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Survey to compare phytoplankton functional approaches: How can these approaches assess River Nile water quality in Egypt?
Egyptian Journal of Aquatic Research (2015) 41, 247–255
H O S T E D BY
National Institute of Oceanography and Fisheries
Egyptian Journal of Aquatic Research http://ees.elsevier.com/ejar www.sciencedirect.com
FULL LENGTH ARTICLE
Survey to compare phytoplankton functional approaches: How can these approaches assess River Nile water quality in Egypt? Mohamad Saad Abd El-Karim National Institute of Oceanography and Fisheries, 101 El-Kasr El-Eny St., Cairo, Egypt Received 15 April 2015; revised 12 July 2015; accepted 19 July 2015 Available online 5 August 2015
KEYWORDS Phytoplankton; River Nile; Water quality assessment; Functional Groups; Morpho-Based Functional Groups; Q(r) index
Abstract The phytoplankton functional group concept (Q(r) index) was used to assess ecological status in the River Nile. Phytoplankton Functional Groups (FGs) and Morpho-Based Functional Groups (MBFGs) were described in the whole river including the main pollution sources. A total of 273 species were classified into 25 FGs and seven MBFGs. The most dominant FGs recorded (D, P, B, M, F, P, J and F) preferred nutrient rich conditions, whereas the appearance of lacustrine groups (W1, T, Y, Lo) reflected an increase of water residence time. Between MBFG groups, VI was the most dominant. The Canonical Corresponding Analysis indicated that the variance explained from environmental conditions is highest for MBFG (78.56%) than for FG (65.57%) and individual species (51.36%). Minimum Q(r) index values were synchronized to spring and the southern section of the river. The least Q(r) values were observed at the discharging point of pollutants and increase toward the highly diluted point. The highest Q(r) values were mainly observed at the upstream and main channel sites. The results suggested that the morphofunctional classification of phytoplankton and Q(r) not only reflects successfully River Nile water quality better than the individual species composition, but can also be used as a monitoring tool to assess the ecological status in the River Nile. ª 2015 National Institute of Oceanography and Fisheries. Hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction Although the water quality monitoring of streams is usually based on the composition of the macroinvertebrate and benthic diatoms, the investigation of the riverine phytoplankton for monitoring purposes is unavoidable (Borics et al., 2007). Indeed, low attentions have been paid to study the biological features of the River Nile as bioindicators and biomonitoring Peer review under responsibility of National Institute of Oceanography and Fisheries.
tools to assess the ecological status of the river. Fishar and Williams (2008) developed a biotic index to assess the river’s ecological status (Nile Biotic Pollution Index; NBPI) that depended on macroinvertebrates. They concluded that NBPI has been shown to provide an excellent biological assessment of organic pollution in the Nile, but it was less useful for chemical monitoring of water quality. Belal (2012) evaluated the River Nile ecological status using epipelon diatoms. She applied four diatom indices and postulated that the diatom indices developed in Europe and elsewhere are less useful to assess water quality in the River Nile.
http://dx.doi.org/10.1016/j.ejar.2015.07.002 1687-4285 ª 2015 National Institute of Oceanography and Fisheries. Hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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Elaboration of a phytoplankton-based quality assessment method needs the evaluation of phytoplankton associations in rivers. An alternative approach to the phylogenic groups is the use of explicit functional classifications ‘association’ that were proposed by Reynolds (1980). He separated 14 species associations in a series of phytoplankton data from a group of lakes in Northwest England. These associations comprised ‘aggregates or groups’ of organisms that exist in the water at the same time and increased or decreased simultaneously. The associations themselves are based on the physiological, morphological and ecological attributes of the species that potentially and alternatively may dominate or co-dominate the system. Later, the scheme has been expanded to accommodate associations from a wider number of lakes (Padisak and Reynolds, 1998). This functional classification was refined and three functional traits were developed; one describes phytoplankton association in deep water lakes (Salmaso and Padisak, 2007), and two functional traits for the turbid shallow water. The first (Functional Groups, FGs), which was refined by Reynolds et al. (2002), Borics et al. (2007) and Padisak et al. (2009), while the second (Morpho-Based Functional Groups, MBFGs) was proposed by Kruk et al. (2010). Functional Groups (FGs) classification comprised a large number of functional groups which were labeled with capital letters, A, B, C, D, P, M. . ., a combination of more than one letter (MP, Lo, NA, . . .) or letters and numbers (X1, X2, S1, S2, . . .). The Morpho-Based Functional Groups (MBFGs) comprised seven groups which were labeled with the Latin numbers; I, II, III, IV, V, VI and VII. These letters and numbers are also called coda and refer to associations or groups. In the past decade, studies on phytoplankton dynamics have proved that the morpho-functional grouping of species may be useful for ecological purposes (Dokulil et al., 2007; Padisak et al., 2009). The functional-groups approach (sensu Reynolds et al., 2002) is one of the most widely accepted forms of grouping phytoplankton species (Padisak et al., 2009). The original idea of the phytoplankton functional group concept (Reynolds et al., 2002) was proposed as a new water quality estimation method for lake phytoplankton (Q index–– Padisak et al., 2006), then for river potamoplankton (Q(r) index––Borics et al., 2007). The Q(r) index is enabled to reflect human impacts at different scales by using specific F factor values for the different functional groups. These factor values were calculated using the following components: (i) nutrient status (from values 0-hypertrophic to 5-oligotrophic), (ii) turbulence (from values 0-standing waters to 5-highly lotic environment), (iii) sufficient time for the development of the given assemblage (from values 0-climax to 5-pioneer assemblages) and (iv) level of risk of functional traits (from values 0-high risk indicating pollution or being able to being toxic to 5-low risk). The specified values of each component were summed, and then the F was calculated for each functional group ranging between 0 and 5 (Borics et al., 2007). QðrÞ ¼
s X ðpiFÞ i¼1
where pi = ni/N, ni is the biomass of the i group, while N is the total biomass. F is the factor number allowing the quality index to range between 0 (the worst) and 5 (the best).
Objectives The present study aims at, (1) comparing the individual species classification with two new phytoplankton functional classifications, (2) which classification of these systems can express more the phytoplankton distribution in the River Nile using different statistical analysis?, (3) which are the dominant functional groups along the River Nile?, (4) how do the point source effluents affect the functional groups?, (5) can Q(r) index be considered as a new ecological status estimation method for the River Nile? Study area The present study covered the Nile section from Aswan Old Dam N 23580 2000 E 32520 4200 till its bifurcation at ElKanater Barrage at N 30100 2500 and E 3180 2000 , for a distance of about 920 km. A total of 36 sites were seasonally sampled at 11 stations (Fig. 1) to represent the different ecological areas of the River Nile including five discharging points of the main pollution sources (Kema; St. 1, Kom Ombo; St. 2, Qus; St. 4, El-Minya; St. 7, and El-Hawamdia; St. 9). At the five stations that receive effluents of the pollution sources, the samples were collected at different sites as follows: Upstream site: Tens of meters upstream to the discharging points of pollution. D site: At the discharging points of pollution. D1 site: Where discharging effluents are partially diluted with river water. D2 site: Where discharging effluents become highly diluted and nearly disappeared. At the stations that do not receive pollution effluents, one littoral site and the main channel were sampled. The river was classified into three sections, the southern section comprised stations from 1 to 4, and the middle section comprised stations from 5 to 7, whereas the northern section comprised stations from 8 to 11. Materials and methods Subsurface water samples were collected seasonally during 2012 and stored in 250-mL plastic bottles, preserved with 4% formalin. Utermo¨hl’s technique (Utermo¨hl, 1958) was used for plankton sedimentation. Species identification and counting were performed in an inverted light microscope (Zeiss, Axiovert 25C) at 10· eyepiece and 400· objectives. Algal biovolume was estimated using formula for geometric shapes (Hillebrand et al., 1999). Phytoplankton taxa were classified into Functional Groups (FGs) according to Reynolds et al. (2002), Borics et al. (2007) and Padisak et al. (2009); and into Morpho-Based Functional Groups (MBFGs) applying Kruk et al. (2010). The ecological status was estimated using F factor values proposed by Borics et al. (2007). Conductivity and pH were determined in the field by means of appropriate probes. Samples for water chemistry were taken simultaneously with phytoplankton samples, stored in special bottles and analyzed in the laboratory. Data of oxygen (DO), biological oxygen demand (BOD), bicarbonate alkalinity, nitrate (N–NO3), nitrite (N–NO2), ammonium (N–NH4) soluble reactive phosphorus (SRP), total phosphorus (TP)
Comparison of phytoplankton functional approaches
Figure 1
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Map showing the 11 sampling stations in the River Nile.
and silicate were available from the report of NIOF, FLD (2012). Statistical analysis Statistical analysis, ANOVA, Spearman correlation and regression analyses were carried out using the SPSS 20.0 package. Variance analysis (one-way ANOVA) was employed for comparing means. Q(r) index, as a dependent variable, was regressed against the different environmental variables, independent variables, using multiple linear regressions. Canonical correspondence analyses (CCA) were used to make a comparison of the three classifications (FGS, MBFGs and individual species). Three canonical correspondence analyses were applied to estimate how much variance of the biovolume of the species and the groups in the two classifications was explained by the environmental variables. CCA analyses were performed using CANOCO version 4.5 (Leps and Smilauer, 2003). The environmental variables were submitted to a stepwise forward selection procedure in which the statistical significance of each variable was tested by the Monte Carlo permutation test (499 permutations) at a cut-off point of P = 0.05. Results Phytoplankton biovolume Ninety-nine algal genera from seven major taxonomic classes were identified. The mean biovolume was least (1.2 mm3 L1) in the southern section (stations 1–4) of the River Nile. The central (stations 4–7) and northern (stations 8–11) sections
had comparable mean biovolumes (4.8 and 4.6 mm3 L1, respectively). The phytoplankton mean biovolume was highest throughout the year at the northern section of the river, except during autumn when the phytoplankton mean biovolume outburst occurred at the central section (Fig. 2). Furthermore, the phytoplankton biomass in the northern section was highest during winter, whereas it was highest in the south and central sections during autumn. At the polluted stations, the phytoplankton biovolume showed changeable behavior. In the south, at stations 1 and 2, the phytoplankton mean biovolume was highest at sites D and D1, respectively; whereas at station 4, phytoplankton biovolume had mean comparable values at upstream and D sites (3.7 and 3.7 mm3 L1, respectively). At station 7, phytoplankton biovolume was highest at upstream site (4.6 mm3 L1) but lower at D and D1 sites (3.3 and 3.7 mm3 L1, respectively). At station 9, phytoplankton biovolume had an outburst at the D site (23.3 mm3 L1), but had least value at the upstream site (3.1 mm3 L1). Phytoplankton FGs and MBFGs distribution Most of the samples involved in this study were mainly dominated by species belonging to diatoms, chlorococcalean green algae and Cyanobacteria. The total species number was 273 which was classified into 25 FGs and seven MBFGs with 48 and 55 descriptor taxa, respectively. The most dominant FGs were D (40.4%), P (20.2%), B (10.4%), M (7.2%) and F (5.1%). The most species rich functional groups were J (45 spp.), F (41 spp.), TC (29 spp.), D (27 spp.) and MP (20 spp.). Marked seasonal variations in representation of each of the 10 main FGs of phytoplankton were observed (Fig. 3). The D, B, P, F and K FGs were the main contributors
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Figure 2
Distribution of total phytoplankton biovolume in the River Nile in 2012.
to the biomass in the Nile River during autumn and winter. On the other side, D, P, MP, F, M, J and C were the most abundant during summer, whereas D, K, F, M, J and C were the most representative of phytoplankton biomass during spring. In the middle and north, there was clear dominance of the three main groups (D, P and B), while at the south there was shared dominancy between the main 10 FGs. The main FGs showed remarkable variations at the polluted sites, and marginally showed seasonal and spatial variations. In an overview, the FGs B (mainly represented by Cyclotella spp.), D (mainly represented by Synedra acus and Synedra delicatissima throughout the river and with Nitzschia subtilis in the central and northern parts), K (mainly represented by Chroococcus limneticus and C. disperses in the south and Chamaesiphon polymorphus in the north), M (mainly represented by Microcystis flos-aquae), P (mainly represented by Aulacoseira granulata, especially in the central and northern parts), F (mainly represented by Kirchneriella spp, specially K. lunaris during spring in the south and Dictyosphaerium pulchellum in the central and northern parts year-round) and J (mainly represented by Scenedesmus ecornis and Tetraedron minimum) contributed most to the total biomass. MBFG data revealed that VI was the leading group with a maximum mean percentage of 87.6% during autumn and a minimum of 78.3% during spring (Fig. 4). Group VI was highest in the middle of the river during autumn and winter, whereas it was highest in the north during spring and summer. At the polluted stations 1, 2 and 9, MBFGs were the highest at D and D1 sites year round, whereas they were highest at upstream and Main Channel sites at stations 4 and 7. MBFGs varied between minimum biomass values in the southern section throughout the year and had maximum biomass in the north of the river except during autumn when they had a maximum in the central section. Canonical correspondence analysis Canonical correspondence analysis (CCA) was performed to reveal the relationships between environmental variables
and new phytoplankton classifications (MBFGs and FGs) and between environmental variables and the dominant species (16 species with biomass P0.5 of the total biomass, Table 1). The CCAs (Fig. 5) indicated that the variance that can be explained from environmental conditions is highest for MBFG (78.6%) than for FG (65.6%) and individual species (51.36%). Not surprisingly, the total variation in the data set (total inertia) decreased from individual species classification (4.0) via FG (1.8) to MBFG (1.4). For the FG, the forward selection of environmental variables selected three significant variables, TP (P < 0.02), pH (P < 0.05) and NH4 (P < 0.03), which together accounted for 46.3% of the total variance. For the MBFG, the forward selection of environmental variables selected three significant variables, TP (P < 0.02), DO (P < 0.03) and NH4 (P < 0.05), which together accounted for 53.7% of the total variance. For the main species, the forward selection of environmental variables selected two significant variables, TP and NH4 (P < 0.05), which together accounted for 23.8% of the total variance. Distribution of Q(r) index The mean Q(r) value was 3.3; the low Q(r) values characterized spring with the least mean value of 2.7, whereas the highest value (3.62) was recorded during autumn (Fig. 6). Spatially, the mean Q(r) was least (3.0) in the southern section of the River Nile. The central and northern sections of the River Nile had the same mean values (3.6). The Q(r) was least throughout the year in the south, whereas it was highest in the north during spring and summer and during winter and autumn in the central section. At the polluted stations, the least Q(r) values were mainly observed at the D site (discharging point of polluted effluents) and increase toward D2 (toward the restoration points, with increase of dilution). The highest Q(r) values were mainly observed at the upstream and main channel points, except in a few cases it was found at the D2 site. The Spearman correlation matrix showed that the Q(r) was negatively correlated with PO4 (0.4), NH4 (0.39),
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Figure 3 Relative biovolume (%) of phytoplankton functional groups, FGs (sensu Reynolds et al., 2002; Padisak et al., 2009) from a winter to d autumn in the River Nile in 2012.
NO3 (0.34) and TP (0.27), whereas it was positively correlated with DO (0.35) and BOD (0.23). The multiple linear regressions of Q(r) and different environmental variables showed an R2 of 0.44.
Discussion The phytoplankton functional-groups approach has been widely used and has proved to be a useful tool for monitoring
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Figure 4 Relative biovolume (%) of phytoplankton Morpho-Based Functional Groups, MBFGs (sensu Kruk et al., 2010) from a winter to d autumn in the River Nile in 2012.
purposes. Moreover, its relationship to the habitat template has been tested and proved since the original work of Reynolds et al. (2002), which gave the habitat description as
well as the tolerances and sensitivities for each codon (Crossetti et al., 2013). Most of the samples, as well as the whole river, are dominated by only a few FGs. In Fig. 3, the
Comparison of phytoplankton functional approaches Table 1
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Main phytoplankton species and their codes used in CCA analysis.
Species name
Species code
Species name
Species code
Aulacoseira granulate (Ehren.) Simon. A. g. var angustissima (Mull.) Simon. Cyclotella kutzingiana Chaum. C. meneghiniana Kutz. C. ocellata Pant. C. operculata (Agard.) Kutz. C. sociales Schutt Nitzschia subtilis Grun.
A. gr. A. gr. ang. C. kut. C. men. C. ocel. C. op. C. soc. Nit
Synedra acus Kutz. S. delicatissima Smith. S. sp. S. ulna (Nit.) Ehren. Chamaesiphon polymorphus Geit. Microcystis flos-aquae (Witt.) Kirch. Micractinium pusillum Fres. Rhadiococcus nimbatus (Wild) Schm.
S. acus. S. del. S. sp. S. ulna Cham. Poly. M. flus. M. pus. R. nim.
dominance of groups D, P, B, M and F is reflected, where other types of dominances (MP, K and J) occur occasionally. Marked seasonal variations in representation of each of the 10 main FGs of phytoplankton were observed, especially at the central and northern sites of the rivers. The synchronization between the morphological responses to environmental variations has been well demonstrated in the case of the River Nile, revealing the spatial heterogeneity in the river. The most important phytoplankton assemblages found in the River Nile are related to the mixed and eutrophic inorganic nutrient rich environments. The main groups, D, B, P, F, K, M, J, C and MP, recorded in the river stretches are sensitive to stratification and well-adapted to mixed and eutrophic/hypertrophic environment as reported by Reynolds et al. (2002) and Padisak et al. (2009). In the present study, the proportion of F, K, M, MP and J increased in the southern section of the river Nile under higher concentrations of inorganic nitrogen and TP. Functional group MP occurred mostly at the north sampling station under higher concentrations of SRP. This group was characterized as occurring in frequently stirred up, inorganically turbid shallow lakes (Padisak et al., 2009), and was firstly described for periphytic diatoms that occasionally occur in lake plankton due to wind suspension (Padisak et al., 2006). In the central and northern sections there was a clear dominance of centric diatoms represented by Cyclotella spp. (groups B), A. granulata (groups P) and pinnate diatom Synedra spp. (groups D). On the other side, the southern section was characterized by shared dominance of different phytoplankton main groups. Centric diatoms, in middle and lower river sections are the most common potamoplankton
taxa in rivers where their size might matter regarding both nutrient and physical conditions (Salmaso and Zignin, 2010; Abonyi et al., 2014). In long rivers, longitudinal succession of phytoplankton is enforced by human modifications on the river bed (Reynolds, 2000). Based on residence time, nutrient availability and light conditions, the maximum phytoplankton production occurs at middle sections of rivers (Reynolds and Descy, 1996) where phytoplankton is mainly dominated by centric diatoms (Istvanovics et al., 2010). Downstream sections of different rivers are dominated by centric diatoms (Reynolds, 2006; Tavernini et al., 2011). One of the most apparent human effects reflected by group distribution in the River Nile is the influence of the eutrophication process that resulted from billions of cubic meters discharged annually. In addition to eutrophication, the water current is regulated by four barrages constructed at Essna, Naga Hammady, Assiut and El-Kanater. Regulated water current modifies the flowing regime, water residence time, nutrient distributions and light conditions (Hart et al., 2002; Abonyi et al., 2012), and therefore the seasonal succession of phytoplankton and species distribution. These can be identified on the River Nile by the eutrophic, groups (P, J and F) resulting in lake type equilibrium assemblages (Naselli-Flores et al., 2003) in this area. The human controlled outlets of dams, Aswan High Dam and Old Aswan Dam, are reflected in the occurrence of lacustrine (i.e. species behave as ones that inhabit shallow lakes) elements downstream groups as W1, T, Y, L0 recorded throughout the River Nile. Despite the presence of the planktonic elements, they cannot maintain persistent dominance downstream, as they are not adapted to survive in lotic environments (Reynolds and Descy, 1996), but are able
Figure 5 Ordination diagram of the canonical correspondence analysis (CCA) for the (a) MBFGs, (b) FGs and (c) main phytoplankton species of annual main biomass of P0.5 in the River Nile.
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Winter 3.8 3.6 3.4 3.2 3 2.8 2.6 2.4 2.2 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0
Autumn
Spring
Summer
Figure 6
Seasonal and regional distributions of the Q(r) index along the River Nile in 2012.
to enrich river phytoplankton with species in additional habitats. This agrees with the results from the River Narva (Estonia), sampled after the Narva Reservoir (Piirsoo et al., 2010). Results of the canonical corresponding analysis revealed that phytoplankton community composition may be predicted best in terms of morphological groups than by traditional phytoplankton species composition. Not surprisingly that a closer relationship between phytoplankton functional approach and environmental conditions in the River Nile was higher between phytoplankton species classification and environmental conditions. The variance explained from environmental conditions is highest for MBFG (78.6%) than for FG (65.6%) and individual species (51.4%). Moreover, the total variation in the data set (total inertia) decreased from individual species classification (4.0) via FG (1.8) to MBFG (1.4). The explained variance for traditional phytoplankton species composition is lower than MBFG and FGs. Kruk et al. (2011) studied the relationship between new concept morphological classifications, traditional species classification and phylogenetic classification with ambient environmental variables. Their results indicated that morphological groups show a closer relationship to environmental conditions than phylogenetic groups and traditional species classification, which suggested that morphology, is a better proxy for ecological functioning than phylogeny and traditional species classification. Put simply, their results indicated that the two species that are morphologically similar but are phylogenetically unrelated and appear more functionally similar than two species that are phylogenetically related but morphologically different. Morphologically similar phytoplankton appears functionally closely related to ecological aspects (Naselli-Flores et al., 2007). By contrast, phylogenetic can lead to closely related taxa to differ widely in ecological features (Lurling, 2003). Due to the specific properties of lowland rivers, such as low hydraulic gradients, high potential for water retention (Schmalz et al., 2008) and relatively larger population within the watershed, reference sites were normally impossible to find,
which was apparently different from the mountain streams (Wu et al., 2012). The low Q(r) index in the south emphasized lack of benthic diatoms dominance which is considered as natural in upstream sections of rivers, groups MP, TD and many members of codon D. The upstream decreasing Q(r) values reflected the increasing amount of groups B, M, F and J, indicating a switch in the primary energy source, as predicted by other studies (Borics et al., 2007; Abonyi et al., 2012). An increasing upstream light availability was reflected by the increasing dominance of these groups, which may influence the longitudinal switch between centric diatoms and green algae as well (Abonyi et al., 2012). Besides the biovolume dominance of these groups at the south of the River Nile, the decreasing of groups D and P also indicates an elevation of the trophic level and decrease of Q(r) levels. The increase of nitrate, ammonium and total phosphorus in the south of the River Nile, many times higher than corresponding values in the middle and north due to the discharging effluents of Kema and Kom Ombo Drians, also enforced the least Q(r) values in the south. Q(r) showed a negative coefficient of determination (R2) with the aforementioned parameters which had higher values in the southern section of the river; similar to the results reported in Loire River (France) by Abonyi et al. (2012). In conclusion, the phytoplankton morphological classification including both FGs and MBFGs reflect River Nile water quality better than the individual phytoplankton species composition. The most dominant FGs recorded in the River Nile (D, P, B, M, F, P, J and F) were known as groups that preferred nutrient rich conditions which reflected the influence of the eutrophication process that resulted from billions of cubic meters discharge annually into the river. At the same time, the appearance of lacustrine groups (i.e. species behaves as one that inhabits shallow lakes) W1, T, Y, Lo reflects the increase of water residence time that resulted from the constructed barrages at Essna, Naga Hammady, Assiut and ElKanater. The environmental conditions can be predicted better in the River Nile via the MBFGs and FGs more than the
Comparison of phytoplankton functional approaches individual species classification, where a closer relationship between phytoplankton functional approach and environmental conditions in the River Nile was higher than between phytoplankton usual individual species classification and environmental conditions. The variance explained from environmental conditions is highest for MBFG (78.6%) than for FG (65.6%) and individual species (51.4%). Finally, Q(r) index can be used to predict the water quality in the River Nile where it had a reasonable correlation with the environmental parameters. References Abonyi, A., Leitao, M., Lanc¸on, A.M., Padisak, J., 2012. Phytoplankton functional groups as indicators of human impacts along the River Loire (France). Hydrobiologia 698, 233–249. Abonyi, A., Leitao, M., Stankovi, I., Borics, G., Varbiro, G., Padisak, J., 2014. A large river (River Loire, France) survey to compare phytoplankton functional approaches: do they display river zones in similar ways? Ecol. Model. 46, 11–22. Belal, M.D., 2012. Epipelic diatoms as a tool for monitoring pollution in River Nile from Aswan to Cairo (M.Sc. thesis). Fac. of Sci., Cairo Uni. Borics, G., Varbıro, G., Grigorszky, I., Krasznai, E., Szabo, S., Kiss, T.K., 2007. A new evaluation technique of potamoplankton for the assessment the ecological status of rivers. Arch. Hydrobiol. Suppl. Large Rivers 161, 465–486. Crossetti, L.O., Beckerb, V., Cardosoc, L., Rodriguesd, L.R., Costad, L.S., Motta-Marques, L., 2013. Is phytoplankton functional classification a suitable tool to investigate spatial heterogeneity in a subtropical shallow lake? Limnologica 43, 157–163. Dokulil, M.T., Donabaum, K., Teubner, K., 2007. Modifications in phytoplankton size structure by environmental constraints induced by regime shifts in an urban lake. Hydrobiologia 578, 59–63. Fishar, M.R., Williams, W.P., 2008. The development of a biotic pollution index for the River Nile in Egypt. Hydrobiologia 598, 17– 34. Hart, D.D., Johnson, T.E., Bushow-Newton, K.L., Horwitz, R.J., Bednarek, A.T., Charles, D.F., Kreeger, D.A., Velinsky, D.J., 2002. Dam removal: challenges and opportunities for ecological research and river restoration. Bioscience 52, 669–681. Hillebrand, H., Durselen, C.D., Kirschtel, D., Pollingher, U., Zohary, T., 1999. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424. Istvanovics, V., Honti, M., Voros, L., Kozma, Z., 2010. Phytoplankton dynamics in relation to connectivity, flow dynamics and resource availability––the case of a large, lowland river, the Hungarian Tisza. Hydrobiologia 637, 121–141. Kruk, C., Huszar, V.L.M., Peeters, H.M., Bonilla, S., Costa, L., Lurling, M., Reynolds, C.S., Scheffer, M., 2010. A morphological classification capturing functional variation in phytoplankton. Freshwater Biol. 55, 614–627. Kruk, C., Peeters, H.M., Van Nes, E.H., Huszar, V.L.M., Costa, L.S., Scheffer, M., 2011. Phytoplankton community composition can be predicted best in terms of morphological groups. Limnol. Oceanogr. 56, 110–118. Leps, J., Smilauer, P., 2003. Multivariate Analysis of Ecological Data Using CANOCO. Cambridge University Press, New York.
255 Lurling, M., 2003. The effect of substances from different zooplankton species and fish on the induction of defensive morphology in the green alga Scenedesmus obliquus. J. Plankton Res. 25, 979–989. Naselli-Flores, L., Padisak, J., Dokulil, M.T., Chorus, I., 2003. Equilibrium/steady-state concept in phytoplankton ecology. Hydrobiologia 502, 395–403. Naselli-Flores, L., Padisak, J., Albay, M., 2007. Shape and size in phytoplankton ecology: do they matter? Hydrobiologia 578, 157– 161. NIOF, FLD, National Institute of Oceanography and Fisheries, Freshwater and Lakes Division, 2012. Strategy to Performing an Integrated Scientific System to Protect the River Nile from Pollution and Development Its Fish Resources, Ecological part. 182 pp. Padisak, J., Reynolds, C.S., 1998. Selection of phytoplankton associations in Lake Balaton, Hungary, in response to eutrophication and restoration measures, with special reference to the cyanoprokaryotes. Hydrobiologia 384, 41–53. Padisak, J., Borics, G., Grigorszky, I., Soruczki-Pinter, E., 2006. Use of phytoplankton assemblages for monitoring ecological status of lakes within the Water Framework Directive: the assemblage index. Hydrobiologia 553, 1–14. Padisak, J., Crossetti, L.O., Naselli-Flores, L., 2009. Use and misuse in the application of the phytoplankton functional classification: a critical review with updates. Hydrobiologia 621, 1–19. Piirsoo, K., Pall, P., Tuvikene, A., Viik, M., Vilbaste, S., 2010. Assessment of water quality in a large lowland river (Narva, Estonia/Russia) using a new Hungarian potamoplanktic method. Estonian J. Ecol. 59, 243–258. Reynolds, C.S., 1980. Phytoplankton associations and their periodicity in stratifying lake systems. Holartic Ecol. 3, 141–159. Reynolds, C.S., 2000. Hydroecology of river plankton: the role of variability in channel flow. Hydrol. Proc. 14, 3119–3132. Reynolds, C.S., 2006. The Ecology of Phytoplankton. Cambridge University Press, New York, pp. 535. Reynolds, C.S., Descy, J.P., 1996. The production, biomass and structure of phytoplankton in large rivers. Arch. Hydrobiol. Suppl. 113, 161–187. Reynolds, C.S., Huszar, V., Kruk, C., Naselli-Flores, L., Melo, S., 2002. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 24, 417–428. Salmaso, N., Padisak, J., 2007. Morpho-functional groups and phytoplankton development in two deep lakes (LakeGarda, Italy, and Lake Stechlin, Germany). Hydrobiologia 578, 97–112. Salmaso, N., Zignin, A., 2010. At the extreme of physical gradients: phytoplankton in highly flushed, large rivers. Hydrobiologia 639, 21–36. Schmalz, B., Bieger, K., Fohrer, N., 2008. A method to assess instream water quality – the role of nitrogen entries in a North German rural lowland catchment. Adv. Geosci. 18, 37–41. Tavernini, S., Pierobon, E., Viaroli, P., 2011. Physical factors and soluble reactive silica affect phytoplankton community structure and dynamics in a lowland eutrophic river (Poriver, Italy). Hydrobiologia 669, 213–225. Utermo¨hl, H., 1958. Zur vervollkommung der quantitativen phytoplankton-methodik. Intern. Verein. fur Theor. Ang. Limnol. 9, 1–38. Wu, N., Schmalz, B., Nicola, F., 2012. Development and testing of a phytoplankton index of biotic integrity (P-IBI) for a German lowland river. Ecol. Indic. 13, 158–167.
H O S T E D BY
National Institute of Oceanography and Fisheries
Egyptian Journal of Aquatic Research http://ees.elsevier.com/ejar www.sciencedirect.com
FULL LENGTH ARTICLE
Survey to compare phytoplankton functional approaches: How can these approaches assess River Nile water quality in Egypt? Mohamad Saad Abd El-Karim National Institute of Oceanography and Fisheries, 101 El-Kasr El-Eny St., Cairo, Egypt Received 15 April 2015; revised 12 July 2015; accepted 19 July 2015 Available online 5 August 2015
KEYWORDS Phytoplankton; River Nile; Water quality assessment; Functional Groups; Morpho-Based Functional Groups; Q(r) index
Abstract The phytoplankton functional group concept (Q(r) index) was used to assess ecological status in the River Nile. Phytoplankton Functional Groups (FGs) and Morpho-Based Functional Groups (MBFGs) were described in the whole river including the main pollution sources. A total of 273 species were classified into 25 FGs and seven MBFGs. The most dominant FGs recorded (D, P, B, M, F, P, J and F) preferred nutrient rich conditions, whereas the appearance of lacustrine groups (W1, T, Y, Lo) reflected an increase of water residence time. Between MBFG groups, VI was the most dominant. The Canonical Corresponding Analysis indicated that the variance explained from environmental conditions is highest for MBFG (78.56%) than for FG (65.57%) and individual species (51.36%). Minimum Q(r) index values were synchronized to spring and the southern section of the river. The least Q(r) values were observed at the discharging point of pollutants and increase toward the highly diluted point. The highest Q(r) values were mainly observed at the upstream and main channel sites. The results suggested that the morphofunctional classification of phytoplankton and Q(r) not only reflects successfully River Nile water quality better than the individual species composition, but can also be used as a monitoring tool to assess the ecological status in the River Nile. ª 2015 National Institute of Oceanography and Fisheries. Hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction Although the water quality monitoring of streams is usually based on the composition of the macroinvertebrate and benthic diatoms, the investigation of the riverine phytoplankton for monitoring purposes is unavoidable (Borics et al., 2007). Indeed, low attentions have been paid to study the biological features of the River Nile as bioindicators and biomonitoring Peer review under responsibility of National Institute of Oceanography and Fisheries.
tools to assess the ecological status of the river. Fishar and Williams (2008) developed a biotic index to assess the river’s ecological status (Nile Biotic Pollution Index; NBPI) that depended on macroinvertebrates. They concluded that NBPI has been shown to provide an excellent biological assessment of organic pollution in the Nile, but it was less useful for chemical monitoring of water quality. Belal (2012) evaluated the River Nile ecological status using epipelon diatoms. She applied four diatom indices and postulated that the diatom indices developed in Europe and elsewhere are less useful to assess water quality in the River Nile.
http://dx.doi.org/10.1016/j.ejar.2015.07.002 1687-4285 ª 2015 National Institute of Oceanography and Fisheries. Hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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Elaboration of a phytoplankton-based quality assessment method needs the evaluation of phytoplankton associations in rivers. An alternative approach to the phylogenic groups is the use of explicit functional classifications ‘association’ that were proposed by Reynolds (1980). He separated 14 species associations in a series of phytoplankton data from a group of lakes in Northwest England. These associations comprised ‘aggregates or groups’ of organisms that exist in the water at the same time and increased or decreased simultaneously. The associations themselves are based on the physiological, morphological and ecological attributes of the species that potentially and alternatively may dominate or co-dominate the system. Later, the scheme has been expanded to accommodate associations from a wider number of lakes (Padisak and Reynolds, 1998). This functional classification was refined and three functional traits were developed; one describes phytoplankton association in deep water lakes (Salmaso and Padisak, 2007), and two functional traits for the turbid shallow water. The first (Functional Groups, FGs), which was refined by Reynolds et al. (2002), Borics et al. (2007) and Padisak et al. (2009), while the second (Morpho-Based Functional Groups, MBFGs) was proposed by Kruk et al. (2010). Functional Groups (FGs) classification comprised a large number of functional groups which were labeled with capital letters, A, B, C, D, P, M. . ., a combination of more than one letter (MP, Lo, NA, . . .) or letters and numbers (X1, X2, S1, S2, . . .). The Morpho-Based Functional Groups (MBFGs) comprised seven groups which were labeled with the Latin numbers; I, II, III, IV, V, VI and VII. These letters and numbers are also called coda and refer to associations or groups. In the past decade, studies on phytoplankton dynamics have proved that the morpho-functional grouping of species may be useful for ecological purposes (Dokulil et al., 2007; Padisak et al., 2009). The functional-groups approach (sensu Reynolds et al., 2002) is one of the most widely accepted forms of grouping phytoplankton species (Padisak et al., 2009). The original idea of the phytoplankton functional group concept (Reynolds et al., 2002) was proposed as a new water quality estimation method for lake phytoplankton (Q index–– Padisak et al., 2006), then for river potamoplankton (Q(r) index––Borics et al., 2007). The Q(r) index is enabled to reflect human impacts at different scales by using specific F factor values for the different functional groups. These factor values were calculated using the following components: (i) nutrient status (from values 0-hypertrophic to 5-oligotrophic), (ii) turbulence (from values 0-standing waters to 5-highly lotic environment), (iii) sufficient time for the development of the given assemblage (from values 0-climax to 5-pioneer assemblages) and (iv) level of risk of functional traits (from values 0-high risk indicating pollution or being able to being toxic to 5-low risk). The specified values of each component were summed, and then the F was calculated for each functional group ranging between 0 and 5 (Borics et al., 2007). QðrÞ ¼
s X ðpiFÞ i¼1
where pi = ni/N, ni is the biomass of the i group, while N is the total biomass. F is the factor number allowing the quality index to range between 0 (the worst) and 5 (the best).
Objectives The present study aims at, (1) comparing the individual species classification with two new phytoplankton functional classifications, (2) which classification of these systems can express more the phytoplankton distribution in the River Nile using different statistical analysis?, (3) which are the dominant functional groups along the River Nile?, (4) how do the point source effluents affect the functional groups?, (5) can Q(r) index be considered as a new ecological status estimation method for the River Nile? Study area The present study covered the Nile section from Aswan Old Dam N 23580 2000 E 32520 4200 till its bifurcation at ElKanater Barrage at N 30100 2500 and E 3180 2000 , for a distance of about 920 km. A total of 36 sites were seasonally sampled at 11 stations (Fig. 1) to represent the different ecological areas of the River Nile including five discharging points of the main pollution sources (Kema; St. 1, Kom Ombo; St. 2, Qus; St. 4, El-Minya; St. 7, and El-Hawamdia; St. 9). At the five stations that receive effluents of the pollution sources, the samples were collected at different sites as follows: Upstream site: Tens of meters upstream to the discharging points of pollution. D site: At the discharging points of pollution. D1 site: Where discharging effluents are partially diluted with river water. D2 site: Where discharging effluents become highly diluted and nearly disappeared. At the stations that do not receive pollution effluents, one littoral site and the main channel were sampled. The river was classified into three sections, the southern section comprised stations from 1 to 4, and the middle section comprised stations from 5 to 7, whereas the northern section comprised stations from 8 to 11. Materials and methods Subsurface water samples were collected seasonally during 2012 and stored in 250-mL plastic bottles, preserved with 4% formalin. Utermo¨hl’s technique (Utermo¨hl, 1958) was used for plankton sedimentation. Species identification and counting were performed in an inverted light microscope (Zeiss, Axiovert 25C) at 10· eyepiece and 400· objectives. Algal biovolume was estimated using formula for geometric shapes (Hillebrand et al., 1999). Phytoplankton taxa were classified into Functional Groups (FGs) according to Reynolds et al. (2002), Borics et al. (2007) and Padisak et al. (2009); and into Morpho-Based Functional Groups (MBFGs) applying Kruk et al. (2010). The ecological status was estimated using F factor values proposed by Borics et al. (2007). Conductivity and pH were determined in the field by means of appropriate probes. Samples for water chemistry were taken simultaneously with phytoplankton samples, stored in special bottles and analyzed in the laboratory. Data of oxygen (DO), biological oxygen demand (BOD), bicarbonate alkalinity, nitrate (N–NO3), nitrite (N–NO2), ammonium (N–NH4) soluble reactive phosphorus (SRP), total phosphorus (TP)
Comparison of phytoplankton functional approaches
Figure 1
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Map showing the 11 sampling stations in the River Nile.
and silicate were available from the report of NIOF, FLD (2012). Statistical analysis Statistical analysis, ANOVA, Spearman correlation and regression analyses were carried out using the SPSS 20.0 package. Variance analysis (one-way ANOVA) was employed for comparing means. Q(r) index, as a dependent variable, was regressed against the different environmental variables, independent variables, using multiple linear regressions. Canonical correspondence analyses (CCA) were used to make a comparison of the three classifications (FGS, MBFGs and individual species). Three canonical correspondence analyses were applied to estimate how much variance of the biovolume of the species and the groups in the two classifications was explained by the environmental variables. CCA analyses were performed using CANOCO version 4.5 (Leps and Smilauer, 2003). The environmental variables were submitted to a stepwise forward selection procedure in which the statistical significance of each variable was tested by the Monte Carlo permutation test (499 permutations) at a cut-off point of P = 0.05. Results Phytoplankton biovolume Ninety-nine algal genera from seven major taxonomic classes were identified. The mean biovolume was least (1.2 mm3 L1) in the southern section (stations 1–4) of the River Nile. The central (stations 4–7) and northern (stations 8–11) sections
had comparable mean biovolumes (4.8 and 4.6 mm3 L1, respectively). The phytoplankton mean biovolume was highest throughout the year at the northern section of the river, except during autumn when the phytoplankton mean biovolume outburst occurred at the central section (Fig. 2). Furthermore, the phytoplankton biomass in the northern section was highest during winter, whereas it was highest in the south and central sections during autumn. At the polluted stations, the phytoplankton biovolume showed changeable behavior. In the south, at stations 1 and 2, the phytoplankton mean biovolume was highest at sites D and D1, respectively; whereas at station 4, phytoplankton biovolume had mean comparable values at upstream and D sites (3.7 and 3.7 mm3 L1, respectively). At station 7, phytoplankton biovolume was highest at upstream site (4.6 mm3 L1) but lower at D and D1 sites (3.3 and 3.7 mm3 L1, respectively). At station 9, phytoplankton biovolume had an outburst at the D site (23.3 mm3 L1), but had least value at the upstream site (3.1 mm3 L1). Phytoplankton FGs and MBFGs distribution Most of the samples involved in this study were mainly dominated by species belonging to diatoms, chlorococcalean green algae and Cyanobacteria. The total species number was 273 which was classified into 25 FGs and seven MBFGs with 48 and 55 descriptor taxa, respectively. The most dominant FGs were D (40.4%), P (20.2%), B (10.4%), M (7.2%) and F (5.1%). The most species rich functional groups were J (45 spp.), F (41 spp.), TC (29 spp.), D (27 spp.) and MP (20 spp.). Marked seasonal variations in representation of each of the 10 main FGs of phytoplankton were observed (Fig. 3). The D, B, P, F and K FGs were the main contributors
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Figure 2
Distribution of total phytoplankton biovolume in the River Nile in 2012.
to the biomass in the Nile River during autumn and winter. On the other side, D, P, MP, F, M, J and C were the most abundant during summer, whereas D, K, F, M, J and C were the most representative of phytoplankton biomass during spring. In the middle and north, there was clear dominance of the three main groups (D, P and B), while at the south there was shared dominancy between the main 10 FGs. The main FGs showed remarkable variations at the polluted sites, and marginally showed seasonal and spatial variations. In an overview, the FGs B (mainly represented by Cyclotella spp.), D (mainly represented by Synedra acus and Synedra delicatissima throughout the river and with Nitzschia subtilis in the central and northern parts), K (mainly represented by Chroococcus limneticus and C. disperses in the south and Chamaesiphon polymorphus in the north), M (mainly represented by Microcystis flos-aquae), P (mainly represented by Aulacoseira granulata, especially in the central and northern parts), F (mainly represented by Kirchneriella spp, specially K. lunaris during spring in the south and Dictyosphaerium pulchellum in the central and northern parts year-round) and J (mainly represented by Scenedesmus ecornis and Tetraedron minimum) contributed most to the total biomass. MBFG data revealed that VI was the leading group with a maximum mean percentage of 87.6% during autumn and a minimum of 78.3% during spring (Fig. 4). Group VI was highest in the middle of the river during autumn and winter, whereas it was highest in the north during spring and summer. At the polluted stations 1, 2 and 9, MBFGs were the highest at D and D1 sites year round, whereas they were highest at upstream and Main Channel sites at stations 4 and 7. MBFGs varied between minimum biomass values in the southern section throughout the year and had maximum biomass in the north of the river except during autumn when they had a maximum in the central section. Canonical correspondence analysis Canonical correspondence analysis (CCA) was performed to reveal the relationships between environmental variables
and new phytoplankton classifications (MBFGs and FGs) and between environmental variables and the dominant species (16 species with biomass P0.5 of the total biomass, Table 1). The CCAs (Fig. 5) indicated that the variance that can be explained from environmental conditions is highest for MBFG (78.6%) than for FG (65.6%) and individual species (51.36%). Not surprisingly, the total variation in the data set (total inertia) decreased from individual species classification (4.0) via FG (1.8) to MBFG (1.4). For the FG, the forward selection of environmental variables selected three significant variables, TP (P < 0.02), pH (P < 0.05) and NH4 (P < 0.03), which together accounted for 46.3% of the total variance. For the MBFG, the forward selection of environmental variables selected three significant variables, TP (P < 0.02), DO (P < 0.03) and NH4 (P < 0.05), which together accounted for 53.7% of the total variance. For the main species, the forward selection of environmental variables selected two significant variables, TP and NH4 (P < 0.05), which together accounted for 23.8% of the total variance. Distribution of Q(r) index The mean Q(r) value was 3.3; the low Q(r) values characterized spring with the least mean value of 2.7, whereas the highest value (3.62) was recorded during autumn (Fig. 6). Spatially, the mean Q(r) was least (3.0) in the southern section of the River Nile. The central and northern sections of the River Nile had the same mean values (3.6). The Q(r) was least throughout the year in the south, whereas it was highest in the north during spring and summer and during winter and autumn in the central section. At the polluted stations, the least Q(r) values were mainly observed at the D site (discharging point of polluted effluents) and increase toward D2 (toward the restoration points, with increase of dilution). The highest Q(r) values were mainly observed at the upstream and main channel points, except in a few cases it was found at the D2 site. The Spearman correlation matrix showed that the Q(r) was negatively correlated with PO4 (0.4), NH4 (0.39),
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Figure 3 Relative biovolume (%) of phytoplankton functional groups, FGs (sensu Reynolds et al., 2002; Padisak et al., 2009) from a winter to d autumn in the River Nile in 2012.
NO3 (0.34) and TP (0.27), whereas it was positively correlated with DO (0.35) and BOD (0.23). The multiple linear regressions of Q(r) and different environmental variables showed an R2 of 0.44.
Discussion The phytoplankton functional-groups approach has been widely used and has proved to be a useful tool for monitoring
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Figure 4 Relative biovolume (%) of phytoplankton Morpho-Based Functional Groups, MBFGs (sensu Kruk et al., 2010) from a winter to d autumn in the River Nile in 2012.
purposes. Moreover, its relationship to the habitat template has been tested and proved since the original work of Reynolds et al. (2002), which gave the habitat description as
well as the tolerances and sensitivities for each codon (Crossetti et al., 2013). Most of the samples, as well as the whole river, are dominated by only a few FGs. In Fig. 3, the
Comparison of phytoplankton functional approaches Table 1
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Main phytoplankton species and their codes used in CCA analysis.
Species name
Species code
Species name
Species code
Aulacoseira granulate (Ehren.) Simon. A. g. var angustissima (Mull.) Simon. Cyclotella kutzingiana Chaum. C. meneghiniana Kutz. C. ocellata Pant. C. operculata (Agard.) Kutz. C. sociales Schutt Nitzschia subtilis Grun.
A. gr. A. gr. ang. C. kut. C. men. C. ocel. C. op. C. soc. Nit
Synedra acus Kutz. S. delicatissima Smith. S. sp. S. ulna (Nit.) Ehren. Chamaesiphon polymorphus Geit. Microcystis flos-aquae (Witt.) Kirch. Micractinium pusillum Fres. Rhadiococcus nimbatus (Wild) Schm.
S. acus. S. del. S. sp. S. ulna Cham. Poly. M. flus. M. pus. R. nim.
dominance of groups D, P, B, M and F is reflected, where other types of dominances (MP, K and J) occur occasionally. Marked seasonal variations in representation of each of the 10 main FGs of phytoplankton were observed, especially at the central and northern sites of the rivers. The synchronization between the morphological responses to environmental variations has been well demonstrated in the case of the River Nile, revealing the spatial heterogeneity in the river. The most important phytoplankton assemblages found in the River Nile are related to the mixed and eutrophic inorganic nutrient rich environments. The main groups, D, B, P, F, K, M, J, C and MP, recorded in the river stretches are sensitive to stratification and well-adapted to mixed and eutrophic/hypertrophic environment as reported by Reynolds et al. (2002) and Padisak et al. (2009). In the present study, the proportion of F, K, M, MP and J increased in the southern section of the river Nile under higher concentrations of inorganic nitrogen and TP. Functional group MP occurred mostly at the north sampling station under higher concentrations of SRP. This group was characterized as occurring in frequently stirred up, inorganically turbid shallow lakes (Padisak et al., 2009), and was firstly described for periphytic diatoms that occasionally occur in lake plankton due to wind suspension (Padisak et al., 2006). In the central and northern sections there was a clear dominance of centric diatoms represented by Cyclotella spp. (groups B), A. granulata (groups P) and pinnate diatom Synedra spp. (groups D). On the other side, the southern section was characterized by shared dominance of different phytoplankton main groups. Centric diatoms, in middle and lower river sections are the most common potamoplankton
taxa in rivers where their size might matter regarding both nutrient and physical conditions (Salmaso and Zignin, 2010; Abonyi et al., 2014). In long rivers, longitudinal succession of phytoplankton is enforced by human modifications on the river bed (Reynolds, 2000). Based on residence time, nutrient availability and light conditions, the maximum phytoplankton production occurs at middle sections of rivers (Reynolds and Descy, 1996) where phytoplankton is mainly dominated by centric diatoms (Istvanovics et al., 2010). Downstream sections of different rivers are dominated by centric diatoms (Reynolds, 2006; Tavernini et al., 2011). One of the most apparent human effects reflected by group distribution in the River Nile is the influence of the eutrophication process that resulted from billions of cubic meters discharged annually. In addition to eutrophication, the water current is regulated by four barrages constructed at Essna, Naga Hammady, Assiut and El-Kanater. Regulated water current modifies the flowing regime, water residence time, nutrient distributions and light conditions (Hart et al., 2002; Abonyi et al., 2012), and therefore the seasonal succession of phytoplankton and species distribution. These can be identified on the River Nile by the eutrophic, groups (P, J and F) resulting in lake type equilibrium assemblages (Naselli-Flores et al., 2003) in this area. The human controlled outlets of dams, Aswan High Dam and Old Aswan Dam, are reflected in the occurrence of lacustrine (i.e. species behave as ones that inhabit shallow lakes) elements downstream groups as W1, T, Y, L0 recorded throughout the River Nile. Despite the presence of the planktonic elements, they cannot maintain persistent dominance downstream, as they are not adapted to survive in lotic environments (Reynolds and Descy, 1996), but are able
Figure 5 Ordination diagram of the canonical correspondence analysis (CCA) for the (a) MBFGs, (b) FGs and (c) main phytoplankton species of annual main biomass of P0.5 in the River Nile.
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Winter 3.8 3.6 3.4 3.2 3 2.8 2.6 2.4 2.2 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0
Autumn
Spring
Summer
Figure 6
Seasonal and regional distributions of the Q(r) index along the River Nile in 2012.
to enrich river phytoplankton with species in additional habitats. This agrees with the results from the River Narva (Estonia), sampled after the Narva Reservoir (Piirsoo et al., 2010). Results of the canonical corresponding analysis revealed that phytoplankton community composition may be predicted best in terms of morphological groups than by traditional phytoplankton species composition. Not surprisingly that a closer relationship between phytoplankton functional approach and environmental conditions in the River Nile was higher between phytoplankton species classification and environmental conditions. The variance explained from environmental conditions is highest for MBFG (78.6%) than for FG (65.6%) and individual species (51.4%). Moreover, the total variation in the data set (total inertia) decreased from individual species classification (4.0) via FG (1.8) to MBFG (1.4). The explained variance for traditional phytoplankton species composition is lower than MBFG and FGs. Kruk et al. (2011) studied the relationship between new concept morphological classifications, traditional species classification and phylogenetic classification with ambient environmental variables. Their results indicated that morphological groups show a closer relationship to environmental conditions than phylogenetic groups and traditional species classification, which suggested that morphology, is a better proxy for ecological functioning than phylogeny and traditional species classification. Put simply, their results indicated that the two species that are morphologically similar but are phylogenetically unrelated and appear more functionally similar than two species that are phylogenetically related but morphologically different. Morphologically similar phytoplankton appears functionally closely related to ecological aspects (Naselli-Flores et al., 2007). By contrast, phylogenetic can lead to closely related taxa to differ widely in ecological features (Lurling, 2003). Due to the specific properties of lowland rivers, such as low hydraulic gradients, high potential for water retention (Schmalz et al., 2008) and relatively larger population within the watershed, reference sites were normally impossible to find,
which was apparently different from the mountain streams (Wu et al., 2012). The low Q(r) index in the south emphasized lack of benthic diatoms dominance which is considered as natural in upstream sections of rivers, groups MP, TD and many members of codon D. The upstream decreasing Q(r) values reflected the increasing amount of groups B, M, F and J, indicating a switch in the primary energy source, as predicted by other studies (Borics et al., 2007; Abonyi et al., 2012). An increasing upstream light availability was reflected by the increasing dominance of these groups, which may influence the longitudinal switch between centric diatoms and green algae as well (Abonyi et al., 2012). Besides the biovolume dominance of these groups at the south of the River Nile, the decreasing of groups D and P also indicates an elevation of the trophic level and decrease of Q(r) levels. The increase of nitrate, ammonium and total phosphorus in the south of the River Nile, many times higher than corresponding values in the middle and north due to the discharging effluents of Kema and Kom Ombo Drians, also enforced the least Q(r) values in the south. Q(r) showed a negative coefficient of determination (R2) with the aforementioned parameters which had higher values in the southern section of the river; similar to the results reported in Loire River (France) by Abonyi et al. (2012). In conclusion, the phytoplankton morphological classification including both FGs and MBFGs reflect River Nile water quality better than the individual phytoplankton species composition. The most dominant FGs recorded in the River Nile (D, P, B, M, F, P, J and F) were known as groups that preferred nutrient rich conditions which reflected the influence of the eutrophication process that resulted from billions of cubic meters discharge annually into the river. At the same time, the appearance of lacustrine groups (i.e. species behaves as one that inhabits shallow lakes) W1, T, Y, Lo reflects the increase of water residence time that resulted from the constructed barrages at Essna, Naga Hammady, Assiut and ElKanater. The environmental conditions can be predicted better in the River Nile via the MBFGs and FGs more than the
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