variability patterns of microphytoplankton communities along ...phycotoxin monitoring network...

MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Vol. 242: 39–50, 2002 Published October 25

INTRODUCTION

The dynamics of coastal marine microphytoplanktoncommunities is characterized by successions of speciesassemblages displaying typical schemes of spatio-temporal variability (Smayda 1980). The succession ofphytoplankton assemblages is mainly governed bylocal environmental conditions and by the response ofphytoplankton populations to their variations (Good-man et al. 1984). The diversity and the dynamics ofphytoplankton populations result from the complexinteraction of hydrodynamical, physicochemical andbiological factors.

Numerous studies of phytoplankton spatio-temporalvariability have concerned holistic variables such astotal biomass or primary production (e.g. Radach &Moll 1993). Such studies deal with the role of primaryproducers in the global functioning of the pelagicecosystem, but have little relevance to other problem-atics. For instance, knowledge of the taxonomic com-position of phytoplankton communities and of theirspatio-temporal patterns is necessary to an under-standing of the predominance of a precisely identifiedpopulation. The dynamics of phytoplankton popula-tions has been the subject of many recent studies con-cerning the development of harmful or toxic species(e.g. Rhodes et al. 1993). Indeed, the negative effectsassociated with the proliferation of some phytoplank-

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*E-mail: [email protected]

Variability patterns of microphytoplanktoncommunities along the French coasts

I. Gailhard1,*, Ph. Gros1, J. P. Durbec2, B. Beliaeff3, C. Belin3, E. Nézan4, P. Lassus3

1IFREMER, BP 70, 29280 Plouzané, France2COM, Faculté des Sciences de Luminy, Case 901, 13288 Marseille Cedex 09, France

3IFREMER, BP 21105, 44311 Nantes Cedex 3, France4IFREMER, 29187 Concarneau Cedex, France

ABSTRACT: Microalgal populations along French coasts (English Channel, Bay of Biscay andMediterranean Sea) have been sampled twice a month since 1987 within the context of the FrenchPhytoplankton and Phycotoxin Monitoring Network (REPHY). This study used these data to charac-terize the large-scale geographical structures of microphytoplankton communities and to determinewhether ‘homogeneous‘ geographical areas exist in which microalgal populations display similar tem-poral variability schemes. Once the temporal variability component shared by all sampled coastal siteswas identified, the ‘residual‘ site-specific component was analyzed. Multivariate ordination methodswere used to determine seasonal and inter-annual variability. The expected temporal pattern commonto all sites was identified and the seasonal cycle of the most frequently observed phytoplankton com-munities along French coasts was described. The between-site analysis, using multitable comparisonmethods (RV-coefficient and multidimensional scaling), allowed the identification of 3 large areas(western English Channel, Bay of Biscay and Mediterranean Sea) according to the temporal variabil-ity patterns of microphytoplankton populations. The results, despite the coastal locations of REPHYsampling sites, indicate that the hydrodynamic characteristics of the different areas play a major rolein the geographical structure of microalgal populations in French coastal waters.

KEY WORDS: Microphytoplankton · French coast · Geographical structure · Temporal variability ·RV-coefficient · Multidimensional scaling

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Mar Ecol Prog Ser 242: 39–50, 2002

ton species (economic losses to aquaculture, fisheriesand tourism as well as an impact on human health) aresuch that the identification of environmental factorsfavoring their development is now of increasing in-terest (Zingone & Enevoldsen 2000). However, themechanisms of bloom species selection within phyto-plankton communities are still unresolved (Smayda &Reynolds 2001). In fact, it is essential to characterizethe large-scale geographical and temporal patternsthat determine the dynamics of phytoplankton com-munities over a wide area rather than those of anisolated population.

Until recently, little attention has been paid to thelarge-scale geographical and temporal patterns ofphytoplankton communities, particularly because ofthe scarcity of long-term series on a large geographicalscale. Successions of phytoplankton species have oftenbeen analyzed in localized areas (e.g. Hallegraeff &Reid 1986), and phytoplankton species compositionhas been characterized and the ‘dominant‘ speciesidentified for different sampling sites (e.g. Belin et al.

1995). However, these studies do not take thecombined effects of temporal and geographi-cal variability into account.

The purpose of this study was to exploit theinformation contained in data collected withinthe context of the French Phytoplankton andPhycotoxin Monitoring Network (REPHY) asa significant basis for the description of thegeographical distribution and temporal pat-terns of microphytoplankton communities. Theobjective was to identify the global temporalvariability patterns of phytoplankton popula-tions observed along French coasts and todetermine biogeographical areas based on thetemporal variability of phytoplankton popula-tions. The methodological approach used forthe analysis was based on descriptive multi-variate techniques allowing multidimensionaldata to be summarized in a geometric space.

MATERIALS AND METHODS

REPHY sampling and pre-processing ofdata. The REPHY monitoring network was setup by IFREMER in 1984 to achieve 3 closelyrelated objectives: (1) obtain knowledge aboutthe temporal development of phytoplanktonpopulations along French coasts, (2) recordall types of unusual phytoplankton-relatedevents in the coastal environment, whethertoxic, harmful or harmless, and (3) detect anyoccurrence of phycotoxin-producing speciesand toxic events within a public health context

(Belin & Raffin 1998). Twenty-nine sites are sampledbimonthly along 3 French coastal areas (English Chan-nel, Atlantic Ocean and Mediterranean Sea). Phyto-plankton identification and counts are performed in 12IFREMER coastal laboratories. The network provides15-year time series, which constituted the basic mater-ial for this study. They were recorded in the IFREMER‘Quadrige‘ database according to a list of 275 taxaranging from species identification level up to the Pro-tista group.

The expertise of REPHY observers has graduallyimproved since 1984, especially through internal train-ing sessions, expert assistance and the use of variousteaching materials such as taxonomic data sheets.However, this progress has sometimes led to disconti-nuities in time series, e.g. 1 phytoplankton species wasclassified into different taxonomic levels between 1987and 2000. Thus, the regrouping of taxonomic units wasconsidered necessary not only because of the diffi-culties in taxonomic identification and confusions be-tween species or genera, but also to obtain homoge-

40

Fig. 1. Geographical location of REPHY sampling sites. Among thesesites, those selected for processing correspond to black symbols: 17 sites

for the temporal window, 1992 to 2000

Gailhard et al.: Phytoplankton variability patterns along the French coasts

neous data sets (i.e. comparable for sampling sites andin time). A panel of experts (see ‘Acknowledgements’)were questioned about the relevance (ecological con-sistency) of regroupings.

A corrected data set for the period 1992 to 2000 with78 taxonomic units was obtained. These units corre-sponded to a species, a regrouping of species, a genus,or a regrouping of genera. Due to problems in dataquality, several taxa could not be retained for theanalysis; consequently, some phytoplankton groups,such as Chlorophyceae, Chrysophyceae, Cryptophy-ceae, Cyanophyceae, Dictyochophyceae, Prasinophy-ceae, Prymnesiophyceae and Raphydophyceae, werenot included in the study.

Monthly gap-free time series were used. As weatherconditions did not always allow bimonthly sampling,a monthly abundance average was computed when2 measurements were obtained in a month. These con-ditions allowed for inclusion of 17 of the 29 REPHYsampling sites (see locations in Fig. 1).

Temporal variability analysis. The notations usedhere are indicated in Table 1. Let Astu be the abun-dance (cells l–1) of taxonomic unit u measured at time t(one of the months of the 1992 to 2000 period) at sam-pling site s (s = 1,…, 17). Among the 78 taxonomic unitsretained after data preprocessing, 44 were selected onthe basis of their occurrence percentages, being atleast 0.20% relative to the total occurrences of allspecies. The list of the 44 taxonomic units selected forthe analysis is indicated in Table 2.

Abundances were log-transformed: Xstu = log10

(Astu + 1). In temperate coastal waters, phytoplanktoncommunities are subject to a marked seasonal trend(Longhurst 1995, 1997). In order to identify the sea-sonal pattern (within-year variability) common to allsites, the mean m.tu and the variance var.tu of Xstu over

all sampling sites were calculated. Thus, 2 T × U (datesby taxonomic units) matrices M and V were computedover sites. Principal component analysis (PCA) wasthen performed to summarize the information con-tained in the average matrix M in order to describe thetemporal variability pattern of taxonomic units on alarge geographical scale.

The structure of the between-year variability ofmicrophytoplankton populations was then exploredafter removal of the seasonal pattern by subtractingthe monthly averages for each taxon from m.tu. Thematrix thus obtained was then summarized by PCA.

Analysis of geographical variability. Geographicalvariability was assessed through a between-site dis-similarity analysis. First, data were standardized inorder to allow for spatial comparison:

Here, it is implicitly assumed that temporal and geo-graphical effects are additive, i.e. that there is no inter-action between time and space. In fact, such inter-action exists; however, the removal of the generaltemporal pattern component shared by all samplingsites will result in a residual spatial trend. The secondstep was to identify the specific patterns of local vari-ability, as each site is now characterized by the timevariations of corrected abundances. For site s (s = 1,...,S), let Z(s) be the U × T matrix with taxonomic units inrows and dates in columns. Due to standardization,column means in Z(s) are 0. In order to examine thebetween-site differences, the site matrices Z(s) need tobe compared. Various methods have been proposedfor statistical analysis of multitables (e.g. 3-mode PCAby Tucker 1964, Kroonemberg 1983; triadic analysis byThioulouse & Chessel 1987; STATIS by Lavit et al.

Z sX m

tustu tu

tu

( ) ..

.

= −var

41

Descriptions

Indices s Sampling sites s = 1,…, S; S = 17t Dates t = 1 ,…, T; T = 100 (time unit is the month; from January 1992 to April 2000 included)u Taxonomic units u = 1 ,…, U; U = 44

Matrices A (u) S × T matrix of abundances for a taxonomic unit u: Astu

X (u) S × T matrix of log-transformed abundances for a taxonomic unit u: Xstu = log(Astu + 1)

M T × U matrix of the site means for taxonomic unit u at date t:

V T × U matrix of between-site variances var.tu associated with means m.tu:

Z(s) T × U matrix of standardized abundances:

D S × S matrix of between-site squared distance values dij2

Z sX m

tustu tu

tu

( ) ..

.

= −var

var. .

–( – )tu stu

s

S

tuS

X m==∑1

1 1

2

m

sXtu stu

s

S

. ==∑1

1

Table 1. Notations used for data description


1994; multifactorial analysis by Escofier & Pagès 1994),but the results given by these methods are difficult tointerpret when the number of tables is too large. Partlyfor this reason, multitable factorial analysis is not fre-quently used in oceanography, although this approachwas used in earlier studies, e.g. on demersal assem-blages (Gaertner et al. 1998), plankton communities(Beaugrand et al. 2000) and zooplankton communities(Licandro & Ibanez 2000). In this study, PCA was usedfirst to summarize each matrix site Z(s), followed by amultitable method based on the RV-coefficient andmetric multidimensional scaling (MDS) to allow com-parison of these various summaries. PCA is a relevanttool for summarizing multidimensional data and hasbeen used especially in oceanography (Manté et al.1995, Gray et al. 1990, Ibanez & Dauvin 1998). WhenEuclidean distance is used, results are not disturbed byrare species, contrary to other methods, such as multi-ple correspondence analysis based on chi-square met-rics (Atchinson 1986, Manté et al. 1995).

Classically, the rows of the Z(s) matrix (taxonomicunits) are represented by points in Euclidean spaceIRT. Let C(Z(s)) be the configuration of points associatedwith Z(s). In order to compare temporal variations of thecorrected abundances over sites, the shapes and rela-tive positions of pairwise C(Z(s)) in IRT were analyzed.Two sites with similar configurations were consideredto present the same temporal variations for all taxo-nomic units. Robert & Escoufier (1976) showed that thedistance between 2 configurations C(Z(s)) (or 2 sam-pling sites) can be estimated as follows:

dij2 = dist2{C(Zi),C(Zj)} = 2[1 – RV(Zi,Zj)] (1)

with(2)

(Escoufier 1973) and Sij, Sii, Sjj are the empirical matri-ces of variance-covariance calculated from Zi and Zj:

(3)

Between-site similarities are represented graphi-cally by MDS. Let D be the Euclidean matrix of be-tween-site squared distances. By using the S(S –1)/2between-site distances, the MDS method allows a con-figuration of S points in IRk (k ≤ S) to be estimated, suchthat the usual Euclidean distance between these pointsis equal to the between-site distances in the D matrix(Krzanowski 1982). The proximities between sitescan be displayed on 1 or several bidimensional plotsaccording to the dimension k, which is necessary toobtain distances between vectors close to those in D.

The RV-coefficient–MDS combination can be con-sidered as a simplified STATIS method (Escoufier1973, Lavit et al. 1994). In our case, the identification of

S Z Z S Z Z S Z Zij i j ii i i jj j jU U U

1

; 1

; 1' ' '= = =

RV( , ) trace( )

trace( ) trace( )2 2Z Z

S S

S Si j

ij ji

ii jj

=×

42

Diatoms

ACHN Achnanthes sp.ASTE Asterionella sp.ASTEGLA Asterionella glacialis

(= A. japonica)BACIPAX Bacillaria paxillifer

(= B. paradoxa)BACT Bacteriastrum sp.BELLITH Bellerochea sp. +

Lithodesmium sp.BIDODON Biddulphia sp. + Odontella sp.CERAPEL Cerataulina pelagicaCHAE Chaetoceros sp.DETLAUD Lauderia sp. + Schroederella sp. +

Detonula sp.DITYBRI Ditylum brightwelliiEUCPZOD Eucampia zodiacusGRAM Grammatophora sp.GUINFLA Guinardia flaccidaHEMI Hemiaulus sp.LEPT Leptocylindrus sp.LICM Licmophora sp.NAVIC Amphora sp. + Diploneis sp. +

Navicula sp.NITZCYL Nitzschia longissima +

Cylindrotheca closteriumPORTHAL Porosira sp. + Thalassiosira sp. +

Coscinosira sp.PRORHIZ Proboscia sp. + Rhizosolenia sp.PSNZ Pseudo-nitzschia sp.RHABSTRI Rhabdonema sp. + Striatella sp.SKELCOS Skeletonema costatumTHAA Thalassionema sp. + Thalassiothrix sp.

Dinoflagellates

ALEX Alexandrium sp.ALEXMIN Alexandrium minutumCERI Ceratium sp.DINOAC Dinophysis acuminata +

Dinophysis sacculus complexDINODI Dinophysis caudataDINOROT Dinophysis rotundataEBRARTRI Ebria tripartitaGONY Gonyaulax sp.GONYSPI Gonyaulax spiniferaGYMN-82 Gymnodinium chlorophorumGYMNNAG Gymnodinium nagasakiense

(= G. mikimotoi = Karenia mikimotoi)GYMNO Warnowia sp. + Nematodinium sp. +

Amphidinium sp. + Cochlodinium sp. + Gyrodinium sp. + Katodinium sp. +Gymnodinium sp.

NOCTSCI Noctiluca scintillansPERID Diplopsalis sp. + Diplopelta sp. +

Diplopsalopsis sp. + Zygabikodiniumsp. + Oblea sp.

PLESGYR Pleurosigma sp. + Gyrosigma sp.POLYSCH Polykrykos schwartziiPROR Prorocentrum sp. (= Exuviaella sp.)PRORLIME Prorocentrum lima + P. marinum +

P. mexicanum

CLEUGLE Euglenophyceae

Table 2. List of 44 taxonomic units selected for data analysis


the taxonomic units responsible for between-site dif-ferences was indirectly examined by computing theratio of the between-site variance to the within-sitevariance of each Xstu. Taxa with a high ratio value dis-played different temporal variability patterns from onesite to another.

RESULTS

Seasonal patterns

Fig. 2a shows representations of individ-uals (dates) in the first factorial plane ofmatrix M PCA, with 40% of variance ex-plained. Date coordinates show obviousintra-annual variability, and variables’ rep-resentation (taxonomic units, figure notshown) in dual space associate the specieswith similar intra-annual variability, al-lowing identification of a seasonal cycleof taxonomic units. Axis 1 opposes taxo-nomic units with summer concentrationmaxima against taxonomic units withwinter concentration maxima, and Axis 2opposes taxonomic units with springconcentration maxima against taxonomicunits with fall concentration maxima. Fig. 3provides an example of the temporal vari-ability of taxonomic units strongly corre-lated with the first and second axes. The‘summer‘ genus Leptocylindrus sp. (LEPTcode) is opposed to the ‘winter‘ generagroup Porosira sp. + Thalassiosira sp. +Coscinosira sp. (PORTHAL code) on Axis 1(Fig. 3a), and the ‘fall‘ genera groupThalassionema sp. + Thalassiothrix sp.(THAA code) is opposed to the ‘spring‘genera group Dinophysis acuminata + D.sacculus (DINOAC code) on Axis 2(Fig. 3b).

Between-year changes

PCA performed on the matrix of sea-sonally adjusted m.tu exhibited a markedbetween-year structure, visible in therepresentation of individuals on both firstmain axes (Fig. 2b). Almost 25% of thevariance is explained by the first 2 princi-pal components. The dual representation(Fig. not shown) allowed the most con-tributing variables in the explanation oftemporal variability structure to be iso-

lated. Fig. 4 shows the inter-annual variability of taxo-nomic units (the genera group Navicula sp. + Diploneissp. + Amphora sp., the genus Pseudonitzschia sp., thespecies Karenia mikimitoi, and the genera regroupingGymnodinium sp. + Amphidinium sp. + Cochlodiniumsp. + Gyrodinium sp. + Katodinium sp. + Warnowia sp.

43

Fig. 2. Temporal variability analysis. (a) Seasonal pattern identification: PCAof the 100 × 44 (dates by taxonomic units) matrix of monthly means of log-transformed abundances over sites. Plot of the month coordinates in the firstfactorial plane (39% of variance). Winter and summer months are opposed onAxis 1, and spring and fall months on Axis 2. (b) Between-year variabilityanalysis: PCA of the 100 × 44 (dates by taxonomic units) matrix of monthlymeans over sites, corrected from the monthly component. Plot of the monthcoordinates in the first factorial plane (25% of variance). Axis 1 shows a break

between 1994 and 1995

b

a


+ Nematodinium sp.) strongly correlated with both firstprincipal components.

Between-site differences

Between-site squared distance values were from 0 to2 per construction, with most of the estimated dis-tances ranging from 1 to 1.5. The distribution of dis-tances (few short distances) and analysis of the stabil-ity of results (calculation of distances between tablesbuilt by inverting rows of the 17 initial tables) showedthat the observed between-site structure was not dueto a random effect. The first 3 eigenvalues of the matrix

used for multidimensional scaling allowed 30% of theoriginal distances to be reconstructed.

Multidimensional scaling results (Fig. 5) point to ageographical effect, i.e. (1) Saint Cast, Paimpol andMorlaix sites in the Western English Channel aregrouped with positive coordinates on Axis 1 andnegative coordinates on Axis 2; (2) Bay of Biscaysites (Douarnenez, Quiberon, Vilaine, Le Croisic,Noirmoutier, Marennes and Ile d’Aix) have similarpositions on Axis 1 (negative coordinates) and Axis 3(positive coordinates); and (3) Mediterranean sites(Bar-cares, Leucate, Marseillan, Thau, Fos andToulon) are grouped on Axes 1 and 3 (negative coor-dinates).

44

Fig. 3. Description of intra-annual variability. (a) Within- and between-year concentrations means (log cell l–1) overall sites for theperiod 1992 to 2000 of LEPT and PORTHAL. The genus Leptocylindrus sp. (LEPT code) shows maximal concentrations duringsummer months (June to August), when the concentrations of the genera Porosira sp. + Thalassiosira sp. + Coscinosira sp. (POR-THAL code) are minimal; these genera reach maximum concentrations during February to March. (b) Within- and between-yearconcentrations means (log cell l–1) overall sites for the period 1992-2000 of THAA and DINOAC. The genera regrouping Thalas-sionema sp. + Thalassiothrix sp. (THAA code) present maximal concentrations during September to October, and D. acuminata

+ D. sacculus complex (DINOAC code) during May to June

b

a


The taxonomic units with the 10 highest ratios of be-tween-site variance to within-site variance, with theirecological characteristics and their geographical distri-bution, are indicated in Table 3. Geographical distribu-tion of these taxa was identified by examining their tem-poral variability at each sampling site (Figs. not shown).Among the 10 taxonomic units, most (7) are diatoms,characterizing, in particular, the western English Chan-nel (Asterionnella glacialis and Rhizosolenia delicatula)and the Mediterranean lagoons (Grammatophora sp.,Licmophora sp., and Navicula sp. + Diploneis sp. + Am-phora sp.). The dinoflagellates class is represented by agenera regrouping abundant in the Bay of Biscay(Gymnodinium sp. + Amphidinium sp. + Cochlodiniumsp. + Gyrodinium sp. + Katodinium sp. + Warnowia sp. +

Nematodinium sp.) and by a species regrouping of thegenus Prorocentrum characteristic of open sea Medi-terranean sites. Note the presence of the Eugleno-phyceae class, mainly composed of freshwater species,and frequent in the Bay of Biscay. From the ecologicalpoint of view, 2 groups can be distinguished: pelagic andbenthic or tychopelagic (fixed on a substrate) species.

DISCUSSION

Seasonal patterns

The examination of seasonality (Figs. 2a & 3) showsthat the temporal variability of phytoplankton popula-

45

Fig. 4. Description of inter-annual variability. (a) Within- and between-year concentrations means (log cell l–1) overall sites for theperiod 1992 to 2000 of NAVIC and PSNZ. The high concentrations of the genera regrouping Navicula sp. + Diploneis sp. +Amphora (code NAVIC) observed between January and April decrease from 1995, whereas for Pseudo-nitzschia sp. genus (codePSNZ) an increase is observed for high spring-summer concentrations from 1995. (b) Within- and between-year concentrationsmeans (log cell l–1) overall sites for the period 1992 to 2000 of GYMNNAG and GYMNO. An increase is observed in high summer

concentrations from 1995, with an exceptional increase in 1995 spring-summer concentrations

b

a


tions in French metropolitan coastal waters is subjectto a marked seasonal cycle, with the exception of somespecies or genera, such as the Navicula sp. + Amphorasp. + Diploneis sp. group (cf. Fig. 4a) which are presentall year-round. This has been confirmed by other stud-ies focussing on changes of phytoplankton populations

in temperate coastal areas (e.g. Longhurst1995, 1997). Fig. 6, which shows the seasonaldistribution of the 44 taxonomic unitsselected, confirms the opposition of winter-summer species (Axis 1) and spring-fall spe-cies (Axis 2), as displayed in Fig. 2a, andallows the succession of communities to bedescribed. The seasonal progression fromdiatom- (Fig. 6b) to dinoflagellate-dominatedcommunities (Fig. 6a) illustrates the basiccharacteristics of phytoplankton succession intemperate coastal waters (Smayda 1980).These typical, well-known successions areusually associated with nutrient enrichment ina well-mixed water column triggering thespring, fall and winter blooms and with theinput of nutrients into a stratified water col-umn for the supply of summer blooms (Halle-graeff & Reid 1986). The summer dinoflagel-late community (codes ALEX to PRORLIME,Fig. 6a) follows the spring diatom bloom(codes THAA to BACT, Fig. 6b). Most dinofla-gellate species are characterized by abun-

dance maxima between May and August, whereasdiatom species are present in high numbers year-round, in particular with a distinct group of winter-fallspecies (code THAA to DETLAUD, Fig. 6b). These sea-sonal patterns have also been described in other stud-ies. For example, diatom species in temperate coastal

46

Code Taxonomic units Geographical distribution Ecological characteristics*

ASTEGLA Asterionella glacialis (pennate diatom) Western English Channel Abundant in cold marine and coastal waters to temperate—pelagic

GYMNO Gymnodinium sp. + Amphidinium sp. + Bay of Biscay Abundant in coastal waters—pelagic Cochlodinium sp. + Gyrodinium sp. + (exception for some Katodinium species)Katodinium sp. + Warnowia sp. + Nematodinium sp. (dinoflagellate)

GRAM Grammatophora sp. Mediterranean lagoons Abundant in warm marine waters to (pennate diatom) temperate—benthic to tychopelagic

CLEUGLE Euglenophyceae Bay of Biscay Mainly freshwater speciesLICM Licmophora sp. (pennate diatom) Mediterranean lagoons Abundant in marine waters, cosmopolite

—benthic to tychopelagicRHIZDEL Rhizosolenia delicatula Western English Channel Abundant in marine waters—pelagic

(centric diatom)NAVIC Navicula sp. + Diploneis sp. + Mediterranean lagoons Abundant in coastal waters—benthic

Amphora sp. (pennate diatom) to tychopelagicNITZCYL Nitzschia longissima + Bay of Biscay Abundant in marine waters—benthic

Cylindrotheca closterium to tychopelagic(pennate diatom)

PRORHIZ Proboscia alata + Rhizosolenia sp. Open Mediterranean Sea Abundant in marine waters—pelagic(centric diatom)

PRORMIN Prorocentrum minimum + P. balticum + Open Mediterranean Sea Abundant in estuarine and coastal P. cordatum (dinoflagellate) waters—pelagic

*From Hendey (1964), Ricard (1987) and Thomas (1996)

Table 3. Inter-intra-sampling site variances: 10 first contributory taxonomic units. Geographical distribution and ecological char-acteristics show that the western English Channel is characterized by 2 pelagic diatoms, and the Bay of Biscay by dinoflagellatesand Euglenophyceaea. Mediterranean sites are divided into lagoons (characterized by benthic diatoms) and open sea sites

(characterized by warm water taxonomic units)

Brest

QuiberonVilaine

DouarnenezLe Croisic

Ile d'AixMarennes

Arcachon

Saint CastPaimpol

Morlaix

Barcares

Leucate

Marseillan

ThauFos

Toulon

Fig. 5. Multidimensional scaling of between-site squared distancematrix. Different text symbols are used to distinguish the 3 Frenchcoastal areas. The size of the characters is proportional to the coordinate on Axis 1. Note the eccentric positions of the Arcachon and Brest sites


waters, such as Skeletonema costatum (SKELCOS) orThalassiosira nordenskioldii (species included in thePorosira sp. + Thalassiosira sp. group, PORTHAL code)have been identified as dominant winter-spring out-bursting species (Levasseur et al. 1984, Marshall &Lacouture 1986). Along French coasts,Rhizosolenia sp. (grouped with Probosciasp., PRORHIZ code) has been describedas a genus blooming in late winter andearly spring, and Dinophysis sp. as asummer genus (late spring-early summerfor DINOAC and DINOROT, and latesummer for DINODI in our results)(Videau et al. 1998, Beliaeff et al. 2001).

Between-year changes

Analysis of inter-annual variabilityrevealed a significant break between1994 and 1995 (Fig. 2b), which was con-firmed by the temporal variability of thetaxonomic units involved (Fig. 4). Inter-pretation is difficult, as only 25% of thevariance could be explained by the firstfactorial plane. Moreover, every speciesor genus concerned would require de-tailed specific interpretation in any givenarea, which was not performed in thisstudy. However, identification of phyto-plankton populations subject to unusualbetween-year temporal variability (e.g.exceptional development of a populationin a given year) would be of particularinterest with respect to harmful taxa andcould be helpful in characterizing theenvironmental factors favorable to theirdevelopment. For example, exceptionalphysical conditions were observed in theBay of Biscay during 1994-1995. Theplumes of 2 large river estuaries (Loireand Gironde) on the French Atlanticcoast, which have an important physicalinfluence on phytoplankton growth,overlapped during these years (Labry etal. 2001). These extraordinary condi-tions, caused by abnormal wind regime,and increase of the Gironde runoff, mayhave been the reason for the break, byaffecting phytoplankton growth throughthe haline stratification (Levasseur et al.1984). PCA results show that the exten-sive development of Karenia mikimotoi(GYMNNAG code) in 1995 at Bay ofBiscay sites was a significant factor ac-

counting for the structure of inter-annual variability(Fig. 4b). The bloom was not limited to its usual areaof occurrence (western Brittany), but extended alongthe entire French Atlantic coast, causing exceptionallosses of marine fauna (Gentien et al. 1998). The same

47

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB

Months

RHABSTRI

BELITH

GRAM

HEMI

EUCPZOD

BACIPAX

CERAPEL

CHAE

GUINFLA

LEPT

LICM

PSNZ

PRORHIZ

BACT

ASTE

ASTEGLA

DETLAUD

ACHN

BIDODON

DITYBRI

NAVIC

NITZCYL

PLESGYR

SKELCOS

PORTHAL

THAA

Taxo

nom

ic U

nits

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB

Months

PRORLIME

DINODI

PERID

PROR

NOCTSCI

GYMN_82

GYMNO

YMNNAGG

DINOROT

DINOAC

CERI

ALEXMIN

ALEX

POLYSCH

GONYSPI

GONY

EBRATRI

CLEUGLE

Taxo

nom

ic U

nits

Fig. 6. Seasonal distribution of (a) dinoflagellate taxonomic units and the Eugleno-phyceae group (CLEUGLE) and (b) diatom taxonomic units recorded along Frenchcoasts for the period 1992 to 2000. Symbol size is proportional to log-transformed

maximum monthly abundance

a

b


1995 event also involved multi-species blooms of suchgenera as Warnowia sp., Nematodinium sp., Amphi-dinium sp., Cochlodinium sp., Gyrodinium sp., Kato-dinium sp. and Gymnodinium sp. (Fig. 4b).

Between-site differences

Removal of the seasonal trend facilitated the identifi-cation of 3 distinct geographical areas (western Eng-lish Channel, Bay of Biscay, Mediterranean Sea; seeFig. 5). Changes in the dynamics of the species thatcontributed significantly to the geographical structure,as identified by the ratio of between-site to within-sitevariance (Table 3), seem to be associated with differentphysical properties of the water column. The state-of-the-art knowledge about the ecology of these speciescorroborated the close association found in our studybetween the distribution of the different phytoplank-ton assemblages and the hydrographic characteristicsof the area (e.g. tidal current and wind regimes).Indeed, there is a close association between speciescomposition of phytoplankton communities and thestratification of the water column, especially on diatoms/dinoflagellates predominance. Turbulence inhibitsdinoflagellate growth by causing physical damages,physiological impairment and behavioral modification,whereas diatoms are considerably less sensitive toturbulence (Thomas & Gibson 1990, Smayda 1997,Smayda & Reynolds 2001). The French western Eng-lish Channel coasts, where low freshwater inputs andphysical forcing by high tidal amplitudes create a verti-cal mixing throughout the year (Garreau 1993), arecharacterized by two diatoms (Asterionnella glacialisand Rhizosolenia delicatula). The predominance ofdiatoms due to the vertical mixing of water column inthis area has been described by other studies (e.g.Maddock et al. 1981, Videau et al. 1998). In contrast tothe western English Channel coast, the French coastsof the Bay of Biscay are subjected to significant fresh-water inputs (mainly from the Loire, Gironde andAdour rivers) accounting for the co-occurrence of typi-cal marine species (e.g. Nitzschia longissima) andfreshwater species (Euglenophyceae) (Dauvin 1997). Inthis area, tidal currents are lower, and the circulationis mainly wind-driven. In summer, the spreading offreshwater over the shelf induced by upwellingfavourable winds from NW, and the occurrence of athermocline (Lazure & Jegou 1998), induce a stratifica-tion of the water column favorable to the developmentof dinoflagellates, such as Gymnodinium sp., Gyro-dinium sp. and Katodinium sp. (Marshall & Cohn1983). Mediterranean sites are divided into 2 groups:open sea and lagoon sites. In open sea sites, hydro-logical characteristics are closely associated with very

small tidal amplitudes, and also marked by waters dis-charged by the Rhône river (Beckers et al. 1997). Thesummer formation and deepening of a seasonal ther-mocline and transient wind-induced upwellings (Johnset al. 1992) allow the development of warm waterdinoflagellates as Prorocentrum minimum (Videau &Leveau 1990). Mediterranean lagoons are describedas diatoms-dominated ecosystems (Jarry et al. 1990).Because of their shallow depths, these lagoons are allyear long well-mixed; this water column mixing isresponsible for the presence of benthic diatoms asLicmophora sp. and Grammatophora sp. (Vaulot &Frisoni 1986).

CONCLUSION

Data collected in the context of the REPHY monitor-ing network constitute a significant basis for examin-ing the large-scale temporal and geographical vari-ability of phytoplankton communities. Mainly due tochanges among the observers responsible for speciesidentification, this study required meticulous pretreat-ment of data, especially with respect to a comprehen-sive regrouping of genera or species by a group ofexperts. Although extremely time-consuming, thisvalidity checking phase was a crucial preliminaryphase in our study: it gave us confidence in our resultsand, in terms of monitoring program management,provided final guidance for phytoplankton identifica-tion and corresponding coding in the database. Ourqualified data set allowed to describe the temporalvariability scheme, and particularly to illustrate theglobal seasonal trend for the most frequently observedspecies along French coasts. Moreover, geographicalareas exhibiting similar phytoplankton populationdynamics were identified. As REPHY sampling sitesare located near the coast, the identification of homo-geneous geographical areas was not a predictableresult. Indeed, in the context of REPHY sampling, localcharacteristics, as anthropogenic impacts, are suppo-sed to dominate. This illustrates the importance ofhydrodynamic properties to the structure of coastalphytoplankton communities. Determination of thelarge geographical scale structure of microphyto-plankton populations along French coasts should facil-itate research concerning the ‘discriminant‘ speciescharacteristics of a geographical area and the effects ofthe environmental factors controlling the dynamics ofthese populations. As phytoplankton populations dis-play similar temporal patterns within large geographi-cal areas, a thorough study of the hydrodynamic char-acteristics of these areas could identify the physicalparameters influencing the dynamics of these popula-tions.

48


Acknowledgements. The authors are grateful to the expertswho contributed to the study: C. Billard (University of Caen,France), J. D. Dodge (University of London, England), V.Martin-Jézéquel (University of Brest, France), G. Paulmier(CREMA, L’Houmeau, France), Y. Rincé (University of Nantes,France) and D. Vaulot (Station Biologique, Roscoff, France).We also acknowledge the assistance of the 12 IFREMERcoastal laboratories responsible for water sample collectionand phytoplankton identification, and B. Raffin (IFREMERNantes) for drawing the maps. This study was supported bythe French National Program for the Coastal Environment(PNEC).

LITERATURE CITED

Atchinson J (1986) The statistical analysis of compositionaldata. Chapman & Hall, London

Beaugrand G, Ibanez F, Reid PC (2000) Spatial, seasonal andlong-term fluctuations of plankton in relation to hydro-climatic features in the English Channel, Celtic Sea andBay of Biscay. Mar Ecol Prog Ser 200:93–102

Beckers JM, Brasseur P, Nihoul JCJ (1997) Circulation of thewestern Mediterranean: from global to regional scales.Deep-Sea Res II 44:531–549

Beliaeff B, Gros P, Belin C, Raffin B, Gailhard I, Durbec JP(2001) ‘Phytoplankton events’ in French coastal watersduring 1987–1997: identification, and qualitative study oflarge-scale spatiotemporal structures. Oceanol Acta 24:425–433

Belin C, Raffin B (1998) Les espèces phytoplanctoniquestoxiques et nuisibles sur le littoral français, résultats duREPHY (réseau de surveillance du phytoplancton et desphycotoxines). Rapp Int IFREMER/RST.DEL/MP-AO 98–16

Belin C, Beliaeff B, Raffin B, Rabia M, Ibanez F (1995) Phyto-plankton time-series data of the French PhytoplanktonMonitoring Network: toxic and dominant species. In: Las-sus P, Arzul G, Erard Le Denn E, Gentien P, MarcaillouLe Baut C (eds) Harmful marine algal blooms—Proli-férations d’algues nuisibles. Paris-France Lavoisier,p 771–776

Dauvin JC (1997) Les biocénoses marines et littorales fran-çaises des côtes Atlantiques, Manche et Mer du Nord: syn-thèses, menaces et perspectives. Laboratoire de Biologiedes Invertébrés Marins et Malacologie—Service du Patri-moine naturel/IEGB/MNHN, Paris

Escofier B, Pagès J (1994) Multiple factor analysis (AFMULTpackage). Comput Stat Data Anal 18:121–140

Escoufier Y (1973) Le traitement des variables vectorielles.Biometrics 29:751–760

Gaertner JC, Chessel D, Bertrand J (1998) Stability of spatialstructures of demersal assemblages: a multitable approach.Aquat Living Resour 11:75–85

Garreau P (1993) Hydrodynamics of the north Brittany coast:a synoptic study. Oceanol Acta 16:469–477

Gentien P, Lazure P, Raffin B (1998) Effect of meteorologicalconditions in spring on the extent of Gymnodinium cf.nagasakiense bloom. In: Reguera B, Blanco J, FernandezML, Wyatt T (eds) Harmful Algae. Xunta de Galiciaand Intergovernmental Oceanographic Commission ofUNESCO, Paris, p 200–203

Goodman D, Eppley RW, Reid FMH (1984) Summer phyto-plankton assemblages and their environmental corre-lates in the Southern California Bight. J Mar Res 42:1019–1049

Gray JS, Clarke KR, Warwick RM, Hobbs G (1990) Detectionof initial effects of pollution on marine benthos: an ex-

ample from the Ekofisk and Eldfisk oilfields, North Sea.Mar Ecol Prog Ser 66:285–299

Hallegraeff GM, Reid DD (1986) Phytoplankton species suc-cessions and their hydrological environment at a coastalstation off Sydney. Aust J Mar Freshwat Res 37:361–377

Hendey NI (1964) An introductory account of the smalleralgae of british coastal waters. Part V. Bacillariophyceae.Her Majesty’s Stationery Office, London

Ibanez F, Dauvin JC (1998) Shape analysis of temporal eco-logical process: long-term changes in English Channelmacrobenthic communities. Coenoses 13:115–129

Jarry V, Fiala M, Frisoni GF, Jacques G, Neveux J, Panouse M(1990) The spatial distribution of phytoplankton in aMediterranean lagoon (Etang de Thau). Oceanol Acta 13:503–512

Johns B, Marsaleix P, Estournel C, Véhil R (1992) On thewind-driven coastal upwelling in the Gulf of Lions. J MarSyst 3:309–320

Kroonemberg PM (1983) Three-mode principal componentsanalysis. DSWO Press, Leiden

Krzanowski WJ (1982) Between-group comparison of princi-pal components—some sampling results. J Statist ComputSimul 15:141–154

Labry C, Herbland A, Delmas D, Laborde P, Lazure P, Froide-fond JM, Jegou AM, Sautour B (2001) Initiation of winterphytoplankton blooms within the Gironde plume waters inthe Bay of Biscay. Mar Ecol Prog Ser 212:117–130

Lavit C, Escoufier Y, Sabatier R, Traissac P (1994) The ACT(STATIS method). Comput Stat Data Anal 18:97–119

Lazure P, Jegou AM (1998) 3D modelling of seasonal evolu-tion of Loire and Gironde plumes on Biscay Bay continen-tal shelf. Oceanol Acta 21:165–177

Levasseur M, Therriault JC, Legendre L (1984) Hierarchicalcontrol of phytoplankton succession by physical factors.Mar Ecol Prog Ser 19:211–222

Licandro P, Ibanez F (2000) Changes of zooplankton commu-nities in the Gulf of Tigullio (Ligurian Sea, westernMediterranean) from 1985 to 1995: influence of hydrocli-matic factors. J Plankton Res 22:2225–2253

Longhurst A (1995) Seasonal cycles of pelagic production andconsumption. Prog Oceanogr 36:77–167

Longhurst A (1997) Ecological geography of the sea. Acade-mic Press, London

Maddock L, Boalch GT, Harbour DS (1981) Populations ofphytoplankton in the western English Channel between1964 and 1974. J Mar Biol Assoc UK 61:565–583

Manté C, Dauvin JC, Durbec JP (1995) Statistical method forselecting representative species in multivariate analysis oflong-term changes of marine communities. Applications toa macrobenthic community from the Bay of Morlaix. MarEcol Prog Ser 120:243–250

Marshall HG, Cohn MS (1983) Distribution and compositionof phytoplankton in northeastern coastal waters of theUnited States. Estuar Coast Shelf Sci 17:119–131

Marshall HG, Lacouture R (1986) Seasonal patterns ofgrowth and composition of phytoplankton in the lowerChesapeake Bay and vicinity. Estuar Coast Shelf Sci 23:115–130

Radach G, Moll A (1993) Estimation of the variability of pro-duction by simulating annual cycles of phytoplankton inthe central North Sea. Prog Oceanogr 31:339–419

Rhodes LL, Haywood AJ, Ballantine WJ, MacKenzie AL(1993) Algal blooms and climate anomalies in north-eastNew Zealand, August–December 1992. NZ J Mar Fresh-wat Res 27(4):419–430

Ricard M (1987) Atlas du phytoplancton marin. Vol. II. Diato-mophycées. Editions du CNRS, Paris

49


Robert P, Escoufier Y (1976) A unifying tool for linear multi-variate statistical methods: the RV-coefficient. Appl Statist25:257

Smayda TJ (1980) Phytoplankton succession. In: Morris (ed)Physiological ecology of phytoplankton I. Blackwell,Oxford, p 493–570

Smayda TJ (1997) Harmful algal blooms: their ecophysiologyand general relevance to phytoplankton blooms in the sea.Limnol Oceanogr 42:1137–1153

Smayda TJ, Reynolds CS (2001) Community assemblyin marine phytoplankton: application of recent modelsto harmful dinoflagellate blooms. J Plankton Res 23:447–461

Thioulouse J, Chessel D (1987) Les analyses multi-tableauxen écologie factorielle. I. De la typologie d’état à la typo-logie de fonctionnement par l’analyse triadique. ActaOecologica 8:463–480

Thomas CR (1996) Identifying marine diatoms and dinoflagel-lates. Academic Press, London

Thomas WH, Gibson CH (1990) Effects of small-scale turbu-lence on microalgae. J Appl Phycol 2:71–77

Tucker LR (1964) The extension of factor analysis to three-dimensional matrices. In: Gullinkson H, Frederiksen N(eds) Contributions to mathematical psychology. Rinehart& Winston, New York, p 110–119

Vaulot D, Frisoni GF (1986) Phytoplanktonic productivity andnutrients in five Mediterranean lagoons. Oceanol Acta 9:57–63

Videau C, Leveau M (1990) Phytoplanktonic biomass andproductivity in the Rhône River plume in spring time. CRAcad Sci Paris Ser III 311:219–224

Videau C, Ryckaert M, L’Helguen S (1998) Phytoplankton inthe Bay of Seine (France): influence of the river plume onprimary productivity. Oceanol Acta 21:907–921 (in Frenchwith English abstract)

Zingone A, Enevoldsen HO (2000) The diversity of harmfulalgal blooms: a challenge for science and management.Ocean Coastal Manage 43:725–748

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Editorial responsibility: Otto Kinne (Editor), Oldendorf/Luhe, Germany

Submitted: March 5, 2002 Accepted: June 26, 2002Proofs received from author(s): October 15, 2002

variability patterns of microphytoplankton communities along ...phycotoxin monitoring network...

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