Table of contents
i
ii
Article No. Contents Page No.Acknowledgements xiii
List of Abbreviations xvii
Abstract xix
Chapter 1
Introduction 1
1.1 General Introduction 1
1.2 Water Quality 5
1.2.1 Climatic Factors 5
1.2.2 Physico-Chemical Parameters 6
1.2.2.1 Physical Parameters 6
1.2.2.2 Chemical Parameters 8
1.2.3 Biological Parameters 11
1.2.3.1 Planktonic Biodiversity 11
1.3 Fish Biodiversity 15
1.4 Fish Morphometry 18
1.5 Aims and Objectives 22
Chapter 2
Materials and Methods 23
2.1 Study Area 23
Figure 2.1 Location map of hill torrents of Suleman Mountain Range, Dera Ghazi Khan, Pakistan
24
Table 2.1 Names and location of sampling sites 27
Table 2.2 Vegetation and soil types of water sampling sites 28
2.2 Water Quality 29
Table 2.3 Dates and weather conditions of each sampling site 30
2.2.1 Climatic Factors 31
2.2.1. a Air Temperature (oC) 31
2.2.1. b Photoperiod (hours) 31
2.2.1. c Humidity (%) 31
Article No. Contents Page No.2.2.2 Physico-Chemical Parameters 31
2.2.2.1 Physical Parametrs 31
2.2.2.1 a Water Temperature (oC) 31
2.2.2.1 b Light Penetration (cm) 31
2.2.2.1 c Total Dissolved Solids (mgL-1) 32
2.2.2.2 Chemical Parametrs 33
2.2.2.2 a pH 33
2.2.2.2 b Electrical Conductivity (dSm-1) 33
2.2.2.2 c Dissolved Oxygen (mgL-1) 33
2.2.2.2 d Free Carbon dioxide (mgL-1) 34
Dedication
To
My Late Loving Mother and My beloved wife
My daughters and my son
iii
Acknowledgements
I would like to pay all my praises to AL-MIGHTY ALLAH, the most Merciful, the most
Beneficent and The Holly Prophet, HAZRAT MOHAMMAD (Peace BeUpon Him) who
showed the right direction and path to humanity and enabled us to recognize our
Creator.
I can not express my gratitude enough to my supervisor Prof. Dr. Muhammad Ali,
Vice Chancellor, Government College University, Faisalabad. It was an absolute
privilege and honour to work under the supervision of Prof. Dr. Muhammad Ali, who
is both exceptional teacher and caring supervisor. As with my Ph.D. the last few years
have been a roller coaster; thankfully with many many more highs than lows, mostly
due to Prof. Dr. Muhammad Ali. If it wasn’t for you I may never have entered the
world of Aquatic Biodiversity, Freshwater fish biodiversity and fish morphometry. For
the last few years you have taken me under your wing, have always shared your
knowledge freely and have allowed me for the completion of my research work. I can
safely say that I feel like I have been your apprentice in the true sense of the word.
Thank you for everything, especially letting me turns into a boomerang every time I
need to scratch my itchy feet. I can only hope you are as proud of this thesis as I am.
We got there in the end!
To Dr. Furhan Iqbal, my co-superviser, I extend my sincere thank you for your swift
comments and feedback. It was great to have someone to talk who knew the ropes so
well. I am eternally indebted to Prof. Dr. Tariq Mahmood Ansari, Dean Facuty of
Science, Bahauddin Zakariya University, Multan and to Prof. Dr. Seema Mehmood,
iv
Director, Institute of Pure and Applied Biology, Bahauddin Zakariya University,
Multan for all your help during this Ph.D. thesis.
I would like to express my sincere thanks to Dr. Muhammad Zubair Hussain,Assistant
professor of Zoology, Govt. Emerson College, Multan, for his accommodative attitude,
patience, sympathetic behaviour during writing and compilation of Ph.D. thesis.I am
also thankful to Dr.Abdul Latif, Assistant professor of Zoology, Govt. Emerson
College, Multan, for his accommodative attitude, patience, sympathetic behaviour
during formatting and proof reading of Ph.D. thesis. I would like to thank to Mrs.
Rehana Iqbal, Assistant Professor of Zoology, Bahauddin Zakariya University, Multan
for her valuable help in providing me all types of facilities and inspiring guidance,
valuable suggestions and dedicated co-operation in the development of this work. I
would like to pay my sincere thanks to Dr. Kashif Umer, Lecturer in Zoology, Govt.
Alamdar Hussain Islamia College, Multan, for his valuable guideline and his help in
analysis of data for the thesis. I would like to pay my special thanks to Dr. Qaswar Ali
Shah for his valuable guidance during thesis writing.
In no particular order, I, would like to thank Dr. Ramzan Mirza, Ex-Professor and Ex-
Head of Zoology Department, GC, University, Lahore, for all his valuable guideline in
fish identification.
I would also like to thank to Mr. Muhammad Aslam, Laboratory analyst, Water and
Soil Laboratory, Dera Ghazi Khan, Department of Agriculture, Punjab, who supported
me in providing facilities for chemical analysis of water samples.
v
My special thanks are to Mr. Abdul Rehman, Mr. Muhammad Khalid Balouch, Mr.
Asmat-Ullah, Mr. Mujahid Niaz Akhtar of Zoology Department, Govt. Emerson
College, Multan and Mr. Muhammad Latif (Ph.D. Scholar) for moral support during
thesis writing.
There are numerous people at the Zoology Department, Govt. Postgraduate College,
Dera Ghazi Khan, who have always offered their assistance both freely and with
kindness. In no particular order, I would like to thank Prof. Muhammad Hussain, Head
of Zoology Department, Mr. Imran Ali, Mr. Abrar Husssain,and Mr. Muhammad
Frooq. Prof. Dr. Amin-Ud-Din, Principal Goverenment College University of
Education, Dera Ghazi Khan. I am also grateful to Dr. Muhammad Atif, Assistant
Professor, Bahauddin Zakariya University, Multan, for providing me help in statistical
analyses of data.
In addition, there have been numerous people from outside of Zoology Department
that have given me support during my Ph.D. I would like to thank Mr. Faiz Bakhsh
Assistant Manager, Stat Life Insurance Corporation, Multan, Pakistan, Dr Muhammad
Imran, Dr Sajjad Hussain, Mr. Alamdar Husssain Bukhari, Hafiz Muhammad Saeed
and Atif Hussain Shah.
As a matter of fact, I, acknowledge all my friends for their support, love, words of
encouragement and advice throughout this particular path – thank you all so much for
your friendship.
vi
Finally I pray and pay my gratitude to late father, my mother and eldest brother and to
my beloved wife, I really could not have done this without you. Your love and support
over the past four years got me through the tough times and made the fun times even
more enjoyable. Thank you for keeping me laughing and for everything you have done
to make my life easier during the writing up stage. I cannot imagine a better person to
have shared this roller coaster with! I can not wait to start our next adventure
together…..
Sajjad Hussain
vii
Abbreviations
ANOVA: Analysis of variance
a: intercept
AFL: anal fin length
AFB: anal fin base
b: slope
BD: body depth
CL: confidence limits
°C: Degree celcius
dd: Double distilled
df: Degree of freedom
DO: Dissolved oxygen
DFL: dorsal fin length
DFB: dorsal fin base
dSm-1: Desi Siemens per meter
EC: Electrical conductivity
ED: eye diameter
EDTA: Ethylenediaminetetraacetic acid
FSS: Fish sampling site
FL: fork length
GL: girth length
Hum: Humidity
HD: head depth
HL head length
viii
K: Condition factor
L: Liter
LLR: Length length relationship
LWR: Length weight relationship
m: Meter
mgL-1: Milligram per liter
ml: Milli liter
mm: Millimeter
PreOL: pre-orbital length
PostOL: post-orbital length
PecFL: pectoral fin length
PecFB: pectoral fin base
PelFL: pelvic fin length
PelFB: pelvic fin base
ppm: Parts per million
r: Correlation coefficient
SAR: Sodium adsorption ratio
SL standard length
TA: Total Alkalinity
TH: Total hardness
TL: total length
WSS: Water sampling site
W: wet body weight
ix
ABSTRACT
Freshwater ecosystems have been playing important role in development and
maintenance of human civilization. Present study depicted the planktonic and
ichthyofaunal biodiversity of Suleman Mountain Range, Dera Ghazi Khan Region of
Pakistan. Water quality was assessed by analyzing physico-chemical parameters and
the ichthyofaunal status of the region was assessed by calculating abundance and
diversity of fish fauna. The fish health status was estimated by measuring several
morphometric characteristics and condition factor.
The analysis of TDS, pH, CO2, CO3-2, HCO3
-1, TA, TH, Na+1, Ca+2, Mg+2, Cl-1, SO4-2,
EC and SAR showed significant site variations while water temperature, light
penetration, pH, DO and CO2demonstrated the significant seasonal variations. Out of
119, 83 phytoplankton and 36 zooplankton genera were recorded wherein chlorophyta
was the most abundant with (RA= 28.80%) and xanthophyta was the least (RA =
0.87%) while Protozoa was the most occuring with (RA = 11.57%) and cladocera was
the least with (RA = 0.80%).
Twenty fish species were explored in which Tor macrolepis was the most and Labeo
calbasu was least abundant in the region. The family Cyprinidae was found to be most
abundant and dominant in Suleman Mountain Range, Dera Ghazi Khan, Region while
three fish species endemic to Pakistan namely Barilius pakistanicus, Labeo dyocheilus
pakistanicus and Salmophasia punjabensis were also observed from this experimental
area.
Total length ranged from 7.80-31.0 cm for Tor macrolepis, 8.08-17.20 for
Schizothorax plagiostomus, 9.0-20.5 for Labeo diplostomus, 9.30-23.60 cm for Labeo
dyocheilus pakistanicus, 8.00-14.30 cm for Cyprinion watsoni, 10.30- 18.90 cm for
x
Ompok pabda and 9.00-14.10 cm were calculated. Similarly, wet body weight ranged
from 5.20-301.90 g for Tor macrolepis, 7.05-67.08 g for Schizothorax plagiostomus,
5.50-90.0 g for Labeo diplostomus, 10.20-125.0 g for Labeo dyocheilus pakistanicus,
7.90-30.70 for Cyprinion watsoni, 8.50-57.0 g for Ompok pabda and 8.60-39.30 g for
Garra gotyla were recorded. New records of maximum total length for Cyprinion
watsoni (14.3 cm), Labeo dyocheilus pakistanicus (22.50 cm), Labeo diplostomus
(22.0 cm) and Tor macrolepis (29.0 cm) were obtained. Condition factors of most
fishes were optimum except Ompok pabda. In general, most of the water quality
parameters of the studied sites were within suitable range for growth of living
organism. The diversity indices value indicated moderate planktonic diversity.
However, ichthyofaunal diversity of the region was poor. The morphometric
characteristics and condition factor values indicated good fish health.
xi
Introduction
1.1 General Introduction
Fresh water is defined as “water with less than 500 parts per million (ppm) of
dissolved salts” (Dobson and Frid, 1998; Gleick, 1993). It comprises nearly 2.5-2.75%
of the total earth water; while remaining 97% is marine water. Naturally, it occurs in
two forms which are surface and ground water. Small amount (1.75-2%) of water
exists in the form of frozen surface water like glaciers, snow and ice. Only 0.01%
water is the available surface water existing in the form of rivers, lakes, swamps etc.
Freshwater habitats include both lentic systems (standing waters) such as ponds, lakes
and swamps; as well as lotic systems (running waters), such as streams and rivers
(Gleick, 1993; Dobson and Frid, 1998).
Freshwater ecosystems are studied under the science of “limnology” a term which
define asthe study of inland freshwater and saline ecosystem. It isconcerned with
structure as well as material and energy balances within these ecosystems with
emphasis on the biogenic materials balance of natural waters (Goldman and Horne,
1983; Schwoerbel, 1987). Limnological studies include three major features of fresh
water ecosystemsi.e.physical, chemical and biological. Physical characteristics include
studies of physical parameters like water temperature, light penetration, water current
etc. Chemical aspects include studies of chemical parameters like dissolved oxygen
(DO), total alkalinity (TA), total hardness (TH) etc. Biological aspects include studies
of biological parameters like diversity of planktons (Ali, 1999).
1
Primarily, freshwater habitats occur as distinct spatial units, and therefore are viewed
as well-defined ecosystems. These are not separate units, but are in continuous
interaction with their surroundings (Jeffries and Mills, 1990). Freshwater ecosystems
are a major resource for vital elements for humans particularly water and nutrients
(Baron et al., 2002; Dudgeon et al., 2006). Majority of the cities and massive
civilizations flourished across the banks of fresh water sources (Van De Meene et al.,
2001).
Biodiversity refers to organisms found within the living world including their number,
variety and variability.In biological terms, it has been defined as the “quantity, variety
and distribution across biological scales ranging through genetics and life forms of
species, populations, communities and ecosystems” (Wilcox, 1984; Mace et al., 2005).
The assessment of the status of species is helpful for monitoringthe trends and losses
of biodiversity. This is an important indicator for the status of biodiversity and in
accordance with the principles of the Convention on Biological Diversity (IUCN
2001). Identification and characterization of those species which are important for
humans is necessary for conservation process. The conservation of biodiversity has
become a core issue for scientific community (Wilson, 1988; Nielsen, 1995).
Freshwater biodiversity comprises of relatively more species richness compared to
terrestrial and marine ecosystems (Dudgeonet al.,2006). Freshwater ecosystem
nourishes 10% of the world existing species including a quarter of all vertebrates even
though, they cover less than 1% of the Earth’s surface (Strayer and Dudgeon, 2010).
Freshwater ecosystems are among the richest and most diverse ecosystems on earth
(Revenga and Mock, 2000). The quality and quantity of available freshwater is of
2
prime importance for estimating the health of ecosystems and sustainability of human
societies worldwide (Dziock et al., 2006; Brazner et al., 2007; Danz et al., 2007).
Biodiversity is important to sustain the ecosystem, maintenance of inclusive
environmental quality, and thereby, helps to understand innate value of all species on
the earth (Ehrlich and Wilson, 1991). However, the importance of freshwater
ecosystems to human culture, welfare and development has resulted in severe and
complex impacts to biodiversity and ecology (Malmqvist and Rundle, 2002).
Our world especially developing countries are facing the problem of water stress due
to rapid growth in population. The world’s hunger population is estimated to be about
923 million (FAO, 2008). This problem will be more aggravated with an expected
addition of 2 billion individuals by the year 2030 (Gany, 2006). The increasing
population will put an increasing demand of food consumed; enhanced water supply
for irrigation and drinking (Steffen et al., 2015; Gerten et al., 2013). Water shortages
due to limited amount and inefficient use of available water are one of the major
causes for lack of food (Laghari et al., 2008).
In Pakistan, aquatic resources are in the form of rivers, canals, drains, lakes, reservoirs,
village ponds, dams, waterlogged areas etc. This great variety of habitats is rightly
inhabited by diverse forms of aquatic life, the fish being a significant constituent
(Mirza and Bhatti, 1999). The largest contiguous gravity flow irrigation system in the
world, which is found in Pakistan, serves as a lifeline for sustaining agriculture and
biodiversity. Based upon pattern of the flow of its rivers and streams, Pakistan can be
divided into three major drainage systems: the Indus drainage, Balochistan coastal
drainage and landlocked drainage. Indus drainage is the largest river system of the
3
country, consisting of the main Indus River and all its associated rivers and streams.
The Balochistan coastal drainage system consists of a number of relatively small and
shallow rivers, all of which emerge from southwestern hills and independently fall into
the Arabian Sea. The landlocked drainage is constituted by a number of small and
shallow landlocked rivers and streams of central and western Balochistan (Rafiq and
Khan, 2012).
However, the mountainous/sub-mountainous areas of north eastern Balochistan,
central and Southern Khyber Pakhtoon Kha and Southwestern Punjab have a very
unique type of freshwater ecosystem, the mountainous streams, named as “Hill
Torrents, locally called Rodhkohi/Nallah”, with associated pits and ponds. The
seasonal torrential rains result in high velocity flowing water which is mostly drained
off either in the form of flood water to nearby areas or drained into nearby River Indus.
However, certain hill torrents have smooth flow of water for certain time of the year.
There have been no previous studies on assessment of water quality and exploration of
planktonic biodiversity of such type of ecosystem in Pakistan.
4
1.2 Water quality
Water quality is “any characteristics of water that influences its beneficial use. It is the
sum of all physical, chemical, biological and aesthetic characteristics of water”. Any
characteristics of water in production system that affect survival, reproduction, growth
and aquaculture species; influences management decision, causes environmental
impacts and reduces product quality and safety can be considered as water quality
variable (Boyd and Tucker, 1998).
Several of available water resources such as rivers, lakes and man-made reservoirs
have been used for water supply to domestic, agriculture, industrial, and aquaculture.
Therefore, assessment of water resources quality from any region is important aspect
for the developmental activities of the region. The thorough assessment of several
physico-chemical and biological characteristics is necessary for sustaining good
quality of water resources. These physico-chemical and biological parameters should
be in optimal range (Postel et al., 1996; Quyang et al., 2006; Gleick et al., 2011).
1.2.1 Climatic Factors
The climatic factors include humidity, air temperature, photoperiod and clouds.
Humidity usually measured as the amount of water vapors in air and the equilibrium
vapor pressure of the water that air can hold at the existing temperature. Humidity
along with temperature and light has an important role in regulating the activities of
organism and in limiting their distribution (Odum, 1971).Photoperiod is the process of
lengthening and shortening of days (Ricklefs and Miller, 2000). Seasonal variations in
day length are used by many organisms to synchronize their life cycle as opposed to
rainfall or temperature and are more reliable (Boyle and Senior, 2002).Clouds are
5
visible accumulations of water droplets or solid ice crystals that float in the earth
troposphere, the lowest part of earth atmosphere moving with the wind
(www.hopkin.dartmooth.edu).
1.2.2 Physico-Chemical Parameters
Several physical characteristics of water like temperature, light penetration, density,
viscosity and solubility of gases are the significant physical factors influencing aquatic
ecosystem. The chemical properties of water like pH, DO, carbonates (CO3-2),
bicarbonates (HCO3-1), salinity and conductivity etc comprise chemical parameters.
Such physico-chemical parameters as well as the biological characteristics
directly/indirectly affect the growth and survival of aquatic species (Boyd, 1998).
Physico-chemical characteristics and nutrients concentration in water significantly
influences the species composition and distribution of plankton (Reynolds, 1984;
Ganai and Parveen, 2014). These factors play important role for abundance and growth
of phytoplankton and consequently zooplankton and other consumer which depend on
phytoplankton for their existence. The seasonal fluctuations in environmental
parameters also significantly influence population density and distribution and of
plants and animals (Odum, 1971).
1.2.2.1 Physical Parameters
The temperature governs all other physical, chemical and biological characteristics of
water bodies and hence is considered as the most important and key factor in aquatic
habitats (Alabaster and Lloyd, 1980). There are variations in water all the year round
due to seasonal fluctuations in solar radiations, air temperature and day length. The
temperature of water depends on sunlight, climate and water depth. It influences the
6
oxygen contents of water being inversely proportional (Boyd, 1998), abundance and
diversity of autotrophs, influencing the percentage of photosynthesis and therefore
indirectly affects the abundance and diversity of heterotrophs (Barnabe, 1994). All
organisms including fish possess limits of temperature tolerance. High temperature
magnifies the impact of toxic substances and increases biodegradation process
(Barnabe, 1990).
Light penetrationis determined by the light intensity which falls on the surface of water
as well as amount of turbidity of water. It is a measure of the extent of eutrophic zone
(Boyd, 1998), and consequently influenes the primary production of water body
(Ramachandra and Solanki, 2007).Various substances present in natural waters such as
silt, clay and suspended particles affect light penetration (Rath, 1993).
Total dissolved solids (TDS) of water are composed of inorganic salts as well as
organic material in small amounts. It constitutes all the floating suspended solids in a
water sample. CO3-2, HCO3
-1, sulphates (SO4-2), chlorides (Cl-1), nitrates (NO3
-2),
potassium (K+1), sodium (Na+1), calcium (Ca+2) and magnesium (Mg+2) are the major
ions which contribute to TDS. Organic material in water may come from natural
sources, industrial discharges and sewage effluent discharges. Dissolved solids
concentration affects osmoregulation of fresh water organisms and reduces solubility
of gases (Boyd, 1998; Kumar and Kakrani, 2000).
7
1.2.2.2 Chemical Parameters
pH measurement accounts for the alkalinity or acidity of water. It is the determination
of hydrogen ion (H+1) and the hydroxyl ion (OH-1) in water. pH indicates the
magnitude of the acidic and basic nature of a solution at a given temperature (APHA,
1989). The rate of daily fluctuations in pH depends upon TA of water and rates of
photosynthesis/respiration (Boyd, 1998). During daytime, carbon dioxide (CO2)is
removed from the water resulting in increase of pH, while CO2 accumulates at night
resulting in decline of pH (Boyd, 1998).
Dissolved oxygen (DO) has a crucial role in determining the potential biological
quality of water. It facilitate degradation of organic detritus, completion of metabolic
pathways and essential for respiration, (Boyd, 1998). Therefore, it is very important
for the survival of fish and other aquatic organisms (Ramchandra and Solanki, 2007).
The concentration of DO in water depends on temperature, partial pressure of oxygen
in contact with water surface and amount of dissolved salts (Boyd, 1981).
Due to respiration of organisms and microbial activity, free carbon dioxide (CO2)
accumulates in water. By dissolving in water, it forms carbonic acid that imparts
acidity to the water. Free CO2 acts as a readily available source of carbon for high
sustainable growth of submerged macrophytes and algae. The dissolved CO2
influences water quality properties such as acidity, hardness and related characteristics
(Ekhande, 2010).
Carbonates (CO3-2) and bicarbonates (HCO3
-1) are the major components of pond
water. Their amounts are described as total alkalinity. There are usually more HCO3-1
8
in natural waters than that which result from ionization of carbonic acid in water
saturated with carbon dioxide (Rath, 1993).
One of the major environmental factors in aquaculture is total alkalinity (TA) due to its
interactions with other factors which affect the fertility of the ecosystem and health of
aquatic animals (Boyd, 1998). It measures the quantity of such ions in water, which
could react to neutralize H+1 (APHA, 1989). The dissolved minerals in water that come
from soil, atmospheric inputs, waste discharge and microbial decomposition of organic
matter provide in water the source of alkalinity. Various ionic substances contributing
to alkalinity include HCO3-1, CO3
-2, OH-1, HPO4-2, and H2PO4
-1 (Abbasi, 1998). In
natural waters TA is correlated with rates of primary production (Boyd, 1998).
Total hardness (TH) is usually expressed as Ca+2 and Mg+2 ions only and described as
equivalent CaCO3. Mostly it exists in the form of bicarbonates of Ca+2 and Mg+2 and
with lesser amount of chlorides and sulphates. TH (Ca+2 and Mg+2) is an important
parameter to detect water pollution (Abbasi, 1998; Boyd, 1979).
Sodium (Na) is found in high concentration in fresh water. Na+1 is essential to human
being and other higher animal. It regulates the composition of body fluids and is
dispensable for many bacteria and most plants. It is found as the principal cation of the
extra cellular fluid. Na+1 is involved in processes occurring outside the cell (Ohta,
2003; Borsani et al., 2003).
Calcium (Ca) is found as Ca+2 and as suspended particulates (mainly CaCO3). It is
essential for metabolism in all living organisms as well as structural/skeletal
9
substances in many animals. Calcium salts in excessive amount result in hard water
(Skipton and Dvorak, 2009). It influences the distribution of diatoms in aquatic
ecosystems. High calcium contents with high temperature cause high abundance of
diatoms (Vidya et al., 2013).
Magnesium (Mg) in water is dissolved from all rocks and soils. Moderate quantities of
Mg+2 have little effect on the usefulness of water for most purposes. Mg+2 also promote
the hardness in water and along with Ca+2 posses the problem of scale formation (EPA,
2012; Vidya et al., 2012). It has been an essential constituent of chlorophyll, without
which no ecosystem can work. Italso plays an important role in metabolism (Skipton
and Dvorak, 2009).
Chlorides (Cl-1) are present as chloride ion (Cl-1) in water and waste water (Trivedi and
Raj, 1992). Cl-1 influences osmotic and salt balance and ion exchange. However,
metabolic utilization does not cause large variations in the spatial and seasonal
distribution of Cl-1 within most lakes. High concentration of Cl-1 indicates the pollution
by sewage or industrial waste (APHA, 1998).
Sulphate (SO4-2) is deposited in water through natural means. All natural waters have
sulphates. Generally, industrial waste water has higher sulphate contents than natural
water. Main sources for sulphates in natural waters are oxidation of pyrite, dolomite
layers and industrial activities (Schippers et al., 1996; Koltuniewicz and Drioli, 2008).
SO4-2 in excess amounts exerts adverse effects on health. Excess amounts of SO4
-2 also
affect aesthetic value of water like odour and taste. It influences economy due to
corrosion of the structures (EPA, 1998; Yalcin and Guru, 2002).
10
Electrical conductivity (EC) in natural water can be measured by its ability to conduct
an electric current. Generally, it is directly proportional to the concentration of ions in
the natural water (Frank et al., 1990; Boyd, 1998). EC levels in water indicate the
amount of joinable substances like phosphate, nitrate and nitrites that are dissolved in
it (Boyd, 1998).
SAR is a more reliable and generally adopted criterion for evaluating sodium risk. It is
used to determine exchangeable value of the soil in equilibrium with irrigation water
(Jivendra, 1995).
1.2.3 Biological Parameters
Biological characteristics of an ecosystem are concerned with density and diversity of
organisms (Barnabe, 1990). There are various communities of living organisms which
survive in an aquatic ecosystem (Declince, 1992; Dolan, 2005) and play an important
role in productivity of aquatic ecosystem. The major groups of organisms inhabiting in
aquatic habitats are phytoplankton, zooplankton, fishes, macrophytes, epiphytes etc.
(Scheffer, 2004).
1.2.3.1 Planktonic Biodiversity
Planktons are “heterogeneous assemblage of pelagic organisms consisting of various
groups that are adapted to suspension in sea and freshwater” (Battish, 1992; Declince,
1992). Planktons are divided into phytoplankton (photosynthetic organisms) and
zooplanktons (heterotrophic organisms) (Battish, 1992; Richmond, 2004).
Phytoplankton consists of several prokaryotic and eukaryotic microscopic organisms.
In fresh water ecosystems major algal groups include diatoms (bacillariophyta), green
11
algae (chlorophyta) and blue green algae (cyanophyta) (Michael and Paerl 1994;
Chang et al., 1995).
Phytoplanktons act as the primary producers thereby comprising the first trophic level
in the food chain. Their total energy fixed in the form of carbon compounds i.e.
primary production forms the basis for oceanic and freshwater food webs (Boyd and
Tucker, 1998). Additionally, phytoplanktons also play a significant role in the material
circulation and energy flow in the aquatic ecosystem. They also exert influence on the
growth, reproductive capacity and population characteristics of other aquatic
organisms (Ariyadej et al., 2008).
The quantitative and qualitative characteristics of phytoplankton are good indicators of
water quality (Boyd and Tucker, 1998). Variations in the phytoplankton of freshwater
lakes act as a good indicator of the environmental quality and trophic status of the
system (Reynolds, 1996). Increase in phytoplankton biomass in surface water is often
related with nutrient enrichment (Smith, 2003). The higher abundance of chlorophyta
indicates productive water (Boyd and Tucker, 1998). Phytoplankton diversity responds
rapidly to changes in the aquatic environment in relation to nutrients (Eggs and
Aksnes, 1992; Chellappa et al., 2008). Floating algae (phytoplankton) are used as
biological indicators for benthic lakes or reservoirs (Michael and Paerl 1994; Chang et
al., 1995).
High sensitivity of planktons to variations in abiotic factors results in changes in their
dominance, abundance and diversity. Therefore, these changes in population dynamics
of planktons are useful indicator of pollution status of water bodies (Basu et al., 2010;
12
Prabhahar et al., 2011). Various phytoplankton species have been used as bioindicators
(Vareethiah and Haniffa, 1998; Bianchi et al., 2003; Tiwari and Chauhan, 2006; Hoch
et al., 2008). Increased growth of certain groups of phytoplankton especially blue
green algae can cause deoxygenation of the waterleading to fish death (Whitton and
Patts, 2000).
Zooplanktons constitute heterotrophic microscopic organisms and include protozoans,
microcrustaceans and other micro invertebrates, which are suspended in water (Omudu
and Odeh, 2006). These serve as important source of food for many other aquatic
organisms (Guy, 1992). They play key role in transferring energy between the
phytoplankton and fish populations (Martin et al, 2006; Holmborn, 2009). The
zooplankton distribution varies spatially as well as temporally within aquatic
ecosystems (Declince, 1992). They also serve as pollution indicators in aquatic
environment (Yakubu et al., 2000).
The assessment of productivity, trophic status and planktonic density of water bodies
is crucial for fisheries management (Peckham et al., 2006). The density and diversity
of planktonic organisms is variable from pond to pond within one locality and from
locality to locality within a region. Greater density and diversity of planktonic
communities indicate successful aquaculture (Bhuiyan et al., 2008).
Various physico-chemical factors like temperature, pH, light penetration, turbidity,
nutrients and salinity regulate species composition, richness of phytoplankton and
zooplankton in aquatic ecosystems (Buzzi, 1999; Veereshakumar and Hosmani, 2006).
The productivity of planktons in an aquatic ecosystem is affected by the physico-
13
chemical characteristics and nutrient level of water (Odum, 1971; Wetzel, 1983). The
optimum level of the physico-chemical factors results in maximum yield of
phytoplankton (Sinha and Srivastava, 1991; Aliet al., 2005; Peerapornpisal et al.,
2004). Variations in physico-chemical and biological characteristics influence several
factors resulting in seasonal changes (Reynold 1984). Seasonal variations in radiations,
temperature, hydraulic output and nutrient status influence composition and abundance
of phytoplankton (Reynolds 1990; Reynolds, 1998; Marinho and Huszar, 2002).
A volume of research work is available to estimate productivity and plankton analysis
in lakes and reservoirs in temperate (Mallin et al., 1994; Watson et al., 1997; Huszar
and Caraco, 1998; Trifonova, 1998), tropical (Figueredo and Giani, 2001; Nogueira,
2000; Talling, 1987) and sub-tropical (Harris and Baxter, 1996; Silva, 2005; Rajagopal
et al., 2010) region. Several studies in Pakistan have described the physico-chemical
characteristics and biodiversity of aquatic ecosystems at various times and places
(Hussainet al., 2014; Chughtai et al., 2011; Chughtai et al., 2013; Khan et al., 2013;
Ghazala and Arifa, 2011; Iqbal et al., 2004; Baloch et al., 2005; Ali et al., 2003; Ali et
al., 2005; Rafique et al., 2002). However, there are no previous studies on hill-torrent
type of ecosystems in this sub-tropical region. This is the first type of study describing
detailed investigations of planktonic diversity with reference to their spatial and
temporal distribution and changes in species composition in relation to environmental
variables.
14
1.3. Fish Biodiversity
Ichthyofaunal diversity refers to “the variety of fish species depending on context and
scale, it could refer to alleles or genotypes within fish population to species of life
forms within a fish community and to species or life forms across aqua regimes”
(Burton et al., 1992). Biodiversity influences the response of living systems to
environmental changes, facilitates functions of ecosystem and provides several
essential nutrients and utilities for human well-being like nutrient cycling and clean
water (Costanza et al., 1997; Hooper et al., 2005; Diaz et al., 2006).
Freshwater fishes constitute a significant proportion of global biodiversity (Reid et al.,
2013). This fact is reflected by the large number of freshwater living fish species. The
number of all fish species is estimated to be 32,500 (Nelson, 2006). Of these, nearly
50% is contributed by freshwater fish species and still the numbers of described
freshwater fishes are increasing (Froese and Pauly, 2010). About 305 new fish species
are described per year since 1976 (Reid et al., 2013). Even though, freshwater makes
less than 0.3% of total available water in the world (Ormerod, 2003). The fishes
demonstrate enormous diversity of morphological, behavioral and physiological
adaptations (Nelson, 1994). By virtue of these adaptations, fishes inhabit a wide
variety of habitats including springs, ponds, pools, lakes, reservoirs, streams, rivers
and sea (Moyle and Cech, 1996).
Fisheries have a significant contribution in the national economy of many countries
(Valdimarsson, 2001; Bostock et al., 2004). The total fish capture has been recorded to
be 149 million tonnes in the year 2010 (FAO, 2012). The developing countries
contribute approximately 94% of all freshwater fisheries (FAO, 2007). Fish meat is a
15
primary source of protein for approximately 1 billion people throughout the globe
(FAO, 2010). The contribution of freshwater fishes to annual animal protein source for
humans is more than 6% (FAO, 2007). Animal protein intake from fish source reaches
up to 40-50% in several developing/underdeveloped countries like Bangladesh,
Indonesia, Philippines, Thailand and Vietnam (Briones et al., 2004).
Being an intrinsic component, fish has a significant contribution in regulation of
energy flow, cycling of nutrients and maintenance of community balance of the
aquatic ecosystem (Nelson, 2006). They act as a key monitor of ecosystem quality due
to their overwhelming impact on the abundance and distribution of other aquatic
organisms (Moyle and Leidy, 1992). The life history characteristics of fishes render
them as appropriate indicators of anthropogenic stress. They are also sensitive to
various other stresses like diseases, parasites and acidification. Several factors of fish
i.e. large body sizes, high growth rates and trophic status make many fishes to
bioaccumulate toxic substances (Holmlund and Hammer, 1999).
The changes in water quality parameters including water temperature, DO contents,
amount of suspended solids, Cl-1, Ca+2, Na+1 and other chemical constituents affect
species composition and relative abundance (RA) of fish communities (Grizzle, 1981).
Therefore, the fishes are highly sensitive to both the quantitative and qualitative
changes in aquatic ecosystems (Laffaille et al., 2005; Kang et al., 2009; Sarkar et al.,
2008). This fact has made freshwater fishes as one of the most threatened taxonomic
groups (Darwall and Vie, 2005).
16
The main factors which cause loss to fish biodiversity include habitat destruction,
damming, industrialization, pollution and other global climatic changes (Prenda et al.,
2006; Collare-Pereira and Cowx, 2004; Allan and Flecker, 1993; Gibbs, 2000; Dawson
et al., 2003; Leveque et al., 2005; Mas-Marti, 2010). The decline in fish biodiversity is
a phenomenon well known from local to global levels (Duncan and Lockwood, 2001;
Cowx and Collares-Pereira, 2002).
IUCN Red List “Categories” and “Criteria” are most commonly used to determine
species status. It also help to detect the extinction risk and provides foundation to
understand status of species which are extinct, threatened, near threatened, least
concern or data deficient. It publishes the results of global assessment, taxanomy,
habitat, distributions and ecology for each species (www.iucnredlist.org).
About 20% of the world’s freshwater fish is either threatened or extinct (IUCN, 2014).
Approximately 1275 fish species have been listed as threatened in the IUCN red list of
2008, including 11 in Pakistan (Rafique and Khan, 2012). The freshwater fish fauna
from various water bodies of Pakistan has been investigated through a number of
comparatively recent studies carried out at different places and times (Mirza, 1990;
Mirza, 2003; Rafique and Qureshi, 1997; Rafique, 2000; Rafique, 2001; Rafique et al.,
2003; Hasan et al., 2013; Iqbal et al., 2013; Akhtar et al., 2014). Much taxonomic
work still remains to be done.Our study (Ali et al., 2010) has previousely described the
fish diversity of Suleman Mountain Range, Dera Ghazi Khan, Region; however,
present study was carried out to explore fish diversity in more detail.
17
1.4 Fish Morphometry
Morphometry describes variations and changes in form i.e. size and shape of organism,
thus facilitating numerical comparison between different forms and description of
complex shapes in a comprehensive manner (Webster, 2006). Different aspects of fish
growth e.g. pattern of shape, variation within and among sample of life stage,
population and species and to manipulate hypothesis about the origin of these
variations in growth patterns are the focus of scientific interest (Shearer, 1994). During
growth, body proportions change resulting in alterations of body form (Le Cren, 1951).
Morphometric characters explain various aspects of body forms and shape in fish
(Turan, 2004). Morphometric analysis utilizes a continuous data set of measurement of
size and shape variation of a population. Morphometrics and meristics are important in
fisheries science because of the extensive use to identify fish species and their habitat
specificity (Geldiay and Balik, 1998, Karatas, 2005), characterize fish stock (Turan et
al., 2004) and assessing the evolutionary adaptations of a species to environment
(Kovac et al., 1999).
Length and weight are two basic components in the biology of fish species and
relationships of such morphological characters of the fish are helpful in predicting the
growth pattern of fish species at individual as well as population level (Pauly, 1993).
Length-weight relation (LWR), Fulton’s condition factor (K) and the relative condition
factor (Kn) are some of the most widely used morphometric indices to assess fish
condition (Froese, 2006).
18
The LWR is a useful tool in fish biology, physiology, ecology and stock assessment
(Oscoz et al., 2005). Body size in fishes is the most easily measured characteristic and
is often measured in terms of body length. The relationship of body length with body
weight has been extensively documented in fisheries research (Froese, 2006; Froese et
al., 2011). Virtually size is more relevant than age as several ecological and
physiological factors are more size-dependent than age-dependent (Santos et al.,
2002). LWRs measurements along with age data can provide information on the stock
composition, age at maturity, life span, mortality, growth and production (Beyer, 1987;
Bolger and Connoly, 1989; King, 1996; Diaz et al., 2000).
LWR information are helpful for determining biomass measures by inter-converting
fish length and weight and/or growth in length to growth in weight equation as well as
prediction of weight during different growth periods (Froese, 1998; Pauly, 1993;
Anderson and Neumann, 1996; Binohlan and Pauly, 1998). The estimation of fish
biomass and information about body weight for regulation of fish catches are
necessary for fishery management and conservation (Froese, 1998; Oscoz, 2005).
Length-weight data indicates the degrees of stabilization of taxonomic characters in
fish species (Pervin and Mortuza, 2008).
The above mentioned factors regarding LWRs are applicable in population dynamics
and aquatics ecology science (Pauly, 1993; Santos et al., 2002). LWRs are useful in
fisheries research to explore life history changes and make morphological comparisons
between and among different fish species or fish populations from different habitats
and geographical areas (Goncalves et al., 1997; Moutopoulos and Stergio, 2002;
Petrakis and Stergiou, 1995). This information is necessary for stock assessment
19
models (Borges et al., 2003; Mendes et al., 2004) and is commonly used in the
ecosystem modeling approach (Pauly et al., 2000). Differences in the parameters of
LWRs show spatial variations (Sparre and Venema, 1998) and the impact of abiotic
factors and availability of food for fish growth (Mommsen, 1998).
The LWRs of a species depend on many factors i.e sex, size range, habitat, food
availability and fishing pressure and season (Froese, 2006; Karachle and Stergio, 2008;
Liousia et al., 2012). In the FishBase, up to now, LWRs of thousands of species
(number ranging to 3584) can be viewed / examined (FishBase, 2012).
In addition to LWRs, relationships between different types of lengths also termed as
length-length relationships (LLRs) are important in fisheries management for
comparative growth studies (Froese and Pauly, 1998; Moutopoulos and Stergiou,
2002). These include measurements of different lengths including: total length
(Gordon, 1978), head length (Coggan et al., 1999; Swan et al., 2003), pre-anus length
(Bergstad, 1990; Kelly et al., 1997), and pre-anal fin length (Jorgensen, 1996). In the
FishBase, LLRs records are available for most of the European and North American
freshwater and marine fishes (Froese and Pauly, 2012; Bagenal and Tesch, 1978;
Petrakis and Stergiou, 1995; Koutrakis and Tsikliras, 2003; Sinovcic et al., 2004;
Leunda et al., 2006; Miranda et al., 2006). However, this record is very poor for
tropical and subtropical fishes (Harrison, 2001; Ecoutin et al., 2005).
Condition factor (K) is extensively used in fisheries and fish biology studies. It
indicates the well-being and health of a certain fish species depending upon the weight
of sampled fish (Pauly 1983; Fafioye and Oluajo, 2005). It is calculated from the
20
relationship between the weight of a fish and its length and reflects the condition of the
individual fish (Froese, 2006). Condition factor indicates the degree of food sources
available, age and sex of same species (Williams, 2000). Therefore, it has been used as
an index of growth and feeding intensity. It decreases with increase in length (Fagade,
1979). It is specific for each species (Fafioye and Oluajo, 2005; Froese, 2006; Treer et
al., 2009).
There have been several studies in the past to describe LWRs, LLRs and condition
factor of several fish species from wild (Narejo, 2006; Narejo et al., 2008; Laghari et
al., 2009; Laghari et al., 2011; Yousaf et al., 2011; Pervaiz et al, 2012); and farmed
fishes (Chatta and Ayub, 2010; Naeem et al., 2012) from various freshwater bodies of
different regions of Pakistan. This is the first study to describe LWRs, LLRs and
condition factor of fishes from Suleman Mountain Range, Dera Ghazi Khan Region,
Pakistan.
21
1.5 Aims and Objectives
The present study explored the aquatic biodiversity of Suleman Mountain Range, Dera
Ghazi Khan Region, Pakistan, with following aims and objectives:
To characterize physico-chemical parameters of water
To evaluate planktons abundance and diversity
To assess the status of water quality of the region
To analyze seasonal effects on aquatic biodiversity
To identify fish community composition
To evaluate fish abundance and diversity
To explore the status of the ichthyofauna of major water bodies of the region
To characterize morphometrics of ichthyofauna
To estimate fisheries potential of the region for future manipulation
22
Materials and Methods
2.1 Study Area
The Suleman Mountain Range in Pakistan encompasses South Waziristan, most of the
Northern Balochistan Province and some of the South Western Punjab Province. To
the East, the mountain range enters the Rajan Pur District (in Punjab Province) and
traverses through Dera Ghazi Khan District (in Punjab Province) to Dera Ismail Khan
District (in Khyber Pakhtun khwa Province). It approaches the Indus River near
Mithan Kot in Rajan Pur District. The eastern slopes drop very quickly to the Indus
River but towards West, the mountain range drops gradually to the Sistan Basin. The
Suleman Mountain Range, Dera Ghazi Khan Region, consists of Dera Ghazi Khan
District and Rajan Pur District (28o 28/ to 31o 18/ North latitude and between 69o 20/
to 70o 55/ East longitudes) covering an area of approximately 12000 Km2. It is
bordered by Musa Khel District and Barkhan District to the West, Dera Ismail Khan
District to the North, Muzaffargarh District and Layyah District to the East and Jakob
Abad District to the South (Figure 2.1).
The Suleman Mountain Range, Dera Ghazi Khan Region, consists of 13 catchments
areas/hill torrents (Malkani, 2010). Of these, ten catchment areas were investigated for
fish biodiversity and four for water quality (Figure 2.1, Table 2.1). The region has sub-
tropical climate with four distinct seasons; the summer from May to August, the post
monsoon from September to October, the winter from November to February and the
spring from March to April (Chaudhry and Rasool, 2004). Mean annual precipitation
in the area is around 350 mm. The climate of the region is extremely dry in the
summer as well as in the winter season both in hilly and plain areas.
23
Figure 2.1: Location map of hill torrents of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan, showing fish sampling
sites (FSS) from FSS-1 to FSS-10 and water sampling sites (WSS) from WSS-1 to WSS-4.
24
The precipitation is in the form of occasional seasonal rain fall particularly during
rainy season (July-September) which turns into hill torrents locally called as
“Nallahs/Rodhkohi”. Along the water course of these hill torrents a number of
associated pits and ponds occur frequently. Besides cold water springs scattered
throughout the region and fewer hot water springs are also found in the region. The
water of these springs drains into associated pits and ponds.
The mean elevation of mountain range is around 1100 m. The minimum elevation is
less than 300 m and maximum elevation exceeds 2000 m. Approximately, 55% of the
area lies between elevation of 900 m and 1300 m. The 65% of total catchment areas of
the mountain range comprise of lime stones, shale and sand stone, 30% gravelly loamy
soils and 5% loamy silt soil cover (Hanson et al., 1995). The geomorphology of the
area is characterized by rugged terrain, large variations in land slope and sharp bends
in natural streams. The stream flow velocity is high that can carry gravels and boulder
sized stones. The high flow velocities are due to high intensity flashy rains resulting in
torrential flows. Almost all the streams are ephemeral in nature flowing during
monsoon or winter rains only. Most of the catchments drain to the Indus River (Hasan
et al., 2001; Malkani, 2010; Malkani, 2011). Around 65% of catchment areas consist
of barren mountains without vegetative cover. The vegetation type of the area is
predominantly xeric (Dasti et al., 2010) (Table 2.2).
Suleman (Middle Indus) Basin represents Mesozoic and Cenozoic strata and have
deposits of sedimentary rocks with radioactive and fuel minerals (Malkani, 2010). The
geologic reports about the presence of reserves oil, gas and minerals makes this area
economically important (Malkani, 2010; Malkani, 2011). Region is a far off area of the
25
country and the transport and communication network is poor and the advent of
modern technology in this area is lagging behind compared to other regions of the
country. There is little industrial human activity like oil, gas and mineral exploration;
including agricultural activity. Human intervention and impact on the ecosystem in the
area is less compared to other regions of the country. Therefore, biodiversity of the
region is expected to be conserved. There have been very few studies on exploration of
flora and fauna of the region (Dasti et al., 2010; Ali et al., 2010) the planktonic
diversity of the region is still unexplored.
26
Table 2.1: Names and location of sampling sites
Site number Hill torrent
name
Site name Altitude*
(Feet)
Geographic coordinates*
Fish sampling Water sampling
FSS-1 WSS-1 Gang Vehowa 1200 31°14′N 70°37′E
FSS-2 WSS-3 Sanghar Barthi 1050 30°55′N 70°36′ E
FSS-3 --- Hingloon Hingloon 3043 30°46′N 70°06′E
FSS-4 WSS-4 Sori Lund Zinda Peer 1500 30°40′N 70°48′E
FSS-5 --- Dalana Jaj 1583 30°08′N 70°34′E
FSS-6 WSS-2 Kaha Harand 1250 29°54′N 70°09′E
FSS-7 --- Siri Siri 2100 30°01′N 70°13′ E
FSS-8 --- Sori Murunj 2300 29°24′N 69°42′E
FSS-9 --- Toe Sar Chitar Wata 1800 31°23′N 70°32′ E
FSS-10 --- Rakhi Rakhi Gaj 2600 29°95′N 70°10′E
27
*(www.latlong.net/c/?lat)
Table 2.2: Vegetation and soil type of water sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan
Site Number Nallah Soil Type* Vegetation Type* Dominant species*
WSS-1 Gang (Vehowa)
Silt in texture, fragmented
rocks and more and more
gravel particles of soil
Almost xeric
Trees + woody shrubs, Acaciamodesta,
Salvodora, Oleodes, Capparis aphylla
WSS-2 Kaha (Harand)Sandy + large gravel particles
and silt also presentXeric
Capparis apylla, Salvadorsp,Prosopis
sp,Aerua persica, Withania coagulens,
Fagonia indica, Rhazya stricta, Suaeda
fructicosa, Phlomis sp, Peganum
hermala, Leptadaenia sp.
WSS-3 Sanghar (Barthi)Silty sand in texture and
almost upper piedmont area
Xeric
and samophytes
species
Prosopis sp, Tamarix aphylla, Capparis
apylla, Calotropis procera, Citrullus
colocynthes and Acacia sp
WSS-4Suri Lund
(Zinda Peer)Calcarious mostly clay
particles and sandy clay in Xeric
Calotropis sp, Peganum sp, Fagoniasp,
Caparis apylla, Acacia jacumontii,
28
textureSalvadora sp, Aeruapersica, Rhazya
stricta, Solanum sp
*(Dasti et al., 2010)
29
2.2 Water Quality
The water sampling was conducted from January 2012 to December 2012 on monthly
basis from four sites (Figure 2.1, Table 2.1). The specific sampling time, date and
weather conditions for each site are given in Table 2.3. The water samples were taken
from sub surface at a depth of 30 cm. The samples were collected from middle of the
standing water bodies. Some of the physico-chemical parameters i.e. air and water
temperature, light penetration, pH, EC and DO were measured at the spot, while other
parameters were determined in the laboratory (Water and Soil Laboratory, Agriculture
Department, Government of the Punjab, Dera Ghazi Khan). For laboratory analysis of
physico-chemical parameters, samples were taken in glass bottles of 1.5 L capacity.
The water samples for biological parameters analysis were taken in plastic bottles of 1
L capacity. The water samples for biological diversity were preserved by adding 100
ml of 4% formaldehyde (Battish, 1992). The bottles were labeled with site number,
date and time with the help of waterproof markers.
Continued availability of water throughout the year facilitated limnological studies at
these sites. The water level fluctuations were observed during study period. The
average depth of the sampling spots varied between 3 to 10 feet. The anthropogenic
activity in the form of agricultural practices was observed in the vicinity of sampling
spots. The sampling sites were selected randomly to represent the whole region. The
selected sampling sites had elevation between 1050 to 1500 feet (Figure 2.1, Table
2.1).
Various climatic and physico-chemical parameters for estimation of water quality of
these sites were determined by methods described as Boyd (1981) and APHA (1998).
30
Table 2.3: Date and weather conditions of each sampling site during study period
Month WSS-1 (12.00 noon*) WSS-2 (1.00 p.m*) WSS-3 (1.00 p.m*) WSS-4 (12.00 noon*)
Date
Cloud Hum
Date
Cloud Hum
Date
Clou
d
Hum
Date
Clou
d
Hum
Jan 10.01.2012 >25 79.50 11.01.2012 >50 76.00 13.01.2012 <50 80.50 14.01.2012 >25 76.50
Feb 10.02.2012 <25 77.00 11.02.2012 >25 73.50 13.02.2012 <25 74.00 14.02.2012 <50 70.00
Mar 10.03.2012 <25 66.50 11.03.2012 np 64.00 13.03.2012 np 64.50 14.03.2012 np 59.50
Apr 10.04.2012 Np 36.00 11.04.2012 np 34.00 13.04.2012 np 39.00 14.04.2012 np 40.00
May 11.05.2012 Np 31.50 12.05.2012 np 35.00 13.05.2012 np 36.50 14.05.2012 np 35.00
Jun 11.06.2012 Np 33.50 12.06.2012 np 38.00 13.06.2012 np 40.50 14.06.2012 np 38.00
Jul 11.07.2012 <25 59.00 12.07.2012 <25 59.00 14.07.2012 >25 58.00 15.07.2012 >25 55.50
Aug 11.08.2012 <50 63.00 12.08.2012 <50 65.00 14.08.2012 <50 66.50 15.08.2012 <50 62.50
Sep 11.09.2012 >50 66.50 12.09.2012 >50 67.50 14.09.2012 >25 67.00 15.09.2012 >25 67.50
Oct 11.10.2012 np 67.00 12.10.2012 np 75.00 14.10.2012 np 73.00 15.10.2012 np 72.00
Nov 10.11.2012 Np 72.50 11.11.2012 np 78.50 13.11.2012 np 78.00 15.11.2012 np 75.00
Dec 10.12.2012 >25 74.00 11.12.2012 <50 79.00 13.12.2012 <50 80.50 15.12.2012 >50 77.00
*Water sampling time; Hum = humidity; np = not present
31
2.2.1 Climatic factors
Following climatic parameters were recorded.
2.2.1 a Air Temperature (oC)
At the time of sampling, the air temperature (oC) was recorded by simple alcoholic
laboratory thermometer.
2.2.1 b Photoperiod (hours)
Noted the sunrise and sunset to calculate the photoperiod (hours).
2.2.1 c Humidity (%)
The data for minimum and maximum humidity (%) was obtained from metereological
department, Multan Division. The mean humidity (%) was calculated.
2.2.2 Physico-Chemical Parameters
2.2.2.1 Physical Parameters
Following physical parameters were recorded.
2.2.2.1 a Water temperature (oC)
Water temperature(oC)was recorded by simple alcoholic laboratory thermometer.
2.2.2.1 b Light Penetration (cm)
Light penetration (cm) was determined using Secchi disc, a metallic disc of 20 cm
diameter with four alternate black and white quarters on the upper surface. The disc
with centrally placed weight at the lower surface was suspended with a graduated cord
32
at the center. Light penetration was measured by gradually lowering the Secchi disc in
water. The depths at which it disappears (A) and reappears (B) in the water were
noted. The light penetration of the water was calculated as follows;
Secchi disc light penetration (cm) = A + B / 2
Where,
A = depth at which Secchi disc disappears
B = depth at which Secchi disc reappears
2.2.2.1 c Total Dissolved Solids (mgL-1)
A clean crucible was ignited in a muffle furnace (RJM 1.8-10A, China) at 550 oC for
30 minutes. Weigh the crucible after cooling it in a vacuum desiccator. Mixed the
water sample and filtered 100 ml. Measured 10 ml of filtrate and poured into crucible.
The contents of crucible were evaporated in an oven at 95 oC, and then, the
temperature of oven was increased to 103 oC for 1 hour. The crucible and residue was
cooled in desiccator and then weighed.The concentration of TDS was calculated as
follows;
Total dissolved solids (mgL-1) = R – D × 1000 sample volume (ml)
Where,
R = weight of empty crucible
D = weight of crucible + water sample
33
2.2.2.2 Chemical Parameters
2.2.2.2 a pH
The pH of water sample was measured by electronic portable pH meter (HANNA 212,
China). pH meter was calibrated with phosphate buffer of known pH. It uses electrodes
that are free from interference. At constant temperature, a pH change produces a
corresponding change in the electrical property of the solution. This change was read
by the electrode and the accuracy was the greatest in the middle pH ranges.
2.2.2.2 b Electrical Conductivity (dSm-1)
The EC (dSm-1) was determined by using EC meter (JENCO 3173 COND, China). The
instrument was standardized with 0.1 N KCl solution (0.7456 g KCl dissolved in 1L of
dd H2O), which has an EC of 1.413 dS m-1at 25 oC. When the 0.1 N KCl solution
gives an EC values different from 1.413 dS m-1, then the sample reading was
multiplied by the cell constant (K). Then the EC of water sample is calculated by the
formula;
EC of Water = Observed EC × K
Where,
K = 1.413
2.2.2.2 c Dissolved Oxygen (mgL-1)
DO (mgL-1) was measured by oxygen meter (JENCO 9250 M, China) after calibration.
5 drops of dd H2O were put into sponge located inside calibration bottle. The excess
water was drained out of the bottle by turning over the bottle. The bottle was screwed
into probe by keeping a space of at least 5 mm between the sponge and probe. The
34
instrument was turned on and waits until reading of DO and temperature become
stabilized. Then pressed the ‘Cal’ key. The local pressure in mbar appears on LCD.
Adjusted it to attain proper pressure value.Then pressed the ‘Enter’ key to check
calibration value of pressure.Again pressed the ‘Enter’ key for salinity calibration
procedure. The salinity of sampling water appears on LCD. Adjusted it to attain the
correct salinity value. The DO meter was calibrated every time after turning it off. The
DO meter was calibrated at temperature close to the temperature of sample water.
2.2.2.2 d Free Carbon Dioxide (mgL-1)
50 ml of water sample were taken in a flask by avoiding its contact with air. 3 ̶ 4 drops
of phenolphthalein indicator (0.5 gm phenolphthalein dissolved in 50 ml of 95%
C2H5OH with 50 ml distilled water) were added in sample water. If sample turns pink,
free CO2 is absent and if it remains colorless, it contains free CO2. Sample containing
CO2 was titrated against 0.045 NNa2CO3solution (2.407 g Na2CO3 dissolved in 1 L of
CO2 free dd H2O) rapidly. A faint pink color which persisted for 30 seconds marked
end point. Concentration of CO2 was calculated by following formula;
CO2 (mgL-1) = ml of titrant (N) (22) × 1000sample volume (ml)
2.2.2.2 e Carbonates and Bicarbonates (mgL-1)
50 ml of water sample was taken in a conical flask for determination of carbonates. 1̶3
drops of phenolphthalein indicator (0.5 g phenolphthalein dissolved in 50 ml of 95%
C2H5OH) added in water sample and titrated it against 0.1 N H2SO4 solution (2.77 ml
of H2SO4 dissolved in 1 L of dd H2O) until the pink color disappear. Noted the volume
of acid used.
35
1̶2 drops of methyl orange (0.1 g methyl orange in 100 ml dd H2O) as an indicator
were added to the same conical flask for the determination of bicarbonates. Titrated it
against 0.1 NH2SO4 until the appearance of light orange color which marks end point.
Noted the volume of acid used. The concentration of carbonates was calculated by
applying the following formula;
Carbonates (mgL-1) = 2R1 × Normality of H2SO4 × 1000 sample volume (ml)
Where,
R1 = Volume of 0.1 N H2SO4 used
Actual volume of 0.1 N H2SO4 used = 2R1 (This reaction is completed in two steps)
Bicarbonates (mgL-1) = R2-R1 × Normality of H 2SO4 × 1000 sample volume (ml)
Where,
R2 - R1 = Volume of 0.1 N H2SO4 (actual amount of acid used for neutralizing
the originally present bicarbonates)
R1 = Amount of the acid used to convert the carbonate into bicarbonates
R2 = Total amount of acid used to neutralize the bicarbonates present in the
sample.
2.2.2.2 f Total Alkalinity (mgL-1)
50 ml of water sample were taken in a conical flask. 4̶5 drops of phenolphthalein
indicator (0.5 g phenolphthalein dissolved in 50 ml of 95% C2H5OH with 50 ml dd
water) were added in sample water. If the color developed pink, the sample was
36
titrated against 0.02 N HCl (1.72 ml of concentrated HCl dissolved in dd H2O with
final volume up to 1L), until the color disappeared. Noted the volume of acid used. 2 ̶3
drops of methyl orange indicator solution (0.5 g of methyl orange dissolved in 50 ml
of dd H2O) were added in the same sample. The titration was continued until end point
with change of color from orange to brick red was observed. The volume of acid
consumed was noted. Total alkalinity (TA) was calculated by using the following
formula;
Total alkalinity (mgL-1) = ml of titrant (N) (50) × 1000 sample volume (ml)
2.2.2.2 g Total Hardness (mgL-1)
EDTA titrimetric method was used for the estimation of total hardness. 1̶2 ml of buffer
solution (17.5 g of NH4Cl added to 142 ml of concentrated ammonia solution with
final volume of 250 ml by adding deionized water) and a pinch of Eriochrome Black-
T, as an indicator, were added in100 ml of water sample. After the appearance of wine
red color, the mixture was titrated against EDTA by stirring continuously till the
change of wine red color to blue which marks end point. The total hardness is
calculated using following formula;
Total Hardness as CaCO3 (mgL-1) = (Volume of EDTA) (N) (100.1) × 1000 sample volume (ml)
Where,
M = molarity of EDTA solution
37
2.2.2.2 h Chlorides (mgL-1)
The chloride contents were determined by argentometric titrimetric method.In the
same conical flask used for the determination of carbonates and bicarbonates [section
3.2.2. e.], added 3̶4 drops of 5% K2CrO4(5.0 g of K2CrO4 dissolved in 50 ml dd H2O
and added l N AgNO3 (169.87 g AgNO3 dissolved in 1 L dd H2O) drop wise until red
precipitate was produced. The solution was filtered and filtrate was diluted to 100 ml.
While stirring, titrated under bright light with 0.05 N AgNO3 (8.494 g AgNO3
dissolved in dd H2O with final volume up to 1 L, and kept in a brown bottle to avoid
photolysis) to a brick red precipitate/permanent reddish brown color.Chlorides were
calculated using following formula;
Cl (meL-1) = volume of AgNO3 used × N × 100 sample volume (ml)
The meL-1 of chlorides was converted to mgL-1 of chloride contents by multiplying
with its equivalent weight.
2.2.2.2 i Sulphates (mgL-1)
The sulphates were determined with the help of spectrophotometer (Spectronic 20D,
Japan). 5 ml of water sample was taken in a 50 ml volumetric flask. 5 ml acid mixture
reagent (125 ml HNO3 was added, 250 ml CH3COOH and 100 ml of 85% H3PO4 with
final volume of 1 L) and 1 ml acid sulphate solution [86 ml concentrated HCl and100
ml sulphate stock solution (100 ppm) withfinal volume up to 500 ml (20 ppm)] were
mixed. 0.5 g BaCl2.2H2O crystals were added in this mixture. This mixture was kept
undisturbed for 3 minutes and then mixed. Then 1 ml gum acacia reagent was added
and mixed. Total volume was made up to 50 ml. Samples and standards were run at
38
420̶450 nm between 3 to 8 minutes after final shaking. Run reagent blank. The
sulphate contents of the water sample were calculated as follows;
Sulphate concentration (ppm) = Sulphate concentration (ppm) × dilution factor
2.2.2.2 j Calcium and Magnesium (mgL-1)
Firstly, the concentration of calcium in water was determined by taking 10 ml of water
sample in a conical flask. 5 drops of 4 N NaOH (160 g of NaOH dissolved in dd H2O
up to final volume of 1 L) and 50 mg of ammonium purporate indicator (0.5 g
C8H4N5O6.NH4 thoroughly mixed with 100 g of K2CrO4) were added in sample water.
Titrated it against 0.01 N EDTA solution (2.0 g of EDTA and 0.05 g of MgCl 2.6H2O
dissolved in dd H2O with final volume of 1L and then standardized against 0.01 N
CaCl2 (0.5 g pure CaCl2 in 10 ml of 3 N 1+3HCl with final volume up to 1L) using
given titration procedure until the approach of end point which is from orange red to
lavender/purple. When closed to the endpoint, ammonium purporate (C8H4N5O6. NH4)
indicator is added drop wise (each drop after 5 − 10 second) as the color change is not
instantaneous.
Then, concentration of both calcium and magnesium together was determined by
taking 10 ml of water sample in a conical flask. 10 drops of NH4Cl/NH4OH buffer
solution (67.5 g NH4Cl dissolved in 570 ml of concentrated NH4OH with final volume
upto 1L) and 3-4 drops of Eriochrome black-T indicator (0.5 g of EBT and 4.5 g
hydroxyl amine hydrochloride was dissolved in 100 ml of 95% ethanol) were added in
sample water. Titrated it against EDTA solution until a change in color from wine red
to bluish green. Concentration of calcium and magnesium was calculated as follows;
39
Ca++ or Ca++ + Mg++ (meL-1) = R1 – R 2 × N × 1000 sample volume (ml)
Mg++ (me L-1) = Ca++ + Mg++ – Ca++ = R1 – R 2 × 0.01 × 1000 = (R1-R2) = R meL-1
10
Where,
R1 = ml of EDTA solution for sample
R2 = ml of EDTA solution for blank
All cations are expressed as meL-1. The meL-1 of calcium and magnesium were
converted to mgL-1 of calcium and magnesium by multiplying with their respective
equivalent weight.
2.2.2.2 k Sodium (mgL-1)
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
40
C2 (ppm required) = V1 × C 1
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.
2.2.2.2 l Sodium Adsorption Ratio (SAR)
The SAR was determined by the following calculations;
SAR = Na + [(Ca++ + Mg++)/2] 1/2
All the cations are expressed as meL-1.
2.2.3 Biological Parameters
2.2.3.1 Planktonic Biodiversity
41
The planktons were identified and counted by taking 1 ml of water sample on
Sedgewick rafter chamber (Pyser – SGI S50, UK) under compound microscope (PW-
BK 5000 China) at magnification 10 × 40x following Stephen et al., 2011. The
identification of phytoplanktons and zooplanktons upto genera level was done
following the standard taxonomic literature;
Ward and Whipple (1959); Prescott (1978); Belcher and Swale (1979); Fritsch (1979);
Tonapi (1980); Chapman and Chapman (1981); Battish (1992); Yunfang (1995) and
APHA (1998).
Relative abundance (%) and biodiversity indices (including Margalefs index, Shanon-
Weiner index, Simpson diversity index, Evenness index and Sorensom index of
similarity) were calculated by the following formulae;
RA (%) = n × 100 (Simpson, 1949) N
Margalef index (d) = (S − 1) (Margalef, 1968) Ln of N
Simpson diversity index (D) = ∑ ni (ni-1) (Simpson, 1949) N (N-1)
Shanon-Weaner index (H) = ∑ ni × Ln (ni/N) (Shannon and Weiner, 1963) N
Evenness index (E) = H (Pielou, 1966) LnS
Sorenson index of similarity (SI) = 2 E (Sorensen, 1948) A + B + C + D
Where,
A = number of species at WSS-1
B = number of species at WSS-2
C = number of species at WSS-3
42
D = number of species at WSS-4
E = number of species common between sites
n = number of individuals of one genera/phyla/division/group
ni = number of ith species
N = total number of individual in division/ phyla/group
S = number of species
Ln = natural logarithm
2.3 Fish biodiversity
Fish samples were collected from different localities of Suleman Mountain Range,
Dera Ghazi Khan (Table 2.1, Figure 2.1). The fishes were collected from standing and
running water bodies. Different gears including cast net (mesh size 2.5 cm2), scoop net
and drag net (mesh size 5 cm2) were used. Fish specimens were counted and then
transferred to laboratory in plastic containers alive. Fish specimens were preserved in
10% formaldehyde solution for identification. Fish specimens were identified
following standard taxonomic keys (Talwar and Jhingran, 1991; Mirza and Sharif,
1996).
Relative abundance (%) and various biodiversity indices [Margalef index (d), Shanon-
Weiner index (H), Simpson diversity index (D)and Evenness index] were calculated
by using formulae as described in section 2.2.3.1 The Jaccard’s index (j) of similarity
was calculated by using formula;
Sj= j (Jaccard, 1912) [(x+y) − j]
43
2.4 Fish External Morphometry
Fishes were anaesthetized and blotted dry on towels and were weighed on a balance
(CHYO 3000, Japan) to the nearest 0.1 g. Total length, standard length, fork length
and 18 other external body morphometric measurements were taken on measuring tray
(Perpex) to the nearst 0.1 mm. The condition factor (K) was calculated by the
following formula;
Condition factor (K) = W × 100 (Pauly, 1983) L3
Total length was taken flat and was taken as the length from tip of the snout to the tip
of the tail. Standard length was taken as the length from tip of the snout to the hidden
base of caudal fin. Head length was taken as the distance from the tip of the snout to
the most distant point on the opercular membrane. Inter-orbital width was taken as the
least distance between the bony rims between inner margins of eyes. Pre-orbital length
was measured from tip of the snout to the anterior hard margin of the orbit. Post-
orbital length was measured from the posterior edge of the orbit to the posterior tip of
the flashy operculum. Eye diameter was taken as the distance between the margins of
the cartilaginous eye ball across the cornea.
The length-weight relationship was calculated from W = aLb
Where,
W = total weight in g
L = total length in centimeters
a = coefficient related to body form
b = an exponent indicating isometric growth when equal to 3
44
The parameters ‘a’ and ‘b’ were estimated by linear regression on the transformed
equation:
log W = log a + b log L.
45
2.5 Statistical Analyses
Statistical software SPSS (version 20) was used for all statistical analyses. The effects
of monthly variations, seasonal variations and site variations on physico-chemical
parameters, planktonic abundance, richness and diversity were analyzed by applying
one way analysis of variance (ANOVA). The assumptions of i) normality of data and
ii) homogeneity of variance were fulfilled when and where required before applying
ANOVA. The Pearson correlations between various water quality parameters were
also calculated. The data for fish biodiversity was statistically analyzed for comparison
of fish abundance, species richness and diversity between various sites and fish
families, by applying one way ANOVA.The data on various morphometric parameters
was log transformed and coefficient of correlation (r) and regression (b) was
determined by least squared linear regression.
46
Results
3.1 Water Quality
3.1.1 Climatic factors
During sampling dates, clouds were observed occassionally at all sampling sites. The
clouds were present during January, February, July, August, September and December
at all sites on sampling dates. The clouds concentration observed was categorized as >
25%, < 25%, > 50% and < 50% (Table 2.3). The clouds were not present in March,
April, May, June, October and November at any of sites. Rain was not observed
during sampling dates at all sites. The day length recorded as photo period (hours) was
maximum (> 14 hours) in June and minimum (< 10.21 hours) in December for all sites
(Table 3.1.1 b). One way ANOVA demonstrated that there was i) no significant
difference between four sites (df = 3, F = 0.002, P > 0.05), ii) a significant effect of
seasonal variations (df = 3, F = 64.14, P < 0.001) (Table 3.1.1 a) and iii) a significant
effect of monthly variations (df = 11, F = 153.23, P < 0.001) (Table 3.1.1 a) for
photoperiod.
The maximum mean relative humidity (%) was observed in January for all four water
sampling sites (79.50% at WSS-1, 79% at WSS-2, 80.50% at WSS-3, 77% at WSS-4)
and minimum in May for three water sampling sites (31.50% at WSS-1, 36.50% at
WSS-3, 35.0% at WSS-4), and in April for WSS-2 (35. 0 %) (Figure 3.1.1 a). One way
ANOVA demonstrated that there was i) no significant difference between four sites
(df = 3, F = 0.41, P > 0.05), ii) a significant effect of seasonal variations (df = 3, F =
38.73, P < 0.001) (Table 3.1.1 a) and iii) a significant effect of monthly variations (df
= 11, F = 25.64, P < 0.001) (Table 3.1.1 b) for mean relative humidity.
47
Maximum air temperature (oC) was observed in July (41.8 oC for WSS-1, 40.6 oC for
WSS-2, 40.5 oC for WSS-3 and 40.8 oC for WSS-4), While minimum air temperature
was observed in January (12.5 oC for WSS-1, 15.4 oC for WSS-2, 18.5 oC for WSS-3
and 23.8 oC for WSS-4) (Figure 3.1.1 b). One way ANOVA demonstrated that there
was i) no significant difference between four sites (df = 3, F = 0.47, P > 0.05), ii) a
significant effect of seasonal variations (df = 3, F = 55.38, P < 0.001) (Table 3.1.1 a)
and iii) a significant effect of monthly variations (df = 11, F = 36.95, P < 0.001) for air
temperature (Table 3.1.1 b).
3.1.2 Physico-Chemical Parameters
The mean values along with standard deviations (SD) of all physico-chemical
parameters at four water sampling sites and in four seasons are presented in table 3.1.2
a and 3.1.2 b respectively. Maximum water temperature was recorded in July (32.8 °C
for WSS-1, 32.7 °C for WSS-2, 31.4 °C for WSS-3 and 34.7 °C for WSS-4)(Figure
3.1.2 a). The minimum water temperature was recorded in January (9.0 oC for WSS-1,
10.2 oC for WSS-2, in December 12.8 °C for WSS-3 and in January 18.2 °C for WSS-
4) (Figure 4.1.2 a). One way ANOVA demonstrated that there was i) no significant
difference between four sites (P >0.05, df = 3, F = 1.19) (Table 3.1.2 a) ii) a significant
effect of seasonal variations (df = 3, F = 50.71, P < 0.001) (Table 3.1.2 b) and iii) a
significant effect of monthly variations (df = 11, F = 28.30, P < 0.001) for water
temperature (Table 3.1.2 c).
Light penetration (cm) was maximum in January (32.5 cm at WSS-1), (30.8 cm at
WSS-2), in May (35.1 cm at WSS-3) and in March (33.4 cm at WSS-4). Light
penetration (cm) was minimum in September (11.8 cm at WSS-1), in August (12.5 cm
48
at WSS-2), in September (16.2 cm at WSS-3) and in August (17.4 cm at WSS-4)
(Figure 3.1.2.b). One way ANOVA demonstrated that there was i) no significant
difference between four sites (P >0.05, df = 3, F = 2.71) (Table 3.1.2 a), ii) a
significant effect of seasonal variations (df = 3, F = 61.37, P < 0.05) (Table 3.1.2 b)
and iii) a significant effect of monthly variations (df = 11, F = 5.26, P < 0.001) for
light penetration (Table 3.1.2 c).
The TDS (mgL-1) were maximum in February (1385 mgL-1 at WSS-1), in May (1430
mgL-1at WSS-2), in June (1360 mgL-1 at WSS-3 and 1795 mgL-1at WSS-4). The TDS
(mgL-1) were minimum in December (870 mgL-1at WSS-1), in August (870 mgL-1at
WSS-2), in September (941 mgL-1at WSS-3) and in January (1450 g at WSS-4)
(Figure 4.1.2 c). One way ANOVA demonstrated that there was i) a significant
difference between four water sampling sites (df = 3, F = 23.51, P < 0.001) (Table
3.1.2 a), ii) a non-significant effect of seasonal variations (df = 3, F = 0.55, P < 0.64)
(Table 3.1.2 b) and iii) a non-significant effect of monthly variations (df = 11, F =
0.46, P > 0.05) (Table 3.1.2 c) for total dissolved solids.
pH was maximum in June (7.5 at WSS-1), in May (8.3 at WSS-2), in August (8.5 at
WSS-3) and in May (8.3 at WSS-4). pH was minimum in October (7.5 at WSS-1), in
January (7.4 at WSS-2), in February (7.6 at WSS-3) and in November (7.4 at WSS-4)
(Figure 4.1.2 d). One way ANOVA demonstrated that there was i) a non-significant
difference between four water sampling sites (df = 3, F = 0.93, P > 0.05) (Table 3.1.2
a), ii) a significant effect of seasonal variations (df = 3, F = 6.47, P < 0.01) (Table
3.1.2 b) and iii) a non-significant effect of monthly variations (df = 11, F = 1.56, P >
0.5) (Table 3.1.2 c) for pH.
49
DO (mgL-1) was maximum in January (9.1 mgL-1at WSS-1), in December (8.9 mgL-1
at WSS-2), in January (8.6 mgL-1 at WSS-3 and 8.6 mgL-1 at WSS-4). DO (mgL-1)
was minimum in July (5.7 mgL-1 at WSS-1), in August (5.8 mgL-1 at WSS-2), in July
(5.8 mgL-1 at WSS-3) and (5.7 mgL-1 at WSS-4) (Figure 4.1.2 e). One way ANOVA
demonstrated that there was i) a non-significant difference between four water
sampling sites (df = 3, F = 1.15, P > 0.05) (Table 3.1.2 a), ii) a significant effect of
monthly variations (df = 11, F = 16.33, P < 0.001) (Table 3.1.2 b) and iii) a significant
effect of seasonal variations (df = 11, F = 32.53, P < 0.001) (Table 3.1.2 c) for DO.
CO2 (mgL-1) was maximum in May (9.7 mgL-1 at WSS-1), in June (8.9 mgL-1 at WSS-
2), in May (8.8 mgL-1 at WSS-3) and in June (8.5 mgL-1 at WSS-4). CO2 (mgL-1) was
minimum in December (6.1 mgL-1 at WSS-1), (5.5 mgL-1 at WSS-2), (5.6 mgL-1 at
WSS-3) and (6.4 mgL-1 at WSS-4) (Figure 4.1.2 f). One way ANOVA demonstrated
that there was i) a non-significant difference between four water sampling sites (df = 3,
F = 0.83, P > 0.05) (Table 3.1.2. a), ii) a significant effect of seasonal variations (df =
3, F = 51.68, P < 0.001) (Table 3.1.2 b) and iii) a significant effect of monthly
variations (df = 11, F = 19.06, P < 0.001) (Table 3.1.2 c) for CO2.
The CO3-2 (mgL-1) were maximum in December (19.8 mgL-1 at WSS-1), in June (24.0
mgL-1 at WSS-2), in January (20.1 mgL-1 at WSS-3) and in June (18.3 mgL-1 at WSS-
4). The CO3-2(mgL-1) were minimum in November (8.4 mgL-1 at WSS-1), in February
(8.4 mgL-1 at WSS-2), in September (9.0 mgL-1 at WSS-3) and in January (18.3 mgL-1
at WSS-4) (Figure 3.1.2 g). One way ANOVA demonstrated that there was i) a
significant difference between four water sampling sites (df = 3, F = 3.44, P < 0.05)
(Table 3.1.2 a), ii) a non-significant effect of seasonal variations (df = 3, F = 0.80, P =
50
0.49) (Table 3.1.2 b) and iii) a non-significant effect of monthly variations (df = 11, F
= 0.908, P = 0.543) (Table 3.1.2 c) for CO3-2.
HCO3-1 (mgL-1) were maximum in October (769.21 mgL-1 at WSS-1), in May (762.5
mgL-1 at WSS-2), in May (658.8 mgL-1 at WSS-3) and in June (433.1 mgL-1 at WSS-4).
HCO3-1(mgL-1) were minimum in December (286.04 mgL-1 at WSS-1), in August (305
mgL-1 at WSS-2), in September (527.65 mgL-1 at WSS-3) and in January (347.7 mgL-1
at WSS-4) (Figure 3.1.2 h). One way ANOVA demonstrated that there was i) a
significant differenceamong four water sampling sites (df = 3, F = 6.30, P = 0.001)
(Table 3.1.2 a), ii) non-significant effect of seasonal variations (df = 3, F = 0.44, P =
0.72) (Table 3.1.2 b) and iii) non-significant effect of monthly variations (df = 11, F =
0.550, P = 0.855) (Table 3.1.2 c) for HCO3-1.
TA (mgL-1) was maximum in October (125 mgL-1 at WSS-1), in May (150 mgL-1 at
WSS-2), in January (646 mgL-1 at WSS-3), in September (452 mgL-1 at WSS-4). TA
(mgL-1) was minimum in December (58 mgL-1 at WSS-1), in August (70 mgL-1 at
WSS-2), in January (538 mgL-1 at WSS-3), in August (358 mgL-1 at WSS-4) (Figure
3.1.2 i). One way ANOVA demonstrated that there was i) a significant difference
between four water sampling sites (df = 3, F = 48.09, P < 0.001) (Table 3.1.2 a.), ii)
non-significant effect of seasonal variations (df = 3, F = 0.27, P = 0.84) (Table 3.1.2 b)
and iii) non-significant effect of monthly variations (df = 11, F = 0.232, P = 0.993)
(Table 3.1.2 c) for TA.
TH (mgL-1) was maximum in April (290 mgL-1 at WSS-1), in May (310 mgL-1 at WSS-
2), in April (239 mgL-1at WSS-3), and in June (268 mgL-1at WSS-4). TH (mgL-1) was
51
minimum in November (210 mgL-1 at WSS-1), in August (230 mgL-1 at WSS-2), in
September (133 mgL-1 at WSS-3) and in January (217 mgL-1 at WSS-4) (Figure 3.1.2
j). One way ANOVA demonstrated that there was i) a significant difference between
four water sampling sites (df = 3, F = 12.05, P < 0.001) (Table 3.1.2 a), ii) no
significant effect of seasonal variations (df = 3, F = 0.70, P = 0.55) (Table 3.1.2 b) and
iii) no significant effect of monthly variations (df = 11, F = 0.855, P = 0.589) (Table
3.1.2 c) for TH.
Na+1 (mgL-1) was maximum in April (154.10 mgL-1 at WSS-1), in April (282.90 mgL-1
at WSS-2), in April and May (210 mgL-1 at WSS-3), in July and November (216.20
mgL-1 at WSS-4). Na+1 (mgL-1) was minimum in December (48.30 mgL-1 at WSS-1), in
January (71.3 mgL-1 at WSS-2), in July (69.0 mgL-1 at WSS-3) and in February (177.10
mgL-1 at WSS-4) (Figure 3.1.2 k). One way ANOVA demonstrated that there was i) a
significant difference between four water sampling sites (df = 3, F = 12.05, P < 0.001)
(Table 3.1.2 a), ii) non-significant effect of seasonal variations (df = 3, F = 0.84, P =
0.47) (Table 3.1.2 b) and iii) non-significant effect of monthly variations (df = 11, F =
0.85, P = 0.58) (Table 3.1.2 c) for Na+1.
Ca+2(mgL-1) was maximum in February (212 mgL-1 at WSS-1), in May (232 mgL-1 at
WSS-2), in April (210 mgL-1 at WSS-3) and in June (250 mgL-1 at WSS-4). Ca+2(mgL-
1) was minimum in November (150 mgL-1 at WSS-1), in August (120.40 mgL-1 at
WSS-2), in September (122 mgL-1 at WSS-3) and in August (206 mgL-1 at WSS-4)
(Figure 3.1.2 l). One way ANOVA demonstrated that there was i) a significant
difference between four water sampling sites (df = 3, F = 10.82, P < 0.001) (Table
3.1.2 a), ii) non-significant effect of seasonal variations (df = 3, F = 0.42, P = 0.73)
52
(Table 3.1.2 b) and iii) non-significant effect of monthly variations (df = 11, F = 0.811,
P = 0.629) (Table 3.1.2 b) for Ca+2.
Mg+2 (mgL-1) was maximum in April (68.40 mgL-1 at WSS-1), in July (66.0 mgL-1 at
WSS-2), in January (68.4 mgL-1 at WSS-3), and in June (74.4 mgL-1 at WSS-4). Mg+2
(mgL-1) was minimum in December (24 mgL-1 at WSS-1), in August (36 mgL-1 at
WSS-2), in September (30 mgL-1 at WSS-3) and in December (51.6 mgL-1 at WSS-4)
(Figure 3.1.2 m). One way ANOVA demonstrated that there was i) a significant
difference between four water sampling sites (df = 3, F = 3.64, P < 0.05) (Table 3.1.2
a), ii) non-significant effect of seasonal variations (df = 3, F = 0.90, P = 0.44) (Table
3.1.2 b) and iii) non-significant effect of monthly variations (df = 11, F = 1.40, P =
0.215) (Table 3.1.2 c) for Mg+2.
Cl-1 (mgL-1) was maximum in December (156.20 mgL-1 at WSS-1), in April (382.69
mgL-1 at WSS-2), in June (120.70 mgL-1 at WSS-3 and422.45 mgL-1 at WSS-4). Cl-1
(mgL-1) was minimum in April (79.87 mgL-1 at WSS-1), in February (86.97 mgL-1 at
WSS-2), in September (74.55 mgL-1 at WSS-3) and in August (323.05 mgL-1 at WSS-
4) (Figure 3.1.2 n). One way ANOVA demonstrated that there was i) a significant
difference between four water sampling sites (df = 3, F = 21.82, P < 0.001) (Table
3.1.2 a), ii) non-significant effect of seasonal variations (df = 3, F = 0.80, P = 0.50)
(Table 3.1.2 b) and iii) non-significant effect of monthly variations (df = 11, F = 0.410,
P = 0.942) (Table 3.1.2 c) for Cl-1.
SO4-2(mgL-1) were maximum in February (306.20 mgL-1 at WSS-1), in May (321.60
mgL-1 at WSS-2), in April and June (321.60 mgL-1 at WSS-3) and in June (427.20
mgL-1 at WSS-4). SO4-2(mgL-1) were minimum in December (148.80 mgL-1 at WSS-1),
53
in August (48.96 mgL-1 at WSS-2), in September (182.40 mgL-1 at WSS-3) and in
August (297.60 mgL-1 at WSS-4) (Figure 3.1.2 o). One way ANOVA demonstrated
that there was i) a significant difference between four water sampling sites (df = 3, F =
18.58, P < 0.001) (Table 3.1.2 a), ii) non-significant effect of seasonal variations (df =
3, F = 0.35, P = 0.78) (Table 3.1.2 b) and iii) non-significant effect of monthly
variations (df = 11, F = 0.696, P = 0.733) (Table 3.1.2 c) for SO4-2.
EC (dS m-1) was maximum in February (2.21 dS m-1 at WSS-1), in May (2.33 dS m-1 at
WSS-2), in January (2.8 dS m-1 at WSS-3) and in June (2.83 dS m-1 at WSS-4). EC (dS
m-1) was minimum in December (1.22 dS m-1 at WSS-1), in August (1.41 dS m-1 at
WSS-2), in September (1.48 dS m-1 at WSS-3) and in August (2.26 dS m-1 at WSS-4)
(Figure 3.1.2 p). One way ANOVA demonstrated that there was i) a significant
difference between four water sampling sites (df = 3, F = 15.40, P < 0.001) (Table
3.1.2 a), ii) non-significant effect of seasonal variations (df = 3, F = 0.46, P = 0.70)
(Table 3.1.2 b) and iii) non-significant effect of monthly variations (df = 11, F = 0.517,
P = 0.879) (Table 4.1.2 c) for EC.
SAR (mgL-1) was maximum in September (2.50 mgL-1 at WSS-1), in April (5.42 mgL-1
at WSS-2), in June (1.92 mgL-1 at WSS-3) and in July (3.90 mgL-1 at WSS-4). SAR
(mgL-1) was minimum in December (0.98 mgL-1 at site 1), in January (1.55 mgL-1 at
WSS-2), in September (1.48 mgL-1 at WSS-3) and in August (2.50 mgL-1 at WSS-4)
(Figure 3.1.2 q). One way ANOVA demonstrated that there was i) a significant
difference between four water sampling sites (df = 3, F = 21.82, P < 0.001) (Table
3.1.2 a), ii) non-significant effect of seasonal variations (df = 3, F = 0.80, P = 0.50)
54
(Table 3.1.2 b) and iii) non-significant effect of monthly variations (df = 11, F = 0.410,
P = 0.942) (Table 3.1.2 c) for SAR.
3.1.3 Planktonic Biodiversity
3.1.3.1 Species richness
A total of 119 planktonic genera including 83 of phytoplankton and 36 of zooplankton
were recorded in this study from four sampling sites of the four catchment areas of
Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan. Among phytoplankton
groups, the cyanophyta contributed 12, chlorophyta 33, chrysophyta 7, Euglenophyta
6, xanthophyta 4, bacillariophyta 12 and pyrrhophyta 9 genera at all sites during this
study (Table 3.1.3. a). Among zooplankton groups, the protozoa contributed 15, rotifer
11 and cladocera 10 genera at all sites during this study. The species richness was
variable at different sites (Table 3.1.3 a, Figure 3.1.3.1 a - Figure 3.1.3.1c), in seasons
(Table 3.1.3 b, Figure 3.1.3.1 d - Figure 3.1.3.1 n) and in months (Table 3.1.3 c, Figure
3.1.3.1o - 3.1.3.1z).
Among major phytoplankton groups, cyanophyta contributed 11 genera at WSS-1 and
WSS-2, 12 at WSS-3 and 9 at WSS-4 (Table 3.1.3 a, Figure 3.1.3.1b). Cyanophyta
were recorded 12 in winter season, 8 in spring season, 10 in summer and post-
monsoon (Table 3.1.3 b). Cyanophyta showed maximum species richness (9 genera) in
October and minimum species richness (7 genera) in several months (Table 3.1.3 c).
One way ANOVA demonstrated that there was i) no significant difference between
four water sampling sites (df = 3, F = 0.51, P = 0.67) (Table 3.1.3 a), ii) a significant
effect of seasonal variations (df = 3, F = 5.37, P = 0.003) (Table 3.1.3 b) and iii) non-
55
significant effect of monthly variations (df = 11, F = 3.40, P < 0.01), (Table 3.1.3. c)
for species richness of cyanophyta.
Chlorophyta contributed 31 genera at WSS-1, 26 at WSS-2, 24 at WSS-3 and 16 at
WSS-4 (Table 3.1.3 a). Chlorophyta were recorded 28 in winter season, 18 in spring
season, 21 in summer season and 15 in post-monsoon(Table 3.1.3 b). Chlorophyta
showed maximum species richness (14 genera) in November and minimum species
richness (9 genera) in June and September (Table 3.1.3 c). One way ANOVA
demonstrated that there was i) a significant difference between four sites (df = 3, F =
4.61, P < 0.01) (Table 3.1.3 a), ii) non-significant effect of seasonal variations (df = 3,
F = 0.47, P = 0.70) (Table 3.1.3 b) and iii) a significant effect of monthly variations (df
= 11, F = 2.25, P = 0.033) (Table 3.1.3 c) for species richness of chlorophyta.
Chrysophyta contributed 7 genera at WSS-1, 5 at WSS-2 and WSS-3 and 3 at WSS-4
(Table 3.1.3 a).Chrysophyta were 5 in winter and 4 in spring, summer, post-monson
season each (Table 3.1.3 b). Chrysophyta showed maximum species richness (5
genera)in April and minimum species richness (2 genera) in November (Table 3.1.3 a,
Table 3.1.3 b). One way ANOVA demonstrated that there was i) no significant
difference between four sites (df = 3, F = 0.59, P = 0.62) (Table 3.1.3 a), ii) no
significant effect of seasonal variations (df = 3, F = 1.55, P = 0.21) (Table 3.1.3 b.) and
iii) non-significant effect of monthly variations (df = 11, F = 0.83, P = 0.60) (Table
3.1.3 c) for species richness of chrysophyta.
Euglenophyta contributed 5 genera at WSS-1, 4 at WSS-2, WSS-3 and WSS-4 (Table
3.1.3 a). Euglenophyta showed 5 genera in winter, 4 in spring, 3 in summer and 4 in
56
post-monson (Table 3.1.3 b). Euglenophyta showed maximum species richness in
January (6 genera) and minimum species richness (2 genera) in July (Table 3.1.3 c).
One way ANOVA demonstrated that there was i) a significant difference between four
sites (df = 3, F = 1.23, P = 0.31) (Table 3.1.3 a), ii) non-significant effect of seasonal
variations (df = 3, F = 1.20, P = 0.32) (Table 3.1.3 b) and iii) non-significant effect of
monthly variations (df = 11, F = 1.65, P = 0.12) (Table 3.1.3 c) for species richness of
euglenophyta.
Xanthophyta contributed 4 genera at WSS-1, 3 at WSS-2 and WSS-3 and 2 at WSS-4
(Table 3.1.3 a). Xanthophyta showed 3 genera in winter, 1 in spring, 3 in summer and
2 in post-monson season (Table 3.1.3 b). Xanthophyta showed maximum species
richness in January (3 genera) while no xanthophyta was observed in September at all
sites (Table 3.1.3 c). One way ANOVA was not applicable for statistical analysis of
species richness of xanthophyta.
Bacillariophyta contributed 11 genera at WSS-1, 12 at WSS-2, 11 at WSS-3 and 9 at
WSS-4 (Table 3.1.3 a). Bacillariophyta were recorded 11 in winter season, and 9 in
spring, summer and post-monsoon season (Table 3.1.3 b). Bacillariophyta showed
maximum species richness (14 genera) in November and minimum species richness (9
genera) in June and September (Table 3.1.3 c). One way ANOVA demonstrated that
there was i) no significant difference between four sites (df = 3, F = 0.88, P = 0.45)
(Table 3.1.3 a), ii) a significant effect of seasonal variations (df = 3, F = 9.38, P
<0.001) (Table 3.1.3 b) and iii) a significant effect of monthly variations (df = 11, F =
7.62, P < 0.001) (Table 3.1.3 c) for species richness of bacillariophyta.
57
Pyrrhophyta contributed 5 genera at WSS-1, 6 at WSS-2, 7 at WSS-3 and 5 at WSS-4
(Table 3.1.3 a). Pyrrhophyta were recorded 4 in winter, spring, post-monson each and
6 in summer (Table 3.1.3 c). Pyrrhophyta showed maximum species richness (4
genera) in April, August and October and minimum (2 genera) in February, July and
September (Table 3.1.3 c). One way ANOVA demonstrated that there was i) no
significant difference between four sites (df = 3, F = 2.04, P = 0.12) (Table 3.1.3 a), ii)
non-significant effect of seasonal variations (df = 3, F = 1.79, P = 0.16) (Table 3.1.3 c)
and iii) non-significant effect of monthly variations (df = 11, F = 1.23, P = 0.30)
(Table 3.1.3 c) for species richness of pyrrhophyta.
Among major zooplankton groups, protozoa contributed 9 genera at WSS-1, 13 at
WSS-2, 10 at WSS-3 and WSS-4 (Table 3.1.3 a). Protozoa were recorded 10 in winter
season, 8 in spring season, 11 in summer season and 7 in post-monsoon (Table 3.1.3
b). Protozoa has maximum species richness (8 genera) in April, July, October and
November, while minimum (5 genera) in June, August and September (Table 3.1.3 c).
One way ANOVA demonstrated that there was i) a significant difference between four
sites (df = 3, F = 3.46, P = 0.024) (Table 3.1.3 a), ii) non-significant effect of seasonal
variations (df = 3, F = 0.94, P = 0.42) (Table 3.1.3 b) and iii) a significant effect of
monthly variations (df = 11, F = 2.80, P = 0.01) (Table 3.1.3 c) for species richness of
protozoa.
Rotifera contributed 11 genera at WSS-1, 8 at WSS-2 & WSS-3 and 7 at WSS-4
(Table 3.1.3 a). Rotifera were recorded 8 in winter and spring season, 6 in summer
season and 5 in post-monsoon season (Table 3.1.3 b). Rotifera has maximum species
richness (7 genera) in March and September while minimum (3 genera) in June and
July (Table 3.1.3 c). One way ANOVA demonstrated that there was i) no significant
58
difference between four sites (df = 3, F = 0.69, P = 0.56) (Table3.1.3 a), ii) a
significant effect of seasonal variations on species richness of rotifera (df = 3, F =
5.43, P = 0.003) (Table 3.1.3 b) and iii) a significant effect of monthly variations (df =
11, F = 4.25, P < 0.001) (Table 3.1.3 c) for species richness of rotifera.
Cladocera contributed 10 genera at WSS-1, 5 at WSS-2 & WSS-3 and 4 at WSS-4
(Table 3.1.3 a). Cladocera were 6 in winter, 5 in spring, 6 in summer and 5 in post-
monson (Table 3.1.3 b). Cladocera has maximum species richness (5 genera) in
September and minimum in April, May, June and July. One way ANOVA
demonstrated that there was i) a significant difference between four sites (df = 3, F =
2.90, P < 0.05) (Table 3.1.3 a), ii) non-significant effect of seasonal variations (df = 3,
F = 0.42, P = 0.73) (Table 3.1.3 b) and iii) a significant effect of monthly variations (df
= 11, F = 2.77, P = 0.01) (Table 3.1.3 c) for species richness of cladocera.
The total number of phytoplankton genera was 74 at WSS-1, 67 at WSS-2, 65 at WSS-
3 and 48 at WSS-4 (Table 3.1.3 a). Total genera of phytoplankton were 68 in winter
season, 48 in spring season, 56 in summer season and 48 in post-monsoon season
(Table 3.1.3 b).The total number of phytoplankton were maximum (42 genera)in
January and minimum (25 genera) in September (Table 3.1.3 c). One way ANOVA
demonstrated that there was i) a significant difference between four sites (df = 3, F =
4.25, P = 0.01) (Table 3.1.3 a), ii) a significant effect of seasonal variations (df = 3, F
= 3.95, P = 0.008) (Table 3.1.3 b) and iii) a significant effect of monthly variations (df
= 11, F = 3.01, P = 0.006) (Table 3.1.3 c) for species richness of total phytoplankton.
59
Total zooplankton genera were 34 at WSS-1, 23 at WSS-2, 27 at WSS-3 and 21 at
WSS-4 (Table 3.1.3 a). Total zooplankton genera were recorded 24 in winter, 21 in
spring, 23 in summer and 19 in post-monson (Table 3.1.3 b). The total number of
zooplankton genera were maximum (15 genera) in September and December and
minimum (12 genera) in January and May (Table 3.1.3. c). One way ANOVA
demonstrated that there was i) no significant difference between four sites (df = 3, F =
1.36, P = 0.26), (Table 3.1.3 a), ii) non-significant effect of seasonal variations (df = 3,
F = 1.70, P = 0.18) (Table 3.1.3 b) and iii) a significant effect of monthly variations (df
= 11, F = 5.03, P < 0.001) (Table 3.1.3 c) for species richness of total zooplankton.
The total number of planktonic genera was 108 at WSS-1, 90 at WSS-2, 92 at WSS-3
and 69 at WSS-4 (Table 3.1.3 a). The total plankton genera recorded were 92 in winter
season, 69 in spring season, 79 in summer season and 67 in post-monsoon (Table 3.1.3
b). The total number of plankton genera were maximum (54 genera) in January and
minimum (40 genera) in September (Table 3.1.3 c). One way ANOVA demonstrated
that there was i) a significant difference between four sites (df = 3, F = 6.60, P =
0.001) (Table 3.1.3 a), ii) non-significant effect of seasonal variations (df = 11, F =
2.53, P = 0.06) (Table 3.1.3 b) and iii) non-significant effect of monthly variations (df
= 11, F = 1.90, P = 0.75) (Table 3.1.3 c) for species richness of total plankton.
60
3.1.3.2 Relative abundance
The relative abundance of planktonic groups was calculated within each site (Table
3.1.5 a) and within all sites (Table 3.1.5 b); as well as within each seasons (Table 3.1.5
c) and within all seasons (Table 3.1.5 d).
The overall RA of phytoplankton was found to be 81.86% and of zooplankton was
18.14% at all sites of study area (Table 3.1.5 a, Figure 3.1.3.2a). Among
Phytoplankton groups, cyanophyta has relative abundance 26.82%, chlorophyta
28.80%, chrysophyta 3.34%, euglenophyta 4.59%, xanthophyta 0.87%, bacillariophyta
13.53%, and pyrrhophyta 3.91%. Among zooplankton groups, protozoa have relative
abundance 11.57%, rotifera 5.78% and cladocera 0.79%. The relative abundance of all
planktonic groups varied at sampling sites (Table 3.1.5 a, Table 3.1.5 b, Figure 3.1.3.2
a - 3.1.3.2 c) in seasons (Table 3.1.5 c, Table 3.1.5 d, Figure 3.1.3.2 d - 3.1.3.2 n) and
in months (Table 3.1.5 e, Figure 3.1.3.2 o - 3.1.3.2 z).
Among phytoplankton groups, cyanophyta has relative abundance of 24.23% at WSS-
1, 27.82% at WSS-2, 28.69% at WSS-3, 26.70% at WSS-4 (Table 3.1.5a). Cyanophyta
has relative abundance 8.75% in winter, 4.21% in spring, 8.73% in summer and 5.12%
in post monsoon (Table 3.1.5 d, Figure 3.1.3.2d). Cyanophyta has maximum relative
abundance in July (2.76%) and minimum in May (1.47%) (Table 3.1.5 e). One way
ANOVA demonstrated that there was i) a significant difference between four water
sampling sites (df = 3, F = 12.25, P < 0.001) (Table 3.1.4 a), ii) non-significant effect
of seasonal variations (df = 3, F = 0.84, P = 0.47) (Table 3.1.4 b) and iii) non-
significant effect of monthly variations (df = 11, F = 1.40, P = 0.21) (Table 3.1.4 c) for
number of organisms of cyanophyta.
61
The chlorophyta was 29.34% at WSS-1, 28.32% at WSS-2, 29.79% at WSS-3 and
27.25% at WSS-4 (Table 3.1.5 a). Chlorophyta has relative abundance 9.94% in
winter, 4.28% in spring, 10.34% in summer and 4.24% in post-monsoon (Table 3.1.5
d). Chlorophyta has maximum relative abundance in July (2.99%) and minimum in
March (1.79%) (Table 3.1.5 e). One way ANOVA demonstrated that there was i) a
significant difference between four sites (df = 3, F = 14.62, P < 0.001) (Table 3.1.4 a),
ii) no significant effect of seasonal variations (df = 3, F = 1.21, P = 0.31) (Table 3.1.4
b) and iii) no significant effect of monthly variations (df = 11, F = 1.00, P = 0.46)
(Table 3.1.4 c) for number of organisms ofchlorophyta.
The chrysophyta was 4.19% at WSS-1, 3.79% at WSS-2, 2.57% at WSS-3 and 2.29%
at WSS-4 (Table 3.1.5 a). Chrysophyta has relative abundance of 1.25% in winter,
0.68% in spring, 1.04% in summer and 0.37% in post monsoon (Table 3.1.5 d).
Chrysophyta has maximum relative abundance in February (0.48%) and minimum in
October (0.11%) (Table 3.1.5 e). One way ANOVA demonstrated that there was i) a
significant difference between four sites (df = 3, F = 9.98, P < 0.001) (Table 3.1.4 a),
ii) nosignificant effect of seasonal variations (df = 3, F = 1.56, P = 0.21) (Table 3.1.4
b) and iii) no significant effect of monthly variations (df = 11, F = 1.24, P = 0.29)
(Table 3.1.4c) for number of organisms of chrysophyta.
The euglenophyta was 5.28% at WSS-1, 4.20% at WSS-2, 4.32% at WSS-3 and 4.51%
at WSS-4 (Table 3.1.5 a). Euglenophyta has relative abundance of 1.73% in winter,
0.81% in spring, 1.34% in summer and 0.72% in post-monsoon (Table 3.1.5 d).
Euglenophyta has maximum relative abundance in March (0.50%) and minimum in
July (0.27%) (Table 3.1.5 e). One way ANOVA demonstrated that there was i) a
62
significant difference among four sites (df = 3, F = 5.26, P = 0.003) (Table 3.1.4 a), ii)
no significant effect of seasonal variations (df = 3, F = 0.85, P = 0.47) (Table 3.1.4 b)
and iii) no significant effect of monthly variations (df = 11, F = 1.14, P = 0.29) (Table
3.1.4c) for number of organisms of euglenophyta.
The xanthophyta was 1.38% at WSS-1, 0.71% at WSS-2, 0.57% at WSS-3 and 0.77%
at WSS-4 (Table 3.1.5 a). Xanthophyta has relative abundance in 0.37% winter, 0.14%
in spring, 0.26% in summer and 0.10% in post monsoon (Table 3.1.5 d.). Xanthophyta
has maximum relative abundance in November (0.13%) and minimum in February
(0.02%) (Table 3.1.5 e). One way ANOVA demonstrated that there was i) a significant
difference between four sites (df = 3, F = 4.12, P = 0.012) (Table 3.1.4 a), ii) no
significant effect of seasonal variations on (df = 3, F =0.61, P = 0.61) (Table 3.1.4 b)
and iii) no significant effect of monthly variations (df = 11, F = 1.45, P = 0.19) (Table
3.1.4 c) for number of organisms of xanthophyta.
The bacillariophyta was 12.49% at WSS-1, 13.73% at WSS-2, 13.16% at WSS-3 and
15.46% at WSS-4 (Table 3.1.5 a). Bacillariophyta has relative abundance7.78% in
winter, 2.00% in spring, 2.24% in summer and 1.49% in post monsoon (Table 3.1.5d).
Bacillariophyta has maximum relative abundance in January (2.87%) and minimum in
May (0.45%) (Table 3.1.5 e). One way ANOVA demonstrated that there was i) no
significant difference between four sites (df = 3, F = 1.66, P = 0.18) (Table 3.1.4 a), ii)
a significant effect of seasonal variations (df = 3, F = 14.02, P < 0.001) (Table 3.1.4 b)
and iii) a significant effect of monthly variations (df = 11, F = 10.23, P < 0.001) (Table
3.1.4 c) for number of organisms of bacillariophyta.
63
The pyrrhophyta was 5.36% at WSS-1, 3.83% at WSS-2, 3.04% at WSS-3 and 2.91%
at WSS-4 (Table 3.1.5 a). Pyrrhophyta has relative abundance of 1.32% in winter,
0.77% in spring, 0.92% in summer and 0.89% in post monsoon (Table 3.1.5 d).
Pyrrhophyta has maximum relative abundance in September (0.46%) and minimum in
July (0.17%) (Table 3.1.5 e). One way ANOVA demonstrated that there was i) a
significant difference between four sites (df = 3, F = 10.80, P < 0.001) (Table 3.1.4 a),
ii) no significant effect of seasonal variations (df = 3, F = 2.87, P = 0.47) (Table 3.1.4
b) and iii) no significant effect of monthly variations (df = 11, F = 0.82, P = 0.61)
(Table 3.1.4 c) for number of organisms of pyrrhophyta.
Among zooplankton groups, protozoa have relative abundance of 9.72% at WSS-1,
11.71% at WSS-2, 12.21% at WSS-3 and 13.45% at WSS-4 (Table 3.1.5a). Among
zooplankton group, protozoa have relative abundance in 4.10% winter, 1.67% in
spring, 3.87% in summer and 1.91% in post monsoon (Table 3.1.5 d). Among major
zooplankton group, protozoa has maximum relative abundance in July (1.17%) and
minimum in March 0.71% (Table 3.1.5 e). One way ANOVA demonstrated that there
was i) a significant difference between four sites (df = 3, F = 6.48, P = 0.003) (Table
3.1.4 a), ii) no significant effect of seasonal variations (df = 3, F = 1.09, P = 0.36)
(Table 3.1.4 b) and iii) no significant effect of monthly variations (df = 11, F = 1.24, P
= 0.29) (Table 3.1.4 c) for number of organisms of protozoa.
The rotifers were 7.21% at WSS-1, 5.07% at WSS-2, 4.89% at WSS-3 and 5.89% at
WSS-4 (Table 3.1.5 a, Figure 3.1.3.1 c). Rotifera have RA of 1.85% winter, 1.11% in
spring, 1.56% in summer and 1.24% in post monsoon (Table 3.1.5 d). Rotifera has
maximum relative abundance in March (0.65%) and minimum in July (0.27%) (Table
64
3.1.5 e). One way ANOVA demonstrated that there was i) a significant difference
between four sites (df = 3, F= 5.55, P = 0.003) (Table 3.1.4 a), ii) no significant effect
of seasonal variations (df = 3, F = 2.17, P = 0.10) (Table 3.1.4b) and iii) no significant
effect of monthly variations (df = 11, F = 1.28, P = 0.27) (Table 3.1.4 c) for number of
organisms of rotifera.
The cladocera were 0.80% at WSS-1, 0.82% at WSS-2, 0.76% at WSS-3 and 0.77% at
WSS-4 (Table 3.1.5 a). Cladocera have RA of 0.23% in winter, 0.19% in spring,
0.25% in summer and 0.17% in post monsoon (Table 3.1.5d). Cladocera has maximum
relative abundance in March (0.11%) and minimum in July (0.02%) (Table 3.1.5 e).
One way ANOVA demonstrated that there was i) no significant difference between
four sites (df = 3, F = 2.58, P = 0.065) (Table 3.1.4 a), ii) a significant effect of
seasonal variations (df = 3, F = 3.04, P = 0.038) (Table 3.1.4 b) and iii) no significant
effect of monthly variations (df = 11, F = 1.97, P = 0.062) (Table 3.1.4 c) for number
of organisms of cladocera.
The total phytoplanktons were 82.27% at WSS-1, 82.40% at WSS-2, 82.14% at WSS-
3 and 79.89% at WSS-4 (Table 3.1.5 a). The total phytoplankton showed relative
abundance of 31.14% in winter, 12.89% in spring, 24.80% in summer and 12.93% in
post monsoon (Table 3.1.4 d). Phytoplankton has maximum relative abundance in
January (8.30%) and minimum in May (5.02%) (Table 3.1.5 e). One way ANOVA
demonstrated that there was i) a significant difference between four sites (df = 3, F =
19.10, P < 0.001) (Table 3.1.4 a), ii) no significant effect of seasonal variations (df = 3,
F = 2.53, P = 0.06) (Table 3.1.4 b) and iii) no significant effect of monthly variations
65
(df = 11, F = 1.05, P = 0.42) (Table 3.1.4 c) for number of organisms of total
phytoplankton.
The total zooplanktons were 17.73% at WSS-1, 17.60% at WSS-2, 17.86% at WSS-3
and 20.11% at WSS-4 (Table 3.1.5 a). The zooplankton showed RA of 6.18% in
winter, 2.97% in spring, 5.68% in summer and 3.33% in post monsoon (Table 3.1.4 d).
Zooplankton has maximum relative abundance in September (1.72%) and minimum in
June (1.22%) (Table 3.1.3 e). One way ANOVA demonstrated that there was i) a
significant difference among four sites (df = 3, F = 9.95, P < 0.001) (Table 3.1.4 a), ii)
no significant effect of seasonal variations (df = 3, F = 0.99, P = 0.40) (Table 3.1.4 b)
and iii) no significant effect of monthly variations (df = 11, F = 0.53, P = 0.86) (Table
3.1.4 c) for number of organisms of total zooplankton.
One way ANOVA demonstrated that there was i) a significant difference among four
sites (df = 3, F = 21.96, P < 0.001) (Table 3.1.4 a), ii) no significant effect of seasonal
variations (df = 11, F = 2.15, P = 0.10), (Table 3.1.4 b) and iii) no significant effect of
monthly variations on number of organisms of total plankton (df = 11, F = 0.85, P =
0.42) (Table 3.1.4 c) for number of organisms of total plankton.
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3.1.3.3 Diversity indices
Simpson diversity index
The overall value of Simpson diversity index was found to be 1.00 for all the plankton
genera at all study sites during study period. It was 0.67 for total phytoplankton and
0.03 for total zooplankton. The observed value for phytoplankton was 0.68 at WSS-1
and at WSS-2, 0.67 at WSS-3 and 0.64 at WSS-4. The observed value for total
zooplankton was 0.03 at WSS-1, WSS-2, WSS-3 and 0.04 at WSS-4. The value of
Simpson index for all plankton was 0.91 in winter, 0.88 in spring, 0.92 in summer and
0.87 in post-monsoon. The value of Simpson index for total phytoplankton was 0.70 in
winter, 0.66 in spring and summer, and 0.63 in post monsoon. The value of Simpson
index for total zooplankton was 0.03 in winter, spring and summer, and 0.04 in post-
monsoon (Table 3.1.6 a and Table 3.1.6 b).
Shannon - Weiner Diversity index
The value of Shannon-Weiner diversity index was found to be 0.16 for phytoplankton
genera and 0.31 for zooplankton genera at all study sites and all seasons during study
period. The overall value for total plankton was 2.38 at WSS-1, 2.31 at WSS-2, 2.26 at
WSS-3 and 2.34 at WSS-4 (Table 3.1.6 c). The observed value for phytoplankton was
1.46 at WSS-1, 1.40 at WSS-2, 1.35 at WSS-3 and 1.36 at WSS-4 (Table 3.1.6 c). The
observed value for zooplankton was 0.45 at WSS-1, 0.44 at WSS-2, WSS-3 and 0.47
at WSS-4 (Table 3.1.6 c). The value of Shannon-Weaner index for total plankton was
0.37 in winter, 0.29 in spring, 0.36 in summer and 0.30 in post monsoon (Table 3.1.6
d). The value of Shannon-Weiner index for total phytplankton was 0.15 in winter, 0.17
in spring, summer and 0.18 in post monsoon (Table 3.1.6 d). The value of Shannon -
67
Weiner index for total zooplankton was 0.30 in winter, 0.31 in spring, summer and
0.30 in post monsoon (Table 3.1.6 d).
Margalef Index (d)
The overall value of Margalef diversity index was found to be 12.13 for all the
plankton genera at all study sites. It was 8.61 for the phytoplankton and 4.37 for
zooplankton. The overall value for all plankton was 12.63 at WSS-1, 10.48 at WSS-2,
10.90 at WSS-3 and 8.53 at WSS-4. The observed value for total phytoplankton was
8.82 at WSS-1, 7.95 at WSS-2, 7.85 at WSS-3 and 6.05 at WSS-4. The observed value
for total zooplankton was 4.89 at WSS-1, 3.25 at WSS-2, 3.92 at WSS-3 and at 3.14
WSS-4 (Table 3.1.6 e). The value of margalef index for all plankton was 10.41 in
winter, 8.63 in spring, 9.14 in summer and 8.34 in post monsoon. The value of
margalef index for phytplankton was 7.83 in winter, 6.12 in spring, 6.60 in summer
and 6.12 in post monsoon. The value of margalef index for zooplankton was 3.31 in
winter, 3.22 in spring, 3.21 in summer and 2.85 in post monsoon (Table 3.1.6 f).
Evenness index (E)
The overall value of evenness index for phytoplankton was 0.04 and 0.09 for
zooplankton for all sites and seasons. The value of Evenness index for total plankton
was found to be 0.51 at WSS-1, WSS-2, 0.50 at WSS-3 and 0.55 at WSS-4. It was
0.34 for phytoplankton at WSS-1, 0.33 at WSS-2, 0.32 at WSS-3 and 0.35 at WSS-4.
It was 0.13 for zooplankton at WSS-1, 0.14 at WSS-2, 0.13 at WSS-3 and 0.15 at
WSS-4(Table 3.1.6 g). The value of evenness index for total plankton was 0.08 in
winter, 0.07 in spring, 0.08 in summer and 0.07 in post monsoon. The value of
evenness index for phytoplankton was 0.04 in winter, spring, summer and 0.05 in post-
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monsoon. The value of evenness index for zooplankton was 0.09 in winter, 0.10 in
spring, summer and 0.11 in post monsoon (Table 3.1.6 h).
Sorenson Similarity index (S)
The value of Sorenson similarity index for all plankton genera among all sites was
found to be 0.33 while it was 0.36 for total phytoplankton and 0.24 for total
zooplankton. It was found maximum (0.87) between WSS-1 and WSS-2, and
minimum (0.65) between WSS-2 and WSS-3 for total plankton genera. It was found
maximum (0.90) between WSS-1 and WSS-2, and minimum (0.62) between WSS-2
and WSS-4 for total phytoplankton genera. It was found maximum (0.85) between
WSS-2 and WSS-3, and minimum (0.77) among WSS-2 and WSS-4 for zooplankton
genera. Among the major planktonic groups, cyanophyta, bacillariophyta and
pyrrhophyta have maximum similarity index value (0.42) between all sites. While
cladocera has minimum value (0.17) between all sites. Cyanophyta showed maximum
value (1) between WSS-1 and WSS-2, and xanthophyta showed maximum value (1)
between WSS-2 and WSS-3. The cladocera has minimum value (0.57) between WSS-
1 and WSS-4 (Table 3.1.6 i).
The similarity index value was maximum (0.84) between winter and post monsoon,
and minimum (0.70) between spring and post monsoon for total plankton genera. It
was maximum (0.85) between winter and post-monsoon, and minimum (0.68) between
spring and post monsoon for total phytoplankton genera. It was maximum (0.84)
between winter and post monsoon, and minimum (0.66) between summer and post-
monsoon for zooplankton genera. Several groups showed maximum value of Sorenson
similarity index (1) among different pairs of seasons. Cyanophyta showed maximum
69
value (1) between summer and post-monsoon, chrysophyta among winter and summer,
xanthophyta between winter and summer, bacillariophyta between spring and summer,
and pyrrhophyta between winter and spring and spring and summer. While
xanthophyta has minimum value (0.5) between winter and spring and cladocera (0.5)
between winter and summer (Table3.1.6 j).
3.1.3.4 Correlation Analysis
A number of strong and significant Pearson correlations were found among different
studied parameters at four study sites.
Correlations between Physico-chemical Parameters
Water temperature was positively correlated with pH at WSS-2 (r = 0.68, P< 0.014), at
WSS-3 (r = 0.86, P < 0.000); with TDS at WSS-4 (r = 0.61, P < 0.05); with CO 2 at
WSS-1 (r = 0.85, P < 0.001),WSS-2 (r = 0.87, P < 0.001), WSS-3 (r = 0.84, P < 0.001),
WSS-4 (r = 0.92, P < 0.001); with CO3-2 at WSS-4 (r = 0.61, P < 0.05); with HCO3
-1 at
WSS-4 (r = 0.57, P < 0.05); with TA at WSS-4 (r = 0.59, P < 0.05); and with SO4-2 at
WSS-4 (r = 0.54, P < 0.05). Water temperature was negatively correlated with DO at
WSS-1 (r = - 0.94, P < 0.001), WSS-2 (r = -0.91, P < 0.001), WSS-3 (r = -0.87, P <
0.001), WSS-4 (r = -0.89,P < 0.001).
Light penetration was positively correlated with TDS at WSS-2 (r = 0.73,P < 0.01),
WSS-3 (r = 0.82,P < 0.001); with DO at WSS-1 (r = 0.69, P < 0.05), at WSS-2 (r =
0.68, P < 0.05), at WSS-4 (r = 0.71, P < 0.01); with CO3-2 at WSS-3 (r = 0.78, P <
0.01); with HCO3-1 at WSS-2 (r = 0.59, P < 0.05), at WSS-3 (r = 0.82, P < 0.01); with
TA at WSS-2 (r = 0.64, P < 0.05), at WSS-3 (r = 0.82, P < 0.01); with TH at WSS-2 (r
= 0.70, P < 0.05), at WSS-3 (r = 0.74, P < 0.01); with Ca+2 at WSS-3 (r = 0.72, P
70
<0.01); with Mg+2 at WSS-3 (r = 0.82, P < 0.01); with SO4-2 at WSS-2 (r = 0.70, P <
0.05), at WSS-3 (r = 0.82, P < 0.01); with EC at WSS-2 (r = 0.72, P < 0.01), at WSS-3
(r = 0.62, P < 0.05); with SAR at WSS-3 (r = 0.88, P < 0.001). Light penetration was
negatively correlated with CO2 at WSS-2 (r = -0.64, P < 0.05) only.
TDS were positively correlated with CO3-2 at WSS-3 (r = 0.94, P < 0.001), at WSS-4 (r
= 0.96, P < 0.001); with HCO3-1 at WSS-1 (r = 0.97, P < 0.001),at WSS-2 (r = 0.59, P <
0.05), at WSS-3 (r = 0.94, P < 0.001),at WSS-4 (r = 0.92, P < 0.001); with TA at WSS-
1 (r = 0.95, P < 0.001),at WSS-2 (r = 0.96, P < 0.001), at WSS-3 (r = 0.95, P < 0.001),
at WSS-4 (r = 0.90, P < 0.001); with TH at WSS-1 (r = 0.92, P < 0.001), at WSS-2 (r =
0.91, P < 0.001), at WSS-3 (r = 0.92, P < 0.001),at WSS-4 (r = 0.94, P < 0.001); with
Na+1 at WSS-1 (r = 0.90, P < 0.001), at WSS-4 (r = 0.91, P < 0.001); with Ca +2 at
WSS-1 (r = 0.89, P < 0.001),at WSS-2 (r = 0.75, P < 0.01), at WSS-3 (r = 0.91, P <
0.001), at WSS-4 (r = 0.93, P < 0.001); with Mg+2 at WSS-1 (r = 0.82, P < 0.001),at
WSS-3 (r = 0.79, P < 0.01),at WSS-4 (r = 0.96, P < 0.001); with Cl -1 at WSS-3 (r =
0.87, P < 0.001), at WSS-4 (r = 0.95, P < 0.001); with SO4-2 at WSS-1 (r = 0.80, P <
0.01),at WSS-2 (r = 0.82, P < 0.001), at WSS-3 (r = 0.91, P < 0.001),at WSS-4 (r =
0.96, P < 0.001); with EC at WSS-1 (r = 0.99, P < 0.001),at WSS-2 (r = 0.99, P <
0.001), at WSS-3 (r = 0.77, P < 0.01), at WSS-4 (r = 0.99, P < 0.001); with SAR at
WSS-1 (r = 0.89, P < 0.001), at WSS-3 (r = 0.96, P < 0.001),at WSS-4 (r = 0.98, P <
0.001). TDS were negatively correlated with Cl-1 at WSS-1 (r = - 0.96, P < 0.001). The
pH waspositively correlated with CO2 at WSS-2 (r = 0.68, P < 0.05),at WSS-3 (r =
0.80, P < 0.01),at WSS-4 (r = 0.60, P < 0.05); with HCO3-1 at WSS-1 (r = 0.57, P <
0.05), at WSS-4 (r = 0.61, P < 0.05); with TA at WSS-4 (r = 0.90, P < 0.001).
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The pH was negatively correlated with DOat WSS-4 (r = 0.61, P < 0.05). The DO
waspositively correlated with CO3-2 at WSS-3 (r = 0.64, P < 0.05); with Mg+2 at WSS-
3 (r = 0.57, P < 0.05); with EC atWSS-3 (r = 0.63, P < 0.05). The DO was negatively
correlated with CO2 at WSS-1 (r = -0.85, P < 0.001),WSS-2 (r = -0.97, P < 0.001), at
WSS-3 (r = - 0.58, P < 0.05), at WSS-4 (r = -0.71, P < 0.01).The CO2 was positively
correlated with CO3-2 at WSS-4 (r = 0.68, P < 0.05); with HCO3
-1 at WSS-4 (r = 0.73, P
< 0.01); with TA WSS-4 (r = 0.74, P < 0.01); with TH WSS-4 (r = 0.61, P < 0.05). The
CO3-2 were positively correlated with HCO3
-1 at WSS-3 (r = 0.86, P < 0.001), at WSS-4
(r = 0.94, P < 0.001); with TA at WSS-3 (r = 0.88, P < 0.001 ), at WSS-4 (r = 0.94, P <
0.001 ); with TH at WSS-3 (r = 0.88, P < 0.001), at WSS-4 (r = 0.94, P < 0.001); with
Ca+2 at WSS-3 (r = 0.86, P < 0.001),at WSS-4 (r = 0.91, P < 0.001); with Mg+2 at
WSS-3 (r = 0.81, P < 0.01 ), at WSS-4 (r = 0.98, P < 0.001); with Cl -1 at WSS-3 (r =
0.82, P < 0.01),at WSS-4 (r = 0.87, P < 0.001); with SO4 at WSS-3 (r = 0.82, P < 0.01),
at WSS-4 (r = 0.97, P < 0.001); with EC at WSS-3 (r = 0.88, P < 0.001), at WSS-4 (r =
0.96, P < 0.001); with SAR at WSS-3 (r = 0.92, P < 0.001), at WSS-4 (r = 0.94, P <
0.001). The HCO3-1 were positively correlated with TA at WSS-1 (r = 0.98, P <
0.001),WSS-2 (r = 0.71, P < 0.01), at WSS-3 (r = 0.99, P < 0.001), at WSS-4 (r = 0.99,
P < 0.001); with TH at WSS-1 (r = 0.91, P < 0.001),WSS-2 (r = 0.67, P < 0.05), at
WSS-3 (r = 0.82, P < 0.01), at WSS-4 (r = 0.94, P < 0.001); with Na+1 at WSS-1 (r =
0.89, P < 0.001), at WSS-4 (r = 0.78, P < 0.01); with Ca+2 at WSS-1 (r = 0.88, P <
0.001), WSS-2 (r = 0.87, P < 0.001), at WSS-3 (r = 0.80, P < 0.01), at WSS-4 (r =
0.93, P < 0.001); with Mg++ at WSS-1 (r = 0.79, P < 0.01), at WSS-3 (r = 0.73, P <
0.01), at WSS-4 (r = 0.98, P < 0.001); with Cl-1at WSS-1 (r = 0.94, P < 0.001),WSS-2
(r = 0.77, P < 0.01), at WSS-3 (r = 0.89, P < 0.001), at WSS-4 (r = 0.87, P < 0.001);
with SO4-2 at WSS-1 (r = 0.73, P < 0.01), WSS-2 (r = 0.83, P < 0.01), at WSS-3 (r =
72
0.81, P < 0.01), at WSS-4 (r = 0.97, P < 0.001); with EC at WSS-1 (r = 0.97, P <
0.001), WSS-2 (r = 0.73, P < 0.01), at WSS-3 (r = 0.69, P < 0.05), at WSS-4 (r = 0.96,
P < 0.001); with SAR at WSS-1 (r = 0.95, P < 0.001), at WSS-3 (r = 0.90, P < 0.001),
at WSS-4 (r = 0.94, P < 0.001).
TA was positively correlated with TH at WSS-1 (r = 0.90, P < 0.001),WSS-2 (r = 0.88,
P < 0.001), at WSS-3 (r = 0.84, P < 0.01), at WSS-4 (r = 0.93, P < 0.001); with Na+1 at
WSS-1 (r = 0.83, P < 0.01), WSS-4 (r = 0.75, P < 0.01); with Ca+2 at WSS-1 (r = 0.89,
P < 0.001), WSS-2 (r = 0.75, P < 0.01), at WSS-3 (r = 0.81, P < 0.01), at WSS-4 (r =
0.91, P < 0.001); with Mg+2 at WSS-1 (r = 0.73, P < 0.01), at WSS-3 (r = 0.74, P <
0.001), at WSS-4 (r = 0.90, P < 0.001); with Cl-1 at WSS-3 (r = 0.88, P < 0.001),at
WSS-4 (r = 0.89, P < 0.001); with SO4-2 at WSS-1 (r = 0.70, P < 0.05),WSS-2 (r =
0.74, P < 0.01), at WSS-3 (r = 0.81, P < 0.01), at WSS-4 (r = 0.95, P < 0.001); with EC
at WSS-1 (r = 0.95, P < 0.001),WSS-2 (r = 0.97, P < 0.001), at WSS-3 (r = 0.71, P <
0.01), at WSS-4 (r = 0.91, P < 0.001); with SAR at WSS-1 (r = 0.94, P < 0.001), at
WSS-3 (r = 0.91, P < 0.001), at WSS-4 (r = 0.97, P < 0.001). TA was negatively
correlated with Cl-1 at WSS-1 (r = - 0.92, P < 0.001).
TH was positively correlated with Na+1 at WSS-1 (r = 0.84, P < 0.001), at WSS-4 (r =
0.89, P < 0.001); with Ca+2 at WSS-1 (r = 0.95, P < 0.001),WSS-2 (r = 0.71, P <
0.001), at WSS-3 (r = 0.99, P < 0.001), at WSS-4 (r = 0.99, P < 0.001); with Mg+2 at
WSS-1 (r = 0.76, P < 0.01), at WSS-3 (r = 0.62, P < 0.05), at WSS-4 (r = 0.91, P <
0.001); with Cl-1 at WSS-1 (r = 0.92, P < 0.001), at WSS-3 (r = 0.72, P < 0.01), at
WSS-4 (r = 0.95, P < 0.001); with SO4-2 at WSS-1 (r = 0.74, P < 0.01),WSS-2 (r =
0.80, P < 0.01), at WSS-3 (r = 0.95, P < 0.001), at WSS-4 (r = 0.91, P < 0.001); with
73
EC at WSS-1 (r = 0.92, P < 0.001), WSS-2 (r = 0.92, P < 0.001), at WSS-3 (r = 0.69, P
< 0.05), at WSS-4 (r = 0.91, P < 0.001); with SAR at WSS-1 (r = 0.84, P < 0.01), at
WSS-3 (r = 0.87, P < 0.001 ),at WSS-4 (r = 0.97, P < 0.001).
The Na+1 was positively correlated with Ca+2 at WSS-4 (r = 0.89, P < 0.001 ); with
Mg+2 at WSS-1 (r = 0.79, P < 0.01), at WSS-3 (r = 0.86, P < 0.001); with Cl-1 at WSS-2
(r = 0.64, P < 0.001), at WSS-4 (r = 0.91, P < 0.001); with SO 4-2 at WSS-1 (r = 0.73, P
< 0.01), at WSS-4 (r = 0.82, P < 0.001); with EC at WSS-1 (r = 0.91, P < 0.001), at
WSS-4 (r = 0.91, P < 0.001); with SAR at WSS-1 (r = 0.82, P < 0.01),WSS-2 (r =
0.96, P < 0.001), at WSS-4 (r = 0.92, P < 0.001). The Na+1 was negatively correlated
with Mg+2 at WSS-2 (r = - 0.58, P < 0.05); with Cl-1 at WSS-1 (r = - 0.90, P < 0.001).
The Ca+2 was positively correlated with Mg+2 at WSS-1 (r = 0.63, P < 0.05 ),WSS-2 (r
= 0.57, P < 0.05), at WSS-3 (r = 0.59, P < 0.05), at WSS-4 (r = 0.89, P < 0.001); with
Cl-1 at WSS-2 (r = 0.67, P < 0.05), at WSS-3 (r = 0.69, P < 0.001), at WSS-4 (r = 0.95,
P < 0.001); with SO4-2 at WSS-1 (r = 0.70, P < 0.05), WSS-2 (r = 0.85, P < 0.001), at
WSS-3 (r = 0.96, P < 0.001), at WSS-4 (r = 0.89, P < 0.001); with EC at WSS-1 (r =
0.88, P < 0.001),WSS-2 (r = 0.73, P < 0.01), at WSS-3 (r = 0.68, P < 0.05), at WSS-4
(r = 0.95, P < 0.001); with SAR at WSS-1 (r = 0.85, P < 0.001), at WSS-3 (r = 0.85, P
< 0.001), at WSS-4 (r = 0.96, P < 0.001). The Ca+2 was negatively correlated with Cl-1
at WSS-1 (r = - 0.89, P < 0.001). The Mg+2 was positively correlated with Cl-1 at WSS-
2 (r = 0.77, P < 0.01), at WSS-3 (r = 0.82, P < 0.01),at WSS-4 (r = 0.87, P < 0.001);
with SO4-2 at WSS-1 (r = 0.73, P < 0.01),at WSS-2 (r = 0.74, P < 0.01), at WSS-3 (r =
0.65, P < 0.05), at WSS-4 (r = 0.97, P < 0.001); with EC at WSS-1 (r = 0.83, P < 0.01),
at WSS-3 (r = 0.77, P < 0.01), at WSS-4 (r = 0.96, P < 0.001).
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Cl-1 were positively correlated with SO4-2 at WSS-3 (r = 0.71, P < 0.01), at WSS-4 (r =
0.88, P < 0.001); with EC at WSS-3 (r = 0.75, P < 0.01 ), at WSS-4 (r = 0.957, P <
0.001); with SAR at WSS-2 (r = 0.70, P = 0.01), at WSS-3 (r = 0.90, P < 0.001), at
WSS-4 (r = 0.97, P < 0.001). Cl-1 were negatively correlated with SO4-2 at WSS-1 (r = -
0.71, P < 0.01), at WSS-2 (r = - 0.61, P < 0.05); with EC at WSS-1 (r = - 0.96, P <
0.001), with SAR at WSS-1 (r = - 0.90, P < 0.001). The SO 4-2 were positively
correlated with EC at WSS-1 (r = 0.80, P < 0.01), at WSS-2 (r = 0.80, P < 0.01), at
WSS-3 (r = 0.61, P < 0.05), at WSS-4 (r = 0.95, P < 0.001); and with SAR at WSS-1 (r
= 0.68, P < 0.05), at WSS-3 (r = 0.89, P < 0.001), at WSS-4 (r = 0.95, P < 0.001). The
EC was positively correlated with SAR at WSS-1 (r = 0.90, P < 0.001), at WSS-3 (r =
0.79, P < 0.01), at WSS-4 (r = 0.98, P < 0.001).
Correlations of Physico-chemical Parameters with number of organisms
Water temperature had negative correlation with density of euglenophyta at WSS-4 (r
= - 0.79, P < 0.01); with bacillariophyta at WSS-1 (r = - 0.62, P < 0.05), at WSS-2 (r =
- 0.90, P < 0.001), WSS-3 (r = - 0.92, P < 0.001), WSS-4 (r = - 0.77, P < 0.01); and
with phytoplankton at WSS-3 (r = - 0.65, P < 0.05), at WSS-4 (r = - 0.64, P < 0.05).
Light penetration had positive correlation with density of chrysophyta at WSS-3 (r =
0.61, P < 0.05); with euglenophyta at WSS-4 (r = 0.61, P < 0.05);with pyrrhophyta at
WSS-4 (r = 0.77, P < 0.01). TDS had negative correlation with density ofchlorophyta
at WSS-1 (r = - 0.66, P < 0.05); with bacillariophyta at WSS-4 (r = - 0.61, P < 0.05),
with protozoa at WSS-4 (r = - 0.58, P < 0.05), with phytoplanktons at WSS-1 (r = -
0.67, P < 0.05).
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The pH was negatively correlated with density of bacillariophyta at WSS-2 (r = - 0.61,
P < 0.05), at WSS-3 (r = - 0.68, P < 0.05); with zooplankton at WSS-1 (r = - 0.59, P <
0.05).DO had positive correlation with density ofeuglenophyta at WSS-4 (r = 0.73, P <
0.01); with pyrrhophyta at WSS-4 (r = 0.58, P < 0.05); with bacillariophyta at WSS-1
(r = 0.59, P < 0.05), at WSS-2 (r = 0.74, P < 0.01), at WSS-3 (r = 0.84, P < 0.001),
WSS-4 (r = 0.82, P < 0.01); with phytoplankton at WSS-3 (r = 0.60, P < 0.05),at WSS-
4 (r = 0.66, P < 0.05). The CO2 had negative correlation with euglenophyta atWSS-1 (r
= - 0.60, P < 0.05), at WSS-4 (r = - 0.79, P < 0.01), with bacillariophyta at WSS-1 (r =
- 0.61, P < 0.05), WSS-2 (r = - 0.66, P < 0.05), WSS-3 (r = - 0.69, P < 0.05). The CO 3-2
had positive correlation with rotifera at WSS-2 (r = 0.67, P < 0.01). The HCO 3-1 had
negative correlation with bacillariophyta at WSS-4 (r = - 0.59, P < 0.05); with protozoa
at WSS-4 (r = - 0.65, P < 0.05); with cladocera at WSS-1 (r = - 0.67, P < 0.05); with
zooplankton at WSS-4 (r = - 0.62, P < 0.05). The HCO3-1 had positive correlation with
bacillariophyta at WSS-1 (r = 0.67, P < 0.05).
The TA had negative correlation with chlorophyta at WSS-1 (r = - 0.63, P < 0.01);
with protozoa at WSS-4 (r = - 0.65, P < 0.01); with phytoplankton at WSS-1 (r = -
0.67, P < 0.05), WSS-2 (r = - 0.64, P < 0.05); and with zooplankton atWSS-4 (r = -
0.62, P < 0.05). The TH had negative correlation with cyanophyta at WSS-1 (r = -
0.64, P < 0.05); at WSS-2 (r = - 0.58, P < 0.05); witheuglenophyta at WSS-3 (r = -
0.60, P < 0.05); with protozoa at WSS-4 (r = - 0.60, P < 0.05); with phytoplankton at
WSS-1 (r = - 0.68, P < 0.05).
The Na+1 had negative correlation with cyanophyta at WSS-2 (r = - 0.59, P < 0.05).
The Ca+2 had negative correlation withcyanophyta at WSS-1 (r = - 0.66, P < 0.05);
76
chlorophytaat WSS-1 (r = - 0.58, P < 0.05); withprotozoa at WSS-4 (r = - 0.60, P <
0.05); with phytoplankton at WSS-1 (r = - 0.76, P < 0.01).The Mg+2 had negative
correlation with bacillariophyta at WSS-3 (r = - 0.58, P < 0.05). The Mg+2 had positive
correlation with xanthophyta at WSS-3 (r = 0.60, P < 0.05). The Cl -1 had negative
correlation with protozoaat WSS-4 (r = - 0.62, P < 0.05).
The Cl-1 had positive correlation with chlorophyta at WSS1 (r = 0.63, P < 0.05); with
phytoplankton at WSS-1 (r = 0.70, P < 0.05). The SO4-2 had negative correlation with
phytoplankton at WSS-1 (r = - 0.65, P < 0.05). The SO4-2 had positive correlation with
chrysophyta at WSS-1 (r = 0.58, P < 0.05). The EC had negative correlation with
chlorophyta at WSS-1 (r = - 0.66, P < 0.05); with bacillariophyta at WSS-4 (r = - 0.58,
P < 0.05); with protozoa at WSS-4 (r = - 0.59, P < 0.05); with phytoplankton at WSS-1
(r = - 0.68, P < 0.05). SAR had negative correlation with chlorophyta at WSS-1 (r = -
0.63, P < 0.05); with phytoplankton at WSS-1 (r = - 0.70, P < 0.05).
Correlations of Physico-chemical Parameters with Species richness
Water temperature had negative correlation with species richness of chrysophyta at
WSS-1 (r = - 0.59, P < 0.05); with bacillariophyta at WSS-3 (r = - 0.63, P < 0.05),
WSS-4 (r = - 0.83, P < 0.01); and with plankton at WSS-2 (r = - 0.59, P < 0.05), WSS-
3 (r = - 0.62, P < 0.05). Water temperature had positive correlation with species
richness euglenophyta at WSS-4 (r = 0.69, P < 0.05). Light penetration had negative
correlation with species richness of cyanophyta at WSS-1 (r = - 0.62, P < 0.05), WSS-2
(r = - 0.77, P < 0.05), WSS-3 (r = - 0.72, P < 0.05); with zooplankton at WSS-1 (r = -
0.65, P < 0.05). Light penetration had positive correlation with chlorophyta at WSS-1
(r = 0.61, P < 0.05); with bacillariophyta at WSS-2 (r = 0.63, P < 0.05), WSS-4 (r =
77
0.60, P < 0.05). TDS had negative correlation with species richness ofcyanophyta at
WSS-2 (r = - 0.73, P < 0.01); with planktons at WSS-1 (r = - 0.63, P < 0.05). TDS had
positive correlation with species richness ofchlorophyta at WSS-2 (r = 0.60, P < 0.05).
The pH was negatively correlated with species richness of bacillariophyta at WSS-3 (r
= - 0.62, P < 0.05); with chlorophyta at WSS-1 (r = - 0.78, P < 0.01); with pyrrhophyta
at WSS-2 (r = - 0.61, P < 0.05) and phytoplankton at WSS-4 (r = -0.61, P < 0.05. The
pH was positively correlated with species richness of euglenophyta at WSS-3 (r = 0.79,
P < 0.01).DO had negative correlation with species richness of euglenophyta at WSS-3
(r = - 0.63, P < 0.05). DO had positive correlation with species richness of
bacillariophyta at WSS-3 (r = 0.59, P < 0.05), WSS-4 (r = 0.84, P < 0.01); with
phytoplankton at WSS-2 (r = 0.61, P < 0.05). The CO2 had negative correlation with
euglenophyta at WSS- (r = - 0.69, P < 0.05), with bacillariophyta at WSS-3 (r = - 0.66,
P < 0.05), WSS-4 (r = - 0.76, P < 0.01) and with phytoplanktons at WSS-3 (r = - 0.63,
P < 0.05). CO3-2 had negative correlation with species richness of pyrrhophyta at WSS-
2 (r = - 0.61, P < 0.05), with cladocera at WSS-2 (r = - 0.62, P < 0.05) and positive
correlation with chlorophyta at WSS-3 (r = - 0.70, P < 0.05). The HCO3-1 had negative
correlation with cyanophyta at WSS-3 (r = - 0.58, P < 0.05); with cladocera at WSS-2
(r = - 0.67, P < 0.05); with zooplankton at WSS-2 (r = - 0.62, P < 0.05); with
phytoplankton at WSS-1 (r = - 0.64, P < 0.05).
The TA had negative correlation with cyanophyta at WSS-2 (r = - 0.64, P < 0.05); at
WSS-3 (r = - 0.58, P < 0.05); and with phytoplankton at WSS-1 (r = - 0.64, P < 0.05).
The TH had negative correlation with cyanophyta at WSS-2 (r = - 0.78, P < 0.01); at
WSS-3 (r = - 0.57, P < 0.05); with bacillariophyta at WSS-1 (r = - 0.67, P < 0.05); with
78
phytoplankton at WSS-1 (r = - 0.74, P < 0.05).The TH had positive correlation with
chlorophyta at WSS-3 (r = 0.61, P < 0.05).
The Na+1 had positive correlation with cyanophyta at WSS-4 (r = 0.59, P < 0.05). The
Ca+2 had negative correlation with chlorophytaat WSS-3 (r = - 0.62, P < 0.05);
withbacillariophyta at WSS-1 (r = - 0.63, P < 0.05); with phytoplankton at WSS-1 (r =
- 0.72, P < 0.01); and with zooplankton at WSS-2 (r = - 0.63, P < 0.05).
The Cl-1 had positive correlation with phytoplankton at WSS-1 (r = 0.57, P < 0.05);
with zooplankton at WSS-2 (r = 0.64, P < 0.05). The SO4-2 had negative correlation
with cyanophyta at WSS-3 (r = - 0.65, P < 0.05). The EC had negative correlation with
cyanophyta at WSS-2 (r = - 0.72, P < 0.01); with zooplankton at WSS-3 (r = - 0.63, P
< 0.05), and positive correlation with chlorophyta at WSS-2 (r = 0.60, P < 0.05); at
WSS-3 (r = 0.78, P < 0.01). SAR had negative correlation with cyanophyta at WSS-3
(r = - 0.57, P < 0.05); with phytoplankton at WSS-1 (r = - 0.62, P < 0.05); and positive
correlation with protozoa at WSS-2 (r = 0.66, P < 0.05).
79
3.2 Fish Biodiversity
3.2 a Species richness
In this study, twenty (20) species were recorded from ten sampling sites belonging to
different catchment areas (hill torrents) of Suleman Mountain Range, Dera Ghazi
Khan Region, Pakistan. These species belong to three orders (Cypriniformes,
Siluriformes and Synbranchiformes) and five families (Bagridae, Cobitidae,
Cyprinidae, Mastacembelidae and Siluridae). Of the sixteen genera, the two genera
Labeo and Barilius each with three species while rest of the genera each with single
species were recorded. Based on data, it was found that 16 species belong to family
Cyprinidae while of the remaining four species; each belongs to one of four families
(Table 3.2.1, Figure3.2.1 a). ANOVA demonstrated a significant difference (P < 0.05;
df = 4; F = 8.205) for occurrence of number of species between fish families in this
region. A Tucky post-hoc test revealed that Cyprinidae family was found to be
significantly different (P < 0.05) from other fish families (Figure 3.2.1 a).
Species richness was variable at sampling sites. Maximum species were found at FSS-
2 (8 species) followed by FSS-1, FSS-3 and FSS-8 (seven species each) and minimum
species were found on FSS-10 (2 species) (Table 3.2.1, Figure3.2.1 b).One way
ANOVA demonstrated that ten sites with respect to number of species were
significantly different (P < 0.05, df = 9, F = 33.55). A post-hoc Tukey test revealed
that FSS-1 (Vehowa) was significantly different (P < 0.001) from most of the studied
sites except FSS-2, FSS-3 and FSS-8 (P > 0.05). Statistical differences among other
sites for number of species, whether significant/non-significant are shown in (Table
3.2.2).
80
Barilius vagra was found to be the most frequent species recorded from eight sites
(FSS-1, FSS-2, FSS-3, FSS-4, FSS-5, FSS-6, FSS-8 and FSS-9). Among other
frequent species, Tor macrolepis recorded from five sites (FSS-1, FSS-2, FSS-6, FSS-
8 and FSS-9); Labeo diplostomus from five fish sampling sites (FSS-1, FSS-3, FSS-5,
FSS-8 and FSS-9), and Garra gotyla from five fish sampling sites (FSS-1, FSS-3,
FSS-4, FSS-5 and FSS-7) were other frequent species. Cyprinion watsoni was
recorded from four fish sampling sites (FSS-3, FSS-4, FSS-5 and FSS-10). Still other
species (Botia birdi, Barlius pakistanicus, Mastacembalus armatus and Securicula
gora) were the least frequent at two sites. The species such as Cirrhinus mrigala,
Labeo calbasu, Ompok pabda, Puntius sophore, Rita rita, Salmophasia punjabensis
and Schizothorax plagiostomus were only recorded from single site (Table 3.2.3,
Figure 3.2.2 c). The studied fish images are given as under:
81
Barilius modustus
Barilius pakistanicus
82
Barilius vagra
Botia birdi
Cirrhinus mrigala
Crossochelus diplocheilus
Cyprinion watsoni
83
Gara gotyla
Labeo calbasu
Labeo diplostomus
Labeo dyocheilus pakistanicus
Mastacembelus armatus
84
Ompok pabda
Puntius sophore
Rita rita
Salmophasia punjabensis
Salmostoma bacaila
85
Schizothorax plagiostomus
Securicula gora
Tor macrolepis
3.2 b Relative abundance (RA)
The recorded fishes in this study were categorized into highly abundant (RA > 10%),
moderately abundant (RA = 2-10%) and least abundanct (RA < 2%). Tor macrolepis
was the most abundant species (20.37%). Other abundant cyprinids species were
Labeo diplostomus (17.00%), Barilius vagra (11.48%) and Cyprinion watsoni
(10.62%) (Table 3.2.1). Abundance of other species of family Cyprinidae i.e. Barilius
modestus (4.17%), Garra gotyla (8.04%), Labeo dyocheilus pakistanicus (7.12%),
Schizothorax plagiostomus (7.55%) and Salmostoma bacaila (2.89%) was moderate.
Ompok pabda (2.46%) belonging to family Siluridae also showed moderate abundance
(RA 2-10%). Rest of the species of family Cyprinidae and other families were quite
rare (Figure 3.2.2 b, Figure 3.2.2 b). One way ANOVA revealed a significant
difference (P < 0.001; df = 19; F = 10.70) between number of individuals of species. A
Tukey post-hoc test revealed that Tor macrolepis was significantly different (P < 0.05)
from most of other fish species. However, no significant difference (P > 0.05) was
found with Labeo diplostomus, Labeo dyocheilus, Schizothorax plagiostomus,
Crossocheilus diplocheilus and Cyprinion watsoni (Table 3.2.1).
The family Cyprinidae showed highest relative abundance (96.40%) in the study.
Among other four families Siluridae contributed (2.45%) relative abundance while rest
of three families contributed 0.25% each (Figure 3.2.2 a). One way ANOVA
demonstrated a significant difference (P ≤ 0.001; df = 4; F = 8.77) for the number of
individuals between different fish families. The post-hoc Tucky test revealed a
significant difference for number of individuals of Cyprinidae from family Cobitidae
(P < 0.01), bagridae (P < 0.05) and Mastacembelidae (P < 0.05) (Figure 4.2.2 a).
86
However, no significant difference (P > 0.05) was observed between number of
individuals of family Cyprinidae and Siluridae.
The highest RA (21.11%) was found at FSS-3 (Hingloon) followed by FSS-1
(Vehowa) with RA (17.80%), FSS-2 (Barthi) with RA (13.50%) and FSS-8 (Murunj)
with RA (13.07%). FSS-4 (Zinda Peer) with RA (5.64%), FSS-5 (Jaj) with RA
(8.22%), FSS-6 (Harand) with RA (7.79%), FSS-7 (Siri) with RA (2.70%) and FSS-9
(Chitar Wata) with RA (8.34%) showed moderate abundance while FSS-10 (Rakhi
Gaj) with RA (1.78%) has lowest abundance (Table 3.2.2). One way ANOVA
demonstrated a significant difference (P < 0.001; df = 9; F = 25.85) for the abundance
of fish individuals between study sites. The post-hoc Tucky test revealed a significant
difference (P < 0.05) for number of fish individuals of FSS-1 from all of the studied
sites except FSS-2 and FSS-8. Similarly, when FSS-3 was compared, it also exhibited
significant difference (P < 0.001) from all sites except FSS-1. FSS-7 also showed
significant difference from all other fish sampling sites except FSS-6 and FSS-10.
FSS-9 showed non-significant difference (P > 0.05) from most of other site, except
FSS-1, FSS-3, FSS-7 and FSS-10 (Table 3.2.2). FSS-10 showed significant difference
with all other sites except FSS-7. All other sites showed variable pattern of
significane / non-significance levels with different sites.
The RA of fish species within each site are presented in (Table 3.2.3 a and Figure
3.2.2. b). At FSS-1, Labeo diplostmus showed maximum RA (29.65%) while
Securicula gora showed minimum RA (1.37%). At FSS-2, Tor macrolepis showed
maximum RA (36.93%) while two species i.e. Labeo calbasu, Mastacembalus
armatus both showed minimum RA (0.90%). At FSS-3, Schizothorax plagiostomus
87
exhibited maximum RA (35.75%) while Botia birdi minimum RA (0.29%). At FSS-4,
Cyprinion watsoni was recorded with maximum RA (40.21%) and Barilius vagra with
minimum RA (13.04%). At FSS-5, Labeo diplostomus was found to exhibit maximum
RA (37.81%) while Garra gotyla indicated minimum RA (15.12%). At FSS-6, Tor
macrolepis showed maximum RA (43.30%) while Mastacembelus armatus indicated
RA (1.60%). At FSS-7, Garra gotyla showed maximum RA (40.90%) while Barilius
pakistanicus indicated minimum RA (20.45%). At FSS-8, Labeo diplostomus showed
maximum RA (29.10%) while Botia birdi showed minimum RA (1.40%). At FSS-9,
Tor macrolepis with maximum RA (49.26%) while Salmophasia punjabensis with
minimum RA (2.94%) was recorded. At FSS-10, Cyprinion watsoni with maximum
RA (65.51%) and Barilius pakistanicus with minimum RA (34.48%) was recorded
(Table 3.2.3 Figure. 3.2.2 b).
It is important to state that RA of each species at different sites showed variations.
Among dominant species, maximum RA (5.03%) of Tor macrolepis was recorded at
FSS-2 and minimum RA (3.38%) at FSS-6. The RA of Labeo diplostomus was
maximum (5.28%) at FSS-1 and minimum (2.27%) at FSS-9. Barilius vagra showed
maximum RA (2.89%) at FSS-1 and minimum RA (0.74%) at FSS-4. The RA of
Cyprinion watsoni was maximum (5.10%) at FSS-3 and minimum (1.17%) at FSS-10.
Garra gotyla showed maximum RA (2.03%) at FSS-7 and minimum (1.10%) at FSS-5
(Figure 3.2.2 c).
3.2 c Diversity indices
The values of various diversity indices are presented in (Table 3.2.2). Simpson
diversity index (D) value was maximum at FSS-10 and minimum at FSS-1; Shannon-
88
Weaner index (H) was maximum at FSS-1 and minimum at FSS-10; Margalef index
(d) was maximum at FSS-2 and minimum at FSS-10. The evenness index (E) value
was maximum at FSS-2 and minimum at FSS-10 (Table 3.2.2). Calculations of the
Jaccards similarity index for comparison of species composition between different
sites showed highest value (0.6) between FSS-4 (Zinda Peer) and FSS-5 (Jaj). The
similarity index value was found to be zero between several pairs of data sets i.e. FSS-
6 (Harand) and FSS-7 (Siri), and FSS-8 (Murunj); and FSS-7 (Siri) and FSS-9 (Chitar
Wata). FSS-10 (Rakhi Gaj) demonstrated no similarity index with several other sites
i.e. FSS-1 (Vehowa), FSS-2 (Barthi), FSS-6 (Harand), FSS-8 (Murunj) and FSS-9
(Chitar wata) (Table 3.2.4). The dendrogram using Jaccards similarity index, based on
single linkage method demonstrated that FSS-4 (Zinda Peer) and FSS-5 (Jaj) showed
maximum similarity level, while FSS-1 and FSS-7 showed minimum similarity level
in species composition (Figure 3.2.3).
89
3. 3 Fish External Morphometry
3.3.1 Tor macrolepis
The central tendency values i.e. means ± standard deviation (SD) along with range of
measured morphometric data are given in table 3.3.1. Total length (TL) ranged from
7.8 – 31 cm with a mean value of 15.96 cm while wet body weight (W) ranged from
5.20 – 301.90 g with a mean value of 43.52 g.
Highly significant correlation values (P < 0.001) were observed for relationships of all
morphometric measurements with TL and W respectively. When least squared linear
regression correlation was calculated by keeping TL on X-axis and other
morphometric values on Y-axis, the maximum correlation (r = 0.984, P < 0.001) was
found for TL vs FL and minimum (r = 0.827 P <0.001) for TL vs PostOL (Table 3.3.2
a, Figure 3.3.1 a, Figure 3.3.1 b). The slope ‘b’ for TL vs W was found to be 2.637,
demonstrating negative allometric growth pattern. For other morphometric
measurements, the ‘b’ value ranged from 0.749 to 1.192. Most of the parameters
showed isometric growth pattern (b = 1), while TL vs ED and TL vs DFB showed
negative allometric growth (b < 1). TL vs PreOL, TL vs PostOL and TL vs CPL
showed positive allometric growth pattern (b > 1). Similarly, when least squared
regression correlation was calculated by keeping W on X-axis and other morphometric
measurements on Y-axis, maximum correlation (r = 0.953, P < 0.001) was found for
W vs BD and minimum (r = 0.863, P < 0.001) for W vs PelFB (Table 3.3.2 a, Figure
3.3.1 c, Figure 3.3.1 d). The slope ‘b’ value showed either isometric growth (b = 0.33)
or positive allometric growth pattern (b > 0.33) for most of the relationships. However,
W vs ED and W vs DFB showed negative allometric growth pattern. The condition
90
factor (K) was found to be 0.875 (Table 3.3.3). The correlation between K and W(r =
0.188); as well as between K and TL (r = 0.430) was significant (P < 0.05).
3.3.2 Schizothorax plagiostomus
The central tendency values i.e. means ± SD along with range of measured
morphometric data are given in Table 3.3.1 TL ranged from 8.08 – 17.20 cm with a
mean value of 14.13 cm while wet body weight (W) ranged from 7.05 – 67.08 g with
mean value of 32.00 g.
Highly significant relationships (P < 0.001) for all morphometric measurements with
both TL and W were observed. When least squared linear regression was calculated by
keeping TL on X-axis and other morphometric values on Y-axis, the maximum
correlation (r = 0.965, P < 0.001) was found for SL vs TL and minimum (r = 0.853, P
< 0.001) for GL vs TL (Table 3.3.2 b, Figure 3.3.2 a, Figure 3.3.2 b). The slope ‘b’ for
TL vs W was found to be 2.90, indicating optimal fish growth. The value of slope ‘b’
ranged from 0.420 – 1.220 for other morphometric measurements. For majority of
parameters, it reflected either isometric growth pattern (b = 1), or negative allometric
growth pattern (b < 1) for most of the parameters. However, AFL vs TL and AFB vs
TL showed positive allometric growth pattern (b > 1). Similarly, When least squared
linear regression was calculated by keeping W on X-axis and other morphometric
measurements on Y-axis, maximum correlation (r = 0.978, P < 0.001) was found for
SL vs W and minimum (r = 0.901, P < 0.001) for FL vs W (Table 3.3.2 b, Figure
3.3.2 c, Figure 3.3.2 d). The value of slope ‘b’ showed either isometric growth pattern
(b = 0.33) or negative allometric growth pattern (b < 0.33) for most of the
relationships. However, BD vs W, GL vs W, AFL vs W and AFB vs W showed
91
positive allometric growth pattern (b > 0.33). The value of K was found to be 1.08
(Table 3.3.3). The correlation between K and W(r = 0.182); as well as betweenK and
TL (r = 0.112) were non-significant (P > 0.05).
3.3.3 Labeo diplostomus
The central tendency values i.e. means ± SD along with ranges of measured
morphometric data are given in Table 3.3.1 TL values ranged from 9.00 – 20.50 cm
with a mean value of 12.72 cm while W ranged from 5.50 – 90.00 g with mean of
24.03 g.
Highly significant (P < 0.001) relationships for all morphometric measurements with
TL and W were recorded. When least squared linear regression correlation was
calculated by keeping TL on X-axis and other morphometric values on Y-axis, the
maximum correlation (r = 0.970, P < 0.001) was found for TL vs SL and minimum (r
= 0.558, P < 0.001) for TL vs GL (Table 3.3.2 c, Figure 3.3.3 c, Figure 3.3.3 d). The
slope ‘b’ for TL vs W found to be 2.601, which indicating optimal growth, while for
other morphometric measurements, it ranged from 0.472 to 1.393. Majority of the
parameters showed either isometric (b = 1) or negative (b < 1) allometric growth,
while TL vs SL and TL vs PecFB showed positive allometric growth (b > 1).
Similarly, when least squared linear regression correlation was calculated by keeping
W on X-axis and other morphometric measurements on Y-axis, maximum correlation
(r = 0.921, P < 0.001) was found between W and FL and W and PreOL and minimum
(r = 0.713, P < 0.001) between W and DFB (Table 4.3.2 c, Figure 4.3.3 c, Figure 4.3.3
d). The value of slope ‘b’ showed either isometric growth (b = 0.33) or negative
allometric growth (b < 0.33) for most of the relationships. However, W vs SL, W vs
92
PreOL, W vs PFB and W vs GL showed positive allometric growth (b > 0.33). The
value of K for Labeo diplostomus was 1.075 (Table 3.3.3). The correlation between K
and W (r = 0.136) was non-significant (P > 0.05) while correlation (0.305) between K
with TL was significant (P < 0.001).
3.3.4 Labeo dyocheilus pakistanicus
The central tendency values i.e. means ± SD along with range of measured
morphometric data are given in Table 3.3.1 TL ranged from 9.30 – 23.60 cm with a
mean value of 16.63 cm while W ranged from 10.20 – 125 g with mean value of
46.26g.
Highly significant relationships (P < 0.001) for all morphometric measurements with
both TL and W were recorded. When least squared regression correlation was
calculated by keeping TL on X-axis and other morphometric values on Y-axis, the
maximum correlation (r = 0.990, P < 0.001) was found for TL vs FL and minimum
(r = 0.648 P < 0.001) for TL vs PostOL (Table 3.3.2 d, Figure 3.3.4 a, Figure 3.3.4 b).
The value of slope ‘b’ for TL vs W was found to be 2.612, demonstrating optimum
growth. The value of slope ‘b’ for other morphometric measurements ranged from
0.727–1.387, reflecting isometric growth pattern (b = 1) for majority of the
parameters. However, for several parameters such as TL vs PostOL, TL vs PectFB, TL
vs AFL and TL vs CPW, the value of slope ‘b’ reflects negative (b < 1) allometric
growth pattern; while for TL vs SL, TL vs PectFL, TL vs PelFL and TL vs DFB, the
value of slope ‘b’ demonstated positive allometric growth pattern (b > 1). Similarly
when least squared linear regression correlation was calculated by keeping W on X-
axis and other morphometric measurements on Y-axis, maximum correlation (r =
93
0.975, P < 0.001) was found between W and FL and minimum (r = 0.640, P < 0.001)
between W and PostOL (Table 3.3.2 d, Figure 3.3.4 c, Figure 3.3.4 d). The value of
slope ‘b’ showed isometric growth pattern (b = 0.33) for majority of the relationships.
Few parameters i.e. W vs PostOL, W vs AFL demonstrated negative allometric growth
pattern (b < 0.33), while other parameters like, W vs SL, W vs PreOL, W vs PecFL, W
vs PelFL and W vs DFB demonstrated positive allometric growth pattern. The value of
K was found to be 0.933 (Table 3.3.3). The correlations between K and W (r = 0.262,
P < 0.05) and between K and TL (r = 0.503, P < 0.001) were significant.
3.3.5 Cyprinion watsoni
The central tendency values including means ±SD along with ranges of measured
morphometric data are given in Table 3.3.1 TL values ranged from 8.00 – 14.30 cm
with a mean value of 10.86 cm and W ranged from 7.90 – 30.70 g with a mean value
of 15.05 g.
Highly significant correlation values were found for relationships of all morphometric
measurements with TL and W. When least squared linear regression correlation was
calculated by keeping TL on X-axis and other morphometric values on Y-axis, the
maximum correlation (r = 0.957, P < 0.001) was found for TL vs FL and minimum (r
= 0.383 P = 0.001) for TL vs PecFL (Table 3.3.2 e, Figure 3.3.5 a, Figure 3.3.5 b). The
value of slope ʽbʼ for TL vs W was found to be 2.732, showing optimum growth,
while for other morphometric measurements, it ranged from 0.557 to 1.127. Most of
the parameters showed either isometric growth (b = 1) or negative allometric growth
(b < 1). Similarly, when least squared regression correlation was calculated by keeping
W on X-axis and other morphometric measurements on Y-axis, maximum correlation
94
(r = 0.929, P < 0.001) was found for W vs FL and W vs CPW and minimum (r =
0.600, P > 0.001) for W vs PecFL (Table 4.3.2 e, Figure 4.3.5 c, Figure 4.3.5 d). The
value of slope ʽbʼ showing either isometric growth (b = 0.33) or negative allometric
growth (b < 0.33) for most of the relationships. However, W vs PreOL and W vs CPL
showed positive allometric growth (b > 0.33). The value of K was found to be 1.136
(Table 3.3.3). The correlation between K and W(r = 0.168); as well as between K and
TL (r = 0.227) was non-significant (P > 0.05).
3.3.6 Ompok pabda
The central tendency values i.e. means ± SD along with ranges of measured
morphometric data are given in Table 3.3.1 TL values ranged from 10.30 – 18.90 cm
with a mean value of 15.06 cm while W ranged from 8.50 – 57.00 g with mean of
26.08 g.
Highly significant correlation values were observed for relationships of most of the
morphometric measurements with TL and W. When least squared regression
correlation was calculated by keeping TL on X-axis and other morphometric values on
Y-axis, the maximum correlation (r = 0.988, P < 0.001) was found for TL vs FL and
minimum (r = 0.194 P > 0.05) for TL vs CPL (Table 3.3.2 f, Figure 3.3.6 a, Figure
3.3.6 b). The value of slope ʽbʼ for TL vs W was found to be 3.048, showing isometric
growth, while for other morphometric measurements, it ranged from -0.864 to 1.630.
Most of the parameters showed isometric growth (b = 1), while TL vs DFB and TL vs
CPL showed negative allometric growth (b < 1). TL vs PostOL, TL vs BG and TL vs
CPW showed positive allometric growth (b > 1). Similarly, when least squared
regression correlation was calculated by keeping W on X-axis and other morphometric
95
measurements on Y-axis, maximum correlation (r = 0.950, P < 0.001) was found
between W vs TL and between W vs FL and minimum (r = 0.154, P > 0.05) between
W and CPL (Table 3.3.2 f, Figure 3.3.6 c, Figure 3.3.6 d). The value of slope ʽbʼ
showing either isometric growth (b = 0.33) or negative allometric growth (b < 0.33)
for most of the relationships. However, W vs Post OL and W vs PecFB and W vs
CPW showed positive allometric growth (b > 0.33). The value of condition factor K
was found to be 0.723 (Table 3.3.3). The correlation between K and W (r = 0.357, P <
0.05) was significant, however, between K and TL (r = 0.048) was non-significant (P
> 0.05).
3.3.7 Garra gotyla
The central tendency values including means ±SD along with ranges of measured
morphometric data are given in Table 3.3.1 TL values ranged from 9.00 – 14.10 cm
with a mean value of 11.06 cm and W ranged from 8.60 – 39.30 g with a mean value
of 17.66 g.
Highly significant correlation values were found for relationships of all morphometric
measurements TL and W. When least squared regression correlation was calculated by
keeping TL on X-axis and other morphometric values on Y-axis, the maximum
correlation (r = 0.991, P < 0.001) was found for TL vs FL and minimum (r = 0.761 P
= 0.001) for TL vs PostOL (Table 3.3.2 g, Figure 3.3.7 a, Figure 3.3.7 b). The value of
slope ʽbʼ for TL vs W was found to be 3.103, showing isometric growth, while for
other morphometric measurements, it ranged from 0.584 to 1.613. Most of the
parameters showed isometric growth (b = 1). Some showed either positive allometric
(b > 1) or negative allometric growth (b < 1). Similarly, when least squared linear
96
regression correlation was calculated by keeping W on X-axis and other morphometric
measurements on Y-axis, maximum correlation (r = 0.986, P < 0.001) was found for
W vs SL and minimum (r = 0.735, P > 0.001) for W vs PostOL (Table 3.3.2 g, Figure
3.3.7 c, Figure 3.3.7 b). The value of slope ʽbʼ showing either isometric growth (b =
0.33) or positive allometric growth (b < 0.33) for most of the relationships. However,
W vs PostOL and W vs ED showed negative allometric growth (b < 0.33). The value
of K was found to be 1.230 (Table 3.3.3). The correlation between K and W (r =
0.335) and between K with TL (r = 0.190) was non-significant (P > 0.05).
97
Table3.1.1 a: Mean values (±SD) of climatic factors at four water sampling sites and in four seasons (January 2012-December 2012) at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Parameter WSS-1 WSS-2 WSS-3 WSS-4
Mean humidity* (%) 60.50±17.20a 62.04±17.06a 63.17±16.23a 60.71±15.42a
Photoperiod* (h) 11.99±1.35a 11.98±1.35a 11.98±1.35a 11.98±1.36a
Air temperature* (oC) 29.26±9.86a 30.72±8.46a 30.74±7.78a 33.31±6.14a
Winter Spring Summer Post-monsoon
Mean humidity** (%) 76.30±3.01a 50.44±14.34a 48.53±13.34b 69.44±3.34b
Photoperiod** (h) 10.51±0.38a 12.00±0.47b 13.54±0.35b 11.82±0.49c
Air temperature** (oC) 21.78±4.96a 30.66±3.44b 39.52±1.34b 32.90±2.01c
Mean values with different superscript letters are significantly different from one another. (LSD Posthoc analysis)*= non significant effect of site/season **= Significant effect of site/season
Table 3.1.1 b: Monthly variations in climatic factors (Mean values±SD) of four water sampling sites (January 2012-December 2012)at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Months Mean humidity** (%) Photoperiod** (h) Air Temperature**(oC)
January 78.13±2.21a 10.28±0.02a 17.55±4.83a
February 73.63±2.87a 11.11±0.02a 22.73±5.11b
March 63.63±2.95ab 11.56±0.02a 27.85±2.02c
April 37.25±2.75bc 12.44±0.02a 33.48±1.58d
May 34.50±2.12c 13.38±0.01b 38.55±0.82e
June 37.50±2.92d 14.06±0.02b 39.90±0.90f
July 57.88±1.65d 13.51±0.02b 41.10±0.65g
August 64.25±1.85d 13.18±0.03c 38.28±0.10h
September 67.13±0.48e 12.27±0.03 c 34.40±1.51i
October 71.75±3.40f 11.36±0.03d 31.40±1.07j
November 76.00±2.80f 10.45±0.03de 26.80±1.20k
December 77.63±2.81f 10.18±0.03e 20.05±3.05l
TheMean values with different superscript letters are significantly different from one another (LSD post-hoc analysis).*= non significant effect of month**= Significant effect of month
98
Table. 3.1.2a: Mean values (±SD) of physico-chemical parameters at four water sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Parameter WSS-1 WSS-2 WSS-3 WSS-4
WT* (°C) 22.73±8.29a 24.10±8.02a 24.62±7.02a 27.82±5.81a
LP*(cm) 23.86±7.04a 23.32±6.42a 28.48±6.63a 26.50±4.88a
TDS**(mgL-1) 1182.50±198.50a 1174.17±179.97b 1209.33±123.11b 1636.58±126.09b
pH* 7.85±0.24a 7.91±0.31a 8.02±0.29a 7.85±0.27a
DO* (mgL-1) 7.30±1.16a 7.29±1.10a 7.03±0.91a 6.63±0.84a
CO2*(mgL-1) 7.84±1.14a 7.18±1.24a 7.35±1.19a 7.34±0.75a
CO3*(mgL-1) 12.08±3.10a 11.81±4.02a 16.06±2.72a 13.78±3.51a
HCO-3**(mgL-1) 623.11±160.61a 596.63±151.09a 600.39±41.69a 386.34±30.14b
TA** (mgL-1) 101.92±22.46a 107.75±25.26b 616.33±44.10c 401.00±35.37c
TH** (mgL-1) 264.17±25.75a 265.00±23.84a 201.58±32.36b 241.25±17.24c
Na** (mgL-1) 105.80±33.33a 125.35±55.46b 118.23±20.48b 200.85±14.73b
Ca** (mgL-1) 195.83±20.81ab 174.27±30.86b 183.50±28.05b 227.33±14.30b
Mg** (mgL-1) 56.50±12.33a 53.37±12.69ab 51.15±12.22b 63.40±8.68b
Cl** (mgL-1) 105.69±25.30a 160.87±99.74b 95.85±15.95c 382.34±37.26c
SO4** (mgL-1) 238.34±43.06a 203.67±79.10b 280.60±40.49bc 361.52±46.98c
EC** (dSm-1) 1.90±0.32a 1.91±0.28b 1.96±0.32b 2.57±0.20b
SAR** (mgL-1) 1.86±0.46a 2.32±1.02b 1.77±0.13bc 3.30±0.46c
TheMean values with different letters are significantly different from one another; (LSD post-hoc analysis)WT (water temperature), LP (light penetration), TDS (total dissolved solids), DO (dissolved oxygen), TA (total alkalinity), TH (total hardness), EC (electrical conductivity), SAR (sodium adsorption ratio).*= non significant effect of site**= Significant effect of site
Table 3.1.2 b: Mean values (±SD) of physico-chemical parameters in four seasons (January 2012- December 2012) of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Parameters Winter Spring Summer Post-monsoonWT**(°C) 16.27±5.07a 25.28±3.44b 32.01±1.55b 27.48±1.84c
LP** (cm) 28.21±3.80a 30.90±2.50a 22.48±7.27b 20.69±6.32b
TDS* (mgL-1) 1270.44±239.06a 1400.13±191.51a 1312.67±287.44a 1259.75±277.15a
pH** 7.73±0.18a 7.98±0.10ab 8.12±0.28bc 7.84±0.28c
DO** (mgL-1) 8.09±0.66a 7.43±0.35b 5.99±0.30c 6.68±0.72d
CO2**(mgL-1) 6.28±0.46a 7.66±0.52b 8.51±0.40b 7.16±0.73c
CO3*(mgL-1) 13.06±4.26a 14.20±2.55a 14.23±4.33a 12.11±1.88a
HCO-3* (mgL-1) 533.94±156.41a 588.57±141.47a 546.19±139.34a 553.42±172.28a
TA* (mgL-1) 303.00±233.10a 324.25±227.86a 326.00±233.98a 285.88±202.06a
TH* (mgL-1) 237.81±28.47a 256.63±39.04a 244.47±35.20a 233.63±48.67a
Na* (mgL-1) 128.05±47.11a 164.45±62.49a 136.28±50.83a 139.21±41.68a
Ca* (mgL-1) 194.35±23.91a 200.50±31.74a 198.16±36.64a 184.90±37.20a
Mg* (mgL-1) 55.91±11.88a 60.60±10.73a 56.45±13.24a 51.92±13.30a
Cl* (mgL-1) 170.84±114.77a 205.54±162.10a 198.34±133.18a 184.47±138.72a
SO4* (mgL-1) 271.50±57.09a 282.54±73.31a 280.56±100.97a 245.22±92.47a
EC* (dSm-1) 2.07±0.42a 2.23±0.30a 2.08±0.44a 2.01±0.41a
SAR** (mgL-1) 2.05±0.69a 2.80±1.29ab 2.38±0.81ab 2.29±0.67b
TheMean values with different letters are significantly different from one another; (LSD Post-hoc analysis)WT (water temperature), LP (light penetration), TDS (total dissolved solids), DO (dissolved oxygen), TA (total alkalinity), TH (total hardness), EC (electrical conductivity), SAR (sodium adsorption ratio)*= non significant effect of season**= Significant effect of season
99
Table 3.1.2 c: Monthly variations in physico-chemical parameters (Mean values±SD) at four water sampling sites(January 2012-December 2012) at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Parameters WT** LP** TDS* pH** DO** CO2** CO3
-2** HCO3-1*
January 12.65±4.10a 31.75±0.88a 1287.75±158.45a 7.68±0.22a 8.65±0.42a 6.17±0.30a 13.58±4.69a 538.9±139.22a
February 18.00±4.85ab 29.68±1.96a 1346.75±114.67a 7.82±0.21a 7.92±0.25ab 6.80±0.24a 11.85±3.70ab 599.2±166.58a
March 22.55±2.23abc 31.48±2.63ab 1377.75±210.00a 7.97±0.10a 7.65±0.33bc 7.40±0.50ab 13.30±1.78ab 613.6±156.22a
April 28.00±1.70abc 30.33±2.61ab 1422.5±200.35a 7.97±0.13ab 7.20±0.22bcd 7.92±0.46ab 15.10±3.14ab 563.5±143.59a
May 30.65±1.48bcd 28.03±5.19ab 1415.0±263.53a 8.10±0.33abc 6.52±0.37cd 8.72±0.71bc 14.40±3.85ab 611.9±143.06a
June 32.10±1.82cd 25.30±6.91abc 1361.25±306.44a 8.10±0.40abc 5.97±0.15d 8.82±0.30c 17.90±5.07ab 578.6±105.69a
July 33.48±1.42d 21.68±6.40bcd 1268.0±331.18a 8.10±0.24 abc 5.77±0.09de 8.52±0.22cd 12.68±3.92ab 520.6±72.71a
August 30.98±0.75e 15.43±3.66bcd 1162.0±270.19a 8.05±0.34 abc 5.90±0.14ef 8.27±0.54de 11.33±1.16ab 488.5±205.88a
September 28.63±1.88ef 15.93±4.62cd 1186.5±334.65a 7.82±0.32 abc 6.30±0.37fg 7.55±0.70de 11.03±1.13ab 502.8±175.43a
October 26.33±0.91f 25.45±3.37d 1333.0±230.06a 7.85±0.28bc 7.05±0.83fg 6.77±0.59ef 13.20±1.94b 604.1±177.88a
November 21.35±1.88g 28.93±0.88e 1268.75±365.88a 7.70±0.21bc 7.52±0.67fg 6.25±0.40ef 12.85±4.96b 525.2±183.43a
December 13.08±3.92g 22.50±1.77e 1178.5±306.88a 7.71±0.10c 8.25±0.73g 5.90±0.42f 13.95±5.17b 472.5±175.18a
TA* TH** Na+1** Ca+2** Mg+2** Cl-1* SO4-2* EC* SAR**
January 301.25±262.13a 245.0±30.10a 122.5±44.40a 197.0±19.63a 62.4±6.72a 165.08±109.59a 278.15±38.43a 2.22±0.45a 2.01±0.42a
February 310.50±246.39a 242.2±39.81a 136.2±32.70ab 202.5±15.11ab 59.4±4.85ab 153.54±118.24a 292.55±36.42a 2.14±0.16a 2.15±0.40ab
March 316.25±236.60a 244.5±50.14ab 142.03±45.20ab 201.0±30.48b 64.2±1.20abc 167.65±160.41a 276.00±70.59a 2.18±0.32a 2.32±0.69 ab
April 332.25±254.93a 268.7±25.50ab 186.9±75.70ab 200.0±37.70ab 57±15.26 abc 243.44±178.17a 289.08±86.26a 2.28±0.30a 3.26±1.40 ab
May 342.50±267.38a 268.5±30.48ab 138.9±54.80ab 222.0±16.57ab 60.3±9.05 abc 194.79±147.58a 319.20±68.61a 2.25±0.40a 2.46±0.73 ab
June 326.50±280.98a 248.5±17.23ab 143.4±53.20ab 202.0±35.67ab 59.58±16.1abc 193.92±152.38a 284.62±117.21a 2.15±0.47a 2.49±0.91 ab
July 303.00±256.57a 246.0±25.11ab 107.5±72.47ab 200.0±29.66ab 58.5±13.30 abc 185.22±156.58a 295.40±93.05a 2.00±0.50a 2.26±0.89 ab
August 276.75±215.96a 221.0±50.98ab 141.4±28.20ab 170.6±45.44ab 46.2±11.36 abc 196.92±125.22a 211.66±111.91a 1.85±0.39a 2.18±0.34 ab
September 277.25±215.96a 213.7±55.64ab 138.7±39.50ab 169.8±45.27ab 44.28±13.49abc 206.34±148.06a 204.84±115.16a 1.89±0.49a 2.25±0.61 ab
October 294.50±220.06a 253.5±37.13ab 139.73±49.90ab 200.0±23.90ab 59.55±8.67 bc 162.59±147.30a 285.60±48.39a 2.13±0.34a 2.32±0.64 ab
November 306.75±265.65a 237.0±32.01ab 127.1±63.30ab 190.5±35.57b 56.47±12.94c 182.29±146.12a 275.52±82.74a 2.01±0.56a 2.11±0.92 ab
December 293.50±267.33a 227.0±16.87b 126.44±62.60b 187.4±28.23b 45.37±15.77c 182.47±133.10a 239.76±68.07a 1.87±0.49a 1.89±0.74b
TheMean values with different letters are significantly different from one another, (LSD post-hoc analysis)*= non significant effect of month**= Significant effect of month
100
Table 3.1.3 a: Species richness (as taxa number) of major planktonic groups at four water sampling sites (January 2012-December 2012)of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta* 11a 11a 12a 9a 12Chlorophyta** 31a 26a 24a 16b 33Chrysophyta* 7a 5a 4a 3a 7Euglenophyta* 5a 4a 4a 4a 6Xanthophyta* 4a 3a 3a 2a 4Bacillariophyta* 11a 12a 11a 9a 12Pyrrhophyta** 5a 6ab 7ab 5b 9Protozoa** 13a 10a 12ab 10b 15Rotifera* 11a 8a 10a 7a 11Cladocera** 10a 5ab 5ab 4b 10Phytoplankton** 74a 67a 65a 48b 83Zooplankton* 34a 23a 27a 21a 36All phyla** 108a 90a 92a 69b 119Thespecies richness values with different letters superscript are significantly different from one another, (LSD post-hoc analysis)*= non significant effect ofsite**= Significant effect of site .
Table 3.1.3 b: Species richness (as taxa number) of major planktonic groups in four seasons(January 2012-December 2012) of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla Winter Spring Summer Post Monsoon All seasons
Cyanophyta** 12a 8ab 10b 10c 12Chlorophyta* 28a 18a 21a 15a 33Chrysophyta** 5a 4ab 4ab 4b 7Euglenophyta* 5a 4a 3a 4a 6Xanthophyta** 3a 1b 3b 2b 4Bacillariophyta** 11a 9b 9b 9b 12Pyrrhophyta** 4a 4ab 6ab 4b 9Protozoa* 10a 8a 11a 7a 15Rotifera** 08a 8a 6a 7b 11Cladocera* 06a 5a 6a 5a 10Phytoplankton** 68a 48a 56a 48b 83Zooplankton** 24a 21a 23a 19b 36All phyla** 92a 69ab 79ab 67b 119Thespecies richness values with different letters superscript are significantly different from one another, (LSD post-hoc analysis).*= non significant effect of season **= Significant effect of season
101
Table 3.1.3 c: Monthly variation in species richness (as taxa number) of major planktonic groups(January 2012-December 2012)of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecCyanophyta** 7a 8ab 8ab 7ab 7ab 7abc 7bcd 8bcd 7cde 9cde 7de 7e
Chlorophyta** 13a 11a 12a 13ab 13ab 9abc 13abc 10babc 9abc 12de 14de 13e
Chrysophyta** 4a 3ab 3ab 5ab 3ab 3abc 3abc 3abc 3bc 3bc 2bc 3c
Euglenophyta** 6a 4ab 5ab 3abc 3abc 3abc 2abc 3bc 3bc 4bc 3c 5c
Xanthophyta** 3a 1ab 1ab 1ab 1abc 1abc 1abc 2bc 0bc 2bc 1bc 2c
Bacillariophyta** 9a 8ab 8bc 6bcd 5cde 6def 6ef 4ef 6ef 7ef 6f 10f
Pyrrhophyta** 3a 2a 3a 4a 3ab 3ab 2ab 4ab 2ab 4ab 3ab 3b
Protozoa** 6a 7ab 6abc 8abc 6abc 5abc 8bc 5bc 5bcd 8cd 8cd 7d
Rotifera** 5a 6ab 7ab 5abc 4abcd 3abcd 3abcde 5bcdef 7cdef 5def 6ef 4f
Cladocera** 3a 3b 4b 2b 2b 2bc 2bc 3bc 5bc 4bc 4bc 4c
Phytoplankton** 42a 33ab 30abc 32bcd 32bcd 31bcde 30bcde 28cde 25de 32de 30ef 36f
Zooplankton* 12a 13a 14a 14a 12a 13a 14a 14a 15a 13a 14a 15a
All phyla** 54a 46ab 44ab 46abc 44abcd 44bcd 44bcd 42bcd 40bcd 45cd 44de 51e
Thespecies richness values with different letters are significantly different from one another, (LSD post-hoc analysis).*= non significant effect of month**= Significant effect of month
102
Table 3.1.4 a: Number of organisms of major planktonic groups at four sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta** 1156a 1350a 1208a 770b 4484Chlorophyta** 1400a 1374a 1254a 786b 4814Chrysophyta** 200a 184a 108b 66b 558Euglenophyta** 252a 204ab 182bc 130c 768Xanthophyta** 66a 34b 24b 22b 146Bacillariophyta* 596a 666a 554a 446a 2262Pyrrhophyta** 256a 186b 128bc 84c 654Protozoa** 464a 568ab 514bc 388c 1934Rotifera** 344a 246b 206b 170b 966Cladocera** 38a 40a 32ab 22b 132Phytoplankton** 3926a 3998ab 3458b 2304c 13686Zooplankton** 846a 854a 752a 580b 3032All phyla** 4772a 4852a 4210b 2884c 16718The values with different letters superscript are significantly different from one another, (LSD post-hoc analysis).*= non significant effect of site**= Significant effect of site
Table 3.1.4 b: Number of organisms of major planktonic groups in four seasons,(January 2012-December 2012)of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla Winter Spring Summer Post Monsoon All seasonsCyanophyta* 1464a 704a 1460a 856a 4484Chlorophyta* 1662a 716a 1726a 710a 4814Chrysophyta* 210a 114a 172a 62a 558Euglenophyta* 286a 136a 224a 122a 768Xanthophyta* 62a 24a 42a 18a 146Bacillariophyta** 1300a 336b 376b 250b 2262Pyrrhophyta** 222a 130ab 152ab 150b 654Protozoa* 686a 280a 648a 320a 1934Rotifera** 310a 186ab 262ab 208b 966Cladocera** 36a 28ab 38bc 30c 132Phytoplankton** 5206a 2160ab 4152ab 2168b 13686Zooplankton* 1032a 494a 948a 558a 3032All phyla** 6238a 2654ab 5100ab 2726b 16718The values with different letters superscript are significantly different from one another, (LSD post-hoc analysis).*= non significant effect of season**= Significant effect of season
103
Table 3.1.4 c: Monthly variation (January 2012-December 2012) in number of organisms of major planktonic groups of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TotalCyanophyta** 350a 362ab 362ab 342ab 246ab 438abc 462abc 314abc 424abc 432abc 398bc 354c 4484Chlorophyta** 360a 454a 300ab 416ab 370ab 474ab 500ab 382ab 356ab 354ab 452ab 396b 4814Chrysophyta** 60a 80ab 58ab 56ab 46ab 28ab 56ab 42ab 44b 18b 34b 36b 558Euglenophyta* 72a 54a 84a 52a 48a 50a 46a 80a 72a 50a 82a 78a 768Xanthophyta** 18a 4ab 18ab 6ab 6ab 14abc 6abc 16abc 0abc 18abc 22bc 18c 146Bacillaphyta** 480a 290ab 224bc 112cd 76de 106de 96de 98e 98e 152e 146e 384e 2262Pyrrhophyta* 48a 60a 70a 60a 48a 44a 30a 30a 78a 72a 64a 50a 654Protozoa** 162a 188a 120ab 160ab 124ab 162ab 196ab 166ab 160ab 160ab 160b 176b 1934Rotifera** 60a 70ab 110ab 76ab 76abc 54abc 46abc 86abc 102abc 106abc 102bc 78c 966Cladocera** 8a 10ab 18abc 10abc 10bcd 4bcd 14bcd 10bcd 16bcd 14cd 10cd 8d 132Phytoplankton** 1388a 1304a 1116a 1044ab 840ab 1154ab 1196ab 962ab 1072ab 1096ab 1198ab 1316b 13686Zooplankton* 230a 268a 248a 246a 210a 220a 256a 262a 278a 280a 272a 262a 3032All phyla** 1618a 1572a 1364a 1290ab 1050ab 1374ab 1452ab 1224ab 1350ab 1376ab 1470ab 1578b 16718The values with differentsuperscriptletters are significantly different from one another, (LSD post-hoc).*= non significant effect of month**= Significant effect of month
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Table 3.1.5 a: Relative abundance (%) of major planktonic groups within each site at four water sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta 24.23 27.82 28.69 26.70 26.82Chlorophyta 29.34 28.32 29.79 27.25 28.80Chrysophyta 4.19 3.79 2.57 2.29 3.34Euglenophyta 5.28 4.20 4.32 4.51 4.59Xanthophyta 1.38 0.71 0.57 0.77 0.87Bacillariophyta 12.49 13.73 13.16 15.46 13.53Pyrrhophyta 5.36 3.83 3.04 2.91 3.91Protozoa 9.72 11.71 12.21 13.45 11.57Rotifera 7.21 5.07 4.89 5.89 5.78Cladocera 0.80 0.82 0.76 0.77 0.79Phytoplankton 82.27 82.40 82.14 79.89 81.86Zooplankton 17.73 17.60 17.86 20.11 18.14All phyla 100 100 100 100 100
Table 3.1.5 b: Relative abundance (%) of major planktonic groups within all sites of Suleman Mountain Range, Dera Ghazi Khan Region,Pakistan.
Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta 6.91 8.08 7.23 4.61 26.82Chlorophyta 8.37 8.22 7.50 4.70 28.80Chrysophyta 1.20 1.10 0.65 0.39 3.34Euglenophyta 1.51 1.22 1.09 0.78 4.59Xanthophyta 0.39 0.20 0.14 0.13 0.87Bacillariophyta 3.57 3.98 3.31 2.67 13.53Pyrrhophyta 1.53 1.11 0.77 0.50 3.91Protozoa 2.78 3.40 3.07 2.32 11.57Rotifera 2.06 1.47 1.23 1.02 5.78Cladocera 0.22 0.24 0.19 0.13 0.79Phytoplankton 23.48 23.91 20.69 13.78 81.86Zooplankton 5.06 5.11 4.49 3.47 18.14All phyla 28.54 29.02 25.18 17.25 100.00
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Table 3.1.5 c: Relative abundance (%) of major planktonic groups within each season (January 2012-December 2012) of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla Winter Spring Summer Post Monsoon All seasonsCyanophyta 23.46 26.52 28.62 31.40 26.82Chlorophyta 26.65 26.97 33.84 26.04 28.80Chrysophyta 3.37 4.29 3.38 2.27 3.34Euglenophyta 4.59 5.12 4.39 4.47 4.59Xanthophyta 0.99 0.91 0.83 0.67 0.87Bacillariophyta 20.84 12.66 7.37 9.17 13.53Pyrrhophyta 3.55 4.89 2.98 5.50 3.91Protozoa 10.99 10.56 12.70 11.73 11.56Rotifera 4.98 7.00 5.14 7.64 5.78Cladocera 0.58 1.06 0.75 1.11 0.80Phytoplankton 83.45 81.38 81.39 79.52 81.86Zooplankton 16.55 18.62 18.59 20.48 18.14All phyla 100 100 100 100 100
Table 3.1.5 d: Relative abundance (%) of major planktonic groups in all seasons (January 2012 -December 2012)of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla Winter Spring Summer Post Monsoon All seasonsCyanophyta 8.75 4.21 8.73 5.12 26.82Chlorophyta 9.94 4.28 10.34 4.24 28.80Chrysophyta 1.25 0.68 1.04 0.37 3.34Euglenophyta 1.73 0.81 1.34 0.72 4.59Xanthophyta 0.37 0.14 0.26 0.10 0.87Bacillariophyta 7.78 2.00 2.24 1.49 13.53Pyrrhophyta 1.32 0.77 0.92 0.89 3.91Protozoa 4.10 1.67 3.87 1.91 11.56Rotifera 1.85 1.11 1.56 1.24 5.78Cladocera 0.23 0.19 0.25 0.17 0.80Phytoplankton 31.14 12.89 24.80 12.93 81.86Zooplankton 6.18 2.97 5.68 3.33 18.14All phyla 37.33 15.89 30.48 16.26 100
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Table 3.1.5 e: Monthly variation (January 2012-December 2012) in relative abundance (%) of major planktonic groups of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total
Cyanophyta 2.09 2.17 2.17 2.05 1.47 2.62 2.76 1.88 2.54 2.58 2.38 2.12 26.82Chlorophyta 2.15 2.72 1.79 2.49 2.21 2.84 2.99 2.28 2.13 2.12 2.70 2.37 28.80Chrysophyta 0.36 0.48 0.35 0.33 0.28 0.17 0.33 0.25 0.26 0.11 0.20 0.22 3.34Euglenophyta 0.43 0.32 0.50 0.31 0.29 0.30 0.28 0.48 0.43 0.30 0.49 0.47 4.59Xanthophyta 0.11 0.02 0.11 0.04 0.04 0.08 0.04 0.10 0.00 0.11 0.13 0.11 0.87Bacillaphyta 2.87 1.73 1.34 0.67 0.45 0.63 0.57 0.59 0.59 0.91 0.87 2.30 13.53Pyrrhophyta 0.29 0.36 0.42 0.36 0.29 0.26 0.18 0.18 0.47 0.43 0.38 0.30 3.91Protozoa 0.97 1.12 0.72 0.96 0.74 0.97 1.17 0.99 0.96 0.96 0.96 1.05 11.57Rotifera 0.36 0.42 0.66 0.45 0.45 0.32 0.28 0.51 0.61 0.63 0.61 0.47 5.78Cladocera 0.05 0.06 0.11 0.06 0.06 0.02 0.08 0.06 0.10 0.08 0.06 0.05 0.79Phytoplankton 8.30 7.80 6.68 6.24 5.02 6.90 7.15 5.75 6.41 6.56 7.17 7.87 81.86Zooplankton 1.38 1.60 1.48 1.47 1.26 1.32 1.53 1.57 1.66 1.67 1.63 1.57 18.14All phyla 9.68 9.40 8.16 7.72 6.28 8.22 8.69 7.32 8.08 8.23 8.79 9.44 100
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Table 3.1.6 a: Simpson diversity index (D) values of major planktonic groups at four water sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta 0.06 0.08 0.08 0.07 0.07Chlorophyta 0.09 0.08 0.09 0.07 0.08Chrysophyta 0.00 0.00 0.00 0.00 0.00Euglenophyta 0.00 0.00 0.00 0.00 0.00Xanthophyta 0.00 0.00 0.00 0.00 0.00Bacillariophyta 0.02 0.02 0.02 0.02 0.02Pyrrhophyta 0.00 0.00 0.00 0.00 0.00Protozoa 0.01 0.01 0.01 0.02 0.01Rotifera 0.01 0.00 0.00 0.00 0.00Cladocera 0.00 0.00 0.00 0.00 0.00Phytoplankton 0.68 0.68 0.67 0.64 0.67Zooplankton 0.03 0.03 0.03 0.04 0.03All phyla 1.00 1.00 1.00 1.00 1.00
Table 3.1.6 b: Simpson diversity index (D) values of major planktonic group in four seasons(January 2012-December 2012)at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla Winter Spring Summer Post-monsoon
All Seasons
Cyanophyta 0.06 0.07 0.08 0.10 0.07Chlorophyta 0.07 0.07 0.11 0.07 0.08Chrysophyta 0.00 0.00 0.00 0.00 0.00Euglenophyta 0.00 0.00 0.00 0.00 0.00Xanthophyta 0.00 0.00 0.00 0.00 0.00Bacillariophyta 0.04 0.02 0.01 0.01 0.02Pyrrhophyta 0.00 0.00 0.00 0.00 0.00Protozoa 0.01 0.01 0.02 0.01 0.01Rotifera 0.00 0.00 0.00 0.01 0.00Cladocera 0.00 0.00 0.00 0.00 0.00Phytoplankton 0.70 0.66 0.66 0.63 0.67Zooplankton 0.03 0.03 0.03 0.04 0.03All phyla 0.91 0.88 0.92 0.87 0.90
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Table 3.1.6 c: Shannon-Weiner index (H) values of major planktonic group at four water sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta 0.34 0.36 0.36 0.35 0.35Chlorophyta 0.36 0.36 0.36 0.35 0.36Chrysophyta 0.13 0.12 0.09 0.09 0.11Euglenophyta 0.16 0.13 0.14 0.14 0.14Xanthophyta 0.06 0.03 0.03 0.04 0.04Bacillariophyta 0.26 0.27 0.27 0.29 0.27Pyrrhophyta 0.16 0.13 0.11 0.10 0.13Protozoa 0.23 0.25 0.26 0.27 0.25Rotifera 0.19 0.15 0.15 0.17 0.16Cladocera 0.04 0.04 0.04 0.04 0.04Phytoplankton 1.46 1.40 1.35 1.36 0.16Zooplankton 0.45 0.44 0.44 0.47 0.31All phyla 2.38 2.31 2.26 2.34 ----
Table 3.1.6 d: Shannon-Weiner index (H) values of major planktonic group in four seasons (January 2012-December 2012) at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla Winter Spring Summer Post-monsoon All SeasonsCyanophyta 0.34 0.35 0.36 0.36 0.35Chlorophyta 0.35 0.35 0.37 0.35 0.36Chrysophyta 0.11 0.14 0.11 0.09 0.11Euglenophyta 0.14 0.15 0.14 0.14 0.14Xanthophyta 0.05 0.04 0.04 0.03 0.04Bacillariophyta 0.33 0.26 0.19 0.22 0.27Pyrrhophyta 0.12 0.15 0.10 0.16 0.13Protozoa 0.24 0.24 0.26 0.25 0.25Rotifera 0.15 0.19 0.15 0.20 0.16Cladocera 0.03 0.05 0.04 0.05 0.04Phytoplankton 0.15 0.17 0.17 0.18 0.16Zooplankton 0.30 0.31 0.31 0.33 0.31All phyla 0.37 0.29 0.36 0.30 ----
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Table 3.1.6 e: Margalef index (d) values of major planktonic groups at four water sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta 1.41 1.38 1.55 1.20 1.31Chlorophyta 4.14 3.45 3.22 2.24 3.77Chrysophyta 1.13 0.76 0.64 0.47 0.95Euglenophyta 0.72 0.56 0.76 0.61 0.75Xanthophyta 0.71 0.56 0.62 0.32 0.60Bacillariophyta 1.56 1.69 1.58 1.31 1.42Pyrrhophyta 0.72 0.95 1.23 1.12 1.23Protozoa 1.95 1.41 1.76 1.50 1.85Rotifera 1.71 1.27 1.68 1.16 1.45Cladocera 2.47 1.08 1.15 0.97 1.84Phytoplankton 8.82 7.95 7.85 6.07 8.61Zooplankton 4.89 3.25 3.92 3.14 4.37All phyla 12.6 10.48 10.90 8.53 12.13
Table 3.1.6 f: Margalef index (d) values of major planktonic group in four seasons (January 2012-December 2012) at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla Winter Spring Summer Post-monsoon All Seasons
Cyanophyta 1.51 1.07 1.24 1.33 1.31Chlorophyta 3.64 2.59 2.68 2.13 3.77Chrysophyta 0.75 0.63 0.58 0.73 0.95Euglenophyta 0.71 0.61 0.37 0.62 0.75Xanthophyta 0.48 0.00 0.54 0.35 0.60Bacillariophyta 1.39 1.38 1.35 1.45 1.42Pyrrhophyta 0.56 0.62 1.00 0.60 1.23Protozoa 1.38 1.24 1.54 1.04 1.85Rotifera 1.22 1.34 0.90 1.12 1.45Cladocera 1.40 1.20 1.37 1.18 1.84Phytoplankton 7.83 6.12 6.60 6.12 8.61Zooplankton 3.31 3.22 3.21 2.85 4.37All phyla 10.41 8.63 9.14 8.34 12.13
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Table 3.1.6 g: Evenness index (E) values of major planktonic group at four water sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta 0.14 0.15 0.14 0.16 0.14Chlorophyta 0.10 0.11 0.11 0.13 0.10Chrysophyta 0.07 0.07 0.06 0.08 0.06Euglenophyta 0.10 0.09 0.09 0.10 0.08Xanthophyta 0.04 0.03 0.03 0.06 0.03Bacillariophyta 0.11 0.11 0.11 0.13 0.11Pyrrhophyta 0.10 0.07 0.06 0.06 0.06Protozoa 0.09 0.11 0.10 0.12 0.09Rotifera 0.08 0.07 0.07 0.09 0.07Cladocera 0.02 0.02 0.02 0.03 0.02Phytoplankton 0.34 0.33 0.32 0.35 0.04Zooplankton 0.13 0.14 0.13 0.15 0.09All phyla 0.51 0.51 0.50 0.55 -----
Table 3.1.6 h: Evenness index (E) values of major planktonic group in four seasons (from January 2012-December 2012) at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Divisions/Phyla Winter Spring Summer Post-monsoon
All Seasons
Cyanophyta 0.14 0.17 0.16 0.16 0.14Chlorophyta 0.11 0.12 0.12 0.13 0.10Chrysophyta 0.07 0.10 0.08 0.06 0.06Euglenophyta 0.09 0.11 0.13 0.10 0.08Xanthophyta 0.05 0.00 0.04 0.04 0.03Bacillariophyta 0.14 0.12 0.09 0.10 0.11Pyrrhophyta 0.09 0.11 0.06 0.12 0.06Protozoa 0.10 0.12 0.11 0.13 0.09Rotifera 0.07 0.09 0.08 0.10 0.07Cladocera 0.02 0.03 0.02 0.03 0.02Phytoplankton 0.04 0.04 0.04 0.05 0.04Zooplankton 0.09 0.10 0.10 0.11 0.09All phyla 0.08 0.07 0.08 0.07 -----
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Table 3.1.6 i: Sorenson index of similarity (SI) for major planktonic groups at four sites from Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Sites Cyanophyta Chlorophyta Chrysophyta Euglenophyta Xanthophyta Bacillariophyta PyrrhophytaWSS-1& WSS-2 1.00 0.894 0.833 0.888 0.857 0.916 0.909WSS-1& WSS-3 0.950 0.836 0.727 0.888 0.857 0.909 0.833WSS-1& WSS-4 0.900 0.680 s0.600 0.666 0.666 0.900 0.900WSS-2 & WSS-3 0.956 0.800 0.888 0.750 1.00 0.956 0.769WSS-2 & WSS-4 0.900 0.714 0.750 0.750 0.800 0.857 0.833WSS-3 & WSS-4 0.857 0.800 0.857 0.750 0.800 0.900 0.920WSS-1, WSS-2 & WSS-3 0.647 0.469 0.500 0.461 0.600 0.588 0.555WSS-1, WSS-2 & WSS-4 0.580 0.410 0.400 0.461 0.444 0.562 0.588WSS-1, WSS-3 & WSS-4 0.500 0.450 0.430 0.220 0.580 0.460 0.500WSS-2, WSS-3 & WSS-4 0.562 0.454 0.500 0.500 0.500 0.562 0.526WSS-1, WSS-2, WSS-3 & WSS-4 0.418 0.309 0.315 0.352 0.333 0.418 0.416
Protophyta Rotifera Cladocera Phytoplanktons Zooplanktons All PhylaWSS-1& WSS-2 0.860 0.842 0.666 0.901 0.807 0.870WSS-1& WSS-3 0.88 0.952 0.666 0.863 0.852 0.860WSS-1& WSS-4 0.782 0.777 0.571 0.764 0.727 0.752WSS-2 & WSS-3 0.819 0.888 0.800 0.863 0.840 0.857WSS-2 & WSS-4 0.800 0.666 0.666 0.620 0.727 0.650WSS-3 & WSS-4 0.909 0.705 0.666 0.789 0.791 0.790WSS-1, WSS-2 & WSS-3 0.514 0.551 0.400 0.533 0.500 0.524WSS-1, WSS-2 & WSS-4 0.484 0.384 0.315 0.484 0.410 0.582WSS-1, WSS-3 & WSS-4 0.460 0.430 0.320 0.470 0.410 0.450WSS-2, WSS-3 & WSS-4 0.50 0.480 0.285 0.500 0.450 0.492WSS-1, WSS-2, WSS-3 & WSS-4 0.311 0.222 0.166 0.362 0.240 0.328
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Table 3.1.6 j: Sorenson index of similarity for major planktonic groups in different seasons (January 2012-December 2012) at Suleman Mountain Range, Dera Ghazi Khan, Region, Pakistan.
Phyla Winter & spring Winter & summer
Winter & post monsoon
Spring & summer Spring & post monsoon
Summer & post monsoon
Cyanophyta 0.80 0.90 0.90 0.73 0.77 1.00Chlorophyta 0.58 0.66 0.60 0.82 0.66 0.61Chrysophyta 0.88 1.00 0.66 0.75 0.75 0.75Euglenophyta 0.88 0.75 0.66 0.85 0.75 0.85Xanthophyta 0.50 1.00 0.80 0.00 0.00 0.80Bacillariophyta 0.85 0.85 0.76 1.00 0.66 0.66Pyrrhophyta 1.00 0.80 0.75 1.00 0.75 0.80Protozoa 0.77 0.76 0.82 0.73 0.80 0.77Rotifera 0.87 0.71 0.93 0.71 0.66 0.72Cladocera 0.54 0.50 0.72 0.54 0.80 0.54Phytoplankton 0.73 0.77 0.85 0.83 0.68 0.75Zooplankton 0.75 0.68 0.83 0.68 0.75 0.66
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Table 3.2.1: Relative abundance (%) and status of each fish species from all sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
Scientific name Common name n* RA (%)
Distributional status
IUCN statusⱡ
Commercial status#
Family CyprinidaeTor macrolepis Mahseer 340 20.87 Indigenous NE HighBarilius vagra Chalwa 187 11.48 Indigenous LC –Barilius modustus Chalwa 68 4.17 Indigenous NE –Labeo diplostomus Pahari rahu 277 17.00 Indigenous LC HighSalmostoma bacaila Choti Chal 47 2.89 Indigenous LC –Garra gotyla Pathar chat 131 8.04 Indigenous LC –Securicula gora Bari Chal 13 0.80 Indigenous LC –Labeo dyocheilusPakistan.icus Pakistani toraki 116 7.12 Indigenous LC High
Crossochelus diplocheilus DograPathar Chat 70 4.30 Indigenous NE –Schizothorax plagiostomus Sawati 123 7.55 Indigenous NE HighCyprinion watsoni Sabzak 173 10.62 Indigenous NE –Labeo calbasu Kalbans 2 0.12 Indigenous LC –Puntius sophore Sophore popara 3 0.18 Indigenous LC –Barilius Pakistan.icus Pakistani chalwa 19 1.17 Endemic NE –Salmophasia punjabensis Punjabi chal 4 0.25 Endemic NE –Cirrhinus mrigala Mori 4 0.25 Indigenous LC HighFamily CobitidaeBotia birdi Birdi loach 4 0.25 Indigenous NE –Family BagridaeRita rita Khaga 4 0.25 Indigenous LC Very HighFamily SiluridaeOmpok pabda Pahari pafta 40 2.46 Indigenous NT –Family MastacembelidaeMastacembalus armatus Bam 4 0.25 Indigenous LC High*n, number of organisms; ⱡ, IUCN status described is worldwide; RA, relative abundance; NE, not evaluated; LC, least concerned; NT, near threatened.ⱡ, # = Rafique and Khan (2012).
Table 3.2.2: Relative abundance (%) and diversity indices values of fish species from fish sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan
Site N SR RA (%) D H d EFSS-1 290a 7a 17.80 0.21 1.68 1.09 0.30FSS-2 220a 8ab 13.50 0.23 1.66 1.30 0.31FSS-3 344b 7ab 21.11 0.23 1.64 1.03 0.28FSS-4 92b 4bc 5.64 0.29 1.29 0.66 0.29FSS-5 134bc 4cd 8.22 0.26 1.36 0.61 0.28FSS-6 127bc 7d 7.79 0.31 1.37 1.24 0.28FSS-7 44bcd 3de 2.70 0.34 1.06 0.53 0.28FSS-8 213cde 7de 13.07 0.22 1.63 1.12 0.30FSS-9 136de 5ef 8.34 0.33 1.27 0.81 0.26FSS-10 29e 2f 1.78 0.53 0.64 0.30 0.19n, number of individuals; RA, relative abundance; D, Simpson diversity index; H, Shannon-Weiner index; d, Margalef index; E, evenness index; SR, Species richnessThe values with different superscript letters are significantly different from one another, (LSD post-hoc analysis).
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Table 3.2.3: Relative abundance (%) of fish species within each fish sampling site from Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan
Species FSS-1 FSS-2 FSS-3 FSS-4 FSS-5 FSS-6 FSS-7 FSS-8 FSS-9 FSS-10Tor macrolepis 27.24 36.93 -- -- -- 44.00 -- 26.76 49.26 --Barilius vagra 16.20 10.81 7.26 13.04 18.48 15.20 -- 10.79 11.02 --Barilius modustus 12.75 -- -- -- -- -- -- 8.45 9.55 --Labeo diplostomus 29.65 -- 13.66 -- 37.81 -- -- 29.10 27.20 --Salmostoma bacaila 5.86 10.81 -- -- -- 4.80 -- -- -- --Garra gotyla 6.89 -- 9.30 30.43 15.12 -- 55.93 -- -- --Securicula gora 1.37 4.05 -- -- -- -- -- -- -- --Labeo dyocheilus Pakistanicus -- 24.77 -- 16.30 -- -- -- 21.59 -- --Crossochelus diplocheilus -- 9.00 9.59 -- -- -- 28.81 -- -- --Schizothorax plagiostomus -- -- 35.75 -- -- -- -- -- -- --Cyprinion watsoni -- -- 24.12 40.21 28.57 -- -- -- -- 65.51Labeo calbasu -- 0.90 -- -- -- -- -- -- -- --Puntius sophore -- -- -- -- -- 2.40 -- -- -- --Barilius Pakistan.icus -- -- -- -- -- -- 15.25 -- -- 34.48Salmophasia punjabensis -- -- -- -- -- -- -- -- 2.94 --Cirrhinus mrigala -- 1.80 -- -- -- -- -- -- -- --Botia birdi -- -- 0.29 -- -- -- -- 1.40 -- --Rita rita -- -- -- -- -- -- -- 1.87 -- --Ompok pabda -- -- -- -- -- 32.00 -- -- -- --Mastacembalus armatus -- 0.90 -- -- -- 1.60 -- -- -- --
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Table 3.3.1: Central tendency values of various body measurements of different fish species from Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan
Body measurements Tor macrolepis Schizothorax plagiostomus Labeo diplostomus Labeo dyocheilus PakistanicusMean±SD Range Means±SD Range Means±SD Range Means±SD Range
Wet body weight (g) 43.52±44.05 5.20-301.90 32.00±12.04 7.05-67.08 24.03±15.64 5.50-90.00 46.26±23.44 10.20-125.00Total length (cm) 15.96±5.12 7.80-31.0 14.13±1.91 8.08-17.20 12.72±2.84 9.00-20.50 16.63±3.22 9.30-23.60Fork length (cm) 13.88±4.44 6.80-27.0 12.89±1.80 7.09-15.80 11.18±2.59 7.50-18.70 14.33±2.73 7.90-20.00Standard length (cm) 12.30±4.05 5.70-24.0 11.16±1.65 6.09-14.10 9.44±2.68 6.00-17.00 12.21±2.87 6.30-18.50Pre-orbital length (cm) 1.27±0.58 0.43-3.75 1.07±0.14 0.75-1.40 0.97±0.26 0.50-1.70 1.26±0.36 0.73-2.53Eye diameter (cm) 0.71±0.16 0.49-1.48 0.48±0.04 0.40-0.60 0.54±0.12 0.24-0.91 0.61±1.49 2.10-11.50Post-orbital length (cm) 1.63±0.81 0.5-5.0 1.16±0.15 0.80-1.60 1.05±0.24 0.60-1.95 1.39±0.40 0.72-2.56Head length (cm) 3.40±1.36 1.10-10.0 2.72±0.34 1.86-3.60 2.53±0.49 1.51-3.60 3.15±0.59 1.57-4.84Head depth (cm) 2.37±0.75 1.23-6.0 1.96±0.26 1.35-2.60 1.90±0.32 1.40-2.90 2.54±0.55 1.47-4.35Pectoral fin length (cm) 2.54±0.82 1.23-6.0 1.97±0.28 1.36-2.60 2.09±0.39 0.79-3.10 2.62±0.64 0.73-4.00Pectoral fin base (cm) 0.57±0.18 0.28-1.5 0.49±0.05 0.38-0.65 0.41±0.14 0.20-0.97 0.62±0.13 0.30-1.00Body depth (cm) 2.93±0.84 1.50-7.0 2.66±0.44 1.62-3.59 2.73±0.45 1.73-3.80 3.21±0.62 1.97-4.62Girth length (cm) 8.14±2.77 4.0-22.0 7.50±1.22 4.00-10.40 0.61±0,23 0.91-2.93 0.85±0.16 0.52-1.30Pelvic fin length (cm) 2.17±0.73 0.90-5.50 1.57±0.15 1.25-20.00 1.91±0.45 0.21-1.15 2.24±0.61 0.59-4.00Pelvic fin base (cm) 0.63±0.27 0.26-2.0 0.48±0.08 0.31-0.71 0.56±0.12 0.34-0.83 0.57±0.13 0.32-1.10Dorsal fin length (cm) 2.98±0.95 1.50-7.30 1.96±0.24 1.34-2.50 3.03±0.44 0.82-2.80 3.28±0.66 1.82-5.06Dorsal fin base (cm) 1.57±0.47 0.70-4.0 1.36±0.21 0.85-1.90 1.49±0.38 2.21-4.30 2.18±0.58 1.04-3.51Anal fin length (cm) 2.34±0.79 1.18-6.0 2.58±0.46 1.48-3.80 2.52±0.52 1.67-3.70 2.56±0.48 1.46-4.00Anal fin base (cm) 0.84±0.32 0.40-2.25 0.98±0.18 0.55-1.40 0.85±0.16 0.60-1.37 1.03±0.20 0.49-1.56Caudal peduncle width (cm) 1.26±0.40 0.70-3.35 1.02±0.14 0.66-1.35 1.16±0.23 0.80-1.93 1.39±0.26 0.82-2.00Caudal peduncle length(cm) 1.99±0.82 0.75-6.0 1.56±0.24 1.00-2.18 2.60±0.63 1.70-3.97 1.90±0.43 1.04-3.10Caudal fin length (cm) 3.80±1.20 1.92-9.50 2.90±0.35 2.00-3.7.00 2.53±0.63 1.70-5.30 4.22±0.91 1.18-6.19
Table 3.3.1: (continued) Central tendency values of various body measurements of different fish species from Suleman Mountain Range,
Dera Ghazi Khan Region, Pakistan.
116
Table 3.3.2 a: Relationship of total length (cm) and wet body weight (g) with different morphometric measurements of Tor macrolepis.
Equation Total length (cm) Equation Wet body weight (g)a b 95 % CL of a 95 % CL of b R a b 95 % CL of a 95 % CL of b r
117
Body measurements Cyprinion watsoni Ompok pabda Garra gotylaMean±S.D Range Means±SD Range Means±SD Range
Wet body weight (g) 15.05±4.51 7.90-30.70 26.08±11.08 8.50-57.00 17.66±8.42 8.60 – 39.30Total length (cm) 10.86±1.16 8.00-14.30 15.06±1.96 10.30-18.90 11.06±1.54 9.00 – 14.1Fork length (cm) 9.55±1.00 7.10-12.50 14.28±2.00 9.50-18.30 9.98±1.45 8.00 – 12.9Standard length (cm) 8.28±0.85 6.20-10.40 13.46±2.03 8.50-17.70 8.48±1.18 6.70 – 10.8Pre-orbital length (cm) 0.76±0.06 0.61-0.91 1.00±0.16 0.60-1.48 1.07±0.27 0.73 – 1.79Eye diameter (cm) 0.49±0.04 0.42-0.61 0.63±0.22 0.36-1.50 0.40±0.06 0.36 – 0.57Post-orbital length (cm) 0.89±0.11 0.69-1.20 1.39±0.34 0.67-2.00 0.70±0.07 0.53 – 0.80Head length (cm) 1.98±0.18 1.68-2.50 2.71±0.41 1.08-3.45 2.10±0.35 1.59 – 2.94Head depth (cm) 1.70±0.25 0.95-2.44 2.17±0.39 1.23-2.80 1.54±0.24 1.13 – 2.06Pectoral fin length (cm) 1.65±0.21 1.20-2.33 1.95±0.27 1.30-2.50 1.92±0.26 1.54 – 2.30Pectoral fin base (cm) 0.44±0.07 0.30-0.62 0.61±0.11 0.39-0.80 0.57±0.12 0.42 – 0.85Body depth (cm) 2.26±0.24 1.64-2.91 2.51±0.46 1.58-3.30 1.81±0.34 1.41 – 2.74Girth length (cm) 0.61±0.07 0.41-0.78 0.68±0.10 0.45-0.90 0.59±0.09 0.46 – 0.80Pelvic fin length (cm) 1.45±0.18 1.00-2.11 0.90±0.13 0.60-1.20 1.85±0.25 1.53 – 2.37Pelvic fin base (cm) 0.49±0.07 0.34-0.69 0.39±0.07 0.20-0.50 0.47±0.08 0.38 – 0.66Dorsal fin length (cm) 1.50±0.21 1.06-2.23 1.42±0.17 0.93-1.70 2.09±0.36 1.60 – 3.07Dorsal fin base (cm) 1.55±0.17 1.25-2.04 0.33±0.34 0.20-2.20 1.44±0.27 1.09 – 2.12Anal fin length (cm) 1.72±0.20 1.40-2.30 1.12±0.12 0.90-1.47 1.73±0.25 1.42 – 2.22Anal fin base (cm) 0.87±0.17 0.50-1.22 7.55±1.01 6.00-9.50 0.70±0.11 0.52 – 0.89Caudal peduncle width (cm) 0.76±0.07 0.65-1.00 0.84±0.19 0.40-1.27 1.01±0.21 0.80 – 1.59Caudal peduncle length (cm) 0.95±0.15 0.70-1.40 0.42±0.47 0.20-2.21 1.23±0.18 0.94 – 1.55Caudal fin length (cm) 2.52±0.35 1.70-3.45 1.67±0.27 1.05-2.27 2.50±0.36 2.08 – 3.32
W = a + b TL 0.022 2.637 0.016-0.032 2.510-2.765 0.952 TL = a + b W 4.753 0.344 4.487-5.035 0.327-0.360 0.952 FL = a + b TL 0.849 1.007 0.851-0.984 0.955-1.008 0.984 FL = a + b W 4.027 0.350 3.750-4.315 0.330-0.371 0.933 SL = a + b TL 0.789 0.990 0.789-0.879 0.951-1.030 0.967 SL = a + b W 3.451 0.363 3.184-3.750 0.340-0.386 0.920 PreOL = a + b TL 0.050 1.152 0.039-0.065 1.062-1.243 0.887 PreOL = a + b W 0.276 0.425 0.248-0.308 0.394-0.456 0.899 ED = a + b TL 0.153 0.557 0.133-0.177 0.504-0.610 0.847 ED = a + b W 0.338 0.214 0.320-0.358 0.198-0.230 0.896 PostOL = a + b TL 0.057 1.192 0.041-0.080 1.070-1.314 0.827 PostOL = a + b W 0.309 0.460 0.271-0.352 0.422-0.498 0.879 HL = a + b TL 0.202 1.012 0.158-0.258 0.922-1.102 0.862 HL = a + b W 0.851 0.389 0.776-0.933 0.362-0.415 0.911 HD = a + b TL 0.240 0.825 0.203-0.285 0.763-0.887 0.895 HD = a + b W 0.785 0.314 0.740-0.836 0.296-0.332 0.937 PecFL = a + b TL 0.203 0.911 0.176-0.236 0.858-0.964 0.932 PecFL = a + b W 0.782 0.335 0.735-0.830 0.318-0.353 0.944 PecFB = a + b TL 0.058 0.823 0.048-0.070 0.752-0.894 0.868 PecFB = a + b W 0.194 0.303 0.178-0.211 0.279-0.328 0.881 BD = a + b TL 0.319 0.801 0.275-0.370 0.748-0.855 0.914 BD = a + b W 1.012 0.304 1.040-1.064 0.289-0.318 0.953 GL = a + b TL 0.662 0.903 0.541-0.811 0.829-0.977 0.878 GL = a + b W 2.410 0.344 2.234-2.600 0.323-0.366 0.922 PelFL = a + b TL 0.153 0.955 0.129-0.181 0.894-1.016 0.921 PelFL = a + b W 0.628 0.350 0.585-0.676 0.330-0.371 0.930 PelFB = a + b TL 0.035 1.038 0.026-0.046 0.938-1.139 0.842 PelFB = a + b W 0.158 0.387 0.140-0.178 0.352-0.421 0.863 DFL = a + b TL 0.252 0.890 0.213-0.297 0.830-0.951 0.912 DFL = a + b W 0.929 0.331 0.867-0.993 0.311-0.350 0.932 DFB = a + b TL 0.197 0.749 0.165-0.236 0.684-0.815 0.866 DFB = a + b W 0.589 0.280 0.546-0.635 0.258-0.301 0.889 AFL = a + b TL 0.207 0.871 0.170-0.254 0.798-0.944 0.874 AFL = a + b W 0.716 0.335 0.667-0.736 0.314-0.355 0.924 AFB = a + b TL 0.057 0.961 0.045-0.074 0.868-1.053 0.843 AFB = a + b W 0.219 0.337 0.200-0.239 0.352-0.403 0.911 CPW = a + b TL 0.122 0.844 0.104-0.142 0.787-0.901 0.914 CPW = a + b W 0.413 0.318 0.390-0.437 0.302-0.334 0.947 CPL = a + b TL 0.089 1.113 0.071-0.111 1.032-1.194 0.901 CPL = a + b W 0.453 0.415 0.413-0.495 0.389-0.441 0.925CFL = a + b TL 0.325 0.887 0.280-0.378 0.832-1.023 0.942 CFL = a + b W 1.194 0.329 1.125-1.268 0.312-0.346 0.945 a = intercept, b = slope, r = correlation coefficient, CL = confidence limits, W = wet body weight, TL = total length, FL = fork length, SL = standard length, PreOL = pre- orbital length, ED = eye diameter, PostOL = post-orbital length, HL = head length, HD = head depth, PecFL = pectoral fin length, PecFB = pectoral fin base, BD = body depth, GL = girth length, PelFL = pelvic fin length, PelFB = pelvic fin base, DFL = dorsal fin length, DFB = dorsal fin base, AFL = anal fin length, AFB = anal fin base, CPW = caudal peduncle Width, CPL = caudal peduncle length, CFL = caudal fin length
118
Table 3.3.2 b: Relationship of total length (cm) and wet body weight (g) with different morphometric measurements of Shizothorax plagiostomus.
Equation Total length (cm) Equation Wet body weight (g)a b 95 % CL of a 95 % CL of b R a b 95 % CL of a 95 % CL of b r
W = a + b TL 0.014 2.900 0.008-0.023 2.707-3.093 0.957 TL = a + b W 4.819 0.315 4.487-5.176 0.294-0.336 0.957 FL = a + b TL 1.009 0.961 0.805-1.265 0.876-1.047 0.926 FL = a + b W 4.498 0.308 4.027-5.023 0.276-0.341 0.901 SL = a + b TL 0.690 1.050 0.586-0.813 0.988-1.112 0.965 SL = a + b W 3.373 0.351 3.184-3.565 0.334-0.367 0.978 PreOL = a + b TL 0.136 0.781 0.108-170 0.694-0.868 0.891 PreOL = a + b W 0.422 0.274 0.394-0.452 0.253-0.294 0.947 ED = a + b TL 0.160 0.420 0.137-0.185 0.363-0.477 0.849 ED = a + b W 0.291 0.150 0.277-0.305 0.136-0.164 0.921 PostOL = a + b TL 0.149 0.775 0.119-0.186 0.690-0.860 0.894 PostOL = a + b W 0.468 0.269 0.432-0.498 0.247-0.290 0.939 HL = a + b TL 0.314 0.814 0.264-0.374 0.749-0.880 0.938 HL = a + b W 1.054 0.278 1.000-1.109 0.263-0.293 0.970 HD = a + b TL 0.202 0.858 0.163-0.251 0.776-0.939 0.917 HD = a + b W 0.705 0.300 0.668-0.743 0.285-0.316 0.973 PecFL = a + b TL 0.187 0.888 0.144-0.242 0.790-0.986 0.893 PecFL = a + b W 0.684 0.310 0.631-0.741 0.286-0.333 0.943 PecFB = a + b TL 0.090 0.645 0.074-0.109 0.572-0.719 0.886 PecFB = a + b W 0.230 0.225 0.216-0.244 0.207-0.243 0.938 BD = a + b TL 0.169 1.038 0.125-0.229 0.925-1.152 0.894 BD = a + b W 0.750 0.370 0.697-0.807 0.349-0.392 0.966 GL = a + b TL 0.533 0.997 0.376-0.759 0.864-1.130 0.853 GL = a + b W 2.153 0.366 1.968-2.355 0.339-0.392 0.949 PelFL = a + b TL 0.349 0.567 0.288-0.423 0.495-0.639 0.864 PelFL = a + b W 0.794 0.199 0.746-0.847 0.181-0.218 0.920 PelFB = a + b TL 0.034 1.002 0.026-0.044 0.904-1.101 0.912 PelFB = a + b W 0.150 0.343 0.138-0.164 0.318-0.369 0.947 DFL = a + b TL 0.265 0.755 0.211-0.331 0.670-0.840 0.889 DFL = a + b W 0.794 0.264 0.741-0.853 0.244-0.285 0.943 DFB = a + b TL 0.102 0.976 0.077-0.135 0.870-1.082 0.896 DFB = a + b W 0.439 0.331 0.394-0.486 0.300-0.362 0.921 AFL = a + b TL 0.107 1.201 0.078-0.146 1.084-1.318 0.913 AFL = a + b W 0.637 0.410 0.571-0.708 0.378-0.441 0.944 AFB = a + b TL 0.039 1.220 0.028-0.054 1.094-1.345 0.905 AFB = a + b W 0.236 0.416 0.210-0.265 0.382-0.450 0.936 CPW = a + b TL 0.119 0.081 0.094-0.152 0.718-0.901 0.888 CPW = a + b W 0.394 0.278 0.362-0.430 0.253-0.303 0.924 CPL = a + b TL 0.118 0.973 0.088-0.158 0.862-1.084 0.887 CPL = a + b W 0.499 0.334 0.450-0.553 0.304-0.365 0.923 CFL = a + b TL 0.388 0.760 0.316-0.475 0.682-0.838 0.906 CFL = a + b W 1.186 0.263 1.112-1.265 0.244-0.281 0.950a = intercept, b = slope, r = correlation coefficient, CL = confidence limits, W = wet body weight, TL = total length, FL = fork length, SL = standard length, PreOL = pre-orbital length, ED = eye diameter, PostOL = post-orbital length, HL = head length, HD = head depth, PecFL = pectoral fin length, PecFB = pectoral fin base, BD = body depth, GL = girth length, PelFL = pelvic fin length, PelFB = pelvic fin base, DFL = dorsal fin length, DFB = dorsal fin base, AFL = anal fin length, AFB = anal fin base, CPW = caudal peduncle Width, CPL = caudal peduncle length, CFL = caudal fin length
Table 3.3.2 c: Relationship of total length (cm) and wet body weight (g) with different morphometric measurements of Labeo diplostomus.
119
Equation Total length (cm) Equation Wet body weight (g)a B 95 % CL of a 95 % CL of b R a b 95 % CL of a 95 % CL of b r
W = a + b TL 0.028 2.601 0.016-0.049 2.381-2.821 0.902 TL = a + b W 4.887 0.313 4.508-5.297 0.286-0.339 0.902 FL = a + b TL 0.838 1.018 0.745-0.942 0.972-1.064 0.969 FL = a + b W 3.999 0.336 3.698-4.315 0.311-0.361 0.921 SL = a + b TL 0.372 1.268 0.322-0.429 1.212-1.324 0.970 SL = a + b W 2.618 0.416 2.377-2.891 0.384-0.448 0.918 PreOL = a + b TL 0.068 1.038 0.046-0.100 0.887-1.189 0.773 PreOL = a + b W 0.258 0.429 0.234-0.285 0.397-0.461 0.921 ED = a + b TL 0.071 0.798 0.052-0.096 0.674-0.921 0.753 ED = a + b W 0.198 0.327 0.181-0.217 0.298-0.357 0.891 PostOL = a + b TL 0.116 0.864 11.246-0.152 0.757-0.970 0.821 PostOL = a + b W 0.384 0.330 0.352-0.417 0.303-0.358 0.905 HL = a + b TL 0.348 0.780 0.282-0.431 0.696-0.864 0.855 HL = a + b W 1.064 0.285 0.986-1.146 0.260-0.309 0.899 HD = a + b TL 0.352 0.664 0.300-0.414 0.600-0.727 0.879 HD = a + b W 0.984 0.216 0.910-1.067 0.189-0.242 0.824 PecFL = a + b TL 0.330 0.725 0.262-0.415 0.634-0.816 0.816 PecFL = a + b W 0.955 0.225 0.935-1.172 0.188-0.262 0.730 PecFB = a + b TL 0.011 1.393 0.008-0.016 1.260-1.526 0.880 PecFB = a + b W 0.117 0.397 0.095-0.143 0.330-0.456 0.724 BD = a + b TL 0.476 0.688 0.412-0.551 0.630-.745 0.905 BD = a + b W 1.352 0.231 1.262-1.449 0.208-0.253 0.875 GL = a + b TL 0.044 1.015 0.022-0.087 0.748-1.282 0.558 GL = a + b W 0.147 0.453 0.116-0.187 0.376-0.531 0.719 PelFL = a + b TL 0.209 0.866 0.149-0.292 0.733-0.999 0.755 PelFL = a + b W 0.738 0.308 0.644-1.183 0.263-0.353 0.775 PelFB = a + b TL 0.076 0.786 0.059-0.098 0.6865-0.887 0.811 PelFB = a + b W 0.252 0.262 0.225-0.283 0.224-0.299 0.778 DFL = a + b TL 0.916 0.472 0.791-0.944 0.414-1.530 0.821 DFL = a + b W 1.914 0.152 1.782-2.051 0.129-0.175 0.761 DFB = a + b TL 0.109 1.022 0.077-0.154 0.885-1.159 0.796 DFB = a + b W 0.555 0.317 0.469-0.656 0.262-0.372 0.713 AFL = a + b TL 0.265 0.885 0.225-0.312 0.821-.949 0.926 AFL = a + b W 1.019 0.296 0.940-1.102 0.270-0.323 0.895 AFB = a + b TL 0.149 0.684 0.120-0.185 0.597-0.770 0.814 AFB = a + b W 0.414 0.235 0.378-0.454 0.204-0.265 0.806 CPW = a + b TL 0.159 0.780 0.133-0.190 0.710-0.851 0.892 CPW = a + b W 0.522 0.260 0.480-0.568 0.233-0.288 0.858 CPL = a + b TL 0.184 1.040 0.151-0.224 0.961--1.118 0.920 CPL = a + b W 0.925 0.337 0.830-1.028 0.302--0.372 0.860 CFL = a + b TL 0.190 1.016 0.161-0.225 0.949-1.082 0.938 CFL = a + b W 0.925 0.328 0.838-1.019 0.295-0.360 0.873a = intercept, b = slope, r = correlation coefficient, CL = confidence limits, W = wet body weight, TL = total length, FL = fork length, SL = standard length, PreOL = pre-orbital length, ED = eye diameter, PostOL = post-orbital length, HL = head length, HD = head depth, PecFL = pectoral fin length, PecFB = pectoral fin base, BD = body depth, GL = girth length, PelFL = pelvic fin length, PelFB = pelvic fin base, DFL = dorsal fin length, DFB = dorsal fin base, AFL = anal fin length, AFB = anal fin base, CPW = caudal peduncle Width, CPL = caudal peduncle length, CFL = caudal fin length
Table 3.3.2 d: Relationship of total length (cm) and wet body weight (g) with different morphometric measurements of Labeo dyocheilus Pakistanicus.
EquationTotal length (cm)
EquationWet body weight (g)
a B 95 % CL of a 95 % CL of b R a b 95 % CL of a 95 % CL of b r
120
W = a + b TL 0.028 2.612 0.019-0.041 2.473-2.750 0.970 TL = a + b W 4.295 0.360 3.999-4.613 0.341-0.380 0.970 FL = a + b TL 0.925 0.975 0.853-0.998 0.946-1.004 0.990 FL = a + b W 3.758 0.357 3.524-4.009 0.339-0.374 0.975 SL = a + b TL 0.372 1.240 0.329-0.421 1.195-1.284 0.986 SL = a + b W 2.254 0.449 2.032-2.500 0.421-0.476 0.961 PreOL = a + b TL 0.044 1.188 0.031-0.062 1.065-1.312 0.899 PreOL = a + b W 0.242 0.436 0.202-0.290 0.388-0.484 0.887 ED = a + b TL 0.034 1.024 0.022-0.053 0.865-1.182 0.808 ED = a + b W 0.153 0.367 0.121-0.194 0.305-0.430 0.781 PostOL = a + b TL 0.146 0.796 0.083-0.255 0.597-0.996 0.648 PostOL = a + b W 0.457 0.292 0.345-0.604 0.217-0.267 0.640 HL = a + b TL 0.267 0.877 0.215-0.333 0.799-0.955 0.923 HL = a + b W 0.933 0.324 0.834-1.045 0.294-0.354 0.916 HD = a + b TL 0.161 0.981 0.119-0.217 0.874-1.088 0.890 HD = a + b W 0.698 0.343 0.585-0.836 0295-0.390 0.837 PecFL = a + b TL 0.068 1.296 0.044-0.104 1.142-1.451 0.873 PecFL = a + b W 0.476 0.450 0.370-0.615 0.382-0.518 0.816 PecFB = a + b TL 0.066 0.793 0.042-0.104 0.633-0.953 0.726 PecFB = a + b W 0.190 0.315 0.123-0.232 0.261-0.370 0.777 BD = a + b TL 0.235 0.930 0.193-0.285 0.860-0.999 0.943 BD = a + b W 0.877 0.346 0.796-0.964 0.320-0.371 0.944 GL = a + b TL 0.075 0.864 0.061-0.091 0.794-0.934 0.935 GL = a + b W 0.239 0.337 0.226-0.252 0.322-0.351 0.980 PelFL = a + b TL 0.045 1.387 0.027-0.074 1.208-1.567 0.855 PelFL = a + b W 0.342 0.496 0.261-0.450 0.423-0.569 0.823 PelFB = a + b TL 0.042 0.920 0.030-0.060 0.796-1.045 0.844 PelFB = a + b W 0.151 0.351 0.129-0.178 0.308-0.394 0.866 DFL = a + b TL 0.236 0.936 0.185-0.300 0.849-1.022 0.918 DFL = a + b W 0.891 0.346 0.789-1.009 0.313-0.313 0.914 DFB = a + b TL 0.069 1.223 0.042-0.112 1.048-1.398 0.831 DFB = a + b W 0.419 0.435 0.321-0.545 0.364-0.506 0.795 AFL = a + b TL 0.331 0.727 0.232-0.472 0.600-0.854 0.773 AFL = a + b W 0.863 0.289 0.738-1.007 0.248-0.331 0.829 AFB = a + b TL 0.084 0.890 0.062-0.114 0.779-0.100 0.864 AFB = a + b W 0.290 0.336 0.251-0.337 0.297-0.375 0.878 CPW = a + b TL 0.141 0.813 0.107-0.185 0.716-0.910 0.873 CPW = a + b W 0.429 0.313 0.381-0.482 0.281-0.344 0.904 CPL = a + b TL 0.097 1.058 0.076-0.123 0.971-1.145 0.934 CPL = a + b W 0.453 0.381 0.394-0.522 0.343-0.419 0.905 CFL = a + b TL 0.280 0.962 0.176-0.447 0.795-1.128 0.776 CFL = a + b W 1.146 0.345 0.899-1.462 0.280-0.410 0.748a = intercept, b = slope, r = correlation coefficient, CL = confidence limits, W = wet body weight, TL = total length, FL = fork length, SL = standard length, PreOL = pre-orbital length, ED = eye diameter, PostOL = post-orbital length, HL = head length, HD = head depth, PecFL = pectoral fin length, PecFB = pectoralfin base, BD = body depth, GL = girth length, PelFL = pelvic fin length, PelFB = pelvic fin base, DFL = dorsal fin length, DFB = dorsal fin base, AFL = anal fin length, AFB = anal fin base, CPW = caudal peduncle Width, CPL = caudal peduncle length, CFL = caudal fin length
121
Table 3.3.2 e: Relationship of total length (cm) and wet body weight (g) with different morphometric measurements of Cyprinion watsoni.
Equation Total length (cm) Equation Wet body weight (g)a B 95 % CL of a 95 % CL of b R a b 95 % CL of a 95 % CL of b r
W = a + b TL 0.021 2.732 0.011-0.041 2.461-3.002 0.922 TL = a + b W 4.732 0.311 4.365-5.140 0.280-0.342 0.922 FL = a + b TL 0.998 0.947 0.849-1.169 0.880-1.014 0.957 FL = a + b W 4.169 0.310 3.855-4.498 0.280-0.338 0.929 SL = a + b TL 0.962 0.903 0.787-1.172 0.820-0.986 0.931 SL = a + b W 0.268 0.298 0.291-0.246 0.267-0.330 0.912 PreOL = a + b TL 0.394 1.127 0.291-0.533 1.000-1.254 0.902 PreOL = a + b W 2.084 0.380 1.754-2.477 0.316-0.444 0.902 ED = a + b TL 0.107 0.637 0.090-0.127 0.566-0.709 0.903 ED = a + b W 0.274 0.217 0.248-0.301 0.181-0.252 0.903 PostOL = a + b TL 0.394 0.858 0.394-0.291 0.672-1.044 0.734 PostOL = a + b W 0.361 0.336 0.274-0.248 0.269-0.402 0.766 HL = a + b TL 0.457 0.721 0.072-0.344 0.617-0.825 0.852 HL = a + b W 0.918 0.288 0.843-1.000 0.255-0.319 0.904 HD = a + b TL 0.141 1.043 0.077-0.256 0.790-1.295 0.696 HD = a + b W 0.745 0.306 0.579-0.959 0.212-0.401 0.606 PecFL = a + b TL 0.188 0.909 0.121-0.294 0.722-1.096 0.752 PecFL = a + b W 0.773 0.283 0.643-0.927 0.215-0.352 0.695 PecFB = a + b TL 0.115 0.557 0.054-0.243 0.242-0.873 0.383 PecFB = a + b W 0.191 0.352 0.157-0.230 0.281-0.423 0.758 BD = a + b TL 0.303 0.841 0.218-0.421 0.703-0.979 0.820 BD = a + b W 1.045 0.288 0.925-1.178 0.243-0.333 0.832 GL = a + b TL 0.068 0.922 0.048-0.095 0.780-1.064 0.837 GL = a + b W 0.256 0.327 0.229-0.286 0.285-0.368 0.879 PelFL = a + b TL 0.258 0.722 0.157-0.422 0.515-0.930 0.633 PelFL = a + b W 0.778 0.231 0.643-0.944 0.159-0.303 0.600 PelFB = a + b TL 0.047 0.980 0.028-0.079 0.762-1.197 0.727 PelFB = a + b W 0.200 0.333 0.164-0.242 0.261-0.406 0.733 DFL = a + b TL 0.293 0.848 0.159-0.540 0.709-0.987 0.821 DFL = a + b W 0.703 0.285 0.590-0.839 0.219-0.351 0.712 DFB = a + b TL 0.166 0.925 0.096-0.286 0.696-1.155 0.688 DFB = a + b W 0.617 0.343 0.551-0.690 0.301-0.385 0.886 AFL = a + b TL 0.114 0.901 0.060-0.220 0.769-1.033 0.849 AFL = a + b W 6.577 0.358 5.888-7.345 0.317-0.399 0.899 AFB = a + b TL 0.070 1.047 0.028-0.175 0.665-1.429 0.541 AFB = a + b W 0.883 0.125 0.849-0.916 0.111-0.139 0.898 CPW = a + b TL 0.068 0.722 0.034-0.135 0.607-0.837 0.827 CPW = a + b W 0.337 0.305 0.313-0.362 0.277-0.333 0.932 CPL = a + b TL 0.116 0.954 0.056-0.239 0.650-1.258 0.593 CPL = a + b W 0.265 0.475 0.234-0.301 0.428-0.522 0.921 CFL = a + b TL 0.225 1.072 0.137-0.372 0.870-1.275 0.780 CFL = a + b W 1.186 0.280 0.575-1.486 0.196-0.364 0.616a = intercept, b = slope, r = correlation coefficient, CL = confidence limits, W = wet body weight, TL = total length, FL = fork length, SL = standard length, PreOL = pre-orbital length, ED = eye diameter, PostOL = post-orbital length, HL = head length, HD = head depth, PecFL = pectoral fin length, PecFB = pectoral fin base, BD = body depth, GL = girth length, PelFL = pelvic fin length, PelFB = pelvic fin base, DFL = dorsal fin length, DFB = dorsal fin base, AFL = anal fin length, AFB = anal fin base, CPW = caudal peduncle Width, CPL = caudal peduncle length, CFL = caudal fin length
Table 3.3.2 f: Relationship of total length (cm) and wet body weight (g) with different morphometric measurements of Ompok pabda.
Equation Total length (cm) Equation Wet body weight (g)
122
a B 95 % CL of a 95 % CL of b R a b 95 % CL of a 95 % CL of b r W = a + b TL 0.006 3.048 0.002-0.017 2.688-3.404 0.950 TL = a + b W 5.834 0.296 5.212-6.531 0.261-0.331 0.950 FL = a + b TL 0.805 1.060 0.684-1.054 1.000-1.120 0.988 FL = a + b W 5.164 0.318 4.571-5.821 0.280-0.355 0.950 SL = a + b TL 0.690 1.094 0.504-0.948 0.978-1.211 0.959 SL = a + b W 4.624 0.333 4.009-5.346 0.288-0.378 0.936 PreOL = a + b TL 0.108 0.816 0.046-0.255 0.500-1.132 0.681 PreOL = a + b W 0.505 0.211 0.354-0.719 0.100-0.322 0.564 ED = a + b TL 0.026 1.157 0.005-0.126 0.577-1.738 0.583 ED = a + b W 0.240 0.289 0.128-0.451 0.091-0.486 0.466 PostOL = a + b TL 0.020 1.559 0.005-0.060 1.152-1.966 0.810 PostOL = a + b W 0.253 0.526 0.182-0.352 0.423-0.630 0.877 HL = a + b TL 0.117 1.155 0.053-0.258 0.864-1.446 0.819 HL = a + b W 0.910 0.339 0.659-1.253 0.239-0.440 0.772 HD = a + b TL 0.097 1.145 0.041-0.225 0.833-1.457 0.797 HD = a + b W 0.692 0.355 0.506-0.948 0.257-0.453 0.793 PecFL = a + b TL 0.177 0.883 0.097-0.324 0.660-1.105 0.819 PecFL = a + b W 0.811 0.273 0.647-1.016 0.202-0.343 0.812 PecFB = a + b TL 0.024 1.186 0.012-0.051 0.911-1.461 0.841 PecFB = a + b W 0.177 0.385 0.139-0.226 0.309-0.462 0.876 BD = a + b TL 0.093 1.211 0.050-0.175 0.978-1.444 0.882 BD = a + b W 0.746 0.377 0.590-0.944 0.303-0.450 0.880 GL = a + b TL 0.044 1.014 0.026-0.072 0.828-1.200 0.891 GL = a + b W 0.252 0.311 0.207-0.308 0.249-0.372 0.875 PelFL = a + b TL 0.061 0.992 0.040-0.092 0.838-1.147 0.918 PelFL = a + b W 0.322 0.319 0.284-0.366 0.280-0.358 0.947 PelFB = a + b TL 0.035 0.889 0.011-0.112 0.456-1.322 0.594 PelFB = a + b W 0.136 0.327 0.093-0.200 0.208-0.447 0.702 DFL = a + b TL 0.234 0.665 0.124-0.444 0.428-0.901 0.712 DFL = a + b W 0.695 0.223 0.561-0.863 0.156-0.291 0.767 DFB = a + b TL 1.500 -0.615 0.086-26.303 -1.673-0.444 0.205 DFB = a + b W 0.482 -0.166 0.167-0.718 -0.498-0.165 0.178 AFL = a + b TL 0.229 0.588 0.148-0.354 0.426-0.749 0.795 AFL = a + b W 0.610 0.192 0.002-0.706 0.146-0.238 0.833 AFB = a + b TL 0.923 0.774 0.524-1.626 0.565-0.984 0.800 AFB = a + b W 3.281 0.260 2.748-3.908 0.205-0.315 0.862 CPW = a + b TL 0.010 1.630 0.005-0.020 1.380-1.880 0.920 CPW = a + b W 0.162 0.509 0.127-0.207 0.433-0.586 0.922 CPL = a + b TL 3.334 -0.864 0.047-2.370 -2.440-0.712 0.194 CPL = a + b W 0.637 0.214 0.131-3.105 -0.709-0.281 0.154 CFL = a + b TL 0.136 0.923 0.066-0.281 0.655-1.191 0.778 CFL = a + b W 0.701 0.269 0.524-0.940 0.178-0.361 0.729a = intercept, b = slope, r = correlation coefficient, CL = confidence limits, W = wet body weight, TL = total length, FL = fork length, SL = standard length, PreOL = pre-orbital length, ED = eye diameter, PostOL = post-orbital length, HL = head length, HD = head depth, PecFL = pectoral fin length, PecFB = pectoral fin base, BD = body depth, GL = girth length, PelFL = pelvic fin length, PelFB = pelvic fin base, DFL = dorsal fin length, DFB = dorsal fin base, AFL = anal fin length, AFB = anal fin base, CPW = caudal peduncle Width, CPL = caudal peduncle length, CFL = caudal fin length
Table 3.3.2 g: Relationship of total length (cm) and wet body weight (g) with different morphometric measurements of Garra gotyla.
Equation Total length (cm) Equation Wet body weight (g)a B 95 % CL of a 95 % CL of b R a b 95 % CL of a 95 % CL of b r
W = a + b TL 0.010 3.103 0.004-0.024 2.723-3.484 0.985 TL = a + b W 4.592 0.313 4.121-5.117 0.274-0.351 0.985 FL = a + b TL 0.818 1.041 0.646-1.035 0.943-1.139 0.991 FL = a + b W 3.954 0.330 3.581-4.365 0.294-0.365 0.977
123
SL = a + b TL 0.766 1.000 0.592-0.991 0.893-1.108 0.989 SL = a + b W 3.483 0.330 3.133-3.873 0.279-0.354 0.986 PreOL = a + b TL 0.022 1.613 0.009-0.051 1.258-1.968 0.954 PreOL = a + b W 0.242 0.524 0.168-0.304 0.443-0.605 0.977 ED = a + b TL 0.068 0.739 0.026-0.177 0.343-1.136 0.796 ED = a + b W 0.202 0.245 0.147-0.280 0.130-0.361 0.832 PostOL = a + b TL 0.172 0.584 0.074-0.399 0.234-0.935 0.761 PostOL = a + b W 0.424 0.179 0.305-0.587 0.063-0.296 0.735 HL = a + b TL 0.136 1.135 0.083-0.223 0.929-1.341 0.968 HL = a + b W 0.748 0.366 0.652-0.859 0.316-0.415 0.982 HD = a + b TL 0.106 1.111 0.055-0.206 0.835-1.387 0.943 HD = a + b W 0.553 0.363 0.466-0.959 0.302-0.425 0.972 PecFL = a + b TL 0.203 0.936 0.103-0.402 0.650-1.218 0.918 PecFL = a + b W 1.208 0.299 0.650-1.057 0.212-0.385 0.925 PecFB = a + b TL 0.029 1.237 0.009-0.090 0.760-1.713 0.877 PecFB = a + b W 0.181 0.404 0.124-0.264 0.269-0.539 0.903 BD = a + b TL 0.118 1.135 0.048-0.290 0.759-1.511 0.905 BD = a + b W 0.634 0.373 0.480-0.838 0.274-0.471 0.936 GL = a + b TL 0.043 1.090 0.027-0.067 0.903-1.278 0.972 GL = a + b W 0.219 0.353 0.198-0.241 0.318-0.388 0.990 PelFL = a + b TL 0.214 0.898 0.120-0.380 0.657-1.138 0.935 PelFL = a + b W 0.824 0.288 1.483 1.005 0.217-0.359 0.944 PelFB = a + b TL 0.036 1.065 0.020-0.066 0.815-1.316 0.949 PelFB = a + b W 0.182 0.338 0.145-0.228 0.257-0.418 0.947 DFL = a + b TL 0.160 1.068 0.070-0.363 0.726-1.409 0.910 DFL = a + b W 0.773 0.353 0.611-0.977 0.269-0.436 0.948 DFB = a + b TL 0.074 1.232 0.036-0.151 0.934-1.530 0.946 DFB = a + b W 0.467 0.399 0.377-0.578 0.323-0.475 0.965 AFL = a + b TL 0.161 0.986 0.090-0.289 0.743-1.229 0.944 AFL = a + b W 0.733 0.305 0.566-0.948 0.213-0.397 0.920 AFB = a + b TL 0.063 1.003 0.025-0.157 0.621-1.385 0.880 AFB = a + b W 0.284 0.321 0.204-0.395 0.203-0.439 0.887 CPW = a + b TL 0.049 1.258 0.019-0.123 0.872-1.645 0.917 CPW = a + b W 0.309 0.419 0.244-0.393 0.334-0.504 0.961 CPL = a + b TL 0.104 1.030 0.055-0.195 0.765-1.295 0.939 CPL = a + b W 2.000 0.320 0.381-0.656 0.223-0.416 0.919 CFL = a + b TL 0.243 0.970 0.381-0.656 0.742-1.197 0.949 CFL = a + b W 1.038 0.313 0.873-1.230 0.252-0.374 0.964a = intercept, b = slope, r = correlation coefficient, CL = confidence limits, W = wet body weight, TL = total length, FL = fork length, SL = standard length, PreOL = pre-orbital length, ED = eye diameter, PostOL = post-orbital length, HL = head length, HD = head depth, PecFL = pectoral fin length, PecFB = pectoral fin base, BD = body depth, GL = girth length, PelFL = pelvic fin length, PelFB = pelvic fin base, DFL = dorsal fin length, DFB = dorsal fin base, AFL = anal fin length, AFB = anal fin base, CPW = caudal peduncle Width, CPL = caudal peduncle length, CFL = caudal fin length
124
Table 3.3.3: Condition factor (K) of various fish species from Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
125
Species Name Condition factor (K)
Tor macrolepis 0.875
Schizothorax plagiostomus 1.080
Labeo diplostomus 1.075
Labeo dyocheilus pakistanicus 0.933
Cyprinion watsoni 1.136
Ompok pabda 0.723
Garra gotyla 1.230
Jan Feb
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p OctNov
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90
Mea
n hu
mid
ity (%
)
Figure 3.1.1 a Monthly variation in mean humidity (%) at four sampling sites(January2012-December 2012).
Figure 3.1.1 b Monthly variation in air temperature (oC) at four sampling sites (January2012-December 2012).
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Wat
er te
mpe
ratu
re (°
C)
Figure 3.1.2 a Monthly variation in water temperature (oC) at four sampling sites(January2012-December 2012).
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p OctNov Dec
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Ligh
t pen
etra
tion
(cm
)
Figure 3.1.2 b Monthly variation in light penetration (cm) at four sampling sites (January2012-December 2012).
126
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tem
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)
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TDS
(mgL
-1)
Figure 3.1.2 c Monthly variation in TDS (mgL-1)at four sampling sites (January2012-December 2012).
Jan Feb
Mar AprMay Jun Jul
Aug Sep Oct
Nov Dec6.8
7
7.2
7.4
7.6
7.8
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8.6
pH
Figure 3.1.2 d Monthly variation in pH at four sampling sites (January2012-December 2012).
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Dec0
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Diss
olve
d ox
ygen
(mgL
-1)
Figure 3.1.2 e Monthly variation in dissolved oxygen (mgL-1)at four sampling sites (January2012-December 2012).
Jan Feb
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Car
bon
diox
ide
(mgL
-1)
Figure 3.1.2 f Monthly variation in carbon dioxide (mgL-1)at four watersampling sites (January2012-December 2012).
127
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p OctNov Dec
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Car
bona
te io
ns (
mgL
-1)
Figure 3.1.2 g Monthly variation in carbonates (mgL-1)at four water sampling sites(January2012-December 2012).
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Bic
arbo
nate
ions
(m
gL-1
)
Figure 3.1.2 h Monthly variation in bicarbonates (mgL-1)at four water sampling sites (January2012-December 2012).
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Tota
l alk
alin
ity (
mgL
-1)
Figure 3.1.2 i Monthly variation in total alkalinity (mgL-1)at four water sampling sites (January2012-December 2012).
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Tota
l har
dnes
s (m
gL-1
)Figure 3.1.2 j Monthly variation in total hardness (mgL-1)at four water
sampling sites (January2012-December 2012).
128
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Sodi
um io
ns (
mgL
-1)
Figure 3.1.2 k Monthly variation in sodium ions (mgL-1)at four water sampling sites (January2012-December 2012).
Jan Feb Mar AprMay Jun Jul
AugSep Oct Nov
Dec0
50
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Cal
cium
ions
(m
gL-1
)
Figure 3.1.2 l Monthly variation in calcium ions (mgL-1)at four water sampling sites(January2012-December 2012).
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Mag
nesi
um io
ns (
mgL
-1)
Figure 3.1.2 m Monthly variation in magnesium ions (mgL-1)at four water sampling sites (January2012-December 2012).
Jan Feb
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Chl
orid
e io
ns (
mgL
-1)
Figure 3.1.2 n Monthly variation in chlorides ions (mgL-1)at four water sampling sites (January2012-December 2012).
129
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Sulp
hate
ions
(m
gL-1
)
Figure 3.1.2 o Monthly variation in sulphate ions (mgL-1) at four water sampling sites from (January2012-December 2012).
Jan Feb
Mar AprMay Jun Jul
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NovDec
0
0.5
1
1.5
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2.5
3
Elec
trica
l con
duct
ivity
(dSm
-1)
Figure 3.1.2 p Monthly variation in EC (mgL-1) at four water sampling sites (January2012-December 2012).
Jan Feb
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SAR
Figure 3.1.2 q Monthly variation in SAR ions at four water sampling sites (January2012-December 2012).
130
WSS-1 WSS-2 WSS-3 WSS-4 All sites0
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Spec
ies r
ichn
ess
Figure 3.1.3.1 a. Variations in species richness (as taxa number) of phytoplankton and zooplankton at four water sampling sites.
WSS-1 WSS-2 WSS-3 WSS-4 All sites0
5
10
15
20
25
30
35
Spec
ies r
ichn
ess
Figure 3.1.3.1 b. Variations in species richness (as taxa number) of major phytoplankton groups at four water sampling sites.
WSS-1 WSS-2 WSS-3 WSS-4 All sites0
2
4
6
8
10
12
14
16
Spec
ies r
ichn
ess
Figure 3.1.3.1 c. Variations in species richness (as taxa number) of zooplankton groups at four water sampling sites.
131
Cyanophyta Chlorophyta Bacillariophyta Protozoa Rotifera0
5
10
15
20
25
30
Spec
ies r
ichn
ess
Figure 3.1.3.1 d. Seasonal variation in species richness (as taxa number) ofmajorplanktonic groups atWSS-1.
Cyanophyta Chlorophyta Bacillariophyta Protozoa Rotifera0
5
10
15
20
25
Spec
ies r
ichn
ess
Figure 3.1.3.1 e. Seasonal variationin species richness (as taxa number) of major planktonic groupsatWSS-2.
Cyanophyta Chlorophyta Bacillariophyta Protozoa Rotifera0
5
10
15
20
25
Spec
ies r
ichn
ess
Figure 3.1.3.1 f. Seasonal variationin species richness (as taxa number) ofmajor planktonic groupsatWSS-3.
Cyanophyta Chlorophyta Bacillariophyta Protozoa Rotifera0
2
4
6
8
10
12
14
16
Spec
ies r
ichn
ess
Figure 3.1.3.1 g. Seasonal variation in species richness (as taxa number) of major planktonic groups at WSS-4.
132
WSS-1 WSS-2 WSS-3 WSS-40
10
20
30
40
50
60
Spec
ies r
ichne
s
Figure 3.1.3.1 h. Seasonal variationin species richness (as taxa number) ofphytoplanktons (January-December 2012) atfour water sampling sites.
WSS-1 WSS-2 WSS-3 WSS-40
5
10
15
20
25
Spec
ies r
ichne
ss
Figure 3.1.3.1 i. Seasonal variationin species richness (as taxa number) ofzooplanktons (January-December2012)
at four water sampling sites.
133
Winter Spring Summer Postmonsoon0
1
2
3
4
5
6
7
8
9
10
Spec
ies r
ichn
ess
Figure 3.1.3.1 j. Seasonal variation in species richness (as taxa number) of cyanophyta (Jan-Dec 2012) at
four water sampling sites.
Winter Spring Summer Postmonsoon0
5
10
15
20
25
30
Spec
ies r
ichn
ess
Figure 3.1.3.1 k. Seasonal variation in species richness (as taxa number) of chlorophyta (Jan-Dec 2012) at four water sampling sites.
Winter Spring Summer Postmonsoon0
2
4
6
8
10
12
Spec
ies r
ichn
ess
Figure 3.1.3.1 l. Seasonal variation in species richness (as taxa number) of bacillariophyta (Jan-Dec 2012) at four water sampling sites.
134
Winter Spring Summer Postmonsoon0
2
4
6
8
10
12
Spec
ies r
ichn
ess
Figure 3.1.3.1 m. Seasonal variation in species richness (as taxa number) of protozoa (January-December 2012)atfour water sampling sites.
Winter Spring Summer Postmonsoon0
1
2
3
4
5
6
7
8
Spec
ies r
ichn
ess
Figure 3.1.3.1 n. Seasonal variation in species richness (as taxa number) ofrotifers (January-December 2012) at four water sampling sites.
135
Jan Feb
Mar AprMay Jun Jul Aug Se
p OctNov Dec
0
1
2
3
4
5
6
7
8
9
10
Spec
ies r
ichn
ess
Figure 3.1.3.1 o. Monthly variation (January-December2012) in species richness (as taxa number) of phytoplankton and zooplankton at WSS-1.
Jan Feb Mar AprMay Jun Jul
AugSep Oct Nov
Dec0
1
2
3
4
5
6
7
8
9
10
Spec
ies r
ichn
ess
Figure 3.1.3.1 p. Monthly variation (January-December 2012) in species richness (as taxa number) of phytoplankton and zooplankton at WSS-2.
Jan Feb
Mar AprMay Jun Jul Aug Se
p OctNov Dec
0
2
4
6
8
10
12
Spec
ies r
ichn
ess
Figure 3.1.3.1 q. Monthly variation (Jan-Dec 2012) in species richness (as taxa number) of phytoplankton and zooplanktonatWSS-3.
Jan Feb
Mar AprMay Jun Jul Aug Se
p OctNov Dec
0
2
4
6
8
10
12
Spec
ies r
ichn
ess
Figure 3.1.3.1 r. Monthly variation (Jan-Dec2012) in species richness (as taxa number) of phytoplankton and zooplanktonatWSS-4.
136
Jan Feb
Mar Apr
May Ju
n Jul
Aug Sep
OctNov Dec
0
2
4
6
8
10
12
Spec
ies
rich
ness
Figure 3.1.3.1 s. Monthly variation (Jan-Dec 2012) in species richness (as taxa number) of major groups of phytoplankton at WSS-1.
Jan Feb
Mar Apr
May Ju
n Jul
Aug Sep
OctNov Dec
0
1
2
3
4
5
6
7
Spec
ies
rich
ness
Figure 3.1.3.1 t. Monthly variation (Jan-Dec2012) in species richness (as taxa number) of major groups of zooplankton at WSS-1.
Jan Feb
Mar AprM
ay Jun Jul
Aug Sep
OctNov Dec
0
2
4
6
8
10
12
Spec
ies r
ichn
ess
Figure 3.1.3.1 u. Monthly variation (Jan-Dec 2012) in species richness (as taxa number) of major groups of phytoplankton at WSS-2.
Jan Feb
Mar AprM
ay Jun Jul
Aug Sep
OctNov Dec
0
1
2
3
4
5
6
7
8
Spec
ies r
ichn
ess
Figure 3.1.3.1 v. Monthly variation (Jan-Dec 2012) in species richness (as taxa number) of major groups of zooplankton at WSS-2.
137
Jan Feb
Mar AprMay Jun Jul Aug Se
p OctNov Dec
0
2
4
6
8
10
12
Spec
ies r
ichn
ess
Figure 3.1.3.1 w. Monthly variation (Jan-Dec2012) in species richness (as taxa number) of major groups of phytoplankton at WSS-3.
Jan Feb
Mar AprMay Jun Jul
Aug Sep Oct
Nov Dec0
1
2
3
4
5
6
7
Spec
ies r
ichn
ess
Figure 3.1.3.1 x. Monthly variation (Jan-Dec 2012) in species richness (as taxa number) of major groups of zooplankton at WSS-3.
Jan Feb
Mar AprMay Jun Jul Aug Se
p OctNov Dec
0
1
2
3
4
5
6
7
8
9
10
Spec
ies r
ichn
ess
Figure 3.1.3.1 y. Monthly variation (Jan-Dec 2012) in species richness (as taxa number) of major groups of phytoplankton at WSS-4.
Jan Feb
Mar AprMay Jun Jul
Aug Sep Oct
Nov Dec0
1
2
3
4
5
6
Spec
ies r
ichn
ess
Figure 3.1.3.1 z. Monthly variation (Jan-Dec 2012) in species richness (as taxa number) of major groups of zooplankton at WSS-4.
138
WSS-1 WSS-2 WSS-3 WSS-4 All sites0
10
20
30
40
50
60
70
80
90
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 a. Variation in relative abundance (%) of phytoplankton and zooplankton at four water sampling sites.
WSS-1 WSS-2 WSS-3 WSS-4 All sites0
5
10
15
20
25
30
35
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 b. Variation in relative abundance (%) of major phytoplankton groups at four water sampling sites.
Site 1 Site 2 Site 3 Site 4 All sites0
2
4
6
8
10
12
14
16
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 c. Variation in relative abundance (%) of major zooplankton groupsat four water sampling sites.
139
Cyano
phyta
Chlorop
hyta
Bacilla
rioph
yta
Protoz
oa
Rotifer
a0
2
4
6
8
10
12
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 d. Seasonal variationin relative abundance (%) of major planktonic groups (Jan-Dec 2012) at WSS-1.
Cyanophyta Chlorophyta Bacillariophyta Protozoa Rotifera0
2
4
6
8
10
12
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 e. Seasonal variation in relative abundance (%) of major planktonic group (Jan-Dec 2012) atWSS-2.
Cyanophyta Chlorophyta Bacillariophyta Protozoa Rotifera0
2
4
6
8
10
12
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 f. Seasonal variationin relative abundance (%) of major planktonic groups (Jan-Dec 2012) atWSS-3.
Cyanophyta Chlorophyta Bacillariophyta Protozoa Rotifera0
2
4
6
8
10
12
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 g. Seasonal variation in relative abundance (%) of major planktonic groups (Jan-Dec 2012) at WSS-4.
140
WSS-1 WSS-2 WSS-3 WSS-40
5
10
15
20
25
30
35
Relati
ve ab
unda
nce (
%)
Figure 3.1.3.2 h. Seasonal variation in relative abundance (%) of phytoplankton (Jan-Dec 2012) at four water
sampling sites.
WSS-1 WSS-2 WSS-3 WSS-40
1
2
3
4
5
6
7
8
9
Relati
ve ab
unda
nce (
%)
Figure 3.1.3.2 i. Seasonal variation in relative abundance (%) of zooplankton (Jan-Dec 2012) at four water samping sites.
141
Winter Spring Summer Postmonsoon0
1
2
3
4
5
6
7
8
9
10
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 j. Seasonal variation in relative abundance (%) of cyanophyta at four water sampling sites.
Winter Spring Summer Postmonsoon0
5
10
15
20
25
30
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 k. Seasonal variationin relative abundance (%) of chlorophyta at four water sampling sites.
Winter Spring Summer Postmonsoon0
2
4
6
8
10
12
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 l Seasonal variationin relative abundance (%) of bacillariophyta at four water sampling sites.
142
Winter Spring Summer Postmonsoon0
2
4
6
8
10
12
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 m. Seasonal variation in relative abundance (%) of protozoa at four water sampling sites.
Winter Spring Summer Postmonsoon0
1
2
3
4
5
6
7
8
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 n. Seasonal variationin relative abundance (%) of rotifera at four water sampling sites.
143
Jan Feb
Mar AprMay Jun Jul Aug Se
p OctNov Dec
0
1
2
3
4
5
6
7
8
9
10
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 o. Monthly variation (Jan-Dec 2012) in relative abundance (%) of phytoplankton and zooplankton at WSS-1.
Jan Feb
Mar AprMay Jun Jul Aug Se
p OctNov Dec
0
1
2
3
4
5
6
7
8
9
10
Rel
ativ
e ab
unda
nce(
%)
Figure 3.1.3.2 p. Monthly variation (Jan-Dec 2012) in relative abundance (%) of phytoplankton and zooplankton at WSS-2.
Jan Feb
Mar AprMay Jun Jul Aug Se
p OctNov Dec
0
2
4
6
8
10
12
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 q. Monthly variation (Jan-Dec 2012) in relative abundance (%) of phytoplankton and zooplankton at WSS-3.
Jan Feb Mar AprMay Jun Jul
AugSep Oct Nov
Dec0
0.2
0.4
0.6
0.8
1
1.2
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 r. Monthly variation (Jan-Dec 2012) in relative abundance (%) of phytoplankton and zooplankton at WSS-4.
144
Jan Feb
Mar AprMay Jun Jul
Aug Sep Oct
Nov Dec0
0.5
1
1.5
2
2.5
3
3.5
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 s. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groups of phytoplankton at WSS-1.
Jan Feb Mar AprMay Jun Jul
AugSep Oct Nov
Dec0
0.2
0.4
0.6
0.8
1
1.2
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 t. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groups of zooplankton at WSS-1.
Jan Feb
Mar AprMay Jun Jul
Aug Sep Oct
Nov Dec0
0.5
1
1.5
2
2.5
3
3.5
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 u. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groups of phytoplankton at WSS-2.
Jan Feb Mar AprMay Jun Jul
Aug Sep Oct NovDec
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 v. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groups of zooplanktonat WSS-2.
145
Jan Feb
Mar AprMay Jun Jul
Aug Sep Oct
Nov Dec0
0.5
1
1.5
2
2.5
3
3.5
4
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 w. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groupsphytoplankton at WSS-3.
Jan Feb
Mar AprMay Jun Jul
Aug Sep Oct
Nov Dec0
0.2
0.4
0.6
0.8
1
1.2
1.4
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 x. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groups of zooplankton at WSS-3.
Jan Feb
Mar AprMay Jun Jul
Aug Sep Oct
Nov Dec0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 y. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groups of phytoplankton atWSS-4.
Jan Feb
Mar AprMay Jun Jul
Aug Sep Oct
Nov Dec0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Rel
ativ
e ab
unda
nce
(%)
Figure 3.1.3.2 z. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groups of zooplanktonaWSS-4.
146
Cyprinidae Cobitidae Bagridae Siluridae Mastacembelidae0
2
4
6
8
10
12
14
16
18
Rela
tive a
buda
nce (
%)
Figure 3.2.1 a. Species richness (as taxa number) of fish families at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
FSS-1 FSS-2 FSS-3 FSS-4 FSS-5 FSS-6 FSS-7 FSS-8 FSS-9 FSS-100
1
2
3
4
5
6
7
8
9
Spec
ies r
ichn
ess
Figure 3.2.1 b. Variation in species richness (as taxa number) at fish sampling sites of Suleman Mountain RangeRegion, Dera Ghazi Khan, Pakistan.
Cyprinidae
Cobitidae
Bagridae
Siluridae
Mastacembelidae
0 10 20 30 40 50 60 70 80 90 100
Species richness
Figure 3.2.2 a Relative abundance (%) of number of individuals of each fish family at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
147
FSS-1 FSS-2 FSS-3 FSS-4 FSS-5 FSS-6 FSS-7 FSS-8 FSS-9 FSS-100
10
20
30
40
50
60
70
80
90
100Mastacembalus arma-tusOmpok pabdaRita ritaBotia birdiCirrhnus mrigalaSalmophasia pun-jabensisBarilius pakistanicusPuntius sophoreLabeo calbasuCyprinion watsoniSchizothorax plagios-tomusCrossochelus diplocheilusLabeo dyocheilus pak-istanicusSecuricula goraGara gotylaSalmostoma bacaila
Rel
ativ
e ab
unda
nce
(%)
Figure 3.2.2 b Relative abundance (%) of each fish species within each site during study period at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
148
Tor m
acrolep
is
Bariliu
s vagr
a
Bariliu
s modustu
s
Labeo
diplostomus
Salmosto
ma baca
ila
Gara go
tyla
Securicu
la gora
Labeo
dyocheilu
s paki
stan...
Crossochelu
s diplocheilu
s
Schizo
thorax plag
iostomus
Cyprin
ion watsoni
Labeo
calbasu
Puntius sophore
Bariliu
s paki
stanicu
s
Salmophasi
a punjab
ensis
Cirrhnus m
rigala
Botia bird
i
Rita rit
a
Ompok pab
da
Mastace
mbalus a
rmatu
s
0
5
10
15
20
25FSS-10FSS-9FSS-8FSS-7FSS-6FSS-5FSS-4FSS-3FSS-2FSS-1
Relati
ve ab
unda
nce (
%)
Figure 3.2.2 c. Relative abundance (%) of twenty fish species at different sites during study period at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.
149
Figure 3.2.3 Dendrogram showing similarity level (based on Jaccard similarity index) in species compositionacross ten fish sampling sites.
150
5 10 15 20 25 30 350
5
10
15
20
25
30
Total length (cm)
Leng
th o
f oth
er v
aria
bles
(cm
)
Figure 3.3.1a Relationship of total length (cm) with length of other variablesof Tor macrolepis.
5 10 15 20 25 30 350
1
2
3
4
5
6
7
8
9
10
Total length (cm)
Fin
leng
ths (
cm)
Figure 3.3.1b. Relationship of total length (cm) with fins length of Tor macrolepis.
0 50 100 150 200 250 300 3500
5
10
15
20
25
30
Wet body weight (g)
Leng
th o
f oth
er v
aria
bles
(cm
)
Figure 3.3.1c. Relationship of wet body weight (g) with length of other variables of Tor macrolepis.
0 50 100 150 200 250 300 3500
1
2
3
4
5
6
7
8
9
10
Wet body weight (g)
Fins
leng
th (c
m)
Figure 3.3.1d. Relationship of wet body weight (g) with fins length of Tor macrolepis.
151
6 8 10 12 14 16 180
2
4
6
8
10
12
14
16
18
Total length (cm)
Leng
th o
f oth
er v
aria
bles
(cm
)
Figure 3.3.2 a Relationship of total length (cm) with length of other variables of Schizothorax plagiostomus.
6 8 10 12 14 16 180
0.5
1
1.5
2
2.5
3
3.5
4
Total length (cm)
Fins
leng
th (c
m)
Figure 3.3.2 b Relationship of total length (cm) with fins lengthof Schizothorax plagiostomus.
0 10 20 30 40 50 60 70 800
2
4
6
8
10
12
14
16
18
Wet body weight (g)
Leng
th o
f oth
er v
aria
bles
(cm
)
Figure 3.3.2 c Relationship of wet body weight (g) with length of other variablesof Schizothorax plagiostomus
0 10 20 30 40 50 60 70 800
0.5
1
1.5
2
2.5
3
3.5
4
Wet body weight (g)
Fins
leng
th (c
m)
Figure 3.3.2 d Relationship of wet body weight (g) with fins length of Schizothorax plagiostomus.
152
8 10 12 14 16 18 20 220
5
10
15
20
25
30
35
40
Total length (cm)
Leng
th o
f oth
er v
aria
bles
(cm
)
Figure 3.3.3 a Relationship of total length (cm) with length of other variablesof Labeo diplostomus.
8 10 12 14 16 18 20 220
1
2
3
4
5
6
7
Total length (cm)
Fins
leng
th (c
m)
Figure 3.3.3 b Relationship of total length (cm) with fins length of Labeo diplostomus.
0 10 20 30 40 50 60 70 80 90 1000
5
10
15
20
25
30
35
40
Wet body weight (g)
Leng
th o
f oth
er v
aria
bles
(cm
)
Figure 3.3.3 c Relationship of wet body weight (g) with length of other variables of Labeo diplostomus.
0 10 20 30 40 50 60 70 80 90 1000
1
2
3
4
5
6
7
Wet body weight (g)
Fins
leng
th (c
m)
Figure 3.3.3 d Relationship of wet body weight (g) with fins length of Labeo diplostomus.
153
8 10 12 14 16 18 20 22 24 260
5
10
15
20
25
Total length (cm)
Leng
th o
f oth
er v
aria
bles
(cm
)
Figure 3.3.4 a Relationship of total length (cm) with length of other variables of Labeo dyocheilus pakistanicus.
8 10 12 14 16 18 20 22 24 260
1
2
3
4
5
6
7
Total length (cm)
Fins
leng
th (c
m)
Figure 3.3.4 b Relationship of total length (cm) with fins length of Labeo dyocheilus pakistanicus.
0 20 40 60 80 100 120 1400
5
10
15
20
25
Wet body weight (g)
Leng
th o
f oth
er v
aria
bles
(cm
)
Figure 3.3.4 c Relationship of wet body weight (g) with length of other variables of Labeo dyocheilus pakistanicus.
0 20 40 60 80 100 120 1400
1
2
3
4
5
6
7
Wet body weight (g)
Fins
leng
th (c
m)
Figure 3.3.4 d Relationship of wet body weight (g) with fins lengthof Labeo dyocheilus pakistanicus.
154
7 8 9 10 11 12 13 14 150
0.5
1
1.5
2
2.5
3
3.5
4
Total length (cm)
Fins
leng
th (c
m)
Figure 3.3.5 a Relationship of total length (cm) with length of other variables of Cyprinion watsoni.
7 8 9 10 11 12 13 14 150
2
4
6
8
10
12
14
Total length (cm)
Leng
th o
f oth
er v
aria
bles
(cm
)
Figure 3.3.5 b Relationship of total length (cm) with fins length of Cyprinion watsoni.
5 10 15 20 25 30 350
2
4
6
8
10
12
14
Wet body weight (g)
Leng
th o
f oth
er v
aria
bles
(cm
)
Figure 3.3.5 c Relationship of wet body weight (g) with length of other variables of Cyprinion watsoni.
5 10 15 20 25 30 350
0.5
1
1.5
2
2.5
3
3.5
4
Wet body weight (g)
Fins
leng
th (c
m)
Figure 3.3.5 d Relationship of wet body weight (g) with fins length of Cyprinion watsoni.
155
8 10 12 14 16 18 200
2
4
6
8
10
12
14
16
18
20
Total length (cm)
Leng
th o
f oth
er v
aria
bles
(cm
)
Figure 3.3.6 a Relationship of total length (cm) with length of other variables of Ompok pabda.
8 10 12 14 16 18 200
0.5
1
1.5
2
2.5
3
Total length (cm)
Fins
leng
th (c
m)
Figure 3.3.6 b Relationship of total length (cm) with fins length of Ompok pabda
0 10 20 30 40 50 600
2
4
6
8
10
12
14
16
18
20
Wet body weight (g)
Leng
th o
f oth
er v
aria
bles
(cm
)
Figure 3.3.6 c Relationship of wet body weight (g) with length of other variables of Ompok pabda.
0 10 20 30 40 50 600
0.5
1
1.5
2
2.5
3
Wet weight (g)
Fin
leng
th (c
m)
Figure 3.3.6 d Relationship of wet body weight (g) with fins length of Ompok pabda.
156
8 9 10 11 12 13 14 150
2
4
6
8
10
12
14
Total length (cm)
Leng
th o
f oth
er v
aria
bles
(cm
)
Figure 3.3.7 a Relationship of total length (cm) with length of other variables of Garra gotyla.
8 9 10 11 12 13 14 150
0.5
1
1.5
2
2.5
3
3.5
Total length (cm)
Fins
leng
th (c
m)
Figure 3.3.7 b Relationship of total length (cm) with fins length of Garra gotyla.
5 10 15 20 25 30 35 40 450
2
4
6
8
10
12
14
Wet body weight (g)
Leng
th o
f oth
er v
aria
bles
(cm
)
Figure 3.3.7 c Relationship of wet body weight (g) with length of other variables of Garra gotyla.
5 10 15 20 25 30 35 40 450
0.5
1
1.5
2
2.5
3
3.5
Wet body weight (g)
Fins
leng
th (c
m)
Figure 3.3.7 d Relationship of wet body weight (g) with fins length of Garra gotyla.
157
Discussion
4.1 Water Quality
4.1.1 Physico-Chemical Factors
Fresh water environments unlike the marine ones are subjected to variations in the
environmental factors such as temperature, DO, light penetration, turbidity, density,
etc. These factors are responsible for distribution of organisms in different fresh water
habitats according to their adaptations, which allow them to survive in that specific
habitat (Jaffries and Mills, 1990). Important climatic and phyico-chemical parameters
were estimated in present study. Several of the physico-chemical parameters showed
variations related either with site/season/months.
Variations in water temperature are usually governed by the climatic conditions.
Rainfall and solar radiations are the major climatic conditions that influence most of
the physico-chemical parameters of water bodies (Kadiri, 2000). Solar radiation is
dependent on the duration and intensity or iridescence received daily by the water
body. The intensity of solar radiations may be naturally modified by variations in
cloud cover, water flow, phytoplankton species composition and diversity, surface
area, depth, wind velocity, solid matter suspension, etc. All these factors influence
daily fluctuations in water temperature (Atoma, 2004). In present study, there were no
significant water temperature variations between sites, but water temperature varied
significantly in different months and seasons. The comparatively wide range of
maximum and minimum water temperature with 32.01±1.55 °C recorded in summer
and minimum 16.27±5.07 °C in winter clearly indicates pronounced seasonal
variations in surface water temperature of this region.
158
Light penetration values showed no significant difference between sites, however,
there was a significant difference between seasons and months. The lower limit of
light penetration is the limit of algal photosynthetic activity that has a major impact on
the primary productivity of the lake (Carpenter et al., 1998; Ask et al., 2009;
Vadeboncoeur et al., 2001). The light penetrationis affected by factors such as time of
the day, clarity of the sky at the time of measurement (cloudy or not) and suspended
solids in water including plankton (Smith et al., 1997; Julianet al., 2008). The mean
values of light penetration ranged from 23.32 cm (WSS-1) to 28.48 cm (WSS-3). It
also varied from 20.69 cm (post monsoon) to 30.90 cm (spring). Lower light
penetrationvaluesare recorded during rainy season when there is turbulence and high
turbidity (USEPA, 1992; APHA, 1992). Higher total solids, TDS and total suspended
solids, resulted in minimum transparency in monsoon season. This complies with the
reports on several water bodies especially in Indian climatic conditions (Zafar, 1964;
Singhai et al., 1990; Kaur et al., 1995; Ekhande, 2010) as well as other tropical
countries like Nigeria (Olele and Ekelemn, 2008). Khan and Chowdhury (1994)
reported that higher transparence occurred, during winter and summer due to absence
of rain, runoff and flood water as well as gradual settling of suspended particles.Light
penetration varying from 30 cm to above 60 cm was acknowledged to be favourable
for fish production (Boyd and Tucker, 1998; Ali et al., 2002). Our study results of
light penetration values also indicate suitable limits for aquatic life at four catchment
areas of Suleman Mountain Range.
TDS values were significantly different between four sampling sites. However, there
was no significant effect of seasonal variations on TDS, though it varied significantly
in months. TDS reduces solubility of gases (like oxygen), utility of water for drinking
159
purpose and also enhances eutrophication of the aquatic ecosystem (Mathur et al.,
2008). High concentration of TDS enriching the nutrient status of water body (Singh
and Mathur, 2005) beyond limit may enhance eutrophication (Mathur et al., 2008).
The TDS values ranged from 1174 to 1636 mgL-1 in present study. A maximum value
of 400 mgL-1 of total dissolved solids is permissible for diverse fish population (Boyd
and Tucker, 1998; Ali et al., 2003). Therefore, the TDS valuesin present study exceed
the recommended limit of suitable waters.
pH values demonstrated significant variations between sites as well as between
seasons. In a balanced ecosystem pH is maintained within the range of 5.5 to 8.5
(Chandrasekhar et al., 2003). Due to diurnal variations in the water temperature of a
system, pH of a water body is a diurnally variable property (Ojha and Mandloi, 2004).
Increased surface pH in water bodies is due to increased metabolic activities of
autotrophs, because in general they utilize the CO2 and liberate O2 thus reducing H+
ion concentration (Kaul and Handoo, 1980; Satpathy et al., 2007). In present study, the
pH values ranged between 7.85 to 8.02 for sites and 7.73 to 8.12 for seasons. Different
reports have given different pH ranges as ideal for supporting aquatic life. However,
all these ranges fall around same point between 6 to 8.5 (ICMR, 1975; Boyd and
Lichtkoppler, 1979; WHO, 1989). pH of lentic water bodies of Suleman Mountain
Range, Dera Ghazi Khan Region, Pakistan fall in ideal range.
DO is essential to all forms of aquatic life.Majority of chemical and biological
processes undergoing in the water body also depend on the presence of
oxygen.Oxygen is also known to affect the solubility and availability of many nutrients
and hence, it is one of the most significant parameter affecting the productivity of
160
aquatic systems (Wetzel, 1983). There was no significant difference between sites,
however, DO varied significantly with season and months. The DO ranged from 6.63
to 7.30 mgL-1 for sampling sites and 5.99 to 8.09 mgL-1 for seasons. The factors
affecting oxygen content in natural waters include input from atmosphere and
photosynthesis and output from respiration, decomposition and mineralization of
organic matter as well as losses to atmosphere (Wetzel, 1983; Ramacandra and
Solanki, 2007; Saravanakumar et al., 2008). The oxygen balance in water bodies
becomes poorer when the input and photosynthetic activity decrease and the metabolic
activities of heterotrophs enhance.The oxygen cycle in water involves a rapid decrease
during summer, a steady increase through autumn till maximum content is reached in
winter following the well-known theory of solubility of gases (Kaul and Handoo,
1980). Ideal DO for the fish, the final stage of productivity in the fresh water
ecosystem, is assumed to be between 6 and 7 mgL-1 (Edmondson, 1960). Low oxygen
concentration will also affect the types of fish and invertebrates that inhabit the area
(Gordon et al, 2004). The minimum limit of DO required for freshwaters as per ICMR
(1975) standards is 5 to 6 mgL-1. Therefore, DO of studied water bodies of Suleman
Mountain Range, Dera Ghazi Khan Region, Pakistan is within normal limits.
Free CO2 amount varied significantly between sites, with season and months. The
values ranged from 7.18 mgL-1 to 7.84 mgL-1for sites and 6.28 mgL-1 to 8.51 mgL-1for
seasons. The free CO2 limit values for drinking water have not been prescribed but the
permissible limit for fish culture is 6 mgL-1. The maximum free CO2 in summer may
be attributed to higher rate of decomposition of organic matter due to comparatively
higher temperature. During daytime due to photosynthesis water is generally CO2 free
(Sahu et al., 1995). Organic decomposition, respiration, photosynthesis, diffusion and
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runoffs could also account for the variations seen in the CO2 levels (Renn, 1968;
APHA, 1995).
An inverse relationships exist between DO and CO2, pH, alkalinity and temperature
the parameter that are directly related to each other. The pH depends upon the free CO2
and bicarbonate-carbonate levels (Michael, 1984).
TA varied significantly between sites, and there was non significant effect of seasonal
variations. TA ranged from 101 mgL-1 to 401 mgL-1 for sites and 285 to 303 mgL-1 for
seasons. The main sources of natural alkalinity are rocks containing carbonate,
bicarbonate, and hydroxide compounds that are abundantly present. TA affects the
primary production and the other metabolic process of aquatic organisms (Singh and
Islam, 2000). It was reported earlier that during summer and winter season water was
alkaline, which is helpful for maximum population dynamics of planktons (Pundir and
Rana, 2002).
The values for TH, CO3-2 and HCO3
-1 were significantly different between sites;
however, there was non-significant effect of seasonal as well as monthly variations on
these parameters. The values of TH ranged from 201 mgL-1 to 265 mgL-1 for sites, CO3-
2 varied from 11.81 mgL-1to 13.78 mgL-1, and HCO3-1 ranged from 386 mgL-1 to 623
mgL-1 for sites. TH values were 146-257.3 mgL-1from River Indus at Beka Swabi
KPK, Pakistan Khan et al., 2014). Chughtai et al., (2013) described TH values 190-
320 mgL-1 at D. G. Khan Canal from Dera Ghazi Khan, Pakistan. These results
coincide with our results.
162
The hardness of water is not a pollution parameter but indicates water quality.Hardness
of water, mainly governed by the Ca+2 and to a lesser extent by Mg+2 contents with ions
such as Fe+2. Waters are often categorized according to degrees of hardness as follows:
0-75 mgL-1 = soft, 75-150 mgL-1 = moderately hard 150-300 mgL-1 = hard above 300
mgL-1 = very hard (WHO, 1996).Waters with more than 60 ppm which is equivalent to
60 mgL-1.CaCO3hardness are classified as ‘nutrient rich’ waters (Spence, 1964). The
desirable limit of hardness in drinking water according to ISI standards is below 300
mgL-1. While hardness levels above 500mgL-1 are generally considered to be
aesthetically unacceptable (WHO, 1996). The results of present study reflect hardness
value of water bodies are within acceptable range.
Calcium (Ca) is one of the most abundant substances of the natural waters. Being
present in higher quantities in rocks, it is leached from these to contaminate water. Ca
is an important element and associated with different cations like CO3-2, HCO3
-1 and
fluorides to exert hardness. As Ca+2 and Mg+2 bond with CO3-2 and HCO3
-1, alkalinity
and water hardness are closely interrelated and produce similar measured levels
(Matini et al., 2012).
Chlorides contents were significantly different between four sites.Higher concentration
of Cl-1 indicates higher degree of organic pollution (Munawar, 1974; Ramakrishna,
1990). Concentration of Cl-1 in sea water is around 20,000 mgL-1, in unpolluted rivers
between 2-10 mgL-1 and in rain water 2 mgL-1. When it is above 200 mgL-1, the water
is unsuitable for human consumption (Koshy and Nayar, 1999). Maximum permissible
limit with regard to Cl-1 content in natural freshwaters is 250 mgL-1(ICMR, 1975; ISI,
1991). In present study, three sites i.e. WSS-1, WSS-2, WSS-3 have lower Cl -1
163
contents than the permissible limit 250 mgL-1as per ISI. However, at WSS-4, higher
Cl-1 contents than permissible limits were recorded. In natural water excessive Cl -1 ions
are usually found associated with Na+1, K+1 and Ca+2 which produce salty taste when
concentration is 100mgL-1 (Kataria et al., 1996; Gowd et al., 1998).
EC values of four sites were significantly different. There was no significant effect of
seasons on EC. Several factors influence the conductivity including temperature, ionic
mobility and ionic valencies. In turn, conductivity provides a rapid mean of obtaining
knowledge of TDS concentration and salinity of water sample (Odum, 1971).
4.1.2 Phytoplankton
The freshwater habitat is a natural condition for the growth of aquatic flora and the
fluxing of the wastes by natural or anthropogenic activities cause disturbance in its
composition, this cause change in the optimum condition favourable for the growth of
the aquatic flora (Jaiswar and Mehta, 2014). Algal communities serve as a natural
oxygenator in any water system.Therefore its conservation is very important to
maintain the biotic community in the water. The present investigation on the
freshwater algal taxa of these unexplored sites of Suleman Mountain Range, Dera
Ghazi Khan Region, Pakistan would form valuable information which would be
further used for the environmental assessment and monitoring of water quality.
In our study, a considerable number of plankton genera i.e. 119 taxa including 83 of
phytoplankton and 36 of zooplankton were recorded from four sampling sites of the
area. A survey of literature has revealed slight or slightly larger variations in number
and types of planktonic genera from several water bodies located at different places in
Pakistan. A total number of 104 genera including 86 of phytoplankton and 18 of
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zooplankton from Chenab River (Chughtai et al., 2011),a total number of 53 genera
including 39 of phytoplankton and 14 of zooplankton from D. G. Khan canal
(Chughtai et al., 2013), a total number of 103 genera including 32 of phytoplankton
and 71 of zooplankton from wetland complex Uchalli (Ali et al., 2007), a total number
of 179 genera including 142 of phytoplankton and 37 of zooplankton from Keenjhar
Lake (Korai et al., 2008), a total number of 169 genera including 125 of phytoplankton
and 44 of zooplankton from River Indus (Ali et al., 2003), a total number of 35
phytoplankton genera from ShaPur Dam (Janjua et al., 2009), a total number of 34
phytoplankton genera from KalaPani stream and adjoining area of stream area from
Mardan (Khan et al., 2011), a total number of 33 genera including 142 of
phytoplankton and 37 of zooplankton from Mangla Reservior (Rafique et al., 2002).
However, a greater variation was also noted when compared to planktonic biodiversity
analysis with other regions of the world. A total number of 54 phytoplankton genera
from Sapanca Lake, Turkey (Yilmaz and Aykulu, 2010), a total number of 39 genera
including 25 of phytoplankton and 14 of zooplankton from a tropical African
Reservior, Nigeria (Mustapha, 2010), a total number of 88 phytoplankton genera from
Lake Mogan, Turkey (Yerli et al., 2012), a total number of 53 species of
phytoplankton from three waterbodies of Satara district, India (Pawar and Sonavane,
2011), a total number of 53 genera of phytoplankton from Zayan Dh-Rood Dam Lake,
Iran (Shams et al., 2012).
In our study, phytoplanktons have been found to be abundant compared to
zooplankton. This is also reported by several other workers from various localities of
Pakistan (Ali et al., 2005; Chughtai et al., 2011; Janjua et al., 2009). The species
composition of phytoplankton was related with site, season and months, however,
165
there was no significant effect of site, season and month on abundance of total
phytoplankton.The composition and biomass of phytoplankton species in reservoirs
depends on a complex combination of factors, such as temperature, light, availability
of nutrients and zooplankton community (Reynolds, 2002). In subtropical regions, the
considerable variation in temperature and other environmental variables produces
predictable changes in the composition of phytoplankton in aquatic systems (Grover
and Chrzanowski, 2006).The composition of phytoplankton is very often an excellent
indication of the trophic state of a water body (Rosen, 1981; Reynolds, 1998). The
differences in number of taxa and number of individuals between sampling sites for
each class of phytoplankton may be due differences in environmental factors.
Chlorophyta demonstrated a significant difference of species richness and abundance
between four sites; however, there was no significant effect of season on species
composition and abundance. The optimal temperature reported for growth of green
algae ranged between 30-40°C (Palmer, 1984). High water temperature supported the
growth of chlorophyceae (Hegde and Sujata, 1997). During the present study
temperature was found to be the most important factor that influenced the other
physicochemical factors by forming positive/negative correlations with other
important environmental factors.
Chlorophyta was the most abundant group of phytoplankton and also with higher
species richness.This has been reported by several workers (Annalakshmi and Amsath,
2012; Jamal et al., 2014; Groga et al., 2014). Thus qualitatively chlorophyceae formed
the largest group and was followed by other groups. Quantitatively also chlorophyceae
dominanted over other groups and contributed as much as (48%) to the total
166
phytoplankton population (Rajagopal et al., 2010).Higher cholorophyceae are a large
and important group of freshwater algae. About 2650 species of chlorophyceae have
been described from the different parts of the world and 350 genera have so far been
authenticated (Ven DenHoeck et al., 1995).
In present study, chlorophyta demonstrated several significant correlations with
environmental variables. These include negative correlation of density of chlorophyta
with TDS at WS-1, with calcium at WSS-1, with EC at WSS-1, with SAR at WSS-1;
and a positive correlation of density of chlorophyta with Cl-1 at WSS-1. While species
composition of chlorophyta has negative correlation with pH at WSS-1, with Ca+2 at
WSS-3, and positive correlation with light penetration at WSS-1, with TDS at WSS-2,
with carbonates at WSS-1, with total hardness at WSS-3, with EC at WSS-2.Several
workers have reported positive correlation of chlorophyta abundance with DO (Kumar
and Oommen, 2009); chlorophyta with temperature (Hegde and Sujata, 1997); with
light penetration, DO and TA (Bhatt et al., 1999) and negative correlation with water
temperature, light penetration and dissolved oxygen (Cordero and Baldia, 2015);
chlorophyta with DO and CO2 (Hegde and Sujata, 1997) with pH (Bhatt et al., 1999).
Chlorophyta has been found to be dominant phyla in several studies. The
chlorophyceae dominance was followed by cyanophyceae (Janjua et al., 2009; Silva,
2005). The Oocystis was the most abundant genera of chlorophyta at WSS-2, WSS-3,
WSS-4 and second abundant genera at SWW-1. At WSS-1, Chlorella was the most
abundant genera. Oocystis was also the dominant genera in summer and post
monsoonseason at all sites, at WSS-2, WSS-3, WSS-4 in winter and at WSS-1 in
167
spring. Similarly, Chlorella was dominant in winter at WSS-1, and at WSS-2, WSS-3,
WSS-4 in spring.
Several genera of chlorophyta were recorded from all sites. These include
Ankistrodesmus, Carteria, Chlamydomonas, Chlorella, Cosmarium, Crucigenia,
Euastrum, Golenkinia, Oocytis, Spirogyra, Staurastrum, Tertaedon and Volvox. While
several species were recorded from single site only. These include Platymonas,
Plerotaenium and Spirotaenia from WSS-1, Treubaria from WSS-2, Heteromastrix
from WSS-3. Development of Oocystis and Sphaerocystis algae are typical of many
clear water bodies (Belkinova et al., 2014). Phacotus is an alga of stagnant inland
waters of various morphometric and ecological states from deep stratified oligotrophic
lakes to shallow polymictic hypertrophic waters (Schlegel et al., 1998).
Members of chlorophyta dominated in spring, summer and autumn. Chlamydomonas
globosa and Chlamydomonas pseudopertyii showed an increase in early spring and
composed of 82.3% of phytoplankton density. Chlamydomonas sp. and Carteria sp.
had been recorded as dominant in Lake Mogan during the spring (Obalı, 1984).
Chlorophyta members were usually found commonly and abundantly in
mesotrophicand eutrophic lakes (Trifonova, 1998). Some ofchlorococcales members
were found mostly in lakes which were heading towards fromoligotrophic period to
eutrophic period (Round, 1984). Scenedesmus, Oocystis and Pediastrum species were
found abundantly in eutrophic lakes and oligomesotrophic reservoirs in Turkey (Obali,
1984; Tas et al., 2002).
168
Cyanophyta demonstrated a non-significant difference of species richness between
four sites; however, there was a significant effect of seasonal variations on species
richness of cyanophyta. The cyanophyta demonstrated a significant difference of
abundance between four sites; however, there was no significant effect of season on
abundance of cyanophyta between sites.
In present study, Cyanophyta demonstrated significant correlations with
physicochemical factors. These include negative correlation of abundance of
cyanophyta with Na+1 at WSS-2, with Ca+2 at WSS-1. There are negative correlations
of species richness of cyanophyta with TDS at WSS-1, with HCO3-1 at WSS-3, with
TA at WSS-2 and WSS-3, with TH at WSS-2 and WSS-3, with SO4-2 at WSS-3, with
SAR at WSS-3, And positive correlation with Na+1 at WSS-4. Several studies showed
positive correlation of cyanophyta with DO (Kumar and Oommen, 2009); cyanophyta
with TH (Iqbal and Katariya, 1995); cyanophyta with CO2 (Johnston and Jacoby,
2003); TA with cyanophyta (Bhatt et al., 1999) and other showed negative correlations
of cyanophyta with temperature and pH (Bhatt et al., 1999). Microcystis was the most
abundant genera of cyanophyta at all sites and in all seasons. Several genera of
cyanophyta were recorded from all sites. These include Anabaena, Aphanothece,
Dactylococcopsis, Gloeocapsa, Microcystis, Ocillatoria, Phormidium and Snowella.
While Aphanizomenon was recorded from WSS-3 in winter season only.
Bacillariophyta demonstrated a non-significant difference of species richness and
abundance between four sites. However, there was a significant effect of seasonal
variations on species richness and abundance of bacillariophyta. In present study,
bacillariophyta was the third abundant group of phytoplankton. It has also been
169
reported to be the most abundant group in several studies (Yilmaz and Aykulu,
2010; Atici and Alas, 2012; Zakariya et al., 2013). However, results of several
studies from various localities of Pakistan show bacillariophyta to be second or third
abundant group (Ali et al., 2005; Korai et al., 2008; Janjua et al., 2009; Lashari et al.,
2014).
Bacillariophyta demonstrated several significant correlations with physico-chemical
factors. These include negative correlation of abundance of bacillariophyta with water
temperature at WSS-1 and WSS-2, with TDS at WSS-4, with pH at WSS-2, WSS-3,
with DO at all sites, with Mg atWSS-3, with EC at WSS-4; and positive correlation
with HCO3-1 at WSS-1. Similarly, there were negative correlations of species richness
of bacillariophyta with water temperature at all sites, with light penetration at WSS-2
and WSS-4, with pH at WSS-3, with DO at WSS-3, with TH at WSS-3, with Ca+2 at
WSS-1. Several studies showed positive correlation of bacillariphyta with temperature
and DO (Cordero and Baldia, 2015); with DO (Sarojini, 1996); with light penetration,
DO and pH (Bhatt et al., 1999); with EC and TDS (Singh et al., 2010) and other
showed negative correlations of bacillariphyta with temperature (Gupta and Pamposh,
2014); with light penetration (Cordero and Baldia, 2015).
Cyclotella was the most abundant genus at WSS-1, WSS-2, and the second abundant
genus at WSS-3 and WSS-4. While, Pinnularia was the most dominant genus at WSS-
3 and WSS-4 while second most abundant group at WSS-1 and WSS-2. Overall,
cyclotella was the most abundant bacillariophycean genus during study period.
Pinnularia was the dominant genus in summer and post-monsoon at all sites,
Cyclotella was the dominant genus at WSS-1, WSS-2, WSS-3 in winter and
170
Pinnularia at WSS-4 in winter. Cyclotella was dominant at WSS-1 in spring,
Stephanodiscus at WSS-2, Pinnularia at WSS-3 and WSS-4 in spring. Several species
of bacillariophyta were recorded from all sites. These include Cyclotella, Fragillaria,
Gomphonema, Melosira, Navicula, Pinnularia, Stephanodiscus, Synedra and
Tabellaria.
Cyclotella meneghinianahas been observed highly in the phytoplankton community
(Yerli et al., 2012).Cyclotella meneghiniana showed a regular distribution during the
study period and were recorded in high densities in summer months and early autumn
(Yerli et al., 2012). Cyclotella spp. are characteristic indicators of oligotrophy in lakes
and reservoirs (Trifonova, 1998), and they have been reported as dominant in several
oligotrophic and mesotrophic lakes and reservoirs of Turkey (Aykulu et al., 1983;
Altuner, 1984; Gonulol and Obali, 1998).
4.1.3 Zooplankton
The composition of total zooplankton did not vary significantly between sites,
however, zooplankton abundance showed significant variations between sites. The
composition of zooplankton was very similar at all sampling sites. Composition and
abundance of total zooplankton showed no significant effect of season. Several
workers have reported a positive correlation of temperature with zooplankton
population growth (Tripathi et al., 2006, Rainaet al., 2013). Temperature causes
zooplankton abundance particularly in shallow freshwaters (Ahangar et al., 2012).
However, in present study, we could not find any correlation of zooplankton
abundance with high temperature. The obvious reason could be the disturbance caused
by flowing water with high velocity, resulting in destabilization of planktonic
171
populations. It was observed that zooplankton diversity and abundance was influenced
by several physico-chemical parameters, showing a positive/negative correlation.
Zooplankton demonstrated severals significant correlations with physico-chemical
parameters. These include a negative correlation of zooplankton with pH and light
penetration at WSS-1; negative correlation of zooplankton with HCO3-1 and positive
correlation with Cl-1 at WSS-2; negative correlation of zooplankton with EC at WSS-3;
And negative correlation of zooplankton with HCO3-1 and TA at WSS-4.
Protozoa was found to be the most abundant group as well as with highest species
richness. Both species composition and abundance varied significantly between sites;
however, no significant seasonal effect was recorded. Several species were recorded
from one site only like Urotrichia and Stentor from WSS-1.Several species were
recorded from all sites like Acanthocystis, Actinophrys, Amoeba, Arcella, Difflugia,
Euglypha, Tintinnidium and Trinema. Centropyxis was found from three sites except
WSS-2. Some of the protozoan species of genus Arcella, Difflugia, Centropyxis and
Paramecium have been related with nutrients enrichment of water (Agarkar et al.,
1994, Wanganeo and Wanganeo, 2006, Kumar et al., 2010). The occurrence of thee
genera from water bodies of this locality suggest nutrient rich waters. Protozoan
demonstrated several significant correlations with physicochemical parameters. These
include negative correlation of protozoan abundance with HCO3- at WSS-4, with TA at
WSS-4, with Ca+2 at WSS-4, with Cl-1 at WSS-4, and with EC at WSS-4., whereas,
protozoan species richness has positive correlation with SAR at WSS-2.
Rotifera was the second abundant group as well as with species richness. Species
composition of rotifer varied significantly between sites; however, rotiferan abundance
172
did not varied significantly between sites. Species composition of rotifer did not varied
significantly between seaonss; however, rotiferan abundance varied significantly
between seasons. Several rotifera species were recorded from all sites. These include
Brachionus, Epiphanes, Keratella, Monotyla and Notholca. Brachionus and Keratella
are typical cosmopolitan genera (Mengestou et al., 1991). Brachionusis considered to
be abundant in eutrophic waters. Several species of Brachionus has high tolerance to
salinity (Mallin et al., 1995). Keratella is dominant rotifer of warm lakes (Fernando,
1980), and also occur with abundanceover whole range of temperatures (May, 1983).
The occurrence of MonostylaandEuchlanis sp. has been related with freshwaterbodies
of low trophic status (Baloch et al., 2004).Rotifera demonstrated a positive correlation
with CO3-2 at WSS-2.
Cladocera occurrence was inconsistent throughout study period at all sites and in all
seasons. Maximum species richness was observed at WSS-1 compared to other sites.
Alona sp. and Pleuroxus sp. was recorded from all sites.
4.1.4 Diversity indices
The major application of indices in plankton studies is their manipulation in pollution
assessment. The species diversity is a role of species richness and evenness in which
organisms are distributed in these species (Margalef, 1968). Present study indicated
diversity indices of phytoplankton and zooplankton are in accordance with the findings
of Ali et al. (2005) in which Margalef index ranged 5.98-8.15 and 1.11-3.85 for
phytoplankton and zooplankton, respectively.But our results of Margalef index differ
with the work of Chughtai et al. (2013) which ranged 2.53-2.99 and 1.08-1.68 for
phytoplankton and zooplankton, respectively. According to Mason (1998) diversity
index value greater than 3 reveals clean water, values from 1 to 3 indicated moderately
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polluted water and having value less than 1 characterized heavily polluted water.
Therefore, our results showed that water conditions are appropriate for growth of
plankton in the region.
Shannon - Wiener Index was used to assess the diversity, this index can have a value
of 0 when there is single species and it is maximum when all species are equal to the
number of individuals (Ludwig and Reynolds, 1988). Shannon-Wiener Index values of
our results can be compared with findings of Onyema et al. (2014) which are ranged
0.73-1.13 and 0.24-0.93 for phytoplankton and zooplankton, respectively. Simpson
diversity index values found in our results for plankton are in agreement with the
results of Mishra et al. (2010) which ranged from 0.80-0.92.
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4.2 Fish Biodiversity
The freshwater fish fauna of Pakistan is very rich and diverse. A number of recent and
comprehensive studies have described the freshwater fish fauna from natural
freshwater bodies at various localities of Pakistan (Mirza, 1994; Mirza, 2003; Rafique
and Qureshi, 1997; Rafique, 2000; Rafique, 2001; Ahmad and Mirza, 2002; Rafique et
al., 2003; Javed et al., 2005; Pervaiz, 2011; Khan et al., 2011). Most of these studies
focus on taxonomic identification thus provide information on fish species
composition and diversity of various water bodies. Mostly, these studies do not
consider other important aspects of fish fauna like population dynamics and
conservation status. Present study describes population dynamics, economic and
conservation status of fish fauna of hill torrents of Suleman Mountain Range, Dera
Ghazi Khan Region, Pakistan.
The freshwater fish fauna of Pakistan is represented by at least 193 fish species,
belonging to infraclass Teleostei of sub-class Actinopterygii, encompassing 3 cohorts,
6 superorders, 13 orders, 30 families and 86 genera (Rafique, 2007). In present study,
twenty fish species belonging to infraclass Teleostei of sub-class
Actinopterygiiencompassing 1 cohort (euteleostei), 2 superorders, 3 orders
(Cypriniformes, Siluriformes and Synbrancheiformes), 5 families (Cyprinidae,
Siluridae, Cobitidae, Bagridae and Mastacembalidae) and 16 genera were identified.
The twenty recorded fish species in present study have also been frequently reported
from other major mountainous, sub-mountainous and plain areas of Pakistan. The
number and type of fish species shared with several studies from other regions of
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Pakistan is variable. Many of the fish species i.e. Bariliuspakistanicus, Barilius vagra,
Barilius modestus, Crossocheilus diplocheilus, Puntius sophore, Garra gotyla,
Schizothorax plagiostomus, Cirrhinus mrigala, Tor macrolepis, Salmophazia
punjabensis, Labeo diplostomus, and Mastacembelus armatus have been recorded
from River Swat at Charsada (Yousafzai et al., 2013). Same species except Barilius
modestus and Labeo diplostomus were reported from River Swat at Manglawar Valley
(Akhtar et al., 2014). Lesser number of fish species i.e. Barilius pakistanicus, Barilius
vagra, Barilius modestus, Crossocheilus diplocheilus, Mastacembelus armatus,
Salmophasia punjabensis and Schizothorax plagiostomus have been found to be
common with fish fauna from different mountainous streams of Bajour Agency (KPK)
(Hasan et al., 2014). Few fish species i.e. Labeo calbasu, Labeo dyocheilus
pakistanicus, Mastacembelus armatus, Cirrhinus mrigala and Puntius sophore were
found to be common with fish fauna from Baran Dam, District Bannu (KPK), major
part of which is included in Suleman Mountain Range (Ullah et al., 2014). Still five
fish species i.e. Cyprinion watsoni, Labeo dyocheilus pakistanicus, Cirrhinus mrigala,
Bariliuspakistanicusand Mastacembelus armatus has been found to be common with
one study from River Zhob, a mountainous region of Baloachistan, adjacent to
Suleman Mountain Range (Kakarabdullahzai and Kakarsulemankhel, 2004).
Four fish species i.e. Cirrhinus mrigala, Rita rita, Labeo calbasu, and Mastacembelus
armatus were found to be shared with Taunsa Barrage at River Indus (Khan et al.,
2012) near drainage place of Nallah Sanghar (Hill torrent of Suleman Mountain
Range, Dera Ghazi Khan Region). Maximum resemblance of fish fauna has been
found with that of River Indus at Attock region, a sub-mountainous area apparently
having no direct land connection, in present, with Suleman Mountain Range, Dera
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Ghazi Khan Region. The common fish species at both studies are., Labeo calbasu,
Labeo diplostomus, Labeo dyocheilus pakistanicus, Cirrhinus mrigala, Tor
macrolepis, Crossocheilus diplocheilus, Salmophasia punjabensis, Securicula gora,
Barilius modestus, Barilius pakistanicus, Barilius vagra, Puntius sophore, Garra
gotyla, Schizothorax plagiostomus, Rita rita, Ompok pabda and Mastacembelus
armatus (Iqbal et al., 2013).
Majority of fish species described in present study are considered as common fish
fauna of the sub-continent region including India (Kumar et al., 2011; Sarkar et al.,
2008; Sarkar et al., 2012), Bangla Desh (Rahman et al., 2012; Galib et al., 2013) and
Nepal (Shrestha, 2001).
The most abundant fish species in our study was Tor macrolepis (RA = 20.87%) and
Labeo diplostomus (RA = 17.00%). Labeo calbasu (RA = 0.12%), Rita rita (RA =
0.25%) and Mastacembelus armatus (RA = 0.25%) were the least abundant species.
While Barilius vagra was the most frequent species recorded from eight fish sampling
sites. In several other studies, the abundance of various fish species is variable.
Crossocheilus diplocheilus (17.07%), Bariliuspakistanicus (16.53%) and Garra gotyla
(13.92%) have been described as the most abundant species while Barilius modestus
(2.98%) and Puntius sophore (2.43%) as least abundant at Rhound stream District
Lower Dir (Ullah et al., 2014). Crossocheilus diplocheilus (13.65%), Schistura
alipidota (10.97%) have been described as the most abundant species and Barilius
modestus (4.25%) as least abundant speceies, in 2010, at different streams of Bajour
Agency, KPK (Hasan et al., 2014)
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The Cyprinidae has been observed the most abundant fish family from this region in
present study while other four fish families have low abundance. The several studies
have reported Cyprinids abundance from various bodies from different localities of
Pakistan. These include 77% Cyprinids from River Swat (Akhtar et al., 2014), 55.56%
Cyprinids from River Swat (Ishaq et al., 2014), 58.33% Cyprinids from River Indus at
Attock (Iqbal et al., 2013) and 85.91% Cypriniformes from Rhound stream Lower Dir
KPK (Ullah et al., 2014). Similarly, several studies from India also verify cyprinid
dominance like 88.67% from River Jammer (Vyas and Vishwakarma, 2013), 75%
from Barna stream network (Vishwakarma et al., 2014).
Two basic components i.e. richness (species number) and evenness (distribution of
relative abundance/biomass among species) dealt under the term “alpha diversity”are
taken into account to describe species diversity of a given area (Huston, 1994; Purvis
and Hector, 2000; Magurran, 2004). These aspects of species diversity are usually
measured by using several population indices as biodiversity indices. A biodiversity
index indicates presence of various taxa in a biological sample/community by a
number (Magurran, 1988). If all the species making up a community structure
contribute equal abundance then diversity is maximum. However, diversity also
depends on the varieties of habitats. Large number of species tends to be found in
more varied habitat compared to less variable habitat. And more species diversity is
found in older habitat compared to younger one. Among other factors resulting in
higher biodiversity are warmer temperatures, availability and stability of food. Still
latitudes and longitudes also affect biodiversity (Mulder et al., 2004; Varrin et al.,
2007; Wittebolle et al., 2009). To better describe the fish diversity including species
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richness and evenness of the species distribution of this area, several biodiversity
indices were considered.
Several biodiversity indices like Simpson’s and Shannon-Weiner’s index reflect both
aspects of alpha diversity. Simpson Indexcalculatesabout the probability whether two
individual fish drawn from a large community will belong to the different species. The
value of this index ranges between 0 and 1, the greater the value, the greater the
sample diversity (Vyas and Vishwakarma, 2013). Simpson diversity index values
ranged from 0.96 to 1.00 at ten sampling sites with an overall value of 0.86 for whole
region, indicating greater diversity. Greater or lesser Simpson diversity index values
have been recorded in several studies from Pakistan like 0.37 at Chashma Reservoir
and 0.48 at Taunsa Reservoir (Khan et al, 2008), from Bangladesh like 0.93 to 0.95 at
upper Halda River (Alam et al., 2013), from India like 0.08 to 0.11 at Betwa River
(Vyas et al., 2012) and 0.72 to 0.98 at Aami River (Shukla and Singh, 2013).
The most commonly used index to compare diversity among various habitats
isShannon’s Index of diversity (Clarke and Warwick, 2001). It is a measure of
heterogeneity taking into account both the species number and their evenness
(Hollenbeck and Ripple, 2007). Increase in diversity of an area is directly proportional
to increase in both factors (Vyas and Vishwakarma, 2013). Shannon’s Index of
diversity is based on the mean value of uncertainty for predicting species richness for a
random sample of community.The uncertainty of species richness is directly
proportional to the number of species present more evenly in a community (Hasan et
al., 2014). Shannon’s Index of diversity is considered sensitive to the presence of rare
species in a sample compared to other indices like Simpson’s index (Krebs, 1989).The
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values of Shannon-Weiner Diversity Index usually range between 1.5 and 3.5. The
higher values indicate greater diversity (Kent and Coker, 1992). Occassionally, the
values of Shannon-Weiner Diversity Index exceed 4.5. A value near 4.6 would
indicate that the numbers of individuals are evenly distributed between all the species.
In our study, Shannon-Weiner Diversity Index value ranged from 0.64-1.68 for ten
fish sampling sites with an overall value of 2.11 for the region. These values imply
lower diversity of the region. Relatively higher values have been reported for fish
biodiversity from other regions of Pakistan including value of 2.91 from River Jehlum,
Pakistan (Mirza et al., 2011), 2.49 from Rhound stream (Ullah et al., 2014) and 1.44 to
3.43 from River Ganga, India.
Another index which is used to measure species richness is the Margalef index which
is highly sensitive to sample size and also accounts for sampling effects (Magurran,
2004). The Margalef index values generally show deviation depending on the species
number when used to compare the sites (Vyas et al., 2012). In our study, the Margalef
index values ranged from 0.30 to 1.30 at ten fish sampling sites with an overall value
of 2.56. Relatively higher values have been found for fish biodiversity from various
regions of Pakistan including 2.19 for Rhound Stream (Ullah et al., 2014), 3.18 from
Chashma reservoir and 3.63 from Taunsa reservoir (Khan et al., 2008); while India
including 2.00-3.89 for Barna streams, Narmada Basin, Central India (Vishwakarma et
al., 2014) and 3.71-6.70 for Betwa River (Vyas et al., 2012).
Apart from above mentioned diversity indices which consider both number of species
and their abundance in a random sample of community, there are evenness indices that
standardize abundance (Smith and Wilson, 1996). Evenness indices range from near 0
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to close to 1. If most of the individuals belong to a few species then values are near 0,
and when species are nearly equally abundantthen values are close to 1 (Smith and
Wilson, 1996). In our study, evenness index ranged from 0.19-0.31 for ten fish
sampling sites with overall value of 0.70. These imply that most of the individuals
belong to a few species at most sites. Variable values of evenness index has been
calculated for fish populations at various sites in Pakistan including 0.69 at Chashma
and 0.74 at Taunsa (Khan et al., 2008), 0.19 at River Jhelum (Mirza et al., 2011), 0.41
at Head Balloki and 0.90 at Head Trimmu (Khan et al., 2011) while India including
0.69-0.72 at Tunga river, India (Naik et al., 2013).
In addition to alpha diversity, beta diversity which is considered as variation in species
composition among different localities (Magurran, 2004) was also calculated. Several
methods are available for estimating beta diversity. There exists a wide variety of
methods for measuring beta diversity. The most widely used are similarity measures
which calculate beta diversity based on data for abundance or presence/absence
(Wolda, 1981; Koleff et al., 2003). One of the classical similarity index i.e. Jaccaard
index was used in this study. It identifies species composition of any of the two or
more sites and the shared species between then (Novotny and Weiblen, 2005).
The freshwater fish fauna of Pakistan is considered to have a highproportion of
endemism.Three fish species endemic to Pakistan i.e. Salmophasia punjabensis (Day,
1872), Barilius pakistamicus (Mirza and Sadiq, 1978) and Labeo dyocheilus
pakistanicus (Mirza and Awan, 1976) were also recorded from this region. While all
other species are considered indigenous species inhabiting the adjacent
countries/regions. Labeo diplostomus, Tor macrolepis, Barilius modestus and Botia
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birdi are indigenous to India and Pakistan; Cirrhinus mrigala and Securicula gora are
indigenous to countries of sub-continent, Crossochilus diplocheilus, Barilius vagra,
Salmostoma bacaila and Cyprinion watsoni are indigenous to countries of south Asia
as well as adjacent countries of Iran and Afghanistan; Ompok pabda, Schizothorax
plagiostomus, Labeo calbasu, Rita rita, Mastacembelus armatus and Garra gotyla are
indigenous either South Asia and South East Asia and Puntius sophore is indigenous
to South Asia as well as Fareast Asia (Froese and Pauly, 2015).
The IUCN status of the two endemic species i.e. Salmophasia punjabensis and
Barilius pakistamicus has not been evaluated yet. Labeo dyocheilus pakistanicus has
also been described as least concern (Rafiq and Khan, 2012). The distributions of
endemic fish species is localised due to dispersal limitation and therefore restricted to
localized areas (Rosenfeld, 2002). The IUCN conservation status of most of the
endemic fish speciestends to be critically endangered or threatened with extinction due
to narrowrange of distribution, decline of population and fewer chances of
reproductive success.
Among indigenous species, IUCN status of Ompok pabda is near threatened; while for
Schizothorax plagiostomus, Tor macrolepis,Barilius modestus, Cyprinion watsoni and
Botia birdi, IUCN status has not been evaluated, and rest of the indigenous fish species
are least concerned (IUCN, 2014).
The commercialfisheries have a significant socio-economic contributionin the life of
peoples of Pakistan for a frequent part of country. Up till now, 31 fish species with
high commercial value has been recorded from fresh water bodies of Pakistan which is
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fairly a high number. One of the commercially important fish species of the
mountainous /sub-mountainous region i.e. Tor macrolepishas been found to be the
most abundant and also the frequent species, being recorded from five sites of this
region. Previously, the population of Tor putitora,a closely related species was
considered to be declining (Rafiq and Khan, 2012). However, the population was
restored after several measures particularly farming. The frequent occurrence and
abundance of Tor macrolepis from various sites of this region indicate a considerable
conservation of population of this species. Tor macrolepis has been reported to be rare
from other regions (Iqbal et al., 2013; Ishaq et al., 2014). The abundance in wild of
this species from this region indicates the potential of this region to contribute in
sustaining the natural fish population of this species in Pakistan.
Schizothorax plagiostomus, essentially acold water fish of high altitudes i.e.
mountainous/sub-mountainous areas has been recorded with moderate abundance.This
species has high commercial importance and has also been described from Northern
hilly areas of Pakistan (Rafiq and Khan, 2012; Yousafzai et al., 2013). But from this
region it has been described for the first time in previous study (Ali et al., 2010) and
current study, only from a specific study site i.e. Hinglon kach.
Among other edible and commercially importantfish species Labeo diplostomus and
Labeo dyocheilus pakistanicus were recorded with moderate abundance in this area. It
may be assumed that the population of these species is conserved in this area and this
area has potential for cultivation of these species. Several other commercially
important fish species i.e.Cirrhinus mrigala, Labeo calbasu, Rita rita and
Mastacembelus armatus were found to be rare in this region.Some of thefish species
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including Puntius sophore, Botia birdi, Barilus vagra, Barilius modestus, Barilius
pakistanicus and Securicula gora have the characteristics which impart them important
for ornamental purpose.
The fish faunal diversity of Pakistan is also attributable to the fact that Pakistan is
comprised of a transitional zone of three zoogeographic regions i.e. Oriental,
Palearctic and Ethiopian, which influence the composition of fish fauna (Mirza, 1994).
The present study area lies in the Oriental region. Ichthyogeograophically, it lies at the
borderline of Yaghistan (western) division and Mehran (eastern) division. Most of its
fish fauna is South Asianin origin (Puntius sophore, Tor macrolepis, Barilius vagra,
Barilius modestus, Cirrhinus mrigala, Crossocheilus diplochilus, Labeo calbasu,
Salmostoma bacaila, Securicula gora, Labeo diplostomus, Garra gotyla, Botia birdi,
Rita rita, Mastacembelus armatus, and Ompok pabda). Among others are West Asian
(Cyprinion watsoni), High Asian (Schizothorax plagiostomus), while Labeo dyocheilus
pakistanicus, Barilius pakistanicus and Salmophasia punjabensis are endemic to
Pakistan (Mirza, 2003; Ahmed and Niazi, 1998).
In present study, two more catchment areas i.e. Sori and Toe Sar were explored for
fish biodiversity compared to previous study (Ali et al., 2010). Alsofor previous
catchment areas, more number of water bodies was explored for fish diversity
compared to previous study (Ali et al., 2010). However, for one catchment area i.e.
Sanghar, new sites were explored for fish biodiversity instead of previous one. Several
species were recorded in present study in addition to previous study. These include
Salmophasia punjabensis, Securicula gora, Labeo dyoceilus pakistanicus, Labeo
calbau, Cirrhinus mrigala, Botia birdi, Rita rita and Mastacembelus armatus. Thus,
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the total number of identified species reaches up to twenty three (23) at Suleman
Mountain Range, Dera Ghazi Khan Region, Pakistan. Three species namely Mystus
cavacius, Schistura sp and Glyptothorax cavia previously found (Ali et al., 2010) but
could not be recorded in present study from the same sites.
Barilius vagra and Labeo diplostomus were the species which were recorded from
maximum catchment areas being recorded from 8 sites and 7 sites respectively,
including both studies. Two species were identified to be recorded from same one
catchment area and site. These include Schizothorax plagiostomus (Hinglon) listed in
red data list as vulnerable, Ompok pabda (Harand) declared to be near threatened in
IUCN and Puntiussophore (Harand). As described above, Tor macrolepis,
Labeodiplostomus, Barilius vagra and Cyprinion watsoni have been among the
abundant species of present study. The previous study documented Cyprinion watsoni,
Garra gotyla, Crossocheilus diplocheilus and Labeo dero, as the abundant species. In
both studies, Hinglon is the most abundant site. Vehowa was the second abundant site
in present study while Harand was the second abundant site in previous site. Barthi
was the third abundant site in this study, while Zinda Peer was the third site in
previous study. Barthi was the site with highest species richness in present study and
Hinglon was the site with highest species richness in previous study.
The frequent presence of two very unique speciesi.e.Mahasheer (Tor macrolepis) and
Snow Carp (Shizothorax plagiostomus) is an indicator of the richness of the
biodiversity of this area.
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4.3 Fish Morphometry
Generally, the fish growth do not obey the cube law as their shape changes with
growth, when the value of slope ‘b’ for length-weight relationship is either lower or
higher than 3, reflecting negative and positive allometric growth. The value of slope
‘b’ remains accurately 3, if fish grows isometrically. This applies to data of fish
growth from natural and commercial fisheries (Martin, 1949; Ricker, 1975).
According to Pauly and Gayanilo (1997) ‘b’ value may range from 2.5 to 3.5 for
length-weight relationship as has been previously described by Carlander (1969). The
variation in ‘b’ value of different species may be due to feeding, state of maturity, sex
and different population of species (Frost, 1945; LeCren, 1951; Jhingran, 1968). In the
present study, ‘b’ value for length-weight relationship of seven freshwater fish species
was between 2.5 to 3.5, the normal range as described by Carlander (1969). The
length-weight relationships were highly significant (P < 0.001) for all seven species
with ‘r’ values greater than 0.93 for most of the studied fish species.
Several workers have reported similar results for ‘b’ values for these species or related
species from different localities. These include, 3.02 for Catla catla (Zafar et al.,
2003), 3.27 for Labeo calbasu (Naeem et al., 2010), 2.52 for Tor puititora (Naeem et
al., 2011), 2.94 for Tor macrolepis from River Indus at Attok (Pervaiz et al., 2012),
2.92 for Labeo bata (Naeem et al., 2012), 3.46 for Labeo gonius (Dars et al., 2010),
2.87 for Ompok pabda(Banik et al., 2012), 3.24 for Garra orentalis (Zhang, 2005),
3.15 for Garra rufa (Hamidan and Britton; 2013), 2.95 for Schizothorax plagiostomus
(Bhat et al., 2010) and 2.86 for Schizothorax plagiostomus (Khan and Sabah, 2013).
186
Condition factor (K) reflects external measures of overall health. It decreases with
increase in length (Bakare, 1970; Fagade 1979). Condition factoris based on the
hypothesis that heavier fish of a given length are in better condition (Bagenal and
Tesch, 1978). Condition factor has been used as an index of growth and feeding
intensity (Fagade, 1979). It reflecs interactions between biotic and abiotic factors in
the physiological condition of the fishes. Environmental factors have been taken into
consideration in order to account for spatial and temporal differences in condition
factor of fish (Bagenal, 1978; Braga, 1986; Ekanem, 2004).Condition factor based on
the LWR is an indicator of the changes in food reserves and therefore an indicator of
the general fish condition (Offem et al., 2007).
The K values for seven fish species in this study were from 0.723 to 1.230. The K
valuesfor most of the studied fishes were ideal except Ompok pabda (0.723) and Tor
macrolepis (0.875). According to Rawal et al., (2013), K for Tor putitora and Labeo
dero was 0.922 and 1.183 respectively. Similarly K for Labeo cylindricus and
Oreochromis niloticus was 1.04 and 1.13 (Elijah et al., 2014). According to Naeem et
al., (2010) the condition factor of the hybrid (Catla catla ♂ × Labeo rohita ♀)
remained constant with increasing length or weight. In our study, most of the condition
factors are within the suitable recommended range for fresh water species. The value
of K in this study indicated good health of fish. This indicates that the environmental
conditions in suleman mountain range D. G. Khan region, Pakistan are favorable for
these fish species.
This study provides first records of length-length and length-weight relationships for
Labeo diplostomus, Labeo dyocheilus pakistanicus. The minimum (5.50 cm) and
187
maximum (90.00 cm) total length for Labeo diplostomus and minimum (14.30 cm) and
maximum (23.60 cm) total length for Labeo dyocheilus pakistanicus are the first
records (FishBase, 2015). Similarly, minimum (5.50 cm) and maximum (90.00 cm) TL
for Tor macrolepis and maximum (14.30 cm) TL for Cyprinion watsoni are also new
records (FishBase 2015).
The experiments explored the basic information on length-weight and length-length
relationships based on various body proportions as well as condition factor (K) for the
fish species of this mountainous area. This study also include information about two
important edible fishes i.e. Tor macrolepis and Schizothorax plagiostomus, the
population of which have now been reported to decline (Rafiq and Khan, 2012; Ishaq
et al., 2014) in Pakistan. According to the best of our knowledge this is the first
morphometric study conducted in this region.The morphometrics results indicate
optimal fish growth reflecting suitable and healthy environmental conditions for fish
culture. For several species including Garra gotyla, Labeo dyocheilus pakistanicus,
Labeo diplostomus, Schizothorax plagiostomus and Barilius pakistanicus, the lengths
and weight measurement are the first reports (FishBase, 2015). The current study will
provide a basic guideline to fishery biologist and conservationists for conducting
sustainable fishery management and conservation in Suleman Mountain Range, Dera
Ghazi Khan Region, Pakistan. However, further research work on morphometrics of
these studied species should be carried out considering different habitats, gender and
size groups.
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4.4 Conclusions
1- Water quality of the area with physico-chemical parameters are within
tolerable limits.
2- The planktonic biodiversity shows variations.
3- Fish biodiversity is conserved very much in this area.The main factor in
this conservation is the behavior of Baloach Tribe. Baloach normally do
not like to consume aquatic life for example fish. Their internal
territorial behavior rarely allows other people to hunt the wildlife
specifically fish. Due to this reason very unique species such as
Mahasheer (Tor macrolepis) and Snow carp (Shizothorax
plagiostomus) are easily found in this area. Especially Shizothorax
plagiostomus is very good indicator of the richness of the biodiversity
of this area. This particular species normally associated with very cold
water and high altitude.
4- Fish morphometry indicates a good health of fishes of the region.
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4.5 Future perspectives
The fisheries potential of the study area needs to be investigated in detail so that it can
be utilized by humans in future. The area receives lot of water in the form of irregular
seasonal rains which is mostly wasted as there is no management of its storage there is
need to adopt measures for the storage of water so that it can be wisely utilized for the
fish production and agriculture. Further, it is suggested that the nutritional status and
market value of the fishes of this area should be thoroughly assessed. Brsides this,
there is dire need for the analysis of water quality parameters of the studied area to
check the suitability of water for commercial fisheries in Suleman Mountain Range,
Drea Ghazi Khan, Region.
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