MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Vol. 352: 53–65, 2007doi: 10.3354/meps07168

Published December 20

INTRODUCTION

Dense blooms of phytoplankton are a widespreadphenomenon of the global coastal ocean. They developin response to favorable conditions for cell growth andaccumulation, and are commonly grouped as harmfulalgal blooms (HABs) because of their deleteriouseffects. In contrast to large-scale blooms that are dom-inated by mesoscale circulation, Mediterranean HABsare a more localized phenomenon commonly related toareas of constrained dynamism, such as bays, lagoons,ports, beaches and estuaries (Garcés et al. 1998, Vila etal. 2001, Lopez-Flores et al. 2006). In these areas,enhanced growth of phytoplankton not only leads to aperceivable water discoloration along the shorelinebut also to a deterioration in water quality. Other

unprecedented ecological effects in the Mediter-ranean, such as fish kills (Garcés et al. 2006) and risksto human health (Penna et al. 2006), have been attrib-uted to toxic algal proliferations in recent years.

The definition of what it is considered to be a bloomvaries, and it is based on several criteria such as cellabundance, biomass, physiological characteristics (e.g.growth rates), toxicity levels and deleterious effects onhuman activities etc. Moreover, the definition of whatis considered to be a bloom rarely includes referencesto biological and environmental conditions leading tothat state (Cartensen et al. 2004). Given that a bloomrepresents a deviation from the normal cycle of bio-mass (Parker & Tett 1987), a summer bloom can tenta-tively be considered to have occurred in the coastalwaters of the western Mediterranean (Balearic Islands)

© Inter-Research 2007 · www.int-res.com*Email: vieagbo@uib.es

Modulation of nearshore harmful algal blooms by in situ growth rate and water renewal

G. Basterretxea1,*, E. Garcés2, A. Jordi3, S. Anglès2, M. Masó2

1Institut Mediterrani d’Estudis Avançats, IMEDEA (UIB-CSIC), Miquel Marqués 21, 07190 Esporles, Baleares, Spain2Institut de Ciències del Mar (CMIMA-CSIC), Pg. Maritim de la Barceloneta 37-49, 08003 Barcelona, Spain

3Marine Sciences Research Center, State University of New York, Stony Brook, 11794 New York, USA

ABSTRACT: Species belonging to the genera Alexandrium and Gymnodinium are amongst thedinoflagellates that regularly cause massive coastal phytoplankton blooms along Mediterraneanbeaches. These episodes encompass a variety of factors favouring bloom development, includingnear-shore nutrient enrichment, enhanced growth and low water renewal. During the summer of2003 the development of a bloom was monitored at 2 nearby beaches, Peguera and Santa Ponça,located at the head of Santa Ponça Bay (Mallorca). Both sites are under the influence of the samephysical regime — which is mainly wind-forced — and present relatively high inorganic nutrient con-centrations for Mediterranean waters during summer (mean dissolved inorganic nitrogen > 1.2 μMand PO4 > 0.18 μM). Total dinoflagellate abundance exhibited a similar trend at both beaches, withremarkable outbursts in late June (>8 × 106 cells l–1). Water exchange calculations, based on 3Dnumerical modeling, yielded low average renewal rates at both sites (<0.08 d–1), and cell growth esti-mations suggested a significant increase in the specific growth rates during the blooming seasonassociated with the seasonal temperature variation. We postulate that both the increased growthrates and the low wind-induced water renewal times are complementary factors and are of keyrelevance to the modulation of these blooms. Diagnostic analyses using a simple phytoplankton-zooplankton (PZ ) model allowed us to observe the effect of growth rate and water renewal on bloomdynamics, and to identify a threshold condition for bloom occurrence.

KEY WORDS: Harmful algal bloom · Water renewal · Growth · Model · Mediterranean

Resale or republication not permitted without written consent of the publisher

Mar Ecol Prog Ser 352: 53–65, 2007

when the chlorophyll concentration reaches values of1 mg m–3 and/or when cell densities are above 104 cellsl–1, which is usually concomitant with sustained coastalwater temperatures exceeding 21°C. These biomassvalues are high when compared with the standingstocks observed in coastal waters (e.g. <0.4 mg m–3,Jansá 1994), and represent a stage where the prolifer-ation begins to become perceptible as a change inwater color.

Despite the fact that in some cases the proliferationof algae may have a natural origin, it is considered thatcoastal blooms are an emerging problem that could berelated to nutrient enrichment of coastal waters(Duarte et al. 2000). Intensive urbanization and recre-ational use of coastal watersheds has resulted in aremarkable increase in sources of nutrients along theMediterranean coasts. This cultural eutrophication ge-nerates a contrast between coastal waters and theopen ocean where, owing to summer stratification andnutrient depletion, oligotrophic conditions prevail inthe upper layer. Nutrient-rich coastal environmentsof the Mediterranean Sea and, in particular, semi-enclosed areas with low turbulence levels constitute anew and unique environment for which several phyto-plankton species with harmful effects may becomedominant. Vertically migrating dinoflagellates withcomplex but distinct life-cycles, such as the genusAlexandrium and Gymnodinium, attain high abun-dances (i.e. >106 cells l–1) during mild summer condi-tions. Proliferations of species of either or both generaare becoming common along the coasts of the Mediter-ranean (Garcés et al. 2005), particularly in areas ofrestricted water exchange such as pocket beaches andports where their swimming ability allows for themaintenance of critical viable patches, even at earlybloom stages.

The intricate coastal geomorphology of the BalearicIslands, with its numerous inlets and pocket beachesand a dynamic tourism industry that has promoted theconstruction of ports along the shore, has provided afavorable environment for HAB expansion. Althoughenrichment is observed in these areas, evaluation ofthe nutrient inputs is difficult owing to their diffusenature. Moreover, when nutrients are added fromanthropogenic sources, relative ratios may be skewedin addition to absolute concentration, favoring the pro-liferation of particular groups (Hodgkiss 1997). In addi-tion, the exact form of the nutrients (e.g. dissolvedorganic nitrogen vs. ammonium vs. nitrate) can be adeterminant in phytoplankton species selection (Colloset al. 2004).

Even though most of the factors involved in theMediterranean nearshore algal outbreaks are known,the mechanisms that underpin their occurrence are notyet well established. Terrestrial nutrient loads, toxin

production, species diversity, grazing pressure, lifecycles and strategies, physical transport, mixing andother factors have all been used to explain the onsetand evolution of phytoplankton blooms. Differences inbloom-controlling mechanisms are also reflected inthe species composition, as reported for other areas(Smayda & Reynolds 2003). It is therefore challengingto understand how all of these different factors com-bine to stimulate and govern outbreaks.

In the case of coastal blooms in semi-enclosed sys-tems, it is generally accepted that the rate of plank-tonic cell growth must exceed the rate of cell dis-persion in order for bloom formation to occur (e.g.Figueiras et al. 2006). Knowledge of the variation ofboth phytoplankton growth rates and water renewal isthus fundamental to our understanding of the pro-cesses and mechanisms leading to bloom formationand to their maintenance at these sites. The contribu-tion of each of these 2 ecosystem variables to near-shore bloom formation can be analyzed with the use ofsimplified biological models. Recognizing that simpli-fied algorithms are generally unable to represent all ofthe variability of the system, they are intended torobustly present the essential foodweb dynamics andto analyze the mechanisms that more sophisticatedapproaches are unable to clarify. In this sense, simplemodels provide the necessary framework for exploringdifferent aspects of HAB occurrence (e.g. Franks 1997,Sarkar et al. 2006).

This study evaluates the importance of dinoflagellategrowth rate and coastal water exchange in the modu-lation of dinoflagellate abundance in Mediterraneannearshore blooms. The analysis is based on field mea-surements of biomass, growth rates and nutrients, andon numerical estimations of the wind-induced waterrenewal. In order to identify the factors that controlbloom formation and, in particular, the effect of growthrates and residence time on bloom modulation, weformulated a simple phytoplankton-zooplankton (PZ )model with an additional factor to account for theadvective losses produced by water renewal.

MATERIALS AND METHODS

Biological sampling. The study was carried out inthe Bay of Santa Ponça (Fig. 1), a small bay located onthe southern coast of Mallorca (Balearic Islands). Thebay is comprised of three beaches, two of which(Peguera and Santa Ponça) are located in semi-enclosed areas that experience recurrent summerphytoplankton blooms. Santa Ponça is a ∼500 m longbeach at the end of a westward facing embaymentwith an average depth of 5.5 m. A shallow reef in aboutthe middle of the embayment restricts the exchanges

54

Basterretxea et al.: Modulation of nearshore HABs

with offshore coastal waters and reduces the total vol-ume to 3.7 × 106 m3. Peguera is a crenulated beach(∼300 m) situated between a headland and a groin.Depths of up to 5 m are reached in the offshore side ofan embayment, which has an approximate volume of1.2 × 105 m3.

With the aim of characterizing the dinoflagellate out-breaks, both sites were monitored from May to Sep-tember 2003. Samples were obtained at intervals ofbetween 3 and 4 d at 3 stations at Peguera and 4 d atSanta Ponça. Stns P1, P2, S1 and S2 were representa-tive of the nearshore environment, and P3, S3 and S4provided information about the conditions at theentrance of the embayments (hereafter offshore sta-tions). The abundance of dinoflagellate species, inor-ganic nutrient concentration, temperature and salinitywere measured each time a water sample was with-drawn. Additionally, 2 temperature loggers recordedthe temperature every half hour at a depth of 1.5 m.

Samples (150 ml) were fixed with lugol (1% finalconcentration) for phytoplankton quantification. Thegeneral procedure for identifying and quantifyingphytoplankton involved sedimentation (24 h) of a sub-

sample in a 50 ml settling chamber andsubsequent counting of cells withinan appropriate area using a Leica-LeitzDM-IRB inverted microscope. Usingthis approach, the minimum speciesabundance detected was 20 cells l–1.Nutrient samples were frozen immedi-ately after collection, and concentra-tions of nitrate, nitrite, ammonia, phos-phate and silicate were measured withan autoanalyzer following Grassoff etal. (1983). For each sample, a 60 ml sub-sample for the quantification of chloro-phyll a (chl a) was filtered through25 mm Whatman GF/F filters. Filterswere then extracted in 8 ml of 90% ace-tone, and concentrations of chl a weremeasured with a Turner Designs fluoro-meter.

Growth rates. In situ growth rateestimations were obtained from anexperiment carried out during thesummer of 2002 at Peguera, when asustained dinoflagellate bloom was ob-served at this site. A detailed descrip-tion of the bloom can be found in Bas-terretxea et al. (2004; see their ‘Results’for development of the summer phyto-plankton biomass). Net rates of changein dinoflagellate cell numbers in watersamples incubated for 24 h under insitu conditions were used as a measure

of the net growth rates of dinoflagellates. A total of 38experiments were carried out prior to and during theevolution of a massive Alexandrium taylori bloom. Ineach experiment, two 1 l plastic cages were filled witha sample of water and submerged to a depth of 0.5 m.All containers were pre-cleaned with 10% HCl-Milli Q water and rinsed 3 times with distilled water.Initial and final subsamples were withdrawn fromeach cage for phytoplankton counts. Accurate datacounts of the incubation samples were made, and spe-cies-specific net growth rates were estimated fromchanges in cell numbers according to Guillard (1973)

(1)

where μn is the net growth rate per day, and N1 and N2

are the initial cellular concentration and the averagefinal cell concentration of the 2 incubations at t1 and t2.

Water renewal. Typical climate conditions in theBalearic Islands present a distinct seasonality withwarm temperatures (∼27°C in August) and low rainfallduring summer (<26 mm), resulting in little riverineinput to the coast. In the absence of any significant sea

μn t tNN

ln=−1

2 1

2

1

55

S1

S2

S3

S4

P4P3

P1

39° 31.88’N

39° 30.81’

2°25.80’E 2°28.58’

Cala SantaPonça

36°

40°

44°N 8° W 4° 0° 4°E 8° 12° 16° 2° 30’ E

39°3

0’N

3° 30’3°

Studyarea

BalearicIslands

Mediterranean Sea

Fig. 1. Study area within the Western Mediterranean, including monitoring sta-tions and coastal bathymetry (in metres). Stns P1, P2, S1 and S2 experience recur-rent nearshore blooms, whereas P3, S3 and S4 are indicative of coastal conditions

Mar Ecol Prog Ser 352: 53–65, 2007

level variations, coastal circulation in southern Mal-lorca is mainly regulated by wind forcing. Breeze con-ditions are prevalent for most of the summer, withintensities rarely exceeding 8 m s–1 (Ramis et al. 1990).This regime is occasionally disrupted by Tramontanaepisodes, a prevailing northerly wind with enhancedintensity. The combination of persistent shorewarddirected seabreeze and an indented coastal geomor-phology limits nearshore water exchange in theBalearic Islands during summer. Under these condi-tions current velocities at the entrance of the beachesare weak and temporally variable, following the diur-nal wind patterns. Even during the wind-enhancedTramontana episodes flushing is restricted, particu-larly along the southward facing coasts.

In order to investigate the effect of water renewal onHAB dynamics in Santa Ponça Bay, a conservativetracer method was used. For each area of interest, res-idence time was estimated from the tracks of neutrallybuoyant particles in the flow, which were obtainedfrom a 3D hydrodynamic numerical model followingMonsen et al. (2002). If a water body with a volume Vand a flushing time τ is considered, the volumetric flowrate q can be expressed as

q = V/τ (2)

In coastal waters q is often unknown, and τ is calcu-lated assuming that any introduction of mass into thewater body is instantaneous and homogeneouslymixed throughout the entire domain. Therefore, τ canbe estimated from the evolution of a tracer over time,implying that mixing inside the water body is infinitelyfast (e.g. Orfila et al. 2005). Assuming that the volumeof the enclosed domain remains constant, the concen-tration (C ) of the tracer over time (t) can be obtainedfrom

C(t) = C0e–t/τ (3)

where C0 is the tracer concentration at t = 0. Flushingtime can then be obtained by the least-square fit of themodeled particle evolution. A total of 27 120 homoge-neously distributed particles were released at Peguera,and 91 200 at Santa Ponça, and allowed to drift for aperiod of up to 800 h.

The algorithm selected for the hydrodynamic modelwas a linear, shallow-water, sigma-coordinate, 3Dfinite element model with spherical-polar extensionsformulated in the frequency domain (FUNDY). Themodel solves the linearized 3D shallow-water equa-tions, and is forced by tidal or other barotropic bound-ary conditions, wind and/or baroclinic pressure gradi-ents. This numerical scheme was previously applied toevaluate the effect of local breezes on coastal circula-tion in previous studies of the area (see Werner et al.1993). Estimations assume that conditions are at steady

state and that wind represents the major forcing thatcontrols water renewal.

Bloom dynamics. A simple 2-component model wasused to analyse the role of water renewal on bloomdevelopment, whereby the population dynamics weredescribed by a growth-loss-transport equation. Themodel, based in that of Steele & Henderson (1992) andTruscott & Brindley (1994), included logistic growthcontrolled by a carrying capacity term (K ) that allowsfor the absence of explicit tracking of nutrient concen-tration, which is convenient for the analysis of systemswith undetermined input fluxes. Moreover, in this way,the uncertainties derived from parameters describingnutrient-uptake kinetics and/or shelf-shading effectsare avoided. The form of the model is:

(4)

(5)

where P and Z are phytoplankton and zooplanktonconcentrations respectively, μ is the specific growthrate for phytoplankton and mz is the specific loss rate ofzooplankton. The model uses a Holling Type III graz-ing function, where γ is the proportion of grazed phyto-plankton that becomes new zooplankton biomass, Gmax

is the maximum grazing rate and kz is the half-satura-tion constant for grazing. In our case a flushing term(q) was included to account for the water renewal rate,which represents an additional source of mortality forboth P and Z.

The model was forced using real temperature dataand a simple analytical expression for the sinusoidalvariation of photoperiod (D), with a specific at summersolstice. Optimal temperature range (Topt) and photo-period (Dopt) were used solely to pulse growth from μmin

to μmax. In this study, the choice of parameters wasbased on available field data. The selected model cur-rency is carbon, and the concentrations of P and Z aregiven in g C m–3 (see Table 1). For conversion betweenC and chl a we used a constant value for the C:chl aratio of 38, and time was measured in days (d). Esti-mates of K were obtained by averaging the peakchlorophyll concentrations at the sampling site overthe last 7 yr. The value for assimilation efficiency ofzooplankton (γ) is similar to the value of Fasham (1955),and the maximum grazing rate (Gmax) is the same asthat of Denman & Peña (1999). The value for mz is inthe range of that provided by Edwards & Brindley(1999), but the half-saturation coefficient for zooplank-ton grazing (kz) is higher than the range provided bythese authors (0.02 to 0.1 g C m–3). In our case, valuesof kz that are higher than those in the oceanic systemimply that zooplankton will adapt more slowly to the

dd maxZt

G ZP

k Pm q Z

zz=

+⎛⎝⎜

⎞⎠⎟ − +( )γ

2

2 2

dd maxPt

PPK

G ZP

k PqP

z

= −( ) −+

−μ 12

2 2

56

Basterretxea et al.: Modulation of nearshore HABs

available food. We used this larger value on the basisthat most microzooplankton species in the nearshoresystem (generally naked ciliates) are incapable ofeffectively grazing large bloom-forming phytoplank-ton cells; hence, a slower response is expected of theseorganisms when P variations occur.

RESULTS

Biological sampling

During the summer of 2003, the coastal temperaturesin the Balearics generally reached ≥26°C, warming upto 29°C in July and occasionally exceeding 30°C in thenearshore environment. As shown in Fig. 2a, bothSanta Ponça and Peguera exhibited the same tempera-ture trend, and only minor variations were observedbetween them. Differences of up to 2°C were experi-

enced between nearshore (S1, S2 and P1) and off-beach stations (S3, S4, P3 and P4) (data not shown).

In keeping with the temperature profile, the near-shore (S1, S2 and P1) phytoplankton biomass (mea-sured as chl a) followed a similar pattern at both sites.An extraordinary outbreak was observed in June(32 and 36 mg m–3), when the coastal water tempera-ture increased from 21 to 26°C at an approximate rateof 1.2°C per week. Maximum cell abundances (8 ×106 cells l–1) were observed on June 20 at Santa Ponça,and 2 d later at Peguera (9 × 106 cells l–1). The onset ofthe bloom was marked by an increase of ×102 cells l–1

in 3 d, and the episode extended over 3 wk. During theoutbreak at Peguera, the microalgal population wascomprised of Alexandrium taylori (64 to 94%) andGymnodinium sp. (species 1-CSIC) (1 to 35%). Thebloom species composition at Santa Ponça differed inthat the species composition was more diverse. Themost important co-blooming dinoflagellates were

Gymnodinium spp., small dinoflagel-lates (>15 μm) and Alexandrium spp.(A. taylori and A. minutum).

Total dinoflagellate cells in the near-shore presented values above 104 cellsl–1 for most of the summer. Fig. 3 re-veals the differences in biomass andcell abundance between offshore andnearshore stations at both samplingsites. Summer offshore chlorophyll con-centrations were almost invariablybelow 0.5 mg m–3 at both sites. Innearshore waters, low concentrationswere observed on 44 and 35% of occa-sions at Peguera and Santa Ponça,respectively, whereas values of 1 to1.5 mg m–3 were recorded in >50% ofsamples. Thresholds in terms of abun-dance appear to be more diffuse. Modalvalues were in the order of 103 cells l–1

in offshore waters and 104 cells l–1 atnearshore stations. In 12% of the obser-vations from Santa Ponça and 33%from Peguera, cell abundances above104 cells l–1 were observed.

Inshore mean concentrations of dis-solved inorganic nitrogen (DIN) excee-ded 2.6 μM at Santa Ponça and 1.25 μMat Peguera over the sampling period,with respective maximum valuesreaching 19.0 and 2.4 μM. Differencesof up to 2 μM were observed betweenaverage onshore and offshore concen-trations at Santa Ponça, whereas inter-mediate DIN concentrations wererecorded at Peguera, with no signifi-

57

b

a

cMay Jun Jul Aug Sep

May Jun Jul Aug Sep

May Jun Jul Aug Sep

Month

30

25

20

15

30

20

10

0

10

8

6

4

2

0

107

105

103

102

100

Cel

ls l–1

Chl

a (m

g m

–3)

Tem

per

atur

e (°

C)

Chl

a (m

g m

–3)

106

cells

l–1

Fig. 2. Variations in (a) midday temperature (grey dots: Santa Ponça; black line:Peguera), (b) chl a and (c) total dinoflagellate cell abundance at Santa Ponça

(grey) and Peguera (black). Insets: data in logarithmic form

Mar Ecol Prog Ser 352: 53–65, 2007

cant differences among stations (Fig. 4). Phosphateoccurred at lower concentrations (mean = 0.20 ±0.1 μM, max = 4.5 μM), with onshore-offshore differ-ences again being observed at Santa Ponça. InorganicN:P ratios varied between 8.4 ± 3.7 at Peguera and18 ± 9.1 at Santa Ponça, with no significant onshore-

offshore variation. Remarkably, these ratios are similarto those in groundwater (9 and 17 respectively),where inorganic nutrient concentrations are an orderof magnitude higher.

In general, nutrient concentrations exhibited greattemporal variability; consequently, no clear pattern orrelationship could be discerned between the phyto-plankton and the concentration of any nutrient. Fur-thermore, nutrient stoichiometry calculations for thebloom based on those of Justic et al. (1995) indicatedvery few cases of potential nutrient limitation of DIN orP-PO4 during the course of the phytoplankton bloom.

Growth rates

Estimated mean net growth rates for Alexandriumtaylori during the pre-bloom stage were low, with val-ues ranging between 0 and 0.32 d–1. This periodextended from the beginning of the experiment to midJune, when dinoflagellate abundances where still bel-low 2000 cells l–1. The experiments performed duringthe bloom period yielded markedly higher values(0.71 ± 0.6 d–1, n = 31) and conspicuous day-to-dayvariability. Exceptional growth values above 2 d–1 wereobtained at the end of July. Mean growth ratesincreased up to 0.9 d–1, when these outlying valueswere considered. The transition between these the 2regimes is not well resolved; however, it is suggested

that a progressive increase in growthrates occurs when temperature risesfrom 21.5 to 26°C in June (Fig. 5).

Water renewal

Tracer analysis under breeze condi-tions reveals that 50% of the particlesreleased at Santa Ponça remained inthe domain after 15 d, whereas renewaloccurred more rapidly at Peguera (50%remained after ∼3 d). Exponential fit-ting of the particle evolution yields τvalues of 19.3 and 13.6 d, respectively,under breeze conditions, decreasing to4.2 and 3.8 d under Tramontana epi-sodes. Nevertheless, as shown in Fig. 6,exponential fits tend to underestimateresidence time in the case of Tramon-tana winds. Moreover, in the case ofPeguera a few particles (20%) remaintrapped under seabreeze forcing, yield-ing an asymptotic behavior. Differencesamong beaches are related to differ-ences in volume between both embay-

58

% o

f occ

urre

nce

Biomass Abundance

Sta. Ponça Sta. Ponça

PegueraPeguera

<0.5 1.5 2.5 >3 10 103 105 107

100

50

0

100

50

0

Fig. 3. Histograms of biomass (mg chl a m–3) and total cellabundance (cells l–1) at Santa Ponça and Peguera at onshore(gray) and offshore (black) stations during the sampling

period. Dashed lines: selected threshold values

S1 S2 S3 S4 P1 P2 P3

Pho

spha

te (µ

M)

PegueraInshore Offshore

Inshore Offshore Inshore Offshore

Inshore Offshore

Median 25–75% Non-outlier range

6

4

2

0

DIN

(µM

)

a

b

Santa Ponça

0.4

0.2

0

Fig. 4. Boxplot of (a) dissolved inorganic nitrogen (DIN) and (b) phosphate concentration at inshore and offshore stations in each embayment

Basterretxea et al.: Modulation of nearshore HABs

ments, and the resultant q for breeze conditions is com-parable in both cases (q = 0.052 d–1 for Santa Ponçaand 0.073 d–1 for Peguera). Under real fluctuating con-ditions (year cycle) q is somewhat higher (0.090 ± 0.08and 0.15 ± 0.08 d–1 respectively), but is still indicativeof slow water renewal at both beaches.

Amongst the different factors that influence waterrenewal, wind direction is probably the most relevant.As shown in Fig. 7, wind patterns included a similarbimodal behavior at both locations, with higher q forwinds blowing from N to SE and markedly reducedvalues of q for other directions. Nonetheless, it shouldbe noted that wind events favoring enhanced renewalwere scarce and episodic during the summer season.

Bloom dynamics

As shown by Truscott (1995), the behaviour of a PZsystem can be explained from the perspective of theexcitability of phytoplankton, i.e. the capability for thispopulation to be subject to rapid changes. The stabilityof the final steady state of P and Z is governed by thegradient dZ/dP of the non-dimensionalized equationsfor P and Z. Briefly, if dZ/dP < 0 then the equilibriumpoint is a stable point, whereas if dZ/dP > 0 then it is anunstable node, and the stable solution is a limit cycle.As depicted in Fig. 8 this changes at points (Pmin, Zmin)and (Pmax, Zmax). When Pmin > P > Pmax, the equilibriumof the system is unstable and a limit cycle is developed.The region of excitability is indicated by the ‘S’ shapeof the curve and, as shown in Fig. 8, this decreases withincreasing q. In our case, the sigmoid shape of thecurve is only preserved for

(6)

where .

Evidently, this solution becomes that of Truscott (1995)

when q = 0.

For the parameters provided in Table 1 the excitabil-ity condition is accomplished for values of q < 0.04 d–1

when μ = μmin, and for q < 0.18 when μ = μmax. Typical conditions for uncoupling between P and Z

occur when μ increases to μmax in response to light,temperature, nutrients or other factors that influencephytoplankton growth. Following the descriptionabove, 3 scenarios can be anticipated depending onthe dZ/dP ratio. If dZ/dP is always negative, then theevolution of the system generates a stable bloom (sameshape independent of μmax) where population is con-

1

3 3

ν =kK

z

01

3 3< <

+ν μ

μ q

59

0

20

40

60

80

0

20

40

60

80

0 5 10 15 20 25 30

Sta. Ponça

Peguera

Time (d)

% o

f par

ticle

s

Breeze

Breeze

Tramontana

Tramontana

4.2 d

19.3 d

3.8 d

13.6 d

Fig. 6. Simulated temporal evolution of tracers remaining atSanta Ponça and Peguera (solid lines), and adjustment lead-ing to water renewal estimates (dashed lines). Specified timepoints: τ in the exponential fit of Eq. (3), where C0 is the initial

concentration of the particles, expressed as %

µ (d

–1)

Temp

erature (ºC)

2.5

2

1.5

1

0.5

0

30

28

26

24

22

20May Jun Jul Aug Sep

Month

Fig. 5. Alexandrium taylori. Mean net growth rate (μ) ob-tained from incubation experiments at Peguera in 2002, andcorresponding lowpass filtered water temperature (greyline). Each point represents 3 incubation experiments.Dashed lines: mean values used for modeling. Grey bar:transition from low to high growth rates during rapid water

temperature warming

Mar Ecol Prog Ser 352: 53–65, 2007

trolled by grazing. Conversely, if dZ/dP is always posi-tive, then the phytoplankton population is relativelyuncontrolled and expands to a high density situationthat is controlled by μmax. Our case represents an inter-mediate situation whereby the variation from μmin toμmax forces a change from dZ/dP > 0 to dZ/dP < 0(assuming low q values); once the bloom is triggered,the behaviour of the system is controlled by the varia-tions of μ and q. As displayed in Fig. 9, under these cir-cumstances the model is able to reasonably reproducethe bloom in both embayments using values within therange of those observed in field experiments. Grosscell growth rates in the model were approximated bymeasured net rates in the belief that they are represen-tative of the lower threshold of phytoplankton growth.

Fig. 10a reveals how the variation in the shape of thebloom is influenced by the increase in μmax. The systemshows little sensitivity for μmax > 0.52 d–1: only the slopeof the bloom initiation increases with increasing μmax.Conversely, lower values produce delayed biomasspeaks, but the system asymptotically tends to a shapedetermined both by the carrying capacity of the system

60

02

46

8100

May Jun Jul Aug Sep Oct

0.1

15

10

5

0

0.2

0.3

0.4

NE

SW

Santa Ponça

Wind directionWind directionWind speed (m s–1 )

Wind speed (m s–1 )

q (d

–1)

Peguera

Win

d (m

s–1

)

0

0.1

0.2

0.3

0.4

02

46

810

NE

SW

Month

a

b

q (d

–1)

0.1 0.15 0.2 0.25

Fig. 7. Calculated flushing rate for Santa Ponça and Peguera under different (a) wind forcing, and (b) wind recorded at Santa Ponça. Grey stripes: Tramontana episodes. Sacle bar in (a) shows the volumetric flow rate (q) d–1

q = 0.3q = 0.2

q = 0.1

q = 0dP/dt

dZ/dt

Bloom

0 0.2 0.4 0.6 0.8 1

1

0.8

0.6

0.4

0.2

0

P/K

Z/K

Fig. 8. Schematic diagram of nullclines for the phytoplankton-zooplankton (PZ ) system using different flushing rates (μ =μmax). Solid lines: dP/dt = 0 (null cline); dashed lines: dZ/dt =0; grey patch: area in which the sigmoidal shape of the curve

is preserved for different q values

Basterretxea et al.: Modulation of nearshore HABs

and by grazing control. As shown in Fig. 10b, thelength of the optimal growth period also influences theevolution of the bloom. Short μmax conditions, resultingeither because temperature is above or below Topt,

generate reduced biomass peaks thatprogressively decay without triggeringsignificant zooplankton responses. Sus-tained blooms with repeated oscilla-tions, similar to that observed at Pe-guera in 2002, are explained bypersistent Topt conditions. Similarly,stagnant conditions generate relativelystable high biomass equilibria wherethe shift in μmax is relatively unimpor-tant. Increased flushing rates weakenthe peak and reduce bloom persis-tence. Fig 10d depicts the response ofthe system to changes in ν. For lowervalues (i.e. 0.05) the system presentsexcitability for any q within the rangeof values considered (the excitability islost for q > 1.5 d–1) and a bloom follow-ing the p-cline will occur (dZ/dP alwaysnegative). This is the evolutiondescribed by Truscott (1995), whereby

q has no effect. Conversely, for high ν values (dZ/dPalways positive), the system is always at a state beyondthe excitabity region and is thus entirely regulated byvariations in μmax. The slope of the curve at the bloomonset is regulated by μmax, whereas the senescence iscontrolled by μmin.

DISCUSSION

Anthropogenically enriched coastal areas of theMediterranean provide a unique environment forphytoplankton growth. These sites are prone to sufferfrom proliferations not only because inorganic nutri-ents are high when compared with offshore conditions,but also because the organic compounds are abun-dant, comprising a variable amount of between 50 and80% of N and P pools (e.g. Lucea et al. 2003). For mostof the year, physical factors (photoperiod, irradiance,mixing, temperature, stratification etc.) have a limitinginfluence on dinoflagellate proliferation; thus, even intimes of enhanced allochthonous nutrient input alongthe coast, blooms are rare. With the onset of summerconditions (between May and June), flagellate out-bursts become a growing economic and environmentalproblem. Calm conditions with less wind are regardedas ideal for biomass accumulation in the nearshore. Inthis study, we presented evidence of simultaneousblooms comprised of different dinoflagellate assem-blages at 2 nearby locations: Peguera and Santa Ponça.Even though these proliferations tend to be almostmonospecific, blooms of different species may stilldevelop if favorable conditions occur (as shown). Thisis suggestive of some sort of non-species-specific syn-

61

Table 1. Variables and parameters used in simulations (unless otherwise noted) and their definitions

Parameter Description Value Unit

C Tracer concentration – %C0 Initial tracer concentration 100 %Dopt Optimal photoperiod >0.60 dGmax Maximum grazing rate 1 d–1

kz Half-saturation constant for grazing 0.17 g C m–3

K Carrying capacity 1.2 g C m–3

μ Specific growth rate for P – d–1

μmin Growth rate of P at pre-bloom phase 0.12 d–1

μmax Growth rate for P during bloom conditions 0.52 d–1

mz Zooplankton mortality rate 0.03 d–1

P Phytoplankton biomass – g C m–3

q Flushing rate 0–0.5 d–1

Topt Optimal temperature range 22–28 °Cγ Zooplankton assimilation efficiency 0.75 dimensionlessV Embayment volume See texta m–3

Z Zooplankton biomass – g C m–3

a‘Materials and methods: Water renewal’

May Jun Jul Aug Sep

102

100

10–2

102

100

10–2

102

100

10–2

a

c

b

Chl

a (m

g m

–3)

Peguera 2002

Peguera 2003

Sta. Ponça 2003

Month

Fig. 9. Measured phytoplankton biomass (black line) and sim-ulated results (grey line). Simulations were performed usingdaily flushing rates calculated from average wind conditions

for each day

Mar Ecol Prog Ser 352: 53–65, 2007

chronization in the mechanisms that lead to the trig-gering of blooms within both systems.

Raimbault et al. (1988) showed that in the Mediter-ranean, the composition of small-sized backgroundphytoplankton remained relatively stable duringblooms when biomass increased beyond 0.5 mg m–3,and that above this density the blooms started to bedominated by larger organisms. Although intended fora spring bloom, this concept appears to be consistentwith the differences observed between inshore andoffshore communities during the bloom. Large dinofla-gellates like Alexandrium spp. and Gymnodinium spp.increase in terms of growth and accumulation,whereas smaller phytoplankton (similar to those off-shore) remain relatively stable. A similar pattern wasreported by Bec et al. (2005) for a Mediterraneanlagoon subjected to summer diatom blooms: they sug-gested that the observed enhanced growth rates of thelarge population fraction (> 2 μm) may rely on newrather than recycled nutrients. Shifts in the dominanceof the different size fractions under the influence ofvariable external nitrogen sources is supported by spe-cies competition experiments (see Hecky & Kilham1988) and food-web structure modeling studies, whichpredict increases in the overall size of the planktoniccommunity in response to increases in the nutrientcontent in the system (Armstrong 1994, Kemp et al.2001). Nutrient enhancement is reported to produce anexponential decline in the ratio of picophytoplanktonto total phytoplankton biomass and primary produc-tion at nitrogen concentrations of >1 μM (Agawin et al.2000), thus favoring proliferation of larger species.

Nevertheless, the tendency for change in size structureis not a ubiquitous feature of the Mediterraneancoastal communities. For example, Vidal & Duarte(2000) reported a low-biomass response to increasednutrient loading on the coast of Blanes (northeastSpain). Furthermore, some evidence suggests that therelationship exists between cell size, growth rate andgrowing conditions, not between cell size and nutrientlevels (Irigoien et al. 2005).

We have proposed a simple PZ model intended toreproduce the dominant dynamics and provide analyt-ical criteria for coastal bloom occurrence. A generalimplication of our study is that nearshore phytoplank-ton blooms are generated by changes in cell growththat destabilize the PZ trophic link, and that the evolu-tion of the system is modulated by the concomitantvariations in physical forcing. Departing from a posi-tive, stable equilibrium — in which the low phyto-plankton growth rates are balanced by biologicallosses and transport — endogenous and exogenouschanges resulting in significant variation in μ deter-mine a new state for the system, manifested either asan episodic outbreak or a more persistent phytoplank-ton biomass enhancement. Freund et al. (2006) statedthat the uncoupling between the phytoplankton bloomand the zooplankton response is rooted, among otherreasons, in the separation of time scales expressed bythe numerical values of the parameters mz and γGmax,which are smaller than the parameters μ and Gmax. Dif-ferences in water renewal modulate the shape of thebloom as determined by the growth rates, and alsoexacerbate the effects caused by the slower response

62

a b

c d

102

100

10–2

102

100

10–2

Chl

a (m

g m

–3)

May Jun Jul Aug Sep May Jun Jul Aug Sep

Month

Fig. 10. Numerical simulations of (a) growth rates (μmax = 0.31, 0.71, 1.11), (b) optimal growth temperature range (Topt = 23–25,23–28, 23–31), (c) flow rates (q × 0.3, 1, 3) and (d) kz/K (0.05, 0.14, 0.5). Grey lines: first case; solid black lines: second case; dot-dash

lines: third case. Simulations were performed using daily flushing rates calculated from average wind conditions for each day

Basterretxea et al.: Modulation of nearshore HABs

of zooplankton. Regarding water renewal, it should benoted that owing to their semi-enclosed nature, it is notonly wind intensity but also wind direction that is adeterminant of the regulation of water exchange in theanalyzed embayments. In this sense, the geographicalorientation of the coast and its relationship with thedirection of prevailing winds is an intrinsic characteris-tic that may be relevant to outbreak occurrence.

As shown in Fig. 10c, high flushing rates reduce sys-tem excitability and cell accumulation; consequently,generalized alongshore blooms are only observed un-der persistent calm weather conditions. In the unlikelysituation of continuously high water renewal, the sys-tem is basically regulated by the equilibrium betweengrowth and water renewal and the role of zooplanktonbecomes secondary. This situation is not well de-scribed by a PZ model because important advectionpossibly conveys strong changes in the carrying capac-ity of the system. In our case, K was considered to be aconstant selected on the basis of observed maximumphytoplankton biomass in these waters, but as demon-strated in the analysis of ν, it plays an important role insetting the threshold for the excitability of the system.Empirical evidence shows that cell abundances above107 cells l–1 are rare, but this threshold includes pro-cesses of distinct nature (either physical, chemical orbiological). Generally, the effect of a variable K de-pends on the rate of change, because an additionaltimescale that interacts with the biological timescalesconsidered in the model is introduced. If K variesslowly in comparison with the biological processes, theevolution of the system will asymptotically adapt tothese variations. If, conversely, a rapid variation is con-sidered, the system will behave by following the aver-age values (the same is valid for μ). A more critical be-havior could be expected from coherent variations of Kand other parameters, or at some stages of the bloom(i.e. when a change from μmin to μmax is forced).

Evidence reveals that owing to their higher compen-sation intensities, some dinoflagellate species appearto be more competitively suited for growth under thehigh summer irradiances experienced in the Mediter-ranean (e.g. Smayda 1997). In our case, the observedrate of chlorophyll increase during the bloom onsetrequired sustained growth rates above 0.5 d–1, which isconsistent with the measured growth rates and withpublished in situ growth rates of single cells of Alexan-drium taylori at other sites (Garcés et al. 1998, 2005).Nonetheless, daily growth rates of some species cangreatly exceed this value for short periods (i.e. up to3.54 d–1, Smayda 1997), and HAB species do not typi-cally exhibit sustained growth rates higher than thoseof other phytoplankton. For example, reported in situgrowth rates of dinoflagellates are generally below<1.0 d–1 (Stolte & Garcés 2006).

As shown by the modeling results, it is the change inμ between the pre-bloom and bloom stages that is rel-evant to the forcing of the system. Various externaland/or internal factors have been proposed in attemptsto explain changes in growth rates in phytoplanktonpopulations. (1) Intraspecific genetic variability chan-ges over time within the same population are possible(Orsini et al. 2002). This suggests that only a fraction ofthe resident population is responsible for the bloom.Furthermore, differences among growth rates of thedifferent strains are possible. (2) Excretion of chemicalsignals produced by the organisms could be responsi-ble for the different physiology. Hastings & Greenberg(1999) suggested that the so-called ‘quorum sensing’hypothesis, which relates to the chemical signals pro-duced by an organism, occurs in bacterial populations.Although this effect has not been documented forphytoplankton, it is known that cell inoculum in batchcultures requires cell filtrates from older cultures to ini-tiate active growth (Fogg 1987). (3) More attention hasbeen devoted to the effect of environmental factors(such as irradiance, nutrients, turbulence, vitaminsand, particularly, temperature) on growth (Guillard1973 and references therein). Temperature is known toalter the enzymatically regulated processes of mostorganisms, and hence should not be disregarded as atriggering factor of the previously mentioned mecha-nisms. Indeed, seasonal temperature variations areknown to play a major role in the regulation of growthrates of coastal communities of the Mediterranean (e.g.Agawin et al. 1998). Most phytoplankton species havean optimal temperature for growth. A temperaturerange of 22 to 28°C was used in our simulations, inwhich no remarkable change in environmental condi-tions between the pre-bloom and bloom period wasobserved (with the exception of the seasonal warm-ing). Obviously, temperature variations do not explainthe observed day-to-day variability within each bloomphase, and other mechanisms should be considered toaccount for this short term variability.

Another possible explanation for unbalancedgrowth is relief from grazing control. Grazing ratesare relevant not only to the control of the biomass, butalso at the initial stages of the outbreak (Uye & Taka-matsu 1990). Planktonic macrograzers, capable ofingesting large-sized cells, are scarce in the coastalenvironment (Calbet et al. 2001), and thus presum-ably relegated to a minor role. In the case of micro-zooplankton, they would mainly control variations ofsmaller-sized cells. Only organisms such as rotifersand heterotrophic protists have comparable growthrates and may hence co-bloom. Moreover, it is wellknown that some bloom-forming phytoplankton spe-cies exhibit ecological and physiological characteris-tics that either reduce zooplankton feeding rates or

63

Mar Ecol Prog Ser 352: 53–65, 2007

hamper egg production and thus population growth(e.g. Turner 2006). Although the reduction of top-down control in microalgal proliferations is situation-specific, the blooming species constitute a parallelfoodweb pathway that is basically modulated byphysical and endogenous factors.

Model performance is lower when these biologicalloss terms become important. Edwards & Yool (2002)demonstrated that the selection of the higher predationclosure term can strongly influence model dynamics.In a PZ model, high zooplankton biomass increasesphytoplankton losses, accelerating the bloom outcome.However, the senescence phase in itself could well beinevitable in either the presence or absence of zoo-plankton. Other factors of phytoplankton mortality,such as population ageing, encystment, infection, spe-cies succession or factors not accounted for in themodel, could play a role in the fate of the bloom.Indeed, evidence reveals that algal evolution substan-tially alters predator-prey dynamics (Yoshida et al.2003). Uncertainties at this stage of the bloom — inwhich cell biology and population life history may beimportant — appear to be greater, and possibly requiremore sophisticated modelling approaches.

Acknowledgements. This study was partially supported bythe EU-financed research projects STRATEGY (EVK·CT 200100046), SEED (GOCE-003875) and THRESHOLDS (IP-003933). We are indebted to Calviá Town Hall, and especiallyto E. Cozar, for collaborating and facilitating fieldwork. Wealso thank B. Casas for assistance with field work and R. Ven-tosa for nutrient analyses. E.G. was supported by a Ramon yCajal contract from the Spanish Ministry of Science and Edu-cation, and A.J. by a MCYT postdoctoral fellowship.

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

Submitted: March 1, 2007; Accepted: July 23, 2007Proofs received from author(s): December 10, 2007