Limnol. Oceanogr., 56(1), 2011, 110–118
2011, by the American Society of Limnology and Oceanography, Inc.
doi:10.4319/lo.2011.56.1.0110
E
Phytoplankton community composition can be predicted best in terms of
morphological groups
C. Kruk,a,1,* E.T.H.M. Peeters,a E. H. Van Nes,a V. L. M. Huszar,b L. S. Costa,b and M. Scheffera
a Aquatic
Ecology and Water Quality Management Group, Department of Environmental Sciences, Wageningen University,
Wageningen, The Netherlands
b Phycology Laboratory, Botany Department, National Museum, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
Abstract
We explored how well the aggregated biovolume of groups of species can be predicted from environmental
variables using three different classification approaches: morphology-based functional groups, phylogenetic
groups, and functional groups proposed by Reynolds. We assessed the relationships between biovolume of each
group and environmental conditions using canonical correlation analyses as well as multiple linear regressions,
using data from 211 lakes worldwide ranging from subpolar to tropical regions. We compared the results of
these analyses with those obtained for single species following the same protocol. While some species appear
relatively predictable, a vast majority of the species showed no clear relationship to the environmental conditions we had measured. However, both the multivariate and the regression analyses indicated that
morphology-based groups can be predicted better from environmental conditions than groups based on the
other classification methods. This suggests that morphology captures ecological function of phytoplankton well,
and that functional groups based on morphology may be the most suitable focus for predicting the composition of
communities.
Phytoplankton are essential for freshwater and marine
ecological and biogeochemical processes, being the basis of
nearly all food webs in aquatic ecosystems (Arrigo 2005).
Their excessive growth can cause significant threats to local
biodiversity and ecosystem functioning, as in the case of toxic
algal blooms (Paerl and Huisman 2009). Consequently, there
is a growing need to understand and predict the responses of
these communities to shifting environmental conditions, such
as climate change, increasing nutrient inputs, and modifications in flow regimes and land use due to an increasing
anthropogenic pressure (Paerl and Huisman 2009).
Aggregated estimators of phytoplankton abundance
such as chlorophyll a levels or total biovolume can be
predicted relatively well from environmental conditions
(McCauley and Murdoch 1987; Scheffer et al. 2003).
However, community composition is less easily predicted.
Nonetheless, species differ widely in aspects such as
edibility or toxicity, making it important to understand
the drivers of community composition, rather than total
biomass. Unfortunately, there are probably thousands of
phytoplankton species in lakes and oceans, and predicting
species’ individual responses to environmental change is
difficult, if not impossible (Hutchinson 1961; Roy and
Chattopadhyay 2007; Benincà et al. 2008). Therefore, it
makes sense to concentrate prediction efforts on an
intermediate level of species aggregation (Doney et al.
2002; Falkowski et al. 2003; Litchman et al. 2006).
A simple and common way of aggregating species is the
use of major phylogenetic groups. Such an approach
assumes that different phylogenies somehow reflect important ecological differences among species (Webb et al.
2002). An alternative approach is the use of explicit
functional classifications (Padisák and Reynolds 1998;
McGill et al. 2006; Litchman and Klausmeier 2008). The
idea is to cluster species with common traits and similar
responses to environmental changes. Such functional
classifications have been proposed for phytoplankton by
several authors (Reynolds et al. 2002; Salmaso and Padisák
2007; Mieleitner et al. 2008). A particularly influential
functional classification is the one proposed by Reynolds et
al. (2002) based not only on individual functional traits, but
also on the ranges of environmental conditions over which
the species are found to occur, as well as co-occurrence
patterns. These groups are often polyphyletic, recognizing
commonly shared adaptive features, rather than common
phylogeny, to be the key ecological driver. Reynolds et al.
(2002) delineated 31 such associations and the basic pattern
of their distinctive ecologies. While this classification
integrates a rich base of information, it also necessarily
relies in part on expert judgment. Therefore, an alternative,
simpler classification has been proposed, based exclusively
on objective morphological aspects such as individual
volume, surface area, and maximum length (Kruk et al.
2010).
So far there has been no consensus on which represents
the best approach to cluster species when it comes to
predicting the effects of environmental change on community composition. Here, we aim to resolve this issue by
exploring how well the biomass of phytoplankton groups
based on different classification systems can be statistically
explained from environmental conditions in a lake, using
information from a very large number of phytoplankton
communities and environmental variables from different
climate zones and continents.
* Corresponding author: ckruk@yahoo.com
1 Present address: Limnology Section, Institute of Ecology and
Environmental Sciences, Faculty of Sciences, Universidad de la
República, Uruguay
110
20
(0–128)
12.2
(,0.3–178)
2492
(,15.0–8955)
85.6
(,10–394)
474
(35–1950)
no
* Sampling in subpolar lakes only included summer season.
25.0
(14.3–33.0)
1.2
(0.2–4.5)
42
Tropical
S
2.7
(0.5–18)
113
(0–2791)
76.7
(,0.3–284)
6660
(,10–23,533)
388
(29–10,087)
4286
(68–37,928)
no
19.6
(10.0–30.1)
0.8
(0.2–3.9)
40
Subtropical
S
3.1
(0.3–7)
142
(0–9318)
31.9
(0.3–595)
2545
(15.4–14,200)
144
(,10–5600)
1515
(63–25,913)
yes
16.0
(0.4–33.0)
1.0
(0.01–4.0)
11
107
Temperate
S
N
8
(0.2–31)
140
(2–791)
20.1
(,0.3–204)
1893
(100–8900)
438
(15–9141)
2106
(52–22,993)
yes
13.4
(10.0–18.6)
1.5
(0.04–6.8)
11
Subpolar*
S
3.6
(0.7–12)
CLAD
(org L21)
Chl a
(mg L21)
SRSi
(mg L21)
TP
(mg L21)
TN
(mg L21)
Ice cover
T
(uC)
ZSD
(m)
Zmax
(m)
Phytoplankton abundance and biovolume calculation—
Phytoplankton populations were counted in random fields
from fixed Lugol samples, using the settling technique
(Utermöhl 1958). We examined the samples at multiple
magnifications and counted until we reached at least 100
individuals of the most frequent species (Lund et al. 1959).
Organisms between 5 and 100 mm were counted at 4003,
larger organisms were counted at 2003, and organisms
between 5 and 2 mm were counted at 10003. We did not
include picoplanktonic species (less than 2 mm) or species
strongly associated with periphytic communities. For all
lakes, we considered the organism as the unit (unicell,
colony, or filament). Cell numbers per colony as well as
organism dimensions, including maximum linear dimension
(MLD, mm) were estimated. Individual volume (V, mm3),
and surface area (S, mm2) were calculated according to
geometric equations (Hillebrand et al. 1999). For colonial
organisms with mucilage, V and S calculations were made
for whole colonies including mucilage. Presence of aerotopes, flagella, mucilage, heterocytes, and siliceous exoskeletal structures were noted for each relevant organism.
Population biomass was estimated as biovolume (mm3 L21)
and calculated as the individual volume of the species
multiplied by the abundance of individuals. More than 80%
Number of
lakes
Database—We compiled a database of 711 species from
211 lakes located within four climate zones in South
America, Europe, and North America, covering a wide
range of environmental characteristics (Table 1). For 107
lakes, information was obtained from published (De León
2000; Mazzeo et al. 2003) and unpublished sources (V. L.
M. Huszar pers. comm. 2006; a 1999 Dutch multilake
survey G. van Geest and F. Roozen pers. comm. 2004). The
remaining 104 lakes were sampled during 2005–2006 by
standard procedures at least once during summer (Kosten
et al. 2009; Kruk et al. 2009). For this study, we included
only one summer sample per lake (211 cases). The sampling
and sample-analyses protocols were comparable among the
lakes. Most lakes were sampled at random points
integrating the water column and covering the whole lake
area. Water samples for nutrients and plankton were taken
integrating the water column with a plastic tube (20 cm
diameter) and combining from 3 to 20 random replicates in
each lake. Phytoplankton samples were fixed in Lugol’s
solution. Zooplankton samples were filtered on a 50-mm
sieve and preserved in a 4% formaldehyde solution.
Environmental variables included temperature (Temp,
uC), inorganic suspended solids (ISS, mg L21), watercolumn mix depth (Zmix, m), light attenuation coefficient
(Kd, m21), conductivity (Cond, mS cm21), alkalinity (Alk,
meq L21), dissolved inorganic nitrogen (DIN, mg L21), total
nitrogen (TN, mg L21), soluble reactive phosphorus (SRP,
mg L21), total phosphorus (TP, mg L21), soluble reactive
silicate (SRSi, mg L21), total zooplankton abundance (TZ,
org L21), cladocera abundance (CLAD, org L21), and
Log10(x + 1) chlorophyll a (LogChl a, mg L21). Details on
sample analysis are provided in Kosten et al. (2009) and
Kruk et al. (2009).
111
Latitude
Methods
Table 1. Mean and range of environmental variables of lakes included in this study for the different regions: subpolar (49u–55uS), temperate (35u–39uS and 41u–52uN),
subtropical (29u–34uS), and tropical (5u–23uS). Zmax, maximum depth; ZSD, Secchi disk depth; T, water temperature; TN, total nitrogen; TP, total phosphorus; SRSi, soluble
reactive silicate; Chl a, chlorophyll a; CLAD, cladocera abundance; S, Southern, and N, Northern Hemispheres.
Phytoplankton groups and environment
112
Kruk et al.
of the samples were analyzed by the same group of
scientists, using the same identification keys, a common
protocol, and fluid communication. However, to diminish
the potential taxonomical discrepancies among different
data sets, we did not include the organisms that were not
identified at least at the class level. Also, organisms from
the same genus but not identified to species level were
grouped together.
Data analysis
Species classification—We excluded species with a
contribution of less than 5% to the total community
biomass in any individual lake (leaving 237 of the original
total of 711 species). We classified the remaining species
into seven morphology-based functional groups (MBFG),
into eight major phylogenetic groups, and into 29 Reynolds
groups (Reynolds) (Table 2). For each group of each
classification, biovolumes were summed per sample.
Overall analysis and comparison of the three classifications and the species—Canonical correspondence analyses
were used to make a global comparison of the three
classifications. Four preliminary detrended correspondence
analyses (DCA) of the biological data were carried out for
the three classifications and for the individual species. All
showed long gradient lengths (i.e., 3.22, 3.38, 3.83, and 10.7
standard deviations for MBFG, phylogenetic groups,
Reynolds, and species, respectively), and therefore the
unimodal response model was regarded as appropriate for
our data set (ter Braak and Smilauer 1998). We applied
four canonical correspondence analyses (CCA) to estimate
how much variance of the biomass of the species and the
groups in the three classifications was explained by the
environmental variables. The original variables included in
all cases were: Temp, ISS, Zmix, Kd, Cond, Alk, DIN, TN,
SRP, TP, SRSi, TZ, CLAD, and LogChl a. We included
LogChl a in the explanatory variables group because it
increased the general predictability without causing a
change in the relative predictability of the species or
groups. The results with and without LogChl a as an
explanatory variable were highly correlated (data not
shown). The importance of each variable was assessed
using the forward selection procedure in a CCA, and only
variables with a significant (p , 0.05) contribution to the
explained variance were included in further analyses (ter
Braak 1986). The variance explained by the environmental
variables was calculated as the variance explained by the
sum of all canonical eigenvalues divided by total inertia.
We tested the significance of each classification result using
Monte Carlo simulations with 499 unrestricted permutations. These analyses were carried out with the software
Canonical Community Ordination (CANOCO) for Windows version 4.5. All phytoplankton data were Log10(x + a)
transformed, where a corresponds to the minimum non
zero value of the variable in each case. All environmental
variables were standardized. Cases with missing values
were removed.
To evaluate the strength of the classifications correcting
for the effect of differences in the number of groups we
compared the results for each classification to those
generated by randomly assigning species into the same
number of groups. One-hundred random versions of each
classification were constructed. The random versions were
evaluated using the explained environmental variables
selected by the forward procedure for the corresponding
classification and using the same CCA protocol. The
analyses were carried out using the {vegan} package in R
software (R Development Core Team 2010). The difference
in the explained variance from the environment between
real and random groups was calculated for each classification. We used the nonparametric Kruskal–Wallis median
test and nonparametric z post-hoc tests to analyze for
differences among classifications.
Regression models of individual species and of each group
within each classification—In addition, we analyzed the
predictability of each group within each classification and
of all individual species from environmental conditions
using ordinary multiple linear regressions. We selected the
best independent variables for each model by a forward
selection procedure. The original variables included in all
cases were: Temp, ISS, Zmix, Kd, Cond, Alk, DIN, TN,
SRP, TP, SRSi, TZ, CLAD, and LogChl a. The results
with and without LogChl a as an explanatory variable were
highly correlated (data not shown). We only considered the
results including LogChl a. We applied the nonparametric
Kruskal–Wallis median test and nonparametric z post-hoc
tests to analyze the differences in the R2 values obtained for
each classification method. All phytoplankton data were
Log10(x + a) transformed. All analyses were performed
using SPSS for Windows version 15.0. We classified the
models for each classification group, and species as wellpredicted when they reached at least 30% of explained
variance following Møller and Jennions (2002).
Results
The CCAs indicated that the variance that can be
explained from environmental conditions is lower for
individual species and phylogenetic groups than for MBFG
and Reynolds’s groups (Table 3). The percentage of
explained variance was highest for the MBFG, reaching
35.9%, followed by the Reynolds classification (Table 3).
Not surprisingly, in view of the different degrees of
aggregation, total variation in the data set (total inertia)
decreased from individual species classification via Reynolds and phylogenetic groups to MBFG. However, the
explained variance using MBFG was more than two times
larger than that using phylogenetic groups despite the latter
had almost the same number of groups. In addition,
Reynolds classification explained a higher variance than
phylogenetic groups. The individual species classification
included the largest number of explanatory variables, while
the phylogenetic groups had a smaller number. Four
variables appeared to be included in the CCA for all four
classification systems: SRSi, CLAD, DIN, and Temp.
These four variables contributed for more than 50% of the
variance explained for each classification, except for the
individual species.
Van Den Hoeck et al. (1997)
except Cyanobacteria
(Komárek and Anagnostidis
1999, 2005) and
Bacillariophyceae (Round
et al. 1992)
Reynolds et al. (2002) and
Reynolds (2006)
Major phylogenetic
groups, n58
Reynolds functional groups,
n528
Kruk et al. (2010)
Reference
MBFG, n57
Classification
Classification method
A, B, C, and D: mostly unicellular Bac from mixed systems
Considering phylogeny,
ordered from low- to high-nutrient concentrations (nitrogen
morphological traits, coand phosphorus). P: Zyg and filamentous Bac from stratified
occurrence, and environment.
mesotrophic and eutrophic systems, respectively. LM and LO:
combined chroococcal Cya and Din in summer epilimnia from
mesotrophic and eutrophic waters, respectively. K and M:
chroococcal Cya from nutrient-rich systems from shallow and
daily mixed layers, respectively. H1 and H2: nostocal Cya with
heterocytes from systems with low nitrogen and carbon and low
nitrogen, respectively. R: Oscillatoriall Cya, mainly Planktothrix
spp. forming maximum in the metalimnion. S1, S2, and SN: mainly
filamentous Cya from mixed conditions. E: Chr from nutrientpoor lakes. J, G, and F: Chl with medium to large size, the first
two from nutrient-rich shallow waters and the third from clear
epilimnia. Q: Gonyostomum from small humic lakes. T: mainly
Zyg from deep, well-mixed epilimnia. W1 and W2: mainly Eug and
some Chr from shallow organic and mesotrophic lakes,
respectively. Z and X1: prokaryote and eukaryote picoplankton,
respectively, from clear mixed layers. X2 and X3: nanoplankton
with and without flagella, respectively, from shallow mixed waters
of mesotrophic and eutrophic conditions, respectively. Y: mainly
Cry from small enriched lakes.
Considering V, S : V, S, MLD,
Small organisms with high S : V (I), small flagellated organisms
and the presence or otherwise
with siliceous exoskeletal structures (II), large filaments with
of aerotopes, flagella, mucilage,
aerotopes (III), organisms of medium size lacking specialized
heterocytes, and siliceous
traits (IV), flagellate unicells with medium to large size (V),
structures.
nonflagellated organisms with siliceous exoskeletons (VI), and
large mucilaginous colonies (VII).
Phylogenetic classes
Bacillariophyceae (Bac), Chlorophyceae (Chl), Chrysophyceae
(Chr), Cryptophyceae (Cry), Cyanobacteria (Cya), Dinophyceae
(Din), Euglenophyceae (Eug), Zygnematophyceae (Zyg).
Groups
Table 2. Phytoplankton species classifications. MBFG, morphology-based functional groups; n, number of groups per classification; V, volume; S, surface area; MLD,
maximum linear dimension.
Phytoplankton groups and environment
113
114
Kruk et al.
Table 3. Results of the canonical correspondence analysis (CCA) relating the species and the different classification systems with the
environmental variables. MBFG, morphology-based functional groups; N groups, number of groups per classification or number of
species when corresponds; N env. var., number of environmental variables included in the analysis; total inertia, total variation obtained
from the sum of all eigenvalues; difference from random %, mean value and range of the difference of environmental explanation between
the real and random classifications; F, F-test values for all canonical eigenvalues with the finally selected environmental variables in all
cases having p , 0.01. Superscripts a and b correspond to significant differences in the post-hoc multiple comparisons tests between the
classifications differences from random affiliation of the species. SRSi, soluble reactive silicate; CLAD, cladocera abundance; DIN,
dissolved inorganic nitrogen; Cond, conductivity; Temp, temperature; LogChl a, log10 chlorophyll a; TN, total nitrogen; SRP, soluble
reactive phosphorus; Kd, light attenuation coefficient; TZ, total zooplankton; TP, total phosphorus; Zmix, mixing depth.
Environmental
explanation (%)
N groups/
N env.var
Total inertia
MBFG
35.9, F255.06
7/8
0.679
Phylogenetic
groups
16.6, F252.99
8/5
1.046
Reynolds groups
22.3, F252.58
29/9
2.394
Species
17.9, F252.26
237/13
Classification
To correct for the potential effect of differences in the
number of groups in each classification, we compared the
results of each classification to those obtained using random
aggregations into the same number of groups. The
differences from random were significant for all classifications and among them (Kruskal–Wallis H2 5 202.9, p ,
0.001) (Table 3). Post-hoc multiple comparisons showed
that the MBFG classification had the explained variance
with largest difference from random (MBFG vs. phylogenetic groups: z 5 13.2, p , 0.001; MBFG vs. Reynolds
groups: z 5 11.3, p , 0.001). No significant differences were
observed between phylogenetic and Reynolds groups.
Qualitatively comparable results were obtained from our
analysis of multiple linear regression models, predicting the
biovolume of groups or species from the same potential
explanatory variables. Note that we obtained a model with
an R2 for each species and for each group in each
classification, and studied the structure in this large set of
results. The differences in average R2 values between
classifications were significant (Kruskal–Wallis H3 5
17.05, p , 0.001). Post-hoc multiple comparisons showed
that only the average value of R2 for the models predicting
MBFG was significantly higher than those of the individual
species (z 5 3.51, p 5 0.02). Phylogenetic and Reynolds
groups were not significantly different from individual
species. In line with the findings from our multivariate
analyses, models for groups in all classifications had on
average higher R2 than models for individual species
(Fig. 1), and the MBFG classification resulted in the
highest R2 values on average.
The frequency distribution of the R2 values for the
different classifications reveals that while the maximum R2
21.56
Included env. var. and
individual explained variance
(%)
SRSi (11.9), CLAD (6.8), DIN
(5.3), Cond (4.4), Temp
(2.1), LogChl a (2.1), TN
(1.6), SRP (1.8)
Cond (5.4), DIN (3.4), SRSi
(3.5), CLAD (2.1), Temp
(2.1)
SRSi (4.4), CLAD (3.3), TN
(3.0), DIN (2.8), Kd (2.5),
TZ (2.7), Temp (1.9),
LogChl a (1.8), TP (1.5)
DIN (4.2), TZ (2.3), SRP (1.9),
Alk (1.9), ISS (1.6), Temp
(1.7), TP (1.3), CLAD (1.1),
TN (1.1), Kd (1.1), LogChl a
(1.0), SRSi (1.0), Zmix (0.9)
Difference from
random (%)
25.7a (19.2–30.1)
8.0b (2.5–12.4)
8.8b (5.2–11.4)
—
values do not differ much, the proportion of relatively low
R2 values varies markedly (Fig. 2). Most of the groups in
the MBFG classification (n 5 7) can be well predicted from
environmental conditions, whereas most individual species
have models with a rather low R2 values. The R2 values for
six of the seven groups in the MBFG classification were
higher than 0.3; only group II had a R2 5 0.26 (p , 0.001,
df 5 71) (Fig. 2). The classification in major phylogenetic
groups yielded significant models for all groups except
Zygnematophyceae. Half of the phylogenetic groups
showed R2 values higher than 0.3 (Cyanobacteria, Chlorophyceae, Cryptophyceae, and Bacillariophyceae), while
the remaining half showed lower R2. When the species were
assembled with Reynolds classification, we obtained
significant models for 20 out of the 29 groups; 11 reached
R2 values higher than 0.3 (Fig. 2). The best explained
groups corresponded to Cyanobacteria and included:
filamentous non–N-fixing groups (S1: R2 5 0.47, p ,
0.001, df 5 71; and S2: R2 5 0.53, p , 0.001, df 5 71),
filamentous N-fixing groups (H1: R2 5 0.67, p , 0.001, df
5 71), colonial large-celled groups (M: R2 5 0.41, p , 0.01,
df 5 71), and colonial small-celled groups (K: R2 5 0.31, p
, 0.001, df 5 71). From the 237 species (5% dominant
species in the 211 lakes), we obtained significant models for
27; 19 of them showed R2 values higher than 0.3 (Fig. 2),
corresponding to 8% of the total studied number.
Discussion
The emerging picture from the results of our regression
analyses as well as the multivariate approach is that
phytoplankton community composition may be predicted
Phytoplankton groups and environment
Fig. 1. The R2 value of multiple linear regression models for
each of the studied phytoplankton classifications in relation to
environmental conditions. Each classification is represented by a
box-whisker plot. The dot is the mean of all the groups in the
corresponding classification. Boxes correspond to the standard
error and the whiskers to the standard deviation. MBFG,
morphology-based functional groups; Reynolds, trait-based functional groups following Reynolds; Phylo, phylogenetic groups;
and the species.
best in terms of morphological groups. The explained
variance for individual species is lower on average than
MBFG and Reynolds groups but not always lower than
phylogenetic groups. While, some species appear relatively
predictable, a vast majority of the species shows no clear
relationship to the environmental conditions we had
measured.
The fact that morphological groups show a closer
relationship to environmental conditions than phylogenetic
groups suggests that morphology is a better proxy for
ecological functioning than phylogeny. Put simply, our
results suggest that two species that look alike but are
phylogenetically unrelated appear more likely to be
functionally similar than two species that are related but
morphologically different. This is in line with a range of
earlier findings (Carpenter et al. 1993; Kruk et al. 2002;
Salmaso and Padisák 2007). Morphology of phytoplankton
appears closely linked to ecological functioning (Weithoff
2003; Naselli-Flores et al. 2007), as shown in relation to
physiological traits and demographic features, as well as
ecological performance (Margalef 1978; Reynolds 1984;
Marañón 2008) and response to grazing (Bergquist et al.
1985; Vanni and Findlay 1990). By contrast, phylogenetic
‘‘overdispersion’’ (Webb et al. 2002) can lead closely related
taxa to differ widely in functioning (Lürling 2003).
Although, in view of this phenomenon, it may seem
straightforward that local conditions favoring groups of
species that share similar adaptive features can result in
associations combining rather different phylogenetic origins (Reynolds et al. 2002; Webb et al. 2002; Salmaso and
Padisák 2007), this is not observed in all natural
communities. In various terrestrial ecosystems, the aggre-
115
gation in phylogenetic groups rather coincides with natural
species assemblages (Vamosi et al. 2009). It has been
suggested that in larger organisms, the occurrence of
phylogenetic clustering may be caused in part by biogeographic history (Hillebrand 2004; Cavender-Bares et al.
2009). Indeed this seems less likely in phytoplankton,
which, because of their small body size, have high rates of
evolution and diversification (Nunn and Stanley 1998;
Schmidt et al. 2006) and disperse easily, causing them to be
largely cosmopolitan (Finlay and Clarke 1999; Padisák
2003; Fenchel and Finlay 2004).
Obviously, some phylogenetic classes in phytoplankton
are broader than others in their range of traits (Sandgren
1988a; Graham and Wilcox 2000). For instance, Cyanobacteria (Paerl 1988) and Chlorophyceae (Happey-Wood
1988) are found under a broad range of environmental
conditions and display a wide variety of growth forms,
ranging from small unicells to filaments with specialized
cells. On the other hand, groups such as Chrysophytes have
a more restricted environmental distribution and array of
morphological variation (Sandgren 1988b). This is reflected
in our results in differences in predictability of groups from
environmental factors. While the seven morphology-based
groups are more or less equally predictable from environmental factors, the phylogenetic groups differ widely when
it comes to the variance explained from environmental
conditions. In fact, even the 29 groups in Reynolds
classification are more homogeneous in terms of explained
variance than the eight phylogenetic groups.
In addition to being more closely related to environmental factors, the morphology-based classification has the
advantage of being transparent and simple. Reynolds
classification follows the traditional phytosociological
approach (Tüxen 1955; Braun-Blanquet 1964) to describe aggregates of organisms that occur together and
respond similarly to environmental changes. This approach has been well received and elaborated further by
phytoplankton ecologists (Padisák and Reynolds 1998;
Padisák et al. 2009). However, the criteria for assigning
species to groups are not formalized, and neither is the
classification independent of phylogenetic affiliation. Furthermore, the high number of groups in such refined
classification systems (40 sensu Padisák et al. 2009) may
pose difficulties when it comes to modeling their dynamics
because it may be challenging to objectively assign
parameter values for each group (Anderson 2005; Le Quéré
et al. 2005).
On a more philosophical level, the question of predictability of ecological community composition is related to
some of the most profound issues in ecology. It has been
argued that even with full knowledge of all species traits, it
might be fundamentally impossible to predict phytoplankton development because dynamics may be ruled by chaos
(Huisman and Weissing 2001; Benincà et al. 2008). Indeed,
our results suggest that the majority of the phytoplankton
species are rather unpredictable. Nevertheless, in line with
suggestions by others (Elliott et al. 2005; Litchman et al.
2006; Mieleitner et al. 2008), our results demonstrate that
the occurrence of groups of species with similar traits is
actually rather predictable from environmental conditions
116
Kruk et al.
Fig. 2. Distribution of the R2 values of regression models for the groups in each
phytoplankton classification against environmental conditions. MBFG, morphology-based
functional groups; Reynolds, trait-based functional groups following Reynolds; phylogenetic
groups; and the species.
and reaches high explained variance when compared with
general ecological models (Møller and Jennions 2002;
McGill et al. 2006). One explanation might be that the
morphology-based functional groups actually correspond
to self-organized groups of similar species, in the sense of
the theory of emergent neutrality (Scheffer and Van Nes
2006). This would imply that while the biomass of such
groups follows from environmental conditions, the dominant species within such a group will remain fundamentally
unpredictable in any particular situation. This is because
the species in any particular group are basically interchangeable and ecologically equivalent. While our results
are consistent with that view, more refined analyses would
be needed to test against predictions of other potential
explanations (Vergnon et al. 2009).
Acknowledgments
We thank Gissell Lacerot and Sarian Kosten for their data
from the South America Lakes Gradient Analysis (SALGA)
project, Néstor Mazzeo, Gerben van Geest, Frank Roozen, and
Valeria Hein for their data, and Angel Segura for his assistance
with the statistical analyses. We thank the Associate Editor
Robert W. Sterner and two anonymous reviewers whose
comments helped us to improve the original submission. This
study was funded by Wetenschappelijk Onderzoek van de Tropen
en Ontwikkelingslanden (WOTRO [Foundation for the Advance
of Tropical Research]), The Netherlands. C. Kruk is also
supported by Sistema Nacional de Investigación-ANII (SNI,
National Investigation System), Uruguay.
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Associate editor: Robert W. Sterner
Received: 23 February 2010
Accepted: 16 September 2010
Amended: 06 October 2010