Effect of Water Quality and Bottom Soil Properties on the Diversity and Abundance of Macrobenthic Fauna in Some Tropical Grow-out Earthen Fish Ponds
Macrobenthic communities can be used in the assessment of environmental quality of earthen fish ponds. This study was conducted to investigate the effect of water quality and bottom soil properties on the diversity and abundance of macrobenthic fauna in some tropical grow-out earthen fish ponds. The aim was to enhance the proper management of soil and water qualities in relation to various groups of benthic organisms found in ponds. Physico-chemical parameters, bottom soil properties and benthic community assemblages were studied in three selected commercial fish farms in Calabar, Cross River State, Nigeria. Results revealed that macrobenthic assemblages were influenced by both the physico-chemical parameters and bottom soil properties in each of the farms. The macrobenthic faunal patterns in earthen fish ponds under investigation showed to be influenced by changes in abiotic parameters. This was caused by seasonality and fish production. Oligochaetes were generally dominant 31(50.82); 21(27.27); 22(31.88) in Aqua Vista farm, UNICAL farm and Akai Efa farm, respectively. From the biotic indicators that show a differential response to organic input in fish earthen ponds, the abundance of Aulophorus spp. (41.99%; 14.29%) in Aqua Vista and UNICAL farms, respectively as well as the diversity (Shannon-Wiener and Margalef species richness), seem to be the best indicators to be used in monitoring studies in similar systems. In general, Farms with optimum physicochemical parameters, high sand and low clay content had the highest assemblages of macrobenthic organisms. Farm managers should pay particular attention to the physico-chemical parameters and soil properties as they are determinant factors of macrobenthic assemblage within the fish ponds. These will enhance high productivity of the grow-out fish ponds since they form the major bulk of fish food.
Received: October 30, 2011;
Accepted: November 17, 2011;
Published: January 21, 2012
When trying to understand river health, aquatic biologists often look not at
fish but rather at fish food: Benthic Macroinvertebrates (BMI) which are effective
integrators of physical, chemical and biological processes (Othman
et al., 2002). Due to various species of BMI found in different consumer
levels in marine food webs, they are used to ascertain the health of river ecosystem.
Consequently, BMI serve as food resources for the myriads of vertebrates including
fish (Varadharajan et al., 2010). In pond ecosystem,
water quality and soil properties play a significant role in the life of benthic
organisms (Tabatabaie et al., 2009). Water exhibits
both physical and chemical properties and the suitability of water for the survival
and growth of fish is governed by a myriad of water quality variables (Boyd,
1982). According to Udo (2007), the quality of water
used in a pond is affected by the chemical properties of the soils on which
it runs. In the course of constructing fish ponds the upper horizons of the
terrestrial soil is usually excavated, thereby exposing the subsoil to water
when ponds are filled. The main requirement is that, soil for pond consists
of a mixture of particles from, within or outside the pond and the accumulation
of sediments which have a direct contact with water can either enhance or hinder
the growth of benthic organism (El-Marakby et al.,
2006). Benthic organisms can be used to monitor the type of soil or the
quality of water used in ponds. Macrobenthos comprises many different groups
of aquatic animals with a large number of species possessing a wide range of
responses to stressors such as sediments, toxicants and organic pollutants (Maryland
Department of Natural Resources, 1999). Benthic organisms offer themselves
as a live food or dead food for culture organisms in their trophic relationship.
They out do artificial feed since they have higher protein content, fats, cellulose,
lignin, starch waxes and oils which are lacking in supplemental feeds. The abundance
and growth of benthic organisms depends on the pond preparation management and
chemical used (Abu Hena et al., 2004). In intensive
fish culture, use of fertilizers, fish feeds or both usually increase production;
the use of high stocking/feeding rates may lead to severe water quality problems
(Green et al., 1989). The effects of inorganic
fertilizer are also reflected in benthic production and abundance (Zorriasatein
et al., 2009). According to Boyd (1995), soft
sediment accumulation makes ponds shallow, encourages anaerobic conditions at
the sediment-water interface and interferes with harvest. This being the case,
it is pertinent to study the relationship between the pond bottom soil properties
and their influence on the macrobenthic assemblage. The objective of this study
therefore, was to assess different water quality parameters and bottom soil
properties and their effects on macrobenthic faunas in some tropical earthen
MATERIALS AND METHODS
Study area: Three study sites were selected. These were: UNICAL, Aqua
Vista and Akai Efa fish farms.
UNICAL farm: This is located in the vicinity of the University of Calabar
staff quarters at approximately 04.56°, 020N and 08.20°, 456E
in Cross River State, Nigeria. The climate of the area is governed by its latitude
and to a large extent by the two dominant winds, the Southwest monsoon and northeast
trade winds common in most parts of West Africa. The area is also characterized
by distinct wet and dry seasons (Akpan et al., 2002;
Asuquo et al., 2004).
Aqua Vista fish farm: This is located along Anantigha Beach at approximately
4°60 N and 8°5 E in Calabar South Local Government Area, Cross River
state, Nigeria. It has a very large catchment area which traverses through fresh
water swamp to mangrove swamp forest forming a tributary of the Cross River
system itself (Moses, 1987).
Akai Efa fish farm: This is located along MCC road in Calabar Municipality, Cross River State, Nigeria at approximately 4°59 N and 8°19 E.
Vegetation of the study areas: They are part of tropical rainforest
areas in West Africa and consists mainly of wood trees and other trees like
palm trees and fruit trees (Asuquo, 1998; Akpan
et al., 2002).
Climate: The study area is characterized by a long wet (April-October)
and a shorter dry season (November to March). Mean annual rainfall is about
2000 mm (Akpan and Offem, 1993). Short period of drought
occurs in the wet season around August/September which is called the August
drought. There is usually a cold, dry and dusty period between December and
January referred to as the harmattan season. Temperatures generally range from
22°C in the wet to 35°C in the dry season. Relative humidity is generally
above 60% at all seasons with close to 90% during the wet season (Akpan,
1993; Akpan and Offem, 1993; Asuquo,
Geology: The soil here, is composed of coastal plains sand; belonging
to tertiary deposits and forms in an island south of Ikot Ekpo, between the
alluvial deposits of the Calabar River and the Great Kwa River. These coastal
plain sand is most preferable for the development of Calabar. The alluvial deposits
usually are on low lying, swampy areas unsuitable for construction work. Also,
the dominant soils are light brown, grey and white sand with clay, grey shales,
carboneous shales, feldsper fragments and pest bands, alternating from 80
downward (Asuquo, 1998).
Human activities: These include farming, hunting, fishing, boat building
and sand mining (Holzlohner et al., 2002).
Sampling: Samples were collected fortnightly for 3 months between November 2010 and January 2011.
Sampling for water parameters: Sampling for water parameters was done in situ at the different sites thrice a month for pH, temperature and transparency while some integrated samples were taken in Winkelrs bottle to the laboratory for the analysis of dissolved oxygen. The pH was measured using a pH meter. A mercury in glass thermometer was lowered into water up to 2 cm below the water surface, allowed to stabilize for 2 min and temperature readings were taken in degree Celsius (°C). A weighted Sechi disc was lowered into each pond until it just disappeared and pulled up until it appeared again. The two readings were recorded and an average value calculated for transparency.
Collection of soil samples: A total of 3 soil samples were collected
from Aqua Vista, Akai Efa and UNICAL farms on each sampling date using an augar
sampler (Abu Hena et al., 2004). Soil samples
were collected between 0-25 cm depths, stored in a well labeled polythene bag
and transported to the laboratory for necessary analysis.
Macrobenthos collection: This was done using a shovel and augar sampling
method; after sieving the soil samples macrobenthoses were handpicked based
on Anderson et al. (1982), stored in a sample
bottle and preserved in 4% buffered formalin prior to identification.
Laboratory studies: Winklers titrimetry was used to estimate each
level of DO2 in mg L-1 for each pond. Soil samples were
dried at room temperature and ground; sieved through 200 Nm mesh screen. Organic
matter was determined using Walkey-Black wet oxidation method. Particle size
distribution was determined by Bougorcos hydrometer method using sodium hexametaphate
as dispersant (Udo and Ogunwale, 1986). Macrobenthoses
were identified by use of identification guide and standard texts based on the
morphological structures of the organisms to the nearest possible taxa (Ingram
et al., 1997).
Determination of numerical abundance of macrobenthic species: To achieve
this, the total number of species was recorded for each week of sampling and
was computed following Ewa-Oboho (1993).
Determination of Relative abundance of macrobenthic species: This was
done according to Amar et al. (2007).
Determination of species richness of the macrobenthic organisms: This
was done using Shannon-Wiener and Margalefs index (d) following Ogbeibu
Determination of species dominance (D): This is the best known index
for the determination of the commonest species in studies involving community
ecology and was done according to Ogbeibu (2005) using
Simpsons index (D). The dominance index gave the probability that two individual
drawn at random from the total population of the macrobenthic organism belong
to the same species.
Determination of species diversity: This was done according to Shokat
et al. (2010) using Shannon-Wiener and Margelefs index (d).
Computation of results: Numerical abundance, relative abundance, richness,
dominance and diversity of the species in the ponds were, respectively calculated
and the results arranged in a tabular form based on Ewa-Oboho
Physico-chemical parameters: The pH mean values were 5.67 in Akai Efa
farm, 5.57 in Aqua Vista farm and 6.20 in UNICAL farm. Mean water temperature
values were observed to vary slightly in the study ponds but with very close
ranges to each other with a value of 29.2°C in Akai Efa farm, 30.0°C
in Aqua Vista farm and 29.8°C in UNICAL farm. Mean dissolved oxygen concentration
was low in all the farms with 2.1 mg L-1 in Akai Efa farm, 1.7 mg
L-1 in Aqua Vista and 2.2 mg L-1 in UNICAL farm. Air temperature
had the same value of 30°C in all the farms studied but transparency was
observed to vary slightly in all the farms with a mean value of 17.5 cm in Akai
Efa farm, 23.0 cm in Aqua Vista farm and 12.0 cm in UNICAL farm (Fig.
Soil characteristics: These were observed to vary in each of the farms. In Akai Efa farm organic matter was 11.82%, while in Aqua Vista a value of 4.74% was obtained and in the UNICAL farm, a value of 10.46% was obtained. Clay content was very low in the UNICAL farm with a value of 2.0%, while in Aqua Vista a clay content of 26.0% was obtained and in Akai Efa it was 8.0%. Sand content was 74.6% in Akai Efa farm, 64.6% in Aqua Vista and 88.6% in the UNICAL farm. Silt content had the same value of 9.4% in Aqua Vista and the UNICAL farm but with a value of 17.4% in Akai Efa farm (Fig. 2).
|| Variation in soil characteristics in different farms studied
Species composition of benthic organisms: Altogether twelve macrobenthic
species were identified during the study period. However, the distribution of
the benthic species was observed to vary in each of the farms (Table
|| Benthic organisms identified from the different fish farms
during the study period
A total of 61 individuals of the benthic organisms were recorded in Aqua vista
farm, 77 in UNICAL farm and 69 in Akai Efa farm. The macrobenthic organisms
identified in Aqua vista farm were: Tubifex, Lumbriculus, Autophorus, Chaetogater,
Chironomus, Dragon fly nymphs, Mayfly nymphs, Dytiscus and Gyrinus.
In the UNICAL farm, the following benthic organisms were recorded: Tubifex,
Lumbriculus, Branchiura, Dro, Autophorus, Chironomus, Culicodes, Dragon
fly nymphs, Mayfly nymphs and Gyrinus, while in Akai Efa farm, Lumbriculus,
Branchuirea, Dro, Chaetogarfer, Chironomus, Culicodes, Dragonfly
nymphs and Gyrinus. On the whole, Aqua vista farm had 9 macrobenthic
species, UNICAL farm 11 and Akai Efa farm 9. The richest farm in terms of macrobenthic
species composition was UNICAL.
Numerical and relative abundance of benthic organisms: Five groups made
up the entire macrobenthic communities in all the farms (Table
2). There were 31 (50.82%) Oligochaetes in Aqua vista farm, 21 (27.27%)
in UNICAL farm and 22 (31.88%) in Akai Efa farm; 5 (8.20%) Diptera, in Aqua
vista farm, 12 (15.58%) in UNICAL farm and 8 (11.59%) in Akai Efa farm; 6 (9.84%)
Odonata, in Aqua vista, 12 (15.58%) in UNICAL farm and 15 (21.74%) in Akai Efa
farm. In the Order Ephemeroptera; 9 (14.75%) individuals were present in Aqua
Vista, 17 (22.10%) in UNICAL farm and 21 (30.43%) in Akai Efa farm while in
the Order Coleoptera, 10 (16.39%) individuals were recorded in Aqua Vista, 15
(19.48%) in UNICAL farm and 3 (4.35%) in Akai Efa farm.
All factors occurring in the pond, whether physical, chemical or biological,
influence the pond ecosystem. The physico-chemical parameters in each of the
fish farms showed normal ranges. According to Boyd (1998),
the optimum pH range for the reduction of algae production is 7.0 to 8.0. In
this study, the minimum and maximum pH recorded in all farms were 5.57 to 6.20
in Aqua vista and UNICAL farms respectively. However, pH of ponds water depends
on a number of factors (Nyam, 1988). First, pH levels
of pond water are known to change depending on the aquatic life within the pond.
|| Major Macrobenthics orders identified during the study period
In the UNICAL farm, a pH value of 6.20 was recorded. This value was however
close to the required pH level in earthen ponds. The varied and reduced values
in pH in these farms may not be unconnected with phytoplankton (algae) level
in them. Low phytoplankton level was observed in Akai Efa and Aqua Vista farm
hence the decreased pH recorded in them. Water temperature observed fall within
acceptable range of between 24°C and 34.0°C for the tropics (Udo,
2007). Ideally, increased temperature causes an increase in the metabolic
activity of organism while reducing the DO, content in the system (Boyd,
1998; Macan, 1978; Udo, 1991).
Little wonder Aqua vista farm with water temperature of 30.0°C had the lowest
dissolved oxygen of 1.7 mg L-1 as against the other ponds. In tropical
earthen ponds a minimum dissolved oxygen of 2.0 mg L-1 has been reported
to sustain aquatic life (Udo, 2007) though, the optimum
range of 5.0-15.0 mg L-1 was reported by Boyd
(1998). In this study Aqua Vista farm had the least DO (mg L-1)
concentration of 1.7 mg L-1 while Akai Efa farm and the UNICAL farm
had a little above the minimum value of DO (mg L-1) during the study
period. The low DO (mg L-1) in this farm might be attributed to high
temperature recorded in the pond. Tave (1999), Boyd
(1998) and Udo (2007), reported that high temperatures
are responsible for reduced DO in water bodies including fish ponds. Transparency
of water bodies indicates the extent to which sunlight can penetrate in order
to cause photosynthesis. The presence of large amount of suspended matter or
high plankton load is known to decrease the amount of light energy entering
the water (Seah et al., 2011). The UNICAL farm
was the most turbid followed by Akai Efa farm each with a transparency value
of 12.0 cm and 17.5 cm, respectively while a value of 23.0 cm was recorded at
Aqua Vista farm. These values were quite below the acceptable values of 2.0-7.5
cm (Boyd, 1998; Nandlal and Pickering,
2004). This was unconnected with catchment area of the farm.
The composition of the soil bottom in relation to organic matter, clay, sand
and silt content in the three farms was observed to vary. Clay content was low
in the UNICAL farm with a value of 2.0%, sand content was rather high with a
value of 88.6%. This was followed by Akai Efa farm with a value of 74.6% and
organic matter with a value of 11.82% in Akai Efa farm and 10.46% in the UNICAL
farm. The least organic matter content was recorded at the Aqua Vista farm with
a value of 4.74%. The nature of pond bottom is known to influence the type of
benthic organisms inhabiting them and hence, the macrobenthic assemblages (Goldman
and Horne, 1983). Most macrobenthic organisms inhabit pond bottom with rich
organic matter which they filter their food. Any aquatic habitat with high percentage
of sand has been reported to have high nutrient seepage capacity (Udo,
2007). Nutrients on the bottom surface seeps to the inner layers of the
sediment (Ingram et al., 1997). When this happens
the benthic organisms burrow into the sediment to derive the nutrients there
in (Boyd, 1990). Ingram et al.
(1997) reported high benthic assemblages in pond bottom with high sand content
in Birmingham ponds and attributed it to nutrient seepage into the under sediment
layers, a result which agrees with that of the present study. UNICAL farm with
the highest sand content has high macrobenthic fauna than either Akai Efa or
Aqua Vista farms. Goldman and Horne (1983) also observed
high benthic assemblages in earthen ponds with sandy bottom in Canada.
The distribution of the macrobenthos in the study ponds was observed to be
influenced by the physico-chemical parameters. More macrobenthos was recorded
in the UNICAL farm with the highest individuals followed by Akai Efa farm. Aqua
Vista recorded the lowest number of benthos with the lowest number of individuals.
These variations in macrobenthic assemblages may be attributed to variations
in the water temperature which invariably influences the amount of DO. In Aqua
Vista, water temperature was 30.0°C with a corresponding reduction in dissolved
oxygen of 1.7 mg L-1 which caused a reduced number of benthic organisms.
As it has been established, temperature causes an increase in the metabolic
activity of organism. Hence, with increased temperature, organisms tend to be
erratic in behaviour, seeking a more comfortable zone in the sediment and go
into hiding or preventing them to be caught during sampling (Needham
and Needham, 1974). The results of the present study is again in agreement
with those of Unanam and Akpan (2006) who reported that
the macrobenthic community in earthen ponds of AKSCOA fish farm in Oruk Anam
Local government, Akwa Ibom State, Nigeria became structurally reduced with
increase in temperature which invariably influence the activity of the benthos,
causing them to go into hiding. Hence, they usually observed reduction in the
number of most of the benthos which cannot withstand high temperature. Carvalho
et al. (2010) also observed that a shift from optimal physicochemical
parameters in fresh water bodies in earthen fish ponds of the Ria Formosa lagoon
resulted in a reduction in the community structure of the macrobenthos, a result
which agrees with that of the present study although the polychaetes were generally
dominant as against the oligochaetes observed in this study.
The ecological parameters, such as species dominance and species diversity
of the benthic organisms were observed to vary. However, there was no difference
in species dominance of the benthos in all the study ponds indicating that all
the earthen ponds experienced similar structural formation as is known in tropical
earthen ponds (Udo, 2007). In terms of species diversity,
it was observed that the benthic assemblages showed differential structural
variation. Unanam and Akpan (2006) observed similar
structural variation in the macrobenthic community in earthen ponds of AKSCOA
fish farm in Oruk Anam, Akwa Ibom State, Nigeria and related it to the varying
degrees of responses of the macrobenthos to the variation in the physico-chemical
parameters in the pond water and nature of the bottom sediment. Voshell
(2002) observed that macroinvertebrates in Maryland streams became reduced
in population structure with a shift from optimal values of the physico-chemical
parameters and denatured pond bottom when studying the behavioural patterns
of the macroinvertebrates in the stream. The general pattern of macrobenthic
community in water bodies is influenced by the prevailing physico-chemical parameter
in the system, hence the usually observed variations in the community structures
in a particular environment (Melntire and Boyd, 1980).
This, in its general perspective causes the different species of organisms inhabiting
different habitats to respond according to the prevailing ecological settings
in the habitat (Odum, 1971; Azizul
et al., 2006). Similar observations were made during the period of
Macrobenthic communities are important to pond ecosystem in that they form food source for most of the fresh water organism especially cultured fishes. Physico-chemical parameters, bottom soil properties and benthic community assemblages were observed to vary in each of the fish farms. Each of the benthic assemblages was influenced by both the physico-chemical parameters and bottom soil properties in each of the farm. Farms with high sand content had the highest number of macrobenthic organisms while those with high organic matter, clay and silt had the lowest abundance. The varied distribution in number and relative abundance were related to both the physico-chemical parameters and bottom soil properties. The present study therefore confirms that runoff from the pond catchment area directly affects the species diversity and indirectly affects aquaculture potentials.
The authors are duly grateful to the Department of Zoology and Environmental Biology, University of Calabar, Calabar, Cross River, Nigeria for providing administrative support.
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