Coastal Phytoplankton Do Not Rest in Winter

Adriana Zingone & Laurent Dubroca & Daniele Iudicone &

Francesca Margiotta & Federico Corato &

Maurizio Ribera d’Alcalà & Vincenzo Saggiomo & Diana Sarno

Received: 6 October 2008 /Revised: 16 February 2009 /Accepted: 28 March 2009 /Published online: 5 May 2009# Coastal and Estuarine Research Federation 2009

Abstract The climatology and interannual variability ofwinter phytoplankton was analyzed at the Long TermEcological Research Station MareChiara (LTER-MC, Gulfof Naples, Mediterranean Sea) using data collected from1985 to 2006. Background winter chlorophyll values (0.2–0.5 μg chl a dm−3) were associated with the dominance offlagellates, dinoflagellates, and coccolithophores. Winterbiomass increases (<5.47 μg chl a dm−3) were oftenrecorded until 2000, generally in association with low-salinity surface waters (37.3–37.9). These blooms weremost often caused by colonial diatoms such as Chaetocerosspp., Thalassiosira spp., and Leptocylindrus danicus. Inrecent years, we observed more modest and sporadic winterbiomass increases, mainly caused by small flagellates andsmall non-colonial diatoms. The resulting negative chl atrend over the time series was associated with positivesurface salinity and negative nutrient trends. Physical andmeteorological conditions apparently exert a strict control onwinter blooms, hence significant changes in winter produc-tivity can be foreseen under different climatic scenarios.

Keywords Phytoplankton .Winter dynamics .

Sverdrup’s theory .Mediterranean Sea . Time series . Climate

Introduction

In the terrestrial environment, winter is in many cases thedormant season, when many higher plants shed their leaves,

going through a period of reduced activity. Low temper-atures, snow or ice, and stronger winds concur to producethis rather typical seasonal pattern. At sea also, winter isperceived as an unfavorable season for autotrophic growth,while the spring bloom is by far regarded as the crucialevent of phytoplankton seasonal cycle. Along with the earlypapers by Gran (1931) and Sverdrup (1953), nutrientconcentrations are restored and maintained at high levelsin the upper layers of the water column in winter, as mixingand low irradiance concurrently prevent high phytoplanktongrowth. These constraints are deemed to be effective also intemperate and subtropical areas, despite winter conditionsare not as extreme as at high latitudes.

Nonetheless, many cases have been reported of phyto-plankton biomass accumulation in the water column priorto the thermal stratification that is assumed to be theprerequisite for the classical spring bloom of the temperateregions (Gran 1931; Sverdrup 1953). Such events havestimulated an intense debate and different interpretations,some of which even questioning the Gran–Sverdrupconceptual scheme (Backhaus et al. 2003; Eilertsen 1993;Huisman et al. 1999; Platt et al. 1994; Smetacek andPassow 1990; Townsend et al. 1992). At lower latitudes thewinter bloom may represent the annual peak, as it is thecase of some regions of the Mediterranean Sea, where thisevent has been defined as the unifying feature (Travers1974; Duarte et al. 1999). With the exception of thosespecific cases, winter is generally relegated to the role of aless important, non-productive season, with no relevantphytoplankton dynamics, to which little attention hasgenerally been paid. Very little is known, for example, onspecies typical for this period of the year, nor has it beendiscussed whether winter conditions may create specificniches for phytoplankton species and assemblages. Even inthe most comprehensive synthesis of seasonal succession of

Estuaries and Coasts (2010) 33:342–361DOI 10.1007/s12237-009-9157-9

A. Zingone (*) : L. Dubroca :D. Iudicone : F. Margiotta :F. Corato :M. Ribera d’Alcalà :V. Saggiomo :D. SarnoStazione Zoologica Anton Dohrn,Naples, Italye-mail: zingone@szn.it

phytoplankton, the Margalef Mandala (Margalef 1978),winter is a time before the appearance of the diatoms at thetransition between mixed and stratified season.

This paper focuses on winter phytoplankton through theanalysis of a dataset gathered at the Long Term EcologicalResearch Station MareChiara, in the Gulf of Naples from1984 to 2006. Climatology and interannual variability ofwinter periods were analyzed with the aim of evaluatingforcings, dynamics and trends of winter biomass build-up.The specific aims of our study were: (1) to assess the entity,frequency, and duration of biomass accumulation eventsand their trends through time, (2) understand the mecha-nisms underlying the temporal patterns of phytoplanktonabundance, and (3) test the existence of a speciesassemblage characteristic of the winter period. The mainquestion is whether blooms occurring in winter are justanticipated spring blooms or they rather have their ownidentity in terms of mechanisms and players.

Study Area

The Gulf of Naples (Fig. 1) is a deep embayment (avg—170 m) covering an area of approximately 870 km2, whichopens onto the Tyrrhenian Sea. The Gulf waters areinfluenced by the land runoff from a very densely populatedregion but, differently from other coastal sites, riverineinputs are limited and highly intermittent and salinity rarelygoes below 37.5. The close proximity of oligotrophicoffshore waters to the coastline results in the coexistence

of two subsystems: a eutrophic coastal zone and anoligotrophic area similar to the offshore Tyrrhenian waters.The location and width of the boundary between the twosubsystems are variable over the seasons (Carrada et al.1980; Carrada et al. 1981; Marino et al. 1984) and theexchange between them at times can be enhanced by localcirculation (Casotti et al. 2000). Freshwater from the riversin the adjacent Gulf of Gaeta at times flows into the Gulf(Carrada et al. 1980) in response to particular atmosphericevents, as confirmed by SeaWiFS data analysis (B.Buongiorno Nardelli, personal communication). The LongTerm Ecological Research Station MareChiara (st. LTER-MC, 40°48.5’ N, 14°15’ E) is located two nautical miles farfrom the coastline, in proximity of the 75 m isobath(Fig. 1), in an area where the influence of the coast is stillpronounced over most of the year. At st. LTER-MC,prominent maxima occur in May in surface waters,followed by other peaks through the summer, but depth-integrated (0–60 m) biomass values are rather stable overthe seasons. In surface waters, phytoplankton populationsare dominated by diatoms and nanoflagellates for most ofthe year, including summer, when blooms of small speciessucceed and overlap each other (Ribera d’Alcalà et al.2004; Scotto di Carlo et al. 1985; Zingone et al. 1990).

Methods

Sampling at st. LTER-MC has been taking place sinceJanuary 1984, with one major interruption from August

Fig. 1 Map of the Gulf ofNaples with the position of thelong-term sampling stationLTER-MC

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1991 through February 1995. The frequency was fortnight-ly until 1991 and weekly from 1995 to date. Here weanalyze data gathered from 1984 to 2006. Each week of theyear was defined as a decimal number (01–53) followingthe international standard for date and time (ISO 8601). Torestrict our analyses to the winter period, we selected 170sampling dates from the 49th week of one year (secondweek of December) through the tenth week of the next year(second week of March). The choice was based on theabsence of thermal stratification of the water column duringthe selected period and in the two flanking weeks (48th and11th). The heat flux reverses and surface water temperaturegradually increases from the 12th week, leading tostratification from the 13th–14th week onwards. Converse-ly, the progressive deepening of the thermocline in autumngenerally leads to complete mixing of the water columnaround the 48th week. Most winters had more than fivesamplings, with a maximum of 14 samplings in winter1997/1998, but only three samplings were available for1983/1984, 1989/1990, and 1994/1995 winters and onlyfour for winter 2006/2007.

From 1984 to 1991, and sporadically in the followingyears, the hydrocast was performed using 5-l Niskin bottlesequipped with reversing thermometers. As of 1995, CTDand fluorescence profiles were obtained with a SBE911mounted on a Rosette sampler equipped with 12-l Niskinbottles. For the entire period, sampling depths were 0.5, 2,5, 10, 20, 30, 40, 50, 60, and 70 m for the analysis ofsalinity, oxygen, and nutrients (NO2, NO3, NH4, PO4, andSiO4). Temperature and salinity data at 2 m depth wereused as representative of surface values, as they were morereliable as compared to 0 m data when obtained from CTD.Mixed layer depth (MLD) was calculated based onindividual density profiles as the depth at which thedifference (Δσθ) with respect to the density value at 10 mdepth was higher than a threshold of 0.03 kg m−3 (DeBoyer Montégut et al. 2004).

The main dataset for meteorological analysis was theObjectively Analyzed air–sea Fluxes (OAFlux: http://oaflux.whoi.edu), which integrates satellite observations withsurface moorings, ship reports, and atmospheric modelreanalyzed surface meteorology. This is the most recentcomprehensive data set for the global oceans. OAFlux timeseries were extracted at the grid point closest to st. LTER-MC. The cloudiness was derived from the InternationalSatellite Cloud Climatology Project data set, which prob-ably represents the best dataset for this very subtleparameter. The intrinsic limitation of these products is therelatively low temporal (day) and spatial (1°) resolution.

Precipitations measured at a meteorological station in thecity of Naples were graciously provided by G. Budillon(Parthenope University, Naples). We focused the analysison the forcings that have the largest impact on the vertical

stratification, i.e. the heat surface flux and the wind.Derived parameters such as wind stress or wind energywere not calculated, since this would make little sense inthe case of daily averages. Finally, given the differentnature of the two datasets used (OAFlux and the meteoro-logical data), we do not discuss the buoyancy fluxesresulting from the combination of heat and freshwaterbudgets but rather consider the data on precipitations as aproxy for the river runoff.

Measurements of photosynthetic available radiation(PAR), both at the coast over the day and underwaterduring sampling, were irregularly obtained by means ofLICOR quantum sensors over the first 5 years of the timeseries. To complement those data, SeWiFS-based daily PARand K490 were used and the latter converted to diffuseattenuation coefficient of PAR (Kd) in the water columnusing the equation

Kd ¼ 0:0085þ 1:624� K490

by Rochford et al. (2001). Time series of SeaWiFSparameters (Feldman et al. 2008) were built averagingvalues for the 16 pixels falling within the Gulf of Naples.

Nutrient concentrations were analyzed according toHansen and Grasshoff (1983) with a TECHNICON IIautoanalyzer. Total chlorophyll a (chl a) concentrationswere determined in samples from selected depths (0.5, 2, 5,10, 20, 40, and 60 m) at the spectrophotometer (Stricklandand Parsons 1972) till 1991 and at the spectrofluorometer(Holm-Hansen et al. 1965; Neveux and Panouse 1987)from 1995 onwards. Phytoplankton samples were collectedfrom the 0.5-m bottle (indicated as 0 m in the following)and, for the first 2 years, at selected additional depths.Phytoplankton samples were fixed with neutralized form-aldehyde (0.8–1.6% final concentration). Cell counts wereperformed at the inverted microscope after sedimentation ofvariable sample volumes (1–100 ml), depending on cellconcentration (Utermöhl 1958), on two transepts represent-ing ca 1/30 of the whole bottom area of the sedimentationchamber, at ×400 magnification. For selected species, theidentification was checked at the electron microscope. Cellssmaller than 2 μm generally escaped detection, unless veryabundant. For carbon content evaluation, linear measure-ments were taken on phytoplankton cells routinely over1 year of sampling and afterwards, occasionally, on selectedsamples for species that were rare or variable in size. Carboncontent was calculated from mean cell biovolumes using theformulas suggested for protist plankton, diatoms anddiatoms >3,000 μm3 by Menden-Deuer and Lessard (2000).

Data Analysis

Weekly time series were built and then combined intoyearly, winter and non-winter, and weekly median. To test

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if the series had increasing or decreasing trends, the non-parametric Spearman correlation coefficient ρ betweenobservations and time was calculated for each variable.Statistical significance was calculated using a blockbootstrap with 999 replications (Efron and Tibshirani1994). Trend results (ρ values) are presented along withthe bootstrapped p-value. It is worth noting that statisticalsignificance tests have been criticized both for the use ofthe null hypothesis (e.g. Nicholls 2001) and for issuesrelated to the autocorrelation of the data (e.g., Santer et al.2000). Consequently, the confidence interval (c.i.) of theSpearman coefficient ρ (covering 99% of the value range)was checked. Trends were considered significant (Table 1,values in bold) only if the associated p-value was ≤0.05 andonly if the 99% c.i. did not include zero.

A statistical characterization of the chl a profiles alongthe water column was performed in order to study thedepth-dependent dynamics of biomass variations. One

hundred fifty-three profiles with no gaps were extractedfrom our initial database and grouped using a hierarchicalclustering based on the Euclidean distance and the Ward’slinkage methods (Ward 1963). The estimation of theoptimal number of clusters in the resultant trees wascomputed with the Gap statistics (Tibshirani et al. 2001).

Two indices of species diversity were used: the numberof species present in each sampling (N) and the Shannondiversity index. The latter index is defined as

H ¼Xn

i¼1

pi� lnb pið Þ

where pi is the proportional abundance of species I, n thenumber of species, and b is the base of the logarithm (herewe use base 10).

Species associations were studied using clusteringanalysis. Rare and very scarcely abundant species were

Table 1 Statistics by winter and non-winter periods for selected parameters by the mean (Avg)±the standard deviation (SD) and the range(minimum and maximum)

Winter Non-winter Winter Non-winterAvg±SD (range) Avg±SD (range) Spearman correlation

coefficient(bootstrapped p-value)

Spearman correlationcoefficient(boostrapped p-value)

Temperature (°C) 2 m 15.20±1.44 (12.37–20.36) 21.17±4.08 (12.59–28.49) 0.23 (0.15) 0.20 (0.21)

60 m 15.30±1.36 (13.11–18.53) 14.95±1.17 (13.07–19.62) 0.12 (0.31) 0.12 (0.30)

Salinity 2 m 37.87±0.21 (37.28–38.30) 37.68±0.35 (36.52–38.32) 0.36 (0.05) 0.11 (0.35)

60 m 37.98±0.15 (37.65–38.40) 37.97±0.11 (37.53–38.41) 0.26 (0.13) −0.02 (0.46)

Chla (µg dm−3) 0 m 0.71±0.73 (0.15–5.47) 2.23±3.26 (0.06–38.95) −0.48 (0.02) −0.34 (0.07)

Int. mean 0.55±0.54 (0.12–4.70) 0.75±0.58 (0.08–4.15) −0.56 (0.01) −0.48 (0.02)

NO3 (µmol dm−3) 0 m 1.61±1.45 (0.01–6.93) 1.06±1.45 (<0.01–9.92) −0.54 (0.01) −0.81 (<0.01)

Int. mean 0.84±0.56 (0.01–3.78) 0.49±0.42 (0.01–2.41) −0.29 (0.10) −0.70 (<0.01)

DIN (µmol dm−3) 0 m 3.44±2.69 (0.15–15.61) 2.38±2.97 (0.03–21.93) −0.36 (0.06) −0.44 (0.03)

Int. mean 1.84±0.98 (0.33–8.29) 1.14±0.62 (0.05–3.72) −0.09 (0.34) −0.58 (0.01)

PO4 (µmol dm−3) 0 m 0.12±0.10 (0.01–0.6) 0.13±0.10 (0.01–0.62) −0.60 (<0.01) −0.52 (0.03)

Int. mean 0.09±0.08 (0.01–0.59) 0.10±0.08 (0.01–0.58) −0.54 (0.01) −0.59 (0.01)

SiO4 (µmol dm−3) 0 m 2.66±1.69 (0.14–9.75) 1.95±1.96 (0.01–16.56) 0.17 (0.24) −0.33 (0.09)

Int. mean 1.82±0.71 (0.12–4.41) 1.46±0.67 (0.11–4.12) −0.04 (0.43) −0.46 (0.03)

Diatoms cells ml−1 388±780 (3–4,563) 7,033±12,484 (3–185,448) 0.22 (0.18) 0.48 (0.01)

µgC l−1 18±42 (0–386) 207±332 (0–4,755) −0.15 (0.26) 0.20 (0.20)

Dinoflagellates cells ml−1 30±29 (1–184) 256±334 (0–3,400) 0.44 (0.02) −0.23 (0.14)

µgC l−1 5±8 (0–45) 54±169 (0–3,346) 0.36 (0.04) −0.46 (0.02)

Coccolitophores cells ml−1 66±40 (8–265) 235±707 (0–10,795) 0.65 (<0.01) 0.12 (0.30)

µgC l−1 2±1 (0–12) 9±32 (0–474) 0.32 (0.08) 0.17 (0.24)

Other flagellates cells ml−1 721±884 (17–5,539) 5,758±6,027 (35–42,619) 0.76 (<0.01) 0.84 (<0.01)

µgC l−1 4±5 (0–29) 35±55 (0–1,049) 0.76 (<0.01) 0.87 (<0.01)

Total cell number cells ml−1 1,206±1,473 (75–8,194) 13,283±17,021 (105–223,385) 0.64 (<0.01) 0.73 (<0.01)

µgC l−1 29±49 (1–421) 305±436 (3–5,002) 0.21 (0.16) 0.19 (0.21)

Shannon index 0 m 1.68±0.43 (0.79–2.75) 1.80±0.43 (0.45–2.86) −0.69 (<0.01) −0.79 (<0.01)

Number of species (N) 0 m 28.83±7.76 (16–57) 29.33±6.68 (12–52) 0.48 (0.01) 0.40 (0.05)

Trends are indicated by the Spearman correlation coefficients and their p-value. Significant coefficients are indicated in bold. “Int. mean” meansvalue averaged along the water column (0–60 m).

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carefully checked by visual inspection and discarded fromthe analyses. Suprageneric groups were also discarded. Theresultant filtered dataset contains 58 taxa. Abundance waslog+1 transformed to decrease the weight of abundant taxain the analysis. Similarity coefficients among taxa werecomputed using a zero-adjusted Bray–Curtis similaritycoefficient, which was selected based on its accuracy incomparison with other indices (Bloom 1981) and on thelarge presence of zero in our dataset (70% of 11,352records, see Clarke et al. 2006 for details). Based on thesimilarity matrix, agglomerative hierarchical clustering ofthe dataset was computed with the flexible method withbeta set at the value of −0.25, in order to have anintermediate solution between single and complete linkage(Lance and Williams 1967). The optimal number of clustersin the resultant trees was estimated with the Gap statistics(Tibshirani et al. 2001).

Principal component analysis (PCA) based on a correlationmatrix was used to investigate the relationships between insitu physical and biological parameters (surface temperature,salinity, chl a, nutrients, and mixed layer depth) andphytoplankton group abundance (diatoms, coccolithophores,dinoflagellates, and other flagellates). Results are presentedusing the diagram of the correlation circles (Saporta 1990).

Data distribution over time (weekly climatology andinterannual variability) and across relevant parameters (i.e.chl a profile types) are presented using box-and-whiskerplots with whisker range fixed at 1.5 time the interquartileinterval (Tukey 1977).

Statistical analyses were performed using the R environ-ment (R Development Core Team 2007).

Results

Hydrography

Sea temperature at 2 m depth ranged from 12.37 to 20.36°C,showing a recurrent decrease from the first to the last weeks ofeach winter period (Fig. 2a). Surface temperature exhibited astrong interannual variability (Fig. 3a), with an alternation ofcolder (e.g. winter 1998/1999, 1999/2000, and 2005/2006)and warmer (1990/1991, from 1995/1996 to 1998/1999,2000/2001, and 2002/2003 to 2003/2004) winters. The watercolumn was homothermal most of the time, with surfacewaters that were rarely slightly warmer or colder thanunderlying water layers (not shown). Winter and non-wintermedian temperature did not show any clear trend (Table 1).

Surface salinity ranged from 37.28 to 38.29, frequentlyshowing the effects of terrestrial or atmospheric inputs, withno clear trend over the winter season (Fig. 2b, Table 1).Values were averagely higher and more stable in the layersunderneath (e.g. 37.7–38.4 at 60 m). Superimposed to short

frequency changes, salinity values over the whole watercolumn were consistently lower in some consecutivewinters (e.g. 1996–1998, 2002–2005) than in others (e.g.from winter 1998/1999 through 2001/2002) (Fig. 3b).Winter surface salinity median values showed a significantpositive trend over the time series (Table 1).

The water column was isopycnal down to 60–70 m on64% of the sampling dates. The rest of the times freshwateraccumulated at surface determining a more or less markeddensity gradient, although these less salty waters were oftenslightly colder than the underlying layers. Over the years, amarked decrease in the number of cases of stratified watercolumn was observed (Fig. 3c).

We used satellite PAR and K490 estimates to determinewhether and when light might be a limiting factor followingthe Sverdrup (1953) model. We took as an estimator for thecommunity compensation irradiance (Ic) the value of 1.3E m−2 d−1 which was derived for a very large area of NorthAtlantic at the same latitude (Siegel et al. 2002). Then werearranged the Sverdrup equation as

I0 ¼ IcKdDcr= 1� exp �KdDcrð Þ½ �imposing to Dcr the value of 75, i.e. the bottom depth at thestudy site, with Kd being the diffuse attenuation coefficient

Fig. 2 Winter weekly cycle of a temperature and b salinity at 2 mdepth (boxplot by week)

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for PAR, derived from the satellite K490 (see Methods).The average Kd was 0.11. For each sampling date, wethen computed the value of the incident light flux I0 thatwould produce an energy flux larger than Ic all over thewater column, i.e. a critical depth deeper than the bottom.

The minimum calculated I0 value satisfying this conditionwas in the order of 11 E m−2 d−1. The actual irradiancewas above this value on 109 over the 130 sampling dates,indicating that light over the water column was sufficientfor net growth in most cases.

The concentration ranges for inorganic nutrients atsurface were 0.01–6.93 µmol dm−3 for NO3, 0.01–0.6 µmol dm−3 for PO4, and 0.14–9.75 µmol dm−3 forSiO4. All nutrient profiles, except that of phosphates,showed the same features: the highest values as well asthe maximum variability were always recorded at thesurface, showing the strong influence of terrestrialinputs. Average concentrations for nitrates and silicateswere consistently higher in winter than in the rest of theyear. Phosphates instead did not show a clear temporalpattern and vertical gradients were not as pronounced asthose observed for the other nutrients. NO3 (at surface)and PO4 (both surface and integrated value) wintermedians exhibited significant negative trends, whereasdissolved inorganic nitrogen (DIN) and SiO4 did notshow any significant trends over the time series (Table 1).

Phytoplankton Biomass

Winter chl a values ranged between 0.15 and 5.47 μg dm−3

in surface waters, with a high weekly and interannualvariability (Fig. 4). The climatology showed a clearincrease of chl a values both at surface (Fig. 4a) and as0–60 m integrated average towards the last weeks of thewinter period (Fig. 4b). The number of blooms markedlyvaried from year to year (Fig. 5), from no bloom in someyears (e.g. in 2000/2001) to one (e.g. 2001/2002) or moredistinct blooms separated by phases of low biomass (e.g.1997/1998, 1999/2000), to more prolonged periods ofrelatively high biomass (e.g. in 1984/1985, 1987/1988,and 2005/2006). A significant negative trend was evident insurface and integrated chl a values during the winterperiod through the time series, matching a negativetrend for the same parameters over the rest of the year(Table 1).

The hierarchical clustering of the chl a profilesresulted in an optimal number of six groups. One of thegroups included only one profile and was hence consid-ered as an outlier and discarded. The profiles associated tothe five remaining groups were plotted along with theirrespective averaged profiles (Fig. 6). Type 1 chl a profilesincluded the most frequent cases (n=79) of homoge-neous vertical distribution, with rather low values(<0.5 μg dm−3). The other 74 cases showed chl a valuesprogressively higher from profile types 2 to 5, with higherconcentrations in the upper layers decreasing gradually(profile type 2, 3, and 5) or more steeply (profile 4) alongthe water column.

Fig. 3 Interannual variability of a temperature and b salinity at 2 mdepth (boxplot by winter); c frequency distribution of winter mixedlayer depth as percentage of >60 m and <60 m MLD

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Phytoplankton Abundance and Composition

Winter phytoplankton abundance (avg 1.2×103cells ml−1)was an order of magnitude lower as compared to the rest ofthe year (avg 13.3×103cells ml−1). A wide variability wasevident at the weekly scale, with higher concentrations (upto 8.2×103cells ml−1) recorded in correspondence withrelatively high chl a values. A clear increase was evidentduring the last 2 weeks of the sampling period (Fig. 7a).Both winter and non-winter cell abundances showed asignificant positive trend over the series (Table 1).

Small flagellates (Fig. 7c) and diatoms (Fig. 7b) were themost important groups in terms of cell concentrations. Theformer were predominant, along with coccolithophores(Fig. 7d) and small naked dinoflagellates (Fig. 7e), in casesof low total phytoplankton abundance. Diatoms werescarcely abundant and diversified in low cell concentrationsamples, but generally prevailed in cell and species numberduring biomass increases. Subsurface phytoplankton pop-ulations examined on a limited number of sampling dateswere not significantly different in composition from thosefrom surface waters, as a consequence of the homogenizingeffect of the vertical turbulence.

The number and intensity of diatom blooms variedamong the years, strongly affecting the average winterphytoplankton abundance and composition (Fig. 8a). Dueto their relatively large biovolume and dominance in therichest samples, diatoms constituted the major part of thewinter-averaged phytoplankton biomass in many years(Fig. 8b). The species reaching high concentrations variedamong the years (Fig. 8c). For example, Chaetocerossocialis, one of the recurrent winter diatoms, contributedsignificantly to the total cell number only in winters 1984/1985, 1996/1997, 1997/1998, and 2004/2005 (Fig. 8c).Other Chaetoceros species, Skeletonema menzelii, andThalassiosira spp. also showed high interannual variabilityfor their abundance. A number of non-diatom speciessporadically reached high cell numbers, among which thechrysophycean Dinobryon coalescens, the autotrophicciliate Myrionecta rubra (=Mesodinium rubrum), and small(<10 μm) unidentified cryptophytes.

Dinoflagellates showed low cell numbers (Fig. 8a) butwere the second most important group in terms of biomass(Fig. 8b). Interestingly, they also increased over the time

Fig. 5 Interannual variability of chlorophyll a concentration a atsurface and b as integrated mean over 60 m (boxplot by winter)

Fig. 4 Winter weekly cycle of chl a concentrations a at surface and bas integrated mean over the 0–60 m water column (boxplot by week)

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series and at times when the total biomass increased.Flagellates and coccolithophores always represented aminor biomass fraction, due to their low cell size. However,the relative importance of these groups markedly increasedin some winters, especially from 2000 onwards. In thatperiod, biomass increases were less marked or less frequentand were characterized by a lower contribution of diatoms.Accordingly, while all the other groups had significantly

Fig. 7 Winter weekly cycle of abundance for total phytoplankton anddifferent species groups (boxplot by week)

Fig. 6 Chl a concentration profile types identified by clusteringanalysis, with ‘n’ indicating the number of profiles available in eachclass. The thick lines represent the averaged profile for each class

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positive trends over the winters in both cell number andbiomass, this was not the case for diatoms (Table 1).

One hundred and twenty-one taxa were tallied in wintersamples over the total of 331 taxa enumerated at the st.LTER-MC. About 25% of these taxa were only found inwinter and another 25% were found more frequently inwinter than in the rest of the year. Most of these winter-exclusive or winter-preferring taxa were rare species, whichmay have been overlooked in other seasons, but 15 taxawere reliably confined to the winter period. These includeda dozen coccolithophores in their heterococcolith (presum-ably diploid) phase, for which the corresponding holococ-colith (aploid) stage was at times found in summer. Thedinoflagellate Thoracosphaera heimii (non-motile plank-tonic stage), the silicoflagellate Dictyocha fibula, and a fewdiatoms (e.g. Odontella mobiliensis, Ditylum brightwellii,

and Bacteriastrum furcatum) were also found almostexclusively during the winter months, but with rather lowconcentrations. Another group of species, including thediatoms Asterionellopsis glacialis, C. socialis, Chaetoceroscurvisetus, and Thalassionema frauenfeldii, were frequentlyfound in winter although their annual peak in surface waterswas generally in spring or summer. In a few cases(Thalassiosira rotula, L. danicus, Leptocylindrus minimus)diatoms species found in winter were instead typicalcomponents of the autumn assemblages of the Gulf ofNaples. The coccolithophore Emiliania huxleyi character-ized all winter samples. This species was often found alsoin other periods of the year and occasionally producedintense blooms at the end of the summer.

The Shannon diversity index in winter varied from 0.79to 2.79, the range being narrower and the average slightly

Fig. 8 Winter-averaged a totalabundances, b total biomass,and c most abundant speciesconcentrations

350 Estuaries and Coasts (2010) 33:342–361

lower than in the non-winter period. The number ofenumerated species per sample (N) was not much differentbetween winter and non-winter samples (average 28.8 and29.3, respectively). While N had a positive trend, theShannon diversity index showed an impressive negativetrend, due to the wider dominance of unidentified flagellatespecies and the demise of the diversified diatom assem-blage over the last years of the time series.

Cluster analysis (Fig. 9) identified two principal speciesassociations, which included the most abundant and the lessabundant species, respectively, with six subgroups at thesecond cut-off. Among the most abundant species, cluster 3comprised those reaching a high abundance rather regularlythroughout the winter (Fig. 10). These were both taxa thatwere present in the majority of the samples, e.g. E. huxleyi,Ceratoneis closterium (=Cylindrotheca closterium), and S.menzelii, and some of the diatoms that were frequentlyassociated with high cell concentrations, e.g. C. socialis,and Chaetoceros spp. Cluster 2 comprised species peakingtowards the end of the winter particularly from 1996/1998to 2000/2001. They included both small, non-colonialdiatoms, such as Chaetoceros tenuissimus, Minidiscuscomicus, and Pseudo-nitzschia galaxiae ‘small morpho-type’ (Cerino et al. 2005), and larger colonial species suchas several Chaetoceros species, Cerataulina pelagica, A.glacialis, and L. danicus, along with the colonial chrys-ophycean Dynobrion coalescens. Cluster 1 compriseddiatoms that were irregularly associated with increases ofspecies of the two previous groups, among which wereThalassiosira mediterranea, C. pelagica, and C. curvisetus.Even less frequent, but still capable of reaching relativelyhigh concentrations, were the species in cluster 4, such asPseudo-nitzschia multistriata, Thalassiosira allenii, T.rotula, and the chrysophycean Apedinella spinifera. Thelast two clusters (5 and 6) included the least abundantspecies considered in this analysis. Those of cluster 5 wererelatively large colonial diatoms almost vanishing in the lastyears, while species in cluster 6 were mostly coccolitho-phores, which showed a slight positive trend over the years(Fig. 10).

Meteorological Data

Winter values of all the atmospheric parameters showed asignificant interannual variability in both mean and vari-ance values. The heat flux (Fig. 11a) oscillated between−150 and −80 W m−2, while the wind speed (Fig. 11b)varied within a range of 5–8 m s−1 and the net shortwaveflux, related to cloudiness, between 70 and 90 W m−2 (notshown). To explore whether the change in winter bloomintensity and species assemblages over the last yearsmatched comparable shifts in the meteorological forcing,the mean heat flux values for the dates corresponding to the

in situ measurements at the st. LTER-MC were computedfor two periods of the time series (1985/1986–1998/1999and 1999/2000–2003/2004). The latter period was charac-terized by an increase in heat loss (average changing from−100 to −140 W m−2), more intense winds and cloudiness(10–20% increase) and larger anomalies for the wind andheat flux variances. Winter precipitations (Fig. 11c) pre-sented marked interannual oscillations over a backgroundvalue of about 250 mm. They were particularly intensein 1984/1985, 1994/1995, 1996/1997, 2000/2001, and in2002/2003. The lowest values were instead observed in1988/1989, 1991/1992, and 2001/2002.

Linking Biological and Environmental Variables

Physical and biological parameters measured on thedifferent sampling dates were grouped according to thechl a profile types described above. The 0–60 m-integratedchl a concentrations associated with the five profile typesshowed distinct ranges of values that increased from chl atypes 1 to 5 (Fig. 12a). Both very low and moderate chl avalues corresponded to a homogeneous water column (type1 and 2). Integrated salinity averages associated to eachprofile type clearly discriminated type 1, 2, and 3 from type4 and 5 profiles (Fig. 12b), the latter two being associatedto lower salinity values at surface and/or over the watercolumn. As temperature did not vary significantly over thewater column, vertical density variations were mainlycaused by salinity gradients. Type 4 chl a profile, whichwas the most stratified, was associated with the largestvertical salinity gradient (Fig. 12c), while the association oftype 5 with low salinity, reduced salinity gradient, and highchl a values at depth suggests that, shortly before themeasurement, mixing events had redistributed high surfacechl a concentrations over the water column. Integratednutrient values for any profile type did not differ signifi-cantly from the average values reported in Table 1, whilesurface values were significantly higher than the average insamplings associated with profile type 2 and 4.

Considering the time lag of the physical forcing effect onthe water column dynamics, the meteorological variables ofthe 3 days before measurements were aggregated for theanalysis of their relationships with chl a profiles. With theexception of type 5 chl a profiles, most of the profiles werelargely associated to net heat losses during the 3 days priorto the sampling. In the case of the type 4 chl a profile, theassociated vertical salinity gradients indicates that heat losscould not overcome the buoyancy gradient resulting fromfreshwater inputs. The type 5 chl a profiles corresponded toa null heat air–sea exchange, and were associated to theweakest winds (below the 5 m s−1 breeze speed level), i.e.to the minimal mechanical stirring of the water column. Asexpected, chl a profiles 1 to 3 were associated to high heat

Estuaries and Coasts (2010) 33:342–361 351

losses and intense winds, with profiles 1 largely coupled tomost of the extreme meteorological events. Low lightavailability was also associated with 1 and 2 profile types,whereas the highest light levels were associated with theprofile type 5, which were the highest in chl a content.Profile types 3 and 4 were not associated with any specificlight conditions.

A clear association was evident between the biomassof single phytoplankton groups and the different chl aprofile types presented in Fig. 6. Diatoms showed thehighest biomass in chl a profile 4 and 5 conditions(Fig. 13a), while coccolithophores had the opposite trend(Fig. 13b). Interestingly, dinoflagellates also had the highestbiomass in type 5 chl a profile (Fig. 13c), whereas the other

Fig. 9 Hierarchical classification of 58 selected winter phytoplankton taxa based on the modified Bray–Curtis similarity coefficient and flexiblelinkage. Dotted lines identify the two hierarchical (cut-off) levels corresponding to the optimal numbers of clusters

352 Estuaries and Coasts (2010) 33:342–361

flagellates progressively increased from profile 1 to 4,decreasing in type 5 chl a profile (Fig. 13d). Speciesclusters described in the previous section (Figs. 9 and 10)also matched chl a types in some cases. For examplespecies of cluster 2 (colonial diatoms peaking at the end ofthe winter) had the highest abundance values in correspon-dence of type 4 and 5 chl a profiles whereas species of

cluster 6 (mostly coccolithophores) had the highest abun-dance in connection with chl a profile type 1, 2, and 3 (notshown).

Differently from chl a profile analysis, principal com-ponent analysis (PCA, Fig. 14) indicated only a weakassociation between biological and environmental parame-ters, as shown by the orthogonal position of these two

Fig. 10 Distribution of the abundance of 58 selected winter phytoplank-ton taxa, as log+1 transformed values. X-axis represents the time and y-axis the taxa. Species were ordered based on the results of the

hierarchical classification (Fig. 11). The black-and-grey bar on topindicates the different winters. Vertical and horizontal lines mark theseparation between winters and between clusters, respectively

Estuaries and Coasts (2010) 33:342–361 353

groups of variables on the plane defined by the first andsecond components, explaining 54% of the variance(Fig. 14a). This is probably linked to the nature of PCA,which only gives an averaged view of the processes at stakeand, in addition, does not take into account possible non

linear relationships. Nevertheless, PCA provides anotheranalytical scale at which bloom processes can be analyzed.Diatoms and chl a were correlated on the second circleexplaining 27% of the variance, while coccolithophores andother flagellates seemed to be partially correlated withvariables associated with mixing (mixed layer depth andsalinity) and negatively with temperature. Dinoflagellatesdid not show any clear association with the other variables.Temperature was orthogonal with nutrients, MLD andsalinity in the first plane but opposed to the latter twovariables in the second plane, confirming that only a smallpart of temperature variability seemed to be associated withMLD and salinity variability, probably also due to thestrong temperature seasonality (Fig. 2a) and the evolutionof meteorological events responsible of stratification pro-cess (e.g. runoff).

Fig. 11 Interannual variability of mean values for selected meteoro-logical parameters; a heat flux; b wind speed; c precipitations. Thegrey area marks the years corresponding to a major interruption in theMC-LTER series

Fig. 12 Distribution of selected parameters by chl a profile types(Fig. 6); depth-averaged (0–60 m) a chl a concentrations and bsalinity; c ΔS as difference between the 2 m and 60 m salinity values(boxplot by profile type)

354 Estuaries and Coasts (2010) 33:342–361

Discussion

Winter Bloom Dynamics

Despite the widespread idea that winter is a season ofslow phytoplankton growth and sluggish biologicalactivity, a diversified species assemblage with markedvariability and frequent biomass highs characterizedwinter phytoplankton dynamics in the Gulf of Naples.Biomass increases did not necessarily manifest asmassive surface accumulation, as in late spring orsummer, but rather as moderate concentrations distributedover a large part or the water column, resulting in depth-integrated biomass values comparable to those found inspring or summer. In fact, winter chl a value averagedover the water column was not so different from that of therest of the year (0.55 vs. 0.75 µg dm−3) while the highestdepth-integrated biomass value in the whole time series(4.7 µg dm−3) was recorded in early March 1985. On theother hand, winter biomass peaks confined to surface layersdid also occur during the time series and were oftenassociated with a decrease in surface salinity. No matterwhether homogeneous over the water column or restrictedto the upper layers, both modes of biomass increase can beconsidered as blooms (e.g., Cloern 1996; Longhurst 2006;Smayda 1997). Such blooms were generally short-lived,

diatoms

dino

cocco

other flagel

temp

sal

chl

mld

DIN

PO4

SiO4 a

diatoms

dino

cocco

other flagel

temp

sal

chl

mld DIN

PO4

SiO4

b

2nd PC

2nd PC

1rst

PC

3rd

PC

Fig. 14 Factorial maps of the correlation circles of a first and secondprincipal components and b second and third principal componentsresulting from a principal component analysis (PCA) performed on thewinter dataset. Cocco coccolithophores, flagel flagellates, dinodinoflagellates, chl chlorophyll a, mld mixed layer depth, sal salinity,temp temperature

Fig. 13 Distribution of phytoplankton group biomass by chl a profiletypes of (Fig. 6) (boxplot by profile type)

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suggesting that some of them might have been missed bythe sampling hence resulting in an overall underestimationof winter production.

The occurrence of winter blooms at the st. LTER-MCmatches similar findings in both inshore and offshoreMediterranean waters. An annual phytoplankton maximumin January–February is a recurrent feature of the AdriaticSea (Bernardi Aubry et al. 2004; Caroppo et al. 1999; Socalet al. 1999; Socal et al. 2002) and a winter peak is regularlyreported from all around the Mediterranean coasts (Duarteet al. 1999; Estrada et al. 1985; Mura et al. 1996; Travers1974). At the long-term offshore station DYFAMED in theLigurian Sea, the yearly peak in chlorophyll is detected inMarch/April at surface, but comparably high integrated chla values are observed in January and February, with a highcontribution of diatoms to total biomass (Marty et al. 2002).Indirect evidence of such production pulses comes from thehigh particulate fluxes most probably associated to diatomsettling that have been recorded in the open north-westernMediterranean Sea at the beginning of the year (Miquel etal. 1994; Stemmann et al. 2002). Reports of winter bloomsor anticipated spring blooms are also frequent outside theMediterranean (Glé et al 2007; Townsend et al. 1992;Townsend et al. 1994).

With the exception of Eilertsen et al. (1995), whoattributed sudden diatom blooms to timed germination ofdormant stages, all interpretations of open sea winterblooms are grounded in the Gran–Sverdrup hypothesisthat, in a nutrient-replete water column, biomass accumu-lation starts when upper layer dynamics relaxes theconstraint of insufficient average light to sustain positivenet growth. This no matter what is the mechanism, being itconvective overturning (Backhaus et al. 2003), moderateturbulent diffusion (Huisman et al. 1999) or absence ofmixing (Townsend et al. 1992). In other words, given anilluminated layer with negligible exchanges with otherlayers, a net accumulation of biomass can occur whendepth-integrated production is higher than depth-integratedconsumption (Sverdrup 1953). To make the argumentquantitative, Sverdrup (1953) introduced two operationalparameters: the compensation depth, whose descriptor wasindeed a minimum energy requirement (Ic) allowingproduction to equal consumption processes, and the criticaldepth (Dcr) which defines the thickness of the layer whereproduction and consumption are at balance. A critical depthdeeper than the mixing layer is exactly the scenariodepicted by Backhaus et al. (2003) for the North Atlanticwhere in winter, despite the thickness of the encompassedlayer (hundreds of meters), convective overturning allowscells in their passage in the photic zone to capture enoughphotons to balance losses.

In coastal waters, winter blooms may apparently bediverse in terms of patterns and mechanisms of formation.

In some cases, the close-to-the-surface bottom entrainsphytoplankton in a well lit layer and cells always receive aphoton dose in excess of the loss terms. In estuarine-likeenvironments local inputs of freshwater and, more ingeneral, the combination of freshwater sources and marinecirculation can prevent excursions of cells to poorlyilluminated depths, promoting the development of a bloom(e.g., Labry et al. 2001; Byun et al. 2005) or modulating it,as in the Scotian Shelf or Gulf of Maine region (Ji et al.2007; Ji et al., 2008) where changes in the intensity of low-salinity inflow significantly influences the winter–springphytoplankton dynamics.

The massive sedimentation events at times recorded inthe Mediterranean Sea (Miquel et al. 1994; Stemmann et al.2002) suggest that winter blooms are scarcely exploited byzooplankton populations, which in that period of the yearare scarcely abundant and are dominated by small specieswhich do not feed on diatoms preferentially (Riberad’Alcalà et al. 2004). The scarcity of suitable mesozoo-plankton grazers is probably a consequence of the shortduration of winter blooms (Legendre and Rassoulzadegan1996), which would rather be a resource for the zoobenthosof the underlying bottom. Many benthic animals have theirannual spawning period in winter (López et al. 1998; Zupoand Mazzocchi 1998), which in the Mediterranean Sea isnot their resting period of the year (Coma et al. 2000),probably as an adaptation to the recurrent food rain fromabove (Calbet et al. 2001; Duarte et al. 1999). Hence, lowgrazing pressure may explain the observed autotrophicbiomass accumulation, but high photosynthetic efficiencyand its dependence on light availability can also concur infavoring relatively high biomass values in winter (Moranand Estrada 2005).

Do winter blooms in coastal Gulf of Naples fit in themechanisms discussed above? One relevant question iswhether the different patterns of vertical biomass distribu-tion derive from different mechanisms or they rathercorrespond to subsequent phases of the same process.Apparently, blooms with marked vertical gradients (chl aprofile type 4) occurred either in a water column stratifiedby low salinity, or in a dynamically stable water columnunder calm and sunny weather. By contrast, a morehomogeneous increase of biomass over the water columnpossibly resulted from phytoplankton growth in an activelymixing water column, as in the original Sverdrup (1953)scenario or, alternatively, from the vertical redistribution ofa surface bloom (chl a profile type 2, 3, and 5). In practice,bloom developing in calm waters may later appear just likeblooms occurring in an overturning water column.

Our data suggest that thin layer-low salinity blooms maybe different from whole water column blooms in terms ofmechanisms of development. In addition, we infer that theformer are exclusive of coastal areas while the latter may

356 Estuaries and Coasts (2010) 33:342–361

occur everywhere, with the timing being latitude-dependentand the occurrence and amplitude being forcing-dependent.The low-salinity surface waters associated with the highestbiomass values were often also characterized by heavynutrient loads. However, we would exclude a role of thosenutrients as a trigger for winter blooms because nutrientconcentrations were always relatively high over the wholewater column, as a consequence of remineralization andmixing processes normally occurring in winter. We wouldrather interpret low-salinity waters at surface as the leadingfactor of biomass accumulation because of the reduced ornull turbulent mixing. In fact, in typical winter conditionsof winds blowing from the NE quadrant and highturbulence, the renewal of water masses in the Gulf issustained (Menna et al 2007) and any vertical gradientwould be quickly dissipated, whereas stable conditionspromoting the retention of low-salinity waters would allowbiomass accumulation in surface waters. Low-salinitywaters could also point at the advection of blooms frommore coastal areas, which we cannot exclude completely.However, phytoplankton studied over 5 years at inshorestations (ca 10 m depth) of the Gulf of Naples did not showvery different winter biomass values and species composi-tion as compared to st. LTER-MC (Siano 2008), confirmingthe existence of high spatial homogeneity and rathersmooth spatial gradients in this season (Carrada et al1980; Carrada et al. 1981).

As far as the other mechanism of bloom formation, i.e.whole column blooms, we concluded that incident light wasin many cases sufficient to promote net plankton growth atour site even in a homogeneous and mixed water column.Obviously, the amount of incident light and the persistenceof favorable light conditions must play a primary role indetermining the intensity of the blooms (Moran and Estrada2005; Sommer and Lengfellner 2008), which finds supportin our observations of more frequent bloom events duringthe last weeks of each winter. In addition, also in this casethe easterly winds mentioned above would promote thetransport from the coast offshore and tend to disperse theblooms.

In conclusion, three basic scenarios can be depicted: (1)in completely sunny days, even in the minimum day-lengthperiod, light is fully utilized in the upper calm layer to anextent to likely overcome consumption thus producing a netbiomass increase; (2) fresh water inputs creates a stratified,often thin, surface layer which favors phytoplanktonbiomass increase directly at the site, or transported by localadvection; and (3) non-limiting light and nutrient condi-tions over an actively mixing water column may occasion-ally be so effective to overcome possible grazing controland allow biomass accumulation homogeneously over thevertical dimension. The environmental and biologicalconditions associated with vertical gradients in chl a

profiles (type 4) clearly point at the second scenariodepicted above. Vertically homogeneous chl a profiles withmoderate concentrations (type 2 and 3) could instead be theresult of the third scenario whereas for high chl aconcentrations over the whole water column (profile type5), a sequence of scenarios from the second to the first oneis hypothesized, with a mixing event in between leading toa redistribution of the freshwater inputs and biomassproduced in the upper layers over the whole depth. Indeedthe three scenarios would often be hardly distinguishable interms of biomass distribution because, as we mentionedbefore, physical forcing might easily distort one intoanother and enhance or prevent biomass accumulationthrough horizontal transport.

Winter Phytoplankton Populations

The biological response to the dynamic winter conditionsdisplayed some interesting differences in terms of phyto-plankton composition. Two rather distinct species assemb-lages alternated in the different phases of the season, with ashift in the dominating taxa accompanying the mostsignificant biomass variations. A flagellate-dominatedpopulation was generally present during the lows, whereasa rather diversified diatom assemblage dominated or co-dominated with flagellates during the highs. Apparently,diatoms were normally kept on check by adverse lightconditions, as they responded to even short-term windowsof opportunity, the so-called loopholes (Bakun and Broad2003; Irigoien et al. 2005), which allowed them to escapegrowth control as soon as the constraint was relaxed in thestratified conditions of the type 2 scenario depicted in theprevious section. Flagellates and coccolithophores werecoping with the worst environmental conditions most of thetime, but they were apparently unable to fully exploit thesame loopholes that favored diatoms, either because underphysiological constraint or because their potential grazers,the ciliates, can promptly control their growth, havingcomparable growth rates. However, flagellates and cocco-lithophores, and at times dinoflagellates, were also occa-sionally capable of a net biomass increase along the wholewater column (type-3 scenario) or at the surface, with asubsequent vertical dispersion (type 1 scenario), thoughwithout producing a massive biomass accumulation as indiatom-dominated type 2 scenario.

The diatom species dominating in correspondence of thehigh winter biomass values belonged to the same generaand species that are responsible for winter blooms in otherMediterranean areas (Estrada et al 1985) or even in muchdistant sites (Marshall and Cohn 1987). For example,colonial Chaetoceros species have been often associatedwith February outbursts along the Catalan coast (Estrada etal 1999; Moran and Estrada 2005), in the Gulf of Marseille

Estuaries and Coasts (2010) 33:342–361 357

(Travers 1974) and at other Mediterranean sites (see Estradaet al. 1985, for a review). The species blooming variedgreatly from one winter to another or over the same winter,but they generally all belonged to medium–large-sizedcolonial diatoms, supporting the view that a certainassemblage is fully predictable based on a given set ofenvironmental conditions whereas the species that will gainare not predictable at all (Reynolds 1998, Smayda andReynolds 2001). In this view, species within the sameassemblage should share some ecophysiological character-istic or life history strategies that allow them to exploit thesame part of the spectrum of environmental variables.

A somewhat surprising result of our study was thefinding of a rather diversified winter phytoplankton speciesassemblage. The major part of these species were notrestricted to the winter period, but a few diatoms, alongwith a number of coccolithophores, silicoflagellates and thedinoflagellate T. heimii appeared as distinctive and uniquefor this apparently unfavorable season of the year. Thesespecies also were similar to those recorded in otherMediterranean areas (e.g. Ziveri et al. 2000) in winter. Asfor flagellates, other studies from the same area indicatethat pelagophytes are only present in surface waters inwinter (McDonald et al. 2007), that the tiny cryptophyteCryptochloris sp. forms recurrent blooms in December(Cerino and Zingone 2006; McDonald et al. 2007), and thatthe picoplanktonic prasinophyte Micromonas pusilla rea-ches relatively high concentrations from December toMarch (Throndsen and Zingone 1994; Zingone et al.1999). These cases show that, even though conditions formassive increase are generally found in late winter–spring,some phytoplankton species have found their optimal nichein winter conditions.

Interannual Variations and Trends

The relatively high frequency of winter biomass increasesover the time series demonstrates that these events are notexceptional but may rather be a recurrent feature for theseason. However, despite the lack of sampling continuity insome years, a high interannual variability for these eventswas evident. As discussed before, a key control of winterblooms and of their interannual variability is exerted by thephysical forcing rather than the chemical (nutrients) andbiological ones (grazing). This suggests that phytoplanktonin this season should promptly respond to variations inmeteorological conditions, and that winter phytoplanktondynamics may be a fine sensor of climate change. Whilemost recent studies point at the impact of temperaturechanges on the biota, the forcing involved in winter bloomdynamics are rather those factors influencing light avail-ability for phytoplankton cells, either directly or through theimpact on the water column structure (Sommer and

Lengfellner 2008). In this view, changes in wind intensity,cloudiness, and rain are all likely to have an importanteffect on biomass accumulation events over the winterseason in both coastal and offshore waters.

In our data series we did observe some significant trends,e.g. in the frequency of fully mixed conditions, whichincreased over the study period in relation with theintensification of winds. This trend was accompanied by anegative trend in biomass and an overall decline in numberand intensity of blooms, which were also associated withthe dominance of small cryptophytes or other unidentifiedflagellates, along with small-sized diatoms that substitutedthe large colonial diatoms of the first part of the time series.Due to the dominance of unidentified groups of species, thewhole phytoplankton community appeared less diversifiedover the last years of the series. Concomitant changes suchas higher surface salinity values, less frequent halostratifi-cation events, and higher wind speed support our recon-struction of bloom dynamics, suggesting that over the lastyears the episodes of higher light availability and stabilitywere too weak or too short to elicit a response from diatomsbut were rather able to stimulate the growth of flagellates.As microplanktonic colonial diatoms are generally associ-ated with bloom settling and biomass export to the bottomcommunities, a strong impact of winter climate change overthe coastal ecosystem of the Gulf of Naples can beforeseen. Interestingly, significant trends have been ob-served also in other Mediterranean sites, yet the effects ofmeteorological variations seem to vary considerably amongdifferent areas in relation with the mechanisms of bloomformation. In the Bay of Calvi (north-western Mediterra-nean), a remarkable decrease of the winter–spring bloomintensity between the years 1979 and 1998 was associatedwith more sunny, warm, and calm winters (Goffart et al.2002), i.e. the same meteorological conditions that favoredhigher winter biomass in the Gulf of Naples. The changesin the north-western Mediterranean were interpreted as theresult of a reduction in mixing causing nutrient depletion inan extremely oligotrophic area.

Concluding Remarks

Our data reinforce other observations of winter hostingactive phytoplankton growth and possible accumulationof biomass. During winter, specific phytoplankton speciesassemblages exhibit a rather lively dynamics. Theprevalent species association of the season, i.e. flagellatesand coccolithophores, seems to be well-adapted to thephysical forcing, firstly coping with it and secondlyprofiting of episodic favorable windows for enhanced netgrowth. However, they are unable to produce massiveaccumulation of biomass like diatoms can episodicallydo.

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Winter blooms have basically similar mechanisms offormation as compared to early spring blooms, yet theyare more episodic and their fate depends on physicalvariables rather than on grazing or nutrient depletion.Incident light, vertical mixing, runoff, and advection areall important in driving biomass accumulation in winter.Accordingly, interannual fluctuations and long-termchanges of climatic factors such as rain, wind, and cloudcoverage may have a much higher impact on productionas compared to changes in winter temperature. As winterblooms apparently fuel the zoobenthos rather than sustainthe mesozooplankton, a possible decrease or increase ofthese events over the years may have a profoundinfluence on the recruitment of this compartment and ingeneral on benthic–pelagic relationships. The strong,poorly explored, biological signature of the winter seasoncalls for an in depth analysis of the traits of occurringspecies and of their potential grazers, which mightultimately lead to a reconsideration of the typical valuesof the loss and gain parameters in the Sverdrup schemeand in other currently used models.

Acknowledgments We wish to thank Augusto Passarelli, CiroChiaese, and Ferdinando Tramontano for the nutrient and chlorophyllanalysis, and the crew of the M/B Vettoria for their assistance in cruisesto the field station LTER-MC. The work at the long-term is entirelysupported by the Stazione Zoologica Anton Dohrn, with the contribu-tion, for the year 2006, of the project MIUR-VECTOR, which alsosupported the retrospective analysis of the st. LTER-MC data. Thisstudy falls in the objectives of the European Network of Excellence forOcean Ecosystems Analysis (EUR-OCEANS). Jim Cloern and theOrganizing Committee are acknowledged for the organization of theChapman Conference on Long Time-Series Observations in CoastalEcosystems, which offered the chance to present and discuss long-termphytoplankton data in a wide scientific context.

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