Interactions between allelopathic properties and growthkynetics in four freshwater phytoplankton species studiedby model simulations

Aldo Barreiro • Vitor Manuel Vasconcelos

Received: 16 August 2013 / Accepted: 4 March 2014 / Published online: 14 March 2014

� Springer Science+Business Media Dordrecht 2014

Abstract We chose four species of freshwater phy-

toplankton: the chlorophyceans Ankistrodesmus falca-

tus, Chlamydomonas reinhardtii and Selenastrum

capricornutum, and the cyanobacteria Oscillatoria sp.

in order to study their competitive abilities for nitrate

and their allelopathic properties. We parameterized

models of nitrate uptake and growth with laboratory

experiments. According to them, the species were

ranked (from the best to the worst competitors): S.

capricornutum, C. reinhardtii, A. falcatus and Oscilla-

toria sp. C. reinhardtii and Oscillatoria sp. were

previously reported as allelopathic. In the present work,

Oscillatoria sp. was allelopathic only against A. falca-

tus. However, none of our species was sensitive to C.

reinhardtii. Additionally, we found an unknown allelo-

pathic effect of A. falcatus against Oscillatoria sp. Our

findings point out the high specificity of allelopathic

interactions. With these data, we constructed a model of

interspecific competition for nitrate, including allelo-

pathic interactions. By performing model simulations,

we studied how three factors influence the outcome of

competition: relative abundance of competing species,

resistance to allelopathy, and nitrate concentration. Our

simulations showed that the initial ratio of species

abundances will significantly determine the outcome of

competition. If the worst competitor was the allelo-

pathic species, the more it needs to outnumber the

competing species, unless it is very sensitive to

allelopathy (not defended). Nitrate has an important

influence, showing a non-intuitive outcome of compe-

tition experiments at low nitrate concentrations, where

the worst competitor (allelopathic species) wins com-

petition in the majority of cases, whereas at interme-

diate concentrations, the better competitor dominates

except for unfavorable ratios of abundances. With the

increased amounts of nitrate, conditions again favor the

worst competitor (the stronger allelopathic species).

Despite the potential for two species coexistence

showed by previous theoretical analysis of systems

was similar to ours, our simulations did not detect this

outcome. We hypothesized that this is due to the strong

allelopathic effect of Oscillatoria sp.

Keywords Chlorophyta � Cyanobacteria �Nitrate � Allelopathy � Interspecific competition

Introduction

Functional traits determine the ability to occupy eco-

logical niches. Production of allelopathic compounds

Handling Editor: Bas W. Ibelings.

A. Barreiro (&) � V. M. Vasconcelos

CIIMAR/CIMAR, Interdisciplinary Centre of Marine and

Environmental Research, University of Porto, Rua dos

Bragas 289, 4050-123 Porto, Portugal

e-mail: aldo.barreiro@gmail.com

V. M. Vasconcelos

Faculty of Sciences, Porto University, Rua do Campo

Alegre, 4069-007 Porto, Portugal

123

Aquat Ecol (2014) 48:191–205

DOI 10.1007/s10452-014-9475-2

seems to be a significant trait present in certain

phytoplankton species. Allelopathy was defined as

‘‘any process involving secondary metabolites pro-

duced by plants, algae, bacteria, or fungi that

influence the growth and development of biological

and agricultural systems’’ (International Allelopathy

Society 1996). But we still do not understand how

allelopathy confers advantage to a phytoplankton

species in order to occupy a niche. We do know this

for other functional traits: Swimming and buoyancy

are related to the competition for light and nutrients

under different regimes of turbulence (Smayda and

Reynolds 2001; Huisman et al. 2004). Affinity for

nutrients determines the occupation of temporal

niches during seasonal succession, due to the special-

ization in the use of different nutrient ratios and

alternative sources of carbon (Tilman et al. 1982;

Sterner 1989; Boit et al. 2012). Specialization in the

use of different wavelengths yields to light niche

differentiation (Stomp et al. 2007). There are several

reasons why the usefulness of the trait ‘‘allelopathy’’ is

not well understood. First, there is not enough

information available about the prevalence of allelop-

athy among phytoplankton species. This happens

because there are many phytoplankton species that

have never been studied in laboratory and because

allelochemical production, mainly involving second-

ary metabolism, depends a lot on physiological

conditions (Legrand et al. 2003). Second, there is still

much uncertainty regarding how the physical–chem-

ical factors of the environment influence the mecha-

nism of allelopathy, despite some remarkable

modeling approaches (Jonsson et al. 2009).

It has been suggested from theoretical studies that

along a gradient from none to high turbulence, low

values would favor the effectiveness of allelochemi-

cals (Hulot and Huisman 2004). Under the conditions

of low turbulence, flagellated or buoyant species

dominate phytoplankton communities, whereas dia-

toms dominate during periods of strong mixing

(Smayda and Reynolds 2001). According to this, it

could be expected that allelopathy would be more

prevalent among motile species, such as some chlo-

rophytes, chrysophytes, cryptophytes, dinoflagellates,

or buoyant species (Microcystis). Filamentous species

of benthic cyanobacteria would also be good candi-

dates as allelopathic species, because of the relatively

low turbulence of their habitat and the motility of

filaments. Among these filamentous or motile species

that dominate phytoplankton during low-turbulence

periods, we can find many examples of genus with

known allelopathic species, such as cyanobacteria:

Anabaena, Nodularia, Aphanizomenon, Cylindro-

spermopsis, Oscillatoria, Microcystis (LeBlanc et al.

2005); small flagellates: Prymnesium, Chrysochrom-

ulina, Ochromonas (Hiltunen et al. 2012), Chlamydo-

monas; dinoflagellates: Alexandrium, Karenia,

Heterosigma, Pfiesteria, Prorocentrum (Xiaoqing

et al. 2011). On the other hand, among those species

that dominate phytoplankton communities during

periods of strong mixing (mainly diatoms), we almost

do not find examples of known allelopathic species

(but see Ribalet et al. 2007; Yamasaki et al. 2010).

In aquatic habitats, mixing is strongly related to

nutrient availability (Sanford 1997) and prolonged

periods of low turbulence cause the lack of nutrient

inputs, leading to limitation by inorganic nutrients. In

such circumstances, allelopathy could be a beneficial

trait to overcome the dominance by species with

higher affinity for inorganic nutrients, as it has been

already suggested (Smayda 1997; Legrand et al. 2003;

Kubanek et al. 2005; Graneli et al. 2008). Then, among

those swimming, buoyant, or filamentous species, the

relatively poorer competitors could be expected to be

the allelopathic ones.

Recently, it has been discussed, based on modeling

approaches, whether allelopathy would be beneficial

at all during bloom forming (Jonsson et al. 2009).

According to this modeling work, allelopathy would

be quite inefficient in aquatic environments. Mainly,

this would be due to the diffusion properties of

chemicals in the water and the relatively low popu-

lation densities of potentially interacting organisms,

which prevents them from close contact. However,

several recent observations might contradict the

applicability of the conclusions from this modeling

approach to all situations. For instance, the unexpected

detection of allelopathic interactions (potentially

caused by cylindrospermopsin and portoamides) at

low cell densities (Leao et al. 2009) may illustrate the

fact that our knowledge about the mode of action of

allelochemicals like those is still limited. In this sense,

this has been recently found in the cyanobacterium

Oscillatoria, two allelochemical compounds, portoa-

mide A and B, that dramatically enhance their

biological activity through synergistic interactions

(Leao et al. 2010). Also, it has been shown that

the function of toxins from Prymnesium parvum

192 Aquat Ecol (2014) 48:191–205

123

(prymnesins) is contact micropredation rather than

exotoxicity (Remmel and Hambright 2012). So,

physical properties of molecules and medium would

not play a determinant role if other allelopathic

interactions follow this same mode of action. Other

variables such as the presence of heterotrophic bac-

teria and grazing intensity also influence the effec-

tiveness of allelopathic compounds (Hulot and

Huisman 2004; Weissbach et al. 2011).

Another feature that makes difficulties in the study

of allelopathy in phytoplankton is the dependency of

allelopathic properties on the target species employed

in an assay. These differential effects on the targets

were shown in laboratory experiments and natural

blooms of allelopathic species: Fistarol et al. (2004)

found that Alexandrium tamarense exudates to change

the relative abundances of species in a natural

phytoplankton community, thus affecting differen-

tially each phytoplankton group, and being allelo-

pathic to ciliates but not to bacteria; Suikkanen et al.

(2005) showed the cyanobateria Nodularia sp., Aph-

anizomenon sp., and Anabaena sp. to inhibit only

cryptophytes among a Baltic Sea phytoplankton

community; Prince et al. (2008) showed Karenia

brevis exudates to inhibit two diatoms and a dinofla-

gellate (Asterionellopsis glacialis, Skeletonema cost-

atum, and Prorocentrum minimum) but not the

dinoflagellate Akashiwo cf. sanguinea; Hattenrath-

Lehmann and Gobler (2011) showed different strains

of Alexandrium fundyense to inhibit, in the natural

environment, nanoflagellates and diatoms, but to

stimulate dinoflagellates.

This specificity on the target or spectrum of action

of each allelopathic species varies from highly specific

(Chlamydomonas allelopathic against Cryptomonas

and Tetrahymena, but not against Ochromonas,

Microcystis, or Paramecium, Barreiro and Hairston

2013) to relatively wide effects (Prymnesium, Nodu-

laria, Alexandrium; see references over this introduc-

tion). So, we cannot draw a general simple conclusion

about target specificity.

The multiplicity of roles of some chemical com-

pounds (allelopathy, anti-predator defense, cell sig-

naling) as found in polyunsaturated aldehydes from

diatoms (Ianora et al. 2011) is also a reason that

complicates the experimental and theoretical study of

allelopathic interactions.

Several authors, from modeling or experimental

approaches, tackled the evolutionary and ecological

aspects of allelopathic interactions (Chao and Levin

1981; Durret and Levin 1997). Though addressing

allelopathy in bacteria, some of their conclusions

apply also to phytoplankton allelopathy: the frequency

dependence on the effectiveness of allelopathy and the

competition with cheaters (individuals of the same

species that do not spend energy producing the

allelochemical). They also showed a strong influence

on the habitat structure that might not be relevant to

planktonic ecosystems, but could be important also in

benthic communities of phytoplankton and periphy-

ton. In the case of interspecific competition in

phytoplankton, theoretical models have shown that

under some conditions, allelopathy can cause species

coexistence on a single limiting resource (Roy 2009).

It comes up that allelopathy has a potential to promote

diversity, and then, it could help to explain the

paradoxical high numbers of coexisting species in

phytoplankton communities (Hutchinson 1961).

The aim of the present work was to determine the

relationship between allelopathy and other functional

traits (motility, affinity for nitrate, and cell growth)

within a set of four phytoplankton species (Oscillato-

ria sp., Ankistrodesmus falcatus, Chlamydomonas

reinhardtii, and Selenastrum capricornutum) framing

our observations on hypothesis based on previous

works, namely the prevalence of allelopathy among

swimming and filamentous species, and the inverse

relationship among nutrient affinity and allelopathy.

Among these species, the strain of Oscillatoria sp. and

other strains of C. reinhardtii were already reported to

be allelopathic (McCracken 1979; Leao et al. 2010;

Barreiro and Hairston 2013). These four species

belong to two ecologically relevant taxonomic groups

in freshwater ecosystems. They often dominate dif-

ferent steps of ecological succession, or alternative

stable states (Scheffer et al. 1997, 2001). Some of

them are ubiquitous (C. reinhardtii, Oscillatoria sp.)

and may coexist in nature. The other two species are

not that relevant in natural environments, but they are

useful for one of our purposes, which is to account for

a wide rage of competitive abilities among our species.

In order to get a relatively constant physiological

status of our species during the tests of allelopathy,

and during the nitrate growth experiments, cultures

were maintained in continuous conditions. In order to

place our experimental data in a broader context, we

constructed an interspecific competition model for

nitrate, including an allelopathic interaction. A variety

Aquat Ecol (2014) 48:191–205 193

123

of models have been used to study allelopathic

interactions in phytoplankton (Maynard-Smith 1974;

Durret and Levin 1997; Hsu and Waltman 2004; Hulot

and Huisman 2004; Sole et al. 2005; Martines et al.

2009; Roy 2009). Our model is similar to those from

Durret and Levin (1997) and Roy (2009), in the sense

that there is no mechanistic structure for the allelop-

athy. This is due to our experimental approach that

does not allow us to estimate the model parameters

needed for such a mechanistic structure. However, we

include the mechanism for resource competition in

chemostat in a similar way than the model in Martines

et al. (2009). In natural freshwater environments, it is

perhaps more frequent to observe limitation by

phosphorus rather than nitrogen. Our choice of

nitrogen was not based on resembling what is more

frequently observed in nature. Rather, it is based in the

fact that it is easier to model nitrogen consumption

than phosphorus. Modeling competition for phospho-

rus in chemostats needs to account for intra-cellular

storage (Passarge et al. 2006), which involves more

complicated equations and more parameters. With

competition for nitrate, we get a model that is similar

to those for ideal or unspecific limiting resources (like

the model from Martines et al. 2009).

Then, we subjected our model to the influence of

several factors (species resistance to allelopathy,

relative abundance of species, and nitrate concentra-

tion) analyzing model behavior along gradients of

these factors.

Methods

Phytoplankton species

The species used in this work were as follows: the

mat-forming cyanobacterium Oscillatoria sp. (strain

LEGE 05292) isolated by members of our laboratory

and cultured for years there, the chlorophytes A.

falcatus (from our laboratory culture collection), C.

reinhardtii (strain CCAP 11/45), and S. capricornu-

tum (strain CCAP 278/4). These organisms were

maintained in batch cultures with growth medium

which composition is detailed in Table 1. The medium

was prepared with distilled water. This medium was

the same used in the forthcoming experiments, varying

only in the concentrations of nitrate and phosphate as

indicated on each case. Culturing conditions were

12:12 h light/dark cycle, *40 lmols m-1s-1, and

20 �C Ta. These conditions were the same in all

experiments, except light cycle, which modifications

are indicated on each case.

Nitrate uptake experiments

We assumed nitrate uptake to follow Michaelis–

Menten kynetics (Eq. 1):

V ¼ VmaxNO2NO3

HNO2þ NO3

ð1Þ

where VmaxNO2is the maximum uptake rate and HNO2

is a half-saturation constant. In order to estimate these

two parameters for each species, we ran short-term

batch culture experiments of nitrate uptake. Prior to

these experiments, a 50-mL flask of batch culture from

each species was precultured during 6 days with the

medium composition shown in Table 1, but with low

nitrate concentration (16 lM). Then, the whole 50-mL

cultures were transferred to a larger flask, adding up to

250 mL of the same medium without nitrate. These

cultures were kept under these conditions during 24 h,

Table 1 Detailed composition of the culture medium

Chemical compound Final lM Final mg L-1

Macronutrients

KNO3 320 32.35

K2HPO4 20 3.48

Micronutrients, iron, trace metals and others

CaCl2�2H2O 250 36.760

MgSO4�7H2O 166 36.970

Fe-EDTA 5.6 (FeCl3�6H2O) ?

7.41 (Na-EDTA)

MnCl2�4H2O 2.100 0.5400

ZnSO4�7H2O 0.073 0.0287

CoCl2�6H2O 0.091 0.0300

Na2MoO4�2H2O 0.074 0.0230

CuSO4�5H2O 0.038 0.0125

KBr 0.100 0.0120

V2O5 0.007 0.0009

H3BO3 74.397 4.560

NaHCO3 150.000 12.600

Vitamins

Thiamine-HCl 0.2965 0.10000

d-Biotin 0.0020 0.00050

Cianocobalamin (B12) 0.0004 0.00055

194 Aquat Ecol (2014) 48:191–205

123

and then, experiment began. At this point, phosphate

was resupplied at 20 lM, and a pulse of approximately

10 lM of nitrate was added to each culture. Then,

nitrate concentration and cell abundances were mon-

itored approximately every 2 h, during 18 h. During

this time, light was permanently on. Three replicates

of 6 mL samples were filtered through 0.22 lm

Millipore�Express PES membrane filters. Nitrate

was analyzed in the filtered medium in a nutrient auto

analyzer Skalar Sanplus using the method Skalar

M461-318 (EPA 353.2). For the cell counts, 1 ml

samples were taken at the same time and counted in an

hemocytometer. In the case of Oscillatoria sp., cells

were sonicated prior to counting in order to separate

the intricate bunches of filaments and counted in

Sedgwick–Rafter chambers. In the case of C. rein-

hardtii, in order to prevent cells to swim, a tiny drop of

Lugol solution was added to the samples. Cell

abundances at the beginning of the experiment were

tried to be equivalent among species regarding total

biovolume. They were the following in average: S.

capricornutum: 300,000 cells ml-1, C. reinhardtii:

35,000 cells ml-1, A. falcatus: 75,000 cells ml-1,

Oscillatoria sp.: 60,000 cells ml-1.

The maximum uptake rate was calculated as the

slope of a linear regression performed between

external nitrate concentration and time. This regres-

sion only takes into account the first hours of the

experiment, when the uptake is faster. Half saturation

constant was fitted to the above equation using a

nonlinear least-squares regression method, with the

nls function coded in the stats package from R (R Core

Team 2012).

Growth experiments

In order to study the population growth of these

species, we performed experiments in continuous

cultures with nitrate as limiting nutrient. We assumed

population growth rate to follow the Monod model

(Eq. 2):

l ¼ lmaxNO3

KNO3þ NO3

ð2Þ

where lmax is the maximum growth rate and KNO2a half

saturation constant. Each phytoplankton species was

cultured in 400-mL vessels with a continuous cultur-

ing system, with dilution rate = 0.3 day-1. Culture

medium composition was the same as in Table 1, but

with a nitrate concentration of 320 lM and phosphate

concentration of 200 lM. Light was on 24 h per day

during the whole duration of the experiments. Cell

abundances and nitrate concentration were monitored

every 24 h, using the same procedures as described

above, except that only two replicates were taken for

the nitrate analysis. Monitoring was stopped a few

days after each species reached steady state (equilib-

rium between medium renewal and microorganism

growth that is reached in a continuous culture certain

time after inoculation and is maintained indefinitely).

The experiments lasted in total 10–14 days.

Parameters from the equation above were fitted

using similar procedures as described for nitrate

uptake. The maximum growth rate was estimated as

the slope of a linear regression performed with cell

densities (log-transformed) against time, during the

period when the cultures showed faster growth. The

half saturation constant was fitted using a nonlinear

least-squares regression method, with the nls function

coded in the stats package from R (R Core Team 2012).

Allelopathy experiments

The allelopathic effect of each species was tested

against the other species, using all possible pair-wise

combinations of them. For this purpose, species were

grown in continuous cultures under the same condi-

tions as in the growth experiments described above,

except that nitrate concentration was 3,200 lM. Tests

of allelopathy were performed with cell-free culture

filtrate from these continuous cultures, after steady

state was reached. In order to obtain this cell-free

medium, culture was filtered through 0.22 lm Milli-

pore�Express PES membrane filters. Experiments

were performed in 5-ml vials containing suspensions

of cells of the target species. Initial abundances of the

target species averaged over our two independent

experiments are shown in Table 2. We aimed to get

similar abundances of the chlorophytes target species

in terms of biomass. In order to achieve similar

biomass, we should get approximately double S.

capricornutum densities than C. reinhardtii and A.

falcatus. As we can see in Table 2, this aim was not

perfectly achieved. However, we do not expect this to

have had an impact in our results. For Oscillatoria sp.,

we aimed to get higher abundances than the others in

order to trying to reduce the inherent high variance in

the cell counts of this species. Cell-free culture filtrate

Aquat Ecol (2014) 48:191–205 195

123

was added to the vials in the following proportions: 0,

10, 25, 50, and 75 % of total vial volume (5 mL). We

also added to the vials 1 mL of sterile culture medium

with high nutrient concentration (medium from

Table 1 with 640 lM of nitrate and 40 lM of phos-

phate final concentrations) so that nutrient limitation

would not have an effect in our results. In order to get

the same volume in all the vials (5 mL), we adjusted

the vial volume with sterile culture medium without

macronutrients. These vials were incubated during the

whole course of the experiment with saturating light

conditions (approx. 60 lmol photons m2s-1). We set

three replicated vials for each of the filtrate propor-

tions. Cell growth was estimated after 24 h for all

species except Oscillatoria sp. for which it was

estimated after 72 h, due to its lower growth rate. Cell

counts were performed following the same procedure

as described above for the nitrate uptake experiments.

pH wasmonitored in the steadystate of thecontinuous

cultures of the species being tested. The average values

were the following: Oscillatoria sp. = 7.9 ± 0.6; A.

falcatus = 9.3 ± 0.8; C. reinhardtii = 9.2 ± 0.6; S.

capricornutum = 9.7 ± 0.5. Values above 9 indicate

CO2 limitation of the cultures. So, probably only

Oscillatoria sp. culture was not limited by CO2. It is

unknown which nutrient was limiting growth in the

continuous cultures of this species.

These experiments were performed in two inde-

pendent dates, in order to obtain a source of variation

for the effect of the cell-free culture filtrate. Table 2

summarizes the initial cell abundances in the

experimental vials and the cell abundances in the

continuous cultures at the moment of the experiments.

The allelopathic effect of each species was esti-

mated by fitting linear regressions between the

dependent variable ‘‘growth rate’’ and the independent

variable ‘‘% of cell-free culture filtrate.’’ The strength

of the allelopathic effect is quantified by the slope of

these regressions. Outliers constituting deviations for

more than 75 % of the mean were removed. This was

approximately 7 % of the data from each regression.

Model of interspecific competition for nitrate

with allelopathy

After parameterizing the equations for nitrate uptake

and growth and the linear regressions for the allelo-

pathic effect, it was possible to construct a two-species

mechanistic model of population growth in continuous

culture under interspecific competition with nitrate as

limiting resource and including an allelopathic inter-

action. Basic model formulation is as follows:

dN

dt¼ o � NI � N

� �� RVCk

ðNÞCk ð3Þ

dC1

dt¼ FC1

� ðNÞC1 � oC1 ð4Þ

dC2

dt¼ FC2

� ðNÞC2 � oC2 � A1C1C2 ð5Þ

where N is the resource (nitrate), NI is the concentra-

tion of limiting resource in the inflow medium, Ck are

Table 2 Cell abundances of the species (tested for allelopathy) in the continuous culture at the moment of the experiment and initial

cell abundances of the target species in the experimental vials

Tested species Chemostat (cells mL-1) Target species Initial (cells mL-1)

Oscillatoria sp. 3,745,000 ± 1,415,362 A. falcatus 23,888 ± 4,490

C. reinhardtii 33,055 ± 6,678

S. capricornutum 35,000 ± 6,318

A. falcatus 11,635,416 ± 1,567,645 Oscillatoria sp. 56,648 ± 6,573

C. reinhardtii 25,601 ± 2,684

S. capricornutum 39,814 ± 8,267

C. reinhardtii 6,038,000 ± 3,565,463 Oscillatoria sp. 59,384 ± 20,118

A. falcatus 19,166 ± 2,463

S. capricornutum 37,546 ± 3,993

S. capricornutum 12,402,500 ± 1,775,113 Oscillatoria sp. 29,379 ± 22,314

A. falcatus 19,490 ± 458

C. reinhardtii 16,851 ± 14,404

Data are the grand mean ± SD of the two independent experiments

196 Aquat Ecol (2014) 48:191–205

123

the competing species of phytoplankton, k = 1,…, n is

the number of competing species (in our case, k = 2),

A1 is the concentration of the allelochemical produced

by species 1 against species 2, q is the dilution rate of

the chemostat, A1 is a constant for the linear mortality

effect of allelopathic compound by species 1. VCk and

FCk are the above-displayed equations for nitrate

uptake and growth, respectively. All these parameters

were obtained experimentally from the experiments

detailed previously in this section.

Model simulations were run in R software (R Core

Team 2012) with a discrete version of this model.

Results

The results of the monitoring of nitrate during uptake

experiments are shown in Fig. 1. By projecting the

curves of nitrate concentration to time = 0 (right after

the 10 lM pulse of nitrate), we can estimate that the

nitrate concentration prior to the pulse lied somewhere

between 0 and 5 lmols for the three chlorophytes and

approximately 30 lmols for Oscillatoria sp. This

suggests a lower affinity for nitrate than the other

species, since all of them went on the same treatment

prior to the experiment. S. capricornutum, C. rein-

hardtii, and A. falcatus consumed most of the nitrate

during the first 5 h of the experiments, at comparable

rates. In these cases, nutrient uptake rate was always

close to maximum, till the bottom of nitrate concen-

tration was reached. On the other hand, Oscillatoria

sp. uptake rate was notably reduced after approxi-

mately 3 h to a nitrate concentration of 30 lmols. For

the three microalgae, cell abundances increased

between 2 and 3 fold while nitrate was available.

Oscillatoria sp. abundance did not double during the

whole course of the experiment. Fitted parameters for

the nitrate uptake model are shown in Table 3.

Maximum uptake rates are reported per cell. Smaller

cells, due to their higher surface/volume relationship,

have a great advantage regarding uptake rate. Half

saturation constants also showed a clear difference in

affinity for nitrate among the species. The cyanobac-

terium was clearly a poorer competitor than the others,

while S. capricornutum was the best competitor for

nitrate uptake. The lack of statistical significance for S.

Fig. 1 Nitrate

concentration in the external

medium during the 20-h

consumption experiments

with the four phytoplankton

species. Data are

mean ± SD (n = 3)

Aquat Ecol (2014) 48:191–205 197

123

capricornutum half saturation constant is not a prob-

lem for the reliability of the estimate, given that the

value of that parameter is itself close to 0, and hence,

the interval defined by standard error of the estimation

is difficult to be kept far from 0. In addition, the p value

is still relatively low.

Results from the growth experiments in continuous

cultures, using nitrate as limiting resource, are shown

in Fig. 2, and the fitted parameters of the growth

model (Eq. 2) in Table 4. Visually inspecting the data

and curves from Fig. 2, we can notice that Oscillatoria

sp. and S. capricornutum are the species with the

lowest maximum growth rate. However, there is an

important difference between these two species: The

chlorophyte shows higher efficiency of growth than

the cyanobacterium at low nitrate concentration. It is

not easy to determine visually from Fig. 4 the

differences in affinity among the three chlorophytes.

In order to do so, we should look at the half saturation

constants from Table 4. The pattern of affinity among

species was the same as for the uptake model

(Table 3). As for the uptake model, the estimate of

the half saturation constant in S. capricornutum was

not significant, although its p value was relatively low.

This was probably due, again, to its low value, which

makes it difficult to exclude 0 from the interval defined

by the standard error of the estimate.

In order to rank species according to their compet-

itive ability for nitrate, we need to determine the

nitrate concentration reached in equilibrium (steady

state) when each species is growing alone in the

chemostat. We will call this nitrate concentration N*.

The species with lower N*, when growing in compe-

tition with the others, will deplete nitrate to levels

below the other species can use it to grow. So, this

Table 3 Fitted parameter values of the nitrate uptake model

for each species

Species Vmax (fmol

cell-1 h-1)

HNO3± SE

(lM)

p (HNO3)

Oscillatoria sp. 0.46 24.4 ± 6.7 \0.01

A. falcatus 0.7 2.88 ± 0.99 \0.01

C. reinhardtii 0.96 1.71 ± 0.55 \0.01

S. capricornutum 1.06 0.14 ± 0.08 0.12

Fig. 2 Daily growth rate of

each species from the

growth experiments

performed in chemostats

with nitrate as limiting

nutrient. The curves plotted

are from a Monod Model

with the parameters shown

in Table 3

198 Aquat Ecol (2014) 48:191–205

123

species will win competition. The N* value is

determined with the following equation (Taylor and

Williams 1975):

N� ¼ KNO3= lmax� dð Þ ð6Þ

where KNO3 the half saturation constant for growth

with nitrate, lmax the maximum growth rate, and d the

dilution rate of the chemostat. Then, for a dilution rate

of 0.5 day-1, the best competitor for nitrate is

Selenastrum capricornutm (N* = 3.6), followed by

C. reinhardtii (N* = 4.5) and A. falcatus (N* = 8.7),

being the cyanobacteria Oscillatoria sp. (N* = 63) far

from the three chlorophytes.

The allelopathic effect of each of our species

against the other ones was tested in two independent

experiments which are summarized in Fig. 3. In order

to interpret the results from our experiments, we will

consider an allelopathic effect when there is a

consistent concentration-dependent effect on the

growth of the target species. This effect might be

positive or negative and should be supported by the

statistical significance of the slope of a linear regres-

sion between filtrate concentration and decrease in

growth (Table 5). According to this, we have only two

negative allelopathic effects, those of Oscillatoria sp.

against A. falcatus and vice versa. There was also a

remarkable positive effect of Oscillatoria sp. on S.

capricornutum. But regarding positive effects, it is

more difficult to elucidate if they were due to a

stimulatory compound released by the allelopathic

species or simply to enrichment in a nutrient that was

provided in sub-optimal concentrations in the culture

medium. For the two negative allelopathic effects

detected, the per capita effects were calculated

dividing the slopes shown in Table 5 by the average

cell abundance of the continuous cultures (Table 2).

This values are -1.31 9 10-7 ± 0.32 9 10-7 (%

decrease 9 allelopathic cell-1) for Oscillatoria sp.

against A. falcatus and -1.13 9 10-7 ± 0.19 9 10-7

for A. falcatus against Oscillatoria sp. The difference

between these two values is small, and the standard

errors of their estimates are a bit overlaid. In order to

compare these per capita effects more accurately, a

linear model was performed using cell-free filtrate

concentration as covariate and ‘‘species’’ as factor. No

significant differences were found for the factor

‘‘species’’ (F1,50 = 0.09; p = 0.77). This means that

although our per capita estimates resulted in a stronger

effect of Oscillatoria sp. against A. falcatus than

otherwise, in statistical terms, the two negative effects

are of equal strength.

In order to study the influence of ecologically

relevant variables in an allelopathic interaction like

those reported from the previous experiments, we

performed simulations with the model of interspecific

competition with allelopathic interaction in continu-

ous culture (Eqs. 3–6) with parameters values esti-

mated from our previous experiments. We simulated

the model along gradients of three variables: ratio of

population densities, strength of allelopathic effect,

and nitrate concentration. Because we chose the only

pair of species showing negative allelopathic interac-

tions (Oscillatoria sp.–A. falcatus), our model had to

be modified in order to take into account the reciprocal

allelopathic effect. So, Eq. 4 now looks:

dC1

dt¼ FC1

Nð ÞC1 � oC1 � A2C2C1

The term A2 is equivalent to A1 for species 2.

We performed two sets of simulations of compe-

tition experiments. For each set, two parameters were

varied along a gradient. In one set, these parameters

were the ratios of species initial abundances and the

allelopathic effect of A. falcatus. In the other set, the

parameters were the ratios of species initial abun-

dances and the initial (and the input) nitrate concen-

tration. More specific details about the simulation

conditions are shown in Table 6. All the parameter

values were obtained from the experiments in this

study. The results of these simulations are shown in

Fig. 4. The upper panel of Fig. 4 shows the simula-

tions that have varied, besides the ratios of species

abundances, the allelopathic effect of A. falcatus. In

this case, it is clear that if the allelopathic effect of A.

falcatus was close to 0, the allelopathic effect of

Oscillatoria sp. would then be very effective against

Table 4 Fitted parameter values of the growth rate model for

each species

Species lmax

(day-1)

KNO3± SE

(lM)

p (HNO3)

Oscillatoria sp. 0.47 73.3 ± 27.7 \0.05

A. falcatus 0.98 14.6 ± 5.8 \0.05

C. reinhardtii 1.05 7.8 ± 3.8 0.05

S. capricornutum 0.74 5.1 ± 3 0.10

Aquat Ecol (2014) 48:191–205 199

123

this competitor, and Oscillatoria sp. would win

competition at any initial population ratio. But this is

not true anymore as we move a little to the right in the

x-axis. This means that an already small allelopathic

effect from A. falcatus would be very effective against

Oscillatoria sp. There is a point in the x-axis at which

A. falcatus always dominates irrespective of the initial

population ratio. With the actual allelopathic effect

of A. falcatus estimated in the present work

(1.13 9 10-9), Oscillatoria sp. would need to be

between 0 and 2 (we cannot estimate this with the

precision in this panel of the plot, but see below the

Fig. 3 Allelopathic effects between all pairs of species against

cell-free filtrate concentration. Allelopathic effect was esti-

mated as % of growth (as growth rate) with respect to a negative

control. Data are grand mean ± SD from the two independent

experiments performed, each one with n = 3 per cell-free

filtrate concentration

200 Aquat Ecol (2014) 48:191–205

123

comments on the low panel) times more abundant at

the beginning of the experiment in order to outcom-

pete A. falcatus. The effect of varying the inflow of

nitrate in the system, shown in the lower panel of

Fig. 4, demonstrates that also this parameter would

have an important effect on the effectiveness of

allelopathy. In this case, there is an interesting

irregular trend of the system along the gradient.

Although Oscillatoria sp. was a poorer competitor

than A. falcatus, under low nitrate concentrations

(between 10 and approx. 70 lmols), conditions might

be too hard for A. falcatus, so it cannot overcome the

allelopathic effect from Oscillatoria. This effect was

not easy to predict a priori. Increasing the nitrate

concentration above 70 lmols then favors the chloro-

phyte, making effective its competitive advantage in

nitrate affinity. However, after an optimal nitrate

concentration of 90 lmols, higher nitrate concentra-

tions would allow Oscillatoria sp. to reach relatively

high population abundances that would produce an

effective allelopathic effect against its competitor. So,

the dominance region of this cyanobacteria spreads to

lower ratios of initial abundances with a constant

trend. At the optimal nitrate concentration for A.

falcatus (90 lmols), the cyanobacteria needs to be less

than 1.2 times more abundant in order to outcompete

the chlorophyte. At 200 lmols of nitrate (fixed value

for the upper panel), the minimum ratio at which

Oscillatoria sp. would outcompete A. falcatus (con-

sidering the estimated value of A2: 1.13 9 10-9) is

approximately 0.5. These results show, from our point

Table 5 Fitted linear regressions for the estimation of the

allelopathic effect on each pair of species

Allelopathic—target species Slope Slope statistics

Oscillatoria sp.—A. falcatus -0.49 t25 = -4.11;

p \ 0.001

Oscillatoria sp.—C. reinhardtii 0.14 t28 = 0.98;

p = 0.33

Oscillatoria sp.—S. capricornutum 0.64 t22 = 3.44;

p \ 0.01

A. falcatus—Oscillatoria sp. -1.32 t24 = -5.96;

p \ 0.001

A. falcatus—C. reinhardtii -0.11 t27 = -0.76;

p = 0.46

A. falcatus—S. capricornutum 0.12 t25 = 1.21;

p = 0.24

C. reinhardtii—Oscillatoria sp. -0.21 t25 = -0.80;

p = 0.43

C. reinhardtii—A. falcatus 0.35 t24 = 2.13;

p = 0.04

C. reinhardtii—S. capricornutum -0.01 t27 = -0.08;

p = 0.93

S. capricornutum—Oscillatoria sp. -0.21 t17 = -0.47;

p = 0.64

S. capricornutum—A. falcatus -0.03 t28 = -0.20;

p = 0.84

S. capricornutum—C. reinhardtii -0.19 t27 = -1.23;

p = 0.23

Table 6 Details of the model simulations performed. In bold

are shown the parameters that were varied on each set of

simulations, and the range of the gradient

Simulation

set 1

Simulation set

2

Number of simulations 2,100 2,100

Time step Day Day

Number of time steps 60 60

Parameters

Inflow nitrate (lM) 200 10–200

Initial nitrate (lM) 200 10–200

Dilution rate (day-1) 0.5 0.5

Vmax Oscillatoria (lmol

cell-1 day-1)

1.032 9 10-5 1.032 9 10-5

Vmax A. falcatus (lmol

cell-1 day-1)

1.68 9 10-5 1.68 9 10-5

HNO2 Oscillatoria (lM) 24.4 24.4

HNO2 A. falcatus (lM) 2.88 2.88

lmax Oscillatoria (day-1) 0.47 0.47

lmax A. falcatus (day-1) 0.98 0.98

KNO2 Oscillatoria (lM) 73.3 73.3

KNO2 A. falcatus (lM) 14.6 14.6

A1 Oscillatoria (ldecrease 9 allelopathic

cell-1)

1.31 9 10-9 1.31 9 10-9

A2 A. falcatus (ldecrease 9 allelopathic

cell-1)

0–3.5 9 10-8 1.13 9 10-9

V initial Oscillatoria

(lmol cell-1 day-1)

1.032 9 10-5 1.032 9 10-5

V initial A. falcatus

(lmol cell-1 day-1)

1.68 9 10-5 1.68 9 10-5

l initial Oscillatoria (day-1) 0.4 0.4

l initial A. falcatus (day-1) 0.9 0.9

Initial abundance

Oscillatoria (cells ml-1)

1,000–20,000 10,000–20,000

Initial abundance A. falcatus

(cells ml-1)

20,000–1,000 10,000–20,000

Aquat Ecol (2014) 48:191–205 201

123

of view, that the defense of A. falcatus is in general

very poor, and the allelopathy from Oscillatoria sp. is

very effective.

Discussion

Within the set of phytoplankton species that we

employed, we have different ‘‘life-forms’’ (Margalef

1978). There are swimming (C. reinhardtii), not

motile (S. capricornutum, A. falcatus), and not swim-

ming but motile species (Oscillatoria sp.). They are

also different regarding cell associations: isolated cells

(S. capricornutum, A. falcatus), isolated and palmella

colonies (C. reinhardtii), and filaments (Oscillatoria

sp.). They show differences in habitat preferences:

planktonic (S. capricornutum, A. falcatus), benthic

and planktonic (C. reinhardtii), and benthic and

tychoplanktonic (Oscillatoria sp.). And they also

show large differences in cell shape and size. The

smallest cells are S. capricornutum (small strain of

2–4 lm in its larger dimension) and Oscillatoria sp.

(3–5 9 1 lm), and the largest C. reinhardtii (3–5 lm

diameter) and A. falcatus (10–20 9 1 lm). The cell

shapes are crescent (S. capricornutum), round (C.

reinhardtii), fuse-form (A. falcatus), or embedded in

filaments (Oscillatoria sp.). Regarding competitive

abilities for nitrate, we have poor (Oscillatoria sp.),

intermediate (A. falcatus), and strong competitors (S.

capricornutum and C. reinhardtii). Considering pre-

vious existing hypothesis about the ecological mean-

ing of allelopathy in phytoplankton (see Introduction),

we could predict, among our species, which are the

best candidates to be allelopathic. Regarding the

criterion of allelopathic species being poor competi-

tors, our options would be Oscillatoria sp. and A.

falcatus. Regarding the criterion of allelopathy being

effective only in low turbulence environments, our

best candidates would be the motile-benthic species

(C. reinhardtii and Oscillatoria sp.). We have detected

Oscillatoria sp. and A. falcatus as the only allelpathic

species. C. reinhardtii was previously reported as

allelopathic (McCracken et al. 1979). A. falcatus was

never previously reported as allelopathic. We also

have to take into account that our test for allelopathy

(significant effect in growth rate of the target species

over 24 h) is quite a strong test, in the sense that only

strong allelopathic effects would be detected. We

cannot rule out the presence of more allelopathic

effects between our set of species that would be

detected only in the long-term experiments.

Our results also point out the high specificity of

allelopathic interactions, already shown by other

authors (Fistarol et al. 2004; Suikkanen et al. 2005;

Prince et al. 2008; Hattenrath-Lehmann and Gobler

2011; Barreiro and Hairston 2013). Our allelopathic

species were allelopathic only against one target. The

lack of knowledge about the range of action of a

specific allelochemical makes more complicated the

Fig. 4 Individual species dominance areas across parameter

values resulting of interspecific model simulations. Light grey

area delimits dominance of Oscillatoria sp. and exclusion of

Ankistrodesmus falcatus, and dark grey the opposite outcome

202 Aquat Ecol (2014) 48:191–205

123

ecological interpretation of allelopathy under any of

the previously mentioned hypothesis. This is because,

besides knowing which species is allelopathic, it is

also necessary to know the spectrum of action of its

allelochemicals. Otherwise, it would be impossible to

understand the usefulness of allelopathy as a strategy

inside the phytoplankton community.

Our simulations show the great potential of alle-

lopathy against competitors. At a fixed nitrate con-

centration, the allelopathic species would dominate in

the majority of situations (Fig. 4 lower panel) unless

the competitor also has a strong allelopathic effect

(Fig. 4, upper panel). Our simulations also showed

that the outcome of a species-to-species allelopathic

interaction varies a lot along a range of nitrate

availability. This statement would be valid for any

other limiting resource for which differences in

affinity could be found between the competing

species. We found a non-intuitive outcome of the

competition experiments at low nitrate concentrations.

In this case, the stronger allelopathic species (Oscill-

atoria sp.) was favored. The reason might be that A.

falcatus growth with low nitrate slows down very

early, and so, cell abundances are low and more easily

killed by allelochemicals. Only intermediate nitrate

concentrations favor this less-allelopathic species

(Fig. 4 lower panel). High nitrate concentrations again

favor the allelopathic species. There is a constant trend

of increase in the dominance area of Oscillatoria sp.

above the optimal nitrate concentration for A. falcatus

of 90 lmols (Fig. 4, lower panel). This is probably due

to the higher population densities reached by the two

species under these conditions. With higher popula-

tion densities, allelopaty is more effective than in any

other situation (Jonsson et al. 2009). Despite this

interesting effect of nitrate in the dynamics of

competition, the variable that most largely influences

these dynamics is still the proportion of species

abundances.

An interesting question is whether these three

variables (proportion of species abundances, strength

of allelopathic effect, and resource availability) are

dynamic and variable at different scales (time and

space). It is not well known how dynamic could be the

trait ‘‘defense against allelopathy’’ or ‘‘allelopathy.’’

However, it should be expected to vary temporally

with physiological conditions and temporally/spatially

with genetic conditions. This potential relation with

physiological conditions is also an indirect link to

resource availability (because resource availability

induces physiological changes in phytoplankton).

Resource availability and species abundance are both

highly dynamic and variable both in time and space.

An important aspect to consider is that our model

simulates growth in a chemostat as the experimental

system. Such a system is good to analyze simple long-

term ecological experiments, but at the same time has

certain features that make it very different from

natural conditions, particularly, the constant ‘‘envi-

ronmental’’ conditions and the high population den-

sities of the interacting species. This aspect might have

artificially increased the effect of allelopathy in the

interaction. Some authors showed in theoretical mod-

els that low population densities such as those

observed in nature would make allelopathy ineffec-

tive, except at bloom population densities (Jonsson

et al. 2009). However, as we pointed out in the

introduction, the mechanism by which allelochemicals

cause damage to target organisms is not yet com-

pletely known, and it is difficult to be simplified in a

theoretical model. Allelopathy can be observed at low

population densities (Leao et al. 2009), allelochemi-

cals could act synergistically (Leao et al. 2010) and

mobile species could release their allelochemicals

during close contact with target cells (Remmel and

Hambright 2012). Regarding this last aspect, we have

observed in mixed cultures of A. falcatus and Oscill-

atoria sp. that despite these species are not active

swimmers, there is close cell-to-cell contact (A.

falcatus cells get attached to the cyanobacteria

filaments). It is possible then that this contact influ-

ences the magnitude of the allelopathic effect. In

principle, this would increase the strength of the effect.

As a consequence, because we measured the allelo-

pathic effect from cell-free filtrate, we might be

underestimating the true effect.

Other works reporting numerical analyses of alle-

lopathy models, similar to ours, showed the existence

of stable coexistence under certain conditions, for

instance, when there is a trade-off between compet-

itive abilities for the limiting resource and allelopathic

effect (Roy 2009; Martines et al. 2009). This would be

the case in our two selected species. However, all of

those models reported that stable coexistence arises

only if other additional conditions were met: Either the

allelopathic effect is relatively weak (Martines

et al.2009) or if, after allelopathic effect exceeds a

threshold, the pressure of competition for the resource

Aquat Ecol (2014) 48:191–205 203

123

is reduced due to a feedback in the species growth

process caused by nutrient recycling associated with

the mortality caused by allelopathy (Roy 2009). The

simulations performed with our system did not detect

stable coexistence. It should not be ruled out the

possibility that coexistence would occur if we used a

different grid of values for our three variables (ratio of

abundances, strength of allelopathic effect, and nitrate

concentration). We chose our grid of parameter values

with the aim to cover a range where we observed

interesting outcomes (actually, the range over which

the dominance of the species changes). In the present

case, coexistence, if it ever happens, it would be a rare

outcome. If we take the allelopathic effects of both

species at fixed values (with the real values estimated

in our experiments), it seems that the mortality effect

of Oscillatoria sp. is very strong, since it dominates in

the majority of abundance ratios and nitrate concen-

trations. Following Martines et al. (2009), this strong

inhibitory effect would explain why coexistence is not

apparent. However, it could still be possible following

the model in Roy (2009). But it might be possible that

Oscillatoria sp. has an extremely strong allelopathic

effect, exceeding the parameter values that support

stable coexistence in Roy (2009) model (see discus-

sion above about the increased effect of allelopathy in

chemostats). Because our model formulation is also a

bit different than those from Martines et al. (2009) and

Roy (2009), it would be possible as well that the

models are not straight forward comparable. Other

models also showed stable (or bistable) coexistence

when an allelopathic interaction is involved in com-

petition, but they studied different biological systems

than ours (Chao and Levin 1981; Durret and Levin

1997; Hsu and Waltman 2004).

In the present work, we conclude that allelopathy

has potential to be an interaction with strong influence

in plankton dynamics. Because of the apparent relation

with other functional traits (low affinity for nutrients,

preference for low turbulence habitats), the high

specificity showed by our group of species also

highlighted in other works (Fistarol et al. 2004,

Suikkanen et al. 2005; Prince et al. 2008; Hattenrath-

Lehmann and Gobler 2011; Barreiro and Hairston

2013) and the dynamics of the outcome of competi-

tion, which are complex and greatly influenced by the

three variables that we have simulated (ratios of initial

population abundances, allelopathic effect of the

target species, and nitrate concentration). Considering

these aspects highlighted in the present work, we

believe that a future challenge for the study of

allelopathy would be to frame it inside the interactions

that shape phytoplankton community dynamics, par-

ticularly at certain steps of the seasonal succession.

Acknowledgments We are very grateful to P. A. Reis for his

technical support with nitrate analysis. A.B. was supported with

the fellowship SFRH/BPD/73286/2010 from FCT, Portugal. This

project was partially funded by PEst-C/MAR/LA0015/2011.

References

Barreiro A, Hairston NG Jr (2013) The effects of resource

limitation on the allelopathic effect of Chlamydomonas

reinhardtii on other unicellular freshwater planktonic

organisms. J. Plankt Res 35:1339–1344

Boit A, Martinez ND, Williams RJ, Gaedke U (2012) Mecha-

nistic theory and modelling of complex food-web dynam-

ics in Lake Constance. Ecol Lett 15:594–602

Chao L, Levin BR (1981) Structured habitats and the evolution

of anticompetitor toxins in bacteria. Proc Natl Acad Sci

USA 78:6324–6328

Durret R, Levin S (1997) Allelopathy in spatially distributed

populations. J Theor Biol 185:165–171

Fistarol GO, Legrand C, Selander E, Hummer C, Stolte W,

Graneli E (2004) Allelopathy in Alexandrium spp.: effect

on a natural plankton community and on algal monocul-

tures. Aquat Microb Ecol 35:45–56

Graneli E, Weberg M, Salomon PS (2008) Harmful algal

blooms of allelopathic microalgal species: the role of

eutrophication. Harmful Algae 8:94–102

Hattenrath-Lehmann TK, Gobler CJ (2011) Allelopathic inhi-

bition of competing phytoplankton by North American

strains of the toxic dinoflagellate, Alexandrium fundyense:

evidence from field experiments, laboratory experiments,

and bloom events. Harmful Algae 11:106–116

Hiltunen TJ, Barreiro A, Hairston NG Jr (2012) Mixotrophy and

the toxicity of Ochromonas in a pelagic food web. Freshw

Biol 57:2262–2271

Hsu SB, Waltman P (2004) A survey of mathematical models of

competition with an inhibitor. Math Biosci 187:53–91

Huisman J, Sharples J, Stroom JM, Visser PM, Kardinaal WEA,

Verspagen JMH, Sommeijer B (2004) Changes in turbulent

mixing shift competition for light between phytoplankton

species. Ecology 85:2960–2970

Hulot FD, Huisman J (2004) Allelopathic interactions between

phytoplankton species: the roles of heterotrophic bacteria

and mixing intensity. Limnol Oceanogr 49:1424–1434

Hutchinson GE (1961) The paradox of the plankton. Am Nat

95:137–145

Ianora A, Bentley MG, Caldwell GS, Casotti R, Cembella AD,

Engstrom-Ost J, Halsband C, Sonnenschein E, Legrand C,

Llewellyn CA, Paldaviciene A, Pilkaityte R, Pohnert G,

Razinkovas A, Romano G, Tillmann U, Vaiciute D (2011)

The relevance of marine chemical ecology to plankton and

ecosystem function: an emerging field. Mar Drugs 9:

1625–1648

204 Aquat Ecol (2014) 48:191–205

123

International Allelopathy Society (1996) Constitution. Drawn

up during the First World Congress on Allelopathy: A

science for the future. Cadiz, Spain. http://www-ias.uca.es/

bylaws.htm#CONSTIIAS

Jonsson PR, Pavia H, Toth G (2009) Formation of harmful algal

blooms cannot be explained by allelopathic interactions.

Proc Nat Acad Sci USA 107:11177–11182

Kubanek J, Hicks MK, Naar J, Villareal TA (2005) Does the red

tide dinoflagellate Karenia brevis use allelopathy to out-

compete other phytoplankton? Limnol Oceanogr 50:883–895

Leao PN, Vasconcelos MTSD, Vasconcelos VM (2009) Alle-

lopathic activity of cyanobacteria on green microalgae at

low cell densities. Eur J Phycol 44:347–355

Leao PN, Pereira AR, Wei-Ting Liuc NJ, Pevzner PA, Dorrestein

PC, Konig GM, Vasconcelos VM, Gerwick WH (2010)

Synergistic allelochemicals from a freshwater Cyanobacte-

rium. Proc Nat Acad Sci (USA) 107:11183–11188

LeBlanc S, Pick FR, Aranda-Rodrıguez R (2005) Allelopathic

effects of the toxic cyanobacterium Microcystis aeruginosa

on duckweed, Lemna gibba L. Environ Toxicol 20:67–73

Legrand C, Rengefors K, Fistarol GO, Graneli E (2003) Alle-

lopathy in phytoplankton-biochemical, ecological and

evolutionary aspects. Phycologia 42:406–419

Margalef R (1978) Life-forms of phytoplankton as a survival

alternatives under an unstable environment. Oceanol Acta

1:493–509

Martines IP, Boukharov HV, Grover JP (2009) A chemostat

model of resource competition and allelopathy. Appl Math

Comput 215:573–582

Maynard-Smith J (1974) Models in ecology. Cambridge Uni-

versity Press, Cambridge

McCracken MD, Middaugh RE, Middaugh RS (1979) A

chemical characterisation of an algal inhibitor obtained

from Chlamydomonas. Hydrobiologia 70:271–276

Passarge J, Hol S, Escher M, Huisman J (2006) Competition for

nutrients and light: stable coexistence, alternative stable

states or competitive exclusion? Ecol Monogr 76:57–72

Prince EK, Myers TL, Kubanek J (2008) Effects of harmful

algal blooms on competitors: allelopathic mechanisms of

the red tide dinoflagellate Karenia brevis. Limnol Ocea-

nogr 53:531–541

Remmel EJ, Hambright KD (2012) Toxin-assisted micropre-

dation: experimental evidence shows that contact microp-

redation rather than exotoxicity is the role of Prymnesium

toxins. Ecol Lett 15:126–132

Ribalet F, Berges JA, Ianora A, Casotti R (2007) Growth inhibition

of cultured marine phytoplankton by toxic algal-derived

polyunsaturated aldehydes. Aquat Toxicol 85:219–227

Roy S (2009) The coevolution of two phytoplankton species on

a single resource: allelopathy as a pseudo-mixotrophy.

Theor Pop Biol 75:68–75

Sanford LP (1997) Turbulent mixing in experimental ecosystem

studies. Mar Ecol Prog Ser 161:265–293

Scheffer M, Rinaldi S, Gragnani A, Muur LR, van Nes EH

(1997) On the dominance of filamentous cyanobacteria in

shallow, turbid lakes. Ecology 78:272–282

Scheffer M, Carpenter S, Foley JA, Folke C, Walkerk B (2001)

Catastrophic shifts in ecosystems. Nature 413:591–596

Smayda TJ (1997) Harmful algal blooms: their ecophysiology

and general relevance to phytoplankton blooms in the sea.

Limnol Oceanogr 42:1137–1153

Smayda TJ, Reynolds CS (2001) Community assembly in

marine phytoplankton: application of recent models to

harmful dinoflagellate blooms. J Plank Res 23:447–461

Sole J, Garcıa-Ladona E, Ruardij P, Estrada M (2005) Model-

ling allelopathy among marine algae. Ecol Model 183:

373–384

Sterner RW (1989) Resource competition during seasonal suc-

cession toward dominance by cyanobacteria. Ecology

70:229–245

Stomp M, Huisman J, Voros L, Pick FR, Laamanen M, Hav-

erkamp T, Sta LJ (2007) Colorful coexistence of red and

green picocyanobacteria in lakes and seas. Ecol Lett 10:

290–298

Suikkanen S, Fistarol GO, Graneli E (2005) effects of cyano-

bacterial allelopchemicals on a natural plankton commu-

nity. Mar Ecol Progr Ser 287:1–9

Taylor PA, Williams JL (1975) Theoretical studies on the

coexistence of competing species under continuous-flow

conditions. Can J Microbiol 21:90–98

Tilman D, Kilham SS, Kilham P (1982) Phytoplankton com-

munity ecology: the role of limiting nutrients. Ann Rev

Ecol Sys 13:349–372

Weissbach A, Rudstrom M, Olofsson M, Bechemin C, Icely J,

Newton A, Tillmann U, Legrand C (2011) Phytoplankton

allelochemical interactions change microbial food web

dynamics. Limnol Oceanogr 56:899–909

Xiaoqing J, Xiaotian H, Li Z, Baijuan Y, Zhiming Y, Jingzhong

Z (2011) Allelopathic interactions between Prorocentrum

micans and Skeletonema costatum or Karenia mikimotoi in

laboratory cultures. Chin J Oceanol Limn 29:840–848

Yamasaki Y, Ohmichi Y, Shikata T, Hirose M, Shimasaki Y,

Oshima Y, Honjo T (2010) Species- specific allelopathic

effects of the diatom Skeletonema costatum. Thalassas

27:27–32

Aquat Ecol (2014) 48:191–205 205

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