Download relative effects of species composition and richness on ecosystem

Ecology, 86(3), 2005, pp. 701–715
q 2005 by the Ecological Society of America
RELATIVE EFFECTS OF SPECIES COMPOSITION AND RICHNESS ON
ECOSYSTEM PROPERTIES IN PONDS
AMY L. DOWNING1
Department of Ecology and Evolution, University of Chicago, 1101 E. 57th Street, Chicago, Illinois 60615 USA
Abstract. Biological diversity is complex and can be described and quantified in various
ways. Research exploring the consequences of biodiversity for ecosystem functioning has
generally focused on the effects of four components of biodiversity: species richness and
composition, and functional group richness and composition. Most of the research to date
has focused on biodiversity effects within single trophic levels. The aim of this study was
to compare the relative effects of these four components of biological diversity across pond
food webs using aquatic mesocosm experiments in which all four community properties
were manipulated independently. Functional groups were defined in terms of trophic groups,
and consisted of aquatic macrophytes, periphyton grazers, and invertebrate predators. Species composition and species richness were manipulated within functional groups. Ecosystem response variables included the final biomass of the manipulated taxa (macrophytes,
grazers, and predators), ecosystem rates (productivity and decomposition), and trophic
structure (phytoplankton and periphyton biomass). Results reveal strong effects of species
composition on a significant proportion of ecosystem response variables. In contrast, species
richness, functional group richness, and functional group composition altered the biomass
of the manipulated taxa, but had no additional effects on other ecosystem variables. Results
suggest that the roles of species in ecosystems, when considered in a food web context,
are often the result of both direct and indirect effects that are difficult to predict based on
the association of a species to a particular functional group. If a goal of biodiversity research
is to predict the response of ecosystems to biodiversity loss, effects of composition and
species losses across food webs must be fully integrated into biodiversity–ecosystem functioning research.
Key words: ecosystem functioning; food web; functional group; herbivores; indirect effects;
macrophytes; pond; predators; species composition; species richness; trophic interactions.
INTRODUCTION
The effects of biodiversity on ecosystem functioning
have received considerable attention over the last decade, largely in response to rapid declines in global
biodiversity (Sala et al. 2000, Kinzig et al. 2002, Loreau et al. 2002). Our current understanding of biodiversity–ecosystem functioning relationships comes primarily from ecosystems such as grasslands that are
dominated by competitive interactions, or from studies
focusing on single trophic levels (see Schmid et al.
2002b for a recent review). These studies generally
characterize biodiversity in terms of species richness
and functional group composition, and to a lesser extent
species composition. Although theoretical and empirical work in more complex communities is slowly increasing (Loreau et al. 2002), we still do not understand
how well theory and empirical results derived from
single trophic-level studies will apply to complex, multitrophic ecosystems (Duffy 2002, Holt and Loreau
Manuscript received 11 April 2003; revised 18 June 2004;
accepted 22 June 2004; final version received 10 August 2004.
Corresponding Editor: J. E. Havel.
1 Present address: Department of Zoology, Ohio Wesleyan
University, Delaware, Ohio 43015 USA.
E-mail: [email protected]
2002, Raffaelli et al. 2002, Thébault and Loreau 2003,
Petchey et al. 2004).
Species richness within a trophic level is known to
influence ecosystem properties primarily via two mechanisms; resource complementarity and the selection effect. Resource complementarity occurs through niche
diversification or facilitative interactions between species within trophic levels, typically maximizing ecosystem processes such as productivity as diversity increases (Aarssen 1997, Tilman et al. 1997, Hooper
1998). The selection effect (or sampling effect) can also
generate diversity–ecosystem functioning relationships
if diverse communities are statistically more likely to
contain species with strong effects on a given ecosystem function (Tilman 1997, Hector 1998, Loreau 1998,
Wardle 1999, Loreau and Hector 2001). Often a combination of both complementarity and selection effects
explains observed asymptotic relationships between
plant richness and productivity in grasslands, and between within-trophic-level richness and ecosystem processes in communities other than grasslands (van der
Heijden et al. 1998, Engelhardt and Ritchie 2001,
Mulder et al. 2001, Cardinale et al. 2002). Importantly,
the majority of these studies have explored the importance of species richness within rather narrowly defined
701
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AMY L. DOWNING
taxa (e.g., communities of plants, mycorrhizal fungi,
macrophytes, bryophytes, or caddisflies).
While complementarity and selection effects can often explain within-trophic-level richness effects, we do
not know if these mechanisms will be important when
richness varies across more complex food webs (Holt
and Loreau 2002, Duffy et al. 2003, Thébault and Loreau 2003, Petchey et al. 2004). In food webs, species
richness is likely to change simultaneously across multiple trophic levels, and will involve more dramatic
changes in species composition than in single trophic
levels. Complementarity/selection effects may still operate within each trophic level or taxonomic group, but
with potentially opposite effects on a particular ecosystem variable. For example, if species are lost across
both plant and grazer communities, plant biomass may
decrease due to the loss of a productive plant species
(selection effect) or through less efficient resource use
(complementarity). By similar reasoning, we might expect grazer biomass to decrease with declining grazer
richness. However, a reduction in grazer biomass may
in turn release plants from grazing pressure, allowing
plant biomass to increase. In addition, factors such as
predator–prey interactions and indirect effects will also
change with species richness in food webs. These additional factors may overwhelm subtle within-trophiclevel effects caused by complementarity or selection
occurring between very similar species.
Functional groups are also used to characterize the
biodiversity of a community, and are useful as a means
to reduce overwhelming complexity of communities.
Functional groups or functional effect groups, as used
in the context of linking species to ecosystems, are
defined as groups of species that share similar traits
relevant to a specific ecosystem function (Dı́az and
Cabido 2001, Hooper et al. 2002). While recent work
has begun to develop methods to define functional
groups based on multiple traits (Petchey and Gaston
2002), most functional groups are based on single traits
of species. For example, in grasslands plant functional
groups are typically defined in terms of resource use
or phenology, and have been useful in predicting ecosystem functioning relationships in grasslands (Lavorel
et al. 1997, Hooper and Vitousek 1998, Dı́az and Cabido 2001). In food webs, functional groups are typically defined by trophic group or feeding guild such as
decomposers, grazers, and predators. Food web functional groups tend to lump species with more dissimilar
traits than plant functional groups (Bengtsson et al.
1996, Naeem 1998, Hulot et al. 2000, Palmer et al.
2000, Duffy et al. 2003). Despite these differences, if
functional groups are defined appropriately for a given
ecosystem variable, changes in functional group composition appear likely to have larger effects on ecosystem variables than random changes in species richness or species composition (Lawton et al. 1998). Functional groups have been useful for predicting biodiversity–ecosystem functioning in grasslands; however
Ecology, Vol. 86, No. 3
in multitrophic systems, traditional definitions of functional groups may leave out important traits such as
predator vulnerability that will determine a species net
effect in an ecosystem.
Species composition (i.e., the particular identity or
assemblage of species) is another important and often
confounding component of biodiversity that is more
difficult to study. Most biodiversity research has not
adequately separated composition effects from richness
effects due to experimental design constraints (Grime
1997, Huston 1997, Allison 1999). Species composition will clearly be important in extreme cases where
communities contain keystone species or ecosystem engineers, but may still be important in communities containing species with less extreme traits (e.g., Symstad
et al. 1998). Data from previous experiments indicate
that compositional effects can indeed be significant as
shown by the typically large variation surrounding the
mean asymptotic increase in ecosystem processes with
richness (Tilman et al. 1997). In multitrophic studies,
species composition may be even more important than
in single trophic level studies. First, the presence of
particular species with strong trophic or indirect effects
may overwhelm more subtle effects of within-trophiclevel richness driven by complementarity and selection
(Duffy et al. 2003). Second, species composition within
functional groups may be important if functional
groups fail to capture the relevant traits for focal ecosystem variables (Hooper et al. 2002).
The goal of this study is to determine if traditional
components of biodiversity, including species richness,
species composition, functional group composition,
and functional group richness, can determine ecosystem responses in pond food webs. Results are presented
here from two experiments conducted simultaneously
in aquatic mesocosms. The ‘‘species experiment’’ manipulated species richness and species composition
within functional groups. The ‘‘functional group experiment’’ manipulated functional group richness,
functional group composition, and species composition
within functional groups. These experiments differ
from the majority of previous experiments in three important ways: richness manipulations occurred across
multiple trophic levels, functional groups are defined
broadly by trophic groups, and all four community attributes are manipulated independently under the same
experimental conditions.
METHODS
Mesocosms
Mesocosms consisted of 300-L plastic cattle watering tanks maintained outdoors at the pond lab facility
of the Kellogg Biological Station (Hickory Corners,
Michigan, USA). Mesocosms were filled with 280 L
of well water enriched with NaNO3 and NaH2PO4 to
approximate average concentrations of 1500 mg N/L
and 150 mg P/L in local ponds. A survey of 30 local
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ECOSYSTEM RESPONSE TO SPECIES COMPOSITION
ponds revealed nutrient levels of 247 6 308 mg P/L
and 1975 6 997 mg N/L (mean 6 1 SD). After the
initial nutrient addition, 500 mg N/L and 50 mg P/L
phosphorus were added biweekly to replenish nutrients
that are lost through processes such as denitrification,
sedimentation, or through nutrients that are converted
to living biomass. Biweekly additions mimic the periodic replenishment of nutrients through groundwater,
rainwater, and allochthonous terrestrial energy inputs.
Mean final nutrient concentrations in the combined experiments were 2347 6 2073 mg N/L, and 162 6 122
mg P/L (mean 6 1 SD).
Sand was added to provide a low-nutrient substrate
for macrophyte root systems. Mesocosms were inoculated with hyper-diverse mixtures of zooplankton,
phytoplankton, periphyton, and bacteria collected from
local ponds in early May. Mesh screen lids were fastened securely to each mesocosm to prevent unwanted
movement of aquatic species. Mesocosms received full
sun, and shading by screens was ,5% as measured by
a light meter.
Experimental pond food web
The experimental pond food web for both experiments consisted of 24 species divided evenly among
three functional groups. The functional groups were
defined broadly in terms of classical trophic position,
and included rooted macrophytes, periphyton grazers,
and invertebrate predators. The latter two groups included macroinvertebrates and larval amphibians (see
Appendix). Determining the appropriate definitions for
functional groups in the context of biodiversity–ecosystem functioning involves a trade-off of competing
goals. First, to reduce the complexity of biodiversity,
it is desirable to use the fewest number of functional
groups to adequately characterize biodiversity. In this
case, functional groups will be defined broadly and may
group somewhat dissimilar species. Alternatively, narrowly defined functional groups will result in functional groups containing more similar species with respect to ecosystem variables, but will not simplify communities to the same extent. Here functional groups are
defined broadly, such that species within each functional group share particular attributes in terms of resource use, but can differ substantially in important
features such as size, reproductive phenology, and
predator vulnerability.
Macrophyte species included Elodea occidentale,
Vallisneria americana, Sagittaria rigida, Potamogeton
crispus, Potamogeton natans, Ceratophyllum demersum, Utricularia vulgaris, and Myriophyllum verticullatum. Macrophytes were washed to remove attached
invertebrates, larvae, and eggs of unwanted species.
These species all develop root systems, but vary in
structural traits such as understory vs. canopy growth
forms, and broad vs. needle-like leaves. The macrophytes in this system are not readily consumed by the
grazers, although grazers feed on the periphyton at-
703
tached to the macrophytes. Macrophytes are important
for providing habitat and structure, and they may enhance phytoplankton and periphyton by pumping nutrients from the sediments, or may decrease algal
growth through shading and competition for nutrients
(Wetzel and Manny 1972, Canfield et al. 1984, SandJensen and Borum 1991).
Periphyton grazers consisted of two snail species
(Helisoma trivolis, Physa gyrina), two amphipod species (Hyallela azteca, Crangonyx richmondensis), two
corixids (Trichocorixa sp., Hesperocorixa sp.), and two
larval amphibians (Rana catesbeiana and Rana clamitans). The majority of their food resources comes from
grazing attached algae and bacteria (Pennak 1989,
Thorp and Covich 1991, Merritt and Cummins 1996),
although the grazers clearly differ in terms of size and
some may consume small amounts of zooplankton (corixids, tadpoles). Periphyton grazers should have direct
negative effects on periphyton (Cattaneo and Mousseau
1995), but may also have positive indirect effects on
algal productivity by recycling nutrients (Vanni 1996,
Kupferberg 1997, McCollum et al. 1998).
Macroinvertebrate predators included pleids ( Neoplea striola), whirligig beetles (Gyrinus sp., Dineutus
sp.), notonectids (Notonecta undulata, Buenoa), water
bugs (Belostoma flumireum, Ambrysus sp.), and dytiscids (Coptotomus). These predators consume primarily
zooplankton (all species), periphyton grazers (all but
pleids), and other predators (all but pleids; Thorp and
Covich 1991, Merritt and Cummins 1996), but clearly
vary in size and foraging activity. Invertebrate predators may have direct negative effects on zooplankton
and periphyton grazers due to predation, and positive
indirect effects on algae via trophic cascades (McCollum et al. 1998).
Species experimental design
To independently test for the effects of species richness and species composition for ecosystems, seven
unique species composition treatments were nested
within the 6-species treatments (m–s) and the 18-species treatments (t–z) for a total of 14 unique species
compositions (Fig. 1, Table 1). Each composition (m–
z) was replicated twice for a total of 28 mesocosms in
the species experiment. Species composition treatments were determined through random draws of either
two or six species per functional group for the 6-species
and 18-species communities, respectively. The species
richness levels of two and six species per functional
group fall within natural ranges as determined through
a survey of 30 natural fishless ponds in Michigan. Species richness ranges from one to 13 per functional
group in natural ponds, with a mean of five (A. Downing, unpublished data), although the survey probably
underestimates natural diversity due to missing rare
species. A significant species composition effect would
indicate that a specific assemblage of species has significantly different effects on ecosystem variables than
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AMY L. DOWNING
Ecology, Vol. 86, No. 3
FIG. 1. Species composition and richness effects on the final biomass of macrophytes, grazers, and predators. Biomass
was measured once at the termination of the experiment. Individual bars are means 6 1 SE for each individual species
composition (m–z). There are seven 6-species combinations, each a unique species composition (m–s), and seven 18-species
combinations, each a unique species composition (t–z; see Methods: Species experimental design). Heavy dashed lines represent
the means for species richness and are shown only for significant richness effects determined through ANOVA.
a different assemblage with the same number of species. The ability for this design to detect compositional
effects provides an advantage over many previous designs that were unable to separate richness from composition effects (Grime 1997, Huston 1997, Allison
1999, Schmid et al. 2002a).
The 24 species pool serves as the regional species
pool from which local communities assemble, and consists of locally abundant, widespread species to reduce
the probability of assembling species combinations
rarely found in nature (Schmid et al. 2002b). Species
were collected from local ponds. On average, each of
the 24 species is found in 40% of local ponds, estimated
from the 30-pond survey; thus it is likely that some of
the species combinations created through random assembly do not occur commonly in nature. However, I
would expect that community interactions would
quickly modify the mesocosm communities to resemble
natural pond communities. Thus, a consequence of such
combinatorial designs is that these communities may
undergo a larger number of extinctions as community
dynamics remove unlikely or unstable species combinations (Tilman et al. 1996, McGrady-Steed et al. 1997,
Naeem and Li 1997, Petchey et al. 1999). Despite this
drawback, the random assembly approach is considered
a valuable null model for making general predictions
about species extinctions (Schmid et al. 2002a), provides a better alternative to nested subsets of species
(Naeem et al. 1994), and results in more general scenarios than richness gradients generated through disturbance such as nutrient addition (Tilman and Downing 1994, Huston 1997).
Functional group experimental design
In a doubly nested design, functional group richness,
functional group composition, and species composition
within functional groups were manipulated independently while keeping the total number of species constant (Table 1). Five functional composition treatments
were nested within three functional richness treatments,
including all biologically plausible combinations of
functional groups. Grazers (G) can be present without
macrophytes (M) because they graze primarily on periphyton growing on the mesocosm walls. Predators
(P) require periphyton grazers because they are a primary food resource for the predators in these experi-
March 2005
TABLE 1.
ECOSYSTEM RESPONSE TO SPECIES COMPOSITION
705
Experimental designs.
Species experiment design
Species richness treatments
Species composition treatments
6
mnopqrs
18
tuvwxyz
P
G
M
Functional group experiment design
Functional group richness
Functional group composition
Species composition
1
M
abc
2
G
def
MG
ghi
GP
jkl
3
MGP
mnopqr
P
G
M
Notes: The ‘‘species experiment’’ has two levels of richness (6 and 18 species) and seven levels of species composition
nested within richness. Composition treatments m–z are each replicated twice for a total of 28 mesocosms. The ‘‘functional
group experiment’’ has three levels of functional richness and either one or two functional group compositions nested within
each functional group richness treatment for a total of five functional group compositions. Species composition treatments
a–r are replicated twice for a total of 36 mesocosms. The two experiments share species compositions m–r and are represented
by the same mesocosms. Key to abbreviations: M 5 macrophyte; G 5 periphyton grazer; P 5 invertebrate predator. The
boxes represent one nested species composition treatment, with each circle indicating a species, to illustrate how species are
distributed within functional groups. See Appendix for a listing of species included in each treatment.
ments. The fifth functional composition, MGP, is by
necessity the only functional composition within the
three functional group richness treatment. An unavoidable result of this design is that functional group richness is partially confounded with functional group
composition due to the MGP functional composition
treatment (Hector 2002).
Three unique species compositions are nested within
each functional group composition treatment, with the
exception of the MGP functional composition which
contains an extra set of three species composition treatments (p–r) to keep the total number of replicates constant across functional richness treatments. Species
composition treatments were determined through random draws of six, three, or two species per functional
group for the one, two, and three functional group richness treatments respectively, keeping species richness
constant across treatments. Species composition treatments m–r represent the same mesocosms and are
shared between the ‘‘species experiment’’ and the
‘‘functional group experiment’’ (Table 1).
Treatment establishment
Treatments for both experiments were established
over a 6-wk period from early May through June, beginning with macrophytes, followed by grazers, and
finally predators. Species were added in a replacement
design, keeping total number of individuals (wet mass
for macrophytes) in each functional group constant
across treatments to provide for equal reproduction and
growth potential; 60 g wet mass of macrophytes, 90
periphyton grazers, and 24 predators. The number of
individuals per functional group approximates densities found in natural ponds, with an added constraint
of ensuring enough individuals of each species for reproduction. Treatments were 80% established by l June,
and 100% established (all species added) by 15 June.
The experiment ran for the majority of the active growing period for the stocked species (certain species of
macrophytes and species such as green and bullfrog
tadpoles are not present until mid May or later in these
systems), and spans the time when the stocked species
are most abundant in local ponds. By late September,
reduced temperatures and short days slow overall activity levels of all organisms in the mesocosms as well
as natural ponds, and serves as an appropriate ending
point for the experiment.
Ecosystem response variables
The goal of the experiment was to determine if the
diversity and composition of the manipulated pond taxa
(macrophytes, grazers, and predators) influenced important aquatic ecosystem variables. The ecosystem response variables included community productivity
rates, decomposition rates, and phytoplankton and periphyton biomass, and were chosen for several reasons.
First, these ecosystem variables can be measured nondestructively through time. Second, macrophytes, periphyton grazers, and predators have strong effects on
other functional or trophic groups in aquatic ecosystems. In order to capture these effects, it is necessary
to monitor the groups likely to respond via food web
interactions. Finally, and perhaps most importantly, the
chosen ecosystem response variables provide widely
used, broad indicators of aquatic ecosystem functioning
targeting the microbial and primary producer communities. Measurements of the ecosystem variables began in early July, approximately five weeks after treatments were fully established. Productivity and decomposition rates were measured approximately weekly,
and phytoplankton and periphyton were measured every three weeks until mid September. The final biomass
AMY L. DOWNING
706
Ecology, Vol. 86, No. 3
TABLE 2. Response of the final biomass of macrophytes, grazers, and predators to species richness, species composition,
and functional group composition.
Macrophyte biomass
Source of variation
Species experiment
Species richness
Species composition
Functional group experiment
Functional group composition
Species composition
Grazer biomass
Predator biomass
df
F
df
F
df
F
1, 12
12, 14
0.152
2.107
1, 12
12, 14
1.955
6.587***
1, 12
12, 14
12.077**
4.42**
2, 12
9, 12
2.020
1.846
3, 16
12, 16
3.474*
10.294***
1, 10
8, 10
8.852*
3.918*
Notes: Data were analyzed with one-way ANOVA. The df and F statistics are reported for each test. In the functional
group experiment, only treatments containing the functional group of interest were compared. An analysis of the effects of
the functional group richness treatment is not possible because not all functional groups are present in each functional group
richness treatment.
* P , 0.05; ** P , 0.01; *** P , 0.001.
of the manipulated functional groups (macrophytes, periphyton grazers, and invertebrate predators) was also
measured at the termination of the experiment.
Community productivity rates were calculated from
diurnal oxygen cycles (Lund 1979, Wetzel and Likens
1991). Oxygen measurements were taken with an oxygen probe (HORIBA International, Irvine, California,
USA) at sunrise and at sunset the same day corresponding to the minimum and maximum oxygen concentrations. Productivity is calculated as the net gain
of oxygen per hour between sunrise and sunset, and
thus measures net community productivity rates. Based
on productivity:biomass ratio calculations, phytoplankton contributes an order of magnitude more to diurnal
oxygen dynamics than do macrophytes and periphyton
in these mesocosms (Downing and Leibold 2002). Decomposition rates were calculated as the loss of dry
mass per day for sugar maple (Acer saccharum) leaves
enclosed in mosquito netting after approximately 21
days. Samples for phytoplankton biomass were obtained from integrated water column samples collected
from 10 locations within each mesocosm and pooled
together. Periphyton biomass samples were obtained
from polyethylene strips placed in each tank at the
beginning of the experiment. Phytoplankton and periphyton biomass were determined fluorometrically
through chlorophyll a extraction (Welschmeyer 1994).
At the termination of the experiment, all stocked
organisms were harvested by draining mesocosms
through a 500-mm sieve. Species were either counted,
counted and measured for length if there was significant
size variation (e.g., snails and tadpoles), or weighed
(macrophytes). In the case of species with significant
size variation, length–mass regressions for each species
were generated to calculate a more precise mass. All
wet mass was converted into dry mass using individually calculated wet:dry ratios determined from a subsample of each species.
Data analysis
Treatment effects were explored using ANOVA and
repeated measures ANOVA. In all statistical analyses,
species richness is treated as a fixed effect. Species
composition is considered a random effect because
compositions were determined randomly from a species pool, providing a representative sample of all possible combinations of six species or 18 species. Functional group composition and richness are fixed effects
because they represent specific and common pond functional groups.
RESULTS
Treatment establishment
All species stocked in the mesocosms reproduced
during the experiment with the exception of macrophytes, the amphipod Crangonyx, and the tadpoles,
which all exhibited growth responses. Although a significant proportion of mesocosms experienced species
extinctions (59% of mesocosms) and unwanted invasions (45% of mesocosms), all treatment differences
were maintained until the termination of the experiment. The majority of species invasions were pleids,
but also included damselflies and other macroinvertebrates. These invaders likely came as eggs and larvae,
which had not been completely washed off the macrophytes, or from occasional insects that successfully
crawled under the mesh lids and laid eggs in the mesocosms. In the ‘‘species experiment’’ (Table 1), final
species richness remained significantly different between species richness treatments (means 5.75 6 1.1
SD and 12.6 6 1.9 SD; ANOVA, F1,12 5 153.3, P ,
0.001), and did not differ significantly between species
composition treatments nested within richness levels
(ANOVA, F12,14 5 1.16, P 5 0.39). In the ‘‘functional
group experiment’’ where initial species richness was
held constant (six) across all treatments, final species
richness did not differ between functional richness
(mean 5.3 6 0.9 SD; ANOVA, F2,13 5 1.51, P 5 0.26),
functional group composition (ANOVA, F2,13 5 0.586,
P 5 0.57), or species composition treatments (ANOVA, F13,18 5 2.00, P 5 0.09). Thus, despite the relatively high rate of invasions and extinctions, all treatments were maintained throughout the experiment. The
March 2005
ECOSYSTEM RESPONSE TO SPECIES COMPOSITION
707
TABLE 3. Effects of species richness and species composition on individual ecosystem variables shown as a mixed-model, hierarchical ANOVA with repeated measures (time).
Variables and factors
Productivity
Between subjects
Species richness
Species composition
Error
Within subjects
Time
Species richness 3 time
Species composition 3 time
Error
Total
Decomposition
Between subjects
Species richness
Species composition
Error
Within subjects
Time
Species richness 3 time
Species composition 3 time
Error
Total
Periphyton†
Between subjects
Species richness
Species composition
Error
Within subjects
Time
Species richness 3 time
Species composition 3 time
Error
Total
Phytoplankton†
Between subjects
Species richness
Species composition
Error
Within subjects
Time
Species richness 3 time
Species composition 3 time
Error
Total
df
SS
MS
F
P
1
12
14
0.15
1.19
0.55
0.15
0.10
0.04
1.49
2.52
0.245
0.051
8
8
96
112
251
6.21
0.10
0.75
0.82
9.76
0.78
0.01
0.01
0.01
99.80
1.53
0.70
0.000
0.231
0.428
1
12
14
0.00006
0.00241
0.00105
0.00006
0.00020
0.00008
0.27
2.69
0.611
0.040
8
8
96
112
251
0.00306
0.00006
0.00182
0.00302
0.01148
0.00038
0.00001
0.00002
0.00003
19.94
0.39
0.70
0.000
0.762
0.893
1
12
14
0.02
8.63
2.96
0.02
0.72
0.21
0.03
3.40
0.874
0.016
2
2
24
28
83
2.23
0.00
3.03
1.08
17.95
1.11
0.00
0.13
0.04
8.82
0.01
3.27
0.001
0.999
0.002
1
12
14
0.23
3.26
2.07
0.23
0.27
0.15
0.86
1.84
0.373
0.137
2
2
24
28
83
0.38
0.94
7.24
3.66
17.78
0.19
0.47
0.30
0.13
0.62
1.55
2.31
0.545
0.173
0.029
Notes: Within-group effects (time interactions) are corrected for violations of sphericity using
the Greenhouse-Geiser (G-G) correction for degrees of freedom. All reported significant univariate tests are also significant in terms of the multivariate statistics (Wilks’ lambda, not
reported). Significant effects are shown in boldface type.
† Data were log-transformed to better meet the assumption of normality.
primary consequence of invasions and extinctions was
to alter the composition of individual replicates through
time, thus generating additional noise in the species
composition treatments as replicates of species composition are less likely to be identical.
Species experiment results
Final predator biomass increased with species richness, whereas macrophyte and grazer biomass did not
change with richness (Table 2, Fig. 1). Both predator
and grazer biomass varied significantly with species
composition treatments (Table 2, Fig. 1). Repeated-
measures ANOVA on ecosystem response variables revealed a significant time effect for most response variables (Table 3), consistent with typical seasonal patterns in temperature and daylight that occur in Michigan between June and September. Species composition
had significant main effects on decomposition, productivity, and periphyton, indicating mean differences
in these ecosystem variables with respect to species
composition through time (Table 3, Fig. 2). Species
composition also had significant time 3 species composition effects for phytoplankton and periphyton, indicating that these ecosystem variables displayed
AMY L. DOWNING
708
Ecology, Vol. 86, No. 3
FIG. 2. Species composition and richness effects on ecosystem variables (decomposition rates [dm 5 dry mass], productivity, and periphyton and phytoplankton biomass [chl a 5 chlorophyll a]) in the ‘‘species experiment.’’ Heavy dashed
lines represent time-averaged means for each species richness treatment, corresponding to among-group effects of the repeatedmeasures ANOVA. Individual bars are time-averaged means 6 1 SE for each individual species composition treatment (m–z).
unique temporal dynamics with respect to species composition (Table 3, Fig. 3). In Fig. 3, measurements began five weeks after treatments were fully established;
therefore initial differences in ecosystem variables indicate early treatment divergence.
Functional group experiment results
Grazer and invertebrate predator biomass each responded to both functional group composition and species composition treatments (Table 2, Fig. 4). Some
replicates in particular functional group treatments
were invaded by species belonging to functional groups
not present in the treatment (e.g., predators in the macrophyte-only treatment). Functional group richness effects on the biomass of macrophytes, periphyton grazers, and invertebrate predators could not be tested because not all taxa were present in all functional richness
levels.
Repeated-measures ANOVA on ecosystem variables
revealed a significant time effect on all ecosystem variables, once again reflecting the seasonal patterns observed in the species experiment (Table 4). Species
composition within functional groups had main effects
on productivity and decomposition rates (Table 4, Fig.
5), indicating that mean rates varied over time with
respect to species composition, similar to the results
observed in the species experiment. Significant time 3
species composition interactions for phytoplankton indicate that temporal patterns varied with species composition (Table 4). Periphyton biomass was the only
variable to vary significantly with functional group
composition, indicating that mean periphyton biomass
differed through time (Table 4, Fig. 5). Periphyton biomass between functional compositions macrophytes
(M) and grazers (G) are statistically different as determined through post-hoc Tukey test (P , 0.05). Func-
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ECOSYSTEM RESPONSE TO SPECIES COMPOSITION
709
FIG. 3. Representative time series data for two ecosystem response variables in the ‘‘species experiment.’’ Repeatedmeasures ANOVA revealed significant time 3 species composition interactions for both phytoplankton and periphyton biomass
(Table 3). Error bars are not shown for clarity. (a, c) Periphyton and phytoplankton biomass (chl a 5 chlorophyll a) through
time for species compositions m–s (6-species treatments); (b, d) periphyton and phytoplankton biomass through time for
species compositions t–z (18-species treatments). Day 0 indicates the first day of measuring response variables and was
approximately five weeks after treatments were fully established. Differences in ecosystem variables at day 0 indicate treatment
effects that have already occurred.
tional group richness had no significant effects on ecosystem responses. Because there was little or no effect
of either functional richness or functional composition
on ecosystem responses, no further analysis was conducted to determine the relative contributions of these
two partially confounded factors due to the experimental design limitations (Hector 2002).
In summary, species composition was very important
in both experiments, species richness and functional
composition had very limited effects, and functional
richness had no significant effects on ecosystem variables. To further investigate the relative importance of
each factor, I calculated the percentage of the total sums
of squares attributed to each manipulated factor in the
two experiments for the ecosystem response variables
in Tables 3 and 4. For each response variable, I combined the sums of squares for the between and within
subject effects for each factor to calculate the percentage sums of squares that each factor contributed
to the total sums of squares. I then averaged the proportion of the sums of squares attributed to each factor
across all response variables. In the species experiment,
species composition accounted for an average of 45%
of the total variance across all response variables,
whereas species diversity accounted for only 2.5%. In
the functional group experiment, species composition
accounted for 37% of the total variance, whereas functional composition accounted for 11%, and functional
richness accounted for 3.8% of the total sums of
squares. Time also contributed significantly to the total
sums of squares for each ecosystem response variable,
again revealing strong average seasonal trends across
all replicates and response variables.
DISCUSSION
Two results from the experiments are particularly
striking; the consistent and strong effects of species
composition in ecosystems, and the general lack of
ecosystem response to functional composition, functional richness, and species richness. Species richness
did increase predator biomass, but did not affect any
other measured ecosystem response variable. Functional group composition altered the biomass of manipulated taxa; predator biomass increased and grazer
biomass decreased in the grazer–predator (GP) functional composition, evidence for a trophic cascade (Fig.
710
AMY L. DOWNING
Ecology, Vol. 86, No. 3
FIG. 4. Functional group composition and species composition effects on the final biomass of macrophytes (M), grazers
(G), and predators (P). Biomass was measured once at the termination of the experiment. Individual bars are means 6 1 SE
for each individual species composition (a–r). Heavy dashed lines represent the means for each functional group composition
and are shown only when significant functional group composition effects were determined through ANOVA. Cases where
the functional group was absent but the biomass of the trophic group is .0 (e.g., predator biomass was .0 in the ‘‘m’’
treatment) indicate invasion events.
4). Interestingly, this pattern disappears in the presence
of macrophytes (MGP). Despite changes in grazer and
predator biomass as grazer and predator richness and
functional group composition changed, other ecosystem responses were unaffected, indicating that the biomass alone of the manipulated taxa is not the primary
determinant of the observed effects.
The lack of a species richness effect runs counter to
results of a previous experiment documenting highly
significant effects of richness (Downing and Leibold
2002). This experiment differs from the earlier experiment in three ways. First, species richness in this experiment reached higher levels, ranging from 6 to 18
species as compared to 3–15 species in the earlier experiment. Second, this experiment contained a total of
28 mesocosms (14 in each richness level), compared
to 84 mesocosms in the earlier experiment, reducing
the power to detect treatment effects. Finally, the experiments presented here were maintained at much
higher productivity levels due to biweekly nutrient ad-
ditions, resulting in communities that are probably
more strongly driven by nutrient additions than the
biota (Downing and Wootton, in press). Despite the
lack of significant richness effects, species rich communities had consistently higher productivity and decomposition rates and periphyton and phytoplankton
biomass, reflecting trends observed in the previous experiment.
Species composition (i.e., unique assemblages of
species) is clearly important. The significant effects of
species composition are not surprising given that species, even within the same functional group, certainly
varied in traits affecting ecosystem variables. The unexpected result is that species composition within functional groups had many more significant effects than
functional composition and richness, even though functional manipulations correspond to changing overall
trophic complexity and structure. The strong effect of
species composition is even more surprising in light of
the number of invasions and extinctions that are likely
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ECOSYSTEM RESPONSE TO SPECIES COMPOSITION
711
TABLE 4. Effects of functional group richness, functional group composition, and species composition on individual ecosystem variables shown as a mixed-model, hierarchical ANOVA with repeated measures (time).
Variables and factors
Productivity
Between subjects
Functional group richness
Functional group composition
Species composition
Error
Within subjects
Time
Functional group richness 3 time
Functional group composition 3 time
Species composition 3 time
Error
Total
Decomposition
Between subjects
Functional group richness
Functional group composition
Species composition
Error
Within subjects
Time
Functional group richness 3 time
Functional group composition 3 time
Species composition 3 time
Error
Total
Periphyton†
Between subjects
Functional group richness
Functional group composition
Species composition
Error
Within subjects
Time
Functional group richness 3 time
Functional group composition 3 time
Species composition 3 time
Error
Total
Phytoplankton
Between subjects
Functional group richness
Functional group composition
Species composition
Error
Within subjects
Time
Functional group richness 3 time
Functional group composition 3 time
Species composition 3 time
Error
Total
df
SS
MS
F
P
2
2
13
18
0.03
0.21
0.10
0.06
0.01
0.11
0.01
0.00
1.83
1.39
2.45
0.200
0.285
0.040
8
16
16
104
144
288
7.30
0.16
0.09
0.62
0.88
9.056
0.91
0.01
0.01
0.01
0.01
152.66
1.71
0.96
0.97
0.000
0.144
0.465
0.534
2
2
13
18
0.00006
0.00002
0.00031
0.00162
0.00003
0.00001
0.00002
0.00009
1.32
0.37
2.69
0.301
0.700
0.027
8
16
16
104
144
288
0.00325
0.00023
0.00052
0.00309
0.00348
0.01057
0.00041
0.00001
0.00003
0.00003
0.00002
13.67
0.48
1.09
1.23
0.000
0.882
0.450
0.190
2
2
13
18
0.13
2.76
2.56
0.49
0.06
1.38
0.20
0.03
0.33
7.02
7.23
0.727
0.009
0.000
2
4
4
26
36
72
6.37
0.37
0.04
2.59
2.62
11.99
3.19
0.09
0.01
0.10
0.07
32.02
0.94
0.09
1.37
0.000
0.409
0.914
0.227
2
2
13
18
0.43
0.24
1.17
1.79
0.22
0.12
0.09
0.10
2.39
1.35
0.91
0.131
0.293
0.561
2
4
4
26
36
72
0.78
0.35
1.33
7.07
3.41
12.93
0.39
0.09
0.33
0.27
0.09
1.43
0.32
1.22
2.87
0.257
0.811
0.327
0.005
Notes: Within-group effects (time interactions) are corrected for violations of sphericity using the Greenhouse-Geiser (GG) correction for degrees of freedom. All reported significant univariate tests are also significant in terms of the multivariate
statistics (Wilks’ lambda, not reported). Significant effects are shown in boldface type.
† Data were log-transformed to better meet the assumption of normality.
to make species composition replicates less similar, and
should therefore bias the results against finding significant species composition effects. Species composition had many significant main (between subject) effects (Tables 3, 4), indicating that different species assemblages maintained average differences in the mean
value of ecosystem variables through time, despite the
high temporal variability in all ecosystem response variables. In addition, significant species composition 3
time interactions for phytoplankton and periphyton biomass indicate that certain species assemblages caused
unique temporal dynamics of these response variables
712
AMY L. DOWNING
Ecology, Vol. 86, No. 3
FIG. 5. Response of ecosystem variables in the ‘‘functional group experiment’’ to functional group richness, functional
group composition, and species composition (M, macrophytes; G, periphyton grazers; P, invertebrate predators). Heavy dashed
lines represent time-averaged means for each functional group richness treatment, and bars are the means 6 1 SE for each
individual species composition (a–r) nested within each functional group composition.
relative to the rest of the experiment. Unique temporal
dynamics between treatments could be driven by
changes in species composition through time due to
species invasions and extinctions, or due to varying
activity levels of the species within the assemblage due
to changes in growth, reproduction, or predation
through time. In either case, significant time 3 species
interactions should occur only when all replicates of a
given compositional treatment display similar dynamics, and indicate an effect of species composition on
ecosystem variables. The highly significant main effect
of time for all ecosystem variables reflects seasonal
trends independent of the treatments imposed in this
study, and is observed every year in both the mesocosms and natural ponds (e.g., Barko et al. 1977,
Downing 2001).
The experimental design provides a distinct advantage over many previous studies because it can determine if specific assemblages of species have unique
effects in ecosystems (Schmid et al. 2002a). The highly
significant effects of species composition could be a
result of the unique assemblage of species, or they
could indicate that one or a few species are having
large effects on ecosystem responses. While the second
scenario is often observed in plant communities where
a competitively dominant species can drive patterns of
aboveground productivity, in pond food webs one species is less likely to simultaneously affect several ecosystem responses, especially given the primarily indirect links between the manipulated species and the
ecosystem responses. Indeed, in a separate analysis designed to detect species-specific effects in these eco-
March 2005
ECOSYSTEM RESPONSE TO SPECIES COMPOSITION
systems, very few species appear to have significant
effects on ecosystem responses (Downing and Wootton, in press). In this analysis, we reduced the ecosystem responses into two broad measures of ecosystem
functioning using principal components analysis. We
then took the final species composition of the mesocosms and asked if the presence of individual species
had significant effects on ecosystem functioning. This
analysis reveals that only two species, the snail Helisoma and the corixid Trichocorixa, have significant
effects on ecosystem responses (Downing and Wootton, in press). Therefore, although these two species
may be important for the measured ecosystem variables, it is unlikely that these two species alone are
responsible for the strong compositional effects across
the entire experiment. Instead, particular assemblages
with unique suites of species interactions are likely
responsible for some of the observed effects. In retrospect this result makes sense because a species is
unlikely to have a consistent effect on ecosystem variables in a background of varying species compositions. For example, a particular species is likely to have
different effects in ecosystems depending on the presence of significant predators or competitors.
Similar reasoning may also explain the surprising
lack of functional group effects in these experiments.
In food webs, functional groups may be difficult to
define in a way that adequately characterizes a species
role in an ecosystem because species differences with
respect to other niche axes may be particularly relevant
for ecosystem variables (Werner and McPeek 1992,
Kupferberg 1997). Functional groups, typically based
on a species direct effect on an ecosystem variable such
as productivity, often ignore other important differences between species such as susceptibility to predation, and the potential suites of indirect effects associated with each species (Yodzis 1988, Wootton
1994, Wilbur 1997, Berlow 1999, Chapin et al. 2000).
Importantly, indirect effects of a particular species on
ecosystem variables are not fixed traits of a species but
will vary with food web context, further complicating
the process of defining functional groups (van der Heijden 1999). The combination of context-dependent interactions and indirect effects of species may ultimately
be more important in determining the effect of a species
in ecosystems than the trait originally used to place the
organism into a functional group (McCann et al. 1998).
Functional groups may still be useful in food webs,
but they may ultimately need to be more narrowly defined than the groups used in this study. Despite the
broad definitions used in this study, the lack of functional group effects in these experiments remains surprising because species within these groups should be
more similar to each other with respect to their effects
on trophic structure (macrophyte, grazer, predator, phytoplankton, and periphyton biomass) and ecosystem
processes (decomposition and productivity) than species in different functional groups. More narrowly de-
713
fined functional groups will tend to group species with
more similar direct and indirect effects on ecosystem
variables because they are more likely to share competitors and predators. Unfortunately, narrowly defined
groups will still suffer from context-dependent effects
of species on ecosystem variables, and will come at
the cost of generality and simplicity. A remaining challenge of biodiversity–ecosystem functioning research
is to determine how narrowly, and with what criteria,
functional groups need to be defined.
The results from this experiment also point out a
limitation of current hypotheses such as the selection
and complementary effects to explain patterns between
diversity and ecosystem functioning in food webs (Loreau and Hector 2001). These two hypotheses generally
consider a single species trait (e.g., resource competitive ability) to predict the species effect in an ecosystem (Holt and Loreau 2002). These mechanisms are
generally based on resource competition dynamics that
occur within trophic levels or within rather narrow taxonomic groups such as plants. As richness changes
across trophic levels, changes in species composition
will be more dramatic than in single trophic levels, and
these subtle mechanisms may become less important
relative to additional factors such as trophic interactions and indirect effects (Duffy 2002, Thébault and
Loreau 2003). Unfortunately, the food web experiments
described here, while broad in scope, are limited in
terms of being able to detect specific mechanisms behind observed effects. In general, food web studies of
biodiversity–ecosystem functioning will face additional challenges in detecting mechanisms because, unlike
single trophic level studies, species in food webs cannot
be grown in monoculture to determine how species
effects differ in monoculture vs. polyculture. However,
more focused experiments on targeted species or species combinations will ultimately be necessary to determine how important mechanisms such as the selection effect, complementarity, trophic interactions, and
indirect effects are for determining the net effect of a
species in an ecosystem.
The results of these experiments show that species
identities in communities may often matter more than
species numbers, especially in food webs. These results
provide a compelling reason to preserve biological diversity because every time a community loses a species, species composition also changes; therefore compositional effects are an integral part of biological diversity (Schwartz et al. 2000). However, substantial
challenges remain in our ability to understand and predict how changes in biodiversity across complex communities will affect ecosystem functioning. A combination of new ‘‘biodiversity–ecosystem functioning’’
theory and classical trophic and food web theory will
ultimately be necessary to make progress in this direction (Duffy 2002, Thébault and Loreau 2003).
ACKNOWLEDGMENTS
I thank M. Leibold, S. Naeem, J.T. Wootton, J. Bergelson,
C. Pfister, and three anonymous reviewers for helpful com-
AMY L. DOWNING
714
ments. J. Chase, J. Shurin, S. Hall, M. Bruns, and G. Cohen
provided invaluable help in the field. The paper is Kellogg
Biological Station contribution 1149, and was supported by
the National Science Foundation (DEB-9972655 to M. Leibold and A. Downing), U.S. Department of Education
GAANN grant, and the University of Chicago Harper Fellowship.
LITERATURE CITED
Aarssen, L. W. 1997. High productivity in grassland ecosystems: effected by species diversity or productive species? Oikos 80:183–184.
Allison, G. W. 1999. The implications of experimental design
for biodiversity manipulations. American Naturalist 153:
26–45.
Barko, J. W., P. G. Murphy, and R. G. Wetzel. 1977. An
investigation of primary production and ecosystem metabolism in a Lake Michigan dune pond. Archives Hydrobiologica 81:155–187.
Bengtsson, J., H. Setälä, and D. W. Zheng. 1996. Food webs
and nutrient cycling in soils: interactions and positive feedbacks. Pages 30–38 in G. A. Polis and K. O. Winemiller,
editors. Food webs: integration of patterns and dynamics.
Chapman and Hall, New York, New York, USA.
Berlow, E. L. 1999. Strong effects of weak interactions in
ecological communities. Nature 398:330–334.
Canfield, D. E., Jr., J. V. Shireman, D. E. Colle, W. T. Haller,
C. E. Watkins, II, and M. J. Maceina. 1984. Prediction of
chlorophyll a concentrations in Florida lakes: importance
of aquatic macrophytes. Canadian Journal of Fisheries and
Aquatic Sciences 41:497–501.
Cardinale, B. J., M. A. Palmer, and S. L. Collins. 2002. Species diversity enhances ecosystem functioning through interspecific facilitation. Nature 415:426–429.
Cattaneo, A., and B. Mousseau. 1995. Empirical analysis of
the removal rate of periphyton by grazers. Oecologia 103:
249–254.
Chapin, F. S., III, E. S. Zavaleta, V. T. Eviner, R. L. Naylor,
P. M. Vitousek, H. H. Reynolds, D. U. Hooper, S. Lavorel,
O. E. Sala, S. E. Hobbie, M. C. Mack, and S. Diaz. 2000.
Consequences of changing biodiversity. Nature 405:234–
242.
Dı́az, S., and M. Cabido. 2001. Vive la différence: plant
functional diversity matters to ecosystem processes. Trends
in Ecology and Evolution 16:646–655.
Downing, A. L. 2001. The role of biological diversity for
the functioning and stability of pond ecosystems. Dissertation. University of Chicago, Chicago, Illinois, USA.
Downing, A. L., and M. A. Leibold. 2002. Ecosystem consequences of species diversity and composition in pond
food webs. Nature 416:837–841.
Downing, A. L., and J. T. Wootton. 2005. Trophic position,
biotic context, and abiotic factors determine species contributions to ecosystem functioning. In P. C. de Ruiter, V.
Wolters, and J. C. Moore, editors. Dynamic food webs—
multispecies assemblages, ecosystem development, and environment change. Elsevier, Amsterdam, The Netherlands.
Duffy, J. E. 2002. Biodiversity and ecosystem function: the
consumer connection. Oikos 99:201–219.
Duffy, J. E., J. P. Richardson, and E. A. Canuel. 2003. Grazer
diversity effects on ecosystem functioning in seagrass beds.
Ecology Letters 6:637–645.
Engelhardt, K. A. M., and M. E. Ritchie. 2001. Effects of
macrophyte species richness on wetland ecosystem functioning and services. Nature 411:687–689.
Grime, J. P. 1997. Biodiversity and ecosystem function: the
debate deepens. Science 277:1260–1261.
Hector, A. 1998. The effect of diversity on productivity: detecting the role of species complementarity. Oikos 82:597–
599.
Ecology, Vol. 86, No. 3
Hector, A. 2002. Biodiversity and the functioning of grassland ecosystems: multi-site comparisons. Pages 71–95 in
A. P. Kinzig, S. Pacala, and G. D. Tilman, editors. The
functional consequences of biodiversity: empirical progress
and theoretical extensions. Princeton University Press,
Princeton, New Jersey, USA.
Holt, R. D., and M. Loreau. 2002. Biodiversity and ecosystem
functioning: the role of trophic interactions and the importance of system openness. Pages 246–262 in A. P. Kinzig, S. Pacala, and G. D. Tilman, editors. The functional
consequences of biodiversity: empirical progress and theoretical extensions. Princeton University Press, Princeton,
New Jersey, USA.
Hooper, D. U. 1998. The role of complementarity and competition in ecosystem responses to variation in plant diversity. Ecology 79:704–719.
Hooper, D. U., et al. 2002. Species diversity, functional diversity, and ecosystem functioning. Pages 195–208 in M.
Loreau, S. Naeem, and P. Inchausti, editors. Biodiversity
and ecosystem functioning: synthesis and perspectives. Oxford University Press, New York, New York, USA.
Hooper, D. U., and P. M. Vitousek. 1998. Effects of plant
composition and diversity on nutrient cycling. Ecological
Monographs 68:121–149.
Hulot, F. D., G. Lacroix, F. Lescher-Montoué, and M. Loreau.
2000. Functional diversity governs ecosystem response to
nutrient enrichment. Nature 405:340–344.
Huston, M. A. 1997. Hidden treatments in ecological experiments: re-evaluating the ecosystem function of biodiversity. Oecologia 110:449–460.
Kinzig, A. P., S. W. Pacala, and G. D. Tilman, editors. 2002.
The functional consequences of biodiversity: empirical progress and theoretical extensions. Princeton University
Press, Princeton, New Jersey, USA.
Kupferberg, S. 1997. Facilitation of periphyton production
by tadpole grazing: functional differences between species.
Freshwater Biology 37:427–439.
Lavorel, S., S. McIntyre, J. Landsberg, and T. Forbes. 1997.
Plant functional classifications: from general groups to specific groups based on response to disturbance. Trends in
Ecology and Evolution 12:474–478.
Lawton, J. H., S. Naeem, L. J. Thompson, A. Hector, and M.
J. Crawley. 1998. Biodiversity and ecosystem function:
getting the Ecotron experiment in its correct context. Functional Ecology 12:848–852.
Loreau, M. 1998. Separating sampling and other effects in
biodiversity experiments. Oikos 82:600–602.
Loreau, M., and A. Hector. 2001. Partitioning selection and
complementarity in biodiversity experiments. Nature 412:
72–76.
Loreau, M., S. Naeem, and P. Inchausti, editors. 2002. Biodiversity and ecosystem functioning: synthesis and perspectives. Oxford University Press, New York, New York,
USA.
Lund, O. T. 1979. Handbook of common methods in limnology. C. V. Mosby, St. Louis, Missouri, USA.
McCann, K., A. Hastings, and G. R. Huxel. 1998. Weak
trophic interactions and the balance of nature. Nature 395:
794–798.
McCollum, E. W., L. B. Crowder, and S. A. McCollum. 1998.
Complex interactions of fish, snails, and littoral zone periphyton. Ecology 79:1980–1994.
McGrady-Steed, J., P. M. Harris, and P. J. Morin. 1997. Biodiversity regulates ecosystem predictability. Nature 390:
162–165.
Merritt, R. W., and K. W. Cummins, editors. 1996. An introduction to the aquatic insects of North America. Third
edition. Kendall/Hunt Publishing Company, Dubuque,
Iowa, USA.
March 2005
ECOSYSTEM RESPONSE TO SPECIES COMPOSITION
Mulder, C. P., D. D. Uliassi, and D. F. Doak. 2001. Physical
stress and diversity–productivity relationships: the role of
positive interactions. Proceedings of the National Academy
of Sciences, USA 98:6704–6708.
Naeem, S. 1998. Species redundancy and ecosystem reliability. Conservation Biology 12:39–45.
Naeem, S., and S. Li. 1997. Biodiversity enhances ecosystem
reliability. Nature 390:507–509.
Naeem, S., L. J. Thompson, S. P. Lawler, J. H. Lawton, and
R. M. Woodfin. 1994. Declining biodiversity can alter the
performance of ecosystems. Nature 368:734–737.
Palmer, M. A., A. P. Covich, S. Lake, P. Biro, J. J. Brooks,
J. Cole, C. Dahm, J. Gibert, W. Goedkoop, K. Martens, J.
Verhoeven, and W. J. Van de Bund. 2000. Linkages between aquatic sediment biota and life above sediments as
potential drivers of biodiversity and ecological processes.
BioScience 50:1062–1075.
Pennak, R. W. 1989. Fresh-water invertebrates of the United
States. Third edition. John Wiley, New York, New York,
USA.
Petchey, O., A. Downing, G. Mittelbach, L. Persson, C. Steiner, D. Raffaelli, P. Warren, and G. Woodward. 2004. Species loss and the structure and functioning of multitrophic
aquatic ecosystems. Oikos 104:467–478.
Petchey, O. L., and K. J. Gaston. 2002. Functional diversity
(FD), species richness and community composition. Ecology Letters 5:402–411.
Petchey, O. L., P. T. McPhearson, T. M. Casey, and P. J. Morin.
1999. Environmental warming alters food-web structure
and ecosystem function. Nature 402:69–72.
Raffaelli, D., M. van der Heijden, W. van der Putten, T. Kennedy, J. Koricheva, G. Lacroix, J. Mikola, L. Persson, O.
Petchey, and D. A. Wardler. 2002. Multi-trophic processes
and ecosystem function. Pages 147–154 in M. Loreau, S.
Naeem, and P. Inchausti, editors. Biodiversity and ecosystem functioning: synthesis and perspectives. Oxford University Press, New York, New York, USA.
Sala, O. E., et al. 2000. Global biodiversity scenarios for the
year 2100. Science 287:1770–1774.
Sand-Jensen, K., and J. Borum. 1991. Interactions among
phytoplankton, periphyton, and macrophytes in temperate
freshwaters and estuaries. Aquatic Botany 41:137–175.
Schmid, B., A. Hector, M. A. Huston, P. Inchausti, I. Nijs, P.
W. Leadley, and D. Tilman. 2002a. The design and analysis
of biodiversity experiments. Pages 61–75 in M. Loreau, S.
Naeem, and P. Inchausti, editors. Biodiversity and ecosystem functioning: synthesis and perspectives. Oxford University Press, New York, New York, USA.
Schmid, B., J. Joshi, and F. Schläpfer. 2002b. Empirical evidence for biodiversity–ecosystem functioning relationships. Pages 120–165 in A. P. Kinzig, S. Pacala, and G. D.
Tilman, editors. The functional consequences of biodiversity: empirical progress and theoretical extensions. Princeton University Press, Princeton, New Jersey, USA.
Schwartz, M. W., C. A. Brigham, J. D. Hoeksema, K. G.
Lyons, M. H. Mills, and P. J. van Mantgem. 2000. Linking
biodiversity to ecosystem function: implications for conservation ecology. Oecologia 122:297–305.
715
Symstad, A. J., D. Tilman, J. Wilson, and J. M. H. Knops.
1998. Species loss and ecosystem functioning: effects of
species identity and community composition. Oikos 81:
389–397.
Thébault, E., and M. Loreau. 2003. Food-web constraints on
biodiversity–ecosystem functioning relationships. Proceedings of the National Academy of Sciences 100:14949–
14954.
Thorp, J. H., and A. P. Covich, editors. 1991. Ecology and
classification of North American freshwater invertebrates.
Academic Press, New York, New York, USA.
Tilman, D. 1997. Distinguishing between the effects of species diversity and species composition. Oikos 80:185.
Tilman, D., and J. A. Downing. 1994. Biodiversity and stability in grasslands. Nature 367:363–365.
Tilman, D., C. L. Lehman, and K. T. Thomson. 1997. Plant
diversity and ecosystem productivity: theoretical considerations. Proceedings of the National Academy of Sciences
94:1857–1861.
Tilman, D., D. Wedin, and J. Knops. 1996. Productivity and
sustainability influenced by biodiversity in grassland ecosystems. Nature 379:718–720.
van der Heijden, M. G. A. 1999. ‘‘Sampling effect,’’ a problem in biodiversity manipulation? A reply to David A. Wardle. Oikos 87:408–410.
van der Heijden, M. G. A., J. N. Klironomos, M. Ursic, P.
Moutoglis, R. Streitwolf-Engel, T. Boller, A. Wiemken, and
I. R. Sanders. 1998. Mycorrhizal fungal diversity determines plant biodiversity, ecosystem variability and productivity. Nature 396:69–72.
Vanni, M. J. 1996. Nutrient transport and recycling by consumers in lake food webs: implications for algal communities. Pages 81–95 in G. A. Polis and K. O. Winemiller,
editors. Food webs: integration of patterns and dynamics.
Chapman and Hall, New York, New York, USA.
Wardle, D. A. 1999. Is ‘‘sampling effect’’ a problem for
experiments investigating biodiversity–ecosystem function
relationships? Oikos 87:403–407.
Welschmeyer, N. A. 1994. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments.
Limnology and Oceanography 39:1985–1992.
Werner, E. E., and M. A. McPeek. 1992. Direct and indirect
effects of predators on two anuran species along an environmental gradient. Ecology 75:1368–1382.
Wetzel, R. G., and G. E. Likens. 1991. Limnological analyses. Second edition. Saunders College Publishing, New
York, New York, USA.
Wetzel, R. G., and B. A. Manny. 1972. Secretion of dissolved
organic carbon and nitrogen by aquatic macrophytes. Verhandlungen. Internationale Vereinigung fur Theoretische
und Angewandte Limnologie 18:162–170.
Wilbur, H. M. 1997. Experimental ecology of food webs:
complex systems in temporary ponds. Ecology 78:2279–
2302.
Wootton, J. T. 1994. The nature and consequences of indirect
effects in ecological communities. Annual Review of Ecology and Systematics 25:443–466.
Yodzis, P. 1988. The indeterminacy of ecological interactions
as perceived through perturbation experiments. Ecology
69:508–512.
APPENDIX
A table listing the species composition of treatments a–z is available in ESA’s Electronic Data Archive: Ecological Archives
E086-036-A1.