Biomarker Responses in Tympanotous Fuscatus Var Radula (L) Inhabiting an Oil-Impacted and Fire-Ravaged Mangrove Ecosystem

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Biomarker Responses in Tympanotous Fuscatus Var Radula (L) Inhabiting an Oil-Impacted and Fire-

Ravaged Mangrove Ecosystem Ochuwa O. George1, Nnamdi H. Amaeze2, Temitope O. Sogbanmu*3, Adebayo A. Otitoloju4

1Global Oceon Engineering Limited, Lekki, Lagos, Nigeria 2-4Ecotoxicology Unit, Department of Zoology, University of Lagos, Akoka, Yaba, Lagos, Nigeria

[email protected]

Abstract - The physico-chemistry, biodiversity and biomarker responses in Tympanotonus fuscatus collected from a mangrove ecosystem in the outskirt of Lagos recovering from the impact of refined petroleum spill and fire outbreak was assessed using a combination of ecotoxicological techniques. The impacted water and sediment were mostly acidic (pH<7) and a combination of high Biochemical Oxygen Demand (10.5±0.5 - 54.0±12.0ppm) and Chemical Oxygen Demand (14.5±0.5 - 165.5±31.5ppm) levels created a hypoxic condition unsuitable for intolerant endemic species to flourish. Dissolved oxygen was lower than national regulatory limit of 5.0mg/L and hydrocarbon concentrations in both media were at least 3 times higher than that of the control station downstream. The concentration of inorganic ions including NO4-, NO3-, NH4, PO4

3- and SO32- were highest at the stations closest to the point

of spill. The heavy metals concentration were in decreasing order of Cu>Fe>Zn>Mn>Pb in water and Fe>Zn>Cu>Pb in the sediment. These physico-chemical changes culminated in low floral and faunal diversity and a preponderance of the invasive and tolerant periwinkles, Tympanotonus fuscatus and Pachymelania aurita. Biomarker studies on the viscera of T. fuscatus revealed some level of DNA damage, and low levels of oxidative stress emphasized Overall, there were no significant changes (p>0.05) in antioxidant enzymes (reduced gluthathione, superoxide dismutase and catalase) and lipid peroxidation product, malondialdehyde (MDA) assayed T. fuscatus collected from the impacted sites compared to the control site. This study provides evidence for the tolerance of the dominant T. fuscatus to the stressed ecosystem and makes a case for the use of biomarkers of stress, together with chemical analysis incorporated into traditional biodiversity assessments for monitoring ecosystem recovery after an oil spill incidence.

Keywords- Physico-Chemistry; Biomarkers; Tolerance; Petroleum Spill; Tympanotonus Fuscatus

I. INTRODUCTION

The magnitude of oil pollution and damage occasioned by multi-national oil companies and sabotage operations in the coastal area of some western parts of Nigeria is a major environmental concern. In Nigeria, 50% of oil spills are due to corrosion, as most of these pipelines are old and poorly maintained leading to leakages, 28% to sabotage and 21% to oil production operations while 1% of oil spills is due to engineering drills, inability to effectively control oil wells, failure of machines and inadequate care in loading and unloading oil [1,2]. Oil spills pose a major threat to the environment in Nigeria and if not managed or checked could lead to total annihilation of surrounding ecosystems [3].

Most oil spillages are followed by fire outbreaks that destroy vegetation and animal life. Occurrence of oil spillages resulting in major fire outbreaks has become a frequent event in Nigeria. The fire outbreaks associated with petroleum products spillages usually cause more damage to the environment than the petroleum product spill alone [4,5]. Currently, there are a range of chemical analysis and standards for monitoring pollution of soil and water in the country. However, chemical analysis provides little information on the impacts of compounds that are rapidly metabolized and excreted by the organisms [6]. Risk assessment cannot be solely based on chemical analysis of environmental samples because this approach does not provide any indication of deleterious effects of contaminants on the biota, hence the use of organisms in biomonitoring of aquatic ecosystems complements the interpretation of the physical and chemical measurement of water quality [7]. Chemical monitoring of environmental health is based on the momentary conditions that exist at the time of sample collection [8]. This procedure often misses the short-term events that may be critical to ecosystem health [9]. In the past, biomonitoring relied heavily on mortality-based acute tests but it has been shown that a chemical that does not cause death within the duration time may still have long- term deleterious impacts on the test organisms [10].

Exposure to endogenous and environmental pollutants like petroleum hydrocarbons can interfere with the normal functioning of an organism, making it less able to grow normally or reproduce successfully in its environment. Apart from being killed, some of the more common but serious effects of environmental stressors on organisms are changes in behaviour, growth, reproduction and deoxyribonucleic acid (DNA) damage which are often indicative of oxidative stress [11]. Early effects of pollution initially occur at the lower levels of biological organization. Changes in protein synthesis, cells, tissues, body chemical processes and basic body functions appear before more severe disturbances occur in populations and ecosystems [12]. These biochemical and molecular effects can be detected as changes in enzyme levels, structure of cell membranes, and genetic material or DNA [13]. All these measurable changes serve as biomarkers of pollutant stress and oxidative stress which amplifies tissue damage and also provides early warnings of environmental damage [14]. [15] defined biomonitoring as a regular, systematic use of living

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organisms to evaluate changes in environment or water quality. [16] identified four levels of biological organisation at which biomonitoring can be done as sub-organismal (using biomarkers), organismal (using bioassays), population level (bioindicators) and ecosystem level (ecological indicators). At sub-organismal level, biomonitoring makes use of biomarkers which are defined by [17] as physiological, biochemical or histopathological alterations that occur as a result of exposure to environmental pollutants. They are sensitive molecular endpoints and help in better understanding of pathways of contaminant metabolism, detoxification and toxic action [18]. For biomarkers of effect, the founding assumption is that some vital biological process (the biomarker) is compromised as a result of pollutant exposure. Thus biomarkers of (adverse) effect must be biological processes that are essential to normal function at cell, tissue or organism level [19]. The main purpose for the use of biomarkers is to give evidence of exposure to pollutants and consequent toxic effects. Biomarkers are essential for assessment of oxidative stress and evaluation of antioxidant capacity in vivo hence their use in this study.

The activity of antioxidant enzymes may be enhanced or inhibited under chemical stress depending on the intensity and the duration of the stress applied, as well as, the susceptibility of the exposed species. The relationship between exposure to toxicants and enzymes such as catalase (CAT), superoxide dismutase (SOD), reduced glutathione (GSH), glutathione-S-transferase (GST) as well as lipid peroxidation products have been the subject of several investigations [19]. The activities of these enzymes are also often linked, making them a useful battery of biomarkers of effect or exposure to xenobiotics albeit general. GSH functions as an important overall modulator of cellular homeostasis, and serves numerous essential functions including detoxification of metals and oxy-radicals [20,21]. An increase in both GSH levels and GSH synthesis has been found in fish exposed to lead [22] and increased hepatic concentration of GSH has been reported in cadmium and fuel oil exposed striped mullet and sediment exposed catfish [23]. It was noted that under acute oxidative stress, the toxic effects of the pollutants may overwhelm the antioxidant defences [24]. Lipid peroxidation, an indicator of damage to cell membranes, occurs when free radicals react with lipids and is a source of cytotoxic products that may damage DNA and enzymes [25]. Contaminants play a key a role in the inhibition of superoxide dismutase enzyme activity, thus preventing the conversion of the superoxide radical to hydrogen peroxide (H2O2). When the superoxides are not removed, it leads to the suppression of catalase enzyme that converts H2O2 to water and molecular oxygen. Catalase also uses H2O2 to breakdown potential harmful toxins, like alchohol, phenol and formaldehyde. Catalase removes reactive oxygen species (ROS) but if it is inhibited, the ROS accumulates leading to lipid peroxidation, an effect of oxidative stress.

In this study, the impact of petroleum products spill and fire outbreak that occurred on the 16th of September 2004 at Imore Village in Oriade local government development area

of Lagos state was investigated several years following rehabilitation of the impacted sites. The village is situated around a mangrove swamp which is a source of fuel wood for the indigenous people but following the oil spillage, the area has been devastated. This mangrove ecosystem which was also reported to be very rich in benthic macroinvetebrates particularly molluscs such as Tympatonus fuscatus var radular (periwinkle), Pachymelina aurita, Graphae gazar (Oyster), Mytilus edilus and crustaceans was completely devastated as the animals and plants were destroyed following a fire outbreak which continued burning for over one week [26]. The Oil spill also destroyed farmlands, polluted ground and drinkable water and caused drawbacks in fishing off the coastal waters. This study investigated the species diversity, environmental chemistry, oxidative stress and DNA damage to the most dominant species surviving in the area.

II. METHODOLOGY

A. Study Location

The study was conducted in Imore Village, Oriade Local Government mangrove swamp located East of Port- Novo creek between (N060.25.495’ and E0030.16.73’) on the outskirts of Lagos State, Nigeria. This study area had been polluted by refined oil spill from pipelines and fire outbreak. A control site was located at about 20km away from the study site around Ijegun Jetty which is another area on the outskirt of the state with similar biogeography.

B. Sampling Design

The selection of sampling points was random but linear falling within 10km radius of the oil spill and fire outbreak site (Fig. 1). A total of five stations including one control station which was located 20km from the oil impacted site were employed to effectively monitor in this study. Sampling was carried out in the rainy (May) and dry (October) seasons of 2010. At each sampling station, flora and fauna as well as sediment and water were sampled for both in situ and ex situ assessment at the Ecotoxicology laboratory of the University of Lagos, Akoka campus, Lagos, Nigeria. Sampling was done in duplicates to reduce the margin for error.

C. Assessment of Surface Water and Sediment Physico-Chemistry

The pH, surface water temperature and total dissolved solids (TDS) level were determined in situ using hand- held probe (Hanna instruments, Model LP200-11). Dissolved oxygen (DO) was determined using Metler Toledo (Inlab720) and total suspended solids (TSS) using HACH (Model 9835). Acidity, Alkalinity, calcium and chlorine hardness were determined gravimetrically (APHA/AWWA/WPCF, 1995). All chemical analyses of water and sediment samples was carried out according to ASTM Standards on Environmental Sampling, 2000. For heavy metals analyses, surface water samples collected from each station were filtered, followed by digestion according

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to APHA/AWWA/WPCF, 1995. The sediment samples were digested following the technique employed by [27] after being oven dried and sieved through a 200µm sieve to homogenize particle size. Both samples were each subjected

to Atomic absorption spectophotometry using Perkin elmer series AAS and their heavy metal concentrations were determined by comparing their absorbance with those of the standards.

Fig. 1: Map of Western Lagos showing the sampling stations in both Imore Village and Ijegun Jetty

D. Biodiversity Studies on Flora and Fauna

Macroinvertebrates were sampled using 1m2 Quadrant thrown at each sampling station in a process repeated twice to minimize random errors and reflect the non-static nature of the ecosystem. Plants were sampled along 10m transects mounted within each station to obtain information of the prevailing flora health. Species diversity and abundance were recorded uniformly and the overall biodiversity calculated according to [28].

E. Biomarker Studies on Most Dominant Fauna (Tympanotonus Fuscatus)

1) Test Animal Collection:

Tympanotonus fuscatus var radula (Periwinkle) (Mollusca, Gastropoda, Mesogastropoda, Potamidae) of similar sizes (shell length: 28-33mm) handpicked from experimental stations (Control and impacted) were used in this assay. The test animals were collected into plastic bowls lined with their habitat sediment, half-filled with the habitat brackish water and were transferred into glass tanks

in the laboratory taking care to avoid overcrowding in preparation for biological assessments.

2) Biochemical Studies - Preparation of Tissue Sample and Analysis:

The shells were each breached at the apex and the periwinkle body mass extracted into normal saline solution to minimize denaturation. The tissues were teased and refrigerated at -40C prior to analysis within 2 days. Each periwinkle tissue sample was retrieved from the freezer, 0.5g weighed out, homogenized cold and dissolved in phosphate buffer (1-10 w/v, that is, to 0.5g sample, 5.0ml of phosphate buffer was added). The homogenate was then centrifuged at 10, 000 revolutions for 10mins, this was assayed by the method of [29]. The supernatant were collected and stored in the fridge (4oC) prior to the analysis.

Total protein concentration was estimated according to [30], Reduced gluthathione (GSH) according to [31], Superoxide dismutase (SOD) according to [32], Catalase (CAT) according to the method of Beers and Sizer as described by [33]. The levels of homogenized tissue malondiadehyde (MDA), as an index of lipid peroxidation

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were determined by thiobarbituric acid reaction (TBARS Assay) using the method of [34].

3) Analysis of Chromosomal DNA Damage by Agarose Gel Electrophoresis:

The periwinkle tissue was homogenized in 5 ml of nuclei lysis buffer (10mM Tris-HCl, 400mM NaCl, 2mM Na2EDTA, pH 8.2). The homogenate was in turn digested overnight at 370C by the addition of 550µl 10% SDS and 55µl of 20 mg/mL Proteinase K solution. 1ml of supersaturated NaCl (6M NaCl) was added to the digestate and shaken vigorously for 20 secs. This was followed by centrifugation at 4000 revolutions per minute (rpm) for 15 mins. The supernatant was then transferred into a new tube. Two volumes of ice-cold absolute ethanol was added to the tube, mixed by inversion (x5) and allowed to stand on ice for 20 mins. Thereafter, centrifugation was done at 8000 rpm for 10 mins., the supernatant was decanted and DNA pellet was washed in 70% ethanol. The DNA pellet was then air-dried for 10h after centrifugation at 8000 rpm for 10 min and decantation of ethanol. The DNA pellet was re-suspended in 100 µl of Tris-EDTA buffer (pH 7.5). A description of the application of agarose gel electrophoresis is given by [35]. DNA Strand breakage was determined according to [36].

F. Statistical Analysis

Species diversity was expressed using the Margalef index [28].

Where, d =diversity, S=Number of species, N= Total number of individuals

The levels of MDA which is an index of lipid peroxidation, antioxidant enzymes activity are presented in µmol/mg protein. Statistical analyses were carried out using SPSS version 10.0. The biochemical results and chromosomal damage data were subjected to one-way analysis of variance (ANOVA) between the different sample station means and the control station. Significant difference was determined at 5% confidence level (P<0.05). When the ANOVA revealed significant differences, post-hoc comparisons of means between stations were carried out using Student Newman Keuls (SNK) test [37] to determine which values differed significantly. Regression analysis was carried out to determine correlation coefficient (r2) between level of DNA damage and the lipid peroxidation. The graphical representations were drawn using Microsoft Excel software package.

III. RESULTS

A. Assessment of Surface Water and Sediment Physico-Chemistry

The surface water physico-chemical characteristics of the refined petroleum impacted area of Imore are shown in Table I. The conductivity of the surface water samples varied widely in the impacted areas from 378.5 ± 214.5 to 6238.5 ± 2871.5 Scm-1. The conductivity values were lower at stations 1 and 2 which were upstream, close to the epicentre of the spill compared to others. Salinity readings were characteristic of brackish water ecosystems (4‰). The mean hydrogen ion concentration (pH) remained slightly acidic in all sampling stations including the control site. The total dissolved solids (TDS) concentration was high, particularly in areas downstream (2785.0±765.0 to 3130.0±1390.0) and control station (2455.0±855.0) compared to sites upstream. Typical of water bodies receiving high amount of organic materials, the Biological oxygen demand was high in the impacted areas relative to the control station and there was a marked decrease in Biological Oxygen Demand (BOD) between the upstream and downstream stations (p<0.05). The average calcium ion content was higher in stations downstream compared to stations closest to the spill site. The average value of chloride ions on the other hand were higher in stations downstream and the control station indicating high influx of chloride ions in the Imore creek from the Lagoon. Observations from surface water analysis indicated that nutrient levels in all the impacted stations at Imore Village were consistently higher than that of the control station (Table I) and a decrease in the measured nutrient levels over sampling times were also noticed. Sulphate, sulphite, Nitrate and Phospahte were higher upstream, although only nitrates decreased significantly (from 8.4±1.0 to 2.6±1.1). Nitrite and Ammonium were not detected in the downstream stations as well as the control site. Total hyhdrocarbon content (THC) expectedly was high in the impacted areas relative to control but did not vary significantly between impacted stations (P>0.05). Heavy metals are often found in crude oil due to its geological proximity with rocks formations and the drilling processes. Copper (Cu) recorded highest mean value amongst the heavy metals of the water at Imore area with Pb being the least in this sequence, Cu>Fe>Zn>Mn>Pb, (Tables I). The control station recorded the least values for all the heavy metals analysed indicating a significant difference (P<0.05). Fe, Zn and Pb concentrations were higher at stations 1 and 2.

TABLE I PHYSICO-CHEMICAL CHARACTERISTICS OF SURFACE WATER IN THE IMPACTED AREAS AND CONTROL STATION

Physico-chemical

Parameters

Sample Stations FEPA Safe

Limit 1 2 3 4 Control

pH 6.25±0.15 6.58±0.03 6.45±0.05 6.68±0.03 6.40±0.1 6-9

Turbidity (NTU) 0.49±0.01 0.69±0.15 0.14±0.01 0.12±0.0 0.12±0.02 10NTU

Conductivity (µScm-1 ) 378.5±214.5 3030.0±850.0 5555.0±155.0 6238.5±2871.5 5118.5±1301.5 NS

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TDS( ppm) 1935.0±65.0 1570.0±430.0 2785.0±765.0 3130.0±1390.0 2455.0±855.0 2000ppm

DO (ppm) 3.15±0.15 3.0±0.4 4.3±0.2 4.5±0.5 4.75±0.6 5.0ppm

BOD (ppm) 49.0±11.0 54.0±12.0 10.0±1.0 10.5±0.5 9.5±0.5 50ppm

COD (ppm) 156.5±33.5 165.5±31.5 14.5±0.5 16.5±0.5 16.0±2.0 NS

Acidity (ppm) 4.15±0.2 3.0±0.0 4.0±0.0 3.0±1.0 5.0±0.0 NS

Alkalinity (ppm) 95.0±1.0 45.0±5.0 38.5±6.5 40±0.0 36.0±4.0 20ppm

Total Hardness (ppm) 148.0±8.0 125.0±25.0 316±0.0 428±0.0 302±2.0 300ppm

Calcium Hardness

(ppm) 88.0±0.0 48.0±0.0 158±62.0 155±1.02 28.0±0.0 NS

Chloride (ppm) 1128±28 856±144 2518±22 3564±1564 1716±500 NS

Sulphate (ppm) 1.13±0.01 1.12±0.01 1.12±0.0 1.16±0.0 1.04±0.0 NS

Sulphite (ppm) 0.40±0.1 0.2±0.1 0.1±0.0 0.1±0.1 0 0.2ppm

Nitrate NO4- (ppm) 6.3±1.0 8.4±1.0 3.4±1.0 3.7±2.0 2.6±1.1 20ppm

Nitrite NO3- (ppm) 0.06±0.01 0.06±0.1 0 0 0 20ppm

Ammonium NH4

(ppm) 0.13±0.25 1.55±0.25 0 0 0 0.02ppm

Phosphate PO43- (ppm) 0.25±0.3 0.34±0.01 0.32±0.1 0.23±0.01 0.14±0.2 5.0ppm

THC (ppm) 2885.5±2088.5 2326.5±1470.5 4149.5±3285.5 2866.0±2025.0 874.0±120.0 NS

Fe (ppm) 0.63±0.01 0.59±0.25 0.35±0.05 0.25±0.15 0.02±0.0 1.0ppm

Cu (ppm) 1.2±0.0 3.4±0.0 4.4±0.0 5.1±0.0 1±0.0 <1.0ppm

Mn (ppm) 0.02±0.0 0.07±0.0 0.03±0.0 0.02±0.0 0.01±0.0 5ppm

Zn (ppm) 0.12±0.0 0.16±0.0 0.1±0.0 0.08±0.0 0.8±0.0 <5ppm

Pb (ppm) 0.11±0.01 0.02±0.02 0.001±0.004 0.01±0.001 0.001±0.001 <1.0ppm

Key: FEPA – federal environmental protection agency, n=2, values presented as mean ± standard deviation

The sediment physico-chemistry of the study areas are presented in Table II. The results showed widespread pollution because the measured physico-chemical characteristics were often different from values obtained in the control sediment samples. Sediment pH values varied from acidic (4.65±0.270) in station 3 to slightly alkaline in station 2 (7.65±0.65). Conductivity reduced among sampling stations (p<0.05) with highest value of

878.0±22.0 and least value of 205.5±3.5µs/cm. Sediment hydrocarbon content remained high across the stations reflecting the recent oil spill situation. Organic ion (Nitrate, Phosphate and Sulphate) concentrations were low, however sulphate ion was the highest in concentration across sampling stations. Heavy metals generally varied as follows Fe>Zn>Cu>Pb.

TABLE II PHYSICO-CHEMICAL CHARACTERISTICS OF SEDIMENT SAMPLES IN THE IMPACTED AREAS AND CONTROL STATION

Sediment Physico-chemistry Sample Stations

1 2 3 4 Control pH 5.4±0.1 7.65±0.65 4.65±0.27 5.90±0.2 5.8±0.2

Conductivity (µS/cm) 878.0±22.0 467.0±27.0 345.5±44.5 299.0±10.0 205.5±3.5 Phosphate PO3

4(ppm) 1.46±0.55 0.94±0.78 1.82±0.19 1.11±0.52 1.79±0.67 Nitrate NO3-(ppm) 3.62±0.5 2.85±0.65 2.10±0.40 1.39±0.53 1.50±0.05 Sulphate SO4(ppm) 4.58±0.13 1.98±0.12 1.96±0.24 2.59±0.32 5.22±0.32

Fe (ppm) 17.87±0.05 19.75±0.05 20.87±0.13 19.64±0.74 15.05±0.55 Zn (ppm) 6.37±1.03 3.48±1.08 6.88±0.38 4.90±0.20 5.33±1.63 Pb (ppm) 0.003±0.001 0.006±0.004 0.011±0.01 0.003±0.001 0.001±0.001 Cu (ppm) 3.0±0.6 2.1±0.6 3.9±0.5 4.8±0.4 1.4±0.3

THC (ppm) 6203.5±1836.5 1408.0±4960.0 9690.0±8350.0 11282.0±9432.5 3060.0±4100.0

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B. Biodiversity of Flora and Fauna

The results of the Flora species abundance and variety are presented in Table III. The red mangrove, Rhizophora racemosa, characteristic of brackish water swamp physiognomy was found to be the most abundant flora while the least was Hibiscus tilaceus species. Stations 1

and 2 which were within the area most impacted recorded the least number of flora species both in abundance and variety. The control station recorded the highest species diversity (1.03) during the study compared to the impacted stations (0.00 - 0.51) indicating a marked difference in ecosystem health.

TABLE III DIVERSITY OF FLORA AT THE SAMPLING STATIONS

Species Sampling Stations

Total number

1 2 3 4 Control

Rhizophora racemosa 1 0 13 25 107 146 Archrosticum aureum 0 0 5 10 50 65

Musa sapientum 1 0 10 15 20 46 Cocos nucifera 0 0 0 5 18 23

Hibiscus tilaceus 0 0 0 2 9 11 Dalbergia ecostaphyllum 0 0 1 0 15 16

Paspalum vaginatum 0 0 0 7 12 19 Total number (N) 2 0 29 64 231 326

Number of Species (S) 2 0 4 6 7

Species Diversity (d) 0.17 0.00 0.51 0.86 1.03

The results of the Fauna species abundance and variety are presented in Table IV. The brackish water periwinkle, T. fuscatus was the most abundant fauna species present in both the control and impacted sites. The least abundant species recorded in the study was the blue crab, Cardiosoma armatum. Molluscans were the dominant class followed by the Crustaceans and they were found in all the stations. However, they

were lower in abundance at the areas close to the oil spill site than at farther stations including the control station. The Ragworm, Neries sp. which thrives in waters with high organic matter was also recorded in the sampling areas. Biodiversity calculations using Margalef’s index showed that stations 1 and 2 had the least biodiversity of 0.52 and 0.25 respectively. The highest faunal diversity was recorded in the control station (0.90).

TABLE IV DIVERSITY OF FAUNA AT THE SAMPLING STATIONS

Species Sampling Stations Total number 1 2 3 4 Control

Phylum - Mollusca Class - Gastropoda

Tympanotunus fuscatus (Brackish water Periwinkle) 168 200 178 200 250 996

Pachymelania aurita (Periwinkle) 28 20 60 170 210 488

Phylum - Arthropoda Class - Crustacea

Sesarma huzardi (Mangrove Marsh Crab) 1 0 10 20 59 90

Clibanarius africanus (Hermit crab) 30 11 70 100 100 311

Callinectes amnicola (Blue crab) 0 0 4 0 32 36

Cardiosoma armatum (Rainbow crab) 0 0 0 14 17 31

Phylum - Chordata Class - Actinopterygii

Periophthalmus sp. (Mudskipper) 0 0 0 38 100 138

Phylum - Annelida Class - Polychaeta

Nereis sp. (Rag worm) 2 0 20 50 150 222

Total Number (N) 229 231 342 592 918 2312 Number of Species (S) 5 3 6 7 8 Species Diversity (d) 0.52 0.25 0.64 0.77 0.90

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C. Biomarker Studies on T. Fuscatus

1) Biochemical Studies - Lipid Peroxidation and Antioxidant Enzymes Analyses:

The Malondialdehyde (MDA) levels varied slightly amongst animals collected from the various stations and at different sampling periods (wet and dry seasons) (Table V). The samples from the dry season sampling period recorded higher MDA levels (in µmol/mg protein) across the stations than those from the rainy season sampling period as shown in Table V. The highest level recorded during dry season sampling period was at station 2 (2.76 ± 0.32), followed by station 1 (2.57 ± 0.18), station 4 (2.31 ± 0.17), station 3 (2.29 ± 0.17) and the least was at the control (2.22 ± 0.20). In rainy season sampling period, the highest value was recorded at station 3 (2.18 ± 0.16) while the least was at station 1 (0.22 ± 0.08). Analysis of variance shows significant difference (p<0.05) in the mean values of Lipid peroxidation activities recorded in the T. fuscatus samples from the dry season sampling period between the stations 1 and 2 and the control station only. Stations 1and 2 were not significantly different (p>0.05) from each other, while stations 3, 4 and Control were not significantly different from one another as well. Overall there was no significant difference across impacted sites compared to the control site (P>0.05)

Superoxide dismutase (SOD) levels (in µmol/mg protein) in periwinkle samples from the rainy season sampling period ranged from 0.07 ± 0.00 (control station) to 0.08 ± 0.00 (stations 1 - 4) while for the dry season sampling period it was from 0.07 ± 0.00 (Stations 2 and 3) to 0.08 ± 0.00 (control, Stations 1 and 3). There was no significant difference (p>0.05) in the levels of SOD in samples collected from the various stations (Table V).

Catalase (CAT) levels (in µmol/mg protein) in periwinkle samples from the rainy season sampling period ranged from 0.74 ± 0.09 (control station) to 0.83 ± 0.05 (station 2) while for the dry season sampling period it was from 0.81 ± 0.06 (Stations 1 and 3) to 0.82 ± 0.05 (control, Stations 2 and 4). There was no significant difference (p>0.05) in the levels of CAT in samples collected from the various stations (Table V).

Reduced Gluthathione (GSH) levels (in µmol/mg protein) in periwinkle samples from the rainy season sampling period and dry season sampling periods were approximately 0.05 ± 0.00 for all the sampling stations. There was no significant difference (p>0.05) in the levels of GSH in samples collected from the various stations (Table V).

TABLE V LEVELS OF BIOCHEMICAL PARAMETERS IN T. FUSCATUS COLLECTED FROM THE VARIOUS SAMPLING STATIONS DURING THE RAINY (MAY) AND DRY (OCTOBER) SEASONS

Biochemical Parameters

(µmol/mg protein)

Sampling Stations

Station 1 Station 2 Station 3 Station 4 Control Rainy Dry Rainy Dry Rainy Dry Rainy Dry Rainy Dry

MDA

0.22 ± 0.08

2.57 ± 0.18

b 2.15 ± 0.09

2.76 ± 0.32 b

2.18 ± 0.16

2.29 ± 0.17 a

2.15 ± 0.10

2.31 ± 0.17 a

2.13 ± 0.36

2.22 ± 0.20a

SOD

0.08 ± 0.00

0.08 ± 0.01

a 0.08 ± 0.00

0.07 ± 0.00

a

0.08 ± 0.00

0.07 ± 0.01

a 0.08 ± 0.00

0.08 ± 0.00

a 0.07 ± 0.00

0.08± 0.00 a

CAT

0.80 ± 0.05

0.81 ±

0.14a 0.83 ± 0.05

0.82 ± 0.05a

0.80 ± 0.05

0.81 ± 0.06

a 0.82 ± 0.04

0.82 ± 0.05

a 0.74 ± 0.09

0.82 ± 0.08

a

GSH 0.05 ± 0.00

0.05 ± 0.00d

0.05 ± 0.00

0.05 ± 0.00cd

0.05 ± 0.00

0.05 ± 0.00bc

0.05 ± 0.00

0.05 ± 0.03b

0.05 ± 0.00

0.05 ± 0.00a

Values with dissimilar letters (a, b, c, d) are significantly different (p<0.05) from each other

2) Chromosomal DNA Damage Assessment

The DNA concentration and purity in T. fuscastus was recorded from the samples collected during the dry season sampling period (Table VI). In Fig. 2, all the graphs peaked at 260nm, suggesting the presence of pure DNA, and the amplitude of peaks indicate yield which varies with plots. Most graphs troughed at 230nm suggesting a fairly pure DNA preparation as evidenced by their absorbance 260/280 ratio in the assay report. The DNA concentration ranged from 830.04 ± 237.8 µg/ml in the samples from station 4 to 135.21 ± 2.48µg/ml in samples from Station 1. DNA mean yield obtained from station 1

and 2 were less than yield obtained from other stations (Fig. 2). Fig. 3 presents DNA fragments of 9.0 and 7.5 which are indicative of possible damage and these bands are not emphasized in the controls (lane 2 and 3). Periwinkles from impacted areas showed definite expression of the bands (lanes 4 - 10). Lanes 11 and 12 (station 2) however showed lighter bands of 7.5 and 9 and this may be linked to lower susceptibility to the pollutants in the sample individuals. Analysis of variance showed significant difference (p<0.05) in the mean values of DNA concentration recorded in the T. fuscatus samples from the impacted stations compared and the control.

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TABLE VI DNA CONCENTRATION IN ANIMAL SAMPLES COLLECTED DURING THE DRY SEASON

Biomarker Sampling Stations Station 1 Station 2 Station 3 Station 4 Control

DNA (µg/ml) 135.21 ± 2.48 c 230.77 ± 25.16a 346.99 ± 26.11a 830.04 ± 237.8 b 460.34 ± 39.85d

Fig. 2 . fuscatus DNA concentration and purity levels

Fig. 3 Agarose gel electrophoresis of chromosomal DNA fragments recovered from periwinkle samples.

Key: Lane 1 = DNA markers, Lanes 2 & 3 = Control, Lane 5 & 11 = Station 1,

Lanes 6, 9 &12 = Station 2 , Lane 7 &10= Station 3 Lane 4 & 8 = Station 4

IV. DISCUSSION

The physico-chemistry of the surface water and sediment studied in the recovering sites of the petroleum impacted and fire ravaged area in Imore village revealed

values indicative of an ecosystem still under stress. The water body was slightly acidic and measured DO content was low suggestive of high levels of microbial degradation activities. Low DO levels have been found to impair survival of aquatic animals because below 5mg/l [38], few

9.0

7.5

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

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oxygen-utilizing species are able to thrive [39]. Water with very low or absent DO (that is, anaerobic conditions) exhibit odour and other aesthetic problems [40]. The COD and BOD values in the oil impacted area were high compared to the surroundings and the control station. This observation was also reported by [41] at an oil impacted site in Atlas Cove. These values suggest a high content of materials requiring chemical and biological oxidation. The increased levels of COD and BOD correlates with the low level of dissolved oxygen in the area close to the impacted site. The Total hydrocarbon content (THC) in both water and sediment in the impacted area were higher in the stations close to the spill and fire outbreak sites compared to the surrounding areas and the control. [42] equally reported higher levels of THC in the areas close to the epicentre than the surrounding areas at Imore village. It is noteworthy that rather than the THC to reduce in the area close to the spill site as reported by [41] in Atlas Cove after 6months of oil spill, the THC level was still high in the area close to the oil spill at Imore village 6 years after. This could be as a result of oil seepage from the lower part of the swamp which probably did not burn out during the fire outbreak or subsequent unreported spillage resulting from compromised pipelines in the area after the initial spillage. [43] had earlier reported high concentration of THC in a previous oil spill site after 19 years due to persistent seepage of crude oil into the area.

The result of the field assessment of Flora in the area showed sparse or no vegetation in some areas 6 years after the incident compared to the areas surrounding the spill site and the control. [26] also reported observation of no vegetation in the recovery assessment at Atlas cove mangrove after the oil impact and fire outbreak. [4,44] had earlier observed a complete destruction of both plant and animal community following a petroleum product spillage and fire outbreak. The vegetation is yet to repopulate at the areas close to the spill probably due to the observed high intolerable content of hydrocarbon in the soil in the impacted site.

Fauna assessment results showed the colonization of the impacted site by tolerant macro-invertebrates. Out of the 8 species observed, 5 were at the impacted area, though in varying numbers. The population was observed to be dominated by the gastropod molluscs, T. Fuscatus (periwinkle), followed by the crustacean, C. africanus (Hermit crab). The periwinkle is known to dominate the lagoons and creeks of southern Nigeria, showing a preference for muddy sediments and an adaptation to low oxygen requirement [45]. [26] had earlier reported that the rapid recovery of the population of C. africanus may be due to their ability to emigrate quickly into the deeper parts of the surrounding water body during inferno or rapid immigration of other population of the animals into the impacted area after rehabilitation activities. [42] observed that the colonization of T. fuscatus in the impacted may be due to their high tolerance to petroleum products. This may be occasioned by their strong protective shell and their natural adaptation for survival under hypoxic conditions in muddy bottoms of sediments. The rapid

colonization of such area as this by C. africanus after rehabilitation could also be their intention to inhabit the shells that were not destroyed by the inferno. The lower number of species observed in the impacted area than the surrounding stations and control could be due the long period required for the recovery to the earlier environmental chemistry as observed by [46].

T. fuscatus collected from the impacted area showed slightly higher levels of MDA than those in the surrounding stations and the control in most cases but the difference were not always significant, indicative of low level of oxidative damage in the tissues which shows some form of tolerance to the polluted and disturbed ecosystem. This result is not agreement with the findings of [47,48] who reported an increase in lipid peroxides in the tissues of fish exposed to petroleum hydrocarbons. The increase in lipid peroxides is usually linked to an inhibitory effect on the mitochondrial electron transport system leading to stimulation in the production of intercellular reactive oxygen species (ROS) [49]. Thus, organisms inhabiting polluted aquatic environments are exposed to a variety of oxyradicals, leading to oxidative damage of lipid or protein biomolecules. Oxidative damage reflects an imbalance between the production of oxidants and scavenging or removal of those oxidants. The intensity of oxidative damage suffered by an organism depends on the fine balance amongst its individual antioxidant enzymes [50]. The findings from this study indicated that there was also no significant increases in the levels of SOD and catalase in the T. fuscatus collected from impacted sites as well as the control sites. The similarity in the trend of activities of SOD and catalase may be linked to their mode of action. SOD protects the cells against free radicals induced damage by converting superoxide radicals (O2-) generated in the peroxisomes and mitonchondria to hydrogen peroxides. The hydrogen peroxide is then removed from the system by the enzyme catalase, which converts it to water and molecular oxygen (O2) [51]. Thus the somewhat direct relationship in their activities could be used to explain the similarity in their responses. The antioxidant mechanism did not seem to have been overwhelmed, given the rather consistent levels of MDA and the activities of anti-oxidative stress enzymes reported in periwinkles from most of the impacted sites compared to the control site. The GSH activity in the periwinkles was observed to be low in animals collected from the impacted area and could be as a result of the tolerance to chronic and persistent exposure of the molluscs in the impacted sites to hydrocarbons. While exposure to pollutants or stressful conditions can result in elevated GSH levels, there is evidence that adverse effects are associated with GSH depletion in marine bivalves [52]. [53] also reported decrease of GSH in the gills of Clarias gariepinus due to the continued exposure of the organ to contaminants. Thus the reason for the survival of T. fuscatus in a seemingly toxic environment can be explained by their natural tendency to survive in muddy bottoms of swamps and lagoons which are characterized by oxygen stress [45].

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The results of the DNA damage analysis in T. fuscatus collected from most of the impacted areas recorded lower DNA fragments than those from the surroundings and the control. This suggests a possible DNA damage of the animals as a result of stress associated with the aftermath of the spill and subsequent fire outbreak. [54] reported that reactive oxygen species (ROS) when continuously generated can damage important biomolecules such as DNA, proteins and lipids. Studies on DNA damage performed on fish and molluscs reveal a direct effect of exposure to different contaminants [55,56]. The free radicals which escaped antioxidant suppression may find their way across nucleolar membranes initiating DNA strand breakages, thus reducing DNA quality and quantity.

The findings of this ecological assessment resounds the long term persistence of pollutants in sites after spills and underscores the need for measures by environmental agencies to ensure effective clean up of oil spill sites. It also emphasizes the incorporation of biomarker studies to typical biodiversity surveys because abundance of macro invertebrates and fishes alone may not be a true reflection of species health. The findings from assessment of the anti oxidative stress enzymes provides supporting evidence for the tolerance of T. fuscatus, one of the dominant animals to this stressed ecosystem. Additional studies such as microbiological assessments and more invasive biomarker studies are recommended in order to further ascertain the true state of the ecosystem.

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