research article variability in the structure of...

Research ArticleVariability in the Structure of Phytoplankton Assemblagesin relation to Human Disturbance in Southern Coast of Tunisia

Lotfi Mabrouk12 Lamia Dammak2 Asma Hamza2

Mabrouka Mahfoudhi2 and Med-Najmeddine Bradai2

1 Faculty of Sciences of Sfax Sfax University Soukra Street km 4 BP 802 3038 Sfax Tunisia2 National Institute of Sciences and Technologies of the Sea Center of Sfax BP 1035 3018 Sfax Tunisia

Correspondence should be addressed to Lotfi Mabrouk lotfi2328yahoofr

Received 23 February 2014 Revised 29 May 2014 Accepted 2 June 2014 Published 25 June 2014

Academic Editor Robert A Patzner

Copyright copy 2014 Lotfi Mabrouk et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

We investigated the impact of industrial effluents on phytoplankton assemblage in southern Tunisia (Skhira) We specificallyaddressed changes in microalgae composition caused by this anthropogenic interference A hierarchical sampling design was usedto compare planktonicmicroalgae structure between one disturbed station and one control station Samples were collected by scubadiving at 5mdepth inAugust 2012 A total of 76microalgae taxa were identified Dinoflagellates abundance was low in the disturbedstation especially Gonyaulacales and Prorocentrales due to P-limitation whereas diatoms and cyanobacteria abundance were lowin control station which is characterized by Si-limitation

1 Introduction

Marine ecosystems are particularly vulnerable to environ-mental change and many are at present severely degraded[1] The availability of good quality water is an indispensablefeature for preserving biodiversity In recent years increasein human population and industrial activity have led tofaster degradation of many coastal ecosystemsThe dischargeof urban industrial and agricultural wastes has added thequantum of various harmful chemicals to the water bodyconsiderably altering their inherent physicochemical charac-teristics [2]

Phytoplankton constitutes the basic components of theaquatic food chain They act as primary producers andrepresent themselves as a direct food source for other aquaticanimals [3] Quantifying the phytoplankton communitycomposition is essential for understanding the structure andthe dynamics of marine ecosystems [4] It is also importantto evaluate the role of the physical and chemical processeson the biological responses of the ecosystem The interplayof physical chemical and biological properties of watermost often leads to the production of phytoplankton whiletheir assemblage (composition distribution diversity andabundance) is also structured by these factors

It is therefore of importance to undertake a studyof the effects of urban and industrial pressure on phyto-plankton dynamics under the impact of human activitiesversus control conditions In southern Tunisia we analyzedthe phytoplankton diversity and abundance and asked ifanthropogenic disturbance such as industrial effluents causestheir variations

We attempted to answer the following question hownutrient enrichment induced by anthropogenic disturbanceaffects abundance and species composition of phytoplanktoncommunity We thus compared phytoplankton assemblagesbetween a station exposed to industrial effluents and a controlstation and examined their variability at spatial scales using ahierarchical sampling design [5]

2 Materials and Methods

21 Study Area The study was carried out in two samplingstations (Figure 1) 100 km apart were chosen in August2012 unpolluted station in the east of Tunisia Mahdia (M)(35∘ 3510158404310158401015840N 11∘ 0510158404210158401015840E) was selected as control stationand Skhira (K) (34∘1910158401791015840N 10∘11101584070610158401015840E) was chosen asdisturbed station localised in the Gulf of Gabes This station

Hindawi Publishing CorporationJournal of Marine BiologyVolume 2014 Article ID 241410 10 pageshttpdxdoiorg1011552014241410

2 Journal of Marine Biology

9∘10

9984008∘20

998400

32∘30

998400

33∘20

998400

34∘10

998400

35∘00

998400

35∘50

998400

36∘40

998400

10∘50

99840011

∘40

99840012

∘30

99840010

∘00

998400

K

Tunisia

M

50km

Figure 1 Map of the study area showing the sampling stations withM Mahdia (control station) and K Skhira (disturbed station)

is exposed since 1988 to industrial effluents fromphosphoricacid and fertilizer production of the ldquoGroupe ChimiqueTunisienrdquo (GCT)These industrial activities affect thismarineecosystem [6] as in deterioration of seagrass bed [7 8] anddecline in fishing [9] The sampling depth in all stations was5m

22 Sampling and Data Collection In accordance with thehierarchical sampling design three sites (500m apart) werechosen randomly at each station At each site three replicatewater samples were collected from each site for nutrientanalyses Samples were taken using a 125mL plastic bottlehaving received prior treatment with hydrochloric acid Onreturning to the surface samples were shaken and thenfiltered with a 045 120583m filter (cellulose acetate 17mm)Samples were frozen in liquid nitrogen for transportationto the laboratory where concentrations of NO

2

minus NO3

minusNH4

+ PO4

minus Si(OH)4 total dissolved nitrogen (TN) and

total dissolved phosphorus (TP) were measured followingstandard colorimetric techniques [10]

In each site temperature salinity and pH were measuredimmediately after sampling using amultiparameter kit (Multi340iSET) For determination of suspended particulate mat-ter (SM) concentration water samples were filtered throughpreweighedGFCWhatman filters (pore size 045 pm) whichwere subsequently dried at 80∘C for about 24 h and reweighed[11]

For phytoplankton enumeration three replicate watersamples (about 100m apart at 5m depth) were selected fromthe same sitesWater column samples using 1-litre glass bottlesampler lowered from the surface to the near bottom wereconducted by scuba diving All samples were collected atnoon

Samples were fixed with Lugolrsquos solution and finallypreserved in 5 formalin All sampling waters were kept

in the dark at ambient temperature until their microscopicobservation Settling long glass tubes used for sedimentationprocedure were 2 cm wide by 21 cm long and have a baseplate that contains a coverslip on which the algae settle Tomix the sample the bottle was gently tilted back and forth10 times before pouring [12] A 50mL subsample was pouredinto the settling chamber and left to settle for 24 h subsampleswere examined in an inverted microscope at medium (times 200)magnification by scanning the entire surface of the settlingchamber to enumerate epiphytic microalgae [12 13] Thetotal number of microalgae individuals (119873) contained in 1litre expressed as number of individuals per litre is obtainedby the following conversion 119873 = (119899 times 1000)(V) with 119899= the number of individuals counted and V = the volumeof the sedimentation chamber (50mL) [13] The identifiedtaxa were divided into groups (diatoms dinoflagellates andcyanobacteria)

23 Data Analysis Data were tested for normality usingthe Kolmogorov-Smirnov test [14] and for heteroscedasticityusing Cochranrsquos 119862 test and transformed if necessary [5]

Relationships between species abundance and abioticparameters were examined using the RELATE procedurein PRIMER RELATE is the equivalent of a nonparametricMantel test [15] it assesses the degree of correspondencebetween matrices and via a randomization test it providesa measure of statistical significance of the relationship [16]the matrix of similarities between phytoplankton speciesabundances (based on Bray-Curtis coefficient from Log(119909 +1)-transformed data) was compared with a matrix of thesimilarity between abiotic parameters (based on Euclideandistance from Log(119909+1)-transformed data)The significanceof any correlation between matrices is assessed with arandomization test

Analyses of similarity (ANOSIM) randomization testswere used to test for differences in community composition(with presenceabsence transformed data) and for differencesin species abundance (with Log(119909 + 1)-transformed data)between control and disturbed stations [17] Differencesfound using ANOSIM were followed up using the SIMPERanalysis to identify which species primarily accounted forthe observed differences between sites SIMPER generates aranking of the species responsible for the significant differ-encesThese analyses used a matrix composed of Bray-Curtissimilarity coefficient generated with Log(119909 + 1)-transformedspecies abundance data [13]

Analysis of variance (ANOVA) was used to test thehypothesis that the abundance of each group of taxa differedbetween disturbed and control stations [13] Factor stationwas fixed The same design was employed for nutrient con-centrations pH temperature and suspended matter TukeyrsquosHSD test was applied for multiple comparisons of means

Canonical correspondence analysis (CCA) a direct gra-dient analysis technique [18] was used to investigate therelationship between microalgae species and environmentalparameters Microalgae abundance data were Log(119909 + 1)-transformed prior to the analysis in order to stabilise thevariance and to optimise the signal-to-noise ratio in the

Journal of Marine Biology 3

Table 1 The mean plusmn SD values of physical and chemical variables measured at all stations during the sampling period Difference betweencontrol station (M) and disturbed station (K) was tested by ANOVA

Variables Unit 119873 Disturbed station (K) Control station (M) 119865(116)

Tukeyrsquos testSM gsdotLminus1 9 1200 plusmn 676 700 plusmn 070 432 nsNO2

minus

120583molsdotLminus1 9 0301 plusmn 012 0283 plusmn 009 0119 nsNO3

minus

120583molsdotLminus1 9 491 plusmn 285 591 plusmn 004 098 nsNH4

+

120583molsdotLminus1 9 2336 plusmn 1680 417 plusmn 019 1042lowast M lt KPO4

3minus

120583molsdotLminus1 9 0307 plusmn 017 0684 plusmn 005 2533lowastlowastlowast K ltMSi(OH)4 120583molsdotLminus1 9 828 plusmn 193 094 plusmn 005 11541lowastlowastlowast M lt KTP 120583molsdotLminus1 9 326 plusmn 084 334 plusmn 034 986lowast M lt KTN 120583molsdotLminus1 9 4529 plusmn 2608 1632 plusmn 047 986lowast M lt KpH 9 797 plusmn 015 732 plusmn 034 2570lowastlowastlowast M lt KSalinity gsdotLminus1 9 3891 plusmn 044 3824 plusmn 088 396 ns119879∘ ∘C 9 3103 plusmn 105 2726 plusmn 096 5902lowast M lt Klowast

119875 lt 005 lowastlowastlowast119875 lt 0001 SM suspended matter ns not significant

data set Downweighting for rare species was performedForward selection and associated Monte Carlo permutationtests (499 unrestricted permutations) were used to identifya subset of environmental variables that contributed mostlyto the species abundances in the data set The environmentalparameters which better described the distribution of thespecies data were a priori identified by forward selection[18] Only significant environmental variables are includedin the model The same procedure was repeated usingspecies abundance and nutrient ratios (Si N N P and Si P)CANOCO 45 (Scientia Software) software was used

3 Results

The disturbed station (K) exhibited quite higher concentra-tions of ammonium Si(OH)

4 TP and TN and higher pH

and temperature Phosphatewas higher in control station (M)(Table 1) We also calculate nutrient ratios NP ratio [DIN(DIN = NO

2

minus

+ NO3

minus

+ NH4

+) to DIP (DIP = PO4

3minus)] indisturbed station (K) was 7573 plusmn 3543 (mean plusmn sd) and1524 plusmn 122 in control station (M) This average in disturbedstationwas higher than the Redfield ratio (16) which suggestspotential P-limitation in this station SiN ratiowas 041plusmn020and 009plusmn001 in disturbed and control stations respectivelySiP ratiowas 2638 plusmn 999 and 139plusmn 014 in disturbed stationand control station respectively which suggests potential Si-limitation in control station

The results of RELATE tests indicated that there is acorrelation between physicochemical parameters and speciesabundance (Spearman rank correlation statistic Rho =0484) None of the 999 random permutations resulted in acorrelation equal to or greater than the measured value of0484 indicating that the correlation was significant at the119875 lt 0001 level

For phytoplankton 76 species were counted including 54in the control station (M) and 74 species in the disturbedstation (K) (Table 2) Analysis of similarity (ANOSIM) ofphytoplankton microalgae species abundances log(119909 + 1)-transformed showed significant differences (119877 = 0954119875 = 001) between the control station and the disturbed

02000400060008000

10000120001400016000

Dinoflagellates Diatoms Cyanobacteria

(ind

L)

KM

Figure 2 Average abundance of planktonic microalgae in pros-pected stations K Skhira (disturbed station) M Mahdia (controlstation)

station Similar results are obtained (119877 = 0913 119875 = 001)using presenceabsence transformed data showing that thedissimilarities between stations are due to both speciescomposition and their abundances

Dinoflagellates diatoms and cyanobacteria were com-mon inwater column (Figure 2) Abundances of those groupswere included in the univariate analyses of variance Thenumber of species (S) was higher in polluted station (F

(117)

= 695 119875 = 0017) Dinoflagellates differed significantlybetween stations (F

(117)= 566 119875 = 0029) with high abun-

dance in control station (Tukeyrsquos test) When dinoflagellateswere grouped in order significant differences were detectedfor Prorocentrales and Gonyaulacales (F

(117)= 617 119875 =

0024 and F(117)

= 1249 119875 = 0003 resp) which weremore abundant in control station Diatoms abundance washigher (F

(117)= 1639 119875 lt 0001) in disturbed station (K)

and specially centric diatoms (F(117)

= 716 119875 = 0016)Cyanobacteria abundance was also higher (F

(117)= 1261

119875 = 0002) in disturbed station (K) No significant differenceswere recorded for total abundance and for H1015840 index

Analyses of similarity percentage (SIMPER) showedthat the average dissimilarity between the control and dis-turbed groups is high (8631) This procedure also alloweddetermining the species that contribute to this dissimilarity


Table 2 Presence-absence species list of planktonic microalgae identified in prospected stations

StationsSkhira (K)

(disturbed station)Mahdia (M)

(control station)Dinoflagellates

Alexandrium minutumHalim 1 1Alexandrium sp 1 1Amphidinium carteraeHulburt 1 1Amphidinium sp 1 1Ceratium candelabrum (Ehrenberg) Stein 1 0Ceratium furca (Ehrenberg) Claparede amp Lachmann 1 1Ceratium fusus (Ehrenberg) Dujardin 1 0Ceratium lineatum (Ehrenberg) Cleve 1 0Coolia monotisMeunier 1 1Dinophysis rotundata Claparede amp Lachmann 1 0Diplopsalopsis sp 1 1Goniodoma polyedricum (Pouchet) Joslashrgensen 1 1Gonyaulax polyedra FStein 1 1Gonyaulax sp 1 1Gonyaulax spinifera (Claparede amp Lachmann) Diesing 1 1Gymnodinium veneficum 1 0Gyrodinium sp 1 1Hermesinum sp 1 1Karenia selliformis AJHaywood KASteidinger amp LMacKenzie in Haywood et al 1 0karlodinium veneficum 1 1Ostreopsis ovata Fukuyo 1 1Peridinium sp 1 1Podolampas palmipes Stein 1 1Polykrikos sp 1 1Prorocentrum gracile Schutt 1 1Prorocentrum lima (Ehrenberg) Dodge 1 1Prorocentrum micans Ehrenberg 0 1Prorocentrum minimum (Pavillard) Schiller 1 1Prorocentrum rathymum (Loeblich) Shirley and Schmidt 1 1Prorocentrum triestinum JSchiller 1 1Protoperidinium divergens (Ehrenberg) Balech 1 1Protoperidinium granii (Ostenfeld) Balech 1 1Protoperidinium ovatum Pouchet 1 1Protoperidinium pellucidum Bergh ex Loeblich Jr amp Loeblich III 1 0Protoperidinium quinquecorne (Abe) Balech 1 1Protoperidinium sp 1 1Protoperidinium steinii (Joslashrgensen) Balech 1 1Protoperidinium diabolus (Cleve) Balech 1 0Pyrophacus sp 1 0Scrippsiella sp 1 1

DiatomsAchnanthes sp 1 0Amphiprora sp 1 0Amphora sp 1 1


Table 2 Continued

StationsSkhira (K)


(control station)Asterionellopsis glacialis (Castracane) Round 1 0Bacteriastrum sp 1 1Biddulphia sp 1 1Bacteriosira fragilis (Gran) Gran 0 0Coscinodiscus sp 1 0Chaetoceros sp 1 1Climacosphenia sp 1 1Guinardia sp 1 1Grammatophora sp 1 1Licmophora sp 1 1Leptocylindrus sp 1 1Melosira sp 1 1Navicula sp 1 1Nitzschia sp 1 1Nitzshoides sp 1 0Plagiotropis sp 1 1Pseudo-nitzschia sp 1 0Pinnularia sp 1 1Pleurosigma sp 1 1Rhizosolenia sp 1 1Rhizosolenia fragilissima Bergon 1 1Rhabdonema sp 1 1Striatella unipunctata (Lyngbye) CAgardh 1 0Skeletonema costatum (Greville) Cleve 1 0Thalassionema nitzschioides (Grunow)Mereschkowsky 1 1Thalassiosira sp 1 0Synedra sp 1 1Triceratium sp 1 0

CyanobacteriaAnabaena sp 1 1Pseudo-anabaena sp 1 0Oscillatoria sp 1 1Spirulina subsalsa Oersted ex Gomont 1 0Croccocus sp 1 0

they are Coscinodiscus sp Nitzshoides sp Chaetoceros spAnabaena sp Gyrodinium sp Biddulphia sp StriatellaunipunctataThalassiosira sp Leptocylindrus sp and Scripp-siella sp Species which were more abundant in disturbedstation (K) are Biddulphia sp Polykrikos sp Pleurosigma spCoolia monotis and Amphidinium carterae (Table 4) Specieswhich had higher abundance in control station areOstreopsisovata Coolia monotis Prorocentrum lima Pleurosigma spPeridinium sp Pinnularia sp and Polykrikos sp

CCA analysis using all measured physicochemicalparameters (Table 5) indicated that the axis I (eigenvalue

1205821= 0493) and axis II (eigenvalue 120582

2= 0154) expressed

475 of the cumulative variance species-environmentalvariable Eigenvalues (119875 value) calculated with the CCAassociated with the analysis in Monte Carlo test were usedto select four environmental variables statistically significant(119875 lt 005) that explain better variations (8188) of speciescomposition they are Si(OH)

4 NH4

+ TN and suspendedmatter (3372 941 1018 and 1186 of total varianceresp) In the triplot diagram (Figure 3) disturbed stationsamples (K) were grouped in the right and are correlatedwith the axis I which is defined by the environmental


66

60

20

4333

73

4847

24

4

22

2829

31

21469 50

68

11

61

NT

SM

5553

76

56

37

M

MM

MM

MM M

M

K

K

K

K

K

KKK K

10

12

minus06

minus04

NH4+

Si(OH)4

Figure 3 Diagram of canonical correspondence analysis (CCA)showing the effects of environmental variables onmicroalgae speciesordination according to the first and second axes Species associatedwith K (disturbed station) samples are as follows 4 Amphidiniumsp 24 Prorocentrum minimum 48 Achnanthes sp 20 Kareniaselliformis 56 Chaetoceros sp 60 Licmophora sp 76Thalassiosirasp 55 Coscinodiscus sp 53 Biddulphia sp 37 Protoperidiniumpellucidum 61 Leptocylindrus sp and 66 Plagiotropis sp Speciesassociated with M (control station) samples are as follows 9Coolia monotis 11 Diplopsalopsis sp 21 Ostreopsis ovata 22Peridinium sp 28 Prorocentrum lima 29 Prorocentrum triestinum50 Amphora sp and 68 Pinnularia sp Species whose coverage andfrequency are less than 40 were eliminated

variables Si(OH)4 NH

4

+ TN and suspended matter(intraset correlation minus0955 minus0613 minus0588 and minus0497resp)

CCA analysis using nutrientsrsquo ratios (Table 5) indicatedthat axis I (eigenvalue 120582

1= 0153) and axis II (eigenvalue

1205822= 0104) expressed 4520 of the cumulative variance

species-environmental variable Monte Carlo test showedthat all nutrient ratios were statistically significant (119875 =0002) and explain 4522 of variation of species ordinationdetailed as Si P (5504) N P (2465) and Si P (203)of total explained variance In the triplot diagram (Figure 4)disturbed station samples (K) were grouped in the rightand are correlated with axis I which is defined by thenutrient ratios N P Si N and Si P (intraset correlation0720 minus0761 and 0877 resp) Most of diatoms specieswere associated with K samples (disturbed station) while Msamples (control station) were associated with dinoflagellatesspecies (Figure 4)

4 Discussion

Our results show that the abundance and composition ofphytoplankton varied between the disturbed and controlstations Dinoflagellates abundance especially the ordersProrocentrales and Gonyaulacales was low in disturbed

10

10

45

45 58

22

29 63

21

119

31

28

41

50

49

54

51 71

2033

N P

Si P

Si N

38

8335

6

19

47

25

minus10

minus06

K

K

K

K

K

K

K

K

K

MMM

MMMM M

M

Figure 4 Diagram of canonical correspondence analysis (CCA)showing the effects of nutrient ratios on microalgae species ordi-nation according to the first and second axes Species associatedwith K (disturbed station) were the dinoflagellates 3 Amphidiniumcarterae 19 Karlodinium veneficum 8 Ceratium candelabrum 6Ceratium fusus 38 Protoperidinium diabolum 34 Protoperidiniumdivergens 20Karenia selliformis and 41 Pyrophacus sp the diatoms51 Climacosphenia sp 47 Biddulphia sp 54 Licmophora sp 49Coscinodiscus sp 50 Chaetoceros sp and 45 Amphora sp andthe cyanobacteria 71 Thalassiosira sp Species associated with M(control station) were the dinoflagellates 9 Coolia monotis 11Diplopsalopsis sp 21 Ostreopsis ovata 22 Peridinium sp 28 Pro-rocentrum lima 31 Protoperidinium sp 29 Prorocentrum triestinumand the diatoms 45Asterionellopsis glacialis 58Navicula sp Specieswhose coverage and frequency were less than 40 were eliminated

station contrary to diatoms that were more abundant Phyto-plankton variation between control and disturbed conditionsis similar to those found by Ben Brahim et al [19] in the southof Tunisia (Gulf of Gabes) byWang et al [20] in BaiyangdianLake (China) and by Davies and Ugwumba [21] in OkpokaCreek (Nigeria) In addition some fertilization experimentshave shown shifts in relative dominance among algal specieswith a shift towards cyanobacteria [2] and diatoms [22] undernutrient enrichment

The high species number in disturbed station reflects anenhanced nutrient enrichment as reported in other studieswith a high abundance of centric over pennate diatoms [2324] The same observation was made in Lebanese coastalwaters [25] Indeed Egge and Aksnes [26] and Fouillaronet al [27] showed that diatoms always numerically dominatedthe phytoplankton community when silicic acid (DSi) con-centrations were above 2120583M Previous studies have shownthat nutrient enrichment was associated with changes inspecies composition [1 21 28] However each microalgalgroup displayed a unique spatial pattern in response to N andP enrichment [2] Our data showed that there is an increaseof diatoms and cyanobacteria in disturbed station comparedto control For insistence Anabaena sp that was abundant in


Table 3 ANOVA on microalgae abundances between controls anddisturbed stationsMMahdia (control station) K Skhira (disturbedstation) ns not significant

DF MS 119865 119875 Tukeyrsquos testTotal abundance

Station 1 781250 0002 0963 nsResiduals 17 356395368

Species numberStation 1 338 6953 0017943 K gtMResiduals 17 48611198671015840


DinoflagellatesStation 1 504560556 5662 0029 K ltMResiduals 17 89109085

GonyaulacalesStation 1 297273472 12488 0003 K ltMResiduals 17 23802884

GymnodinialesStation 1 68056 0036 085 nsResiduals 17 1850408

Total 18Dinophysiales


ProrocentralesStation 1 40951250 6177 0024 K ltMResiduals 17 6628897

PeridinialesStation 1 1250 0001 0974 nsResiduals 17 1148603

DiatomsStation 1 481119864 + 08 16398 gt0001 K gtMResiduals 17 293119864 + 07

Pennate diatomsStation 1 48840139 0924 035 nsResiduals 17 52850433

Centric diatomsStation 1 196020000 7158 0016 M lt KResiduals 17 27384118

CyanobacteriaStation 1 533889 12613 0002 K gtMResiduals 17 42326

Skhira station (Table 3) is pollution indicator species usuallyassociated with eutrophic water bodies [29] and has beenrecorded inOkpoka Creek (Niger) due to the increased influxof nutrients from anthropogenic inputs [21]

Phytoplankton species respond in different ways to nutri-ent enrichment probably based on their life cycle charac-teristics such as growth rate and their absorptive capacity[30] According to Lepoint et al [31] it is difficult to predictwhich group would be favored by the moderate intake of

nutrients For example Lin et al [32] found that nutrientenrichment (NO

3

minus NH4

+ and PO4

minus either alone or incombination) did not result in an increase in the abundanceof phytoplankton These communities respond in complexways to the addition of nutrients The seasonal influence onthe response of microalgae to nutrient enrichment seemsimportant [33]

Our results showed that Si(OH)4and nitrogen (NH

4

+NT) are factors that contribute to differences of speciesabundance between stations (CCA Figure 3) Indeed silicon(Si) played the most important role in the growth anddevelopment of diatoms while dinoflagellates were mostlycontrolled by phosphorus (P) availability [34 this study]When phosphorus loading increased a shift from diatoms todinoflagellates was observed [35] Krumme and Liang [36]and Wear et al [28] have found that diatoms were highin terms of abundance and species composition in nutrientenrichment station Pennate and centric diatoms responddifferently to disturbance Indeed our results showed thatcentric diatoms were higher in disturbed station result foundalso by Harrison et al [37] Indeed diatoms have the capacityof rapid cell division with growth rates (up to 59 dayminus1)generally much higher than those for dinoflagellates (up to27 day minus1) based on equivalent body mass [38]

The canonical molar Redfield ratio of 16 1 (N P) inthe water column has traditionally been considered thedetermining 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 todissolved inorganic P (DIP) is greater than 16 1 and nutrientsare limiting the system is considered P-limited [41] Ourdata allow us to highlight that disturbed station was char-acterized by a slight P-limitation which is known to inducethe synthesis of alkaline phosphatases AP for numerousphytoplankton species such as Alexandrium catenella [42]Ptychodiscus brevis [43] Karenia mikimotoi [44] Gymno-dinium catenatum and Alexandrium tamarense [45] AP isan enzyme used to convert DOP (dissolved organic P) tobioavailable DIP (dissolved inorganic P) by cleaving P fromthe DOP molecule [42 46] Some diatoms also do produceAP includingPseudo-nitzschiaChaetoceros and Skeletonema[41 47] But previous studies [41 47] have indicated that somephytoplankton groups (dinoflagellates and coccolithophores)may have a greater P-requirement and utilize DOP morereadily than diatoms through the production of AP

We also found a strong Si-limitation in the control stationwhich seems to be the main driver of differences in diatomsrsquoassemblages between control and disturbed conditions Thisstation showed low abundance of diatoms (Figure 4 Table 4)compared to disturbed station This result was also found byprevious study [1 21 34 35] that showed that increase in theN Si ratio was proportional with the increase in flagellatesand the decrease of diatoms abundance [1]

Differences in species abundance and compositionbetween the two stations cannot be assigned only to nutri-ents enrichment many other factors could influence theirdistributions such as watermotion [1] light intensity [2] andpollution by heavymetals For instance in Skhira station (K)


Table 4 SIMPER results

Species Group K (disturbed station) Group M (control station) Av Diss DissSD Contrib CumAv abund Av abund

Coscinodiscus sp 556667 0 1547 114 1793 1793Ostreopsis ovata 3333 456667 1393 389 1614 3407Coolia monotis 0 384444 114 198 1321 4728Prorocentrum lima 7778 366667 1088 211 1261 5988Nitzshoides sp 241667 0 535 046 619 6608Nitzschia sp 93333 12222 258 126 299 6907Navicula sp 163333 144444 248 138 288 7195Pleurosigma sp 300 83889 197 115 228 7424Peridinium sp 13333 53889 149 129 172 7772Pinnularia sp 5556 48333 138 161 16 7932Prorocentrum rathymum 47778 0 128 134 148 808Karenia selliformis 400 0 091 052 106 8186Polykrikos sp 25556 33889 09 145 105 8291Licmophora sp 31111 0 089 149 103 8393Chaetoceros sp 24444 0 075 062 087 848Anabaena sp 28889 11667 064 143 075 8555Protoperidinium sp 8889 150 057 128 066 8693Gyrodinium sp 11111 6111 054 056 062 8756Biddulphia sp 16667 0 051 058 059 8815Striatella unipunctata 200 0 049 088 057 8872Thalassiosira sp 16667 0 045 088 052 8923Leptocylindrus sp 13333 0 042 057 049 8972Scrippsiella sp 13333 0 041 071 048 902

Table 5 CCA results

Using environmental variables Using nutrient ratiosAxis 1 Axis 2 Axis 1 Axis 2

Eigenvalues 0493 0154 0153 0104Species-environment correlations 0994 0943 0975 0935Cumulative percentage variance of species data 297 389 388 452Cumulative percentage variance of species-environment relation 362 475 858 1000Sum of all eigenvalues 1662 1623Sum of all canonical eigenvalues 1361 0734Explained variance by CCA 8188 4522Intraset correlations of variables with axes

NO2

minus

minus0135 minus0450Si(OH)4 minus0955 minus0026NO3

minus 0281 minus0339NH4

+

minus0613 minus0445PO4

3minus 0832 minus0060NT minus0588 minus0384PT 0145 0210119879 minus0879 minus0072Salinity minus0386 0143pH minus0821 0033SM minus0497 minus0411N P 07204 06059Si N 07606 minus03145Si P 08765 02950


there is phosphate treatments factory that contaminateswaterand sediments by cadmiummercury arsenic and so forth asreported by Smaoui-Damak et al [6]

The phytoplankton-grazer interaction also plays animportant role in controlling abundance and diversity ofmicroalgae Indeed plankton is a food source for a range ofgrazers which influence their diversity and their abundance[48]

The present study underlines the use of phytoplanktonassemblages as general indicators of anthropogenic distur-bance in marine ecosystems It was clear that the effectof nutrient loading and turbidity could be depicted inthe community structure and diversities of phytoplank-ton During period of study (August) nutrient enrichment(mainly Si(OH)

4and NH

4

+) and turbidity were associatedwith increase of diatoms and cyanobacteria and decreaseof dinoflagellates abundance especially Gonyaulacales andProrocentrales The existence of Si- and P-limitation instudy sites seems to be the main driver of differences inphytoplankton assemblages between control and disturbedconditions

Appendix

See Table 2

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

Theauthors wish to acknowledge use of theMaptool programfor analysis and graphics in this paper They thank DrIdriss and Ahmed Omar Mabrouk and Groupe ChimiqueTunisien (CGT Skhira factory) for essential help They alsothank Professor Robert A Patzner and the reviewer forhis comments which improved the quality of the paper Inaddition they thank all the team of the Journal of MarineBiology for their professionalism and rapid response

References

[1] T J Smayda D G Borkman G Beaugrand andA G BelgranoldquoEcological effects of climate variation in the North Atlanticphytoplanktonrdquo in Ecological Effects of Climate Variations in theNorth Atlantic N C Stenseth G Ottersen J Hurrell and ABelgrano Eds Oxford University Press 2004

[2] A R Armitage T A Frankovich and J W FourqureanldquoVariable responses within epiphytic and benthic microalgalcommunities to nutrient enrichmentrdquo Hydrobiologia vol 569no 1 pp 423ndash435 2006

[3] A R Price ldquoThe marine food chain in relation to biodiversityrdquoTheScientificWorldJournal vol 1 pp 579ndash587 2001

[4] F Gomez and G Gorsky ldquoAnnual microplankton cycles inVillefranche Bay Ligurian Sea NWMediterraneanrdquo Journal ofPlankton Research vol 25 no 4 pp 323ndash339 2003

[5] A J Underwood ldquoBeyond BACI the detection of environmen-tal impacts on populations in the real but variable worldrdquoJournal of ExperimentalMarine Biology and Ecology vol 161 no2 pp 145ndash178 1992

[6] W Smaoui-Damak A Hamza-Chaffai B Berthet and J CAmiard ldquoPreliminary study of the clam Ruditapes decussatusexposed in situ to metal contamination and originating fromthe gulf of Gabes Tunisiardquo The Bulletin of EnvironmentalContamination and Toxicology vol 71 no 5 pp 961ndash970 2003

[7] M Ben Brahim A Hamza I Hannachi et al ldquoVariability inthe structure of epiphytic assemblages of Posidonia oceanica inrelation to human interferences in the Gulf of Gabes TunisiardquoMarine Environmental Research vol 70 no 5 pp 411ndash421 2010

[8] A Hattour K Ben Mustapha A El Abed and M ChaouchldquoLrsquoecosysteme du golfe de Gabes degradation de son couvertvegetal et de sa pecherie benthiquerdquo Bulletins de lrsquoInstitutOceanographique et de Peche de Salammbo vol 25 pp 5ndash401998

[9] K Ben Mustapha ldquoThe Gulf of Gabes a case study in theMediterranean decline in fishing out the Mediterraneanrdquo inProceedings of the 21st Session of the General Fisheries Commis-sion for the Mediterranean (GFCM rsquo95) Greenpeace Interna-tional Ed pp 8ndash9 The Netherlands 1995

[10] K Grasshoff M Ehrhardt and K Kremling Methods ofSeawater Analysis Chemie GmbH Weinheim Germany 1983

[11] D M Moore and R C Reynolds X-Ray Diflraction and TheIdentification and Analysis of Clay Minerals Oxford UniversityPress Oxford UK 1989

[12] H Utermohl ldquoZur vervollkommung der quantitativen Phy-tomicroorganisms-MethodikrdquoMitteilungen der InternationalenVereinigung furTheoretische und Angewandte Limnologie vol 9pp 1ndash38 1958

[13] L Mabrouk A Hamza and M N Bradai ldquoVariability inthe structure of planktonic microalgae assemblages in watercolumn associated with Posidonia oceanica (L) bed in TunisiardquoJournal of Marine Biology vol 2014 Article ID 621238 7 pages2014

[14] J H Zar Biostatistical Analysis Prentice Hall Upper SaddleRiver NJ USA 4th edition 1999

[15] P J Somerfield K R Clarke and F Olsgard ldquoA comparisonof the power of categorical and correlational tests applied tocommunity ecology data from gradient studiesrdquo Journal ofAnimal Ecology vol 71 no 4 pp 581ndash593 2002

[16] K R Clarke and R M Warwick Change in Marine Commu-nities An Approach to Statistical Analysis and InterpretationPRIMER-E Plymouth UK 2001

[17] K R Clarke ldquoNon-parametric multivariate analyses of changesin community structurerdquo Australian Journal of Ecology vol 18no 1 pp 117ndash143 1993

[18] C J F ter Braak and P F M Verdonschot ldquoCanonical corre-spondence analysis and related multivariate methods in aquaticecologyrdquo Aquatic Sciences vol 57 no 3 pp 255ndash289 1995

[19] M Ben Brahim A Hamza S Ben Ismail L Mabrouk ABouain and L Alaya ldquoWhat factors drive seasonal variationof phytoplankton protozoans and metazoans on leaves ofPosidonia oceanica and in the water column along the coast ofthe Kerkennah Islands TunisiardquoMarine Pollution Bulletin vol71 no 1-2 pp 286ndash298 2013

[20] X Wang Y Wang L Liu J Shu Y Zhu and J Zhou ldquoPhyto-plankton and eutrophication degree assessment of BaiyangdianLake Wetland Chinardquo The Scientific World Journal vol 2013Article ID 436965 8 pages 2013


[21] O A Davies andO A Ugwumba ldquoTidal influence on nutrientsstatus and phytoplankton population of Okpoka Creek UpperBonny Estuary Nigeriardquo Journal of Marine Biology vol 2013Article ID 684739 16 pages 2013

[22] T A Frankovich A R Armitage A HWachnicka E E Gaiserand J W Fourqurean ldquoNutrient effects on seagrass epiphytecommunity structure in florida bayrdquo Journal of Phycology vol45 no 5 pp 1010ndash1020 2009

[23] X N Verlecar S R Desai A Sarkar and S G Dalal ldquoBiologicalindicators in relation to coastal pollution along KarnatakaCoast Indiardquo Water Research vol 40 no 17 pp 3304ndash33122006

[24] K P Raveesha A Mohan N Chethan R J Katti and H R VReddy ldquoCommunity structure of net phytoplankton along surfzone of Mangalore during southwest monsoon seasonrdquo IndianJournal of Marine Sciences vol 39 no 3 pp 445ndash448 2010

[25] M Abboud Abi Saab M Fakhri M T Kassab and N MatarldquoPhenomene exceptionnel drsquoeaux colorees au printemps dans lazone cotiere Libanaise de Zouk-Nahr El Kelbrdquo Lebanese ScienceJournal vol 9 pp 61ndash70 2007

[26] J K Egge and D L Aksnes ldquoSilicate as regulating nutrientin phytoplankton competitionrdquoMarine Ecology Progress Seriesvol 83 no 2-3 pp 281ndash289 1992

[27] P Fouillaron P Claquin S LHelguen et al ldquoResponse ofa phytoplankton community to increased nutrient inputs amesocosm experiment in the Bay of Brest (France)rdquo Journal ofExperimental Marine Biology and Ecology vol 351 no 1-2 pp188ndash198 2007

[28] D J Wear M J Sullivan A D Moore and D F Millie ldquoEffectsof water-column enrichment on the production dynamics ofthree seagrass species and their epiphytic algaerdquoMarine EcologyProgress Series vol 179 pp 201ndash213 1999

[29] D I Nwankwo ldquoSeasonal changes in phytoplankton composi-tion and diversity in the Epe lagoon Nigeriardquo Acta Hydrobiol-ogy vol 40 no 2 pp 83ndash92 1998

[30] C J M Philippart G C Cadee W Van Raaphorst andR Riegman ldquoLong-term phytoplankton-nutrient interactionsin a shallow coastal sea algal community structure nutrientbudgets and denitrification potentialrdquo Limnology andOceanog-raphy vol 45 no 1 pp 131ndash144 2000

[31] G Lepoint J Jacquemart J-M Bouquegneau V Demoulinand S Gobert ldquoField measurements of inorganic nitrogenuptake by epiflora components of the seagrass Posidonia ocean-ica (Monocotyledons Posidoniaceae)rdquo Journal of Phycologyvol 43 no 2 pp 208ndash218 2007

[32] H-J Lin SWNixon D I Taylor S L Granger and B A Buck-ley ldquoResponses of epiphytes on eelgrass Zostera marina L toseparate and combined nitrogen and phosphorus enrichmentrdquoAquatic Botany vol 52 no 4 pp 243ndash258 1996

[33] H A Neckles ldquoThe role of epiphytes in seagrass productionand survival microcosm studies and simulation modellingrdquoin Proceedings and Conclusions of Workshops on SubmergedAquatic Vegetation and Photosynthetically Active Radiation LJ Morris and D A Tomasko Eds Special Publication St JohnsRiver Water Management District Palatka Fla USA 1993

[34] M A Chikhaoui A S Hlaili and H H Mabrouk ldquoSeasonalphytoplankton responses to NSiP enrichment ratio in the Biz-erte Lagoon (southwesternMediterranean)rdquoComptes RendusmdashBiologies vol 331 no 5 pp 389ndash408 2008

[35] I J Hodgkiss ldquoThe NP ratio revisitedrdquo in Prevention andManagement of Harmful Algal Blooms in the South China Sea

K C Ho and Z D Wang Eds pp 344ndash355 School of Scienceand Technology The Open University of Hong Kong 2001

[36] U Krumme and T-H Liang ldquoTidal-induced changes in acopepod-dominated zooplankton community in a macrotidalmangrove channel in northern Brazilrdquo Zoological Studies vol43 no 2 pp 404ndash414 2004

[37] P J Harrison P J Clifored W P Cochlan et al ldquoNutrient andplankton dynamics in the Fraser River plume Strait of GeorgiaBritish ColumbiardquoMarine Ecology Progress Series vol 70 no 3pp 291ndash304 1991

[38] T J Smayda ldquoHarmful algal blooms their ecophysiologyand general relevance to phytoplankton blooms in the seardquoLimnology and Oceanography vol 42 no 5 pp 1137ndash1153 1997

[39] A C Redfield ldquoThe biological control of chemical factors in theenvironmentrdquo American Scientist vol 46 no 3 pp 205ndash2211958

[40] J Beardall E Young and S Roberts ldquoApproaches for determin-ing phytoplankton nutrient limitationrdquoAquatic Sciences vol 63no 1 pp 44ndash69 2001

[41] D Nicholson S Dyhrman F Chavez and A Paytan ldquoAlkalinephosphatase activity in the phytoplankton communities ofMonterey Bay and San Francisco Bayrdquo Limnology and Oceanog-raphy vol 51 no 2 pp 874ndash883 2006

[42] C Jauzein C Labry A Youenou J Quere D Delmasand Y Collos ldquoGrowth and phosphorus uptake by the toxicdinoflagellateAlexandrium catenella (dinophyceae) in responseto phosphate limitationrdquo Journal of Phycology vol 46 no 5 pp926ndash936 2010

[43] G A Vargo and E Shanley ldquoAlkaline phosphatase activity inthe red-tide dinoflagellate Ptychodiscus brevisrdquoMarine Ecologyvol 6 no 3 pp 251ndash264 1985

[44] H Yamaguchi H Sakou K FukamiM AdachiM Yamaguchiand T Nishijima ldquoUtilization of organic phosphorus and pro-duction of alkaline phosphatase by the marine phytoplanktonHeterocapsa circularisquama Fibrocapsa japonica and Chaeto-ceros ceratosporumrdquo Plankton Biology and Ecology vol 52 no2 pp 67ndash75 2005

[45] J O Seok T Yamamoto Y Kataoka OMatsuda YMatsuyamaand Y Kotani ldquoUtilization of dissolved organic phosphorusby the two toxic dinoflagellates Alexandrium tamarense andGymnodinium catenatum (Dinophyceae)rdquo Fisheries Science vol68 no 2 pp 416ndash424 2002

[46] A D Cembella N J Antia and P J Harrison ldquoThe utilizationof inorganic and organic phosphorus compounds as nutrientsby eukaryotic microalgae a multidisciplinary perspective Part2rdquoCritical Reviews inMicrobiology vol 11 no 1 pp 13ndash81 1984

[47] M B Peacock and R M Kudela ldquoA method for determiningalkaline phosphatase activity in marine phytoplankton usingspectrofluorometryrdquo Journal ofMicrobiologicalMethods vol 89no 3 pp 209ndash212 2012

[48] O Sarnelle K W Kratz and S D Cooper ldquoEffects of aninvertebrate grazer on the spatial arrangement of a benthicmicrohabitatrdquo Oecologia vol 96 no 2 pp 208ndash218 1993

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


9∘10

9984008∘20

998400

32∘30

998400

33∘20

998400

34∘10

998400

35∘00

998400

35∘50

998400

36∘40

998400

10∘50

99840011

∘40

99840012

∘30

99840010

∘00

998400

K

Tunisia

M

50km

Figure 1 Map of the study area showing the sampling stations withM Mahdia (control station) and K Skhira (disturbed station)

is exposed since 1988 to industrial effluents fromphosphoricacid and fertilizer production of the ldquoGroupe ChimiqueTunisienrdquo (GCT)These industrial activities affect thismarineecosystem [6] as in deterioration of seagrass bed [7 8] anddecline in fishing [9] The sampling depth in all stations was5m

22 Sampling and Data Collection In accordance with thehierarchical sampling design three sites (500m apart) werechosen randomly at each station At each site three replicatewater samples were collected from each site for nutrientanalyses Samples were taken using a 125mL plastic bottlehaving received prior treatment with hydrochloric acid Onreturning to the surface samples were shaken and thenfiltered with a 045 120583m filter (cellulose acetate 17mm)Samples were frozen in liquid nitrogen for transportationto the laboratory where concentrations of NO

2

minus NO3

minusNH4

+ PO4

minus Si(OH)4 total dissolved nitrogen (TN) and

total dissolved phosphorus (TP) were measured followingstandard colorimetric techniques [10]

In each site temperature salinity and pH were measuredimmediately after sampling using amultiparameter kit (Multi340iSET) For determination of suspended particulate mat-ter (SM) concentration water samples were filtered throughpreweighedGFCWhatman filters (pore size 045 pm) whichwere subsequently dried at 80∘C for about 24 h and reweighed[11]

For phytoplankton enumeration three replicate watersamples (about 100m apart at 5m depth) were selected fromthe same sitesWater column samples using 1-litre glass bottlesampler lowered from the surface to the near bottom wereconducted by scuba diving All samples were collected atnoon

Samples were fixed with Lugolrsquos solution and finallypreserved in 5 formalin All sampling waters were kept

in the dark at ambient temperature until their microscopicobservation Settling long glass tubes used for sedimentationprocedure were 2 cm wide by 21 cm long and have a baseplate that contains a coverslip on which the algae settle Tomix the sample the bottle was gently tilted back and forth10 times before pouring [12] A 50mL subsample was pouredinto the settling chamber and left to settle for 24 h subsampleswere examined in an inverted microscope at medium (times 200)magnification by scanning the entire surface of the settlingchamber to enumerate epiphytic microalgae [12 13] Thetotal number of microalgae individuals (119873) contained in 1litre expressed as number of individuals per litre is obtainedby the following conversion 119873 = (119899 times 1000)(V) with 119899= the number of individuals counted and V = the volumeof the sedimentation chamber (50mL) [13] The identifiedtaxa were divided into groups (diatoms dinoflagellates andcyanobacteria)

23 Data Analysis Data were tested for normality usingthe Kolmogorov-Smirnov test [14] and for heteroscedasticityusing Cochranrsquos 119862 test and transformed if necessary [5]

Relationships between species abundance and abioticparameters were examined using the RELATE procedurein PRIMER RELATE is the equivalent of a nonparametricMantel test [15] it assesses the degree of correspondencebetween matrices and via a randomization test it providesa measure of statistical significance of the relationship [16]the matrix of similarities between phytoplankton speciesabundances (based on Bray-Curtis coefficient from Log(119909 +1)-transformed data) was compared with a matrix of thesimilarity between abiotic parameters (based on Euclideandistance from Log(119909+1)-transformed data)The significanceof any correlation between matrices is assessed with arandomization test

Analyses of similarity (ANOSIM) randomization testswere used to test for differences in community composition(with presenceabsence transformed data) and for differencesin species abundance (with Log(119909 + 1)-transformed data)between control and disturbed stations [17] Differencesfound using ANOSIM were followed up using the SIMPERanalysis to identify which species primarily accounted forthe observed differences between sites SIMPER generates aranking of the species responsible for the significant differ-encesThese analyses used a matrix composed of Bray-Curtissimilarity coefficient generated with Log(119909 + 1)-transformedspecies abundance data [13]

Analysis of variance (ANOVA) was used to test thehypothesis that the abundance of each group of taxa differedbetween disturbed and control stations [13] Factor stationwas fixed The same design was employed for nutrient con-centrations pH temperature and suspended matter TukeyrsquosHSD test was applied for multiple comparisons of means

Canonical correspondence analysis (CCA) a direct gra-dient analysis technique [18] was used to investigate therelationship between microalgae species and environmentalparameters Microalgae abundance data were Log(119909 + 1)-transformed prior to the analysis in order to stabilise thevariance and to optimise the signal-to-noise ratio in the





minus


minus


+


3minus




3 Results




2

minus

+ NO3

minus

+ NH4





02000400060008000

10000120001400016000


(ind

L)

KM




(117)


(117)= 566 119875 = 0029) with high abun-


(117)= 617 119875 =

0024 and F(117)





(117)= 1261





StationsSkhira (K)






Table 2 Continued

StationsSkhira (K)







2= 0154) expressed


4 NH4



66

60

20

4333

73

4847

24

4

22

2829

31

21469 50

68

11

61

NT

SM

5553

76

56

37

M

MM

MM

MM M

M

K

K

K

K

K

KKK K

10

12

minus06

minus04

NH4+

Si(OH)4



4






4 Discussion


10

10

45

45 58

22

29 63

21

119

31

28

41

50

49

54

51 71

2033

N P

Si P

Si N

38

8335

6

19

47

25

minus10

minus06

K

K

K

K

K

K

K

K

K

MMM

MMMM M

M
























3

minus NH4

+ and PO4



4









Table 5 CCA results



NO2

minus



+







4and NH

4


Appendix

See Table 2



Acknowledgments


References


























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





minus


minus


+


3minus




3 Results




2

minus

+ NO3

minus

+ NH4





02000400060008000

10000120001400016000


(ind

L)

KM




(117)


(117)= 566 119875 = 0029) with high abun-


(117)= 617 119875 =

0024 and F(117)





(117)= 1261





StationsSkhira (K)






Table 2 Continued

StationsSkhira (K)







2= 0154) expressed


4 NH4



66

60

20

4333

73

4847

24

4

22

2829

31

21469 50

68

11

61

NT

SM

5553

76

56

37

M

MM

MM

MM M

M

K

K

K

K

K

KKK K

10

12

minus06

minus04

NH4+

Si(OH)4



4






4 Discussion


10

10

45

45 58

22

29 63

21

119

31

28

41

50

49

54

51 71

2033

N P

Si P

Si N

38

8335

6

19

47

25

minus10

minus06

K

K

K

K

K

K

K

K

K

MMM

MMMM M

M
























3

minus NH4

+ and PO4



4









Table 5 CCA results



NO2

minus



+







4and NH

4


Appendix

See Table 2



Acknowledgments


References


























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



StationsSkhira (K)






Table 2 Continued

StationsSkhira (K)







2= 0154) expressed


4 NH4



66

60

20

4333

73

4847

24

4

22

2829

31

21469 50

68

11

61

NT

SM

5553

76

56

37

M

MM

MM

MM M

M

K

K

K

K

K

KKK K

10

12

minus06

minus04

NH4+

Si(OH)4



4






4 Discussion


10

10

45

45 58

22

29 63

21

119

31

28

41

50

49

54

51 71

2033

N P

Si P

Si N

38

8335

6

19

47

25

minus10

minus06

K

K

K

K

K

K

K

K

K

MMM

MMMM M

M
























3

minus NH4

+ and PO4



4









Table 5 CCA results



NO2

minus



+







4and NH

4


Appendix

See Table 2



Acknowledgments


References


























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Table 2 Continued

StationsSkhira (K)







2= 0154) expressed


4 NH4



66

60

20

4333

73

4847

24

4

22

2829

31

21469 50

68

11

61

NT

SM

5553

76

56

37

M

MM

MM

MM M

M

K

K

K

K

K

KKK K

10

12

minus06

minus04

NH4+

Si(OH)4



4






4 Discussion


10

10

45

45 58

22

29 63

21

119

31

28

41

50

49

54

51 71

2033

N P

Si P

Si N

38

8335

6

19

47

25

minus10

minus06

K

K

K

K

K

K

K

K

K

MMM

MMMM M

M
























3

minus NH4

+ and PO4



4









Table 5 CCA results



NO2

minus



+







4and NH

4


Appendix

See Table 2



Acknowledgments


References


























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


66

60

20

4333

73

4847

24

4

22

2829

31

21469 50

68

11

61

NT

SM

5553

76

56

37

M

MM

MM

MM M

M

K

K

K

K

K

KKK K

10

12

minus06

minus04

NH4+

Si(OH)4



4






4 Discussion


10

10

45

45 58

22

29 63

21

119

31

28

41

50

49

54

51 71

2033

N P

Si P

Si N

38

8335

6

19

47

25

minus10

minus06

K

K

K

K

K

K

K

K

K

MMM

MMMM M

M
























3

minus NH4

+ and PO4



4









Table 5 CCA results



NO2

minus



+







4and NH

4


Appendix

See Table 2



Acknowledgments


References


























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





















3

minus NH4

+ and PO4



4









Table 5 CCA results



NO2

minus



+







4and NH

4


Appendix

See Table 2



Acknowledgments


References


























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





Table 5 CCA results



NO2

minus



+







4and NH

4


Appendix

See Table 2



Acknowledgments


References


























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





4and NH

4


Appendix

See Table 2



Acknowledgments


References


























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

research article variability in the structure of...

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