Journal of Experimental Marine Biology and Ecology 461 (2014) 323330

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Journal of Experimental Marine Biology and Ecology

j ourna l homepage: www.e lsev ie r .com/ locate / jembeAre microcosm volume and sample pre-filtration relevant to evaluatephytoplankton growth?Patrcia Nogueira a, Rita B. Domingues a,b,, Ana B. Barbosa a

a Centro de Investigao Marinha e Ambiental, Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugalb Centro de Oceanografia, Faculdade de Cincias, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal Corresponding author at: FCT, ed. 7, Universidade do8005-139 Faro, Portugal. Tel.: +351 289 800 900.

E-mail addresses: patricianogueira.pn@gmail.com (P. Nrbdomingues@ualg.pt (R.B. Domingues), abarbosa@ualg.p

http://dx.doi.org/10.1016/j.jembe.2014.09.0060022-0981/ 2014 Elsevier B.V. All rights reserved.a b s t r a c ta r t i c l e i n f oArticle history:Received 8 July 2014Received in revised form 9 September 2014Accepted 10 September 2014Available online xxxx

Keywords:Bottle effectsBottle volumeMicrocosmsPhytoplanktonPre-filtrationTrophic cascadeBottle effects are one of the most deeply rooted concerns of phytoplankton microcosm studies and are mainlyrelated to incubation time and sample volume. Sample pre-filtration to remove larger grazers is also a commonprocedure in experimental phytoplankton ecology studies, particularly in nutrient enrichment experiments.However, the effects of bottle volume and sample pre-filtration on the outcomes of such experiments, particular-ly on the net growth rates of specific phytoplankton taxa, have never been addressed. Therefore, this study aimsto evaluate the effects of different bottle volumes and sample pre-filtration on phytoplankton net growth rates inmicrocosm experiments.To accomplish this goal, unfiltered and filtered (b100 m)water samples, collected in theGuadiana estuary,werenutrient-enriched to avoid nutrient limitation and incubated for 3 days in polycarbonate microcosms withdifferent volumes (0.5 L8.0 L), inside a plant growth chamber. Phytoplankton composition, abundance, biomassand taxon-specific net growth rates were evaluated throughout the experiment. No systematic significant effectsof bottle volume were detected in phytoplankton growth rates. However, sample filtration caused significantchanges in phytoplankton composition, with a decline of diatom abundance. Moreover, the removal oflarge-sized predators and large-sized phytoplankton (diatoms) after sample filtration cascaded down thefood web, affecting taxon-specific net growth rates differently. Net growth rates of green algae and eukaryoticpicophytoplankton were significantly higher in filtered treatments in respect to unfiltered treatments. Con-versely, both diatoms and cryptophytes presented higher net growth rates in unfiltered treatments whilenet growth rates of picoplanktonic cyanobacteria and plastidic nanoflagellates were not affected by samplefiltration. We conclude that, while microcosm volume does not affect results in phytoplankton microcosms,sample pre-filtration may significantly alter the structure of the original phytoplankton community andhence increase the problems associated with the extrapolation of experimental outcomes to the naturalenvironment.

2014 Elsevier B.V. All rights reserved.1. Introduction

Containing phytoplankton inside closed bottles is a common proce-dure to evaluate the effects of one or more environmental variables onnatural phytoplankton communities. The outcomes of these in vitro ex-periments are then extrapolated to natural systems and used to predictchanges in phytoplankton composition and growth following specificenvironmental scenarios (Domingues et al., 2011a). Phytoplanktonmicrocosms have been successfully used to assess, for instance, nutrientand light limitation in numerous aquatic ecosystems (Altman and Paerl,2012; Domingues et al., 2011a, 2011b, 2011c; Quiblier et al., 2008).Algarve, Campus de Gambelas,

ogueira),t (A.B. Barbosa).Microcosms are typically small experimental containers (volumes:12 L), such as bottles and bags, incubated over 2 to 5 days(Domingues et al., 2011a; Duarte et al., 1997; Gobler et al., 2006;Rudek et al., 1991; Xu et al., 2010). However, the containment ofplanktonic microorganisms inside small experimental units isknown to induce environmental changes in respect to the naturalenvironmental conditions that microorganisms would normally ex-perience, raising concerns in the extrapolation of experimental resultsto nature (Domingues et al., 2011a; Duarte et al., 1997). Indeed, physical(e.g., light intensity, spectral composition, turbulence), chemical(e.g., nutrients, dissolved gases, organic matter concentration), andbiological variables (e.g., abundance of phytoplankton predators andparasites) inside closedmicrocosms are different from natural environ-mental conditions (e.g., Fahnenstiel and Scavia, 1987; Venrick et al.,1977), and these differences are aggravated over the incubation period.Moreover, dissolved compounds, including inorganic nutrients, organic

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324 P. Nogueira et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 323330matter and dissolved gases (CO2, O2) produced by non-planktonicsources (e.g., benthic organisms) or advected into the study area,will be excluded from nutrient enrichment experiments (Prins andSmaal, 1994; Trguer and De La Rocha, 2013). The existence ofsolid surfaces may also promote the adsorption of organic matterand the activity and adhesion of specific microbes (e.g., pennate di-atoms, stalked ciliates) inside the experimental bottles (Zobell,1943). Overall, in vitro approaches are subjected to impacts ofthe container itself, and these, collectively called bottle effects(Robinson and Williams, 2005), can potentially affect the outcomeof the experiments.

A large fraction of bottle effect literature specifically emphasizesthe volumetric bottle effects, i.e., the effects of the size (orsurface-to-volume ratio) of the experimental unit. It is usually con-sidered that the smaller the volume used, the larger the bottle effectson growth bioassays (Hammes et al., 2010 and references therein).In fact, several studies reported significant effects of bottle volumeon phytoplankton production or growth for small-sized microcosms,over the range of 30mL to 4 L (Gieskes et al., 1979). Yet, some studiesdid not observe evidence for significant volumetric bottle effectsupon heterotrophic bacterioplankton growth (microcosm volumes:20mL1 L: Hammes et al., 2010), phytoplankton primary production(microcosm volumes: 50 mL8 L: Fogg and Calvario-Martinez, 1989;Williams and Purdie, 1991), phytoplankton net growth and planktonrespiration (microcosm volumes: 50 mL570 mL: Garca-Martnet al., 2011).

A procedure sometimes associated with phytoplanktonmicrocosmsis a sample pre-filtration stage (Duarte et al., 1997; Venrick et al., 1977),before the beginning of the experiment. This procedure aims to removephytoplankton grazers present in the sample and minimize grazing-induced phytoplankton mortality, therefore enabling a more accu-rate evaluation of direct effects on phytoplankton (instantaneous)growth rates (Carrillo et al., 1990; Tang et al., 2009). However, themost important phytoplankton grazers are phagotrophic protists,which can remove on average 67% of phytoplankton daily produc-tion (Calbet and Landry, 2004), and these organisms cannot becompletely removed using this strategy, due to a clear overlap be-tween cell dimensions of phytoplankton and phagotrophic protists(e.g., ciliates, aplastidic nanoflagellates, heterotrophic dinoflagel-lates and amoeba: Hansen and Bjornsen, 1994; Naustvoll, 2000): Insome cases, grazers are even smaller than their phytoplanktonicprey (e.g., pallium feeding heterotrophic dinoflagellates; Naustvoll,2000). The existence of several mixotrophic groups (Hammer andPitchford, 2005 and references therein) further complicates thephysical separation of phytoplankton and their herbivores. In addi-tion to the differential elimination of some phytoplankton grazers,sample filtration also removes large-sized phytoplankton, such aschain-forming diatoms, may break chain-forming phytoplanktonand may also affect cell physiological state (see Domingues et al.,2011b). The removal of larger cells may cascade down the foodweb, disrupting the normal functioning of microbial food webs andleading to changes in phytoplankton community (Bell et al., 2003;Carpenter et al., 1985; Ellis et al., 2011).

In this context, the present study aims to evaluate the effects ofmicrocosm volume and sample pre-filtration on phytoplankton netgrowth and community composition in microcosm experiments,using natural phytoplankton assemblages from the Guadiana estu-ary. We hypothesize that microcosm volume does not affect phyto-plankton growth (Fogg and Calvario-Martinez, 1989; Garca-Martnet al., 2011; Hammes et al., 2010; Williams and Purdie, 1991),while pre-filtration of samples will significantly alter phytoplanktonnet growth (Domingues et al., 2011a) due to changes in grazing pres-sure and phytoplankton community structure. This is, to our knowl-edge, the first study that evaluates simultaneously the effects ofmicrocosm volume and pre-filtration on taxon-specific phytoplank-ton net growth rate.2. Materials and methods

2.1. Sampling strategy

Sampling was conducted in November 2011, in the freshwater tidalzoneof theGuadiana estuary (Fig. 1). A description of the study area andassociated phytoplankton dynamics is given by Barbosa et al. (2010)and Domingues et al. (2014). Due to space limitations inside the growthchamber, sub-superficial water samples were collected on differentdays (18 and 22 November) for testing experimental units 2 L andN2 L, respectively. Ten-liter polycarbonate bottles were used for samplecollection and transportation, under cold and dark conditions. Watertemperature was measured in situ with a MSI probe.

2.2. Experimental strategy

Water samples were incubated inside Nalgene transparent polycar-bonate bottles of four different volumes: 0.5 L, 1 L, 2 L and 8 L. For eachbottle volume, both unfiltered samples (NF) and samples filteredthrough a 100 m mesh (F) were used. Eight experimental treatmentswere thus obtained (0.5 NF, 0.5 F, 1 NF, 1 F, 2 NF, 2 F, 8 NF, 8 F), andeach treatment was prepared in triplicate. At the beginning of theexperiment, dissolved inorganic macronutrients were added to all ex-perimental treatments, in a single, saturating pulse, according toRedfield and Brzezinski ratios (Brzezinski, 1985; Redfield et al., 1963).Nutrient concentrations added were +160 M of nitrogen (as nitrate,NO3), +10 M of phosphorus (as phosphate, PO4) and +160 M ofsilicon (as silicate, SiO4). The experimental treatments were incubatedfor 3 days inside a plant growth chamber (Fitoclima S600), under a lightintensity of 120 mol photos m2 s1. Nutrient concentrations andlight intensity values used in the experiments were slightly higherthan typical values for the freshwater tidal zone of the Guadianaestuary, to prevent the occurrence of both nutrient and light limitations(Domingues et al., 2011a, 2011b).

Sampleswere takendaily fromeach experimental treatment to eval-uate chlorophyll a in vivo fluorescence. Samples for evaluation of phyto-plankton abundance, composition, and chlorophyll a concentrationwere also taken from each experimental treatment at the beginningand end of incubation.

2.3. Chlorophyll a concentration, phytoplankton abundance and composition

Chlorophyll a in vivo fluorescence, was measured daily with anAU-10 Turner Designs fluorometer and used to verify the occurrenceof exponential growth of phytoplankton during incubation. Chlorophylla concentration (Chl a) was determined spectrophotometrically(Parsons et al., 1984), using glass fiber filters (Whatman GF/F, nominalpore diameter = 0.7 m) and used as a proxy for phytoplankton bio-mass (but see Domingues et al., 2008). Chlorophyll a was extractedovernight at 4 C, with 90% acetone. After centrifugation, absorbanceof the supernatant was measured in a spectrophotometer (HitachiU-2000) at 750 and 665 nm, before and after acidification with HCl.

Phytoplankton abundance and composition were analyzed usingtwo distinct methods: inverted microscopy (Utermhl, 1958) formicrophytoplankton (N20 m), and epifluorescence microscopy(Haas, 1982) for picophytoplankton (b2 m) and nanophytoplankton(220 m). Samples for microphytoplankton analyses were preservedwith acid Lugol's solution, settled in sedimentation chambers(510 mL, depending on suspended particulate matter; sedimentationtime = 24 h), and observed using a Zeiss AxioObserver S1 invertedmicroscope, at 400 magnification. Samples for picophytoplanktonand nanophytoplankton were preserved with glutaraldehyde (2% finalconcentration), stained with proflavin and filtered (12 mL, dependingon the amount of suspended matter) onto black polycarbonatemembrane filters (Whatman, nominal pore diameter = 0.4 m).Preparations were made within 24 h of sampling, using glass slides

Fig. 1. Location of the Guadiana estuary and sampling station (Alcoutim).

325P. Nogueira et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 323330and immersion oil Cargille type A, and were frozen (20 C) in darkconditions to minimize loss of autofluorescence. A Leica DM LBepifluorescencemicroscope, at 787.5magnification,was used for sam-ple observation. For both methods, a minimum of 50 random visualfields, at least 400 cells in total and 100 cells of the most commontaxon were enumerated. Assuming cells were randomly distributed,the counting precision was 10% (Venrick, 1978).

2.4. Data analysis

Data analysis was performed using GraphPad Prim 5 software.Differences in Chl a and phytoplankton abundance between unfilteredand filtered water samples at the beginning of the experiment and be-tween sampling days were assessed using an unpaired t-test. Variationof Chl a and phytoplankton abundance over the incubation period wereused to estimate phytoplankton net growth rate (d1) for each experi-mental treatment, as (lnNt lnN0) / t, where Nt represents cellabundance or Chl a concentration at time t (days) and N0 is cell abun-dance or Chl a concentration at the beginning of the experiment.Table 1Initial conditions in non-filtered (NF) and filtered (F) experimental treatments, and different mperiments. Samples were collected in the Guadiana upper estuary on 18th November 2011 (V0Significant differences between non-filtered and filtered treatments are denoted by a and signiand underlined letter is p b 0.01.

Units November 18th

NF

Temperature C 15Total phytoplankton 106 cells L1 13.6 1.1b

Diatoms 103 cells L1 596.8 59.7ab

Aulacoseira granulata 103 cells L1 327.6 39.2Green algae 103 cells L1 429.8 57.0b

Cryptophytes 103 cells L1 88.7 9.9Cyanobacteria 106 cells L1 6.0 0.8b

Eukaryotic picophytoplankton 106 cells L1 3.7 0.3b

Plastidic nanoflagellates 106 cells L1 2.8 0.5Chlorophyll a g L1 4.7 0.4Phytoplankton net growth rates in each treatment were statisticallycompared using a 2-way ANOVA with replication (factors: bottle vol-ume and sample filtration). A Bonferroni post-hoc test was applied toassess significant differences between each experimental treatment.All statistical analyses were considered at a 0.05 significance level.

3. Results

3.1. Initial conditions

Water temperature ranged between 15 and 16 C and Chl a concen-tration between 3.30 and 6.13.9 g L1 (see Table 1). Cyanobacteria(dominated by Synechococcus-like picoplanktonic forms), plastidicnanoflagellates and eukaryotic picophytoplankton represented, onaverage, 39.6%, 25.1% and 23.0% of total phytoplankton abundance,respectively. By contrast, the contributions of diatoms (dominatedby the centric colonial species Aulacoseira granulata), green algae(dominated by non-motile coenobial species Scenedesmus) andcryptophytes (dominated by microplanktonic forms) were lower,icrocosm volumes (V0.5 = 0.5 L, V1 = 1 L, V2 = 2 L, V8 = 8 L) at the beginning of ex-.5, V1 and V2) and 22nd November 2011 (V8). Values are mean 1 standard deviation.ficant differences between sampling days by b. Non-underlined letter represents p b 0.05

November 22nd

F NF F

1612.7 0.4b 6.7 0.2b 5.0 0.6b

441.7 62.8ab 397.4 29.0ab 204.2 21.8ab

189.5 32.4 195.9 21.7 84.06 18.9413.2 40.6b 297.6 9.9b 341.9 29.7b

107.5 12.0 61.7 13.4 79.2 7.35.7 0.5b 2.4 0.4b 2.0 0.2b

3.3 0.5b 1.3 0.3b 1.2 0.4b

2.7 0.4 2.0 0.4 1.5 0.36.1 0 3.3 0 6.1 3.9

image of Fig.1

A

326 P. Nogueira et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 323330accounting for 5.1%, 3.8%, and 0.8% of total phytoplankton abun-dance, respectively. Statistically significant differences were foundin phytoplankton abundance (total abundance, diatoms, greenalgae, cyanobacteria and eukaryotic picophytoplankton) between thetwo sampling days (Table 1). Sample pre-filtration induced a significantdecline in the abundance of diatoms, on average the largest phytoplank-ton functional group evaluated, in respect to non-filtered samples(p b 0.05, Table 1). The initial abundance of the other phytoplanktonicgroups was not affected by sample filtration.B

Fig. 2. Phytoplankton community net growth rates (d1) based on (A) chlorophyll a con-centration and (B) total phytoplankton abundance for unfiltered (NF) and filtered(F) experimental treatments and different microcosm volumes (V0.5 = 0.5 L, V1 = 1 L,3.2. Effects of sample pre-filtration and bottle volume on phytoplankton netgrowth

Overall, sample pre-filtrationwas themain factor responsible for thevariability observed in phytoplankton net growth rates in the experi-ments (Table 2). Bottle volume and interactions between bottle volumeand pre-filtration significantly affected phytoplankton net growth ratesbased on chlorophyll a concentration, and net growth rates of diatomsand cryptophytes. However, the exclusion of 8-L volume treatments(samples collected in different days displayed significant phytoplanktondifferences; see Materials and methods and Table 1), leads to no sig-nificant effects of bottle volume and interactions between the twofactors on phytoplankton net growth rates (data not shown). Indeed,Bonferroni post-hoc tests confirmed that volume differences wereobserved only between 8 L bottles and the other bottles volumes,particularly for filtered treatments.

On the contrary, sample pre-filtration significantly affected netgrowth rates of most phytoplankton groups, except autotrophicnanoflagellates (0.30 0.03 d1 and 0.47 0.05 d1, Fig. 3D) andcyanobacteria (0.160.04 d1 and 0.300.04 d1, Fig. 3F), and growthrates based on total phytoplankton abundance (0.29 0.02 d1 and0.38 0.04 d1, Fig. 2B).Table 2Results of two-way ANOVA used to test the effects of sample pre-filtration (unfiltered andfiltered), bottle volume (0.5, 1, 2 and 8 L) and interactions between the two variables onthe net growth rates of phytoplankton community based on chlorophyll a concentration(PHYTO Chla) and on total abundance (PHYTO TotAb), and net growth rates of specificphytoplankton groups: diatoms (DIAT), cryptophytes (CRYPT), green algae (GREEN), au-totrophicnanogflagellates (ANFL), eukaryotic picophytoplankton (EPP) and cyanobacteria(CYA). Right column represents p-value of two-way ANOVA for unfiltered and filteredtreatments and volumes from 0.5 L to 2 L; the other columns represent results for allbottle volumes tested (0.5 to 8 L).

SS df F p-value p-value(excl. V8)

PHYTOChla

Sample filtration 1.7253 1 188.9488 b0.0001 b0.0001Bottle volume 0.1165 3 4.2530 b0.05 n.s.Interactions 0.2837 3 10.3549 b0.0005 n.s.

PHYTOTotAb

Sample filtration 0.0104 1 1.5871 n.s. n.s.Bottle volume 0.0158 3 0.8053 n.s. n.s.Interactions 0.0071 3 0.3627 n.s. n.s.

DIAT Sample filtration 0.5089 1 21.5512 b0.0005 b0.005Bottle volume 0.0975 3 1.3769 n.s. n.s.Interactions 0.0673 3 0.9497 b0.05 n.s.

CRYP Sample filtration 2.5706 1 134.3169 b0.0001 b0.0001Bottle volume 0.2032 3 3.5384 b0.05 n.s.Interactions 0.0160 3 0.2784 n.s. n.s.

GREEN Sample filtration 0.1238 1 7.0660 b0.05 b0.05Bottle volume 0.1127 3 2.1447 n.s. n.s.Interactions 0.0497 3 0.9464 n.s. n.s.

ANFL Sample filtration 0.0059 1 0.3366 n.s. n.s.Bottle volume 0.0052 3 0.1000 n.s. n.s.Interactions 0.0171 3 0.3273 n.s. n.s.

EPP Sample filtration 0.0735 1 20.0360 b0.0005 b0.0001Bottle volume 0.0016 3 0.1446 n.s. n.s.Interactions 0.0086 3 0.7823 n.s. n.s.

CYA Sample filtration 0.0818 1 2.1608 n.s. n.s.Bottle volume 0.1162 3 1.0230 n.s. n.s.Interactions 0.0777 3 0.6838 n.s. n.s.

V2 = 2 L, V8 = 8 L). Vertical lines represent 1 SE (n = 3).The effects of sample pre-filtration were different between groups.Net growth rates of thephytoplankton community based on chlorophylla were significantly (p b 0.0001) higher in unfiltered treatments, vary-ing between 0.610.03 d1 and 0.750.06 d1 in unfiltered samples,and 0.02 0.06 d1 and 0.28 0.14 d1 in filtered samples (Fig. 2A).

Diatom (Bacillariophyceae) net growth rates (Fig. 3A) varied between0.04 0.04 and 0.57 0.03 d1, and significantly (p b 0.0005) highernet growth rates were also detected in unfiltered treatments in relationto filtered treatments. Cryptophytes (Cryptophyceae) net growth rates(Fig. 3B) ranged between 0.48 0.03 and 0.17 0.05 d1 and, asfor diatoms, net growth rates were significantly (p b 0.0001) higherfor unfiltered treatments in relation tofiltered treatments, being consis-tently negative for the later experimental treatments.

In contrast to diatoms and cryptophytes, green algae (Chlorophyceae)net growth rates (Fig. 3C) varied between 0.36 0.01 and 0.71 0.05 d1, and significantly (p b 0.05) higher net growth rates weredetected for filtered samples. Eukaryotic picophytoplankton net growthrates (Fig. 3E) varied between 0.23 0.03 and 0.35 0.03 d1, andexcept for 8 L of microcosms, this group also exhibited significantlyhigher net growth rates in filtered treatments in relation to unfilteredtreatments (p b 0.05).

4. Discussion

Sample pre-filtration through a 100 m mesh will, theoretically, re-tain planktonic organisms or colonies with sizes above 100 m. Indeed,sample pre-filtration induced significant changes only for diatoms, onaverage the largest phytoplankton functional group observed, whichshowed significantly lower abundances after sample filtration. Thispartial removal of diatomswas probably concomitant with the removalof N100 m-sized predators, including metazooplankton and the larger

image of Fig.2

A B

C D

E F

Fig. 3. Net growth rates (d1) of specific phytoplankton groups: (A) diatoms, (B) cryptophytes, (C) green algae, (D) autotrophic nanoflagellates, (E) eukaryotic picophytoplanktonand (F) cyanobacteria, for unfiltered (NF) and filtered (F) experimental treatments and different microcosm volumes (V0.5 = 0.5 L, V1 = 1 L, V2 = 2 L, V8 = 8 L). Verticallines represent 1 SE (n = 3).

327P. Nogueira et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 323330phagotrophic protists, a dominant phytoplankton mortality source(Barbosa, 2006; Calbet and Landry, 2004).

The effects of sample pre-filtration on phytoplankton communitynet growth rate varied according to the strategy used to estimate it.Overall, phytoplankton community net growth rates based on Chl awere significantly lower for filtered treatments in respect to unfilteredtreatments. Since Chl a was used as a proxy for total phytoplanktonbiomass (Jesus et al., 2006; Poikne et al., 2010; Stow and Cha, 2013),large phytoplanktonic cells, that usually account for a greater percent-age of phytoplankton chlorophyll and biomass, had probably a greatercontribution to these differences (Domingues et al., 2008). Indeed, thispattern of decreased net growth rates for filtered treatments was alsoobserved for diatoms. By contrast, filtration showed no significanteffects on phytoplankton community net growth rates based on totalphytoplankton abundance. This estimate was strongly conditioned bythe response of Synechococcus-like picoplanktonic cyanobacteria thatrepresented the dominant phytoplankton taxon in terms of abundance(approx. 40% of total phytoplankton abundance). Indeed, net growthrate of cyanobacteria was not affected by sample filtration. Overall, theuse of phytoplankton community net growth rates to evaluate filtrationeffects is biased toward the response of dominant taxa in terms of bio-mass (Chl a-based estimates) or abundance (total abundance-basedestimates). Then, in order to investigate the responses of differentphytoplankton functional groups, the analysis of specific groups basedeither on taxa abundance (this study; Morris et al., 2006) or specificpigment markers (e.g., Zhao and Quigg, 2014) is fundamental.

Indeed, the responses of net growth rates to sample filtration variedamong different phytoplankton taxa. Net growth rates of green algaeand eukaryotic picophytoplankton were significantly higher in filteredtreatments in respect to unfiltered treatments. Green algae, includingScenedesmus, are usually grazed by large-sized protists (e.g., ciliates:Goulder, 1972), rotifers, cladocerans and copepods (Chow-Fraser,1986; Mayeli et al., 2004). Consequently, the removal of large-sizedplanktonic predators may directly explain increased net growth rateof green algae after filtration, through decreased mortality. Moreover,sample pre-filtration induced a significant decline in diatom abundance.Then, any antagonistic relationship between green algae and diatoms,including allelopathic interactions (Leflaive and Ten-Hage, 2007;Legrand et al., 2003) and competition for nutrients (Roelke et al., 1999;Tilman et al., 1986), would favor green algae in filtered treatments. Infact, in the Guadiana estuary, recurrent late-spring green algae bloomssystematically follow the dissipation of diatom blooms, suggesting acompetitive interaction between these two groups (Barbosa et al.,2010; Domingues et al., 2005).

image of Fig.3

328 P. Nogueira et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 323330However, this explanation does not apply to eukaryoticpicophytoplankton, usually grazed by smaller predators. The removalof large-sized planktonic predators after sample filtration probablypromoted an increase in medium-sized predators (e.g., micro-sizedciliates and dinoflagellates) that further induced a decline innanoplanktonic protists (e.g., Solic and Krstulovic, 1994). Since eukary-otic picophytoplankton are usually ingested by nanoplanktonic protists(Barbosa, 2006; Hansen and Bjornsen, 1994; Hirose et al., 2008), theirreduction possibly decreased eukaryotic picophytoplankton mortality,thereby increasing net growth rates in filtered treatments. Similartrophic cascades were also observed in natural aquatic ecosystemsand in experimental manipulation studies (Calbet and Landry, 1999;First et al., 2009; Kuuppo-Leinikkil et al., 1994; Lonsdale et al., 1996;Pace et al., 1998; Reckermannl and Veldhuis, 1997; Samuelsson andAndersson, 2003; Vidussi et al., 2011).

In contrast to green algae and eukaryotic picophytoplankton,both diatoms and cryptophytes presented higher net growth ratesin unfiltered treatments. Since these groups were dominatedby microplanktonic forms, that sometimes even exceed 100 m(e.g., chain-forming centric diatom A. granulata), the detrimental ef-fects of pre-filtration could be related to direct impairment of cellphysiological state induced by the filtration procedure (Venricket al., 1977). This impairment, associated with the phytoplanktoncommunity changes (reduction of diatoms) induced by sample fil-tration, can affect the response of phytoplankton to the variablesbeing experimentally tested, such as nutrient enrichments, therebyeventually altering the outcome of the experiments. Moreover, samplepre-filtration also removed large-sized planktonic predators, whichpossibly caused an increase in medium-sized phytoplankton predators,such as ciliates and heterotrophic/mixotrophic dinoflagellates, impor-tant grazers of cryptophytes and diatoms (Aberle et al., 2007; Jeonget al., 2010; Urrutxurtu et al., 2003). Hence, their increase may haveenhanced phytoplankton mortality, leading to lower net growth ratesfor the filtered treatments. In the absence of any detrimental effects offiltration on cell physiology, it is thus possible that the dominantpredators of diatoms and cryptophytes are smaller than 100 m. Thedifferential removal of large-sized phytoplankton and their predatorsmay cascade down the food web and disrupt the normal functioningof the microbial food web (Bell et al., 2003; Carpenter et al., 1985;Ellis et al., 2011). Therefore, changes induced by sample pre-filtrationto the original phytoplankton community are likely to affect the out-comes of microcosm experiments.

Net growth rates of cyanobacteria and plastidic nanoflagellates,unlike the other groups, were not significantly affected by samplepre-filtration. Cyanobacteria, dominated by Synechococcus-likepicoplanktonic forms, were the dominant phytoplankton taxon interms of abundance. Although this group shares predators with eukary-otic picophytoplankton (Apple et al., 2011; Guillou et al., 2001; Weisse,1993), the effects of sample filtration on the net growth rates of thesegroups were different. Indeed, phagotrophic protists usually exhibit aselective feeding behavior (Montagnes et al., 2008) and cyanobacteriaare usually considered less vulnerable to grazing than other phyto-plankton groups (Agrawal and Agrawal, 2011; Apple et al., 2011;Sterner, 1989).

Regarding bottle volume, significant effects of bottle size on netphytoplankton growth were restricted to differences between 8.0 Land other bottle volumes (only for cryptophytes, green algae and netcommunity growth based on Chl a). Since phytoplankton communityin the water samples used to evaluate 8-L volume treatments weresignificantly different from those used to test smaller bottle volumes,differences between net growth rates in 8-L and 2-L bottles cannotbe solely attributed to bottle volume, but instead to the different ini-tial phytoplankton assemblages. No significant differences werefound between bottle volumes 2 L, which is in accordance withother studies that investigated the effects of bottle volume (volumerange: 50 mL8.0 L) on phytoplankton primary production (Foggand Calvario-Martinez, 1989; Williams and Purdie, 1991), phyto-plankton net growth and plankton respiration (Garca-Martn et al.,2011). Yet, this result contrasts to studies that referred significantvolumetric bottle effects (volume range 30 mL4.0 L and incubationtime ranging between 12 h to 14 days) on phytoplankton production(e.g., Gieskes et al., 1979).

The absence of volumetric bottle effects in our study does notexclude the existence of other bottle effects. Indeed, mimicking theenvironmental conditions to which phytoplankton is naturally exposedwithin a non-permeable experimental unit, incubated over 72 h, is anunattainable task and experimental artifacts are unavoidable. Indeed,phytoplankton net growth rate represents the interplay between phy-toplankton mortality and phytoplankton growth, a biological processthat cannot proceed unaltered when phytoplankton are removed fromtheir natural surroundings (Cullen, 2009). Thus, the extrapolation ofexperimental results to natural conditions is not straightforward andshould thus be done with caution (Domingues et al., 2011a; Duarteet al., 1997).

5. Conclusions

According to our experiments using phytoplankton communitiesfrom the Guadiana estuary, different bottle volumes did not systemati-cally affect phytoplankton net growth rate. The removal of largergrazers by sample pre-filtration was not an efficient predator removalstrategy for some phytoplankton groups, and it caused a significantalteration in phytoplankton composition due to the retention of large-sized cells. Sample pre-filtration caused changes in the size structureof the microbial food web. The removal of large-sized predators andlarge-sized phytoplankton (diatoms) cascaded down the food web,affecting differently taxon-specific net growth rates. Sample pre-filtration may, therefore, significantly alter the structure of the orig-inal phytoplankton community and hence increase the problemsassociated with the extrapolation of experimental outcomes to thenatural environment.

Acknowledgments

This work was financially supported by the Portuguese Founda-tion for Science and Technology (FCT) through project PHYTORIA(PTDC/MAR/114380/2009). FCT provided funding for R.B.D. througha postdoctoral fellowship (SFRH/BPD/68688/2010). [SS]

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Are microcosm volume and sample pre-filtration relevant to evaluate phytoplankton growth?1. Introduction2. Materials and methods2.1. Sampling strategy2.2. Experimental strategy2.3. Chlorophyll a concentration, phytoplankton abundance and composition2.4. Data analysis

3. Results3.1. Initial conditions3.2. Effects of sample pre-filtration and bottle volume on phytoplankton net growth

4. Discussion5. ConclusionsAcknowledgmentsReferences