Biology and Body Composition of Channa Marulius (Soul) from Two Natural Habitats of Pakistan

Biology and Body composition of Channa marulius (Soul) from Two natural habitats of Pakistan

A thesis submitted in partial fulfillment of the requirements for The degree of Ph.D In the subject of ZOOLOGY

Muhammad Latif PIN No: 085-13075-Bm5-084

Institute of Pure and Applied Biology (Zoology Division) Bahauddin Zakariya University, Multan 2015

This thesis Submitted by Muhammad Latif is accepted in its present form by Institute of Pure and Applied Biology, Bahauddin Zakariya University, Multan (Pakistan) as satisfying the thesis requirement for the degree of Ph.D in the field of Zoology.

SUPERVISORY BOARD

1. Dr. Furhan Iqbal (Internal Examiner)

2. Prof. Dr. Muhammad Ali (Internal Examiner)

3. Dr. Seema Mehmood (Director IP&AB)

4. External Examiner

Dedication

I dedicated this

dissertation to My

Dear Parents,

Teachers, Family and

Friends

CONTENTS LIST Contents Page No.

Acknowledgements I

List of Abbreviations Iii

List of Figures Iv

List of Tables V

Summary Xiii

1. Introduction

1.1 Introduction Water 1

1.2Water quality 3

1.3Soul (Channa marulius) 7

1.4Body composition 12

1.5Blood 13

1.6Hematological parameters 14

1.7Serological parameters 17

1.8 Elements 19

2. Material Method

2.1Water quality Analysis 23

2.2 Measurement of wet weight and total length of Channa marulius 29

2.3 Estimation of body / fillet Composition parameters 30

2.4 Blood and serum collection 34

2.5 Estimation of Serum parameters 36

2.6 Estimation of elemental contents in fish powder 37

2.7 Statistical Analysis 39

3. Results

3.1 Results of water Quality Parameter Analysis 41

3.2 Body Composition 46

3.3 Fillet Composition 48

3.4 Results of Hematological Parameters of Channa marulius 50

3.5 Results of Elements Composition of Channa marulius 54

4. Discussion

Discussion 108

5. References

References 117

Appendix 132

Acknowledgements

I express my heartiest gratitude to Almighty Allah who provided me the opportunities to complete my research work and the achievements I have gained throughout my life. Hazrat Mohammad (PBUH) conveyed the message of Allah to the humanity. Gain of knowledge and education is one and the foremost command of Allah conveyed through the Prophet of Islam (PBUH). I am extremely grateful to him for this message and all the teachings of Islam.

In preparing and completing this thesis, I have received inspiration and practical help from many colleagues. First of all, I would like to express appreciation and thanks to my respectable teacher and supervisor Prof. Dr. Mohammad Ali, Vice Chancellor, Government College University Faisal Abad. In spite of his multifarious engagements in the University, he had been kind enough to provide proper guidance whenever I requested him. I wish to express my gratitude to my friend and respectable supervisor, Dr. Furhan Iqbal, Assitant Professor of Zoology at Institute of Pure and Applied Biology. This is due to his matchless guidance that I have been able to complete my work. I am grateful to him from the core of my heart. I express my sincere gratitude to Prof. Dr. Seema Mehmood, Director, Institute of Pure and Applied Biology, Bahauddin Zakariya University, Multan, for providing me all possible facilities and good academic environment during the course of my research work.

I am highly grateful to Higher Education Commission (HEC) of Pakistan for awarding me an indeginous scholarship and provided me an opportunity to complete my doctoral studies. I am also thankful to my fellow HEC scholars in labs for being with me in good as well as in hard times. I especially acknowledge my research fellow, Shahid Iqbal, who was always in the lime light while we worked in the laboratories. I am extremely grateful to him. I also grateful to Ms. Rehana Iqbal and Dr. Kashif Umer for their help during my studies.

I acknowledge the efforts of my brother in Law, Mr Abdul Rrasheed (Late). He always provided me help throughout my studies and all other affairs of my life. I have gained courage and confidence from him. He is a constant source of inspiration for me. May his soul rest in peace aamen. I acknowledge the efforts of my Brother Mr Hanif. I have always stepped in his footprints. He has the most important contribution in formulating my life. i

I am grateful to my sisters Iqbal, Shamim (Late), Nasreen, Parveen and my brothers Tanveer and Zeeshan Nasir for the appreciation they provided to me throughout my work. I am really thankful to my niece Sumreen Iqbal for her prayers. I acknowledge the support of my father, Ghulam Muhammad Bhatti. My mother, Zainab, has also been a source of inspiration throughout my life. Last but not the least, I want to express my gratitude to my wife Sobia and my two little Kids Eshaal And Abdul Rafeh who sacrificed their time during the course of my studies.

Muhammad Latif

List of Abbreviations ALT Alanine Aminotransferase ANOVA Analysis of Variance AST Aspartate Transaminase EC Electric Conductivity Hb Haemoglobin IUCN International union of conservation of nature MCH Mean Corpuscular Haemoglobin MCHC Mean Corpuscular Haemoglobin Concentration MCV Mean Corpuscular Volume PCV Pack Cell Volume RBC Red Blood Cells SD Standard Deviation TDS Total dissolved solids TWBC Total white Blood Count WBC White Blood Cells

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List of Figures

Figure No. Contents Page No. 1.1 Channa marulius 8 1.2 Distribution of Channa marulius 9

List of Tables

Table No. Contents Page No. 3.1 Seasonal variations in studied water quality parameters in water samples 55 from River Sindh during 2010-2011 sampling season. 3.2 Seasonal variations in studied water quality parameters in water samples 56 from River Sindh during 2011-2012 sampling season. 3.3 Seasonal variations in studied water quality parameters in water samples 57 from River Chenab during 2010-2011 sampling season. 3.4 Seasonal variations in studied water quality parameters in water samples 58 from River Chenab during 2011-2012 sampling season. 3.5 Analysis of variance for pH in River Sindh and Chenab Waters during 59 sampling season 2010-11. 3.6 Analysis of variance for Calcium (mgL-1) in River Sindh and Chenab 59 Waters during sampling season 2010-11. 3.7 Aanalysis of variance for Sodium (mgL-1) in River Sindh and Chenab 60 Waters during sampling season 2010-11. 3.8 Analysis of variance for Electric conductivity (dsc M-1) in River Sindh 60 and Chenab Waters during sampling season 2010-11. 3.9 Analysis of variance for Chloride (mgL-1) in River Sindh and Chenab 61 Waters during sampling season 2010-11. 3.10 Analysis of variance for Total Alkalinity (mgL-1) in River Sindh and 61 Chenab Waters during sampling season 2010-11. 3.11 Analysis of variance for Dissolved oxygen (mgL-1) in River Sindh and 62 Chenab Waters during sampling season 2010-11. 3.12 Analysis of variance for Magnesium (mgL-1) in River Sindh and Chenab 62 Waters during sampling season 2010-11. 3.13 Analysis of variance for Total dissolved solids (mgL-1) in River Sindh 63 and Chenab Waters during sampling season 2010-11. 3.14 Analysis of variance for Temperature (C0) in River Sindh and Chenab 63 Waters during sampling season 2010-11.

Table No. Contents Page No. 3.15 Analysis of variance for pH in River Sindh and Chenab waters during 64 sampling season 2011-12. 3.16 Analysis of variance for Electric conductivity (dsM-1) in River Sindh and 64 Chenab waters during sampling season 2011-12. 3.17 Analysis of variance for Calcium (mgL-1) in River Sindh and Chenab 65 waters during sampling season 2011-12. 3.18 Analysis of variance for Magnecium (mgL-1) in River Sindh and Chenab 65 waters during sampling season 2011-12. 3.19 Analysis of variance for Sodium (mgL-1) in River Sindh and Chenab 66 waters during sampling season 2011-12. 3.20 Analysis of variance for Chloride (mgL-1) in River Sindh and Chenab 66 waters during sampling season 2011-12. 3.21 Analysis of variance for Total Alkalinity (mgL-1) in River Sindh and 67 Chenab waters during sampling season 2011-12. 3.22 Analysis of variance for Dissolved oxygen (mgL-1) in River Sindh and 67 Chenab waters during sampling season 2011-12. 3.23 Analysis of variance for Total dissolved solids (mgL-1) in River Sindh 68 and Chenab waters during sampling season 2011-12. 3.24 Analysis of variance for Temperature (Co) in River Sindh and Chenab 68 waters during sampling season 2011-12. 3.25 Comparison of various parameters determining body composition of 69 Channa marulius from River Sindh during sampling season 2010-11. 3.26 Comparison of various parameters determining Body composition of 70 Channa marulius from River Sindh during sampling season 2011-12. 3.27 Comparison of various parameters determining body composition of 71 Channa marulius from River Chenab during sampling season 2010-11. 3.28 Comparison of various parameters determining body composition of 72 Channa marulius from River Chenab during sampling season 2011-12.

Table No. Contents Page No. 3.29 Analysis of variance for % Water in the Body composition of Channa 73 marulius from River Sindh and Chenab waters during sampling season 2010-11. 3.30 Analysis of variance for % Ash (wet weight) in the Body composition 73 of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. 3.31 Analysis of variance for % Organic contents (wet weight) in the Body 74 composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. 3.32 Analysis of variance for % Fat (wet weight) in the Body composition of 74 Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. 3.33 Analysis of variance for % Protein (wet weight) in the Body 75 composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. 3.34 Analysis of variance for Condition Factor in the Body composition of 75 Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. 3.35 Analysis of variance for % Dry weight in the Body composition of 76 Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. 3.36 Analysis of variance for % Water in the Body composition of Channa 76 marulius from River Sindh and Chenab waters during sampling season 2011-12. 3.37 Analysis of variance for % Dry weight in the Body composition of 77 Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. 3.38 Analysis of variance for % Ash (wet weight) in the Body composition of 77 Channa marulius from River Sindh and Chenab waters during sampling

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Table No. Contents Page No. season 2011-12. 3.39 Analysis of variance for % Organic Contents (wet weight) in the Body 78 composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. 3.40 Analysis of variance for % Fat (wet weight) in the Body composition of 78 Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. 3.41 Analysis of variance for % Protein (wet weight) in the Body 79 composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. 3.42 Analysis of variance for Condition Factor in the Body composition of 79 Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. 3.43 Comparison of various parameters determining Fillet composition of 80 Channa marulius from River Sindh during sampling season 2010-11. 3.44 Comparison of various parameters determining Fillet composition of 81 Channa marulius from River Sindh during sampling season 2011-12. 3.45 Comparison of various parameters determining Fillet composition of 82 Channa marulius from River Chenab during sampling season 2010-11. 3.46 Comparison of various parameters determining Fillet composition of 83 Channa marulius from River Chenab during sampling season 2011-12. 3.47 Analysis of variance for % Water in the Fillet composition of Channa 84 marulius from River Sindh and Chenab waters during sampling season 2010-11. 3.48 Analysis of variance for % Dry weight in the Fillet composition of 84 Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. 3.49 Analysis of variance for % Organic contents (wet weight) in the Fillet 85 composition of Channa marulius from River Sindh and Chenab waters

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Table No. Contents Page No. during sampling season 2010-11. 3.50 Analysis of variance for % Fat (wet weight) in the Fillet composition of 85 Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. 3.51 Analysis of variance for % Protein (wet weight) in the Fillet composition 86 of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. 3.52 Analysis of variance for % Ash (wet weight) in the Fillet composition of 86 Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. 3.53 Analysis of variance for % Water in the Fillet composition of Channa 87 marulius from River Sindh and Chenab waters during sampling season 2011-12. 3.54 Analysis of variance for % Dry weight in the Fillet composition of 87 Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. 3.55 Analysis of variance for % Ash (wet weight) in the Fillet composition of 88 Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. 3.56 Analysis of variance for % Organic contents (wet weight) in the Fillet 88 composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. 3.57 Analysis of variance for % Protein (wet weight) in the Fillet composition 89 of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. 3.58 Analysis of variance for % Fat (wet weight) in the Fillet composition of 89 Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. 3.59 Seasonal variations in the hematological parameters of Channa marulius 90

Table No. Contents Page No. captured from River Sindh during 2010-2011 sampling season. 3.60 Seasonal variations in the hematological parameters of Channa marulius 91 captured from River Sindh during 2011-2012 sampling season. 3.61 Seasonal variations in the hematological parameters of Channa 92 marulius captured from River Chenab during 2010-2011 sampling season. 3.62 Seasonal variations in the hematological parameters of Channa marulius 93 captured from River Chenab during 2011-2012 3.63 Analysis of variance for Haemoglobin (gd L-1) of Channa marulius in 94 River Sindh and Chenab waters during sampling season 2010-11. 3.64 Analysis of variance for RBC (10*12 (L-1)) of Channa marulius in River 94 Sindh and Chenab waters during sampling season 2010-11. 3.65 Analysis of variance for PCV (%) of Channa marulius in River Sindh 95 and Chenab waters during sampling season 2010-11. 3.66 Analysis of variance for Platelets (10*9 (L-1)) of Channa marulius in 95 River Sindh and Chenab waters during sampling season 2010-11. 3.67 Analysis of variance for MCV (fl) of Channa marulius in River Sindh 96 and Chenab waters during sampling season 2010-11. 3.68 Analysis of variance for MCH (pg) of Channa marulius in River Sindh 96 and Chenab waters during sampling season 2010-11. 3.69 Analysis of variance for MCHC (gd L-1) of Channa marulius in River 97 Sindh and Chenab waters during sampling season 2010-11. 3.70 Analysis of variance for WBC (10*9 L-1) of Channa marulius in River 97 Sindh and Chenab waters during sampling season 2010-11. 3.71 Analysis of variance for Haemoglobin (gd L-1) of Channa marulius in 98 River Sindh and Chenab waters during sampling season 2011-12. 3.72 Analysis of variance for RBC (10*12 L-1) of Channa marulius in River 98 Sindh and Chenab waters during sampling season 2011-12. 3.73 Analysis of variance for PCV (%) of Channa marulius in River Sindh 99

Table No. Contents Page No. and Chenab waters during sampling season 2011-12. 3.74 Analysis of variance for Platelets (10*9 L-1) of Channa marulius in 99 River Sindh and Chenab waters during sampling season 2011-12. 3.75 Analysis of variance for MCH (pg) of Channa marulius in River Sindh 100 and Chenab waters during sampling season 2011-12. 3.76 Analysis of variance for MCHC (gdL-1) of Channa marulius in River 100 Sindh and Chenab waters during sampling season 2011-12. 3.77 Analysis of variance for WBC (10*9 L-1) of Channa marulius in River 101 Sindh and Chenab waters during sampling season 2011-12. 3.78 Analysis of variance for Total Cholestrol of Channa marulius in River 101 Sindh and Chenab waters during sampling season 2011-12. 3.79 Analysis of variance for ALT (IUL-1) of Channa marulius in River 102 Sindh and Chenab waters during sampling season 2011-12. 3.80 Analysis of variance for AST (IUL-1) of Channa marulius in River 102 Sindh and Chenab waters during sampling season 2011-12. 3.81 Analysis of variance for Total Protein (gdL-1) of Channa marulius in 103 River Sindh and Chenab waters during sampling season 2011-12. 3.82 Analysis of variance for MCV (fl) of Channa marulius in River Sindh 103 and Chenab waters during sampling season 2011-12. 3.83 Comparison of various elements of Channa marulius from River Sindh 104 during sampling season 2011-12. 3.84 Comparison of various elements of Channa marulius from River Chenab 105 during sampling season 2011-12. 3.85 Analysis of variance for Nickle element of Channa marulius in River 106 Sindh and Chenab waters during sampling season 2011-12. 3.86 Analysis of variance for Zinc element of Channa marulius in River 106 Sindh and Chenab waters during sampling season 2011-12. 3.87 Analysis of variance for Manganese element of Channa marulius in 107 River Sindh and Chenab waters during sampling season 2011-12.

Table No. Contents Page No. 3.88 Analysis of variance for Iron element of Channa marulius in River 107 Sindh and Chenab waters during sampling season 2011-12. 4.1 Safe Water quality parameters 110,111

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Summary

The present study was conducted to determine the serum biochemistry, body composition, hematology and elemental composition in muscles of Channa marulius on monthly basis for two consecutive years (2010-2012) from River Sindh and Chenab sites in southern Punjab. Water samples along with Channa marulius specimens were collected from both sampling sites on monthly basis during November 2010 to April 2011 and then from November 2011 to April

2012.Standard parameters of water quality (pH, Dissolved oxygen, Calcium, Total dissolved solids, Sodium, Magnecium, Chloride, Total alkalinity, Electric conductivity and Temperature) were dwtermined and their correlation were determined with studied parameters of proximate composition and studied parameters of hematology and serum biochemistry. Blood was sampled on spot through direct cardiac punctures and divided into two parts to study the hematological parameters (RBCs, WBCs, PCV, platelets, MCV, MCH, MCHC and Hb) and parameters of serum biochemistry (Total protein, Cholestrol, ALT and AST) respectively. Concentration of certain metals (Zinc, Nickle, Manganese and Iron) were also determined in fish muscles.

We have observed that water quality parameters of both river (Chenab and Sindh) followed the standard patterns of variations with season and the water quality parameters varied with climatic changes and biological activities at the sampling sites during both sampling sessions. Analysis of our results indicating that Channa marlius is nutritionally very important fresh water fish as it contained higher protein contents as compared to Channa punctata (belonging to the same genera) and also to some other fresh water carnivorous fish species. The studied parameters of body composition showed that fish from river Sindh had better nutrional quality than Channa marulius from river Chenab.

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Reverse was the situation in months with low environmental temperature for both sampling sites during two consecutive seasons. While comparison of elements in fish bodies from both sampling sites revealed no significant differences indicating that water quality parameters remained within safe limits leading to normal elemental concentration in fish body.

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1.1 Water

Fresh water is used for fishing, irrigation, drinking, industry, transportation, recreation, hunting, supporting biodiversity and an important source from esthetic sense (Carpenter et al, 1998). From primeval times, people reside close to water, settling in along coastlines, river valleys and beside lakes. The inspirations of water are as miscellaneous as human needs and aspirations. For as long as humans have lived near waterways, they have also used water to wash away and attenuate the general public’s wastes and pollutants (Brack et al, 2002).

If we have to consign a monetary value on all recourses which are needed for our endurance, air would be most extroverted and water is important after air. All body functions, including waste eradication, digestion, respiration, growth, toxin dilution, reproduction and for many other functions water is utmost important. Water minimizes the hunger and also aid body to digest the accumulated lipids in body. It has been proven through many studies that water low consumption might increase the lipid deposits, while abundance of water in the body could in fact minimizes the lipid deposits (Heinriches, 2005).

1.1.1 Fresh water

Clean and hygienic water is a very significant resource from visual, cultural, economic, educational and scientific sense (Dudgeon et al, 2006). Freshwater is obtainable as ground water, surface water, rainfall and atmosphere moisture (Verma et al, 2009). 70% of freshwater is not available for human use as it is present in the form of ice and snow (Chapin et al., 2009). The subsurface water is a vital source of freshwater accessible for human use and it comprise almost 30% of total freshwater (Delleur, 1999; Raghunath, 2007). Rivers and lakes are remarkable source of fresh water but they make up only 1% of it. Freshwater is also present in a small amount in wetlands, soil moisture marshes, and atmosphere (Petersen et al., 2010). Freshwater has enormous biodiversity (Allan and Flecker, 1993) and some rivers are called as global biodiversity ‘hotspots’ (Myers et al., 2000). One-third of vertebrate species; together with fish, amphibians, aquatic reptiles, and mammals and an immense number of invertebrates are present in freshwater (Dudgeon et al, 2006). Pakistan has been gifted by God with wealthy water assets, these rivers running down the Himalayas and Karakoram heights from the world’s biggest glaciers and free and distinctive gift for this land. Pakistan is fundamentally an agricultural 1 market. Mainly two types of assets of water are found in Pakistan, natural and artificial. These God gifted assests comprises of karez, ponds, rainfall, lakes, streams, rivers, glaciers and wells etc.

1.1.1.1 River Indus

In Asia River Indus is a chief river which moves all the way through Pakistan. All along the way it flows to Tibet and Northern India . Beginning in the Tibetan Plateau in the locality of Lake Mansarovar, the river flows a line from side to side the Ladakh area of Jammu and Kashmir, approaching Gilgit and Baltistan and then runs from the south direction along the whole span of Pakistan to unite into the Arabian Sea in Sindh close to seaport metropolitan of Karachi. The entire measurement lengthwise of the river is 1980 miles which is 3,180 in kilo meters. River Indus is the largest river of Pakistan. Four countries (India, Afghanistan, China, and Pakistan) different parts spanned by the Indus river basin that is larger than 30% arid, and most of that drier than the adjusting basin of Ganges river (World Resource International, 2003).

Pakistan’s 80% irrigates which include agricultural land of 21.5 million the Indus River is very important for 160 million people (Rizvi, 2001). The water flowing passage of river Indus is a spot of prosperous biodiversity, Arabian Sea is its final destination. The Indus river basin is a extremely fruitful part for animals living in freshwater and an central area for water birds (Ramsar Convention on Wetlands 2003). The Indus is a habitat of fish species in total of 147 and 22 is specifically found here and not present in any other part of the world and also 25 amphibian species. It accommodates the exceptional Indus River blind Dolphin, the world’s unique mammals, not larger than 1,100 population of this mammal (World Resource International, 2003; Ramsar Convention on Wetlands 2003). River Indus supports a larger number of the population living in this piece of the world. River Indus is called the life line of Pakistan because more than 80 per cent of farming capital and food production of Pakistan is consequent from the Indus River (Meadows and Meadows, 1999).

1.1.1.2 River Chenab

Himachal Pradesh the Indian province river Chenab originates. Equal to its merger with the River Indus in Pakistan, it moves along nearly 1240 km with approximately yearly flow of 5.29 billion cubic meters (BCM) 67500 km2 of the total drainage area of the river Indus out of which (38500 km2 present in Pakistan). In Pakistan after its entrance, in the Punjab province the River Chenab tavels a number of densely inhabited industrial cities (e.g. Gujrat, Faisalabad, Sialkot, Gujranwala, Sargodha, Hafizabad, and Jhang).

The river Chenab starts flowing from Jammu and Kashmir. After getting several tributaries, it enters in Punjab near district Sialkot. This river flows through Gujrat, Sargodha and Gujranwala districts in the Punjab. It collects river Jhelum at Trimmu in district Jhang and river Ravi at Sidhnai in district Khanewal. It then flows through districts of Multan and Muzaffargarh and unites river Sutluj in district Muzaffargarh (Salam and Rizvi, 1999). The river Chenab is chief as it contains 33 fish species which have been identified (Ali et al, 2005).

1.2 Water Quality

The recent available data about the amounts of a range of solutes at a particular time and place is provided by water quality. Water quality parameters provide the foundation for analyzing the appropriateness of designated uses of water and to improve presented circumstances. For most advantageous development and management for favorable uses, recent information is required as suggested by programmers of water quality. The major concern in terms of water quantity and quality are imbalanced water circulation on the plane of the earth and rapid declining availability of usable fresh water. (Shinde et al, 2011). The physico-chemical characteristics are altered owing to the activities of the aquatic biota predominantly the metabolism of the aquatic organisms. Any amendment in the environmental parameters may bring in an unwanted aquatic condition which may lead to the aquatic pollution (Waghmave et al, 2012).

1.2.1 Water temperature

Water temperature influences many abiotic and biotic components of aquatic ecosystem directly and indirectly. It also transmits to the dynamics of the organisms such as metabolic and physiological conduct of aquatic ecosystem (Sahni and Yadav, 2012).

One of the most important environmental aspect is temperature. It controls behavioral features of organisms, incorporating gases and salts in water. The basis of lifelong working is multifaceted set of biochemical reactions that are subjective by physical element e.g. temperature. Temperature has also resistance against diseases. Microbial action increases with the increasing temperature. Temperature increase may be a hindrance for fish to migrate and resultantly species reproduction affected (Ram et al, 2011).

1.2.2 pH

The important basis for controlling all the aquatic chemical and biological processes is Hydrogen ion concentration (pH). Fluctuations in pH range afar to the mainly acceptable values might affect microbial bodily processes (Hassanin, 2006). Also, natural waters pH might be affected chemical and biological reactions, would also influenced the aquatic life because solubility of ions are affected. The aquatic life has suitable pH range between 6.5 - 9.0, and 6.5 - 8.5 for fresh water ecosystem (Chin, 2000). pH is one of the most important chemical parameter of water since aquatic organisms are tailored to an average pH. pH also influences other factors like conductivity, bicarbonates, salinity, phosphate, chloride, magnesium and hardness (Sheeja, 2005). pH is the quantify of the concentration of hydrogen ions, which affects acidity or alkalinity of a water or solution (Patil, 2012).

1.2.3 Dissolved oxygen

The health and ability of water body is indicated by dissolved oxygen content to purity itself through biochemical processes. Oxygen is also essential for many chemical reactions that are

4 important to lake functioning, such as oxidation of metals, decomposition of dead and decaying matters etc. (Ramachandra and Solanki, 2007). The essential element for aquatic life is dissolved oxygen, as it influence their whole life processes, oxidation of macrobiotic matter and gaseous exchange of animals in water. These complex macrobiotic matter are changed into simple dissolved inert salts that could be worn by the micro - and macrophyte (Okbah and Tayel, 1999). In most of the cases, the DO for warm water aquatic living organisms in their early life stage it is equal to 6 mg/l and in their other life stages it is equal to 5.5 mg/l and for the aquatic living organisms who lives in cold water at their early life stages the DO requirement = 9.5 mg/l, other life stages = 6.5 mg/l]. This is cited by USEPA, (1999) in most stations of canal waters.

1.2.4 Electrical Conductivity

Another major factor is electrical Conductivity that determines the quality of water. It is a appraise of purity of water (Sahni and Yadav, 2012). EC determines the capacity of water solution to carry out electric current. There is higher conductivity of majority of inorganic compounds solutions and abundant ions (APHA, 1998). The maximum suitable electric conduction is 1.25 m. mhos/cm which is widely used for irrigation purpose. It is to proportionate to total dissolved solids. Natural waters electric conductivity is between 20-1500m.mhos/cm. EC above 400 m.mhos/cm does not influence yield but yield does not enhance with escalating EC.

1.2.5 Calcium

This element is essential for humans and proper ingestion of calcium is vital for appropriate health and growth of body. Intake of calcium to a higher side must be the order of 1 - 2 grams and obtains chiefly from dairy products like yogurt. If water with high level of hardness is used then occurrence of heart diseases is reduced in those areas. The calcium is basic element, so its mixing in water useful to health. Calcium is the major cation in the study of water quality and calcium content above 25 mg/L is called as calcium rich water. Calcium is one of the most important elements, which influence the distribution of diatoms in water bodies, high calcium content favors rich growth of diatoms when followed with high temperature (Vidya et al, 2012).

1.2.6 Magnesium

Magnesium is major dietary component of humans (0.3-0.5 g/day). It is basic cause of hardness of second level and it contains 15-20 % of the total hardness articulated as CaCO3. In combination with sulphate it becomes more important. Magnesium relatively non-toxic in concentration normally encountered in nature. Magnesium also adds to the hardness of water and along with calcium due to which the problem of scale formation is appeared (Vidya et al, 2012).

1.2.7Alkalinity

Due to existence of salts of weak acids and strong bases water is having alkalinity. Alkalinity in water occurs due to presence of carbonates, bicarbonates, and hydroxides. The alkalinity causes buffering capacity of a water body. It is responsible for measuring the ability of water bodies to defuse acids and bases so maintain the pH. Water is a good buffer containing bicarbonates, carbonates, and hydroxides, which combine with hydrogen ions from the water as a result increasing the pH of water (Virendra et al, 2013). There can be long-term ups and downs in the alkalinity of rivers and streams in response to human disturbances (Kausha et al, 2013).

1.2.8 Chloride

Natural waters contain chloride ions. High chlorides level has damaging effect on agricultural crops (Ramachandran et al, 2006). Salts such as sodium chloride are often very soluble in water (Green et al, 2001). It is essential electrolyte needed for maintaining osmoregulation, regulate fluids intake and exit from cells and also conduction of nerve impulses (Fomous and Miller, 2011). Usually the range of chloride is 15-35 mg/l naturally in rivers and other fresh waters, though which is much lower from the standards of drinking water quality. This thing must be considered that we obtained these results from series of level ranging from one sampling site to another (environment protection agency, 2001)

1.2.9 Sodium

Drinking water contains a mineral which is called sodium. Sodium is poured in water to make it soften. Sodium range in water is primarily important to people who have to pay attention their sodium ingestion for their health reasons and there is need an increase in Na amount in water decreases the power of tolerance in the person and there is need to increase the Na level in drinking water. The inhabitants of areas with high Na level in water possess high BP than the People living in areas with low Na levels in drinking water (ASAE, 1989). Salt usage particularly NaCl it has been reported that elevated use of salts, might be severe for the progress of high blood pressure and high threat for stroke, asthma, left ventricular hypertrophy, osteoporosis, and renal stones (McCarthy, 2004). As for as data available, renal stones and asthmatic problems have high ratios in people who used water with high salinity (McCarthy, 2004).

1.2.10 Total Dissolved solids

Minerals like Salts, metals, cations or anions dissolved in water are collectively called total dissolved solids (TDS). Neutral water contains salts like carbonates, bicarbonates, chlorides, sulphates, phosphates and nitrates of calcium, magnesium, sodium, potassium, iron etc. The density of water amplified due to high contents of dissolved solids increases. Total Dissolved solids designate mainly various kinds of mineral present in the water (Virendra et al, 2013). the Total quantity of inorganic chemicals in a particular solution determined the total dissolved solids. The fraction of dissolved solids has sulphates, calcium, carbonates, sodium, bicarbonates and chlorides. A maximum range of TDS is 400 mg L-1 which is acceptable for diverse population of fish (Chhatwal, 1998).

1.3 Channa marulius (Soul)

Fish are mostly considered for the evaluation of aquatic environment quality and are established as bio-indicators of ecological contamination (Borkovic et al, 2008). Fish, in fact, live in much

7 cherished contact with their environment, and are therefore very likely to physical and chemical changes which may be shown in their blood components. It should be noted that hematological represents are of different sensitivity to many kinds of environmental factors and chemicals (Adeyemo, 2007).

Fig 1.1 Morphological features of Channa marulius (Hamilton, 1822)

1.3.1 Distribution of Channa marulius

Sol (Channa marulius) also called in different names like Indian snakehead, giant snakehead, cobra snakehead, great snakehead, bullseye snakehead, related to the family Channidae (superclass Osteichthyes, suborder Channoidei). Sol is recognized by different native names in diverse parts of its presence range in Pakistan it is called Sol, in India it is recognized as Soal, in Sri Lanka it is called Ara, in Cambodia it is called Trey Raws and in Thailand it is called Pla Chon Ngu Hao (Smith, 1945; Talwar and Jhingran, 1992).

Snakeheads related to Channidae (family) and are resident to tropical Africa and Asia. Snakeheads are also called propel predators and reasonably air breathing freshwater fishes. They Majority of them are fish eaters. Because of possessing definite snake-like appearance having lengthened, cylindrical bodies, big scales occurrence on head in majority of the species and compressed head. The position of the eyes on head is dorso-lateral on the anterior part. The size of snakeheads differ greatly, some of the snakeheads can reach up to under 17 cm in length while majority of the snakehead has enormous size grow capability and can acquire a length up to 180

8 cm. Snakeheads considered as high-quality foodstuff and fish for aquarium, can obtained top market value. Asian people considered Snakeheads for long a precious food fish and in most of the markets it has been well-liked. In Jamshedabad, India (Rao and Durve, 1989). Channa marulius consider as essential food fish in Andhra Pradesh, India (Talwar and Jhingran, 1992). Sol (Channa marulius) being the important representative of the Channidae, it has its native distribution is in Pakistan (Rafique, 2007), some parts of South East Asia. Sol (Channa marulius) achieved the growth up to 183 cm in length and 30 kg in weight. Just because of big size and healthier growth, and also the capability of its endurance below low oxygen desolation because of its fractional reliance through air breathing, it is well marked as a suitable fish species to be introduced into the Pakistan farming system. The resident range of this species extends from Myanmar, China, Sri Lanka, Laos, Bangladesh , Nepal, Thailand, Cambodia and Pakistan through India (Courtenay and Williams, 2004).

Fig 1.2 Distribution of Channa marulius in Asia (Courtenay and Williams, 2004)

1.3.2 Discription of Channa marulius

The description of C. marulius which has been appeared in literature (Hamilton, 1822; Talwar and Jhingran, 1992; Fuller and Benson, 2010), constituted the foundation for forming the

9 subsequent portrayal of the species. Channa marulius is a huge sized fish species, lengthened and cylindrical body, dark grey above and yellowish-white below. Along the lateral line Dark patches can be viewed and the spotted fins are also present. Caudal peduncles have typical organ spot. The dorsal outline of the body which rises from the tip of snout toward the dorsal fin front, then forward to this it is almost straight. From the appearance of anal fin faintly domed ventral profile is seen, whence forward it is some extent in a straight line. Therefore deepest body is seen in frontage of dorsal fin, wherever the breadth is smaller and its depth is larger. Large compressed head which is dorso-ventrally in position and narrowing. Scales are also present on the peak of head and their size is also reasonable with a badge of head scales, emerging between orbits, and no bit of scales on gular region. Snout is a bit sharp and a little smaller than the inter- orbital thickness. Big and round eyes are located in dorso-lateral place, nearer to the top of the nose than the latter ending of operculum. Fissure of mouth lengthen upto the latter ends of the eyes and no barble is present. Big mouth which is more or less sloping, inferior jaw rather protrude, the number of canine teeth are 7-18 ehich has position just at the back a only row of villiform teeth, widen into 5-6 rows at jaw symphysis. Prevomer have only teeth while its absent on palatines. The length is pectoral fin is small its about half the length of head Talwar and Jhengran (loc cit). Ventral fin approaches the anal opening and the dorsal fin extend further than the caudal bottom. The shape of caudal fin is round. The explanation of Talwar and Jhengran (loc cit) has specified the meristic count of fin rays (caudal = 13-16, anal =28-36, pelvic =.6, dorsal =45-55, pectoral =16-18), pre-dorsal scales (16) and the scales on the lateral line (60-70) of this fish specie (Talwar and Jhingran, 1992). The lines of scales situated among latter edge of the orbit and pre-opercular area are located at 10o angle, and sideways line scales fall in the shape of 2 rows among 16th - 18th perforated scale.

1.3.3 Biology of Channa marulius

Channa marulius mostly tailored to survive in slow-moving water in swamps ,canals and lakes having underwater vegetation (Rainboth., 1996). Though this species occurrence has been

10 reported from flood forests, swamps (Jhingran., 1984) and from ponds (Roberts, 1993), but the demand of this species is fresh water with rocky substrate or sandy (Talwar and Jhingran, 1992; Pethiyagoda, 1991) described the deep pools in rivers and infrequently the deep lakes as ideal environment for the Channa maruliu. The juvenile fish are entirely air breather, but their adults are obligate air breather. Channa marulius is a meat eaters fish kind (Jhingran, 1984) which entirely depend upon the foodstuff coming from a range of animal origin, including, tadpoles, insects, frogs, snakes, earthworms and fish (Rahman, 1989), and also include aquatic rodents and birds. Channa maruliu (Sol) has strong canine like-teeth which might be used to bite a man. In this species cannibalism is also reported (Devaraj, 1973a), which might result in a reduced endurance rate of the juvenile ones, which might create few troubles in its culturing (Devaraj, 1973b; Mirza and Bhatti, 1993).

1.3.4 Classification of Channa marulius

Kingdom Animalia

Phylum Chordata

Class Actinopterigii

Order Perciformes

Family Channidae

Genus Channa

Specie C. marulius

(Hamilton, 1822)

1.3.5 Texonomic Key of Channa marulius

Abdomen non keeled and non serrated, caudal non confluent with anal Cohort euteleostei

Head Scaly, circumoral barbell absent Super order Acanthopterygii

Fins with Spines Series percomorpha

Body not eel like, dorsal without spiny and soft portions Order channiformes

Family channidae

Genus Channa

Pre-dorsal scales 16,dorsal with 40 rays ,lateral line scales 60, black white edged ocellus on basal portion of caudal fin present Species Channa marulius

1.4 Body Composition

For the physiological condition of fish body composition is considered a good mark. The proportion of water is a fine means of its comparative content of lipids, proteins and energy. The elevated protein and lipid contents and percentage of water is smaller but energy density of fish is larger (Dempson et al, 2004; Ali et al, 2005).

Body composition is an excellent demonstration of fish physiology condition but it is comparatively time taking to measure. Analysis of protein, ash, fat and water contents of fish is called proximate body composition. For usual analysis carbohydrates and non-protein compounds are generally unnoticed because it is present in infrequent amount (Cui and Wootton, 1988). However, these values differ by far in-between and among species, physical activity, sexual condition, size and feeding season. Protein as a vital component remains small in good physical shape fish (Weatherly and Gills, 1987).

Food quality (nutritional) measurement must be covered when we analyzed body composition which include (biological and chemical) all the essential things like protein, fat and ash. For determination of worth of foodstuff these all parameters are very necessary (Kamal et al, 2007).

1.4.1 Water Contents

The water fraction in fishes differs between 74-83% which has been reported on the net weight basis. The relative content of energy, protein and lipid depends upon the amount of water in fishes. Lipid and protein content will be greater when water quality will be low, and on the contrary density of fish will be higher. It seems one can automatically calculate energy if one knows the content of water in fish (Jonsson and Jonsson, 1998).

1.4.2 Protein Contents

Proteins are important biochemical compounds which are there in living organisms. The range of proteins lies approximately upto 12-25% / grams, which more or less accounts for one third of an adult needs. Considering the biological aspect fish protein has an immense worth. It gives information that what types of proteins will hold essential amino acids. Tryptophan is the amino acid which is not present in considerable amount (Balachandra, 2001).

1.4.3 Lipid contents

Lipids maintain the structural integrity in cell membranes and also serve as a basis of metabolic energy (ATP). Carnivorous fish insistent on zooplanktons have expected diet that comprises of wax esters about one-third of its dry weight. Triglycerols counts for around 10-26% of dry weight among the clupeoid fish which usually have neutral lipid (Hoar et al, 1979). The lipid concentration found in fishes tends to differ between 80-94% of dry weight (Umer et al, 2011; Naeem et al, 2011). Fish lipids considered huge dietary importance that prevents the progress of rheumatoid arthritis and cardiovascular disease (Shahidi and Boota, 1994).

1.5 Blood

Oxygen is transported from lungs to various parts of body through blood which is a type of connective tissue and from various body organs blood transported CO2 to lungs. The other important function of blood is to transport nourishment from digestion site and Hormones from

13 glands. This is basically the fluid of healthiness which delivering excretory products to kidney and also transport those substances which fight against diseases to tissues (Robert et al, 2006). By studying the blood cell characteristics one could assess the disease condition of fish (Anderson, 2003). The cause of heavy metal on blood biochemistry of fish or blood was the active research topic for last decade or so and blood was well thought-out to be a superb health forecaster. Several groups have reported various heavy metals effects on variety of cultured fish species under variable experimental situations (Cavas et al, 2005; Carvalho and Fernandes, 2006).

1.6 Hematological parameters

The study of blood, the blood forming organs and blood diseases is come under the branch of medicine which is called hematology. Hematology also included treatment, etiology, prognosis, diagnosis study and also blood diseases prevention (Hoyle et al, 2007). The production of blood and its components such as glucose, blood proteins, blood cells, hemoglobin, the mechanism of coagulation etc can be affected by blood diseases (Aaron et al, 2003). Fish can adapt themselves to bad environmental conditions by changing their physiological activities. Different stress factors might have to be faced by fish such as pollution, malnutrition diseases and varied water qualities. The qualitative and quantitative variations in hematological parameters which include cell proportions of leukocyte, the red blood cell (RBC) counts, the amount of hemoglobin (Hb), white blood cell (WBC) counts, , and the size of RBC and WBC are mainly significant findings regarding diagnosis (Anonymous, 2000). Environmental factors, age, nutritional, sex, ethnic background, body build, social and especially, altitudes are such number of factors which affect hematological parameters values even in apparently in good physical shape populations (Mohsen et al, 2001).

1.6.1 Red Blood Cells (RBC)

These are also named as erythrocytes the most plentiful cells of blood in body. Erythrocytes include Hb protein which is responsible for oxygen transport (Michael et al, 2007). Mainly health is affected by hemoglobin concentration in our blood. When hemoglobin level is too small Fatigue and short of breath is experienced because oxygen supplied to tissues is not sufficient.

This condition is called anemia. Anemia can be prohibited by enhancing body’s production of red blood cells with a blood transfusion or with drugs. On the other hand high red blood cells in our blood can be a symbol of a hidden lung pr heart problem (Dondorp et al, 1999).

1.6.2 White Blood Cells (WBC)

Nucleated heterogeneous group of cells are called White Blood Cells (WBC). Per micro liter of blood contains 4000 to 10,000 (WBC) in their normal concentration. Their vital role is defense against infection in phagocytosis and immunity. Both disorders of WBC can be seen either quantitative or qualitative. In quantitative abnormalities, all cells seems to be normal but there quantities are present in unusual manner either higher than the average values or in fault, of usual values. While in qualitative abnormalities, nonstandard appearing cells are found in blood flow (Blumenreich, 1990).

WBC count might be considered an important biomarker for a variety of disease processes e.g. the WBC number decreases during hemodialysis (Ksiaze et al, 1984). Elevated WBC levels might consider a risk factor for stroke, acute myocardial Infarction (MI) and coronary artery disease (Brown et al, 2001).

1.6.3 Hemoglobin (Hb)

In vertebrates hemoglobin is the main constituent except in family Channichthyidae of fish (Sidell and Ycristin, 2006). The hemoglobin structure was first revealed in 1959 (Kumar et al, 2007). Oxygen binding capacity of blood increases to 70 times more by Hemoglobin and 4 molecules of oxygen can bind with mammalian hemoglobin (Castanzo, 2007). Oxygen as well as other gases is transported by hemoglobin also carbon dioxide is transported as carbaminohemoglobin about 10% of total amount (Connie, 1998). The binding capacity of hemoglobin with CO is 250 times more than for oxygen (Hall, 2010). Standard range for Hb in females is 12-15 g/dL; in men is 13.8-18 g/dL; in infants the range is 11-16 g/dL and in expectant women is 11-14 g/dL (Robert et al, 2003).

1.6.4 Packed Cell Volume (PCV)

Hematocrit measurement is so valuable in any hematologic workup that its occurence in blood test panels is taken for granted. It is just like a quality control programs in the hematology laboratory. Whole blood viscosity most essentially determined by Packed cell volume (PCV) (Akinnuga et al, 2011). Certain diseases like Polycythemia, anemia, hemolytic anemia and Blood loss can be well determined by PCV values (Bull et al, 2001).

1.6.5 Mean Corpuscular Volume (MCV)

In the given blood volume the average volume of RBC’s is actually called MCV (NCCLS, 2000). standard value of mean corpuscular volume is 80-96 fL (Dasgupta, 2011). The levels of MCV are actually the indicator of anemia. Macrocytic anemia is that kind of anemia when there is increases level of MCV (Estridge et al, 2000). When there is normal value of mean corpuscular volume in this case the anemia is called normocytic anemia and when the MCV is lower than the normal value it is called microcytic anemia (Uthman, 1998).

1.6.6 Mean Corpuscular Hemoglobin (MCH)

In an average red blood cell volume hemoglobin weight is called Mean corpuscular hemoglobin (Morton, 2002). 27-31 pg per cell of MCH is the standard value in human (Lippi et al, 2012). Infants have range of MCH between 32-34 pg (Pagana and Pagana, 2012). During anemia the level of MCH decreases which also decreases hemoglobin mass per red blood cell (Greer et al, 2009).

1.6.7 Mean Corpuscular Hemoglobin Concentration (MCHC)

The concentration of hemoglobin in the RBC’s in relation to size and volume designated as MCHC (Estridge et al, 2000). In humans standard ranges of mean corpuscular hemoglobin concentration is 32-36% (Turgeon, 2005). Fluctuations of MCHC from its average range can be the sign of an alteration in RBC’s physiology. MCHC lowered during severe anemia case

(Morton, 2002). Both these diseases like sickle cell anemia and homozygous hemoglobin C have elevated of mean corpuscular hemoglobin (David and Cohen, 2002; Sircar, 2008).

1.6.8 Platelets

Platelets also called thrombocytes are actually a disk shape cell fragments called megakaryotes of small size. The platelets have life span of 5-9 days and their diameter is 2-3 µm. In healthy individual platelets production rate is very high about 10¹¹ platelets per day (Campbell, 2008). Standard values of thrombocytes is 150,000- 450,000 /µL of blood (Kumar and Clark, 2005). Platelets discharge growth factors, Platelet derived growth factor (PDGF) and TGF beta (O’Connell et al, 2008). These two have a vital role to take part in repair and regulation of connective tissue (Sanchez et al., 2007). Along with leukocytes thrombocytes also sustain homeostasis; regulate inflammatory process at the site of injuries (Weyrich and Zimmerman, 2004).

Bleeding results if the concentrations of platelets are low and on the other hand if the concentrations of platelets are high it can cause blood clotting, which can be the result of myocardial infarction, pulmonary embolism and stroke. Thrombocytopathy is the study of disease or abnormalities of platelets (Van Veen et al, 2010). While thrombocytopenia is the disease in which concentration of platelets is less than 50,000 / µL (Cheung, 2005).

1.7 Serological parameters

Serology is the branch of hematology in which we study about the blood serum and other fluids of body. The term serology usually applies to the diagnostic identification of antibodies in the serum. Such kind of antibodies are usually produced in reaction to an infection (some microorganism) against other foreign proteins (which results from wrong matched blood transfer), or to one's own proteins (in case of autoimmune disease) (Washington, 1996).

Checking an individual's blood type for many other situations and also for the diagnostic purposes in rheumatic illnesses serological tests may be performed. Certain immune deficiencies associated with the lack of antibodies can be diagnosed with serology blood tests in patients.

Several serology techniques including ELISA, agglutination, precipitation, complement-fixation and fluorescent antibodies can be used depending on the antibodies being studied. Some serological tests have (generally) comparable properties to serum which can execute on semen and saliva (Ryan and Ray, 2004).

1.7.1 Total protein

In performing different functions such like assuring glucose through glycogen synthesis, the regulating of colloid osmotic pressure, as a rapid exchange for indispensable amino acids, in hormones and minerals transportation, to make enzymes and immune system blood plasma protein regulate all these in the organism. Consequently, blood plasma proteins have an extraordinary importance in maintaining internal body conditions at equilibrium and it could be fine mark of healthiness or disease under standard and also during strain conditions (Filipovic et al, 2007).

1.7.2 Cholesterol

Certain crucial functions in the body are performed by cholesterol which is wrongly traditionally known as a powerful enemy of health. It is the cholesterol who takes part in synthesis of bile acids which are crucial for the assimilation of fats and also cholesterol is also required for many organic substances (hormones) such as progesterone, cortisol, dihydroepiandrosterone, testosterone and estrogen (Girao et al, 1999). Cholesterol also needed in producing fat soluble vitamin like vitamin D. Cholesterol is an integral part of cell membranes where its function is in providing structural support and also it serves as a protecting antioxidant. Cholesterol is also important for the conduction of nerve impulses, chiefly at the synapse level. A great deal of medically important health diseases predominantly the diseases associated with cardiovascular system has been associated with abnormal cholesterol level therefore cholesterol level can serve as fine vigor sign ( Alberts et al, 1994).

1.7.3 Alanine Transaminase (ALT)

Serum alanine aminotransferase (ALAT) or pyruvic transaminase (SGPT) assists in the alteration of alanine and α-ketoglutarate to pyruvate and glutamate (Tolman et al, 1999). Two

18 important processes of body metabolism like gluconeogenesis and amino acid metabolism ALT has an important role to play. ALT is the essential component of pancreas tissues, heart, muscle, liver and to a lesser extent in kidney. When the ALT levels are too high it can generally be serve as forecaster of abnormal liver functions (Liu et al, 2008).

1.7.4 Aspartate Transaminase (AST)

The conversion of aspartate and α-ketoglutarate to oxaloacetate and glutamate is facilitated by aspartate aminotransferase (ASAT/AAT) also be called as serum glutamic oxaloacetic transaminase (GOT) (Pratt and Kaplan, 2000). In humans aspartate tramsaminase has two isoenzymes namely cytosolic isoenzyme can be called as GOT 1 which derived from RBC’s and heart while its second enzymes mitochondrial isoenzyme known as GOT 2 is found primarily in the liver. During liver and muscles diseases the level of AST rises. AST is a very important investigative test for severe burns, liver function, acute renal disease, myocardial infarction, acute pancreatitis, acute hemolytic anemia and trauma (Bowers and McComb, 1984).

1.8 Elements

Evaluation of fishes to determine the concentration of many other heavy metals, that are non- essential and may be body burden to human due to accumulation from fish consumption, is mostly crucial to be certain that they fulfill the edible and commercial needs (Watermann, 2000). Report has shown that heavy metals are major pollutants that pose serious health risk and environmental concerns (Onyia et al, 2007). Water pollution has become a universal problem due to dangerous accumulation of metals in it. They have toxic and fatal effect to aquatic life (Sen et al, 2011). Among ecological pollutants, metals are specific centered, due to their possible noxious effects and ability to bioaccumulates in water ecosystems (Gledhill et al, 1997). Blood is main carrier of these metals in fish where the ions are often bound to protein (Kaoud and El- Dahshan, 2010). Some animals have capability to build up heavy metals to higher levels than the acceptable metal concentrations without any evident bad effects on their normal physiological functions (Otitoloju et al, 2007). Vital organs and systems of human body can be extremely affected if the concentrations of heavy metals are high in human body. Damages have been reported which include enhanced lipid peroxidation, DNA damage, enzyme inactivity and the

19 oxidation of protein sulfydryl groups (Taiz and Zeiger, 1998). Noxious heavy metal can also be reason of neurological effects ,internal organs cancers, diabetes and anaemia, dermatological diseases, skin cancer and cardiovascular disease as well as reproductive, developmental, immunological responses in the human body (Kaoud and El-Dahshan, 2010). Oxidative damages to living tissues can be caused by the toxicity of these elements (Kaoud and El-Dahshan, 2010).

1.8.1 Copper (Cu)

Copper is important element for the very well progress and development of humans (Ralph and McArdle, 2001). The liver controls homeostasis, it also discharges the copper for its hepatic control regulation, over extra hepatic copper (Harris, 2000). Cu also plays essential function in metabolism of fish (Canli et al, 2002). Unpolluted water which contains Cu is a source of food for fish (Ogino and Yang, 1980) and gills of fish also uptake slight contribution of Cu to the whole fish body. Whereas in contaminated water sources gills accumulate Cu 10 times higher in few hours which are suddenly appeared in blood (Grosell et al, 1997).

1.8.2 Iron (Fe)

DNA synthesis, immune system, oxygen transport and respiratory reactions, Iron (Fe) is responsible for all these processes (Bury et al, 2011) and also the essential component of ribonucleotide reductase cofactor. Fe is necessary element of teleost fish for transportation of oxygen and cellular respiration (Bury et al, 2003) but in surplus Fe is deadly (Crichton et al, 2002).

1.8.3 Manganese (Mn)

Manganese (Mn) is an important microelement not being part of aquatic ecosystems naturally but necessary for all life forms. Many enzymes like oxidoreductases, transferases, isomerases hydrolyses and ligases etc. manganese is present as cofactor. Reverse transcriptase of many retroviruses also contains (Mn). Arginase and diphtheria toxin contain it in polypeptide chain

(Law et al, 1998). Mn in surplus amount can be the source of the toxicity of brain resulting in Parkinson disease (Aschner, 2000).

1.8.4 Zinc (Zn)

Zinc is a second abundant trace mineral of living organisms which is very necessary for living organisms. Zinc primarily stores in muscles, however large quantity of zinc is also found in kidney, eye (retina), red and white blood cells, liver, pancreas, skin and bones. Zinc plays crucial role in the taste, stress level, immunity, appetite balance and smell (Belongia et al, 2001). Brain stores zinc in glutmatergic neurons which is in synaptic vesicles (Bitanihirwe and Cunningham, 2009) and the functions of this is to regulate the excitability of brain (Hambidge and Krebs, 2007), synaptic plasticity and learning (Nakashima and Dyck, 2009). For aquatic organisms zinc is vital element but excess quantity of zinc in aquatic environment may lead to pollution. Zinc is chiefly absorbed by intestine (Glover and Hogstrand, 2003). The complete fish body and gills has minimum accumulation of Zn when the water is hard which resulted in the minimizing of the overall severe toxicity of Zn (Everall et al, 1989).

AIMS AND OBJECTIVES

The aims and objectives of present study are as follows:

1 To determine the water quality of river Sindh and Chenab for two consecutive year and to recommend the suggestions for their improvement, if the parameters are not within safe limits.

2 To report the body and fillet composition of Channa murulius from two habitats i.e. Chenab and Sindh rivers.

3 To determine the complete blood count and variations in blood chemistry of Channa murulius on seasonal basis.

4 To report the monthly variation in the concentration of studied elements in Channa murulius muscles on seasonal basis.

5 To report the effect of water quality on biology of Channa murulius on seasonal basis.

6 To assess the market value of Channa murulius as compared to other commercially available fish in pakistan

Materials and methods

Present study was carried out during 2010-12 at the River Indus and Chenab. For River Indus, Sampling site was Ghazi Ghat (N 30 03 852; E 070 48 540) in Dera Ghazi Khan District approximately 80 km in west from Multan. For River Chenab, Samples were collected from Head Muhammad wala (N 30° 18' 1; E 71° 20' 41) in Muzafargarh District approximately 50 km away from Multan.

2.1 Water Quality Analysis

Water samples were collected from both sampling sites (River Sindh and Chenab) on monthly basis from November 2010 to April 2011 (Season 1) and November 2011 to April 2012 (Season 2). Water temperatures and pH were determined at the sampling sites. The water samples were collected from just below the surface from each sites in plastic bottles of 1.5 liter capacity and divided into two parts. One part of samples was used to study all physico-chemical parameters while second part was fixed using MnSO4 and alkaline iodide for the determination of dissolved oxygen. All water samples were analyzed according to standard and widely accepted methods (Boyd, 1981).

2.1.1 Temperature

The water and air temperature were measured at the time of sampling by using simple alcoholic thermometer.

2.1.2 pH

By using buffer solution pH meter ( Eutech, Singapore) was caliberated and pH was measured by dipping pH electrode in water samples.

2.1.3 Dissolved Oxygen (Iodometric Method)

2.1.3.1 Reagents

1) MnSO4 Solution was prepared by dissolving 36.4 gms MnSO4 in 100 ml of distilled water 2) For the preparation of solution A 50.0 g of sodium hydroxide and 15 g potassium iodide were mixed in 100 ml of distilled water. For the preparation of solution B 2 g Sodium Azide was mixed in 8 ml of distilled water. Both solution were mixed to prepare Alkali Iodide Azide reagent.

3) Concentrated Sulphuric Acid (H2SO4)

4) 0.025 M sodium thiosulphate was prepared by dissolving 6.3 g Na2S2O3.5H2O in one liter of boiled distilled water. 5) 2% Starch indicator was made by mixing 2 gram Starch in 100 ml distilled water. Mixture was heated during stirring until it became transparent 0.5 ml formalin was added as preservative.

Protocol

Sample (4) was taken in a 250 ml flask and 1 ml of MnSO4 followed by 1 ml alkali iodide azide reagent was add and a brown precipitate was formed to which I ml of concentrated H2SO4 was added and titrated the mixture against sodium thiosulphate solution until pale straw colored appeared. Noted the volume of Na2S2O3.5H2O used and added 8 drops of 2% starch solution and titrated again till blue color of starch disappeared. Recorded the total volume of sodium thiosulphate used. The formula given below was used to calculate the dissolved oxygen in each sample.

DO (mg l-1) = (ml of titrant used) (N) (8) (1000) Sample Volume (ml)

2.1.4 Total Alkalinity

2.1.4.1 Reagents

1) 0.02 N H2SO4 mixture was prepared by mixing 2.8 ml H2SO4 in one liter distilled water. 2) For the preparation of phenolphthalein indicator 0.5 g of phenolphthalein was mixed in 50 ml of 95% ethanol followed by adding up of 50 ml distilled water.

2.1.4.2 Procedure

To 50 ml of taster water in a flask 5 drops of phenolphthalein Indicator was added. Upon turning the water pink, it was titrated with 0.02 N H2SO4 till the point that the color disappeared. Volume of the water that was used during titration was recorded. Now 5-8 drops of methyl orange indicator was added in the same sample and titrated it against 0.02 N H2SO4 until the orange color appeared and recorded.

Alkalinity of the water sample was calculated by the following formula,

Total Alkalinity = (ml of titrant) (N) (8) (1000) Sample Volume (ml)

2.1.5 Total dissolved Solids (TDS)

Procedure

Ignited a clean crucible in a muffle furnance (RJM 1.8-10A, China) at 550 0C for 30 minutes. Crucible were cooled in desiccators and weighed it. Mixed the sample and filtered 100 ml of it. 10 ml of filtrate was measured and poured it into crucible. Evaporated the contents of crucible in an oven at 95 0C. Increased the temperature to 103 oC for 1 hour. The crucible and residue were cooled down in a desiccators and then weigh.

Total dissolved solids were calculated by using this formula; Total dissolved solids (mg 1-1) = (R-A) (1000) Sample volume (ml) Where R = Weight of crucible and residue (mg). D = Weight of crucible (mg).

2.1.6 Cholrides

2.1.6.1 Reagents

1) For the preparation of 0.014 N silver nitrate (AgNO3) solution 2.3950 g of AgNO3 was dissolved in 200 ml of distilled water. 2) Potassium chromate indicator was prepared by dissolving 50 g potassium chromate in 500 ml of distilled water.

2.1.6.2 Protocol

Took 100 ml of sample water in a beaker and dissolved 1 ml of (K2Cr3) marker was poured into it. Sample was titrated beside silver nitrate solution until the solution turned orange.

Chloride concentrations were calculated by using formula; Chloride (mg/ml = (A-0.2) X N X 354.5 Where, A = Standard silver nitrate used (ml)

0.2 = Blank value attributed to potassium chromate N = Normality of silver nitrate 354.5 = Constant

2.1.7 Electrical Conductivity

Electrical conductivity of sample water was expressed with the help of TOA conductivity Meter (CM. ET, Tokyo)

2.1.8 Calcium

2.1.8.1 Reagents

1) NH4Cl-NH4OH buffer was made by mixing 67.5 g of NH4Cl in 570 ml of NH4OH solution

or NH3 solution and raised the volume up to 1 litre by adding distilled water to produce

NH4Cl solution.

2) NaOH (4N)

160 g of NaOH was dissolved in 1 liter of water to prepare 4N solution of NaOH.

3) Standard CaCl2 solution (0.01N)

CaCo3 (0.5 g) was dissolved in 3N HCl (10 ml) and volume was increased to 1 liter by adding distilled water.

4) Ammonium purpurate as an indicator

Thoroughly mixed 0.5 g of Ammonium purpurate with 100 gm of minced K2SO4.

5) EDTA (0.01N)

Dissolved EDTA (2.0 g) and MgCl2.6H2O (0.05 g) in distilled water and dilute it to 1 liter.

Standardize this solution against CaCl2 solution using titration procedure. The solution is standardized using both the indicator and the exact normality of EDTA is calculated.

2.1.8.2 Protocol

To 1 ml of water sample in a titration flask, 25 ml of distilled water was added along with 5 drops of NaOH. Also add approximately 50 g of Ammonium purpurate indicator in flask. Titrate it against EDTA taken in burette till the end point that is from orange red to purple. Below mentioned formula was used to determine the calcium concentration in each sample;

Ca ++ (mg /L) = EDTA volume consumed X EDTA Normality Sample water volume

2.1.9 Magnecium

2.1.9.1 Reagents

1) 67.5 g of NH4Cl was dissolved in 570 ml of concentrated NH4OH or NH3 solution and

volume was raised to 1 liter finally by adding distilled water to prepare NH4Cl-NH4OH buffe.

2) NaOH (4N)

160 g of NaOH was dissolved in 1 liter of water to prepare 4N solution of NaOH.

3) Erichrome Black-T (EBT Indicator) was made by mixing 0.5 g of EBT and 4.5 g hydroxylamine hydrochloride in 100 ml of 95% ethanol.

4) CaCl2 solution (0.01N) was made by mixing 0.5 g of pure CaCo3 in 10 ml of HCl (3N). Total volume was raised up to 1 litre by adding pure water.

5) EDTA (0.01N)

Dissolve 2 g of EDTA and 0.05 gm of MgCl2.6H2O (hexa hydrated) were dissolved in 1 litre of water. Standardized this solution against CaCl2 solution by titration. The solution is standardized using both the indicator and the exact normality of EDTA is calculated.

2.1.9.2 Protocol

25 ml of distilled water and 10 drops of NH4Cl-NH4OH buffer solution was added to 1 ml of sample and 3-4 drops of EBT indicator. Titrated it against EDTA in burette until colour change from vine to bluish green.

Ca++ was calculated by using formula

Ca++ + Mg /lit = EDTA volume consumed X EDTA Normality Water sample volume

Mg++ mg/lit = (Ca++ + Mg ++) Ca++

2.1.10 Sodium 2.1.10.1 Protocol Sodium concentration was determined by using flame Photometer (Jenway PFP7, China).

Stock solution of Na (1000 ppm) was prepared by dissolving 2.5435 g dry NaCl in 1 L of

dd H2O and stored in a cool and dry place. Standards solutions of 10-100 ppm were

prepared from this stock solution by using the following formula;

C1V1 = C2V2

Where,

C1 = Concentration of stock solution in ppm

V1 = Volume to be taken of stock solution in ml

C2 = Concentration of Na or K to be required in ppm

V2 = Total volume to be required in ml

C2 (ppm required) = V1× C1 V2

Where, V1 = ml of known solution

C1 = ppm of known solution

V2 = total vokume to be made

The fuel supply of flame photometer was turned on and it was ignited by rotating the fuel knob clockwise. After ignition the knob was rotated anticlockwise in such a way that small cones were formed in flame. First aspirated the dd H2O for at least 10-15 minutes. Adjusted the required filter i.e. sodium filter by the knob provided. Adjusted the zero on the scale by rotating the knob “Blank”. Aspirated the standard working solution and recorded the reading in ppm. Again aspirated the distilled water and checked whether there is zero on the scale, which indicates that instrument is standardized according to the given sodium filter.

Distilled water was aspirated before aspirating the next sample. Distilled water was aspirated for at least 15 minutes for cleaning after finishing the work. The concentration of sodium was determined as follows;

Na (ppm) = ppm from calibration curve of standards.

2.2 Measurement of wet weight and total length of Channa marulius

For the weighing of all fish samples digital balance (Shimadzn, Japan) was used to the nearest 0.01 g. Perspex measuring tray containing mm scale was used to determine the body length (0.01 cm). All measurements were made from the proximal maxillary tip to the largest ray of caudal fin.

2.3 Estimation of body / fillet Composition parameters

2.3.1 Estimation of water contents

To determine the contents of water in every individual fish in already weighed dead and cleaned fish were put in already weighed aluminum foil plate for desiccation in an oven (Yiheng, China) at 60-65°C. For the estimation of whole water quantity in the body of the fish following formula was applied.

Water content in fish body = Body Weight (wet) – Body weight (dry)

% water in fish body was calculated by the formula mentioned below:

Total water in body % Water = ______x 100 Fish wet weight

Following formula was used for the calculation of percent dry weight:

Fish weight (dry) Dry Weight of fish (%) = ______x 100 Fish weight (wet)

For more analysis powdered material was needed and it was made possible with the help of electric blender (Moulinex, Japan) which powered and homogenized that dry carcass.

2.3.2 Calculation of ash contents

For the analyze of ash content in every single fish, 50.0 mg of dry powdered fish was obtained in a already weighed, China clay crucible which is heat resistant and put it for 7 hrs at 500°C into a Muffle furnace (Sybron thermolyne 1300) for ashing and again weighed following cooling to determine ash content in fish. By applying the following formula % of ash contents was determine.

Ash Weight in fish sample Ash (% of dry fish mass) = ______x 100 Fish dry weight

Quantity of ash in desiccated fish mass of fish was calculated through the standard formula mentioned below.

Mass of ash per sample Ash content in fish mass = ______x Dry fish mass fish Sample weight

Following formula was use to calculate % ash in wet weight of fish:

Total ash in dry weight of fish % ash in fish weight (wet) = ______x 100 Wet fish weight (wet)

2.3.3 Organic content estamination in fish body

To calculate organic contents in fish following formulas were applied;

Organic content in fish body = Fish dry mass –Ash contents in fish body

% organic content in dry mass of fish were determined by equation given below.

% Organic content in dry fish mass = 100 – Ash content % dry fish weight

Whereas percent organic content in wet fish weight were determined by applying the formula:

Organic contents in fish mass % Organic contents in wet fish weight) = ______x 100 Fish weight (wet)

2.3.4 Fat contents estimation in fish body

Dry tissue was used for the estimation of the fat contents by dry extraction method during which a Methanol and Chloroform mixture (2:1) was taken adopting the technique of Salam & Davies (1994), and 500.0 mg of finely grinded dry tissue was dissolved into 10.0 ml of above mentioned mixture and stirred by using glass rod. Following centrifugation this mixture was left over night. Following the centrifugation, the transparent supernatant was taken with great care into dried, washed and already weighed small glass bottles. Lipid fraction obtained by drying these bottles in an oven at 40─50°C to evaporate the solvent. Electronic balance with sensitivity 0.0001 g was used for the estimation of lipids.

Fat (%) in dry fish mass was determined by equation given below.

Mass of fat per sample Fat (%) in fish dry mass = ______x 100 Weight of sample

Following equation was used to calculate the amount of of fat in dry weight of fish:

Fat per sample Total fat content in fish mass = ______x Fish body weight (dry) Weight of sample

While % fat in fish body was calculated by using the equation below:

Fat in dry fish mass % Fat in fish body (wet weight) = ______x 100 Fish weight (wet)

2.3.5 Estimation of protein contents in fish body

Protein contents in dry fish mass were calculated either by subtracting ash + fat contents from dry fish mass;

Protein in fish = Dry fish mass – (Ash + fat contents in fish)

Or by the formula mentioned below,

Protein content = Organic –fats in fish mass

Protein (%) in dry mass of fish were determined by using the formula:

Protein in dry weight of mass Protein (% ) in fish dry mass = ______x 100 Dry fish weight

% protein (wet fish weight) was determined by using the following formula: Protein in fish mass ( dry ) Protein (% ) in wet fish weight = ______x 100 Wet mass of fish

2.3.6 Calculation of carbohydrates

Carbohydrate contents are generally in negligible concentrations in fish mass and are thus neglected (Salam and Davies, 1994). Carbohydrates in fish body were not determined during present investigation.

2.4 Blood and serum collection

Blood from each fish specimen was collected directly through heart puncture and aliquoted into two halves; one for the study of haematological parameters and analysis of selected serum biochemical parameters were carried out from second aliquot.

2.4.1 Determination of hematological parameters

Blood samples were analyzed to work out red blood cell (RBC), hemoglobin (Hb), mean corpuscular hemoglobin (MCH), packed cell volume (PCV), mean corpuscular volume (MCV), platelet count, and mean corpuscular hemoglobin concentration(MCHC).

2.4.1.1 Haemoglobin (Hb)

For the determination of Hb quantitatively MICRO-LAB 3000 (Merck, Germany)was used. The wavelength of MICRO-LAB 3000 was adjusted at 1540 nm. 5 ml hemoglobin solution was put in a funnel and dissolved with 20 µl of the complete sample. Solution was kept at room warmth for 5 minutes and Hb concentration was estimated in g/dl.

2.4.1.2 Packed cell volume (PCV)

To determined (PCV) ¾ the of micro hematocrit capillary tube was filled with blood and centrifuged at ten thousand rpm for 5 minutes. The hematocrit was calculated by comparing the total blood height with that of plasma and the height of the blood cell column to the nearest 0.5 mm. Following formula was used to determine PCV in each blood sample;

Packed Red Cells Height (mm) PCV (%) = x 100 Packed Red Cells and Plasma Height (mm)

2.4.1.3 Total Red Blood Cell Count (TRBC)

Neubaur Ruling Hemocytometer was used to calculate total red blood cell count (TRBC) . Blood was watered down at the proportion of 1: 200 as 20 ul of blood was assorted with diluting solution (4 ml) in a standard micropipette for red blood cell with diluting solution and left for 5 minutes. A little volume of this solution was inserted in estimation chamber. Red cells were counted four small squares at corners and one in center of the large central squares. RBC numbers can be calculated by Standard formula given under.

Volume Factor (50) x Dillution Factor (200) x Counted cell number RBC (%) = 100 if 524 cells were counted, the RBC would be 5.24 millions /mm3.

2.4.1.4 Total White Blood Cell Count (TWBC)

Enhanced Neubaur Ruling Hemacytometer for the estimation of Total White Blood Cell Count (TWBC) was used. Blood was watered down at the relation of 1: 20 in a typical red blood cell micropipette with diluting solution. White cells were counted in all four large squares. WBC numbers can be measured by Standard formula given below.

White Blood Cells (cells /mm3) = Cells counted x dilution factor (20) / Volume (0.4mm)

2.4.1.5 Mean Corpuscular Volume (MCV)

Conventional formula was used to calculate mean corpuscular volume (MCV). Hematocrit (%) MCV (fempto liters)= x 10 Red Blood Cell count (millions /µL )

2.4.1.6 Mean Cell Hemoglobin (MCH)

Following formula was used to calculate mean cell hemoglobin (MCH).

Hemoglobin (g/dl) MCV (pico grams) = x 10 Red Blood Cell count (millions /µL)

2.4.1.7 Mean Cell Hemoglobin Concentration (MCHC)

MCHC was calculated by the following formula.

Hemoglobin (g/dl) Mean Cell Hemoglobin Concentration (g/dl) = x 100 Packed Cell Volume

2.4.8 Platelet counting

For the estimation of the number of platelets (per µL), 10 squares, on either side, of a Neubaur hemocytometer were counted and multiplied with 10000.

2.5 Estimation of Serum parameters

Blood in serum cups was centrifuged at 13000 RPM for 15 minutes for plasma separation from the blood cells. Plasma was transferred into labelled tubes for the estimation of total proteins, aspartate aminotransferase, alanine aminotransferase and cholesterol.

2.5.1 Total protein

Total protein could be measured quantitatively using MICRO-LAB 3000 (Merck, Germany) by using the diagnostic kits manufactured by Diasys (Germany). The kit contained a operational solution (R) and a typical solution (8 mg/dl).20 µl of serum sample was added to 1 ml of working solution. The samples were assorted and kept at 37oC for 10 minutes. The wavelength of the MICRO-LAB 3000 was adjusted at 578 nm. Pure water was used for the adjustment of equipment at 0, TPC (mg/dl) was estimated in this order; 1- Blank, 2- standard sample, 3- Test sample.

2.5.2 Aspartate transaminase (AST)

The quantitative estimation of AST was conceded out on MICRO-LAB 3000 (Merck, Germany). Diagnostic kit manufactured by Diasys (Germany) was used to determine AST concentration in serum samples. The wavelength of the instrument was attuned at 340 nm. The AST kit contained two reagents R1 and R2. Four ratio one of R1 and R2 reagents were assorted to prepare the operational reagent. 1 ml of the effective reagent was added to 100 µl of each plasma sample, mixed and analysed. AST concentration was measured in IU/Lit and AST factor was 1746.

2.5.3 Alanine transaminase (ALT)

ALT values determined by using a diagnostic kit manufactured by Diasys (Germany) following a similar protocol as mentioned above in section 2.5.2.

2.5.4 Cholesterol

For the estimation of cholesterol, quantitatively, diagnostic kit manufactured by Biomed (Germany) and MICRO-LAB 3000 (Merck, Germany) were used. . An arranged reagent (R) and a standard solution (200 mg/dl) was provided with the kit. Serum sample (10 µl) was added to 1 ml of R, mixed and kept at 37oC for 10 minutes and analyzed at 546 nm MICRO-LAB 3000 (Merck, Germany). 2.6 Estimation of elemental contents in fish powder

For the determination of elements in fish mass, fish were weighted and wrapped in aluminium paper of known mass and placed in an electric oven (Memmert-Riefler Grundstucks-GmbH, Germany) at 70°C until even weight was attained. Dry carcasses was powdered in electric blender (Moulinex, China). 1 g of fish powder was ashed in a muffle furnace (China) at 500°C for 12 h. 10 ml of 70% nitric acid was used to homogenized the ash contents on a warm plate at 100°C and volume was raised up to 25 ml with deionized water. The resultant solutions were aspirated in to atomic absorption spectrophotometer (Hitachi, Japan) and absorbance were measured individually for studied elements (Zinc, Nickle, Manganese, Iron) by using specific conditions for each element in flame atomization mode. Solutions of known concentrations were used to calibrate the instrument and each sample was analyzed in triplicate Standard solutions were run after every 10 samples following Yousaf et al. (2012).

2.7 Statistical Analysis

Results were analyzed by statistical package Minitab (Version 16, USA). Data was presented as Mean ± Standard deviation (SD) of mean. One way analysis of variance (ANOVA) was applied to compare various parameters of body/fillet composition, elemental concentration, hematology, serum biochemical profile of Channa marulius and water quality of River Sindh and Chenab

39 between the sampling months and each season. Analysis of variance (ANOVA) for different body/fillet composition, elemental concentration, hematology, serum biochemical profile of Channa marulius and water quality between River Sindh and Chenab during the sampling months and each season to determine the effects of sampling seasons with studied parameters.

Results 3.1 Results of water Quality Parameter Analysis

3.1.1. River Sindh Waters

3.1.1.1. Sampling Season 2010-2011

Analysis of results of studied water quality parameters of River Sindh waters indicated that all the studied parameters varied significantly during the sampling season except for pH (P = 0.32) (Table 3.1).

Data analysis revealed that electrical conductivity (EC) varied significantly (P ˂ 0.001) during the present study with minimum EC value were observed in January while maximum EC value were reported in April 2011. Total Dissolved Solids (P ˂ 0.001) and temperature (P ˂ 0.001) also showed a similar variation pattern as observed for EC (Table 3.1).

For Sodium (P ˂ 0.001), chloride (P ˂ 0.001) and alkalinity (P ˂ 0.001) maximum values were observed in April 2011 while minimum values were detected in December 2010 and all these parameters showed significant variations during the sampling season (Table 3.1).

Magnesium (P ˂ 0.001) and dissolve oxygen (P = 0.01) in Sindh waters also varied significantly with season and both parameters had minimum concentrations in November 2010 while maximum Magnesium values were observed in March 2011 and those of dissolve oxygen were detected in December 2010 (Table 3.1).

Calcium concentrations (P ˂ 0.001) also varied significantly during sampling season 2010-11 with maximum value in February and minimum concentrations were detected in January (Table 3.1).

3.1.1.2 Sampling season 2011-2012

Analysis of results of studied water quality parameters from River Sindh waters indicated that all the studied parameters varied significantly with the sampling season except for pH (P = 0.82) and dissolved oxygen (P = 0.11) (Table 3.2).

Data analysis revealed that electrical conductivity (EC) and total dissolved solids varied significantly (P˂0.001) during the sampling season with minimum values were observed in December 2011 while maximum values were detected in April 2012 (Table 3.2).

The maximum values for calcium (P ˂ 0.001) and magnesium (P ˂ 0.001) were observed in March 2012 while minimum values were observed in December 2011 and both these parameters varied significantly during the sampling season (Table 3.2).

Total alkalinity (P ˂ 0.001) in Sindh waters also varied significantly with season. This parameter had maximum concentrations in November 2011 while minimum values were observed in March 2012. Chloride (P ˂ 0.001) also varied significantly within the sampling season but had opposite results as compared to total alkalinity with maximum values in March 2012 while minimum values in November 2011 (Table 3.2).

Sodium (P ˂ 0.001) and temperature (P ˂ 0.001) in Sindh waters also showed significant variations during sampling season 2011-12. Both parameters had minimum concentrations in January while maximum values were detected in April 2012 (Table 3.2).

3.1.2. River Chenab Water Quality

3.1.2.1. Sampling Season 2010-2011

Analysis of results of studied water quality parameters of River Chenab waters indicated that all the studied parameters varied highly significantly (P < 0.001) during the sampling season except for pH (P = 0.82) and dissolved oxygen (P = 0.11) (Table 3.3 ).

Data analysis revealed that electrical conductivity (EC) and total dissolved solid (P ˂ 0.001) varied significantly during the present study with minimum EC value observed in November 2010 while maximum value were detected in March 2011 (Table 3.3).

Magnecium, chloride, total alkalinity and calcium had maximum values observed in January 2011 while minimum values were detected in April 2011 and all these parameters showed highly significant variations (P < 0.001) during the sampling season (Table 3.3).

Sodium and temperature of Chenab waters also varied significantly (P < 0.001) with season both parameters had maximum concentrations in March 2011 while minimum sodium value was observed in April 2011 and temperature had minimum value in February 2011 (Table 3.3).

3.1.2.2. Sampling Season 2011-2012

Analysis of results of studied water quality parameters of River Chenab waters indicated that all the studied parameters varied significantly with the sampling season except for pH (P = 0.69) and dissolved oxygen (P = 0.12) a trend similar to previous sampling season (Table 3.4).

Data analysis revealed that electrical conductivity (EC) varied highly significantly (P ˂ 0.001), during the present study with minimum value was observed in November 2011 while maximum (EC) value were detected in November April 2012. Total dissolved solids (P ˂ 0.001) and Magnesium (P ˂ 0.001) showed a similar variation pattern as observed for EC during this sampling season (Table 3.4 ).

For total alkalinity (P ˂ 0.001) and Chloride (P ˂ 0.001) maximum values were observed in January 2012 while minimum values were detected in April 2012 and both these parameters varied highly significantly during the sampling season (Table 3.4).

Sodium (P ˂ 0.001) in Chenab waters also varied significantly (P ˂ 0.001) with season and this parameters had maximum concentration in March 2012 while minimum sodium value were observed in November 2011 (Table 3.4 ).

For calcium (P < 0.001) and temperature (P = 0.002) minimum values were observed in February 2012 while maximum values of temperature were detected in November 2011 and those of calcium in December 2011 and both these parameters showed significant variation during the sampling season (Table 3.4).

3.1.3.1 Analysis of Variance of water quality parameters of River Sindh and River Chenab during sampling season 2010-2011.

Results of ANOVA revealed that pH of water varied significantly (P < 0.01) between sites and also highly varied during sampling months (P < 0.001). The interactions of sites and months were also highly significant (P < 0.001) for this parameter during the sampling season 2010-11 between Sindh and Chenab waters (Table 3.5).

Calcium, Sodium, Electric conductivity and Chloride of multi factorial ANOVA revealed that all these parameters varied least significantly (P < 0.05) between the two sites and also least significantly varied during sampling months (P < 0.05). The interactions of sites and months were also least significant (P < 0.05) for all these parameter (Table 3.6 - 3.9).

Total Alkalinity, dissolved oxygen, magnesium and total dissolved solids in water varied significantly (P < 0.01) between sites and least significantly varied during sampling months (P < 0.05). The interactions of sites and months was also least significant (P < 0.05) for this parameter (Table 3.10 - 3.13).

Results of ANOVA revealed that temperature of water varied significantly (P < 0.01) between sites and least significantly varied during sampling months (P < 0.05). The interactions of sites and months was also least significant (P < 0.05) for this parameter (Table 3.14).

3.1.3.2 Analysis of variance of water quality parameters of River Sindh and River Chenab during sampling season 2011-2012.

ANOVA was also calculated to determine the interactions of each studied parameter with site, sampling months and with with both sites and months together. Results of multi factorial ANOVA revealed that pH of water varied significantly (P < 0.01) between sampling sites and sampling months (P < 0.001). The interactions of sampling sites and months was also highly significant (P<0.001) for this parameter during the sampling season 2011-12 between Sindh and Chenab waters (Table 3.15).

Analysis of variance results revealed that electric conductivity and calcium in water varied significantly (P < 0.01) between sites and least significant between sampling months (P< 0.05). The interactions of sites and months was also significant (P < 0.01) for this parameter (Table3.16, 3.17).

Results of ANOVA revealed that Magnesium, Sodium and chloride in water varied least significantly (P < 0.05) between sites and sampling months (P < 0.05).The interactions of sites and months was also significant (P < 0.05) for these parameters (Table 3.18. 3.19, 3.20).

Results of ANOVA revealed that total alkalinity of water varied significantly (P < 0.01) between sites and least significantly varied during sampling months (P < 0.05).The interactions of sites and months was also least significant (P < 0.05) for this parameter (Table 3.21).

Data analysis for multi factorial ANOVA revealed that dissloved oxygen, total dissolved solids and temperature of water varied significantly (P < 0.01) between sites and least significantly

45 varied during sampling months (P < 0.05).The interactions of sites and months was significant (P < 0.01) for these parameters (Table 3.22 - 3.24).

3.2 Body Composition

3.2.1. Body Composition of Channa marulius from River Sindh Waters

3.2.1.1. Sampling Season 2010-2011

Analysis of the results indicated that most the studied parameters varied non significantly during the sampling season except % Ash (wet weight) (P < 0.001), Condition factor (P < 0.001) and % fat (wet weight) (P = 0.004). The maximum % Ash (wet weight) were observed in March 2011 while minimum levels were detected in January 2011. % fat (wet weight) had maximum values in January 2011 while minimum values were reported in February 2011. For condition factor the maximum value were observed in December 2010 while minimum value were detected in March 2011 (Table 3.25).

3.2.1.2. Sampling Season 2011-2012

Analysis of the results indicated that all the studied parameters varied non significantly (P > 0.05) during the sampling season November 2011 to April 2012 in body composition of Channa marulius sampled from River Indus (Table 3.26).

3.2.2. Body Composition of Channa marulius from River Chenab Waters

3.2.2.1. Sampling Season 2010-2011

Analysis of the results indicated that all the studied parameters of body composition of Channa marulius from river Chenab varied non significantly within the sampling season except % fat (wet weight) (P < 0.001) and Condition factor (P < 0.001) which varied significantly during sampling season November 2010 to April 2011. The maximum % fat (wet weight were observed in December 2010 while minimum levels were reported in April 2011. For condition factor the maximum value were observed in November 2010 while minimum value were detected in February 2011 (Table 3.27).

3.2.2.2. Sampling Season 2011-2012

Analysis of the results indicated that all the studied parameters varied non significantly (P = 0.05) within the sampling season November 2011 to April 2012 of body composition of Channa marulius sampled from River Chenab except % ash (wet weight) (P = 0.03) having maximum reported value in November (2011) and minimum in January 2012 (Table 3.28).

3.2.3.1 Analysis of variance of Body composition parameters of Channa marulius from River Sindh and Chenab during sampling season 2010-2011.

Results of ANOVA for various body composition parameters of Channa marulius revealed that % water, % ash (wet weight), % organic contents (wet weight), % fat (wet weight), % protein (wet weight) and condition factor varied least significantly (P < 0.05) between sites and varied significantly (P < 0.01) during sampling months. The interaction of sites and months was also significant (P < 0.01) for these parameters during the sampling season from November 2010 to April 2011 (Table 3.29 - 3.34).

Results of ANOVA of body composition parameters of Channa marulius revealed that % dry weight varied least significantly (P < 0.05) between sites and varied least significantly (P < 0.05) during sampling months. The interaction of sites and months was significant (P < 0.01) for this parameter during the sampling season from November 2010 to April 2011 (Table 3.35).

3.2.3.2 Analysis of variance for Body composition parameters of Channa marulius from River Sindh and Chenab during sampling season 2011-2012.

Results of ANOVA for body composition parameters [% water, % dry weight, % ash (wet weight), % organic contents (wet weight), % fat (wet weight), % protein (wet weight) and

47 condition factor] of Channa marulius revealed that these parameters varied least significantly (P < 0.05) between sites and also varied least significantly (P < 0.05) during sampling months. The interaction of sites and months was also least significant (P < 0.05) for these parameter during the sampling season from November 2011 to April 2012 (Table 3.36 -3.42).

3.3 Fillet Composition

3.3.1. Fillet Composition of Channa marulius from River Sindh Waters

3.3.1.1. Sampling Season 2010-2011

Analysis of the results indicated that all the studied parameters varied non significantly (P > 0.05) within the sampling season except % fat (wet weight) (P = 0.01) which was significantly different in Channa marulius sampled from River Sindh during November 2010 to March 2011.The maximum % fat (wet weight) were observed in December 2010 while minimum values were determined in November 2010 ( Table 3.43).

3.3.1.2. Sampling Season 2011-2012

Analysis of the results indicated that all the studied parameters varied non significantly (P > 0.05) during the sampling season except % fat (wet weight) (P = 0.01) which was significantly different in fillet composition of Channa marulius sampled from River Sindh during November 2011 to March 2012. The maximum % fat (wet weight) values were observed in March 2012 while minimum were detected in January 2012 (Table 3.44).

3.3.2. Fillet Composition of Channa marulius from River Chenab Waters

3.3.2.1. Sampling Season 2010-2011

Analysis of the results indicated that all the studied parameters varied non significantly (P > 0.05) in fillet composition of Channa marulius sampled from river Chenab during November 2010 to March 2011 (Table 3.45).

3.3.2.2. Sampling Season 2011-2012

Analysis of the results indicated that all the studied parameters varied non significantly (P >0.05) in fillet composition of Channa marulius sampled from river Chenab during November 2011 to March 2012 (3.46).

3.3.3.1 Analysis of variance for Fillet composition parameters of Channa marulius from River Sindh and Chenab during sampling season 2010-2011.

Results of ANOVA of fillet composition parameters of Channa marulius revealed that % water, % dry weight, % organic contents (wet weight), % fat (wet weight), and % protein (wet weight) varied least significantly (P < 0.05) between sites and varied significantly (P < 0.01) during sampling months. The interaction of sites and months was also significant (P < 0.01) for these parameters during the sampling season from November 2010 to April 2011 (Table 3.47 - 3.51).

Results of ANOVA of fillet composition parameters of Channa marulius revealed that % ash (wet weight) varied not significantly (P > 0.05) between sites and varied least significantly (P < 0.05) during sampling months. The interaction of sites and months was least significant (P < 0.05) for this parameter during the sampling season from November 2010 to April 2011 (Table 3.52).

3.3.3.2 l Analysis of variance for Fillet composition parameters of Channa marulius from River Sindh and Chenab during sampling season 2011-2012.

Results of ANOVA of fillet composition parameters of Channa marulius revealed that % water, % dry weight, % ash (wet weight), % organic contents (wet weight) and % protein (wet weight) varied least significantly (P < 0.05) between sites and also varied least significantly (P < 0.05)

49 during sampling months. The interaction of sites and months was significant (P < 0.01) for this parameter during the sampling season November 2011 to April 2012 (Table 3.53 - 3.57).

Results of ANOVA of fillet composition parameters of Channa marulius revealed that % fat (wet weight) varied least significantly (P < 0.05) between sites and also varied least significantly (P < 0.05) during sampling months. The interaction of sites and months was also least significant (P < 0.05) for this parameter during the sampling season from November 2011 to April 2012 (Table 3.58).

3.4 Results of Hematological Parameters of Channa marulius

3.4.1. Hematological Parameters of Channa marulius River Sindh Waters

3.4.1.1. Sampling Season 2010-2011

Analysis of the results of the studied hematological parameters of Channa marulius from River Sindh waters indicated that all the studied parameters varied significantly with the sampling season except for pack cell volume (PCV) (P = 0.1) and Mean corpuscular hemoglobin concentration (MCHC) (P = 0.6) (Table 3.59).

Data analysis revealed that hemoglobin (P = 0.02), white blood cell (P = 0.03) and red blood cell count (P = 0.009) Varied significantly during the sampling season. All these parameters had maximum concentrations in December 2010 while minimum values of red blood cell and white blood cell were detected in March 2012 while those of hemoglobin were detected in February 2011 (Table 3.59). Platelets (P = 0.01), mean corpuscular volume (MCV) (P = 0.02) and mean corpuscular hemoglobin MCH (P = 0.03) also varied significantly with the sampling season. All these parameters had minimum concentration in December 2010 while maximum values of MCV and MCH were observed in March 2012 and those of platelets were detected in April 2012 (Table 3.59). 3.4.1.2. Sampling season 2011-2012

Analysis of the results of studied hematological parameters of Channa marulius from river Sindh waters indicated that all the studied parameters varied significantly with the sampling season except platelets (P = 0.1), mean corpuscular hemoglobin (MCH) (P = 0.5), pack cell volume (PCV) (P = 0.1) and mean corpuscular hemoglobin concentration (MCHC) (P = 0.08) (Table 3.60).

Data analysis revealed that hemoglobin (P = 0.03), white blood cell (P = 0.04) red blood cell (P = 0.05) and mean corpuscular volume (MCV) (P = 0.01) varied significantly during the sampling season. All these parameters had minimum concentration in April 2012 while maximum concentration was detected in February 2012 (Table 3.60).

Analysis of the results of serum biochemistry of Channa marulius indicated that all the studied parameters total cholestrol (P = 0.5), alanine transaminase (ALT) (P = 0.8), aspartate transaminase (AST) (P = 0.3) and total protien (P = 0.1) varied non significantly with the sampling season. Total cholesterol had minimum concentration in December 2011 while maximum concentration was detected in April 2012. AST and total protein had minimum concentration in April 2012 while maximum values were detected in February 2012. ALT had minimum concentration in January 2012 while maximum ALT value was detected in March 2012 (Table 3.60).

3.4.2. Hematological Parameters of Channa marulius River Chenab Waters

3.4.2.1. Sampling Season 2010-2011

Analysis of the results of studied hematological parameters of Channa marulius from river Chenab waters indicated that most of the studied parameters varied non significantly within the sampling season [hemoglobin (P = 0.07), red blood cell (P = 0.08), platelets (P = 0.2),mean

51 corpuscular volume (MCV) (P = 0.1) and mean corpuscular hemoglobin (MCH) (P = 0.6)] (Table 3.61).

Data analysis revealed that white blood cell (P = 0.03), mean corpuscular hemoglobin concentration MCHC (P = 0.04) and pack cell volume (PCV) (P = 0.009) varied significantly with the sampling season. PCV and WBC had minimum concentrations in March 2011 and maximum values were observed in December 2011. While mean corpuscular hemoglobin concentration (MCHC) had maximum concentration observed in March 2011 and minimum value were detected in December 2010 (Table 3.61).

3.4.2.2. Sampling Season 2011-2012

Analysis of the results of studied hematological parameters of Channa marulius from river Chenab waters indicated that most of the studied parameters non-significantly varied in the sampling season [platelets (P = 0.7), mean corpuscular hemoglobin (MCH) (P = 0.9) and mean corpuscular hemoglobin concentration (MCHC) (P = 0.4)] (Table 3.62).

Data analysis revealed that hemoglobin (P = 0.001), white blood cell (P = 0.009), red blood cell (P < 0.001), pack cell volume (PVC) (P = 0.001) and mean corpuscular volume (MCV) (P = 0.05) varied significantly within the sampling season. All parameters had maximum concentrations in April 2012 and minimum values were detected in December 2011 (Table 3.62). Analysis of the results of serum biochemistry of Channa marulius indicated that all the studied parameters total cholestrol (P = 0.19), alanine transaminase (ALT) (P = 0.87), aspartate transaminase (AST) (P = 0.34) and total protien (P = 0.07) are not significant within the sampling season. Total cholesterol and ALT had minimum concentration in November 2011 while maximum values were detected in January 2012. For AST and total protein maximum values were detected in February 2012 while AST had minimum value reported in March 2012 and those of total protein had minimum value in November 2011 (Table 3.62).

3.4.3.1. Analysis of variance from both sites of hematological parameters of Channa marulius during season 2010-11

ANOVA revealed that all the studied parameters (hemoglobin, RBC, PCV, platelets, MCV, MCH, MCHC and WBC) varied least significantly between the two sampling sites (P < 0.05) and had significant variation (P < 0.01) with the sampling season 2010-11. The interaction of sites and season was also significant (P < 0.01) for all these studied parameters (Tables 3.63 - 3.70).

3.4.3.2. Analysis of variance from both sites of hematological parameters of Channa marulius during season 2011-12

Results of ANOVA for hemoglobin of Channa marulius revealed that hemoglobin concentration varied least significatly (P < 0.05) between sites and varied significantly (P < 0.01) during sampling months. The interaction of sites and months was least significant (P < 0.05) for this parameter during the sampling season 2011- 2012 (Table 3.71).

Results of ANOVA for RBC, PCV, platelets, MCH, MCHC, WBC, total cholesterol, ALT, AST and total protein in Channa marulius revealed that all these parameters varied significantly (P < 0.01) between sites and varied least significantly (P < 0.05) during sampling months. The interaction of sites and months was also significant (P < 0.01) for all these parameters during the sampling season from 2011- 2012 (Table 3.72 - 3.81).

Results of ANOVA for MCV in Channa marulius revealed that mean corpuscular volume (MCV) concentration varied least significantly (P < 0.05) between sites and varied least significantly (P < 0.05) during sampling months. The interaction of sites and months was also least significant (P < 0.05) for this parameter during the sampling season 2011- 2012 (Table 3.82).

3.5 Results of Elements Composition of Channa marulius

3.5.1. Elements Composition of Channa marulius from River Sindh Waters

3.5.1.1. Sampling Season 2011-2012

Analysis of the results indicated that all the studied parameters varied non significantly (P > 0.05) in muscles of Channa marulius sampled from river Sindh during November 2011 to March 2012 (Table 3.83).

3.5.2. Elements Composition of Channa marulius from River Chenab Waters

3.5.2.1. Sampling Season 2011-2012

Analysis of the results indicated that all the studied parameters varied non significantly (P > 0.05) in muscles of Channa marulius sampled from river Chenab during November 2011 to March 2012 (Table 3.84).

3.5.3.1. Analysis of variance for elements composition in muscles of Channa marulius from River Sindh and Chenab waters during season 2011-12.

Results of multifactorial ANOVA for nickle, zinc, manganese and iron in muscles of Channa marulius revealed that all these studied parameters varied non significantly (P > 0.05) between sites and sampling months. The interaction of sites and months was significant (P < 0.01) for these parameters during the sampling season 2011- 2012 (Table 3.85 - 3.88).

Table 3.1 Seasonal variations in studied water quality parameters in water samples from River Sindh during 2010-2011 sampling season. Data is expressed as Mean ± Standard deviation Minimum-maximum range for each parameter is mentioned in parenthesis. P – value indicates the results on one way ANOVA test.

Water quality November December January February March April P- Value Parameters 2010 2010 2011 2011 2011 2011 pH 8.9 ± 0.1 8.6 ± 1 8.6 ± 1 7.5 ± 1 7.5 ± 1 8 ± 1 (8.8-9) (7.6-9.6) (.7.6-9.6) (6.5-8.5) (6.5-8.5) (7-9) 0.32

Electrical 1.304 ± 0.1 0.65 ± 0.2 0.606 ± 0.1 2.29 ± 0.15 2.34 ± 0.1 2.35 ± 0.1 conductivity (0.93-1.13) (0.55-0.75) (0.506-0.706) (2.12-2.42) (2.24-2.44) (2.25-2.45) P˂0.001 *** (dscM-1 ) Calcium 45.4 ± 1 74 ± 1 64 ± 1 190 ± 1 184 ± 1 142 ± 1 ( mgL-1 ) (44.4-46.4) (73-75) (63-65) (186-119) (183-185) (141-143) P˂0.001 ***

Magnesium (mgL- 13.2 ± 1 20.14 ± 1 15.36 ± 1 48.12 ± 1 57.36 ± 1 36.6 ± 1 1 ) (12.2-14.2) (19.4-21.4) (14.36-16.36) (47.12-49.12) (56.36-58.36) (35.6-37.6) P˂0.001 ***

Sodium(Na) 158.01 ± 19.7 24.38 ± 23 36.34 ± 23 222.87 ± 23 238.7 ± 23 307.05 ± 23 (mgL-1 ) (137.31- (1.38-47.38) (13.34-59.34) (199.87- (215.47- (284.05- P˂0.001 *** 176.41) 245.87) 261.74) 330.05) Total Alkalinity 368.44 ± 61 290.36 ± 61 290.36 ± 61 296.46 ± 61 294.02 ± 61 561.2 ± 61 (mgL-1 ) (307.44- (229.36- (229.36- (235.46- (233.02- (500.2-622.2) 0.001 ** 429.44) 351.36) 351.36) 357.46) 355.02) Chloride 118.925 ± 68.87 ± 35.5 68.87 ± 35.5 165.07 ± 35.5 136.91 ± 35.5 230.75 ± (mgL-1 ) 35.5 (33.37- (33.37- (129.58- (121.41- 36.26 0.001 ** (83.43- 104.37) 104.37) 200.58) 192.41) (199.51- 154.43) 270.51) Dissolved Oxygen 5.32 ± 1 8.32 ± 1 7.98 ± 1 6.82 ± 0.71 6.01 ± 1 5.65 ± 1 (mgL-1 ) (4.32-6.32) (7.32-9.32) (6.98-8.98) (6.98-8.98) (5.01-7.01) (4.64-±6.65) 0.01 *

Total Dissolved 661.7 6± 64 413.44 ± 64 387.84 ± 64 1463.47 ± 1497.64 ± 1504 ± 64 solids (597.76- (349.44- (323.84- 97.76 (1433.6- (1440-1568) P˂0.001 *** (mgL-1 ) 725.76) 177.44) 451.84) (1356.8- 1561.6) 1548.8) Temperature 19 ± 1 18 ± 1 16 ± 1 18 ± 1 23 ± 1 25 ± 1 ( C0 ) (18-20) (17-19) (15-17) (15-19) (22-24) (24-26) P˂0.001 ***

P > 0.05 = Non significant; P < 0.05 = Least significant (*); P < 0.01 = Significant (**); P < 0.001 = Highly significant (***)

Table 3.2 Seasonal variations in studied water quality parameters in water samples from River Sindh during 2011-2012 sampling season. Data is expressed as Mean ± Standard deviation Minimum-maximum range for each parameter is mentioned in parenthesis. P – value indicates the results on one way ANOVA test.

Water quality November December January 2012 February March 2012 April 2012 P- Value Parameters 2011 2011 2012 pH 8.5 ± 1 7.8 ± 1 8.7 ± 1 8.5 ± 1 7.9 ± 1 8 ± 1 0.82 (7.5-9.5) (6.8-8.8) (7.7-9.7) (7.5-9.5) (6.9-8.9) (7-9)

Electrical 0.707 ± 0.1 0.36 ± 0.01 0.703 ± 0.1 0.705 ± 0.1 0.86 ± 0.01 0.89 ± 0.18 P˂0.001 *** conductivity (0.607-0.807) (0.35-0.37) (0.603-0.803) (0.605- (0.85-0.87) (0.71-1.06) (dscM-1) 0.805)

Calcium 85 ± 18.2 40.4 ± 1 77.4 ± 1 90 ± 1 120.4 ± 1 97 ± 1 P˂0.001 *** ( mgL-1 ) (64-96) (39.4-41.4) (76.4-78.4) (89-91) (119.4-121.4) (96-98) Magnesium 24.36 ± 1 12 ± 1 15.6 ± 1 26.64 ± 1 37.2 ± 1 25.44 ± 1 P˂0.001 *** (mgL-1 ) (23.36-23.36) (11-13) (14.6-16.6) (25.64- (36.2-38.2) (24.44- 27.64) 26.44) Sodium 6.67 ± 1 13.34 ± 1 6.44 ± 1 14.49 ± 1 27.6 ± 1 28.29 ±1 P˂0.001 *** (mgL-1 ) (5.67-7.67) (12.34- (5.44-14.34) (27.29- (26.6-28.6) (27.29- 14.34) 29.29) 29.29) Total 396.5 ± 1 164.7 ± 1 237.8 ± 1 395.28 ± 1 64.09 ± 1 280.7±1 P˂0.001 *** Alkalinity (395.5 - 397.5) (163.7- (230.8-23.8) (394.28- (163.01- (285.7- (mgL-1 ) 165.7) 396.28) 165.09) 287.7) Chloride 14.2 ± 1 21.3 ± 1 20.59 ± 1 14.84 ± 1 110.76 ± 1 88.04 ± 1 P˂0.001 *** (mgL-1 ) (13.2-15.2) (20.3-22.3) (19.59-21.59) (13.85- (109.76- (87.04- 15.84) 111.76) 89.04) Dissolved 6.45 ± 1 6.9 ± 1 7.5 ± 1 6.8 ± 1 6.1 ± 1 5.3±1 0.11 Oxygen (mgL- (5.45-7.45) (5.9-7.9) (6.5-8.5) (5.8-7.8) (5.1-7.1) (4.3-6.3) 1 ) Total 452.48 ± 1 230.4 ± 1 450.92 ± 1 451.2 ± 1 550.4 ± 1 579.84 ± 1 P˂0.001 *** Dissolved (451.48-453.48) (229.4- (449.92-451.32) (450.2- (549.4-552.4) (578.84- solids ( mgL-1 ) 231.4) 452.2) 580.84)

Temperature 23±1 20 ± 1 18 ± 1 19 ± 1 24 ± 1 26 ±1 P˂0.001 *** ( C0 ) (22-24) (19-21) (17-19) (12-20) (23-25) (25-27) P > 0.05 = Non significant; P < 0.001 = highly significant (***)

Table 3.3 Seasonal variations in studied water quality parameters in water samples from River Chenab during 2010-2011 sampling season. Data is expressed as Mean ± Standard deviation Minimum-maximum range for each parameter is mentioned in parenthesis. P – value indicates the results on one way ANOVA test.

Water quality November 2010 December January February 2011 March 2011 April 2011 P- Parameters 2010 2011 Value

pH 8.5 ± 1 7.6 ± 1 7.3 ± 1 7.6 ± 1 7.5 ± 1 9.5 ± 1 0.22 (7.5-9.5) (6.5-8.6) (6.3-8.5) (6.6-8.6) (6.5-8.5) (8.2-10.2) Electrical 0.297 ± 0.1 0.718 ± 0.1 1.6 ± 0.1 1.3 ± 0.1 1.9 ± 0.1 1.4 ± 0.01 P˂0.001 conductivity (0.197-0.397) (0.618- (1.5-1.7) (1.2-1.4) (1.8-2) (0.42-1.44) *** (dscM-1 ) 0.818) Calcium 17.4 ± 15 76.8 ± 15 186 ± 2 180 ± 15 175.05 ± 15 160.5 ± 10 P˂0.001 ( mgL-1 ) (2.4-32.4) (61.8-91.8) (184-188) (165-195) (160.05-190.05) (150.5- *** 170.5)

Magnesium 3.48 ± 3 15.36 ± 3 42 ± 1.2 36 ± 3 35.0 ± 3 1.84 ± 1.5 P˂0.001 (mgL-1 ) (0.48-6.48) (12.4-18.4) (40.8-43.2) (33-39) (32.01-38.01) (0.21-3.21) *** Sodium 41.63 ± 23 47.38±23 280.6 ± 23 322 ± 23 331.89 ± 23 8.2 ± 0.23 P˂0.001 (mgL-1 ) (18.6-64.6) (24.3-70.3) (278.3- (299-345) (368.9-354.9) (8.05-8.51) *** 282.9)

Total Alkalinity 40.8 ± 30 120 ± 30 191.4 ± 30 40.4 ± 30 139.2 ± 30 17.4 ± 0.3 P˂0.001 (mgL-1 ) (10.8-70.8) (90-150) (161.4- (110.4-170.4) (109.2-169.2) (17.1-17.7) *** 221.4)

Chloride 31.5 ± 3.5 108.1 ± 35 283.5 ± 3.8 278.3 ± 36.8 269.8 ± 35 21 ± 3.5 P˂0.001 (mgL-1 ) (28-35) (73.1-143.1) (280-287) (234.1-307.6) (234.8-304.8) (17.5-24.5) *** Dissolved Oxygen 4.56 ± 1 6.47 ± 1 6.77 ± 1 6.02 ± 1 5.03 ± 1 5.24 ± 1 0.11 (mgL-1 ) (3.56-5.56) (5.47-7.41) (5.77-7.77) (5.02-7.02) (4.03-6.03) (4.24-6.24)

Total Dissolved 190.08 ± 64 459.52 ± 64 1024 ± 64 832 ± 64 1216 ± 64 915.2 ± 6.4 P˂0.001 solids (126.08-254.08) (395.52- (960-1088) (768-896) (1152-1280) (908.8- *** ( mgL-1 ) 523.52) 921.6) Temperature 21 ± 1 20 ± 1 19 ± 1 18 ± 1 23 ± 1 21 ± 1 P˂0.001 ( C0 ) (21-23) (19-21) (18-20) (17-19) (22-24) (20-22) *** P > 0.05 = Non significant; P < 0.001 = highly significant (***)

Table 3.4 Seasonal variations in studied water quality parameters in water samples from River Chenab during 2011-2012 sampling season. Data is expressed as Mean ± Standard deviation Minimum-maximum range for each parameter is mentioned in parenthesis. P – value indicates the results on one way ANOVA test.

Water quality November December January February March April P- Value Parameters 2011 2011 2012 2012 2012 2012 pH 8 ± 1 7.5 ± 1 8.5 ± 1 7.6 ± 1 7.9 ± 1 8.6 ± 1 0.69 (7-9) (6.5-8.5) (7.5-9.5) (6.6-8.6) (6.9-8.9) (7.6-9.6) Electrical 0.197 ± 0.001 0.518 ± 0.001 1.2 ± 0.1 1.3 ± 0.1 1.4 ± 0.1 1.6 ± 1.1 P˂0.001 conductivity (0.196-0.198) (0.517-0.519) (1.1-1.3) (1.2-1.4) (1.3-1.5) (1.5-1.7) *** (dscM-1 ) Calcium 14.3 ± 0.15 60.8 ± 0.1 120 ± 1 1.31 ± 1 134.3 ± 4.9 150 ± 10 P˂0.001 ( mgL-1 ) (4.1-14.4) (60.7-60.9) (119-121) (13.0-13.2) (131-140) (140-160) *** Magnesium 3.14 ± 3.1 12.36 ± 0.01 30.31 ± 0.90 32.1 ± 0.1 33.17 ± 2.5 40.63 ± 4.5 P˂0.001 (mgL-1 ) (0.48-6.48) (12.5-12.37) (29.2-31) (32-32.2) (31.5-36.01) (36.2-45.2) *** Sodium 33.7 ± 1.499 47.4 ± 0.5 180 ± 1 220.3 ± 0.5 280 ± 1 36.2 ± 0.1 P˂0.001 (mgL-1 ) (32.1-35.6) (47-48) (179-181) (220-221) (279-281) (36.1-36.3) *** Total 40.7 ± 0.1 120 ± 1 181 ± 0.5 141.4 ± 1 139.2 ± 30 17.4 ± 0.3 P˂0.001 Alkalinity (40.6 - 40.8) (119-121) (180.4-181.5) (140.4-142.4) (109.2-169.2) (17.1-17.7) *** (mgL-1 ) Chloride 35 ± 1 109.1 ± 1 280 ± 0.6 271.4 ± 0.74 269.8 ± 35 21 ± 35 P˂0.001 (mgL-1 ) (34-36) (108.1-110.1) (280-811) (270.65-272.1) (234.85-304.85) (17.5-24.5) *** Dissolved 5.36 ± 1 7.23 ± 1 6.77 ± 1 7.02 ± 1 6.03 ± 1 5.24 ± 1 0.12 Oxygen (4.36-6.36) (6.23-8.23) (5.77-7.77) (6.02-8.02) (5.03-7.03) (4.24-6.24) (mgL-1 ) Total 126.08 ± 0.64 331.52 ± 0.64 768 ± 64 832 ± 64 896 ± 64 1024 ± 64 P˂0.001 Dissolved (125.4-126.7) (330.8-332.1) (704-832) (768-896) (832-960) (960-1088) *** solids ( mgL-1 ) Temperature 22 ± 1 18 ± 1 19 ± 1 18 ± 1 20 ± 1 21 ± 1 0.002 ** ( C0 ) (21-23) (17-19) (18-20) (17-19) (19-21) (20-22)

P > 0.05 = Non significant; P < 0.05 = Least significant (*); P < 0.01 = Significant (**); P < 0.001 = Highly significant (***)

Table 3.5Analysis of variance for pH in River Sindh and Chenab Waters during sampling season 2010-11.

Degree of Sum of Source of Variation freedom Squares Mean Squares F-value

Sites 1 11715.74 11715.74 6.54 **

Months 5 7456541 1491308 833.33 ***

Sites*Months 5 -7470010 -1494002 -834.83 ***

ERROR 25 1789.575

TOTAL 36 36.2 P < 0.01 = Significant (**); P < 0.001 = Highly significant (***)

Table 3.6 Analysis of variance for Calcium (mgL-1) in River Sindh and Chenab Waters during sampling season 2010-11.

Degree of Mean Source of Variation freedom Sum of Squares Squares F-value

Sites 1 2261945.3 2261945 4.86 *

Months 5 9750813 1950163 4.19 *

Sites*Months 5 -12309984 -2461997 -5.29 *

ERROR 25 464708.97

TOTAL 36 167483.74

P < 0.05 = Least significant (*)

Table 3.7 Analysis of variance for Sodium (mgL-1) in River Sindh and Chenab Waters during sampling season 2010-11.

Degree of Mean Source of Variation freedom Sum of Squares Squares F-value

Sites 1 5099265.069 5099265 4.28 *

Months 5 22400308.99 4480062 3.76 *

Sites*Months 5 -28125500.3 -5625100 -4.72 *

ERROR 25 1190311.853

TOTAL 36 564385.5903 P < 0.05 = Least significant (*)

Table 3.8 Analysis of variance for Electric conductivity (dsc M-1) in River Sindh and Chenab Waters during sampling season 2010-11.

Source of Degree of Sum of Variation freedom Squares Mean Squares F-value

Sites 1 341.9993948 341.9993948 5.32 *

Months 5 1373.535607 274.7071214 4.27 *

Sites*Months 5 -1761.4804 -352.2960791 -5.48 *

ERROR 25 64.28297258

TOTAL 36 18.33757875

P < 0.05 = Least significant (*)

Table 3.9 Analysis of variance for Chloride (mgL-1) in River Sindh and Chenab Waters during sampling season 2010-11.

Mean Source of Variation Degree of freedom Sum of Squares Squares F-value

Sites 1 4067668.111 4067668 4.77 *

Months 5 15751949.05 3150390 3.70 *

Sites*Months 5 -20347869 -4069574 -4.78 *

ERROR 25 851217.7494

TOTAL 36 322965.945 P < 0.05 = Least significant (*)

Table 3.10 Analysis of variance for Total Alkalinity (mgL-1) in River Sindh and Chenab Waters during sampling season 2010-11. Source of Degree of Mean Variation freedom Sum of Squares Squares F-value

Sites 1 12614288.04 12614288 6.16 **

Months 5 32703599.01 6540720 3.19 *

Sites*Months 5 -46544263.7 -9308853 -4.54 *

ERROR 25 2046562.196

TOTAL 36 820185.534 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.11 Analysis of variance for Dissolved oxygen (mgL-1) in River Sindh and Chenab Waters during sampling season 2010-11.

Degree of Source of Variation freedom Sum of Squares Mean Squares F-value

Sites 1 6485.953 6485.953 5.95 **

Months 5 22630.33 4526.065 4.15 *

Sites*Months 5 -30073.7 -6014.74 -5.52 *

ERROR 25 1088.475

TOTAL 36 131.0278 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.12 Analysis of variance for Magnesium (mgL-1) in River Sindh and Chenab Waters during sampling season 2010-11.

Sum of Source of Variation Degree of freedom Squares Mean Squares F-value

Sites 1 136745.5 136745.5 4.92 **

Months 5 565833.9 113166.8 4.07 *

Sites*Months 5 -719710 -143942 -5.18 *

ERROR 25 27786.1

TOTAL 36 10655.25 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.13 Analysis of variance for Total dissolved solids (mgL-1) in River Sindh and Chenab Waters during sampling season 2010-11.

Source of Degree of Sum of Variation freedom Squares Mean Squares F-value

Sites 1 142022775.5 142022775.5 5.36 **

Months 5 565185882.6 113037176.5 4.27 *

Sites*Months 5 -726330342.8 -145266068.6 -5.49 *

ERROR 25 26449002.05

TOTAL 36 7327317.322 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.14 Analysis of variance for Temperature (C0) in River Sindh and Chenab Waters during sampling season 2010-11.

Degree of Sum of Source of Variation freedom Squares Mean Squares F-value

Sites 1 73229 73229 6.55 **

Months 5 252065 50413 4.50 *

Sites*Months 5 -336217 -67243.3 -6.01 **

ERROR 25 11179.5

TOTAL 36 257 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.15 Analysis of variance for pH in River Sindh and Chenab waters during sampling season 2011-12.

Degree of Sum of Source of Variation freedom Squares Mean Squares F-value

Sites 1 11885.35 11885.35 6.54 **

Months 5 6036836 1207367 664.40 ***

Sites*Months 5 -6050502 -1210100 -665.91 ***

ERROR 25 1817.21

TOTAL 36 36.2 P < 0.01 = Significant (**); P < 0.001 = Highly significant (***)

Table 3.16 Analysis of variance for Electric conductivity (dsM-1) in River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Sum of Variation freedom Squares Mean Squares F-value

Sites 1 142.1955712 142.1955712 5.64 **

Months 5 527.3430899 105.468618 4.18 *

Sites*Months 5 -688.457514 -137.6915028 -5.46 **

ERROR 25 25.20249667

TOTAL 36 6.283643889 Least significant (*); P < 0.01 = Significant (**); P < 0.001

Table 3.17Analysis of variance for Calcium (mgL-1) in River Sindh and Chenab waters during sampling season 2011-12.

Degree of Sum of Mean Source of Variation freedom Squares Squares F-value

Sites 1 1584740.5 1584741 5.71 **

Months 5 5998694.8 1199739 4.32 *

Sites*Months 5 -7805227 -1561045 -5.62 **

ERROR 25 277359.06

TOTAL 36 55567.75 Least significant (*); P < 0.01 = Significant (**); P < 0.001

Table 3.18 Analysis of variance for Magnecium (mgL-1) in River Sindh and Chenab waters during sampling season 2011-12.

Sum of Source of Variation Degree of freedom Squares Mean Squares F-value

Sites 1 107311.8 107311.8 5.53 *

Months 5 416228 83245.6 4.29 *

Sites*Months 5 -538540 -107708 -5.55 *

ERROR 25 19376.36

TOTAL 36 4376.269

P > 0.05 = Non significant; P < 0.05 = Least significant (*); P < 0.01 = Significant (**); P < 0.001 = Highly significant (***)

Table 3.19 Analysis of variance for Sodium (mgL-1) in River Sindh and Chenab waters during sampling season 2011-12.

Degree of Mean Source of Variation freedom Sum of Squares Squares F-value

Sites 1 1737115.889 1737116 4.64 *

Months 5 5038125.815 1007625 2.69 *

Sites*Months 5 -6850731.65 -1370146 -3.66 *

ERROR 25 373707.7023

TOTAL 36 298217.7611 Least significant (*); P < 0.01

Table 3.20. Analysis of variance for Chloride (mgL-1) in River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Mean Variation freedom Sum of Squares Squares F-value

Sites 1 2744870.279 2744870 4.67 *

Months 5 9020691.006 1804138 3.07 *

Sites*Months 5 -11965280.7 -2393056 -4.07 *

ERROR 25 587487.8624

TOTAL 36 387768.4398 P < 0.05 = Least significant (*)

Table 3.21 Analysis of variance for Total Alkalinity (mgL-1) in River Sindh and Chenab waters during sampling season 2011-12. Sum of Mean Source of Variation Degree of freedom Squares Squares F-value

Sites 1 7989281 7989281 5.99 **

Months 5 23410610 4682122 3.51 *

Sites*Months 5 -3.2E+07 -6450840 -4.83 *

ERROR 25 1333712

TOTAL 36 479402.5 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.22 Analysis of variance for Dissolved oxygen (mgL-1) in River Sindh and Chenab waters during sampling season 2011-12.

Degree of Sum of Source of Variation freedom Squares Mean Squares F-value

Sites 1 7163.353 7163.353 6.42 **

Months 5 24740.28 4948.056 4.43 *

Sites*Months 5 -32972.5 -6594.5 -5.91 **

ERROR 25 1115.333

TOTAL 36 46.4879

P > 0.05 = Non significant; P < 0.05 = Least significant (*); P < 0.01 = Significant (**); P < 0.001 = Highly significant (***)

Table 3.23 Analysis of variance for Total dissolved solids (mgL-1) in River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Sum of Variation freedom Squares Mean Squares F-value

Sites 1 58382963.24 58382963.24 5.66 **

Months 5 216876674.3 43375334.87 4.21 *

Sites*Months 5 -283035817.7 -56607163.55 -5.49 **

ERROR 25 10299821.57

TOTAL 36 2523641.4 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.24 Analysis of variance for Temperature (Co) in River Sindh and Chenab waters during sampling season 2011-12. Degree of Sum of Mean Source of Variation freedom Squares Squares F-value

Sites 1 77096 77096 6.57 **

Months 5 264128 52825.6 4.50 *

Sites*Months 5 -352700 -70540 -6.01 **

ERROR 25 11724

TOTAL 36 248 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.25 Comparison of various parameters determining body composition of Channa marulius from River Sindh during sampling season 2010-11. Data is expressed as Mean ± Standard deviation. P – value indicates the results of one way ANOVA test.

Body November December January February 2011 March April P- Value Composition 2010 2010 2011 2011 2011 Parameters % water 76.84 ± 77.19 ± 1.26 73.63 ± 0.68 76.03 ± 2.58 75.21 ± 1.02 76.01 ±0.97 0.45 3.06 (75.64-78.73) (72.86-74.16) (73.62-78.75) (74.11-76.11) (75.33-77.12) (71.92- 81.81)

% Dry weight 18.19 ± 22.81 ± 1.26 26.47 ± 0.65 23.97 ± 2.58 25.79 ± 1.16 23.99 ± 0.97 0.45 3.06 (21.27-24.36) 25.84-27.14 21.25-26.38 24.58-26.89 22.88-24.67 18.19-28.08 % Ash (wet 4.22 ± 0.60 4.08 ± 0.26 2.65 ± 0.81 4.97 ± 0.63 6.39 ± 1.04 5.41 ± 0.46 P˂0.001 weight) (3.40-5.13) (3.83-4.45) 1.78-3.4 4.52-5.69 5.55-7.55 5.02-5.92 ***

% Organic 22.94 ± 22.72 ± 1.28 26.59 ± 0.68 23.86 ± 2.6 25.71 ± 1.15 23.9 ± 1.05 0.41 content (wet 3.04 (21.15-24.28) (25.83-27.13) (21.15-26.34) (24.51-26.81) (22.7-24.63) weight) (17.96- 27.78) % Fat (wet 3.16 ± 0.66 4.03 ± 0.35 5.38 ± 0.28 2.91 ± 0.86 3.67 ± 1.07 3.26 ± 0.7 0.004 * weight) (2.3-4.4) (3.69-4.38) (5.17-5.7) (2.2-3.87) (2.85-4.88) (2.59-3.99)

% Protein (wet 19.78 ± 18.69 ± 1 21.58 ± 1.01 20.95 ± 1.81 22.72 ± 1.38 20.64 ± 0.69 0.31 weight) 2.46 (17.46-19.9) (20.66-22.66) (18.95-22.47) (21.24-23.97) (20.1-21.42) (15.65- 23.38) Condition Factor 0.71 ± 0.06 0.88 ± 0.03 0.78 ± 0.02 0.69 ± 0.03 0.65 ± 0.01 0.66 ± 0.02 P˂0.001 (0.6-0.78) (0.85-0.91) (0.76-0.8) (0.66-0.72) (0.64-0.67) (0.64-0.68) ***

P > 0.05 = Non significant; P < 0.05 = Least significant (*); P < 0.001 = Highly significant (***)

Table 3.26 Comparison of various parameters determining Body composition of Channa marulius from River Sindh during sampling season 2011-12. Data is expressed as Mean ± Standard deviation. P – value indicates the results of one way ANOVA test.

Body Composition November December January February March April P- Value Parameters 2011 2011 2012 2012 2012 2012 % water 77.37 ± 0.87 77.54 ± 1.29 74.93 ± 1.15 76.39 ± 1.3 76.37 ± 0.95 76.88 ± 1.85 0.22 (76.58-78.3) (76.38-78.93) (73.62-75.78) (75.57-77.89) (75.42-77.32) (75.33-78.93)

% Dry weight 22.63 ± 0.87 22.49 ± 1.29 25.07 ± 1.15 23.61 ± 1.3 23.63 ± 0.95 23.12 ± 1.85 0.22 21.7-23.42 (21.07-23.62) (24.22-26.38) (22.11-24.43) (22.68-24.58) (21.07-24.67)

% Ash 5.08 ± 0.28 5.03 ± 0.28 4.37 ± 0.2 4.8 ± 0.77 5.28 ± 0.43 5.22 ± 0.34 0.17 (wet weight) (4.78-5.32) (4.86-5.35) (4.26-4.6) (4.01-5.55) (4.82-5.69) (4.97-5.6)

% Organic 22.56±0.89 22.39-1.29 25.02 ± 1.17 23.46 ± 1.36 23.56 ± 0.96 23.07 ± 1.86 0.23 content (wet (21.6-23.37) (21.01-23.57) (24.16-26.35) (21.9-24.39) (22.59-24.51) (21.01-24.63) weight)

% Fat 2.57 ± 0.23 3.31±0.68 3.01 ± 0.42 2.6 ± 0.4 2.84 ± 0.67 3.55 ± 0.45 0.18 (wet weight) (2.43-2.83) (2.57-3.9) (2.58-3.41) (2.31-3.06) (2.21-3.55) (3.1-3.99)

% Protein 19.99 ± 0.89 19.08±1.71 22.01 ± 0.91 20.86 ±1.23 20.72 ± 0.92 19.52 ± 2.04 0.18 (wet weight) (19.17-20.94) (17.11-20.11) (21.12-22.94) (19.46-21.78) (20.01-21.75) (17.45-21.53)

Condition 0.69 ± 0.04 0.69 ± 0.04 0.69 ± 0.01 0.68 ± 0.03 0.66 ± 0.02 0.69 ± 0.04 0.81 Factor (0.66-0.73) (0.66-0.74) (0.68-0.7) (0.66-0.71) (0.64-0.68) (0.66-0.74)

P > 0.05 = Non significant; P < 0.05

Table 3.27 Comparison of various parameters determining body composition of Channa marulius from River Chenab during sampling season 2010-11. Data is expressed as Mean ± Standard deviation. P – value indicates the results of one way ANOVA test.

Body Composition November December January February March April P- Value Parameters 2010 2010 2011 2011 2011 2011 % water 75.61 ± 2.05 77.37 ± 2.82 74.84 ± 1.51 76.15 ± 2.33 77.45 ± 2.41 79.67 ± 4.22 0.20 (73.61-78.48) (74.4-80.95) (72.6-76.54) (73.7-78.33) (74.71-79.27) (74.88-82.86)

% Dry weight 24.39 ± 2.05 22.63 ± 25.16 ± 1.51 23.85 ± 2.33 22.55 ± 2.41 20.33 ± 4.22 0.20 (24.52-26.39) 2.82 (23.46-27.4) (21.67-26.3) (20.73-25.29) (25.12-17.14) (19.05-25.6)

% Ash 3.96 ± 0.86 4.35 ± 1.42 3.26 ± 1.2 4.62 ± 0.59 3.78 ± 0.52 4.03 ± 0.25 0.49 (wet weight) (3.1-4.96) (2.87-5.93) (1.43-4.46) (4.24-5.3) (3.36-4.36) (3.88-4.31)

% Organic 24.38 ± 2.05 22.6 ± 2.82 25.13 ± 1.51 23.75 ± 2.35 22.47 ± 2.45 20.24 ± 4.28 0.20 content (21.5-26.37) (19.03-25.57) (23.44-27.38) (21.54-26.22) (20.64-25.26) (17.04-25.09) (wet weight) % Fat 4.25 ± 0.46 6.93 ± 1.64 4.68 ± 0.99 3.04 ± 0.25 3.63 ± 0.51 2.88±0.81 P˂0.001 (wet weight) (3.73-4.84) (5.12-9.52) (3.04-5.55) (2.89-3.33) (3.04-3.96) (2.17-3.77) ***

% Protein 20.12 ± 1.63 15.68 ± 4.28 20.45 ± 1.49 20.71 ± 2.12 18.54 ± 2.13 17.36 ± 3.47 0.09 (wet weight) (17.77-21.54) (9.5-20.45) (18.83-22.08) (18.65-22.88) (17.6-21.3) (14.86-21.33)

Condition 0.8 ± 0.03 0.75 ± 0.03 0.73 ± 0.04 0.66 ± 0.05 0.68 ± 0.01 0.67 ± 0.03 P˂0.001 Factor (0.78-0.54) (0.71-0.79) (0.7-0.79) (0.6-0.7) (0.67-0.7) (0.65-0.7) ***

P > 0.05 = Non significant; P < 0.001 = Highly significant (***)

Table 3.28 Comparison of various parameters determining body composition of Channa marulius from River Chenab during sampling season 2011-12. Data is expressed as Mean ± Standard deviation. P – value indicates the results on one way ANOVA test.

Body November December January February March April Composition 2011 2011 2012 2012 2012 2012 P- Value Parameters % water 78.77 ± 2.06 78.58 ± 1.08 78.86 ± 2.9 77.65 ± 0.99 78.45 ± 0.2 77.67 ± 1.53 0.90 (76.4-80.11) (77.36-79.44) (76.94-82.2) (76.55-78.47) (78.33-78.68) (76.41-79.37)

% Dry weight 21.23 ± 2.06 21.42 ± 1.08 21.14 ± 2.9 22.35 ± 0.99 21.55 ± 0.2 22.33 ± 1.53 0.90 (19.89-23.6) (20.56-22.64) (17.8-23.06) (21.35-23.45) (21.32-21.67) (20.63-23.59)

% Ash 5.72 ± 0.21 5.19 ± 0.28 5.03 ± 0.08 5.22 ± 0.32 5.46 ± 0.31 5.6 ± 0.23 0.03 * (wet weight) (5.53-5.94) (4.93-5.49) (4.96-5.49) (4.86-5.43) (5.11-5.63) (5.43-5.86)

% Organic 21.12 ± 2.1 21.35 ± 1.1 21.07 ± 2.92 22.29 ± 0.98 21.47 ± 0.23 22.25 ± 1.53 0.90 content (19.76-23.54) (20.48-22.59) (17.71-23.02) (21.47-23.37) (21.21-21.61) (20.55-23.5) (wet weight)

% Fat 2.02 ± 0.42 3.75 ± 2.6 2.87 ± 1.24 3.33 ± 0.51 3.18 ± 0.65 2.51 ± 0.33 0.60 (wet weight) (1.74-2.5) (1.68-6.66) (1.94-4.27) (2.87-3.87) (2.43-3.6) (2.27-2.88)

% Protein 19.1 ± 1.69 17.61 ± 3.57 18.2 ± 4.12 18.96 ± 1.28 18.29 ± 0.42 19.74 ± 1.28 0.90 (wet weight) (17.93-21.04) (13.82-20.92) (13.44-20.62) (17.6-20.13) 18.02-18.78 18.28-20.62

Condition 0.7 ± 0.03 0.72 ± 0.02 0.7 ± 0.02 0.72 ± 0.01 0.7 ± 0.06 0.68 ± 0.01 0.75 Factor (0.67-0.74) (0.68-0.72) (0.68-0.72) (0.7-0.73) (0.65-0.76) (0.68-0.69)

P > 0.05 = Non significant; P < 0.05 = Least significant (*)

Table 3.29Analysis of variance for % Water in the Body composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11.

Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 1292006 1292006 7.71 *

Months 5 8399088 1679818 10.02 **

Sites*Months 5 -9858312 -1971662 -11.77 **

ERROR 32 167489.9

TOTAL 43 271.0271 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.30 Analysis of variance for % Ash (wet weight) in the Body composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11.

Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 3923.504 3923.504 7.10 *

Months 5 24895.46 4979.091 9.01 **

Sites*Months 5 -29326.2 -5865.24 -10.62 **

ERROR 32 552.2693

TOTAL 43 45.01226 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.31 Analysis of variance for % Organic contents (wet weight) in the Body composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11.

Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 122070 122070 7.56 *

Months 5 789302.4 157860.5 9.78 **

Sites*Months 5 -927234 -185447 -11.49 **

ERROR 32 16136.86

TOTAL 43 275.583 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.32 Analysis of variance for % Fat (wet weight) in the Body composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. Degree of Sum of Mean Source of Variation freedom Squares Squares F-value

Sites 1 3670.346 3670.346 6.99 *

Months 5 26127.03 5225.407 9.95 **

Sites*Months 5 -30230.8 -6046.15 -11.52 **

ERROR 32 524.7623

TOTAL 43 91.37755 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.33 Analysis of variance for % Protein (wet weight) in the Body composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 83845.44 83845.44 7.48 *

Months 5 536626.1 107325.2 9.57 **

Sites*Months 5 -631370 -126274 -11.26 **

ERROR 32 11209.16

TOTAL 43 311.1543 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.34 Analysis of variance for Condition Factor in the Body composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 117.7882 117.7882 7.67 *

Months 5 805.8941 161.1788 10.49 **

Sites*Months 5 -938.802 -187.76 -12.22 **

ERROR 32 15.35674

TOTAL 43 0.236903 P < 0.05 = Least significant (*); P < 0.01 = Significant (**); P < 0.001

3.35 Analysis of variance for % Dry weight in the Body composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 122978 122978 7.57 *

Months 5 796009.1 159201.8 9.80 *

Sites*Months 5 -934957 -186991 -11.51 **

ERROR 32 16241.24

TOTAL 43 271.0271 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.36 Analysis of variance for % Water in the Body composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 1080079 1080079 6.66 *

Months 5 3671846 734369.1 4.53 *

Sites*Months 5 -4913900 -982780 -6.06 *

ERROR 24 162074.8

TOTAL 35 99.02265 P < 0.05 = Least significant (*)

3.37 Analysis of variance for % Dry weight in the Body composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. Degree of Sum of Mean Source of Variation freedom Squares Squares F-value

Sites 1 91641.73 91641.73 6.63 *

Months 5 311160.2 62232.05 4.50 *

Sites*Months 5 -416512 -83302.4 -6.03 *

ERROR 24 13809.3

TOTAL 35 99.02264 P < 0.05 = Least significant (*)

3.38 Analysis of variance for % Ash (wet weight) in the Body composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 4813.666 4813.666 6.62 *

Months 5 16379.17 3275.833 4.50 *

Sites*Months 5 -21912.5 -4382.5 -6.02 *

ERROR 24 726.8059

TOTAL 35 7.157971 P < 0.05 = Least significant (*)

3.39 Analysis of variance for % Organic Contents (wet weight) in the Body composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 91021.61 91021.61 6.63 *

Months 5 309049.4 61809.88 4.50 *

Sites*Months 5 -413688 -82737.6 -6.03 *

ERROR 24 13717.5

TOTAL 35 100.4095 P < 0.05 = Least significant (*)

3.40 Analysis of variance for % Fat (wet weight) in the Body composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12 Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 1577.726 1577.726 5.97 *

Months 5 5447.515 1089.503 4.12 *

Sites*Months 5 -7259.89 -1451.98 -5.49 *

ERROR 24 264.1218

TOTAL 35 29.47273 P < 0.05 = Least significant (*)

3.41 Analysis of variance for % Protein (wet weight) in the Body composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 68647.43 68647.43 6.63 *

Months 5 233066 46613.2 4.50 *

Sites*Months 5 -311974 -62394.8 -6.03 *

ERROR 24 10340.23

TOTAL 35 79.84146 P < 0.05 = Least significant (*)

3.42 Analysis of variance for Condition Factor in the Body composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 86.40423 86.40423 6.65 *

Months 5 293.7211 58.74422 4.52 *

Sites*Months 5 -393.079 -78.6159 -6.05 *

ERROR 24 12.98628

TOTAL 35 0.032205 P < 0.05 = Least significant (*)

3.43 Comparison of various parameters determining Fillet composition of Channa marulius from River Sindh during sampling season 2010-11. Data is expressed as Mean ± Standard deviation. P – value indicates the results of one way ANOVA test.

Body Composition November December January February March P- Value Parameters 2010 2010 2011 2011 2011

77.99 ± 8.18 77.90 ± 0.87 76.06 ± 0.98 76.42 ± 1.42 75.82 ± 1.44 0.98 % water (60.16-89.94) (76.90-79.04) (75.06-77.04) (74.81-77.54) (74.62-77.42)

22.00 ± 8.18 22.09 ± 0.87 23.96 ± 0.98 23.57 ± 1.42 24.17 ± 1.44 0.98 % Dry weight (10.05-39.83) (20.95-23.09) (22.95-24.93) (22.45-25.18) (22.57-25.37)

2.32 ± 0.93 1.83 ± 0.27 2.42 ± 0.64 2.97 ± 0.34 2.59 ± 0.39 0.62 % Ash (1.20-4.43) (1.41-1.98) (1.94-3.16) (2.69-3.36) (2.16-2.93) (wet weight)

21.76 ± 21.86 ± 0.94 23.92 ± 1.00 23.49 ± 1.45 24.13 ± 1.44 0.97 % Organic 8.12 (20.80-23.05) (22.93-24.93) (22.41-25.15) (22.53-25.34) content (wet (9.83-39.4) weight)

3.12 ± 1.02 5.24 ± 0.43 4.21 ± 0.35 3.47±0.62 4.24±0.72 0.01 * % Fat (1.44-4.91) (4.65-5.61) (3.82-4.50) (3.07-4.19) (3.63-5.04) (wet weight)

18.63 ± 7.21 16.62 ± 1.03 19.60 ± 0.50 20.01 ± 0.85 19.22 ± 2.20 0.91 % Protein (8.39-34.48) (15.28-17.44) (19.10-20.10) (19.27-20.95) (17.49-21.70) (wet weight)

P > 0.05 = Non significant; P < 0.05 = Least significant (*)

3.44 Comparison of various parameters determining Fillet composition of Channa marulius from River Sindh during sampling season 2011-12. Data is expressed as Mean ± Standard deviation. P – value indicates the results of one way ANOVA test.

Body

Composition November December January February March April P- Value Parameters 2011 2011 2012 2012 2012 2012

% water 76.90 ± 1.99 78.34 ± 0.45 78.27 ± 0.96 78.41 ± 0.9 77.26 ± 2.41 76.99 ± 1.92 0.69

(74.63-78.31) (77.84-78.71) (77.67-79.37) (77.67-79.42) (74.63-79.37) (74.82-78.48)

% Dry weight 23.10 ± 1.99 21.66 ± 0.45 21.73 ± 0.96 21.59 ± 0.9 22.74 ± 2.41 23.01 ± 1.92 0.69

(21.69-25.37) (21.29-22.16) (20.63-22.33) (20.58-22.33) (20.63-25.37) (21.52-25.18)

% Ash 3.24 ± 0.73 2.84 ± 0.57 2.53 ± 0.2 2.64 ± 0.09 3.04 ± 0.89 3.04±0.23 0.59

(wet weight) (2.64-4.06) (2.18-3.2) (2.3-2.65) (2.55-2.73) (2.48-4.06) (2.81-3.27)

% Organic 23.02 ± 2.02 21.58 ± 0.46 21.67 ± 0.94 21.5 ± 0.92 22.7 ± 2.42 22.95 ± 1.94 0.69

content (21.55-25.33) (21.19-22.09) (20.58-22.24) (20.47-22.25) (20.58-25.33) (21.47-25.15)

(wet weight)

% Fat 3.13 ± 0.16 2.8 ± 0.72 2.1 ± 0.82 2.33 ± 0.05 3.49 ± 0.63 3.53 ± 0.09 0.02 *

(wet weight) (2.95-3.24) (2.17-3.59) (1.32-2.95) (2.28-2.38) (3.06-4.22) (3.44-3.63)

% Protein 19.89 ± 1.99 18.78 ± 0.26 19.57 ± 1.22 19.17 ± 0.92 19.2 ± 2.54 19.42 ± 1.87 0.97

(wet weight) (18.31-22.12) (18.5-19.02) (18.55-20.93) (18.12-19.87) (17.52-22.12) (17.94-21.52

P > 0.05 = Non significant; P < 0.05 = Least significant (*)

3.45 Comparison of various parameters determining Fillet composition of Channa marulius from River Chenab during sampling season 2010-11. Data is expressed as Mean ± Standard deviation. P – value indicates the results of one way ANOVA test.

Fillet November December January February March April P- Value

Composition 2010 2010 2011 2011 2011 2011 Parameters

% water 76.69 ± 1.51 77.32 ± 2.15 79.60 ± 3.53 79.05 ± 3.97 77.08 ± 2.08 79.06 ± 0.33 0.51

(74.43-77.66) (73.81-79.68) (75.99-83.21) (75.75-83.47) (75.41-79.41) (78.70-79.36)

% Dry weight 23.30 ± 1.51 22.67 ± 2.15 20.39 ± 3.53 20.95 ± 3.97 22.91 ± 2.08 20.93 ± 0.33 0.51

(22.33-25.56) (20.31-26.18) (16.78-24) (16.52-24.24) (20.58-24.58) (20.63-21.29)

% Ash 2.04 ± 0.81 1.81 ± 0.57 2.31 ± 0.47 2.38 ± 0.60 3.25 ± 0.68 2.35 ± 0.50 0.11

(wet weight) (1.36-3.12) (1.10-2.29) (1.88-2.98) (1.86-3.04) (2.49-3.82) (1.82-2.83)

% Organic 23.29 ± 1.51 22.65 ± 2.15 20.38 ± 3.53 20.88 ± 4.04 22.86 ± 2.12 20.81± 0.41 0.50 content (22.31-25.55) (20.29-26.16) (16.76-23.98) (16.38-24.21) (20.4-24.55) (20.45-21.27) (wet weight)

% Fat 4.68 ± 1.79 5.10 ± 1.36 3.72 ± 0.57 3.12 ± 0.65 3.66 ± 0.81 2.86 ± 0.71 0.10

(wet weight) (2.15-6.41) (3.75-7.35) (3.18-4.40) (2.36-3.53) (2.88-4.50) (2.40-3.69)

% Protein 18.61 ± 2.12 17.55 ± 2.98 16.65 ± 3.01 17.75 ± 3.42 19.19 ± 1.38 17.95 ± 0.33 0.82

(wet weight) (16.20-20.61) (12.91-21.19) (13.57-19.98) (14.02-20.74) (17.59-20.04) (17.57-18.22)

P > 0.05 = Non significant

3.46 Comparison of various parameters determining Fillet composition of Channa marulius from River Chenab during sampling season 2011-12. Data is expressed as Mean ± Standard deviation. P – value indicates the results of one way ANOVA test.

Body November December January February March April P- Value

Composition 2011 2011 2012 2012 2012 2012 Parameters

% water 77.08 ± 2.08 77.22 ± 2.95 76.9 ± 2.95 79.05 ± 3.98 76.33 ± 1.37 78.01 ± 1.17 0.83

(75.42-79.42) (73.81-79.13) (73.81-79.69) (75.76-83.47) (75.17-77.83) (77.3-79.37)

% Dry weight 22.92 ± 2.08 22.78 ± 2.95 23.1 ± 2.95 20.95 ± 3.98 23.67 ± 1.37 21.99 ± 1.17 0.83

(20.58-24.58) (20.87-26.19) (20.31-26.19) (16.53-24.24) (22.17-24.83) (20.63-22.7)

% Ash 3.25 ± 0.68 2.28 ± 0.51 2.12 ± 0.21 2.38 ± 0.61 2.39 ± 1.11 2 ± 0.56 0.32

(wet weight) (2.49-3.82) (1.83-2.83) (1.89-2.29) (1.86-3.05) (1.11-3.09) (1.36-2.42)

% Organic 22.86 ± 2.13 22.72 ± 3 23.08 ± 2.95 20.88 ± 4.04 23.65 ± 1.36 21.92 ±1.27 0.83

content (20.48-24.55) (20.73-26.17) (20.29-26.17) (16.39-24.22) (22.16-24.81) (20.45-22.69) (wet weight)

% Fat 2.2 ± 0.49 2.23 ± 0.74 3.35 ± 0.95 1.88 ± 0.39 2.75 ± 0.3 2.81 ± 1.23 0.26

(wet weight)) (1.73.2.7) (1.5-2.99) (2.64-4.43) (1.42-2.12) (2.4-2.93) (1.44-3.85)

% Protein 20.66 ± 1.67 20.49 ± 2.33 19.73 ± 3.68 19.01 ±3.67 20.9±1.45 19.11±0.41 0.89

(wet weight) (18.75-21.85) (19.06-23.18) (15.86-23.18) (14.97-22.13) (19.23-21.88) (18.77-19.56)

P > 0.05 = Non significant

3.47 Analysis of variance for % Water in the Fillet composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. Degree of Sum of Mean Source of Variation freedom Squares Squares F-value

Sites 1 916204.6 916204.6 7.29 *

Months 4 8849287 2212322 17.60 **

Sites*Months 4 -9890516 -2472629 -19.67 **

ERROR 28 125646.4

TOTAL 37 622.3214 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.48 Analysis of variance for % Dry weight in the Fillet composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. Degree of Sum of Mean Source of Variation freedom Squares Squares F-value

Sites 1 76026.59 76026.59 6.84 *

Months 4 736338.1 184084.5 16.57 **

Sites*Months 4 -822850 -205712 -18.52 **

ERROR 28 11107.09

TOTAL 37 622.3214 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.49 Analysis of variance for % Organic contents (wet weight) in the Fillet composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 75351.54 75351.54 6.82 *

Months 4 727756.5 181939.1 16.48 **

Sites*Months 4 -813522 -203381 -18.43 **

ERROR 28 11033.44

TOTAL 37 619.038 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.50 Analysis of variance for % Fat (wet weight) in the Fillet composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11. Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 2460.264 2460.264 6.25 *

Months 4 24316.39 6079.098 15.45 **

Sites*Months 4 -27115.7 -6778.92 -17.23 **

ERROR 28 393.4034

TOTAL 37 54.3658 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.51 Analysis of variance for % Protein (wet weight) in the Fillet composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11.

Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 50623.68 50623.68 6.784943 *

Months 4 489604.3 122401.1 16.40506 **

Sites*Months 4 -547163 -136791 -18.3337 **

ERROR 28 7461.179

TOTAL 37 525.879 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.52 Analysis of variance for % Ash (wet weight) in the Fillet composition of Channa marulius from River Sindh and Chenab waters during sampling season 2010-11.

Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 1260.151 1260.151 2.48

Months 4 11557.41 2889.354 5.70 *

Sites*Months 4 -12964.1 -3241.01 -6.40 *

ERROR 28 506.1969

TOTAL 37 359.7116 P > 0.05 = Non significant; P < 0.05 = Least significant (*)

3.53 Analysis of variance for % Water in the Fillet composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. Degree of Sum of Mean Source of Variation freedom Squares Squares F-value

Sites 1 1049764 1049764 6.62 *

Months 5 3574208 714841.6 4.51 *

Sites*Months 5 -4781235 -956247 -6.03 **

ERROR 24 158466.1

TOTAL 35 1202.732 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.54 Analysis of variance for % Dry weight in the Fillet composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. Degree of Sum of Mean Source of Variation freedom Squares Squares F-value

Sites 1 100854.2 100854.2 6.25 *

Months 5 347913.9 69582.77 4.31 *

Sites*Months 5 -463695 -92739 -5.74 **

ERROR 24 16129.55

TOTAL 35 1202.732 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.55 Analysis of variance for % Ash (wet weight) in the Fillet composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 1402.821 1402.821 6.07 *

Months 5 4867.563 973.5125 4.21 *

Sites*Months 5 -6472.43 -1294.49 -5.60 **

ERROR 24 231.0832

TOTAL 35 29.03929 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.56 Analysis of variance for % Organic contents (wet weight) in the Fillet composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12.

Degree of Sum of Mean Source of Variation freedom Squares Squares F-value

Sites 1 100089.1 100089.1 6.26 *

Months 5 345109.8 69021.96 4.31 *

Sites*Months 5 -460022 -92004.3 -5.75 **

ERROR 24 15981.51

TOTAL 35 1158.81 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.57 Analysis of variance for % Protein (wet weight) in the Fillet composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 77100.77 77100.77 6.27 *

Months 5 266086.8 53217.35 4.32 *

Sites*Months 5 -354625 -70925.1 -5.77 **

ERROR 24 12290.55

TOTAL 35 852.7601 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.58 Analysis of variance for % Fat (wet weight) in the Fillet composition of Channa marulius from River Sindh and Chenab waters during sampling season 2011-12. Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 1508.212 1508.212 5.621898 *

Months 5 5197.147 1039.429 3.8745 *

Sites*Months 5 -6921.79 -1384.36 -5.16023 *

ERROR 24 268.2745

TOTAL 35 51.84188 P < 0.05 = Least significant (*)

Table 3.59 Seasonal variations in the hematological parameters of Channa marulius captured from River Sindh during 2010-2011 sampling season. Minimum-maximum range for each parameter is mentioned in parenthesis. Data is expressed as Mean ± Standard deviation. P – value indicates the results on one way ANOVA test.

Blood Parameters December January February 2011 March April P- Value 2010 2011 2011 2011

Hemoglobin 14.3 ± 0.88 14.2 ± 1.01 12.0 ± 2.14 12.2 ± 2.13 12.2 ± 0.74 0.02 * (gdl -1) (12.8-15.4) (12.8-15.4) (8.3-14.6) (8.3-14.6) (11.4-12.8)

RBC 10*12 2.9 ± 0.18 2.8 ± 0.24 2.3 ± 0.46 2.3 ± 0.43 2.4 ± 0.19 0.009 ** (L-1) (2.63-3.1) (2.42-3.1) (1.51-2.8) (1.51-2.8) (2.2-2.61)

PCV 48.2 ± 3.74 50.0 ± 4.39 42.0 ± 8.95 42.4 ± 9.13 42.0 ± 5.38 0.14 (%) (44.4-52.9) (44.4-54.7) (24.7-50.5) (24.7-50.5) (37.2-47.8)

Platelets 10*9 36.7 ± 11.40 38.8 ± 17.69 86.9 ± 55.91 134.8 ± 96.82 137.7 ± 67.41 0.01 * (L -1 ) (27-57) (21-63) (22.6-161) (22.6-256) (60-181)

MCV 166.1 ± 7.35 178.5 ± 14.08 185.1 ± 16.43 189.6 ± 15.34 177.1 ± 9.16 0.02 * (fl) (156.1-175.5) (156.1-193.8) (163.8-207.6) (163.8-207.6) (166.5-182.8)

MCH 49.4 ± 2.20 51.4 ± 3.29 53.1 ± 1.91 53.6 ± 2.50 50.3 ± 2.03 0.03 * (pg) (46.5-52.9) (46.5-54.7) (50.5-55.6) (50.5-56.8) (48-51.7)

MCHC 29.7 ± 1.51 28.6 ± 1.54 28.9 ± 2.63 28.7 ± 2.46 29.4 ± 17.33 0.66 (gdl -1) (27.4-31.2) (26.9-31.2) (26.4-33.5) (26.4-33.5) (68.2-20.6)

WBC 10*9 79.9 ± 6.45 72.8 ± 9.84 56.7 ± 18.68 54.9 ± 17.33 69.0 ± 12.95 0.03 * (L-1) (67.1-83.6) (56.1-83.6) (20.6-68.2) (20.6-68.2) (54.1-77.2)

P > 0.05 = Non significant; P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

3.60 Seasonal variations in the hematological parameters of Channa marulius captured from River Sindh during 2011-2012 sampling season. Minimum-maximum range for each parameter is mentioned in parenthesis. Data is expressed as Mean ± Standard deviation. P –value indicates the results on one way ANOVA test.

Blood Parameters December January February March April P- Value 2011 2012 2012 2012 2012 Hemoglobin 13.7 ± 0.93 14.0 ± 0.95 14.6 ± 0.10 14.1 ± 0.86 12.4 ± 0.75 0.03 * (gdl -1) (12.9-14.7) (12.9-14.8) (14.5-14.7) (13.2-14.9) (11.6-13.1) RBC 10*12 2.7 ± 0.42 2.6 ± 0.28 3.0 ± 0.06 2.7 ± 0.26 2.3 ± 0.16 0.05 * -1) (L (2.18-2.93) (2.18-2.76) (2.9-3.01) (2.42-2.94) (2.13-2.45) PCV 44.0 ± 14.33 35.0 ± 4.85 49.0 ± 3.38 49.2 ± 4.04 30.3 ± 3.56 0.03 * (%) (27.7-54.7) (27.7-38) (47-52.9) (44.8-52.7) (26.6-33.7) Platelets 10*9 252.0 ± 48.03 225.0 ± 37.62 185.7 ± 62.58 182.3 ± 24.50 159.0 ± 34.51 0.13 (L -1 ) (205-301) (182-259) (125-250) (158-207) (125-194) MCV 162.2 ± 32.07 133.9 ± 5.13 164.4 ± 10.00 183.0 ± 11.87 132.4 ± 6.71 0.01* (fl) (127.2-190.2) (127.2-139.7) (156.1-175.5) (170.3-193.8) (124.8-137.5) MCH 52.1 ± 6.75 50.8 ± 3.91 49.1 ± 1.24 52.6 ± 3.44 54.4 ± 3.43 0.59 (pg) (45.9-59.3) (45.9-54.1) (48.3-50.5) (48.6-54.7) (51.5-58.2) MCHC 33.5 ± 11.32 35.9 ± 6.57 29.8 ± 2.11 28.7 ± 0.62 41.2 ± 4.67 0.08 (gdl -1) (26.9-46.6) (26.9-41.5) (27.4-31.2) (28.2-29.4) (38.1-46.6) WBC 10*9 53.2 ± 24.90 54.0 ± 18.70 81.8 ± 2.20 68.8 ± 10.97 36.5 ± 16.26 0.04* ( L -1 ) (30-79.5) (30-72.8) (79.3-83.2) (56.1-75.4) (18.4-49.8) Total Cholesterol 188.0 ± 36.39 218.8 ± 38.34 225.3 ± 16.20 223.0 ± 37 236.3 ± 60.91 0.52 (mgdL-1) (166-230) (166-250) (215-244) (183-256) (166-272) ALT 45.7 ± 17.62 35.5 ± 27.74 39.3 ± 32.75 60.3 ± 30.66 36.3 ± 21.22 0.82 (IUL-1 ) (26-60) (13-76) (13-76) (26-85) (19-60) AST 250.0 ± 50.27 264.8 ± 46.61 278.7 ± 31.56 264.7 ± 53.43 221.7 ± 19.86 0.337 (IUL-1 ) (203-303) (203-315) (258-315) (203-297) (206-244) Total Protein 3.9 ± 0.30 4.1 ± 0.46 4.4 ± 0.26 4.0 ± 0.46 3.6 ± 0.23 0.101 (gdL-1 ) (3.6-4.2) (3.6-4.7) (4.2-4.7) (3.6-4.5) (3.5-3.9)

P > 0.05 = Non significant; P < 0.05 = Least significant (*)

Table 3.61 Seasonal variations in the hematological parameters of Channa marulius captured from River Chenab during 2010-2011 sampling season. Minimum-maximum range for each parameter is mentioned in parenthesis. Data is expressed as Mean ± Standard deviation. P – value indicates the results on one way ANOVA test.

Blood Parameters December January February March April P- Value 2010 2011 2011 2011 2011 Hemoglobin (gdl - 12.2 ± 0.96 11.5 ± 2 12.3 ± 3.07 8.6 ± 2.27 11.1 ± 3.16 0.07 1) (10.4-13) (7-13.5) (6.9-14.8) (6.6-12.9) (5-13.8) RBC 10*12 (L-1) 2.6 ± 0.16 2.5 ± 0.48 2.6 ± 0.70 1.8 ± 0.51 2.3 ± 0.66 0.08 (2.46-2.8) (1.39-2.93) (1.35-3.19) (1.41-2.76) (1.12-3.07) PCV (%) 42.4 ± 5.29 37.5 ± 12.43 32.5 ± 9.40 22.2 ± 5.47 29.8 ± 10.21 0.009 ** (31.7-45.4) (14.2-49.5) (16.6-41.9) (16.2-31.7) (13.2-42.1) Platelets 10*9 (L - 22.2 ± 8.66 33.5 ± 18.57 71.7 ± 67.52 51.2 ± 48.70 74.7 ± 67.30 0.21 1 ) (9-31) (13-66) (24-204) (25-150) (20-208) MCV (fl) 157.6 ± 22.48 132.5 ± 42.57 124.8 ± 5.57 120.6 ± 6.67 126.9 ± 23.94 0.15 (114.6-179) (45.1-169.1) (119.8-135.8) (112.2-127.1) (111.7-173.7) MCH (pg) 46.3 ± 2.63 44.3 ± 7.07 47.7 ± 1.77 47.0 ± 1.69 47.0 ± 3.68 0.60 (44.1-51.2) (26.7-51) (46.5-51.2) (43.8-48.6) (43.9-53.1) MCHC 29.8 ± 5.34 33.9 ± 9.16 38.3 ± 2.32 39.0 ± 2.47 37.6 ± 4.76 0.04 * (gdl -1) (27.1-40.6) (27.1-49.6) (34.7-41.6) (37.1-43.3) (30.5-44.6) WBC 10*9 ( L 72.2 ± 16.18 58.6 ± 29.29 48.2 ± 16.67 32.8 ± 22.48 52.8 ± 15.58 0.02 * -1 ) (43.2-88.2) (7.4-89.5) (25.4-77.1) (10.3-63.8) (33.5-79.5)

P > 0.05 = Non significant; P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.62 Seasonal variations in the hematological parameters of Channa marulius captured from River Chenab during 2011-2012 sampling season. Minimum-maximum range for each parameter is mentioned in parenthesis. Data is expressed as Mean ± Standard deviation. P – Value indicates the results on one way ANOVA test.

Blood November December January February March April P- Value Parameters 2011 2011 2012 2012 2012 2012 Haemoglobin 13.9 ± 0.68 11.9 ± 0.44 12.1 ± 0.72 14.5 ± 0.98 14.8 ± 1.23 15.4 ± 0.65 0.001 ** (gdl -1) (13.1-14.4) (11.6-12.4) (11.6-12.9) (13.4-15.3) (13.4-15.7) (14.7-16) RBC 10*12 2.7 ± 0.20 2.3 ± 0.12 2.3 ± 0.10 2.7 ± 0.05 2.7 ± 0.02 3.0 ± 0.13 P˂0.001 *** (L-1) (2.45-2.81) (2.13-2.37) (2.18-2.37) (2.67-2.76) (2.68-2.72) (2.86-3.1) PCV 36.1 ± 2.14 29.1 ± 2.16 29.4 ± 1.53 37.0 ± 1 36.4 ± 1.39 45.8 ± 7.62 0.001 ** (%) (33.7-37.8) (26.6-30.6) (27.7-30.6) (36-38) (35.5-38) (37.7-52.8) Platelets 10*9 109.0 ± 14 144.0 ± 58.28 191.7 ± 110.52 216.0 ± 39.28 174.3 ± 88.75 139.3 ± 180.89 0.79 (L -1 ) (99-125) (80-194) (80-301) (182-259) (82-259) (27-348) MCV 135.1 ± 3.63 128.7 ± 5.41 129.5 ± 4.66 136.1 ± 3.12 134.4 ± 4.84 154.4 ± 20.34 0.05 * (fl) (130.9-137.5) (124.8-134.9) (126.5-134.9) (134.2-139.7) (130.2-139.7) (131.5-170.2) MCH 52.1 ± 1.60 53.1 ± 4.56 53.4 ± 5.18 53.4 ± 3.86 54.8 ± 4.65 52.1 ± 3.43 0.96 (pg) (50.4-53.6) (49.5-58.2) (49.5-59.3) (49.4-57.1) (49.4-57.7) (49.7-56) MCHC 38.5 ± 0.46 41.3 ± 4.65 41.3 ± 4.65 39.3 ± 3.66 40.8 ± 4.84 34.3 ± 7.26 0.47 (gdl -1) (38.1-39) (38.1-46.6) (38.1-46.6) (35.3-42.5) (35.3-44.3) (29.1-42.6) WBC 10*9 56.4 ± 13.36 34.7 ± 15.74 34.8 ± 15.83 62.0 ± 11.86 52.2 ± 11.87 78.9 ± 4.96 0.009 ** ( L -1 ) (41.4-67.1) (18.4-49.8) (18.4-50) 49.3-72.8) (42.1-65.3) (73.7-83.6) Total 0.19 Cholesterol 216.0 ± 29.46 242.7 ± 67 290.0 ± 25.51 242.7 ± 27.02 221.0 ± 7.81 251.7 ± 19.86 (mgdL-1) (198-250) (166-290) (271-319) (215-269) (216-230) (230-269) ALT 20.7 ± 8.50 37.7 ± 19.66 39.0 ± 28 33.7 ± 36.67 27.3 ± 1.53 41.3 ± 24.85 0.87 (IUL-1 ) (11-27) (23-60) (19-71) (12-76) (26-29) (26-70) AST 229.0 ± 48.51 218.0 ± 22.54 257.7 ± 44.61 286.0 ± 26.51 216.0 ± 37.24 226.7 ± 61.99 0.34 (IUL-1 ) (181-278) (204-244) (215-304) (263-315) (187-258) (180-297) Total Protein 3.5 ± 0.42 3.7 ± 0.21 4.1 ± 0.51 4.6 ± 0.40 3.9 ± 0.38 4.0 ± 0.45 0.07 (gdL-1 ) (3.2-4) (3.5-3.9) (3.5-4.5) (4.2-5) (3.6-4.3) (3.6-4.5) P > 0.05 = Non significant; P < 0.05 = Least significant (*); P < 0.01 = Significant (**); P < 0.001 Highly significant (***)

Table 3.63 Analysis of variance for Haemoglobin (gd L-1) of Channa marulius in River Sindh and Chenab waters during sampling season 2010-11. Degree Source of of Sum of Mean variance freedom Squares squares F-value

Sites 1 32279.97 32279.97 9.18 *

Months 4 8199.738 1639.948 0.46 **

Sites*Months 4 -43671.2 -8734.24 -2.48**

ERROR 46 3513.65

TOTAL 55 322.158 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.64 Analysis of variance for RBC (10*12 (L-1)) of Channa marulius in River Sindh and Chenab waters during sampling season 2010-11.

Source of Degree of Sum of Mean variance freedom Squares squares F-value

Sites 1 1353.081 1353.081 9.31 *

Months 4 22579.08 4515.816 31.08 **

Sites*Months 4 -24063.563 -4812.71 -33.12 **

ERROR 46 145.2957

TOTAL 55 13.89331 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.65 Analysis of variance for PCV (%) of Channa marulius in River Sindh and Chenab waters during sampling season 2010-11.

Source of Degree of Sum of Mean Variation Freedom Squares Squares F-Value

Sites 1 339699.5 339699.5 8.71 *

Months 4 5636527 1127305 28.90 **

Sites*Months 4 -6008345 -1201669 -30.81 **

ERROR 46 38996.22

TOTAL 55 6878.12 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.66 Analysis of variance for Platelets (10*9 (L-1)) of Channa marulius in River Sindh and Chenab waters during sampling season 2010-11.

Source of Degree of Sum of Variation Freedom Squares Mean Squares F-Value

Sites 1 980991.7 980991.7 3.55 *

Months 4 14869373 2973874.6 10.77 **

Sites*Months 4 -15925904 -3185180.86 -11.54 **

ERROR 46 275910.88

TOTAL 55 200371.3 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.67 Analysis of variance for MCV (fl) of Channa marulius in River Sindh and Chenab waters during sampling season 2010-11.

Source of Degree of Sum of Variation Freedom Squares Mean Squares F-Value

Sites 1 5333989 5333989 8.99 *

Months 4 84067972 16813594.4 28.34 **

Sites*Months 4 -89934675 -17986934.93 -30.32 **

ERROR 46 593185.75

TOTAL 55 60472.12 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.68 Analysis of variance for MCH (pg) of Channa marulius in River Sindh and Chenab waters during sampling season 2010-11.

Source of Degree of Sum of Mean Variation Freedom Squares Squares F-Value

Sites 1 528993.6 528993.6 9.61 *

Months 4 8427182 1685436.4 30.63 ** - - Sites*Months 4 9010035.77 1802007.154 -32.75 **

ERROR 46 55018.221

TOTAL 55 1158.05 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.69 Analysis of variance for MCHC (gd L-1) of Channa marulius in River Sindh and Chenab waters during sampling season 2010-11.

Source of Degree of Sum of Mean Variation Freedom Squares Squares F-Value

Sites 1 242780.8 242780.8 10.2779 *

Months 4 3797293 759458.5 32.15096 **

Sites*Months 4 -4062537 -812507 -34.3967 **

ERROR 46 23621.64

TOTAL 55 1951.774 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.70Analysis of variance for WBC (10*9 L-1) of Channa marulius in River Sindh and Chenab waters during sampling season 2010-11.

Source of Degree of Sum of Variation Freedom Squares Mean Squares F-Value

Sites 1 811070.66 811070.66 8.15 *

Months 4 13909874 2781974.704 27.97 **

Sites*Months 4 -14796001 -2959200.296 -29.75 **

ERROR 46 99436.722

TOTAL 55 24379.422 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.71 Analysis of variance for Haemoglobin (gd L-1) of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12. Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 22266.13 22266.13 5.66 *

Months 5 94700.21 18940.04 4.81 **

Sites*Months 5 -120852 -24170.4 -6.14 *

ERROR 18 3931.128

TOTAL 29 45.468 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.72 Analysis of variance for RBC (10*12 L-1) of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12 Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 809.483 809.483 5.648397 **

Months 5 3441.171 688.2342 4.802349 *

Sites*Months 5 -4391.7 -878.34 -6.12887 **

ERROR 18 143.312

TOTAL 29 2.266 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.73 Analysis of variance for PCV (%) of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 173995.1 173995.1 5.512194 **

Months 5 736557.7 147311.5 4.666854 *

Sites*Months 5 -940090 -188018 -5.95644 **

ERROR 18 31565.49

TOTAL 29 2027.807 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.74 Analysis of variance for Platelets (10*9 L-1) of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 4134070.7 4134070.7 5.047723 **

Months 5 17435455 3487091.074 4.257758 *

Sites*Months 5 -22270556 -4454111.16 -5.43849 **

ERROR 18 818997.1

TOTAL 29 117967.37 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.75 Analysis of variance for MCH (pg) of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12. Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 333837.3 333837.3 5.67 **

Months 5 1419234 283846.8 4.82 *

Sites*Months 5 -1811463 -362293 -6.16 **

ERROR 18 58787.51

TOTAL 29 396.076 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.76 Analysis of variance for MCHC (gdL-1) of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 168536 168536 5.57 **

Months 5 715001.5 143000.3 4.73 *

Sites*Months 5 -912622 -182524 -6.04 **

ERROR 18 30209.01

TOTAL 29 1124.609 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

100

Table 3.77 Analysis of variance for WBC (10*9 L-1) of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 358877.7 358877.7 5.08 **

Months 5 1530047 306009.3 4.33 *

Sites*Months 5 -1948757 -389751 -5.52 **

ERROR 18 70573.9

TOTAL 29 10741.7 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.78 Analysis of variance for Total Cholestrol of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Mean Variation freedom Sum of Squares Squares F-value

Sites 1 6482322.8 6482323 5.53 **

Months 5 27657489.47 5531498 4.71 *

Sites*Months 5 -35265955.2 -7053191 -6.01 **

ERROR 18 1171976.396

TOTAL 29 45833.466 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

101

Table 3.79 Analysis of variance for ALT (IUL-1) of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12. Source of Degree of Sum of Mean Variation freedom Squares Squares F-value

Sites 1 7555219 7555218.96 5.54 **

Months 5 32275554 6455110.86 4.73 *

Sites*Months 5 -41144280 -8228856.09 -6.03 **

ERROR 18 1363544.5

TOTAL 29 50036.3 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.80 Analysis of variance for AST (IUL-1) of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Sum of Variation freedom Squares Mean Squares F-value

Sites 1 7555219 7555218.967 5.54 **

Months 5 32275554 6455110.86 4.73 *

Sites*Months 5 -41144280 -8228856.094 -6.03 **

ERROR 18 1363543.8

TOTAL 29 50036.3 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

102

Table 3.81 Analysis of variance for Total Protein (gdL-1) of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Sum of Variation freedom Squares Mean Squares F-value

Sites 1 1920.327 1920.327 5.62 **

Months 5 8192.16 1638.432 4.80 *

Sites*Months 5 -10447.5 -2089.5 -6.12 **

ERROR 18 341.133

TOTAL 29 6.12 P < 0.05 = Least significant (*); P < 0.01 = Significant (**)

Table 3.82 Analysis of variance for MCV (fl) of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12.

Source of Degree of Sum of Variation freedom Squares Mean Squares F-value

Sites 1 2514591.4 2514591.39 4.557362 *

Months 5 10659804 2131960.88 3.863896 *

Sites*Months 5 -13608193 -2721638.6 -4.93261 *

ERROR 18 551764.62

TOTAL 29 117967.37 P < 0.05 = Least significant (*)

103

Table 3.83 Comparison of various elements of Channa marulius from River Sindh during sampling season 2011-12. Data is expressed as Mean ± Standard deviation. P – value indicates the results on one way ANOVA test.

Elements November 2011 December 2011 January 2012 February 2012 March 2012 P- Value

Zinc 0.0833 ± 0.0247 0.0983 ± 0.0187 0.0627 ± 0.0332 0.0679 ± 0.0285 0.0928 ± 0.0144 0.29

(µg/g) (0.0507-0.1056) (0.0704-0.1094) (0.0264-0.0970) (0.0379-0.0948) (0.0815-0.1091)

Nickel 0.0101 ± 0.0021 0.0084 ± 0.0039 0.0106 ± 0.0021 0.0110 ± 0.0010 0.0103 ± 0.0002 0.65

(µg/g) (0.0086-0.0131) (0.0025-0.0108) (0.0083-0.0129) (0.0104-0.0123) (0.0101-0.0105)

Manganese 0.0178 ± 0.0084 0.0150 ± 0.0080 0.0120 ± 0.0079 0.0144 ± 0.0088 0.0197 ± 0.0061 0.73

(µg/g) (0.0099-0.0278) (0.0068-0.0226) (0.0044-0.0226) (0.005-0.0226) (0.0153-0.0268)

Iron 0.0480 ± 0.0122 0.0381 ± 0.0079 0.0354 ± 0.0069 0.0344 ± 0.0083 0.0506 ± 0.0087 0.11

(µg/g) (0.0306-0.0567) (0.0313-0.0490) (0.0297-0.0452) (0.0256-0.0423) (0.0423-0.0598)

P > 0.05 = Non significant

104

Table 3.84 Comparison of various elements of Channa marulius from River Chenab during sampling season 2011-12.Data is expressed as Mean ± Standard deviation. P – value indicates the results on one way ANOVA test.

Elements November 2011 December 2011 January 2012 February 2012 March 2012 P- Value

Zinc 0.0818 ± 0.0338 0.0836 ± 0.0117 0.0639 ± 0.0304 0.0890 ± 0.0336 0.0611 ± 0.0147 0.53

(µg/g) (0.0543-0.1196) (0.0725-0.0959) (0.0378-0.0103) (0.0563-0.1345) (0.0440-0.0801)

Nickel 0.0132 ± 0.0023 0.00971 ± 0.0028 0.0124 ± 0.0012 0.0114 ± 0.0021 0.0096 ± 0.0028 0.24

(µg/g) (0.0113-0.0159) (0.0066-0.0122) (0.0105-0.0132) (0.0083-0.0129) (0.0057-0.0117)

Manganese 0.0221 ± 0.0118 0.0214 ± 0.0034 0.0160 ± 0.0109 0.0167 ± 0.0071 0.0116 ± 0.0023 0.44

(µg/g) (0.0119-0.0351) (0.0186-0.0253) (0.0073-0.0302) (0.0114-0.027) (0.0101-0.0150)

Iron 0.0576 ± 0.0232 0.0481 ± 0.0172 0.0406 ± 0.0294 0.0426 ± 0.0094 0.0260 ± 0.0058 0.32

(µg/g) (0.0329-0.079) (0.0283-0.0600) (0.0242-0.0847) (0.0245-0.0548) (0.0219-0.0344)

P > 0.05 = Non significant

105

Table 3.85 Analysis of variance for Nickle element of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12.

P > 0.05 = Non significant (*); P < 0.01 = Significant (**) Degree of Sum of Source of Variation freedom Squares Mean Squares F-value

Sites 1 0.01647 0.01647 5.89

Months 4 0.105197 0.026299 9.41

Sites*Months 4 -0.12425 -0.03106 -11.11 **

ERROR 26 0.002795

TOTAL 35 0.000209

Table 3.86 Analysis of variance for Zinc element of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12. Degree of Sum of Source of Variation freedom Squares Mean Squares F-value

Sites 1 0.881622 0.881622 5.45

Months 4 5.493432 1.373358 8.50

Sites*Months 4 -6.51266 -1.62816 -10.08 **

ERROR 26 0.161483

TOTAL 35 0.02388 P > 0.05 = Non significant (*); P < 0.01 = Significant (**)

106

Table 3.87 Multifactorial analysis of variance for Manganese element of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12. Degree of Sum of Source of Variation freedom Squares Mean Squares F-value

Sites 1 0.03883 0.03883 4.74

Months 4 0.24301 0.060753 7.41

Sites*Months 4 -0.28795 -0.07199 -8.79 **

ERROR 26 0.008188

TOTAL 35 0.002075 P > 0.05 = Non significant (*); P < 0.01 = Significant (**)

Table 3.88 Analysis of variance for Iron element of Channa marulius in River Sindh and Chenab waters during sampling season 2011-12. Degree of Source of Variation freedom Sum of Squares Mean Squares F-value Sites 1 0.248708 0.248708 5.24 Months 4 1.57508 0.39377 8.30 Sites*Months 4 -1.86285 -0.46571 -9.81 ** ERROR 26 0.04743 TOTAL 35 0.00837 P > 0.05 = Non significant (*); P < 0.01 = Significant (**)

107

4. Discussion Channa marulius (Hamilton) also known as giant snakehead has a very rapid growth rate having huge market demand and purchaser preference in Pakistan (Khan et al, 2012). The distributional status of this fish species is native and according to IUCN status Channa marulius is near to be vulnerable (Rafique and Khan, 2012). Channa marulius can become an important member of fish culture due to variety of characteristics such as carnivorous food habitat, huge market price and forbearance to a range of habitats. Hence understanding of their biology and ecological requirements is worthwhile to study (Dua and Kumar, 2006). The present study was conducted to describe the morphometery, serum biochemistry, body composition, hematology and elemental composition in muscles of Channa marulius on monthly basis for two consecutive years (2010- 2012) from River Sindh and Chenab sites in southern Punjab. The water quality of sampling sites was also analysed in order to determine the effect of seasonal variations in water quality on the biology of Channa marulius.

All living organisms on our earth are so intimately connected with water that life on this planet is believed to have evolved in and around water (Patil, 2012). Fitness of water for its selected uses and also to improve existing conditions, water quality markers give the foundation for analyzing (Shinde et al, 2011). Study of these water parameters is very important for the understanding of the metabolic events going on in aquatic ecosystem as these parameters influence each other and also influence the sediment parameters, as well as they govern the abundance and distribution of the flora and the fauna (Kumar and Sharma, 2001).

Analysis of our results indicated that the concentration of calcium, sodium, total dissolved solids, magnesium, electrical conductivity and water temperature of river Indus and Chenab was lowest in winter (December) while their highest values were detected in spring (March) during 2011- 2012 sampling year (Table 3.2, 3.4). Our results are in agreement with Sahni and Yadav, (2012), who had reported the lowest values of calcium and magnesium in winter season of Bharawas Pond Rewari in Haryana India. Sahu et al. (2007) reported the elevated value of chloride in the Hot period are may be due to enlarged temperature, water with low level and manure mixing while Sahni and Yadav (2012) has reported the higher concentration of chloride in pond Rewari

108 in Haryana (india) is measured to be a marker of elevated pollution due to the higher organic waste of animal origin. These observations are consistent with our results as we have also observed the increased concentrations of calcium in river waters with increasing temperatures which correlates the fact that human and animal activities increase in our study area with increasing temperature. Our results are in agreement with Patil (2012) who observed that total dissolved solids in water of the Adan dam reservoir, Karanja (lad), District Washim (India) were higher in summer and lower in winter. Similar seasonal peak was recorded by Manimegalai et al. (2010) in waters of Walayar reservoir, Pelghat, Kerala (india). Karne and Kulkarni (2009) has mentioned that this total solids peak in summer is may be due to low water flow into water body and higher evaporation rate due to increasing temperatures in summer months.

During the present study, lowest values of alkalinity and chloride were observed during low temperature season (December) while highest concentration was observed in high temperature season (April) in river Sindh waters while opposite was the situation for river Chenab waters during the two consecutive years of sampling (Table 3.1-3.4). Our results are in agreement with Karne and Kulkarni (2009) who had reported maximum value of chloride in summer while minimum in winter from freshwater bodies in Khatau Tahsil, India. This difference in chloride concentrations in river Chenab and Sindh waters is due to the different geographical distribution of two sampling sites along with different magnitude of human and animal activities at both sites with more activity near River Sindh as compared to Chenab.

Data analysis revealed that dissolved oxygen had inverse corelation with water temperature. For both sampling sites river sindh and river Chenab, it was observed that during the present study during the months with lower temperature, water had high oxygen concentration during both the sampling year 2010-2012 and vice versa (Table 3.1-3.4). Our results are in agreement with Ali et al. (2004) who had reported the maximum values of dissolved oxygen in winter seasons (November) while minimum values were reported in high temperature season (March) from fresh water bodies of Indus River at Ghazi Ghat, Muzaffargarh, Pakistan.

The environmental factors like density, light penetration, turbidity, temperature and dissolved oxygen etc. are subjected to variations in the fresh water environments unlike that of marine

109 environment. Different organisms are distributed in various fresh water bodies according to their survival in these fresh water systems and these discussed factors enable them to live in specific environment (Iqbal et al, 2004). The distribution of a fish, consequently, depends completely on its ability to hold itself to a range of physical conditions and amount of strength by which it is make possible to live under more or less rapid changes (Ali et al, 2005). Comparison of our water quality results with the standard values (Table 4.1) revealed that despite of seasonal fluctuations, most of the studied water quality parameters remained within safe levels during both sampling seasons.

Table 4.1. Safe water quality standards

Parameters Safe levels pH 6.5-9 for variety of fish production 6.0-8.2 irrigation use 4.0 death point acidic 4.0-5.0 not suitable for any reproduction 4.0-6.5 sluggish growth 11.0 death point alkaline Dissolve Oxygen The more appropriate tolerable level is 5.0 mg/liter for reproduction of desirable fish. 0.0 mg/liter little fish stay alive- if exposed for shorter period 0.3-1.0 mg/liter deadly if exposed for long duration 3.5mg/liter lethal to most of the fish species inside 20 hours. Turbidity Turbidity influence planktonic growth if it’s less than 2.5mg/liter 12.8 times more planktonic growth also if turbidity exceed 100mg/litre the fish production increases up to 5.5 times. Electric Conductivity For irrigation uses the most tolerable level of electric conductivity is 1.25 m.mhos. /cm. E.C always proportional to total dissolved solids. 20-1500m.mhos/cm is the electric conductivity of normal waters. Productivity limit does not influence with EC above 400m. mhos/cm and also there is no effect of production on increasing EC.

110

Alkalinity 0-0.2 mg/liter low fish production 20-40 mg/liter medium fish production 40-90 mg/liter high fish production Less than 10 mg/liter rarely produce large carps. Acidity For suitable fish production the acid waters are limed to enhance alkalinity more than 20 mg/litre. When pH is Low (Below 4.5) Acidity is high and when pH is High (Above 8.0) Acidity is low. Total Hardness Total Hardness range above 15mg/liter is ideal for fish growth. When its concentration decreases below 15 mg/liter it can cause slow fish growth and liming is needed to enhance fish production. If its level decreases up to 5 mg/liter hardness it causes death of fish. Total Solids Waters with less than 2.5 mg/liter of total solids cause 5.5 time more production of fish than the waters with total solids exceeding 100 mg/liter. Carbonates and Their Carbonates and bicarbonates existence in water maintains equilibrium, It Bicarbonates also stop fluctuations in water pH and does not permit falling of pH below 4.5 and rising above 8.3.

Light penetration Turbidity and Light penetration has opposite relationship. Turbidity of waters below 2.5 mg/liter has high light penetration, fish production increases up to5.5 times and planktons growth increases up to12.8 times. While Turbidity beyond 100mg/liter of waters have little light penetration and fish production. The range of Light penetration above 600mm is characteristic of good water. More than 300mm is the sign of satisfactory Light penetration. While the bad quality waters have more than 100mm Light penetration. Total dissolved solids TDS is the measure of all the mineral contents, which might or might not be lethal. The diversity of fish is very high when there are low total dissolved solids while diversity of fish reaches to its peak when TDS is 400mg/liter.

111

Fish is widely acceptable for consumption because of its high palatability, low cholesterol and tender flesh (Eyo, 2001). In fisheries the attention of researchers have not emphasized on proximate composition of the freshwater fish as seen through different studies; therefore the purchaser and workers of fishery are missed with limited or scarcity of information’s on the significance of specific fish species in their routine diet (Adewoye et al., 2003). Protein and fat constitute the main nutrient in fish and their level in diet helps us to describe the dietary status of a particular organism (Aberoumad and Pourshafi, 2010). Different body composition parameters measurements like ash percentage, protein contents, moisture contents, carbohydrates and lipids are frequently essential to make certain that they fulfill the needs of commercial specifications and food regulations (Watermann, 2000).

Analysis of results regarding the proximate body Composition of Channa marulius obtained from River sindh for the sampling year 2010-2012 revealed fish body was constituted % water contents 76% , protein contents (wet weight) 21%, ash contents 4.5 % (wet weight) and Lipid contents were 3.5% (wet weight) for both the sampling year (Table, 3.25-26). Data obtained from river Chenab showed that water contents was the largest (77%) during both the sampling seasons (2010-2012) analyzed from muscles of Channa marulius. The % Fat (wet weight) was 3.5 and % ash (wet weight) was 4.5 (Table, 3.27, 3.28). Proteins contents (wet weight) was 18.5% lowered from Channa marulius captured from River Sindh water. However the protein contents detected in fisf body during our study were very high compared to other carnivore species like Wallago attu captured and reported from river Sindh by yousaf et al. (2011) who had studied body composition of freshwater Wallago attu in relation to body size, condition factor and sex from southern Punjab, Pakistan. Yousaf et al. (2011) had reported 13.32% proteins in wet body weight of Wallago attu as compared to 21% in our results whereas % fat was little higher in our study 3.5% compared to 2.24% which was reported from Wallago attu. Analysis of the results also showed that the moisture contents were little higher 77% in Channa marulius compared to Channa punctata 75% captured from two different localities by Ali et al. (2001) who studied the effect of environmental variables on body composition parameters of Channa punctata. The protein contents (wet weight) were also higher in C. marulius 21% in fish from river Sindh and 18.5% in fish from river Chenab where as in Channa punctata the protein contents were just 14.84 % and 12.3% from fresh water and contaminated water bodies

112 respectively confirming that environment directly effects the composition. The % fat (wet weight) was 3.5 in Channa marulius which was much lowered as compared to Channa punctata (8.94% and 6.01%) reported by Ali et al. (2001) indicating intraspecific variations for body composition parameters. The difference in the proximate composition are due to different species and different sampling seasons. The Channa marulius showed higher protein contents as compared to Channa punctata belonging to the same genus and also from Wallago attu (which is also a carnivore specie) indicating its nutrional importance.

Fish are intimately associated with the aqueous environment; physical and chemical changes in the environment are rapid and reflected as measurable physiological changes in fish (Fazio et al., 2013). Information about the hematological profile is an important tool which can be used for effective and sensitive monitoring of physiological and pathological state of a fish (Kohanestani et al., 2013). Hematological parameters of fish are closely related to the response of fish to environmental and biological factors (Steinhagen et al, 1990; Fernandes, and Mazon, 2003). Many factors can seasonally affect blood parameters changes in fish such as the reproduction cycle (Svoboda, et al., 2001; Bayir, 2005), diet (Guijarro et al, 2003), temperature (Sandnes et al, 1988), pH (Wilkie et al., 1996) and photoperiod (Kavadias et al, 2003).

In present study, values of hematocrit, hemoglobin and number of red blood cell showed interesting variations during both sampling seasons (Table, 3.59-60) with higher values in months having lower temperatures (December-February) while there was a tendency of decreasing concentrations of hematocrit, hemoglobin and number of red blood cells with an increase in environmental temperature (Table, 3.59-60). Dissolved oxygen in water of river Sindh was lowest in April indicating fluctuations in hematological response of fish with changing water temperature and dissolved oxygen concentrations. Our results are contradictory to Orun et al. (2003) who had reported that some of hematological parameters, like total hematocrit, hemoglobin, erythrocytes, total leucocytes and lymphocyte of Chalcalburnus mossulensts, Cyprinion macrostomus and Alburnoides bipunctatus had increased concentrations in summer and suggested that these changes are due to seasonal water temperature and dissolved oxygen variations. The difference in observations is may be due to different geographical, climatic conditions as well as due to different fish species under study having different

113 physiology and ecological demands. Our results are in agreement with those of Zaragabadi et al. (2009) who had studied effect of rearing temperature on hematological parameters of Husohuso juvenile and had reported that some hematological parameters like hematocrit and number of eosinophil increased while white blood cell count decreased with increasing temperatures. Most of fish including Channa marulius breeds during (spring and summer) with rising environmental temperature. Khadjeh et al. (2010) and Kohanestani et al. (2013) has reported that some of the blood parameters levels may increase to full fill high energy demand of fish during their breeding seasons.

Hematocrit, white blood cell, red blood cell, pack cell volume and mean corpuscular volume showed interesting variations during both sampling seasons for Channa marulius captured from River Chenab and showed these parameters showed higher values in high temperature seasons (April) and lower values during low temperature seasons (December) (Table, 3.61 - 3.62). Furthermore, this study has suggested that most of the hematological parameters showed higher value during summer time due to high ambient temperature and also because of high body metabolic rate and reproductive activities than other seasons. On the contrary the hematological parameters showed lowest value during winter which might be due to low metabolic rate and low ambient temperature. Our results are in agreement with Orun et al. (2003) who had reported that some of hematological parameters, like total hematocrit, hemoglobin, erythrocytes, total leucocytes and lymphocyte of Chalcalburnus mossulensts, Cyprinion macrostomus and Alburnoides bipunctatus had increased concentrations in summer and suggested that these changes are due to seasonal water temperature and dissolved oxygen variations.

Analysis of the results of serum biochemistry of Channa marulius during the year 2011-12 in river Sindh waters showed that total cholesterol remained on the average 220 range and the range of total protein is 4 gdL-1. The level of ALT and and AST remained 40 and 250 IUL-1 (Table 3.60).Whereas almost similar findings were observed on serum biochemistry of Channa marulius from river Chenab water during the same year 2011-2012. Malathi et al, (2012) observed during their comparative haematological studies on fresh water fishes Channa punctata and Channa striatus (Bloch) in Cauvery delta in and around Thanjavur the average chlolestrol were 198 Mg/dl among the Channa punctata species and 187 mg/dl among Channa

114 striatus species being the same genus our results showed little higher value of total cholesterol among Channa marulius which were 220 mg/dl on the other hand Total protein were little lower in Channa marulius 4 gdL-1 whereas in Channa punctata it was 5.1 and in Channa striatus it was 4.9 gdL-1. The difference of the results may be due to different fish species

Aquatic systems are the graveyard of various types of metals which regularly released from human activities and also from natural sources, these have very deleterious effects in the food chain because of their bio-magnification, toxicity, long persistence and bioaccumulation (Eisler, 1988). For trace metals assessment Fish are known be one of the most analytical factors in freshwater ecosystems, for the of trace metals contamination (Rashed, 2001). Fish may accumulate large amounts of metals from the water due to its presence at the high trophic level of the food web and frequently fish body have metals concentrations several times higher than in the ambient water (Yousafzai et al, 2010). Heavy metals like copper, iron and zinc are essential for fish metabolism, while some others such as mercury, cadmium and lead have no known role in biological systems. For normal metabolism the essential metals must be taken up from water or food, but excessive intake of the essential metals can produce toxic effects (Yousafzai, 2004). Heavy metals are taken up through different organs of the fish because of the affinity between them. In this process, many of these heavy metals are concentrated at different levels in different organs of the fish body (Rao and Padmaja, 2000; Bervoets et al, 2001).

Copper, zinc, iron and manganese were detected in Channa marulius body during the present study and the results were remained statistical non significant when compared between the fish from two different rivers of Pakistan indicating that the two sampling sites might not be polluted because the water quality parameters of both rivers remained within the safe ranges (Table 4.1)

In conclusion, we have observed that water quality parameters of both river (Chenab and Sindh) followed the standard patterns of variations with season and the water quality parameters varied with climatic changes and biological activities at the sampling sites during both sampling sessions. Analysis of our results indicating that Channa marlius is nutritionally very important fresh water fish as it contained higher protein contents as compared to Channa punctata (belonging to the same genera) and also to some other fresh water carnivorous fish species. The

115 studied parameters of body composition showed that fish from river Sindh had better nutrional quality than Channa marulius from river Chenab.

Analysis of results revealed variations in blood chemistry of Channa marulius with season. Most of the hematological parameters had their highest values in months with higher temperatures that correspond to high metabolic rate of fish due to ambient temperature and reproductive activities. Reverse was the situation in months with low environmental temperature for both sampling sites during two consecutive seasons. While comparison of elements in fish bodies from both sampling sites revealed no significant differences indicating that water quality parameters remained within safe limits leading to normal elemental concentration in fish body.

116

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CURRICULUM VITAE

MUHAMMAD LATIF

PERSONAL PROFILE Father’s Name: Ghulam Muhammad Bhatti Date of Birth: 12-11-1979 N.I.C. No: 36302-5256681-5 Marital Status: Married Domicile: Multan (Punjab) Address: House No. 2, Street No. 17, New Shahdab Colony No. 2, Near Pull Bararan, Multan. Ph No: +92061-4781511 Cell No: +920333-6136965 E-Mail: [email protected]

ACADEMIC QUALIFICATION Degree Year Board/University %age Division Ph. D (Zoology) 2016 B.Z.U. Multan

M. Phil (Zoology) 79.3 2005 B.Z.U. Multan 1st

M.Sc. (Zoology) 2002 B.Z.U. Multan 57.4 2nd B.Sc. (Zol, Bot, Chem) 2000 B.Z.U. Multan 58.6 2nd F.Sc. (Pre-Medical) 1998 Multan Board 66.6 1st Matric (Science) 1995 Multan Board 76.6 1st

COMPUTER SKILLS  4 weeks Computer Literacy Programme from Bahauddin Zakariya University, Multan.  MS-Office, Windows, Internet

Conferences/Workshop: 132

Two Days Conference of National Core Group in Life Sciences Held in 15-16

November 2006 at Bahauddin Zakariya University Multan Pakistan.

Zoological Congress held in 27th Feb to 1st march 2007 at Bahauddin Zakariya

University Multan Pakistan.

RESEARCH INTEREST

 Human diseases and genetics

 Fisheries

EXPERIMENTAL EXPERTISE

 Advanced analytic balance

 FTR

 Centrifugation

 Protein and lipid analysis

 Bom-calrimeter

PUBLICATIONS

 Latif M, Muhammad Ali M and Iqbal F 2015. Seasonal Variations in Hematological and Serum Biochemical Profileof Channa marulius are Complementary to the Changes in Water Quality Parameters of River Chenab in Pakistan. Pakistan J. Zool. 47(6):1699-1707. (IF 0.404)  Taqddus A, Saad ABA, Pasha B, Latif M, Safdar S, Shaikh RS, Ali M, Iqbal F 2014. Association of Endothelial Nitric Oxide Synthase (eNOS) Gene Polymorphism (Glu 298 Asp) with Coronary Artery Disease in Subjects from Multan (Pakistan). Pakistan Journal of Pharmaceutical Sciences. . 27(2): 357-363. (IF 0.684)  Zulfiqar S, Shahnawaz S, Ali M, Bhutta AM, Iqbal S, Hayat S, Qadir S, Latif M, Kiran N, Saeed A, Ali M, Iqbal F 2012. Detection of Babesia bovis in blood samples and its effect on the hematological serum biochemical profile in large ruminants from Southern Punjab (Pakistan). Asian Pacific Journal of Tropical Biomedicine. 2(2): 104-108.

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 Umer, K., Iqbal, F., Iqbal, R., Naeem, M., Qadir, S., Latif, M., Shaikh, R. S. and Ali, M., 2011. Effect of Various Nutrient combination and growth Body Composition of Rohu (Labeo Rohita). African Journal of Biotechnology. 10(62): 13605-13609.  Tassaduqe, K., Ali, M., Salam, A., Latif, M., Afroz, N., Masood, S. and Umar, S. (2004) Studies on the Chemical Composition and Presentation of Gallstones in Relation to Sex and Age among Human Population of Multan, Pakistan. J. Biol. Sci. 4(4): 470-473.  Tassaduqe, K., Ali, M., Salam, A., Latif, M. and Zahra, T. (2004) Study of the Seasonal Variations in the Physico Chemical and Biological Aspects of Indus River Pakistan. Pak. J. Biol. Sci. 6(21): 1795-1801.  Tassaduqe, K., Ali, M., Salam, A., Kanwal, L., Afroz, N., Masood, S., Latif, M., and Umar, S. (2003) Studies on the Chemical Composition and Presentation of Urinary Stones in Relation to Sex and Age Among Human Population of Multan, Pakistan. J. Med. Sci. 3(5-6): 401-410.  Ali, M., Salam, A., Gohar, S., Tassaduqe, K. and Latif, M. (2004) Studies on Fillet Composition of Fresh Water Farmed Labeo rohita in Relation to Body Size, Collected from Government Fish Seed Hatchery Mian Channu, Pakistan. J. Biol. Sci. 4(1): 40-46.  Tassaduqe, K., Ali, M., Salam, A., Latif, M., Afroz, N., Masood, S. and Umar, S. (2004) Hypertension in Relation to Obesity, Smoking, Stress, Family history, Age and Marital Status among Human Population of Multan, Pakistan. J. Med. Sci. 4(1): 30-35. EXTRA CURRICULAR ACTIVITIES  Gardening  Internet References Dr Furhan Iqbal Assistant Professor of Zoology Bahauddin Zakariya University, Multan Pakistan. Ph No 03315657685

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E mail: [email protected]

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