phytoplankton community as biomonitors in a tropical freshwater biome the benoe stream, south west...

International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 5 Issue 3, March-April 2021 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470

@ IJTSRD | Unique Paper ID – IJTSRD40041 | Volume – 5 | Issue – 3 | March-April 2021 216

Phytoplankton Community as Biomonitors in a Tropical Freshwater Biome: The Benoe Stream, South-West Cameroon

Daniel Brice Nkontcheu Kenko1, 2, Patricia Bi Asanga Fai2, 3, Norbert Ngameni Tchamadeu2, Mpoame Mbida2

1Zoology Laboratory, Faculty of Science, University of Buea, Buea, Cameroon 2Biology and Applied Ecology Research Unit, Dschang School of Science and Technology,

University of Dschang, Dschang, Cameroon 3Environmental Toxicology and Risk Assessment Research Unit,

College of Technology, University of Bamenda, Bamenda, Cameroon

ABSTRACT The aquatic ecosystem is being threatened by many sources of pollution that may lead to reduced economic potential and severe consequences on human health. When assessing the issue, chemical analysis alone is not very informative. Moreover, most of the models used in the assessment are based on data from non-tropical species. This study was then aimed at using the phytoplankton community structure as a tool for the biomonitoring of the Benoe stream in the Southwest region of Cameroon. For this purpose, water physicochemical and hydrological parameters, along with the phytoplankton abundance and diversity were measured from June 2016 to May 2017 in eight sites along the stream. Temperature, Total Dissolved Solids (TDS), conductivity, Dissolved Oxygen (DO), pH, salinity, velocity and discharge were measured in situ. Turbidity, color, orthophosphates, nitrates and nitrites were measured in the laboratory. Shannon and Pielou diversity indices were used to characterize the biotic community. Water temperature, DO, orthophosphates and flow velocity significantly fluctuated across sampling stations. Salinity, EC, TDS, pH, color, Suspended Solids, nitrites, nitrates, turbidity and flow rate exhibited no significant spatial change. Nevertheless, turbidity in four stations (B1, B4, B6 andB8) was above the WHO maximum tolerable value. Three dominant families, the Coscinoiscoidea, the Tabellariaceae and the Fragilariaceae may be exploited as potential biomonitors while four main genera Spirogyra, Pleurosira, Diatoma and Gyrosigma may be exploited as biological monitors in tropical freshwater biomes. Water turbulence (high flow velocity and flow rate) were not favorable to phytoplankton proliferation.

KEYWORDS: Biomonitors, Plankton, Community, Diversity, Freshwater, Tropical, Biome

How to cite this paper: Daniel Brice Nkontcheu Kenko | Patricia Bi Asanga Fai | Norbert Ngameni Tchamadeu | Mpoame Mbida "Phytoplankton Community as Biomonitors in a Tropical Freshwater Biome: The Benoe Stream, South-West Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3, April 2021, pp.1216-1227, URL: www.ijtsrd.com/papers/ijtsrd40041.pdf Copyright © 2021 by author (s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

1. INTRODUCTION Water is an indispensable natural resource and freshwater streams in particular are an important natural resource for humans, providing water for agriculture, industry and domestic use (El Morhit & Mouhir, 2014; Mahar, 2003).Water bodies harbor a very rich biodiversity, ranging from viruses to mammals (Roth, 2009)and are used for many purposes: fishing, production of electricity, agriculture, navigation, tourism, water supply to urban and industrial areas, and disposal of wastes (Moss, 2010). Freshwater provides many ecosystem services to local populations; drinking, domestic purposes, irrigation, transport, recreational and cultural activities (Egbe et al., 2019). Unfortunately, the aquatic ecosystem is being threatened by many sources of pollution that may lead to reduced economic potential and severe consequences on human health (El Morhit, 2009). Pollution, physical degradation and exploitation are the three main eroding factors associated with anthropogenic pressure (Brugneaux et al., 2004). Surface waters suffer from overfishing, eutrophication, introduction of alien species, drainage and toxic pollution

(Aboyeji, 2013; Bernauer & Kalbhenn, 2010; Castello et al., 2013; Moss, 2010).

In developing countries, 90% of wastewaters are poured without any treatment into water bodies (Chevalier et al., 2003). In Cameroon for example, only 20.6% of inhabitants have facilities to handle domestic wastes (INS, 2013) and as a consequence, most often water bodies serve as places to dump domestic wastes. The Benoe stream is used by the local population and the CDC (Cameroon Development Corporation) for several purposes (swimming, drinking, irrigation, washing) making it subject to pollution. About 107 pesticide formulations corresponding to 54 active ingredients are used in plantations around that stream (Kenko, Patricia, et al., 2017); unfortunately, there is a high risk to water, fish, Daphnia and amphibians (Amietophrynus regularis Reuss, 1833) associated with the use of pesticides near water bodies (Kenko, Fai, et al., 2017; Kenko, Tchamadeu, et al., 2017). Moreover, in a study carried out in the Ndop plain (North-West Cameroon), the agricultural

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pesticides chlorpyriphos-ethyl, chlorothalonil and cypermethrin have been reported to be very risky to streams (Fai et al., 2019). In order to assess to which extent human activities can impair aquatic species diversity and productivity, there is a need to study the organization of aquatic life.

Chemical analysis alone is expensive and not sufficient for the limnological characterization of water bodies since there is a need to link water perturbation to the biotic community. Abundant in freshwater, forming the base of the aquatic food chain and playing a tremendous role in freshwater productivity, algae (planktonic and benthic) can be used to set up such a linkage; algae have long been used as ecological bio-indicators worldwide (Al-Homaidan, 2006; Amado Filho et al., 1999; Bailey & Stokes, 1985; Dokulil, 2003; Fonge et al., 2012; Fonge et al., 2015; Kengne et al., 2014; Lavoie et al., 2012; Mahadev & Hosamani, 2005; Menye et al., 2012; Nguetsop et al., 2009; Njine et al., 2007; Poikolainen et al., 1998; Żbikowski et al., 2007). These microscopic organisms plays a key role in aquatic ecosystems; they accumulates over days the effects of hourly changes in water quality (Suthers et al., 2019). However, limited research has been conducted in the South-West Region of the country especially in relation to the contribution of CDC farms to water quality and biota. Moreover, companies such as the CDC have never set up any appropriate environmental health or monitoring scheme, hence the necessity to carry out this work, aimed at using the phytoplankton community structure for the biomonitoring of the Benoe stream, a typical tropical freshwater biome.

2. Material and Methods 2.1. The Benoe Stream The study was carried out in the Benoe stream, located in the Fako Division, South West Region of Cameroon. The stream has its sources around Small-Soppo (Buea), passes through Mutengene and Tiko where it enters the forest to join the sea around Bwenda after crossing CDC Ndongo (2275ha) and CDC Holforth (more than 3000ha) Estates. The CDC is an

agro-industrial complex created in 1947 aimed at acquiring, developing and operating extensive cultivation of tropical crops. An appeal for data collection was addressed to the CDC and authorization to carry out the work was obtained.

2.2. Climate of The Study Area The Fako division has an equatorial climate. The dry season runs from November to February and the rainy season from March to October. On average, rainfall is much more than the rest of the country; this is because the site is located at the base of Mount Cameron and is close to the Atlantic coast of Cameroon, resulting in a humid climate (INS, 2013).

2.3. Choice and Description of Sampling Stations Based on land use, point sources, tributaries and the location of farms, eight stations were chosen. A GPS (Garmin ETREX-H™) was used to get the coordinates and elevation of all the sampling stations. Sampling stations were named as follows: Benoe 1 (B1) up to Benoe 8 (B8).

Station B1 was close to the source and has little influence from urbanization and extensive agriculture; it served as the reference site. Station B2 is located after the Mutengene abattoir just before the CDC water capture; swimming and car washing activities take place around. Located near small-scale farms, station B3 was after the CDC water capture area; it was intended to give account of the impact of the Mutengene capture and the contribution of a tributary from Mutengene. With no human habitation, station B4 was located at the entrance of the CDC Ndongo farm. Located between the CDC Ndongo and Holforth banana farm, station B5 receives wastes water from the Ndongo parking house, where banana is processed after harvesting, and receives pollutant residues from the Ndongo farm. Toward the middle of the Holforth farms, station B6 is subject to wastewaters from Holforth parking house and a tributary from the Holforth farm. Situated towards the end of the Holforth farm, station B7 assessed the contribution of a tributary from the said plantation. Station B8 was after the “Cabbe Bridge” and was the exit point at which the stream enters the forests to join the sea (Figure 1).

Figure 1: Location of sampling stations along the Benoe stream

2.4. Analysis of The Hydrological and Physicochemical Quality of Water Temperature, Total Dissolved Solids (TDS) and conductivity were measured in situ using a COM-100™ Multiparameter. The amount of dissolved oxygen (DO) was measured in situ with a Milwaukee MW600™ Dissolved Oxygen Meter. Water pH was measured in situ with a pH meter (Dr. Meter™). Salinity was measured in situ with an EC70™ salinity meter. Flow velocity and flow rate were determined in situ using the float method (Michaud & Wierenga, 2005).



For parameters measured in the laboratory, water samples were collected just below the surface water using 2litersplastic bottles and transported to the laboratory using an icebox in order to maintain the samples around 4°C.Turbidity (turbidity meter), color, orthophosphates (molybdenum blue method), suspended solids (differential weighing) were determined following (APHA, 1992; Pauwels et al., 1992). Nitrogenous compounds (nitrates and nitrites) were measured using the Kjeldahl method (Bremner, 1996).

2.5. Phytoplankton Collection and Identification Phytoplankton samples were collected by LaMotte™ plankton net, mesh size 30µm (Mahar, 2003). Samples were preserved in a 3% formalin solution (Suthers et al., 2019), taken to the laboratory for identification and counting the number of phytoplankton units (cell, colony, filament) using a Sedgwick-Rafter counting chamber under a compound microscope equipped with a micrometer. The identification was done using phytoplankton identification keys (Bellinger & Sigee, 2010; Mahar, 2003; Suthers et al., 2019; Van Vuuren et al., 2006; Verlecar & Desai, 2004; Yamaguchi & Bell, 2007).

2.6. Data Processing and Analysis Data was compiled using Microsoft Excel 2016; the programs, SPSS Version 21 and XLSTAT 2014 were used for statistical analyses.

Univariate analysis (Mean; Standard Error of Mean) was used to describe and assess the variation of water physicochemical and hydrological variables among stations after what, data was checked for normal distribution using the Shapiro-Wilk test (α=0.05). Following the normality test, ANOVA was performed to determine significant differences in water quality parameters among stations; multiple comparison (Least Significant Difference) test was performed to determine significant different means among stations at 0.05 significant level.

The list of identified plankton taxa was established and two main diversity indices were computed:

The Shannon Diversity Index (Shannon, 1948) H’ = -Σ Pi Log2Pi

H’ is the Shannon diversity index, Pi is the proportion (n/N) of individuals of one particular species found (n) divided by the total number of individuals found (N). Σ is the sum of the calculations. The Shannon index usually ranges between 1.5 (heavily polluted water) and 4 (mildly polluted water) (Shekhar et al., 2008).

The Evenness Index (Pielou, 1969)

E is the Pielou Index, H’ is the Shannon diversity index while S is the total number of species or taxonomic richness. The Pielou Index varies between 0 (one species dominates) and 1 (all the species tend to have the same abundance).

The relationship between dominant phytoplankton taxa and water abiotic variables was evaluated with the Principal Component Analysis (PCA).

3. Results 3.1. Water Physicochemical Quality Water temperature fluctuated between 24.66±0.35°C in Station B1 and 27.04±0.79°C in station B5. As compared to the reference (Station B1), Station B5 had a significant increase in the mean water temperature (p<0.01), the same as B6 (p<0.01) and B7 (p=0.04). The pair wise comparison revealed that Station B5 (p<0.01) and Station B6 (p=0.03) had a significant increase in water temperature as compared to Station B2. Water temperature significantly increased from Station B4 to Station B5 (p=0.01). Water color, turbidity, suspended solids and electrical conductivity exhibited no significant (p≥0.05) changes across stations (Table 1).

Table 1: Distribution of water hydrological and physicochemical parameters according to sampling stations Abiotic

Variables Sampling Stations

B1 B2 B3 B4 B5 B6 B7 B8 Temperature (°C) 24.70± 0.4a 25.30±0.4a 25, 80±0.3abc 25.5±0.4ab 27.0± 0.8c 26, 70±0.4bc 26.0±0.4abc 25.8±0.3abc EC (µS/cm) 205.8 ± 5.2 211.3 ± 5.1 212.6 ± 5.5 209.0 ± 6.5 207.8 ± 5.2 205.4 ± 7.7 205.3 ± 5.0 209.4 ± 4.8 TDS (ppm) 103.8 ± 2.8 106.6 ± 2.6 106.8 ± 2.8 105.5 ±3.2 104.4 ± 2.5 103.0 ± 3.7 103.6 ± 2.5 105.2 ± 2.5 Salinity (ppt) 0, 09 ±0.00 0, 09 ±0.00 0, 10 ± 0.00 0.09 ± 0.00 0.09 ± 0.00 0.09 ± 0.00 0.09 ± 0.00 0.09 ± 0.00 DO (ppm) 8.0±0.1 7.9 ±0.1 7.8 ± 0.1 7.9 ± 0.1 8.3 ± 0.4 7.7 ± 0.1 8.1 ± 0.1 8.3 ± 0.1 pH 8.09 ± 0.29 8.00 ± 0.21 7.96 ± 0.18 7.75 ± 0.20 8.16 ± 0.20 8.26 ± 0.18 8.13 ± 0.18 7.96 ± 0.17 Color (Pt-Co) 10 ± 2 10 ± 1 11 ± 1 11 ± 1 10 ± 1 11 ±1 10 ± 1 13 ± 2 Suspended Solids (ppt)

0.86 ± 0.61 0.64 ± 0.61 0.66 ± 0.63 1, 46 ±0.96 0, 78 ±

0.67 0.86 ± 0.67 0.86 ± 0.65 1.79 ± 1.06

Nitrites (ppm) 1.21 ± 1.14 0.21 ± 0.15 0.12 ± 0.08 0.20 ±0.15 0.12 ± 0.08 0.59 ± 0.53 0.14 ± 0.07 0.27 ± 0.23 Orthophosphates (ppm)

1.69 ± 0.75 1.51 ±0.94 6.21 ± 4.52 2.91 ± 1.59 2.65 ± 1.13 1.28 ± 0.64 0.89 ± 0.47 2.11 ± 0.85

Turbidity (NTU) 4.80 ± 1.80 3.50 ±0.50 3.40 ± 1.00 5.10 ±1.80 4.00 ± 1.70 4.40 ± 1.60 3.90 ± 1.40 7.30 ± 2.40 Nitrates (ppm) 1.63 ± 1.54 0.26 ± 0.20 0.19 ± 0.10 0.25 ±0.20 0.16 ± 0.10 0.77 ± 0.72 0.18 ± 0.10 0.34 ± 0.31 Velocity (m/s) 0.50±0.07 0.58 ± 0.09 0.71 ± 0.22 0.54 ±0.14 0.49 ± 0.13 0.62 ±0.09 0.47 ± 0.07 0.34 ± 0.04 Discharge (m3/s) 1.41±0.25 1.60 ±0.33 2.11 ± 0.62 1.66 ±0.64 1.97 ± 0.65 1.50 ± 0.22 1.52 ± 0.30 1.78 ± 0.32



Note: ppm = part per million; ppt = part per thousand; on the same line values carrying the same letter are not significantly different (α = 0.05); results are expressed as Mean ± SEM (Standard Error of Mean)

Dissolved Oxygen (DO) values ranged from 7.72 ± 0.13ppm in B6 to 8.32 ± 0.12ppm in B8. No significant difference was recorded between the reference Station B1 and other stations. In a pairwise comparison, Station B5 had a significantly higher amount of DO (p = 0.04) as compared to Station B6, which also had a significantly lower amount as compared to Station B8 (p = 0.02).

Orthophosphates significantly (p<0.05) changed across stations. Station B3 had the highest amount of phosphate (6.21±4.52ppm) while the lowest occurred in station B7 (0.89±0.47ppm). No significant difference was observed between the amounts of phosphates of the reference Station (B1) as compared to subsequent stations (p≥0.05). The pairwise comparison revealed a significant (p =0.04) decrease from Station B3 to Station B7. Salinity, pH, nitrites and nitrates did not vary significantly (p≥0.05) across stations (Table 1).

3.2. Water Hydrology Water velocity fluctuated from 0.34±0.04m/s in Station B8 to 0.71±0.22m/s in Station B3. No significant difference was observed between the water flow velocities of the reference station (B1) as compared to that of subsequent stations (p≥0.05). The pairwise comparison revealed a significant decrease in station B8 as compared to station B3 (p = 0.03). The flow rate (discharge) exhibited no significant spatial fluctuation (Table 1).

3.3. Phytoplankton Community The phytoplankton community consisted of 84 species distributed in 51 families (Table 2). The diatom Pleurosira laevis had the highest relative abundance. The most abundant taxa per station were P. laevis in B1, Bacteriastrum sp.at B2, Synedra ulna in B3 and B4, Oscillatoria sp. in B5 and B7, Diatoma vulgaris in B6 and B8. Station B1 had the highest species abundance (31683) as well as the highest number of taxa (45).

3.4. Phytoplankton Diversity Indices The diversity level of the stream was assessed by computing 2 main diversity indices: the Shannon Diversity Index (H’) and the Pielou Evenness Index (E) (Figure 2).

Figure 2: Distribution of the Shannon and Pielou Diversity Indices in sampling stations

The highest value of the Shannon diversity index (H’=4) was found in stations B3 and B8. Station B2 came at the second position with H’=3.9. The smallest value occurred at station B1 (H’=3.3), giving a variation amplitude of 0.7. The Pielou Evenness Index ranged from the smallest value in Station B1 (E=0.60) to the highest value in Station B3 (E=0.75), giving a variation amplitude of 0.15. Stations B8 and B3 occupied the second position with E=0.74.

Table 2: Abundance (number of units/m3) of phytoplankton taxa in all the sampling stations Family Name B1 B2 B3 B4 B5 B6 B7 B8

Amphipleuraceae Amphipleura sp. 358 43 129 87 90 88 59 30 Diadesmidiaceae Diadesmis sp. 15 15 0 0 0 0 0 0 Diploneidaceae Diploneis sp. 129 0 42 0 0 0 0 0

Naviculaceae Caloneis sp. 0 0 43 0 0 0 0 0 Navicula bryophila 774 475 273 501 0 360 0 144

Navicula longa 201 548 559 445 1215 774 430 259 Navicula rhynchocephala 1416 2721 316 402 859 30 732 1359

Navicula subtiltissima 1145 391 989 15 674 142 145 1022 Pleurosigma sp. 0 0 29 0 0 15 15 0

Neidiaceae Neidium sp. 0 0 0 0 0 0 15 0



Pinnulariaceae Pinularia sp. 159 15 185 603 259 60 165 593 Stauroneidaceae Craticula ambigua 201 0 0 0 60 0 30 0

Stauroneis sp. 0 0 144 0 0 0 101 0 Bacillariaceae Nitzchia acicularis 116 0 0 28 30 0 0 0

Nitzchia linearis 547 89 43 0 43 100 15 0 Nitzchia palea 30 30 15 213 0 0 0 0

Catenulaceae Amphora sp. 160 189 15 0 174 173 130 89 Skeletonemataceae Skeletonema sp. 0 0 0 229 0 0 0 0

Cocconeidaceae Cocconeis sp. 43 0 43 0 15 0 0 0 Coscinoiscoideae Cyclotella sp. 0 0 0 60 0 0 15 0

Cymbella sp. 1394 694 301 403 304 216 249 403 Frustulia sp. 0 115 15 0 0 0 15 57

Gyrosigma baltium 124 15 501 1473 948 519 992 2912 Gomphonemataceae Encyonema sp. 57 0 0 0 0 0 0 0 Rhoicospheniaceae Gomphonema sp. 60 144 143 0 88 0 187 44

Rhoicosphenia sp. 0 172 0 0 0 44 29 116 Peroniaceae Peronia sp. 0 0 0 0 0 0 15 0 Eunotiaceae Eunotia forma 0 87 0 670 45 28 288 58

Fragilariaceae Asterionalla sp. 30 86 29 28 116 129 14 71 Fragilaria sp. 14 0 15 0 0 1086 343 30 Synedra ulna 3202 2201 2205 3648 1933 2405 889 1306

Hemidiscaceae Actinocyclus sp. 0 30 29 28 0 15 0 0 Striatellaceae Strialla sp. 0 0 0 0 0 0 0 15 Surirellaceae Cymatopleura sp. 129 159 0 0 0 0 0 0

Surinella sp. 58 0 0 0 0 0 0 0 Tabellariaceae Diatoma vulgaris 87 87 119 73 102 4807 1304 4162

Tabellaria sp. 0 0 43 0 373 0 0 3846 Tribonemataceae Tribonema sp. 0 0 0 0 0 0 944 0

Triceratioceae Pleurosira laevis 14246 3595 915 918 3534 988 1788 1961 Probosciaceae Proboscia alata 0 0 0 0 30 0 0 0 Melosiraceae Melosiria varians 387 1628 1087 0 2220 87 0 416

Leptocylindraceae Leptocylindrus danu 0 0 0 0 0 0 43 0 Chaetocerotaceae Bacteriastrum sp. 0 4948 0 0 0 0 0 0

Chaetoceros sp. 0 0 0 0 60 0 15 0 Chaetophora sp. 0 0 0 0 0 0 0 86 Stigeoclonum sp. 689 216 0 0 58 0 101 1001

Chlamydomonadaceae Chlamydomonas sp. 29 143 0 0 14 0 0 29 Chlorellaceae Chlorella sp. 204 104 88 171 160 87 606 231 Desmidiaceae Desmidium sp. 0 759 74 1580 176 2640 0 729

Actinotaenium sp. 675 117 58 0 15 45 14 29 Closterium sp. 274 0 30 59 58 74 14 101 Cosmarium sp. 0 15 14 29 0 73 72 30

Tetmemorus sp. 0 0 0 0 0 0 29 0 Oedogoniaceae Bulbochaete sp. 0 601 1506 0 0 0 346 1063 Selenastraceae Ankistrodesmus sp. 0 0 0 0 0 0 0 15

Monoraphidium sp. 29 0 14 0 0 0 115 45 Selenastrum sp. 0 0 0 15 0 43 0 0

Chladophoraceae Cladophora sp. 0 0 0 0 30 744 1205 0 Coelosphaeriaceae Coelosphaerium sp. 0 0 0 0 0 29 0 0

Nostocaceae Anabaena sp. 0 0 29 0 0 15 0 14 Aulosira prolifera 115 0 0 0 0 0 0 0

Cylundrospermum sp. 701 0 43 0 0 0 0 0



Oscillatoriaceae Lyngbya sp. 0 0 0 0 0 43 15 0 Oscillatoria sp. 0 0 0 0 3945 2946 8600 0

Rivulariaceae Rivularia sp. 0 145 43 0 0 101 191 30 Phormidiaceae Planktothrix sp. 0 0 0 15 0 0 0 0

Chroococcaceae Microcystis sp. 0 0 0 0 0 0 0 15 Oocystaceae Oocystis parsilla 230 0 57 43 146 59 14 87

Scenedesmaceae Actinastrum sp. 0 0 0 0 0 0 129 0 Ceratiaceae Ceratium sp. 43 0 0 0 0 0 0 15

Peridiniaceae Peridinium sp. 14 0 58 0 0 0 0 14 Protoperidiniaceae Boreadinium sp. 29 0 0 0 0 0 0 186

Dinophysaceae Dinophysis sp. 0 0 0 0 15 0 0 0 Gymnodiniaceae Gymnodinium sp. 0 0 0 0 0 0 0 15

Dinobryaceae Dinobryon sp. 0 0 2073 0 0 0 0 0 Euglenaceae Eudorina elegans 29 0 0 29 0 0 0 0

Gonium sp. 30 0 0 0 0 0 0 0 Euglena acus 331 934 57 103 1156 347 145 503

Trachelomonas sp. 0 29 57 59 58 94 30 0 Acrochaetiaceae Audouinella sp. 244 189 0 0 0 0 0 0 Zygnemataceae Spirogyra porticalis 2805 629 432 186 3273 445 2903 1562

Zygnema sp. 15 186 0 0 0 0 115 371 Mougeotia sp. 115 902 516 0 0 1030 401 0

Peciaceae Penium sp. 0 0 28 0 0 0 0 15

3.5. Association Between Water Physicochemical and Hydrological Quality and Phytoplankton Abundance The first three factor axes F1 (20.32%), F2 (13.58%) and F3 (11.12%) expressed 45.02% of the total variance (Figure 3). The F1 axis grouped EC, TDS, Nitrates, Nitrites, Turbidity, Suspended Solids and Color. There is evidence of a strong association between water temperature and two families (Coscinoiscoidea and Tabellariaceae) and a weaker positive association between temperature and two other phytoplankton families (Oscillatoriaceae and Zygnemataceae). Dissolved Oxygen had a positive strong correlation with Oscillatoriaceae and Zygnemataceae and a weak positive correlation with Coscinoiscoidea and Tabellariaceae. The F2 axis confirms that flow velocity and flow rate are not favourable to the proliferation of phytoplankton families while the Fragilariaceae had a negative association with DO.

Figure3: Association between the abundance of dominant phytoplankton families and water physicochemical

quality (Q: Flow Rate, Vel: Velocity, NO2: Nitrites; NO3: Nitrates; Turb: Turbidity; SS: Suspended Solids; Col: Color; Temp: Temperature; EC: Electrical Conductivity; TDS: Total Dissolved Solids; Sal: salinity; DO: Dissolved Oxygen;

PO4: Orthophosphates; Nav: Naviculaceae; Cosc: Coscinoiscoidea; Fragil: Fragilariaceae; Tab: Tabellariaceae; Tricer: Triceratioceae; Oscil: Oscillatoriaceae; Zygn: Zygnemataceae)



Figure 4 shows that, the factors F1 (22.08%), F2 (15.49%) and F3 (8.09%) expresses 45.66% of the total variance. The F1 axis shows that pH is strongly and positively associated with Spirogyra and has a weaker positive association with Pleurosira. Moreover, Pleurosira was positively associated to nitrogenous compounds (nitrites and nitrates). Diatoma and DO have a positive association while Gyrosigma exhibits a strong positive correlation with turbidity, color, suspended solids, TDS and EC. The F3 axis show that flow velocity and flow rate are negatively correlated with phytoplankton genera

Figure 4: Association between the abundance of dominant phytoplankton genera and water physicochemical

quality (Q: Flow Rate, Vel: Velocity, NO2: Nitrites; NO3: Nitrates; Turb: Turbidity; SS: Suspended Solids; Col: Colour; Temp: Temperature; EC: Electrical Conductivity; TDS: Total Dissolved Solids; Sal: salinity; DO: Dissolved Oxygen; PO4: Orthophosphates; Nav: Navicula spp.; Gyr: Gyrosigma sp., Syn: Synedra ulna, Diat: Diatoma vulgaris,

Pleur: Pleurosira laevis, Osc: Oscillatoria sp.; Spir: Spirogyra sp.)

4. Discussion 4.1. Water Physicochemical Quality Water temperature significantly increased in downstream sampling sites. In facts, running waters exhibit a wide range of thermal regimes in time and space (Allan & Castillo, 2007). Water temperature is influenced by stream flow, vegetation, ground water inputs and effluents from industries. This variation in space may also determine variation in aquatic organisms as temperature influences aquatic life via metabolic rates and concentration of dissolved gases (Ganai & Parveen, 2014).

Water turbidity failed to show significant spatial fluctuations. Nevertheless, turbidity in stations B1, B4, B6 and B8 was above 4NTU, the highest recommended value (World Health Organization, 2017), making these sampling sites polluted. Turbidity has two main components: inorganic turbidity created by particles that come from the erosion of soils in the catchment area and re-suspension of bottom sediments and organic turbidity associated with large concentration of phytoplankton, humic substances, pigmented bacteria and micro-crustacean in water (Salonen, 2012). The case of station B1 may be due to high plankton biomass recorded at this site. High turbidity in station B4 may be related to abattoir discharges, washing and dumping of domestic wastes from Mutengene. Swimming activities as well as erosion may explain the case of station B6; it is not near human habitations but bordered by CDC plantations. Wastes dumped into water and erosion are two factors rendering water more turbid (Sofi et al., 2018). Station B8 was the most turbid site; this may be related to surface runoff after irrigation of CDC farms and the continuum

concept model. Towards the mouth of a stream, this model foresees a rise in turbidity as compared to the source due to gradual loading of particulate material from effluents and surrounding terrestrial ecosystems (Salomoni et al., 2007).

Suspended solids followed the same distribution as turbidity with no significant spatial fluctuation. Low values of suspended solids may be due to stream epuration capacity and low values of nutrients. In facts, increase in suspended solids is most often related to erosion and human activities such as abattoir wastes, poor agricultural practices (Allan & Castillo, 2007; Chevalier et al., 2003; Fawzi et al., 2001). Color in all the sampling stations was within acceptable range (<15Pt-Co) (World Health Organization, 2017) and showed no significant spatial trends. Color is due to the presence of dissolved salts (Patil et al., 2012).

Electrical Conductivity did not show any significant spatial trends and values were within the accepted range, less than 800µS/cm (World Health Organization, 2017). EC is a very good predictor of salinity since some water dissolved solids act as conductors (Zinabu et al., 2002).

Salinity was the most constant parameter with a negligible variation of amplitude across sampling sites. Salinity is the sum of dissolved ions in water including Na+, K+, Mg2+, Ca2+, Cl-, SO42- and CO3-. It is influenced by such factors as soil type, climate, agriculture and mining (Williams, 2001). Salinity values obtained in all the stations were within the recommended 0.065-0.3pptrange (World Health Organization, 2017). The organisms living in the Benoe stream may then be described as halophobs.



TDS values showed no significant spatial fluctuation and remained within an acceptable range, i.e. less than 500ppm (World Health Organization, 2017).TDS is controlled by organic and inorganic substance dissolved and washed into the stream by runoffs (Anago et al., 2013). Moreover, TDS contain both organic and mineral compounds that can either be beneficial in the case of nutrients or toxic, in the case of metals, organic pollutants (Weber-Scannell & Duffy, 2007). Plankton and aquatic organisms greatly contribute to TDS reduction (Ganai & Parveen, 2014). Dilution and low evaporation rate are not favorable to an increase in TDS, EC and salinity (Inyang & Wang, 2020).

Dissolved oxygen (DO) alone can give a good account of the pollution status of a water body. DO showed significant spatial fluctuations. The significant drop in DO in station B6 may be an indication of pollution pressure as station B6 may be subject to runoffs from the CDC Ndongo Estate. Wastes are not favorable to an increase in the amount of DO (Arimoro et al., 2008; Sofi et al., 2018), since microorganisms use oxygen to degrade organic and mineral matters via oxidation reactions (Anago et al., 2013; Salomoni et al., 2007). After B6, DO showed an increase in station B8; water at this point may have a high capacity to hold oxygen, or there may be low oxygen demand, assuming that organic matter inputs from previous sites might have gone through oxidation before reaching this station.

Benoe stream pH was slightly alkaline and did not vary significantly from one site to the next. Natural water bodies sometimes have an alkaline tendency (El Morhit & Mouhir, 2014; Hassan & Shaawiat, 2016; Prudence et al., 2015; Ramakrishnan, 2003) with little spatial variation (Offem et al., 2011). Above all, pH values obtained in all the stations were within the recommended range (6.5 to 8.5) (World Health Organization, 2017). Water pH is influenced by the geology, soil and vegetation around the stream. Alkaline pH may be related to calcareous and dolomite soils. The absence of significant spatial variation may due to water buffer capacity (El Morhit & Mouhir, 2014)which promotes the CO2-HCO3-CO32- equilibrium, a major freshwater buffering mechanism (Allan & Castillo, 2007).The buffer capacity is a measure of the ability of the solution to resist a pH change when a strong acid or base is added. Water pH controls most of the chemical reactions taking place in water (Mebarkia, 2011).

The Benoe stream nitrite content showed no significant spatial change throughout the study area and generally, it was low; this is normal since nitrites are intermediate compounds. All the values were within the accepted limit, i.e., less than 3mg/L (World Health Organization, 2017)They come from agricultural fertilizers and municipal wastes (Chevalier et al., 2003).Their value may increase if there is an imbalance between the oxygen level and bacterial activity (Bouamrane, 2008).

Nitrates did not show a significant spatial fluctuation and the concentration in all the sampling stations was within the recommended range (<10ppm) (World Health Organization, 2017). Most mountain streams like Benoe tend to be oligotrophic (nutrient poor, high clarity, low algal biomass and oxygen rich). Nitrates derive from fertilizers, domestic wastes and industrial wastes (Chevalier et al., 2003; Festy et al., 2003). Their concentration increases when there is decomposition of organic matter due to high temperature and entry of nitrogen fertilizers from catchment areas (Boughrous, 2007).

The concentration of orthophosphates fluctuated significantly across sampling sites. The peak observed in station B3 may be related to sediment re-suspension, especially in shallow water bodies (Li et al., 2013)or to pollution pressure related to sewage, decay and manure (Bužančić et al., 2016; Offem et al., 2011). The lowest value was found in station B7 located downstream (stream auto-epuration capacity) and the outlet (Station B8) witnessed a slight increase as compared to station B7. Higher orthophosphate concentration is related to domestic wastes and excreta (Omotoriogun et al., 2012) but as B8 has no surrounding human habitations, this relative increase may be related to the breakdown of phosphate fertilizers and organophosphates pesticides from the CDC Holforth Estate. Pesticide use around the Benoe stream is very risky to the aquatic ecosystem; able to pose acute and chronic risk (Kenko, Fai, et al., 2017). Leaching of farms along water bodies and runoff on cultivated lands may cause an increase in the amount of phosphates (Fawzi et al., 2001; Offem et al., 2011; Some, 2008).

4.2. Hydrological Parameters The Benoe stream flow velocity fluctuated significantly across sampling sites. Water flow velocity is never stable (Moss, 2010) since natural stream channels vary greatly in their physical form (Roth, 2009). The gradual significant (p<0.05) decrease from B3 to B8 may be related to changes in the slope; the slope is lower in B8, leading to high residence time. Velocity is influenced by slope, channel irregularities and water viscosity (Roth, 2009)and decreases as a function of the logarithm of depth (Allan & Castillo, 2007). Water velocity varies too because of the contribution of tributaries and point sources.

Discharge showed no significant spatial change. The fluctuation in water flow rate depends on the flow velocity, water width and water depth; changes in these hydrologic characteristics will bring about changes in the flow rate (Garizi et al., 2011).A low flow rate leads to reduced re-aeration and pollutant dilution capacity (Salomoni et al., 2007).

4.3. Phytoplankton Diversity Indices The distribution of the Shannon diversity index in various sampling sites of the Benoe stream makes this stream a mildly polluted one with H’ values between 3 and 4 (Shekhar et al., 2008). The calculation of diversity indices took into account all the species sampled in the area (abundant and rare species).Rare species greatly influence the overall species diversity (Inyang & Wang, 2020).

4.4. Association Between Dominant Phytoplankton Families and Water Abiotic Variables

The Oscillatoriaceae and the Zygnemataceae had a weak positive association with temperature while the Coscinoiscoidea and the Tabellariaceae had a strong positive link with water temperature. The thermophilic tendency of these taxa may be related to their metabolic requirements as water temperature is essential for species distribution. Most cellular processes are temperature-dependent and their rate typically increases as an exponential function of temperature. This makes temperature one of the main limiting factors for the proliferation of plankton over time (Lv et al., 2011). Further studies are needed for the exploitation of the Coscinoiscoidea and the Tabellariaceae as indicators of thermal pollution.



The Fragilariaceae had a negative association with water dissolved oxygen content; This family may be able to survive in oxygen poor environments and may be suitable to indicate organic pollution.

4.5. Association Between Dominant Phytoplankton Genera and Water Abiotic Variables

Spirogyra and Pleurosira may indicate alkaline water bodies as their abundance significantly increased with an increase in pH values. Moreover, Pleurosira may indicate eutrophic water bodies because of its positive association with nitrogenous compounds. Pleurosira is able to change its morphology to fit the milieu. Four different morphological valve shapes have been described in Pleurosira leavis making it a potential bioindicator of polluted environments (El Awamri, 2008).

Diatoma vulgaris may proliferate in well oxygenated waters. DO is one of the most important water parameters, able to give direct and indirect information. Gyrosigma appears to be a pollution-tolerant taxon showing positive association with turbidity, color, suspended solids, TDS and EC. In facts, some of the components accounting for turbidity (organic and inorganic matter) favour plankton growth. In fact, the presence of essential nutrients as part of suspended matters is critical for the growth and reproduction of aquatic organisms (Sorensen, 1977). Suspended solids usually contain a great amount of organic matter favorable to the proliferation of biota (Bianchi et al., 2003). This genus may also be able to withstand the effects of toxic chemicals found in suspended matters. Therefore, Gyrosigma sp. may be a pollution-tolerant taxon likely to indicate aquatic ecosystems subject to organic, mineral and nutrient pollution as well as turbid waters.

As a general rule, flow velocity and flow rate were not favorable to phytoplankton proliferation. High velocity and discharge carry plankton toward downstream leading to increase in the abundance in areas where water is relatively stationary. Small streams in particular are characterized by high turbulence, short residence time and deficient in nutrients; these factors do not favor an autochthonous planktonic development (Akopian et al., 2002).

5. Conclusion This study revealed that anthropogenic pressure had a negative impact on water characteristics since abiotic variables exhibited spatial change. Turbidity in stations B1, B4, B6 and B8 was higher than the recommended value for surface water. The phytoplankton community consisted of 84 species with diatoms occupying the top position. The distribution of phytoplankton families was significantly influenced by water temperature and dissolved oxygen. Water pH, nitrates, nitrites, DO, turbidity, color, suspended solids, TDS and EC significantly influenced the abundance of phytoplankton genera. Three dominant families, the Coscinoiscoidea, the Tabellariaceae and the Fragilariaceae may be exploited as potential biomonitors while four main genera Spirogyra, Pleurosira, Diatoma and Gyrosigma may be exploited as biological monitors in tropical freshwater biomes. Water turbulence (high flow velocity and flow rate) were not favorable to phytoplankton proliferation.

Acknowledgments The research team is very grateful to Pr. Fonge Beatrice for her assistance and to the Cameroon Development Corporation (CDC) who gave authorization to access their Estates.

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