cell size, photosynthesis and the package effect: an ... · malerba etal. (2018). brieﬂy,...

Cell size, photosynthesis and the package effect: an artificialselection approach

Martino E. Malerba1 , Maria M. Palacios2, Yussi M. Palacios Delgado3, John Beardall3 and Dustin J. Marshall1

1Centre of Geometric Biology, School of Biological Sciences, Monash University, Melbourne, Vic. 3800, Australia; 2Department of Marine Biology and Aquaculture, ARC Centre of Excellence

for Coral Reef Studies, James Cook University, Townsville, Qld 4811, Australia; 3School of Biological Sciences, Monash University, Melbourne, Vic. 3800, Australia

Author for correspondence:Martino E. Malerba

Tel: +61 03 9905 1576Email: [email protected]

Received: 16 January 2018

Accepted: 8 March 2018

New Phytologist (2018) 219: 449–461doi: 10.1111/nph.15163

Key words: allometric size-scaling, artificialselection, evolutionary size-shift,experimental evolution, geometric biology,metabolism, oxygen evolution, primaryproduction.

Summary

� Cell size correlates with most traits among phytoplankton species. Theory predicts that

larger cells should show poorer photosynthetic performance, perhaps due to reduced intracel-

lular self-shading (i.e. package effect). Yet current theory relies heavily on interspecific corre-

lational approaches and causal relationships between size and photosynthetic machinery have

remained untested.� As a more direct test, we applied 250 generations of artificial selection (c. 20 months) to

evolve the green microalga Dunaliella teriolecta (Chlorophyta) toward different mean cell

sizes, while monitoring all major photosynthetic parameters.� Evolving larger sizes (> 1500% difference in volume) resulted in reduced oxygen production

per chlorophyll molecule – as predicted by the package effect. However, large-evolved cells

showed substantially higher rates of oxygen production – a finding unanticipated by current

theory. In addition, volume-specific photosynthetic pigments increased with size (Chla+b),

while photo-protectant pigments decreased (b-carotene). Finally, larger cells displayed higher

growth performances and Fv/Fm, steeper slopes of rapid light curves (a) and smaller light-

harvesting antennae (rPSII) with higher connectivity (q).� Overall, evolving a common ancestor into different sizes showed that the photosynthetic

characteristics of a species coevolves with cell volume. Moreover, our experiment revealed a

trade-off between chlorophyll-specific (decreasing with size) and volume-specific (increasing

with size) oxygen production in a cell.

Introduction

Living organisms have evolved a seemingly endless range ofgeometries. Yet, at a fundamental level, all sizes and shapes con-form to certain physical laws and principles to optimise the fluxof resources and energy (Beardall et al., 2009; Niklas & Cobb,2017). Differing by as much as nine orders of magnitude, the cellsize of single-celled phytoplankton species strongly covaries withvirtually all aspects of their physiology and ecology, especiallyphotosynthetic performance (Litchman & Klausmeier, 2008;Maranon, 2015; Ward et al., 2017). For instance, smaller cellsusually have a higher mass-specific photosynthetic rate than largercells – with a cell-specific size-scaling between ¾ and 1 (Finkelet al., 2004; Lopez-Urrutia et al., 2006; Maranon, 2008; Malerbaet al., 2017).

Cell size also correlates with most light harvesting characteris-tics among phytoplankton species. For example, smaller cells usu-ally have higher chlorophyll density per unit mass (Agusti, 1991;Finkel et al., 2004; Wu et al., 2014), higher light absorption rela-tive to their volume (Agusti, 1991), higher absorption per unitchlorophyll (Finkel et al., 2004), higher carbon-dense biomass(Maranon et al., 2013), lower carbon-to-nitrogen ratio (Maranon

et al., 2013) and are more efficient at absorbing photons (Wil-helm et al., 2014). Phytoplankton alone are responsible for mostof the photosynthesis in the oceans and half of that on the planet(Field et al., 1998; Maranon, 2015). Unsurprisingly, understand-ing the basic rules which regulate the physiology and ecology ofphytoplankton species has been a major goal in biology for manydecades (Banse, 1976; Berner et al., 1989; Agusti, 1991), butsuch efforts have taken on a new urgency as it appears that cli-mate change could alter the size structure of phytoplankton astemperatures increase (Daufresne et al., 2009; Gardner et al.,2011; Forster et al., 2012).

The optical principle explaining why so many photosyntheticparameters correlate with cell size is the ‘package effect’. Specifi-cally, all other things being equal, the efficiency of pigmentmolecules to harvest photons decreases as cell volume increases(Kirk, 1975, 1994; Raven, 1984; Raven & Beardall, 2016). Pro-posed over 30 yr ago, the package effect is today recognized as aconvincing explanation for why smaller species should be supe-rior competitors for light than larger species (Morel & Bricaud,1981; Geider et al., 1986; Geider & Osborne, 1987; Berneret al., 1989; Finkel & Irwin, 2000). Mechanistic models showthat a decline in chlorophyll-specific absorption is due to

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increasing self-shading among pigments, as photons penetratethroughout a cell (Finkel & Irwin, 2000; Key et al., 2010). More-over, several size-related trends of the photosynthetic characteris-tics of a species can be seen as evolutionary adaptations tominimize the package effect (Geider et al., 1998; Finkel & Irwin,2000; Ross & Geider, 2009). First, larger species usually developlower pigment densities than smaller species (Geider et al., 1986;Agusti, 1991; Finkel & Irwin, 2000). Second, larger species havefewer chlorophyll pigments associated with each reaction centrein the thylakoid membrane (i.e. a smaller light-harvestingantenna; Mauzerall, 1986; Suggett et al., 2009; Key et al., 2010;Kirst et al., 2014; Suggett et al., 2015). Third, for a given concen-tration of ultraviolet radiation, larger cells are less susceptible toUVB damage due to the longer path length and increased poten-tial for photon absorption, which also reduces the need for pro-tective ‘sunscreen’ pigments such as b-carotene (Garcia-Pichel,1994; Key et al., 2010). All these common adaptations of largerphytoplankton species are thought to optimize photosyntheticperformance by reducing light gradients and enabling deeperlight penetration in the cell (Raven, 1984, 1998).

Hypotheses such as the package effect rely heavily on inter-specific comparisons of cells of different sizes. While they areinformative, such comparisons necessarily confound other traitsthat covary with cell size: growth rate, pigment content/diversity/ratio, extrinsic or intrinsic proteins of photosystem II, activitiesof various enzymes, chemical composition, genome size and otherlife-history traits. As a result, species of similar size can differ bymore than one order of magnitude in their photosynthetic per-formance (Moloney & Field, 1989; Finkel et al., 2004, 2010).Worse, trait covariance with size makes it difficult to determinewhich trait is driving the underlying differences in photosyntheticperformance among species (Niklas, 1997; Litchman & Klaus-meier, 2008; Maranon, 2015). Are species of a certain sizebecause of specific features of their photosynthetic apparatus, oris the light-harvesting apparatus able to adjust to the size of thecell? Hence, while it is an important first step, interspecific stud-ies provide limited insights into the contribution of cell size tophotosynthetic performance. An alternative approach to morereliably establish cause-and-effect relationships would be tochange only the size of a species and to examine if other charac-teristics coevolve as predicted by current theory.

Perhaps the most direct way to examine the effect of size onthe photosynthetic performance of phytoplankton species is toengineer genetic differences in mean cell size via artificial selec-tion and evaluate its consequences. Artificial selection experi-ments have often been used to investigate the heritability andevolutionary potential of many macroscopic model species(Tobler & Nijhout, 2010; Voje et al., 2014; Bolstad et al., 2015),but have been utilised more rarely in photosynthetic micro-organisms. The aim of the present study was to assess how photo-synthetic characteristics change as cells are evolved toward differ-ent sizes (measured as the mean cell volume within a culture),and if these changes are consistent with theoretical expectationsof optimizing light capturing and minimizing the package effectof larger cells. To this end, we extended an artificial selection pro-gramme (Malerba et al., 2018) to 250 generations of artificial

selection (c. 20 months) to generate differences of over 1500% inthe cell volume of the green microalga Dunaliella tertiolecta(Chlorophyta). While Malerba et al. (2018) documented differ-ences in energy use among our cell lineages, this study looks atthe physiological underpinnings of size-dependent photosyn-thetic rates, by unpacking the mechanistic drivers leading to dif-ferent energy utilizations. As cells evolved to different sizes, werepeatedly assessed all major photosynthetic parameters: pigmentcontent (Chla and b, b-carotene), photosynthetic oxygen produc-tion, maximum quantum yield, the initial slope of the rapid lightcurve, antenna size and the connectivity of the alpha centres ofphotosystem II (PSII a). We also measured population growthrates. We found that small-evolved cells showed greater rates ofchlorophyll-specific oxygen production – as predicted by thepackage effect. However, large-evolved cells upregulated theirvolume-specific Chla and b concentrations and improved theirphotosynthetic production, as well as their growth performance –a finding that was not anticipated by current theory.

Materials and Methods

Model species

As a model species, we chose the cosmopolitan, fast growingmarine green alga Dunaliella tertiolecta (Butcher). This specieshas intermediate cell volumes (relative to other phytoplanktonspecies) and grows well under laboratory conditions. Monoclonalbatch cultures were sourced from the Australian National AlgaeCulture Collection (ANACC; strain code CS-14) and reared instandard autoclaved f/2 algal medium (without silica), preparedwith 0.45 lm filtered seawater (Guillard, 1975). All experimentalcultures were grown at 21� 2°C with a 14 h : 10 h, day : nightcycle, at a light intensity of 150 lmol quanta m�2 s�1. Bacterialload in the cultures was kept to minimal levels by resuspendingcells in autoclaved media twice a week and by handling allsamples with sterile material in a laminar-flow cabinet (CF23S,NATA certified; Gelman Sciences, Melbourne, Vic., Australia).

Cell size evolution

For a detailed description of the artificial selection protocols seeMalerba et al. (2018). Briefly, artificial selection on cell size wasachieved through differential centrifugation. The method relieson cells larger in volume sinking and forming a pellet at the bot-tom of a test tube at lower centrifugal forces compared to smallercells, which instead will remain in the supernatant. A total of 72lineages were created and kept independent throughout the evo-lutionary experiment: 30 lineages were selected for smaller cellvolumes, 30 for larger cell volumes, and 12 as control. Controlcultures experienced identical conditions (including centrifuga-tion) but no size was selected for. Starting on 25 April 2016,selection routines were repeated twice a week, every Monday andThursday. Day–night cycles are known to synchronize the circa-dian rhythm of phytoplankton populations, with cell divisionoccurring mostly at night. To control for the influence of photo-cycle on selection outcome, we carried out artificial selection in

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the morning on newly divided cells. Pilot studies showed that, onaverage, cells divide three times a week, which means betweenone and two times before each round of selection. In this way,the selection frequency also allowed a substantial opportunity fornatural selection on traits providing a fitness benefit at the newlyevolved cell volumes. At the end of each selection round, all cul-tures were reinoculated into fresh f/2 medium at comparable bio-volume densities. Every Thursday, cultures were moved to newlysterilized 75 cm3 plastic cell culture flasks (Canted Neck, Nonpy-rogenic; Corning Inc., Corning, NY, USA).

Experimental design

Photosynthetic traits were measured after 50, 100 and 200 gener-ations of size-selection (4, 8 and 16 months, respectively).Growth performances were measured after 150 generations(12 months). For each experiment, 12 randomly selected lineageswere sampled and analysed for each size-selection treatment. Toremove any environmental conditioning from the selection pro-tocols, all experimental cultures were grown for three generations(a week) under neutral selection (i.e. with no centrifugation)before collecting any data. Cells were maintained under nutrient-replete conditions during neutral selection, by adding newmedium 3 d before and on the same day of the assays. All assayswere carried out after standardizing all lineages to the sameblank-corrected optical density, as we had previously shown thatthis represents a reasonable proxy for biovolume (Malerba et al.,2018).

Cell size

The mean cell volume of each lineage was calculated after mea-suring at least 200 cells with optical light microscopy at 9400,after staining with Lugol’s iodine at 2%. Cell area was measuredwith IMAGEJ and FIJI (Schindelin et al., 2012) and cell biovolumewas calculated assuming a prolate spheroid shape, as recom-mended for this species by Sun & Liu (2003).

Flow-cytometric analysis

The total number of cells per ll in each culture was measuredby flow cytometer (FlowCore, BD LSRII; BD Biosciences,Franklin Lakes, NJ, USA) using CountBright absolute countingbeads (Thermo Fisher, Waltham MA, USA) as internal stan-dards in each sample. Mean flowcytometric optical properties ofsingle cells were also recorded (using a blue laser at 488 nm), asforward scatter, side scatter and red fluorescence (using a 650–670 nm detection filter). Optical variables were represented bythe means of the cytometric histograms of the cell populationsnormalized by the mean values of the CountBright beads, asdescribed in Malerba et al. (2016, 2018). Flow-cytometric tech-niques were also used to quantify DNA content and ploidylevel, by staining live cells with Hoechst fluorescence dye andchecking the distribution of the fluorescence histogram (laserexcitation at 405 nm and fluorescence measurement at 460 nm;Jayat & Ratinaud, 1993).

Ash-free dry weight

Ash-free dry weights were measured using standard protocols(Zhu & Lee, 1997; Malerba et al., 2012). A 15 ml aliquotof each culture was gently filtered through filter papers(Whatman GF/C, diameter 47 mm, precombusted overnightat 550°C) and dried for 2 d in an oven at 100°C (UNB400; Memmert, Schwabach, Germany). Dry weights weremeasured to the closest 0.1 mg after cooling for 30 min ina desiccation chamber with silica beads. The samples werethen placed in a muffle furnace (Lindberg/Blue M828;Thermo Fisher) at 550°C for 24 h and weighted a secondtime. The ash-free dry weight of each sample was calculatedas the difference between weight after drying oven andweight after muffle furnace. All values were correcting forsalt content by subtracting the mean ash-free dry weightfrom seven samples with the same volume of medium with-out cells (salt blanks).

Pigment analysis

A 4ml aliquot of culture from each lineage was concentrated andresuspended in 1 ml of ethanol (ACS reagent grade > 99.9%;Sigma–Aldrich) and stored overnight at 4°C. After ensuring therewas a colourless particulate matter and successful pigment extrac-tion, absorbance spectra were measured from 400 to 700 nm at1 nm steps with a SPECTROstar® Nano microplate reader(BMG Labtech, Aylesbury, UK), after loading three 250 ll sam-ples per lineage into 96-well plates (Corning® polystyrene, flatbottom, nontreated; Sigma-Aldrich). The concentrations ofChla, Chlb and b-carotene were calculated using the spectraldeconvolution method described in Thrane et al. (2015). Briefly,the method uses Gaussian functions to reconstruct (usingnon-negative linear models) the total absorbance spectrum of asample, as a weighted sum of all its individual pigmentspectra. Finally, the chlorophyll-specific oxygen production(fmol O2 d

�1 mg�1) was calculated by dividing the per-cell dailynet oxygen production (fmol O2 d�1 per cell) by per-cell Chla+bcontent (mg per cell).

PSII fluorescence measurements

Chlorophyll fluorescence measurements were performed using aPhyto-PAM phytoplankton analyser (Heinz Walz, Effeltrich,Germany) with the software PHYTOWIN v.3 (Heinz Walz). Allsamples were resuspended in fresh medium and standardized atthe same optical density (at 750 nm). After 15 min of dark accli-matisation, minimum (F0) and saturating (Fm) fluorescence val-ues were measured. Maximum quantum yield of photosyntheticenergy conversion in PSII (Fv/Fm) was then calculated, as(Fm� F0)/Fm. We also used rapid light curves to quantify the ini-tial slope (a) of the increase in relative electron transport rate(rETR) as a function of light exposure, proportional to the effi-ciency of a cell to transform incident photons into photosyntheticenergy (Liang et al., 2006; Zhang et al., 2017). The a parameterwas estimated with PHYTOWIN by exposing each culture to 18


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light irradiances (0–600 lmol m�2 s�1) and fitting a Platt curvebetween rETR and the light intensity.

The effective absorption cross-sectional area (hereafter referredto as antenna size: rPSII) and the connectivity (q) of the a com-ponent of the alpha centres of photosystem II (PSII a) were esti-mated using Flash Fluorescence Induction (FFI) curves with adouble-modulation fluorometer (Photon Systems Instruments,Brno, Czech Republic). Cultures were dark-acclimatized for5 min before being exposed to a single saturating turnover flash.The fluorescence increase of the FFI curve was then analysed asdescribed by Nedbal et al. (1999) and Pierangelini et al. (2014).All fluorescence values were blank corrected.

Light and dark metabolism

The rate of oxygen evolution (VO2) was measured for all lineagesafter 200 generations of size-selection at 19°C with a 49 24-channel PreSens Sensor Dish Reader (SDR; AS-1 Scientific,Wellington, New Zealand). All methods were adapted fromMalerba et al. (2017, 2018). The metabolic rates of this species atearlier generations can be found in Malerba et al. (2018). Beforethe experiment, all SDR were calibrated with air-saturated (AS)sea-water (100% AS) and water containing 2% sodium sulfite(0% AS). The lineages were placed in 5 ml sealed vials, beingcareful to remove any bubbles from the inside of the vials.Sodium bicarbonate (2 mM) was added to the medium to ensurephotosynthesis was not limited by carbon availability. At leastthree vials were used as blanks and filled with filtered supernatantin each SDR. All vials were then placed horizontally under thelight source, to avoid cell deposition on the top of the oxygensensor at the base of the vial. Samples were randomly allocated tothe top row of each SDR, while blanks were placed in the bottomrow (pilot studies showed there was no row effect on blank mea-sures). VO2 measurements were taken in the dark under a satu-rating light regime (300 lmol quanta m�2 s�1), each replicatedfour times. Previous studies (Malerba et al., 2018) and morerecent pilot studies have shown that such light intensities wereenough to saturate cell photosynthetic rates for all size-selectedtreatments. Rates of light and dark metabolism for each samplewere calculated from linear rates of O2 change over time.Metabolism was then standardized to total cell biovolume (i.e.cell number9mean cell biovolume).

Population growth rates

Maximum growth rate and carrying capacity were quantified from10 randomly selected lineages per size-selection treatment after150 generations of artificial selection. More methodological detailscan be found in Malerba et al. (2018). Briefly, after three genera-tions of neutral selection, the optical densities (750 nm) of three250 ll independent cultures per lineage in separate 96-well plateswere monitored with SPECTROstar® Nano (BMG labtech) untilthey reached carrying capacity (c. 7 d). Optical density values wereconverted to units of total biovolume (using log-linear calibrationcurves) and fitted with logistic growth curve models to estimatemax. growth rate (r) and carrying capacity (K ) for each lineage.

Scanning election microscopy (SEM)

Cells were sampled after 250 generations of artificial selectionand fixed with 2.5% glutaraldehyde in 0.1M sodium cacodylatebuffer for 1 h at room temperature. Cells were washed three timesin cacodylate buffer (10 min each) and fixed in 1% osmiumtetroxide in buffer solution for 1 h at room temperature. Afterthree washes in distilled water (10 min each), 200 ll of fixed cellswere incubated on polyester coverslips (NuncTM ThermanoxTM

Coverslips) for 30 min. Cells were dehydrated through a gradedethanol series (30%, 50%, 70%, 90% and 100%, 10 min each),followed by two more washes in 100% ethanol (30 min each).Coverslips were then placed in mesh baskets in fresh ethanol.Cells were dried on coverslips using a Bal-Tec CPD 030 (Bal-Tec, Los Angeles, CA, USA). Coverslips were mounted on alu-minium SEM stubs using double-sided sticky carbon tabs. Sam-ples were gold coated using a Bal-Tec SCD 005. Finally, cellswere viewed in a Nova NanoSEM 450 scanning electron micro-scope.

Statistical analysis

All analyses were carried out in R (R Core Team, 2018). Logisticgrowth curve models were used to quantify population growthrates (NLS package in R). Ordinary least-squares linear modelswere used to assess the effects of cell volume (lm3) on each vari-able of interest: C-content (g per cell), fluorescent measurements(Fv/Fm, alpha, and antenna size), light and dark metabolism(fmol O2 per cell), pigment content (mg per cell), optical density,flow-cytometric cell optical properties (forward scatter, side scat-ter and red fluorescence) and population growth performance(maximum growth rate and carrying capacity). For the analysis oflog-log size-scaling relationships, traits were deemed to scale allo-metrically with cell volume if the slope coefficient did not include1 in its 95% confidence interval; otherwise the relationship wasconsidered not significantly different from isometry. While otherstudies have also used Reduced Major Axis (RMA) to calculatesize-scaling exponents, Kilmer & Rodriguez (2017) recom-mended against this technique and in favour of ordinary least-squares linear models when the uncertainty in the x-axis (i.e.mean cell volume) is lower than that on the y-axis. Because ourestimates of mean cell size were precise (CV < 5%), size-scalingrelationships were analysed with ordinary least-square linearmodels. Data transformations were employed whenever the resid-uals departed from normality. In addition, in cases where treat-ments displayed unequal variances, treatment-specific variancecoefficients were included in the model (VARIDENT function inR). A blocking variable describing whether the culture was mea-sured at generation 50, 100 or 200 was added in the model tocontrol for differences due to instrument calibrations amongrounds of data collection across the experimental period (> 1 yr).Finally, evolutionary rates of cell volume were quantified usingHaldane units, as d /(SD9 t) where d is the difference of loge-transformed cell volumes between two means, SD is the pooledstandard deviation of all loge-transformed cell volumes and t isthe generation interval (see Supporting Information Fig. S1B).



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One Haldane unit is defined as a change by a factor of one stan-dard deviation per generation (Haldane, 1949; Gingerich, 1993).

Data accessibility

All data and codes generated in this study are available in Dryad(doi: 10.5061/dryad.642jt90). Also contained in the Dyrad fileare the photos from optical microscopy and scanning electronmicroscopy of the size-evolved cells.

Results

Cell size evolution

Size-selection on a common ancestor of D. tertiolecta led toincreasing differences in the mean cell volume between small-and large-selected lineages (Figs 1, 2). After 50, 100 and 200generations, the mean cell volume among large-selected cultureswas on average 81%, 142% and 248% larger than that ofsmall-selected cultures, respectively (Fig. 1). The range of sizedifferences among lineages increased throughout generations:130% after 50 generations, 230% after 100 generations and500% after 200 generations (Fig. 1). At generation 250, the twolineages with the smallest and largest mean cell volumesspanned by as much as 1567% (from 854 to 14 241 lm3;Fig. 1). Control lineages recorded intermediate sizes, butremained more similar in volume to small-selected than tolarge-selected lineages (Fig. 1). Cell size trajectories showed sig-nificant trends over time for both small- (generalized additivemodels; Fedf: 2.11 = 7.61, P < 0.001) and large-selected lineages(F7.07 = 20.42, P < 0.001), but not for control lineages(F6.48 = 1.91, P = 0.06). Moreover, there was a positive linearcorrelation between the mean and variance of loge-transformedcell volumes among lineages (t52 = 10.57, P < 0.001, r = 0.83),with lineages of larger cells showing more size variability thansmall-selected and control cultures. Finally, all conclusionsremained consistent when analysing median cell volumes insteadof mean cell volumes among lineages.

Evolutionary rates of cell volume over time were highest forlarge-selected lineages (increasing by 0.02 standard deviations pergeneration) than the control (0.003 SD per generation) and small(�0.003 SD per generation lineages (F2,15 = 196.18, P < 0.001;Fig. S1B). The range of cell volumes in the ancestral population(at generation 0) showed very little overlap with the range of size-selected lineages after 250 generations of selection (Fig. S1A). Inaddition, there was no difference in mean (F1,10 = 2.41, P = 0.14)and median (F1,10 = 2.04, P = 0.18) DNA content among size-selected treatments, with the majority of cells in haploid stage(data not shown).

Consistently with observed increases in cell volume, all opticalproperties measured from single cells using flow cytometry (i.e.forward scatter, side scatter and red fluorescence) systematicallyincreased with cell size (Fig. S2). Finally, there was a positive rela-tionship between the mean cell volume of a lineage and the per-cell optical density at 750 nm recorded with spectrophotometry(Fig. S3).

Carbon mass

The carbon (C) mass of individual cells spanned from 1.5 to10 ng C per cell (Fig. 3). Interestingly, the 95% confidence inter-val for the size-scaling exponent largely overlapped 1, indicating aproportional (isometric) relationship between cell volume andcell C mass (Fig. 3). Below we use cell volume as proxy for cellsize, as it can be measured more accurately and precisely than Cmass. However, the isometric scaling between cell volume andcell C mass indicates that the same qualitative results can beobtained from standardizing for cell C mass.

Pigment composition

Per-cell chlorophyll content varied by up to two orders of magni-tude among size-selected cells, from 7 to 295 pg per cell for Chlaand from 0.6 to 100 pg per cell for Chlb (Fig. 4a,b). By contrast,b-carotene increased by almost fourfold, from 8 to 32 pg per cell(Fig. 4c). Size-scaling for Chla and b were both hyper-allometric(size-scaling exponent significantly > 1), while b-caroteneincreased hypo-allometrically (size-scaling exponent significantly< 1; Fig. 4a–c); as cells increased in size, volume-specific chloro-phyll content increased while b-carotene content decreased. Onaverage, the volume-specific pigment concentration of cells in thesmall-selected treatment recorded 16% and 23% less Chla and b,respectively, but also a 36% increase in b-carotene, compared tocells in the large-selected treatment (Fig. 4d–f). Pigment compo-sition was statistically indistinguishable between cells in small-selected and control treatments (Fig. 4d–f).

Photosynthetic characteristics

Both the maximum PSII quantum yield (Fv/Fm) and the initialslope (a) of the rapid light curve increased with size, with large-selected lineages recording up to 22% and 36% higher Fv/Fm anda values than small-selected lineages, respectively (Fig. 5a,b;Table 1). By contrast, the size of the photosynthetic antenna(rPSII) decreased with cell size, with large-selected lineagesshowing a 16% decrease in mean antenna size compared tosmall-selected lineages (Table 1). Finally, there was an inverserelationship between the size (rPSII) and the connectivity (q) ofthe antennae in PSII a: smaller antennae of larger cells showedhigher q than the larger antennae of smaller cells (Fig. S4).

Light and dark metabolism

The photosynthetic oxygen production of a cell scaled hyper-allometrically with its cell volume at 1.23 (95% CI of size-scalingexponent > 1; Fig. 6a). Specifically, for a 525% increase in cellvolume corresponded to a disproportionally higher (889%)increase in oxygen production (Table 1). The mean size-scalingrelationship of dark respiration was hyper-allometric at 1.2: themetabolism increases by 754% for a 525% increase in cell size(Table 1). While the size-scaling exponent for dark respirationdoes not exclude an isometric scaling with 95% statistical confi-dence (CI includes 1; Fig. 6b; Table S1), there are several lines of





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evidence showing that an increase in sample size and further evo-lution may confirm the hyper-allometric pattern (as for otherrates of oxygen evolution): (1) the lower estimate for the 95%confidence interval for the parameter best-estimate of thesize-scaling of dark respiration is very close to 1 (0.97), (2) theparameter best-estimate is substantially higher than 1 (1.19) and(3) isometric scaling is excluded with 93% confidence (Fig. 6b;Table S1).

Using a 14 h : 10 h, day : night photoperiod, we can quantifythe daily photosynthetic production of a cell as the differencebetween oxygen produced during the day-phase minus oxygenconsumed during the night-phase. The daily net oxygen produc-tion scaled hyper-allometrically at 1.25 (95% CI of size-scalingexponent > 1; Fig. 6c; Table S1): large-selected cells are 525%larger than small-selected cells but produced 889% more oxygen(Table 1).

All rates of oxygen production scaled hypo-allometrically (be-tween 0.73 and 0.76) with the concentration of light-harvestingpigments (i.e. Chla and b) of a cell (Fig. 6d–f; Table S1). Hence,the efficiency of a pigment to produce oxygen decreased – by asmuch as 41% – as the concentration of pigments increasedwithin a cell (Table 1). In the same way, standardizing the netoxygen production of a cell by its pigment concentration showeda negative relationship with the size of the cell (Fig. S5).

Population growth rate

After 150 generations of artificial selection, large-selected lineagesshowed faster maximum growth rates (F2,27 = 37.05, P < 0.001)and higher carrying capacities (F2,27 = 1739, P < 0.001)

compared to both small-selected and control lineages, respec-tively (Fig. S6). Population growth rates measured in previousgenerations are reported in Malerba et al. (2018).

Discussion

In this study we showed that all traits measured for the greenmicroalga D. tertiolecta coevolved in response to selection on cellsize. In Malerba et al. (2018), we reported the first 100 genera-tions for this model system – here we have extended this programfor 150 more generations. While cell carbon mass increased pro-portionally to the cell volume (isometric size-scaling), most otherproperties did not (allometric size-scaling). For instance, volume-specific concentrations of Chla and b increased as cells evolved tolarger sizes (hyper-allometric size-scaling), while volume-specificb-carotene decreased (hypo-allometric size-scaling). Overall, wefound that smaller cells are 41% more efficient at producing oxy-gen per unit chlorophyll – as predicted by the package effect –but larger cells have 60–100% more chlorophyll per unit volumeand their per-cell oxygen production increased by 889% com-pared to smaller cells. Consistently, larger cells showed a 22%increase in maximum quantum yield (Fv/Fm) and a 36% increasein the initial slope of the rapid light curve (a). Furthermore,larger cells displayed smaller effective absorbance cross-section(rPSII), but higher connectivity (q)of PSII a. Finally, large-selected cultures showed significantly faster maximum growthrates and higher carrying capacities compared to small-selectedand control populations, which is consistent with the results fromearlier generations reported in Malerba et al. (2018). Hence, byevolving a common ancestor to different sizes, we showed that

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Fig. 1 Effect of artificial selection on mean cell volume of Dunaliella tertiolecta among size-selection treatments. Each point represents the mean cellvolume of an independent lineage after measuring c. 200 cells per lineage. Lines indicate the mean for each treatment over time. Photographs on the rightshow a specimen for each treatment after 220 generations, taken with light microscopy. Both panoramic (lugol-stained at9 400) and close-up (unstainedat 9 600) photos are in scale and display mean cell volumes of 841, 3021 and 7313 lm3 for small, control and large, respectively.



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altering the size of a cell profoundly alters many fundamentaltraits of algal physiology and ecology.

Coevolution of photosynthetic characteristics with size

We only exerted artificial selection on the cell volume, but foundthat many other traits systematically also coevolved with thisproperty. As such, our findings provide strong evidence that bodysize is an important evolutionary driver for many photosyntheticcharacteristics of a species. Even after 200 generations of selection(c. 16 months), cells of similar size showed similar photosyntheticcharacteristics – further evidence that it is size (not generation,nor time) driving many of the physiological characteristics in ourstudy system. In particular, our results showed that large cells canstill produce more photosynthetic energy than small cells, eventhough their chlorophyll-specific oxygen production decreaseswith size. These results suggest that adaptive evolution can morethan compensate for the inevitable self-shading associated withan increase in cell size. Perhaps the high-light regime we used inthe laboratory was the reason for these results: by reducing thecosts of intracellular light penetration at increasing cell sizes,larger cells may need to dissipate less excess excitation energyas heat through nonphotochemical quenching mechanisms,

compared to smaller cells (Dubinsky & Stambler, 2009). Simi-larly, growing our cells in a nutrient-rich medium may havefavoured the fitness of cells with more intracellular space andhigher capacity for biosynthesis (Raven, 1998; Maranon, 2015).

Costs and benefits of cell size

Evolving our study species toward different cell sizes revealed atrade-off between more efficient (small-selected) and more pro-ductive (large-selected) cells, with respect to their photosyntheticperformance. Specifically, by becoming smaller, cells developeda greater efficiency at producing oxygen with each chlorophyllmolecule (+41%); by becoming larger, cells massively increasedtheir volume-specific chlorophyll a (+847%) and b (+1075%)concentrations and upregulated their total daily photosyntheticproduction, both per-volume (+69%) and per-cell (+889%).However, this superior photosynthetic performance of largercells also comes at a cost. For instance, larger cells also substan-tially increased their oxygen demand in the dark (+791%). Fol-lowing this, we would predict that low light regimes woulddisproportionally affect the fitness of larger cells compared tosmaller cells (as was already shown for this species in Malerbaet al. (2018)). In addition, the higher photosynthetic perfor-mances of larger cells coincided with a substantial increase inchlorophyll synthesis and with an improvement in growth per-formance. Given that chlorophyll molecules are highly rich innutrients such as nitrogen and iron (Turpin, 1991), we wouldalso predict that nutrient regimes impairing the synthesis of pig-ment would reduce net oxygen production more in larger thansmaller cells. Finally, b-carotene is an important nonenzymaticantioxidant (as well as a light harvesting component) and itplays a major role in protecting the photosynthetic apparatusagainst photo-oxidative damage by deactivating reactive oxygenspecies (ROS) formation, in Dunaliella sp. (Fu et al., 2013;Kim et al., 2013) and most other photosynthetic organisms(Frank & Cogdell, 1996). As such, the faster rates of electronturnover could make larger cells more vulnerable to ROS build-up. Overall, our results indicate that the costs and benefits ofcell size are case-specific and must consider the interplaybetween physiology, ecology and environment.

Cell size, package effect and pigment concentrations

We used the approach described in Raven (1984) to quantify thepredicted package effect solely based on cell size and pigmentcontent. As cells evolved from 800 to 5000 lm3 in volume, wewould expect that pigments in larger cells would absorb up to200% fewer photons compared to smaller cells (Fig. S7 andMethod S1). Instead, our observations showed only a 41%decrease (Table 1). Thus, larger cells appeared to have evolvedcompensatory mechanisms to maximize photon harvesting whileminimizing the negative consequences of the package effect. Forinstance, large-selected cells showed adaptations in their light-harvesting machinery that were consistent with both algae andplants adapting to low-light conditions: increased photosyntheticpigments (mainly Chla and b), decreased photo-protectant

5 µm

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LargeFig. 2 Scanning electron microscopy (SEM) of individual cells of Dunaliellatertiolecta from each of the three size-selection treatments after 250generations of size selection.





pigments (e.g. b-carotene), increased Chla to b-carotene ratios,and a reduction in effective absorption cross-section area in PSIIa (Givnish, 1988; Mussgnug et al., 2007; Dubinsky & Stambler,2009; Kirst et al., 2014). Moreover, it is likely that larger cellsreduced the impact of the package effect by altering not just thesize but also the number of reaction centres in PSII a – anothercommon light adaptation strategy (Falkowski & Owens, 1980;Richardson et al., 1983).

Size and nutrient storage

Phytoplankton species with larger cell sizes are placed at anadvantage because of their ability to continue growth even afternutrient concentrations are limited, by relying on internal nutri-ent storage (or cell ‘quota’). Although we did not measure nutri-ent quota, there is some evidence showing that the intensity ofthe cell flow-cytometric red fluorescence is proportional to thecell nitrogen quota (Malerba et al., 2016). Here we found thatthe intensity of red fluorescence emitted from larger cells is upto an order of magnitude higher than in smaller cells(Fig. S2C). This suggests an increased ability to store limitingnutrients, which could lead to important advantages in evolvinglarger cell sizes, especially under fluctuating nutrient regimes(Grover, 1991; Holt, 2008; Barton et al., 2013). In contrast, we

would also anticipate parallel costs from increasing cell size, inparticular increased sinking rates, reduced swimming perfor-mances and higher nutrient requirements (Maranon, 2015;Grover, 2017; Malerba et al., 2018). Testing competitive inter-actions between size-evolved treatments could provide an insightinto the size-mediated mechanisms which maintain phytoplank-ton diversity.

Artificial selection vs macro-evolutionary patterns

While our results mostly confirmed the theory of the packageeffect, some of findings on the role of size from our artificialselection program were not consistent with among-species allo-metric studies. In particular, we found here that large-selectedcells produced more photosynthetic energy per unit volume com-pared to small-selected cells (i.e. size-scaling exponent of > 1).However, many previous among-species studies showed that size-specific primary production and respiration decreased with size(i.e. size-scaling exponent < 1; Brown et al., 2004; Finkel et al.,2004; Lopez-Urrutia et al., 2006). Furthermore, Malerba et al.(2017) analysed energy use among 21 phytoplankton speciesusing the same laboratory conditions and the same instrumentsas those here and found photosynthetic energy flux to scale at < 1with cell size. These contrasting findings between within- and

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Small

Fig. 3 Size-scaling relationship between cell volume and cell carbon mass of Dunaliella tertiolecta. Each symbol represents the mean value of anindependent phytoplankton lineage, whose colour indicates the artificial size-selection treatment, and shape indicates the generation at the time of thesampling. Solid line (� 95% confidence intervals) shows the fit of the allometric model (F3,89 = 42.52, P < 0.001), with mean estimate (� 95% confidenceintervals) and coefficient of determination also reported. Because the 95% confidence interval of the size-scaling exponent includes 1, the relationship isisometric and changes in cell volumes are proportional to changes in cell carbon mass. Boxplots represent the distribution of all cell volumes (top) andcarbon mass (right) for each size-selection treatment (S, small; C, control; L, large). The dots in the boxplot are observations outside 1.5-fold theinterquartile range, either above the upper quartile or below the lower quartile.



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Fig. 4 Size-scaling relationships for per-cell(a) chlorophyll (Chl)a, (b) Chlb, and (c) b-carotene as a function of mean cell volume,and per-volume (d) Chla, (e) Chlb, and (f) b-carotene as a function of the size-selectiontreatment of Dunaliella tertiolecta. (a–c)Solid lines (� 95% confidence intervals)indicate the fit of the allometric model (a,F3,89 = 242, P < 0.001; b, F3,89 = 241,P < 0.001; c, F3,89 = 192, P < 0.001), whosemean estimates (� 95% confidenceintervals) and coefficients of determinationare also reported. (d–f) Black circles indicatethe mean volume-standardized pigmentcontent (� 95% confidence intervals) amongall independent size-selected lineages(coloured symbols). All model results weresignificant (d, F2,90 = 4.63, P = 0.012; e,F2,90 = 6.63, P = 0.002; f, F2,90 = 22.31,P < 0.001). Letters indicate groupingsfollowing Tukey’s post hoc test.

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Fig. 5 Relationships between cell volume andphotosynthetic characteristics of Dunaliellatertiolecta: (a) maximum quantum yield (Fv/Fm, rel. units); (b) slope of relative electrontransport rate (rETR) as a function of lightexposure (a, rel. units); (c) effectiveabsorption cross-sectional area of PSII a(rPSII;�A); (d) antenna connectivity parameterof PSII a (q; rel. units). Solid lines (� 95%confidence intervals) indicate the fit of thelinear model (a, F3,87 = 11.21, P < 0.001; b,F3,79 = 28.2, P < 0.001; c, F3,88 = 249.6,P < 0.001; d, F3,89 = 69.34, P < 0.001).





among-species suggest: (1) among-species comparisons may notreflect mechanistic relationships between traits because too manytraits can vary simultaneously, and (2) that our experimental

approach may have relaxed the selection against very large cells,by providing a high light environment and by not requiring cellsto swim effectively to remain in the photic zone. Regardless, it

Table 1 Summary of photosynthetic characteristics estimated using linear models presented in the Results section and Supporting Information Figs S2, S3and S5 for a single small (800 lm3) and large (5000 lm3) cell of Dunaliella tertiolecta

Biological variables (units) Small cell Large cellPer-cell change fromsmall to large (%)

Per-vol. change fromsmall to large (%)

Cell volume (lm3) 800 5000 525 0Chlorophyll a (mg per cell) 2.629 10�8 2.489 10�7 847 61Chlorophyll b (mg per cell) 7.759 10�9 9.109 10�8 1075 105b-Carotene (mg per cell) 8.339 10�9 2.799 10�8 235 �55

Max. PSII quantum yield (Fv/Fm) 0.582 0.710 22Slope of rapid light curve (a) 0.213 0.290 36 �93

Antenna size (rPSII) 185 155 �16 �103

Antenna connectivity (q) 0.579 0.822 42O2 produced in light (fmol O2min�1 per cell) 158 1493 847 61O2 consumed in dark (fmol O2min�1 per cell) 27 242 791 51Daily net O2 produced (fmol O2 d

�1 per cell) 1.139 105 1.119 106 889 69Daily net O2 produced per Chla+b (fmol O2 d

�1 mg�1) 4.419 1012 2.599 1012 �41 �108Cell optical density 0.005 0.016 232 �56

For each category, the relative change was calculated as the difference as cells evolved from small to large cells for each biological variable, estimated perunit cell and per unit volume. Positive (bold) values indicate positive relationships between each (per-cell or per-vol.) variable and cell size, while negative(italic) numbers indicate negative relationships. Because Fv/Fm and q are parameters independent of cell size, we only calculated per-cell changes. Units arereported unless the variable is in relative units. See Table S1 for model parameters, confidence intervals, coefficient of determinations, and figure numberfor each size-scaling relationship.

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Size-scaling: 1.23 (1.00–1.45)r 2 = 0.81

Size-scaling: 1.19 (0.97–1.42)r 2 = 0.76

Size-scaling: 1.25 (1.01–1.49)r 2 = 0.79

Size-scaling: 0.75 (0.62–0.88)r 2 = 0.82

Size-scaling: 0.73 (0.60–0.86)r 2 = 0.81

Size-scaling: 0.75 (0.61–0.9)r 2 = 0.80

(d)

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r ce

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Dai

ly n

et O

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oduc

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1

Fig. 6 Size-scaling relationships of cellphotosynthetic rates of Dunaliella tertiolectaas a function of cell volume (a–c) or cell totalchlorophyll (Chl) a and b content (d–f) for (a,d) net O2 produced at saturating light (i.e.300 lmol m�2 s�1), (b, e) net O2 consumedin the dark, and (c, f) net O2 producedthroughout the day, as the differencebetween O2 produced during the day and O2

consumed at night (assuming 14 h : 10 h,day : night cycle). Rates of net O2 consumed(i.e. dark respiration) are here transformedinto positive rates to allow logarithmictransformation, as log10 (–per-cell O2 rates).Solid lines (� 95% confidence intervals)indicate the fit of each allometric model (a,F1,30 = 124.1, P < 0.001; b, F1,30 = 92.6,P < 0.001; c, F1,30 = 104.5, P < 0.001; d,F1,30 = 141.3, P < 0.001; e, F1,30 = 128.2,P < 0.001; f, F1,30 = 108.8, P < 0.001), whosesize-scaling exponent (� 95% confidenceintervals) and coefficient of determinationare also reported. Each symbol represents themean value calculated from four replicatemeasurements of an independent lineageafter 200 generations of size-selection.



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seems that inferences about interspecific size relationships do notreflect intraspecific relationships.

Caveats

Other than adaptive evolution, it is possible that pleiotropy and/or linkage disequilibrium could also have contributed to thecoevolution of photosynthetic characteristics with body size(Lande, 1980; Hansen, 2006). If this was the case, our resultsmay be explained by developmental or functional constraintsrather than solely by natural selection on fitness-enhancing traitsand may not be generalizable to other species. Unfortunately,there is no information on the genetic correlation (G matrix)between size and photosynthesis in the ancestral algal population,as the nature of the system (this algal species appears to reproduceasexually) makes such estimates difficult to obtain. Furthermore,the G matrix evolves over time and any estimate of genetic corre-lations in the ancestral, unselected population may not be infor-mative 200 generations later (Steppan et al., 2002). We cannotrule out pleiotropy and/or linkage disequilibrium, but adaptiveevolution seems to be the most important driver of trait co-evolution, especially given an intervening round of natural selec-tion between each round of artificial selection. In this regard, wemust let the reader decide for themselves what the likely driversof coevolution were. Extending similar artificial selection pro-grammes to other species would certainly help with confirmingthe causal effects of size on species traits, but, for now, our studyremains the only one.

Conclusions

Global temperature increases are modifying ocean conditionsand reducing body sizes among phytoplankton species, withlikely cascading consequences on marine food web dynamics(Daufresne et al., 2009; Gardner et al., 2011; Forster et al.,2012). Our experiment showed that evolving larger cells ofD. tertiolecta improved their growth performance and the oxygenproduction of the species, compared to small-evolved cells. Ifthese same mechanisms were also found to apply to other species,we would expect a general reduction in the ability of phytoplank-ton biomass to fix carbon. However, it is very likely that thesesize-related differences in oxygen production were also dependenton experimental conditions, in particular nutrient and lightregimes. Smaller cells are more efficient at producing oxygen perunit chlorophyll, which would make them better adapted to low-resource conditions. Future research will better resolve size-related effects across treatments of nutrient conditions, lightregimes and temperatures but for now it seems likely that anyshifts in size will have profound consequences for global carboncycles.

Acknowledgements

We thank Lucy Chapman, Dr Liz Morris, Belinda Comerford,and Annie Guillaume for their help with laboratory procedures.We also want to thank the Monash Centre for Electron

Microscopy for their help with cell imaging, in particular DrGeorg Ramm, Dr Simon Crawford and Dr Joan Clark. Weacknowledge Dr Michael McDonald and Prof. Anastasios Melisfor insightful suggestions and well as two anonymous reviewersand Prof. Douglas Campbell. We would like to express our grati-tude to Ricardo San Martin, Stewart Crowley and John Arvani-takis for logistical support at Monash University. Finally, we areparticularly grateful to the Australian Research Council for finan-cial support (grant DP110103529 to D.J.M.).

Author contributions

M.E.M., D.J.M. and J.B. designed the study. M.E.M., M.M.P.and Y.M.P.D. conducted the experiment and collected the data.M.E.M. carried out statistical analyses and wrote the initial draftof the manuscript, while all other co-authors provided substantialfeedback. All authors gave final approval for publication.

ORCID

Martino E. Malerba X http://orcid.org/0000-0002-7480-4779

References

Agusti S. 1991. Allometric scaling of light-absorption and scattering by

phytoplankton cells. Canadian Journal of Fisheries and Aquatic Sciences 48:763–767.

Banse K. 1976. Rates of growth, respiration and photosynthesis of unicellular

algae as related to cell-size – review. Journal of Phycology 12: 135–140.Barton AD, Finkel ZV, Ward BA, Johns DG, Follows MJ. 2013.On the roles of

cell size and trophic strategy in north Atlantic diatom and dinoflagellate

communities. Limnology and Oceanography 58: 254–266.Beardall J, Allen D, Bragg J, Finkel ZV, Flynn KJ, Quigg A, Rees TAV,

Richardson A, Raven JA. 2009. Allometry and stoichiometry of unicellular,

colonial and multicellular phytoplankton. New Phytologist 181: 295–309.Berner T, Dubinsky Z, Wyman K, Falkowski PG. 1989. Photoadaptation and

the “package” effect in Dunaliella tertiolecta (Chlorophyceae). Journal ofPhycology 25: 70–78.

Bolstad GH, Cassara JA, Marquez E, Hansen TF, van der Linde K, Houle D,

Pelabon C. 2015. Complex constraints on allometry revealed by artificial

selection on the wing of Drosophila melanogaster. Proceedings of the NationalAcademy of Sciences, USA 112: 13284–13289.

Brown JH, Gillooly JF, Allen AP, Savage VM, West GB. 2004. Toward a

metabolic theory of ecology. Ecology 85: 1771–1781.Daufresne M, Lengfellner K, Sommer U. 2009. Global warming benefits the

small in aquatic ecosystems. Proceedings of the National Academy of Sciences,USA 106: 12788–12793.

Dubinsky Z, Stambler N. 2009. Photoacclimation processes in phytoplankton:

mechanisms, consequences, and applications. Aquatic Microbial Ecology 56:163–176.

Falkowski PG, Owens TG. 1980. Light–shade adaptation two strategies in

marine phytoplankton. Plant Physiology 66: 592–595.Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. 1998. Primary

production of the biosphere: integrating terrestrial and oceanic components.

Science 281: 237–240.Finkel ZV, Beardall J, Flynn KJ, Quigg A, Rees TAV, Raven JA. 2010.

Phytoplankton in a changing world: cell size and elemental stoichiometry.

Journal of Plankton Research 32: 119–137.Finkel ZV, Irwin AJ. 2000. Modeling size-dependent photosynthesis: light

absorption and the allometric rule. Journal of Theoretical Biology 204: 361–369.





http://orcid.org/0000-0002-7480-4779

http://orcid.org/0000-0002-7480-4779

http://orcid.org/0000-0002-7480-4779

Finkel ZV, Irwin AJ, Schofield O. 2004. Resource limitation alters the 3/4 size

scaling of metabolic rates in phytoplankton.Marine Ecology Progress Series 273:269–279.

Forster J, Hirst AG, Atkinson D. 2012.Warming-induced reductions in body

size are greater in aquatic than terrestrial species. Proceedings of the NationalAcademy of Sciences, USA 109: 19310–19314.

Frank HA, Cogdell RJ. 1996. Carotenoids in photosynthesis. Photochemistry andPhotobiology 63: 257–264.

Fu W, Guðmundsson �O, Paglia G, Herj�olfsson G, Andr�esson �OS, Palsson

BØ, Brynj�olfsson S. 2013. Enhancement of carotenoid biosynthesis in the

green microalga dunaliella salina with light-emitting diodes and adaptive

laboratory evolution. Applied Microbiology and Biotechnology 97: 2395–2403.

Garcia-Pichel F. 1994. A model for internal self-shading in planktonic organisms

and its implications for the usefulness of ultraviolet sunscreens. Limnology andOceanography 39: 1704–1717.

Gardner JL, Peters A, Kearney MR, Joseph L, Heinsohn R. 2011. Declining

body size: a third universal response to warming? Trends in Ecology & Evolution26: 285–291.

Geider R, Osborne B. 1987. Light absorption by a marine diatom: experimental

observations and theoretical calculations of the package effect in a small

Thalassiosira species.Marine Biology 96: 299–308.Geider RJ, MacIntyre HL, Kana TM. 1998. A dynamic regulatory model of

phytoplanktonic acclimation to light, nutrients, and temperature. Limnologyand Oceanography 43: 679–694.

Geider RJ, Platt T, Raven JA. 1986. Size dependence of growth and

photosynthesis in diatoms – a synthesis.Marine Ecology Progress Series 30: 93–104.

Gingerich PD. 1993.Quantification and comparison of evolutionary rates.

American Journal of Science 293a: 453–478.Givnish TJ. 1988. Adaptation to sun and shade: a whole-plant perspective.

Functional Plant Biology 15: 63–92.Grover JP. 1991. Resource competition in a variable environment:

phytoplankton growing according to the variable-internal-stores model.

American Naturalist 138: 811–835.Grover JP. 2017. Sink or swim? Vertical movement and nutrient storage in

phytoplankton. Journal of Theoretical Biology 432: 38–48.Guillard RRL. 1975. Culture of phytoplankton for feeding marine invertebrates.

In: Smith WL, Chanley MH, eds. Culture of marine invertebrate animals. New

York, NY, USA: Plenum Press, 26–60.Haldane JBS. 1949. Suggestions as to quantitative measurement of rates of

evolution. Evolution 3: 51–56.Hansen TF. 2006. The evolution of genetic architecture. Annual Review ofEcology Evolution and Systematics 37: 123–157.

Holt RD. 2008. Theoretical perspectives on resource pulses. Ecology 89: 671–681.

Jayat C, Ratinaud M-H. 1993. Cell cycle analysis by flow cytometry: principles

and applications. Biology of the Cell 78: 15–25.Key T, McCarthy A, Campbell DA, Six C, Roy S, Finkel ZV. 2010. Cell size

trade-offs govern light exploitation strategies in marine phytoplankton.

Environmental Microbiology 12: 95–104.Kilmer JT, Rodriguez RL. 2017.Ordinary least squares regression is indicated

for studies of allometry. Journal of Evolutionary Biology 30: 4–12.Kim S-H, Liu K-H, Lee S-Y, Hong S-J, Cho B-K, Lee H, Lee C-G, Choi H-K.

2013. Effects of light intensity and nitrogen starvation on glycerolipid,

glycerophospholipid, and carotenoid composition in Dunaliella tertiolectaculture. PLoS ONE 8: e72415.

Kirk JTO. 1975. A theoretical analysis of the contribution of algal cells to the

attenuation of light within natural waters. New Phytologist 75: 11–20.Kirk JT. 1994. Light and photosynthesis in aquatic ecosystems. Cambridge, UK:

Cambridge University Press.

Kirst H, Formighieri C, Melis A. 2014.Maximizing photosynthetic efficiency

and culture productivity in cyanobacteria upon minimizing the phycobilisome

light-harvesting antenna size. Biochimica Et Biophysica Acta-Bioenergetics 1837:1653–1664.

Lande R. 1980. The genetic covariance between characters maintained by

pleiotropic mutations. Genetics 94: 203–215.

Liang Y, Beardall J, Heraud P. 2006. Effect of UV radiation on growth,

chlorophyll fluorescence and fatty acid composition of Phaeodactylumtricornutum and chaetoceros muelleri (bacillariophyceae). Phycologica 45: 605–615.

Litchman E, Klausmeier CA. 2008. Trait-based community ecology of

phytoplankton. Annual Review of Ecology Evolution and Systematics 39: 615–639.

Lopez-Urrutia A, San Martin E, Harris RP, Irigoien X. 2006. Scaling the

metabolic balance of the oceans. Proceedings of the National Academy of Sciences,USA 103: 8739–8744.

Malerba ME, Connolly SR, Heimann K. 2012. Nitrate-nitrite dynamics and

phytoplankton growth: formulation and experimental evaluation of a dynamic

model. Limnology and Oceanography 57: 1555–1571.Malerba ME, Connolly SR, Heimann K. 2016. Standard flow cytometry as a

rapid and non-destructive proxy for cell nitrogen quota. Journal of AppliedPhycology 28: 1085–1095.

Malerba ME, White CR, Marshall DJ. 2017. Phytoplankton size-scaling of net-

energy flux across light and biomass gradients. Ecology 98: 3106–3115.Malerba ME, White CR, Marshall DJ. 2018. Eco-energetic consequences of

evolutionary shifts in body size. Ecology Letters 21: 54–62.Maranon E. 2008. Inter-specific scaling of phytoplankton production and cell

size in the field. Journal of Plankton Research 30: 157–163.Maranon E. 2015. Cell size as a key determinant of phytoplankton metabolism

and community structure. Annual Review of Marine Science 7: 241–264.Maranon E, Cermeno P, Lopez-Sandoval DC, Rodriguez-Ramos T, Sobrino C,

Huete-Ortega M, Blanco JM, Rodriguez J. 2013. Unimodal size scaling of

phytoplankton growth and the size dependence of nutrient uptake and use.

Ecology Letters 16: 371–379.Mauzerall D. 1986. The optical cross section and absolute size of a

photosynthetic unit. Photosynthesis Research 10: 163–170.Moloney CL, Field JG. 1989. General allometric equations for rates of nutrient-

uptake, ingestion, and respiration in plankton organisms. Limnology andOceanography 34: 1290–1299.

Morel A, Bricaud A. 1981. Theoretical results concerning light absorption in a

discrete medium, and application to specific absorption of phytoplankton. DeepSea Research Part A, Oceanographic Research Papers 28: 1375–1393.

Mussgnug JH, Thomas-Hall S, Rupprecht J, Foo A, Klassen V, McDowall A,

Schenk PM, Kruse O, Hankamer B. 2007. Engineering photosynthetic light

capture: impacts on improved solar energy to biomass conversion. PlantBiotechnology Journal 5: 802–814.

Nedbal L, Trtilek M, Kaftan D. 1999. Flash fluorescence induction: a novel

method to study regulation of photosystem II. Journal of Photochemistry andPhotobiology B-Biology 48: 154–157.

Niklas KJ. 1997. The evolutionary biology of plants. Chicago, IL, USA: University

of Chicago Press.

Niklas KJ, Cobb ED. 2017. Size-dependent variation in plant form. CurrentBiology 27: R900–R905.

Pierangelini M, Stojkovic S, Orr PT, Beardall J. 2014. Photosynthetic

characteristics of two Cylindrospermopsis raciborskii strains differing in their

toxicity. Journal of Phycology 50: 292–302.R Core Team. 2018. R: A language and environment for statistical computing.Vienna, Austria: R Foundation for Statistical Computing.

Raven JA. 1984. A cost-benefit analysis of photon absorption by photosynthetic

unicells. New Phytologist 98: 593–625.Raven JA. 1998. The twelfth tansley lecture. Small is beautiful: the

picophytoplankton. Functional Ecology 12: 503–513.Raven JA, Beardall J. 2016.Microalgal production for biomass and high-value

products. In: Slocombe SP, Benemann J, eds. Algal photosynthesis andphysiology. Boca Raton, FL, USA: CRC Press, 1–20.

Richardson K, Beardall J, Raven J. 1983. Adaptation of unicellular algae to

irradiance: an analysis of strategies. New Phytologist 93: 157–191.Ross ON, Geider RJ. 2009. New cell-based model of photosynthesis and photo-

acclimation: accumulation and mobilisation of energy reserves in

phytoplankton.Marine Ecology Progress Series 383: 53–71.Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T,

Preibisch S, Rueden C, Saalfeld S, Schmid B et al. 2012. Fiji: an open-source

platform for biological-image analysis. Nature Methods 9: 676–682.



Research

NewPhytologist460

Steppan SJ, Phillips PC, Houle D. 2002. Comparative quantitative genetics:

evolution of the g matrix. Trends in Ecology & Evolution 17: 320–327.Suggett DJ, Goyen S, Evenhuis C, Szabo M, Pettay DT, Warner ME, Ralph PJ.

2015. Functional diversity of photobiological traits within the genus

Symbiodinium appears to be governed by the interaction of cell size with cladal

designation. New Phytologist 208: 370–381.Suggett DJ, Moore CM, Hickman AE, Geider RJ. 2009. Interpretation of fast

repetition rate (FRR) fluorescence: signatures of phytoplankton community

structure versus physiological state.Marine Ecology Progress Series 376: 1–19.Sun J, Liu DY. 2003. Geometric models for calculating cell biovolume and

surface area for phytoplankton. Journal of Plankton Research 25: 1331–1346.Thrane JE, Kyle M, Striebel M, Haande S, Grung M, Rohrlack T, Andersen T.

2015. Spectrophotometric analysis of pigments: a critical assessment of a high-

throughput method for analysis of algal pigment mixtures by spectral

deconvolution. PLoS ONE 10: e0137645.

Tobler A, Nijhout HF. 2010. Developmental constraints on the evolution of

wing-body allometry in manduca sexta. Evolution & Development 12: 592–600.Turpin DH. 1991. Effects of inorganic n availability on algal photosynthesis and

carbon metabolism. Journal of Phycology 27: 14–20.Voje KL, Hansen TF, Egset CK, Bolstad GH, Pelabon C. 2014. Allometric

constraints and the evolution of allometry. Evolution 68: 866–885.Ward BA, Mara~n�on E, Sauterey B, Rault J, Claessen D. 2017. The size

dependence of phytoplankton growth rates: a trade-off between nutrient uptake

and metabolism. American Naturalist 189: 170–177.Wilhelm C, Jungandreas A, Jakob T, Goss R. 2014. Light acclimation in

diatoms: from phenomenology to mechanisms.Marine Genomics 16: 5–15.Wu Y, Campbell DA, Irwin AJ, Suggett DJ, Finkel ZV. 2014.Ocean

acidification enhances the growth rate of larger diatoms. Limnology andOceanography 59: 1027–1034.

Zhang W, Jeppesen E, Wang M, Xu X, Wang L. 2017. Allelopathic effect boosts

Chrysosporum ovalisporum dominance in summer at the expense ofMicrocystispanniformis in a shallow coastal water body. Environmental Science andPollution Research 24: 4666–4675.

Zhu CJ, Lee YK. 1997. Determination of biomass dry weight of marine

microalgae. Journal of Applied Phycology 9: 189–194.

Supporting Information

Additional Supporting Information may be found online in theSupporting Information tab for this article:

Fig. S1 Cell size distribution and rates of evolutionary changefrom the ancestral population (at generation 0) to 250 genera-tions of artificial selection.

Fig. S2 Changes in optical properties of size-selected cells as mea-sured by flowcytometer (i.e. forward scatter, side scatter and redfluorescence).

Fig. S3 Relationship between total optical density (750 nm) ofthe culture – standardized for population density – and mean cellvolume of the culture.

Fig. S4 Relationship between antenna size of PSII a (�A) andantenna connectivity parameter of PSII a.

Fig. S5 Allometric relationships for chlorophyll-standardized(Chla + b) per-cell O2 evolution rates as a function of cell volume

Fig. S6 Maximum growth rate and carrying capacity across size-evolved lineages after 150 generations of artificial selection.

Fig. S7 Predicted photon harvesting efficiencies standardized perunit as a function of cell volume.

Table S1 Summary of all allometric size-scaling relationships

Methods S1 Quantifying the magnitude of the packaging effecton a cell.

Please note: Wiley Blackwell are not responsible for the contentor functionality of any Supporting Information supplied by theauthors. Any queries (other than missing material) should bedirected to the New Phytologist Central Office.





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