Changes in plankton size structure and composition, during the ...

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Changes in plankton size structure and composition, during the generation of a phytoplankton bloom, in the central Cantabrian sea LUCIA ZARAUZ*, XABIER IRIGOIEN AND JOSE A. FERNANDES MARINE RESEARCH DIVISION, AZTI TECNALIA FOUNDATIONTXATXARRAMENDI UGARTEA Z/G, SUKARRIETA BIZKAIA

48395,

SPAIN

*CORRESPONDING AUTHOR: [email protected] Received November 21, 2007; accepted in principle October 20, 2008; accepted for publication October 24, 2008; published online 25 November, 2008 Corresponding editor: Roger Harris

Six stations situated on a transect perpendicular to the coast were sampled eight consecutive times in the central Cantabrian Sea, during February – March of 2005. A contrast was observed in the timing, magnitude and size structure of a phytoplankton bloom between coastal and oceanic stations, probably due to differences in the depth and mixing of the water column. The increase of biomass during the bloom occurred through the addition of “new” size classes of larger cells. An increase of small particle biomass was also observed at oceanic stations. In view of the results, it is proposed that at coastal stations, improved environmental conditions (nutrients and light) enhanced productivity and increased the number of both small and large cells. Because predation susceptibility is related to size, only large diatoms were able to escape from the predatory pressure exerted by microzooplankton. The results obtained indicate that imaging technology, combined with automatic recognition techniques, constitutes a powerful approach to describe plankton distributions at a fine temporal scale.

I N T RO D U C T I O N During phytoplankton blooms, photoautotrophic biomass may increase by orders of magnitude in the course of a few days. This primary production represents a pulsed source of organic carbon which is fundamental to ecosystem productivity and carbon flux. It may be transferred to higher levels of the food web (Ryther, 1969) or exported to sediments as sinking cell aggregates (Billett et al., 1983) and faecal pellets of large grazers (Turner et al., 2000). The influence of size in the regulation of these processes has been widely recognized. Size is closely related to sedimentation dynamics (Smayda, 1970; Richardson and Jackson, 2007), and it is one of the main factors determining the type of trophic interactions within the planktonic food web (Cushing, 1989). It has been widely observed that marine phytoplankton blooms are usually dominated by large species

(Kiørboe, 1993; Irigoien et al., 2004). This is becoming regarded as a fundamental assumption of planktonic food web models, especially those using allometric scaling (Moloney and Field, 1991; Carr, 1998). However, the underlying mechanisms explaining the dominance of large-sized cells in phytoplankton blooms are not well understood. Traditionally, two main theories have tried to explain this fact. On the one hand, the larger size of blooming phytoplankton species has been interpreted as a strategy to escape from microzooplankton predatory control. It is now accepted that microzooplankton are the main source of phytoplankton mortality in the sea (Calbet and Landry, 2004). Therefore, a decrease in microzooplankton feeding efficiency caused by the larger size of phytoplankton could open a “loophole” in which blooming cells can multiply rapidly (Irigoien et al., 2005). On the other hand,

doi:10.1093/plankt/fbn107, available online at www.plankt.oxfordjournals.org # The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please email: [email protected]

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there are studies showing that large-sized phytoplankton can attain higher growth rates (Frenette et al., 1996; Crosbie and Furnas, 2001) and higher carbon-specific photosynthesis (Cermen˜o et al., 2005) than small phytoplankton under favourable conditions of nutrients and light. This raises the possibility that, in addition to trophic mechanisms, purely physiological factors could also explain the dominance of large-sized phytoplankton under suitable conditions for growth (Irwin, 2006). Many studies have been performed to study marine and freshwater phytoplankton bloom dynamics, but probably due to technical limitations, most of them are the result of a restricted number of observations (Fernandez et al., 1991); modelling approaches (Townsend et al., 1992; Huisman, 1999; Backhaus et al., 2003) or blooms induced in laboratory microcosms (Marrase´ et al., 1989; Fernandez and Acun˜a, 2003; Co´zar and Echevarria, 2005). Fewer studies have investigated the generation of plankton blooms with small-scale temporal and vertical resolution (Echevarrı´a and Rodrı´guez, 1994). In particular, studies where phytoplankton size structure and the main predator biomass

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(microzooplankton) are sampled at high temporal resolution during the bloom formation process are unusual, due to the highly laborious techniques involved in nanomicroplankton analysis. The objective of this study is to investigate the changes in the size structure and composition of nano-microplankton (diatom chains, ciliates and unidentified particles were distinguished) during the generation of a phytoplankton bloom.

METHOD Study area The study site was located in the central Cantabrian Sea (5.58W longitude and from 44.28 to 43.68 latitude; Fig. 1). Six stations were sampled on a transect perpendicular to the coast, with a distance of 10 nm between them. Coastal (St1, St2), shelf break (St3, St4) and oceanic waters (St5, St6) were covered. This transect was repeated eight times between February and March 2005 (24th and 25th February; 2nd, 8th, 11th, 14th, 17th and 20th

Fig. 1. General location of the study area. 200, 1000 and 2000 isobaths are shown.

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Table I: Julian day and date corresponding were considered to be underrepresented because of the relative low volumes analysed by the FlowCAM. to the eight transects sampled Transect transect transect transect transect transect transect transect transect

1 2 3 4 5 6 7 8

Julian day

Date

Consequently, nano-microplankton biomass was estimated for the size range 7 – 174 mm ESD.

55 56 61 67 70 73 76 79

24 February 2005 25 February 2005 02 March 2005 08 March 2005 11 March 2005 14 March 2005 17 March 2005 20 March 2005

Automatic classification

March). Hereafter, transects 1 to 8 will be used to refer to the eight consecutive samplings. The date and Julian day corresponding to each transect are shown in Table I.

Sample collection Hydrography and nutrients Water column profiles of temperature, salinity and density were obtained using a CTD coupled with a rosette system. Mixed layer depth (Kara et al., 2000) was computed for each station. Water samples were collected at the surface and at a depth of 20 m using Niskin bottles fitted to the rosette. Analyses for nutrients were performed using an autoanalyzer Skalar Scan Plus System, following the standard methods described by Grashoff et al. (Grashoff et al., 1983).

Nano-microplankton Water samples for the nano-microplankton analysis were collected at the surface and at a depth of 20 m. Particles were counted and imaged on board using a FlowCAM (Sieracki et al., 1998), in order to determine the biomass, size structure and composition of the plankton community. Fluorescence measurements were not included in the analysis; therefore, every particle ( phytoplankton, zooplankton, detritus and inorganic) was considered. For each sample, a fixed volume of 20 mL was analysed. A 4X objective was used and the instrument was calibrated using beads of a known size. Invalid recordings (i.e. bubbles and repeated images) were removed from the image database through visual recognition. The biovolume of each particle was calculated from its equivalent spherical diameter (ESD), assuming a spherical shape. Biovolume was converted into biomass using the equation given by Montagnes et al. (Montagnes et al., 1994) for marine phytoplankton: Biomass = 0.109  Volume0.991. Where Biomass is the carbon cell content expressed in pg C cell21; and Volume is the cell biovolume expressed in mm3. Very small sizes were considered to be below the confidence limits of the FlowCAM. Very large sizes

Nano-microplankton particles, measuring 17 mm ESD or smaller, were identified as “small particles”. Particles larger than 17 mm ESD were automatically classified into three broad groups (diatom chains, ciliates and large unidentified particles) using WEKA toolkit (GNU license, http://www.cs.waikato.ac.nz/ml/weka Witten and Frank, 2005). Two different algorithms were tested: Random Forest (RF; Breiman, 2001) and Tree Augmented Naive Bayes (TAN; Friedman, 1997). These techniques search for decision trees (RF) and probabilistic relationships between the predictor variables (TAN), which are learnt using a training set where the items are labelled to their corresponding class. In the present study, each object was described by five morphological parameters: cell area, cell ESD, maximum length, minimum length and number of particles per chain. The majority of data used to build the training set came from samples analysed in the present campaign, but data from other stations sampled in the Bay of Biscay were also included. All stations were selected due to their high abundance and diversity of plankton organisms. Every particle larger than 17 mm ESD measured at these stations was classified by a human expert. The training set used to build the classifier was composed of 138 diatoms, 180 unidentified particles and 252 ciliates. The classifier was evaluated using 10-fold crossvalidation. In 10-fold cross validation, the data are split into ten randomly chosen and approximately equal partitions. Each part is held out in turn and the learning scheme is trained on the remaining nine-tenths; the error rate is then calculated on the holdout set. This learning procedure is performed a total of 10 times, omitting a different subset each time. Finally, the 10 error estimates are averaged to yield an overall error estimate (Witten and Frank, 2005). A simple way to estimate the error of our classification is to calculate the percentage of particles which have been classified correctly (overall accuracy). However, the main objective of the present classification was to distinguish ciliates and diatoms, so total accuracy was not enough to evaluate the performance of the classifier. A confusion matrix, precision and recall for each class were calculated to evaluate the classification of the two different algorithms.

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The confusion matrix is a table where the true counts (manual counts) for each group are presented in the rows, while counts by automatic identification are given in the columns. Good results correspond to large numbers down the main diagonal and small, ideally zero, off-diagonal elements. Precision is defined as the number of correct results divided by the number of all results returned as positive: tp p¼ tp þ fp Recall rate is defined as the number of correct results divided by the number of results that should have been returned as positive: tp r¼ tp þ fn Where tp are true positive instances, fp are false positives and fn false negatives. It must be pointed out that the categories “small particles” and “large unidentified particles” comprise both autotrophic and heterotrophic organisms. “Small particles” embrace all organisms smaller than 17 mm, including nanoflagellates, dinoflagellates, small diatoms etc. In the class “large unidentified particles”, the fraction of microplankton which could not be identified due to the limited resolution of the images obtained with the FlowCAM is grouped. As the FlowCAM does not discriminate between living and inert particles, these classes may also include detrital and inorganic material. An example of the nanomicroplankton images is given in Fig. 2.

Normalized biomass-size spectra Nano-microplankton biomass data were arranged in base 2 logarithmic size intervals. They were fitted using normalized biomass-size (NB-S) spectra, which conform to a power law: Bm ¼ amb Dm or logð

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to the NB-S spectra; that is, the intercept (a), the slope (b), and the correlation coefficient of the regression (r 2). To calculate these parameters, any size class with zero biomass was not included. The upper size limit was set when two consecutive size classes with zero biomass were found. Analysis of the covariance (ANCOVA) was performed, in order to evaluate the effect of three different factors on the slope and the intercept of the NB-S spectra. The selected explanatory factors were: time (transect 1 versus transect 8); distance to the coast (coastal versus oceanic stations); and depth (0 versus 20 m samples). For the ANCOVA analysis, three models were compared: log(Bm/Dm)m, which assigns the same regression line for the different values of the explanatory factors; log(Bm/Dm)m+explanatory factor, which define separate regression lines for each group, but with the same slope; and log(Bm/Dm)m *explanatory factor, where different slopes and intercepts are defined for each regression line.

R E S U LT S Hydrography The vertical distribution of salinity and temperature in the study area shows a variable surface layer. Thermal inversion, maintained by saline stratification, characterized the water column during the first days of sampling. This situation was followed, first, by a mixing period and, second, by the beginning of a shallow thermal stratification. At oceanic stations, thermal stratification began slightly earlier than at the coastal stations. Thermal inversion in St1 was associated with the lowest values of temperature and lasted until transect 6 (Fig. 3). During the survey, temperature ranged from 10.5 to 138C, and salinity from 31.6 to 36.4. All stations had a deep mixed layer depth (MLD). At coastal stations MLD was limited by bottom depth, whereas at oceanic stations it reached depths of 500 m (Fig. 4). A decrease of MLD was observed on transect 8 at stations 2, 4, 5 and 6, showing the beginning of thermal stratification.

Nutrients

Where Bm is the total biomass in carbon per size class m, a and b are constants and Dm is the size class interval (Platt and Denman, 1978). A maximum of 15 size classes were considered for each sampling station, ranging from 7 to 174 mm ESD. The analysis of the plankton size structure was based on the parameters derived from the straight line fitted

Differences were observed in nutrient absolute values (Table II). NH3 showed the highest levels, ranging from 0.03 to 12.81 mm L21. NO2 concentrations were the lowest and varied from 0.01 to 0.3 mm L21. The concentration of P, Si and NO3 increased during the first days of sampling, reaching maximum values at transects 4 and 5. Concentration of NO2 remained constant until transect 7, and NH3 distribution showed a

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Fig. 2. Examples of the identified nano-microplankton plankton groups: (A) diatom chains, (B) ciliates, (C) large unidentified particles, (D) small unidentified particles.

peak on transect 5, at stations 2 and 6. On transect 8, the concentrations of NO2, NO3, P and Si decreased drastically in coastal surface waters. A decrease of P was also observed in oceanic waters (Fig. 5). Kruskal – Wallis test showed that the concentration of NO2 was significantly higher in coastal than in oceanic waters (P , 0.001). The other nutrients did not show

significant differences between neritic and oceanic areas (P . 0.001).

Accuracy of nano-microplankton classification Although overall accuracy levels were slightly higher when using RF, the significance test did not show

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Fig. 3. Time evolution of temperature (ºC) and salinity vertical profiles, for the six stations sampled.

Fig. 4. Time evolution of mixed layer depth (MLD), for the six stations sampled.

Table II: Nutrient concentration range and mean values (in brackets), for the six stations sampled Station

NO2 (mm L21)

NO3 (mm L21)

NH3 (mm L21)

P (mm L21)

Si (mm L21)

1 2 3 4 5 6

0.03 –0.2 (0.16) 0.01 –0.2 (0.15) 0.15 –0.3 (0.22) 0.09 –0.25 (0.14) 0.07 –0.15 (0.11) 0.06 –0.14 (0.1)

0.03 –6.55 0.29 –5.95 2.91 –6.68 2.38 –6.73 3.31 –6.63 3.24 –6.33

0.2 –2.03 (0.84) 0.08 –3.28 (1.02) 0.04 –6.9 (2.78) 0.11 –12.81 (4.84) 0.04 –2 (0.78) 0.01 –7.85 (1.47)

0.18 –0.4 (0.32) 0.09 –0.39 (0.3) 0.29 –0.43 (0.36) 0.1 –0.48 (0.36) 0.15 –0.46 (0.34) 0.35 –0.5 (0.42)

0.17– 2.74 0.14– 2.47 1.48– 2.21 0.82– 2.41 1.68– 2.32 1.75– 2.33

(3.99) (3.7) (4.61) (4.96) (4.63) (4.7)

significant differences between RF and TAN algorithms (Table III). RF has been described previously as an efficient algorithm to perform plankton classification (Grosjean et al., 2004), and was selected to train the classifier and perform nano-microplankton classification in the present study.

(1.84) (1.7) (1.94) (1.86) (2.18) (2.07)

The results of the 10-fold cross validation showed an overall accuracy of 91.7% (Table IV). Diatoms and ciliates showed the highest precision (93.5 and 94.7%, respectively) and recall scores (94.2 and 92.5%, respectively). The recall score represents the percentage of instances of a given class which are correctly classified

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Fig. 5. Time evolution of nutrient concentration, for the six stations sampled, at the surface and at 20 m depth. Coastal (St1, St2) and oceanic (St5, St6) stations are plotted.

Nano-microplankton biomass

Table III: Output of the significance test Iteration

RF

TAN

1 2 3 4 5

90.88 90.7 90 91.05 91.75 (v/ /*)

88.95 88.77 89.12 89.3 88.95 (0/5/0)

The percent of correctly classified instances is compared with the classifications performed using RF and TAN algorithms. Annotation (v/ /*) corresponds to the number of iterations in which the TAN algorithm is significantly better (v), similar () or worse (*) than RF.

Table IV: Confusion matrix for the classifier based upon Random Forest algorithm Diatoms Unidentified Ciliates Precision

As diatoms

As unidentified

As ciliates

Recall

130 9 0 0.935

6 160 19 0.865

2 11 233 0.947

0.942 0.889 0.925 91.75%

True counts (manual counts) for each group are presented in the rows, while counts by automatic identification are given in the columns. Good results correspond to large numbers in the main diagonal, and ideally zero off-diagonal elements. Precision, recall and overall accuracies (bolded) have been computed using 10-fold cross validation.

Total nano-microplankton biomass varied from 21 to 1299 mg C m23 at 0 m, and from 21 to 1805 mg C m23 at a depth of 20 m. Lower values of biomass were found at the beginning of our study, and increased during transects 7 and 8 (Fig. 6). This increase of total plankton biomass was due to a higher contribution of large particles, as can be seen in the logarithmic models fitting the percentage of particles larger than 17 mm versus total biomass (Fig. 7A). However, the relationship between small and large particles differs along the coast–ocean transect studied. At coastal stations, large particles show a wide range of variation during the bloom, whereas small particles remain almost constant. At these stations, no correlation was found between the log-transformed biomass of large and small particles (Fig. 7B). At oceanic stations, the slope of the log–log relationship between the biomass of large particles and the biomass of small particles is .1, which denotes that large particles increased faster than small particles (Fig. 7C). The differences in the dynamics of nanomicroplankton biomass between coastal and oceanic stations can be summarized as follows: (i)

in that category, in relation to the total amount of instances that correspond to that class. This is slightly different to precision which evaluates the percentage of instances that really correspond to the class in relation to the total instances classified in a given class. These results are well illustrated by the confusion matrix (Table IV).

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At coastal stations, total biomass reached maximum levels (.1200 mg C m23). This increase of total biomass was observed on transects 7 and 8, and was due to a bloom of large chain-forming diatoms (Figs 6B and D and 8). Strikingly, a shift in the size structure of the plankton community was observed before the bloom of biomass occurred. This shift occurred on transect 5 and was characterized by an

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Fig. 6. Time evolution of (A and C) total biomass (mg C m23) and (B and D) percentage of large particle biomass, for the six stations sampled, at the surface (A and B) and at 20 m depth (C and D). The contribution of diatoms, ciliates and large unidentified particles to the biomass of large particles is plotted. Note the absence of transect 2 (Julian day 56) in the majority of stations.

(ii)

increase in the contribution of large particles, mainly diatoms which accounted for more than a 60% of the total biomass (Fig. 6). At oceanic stations, the increase of total nanomicroplankton biomass was lower, and took place on transect 8, simultaneously with the shift in the size structure of the community (Fig. 6A and C). At St5, the biomass increase was due to an increase of diatom chains biomass. At St6, however, it was due to ciliates (Figs 6B and D and 8). It must be highlighted that at oceanic stations, both small and large cells contributed to the biomass increase, although large cells increased faster and had a larger contribution that resulted in the final size structure (Fig. 7 and Fig. 8).

Between these two situations, shelf-break stations showed intermediate features, as a transition between

coastal and oceanic areas. On the other hand, the increase of biomass due to the bloom was lower at 20 m than at the surface, but the community structure followed the general pattern described above (Figs 6 and 8). The only exception was St 6, where the dominance of ciliates observed at the surface during transect 8, was not present at a depth of 20 m.

Nano-microplankton size spectra The analysis of the nano-microplankton size structure showed NB-S spectra which can be divided into two parts: (i) the spectra corresponding to the smallest fraction of the community (17 mm ESD), which remains linear at all stations and during all the sampling period. At oceanic stations, an increase of the mean elevation of this part of the spectrum is observed. And (ii) more complex NB-S spectra, which follow the linear spectra

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Fig. 7. (A) Percentage of large particles (.17 mm ESD) biomass (mg C m23) in relation to total biomass (mg C m23). The solid line represents the linear regression for coastal stations (St1 and St2, y = 20.37 ln(x)244.77, r 2 = 0.61, P , 0.005). The dashed line represents the linear regression for oceanic stations (St5 and St6; y = 10.88 ln(x)28.21, r 2= 0.47, P , 0.005). (B) Log–log plot between the biomass (mg C m23) of large and small particles at coastal stations. The solid line represents the fitted regression (ln(y)=0.73 ln(x) 21.66, r 2 = 0.05, P . 0.1). The dotted line represents a 1:1 relationship. (C) Log– log plot between the biomass (mg C m23) of large and small particles at oceanic stations. The dashed line represents the fitted regression (ln(y)=1.4 ln(x)22.16, r 2 = 0.75, P , 0.001). The dotted line represents a 1:1 relationship.

Fig. 8. Biomass difference (mg C m23) estimated as biomass on transect 8 minus biomass on transect 1, for diatom chains, ciliates, large unidentified particles and small particles. Calculated for the six stations sampled, at the surface and at 20 m depth.

Table V: Mean slope, intercept and r2 coefficient on each transect, for coastal (St1, St2) and oceanic stations (St5, St6) 0m Station Coastal

described by small particles at the beginning of the sampling, but progressively deviates from linearity and describes a dome centred at 33.5 mm ESD. This deviation from linearity first affects a limited number of size classes but it becomes more pronounced in the last transects, covering all the size classes larger than 17 mm (Figs 9 and 10). The slopes and intercepts defining the NB-S spectra of coastal and oceanic stations are shown in Table V. The slope of the NB-S spectra decreased significantly at all stations, during the bloom, (Table VI and Fig. 11). On the first transect, the size structure of the nanomicroplankton community at coastal and oceanic stations was described by spectra with similar slopes, but significantly different intercepts. On transect 8, however, the slopes of the spectra in coastal and oceanic waters differed significantly (Table VI and Fig. 11). The

Mesoscale Slope

1 2 3 4 5 6 7 8 Oceanic 1 2 3 4 5 6 7 8

20 m Intercept r

21.21 4.42 21.44 5.62 21.17 4.44 21.16 4.46 20.88 2.16 20.75 0.82 20.72 2.5 20.65 2 21.3 3.81 – – 21.28 3.88 21.34 3.74 21.23 3.35 21.25 4.18 21.32 5.62 21.05 5.76

2

0.92 0.99 0.95 0.96 0.86 0.81 0.93 0.95 0.96 – 0.93 0.97 0.97 0.94 0.93 0.91

Slope

Intercept r 2

21.37 5.57 21.2 3.69 21.41 6.01 21.16 3.8 20.88 2.6 20.93 2.88 20.7 1.77 20.8 2.93 21.36 5.57 – – 21.69 6.53 21.45 4.73 21.17 3.17 21.49 5.68 21.29 4.85 21.03 4.45

0.94 0.93 0.95 0.95 0.93 0.92 0.91 0.96 0.95 – 0.98 0.98 0.95 0.98 0.89 0.94

NB-S spectra for data collected at the surface and at a depth of 20 m were similar; excepting at oceanic stations, during the last transect, where the NB-S spectrum corresponding to surface waters showed a significantly higher intercept (Table VI and Fig. 11).

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Fig. 9. Time evolution of the normalized biomass-size spectra, for the six stations sampled, at the surface. Vertical dashed line in St1 separates small particles (17 mm ESD) from larger ones (.17 mm ESD).

DISCUSSION Initial microplankton biomass found during the study coincides with that previously described in the region (Quevedo and Anadon, 2000; Zarauz et al., 2007). During the bloom, biomass levels agree with the biomass described by Irigoien et al. (Irigoien et al., 2005) for many oceanic regions. The size distribution of the plankton community agrees also with the general observation that the increase in biomass during a bloom does not occur through accumulation of small phytoplankton, but through the addition of “new” size classes of larger cells (Fig. 7; Strom et al., 2001; Irigoien et al., 2004). During the survey, no significant differences were found in the vertical distribution of plankton size spectra, at 0 and 20 m depth (Table VI); excepting the

high values of ciliate biomass observed at St6 surface waters during transect 8. The hydrographic profiles did not show the intrusion of new water masses at this station, and it was decided to interpret this event as a microzooplankton patch sampled accidentally. The data analysed in the present survey show that there is a clear contrast in the timing and magnitude of the phytoplankton bloom, between coastal and oceanic waters. The reasons for these differences cannot be assigned to nutrients, as no significant variation in nutrient concentration was found between coastal and oceanic stations until transect 8 (Kruskal – Wallis, P . 0.001; Fig. 5). Moreover, the depleted concentrations observed in coastal stations on transect 8, suggested that nutrients were more relevant to bloom termination than

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Fig. 10. Time evolution of the normalized biomass-size spectra, for the six stations sampled, at 20 m depth. Vertical dashed line in St1 separates small particles (17 mm ESD) from larger ones (.17 mm ESD).

initiation. Alternatively, we propose that differences in the depth and the mixing of the water column could give rise to the coast– ocean contrast. On the coast, the water column is mixed, but MLD is limited by bottom depth (Fig. 4). This prevents losses of phytoplankton below the critical depth (Sverdrup, 1953), and allows the development of the bloom even without stratification (Huisman, 1999; Huisman, 2002). In oceanic waters, however, light conditions are favourable for photosynthesis, only if the upper water column is sufficiently shallow that deep integrated production exceeds the deep integrated losses (Sverdrup, 1953). This requires a relaxation of mixing, which in the present study was marked by the beginning of thermal stratification during transects 7 and 8 (Figs 3 and 4). These arguments suggest that the bloom started earlier on the coast, where MLD is limited by bottom depth;

whereas in offshore waters, it was going through an initial phase related to stratification. A more striking result is that, although the bloom was characterized by a proliferation of large cells in all stations, the dynamics contributing to this shift in the size structure were different at coastal and oceanic waters (Fig. 6). At coastal stations, the bloom dynamics involved a two-step process: firstly, a shift in the size structure of plankton was observed (Fig. 6; transect 5). The change consisted of an increase in the contribution of large diatoms, which accounted for more than a 60% of the total plankton biomass, but was not translated into an increase in biomass. Secondly, the bloom of plankton biomass took place (Fig. 6A; transect 7), maintaining the high levels of large diatoms. At oceanic stations, however, the shift in the size structure was simultaneous with the increase in biomass (Fig. 6). At these

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Table VI: ANCOVA results showing the effect of: time, distance to the coast and depth, on the slope and the intercept of NB-S spectra Distance: coast Model

Res.Df

RSS

Df

Sum of Sq

Explanatory factor: time (transect 1 versus transect 8) Distance: coast Depth: 0 m 45 141.17 log(Bm/Dm) m 44 72.42 1 68.75 log(Bm/Dm) m + time 43 35.95 1 36.47 log(Bm/Dm) m *time Distance: ocean log(Bm/Dm) m log(Bm/Dm) m + time log(Bm/Dm) m *time Explanatory factor: distance Time: transect 1 log(Bm/Dm) m log(Bm/Dm) m + distance log(Bm/Dm) m *distance Time: transect 8 log(Bm/Dm) m log(Bm/Dm) m + distance log(Bm/Dm) m *distance

37 36 35

198.66 49.14 43.29

1 1

149.52 5.86

to the coast (coastal versus oceanic) Depth: 0 m 34 47.69 33 33.77 1 13.92 32 33.65 1 0.12 48 47 46

63.94 63.84 45.59

Explanatory factor: depth (0 versus 20 m) Time: transect 1 35 40.60 log(Bm/Dm) m 34 40.46 log(Bm/Dm) m + depth 33 39.08 log(Bm/Dm) m *depth Time: transect 8 49 39.10 log(Bm/Dm) m 48 36.60 log(Bm/Dm) m + depth 47 34.61 log(Bm/Dm) m *depth

1 1

0.10 18.25

F

p

Res.Df

RSS

Df

82.23 43.62

,0.001*** ,0.001***

39 38 37

117.95 70.47 37.73

1 1

120.90 4.73

,0.001*** ,0.05*

35 34 33

112.01 49.38 39.75

1 1

13.24 0.12

,0.001*** 0.7335

29 28 27

46.33 30.50 30.19

1 1

0.10 18.41

0.7477 ,0.001***

45 44 43

63.14 57.65 47.29

1 1

26.55 26.08 24.77

1 1

75.22 58.39 58.27

1 1

Sum of Sq

F

p

47.48 32.74

46.56 32.10

,0.001*** ,0.001***

62.63 9.63

51.99 7.99

,0.001*** ,0.01**

15.84 0.30

14.16 0.27

,0.001*** 0.6064

5.49 10.36

4.99 9.42

,0.05* ,0.01**

0.47 1.32

0.49 1.38

0.4901 0.2503

16.83 0.12

12.13 0.08

,0.01** 0.7747

Depth: 20 m

Depth: 20 m

Distance: coast

Distance: ocean

1 1

0.14 1.38

0.12 1.16

0.7331 0.2884

28 27 26

1 1

2.50 1.99

3.39 2.70

0.0718 0.1067

44 43 42

Bm is the total biomass in carbon per size class m, and Dm is the size class interval, as defined in the NB-S spectra formula (see Methods). ***0.001. **0.01. *0.05.

stations, a higher contribution of large cells was observed during the bloom, but an increment in the biomass of small particles was also evident (Fig. 8). Between these two situations, there was a gradient that could be observed in intermediate stations and was well illustrated by the size spectra (Figs 9 and 10). With the data available, it is not possible to elucidate clearly the reasons underlying these ocean – coast differences in the size structure shift process. However, some hypotheses can be posed. The results collected in oceanic waters show that when the environmental conditions improve (during the last transects), the biomass of both large and small cells increases (Fig. 8). Nevertheless, at coastal stations small particles were not able to translate the improved conditions into a biomass increase. We propose that this may be due to predation control exerted by microzooplankton. At coastal stations, when the growth conditions were favourable, small particles were rapidly controlled by microbial predation. At oceanic stations, the bloom was starting when our sampling finished, and it was too early to see the effects

of microzooplankton. As predation susceptibility is related to size, larger diatoms were fed upon less efficiently, giving rise to the shift in size composition observed in coastal waters during transect 5. These diatoms became increasingly larger (Figs 9 and 10), and finally they were able to escape from the predatory pressure exerted by microzooplankton and form a dense biomass bloom (transects 7 and 8). This analysis strengthens the theory of trophic control in the generation of plankton blooms. It is now widely accepted that microzooplankton grazing exerts a tight grazing control over phytoplankton, consuming 60– 70% of total primary production in the oceans (Calbet and Landry, 2004). Their short generation times constitute a decisive advantage over large metazoans, in their ability to exploit ephemeral changes in algal biomass (Banse, 1992). In such a context, blooms may be considered as events generated by a failure of the microzooplankton grazers to contain phytoplankton production. Or, in other words, blooming species will be those which are able to escape control by microzooplankton.

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Fig. 11. Summary plot of the normalized biomass-size spectra, showing the differences with time, distance to the coast and depth. The NB-S spectra are represented at day 55 (transect 1) and day 79 (transect 8); for coastal (St1 and St2) and oceanic (St5 and St6) stations; at the surface and at 20 m depth.

In fact, these organisms have developed a combination of predation avoidance mechanisms (i.e. larger size, spines, colonies and toxic compounds) which have been shown to be an effective defence against microheterotrophs (Fenchel, 1980; Strom et al., 2003). Cell size in particular, is an important factor in food selection. The linear size ratio between predators and their optimal prey are 8:1 for ciliates and 3:1 for the majority of dinoflagellates (Hansen et al., 1994). Therefore, although microzooplankton are capable of feeding on bloom-forming diatoms using various feeding strategies (Monger and Landry, 1991; Strom and Buskey, 1993; Strom et al., 2001), a lower feeding efficiency is expected with increasing phytoplankton size (Strom et al., 2007). This slowing down of their feeding efficiency alone, is enough to open the “loophole” (Irigoien et al., 2005) into which blooming species can explode. The results obtained indicate that imaging technology, combined with automatic recognition techniques, constitutes a powerful approach to describe plankton distributions at a fine temporal scale. However, it must be remembered that this study presents a number of methodological limitations. On the one hand, the categories into which plankton have been classified are very broad, and no information is provided about the

contribution of non-living material to our estimates. As such, they provide only a general view of the existing plankton community, and limit our understanding of the ecological processes therein. Additionally, as only simple shape descriptors were used in the classification, difficulties were found to distinguish certain groups of similar size and shape, such as ciliates, large flagellates and circular diatoms. It may be possible to improve identification performance by using other contour representations, moment-based and texture-based features (Blaschko et al., 2005; Hu and Davis, 2006), as well as features specific to particular taxa (Blaschko et al., 2005). Better taxonomic grouping will provide an improved understanding of the composition and the mechanisms involved in the functioning of the planktonic ecosystem. On the other hand, when constructing a size spectrum which covers a wide size range of particles, it is operationally impossible to conduct perfect sampling, and to choose ideal carbon/volume conversion factors, for each of the size classes throughout the whole sizespectrum (Quin˜ones et al., 2003). In the present analysis, the abundance of large microplankton is small, in such a way that often it can be defined in terms of presence – absence (Fig. 8). Due to their large size, a

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difference of just one organism sampled has a significant impact on the shape of the biomass spectra (more positive slopes), which may cause an overestimation of large microzooplankton biomass. Additionally, the 7 –174 mm size range used to describe the plankton community leaves aside the picoplankton size fraction. Yet the estimated contribution of picoplankton to total algae has been shown to be high under late winter-early spring conditions, indicating the important role of small cells at the onset of the spring bloom (Calvo-Diaz et al., 2004). These limitations suggest caution in biomass size spectra interpretation. This is the reason why our study was focused on the analysis of the relative differences between the NB-S spectra of the different plankton communities sampled, and not on their absolute values. Finally, the dynamic nature of marine systems is a well-known problem in oceanography. Due to prevailing currents and advection of new water masses, measurements made from fixed stations represent a mix of sequences of plankton populations passing by (Huntley and Niiler, 1995). In particular, the northern shelf of the Iberian Peninsula is a region of great hydrodynamic activity. During winter and early spring, the hydrography of this area is strongly influenced by a poleward slope current of high salinity waters (Frouin et al., 1990). This water body generates a sharp thermohaline front between coastal and offshore waters (Botas et al, 1989, Fernandez et al., 1991), which can affect plankton distribution and production (Fernandez et al., 1991, 1993). During the survey, the hydrographic profiles observed remained consistent (Fig. 3) and we did not identify any significantly different water masses passing by, and therefore it was considered that the water mass sampled during the survey was sufficiently homogeneous so as to interpret the bloom as a continuous event.

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FUNDING This research was supported by a project funded by the Spanish Ministry of Education and Research (DINAPROFIT). L.Z.’s work was supported by a doctoral fellowship of the Education, Universities and Research Department of the Basque Country Government. X.I. was supported partially by a Ramon y Cajal Grant from the Spanish Ministry of Education and Research. J.A.F. was supported by a doctoral fellowship from the Fundacio´n Centros Tecnolo´gicos In˜aki Goenaga.

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