STUDY OF THE FISH FAUNA AND PHYSICO-CHEMICAL PARAMETERS OF IKPOBA RIVER AT BENIN CITY EDO STATE

Water is essential in life, it is important to both plants and animals. It supports a vast variety of life and provides a home for many living organisms. It is important for the survival and distribution of fish. The physico­chemicalcharacteristics of water play a large role in determining the fish fauna in a water body.  Physicochemical parameters of a water body are those physical and chemical factors that affect water quality. Parameters such as temperature, salinity, dissolved oxygen, and nutrients have biological significance and are used as population indicators (Grace, 2015).

Good water quality supports and provides good comfort for a large diversity of fish species. Aquatic organisms need a healthy environment to live in and adequate nutrients for their growth. Quality of water refers to the component of water, which is to be present at the optimum level for the suitable growth of plants and animals.

Fish productivity depends on the physicochemical characteristics of the water body which also affects the fish fauna. Temperature for example is crucial in the movement and distribution of fish and this is usually more noticeable in the temperate regions where there is marked variation in the seasonal changes of temperature (Araoye, 2009).

High and low temperatures that are lethal to the individual organism of a species determine the distribution and abundance of its populations this is because fish that cannot survive or tolerate a particular temperature tend to migrate to a favourable habitat leaving those that can tolerate the temperature range behind. Not only that temperature affects the distribution of fish, but it also affects directly or indirectly other limnological parameters such as dissolved gases, transparency, viscosity, pH, Total dissolved solids, and conductivity (Araoye, 2009).

These parameters in turn affect the distribution and abundance of fish. Water temperature controls the rate of all chemical reactions, and affects fish growth, reproduction, and immunity (Mulongaibalu et al. 2014). The biota can be affected in many ways by Changes in water quality and river habitat and can impact stream-resident fishes as well as migratory species (Joanna and Daniel, 2002).

Fish fauna of a water body simply means the diversity and population of fish (Kantaraj et al., 2011). It is the different type of fish species and their population that is present in a given water body. Some factors such as irrational fishing practices, and degradation of the environment like increased sedimentation, increasing drought, water abstraction, and pollution for a long period of time have reduced the diversity of fish and some species have been lost from the freshwater ecosystem and the number of some fish are being threatened (Kantaraj et al., 2011).

Effluents from industries are being discharged into most rivers in urban areas of developing countries (Patil et al., 2012). Ikpoba river of Edo state Nigeria is not an exception to that as the river is located very close to the Guinness brewery, the water receives pollutants from the company, and this effluent tends to alter the biotic and abiotic condition of the river and hence the fish fauna in the water body.

Different works had been carried out on the river to determine the physical-chemical parameters of the river. Beckley et al. (2014) studied the physicochemical and microbial properties of wastewater discharged into the Ikpoba rivers as well as water samples obtained from the river at different points of collection.

Etiosa and Agho (2006) in participating in world water monitoring day studied the physicochemical parameters of the river. Ogbeibu and Oribhabor (2001) studied the water quality, and the ecological impact of stream regulation using benthic macroinvertebrates as indicators designed to investigate all possible anthropogenic impacts on the water quality and fauna of the Ikpoba River.  Ekhaise and Anyansi (2005) reported high counts of the bacterial population in the River.

The majority of the work carried out on the river is centered mostly on just the physicochemical parameter of the river, much work has not been documented on the effect of the physicochemical parameter of the water on fish distribution in the river. This is where the zeal for this study was borne. Having earlier noted that the Ikpoba River receives effluents from the brewery industry, it is important to critically study the effect of water quality characteristics on the fish population and diversity of the Ikpoba river, for proper sustainability and decision-making process.

Many works have been published on the test for the effect of physicochemical characteristics of different water bodies on the distribution of fish. Daniel et al. (2013) state that several parameters influence the distribution of both the juveniles and adults of fish species.  Dhirendra et al. (2009) noted that when a river is polluted, the chemical quality is first affected before the community is systematically destroyed. He went further to state that to shape a sound public policy and to implement water quality improvement programs efficiently, accurate and timely information on the quality of water is necessary.

Aftab et al. (2005) studied various physicochemical parameters and analyses of untreated fertilizer effluent. His result revealed that parameters like Electrical conductivity, Total Dissolve Solid, Biological Oxygen Demand, Chemical Oxygen Demand, and ammonia are high compared to the permissible limits of the Central Pollution Control Board CPCB (1995). Dey et al. (2005) while studying the various physicochemical parameters on the samples drawn from the river Koel, Shankha, and Brahmani, observed that dilution during the rainy season decreases the metal concentration level to a considerable extent.

Premlata (2009) studied the physicochemical characteristics of the Pichhola lake, various water parameters like air and water temperature, pH, free CO2, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, conductivity, total dissolved solids, hardness, total alkalinity, chloride, nitrate, phosphate, and sulfate were studied. The results revealed that the values of conductivity, Chemical Oxygen Demand, and sulfate were found to cross the standard limits in water samples. The coefficient of correlation (r) among various physicochemical parameters was also made.

Gupta et al. (2009) analyzed water samples from 20 sampling points of Kaithal for their physicochemical characteristics. Analysis of samples for pH, Colour, Odour, Hardness, Chloride, Alkalinity, and Total Dissolve Solid was carried out, by comparing the results obtained against drinking water quality standards laid by the Indian Council of Medical Research (ICMR) and the World Health Organization (WHO), it was found that some of the water samples are non-potable for human being due to high concentration of one or the other parameter.

Saravanakumar and Ranjith (2011) present paper studies about groundwater quality of the Ambattur industrial area in Chennai City. They studied parameters such as pH, total alkalinity, total hardness, turbidity, chloride, sulphate, fluoride, total dissolved solids, and conductivity. It was observed that there was a slight fluctuation in the physicochemical parameters among the water samples studied. A comparison of the physicochemical parameters of the water sample with the World Health Organization (WHO) and Indian Council of Medical Research (ICMR) limits showed that the groundwater is highly contaminated and accounts for health hazards for human use.

Manjare et al. (2010) studied the Physicochemical Parameters of the Tamadalge Water Tank in Kolhapur District, Maharashtra, monthly Changes in Physical and Chemical Parameters Such as Water Temperature, Transparency, Turbidity, Total Dissolved Solids, pH, Dissolved Oxygen, Free Carbon dioxide, and Total Hardness, Chlorides, Alkalinity, Phosphate and Nitrates were analyzed for a period of one year. The results obtained were within the Permissible limits and the results indicate that the tank is non-polluted and can be used for Domestic and Irrigation purposes.

Mulongaibalu et al. (2014) studied the water quality standards of river Ishasha and lake Edward to demonstrate their ability to support fish species in selected sites. In their studies, the mean values of the parameters remained within the permissible limits of water quality standards for most aquatic species. Venkatesharaju et al. (2010) state that the maintenance of a healthy aquatic ecosystem is dependent on physicochemical properties and biological diversity.

Aghoghovwia (2011) studied the Physicochemical Parameters of the Warri river in the Niger Delta region of Nigeria, the values recorded in his studies were lower than those reported by Egborge (1994) respectively on the Warri River but higher than those documented by Ogbeibu and Ezeunara (2002) for Ikpoba River in Benin City. He went further to state that the result was because of the several industries, sawmills, and markets along the shores of the Warri River which are more compared to Ikpoba River in Benin City which is bounded by the Nigerian brewery as the only industry that discharges effluents and pollutant into the river.

He further stated that Gas flaring as well as the release of carbon by vehicles, and industries that depend on generating sets owing to poor supply of power from the nation’s energy sector in and around Warri may have generated acid rains. Ogbeibu and Edutie (2002) studied the physical properties of the Ikpoba river and they review that increased total dissolved solids (TDS), turbidity, NaCl, reduced transparency, and dissolved oxygen indicate inefficient effluent treatment in the brewery.

The flow of heat and fluctuation of temperature determines species that will live and thrive in water. As such it influences the chosen habitat of a variety of aquatic life (Fondriest, 2014). Fish may locate farther from optimal feeding sites, or away from refuges such as large woody debris, overhanging banks, or rocks when searching for appropriate temperatures and dissolved oxygen levels. This may result in increased mortality, due to low growth rates and increased vulnerability to size-selective predation, or due to increased predation as fish spend less time in refuges in order to feed (Lisa et al.,2006).

An experiment conducted by Joanna and Daniel (2002), indicated that temperature was the best single predictor of fish species richness, lower densities of several cold-water fish species, specifically brown trout (Salmo trutta), brook trout (Salvelinus fontinalis) and slimy sculpin (Cottus cognatus) coincided with increasing temperatures downstream, they added that regression analysis on the relationship between changes in fish species richness between stream sections and changes in habitat showed that changes in richness were most related to the amount of warming downstream of the dams.

Temperature is known to have a significant effect not only on the biological functions of aquatic organisms but also on other physicochemical parameters (Mulongaibalu et al. 2014). Selleslagh and Amara (2008) stated that fish have a thermal preference that optimizes physiological processes. Daniel et al. (2013) found water temperature to be the best predictor of temporal changes in fish abundance and species composition in the Elbe estuary (Germany). Different species have different optimal temperatures for migration, spawning, egg incubation, and juvenile growth, and also different lethal-temperature (Thompson and Larsen, 2004).

Light plays a very important role in lotic systems because it provides the energy necessary for photosynthesis to occur. The shadows it casts provide refuge for prey species (Fondriest, 2014). He further stated that the availability of light is influenced by factors such as altitude, cloud cover, and geographic position and that suspended particles present in water absorb heat from solar radiation which can be transferred from the particles to water molecules thereby increasing the temperature of the surrounding water.

As a consequence of increased sediment loads, an increase in turbidity occurs under both natural and anthropogenic conditions; however, increasing levels of turbidity caused by eutrophication, agricultural runoff, and other anthropogenic activities became a serious ecological concern worldwide. Mulongaibalu et al. (2014) review that a maximum value of 400 mg L-1 of total dissolved solids is permissible for diverse fish populations.

The inorganic substrate of lotic systems is composed of the geologic material present in the catchment that is eroded, transported, sorted, and deposited by the current. Substrates can also be organic and may include fine particles, autumn shed leaves, submerged wood, moss, and more evolved plants. Substrate deposition is not necessarily a permanent event, as it can be subject to large modifications during flooding events (Fondriest, 2014).

In studying the relationship between depth and fish density, Wilson et al. (2006) noted that Fish density ranged from zero to over two fish per cubic meter. Fish density data varied significantly with water depth and region. Fish densities were highest in the 20 m strata and lowest near the bottom. Fish density data followed the general pattern of acoustic biomass and was highest near the surface (Stanley and Wilson, 2000). Wilson et al. (2006) state that the composition of fish communities has also been shown to change with location and water depth. The increase in trout density in higher-elevation pools may indicate that trout are moving upstream toward cooler waters, or out of riffles and into pools in search of depth (cover) or food (Lisa et al.,2006).

Stratified pools may have cooler temperatures at the bottom, but also lower dissolved-oxygen concentrations with increased depth (Mulongaibalu et al. 2014). This will determine the population of fish found in a particular depth. Stanley and Wilson (2000) reported that, at a platform in 219 m of water, pelagic Bluewater fishes were found principally in the upper 100 m of the water column and fish density was essentially zero below 100 m depth. At a platform situated at a water depth of 60m, they found fishes throughout the water column, but fish density was highest near the surface and adjacent to the bottom; the fish community was characterized as reef-associated shelf species. At a nearshore platform at 20 m depth, fishes were found throughout the water column and the population was characterized as estuarine.

Conductivity is a measure of the ability of water to pass an electrical current. Conductivity in water is affected by the presence of inorganic dissolved solids such as chloride, nitrate, sulphate, and phosphate anions or sodium, magnesium, calcium, iron, and aluminum cations. Organic compounds like oil, phenol, alcohol, and sugar do not conduct electrical current very well and therefore have a low conductivity when in water. Conductivity is also affected by temperature the warmer the water, the higher the conductivity (Himangshu et al., 2012). Conductivity Studies of inland freshwaters indicate that streams supporting well-mixed fisheries have a range between 150 and 500 μhos/cm (Himangshu et al., 2012). Total dissolved solids (TDS) are a measure of dissolved matter (salts, organic matter, minerals, etc.) in water.

Water in irrigation canals can flow or seep to an underlying aquifer, which eventually discharges water to rivers, thereby sustaining streamflow essential for the maintenance of fish populations (Konrad et al., 2003). Water flow is usually fast at the source and has a high current, the current decreases downstream and also with depth. Water current decrease from the mid sea towards the bank. Shallow waters are swift while deep waters are calm and sluggish. Current can influence the fauna of a water body this is because some organisms that lack self-propulsion or adaptation for survival in a region of high current cannot withstand the high current impacted on them hence making these organisms to relocate or being swept away by water current.

Fondriest (2014) states that oxygen is likely the most important chemical constituent of running water, as all aerobic organisms require it for survival. He went further to state that the solubility of oxygen in water decreases as water pH and temperature increases and that fast, turbulent streams expose more of the water’s surface area to the air and tend to have low temperatures and thus more oxygen than slow, backwaters. Gee et al. (1978) noted that when freshwaters become hypoxic some fishes respond by breathing air facultatively while some remain aquatic breathers, rising to the surface where they irrigate their gills with relatively oxygen-rich surface film.

An adequate supply of dissolved oxygen is important to fish, especially salmonids such as salmon and trout, during all stages of life (Bjornn and Reiser, 1991). The local fish community structure can be dramatically altered when fish are faced with low dissolved oxygen, this is because many fish species will migrate to more tolerable regions. Aghoghovwia (2011) noted that oxygen is an essential and limiting factor for maintaining life. It is an important factor limiting abundance, distribution composition and survival of aquatic organisms.

The pH of a water is another factor that affects fish distribution, it is the measurement of the acidity or basic quality of water (Himangshu et al., 2012). When the pH of a water body shifts beyond acceptable range, aquatic organisms might be affected. Other studies have determined that fish move away from alkaline waters when pH levels approach 9.06 – 10.0, unless more important survival factors such as food availability or lower predation levels outweigh avoidance (Scott et al. 2005). According to Himangshu et al. (2012), water becomes unsuitable at extremely high or low pH levels (for example 9.6 or 4.5), for most organisms. They further emphasized that aquatic organisms adapt to a specific pH level and may die if the pH of the water changes even slightly. Gas flaring as well as the release of carbon by vehicles, small and medium scale industries in and around Warri may have generated acid rains (Aghoghovwia, 2011). This can as well lower the pH of the receiving water body.

Alkalinity of a water is the buffering capacity of the water or it is the ability of water to resist changes in pH when acids or bases is added to the water. It is the total quantity of base present in water (Himangshu et al., 2012). They went further to state that 75 and 200 mg/L CaCO3 is the desirable range of total alkalinity for fish culture. The most common and most important components of alkalinity are Carbonates and bicarbonates. Brian (2014) noted that alkalinity is important for fish and aquatic life because it protects or buffers against rapid pH changes.

He further noted that alkalinity of natural water is determined by the soil and bedrock through which it passes that the main sources for natural alkalinity are rocks which contain carbonate, bicarbonate, and hydroxide compounds and that limestone is rich in carbonates, so waters flowing through limestone regions or bedrock containing carbonates generally have high alkalinity hence good buffering capacity. Total alkalinity is expressed as milligrams per liter or parts per million calcium carbonate (mg/L or ppm CaCO3). Living organisms, especially aquatic life, function best in a pH range of 6.0 to 9.0 The main source of alkalinity is usually from limestone which are mostly CaCO3 hence alkalinity is often related to hardness. If CaCO3 actually accounts for most of the alkalinity, hardness in CaCO3 is equal to alkalinity.

When water passes through or over deposits such as limestone, the levels of Ca2+, Mg2+, and HCO3– ions present in the water can greatly increase and cause the water to be hard. Vernier (2007) noted that though calcium is an important composition of cell walls of aquatic plants, and of the bones or shells of aquatic organisms, high levels of total hardness are not considered a health concern. Calcium and magnesium ions are the major contributors to scale formation in water supplies. Water having hardness value more than 300mg/l is undesirable for dying and textile industries and also for high temperature boilers. Water with a total hardness in the range of 0 to 60 mg/L is termed soft, from 60 to 120 mg/L moderately hard, from 120 to 180 mg/L hard, and above 180 mg/L very hard (Himangshu et al., 2012).

The alkalinity of the sample was determined by measuring 100ml of the water sample into a 250ml volumetric flask 3 drops of methyl orange indicator was added and the mixture titrated against 0.02M of hydrochloric acid (HCL).

Calculations

Alkalinity = Titre value × M × 100 Mg/L

                             Volume of sample

Where M= molarity of acid (0.02)

2.4.5 Determination of pH.

Determination of the pH of the water was done by the use of the pHep.  The procedure used was that of Sa’eed and Mahmoud (2014). The pHep meter was switched on and was immersed into a beaker containing a buffer of neutral pH (pH 7) up to the maximum immersion level and then calibrated to pH 7 with the help of a small screwdriver. The pH meter was inserted into the test sample, stirred for some seconds and the value recorded when the reading was stable.

2.4.6 Determination of temperature

 The temperature of the river was determined using a mercury in glass thermometer with 0°C to 100°C calibration. This was done by immersing the thermometer into the water body for a sufficient period of time till the reading stabilizes and the value recorded (Beckley et al., 2014).

2.4.7 Determination of water transparency

           Water transparency was determined using secchi disc. The disc is suspended with a graduated cord at the center. The depth at which the secchi disc disappeared when it was lowered into water was recorded and the depth at which it reappeared when the disc was slowly withdrawn from the water was also taken (Araoye, 2009). This was repeated three times to get a maximum accuracy. The transparency of the water body was computed as follows:

Where X1= depth at which secchi disc disappear

            X2= depth at which secchi disc reappear

2.4.8 Determination of water conductivity

The conductivity of the water was tested with the aid of HARCH conductivity meter. After the meter has been switched on and calibrated, the probe of the meter was rinsed with distilled water and then inserted into a beaker containing 50ml of the test sample and the value recorded (Sa’eed and Mahmoud, 2014).

2.4.9 Determination of depth

The depth of the river was obtained by the use of a meter rule. The meter rule was dipped into different location at each study stations.

2.4.10 Determination of water current.

The water current was determined by calculating the time required for a weighted cork to cover a distance of hundred meters (Mustapha and Omotosho, 2005). A 100- meter length was mapped out using a measuring tape in a region of the water free of substances such as leaves, suspended logs and floating plants which might hinder the movement of the cork, the cork was placed some distance away at the upper stream from the hundred-meter length and was allowed to flow downwards towards the portion marked out. When the cork got to the starting point of the length, a stop watch was switched on and when the cork got to the end of the length, the stop watch was switched off and the time record.

Calculations:

Water current(m/s) = Distance covered (m)

                                       Time taken (s)

2.4.11 Determination of heavy metals

Heavy metals were analyzed using the Atomic Absorption Spectrophotometer (AAS). Each metal has a hollowed cathode lamp for its determination. The water sample is sprayed through a nebulizer into an air-acetylene flame resonance line in element, which was generated in a hollow cathode lamp and was simultaneously passed through the flame (Limgis, 2001). Since metals have their own characteristic absorption wavelength, a source lamp composed of that element is used, making the method relatively free from spectral or radiational interferences. The amount of energy of the characteristic wavelength absorbed in the flame is proportional to the concentration of the element in the sample (Limgis, 2001).

The sample is thoroughly mixed by shaking, and 100ml of it is transferred into a glass beaker of 250ml volume, to which 5ml of conc. nitric acid is added and heated to boil till the volume is reduced to about 15-20ml, by adding conc. nitric acid in increments of 5ml till all the residue is completely dissolved. The mixture is cooled, transferred and made up to 100ml using metal free distilled water. Lamp for the element to be detected is selected, operating current is suitably adjusted, the lamp is aligned for the visible beam to fall on the slit of the monochromator Appropriate wavelength for the element to be detected is selected.

The wavelength controller is moved clockwise or anti-clockwise slowly to get maximum percentage of transmittance.   The slits are adjusted to get closest to the required wavelength and avoid excess stray light.  On selecting suitable wavelength, the acetylene-air mixture is lit at the recommended pressure the burner level is so adjusted that the beam from the cathode crosses 1cm from the top of the burner and the beam is stabilized. A calibration graph is obtained by feeding the standard solutions of suitable concentration the samples are aspirated by feeding them through the capillary and the readings are noted.

Calculations can be done using the formular give below

Absorbance of sample = Concentration of sample × Absorbance of standard

Concentration of standard

2.5 Statistical Analysis                                                                

All data collected during the sampling period were statistically analyzed using Statistical packages for Social Sciences (SPSS) version 20 (IBM Corp., Armonk, USA) and Microsoft Excel (Microsoft Inc., Redmond, USA). Percentage abundance, diversity and richness indices were calculated. Simpson’s index, Gini-Simpson, Reciprocal Simpson, Shannon-Wiener diversity index and Hill’s family of numbers (N0, N1 and N2) were all calculated according to the formulae listed by Ludwig and Reynolds (1988) and Krebs (2014). The formulae used include:

Simpson’s index, D = Ʃ^s_i=1Ƥ²

Where p is the proportional abundance of ith species

Gini-Simpson index = 1- D

Reciprocal Simpson or Hill’s N2 = 1/D

Where D is Simpson’s original index

RESULTS

3.1 Physicochemical Characteristics of Ikpoba River

The mean monthly temperature of Ikpoba River ranged from 26 ºC – 28 ºC for the duration of the study. The monthly temperature between May, June and July were similar at each of the stations. Only at station C was the temperature of the River in July less than the months of June and May significantly (p < 0.05). The temperature of the River in the months of May and June was significantly different between station A and station C (p < 0.05).

The pH of the River for the duration of the study ranged from 5.40 ± 0.31 to 7.40 ± 0.31. At station A (Lucky way) the pH was not different significantly between the months (p > 0.05). The pH of May and July at station B (Ikpoba bridge) was significantly higher than the pH of June (p < 0.05). The pH for the months of May, June and July were not significantly different at station C (p > 0.05). In the month of June, the pH at station A (Lucky way) and station C (Idogbo community) were significantly higher than that at station B (p < 0.05) (Table 2).

At each of the three stations, the dissolved oxygen (DO) magnitude was in the order, July > June > May. The difference was significant due mainly to the very high July DO content of the Ikpoba River (Table 3). Also, the DO of Ikpoba River decreased at station C (Idogbo community) significantly in the months of May and July compared to that of station A (p < 0.05).

The monthly total alkalinity of Ikpoba River at the three stations did not show a similar pattern. At station A (Lucky way), total alkalinity decreased from May through June to July (Table 4). The total alkalinity of the River at station B (Ikpoba bridge) also declined from May to July. While in station C (Idogbo community), the total alkalinity of July was highest. But only at stations A (Lucky way) and B (Ikpoba bridge) was the difference in total alkalinity between the stations significant (p < 0.05).

Table 1: Changes in temperature of Ikpoba River
	Station A	Station B	Station C
May	26.50 ± 0.29a1	27.10 ± 0.21a12	27.57 ± 0.30a2
June	26.23 ± 0.39a1	27.50 ± 0.50a12	27.93 ± 0.07a2
July	26.43 ± 0.30a1	26.50 ± 0.40a1	26.67 ± 0.33b1
Values with different alphabet superscript down a column were significantly different (p < 0.05). Values with different numeric superscript across a row were significantly different (p < 0.05).

Table 2: Changes in pH of Ikpoba River
	Station A	Station B	Station C
May	7.23 ± 0.26a1	7.10 ± 0.38a1	6.63 ± 0.32a1
June	7.07 ± 0.34a2	5.40 ± 0.31b1	7.30 ± 0.31a2
July	7.12 ± 0.24a1	6.67 ± 0.17a1	7.40 ± 0.31a1
Values with different alphabet superscript down a column were significantly different (p < 0.05). Values with different numeric superscript across a row were significantly different (p < 0.05).

Table 3: Changes in dissolved oxygen (mg/l) of Ikpoba River
	Station A	Station B	Station C
May	9.03 ± 0.58b2	8.17 ± 0.93b2	5.67 ± 0.35b1
June	13.12 ± 3.77b1	7.60 ± 1.82b1	7.83 ± 2.72b1
July	33.48 ± 3.26a2	25.52 ± 5.25a12	18.83 ± 1.50a1
Values with different alphabet superscript down a column were significantly different (p < 0.05). Values with different numeric superscript across a row were significantly different (p < 0.05).

Table 4: Changes in total alkalinity (Mg/LCaCO3) of Ikpoba River
	Station A	Station B	Station C
May	69.87 ± 0.44a2	72.20 ± 4.02a2	36.33 ± 4.23a1
June	61.70 ± 9.03ab1	38.56 ± 4.46b1	46.77 ± 5.46a1
July	43.73 ± 1.13b1	33.07 ± 3.13b1	53.90 ± 11.19a1
Values with different alphabet superscript down a column were significantly different (p < 0.05). Values with different numeric superscript across a row were significantly different (p < 0.05).

The free CO₂ content of Ikpoba River increased from station A (Lucky way) to station C (Idogbo community) for the three months duration of the study. Station B (Ikpoba bridge) and station C free CO₂ content were both significantly higher than that of station A (Lucky way) for the three months, May to July (p < 0.05). Also, the water appeared to have the highest free CO₂ content in the month of June at the three stations (Table 5).

The River was deeper in June and July than in May at all three stations. The depth of the River was also higher at station C compared to the two other sampled stations (Table 6). The difference between the depth of station C and the two other stations was significant (p < 0.05). The hardness of the River was similar between the three stations; though in May, station C had a higher water hardness value than Station B (Ikpoba bridge) and station A (Lucky way) which was significant at p < 0.05. Monthly comparisons at the three stations only indicated significant between months variation in hardness at Station B (Ikpoba bridge) (Table 7).

Apart from the month of July, secchi disc transparency at station A (Lucky way) was clear. At Station B (Ikpoba bridge) and C, secchi disc transparency was significantly higher compared to station A (Lucky way). The trend in secchi disc transparency magnitude was station C > Station B (Ikpoba bridge) > station A (Lucky way) (Table 9). Between the months at each of the stations, there was no significant difference in the transparency values (p > 0.05).

Conductivity of Ikpoba River was highest at Station B (Ikpoba bridge) compared to the two other stations. The conductivity of the River at station A (Lucky way) was not different from that at station C significantly (p > 0.05). At Station B (Ikpoba bridge), conductivity was not different significantly between the months (p > 0.05). But at Station B (Ikpoba bridge) and C, the case was different (Table 10).

Table 5: Changes in free CO2((Mg/l) of Ikpoba River
	Station A	Station B	Station C
May	15.58 ± 0.38ab1	21.46 ± 0.94b2	31.32 ± 1.06a3
June	17.64 ± 0.43a1	27.53 ± 0.93a2	33.79 ± 0.99a3
July	13.18 ± 1.65b1	18.12 ± 1.41b2	24.69 ± 0.47b3
Values with different alphabet superscript down a column were significantly different (p < 0.05). Values with different numeric superscript across a row were significantly different (p < 0.05).

Table 6: Changes in depth(M) of Ikpoba River
	Station A	Station B	Station C
May	1.88 ± 0.32b1	4.60 ± 0.57a2	9.40 ± 0.83b3
June	4.90 ± 0.90a1	7.93 ± 0.74a1	16.17 ± 2.09a2
July	5.07 ± 0.97a1	8.43 ± 1.95a1	13.47 ± 0.79ab2
Values with different alphabet superscript down a column were significantly different (p < 0.05). Values with different numeric superscript across a row were significantly different (p < 0.05).

Table 7: Changes in water hardness of Ikpoba River
	Station A	Station B	Station C
May	4.83 ± 0.26a1	6.80 ± 0.61b1	9.60 ± 0.97a2
June	7.30 ± 0.64a1	13.77 ± 1.70a1	13.55 ± 3.89a1
July	10.79 ± 6.11a1	6.16 ± 0.82b1	10.56 ± 2.65a1
Values with different alphabet superscript down a column were significantly different (p < 0.05). Values with different numeric superscript across a row were significantly different (p < 0.05).

The water current of Ikpoba River was significantly higher in July than May and June at the three stations sampled (p < 0.05). The water current in the months of May and June declined significantly from station A (Lucky way) to station C at p < 0.05 (Table 8).

Table 8: Changes in water current of Ikpoba River
	Station A	Station B	Station C
May	0.59 ± 0.12b3	0.36 ± 0.22c2	0.09 ± 0.00b1
June	0.98 ± 0.02b3	0.76 ± 0.04b2	0.17 ± 0.01b1
July	1.83 ± 0.33a1	1.40 ± 0.15a1	1.17 ± 0.17a1
Values with different alphabet superscript down a column were significantly different (p < 0.05). Values with different numeric superscript across a row were significantly different (p < 0.05).

Table 9: Changes in secchi disc transparency of Ikpoba River
	Station A	Station B	Station C
May	0.00 ± 0.00a1	0.96 ± 0.03a2	1.64 ± 0.09a3
June	0.00 ± 0.00a1	0.90 ± 0.23a2	1.03 ± 0.24a3
July	0.67 ± 0.38a1	0.77 ± 0.29a1	1.49 ± 0.66a1
Values with different alphabet superscript down a column were significantly different (p < 0.05). Values with different numeric superscript across a row were significantly different (p < 0.05).

Table 10: Changes in conductivity(µscm-1) of Ikpoba River
	Station A	Station B	Station C
May	24.67 ± 0.67b1	50.67 ± 5.36a2	33.00 ± 2.08ab1
June	38.33 ± 4.41a1	51.67 ± 1.67a1	46.67 ± 7.27a1
July	27.67 ± 0.67b1	45.33 ± 3.28a2	24.30 ± 2.97b1
Values with different alphabet superscript down a column were significantly different (p < 0.05). Values with different numeric superscript across a row were significantly different (p < 0.05).

3.2 Heavy Metal Constituent of Ikpoba River

The heavy metals evaluated at Ikpoba River were mercury, cadmium and lead. Mercury, cadmium and lead were detected at the three stations (Tables 11 – 13). Mercury content at station A (Lucky way) was significantly higher in May and June than July; and significantly higher in July than May and June at Station B (Ikpoba bridge). There was no significant monthly variation in the mercury content of Ikpoba River at station C (Table 11). The mercury content of Station B (Ikpoba bridge) was significantly higher (p < 0.05) than those of stations A (Lucky way) and C in the months of May and July. The cadmium content of Ikpoba River at station A (Lucky way) was not significantly different between the months.

Also, cadmium content of the River at station C was not significantly different between the months (p > 0.05). At Station B (Ikpoba bridge), the cadmium content of the River was significantly higher (p < 0.05) in July compared to the earlier two months between the station, only in July was the Station B (Ikpoba bridge) cadmium level significantly higher than that of the other two stations (Table 12). The lead content of Ikpoba River was higher at Station B (Ikpoba bridge) than stations A (Lucky way) and C in the months of May and June. The lead content of the River at Station B (Ikpoba bridge) was also higher in the months of May and June than July (Table 13). The difference was significant (p < 0.05).

Table 11: Mercury(mg/l) content of Ikpoba River
	Station A	Station B	Station C
May	0.02 ± 0.00a1	0.04 ± 0.00b2	0.02 ± 0.01a1
June	0.02 ± 0.01a1	0.04 ± 0.02b1	0.03 ± 0.01a1
July	0.01 ± 0.00b1	0.12 ± 0.04a2	0.04 ± 0.01a1
Values with different alphabet superscript down a column were significantly different (p < 0.05). Values with different numeric superscript across a row were significantly different (p < 0.05).

Table 12: Cadmium(mg/l) content of Ikpoba River
	Station A	Station B	Station C
May	0.01 ± 0.00a1	0.02 ± 0.01b1	0.02 ± 0.01a1
June	0.02 ± 0.01a1	0.02 ± 0.00b1	0.04 ± 0.01a1
July	0.03 ± 0.01a1	0.11 ± 0.03a2	0.02 ± 0.01a1
Values with different alphabet superscript down a column were significantly different (p < 0.05). Values with different numeric superscript across a row were significantly different (p < 0.05).

Table 13: Lead (mg/l) content of Ikpoba River
	Station A	Station B	Station C
May	0.04 ± 0.01a1	0.07 ± 0.01a2	0.02 ± 0.00a1
June	0.02 ± 0.01a1	0.08 ± 0.01a2	0.03 ± 0.01a1
July	0.16 ± 0.10a1	0.10 ± 0.04a1	0.04 ± 0.02a1
Values with different alphabet superscript down a column were significantly different (p < 0.05). Values with different numeric superscript across a row were significantly different (p < 0.05).

3.3 Fish Species Diversity and Abundance at Ikpoba River

Three species of fish were found in Ikpoba River. They were Oreochromis niloticus, Clarias gariepinus and Malapterurus electricus. O. niloticus had the highest mean abundance (Table 14).

O. niloticus was the highest number of a fish species per month caught. C. gariepinus was the most abundant at the three stations (Figure 2).

O. niloticus abundance decreased from stations A (Lucky way) through Station B (Ikpoba bridge) to station C (Idogbo community). Malapterurus electricus on the other hand was higher in station C (Idogbo community) than at stations A (Lucky way) and B (Ikpoba bridge). The monthly richness and diversity indices of the station and of Ikpoba River are presented in Table 15.

The species richness of station C in the month of May based on Margalef’s and Menhinick’s indices was assigned values of 0.738539 and 0.774597 respectively. This is higher than the values that were assigned to station A (Lucky way) (0.378923 and 0.534522 respectively), Station B (Ikpoba bridge) (0.531745 and 0.457496 respectively), and the Ikpoba River (0.46765 and 0.353553 respectively).

Still in the month of May, Simpson’s and Gini-Simpson diversity indices assigned a greater diversity level to station C where only 15 fish samples comprised three different species. Compared to station C, 14 fish samples at station A (Lucky way) which is almost equal to 15 comprised only two species, while 43 samples from Station B (Ikpoba bridge) was only made up of 3 species; in the River as a whole, 72 samples contained only three species in the month of May. Still in the month of May, following the reciprocal Simpson’s index or Hill’s second order value of numbers, approximately 3 of the species at station C were in very high abundance (N2 = 2.777778, see Figure 2).

Table 14: Fish species abundance in Ikpoba River
Fish Species	Station A	Station B	Station C	Total	Mean
May
Oreochromis niloticus	10	25	7	42	14.00
Clarias gariepinus	4	17	4	25	8.33
Malapterus electricus		1	4	5	2.5
Total	14	43	15	72	24.00
June
Oreochromis niloticus	17	20	5	42	14.00
Clarias gariepinus	6	19	15	40	13.33
Malapterus electricus	4	3	10	17	5.67
Total	27	42	30	99	33.33
July
Oreochromis niloticus	11	28	5	44	14.67
Clarias gariepinus	2	15	20	37	12.33
Malapterus electricus		5	6	11	5.5
Total	13	48	31	92	30.67

 In the month of June, the Margalef’s and Menhinick’s indices of richness almost had equal values for all the stations. But station A (Lucky way) was assigned a slightly higher value by both indices: station A (Lucky way) had 27 samples containing 3 species compared to the others. Station A (Lucky way) was only matched in species richness by station C where 30 samples comprised 3 species. Station C had approximately three of the species in very high abundance based on the reciprocal Simpson’s index (N2 = 2.571429) while only 2 species approximately were in very high abundance at station A (N2 = 2.13783).

Also, only two species approximately were in moderate abundance as well at station A (Lucky way) (N1 = 2.480389) compared to three at station C (N1 = 2.749457). In Ikpoba River approximately 3 species were in high to very high abundance in the month of June. In July, species richness based on Margalef’s and Menhinick’s indices were similar between the stations. Species diversity in the month of July was generally low based on Simpson’s and Gini-Simpson’s indices. Diversity was however relatively high in stations B and C based on these indices.

Table 15: Diversity and richness indices
MAY	Station A	Station B	Station C	Total
Richness
Margalef	0.378923	0.531745	0.738539	0.46765
Menhinick	0.534522	0.457496	0.774597	0.353553
Diversity
Simpson’s	0.591837	0.494862	0.36000	0.465664
Gini-Simpson	0.408163	0.505138	0.64000	0.534336
Reciprocal Simpson (N2)	1.689655	2.020765	2.77778	2.147473
Hill 1st Order No. (N1)	1.1818969	2.159016	2.888106	2.379586
Shannon-Wiener	0.59827	0.769653	1.060602	0.866927
Others
Sample size	14	43	15	72
Species	2	3	3	3
JUNE
Richness
Margalef	0.606826	0.531745	0.588028	0.435244
Menhinick	0.577350	0.457496	0.547723	0.301511
Diversity
Simpson’s	0.467764	0.436508	0.388889	0.372717
Gini-Simpson	0.532236	0.563492	0.611111	0.627283
Reciprocal Simpson (N2)	2.137830	2.290909	2.571429	2.683000
Hill 1st Order No. (N1)	2.480389	2.461201	2.749457	2.808003
Shannon-Wiener	0.908416	0.900650	1.011404	1.032474
Others
Sample size	27	42	30	99
Species	3	3	3	3
JULY
Richness
Margalef	0.389871	0.531745	0.582413	0.442303
Menhinick	0.554700	0.457496	0.538816	0.312772
Diversity
Simpson’s	0.739645	0.448785	0.479709	0.404773
Gini-Simpson	0.260355	0.551215	0.520291	0.595227
Reciprocal Simpson (N2)	1.352000	2.228240	2.084599	2.470520
Hill 1st Order No. (N1)	1.536217	2.493030	2.447035	2.645966
Shannon-Wiener	0.429323	0.913500	0.894878	0.973037
Others
Sample size	13	43	31	92
Species	2	3	3	3

DISCUSSION AND CONCLUSION

4.1 Discussion

The mean temperature of the river during the three months study period range from 26°C -28°C. This temperature falls within the range stated by Okayi (2003) in the study of Warri river as the minimum and maximum temperature that is normal for tropical waters for optimal growth of organisms. The temperature at the month of July was significantly less than that of May and June in station C (Idogbo community) this low temperature might be attributed to increasing rain fall, colder weather and more vegetation covering the water (Fondriest, 2014).

The pH of the river was between the range of 5.40 ±0.31 – 7.40 ±0.31 this value is close to the work conducted by Etiosa and Agho (2006) on the Ikpoba river in which their result fall within pH of 6.0 – 7.0. The pH at station A (Lucky way) remain significantly constant during the months. The pH of station B (Ikpoba bridge) was significantly higher in May and July than in the month of June, the decrease in pH at station B (Ikpoba bridge) might be attributed to acidic runoff and brewery effluent carried from uptown to the river at station B (Ikpoba bridge) making the water to be acidic. Also at this month, the free carbon(IV)oxide (CO2) was higher and when it dissolves in water it can form carbonic acid thereby decreasing the pH of the water (Bioworld, 2013).

The very high July dissolve oxygen might be due to decrease in temperature in the month of July as the solubility of oxygen increase with decrease in temperature (Fondriest, 2014). Increase in dissolve oxygen might also be as a result of increase in phytoplankton population during the rainy season that release oxygen during photosynthesis. The decrease in dissolve oxygen in station C (Idogbo community) in the month of May might be as a result of the high temperature recorded during this period (Fondriest, 2014). In July, the decrease might be attributed to the water not being as fast as station A (Lucky way) this is because fast turbulent water increase oxygen solubility in river (Mulongaibalu et al., 2014).

There was a significant difference between total alkalinity in station A (Lucky way) and station B (Ikpoba bridge). There was a decrease in total alkalinity in both station A (Lucky way) and station B (Ikpoba bridge) from May to July, this decrease may be as a result of the progressive increase in water volume during the month causing the dilution of the bicarbonate component of the river which is the source of the alkalinity. Increase in alkalinity in station C (Idogbo community) in the month of July might be attributed to the kind of substrate the water is flowing through it might be due to different substrate type containing limestone (Brian 2014). The free carbon(IV)oxide present in the water at station B and C might be due to higher commercial vehicle releasing carbon(IV)oxide around station B (Ikpoba bridge) this is in concordance with the statement made by Aghoghovwia (2011) while studying the Warri river.

While station C (Idogbo Community) being the highest in concentration of free carbon(IV)oxide might be attributed to higher decomposition rate in the station and also to the decrease in the volume of dissolve oxygen present in the water (Shahid and Satyendra, 2014). The increase in the depth of the river across each station from station A to C and from May to July might be due to higher rainfall during the month of June and July.

Station A (Lucky way) secci disc transparency indicate that the water was clear, the clarity of the water at station B and C decrease significantly this might be related to the washing of sediment into the water during the raining seasons this is in concordance with the report of Ogbeibu and Edutie (2002) in Ikpoba river. Higher conductivity at station B (Ikpoba bridge) than at station A (Lucky way) and station C (Idogbo community) might be due to the higher dissolve solute from runoff and brewery effluent into station B (Ikpoba bridge). Water current being significantly higher in July than in May and June might be as a result of decrease in depth in station A (Lucky way) as water current increase with decrease in depth (korald et al., 2003).

Also, it might be due to increase in rainfall during the month of July (Mustapha and Omotosho, 2005). The decrease in mercury from May to July in station A (Lucky way) might be as a result of dilution due to increase in water volume in this station from May to July, this observation is in concordance with the result obtained by Dey et al. (2005) while studying the various physicochemical parameters on the samples drawn from the river Koel, Shankha and Brahmani. While the higher value recorded in the rainy month of July in station B (Ikpoba bridge) might be as a result of run off from both side of the river carrying sediments and effluent from the Guinness brewery.

Oreochromis niloticus, Clarias gariepinus and Malapterurus electricus were the three species of fish recorded during the three months study. A total of two hundred and sixty-three (263) fishes was recorded during the study period belonging to the class of Actinopterygii and two orders of Perciformes and Siluriformes.

O. niloticus was the highest number of a fish species per month caught but C. gariepinus being the most abundant at the three stations might be as a result of C. gariepinus to survive in different water condition. O. niloticus abundance decreased from stations station A (Lucky way) through Station B (Ikpoba bridge) to station C (Idogbo community) this might be associated to the fact that they prefer shallow water. Malapterurus electricus on the other hand was higher in station C than at stations A (Lucky way) and Station B (Ikpoba bridge) this might be as a result of them to be able to live in habitat with less dissolve oxygen and muddy floor.

The species richness of station C in the month of May based on Margalef’s and Menhinick’s indices was assigned values of 0.738539 and 0.774597 respectively. This is higher than the values that were assigned to station A (Lucky way) (0.378923 and 0.534522 respectively), Station B (Ikpoba bridge) (0.531745 and 0.457496 respectively), and the Ikpoba River (0.46765 and 0.353553 respectively).

Simpson’s and Gini-Simpson diversity indices assigned a greater diversity level to station C where only 15 fish samples comprised three different species in the month of May. Compared to station C, 14 fish samples at station A (Lucky way) which is almost equal to 15 comprised only two species, while 43 samples from Station B (Ikpoba bridge) was only made up of 3 species; the diversity in species in the month of May in this station, might be that the three species have a common factor such as food presence in station C. during this period in the River as a whole, 72 samples contained only three species in the month of May. In the month of June, the Margalef’s and Menhinick’s indices of richness almost had equal values for all the stations.

But station A (Lucky way) was assigned a slightly higher value by both indices. Species diversity in the month of July was generally low based on Simpson’s and Gini-Simpson’s indices. But diversity was relatively high in stations B (Ikpoba bridge) and C (Idogbo community)  based on these indices.

4.2 Conclusion

The physicochemical parameters of the River were within permissible limits, monthly variations in the water parameters affected the abundance as well as the diversity of fish fauna in the water.   Supported by the fact that Simpson’s and Gini-Simpson diversity indices assigned a greater diversity level to station C where only 15 fish samples comprised three different species in the month of May, and 14 fish samples at station A (Lucky way) which is almost equal to 15 comprised only two species, while 43 samples from Station B (Ikpoba bridge) was only made up of 3 species.

Station B (Ikpoba bridge) although supports a considerable number of fishes, the species diversity supported in this station is low especially in the month of May this might be due to much impact from anthropogenic activities around this station. Proper care for this position and enactment of regulations will increase the species abundance and diversity.

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