Journal of Marine Sciences

Journal of Marine Sciences / 2014 / Article

Research Article | Open Access

Volume 2014 |Article ID 241410 |

Lotfi Mabrouk, Lamia Dammak, Asma Hamza, Mabrouka Mahfoudhi, Med-Najmeddine Bradai, "Variability in the Structure of Phytoplankton Assemblages in relation to Human Disturbance in Southern Coast of Tunisia", Journal of Marine Sciences, vol. 2014, Article ID 241410, 10 pages, 2014.

Variability in the Structure of Phytoplankton Assemblages in relation to Human Disturbance in Southern Coast of Tunisia

Academic Editor: Robert A. Patzner
Received23 Feb 2014
Revised29 May 2014
Accepted02 Jun 2014
Published25 Jun 2014


We investigated the impact of industrial effluents on phytoplankton assemblage in southern Tunisia (Skhira). We specifically addressed changes in microalgae composition caused by this anthropogenic interference. A hierarchical sampling design was used to compare planktonic microalgae structure between one disturbed station and one control station. Samples were collected by scuba diving at 5 m depth in August 2012. A total of 76 microalgae taxa were identified. Dinoflagellates abundance was low in the disturbed station, especially Gonyaulacales and Prorocentrales due to P-limitation, whereas diatoms and cyanobacteria abundance were low in control station which is characterized by Si-limitation.

1. Introduction

Marine ecosystems are particularly vulnerable to environmental change and many are, at present, severely degraded [1]. The availability of good quality water is an indispensable feature for preserving biodiversity. In recent years, increase in human population and industrial activity have led to faster degradation of many coastal ecosystems. The discharge of urban, industrial, and agricultural wastes has added the quantum of various harmful chemicals to the water body considerably altering their inherent physicochemical characteristics [2].

Phytoplankton constitutes the basic components of the aquatic food chain. They act as primary producers and represent themselves as a direct food source for other aquatic animals [3]. Quantifying the phytoplankton community composition is essential for understanding the structure and the dynamics of marine ecosystems [4]. It is also important to evaluate the role of the physical and chemical processes on the biological responses of the ecosystem. The interplay of physical, chemical, and biological properties of water most often leads to the production of phytoplankton, while their assemblage (composition, distribution, diversity, and abundance) is also structured by these factors.

It is, therefore, of importance to undertake a study of the effects of urban and industrial pressure on phytoplankton dynamics under the impact of human activities versus control conditions. In southern Tunisia, we analyzed the phytoplankton diversity and abundance and asked if anthropogenic disturbance such as industrial effluents causes their variations.

We attempted to answer the following question: how nutrient enrichment induced by anthropogenic disturbance affects abundance and species composition of phytoplankton community? We thus compared phytoplankton assemblages between a station exposed to industrial effluents and a control station and examined their variability at spatial scales using a hierarchical sampling design [5].

2. Materials and Methods

2.1. Study Area

The study was carried out in two sampling stations (Figure 1), 100 km apart, were chosen in August 2012; unpolluted station in the east of Tunisia, Mahdia (M) (35° 35′43′′N 11° 05′42′′E), was selected as control station, and Skhira (K) (34°19′.179′N 10°11′.706′′E) was chosen as disturbed station, localised in the Gulf of Gabes. This station is exposed, since 1988, to industrial effluents from phosphoric acid and fertilizer production of the “Groupe Chimique Tunisien” (GCT). These industrial activities affect this marine ecosystem [6] as in deterioration of seagrass bed [7, 8] and decline in fishing [9]. The sampling depth in all stations was 5 m.

2.2. Sampling and Data Collection

In accordance with the hierarchical sampling design, three sites (500 m apart) were chosen randomly at each station. At each site, three replicate water samples were collected from each site for nutrient analyses. Samples were taken using a 125 mL plastic bottle, having received prior treatment with hydrochloric acid. On returning to the surface, samples were shaken and then filtered with a 0.45 μm filter (cellulose acetate, 17 mm). Samples were frozen in liquid nitrogen for transportation to the laboratory, where concentrations of , , , , Si(OH)4, total dissolved nitrogen (TN), and total dissolved phosphorus (TP) were measured following standard colorimetric techniques [10].

In each site, temperature, salinity, and pH were measured immediately after sampling using a multiparameter kit (Multi 340i/SET). For determination of suspended particulate matter (SM) concentration, water samples were filtered through preweighed GF/C Whatman filters (pore size 0.45 pm), which were subsequently dried at 80°C for about 24 h and reweighed [11].

For phytoplankton enumeration, three replicate water samples (about 100 m apart at 5 m depth) were selected from the same sites. Water column samples, using 1-litre glass bottle sampler lowered from the surface to the near bottom, were conducted by scuba diving. All samples were collected at noon.

Samples were fixed with Lugol’s solution and finally preserved in 5% formalin. All sampling waters were kept in the dark at ambient temperature until their microscopic observation. Settling long glass tubes used for sedimentation procedure were 2 cm wide by 21 cm long and have a base plate that contains a coverslip on which the algae settle. To mix the sample, the bottle was gently tilted back and forth 10 times before pouring [12]. A 50 mL subsample was poured into the settling chamber and left to settle for 24 h. subsamples were examined in an inverted microscope at medium (× 200) magnification by scanning the entire surface of the settling chamber to enumerate epiphytic microalgae [12, 13]. The total number of microalgae individuals () contained in 1 litre expressed as number of individuals per litre is obtained by the following conversion: = (  × 1000)/, with = the number of individuals counted and = the volume of the sedimentation chamber (50 mL) [13]. The identified taxa were divided into groups (diatoms, dinoflagellates, and cyanobacteria).

2.3. Data Analysis

Data were tested for normality using the Kolmogorov-Smirnov test [14] and for heteroscedasticity using Cochran’s test and transformed if necessary [5].

Relationships between species abundance and abiotic parameters were examined using the RELATE procedure in PRIMER. RELATE is the equivalent of a nonparametric Mantel test [15]; it assesses the degree of correspondence between matrices, and, via a randomization test, it provides a measure of statistical significance of the relationship [16]: the matrix of similarities between phytoplankton species abundances (based on Bray-Curtis coefficient from -transformed data) was compared with a matrix of the similarity between abiotic parameters (based on Euclidean distance from -transformed data). The significance of any correlation between matrices is assessed with a randomization test.

Analyses of similarity (ANOSIM) randomization tests were used to test for differences in community composition (with presence/absence transformed data) and for differences in species abundance (with -transformed data) between control and disturbed stations [17]. Differences found using ANOSIM were followed up using the SIMPER analysis to identify which species primarily accounted for the observed differences between sites. SIMPER generates a ranking of the species responsible for the significant differences. These analyses used a matrix composed of Bray-Curtis similarity coefficient generated with -transformed species abundance data [13].

Analysis of variance (ANOVA) was used to test the hypothesis that the abundance of each group of taxa differed between disturbed and control stations [13]. Factor station was fixed. The same design was employed for nutrient concentrations, pH, temperature, and suspended matter. Tukey's HSD test was applied for multiple comparisons of means.

Canonical correspondence analysis (CCA), a direct gradient analysis technique [18], was used to investigate the relationship between microalgae species and environmental parameters. Microalgae abundance data were -transformed prior to the analysis in order to stabilise the variance and to optimise the signal-to-noise ratio in the data set. Downweighting for rare species was performed. Forward selection and associated Monte Carlo permutation tests (499 unrestricted permutations) were used to identify a subset of environmental variables that contributed mostly to the species abundances in the data set. The environmental parameters which better described the distribution of the species data were a priori identified by forward selection [18]. Only significant environmental variables are included in the model. The same procedure was repeated using species abundance and nutrient ratios (Si : N, N : P, and Si : P). CANOCO 4.5 (Scientia Software) software was used.

3. Results

The disturbed station (K) exhibited quite higher concentrations of ammonium, Si(OH)4, TP, and TN and higher pH and temperature. Phosphate was higher in control station (M) (Table 1). We also calculate nutrient ratios; N/P ratio [DIN (DIN = ) to DIP (DIP = )] in disturbed station (K) was (mean ± sd) and in control station (M). This average in disturbed station was higher than the Redfield ratio (16), which suggests potential P-limitation in this station. Si/N ratio was and in disturbed and control stations, respectively. Si/P ratio was and in disturbed station and control station, respectively, which suggests potential Si-limitation in control station.

VariablesUnit Disturbed station (K)Control station (M) Tukey’s test

SMg·L−1912.00 ± 6.767.00 ± 0.704.32n.s.
mol·L−190.301 ± 0.120.283 ± 0.090.119n.s.
mol·L−194.91 ± 2.855.91 ± 0.040.98n.s.
mol·L−1923.36 ± 16.804.17 ± 0.1910.42*M < K
mol·L−190.307 ± 0.170.684 ± 0.0525.33***K < M
Si(OH)4 mol·L−198.28 ± 1.930.94 ± 0.05115.41***M < K
TP mol·L−193.26 ± 0.843.34 ± 0.349.86*M < K
TN mol·L−1945.29 ± 26.0816.32 ± 0.479.86*M < K
pH97.97 ± 0.157.32 ± 0.3425.70***M < K
Salinityg·L−1938.91 ± 0.4438.24 ± 0.883.96n.s.
°C931.03 ± 1.0527.26 ± 0.9659.02*M < K

; ; SM: suspended matter; n.s.: not significant.

The results of RELATE tests indicated that there is a correlation between physicochemical parameters and species abundance (Spearman rank correlation statistic, Rho = 0.484). None of the 999 random permutations resulted in a correlation equal to or greater than the measured value of 0.484, indicating that the correlation was significant at the level.

For phytoplankton, 76 species were counted including 54 in the control station (M) and 74 species in the disturbed station (K) (Table 2). Analysis of similarity (ANOSIM) of phytoplankton microalgae species abundances -transformed showed significant differences (; ) between the control station and the disturbed station. Similar results are obtained (; ) using presence/absence transformed data showing that the dissimilarities between stations are due to both species composition and their abundances.

Skhira (K)
(disturbed station)
Mahdia (M)
(control station)

Alexandrium minutum Halim11
Alexandrium sp.11
Amphidinium carterae Hulburt11
Amphidinium sp.11
Ceratium candelabrum (Ehrenberg) Stein10
Ceratium furca (Ehrenberg) Claparède & Lachmann11
Ceratium fusus (Ehrenberg) Dujardin10
Ceratium lineatum (Ehrenberg) Cleve10
Coolia monotis Meunier11
Dinophysis rotundata Claparède & Lachmann10
Diplopsalopsis sp.11
Goniodoma polyedricum (Pouchet) Jørgensen11
Gonyaulax polyedra F.Stein11
Gonyaulax sp.11
Gonyaulax spinifera (Claparède & Lachmann) Diesing11
Gymnodinium veneficum 10
Gyrodinium sp.11
Hermesinum sp.11
Karenia selliformis A.J.Haywood, K.A.Steidinger & L.MacKenzie in Haywood et al. 10
karlodinium veneficum 11
Ostreopsis ovata Fukuyo11
Peridinium sp.11
Podolampas palmipes Stein11
Polykrikos sp.11
Prorocentrum gracile Schütt11
Prorocentrum lima (Ehrenberg) Dodge11
Prorocentrum micans Ehrenberg01
Prorocentrum minimum (Pavillard) Schiller11
Prorocentrum rathymum (Loeblich) Shirley and Schmidt11
Prorocentrum triestinum J.Schiller11
Protoperidinium divergens (Ehrenberg) Balech11
Protoperidinium granii (Ostenfeld) Balech11
Protoperidinium ovatum Pouchet11
Protoperidinium pellucidum Bergh ex Loeblich Jr. & Loeblich III10
Protoperidinium quinquecorne (Abé) Balech11
Protoperidinium sp.11
Protoperidinium steinii (Jørgensen) Balech11
Protoperidinium  diabolus (Cleve) Balech10
Pyrophacus sp.10
Scrippsiella sp.11

Achnanthes sp.10
Amphiprora sp.10
Amphora sp.11
Asterionellopsis glacialis (Castracane) Round10
Bacteriastrum sp.11
Biddulphia sp.11
Bacteriosira fragilis (Gran) Gran00
Coscinodiscus sp.10
Chaetoceros sp.11
Climacosphenia sp.11
Guinardia sp.11
Grammatophora sp.11
Licmophora sp.11
Leptocylindrus sp.11
Melosira sp.11
Navicula sp.11
Nitzschia sp.11
Nitzshoides sp.10
Plagiotropis sp.11
Pseudo-nitzschia sp.10
Pinnularia sp.11
Pleurosigma sp.11
Rhizosolenia sp.11
Rhizosolenia fragilissima Bergon11
Rhabdonema sp.11
Striatella unipunctata (Lyngbye) C.Agardh10
Skeletonema costatum (Greville) Cleve10
Thalassionema nitzschioides (Grunow) Mereschkowsky 11
Thalassiosira sp.10
Synedra sp.11
Triceratium sp.10

Anabaena sp.11
Pseudo-anabaena sp.10
Oscillatoria sp.11
Spirulina subsalsa Oersted ex Gomont10
Croccocus sp.10

Dinoflagellates, diatoms, and cyanobacteria were common in water column (Figure 2). Abundances of those groups were included in the univariate analyses of variance. The number of species (S) was higher in polluted station (F(1;17) = 6.95, ). Dinoflagellates differed significantly between stations (F(1;17) = 5.66, ) with high abundance in control station (Tukey’s test). When dinoflagellates were grouped in order, significant differences were detected for Prorocentrales and Gonyaulacales (F(1;17) = 6.17, and F(1; 17) = 12.49, , resp.) which were more abundant in control station. Diatoms abundance was higher (F(1;17) = 16.39, ) in disturbed station (K) and specially centric diatoms (F(1;17) = 7.16, ). Cyanobacteria abundance was also higher (F(1;17) = 12.61, ) in disturbed station (K). No significant differences were recorded for total abundance and for H′ index.

Analyses of similarity percentage (SIMPER) showed that the average dissimilarity between the control and disturbed groups is high (86.31%). This procedure also allowed determining the species that contribute to this dissimilarity; they are Coscinodiscus sp., Nitzshoides sp., Chaetoceros sp., Anabaena sp., Gyrodinium sp., Biddulphia sp., Striatella unipunctata, Thalassiosira sp., Leptocylindrus sp., and Scrippsiella sp. Species which were more abundant in disturbed station (K) are Biddulphia sp., Polykrikos sp., Pleurosigma sp., Coolia monotis, and Amphidinium carterae (Table 4). Species which had higher abundance in control station are Ostreopsis ovata, Coolia monotis, Prorocentrum lima, Pleurosigma sp., Peridinium sp., Pinnularia sp., and Polykrikos sp.

CCA analysis using all measured physicochemical parameters (Table 5) indicated that the axis I (eigenvalue = 0.493) and axis II (eigenvalue = 0.154) expressed 47.5% of the cumulative variance species-environmental variable. Eigenvalues ​​( value) calculated with the CCA associated with the analysis in Monte Carlo test were used to select four environmental variables statistically significant () that explain better variations (81.88%) of species composition: they are Si(OH)4, , TN, and suspended matter (33.72%, 9.41%, 10.18%, and 11.86% of total variance, resp.). In the triplot diagram (Figure 3) disturbed station samples (K) were grouped in the right and are correlated with the axis I, which is defined by the environmental variables Si(OH)4, , TN, and suspended matter (intraset correlation −0.955, −0.613, −0.588, and −0.497, resp.).

CCA analysis using nutrients' ratios (Table 5) indicated that axis I (eigenvalue = 0.153) and axis II (eigenvalue = 0.104) expressed 45.20% of the cumulative variance species-environmental variable. Monte Carlo test showed that all nutrient ratios were statistically significant () and explain 45.22% of variation of species ordination detailed as Si : P (55.04%), N : P (24.65%), and Si : P (20.3%) of total explained variance. In the triplot diagram (Figure 4) disturbed station samples (K) were grouped in the right and are correlated with axis I, which is defined by the nutrient ratios N : P, Si : N, and Si : P (intraset correlation 0.720, −0.761, and 0.877, resp.). Most of diatoms species were associated with K samples (disturbed station), while M samples (control station) were associated with dinoflagellates species (Figure 4).

4. Discussion

Our results show that the abundance and composition of phytoplankton varied between the disturbed and control stations. Dinoflagellates abundance, especially the orders Prorocentrales and Gonyaulacales, was low in disturbed station contrary to diatoms that were more abundant. Phytoplankton variation between control and disturbed conditions is similar to those found by Ben Brahim et al. [19] in the south of Tunisia (Gulf of Gabes), by Wang et al. [20] in Baiyangdian Lake (China), and by Davies and Ugwumba [21] in Okpoka Creek (Nigeria). In addition, some fertilization experiments have shown shifts in relative dominance among algal species, with a shift towards cyanobacteria [2] and diatoms [22] under nutrient enrichment.

The high species number in disturbed station reflects an enhanced nutrient enrichment, as reported in other studies with a high abundance of centric over pennate diatoms [23, 24]. The same observation was made in Lebanese coastal waters [25]. Indeed, Egge and Aksnes [26] and Fouillaron et al. [27] showed that diatoms always numerically dominated the phytoplankton community when silicic acid (DSi) concentrations were above 2 μM. Previous studies have shown that nutrient enrichment was associated with changes in species composition [1, 21, 28]. However, each microalgal group displayed a unique spatial pattern in response to N and P enrichment [2]. Our data showed that there is an increase of diatoms and cyanobacteria in disturbed station compared to control. For insistence, Anabaena sp. that was abundant in Skhira station (Table 3) is pollution indicator species usually associated with eutrophic water bodies [29] and has been recorded in Okpoka Creek (Niger) due to the increased influx of nutrients from anthropogenic inputs [21].

DFMS Tukey’s test

Total abundance
Species number
 Station13386.9530.017943K > M
 Station15045605565.6620.029K < M
 Station129727347212.4880.003K < M
 Station1409512506.1770.024K < M
 Station1 16.398>0.001K > M
Pennate diatoms
Centric diatoms
 Station11960200007.1580.016M < K
 Station153388912.6130.002K > M

SpeciesGroup K (disturbed station)Group M (control station)Av. Diss.Diss./SDContrib.%Cum.%
Av. abund.Av. abund.

Coscinodiscus sp.5566.67015.471.1417.9317.93
Ostreopsis ovata 33.334566.6713.933.8916.1434.07
Coolia monotis 03844.4411.41.9813.2147.28
Prorocentrum lima 77.783666.6710.882.1112.6159.88
Nitzshoides sp.2416.6705.350.466.1966.08
Nitzschia sp.933.33122.222.581.262.9969.07
Navicula sp.1633.331444.442.481.382.8871.95
Pleurosigma sp.300838.891.971.152.2874.24
Peridinium sp.133.33538.891.491.291.7277.72
Pinnularia sp.55.56483.331.381.611.679.32
Prorocentrum rathymum 477.7801.281.341.4880.8
Karenia selliformis 40000.910.521.0681.86
Polykrikos sp.255.56338.890.91.451.0582.91
Licmophora sp.311.1100.891.491.0383.93
Chaetoceros sp.244.4400.750.620.8784.8
Anabaena sp.288.89116.670.641.430.7585.55
Protoperidinium sp.88.891500.571.280.6686.93
Gyrodinium sp.111.1161.110.540.560.6287.56
Biddulphia sp.166.6700.510.580.5988.15
Striatella unipunctata 20000.490.880.5788.72
Thalassiosira sp.166.6700.450.880.5289.23
Leptocylindrus sp.133.3300.420.570.4989.72
Scrippsiella sp.133.3300.410.710.4890.2

Using environmental variablesUsing nutrient ratios
Axis 1Axis 2Axis 1Axis 2

Species-environment correlations0.9940.9430.9750.935
Cumulative percentage variance of species data29.738.938.845.2
Cumulative percentage variance of species-environment relation36.247.585.8100.0
Sum of all eigenvalues1.6621.623
Sum of all canonical eigenvalues1.3610.734
Explained variance by CCA81.88%45.22%
Intraset correlations of variables with axes
 pH −0.8210.033
 SM −0.497−0.411
 N : P0.72040.6059
 Si : N0.7606−0.3145
 Si : P0.87650.2950

Phytoplankton species respond in different ways to nutrient enrichment, probably based on their life cycle characteristics, such as growth rate and their absorptive capacity [30]. According to Lepoint et al. [31], it is difficult to predict which group would be favored by the moderate intake of nutrients. For example, Lin et al. [32] found that nutrient enrichment (, , and , either alone or in combination) did not result in an increase in the abundance of phytoplankton. These communities respond in complex ways to the addition of nutrients. The seasonal influence on the response of microalgae to nutrient enrichment seems important [33].

Our results showed that Si(OH)4 and nitrogen (, NT) are factors that contribute to differences of species abundance between stations (CCA; Figure 3). Indeed, silicon (Si) played the most important role in the growth and development of diatoms while dinoflagellates were mostly controlled by phosphorus (P) availability [34, this study]. When phosphorus loading increased, a shift from diatoms to dinoflagellates was observed [35]. Krumme and Liang [36] and Wear et al. [28] have found that diatoms were high in terms of abundance and species composition in nutrient enrichment station. Pennate and centric diatoms respond differently to disturbance. Indeed, our results showed that centric diatoms were higher in disturbed station, result found also by Harrison et al. [37]. Indeed, diatoms have the capacity of rapid cell division with growth rates (up to 5.9 day−1) generally much higher than those for dinoflagellates (up to 2.7 day −1) based on equivalent body mass [38].

The canonical molar Redfield ratio of 16 : 1 (N : P) in the water column has traditionally been considered the determining factor for whether there is potential for N- or P-limitation in phytoplankton for a specific ecosystem [39, 40]. If the ratio of dissolved inorganic N (DIN) compared to dissolved inorganic P (DIP) is greater than 16 : 1 and nutrients are limiting, the system is considered P-limited [41]. Our data allow us to highlight that disturbed station was characterized by a slight P-limitation, which is known to induce the synthesis of alkaline phosphatases, AP, for numerous phytoplankton species such as Alexandrium catenella [42], Ptychodiscus brevis [43], Karenia mikimotoi [44], Gymnodinium catenatum, and Alexandrium tamarense [45]. AP is an enzyme used to convert DOP (dissolved organic P) to bioavailable DIP (dissolved inorganic P) by cleaving P from the DOP molecule [42, 46]. Some diatoms also do produce AP, including Pseudo-nitzschia, Chaetoceros, and Skeletonema [41, 47]. But previous studies [41, 47] have indicated that some phytoplankton groups (dinoflagellates and coccolithophores) may have a greater P-requirement and utilize DOP more readily than diatoms through the production of AP.

We also found a strong Si-limitation in the control station which seems to be the main driver of differences in diatoms’ assemblages between control and disturbed conditions. This station showed low abundance of diatoms (Figure 4, Table 4) compared to disturbed station. This result was also found by previous study [1, 21, 34, 35] that showed that increase in the N : Si ratio was proportional with the increase in flagellates and the decrease of diatoms abundance [1].

Differences in species abundance and composition between the two stations cannot be assigned only to nutrients enrichment; many other factors could influence their distributions, such as water motion [1], light intensity [2], and pollution by heavy metals. For instance, in Skhira station (K), there is phosphate treatments factory that contaminates water and sediments by cadmium, mercury, arsenic, and so forth, as reported by Smaoui-Damak et al. [6].

The phytoplankton-grazer interaction also plays an important role in controlling abundance and diversity of microalgae. Indeed, plankton is a food source for a range of grazers which influence their diversity and their abundance [48].

The present study underlines the use of phytoplankton assemblages as general indicators of anthropogenic disturbance in marine ecosystems. It was clear that the effect of nutrient loading and turbidity could be depicted in the community structure and diversities of phytoplankton. During period of study (August), nutrient enrichment (mainly Si(OH)4 and ) and turbidity were associated with increase of diatoms and cyanobacteria and decrease of dinoflagellates abundance, especially Gonyaulacales and Prorocentrales. The existence of Si- and P-limitation in study sites seems to be the main driver of differences in phytoplankton assemblages between control and disturbed conditions.


See Table 2.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.


The authors wish to acknowledge use of the Maptool program for analysis and graphics in this paper. They thank Dr. Idriss and Ahmed Omar Mabrouk and Groupe Chimique Tunisien (CGT, Skhira factory) for essential help. They also thank Professor Robert A. Patzner and the reviewer for his comments which improved the quality of the paper. In addition, they thank all the team of the Journal of Marine Biology for their professionalism and rapid response.


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