Preliminary assessment of the deteriorating state of a dam in north-western Nigeria using phytoplankton structural assemblage and environmental factors

ABSTRACT Phytoplankton community structure in relation to some physico-chemical parameters in Kalgwai Dam, north-western Nigeria was undertaken monthly in three stations for a period of six months spanning from January to June 2018. Physico-chemical parameters and phytoplankton were collected once in a month on each sampling occasion. Physico-chemical parameters such as Dissolved Oxygen (DO) concentration was lowest in Station 1 (3.79 ± 0.53 mgl−1), and phosphate, pH and turbidity were highest in Station 3. Tukey’s Honestly Significant Difference (HSD) test showed that the mean values of DO, TDS, EC, and turbidity were significantly different (p < .05) among the three sampled stations. A total of 379 phytoplakton individuals belonging to 6 divisions and 41 taxa were identified in the study area, and Baccillariophyta was the most predominant division of phytoplankton in the dam. Slightly higher abundance of phytoplankton was recorded in the dry season than in wet season. Analysis of variance (ANOVA) calculated for biological indices showed no significant difference among the the months sampled (p> .05). Canonical Correspondence Analysis (CCA) model revealed a weak relationship between phytoplankton community structure and measured physico-chemical parameters. Species such as Microcystis sp., Melosira sp., Closterium sp., and Ulothrix sp. indicated a strong positive correlation with increasing physico-chemical parameters such as nutrients and BOD5 at Station 1. These species were considered indicators of prevailing environmental conditions in the study area. Cluster analysis (Jaccard similarity) showed species of phytoplankton to be clustered by season rather than by stations. The study revealed anthropogenic pressures to contribute to the current deteriorating state of the dam.


Introduction
Aquatic ecosystems in sub-Sahara Africa are under severe habitat alteration and deteriorating water quality due to population growth, urbanization, industrialization, and technological progress (Edegbene, Arimoro, & Odume, 2019;Parienté, 2017). In urban areas, water pollution negatively impacts the biological structure and function of freshwater ecosystems, including freshwater reservoirs, e.g. dams (Znachor et al., 2020). In Nigeria, freshwater ecosystems draining urban catchments are often poorly managed resulting to severe pollution influx into the ecosystems (Emenike, Neris, Tenebe, Nnaji, & Jarvis, 2020a;Emenike et al., 2020b;Ighalo & Adeniyi, 2020). Freshwater ecosystems in the north-western part of Nigeria is no exception to pollution as they receive pollution inputs from farming (agricultural) actvities which include crop production and cattle grazing. Farming activities in the region primarily rely on the use of fertilizer and pesticides, which contribute high amounts of nutrients and heavy metals into the freshwater ecosystems (Edegbene, 2020). Additionally farming practices within freshwater ecosystems in the region are poorly regulated and the existing laws and regulations guiding inland waters in the northern part of Nigeria, north-western region inclusive are poorly enforced or unenforced by the appropriate authorities (Edegbene, Arimoro, Odoh, & Ogidiaka, 2015), thus exacerbating the inputs of pollutants into inland waters in the region, and the Kalgwai Dam is no exception. The exacerbating inputs of pollutants into the inland waters have a deleterious effects on the structural assemblages of resident aquatic biota.
Resident aquatic biota, including microorganisms, phytoplankton, zooplankton, macroinvertebrates, and fish are widely used to monitor the levels of freshwater pollution worldwide (Arimoro, Ikomi, Nwadukwe, Eruotor, & Edegbene, 2014;Patang, Soegianto, & Hariyanto, 2018). Among aquatic biota used in monitoring freshwater ecosystems, phytoplankton are primary producers, and therefore they constitute the base of aquatic food webs underpinning reservoir ecosystem stability and function (Geider et al., 2001). Anthropogenic nutrient enrichment significantly accelerates phytoplankton productivity and biomass in freshwater ecosystems (Hatt, Fletcher, Walsh, & Taylor, 2004;Heino, 2013;Krynak & Yates, 2018). Therefore understanding the effects of environmental parameters on phytoplankton communities is important to assess the effects of anthropogenic impacts on reservoir ecosystems (Znachor et al., 2020) and to develop strategies for water quality monitoring.
Additionally, phytoplankton is seen as important biological indicators of water quality changes due to their short life spans, quick response to pollutants and ease in determining their numbers (Han, Krause, Mccormick, Carrichk, & Schelscke, 2012), unlike other biodindicators. Despite the increasing use of phytoplankton for water pollution monitoring (Jasim, Sheimmaa, & Ahmad, 2013;Nweze, 2012;Shakila & Natarajan, 2012), only few studies have used phytoplankton as indicator of water quality in Nigeria, especially freshwater reservoirs (Guy et al. 2004;Davies & Ugwumba, 2013;Dimowo, 2013;Effiong & Inyang, 2016). Thus, this study is an addition to the already existing studies using phytoplankton assemblage structure for biological monitoring of freshwater ecosystems globally and Nigeria in particular. Therefore, in the present study, phytoplankton assemblage structure and their relationship with selected physico-chemical parameters were assessed to determine the current deteriorating state of Kalgwai Dam in north-western Nigeria.
Kalgwai Dam was built by the Federal Government of Nigeria in collaboration with a nongovernmental organization to supply water for irrigation purposes to farmers around the dam (Edegbene, 2020). However, the aim of the project has been abused by the riparian communities, as the dam now serves as a refuse dump site, place of convenience (toilet) where they defecate, bath and conduct other domestic activities. These activities have a debilitating effect on the inherent biota structural and functional assemblages of the Kagwai Dam. Therefore, this study was conducted to ascertain the impact of these activities on the dam by using phytoplankton community structural assemblage and selected physico-chemical parameters.

Study area
Kalgwai Dam, an irrigation dam is located few meters away from Kalgwai village in Auyo Local Government Area of Jigawa State, Nigeria ( Figure 1). It is a perennial dam, located on the interception of latitude 12°35ʹN -10°7ʹE and longitude 12.230°N -10.022°E of the equator (Edegbene, 2020). Kalgwai Dam is a storage pond constructed across the Hadejia River for storage. Details of the study area description are contained in Edegbene (2020). The study was carried out seasonally (dry and wet seasons) in three marked out stations over a period of six months between January and June 2018.
The three stations (Station 1: Gajegumi; Station 2: Galadimawa; and Station 3: Yanruwa) were selected based on community and anthropogenic activities (see Edegbene, 2020 for details of selected stations description).

Physico-chemical parameters sampling and analysis
The following physico-chemical parameters were sampled and analyzed in the present study: water temperature, water depth, electrical conductivity (EC), salinity, total dissolved solids (TDS), pH, dissolved oxygen (DO), five days biochemical oxygen demand (BOD 5 ), phosphate, and nitrate. At each of the sampling stations on every sampling occasion, subsurface water temperature was determined using a mercury-in-glass thermometer. Water depth was measured using a calibrated rod measured in meters, according to Gordon, McMahon, and Finlayson (1994) method, and EC, salinity and TDS were measured using a conductivity meter (model DDSJ-308A). Portable turbidity meter and pH meter (HANNA HI 9828 multi-probe meter manufactured by HANNA instruments) were used in measuring turbidity and pH of water at each sampling station. DO was measured using hand-held dissolved oxygen meter (model YSI 55). Separate water samples were collected in 50 ml sterile bottles for the measurement of BOD 5, phosphate and nitrate and they were analyzed according to (American Public Health Association, APHA, 2005) methods.

Phytoplankton sampling and processing
Phytoplankton were collected using a plankton net with a small plastic bottle container of 20 ml attached to a narrow end. The net was tied to a metal rod, towed horizontally for a fixed distance of 1 m, and then it was hauled out of water. Five replicates of the samples were collected in different flow regime in each of the stations. Water (containing plankton) was poured into a plastic bottle of 20 ml and immediately fixed with 2 ml of 4% formalin in order to preserve the phytoplankton (Gaswani, 2004). The samples were taken to the laboratory for identification and counting. Before identification, the samples were allowed to settle for 10 minutes and thereafter, 2/3 of each sample was decanted. Two drops of the concentrated sample were taken using a pipette and dropped into a glass slide and viewed under a compound microscope using ×10 magnification and then confirmed with ×40 magnification for identification confirmation. Phytoplankton species were directly counted under a microscope and identified to the lowest possible taxonomic level using identification guide (Han, 1978).

Statistical analyzes
Descriptive statistics (mean ± standard deviation) were computed for the three sampling stations for the six months sampling period using univariate analysis on Paleontological Statistical Package (PAST- Hammer, Harper, & Ryan, 2001). Two-way analysis of variance (ANOVA) was carried out to determine the level of significance of physico-chemical parameters among the three stations. When significant difference occurred (at p< .05), Tukey Honestly Significant Difference (HSD) was used to determined which stations differs. ANOVA and HSD were computed using PAST (Hammer et al., 2001). The absolute and relative abundance of major divisions of phytoplankton collected in both dry (January to April) and wet (May to June) seasons were represented in bar charts. Student t-test was computed to show if there is a significant difference in the structural assemblage of phytoplankton between dry and wet seasons, and student t-test was computed using PAST (Hammer et al., 2001). Diversity indices which include; Simpson dominance, (1-D), Shannon Weiner Index (H), evenness, and Margalef index were determined using PAST (Hammer et al., 2001). Oneway ANOVA was used to show the significant differences in the diversity indices among the three sampling stations, and was computed using PAST (Hammer et al., 2001). Canonical correspondence analysis (CCA) was used to evaluate associations between phytoplankton community and physico-chemical parameters. Prior to CCA, rare taxa occurring less than 1 % of the overall sample collected per sampling station were removed from the computation of the CCA model. Physico-chemical parameters used for the CCA analysis were logarithmically transformed to prevent the undue influences of extreme values on the final model. A Monte-Carlo permutation test at 999 permutations argument (Jckel, 1986) was used to assess the significance of the first three CCA axes. Cluster analysis based on the Jaccard similarity index was used to ascertain whether the phytoplankton community assemblage structure was influenced either by differences in sampling stations or months. The CCA model, Monte-Carlo test, and cluster analysis were computed using PAST statistical package (Hammer et al., 2001).

Physico-chemical parameters
Summary of physico-chemical parameters are shown in Table 1. Significant differences were noted among the months and sampling stations for BOD 5 and EC (p< .05) while water temperature, pH, transparency, water depth, DO and nitrate showed no significant differences among the months and sampling stations (p> .05). The DO concentration was lower in Station 1 (3.79 ± 0.53 mgl −1 ) than other stations, while BOD 5 was higher in Station 1 (2.33 ± 0.93mgl −1 ), compared to the other two stations. Station 3 had the highest mean value of pH, phosphate and turbidity. Tukey HSD test computed showed stations means of DO, TDS, EC, and turbidity to be significantly different (p> .05).

The phytoplankton assemblage structure
A total of 379 individuals of phytoplankton belonging to six divisions, 41 taxa were identified during the study ( Table 2). The division Baccillariophyta was the most dominant phytoplankton in the dam with 17 taxa, followed by Chlorophyta, whereas Xanthophyta was the least abundant division. Absolute abundance of taxonomic groups at the divisions level revealed that Cyanophyta was the most occurring at Station 1 with 60 individuals ( Figure  2). Euglenophyta and Pyrophyta were sparingly represented in the three sampling stations. The highest relative abundance was recorded in Station 1 in January ( Figure 3). Station 3 did not show much spatio-temporal variation in phytoplankton except in April (dry season) and June (wet season). Overall, slightly higher abundance was recorded in the dry season (January-April) than in the wet season (May-June). Student t-test showed a significant difference in the structural assemblage of phytoplankton between the two seasons (p < .05).

Phytoplankton diversity
Summaries of biological diversity metrics are shown in Table 3. The abundance, Simpson dominance, Shannon Weiner diversity, evenness and taxa richness were slightly higher in Station 1 compared to Stations 2 and 3 (Table 3). Generally, the mean value of biological diversity metrics calculated showed no significant difference among the sampling stations (p> .05). The biological indices were not significantly different for the months sampled (p> .05) but significantly different for the stations (p< .05) except for abundance, dominance, Simpson dominance, and evenness.

Relationship between phytoplankton and selected physico-chemical parameters
A CCA ordination revealed a weak relationship between phytoplankton species communities and measured physico-chemical parameters. The Eigenvalues for Axes 1 and 2 are 0.30 and 0.45 respectively ( Table 4). The first CCA axis accounted for over 87% of the variation in the data set. A Monte-Carlo permutation showed that the first two canonical axes were not significantly different (p> .05). Axis 1 was slightly associated with Microcystis sp., Dactylococcopis sp., and Melosira sp. and they were mostly found in Station 1 and explained by increasing concentrations of BOD 5 , nitrate and TDS. These species of phytoplankton can be suggested as pollution tolerant species due to their association with increasing concentrations of BOD 5 and nitrate. Ulothrix sp. and Closterium sp. were positioned on the center of the CCA model and they were explained by increasing water transparency. Station 2 had no definite cluster of phytoplankton species around it, and this show that weak relationship existed between Station 2 and the phytoplankton collected during this study. Anabaenopsis sp., Fragilaria sp. and Tribonema sp. were shared between Stations 2 and 3. These organisms distribution on the CCA were mainly associated with pH, DO, turbidity, and EC. Anabaenopsis sp. was noticed to be strongly positively correlated with increasing DO concentration. Axis 2 of the CCA triplot was associated mainly with physico-chemical parameters that were seasonally influenced including; water depth, phosphate and EC. Nitrate, BOD 5 , and TDS were strongly associated with Axis 1 (Table 4). Cluster analysis (Jaccard similarity) produced based on phytoplankton log(x + 1) transformed data showed that species were clustered by season rather than by stations. Samples collected from the same season were closely associated ( Figure 5). The highest similarity was noticed in the cluster between January and March while the least similarity was found in the cluster between March and April in Stations 1 and 3, followed by February and April cluster in Station 3.

Physico-chemical parameters
Physico-chemical parameters of freshwater ecosystems can be influenced by varied factors such as catchment size, vegetation, soil types, land use patterns, and other anthropogenic activities. These factors can alter the community structure of aquatic biota inhabiting the ecosystem (Arimoro, Odume, Uhunoma, & Edegbene, 2015). For Effective planning of the structural design of a dam or any other man-made aquatic system, it is better to put into consideration these factors that have the potential to either positively or negatively influence the aquatic organisms in the system. The Kalgwai Dam is a very significant water source for irrigation and other domestics activities in Jigawa State in north-western Nigeria. Therfore, the high values of TDS, EC, and turbidity, especially in Station 1 are indicators of the results of human influences on the dam. Local dwellers residing along the banks of the dam use the dam for washing, bathing, and fertilizer runoff from farms around the dam catchments are also evident. These activities may likely be the cause of the deteriorating state of the water quality of the dam. Station 1 had the highest BOD 5 mean value, and this may be attributed to the incessant anthropogenic activities carried out within and around the dam such as crop farming, cattle grazing and domestics activities. Station 3 with the lowest BOD 5 mean value, is as expected as the station is relatively not disturbed due to the absence of farms lands. It could also favorably be argued that the presence of concrete dyke might have prevented much human influences in Station 3, which would have increased the deteriorating state of the station. Nitrate values were relatively low (0.15-0.50 mgl −1 ) and could be an indication of insufficient inorganic nutrients accumulation. Although farmers around the dam bank use fertilizer for crops farming, but probably the concentration of the fertilizer used in the crop farming may be minimal, hence the relatively low concentration of nutrients in the dam. Further, nutrients uptake by phytoplankton and aquatic macrophytes as well as microbial activities might have contributed to the low nutrients concentration in the dam . The pH values recorded in the present study indicate an alkaline type of water. This result is contrary to the concentration of pH recorded in similar studies in southern parts of Nigeria, where pH is slightly acidic to neutral . Most freshwater ecosystems catchments in southern parts of Nigeria are forested, hence the acidic state of the waters unlike the Sahel/Sudan savannah catchments of freshwater ecosystems in the north-western part of Nigeria.
Most freshwater ecosystems in Nigeria are subjected to discharges of organic and inorganic materials from activities within the ecosystems catchments (Andem, Okorafor, Eyo, & Ekpo, 2014). In this study, variation in most pollution indicating physico-chemical parameters between Station 3 and the remaining two stations may have contributed to differences in phytoplankton assemblage structure between the sampling stations. The concentrations of phosphate recorded in this dam were slightly high compared with relatively unperturbed freshwater ecosystems elsewhere (Chapman, 1996;Radojevic & Bashkin, 1999)  and similar to phosphate recorded in perturbed freshwater ecosystems in the sub-Sahara Africa (Adeyemo, Adedokun, Yusuf, & Adeleye, 2008;Ighalo & Adeniyi, 2020).

Phytoplankton assemblage structure and diversity
A total of 379 phytoplankton individuals belonging to six divisions were recorded during the study period in  Kalgwai Dam. This number is low compared to the number of individuals recorded for other water bodies in Nigeria (Davies & Ugwumba, 2013;Effiong & Inyang, 2016;George, Samuel, & Andem, 2012), but higher than species number recorded elsewhere (Jasim et al., 2013;Lasakar & Gupta, 2013).
Of the phytoplankton division recorded in this study area, Baccillariophyta was found to be the dominant division. Earlier, a study has also reported the dominance of Baccillariophyta (Effiong & Inyang, 2016) and their dominance were hinged on accumulated organic pollution in the riverine system. Eleven (11) taxa of Chlorophyta were recorded, and this may not be unconnected to the low nitrate concentration in the dam (Ewebiyi, Appah, & Ajibade, 2015), which would have hampered the productivity of Chlorophyta if in high concentration. Cyanophyta was represented by pollution tolerant taxa (Microcystis sp. and Anabaenopsis sp.), and they have been reported to strive well in eutrophic waters (Davies & Ugwumba, 2013) (Figure 4).
The relative abundance of phytoplankton in the dry season was higher than that of the wet season. Similar studies in Nigeria reservoirs and streams have reported the same trend in phytoplankton abundance (Balogun & Ajani, 2015;Ewa, Iwara, Alade, & Adeyemi, 2013), and they attributed this to increased photic depth and reduction in the input of turbid materials during the dry season months (Ewa et al., 2013). In the Kalgwai Dam, similar conditions were noticed during the sampling period in the dry season months (March and April).

Relating phytoplankton with environmental variables
A CCA ordination showed a weak association between the selected physico-chemical parameters and phytoplankton in the present study. Microcystis sp. and Melosira sp. are suggested as pollution indicating taxa due to their association with pollution indicating physico-chemical parameters such as BOD 5 and nutrients (Dimowo, 2013;Ewebiyi et al., 2015;Shakila & Natarajan, 2012). Similarly, study elsewhere reported correlations of some phytoplankton group with pollution indicating physico-chemical parameters such as increasing concentration of nitrate and ammonia, and decreasing water transparency (Lasakar & Gupta, 2013). Ulothrix sp. and Closterium sp. in this study were found to be strongly positively associated with increasing water transparency, suggesting that they may be sensitive to pollution (Lasakar & Gupta, 2013).

Conclusion
This present study revealed a deteriorating water quality of Kalgwai Dam and the level of deterioration experienced were occasioned by anthropogenic activities such as crop farming, cattle grazing, bathing, washing, and open defecation. Phytoplankton species such as Microcytis sp., Melosira sp. and Ulothrix sp. were considered as potential indicators of water quality. This research serves as a baseline study for further studies in the use of phytoplankton structural assemblage and environmental factors in assessing the deteriorating state of freshwater ecosystems such as dams, lake, rivers and streams in north-western Nigeria and Nigeria in general.

Declaration of interest
We declare that no conflicting interest in this work.