“Trophic overyielding”: Phytoplankton diversity promotes zooplankton productivity

Ecology, 93(12), 2012, pp. 2719–2727� 2012 by the Ecological Society of America

‘‘Trophic overyielding’’: Phytoplankton diversity promoteszooplankton productivity

MAREN STRIEBEL,1,2,6 GABRIEL SINGER,2,3 HERWIG STIBOR,4,5 AND TOM ANDERSEN1

1Department of Biology, University of Oslo, P.O. Box 1066, Blindern, 0316 Oslo, Norway2WasserCluster Lunz, Dr. Carl-Kupelwieser-Promenade 5, 3293 Lunz am See, Austria

3Department of Limnology, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria4Department Biology II, Ludwig-Maximilians University Munich, Grosshaderner Strasse 2, 82152 Planegg-Martinsried, Germany

5European Institute for Marine Studies, Technopole Brest-Iroise, Plouzane, France

Abstract. Diversity–productivity relationships at the primary producer level have beenextensively studied, especially for terrestrial systems. Here, we explore whether the diversity ofaquatic primary producers (phytoplankton) has effects on higher trophic levels (zooplankton).We investigated the effect of phytoplankton diversity on an artificial zooplankton communityin a laboratory experiment where phytoplankton biomass and elemental composition (carbon-to-phosphorus ratio) were kept constant. Phytoplankton diversity increased the means of bothzooplankton growth rate and abundance while suppressing their variability, and sustainedhigher zooplankton diversity. Likely explanations include resource complementarity effectsamong phytoplankton species as food entities, as well as niche complementarity effects amongDaphnia species as competitors. By affecting the productivity as well as the variability of thenext trophic level, biodiversity of primary producers may have far-reaching consequences inaquatic food webs.

Key words: biodiversity; complementarity; facilitation; plankton; predictability; productivity; reliabil-ity; trophic transfer.

INTRODUCTION

The basic idea of diversity–productivity relationships

dates back to Darwin (1859) who observed that greater

plant diversity leads to greater primary productivity, an

observation that was later experimentally supported by

Tilman et al. (1996) and Hector et al. (1999) in the same

type of grassland system. Until today, grassland

communities remain the ecosystems where most exper-

iments targeting the relationships between diversity and

productivity have been done (Hector et al. 1999, Tilman

et al. 2001, Hooper and Dukes 2004). Consequently, our

current knowledge about diversity–productivity rela-

tionships is much limited to primary producers in

terrestrial systems. This seems a serious shortcoming,

given that 50% of primary production on earth occurs in

aquatic environments (Falkowski et al. 1998) and

considering the high diversity often encountered at the

base of aquatic ecosystems (Hutchinson 1961). Aquatic

communities are also characterized by short generation

times and efficient trophic transfer (Sommer et al. 1986)

along potentially long food chains (Post et al. 2000).

These characteristics turn aquatic ecosystems into the

obvious place to test if diversity at the level of primary

producers has effects on productivity at higher trophic

levels.

Among the mechanisms proposed to explain the

positive effects of diversity on productivity at a single

trophic level are niche complementarity (i.e., resource

use partitioning), facilitative mechanisms and/or sam-

pling effects (Srivastava and Vellend 2005). Niche

complementarity occurs when differentiation in individ-

ual traits allows a diverse community to use resources

more efficiently, thus leading to higher community

productivity. Facilitation describes a positive effect of

a species on the functional capability of another species,

which may again result in better performance of the

mixture compared to monocultures. The sampling effect

refers to the increased probability of including a

particularly productive species when the total number

of species increases. These mechanisms may result in

overyielding; a term used when multi-species assemblag-

es perform better than expected from the performances

of its member species in monoculture (Hector et al.

2002).

When considering only the quantity of resources

available to consumers, overyielding of the primary

producer community may result in a simple bottom-up

driven increased consumer productivity. However, the

increased resource use efficiency in a functionally diverse

primary producer assemblage may also have negative

consequences for consumers by decreasing the stoichio-

metric quality of their food, i.e., increasing the mismatch

in nutrient stoichiometry between autotrophs and

zooplankton consumers (Andersen and Hessen 1991,

Main et al. 1997, Elser et al. 2000). For example, in a

Manuscript received 2 January 2012; revised 19 April 2012;accepted 12 July 2012. Corresponding Editor: E. van Donk.

6 E-mail: [email protected]

2719

phytoplankton community, an increase of functional

diversity in terms of photosynthetic pigments was shown

to not only increase phytoplankton productivity but also

influence its biomass stoichiometry (Striebel et al.

2009a, b). Thus, for a reductionist test of the effect of

primary producer diversity on zooplankton productiv-

ity, food quantity and stoichiometric quality must be

kept constant.

We performed a laboratory experiment with a diverse

zooplankton community (different Daphnia species)

under controlled food conditions where phytoplankton

biodiversity was manipulated while food quantity and

food quality in terms of biomass stoichiometry were

attempted to be maintained constant. Daphnia was

chosen due to its non-selective feeding nature, which

helps to avoid additional difficulties like specific

selection of food.

METHODS

We used an initial zooplankton community composed

of two individuals of each of three Daphnia species

originating from lakes and ponds in Germany, and fed

them with artificial phytoplankton communities of

differing biodiversity. The three zooplankton species

were Daphnia magna, Daphnia similis, and Daphnia

hyalina (all clones from Munich). The zooplankters were

kept in 500 mL Daphnia medium (Kluttgen et al. 1994),

and transferred to fresh medium every third day to

minimize growth of bacteria and algae. A constant

amount of food (0.4 mg C/L) was added every day. We

maintained a constant temperature (228C) and light (100

lmol photons�m�2�s�1) during the entire experiment.

Abundance and biomass (based on carbon measured by

combustion and infrared spectrometry; C-Mat 5500;

Strohlein, Korschenbroich, Germany) for the three

Daphnia species were determined from aliquots at the

beginning and from all experimental units at the end of

the experiment (i.e., after three weeks). Specific carbon-

based growth rates of zooplankters (averages for whole

communities) were calculated as ln ([Cend/Cinit]/[Tend �Tinit]), where Cend is the zooplankton carbon concentra-

tion (in mg) at the end of the experiment, Cinit the

zooplankton carbon concentration (in mg) at the

beginning, and (Tend � Tinit) the duration of the

experiment in days. Daphnia diversity was expressed as

the Shannon diversity number [exp(H ), i.e., the effective

number of species (Jost 2006)] of Daphnia biomass:

expðHÞ ¼ exp �XD

d¼1

pdlnðpdÞ !

with pd ¼Bd

XD

d¼1

Bd

where pd is the relative biomass B of surviving Daphnia

species d, and D is maximally 3.

Artificial phytoplankton communities with identical

total biovolume (equivalent to 0.4 mg C/L) but different

biodiversity (1–10 species) were used as food for our

zooplankton communities. Species were added as equal

biovolume contributions (5.0 3 106 lm3/mL) in all the

artificial phytoplankton communities. The 12 different

phytoplankton strains used in this experiment were pre-

cultivated as monocultures in semi-batch cultures (10%exchange per day) with standardized growth medium(WC-medium after Guillard and Lorenzen 1972) for

months. Biovolume concentrations of all phytoplankton

cultures were determined daily during the experiment

using an electronic particle counter (CASY counter;

Scharfe Systems, Reutlingen, Germany). Artificial phy-

toplankton communities were randomly assembled fromthis total pool of 12 species. We used 10 different

mixtures of 2, 4, 7, and 10 species and all 12

monocultures yielding a total of 52 phytoplankton–food

combinations as replicates for a regression approach (see

Appendix: Table A1 for exact food composition).

Particulate organic carbon (POC) and particulatephosphorus (PP) concentrations of the resulting food

mixtures were determined three times during the

experiment for control purposes. This was done by

filtration of samples onto precombusted and acid-

washed glass-fiber filters (Whatman GF/F) followed

by combustion and infrared spectrometry (C-Mat 5500,Strohlein) to determine POC, and by molybdate reaction

after sulfuric acid digestion (Wetzel and Likens 2003) to

measure PP. Mean food POC was 0.4 6 0.01 mg C/L

(mean 6 SE) and mean food PP was 3.6 6 0.24 lg P/L,

resulting in a mean molar C:P ratio of 115.8 6 3.4

during the experiment.

Since all phytoplankton species were added to multi-species food treatments at known (equal) biovolumes, it

was possible to compute a predicted Daphnia growth

rate for each multi-species diet as the average growth

rate observed for the corresponding monoculture foods.

To describe effects of phytoplankton community diver-

sity on Daphnia growth rate we plotted the observed

Daphnia growth rate against the predicted growth rate.In the same manner, we computed predicted abundances

from monoculture data.

Daphnia diversity was predicted from monoculture

data by first summing up species-specific abundances

across the respective monocultures used in a mixture,

followed by computing exp(H )pred once from the

cumulative distribution

expðHÞpred ¼ exp �XD

d¼1

pcumdlnðpcumdÞ !

with

pcumd ¼

XM

m¼1

Bd;m

XM

m¼1

XD

d¼1

Bd;m

where pcumd is the relative abundance of Daphnia

species d cumulated across M monocultures.

The question of Daphnia species niche preferences was

also pursued in a combined analysis of community

compositions of the phytoplankton treatments and the

MAREN STRIEBEL ET AL.2720 Ecology, Vol. 93, No. 12

surviving Daphnia communities using a dissimilarity-

based approach. For this purpose, pairwise dissimilarity

between two phytoplankton communities was computed

by the Jaccard coefficient (Legendre and Legendre 1998),

i.e., based on presence/absence of the various phyto-

plankton species and assuming equal biomass contribu-

tion. For surviving Daphnia communities, pairwise

dissimilarities were computed based on the abundance

and biomass distributions at the end of experiments by

the Bray-Curtis dissimilarity index (Bray and Curtis

1957). We then tested for existence of an association

between phytoplankton and zooplankton community

compositions using a Mantel test (Manly 2006).

It was evident that the variability in the Daphnia

responses to the food treatment gradient of increasing

phytoplankton species richness decreased systematically

with increasing species richness of the phytoplankton

prey community. Since decreasing variance with increas-

ing mean cannot be alleviated by standard transforma-

tions, we used a more general approach where both

mean and variance can be modeled as linear functions of

the explanatory variables (Rigby and Stasinopoulos

2005) using the GAMLSS package in R (generalized

additive models for location, scale, and shape; R

Development Core Team 2011). We thereby modeled

Daphnia biomass or growth rate as linear functions of

phytoplankton diversity, but with a non-constant

variance log-linearly related to the same explanatory

variable. This gave a model with 4 degrees of freedom

(instead of 3 for a linear regression with a constant

variance), which is fitted by maximum likelihood instead

of ordinary least squares. Competing GAMLSS models

can be compared using the Akaike information criterion

(AIC) in the same manner as ordinary linear models.

RESULTS

The food supply rate (i.e., POC, daily addition of 0.4

mg C/L) and stoichiometric food quality (PP and C:P)

were attempted to be maintained constant across all

food treatments and throughout the duration of the

experiment. While their means did not show any trend

along the food treatment gradient of phytoplankton

diversities, their variances decreased significantly with

increasing phytoplankton species richness within the

implemented GAMLSS models (Appendix: Table A2

and Fig. A1). We found no significant relationship

between Daphnia growth rates and POC, PP, nor molar

C:P ratios (Fig. 1), in agreement with our experimental

planning. Similarly, no relationships of food quantity or

quality indicators were found with Daphnia abundance

or Daphnia diversity (data not shown).

However, Daphnia growth rate was significantly

related to phytoplankton species richness (Fig. 2a) and

increased by 0.0017 6 0.0004 d�1 (mean slope 6 SE) per

phytoplankton species added, while the variance in

growth rate decreased by 14.7% per phytoplankton

species added (95% confidence interval [CI]:

9.5%�19.7%). The model with variable variance was

substantially better (AIC ¼ �304.9) than one with

constant variance (AIC ¼ �281.8). When predicting

Daphnia growth rate for the experiments with higher

phytoplankton species richness from growth on mono-

cultures (Fig. 2b), we found no increase of Daphnia

growth rate with increasing phytoplankton species

richness (slope �0.0003 6 0.00069, P ¼ 0.66), but a

decrease in variance by 8.7% per added species (CI ¼2.1%–14.9%). Also in this case, the model with variable

variance was better (AIC ¼ �268.6) than one with

constant variance (AIC ¼�263.9). Confidence intervals

for the regression coefficient for a variable variance

overlapped for the observed and predicted Daphnia

growth rate models, thus indicating no significant

difference in the variance trend.

Daphnia abundance (Fig. 2c) increased by 7.3 6 2.8

individuals/L per phytoplankton species added, while its

variance decreased by 11.3% per phytoplankton species

added (CI ¼ 5.7% �16.6%). The model with variable

variance was substantially better (AIC¼ 602.3) than one

FIG. 1. (a) Food carbon content, (b) food phosphorus content, and (c) food molar C:P ratios related to Daphnia growth rate.Data for food quantity and quality are mean values obtained from three measurements during the experiment.

December 2012 2721TROPHIC OVERYIELDING

with constant variance (AIC ¼ 614.4). Here too,

predicted Daphnia abundance (Fig. 2d) showed no

increase of the mean Daphnia abundance with increasing

phytoplankton species richness (slope 0.12 6 1.95, P ¼0.95), but a decrease in variance by 17.8% per added

species (CI ¼ 12.2%�23.1%). The model with variable

variance was again better (AIC¼ 564.14) than one with

constant variance (AIC ¼ 592.53.9). Again, confidence

intervals for the regression coefficient for a variable

variance overlapped for the observed and predicted

Daphnia abundance models, thus indicating no signifi-

cant difference in the variance trend.

Out of 40, 35 Daphnia growth rates on multi-species

foods were higher than predicted from growth on the

corresponding monoculture diets (Fig. 3a), with the

two- and four-species treatments being closer to the 1:1

line than the seven- and 10-species treatments. We

computed a potential diversity effect on the Daphnia

growth rate as the ratio of observed growth

rate : predicted growth rate. This ratio was significantly

different among the species richness treatments (Fig. 3b;

one-way ANOVA, F3,36 ¼ 16.88, P , 0.001). As

variances were obviously unequal among treatments

(especially the seven-species mixture) we also performed

a nonparametric Kruskal-Wallis test with identical

outcome (v2 ¼ 21.5, P , 0.001). The 95% CI for the

effect sizes (group means) were 0.999–1.087 for two

species, 0.974–1.062 for four species, 1.174–1.262 for

seven species, and 1.061–1.150 for 10 species. Thus,

effect sizes are significantly different from 1 for the

seven- and 10-species mixtures.

Daphnia diversity (exp(H )) significantly increased by

0.038 6 0.016 units per phytoplankton species added

(Fig. 4a). Also here, the model with variable variance

was better (AIC ¼ 42.9) than the one with constant

variance (AIC ¼ 44.6). Excluding the highest value for

FIG. 2. Phytoplankton species richness determines (a) Daphnia growth rate and (c) Daphnia abundance after three weeks ofgrowth. Predicted effects of phytoplankton species richness on (b) Daphnia growth rate and (d) Daphnia abundance are calculatedfrom monoculture data. All data shown were fitted with GAMLSS (generalized additive models for location, scale, and shape)models where the heavy black line represents the conditional expectation and gray lines represent percentiles of the conditionaldistribution. Outermost gray lines correspond to 95% confidence intervals for the fitted conditional distributions. See Table 1 forparameter estimates of the fitted models.


Daphnia diversity (phytoplankton species richness of

10), the relationship between phytoplankton species

richness and Daphnia diversity was no longer statistical-

ly significant (P ¼ 0.22) but showed a similar positive

trend. When computing a predicted Daphnia diversity

there was a slight increase with increasing phytoplank-

ton species richness (slope 0.0113 6 0.0048, P , 0.05),

and the decrease in variance with increasing phyto-

plankton species richness was significant (CI ¼13.5%�23.3%; Fig. 4b).

In the monocultures, on average only 1.75 6 0.2

(mean 6 SE) of the three Daphnia species survived until

the end of the experiment and extinction probability was

not evenly distributed across three Daphnia species.

While D. magna survived in all monoculture experi-

ments, D. similis and D. hyalina showed survival rates of

25% and 58%, respectively. In treatments with a

phytoplankton species richness of 7 and 10, species

richness was slightly higher (mean 6 SE for seven

species, 2.0 6 0.1, and for 10 species, 2.1 6 0.15) and

also evenness was slightly but insignificantly higher with

higher phytoplankton species richness (Fig. 4c and d).

At the end of the experiment, the relative abundance

(and biomass) of the three Daphnia species differed

across treatments with D. magna being over-propor-

tionately abundant throughout all experiments and

treatments (Appendix: Fig. A2).

The surviving zooplankton community composition

was significantly associated with the community compo-

sition of the phytoplankton treatments (Mantel tests for

abundance and biomass, r¼ 0.20, P , 0.05 and r¼ 0.28,

P , 0.001, respectively). This relationship remained

significant when the differences in species richness

between phytoplankton community pairs were included

as a covariate in a partial Mantel test (abundance, r ¼0.20, P , 0.05; biomass, r¼ 0.27, P , 0.001).

DISCUSSION

Phytoplankton diversity increases zooplankton growth,

abundance, and diversity

Our experiment tested effects of phytoplankton

diversity on zooplankton growth, abundance, and

TABLE 1. Fitted linear models with phytoplankton species richness (SR) as the independentvariable.

Parameter Equation P 95% CI (%)

Daphnia growth rate 0.1958 þ 0.0017SR ,0.001 9.5�19.7Predicted Daphnia growth rate 0.1908 � 0.0003SR 0.66 2.1�14.9Daphnia abundance 246.867 þ 7.316SR ,0.05 5.7�16.6Predicted Daphnia abundance 222.29 þ 0.1193SR 0.95 12.2�23.09Daphnia diversity, exp(H ) 1.26 þ 0.038SR ,0.05 �11.88�0.4Predicted Daphnia diversity, exp(H ) 1.17 þ 0.012SR ,0.05 13.5�23.3

Notes: For Daphnia growth rate (Fig. 2a), predicted Daphnia growth rate (Fig. 2b), Daphniaabundance (Fig. 2c), predicted Daphnia abundance (Fig. 2d), Daphnia diversity (Fig. 4a), andpredicted Daphnia diversity (Fig. 4b), the model was fitted allowing variable variances (generalizedadditive models for location, scale, and shape [GAMLSS]). The last column refers to the 95%confidence interval for the relative decrease in variance per phytoplankton species added (i.e., thelog-linear slope of the GAMLSS model for the variance) (n ¼ 52 replicates).

FIG. 3. (a) Predicted and observed data for Daphnia growthrates displayed with a line showing a 1:1 ratio and with symbolsindicating the phytoplankton species richness treatments (2, 4,7, and 10). (b) Box plots showing the relative change in specificDaphnia growth rate (observed/predicted), grouped by numberof species in the mixture (one-way ANOVA, F3,36¼ 16.88, P ,0.001). Box plots give the median (central line), first and thirdquartiles (box limits), and most extreme values within intervals1.5 times the box height away from the box limits (whiskers).


diversity under constant light conditions. Zooplankton

growth clearly increased with phytoplankton diversity

(Fig. 2a), while it was not related to phytoplankton

biomass or stoichiometry (Fig. 1). Zooplankton growth

rates predicted from monocultures (Fig. 2b) showed no

effect of phytoplankton diversity other than a reduction

in variance due to statistical averaging (Fig. 2b and d).

Similar to zooplankton growth, zooplankton abundance

(Fig. 2c), and Daphnia diversity (Fig. 4a) both increased

with phytoplankton diversity. When using phytoplank-

ton mixtures of more than four species, the Daphnia

communities grew considerably better than predicted

from monocultures (Fig. 3). We interpret this increased

performance of a zooplankton community feeding on a

diverse phytoplankton community (see Plate 1) as trophic

overyielding, i.e., a specific form of overyielding involving

two trophically interacting diverse assemblages. The

main difference from overyielding as defined by Hector

et al. (2010) is that performance is measured at the next

trophic level, i.e., zooplankton growth is considered as

the response function instead of the net primary

productivity of the plant community. Behind this effect,

there could be multiple mechanisms at both trophic

levels. At the phytoplankton level complementarity

among phytoplankton traits with regard to zooplankton

requirements is a likely mechanism; based, for instance,

on the diversity of cell sizes, structures, shapes, and

nutritional content (e.g., fatty acid composition), which

translate into packing density, digestibility, and nutri-

tional quality of phytoplankton species. Multiple phyto-

plankton species, respectively their traits, could also

interact positively to fuel increased function at the next

trophic level, for example in a situation of zooplankton

co-limitation by multiple nutritional components pro-

FIG. 4. Phytoplankton species richness determines (a) Daphnia diversity [exp(H )], (c) Daphnia species richness, and (d) Daphniaevenness. (b) Predicted data for effects of phytoplankton species richness on Daphnia diversity are calculated from monoculturedata. See Table 1 for parameter estimates of the fitted models [panels (a) and (b)]. Data for Daphnia species richness and evennessare displayed as means 6 SE.


vided by different phytoplankton species (Sperfeld et al.

2012). In the light of classically discussed mechanisms for

biodiversity–ecosystem-functioning relationships, we un-

derstand our suggested complementarity at phytoplank-

ton level as inclusive of such potential facilitative

interactions (see also Loreau and Hector 2001). It is also

possible that individual phytoplankton traits translate

into exceptional nutritional value, thereby giving over-

proportional importance to single phytoplankton species

in sustaining zooplankton growth. Further selection of

phytoplankton species during the ingestion process by

zooplankton is possible, but unlikely given the unselec-

tive feeding behavior for Daphnia. Still, such a sampling

or selection effect could indeed play a role in our

experiments, but unfortunately the classical approach

partitioning selection and complementarity (Loreau and

Hector 2001) is not applicable in our situation as we have

no knowledge about phytoplankton species-specific

yields, measured at zooplankton level, for the mixture

food treatments. This would require an idea about the

distribution of ingestion probabilities and knowledge

about actual phytoplankton species-specific absorption

efficiency.

Importantly, these phytoplankton trait-based mecha-

nisms can already beneficially affect single zooplankton

species or even individuals. However, the increased

heterogeneity in zooplankton resource requirements

given by multiple zooplankton species or even just

different life stages of single species may further have

amplified this consumer productivity-enhancing effect

given by phytoplankton traits. Seen from this point of

view, higher phytoplankton diversity is equivalent to

increased resource heterogeneity, i.e., wider niche space,

which can be better exploited by various zooplankters

complementing each other in body sizes, filtration

characteristics and other species- or life-stage-specific

traits. Our results could thus also partly be a conse-

quence of increasingly complementary resource use by

the same (minimally diverse) zooplankton community

along a gradient of increasing resource heterogeneity.

The coexistence of several species competing for several

resources in a heterogeneous environment was predicted

to lead to overyielding (Tilman 1999), and resource

heterogeneity was also empirically shown to lead to

steeper diversity–function relationships (Tylianakis et al.

2008). In our experiment zooplankton diversity showed

a tendency to be maintained to a larger extent in

treatments with higher phytoplankton (Fig. 4a). Evi-

dence for niche separation of the three Daphnia species

along the experimental food gradient of 12 phytoplank-

ton species is given by the increasing trend of mean

predicted diversity with phytoplankton species richness.

This result is also in agreement with the differential

survival of D. similis and D. hyalina in the various

monocultures. The much higher survival rate and

abundances of D. magna at the end of the experiment

may simply be due to higher body size and thus higher

numbers of juvenile offspring. Irrespective of this

competitive advantage of D. magna, a last evidence for

niche separation is provided by the significant relation-

PLATE 1. Diverse phytoplankton community as seen under an inverted microscope. Photo credit: M. Striebel.


ship of the surviving zooplankton community composi-

tion with the experimental phytoplankton treatments

provided by the Mantel tests. We are confident that

these test results truly reflect an association of commu-

nity compositions between zoo- and phytoplankton, as

results were almost identical when the diversity treat-

ment gradient was accounted for in the respective partial

Mantel tests.

We thus conclude that there is evidence for differences

in Daphnia species traits, which could support comple-

mentarity among Daphnia species as an important

mechanism in our experiment.

Trophic overyielding could thus be based on both a

diverse phytoplankton and a diverse zooplankton

assemblage, whose potentials for complementarity

effects could in fact interact multiplicatively. Our

experimental setup used only one constant zooplankton

assemblage and we are thus unable to separate

complementarity effects based on phytoplankton traits

from those based on zooplankton traits.

The variance in zooplankton growth is affected

by phytoplankton diversity

In our experiment, the variance of the zooplankton

growth rate and the variance of the predicted growth

rate decreased with increasing phytoplankton diversity

(by 14.7% and 8.7% per phytoplankton species added

for growth rate and predicted growth rate, respectively;

Fig. 2a and b). The same pattern existed with respect to

Daphnia abundance; here the variance in observed

abundance decreased by 11.3% per phytoplankton

species added (CI ¼ 5.7–16.6; Fig. 2c) and the variance

in predicted abundance decreased by 17.8% per phyto-

plankton species added (CI ¼ 12.2–23.09, Fig. 2d). A

similar statistical averaging mechanism is also known as

the driver of the ‘‘portfolio effect,’’ which attributes a

higher stability to more diverse communities (Doak et

al. 1998, Tilman 1999). In the context of temporal

variability and stability statistical averaging means that

the coefficient of variation of a group of species is

necessarily less than the average coefficient of variation

of the individual species (Tilman et al. 1998). A temporal

scale was not included in our experiments, but we can

interpret the variance among treatments at the same

diversity level as a measure of ecosystem reliability

(Naeem and Li 1997) or ecosystem predictability, which

is inversely related to temporal and spatial variation of

an ecosystem process (McGrady-Steed et al. 1997).

Then, the observed effect of phytoplankton diversity on

the variability of zooplankton growth suggests a far-

reaching influence of phytoplankton diversity on the

reliability of resource provision along the food chain.

CONCLUSION

Our results show that phytoplankton diversity affects

zooplankton growth. The experiment demonstrated that

when controlling for resource quantity and resource C:P

stoichiometry, phytoplankton diversity per se can be an

important predictor of zooplankton growth rate, thus

constituting an important basal parameter for explain-

ing zooplankton population dynamics. This is in line

with previous studies (Tilman 1999) that have proposed

to include biodiversity in addition to disturbance,

nutrient supply, and climate as major controllers of

population and ecosystem dynamics. Furthermore, we

were able to reveal in an experimental setup what Yachi

and Loreau (1999) showed theoretically; we found (1) a

buffering effect, a reduction in variance in zooplankton

growth and abundance with increasing phytoplankton

diversity, and (2) a performance-enhancing effect, an

increase of the mean growth rate, abundance, and

diversity of zooplankton with increasing phytoplankton

diversity. Thus, by affecting the productivity as well as

the variability of the next trophic level, biodiversity of

primary producers may have far-reaching consequences

within the whole food web. As most experimental

studies dealing with the relationship between diversity

and ecosystem functions cover a narrow range of

richness relative to natural variation due to methodo-

logical constraints on manipulating large numbers of

species (Cottingham et al. 2001), a major future

challenge is to determine how biodiversity dynamics,

ecosystem processes, and abiotic factors interact in

natural and/or large scale systems as first examples

demonstrate (see, e.g., Ptacnik et al. 2008, 2010).

ACKNOWLEDGMENTS

This study was supported by funding from the NorwegianResearch Council (project COMSAT; 196336/S30) and apersonal grant for M. Striebel from the DAAD. We thankMechthild Kredler, Margit Feibel, Angelika Wild, and AchimWeigert for technical support and Sabine Dietrich for helpduring the experiment.

LITERATURE CITED

Andersen, T., and D. O. Hessen. 1991. Carbon, nitrogen, andphosphorus-content of fresh-water zooplankton. Limnologyand Oceanography 36:807–814.

Bray, R. J., and J. T. Curtis. 1957. An ordination of the uplandforest communities of southern Wisconsin. EcologicalMonographs 27:325–349.

Cottingham, K. L., B. L. Brown, and J. T. Lennon. 2001.Biodiversity may regulate the temporal variability ofecological systems. Ecology Letters 4:72–85.

Darwin, C. 1859. On the origin of species by means of naturalselection, or the preservation of favoured races in the strugglefor life. First edition. John Murray, London, UK.

Doak, D. F., D. Bigger, E. K. Harding, M. A. Marvier, R. E.O’Malley, and D. Thomson. 1998. The statistical inevitabilityof stability–diversity relationships in community ecology.American Naturalist 151:264–276.

Elser, J. J., W. F. Fagan, R. F. Denno, D. R. Dobberfuhl, A.Folarin, A. Huberty, S. Interlandi, S. S. Kilham, E.McCauley, K. L. Schulz, E. H. Siemann, and R. W. Sterner.2000. Nutritional constraints in terrestrial and freshwaterfood webs. Nature 408:578–580.

Falkowski, P. G., R. T. Barber, and V. Smetacek. 1998.Biogeochemical controls and feedbacks on ocean primaryproduction. Science 281:200–206.

Guillard, R. R., and C. J. Lorenzen. 1972. Yellow-green algaewith chlorophyllide c. Journal of Phycology 8:10–14.


Hector, A., E. Bazeley-White, M. Loreau, S. Otway, and B.Schmid. 2002. Overyielding in grassland communities: testingthe sampling effect hypothesis with replicated biodiversityexperiments. Ecology Letters 5:502–511.

Hector, A., et al. 1999. Plant diversity and productivityexperiments in European grasslands. Science 286:1123–1127.

Hector, A., et al. 2010. General stabilizing effects of plantdiversity on grassland productivity through populationasynchrony and overyielding. Ecology 91:2213–2220.

Hooper, D. U., and J. S. Dukes. 2004. Overyielding amongplant functional groups in a long-term experiment. EcologyLetters 7:95–105.

Hutchinson, G. E. 1961. The paradox of the plankton.American Naturalist 95:137–145.

Jost, L. 2006. Entropy and diversity. Oikos 113:363–375.Kluttgen, B., U. Dulmer, M. Engels, and H. Ratte. 1994.ADaM, an artificial freshwater for the culture of zooplank-ton. Water Research 28:743–746.

Legendre, P., and L. Legendre. 1998. Numerical ecology. Secondedition. Elsevier Science, Amsterdam, The Netherlands.

Loreau, M., and A. Hector. 2001. Partitioning selection andcomplementarity in biodiversity experiments. Nature413:548.

Main, T. M., D. R. Dobberfuhl, and J. J. Elser. 1997. N:Pstoichiometry and ontogeny of crustacean zooplankton: Atest of the growth rate hypothesis. Limnology and Oceanog-raphy 42:1474–1478.

Manly, B. F. J. 2006. Randomization, bootstrap and MonteCarlo methods in biology. Third edition. Chapmann andHall, London, UK.

McGrady-Steed, J., P. M. Harris, and P. J. Morin. 1997.Biodiversity regulates ecosystem predictability. Nature390:162–165.

Naeem, S., and S. Li. 1997. Biodiversity enhances ecosystemreliability. Nature 390:507–509.

Post, D. M., M. L. Pace, and N. G. J. Hairston. 2000.Ecosystem size determines food-chain length in lakes. Nature405:1047–1049.

Ptacnik, R., T. Andersen, P. Brettum, L. Lepisto, and E.Willen. 2010. Regional species pools control communitysaturation in lake phytoplankton. Proceedings of the RoyalSociety B 277:3755–3764.

Ptacnik, R., A. Solimini, T. Andersen, T. Tamminen, P.Brettum, L. Lepisto, E. Willen, and S. Rekolainen. 2008.Diversity predicts stability and resource use efficiency innatural phytoplankton communities. Proceedings of theNational Academy of Sciences USA 105:5134–5138.

R Development Core Team. 2011. R: a language andenvironment for statistical computing. R Project for Statis-tical Computing, Vienna, Austria. www.R-project.org

Rigby, R. A., and D. M. Stasinopoulos. 2005. Generalizedadditive models for location, scale and shape (with discus-sion). Journal of the Royal Statistical Society 54:507–554.

Sommer, U., Z. M. Gliwicz, W. Lampert, and A. Duncan. 1986.The PEG-model of seasonal succession of planktonic eventsin fresh waters. Archiv fur Hydrobiologie 106:433–471.

Sperfeld, E., D. Martin-Creuzburg, and A. Wacker. 2012.Multiple resource limitation theory applied to herbivorousconsumers: Liebig’s minimum rule vs. interactive co-limita-tion. Ecology Letters 15:142–150.

Srivastava, D. S., and M. Vellend. 2005. Biodiversity-ecosystemfunction research: Is it relevant to conservation? AnnualReview of Ecology, Evolution, and Systematics 36:267–294.

Striebel, M., S. Behl, S. Diehl, and H. Stibor. 2009a. Spectralniche complementarity and carbon dynamics in pelagicecosystems. American Naturalist 174:141–147.

Striebel, M., S. Behl, and H. Stibor. 2009b. The coupling ofbiodiversity and productivity in phytoplankton communities:consequences for biomass stoichiometry. Ecology 90:2025–2031.

Tilman, D. 1999. The ecological consequences of changes inbiodiversity: a search for general principles. Ecology80:1455–1474.

Tilman, D., C. L. Lehman, and C. E. Bristow. 1998. Diversity–stability relationships: statistical inevitability or ecologicalconsequence? American Naturalist 151:277–282.

Tilman, D., P. B. Reich, J. Knops, D. Wedin, T. Mielke, and C.Lehman. 2001. Diversity and productivity in a long-termgrassland experiment. Science 294:843–845.

Tilman, D., D. Wedin, and J. Knops. 1996. Productivity andsustainability influenced by biodiversity in grassland ecosys-tems. Nature 379:718–720.

Tylianakis, J. M., T. A. Rand, A. Kahmen, A.-M. Klein, N.Buchmann, J. Perner, and T. Tscharntke. 2008. Resourceheterogeneity moderates the biodiversity–function relation-ship in real world ecosystems. PLoS Biology 6:e122.

Wetzel, R. G., and G. E. Likens. 2003. Limnological analyses.Springer-Verlag, Berlin, Germany.

Yachi, S., and M. Loreau. 1999. Biodiversity and ecosystemproductivity in a fluctuating environment: the insurancehypothesis. Proceedings of the National Academy of SciencesUSA 96:1463–1468.

SUPPLEMENTAL MATERIAL

Appendix

A table summarizing all experimental treatments and main characteristics of the algae used as food for our experiment; a tableshowing carbon biomass, phosphorus content, and stoichiometry of the phytoplankton communities; a figure showingphytoplankton richness related to POC, POC predicted, PP, PP predicted, C:P, and C:P predicted; and a figure showing averagezooplankton community composition at the outcome of the experiment as stacked bar plots for each phytoplankton diversitytreatment (Ecological Archives E093-252-A1).


Documents

“Trophic overyielding”: Phytoplankton diversity promotes zooplankton productivity