Weithoff Funcional Classification of PhytoplanktonBy

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OPINION

The concepts of plant functional types and functionaldiversity in lake phytoplankton a new understanding

of phytoplankton ecology?

GU NTR A M W EI THO F F

Department of Ecology and Ecosystem Modeling, Institute for Biochemistry and Biology, University of Potsdam, Potsdam,

Germany

SU M M A R Y

1. This is a discussion of the applicability to the phytoplankton of the concepts of plant

functional types (PFTs) and functional diversity (FD), which originated in terrestrial

plant ecology.

2. Functional traits driving the performance of phytoplankton species reflect importantprocesses such as growth, sedimentation, grazing losses and nutrient acquisition.

3. This paper presents an objective, mathematical way of assigning PFTs and measuring

FD. Ecologists can use this new approach to investigate general hypotheses [e.g. the

intermediate disturbance hypothesis (IDH), the insurance hypothesis and synchronicity

phenomena] as, for example, in its original formulation the IDH makes its predictions

based on FD rather than species diversity.

Keywords: functional diversity, functional traits, life strategies, ordination techniques, phytoplankton

Introduction

The classification of species aids our understanding of

the complexity of nature. Such classification is based

mainly on the morphology and has only recently been

assisted by genetics. At the same time, species have

also been classified according to their functional/

structural properties but, without using distinctly

measurable characters, such classification has had

limited application. The classification of the ecological

strategies of plants proposed by Grime (1977) has

been influential. He assigned species to three groups

based on their evolutionary responses to disturbance

and stress. This CRS concept states that in situationswith a low intensity of disturbance and a low intensity

of stress, such as nutrient limitation, competitive

species dominate, the C-strategists. At low distur-

bance intensity and in a high-stress environmentstress-tolerant species (S-strategists) are expected to

outcompete others. So-called ruderals (R-strategists)

dominate in low-stress and high disturbance environ-

ments. High-stress and high-disturbance environ-

ments are too hostile for any species to persist. This

concept has been transferred and adapted to the

phytoplankton by Reynolds (1988), further modified

(Reynolds, 1997) and has provided a framework to

explain phytoplankton succession with respect to

water column mixing (Reynolds, 1993). It has been

applied both to natural phytoplankton communities

(e.g. Huszar & Caraco, 1998; Melo & Huszar, 2000;Weithoff, Lorke & Walz, 2001) and to experimental

studies (Weithoff, Walz & Gaedke, 2000).

Besides the CRS concept, Reynolds (1980) intro-

duced a functional classification by assigning 14

phytoplankton associations to sets of environmental

conditions, such as lake size, mixing regime, nutri-

ents, light and carbon availability, etc. Over the past

Correspondence: Guntram Weithoff, Department of Ecology and

Ecosystem Modeling, Institute for Biochemistry and Biology,

University of Potsdam, Maulbeerallee 2, D-14469 Potsdam,

Germany. E-mail: [email protected]

Freshwater Biology (2003) 48, 16691675

2003 Blackwell Publishing Ltd 1669


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two decades this approach has been refined and an

upgraded classification was presented recently by

Reynolds et al. (2002). In its present form, 31 associa-

tions are described but the new groupings were

accommodated on intuitive grounds (Reynolds et al.,

2002). However, the different algae forming a single

group have similar morphological features which arepowerful predictors of optimum dynamic perform-

ance (Reynolds & Irish, 1997), although algae with

different ecological strategies might be well adapted to

similar environmental conditions. Nevertheless, the

overall relationships between the algal associations

and their habitats are quite well founded.

Recently, terrestrial plant ecologists have revived

the idea of a functional classification in order to

predict possible changes in the vegetation as a result

of global climate change (Lavorel et al., 1997; Smith,

Shugart & Woodward, 1997). The term plant func-

tional types (PFTs) was coined (Smith et al., 1993)

and defined as sets of species showing similar

responses to the environment and similar effects on

ecosystem functioning (Gitay & Noble, 1997). In

terrestrial environments PFTs differ among habitats

because habitats, ranging from deserts to marshes or

forests, are inhabited by very different kinds of

plants, whereas the pelagic phytoplankton commu-

nities from different water bodies are relatively

similar. Relevant functional traits of species were

proposed to be included in the set of functional

characteristics (Weiher et al., 1999). A functional traitin this context is a feature or property of an organism

which is measurable and influences one or more

essential functional processes such as growth, repro-

duction, nutrient acquisition, etc.

The concept of functional diversity (FD) touches

another aspect of the functional characterisation of

species and communities. FD reflects the functional

multiplicity within a community rather than the

multiplicity of species. A simple measure of FD is

the number of co-occurring functional types (Marti-

nez, 1996), analogous to species richness at the species

level. A more refined quantification of FD includes a

distance measure. In such a case the distance between

species based on their functional traits is calculated,

i.e. FD is high when species with widely differing

functional traits are present in the same community.

Although in many diversity studies a FD is implied,

species diversity or species richness is used as a

diversity measure, which may lead to misinterpreta-

tions. Thus, a new and mathematically quantifiable

measure for FD is needed. In the following section I

describe the basis upon which functional traits should

generally be selected and which traits are useful for

investigating PFT and FD in phytoplankton. After that

I discuss a number of aspects in general ecology that

could be investigated by using the proposed approach.

Selection of functional traits

The selection of traits is of crucial importance because

all subsequent classifications or calculations of eco-

logical distance (see below) depend on them (Gitay &

Noble, 1997). The abundance and dynamics of any

population are driven by ambient standing stock,

growth and loss processes. For phytoplankton, net

growth is the sum of intrinsic growth, sedimentation,

grazing losses and some other less important loss

factors. Therefore, the traits selected for phytoplank-

ton should reflect these three main processes, which

are a surrogate for population performance. In

general, all traits should be easily measurable (Keddy,

1992; McIntyre et al., 1999; Walker, Kinzig & Lan-

gridge, 1999). Thus, for this purpose the capability of a

species to acquire a particular nutrient through

N-fixation or phagotrophy, for example, or the

demand for another like silica, is the appropriate

information, especially as such information is avail-

able even for less well-known species. Resource-

dependent growth rates are scarce in the literatureand more useful for dynamic modelling approaches,

e.g. in the PROTECH model (Reynolds et al., 2001).

In the following, I propose six traits as valuable for

characterising functional aspects of phytoplankton.

Most of them refer to processes which are also

reflected in the dynamic PROTECH model (Elliott

et al., 1999).

Size

According to allometric theory, size is a determinant

of specific physiological activities such as growth, and

the size range of phytoplankton covers more than five

orders of magnitude, ranging from autotrophic pico-

plankton (50 000 lm3). Size in combination with shape also

affects edibility. For Cladocera, the maximum-sized

particle that can be ingested depends directly on the

body size of the animal (Burns, 1968). Additionally,

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size and shape determine the surface to volume ratio

that in turn influences nutrient uptake.

Nitrogen fixation

The potential for nitrogen fixation (e.g. in Nostocales,

Cyanoprokaryota) gives a competitive advantageunder nitrogen-limiting conditions.

Demand for silica

Aside from diatoms, which need silica for their frus-

tules, Chrysophyceae and Synurophyceae form stato-

spores (e.g. Ochromonas), bristles and scales (e.g. Synura

or Mallomonas) also made of silica (Lee, 1999). In addi-

tion, silica increases the specific weight, which leads to

higher sedimentation rates, especially in diatoms.

Phagotrophy

The ability to ingest bacteria serves as an additional

source of nutrients and energy for phagotrophs.

Particularly under nutrient-poor conditions the up-

take of bacteria may contribute significantly to the

phosphorus budget of the cell.

Motility

Mobile organisms can migrate into favourable patches

and counteract sedimentation. This may be of partic-ular advantage in an environment exhibiting steep

gradients, such as the chemocline in stratified lakes

(Gervais, 1997). Being non-motile is not a disadvan-

tage per se as, in shallow waters, sedimentation may

allow nutrient-depleted diatoms to take up minerals

from the sediment surface and begin photosynthesis

again soon after resuspension (Sicko-Goad, Stoermer,

& Fahnenstiel, 1986). This meroplanktic behaviour can

be seen as an efficient life strategy in shallow waters

(Carrick, Aldridge & Schelske, 1993). In addition,

motility effects nutrient deficiency as the movement of

cells minimises the hydrate envelope and, thus, the

diffusive boundary layer for nutrients around the cells

(Pasciak & Gavis, 1974).

Shape

The shape of a cell or colony is important with respect

to their susceptibility to zooplankton grazing. A

suitable measure for ingestibility for filter-feeding

zooplankton is the longest linear dimension (LLD) of

the food item. As mentioned above, the shape together

with size also influences the surface to volume ratio,

which has been used in the PROTECH model to

predict the maximum specific replication rate (Elliott

et al., 1999; Reynolds, Irish & Elliott, 2001). In thepresent study, shape and size are treated separately.

All the above proposed traits are relatively easy to

determine or data are available in the literature.

Extensive and time-consuming preparations for the

taxonomic determination of diatoms or dinoflagellates

are not necessary and the proposed procedure is

robust against new taxonomic findings that have

accelerated recently supported by molecular methods.

The set of traits selected may differ from one inves-

tigator to another and some variation is reasonable

depending on the question under consideration in a

particular study.

Data processing and the assignment of functional

groups and functional diversity

In this section I discuss different mathematical proce-

dures for assigning plants to PFTs and recommend a

promising procedure for calculating FD based on the

above selected traits. The values for the selected

functional traits can be categorised as either binary or

other codes. For some traits, such as silica demand,

the capability of nitrogen fixation or bacterivory,binary data (0, 1) are recommended. For others a

categorisation into size classes (size, LLD) is sugges-

ted. Motility can be classified as: 0, non-motile; 0.5,

buoyancy regulation (through gas vacuoles); and 1,

flagellated species which can move in three-dimen-

sional space. From these data a species-trait matrix is

created. As all traits are standardised in a range of 0

1, all traits are regarded to be of equal importance.

This is not a prerequisite for this approach, because an

a priori weighting is reasonable when some traits are

regarded as ecologically more important than others.

Common methods for determining functional groups

are multivariate statistics, such as clustering tech-

niques and principal component analysis (PCA),

correspondence analysis (CA), canonical correspon-

dence analysis (CCA) or principal coordination ana-

lysis (PCoA) (e.g. Jaksic & Medel, 1990; Leishman &

Westoby, 1992; Pillar, 1999; Usseglio-Polatera et al.,

2000; Kruk et al., 2002). CCA is a valuable option

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when additional environmental data are available. In

this case species are ordinated and environmental

factors are displayed as vectors within the ordination

(e.g. for phytoplankton: Fangstrom & Willen, 1987;

Kruk et al., 2002). Potential pitfalls in CCA with

respect to a functional classification may occur when

functionally different species co-occur under similarenvironmental conditions, e.g. small cryptophytes

and large cyanobacteria colonies in eutrophic lakes

(Sommer et al., 1986).

For cluster analysis, the species-trait matrix is

transformed into a distance matrix. On this basis the

species are clustered and the outcome of the cluster

analysis is a dendrogram, which can be used to assign

species into groups. Cutting the dendrogram into

sub-dendrograms (branches) leads to a number of

functional groups (Petchey & Gaston, 2002). This

procedure can be used arbitrarily depending on the

resolution in question, i.e. whether many or few

groups should be created. The sum of all branch

lengths is taken as the FD (Petchey & Gaston, 2002). It

may be normalised for species number (s) as

FDnorm FD/s, which reflects the mean branch

length and therefore allows comparisons between

samples with different s.

Ordination techniques such as PCA, CA or PCoA

result in biplots based on species traits. Species with

similar ecological traits are located closely together

whereas widely differing species are distant from

each other. These techniques aim for a reduction offactors and thus inherit a weighting of traits. In PCA,

abundant binary data may lead to misinterpretations

but categorised variables are suitable and, therefore,

PCA is not a suitable technique for the proposed

traits. For the generation of biplots in PCA (e.g. PC1

versus PC2 or PC3 versus PC1) the euclidean distance

between species is maintained. In contrast to that, the

resulting biplots from CA display the v2 distance

between species. Different distance measures, of

course, influence the outcome of the functional clas-

sification. In PCoA, the species-trait matrix is trans-

formed into a distance matrix prior to ordination. This

means the suitable distance measure for a particular

investigation can be chosen. This, and robustness even

when using binary data, makes PCoA a suitable

technique for the approach suggested. The first three

axes generated by all three techniques ideally explain

a large amount of the variation in the data set. Thus a

three-dimensional ordination can be seen as an

ecological trait space spanned by the species accord-

ing to their traits. As in dendrograms, functional

groups can be created by dissecting the trait space into

different sub-spaces containing the species forming a

group. These groups can then be compared with the

functional classification proposed by Reynolds et al.

(2002). Additionally, each species has distinct coordi-nates within this ecological trait space, which enables

us to calculate the variation around the community

mean as a measure of FD. In other words, the centre of

gravity of the species present forms the community

mean and the size of the space around it is a measure

of the FD. This centre of gravity represents the

location of a system based on the function of its

inhabitants and community shifts can be observed

according to the directional movements of this centre.

An exemplar comparison of species diversity calcula-

ted according to the Shannon index, and FD for the

phytoplankton of Lake Constance in southern Ger-

many (Gaedke, 1998) is shown in Fig. 1. The species-

trait matrix was generated as described above and

was transformed into a distance matrix using the v2

distance. From the distance matrix a PCoA was run

and the species coordinates were extracted. For FD the

sum of the biomass weighted squared distances of

each species present from the community mean was

calculated for each of the first three axes (weighted

according to the eigenvalue of each axis). Despite

some relation, this figure clearly shows that a high

species diversity does not necessarily coincide with ahigh FD and at intermediate species diversity FD is

0.0 0.2 0.40

1

2

3

4

5

Species

diversity

FD

Fig. 1 Comparison of species diversity and FD from 845 phy-

toplankton samples from Lake Constance over a 21-year period.

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highly variable. This proposed measure is new and

based on distinct functional traits determining the

functional distance between species. Therefore, it

overcomes existing problems with traditional diver-

sity indices (Hurlbert, 1971) and enables new insights

into phytoplankton and general ecology.

Applications of functional classifications and FD

The intermediate disturbance hypothesis

and ecosystem function

Over the past few decades ecologists have searched

for the main factors driving diversity. Among others,

the frequency and intensity of disturbances (the

intermediate disturbance hypothesis (IDH); Connell,

1978), the productivity of a system (e.g. Rosenzweig,

1995) and predation (including herbivory) have been

shown to influence diversity directly. The IDH states

that diversity peaks at an intermediate frequency or

intensity of disturbance as a result of the co-existence

of pioneers, stress-tolerants and ruderals. Thus, the

IDH relies on the concept of FD, but empirical studies

testing this hypothesis have concentrated almost

entirely on species diversity (but see Weithoff et al.,

2001). This same inconsistency appears when species

richness (species number per unit area/volume) is

considered. Calculation of FD overcomes this incon-

sistency and ecologists are encouraged to perform

studies on the IDH in its original sense using FD.In recent years the role of diversity has been

investigated with respect to ecosystem function (e.g.

Schulze & Mooney, 1993; Daz & Cabido, 2001; Kinzig,

Pacala & Tilman, 2001), but no defined measure of FD

was used. I suggest the use of FD, in the way proposed

in this paper, to compare different studies and provide

a more objective way of characterising FD. In aquatic

sciences, FD has often been neglected and the conse-

quences for ecosystem processes of FD in the phyto-

plankton should be investigated in the future.

The insurance hypothesis and functional redundancy

Important questions in community ecology are Why

are there so many species? (Hutchinson, 1961) and

what role do the rare species play in a given

community? (Gaston, 1994, 1996). In many habitats,

a few species dominate the biomass of a community

whilst many others share a very limited amount. The

question arises, therefore, as to whether the rare

species are specialised and have found their ecological

niche or whether they are in a transient state of being

outcompeted or having recently colonised the habitat.

Occupying a distinct niche makes a species less

susceptible to competitive exclusion. The insurance

hypothesis states that rare species form a functionalbackup for dominant species, and that they can

respond quickly to disturbance thus increasing the

resilience of ecosystem processes. There is theoretical

and experimental evidence from plankton and other

communities supporting this hypothesis (McGrady-

Steed, Harris & Morin 1997; Naeem & Li, 1997; Yachi

& Loreau, 1999; Fonseca & Ganade, 2001). Studies on

the insurance hypothesis should be based on quanti-

fied relative functional redundancy among species, as

is possible if the proposed procedure is applied.

Ecosystem shifts

Ecosystems continuously undergo changes caused by

season, climate change or multiple anthropogenic

impacts, such as eutrophication, chemical pollution or

disturbances in the cycling of nutrients or water. Such

changes may cause gradual or rapid shifts from one

state to another (Scheffer et al., 2001). In each case, the

community exhibits a directional change which can be

traced by ordinating the community or its centre of

gravity at any given time in an ecological space based

on the abundance of the functional entities present. Afunctional analysis of phytoplankton long-term data

may reveal such directional shifts, and inter-lake

comparisons are facilitated by the functional ataxo-

nomical approach. Such shifts in communities may

then be related to climate change, eutrophication or

other factors on a broader geographical scale. This

will increase our understanding of perturbed systems

and provide a guide to better management.

Conclusions

The aim here is to encourage phytoplankton ecologists

to adopt functional concepts and to apply them in

phytoplankton research. The selection of functional

traits is crucial and requires broad discussion among

researchers working in different types of lakes. The

traits proposed in this contribution are also open to

debate and different traits or different modalities may

in fact prove more useful. One of the trait selection




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criteria was practicability. Many phytoplankton data

sets already exist and the proposed traits could offer

new insights into phytoplankton and general ecology,

without laboriously collecting new data. Compared

with higher plants, generation times in algae are very

short and, even within a season, true succession takes

place (Sommer et al., 1986). This should allow plank-ton ecologists to investigate hypotheses of general

ecological interest and lead to a better perception of

limnological studies within the general ecological

community (Reynolds, 1998). The application of func-

tional classifications and FD is by no means an

argument for neglecting a careful species determin-

ation and/or autecological research, although it is seen

as a very valuable addition to traditional taxonomic

approaches and to the list of phytoplankton associa-

tions proposed by Reynolds et al. (2002).

Acknowledgments

This manuscript benefited greatly from the comments

by F. Gervais, G. Fussmann, U. Gaedke, V. Bissinger

and E.M. Bell. A. Hildrew helped with clarity. Th.

Kumke gave valuable statistical advice. The data in

Fig. 1 were acquired within the Integrated Research

Project (SFB) 248 The cycling of matter in Lake

Constance and successor projects.

References

Burns C.W. (1968) The relationship between body size of

filter-feeding Cladocera and the maximum size of

particles ingested. Limnology and Oceanography, 33,

675678.

Carrick H.J., Aldridge F.J. & Schelske C.L. (1993) Wind

influences phytoplankton biomass and composition in

a shallow, productive lake. Limnology and Oceanogra-

phy, 38, 11791192.

Connell J.H. (1978) Diversity in tropical rain forests and

coral reefs. Science, 199, 13021310.

Daz S. & Cabido M. (2001) Vive la difference: plant

functional diversity matters to ecosystem processes.Trends in Ecology and Evolution, 16, 646655.

Elliott J.A., Reynolds C.S., Irish A.E. & Tett P. (1999)

Exploring the potential of the PROTECH model to

investigate phytoplankton community theory. Hydro-

biologia, 414, 3743.

Fangstrom I. & Willen E. (1987) Clustering and canonical

correspondence analysis of phytoplankton and envir-

onmental variables in Swedish lakes. Vegetatio, 71, 8795.

Fonseca C.R. & Ganade G. (2001) Species functional

redundancy, random extinctions and the stability of

ecosystems. Journal of Ecology, 89, 118125.

Gaedke U. (1998) Functional and taxonomical properties

of the phytoplankton community: interannual variab-

ility and response to re-oligotrophication. Archiv fur

Hydrobiologie Special Issues: Advances in Hydrobiology,

53, 119141.

Gaston K.J. (1994) Rarity. Chapman & Hall, London.

Gaston K.J. (1996) Biodiversity A Biology of Numbers and

Differences. Blackwell, Oxford.

Gervais F. (1997) Diel vertical migration of Cryptomonas

and Chromatium in the deep chlorophyll maximum of a

eutrophic lake. Journal of Plankton Research, 19, 533550.

Gitay H. & Noble I.R. (1997) What are functional types and

how should we seek them? In: Plant Functional Types

Their Relevance to Ecosystem Properties and Global Change

(Eds T.M. Smith, H.H. Shugart & F.I. Woodward), pp.

319. Cambridge University Press, Cambridge.

Grime J.P. (1977) Evidence for the existence of threeprimary strategies in plants and its relevance to

ecological and evolutionary theory. American Natural-

ist, 111, 11691194.

Hurlbert S.H. (1971) The nonconcept of species diversity:

a critique and alternative parameters. Ecology, 52, 577

586.

Huszar V.L.M. & Caraco N. (1998) The relationship

between phytoplankton composition and physical

chemical variables: a comparison of taxonomic and

morphologicalfunctional approaches in six temperate

lakes. Freshwater Biology, 40, 118.

Hutchinson G.E. (1961) The paradox of the plankton.American Naturalist, 95, 137147.

Jaksic F.M. & Medel R.G. (1990) Objective recognition of

guilds: testing for statistically significant species

clusters. Oecologia, 82, 8792.

Keddy P.A. (1992) A pragmatic approach to functional

ecology. Functional Ecology, 6, 621626.

Kinzig A.P., Pacala S.W. & Tilman D. (2001) The Func-

tional Consequences of Biodiversity. Princeton University

Press, Princeton.

Kruk C., Mazzeo N., Lacerot G. & Reynolds C.S. (2002)

Classification schemes for phytoplankton: a local

validation of a functional approach to the analysis of

species temporal replacement. Journal of PlanktonResearch, 24, 901912.

Lavorel S., McIntyre S., Landsberg J. & Forbes T.D.A.

(1997) Plant functional classifications: from general

groups to specific groups based on response to

disturbance. Trends in Ecology andEvolution, 12, 474478.

Lee R.E. (1999) Phycology. Cambridge University Press,

Cambridge.

1674 G. Weithoff



7/7

Leishman M.R. & Westoby M. (1992) Classifying plants

into groups on the basis of associations of individual

traits evidence from Australian semi-arid wood-

lands. Journal of Ecology, 80, 417424.

Martinez N.D. (1996) Defining and measuring functional

aspects of biodiversity. In: Biodiversity: A Biology of

Numbers and Differences (Ed. K.J. Gaston), pp. 114148.

Blackwell, Oxford.

McGrady-Steed J., Harris P.M. & Morin P.J. (1997)

Biodiversity regulates ecosystem predictability. Nature,

390, 162165.

McIntyre S., Daz M., Lavorel S. & Cramer W. (1999)

Plant functional types and disturbance dynamics

introduction. Journal of Vegetation Sciences, 10, 604

608.

Melo S. & Huszar V.L.M. (2000) Phytoplankton in an

Amazonian flood-plain lake (Lago Batata, Brasil): diel

variation and species strategies. Journal of Plankton

Research, 22, 6376.

Naeem S. & Li S. (1997) Biodiversity enhances ecosystemreliability. Nature, 390, 507509.

Pasciak W.J. & Gavis J. (1974) Transport limitation of

nutrient uptake in phytoplankton. Limnology & Ocea-

nography, 19, 881888.

Petchey O.L. & Gaston K.J. (2002) Functional diversity

(FD), species richness and community composition.

Ecology Letters, 5, 402411.

Pillar V.D. (1999) On the identification of optimal plant

functional types. Journal of Vegetation Sciences, 10, 631

640.

Reynolds C.S. (1980) Phytoplankton assemblages and

their periodicity in stratifying lake systems. HolarcticEcology, 3, 141159.

Reynolds C.S. (1988) Functional morphology and the

adaptive strategies of freshwater phytoplankton. In:

Growth and Reproductive Strategies of Freshwater Phyto-

plankton (Ed. C.D. Sandgren), pp. 388433. Cambridge

University Press, Cambridge.

Reynolds C.S. (1993) Scales of disturbance and their role

in plankton ecology. Hydrobiologia, 249, 157171.

Reynolds C.S. (1997) Vegetation Processes in the Pelagic. A

Model for Ecosystem Theory. ECI, Oldendorf.

Reynolds C.S. (1998) The state of freshwater ecology.

Freshwater Biology, 39, 741753.

Reynolds C.S. & Irish A.E. (1997) Modelling phytoplank-ton dynamics in lakes and reservoirs: the problem of

in-situ growth rates. Hydrobiologia, 349, 517.

Reynolds C.S., Irish A.E. & Elliott J.A. (2001) The

ecological basis for simulating phytoplankton re-

sponses to environmental change (PROTECH). Ecolo-

gical Modelling, 140, 271291.

Reynolds C.S., Huszar V., Kruk C., Naselli-Flores L. &

Melo S. (2002) Towards a functional classification of

the freshwater phytoplankton. Journal of Plankton

Research, 24, 417428.

Rosenzweig M.L. (1995) Species Diversity in Space and

Time. Cambridge University Press, Cambridge.

Scheffer M., Carpenter S.R., Foley J.A., Folke K. & Walker

B. (2001) Catastrophic shifts in ecosystems. Nature, 413,

591596.

Schulze E.D. & Mooney H.A. (1993) Biodiversity and

Ecosystem Function. Springer-Verlag, New York.

Sicko-Goad L., Stoermer E.F. & Fahnenstiel G. (1986)

Rejuvenation of Melosira granulata (Bacillariophyceae)

resting cells from anoxic sediments of Douglas Lake,

Michigan. 1. Light microscopy and 14C uptake. Journal

of Phycology, 22, 2228.

Smith T.M., Shugart H.H., Woodward F.I. & Burton P.J.

(1993) Plant functional types. In: Vegetation Dynamics

and Global Change (Eds A.M. Solomon & H.H. Shugart),

pp. 272292. Chapman & Hall, New York.

Smith T.M., Shugart H.H. & Woodward F.I. (1997) Plant

Functional Types Their Relevance to Ecosystem Propertiesand Global Change. Cambridge University Press, Cam-

bridge.

Sommer U., Gliwicz Z.M., Lampert W. & Duncan A.

(1986) The PEG-model of seasonal succession of

planktonic events in fresh waters. Archiv fur Hydro-

biologie, 106, 433471.

Usseglio-Polatera P., Bournaud P., Richoux P. & Tachet

H. (2000) Biological and ecological traits of benthic

freshwater macroinvertebrates: relationships and defi-

nition of groups with similar traits. Freshwater Biology,

43, 175205.

Walker B., Kinzig A. & Langridge J. (1999) Plant attributediversity, resilience, and ecosystem function: the

nature and significance of dominant and minor

species. Ecosystems, 2, 95113.

Weiher E., van der Werf A., Thompson K., Roderick M.,

Garnier E. & Eriksson O. (1999) Challenging Theo-

phrastus:a commoncore list of plant traitsfor functional

ecology. Journal of Vegetation Sciences, 10, 609620.

Weithoff G., Lorke A. & Walz N. (2000) Effects of water-

column mixing on bacteria, phytoplankton, and

rotifers under different levels of herbivory in a shallow

eutrophic lake. Oecologia, 125, 91100.

Weithoff G., Walz N. & Gaedke U. (2001) The inter-

mediate disturbance hypothesis species diversity orfunctional diversity? Journal of Plankton Research, 23,

11471155.

Yachi S. & Loreau M. (1999) Biodiversity and ecosystem

productivity in a fluctuating environment: the insur-

ance hypothesis. Proceedings of the National Academy of

Sciences USA, 96, 14631468.

(Manuscript accepted 23 June 2003)



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