

Table 6. ISSR Primers for Amblyceps mangois 107

Table 7. Retrieved publicly available 25 number of Badis badis COI gene sequence (No. 1-25) from NCBI database and ten number of studied sequences (No. 26-35) . 121

Table 8. Retrieved publicly available four number of Amblyceps mangois COI gene sequence (No. 1-4) from NCBI database and ten number of studied sequences (No. 5-14) 122

Table 9. Software Used for Statistical and Genetic Diversity Analyses 124

Results

Table 10. Spectrophotometric assessment of gDNA yields and purity in both live and frozen tissues from a carp (Labeo bata) and a catfish (Heteropneustes fossilis) after treatment with RNase A. 132

Table 11. Intra-population genetic diversity indices based on RAPD analyses of Badis badis of Mahananda-Balason river system. 135

Table 12. Intra-population genetic diversity indices based on RAPD analyses of Badis badis of Teesta river system. 136

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Table 13. Intra-population genetic diversity indices based on RAPD analyses of Badis badis of Jaldhaka river system. 137

Table 14. Intra-population genetic diversity indices based on ISSR analyses of Badis badis of Mahananda-Balason river system. 138

Table 15. Intra-population genetic diversity indices based on ISSR analyses of Badis badis of Teesta river system. 139

Table 16. Intra-population genetic diversity indices based on ISSR analyses of Badis badis of Jaldhaka river system. 140

Table 17. Intra-population genetic diversity indices based on RAPD+ISSR analyses of Badis badis of Mahananda-Balason river system. 141

Table 18. Intra-population genetic diversity indices based on RAPD+ISSR analyses of Badis badis of Teesta river system. 142

Table 19. Intra-population genetic diversity indices based on RAPD+ISSR analyses of Badis badis of Jaldhaka river system. 143

Table 20: Matrix showing values of Nei's (1978) unbiased measures of genetic similarity (above diagonal) and genetic distances (below diagonal). The square boxes indicate the highest (green box) and lowest (blue box) genetic similarity and genetic distance between two pair of population. 153

Table 21: SHE analyses 157

Table 22: Population genetic diversity parameters based on Badis badis MtDNA COI partial coding sequences. 172

Table 23. Intra-population genetic diversity indices based on RAPD analyses of Amblyceps mangois of Mahananda-Balason river system. 178

Table 24. Intra-population genetic diversity indices based on RAPD analyses of Amblyceps mangois of Teesta river system. 179

Table 25. Intra-population genetic diversity indices based on RAPD analyses of Amblyceps mangois of Jaldhaka river system. 180

Table 26. Intra-population genetic diversity indices based on ISSR analyses of Amblyceps mangois of Mahananda-Balason river system. 181

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Table 27. Intra-population genetic diversity indices based on ISSR analyses of Amblyceps mangois of Teesta river system. 182

Table 28. Intra-population genetic diversity indices based on ISSR analyses of Amblyceps mangois of Jaldhaka river system. 183

Table 29. Intra-population genetic diversity indices based on RAPD + ISSR analyses of Amblyceps mangois of Mahananda-Balason river system. 184

Table 30. Intra-population genetic diversity indices based on RAPD + ISSR analyses of Amblyceps mangois of Teesta river system. 185

Table 31. Intra-population genetic diversity indices based on RAPD + ISSR analyses of Amblyceps mangois of Jaldhaka river system. 186

Table 32. Matrix showing values of Nei's (1978) unbiased measures of genetic similarity (above diagonal) and genetic distances (below diagonal). The square boxes indicate the highest (green box) and lowest (blue box) genetic similarity and genetic distance between two pair of population. 195

Table 33. SHE analyses 199

Table 34. Population genetic diversity parameters based on Amblyceps mangois MtDNA COI partial coding sequences. 213

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List of Figures

Introduction

Figure 1: Processes underlying potential direct and indirect impacts of genetic diversity on the ecological functioning. Solid black lines indicate direct ecological consequences of genetic diversity; dotted black lines indicate effects of natural selection, which depend on genetic diversity. 10

Figure 2. The island model. Each population receives and gives migrants to each of the other population at the same rate m. Each population (A, B, C, D, and E) is also composed of the same number of individuals. Modified from Wright (1931). 18

Figure 3. The process of habitat fragmentation. Black areas represent habitat and the white areas represent matrix (Wilcove et al., 1986). 26

Figure 4: Patch-scale study: each observation represents the information from a single patch. Only one landscape is studied, so sample size for landscape- scale inferences is one. Landscape-scale study: each observation represents the information from a single landscape. Multiple landscapes, with different structures, are studied. Here, sample size for landscape- scale inferences is four (adaptation from Brennan et al. 2002) 27

Figure 5: Electrophoretic pattern for allozymes: a) Monomeric enzyme; b) Dimeric enzyme and c) Tetrameric enzyme. Homozygotes are represented by aa, bb and heterozygotes are represented by ab (adapted from Bader, 1998). 42

Study Area

Figure 6. Detailed map of the Study area showing sample collection sites. 77

Materials and Methods

Figure 7. Photographic records of the sample collection sites. 90

A. Balasan River, Palpara, Matigara B. Balasan River, Tarabari.

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C. Mahananda River, Champasari, Siliguri. D. Mahananda-Panchnoi River Junction, Siliguri. E. Panchnoi River, Siliguri. F. Mahananda Barrage, Fulbari, Siliguri. G. Teesta River, Sevoke, Near Siliguri H. Ghish River, Jalpaiguri I. Chel River J. Neora River. K. Teesta River, Jalpaiguri. L. Teesta Barrage, Gajoldoba M. Jaldhaka River. N. Murti River O. Ghotia River. P. Diana River.

Figure 8. Sample collection procedure of fishes from rivers with scoop net. 93

Figure 9. Samples of Badis badis 95

Figure 10. Samples of Amblyceps mangois 96

Figure 11: Hypothetical Genetic hierarchical model of seven different populations. The populations are arranged in a hierarchical order, viz., first order, second order, third order and fourth order populations. The different shaded areas represent different orders of the hierarchy. The triangles indicate the collection spots and curved line indicate the river streams (adapted from Meffe and Carroll, 1997). 117

Results and Discussion

Figure 12. 0.7 % agarose gel electrophoreses of genomic DNAs (gDNAs) isolated from Live and Frozen scale and fin samples of L. bata and H. fossilis. a Lane 1 Lambda DNA/Hind III Digest DNA size marker (kb), lanes 2–6 5, 10, 20, 30, 50 pieces of scale gDNA preparations from live L. bata, lanes 8–12 5, 10, 20, 30, 50 pieces of scale gDNA preparations from frozen L. bata. b Lane 1 Lambda DNA/Hind III Digest DNA size marker (kb), lanes 2–6 1, 5, 10, 20, 30 mg of fin gDNA preparations from live L. bata, Page | XXII

lanes 8–12 1, 5, 10, 20, 30 mg of fin gDNA preparations from frozen L. bata. c Lane 1 lambda DNA/Hind III Digest DNA size marker (kb), lanes 2–6 1 mg, 5, 10, 20, 30 mg of fin gDNA preparations from live H. fossilis, lanes 8–12 1, 5, 10, 20, 30 mg of fin gDNA preparations from frozen H. Fossilis. 131

Figure 13. RAPD banding patterns in 1.4 % agarose gels from L. bata and H. fossilis using decamer primers OPA02 and OPA07. a Lane 1 100 bp DNA ladder (kb), lanes 2–3, 6–7 RAPD profile from 5 and 50 pieces of scale respectively from live L. bata, lanes 4–5, 8–9 RAPD profile from 5 and 50 pieces of scale respectively from frozen L. bata. b Lane 1 100 bp DNA ladder (kb), lanes 2–3, 6–7 RAPD profile from 1 and 30 mg of fins respectively from live L. bata, lanes 4–5, 8–9 RAPD profile from 1 and 30 mg of fin respectively from frozen L. bata. c Lane 1 100 bp DNA ladder (kb), lanes 2–3, 6–7 RAPD profile from 1 and 30 mg of fin respectively from live H. fossilis, lanes 4–5, 8–9 RAPD profile from 1 and 30 mg of fin respectively from frozen H. fossilis. 133

Figure 14. Badis badis collection sites. Also refer to Table 2 for the geographicoordinates of the collection spots for Badis badis. 134

Figure 15: Representative Gel of Badis badis after RAPD (1.4% agarose) and ISSR (1.8% agarose) amplification. RAPD primer OPA16 and ISSR primer ISSR02 primers are used for amplification. M=100 base pair DNA size marker. A. RAPD gel of Mahananda River system; B. RAPD gel of Teesta river system; C. RAPD gel of Jaldhaka river system; D. ISSR gel of Mahananda River system; E. ISSR gel of Teesta river system; F. ISSR gel of Jaldhaka river system. BTR1-BTR6 are the populations from Mahananda river system, BDR1-BDR7 are the populations of Teesta river system, BDR8-BDR11 are the populations of Jaldhaka river system. Each population consist of ten individuals. Each lane represents each individual’s RAPD and ISSR banding pattern. 146

Figure 16: Comparison of genetic diversity between three river system populations of Badis badis by A. RAPD; B. ISSR and C. RAPD + ISSR based analyses. S=observed number of alleles, H= Nei's gene diversity, H´ or

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I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index. 152

Figure 17. UPGMA dendrogram based on Nei’s (1978) unbiased genetic distance matrix. The Green and Brown square box indicates the clustering of Terai and Dooars region Badis badis populations. 154

Figure 18: Principal Component Analysis based on covariance matrix without data standardization of Badis badis populations of three river system based on RAPD and ISSR analyses. Blue, Brown and Green circles represent clustering of Mahananda, Teesta and Jaldhaka river populations. Coordinates 1 and 2 explain 48.97 % and 23.61 % of the variations respectively. 155

Figure 19: SHE analyses showing observed patterns of diversity changes of Amblyceps mangois in three the river system populations based on RAPD and ISSR marker. Plot A, B, C represents the Mahananda river system; Plot D, E, F, G represents Teesta river system and Plot H, I, J represents Jaldhaka river system. 160

Figure 20: Genetic hierarchical model of seventeen different populations of Badis badis. The populations are arranged in hierarchical orders as first, second,

third, fourth, fifth order populations. FST = Population genetic differentiation, Nm= Estimated gene flow, AMOVA= Analysis of molecular variance, PhiPT= Estimated variance among population/(Estimated variance within population + Estimated variance among population), probability values based on 999 permutations. # indicates the comparison between populations or groups of populations. 164

Figure 21: A. Amplification of COI gene of Badis badis by specific primer. B. HindIII digestion of the amplified product. M= 100 base pair DNA sizer marker, Lane No. 1-10= Amplified product of COI gene of Badis badis. 168

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shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Joining and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 0.4967)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 36.5108% sites). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 35 nucleotide sequences. All positions containing gaps and missing data were eliminated. There were a total of 556 positions in the final dataset. Evolutionary analyses were conducted in MEGA7 (Kumar et. al., 2016). 176

Figure 23. Amblyceps mangois collection sites. Also refer to Table 2 for the geographicoordinates of the collection spots for Amblyceps mangois. 177

Figure 24: Representative Gel of Amblyceps mangois after RAPD (1.4% agarose) and ISSR (1.8% agarose) amplification. RAPD primer OPA16 and ISSR primer ISSR04 primers are used for amplification. M=100 base pair DNA size marker. A. RAPD gel of Mahananda River system; B. RAPD gel of Teesta river system; C. RAPD gel of Jaldhaka river system; D. ISSR gel of Mahananda River system; E. ISSR gel of Teesta river system; F. ISSR gel of Jaldhaka river system. ATR1-ATR3 are the populations from Mahananda river system, ADR1-ADR7 are the populations of Teesta river system, ADR8-ADR11 are the populations of Jaldhaka river system. Each population consist of ten individuals. Each lane represents each individual’s RAPD and ISSR banding pattern. 189

Figure 25: Comparison of genetic diversity between three river system populations of Amblyceps mangois by A. RAPD; B. ISSR and C. RAPD + ISSR based analyses. S=observed number of alleles, H= Nei's gene diversity,

H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index. 194

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Figure 26. UPGMA dendrogram based on Nei’s (1978) unbiased genetic distance matrix. The Green and Brown square box indicates the clustering of Terai and Dooars region populations. 196

Figure 27: Principal Component Analysis based on covariance matrix without data standardization of Amblyceps mangois populations of three river system based on RAPD and ISSR analyses. Blue, Brown and Green circles represent clustering of Mahananda, Teesta and Jaldhaka river populations. Coordinates 1 and 2 explain 48.97 % and 23.61 % of the variations respectively. 197

Figure 28: SHE analyses showing observed patterns of diversity changes of Amblyceps mangois in three the river system populations based on RAPD and ISSR marker. Plot A and B represents the Mahananda river system; Plot C, D, E, F represents Teesta river system and Plot G, H, I represents Jaldhaka river system. 201

Figure 29: Genetic hierarchical model of fourteen different populations of Amblyceps mangois. The populations are arranged in hierarchical orders

as first, second, third, fourth, fifth order populations. FST = Population

genetic differentiation, Nm= Estimated gene flow, AMOVA= Analysis of molecular variance, PhiPT= Estimated variance among population/(Estimated variance within population + Estimated variance among population), probability values based on 999 permutations. # indicates the comparison between populations or groups of populations. 205

Figure 30: A. Amplification of COI gene of Amblyceps mangois by specific primer. B. HindIII digestion of the amplified product. M= 100 base pair DNA sizer marker, Lane No. 1-10= Amplified product of COI gene of Amblyceps mangois. 208

Page | XXVI search were obtained automatically by applying Neighbor-Joining and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 14 nucleotide sequences. Codon positions included were 1st+2nd+3rd+Noncoding. All positions containing gaps and missing data were eliminated. There were a total of 609 positions in the final dataset. Evolutionary analyses were conducted in MEGA7. (Kumar et. al., 2016). 216

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List of Abbreviations aDNAs = ancient DNAs

AFLP = Amplified Fragment Length Polymorphism

AICc = Akaike Information Criterion corrected

AMOVA = Analysis of Molecular Variance

AMSL = Above Mean Sea Level

ANOVA = Analysis Of Variance

AP-PCR = Arbitrarily Primed Polymerase Chain Reaction

BIC = Bayesian Information Criterion

BLAST = Basic Local Alignment Search Tool

CAMP = Conservation Assessment and Management Plan

CBD = Convention on Biological Diversity

CDS = Coding Sequences

COI or COX1 = Cytochrome Oxidase subunit 1

COII or COXII = Cytochrome Oxidase subunit 2

CoRAP = Conserved Region Amplification Polymorphism

Cytb = Cytochrome b

CytP450 = Cytochrome P450

DAF = DNA Amplification Fingerprinting

DArT = Diversity array technology dATPs = deoxy adenosine tri-phosphate dCTPs = deoxy cytidine tri-phosphate

DDBJ = DNA Data Bank of Japan dGTPs = deoxy guanosine tri-phosphate dNTPs = deoxy nucleotide tri-phosphate dTTPs = deoxy thymidine tri-phosphate

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ED = Evolutionary Distinctiveness

EDGE = Evolutionarily Distinct and Globally Endangered species

EDTA = Ethylenediaminetetraacetic Acid

EHeip = Heip’s evenness index.

EMBL = European Molecular Biology Laboratory

EN = Endangered

EST = Expressed Sequence Tag

ESU = Evolutionary Significant Units

EtBr = Ethidium Bromide

FST = Genetic Differentiation gDNA = Genomic DNA

GPS = Global Positioning Syatem

H´ = Shannon's Information index h2 = Heritability

HCL = Hydrochloric Acid

HindIII = Haemophilus influenza, strain D, third isolated enzyme

HWT = Hardy-Weinberg equilibrium Test

ISSR = Inter Simple Sequence Repeat

ITS = Internal Transcribed Spacer

IUCN = International Union for Conservation of Nature

KCl = Potassium Chloride

LGT = Lateral Gene Transfer

LSA = Locus Specific Amplification

MgCl2 = Magnesium Chloride

ML = Maximum Likelihood

MOTU = Molecular Operational Taxonomic Units

Page | XXX mtCOI = Mitochondrial Cytochrome Oxidase subunit 1

NaCl = Sodium Chloride

NaOH = Sodium Hydroxide

NBFGR = National Bureau of Fish Genetic Resources

NCBI = National Center for Biotechnology Information

Ne = Effective population size

NGS = Next Generation Sequencing

NJ = Neighbor Joining

Nm = Amount of Gene flow

Np = Number of Polymorphic Loci

Nper = Percentage of Polymorphic Loci nsSNPs = non-synonymous SNPs

OD = Optical Density

PAGE = Polyacrylamide Gel Electrophoresis

PCoA = Principle Coordinate/Component Analyses

PCR = Polymerase Chain Reaction

PD = Phylogenetic Diversity

QST = Genetic Differentiation

RAD = Restriction site-Associated DNA

RAPD = Random Amplified Polymorphic DNA rDNA = Ribosomal DNA

RFLP = Restriction Fragment Length Polymorphism

RNase A = Ribonuclease A

RRSS = Reduced Representation Shotgun Sequencing

SDS = Sodium Dodecyl Sulphate or Sodium Lauryl Sulphate

SET = Serial Endosymbiotic Theory

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SFP = Single Feature Polymorphism

SLOSS = Single Large Or Several Small

SNP = Single-Nucleotide Polymorphism

SRAP = Sequence Related Amplified Polymorphism

SSR = Simple Sequence Repeats

SSU rRNAs = Small Sub Unit rRNAs

STR = Short Tandem Repeats

TAE = Tris Acetic acid EDTA

Taq = Thermus aquaticus tRNA = Transfer RNA

UPGMA = Unweighted Pair Group Method with Arithmatic mean

UTR = Untranslated Region

VNTR = Varying Number of Tandem Repeats

VU = Vulnerable

WGSS = Whole Genome Shotgun Sequencing

πn = Nucleotide Diversity

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CHAPTER-I Introduction

1. INTRODUCTION ______1.1 What is genetic diversity? Genetic diversity is the result of evolutionary processes, the basic and fundamental material on which adaptation and speciation depend. High levels of variability is considered as healthy, conferring the ability to respond to threats such as disease, challenges from parasites and predators, environmental change and as reproductively fit. Conversely, low levels of variability are considered to limit a species' ability to respond to these threats in wide range. The study of genetic diversity is carried out to characterize an individual or whole population compared to other individuals or populations. It quantifies the magnitude of genetic variability available within an individual or population and is a fundamental source of biological diversity/ biodiversity. The fundamental unit of biodiversity in gene and it is the raw material for evolution and the source of the enormous variety of life on earth from plants, animals, populations, communities to ecosystems that we hunt for conservation, management and utilization for our better livelihood. Genetic variation shapes and defines individuals, populations, subspecies, species, genus and ultimately the whole kingdoms of life on earth. Genetic diversity among individuals reflects the presence of different variation of alleles present in the gene pool, and hence constitutes genotypic plasticity within the populations. Genetic diversity should be distinguished from genetic variability, which describes the tendency of variation of genetic traits found within populations (Laikre et al., 2009). Since at the advent of twentieth century, the detailed study of genetic diversity of life has became the major focus of the core evolutionary and conservation biology. The theoretical metrices such as genetic variance and heritability provided the quantitative standards necessary for the study of evolutionary and conservation synthesis (Fisher, 1930; Wright, 1931). Further research has focused on the origin of genetic diversity, its cause, its maintenance and its fundamental role in evolutionary process. Some elementary questions such as ―who breeds with whom?‖ originated the further studies on the relatedness of the individuals, as well as different populations. These investigations led to the development of meta-population theory, where a group of spatially isolated populations of the same species interact at some level and form with a coherent larger groups (Hanski, 1998). The discovery of spatial arrangement in populations was a key element in the early concepts and models of population ecology, conservation genetics and adaptive evolution (Wright, 1931; Andrewartha and Birch, 1954).

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How exceptional tiers of genetic version affect the rate of evolutionary exchange inside populations has additionally been intensively studied. Subsequently the detection of genetic version has end up extra sensitive but inevitable for conservation purpose; firstly, through the utilization and detection of variation in enzymes (i.e., allozymes) after which through Polymerase Chain Reaction (PCR)-based molecular marker system, allowing direct assessment of DNA sequence variantion. The unique detection of genetic variation/range has substantially more advantageous for intricate studies of evolution; and its motive and impact. There is no uncertainty that genetic variation affects the fitness of individuals, and that that is pondered in natural selection. In this regard, the plasticity within genotypes has to vary in ecologically vital ways. Ecological model suggested that the genotypic variation of individuals reside within the natural habitat is a massive element (as an instance, in variety expansion of different species) for its survivability in nature. Species having various genotypes conferring the best stages of fitness are predicted to live on and reproduce better and modify the gene pool in the direction of higher adaptive allele‘s frequencies that ultimately results in the more successful genotypes and fitness (Ward et al., 2008). Fisher (1930) reported that the increased rate of survivability and reproductive fitness of population positively affect the genetic diversity which could enhance the growth rate of population, but the population is free from any directional selection and not regulated by other evolutionary factors. Although the genetic variation is exist in ecologically significant traits but relatively little is known about the range of potential ecological consequences of genetic diversity on population dynamics, species interactions and ecosystem processes (Hughes et al., 2008). This has led to the thrust in the field of molecular ecology, which integrates the application of sophisticated aspects of molecular population genetics, phylogenetics and genomics to solve all the ecological questions relating to the genetic diversity. Information about genetic diversity is necessary for the development of appropriate management strategies in conservation biology as well as in many other implimented studies. From an evolutionary perspective, genetic variation is believed to be vital for the evolutionary capacity of a species and its fitness. Researchers have meant to investigate the evolutionary insights of population structure into the demographic dynamics of numerous organisms (Milligan et al., 1994). Molecular phylogenetics and genetic diversity analysis can help to clarify the taxonomic uniqueness and evolutionary relationships of the natural organisms. These approaches can also assist to prevent miss-identification, and carefully plan effective germplasm management techniques. Variability and genetic diversity are important

Page | 4 factors in applied sciences because they determine the responses of a given organism to different environmental stress, natural selection and susceptibility to different diseases.

1.2. Importance of genetic diversity All living organisms carry a fundamental genetic blueprint or specific genetic fingerprint regardless of whether they are short- or long-lived animals, plants, or fungi, and whether they reproduce sexually or asexually. Therefore, conservation, genetics and conservation genetics play a vital role for the restoration and survival of living organisms. Diversity can occur at three levels: genetic diversity (variation in genes and genotypes), species diversity (species richness) and ecosystem diversity (communities of species and their environment). Genetic diversity is a gene variation lies within either an individual or a population. Genetic variation characterized by the percentage of heterozygous alleles in diploid organisms (Nei, 1973). The genetic variability has an immense effect and has a great importance on the survival and reproduction of any organisms as well as populations. They can be summarized as follows:

1.2.1 Effects on the individuals The deficiency of genetic diversity within the individuals can affect their survivalbility and reproductive fitness. In diploid organisms expression of the homozygous gene may incorporate characteristics having less useful for survival or proliferation in certain circumstances. These may cause several physiological, biochemical or behavioral problems of genetic origin, like twisted physical structure, lessened biochemical functions, inappropriate organ development and capacity, modified social conduct, and weakness to various infections and so on (Chai, 1976). Homozygosity or lack of heterozygosity is an eventual result of inbreeding (sexual reproduction between closely related individuals) which leads to the possibility of two alleles of a locus being identical by descent from a common ancestor of both the parents or by genetic drift. Small sized populations having little dispersal or migration range or are constrained allopatrically from gene flow with other populations can be particularly susceptible to inbreeding depression, which leads to reduced rate of overall survivability and reproduction. Inbreeding depression evolves from a variety of causes, including expression of adverse or deleterious alleles, and often leads to lower survival and birth or reproductive rates (Meffe and Carroll 1997; Husband and Schemske

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1996). Inbreeding has a first-rate effect on organism‘s survivability and mortality. It causes better mortality, decrease fecundity, decreased reproductive potentiality, decreased growth rate, developmental instability, and reduces adaptive potential to face up to environmental strain and decrease competitive capacity (Allendorf and Leary, 1986; Falconer, 1989; Ledig, 1986 and Wright, 1977). However, heterozygosity is not always tremendous, nor does inbreeding always have negative outcomes. In some occasions, a population can be so well tailored to its nearby conditions that introducing alleles from different populations truly reduces its performance (outbreeding depression) (Templeton, 1991).

1.2.2 Effects on the population Genetic variability/ variety affect any population in numerous ways. The growth rate of any population is diminished because of decreased fecundity and survival of the inbreed individuals, which in turn will increase the hazard of extinction of the population concern due to stochastic phenomena in near future (Goodman, 1987). Lower capability for population growth also reduces the potentiality of a population to recover from demographic catastrophe because of several environmental stress and genetic bottlenecks, which causes similarly inbreeding depression inside the population (Schmitt and Ehrhardt, 1990; Miller, 1994; Keller et al., 1994). Regardless of the truth that reduces variation has little effect on a population‘s fitness in its present surroundings or if the reduction in the population growth is not good enough, a decline in variability will decrease the capability of the population to evolve with the evolving environment. Fisher (1930) said that the heritability of a genetic trait is immediately proportional to the evolutionary response to natural selection, which is in turn proportional to the expected heterozygosity of that particular trait (Falconer, 1989). James (1971), has suggested that populations having genetic polymorphism in several alleles or having multiple alleles for a specific trait showed amazing evolutionary adaptability and advantageous positive natural selection. Thus population with lower heritability, reduced heterozygosity, and fewer polymorphic loci will adapt very slowly and have lower adaptatibility than the population which is more diverse and having greater genetic diversity. Therefore surprising fluctuation in genetic variability in small populations can reduce the rate of adaptability accurately to cause small populations to head extinct inside the face of environmental alteration to which huge populations would be able to adapt (Burger and Lynch, 1995). Therefore, every population should have an effective viable population size to withstand genetic erosion due to genetic drift/ genetic bottlenecks to preserve long term survival and reproductive potentiality. Mutation can basically affect the small populations by

Page | 6 bringing on inbreeding depression, by maintaining adaptive genetic variation in quantitative characters, and through the disintegration of fitness by using accumulation of mildly unfavorable mutations. Russell Lande (1995) has suggested that highly harmful mutations have low effect to increase the genetic variation because overall mutation rate in populations is much higher than the incorporation of new genetic variation. Therefore thinking about the mutations aren't damaging, the effective population size will need to be so as of 5000 to ensure long time viability of the population (Lande, 1995). Random genetic drift is the principal driving force that influences to exchange the allele frequencies in small populations. Therefore, deleterious mutations on occasion get fixed in small populations changing the available adaptable alleles. As the deleterious mutations accumulate in the populations the populace size may also decreases and genetic drift become even extra pronounce. This remarks phenomenon termed as Genetic Meltdown (Kimura, 1983; Lacy, 1987).

1.2.3 Phenotypes and functions of organisms are depends on genetic diversity Biologists talk over with simple expressions of variability, the genotype and the phenotype. The genotype refers back to the genetic make-up of an organism; in organisms with nucleated cells (multi-cell plant life, fungi and animals), the genetic content material is located inside the nucleus (chromosomal DNA) of each cell. Additional genetic content material is present in other organelles inside the cell, such as mitochondria (mitochondrial DNA in animals) and chloroplasts (chloroplast DNA in plant life). A massive number of genes constitute the organism's genotype, and genes can be discovered at more than one loci on chromosomes. In higher flora and animals (however no longer fungi, bryophytes, and many marine invertebrates), the grownup organism has copies of each gene, one of every derived from every single parent. When the two copies are the identical, the organism is homozygous for that gene; if the two copies are one of a kind, the organism is heterozygous for that gene. The diverse forms of a gene are referred to as alleles; when the forms are equal across a population, the gene is considered as monomorphic in nature, and if multiple allele exists of that precise gene, the gene is taken into consideration as polymorphic in nature. Phenotype is the expression of those genes in a specific environment, and is prompted by environmental context at every degree from the cell to the complete organism. It may be very tough to split the phenotypic variation in an organism‘s trait with a genetic foundation. Sometimes errors are typically occurring assuming a genetic foundation of a trait while the observed variant is purely due to phenotypic plasticity. Genetic variation isn't always entirely

Page | 7 affecting the fitness of individuals or populations as an alternative both the genes and the environment will in the end make a contribution to the fitness and survivability. A variety of research has been carried out that suggested that how the genetic composition and genetic variability influences the shape and feature of organisms (Hamrick et al., 1979; Hedrick, 1985; Primack and Kang, 1989; Rehfeldt, 1990; Allen et al., 1996; Hartl and Clark 1997). In truth, the detection and estimation of genetic variation among organisms became a primary perception that led to the formulation of evolutionary concept as we realize it these days (Freeman and Herron, 1998). Genes alter body size, shape, physiological strategies, behavioral traits, reproductive characteristics, tolerance of environmental extremes, dispersal and colonizing potential, the timing of seasonal and annual cycles , disorder resistance, and plenty of other trends (Raven et al., 1986). Therefore, genetic variation in ecology is one of the fundamental forces that form the biology and survival of dwelling organisms.

1.2.4 Genetic diversity helps to adapt to the environmental variabilities Organisms exist in environments that vary in spatial and temporal scale. Such variation is regularly described in phrases of several environmental conditions which include climate, disturbance activities, aid availability, population sizes of competitors, and so forth (White and Walker, 1997). If a collection of organisms had been to stay in a very solid physical and organic surroundings, then a notably narrow variety of phenotypic versions is probably optimally tailored to the ones various situations. Under those situations, organisms might benefit more by using keeping a slim range of genotypes tailored to winning conditions, and allele frequencies might ultimately attain equilibrium. By evaluation, if the surroundings is patchy, heterogeneous, unpredictable over time, or consists of a wide and changing nature of diseases, predators, and parasites, then diffused variations amongst people increase the probability that some individuals and not others will live on to breed i.e., the traits of the organisms are exposed to natural selection. Since differences among individuals are decided as a minimum partly through genotype, population genetic idea predicts that, in variable environments a broader range of genetic variation or better heterozygosity will persist over time (Cohen, 1966; Chesson, 1985; Tuljapurkar, 1989; Tilman, 1999). The traits with a genetic foundation for tolerance of environmental variant is critical for resistant/resilience work which include tolerance of freezing, drought or inundation, high or low mild availability, salinity, heavy metals, soil nutrient deficiencies, and excessive soil pH

Page | 8 values in plant life; resilience to fluctuating temperature, dissolved oxygen, and nutrient availability in aquatic organisms; and resistance to novel diseases in all organizations of organisms (Huenneke, 1991).

A diverse array of genotypes appears to be especially important in disease resistance (Schoen and Brown, 1993; McArdle, 1996). Genetically uniform populations are famously vulnerable to illnesses and pathogens and such uniformity also predisposes a population to transmit diseases from one character to some other via direct contact or proximity. More numerous populations are much more likely to encompass organisms resistant against specific diseases; furthermore, infected individuals arise at decrease density, and thus diseases or pathogens may flow extra slowly through the population. Finally, genetic variation is a factor that helps individuals in competition among other individuals in real ecological communities. Among animals, behavioral tendencies can also regulate interspecific competition. Since organisms make active or life records tradeoffs among traits (as an instance, allocating energy between growth and reproduction), genetic variability is a vital issue that elaborate how populations function (Thompson and Plowright, 1980; Fowler 1981; Gurevitch, 1986; Goldberg, 1987; Manning and Barbour, 1988; Welden et al., 1988; Grace and Tilman, 1990; Tilman and Wedin, 1991; Wilson and Tilman, 1993; Delph et al., 1998).

1.3 Genetic diversity and Ecology Genetic diversity is a degree that quantifies the significance of genetic variability inside the natural populations and is the essential source of biological variability in nature. Over eight decades the study of genetic diversity has been taken into consideration as a primary domain for evolutionary and conservation biologists (Wright, 1920 and Fisher, 1930). The genetic diversity provides the raw material for evolution and influenced by natural selection, affecting the individual genotypes and reproductive fitness vary in ecologically important ways (Fisher, 1930; Ford, 1964; Endler, 1986). However, the easy presence of heritable trait variation does now not imply that distinct degrees of genetic diversity may have predictable ecological effects. For instance, with increasing fitness, genetic diversity can increase in population if the population isn't regulated via different evolutionary elements, such as genetic drift, gene flow and natural selection (Fisher, 1930). Thus, regardless of the apparent presence of genetic variation for ecologically vital traits, we have recognized exceptionally little about the the range of potential ecological effects of genetic diversity for population dynamics, species interactions and ecosystem tactics (Figure 1). Frankham et al.

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(2002) have reported short term ecological effects of genetic variety in small or endangered populations. Several agricultural practices have been performed where genetically varied plants are harvested for higher yield and massive scale manufacturing, as well as decreased chance of herbivores and pathogens (Zhu et al., 2000). Mainly three lines of evidences ease the pathway for the study of the ecological effects on genetic diversity. First, the ecological results of genetic diversity have centered on how the number of species and purposeful functional groups like the trophic structure within communities influences the stability and functioning of the surrounding ecosystem (Hooper et al., 2005). Second, there may be a growing research on the ecological effects of the variance component around the average in the experimental or observable units of a selected describable variable (Inouye, 2005). From lengthy back numerous research have been carried out that offer detailed facts concerning how the genetic differences between organisms have encouraged the species interplay; and the interaction between the genetic and ecological dynamics (Turkington and Harper 1979). Finally, the community genetics has bridged the fields like evolutionary biology, population genetics and community ecology (Whitham et al., 2003). Community genetics focuses that the genetic variety is a hierarchical idea and isn't always constrained to single taxonomic and genetic stage. Therefore, the variation in ecologically essential traits together with growth and reproduction rate, competitive potential, immune feature, virulence, and so on, the amount of genetic diversity at any degree of population could have vital ecological results.

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Genetic diversity will have an effect on the community at identical trophic level (Figure 1). As an example, multi-species communities of grassland plants with higher genetic diversity per species maintained higher species diversity over time than the communities with lower genetic diversity (Booth and Grime, 2003). This system suggested that the genotypic interactions between species contribute to the consequences of overall genetic diversity (Whitlock et al., 2007). Mechanisms underlying the consequences of genetic diversity on community could apply equally to the consequences of species diversity on community, however mechanisms specific to genetic diversity may also contribute to community-level impacts. The effect of genetic diversity on kin recognition has been observed in the successful invasion of the Argentine ant (Linepithema humile); the low allelic diversity of the invader decreased the accuracy of the recognition system, allowing this species to form large, competitively dominant super-colonies (Tsutsui et al., 2003). Genetic diversity also appears to influence behavior in this species, with ants from low diversity colonies showing greater aggression and lower mortality in encounters with ants from high-diversity colonies (Tsutsui, 2004). Several researches had been executed to show how the genetic variation affects the better trophic structure in an ecosystem. The range of plant genotypes affects the arthropod network, translating into superb results on total arthropod species richness however not on total abundance (Johnson et al., 2006). The mechanisms underlying those results differed relying on arthropod trophic stage, increases with predator species richness and abundance can be attributed to spatial and temporal niche complementarity amongst genotypes contributed to the growth in abundance of arthropod species. In study stated above suggested that the genotypic richness accelerated the abundance of plant-related species by using growing plant abundance (Reusch et al., 2005), at the same time as genotypic richness undoubtedly affected arthropod species richness by growing each the entire abundance and the variability of plant resources availability (Crutsinger et al., 2006). Another study additionally indicates the effects of plant genetic variation on the abundance and/or variety of invertebrate communities (Tovar-Sanchez and Oyama 2006). Effects of genetic variety on community dynamics throughout trophic levels also can occur because of fast evolution, as illustrated with the aid of modified predator–prey and host–pathogen dynamics (Pimentel 1968). Genetic diversity in dominant plant species can also influence the fluxes of nutrients cycle and energy cycle, i.e., ecosystem-level processes (Madritch et al., 2006). Moreover, genetic diversity is not the only driving force to affect the ecological processes. However,

Page | 11 genetic diversity influences the ecological processes only when the four conditions are available. The situations are as follows: Firstly, while a community or atmosphere is dominated via one or some primary habitat-structuring species, the position of genetic variety is much like that of species diversity of the ecosystem (Crutsinger et al., 2006). Interestingly, a particularly few studies have been done which targeted on the genetic variation of the foundation species. However, the changes within the genetic variety in the habitat forming species have a massive effects at the ecosystem level and adjustments within the genetic diversity inside the habitat-forming species suggesting that greater focus should be positioned at the genetic diversity of these dominant species for correct knowledge of the ecosystem functioning. Second, when genetic variety in one species influences the abundance or distribution of a keystone species (i.e. a species with an effect disproportionate to its biomass within the community), it could have massive oblique ecological influences (Crawford et al., 2007). Third, a critical prediction is that the genetic variation will have prominent ecological results for species that exhibit measurable genetic diversity inside populations for applicable traits, and thus those effects cannot be assumed without the information about the genetic variation. For example, populations that are distinctly inbreed or have experienced a current selective force for genes controlling ecologically essential developments will have a likelihood to exhibit low genetic diversity. Finally, the maintenance and inspecting of the genetic diversity and species diversity of any ecosystem is very crucial for the stability and disturbance responses of the concerned ecosystem. Therefore examining and investigating the genetic diversity is the most relevant subject in highly variable environments or those subjected to rapid anthropogenic and natural modifications (Reusch et al., 2005; Hooper et al., 2005). Therefore, species diversity and genetic diversity can be both a cause and consequence of ecological processes (Vellend and Geber, 2005). For instance, genetic diversity allows prey populations to evolve, which may have an effect on predator population dynamics and successively drive additional ecological and biological process changes among the prey population, resulting in inevitable predator–prey and eco-evolutionary dynamics (Yoshida et al., 2003). Moreover, genetic variation in a single species can permit for coexistence with its competitor species, while at the equal time, competitor species variation maaintain this genetic variability (Lankau and Strauss, 2007). There are certainly several additional reciprocal effects among genetic variety and ecological elements, as genetic diversity and evolutionary processes can have an impact on a number population, community network and surroundings ecosystem responses (Fussmann et al., 2007). It is clear that the level of genetic

Page | 12 diversity within a population can affect the productivity, growth and stability of that local population, inter-specific interactions within communities, and ecosystem-level processes.

1.4 Genetic diversity and Evolutionary processes There were two absolutely contrasting schools of thought to determine the extent of genetic variability in natural population. In 1974, Richard Charles Lewontin had argued that 'it is a common myth of science that scientists gather evidence on some issues and then by logic and perception form what seems to them was the most reasonable interpretation of the facts'. According to the Classical hypothesis proposed by H. J. Muller (1950), two randomly chosen individuals from a population would be expected to be nearly identical nearly all of their genetic loci, so they are known as wild type alleles which are predicted to turn out to be fixed at most loci. One of them may sometimes have an extraordinary mutant allele in location of one in every of its wild type alleles, which is not likely in each the individuals selected randomly at the identical time to have the same mutant alleles. For the same motive, it would be difficult to discover an organism which could be homozygous for the mutant allele. Mutant alleles are anticipated to get up by means of mutation and are anticipated to be eliminated from the population by natural selection because most mutant alleles are deleterious. Mutant alleles which are higher/beneficial than the wild kind, are anticipated to get constant within the population, but then they would be referred to as wild types. In different words, most individuals are predicted to be monomorphic and homozygous for the wild type allele at most in their loci. The ―Balance‖ speculation, proposed by means of Dobzhansky (1955), makes a completely distinctive prediction approximately two randomly selected individuals from a population and expects that some of distinctive alleles exist for every locus in the population and most of the loci of the two individuals randomly chosen are predicted to have different alleles. In different words, there is no such element as a wild type. Many loci are anticipated to be polymorphic and any individual is predicted to be heterozygous at a big proportion of its loci. Richard Charles Lewontin (1974) finally gave an optimistic view of the Balance hypothesis and it states that the natural evolution is a essentially progressive phenomena which leads to harmony between living systems and the existing conditions.

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1.4.1. Mutation Mutations are the heritable modifications inside the genetic material of any organism. These phenomena are very uncommon process and it provides the raw material for genetic variability on which the natural forces of selection and evolution perform. The system of mutation ranges from a single base pair adjustments to the exchange of the chromosome variety in the gamete. Mutations are classified according to their mode of characteristic viz. Deleterious or lethal, some are impartial or neutral and few may be beneficial for the carrier. The useful mutations are beneficial for the survivability and reproductive fitness of the provider which ultimately leads to natural selection and evolution of the organisms. There are numerous natural mutations which are definitely indistinguishable in phenotypes and can be detected only even with the help of exclusive types of genetic techniques. Mutations are of very remarkable concern for conservation biologists. Firstly, the presence of very rare and deleterious alleles has an incredible effect at the closed captive populations; and secondly, the artificial selection of uncommon alleles is once in a while the fundamental focus of synthetic breeding programmes of captive populations (Frankham et al, 2010). Most population lose genetic variation both by means of population extirpation or genetic erosion. Therefore, conservation geneticists are a long way more involved with the deleterious effect of mutations in addition to with the lack of genetic variability in the small populations than that in large populations. Population extinction due to genetic phenomena cannot be ignored due to the fact that natural selection eliminates the deleterious alleles from populations nearly without delay however mildly deleterious, near-neutral mutations will progressively be increased in frequency over hundred of generations and turn out to be a severe problems when their frequencies exceed 0.05 or 1/(2Ne). These mildly deleterious alleles will tend ultimately to diminish the long-term viability of threatened taxa (Hedrick, 2011). Therefore, conservation biologist has to clear up this problem via two techniques i.e., first of all by means of maximizing the genetic variation of the captive threatened taxa earlier than introducing the taxa from captivity to the wild or secondly, by eliminating the deleterious mutations for the ones populations which need to be contained in captivity for plenty generations and cannot be back to the wild (Woodruff, 2001).

1.4.2 Mating system The population genetics theories are completely primarily based at the principle of ―random mating‖ but this phenomenon is hardly ever observed in natural populations.

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Moreover, related species once in a while display extraordinary sorts of mating system. Self fertilization in monoecious organisms and outbreeding mechanisms in dioecious organism are the two severe variety of mating behaviours found in living organisms. The most hard hassle arises in mating system all through conservation of organisms whilst the last surviving individuals in a sexually reproducing population are all belong to the same sex (Woodruff, 2001). Animal mating systems are relatively variable and had been most typically classified as monogamous (a single male and single female mate solely with one another) or polygamous (adult males and/or females mate with numerous individuals). Moreover, there are three regular polygamous mating systems have been classified viz., polygyny (a male associates with more than one female), polyandry (a girl mate with a couple of males) and polygynandry (each men and women have a couple of mate). Mating systems are stimulated via many natural factors, inclusive of the spatial and temporal distribution of sexually receptive male and female, resource availability and distribution, individual‘s life history patterns, sexual selection and parental care (Klug et al., 2011).

1.4.3 Inbreeding Inbreeding is the mating of individuals associated with common ancestry. It is a potentially extreme trouble and whose opportunity of occurrence increases in the small, fragmented or isolated populations, that is regular for many threatened species (Frankham et al., 2010), and may lead to a significant decline in population fitness and reproducibility (Keller and Waller, 2002). Inbreeding results in a predictable rise in homozygosity and its ends in inbreeding depression, such as decreased fecundity, reduced offspring size, growth and survivorship; physiological malfunction, modifications in the age of adulthood and physical deformities (Falconer, 1989). The decline in trait values due to inbreeding has been extensively studied in captive (Ralls et al., 1988), laboratory (Charlesworth and Charlesworth 1987; 1999; Falconer 1989; Roff 1997) and in wild populations (Keller and Waller 2002). Inbreeding has the deleterious effects on organisms fitness and reproducibility (Roff 1997), and can be a crucial element contributing to population extinction (Frankham 2005). Although experimental proof regarding the feasible involvement of inbreeding in extinction hazard is inadequate, elevated extinction hazard due to inbreeding has been demonstrated in a restrained variety of research (Reed et al., 2002, 2003). Furthermore, it is tough to isolate genetic consequences from ecological outcomes in natural populations (Bijlsma et al., 2000) but there was a direct possible connection present among inbreeding and extinction risk (Saccheri et al. 1998). Three lines of hypothesis were proposed for the mechanism of

Page | 15 inbreeding depression; firstly the inbreeding acts to change the frequency of genotypes in a population, growing homozygote frequency in spite of the heterozygotes, and as a result inbreeding depression occurs. Secondly, the partial dominance hypothesis states that inbreeding depression may result from the elevated expression of deleterious recessive or partially recessive alleles, which are masked in heterozygotes but are exposed as homozygosity will increase. Thirdly, the overdominance hypothesis states that the heterozygotes may additionally have superior fitness relative to either homozygote and inbreeding depression arises from the loss of favorable heterozygote mixtures (Charlesworth and Charlesworth 1987, 1999). The partial dominance idea is usually considered to be the most vital cause of the observed inbreeding depression (Charlesworth and Charlesworth 1987, 1999; Roff 2002), however there are evidences which helps the overdominance hypothesis inflicting inbreeding depression (Li et al. 2001), and every so often both partial and over-dominance mechanisms might also occur concurrently (Kristensen and Sorensen 2005). Inbreeding depression and inbreeding avoidance both clear out gene pool as it passes into the subsequent generation(Amos and Harwood, 1998). Inbreeding depression acts on individual genes and gene complexes, and inbreeding avoidance acts on whole units of chromosomes. There are two opposing consequences that have an impact on the two above phenomena that have an effect on the stages of genetic variability (Amos and Harwood, 1998). Firstly, by way of lowering the representation of some elements of the gene pool in future generations and as a result variability can be decreased. On the other hand, those individuals that do live to survive to breed will have a tendency to be outbred, and as a result, will convey more variety than would be anticipated. Inbreeding depression has the capability and potential importance for the control and conservation of endangered species (Hedrick and Kalinowski 2000), and it's far critical that the causes, expenses and patterns of inbreeding depression has to be nicely understood.

1.4.4 Outbreeding It is the method of crossing between unrelated people, a full-size phenomenon in nature. It is assumed that sexual reproduction advanced due to the fact the chromosomal crossing over and genetic recombination facilitated through outbreeding process produces extra genetic variability than do different mating structures. Outbreeding depression occurs whilst organisms of the various geographic vicinity are mated and the produced hybrids or offsprings have decreased fertility and decrease survivability (Woodruff, 2001). There are

Page | 16 two most important reasons for outbreeding depression viz., first of all, co-adaptation consequences whilst a nearby population evolves a gene pool that is they're reproductively balanced internally (e.g., the procedure of gamete formation requires a matched set of chromosomes and failure of those leads to fertility issues); and secondly, local adaptation takes place when populations adapt to their constrained nearby surroundings and hybrids from different local populations are not adapted to any or wrong nearby environment (Meffe and Carroll, 1997).

1.4.5 Hybridization Hybridization occurs between special species and semi-species of animal and plants. Hybrids are exciting in evolutionary and conservation biology due to the fact they display that evolution of many vegetation and animals includes each lineage splitting and lineage anastomosis (Woodruff, 2001). Fertile hybrids allow gene flow between specific species. Hybridization may also restore the viability of small, isolated and inbred populations. Understanding the key elements that make contributions to constructive as opposed to destructive consequences of hybridization is of great importance for handling conservation of the populations. Hybridization may also drive rare and endangered taxa to extinction through ―genetic swamping or genetic pollution‖ (is a technique which leads to homogenization of the wild genotypes), where the rare individuals are replaced by potential hybrids, or with the aid of demographic swamping, wherein population growth rates are decreased because of the production of much less adaptive hybrids (Todesco et al., 2016).

1.4.6 Gene flow Gene flow is the fundamental mechanism that forces evolution based on the dispersal of genes between populations of a species. Gene flow is the mechanisms resulting in the movement of genes from one population to another; from the movement of extranuclear genetic elements present in mitochondria or the mitochondria as a whole to the extinction of an entire population followed by recolonization. It is the common gene flow over generations that decide the quantity to which the dynamics of genes in unique populations are dependent (McDermott and McDonald, 1993). (McDermott and McDonald, 1993). Gene flow involves not only the migration of individuals or genotypes but also successful establishment of immigrants in the new population. Continuous gene flow leads to homogenization of the genotypes within the linked population, whereas lack of gene flow tends to differentiation or isolation of the populations. It is very important for conservation biologists to estimate the

Page | 17 pattern and rate of gene flow, and the rate at which migration among populations occurs though allele frequency data and are examined in terms of number of migrants per generation (Woodruff, 2001). Slatkin (1977, 1985, and 1987) has reviewed extensively regarding the theory of gene flow. The important aspect of gene flow is to prevent the complete fixation of different alleles in different local populations (Wright, 1931). Wright (1931) also introduced a simple model of population structure, called the Island model, which predicts a simple relationship between the number of migrants a population receives per generation and genetic differentiation (FST)

(Figure 2). Under the assumption of the island model, FST ≈ 1/(4Nm+1), where N is the effective population size of each population and m is the migration rate between populations.

E B

D C

Figure 2. The Island model. Each population receives and gives migrants to each of the other population at the same rate m. Each population (A, B, C, D, and E) is also composed of the same number of individuals [Modified from Wright (1931)].

Perhaps an average of one migrant or more between populations is sufficient to prevent different neutral alleles to become nearly fixed in different populations. In other words, the movement of one individual per generation between populations is sufficient to prevent substantial differentiation between those populations. This result is independent of population size because the force of gene flow, which is measured by the fraction of migrants (m) in a population, is counteracted by the force of genetic drift, which is proportional to the inverse of the population size (N). Generally, if Nm < 1, then local populations differentiation

Page | 18 will occur, and if Nm > 1, then there will be little differentiation occur among populations (Wright, 1951). Haldane (1930) first characterized the balance between gene flow and selection. According to him, in a population where an allele was favoured by selection strength (s), the favoured allele would be maintained in a high frequency against the force of gene flow if m < s. It is difficult to maintain the favoured allele at low frequency under the balance between gene flow and selection (Nagylaki, 1975). Not all gene flow involves the movement of genetic constituents among established populations rather the extinction and recolonization is likely to be an important source of gene flow in populations. Slatkin (1977) observes that two consequences results from the extinction and recolonization process: firstly, the "founder effect" results from the survival of only a small fraction of the individuals from the source population; secondly, a potential component of gene flow arises due to random mixing and survival of individuals from several populations to constitute the new population. Slatkin (1977) had proposed two extreme models on gene flow, extinction and recolonization, viz., the ‗propagule pool‘ and the ‗migrant pool‘. In the first model, the propagule pool is formed by sampling individuals from a single source population. In this case, genetic drift overshadows the effect of gene flow, resulting in greater local differentiation among populations. In the migrant pool model, the local population is made up of a random sample of genotypes from many populations, collectively termed the "metapopulation". In this case, local mutation tends to increase local differentiation, while gene flow tends to decrease local differentiation. Whitlock and McCauley (1990) have elaborated on Slatkin‘s models and allow greater flexibility in the formation of founding populations. According to them the propagule model states that the extinction and recolonization process increases the differentiation among populations (relative to the case with no extinction) if k< N; where k is the number of individuals comprising the founding group and N is the effective population size after colonization (McCauley, 1991). Under the migrant pool model, local differentiation can be either increased or decreased depending on the relationship among the parameters. This results from an interaction between the effects of genetic drift and the gene flow from different sources in the metapopulation. Generally, differentiation among populations will increase if (4Nm + 1) > 2k; where m is the migration rate among surviving populations (McCauley, 1991). The metapopulation concept (Olivieri et al., 1990, McCauley, 1991) where a "regional population" contributes individuals to local populations is likely to play an important role in understanding the dynamics and migration of genes in populations.

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1.4.7 Genetic drift Genetic drift is a random sudden change in the gene frequencies in the small populations attributable to sampling error. That is, in small populations, by chance alone some alleles will not be ―sampled or selected‖ and represented in the next generation. Random fluctuations in the allele frequencies in small populations reduce genetic diversity, leading to increased homozygosity and loss of heterozygosity as well as evolutionary adaptability. The rate at which alleles are lost from a sexually reproducing population by genetic drift can be detected. Wright (1931) developed the basic theoretical model to show how the rate varies according to the effective population size (Ne). This model postulated that closely related individuals will share alleles by common descent. Monozygotic twins are genetically identical and therefore should be counted as one individual rather than two. Siblings share half their genes with each other and half with their parents and are therefore is not equivalent to two genetically unrelated individuals. The effective number (Ne) of individuals in a population is therefore almost always less than the total number of individuals (N) estimated. Ne thus under some breeding system, be one or two magnitude less than N (Lande and Barrowclough, 1987). Wright (1969) reported that the individuals present within the effective population (Ne) would experience same rate of genetic drift as that of the actual population (N). Ne can be estimated in various ways using temporal ecological data, DNA sequences and various methods of estimating migration rate.

1.4.8 Population bottleneck Sudden population decline and subsequent restoration are known as population or demographic bottlenecks. The bottleneck may be due to numerous occasions, inclusive of an environmental disaster, the hunting of a species to the point of extinction, or habitat destruction that consequences within the deaths of organisms. They may have an instantaneous effect on variability at genetic loci. A bottleneck can without a doubt result in a brief term increase in population variant because epistatic variation within the loci is converted into additive variation (Woodruff, 2001). A sudden reduction in population, as well as variation outcomes in a lack of fitness except there, maybe a fast and sustained recovery. Gradual reduction, however, permits natural selection to take away recessive lethal mutations and avoid huge a part of inbreeding depression (Woodruff, 2001). The population bottleneck produces a lower inside of the gene pool of the population because many alleles, or gene variants, that had been present inside the unique population are misplaced. Therefore, the final population has a totally low degree of genetic diversity, this means that that the

Page | 20 populace as a whole has a few genetic traits. Following a population bottleneck, the remaining population faces a better degree of genetic drift, which describes random fluctuations in the presence of alleles in a populace. In small populations, infrequent alleles face a greater hazard of being lost that may, in addition, lower the gene pool. Due to the loss of genetic variation, the new population can emerge as genetically distinct from the original population, which has brought about the hypothesis that populace bottlenecks can cause the evolution of new species (Provine, 2004).

1.4.9 Genetic erosion Genetic erosion is the decrease in the population variation due to genetic drift and inbreeding depression which is evident in the small isolated population which lead to endangerment of the population. Population genetic theory revealed the fact that variation is lost regularly by genetic drift. In closed isolated population without the factors that promote genetic variation (viz., mutation and gene flow) the expected rate of loss of heterozygosity or rate of loss of genetic variance in the quantitative characters is 1/2Ne per generation. Little amount of variation is lost in any one generation but if a small population (N) sustains for several successive generations, the genetic variability is severely depleted (Wright, 1969). Therefore, small isolated population have a higher tendency to loss the genetic variation or heterozygosity than that of large continuously distributed populations.

1.4.10 Natural selection Natural selection is a microevolutionary change that leads to differential ability of survival and reproductive fitness of some genotypes or individuals over others. Possibly because of two important reasons it is a major focus for conservation biologists (Woodruff, 2001). Firstly, the artificial selections where human activities can affect the selection process in both natural and managed populations. Secondly, a challenging area of research for conservation biologist is that the species are continuously adapted to the ongoing global climatic changes. In the past, natural selection (without anthropogenic interferences) was the main active force which favoured wild individuals to adapt to natural changes and this led many species to shift their niche ranges to accommodate major changes for their survival. Quantitative characters are mainly under the directional or stabilizing selection presure and are evolved in response to the environmental changes (Falconer, 1989). These characters balance the present fitness and the future adaptability. Lande (1999) has reported that

Page | 21 effective population size (Ne) =5000 is required to maintain the genetic variability of endangered or threatened population to naturally select and survivability.

1.5 Neutral vs. Adaptive genetic diversity Manel et al. (2003) stated that landscape genetics is the aggregate of molecular population genetics and landscape ecology which goals to offer information about the interplay among landscape features and evolutionary techniques within species, consisting of gene drift or local adaptation model. Moreover, the distinct information of such processes requires substantial knowledge of how landscape traits affect the local gene pools of populations, which requires robust data on the genetic diversity and population differentiation. Genetic differentiation can be defined as how much of the genetic diversity present in a sample of several populations (among population) vs. within these populations (Pearse and Crandall, 2004). There are two diverse types of genetic diversity available in natural population. Firstly, the neutral genetic variation, which is simple to measure in the laboratory with the help of the ever-growing collection of molecular-genetic markers. Secondly, the adaptive or selective genetic variation, which is more difficult to estimate but can be estimated especially with the help of quantitative genetic experiments. This distinction between neutral and adaptive genetic variation is largely unrecognised outside the scope of of population or conservation genetics (Pearman, 2001). Therefore it is important to know the distinction between neutral and adaptive genetic diversity for suitable conservation and management of natural populations.

1.5.1. Neutral genetic diversity The term ‗neutral‘ refers to a gene (or a locus) that has no (or nearly no) impact on fitness and survival. A gene having simplest two alleles, specifically a and b, can occur in a diploid species in three specific genotypic forms, namely the homozygotes aa and bb and the heterozygote ab. However, it does not matter for a given individual fitness which of those three genotypes it carries, since this has no impact on its performance. As natural selection does now not act upon these alleles, they may be of no direct adaptive value and are selectively neutral (Kimura, 1983; Conner and Hartl, 2004). Neutral genetic variation is the genetic variation that is estimated at such neutral genes. The most commonly used statistical measure of neutral genetic variation of a population is gene diversity, He. The gene diversity (He) can be interpreted as the probability of sampling two different genes from a population (Hartl and Clark 1997). Gene diversity,

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He, is usually calculated per locus and subsequently averaged over several loci and it represents a measure of the genetic variation of a population. Several statistical measurements are available to estimate genetic variation of a population which are either universal or molecular marker specific and depends on the specific mode of inheritance or mutational change (Lowe et al. 2004). Population geneticists use many different measurements to refer to population differentiation (Lowe et al. 2004). The most commonly used measure of genetic differentiation is Wright‘s F-statistics (Wright, 1951; Conner and

Hartl, 2004). F-statistics (FST) gives an estimate of the amount of genetic variation found among populations and refers to the genetic differentiation or the genetic structure of these populations. If the value of FST is more than zero, all genetic variation is found among the populations (i.e. population is differentiated, different alleles are found in the different populations) and if FST is zero, the populations are not differentiated at all (i.e. the same alleles are found in the same frequencies in all the populations) (Wright, 1951). FST can either be calculated as mean differentiation over the whole sample set of populations or in a pairwise fashion for each pair of populations. Gene flow homogenises the genetic diversity and thus leads to a decrease genetic differentiation. An often used estimate the gene flow, Nm (i.e. number of migrants exchanged among populations per generation), relies on the differentiation of populations, FST, under the assumptions of Wright‘s island model (Conner and Hartl, 2004; Vandewoestijne and Baquette, 2004).

1.5.2. Adaptive genetic diversity The terms ‗adaptive‘ or ‗selective‘ refer to a gene (or a quantitative trait) that has an substantial effect on individual fitness and survival. If there are only two alleles for a given gene, namely a and b; there will be three different genotypes, namely the homozygotes ‗aa‘ and ‗bb‘ and the heterozygote ‗ab‘. These three genotypes affect the individual and population fitness because they are selectively non-equivalent and most importantly adaptive in nature. For instance, organisms carrying the genotype ab might have a higher level of fitness than the genotypes aa or bb (the homozygotes). Hence, natural selection will directly act and favour on ab genotypes. The genotypes are thus of adaptive or selective significance for survival for the concerned organism (Conner and Hartl, 2004). It is rarely possible to directly study the alleles of genes that are responsible for adaptive genetic variation in most organisms (Jackson et al., 2002). The variation of traits having potential adaptive value, such as body size in animals has to be investigated through quantitative genetic experiments. Moreover, most of the quantitative traits are determined by several genes, therefore, are

Page | 23 polygenic characters (Conner and Hartl, 2004). Alleles may therefore be additive in their effects across many genes and quantitative traits are not affected by selection and are effectively neutral (Conner and Hartl, 2004). To analyze the quantitative traits which is influenced by natural selection, individuals with known genetic relationship are grown under constant environmental conditions.

1.5.2.1 Measurement of Adaptive genetic diversity

Gene diversity (He) is a measurement of the genetic variation at neutral genes, whereas heritability, h2, is used as a measurement of genetic variation of a population of an adaptive gene or a quantitative trait. It is usually called a narrow-sense heritability and presented as: 2 h = VA/VP;

where, VA is the additive genetic variance, and VP is the phenotypic variance of a trait that varies with genotype and environment. Additive genetic variance is the effects of the alleles in a genotype that can be summed up to determine the total effect on the phenotype (Conner and Hartl, 2004). The additive genetic variance is responsible for the evolutionary potentiality of populations. To calculate h2, it is necessary to separate genetic variance from that of environmental or non-genetic variances. A typical approach is to estimate variance components from an Analysis Of Variance (ANOVA). Heritability (h2) is specific to a particular trait within a particular environment (Conner and Hartl 2004). As mentioned earlier, FST represents population differentiation based on neutral genes. The equivalent measurement referring to population differentiation of adaptive genes is QST, which can be written as:

QST =VG/( VG+ 2VA)

where, VG is the between-population variance component, and VA is now the average additive genetic variance within populations (Savolainen et al. 2004). Like FST, QST is also used to estimate the genetic differentiation; higher values of QST (QST > 0) indicate complete differentiation at quantitative adaptive traits and zero (QST = 0) indicates genetic homogeneity or less differentiation of populations (Latta, 2003). Reed and Frankham (2001) performed a meta-analysis of 71 published data sets which provided estimates of gene diversity (He) measured by neutral genetic markers and the adaptive genetic variation measured by heritability (h2) for each of the populations. Then,

Page | 24 pair-wise values per population were correlated across all populations per species. They found a significant overall correlation coefficient of r = 0.217 in the 71 studies surveyed. Another study carried out by Merila and Crnokrak (2001) used data from 14 studies providing estimates of FST and QST for the published date sets. They found a significant and strong correlation between FST and QST (r = 0.75). Whereas, McKay and Latta (2002), in a survey of 29 species, detected an only marginally significant correlation of these variables (r = 0.363). Furthermore, Reed and Frankham (2002) concluded that molecular measurements of genetic diversity through neutral marker have a very limited potentiality to predict quantitative genetic variation in natural population. Although a correlation exist between neutral and adaptive genetic variation but the former cannot be used as a substitute for the otherone, because the quantitative genetic data are needed for population‘s evolutionary or adaptive potentiality. However, neutral genetic variation at the level of heterozygosity is correlated with individual fitness (Reed and Frankham 2002).

1.6 Habitat fragmentation and genetic diversity

1.6.1 Fragmentation process

Habitat fragmentation is described as a process all through which ―a huge stretch of habitat is altered into a number of smaller patches of smaller general place, isolated from every different by using a matrix of habitats in contrast to the unique habitat‖ (Wilcove et al., 1986) (Figure three). By this definition, a habitable landscape may be qualitatively categorized as both continuous (containing non-stop habitat) or fragmented, wherein the fragmented landscape represents the outcome of the process of fragmentation process. The continuous landscape represents a landscape earlier than fragmentation manner and the fragmented landscape represents a landscape following fragmentation. Although these technique for outlining the system of fragmentation have two inherent weakness. First, due to the fact habitat fragmentation is a landscape scale manner, two factors determine the impact of habitat fragmentation on biodiversity i.e., one non-stop landscape and one fragmented landscape (McGarigal and Cushman 2002). The inferences about the results of fragmentation are susceptible on this type of observe layout; however the detectable results of fragmentation are because of different differences among the landscapes. Secondly, the characterization of habitat fragmentation is strictly qualitative, i.e., each landscape both may be continuous or fragmented. This opportunity does not permit one to investigate the relationship between the

Page | 25 degree of habitat fragmentation and the extent of the fragmentation-outcomes on biodiversity. This perception requires measuring the pattern of habitat fragmentation.

Figure 3. The process of habitat fragmentation. Black areas represent habitat and the white areas represent matrix (Wilcove et al., 1986).

The most obvious effect of the process of habitat fragmentation is the division of habitat into smaller patches or fragments (Figure 3). Therefore, the degree of habitat fragmentation can be measured simply by the amount of habitat remaining on the landscape (Carlson and Hartman, 2001; Fuller, 2001, Summerville and Crist, 2001; Virgós, 2001). Fragmentation results into the small and isolated patch which in turn also changes the properties of the remaining habitat (van den Berg et al., 2001). In addition to habitat loss, the process of habitat fragmentation results in three other effects: increase in number of patches, decrease in patch sizes, and increase in isolation of patches. There are several other measurements available to analyze the fragmentation process (e.g., amount of edge available) and these measurements are strongly related to the amount of habitat remaining (Bélisle et al., 2001; Boulinier et al., 2001; McGarigal et al., 2002). Some researchers do not separate the effects of habitat loss from the compositional effects of fragmentation which leads to ambiguous conclusions regarding the effects of habitat organization on biodiversity (Summerville and Crist, 2001). Being a landscape-scale process fragmentation measurements are correctly made at the landscape scale (McGarigal and Cushman, 2002). As pointed out by Delin and Andrén (1999), when a study is at the patch scale, the sample size at the landscape scale is only one, which means that landscape-scale inference is not possible (Brennan et al. 2002) (Figure4).

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Figure 4: Patch-scale study: each observation represents the information from a single patch. Only one landscape is studied, so sample size for landscape-scale inferences is one. Landscape-scale study: each observation represents the information from a single landscape. Multiple landscapes, with different structures, are studied. Here, sample size for landscape-scale inferences is four (Brennan et al. 2002).

The connection between the patch size and fragmentation is unsure because each habitat loss and habitat fragmentation bring about smaller patches. Using patch size as a measure of habitat fragmentation it's far assumed that patch size is impartial of quantity of habitat remaining at the landscape scale (Niemela, 2001). However, areas where patches are large often correspond to areas in which there may be more habitats remain (Fernandez- Juricic 2000). Patch isolation is considered as a measure of the shortage of habitat inside the landscape surrounding the patch, extra isolated a patch is, the much less habitat that surrounds it. The most common measure of the degree of patch isolation is the distance to the next-nearest patch, or ―nearest-neighbour distance‖ (Haig et al., 2000). Patches with small nearest-neighbour distances are commonly located in landscapes containing extra habitat than are patches with large nearest-neighbour distances, so in maximum conditions this measure of isolation is associated with habitat quantity within the landscape. Another commonplace estimation of patch isolation is the inverse of the quantity of habitat within some distance of the patch (Magura et al. 2001). In other words, patch isolation is measured as habitat amount remain after fragmentation on the landscape scale. All other measures of patch isolation are a

Page | 27 aggregate of distances to other patches and sizes of those patches (or the populations they comprise) in the surrounding landscape (Bender et al. 2003).

1.6.2 Effect of Habitat Loss on Genetic Diversity Habitat loss has large and negative effect on genetic diversity as well as on genetic diversity. The negative effect of habitat loss apply not only to the directs measures of biodiversity such as species richness, population abundance, distribution and genetic diversity but also to the indirect measures of genetic diversity and factors affecting genetic diversity (Best et al., 2001; Guthery et al., 2001; Gibbs, 2001; Schmiegelow and Monkkonen, 2002; Steffan-Dewenter et al., 2002). Moreover, habitat loss also reduces the trophic food-chain length, alter species interactions, breeding pattern, dispersal rate of different species, predation rate, foraging success rate, and behaviour of animals (Komonen et al., 2000; Taylor and Merriam, 1995; Kurki et al., 2000; Belisle et al., 2001; With and King, 1999; Bergin et al., 2000; Mahan and Yahner, 1999). Patch isolation also has negative effect on species richness (Rukke, 2000; Virgos, 2001). Tischendorf et al. (2003) found that the ―buffer‖ measures (i.e., amount of habitat present within a given region around the patch) was the best measure to analyse the effect of patch isolation on the biodiversity and suggest the effects of habitat-amount on the movement of animals between the patches. Smaller patches generally contain fewer numbers of species than the larger patches and the species on the smaller patches is more prone to extinction than the larger patches (Debinski and Holt, 2000; Vallan, 2000). The variety of individuals of any species that a landscape can support have to be a positive function of the quantity of habitat available, but research showed that there was threshold habitat level beneath which the population cannot sustain itself, termed the extinction threshold (Fahrig, 2001; Flather and Bevers, 2002). Several research showed that the effect of habitat fragmentation is vulnerable relative to the habitat loss (Collingham and Huntley, 2000; Flather and Bevers, 2002), although some different modeling research predicts a much larger effect of habitat fragmentation of diversity (Urban and Keitt, 2001; Fahrig, 2002). Studies endorse that the effects of fragmentation need to become apparent best at low ranges of habitat quantity under approximately 20-30% habitat present on the landscape (Flather and Bevers, 2002). The fragmentation has a negative effect in all likelihood because of two major reasons; firstly, fragmentation leads to the formation of a large variety of small patches so that it will be too small to sustain the local population and species which can be unable to migrate the non-habitat region could be constrained to the

Page | 28 small patches and ultimately reducing the general population size. Secondly, significantly fragmented landscapes have extra edges which lead to negative edge effect (Fahrig 2002). Moreover, the amount of time spent in the matrix (i.e., the interpatch migration through non- liveable portion) in the greater fragmented landscape is large which could cause an increase of mortality rate and thus decrease in reproductive success of the population (Fahrig, 2002). However, several researches additionally reported that habitat fragmentation should have fine results at the biodiversity. Huffaker‘s (1958) advised that fragmentation of habitat into many small range patches can enhance the persistence of a predator-prey dynamics. Some theoretical studies advocate that habitat fragmentation enhances the steadiness of species- species competition (Levin, 1974; Shmida and Ellner, 1984) and additionally coexistence of competing species might be prolonged by fragmentation of the habitat (Atkinson and Shorrocks, 1981). Other researchers cautioned that habitat fragmentation could even stabilize single-species population dynamics whilst nearby disturbances are asynchronous with the aid of decreasing the possibilities of simultaneous extinction of the complete population (Reddingius and den Boer, 1970; den Boer, 1981). Bowman et al. (2002) argued that the overall immigration rate needs to be better while the landscape is produced from a bigger quantity of smaller patches than while it is far created from a smaller wide variety of large patches. This is because the immigration rate relies upon the quantity of patches available in preference to the vicinity of the patches. The species which require multiple habitats for its survival and reproduction, the proximity of different required habitat or patches will be advantageous for them to migrate among those patches (Law and Dickman 1998). Positive edge effects could be responsible for positive effects of habitat fragmentation on abundance or distribution of some species (Carlson and Hartman, 2001; Laurance et al., 2001).

1.7 Meta-population dynamics and genetic diversity More than three decades ago the term ‗metapopulation‘ was coined by Levins (1969), who defined it as a ‗population of populations‘ which go extinct locally and recolonize further (Levins, 1969; 1970), an analogous concept with respect to the births and deaths cycle of individual organisms. The concept of metepopulation has been elaborated recently and defined as ‗any assemblage of discrete local populations with continuous migration among them is considered to be a metapopulation, regardless of the rate of population turnover‘ (Hanski and Gilpin 1997). The current metapopulation theory is based on the spatial structure of populations and depends on regional processes of recolonization and extinction that is of

Page | 29 major importance in the dynamics of animal and plant populations. Metapopulation theory has postulated the concept that the long-term persistence of a local population which is dependent on the dynamics of immigration and emigration. The system of a metapopulation is the result of local and regional population dynamics. The local dynamics depend on habitat patch size and habitat quality and regional dynamics depend on landscape configuration, i.e. habitat patch position, patch connectivity, and large-scale environmental forces. The balance between these two levels of dynamics stabilizes the persistence of the metapopulation (Harrison, 1991). To satisfy the metapopulation concept, the habitat patches must meet three basic conditions (Hanski et al., 1995; Hanski and Gilpin, 1997) (i) the local populations lives within a discrete habitat patches that are separated from the rest of the landscape by some unsuitable habitat portion, called the matrix, (ii) asynchronous dynamics of the local populations and thus making the local populations susceptible to become extinct (iii) the habitat patches are in proximity with each other to allow dispersal resultantly the populations become isolated local populations. These three conditions lead to the local populations to behave as a metapopulation. The rate of dispersal, spatial variation of the patch size and quality of the habitat patches determine the metapopulation dynamics (Thomas and Hanski, 1997). In nature different types metapopulations structure has been found and sometimes several features of different types combine and influence each other to constitute a heterogeneous dynamic structure (Harrison and Taylor, 1997). The landscape is heterogeneous in nature and the dispersal of a species in relies upon at the ability of the emigrants to move although the unique landscape to find appropriate habitat patch. This phenomenon is interaction among the physical functions of the given landscape and the species migratory behaviour and mobility. This migratory behaviour is at once associated with the journey cost both directly i.e. mortality of the species by using predators or indirectly, i.e. lack of time for reproduction inside the destination patch (Stamps et al., 2005). The connection among the species attributes and the habitat patch is complicated. Sometimes inside the same habitat patch different species may present and they showed different level of functional activity and dispersed according to the resource availability or populations of the same species dispersed depending on the features of the habitat patches (Taylor et al., 1993; Schtickzelle et al., 2006). Moreover there may be a temporal and spatial variation present in the dispersal of the species with the spatially structured habitat patches. The Levins model showed that the dispersal between the isolated populations forms a patchy panmictic populations and the rate of change through time of the fraction p of the patches occupied at

Page | 30 any time t is given by dp/dt= mp (1-p) - ep, where m and e are the colonization and extinction probabilities respectively (Levins 1969, 1970). When the recolonization is high enough to balance the local extinction (e/m < 1) the landscape system is in a stable equilibrium. Whereas, when recolonizations are not frequent enough to balance local extinctions (e/m ≥ 1), the system is in a non-equilibrium state and is propel towards extinction. These circumstances occur when habitat destruction decreases the connectivity between the habitats which lead to high rate of extinctions, which ultimately limit the recolonization process. Consequently, the system lies in disequilibrium without reaching its new equilibrium state and leads towards the extinction, called the ‗extinction debt‘ (Tilman et al., 1994). At the other end, dispersal may occur more frequently and populations may receive enough immigrants to prevent local extinctions (e/m ~ 0) which is known as the so-called ‗rescue effect‘ (Brown and Kodric-Brown, 1977). Sometimes the fragmented habitats are less connected with each other then they receive fewer immigrants per unit of time and their emigrants may encounter higher mortality during dispersal through the matrix. Therefore, a core area of well connected patches functions as a patchy population and the surrounding less-connected patches functions like a metapopulation. These processes of dispersal events strongly affect the temporal and spatial dynamics of the local populations resulting in a metapopulation (Stacey et al., 1997; Liebhold et al., 2004). Very large population occupying a bigger habitat patches is less likely to become extinct (Lande, 1993). This model is solely determined by the perseverance of large populations and called mainland-island or core- satellite model and derived from the McArthur-Wilson model of island biogeography (Boorman and Levitt, 1973). The first condition to satisfy the metapopulation dynamics concept is that the population must be discrete because metapopulation theory is unacceptable in the cases where habitat structure varies continuously. In a continuous habitat structure, the local populations are not fragmented or isolated from each other; therefore a metapopulation structure doesn‘t develop. The habitat is a place where the species complete its life cycle i.e., a set of biotopes used by some or all life stages to find resources they need for survival. For many species one biotope is enough to get all the resources for survival and complete the life cycle and the definition of habitat is straightforward here. But for anadromous fishes, different stages of life cycle occupy totally distinct biotopes sometimes several kilometres apart from the one to another (Schaefer, 2006). The second condition for the existence of metapopulation structure is that the dynamics of all local population are asynchronous; otherwise the persistence of the metapopulation is simply like any local populations.

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However, the dynamics of metapopulation can be synchronous i.e., some populations that experience the same or comparable environmental conditions frequently responds in a similar way or sometimes the immigration is high enough compared to the inhabitants (Liebhold et al., 2004). Biocomplexity is defined as the heterogeneity in the habitat structure and the life history traits, also determined the metapopulation dynamics (Hilborn et al., 2003). The third and most important determinant of any metapopulation dynamics is the existence of dispersal between the local populations. In many terrestrial organisms, the extent of dispersal depends primarily on the distance between the habitat containing the populations and which is influenced by the existence of corridors (habitable) or barriers (inhabitable) in the landscape matrix and the mobility of the organisms throughout the landscape. Moreover, a meta-community is a set of spatially structured local communities that are connected by dispersal i.e., a community of metapopulations (Hanski and Gilpin, 1991). Meta-community is used to explain the significance of species interactions within the community structure. Naturally, the meta-community would be composed of local communities that differ in species composition and formed due to the initial differences in species assemblages (Wilson, 1992). Leibold et al. (2004) identified four theoretical models to explain metacommunity structure and dynamics; these were ‗patch dynamics‘, ‗species sorting‘, ‗mass effects‘ and ‗neutral‘. ‗Patch dynamics‘ emphasizing local colonization and extinction dynamics and species interactions in a set of similar habitat patches. Patch dynamics theory is a best approach to study the relationship between the landscape structure and food chain or food web within a community (Holt, 2002). In a fragmented patchy habitat, the species at higher trophic level decline to extinction than the lower trophic level because they experience greater resource exhaustion in terms of total amount of the resource and the distances among resource patches. The second meta-community model or ‗species sorting‘ is based on the concept of ecological niche in a heterogeneous landscape. ‗Mass effects‘ describes the relationship between mobile migratory species with that of resource utilization and exhibit source–sink dynamics. Finally, according to the ‗neutral‘ model all species are equality competitive and so the assemblage of species in local communities is random. The source-sink effect of a metapopulation depends on the stability between the cost to the source population (increased risk of local extinction within the source) and the benefit to the sink population (decreased risk of local extinction within the sink). There are some pseudo-sink populations that do not depend on the arrival of emigrants from sources. It may even benefit from a reduced influx of immigrants from the nearby source population (Watkinson and Sutherland, 1995). Other considerations include the presence of the corridors

Page | 32 which may provide a continuous stretch of habitat between habitat patches or discontinuous patches that improve connectivity in ‗stepping-stone‘ fashion and function as a buffer against catastrophic events. A corridor for movement in one direction may simultaneously act as a barrier in the perpendicular direction (Rondinini and Doncaster, 2002). Hanski (1994) predicted that generally higher extinction rates are found in smaller populations, and the probability of rescue is very low by immigration in more isolated population. This observation was mainly called as the ―SLOSS” debate, i.e., whether a Single Large Or Several Small (SLOSS) populations are better to protect the species. In one hand several small populations may have a lower extinction risk than one large population, if the rate of dispersal is high and the environmental pressure on the habitat patch is low. This is because a single large population will not get benefited from uncorrelated environmental conditions; therefore if it becomes extinct, and it cannot be recolonized. On the other hand, several factor like demographic stochasticity and environmental fluctuations and edge effects leads to the extinction of the small population. Thus, if they become extinct at the same time, or if the extinct ones cannot be recolonized from other source populations, a metapopulation consisting of several small populations may face a higher extinction risk than a single large population (Akcakaya et al., 1999). Models are particularly important to the conservation of metapopulation, because it is useful in evaluating management actions at large spatial scales, where the endangered status of a species makes experimental approaches unreasonable. Frequently, management of a metapopulation means management of the species and its habitat as a whole (Schtickzelle and Baguette, 2004). Most of the issues and decisions regarding conservation of metapopulation is relied on some interdependent and correlated factors, such as number of populations, spatial relationship, dispersal and density of the organism. Because many of these factors involve interactions between populations, there is no simple way to deduce the metapopulation dynamics models in a simplified manner (Doncaster, 2000). Therefore, the only way is to incorporate all these factors simultaneously including all populations and their interactions in one model, in other words, to use a ‗metapopulation model‘. There are several different types of metapopulation models available and each has their own set of hypothesis and constrains (Breininger et al., 2002). Patch-occupancy model describe each population as present or absent within a particular patch (Hanski, 1994). In structured or frequency-based models an intermediate level of complexity is found which describe the abundances of age classes or life-history patterns of each population (Akcakaya 2000a). These models describe the spatial structure by modelling the correlation between

Page | 33 dispersal and temporal dynamics among populations (Akcakaya and Baur, 1996). Finally, the individual-based models describe the spatial structure individuals within population or within the territories (Lacy, 2000). Some earlier models use a regular network where each cell of the network can be modelled as a potential territory (Pulliam et al., 1992). Sometimes, a suitable habitat map is also used to determine the spatial structure of the metapopulation (Akcakaya and Atwood, 1997). Some models of metapopulation suggest that there is an equilibrium state with respect to extinction and recolonization of patches (Hanski, 1999). However, some small and highly heterogeneous metapopulations are found naturally in a un-equilibrium state (Baguette, 2004). Moreover, some metapopulations that persist over long time period showed density- dependent regulation at the regional scale, often assumed to be in colonization rate. Therefore, the metapopulation equilibrium models can play a useful role for analyzing the processes (like habitat loss, habitat fragmentation, exploitation and alien invasions) that may threaten viability of metapopulation concern. The metapopulation may still decline if the rate of local extinctions due to environmental fluctuations and demographic stochasticity exceeds colonization rates, because of factors such as limited dispersal, or ‗Allee‘ effects on small populations, or correlated environments. The equilibrium assumption is sometimes mistakenly believed to apply to the metapopulation concept in general, yet several metapopulation models and approaches do not make this assumption (Akcakaya and Sjogren- Gulve, 2000). Metapopulation models assume constant number and location of habitat patches but the natural landscapes are dynamic in nature. The spatial structure changes according to the seasonal and climatic fluctuations and anthropogenic interventions. Therefore, the viability of metapopulation depends on the patch turnover and the quality, stability and quantity of the habitat patches (Keymer et al., 2000). Some metapopulation models incorporate spatially and temporarily distributed community succession which determines a critical habitat for certain species (Johnson, 2000; Hastings, 2003). Other models incorporate changes in carrying capacity over time, either deterministically or stochastically (Johst et al., 2002; Keith, 2004). A recent approach has developed which deals with linking a landscape model and a metapopulation model (Akcakaya et al., 2005). Kindvall and Bergman (2004) analyzed the long term extinction risk under different landscape condition by the help of metapopulation models and demonstrated the importance of landscape dynamics in the variability o different species.

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1.8 Background and origin of research problem Genetic variation is the raw material of evolution. A change in genetic composition of population and species is the primary mechanism of evolutionary change within species. Genetic differences among the individuals of a species are the basis of the phenotypic variation within that species. Genetic diversity may be defined as the heritable variations within and between populations of organisms. The pool of genetic diversity of species can exist at three levels - genetic diversity within individuals (or heterozygosity), genetic diversity among different individuals within a population, and genetic variations among different populations. New genetic variations can arise in individuals of a species by gene and chromosomal mutations. In a sexually reproducing population, these variations spread through recombination, and are ultimately acted upon by natural selection to produce evolution of the population. Thus, genetic diversity contributes to all the diverse forms of life on earth. The loss of genetic diversity reduces the capability for adaptation and increases the risk of extinction (Frankham, 1995; Caughley and Gunn, 1996; Avise and Hamrick, 1996; Landweber and Dobson, 1999). Inbreeding has been implicated in the erosion of genetic variation within natural populations (especially in small populations) by reducing the number of heterozygotes and hence reducing the mean phenotypic values of useful traits, which can result either through an increased chance of sharing parental genes from inbreeding itself or through a loss of alleles from random genetic drift (Wang et al., 2002). Therefore, loss of genetic diversity through inbreeding or loss of heterozygosity is of great importance in the cases of potential ornamental fishes of the country. Therefore, a firsthand knowledge of the genetic structure of a species is essential for assessing the genetic status and subsequently implementing programs on conservation of the ―Evolutionary Significant Unit‖ as well as selective improvement of the genetic stock of natural populations. Based on IUCN categories, the CAMP workshop (Molur and Walker, 1998) identified several fish species which have attained the threatened status in India. However, there have been insufficient studies with regard to the details of endemism and status of genetic diversity of many fish species, especially in the North-East India and northern West Bengal alike. A detailed study related to germplasm inventory, evaluation of genetic hierarchy in the river systems and ascertaining the genetic diversity of freshwater fishes would not only help us in prioritization for conservation measures for threatened fishes, but also fulfil our national obligation to Convention on Biological Diversity (CBD). North Bengal region being in the

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Eastern Himalaya Hotspot, this type of study with regard to threatened fish fauna, especially which have ornamental values, will be of much value from the standpoint of conservation, sustainability, economics and human livelihood. The Indian Torrent Catfish, Amblyceps mangois (Hamilton-Buchanan 1822, Family: Amblycipitidae), Ambassis ranga (Hamilton-Buchanan 1822, Family: Ambassidae), Nemacheilus sp (Bleeker 1863, Family: Balitoridae) and Badis sp (Hamilton-Buchanan 1822, Family: Nandidae), Channa amphibius (McClelland 1845, Family: Channidae), Ompok pabo (Hamilton-Buchanan 1822, Family: Siluridae), Sisor rhabdophorus (Hamilton-Buchanan 1822, Family: Sisoridae), Ctenops nobilis (McClelland 1845, Family: Belontiidae), Glyptothorax telchitta (Hamilton-Buchanan 1822, Family: Sisoridae), Mystus montanus (Jerdon 1849, Family: Bagridae) are important freshwater fishes found in different rivers in the northern region of West Bengal, India. Among these fishes, Badis badis [VU] and Amblyceps mangois [EN] have important noteworthy ornamental, thus commercial values (MPEDA website, www.mpeda.com) and have been included in the list of threatened freshwater fishes of India by NBFGR (Lakra et al., 2010). Over last few years, wild populations of these fishes have declined steadily probably due of overexploitation, habitat loss, pollution, and destructive fishing procedures. While, the fish fauna is yet to be assessed from the viewpoint of wildlife conservation in West Bengal, it is known that the several hill stream fishes, especially goonch, common mahaseer, tor mahaseer, mosal mahaseer, etc., are becoming rare due to intervention by dams and barrages. Therefore, I wanted to ascertain the status of genetic diversity in fishes that have potential ornamental uses and export values in a region of India where proper exploitation of such ichthyofauna with regard to sustainable aquaculture can substantially improve human livelihood and also to conserve the germplasms in the wild. Moreover, I wanted to ascertain spatial hierarchic genetic structure of geographic populations of two threatened fish species having great ornamental values, namely Badis badis and Amblyceps mangois in the stream and river systems of the Terai and Dooars region of northern West Bengal, popularly known as North Bengal. In my opinion, the study would give a firsthand picture of these aspects in this region for the first time. The study is expected throw light on the present level of genetic variation of two important threatened fishes (Badis badis and Amblyceps mangois) and ascertain potential local (river-wise) populations deemed for conservation. The area of study is within the sub-Himalayan hotspot and thus the study is important from the stand point of endemic morphological variations in these ornamental species.

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1.9 Review of literature 1.9.1 Origin of ichthyofaunal conservation. The ‗diversity crisis‘ was the main force that pushed ichthyologists to think about the conservation and management of ichthyofaunal diversity. The threat of the diversity crisis focused interest in the mechanism of extinction and in population viability and this approach drew upon population genetics, population ecology and conservation genetics together. With the increasing global population, the demand for high quality protein is also increasing, especially from aquatic resources. Over-exploitation, habitat alteration, introduction of exotic fish species and overharvesting toward certain sizes or age classes are the major causative factor for the considerable depletion of almost all economically important species in fish populations. So an urgent need for conservation of fish genetics has been recognised by fishery scientists and aquaculturists worldwide (Lakra et al., 2007). Fish living spaces are devastated as an outcome of numerous stochastic and natural components, for example, deforestation, watershed disintegration, and silting forms. Farming run-offs, pesticides, fertilizers, sewage, and chemical contaminants have added additional pressure for the declining fish populations. Construction of dams within the rivers additionally creates barriers to the natural dispersal pathways of migratory fish species, and eliminates opportunities for intermixing as well as gene flow among fish populations. Moreover, introduction of some exotic fish species may cause in hybridization between exotic and naturally occurring species that leads to modification of the genetic composition.This alternation finally lead to collapse the locally adapted genetic architecture of native populations. Moreover, exotic species compete with native species for habitat and food; and they often reproduce fast and quickly replace the native fishes (Banerjee et al., 2008). The basic aim of natural fisheries management should be aimed at deriving a long term sustainable benefit through multidisciplinary scientific concepts involving population biology, ecology, evolution, genetics, social, political and economic aspects to manage and domestication of the natural fish stocks. The fundamental goal of protection is to keep up thethe genetic integrity and variability of the fish species in their natural common habitats. Therefore, a legitimate documentation of genetic variability and diversity is of critical importance for sustainable management fishes especially economically important fishes with long term potential impact.

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Additionally, some hypothetical issues are there and some basic inquiries must be replied before organizing any conservation practice. The risky region and objectives has to be made as per the following: 1. To identify any critical species or species group as a bioindicator in ascertaining the biological diversity (Pearson and Cassola, 1992). 2. To identify and ensure the minimum thresholds of diversity before ecological systems lose their functional integrity or evolutionary adaptability. This is the question of redundancy of ‗diversity‘ or ‗heterogeneity‘ and a limit of diversity has to be set to maintain the integrity (Walker, 1992).

1.9.2 Molecular techniques to study genetic diversity The most powerful catalyst for multiplied analysis efforts within the field of conservation has been most likely the nice advances in genetic and molecular technologies, resulting in associate degree more and more wider style of molecular methodologies for application in conservation genetic and population genetic studies. To date, genetic and molecular ways are applied immensely in conservation biology primarily as by selection neutral molecular tools for partitioning the empirical queries of conservation and organic process connectedness (Primmer, 2009).

1.9.2.1 Molecular genetics methods in conservation biology Frankel and Soule (1980) reviewed the applying of population genetics and evolutionary biology theory, and also the methodology to deal with the problems of conservation concern features a long history. The primary step of molecular biological technologies within the field of conservation biology was taken within the 1960s with the introduction of macromolecule polymorphism analysis (Ridgeway, 1962), followed by the mitochondrial DNA (mtDNA), Restriction Fragment Length Polymorphism (RFLP) methodologies (Bowen and Avise, 1990), Randomly Amplified Polymorphic DNA (Williams et al., 1990), and most recently microsatellite (Taylor et al., 1994) and Inter Simple Sequence Repeat markers (Zietkiewicz et al., 1994). every of those molecular-markers was seen as a revolution at the time once it had been developed, because of the new potentialities that every new development created compared to antecedently available markers (e.g., high-quality DNA vs. low-quality DNA; high polymorphism vs. Low polymorphism, recombining vs. non-recombining, neutral vs. presumably selected) (De Young and Honeycutt, 2005). Therefore, the applications of particular forms of genetic markers are getting more and more

Page | 38 specialised to attain a specific goal to answer/solve the particular queries of conservation concern (Schlotterer, 2004). To date, the most use of genetic markers in conservation biology has been as molecular tools for ansawring varied queries, including: estimating parentage or relatedness in ex situ breeding programs (McLean et al., 2008), estimating the effective population size (Vera et al., 2008), investigating population bottlenecks, likewise as genetic drift (Luikart,1998), determining the population structure for the improvement of management process or to spot the evolutionary significant units (ESUs) (Fraser and Bernatchez, 2001), and for investigating hybridization between different groups (Barilani, 2005). Moreover, the analysis of neutral markers are often accustomed to assess the evolution of a specific loci in response to the changes in environmental processes and this successively may well be accustomed to assess the potential for populations to adapt the longer term changes of the environment (Bonin et al., 2007).

1.9.2.2 Genomic approaches in conservation biology The genomic era was started when the genome of the evolutionary, ecologically and commercially important model organisms were successfully sequenced (Kohn et al., 2006). This genomic information has been used for the study of organism having great economic value and as well as of their relationship with other important taxa. Vasemagi et al. (2005a) has reported the population genetic structure and evolutionary analyses of wild Atlantic salmon population with the help of genomic information available from the Rainbow trout (Oncorhynchus mykiss) andAtlantic salmon (Salmo salar). There are several benefits of focusing the conservation towards genomic approach. Firstly, it provides a raw material to develop a larger number of molecular markers of a wide variety of organisms and also is expected to solve the uncertainties reflected in population structure towards a specific conclusion (Koskinen et al., 2004). Secondly, the single- nucleotide polymorphism (SNPs) markers have the potential advantages to develop a highly sophisticated marker procedure that enable to analyse the genomic information more sofistacally even the DNA is highly degraded that is hardly possible with conventional PCR- based markers. (Sanchez and Endicott, 2006). Thirdly, this genomic data has enabled a novel way to analyze the conservation genetic issues with the help of neutral markers, therefore enhancing conservation of functionally relevant genetic variation (Kohn et al., 2006). The superfluity of data provided by various genome projects, advances in genomics and transgenic analysis have supplemental nice resources within the field of biotechnology and recombinant DNA technology. These resources open a wider way in the field of fisheries

Page | 39 and aquaculture to realize many goals like multiplied food production, development of recent natural resources, awareness concerning decreasing genetic diversity and to halt the injurious impact of contemporary civilization on our surroundings and environment. Melamed et al. (2002) delineated some biotechnological approaches to fisheries and aquaculture science. Expressed Sequence Tag (EST) libraries have been used as a precious resource for the study of conservation and evolutionary genetics. The EST has various homologous sequences, which can be aligned with overlapping sequences to spot polymorphic sites and at the same time that could develop the SNP marker using bioinformatics tools by CASCADE databases, known as in silico SNP mining pipeline (Guryev et al., 2005). Those ESTs having microsatellite sequences at the un-translated region has been applied to develop PCR primers using straightforward bioinformatics tools to observe gene polymorphisms (Vasemagi et al., 2005a). In 2005b, Vasemagi et al. used this approach to develop 75 polymorphic EST- associated markers in five salmonid species. Recently, genomic technological advances like next generation sequencing (NGS) technologies and ―deep sequencing‖ or ―ultra-high turnout sequencing‖ technologies (Mardis, 2008; Holt and Jones, 2008) give a wider path to know the empirical queries concerning the conservation genetic architecture of model also as nonmodel organisms.

1.9.2.3 Types of molecular markers Molecular markers can be classified into Type I and Type II markers on the basis of gene function. Genes of known function are analysed with Type I markers whereas, while type II markers are associated with genes having unknown genomic function as well as regions. Therefore, on the basis of this classification of markers allozyme markers are Type I markers because they encode the protein has known functions. RAPD and ISSR markers are Type II markers because these markers give bands which are amplified from anonymous genomic regions with random/arbitrary primers via the polymerase chain reaction (PCR). Microsatellite markers are also Type II markers unless they're related to genes of better known function. In general, type II markers are selectively neutral in nature because these markers such as RAPDs, ISSR, microsatellites, and AFLPs are considered as non-coding markers. Such neutral markers have been widely used in population genetic studies to characterize genetic divergence and genetic diversity within and among the populations or species (O‘Brien, 1991). Earlier reports suggested that there are six different types of DNA polymorphisms used for population genetic studies, viz., RFLP, minisatellites (Varying Number of Tandem Repeats or VNTRs), microsatellites (Short Tandem Repeats or STRs;

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Simple Nucleotide Repeats or SSRs), RAPD, AFLP and SNP (Altukhov and Salmenkova, 2002).

1.9.2.4 Criteria for choosing molecular markers To study population genetics of any organism it is very much essential to choose a genetic marker with appropriate characteristics. The most problems for selecting a molecular marker to resolve given questions of conservation concern are in brief delineating below:

1.9.2.4.1 Sensitivity and reliability In order to solve the particular question regarding the population genetics the marker should have a correct sensitivity and reliability. Based on the data available throughout the studies (either too several or too little), the accumulated information have given the concept regarding what kind of markers needs to be used for correct analysis.

1.9.2.4.2 Multi-locus or Single-Locus Usually there is fascinating however incompatible relationship between the utility and accuracy of genetic markers. This dichotomous relationship has been found within the Multi- locus (RAPD, ISSR, and AFLP) and Single locus (microsatellite) techniques. Multi-locus markers are technically convenient however imprecise with some drawbacks, together with the variations they detect is non-heritable. A elementary limitation is the dominant inheritance, as a result of this DNA fragments is scored solely on the basis of presence or absence of bands (1 or 0), in distinction to co-dominant inheritance wherever every of the two alleles at a locus in an individual is identified and so analyzed more acurately (Burt et al., 1996). Against this, single-locus markers are much more versatile and informative because they can be analyzed as genotypic arrays, as alleles with frequencies and as gene genealogies (Sunnucks et al., 1997). However, multi-locus RAPD bands can be cloned and regenerate to single locus co-dominant marker (Fa et al., 2010). The RAPD and ISSR techniques are low cost and really quick method to estimate or measure the genetic structure of any species into account. Moreover, these two ways don't need any previous data of the genetic design of the concern species. Although, these ways have limitations and therefore the obtained results could vary from laboratory to laboratory, the utilization of various sets of primers and perennial experimentation will scale back the error rate considerably to achieve a primary data of the genetic variability of any species very simply. This is often the explanation why we've chosen these two markers i.e., RAPD and ISSR in our study.

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1.9.2.4.3 Gene genealogies and Frequencies Mitochondrial or nuclear DNA sequences are affected by mutation rates, population parameters and natural selection. Genealogies describe the population processes, phylogeographic phenomenon, speciation and evolutionary relationships. Molecular phylogenies can facilitate to untangle the present structure from the effects of historical events (Templeton, 1998). Therefore, markers that are capable of analysing gene genealogies have great advantages over other possible markers.

1.9.2.4.4 Organelle and Nuclear DNA The eukaryotic cells get nuclear DNA biparentally however mitochondrial or plastid DNA are uni-parental. This mode of transmission and also the variations within the pattern of evolution helps to know the population biology and evolutionary history of organelle DNA and nuclear DNA gene genealogies. Mitochondrial DNA features a lower Ne (effective variety of alleles) than nuclear markers therefore mitochondrial DNA markers are accustomed to determine the taxa (Sunnucks, 2000). The technical advancement of molecular markers has crystal rectifier to the inflorescence of genetic analysis of populations. However, genetic markers are indiscriminately used for population genetic science to conservation genetics studies but sometimes probably result in inappropriate interpretations. This is due to the two contradictory variations within the study question (historic/contemporary genetic variation), population size (big/small populations), time window (over past/recent events), and purpose (understanding evolutionary forces/exploring effects of diversity on survival) (Wan et al., 2004). Population genetics emphasizes the role of various evolutionary forces that play over time and conservation genetics highlights the consequences of genetic structure to preserve any species. Each of which might be deduced with the assistance of acceptable molecular markers.

1.9.2.5 Applications of different molecular markers/tools in ichthyofaunal conservation. The most powerful catalyst with in the field of conservation biology has been the advances in genetic and molecular technologies and experimentations, resulting in a vast variety of molecular methodologies for application in conservation and population genetic studies. To date, molecular methods have been applied immensely in conservation biology primarily as selectively neutral molecular tools for resolving the empirical questions of conservation, management and evolutionary relevance (Primmer, 2009). Molecular genetic

Page | 42 markers have been used as powerful and high resolution molecular genetic tools to detect the genetic variability as well as uniqueness of individuals, populations and species and have a revolutionized analytical power to explore the genetic diversity (Lakra et al., 2007). The data gathered from genetic diversity study will help to implement the conservation and management programmes towards natural resources. This genetic diversity and variation within/between population can provide clues to the population‘s life histories, divergence and degree of evolutionary isolation. Different types of molecular markers available for studying the fish population may basically be divided into 2 types of markers viz. Protein and DNA, and there are three general classes of molecular markers used in population genetic and phylogenetic studies: (1) allozymes, (2) mitochondrial DNA and (3) nuclear DNA (Parker et al., 1998).

1.9.2.5.1 Allozyme The term ―allozyme‖ refers to different allelic forms of nuclear gene -encoded enzymes, whereas ‗‗isozyme‖ is a more general term referring to different biochemical forms of an enzyme which is identified by electrophoresis. Therefore, Allozyme electrophoresis technique denotes the procedure for identifying the genetic variation at the level of enzymes thus protein level, which are directly encoded by DNA. The allelic variants that encode different types of protein enzymes known as allozymes are differ slightly in electrical charges and can be separated by enzyme electrophoresis technique. Moreover, the allelic variants are inherited in a Mendelian fashion (both alleles are expressed in homozygous as well as heterozygous condition), thus Allozymes are co-dominant markers and the character variants passed from parent to offspring in a predictable manner. The allozyme variation provides data regarding single locus genetic variation and this locus specific genetic data allow us to answer many basic questions regarding the variability available in fish populations (May, 2003). Each of these allozymes is a product of a unique allele whose amino acid sequence is slightly different from each other. In diploid organisms, there will be a combination of two alleles at each locus, designated as F (fast) and S (slow) or as ―a‖ and ―b‖ to distinguish between them. Therefore, the genotype at a gene locus coding for an enzyme can be deduced for each individual in the sample from the number and position of the bands observed on the electrophoretic gels. If the enzyme is a monomer (the complete enzyme consists of only one polypeptide) the electrophoretic band pattern shown in Figure 5a would result for homozygotes and heterozygotes. An enzyme whose quaternary structure is a dimer (the

Page | 43 complete enzyme consists of two polypeptides) would show the electrophoretic band pattern demonstrated in Figure 5b. In an individual, heterozygous at a dimeric enzyme locus for slow (a) and fast (b) alleles, three enzyme associations will be evident (aa, ab, bb). The heterozygote will show up twice as dark because both forms of each polypeptide will randomly associate, meaning that there is only one combination that will yield aa or bb, but there are two combinations which will yield ab. The electrophoretic pattern for a tetrameric enzyme is given in Figure 5c (Bader, 1998).

The allozyme electrophoresis technique is comparatively pricey, it needs relatively specialised instrumentation and also the general applicability of this method has created this the foremost studied style of molecular variation (Park and Moran, 1995). This method is extremely a lot of advantageous for screening of an outsized variety of loci typically over 30- 40 and no marker development part is critical and also the analysis will begin for any species straightaway once the samples are collected from the field (Okumus and Ciftci, 2003). Moreover, comparable information from previous studies and a wealth of normal statistical procedures create allozymes appealing for studies of each fine and broad scale genetic variation (Weir, 1990). Beside these benefits there are few limitations. one in all the main limitations of this has been the inability to read genotypes from little quantities of tissue, that makes allozymes unsuitable for tiny organisms (e.g. larvae) and conjointly in vulnerable species; moreover, the main disadvantage that is tough to overcome is that solely atiny low fraction of protein loci seem to be allozymically polymorphic in several species (Okumus and Ciftci, 2003). Additionally, a given modification in nucleotide sequence might not result in a modification in amino acid at all (synonymous), and so wouldn't be detected by protein electrophoresis.Furthermore, often a change in the DNA that results in a change in an amino acid may not result in a change in the overall charge of the protein and, therefore, would also not be detected (Park, 1994).

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In spite of these drawbacks, allozyme electrophoresis analyses have a more profound impact on fisheries research and management than most of all other genetic tools. There are widespread applications of allozymes in fisheries, which include allozyme variation in Brown trout, Salmo trutta from Central Spain (Machordom et al., 1999), in Chiloglanis paratus and Chiloglanis pretoriae from the Limpopo River system, Southern Africa (Engelbrecht and Mulder, 2000), Johnston‘s topminnow, Aplocheilichthys johnstoni, population from the Zambezi River system (Steenkamp et al., 2001), Australian rainbow trout, Oncorhynchus mykiss (Farrington et al., 2004), Spotted Murrel, Channa punctatus from South Indian river system (Haniffa et al., 2007), Armored Catfish Hypostomus regani from Brazil (Zawadzki et al., 2008), and Nandus nandus populations from Bangladesh (Zohora et al., 2010). The allozyme electrophoresis study can also be used for the study of population structure and genetic diversity analysis in different populations of Atlantic Salmon (Cordes et al., 2005), left eyed founder Paralichthys olivaceus (Feng et al., 2007), endemic and endangered Yellow Catfish, Horabagrus brachysoma (Muneer et al.,2007).

1.9.2.5.2 Restriction Fragment Length Polymorphism (RFLP) A small portion (< per cent 1) of the DNA of eukaryotic cells is non-nuclear; it is located within organelles in the cytoplasm called mitochondria. The major features of mtDNA: (a) in general maternally inherited haploid single molecule; (b) the entire genome is transcribed as a unit; (c) not subject to recombination and provides homologous markers; (d) mainly selectively neutral and occurs in multiple copies in each cell; (e) replication is continuous, unidirectional and symmetrical without any apparent editing or repair mechanism; and (f) optimal size, with no introns present (Billington, 2003). Like nuclear DNA the genome includes coding and non-coding regions and later evolves much faster than coding regions of DNA. One of the major consequences of maternal transmission of mtDNA is that the effective population size for mtDNA is smaller than that of nuclear DNA, so that mtDNA variation is a more sensitive indicator of population phenomena such as genetic drift, bottlenecks and hybridizations (Avise, 1994). The rapid rate of evolution, the maternal mode of inheritance and the relatively small size of mtDNAs make the RFLP analysis of this molecule one of the methods of choice for many population studies (Ferguson et al., 1995). RFLP reveals polymorphism in an individual defined by restriction fragment sizes of distinctive lengths produced by a specific restriction endonuclease. Variation at mtDNA may be analyzed mainly with two different approaches: (a) RFLP analysis of whole purified mtDNA obtained from fresh tissue of

Page | 45 organisms (usually liver or gonad) by digesting it with different restriction endonucleases; (b) or DNA sequencing of small segments of the mtDNA molecule obtained by means of PCR amplification of the fragments generated after restriction digestion (Billington, 2003). These techniques yields maximum resolution and information that have made examination of mtDNA variations considerably easier and faster (Ferguson et al., 1995). RFLP analyses of mtDNA in population genetic studies have several advantages, including detection of quite a high levels of potential variation, evolving rate of a mtDNA is high , high rate of genetic drift and low level of gene flow, possibility of reconstructing the phylogenetic history of organisms and populations after mtDNA analyses, analysis can be carried out with a very small amount tissues (Hansen and Mensberg, 1998; Nielsen et al., 1998; Hansen et al., 2000). Variations in the characteristic pattern of a RFLP digest can be caused by base pair deletions, mutations, inversions, translocations and transpositions which result in the loss or gain of a recognition site of the restriction endonucleases resulting in a fragment of different length and and thus polymorphism occurs. Several sets of ―universal primers‖ have been developed to analyse the same mtDNA segments in a variety of fish species. In initial studies with fish carried out with salmonids, universal primers for analysing the mtDNA ND-1 and ND-5/6 segments were used (Cronin et al., 1993). The only non-coding region in the entire mtDNA of vertebrates is the D-loop region. Some markers have targeted the D-loop region which is highly variable in mammals but for some fish species little polymorphism is shown in the D-loop region (Okumus and Ciftci, 2003). Studies with a number of fish species Salmo trutta (Hall and Nawrocki, 1995), S. salar (McConnell et al., 1995), Anguilla anguilla (Daemen et al., 1996), Hirundichthys affinis (Gomes et al., 1999), and Liporinus elongates (Martins et al., 2003) have actually shown less variability in the non-coding D-loop region rather than elsewhere in the mitochondrial genome. Thus the cytochrome b, cytochrome oxidase gene, 12S and 16S rRNA genes and dehydrogenase genes were widely examined to study genetic variability in Atlantic cod (Carr and Marshall, 1991), Tor tambroides Valenciennes (Esa et al., 2008), Mekong giant cat fish (Nakorn et al., 2006), six flatfish species (Kartavtsev et al., 2007), anemone fishes (Ghorashi et al., 2008) and Barilius bendelisis (Sah et al., 2011). mtDNA is also useful for barcoding of different fish species like Channa marulius (Lakra et al., 2011). Genetic divergence study in six Labeo species from nine Indian river system has been also carried out with mtDNA Cytb gene sequence analyses (Luhariya et al., 2012). Mitochondrial DNA (mtDNA) sequence variations have also been studied in two highly endangered fish Chitala chitala and Anaecypris hispanica (Mandal et al., 2011 and Alves et al., 2001). The

Page | 46 mtDNA have a number of applications in fisheries biology, management and aquaculture and has attracted a lot of attention in many species, especially for population and evolutionary studies to provide critical information for use in the conservation and rehabilitation programmes.

1.9.2.5.3 Amplified Fragment Length Polymorphism (AFLP) Amplified Fragment Length Polymorphisms (AFLP)-based genomic DNA fingerprinting is a technique used to detect DNA polymorphism. AFLP is a polymerase chain reaction (PCR)-based technique, and has been reliably used for determining genetic diversity and phylogenetic relationship between closely related species or organism (Vos et al., 1995). AFLP analysis combines both the reliability of restriction fragment length polymorphism (RFLP) and the convenience and rapidness of PCR-based fingerprinting methods. AFLP markers are generally dominant in nature and importantly do not require prior knowledge of the genomic composition of the concerned species. The bands generated after AFLPs are in great numbers and are reproducible. AFLP analysis is based on selective amplification of fragments obtained by restriction of the genomic DNA. This method involves three steps: (a) DNA digestion (typically by two specific restriction enzymes) and binding of sticky fragment ends with specific oligonucleotides adapters by a ligase; (b) selective PCR amplification of the set of restriction fragments; and (c) electrophoretic analysis of the amplified fragments (d) scoring of the amplified fragment and analyses The nucleotide sequence of the adapter and an flanking restriction site serves as a target for the complimentary annealing of the primer and the primer sequence is elongated at the 3' end by several arbitrary nucleotides. This allows selective amplification of only the fragments whose restriction sites are flanked by the corresponding complementary nucleotides (Mueller and Wolfenbarger, 1999). The AFLP can be applied to all species and give very reproducible results; and it has the advantage of the extensive coverage of the genome of the organism under study. In addition, the complexity of the bands can be reduced by adding selective bases to the primers during PCR amplification. Although there are several disadvantages of this technique i.e., the alleles are not easily recognized from homozygous to heterozygous condition, have medium level of reproducibility, labor intensive and have high instrumental, chemical and development costs (Karp et al., 1997). Moreover, AFLP requires a firsthand knowledge of the genomic sequence to design primers with specific selective bases. The efficiency of AFLP seems to be much more than the microsatellite loci and it was used to discriminate the population differentiation in whitefish (Coregonus clupeaformis)

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(Campell et al., 2003). The researchers conjointly noted that the AFLP resulted in higher level of success in all levels of stringency and also the probability distinction between populations may be studied befittingly through AFLP marker. Like RAPD, AFLP analysis permits the screening of the many additional loci within the genome in a relatively within a short time and in an inexpensive manner. AFLP markers show dominant nature of inheritance, therefore, it limits their utility for application in population genetics. Sometimes, AFLP markers have been used for intra and inter population mtDNA analysis. The AFLP methodology sometime become difficult to analyze due to the large number of unrelated fragments are visible on the electrophorectic gel along with the polymorphic fragments (Okumus and Ciftci, 2003). There are several advantage of this technique: (a) multiple polymorphic bands are produced per experiment, (b) these bands are generated from all over the genome, (c) the technique is reproducible (Blears et al., 1998; Vos and Kuiper, 1997), (d) this technique have highly discriminatory and differentiation power, and (e) the data can be stored in database like AmpliBASE MT (Majeed et al., 2004) for comparison purposes.

1.9.2.5.4 Random Amplified Polymorphic DNA (RAPD) RAPD is a random amplification of anonymous loci by PCR. It has several advantages and has been quite widely employed in fisheries and other organismal studies. The method is relatively simple, rapid and cheap, it reveals high degree polymorphism, only a small amount of genomic DNA is required and most importantly, no prior knowledge of the genetic make-up of the organism in question is required (Hadrys et al., 1992). RAPD markers allow creation of genomic markers from scarcely known species of which little is known about target sequences to be amplified. The technique is a based on the PCR amplification of discrete regions of genome with short oligonucleotide primers of arbitrary or random sequence. A small amount of genomic DNA, one or more oligonucleotide primer (usually about 10 oligonucleotide in length), free nucleotides (dATP, dGTP, dCTP and dTTP) and Taq DNA polymerase with a suitable reaction buffer are major requirements. The main drawback with RAPDs is that the resulting pattern of bands is very sensitive to a number of factors, like reaction conditions, genomic DNA quality and the PCR temperature profile (Williams et al., 1990; Welsh and McClelland, 1990). Even if the researcher is able to control the major parameters, other drawbacks of RAPD will remain: homozygous and heterozygous conditions of the alleles cannot be differentiated and the patterns are very sensitive to slight changes in amplification conditions, giving problems of reproducibility from laboratory to laboratory (Ferguson et al., 1995).

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RAPD markers is a type II markers have been used for phylogenetic studies for species and subspecies identification of fish, for gynogenetic identification of fish, analysis of inter-specific gene flow, paternity analysis, kinship relationships and for gene mapping studies in fish. In aquatic organisms DNA polymorphisms have been extensively employed to investigate and assess genetic diversity.. RAPD-PCR fingerprinting offers a rapid, reliable and efficient method for generating a new series of inheritable DNA markers in fishes (Foo et al., 1995; Ali et al., 2004). There are two modifications of detecting RAPD markers have been described as DNA Amplification Fingerprinting (DAF) and Arbitrarily Primed Polymerase Chain Reaction (AP-PCR). DAF utilizes short random primers of 5-8 bp and a great number of amplified polymorphic fragments are visualizes by Polyacrylamide Gel Electrophoresis (PAGE) and subsequent silver staining. AP-PCR uses slightly longer primers and amplified polymorphic fragments are radioactively tagged and finally resolved by PAGE analyses (Hadrys et al., 1992). Assessment of genetic diversity of Tilapia stock under aquacultural practices in Fiji has been carried out with the help of RAPD markers to ascertain polymorphism and the potential impacts of current management practices on genetic diversity and gene introgression among the tilapia stocks (Appleyard and Mather, 2002). Similar studies in flounder Paralichthys olivaceus, in China, has been done to investigate gradually diminishing physiological adaptability, slower growth rate with higher mortality and lower reproduction capability in the cultured stock in comparison to those of natural and wild ones in spite of extensive fishery management efforts (Feng et al., 2007). RAPD technique was used to examine the genetic variability and status of Brazilian endemic fish, Brycon lundii, Astyanax altiparanae and a migratory fish Prochilodus marggravii. The studies suggested the occurrence of a distinct structured populations and important implications for the conservation of the genetic variability of distinct natural stocks (Wasko and Galetti, 2002; Leuzzi et al., 2004; Hatanaka and Galetti, 2003). Genetic stock structure of Indian carp Labeo rohita was investigated with the help of RAPD in Bangladesh (Islam and Alam, 2004). More recently, in a similar study in Bangladesh, the genetic structure has been ascertained in freshwater mud eel, Monopterus cuchia, and a baseline for any conservation approach towards threatened or near-threatened species (Alam et al., 2010). RAPD has also been used for species identification. Species identifications of the Pacific lamprey Entosphenus tridentatus from four other Japanese lampreys, Lethenteron japonicum, L. kessleri, and two undescribed Lethenteron species, were carried out on the basis of PCR-based RAPD (Yamazaki et al., 2005). The interspecific genetic variability and

Page | 49 genetic relatedness among five Indian sciaenids namely Otolithes cuvieri, Johnieops sina, Johnieops macrorhynus, Johnieops vogleri and Protonibea diacanthus was studied for the first time using RAPD marker (Lakra et al., 2007). A RAPD-based molecular identification and assessment of intraspecific genetic similarity in two ornamental fishes, Badis badis and Dario dario provided an excellent guideline for taxonomic identification of the fishes (Brahmane et al., 2008). We have also carried out RAPD-PCR analysis to delineate the genetic structure of Badis badis from three major river streams of the Terai region of West Bengal, India (Mukhopadhyay and Bhattacharjee, 2013). Moreover, this technique can be used for phylogenetics, linkage group identification, chromosome and genome mapping, analysis of interspecific gene flow and hybrid speciation, and analysis of mixed genome samples (Hadrys et al., 1992).

1.9.2.5.5 Inter Simple sequence Repeat (ISSR) The Inter Simple Sequence Repeat (ISSR) technique is another PCR-based method that utilizes microsatellites sequence (usually 15-25 oligonucleotide long) as primers in a single-primer PCR amplification reaction that targets amplification of multiple loci of genomic DNA segment present at an amplifiable distance in between two identical microsatellite repeat regions within the geomic DNA (inter- SSR sequences) oriented in opposite direction (Reddy et al., 2002). The microsatellite repeats can be di-nucleotide, tri- nucleotide, tetranucleotide or penta nucleotide repeat sequence. ISSRs have high reproducibility possibly due to the use of longer primers (16–25 oligonucleotide long) as compared to RAPD primers (10 oligonucleotide long) which permits the subsequent use of high annealing temperature (45 - 60ºC) leading to higher stringency and accuracy (Reddy et al., 2002). The primers used can be either unanchored (Meyer et al., 1993; Gupta et al., 1994; Wu et al., 1994) or more usually anchored at its 3‘ or 5‘ ends with 1 to 4 degenerate bases extended into the flanking sequences of the primer of either direction (Zietkiewicz et al., 1994). This sort of anchoring makes the primer more stringent and high level of reproducibility can be attained. ISSR markers are segregated generally as a dominant marker (Gupta et al., 1994; Wang et al., 1998). However, they have also been shown to segregate as co-dominant markers in some cases that enable to differentiate between homozygotes and heterozygotes conditions (Wu et al., 1994; Akagi et al., 1996; Wang et al., 1998; Sankar and Moore, 2001).

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ISSRs have been successfully used in many organisms to estimate the extent of genetic diversity at inter- and intra-specific level. Over the last few years ISSR markers have been widely used in the population genetic studies and stock identification in fishes including Japanese flounder (Liu et al., 2006), Brycon sp (Antunes et al., 2010), Botia superciliaris (Liu at al., 2012), four Tilapia species (Oreochromis niloticus, Oreochromis aureus, Tilapia zillii and Sarotherodon galilaeus) (Saad et al., 2012), grouper, cobia and red coral trouts (Chiu et al., 2012).

1.9.2.5.6 VNTRs Variable Number of Tandem Repeat or VNTR are basically the short sequences found with in the nuclear genome of eukaryotes, including fishes, that are repeated tens or even hundreds to thousands of time. . These repeated sequences are the most important class of repetitive DNA sequences found in the eukaryotic genome (O‘Reilly and Wright, 1995). The tandem repeats vary in number at different loci in different individuals and dispersed throughout the genome. Based on the size of the repeat unit two main classes of this repetitive and highly polymorphic DNAs have been distinguished: a) Minisatellite DNA, which refers to genetic loci with repeats of smaller length (9-65 bp) concentrated near the centromeric region, and b) Microsatellite DNA, in which the repeat unit is only 2 to 8 bp long dispersed throughout the genome. The term VNTR is frequently used for both mini- and microsatellite DNAs (Magoulas, 1998). Most importantly, microsatellites repeats are much more numerous in the genome of vertebrates than minisatellites. Minisatellites are also characterized as multilocus and single locus marker. Many minisatellite loci are highly variable and useful in parentage analysis and utilized for individual geneologies studies. In addition to high level of polymorphism, the major advantages are generation of many informative bands per PCR amplification reaction and high rate of reproducibility. Taggart and Ferguson (1990) was first developed the single locus minisatellite marker for Atlantic salmon and tilapia species. Unfortunately the extremely variable loci are less helpful for discriminating populations unless massive sample sizes are used. Great numbers of alleles can even result in difficulties in evaluation and interpretation of the data. Moreover, another limitation is that the complicated mutation process (O‘Reilly and Wright, 1995). Despite of several technical issues associated with using minisatellite markers, they have been widely used and very successful in detecting genetic variations within and between fish populations (Taggart et al., 1995 and Taylor, 1995), micro-geographical (between different tributaries of a river) population differences and gene flow (Galvin et al., 1995) and

Page | 51 reproductive success of farm organisms (Ferguson et al., 1995). Recently minisatellites have been applied in fisheries for genetic identity, parentage analyses, forensics, identification of stock and wild varieties, estimate mating success and conforming gynogenesis etc. (Jeffreys et al., 1985).

1.9.2.5.7 Microsatellite or SSRs Simple sequence repeat (SSR) markers are repeats of short nucleotide sequences, usually ≤six bases in length that vary in number throught the genome (Rafalski et al., 1996; Reddy et al., 2002). SSR has been greatly used and very important molecular markers in both animal and plant genetic studies. They are also known as microsatellites. SSR are stretches of 2 to 8 nucleotide units repeated in tandem and spread randomly and dispersely in eukaryotic genomes. Due to high rate of mutational process that affects the number of repeat units, SSR markers are very polymorphic in nature.. Such length-polymorphisms can be easily detected on high resolution gels (e.g. sequencing gels). It is suggested that the variation or polymorphism of SSR markers are a result of polymerase/replicational slippage during DNA replication or unequal crossovers (Levinson and Gutman, 1987). SSRs are are hypervariable for the relative numbers of repetitive DNA segments motifs in the eukaryote genome (Rallo et al., 2000). SSR have several advantages over other molecular markers as (a) microsatellites allow the identification of many alleles simultaneously at a single locus, (b) they are evenly distributed all over the genome of eukaryotes, (c) microsatellites can reveal more detailed picture of population genetic architecture of concerned organism, (d) they are co-dominant, highly polymorphic and specific (Jones et al., 1997), (e) highly repeatable, reproducible, little amount of DNA is required and also very inexpensive and easy to carry on, (f) need a small amount of medium quality DNA and (g) the analysis can be semi-automated and performed without the need of radioactivity and high expertise (Guilford et al., 1997), (h) with the advancement of DNA isolation technology, it is possible to identify loci in highly degraded ancient DNAs (aDNAs), where traditional isolation and amplification procedures have been unsuccessful (Allentoft et al., 2009), (i) with the development of high-throughput sequencing platforms, SSR has recently become faster, reliable and efficient (Santana et al., 2009). However, since genomic sequencing is needed to design specific primers, it is not very cost effective and also requires less knowledge and optimization for each species before use. Microsatellites are inherited in a Mendelian fashion can be repeated up to ~100 times at a locus. They are the fastest evolving genetic markers with 10–3-10–4 mutations/generation and their high polymorphism rate and PCR based analysis has made them one of the most

Page | 52 popular genetic markers (Goldstein et al., 1995). Some microsatellite loci have very high numbers of alleles per locus (>20) which make them possible for parent-offspring identification in mixed populations, while others microsatellite loci have lower numbers of alleles and they are suitable for population genetics and phylogenetic analyses (O‘Connell and Wright, 1997). Primers developed for one species can usually cross-amplify microsatellite loci in closely connected species (Okumus and Ciftci, 2003). The much higher variability at microsatellites leads to enlarged power for variety of applications (Luikart and England, 1999). Solely tiny amounts of tissue are needed for microsatellites typing and these markers are assayed using non-lethal fin clips or archived scale samples, facilitating retrospective analyses and also the study of depleted populations (McConnell et al., 1995). One amongst the most issues is that the presence of questionable ―null alleles‖ and this null alleles occur once mutations come about within the primer binding regions of the microsatellite locus, i.e. not within the microsatellite DNA itself (O‘Reilly and Wright, 1995; Jarne and Lagoda, 1996). Albeit microsatellites have already proved to be powerful single locus markers for a variety of genetic studies, the requirement to develop species-specific primers for PCR amplification of alleles is valuable. However, primers developed to amplify markers in one species could amplify the homologous markers in connected species also (Morris et al., 1996). Another necessary disadvantage of microsatellite allelomorphs is that amplification of associate allele via PCR usually generates a ladder of bands (1 or a pair of bp apart) once resolved on the standard denaturing polyacrylamide gels. These accessory bands (also known as stutter or shadow bands) are thought to be due to slipped-strands impairing during PCR (Tautz, 1989) or incomplete denaturation of amplification products (O‘Reilly and Wright, 1995). The sensible outcome of PCR stutter bands is that it should cause issues while scoring the alleles. The main application of microsatellite in fisheries and aquaculture are phylogenetics and phylogeography (Hansen, 2002), population genetic structure analyses (Nielsen et al., 1999), conservation of biodiversity and estimating the effective population size (Reilly et al., 1999), hybridization and stocking impacts (Hansen, 2002; Ruzzante et al., 2001), inbreeding (Tessier et al., 1997), studies of kinship and behavioural patterns (Bekkevold et al., 2002). Microsatellites are also widely utilised in forensic identification of individuals, determination of parentage and relatedness, genome mapping, gene flow and effective population size analysis (Withler et al., 2004).

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1.9.2.5.8 Single Nucleotide Polymorphisms (SNPs) Single nucleotide polymorphism (SNP) utilizes DNA sequences that differ by a single base when two or more individuals are compared. The single nucleotide polymorphisms are responsible for specific traits or phenotypes of an organism that represents neutral type of variation which is useful for evaluating diversity in the context of evolution. SNPs are the most widespread type of sequence variation found within genomes discovered so far. About 90% of sequence variants in humans are differences in single bases of DNA (Collins, 1998). Several disciplines such as population ecology and conservation and evolutionary genetics have benefitted from SNPs as genetic markers. There is a widespread interest to find SNPs as a result of they are varied in nature, more stable and probably easier to attain. Within the coding regions there are on average, four SNPs per gene with a frequency >1% and about half of these SNPs cause amino acid substitutions. Therefore they are known as termed non- synonymous SNPs (nsSNPs) (Cargill et al., 1999). SNPs are rapidly replacing simple sequence repeats (SSRs) as the DNA marker of choice for operational use in conservation and population genetic studies because they are more abundant, highly polymorphic in nature, stable/reproducible, amenable to automation, efficient, and increasingly inexpensive (Edwards and Batley, 2010). There are limitations to the invention of SNP‘s within the non-model organisms due to the expenses and technical difficulties concerned within the presently available SNP isolation methods (Brumfield, 2003; Seddon et al., 2005). Conventional direct SNP discovery strategies involves sequencing of Locus Specific Amplification (LSA) products from multiple individuals or determination of sequence of Expressed Sequence Tags (EST-sequencing) (Suh and Vijg, 2005; Twyman, 2004). Other direct strategies include whole genome Shotgun Sequencing (WGSS) and Reduced Representation Shotgun Sequencing (RRSS) approaches. If comparative sequence data are available in public or alternate databases, various sequence comparison algorithms can be employed that identify nucleotide differences and provide an alternative way to empirically discover SNP variations (Guryev et al., 2005).

The software applications like ‗PyroBayes‘ and ‗Mosaik‘ were widely used to differentiate between true polymorphism and sequence errors (Hillier et al., 2008). The new development in bioinformatic technologies that collect high-throughput data contributes substantially in the progress in evolutionary and population genomics (Gilad et al., 2009). The next generation sequencing (NGS) technologies have the great potential to revolutionize the genomic and genetic research; thus enable investigator to focus on a varied number of

Page | 54 outstanding questions more sophistically (Rafalski, 2002). The NGS provides the capacity for high-throughput sequencing of whole genomes at low cost. They have advantage of improving the capability to finding novel variations that are not possible by genotyping arrays (Imelfort et al., 2009).

1.9.2.5.9 Other advanced DNA Markers Diversity array technology (DArT) is a microarray hybridization-based technique that can simultaneously screen thousands of polymorphic loci in a high level of multiplexing without any prior information about the sequence. DArT assays generate afingerprints of whole genome by scoring the presence versus absence of DNA fragments generated from genomic DNA samples through the method of complexity reduction (Jaccoud et al., 2001). Restriction site-Associated DNA (RAD) is a high throughput technique which involves digesting the genomic DNA with a particular restriction enzyme and then ligating a biotinylated probe/adapters to the overhanging position of the fragment and randomly shearing the DNA into fragments which is much smaller than the average distance between restriction sites, and finally the biotinylated fragments are isolated by using streptavidin beads (Miller et al., 2007a). Single Feature Polymorphism (SFP) is carried out by labelling target genomic DNA with adapters and then hybridizing to arrayed oligonucleotide probes that are complementary with the indel loci. The SFPs can be generated through sequence alignments or by hybridization of genomic DNA with whole genome microarrays. The presence or absence of a hybridization signal is scored for each SFP with its corresponding oligonucleotide probe on the array (Borevitz et al, 2003; Cui et al, 2005). Nucleolar ribosomal DNA contains two internally transcribed spacers viz., ITS- 1,which is located between the small subunit and 5.8S rRNA cistronic regions, and ITS-2 which is located between the large subunit and 5.8S rRNA cistronic regions. The two spacers and the 5.8S subunit are collectively known as the Internal Transcribed Spacer (ITS) region. The ITS regions are fast evolving due to high mutation rate and therefore may vary in length and sequences across individuals. The flanking regions of ITS are usually utilized to design universal PCR primers to ensure easy amplification of ITS regions. The number of copies of rDNA repeats is up to 30000 per cell (Dubouzet and Shinoda, 1999). Cytochrome P450 (Cyp450) mono-oxygenases gene are widely found in animals, plants and microorganisms (Shalk et al., 1999; Somerville and Somerville, 1999). These

Page | 55 findings lead to development of a technique called Cytochrome P450 Based Analog (PBA) markers by Yamanaka et al. (2003) to create highly polymorphic fingerprints to characterize genetic diversity and genetic differentiation within and among populations of a wide verity of organisms. In the method universal forward and reverse primer pairs were designed to anneal to specific conserved exon regions flanking the intron of Cyp450 genes to initiate PCR amplification. The sequence-related amplified polymorphism technique (SRAP) was developed by Li and Quiros (2001), which uses arbitrary primers (17-21 oligonucleotide in length) to generate a specific banding pattern. The forward and reverse primers are designed to contain GC-rich sequences and AT-rich sequences near the 3‘-end respectively. This is based on the fact that protein coding exons generally contain GC-rich codons, while 3‘UTRs frequently consist of AT-rich stretches (Lin et al., 1999). Due to several advantages this technique got much popularity: (a) a large number of polymorphic fragments could generated in single reaction and highly reproducible (b) no need for prior knowledge about sequences, cost effective and easy to perform (c) primers can be applied to any species, (d) PCR products can be directly sequenced using the original primers without cloning into a vector. The CoRAP technique (Wang et al., 2009) utilizes two different type of primers i.e., a fixed one and an arbitrary one, to detect polymorphism. The technique incorporated the sequence motifs in the arbitrary primer. Mobile element based markers have been widely used molecular tool with a great potentiality for investigating numerous aspects of molecular ecology, including population structure, conservation genetics, the genetics of speciation, phylogeography and phylogeny (Ray, 2007). Retrotransposons is the important group of mobile elements, provides an excellent basis for the development of markers systems. Retrotransposons replicate much like retroviruses by successive reverse transcription, and insertion of the new cDNA copies back into the genome, (Scheifele et al., 2009). The structure and replication strategy of retrotransposons make them very usefull as a molecular markers (Kalendar et al., 1999).

1.9.3 Mitochondrial DNA (MtDNA) and Genetic Diversity 1.9.3.1 Origin and Evolution of Mitochondria

One of the distinguishing features of a eukaryotic cell is the presence mitochondria. One theory proposed that previously the ancestors of modern eukaryotes did not have mitochondria or similar organelles but later acquired them from an outside source and

Page | 56 ultimately gets into the host cell. This theory is popularly known as the Serial Endosymbiotic Theory (SET), proposed by Lynn Margulis (Margulis, 1996) which stated the xenogenous origin of mitochondria in eukaryotic cells. The endosymbiosis is usually a process of endocytosis of aerobic alpha- proteobacterium into the host cell, which ultimately lead to the formation of mitochondria (Martin and Müller, 1998; Lang et al., 1999). The endosymbiosis theory has been well supported by various recent organeller genetic and genome sequencing data which suggested that mitochondria are not only evolved from a common bacterial ancestor but more likely arose from a single ancestral source (Gupta, 2000; Lang et al, 1999). Two contrasting hypothesis were put forward for the origin of eukaryotic cell by endosymbiosis which helps to understand the evolution of modern eukaryotes. Firstly, if the endosymbiosis happened very early and when the host cell was a primitive archeaon, then the endosymbiosis of the alpha proteobacterium could have had a crucial role in evolution of eukaryotic cells (Martin and Müller, 1998; Lang et al, 1999). Secondly, if the endosymbiosis occured later stages, perhaps after the early eukaryote like cells had already developed true eukaryotic properties and phagocytic cytoskeletal system then maybe the endosymbiosis wasn‘t truly a major factor in eukaryogenesis (Cavalier-Smith, 2002). However, there are some competing hypotheses viz., the Archezoa hypothesis, the hydrogen hypothesis, and the syntrophic hypothesis about how the process of endosymbiosis actually occurred during mitochondria evolution and about what specific types of ancient cells were involved in the process. The Archezoa Hypothesis suggests that there was an amitochondriate eukaryote and it was a direct descendent of a prokaryote (domain-archaea) and it was proved by the fact that the nuclear genes of the recent eukaryotes are very similar with that of the archaea (Margulis, 1996; Doolittle, 1999). The study of the small-sub-unit rRNAs (SSU rRNAs) of the archaeal lineage shows a striking similarity in replication and protein synthesis system to eukaryotes (Doolittle, 1999); and genes for enzymes involved in cytosolic metabolism in the eukaryote lineages are of bacterial origin or homologs to bacterial genes (Doolittle, 1999). The presence of these bacterial genes is attributed to lateral gene transfer (LGT), where the archeaon would engulf the bacteria and acquire small amount of bacterial genetic material, the process is known as ―ratcheting‖ (Doolittle, 1998). The most common occurrences of this LGT are between the housekeeping and catabolic genes (Doolittle, 1999). The Syntrophic hypothesis suggests that ―synthophy‖ (first symbiosis) of two proteobacteria endosymbionts (one methanogenic archaea and other sulphate-reducing myxobacteria or delta- proteobacterium) were involved in the generation of mitochondria (López-García, 1999). This concept of syntrophic endosymbiosis was evident

Page | 57 from the classic fusion model which describes exchange of many homologous gene viz., genes involved in signalling pathways, lipid systhesis pathways, DNA related enzymes and histones (Gupta, 2000). The hydrogen hypothesis suggests there is only one endosymbiosis event took place between proteobacterium i.e., between an alpha-proteobacterium and an obligatory anaerobic autotrophic methanogenic archaeon that required hydrogen and CO2 from the environment for its survival (Martin and Müller, 1998). The autotrophic archaeon receives hydrogen and CO2 from the alpha-proteobacterium and the alpha-proteobacterium received methane from autotrophic archeon in turn. Consequently, the symbiont gave rise to the nuclear envelope, mitochondria, and other features of hypothetical early eukaryotic cell all at once.

1.9.3.2 Nuclear integration of Mitochondrial genome Mitochondrial genomes are the descendants of free-living non-symbiotic proteobacteria and most of the coding sequences have been lost from the proteobacteria (Gray et al., 1999). Two distinct hypothesis have been put forward regarding the modes of genetic loss and this reduced genetic potentiality have further curtails the coding capacities of mitochondrial genomes from the non-symbiotic ancestor to modern mitochondrion. One is that the reduction of genes that encode the biosynthetic activities of the cell could be eliminated from the mitochondrial genome by random mutations that are finally eliminated by purifying selection (Gray 1992). The second mode of genetic loss involves the lateral transfer of essential genes from the mitochondrial genome to the nuclear genome. Indeed, nearly all of the genes needed for the function and propagation of mitochondria unremarkably reside within the host nucleus (Gray 1992). It was reported in several vertebrates and invertebrates the mitochondrial-like sequences were observed in the nuclear genomes. This incorporation contains several genes encoding mitochondrial proteins, ribosomal RNAs (r-RNA), transfer RNAs (tRNA) and also a deleted D-loop region (Smith et al., 1992; Lopez et al., 1994; Collura et al., 1995; Zischler et al., 1995; Sunnucks and Hales, 1996; Gellissen et al., 1983; Hadler et al., 1983; Tsuzuki et al., 1983; Quinn, 1992). Such a nuclear integration are the result of a relatively recent (about two million years ago) inter-genomic transfer. In human nuclear genome several mitochondrial gene like sequences have also been found and some insertions were observed to have very high number of copies about hundreds to thousands (Fukuda et al., 1985 and Kamimura et al., 1989).

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Nuclear insertions show numerous degrees of similarity with their mitochondrial counterparts; for instance, the nucleotide divergence between the mitochondrial COI-COII gene sequence and its nuclear homologue in aphids was less than 1% (Sunnucks and Hales, 1996) and in primates the Cytb sequences shows a divergence of greater than 25% (Collura and Stewart, 1995). The presence of homology of DNA sequences of mitochondrial origin with that of nuclear genome through the result of intergenomic transfer is the critical corollary of the endosymbiotic phenomenon of the origin of mitochondria.

1.9.3.3 Biology and genetics of Mitochondrial genome Mitochondrial DNA (mtDNA) has been widely used as a molecular marker to study molecular diversity in animals by population geneticists and molecular taxonomists (Avise et al., 1987 and Moritz et al., 1987). All the molecular study of animal species has been carried out by haplotyping of mtDNA and the mitochondrial cytochrome oxidase subunit 1 (COX 1 or COI) has been widely accepted molecular tool for molecular taxonomical and identification studies (Ratnasingham and Hebert, 2007). The main reason behind selecting mtDNA as a molecular tool is that the cistron content of mitochondrial DNA is powerfully preserved across animals; with little or no duplication, lack of intron, and really short intergenic regions (Gissi et al. 2008). mtDNA has terribly high rate of mutations and due to this, it shows high level of variability across natural populations, which may generate some signal regarding population history over a brief period of time. The highly variable regions are flanked by extremely preserved regions (e.g. ribosomal DNA), this conserved regions are used to design the specific primers for amplification of the variable region (Bensasson et al. 2001). Thus mtDNA is the most convenient, reliable and least expensive molecular tool to research or genetically explore any new species within the wild.

Several specific and fundamental biological properties of mtDNA make it an appropriate marker of molecular biodiversity studies (Ballard & Whitlock 2004; Ballard & Rand 2005). Firstly its clonal/maternal mode of inheritance, which means that the whole mtDNA genome acts like a common-nonrecombining geneological unit. Thus helps the investigator to analysis the intra-species variation data. Secondly, it‘s the neutral mode of evolution; and involvement in basic metabolic and cellular functions. Therefore mitochondrial genes have been considered as more adaptive than other cellular genes. Finally, the clock-like evolutionary rate of mtDNA genome; thus only neutral and slightly deleterious mutations accumulate in time by positive selection process.

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1.9.3.3.1 mtDNA is clonally inherited: In animals the mtDNA is maternally transmitted, and therefore it shows clonal inheritance, (Birky 2001). The diversity of organisms is negatively affected due to the strong bottlenecking effect in the female germline (Shoubridge & Wai 2007). A distinct mtDNA lineages can co-occur within the zygote because of paternal inheritance is missing, which prevents effective recombination process. The lack recombinational process the within- species evolutionary history can be appropriately represented by a unique phylogenetic/ phylogeographic tree based on mtDNA sequence analyses, which traces the ancient origins and geographic migration of maternal lineages (Avise et al. 1987). Although mtDNA recombination was reported in many population based studies like in mussel (Ladoukakis & Zouros 2001), a butterfly (Andolfatto et al. 2003), scorpions (Gantenbein et al. 2005), a lizard (Ujvari et al. 2007) and fish (Hoarau et al. 2002; Ciborowski et al. 2007), based either on linkage disequilibrium or discovery of noticeable recombinants.

1.9.3.3.2 mtDNA is neutral in nature: The neutral nature of mtDNA is widely assumed during the population genetic studies especially when diversity patterns are ascertained in terms of demography, migrations, genetic diversity and founding events. The neutral, adaptive or deleterious mutations mtgenome are spread through positive selection but rapidly removed by the purifying selection. But the linked sites are unaffected as these are involved in the genetic variability. Neutralist theory of molecular evolution suggested that the variation observed in mitochondrial genome is due to the neutral processes (Kimura 1983). A meta-analytical study over 1600 vertebrates and invertebrates suggested that he average intra-species mtDNA diversity is significantly similar across all animal phyla (Bazin et al., 2006). Theoretical relationship showed a contrasting correlation between population size and genetic diversity between nuclear and mitochondrial genome. The adaptive evolution or recurrent selective sweep theory suggested that the nuclear genomic diversity is greater in invertebrates, in marine animals and in small organisms than in vertebrates, in terrestrial animals and in large organisms respectively (Bazin et al., 2006). This hypothesis is in corcordance with the Gillespie‘s (2000) ‗genetic draft‘ model which explains the selective sweep increased with genetic hitch-hiking that affect the mtDNA evolution in species with larger population sizes which lead to recurrent decline in diversity of whole genome level.

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1.9.3.3.3 Mutation rate of mtDNA is constant: High substitution rate of mtDNA make it a widely used phylogenetic marker to construct evolutionary tree and molecular dating. The earlier attempt to calibrate mitochondrial molecular clock has been carried out in primates (Brown et al., 1979). They used a logical reference of ―2% per million years‖ for calibration when relevant fossil data was missing in mammals including in vertebrates. A vast array of phylogenetic analyses showed that the evolutionary substitution rate of mtDNA is insignificantly variable (between fast evolving and slow-evolving lineages) across all taxa in vertebrates (Martin 1995; Gissi et al. 2000; Bininda-Emonds 2007).

1.9.3.4 mtCOI gene and Barcoding The planet earth carries range of life forms and it underpins all biological studies, but it is also make a extreme burden. Moreover, biologists cope with with the aid of hundreds of thousands of species and their characterisation isn't always an easy assignment. Hawksworth & Kalin-Arroyo (1995) had suggested that taxonomists can seriously perceive more than 0.01% of the expected 10-15 million species. Moreover, the identity of this quantity of species has 4 great limitations. First, the genetic and phenotypic plasticity within the characters employed for species characterization can cause wrong identifications. Second, this identification technique fails to check the morphologically cryptic taxa, which are not unusual in many organizations of species (Jarman & Elliott 2000). Third, seeing that morphological system of identification is often powerful simplest for a selected degree of existence or gender, many people can't be diagnosed or faultily identified. Finally, although modern-day interactive variations and advances of species identification and the usage of keys often need any such excessive stage of understanding that misdiagnoses are common. The barriers in morphology-based identification systems glide the identity science to a new technique to taxon popularity. Microgenomic stage identity systems permit discrimination of taxons via the analysis of a small phase of the genome; constitute an incredibly promising technique to the analysis of organic diversity. This concept has already applied for viruses, micro organism and protists, wherein morphological identification is debatable (Allander et al. 2001; Hamels et al. 2001). In reality, studies confirmed that there are huge evidences wherein DNA-based totally identification structures have been carried out to better organisms (Trewick, 2000; Vincent et al., 2000). Genomic techniques take advantage of the massive DNA sequences to pick out the taxon variety and the sequences used as a genetic ―Barcode‖ that is embedded in each cell of

Page | 61 the individuals (Wilson, 1995). Genomic barcodes have best 4 alternate nucleotides bases at each function and the massive stretch of DNA have been used for investigation. Therefore, the survey of simply 15 of those nucleotide bases creates the opportunity of 415 (1 billion) codes, a hundred times the range that would be required to discriminate the numerous lifeforms forms if every taxon was uniquely barcoded. However, the estimation of the nucleotide variation needs to be very robust due to the fact there are some constant functional constraints lie within the nucleotide positions. The impact of useful constraints can be minimized by using pinpointing in a protein-coding gene, because most shifts at the third nucleotide function of codons are weakly limited by selection pressure due to their 4-fold degeneracy. Naturally a large fragment of DNA stretch of hundred of base pair is used for sequence evaluation that appreciably lessen the useful constraint. Two different aspects of longer sequences needs to be taken into consideration while analyzing the animal taxa viz., firstly nucleotide composition at third-position nucleotide is regularly biased (A–T in arthropods, C–G in chordates), that lessen the information content of the sequence in problem and secondly maximum nucleotide positions are steady in comparisons to closely associated species that lessen the potential information capability (Hebert, 2003). However, no simple formulation is available that can expect the length of the sequence that ought to be analyzed to ensure species investigation, because molecular evolutionary rate differ across segments of the genome and taxa. Moreover the evaluation of unexpectedly evolving gene areas across taxa will help the prognosis of lineages with brief evolutionary histories of reproductive isolation (Hebert, 2003). The mitochondrial genome of animals is a higher target for analysis of genetic variability than the nuclear genome due to its lack of introns, its confined exposure to recombination methods and its uniparental mode of inheritance (Saccone et al. 1999). Moreover, development of precise primers additionally permits the standard amplification of specific segments of the mitochondrial genome for analyses (Simmons & Weller 2001). The 13 protein-coding genes within the animal mitochondrial genome are higher targets because insertions and deletions are uncommon due to the fact most lead to a shift inside the reading frame. A quick DNA collection of 600 base pairs (bp) in the mitochondrial gene encoding cytochrome c oxidase subunit 1 (CO1) has been regular as a sensible, standardized, species- level DNA barcode for plenty groups of animals (Hebert and Barret, 2005). Moreover, the cytochrome c oxidase I gene (COI) does have two crucial benefits. First, universal primers can be used that permit to extend of its 5ʹ end from representatives of maximum animal phyla (Folmer et al. 1994; Zhang & Hewitt 1997). Second, COI genes have a greater range of

Page | 62 phylogenetic informations than some other mitochondrial gene. Moreover the nucleotide substitution of its third position is greater than that of 12S or 16S rDNA genes and this lead to about three times greater rate of molecular evolution (Knowlton & Weigt, 1998). Importantly, the rate of evolution of this COI gene is rapid enough to allow the discrimination of not only closely related species, but also phylogeographic or allopatric groups within a single species (Cox & Hebert, 2001; Wares and Cunningham 2001). Recent population divergence can be resolved by using this gene very sophisticatedly because this gene is more likely to provide clearer phylogenetic insights than different options together with cytochrome b (Simmons & Weller 2001), due to the fact the amino-acid sequence changes occur slowly than different alternative of mitochondrial genes (Lynch & Jarrell 1993).

1.9.3.5 mtCOI in fish genetic diversity study Recent studies have suggested that a DNA-based identification system, based on the mitochondrial cytochrome oxidase subunit 1 (COI) gene can aid the resolution of this diversity. Hebert et al. (2003) shed light on this discussion by proposing that a DNA barcoding system for animal life could be based upon sequence diversity in COI gene. They established that diversity in the amino acid sequences coded by the 5ʹ section of this mitochondrial gene was sufficient to reliably place species into higher taxonomic categories (from phyla to orders). They also argued that a COI-based identification system could be developed for all animals groups. The use of classical biodiversity indices viz., species richness, Simpson‘s index and Shannon‘s index, could be complemented by new indices evolved to take advantage of the information contained in entire collection sets obtained from a single source. Thus, the estimation of biodiversity indices may be based totally on Molecular Operational Taxonomic Units (MOTU) detected the usage of the barcoding technique wherein the relative abundances of each sort of DNA series (MOTU) update the classical relative abundance of each species envisioned from the range of individuals (Blaxter et al., 2005). COI gene sequence has been analyzed to investigate the phylogenetic relationship and variation patterns in the yellowfin (Thunnus albacares) and longtail tunas (Thunnus tonggol) (Kunal and Kumar, 2013), seven sturgeon species (Acipense rschrenckii, A. baerii, A. gueldenstaedti, H. dauricus, A. ruthenus, A. sinensisand A. stellatus), ten interspecific hybrids in China (Zhang et. al., 2013) and five Indian Mahseer (Tor putitora, Tot tor, Tor khudree, Tor chelynoids and Neolissochilus hexagonolopis) (Sati et. al., 2013). COI gene sequence has been used to study the genetic as well as mitochondrial gene diversity of Malaysian indigenous Mahseer, Tor douronensis from eight different populations in Sarawak

Page | 63 river basins (Nadiatul et. al., 2011), Humpback Grouper (Cromileptes altivelisin) Asia Pacific region (Susanto et. al., 2011), Tor tambroides Valenciennes (Cyprinidae) from five natural populations in Malaysia (Esa et. al., 2008). Mitochondrial DNA CO1 gene region was sequenced for 7 scorpion fish species from the Far East of Russia and compared with 15 other sequences of Scorpaeniformes comprising altogether 29 scorpion-like fish sequences and two outgroup sequences (Cypriniformes) to analyze the phylogenetics status and taxonomic categorization. (Kartavtsev et. al., 2009). COI-primarily based DNA barcoding is extensively used to discover and study distinctive specimens to recognize and compare genetic range of species inside itself and in an atmosphere. Moreover, DNA barcodes offer beneficial facts for conservation and management, considering the fact that taxonomist ought to become aware of and decide species and its groups quickly and conservationist may want to use barcode facts to determine the right measures to prioritise conservation practices (Francis et al. 2010). Subsequently, the most great contributions of DNA barcoding are in the conservation of known biodiversity. Rubinoff (2006) said that mitochondrial DNA (mtCOI gene) may be used as a powerful device to discover species, to analyze their phylogenetic relationships with each other, and threatened or endangered populations with divergent genetic architecture for conservation interest. Indeed, the use of mtDNA for species identification has been claimed to have high rate of achievement e.g., a 93 % achievement was observed in figuring out Canadian freshwater fish (Hubert et al., 2008). The finest strength for DNA barcoding initiatives is that genetic and molecular information have the capacity to beautify conservation techniques at exceptional levels of analysis (DeSalle and Amato, 2004); from nice-scale control of coral reefs (Neigel et al., 2007) to regional management of fisheries (Swartz et al., 2008). The most vital contribution of barcoding to biodiversity conservation is facilitating the management of species in extinction/ extirpation cascade and evaluation of biodiversity in affordably and fast in which monetary assets are restrained. This is especially important because in our country India and in other developing countries, where resources for comprehensive biodiversity assessments are lacking. There have been several notable conservation successes for rapid classification of 99 % of 210 chondrichthyan species from 36 families in Australia using barcoding technique (Ward et al. 2008 Barcoding can also act as a device to actively prevent or punish environmental criminals offering forensic evidence, for example the illegal sharkfin trade, which is a significant threat to biodiversity in many coastal African countries where barcoding technique can identify different critically endangered and legally protected shark species (Swartz et al. 2008). Faith and Williams

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(2005) reported that DNA barcoding is critically used to measure the phylogenetic diversity by improving and speeding up the process of diversity assessments. Therefore, a venture for DNA barcoding may be to combine genetic information into the broader context of clinical, social, reasonable and political elements for powerful conservation and management of biodiversity.

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1.10 Objectives of the study

The objectives of my study were:

I. To standardize isolation of genomic DNA (gDNA) from minute quantity

of fish tissue samples for PCR-based genetic analyses.

II. To standardize and perform Random Amplified Polymorphic DNA–PCR

(RAPD-PCR) amplification with selective random/arbitrary decamer

primers.

III. To standardize and perform Inter Simple Sequence Repeat–PCR (ISSR-

PCR) amplification with selective ISSR/microsatellite primers.

IV. To ascertain the genetic diversity within individuals (heterozygosity),

intra- and inter population diversity at significant number of loci through

RAPD-PCR and ISSR-PCR analyses in different populations of

threatened ornamental fishes Badis badis and Amblyceps mangois in the

river systems of Terai and Dooars region of North Bengal, India.

V. To map genetic hierarchy and perform cluster analyses in these two

species through the assessment of among-population divergence in

selected geographic populations along the major river systems of Terai

and Dooars region of North Bengal.

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CHAPTER-II Study Area

2. STUDY AREA ______2.1 Study area (sub-Himalayan hotspot region of North Bengal, West Bengal, India). North Bengal (In Bengali language: ―Uttarbanga‖) is a region that encompass Northern part of West Bengal and denotes Cooch Behar, Darjeeling, Jalpaiguri, Alipurduar, North Dinajpur, South Dinajpur and Malda districts collectively. It additionally includes parts of Darjeeling Hills. Traditionally, the Hooghly River divides West Bengal into North and South Bengal. North Bengal is in addition divided once more into Terai and Dooars regions. North Bengal can be broadly divided in three sections i.E., the hills consisting of Darjeeling, Kalimpong, Kurseong and Mirik, the plains of Cooch Behar and Malda and the ultimate phase is the Terai place which include Dooars which is the stretch from of the foothills of Himalaya, additionally called sub-Himalayan place of West Bengal. The snow covered mountain peaks, the dense foothill, the luxuriant green forests, the frothing rivers, the luxurious crop fields, vibrant green tea gardens makes the terai and dooars of North Bengal a hotbed of bio-variety or biodiversity hotspot. Siliguri is the centre for buying and selling, tourism, tea and wood; and Jalpaiguri is the headquarter of Dooars. Siliguri is the gateway to the complete of North-East India that consists of seven states and also 4 neighbouring international locations (Nepal, Bangladesh, Bhutan, Tibet and China); and they have their global buying and selling course through Siliguri. This vicinity has many the worlds over famous locations and growing very fast and is hooked up to all of the most important cities or town all throughout the globe. The predominant reason for its infrastructural and the industrial increase is its strategic place. Tea, tourism, wood, transport and trading is probably the possible cause for the increasing development of this location. The region covers the moist and dense riverine forests of the Bengal Dooars (Duars) and the foothills of the snow covered Kanchenjunga range. The specific climatic and ecological situations make North Bengal a completely unique habitat for a massive wide variety of mega fauna and flowers. The altitudanal zones of plants run from tropical, sub tropical, moderate to Alpine – a few locations just 10 km in a right away line isolates the palm developing valleys from ceaseless snow.

2.1.1 Terai The Terai ("moist land") is a belt of mucky meadows, savannas, and woodlands at the bottom of the Himalaya that run in India, Nepal, and Bhutan, from the Yamuna River within

Page | 69 the west to the Brahmaputra River in the east. Over the Terai belt lies the Bhabhar is made up of replacement layers of mud and sand; a forested belt of rock and soil disintegrated from the Himalaya mountain tiers. The excessive water table that lies from 5 to 37 meters down makes numerous springs and wetlands. The Terai zone is immersed every year by way of the rainstorm swollen river streams of the Himalaya. Underneath the Terai lies the good sized alluvial undeniable of the Yamuna, Ganges, Brahmaputra, and their tributaries. [Source: http://biodiversity-mohanpai.blogspot.in/2008/12/biodiversity-north-bengal.html]

2.1.2 Dooars The Dooars or Duars are flood plains at the foothills of the jap Himalayas in North- East India around Bhutan. In both Assamese and Bengali languages Duar means ―Door‖. It is the gateway to the North Eastern place of India. This area is divided via the Sankosh river into the eastern and the western Dooars, consisting of an area of 800 square kilometre. This vicinity became part of the Indian states of Assam and West Bengal. There are innumerable streams and rivers flowing thru those plains from the mountains of Bhutan make this undeniable very fertile. In Assam the foremost rivers are Brahmaputra and Manas, and in northern West Bengal the major river is the Teesta and plenty of others like the Jaldhaka, Murti, Ghotia, Torsha, Sankosh, Diana, Raidak, Kaljani etc. The forested regions of northern West Bengal gift a plenty of Wildlife. This blended dry deciduous woodland noticed with fields, harbors the biggest assorted kind of principal fauna in West Bengal. A huge range of foothill forest in North Bengal is referred to as Dooars. Tea Gardens, snow covered landscapes, honest streams; National Parks and the Wildlife Sanctuary make this area a paradise. Wonderful motorable streets slice via profound forest, wealthy with wild lifes. Gentle slopes stay toward the cease of velvet inexperienced fields and the woodland is echoed with the music of winged animals. Dooars is the territory of the unusual Toto clans. The maximum superb passage factor to Dooars is thru Siliguri by street. The primary spots of enthusiasm for Dooars are Jaldapara National Park and Gorumara National Park, Buxa National Park, Mahananda Wildlife Sanctuary, Rasik Bill Bird Sanctuary, Chapramari Wildlife Reserve, Chilapata forest, Jayanti hills, Bindu, Rocky Island. [Source: http://www.northeastindiatours.in]

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2.2 River systems The sub-Himalayan North Bengal region is intersected via numerous streams, rivers and rivulets. All their resources are the Darjeeling Himalaya in the north. The rivers in this place are in particular snow fed and rain endorsed. Due to their big catchment location most of the rivers drain good sized quantity of water throughout wet season. All rivers convey massive load of sediment with their water due to excessive price of soil disintegration occur inside the hill location. The greater part of the streams has excessive slope and big measure of water so they're used for the technology of hydroelectricity. Scarcely any hydroelectric ventures are brought in the sloping components of the streams. The streams are separated into high run-off region, excessive infiltration quarter and recharge quarter. Unreasonable rain, expansive catchment territory, low wearing limit and snow melted water all activates frequent floods within the streams. The rivers often changed their course of glide due to unexpected bursting of artificial water garage by using landslides and bank disintegration. High deforestation occurred due to human interference inside the hills and adjacent region of the Darjeeling place ends in soil degradation, erosion and common landslides which is the characteristic of Darjeeling hills. Many forests are cleared for tea plantation and terrace cultivation for production of plants; leaves last forest inside a danger because of populace enlargement changed into due to excessive rate of immigration from neighboring states and international locations. The rivers with their higher catchment inside the hills have funneled fashioned basin with narrow outlets on the foothills; inside the plains the rivers falls notably and unexpectedly widens. So to resist this condition the rivers adjust themselves by means of converting their courses from erosional to depositional nation to adjust the brand new hydrogeographical necessities brought on by way of the exchange of the gradients as rivers glide from excessive to lower altitude. Development of new channels, shifting of the channels and forsaking the old one are the most common inside the rivers of this place. Often, overflowing of water on one river reasons flood inside the catchment region of nearby rivers. The construction of longitudinal embankment along the rivers has reasons the rise of the river bed level that results in deposition of sediments in many locations of the river bed. This problem is augmented by way of careless diversion of water from rivers for irrigation inside the agricultural fields and different family purposes; these outcomes the rivers incompetent to hold silts and particles. Removal of boulders and twine-guards from the embankment weakens the embankment; consequently, the rivers end up unable to carry massive quantity of water at some point of wet season lead to floods within the nearby region. This activities reason intense financial institution erosion and therefore inundate the adjacent regions.

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Scarcity of appropriate habitats pressured the lowland inhabitants to settle their everlasting homes at the stretches river mattress or facets of the banks due to the fact the whole highlands are dedicated to cultivation of various vegetation and teas. Peoples with out a know-how approximately the nature of the North Bengal rivers also build their citizens at the lands which can be generally the beds of vintage abandoned channels.

2.2.1 Mahananda-Balasan River System Mahananda River is one of the essential tributary of the Ganga River within the eastern a part of India. The Mahananda River is a trans-boundary river and flows thru the Indian states of West Bengal, Bihar, and crosses Indian boundary to go into Bangladesh. Its proper bank tributary, the Mechi River paperwork part of Nepal's jap boundary with West Bengal, India; while the Kankai flows out of Nepal. The Mahananda originates within the Himalaya‘s Paglajhora Falls on Mahaldiram Hill near Chimli, east of Kurseong in Darjeeling district at an elevation of two,one hundred metres (6,900 ft) (Jain et al., 2010; "Rivers in Siliguri". Mahananda River. Siliguri on line. Retrieved 2010-05-14; "Rivers". Darjeeling News.Net. Retrieved 2010-05-14). It flows across the Mahananda Wildlife Sanctuary and descends to the fotthills of Himalayan plains near Siliguri and touches Jalpaiguri district ("Mahananda Wildlife Sanctuary". nature beyond. Retrieved 2010-05-14). It enters Bangladesh near Tentulia in Panchagarh District, flows for 3 kilometres after Tentulia and returns to India ("News from Bangladesh". Retrieved 2010-05-14.). After flowing through Uttar Dinajpur district in West Bengal and Kishanganj and Katihar districts in Bihar, it again enters Malda district in West Bengal ("Uttar Dinajpur district". Uttar Dinajpur district administration. Retrieved 2010-05-14.; "Kishanganj district". Kishanganj district administration. Retrieved 2010-05-14.). The Mahananda divides the district into two regions — the jap region, known as Barind, consisting specifically of antique alluvial and relatively infertile soil, and the western location, that is in addition subdivided with the aid of the river Kalindri into areas, the northern location is known as "Tal" that is low-mendacity and usually inundated all through wet season; the southern area is "Diara", includes very fertile land and is thickly populated ("Malda district". Malda district administration. Retrieved 2010-05-14.). It merges with the Ganges at Godagiri in Nawabganj district in Bangladesh after flowing for 360 kilometers (Jain et al., 2010). The total length of the Mahananda is 360 kilometres ("Mahananda River". Britannica. Retrieved 2010-05-14.) out of which 324 kilometres are in India and 36 kilometres are in Bangladesh. The total drainage area of the Mahananda is 20,600 square

Page | 72 kilometres out of which 11,530 square kilometres are in India (Jain et al., 2010). The main tributaries of the Mahananda are Balason, Mechi, Ratwa, Kankai and Kalindri (Jain et al., 2010).

2.2.2 Teesta River System and important tributaries

The 'Teesta' or 'Tista' is the greatest and high tempestuous ice sheet encouraged stream of Darjeeling area. The term 'Teesta' or 'Tista' is a gotten from the Sanskrit word 'Tri- srota', which means, it has three 'srotas' or flows. This most likely in light of the fact that the waterway once (before 1787 A.D.) moved through three diverse stream streams viz., the Atrai, Punarbhaba, and Karatoya, every one of which pursued free courses to the Ganges and Meghna. The Bhutia name for this waterway is 'Tsang-Chhu' or the unadulterated water, while the Lepcha consider it the 'Rangnyung' or the extraordinary straight going water delineates actuality that it proceeds in a straight unaltered course regardless of accepting an incredible load to its water from the Rangit stream joining to it at straight points.

The waterway accepts its ascent as 'Chumbu chu' (the upper most Teesta) in the North-East edges of North Sikkim from Pouhunri (Pouhungri) icy mass at an elevation around 6200 meter. Teesta river out falls into the waterway Brahmaputra (Jamuna) at Kamarganj in Rangpur region of Bangladesh. The waterway is amplified by the converging of a significant expansive number of tributaries in the Himalayan and sub-Himalayan areas. The waterway coursing through the sloping catchment of the Sikkim state, gets its name Teesta underneath the intersection of two streams, Lachung chhu from the north-eastern bearing and Lachen chhu from the north-western heading. The Lachen chhu is bolstered by a snow-encouraged right bank stream of Zemu chhu and the Lachung chhu is nourished by left bank tributary of Sebzung chhu. The Lachung chhu and Lachen chhu, subsequent to rising up out of, Chungthang joins to stream with the name of Teesta River. The Teesta shapes the limit of the Darjeeling region from the point where it is joined by the Rangpo chhu to its intersection with the incomparable Rangit. After that it streams completely through a profound canyon of Sivok-Gola go inside the Darjeeling locale until the point when it abandons it at Sivok. Through its course, the Teesta waterway has removed crevasses and gulches in Sikkim twisting through the slants of Kalimpong lying essentially off the stream. Variegated vegetation can be seen along this course. At lower statures, tropical deciduous trees and brambles cover the enveloping inclines; elevated vegetation is seen at the upper statures. The

Page | 73 stream bank is flanked by white sand and rock which is utilized for the development business in this district. Huge rocks are found around the waters make it perfect for waterway raftingrafting aficionados. Close to the Rangpo town the railroad connects (prevalently called Lohapul or iron extension) was developed as it enters the fields at Sevoke. Towns like Teesta Bazaar and MelliMelli stuated here. Despite the fact that the stream looks gentle in this district yet hidden current is extremely solid. Frequently, a pontoon struck a halfway concealed stone underneath the waterway water and was sucked in by a whirlpool, leaving no trace of the occupants. In the midst of the rainstorm, Teesta stream develops its banks; both in size and unsettling influence. Torrential slides in this region as often as possible dam up parts of the conduit in this season. Amid the period since 1500 CE, amazing changes have happened over the range of a bit of the streams in Bengal and the bordering zones. Albeit positive verification is missing, similar changes can be acknowledged in the remoter past. The Teesta River is one of the streams that have changed consistently (Majumder, 1971). Already three channels, viz., the Karatoya toward the east, the Punarbhaba in the west and the Atrai in the middle kept running from Jalpaiguri conceivably gave the name to the waterway as Trisrota "had of three streams" which has been abbreviated to Teesta. Later on the Punarbhaba joined the Mahananda. In the wake of going through a huge boggy territory known as Chalan Beel, Atrai waterway joined the Karatoya and the assembled stream converged to the Padma (Ganges) close Jafarganj. After the dangerous surges of 1787, the Teesta stream relinquishes its old channel and streaming south-east to join the Brahmaputra. As indicated by the guide developed by James Rennell (1764 and 1777) Teesta is appeared as coursing through North Bengal in a few branches—Punarbhaba, Atrai, Karatoya, and so on. Every one of these streams consolidated let down with the Mahananda in North Bengal, and taking the name of Hoorsagar at long last released into the Ganges at Jafarganj. The Hoorsagar waterway still in nearness, being the united outfall of the Baral, a spill channel of the Ganges, the Atrai, the Jamuna or Jamuneswari, and the Karatoya, rather than falling into the Ganges, it falls into the essential Jamuna, several miles over its crossing point with the Padma at Goalundo (Majumder, 1941).

2.2.3 Jaldhaka River System and important tributaries The Jaldhaka River similarly alluded to previously as Dichu River, is a trans-restrict conduit with a length of 192 kilometers that starts from the Kupup or Bitang Lake in southeastern Sikkim in the eastern Himalayas and moves through Bhutan; and the Kalimpong, Jalpaiguri and Cooch Behar districts of West Bengal, India (Sharma and

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Aharma, 2005). By then the stream enters Bangladesh through the Lalmonirhat District and after that joins with the Dharla River until the point when the moment that the Dharla converges into the Brahmaputra River near the Kurigram District. The waterway's floating more than a few universal fringes, hence, just a little length of the stream exists in Bangladesh (Murshed, 2012). The Jaldhaka River is surrounded by the mix of three streams at Bindu, the end reason for the Jaldhaka Police Station at Kalimpong district in West Bengal. The three streams are known as Bindu Khola, Dudh Pokhri and Jaldhaka that starts from the Kupup Lake, a little sub zero lake in Sikkim. The joined streams meet at Bindu to shape the Jaldhaka River, along these lines surrounding a riverine restrict with India and Bhutan in the left bank. The crucial tributaries that join the stream in its right bank are the Murti, the Naksal Khola, the Sutunga and the Jarda in the lower reach. The Diana, Rehti-Duduya and Mujnai are the principal left bank tributaries. The stream courses through the three North Bengal regions of Kalimpong, Jalpaiguri and Cooch Bihar. The entire watershed is the most productive provincial zone close by the Teesta Basin. The upper course is commended for harvests like ginger, remedial herbs and normal items like oranges and pomegranate. The inside course containing Jalpaiguri region is by and large tea and corn overpowered and the lower course is governed by rice, jute and tobacco. The between stream molded territories are produced with harvests like bamboo and tangle sticks. In the lower bowl, the between stream lands are produced with banana. The stream enters Bangladesh at Ghoksadanga area to meet the Brahmaputra or the Jamuna. Through its course, the Teesta waterway has removed chasms and gorge in Sikkim twisting through the slants of Kalimpong lying basically off the stream. Variegated vegetation can be seen along this course. At lower statures, tropical deciduous trees and shrubs cover the including inclines; high vegetation is seen at the upper statures. The stream bank is flanked by white sand and rock which is utilized for the development business in this locale. Huge stones are found around the waters make it perfect for waterway raftingrafting devotees. Next to the Rangpo town the railroad connect (famously called Lohapul or iron extension) was developed as it enters the fields at Sevoke. Towns like Teesta Bazaar and MelliMelli stuated here. Despite the fact that the waterway looks gentle in this locale however, fundamental momentum is exceptionally solid. Regularly, a vessel struck a halfway concealed rock underneath the waterway water and was sucked in by a whirlpool, leaving no trace of the occupants. In the midst of the rainstorm, Teesta stream grows its banks; both in size and unsettling influence. Torrential slides in this area often dam up parts of the conduit

Page | 75 in this season. Amid the period since 1500 CE, amazing changes have happened over the range of a part of the waterways in Bengal and the abutting zones. Albeit positive confirmation is missing, similar changes can be acknowledged in the remoter past. The Teesta River is one of the streams that have changed consistently (Majumder, 1971). Already three channels, viz., the Karatoya toward the east, the Punarbhaba in the west and the Atrai in the inside kept running from Jalpaiguri conceivably gave the name to the waterway as Trisrota "had of three streams" which has been abbreviated to Teesta. Later on the Punarbhaba joined the Mahananda. In the wake of going through an immense boggy territory known as Chalan Beel, Atrai waterway joined the Karatoya and the assembled stream converged to the Padma (Ganges) close Jafarganj. After the ruinous surges of 1787, the Teesta waterway forsakes its old channel and streaming south-east to join the Brahmaputra. As per the guide developed by James Rennell (1764 and 1777) Teesta is appeared as moving through North Bengal in a few branches—Punarbhaba, Atrai, Karatoya, and so on. Every one of these streams joined let down with the Mahananda in North Bengal, and taking the name of Hoorsagar at last released into the Ganges at Jafarganj. The Hoorsagar stream still in nearness, being the solidified outfall of the Baral, a spill channel of the Ganges, the Atrai, the Jamuna or Jamuneswari, and the Karatoya, rather than falling into the Ganges, it falls into the essential Jamuna, a few miles over its crossing point with the Padma (Majumder,1941).

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2.3 Detailed Map of the Study Area showing sample collection sites

Northern West Bengal

Figure 6. map of the Study area showing sample collection sites Page | 77

2.4 Local Names of sample collection site showing geographical coordinates of each site.

Table 1. Geographical coordinates recorded with the help of hand-held GPS device Population Name of the River and Geographic position and Sl/No. Code adjacent place GPS Record 26°38.884Ń; 88°24.125É; 1 TR-1 Mahananda Barrage, Fulbari. Elevation 319 AMSL Mahananda-Panchanoi River 26°42.125Ń; 88°24.717É; 2 TR-2 Junction, Siliguri. Elevation 620 AMSL 26°43.177Ń; 88°22.825É; 3 TR-3 Balason River, Palpara, Matigara. Elevation 646 AMSL 26°43.007Ń; 88°24.396É; 4 TR-4 Panchanoi River, Siliguri. Elevation 666 AMSL Mahananda River, Champasari, 26°44.452Ń; 88°25.497É; 5 TR-5 Siliguri. Elevation 717 AMSL 26°45.632Ń; 88°18.912É; 6 TR-6 Balason River, Tarabari. Elevation 731 AMSL N 26°53´043, E 88°28´367 7 DR-1 Sevok (Teesta River) Elevation 480 AMSL N 26°52´327, E 88°36´355 8 DR-2 Ghish River Elevation 536 AMSL N 26°44´584, E 88°35´314 9 DR-3 Gajoldoba (Teesta River Barrage) Elevation 354 AMSL N 26°51´499, E 88°38´048 10 DR-4 Chel River Elevation 522 AMSL N 26°52´486, E 88°46´205 11 DR-5 Neora River Elevation 527 AMSL N 26°40´496, E 88°44´126 12 DR-6 Dharla River Elevation 299 AMSL N 26°33´499, E 88°45´369 13 DR-7 Jalpaiguri (Teesta River) Elevation 274 AMSL 26°34´13.17 N, 88°56´14.26 E 14 DR-8 Jaldhaka River Elevation 267 AMSL 26°52´57.73 N, 88°49´44.98 E 15 DR-9 Murti River Elevation 578 AMSL 26°52´14.89 N, 88°53´37.98 E 16 DR-10 Ghotia River Elevation 540 AMSL 26°51´37.96 N, 89°00´07.40 E 17 DR-11 Diana River Elevation 647 AMSL *AMSL= Above Mean Sea Level

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CHAPTER-III Materials and Methods

3. MATARIALS & METHODS ______3.1 Survey of Major River Systems of Terai and Dooars Region A detailed survey has been carried out in the sub-Himalayan region of the West Bengal, India especially in the Terai and Dooars region. I have investigated the rivers, flowing through this region and the nearby landing centres to get the detailed picture of the habit and habitats of the studied fishes viz., Badis badis and Amblyceps mangois. After getting enough information about the fishes the major rivers of the sub-Himalayan Terai and Dooars region of the West Bengal India were considered for surveys and sample collection. The rivers under consideration were Mahananda river, Balason river, Teesta river and Jaldhaka river. The main tributaries of the rivers were also targeted for sample collection and to study the natural hierarchical distribution of the fishes. The collection sites with their geographic coordinates were recorded by hand-held Garmin GPS (eTrex Vista HCX, Garmin, USA) and are depicted in Figure 7 and Table 2.

A. Balasan River, Palpara, Matigara.

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B. Balasan River, Tarabari.

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C. Mahananda River, Champasari, Siliguri.

Panchnoi River

Mahananda River

River Junction

D. Mahananda- Panchnoi River Junction, Siliguri.

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E. Panchnoi River, Siliguri.

F. Mahananda Barrage, Fulbari, Siliguri.

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G. Teesta River, Sevoke, Near Siliguri.

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H. Ghish River, Jalpaiguri.

I. Chel River.

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J. Neora River.

K. Teesta River, Jalpaiguri.

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L. Teesta Barrage, Gajoldoba.

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M. Jaldhaka River.

N. Murti River.

O. Ghotia River.

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P. Diana River.

Figure 7. Photographic records of the sample collection sites (A to P).

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Table 2. Geographical coordinates recorded with the help of hand-held GPS device Population Name of the River and Geographic position and Sl/No. Code adjacent place GPS Record 26°38.884Ń; 88°24.125É; 1 TR-1 Mahananda Barrage, Fulbari. Elevation 319 AMSL Mahananda-Panchanoi River 26°42.125Ń; 88°24.717É; 2 TR-2 Junction, Siliguri. Elevation 620 AMSL 26°43.177Ń; 88°22.825É; 3 TR-3 Balason River, Palpara, Matigara. Elevation 646 AMSL 26°43.007Ń; 88°24.396É; 4 TR-4 Panchanoi River, Siliguri. Elevation 666 AMSL Mahananda River, Champasari, 26°44.452Ń; 88°25.497É; 5 TR-5 Siliguri. Elevation 717 AMSL 26°45.632Ń; 88°18.912É; 6 TR-6 Balason River, Tarabari. Elevation 731 AMSL 26°53´043 N, 88°28´367E 7 DR-1 Sevok (Teesta River) Elevation 480 AMSL 26°52´327N, 88°36´355E 8 DR-2 Ghish River Elevation 536 AMSL 26°44´584N, 88°35´314E 9 DR-3 Gajoldoba (Teesta River Barrage) Elevation 354 AMSL 26°51´499N, 88°38´048E 10 DR-4 Chel River Elevation 522 AMSL 26°52´486N, 88°46´205E 11 DR-5 Neora River Elevation 527 AMSL 26°40´496N, 88°44´126E 12 DR-6 Dharla River Elevation 299 AMSL 26°33´499N, 88°45´369E 13 DR-7 Jalpaiguri (Teesta River) Elevation 274 AMSL 26°34´13.17 N, 88°56´14.26 E 14 DR-8 Jaldhaka River Elevation 267 AMSL 26°52´57.73 N, 88°49´44.98 E 15 DR-9 Murti River Elevation 578 AMSL 26°52´14.89 N, 88°53´37.98 E 16 DR-10 Ghotia River Elevation 540 AMSL 26°51´37.96 N, 89°00´07.40 E 17 DR-11 Diana River Elevation 647 AMSL *AMSL= Above Mean Sea Level

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3.2 Habit and Habitat Identification of the Fishes The fishes are mainly found in the river streams having moderate current in water. They are found mainly in shallow water near the river banks, among vegetation and under the stony rubbles. They are found mainly after the monsoon season. The Badis badis are mainly found hiding underneath the boulders, gravels, bushes or wooden logs distributed along the river banks. The Amblyceps mangois are mainly found hiding in the bushes, garbage and debris distributed along the river banks. The fishes are small in size (1-2inch in length). They are also very fast swiming fishes and difficult to net.

3.3 Sample collection and Preservation 3.3.1 Collection Procedure I have used scoop net for collection of the samples. The scoop net was constructed by cutting the mosquito net and then tied it with a round shaped metal rod. A long bamboo-cane handle was attached with the metal rod for proper griping purpose. The scoop net was waved against the water corrent or placed underneath/or beside the boulders and gently flapped the water around the boulder or bushes to trap the fishes (Figure 8).

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Boulders

Boulders and Scoop Net

gravels

Wooden tree log Bushes

Figure 8. Sample collection procedure of fishes from rivers with scoop net.

3.3.2. Preservation

Some of the fishes were collected to the laboratory within water bottle. It was ensured that the water inside the bottle get aerated all the time. Therefore, the mouth of the bottle was loosely tightened to get enough air for respiration of the fishes. Fishes were brought to the laboratory and proper identified. After identification, tiny amount of fin clips were taken noninvasively from the fishes and kept within the aquarium with proper aeration and adequate amount fish food. This was carried out to check whether cutting-out of tiny portion of fin clips affect/harm the fishes any way or not. But the fins were regenerated fully within 30-40 days and the fishes were healthy. Some type specimen of the fishes were kept in plastic pouches and stored at -20°C for preservation and record.

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3.4 Identification of Samples 3.4.1 Badis badis (Hamilton-Buchanan, 1822) [Shaw and Shebbeare, 1934, Talwar and Jhingran, 1991; Jayaram, K.C., 1994] Etymology: Badis: Latin, badius = the colour of swift (Romero, 2002)

3.4.1.1 Morphometric Measurement 1. Total number of Dorsal spines 16-20 2. Total number of soft rays 8-10 and Anal soft rays 6-9 3. Total vertebrae 26-28. 4. Body depth 30.7-38.9% SL. 5. Interorbital width 6,5-8,3% SL 6. Scales in lateral row 26-28 7. Pectoral rays 12-14 8. Contain a series of dark blotches along the dorsal fin base and/or a series of dark blotches along middle of dorsal fin and has an indistinct bars on side.

3.4.1.2 Taxonomic Categorization Kingdom- Animalia Phylum-Chordata Class-Actinopterygii (Ray-finned fishes) Order-Perciformes (Perch-likes) Familiy-Badidae (Chameleon fishes) Genus-Badis Species- B. badis

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3.4.1.3 Photographs

Figure 9. Samples of Badis badis

3.4.2 Amblyceps mangois (Hamilton-Buchanan, 1822) [Shaw and Shebbeare, 1934, Talwar and Jhingran, 1991; Jayaram, K.C., 1994] Etymology: Amblyceps: Greek, amblys = soft, darkness + Latin, ceps = head (Romero, 2002)

3.4.2.1 Morphometric Measurement 1. Short body with 34-36 vertebrae. 2. Caudal fin with upper and lower lobes of distinctly different shapes. 3. Pectoral spine smooth. 4. Proximal lepidotrichia of the median caudal-fin rays has strongly-developed projections.

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3.4.2.2 Taxonomic Categorization Kingdom- Animalia Phylum-Chordata Class-Actinopterygii (Ray-finned fishes) Order-Siluriformes (Catfish) Familiy-Amblycipidae (Torrent catfishes) Genus-Amblyceps Species- A. mangois 3.4.2.3 Photographs

Figure 10. Samples of Amblyceps mangois

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3.5 Genomic DNA Extraction 3.5.1 Materials 3.5.1.1 Phenol-Chloroform Procedure: 1. Fish Tissue (Fin/Scale) 2. Filter paper 3. Micro-centrifuge tube (1.5ml and 2.0 ml) 4. Centrifuge Machine 5. Water Bath 6. Lysis buffer a. Composition of Lysis buffer: i. 200 mM Tris HCl, pH 8.0 ii. 100 mM EDTA, pH 8.0 iii. 250 mM NaCl b. Preparation of Lysis buffer: i. 200 mM Tris HCl (pH 8.0) was prepared by dissolving 24.22gm of Tris base in

800 ml of distilled H2O. The pH was adjusted to the desired value of 8.0 by adding concentrated HCL. The solution was allowed to cool to room temperature before making final adjustment to the pH. The volume of the solution was adjusted to 1 litre

with distilled H2O. The solution was dispensed into aliquots and sterilised by autoclaving. The pH of the solution is temperature dependent and decreases ~0.03 pH units for each 1°C increase in temperature. The solution was stored at 4°C.

ii. 100 mM EDTA was prepared by dissolving 37.22gm of disodium EDTA.2 H2O

to 800ml of distilled H2O and stirred vigorously on the magnetic stirrer. The pH of the solution was adjusted to 8.0 with NaOH pellets and sterilised by autoclaving. The solution was stored at 4°C. [Note: The disodium salt of EDTA will not go into solution until the pH of the solution is adjusted to ~8.0 by the addition of NaOH.] iii. 250 mM NaCl was prepared by adding 14.6gm of NaCl into 800ml of distilled

H2O. The volume was adjusted to 1lt with distilled H2O. Then the solution was sterilised by autoclaving and stored at room at 4°C. 7. Proteinase K (20 mg/ml)

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Preparation of Proteinase K: Proteinase K was was initially centrifuged at at 12000rpm for 30sec. The concentration of the solution adjusted to 20mg/ml by adding required amount of sterilized Milli-Q water.. The solution divided into small aliquots of 1 ml and stored at -20°C and store at -20°C.

8. SDS (20% w/v) Preparation of SDS: SDS is also called sodium lauryl sulphate. 200gm of electrophoresis grade SDS was dissolved in 900 ml distilled H2O. The solution was heated to 68°C and stirred with a magnetic stirrer to assist dissolution. The pH of the solution was adjusted to 7.2 by adding few drops of concentrated HCl. The volume was adjusted to 1lt with distilled

H2O and stored at room temperature. [Note: Sterilization and autoclaving is not required]

9. Water Saturated Phenol 10. Chloroform 11. Iso-amyl alcohol 12. Ammonium acetate (10 M) Preparation of Ammonium acetate: 77 gm of ammonium acetate was added to 70 ml of distilled H2O and the volume was adjusted to 100 ml with distilled H2O at room temperature. The solution was sterilized by filtration by passing through a 0.22 µm filter and stored the solution in tightly sealed bottles at 4°C or at room temperature. [Note: Ammonium acetate decomposes in hot H2O and solution containing it should not be autoclaved.]

13. Ethanol (70%) Preparation of Ethanol: 70 ml of dehydrated ethanol was added to 30 ml of distilled

H2O and stored at room temperature. Before The ethanol was stored at -20°C to make it chilled before gDNA washing and precipitation.

14. Milli-Q water Type I water was obtained from Elix pre-filtration and Milli-Q filtration unit.

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3.5.1.2 DNA extraction Kit (DNAse Easy Blood and Tissue DNA extraction kit, Qiagen) 1. ATL Buffer 2. Proteinase K 3. ALBuffer 4. Ethanol 5. DNeasy mini spin Column 6. Collection tube 7. AW1Buffer 8. AW2Buffer 9. AE Buffer

3.5.2 Methods 3.5.2.1 Phenol-Chloroform Procedure: A little portions of the fin (20-30 mg) were Clipped off (from the Caudal and ventral portion of the body) and the scales (30-50 pieces) were noninvasively (by gentle scraping from the dorsal portion of the body) from the live and frozen specimens (fish). The samples were dried on a filter paper before further processing. Genomic DNA extraction was carried out following Kumar et al. (2007) with minor modifications. A lysis buffer containing 200 mM Tris HCl, pH 8.0; 100 mM EDTA, pH 8.0; 250 mM NaCl was prepared and warmed at 50°C for 5–10 min. The tissues were then chopped and placed in a 2 ml micro-centrifuge tube containing 955 µl pre- warmed lysis buffer, 15 µl Proteinase K (20 mg/ml) (NEB, USA) and 30 µl 20 % SDS. Then the mixture was incubated at 52°C for 60–70 min in a water bath and. the tubes were gently stirred occasionally during incubation period. After the incubation equal volume of phenol: chloroform: isoamyl alcohol (25:24:1, v/v/v) was added to the tubes and mixed by gently inverting the tubes for about 15 min. The tubes were then centrifuged for 12 min at 9,500 rpm at 4°C in an Eppendorf refrigerated Centrifuge (Model-5424R, Germany). After centrifugation clear supernatants were taken in fresh tubes and equal volume of isopropyl alcohol and 0.2 volume of 10 M ammonium acetate were added. The mixture was incubated for 30–40 min at -20°C to precipitate the gDNAs. The gDNAs were pelleted by centrifugation at 13,500 rpm for 12 min at 4°C. The supernatants were decanted gently and pellets were washed once in 500 µl chilled 70% ethanol, air dried and re-suspended in 100 µl sterile Milli-Q water.

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3.5.2.2 DNA extraction Kit (DNAse Easy Blood and Tissue DNA extraction kit, Qiagen) A little portion of the fin (20-30 mg) were clipped-off (from the caudal and ventral portion of the body) and the scales (30-50 pieces) were taken non-invasively (by gentle scraping from the dorsal portion of the body). Then the tissues were placed in a 1.5 ml micro-centrifuge tube and 180 µl of buffer ATL was added followed by 20 µl of Proteinase K. The samples were mixed thoroughly by vortexing and incubate at 56ºC until the tissue was completely lysed. Then 200 µl of buffer AL was added to the sample and mixed thoroughly by vortexing. The tube was incubate at 560C for 10 min. 200 µl of ethanol (96-100%) was added and mixed again thoroughly by vortexing. In the next step the mixture was pipetted out (including any precipitate) into a DNeasy mini spin column placed in a 2 ml collection tube. The tube was centrifuged at 8000 rpm for 1 min at 40C. The collection tube was replaced and 500 µl buffer AW1 was added. Then the sample was centrifuged at 8000 rpm for 1min at 40C followed by another replacement of the collection tube. In the same tube 500 µl buffer AW2 was added and centrifuged at 14000 rpm for 3 min at 40C (to dry DNeasy membrane). Finally the collection tube is replaced by a clean 1.5ml micro-centrifuge tube and 200 µl of buffer AE was added directly onto the DNeasy membrane. Incubation was done at room temparature for 1 min followed by centrifugation at 8000 rpm for 1 min at 40C (to elute the DNA). Then the extracted gDNAs samples were stored in 1.5ml micro- centrifuge tube in -200C refrigerator.

3.6 Characterization of genomic DNA 3.6.1 Integrity checking by electrophoresis 3.6.1.1 Materials 1. Ethidium Bromide/EtBr (10mg/ml) [Stock Solution]

Preparation of EtBr: 1gm of Ethidium bromide was added to 100 ml of Milli-Q H2O. The solution was stirred on a magnetic stirrer for several hours to ensure that the dye has completely dissolved. The container was wrapped in aluminium foil and then the solution aws transferred to a dark bottle and stored at room temperature.

2. Agarose Gel (0.7%) (Lonza, Basel, Switzerland)

Preparation of gel: 0.7gm agarose was added in 98 ml distilled H2O. Then 2ml TAE buffer was added from 50X TAE stock solution. The solution was heated in microwave oven to

Page | 100 dissolve the agarose. Required amount of ethidium bromide was added from stock solution to ensure the final concentration of 0.5 µg/ml in the final gel solution.

3. TAE Buffer (50X) [Stock Solution] Composition for 1 liter: i. 242gm Tris base ii. 57.1 ml glacial acetic acid iii. 100ml of 0.5M EDTA: 186.1 gm of disodium EDTA.2 was dissolved in 800ml of

distilled H2O. The solution was stirred vigorously on the magnetic stirrer. The pH was adjusted to 8.0 with NaOH pellets and sterilised by autoclaving. The disodium salt of EDTA was not dissolved into solution until the pH of the solution was adjusted to ~8.0 by the addition of NaOH. The solution was stored at 4°C).

4. Gel casting tray (BenchTop Labsystems BT-MS- 300, Taiwan) 5. Electrophoretic Apparatus with power pack (BenchTop Labsystems BT-MS- 300, Taiwan) 6. 6X DNA loading Dye Composition: Buffer Type Composition Storage 0.25% (w/v) bromophenol blue I 4°C 0.25% (w/v) xylene cyanol FF 40% (w/v) sucrose in H O 2 0.25% (w/v) bromophenol blue III 4°C 0.25% (w/v) xylene cyanol FF 30% (v/v) glycerol in H2O

7. Lambda DNA/Hind III Digest DNA size marker (New England Biolabs, USA)

8. UV-transilluminator (Spectroline BI-O-Vision, NY, USA)

10. Digital Camera [Nikon DSLR Camera (Model-D3100 with WF-S-18-55 VR Kit]

3.6.1.2 Methods

The integrity of the gDNAs was checked by loading 10 µl extracted DNA with 1X DNA loading dye in ethidium bromide (0.5 µg/ml) pre-stained 0.7 % agarose gel (Lonza, Basel,

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Switzerland) in 1X TAE tank buffer. Lambda DNA/Hind III double digest DNA size marker were used to estimate the expected size of the extracted genomic DNA. The gel was subjected to electrophoresis in constant current (90 mA) and voltage (90 V). The gels were checked on the UV-transilluminator (Spectroline BI-O-Vision, NY, USA) and then photographed using Nikon D3100 digital Camera.

3.6.2 Purity Checking The gDNA samples were subjected to spectrophotometric assessment for purity checking at A260/A280 in Rayleigh UV2601 Spectrophotometer, Beijing,China. The ratio between ~1.7- ~1.8 means the DNA is pure. If the ration is lower, it indicates the presence of protein or phenol or other contaminants.

3.6.3 Quantification of PCR amplification The gDNAs isolated from fish samples were treated with RNase A (USB, USA) at 37°C followed by phenol–chloroform purification and were quantified spectrophotometrically. Concentration of the extracted gDNA was estimated by the formula Concentration of DNA (µg/ml) = OD at 260nm x dilution factor x 50/1000 and adjusted to ~50 ng/µl for PCR amplifications.

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[*Note: Spectrophotometric Analyses of DNA (Sambrook and Russell, 2001)

For quantifying the amount of DNA, reading was taken at wavelength of 260nm and 280nm. The reading at 260nm allows calculation of the concentration of nucleic acid in the sample. The absoption spectra of DNA and RNA are maximal at 260nm; absorbance data for nucleic acids were almost always expressed in A260 or OD260. An OD of 1 corresponds to ~50µg/ml for double stranded DNA, ~40 µg/ml for single stranded DNA and RNA, and ~33µg/ml for single stranded oligonucleotides.

The molar extinction co-efficient or molar absorption co-efficient () decreases as the ring system of adjacent purines and pyrimidines become stacked in a polynucleotide chain. The value of  decreases in the following series

Free base

Small oligonucleotide

Single-stranded nucleotide

Double-stranded nucleotide

The concentration of stock DNA was determined by the following formula; Concentration of DNA (µg/ml) = OD at 260nm x dilution factor x 50 / 1000.

The ratio between the reading at 260nm and 280nm (OD260: OD280) provides the purity of the nucleic acid. Pure preparations of DNA and RNA have OD260: OD280 values of 1.8 and 2.0 respectively. If there was a significant contamination of phenol or protein, the OD260: OD280 will be less than the values given above.]

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3.7 RAPD-PCR amplification 3.7.1 Selection of primers Forty arbitrary decamer RAPD primers of random sequences (Kit-A and Kit-B, twenty primers from each kit) were purchased from Imperial Life Science Pvt. Ltd., India. Firstly, all the populations were screened with the forty primers and finally twenty (ten primers from Kit-A and ten primers from Kit-B) were selected for further analyses of Badis badis and Amblyceps mangois population on the basis of the variability and reproducibility of the bands obtained. The GC content of the primers was between 60-70%. The selected primer name and sequence for the study of Badis badis and Amblyceps mangois population were given in Tables 3 and 4.

Table 3. RAPD Primers for Badis badis Sequence G+ C Content Annealing Sl/No. Primer (5´ 3´) (%) temperature 1 OPA-01 CAGGCCCTTC 70 2 OPA-02 TGCCGAGCTG 70 3 OPA-04 AATCGGGCTG 60 4 OPA-07 GAAACGGGTG 60 5 OPA-09 GGGTAACGCC 70 6 OPA-10 GTGATCGCAG 60 7 OPA-13 CAGCACCCAC 70 8 OPA-16 AGCCAGCGAA 60 9 OPA-19 CAAACGTCGG 60 10 OPA-20 GTTGCGATCC 60 36ºC 11 OPB-01 GTTTCGCTCC 60 12 OPB-03 CATCCCCCTG 70 13 OPB-04 GGACTGGAGT 60 14 OPB-06 TGCTCTGCCC 70 15 OPB-07 GGTGACGCAG 70 16 OPB-11 GTAGACCCGT 60 17 OPB-12 CCTTGACGCA 60 18 OPB-15 GGAGGGTGTT 70 19 OPB-17 AGGGAACGAG 60 20 OPB-18 CCACAGCAGT 60

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Table 4. RAPD Primers for Amblyceps mangois Sequence G+ C Content Annealing Sl/No. Primer (5´ 3´) (%) temperature 1 OPA-01 CAGGCCCTTC 70 2 OPA-02 TGCCGAGCTG 70 3 OPA-04 AATCGGGCTG 60 4 OPA-06 GGTCCCTGAC 60 5 OPA-08 GTGACGTAGG 60 6 OPA-10 GTGATCGCAG 60 7 OPA-12 TCGGCGATAG 60 8 OPA-13 CAGCACCCAC 70 9 OPA-14 TCTGTGCTGG 60 10 OPA-16 AGCCAGCGAA 60 36ºC 11 OPB-01 GTTTCGCTCC 60 12 OPB-04 GGACTGGAGT 60 13 OPB-06 TGCTCTGCCC 70 14 OPB-07 GGTGACGCAG 70 15 OPB-08 GTCCACACGG 70 16 OPB-09 TGGGGGACTC 60 17 OPB-10 CTGCTGGGAC 60 18 OPB-11 GTAGACCCGT 60 19 OPB-12 CCTTGACGCA 60 20 OPB-17 AGGGAACGAG 60

3.7.2 PCR reaction mixture RAPD-PCR amplifications were performed in a 96 well Eppendorf® thermal cycler (USA Scientific, USA) and 96 well Peltier Thermal Cycler (Applied Biosystems 2720, Life Technologies, USA) in a final reaction volume of 25 µl, each containing a final concentrations of ~100-150 ng of isolated gDNA, 1.6 pM of oligonucleotide primers (RAPD), standard Taq polymerase buffer (10mM Tris-HCl, pH 8.3, 50 mM KCl, 1.5 mM MgCl2) (NEB, USA), 200 µM of each dNTPs (dATP, dTTP, dCTP, dGTP) (NEB, USA), and one unit of Taq DNA Polymerase (NEB, USA). After the standardization of each reaction regime, all PCR amplifications were replicated to verify reproducibility and authenticity of the DNA bands.

3.7.3 PCR program

PCR cycling programs were as follows: initial denaturation at 94º C for 5 min followed by 50 cycles of 94ºC, 1 min for denaturation; 36ºC, 1 min for annealing; 72 ºC, 2 min for elongation and finally an extension at 72 ºC for 10 min.

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94 ºC, 5min 94 ºC, 1min Initial Denaturation 72 ºC, 2min 72 ºC, 10min denaturation Elongation Final extension 36 ºC, 1min Annealing 4 ºC 50 Cycles Forever

3.8 ISSR PCR amplification 3.8.1 Selection of primers Twenty-one ISSR primers, purchased from Xcelris Genomics, Ahmadabad, Gujarat, India, were used to screen all the populations; and finally, fifteen ISSR primers for Badis badis and twelve ISSR primer for Amblyceps mangois population study were selected for further analyses on the basis of the variability and reproducibility. The primer names, sequences and GC contents are depicted in Tables 5 and 6.

Table 5. ISSR Primers for Badis badis Sequence Annealing Sl/No. Primer G+ C Content (%) (5´ 3´) temperature 1 ISSR-1 (CT)8 TG 50 2 ISSR-2 (CT)8 AC 50 45ºC 3 ISSR-3 (CT)8 GC 55.6 4 ISSR-4 (CA)6 AG 50 5 ISSR-5 (CA)6 AC 50 6 ISSR-7 (CAC)3 GC 72.7 33ºC 7 ISSR-8 (GAG)3 GC 72.7 8 ISSR-9 (GTG)3 GC 72.7 9 ISSR-10 (GGAC)3 T 69.2 42ºC 10 ISSR-11 (GA) CC 57.1 6 37ºC 11 ISSR-13 (GT)6 CC 57.1 12 ISSR-15 (ACC)6 66.66 33ºC 13 ISSR-19 (GGAC) A 69.2 3 42ºC 14 ISSR-20 (GGAC)3 C 76.9 15 ISSR-21 (GGAC)4 75 45ºC

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Table 6. ISSR Primers for Amblyceps mangois Sequence G+ C Content Annealing Sl/No. Primer (5´ 3´) (%) temperature 1 ISSR-1 (CT)8 TG 50 2 ISSR-2 (CT)8 AC 50 45ºC 3 ISSR-3 (CT)8 GC 55.6 4 ISSR-4 (CA)6 AG 50 5 ISSR-5 (CA)6 AC 50 6 ISSR-7 (CAC)3 GC 72.7 33ºC 7 ISSR-8 (GAG)3 GC 72.7 8 ISSR-9 (GTG)3 GC 72.7 9 ISSR-13 (GT)6 CC 57.1 37ºC 10 ISSR-19 (GGAC) A 69.2 3 42ºC 11 ISSR-20 (GGAC)3 C 76.9 12 ISSR-21 (GGAC)4 75 45ºC

3.8.2 PCR reaction mixture ISSR-PCR amplifications were performed in a 96 well Eppendorf® Thermal Cycler (USA Scientific, USA) and 96 well Peltier Thermal Cycler (Applied Biosystems 2720, Life Technologies, USA) in a final reaction volume of 25µl, each Containing a final concentrations of ~100-150 ng of isolated gDNA, 1.6 pM of oligonucleotide primers, standard Taq polymerase buffer (10mMTris-HCl, pH 8.3, 50 mM KCl, 1.5 mM MgCl2) (NEB, USA), 200 µM of each dNTPs (dATP, dTTP, dCTP, dGTP) (NEB, USA), and one unit of Taq DNA Polymerase (NEB, USA). PCR amplifications were replicated to verify reproducibility and authenticity of the DNA bands.

3.8.3 PCR program PCR cycling programs were as follows: initial denaturation at 94ºC for 5 min followed by 40Cyclesof 94ºC, 1 min for denaturation; 38ºC - 47ºC (specific and optimal annealing temperature for each primer), 1 min for annealing; 72 ºC, 2 min for elongation and finally an extension at 72 ºC for 10 min.

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94 ºC, 5min 94 ºC, 1min Initial Denaturation 72 ºC, 2min 72 ºC, 10min denaturation Elongation Final extension 33-45 ºC, 1min Annealing

4 ºC 50 Cycles Forever

3.9 Electrophoresis and Checking of amplified products 3.9.1 Materials 1. Ethidium bromide (10mg/ml) 2. Agarose (1.4 % for RAPD analyses and 1.6% for ISSR analyses) (Lonza, Basel, Switzerland)

Preparation: 1.6 gm agarose was added in 98 ml distilled H2O. 2ml TAE buffer was added from 50X stock solution. The solution was heated in microwave oven or electric heater to dissolve the agarose. Required amount of ethidium bromide was added from the stock solution to ensure the final concentration of 0.5 µg/ml in the final gel solution. 3. TAE Buffer (50X ) 4. Electrophoretic apparatus with power pack (BenchTop Labsystems BT-MS- 300, Taiwan). 5. Standard 100bp DNA size marker (100-1500 bp, New England Biolabs, USA ). 6. DNA loading dye (6X)

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Composition:

Buffer Type Composition Storage 0.25% (w/v) bromophenol blue I 0.25% (w/v) xylene cyanol FF 4°C 40% (w/v) sucrose in H2O 0.25% (w/v) bromophenol blue III 0.25% (w/v) xylene cyanol FF 4°C 30% (v/v) glycerol in H2O

3.9.2 Methods I. The amplified products were electrophoresed in an ethidium bromide (0.5µg/ml) pre- stained 1.4 % (for RAPD) and 1.6% (for ISSR) (w/v) agarose gel (Lonza, Basel, Switzerland) at a Constant voltage 100 V and Current 100 mAmp in 1X TAE buffer.. The molecular weight of each band was estimated using a standard 100 base pair ladder. Reproducibility of the amplification pattern in each primer was checked by repeating each reaction thrice without deliberately altering the protocol.

3.9.3 Photographic records

The gels were visualized on the UV-transilluminator (Spectroline BI-O-Vision®NY, USA) and photographed using a Nikon D3100 Digital Camera.

3.10 Statistical and Analytical Procedures to study genetic diversity Polymorphism data can be scored in presence/absence matrices manually or with the aid of specific software. The banding patterns produced, largely depended on the nature of the methods and whether they generate dominant or co-dominant markers. There are two features fingerprinting markers that considerably constrain how they can be statistically analyzed. Firstly, polymorphic marker loci are generally scored as two alleles, the ―band-presence‖ allele and the ―band absence‖ allele. Secondly, the banding patterns produced by each technique are treated as dominant markers during the evaluation process, and the methods are described accordingly. Thus, it is difficult to distinguish heterozygous individuals from homozygous ones with respect to the band-presence allele.

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3.10.1 Band-based approaches The easiest way to analyze banding patterns and to measure the diversity represented by them is to focus on band presence or absence and to compare it between samples. This method is routinely used at the level of the individual, and produces distances generated from the bands rather than taking into account the diversity of the population. The disadvantage is that it treats the data as polymorphism produced by the markers rather than describing the genetic diversity of the organism.

3.10.2 Polymorphism or polymorphic loci A locus is considered polymorphic when the band is present at a frequency of between 5% and 95%. The polymorphism represented in the banding patterns can be extracted simply by observing the total number of polymorphic bands (PB) and then calculating the percentage of polymorphic bands (PP) present in any individual based on this number. This ―band informativeness‖ (Ib) can be represented on a scale ranging from 0 to 1 according to the formula:

Ib = 1 – (2 × |0.5 – p|), where p is the portion of genotypes containing the band.

3.10.3 Hardy-Weinberg equilibrium Test (HWT) Hardy- Weinberg equilibrium holds that the both gene and genotype frequencies will be constant from generation to subsequent next generations (Lebate, 2000). Chi-square test is useful for determining whether the allelic frequencies are in HW equilibrium. The statistical test

퐎 −퐄 ퟐ follows this formula (Guo and Thompson, 1992): HWT= 퐢 퐢 ; when: HWT = 퐄퐢

Statistical test, Oi = Observed frequencies, Ei = Expected frequencies, df = Degree of freedom. If 2 2 X cal ≤ X tab then H0 hypothesis is accepted, it means that allele frequencies for loci in a given 2 2 population are HWT equilibrium, if X cal ≥ X tab then H0 hypothesis is rejected (Hartl and Clark, 1997).

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3.10.4 Average number of Alleles per locus These measures provide complementary information to that polymorphism (Kimura and

1 푘 Crow, 1964): N = n , where k = Number of loci, ni = Number of alleles detected by 푘 i=1 i locus. This parameter has the best application in co-dominant markers.

3.10.5 Effective Number of Alleles The measure explains about the number of alleles that would be expected in a locus in

ퟏ 2 each population (Kimura and Crow, 1964): 푨 = , where pa is the frequency of 풆 풌 ퟐ 풂=ퟏ 풑풂 the ath of k alleles. By taking allele frequencies into account, this descriptor of allelic richness is less sensitive to rare alleles.

3.10.6 Average Expected Heterozygosity (He) Average expected heterozygosity is the probability that at a single locus of a diploid organism any two alleles, chosen at random, are different from each other. It is an indicator of genetic diversity in a population (Kimura and Crow, 1964): 풎 풌 ퟏ 푯 = ퟏ − 풑 ퟐ 풆 풎 풂 퐢=ퟏ 퐚=ퟏ where, m is the number of analyzed loci. The range of this parameter from 0-1 and it is maximized when there are many alleles at equal frequency.

3.10.7 Shannon‘s Information Index ( H´ or I) Shannon‘s information index is a diversity coefficient widely used to quantify the degree if bad polymorphism provided by banding patterns (Lewontin, 1972). It can be calculated as

I = –∑ pi log2 pi , where I is diversity and pi is the frequency of a particular RAPD band. It is assumed that (a) the population is large, (b) random mating takes place, (c) the population is isolated from others, and (d) the effects of migration, mutation and selection are not relevant. Allele-frequency statistics are commonly supplemented with another estimation that allows for the distortion from HWE. These methods depend on the extraction of allelic frequencies from the data, which can be accomplished by using a number of different procedures (Lynch and

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Milligan, 1994; Stewart and Excoffier, 1996). The value of the Shannon index obtained from the data usually ranges between 1.5 and 3.5, and sometimes surpasses 4 (Margalef, 1972). It is only when there is a huge number of species in the samples a high value of the index is produced. The fact that the Shannon index is so narrowly constrained in most cases can make interpretation difficult. Therefore, some investigators sidestep the problem by using eI instead of I (Kaiser et al, 2000).

3.10.8 Allelic Diversity (A) One of the easiest ways to measure genetic diversity is to quantify the number of alleles present. Allelic diversity (A) is the average number of alleles per locus and is used to describe genetic diversity (Frankham et al., 2002). When there is more than one locus, A is calculated as

the number of alleles averaged across all loci: A = ni / nl, where ni is the total number of alleles

over all loci and nl is the number of loci.

3.10.9 Heterozygosity (H) Heterozygosity can be regarded as the average portion of loci with two different alleles at a single locus within an individual. It is commonly extended to the whole or a sub-portion of an entire population and differentiated into observed and expected heterozygosity. Expected

heterozygosity (HE) or Nei‘s gene diversity (D) is the expected probability that an individual will be heterozygous at a given locus, in multi-locus systems over the assayed loci. In other words, it is the estimated fraction of all individuals that would be heterozygous for any randomly chosen locus (Nei, 1973). It is often calculated based on the square of the frequency of the null 풏 (recessive) allele as follows: H = 1 – 풑ퟐ, where p is the frequency of the ith allele. E 퐢 퐢 i

Observed heterozygosity (HO) is the portion of genes that are heterozygous in a population. It is calculated for each locus as the total number of heterozygotes divided by sample size. Typically,

values for HE and HO range from 0 (no heterozygosity) to nearly 1 (a large number of equally frequent alleles). Expected heterozygosity is usually calculated when describing genetic

diversity, as it is less sensitive to sample size than observed heterozygosity. If HO and HE are similar (they do not differ significantly) mating in the populations is approximately random. If

HO is less then HE, the population is inbreeding; if HO exceeds HE, the population has a mating system avoiding inbreeding.

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3.10.10 F-statistics F-statistics, or fixation indices were originally designed to measure the amount of allelic fixation by genetic drift.. It can describe the inbreeding (coefficient) of an individual relative to

the total population (FIT) or the inbreeding (coefficient) of an individual relative to the

subpopulation (FIS), and can also express the ―fixation‖ (index) resulting from comparing

subpopulations to the total population (FST) (Wright, 1977). Moreover, inbreeding and genetic drift allow determining fixation indices when expected and observed heterozygosity are known.

Thus the three indexes can be calculated as: FIT = 1 – (HI/HT); FIS = 1 – (HI/HS) and FST

= 1 – (HS/HT); where HI is the average observed heterozygosity within each population, HS is the average expected heterozygosity of subpopulations, assuming random mating within each

population, and HT is the expected heterozygosity of the total population, assuming random mating within subpopulations and no divergence of allele frequencies among subpopulations.

Fixation indices are interrelated as follows: 1 – FIT = (1 – FST) (1 – FIS) (Wright, 1977). It varies from 0 (no isolation) to 1 (complete isolation), but in the case of a bias towards polymorphic loci, lower values (e.g. 0.5) may indicate complete isolation between populations.

3.10.11 Gene Flow (Nm) The parameter of gene flow (Nm) is the product of the effective size of individual populations (N) and the rate of migration among them (m). However, commonly used approach is to make an estimation of Nm is based on the degree of genetic differentiation among

populations by calculating F-statistics. FST is related to population size and migration rate, based on a standard island model, which is not applicable to every natural population, but provides an

easy way to calculate gene flow as the following: Nm = (1/FST – 1) (McDonald and McDermott,1993). Gene flow among fragmented populations is related to dispersal ability, so it is expected to provide information about the dispersal ability of the organism and the degree of isolation among populations.

3.10.12 Shannon‘s Evenness measure The degree of evenness in species abundance is calculated by the Shannon‘s evenness

measure. The maximum diversity (Hmax) could occur when all species has equal abundance, in

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other words if H'=Hmax =ln S. The ratio of observed diversity to maximum diversity can,

therefore, be used to measure evenness (J') (Pielou, 1975) therefore, J'= H'/Hmax = H'/ln S.

3.10.13 Heip‘s Index of Evenness Heip (1974) reported that the evenness measure should be independent of species richness and that they should have a low value in contexts where evenness in very low. The H' proposed measured was EHeip= (e - 1)/ (S-1). The minimum value of Heip‘s measure is 0 and that it registers 0.006 when an extremely uneven community is used (Smith and Wilson, 1996).

3.10.14 SHE Analysis Buzas and Hayek (1996) and Hayek and Buzas (1997) pointed out that Shannon‘s index is simply the sum of the natural log of evenness value (lnE) [where E= eH/S] and natural log of species richness (lnS). This decomposed the Shannon‘s index into its two components H'= ln E + ln S. This allows the investigators to interpret the changes in diversity pattern in a spatial or temporal scale. The SHE analysis describes a relationship between the S (species richness), H (diversity measured by Shannon‘s index) and E (evenness). When there is a large number of samples are counted or estimated there are five scenarios possibly occur (Hayek and Buzas, 1997). i. S1=S2, H1=H2, E1=E2; identical richness, evenness and relative abundance. ii. S1=S2, H1≠H2, E1≠E2; richness remains constant but evenness changes. iii. S1≠S2, H1=H2, E1≠E2; H remains constant because changes in S and E offset each other. iv. S1≠S2, H1≠H2, E1=E2; E remains constant but S and therefore H changes v. S1≠S2, H≠H2, E1≠E2, H changes because differences in S and E do not offset each other. I have divided the seventeen riverine populations in case of Badis badis into ten groups and fourteen riverine populations of Amblyceps mangois into nine groups according to the continuity of the water flow through the river system from upstream to downstream. These groupings form a altitudinal gradient throughout the river system and easily observed the

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relationship between the diversity index with that of richness and evenness. This allows interpreting the changes in diversity pattern of the two studied fishes within the three river system of the Terai and Dooars region of West Bengal, India.

3.10.15 Phylogenetic Diversity or Phylogenetic Tree Besides genetic diversity estimation it is also obvious in molecular ecological studies to represent the genetic structure of the population(s) examined. Such information is extremely valuable because it can address many important issues such as estimation of migration pattern, identification of conservation units, or resolution of biogeographical patterns (Mantel et al., 2005). When geographical locations are available for populations and sampling allows the detection of spatial structure, then tree-based methods should be considered. These methods allow graphical representation of the relatedness of individuals and exploration of information on the spatial structure of populations. The simultaneous evaluation of the results of tree-based methods coupled with the distribution of individual markers within and among identified groups provides some information on the distribution of genetic diversity (Bonin et al., 2007). Knowledge of phylogenetic structure of genetic variation in populations is essential for understanding what the genetic diversity within a given species is likely to represent as part of the total diversity (Pleijel and Rouse, 2000). Faith (1992) showed how the sum of the branch lengths on a minimum-spanning path between any set of terminals on a cladogram could be used to calculate a metric he termed Phylogenetic Diversity (PD) for that set. PD is important not only because species in isolated, monotypic lineages usually represent much more unique genetic diversity than ones in younger, more speciose groups, but also because the very nature of these species means that they may actually be disproportionately threatened by extinction (Purvis et al., 2000). Isaac et al. (2007) devised a measure they termed Evolutionary Distinctiveness (ED), which represents the relative contribution of an individual species to a clade‘s PD. They further showed how extinction risk could be combined with ED to identify species that are both Evolutionarily Distinct and Globally Endangered (EDGE) species. UPGMA (Unweighted Pair Group Method with Arithmatic mean) dendrogram based on Nei‘s (1978) unbiased genetic distance matrix was constructed by POPGENE v 1.2.1 software using default parameters (Yeh and Boyle, 1997).

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3.10.16 Hierarchical Gene Diversity Analyses

Genetic hierarchy occurs because the divergence component of diversity (Dpt) can be subdivided into geographic hierarchy in the distribution of species. Therefore, genetic divergence exists between different populations because of different level of gene flow among them. Populations that are geographically proximate show continuous gene flow and are genetically more similar than the more distant populations. Thus a species can be visualized as having a spatial genetic architecture. Perhaps the clearer picture of genetic and geographic hierarchy is observed in riverine species because river forms a geographic structure with natural hierarchy (Meffe and Carroll, 1997). The river forms the first order, second order and so on hierarchical structure. The first order stream joins to form second order streams, and the second order streams join to form the third order streams and so on. In Figure 11, the first order hierarchy constitutes the A and B populations, and C and D populations. The second order hierarchy constitutes the A, B and C populations, and D, E and F populations. The third order hierarchy constitutes the A, B, C and D, E, F populations. Whereas the fourth order hierarchy constitute the A, B, C, D, E, F and G populations (Figure 11). Therefore, the overall diversity of any river stream is determined by calculating genetic diversity and genetic divergence of the individual hierarchical order of the river stream and finally adding the values of genetic diversity and genetic divergence of the

individual first, second, third and fourth hierarchical orders. FST, Nm and AMOVA (Amalyses of Molecular Variance) among and within population was calculated to analyze the genetic hierarchical structure of the studied fishes across river system using GenAlex v 6.5 software (Peakall and Smouse, 2006; Peakall and Smouse, 2012). The genetic differentiation between populations was determined using PhiPT values.

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Hierarchical Populations Structure First Order A and B

First Order C and D

Second Order A, B and C

Second Order D, E and F

Third Order A, B, C and D, E, F

A, B, C, D, E, F and Fourth Order G

Figure 11: Hypothetical genetic hierarchical map of seven different populations. The populations are arranged in a hierarchical order, viz., first order, second order, third order and fourth order populations. The different shaded areas represent different orders of the hierarchy. The triangles indicate the collection spots and curved line indicate the river streams (adapted from Meffe and Carroll, 1997).

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3.11 mtCOI amplification and analyses Analyses of Mitochondrial Cytochrome Oxidase subunit I gene was undertaken as additional objective to support and substatantiate RAPD-ISSR-based data.

3.11.1 Primer Selection

The primers were chosen from previously published report (Ward et al. 2005) and selected after nr-blast in NCBI database to amplify the cytochrome oxidase subunit 1 (COI) gene (approximately 700 bp long). The primers were synthesized by Imperial Life Science (Gurgaon, India). The primer sequences are follows:

Forward: 5′- TCAACCAACCACAAAGACATTGGCAC-3′ (GC Content 42.30%) Reverse: 5′- TAGACTTCTGGGTGGCCAAAGAATCA-3′ (GC Content 46.15%).

3.11.2 PCR reaction mixture

Mitochondrial COI gene amplification was performed in a 96 wells Eppendorf® (Germany) Peltier thermal cycler. The final reaction volumes were 30µl, each containing a final concentration of ~100-150 ng of isolated gDNA, 1pM of each oligonucleotides primers (Forward and Reverse), 1X standard Taq Polymerase buffer (10mM Tris-HCl, pH 8.3, 50 mMKCl, 1.5 mM MgCl2) (NEB, USA), 200 µM of each dNTPs (dATP, dTTP, dCTP, dGTP) (NEB, USA), and one unit of Taq DNA Polymerase (NEB, USA).

3.11.3 PCR Programme PCR cycling programs were as follows: initial denaturation at 94ºC for 5 min followed by 40 cycles of 94ºC, 1 min for denaturation; 50ºC, 45 sec for annealing; 72ºC, 1 min for elongation and finally an extension at 72ºC for 10 min.

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94 ºC, 5min 94 ºC, 1min Initial Denaturation 72 ºC, 1min 72 ºC, 10min denaturation Elongation Final ex tension

50 ºC, 45sec Annealing

40 Cycles 4 ºC

Forever

3.11.4 Checking and documentation of amplified product The amplified products were electrophoresed in an ethidium bromide (0.5µg/ml) pre- stained 1.5% (w/v) agarose gel (Lonza, Switzerland) at a constant voltage 100 V and current 100 mA in TAE buffer (40 mM Tris-HCl, pH 8.0; 20 mM Acetic acid; 1 mM EDTA, pH 8.0) using BenchTop Labsystems BT-MS-300 (Taiwan) electrophoretic apparatus. Molecular weight of each band was estimated using a standard 100 base pair ladder (NEB, USA). The gels were visualized on the UV-transilluminator (Spectroline BI-O-Vision®NY, USA) and photographed using a Nikon D3100 camera (Fig 2).

3.11.5 Purification of PCR product, sequencing and submission of Badis badis and Amblyceps mangois cytochome oxidase I (COI) partial coding sequences (CDS) to public database The amplified products were purified using Invitrogen PurelinkTM PCR Purification Kit (Thermofisher Scientific, India), following manufacturer‘s protocol. The amplified products were confirmed as COI partial coding gene by restriction digestion using Hind III restriction enzyme (Hind III cutting point between 252th-253th bp). Each PCR product was sequenced at least twice by dye-dideoxy automated chain termination method (MWG-Biotech Pvt. Ltd., Bangalore India). All the COI partial CDS were used separately to search the GB/EMBL/DDBJ combined nr database in NCBI BLAST search through the implementation of Blastn and Blastx, optimized for highly similar sequences (Megablast), using stringent expect threshold (0.01) and excluding low complexity regions within the combined database. The curated sequences (approximately 650-690 bp) were submitted to GenBank and accession numbers were obtained (Table 7 and 8).

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3.11.6 Retrieval of publicly available Badis badis and Amblyceps mangois COI partial CDS Publicly available twentyfive Badis badis COI partial CDS corresponding to isolates submitted from northern, southern, north-easten India and Dhaka, Bangladesh were retrieved from GenBank (NCBI; National Centre for Biotechnology Information). The isolate names and their accession numbers are presented in Table 7. Moreover, publicly available four Amblyceps mangois COI partial CDS corresponding to isolates submitted from north-eastern India and Dhaka, Bangladesh were retrieved from GenBank (NCBI; National Centre for Biotechnology Information). The isolate names and their accession numbers are presented in Table 8.

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Table 7: Retrieved publicly available 25 number of Badis badis COI gene sequence (No. 1-25) from NCBI database and ten number of presently studied sequences (No. 26-35) . Broad No. Name Accession No Location Location Institute Division 1 GPCR 5BB KU043316.1 2 GPCR 6BB KU043317.1 3 GPCR 7BB KU043318.1 Ganges &Yamuna rivers, uttarkashi, Zoology, Pt. LMS Govt. PG 4 GPCR 8BB KU043319.1 garhwalhimalaya,uttarak North India College, Rishikesh, Delhi Road, 5 GPCR 141BB KR809715.1 hand. Rishikesh, Uttarakhand, India. 6 GPCR 282BB KR809716.1 7 GPCR 284BB KR809717.1 8 GPCR 287BB KR809718.1 9 NBFGR: BB8079A FJ459452.1 International Centre For DNA 10 NBFGR: BB8079B FJ459453.1 Barcoding,National Bureau of Fish Kulsi river, Rampur, Genetic Resources, Canal Ring 11 Assam NBFGR: BB8079C FJ459454.1 Road, P. O.Dilkusha, Lucknow, Uttar Pradesh 226002, India 12 Resource Management & Environment Section,Institute of Ranganadi river, Advanced Study in Science & IASST F169 KY823511.1 Lakhimpur, Assam. Technology, Vigyan Path,PaschimBoragaon, Garchuk, Guwahati, Assam 781035, India 13 DOF 72 JN815304.1 N-E India Assam University (Central Sunai river, karimganj, 14 DOF 73 JN815305.1 University), Durgakona, Silchar, Assam 15 DOF 89 JN815311.1 Assam 788011, India 16 BDB 1001 KJ936835.1 Jinjiram River, College of Fisheries, Central 17 BDB 1002 KJ936836.1 Lembucherra, Tripura AgriculturalUniversity, 18 BDB 1003 KJ936837.1 West, Tripura Lembucherra, Tripura West, Tripura 19 BDB 1004 KJ936838.1 799210, India 20 BDB 1005 KJ936839.1 21 Assam University (Central Tuipi river, Pherzawl, SGBK OFF29F JQ713858.1 University), Durgakona, Silchar, Manipur Assam 788011, India 22 Indian Institute of ScienceEducation Cauvery river, Mysore, and Research, Pune, G1 Block, Dr. WILD 15 PIS 144 KP666032.1 Karnataka HomiBhabha Road,Pashan, Pune, Maharashtra 411008, India South India 23 BabaH1 KC774640.1 Kochi Centre, National Bureau of 24 Fish Genetic Resources, CMFRI Kochi, Kerala BabaH2 KC774641.1 Campus, PO. Ernakulam North, Kochi, Kerala682 018, India 25 Tanguar Haor, Dhaka, Department of Zoology, University DUZM 196 KT364764.1 Sunamganj, Dhaka, Banglades of Dhaka,Karzon Hall, Dhaka, Bangladesh h Dhaka 1000, Bangladesh. 26 DRBB 1 MF774665.1 27 DRBB 3 MF774666.1 28 DRBB 6 MF774667.1 THIS STUDY Dooars region, Sub- 29 DRBB 7 MF774668.1 Sub- Himalayan West Bengal. Cell and Molecular Biology 30 DRBB 8 MF774669.1 Himalayan Laboratory, Dept. of Zoology, 31 DRBB 9 MF774670.1 Terai and University of North Bengal, Raja Dooars 32 DRBB 11 MF774671.1 Rammohanpur, Darjeeling, West 33 TRBB 1 MF774672.1 Terai region, Sub- Bengal, India. 34 TRBB 5 MF774673.1 Himalayan West Bengal. 35 TRBB 6 MF774674.1

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Table 8: Retrieved publicly available four number of Amblyceps mangois COI gene sequence (No. 1-4) from NCBI database and ten number of presently studied sequences (No. 5-14). Accession Broad Location No. Name Location Institute No Division UMMZ 1 DQ508067.1 NA NA NA 244760 Noadihang river, 2 BCF11 JX105483.1 Assam, India N-E India NA SGBK- 3 JQ713859.1 Thingou, Manipur OF31F Department of Zoology, University of Sunamgan,Tanguar 4 DUZM140 KT762370.1 Bangladesh Dhaka, Haor,Bangladesh Karzon Hall, Dhaka 1000, Bangladesh 5 TRAM1 MK012251 Sub-Himalayan Terai 6 TRAM2 MK012252 region MK012253 7 DRAM4 THIS STUDY 8 DRAM6 MK012254 Sub-Himalayan 9 DRAM7 MK012255 Cell and Molecular Biology Laboratory, Terai and Dooars, MK012256 Dept. of Zoology, University of North 10 DRAM8 Sub-Himalayan Dooars Inmdia 11 DRAM9 MK012257 region Bengal. Raja Rammohanpur, Darjeeling, 12 DRAM10 MK012258 West Bengal, India. 13 DRAM12 MK012259 14 DRAM14 MK012260

3.11.7 Genetic diversity and population structure of two species Levels of genetic variation of Badis badis and Amblyceps mangois within the Mahananda, Teesta, Jaldhaka and other populations were estimated in terms of the number of variable sites (S), total number of mutations (Eta), number of haplotypes (H), haplotype diversity

(H), nucleotide diversity (πn) and average number of nucleotide differences. These indices were calculated by freely available DnaSP ver. 5.1 software (Librado and Rozas 2009). The genetic differentiation (FST) and gene flow (Nm) were also calculated following the method of Wright (1978), Govindaraju (1989) and Low et al. (2014). Tajima‘s D (Tajima 1989), Fu and Li‘s D and Fu and Li‘s F (Fu 1997) were calculated to verify selective neutrality in relation to mtDNA sequences, which would help to accumulate information regarding the demographic history of the population and used to deduce whether a population has undergone a sudden population expansion or construction.

3.11.8 Preparation of sequence dataset and phylogenetic analyses of mtDNA COI sequences Raw sequence reads, each PCR product sequenced at least twice, were curated in BioEdit ver. 7.0.9 (Hall, 1999) for character uncertainty and then assembled in Geneious R7 ver 7.0.6 (trial) (Rozen and Skaletsky, 2000) using pro features and retrieved the final sequences as Fasta

Page | 122 files. Multiple sequence alignment was implemented by CLUSTALX in MEGA ver 7.10 (Kumar et. al., 2016) using gap opening penalty 50, gap extension penalty 20, transition weight 0.5 and delay divergent cut-off 30%. The best model of DNA substitution selected was implemented in MEGA ver 7.10 (Kumar et. al., 2016) following software default parameter settings.

3.11.9 Phylogenetic analyses based on a MtCOI partial CDSs of Badis badis and Amblyceps mangois The evolutionary relationship between thirty five Badis badis (ten number of studied sequences and twenty five number of published sequences from NCBI ) and fourteen Amblycecps mangois (ten number of studied sequences and four number of published sequences from NCBI) mitochondrial cytochrome oxidase I sequences was estimated using phylogenetic inference and Maximum Likelihood (ML) method. The best nucleotide substitution model for phylogenetic analyses of Badis badis was selected by model testing programme in MEGA7.10. The best nucleotide substitution model obtained was Kimura 2-parameter the following scores: Bayesian Information Criterion (BIC)=5863.595, Akaike Information Criterion corrected (AICc)= 5132.779, Maximum likelyhood value (InL) = -2586.133, Assumed or estimated values of transition and transversion (R)=5.12, nucleotide frequencies (f): f(A)=0.250, f(T) =0.250, f(C) =0.250, f(G) =0.250; rates of base substitution (r): r(AT)=0.020, r(AC)=0.020, r(AG)=0.209, r(TA)=0.020, r(TC)=0.209, r(TG)=0.020, r(CA)=0.020, r(CT)=0.209, r(CG)=0.020, r(GA)=0.209, r(GC)=0.020, r(GT)=0.020. Kimura 2-parameter nucleotide substitution model was selected and total 500bootstrap replication was carried out during phylogenetic tree construction. The following default parameters are selected: uniform rates, 1st+2nd+3rd+noncoding codon positions, ML Heuristic method=Subtree-Pruning-Regrafting - Extensive (SPR level 5), Initial tree for ML= Make initial tree automatically (Default - NJ/BioNJ), strong branch swap filter and one number of thread. The best nucleotide substitution model for phylogenetic analyses of Amblyceps mangois was selected by model testing programme in MEGA7.10. The best nucleotide substitution model obtained was Jukes-Cantor having the following scores: Bayesian Information Criterion (BIC)=2398.872, Akaike Information Criterion corrected (AICc)= 2222.754, Maximum likelyhood value (InL) = -1086.300, Assumed or estimated values of transition and transversion

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(R)=0.50, nucleotide frequencies (f): f(A)=0.250, f(T) =0.250, f(C) =0.250, f(G) =0.250; rates of base substitution (r): r(AT)=0.083, r(AC)=0.083, r(AG)=0.083, r(TA)=0.083, r(TC)=0.083, r(TG)=0.083, r(CA)=0.083, r(CT)=0.083, r(CG)=0.083, r(GA)=0.083, r(GC)=0.083, r(GT)=0.083. Jukes-Cantor nucleotide substitution model was selected and total 500bootstrap replication was carried out during phylogenetic tree construction. The following default parameters are selected: uniform rates, 1st+2nd+3rd+noncoding codon positions, ML Heuristic method=Subtree-Pruning-Regrafting - Extensive (SPR level 5), Initial tree for ML= Make initial tree automatically (Default - NJ/BioNJ), strong branch swap filter and one number of thread. All the trees were built as midpoint-rooted consensus trees using FIGTREE v1.4.1 (http://tree.bio.ed.ac.uk/software/figtree/).

3.12 Table 9. Software used for statistical and genetic diversity analyses

Software Sl. Operating Implemented Program Source Description Reference No. Platform In me Arlequin1 Windows Freeware from It can handle RFLPs, Calculation of Schneider et v. 1.1 http://anthropol microsatellites, genetic al.(1997) o allozymes, RAPDs, diversity gie.unige.ch/arl AFLPs,allele indices, equin frequencies, DNA AMOVA. sequences data. Calculates gene and nucleotide diversity, mismatch distribution, haplotype frequencies, linkage disequilibrium, tests of Hardy-Weinberg equilibrium, neutrality tests, pairwise genetic distances, analyses of molecular variance (AMOVA).

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Software Sl. Operating Implemented Program Source Description Reference No. Platform In me Fstat2 DOS/ Freeware by It can handle Genetic Goudet (1995) v. 1.2 Windows writing to J. Allozymes, diversity Goudetatjerome microsatellites, indices [email protected] mtDNA RFLPs data. nil.ch Calculates gene diversity statistics of Weir and Cockerham (Weir, 1996). Computes jackknife and bootstrap confidence intervals of the statistics or can test gene diversity statistics using a permutation algorithm. POPGEN3 Windows Freeware from It can handle Calculation of Yeh and Boyle E http://www.ualb Codominant or Polymorphic (1997) v. 1.21, erta.ca/~fyeh/in dominant markers loci, Nei‘s dex.htm using haploid or genetic diploid data. diversity, Calculates standard Shannon‘s genetic diversity information measures, tests of index, Hardy-Weinberg UPGMA Equilibrium, Wright‘s cluster Fstatistics, genetic analyses. distances, UPGMA dendrogram, neutrality tests, linkage disequilibrium. TFPGA4 Windows Freeware from It can handle Calculation of Miller (1997) http://herb.bio.n Codominant F statistics, au.edu/~miller (allozyme) and UPGMA dominant (RAPD, cluster AFLP) genotypes analyses. data. Calculates descriptive statistics, genetic distances, and F-statistics. Performs tests for Hardy- Weinberg equilibrium, exact tests for genetic differentiation, Mantel tests, and UPGMA cluster analyses.

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Software Sl. Operating Implemented Program Source Description Reference No. Platform In me GenAlEx5 Windows, Freeware from GenAlEx offers Calculation of Peakall R and 6.5 Mac OS X http://biology.a analysis of F statistics and Smouse PE nu.edu.au/GenA codominant, haploid AMOVA, (2006); Peakall R lEx and binary genetic loci PhiPT values. and Smouse PE and DNA sequences. (2012) Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. In GenAlEx 6.5 introduce exciting new features including calculation of new estimators of population structure: G’ST, G’’ST, Jost‘s Dest, and F’ST via AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data, and heterogeneity tests for spatial autocorrelation analysis. Phylip6 Windows, Freeware from Data types that can be Felsenstein (2005) ver. 3.69 Mac OS X, http://evolution. handled include UNIX genetics.washin molecular sequences, gton.edu/phylip. gene frequencies, html. restriction sites, distance matrices, and 0/1 (binary) discrete characters. Methods that are available in the package include parsimony, distance matrix, and likelihood methods, including bootstrapping and consensus trees.

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Software Sl. Operating Implemented Program Source Description Reference No. Platform In me FigTree7 Window, Freeware from FigTree is designed as Construction Rambaut and ver.1.3.1 UNIX, http://tree.bio.e a graphical viewer of of graphical Drummond (2010) Linux, Mac d.ac.uk/ phylogenetic trees and viewer and OS X as a program for publication producing publication- ready ready figures. phylogenetic trees Geneious8 Windows, Trial Version: Sequence editing Sequence Rozen and R7 ver UNIX, Freeware from editing, Skaletsky (2000) 7.0.6 Linux, Mac http://www.gen alignment (Trial OS X eious.com Version) BioEdit9 Windows, Freeware from Sequence editing Sequence Hall (1999) ver. 7.0.9 UNIX, www.mbio.ncsu editing, Linux, Mac .edu/BioEdit/bi alignment OS X oedit.html MEGA1 Windows, Freeware from Sequence alignment Sequence Kumar et. al., ver0 7.10 UNIX, http://www.meg and analyses alignment, (2016) Linux, Mac asoftware.net analyses of OS X different indices and phylogenetic analyses. 11 . DnaSP Windows, Freeware from DNA polymorphism Sequence Librado and ver. 5 UNIX, www.ub.edu/dn analyses from single variation/ Rozas (2009) Linux, Mac asp locus and multiple polymorphism OS X loci. Measurement of analyses, sequence variation haplotype, within and between nucleotide populations. Perform diversity Neutrality tests. analyses. Neutrality test.

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CHAPTER-IV Results and Discussion

4.1 Standardization of genomic DNA extraction from fish tissue The fishes under study viz., Badis badis and Amblyceps mangois have been classified as threatened in category as reported by NBFGR. Therefore it would be unethical to standardize the genomic DNA (gDNA) isolation and RAPD PCR experiments with these fish samples. For this reason closely related fish samples (Labeo bata related to Badis badis and Heteropneustes fossilis related to Amblyceps mangois) were chosen for the standardization of sufficient amount of genomic DNA extractraction from minute quantity of tissue samples from these fishes. The DNA isolation method employed in this study showed that sufficient amounts of high molecular weight gDNAs could be purified from fish scales/fins (Figure 12). While the integrity of the isolated gDNAs appeared satisfactory ample amount of RNAs were present in the isolated gDNA samples (Figure 12 A–C). The figure showed that the yields of high molecular weight gDNAs from live scale and fin samples were substantially higher than that from frozen samples. However the gDNAs isolated from different quantities of fin clips of both live and frozen Heteropneustes (scaleless species) were more degraded than that from scales or fins of Labeo (Figure 12 C).

Live Frozen Live Frozen Live Frozen

A. Labeo bata (Scale) B. Labeo bata (Fin) C. Heteropneustes fossilis (Fin) Figure 12. 0.7 % agarose gel electrophoreses of genomic DNAs (gDNAs) isolated from Live and Frozen scale and fin samples of L. bata and H. fossilis. A. Lane 1 Lambda DNA/Hind III Digest DNA size marker (kb), lanes 2–6: 5, 10, 20, 30, 50 pieces of scale gDNA preparations from live L. bata, lanes 8–12: 5, 10, 20, 30, 50 pieces of scale gDNA preparations from frozen L. bata. B. Lane 1 Lambda DNA/Hind III Digest DNA size marker (kb), lanes 2–6: 1, 5, 10, 20, 30 mg of fin gDNA preparations from live L. bata, lanes 8–12: 1, 5, 10, 20, 30 mg of fin gDNA preparations from frozen L. bata. C. Lane 1 lambda DNA/Hind III Digest DNA size marker (kb), lanes 2–6: 1 mg, 5, 10, 20, 30 mg of fin gDNA preparations from live H. fossilis, lanes 8–12: 1, 5, 10, 20, 30 mg of fin gDNA preparations from frozen H. fossilis. Quantification of gDNA yields was done after treating each isolate with RNase A. The total yield of gDNAs from 5 and 50 pieces of scales were 8.40 and 80.0 µg respectively

Page | 131 and from 1 and 30 mg fin were 4.65 and 123.50 µg respectively in live L. bata. In live H. fossilis the total yield were 14.60 and 132.60 µg from 1 and 30 mg fin respectively. However, the yield of gDNAs from frozen samples of both L. bata and H. fossilis were relatively low in comparison to that from the live ones (Table 10).

Table 10. Spectrophotometric assessment of gDNA yields and purity in both live and frozen tissues from a carp (Labeo bata) and a catfish (Heteropneustes fossilis) after treatment with RNase A. Specimen Tissue Sample OD260 OD280 OD260/ Concentration Total type OD280 (µg/ll) yield (µg) 5 pieces 0.0056 0.0032 1.75 0.084 8.40 Scale Labeo bata 50 pieces 0.0530 0.0243 2.18 0.800 80.0 (live) 1 mg 0.0031 0.0016 1.94 0.0465 4.65 Fin 30 mg 0.0823 0.0446 1.85 1.235 123.50 5 pieces 0.0021 0.0011 1.91 0.032 3.20 Scale Labeo bata 50 pieces 0.0190 0.0102 1.86 0.285 28.5 (frozen) 1 mg 0.0009 0.0005 1.80 0.0135 1.35 Fin 30 mg 0.0240 0.0137 1.75 0.36 36.0 Heteropneustes 1 mg 0.0097 0.0049 1.98 0.146 14.60 Fin fossilis (live) 30 mg 0.0884 0.0463 1.86 1.326 132.60 Heteropneustes 1 mg 0.0011 0.0006 1.83 0.0165 1.65 Fin fossilis (frozen) 30 mg 0.0165 0.0093 1.77 0.2475 24.75

The gDNAs isolated from lowest (5 pieces) and highest (50 pieces) numbers of scales of L. bata and that from lowest (1 mg) and highest (30 mg) amounts of fin tissues of the fishes were used as templates in RAPD-PCR analyses (Figure. 13). In live L. bata scale and fin, OPA02 primer produced nine bands and OPA 07 produced seven bands respectively [Figure. 13 A (lanes 2, 3, 6, 7), 2B (lanes 2, 3, 6, 7)]. But in frozen L. bata scale and fin the result was different, the bands appeared faint, OPA02 primer produced three bands and OPA07 primer produced four bands [Figure 13 A (lanes 4, 5, 8, 9), b (lanes 4, 5, 8, 9)]. In live H. fossilis fin OPA 02 gives eight bands and OPA07 gives four bands (Figure 13 C lanes 2, 3, 6, 7) whereas in frozen sample the numbers of bands were relatively less, both OPA 02 produced six bands and OPA07 produced three bands (Figure 13 C lanes 4, 5, 8, 9).

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A. Labeo bata (Scale) B. Labeo bata (Fin) C. Heteropneustes fossilis (Fin)

Figure 13. RAPD banding patterns in 1.4 % agarose gels from L. bata and H. fossilis using decamer primers OPA02 and OPA07. A Lane 1 100 bp DNA ladder (kb), lanes 2–3, 6–7 RAPD profile from 5 and 50 pieces of scale respectively from live L. bata, lanes 4–5, 8–9 RAPD profile from 5 and 50 pieces of scale respectively from frozen L. bata. B Lane 1 100 bp DNA ladder (kb), lanes 2–3, 6–7 RAPD profile from 1 and 30 mg of fins respectively from live L. bata, lanes 4–5, 8–9 RAPD profile from 1 and 30 mg of fin respectively from frozen L. bata. C Lane 1 100 bp DNA ladder (kb), lanes 2– 3, 6–7 RAPD profile from 1 and 30 mg of fin respectively from live H. fossilis, lanes 4–5, 8–9 RAPD profile from 1 and 30 mg of fin respectively from frozen H. fossilis.

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4.2 Genetic diversity study of Badis badis

A detailed survey was carried out in the Mahananda (Terai region), Teesta and Jaldhaka (Dooars region) river systems for the collection of Badis badis samples. Total seventeen spots were selected for collection purpose (Figure 14) and samples were collected from the selected sites with the help of scoop net. The fourteen collection spots were as follows, viz., Mahananda Barrage, Fulbari (BTR-1), Mahananda-Panchanoi River Junction (BTR-2), Balason River, Palpara, Matigara (BTR-3), Panchanoi River (BTR-4), Mahananda River, Champasari (BTR-5), Balason River, Tarabari (BTR-6) from Mahananda river system; Sevoke (BDR-1), Ghish river (BDR-2), Gajoldoba-Teesta Barrage (BDR-3), Chel river (BDR-4), Neora river (BDR-5), Dharla river (BDR-6), Jalpaiguri (BDR-7) from Teesta river system; and Jaldhaka River (BDR-8), Murti River (BDR-9), Ghotia River (BDR-10), Diana River (BDR-11) from Jaldhaka river system. See the map below for detail:

BDR-2 BDR-5 BDR-1 BDR-4 BDR-11 BDR-10 BDR-9

BTR-5 BTR-6 BTR-4 Teesta River System Jaldhaka River System Mahananda River System BDR-3 BTR-3

BTR-2 Water channel BDR-6 BTR-1

N sampling site BDR-8 BDR-7 10 Km Figure 14. Badis badis collection sites. Also refer to Table 2 for the geographic coordinates of the collection spots for Badis badis.

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4.2.1 Findings based on RAPD based analyses 4.2.1.1 Intra-Population Genetic Diversity Study 4.2.1.1.1 Mahananda River System Table 11. Intra-population genetic diversity indices based on RAPD analyses of Badis badis of Mahananda-Balason river system. Populations RAPD Markers Diversity Indices H´ H´ Np Nper S H H´ or I E= e /S EHeip=(e - 1/S-1) Mahananda Barrage, 40 28.37% 1.2837± 0.1061± 0.1562± 0.910696 0.595911 Fulbari (BTR-1) 0.4524 0.1841 0.2637 Mahananda-Panchanoi 35 24.82% 1.2482± 0.0898± 0.1330± 0.915118 0.573127 River Junction (BTR-2) 0.4335 0.1718 0.2471 Balason River, Palpara, 34 24.11% 1.2411± 0.0865± 0.1283± 0.916037 0.567789 Matigara 0.4293 0.1690 0.2433 (BTR-3) Panchanoi River 36 25.53% 1.2553± 0.0946± 0.1395± 0.915876 0.586364 (BTR-4) 0.4376 0.1766 0.2533 Mahananda River, 37 26.24% 1.2624± 0.0953± 0.1409± 0.912001 0.576637 Champasari 0.4415 0.1764 0.2531 (BTR-5) Balason River, Tarabari 44 31.21% 1.3121± 0.1166± 0.1717± 0.904902 0.600197 (BTR-6) 0.4650 0.1890 0.2709 Mahananda river 53 37.59% 1.3759± 0.1207± 0.1836± 0.873272 0.53614 system 0.4861 0.1789 0.2600 Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index.

Based on the RAPD profile of Badis badis from Mahananda-Balason river the number of polymorphic loci and the percentage of polymorphic loci vary across six populations (BTR-1 to BTR-6) (Table 11 and Figure 15 A). The highest number of polymorphic loci was observed in BTR-6 population (44 numbers) and the percentage of polymorphism was 31.21. The lowest number of polymorphic loci was observed in BTR-3 population (34 numbers) and the percentage of polymorphism was 24.11. The observed number of alleles or allelic richness (S) varies from 1.2411 ± 0.4293 in BTR-3 population to 1.3121 ± 0.4650 in BTR-6 population (Table 11). The Nei’s genetic diversity (H) was highest (0.1166 ± 0.1890) in BTR-6 population and lowest (0.0865 ± 0.1690) in BTR-3 population (Table 11). The Shannon’s information index (H´ or I) was highest (0.1717 ± 0.2709) in BTR-6 population and lowest (0.1283 ± 0.2433) in BTR-3 population (Table 11). However, the measure of evenness (E) was highest (0.916037) in BTR-3 population and lowest (0.904902) in BTR-6 population (Table 11). The Heip’s measure of evenness was highest (0.600197) in BTR-6 population and lowest (0.567789) in BTR-3 population (Table 11).

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4.2.1.1.2 Teesta River System Table 12. Intra-population genetic diversity indices based on RAPD analyses of Badis badis of Teesta river system. RAPD Markers Diversity Indices Populations E =(e N N S H H´ or I E= e H´/S Heip p per H´-1/S-1) Sevoke 1.3333± 0.1191± 0.1756± (Teesta River) 47 33.33% 0.893993 0.575941 0.4731 0.1925 0.2735 (BDR-1) Ghish River 1.2553± 0.0984± 0.1442± 36 25.53% 0.92019 0.60758 (BDR-2) 0.4376 0.1800 0.2584 Gajoldoba (Teesta River 1.2128± 0.0762± 0.1130± 30 21.28% 0.923179 0.562181 Barrage) 0.4107 0.1611 0.2322 (BDR-3) Chel River 1.3475± 0.1291± 0.1897± 49 34.75% 0.897133 0.601113 (BDR-4) 0.4779 0.1964 0.2802 Neora River 1.2411± 0.0820± 0.1223± 34 24.11% 0.910558 0.539581 (BDR-5) 0.4293 0.1655 0.2378 Dharla River 1.2199± 0.0801± 0.1187± 31 21.99% 0.923053 0.573134 (BDR-6) 0.4156 0.1641 0.2366 Jalpaiguri 1.2411± 0.0932± 0.1355± (Teesta River) 34 24.11% 0.922657 0.601863 0.4293 0.1805 0.2566 (BDR-7) Teesta river 1.8227± 0.2929 ± 0.4361± 116 82.27% 0.848556 0.664475 system 0.3833 0.1825 0.2514 Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index.

The RAPD analyses of Badis badis of Teesta river showed that the diversity indices were varied across populations from BDR-1 to BDR-7 collected from the Teesta River (Table 12 and Figure 15B). The highest number of polymorphic loci (49) was observed in BDR-4 population and the percentages of polymorphism were found to be 34.75 in RAPD. The lowest number of polymorphic loci (30) was observed in BDR-3 population and the percentages of polymorphism were found to be 21.28. The observed number of alleles or allelic richness (S) varied from 1.2128 ± 0.4107 in BDR-3 population to 1.3475 ± 0.4779 in BDR-4 population (Table 12). The Nei’s genetic diversity (H) was highest (0.1291 ± 0.1964) in BDR-4 population and lowest (0.0762 ± 0.1611) in BDR-3 population in RAPD (Table 12). The Shannon’s information index (H´ or I) was highest (0.1897 ± 0.2802) in BDR-4 population and lowest (0.1130 ± 0.2322) in BDR-3 population (Table 12). Whereas, the measure of evenness (E) was highest (0.923179) in BDR-3 population; and lowest (0.897133) in BDR-4 population after RAPD analyses (Table 12). The Heip’s measure of evenness was highest (0.607581) in BDR-2 population and lowest (0.539581) in BDR-5 population (Table 12).

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4.2.1.1.3 Jaldhaka River System Table 13. Intra-population genetic diversity indices based on RAPD analyses of Badis badis of Jaldhaka river system. RAPD Markers Diversity Indices Populations H´ Np Nper S H H´ or I E= e /S EHeip= (e H´-1/S-1) Jaldhaka River 1.3171 ± 0.1048 ± 0.1652 ± 0.895626 0.566474 51 36.17 % (BDR-8) 0.4811 0.1855 0.2788 Murti River 1.3972 ± 0.1378 ± 0.2052 ± 56 39.72 % 0.878736 0.573441 (BDR-9) 0.4911 0.1955 0.2791 Ghotia River 1.3471 ± 0.1068 ± 0.1756 ± 52 36.88 % 0.884835 0.553043 (BDR-10) 0.4656 0.1756 0.2588 Diana River 1.4326± 0.1436 ± 0.2150± 61 43.26 % 0.865463 0.554466 (BDR-11) 0.4972 0.1963 0.2794 Jaldhaka river 1.4965 ± 0.1471 0.2247± 70 49.65% 0.836583 0.507446 system 0.5018 ±0.5018 0.5018

Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index.

The RAPD analyses showed that the polymorphism varied across four populations of Badis badis from Jaldhaka river system (Table 13 and Figure 15C). The highest number of polymorphic loci (61) was observed in BDR-11 population and the percentages of polymorphism were found to be 43.26. The lowest number of polymorphic loci (51) was observed in BDR-8 population and the percentages of polymorphism were found to be 36.17. The observed number of alleles or allelic richness (S) varied from 1.3171 ± 0.4811 in BDR-8 population to 1.4326 ± 0.4972 in BDR-11 population (Table 13). The Nei’s genetic diversity (H) was highest (0.1436 ± 0.1963) in BDR-11 population and lowest (0.1048 ± 0.1855) in BDR-8 population (Table 13). The Shannon’s information index (H´ or I) was highest (0.2150 ± 0.2794) in BDR-11 population and lowest (0.1652 ± 0.2788) in BDR-8 population (Table 13). Whereas, the measure of evenness (E) was highest (0.895626) in BDR-8 population and lowest (0.865463) in BDR-11 population after RAPD analyses (Table 13). The Heip’s measure of evenness was highest (0.573441) in BDR-9 population and lowest (0.553043) in BDR-10 population (Table 13).

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4.2.2 Findings based on ISSR based analyses 4.2.2.1 Intra-Population Genetic Diversity Study 4.2.2.1.1 Mahananda River System Table 14. Intra-population genetic diversity indices based on ISSR analyses of Badis badis of Mahananda-Balason river system. ISSR Markers Diversity Indices

Populations EHeip= S H´ H´ Np Nper H H´ or I E= e /S (e -1/ S-1) Mahananda Barrage, Fulbari 1.2931± 0.1024± 0.1534± 34 29.31% 0.910696 0.595911 (BTR-1) 0.4572 0.1751 0.2540 Mahananda-Panchanoi River 1.2328± 0.0806± 0.1204± Junction 27 23.28% 0.915118 0.573127 0.4244 0.1629 0.2354 (BTR-2) Balason River, Palpara, 1.2241± 0.0767± 0.1149± 26 22.41% 0.916037 0.567789 Matigara (BTR-3) 0.4188 0.1593 0.2305 Panchanoi River 1.2586± 0.0924± 0.1372± 30 25.86% 0.915876 0.586364 (BTR-4) 0.4398 0.1733 0.2493 Mahananda River, 1.259 ± 0.0959± 0.1428± 32 27.59% 0.912001 0.576637 Champasari (BTR-5) 0.4489 0.1749 0.2513 Balason River, Tarabari 1.3276± 0.1136± 0.1704± 38 32.76% 0.904902 0.600197 (BTR-6) 0.4714 0.1806 0.2619 1.3793 ± 0.1092± 0.1685± Mahananda river system 44 37.93% 0.4873 0.1718 0.2490 0.858064 0.48386

Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness,

EHeip= Heip’s evenness index.

In the ISSR-based analysis, the highest number of polymorphic loci in Badis badis from Mahananda-Balason river system was observed in BTR-6 population (38 numbers) and the percentage of polymorphism was 32.76 (Table 14 and Figure 15D). The lowest number of polymorphic loci was observed in BTR-3 population (26 numbers) and the percentage of polymorphism was 22.41. The observed number of alleles or allelic richness (S) varies from 1.2241 ± 0.4188 in BTR-3 population to 1.3276 ± 0.4714 in BTR-6 population (Table 14). The Nei’s genetic diversity (H) was highest (0.1136 ± 0.1806) in BTR-6 population and lowest (0.0767 ± 0.1593) in BTR-3 population (Table 14). The Shannon’s information index (H´ or I) was highest (0.1704 ± 0.2619) in BTR-6 population and lowest (0.1149 ± 0.230) in BTR-3 population (Table 14). However, the measure of evenness (E) was highest (0.916037) in BTR-3 population and lowest (0.904902) in BTR-6 population (Table 14). The Heip’s measure of evenness was highest (0.600197) in BTR-6 population and lowest (0.567789) in BTR-3 population (Table 14).

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4.2.2.1.2 Teesta River System Table 15. Intra-population genetic diversity indices based on ISSR analyses of Badis badis of Teesta river system. ISSR Markers Diversity Indices Populations E =(e H´- N N S H H´ or I E= e H´/S Heip p per 1/S-1) Sevoke 1.3448 ± 0.1207 ± 0.1785 ± (Teesta River) 40 34.48% 0.888922 0.566772 0.4774 0.1924 0.2734 (BDR-1) Ghish River 1.2672 ± 0.0999 ± 0.1472 ± 31 26.72 % 0.914288 0.593509 (BDR-2) 0.4444 0.1793 0.2579 Gajoldoba (Teesta River 1.2155 ± 0.0726 ± 0.1071 ± 25 21.55% 0.917543 0.534914 Barrage) 0.4130 0.1549 0.2248 (BDR-3) Chel River 1.3448 ± 0.1242 ± 0.1838 ± 40 34.48% 0.893646 0.585196 (BDR-4) 0.4774 0.1917 0.2746 Neora River 1.2500 ± 0.0807 ± 0.1214 ± 29 25.00% 0.903261 0.516306 (BDR-5) 0.4349 0.1629 0.2342 Dharla River 1.2256 ± 0.0746 ± 0.1091 ± 26 22.41% 0.908164 0.501088 (BDR-6) 0.4211 0.1649 0.2028 Jalpaiguri (Teesta 1.2376 ± 0.0786 ± 0.1151 ± River) 27 23.27% 0.906582 0.513408 0.4111 0.1675 0.2228 (BDR-7) Teesta river 1.7586 ± 0.2406± 0.3639± 88 75.86% 0.818225 0.578606 system 0.4298 0.1929 0.2680 Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness,

EHeip= Heip’s evenness index. The ISSR analyses in Badis badis from Teesta river showed that the diversity indices were varied across populations from BDR-1 to BDR-7 (Table 15 and Figure 15E). The highest number of polymorphic loci (40) was observed in BDR-1 and BDR-4 population, and the percentages of polymorphism were found to be 34.48. The lowest number of polymorphic loci (25) was observed in BDR-3 population and the percentages of polymorphism were found to be 21.55 in ISSR analyses. The observed number of alleles or allelic richness (S) varied from 1.2155 ± 0.4130 in BDR-3 population to 1.3448 ± 0.4774 in BDR-1 and BDR-4 populations (Table 15). The Nei’s genetic diversity (H) was highest (0.1242 ± 0.1917) in BDR-4 population and lowest (0.0726 ± 0.1549) in BDR-3 population (Table 15). The Shannon’s information index (H´ or I) was highest (0.1838 ±0.2746) in BDR-4 population and lowest (0.1071±0.2248) in BDR-3 population (Table 15). Whereas, the measure of evenness (E) was highest (0.917543) in BDR-3 population and lowest (0.888922) in BDR-1 population after ISSR analyses (Table 15). The Heip’s measure of evenness was highest (0.593509) in BDR-2 population and lowest (0.501088) in BDR-6 population (Table 15).

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4.2.2.1.3 Jaldhaka River System Table 16. Intra-population genetic diversity indices based on ISSR analyses of Badis badis of Jaldhaka river system. ISSR Markers Diversity Indices Populations EHeip= H´ H´ Np Nper S H H´ or I E= e /S (e -1/ S-1) 1.3966 0.2017 Jaldhaka River 0.1348 ± 46 39.66% ± ± 0.876042 0.563492 (BDR-8) 0.1927 0.4913 0.2756 1.4138 Murti River 0.1453 ± 0.2169 ± 48 41.38% ± 0.878639 0.585355 (BDR-9) 0.1960 0.2810 0.4946 1.4052 Ghotia River 0.1374 ± 0.2049 ± 47 40.52% ± 0.873472 0.561212 (BDR-10) 0.1962 0.2789 0.4931 1.4224 Diana River 0.1409 ± 0.2109 ± 49 42.24% ± 0.868102 0.555845 (BDR-11) 0.1954 0.2785 0.4961 1.4828 Jaldhaka river 0.1455 ± 0.2219± 56 48.28% ± 0.841952 0.514595 system 0.1892 0.2706 0.5019 Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index. The ISSR analyses Badis badis from Jaldhaka river showed that the polymorphism varied across four populations of Jaldhaka river system (Table 16 and Figure 15F). The highest number of polymorphic loci (49) was observed in BDR-11 population and the percentages of polymorphism were found to be 42.24 in ISSR analyses. The lowest number of polymorphic loci (46) was observed in BDR-8 population and the percentages of polymorphism were found to be 39.66. The observed number of alleles or allelic richness (S) varied from 1.3966 ±0.4913 in BDR-8 population to 1.4224 ±0.4961 in BDR-11 population by ISSR analyses (Table 16). The Nei’s genetic diversity (H) was highest (0.1409 ± 0.1954) in BDR-11 population and lowest (0.1348 ± 0.1927) in BDR-8 population (Table 16). The Shannon’s information index (H´ or I) was highest (0.2109 ± 0.2785) in BDR-11 population and lowest (0.2017 ± 0.2756) in BDR-8 population (Table 16). Whereas, the measure of evenness (E) was highest (0.878639) in BDR-9 population and lowest (0.868102) in BDR-11 population after ISSR analyses (Table 16). The Heip’s measure of evenness was highest (0.585355) in BDR-9 population and lowest (0.555845) in BDR-11 population (Table 16).

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4.2.3 Findings based on RAPD + ISSR based analyses 4.2.3.1 Intra-Population Genetic Diversity Study 4.2.3.1.1 Mahananda River System Table 17. Intra-population genetic diversity indices based on RAPD+ISSR analyses of Badis badis of Mahananda-Balason river system. RAPD+ISSR Markers Diversity Indices Populations EHeip= H´ H´ Np Nper S H H´ or I E= e /S (e -1/ S-1) Mahananda Barrage, 1.2879± 0.1044± 0.1549± 74 28.79 % 0.906546 0.581942 Fulbari (BTR-1) 0.4537 0.1797 0.2589 Mahananda- 1.2412± 0.0857± 0.1273± Panchanoi River 62 24.12 % 0.915048 0.562843 0.4287 0.1676 0.2415 Junction (BTR-2) Balason River, 1.2335 ± 0.0821± 0.1223± Palpara, Matigara 60 23.35 % 0.916168 0.557144 0.4239 0.1645 0.2372 (BTR-3) Panchanoi River 1.2568± 0.0936± 0.1384± 66 25.68 % 0.913777 0.578017 (BTR-4) 0.4377 0.1748 0.2510 Mahananda River, 1.2724± 0.0973± 0.1442± Champasari 70 27.24 % 0.907824 0.569439 0.4460 0.1765 0.2534 (BTR-5) Balason River, 1.3230 ± 0.1172± 0.1739± 83 32.30 % 0.899423 0.588039 Tarabari (BTR-6) 0.4685 0.1861 0.2680 Mahananda river 1.3813 ± 0.1163 ± 0.1782± 98 38.13 % 0.865174 0.511577 system 0.4867 0.1755 0.2547 Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index. Based on the RAPD+ISSR profile the number of polymorphic loci and the percentage of polymorphic loci vary across six populations (BTR-1 to BTR-6) in the Mahananda- Balason river system (Table 17). The highest number of polymorphic loci was observed in BTR-6 population (83 numbers) and the percentage of polymorphism was 32.30. The lowest number of polymorphic loci was observed in BTR-3 population (60 numbers) and the percentage of polymorphism was 23.35. The observed number of alleles or allelic richness (S) varies from 1.2335 ± 0.4239 in BTR-3 population to 1.3230 ± 0.4685 in BTR-6 population (Table 17). The Nei’s genetic diversity (H) was highest (0.1172 ± 0.1861) in BTR-6 population and lowest (0.0821 ± 0.1645) in BTR-3 population (Table 17). The Shannon’s information index (H´ or I) was highest (0.1739 ± 0.2680) in BTR-6 population and lowest (0.1223 ± 0.2372) in BTR-3 population (Table 17). However, the measure of evenness (E) was highest (0.916168) in BTR-3 population and lowest (0.899423) in BTR-6 population (Table 17). The Heip’s measure of evenness was highest (0.588039) in BTR-6 population and lowest (0.557144) in BTR-3 population (Table 17).

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4.2.3.1.2 Teesta River System Table 18. Intra-population genetic diversity indices based on RAPD+ISSR analyses of Badis badis of Teesta river system. RAPD+ISSR Markers Diversity Indices Populations E =(e H´- N N S H H´ or I E= e H´/S Heip p per 1/S-1) Sevoke 34.24 1.3424± 0.1218± 0.1797± (Teesta River) 88 0.891581 0.574936 % 0.4754 0.1931 0.2745 (BDR-1) Ghish River 26.07 1.2607 ± 0.0991 ± 0.1456± 67 0.917533 0.601202 (BDR-2) % 0.4399 0.1793 0.2577 Gajoldoba 1.2179 (Teesta River 21.79 0.0765± 0.1140 ± 56 ± 0.920233 0.554163 Barrage) % 0.1600 0.2311 0.4136 (BDR-3) Chel River 34.63 1.3463 ± 0.1269± 0.1870± 89 0.895512 0.593784 (BDR-4) % 0.4767 0.1939 0.2772 Neora River 24.51 1.2451± 0.0814± 0.1219± 63 0.907269 0.528932 (BDR-5) % 0.4310 0.1640 0.2357 Dharla River 30.74 1.3074± 0.1144 ± 0.1685± 79 0.905253 0.597034 (BDR-6) % 0.4623 0.1880 0.2694 Jalpaiguri 26.46 1.2646 ± 0.0962± 0.1415 ± (Teesta River) 68 0.91096 0.574454 % 0.4420 0.1791 0.2554 (BDR-7) Teesta river 1.7977 ± 0.2700 ± 0.4047 ± 205 79.77 0.833761 0.625364 system 0.4025 0.1881 0.2599 Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index. The RAPD+ISSR analyses of Badis badis from Teesta river showed that the diversity indices were varied across populations from BDR-1 to BDR-7 (Table 18). The highest number of polymorphic loci (89) was observed in BDR-4 population, and the percentages of polymorphism were found to be 34.63. The lowest number of polymorphic loci (56) was observed in BDR-3 population and the percentages of polymorphism were found to be 21.79 in RAPD+ISSR analyses. The observed number of alleles or allelic richness (S) varied from 1.2179 ± 0.4136 in BDR-3 population to 1.3463 ± 0.4767 in BDR-4 population (Table 18). The Nei’s genetic diversity (H) was highest (0.1269 ± 0.1939) in BDR-4 population and lowest (0.0765 ± 0.1600) in BDR-3 population (Table 18). The Shannon’s information index (H´ or I) was highest (0.1870 ± 0.2772) in BDR-4 population and lowest (0.1140 ± 0.2311) in BDR-3 population (Table 18). Whereas, the measure of evenness (E) was highest (0.920233) in BDR-3 population and lowest (0.891581) in BDR-1 population after RAPD+ISSR analyses (Table 18). The Heip’s measure of evenness was highest (0.601202) in BDR-2 population and lowest (0.528932) in BDR-5 population (Table 18).

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4.2.3.1.3 Jaldhaka River System Table 19. Intra-population genetic diversity indices based on RAPD+ISSR analyses of Badis badis of Jaldhaka river system. RAPD+ISSR Markers Diversity Indices Populations EHeip= H´ H´ Np Nper S H H´ or I E= e /S (e -1/ S-1) Jaldhaka River 1.3969 ± 0.1364 0.2036 ± 102 39.69 % 0.87752 0.568929 (BDR-8) 0.4902 ±0.1939 0.2770 Murti River 1.4047± 0.1438 ± 0.2140± 104 40.47 % 0.88177 0.589629 (BDR-9) 0.4918 0.1962 0.2811 Ghotia River 1.4008± 0.1388± 0.2063± 103 40.08 % 0.877443 0.571661 (BDR-10) 0.4910 0.1972 0.2804 Diana River 1.4280 ± 0.1424 ± 0.2131± 110 42.80 % 0.866603 0.554926 (BDR-11) 0.4958 0.1955 0.2784 Jaldhaka river 1.4903 ± 0.1464 ± 0.2235 ± 126 49.03% 0.839056 0.510801 system 0.5009 0.1887 0.2698 Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index.

The RAPD+ISSR analyses in Badis badis showed that the polymorphism varied across four populations of Jaldhaka river system. The highest number of polymorphic loci (110) was observed in BDR-11 population and the percentages of polymorphism were found to be 42.80. The lowest number of polymorphic loci (102) was observed in BDR-8 population and the percentages of polymorphism were found to be 39.69. The observed number of alleles or allelic richness (S) varied from 1.3969 ± 0.4902 in BDR-8 population to 1.4280 ± 0.4958 in BDR-11 population by RAPD+ISSR analyses (Table 19). The Nei’s genetic diversity (H) was highest (0.1424 ± 0.1955) in BDR-11 population and lowest (0.1364 ±0.1939) in BDR-8 population (Table 19). The Shannon’s information index (H´ or I) was highest (0.2131 ± 0.2784) in BDR-11 population and lowest (0.2036 ± 0.2770) in BDR-8 population (Table 19). Whereas, the measure of evenness (E) was highest (0.88177) in BDR-9 population and lowest (0.866603) in BDR-11 population after RAPD+ISSR analyses (Table 19). The Heip’s measure of evenness was highest (0.589629) in BDR-9 population and lowest (0.554926) in BDR-11 population (Table 19).

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M BTR1 BTR2 BTR3 BTR4 BTR5 BTR6

M BDR1 BDR2 BDR3 BDR4 BDR5 BDR6 BDR7

M BDR8 BDR9 BDR10 BDR11

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M BTR1 BTR2 BTR3 BTR4 BTR5 BTR6

M BDR1 BDR2 BDR3 BDR4 BDR5 BDR6 BDR7

M BDR8 BDR9 BDR10 BDR11

F Page | 145

Figure 15: Representative Gel of Badis badis after RAPD (1.4% agarose) and ISSR (1.8% agarose) amplification. RAPD primer OPA16 and ISSR primer ISSR02 primers are used for amplification.

M=100 base pair DNA size marker. A. RAPD gel of Mahananda River system; B. RAPD gel of Teesta river system; C. RAPD gel of Jaldhaka river system; D. ISSR gel of Mahananda River system; E. ISSR gel of Teesta river system; F. ISSR gel of Jaldhaka river system.

BTR1-BTR6 are the populations from Mahananda river system, BDR1-BDR7 are the populations of Teesta river system, BDR8-BDR11 are the populations of Jaldhaka river system. Each population consists of ten individuals. Each lane represents each individual’s RAPD and ISSR banding pattern.

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4.2.4 Comparative discussion based on RAPD, ISSR and RAPD+ISSR based analyses

In the study carried out by Mishra et al. (2012) on Barilius barna species in Uttarakhand, India, eight RAPD primers generated total 35 bands of which 14 bands were polymorphic in nature. Maximum polymorphism (50.00%) was observed after amplification with OPB08 and OPB12 primer whereas minimum polymorphism (33.33%) was observed by OPA18, OPB15, OPB18, and OPH03 primers. We have also compared our data with other studies carried out with some other vulnerable species found in geographically related areas. The study carried out by Kader et al. (2013) on three Tilapia species (Oreochromis niloticus, Oreochromis aureus, and Tilapia zilli) in Egypt and by Chandra et al. (2010) on vulnerable Eutropiichthys vacha in India, 15 RAPD primers generated 201 and 45 polymorphic amplified fragments, respectively. In our study we have found total 124 fragments after amplification with ten RAPD decamer primers, out of which 111 fragments were polymorphic in Barilius barna (Paul et al. 2016). In a study carried out Astyanax fasciatus (Teleostei, Characidae), the amplification resulted in 59 RAPD loci showed 88.14% polymorphism. The ISSR amplifications resulted in being 91.43% polymorphism (Pazza et al., 2007). Liu et al., (2006) carried out a study by ISSR marker on Japanese Flounder (Paralichthys olivaceus), they have found total 36 number of polymorphic loci (33.92%) in common population, 27 number (28.15%) in susceptible population and 26 number (27.45%) in resistant population. Another study carried out by Liu et al., (2009) on Cynoglossus semilaevis using ISSR markers the number of bands, number of polymorphic bands and percentage of polymorphic bands varied from 113 to 137, from 47 to 62 and from 0.4159±0.08 to 0.4526±0.07 respectively. Liu et al., (2012) carried out a study on Botia superciliaris of Yangtze river where they found the polymorphic loci varied from 33 to 41. A study carried out on four Tilapia species of Egypt the number of polymorphic bands varied across different populations i.e. in Sarotherodon galilaeus= 27, Oreochromis niloticus =29, Oreochromis aureus=34 and Tilapia zillii=67) (Saad et al., 2012).

In the present study, I have found total 53 (Mahananda river system, Table 11), 116 (Teesta river system, Table 12) and 70 (Jaldhaka river system, Table 13) polymorphic fragments after RAPD amplification; 44 (Mahananda river system, Table 14), 88 (Teesta river system, Table 15) and 56 (Jaldhaka river system, Table 16) polymorphic fragments after ISSR amplification; and 98 (Mahananda river system, Table 17), 205 (Teesta river system,

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Table 18), 116 (Jaldhaka river system, Table 19) polymorphic fragments after combining the RAPD and ISSR data. The total number of fragments and polymorphic fragments generated by ISSR amplifications were comparatively lower than that by RAPD primers. This might be because of lower number of priming sites of the ISSR primers or owing to the fact that ISSR primers bind to the genomic DNA comparatively specific site than that by the RAPD primers.

In a different study on Barilius barna isolated from Teesta river we have reported that the Nei’s genetic diversity ranged from 0.172 ± 0.189 to 0.293 ± 0.164 and the Shannon’s information index (I) ranged from 0.265 ± 0.268 to 0.445 ± 0.220 (Paul et al. 2016). In the study carried out on B. barna in Uttarakhand, India, Mishra et al. (2012) reported that the Nei’s genetic diversity of B. barna species was highest (0.4972) at the locus OPB18 while the lowest (0.1339) at the loci OPB12 and the mean value of gene diversity was found to be 0.1606. Mwanja et al. (2008) reported that the genetic diversity of Oreochromis niloticus in Africa ranges from lowest (0.14) to highest (0.27) values. Mishra et al. (2012) found that Shannon’s information index values for all the polymorphic loci in B. barna populations ranged from 0.2592 to 0.6904 with a mean value of 0.2331. Moreover, Kader et al. (2013) reported that the Shannon’s index was higher in Tilapia (0.363) population compared to two species of Oreochromis (0.318 and 0.347) populations. A study carried out by ISSR marker on Japanese Flounder (Paralichthys olivaceus) the Shannon index (I) in common population was 0.1431 which significantly higher than susceptible (0.1192) and resistant (0.1170) populations (P< 0.05) (Liu et al., 2006). Another study carried out by Liu et al., (2009) on Cynoglossus semilaevis using ISSR markers the average heterozygosity ranged from 0.0696±0.01 to 0.0814±0.02 and Shannon’s index ranged from 0.1089±0.02 to 0.1146±0.06. A study carried out by Liu et al., (2012) on Botia superciliaris of Yangtze river China, the heterozygosity and Shannon’s index varied from 0.1328 to 0.1572 and from 0.2069 to 0.2469 respectively. The range of Nei’s genetic diversity ranges from 0 to 1 (Nei 1973) and Shannon’s Information index ranges from 1.5 to 3.5 (Lewontin 1972).

In our present study we have found the Nei’s genetic diversity and Shannon’s information index were 0.1207±0.1789 and 0.1836±0.2600 (in Mahananda river system, Table 11), 0.2929±0.1825 and 0.4361± 0.2514 (in Teesta river system, Table 12), 0.1471±0.5018 and 0.2247±0.5018 (in Jaldhaka river system, Table 13) after RAPD analyses; 0.1092±0.1718 and 0.1685±0.2490 (in Mahananda river system, Table 14), 0.2406± 0.1929 and 0.3639±0.2680 (in Teesta river system, Table 15), 0.1455 ± 0.1892 and 0.2219±0.2706 (in Jaldhaka river system, Table 16) after ISSR analyses; 0.1163 ± 0.1755 and 0.1782±

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0.2547 (in Mahananda river system, Table 17), 0.2700 ± 0.1881 and 0.4047 ± 0.2599 (in Teesta river system, Table 18), 0.1464 ± 0.1887 and 0.2235 ±0.2698 (in Jaldhaka river system, Table 19) after RAPD+ISSR analyses.

Therefore, in comparison with other studies, we have found that the genetic diversity was comparatively lower in three river system viz. Mahananda, Teesta and Jaldhaka of the study region. Although the genetic diversity Badis badis was low in the three main riverine system of the Terai and Dooars region of West Bengal but the Teesta population showed a high level of genetic diversity compared to other two river populations.

4.2.5. Comparison of genetic diversity between three river system populations of Badis badis.

The RAPD, ISSR and RAPD+ISSR based analyses showed that the Teesta river system has highest detectable polymorphic loci i.e. 116, 88 and 205 in number respectively (Table 12, 15, 18) in Badis badis. In contrast, the Mahananda river system showed lower number of polymorphic loci i.e., 53, 44 and 98 based on RAPD, ISSR and RAPD+ISSR analyses respectively (Table 11, 14, 17) in Badis badis. The observed number of alleles (S), Nei's gene diversity (H) and Shannon's Information index (H´ or I) showed highest values in the Teesta river system i.e., 1.8227 ± 0.3833, 0.2929 ± 0.1825 and 0.4361 ± 0.2514 respectively by RAPD analysis (Table 12 and Figure 16) in Badis badis ; 1.7586 ± 0.4298, 0.2406 ± 0.1929 and 0.3639 ± 0.2680 respectively by ISSR analysis (Table 15 and Figure 16); 1.7977 ± 0.4025, 0.2700 ± 0.1881 and 0.4047 ± 0.2599 respectively by RAPD + ISSR analyses (Table 18 and Figure 16); and lowest in the Mahananda river system i.e., 1.3759±0.4861, 0.1207±0.1789 and 0.1836±0.2600 respectively by RAPD analyses (Table 11 and Figure 16); 1.3793 ± 0.4873, 0.1092±0.1718 and 0.1685±0.2490 respectively by ISSR analyses (Table 14 and Figure 16); 1.3813 ± 0.4867, 0.1163 ± 0.1755 and 0.1782±0.2547 respectively by RAPD + ISSR analyses (Table 17 and Figure 16). The genetic diversity is comparatively high as evident from different diversity indices within the Badis population of Teesta river system, than found in the Mahananada and Jaldhaka river systems, after considering the whole river system as a unit. But when we considered the individual collection spot as a unit to measure the genetic diversity the indices showed higher value in case of Jaldhaka river system than Teesta river system. Whereas, the Mahananda river system

Page | 149 showed constantly lower values for all the diversity indices. This lower level of diversity in Mahananda river system is attributed to different anthropogenic pressures viz., high level of sand and pebbles excavation for building purpose, a large amount of urban and factory effluents wastes are deposited into the river, pesticide and toxic chemicals depositions. Whereas a high level of overall Teesta river system genetic diversity is found because of a number of tributaries joined with the Teesta river (Figure 14), and cause the overall diversity of Teesta river system to rise. But the individual collection spots showed low level of diversity because of high pesticide run-offs, fish catching practices and dam building. Whereas, the Jaldhaka river is relatively free from all those anthropogenic pressures as occurred in Mahananda and Teesta river system, therefore the individual collection spot’s genetic diversity was comparatively higher than other two river system.

Heip’s measure of evenness was used for better interpretation of the measure of evenness, since a population having higher diversity may have lower evenness or vice versa. It is expected that when Heip’s measure of evenness is low evenness is low. Smith and Wilson (1996) showed that the minimum value of Heip’s measure is 0 and that it registered 0.006 when an extremely uneven population structure is found. We have found that the Teesta river system was more even in genetic diversity distribution than other populations (Figure 16).

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2 8

2 .

6 9

1.8 9

1 3

1.6 .

1 2

1.4 6 3

7 S

8 5

1.2 6

4 7

3 H

0 0

1 4

4 6

6 I

0 0

0.8 6

9 0

4 E

9 0

0.6 4

2 4

0 EHeip

2 .

0.4 1

. 0 0.2 0 0 Mahananda Teesta Jaldhaka

6 B

1 8

1.8 2

7 1

1.6 3

. 1

1.4

2 0

9 S 2

1.2 8

0 .

5 H 0

1 0

4 3

5 I

0.8 1

6 0

0.6 9 E

1 0

0.4 0 EHeip

. 0 0.2 0 0 Mahananda Teesta Jaldhaka

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7 C

1 3

1.8 0

8 1

1.6 3

. 1

1.4

1 0

1.2 7 S

6 .

. H 0

1 3

1 0

0.8 . I

0 0

4 E 5

0.6 .

7 2

6 EHeip

4 0

0.4 1

. 0 0.2 0 0 Mahananda Teesta Jaldhaka

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4.2.6. Genetic relationship between populations of Badis badis:

Table 20. Matrix showing values of Nei's (1978) unbiased measures of genetic similarity (above diagonal) and genetic distances (below diagonal). The square boxes indicate the highest (blue box) and lowest (orange box) genetic similarity and genetic distance between two pair of population.

======pop ID BTR- 1 BTR- 2 BTR- 3 BTR- 4 BTR- 5 BTR- 6 BDR-1 BDR-2 BDR-3 BDR-4 BDR-5 BDR-6 BDR-7 BDR-8 BDR-9 BDR-10 BDR-11 ======BTR-1 **** 0.9590 0.9566 0.9569 0.9609 0.9511 0.7553 0.9439 0.9536 0.7677 0.7426 0.7151 0.7068 0.6230 0.6223 0.6325 0.6287 BTR-2 0.0419 **** 0.9957 0.9878 0.9953 0.9697 0.7737 0.9775 0.9884 0.7745 0.7423 0.7038 0.6871 0.6104 0.6098 0.6171 0.6193 BTR-3 0.0444 0.0043 **** 0.9837 0.9938 0.9710 0.7772 0.9837 0.9945 0.7777 0.7439 0.7066 0.6889 0.6026 0.6022 0.6101 0.6132 BTR-4 0.0440 0.0122 0.0164 **** 0.9887 0.9654 0.7787 0.9683 0.9786 0.7660 0.7463 0.7022 0.6909 0.6171 0.6155 0.6242 0.6251 BTR-5 0.0399 0.0047 0.0062 0.0114 **** 0.9732 0.7778 0.9789 0.9911 0.7777 0.7438 0.7095 0.6884 0.6133 0.6128 0.6207 0.6228 BTR-6 0.0502 0.0307 0.0294 0.0352 0.0272 **** 0.7704 0.9542 0.9669 0.7681 0.7307 0.7085 0.6947 0.6074 0.6062 0.6144 0.6141 BDR-1 0.2806 0.2566 0.2521 0.2502 0.2513 0.2608 **** 0.7754 0.7738 0.8382 0.8480 0.7443 0.7784 0.6712 0.6771 0.6758 0.6779 BDR-2 0.0577 0.0227 0.0164 0.0322 0.0213 0.0469 0.2544 **** 0.9882 0.7809 0.7372 0.7099 0.6909 0.6070 0.6075 0.6155 0.6210 BDR-3 0.0475 0.0117 0.0055 0.0216 0.0089 0.0337 0.2565 0.0118 **** 0.7829 0.7397 0.7029 0.6895 0.5927 0.5931 0.6011 0.6064 BDR-4 0.2644 0.2556 0.2515 0.2665 0.2514 0.2639 0.1765 0.2473 0.2447 **** 0.8611 0.8263 0.7915 0.7586 0.7584 0.7667 0.7613 BDR-5 0.2976 0.2981 0.2958 0.2926 0.2959 0.3137 0.1649 0.3049 0.3015 0.1495 **** 0.8057 0.8165 0.7204 0.7208 0.7254 0.7197 BDR-6 0.3353 0.3513 0.3473 0.3535 0.3432 0.3446 0.2953 0.3426 0.3526 0.1908 0.2160 **** 0.7457 0.6896 0.6912 0.6948 0.6923 BDR-7 0.3470 0.3753 0.3727 0.3697 0.3735 0.3642 0.2506 0.3697 0.3718 0.2338 0.2028 0.2935 **** 0.6315 0.6357 0.6349 0.6326 BDR-8 0.4732 0.4937 0.5066 0.4828 0.4890 0.4986 0.3987 0.4992 0.5231 0.2763 0.3280 0.3717 0.4597 **** 0.9959 0.9914 0.9873 BDR-9 0.4743 0.4946 0.5071 0.4853 0.4897 0.5005 0.3900 0.4983 0.5224 0.2765 0.3274 0.3694 0.4530 0.0041 **** 0.9898 0.9862 BDR-10 0.4580 0.4828 0.4941 0.4713 0.4769 0.4871 0.3918 0.4854 0.5090 0.2656 0.3210 0.3642 0.4543 0.0086 0.0102 **** 0.9933 BDR-11 0.4641 0.4792 0.4891 0.4698 0.4735 0.4876 0.3887 0.4764 0.5002 0.2727 0.3289 0.3678 0.4579 0.0128 0.0139 0.0068 **** ======

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BTR-1

BTR-2

BTR-3

BTR-5

BDR-3

BTR-4

BDR-2

BTR-6

BDR-1

BDR-4

BDR-5

BDR-7

BDR-6

BDR-8

BDR-9

BDR-10

BDR-11

3.0

Figure 17. UPGMA dendrogram based on Nei’s (1978) unbiased genetic distance matrix. The Green and Brown square box indicates the clustering of Terai and Dooars region Badis badis populations. BDR-Badis badis from Dooars; BTR-Badis from Terai region. The shaded area represent two different clade i.e., blue portion represent Mahananada-Balasan river system and Teesta river system; pink portion represent the Jaldhaka river system.

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Principal Coordinates (PCoA)

BDR-7

BDR-5

BDR-6 BDR-1 BDR-4

BTR-6 BDR-2 Coordinate 2 (20.45 (20.45 2 %) Coordinate BTR-2 BTR-1 BTR-5 BTR-4 BDR-3 BTR-3

BDR-10 BDR-9 BDR-11 BDR-8

Co-ordinate 1 (57.12 %)

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Based on the RAPD and ISSR analyses, the Nei’s genetic distance was highest between BDR-3 and BDR-8 populations (0.5231) and lowest between BDR-8 and BDR-9 population (0.0041) (Table 20). The genetic identity was highest between BDR-8 and BDR-9 (0.9959) and lowest between BDR-3 and BDR-8 populations (0.5927) (Table 17). The UPGMA-based dendrogram, based on the Nei’s unbiased genetic distance and identity matrix after RAPD and ISSR analyses, showed clear representation of genetic relationship of seventeen populations of Badis badis from the three major riverine systems (Mahananda, Teesta and Jaldhaka of the sub-Himalayan West Bengal. The dendrogram based on RAPD and ISSR analyses showed that the Mahananda and Teesta river populations (BTR-1 to BTR- 6 and BDR-1 to BDR-7) formed a distinct group from the remaining Jaldhaka river population (BDR-8 to BDR-11) (Figure 17). Moreover, BDR-2 and BDR-3 population of Dooars region formed cluster with Terai region populations group. The close association of Mahananda river population with the Teesta river population especially with the BDR-2 and BDR-3 population was mainly because of the connection of Mahananda riverine system with the Teesta river system by a water channel (Figure 14). The Mahananda river system has converged to the Fulbari Barrage (BTR-1 sample collection site) and this Barrage connects to the Teesta river system at the Teesta Barrage (BDR-3 sample collection site) via narrow channel (Figure 14). This channel causes admixture of Mahananda river population with the Teesta river population (Figure 17). The principal component analyses clearly showed the clustering of seventeen populations into distinct three groups; two groups for Mahananda and Teesta river populations and separate group for Jaldhaka river population (Figure 18). In this case also the two Teesta river population BRD-2 and BDR-3 form a close association with Mahananda river populations.

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4.2.7 SHE Analyses

Table 21: SHE analyses based on RAPD + ISSR analyses. Population lnS H´ lnE Mahananda Barrage, Fulbari 0.253013 0.1044 -0.09811 (BTR-1) Mahananda-Panchanoi River 0.216079 0.0857 -0.08878 Junction (BTR-2) Balason River, Palpara, Matigara 0.209856 0.0821 -0.08756 (BTR-3) Panchanoi River (BTR-4) 0.228569 0.0936 -0.09017 Mahananda River, Champasari 0.240905 0.0973 -0.09670 (BTR-5) Balason River, Tarabari (BTR-6) 0.279902 0.1172 -0.10600 Sevoke (Teesta River), (BDR-1) 0.294459 0.1218 -0.11476 Ghish River, (BDR-2) 0.231667 0.0991 -0.08607 Gajoldoba (Teesta River Barrage), 0.197128 0.0765 -0.08313 (BDR-3) Chel River, (BDR-4) 0.297360 0.1269 -0.11036 Neora River, (BDR-5) 0.219216 0.0814 -0.09732 Dharla River, (BDR-6) 0.268040 0.1144 -0.09954 Jalpaiguri (Teesta River), (BDR-7) 0.234756 0.0962 -0.09326 Jaldhaka River, (BDR-8) 0.334255 0.1364 -0.13066 Murti River, (BDR-9) 0.339824 0.1438 -0.12582 Ghotia River, (BDR-10) 0.337044 0.1388 -0.13074 Diana River, (BDR-11) 0.356275 0.1424 -0.14317 [ lnS= natural logarithm of richness (allelic richness); H´= Shannon's Information index; lnE= natural logarithm of evenness]

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0.3 0.3 0.25 0.25 0.2 0.2 0.15 0.15 0.1 0.1 0.05 0.05 BTR-6 BTR-3 BTR-1 BTR-4 BTR-2 BTR-1 0 0 -0.05 0 1 2 3 4 -0.05 0 1 2 3 4 -0.1 -0.1 -0.15 -0.15 A B

0.3 0.25 0.2 0.15 0.1 0.05 BTR-5 BTR-2 BTR-1 0 -0.05 0 1 2 3 4 -0.1 -0.15 C

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0.35 0.3 0.3 0.25 0.25 0.2 0.2 0.15 0.15 0.1 0.1 0.05 0.05 BDR-1 BDR-3 BDR-7 BDR-2 BDR-3 BDR-7 0 0 0 1 2 3 4 -0.05 0 1 2 3 4 -0.05 -0.1 -0.1 -0.15 D -0.15 E

0.35 0.3 0.3 0.25 0.25 0.2 0.2 0.15 0.15 0.1 0.1 0.05 0.05 BDR-5 BDR-6 BDR-7 BDR-4 BDR-6 BDR-7 0 0 0 1 2 3 4 -0.05 0 1 2 3 4 -0.05 -0.1 -0.1 -0.15 F -0.15 G

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0.4 0.4

0.3 0.3

0.2 0.2

0.1 0.1 BDR-11 BDR-8 BDR-10 BDR-8 0 0 0 1 2 3 4 0 1 2 3 4 -0.1 -0.1

-0.2 H -0.2 I

0.4

0.3

0.2

0.1 BDR-9 BDR-8 0 0 1 2 3 4 -0.1

-0.2 J

Figure 19: SHE analyses showing observed patterns of diversity changes of Badis badis in three the river system populations based on RAPD and ISSR markers. Plot A, B, C represents the Mahananda river system; Plot D, E, F, G represents Teesta river system and Plot H, I, J represents Jaldhaka river system. ( = lnS, =H´, = lnE )

SHE analyses revealed a clear distribution of three biodiversity components richness (S), diversity (H´) and evenness (E) of the Badis badis gene pool into seventeen different riverine populations of the Terai and Dooars regions. We found that the lnS and H´ components were highest in BDR-11 population (lnS=0.356275) and BDR-9 population (H´=0.1438) respectively and lowest in BDR-3 population (lnS=0.197128) and BDR-5 population (H´=0.0814) respectively (Table 21). The lnE value was highest in BDR-11 population (-0.14317) and lowest in BDR-3 population (-0.08313) (Table 21). The SHE analysis plot revealed the observed pattern for distribution of three components viz., S

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(richness), H´ (Shannon's Information index) and E (evenness) in relation to seventeen different populations.

We had divided the seventeen riverine populations into ten groups according to the continuity of the water flow through the river from upstream to downstream viz., BTR-6, BTR-3 and BTR-1 constituting first group (Plot A); BTR-4, BTR-2 and BTR-1 constituting second group (Plot B); BTR-5, BTR-2 and BTR-1 constituting third group (Plot-C); BDR-1, BDR-3 and BDR-7 constituting fourth group (Plot-D); BDR-2, BDR-3 and BDR-7 constituting fifth group (Plot-E); BDR-4, BDR-6 and BDR-7 constituting sixth group (Plot- F); BDR-5, BDR-6 and BDR-7 constituting seventh group (Plot-G); BDR-11 and BDR-8 constituting eighth group (Plot-H); BDR-10 and ADR-8 constituting ninth group (Plot-I) and BDR-9 and BDR-8 constituting tenth group (Plot-J) (Figure 19).

We have found that as the river flows downward, in most of the cases the diversity has decreased but evenness increased (Plot- D, F, H); or diversity and evenness both increased (Plot-G); or diversity and evenness both decreased (Plot-J); or diversity increased and evenness decreased (Plot-A, B,C, E); or remain same (Plot-I) (Figure 19). This fluctuation in genetic diversity in attributed to different degree of anthropogenic and natural pressures that acted upon the river stream as the river flows downward. The Terai region river i.e. the Mahananada and Balasan river is heavily exploited for sand and pebbles excavation for concrete building purposes and nearby city and urban domestic and factory sewage runoffs are responsible most of the river pollution. This may cause the break in the diversity pattern of the Badis population in this river streams. Moreover, the major farming practices of the Dooars region is tea plantation, so the most of the foothills of this regions is utilized for tea plantation. Additionally this region has a perfect climatic and geographic infrastructure for tea farming. Therefore lots of pesticides are also used for developing better quality and quantity of tea. This causes a huge amount of pesticide run-off to the nearby rivers during the monsoon season. This may cause the fluctuation of the diversity of aquatic life including the studied ichthyofauna. Moreover, due to the construction of Dams for hydroelectric power project the course of the river is also changed and over fishing practices in the rivers also affects the genetic structure of the fishes. Finally as all the rivers are hill streams so an altitudinal variation of river flow may be an important factor that also affects the genetic diversity of the studied fishes.

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4.2.8. Genetic Hierarchical Structure based on RAPD + ISSR analyses

BTR-4 BTR-5 BTR-6

BTR-3 BTR-2

Water flow

BTR-1

Mahananda River System (hypothetical map)

=Collection spots, = River streams AMOVA Hierarchic Source of variation between Among Within al FST Nm PhiPT populations population population Structure (p value) (%) (%) 0.118 1st Order BTR-6 # BTR-4 0.1281 3.4021 1.802(12) 13.528(88) (0.001) 11.983 -0.002 1st Order BTR-4 # BTR-5 0.0509 9.3287 0.000(0) (100) (0.497) 11.681 0.015 2nd Order BTR-4, BTR-5 # BTR-2 0.0582 8.0982 0.000(0) (100) (0.677) 0.104 3rd Order BTR-4, BTR-5, BTR-2 # BTR-1 0.1522 2.7856 1.416(10) 12.147(90) (0.001) 0.149 3rd Order BTR-6, BTR-3 # BTR-1 0.1934 2.0851 2.287(15) 13.380(85) (0.001) BTR-6, BTR-3, BTR-4, BTR-5, 0.088 4th Order 0.1685 2.4673 1.196(9) 12.387(91) BTR-2 # BTR-1 (0.001)

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BDR-2 BDR-4 BDR-5 BDR-1

BDR-6 BDR-3

Water flow

BDR-7

Teesta River System (hypothetical map) =Collection spots, = River streams AMOVA Hierarchic Source of variation between Among Within al FST Nm PhiPT populations population populatio Structure (p value) (%) n (%) 0.643 1st Order BDR-1 # BDR-2 0.4749 0.5527 24.097(64) 13.480(36) (0.001) 0.635 1st Order BDR-2 # BDR-4 0.4625 0.5810 24.320(64) 13.950(36) (0.001) 0.543 1st Order BDR-4 # BDR-5 0.3744 0.8353 16.107(54) 13.533(46) (0.002) 0.566 2nd Order BDR-1, BDR-2 # BDR-3 0.4817 0.5380 15.785(57) 12.081(43) (0.001) 0.568 2nd Order BDR-4, BDR-5 # BDR-6 0.4833 0.5345 18.155(57) 13.819(43) (0.001) BDR-1, BDR-2, BDR-3 # BDR-4, 0.635 3rd Order 0.5980 0.3361 22.497(63) 12.950(37) BDR-5, BDR-6 (0.001) 0.672 4th Order BDR-1, BDR-2, BDR-3 # BDR-7 0.5987 0.3351 24.471(67) 11.964(33) (0.001) 0.613 4th Order BDR-4, BDR-5, BDR-6 # BDR-7 0.5521 0.4056 21.028(61) 13.267(39) (0.001) BDR-1, BDR-2, BDR-3, BDR-4, 0.655 5th Order 0.6210 0.3052 24.220(65) 12.759(35) BDR-5, BDR-6 # BDR-7 (0.001)

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BDR-9 BDR-11 BDR-10

Water flow

BDR-8

Jaldhaka River System (hypothetical map)

=Collection spots, = River streams AMOVA Hierarchic Source of variation between Among Within al FST Nm PhiPT populations population population Structure (p value) (%) (%) -0.090 1st Order BDR-11 # BDR-10 0.0202 24.2831 0.000(0) 18.494(100) (1.000) -0.066 1st Order BDR-10 # BDR-9 0.0300 16.1782 0.000(0 18.700(100) (1.000) -0.081 2nd Order BDR-11, BDR-10 # BDR-8 0.0370 12.9981 0.000(0 18.489(100) (1.000) -0.092 2nd Order BDR-9 # BDR-8 0.0130 39.3991 0.000(0 18.800(100) (1000) BDR-11, BDR-10, BDR-9 # -0.079 3rd Order 0.0412 11.6381 0.000(0 18.647(100) BDR-8 (1.000)

Figure 20: Genetic hierarchical model of seventeen different populations of Badis badis based on RAPD + ISSR analyses. The populations are arranged in hierarchical orders as first, second, third, fourth, fifth order populations. FST = Population genetic differentiation, Nm= Estimated gene flow, AMOVA= Analysis of molecular variance, PhiPT= Estimated variance among population/( Estimated variance within population + Estimated variance among population), probability values based on 999 permutations. # indicates the comparison between populations or groups of populations.

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The three river system has different natural hierarchical genetic-spatial structure. Therefore, we have divided the three river system into 1st to 4th order (for Mahananda river system), 1st to 5th order (for Teesta river system) and 1st to 3rd order (for Jaldhaka river system) hierarchy. This division helped us to compare the riverine Badis populations constituting the different order of hierarchy sophisticatedly (Figure 20).

In the Mahananda river system, the first order hierarchical group (between population

BTR-4 and BTR-5) showed lower value of genetic differentiation (FST = 0.0509) and higher value of gene flow (Nm = 9.3287) than other hierarchical population (Figure 20). The among population variance component and PhiPT value of first order hierarchical group (between population BTR-4 and BTR-5) were low i.e., 0.000 (0%) and -0.002 (p value = 0.497) respectively than other hierarchical population (Figure 20).

In the Teesta river system the first order hierarchical groups (between population

BDR-4 and BDR-5) showed lower value of genetic differentiation (FST = 0.3744) and higher value of gene flow (Nm = 0.8353) than other hierarchical orders of population (Figure 20). The among-population variance component and PhiPT value of the first order hierarchical groups were also lower i.e., 16.107 (54%) and 0.543 (p value = 0.002) than the other hierarchical orders of populations (Figure 20).

In Jaldhaka river system all three hierarchical groups i.e., first order, second order and third order showed very low genetic differentiation (FST =0.0202, 0.0300, 0.0370, 0.0125 and

0.0412); and high amount of gene flow (Nm =24.2831, 16.1782, 12.9981, 39.3991 and

11.6381) (Fig. 5). Although second order populations (ADR-9 and ADR-8) showed low FST and high gene flow among other hierarchical orders but the there was no among populations variance in any hierarchical orders and there was a significant negative PhiPT values in all hierarchical orders of Jaldhaka populations (Figure 20).

The most appropriate measure for genetic differentiation or divergence among subpopulation is the Fixation index or FST which ranges from 0 (when all subpopulations have equal allele frequencies) to 1 (when all the subpopulations are fixed for different alleles) (Allendorf et al., 2013). In our study we have detected a low to high (0.0130 to 0.6210) level of genetic differentiation (FST) across different populations of Badis badis in three river system in a hierarchical approach and the gene flow was ranged from low to high between different populations. Although a sufficient level of genetic admixture was carried out through narrow channels between different river populations because of the submergence of

Page | 165 the areas during monsoon season and some river streams are connected with man-made irrigation process. A high level of among population variance and genetic differentiation

(FST) was detected in the third order hierarchical population (consisting of populations BTR- 6, BTR-3 and BTR-1) of Mahananda river system (Figure 20) and fifth order hierarchical population (consisting of populations BDR-1, BDR-2, BDR-3, BDR-4, BDR-5, BDR-6 and BDR-7) of Teesta river system, which indicate that these hierarchical group are genetically and reproductively isolated and there was a irregular gene flow occurred (Figure 20). Whereas, in the Jaldhaka river system the observed among population variance was nil and very low amount of genetic differentiation with a high amount of gene flow indicates that the sub-populations of this river system is genetically analogous (Figure 20). The PhiPT is another population genetic statistical tool to detect the population differentiation; the results of PhiPT were also in accordance with the results of FST. Gene flow (Nm) is the most important determinant of the population structure, because it determines to which extent each local population of a species is an independent evolutionary unit (Slatkin, 1993). Therefore,

Nm provides the measures of the number of migrants per generation, also provides an indication of the differentiation among populations. A study carried out to analyze the genetic variability between wild and cultured Tilapia zilli species collected from New Bussa, Niger State through RAPD markers revealed that PhiPT values and data value for wild and cultured T. zilli were 0.302 and 0.001 respectively which shows significant variation among cultured and wild T. zilli (Yisa et al., 2016). Li et al., (2017) studied the genetic diversity of Hemibarbus maculates, a common and economically important species is widely distributed in the rivers and lakes of China through microsatellite marker. They have found the parameters of genetic differentiation among populations, pair-wise FST values, ranged from 0.001 to 0.214, while RST values ranged from

0.001 to 0.709. Pairwise comparisons of RST and FST values among populations reflected identical patterns of genetic differentiation among populations. Results of AMOVA analysis on wild and cultured populations showed that most variations within individuals explained total variance. AMOVA analysis revealed that 77.72% of the total variation was within individuals, 3.28% was among individuals within populations, and 12.81% was among groups (Li et al., 2017). A different study carried out by Garg et al., (2014) on Sperata seenghala from five different reservoirs (hadbada reservoir, Mohinisagar reservoir, Bansagar reservoir, Bargi reservoir and Gandhisagar reservoir) of India through RAPD revealed the estimated relative differentiation (GST) was 0.3993 and Estimate gene flow (Nm) was 0.7523 (Garg et al., 2014). Another study on Brycon hilarii is a migratory fish widely distributed

Page | 166 throughout the Paraguay River Basin, Sanches and Galetti (2007) reported that a significant genetic differentiation was found between populations (ST = 0.034; P < 0.0032). Of the total variation, 3.44% was attributed to interpopulation divergence and 96.56% to the individual differences within populations. A higher genetic differentiation between populations (ST = 0.108; P = 0.0002) was detected in the non-reproductive season and, in contrast to the spawning season, there was a considerable rise in the participation of interpopulation divergences in the total variation (10.81%) and a consequent reduction in intrapopulation variation (89.19%) (Sanches and Galetti, 2007).

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4.2.9 mtCOI based findins of Badis badis

4.2.9.1 Amplification and Checking of amplified fragments

The COI gene was amplified with the specific primers from ten different individuals from Terai (three individuals) and Dooars region (seven individuals). After amplification I have found distinct sharp bands with approximately 647bp size. The Hind III digestion produced two distinct bands of approximately 250bp in size and 400bp in size. (Figure 21)

M 1 2 3 4 5 6 7 8 9 10

1000bp -----

650bp ------

500bp------

M 1 2 3 4 5 6 7 8 9 10

1000bp -----

400bp ------

250 bp------

Figure 21: A. Amplification of COI gene of Badis badis by specific primer. B. Hind III digestion of the amplified product. M= 100 base pair DNA sizer marker, Lane No. 1-10= Amplified product of COI gene of Badis badis.

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4.2.9.2 Ten Cytochrome oxidase subunit 1 (COI) gene sequences of Badis badis and their NCBI Accession No.

Badis badis COI gene sequences

>DRBB1 (Accn. No. MF774665.1) AATTTTTGGTGCATGAGCCGGGATAGTAGGCACAGCCCTGAGCCTTCTTATTCGA GCCGAACTTAGTCAACCAGGAACCCTCTTAGGCGATGACCAAATTTATAATGTAA TCGTTACGGCACATGCTTTCGTGATAATTTTCTTTATAGTAATACCCATCATGATT GGAGGCTTTGGAAACTGACTGCTCCCGTTGATGATTGGCGCCCCCGATATAGCAT TTCCTCGAATAAACAACATAAGCTTTTGGCTTCTTCCCCCATCCTTCCTGCTTCTT TTGGCTTCTTCTGGAGTAGAAGCAGGAGCCGGAACCGGATGAACAGTATATCCG CCTTTGGCGGGCAATTTAGCGCACGCAGGTGCTTCCGTAGATCTAACCATCTTTT CCCTACACTTGGCAGGAGTATCTTCAATTTTAGGCTCAATCAATTTTATTACCACT ATTCTCAACATAAAACCCCCCGCGCTTTCCCAGTATCAAACCCCACTATTTGTCTG AGCCCTCCTGGTAACTACTGTTCTTCTTTTACTCTCGCTACCTGTTTTAGCCGCCG GCATTACAATACTTCTAACAGATCGAAACCTAAATACCTCCTTTTTTGACCCAGC CGGTGGTGGAGACCCTATTCTTTACCAACACCTGTTTT

>DRBB3 (Accn. No. MF774666.1) AATTTTTGGTGCATGAGCCGGGATAGTGGGTACAGCCCTGAGCCTTCTTATTCGA GCCGAACTTAGTCAACCAGGGACCCTTTTAGGCGATGACCAAATTTATAATGTAA TCGTTACGGCACATGCTTTCGTGATAATTTTCTTTATAGTAATACCCATCATGATT GGAGGCTTTGGAAACTGACTACTCCCGTTGATAATCGGCGCCCCCGATATAGCAT TTCCTCGAATAAACAACATAAGCTTTTGGCTACTTCCCCCATCCTTCCTGCTTCTT TTGGCTTCTTCTGGGGTAGAAGCAGGAGCCGGAACCGGGTGAACAGTATACCCG CCCTTGGCGGGCAATTTGGCGCATGCAGGTGCTTCCGTAGATCTAACCATCTTCT CCCTCCACTTGGCAGGAGTATCTTCAATTTTAGGTTCAATCAATTTCATTACCACC ATTCTAAACATAAAACCCCCCGCGCTTTCCCAGTATCAAACTCCTCTATTTGTCTG AGCCCTCCTGGTAACTACTGTTCTTCTTTTACTTTCTCTACCTGTCTTAGCCGCTGG CATCACAATGCTTCTAACAGATCGAAACCTAAATACCTCCTTTTTTGACCCAGCC GGTGGGGGAGATCCTATTCTTTACCAACACCTGTTTT

>DRBB6 (Accn. No. MF774667.1) AATTTTTGGTGCATGAGCCGGGATAGTAGGTACGGCCCTAAGCCTCCTAATTCGA GCCGAACTCAGTCAGCCAGGAACCCTCTTGGGTGATGATCAGATTTACAATGTAA TTGTTACGGCACACGCTTTTGTAATAATTTTCTTTATAGTAATACCCATCATGATC GGGGGGTTCGGAAACTGATTACTCCCACTAATAATCGGCGCCCCCGACATAGCAT TCCCTCGAATAAACAACATAAGCTTCTGACTCCTCCCCCCATCCTTTCTGCTTCTT CTGGCCTCTTCTGGGGTAGAAGCAGGTGCCGGAACCGGATGAACAGTATATCCC CCTTTAGCGGGTAATCTAGCACACGCAGGCGCATCCGTAGATCTAACCATCTTTT CCCTTCACTTAGCAGGGGTGTCTTCAATTTTGGGCTCTATTAATTTTATTACTACC ATCCTCAACATAAAACCCCCCGCACTCTCTCAGTATCAAACCCCGCTATTTGTTTG AGCCCTCCTGGTAACCACTGTTCTCCTCCTACTTTCCTTACCCGTGCTAGCCGCCG GTATTACAATGCTTCTAACAGATCGAAACCTAAATACCTCCTTTTTTGACCCAGC CGGTGGCGGAGACCCCATTCTTTACCAACACTTGTTTT

>DRBB7 (Accn. No. MF774668.1) AATTTTTGGTGCATGAGCCGGGATAGTAGGTACGGCCCTAAGCCTCCTAATTCGA GCCGAACTCAGTCAGCCAGGAACCCTCTTGGGTGATGATCAGATTTACAATGTAA

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TTGTTACGGCACACGCTTTTGTAATAATTTTCTTTATAGTAATACCCATCATGATC GGGGGGTTCGGAAACTGATTACTCCCACTAATAATCGGCGCCCCCGACATAGCAT TCCCTCGAATAAACAACATAAGCTTCTGACTCCTCCCCCCATCCTTTCTGCTTCTT CTGGCCTCTTCTGGGGTAGAAGCAGGTGCCGGAACCGGATGAACAGTATATCCC CCTTTAGCGGGTAATCTAGCACACGCAGGCGCATCCGTAGATCTAACCATCTTTT CCCTTCACTTAGCAGGGGTGTCTTCAATTTTGGGCTCTATTAATTTTATTACTACC ATCCTCAACATAAAACCCCCCGCACTCTCTCAGTATCAAACCCCGCTATTTGTTTG AGCCCTCCTGGTAACCACTGTTCTCCTCCTACTTTCCTTACCCGTGCTAGCCGCCG GTATTACAATGCTTCTAACAGATCGAAACCTAAATACCTCCTTTTTTGACCCAGC CGGTGGCGGAGACCCCATTCTTTACCAACACTTGTTTT

>DRBB8 (Accn. No. MF774669.1) AATTTTTGGTGCATGAGCCGGGATAGTGGGTACAGCCCTGAGCCTTCTTATTCGA GCCGAACTTAGTCAACCAGGGACCCTTTTAGGCGATGACCAAATTTATAATGTAA TCGTTACGGCACATGCTTTCGTGATAATTTTCTTTATAGTAATACCCATCATGATT GGAGGCTTTGGAAACTGACTACTCCCGTTGATAATCGGCGCCCCCGATATAGCAT TTCCTCGAATAAACAACATAAGCTTTTGGCTACTTCCCCCATCCTTCCTGCTTCTT TTGGCTTCTTCTGGGGTAGAAGCAGGAGCCGGAACCGGGTGAACAGTATACCCG CCCTTGGCGGGCAATTTGGCGCATGCAGGTGCTTCCGTAGATCTAACCATCTTCT CCCTCCACTTGGCAGGAGTATCTTCAATTTTAGGTTCAATCAATTTCATTACCACC ATTCTAAACATAAAACCCCCCGCGCTTTCCCAGTATCAAACTCCTCTATTTGTCTG AGCCCTCCTGGTAACTACTGTTCTTCTTTTACTTTCTCTACCTGTCTTAGCCGCTGG CATCACAATGCTTCTAACAGATCGAAACCTAAATACCTCCTTTTTTGACCCAGCC GGTGGGGGAGATCCTATTCTTTACCAACACCTGTTTT

>DRBB9 (Accn. No. MF774670.1) AATTTTTGGTGCATGAGCCGGGATAGTGGGTACAGCCCTGAGCCTTCTTATTCGA GCCGAACTTAGTCAACCAGGGACCCTTTTAGGCGATGACCAAATTTATAATGTAA TCGTTACGGCACATGCTTTCGTGATAATTTTCTTTATAGTAATACCCATCATGATT GGAGGCTTTGGAAACTGACTACTCCCGTTGATAATCGGCGCCCCCGATATAGCAT TTCCTCGAATAAACAACATAAGCTTTTGGCTACTTCCCCCATCCTTCCTGCTTCTT TTGGCTTCTTCTGGGGTAGAAGCAGGAGCCGGAACCGGGTGAACAGTATACCCG CCCTTGGCGGGCAATTTGGCGCATGCAGGTGCTTCCGTAGATCTAACCATCTTCT CCCTCCACTTGGCAGGAGTATCTTCAATTTTAGGTTCAATCAATTTCATTACCACC ATTCTAAACATAAAACCCCCCGCGCTTTCCCAGTATCAAACTCCTCTATTTGTCTG AGCCCTCCTGGTAACTACTGTTCTTCTTTTACTTTCTCTACCTGTCTTAGCCGCTGG CATCACAATGCTTCTAACAGATCGAAACCTAAATACCTCCTTTTTTGACCCAGCC GGTGGGGGAGATCCTATTCTTTACCAACACCTGTTTT

>DRBB11 (Accn. No. MF774671.1) AATTTTTGGTGCATGAGCCGGGATAGTGGGTACAGCCCTGAGCCTTCTTATTCGA GCCGAACTTAGTCAACCAGGGACCCTTTTAGGCGATGACCAAATTTATAATGTAA TCGTTACGGCACATGCTTTCGTGATAATTTTCTTTATAGTAATACCCATCATGATT GGAGGCTTTGGAAACTGACTACTCCCGTTGATAATCGGCGCCCCCGATATAGCAT TTCCTCGAATAAACAACATAAGCTTTTGGCTACTTCCCCCATCCTTCCTGCTTCTT TTGGCTTCTTCTGGGGTAGAAGCAGGAGCCGGAACCGGGTGAACAGTATACCCG CCCTTGGCGGGCAATTTGGCGCATGCAGGTGCTTCCGTAGATCTAACCATCTTCT CCCTCCACTTGGCAGGAGTATCTTCAATTTTAGGTTCAATCAATTTCATTACCACC ATTCTAAACATAAAACCCCCCGCGCTTTCCCAGTATCAAACTCCTCTATTTGTCTG AGCCCTCCTGGTAACTACTGTTCTTCTTTTACTTTCTCTACCTGTCTTAGCCGCTGG

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CATCACAATGCTTCTAACAGATCGAAACCTAAATACCTCCTTTTTTGACCCAGCC GGTGGGGGAGATCCTATTCTTTACCAACACCTGTTTT

>TRBB1 (Accn. No. MF774672.1) AATTTTTGGTGCATGAGCCGGGATAGTAGGTACGGCCCTAAGCCTCCTAATTCGA GCCGAACTCAGTCAGCCAGGAACCCTCTTGGGTGATGATCAGATTTACAATGTAA TTGTTACGGCACACGCTTTTGTAATAATTTTCTTTATAGTAATACCCATCATGATC GGGGGGTTCGGAAACTGATTACTCCCACTAATAATCGGCGCCCCCGACATAGCAT TCCCTCGAATAAACAACATAAGCTTCTGACTCCTCCCCCCATCCTTTCTGCTTCTT CTGGCCTCTTCTGGGGTAGAAGCAGGTGCCGGAACCGGATGAACAGTATATCCC CCTTTAGCGGGTAATCTAGCACACGCAGGCGCATCCGTAGATCTAACCATCTTTT CCCTTCACTTAGCAGGGGTGTCTTCAATTTTGGGCTCTATTAATTTTATTACTACC ATCCTCAACATAAAACCCCCCGCACTCTCTCAGTATCAAACCCCGCTATTTGTTTG AGCCCTCCTGGTAACCACTGTTCTCCTCCTACTTTCCTTACCCGTGCTAGCCGCCG GTATTACAATGCTTCTAACAGATCGAAACCTAAATACCTCCTTTTTTGACCCAGC CGGTGGCGGAGACCCCATTCTTTACCAACACTTGTTTT

>TRBB5 (Accn. No. MF774673.1) AATTTTTGGTGCATGAGCCGGGATAGTAGGCACAGCCCTGAGCCTTCTTATTCGA GCCGAACTTAGTCAACCAGGAACCCTCTTAGGCGATGACCAAATTTATAATGTAA TCGTTACGGCACATGCTTTCGTGATAATTTTCTTTATAGTAATACCCATCATGATT GGAGGCTTTGGAAACTGACTGCTCCCGTTGATGATTGGCGCCCCCGATATAGCAT TTCCTCGAATAAACAACATAAGCTTTTGGCTTCTTCCCCCATCCTTCCTGCTTCTT TTGGCTTCTTCTGGAGTAGAAGCAGGAGCCGGAACCGGATGAACAGTATATCCG CCTTTGGCGGGCAATTTAGCGCACGCAGGTGCTTCCGTAGATCTAACCATCTTTT CCCTACACTTGGCAGGAGTATCTTCAATTTTAGGCTCAATCAATTTTATTACCACT ATTCTCAACATAAAACCCCCCGCGCTTTCCCAGTATCAAACCCCACTATTTGTCTG AGCCCTCCTGGTAACTACTGTTCTTCTTTTACTCTCGCTACCTGTTTTAGCCGCCG GCATTACAATACTTCTAACAGATCGAAACCTAAATACCTCCTTTTTTGACCCAGC CGGTGGTGGAGACCCTATTCTTTACCAACACCTGTTTT

>TRBB6 (Accn. No. MF774674.1) AATTTTTGGTGCATGAGCCGGGATAGTAGGCACAGCCCTGAGCCTTCTTATTCGA GCCGAACTTAGTCAACCAGGAACCCTCTTAGGCGATGACCAAATTTATAATGTAA TCGTTACGGCACATGCTTTCGTGATAATTTTCTTTATAGTAATACCCATCATGATT GGAGGCTTTGGAAACTGACTGCTCCCGTTGATGATTGGCGCCCCCGATATAGCAT TTCCTCGAATAAACAACATAAGCTTTTGGCTTCTTCCCCCATCCTTCCTGCTTCTT TTGGCTTCTTCTGGAGTAGAAGCAGGAGCCGGAACCGGATGAACAGTATATCCG CCTTTGGCGGGCAATTTAGCGCACGCAGGTGCTTCCGTAGATCTAACCATCTTTT CCCTACACTTGGCAGGAGTATCTTCAATTTTAGGCTCAATCAATTTTATTACCACT ATTCTCAACATAAAACCCCCCGCGCTTTCCCAGTATCAAACCCCACTATTTGTCTG AGCCCTCCTGGTAACTACTGTTCTTCTTTTACTCTCGCTACCTGTTTTAGCCGCCG GCATTACAATACTTCTAACAGATCGAAACCTAAATACCTCCTTTTTTGACCCAGC CGGTGGTGGAGACCCTATTCTTTACCAACACCTGTTTT

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Table 22: Population genetic diversity parameters based on Badis badis mtDNA COI partial coding sequences.

No. of Haplotype Nucleotide diversity Avg. No. of Total No. of No. of variable (gene) nucleotide Populations mutations Haplotypes (Pi±SD) Tajima’s D Fu and Li’s D Fu and Li’s F sites (S) diversity differences (Eta) (h) (Hd±SD) (k) 0.667± 0.07728± Terai Region 75 75 2 50.00000 - 0.314 0.03643 0.72029 1.17998 (Not 1.19329 (Not Dooars 0.667± 0.06874± (Not 91 97 3 44.47619 significant, P significant, P region significant, P 0.160 0.01888 > 0.10) > 0.10) > 0.10) Sub- 1.56646 (Not 1.65454 1.84497 Himalayan 0.733± 0.06976± 91 97 3 45.13333 significant, P (significant P (significant P region (Terai 0.076 0.01217 > 0.10) < 0.02) < 0.02) + Dooars)

North India 0.893± 0.00324± 0.33640 (Not 0.12651 (Not 0.19295 (Not 5 5 5 2.07143 significant, P significant, P significant, P 0.086 0.00081 > 0.10) > 0.10) > 0.10) -1.90732 -2.33549 -2.51827 N-E India 95 95 5 0.756±0.130 0.03713±0.02396 20.644 (significant P (significant P (significant P

< 0.05) < 0.02) < 0.02) South India 23 23 3 1.000±0.272 0.02518±0.01136 15.33333 - * COI, Cytochrome oxidase subunit I; SD, standard deviation; Four or more sequences are need to compute Tajima's and Fu and Li's statistics (Terai and South India population have three sequences each).

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4.2.9.3 Genetic diversity and population structure of Badis badis: The mtDNA-based genetic diversity analysis was carried out to determine the available genetic diversity in the mitochondrial COI partial CDS of Badis badis of the Mahananda, Teesta and Jaldhaka river system of the Terai and Dooars region of sub- Himalayan West Bengal, India using DnaSP software. The total number of variable sites were 75 and 91 in Terai and Dooars region respectively, and 91 when two regions were considered together i.e., sub-Himalayan region (Table 22). Total numbers of mutations were 75 and 97 in Terai and Dooars region populations respectively (Table 22). Total 3 haplotypes were found in the sub-Himalayan Terai and Dooars region population (Terai region=2 haplotypes and Dooars region=3 haplotypes). Total number of variable sites, mutations and haplotypes in north Indian populations were 5, 5 and 5 ; in north-eastern Indian population were 95, 95 and 5; and south Indian population were 23, 23 and 3 respectively (Table 22). The haplotype diversity and nucleotide diversity of Terai region population were 0.667±0.314 and 0.07728±0.03643; Dooars region populations were 0.667±0.160 and 0.06874±0.01888; and 0.733±0.076 and 0.06976±0.01217 in sub-Himalayan region respectively (Table 22). The haplotype diversity and nucleotide diversity of north Indian, north-eastern Indian and south Indian population were shown in Table 22. The mitochondrial DNA COI gene sequences are highly polymorphic in nature, as reported in many genetic studies of geographically isolated populations of different organisms. In the study of 616 bp COI gene sequences of the Japanese anchovy Engraulis japonicus sampled from the Yellow Sea and East China Sea, Yu et al. (2005) found that 32 haplotypes in 44 specimens and 47 nucleotide positions were variable. Another study by Worheide (2006) reported 3 haplotypes from 55 specimens and 48 polymorphic sites in an analysis of the 479 bp COI genes in Astrosclera willeyana populations collected from the Red Sea to the central Pacific. Inoue et al. (2007) reported that the haplotype diversity was 0.923 ± 0.012 and nucleotide diversity was 0.0090 ± 0.0048 for Panulirus japonicus collected from three locations in Japan. In a study carried out by Thirumaraiselvi et al. (2015) on 614 bp of mtCOI gene of Eleutheronema tetradactylum from south Asian countries, the authors found a total of 18 haplotypes from 30 sequences where, South China population had the lowest number (one) of haplotype and the lowest genetic diversity (haplotype diversity = 0.00±0.000, nucleotide diversity = 0.00000±0.00000), and the Bay of Bengal population showed the highest values for all the indices (number of haplotypes = 6, haplotype diversity = 0.952±0.096, nucleotide diversity =0.01536±0.00312). Therefore, our present study reveals that the genetic diversity (number of haplotypes, haplotype diversity and nucleotide diversity) is diminishing in Badis

Page | 173 badis population as evident from the mitochondrial COI study. The present mtDNA based result is also in accordance with the study on Badis badis, where we have found that the overall genetic diversity (polymorphism, Nei’s genetic diversity and Shannon’s information index) was low as revealed by RAPD and ISSR marker analyses (see Tables 22 and 11 to 19). The observed values of Tajima’s D, Fu and Li’s D and Fu and Li’s F analyses of Dooars region were 0.72029 (Not significant, P > 0.10), 1.17998 (Not significant, P > 0.10) and 1.19329 (Not significant, P > 0.10); and sub-Himalayan region population were 1.56646 (Not significant, P > 0.10), 1.65454 (significant P < 0.02) and 1.84497 (significant P < 0.02) respectively (Table 22). The observed values of Tajima’s D, Fu and Li’s D and Fu and Li’s F analyses of north Indian and north-eastern Indian and south Indian population were shown in Table 22. Tests for neutral evolution were performed to ascertain the evidence of purifying or balancing selections. Both the population i.e., Terai and Dooars gave positive values for Tajima’s D, Fu and Li’s D and Fu and Li’s F test, which indicated that the population showed balancing selection and sudden population contraction. (Tajima 1989; Fu and Li 1993).

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4.2.9.4 Phylogenetic relationship of different populations of Badis badis based on mtDNA COI gene.

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Figure 22. Molecular Phylogenetic analysis of Badis badis by Maximum Likelihood method. The evolutionary history was inferred by using the Maximum Likelihood method based on the Kimura 2- parameter model (Kimura, 1980). The tree with the highest log likelihood (-1764.8487) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 0.4967)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 36.5108% sites). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 35 nucleotide sequences. All positions containing gaps and missing data were eliminated. There were a total of 556 positions in the final dataset. Evolutionary analyses were conducted in MEGA7 (Kumar et. al., 2016). TRBB-Terai region Badis badis; DRBB-Dooars region Badis badis.

The maximum likelyhood method of phylogenetic analysis showed that four taxa (DRBB3, DRBB8, DRBB9 and DRBB11) from Dooars region form a distinct group, and another group formed by DRBB1, TRBB5 and TRBB6 (Figure 22). Moreover DRBB6, DRBB7 and TRBB1 form a cluster with the north Indian Badis populations (Figure 22). The DRBB1 from Dooars region show a close relation with TRBB5 and TRBB6, this is due to the connection of Dooars region rivers with that of Terai region through narrow man-made canals. Moreover during monsoon season the submergence of nearby river banks cause intermixing of different river populations. This population indicate a significant level of gene flow between the Terai and Dooars region. Interestingly, the phylogenetic analysis showed that a close association exist between the Badis population of Ganges and Yamuna rivers, Uttarakhand with that of few individual of sub-Himalayan Terai and Dooars region (TRBB1, DRBB6 and DRBB7) West Bengal. This phylogenetic association may be attributed to accidental or intentional introduction of some Badis badis species from sub-Himalayan region to Ganga and Yamuna river of Uttarakhand or vice-versa in the past.

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4.3 Genetic diversity study of Amblyceps mangois

A detailed survey has been carried out in the Mahananda (Terai region), Teesta and Jaldhaka (Dooars region) river system for the collection of Amblyceps mangois samples. Total seventeen spots were selected for collection purpose (Figure 23; Table 2). Out of seventeen collection spots, the samples finally collected from thirteen spots because of the unavailability of the particular species on the remaining locations. The catch frequency was very low. It takes 3-4hours to collect about 8-10 samples from each collection site with the help of scoop net. The thirteen collection spots were as follows, viz., Mahananda Barrage, Fulbari (ATR-1), Mahananda River, Champasari (ATR-2), Balason River, Tarabari (ATR-3) from Mahananda river system; Sevoke (ADR-1), Ghish river (ADR-2), Gajoldoba-Teesta Barrage (ADR-3), Chel river (ADR-4), Neora river (ADR-5), Dharla river (ADR-6), Jalpaiguri (ADR-7) from Teesta river system; and Jaldhaka River (ADR-8), Murti River (ADR-9), Ghotia River (ADR-10), Diana River (ADR-11) from Jaldhaka river system. See the map below for detail:

Figure 23. Amblyceps mangois collection sites. Also refer to Table 2 for the geographic coordinates of the collection spots for Amblyceps mangois.

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4.3.1 RAPD based analyses 4.3.1.1 Intra-Population Genetic Diversity Study 4.3.1.1.1 Mahananda River System

Table 23. Intra-population genetic diversity indices based on RAPD analyses of Amblyceps mangois of Mahananda-Balason river system. RAPD Markers Diversity Indices

Populations E = N N S H H´ or I E= e H´/S Heip p per (e H´-1/ S-1) Mahananda 1.3050± 0.1159± 0.1701± Barrage, Fulbari 43 30.50% 0.90837 0.607946 0.4620 0.1899 0.2718 (ATR-1) Mahananda River, 1.2837± 0.1052± 0.1549± Champasari 40 28.37% 0.909513 0.590558 0.4524 0.1837 0.2629 (ATR-2) Balason River, 1.3404± 0.1290± 0.1896± Tarabari 48 34.04% 0.901795 0.613296 0.4755 0.1954 0.2798 (ATR-3) Mahananda 1.4043± 0.1485± 0.2200± 57 40.43% 0.887329 0.608649 River System 0.4925 0.1988 0.2853 Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index. Based on the RAPD profile the number of polymorphic loci and the percentage of polymorphic loci in Amblyceps mangois of Mahananda-Balason rivers vary across three populations (ATR-1 to ATR-3) (Table 23; Figure 24A). The highest number of polymorphic loci was observed in ATR-3 population (48 numbers) and the percentage of polymorphism was 34.04. The lowest number of polymorphic loci was observed in ATR-2 population (40 numbers) and the percentage of polymorphism was 28.37. The observed number of alleles or allelic richness (S) varies from 1.2837 ± 0.4524 in ATR-2 population to 1.3404 ± 0.4755 in ATR-3 population (Table 23). The Nei’s genetic diversity (H) was highest (0.1290 ± 0.1954) in ATR-3 population and lowest (0.1052 ± 0.1837) in ATR-2 population (Table 23). The Shannon’s information index (H´ or I) was highest (0.1896±0.2798) in ATR-3 population and lowest (0.1549 ± 0.2629) in ATR-2 population (Table 23). However, the measure of evenness (E) was highest (0.909513) in ATR-2 population and lowest (0.901795) in ATR-3 population (Table 23). The Heip’s measure of evenness was highest (0.613296) in ATR-3 population and lowest (0.590558) in ATR-2 population (Table 23).

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4.3.1.1.2 Teesta River System Table 24. Intra-population genetic diversity indices based on RAPD analyses of Amblyceps mangois of Teesta river system. RAPD Markers Populations Diversity Indices E =(e N N S H H´ or I E= e H´/S Heip p per H´-1/S-1) Sevoke 1.3404± 0.1224± 0.1802± (Teesta River) 48 34.04% 0.893358 0.580073 0.4755 0.1944 0.2761 (ADR-1) Ghish River 1.2695± 1.1050± 0.1535± 38 26.95% 0.918399 0.615613 (ADR-2) 0.4453 0.1848 0.2648 Gajoldoba (Teesta River 1.2340± 0.0861± 0.1271± 33 23.40% 0.920203 0.579190 Barrage) 0.4249 0.1704 0.2447 (ADR-3) Chel River 1.2411± 0.0884± 0.1308± 34 24.11% 0.91833 0.579593 (ADR-4) 0.4293 0.1710 0.2460 Neora River 1.2553± 0.08070± 0.1299± 36 25.53% 0.907125 0.543339 (ADR-5) 0.4376 0.1685 0.2425 Dharla River 1.3546± 0.1326± 0.1946± 50 35.46% 0.896815 0.605823 (ADR-6) 0.4801 0.1984 0.2829 Jalpaiguri (Teesta 1.2553± 0.0998± 0.1448± River) 36 25.53% 0.920743 0.610295 0.4376 0.1855 0.2634 (ADR-7) Teesta River 1.8369± 0.2991± 0.4457± 118 83.69% 0.850119 0.671028 System 0.3708 0.1780 0.2441 Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index. Based on the RAPD profile the number of polymorphic loci and the percentage of polymorphic loci in Amblyceps mangois of Teesta river vary across three populations (ADR- 1 to ADR-7) (Table 24; Figure 24B). The highest number of polymorphic loci was observed in ADR-6 population (50 numbers) and the percentage of polymorphism was 35.46. The lowest number of polymorphic loci was observed in ADR-3 population (33 numbers) and the percentage of polymorphism was 23.40. The observed number of alleles or allelic richness (S) varies from 1.2340±0.4249 in ADR-3 population to 1.3546 ± 0.4801 in ADR-6 population (Table 24). The Nei’s genetic diversity (H) was highest (0.1326 ± 0.1984) in ADR-6 population and lowest (0.0861±0.1704) in ADR-3 population (Table 24). The Shannon’s information index (H´ or I) was highest (0.1946 ± 0.2829) in ADR-6 population and lowest (0.1271 ± 0.2447) in ADR-3 population (Table 24). However, the measure of evenness (E) was highest (0.920743) in ADR-7 population and lowest (0.893358) in ADR-1 population (Table 24). The Heip’s measure of evenness was highest (0.615613) in ADR-2 population and lowest (0.579190) in ADR-3 population (Table 24).

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4.3.1.1.3 Jaldhaka River System

Table 25. Intra-population genetic diversity indices based on RAPD analyses of Amblyceps mangois of Jaldhaka river system. RAPD Markers Diversity Indices Populations E = E= e Heip N N S H H´ or I (e H´-1/ p per H´/S S-1) Jaldhaka 1.3972± 0.1378± 0.2052± River 56 39.72% 0.878736 0.573441 0.4911 0.1955 0.2791 (ADR-8) Murti River 1.3992± 0.1426± 0.2117± 57 40.72% 0.884467 0.593598 (ADR-9) 0.4927 0.1972 0.2822 Ghotia River 1.4032± 0.1430± 0.2075± 58 40.79% 0.88076 0.580558 (ADR-10) 0.4952 0.1987 0.2856 Diana River 1.4326± 0.1436± 0.2150± 61 43.26% 0.865463 0.554466 (ADR-11) 0.4972 0.1963 0.2794 Jaldhaka 1.4965± 0.1471± 0.2247± River 70 49.65% 0.836583 0.507446 0.5018 0.1891 0.2701 System Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index. Based on the RAPD profile the number of polymorphic loci and the percentage of polymorphic loci in Amblyceps mangois of Jaldhaka river vary across three populations (ADR-8 to ADR-11) (Table 25; Figure 24C). The highest number of polymorphic loci was observed in ADR-11 population (61 numbers) and the percentage of polymorphism was 43.26. The lowest number of polymorphic loci was observed in ADR-8 population (56 numbers) and the percentage of polymorphism was 39.72. The observed number of alleles or allelic richness (S) varies from 1.3972±0.4911 in ADR-8 population to in 1.4326 ± 0.4972 ADR-11 population (Table 25). The Nei’s genetic diversity (H) was highest (0.1436 ± 0.1963) in ADR-11 population and lowest (0.1378 ± 0.1955) in ADR-8 population (Table 25). The Shannon’s information index (H´ or I) was highest (0.2150 ± 0.2794) in ADR-11 population and lowest (0.2052 ± 0.2791) in ADR-8 population (Table 25). However, the measure of evenness (E) was highest (0.884467) in ADR-9 population and lowest (0.865463) in ADR-11 population (Table 25). The Heip’s measure of evenness was highest (0.593598) in ADR-9 population and lowest (0.554466) in ADR-11 population (Table 25).

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4.3.2 ISSR based analyses 4.3.2.1 Intra-Population Genetic Diversity Study 4.3.2.1.1 Mahananda River System Table 26. Intra-population genetic diversity indices based on ISSR analyses of Amblyceps mangois of Mahananda-Balason river system. ISSR Markers Diversity Indices Populations EHeip= H´ H´ Np Nper S H H´ or I E= e /S (e -1/ S-1) Mahananda Barrage, 1.3043± 0.1055± 0.1580± 28 30.43% 0.897927 0.562492 Fulbari 0.4627 0.1775 0.2568 (ATR-1) Mahananda River, 1.2826± 0.0992± 0.1474± 26 28.26% 0.903491 0.561987 Champasari 0.4527 0.1783 0.2554 (ATR-2) Balason River, 1.3370± 0.1186± 0.1773± Tarabari 31 33.70% 0.893036 0.575636 0.4753 0.1844 0.2667 (ATR-3)

1.3913± 0.1239± 0.1891± Mahananda 36 39.13% 0.868369 0.531975 0.4907 0.1794 0.2609 River System Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index. The highest number of polymorphic loci in Amblyceps mangois of Mahananda- Balason rivers was observed in ATR-3 population (31 numbers) and the percentage of polymorphism was 33.70. The lowest number of polymorphic loci was observed in ATR-2 population (26 numbers) and the percentage of polymorphism was 28.26 (Table 26, Figure 24D). The observed number of alleles or allelic richness (S) varies from 1.2826 ± 0.4527 in ATR-2 population to 1.3370±0.4753 in ATR-3 population (Table 26; Figure 24D). The Nei’s genetic diversity (H) was highest (0.1186 ± 0.1844) in ATR-3 population and lowest (0.0992 ± 0.1783) in ATR-2 population (Table 26). The Shannon’s information index (H´ or I) was highest (0.1773 ± 0.2667) in ATR-3 population and lowest (0.1474 ± 0.2554) in ATR-2 population (Table 26). However, the measure of evenness (E) was highest (0.903491) in ATR-2 population and lowest (0.893036) in ATR-3 population (Table 26). The Heip’s measure of evenness was highest (0.575636) in ATR-3 population and lowest (0.561987) in ATR-2 population (Table 26).

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4.3.2.1.2 Teesta River System Table 27. Intra-population genetic diversity indices based on ISSR analyses of Amblyceps mangois of Teesta river system. ISSR Markers Diversity Indices Populations EHeip=(e H´ H´ Np Nper S H H´ or I E= e /S -1/ S-1) Sevoke 1.3804± 0.1382± 0.2035± (Teesta River) 35 38.08% 0.88792 0.593284 0.4882 0.2009 0.2855 (ADR-1) Ghish River 1.2500± 0.0978± 0.1433± 23 25% 0.923261 0.616304 (ADR-2) 0.4354 0.1795 0.2583 Gajoldoba (Teesta River 1.2065± 0.0699± 0.1048± 19 20.65% 0.920421 0.535053 Barrage) 0.4070 0.1533 0.2221 (ADR-3) Chel River 1.3261± 0.1148± 0.1700± 30 32.61% 0.893828 0.568245 (ADR-4) 0.4713 0.1880 0.2688 Neora River 1.2391± 0.0774± 0.1164± 22 23.91% 0.906662 0.516291 (ADR-5) 0.4289 0.1602 0.2307 Dharla River 1.4130± 0.1551± 0.2271± 38 41.30% 0.888150 0.617325 (ADR-6) 0.4951 0.2082 0.2954 Jalpaiguri (Teesta 1.2391± 0.0774± 0.1164± River) 22 23.91% 0.906662 0.516291 0.4289 0.1602 0.2307 (ADR-7) Teesta River 1.7174± 0.2295± 0.3461± 66 71.74% 0.823072 0.576448 System 0.4527 0.1965 0.2759 Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index. The diversity indices based on the ISSR analyses in Amblyceps mangois of Teesta river were also in accordance with the RAPD data. The highest number of polymorphic loci was observed in ADR-6 population (38 numbers) and the percentage of polymorphism was 41.30 (Table 27, Figure 24E). The lowest number of polymorphic loci was observed in ADR- 3 population (19 numbers) and the percentage of polymorphism was 20.65. The observed number of alleles or allelic richness (S) varies from 1.2065 ± 0.4070 in ADR-3 population to 1.4130 ± 0.4951 in ADR-6 population (Table 27). The Nei’s genetic diversity (H) was highest (0.1551 ± 0.2082) in ADR-6 population and lowest (0.0699 ± 0.1533) in ADR-3 population (Table 27). The Shannon’s information index (H´ or I) was highest (0.2271 ± 0.2954) in ADR-6 population and lowest (0.1048 ± 0.2221) in ADR-3 population (Table 27). However, the measure of evenness (E) was highest (0.923261) in ADR-2 population and lowest (0.88792) in ADR-1 population (Table 27). The Heip’s measure of evenness was highest (0.617325) in ADR-6 population and lowest (0.516291) in ADR-5 and ADR-7 populations (Table 27).

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4.3.2.1.3 Jaldhaka River System

Table 28. Intra-population genetic diversity indices based on ISSR analyses of Amblyceps mangois of Jaldhaka river system. ISSR Markers Diversity Indices Populations EHeip= H´ H´ Np Nper S H H´ or I E= e /S (e -1/ S-1) Jaldhaka River 1.3913± 0.1290± 0.1935± 36 39.13% 0.872198 0.54559 (ADR-8) 0.4907 0.1913 0.2725 Murti River 1.4022± 0.1314± 0.2058± 37 40.22% 0.876129 0.568144 (ADR-9) 0.4930 0.1940 0.2772 Ghotia River 1.4022± 0.1314± 0.2058± 37 40.28% 0.868105 0.540172 (ADR-10) 0.4930 0.1944 0.2772 Diana River 1.4130± 0.1320± 0.2102± 38 41.30% 0.861901 0.52752 (ADR-11) 0.4951 0.1910 0.2716 Jaldhaka 1.4783± 0.1383± 0.2117± 44 47.83% 0.835945 0.492948 River System 0.5023 0.1875 0.2674 Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index.

The highest number of polymorphic loci in Amblyceps mangois of Jaldhaka river was observed in ADR-11 population (38 numbers) and the percentage of polymorphism was 41.30 (Table 28, Figure 24F). The lowest number of polymorphic loci was observed in ADR- 8 population (36 numbers) and the percentage of polymorphism was 39.13. The observed number of alleles or allelic richness (S) varies from 1.3913 ± 0.4907 in ADR-8 population to 1.4130 ± 0.4951 in ADR-11 population (Table 28). The Nei’s genetic diversity (H) was highest (0.1320 ± 0.1910) in ADR-11 population and lowest (0.1290 ± 0.1913) in ADR-8 population (Table 28). The Shannon’s information index (H´ or I) was highest (0.2102 ± 0.2716) in ADR-11 population and lowest (0.1935 ± 0.2725) in ADR-8 population (Table 28). However, the measure of evenness (E) was highest (0.876129) in ADR-9 population and lowest (0.861901) in ADR-11 population (Table 28). The Heip’s measure of evenness was highest (0.568144) in ADR-9 population and lowest (0.52752) in ADR-11 population (Table 28).

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4.3.3 RAPD + ISSR based analyses 4.3.3.1 Intra-Population Genetic Diversity Study 4.3.3.1.1 Mahananda River System Table 29. Intra-population genetic diversity indices based on RAPD + ISSR analyses of Amblyceps mangois of Mahananda-Balason river system. RAPD + ISSR Markers Diversity Indices Populations E = E= e Heip N N S H H´ or I (e H´-1/ p per H´/S S-1) Mahananda 1.3047± 1.1960± 0.1653± Barrage, Fulbari 71 30.47% 0.904229 0.589915 0.4613 0.3417 0.2655 (ATR-1) Mahananda River, 1.2833± 1.1821± 0.1519± 66 28.33 % 0.907071 0.579046 Champasari 0.4516 0.3378 0.2595 (ATR-2) Balason River, 1.3391± 1.2186± 0.1847± Tarabari 79 33.91 % 0.898258 0.598223 0.4744 0.3523 0.2742 (ATR-3) Mahananda 1.3991± 0.1388± 0.2078± 93 39.91% 0.879828 0.578719 River System 0.4908 0.1914 0.2758 Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index. The highest number of polymorphic loci in Amblyceps mangois of Mahananda- Balason river was observed in ATR-3 population (79 numbers) and the percentage of polymorphism was 33.91 (Table 29). The lowest number of polymorphic loci was observed in ATR-2 population (66 numbers) and the percentage of polymorphism was 28.33. The observed number of alleles or allelic richness (S) varies from 1.2833 ± 0.4516 in ATR-2 population to 1.3391 ± 0.4744 in ATR-3 population (Table 29). The Nei’s genetic diversity (H) was highest (1.2186 ± 0.3523) in ATR-3 population and lowest (1.1821 ± 0.3378) in ATR-2 population (Table 29). The Shannon’s information index (H´ or I) was highest (0.1847 ± 0.2742) in ATR-3 population and lowest (0.1519 ± 0.2595) in ATR-2 population (Table 29). However, the measure of evenness (E) was highest (0.907071) in ATR-2 population and lowest (0.898258) in ATR-3 population (Table 29). The Heip’s measure of evenness was highest (0.598223) in ATR-3 population and lowest (0.579046) in ATR-2 population (Table 29).

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4.3.3.1.2 Teesta River System Table 30. Intra-population genetic diversity indices based on RAPD + ISSR analyses of Amblyceps mangois of Teesta river system. RAPD + ISSR Markers Diversity Indices Populations H´ H´ Np Nper S H H´ or I E= e /S EHeip=(e - 1/S-1) Sevoke 1.3562± 1.2303± 0.1894± (Teesta River) 83 35.62 % 0.891111 0.585413 0.4799 0.3695 0.2795 (ADR-1) Ghish River 1.2618± 1.1813± 0.1495± 61 26.18 % 0.920315 0.615941 (ADR-2) 0.4406 0.3355 0.2618 Gajoldoba (Teesta River 1.2232± 1.1396± 0.1183± 52 22.32 % 0.920194 0.562642 Barrage) 0.4173 0.3013 0.2358 (ADR-3) Chel River 1.2747± 1.1741± 0.1463± 64 27.47 % 0.908091 0.573511 (ADR-4) 0.4473 0.3299 0.2554 Neora River 1.2489± 1.1449± 0.1246± 58 24.89 % 0.906954 0.533127 (ADR-5) 0.4333 0.3041 0.2375 Dharla River 1.3777± 1.2529± 0.2074± 88 37.77 % 0.893137 0.610206 (ADR-6) 0.4859 0.3778 0.2877 Jalpaiguri (Teesta 1.2489± 1.1631± 0.1336± River) 58 24.89 % 0.915154 0.574269 0.4333 0.3287 0.2509 (ADR-7) Teesta River 1.7897± 0.2716± 0.4064± 184 78.97% 0.838913 0.634928 System 0.4084 0.1883 0.2611 Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index. The highest number of polymorphic loci in Amblyceps mangois of Teesta river was observed in ADR-6 population (88 numbers) and the percentage of polymorphism was 37.77 (Table 30). The lowest number of polymorphic loci was observed in ADR-3 population (52 numbers) and the percentage of polymorphism was 22.32. The observed number of alleles or allelic richness (S) varies from 1.2232 ± 0.4173 in ADR-3 population to 1.3777 ± 0.4859 in ADR-6 population (Table 30). The Nei’s genetic diversity (H) was highest (1.2529 ± 0.3778) in ADR-6 population and lowest (1.1396 ± 0.3013) in ADR-3 population (Table 30). The Shannon’s information index (H´ or I) was highest (0.2074 ± 0.2877) in ADR-6 population and lowest (0.1183 ± 0.2358) in ADR-3 population (Table 30). However, the measure of evenness (E) was highest (0.920315) in ADR-2 population and lowest (0.891111) in ADR-1 population (Table 30). The Heip’s measure of evenness was highest (0.615941) in ADR-2 population and lowest (0.533127) in ADR-5 populations (Table 30).

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4.3.3.1.3 Jaldhakaa River System Table 31. Intra-population genetic diversity indices based on RAPD + ISSR analyses of Amblyceps mangois of Jaldhaka river system. RAPD + ISSR Markers Diversity Indices Populations EHeip= H´ H´ Np Nper S H H´ or I E= e /S (e -1/ S-1) Jaldhaka River 1.3948± 1.2354± 0.2006± 92 39.48 % 0.876209 0.562654 (ADR-8) 0.4899 0.3636 0.2760 Murti River 1.3991± 1.2417± 0.2079± 93 39.91 % 0.881237 0.583658 (ADR-9) 0.4908 0.36371 0.27590 Ghotia River 1.3991± 1.2419± 0.2032± 93 39.91 % 0.87579 0.564564 (ADR-10) 0.4908 0.3717 0.2792 Diana River 1.4249± 1.2457± .0.2094± 99 42.49 % 0.863983 0.543869 (ADR-11) 0.4954 0.3657 0.2797 Jaldhaka 1.4893± 0.1436± 0.2196± 114 48.93% 0.836352 0.501897 River System 0.5010 0.1881 0.2685 Note: Np=number of polymorphic loci, Nper=percentage of polymorphic loci, S=observed number of alleles, H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index. The highest number of polymorphic loci in Amblyceps mangois of Jaldhaka river was observed in ADR-11 population (99 numbers) and the percentage of polymorphism was 42.49 (Table 31). The lowest number of polymorphic loci was observed in ADR-8 population (92 numbers) and the percentage of polymorphism was 39.48. The observed number of alleles or allelic richness (S) varies from 1.3948 ± 0.4899 in ADR-8 population to 1.4249 ± 0.4954 in ADR-11 population (Table 31). The Nei’s genetic diversity (H) was highest (1.2457 ± 0.3657) in ADR-11 population and lowest (1.2354 ± 0.3636) in ADR-8 population (Table 31). The Shannon’s information index (H´ or I) was highest (0.2094 ± 0.2797) in ADR-11 population and lowest (0.2006 ± 0.2760) in ADR-8 population (Table 31). However, the measure of evenness (E) was highest (0.881237) in ADR-9 population and lowest (0.863983) in ADR-11 population (Table 31). The Heip’s measure of evenness was highest (0.583658) in ADR-9 population and lowest (0.543869) in ADR-11 population (Table 31).

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M ATR1 ATR2 ATR3

M ADR1 ADR2 ADR3 ADR4 ADR5 ADR6 ADR7

M ADR8 ADR9 ADR10 ADR11

C Page | 187

M ATR1 ATR2 ATR3

M ADR1 ADR2 ADR3 ADR4 ADR5 ADR6 ADR7

M ADR8 ADR9 ADR10 ADR11

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Figure 24: Representative Gel of Amblyceps mangois after RAPD (1.4% agarose) and ISSR (1.8% agarose) amplification. RAPD primer OPA16 and ISSR primer ISSR04 primers are used for amplification.

ATR1-ATR3 are the populations from Mahananda river system, ADR1-ADR7 are the populations of Teesta river system, ADR8-ADR11 are the populations of Jaldhaka river system. Each population consists of ten individuals. Each lane represents each individual’s RAPD and ISSR banding pattern.

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4.3.4 Comparative discussion based on RAPD, ISSR and RAPD+ISSR based analyses To our knowledge, the present study is the first endeavour to explore the present status of population categorical genetic background of this threatened fish fauna in the major river streams in the Terai and Dooars region of this sub-Himalayan hotspot of North Eastern India. The data obtained after amplification revealed the level of genetic diversity was dwindling within fourteen populations of Amblyceps mangois of the major river streams of the study region. RAPD and ISSR technique-based estimations of genetic diversity may be suitable for assessing the impact of different stresses upon a population as well as wide range of ecosystems. Since other popular sophisticated markers are not available in this fish species, we have used both RAPD and ISSR techniques for the estimation of genetic diversity of Amblyceps mangois species to increase the robustness of the study protocol. Both the techniques have advantages but ISSR is more robust than RAPD.

The study carried out in Bangladesh with two species of vulnerable Mud eel (Alam et al. 2010; Miah et al. 2013), endemic yellow catfish Horabagrus brachysoma (Muneer et al. 2009) and Clarias batrachus (Khedkar et al. 2010) the proportion of polymorphism was 76.92%, 32.87%, 60.48% and 77.49% respectively. Another study carried out in Bangladesh in catfish Mystus vittatus the polymorphism observed were 88.64% (Chalan beel), 84.09% (Mohanganj haor) and 90.91% (Kangsha river) (Tamanna et al, 2012). A study carried out in Brazil on Pimelodus maculatus the proportions of polymorphic loci estimated for the lower, middle and the upper Tietê river were 60.19%, 51.94%, and 52.43%, respectively; and 56.49%, 54.81%, and 61.51% for the lower, middle and upper Paranapanema, respectively (Almeida et al , 2003). In our earlier study with Badis badis the the proportion of polymorphic loci varies from 0.4371 (43.71%) in overall population to 0.6733 (67.33%) in between populations (Mukhopadhyay and Bhattacharjee, 2014a). In the present study we have found that the individual population polymorphism was lower to moderate in Teesta river system (23.40% in ADR-3 to 35.46% in ADR-6), moderate in Mahananda river system (28.37% in ATR-2 to 34.04% in ATR-3 population) and moderate in Jaldhaka river system (39.72% in ADR-8 to 43.26% in ADR-11 population). Therefore our present study revealed that a substantial decline of genetic variability within Amblyceps mangois population of the sub-Himalayan Terai and Dooars region.

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Our present study revealed that the Nei’s genetic diversity, Shannon’s information index varied across three river system (Table 23 to 31 and Figure 25). Although considering the intra population genetic variation the Jaldhaka river system population showed high level of genetic variation than the Mahananda and Teesta river system populations (Table 24, 27, 30). In the study carried out on vulnerable Monopterus cuchia in Bangladesh (Alam et al. 2010) and on endemic species Horabagrus brachysoma in India (Muneer et al. 2009) the Nei’s genetic diversity was 0.285 and 0.222, respectively. These results are in accordance with the findings of Chandra et al. (2010) on Eutropiichthys vacha, where the Shannon’s Information Index was 0.280 and 0.300 in two different geographical populations. In two different studies carried out by Alam et al. (2010) and Miah et al. (2013) on mud eel Monopterus cuchia in Bangladesh, the Shannon’s indices were 0.423 and 0.213, respectively. Another study carried out in Bangladesh in catfish Mystus vittatus the genetic diversity were found to be 0.259±0.163, 0.198±0.136 and 0.216±0.138; and Shannon’s Information Index were 0.403±0.03, 0.327±0.03 and 0.354±0.02 in Chalan beel, Mohanganj haor and Kangsha river respectively (Tamanna et al, 2012). Another separate study of ours revealed that the Nei’s genetic diversity (퐻) of Badis badis Mahananda and Balason river population was 0.1654 and 0.1983, respectively and the Shannon’s information index (퐻´) was calculated to be 0.2450 ± 0.2907 in Mahananda river and 0.2901 ± 0.3037 in Balason river (Mukhopadhyay and Bhattacharjee, 2014a) ; and the Shannon’s information index ranged from 0.1648 ± 0.2691 to 0.2205 ± 0.2950 in the Terai region of West Bengal India. In a different study on Barilius barna isolated from Teesta river we have found that the Nei’s genetic diversity ranged from 0.172 ± 0.189 to 0.293 ± 0.164 and the Shannon’s information index (I) ranged from 0.265 ± 0.268 to 0.445 ± 0.220 (Paul et al. 2016). The range of Nei’s genetic diversity ranges from 0 to 1 (Nei 1973) and Shannon’s Information index ranges from 1.5 to 3.5 (Lewontin 1972). Therefore, in comparison with other studies, we have found that the genetic diversity was comparatively lower in three river system viz. Mahananda, Teesta and Jaldhaka of the study region.

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4.3.5. Comparison of genetic diversity between three river system populations.

The RAPD, ISSR and RAPD+ISSR based analyses showed that the Teesta river system have highest detectable polymorphic loci i.e. 118, 66 and 184 in number respectively (Table 24, 27, 30). In contrast, the Mahananda river system showed lower number of polymorphic loci i.e., 57, 36 and 93 based on RAPD and ISSR analyses respectively (Table 23, 26, 29). The observed number of alleles (S), Nei's gene diversity (H) and Shannon's Information index (H´ or I) showed highest values in the Teesta river system i.e., 1.8369±0.3708, 0.2991±0.1780 and 0.4457± 0.2441 by RAPD analysis; and 1.7174±0.4527, 0.2295±0.1965 and 0.3461±0.2759 by ISSR analysis respectively (Table 24, 27, 30 and Figure 25) and lowest in the Mahananda river system i.e.,1.4043±0.4925, 0.1485±0.1988 and 0.2200±0.2853 by RAPD analysis; and 1.3913±0.4907, 0.1239±0.1794 and 0.1891±0.2609 by ISSR analysis respectively (Table 23, 26, 29 and Figure 25). Heip’s measure of evenness was used for better interpretation of the measure of evenness. We have found that the Teesta river system was more even in genetic diversity distribution than other populations (Figure 19).

The genetic diversity is comparatively high as evident from different diversity indices within the Amblyceps population of Teesta river system, than those of Mahananada and Jaldhaka river system, after considering the whole river system as a unit. But when we consider the individual collection spot as a unit to measure the genetic diversity the indices showed higher value in case of Jaldhaka river system than that of Teesta river system. Whereas, the Mahananda river system showed constantly lower values for all the diversity indices after RAPD, ISSR and RAPD+ISSR analyses. This lower level of diversity in Mahananda river system is attributed to different anthropogenic activities viz., high level of sand and pebbles excavation for house building purpose, a significant amount of city and factory wastes are deposited into the river, pesticide run-off and toxic chemicals depositions into the river from nearby tea gardens. Whereas a high level of overall genetic diversity of Teesta river system is found because of a number of tributaries viz. Chel, Neora etc joined with the Teesta river (Figure 23), and cause the overall diversity of Teesta river system to increase. But the individual collection spots showed low level of diversity because of high pesticide run-offs, fish catching practices and hydroelectric dam building. Whereas, the Jaldhaka river is comparatively free from all those anthropogenic pressures as occurred in

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Mahananda and Teesta river system, therefore the individual collection spot’s genetic diversity was comparatively higher than other two river system.

Heip’s measure of evenness was used for better interpretation of the measure of evenness. It does on the other hand satisfy the probability of attaining a low value when evenness in low. Smith and Wilson (1996) showed that the minimum value of Heip’s measure is 0 and that it registered 0.006 when an extremely uneven population structure is found. We have found that the Teesta river system was more even in genetic diversity distribution than other populations with respect to Amblyceps mangois (Figure 25).

2 1.8369 1.8

1.6 1.4965 1.4043 1.4 S

1.2 H

0.887329 0.850119 1 0.836583 I

0.8 0.671028 0.608649

0.507446 E

0.6 0.4457

0.2991 EHeip 0.2247

0.4 0.22 0.1485 0.2 0.1471 0 Mahananda Teesta Jaldhaka

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1 8

1.8 3

1 3

1.6 . 1

1.4 9

7 3

9 S

0 8

1.2 5

4 .

0 H

7 0

1 4

2 3

5 I 9

0.8 .

5 7

0.6 1 E

. 2

0.4 . 0

1 EHeip

. 0 0.2 0 0 Mahananda Teesta Jaldhaka

9 C

3 9

1.8 1

1 3

1.6 . 1

1.4 8

8 9 3 S

1.2 9

9 1

0 H

7 0

1 0

1 .

5 I 4

0.8 0

1 .

0.6 8 E

. 0

0.4 . EHeip

. 0 0.2 0 0 Mahananda Teesta Jaldhaka

Figure 25: Comparison of genetic diversity between three river system populations of Amblyceps mangois by A. RAPD; B. ISSR and C. RAPD + ISSR based analyses. S=observed number of alleles,

H= Nei's gene diversity, H´ or I= Shannon's Information index, E= measure of evenness, EHeip= Heip’s evenness index

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4.3.6. Genetic relationship between populations based on RAPD and ISSR analyses Table 32: Matrix showing values of Nei's (1978) unbiased measures of genetic similarity (above diagonal) and genetic distances (below diagonal). The square boxes indicate the highest (green box) and lowest (blue box) genetic similarity and genetic distance between two pair of population. ======pop ID ADR-1 ADR-2 ADR- 3 ADR-4 ADR-5 ADR- 6 ADR-7 ADR-8 ADR-9 ADR-10 ADR-11 ATR-1 ATR-2 ATR-3 ======ADR-1 **** 0.7524 0.7524 0.7530 0.8622 0.8601 0.7923 0.6897 0.6971 0.6949 0.6941 0.7325 0.7543 0.7536 ADR-2 0.2845 **** 0.9874 0.7281 0.7490 0.7884 0.6906 0.6196 0.6212 0.6293 0.6323 0.9371 0.9768 0.9504 ADR-3 0.2845 0.0126 **** 0.7210 0.7539 0.7929 0.6906 0.6078 0.6090 0.6170 0.6197 0.9483 0.9890 0.9634 ADR-4 0.2837 0.3173 0.3271 **** 0.7755 0.8062 0.7238 0.6526 0.6556 0.6600 0.6575 0.7304 0.7252 0.7324 ADR-5 0.1483 0.2891 0.2825 0.2542 **** 0.8533 0.8044 0.7115 0.7126 0.7163 0.7116 0.7553 0.7565 0.7386 ADR-6 0.1507 0.2377 0.2321 0.2155 0.1586 **** 0.7779 0.7660 0.7660 0.7743 0.7708 0.7820 0.7855 0.7744 ADR-7 0.2328 0.3702 0.3702 0.3232 0.2176 0.2512 **** 0.6151 0.6202 0.6178 0.6170 0.7092 0.6897 0.6957 ADR-8 0.3715 0.4786 0.4979 0.4268 0.3404 0.2666 0.4861 **** 0.9960 0.9907 0.9871 0.6290 0.6200 0.6173 ADR-9 0.3608 0.4762 0.4960 0.4222 0.3388 0.2666 0.4778 0.0041 **** 0.9894 0.9863 0.6295 0.6208 0.6173 ADR-10 0.3641 0.4631 0.4828 0.4154 0.3337 0.2558 0.4816 0.0093 0.0106 **** 0.9936 0.6401 0.6289 0.6258 ADR-11 0.3651 0.4584 0.4785 0.4193 0.3403 0.2604 0.4829 0.0130 0.0138 0.0064 **** 0.6334 0.6281 0.6230 ATR-1 0.3112 0.0650 0.0531 0.3142 0.2807 0.2459 0.3436 0.4637 0.4629 0.4461 0.4566 **** 0.9571 0.9441 ATR-2 0.2820 0.0235 0.0111 0.3213 0.2790 0.2415 0.3715 0.4780 0.4768 0.4639 0.4650 0.0439 **** 0.9688 ATR-3 0.2829 0.0509 0.0372 0.3114 0.3029 0.2557 0.3629 0.4824 0.4824 0.4688 0.4733 0.0576 0.0317 ****

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ADR-1

ADR-5

ADR-6

ADR-7

ADR-4

ADR-2

ADR-3

ATR-2

ATR-3

ATR-1

ADR-8

ADR-9

ADR-10

ADR-11

Figure 26. UPGMA dendrogram based on Nei’s (1978) unbiased genetic distance matrix. The Green and Brown square box indicates the clustering of Terai and Dooars region populations. ADR- Amblyceps mangois from Dooars; ATR- Amblyceps mangois from Terai region. The shaded area represent two different clade i.e., pink portion represent Mahananada-Balasan river system and Teesta river system; purple portion represent the Jaldhaka river system.

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Principal Coordinates (PCoA)

ADR-7

ADR-5 ) ADR-4

.61% ADR-1 2 (23 2

ADR-6

d Coor ATR-1 ATR-2 ADR-11 ATR-3 ADR-10 ADR-8 ADR-2 ADR-3 ADR-9 Coordinate 1 (48.97%)

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Based on the RAPD and ISSR analyses, the Nei’s genetic distance was highest between ADR-3 and ADR-8 populations (0.4979) and lowest between ADR-8 and ADR-9 population (0.0041) (Table 32). The genetic identity was highest between ADR-8 and ADR-9 (0.9960) and lowest between ADR-3 and ADR-8 populations (0.6078) (Table 32). The UPGMA based dendrogram based on the Nei’s unbiased genetic distance and identity matrix after RAPD and ISSR analyses showed clear representation of genetic relationship of fourteen populations of Amblyceps mangois of the three major riverine systems (Mahananda, Teesta and Jaldhaka of the sub-Himalayan West Bengal. The dendrogram based on RAPD and ISSR analyses showed that the Mahananda and Teesta river populations (ATR-1 to ATR- 3 and ADR-1 to ADR-7) formed a distinct group from the remaining Jaldhaka river population (ADR-8 to ADR-11) (Figure 26). The principal component analyses clearly showed the clustering of fourteen populations into distinct three groups; two groups for Mahananda and Teesta river populations and separate group for Jaldhaka river population (Figure 27). The ADR-2 and ADR-3 population of Teesta river system form cluster with the Mahananda river populations i.e., ATR-1, ATR-2 and ATR-3. This clustering seems to be obvious because there is situated a man-made water-canal that joins this two (Mahanada and Teesta) river system. This canal was mainly constructed for irrigation purpose in the nearby agricultural fields and to channelize the extra water when there is a flood condition in the rivers during monsoon season. This canal causes the admixture of Amblyceps gene pool of this two river system i.e., Mahananda and Teesta and grouping of the populations into a single cluster (Figure 23). Whereas the Jaldhaka river system is allopatrically isolated from the other two neighbouring river systems, therefore the Jaldhaka river populations formed a distinct cluster separated from Mahananda and Teesta river system cluster.

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4.3.7. SHE analyses

Table 33: SHE analyses based on RAPD + ISSR analyses Population lnS H´ lnE Mahananda Barrage, Fulbari, (ATR-1) 0.265973129 0.1653 -0.100673129 Mahananda River, Champasari, (ATR-2) 0.249434885 0.1519 -0.097534885 Balason River, Tarabari, (ATR-3) 0.291997747 0.1847 -0.107297747 Sevoke(Teesta River), (ADR-1) 0.304686671 0.1894 -0.115286671 Ghish River, (ADR-2) 0.232539273 0.1495 -0.083039273 Gajoldoba(Teesta River Barrage), (ADR-3) 0.201470376 0.1183 -0.083170376 Chel River, (ADR-4) 0.242710857 0.1463 -0.096410857 Neora River, (ADR-5) 0.222263164 0.1246 -0.097663164 Dharla River, (ADR-6) 0.320415442 0.2074 -0.113015442 Jalpaiguri (Teesta River), (ADR-7) 0.222263164 0.1336 -0.088663164 Jaldhaka River, (ADR-8) 0.332751036 0.2006 -0.132151036 Murti River, (ADR-9) 0.335829173 0.2094 -0.126429173 Ghotia River, (ADR-10) 0.335829173 0.2032 -0.132629173 Diana River, (ADR-11) 0.354101636 0.2079 -0.146201636 [ lnS= natural logarithm of richness (allelic richness); H´= Shannon's Information index; lnE= natural logarithm of evenness]

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0.35 0.3 0.3 0.25 0.25 0.2 0.2 0.15 0.15 0.1 0.1 0.05 ATR-2 ATR-1 0.05 ATR-3 ATR-1 0 0 -0.05 -0.05 -0.1 -0.1 -0.15 A -0.15 B

0.35 0.25 0.3 0.2 0.25 0.15 0.2 0.15 0.1 0.1 0.05 ADR-2 ADR-3 ADR-7 0.05 ADR-1 ADR-3 ADR-7 0 0 -0.05 -0.05 -0.1 -0.1 -0.15 C -0.15 D

0.35 0.35 0.3 0.3 0.25 0.25 0.2 0.2 0.15 0.15 0.1 0.1 0.05 ADR-4 ADR-6 ADR-7 0.05 ADR-5 ADR-6 ADR-7 0 0 -0.05 -0.05 -0.1 -0.1 -0.15 -0.15 E F

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0.4 0.4

0.3 0.3

0.2 0.2

0.1 0.1 ADR-11 ADR-8 ADR-10 ADR-8 0 0

-0.1 -0.1

-0.2 G -0.2 H

0.4

0.3

0.2

0.1 ADR-9 ADR-8 0

-0.1

-0.2 I

SHE analyses revealed a clear distribution of three biodiversity components richness (S), diversity (H´) and evenness (E) of the Amblyceps mangois gene pool into fourteen different riverine populations of the Terai and Dooars regions. We found that the lnS and H´ components were highest in ADR-9 population (0.335829173 and 0.2094 respectively) and lowest in ADR-3 population (0.201470376 and 0.1183 respectively) (Table 33). The lnE value was highest in ADR-11 population (-0.146201636) and lowest in ADR-2 population (- 0.083039273) (Table 33). The SHE analysis plot revealed the observed pattern for

Page | 201 distribution of three components viz., S (richness), H´ (Shannon's Information index) and E (evenness) in relation to fourteen different populations.

We had divided the fourteen riverine populations into nine groups according to the continuity of the water flow through the river from upstream to downstream viz., ATR-3 and ATR-1 constituting first group (Plot A); ATR-2 and ATR-1 constituting second group (Plot B); ADR-1, ADR-3 and ADR-7 constituting third group (Plot-C); ADR-2, ADR-3 and ADR- 7 constituting fourth group (Plot-D); ADR-4, ADR-6 and ADR-7 constituting fifth group (Plot-E); ADR-5, ADR-6 and ADR-7 constituting sixth group (Plot-F); ADR-11 and ADR-8 constituting seventh group (Plot-G); ADR-10 and ADR-8 constituting eighth group (Plot-H); and ADR-9 and ADR-8 constituting ninth group (Plot-I) (Figure 28 upper panel).

We have found that as the river flows downward, in most of the cases the diversity has decreased but evenness increased (Plot- A, C, E, I); or diversity and evenness both increased (Plot-F); or diversity and evenness both decreased (Plot-D, G); or diversity increased and evenness decreased (Plot-B); or remain same (Plot-H) (Figure 28 upper panel). The flow pattern disturbance and human interferences (such as fishing and pesticide run-offs) as the river streams flow from higher to lower altitudes may cause the overall fluctuation and break in diversity and richness pattern within the gene pool of the Amblyceps population. The Terai region rivers viz. Mahananda and Balasan are exploited for sand excavation, and polluted by the nearby urban effluents and sewage run-off. Whereas, Dooars regions rivers viz., Jaldhaka, Teesta and its tributaries are greatly polluted by pesticides surplus from nearby tea gardens during monsoon season. Moreover hydroelectric Dam construction and over fishing also affect the diversity pattern of this fish species. All of these causes can culminate in to the observed decline, modification and change in diversity pattern and richness in Amblyceps mangois populations across the river stream along the altitudinal gradient.

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4.3.8. Genetic Hierarchical Structure

ATR-2 ATR-3

Water flow

ATR-1

Mahananda River System (hypothetical map)

=Collection spots, = River streams AMOVA

Hierarchical Source of variation Among Within FST Nm PhiPT Structure between populations population population (p value) (%) (%) 13.217 0.069 1st Order ATR-3 # ATR-2 0.1084 4.1117 0.983 (7) (93) (0.006)

ATR-3, ATR-2 # 13.259 0.141 2nd Order 0.1847 2.2068 2.181 (14) ATR-1 (86) (0.001)

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ADR-2 ADR-4 ADR-5 ADR-1

ADR-6 ADR-3

Water flow

ADR-7

Teesta River System (hypothetical map) =Collection spots, = River streams AMOVA Hierarchic Source of variation between Among Within al FST Nm PhiPT populations populatio populatio Structure (p value) n (%) n (%) 0.487 0.526 0.595 1st Order ADR-1 # ADR-2 18.36 (63) 12.65 (37) 0 8 (0.001) 0.618 0.410 0.717 1st Order ADR-2 # ADR-4 28.70 (72) 11.31 (28) 7 9 (0.001) 0.528 0.446 0.668 1st Order ADR-4 # ADR-5 22.17 (67) 11.02 (33) 5 1 (0.001) 0.492 0.514 0.580 2nd Order ADR-1, ADR-2 # ADR-3 15.82 (58) 11.47 (42) 8 5 (0.001) 0.510 0.480 0.657 2nd Order ADR-4, ADR-5 # ADR-6 24.30 (66) 12.48 (40) 2 1 (0.001) ADR-1, ADR-2, ADR-3 # ADR-4, 0.590 0.346 0.628 3rd Order 20.20 (63) 11.98 (38) ADR-5, ADR-6 7 5 (0.001) 0.599 0.334 0.669 4th Order ADR-1, ADR-2, ADR-3 # ADR-7 22.65 (67) 11.20 (33) 2 5 (0.001) 0.577 0.366 0.637 4th Order ADR-4, ADR-5, ADR-6 # ADR-7 21.02 (64) 11.96 (36) 0 6 (0.001) ADR-1, ADR-2, ADR-3, ADR-4, 0.548 0.308 0.652 5th Order 22.02 (65) 11.74 (35) ADR-5, ADR-6 # ADR-7 9 1 (0.001)

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ADR-9 ADR-11 ADR-10

Water flow

ADR-8

Jaldhaka River System (hypothetical map)

=Collection spots, = River streams AMOVA Hierarchic Source of variation between Among Within al FST Nm PhiPT populations populatio populatio Structure (p value) n (%) n (%) 0.019 25.024 16.35 -0.088 1st Order ADR-11 # ADR-10 0.0 (0) 6 7 (100) (1.000) 0.031 15.236 16.59 -0.062 1st Order ADR-10 # ADR-9 0.0 (0) 8 5 (100) (0.991) 0.038 12.433 16.37 -0.079 2nd Order ADR-11, ADR-10 # ADR-8 0.0 (0) 7 0 (100) (1.000) 0.012 39.344 16.67 -0.090 2nd Order ADR-9 # ADR-8 0.0 (0) 5 7 (100) (0.999) 0.042 11.213 16.51 -0.077 3rd Order ADR-11, ADR-10, ADR-9 # ADR-8 0.0 (0) 7 6 (100) (1.000)

Figure 29: Genetic hierarchical model of fourteen different populations of Amblyceps mangois. The populations are arranged in hierarchical orders as first, second, third, fourth, fifth order populations. FST = Population genetic differentiation, Nm= Estimated gene flow, AMOVA= Analysis of molecular variance, PhiPT= Estimated variance among population/ (Estimated variance within population + Estimated variance among population), probability values based on 999 permutations. # indicates the comparison between populations or groups of populations.

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The three river system has different natural hierarchical genetic-spatial structure. Therefore, we have divided the three river system into 1st and 2nd order (for Mahananda river system), 1st to 5th order (for Teesta river system) and 1st to 3rd order (for Jaldhaka river system) hierarchy. This division helped us to compare the riverine populations constituting the different order of hierarchy sophisticatedly (Figure 29).

In the Mahananda river system the first order hierarchical group (between population

ATR-3 and ATR-2); showed lower value of genetic differentiation (FST = 0.1084) and higher nd value of gene flow (Nm = 4.1117) than the 2 order hierarchical population (Figure 29). The among population variance component and PhiPT value of first order hierarchical group were also lower i.e., 0.983 (7%) and 0.069 (p value = 0.006) respectively than the 2nd order hierarchical population (Figure 29).

In the Teesta river system the first order hierarchical group (between population ADR-1 and ADR-2) and the second order hierarchical group (between population ADR-1,

ADR-2 and ADR-3); showed lower value of genetic differentiation (FST = 0.4870 and 0.4928 respectively) and higher value of gene flow (Nm = 0.5268 and 0.5145) than other hierarchical orders of population (Figure 29). The among population variance component and PhiPT value of first order hierarchical group (between population ADR-1 and ADR-2) and the second order hierarchical group (between population ADR-1, ADR-2 and ADR-3) were also lower i.e., 18.36 (63%) and 15.82 (58%); and 0.595 (p value = 0.001) and 0.580 (p value = 0.001) respectively than the other hierarchical orders of populations (Figure 29).

In Jaldhaka river system all three hierarchical groups i.e., first order, second order and third order showed very low genetic differentiation (FST = 0.0196, 0.0318, 0.0387, 0.0125,

0.0427); and high amount of gene flow (Nm = 25.0247, 15.2365, 12.4330, 39.3447 and 11.2136) (Figure 29). Although second order populations (ADR-9 and ADR-8) showed low

FST and high gene flow among other hierarchical orders but the there was no among populations variance in any hierarchical orders and there was a significant negative PhiPT values in all hierarchical orders of Jaldhaka populations (Figure 29). .

Fixation index or FST is a measure of genetic divergence among subpopulations that ranges from 0 (when all subpopulations have equal allele frequencies) to 1 (when all the subpopulations are fixed for different alleles) (Allendorf et al., 2013). In our study we have detected a low to high (0.0125 to 0.6187) level of genetic differentiation (FST) across different populations of Amblyceps mangois in three river system in a hierarchical manner and the

Page | 206 gene flow was low to high between different populations. Although a residual level of genetic admixture was occurred through narrow channels between different populations because of the submergence of the channels during monsoon season. A direct evidence of population differentiation was revealed by the AMOVA, which detected significant differentiation of populations in each hierarchical order among the populations. A high level of among population variance and genetic differentiation (FST) was detected in the second order hierarchical population (consisting of populations ATR-3, ATR-2 and ATR-1) of Mahananda river system and second order hierarchical population (consisting of populations ADR-2 and ADR-4) of Teesta river system, which indicate that these hierarchical group are genetically and reproductively isolated and there was a sporadic gene flow occurred (Figure 29). Moreover the Jaldhaka river system the observed among population variance was nil and very low amount of genetic differentiation with a high amount of gene flow indicates that this river system is made up of genetically similar sub populations (Figure 29). The PhiPT is another population genetic statistical tool to detect the population differentiation; the results of PhiPT were also congruent with the results of FST. The population genetic parameter Nm, which measures the number of migrants per generation, also provides an indication of the differentiation among populations. A study carried out in Bangladesh in catfish Mystus vittatus in Chalan beel, Mohanganj haor and Kangsha river population differentiation (PhiPT) values were found to be insignificant indicating that there were no significant differentiation among three studied populations of Mystus vittatus (Tamanna et al, 2012). Another study carried out in Brazil on Pimelodus maculatus there was a significant genetic differentiation between the upper Paranapanema subpopulation and those of the lower and middle parts, and a greater similarity between the lower and middle Paranapanema subpopulations. The gene flow estimates for P. maculatus in the Paranapanema river were 4.4646, 2.1732, and 1.8776 between the lower and middle, lower and upper, and middle and upper parts, respectively (Almeida et al , 2003). According to Wright (1978), genetic differentiation estimates with probabilities between 0.05 and 0.15 are considered indicative of moderate population structuring, and with probabilities between 0.15 and 0.25 of high population structuring. Structured populations usually show a dynamic equilibrium between factors which favor differentiation (mutation, drift, and directional or disruptive natural selection, differing in each area) and homogenizing factors (migration, purifying natural selection, and balanced or differential natural selection, uniform in each area) (Solé-Cava, 2001). The study carried out in Bangladesh with endemic yellow catfish Horabagrus brachysoma the maximum value of

FST (0.219), was shown between Meenachil and Nethravathi populations and the minimum value of FST (0.045) between Meenachil and Chalakkudi populations (Muneer et al, 2009).

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4.3.9 mtCOI based findings of Amblyceps mangois

4.3.9.1 Amplification and Checking of amplified fragments

The COI gene was amplified with the specific primers from ten different individuals from Terai (two individuals) and Dooars region (eight individuals). After amplification I have found distinct sharp bands with approximately 687bp size. The HINDIII digestion gave two distinct bands of approximately 270bp in size and 420bp in size (Figure 30).

M 1 2 3 4 5 6 7 8 9 10

1000bp -----

650bp ------

500bp------

M 1 2 3 4 5 6 7 8 9 10

1000bp -----

400bp ------

250bp------

Figure 30: A. Amplification of COI gene of Amblyceps mangois by specific primer. B. Hind III digestion of the amplified product. M= 100 base pair DNA sizer marker, Lane No. 1-10= Amplified product of COI gene of Amblyceps mangois.

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4.3.9.2 Ten Cytochrome oxidase subunit 1 (COI) gene sequences of Amblyceps mangois and their NCBI.

>TRAM1 (Accn. No.MK012251) CCCAAAGACATTGGCACCCTTTATTTAGTCTTTGGTGCTTGAGCCGGAATAGTTG GTACAGCCCTTAGCCTGCTAATTCGGGCGGAGCTGGCCCAACCTGGCGCTCTCTT AGGAGACGACCAAATTTATAATGTTATTGTTACTGCCCATGCTTTTATTATAATTT TCTTTATAGTAATACCAATCATAATTGGCGGGTTCGGAAACTGACTTGTCCCTTTA ATGATCGGGGCTCCTGATATGGCATTCCCCCGAATAAACAACATAAGCTTCTGAC TTCTCCCCCCTTCTTTCCTGTTGCTACTTGCCTCTTCTGGGGTCGAAGCAGGCGCA GGAACCGGGTGAACTGTCTACCCCCCTCTTGCTGGTAACTTAGCACATGCCGGGG CCTCCGTAGACTTAACTATCTTTTCACTTCACCTTGCTGGTGTCTCCTCAATTTTA GGTGCCATTAACTTTATCACAACTATCATTAACATGAAACCCCCTGCAATTTCAC AGTATCAAACTCCACTCTTTGTCTGAGCAGTACTAATTACAGCCGTGCTCCTTCTA CTATCTCTGCCCGTACTGGCTGCCGGCATCACAATACTACTAACAGACCGAAACT TAAATACCACCTTCTTTGACCCAGCAGGGGGAGGGGATCCCATCCTCTACCAACA CCTGTTTTGATTCTTTGGCCAC

>TRAM2 (Accn. No.MK012252) CCCAAAGACATTGGCACCCTTTATTTAGTCTTTGGTGCTTGAGCCGGAATAGTTG GTACAGCCCTTAGCCTGCTAATTCGGGCGGAGCTGGCCCAACCTGGCGCTCTCTT AGGAGACGACCAAATTTATAATGTTATTGTTACTGCCCATGCTTTTATTATAATTT TCTTTATAGTAATACCAATCATAATTGGCGGGTTCGGAAACTGACTTGTCCCTTTA ATGATCGGGGCTCCTGATATGGCATTCCCCCGAATAAACAACATAAGCTTCTGAC TTCTCCCCCCTTCTTTCCTGTTGCTACTTGCCTCTTCTGGGGTCGAAGCAGGCGCA GGAACCGGGTGAACTGTCTACCCCCCTCTTGCTGGTAACTTAGCACATGCCGGGG CCTCCGTAGACTTAACTATCTTTTCACTTCACCTTGCTGGTGTCTCCTCAATTTTA GGTGCCATTAACTTTATCACAACTATCATTAACATGAAACCCCCTGCAATTTCAC AGTATCAAACTCCACTCTTTGTCTGAGCAGTACTAATTACAGCCGTGCTCCTTCTA CTATCTCTGCCCGTACTGGCTGCCGGCATCACAATACTACTAACAGACCGAAACT TAAATACCACCTTCTTTGACCCAGCGGGGGGAGGGGATCCCATCCTCTACCAACA CCTGTTTTGATTCTTTGGCCAC

>DRAM4 (Accn. No.MK012253) CCCAAAGACATTGGCACCCTTTATTTAGTATTTGGTGCTTGAGCCGGAATAGTTG GTACAGCCCTTAGCCTGCTAATTCGGGCGGAGCTGGCCCAACCTGGCGCTCTCTT AGGAGACGACCAAATTTATAATGTTATTGTTACTGCCCATGCTTTTATTATAATTT TCTTTATAGTAATACCAATCATAATTGGTGGGTTCGGAAACTGACTTGTCCCTTTA ATGATCGGGGCTCCTGATATGGCATTCCCCCGAATAAACAACATAAGCTTCTGAC TTCTTCCCCCTTCTTTCCTGTTGCTACTTGCCTCTTCTGGGGTCGAAGCAGGCGCA GGAACCGGGTGAACTGTCTACCCCCCTCTTGCTGGTAACTTAGCACATGCCGGGG CCTCCGTAGACTTAACTATCTTTTCACTTCACCTTGCTGGTGTCTCCTCAATTTTA GGCGCCATTAACTTTATCACAACTATCATTAACATGAAGCCCCCTGCAATTTCAC AGTATCAAACTCCACTCTTTGTCTGAGCAGTACTAATTACAGCCGTGCTCCTTCTA CTATCTCTGCCCGTACTGGCTGCCGGCATCACAATACTACTAACAGACCGAAACT TAAATACTACCTTCTTTGACCCAGCAGGGGGAGGAGATCCCATCCTCTACCAACA CCTGTTTTGATTCTTTGGCCAC

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>DRAM6 (Accn. No.MK012254) CCCAAAGACATTGGCACCCTTTATTTAGTATTTGGTGCTTGAGCCGGAATAGTTG GTACAGCCCTTAGCCTGCTAATTCGGGCGGAGCTGGCCCAACCTGGCGCTCTCTT AGGAGACGACCAAATTTATAATGTTATTGTTACTGCCCATGCTTTTATTATAATTT TCTTTATAGTAATACCAATCATAATTGGCGGGTTCGGAAACTGACTTGTCCCTTTA ATGATCGGGGCTCCTGATATGGCATTCCCCCGAATAAACAACATAAGCTTCTGAC TTCTCCCCCCTTCTTTCCTGTTGCTACTTGCCTCTTCTGGGGTCGAAGCAGGCGCA GGAACCGGGTGAACTGTCTACCCCCCTCTTGCTGGTAACTTAGCACATGCCGGGG CCTCCGTAGACTTAACTATCTTTTCACTTCACCTTGCTGGTGTCTCCTCAATTTTA GGTGCCATTAACTTTATCACAACTATCATTAACATGAAACCCCCTGCAATTTCAC AGTATCAAACTCCACTCTTTGTCTGAGCAGTACTAATTACAGCCGTGCTCCTTCTA CTATCTCTGCCCGTACTGGCTGCCGGCATCACAATACTACTAACAGACCGAAACT TAAATACCACCTTCTTTGACCCAGCAGGGGGAGGGGATCCCATCCTCTACCAACA CCTGTTTTGATTCTTTGGCCAC

>DRAM7 (Accn. No.MK012255) CCCAAAGACATTGGCACCCTTTATTTAGTATTTGGTGCTTGAGCCGGAATAGTTG GTACAGCCCTTAGCCTGCTAATTCGGGCGGAGCTGGCCCAACCTGGCGCTCTCTT AGGAGACGACCAAATTTATAATGTTATTGTTACTGCCCATGCTTTTATTATAATTT TCTTTATAGTAATACCAATCATAATTGGTGGGTTCGGAAACTGACTTGTCCCTTTA ATGATCGGGGCTCCTGATATGGCATTCCCCCGAATAAACAACATAAGCTTCTGAC TTCTTCCCCCTTCTTTCCTGTTGCTACTTGCCTCTTCTGGGGTCGAAGCAGGCGCA GGAACCGGGTGAACTGTCTACCCCCCTCTTGCTGGTAACTTAGCACATGCCGGGG CCTCCGTAGACTTAACTATCTTTTCACTTCACCTTGCTGGTGTCTCCTCAATTTTA GGCGCCATTAACTTTATCACAACTATCATTAACATGAAGCCCCCTGCAATTTCAC AGTATCAAACTCCACTCTTTGTCTGAGCAGTACTAATTACAGCCGTGCTCCTTCTA CTATCTCTGCCCGTACTGGCTGCCGGCATCACAATACTACTAACAGACCGAAACT TAAATACTACCTTCTTTGACCCAGCAGGGGGAGGAGATCCCATCCTCTACCAACA CCTGTTTTGATTCTTTGGCCCC

>DRAM8 (Accn. No.MK012256) CCCAAAGACATTGGCACCCTTTATTTAGTATTTGGTGCTTGAGCCGGAATAGTTG GTACAGCCCTTAGCCTGCTAATTCGGGCGGAGCTGGCCCAACCTGGCGCTCTCTT AGGAGACGACCAAATTTATAATGTTATTGTTACTGCCCATGCTTTTATTATAATTT TCTTTATAGTAATACCAATCATAATTGGTGGGTTCGGAAACTGACTTGTCCCTTTA ATGATCGGGGCTCCTGATATGGCATTCCCCCGAATAAACAACATAAGCTTCTGAC TTCTTCCCCCTTCTTTCCTGTTGCTACTTGCCTCTTCTGGGGTCGAAGCAGGCGCA GGAACCGGGTGAACTGTCTACCCCCCTCTTGCTGGTAACTTAGCACATGCCGGGG CCTCCGTAGACTTAACTATCTTTTCACTTCACCTTGCTGGTGTCTCCTCAATTTTA GGCGCCATTAACTTTATCACAACTATCATTAACATGAAGCCCCCTGCAATTTCAC AGTATCAAACTCCACTCTTTGTCTGAGCAGTACTAATTACAGCCGTGCTCCTTCTA CTATCTCTGCCCGTACTGGCTGCCGGCATCACAATACTACTAACAGACCGAAACT TAAATACTACCTTCTTTGACCCAGCAGGGGGAGGAGATCCCATCCTCTACCAACA CCTGTTTTGATTCTTTGGCCAC

>DRAM9 (Accn. No.MK012257) CACAAAGACATTGGCACCCTTTATTTAGTATTTGGTGCTTGAGCCGGAATAGTTG GTACAGCCCTTAGCCTGCTAATTCGGGCGGAGCTGGCCCAACCTGGCGCTCTCTT AGGAGACGACCAAATTTATAATGTTATTGTTACTGCCCATGCTTTTATTATAATTT TCTTTATAGTAATACCAATCATAATTGGTGGGTTCGGAAACTGACTTGTCCCTTTA

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ATGATCGGGGCTCCTGATATGGCATTCCCCCGAATAAACAACATAAGCTTCTGAC TTCTTCCCCCTTCTTTCCTGTTGCTACTTGCCTCTTCTGGGGTCGAAGCAGGCGCA GGAACCGGGTGAACTGTCTACCCCCCTCTTGCTGGTAACTTAGCACATGCCGGGG CCTCCGTAGACTTAACTATCTTTTCACTTCACCTTGCTGGTGTCTCCTCAATTTTA GGCGCCATTAACTTTATCACAACTATCATTAACATGAAGCCCCCTGCAATTTCAC AGTATCAAACTCCACTCTTTGTCTGAGCAGTACTAATTACAGCCGTGCTCCTTCTA CTATCTCTGCCCGTACTGGCTGCCGGCATCACAATACTACTAACAGACCGAAACT TAAATACTACCTTCTTTGACCCAGCAGGGGGAGGAGATCCCATCCTCTACCAACA CCTGTTTTGATTCTTTGGCCAC

>DRAM10 (Accn. No.MK012258) CCCAAAGACATTGGCACCCTTTATTTAGTATTTGGTGCTTGAGCCGGAATAGTTG GTACAGCCCTTAGCCTGCTAATTCGGGCGGAGCTGGCCCAACCTGGCGCTCTCTT AGGAGACGACCAAATTTATAATGTTATTGTTACTGCCCATGCTTTTATTATAATTT TCTTTATAGTAATACCAATCATAATTGGTGGGTTCGGAAACTGACTTGTCCCTTTA ATGATCGGGGCTCCTGATATGGCATTCCCCCGAATAAACAACATAAGCTTCTGAC TTCTTCCCCCTTCTTTCCTGTTGCTACTTGCCTCTTCTGGGGTCGAAGCAGGCGCA GGAACCGGGTGAACTGTCTACCCCCCTCTTGCTGGTAACTTAGCACATGCCGGGG CCTCCGTAGACTTAACTATCTTTTCACTTCACCTTGCTGGTGTCTCCTCAATTTTA GGCGCCATTAACTTTATCACAACTATCATTAACATGAAGCCCCCTGCAATTTCAC AGTATCAAACTCCACTCTTTGTCTGAGCAGTACTAATTACAGCCGTGCTCCTTCTA CTATCTCTGCCCGTACTGGCTGCCGGCATCACAATACTACTAACAGACCGAAACT TAAATACTACCTTCTTTGACCCAGCAGGGGGAGGAGATCCCATCCTCTACCAACA CCTGTTTTGATTCTTTGGCCAC

>DRAM12 (Accn. No.MK012259) CACAAAGACATTGGCACCCTTTATTTAGTATTTGGTGCTTGAGCCGGAATAGTTG GTACAGCCCTTAGCCTGCTAATTCGGGCGGAGCTGGCCCAACCTGGCGCTCTCTT AGGAGACGACCAAATTTATAATGTTATTGTTACTGCCCATGCTTTTATTATAATTT TCTTTATAGTAATACCAATCATAATTGGTGGGTTCGGAAACTGACTTGTCCCTTTA ATGATCGGGGCTCCTGATATGGCATTCCCCCGAATAAACAACATAAGCTTCTGAC TTCTTCCCCCTTCTTTCCTGTTGCTACTTGCCTCTTCTGGGGTCGAAGCAGGCGCA GGAACCGGGTGAACTGTCTACCCCCCTCTTGCTGGTAACTTAGCACATGCCGGGG CCTCCGTAGACTTAACTATCTTTTCACTTCACCTTGCTGGTGTCTCCTCAATTTTA GGCGCCATTAACTTTATCACAACTATCATTAACATGAAGCCCCCTGCAATTTCAC AGTATCAAACTCCACTCTTTGTCTGAGCAGTACTAATTACAGCCGTGCTCCTTCTA CTATCTCTGCCCGTACTGGCTGCCGGCATCACAATACTACTAACAGACCGAAACT TAAATACTACCTTCTTTGACCCAGCAGGGGGAGGAGATCCCATCCTCTACCAACA CCTGTTTTGATTCTTTGGCCAC

>DRAM14 (Accn. No.MK012260) CCCAAAGACATTGGCACCCTTTATTTAGTATTTGGTGCTTGAGCCGGAATAGTTG GTACAGCCCTTAGCCTGCTAATTTTGGCGGAGTTGGCCCAACCTGGCGCTCTTTT AGGAGACGACCAAATTTATAATGTAATTGTTACTGTCCATGCTTTTATTATAATTT TGTTTATAGTAATACCAATCATAATTGGTGGGTTCGGAAACTGACTTGTCCCTTTA ATGATGGGGGCTCCTGATATGGCATTCCCCCGAATAAACAACATAAGCTTGTGAC TTCTCCCCCCTTCTTTCCTGTTGTTACTTGCCTTTTGGGGGGTGGAAGCAGGCGCA GGAACCGGGTGAACCGTCTACCCCCCTCTGGCTGGTAACTTAGCACATGCCGGGG CCTCCGTAGACTTAACTATCTTTTCACTTCACCTTGATGGTGTCTCCTCAATTTTA GGCGCCATTAACTTTATCACAACTATCATTAACATGAAGCCCCCTGCAATTTCAC

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AGTATCAAACTCCAGTCTTTGTGTGAGCAGTACTAATTACAGCCGTGCTCCTTGT ACTAAGAATGCCCGTACTGGCTGCCGGCATCACAATAGTATTAACAGACCGAAA CTTAAATACTACCTTCTTTGACCCAGCAGGGGGAGGGGATCCCATCCTCTACCAA CACCTGTTTTGATTCTTTGGCCAC

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Table 34. Population genetic diversity parameters based on Amblyceps mangois mtDNA COI partial coding sequences. Number Total Average Haplotype Nucleotide of number Number of number of (gene) diversity Tajima’s Fu and Fu and Populations variable of Haplotypes nucleotide diversity (Pi±SD) D Li’s D Li’s F sites (S) mutations (h) differences (Hd±SD) (Eta) (k) Terai Region 1 1 2 1.000±0.500 0.00146±0.00073 1.00000 - -1.62915 -1.70051 -1.87606 Not Not Not Dooars region 31 31 5 0.857±0.108 0.01206±0.00626 8.28571 significant, significant, significant, 0.10 > P > 0.10 > P > 0.10 > P > 0.05 0.05 0.05 Sub- -1.24832 -1.48896 -1.61277 Himalayan Not Not Not 33 33 7 0.911±0.077 0.01258±0.00512 8.64444 region (Terai + significant, significant, significant, Dooars) P > 0.10 P > 0.10 P > 0.10 N-E India 4 4 2 1.000±0.500 0.00657±0.00328 4.00000 - * COI, Cytochrome oxidase subunit I; SD, standard deviation; Four or more sequences are need to compute Tajima's and Fu and Li's statistics (Terai and North-East India population has two sequences each).

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4.3.9.3 Genetic diversity and population structure of Amblyceps mangois The genetic diversity analysis was undertaken to determine the available genetic diversity in the mitochondrial COI partial CDS of Amblyceps mangois of the major river system of the Terai and Dooars region of sub-Himalayan West Bengal, India. The total number of variable sites were 1 and 31 in Terai and Dooars region respectively, and 33 when two regions were considered together i.e., sub-Himalayan region (Table 34). Total numbers of mutations were 1 and 31 in Terai and Dooars region populations respectively (Table 34). Total 7 haplotypes were found in the sub-Himalayan Terai and Dooars region population (Terai region=2 haplotypes and Dooars region=5 haplotypes). Total number of variable sites, mutations and haplotypes in north-east Indian populations were 4, 4 and 2. (Table 34). The haplotype diversity and nucleotide diversity of Terai region population were 1.000±0.500 and 0.00146±0.00073; Dooars region population were 0.857±0.108 and 0.01206±0.00626; and 0.911±0.077 and 0.01258±0.00512 in sub-Himalayan region respectively (Table 34). The haplotype diversity and nucleotide diversity of north-east Indian population were shown in Table 34. A study carried out by Boonkusol et al. (2016) on snakehead fish from Thailand and they have found total 33 haplotypes among 60 individuals and 14 haplotypes were shared by multiple populations and 19 haplotypes have been found to be population specific. They also found that haplotype and nucleotide diversity ranged from 0.750±0.096 to 1.000±0.052 and 0.00442±0.00083 to 0.2672±0.00216. In the study carried out by Du et al. (2009) on COI gene sequences of Sipunculus nudus sampled from three ecological regions along the southern coast of China, 16 haplotypes were defined for 26 individuals, and 71 polymorphic sites were observed. They also found the mean haplotype diversity and nucleotide diversity of the three populations of Sipunculus nudus analyzed were 0.9754 ± 0.0168 and 0.0035 ± 0.0018, respectively. Therefore, our present study reveals that the genetic diversity (number of haplotypes, haplotype diversity and nucleotide diversity) was dwindling in Amblyceps mangois population as evident from the mitochondrial COI study. The present findings is also in accordance with our previous study on Amblyceps mangois, where we have found that the overall genetic diversity (polymorphism, Nei’s genetic diversity and Shannon’s information index) was low as revealed by RAPD and ISSR marker analyses. The observed values of Tajima’s D, Fu and Li’s D and Fu and Li’s F analyses of Dooars region were -1.62915 (Not significant, 0.10 > P > 0.05), -1.70051 (Not significant, 0.10 > P > 0.05) and -1.87606 (Not significant, 0.10 > P > 0.05) and in sub-Himalayan region population were -1.24832 (Not significant, P > 0.10), -1.48896 (Not significant, P > 0.10) and

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-1.61277 (Not significant, P > 0.10) respectively (Table 34). The neutral test was performed to ascertain the evidence of purifying or balancing selction. All the population gave negative values for Tajima’s D, Fu and Li’s D and Fu and Li’s F test, which indicated that the recent selective sweep, population expansion after a recent bottleneck (Tajima 1989; Fu and Li 1993). Thirumaraiselvi and Thangaraj (2015) studied six Eleutheronema tetradactylum populations from South Asian countries, and they found negative values for the tests and this indicated sudden population expansion of the Eleutheronema tetradactylum population.

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4.3.9.4 Phylogenetic relationship of different populations of Amblyceps mangois based on COI gene.

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Figure 31. Molecular Phylogenetic analysis of Amblyceps mangois by Maximum Likelihood method. The evolutionary history was inferred by using the Maximum Likelihood method based on the Jukes-Cantor model (Jukes and Cantor, 1969). The tree with the highest log likelihood (- 1086.2810) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 14 nucleotide sequences. Codon positions included were 1st+2nd+3rd+Noncoding. All positions containing gaps and missing data were eliminated. There were a total of 609 positions in the final dataset. Evolutionary analyses were conducted in MEGA7. (Kumar et. al., 2016).

The maximum likelyhood method of phylogenetic analysis showed that six taxa (DRAM4, DRAM7, DRAM8, DRAM9, DRAM10 and DRAM12) from Dooars region form a distinct group (Figure 31). Whereas, DRAM14 appeared as an outlier individual completely isolated from others. Moreover, DRAM6, TRAM1 and TRAM2 form a cluster with the Bangladesh Amblyceps mangois sequence (i.e., DUZM140) and north-east Indian Amblyceps mangois populations (i.e., SGBK-OF31F and BCF11) make one distinct cluster (Figure 31). Interestingly, the phylogenetic analysis showed that a close association exist between the Amblyceps mangois population of Bangladesh with that of sub-Himalayan Terai region (TRAM1 and TRAM2), West Bengal. This phylogenetic association may be attributed to introduction of some Amblyceps mangois species from sub-Himalayan region to Bangladesh or vice-versa during past years.

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CHAPTER-V Comprehensive Discussion

5. COMPREHENSIVE DISCUSSION 5.1 Standardization of Extraction of Genomic DNA Fish scales and fin are reliable sources of gDNA and have been used earlier to isolate gDNA from some fish species (Shiozawa et al. 1992; Taggart et al. 1992; Whitemore et al. 1992; Zhang et al. 1994; Nielsen et al. 1997, 1999; Adcock et al. 2000). Since 1990s however, those isolation techniques were not standardized quantitatively and were either elaborate or costly (Nelson et al. 1998; Yue and Orban 2001). Isolation of gDNAs from minute quantities of fish scale and fin is less likely to cause mortality of the fish and being the non-destructive method that can be employed to threatened category fish species for subsequent DNA-based studies. In our case the two fish species viz., Badis badis and Amblyceps mangois are both under threatened category according to the report published by NBFGR, India. Therefore, it would be unethical to kill the study fishes for genetic diversity study. So it is probable to standardize the extraction of genomic DNA as well as RAPD PCR amplification from very small quantity of tissue samples from easily available fishes and also importantly the model fishes should be very close phylogenetically. In my case the scale and fin tissues were used and the model fishes taken for standardization were Labeo bata and Heteropneustes fossilis. The gDNAs isolated from different quantities of fin clips of both live and frozen Heteropneustes fossilis were more degraded than that from gDNAs isolated from scales or fins of Labeo bata. This could result due to the soft nature of the tissue itself, which is also evident in Labeo fin gDNAs (Figure 12B). However, it is clear from the gDNA gel (Figure 12C) that the yields of gDNAs were much higher from the live samples than that from the frozen samples of Heteropneustes, and these fin-gDNAs could also support several rounds RAPDPCR analyses. Moreover, the gDNA yields in frozen samples of both Labeo bata and Heteropneustes fossilis were relatively lower in comparison to that from the live ones (Table 10). Wasko et al. (2003) used 0.075 mg/ml Proteinase K in 10 h incubation period for tissue digestion. In the present study, 0.3 mg/ml final concentration of Proteinase K was used in tissue digestion at 52ºC for 1 h for each preparation. While Wasko et al. (2003) suggested that gDNA should be free of RNA contamination for efficient PCR amplification our data showed that successful RAPD amplifications could be carried out in the presence of contaminating RNA in gDNA preparations, thus suffice to state that RNA could not hamper our RAPD amplifications and distinct scorable bands were found in each replicate. All gDNA preparations were treated with RNase A to accurately quantify gDNA yields. Spectrophotometric data showed that the quantity of gDNA isolated from 5 scales was

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8.40 µg and from 1 mg fin clip was 4.65 µg in live Labeo bata. The quantity of gDNA isolated from 1 mg fin clip was 14.60 µg in live Heteropneustes fossilis (Table 10). We have found that even a single piece of scale or less than 1 mg of fin clip were sufficient to produce sufficient amount of gDNA (data not shown). The quantities and quality of gDNAs isolated from live scales and fins were better than that from frozen scales and fins as evident from spectrophotometric analyses and RAPD-PCR (Table 10; Figure 13). In this study the quantities of isolated gDNAs were higher as compared to the studies undertaken by Yue and Orban (2001) and Kumar et al. (2007). The high molecular weight gDNAs were compared with the 23.13 kb band of Lambda/Hind III digest size markers. The isolated gDNAs were dissolved in Type I Milli-Q water which contained no PCR inhibitors. The spectrophotometric data provided the purity factor (A260/A280) that ranged from 1.75 to 2.18 indicating low protein contamination (Table 1). The RAPD-PCR analyses showed that gDNAs isolated from minute tissue samples were able to produce banding patterns comparable to that from templates isolated from larger tissue samples (Figure 13). The number and quality of RAPD bands from the frozen samples were less and this indicated that gDNAs isolated from live samples will be more informative than that from frozen or preserved tissue samples in DNA-based studies. Moreover, no positive control has been used in RAPD-PCR analysis because no such proper control can be included in RAPD-PCR as the banding pattern differs depending on the gDNAs used. In summary, the yield of gDNA was substantially high from the live than that from the frozen fish tissue samples. The gDNA samples isolated by this method from fresh fish scale and fin tissue of live individuals of two species (Labeo bata and Heteropneustes fossilis) were successfully used in RAPD-PCR using two random primers (OPA02 and OPA07) and the results indicated that the quality and quantities of the gDNAs were quite satisfactory to support several rounds of RAPD-PCR amplification. The banding pattern in live and frozen specimen showed a marked difference, which might be due to fragmentation of the genome in frozen condition and the primers could not found a continuous long region with considerable length to amplify. Although there are reports of non-destructive methods of gDNA isolation from fishes our study not only compared the yields of gDNAs from live and frozen/preserved tissue samples but also determined the lowest amount of tissue samples required for DNA isolation. Our procedure described here thus offers an inexpensive and reliable method to isolate sufficiently good quantity and quality of gDNAs from minute quantities of fish scale and fin tissues that can serve as templates for RAPD-based Polymerase Chain Reaction and other gDNA based

Page | 222 studies. This study has a great implication in case of small fish species which are rare or endangered, where killing or sacrificing of the concerned species will not be acceptable.

5.2 Genetic Architecture of Badis badis and Amblyceps mangois Several PCR-based molecular methods of genotype analysis have been developed for the genetic analysis of fish (Liu and Cordes, 2004). Sequence analysis of specific mitochondrial or ribosomal DNA fragments, microsatellite marker and multiplex PCR of strain conserved DNA fragments is efficient for fish strain identification (Gharrett et al., 2001; Noell et al., 2001; Wasko et al., 2001; Sekino et al., 2005). Compared with the microsatellite marker and DNA sequence methods, RAPD-based DNA fingerprinting is quicker and cheaper in spite of lower efficiency. In addition, the experimental requirements are lower (Cordes et al., 2001). However, RAPD provides low reproducibility (Liu et al., 2004), and its power for the detection of polymorphism is not very high (Cubeta et al., 1993; Cespedes et al., 1998). ISSR has the advantage over RAPD for its high reproducibility, as well as its great power for the detection of polymorphism. A large number of bands can be produced rapidly with a limited number of primers. Clearly, ISSR is one of the powerful approaches for the assessment of genetic variation among populations, especially for species in which no molecular genetic information was previously available. In addition, variable ISSR patterns have potentials as dominant markers for studying genetic diversity of many fishes (Tong et al., 2005). I have studied total seventeen different populations of Badis badis and fourteen different populations of Amblyceps mangois from major river system and its tributaries viz., Mahananda-Balasan river system, Teesta river system, Jaldhaka river system from the sub- Himalayan Terai and Dooars region of North Bengal, West Bengal, India. This region is the situated just at the foothills of the North Eastern Himalaya range and the major rivers flowing through this region makes the region very fertile. Moreover the region has a good climatic and geographic status for tea plantation. And the region lies beside the river bed and catchment area is very well developed and productive for different crop plantation like rice, wheat, jute and other vegetables. Moreover the rivers are the habitat for different fishes, so fish catching and selling in the fish market become one the most valuable business for rural inhabitants for daily earning. The snow covered mountain peaks, the dense foothill enveloped with the luxuriant forests, snow fed rivers, lush green paddy fields, vibrant green tea gardens adjacent to the hills makes the Terai and Dooars of North Bengal a hotbed of bio-diversity or

Page | 223 a biodiversity hotspot. The unique climatic and ecological conditions make North Bengal a unique habitat for a large number of mega fauna and flora. RAPD and ISSR techniques can be utilized as efficient molecular tools to differentiate spatially and/or genetically isolated populations and have been widely used to characterize and estimate the available gene pool/genetic architecture of locally adapted populations within a species that may have arisen either through genetic selection under different environmental conditions, some anthropogenic pressure or as a result of genetic drift (Fuchs et al. 1998). To our knowledge, the present study is the first endeavour to explore and compare the present status of the genetic background of this fish fauna in the major river streams of the Terai and Dooars region of this sub-Himalayan hotspot. A total of twenty decamer RAPD primers and fifteen ISSR primers were used to characterize total seventeen different populations of Badis badis species for the study. And a total of twenty decamer RAPD primers and twelve ISSR primers were used to characterize total fourteen different populations of Amblyceps mangois species for the study. The total analysis was based on the reproducibility of banding patterns. RAPD and ISSR technique-based estimations of genetic diversity may be suitable for assessing the impact of different stresses upon a population as well as wide range of ecosystems. Since homozygotes cannot be distinguished from heterozygotes by the RAPD and ISSR techniques, the absence of amplification of a band in two genotypes does not necessarily represent genetic similarity between them. Since other popular sophisticated markers are not available in this fish species, I have used both RAPD and ISSR techniques separately and jointly for the estimation of genetic diversity of Badis badis species to increase the robustness of the study protocol. Different diversity indices like total number of polymorphic loci, percentage of polymorphic loci, Nei‘s genetic diversity, Shannon‘s information index, and measure of evenness are used to study the genetic architecture of two studied fishes.

After compiling all the data and comparing with some other related studies it was found that the genetic diversity of the studied fishes were reasonably positioned within the range of low to moderate. Different level of anthropogenic pressures and several natural processes lead to the decline of the genetic diversity of the studied ichthyofauna the three river system of the sub-Himalayan Terai and Dooars region. Although, the Teesta river system populations showed the comparatively greater level of genetic diversity than the two river system populations viz., Mahananda-Balasan river system and Jaldhaka river system. But the individual population of the Jaldhaka river system showed greater level of diversity

Page | 224 than other river system populations but the Teesta river system showed overall higher level of genetic diversity after considering all the populations together. This is happened due to joining of different tributaries to the Teesta river which causes the overall genetic diversity of the Teesta river system population to increase. This situation occurred in the both the studied fishes.

The Nei‘s genetic distance and the principle component analyses are used to observe the genetic relationship between different populations of Badis badis and Amblyceps mangois from three major river streams of Terai and Dooars region. In both the studied fishes it was found that the three river populations form three distinct groups but the some populations from Mahananda river system showed close relationship with that of the Teesta river system. This is happen due to the connection of Mahananda river system with that of Teesta river system via a narrow channel. This channel extends from Mahananda Barrage (TR1) to the Teesta Barrage (DR3) (Figure 6) and leads to the admixture of Mahananda population with that of Teesta river population. Therefore, a substantial genetic exchange is suspected to have occurred between Mahananda and Teesta river system population. Moreover, excessive rain during monsoon season, large catchment area, low carrying capacity of the rivers and snow melt water, all leads to frequent flood in the rivers. This flood also leads to mixing of river water with nearby tributaries and genetic exchange occurred. This ultimately led to the close association of the Mahananda and Teesta river system which is reflected by the UPGMA based dendrogram and principal component analyses. Whereas, the Jaldhaka river system is free from any connection other two river systems so the Jaldhaka river system forms a separate group and the populations showed very close association with each other (Figure 14 and 23).

Buzas and Hayek (1996) reported that the Shannon‘s index of diversity can be decomposed into two metric components namely richness (E) and evenness (S) i.e., (H´= lnE+ lnS) but sometimes it become difficult to ascribe whether the diversity component is influenced by greater/lower richness or greater/lower evenness values or both. However, this decomposition also allows investigator to describe the change in the diversity pattern though different subpopulations in a spatial and temporal scale. In my study we have found that the three components i.e., the pattern of diversity (H´), richness (S) and evenness (E) have varied across all seventeen populations of Badis badis and fourteen populations of Amblyceps mangois in a substantial manner as the river flows from higher to lower altitude (Figure 14 and Figure 23) which are most obvious in naturally subdivided populations of hill streams.

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Therefore, the SHE analysis can deduce the change or break in the diversity pattern of the populations along a distinct spatial gradient (Buzas and Hayek, 1998). Hayek and Buzas (1997) pointed out that often the diversity (H´) changes because of the differences between richness (S) and evenness (E) do not compensate each other (i.e., H´1 ≠ H´2 , S1 ≠ S2, E1 ≠ E2 , where 1 and 2 in suffix are any two population) and such SHE plot is log normal one. SHE analysis appears to be a useful approach for defining the diversity pattern. My data revealed that as the river streams converged from higher to lower altitude, five scenarios have been observed 1) diversity has decreased but evenness increased; 2) diversity and evenness both increased; 3) diversity and evenness both decreased; 4) diversity increased and evenness decreased; 5) diversity and evenness remain same. These scenarios have been observed in both the studied fishes. Several anthropogenic, climatic and geographic factors are responsible for this observed change and alternation of the diversity pattern in the studies fish populations. Most of the rivers have high gradient and huge amount of water at the upper reaches so they are utilized for the production of hydroelectricity. Few hydroelectric projects are installed in the hilly parts of the rivers. Therefore, creation of hydroelectric dam causes the disturbances in the flow pattern of the rivers. High deforestation for tea plantation and terrace cultivation in the hills and adjacent area of this region leads to soil degradation, erosion and landslides. In the plains the rivers falls significantly and the rivers suddenly widens. This causes flow pattern disturbances. So to counterbalance this condition the rivers adjust themselves by changing their processes and flow pattern from erosional to depositional state to meet with the new hydrological requirements induced by the change of the gradients as rivers flow from high to lower altitude. Therefore, the river shifted to a new channel and abandoning the old one, this is the most common phenomena in the river of this region. Sometimes careless diversion of water from the rivers for irrigation in the agricultural fields and for other domestic or household purposes results the rivers unable to carry silts and debris. Removal of boulders from the embankment weakens the structures which make the rivers incapable to carry huge amount of water during rainy season lead to floods in the nearby catchments. Moreover the rivers of the Terai region heavily exploited for sand and pebbles excavation for concrete building purposes; and nearby city, urban, domestic and factory sewage runoffs are responsible for most of the river pollution. Additionally, this region, mostly the Dooars reion, has a suitable climatic and geographic position for tea plantation. So, huge amount pesticides are used for the production of good quantity of tea; which results in a significant amount of pesticide run-off to the nearby rivers during the

Page | 226 monsoon season. Scarcity of suitable habitats compelled the lowland and tribal peoples to settle their permanent houses on the river bed or sides of the banks. Peoples with no knowledge about the nature of this region‘s rivers also settle their residents and overexploit the river resources which ultimately affect the river geography and aquatic life. All of these factors can culminate in to the observed decline and change in diversity pattern and richness in Badis badis and Amblyceps mangois populations across the river stream along the altitudinal gradient. Genetic variation is non-randomly distributed among different populations, species and higher taxa (Hamrick et al., 1979). This spatial and temporal distribution of alleles and genotypes; and its variation is often referred to as the genetic architecture of a population (Loveless and Hamrick, 1984). In general, inbreeding species commonly have lower levels of genetic diversity and differentiation between populations than out-crossing species (Hamrick and Godt, 1996). In other words, different F-coefficients explain the correlation of genes within individuals over all populations (FIT), the correlation of genes of within individuals within populations (FIS), and the correlation of genes of different individuals in the same population (FST). For a given species this can be interpreted as the overall inbreeding (FIT), the inbreeding within taxa (FIS) and the coefficient of co-ancestry (FST), which provides an estimate of inter-specific genetic differentiation (Wright, 1977). Since inbreeding increases the frequency of homozygotes, as a consequence, it decreases the frequency of heterozygotes, therefore population substructure results in a reduction of heterozygosity. Fixation index or

FST is a measure of genetic divergence among subpopulations that ranges from 0 (when all subpopulations have equal allele frequencies) to 1 (when all the subpopulations are fixed for different alleles) (Allendorf et al., 2013). Although a residual level of genetic admixture was happened through narrow channels between different riverine populations because of the construction of canals for irrigation purpose and submergence of the different channels during monsoon season. A direct verification of population differentiation was revealed by the AMOVA (Analyses of Molecular Variance), which detect significant differentiation of populations in each hierarchical order among the populations. Estimation of gene flow (Nm) is one of the most important analyses to study the population genetic structure of any population because it determines to which extent each local population of a species acts as an independent evolutionary unit (Slatkin, 1993). So, if gene flow among allopatric populations is intense, they evolve together, while, if it is small, each population evolves in a virtually independent manner. Therefore, the population genetic parameter Nm, provides the measures of migrants per generation, also provides an indication of the differentiation among Page | 227 populations. According to Wright (1978), genetic differentiation ranged from moderate (0.5 to 0.15) to high (0.15 to 0.25). Structured populations usually show a dynamic equilibrium between factors which favor differentiation (mutation, drift, and directional or disruptive natural selection, differing in each area) and homogenizing factors (migration, purifying natural selection, and balanced or differential natural selection, uniform in each area) (Solé- Cava, 2001). One of the easiest approaches to define the Evolutionary Significant Unit (ESU) is to ascertain the hierarchical gene diversity analysis and it is based on that species is situated at a spatially genetic hierarchical structure. Therefore, genetic diversity can be partitioned into within population and among population components and determine where biologically significant breaks occur into the genetic diversity. At the lowest level of hierarchy inbreeding individuals are genetically more similar. As move through the hierarchy the genetic differences among more geographically separated populations are greater (Meffe and Carroll, 1997). In our present study a significant levels of genetic divergence and population differentiation occurred in the Badis badis and Amblyceps mangois populations of Mahananda, Teesta and Jaldhaka river system; and therefore populations are highly structured. This high level of differentiation is attributed to different types of anthropogenic and natural pressures which resulted in break or fluctuation in the diversity pattern as evident by the SHE analyses. That is, a population having local breeding units in each stream have high genetic divergence from similar breeding units in other streams, and need to be managed as evolutionary significant units (ESU) for conservation purpose. Therefore, in Mahananda river system there is a need to manage the whole river system as evident from the hierarchical analyses to conserve the Badis badis and Amblyceps mangois population; and in Teesta and Jaldhaka river system it is necessary to manage the first and second order streams to conserve the gene pool of Badis badis and Amblyceps mangois of the whole river system. Application of mitochondrial data in conservation of endangered species has been widely practiced in various organism groups at the national and international level for a long time. Mitchondrial DNA (mtDNA) has been widely adopted for population genetic studies and it being almost exclusively maternally inherited has some advantages over the nuclear DNA (Billington, 2003). Mitochondrial DNA is highly variable in natural populations because of its elevated mutation rate and loss of recombination which can generate some information about population history over short time frames. Being involved in basic metabolic functions (cellular respiration), mitochondrial genes have been considered as less

Page | 228 likely than other genes to be involved in adaptive processes (Galtier et al. 2009). One consequence of maternal transmission and neutral in nature is that the effective population size for mtDNA is smaller than that of nuclear DNA, so that mtDNA variation is a more sensitive indicator of population phenomena (Avise 1994). Mitochondrial genes like the cytochrome b, cytochrome oxidase, 12S and 16S rRNA genes and dehydrogenase genes may be examined in a number of species (Carr and Marshall 1991, Sah et al. 2011). Among the most frequently used mitochondrial genes for detecting genetic data, the one coding for cytochrome oxidase subunit I (COI) can be easily amplified using polymerase chain reaction methods using conserved primers (Folmer et al. 1994). Hebert et al., 2003 argued that a COI- based identification system could be developed for all animal taxa and can be used to estimate different biodiversity indices viz., species richness, Simpson‘s index and Shannon‘s index. Thus, the estimation of biodiversity indices can be utilized to develop the molecular operational taxonomic units (MOTU) (Blaxter et al., 2005). Moreover, COI gene sequence has been analyzed to investigate the phylogenetic relationship and variation patterns of different geographically isolated populations. My study revealed that the mtCOI diversity is low and dwindling in both the species i.e., Badis badis and Amblyceps mangois in the studied river system evidenced in.RAPD and ISSR analyses. Comparing with the other geographically isolated population it was found that there is a close association or genetic relationship of Badis badis with that of Ganga and Yamuna river of Uttarakhand, India and Amblyceps mangois with that of the rivers in Bangladesh. This close genetic relationship occurred due to substantial amount of gene flow that may occurred through natural or anthropogenic introduction of fish stocks from this region to the others or vice-versa. Sudden population contraction in case of Badis badis and sudden expansion of Amblyceps mangois population was attributed to several natural and anthopogenic factors. Moreover the loss of diversity and break in the diversity pattern as the river flows from higher to lower altitude may affect the population dynamics of the fishes as evident from the RAPD and ISSR analyses. Therefore, our recent data substantially harmonize the mitochondrial COI gene sequence analyses.

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CHAPTER-VI Summary and Conclusion

6. SUMMARY AND CONCLUSION

6.1 Genomic DNA extraction based study

The molecular and genetic based study required sufficient quantity and quality of

genomic DNA which is extracted from the concerned animal for amplification and

characterization with specific or arbitrary primers.

But if the species concern is threatened in category or its population size is very small

then it is necessary to extract the genomic DNA from that species without killing or

noninvasively.

The present study was basically on two fish species viz., Badis badis and Amblyceps

mangois which are in threatened category as reported by a prestigious fish research

institute of India, NBFGR (National Bureau of Fish Genetic Resources, Lucknow,

India.

The study was carried out with the help of two molecular markers viz., RAPD and

ISSR; and it have so many advantages over other molecular markers

These markers are cheap, inexpensive, and easy to perform and require very little

laboratory expertise in moderately equipped ordinary laboratories and importantly,

these markers do not require any prior knowledge of the DNA sequence.

Therefore, at first stage of my research I have standardised the gDNA extraction and

PCR apmlification from two commonly available and phylogenetically related fish

species.

This allows me to find out minimum quantity of gDNA required to perform sufficient

round of PCR amplification for genetic diversity study. Moreover, the protocol which

I have modified from previously used protocols is quite efficient and robust and less

time consuming. Therefore this procedure can be used not only for the studied species

but all other species too.

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6.2 Study of the genetic Architecture of Badis badis and Amblyceps mangois

The primary goal of my study was to estimate the available gene pool present within

the ornamental and threatened fish Badis badis and Amblyceps mangois at the rivers

of Terai and Dooars region of North Bengal, India.

The study region i.e., the Terai and Dooars region is situated at the foothills of

Himalaya and different rivers are flowing across this region and the geo-physical

conditions make this region a biodiversity hotspot.

So the estimation the genetic diversity of this threatened fauna is utmost important for

conservation of the genetic resources of this region. Since the population genetic

architecture of this ichthyofauna is totally unexplored in that region, therefore, to gain

first-hand knowledge of this ichthyofauna of this region is very much essential for

their proper management and rehabilitation within the wild.

Three major river stream systems and their tributaries are targeted for study viz.,

Mahananda, Teesta and Jaldhaka. As these rivers are hill streams, so the rivers have a

natural altitudinal gradient and monsoon flood sometimes cause admixture of the river

waters through overflowing. The irrigation practice and some human made narrow

channels are also causative agent for mixing of water of different rivers.

The total seventeen populations of Badis badis (six population from Mahananda river

system, seven population from Teesta river system, four populations from Jaldhaka

river system) and fourteen populations of Amblyceps mangois (three populations from

Mahananda rier system, seven populations from Teesta river system, four populations

from Jaldhaka river system) were studied for estimation of available genetic diversity.

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Different diversity indices were used for statistical analyses of the data that were generated after amplification by RAPD and ISSR amplification. Additionally, after achieving the specified objectives of my study, Mitochondrial Cytochrome oxidase

(mtCOI) gene of both the species was amplified with specific primer to investigate the genetic diversity and phylogenetic relationship with other geographically/allopatrically isolated populations.

After analysing it was found that the both the fish populations Badis badis and

Amblyceps mangois are dwindling across different river streams. But Teesta river system showed highest level of genetic diversity than other two river systems.

Although in the Jaldhaka river system the within population diversity is high. But the whole river system population was low compared to the Teesta river system. In Teesta river system the within population diversity was low but the whole river system diversity was high. This situation arises because more number of tributaries are converged to the Teesta river which ultimately lead to the high level of diversity within the Teesta river system.

It was observed that there is a break in the diversity pattern as the river flows downward. The richness, evenness and diversity index was altered as the river flows from hilly region to the plain. This is occurred due to different types of anthropogenic and natural pressures which modify or alter the pattern of the diversity.

Moreover the river streams hierarchy showed that in the Teesta and Jaldhaka population genetic differentiation was high and significant level of gene flow also occurs between different populations of river systems. Because mixing of waters from different rivers causes significant number of individuals in reproductive proximity which lead to a great amount of gene flow.

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It was also observed that the first order streams of Teesta and second order streams of

Jaldhaka have high level of diversity and high degree of differentiation. Therefore

these river streams of Teesta and Jaldhaka river system can be considered as an

evolutionary significant unit or ESU for proper management and conservation of this

ichthyofauna.

Moreover, conservation of this ichthyofauna is very much significant and essential for

the social and economical upliftment of local inhabitants and also biological diversity.

Page | 236

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INDEX

A adaptation, 3, 17, 22, 35, 271, Adaptive genetic diversity, 22, 23, 24, Allee effects, 34, allozymes, 4, 43, 44, 45, 124, 240, Amblyceps mangois, 36, 66, 81, 92, 95, 96, 104, 105, 106, 107, 119, 120, 122, 123, 131, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 189, 190, 193, 194, 196, 197, 198, 201, 202, 205, 206, 208, 209, 213, 214, 216, 217, 221, 223, 224, 225, 228, 229, 233, 234, 235, Amplified Fragment Length Polymorphism (AFLP), 47, ANOVA, 24, Arbitrarily Primed Polymerase Chain Reaction (AP-PCR), 49, Archezoa hypothesis, 57, artificial selection, 14, 252, 255,

Badis badis, 36, 50, 66, 81, 92, 94, 95, 104, 106, 114, 119, 120, 121, 123, 131, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 146, 149, 152, 153, 154, 155, 156, 160, 164, 165, 168, 169, 172, 173, 174, 175, 176, 191, 221, 223, 224, 225, 228, 229, 233, 234, 235, 243, 265, Balance hypothesis, 13, Balance speculation, Barcode, 61, 247, 269, Barcoding, 61, 64, 121, 247, bioindicator, 38, BLAST, 119, bottleneck, 20, 21, 215, 257,

CBD, 35, Community genetics, 10, conservation genetics, 3, 5, 22, 37, 42, 56, 247, 249, 257, 258, CoRAP technique, 56, Cytb, 46, 59, cytochrome oxidase subunit 1 (COX 1 or COI), Cytochrome P450 (Cyp450), 55,

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D deleterious alleles, 14, dissolved oxygen, 9, Diversity array technology (DArT), 55, D-loop region, 46, 58, DNA Amplification Fingerprinting, 49, Dooars or Duars, 70,

E edge, 26, 29, 33, effective population size, 7, 18, 19, 20, 22, 39, 45, 53, 229, essentially progressive phenomena, 13, Evolutionary Significant Unit, 228, Expressed Sequence Tag (EST), 40, extinction, 6, 14, 15, 17, 19, 20, 28, 29, 31, 32, 33, 34, 35, 37, 64, 103, 115, 242, 243, 244, 246, 247, 249, 251, 253, 259, 263, 268, 269, 270, 275, 276, 279,

F founder effect, 19, 255, F-statistics, 23, 113, 125, 126,

Gene flow, 17, 23, 113, 166, 259, 264, 273, Gene Flow (Nm), 113, gene pool, 3, 16, 17, 20, 21, 160, 198, 201, 202, 228, 234, genealogies, 41, 42, genetic bottlenecks, 6, Genetic differentiation, 22, 252, 269, Genetic diversity, 3, 5, 8, 9, 11, 13, 35, 122, 134, 173, 177, 214, 245, 256, 266, 270, 276, 281, , genetic draft, 60, genetic drift, 5, 6, 7, 9, 18, 19, 20, 21, 35, 39, 45, 46, 113, 224, 273, Genetic erosion, 21, genetic fingerprint, 5, Genetic Meltdown, 7,

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Genetic variation, 3, 7, 35, 227, 243, 248, 271, 274, 275, 281,

Habitat fragmentation, 25, 279, habitat loss, 26, 27, 28, 34, 36, Hardy-Weinberg equilibrium Test (HWT), 110, Heip’s Index, 114, heritability, 3, 6, 24, heterogeneity, 32, 38, 126, 260, heterogeneous, 8, 30, 32, 34, 247, heterozygosity, 5, 6, 8, 20, 21, 25, 35, 66, 111, 112, 113, 126, 148, 227, Hierarchical Gene Diversity Analyses, 116, homozygosity, 15, 16, 20, Homozygosity, 5, homozygous, 5, 7, 13, 43, 47, 48, 109, Hybridization, 17, 264, hydrogen hypothesis, 57, 58,

Inbreeding, 5, 6, 15, 16, 35, 245, 254, 257, 258, 269, 270, inbreeding depression, 5, 7, 15, 16, 20, 21, 245, 255, 260, 265, 272, Inter Simple sequence Repeat (ISSR), 50, island biogeography, 31, Island model, 18,

Jaldhaka River System, 74, 134, 137, 140, 143, 164, 180, 183, 205,

K keystone species, 12, kin recognition, 11,

L lateral gene transfer (LGT), 57,

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Mahananda-Balasan River System, 72, mass effects, 32, matrix, 25, 26, 29, 30, 31, 32, 115, 126, 154, 155, 156, 176, 196, 197, 198, 217, meta-community, 32, metapopulation, 19, 29, 30, 31, 32, 33, 34, 239, 241, 243, 253, 257, 267, 270, meta-population, 3 Microsatellite DNA, 51, 267, migrant pool, 19, Minisatellite DNA, 51, mitochondrial DNA, 7, 38, 43, 59, 64, 173, 240, 241, 244, 246, 250, 252, 253, 258, 262, 265, 270, 278, molecular clock, 61, Molecular Operational Taxonomic Units (MOTU), 63, monogamous, 15, monomorphic, 7, 13, Mutant alleles, 13, Mutation, 6, 14, 61, 259, 273,

Natural selection, 21, natural selection,, 6, 9, 10, 24, 207, 228, nearest-neighbour distance, 27, Nei’s gene diversity, 135, 136, 137 next generation sequencing (NGS), 40, 54, noninvasively, 93, 99, 233, nuclear DNA, 42, 43, 45, 228, 229, 252, 276,

Outbreeding, 16, outbreeding depression, 6, 17, overdominance hypothesis, 16,

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P panmictic populations, 30, partial dominance, 16, patch dynamics, 32, patch isolation, 27, 28, 242, 275, pH, 8, 97, 98, 99, 101, 105, 107, 118, 119, Phenol-Chloroform Procedure, 97, 99, phenotypic plasticity, 7, Phylogenetic Diversity (PD), 115, Phylogenetics, 273, Polyandry, 15, Polygamous, 15, Polygyny, 15, polymorphic, 6, 7, 13, 40, 44, 48, 49, 51, 52, 54, 55, 56, 109, 110, 113, 135, 136, 137, 138, 139, 140, 141, 142, 143, 147, 148, 149, 173, 178, 179, 180, 181, 182, 183, 184, 185, 186, 190, 192, 214, 224, 239, 240, 250, 252, 255, 256, 266, 272, 274, 280, Polymorphism, 38, 45, 47, 55, 109, 110, 240, , , population genetics, 4, 10, 14, 22, 37, 38, 41, 48, 53, 241, 263, 267, populations differentiation, 18, propagule pool, 19,

Random Amplified Polymorphic DNA (RAPD), 48, random mating, 111, 113, ratcheting, 57, recolonization, 17, 19, 29, 31, 34, 263, recombinant DNA technology, 39, Reduced Representation Shotgun Sequencing (RRSS), 54, regional population, 19, 30, reproductive fitness, 4, 5, 9, 14, 21, rescue effect, 31, restriction fragments, 47, Restriction site-Associated DNA (RAD), 55, RFLP, 38, 40, 45, 46, 47, 246, 266,

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S sequence-related amplified polymorphism technique (SRAP), 56, Serial Endosymbiotic Theory (SET), 57, Shannon’s Information Index, 191, SHE Analysis, 114, Simple sequence repeat (SSR), 52, SLOSS” debate, 33, SNPs, 39, 54, 272, Speciation, 42, 50, 56, species sorting, 32, sub-Himalayan hotspot, 36, 69, 190, 224, synthophy, 57, syntrophic hypothesis, 57,

Teesta River System, 73, 134, 136, 139, 142, 163, 179, 182, 185, 204, Terai, 36, 50, 66, 69, 70, 81, 115, 121, 122, 134, 149, 154, 156, 160, 161, 168, 172, 173, 174, 176, 177, 190, 191, 196, 201, 202, 208, 213, 214, 217, 223, 224, 225, 226, 234, 265, , ,

U universal primers, 46, 62,

Variable Number of Tandem Repeat or VNTR, 51,

W whole genome Shotgun Sequencing (WGSS), 54, wild type alleles, 13,

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APPENDICES

Appendix: A [List of publications]

1. Mukhopadhyay, T. & Bhattacharjee, S. 2013. Assessment of intra-population diversity of the threatened ornamental fish Badis badis (Hamilton-Buchanan, 1822) from the Terai region of West Bengal. NBU J. Anim. Sc. 7:13-23. ISSN-0975-1424. 2. Mukhopadhyay, T. & Bhattacharjee, S. 2014. Standardization of genomic DNA isolation from minute quantities of fish scales and fins amenable to RAPD-PCR. Proc. Zool. Soc. 67(1):28-32. ISSN: 0974-6919 (electronic version), Springer-Verlag, Germany & Zoological Society, Kolkata, India. 3. Mukhopadhyay, T. & Bhattacharjee, S. 2014. Study of the genetic diversity of the Ornamental fish Badis badis (Hamilton-Buchanan, 1822) in the Terai region of sub- Himalayan West Bengal, India. Int. J. Biodiv. :1-10, [ISSN: 2314-4149 (Print)/2314- 4157(Online), DOI. http://dx.doi.org/10.1155/2014/791364. Hindawi Publishing Corporation, USA. 4. Mukhopadhyay, T. & Bhattacharjee, S. 2015. Population and hierarchical genetic structure of Badis badis (Hamilton-Buchanan, 1822) in sub-Himalayan Terai region of West Bengal, India. Int. J. Aqua. 5(27): 1-10. ISSN 1927-5773, BioPublisher & Sophia Publishing Group Inc., British Columbia, Canada. 5. Mukhopadhyay, T. & Bhattacharjee, S. 2016. Locus specific genetic divversity and population differentiation analyses of Badis badis (Hamilton-Buchanan, 1822) in sub- Himalayan West Bengal, India. Int. J. Aqua. 6(4): 1-9. ISSN 1927-5773, BioPublisher & Sophia Publishing Group Inc., British Columbia, Canada. 6. Paul, A., Mukhopadhyay, T. & Bhattacharjee, S. 2016. Genetic characterization of Barilius barna (Hamilton, 1822) in the Teesta river of sub-Himalayan West Bengal, India, through RAPD and ISSR fingerprinting. Proc. Zool. Soc.:1-10, DOI: 10.1007/s12595-016-0191-x. ISSN: 0974-6919 (electronic version), Springer-Verlag, Germany & Zoological Society, Kolkata, India 7. Mukhopadhyay, T., & Bhattacharjee, S. Analyses of Genetic diversity Badis badis (Hamilton- Buchanan 1822) using RAPD and ISSR fingerprinting from three riverine systems in sub- Himalayan biodiversity hotspot, West Bengal, India. Genetika, Belgtade (Accepted). 8. Mukhopadhyay, T. & Bhattacharjee, S. Genetic diversity and population structure analyses of threatened Amblyceps mangois from biodiversity-rich sub-Himalayan West Bengal, India, through RAPD and ISSR fingerprinting. (In Communication) 9. Mukhopadhyay, T. & Bhattacharjee, S. 2016. Genetic Diversity: Its Importance and Measurements. In Conserving Biological Diversity: A Multiscaled Approach, eds. Aabid Hussain Mir and Nazir Ahmad Bhat. Research India Publication, New Delhi, pp 251-295. (ISBN: 978-93-86138-00-2). 10. Mukhopadhyay, T. & Bhattacharjee, S. (2015). DNA Fingerprinting in Conservational Research on Ichthyofauna. In Animal Diversity, Natural History and Conservation, eds. V. K. Gupta and A. K. Verma, Vol. 5 (Chapter 13): 177-211. Daya Publishing House, New Delhi (Indian ISBN: 9789351246169; International ISBN: 9789351306610).

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Appendix: B [Seminar & Conferences]

1. Mukhopadhyay, T. and Bhattacharjee, S. (2012). Standardization Of Genomic DNA Isolation from Minute Quantity of Fish Scales and Fins Amenable to RAPD-PCR Based Genetic Diversity Assessment. UGC Sponsored National Seminar on ―BIODIVERSITY AND SUSTAINABILITY VIS-À-VIS ECONOMIC DEVELOPMENT IN THE NORTHERN PARTS OF WEST BENGAL‖, jointly organized by Department of Botany, Raiganj Surendranath Mahavidyalaya, Uttar Dinajpur and Department of Zoology, Alipurduar College, Jalpaiguri, West Bengal, August 26-27, 2012. [Oral Presentation]

2. Mukhopadhyay, T. and Bhattacharjee, S. (2012). RAPD-PCR based Assessment of Genetic Diversity in the Threatened Ornamental Fish Badis from Terai Rivers of North Bengal. UGC Sponsored National Seminar on ―RECENT ADVANCES IN LIFE SCIENCE APPLICATIONS‖, jointly organized by PG Department of Zoology, A. B. N. Seal College, Coochbehar and Department of Agricultural Entomology, UBKV Pundibari, Coochbehari, West Bengal, December 08-09, 2012. [Oral Presentation]

3. Mukhopadhyay, T. and Bhattacharjee, S. (2013). Assessment of Genetic Relatedness in the Threatened Ornamental fish Badis badis (Hamilton 1822) from Terai rivers of North Bengal. UGC-Merged Scheme National Symposium on ―MAN, ANIMAL AND ENVIRONMENT INTERACTION IN THE PERSPECTIVE OF MODERN RESEARCH‖, at Department of Zoology, University of North Bengal, March 8-9, 2013. [Oral Presentation]

4. Mukhopadhyay, T. and Bhattacharjee, S. (2014). Intra-Population Diversity of the Threatened Ornamental Fish Badis Badis (Hamilton-Buchanan, 1822) from the Terai Region of West Bengal. ―SECOND NATIONAL BIODIVERSITY CONGRESS 2013‖, Kolakata, January 16-18, 2014. [Oral Presentation]

5. Mukhopadhyay, T. and Bhattacharjee, S. (2014). Assessment of genetic diversity of threatened ornamental fish Badis badis (Hamilton-Buchanan, 1822) through RAPD fingerprinting in sub-Himalayan North-Eastern India. UGC Sponsored Seminar on ―BIOLOGICAL RESEARCH IN HUMAN WELFARE‖, February 07, 2014 at Department of Zoology, University of North Bengal. [Poster Presentation]

6. Mukhopadhyay, T. and Bhattacharjee, S. (2014). RAPD Fingerprinting Delineates the Genetic Diversity of Threatened Ornamental Fish Badis badis (Hamilton-Buchanan, 1822) in sub-Himalayan Terai Region of North-Eastern India. ―INTERNATIONAL CONFERENCE ON ENVIRONMENTAL BIOLOGY AND ECOLOGICAL MODELLING (ICEBEM–2014)‖, at Department of Zoology, Centre for Advanced Studies, Visva-Bharati, Santiniketan-731235, West Bengal, INDIA, February 24-26, 2014. [Poster Presentation]

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7. Mukhopadhyay, T. and Bhattacharjee, S. (2015). Hierarchical Genetic Diversity Analyses of Badis Badis (Hamilton-Buchanan, 1822) in the Sub-Himalayan Dooars Region of West Bengal, India. UGC Sponsored National Seminar on ―ADVANCES IN BIOLOGY: EASTERN HIMALAYAN PERSPECTIVE‖, October 03 to October 04, 2015 at Department of Zoology, Kalimpong College, Kalimpong. [Oral Presentation]

8. Mukhopadhyay, T. and Bhattacharjee, S. (2015). Genetic Diversity Analyses of Badis Badis (Hamilton-Buchanan, 1822) in the Sub-Himalayan Dooars Region of West Bengal, India. National Conference on ―NATURAL RESOURCES, DIVERSITY AND SUSTAINABLE DEVELOPMENT‖, December 11 to December 12, 2015, Organized by Foundation for Science and Environment, Kolkata and West Bengal Biodiversity Board at Department of Zoology, University of North Bengal. [Oral Presentation]

9. Paul, A., Mukhopadhyay, T. and Bhattacharjee, S. (2017). Genetic characterization and assessment of population structure of Barilius barna (Hamilton, 1822) in the Teesta river of sub-Himalayan Dooars region of West Bengal through RAPD, ISSR and MtCOI sequence analyses. ZooCon2017, February 11th to 12th, 2017, Organized by Department of Zoology, University of North Bengal, West Bengal, India. [Oral Presentation]

10. Paul, A., Mukhopadhyay, T. and Bhattacharjee, S. (2017). Assessment of genetic diversity and population structure of Barilius barna (Hamilton, 1822) in the sub- Himalayan Teesta River of northern West Bengal, India. The 8TH STUDENTS CONFERENCE ON CONSERVATION SCIENCE, BENGALURU, September 21st to 24th, 2017, Organized by Indian Institute of Science, Bengaluru, India. [Poster Presentation]

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Author's personal copy OGICAL L S O O O C Z I E

Proc Zool Soc T H Y DOI 10.1007/s12595-013-0065-4 T

K O LK ATA RESEARCH ARTICLE

Standardization of Genomic DNA Isolation from Minute Quantities of Fish Scales and Fins Amenable to RAPD-PCR

Tanmay Mukhopadhyay • Soumen Bhattacharjee

Received: 22 November 2012 / Revised: 25 February 2013 / Accepted: 18 March 2013 Ó Zoological Society, Kolkata, India 2013

Abstract The main focus of this study was to standardize organisms threatened with extinction (O’Brien 1994 ; Mit- a non-destructive procedure for extraction of genomic ton 1994 ; Sunnucks 2000 ). These studies require extraction DNA (gDNA) from minute quantities of scales and fins of of genomic DNA (gDNA) from the species concerned two commonly available fishes Labeo bata and Hete- using a method which is easy, inexpensive, rapid and ropneustes fossilis and also to compare the gDNA yields reliable (Elphinstone et al. 2003 ; Mogg and Bond 2003 ). A from live and as well from frozen samples. The spectro- non-destructive method of gDNA isolation is highly pre- photometric and electrophoretic analyses revealed a sig- ferred to in the species that are threatened with extinction, nificant difference in the DNA yields from live and frozen aiming avoidance of their indiscriminate sacrifice. samples. The isolated gDNAs were used as templates for Although DNA can successfully be extracted from the RAPD-PCR. The quality and consistency of banding pat- muscles (Hilsdorf et al. 1999 ) and blood (Cumming and tern showed that gDNA templates from live tissues per- Thorgaard 1994 ; Estoup et al. 1996 ; Martinez et al. 1998 ) formed better than that from frozen tissue samples. It was without sacrifice, there are some non-destructive and non- also found that the minute quantities of fresh scales or fin invasive techniques available for obtaining gDNA from tissues from live fish provided satisfactory quantity and fish (Wasko et al. 2003 ; Kumar et al. 2007 ). However, quality of gDNAs that could support several rounds of there is a lack of information with regard to a comparative RAPD-PCR. This non-destructive sampling has a great analysis of gDNA yields from live and preserved tissue implication in gDNA based population genetic studies in samples and also of the performance of isolated DNAs as endangered and vulnerable species of fishes, where killing templates in RAPD-PCR-based analyses. or sacrificing is an ethical issue. The main objectives of the study were (1) to focus on a non-destructive method to extract gDNA from minute Keywords gDNA extraction Á Non-destructive Á quantities of fin and minimal numbers of scales of two Fish scale Á Fish fin Á RAPD-PCR commonly available fishes, Labeo bata and Heteropneustes fossilis without sacrificing the species, (2) to compare gDNA yields from live specimens as against frozen sam- Introduction ples, and (3) to check performance of RAPD-PCR using these gDNA as templates. We have standardized a non- DNA markers are widely used in the study of biodiversity, invasive and non-destructive method of gDNA isolation conservation and population genetics of different from minute number/quantities of scale and fin tissues from two commonly available fishes without sacrificing the individuals. We have also found that the gDNA yields were T. Mukhopadhyay Á S. Bhattacharjee ( &) relatively high in live specimens in comparison to that from Cell and Molecular Biology Laboratory, Department of Zoology, frozen ones. When isolated gDNAs from live and frozen University of North Bengal, P.O. North Bengal University, Raja tissues were used as templates in Random Amplified Rammohunpur, Siliguri, District Darjeeling 734 013, West Bengal, India Polymorphic DNA-Polymerase Chain Reaction (RAPD- e-mail: [email protected] PCR)-based amplifications the RAPD banding patterns

123 Author's personal copy

Proc Zool Soc showed a marked difference between live and frozen well Peltier Thermal Cycler (Applied Biosystems 2720, samples. Our results also showed that the gDNA from Life Technologies, USA) in a reaction volume of 25 ll, minute quantities of fresh scales and fins were sufficient for containing a final concentration of 50–100 ng of isolated several rounds of RAPD-PCR and other gDNA-based gDNA (from 5 and 50 pieces of scales of L. bata and from experiments in comparison to larger tissue sizes. 1 and 30 mg of fin tissue of both L. bata and H. fossilis ), 1.6 pM of OPA primers, 1 9 standard Taq Polymerase Buffer (NEB, USA), 200 lM of each dNTP (NEB, USA), Materials and Methods and one unit of Taq DNA Polymerase (NEB, USA). Amplification conditions were 94 °C for 5 min followed by Labeo bata (Hamilton) and H. fossilis (Bloch) samples 40 cycles at 94 °C for 1 min, 35 °C for 1 min, 72 °C for were collected from local fish markets and were kept in an 2 min and then subjected to a final extension of 72 °C for aquarium and in frozen condition accordingly. The scales 10 min. The amplified products were electrophoresed in (5, 10, 20, 30 and 50 pieces) were noninvasively collected 1.4 % agarose gel at a constant voltage 100 V and current from live and frozen specimens separately by gentle 100 mA using BenchTop Labsystems BT-MS-300, Tai- scraping from the dorsal portion of the body of L. bata and wan. Molecular weight of each band was estimated using a little portions of the fin (1, 5, 10, 20 and 30 mg) were standard 100 base pair ladder (NEB, USA). Reproducibility clipped off from the caudal and ventral fins from L. bata as of the amplification pattern in each primer was checked by well as H. fossilis. The samples were dried on a filter paper repeating each reaction thrice without deliberately altering before further processing. the protocol. Genomic DNA extraction was done following Kumar The gDNAs isolated from 5 and 50 pieces of scales of L. et al. ( 2007 ) with minor modifications. A lysis buffer con- bata and from 1 and 30 mg of fin of both L. bata and H. taining 200 mM Tris HCl, pH 8.0; 100 mM EDTA, pH 8.0; fossilis were treated with RNase A (USB, USA) at 37 °C 250 mM NaCl was prepared and warmed at 50 °C for followed by phenol–chloroform purification and were 5–10 min. The tissues were then chopped and placed in a quantified spectrophotometrically and purity checked at Ò 2 ml microcentrifuge tube containing 955 ll pre-warmed A260 /A 280 in Rayleigh UV2601 Spectrophotometer, Bei- lysis buffer, 15 ll Proteinase K (20 mg/ml) (NEB, USA) and jing, China. 30 ll 20 % SDS and incubated at 52 °C for 60–70 min in a water bath. The tubes were gently stirred occasionally during incubation period. After the incubation equal volume of Results phenol:chloroform:isoamyl alcohol (25:24:1, v/v/v) was added to the tubes and mixed by gently inverting the tubes The DNA isolation method employed in this study showed for about 15 min. The tubes were then centrifuged for that sufficient amounts of high molecular weight gDNAs 12 min at 9,500 rpm at 4 °C in an Eppendorf refrigerated could be purified from fish scales/fins (Fig. 1). While the centrifuge (Model-5424R, Germany). After centrifugation integrity of the isolated gDNAs appeared satisfactory clear supernatants were taken in fresh tubes and equal vol- ample amount of RNAs were present in the isolated gDNA ume of isopropyl alcohol and 0.2 volume of 10 M ammo- samples (Fig. 1a–c). The figure showed that the yields of nium acetate were added and incubated for 30–40 min at high molecular weight gDNAs from live scale and fin -20 °C to precipitate the gDNAs. The gDNAs were pel- samples were substantially higher than that from frozen leted by centrifugation at 13,500 rpm for 12 min at 4 °C. samples. However the gDNAs isolated from different The supernatants were decanted gently and pellets were quantities of fin clips of both live and frozen Heteropn- washed once in 500 ll chilled 70 % ethanol, air dried and eustes (scaleless species) were more degraded than that resuspended in 100 ll sterile Milli-Q water. The integrity of from scales or fins of Labeo (Fig. 1c). the gDNAs was checked by loading 10 ll extracted DNA in Quantification of gDNA yields was done after treating ethidium bromide (0.5 lg/ml) prestained 0.7 % agarose gel each isolate with RNase A. The total yield of gDNAs from (Lonza, Basel, Switzerland) in TAE buffer (40 mM Tris– 5 and 50 pieces of scales were 8.40 and 80.0 lg respec- HCl, pH 8.0; 20 mM Acetic acid; 1 mM EDTA, pH 8.0). tively and from 1 and 30 mg fin were 4.65 and 123.50 lg The gels were checked on the UV-transilluminator respectively in live L. bata . In live H. fossilis the total yield (Spectroline BI-O-Vision Ò NY, USA) and then photo- were 14.60 and 132.60 lg from 1 and 30 mg fin respec- graphed. The DNAs were stored at -20 °C till PCR analysis. tively. However, the yield of gDNAs from frozen samples RAPD analyses were done using two decamer primers of both L. bata and H. fossilis were relatively low in viz., OPA02 (5 0 TGCCGAGCTG 3 0) and OPA07 (5 0GA- comparison to that from the live ones (Table 1). AACGGGTG 3 0) (Imperial Life Science Pvt. Ltd., India). The gDNAs isolated from lowest (5 pieces) and highest The PCR amplification reaction was carried out in a 96 (50 pieces) numbers of scales of L. bata and that from

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Live Frozen Live Frozen Live Frozen 4 5 6 7 1 2 3 8 9 10 11 12 2 3 4 7 8 9 1 5 6 10 11 3 4 7 8 10 12 12 1 2 5 6 9 11

(a) L. bata (Scale) (b) L. bata (Fin) (c) H. fossilis (Fin)

Fig. 1 0.7 % agarose gel electrophoreses of genomic DNAs 30 mg of fin gDNA preparations from live L. bata , lanes 8–12 1, 5, (gDNAs) isolated from Live and Frozen scale and fin samples of L. 10, 20, 30 mg of fin gDNA preparations from frozen L. bata . c Lane 1 bata and H. fossilis . a Lane 1 Lambda DNA/Hind III Digest DNA size lambda DNA/Hind III Digest DNA size marker (kb), lanes 2–6 1 mg, marker (kb), lanes 2–6 5, 10, 20, 30, 50 pieces of scale gDNA 5, 10, 20, 30 mg of fin gDNA preparations from live H. fossilis , lanes preparations from live L. bata , lanes 8–12 5, 10, 20, 30, 50 pieces of 8–12 1, 5, 10, 20, 30 mg of fin gDNA preparations from frozen H. scale gDNA preparations from frozen L. bata . b Lane 1 Lambda fossilis DNA/Hind III Digest DNA size marker (kb), lanes 2–6 1, 5, 10, 20, lowest (1 mg) and highest (30 mg) amounts of fin tissues Orban 2001 ). Isolation of gDNAs from minute quantities of of the fishes were used as templates in RAPD-PCR anal- fish scale and fin is less likely to cause mortality of the fish yses (Fig. 2). In live L. bata scale and fin, OPA02 primer and being the non-destructive method can be employed to produced nine bands and OPA 07 produced seven bands threatened category fish species for subsequent DNA-based respectively [Fig. 2a (lanes 2, 3, 6, 7), b (lanes 2, 3, 6, 7)]. studies. But in frozen L. bata scale and fin the result was different, The gDNAs isolated from different quantities of fin clips the bands appeared faint, OPA02 primer produced three of both live and frozen Heteropneustes were more degra- bands and OPA07 primer produced four bands [Fig. 2a ded than that from gDNAs isolated from scales or fins of (lanes 4, 5, 8, 9), b (lanes 4, 5, 8, 9)]. In live H. fossilis fin Labeo . This could result due to the soft nature of the tissue OPA 02 gives eight bands and OPA07 gives four bands itself, which is also evident in Labeo fin gDNAs (Fig. 1b). (Fig. 2c lanes 2, 3, 6, 7) whereas in frozen sample the However, it is clear from the gDNA gel (Fig. 1c) that the numbers of bands were relatively less, both OPA 02 pro- yields of gDNAs were much higher from the live samples duced six bands and OPA07 produced three bands (Fig. 2c than that from the frozen samples of Heteropneustes , and lanes 4, 5, 8, 9). these fin-gDNAs could also support several rounds RAPD- PCR analyses. Moreover, the gDNA yields in frozen samples of both L. bata and H. fossilis were relatively Discussion lower in comparison to that from the live ones (Table 1). Wasko et al. ( 2003 ) used 0.075 mg/ml Proteinase K in Fish scales and fin are reliable sources of gDNA and have 10 h incubation period for tissue digestion. In the present been used earlier to isolate gDNA from some fish species study, 0.3 mg/ml final concentration of Proteinase K was (Shiozawa et al. 1992 ; Taggart et al. 1992 ; Whitemore used in tissue digestion at 52 °C for 1 h for each prepa- et al. 1992 ; Zhang et al. 1994 ; Nielsen et al. 1997 , 1999 ; ration. While Wasko et al. ( 2003 ) suggested that gDNA Adcock et al. 2000 ). Since 1990s however, those isolation should be free of RNA contamination for efficient PCR techniques were not standardized quantitatively and were amplification our data showed that successful RAPD either elaborate or costly (Nelson et al. 1998 ; Yue and amplifications could be carried out in the presence of

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Table 1 Spectrophotometric Specimen Tissue Sample OD OD OD / Concentration Total yield assessment of gDNA yields and 260 280 260 type OD (lg/ ll) (lg) purity in both live and frozen 280 tissues from a carp ( Labeo bata ) Labeo bata (live) Scale 5 pieces 0.0056 0.0032 1.75 0.084 8.40 and a catﬁsh ( Heteropneustes fossilis ) after treatment with 50 pieces 0.0530 0.0243 2.18 0.800 80.0 RNase A Fin 1 mg 0.0031 0.0016 1.94 0.0465 4.65 30 mg 0.0823 0.0446 1.85 1.235 123.50 Labeo bata (frozen) Scale 5 pieces 0.0021 0.0011 1.91 0.032 3.20 50 pieces 0.0190 0.0102 1.86 0.285 28.5 Fin 1 mg 0.0009 0.0005 1.80 0.0135 1.35 30 mg 0.0240 0.0137 1.75 0.36 36.0 Heteropneustes fossilis (live) Fin 1 mg 0.0097 0.0049 1.98 0.146 14.60 30 mg 0.0884 0.0463 1.86 1.326 132.60 Heteropneustes fossilis (frozen) Fin 1 mg 0.0011 0.0006 1.83 0.0165 1.65 30 mg 0.0165 0.0093 1.77 0.2475 24.75

OPA02 OPA07 OPA02 OPA07 OPA02 OPA07

Live Frozen Live Frozen Live Frozen Live Frozen Live Frozen Live Frozen

(a) L. bata (Scale) (b) L. bata (Fin) (c) H. fossilis (Fin)

Fig. 2 RAPD banding patterns in 1.4 % agarose gels from L. bata 1 and 30 mg of fins respectively from live L. bata , lanes 4–5 , 8–9 and H. fossilis using decamer primers OPA02 and OPA07. a Lane 1 RAPD profile from 1 and 30 mg of fin respectively from frozen 100 bp DNA ladder (kb), lanes 2–3 , 6–7 RAPD profile from 5 and 50 L. bata . c Lane 1 100 bp DNA ladder (kb), lanes 2–3 , 6–7 RAPD pieces of scale respectively from live L. bata , lanes 4–5 , 8–9 RAPD profile from 1 and 30 mg of fin respectively from live H. fossilis , profile from 5 and 50 pieces of scale respectively from frozen L. bata . lanes 4–5 , 8–9 RAPD profile from 1 and 30 mg of fin respectively b Lane 1 100 bp DNA ladder (kb), lanes 2–3 , 6–7 RAPD profile from from frozen H. fossilis

contaminating RNA in gDNA preparations, thus suffice to were sufficient to produce sufficient amount of gDNA (data state that RNA could not hamper our RAPD amplifications not shown). The quantities and quality of gDNAs isolated and distinct scorable bands were found in each replicate. from live scales and fins were better than that from frozen All gDNA preparations were treated with RNase A to scales and fins as evident from spectrophotometric analyses accurately quantify gDNA yields. Spectrophotometric data and RAPD-PCR (Table 1; Fig. 2). In this study the quan- showed that the quantity of gDNA isolated from 5 scales tities of isolated gDNAs were higher as compared to the was 8.40 lg and from 1 mg fin clip was 4.65 lg in live L. studies undertaken by Yue and Orban ( 2001 ) and Kumar bata . The quantity of gDNA isolated from 1 mg fin clip et al. ( 2007 ). The high molecular weight gDNAs were was 14.60 lg in live H. fossilis (Table 1). We have found compared with the 23.13 kb band of Lambda/Hind III that even a single piece of scale or less than 1 mg of fin clip digest size markers. The isolated gDNAs were dissolved in

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Type I Milli-Q water which contained no PCR inhibitors. Cumming, S.A., and G.H. Thorgaard. 1994. Extraction of DNA from The spectrophotometric data provided the purity factor fish blood and sperm. BioTechniques 17: 426–430. Elphinstone, M.S., G.N. Hinten, M.J. Anderson, and C.J. Nock. 2003. (A 260 /A 280 ) that ranged from 1.75 to 2.18 indicating low An inexpensive and high-throughput procedure to extract and protein contamination (Table 1). purify total genomic DNA for population studies. Molecular The RAPD-PCR analyses showed that gDNAs isolated Ecology Notes 3: 317–320. from minute tissue samples were able to produce banding Estoup, A., C.R. Largiader, E. Perrot, and D. Chourrout. 1996. Rapid one tube DNA extraction for reliable PCR detection of fish patterns comparable to that from templates isolated from polymorphic markers and transgenes. Molecular Marine Biology larger tissue samples (Fig. 2). The number and quality of and Biotechnology 5: 295–298. RAPD bands from the frozen samples were less and this Hilsdorf, A., D. Caneppele, and J.E. Krieger. 1999. Muscle biopsy indicated that gDNAs isolated from live samples will be technique for electrophoresis analysis of fish genus Brycon . Genetics and Molecular Biology 22: 547–550. more informative than that from frozen or preserved tissue Kumar, R., P.J. Singh, N.S. Nagpure, B. Kushwaha, S.K. Srivastava, samples in DNA-based studies. Moreover, no positive and W.S. Lakra. 2007. A non invasive technique for rapid control has been used in RAPD-PCR analysis because no extraction of DNA from fish scales. Indian Journal of Exper- such proper control can be included in RAPD-PCR as the imental Biology 45: 992–997. Martinez, G., E.M. Shaw, M. Carrillo, and S. Zanuy. 1998. Protein banding pattern differs depending on the gDNAs used. salting-out method applied to genomic DNA isolation from fish In summary, the yield of gDNA was substantially high whole blood. BioTechniques 24: 238–239. from the live than that from the frozen fish tissue samples. Mitton, J.B. 1994. Molecular approaches to population biology. The gDNA samples isolated by this method from fresh fish Annual Review of Ecological Systematics 25: 45–69. Mogg, R.J., and M.J. Bond. 2003. A cheap, reliable and rapid method scale and fin tissue of live individuals of two species ( L. of extracting high-quality DNA from plants. Molecular Ecology bata and H. fossilis ) were successfully used in RAPD-PCR Notes 3: 666–668. using two random primers (OPA02 and OPA07) and the Nelson, R.J., T.D. Beacham, and M.P. Small. 1998. Microsatellite results indicated that the quality and quantities of the analysis of the population structure of Vancouver Island sockeye salmon Oncorynchus nerka stock complex using non-denaturing gDNAs were quite satisfactory to support several rounds of gel electrophoresis. Molecular Marine Biology and Biotechnol- RAPD-PCR amplification. The banding pattern in live and ogy 7: 312–319. frozen specimen showed a marked difference, which might Nielsen, E.E., M.M. Hansen, and V. Loeschcke. 1997. Analysis of be due to fragmentation of the genome in frozen condition microsatellite DNA from old scale samples of Atlantic salmon Salmo salar : a comparison of genetic composition over 60 years. and the primers could not found a continuous long region Molecular Ecology 6: 487–492. with considerable length to amplify. Although there are Nielsen, E.E., M.M. Hansen, and V. Loeschcke. 1999. Analysis of reports of non-destructive methods of gDNA isolation from DNA from old scale sample: technical aspects, applications and fishes our study not only compared the yields of gDNAs perspective for conservation. Heriditas 130: 265–276. O’Brien, S.J. 1994. A role of molecular genetics in biological from live and frozen/preserved tissue samples but also conservation. Proceedings of National Academy of Science USA determined the lowest amount of tissue samples required 91: 5748–5755. for DNA isolation. Our procedure described here thus Shiozawa, D.K., J. Kudo, R.P. Evans, S.R. Woodward, and R.N. offers an inexpensive and reliable method to isolate suffi- Williams. 1992. DNA extraction from preserved trout tissue. Great Basin Naturalist 52: 29–34. ciently good quantity and quality of gDNAs from minute Sunnucks, P. 2000. Efficient genetic markers for population biology. quantities of fish scale and fin tissues that can serve as Tree 155: 199–203. templates for RAPD-based Polymerase Chain Reaction and Taggart, J.B., R.A. Hynes, P.A. Prodohol, and A. Ferguson. 1992. A other gDNA based studies. This study has a great impli- simplified protocol for routine total DNA isolation from salmonoid fishes. Journal of Fish Biology 40: 963–965. cation in case of small fish species which are rare or Wasko, A.P., C. Martins, C. Olivera, and F. Foresti. 2003. Non endangered, where killing or sacrificing of the concerned destructive genetic sampling in fish: an improved method for species will not be acceptable. DNA extraction from fish fins and scales. Heriditas 138: 161–165. Acknowledgments The work was supported by a research grant Whitemore, D.H., T.H. Thai, and C.M. Craft. 1992. Gene amplifi- from University Grants Commission (UGC), India [Sanction No.: cation permits minimally invasive analysis of fish mitochondrial 40-289/2011 (SR)] awarded to corresponding author. DNA. Transactions of the American Fish Society 121: 170–177. Yue, G.H., and L. Orban. 2001. Rapid isolation of DNA from fresh and preserved fish scales for polymerase chain reaction. Marine Biotechnology 3: 199–204. References Zhang, Q.Y., T.R. Tiersch, and R.K. Cooper. 1994. Rapid isolation of DNA for genetic screening of catfishes by polymerase chain Adcock, G.J., J.H.B. Ramirez, L. Hauser, P. Smith, and G.R. reaction. Transactions of the American Fish Society 123: Carvalho. 2000. Screening of DNA polymorphism in samples of 997–1001. archived scales from New Zealand snapper. Journal of Fish Biology 56: 1283–1287.

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Hindawi Publishing Corporation International Journal of Biodiversity Volume 2014, Article ID 791364, 10 pages http://dx.doi.org/10.1155/2014/791364

Research Article Study of the Genetic Diversity of the Ornamental Fish Badis badis (Hamilton-Buchanan, 1822) in the Terai Region of Sub-Himalayan West Bengal, India

Tanmay Mukhopadhyay and Soumen Bhattacharjee

Cell and Molecular Biology Laboratory, Department of Zoology, University of North Bengal, P.O. North Bengal University, Raja Rammohunpur, Siliguri, Darjeeling District, West Bengal FGH IJG, India

Correspondence should be addressed to Soumen Bhattacharjee; [email protected]

Received ;< August ;>?@; Accepted A October ;>?@; Published D November ;>?@

Academic Editor: Devon Keeney

Copyright © ;>?@ T. Mukhopadhyay and S. Bhattacharjee. Ois is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Dwarf chameleon Tsh or Badis badis , a lesser known ornamental freshwater Tsh, has recently been included in the Indian threatened category of Tsh list. Oere are insuVcient studies with regard to the assessment of genetic background of this ichthyofauna, especially in the western sub-Himalayan region of West Bengal, India, popularly known as the Terai. Oe present study is the Trst attempt to investigate the present status of the genetic background of this species in the Mahananda and Balason rivers, major streams of this region. Twenty-one selective RAPD primers generated Z[ and D> polymorphic fragments in the Mahananda and Balason populations, respectively. Oe proportion of polymorphic loci, Nei’s genetic diversity ( H), and Shannon’s index (! ὔ) were >.@@?D, 0.1654 ± 0.2023 , and 0.2450 ± 0.2907 , respectively, in Mahananda river population and were >.Z>@?, 0.1983 ± 0.2126 , and 0.2901 ± 0.3037 , respectively, in Balason river population. Inbreeding coeVcient and degree of gene di`erentiation were also calculated. Oe H and !ὔ were found to be 0.1601±0.1944 and 0.2363±0.2782 , respectively, in overall Mahananda-Balason river system. Our study revealed considerable lack of genetic variation among the individuals of Badis badis . Oe genetic data obtained from the present study lend support to the view that there is a scope of stock improvement for this ichthyofauna.

1. Introduction Genetic variability or diversity is an essential characteristic of any population for the Ttness of individuals as well Badis badis (Hamilton-Buchanan, ?a;;) (Actinopterygii, Per- as survival of the whole population, permitting adaptation to ciformes, Badidae) or dwarf chameleon Tsh is a tropical, ben- the changing environmental conditions and stress. Oerefore thopelagic freshwater species that attains up to Z cm in total the degradation of genetic diversity of a species reduces length. It has noteworthy ornamental and thus commercial its capability for adaptation and increases the risk of its value and has recently been included within the vulnerable extinction [ ;–Z]. Inbreeding is implicated in the erosion of category in the list of threatened freshwater Tshes of India genetic variation within natural populations, especially in by National Bureau of Fish Genetic Resources (NBFGR, Luc- small populations, by reducing the number of heterozygotes know, India) [ ?]. However, there have been insuVcient studies and hence reducing the mean phenotypic values of useful with regard to the estimation of genetic diversity of this Tsh traits [ D]. Oe endangered/vulnerable organisms having small species, especially in the eastern sub-Himalayan region of population size may experience a continuous reduction in the West Bengal, India, popularly known as Terai. Oe region level of genetic variation. Oerefore, the study of genetic vari- being included within the Eastern Himalayan hotspot, our ability is of major importance to conservation, sustainability, results will be important from the standpoint of validation and management of wild population which depends on the of its threatened status and its conservation/sustainability of Trsthand knowledge of the amount of variation existing in a wild population, if required. local reproductive population [ <]. ! International Journal of Biodiversity

PCR-based random ampli:ed polymorphic DNA ;.;. Isolation of High Molecular Weight DNA and QuantiG- (RAPD-PCR) is a widely used molecular tool in detection cation. Genomic DNA (gDNA) extraction was done from and characterization of genetic polymorphisms in natural tiny amount of tissue samples (NO–NU mg of :n clips from populations with higher speed and eEciency [ G, I]. RAPD- the caudal and ventral portions and/or !U–RO pieces of scales PCR ampli:es DNA segments of variable lengths and from the dorsal portion were collected noninvasively) from such length polymorphisms are inherited in a Mendelian each :sh following Mukhopadhyay and Bhattacharjee [ !W ]. fashion and thus can be used as genetic markers [ NO ]. Ze DNA samples were resuspended in NOO !L sterile Type- RAPD analysis has been extensively used to evaluate N Milli-Q water and stored at −!O ∘C till further analysis. genetic diversity in Brycon lundii, Astyanax altiparanae and Ze extracted DNA samples were subjected to spectropho- Prochilodus marggravii [NN –NR ], Sounder [ NT ], Labeo rohita tometric analysis for quanti:cation in Rayleigh UV!VON [NU ], cat:sh [ NV , NW ], mud eel [ NG ], ornamental reef :shes Spectrophotometer, Beijing, China, and the concentration [NI ], crocodile [ !O ], black rat snake [ !N ], and hilsa [ !! ]; of extracted DNA was adjusted to UO ng/ !L aaer suitable and in subspecies identi:cation of tilapia [ !R ], Badis badis , dilution of the extracted DNA samples for each PCR ampli:- Dario dario [!T ], and lamprey [ !U ]. A very limited genomic cation. research has been carried out in Badis badis species till date and insuEcient genomic information is available to ;.I. Primer Selection. Forty arbitrary decamer primers of perform other sophisticated :ngerprinting techniques where random sequences (Kit-A and Kit-B, twenty primers from whole genomic sequence is necessary; therefore, RAPD each kit) were purchased from Imperial Life Science Pvt. :ngerprinting is the best choice for genetic diversity analysis Ltd., India. Twenty-one primers (NO from Kit-A and NN from in this species. Kit-B) were selected for further analyses on the basis of Ze main objectives of our study was to (N) ascertain the the numbers, variability, and reproducibility of the bands present status of the genetic background of the Badis badis obtained ( Table N ). species in the Terai region (especially in the Mahananda and Balason river system) of sub-Himalayan Terai region of West Bengal, India, (!) determine the genetic diversity within and ;.J. RAPD-PCR and Documentation of AmpliGed Products. between the populations of Badis badis , (R) determine the RAPD analyses were performed in a IV-well Peltier Zermal total genetic diversity of Mahananda-Balason river system Cycler (Applied Biosystems !W!O, Life Technologies, USA) of Terai region of sub-Himalayan West Bengal, India. Our in a :nal reaction volume of !U !L, each containing :nal results provide :rsthand information that may be used in the concentrations of ∼NOO–NUO ng of isolated gDNA, N.V pM of proper maintenance and conservation of this :sh fauna as OPA or OPB primer, NX Standard Taq Polymerase buèr well as in other dwindled populations. (NO mM Tris-HCl, pH G.R, UO mM KCl, and N.U mM MgCl 2) (NEB, USA), !OO !M of each of dNTPs (dATP, dTTP, dCTP, and dGTP) (NEB, USA), and one unit of Taq DNA Poly- merase (NEB, USA). PCR cycling programs were as follows: 2. Materials and Methods initial denaturation at IT ∘C for U min followed by TO cycles ∘ ∘ ;.=. Survey and Sample Collection. A detailed survey has of IT C, N min for denaturation; RU C, N min for annealing; ∘ ∘ been carried out in dièrent spots of the major streams W! C, ! min for elongation; and :nally an extension at W! C of the Terai region of sub-Himalayan West Bengal, India. for NO min. Ze ampli:ed products were electrophoresed A total of twenty samples of Badis badis were collected in N.T% (w/v) agarose gel (Lonza, Basel, Switzerland) pre- mainly during the monsoon and aaer monsoon period stained with ethidium bromide (O.U !g/mL) at a constant from Mahananda and Balason rivers (ten samples from voltage NOO V and current NOO mA in TAE buèr (TO mM each river system) (Hamilton-Buchanan NG!!) during !ON!- Tris-HCl, pH G.O; !O mM acetic acid; and N mM EDTA, !ONR and geographic coordinates of the collection spots pH G.O) using BenchTop Labsystems BT-MS-ROO, Taiwan were recorded with the help of handheld GPS (eTrex Vista electrophoretic apparatus. Molecular weight of each band HCx, Garmin, USA). Individuals mostly measuring T cm was estimated using a standard NOO base pair ladder (NEB, were obtained with the help of a “scoop net” from the USA) and/or Lambda EcoRI/HindIII double digest DNA submerged vegetation/gravels along the sides of the streams. size marker (NEB, USA). Ze gels were visualized on the Ze collection spots were as follows: in Mahananda River UV-transilluminator (Spectroline BI-O-Vision NY,USA) and [MN = !V ∘T!.N!U ὔN; GG ∘!T.WNW ὔE, Elevation-V!O a AMSL and photographed using a digital camera. M! = !V ∘TT.TU! ὔN; GG ∘!U.TIW ὔE, Elevation-WNW a AMSL]; in Balason River [BN = !V ∘TR.NWW ὔN; GG ∘!!.G!U ὔE, Elevation- ;.L. RAPD Data Analyses. RAPD data were analyzed for VTVa AMSL and B! = !V ∘TU.VR! ὔN; GG ∘NG.IN! ὔE, Elevation- assessing within and between populations genetic variability WRNa AMSL]; P = !V ∘TR.OOW ὔN; GG ∘!T.RIV ὔE, Elevation-VVV a of Badis badis in the two major rivers. Ze RAPD marker pro- AMSL and F = !V ∘RG.GGT ὔN; GG ∘!T.N!U ὔE, Elevation-RNI a :les were determined by direct comparison of the ampli:ed AMSL ( Figure N ). Ze MN-M!, BN-B!, P, and F constitute pro:les and the data obtained were computed and analyzed together the whole Mahananda-Balason river system of in the form of binary variables (N = band present or O = band the Terai region of West Bengal, India. Fishes were iden- absent). Each locus was treated as a two-allele system, where ti:ed according to Talwar and Jhingran [ !V ] and were only one of the alleles per locus was ampli:able by PCR and photographed. each fragment represented a Mendelian locus in which the International Journal of Biodiversity 2

∘ $ 88 25 E

TERAI region

West Bengal M2 B2 ∘ $ 26 45 N Mahananda R. B1 P Panchnoi R.

M1 Balason R.

India

F River 2 km Road 26 ∘38 $ N 2 Mi Sampling site Mahananda Barrage

F45678 9: Map showing collection spots of Badis badis from the major streams of the Terai region of West Bengal, India. Geographical coordinates and altitudes, recorded by hand-held Garmin eTrex Vista HCx GPS, are as follows: M9 = QR ∘SQ.9QT ὔN; WW ∘QS.X9X ὔE; RQZ [ AMSL; MQ = QR ∘SS.STQ ὔN; WW ∘QT.S^X ὔE; X9X [ AMSL; B9 = QR ∘S2.9XX ὔN; WW ∘QQ.WQT ὔE; RSR [ AMSL and BQ = QR ∘ST.R2Q ὔN; WW ∘9W.^9Q ὔE; X29 [ AMSL, P = QR ∘S2.ZZX ὔN; WW ∘QS.2^R ὔE; RRR [ AMSL and F = QR ∘2W.WWS ὔN; WW ∘QS.9QT ὔE; 29^ [ AMSL.

visible “dominant” allele was in Hardy-Weinberg equilibrium and $# is the frequency of a particular RAPD band) [ 2T ]. fe (HWE) with the corresponding “recessive” null allele or the rates of polymorphism were calculated using the criterion for absent fragment [ ^, QW ]. fe binary scores obtained from polymorphism in which the frequency of the most common all the twenty-one primers in the RAPD analyses were then allele was ≤Z.^T or ≤Z.^^. fe maximum diversity has been pooled for constructing a single data matrix for further found where all RAPD bands had equal abundance. For better analysis. Each set of PCR was repeated for at least three times interpretation of Shannon’s information index we have used to check the reproducibility of the banding pattern generated. ὔ the exponential function of Shannon’s index, that is, $ , and fe RAPD data was analysed using three so[ware appli- ) $ὔ cations, namely, Popgene ver. 9.2Q [ Q^ ], TFPGA (Tools for subsequently calculated the measures of evenness ( * = ) /+ , Population Genetic Analysis) ver. 9.2 [ 2Z ], and GenAlEx R.T where + is the observed number of alleles). fe proportion [29 , 2Q ]. Total number of RAPD bands, monomorphic bands, of total inbreeding within the population was assessed by and polymorphic bands within and between populations the formula ,%& = , %' − , &' /1 − , &' (where ,%& is the were calculated manually by directly scoring the RAPD measure of inbreeding or departure from Hardy-Weinberg amplijed banding projles from gel photographs. Dikerent (HW) proportions within local demes or subpopulations, indices of diversity measurement were used for assessing ,&' is the measure of allele frequency divergence among the genetic background of Badis badis species. fe data subpopulations relative to metapopulation, and ,%' is the matrix was used to estimate the observed number of alleles measure of the overall departure from HW proportions for individuals with the metapopulation) [ 2R ]. [( 1/! )∑ # #, where ! is the number of loci and ## is the number of alleles detected per locus], ekective number of alleles To analyze the population genetic dikerentiation of Badis 2 badis , values were calculated using the formula (1/ ∑ $ # , where $# is frequency of particular RAPD band) ,&' ,&' = [22 ], number of polymorphic loci, proportion of polymorphic 1 − % &/% ' (where %& is the average expected heterozygosity loci, Nei’s genetic diversity ( %) [ 2S ], and Shannon’s informa- estimated from each subpopulation and %' is the total gene ὔ ὔ tion index ( % or & = −∑$ # log 2$ #, where % or & is diversity diversity or expected heterozygosity in the total population ! International Journal of Biodiversity

T4567 8: Code and sequence of random primers used for detection of polymorphism in B. badis populations.

Sl/No. Primer Sequence (F ὔ → Gὔ) G + C Content (%) 8 OPA-O8 CAGGCCCTTC PO Q OPA-OQ TGCCGAGCTG PO G OPA-O! AATCGGGCTG RO ! OPA-OP GAAACGGGTG RO F OPA-OS GGGTAACGCC PO R OPA-8O GTGATCGCAG RO P OPA-8G CAGCACCCAC PO T OPA-8R AGCCAGCGAA RO S OPA-8S CAAACGTCGG RO 8O OPA-QO GTTGCGATCC RO 88 OPB-O8 GTTTCGCTCC RO 8Q OPB-OG CATCCCCCTG PO 8G OPB-O! GGACTGGAGT RO 8! OPB-OF TGCGCCCTTC PO 8F OPB-OR TGCTCTGCCC PO 8R OPB-OP GGTGACGCAG PO 8P OPB-88 GTAGACCCGT RO 8T OPB-8Q CCTTGACGCA RO 8S OPB-8G TTCCCCCGCT PO QO OPB-8P AGGGAACGAG RO Q8 OPB-8T CCACAGCAGT RO as estimated from the pooled allele frequencies) [ GR ]. We bands obtained between the M8-MQ population and the B8- diversity among populations ( ""# = # # − # ") was also BQ population ranged from 8O (OPB8G) to G (OPB8T), from R calculated to ascertain the interpopulation diversity between (OPAO8) to O (OPBOG and OPB8G), and from S (OPB8Q) to 8 di[erent pairwise populations. (OPA8O, OPA8G, and OPB8T), respectively, ( Table Q ).

3. Results (.3. Within Population Diversity. We total number of monomorphic and polymorphic bands generated ader amplie- RAPD data were analyzed to ascertain within and between cation with twenty-one RAPD primers in M8-MQ popula- populations genetic variability as well as genetic di[erences tion was RP (average G.8S) and FG (average Q.FQ), respec- within and between the Badis badis populations of two tively, ( Table Q ). We total number of monomorphic and major river streams of the Terai region of West Bengal India. polymorphic bands generated in B8-BQ population was R8 We banding patterns generated through RAPD assay in the (average Q.SO) and RO (average Q.TR), respectively, ( Table Q ). present study were used to evaluate the genetic variation Diversity and evenness indices were estimated in the two within and between the populations of B. badis from the two Badis badis populations separately ( Table G ). We richness major rivers of Terai region. or observed number of alleles and e[ective number of alleles of M8-MQ population were 8.!FOO ± O.!SSR and (.). RAPD Pro0le. Twenty-one RAPD primers generated in 8.QTRG ± O.GPSP, respectively. We proportion of polymorphic total 8QO (average F.P8) bands in M8-MQ population and 8Q8 loci was O.!!8R (!!.8R%). We Nei’s genetic diversity ( #) (average F.PR) bands in B8-BQ population ( Figure Q ). We and Shannon’s information index ( #ὔ or &) were O.8RF! ± number of bands ranged from S (OPA8R, OPBO!, and OPB8P) O.QOQG and O.Q!FO ± O.QSOP, respectively. We richness or to G (OPB88 and OPB8T) in M8-MQ population and from observed number of alleles and e[ective number of alleles S (OPBO!) to G (OPB8T) in B8-BQ population, respectively, of B8-BQ population were 8.FO!8 ± O.FOQ8 and 8.GFO8 ± (Table Q ). We number of monomorphic bands ranged from F O.GS!R, respectively. We proportion of polymorphic loci (OPAO8, OPBO!, and OPBOR) to 8 (OPBOG) in M8-MQ popula- was O.FO!8 (FO.!8%). We Nei’s genetic diversity ( #) and tion and from F (OPA8G and OPBO8) to O (OPBOG and OPB8G) Shannon’s information index ( #ὔ or &) were O.8STG ± O.Q8QR in B8-BQ population, respectively, ( Table Q ). We number of and O.QSO8 ± O.GOGP, respectively. We measure of evenness polymorphic bands ranged from R (OPB8P) to O (OPB8T) (') was O.TT88 and O.TTTR in M8-MQ and B8-BQ popula- in M8-MQ population and from F (OPB8P) to O (OPA8G and tions, respectively. We inbreeding coehcient ( ($" ) of M8-MQ OPB8T) in B8-BQ population, respectively, ( Table Q ). We total and B8-BQ populations was O.8GTP and O.8G8R, respectively number of bands, monomorphic bands, and polymorphic (Table G ). International Journal of Biodiversity 2

Mahananda River (M1-M2) Balason River (B1-B2) m1 1 2 3 4 5 6 7 8 9 10 m2 m1 1 2 3 4 5 6 7 8 9 10 m2 OPA09

(a) (e) m1123456 7 8 9 10 m2 m112345 6 7 8 9 10 m2 OPA10

(b) (f) m1 1 2 3 4 5 6 7 8 9 10 m2 m1 1 23 4 5 6 7 8 9 10 m2 OPB01

(d) (h)

F45678 9: Representative =.?% agarose gel showing RAPD fragment patterns generated using OPAIJ, =I and OPBI=, IL primers from Mahananda (M= and M9) (a) to (d) and Balason river populations (B= and B9) (e) to (h). m=: =II base pair DNA ladder; m9: Lambda DNA EcoRI/HindIII double digest. Lanes = to =I indicate =I individuals from each river populations

!.!. Between Population Diversity and Genetic Di7erentiation. observed number of alleles and e]ective number of alleles Xe total number of RAPD fragments scored between the of among the Mahananda and Balason populations were Mahananda (M=-M9) and the Balason (B=-B9) populations 1.6733 ± 0.4706 and 1.3752 ± 0.3642 , respectively, ( Table L ). was =2I (average Z.=?) and the number of monomorphic Xe proportion of polymorphic loci was I.^ZLL (^Z.LL%) and polymorphic bands scored was ?J (average 9.LL) and (Table L ). Xe Nei’s genetic diversity ( ") and Shannon’s =I= (average ?.[=), respectively, ( Table 9 ). Xe richness or information index ( "ὔ or #) were 0.2228 ± 0.1934 and I.LL^? ! International Journal of Biodiversity

T4567 8: RAPD band pro@les for the twenty-one primers in Badis badis population from two diEerent locations of the Terai region of West Bengal.

Populations “ML-M8” “BL-B8” “BetweenML-M8andBL-B8” Primers Total Total Total Total Total Total Total number of number of Total number of number of Total number of number of number of monomor- polymor- number of monomor- polymor- number of monomor- polymor- bands phic phic bands phic phic bands phic phic bands bands bands bands bands bands OPA-OL P Q R P S S T ! R OPA-O8 Q R 8 ! 8 S U R S OPA-OS Q R 8 Q 8 R U R S OPA-OU U R S Q R 8 U 8 Q OPA-OT Q R 8 ! S 8 ! R R OPA-LO S R L S L R Q S L OPA-LR ! S 8 Q Q O ! Q L OPA-L! T S Q Q S L T 8 U OPA-LT Q 8 R Q 8 R P 8 ! OPA-8O U R S ! 8 S P R Q OPB-OL S L R U Q 8 P L U OPB-OR S L R S O S Q O Q OPB-OS Q Q O T Q S T 8 U OPB-OQ ! S 8 P S S P R Q OPB-O! Q Q O Q R 8 ! R R OPB-OU T S Q U R S T L P OPB-LL R 8 L Q 8 R ! L Q OPB-L8 U S R ! S 8 LO L T OPB-LR S 8 8 S O S Q O Q OPB-LU T R ! P R Q T 8 U OPB-LP R R O R R O R 8 L Total !"# $% &' !"! $! $# !&# () !#! Average &.%! '.!) ".&" &.%$ ".)# ".*$ %.!( ".'' (.*! ML and M8 sites are from Mahananda river; BL and B8 sites are from Balason river.

T4567 R: Within and between populations genetic diversity, evenness, and inbreeding coeXcient of B. badis based on RAPD analyses.

EEective number Proportion of ὔ Population name ! (±SD) # (±SD) #ὔ or $ (±SD) % = & " /! ' of alleles ( ±SD) polymorphic loci #$ ML/M8 L.SQOO ± O.STT! L.8P!R ± O.R!TU O.SSL! (SS.L!%) O.L!QS ± O.8O8R O.8SQO ± O.8TOU O.PPLL O.LRPU BL/B8 L.QOSL ± O.QO8L L.RQO8 ± O.RTS! O.QOSL (QO.SL%) O.LTPR ± O.8L8! O.8TOL ± O.RORU O.PPP! O.LRL! ML/M8 and BL/B8 L.!URR ± O.SUO! L.RUQ8 ± O.R!S8 O.!URR(!U.RR%) O.888P ± O.LTRS O.RR!S ± O.8USU O.PR!O O.L8TO Mahananda- Balason river L.SRU8 ± O.S!PO L.8PTS ± O.RU8S O.SRUL (SR.UL%) O.L!OL ± O.LTSS O.8R!R ± O.8UP8 O.PPU! O.OPPS system ὔ $ : Observed number of alleles or richness; ": Nei’s gene diversity; " or #: Shannon’s information index; %: measure of evenness; &#$ = &#' − & $' /L −& $' = Inbreeding coeXcient.

± O.8USU, respectively. ce measure of evenness ( %) was respectively, ( Table S ). ce diversity among the Mahananda O.PR!O ( Table R ). ce expected ( #') and mean heterozygosity and Balason populations was O.OSUO ( Table S ). ce degree (#$), diversity among populations ( ($' ), and degree of gene of gene diEerentiation between the Mahananda and Bala- diEerentiation ( '$' ) were estimated between Mahananda and son populations was calculated to be O.8LOT ( Table S ). ce Balason populations. ce expected and mean heterozygosity inbreeding coeXcient ( '#$ ) between populations was found were found to be 0.2228 ± 0.0374 and 0.1758 ± 0.0289 , to be O.L8TO ( Table R ). International Journal of Biodiversity 2

T4567 8: Between populations diversity and gene di=erentiation in results with some evolutionary related species as well as some two di=erent populations of B. badis based on RAPD analyses. vulnerable species found in geographically related areas. In a study carried out by Kader et al. [ ML ] on three Tilapia species Populations !! !" ""! #"! (Oreochromis niloticus, O. aureus, and Tilapia zilli ) in Egypt Mahananda and by Chandra et al. [ MO ] on vulnerable Eutropiichthys vacha (MG/MI) and K.IIIL ± K.KM28 K.G2NL ± K.KILO K.K82K K.IGKO in India, GN RAPD primers generated IKG and 8N polymorphic Balason amplieed fragments, respectively. In a di=erent study carried (BG//BI) out by Alam et al. [ GL ] and Miah et al. [ 8K ] on another : expected heterozygosity in random mating total population; : #! #" locally vulnerable species of Mud eel ( Monopterus cuchia ) mean expected heterozygosity within random mating subpopulations; : $"! in Bangladesh, the number of polymorphic bands generated diversity among populations; %"! : degree of gene di=erentiation among populations in terms of allele frequencies. were MK out of MO bands and I8 out of 2M bands, respectively. Another study on endemic catesh ( Horabagrus brachysoma ) in the Western Ghat region of South India and the Clarias batrachus in Indian riverine system reported the number of '.). Total Genetic Diversity of Mahananda-Balason River Sys- polymorphic bands to be 2N and MNL, respectively [ 8G , 8I ]. Ve tem. Ve observed number of alleles (richness) and e=ective di=erence in the number and sizes of the amplieed fragments number of alleles of the Mahananda-Balason river system can be ascribed to the sequence of the primers used and the (combining all the sites, namely, MG-MI, BG-BI, P, and F) source of the template DNA when compared to comparable were found to be and , 1.4372 ± 0.4680 1.2894 ± 0.3724 studies. We have chosen a large number of decamer primers respectively, ( Table M ). Ve proportion of polymorphic loci to provide robustness to our results [ 8M ]. was K.8M2G (8M.2G%) ( Table M ). Ve Nei’s genetic diversity ( ) ! Ve possible reason behind the observed high between and Shannon’s information index ( ὔ or ) were ! % 0.1601 ± populations allelic richness may be ascribed to the spatial and , respectively. Ve measure of 0.1944 0.2363 ± 0.2782 distribution of the two populations ( Figure G ). In a study evenness ( ) was K.LL2^. Ve inbreeding coe_cient ( ) of & #'" on three Tilapia species in Egypt, the proportion of poly- the whole river system population was calculated to be K.KLL8 morphic loci was NG.L%, NL.^%, and ^K.M%, respectively (Table M ). [ML ] and the proportion of polymorphic lociof Tilapia zilli and Oreochromis niloticus populations in Lake Victoria of 4. Discussion Africa was ^N.K% and NO.K%, respectively [ 88 ]. In the studies on vulnerable Mud eel of Bangladesh, the proportion of RAPD-PCR can be used as an e_cient molecular tool to polymorphic loci was 2^.OI% [ GL ] and MI.L2% [ 8K ], while in di=erentiate allopatrically and/or sympatrically isolated pop- the studies on endemic yellow catesh Horabagrus brachysoma ulations and has been widely used to delineate the available and Clarias batrachus the proportion of polymorphic loci gene pool of locally adapted populations of a species that was ^K.8L% [ 8G ] and 22.8O% [ 8I ], respectively. In the present may have arisen either by means of genetic selection under study the proportion of polymorphic loci varies from K.8M2G di=erent environmental pressure or as a result of genetic dria (8M.2G%) in overall population to K.^2MM (^2.MM%) in between [M2 ]. To our knowledge, the present study is the erst attempt populations ( Table M ). Ve overall proportion of polymorphic to explore the present status of population speciec genetic loci (8M.2G%) is lower than that found in phylogenetically background of this esh fauna in the major river streams in the related and vulnerable esh fauna indicating a substantial Terai region of this sub-Himalayan hotspot of North Eastern decline of genetic variability within Badis badis population India. Our results showed that there is considerable lack of of the sub-Himalayan Terai region. genetic variation among populations of Badis badis collected Our study revealed that the Nei’s genetic diversity ( !) from the Mahananda-Balason river system. within the Mahananda (MG-MI) and Balason (BG-BI) pop- Forty decamer primers were initially used to screen two ulations is K.G^N8 and K.GOLM, respectively, whereas genetic di=erent populations, and twenty-one primers were enally diversity between populations is K.IIIL ( Table M ). Ve overall chosen for the study based on reproducibility of banding pat- genetic diversity of the river system was K.G^KG ( Table M ). An terns ( Table G ). Ve obtained results showed that the analyzed earlier study carried out by Mwanja et al. [ 88 ] on Oreochromis decamer primers permitted the detection of polymorphic niloticus populations in eastern Africa revealed that the fragments in Badis badis , revealing lower levels of genetic genetic diversity ranged from highest (Lake Kachira = K.I2 variability within and between two populations of major river and Lake Nabugabo = K.IO) to intermediate (Lake Albert = streams of the study region. In the present study the number K.G2 and Lake Victoria = K.IK) and to lowest (Lake Edward of amplieed fragments generated by primer OPBGI was ^– = K.G8) values. Ve studies carried out by Appleyard and GK, L-O by primer OPBG2, and M by primer OPBGL, which Mather [ 8N ] in Oreochromis mossambicus populations in Fiji were comparable with the amplieed fragments observed by islands displayed a lower (K.KOG to K.GGG) to highest (K.G^N to Brahmane et al. [ I8 ] (OPBGI = 8–^ bands, OPBG2 = M–^ K.GL2) levels of observed heterozygosity. Moreover, Kader et bands, and OPBGL = M-8 bands) in Badis badis populations al. [ ML ] found that the genetic diversity was higher in Tilapia collected from three districts of West Bengal namely, Nadia, zilli (K.I8O) than in the O. niloticus and O. aureus populations North I8 Parganas, and Coochbehar. While there is an (K.IGO and K.IML, resp.). In the study carried out on vulnerable absence of comparable data on Badis badis and other related Monopterus cuchia in Bangladesh and on endemic species species from other Indian rivers, we have compared our Horabagrus brachysoma in India [ 8G ] Nei’s genetic diversity ! International Journal of Biodiversity

was 4.6!7 and 4.666, respectively. ;erefore in comparison it the &$% value was 4.4UV and the $$% value was 4.6G4N can be said that genetic diversity of Badis badis population (Table U ). In a diWerent study carried out by Abdul Muneer in the Mahananda-Balason river system ranges from lower to et al. [ UG ] on Horabagrus brachysoma the $$% value ranged intermediate values. from the maximum (4.6GN) between (4.4U7) Meenachil and In our study the Shannon’s information index ( !ὔ) was Nethravathi populations to the minimum between (4.4U7) calculated to be 0.240 ± 0.2907 in Mahananda (MG-M6), Meenachil and Chalakkudi populations. ;e lower value of 0.2901 ± 0.3037 in Balason (BG-B6), and 0.3364 ± 0.2747 &$% in the present study indicates that the diversity between in Mahananda-Balason taken together, respectively, whereas these two populations was low and a higher $$% value the !ὔ of overall river system was 0.2363 ± 0.2782 (Table J ). indicates that gene diWerentiation was high. ;e observed ;ese results are similar to the Kndings of Chandra et al. &$% and $$% values may be attributed to several factors, [JN ] on Eutropiichthys vacha , where the Shannon’s index namely, lack of migration, lower level of gene ]ow, water was 4.6!4 and 4.J44 in Patnaand Madhepura populations, ]ow pattern, water volume, Kshing extent, and other possible respectively. Moreover, Kader et al. [ J! ] reported that Tilapia anthropogenic and/or geological activities. ;is decrease in zilli population had a higher proportion of heterozygous genetic diversity and variability within and between the gene genotypes ( !ὔ = 4.JTJ) than O. niloticus (!ὔ = 4.JG!) and pools of the Badis population in the study area may result due O. aureus populations ( !ὔ = 4.JUV). In two diWerent studies to ]ow pattern disturbances owing to sand/stone excavation, carried out by Alam et al. [ G! ] and Miah et al. [ U4 ] on mud eel indiscriminate Kshing and pesticide run-oWs from adjacent Monopterus cuchia in Bangladesh, the Shannon’s information tea gardens in the hilly areas of lower Himalayas, as the index was 4.U6J and 4.6GJ, respectively. Comparing our data river streams ]ow from higher to lower altitudes. All of these with that of Oreochromis sp. and Eutropiichthys vacha it was causes can culminate into the observed lower level of the found that the genetic diversity of Badis badis in the major genetic diversity pattern and genetic erosion in Badis badis river streams (Mahananda and Balason) of Terai region is populations across the Mahananda and Balason river system. lower. However there was a weak point in this study, as a limited We have used this exponential function of Shannon’s number of (ten samples from each river stream) individuals "ὔ were involved for genetic analyses, primarily because of the index (i.e., # ) to calculate the measure of evenness [ UT ]. dwindling population structure of the species and also to have ;e measure of evenness was more or less similar in these ease in data handling and analyses. However a large number two populations: 4.!!GG in Mahananda population and 4.!!!T of decamer primers were used to provide robustness to the in Balason population ( Table J ). ;erefore, this indicates that study. ;e present study using twenty-one RAPD primers the gene pool of these two populations is genetically quite revealed that a lower level of genetic diversity exists in the even in distribution. ;is might happen as an outcome of Badis badis populationof the Mahananda-Balason river sys- addition of juveniles due to the joining of the major streams tem in the Terai region. Several anthropogenic interferences with small tributaries, especially during the monsoonal like indiscriminate Kshing, eguents from adjacent house- breeding season, which may result in substantial amount of holds and factories, heavy use of fertilizers and pesticides in gene ]ow between these two major river stream populations. the nearby tea gardens and their subsequent run-oWs into the While the value of $#$ ranged from −G.4 (all the individ- hilly parts of the streams, or excavation of river bed along uals within a subpopulation being heterozygotes) to +G.4 (no the banks for sand and gravel, dam building activities at the observed heterozygotes are present within a subpopulation), upper reaches of the rivers all may be responsible for the lack we have found that the Mahananda and Balason river of the overall diversity of this threatened and ornamental Ksh system showed positive values of the inbreeding coe_cient species in this region. ;erefore, conservational interventions [Mahananda population (MG-M6) = 4.GJ!V, Balason popu- may be directed towards the Terai population. Proper steps lation (BG-B6) = 4.GJGT, between Mahananda and Balason should be taken to improve the environment to avoid further population (MG-M6 and BG-B6) = 4.G6N4; and 4.4!!U in losses. ;e genetic data obtained from the present study lend the overall Mahananda-Balason river system] indicated a support to the view that there is a scope of stock improvement lower number of heterozygotes within these two populations. for this threatened ornamental Ksh. Abdul Muneer et al. [ UG ] also reported that the $#$ value of Horabagrus brachysoma was 4.74U indicating a deKciency of heterozygotes. Nonrandom mating or inbreeding, spatial Conflict of Interests Wahlund eWect, and expression of null alleles are the leading causes of heterozygotes deKciency in small populations. ;e authors declare that there is no con]ict of interests However, in small populations sexual diWerences in allele regarding the publication of this paper. frequencies are likely to cause an increase in heterozygotes relative to Hardy-Weinberg proportions and therefore there Acknowledgments is a tendency for the excessof heterozygotes [ UV ].

Fixation index or $$% is a measure of genetic divergence ;e work was supported by a research grant from University among subpopulations that ranges from 4 (when all sub- Grants Commission (UGC), India (MRP Sanction no. U4- populations have equal allele frequencies) to G (when all the 6!N/64GG (SR)) awarded to corresponding author. ;e authors subpopulations are Kxed for diWerent alleles) [ UV ]. We have are thankful to the Department of Zoology, University of found that between the Mahananda and Balason populations North Bengal, for providing necessary facility and Dr. Ranjan International Journal of Biodiversity 2

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Research Article Open Access Population and hierarchical genetic structure of Badis badis (Hamilton- Buchanan, 1822) in sub-Himalayan Terai region of West Bengal, India Mukhopadhyay T., Bhattacharjee S. Cell and Molecular Biology Laboratory, Department of Zoology, University of North Bengal, P.O. North Bengal University, Siliguri 734013, West Bengal, INDIA Corresponding author: [email protected] International Journal of Aquaculture, 2015, Vol.5, No.27 doi : 10.5376/ija.2015.05.0027 Received: 20 Jun, 2015 Accepted: 21 Jul., 2015 Published: 09 Sep., 2015 Copyright © 2015 Mukhopadhyay T. and Bhattacharjee S., This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article : Mukhopadhyay T. and Bhattacharjee S., 2015, Population and hierarchical genetic structure of Badis badis (Hamilton- Buchanan, 1822) in sub-Himalayan Terai region of West Bengal, India, International Journal of Aquaculture, 5(27): 1-10

Abstract Badis badis is a threatened ornamental freshwater fish in the Indian scenario. But the population genetic background of this ichthyofauna is largely unexplored in the eastern sub-Himalayan hotspot region of West Bengal state of India, known as the Terai. We have studied six populations from two major streams (Mahananda and Balason) of the region through RAPD fingerprinting. The allelic richness, Shannon’s Information index and a measure of evenness were calculated for each population. The SHE analysis revealed the change in diversity pattern in a spatial scale. Our results indicated that the allelic richness decreased and the evenness increased in the populations as the streams drained from higher to a lower altitude. The Nei’s genetic distan ce, genetic identity and UPGMA dendrogram revealed that the six populations formed two distinct groups. The principal component analysis also supported the UPGMA dendrogram. The pair-wise gene differentiation and gene flow between the populations showed the hierarchical genetic structure of Badis badis in the studied region. Keywords Badis badis ; Threatened ornamental fish; SHE analysis; Genetic hierarchy; RAPD fingerprinting

Introduction if required. The study of genetic variability within and Badis badis (Hamilton-Buchanan 1822) (Actinopterygii, between local populations is extremely useful to gain Perciformes, Badidae) a tropical, benthopelagic, information on the individual identity, breeding ornamental freshwater fish has recently been included patterns, the degree of relatedness and genetic within the vulnerable category in the list of threatened variability within as well as between them. Genetic freshwater fishes of India by National Bureau of Fish variations can be assessed by means of DNA Genetic Resources (NBFGR, Lucknow, India) (Lakra polymorphisms. RAPD fingerprinting has been used et al, 2010). However, there have been insufficient to evaluate genetic diversity and also in subspecies studies with regard to the spatial genetic architecture identification (Wasko et al., 2002; Leuzzi et al., 2004; of this fish species, especially in the eastern Hatanaka and Galetti, 2003; Feng et al., 2007; Islam sub-Himalayan hotspot region of West Bengal, India, and Alam, 2004; Garg et al., 2009; Garg et al., 2010; known as the Terai. Alam et al., 2010; Affonso and Galetti, 2007; Amavet Genetic variability or diversity is an essential for the et al., 2009; Prior et al., 1997; Brahmane et al., 2006; fitness as well as survival of the whole population. Bardakci and Skibinski, 1994; Brahmane et al., 2008; Therefore the loss of genetic diversity of a species Yamazak et al., 2005). A very limited genomic reduces its capability for adaptation and increases the research has been carried out in Badis badis species risk of extinction (Frankham, 1995; Caughley and till date and insufficient genomic information is Gunn, 1996; Avise and Hamrick, 1996; Landweber available to perform other sophisticated fingerprinting and Dobson, 1999). The endangered/vulnerable techniques, where whole genomic sequence is necessary; organisms having small population size may experience therefore, RAPD fingerprinting was the best a continuous reduction in the level of genetic variation. alternative for genetic diversity analyses. A number of However, maintaining genetic variation in endangered diversity indices have been widely used by different species is essential to ensure its adaptation, expansion investigators to quantitatively express diversity. and proper reestablishment in natural conditions, Shannon’s index of diversity ( H´ or I) can be 1

International Journal of Aquaculture, 2015, Vol.5, No.27 1 10 http://ija.biopublisher.ca decomposed into two functional components viz.; populations in the Terai region of West Bengal, India species richness ( E) and evenness ( S). These three (Mukhopadhyay and Bhattacharjee, 2014a). We have components add up to SHE analysis that may enable also reported the genetic diversity within and between the delineation of change in the diversity pattern the populations of Badis badis and also determined (Buzas and Hayek, 1996). This method allows the total available genetic diversity present in the researchers to examine the evenness component Mahananda-Balason river system of the Terai region separately from the richness and vice-versa in a single of sub-Himalayan West Bengal, India. Based on this step process (Buzas and Hayek, 1998). firsthand information we have carried out further analyses to extract as much as possible genetic To answer the questions regarding the genetic information regarding this threatened ichthyofauna. variation in any subdivided population or in the The objectives of the present study was to (1) metapopulation, it is very essential to focus our ascertain the genetic distance and genetic relatedness attention towards the “evolutionary functional unit” of the different populations of Badis badis from the and a population seems the most reasonable level at major river streams of the Terai region of which genetic conservation intervention should take sub-Himalayan West Bengal, India, (2) determine the place. The population is where the local adaptation changes in the diversity pattern through SHE analysis and genetic changes occur over generations, therefore among different populations of Badis badis from the allopatric/sympatric population is of great interest. In streams of the region, and (3) ascertain hierarchical a noteworthy book on conservation biology, Meffe genetic structure among different Badis populations. and Carroll in 1997 pointed out one approach to 1 Results define ‘evolutionary functional unit’ in a genetic 1.1 RAPD Profile perspective through a hierarchical genetic analysis of Twenty-two RAPD primers generated in total 199 subdivided populations. amplified fragments from thirty individuals, We have previously investigated the present status of corresponding to 6 separate riverine collection sites the available genetic diversity in the Badis badis (Table 1). The number of amplified fragments ranged

Table 1 Number and size range of fragments amplified by different RAPD primers Sl/No. Primer Sequence (5´ 3´) G + C Content (%) Total no. of fragments scored Size range of fragments (bp) 1 OPA-01 CAGGCCCTTC 70 11 200-1700 2 OPA-02 TGCCGAGCTG 70 11 200-1500 3 OPA-04 AATCGGGCTG 60 8 300-1000 4 OPA-07 GAAACGGGTG 60 12 150-2000 5 OPA-09 GGGTAACGCC 70 6 500-1300 6 OPA-10 GTGATCGCAG 60 7 250-1200 7 OPA-13 CAGCACCCAC 70 6 400-1200 8 OPA-16 AGCCAGCGAA 60 13 500-2200 9 OPA-19 CAAACGTCGG 60 9 400-1600 10 OPA-20 GTTGCGATCC 60 10 400-1800 11 OPB-01 GTTTCGCTCC 60 10 300-1400 12 OPB-03 CATCCCCCTG 70 8 600-1400 13 OPB-04 GGACTGGAGT 60 9 250-1600 14 OPB-05 TGCGCCCTTC 70 8 500-1600 15 OPB-06 TGCTCTGCCC 70 11 250-1500 16 OPB-07 GGTGACGCAG 70 9 500-1500 17 OPB-11 GTAGACCCGT 60 6 300-1700 18 OPB-12 CCTTGACGCA 60 11 150-1800 19 OPB-13 TTCCCCCGCT 70 7 400-2000 20 OPB-15 GGAGGGTGTT 60 12 200-1900 21 OPB-17 AGGGAACGAG 60 9 500-1700 22 OPB-18 CCACAGCAGT 60 6 250-1200 Total = 199 Range=150 – 2200

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Supplementary Table 1 Pair-wise inter-population diversity, gene differentiation and estimate of gene flow of six different populations of Badis badis

Population Pairs HT HS DST FST Nm TR-1and TR-2 0.2954 ± 0.0386 0.1183 ± 0.0178 0.1771 0.5996 0.3339 TR-1and TR-3 0.2966 ± 0.0372 0.1379 ± 0.0188 0.1587 0.5352 0.4343 TR-1and TR-4 0.2822 ± 0.0385 0.1280 ± 0.0178 0.1542 0.5466 0.4147 TR-1and TR-5 0.3124 ± 0.0362 0.1276 ± 0.0185 0.1848 0.5917 0.3450 TR-1and TR-6 0.3032 ± 0.0390 0.1280 ± 0.0198 0.1752 0.5778 0.3653 TR-2 and TR-4 0.1848 ± 0.0367 0.1220 ± 0.0195 0.0628 0.3402 0.9698 TR-2 and TR-5 0.1962 ± 0.0404 0.1216 ± 0.0220 0.0746 0.3803 0.8148 TR-2 and TR-3 0.2135 ± 0.0395 0.1319 ± 0.0219 0.0816 0.3822 0.8081 TR-2 and TR-6 0.2037 ± 0.0413 0.1220 ± 0.0225 0.0817 0.4012 0.7463 TR-3 and TR-4 0.1988 ± 0.0406 0.1415 ± 0.0276 0.0573 0.2881 1.2355 TR-3 and TR-5 0.2045 ± 0.0367 0.1411 ± .0214 0.0634 0.3098 1.1138 TR-3 and TR-6 0.2030 ± 0.0377 0.1416 ± 0.0229 0.0614 0.3025 1.1529 TR-4 and TR-5 0.1764 ± 0.0387 0.1312 ± 0.0261 0.0452 0.2560 1.4534 TR-4 and TR-6 0.1955 ± 0.0403 0.1317 ± 0.0239 0.0638 0.3263 1.0321 TR-5 and TR-6 0.2003 ± 0.0391 0.1313 ± 0.0227 0.0690 0.3445 0.9514 All Populations 0.2983 ± 0.0203 0.1304 ± 0.0096 0.1010 0.5629 0.3883

HT = Expected heterozygosity in random mating total population; H S = Mean expected heterozygosity within random mating

subpopulations ; D ST = diversity among populations; F ST = Degree of gene differentiation among populations in terms of allele frequencies; Nm = Estimate of gene flow from 6 (OPA-09, OPA-13, OPB-11 and OPB-18) to 13 populations of TR-1 and TR-5 and the lowest value (OPA-16). Considering all the primers, the size ranges (0.0452) was observed between the populations of of the amplified fragments ranged from 150 to 2200 TR-4 and TR-5 (Supplementary Table 1). The degree base pairs (Table 1). of gene differentiation was highest (0.5996) between the populations of TR-1 and TR-2 whereas the degree 1.2 Within and between- population diversity and of gene differentiation between the populations of genetic differentiation TR-4 and TR-5 was lowest (0.2560) (Supplementary The allelic richness or observed number of alleles Table 1 and Figure. 2). Moreover the populations of ranged from 1.2915± 0.4556 in TR-2 to 1.3769 ± 0.4858 in TR-3 popu lations. While Shannon’s TR-4 and TR-5 showed maximum level of gene flow Information index ( H´ or I) was highest (0.2205 ± (1.4534) between them while the extent of gene flow 0.2950) in the TR-3 population (Table 2) and the TR-2 was minimum between the populations of TR-1 and population showed its lowest value (0.1648 ± 0.2691) TR-2 (0.3339) (Supplementary Table 1 and Figure. 2). (Table 2). The highest value of evenness (0.922561) When total populations were considered the total was found in the TR-1 population and the lowest genetic diversity ( HT) was 0.2983 ± 0.0203 and the value of evenness (0.904002) was found in the TR-6 total genetic differentiation coefficient ( FST ) among population (Table 2). the populations was 0.5629 (Supplementary Table 1). 1.3 Genetic distance and relatedness The expected ( HT) and mean heterozygosity ( HS); The Nei’s genetic distance between the populations of diversity among populations ( DST ); degree of gene differentiation ( FST ); and estimates of gene flow ( Nm ) TR-1 and TR-5 was maximum (0.5350) and the were estimated in pairwise populations. The expected distance between the populations of TR-4 and TR-5 heterozygosity and mean heterozygosity ranged from was minimum (0.0928) (Figure 3A). The genetic 0.1764 ± 0.0387 to 0.3124 ± 0.0362 and from 0.1183 identity was highest (0.9114) between the TR-4 and ± 0.0178 to 0.1416 ± 0.0229 respectively TR-5 populations but the identity between the TR-1 (Supplementary Table 1). The highest observed value and TR- 5 populations was lowest (0.5857) (Figure of inter-population diversity (0.1848) was between the 3A). Thus it was observed from the Nei ’s genetic distance

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Table 2 Within population allelic richness, Shannon's index, evenness Population Name S (±SD) H´or I (±SD) e H´ E= e H´ /S TR-1 1.2965 ± 0.4579 0.1791 ± 0.2848 1.1961 0.922561 TR-2 1.2915 ± 0.4556 0.1648 ± 0.2691 1.1792 0.913047 TR-3 1.3769 ± 0.4858 0.2205 ± 0.2950 1.2467 0.905440 TR-4 1.3417 ± 0.4755 0.1934 ± 0.2811 1.2134 0.904375 TR-5 1.3216 ± 0.4683 0.1899 ± 0.2859 1.2091 0.914876 TR-6 1.3417 ± 0.4755 0.1930 ± 0.2826 1.2129 0.904002 S = Observed number of alleles or allelic richness; H´or I= Shannon's Information index; E= Measure of evenness. SD= Standard deviation

(Figure 3B). The dendrogram indicated that the six populations formed two groups, one group consisting five populations viz., TR-2, TR-3, TR-4, TR-5 and TR-6 whereas the population of TR-1 formed a single outgroup (Figure 3B). The two-dimensional PCoA

plot (Figure 4) gave a clearer picture of clustering of 6

Badis badis populations in this region. The four

populations viz., TR-3; TR-4; TR-5; TR-6 formed a

cluster whereas TR-1 and TR-2 constituted two separate groups (Figure 4). Therefore, the PCoA analysis was largely in agreement and confirmation with the UPGMA dendrogram.

1.4 SHE analysis

SHE analysis revealed a clear distribution of three

biodiversity components richness ( S), diversity ( H´) and evenness ( E) of the Badis badis gene pool into six

Figure 1 Map showing collection spots of Badis badis from the streams of the Terai region in the northern part of West Bengal, India. Geographical locations and altitudes were recorded by hand-held GPS. The alphabets in capital bold case indicate the collection spots. TR-1. Mahananda Barrage, Fulbari [26º38.884 Ń; 88º24.125É; 319 ft AMSL]; TR-2. Mahananda-Panchanoi River Junction [26º42.125Ń; 88º24.717É; 620 ft AMSL]; TR-3. Balason River, Palpara, Matigara [26º43.177Ń; 88º22. 825É; 646ft AMSL]; TR-4. Panchanoi River [26º43.007Ń; 88º24.396É; 666 ft AMSL]; TR-5. Mahananda River, Champasari [26º44.452Ń; 88º25.497É; 717 ft AMSL]; TR-6. Balason River, Tarabari [26º45.632Ń; 88º18.912É; 731 ft AMSL] and identity as well as from the UPGMA dendrogram that the TR-1 and TR-5 populations were more distant from each other while the TR-4 and TR-5 Figure 2 Pair-wise gene differentiation and estimate of gene populations were more closely related to each other flow of six different populations of Badis badis 4

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viz., S (richness), H´ (Shannon's Information index) and E (evenness) in relation to six different populations. We divided the six riverine populations in three groups [TR-1, TR-3, TR-6 constituting first group (Plot A); TR-1, TR-2, TR-5 constituting second group (Plot B); TR-1, TR-2, TR-4 constituting third group (Plot C)] based on the continuity of the water flow through the river stream (Figure 5). Since water flows A from higher to lower altitude there is an expected

trend of increase in S (richness) and H´ (Shannon's

Information index) but decrease in E (evenness) in all

the three analyses as one goes upstream (Figure 5

upper panel).

2 Discussions RAPD technique has been widely used to ascertain the available gene pool of different subdivided populations of a species that may have arisen either by means of selection pressure or as a result of genetic drift (Fuchs et al., 1998). The ornamental fish Badis Figure 3 A. Matrix showing values of Nei's (1978) unbiased measures of genetic similarity (above diagonal) and genetic badis has been considered to be a vulnerable fish distance (below diagonal). B. UPGMA dendrogram based on species in the Indian scenario demanding conservation, Nei’s (1978) unbiased genetic distance matrix. The numbers management and stock enhancement in the Terai above the branches denote the branch lengths region of West Bengal, India. The region is within a biodiversity hotspot; therefore, acquiring information regarding the population genetic structure of this species might be helpful to the development of suitable conservation strategies. To our knowledge, the present study is the first attempt to explore and also to investigate the present status of population- specific genetic relationship of this species, changes in the diversity pattern and also to map the genetic hierarchy in this sub-Himalayan hotspot region of northern West Bengal, India.

Since Shannon’s index of diversity can be decomposed Figure 4 Principal Coordinate Analysis indicates spatial genetic into two metric components, namely species richness structure of six Badis badis populations based on a population genetic distance matrix (Nei 1978) with data standardization. (E) and evenness ( S) i.e., ( H´= lnE+ lnS ) (Buzas and Coordinates 1 and 2 explain 84.06 % and 7.54 % of the Hayek, 1996), sometimes it is difficult to interpret variations respectively whether the diversity index is influenced by greater/lower richness or greater/lower evenness different riverine populations of the Terai region. values or both. However, this decomposition also We found that the lnS and H´ were highest in TR-3 allows us to analyze the change in diversity pattern (0.319835 and 0.2205) and lowest in TR-2 population though different subpopulations. We have found that (0.255804 and 0.1648 respectively) (Figure 5 lower the diversity ( H´ ), richness ( S) and evenness ( E) have panel). The lnE value was highest in TR-1 population varied across all six populations ( Figure 5 and 6) (-0.8060) and lowest in TR-6 population (0.10092) which are most obvious in naturally subdivided (Figure 5 lower panel). The SHE analysis plot revealed populations. Therefore the SHE analysis can deduce the observed pattern for distribution of three components the change in the diversity pattern of populations along 5

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Figure 5 SHE analysis plots showing expected patterns of diversity

S1 ≠ S 2, E1 ≠ E 2 , where 1 and 2 in suffix are any two population) and such SHE plot is log normal one. SHE analysis appears to be a useful approach for defining the diversity; moreover, it allows a high resolution visualization of the changes in diversity in a temporal as well as spatial scale. Our data revealed that as the river streams converged from higher to lower altitude, the diversity and richness of the Badis badis populations decreased and evenness increase d (Figure 5, Plots A, B and C). This decrease in diversity and richness within the gene pool of the Badis population may be due to flow pattern disturbances and human

interferences (such as fishing and pesticide run-offs from adjacent tea gardens in the hilly areas of lower

Figure 6 Genetic hierarchical model of six different populations Himalayas) as the river streams flow from higher to of Badis badis . The dotted arrows indicate the gene lower altitudes. All of these causes can culminate in to differentiation ( FST ) and gene flow ( Nm) (within parentheses) the observed decline and change in diversity pattern (see inner box). The shaded circles indicate the collection sites; and richness in Badis badis populations across the S= richness; H= Shannon Information index ( H´ ); E= measure river stream along the altitudinal gradient. of evenness. Major streams are marked in dark lines and minor Fixation index or F is a measure of genetic streams are indicated in faded lines ST divergence among subpopulations that ranges from 0 a gradient and also look for the breaks in the pattern (when all subpopulations have equal allele that indicate the change in diversity of the population frequencies) to 1 (when all the subpopulations are

(Buzas and Hayek, 1998). Hayek and Buzas (1997) fixed for different alleles) (Allendorf et al., 2013). pointed out that often the diversity ( H´ ) changes We found that the FST value was highest between the because the differences between richness ( S) and TR-1 and TR-2 population and consequently lowest gene evenness ( E) do not offset each other (i.e., H´ 1 ≠ H´ 2, flow existed between them (Supplementary Table 1 and

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Figure 5, 6). The FST and Nm values between TR-1 We have involved a population of five individuals and TR-5 population (0.5917 and 0.3450) and that each from each collection site primarily because of the between TR-1 and TR-4 population (0.5466 and dwindling population structure of the species. 0.4147) indicate that there is reduced gene flow from However to compensate this weak point of the study, the Mahananda (TR-2 and TR-5 population) and we have used a set (twenty-two) primers to generate a Panchanoi (TR-4 population) rivers to the Fulbari robust data matrix. Our study revealed a distinct barrage (TR-1) population (Supplementary Table 1 pattern of genetic diversity and genetic divergence and Figure 5, 6). However in contrast, higher gene that exists across the six Badis badis populations in flow is observed between Balason (TR-3 and TR-6) the study area. The populations were allopatrically and Fulabari barrage (TR-1) population (Supplementary distinct or separated from each other, although gene Table 1 and Figure 5, 6). The differences observed in flow was still evident in the populations which may prevent the population from inbreeding depression and the FST and Nm values between Mahananda, Panchanoi and Balason river populations may be consequently maintaining their survival. Moreover, it attributable to several factors, viz, water flow pattern, was also found with the help of SHE analysis, FST water volume, fishing extent and other possible analysis and dendrogram of the six populations of Badis badis , that a genetic hierarchical structure was anthropogenic and/or geological activities. The FST value was lowest (0.2560) and gene flow (1.4534) was present in the populations. SHE analysis was highest between the Panchnoi River (TR-4) and employed to separate species diversity into its richness Champasari (TR-5) populations (Supplementary Table and evenness components. The SHE analysis revealed 1 and Figure 5, 6). Although not linked at the that the change in the genetic diversity of the Badis upstream region there is a possibility that these two populations was due to altitudinal variation in the genetic richness and evenness in the populations. We regions might be linked during the monsoon season have previously reported that the genetic diversity of though flood plains causing mixing of individuals the Badis badis population in the major stream between these two regions. This intermixing of (Mahananda and Balason rivers) of Terai region was individuals as well as inter-breeding may cause lower declining. Several anthropogenic interferences like gene differentiation and high rates of gene flow indiscriminate fishing, effluents from adjacent among these two sites (TR-4 and TR-5). Moreover, households and factories, heavy use of fertilizers and when we compared the six populations in a pair-wise pesticides in the nearby tea gardens or excavation of manner, we found that the TR-1 population lies more river beds along the banks for sand and gravel all may divergent from other five populations and gene flow be responsible for the decline of the overall diversity was also lowered between TR-1 with other five of this threatened ornamental fish species in the Terai populations. The same was also true for TR-2 with region of West Bengal, India. Therefore, proper steps other five populations. This confirms an should be taken to improve the environment to avoid independent and gradually isolated genetic further losses. The genetic data obtained from the differentiation across the population of Badis badis . present study lend support to the view that there is a The independent genetic differentiation of this scope of stock improvement for this threatened population may be attributed to the geographical ornamental fish species. distance. The Nei’s genetic distance and genetic similarity exposed a distinct spatial relationship 3 Materials and Method between the six Badis badis populations. In the 3.1 Survey and Sample Collection UPGMA based dendrogram TR-1 population formed An extensive survey has been carried out in different an outgroup which might be due to the convergence of spots of the major streams (i.e. Mahananda and all other three river streams along with the Badis badis Balason rivers) of the Terai Region of West Bengal, gene pool to the barrage before being released to India. A total thirty fish (five individuals from each Bangladesh. In general, this spatial genetic collection spots) tissue samples of Badis badis were architecture delineates the genetic hierarchical collected from six different spots and geographic structure of Badis badis populations across the main co-ordinates were recorded with the help of GPS river streams of the Terai region. (eTrex Vista HCx, Garmin, USA). We have previously 7

International Journal of Aquaculture, 2015, Vol.5, No.27 1 10 http://ija.biopublisher.ca reported that the genetic diversity of this fish cycles of 94 ºC, 1 min for denaturation; 35 ºC, 1 min population was low (Mukhopadhyay T, Bhattacharjee, for annealing; 72 ºC, 2 min for elongation and finally 2014a ). Therefore, a limited number of individuals an extension at 72 ºC for 10 min. The amplified were involved for further population genetic analyses products were electrophoresed in an ethidium bromide primarily because of the dwindling population (0.5 µg/ml) pre-stained 1.4 % (w/v) agarose gel structure of this species in the study region. The (Lonza, Basel, Switzerland) at a constant voltage 100 collection spots were as follows: TR-1, TR-2, TR-3, V and current 100 mA in TAE buffer (40 mM TR-4, TR-5 and TR-6. The local names and Tris-HCl, pH 8.0; 20 mM Acetic acid; 1 mM EDTA, geographical co-ordinates of the collection spots are pH 8.0) using BenchTop Labsystems BT-MS-300, mentioned in Figure 1. Fishes were identified according Taiwan electrophoretic apparatus. Molecular weight to Talwar and Jhingran (1991). of each band was estimated using a standard 100 base pair ladder (NEB, USA) and/or Lambda EcoRI/ 3.2 Isolation of high molecular weight DNA and HindIII double digest DNA size marker (NEB, USA). Quantification The gels were visualized on the UV-transilluminator Genomic DNA (gDNA) was extracted noninvasively (Spectroline BI-O-Vision ®NY, USA) and photographed from tiny amount of tissue samples (10-15 mg of fin using a digital camera. clips from the caudal and ventral portions and/or 25-30 pieces of scales from the dorsal portion) from 3.5 RAPD data analyses the live Badis badis following Mukhopadhyay and A robust RAPD dataset from six Badis badis Bhattacharjee (2014b). Briefly, tissues were chopped populations were analyzed for assessing inter-population finely and then incubated at 52ºC for 60 min in a lysis genetic variability, genetic differentiation, genetic buffer containing 15 µl Proteinase K (20 mg/ml) and distance and genetic hierarchy between six populations 30 µl 20% SDS. After the incubation, each lysate was of Badis badis of Terai region. The RAPD purified by phenol:chloroform:isoamyl alcohol fingerprinting profile were computed and analyzed in (25:24:1, v/v/v). the form of binary variables (1= band present or 0= band absent) by direct comparison of the amplified 3.3 Primer Selection pattern. The binary scores obtained from all the Forty arbitrary decamer primers of random sequences twenty-two primers in the RAPD analyses were then (Kit-A and Kit-B, twenty primers from each kit) were pooled for constructing a single data matrix. The purchased from Imperial Life Science Pvt. Ltd., India. RAPD data was analysed using three software viz., Firstly, six different populations were screened with Popgene ver. 1.32 (Yeh et al., 1999), TFPGA ver.1.3 the forty primers and finally twenty-two (10 primers (Miller, 1997) and GenAlEx 6.5 (Peakall and Smouse, from Kit-A and 12 primers from Kit-B) were selected 2006; Peakall and Smouse, 2012). The data matrix for further analyses on the basis of the variability and was used to estimate the observed number of alleles or reproducibility of the bands obtained (Table 1). allelic richness [ (1/K) ∑ n i, where K= number of loci 3.4 RAPD-PCR and documentation of amplified and ni = the number of alleles detected per locus], products Shannon’s Information Index ( H´ or I= -∑ pi log2 pi, RAPD analyses were performed in a 96 well Peltier where H´ or I is diversity and pi is the frequency of a Thermal Cycler (Applied Biosystems 2720, Life particular RAPD band) (Lewontin, 1972). To calculate Technologies, USA) in a final reaction volume of 25 the measure of evenness ( E) we have used the H´ µl, each containing a final concentrations of ~100-150 exponential function of Shannon’s Index i.e, e and ng of isolated gDNA, 1.6 pM of OPA or OPB primer, subsequently divided it by allelic richness (S) or H´ 1X Standard Taq Polymerase buffer (10mM Tris-HCl, observed number of alleles ( E= e /S , where S is the observed number of alleles or allelic richness). The pH 8.3, 50 mM KCl, 1.5 mM MgCl 2) (NEB, USA), 200 µM of each dNTPs (dATP, dTTP, dCTP, dGTP) Shannon’s Information index ( H´ ), measure of (NEB, USA), and one unit of Taq DNA Polymerase evenness ( E) and observed number of alleles i.e. (NEB, USA). PCR cycling programs were as follows: richness ( S) sum up to SHE analysis (H´= lnE+ lnS ) initial denaturation at 94º C for 5 min followed by 40 (Hayek and Buzas, 1997; Magurran, 2004). To 8

International Journal of Aquaculture, 2015, Vol.5, No.27 1 10 http://ija.biopublisher.ca analyze the inter-population genetic differentiation Amplified Polymorphic DNA (RAPD) Marker, International Journal of Biotechnology and Biochemistry, 6(2): 271-278 between six different populations of Badis badis , Allendorf F.W., Luikart G., Aitken S.N., 2013, Conservation and the pair-wise FST values were calculated using the formula Genetics of Populations, A John Wiley & Sons, Ltd., Publication, UK Amavet P., Vilardi J.C., Rosso E. and Saidman B., 2009, Genetic and FST = 1- HS/H T (where HS is the average expected Morphometric Variability in Caiman latirostris (Broad-Snouted heterozygosity estimated from each subpopulation and Caiman), Reptilia, Alligatoridae, Journal of Experimental Zoology, 311 HT is the total gene diversity or expected heterozygosity A : 258-269 in the total population as estimated from the pooled Avise J.C. and Hamrick J.L., 1996, Conservation and Genetics: Case Histories from Nature, Chapman & Hall, New York allele frequencies) (Wright, 1969). The FST is http://dx.doi.org/10.1007/978-1-4757-2504-9 equivalent to the coefficient of gene differentiation Bardakci F. and Skibinski D.O.F., 1994, Application of the RAPD technique G (G =D /H ). The diversity among populations in tilapia fish sps and subspecies identification, Heredity, 73:117-123 ST ST ST T http://dx.doi.org/10.1038/hdy.1994.110 (DST = H T - HS) was also calculated to ascertain the Brahmane M.P., Mitra K. and Misra S.S., 2008, RAPD fingerprinting of the inter-population diversity between different pair-wise ornamental fish Badis badis (Hamilton 1822) and Dario dario (Kullander and Britz 2002) (Perciformes, Badidae) from West Bengal, populations. The estimated gene flow ( Nm ) between India,. Genetics and Molecular Biology, 31:789-792 the pair-wise populations was also calculated by the http://dx.doi.org/10.1590/S1415-47572008000400028 Brahmane M.P., Das M.K., Sinha M.R., Sugunan V.V., Mukherjee A., Singh formula Nm=0.5(1-GST )/G ST (McDonald and McDermott, S.N., Prakash S., Maurye P. and Hajra A., 2006, Use of RAPD 1993). The binary matrix prepared from all scored fingerprinting for delineating populations of hilsa shad Tenualosa ilisha fragments were used to generate Nei's unbiased (Hamilton, 1822), Genetics and Molecular Research, 5(4):643-652 measures of genetic identity and genetic distance Buzas M.A. and Hayek L.A.C. 1996, Biodiversity resolution: an integrated approach, Biodiversity Letters 3:40-43 matrix (Nei, 1978) using the software Popgene ver. http://dx.doi.org/10.2307/2999767 1.32 and the output data matrix was also verified Buzas M.A. and Hayek L.A.C., 1998, SHE analysis for biofacies identification, Journal of Foriminiferal Research, 28:233-239 separately using the software TFPGA ver. 1.3 and Caughley G. and Gunn A., 1996, Conservation Biology in Theory and Arlequin ver. 3.1 (Excoffier and Schneider, 2005). Practice. 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SB designed http://dx.doi.org/10.1111/j.1439-0426.1998.tb00613.x Garg R.K., Sairkar P., Silawat N., Batav N. and Mehrotra N.N., 2010, study, defined intellectual content and performed data analyses, Assessment of genetic diversity of Clarias batrachus using RAPD manuscript editing and review. markers in three water bodies of Bhopal, Journal of Environmental Biology, 31: 749-753 Acknowledgments Garg R.K., Silawat N., Sairkar P., Vijay N. and Mehrotra N.N., 2009, The work was supported by a research grant from University RAPD analysis for genetic diversity of two populations of Mystus vittatus (Bloch) of Madhya Pradesh, India, African Journal of Grants Commission (UGC), India [MRP Sanction No.: Biotechnology, 8: 4032-4038 40-289/2011 (SR)] awarded to corresponding author. 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10 Research Article NBU J. Anim. Sc. 7: 13–23 (2013) ISSN 0975-1424

ASSESSMENT OF INTRA-POPULATION DIVERSITY OF THE THREATENED ORNAMENTAL FISH BADIS BADIS (HAMILTON-BUCHANAN, 1822) FROM THE TERAI REGION OF WEST BENGAL

Tanmay Mukhopadhyay and Soumen Bhattacharjee* Cell and Molecular Biology Laboratory, Department of Zoology, University of North Bengal, P.O. North Bengal University, Raja Rammohunpur, Siliguri, District Darjeeling 734 013, West Bengal, INDIA.

Received : August 30, 2013 Revised & Accepted : October 31, 2013

ABSTRACT Random Amplified Polymorphic DNA (RAPD) was used to delineate the genetic diversity of threatened ornamental fish Badis badis populations sampled from the six riverine sites of three major streams of the Terai region of West Bengal. The sites were Mahananda Barrage at Fulbari, Mahananda-Panchanoi River junction, Balason River at Palpara, Panchanoi River, Mahananda River at Champasari and Balason River at Tarabari. Twenty-two degenerate decamer primers were used to generate the RAPD bands from the DNA samples collected from five individuals of each of six groups. All primers were highly polymorphic (> 95%) and generated 199 amplification products. The study revealed that the Balasan (Palpara) population possessed highest proportions of polymorphic loci (37.69%), observed number of alleles (1.3769±0.4858), effective number of alleles (1.2729±0.3918), Nei’s genetic diversity (0.151±0.015), Shannon’s diversity index (0.2205±0.2950) and Shannon’s measure of evenness (0.071359). On the other hand the Mahananda-Panchnoi Junction population showed lowest values for each diversity indices i.e., degree of polymorphism (29.65%), observed number of alleles (1.2915±0.4556), effective number of alleles (1.2007±0.3545), Nei’s genetic diversity (0.112±0.013), Shannon’s diversity index (0.1648±0.2691) and Shannon’s measure of evenness (0.053333). The Analysis of Molecular Variance (AMOVA) for the populations studied showed significant variances: where 54% of the differentiation attributed to among populations with an estimated variance of 18.422 and 46% attributed to within population with an estimated variance of 15.717. To our knowledge, the present study is the first attempt to explore and also to determine the river specific genetic background of this species available in this sub-Himalayan hotspot region of North-Eastern India. This preliminary study revealed that RAPD technique could be an effective tool in the assessment of population genetic structure of a genomic information deficient species, Badis badis .

Key Words: Badis badis , threatened ornamental fish, genetic diversity, RAPD-PCR

INTRODUCTION sub-Himalayan region of West Bengal, known as Badis badis (Hamilton-Buchanan, 1822) Terai and Dooars. The region being within the (Actinopterygii, Perciformes, Badidae) is an important Eastern Himalayan Hotspot, this type of study with ornamental fish and has potential commercial value regard to threatened fish fauna having ornamental (MPEDA website, www.mpeda.com). The Bureau values will be of much importance from the of Fish Genetic Resources (NBFGR) has included standpoint of conservation, sustainability and human this fish within the vulnerable (VU) category in livelihood. the list of threatened freshwater fishes of India (Lakra et al. , 2010). However, a detailed study of Genetic variability is an essential characteristic genetic diversity of this fish has not been carried of any population and also permits the population to out with regard to species, especially in the eastern adapt and evolve with the changing environmental *Author for correspondence: Dr. S. Bhattacharjee ([email protected])

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