ABSTRACT
The hydrobiological characteristics of the Imo River Estuary in Nigeria as well as the land cover dynamics of it’s surrounding wetlands, were investigated. Selected physico-chemical parameters (Temperature, DO, BOD, Conductivity, Nitrates and Nitrites, Salinity, Phosphates, TSS, pH, TDS, Transparency, NH4, Pb, Zn, Cu and Cd) and the biological parameters which includes phytoplankton, zooplankton and fish species were analysed to determine the present condition of the water body and how it affects fish catch, abundance and diversity. Based on these findings, land cover change was analysed to ascertain any changes in the intergrity of the estuary within a thirty years span, to detect any perturbations that could have affected the intergrity of the river. Samples were collected bi-monthly between April 2015 and March 2016, covering the dry and wet seasons of the year at 3 stations namely, Kalibiama, Opobo and Queenstown.. Variations observed in the physicochemical parameters were affected more in the seasonal scale than the spatial, as Pb, PO4, and TSS were the only parameters significantly different at p≤0.05 between the stations. The water quality of the estuary was examined by comparing means of parameters to Federal Environmental Protection Agency, Federal Ministry of Environment and World Health Organisation’s acceptable standards. Parameters that exceeded regulatory units were cadmium, lead and nickel against FMENV standards of 0.33, 0.05 and 0.5mg/l respectively, suggesting a slightly chemically polluted environment. A total of 2,738 individuals from 4 phyla (Bacillariophyta, Chlorophyta, Euglenophyta and Cyanophyta) were recorded for phytoplankton. Shannon diversity index were used to determine abundance and diversity of plankton and fish. The diversity results showed values which suggests a slightly polluted ecosystem. The correlation of the plankton and environmental parameters indicated that phytoplankton correlated both positively and negatively with parameters such as TDS (P<0.003), salinity(P<0.002), cadium(p<0.038), zooplankton(P<0.048), pH( p<0.006), conductivity(P<0.003) and negatively with BOD at (P<0.032). A total of 1950 individuals in 22 fish species consisting of 13 families were collected during the study period from artisanal fisheries landings. The Carrangids and Clupeids made up the dominant fisheries of the estuary. A Remote Sensing analysis of the study area within a period of thirty years, 1986, 2000 and 2016 showed that the mangrove ecosysytem had been depleted by 17% to crop and grassland within this period, the implication of this is an increase in silt sediment and nutrient load from run off into the river by the nearby crop and grassland. Also an increase in the human settlements was witnesed at 3.4% total area (723.456 hectares) causing increased waste discharges into the river. It is recommended that constant policy regulation and compliance checks should be carried out on coastal wetlands to reduce degradation.
TABLE OF CONTENT
Title Page i
Declaration ii
Dedication iii
Certification iv
Acknowledgement v
Table of Content vii
List of Tables xi
List of Figures
xv
List of Plates xvii
List of Abbreviation and
Acronyms xviii
Abstract xix
CHAPTER 1: INTRODUCTION
1.1 Background of the Study 1
1.2 Statement of the Problem 5
1.3 Justification of the Study 6
1.4 Aim and Objectives of the Study 7
1.5 Scope of the Study
8
CHAPTER 2: LITERATURE REVIEW
2.1 Overview 9
2.2 Monitoring the Aquatic Ecosystem
11
2.3 Applied Strategies and Tools for
Fisheries Development. 15
2.4 Commercial
Fish Stocks of The Nigerian Brackish Waters 16
2.4.1 The clupeidae 18
2.4.2 The carangidae 18
2.4.3 The sciaenidae 19
2.5 Environmental Factors in Fish Abundance
and Distribution. 20
2.6 Mangroves:
A Coastal Resource
23
2.7 Micro-Invertebrates and Plankton as Bio-Indicators
25
2.8 The
Role of GIS and Remote Sensing in Aquatic Resource Management 26
2.8.1 Description
of the Landsat satellite sensor 29
CHAPTER 3: MATERIALS AND METHODS
3.1 The Study Area 31
3.2 Experimental
Procedure And Analysis
34
3.2.1 Experimental design
34
3. 2 .2 Data collection
34
3.2.3 Experimental procedure
34
3.2.4 Sample
collection 40
3.3 Laboratory
Procedures 41
3.3.1 Determination
of physico-chemical parameters 41
3.3.2 Determination
of fish catch 43
3.4 Image
Data Processing 44
3.5 Data Analysis 53
3.5.1 Statistical analysis 53
3.5.2 Fish data
analysis 61
3.5.3 Fish
diversity index 61
CHAPTER 4: RESULTS
AND DISCUSSION
4.1 Spatial
and Temporal Variations in Physico-chemical Parameters of Imo River Estuary 57
4.1.1 Spatial
variation 57
4.1.2 Temporal
variation 57
4.1.3 Principal component analysis 62
4.1.4 Spatial correlation of Physico-chemical parameters 71
4.1.5 Temporal correlations of Physico-chemical
parameters 79
4.2 The
Water Quality of the Imo River Estuary 83
4.2.1 Temperature 102
4.2.2 pH 102
4.2.3 Dissolved oxygen 103
4.2.4 Biochemical oxygen demand (BOD) 103
4.2.5 Transparency 104
4.2.6 Conductivity 104
4.2.7 Salinity 105
4.2.8 Total dissolved solids (TDS) 105
4.2.9 Ammonium Ion 106
4.2.10 Nitrates, nitrites, and phosphates 107
4.2.11 Total suspended solids 107
4.2.12 The metals (Copper, Zinc, Lead, Cadmium and
Nickel) 108
4.3 The
Plankton of Imo River Estuary. 111
4.4 Fish
Catch, Abundance, and Species Diversity 120
4.5. Length/Weight Relationship and Condition
Factor of Fish Catch in the Imo River Estuary 126
4.6 The
Effect of Environmental Parameters on Plankton Abundance 140
4.7 Remote
Sensing 146
4.7.1 Land cover change 146
4.7.2 Accuracy
assessment 156
4.7.3 Mapping coastal erosion
through suspended sediment index. 159
CHAPTER 5: CONCLUSION
AND RECOMMENDATIONS
5.1 Conclusion 162
5.2
Recommendations 163
5.3
Contribution to Knowledge 164
REFERENCES
APPENDICES
LIST OF TABLES
4.1 Summary
of analysis of variance for spatial
variations in the
physico-chemical parameters of the estuary. 58
4.2 Summary for seasonal variations in the physico-chemical parameters
of the estuary 60
4.3 Table
of component weights for the physico-chemical parameters. 65
4.4 Physico-chemical
parameters that significantly correlate with pc1 65
4.5 Physico-chemical
parameters that significantly correlate with pc2 66
4.6 Pearson’s correlation matrix for kalibiama station in the Imo
R iver estuary 80
4.7 Pearson’s correlation matrix for opobo station in the Imo
River estuary 75
4.8 Pearson’s correlation matrix for queens town station in the
Imo River estuary.76
4.9 Pearson’s correlation matrix for
dry season in the Imo River estuary 77
4.10 Pearson’s correlation matrix for the wet
season in the Imo River estuary 81
4.11 Spatial variation in plankton diversity in
Imo River estuary 113
4.12 Temporal variation in plankton diversity in
Imo River estuary. 114
4.13 Abundance and diversity of fish species per
station in the
Imo
River estuary 121
4.14 Showing abundance and diversity of fish species per station
in the
Imo River estuary 122
4.15 Length-weight
relationship and condition factor of cynoglossus in Kalibiama,
Opobo and Queen town
during wet and dry season. 127
4.16 Length-weight
relationship and condition factor of carangidae
in Kalibiama,
Opobo and Queentown during the wet and dry season 127
4.17 Length-weight
relationship and condition factor of clupeidae in Kalibiama,
Opobo
and Queentown during the wet and dry season 128
4.18 Length-weight
relationship and condition factor of lutjanidae in Kalibiama,
Opobo and Queentown during the wet
and dry season 129
4.19 Length-weight
relationship and condition factor of mugilidae in Kalibiama,
Opobo and Queentown
during the wet and dry season 130
4.20 Length-weight
relationship and condition factor of clarotidae in Kalibiama,
Opobo and Queen town during the wet
and dry season 131
4.21 Length-weight
relationship and condition factor of sciaenidae in Kalibiama,
Opobo and Queentown during the wet
and dry season 132
4.22 Length-weight
relationship and condition factor of cichlidae in Kalibiama,
Opobo and Queentown during the wet
and dry season 133
4.23 Length-weight
relationship and condition factor of trichiudae in Kalibiama,
Opobo and Queentown during the wet
and dry season. 134
4.24 Length-weight
relationship and condition factor of sphyraenidae in Kalibiama,
Opobo and Queentown during the wet
and dry season. 135
4.25 Length-weight
relationship and condition factor of polynemidae in
Kalibiama,
Opobo and Queentown during the wet
and dry season. 136
4.26 Length-weight
relationship and condition factor of carcharhinidae in Kalibiama,
Opobo and Queentown during the wet
and dry season 137
4.27 Length-weight
relationship and condition factor of eleotridae in Kalibiama,
Opobo and Queento during the wet and
dry season. 138
4.28 The effect of physico-chemical parameters
on phytoplankton 142
4.29
The effect of heavy metals on
phytoplankton. 143
4.30 Pearson’s
correlation matrix for physicochemical parameters, heavy metals
and plankton, in the Imo River estuary. 144
4.31 Change
detected in pixel percent by year, in the Imo River estuary
150
4.32 Confusion
matrix, computed using envi software, for 1986 image 156
4.33 Confusion
matrix, computed from envi software for 2000 image. 157
4.34 Confusion matrix, computed from envi software for 2016
image. 158
LIST OF FIGURES
3.1 Map of
the study area 32
3.2 Monthly
rainfall data for study area.
35
3.3 Steps
taken for processing the remote sensing images. 47
4.1 PCA
biplot of the physico-chemical variables based PC1 and PC2.
64
4.2 Variation
in physico-chemical parameters of the studied stations in
Imo
River estuary based PC1 and PC3 67
4.3 Seasonal
variation (wet and dry seasons) in physico-chemical parameters
of the Imo River estuary based on PC1 and PC2. 68
4.4 Temporal
variation (months) in physico-chemical parameters of the in
Imo River estuary based on PC1 and
PC2 69
4.5 Spatial and
temporal variations in temperature (̊c). 84
4.6 Spatial
and temporal variations in ph 85
4.7 S
patial and temporal variations in dissolved oxygen (mg/l) 86
4.8 Spatial
and temporal variations in biochemical oxygen demand (mg/l) 87
4.9 Spatial and temporal variations in
transparency (cm). 88
4.10
Spatial and temporal variations conductivity (µs/cm) 89
4.11 Spatial and temporal variations salinity (ppt). 90
4.12 Spatial and temporal variations total dissolved solids (mg/l) 91
4.13 Spatial and
temporal variations in phosphates (mg/l) 92
4.14 Spatial and temporal
variations in ammonium ion (NH4+ ) mg/l 93
4.15
Spatial
and temporal variations in nitrate (N03)
mg/l 94
4.16 Spatial
and temporal variations in nitrite (N02)
mg/l. 95
4.17 Spatial and temporal variations in total suspended solids (tss) mg/l. 96
4.18 Spatial and temporal variations in copper (Cu) mg/l 97
4.19 Spatial and temporal variations in zinc (Zn) mg/l 98
4.20 Spatial and temporal variations in cadmium(Cd) mg/l 99
4.21 Spatial and temporal variations in lead(Pb) mg/l. 100
4.22 Spatial and temporal variations in nickel(Ni) mg/l 101
4.23 Phytoplankton phyla per sampling station in the dry season 115
4.24 Phytoplankton phyla per sampling station in the wet season. 116
4.25 Frequency of zooplankton
phyla per sampling station in the dry season 117
4.26 Frequency
of zooplankton phyla per sampling station in the wet season. 118
4.27 Spatial total
suspended sediment rates in the Imo River estuary, showing bank erosion in
queenstown (sample area 3a). 160
LIST OF PLATES
3.1 The Kalibiama sampling station 36
3.2 The Opobo sample station, showing the
jetty area. 36
3.3 Opobo sample station, showing a refuse
dump by the river. 37
3.4 Queens town sample station, showing an
abandoned fishing village. 38
3.5 Queens town sample station, showing a
defunct oil well.
39
3.6 Samples of Lutjanus goreensis 45
3.7 A catch of Psuedotolitus species. 46
3.8 Landsat5 TM raw spatial images of the
Bight of Bonny in 1986, in true and
false
colour composite. source: United States Geological Survey ( USGS).49
3.9 Landsat 7 ETM raw spatial images of the
Bight of Bonny in 2000, in true and
false colour composite. source: United States Geological Survey ( USGS). 49
3.10
Landsat 8, raw spatial images of the Bight of Bonny in 2016, in true and
false
colour composite. source:
United States Geological Survey ( USGS). 50
3.11 Subset images of
2000 Landsat7 TM, in a true and false
colour composite. 50
3.12
USGS 2016 digital image of the Imo River estaury, classified
using the ENVI software. 140
3.13
USGS 2000 digital image of the Imo River estaury, classified
using the ENVI software. 141
3.14 USGS 1986 digital image of the Imo River estaury,
Classified using the ENVI Software. 142
4.1
USGS 2016 digital image of the Imo River estaury, classified
using the ENVI software. 147
4.2
USGS 2000 digital image of the Imo River estaury, classified
using the ENVI software. 148
4.3
USGS 1986 digital image of the Imo River estaury, classified
using the ENVI software. 149
4.4 Mangrove
(Rhizophora
mangle) on the
bank of the Imo River estuary at Akwa-Ibom State, Nigeria 153
4.5 Mangrove (Rhizophora racemosa) by the bank of the estuary at Utaewa,
Akwa-Ibom State, Nigeria. 154
4.6 Nypa palm (Nypa fruiticans) by the bank of the
Imo River estuary
at Opobo, Rivers State. 155
4.7 Processed
Images of normalised difference suspended sediment
index (NDSSI), showing radiance values 159
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND
OF THE STUDY
Over the years, the anthropogenic effects of man's activities have
been recorded by the United Nations Framework Convention on Climate Change
(UNFCCC), as the cause of certain environmental changes seen today (UNEP,2011).
These activities range from developmental to socio-economic, and cultural
values which contribute to degradation, and therefore the need to build and
support sustainable development through ecosystem management. The aquatic
habitats are one of the most exploited and have seen immense degradation as
man's dependence on fish and aquatic resources for food and development are insatiable.
The
assumption that the decline of the fisheries and fish resources in most water
bodies is because of overfishing, has been challenged by authors in different
parts of the world as management practices and regulations which were put in place to reduce fishing pressure has
yielded limited results (Beamish and Bouillon (1993) as cited in (Mann,
2000).They went ahead to suggest that the synchronous variations in depth,
nutrient fluxes, recruitments, and climate patterns, were considered
responsible for the changes in catches of sardines from stocks of the Pacific
coasts. Therefore, species abundance can be attributed to fishing regulations
and management practices which help to keep stocks. Also, spatial and temporal
variations in physicochemical parameters vary water quality and when similar
within regions, produces the same pattern of abundance and catch.
Coastal wetlands are characterised by
mangrove swamps within the tropics and play huge roles in aquatic habitats
(Barg,1992). These watersheds serve as breeding grounds, a refuge for juvenile
fishes to avoid predation and sink for alluvial deposits. Therefore, the
sustainable management of coastal watersheds and estuaries will help in keeping
the integrity of our wetlands, to support a stable and sustainable economy
through fisheries. Coastal communities and water bodies have to be protected
from the impacts of climate change and other anthropogenic sources of
degradation. To reduce the vulnerability of our coastal wetlands to climate
change, a lot of attention is needed in other to develop mitigation measures
and create strategies to maintain a sustainable balance in water bodies and
coastal wetlands. Some of these strategies involve a study of the hydrobiology of the water body and its
hydrological processes.
Rivers transport substances from
runoff through its course which is eventually deposited downstream. The physicochemical
characteristics of these deposits, as well as tidal fluctuations, accounts for
the dynamic nature of estuaries (Zheng et
al., 2002). Hydrobiological studies, therefore, help in monitoring the
quality and quantity of spatial and temporal variations of the aquatic
ecosystems and how the impact of these properties affect biotic communities and
diversities of species. While
hydrobiology is the study of life, in water, hydrology refers to the movement
and volume of water in and around a wetland and how they influence ecology
which includes plant growth, fauna distribution and species abundance (Glamore,
2013).
The River continuum of a water body
is the varying gradients of the water body along its course. This also
determines the energy and nutrient content as different spatial conditions give
varying results with regards to biota. (Odum,1980). Seasonal variations
determined by rainfall and weather conditions alter nutrient influxes and
restructure aquatic assemblages. There is also the factor of biogeochemical
cycles, anthropogenic factors which affect species diversity or alter the
composition of water quality. Changes from regenerated habitats caused by
mangrove deforestation and destruction of aquatic vegetation bring about the introduction
of non-native species which alter these communities and declination of flora
and fauna.
Aquatic organisms depend on each
other as well as their environment to live. When the ecosystem is degraded, its
ability to absorb stresses becomes exceeded.
Physical and chemical alterations are from changes in temperature, flow,
bio-stimulatory nutrients and toxins in form of heavy metals while biological
alterations could emerge from introduction of exotic species or over-harvesting
of stock (Oguntade et al., 2014;
Chilaka et al., 2014 and Olopade et al., 2017). On the other hand, when
these parameters are at optimal levels, maximum productivity is witnessed and
the integrity of the ecosystem is assured.
Habitat loss and its pressure on the
integrity of water resources have brought about improved practices in the
traditional methods of water quality monitoring. These practices, for instance,
the use of biomonitors like fish and micro-invertebrae monitoring tools give
reliable and early warning signs to the condition of the water resource. Stock
assessment is also an avenue for monitoring habitat condition. Masese et al. (2013) indicate that using biota
for monitoring is more cost-effective and indicative of environmental
degradation in these aquatic habitats than traditional water quality practices.
In estuarine environments, the main
component of hydrology is tide and the influence of tide cuts across all the
habitats in the wetland which might be a determinant factor as to the water
quality, growth, and abundance of floral and faunal species. Intertidal
wetlands, which consists of various categories of biomes are not overlooked as
they play a major role in tropical estuarine systems and brackish environments. Mangroves are a group of
highly adapted halophytes occupying the intertidal zone in estuaries, lagoons,
and coastal mudflats of tropical and subtropical areas. These salt-tolerant
marine tidal evergreen forests include trees, shrubs, palms, epiphytes and
ferns in most areas of tropical and subtropical latitudes. They have a prominent role in this community
as they fuel the trophic web with withered leaves and detrital matter. They
have been found to enhance and sustain the natural biomass of coral reef fish
as well as breeding grounds for many fish fauna. Furthermore, mangrove forests
enhance water quality by trapping nutrients and heavy metals (Somero, 2012).
Nonetheless, all over the world mangrove
ecosystems and the hydrological integrity of coastal systems are threatened
with destruction through various forms of anthropogenic activities, in
particular, utilization of coastal resources, pollution, and land reclamation (Omogoriola
et al.,
2012). Within the lists of human-induced pressure on the mangrove forests in
Nigeria, pollution from crude oil activities tops the chart and is still a
source of concern for ecologists. However, the presence of vast expanse of
wetland vegetation within a coastal environment serves as a sink for these
heavy metals and therefore a mitigating factor (Held et al., 2003). They argue that the species richness of mangroves in
many geographical areas is decreasing over time as a result of socio-economic
activities bringing about overexploitation by traditional users. Replacement
and degradation as a consequence of development are all major problems of
mangrove environments and have been predicted by the Intergovernmental Panel on
Climate Change (IPCC) that climate change will have more effect in Sub-Saharan
Africa. Thus the need to use natural resource management tools like Remote
sensing which analyse vast expanse of land-cover with little or no contact to
the sites. This space-borne\airborne sensor system data acquisition and
observation have profoundly changed the practice, monitoring, and understanding
of the dynamics of coastal environments.
Scientists in developing countries around
the coastal environments are faced with the challenge of fighting the effects
of climate change and other anthropogenic degradation using remote sensing
techniques so as to retain the services rendered by these natural habitats to
the environment among many others such as maintaining water quality, absorbing
inland floodwaters, protecting the shoreline and the aforementioned nursery for
terrestrial and marine species as well as retaining bio-diversity (McCoy, 2005). Therefore, these and many more
ecological values are the reasons why mangrove and tidal wetland conservation
are key factors in aquatic resource management.
1.2
STATEMENT OF THE PROBLEM
The Imo River has over
the years been a source of aquatic resource to four Nigerian states which it
passes through before it empties into the sea. This River and its tributaries
are affected by anthropogenic sources of pollution through land-use. Runoff
from agricultural lands, refuse disposal by urban and indigenous communities,
industrial effluent discharges and many more are the point and non-point
pollution sources which end up affecting its habitat quality, especially the
estuary which is the downstream area and the sink where sedimentation process
take place. The choice of the Imo River estuary for this research was informed
by past and present industrial activities around its wetlands which range from
an Aluminum company to Oil wells and refinery activities in the downstream area
of the River within the past few decades. Even though oil exploration activities around the Imo River communities have been
discontinued for over a decade, there
has been a reduction of ichytofauna in aquatic communities within its reaches
due to acid rain (Etesin et al.,
2013).
It has become increasingly important to identify and inventory the
current extent and condition of coastal wetlands and its River basins, especially
in Nigeria where spatial and in-situ data on wetlands are relatively scarce and
still evolving. Few studies have been carried out on the Imo River Estuary.
Akpan (2013) studied the species composition and Abundance in the Utaewa creek,
a major tributary to the Imo river estuary, Akoma and Osondu (2008) studied the
phytoplankton and nutrient dynamics of the Imo river estuary. Etesin et al.(2013) carried out studies on the
seasonal variation of physicochemical parameters of the Iko River which is also a tributary of
the Imo river . However, none has studied the hydrobiology of the estuary in
relation to its changing landcovers. This study fills
the gap in research through mixed scale methods built on descriptive
statistics, GIS and remote sensing techniques by analyzing spatially referenced
satellite data to buttress ecological findings on the Imo river estuary
relating to fish and its habitat.
1.3
JUSTIFICATION OF THE STUDY
Sub-Saharan Africa and their coastal areas
are most prone to the effects of climate change which include accelerated
erosion, sea-level rise, saltwater intrusion into inland waters, stronger
storms and warmer ocean temperatures which are likely to disturb sensitive
marine ecosystems and damage the coastal environment as well as public
infrastructure (Akhionbare, 2009). Presently, various
coastal States in advanced economies are preparing
for and have set up mitigation strategies against the impacts of climate
change. The creation of climate-ready estuaries and improving fishing practices
are key to a sustainable fisheries economy (NOAA, 2013). It is, therefore,
necessary to carry out research on the present state of our estuaries in order
to identify early, the changes in their conditions.
This study will provide local officials,
researchers, and the state government, an overview of the riparian corridors
draining into the estuary, an understanding of the land-cover trends within
these areas, the water quality of the estuary and the effects of these trends
on the fisheries and abundance of aquatic flora and fauna. It will also serve
as a baseline study upon which subsequent studies will be hinged on to provide
best management practices for our estuaries in Nigeria.
1.4 AIM AND OBJECTIVES OF THE STUDY
The aim of this work is to study the physicochemical
parameters of the estuary in order to
determine whether these traits show differences in space and time so as to know
the hydrobiological status of the estuary and how
these attributes affect flora and fauna.
To achieve the general aim of the study,
the following objectives were developed.
i.
Determine the spatial and temporal variations in the physicochemical
parameters and the water quality of the estuary.
ii.
Investigate the distribution and diversity of plankton
within the estuary;
iii.
Identify the relative abundance and diversity of important
commercial fish species present in the estuary;
iv.
Assess the condition factor of the fish species caught
within this estuary;
v.
Examine the relationship between the physico-chemical parameters
and phytoplankton abundance;
vi.
To assess the land-cover trends and change dynamics of the
Imo River estuary;
1.5 SCOPE OF THE STUDY
The study
was undertaken for a period of twelve months to cover the wet and dry season of
the year, April 2015 to March 2016 in the Imo River Estuary. The physico-chemical
parameters, plankton and fish of the estuary were analysed. An analysis of the
land cover of the estuary and its wetlands was also carried out to determine
physical changes that might have occurred in the estuary which could affect its
integrity. The study is focused on the effect of all these environmental
parameters on the present fish catch.
Login To Comment