Changes of Zooplankton Communities in the Gulf of Tigullio (Ligurian Sea, Western Mediterranean) from 1985 to 1995

Journal of Plankton Research Vol.22 no.12 pp.2225–2253, 2000

Changes of zooplankton communities in the Gulf of Tigullio (Ligurian Sea, Western Mediterranean) from 1985 to 1995. Inﬂuence of hydroclimatic factors

Priscilla Licandro and Frédéric Ibanez1 Dipartimento per lo studio del Territorio e delle sue Risorse, Università di Genova, Corso Europa, 26, 16132 Genova, Italy and 1Laboratoire d’Océanographie Biologique et Ecologie du Plancton Marin, ESA 7076, BP 28, 06230 Villefranche-sur-mer, France

Abstract. Year-to-year variations in abundance and composition of zooplankton were studied in the Ligurian Sea at a station sampled two times a month between 1985 and 1995. As a break of 2 years (April 1989–December 1990) occurred in the time series, the STATIS method was chosen instead of time series analysis. Each of the nine sampled years was a single table of monthly or seasonal average densities of 26 plankton taxa. STATIS allowed (i) estimation of similarity between each yearly table, (ii) visualization of the trajectories of both species and observations (seasons) from one year to another, and (iii) associations of particular species, which showed similar temporal variations, to be determined. A strong seasonal variation was evident for most species, and years 1987, 1992 and 1994 were different from the others. Trajectories indicated which species were stable and which were characterized by small or large ﬂuctuations during the nine years. Five different taxa associations were detected. For each association, the most representative period was identiﬁed, where each period was a group of months obtained by clustering on species abundances. Taking into account hydro-climatic factors in the representative periods, a contingency discriminant analysis allowed us to identify and characterize the most discriminant environmental parameters associated with each group of species. Environmental factors that best discriminated the different representative periods were atmospheric pressure, current speed and direction, and water temperature.

Introduction Numerous long-term studies carried out in the Atlantic and the Pacific have clearly shown the influence of hydroclimatic factors on plankton year-to-year variability [(McGowan, 1990) for a review; (Fromentin and Ibanez, 1994; Mullin, 1994; Le Fèvre-Lehoërff et al., 1995; Fromentin and Planque, 1996)]. However, similar studies in the Mediterranean Sea are somewhat rare because of the lack of zooplankton long-term datasets (Pucher-Petković et al., 1971; Goy et al., 1989; Ménard et al., 1994, 1997; Cataletto et al., 1995; Šolić et al., 1997). Relationships between climate and water circulation in the Western Mediter- ranean Sea have been detected over the last decade. This has led to identification of climatic parameters that influence hydrography in this region. Meteorological factors have been shown to involve oscillations of sea-water level (Vilibić and Leder, 1996) or variations in sea surface-water properties (Grbec, 1997). Seasonal and year-to-year variability of dominant forcing factors (e.g. atmospheric pressure, winds) may also have induced variations in flow and circulation dynamics (La Violette, 1995). Year-to-year variability of mesozooplankton was analysed at a pilot station over the eastern Ligurian Continental Shelf between 1985 and 1995. The goal was to examine relationships between hydroclimatic factors and zooplankton at the

© Oxford University Press 2000 2225 P.Licandro and F.Ibanez interannual scale. A break of about two years (April 1989–December 1990) in the sampling collection prevented the use of time series analyses which require regular sampling intervals. Instead, the multivariate method STATIS (Robert and Escouﬁer, 1976) was used to compare changes between the nine sampled years. The basic questions were: what are the main patterns of variation of species during the nine years; are changes in mesozooplankton communities more sensi- tive to year-to-year or to seasonal environmental variations; what is the latent hierarchy between climatic forcing and hydrography on abundance and composition of zooplankton?

Method Study site The Gulf of Tigullio is located along the Italian coast of the Ligurian Sea, 40 km east of Genoa (Figure 1). The Ligurian Current principally drives the water mass circulation within the gulf westwards. However, inversion in the direction of this current seasonally occurs in the upper layers (Bossolasco and Dagnino, 1957; Hela, 1963). The wind mainly blows southwards while SSE winds stronger than 10 km h–1 are considered responsible for storms (Papa, 1984). Previous studies on the productivity of coastal waters in the Ligurian Sea (Albertelli et al., 1982) showed that the pilot zone (including the sampling station ‘C’) in the Gulf of Tigullio may be considered as representative of the whole Ligurian coast with respect to hydrology and nutrients (Fabiano, 1984; Albertelli et al., 1994), particulate matter and planktonic biomasses (Fabiano et al., 1984; Zunini Sertorio et al., 1985; Fabiano and Zunini Sertorio, 1993).

Fig. 1. Study area in the Gulf of Tigullio, on the Ligurian coast 40 km east of Genoa (Ligurian Sea). At the sampling station C (09°16E, 44°17N), zooplankton was collected twice a month from March 1985 to March 1989 and from January 1991 to December 1995. Bathymetry of the coast is also indicated.

2226 Zooplankton in the Gulf of Tigullio

Sampling Zooplankton samples were collected between March 1985 and March 1989, and between January 1991 and December 1995, at station C (Figure 1). This is a coastal station (maximum depth of 80 m) not far from the harbour of Chiavari and the estuary of the river Entella. Twice a month, a Bongo sampler with two nets of 335 µm mesh size (90 cm long, with a 20 cm mouth diameter) was hauled obliquely at approximately 1 knot speed from west to east throughout a 60 m water column. A ‘General Oceanic’ ﬂowmeter model 2030 inside the mouth measured the volume of water ﬁltered. Two simultaneous samples were obtained between 10:00 and 15:00 h. Zooplankton samples were preserved in 4% buffered formalin seawater.

Data records Hydrological and meteorological parameters. Temperature was measured in the water column by a bathythermograph (Model Belfort OC1) until July 1993, and thereafter by an electronic probe (Sea Bird Electronics Seacat 19–03). Only surface sampling for salinity measurements was carried out from March 1985 to March 1986. Salinity sampling was carried out throughout the water column from September 1987 onwards. Salinity measurements were made on water samples in the laboratory according to Strickland and Parsons (Strickland and Parsons, 1968), by a Salinometer Aanderaa-Model 3012-Sensor 2975 and by an electronic probe after July 1993. Water transparency was evaluated with a Secchi disk. Currents were measured from March 1987 between 09:00 and 15:00 h, at 8 min intervals for 4 h 50 min, at 5 m, 45 m and 75 m, using three current meters (Model SD 2000 Sensordata, Bergen) ﬁxed near the bottom. The ‘East-West component’ of the currents at the three depths was estimated by the variable [ sin()], where is the most frequent current direction of the day (degrees) and its daily average speed (cm s–1). Positive values of this function indicate current eastwards while negative values correspond to westward current. Several meteorological parameters were considered during the whole sampling period (Meteoclimatic Observatory of Chiavari): atmospheric pressure, air temperature, precipitation (monthly sum), sky cover and number of monthly days with N and SE winds stronger than 10 km h–1.

Biological descriptors. Only the most abundant mesozooplanktonic taxa of the Gulf of Tigullio were considered, according to previous studies in this area (Bogli- olo et al., 1979). Thirty-one species and nine genera of medusae, siphonophores, ctenophores, cladocerans, copepods, chaetognaths, appendicularians and thaliacea were counted in a total of 213 collected samples. The copepod genus Clauso- calanus was classiﬁed in three size classes, A, B and C, according to decreasing size (Boucher et al., 1987). Zooplankton was counted in a fraction of the total sample where at least 40 individuals for each category could be identiﬁed. The total number in each sample was normalized per cubic meter. Two different tables of zooplankton densities were built, one with the monthly

2227 P.Licandro and F.Ibanez average and one with the seasonal average. Seasonal abundances were arbitrarily estimated averaging the following months: January, February and March for winter; April, May and June for spring; July, August and September for summer; October, November and December for autumn.

Numerical methods Figure 2 shows the successive steps of the numerical procedure.

1. Biological variables

Step 1 – Selection of representative species. The ﬁrst step was to select among the 40 zooplankton categories considered (31 species and nine genera) the most

Fig. 2. Summary of the numerical procedures computed in the present study.

2228 Zooplankton in the Gulf of Tigullio representative for the following numerical analysis. Percentages of null values (from 0 to 99%) were plotted against number of species (from 1 to 40). A change in the slope of the curve separated most abundant species (with a percentage of zeros below the curvature point) from the rarest ones (Ibanez et al., 1993). Following this procedure, 26 dominant species or genera, below the slope, were selected.

Step 2 – Tables of data. Tables with the seasonal and monthly abundance of the 26 selected species were built for each year. The abundance was then log- transformed.

Step 3 – STATIS. The nine annual tables were compared using the STATIS method (L’Hermier des Plantes, 1976; Robert and Escoufier, 1976; Lavit, 1988; Lavit et al., 1994; Dazy et al., 1996). STATIS had already been used in ecology to study diversity of benthic macrofauna in different stations in a French Lagoon (Amanieu et al., 1981) and spatial structures of demersal fish species (Gaertner et al., 1998). STATIS enables tables having at least one common dimension (e.g. a constant number of observations or a constant number of descriptors) to be analysed. Here, the 26 most abundant species were present during the nine years actually sampled (corresponding to 109 months or 36 seasons). The algorithm of STATIS derives from the Principal Component Analysis (PCA); a simplified scheme of the numerical procedure is showed in Appendix 1. Each year-table Xt is scaled for each descriptor (here, the species) to a zero mean and one standard deviation. The correlation matrices between descriptors Wt (t = 1,2…T, with T = 9) are then computed.

1. Interstructure analysis: Ordination of years (Appendix 1 and Figure 2).

Association coefficients between pairs of Wt (Wi and Wj) were defined by the coefficient RV of Escoufier (Escoufier, 1973):

12/ 22 RVij= tr_ W i W j i /a trWii trW k where tr corresponds to the trace of a matrix. The RV varies between 0 and 1 and indicates the vectorial correlation between two Wt. The T elements of eigenvectors of the matrix RV (the matrix containing all the RVij) allow the proximities between Wt to be displayed. This ordination also reﬂects similarity between the different year-tables Xt. As the diagonalized matrix RV has values ranging from 0 to 1, the ﬁrst eigenvector has only positive elements. 2. Intrastructure analysis: Ordination of the species and trajectories (Appendix 1 and Figure 2).

A compound matrix Wc is built by summing all the Wt weighted by the ﬁrst eigenvalue of the RV matrix:

2229 P.Licandro and F.Ibanez

1 T WWct= ! m1 t = 1 with 1 the greatest eigenvalues of the RV matrix. PCA on Wc, called ‘the compromise table’ because it summarizes all the tables Wt, leads to an ordination representing the global relationships between the species. Therefore, the space deﬁned by the Wc matrix is called ‘space of compromise’ because the position of each species corresponds to its average position during the entire period of nine years. In the ‘space of compromise’, proximity of both observations and species can be represented for each year-table. The ﬁrst representation is based on the correlations between each observation i and each principal ‘compromise axis’ x:

rXVix= t x

where rix is the vector of correlations between the observation i of the table Xt and the axis x, Vx corresponding to the eigenvector x of the compound matrix Wc. The drawing of all the rix allows visualization of the trajectories of the observations through the different T tables. The second representation is obtained by considering projection of each species j on the axes:

Posjx= W t V x/m x where Posjx is the projection vector of species j of the table Xt on axis x, Wt is the correlation matrix derived from table Xt, x and Vx are the eigenvalue and eigenvector, respectively, extracted from the matrix Wc. The drawing of each Posjx allows the trajectories of the species through the different T tables to be shown.

Step 4 – Detection of groups of species. K groups of species having a similar temporal variability were identified by classification on the Euclidean distances between the positions of the 26 descriptors in the ‘compromise space’. Two distinct clusterings were performed on the 26 co-ordinates in the first factorial plane obtained from monthly and from seasonal intrastructure analyses. Average linkage hierarchical agglomerative clustering (Lance and Williams, 1967) with = 0.3 (Legendre and Legendre, 1984, 1998) was used. Clusters obtained from monthly intrastructure were used in step 6 in order to compare zooplankton variability with monthly variations of environmental factors.

Step 5 – Groups of observations: P periods. Clustering as above was performed on a Bray–Curtis (Bray and Curtis, 1957) matrix of distance between monthly observations (109 months). Looking at the dendrogram at a particular cut-off level, P periods or groups of observations were identiﬁed. These groups did not show any temporal connection.

2230 Zooplankton in the Gulf of Tigullio

Step 6 – Pattern of each K group of species in each P period. K species assemblages were separated by the ‘intrastructure’ analysis of STATIS as explained by step 4. A ‘pattern variable’ of each one of these K groups was constructed following Depiereux et al. (Depiereux et al., 1983). The ‘pattern variable’ for a group k indicates its temporal variability (here, throughout the 109 months). This parameter was estimated in several steps:

1. The values of each species j was transformed in percentages:

l n xxij= ij/ ! x ij i = 1,2. . .n total observations; j = 1,2,. . . s total species; i = 1 where xij is the value of each species j for the observation i.

2. The ‘pattern variable’ for each k group is the average of percentages of the m species forming the group: J N K m l O ptik= K ! x ij O/ m m = number of species of group k L j = 1 P where ptik is the value of the ‘pattern variable’ of the group k for the observation i and x’ij the percentage of species j at observation i.

Step 7 – Selection of the most representative periods for the K groups of species. The average of each pattern variable ptk deﬁnes a ‘global mean’ for the whole series. On the contrary, k ‘local means’ are calculated in each period Pi, when the average is calculated considering just the observations in Pi. For each Pi the differ- ence between the global mean and the k local means estimates the indicative values of the K groups. One or several k assemblages of species were considered characteristic for a period Pi if positive high deviations between their local mean in Pi and the global mean were detected.

2. Discrimination by environmental variables

Contingency discriminant analysis was applied on monthly environmental factors matrix. This non-linear type of discriminant analysis is based on information theory (Legendre and Legendre, 1984, 1998). It allows observations previously partitioned in P periods in the biological space to be interpreted by hydroclimatic parameters.

Step 1. Partitioning hydroclimatic parameters into discrete states. The range of each ordered parameter is partitioned stepwise into several states (Legendre and Legendre, 1983). For each cut-off level, a contingency table T is computed. The best partition in 2, 3…n states is that which maximizes entropy of the classiﬁcation of P biological periods by discriminant environmental descriptor (Legendre and Legendre, 1984, 1998). Then, for each parameter, a new contingency table T is 2231 P.Licandro and F.Ibanez constructed in which columns are the P periods, rows the n observations and the values corresponding to the coded states of the parameter.

Step 2. Contingency table with all the combinations of states of the parameters. The entropy of T is estimated stepwise by combining simultaneously the different states of two, and then successively more discriminant descriptors, in order to ﬁnd the combinations of environmental parameters which better discriminate the different P periods. At each step, entropy is calculated for all possible combinations of descriptors. The iteration is stopped when, after adding a new combination of discriminating parameters, the entropy does not increase, or when it represents a high percentage of the total entropy computed with all the possible combinations.

Step 3. Characterization of the P periods by the best combination of states of environmental parameters. For each significant combination in the multiple contingency table, conditional probability is compared with unconditional probability (Legendre and Legendre, 1998). If the former is greater than the latter, the states of the parameters are characteristic of the considered group of observations. Therefore, each period is distinguished by significant states of environmental parameters. Finally, the K assemblages of species defined for preferential periods are characterized by combinations of ranges of environmental parameters.

Results Selection of biological variables In the Gulf of Tigullio from 1985 to 1995, copepods represented an average of 63.6% of the total number of all taxa, followed by cladocerans, appendicularians, thaliacea, chaetognaths, siphonophores, medusae and ctenophores. Dominant zooplanktonic taxa were chosen among the 31 species and nine genera, and 26 categories were selected having a percentage of zeros lower than 50% (Table I), which is the estimated threshold separating most abundant species from the rarest ones.

STATIS The STATIS method was applied to monthly and seasonal log-transformed abundance of selected species shared out into nine annual tables. The analysis performed on the seasonal abundance dataset is appropriate to visualize zooplankton interannual variation. Analysis on a monthly dataset permitted us to relate results of STATIS to monthly variation of environmental factors.

Ordination of years. Results of interstructure analyses are presented in Figure 3. At a monthly level (Figure 3A), the ﬁrst and second axes of the interstructure represented 45.3% and 9.6% of the total variance, respectively. Two different groups of years were separated. The ﬁrst encompassed the years 1985, 1986, 1987, 2232 Zooplankton in the Gulf of Tigullio

Table I. Gulf of Tigullio (1985–1995). Interannual mean, standard deviation and maximum concentrations (ind. m–3) of the zooplankton species or genera. Percentages of zeros during the nine years are also indicated

No Species Mean SD Max % Zero values

Hydromedusae 1 Aglaura hemistoma 0.42 0.65 4.07 15.6 2 Liriope tetraphylla 0.24 0.53 2.59 28.4 Siphonophora 3 Abylopsis tetragona 0.04 0.06 0.42 45.9 4 Lensia subtilis 0.50 0.67 3.15 4.6 5 Muggiaea atlantica 1.19 2.84 18.10 16.5 6 Muggiaea kochi 0.25 0.63 5.31 29.4 Cladocera 7 Evadne spinifera 3.23 5.83 27.81 17.4 8 Podon spp. 1.82 7.28 73.42 14.7 Copepoda 9 Calanus minor 0.74 1.32 8.77 2.8 10 Paracalanus parvus 0.97 2.22 12.94 8.3 11 Clausocalanus spp. B 5.77 4.82 23.79 0.0 12 Clausocalanus spp. C 1.34 2.29 17.19 2.8 13 Centropages typicus 34.95 63.75 395.31 0.0 14 Temora stylifera 7.20 13.31 117.36 2.8 15 Isias clavipes 1.35 3.90 35.26 9.2 16 Acartia clausi 19.20 49.86 311.36 0.0 17 Oithona helgolandica 1.83 1.91 8.24 0.0 18 Oncaea spp. 0.14 0.15 0.73 8.3 19 Corycaeus limbatus 0.81 0.72 3.83 5.5 Chaetognatha 20 Sagitta enﬂata 0.65 1.20 6.17 25.7 21 Sagitta setosa 0.47 0.95 5.05 21.1 22 Sagitta spp. 1.29 1.56 9.91 2.8 Appendicularia 23 Oikopleura spp. 7.63 5.64 37.97 0.0 24 Fritillaria spp. 1.60 2.36 13.07 4.6 Thaliacea 25 Doliolum spp. 3.99 10.56 90.14 15.6 26 Thalia democratica 0.62 3.33 34.06 47.7

1991 and 1994 and the second clustered the years 1988, 1992, 1993 and 1995 (Figure 3A). Considering seasonal abundance, axes 1 and 2 explained 37.4% and 12.4% of the total variance, respectively, and the years 1987, 1992 and 1994 were different from the other years (Figure 3B).

Ordination of species. The intrastructure analysis shows the average positions of species during the nine years in the ‘compromise space’ (Figure 4). Intrastructure calculated on monthly densities of species is displayed in Figure 4A. At a monthly level, a cluster analysis detected ﬁve species associations (M1–M5) at a cut-off of 0.23 (Table IIA). The ﬁrst axis (16.1% of total variance) opposed M2 to M3 and the second (11.6% of total variance), M1 to M5 (Figure 4A).

2233 P.Licandro and F.Ibanez

Fig. 3. STATIS. ‘Interstructure analysis’. Proximity between the tables (years) calculated from (A) monthly and (B) seasonal abundances (individuals m–3) of 26 zooplankton species or genera. The correlation between tables is calculated by the coefﬁcient RV of Escouﬁer.

Fig. 4. STATIS. ‘Intrastructure analysis’. Proximity of the 26 zooplankton species or genera calculated considering their (A) monthly and (B) seasonal abundances (individuals m–3) and represented in the ‘compromise space’. Five groups of species are separated by classiﬁcation on the 26 co- ordinates and they are indicated as ‘M1–M5’ and ‘S1–S5’ in monthly and seasonal analyses, respectively.

At a seasonal level, cluster analysis allowed the detection of ﬁve groups of species (S1–S5) at a cut-off of 0.28 (Table IIB). Intrastructure analysis calculated from seasonal densities is shown in Figure 4B. Axis 1 (28.5% of total variance) separated S2 and S3 and axis 2 (18.2% of total variance) opposed the species associations S1 and S5. Differences between monthly (Figure 4A) and seasonal (Figure 4B) intrastruc- tures are due to distinct composition of groups 1 and 4. Groups S3 and S5 are identical to M3 and M5, respectively, and S2 and M2 just differ by the presence of Thalia democratica in group S2. Group S4 is composed of a greater number of species than M4 and encloses zooplanktonic taxa belonging to group 1 in monthly intrastructure.

Interpretation of the ‘compromise axes’: trajectories of observations. To inter- pret ordination of different associations of species correctly, it is necessary to determine correlations between observations and principal ‘axes of compromise’. 2234 Zooplankton in the Gulf of Tigullio spp. spp. ) seasonal abundances B Lensia subtilis Aglaura hemistoma Evadne spinifera Corycaeus limbatus Aglaura hemistoma Sagitta setosa Oikopleura Oikopleura Sagitta setosa Lensia subtilis Evadne spinifera Corycaeus limbatus 1 1 4 7 19 21 ) monthly and ( A spp. B 4 spp. B 19 spp. 7 spp. 23 Clausocalanus Oncaea Oncaea Liriope tetraphylla Clausocalanus Liriope tetraphylla Isias clavipes Abylopsis tetragona Abylopsis Muggiaea kochi 2 2 3 6 11 11 a18 spp. 18 spp. 23 spp. 21 spp. 15 Temora stylifera Temora Oithona helgolandic Calanus minor Sagitta enflata Calanus minor Doliolum Sagitta Doliolum Temora stylifera Temora Oithona helgolandica Sagitta enflata Sagitta 9 9 17 20 25 22 25 17 20 22 spp. 14 spp. 14 Paracalanus parvus Paracalanus Podon Muggiaea atlantica Centropages typicus Muggiaea atlantica Thalia democratica Acartia clausi Paracalanus parvus Paracalanus Centropages typicus Acartia clausi Podon 8 5 13 26 10 13 16 spp. C 10 spp. C 5 spp. 16 spp. 8 Species composition of the five groups separated by classification from ‘intrastracture analysis’ on ( ). The numbers corresponding to each species as indicated in Figure 4, are also specified –3 Clausocalanus Muggiaea kochi Abylopsis tetragona Abylopsis Isias clavipes Fritillaria Fritillaria Clausocalanus Thalia democratica 6 3 12 15 B 24 A 24 Table II. Table M1S1 M212 S2 M3 M4 S3 M5 S4 S5 (ind. m 26

2235 P.Licandro and F.Ibanez

Fig. 5. STATIS. Correlations between the seasons of the nine years studied and the axes 1 and 2 of the ‘compromise space’. Sp = spring; S = summer; A = autumn; W = winter.

However, in order to achieve a more comprehensive result, only the trajectories of the 36 seasons were considered (Figure 5). In seasonal intrastructure, the first axis opposed autumn to spring and the second separated summer from winter. Zooplankton of S2 and S3 was characteristic of spring and autumn, respectively; those of S1 appear mostly in winter while S5 was found typically in summer. The position of species in group S4 indicated that there was no seasonal variability. The seasons of maximum abundance of zooplankton taxa found with STATIS are generally conﬁrmed by their multi-year averages (monthly averages of 1985–1995, see Appendix 2) shown in Figure 6. Species of S1 are more abundant in winter, those of S2 are mainly present between February and July while those of S3 are present between September and December, excluding Doliolum spp. and Calanus minor which present a seasonal maximum in July. S4 is composed of species that have maximum densities in different seasons; some are most abundant between April and June (Muggiaea kochi), some between May and August (Isias clavipes and Abylopsis tetragona) and others during autumn–winter (Liriope tetraphylla, Clausocalanus spp. B, Oncaea spp.). Species in S5 are more abundant between June and August, excluding Corycaeus limbatus, Oikopleura spp. and Lensia subtilis which have their maximum densities in December, September and between March and May, respectively.

Trajectories of the species. Trajectories of zooplankton allowed their detailed changes during the nine years to be detected (Figure 7). For each species, the season of maximum abundance was determined for every single year. Three different patterns were identiﬁed. 2236 Zooplankton in the Gulf of Tigullio

–3 Fig. 6. Gulf of Tigullio, 1985–1995. Monthly densities [log10(ind. m +1)] of zooplankton species or genera of each group S1–S5 (average 1985–1995). Note that scales are different. 2237 P.Licandro and F.Ibanez

Fig. 7. Trajectories of Centropages typicus, Thalia democratica and Sagitta setosa, calculated from seasonal abundances (ind. m–3) and projected on axes 1 and 2 of the ‘compromise space’ (the axes can be interpreted on the basis of Fig. 5). These three species are examples of (A) constant, (B) quite constant and (C) variable seasonal cycles. Note that scales are different.

(i) Species with a constant seasonal cycle throughout the years: Muggiaea atlantica, Evadne spinifera, Podon spp., Fritillaria spp., Doliolum spp., Calanus minor, Centropages typicus (Figure 7A) and Oithona helgolandica. For example, C.typicus was always found in spring. (ii) Species that were typical of a certain season even if in one, two or three of the nine years the periods of their major presence changed: Aglaura hemistoma, Liriope tetraphylla, Lensia subtilis, Sagitta enﬂata, Sagitta spp., Oikopleura spp., Thalia democratica (Figure 7B), Paracalanus parvus, Clausocalanus spp. B, Clausocalanus spp. C, Temora stylifera, Isias clavipes, Acartia clausi and Corycaeus limbatus. For example, T.democratica was commonly a spring species, except from 1992 to 1994. (iii) Species that were the most abundant in different seasons depending on the years: Abylopsis tetragona, Muggiaea kochi, Sagitta setosa (Figure 7C) and Oncaea spp. For example, highest abundances of S.setosa were found during 2238 Zooplankton in the Gulf of Tigullio summer in 1985–86 and 1995, in autumn during 1987 and 1994, in winter during 1992 and in spring during 1988, 1991 and 1993.

Selection of the most representative periods for each group of species A ‘pattern variable’ was constructed for each group of species defined by the intrastructure analysis of STATIS on monthly densities (Table IIA). This was the first step of such a procedure carried out to identify the most representative period for each one of the five groups that will be then characterized by hydroclimatic factors. To prevent an excessive smoothing of physical parameters, monthly observations were considered. The patterns ptk of the five groups M1–M5 (Figure 8) represented their variability during the 109 months. Therefore, M1 was mostly present in 1986 and

Fig. 8. Gulf of Tigullio, 1985–1995. ‘Pattern variables’ ptk of the zooplanktonic groups M1–M5 throughout the 109 months. Note that scales are different.

2239 P.Licandro and F.Ibanez

Fig. 9. (A) Groups of months separated by a hierarchical agglomerative ﬂexible clustering from Bray–Curtis, on log-transformed monthly abundances of the 26 zooplankton species or genera. Each period ‘P’ is constituted of months not necessarily in chronological order. The grey cases indicate the observations considered in the contingency discriminant analysis. (B) In each period P1–P7 and for each group of species M1–M5, the differences between the ‘global mean’ and ‘local mean’ of each pattern ptik are represented. When positive high deviation is detected for a group Mi in a period Pi, this group is considered characteristic for such a period.

1994, M2 in 1994 and 1995, and M3 between 1985 and 1988. M4 was more abundant in 1987, 1991 and 1994, while M5 appeared more constant throughout the years, but decreased in 1987, 1991 and 1995. Ordination on a matrix of Bray–Curtis distances between the 109 monthly densities of the 26 species separated, for a distance threshold of 0.8, seven periods Pi. All P1–P7 were composed of months not necessarily in chronological order (Figure 9A). For each pattern ptik, differences between the global mean (i.e. the mean during the whole period) and local means in each Pi, identiﬁed the representative P period of each k group of species (Figure 9B). Hence, M3 and M4 were characteristic of P1, species of M1 and M2 of P6, and species of M5 were characteristic of P7.

Discrimination by environmental factors The most discriminant hydroclimatic parameters characterizing each representative period were identiﬁed using a contingency discriminant analysis on monthly values of environmental factors. In order to take into account as many parameters as possible, discriminant analysis was performed considering 85 (from March 2240 Zooplankton in the Gulf of Tigullio

1987 onward) out of 109 months. Parameters (recorded during the months of P1, P6 and P7) used were: atmospheric pressure, precipitation, N and SSE winds, surface water temperature and salinity, and currents at 5 m and 75 m depths. Air temperature and water transparency were not considered because of their correlation with surface water temperature (r = 0.89** and 0.67**, respectively, with P ≤ 1%), as well as current at 45 m which correlated with current at 5 m depth (r = 0.44**). Similarly, ∆T values (the vertical gradients of temperature between the surface and 60 m depth) were excluded because they were highly correlated with surface water temperature (r = 0.95**). ∆S values between 0 and 60 m were not considered because of the large ammount of missing data. The number of observations significantly discriminated in the three periods P1, P6 and P7 are indicated in Table III. The number of states and their corresponding values obtained by partitioning every hydroclimatic parameter are also indicated. Atmospheric pressure varied between 755.3 and 771.3 mmHg, while 760 mmHg was the critical value which separated the two states of this important parameter. The great number of states of current values at 75 m (eight states) and 5 m (five states) depth highlighted the occurrence of different circulation conditions. Critical values of surface water temperature were 14, 16 and 21°C, while rain ranged between 0 and 308 mm, with upper classes of large ranges. Frequencies of SSE wind were partitioned into two states of 0–1 and 1–9 windy days month–1. The results of contingency discriminant analysis indicated that atmospheric pressure, currents at 75 m and at 5 m, and surface water temperature were the most discriminating variables (they accounted for 92% of discrimination). These hydroclimatic parameters are hierarchically ordered according to their power of discrimination. The percentage of discrimination of their different combinations is indicated in Table III. The curve of the percentage of discrimination (Table III) shows that a saturation level of 92% is reached, taking into account the 4th parameter (surface water temperature), while the last four factors seem to be less important for the discrimination. Figure 10 shows the characteristic states of the most discriminant hydro-climatic parameters in each P period considered. These are calculated from observations significantly discriminated (i.e. only when the conditional probability is greater than the unconditional probability, see section 2.3 of ‘Numerical methods’). For months of P1 (representative period of species associations M3 and M4) where high values of atmospheric pressure were recorded (anticyclonic conditions), weak westward currents (1.4–4.8 cm s–1) drove water mass circulation at the surface. P6 (representative of M1 and M2) was characterized by a combination of very low atmospheric pressures (cyclonic conditions) and strong surface eastward countercurrents (0–18.4 cm s–1). P7 (representative period of M5) was constituted by months with varying atmospheric pressures while the Ligurian–Provencal current (westward), which attained higher values than in P1 (1.4–9.4 cm s–1), drove the surface circulation. In the three periods P1, P6 and P7, water masses at 75 m depth were directed eastwards with the same speed (0–6.8 cm s–1) or westwards, with the highest values in P7 (6.5–18 cm s–1). Temperature had the same range of variability for all observation groups. This is probably 2241 P.Licandro and F.Ibanez

Table III. Gulf of Tigullio. Results of contingency discriminant analysis

2242 Zooplankton in the Gulf of Tigullio

Fig. 10. Characteristic states of the most discriminating hydroclimatic parameters in the three periods considered. The groups of species M1–M5 characteristic of P1, P6 and P7 are speciﬁed. because only observations with a signiﬁcant power of discrimination were considered in all P periods.

Multi-year variations of most relevant environmental parameters Multi-year variations of the most discriminant hydroclimatic factors are shown in Figure 11. Interannual averages and their boundaries for each month are indicated in Table IV. Atmospheric pressure generally decreases to minimum values between Febru- ary and June and rises to maximum pressures between October and January (Figure 11 and Table IV). However, very low pressure values were recorded during summer in 1993, 1994 and 1995. Surface water temperature showed a marked annual cycle. The maximum and minimum values were reached in August and February, respectively (Figure 11 and Table IV). The vertical gradient of temperature at station C followed each year a classical scheme of variation, with a stable thermocline from the end of May to September, a strong instability due to vertical mixing in October, and homogeneous temperature in the whole water column from November to April. Between 1987 and 1995, average speeds of currents were 6.3 cm s–1 and 4.2 2243 P.Licandro and F.Ibanez ) –1 ) Current E–W at 75 m (cm s. –1 Mean Min Max Mean Min Max Mean Min Max Mean Min Max Interannual monthly mean, standard deviation, minimum and maximum values of most discriminant environmental variables Table IV. Table Months Atmospheric pressure (mm)JanuaryFebruaryMarch 766.1 ± 3.45 761.3 ±April Surface water temperature (ºC) 3.27May 762.1 755.3 Current E–W at 5 m (cm s. 759.8 ±June 5.36July 760.4 ± 771.3 746.4 3.30August 766.6 759.2 ±September 1.91 754.7 760.3 ± 3.09October 13.8 ± 764.8 0.49 761.2 ± 756.2 12.8 ± 2.18 759.8 ± 0.68 2.46 760.2 ±November 1.39 755.3 764.1 12.9 757.1December 11.6 763.9 ± 755.8 762.9 ± 2.31 13.3 ± 758.1 4.06 0.55 761.5 762.2 ± 14.5 2.41 764.2 761.5 14.8 ± 757.2 13.8 0.74 12.6 764.5 763.6 759.1 761.9 18.1 ± 1.48 13.7 14.3 21.3 ± 1.19 7.9 ± 768.4 770 6.48 22.9 ± 5.0 ± 15.9 0.96 3.30 25.4 ± 1.22 25.2 ± 16.1 766.5 0.69 20 0.8 22 20.3 ± 1.4 20 23.8 6.6 ± 0.76 24 2.73 17.4 ± 0.63 15.8 ± 0.62 6.8 ± 24.1 19.2 18.4 3.90 27.3 2.8 10.9 24.6 16.4 15.1 26.1 0 4.1 ± 21.3 4.40 10.2 18.2 3.9 ± 3.06 17.3 6.3 ± 5.9 ± 4.95 3.70 6.1 ± 5.03 5.6 ± 0 6.14 7.7 ± 4.42 11.2 4.8 ± 1.4 2.66 0 0.5 0.1 9.3 ± 5.3 ± 6.06 0.9 7.5 ± 2.95 0.9 4.36 12.1 0.6 8.1 3.3 12.9 6.0 ± 2 0.5 13.4 3.94 14.3 9.8 18 9 17.5 3.9 ± 2.2 2.99 31. ± 13.4 3.6 ± 3.14 2.92 9.3 1.4 ± 0.54 4.0 ± 3.97 0.3 0 13.1 3.5 ± 0.2 2.76 3.4 ± 0.5 4.00 0.3 5.0 ± 5.61 1.2 7.6 0.5 1.6 7.6 12.5 1.8 8.5 12 8.4 17.5

2244 Zooplankton in the Gulf of Tigullio

Fig. 11. Gulf of Tigullio, 1985–1995. Monthly values of the most discriminant hydroclimatic factors: atmospheric pressure, surface water temperature and currents at 5 m and 75 m. For currents, which were measured from March 1987 onward, positive values of the ‘east–west component’ indicate current eastward while negative values corresponded to westward current. cm s–1 at depths of 5 m and 75 m, respectively (see also interannual monthly averages in Table IV). Generally, the prevalent current direction is westward (Figure 11) but during spring–summer (May–August), and sometimes in autumn (November–December), a counter-current directed eastwards was recorded, mainly in the upper layers. Eastward ﬂow was also observed during the winters (January–March) of 1992 and 1993.

Discussion Statistical methods In this study, the use of exploratory statistical analysis was chosen because a large break existed in our data. The STATIS method is an alternative tool when time series analysis cannot be used. Each year was considered as a single table (e.g. species season or species month). The method shows the dissimilari- ties between the successive tables and allowed a visualization of changes in zooplankton population abundance from one year to another. To analyse how

2245 P.Licandro and F.Ibanez environmental parameters inﬂuence the zooplankton interannual variability, the contingency discriminant analysis was chosen instead of the parametric discriminant analysis (Saporta, 1990). The latter involves numerous basic assumptions: discriminant descriptors (multi) normally distributed, homogeneity of within dispersion matrices and linear interaction between descriptors. The contingency discriminant analysis is a distribution-free procedure, which takes into account non-linear relationships and handles any type of ecological variable, even quali- tative ones. This method permits the detection of the range of discriminant power of each descriptor.

Zooplankton interannual variability and influence of hydroclimatic factors on zooplankton variations In the Gulf of Tigullio between 1985 and 1995, mesozooplankton abundance and composition mostly followed a seasonal pattern of variation, which is well known for zooplankton in different areas all around the world. However, 1987, 1992 and 1994 appeared as ‘special years’. In these three years, the zooplankton population was particularly abundant and 65% of the 26 species or genera showed their maximum interannual concentrations. Using STATIS, we found five different associations of zooplankton species of different taxa, which showed a similar evolution in time during the nine years. Some species were typical of winter (Clausocalanus spp. C, Fritillaria spp.), others of spring (Muggiaea atlantica, Podon spp., Paracalanus parvus, Centropages typicus, Acartia clausi, Thalia democratica), others of autumn (Calanus minor, Temora stylifera, Oithona helgolandica, Sagitta enflata, Sagitta spp., Doliolum spp.) or summer (Aglaura hemistoma, Lensia subtilis, Evadne spinifera, Corycaeus limbatus, Sagitta setosa, Oikopleura spp.). Liriope tetraphylla, Abylop- sis tetragona, Muggiaea kochi, Clausocalanus spp. B, Isias clavipes and Oncaea spp. showed a similar interannual variability which did not appear to be correlated with seasons. Other authors who have studied zooplankton long-term series in Mediter- ranean waters sometimes found the same associations of species. High concentrations of A.clausi, C.typicus and P.parvus in spring or in late spring–summer were found in a multi-year series in Trieste (Cataletto et al., 1995) and in Naples (Mazzocchi and Ribera d’Alcalà, 1995). The associations between T.stylifera and Oithona spp., E.spinifera and Oikopleura, A.clausi and Podon also appeared in the Gulf of Naples (Della Croce, 1962; Carrada et al., 1980). Sagitta enflata and S.setosa had different periods of maximum concentrations at station C, as well as in the Bay of Villefranche (Ibanez and Dallot, 1969). All of these observations suggest that the recurrence of some zooplanktonic associations may involve a similar response to properties of the environment, and/or might be the conse- quence of the physiology and behaviour of species. A comparison with other multi-year or annual data series in different coastal areas of the Mediterranean generally confirms the periods of maximum abundance of zooplanktonic genera/species found in the present study (Table V). Although zooplankton in the Gulf of Tigullio mainly showed a seasonal cycle, 2246 Zooplankton in the Gulf of Tigullio . (1995) . (1997) (1980) . (1994) . (1997) et al et al Period et al et al et al. (1995) ‘66; Sept. 1966–Dec. ‘67 8) 1976 15) 1967–1990 8) Carrada 1987 10) Mar. 1986–Febr. 6) Oct. 1960–Sept. ‘61; Apr. 12) 1970–1980 13) 1984–1990 14) 1966–1993 1) Ghirardelli (1952) 11) Ménard 13) Mazzocchi & Ribera d’Alcalà 14) Buecher 3) Fenaux (1963) 3) Fenaux 6) Gaudy (1972) 7) Thiriot (1972–73) 5) 1967 4) Carli & Sertorio (1964) 15) Ménard 3) 1959–1961 4) 1957–1958 W (12) 1979 1978–June 9) June –March (8) 2) 1951 April (13) 2) Massuti (1954) October (8) 7) 1965–1969 Aug.–November (13)Aug.–November 1962–Mar. ‘64; Nove. 1964–Mar. D. nationalis D. F. pellucida & borealis – F. late Sp & A (7) 11) 1967–1990 P. polyphemoides –P. polyphemoides – P. any particular season (7) (8) June 9) Dauby (1980) & – - S (3) S (10) Sp (10) Sp (10) July (5) July A(10&14) A–W (6) dioica F. pellucida F. July–Aug. (5)July–Aug. Oct.–Dec.(2) (1) July–Aug. 1) 1948 O.longicauda March–July (11) March–July – S (4) Sp (9) Sp-S (6) (13) (8); April–July June S (12) 12) Cataletto A (4) Sept.–Oct. (9)Aug.–Sept.(6) Oct. (13) A–W (12) Sp (4) April (9) Sp (4) Sp (9) Sp (6) May (4) A(6) Sept. (4) (3) Febr.–March March (8) 5) Ibanez & Dallot (1969) W-sp (4) W-sp Febr. (4) Febr. April (4) April (4) Sp-S (6) (13) (8); July–Sept. June Sp (12) 10) Dowidar (1992) Febr.–April (4) Febr.–April Fritillaria spp. S A W Sp Tigullio Genoa Villefranche Calvi Marseille Castellón Banyuls Naples Trieste in a chronological order July–Aug. This study May–Aug. Jan.–April Sept.–Nov. Dec.–April April–June B” spp. S (4) Characteristic periods of some zooplanktonic forms in different coastal areas the Mediterranean Sea spp. (4) June S–A (15) spp. ) spp. spp. furcatus) paululus Oncaea Group S5 Evadnme spinifera Corycaeus limbatus Clausocalanus “ ( Isias clavipes Sagitta setosa Oikopleura Paracalanus parvus Paracalanus Thalia democratica Cerntropatges typicus Clausocalanus “C” ( Acartia clausi Group S3 Fritillaria Muggiaea atlantica Podon Muggiaea kochi Group S2 Calanus minor stylifera Temora Oithona helgolandica SEAGroup S1 LIGURIAN THYRRENIAN ADRIATIC Authors Sagitta enflata Doliolum Group S4 Liriope tetraphylla tetragona Abylopsis Sp, spring; S, summer; A, autumn; W, winter. summer; A, autumn; W, Sp, spring; S, Table V. Table

2247 P.Licandro and F.Ibanez species trajectories indicated which taxa showed a stable seasonal cycle during the nine years and which forms underwent large multi-year fluctuations. Three different behaviour patterns were identified (Figure 7): species like C.typicus which showed a constant seasonal cycle throughout the years; species like T.democratica that, although typically found during a certain season, showed major concentrations during other periods in one, two or three years; species like S.setosa which were most abundant in different seasons depending on the years. Contingency discriminant analysis allowed the effects of hydro-climatic parameters on interannual zooplankton variability to be analysed. Atmospheric pressure, currents and water temperature better discriminate the representative periods of the five species associations in the Gulf of Tigullio. The results of discriminant analysis are probably affected by the lack of important environmental data such as primary production during the sampling period. However, parameters that determine long-term changes are not necessarily those that explain seasonal cycles. Surface temperature, although an important discriminating variable, presented the same range of values in the three periods P1, P6 and P7 (Figure 10). In contrast, atmospheric pressure, current strength and direction might have different values. The atmospheric pressure directly influences the stability of climate and the stability and the dynamics of the marine environment; the critical value, which induced different environmental conditions in the Gulf of Tigullio, is 760 mmHg. The representative months for species of groups M1 and M2 are characterized by cyclonic conditions and strong surface countercurrents directed to the east, and those for species of M3 and M5, by anticyclonic conditions and surface circulation characterized by a weak Ligurian–Provençal current (from east to west). Species of group M5 are characteristic of months with varying atmospheric pressures and strong Ligurian–Provençal current at the surface and at 75 m depth. It is known that the diffusive and advective properties of the marine environment must play a critical role in population patterns and richness, and in its temporal variability (Sinclair and Iles, 1989). The importance of Mediterranean circulation dynamics in the diffusion of zooplankton species from the Atlantic to Mediterranean waters has been reported (Furnestin, 1968), as well as in the determination of different zooplankton associations found in the Gulf of Trieste (Cataletto et al., 1995) and in the Gulf of Naples (Carrada et al., 1980). In the last decade, a prominent seasonal variability of water circulation in the Ligurian–Provençal Basin was found after studying multi-annual data (Astraldi et al., 1995). The energy of the Ligurian–Provençal current markedly increases from November to May when the horizontal circulation links the eastern and western part of the basin, while during summer and autumn, there is an inde- pendent circulation in the Ligurian Sea and in the Gulf of Lions. A similar pattern was found in the Gulf of Tigullio and this influences the taxa associations that dominate zooplankton populations in different periods. A comparison with multi-annual data recorded in other coastal areas of the Mediterranean Sea enable us to verify synchrony of zooplankton time fluctuations. Furthermore, spatio-temporal analysis of existing biological time series is 2248 Zooplankton in the Gulf of Tigullio necessary to confirm the influence of atmospheric pressure and water circulation on interannual fluctuations of most abundant coastal zooplankton species.

Acknowledgements The authors acknowledge T.Sertorio Zunini and S.Souissi for their constructive comments and helpful suggestions, and M.Giallain for the invaluable help in the plankton counting. Thanks to P.Chang and to G.Beaugrand for the help in correcting the English text. Zooplankton samples were collected in the frame- work of the project ‘Estimation of pelagic resources’ of the Department of the Italian Merchant Marine. This study was part of two research programmes: PNEC art.4 (Programme National d’Environnement Côtier) topic ‘variabilité spatio-temporelle, évolution et tendance à long-terme’ and SINAPSI (Seasonal Interannual and decAdal variability of the atmosPhere oceanS and related marIne ecosystems).

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Received on August 17, 1999; accepted on June 18, 2000

Appendix 1

Simpliﬁed scheme of the different steps of the STATIS method. Xt = initial table; Wti = correlation matrix between species; RV = Escouﬁer’s correlations matrix; Wc = ‘compromise table’.

Standardised data

2251 P.Licandro and F.Ibanez

1. Interstructure Analysis

Correlation matrix between species for each Xt table Escouﬁer’s correlations between Wt tables

Ordination by PCA on RV matrix

2. Intrastructure Analysis (‘Compromise Space’)

Compound matrix Wc (‘Compromise Table’): weighted sum of Wt by the ﬁrst eigenvalue 1 of RV matrix 1 T WWct= ! m1 t = 1

PCA on Wc: Global ordination of the species

Trajectories from table to table

1. Trajectories of the observations: 2. Trajectories of species: correlations with the Principal Axes of Wc projections of the species of each t table on the principal axes of Wc

2252 Zooplankton in the Gulf of Tigullio 2.73 0.07 1.08 5.15 4.1 0.1 1.4 9.2 0.72 0.3 0.22 0.5 0.57 2.4 3.69 0.9 0.83 1.95 3.5 1.66 2.3 1.31 3.01 2.1 1.56 1.1 0.57 33.39 9.9 9.12 5.3 3.18 0.7 5.3 2.4 31.9 0.24 0.2 0.28 0.2 0.35 0.3 0.49 1.89 0.9 0.72 1.0 1.05 0.8 0.70 1.86 1.8 1.82 1.7 1.84 0.7 0.45 5.44 9.9 6.16 6.5 3.62 6.5 3.03 0.2 1.9 1.8 11.9 11.67 0.8 0.63 1.8 1.87 1.8 3.99 1.0 2.04 4.1 0.23 0.6 0.59 1.3 1.50 1.3 1.24 1.9 2.43 2.1 1.73 0.14 0.1 0.08 0.0 0.06 0.0 0.04 0.0 0.04 0.0 0.02 0.03 0.3 0.86 0.4 0.74 0.5 0.65 28.70 6.2 9.87 5.0 9.87 9.4 11.06 7.1 8.40 2.9 3.67 0.2 0.1 0.0 14.6 1.88 0.7 1.09 0.2 0.19 0.6 0.70 0.4 0.54 0.3 0.63 0.2 0.19 1.12 0.7 0.85 0.3 0.10 0.9 1.22 0.4 0.22 0.2 0.14 0.2 0.24 10.53 10.2 7.62 3.3 4.57 2.8 3.08 3.2 3.82 0.9 1.39 0.1 0.08 1.6 1.0 12.7 0.33 0.3 0.37 0.4 0.29 0.8 0.69 0.89 0.41 1.5 0.89 1.0 0.89 1.0 1.13 6.26 4.1 4.41 0.8 1.24 0.2 0.22 0.3 0.16 0.2 0.18 0.1 0.10 0.1 0.13 0.92 4.8 5.82 5.9 9.11 5.8 8.19 10.2 7.38 0.14 0.6 0.69 0.9 0.78 1.8 1.52 3.3 2.40 0.06 0.1 0.15 0.1 0.05 0.1 0.11 0.2 0.13 0.2 0.23 0.1 0.09 0.2 0.13 5.43 4.5 3.85 4.8 2.14 5.9 4.82 4.9 2.95 4.4 4.28 5.1 2.17 1.73 0.3 0.45 0.1 0.16 0.2 0.22 0.3 0.40 0.3 0.31 0.2 0.18 0.2 0.25 11.13 0.4 0.49 0.2 0.23 0.2 0.49 0.1 0.11 0.2 0.46 0.0 0.11 0.0 0.08 146.78 55.0 37.65 25.5 24.06 12.1 10.51 7.6 6.22 6.1 5.73 6.1 7.24 0.2 4.5 5.3 1.0 0.3 0.1 4.0 0.7 140.7 5.60 1.15 0.7 0.36 0.4 0.31 0.6 0.67 0.3 0.20 0.6 0.38 0.5 0.66 0.3 0.41 0.3 0.25 57.87 44.3 65.67 41.8 101.30 4.9 4.47 4.8 7.48 1.9 1.72 4.0 1.3 46.7 4.56 1.3 1.36 1.6 2.77 0.9 1.32 0.3 0.46 0.4 0.63 0.06 0.0 0.06 0.1 0.22 0.2 0.36 0.2 0.23 1.1 1.35 ) and standard deviations of the zooplankton species or genera over 1985–1995 period. Maximum 22.62 3.2 4.18 2.6 4.28 1.3 2.21 1.2 1.36 1.4 2.19 1.0 1.71 0.1 0.19 0.1 0.13 –3 2.9 9.2 0.0 0.02 0.4 1.04 1.5 1.73 0.05 0.6 0.86 2.6 2.68 4.04 3.2 3.26 0.8 0.84 0.3 0.44 0.9 2.15 0.07 0.1 0.10 0.1 0.10 0.1 0.24 0.2 0.13 1.8 3.01 1.4 1.58 0.06 2.87 6.7 5.04 9.5 11.59 7.2 3.53 7.9 4.76 8.1 5.56 8.7 5.46 0.200.09 0.40.04 0.70.32 0.78 0.2 1.24 0.5 0.2 0.41 0.41 1.0 0.27 0.9 0.4 1.88 0.95 0.35 4.7 1.3 4.87 1.07 0.9 0.78 0.8 0.69 0.5 0.44 0.7 0.52 0.7 0.45 1.0 0.75 0.39 0.5 0.89 1.0 1.37 1.1 1.25 2.0 1.69 1.0 1.21 1.7 1.50 1.9 1.83 0.03 0.10.01 0.28 0.0 0.2 0.04 0.21 0.0 0.1 0.04 0.16 0.0 2.6 0.02 3.85 0.1 0.05 0.0 0.0 3.6 0.1 0.0 4.0 0.1 0.1 0.0 0.4 0.3 0.0 0.0 0.04 0.04 0.02 0.1 0.18 0.0 0.09 0.5 0.99 1.0 1.53 0.04 0.44 0.9 1.09 0.4 0.72 0.4 0.37 1.5 3.03 2.8 4.14 0.4 0.61 0.23 0.2 0.12 0.2 0.20 0.1 0.13 0.03 0.0 0.06 0.1 0.19 0.6 1.07 4.1 6.90 46.3 83.00 30.3 54.62 0.0 JanuaryMean SD February Mean SD March Mean SD Mean April SD Mean SD May Mean SD Mean June SD Mean SD July Mean SD Mean SD August Mean SD Mean SD September October November December 0.48.5 1.12 11.19 2.6 18.5 21.05 4.17 39.4 49.75 95.1 70.99 0.0 2.7 3.51 4.1 6.23 3.4 6.14 1.8 1.75 0.4 0.49 1.7 1.29 0.8 0.50 0.7 0.77 0.8 0.78 0.2 0.16 0.0 0.0 0.20.1 0.31 0.8 0.11 0.41 0.4 0.2 0.0 0.0 0.03 0.1 0.14 0.0 0.04 0.2 0.66 0.3 0.80 0.1 0.09 0.0 0.02 Mean monthly densities (ind. m spp. C 0.8 0.59 1.8 1.54 3.3 3.68 spp. B 6.3 5.35 5.6 6.02 7.2 5.26 7.3 7.64 spp. 4.7 3.61 spp. 0.1 0.22 spp. 2.7 3.47 spp. spp. 0.3 0.26 spp. 0.1 0.12 0.9 1.51 Acartia clausi Fritillaria Thalia democratica Group S1 Clausocalanus Paracalanus parvus Paracalanus Centropages typicus Group S2 Muggiaea atlantica Podon Temora stylifera Temora Group S3 Calanus minor Oithona helgolandica Oikopleura Sagitta enﬂata Sagitta Sagitta setosa Evadne spinifera Group S5 Aglaura hemistoma Lensia subtilis Corycaeus limbatus Isias clavipes Clausocalanus Oncaea Doliolum Muggiaea kochi Abylopsis tetragona Abylopsis Group S4 Liriope tetraphylla Appendix 2. minimum values are in bold.

2253