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Journal of Animal
Ecology 2001
70, 80 – 90
Effects of intra- and interspecific interactions on
species responses to environmental change
Blackwell Science, Ltd
JEREMY W. FOX* and PETER J. MORIN
Department of Ecology, Evolution, and Natural Resources, Cook College, Rutgers University, 14 College Farm Rd,
New Brunswick, NJ 08901–8551, USA
Summary
1. The extent of directional environmental change expected in the next century underscores the need to understand density-dependent population regulation.
2. Direct density dependence generated by intraspecific competition and /or predator–
prey interactions should buffer environmentally produced changes in density-independent
growth rates (r). Density dependence generated by interspecific competition should
magnify sensitivity to changes in r.
3. We tested these predictions by assembling protist communities in laboratory
microcosms and subjecting the communities to directional environmental change.
Two experiments used pairs of competing protists (Colpidium striatum Stein and
Tetrahymena thermophila Nanney & McCoy, and Paramecium tetraurelia Sonneborn
and Blepharisma americanum Suzuki) along with single-species controls. Two experiments used predator–prey pairs (Didinium nasutum Müller or Euplotes patella
Ehrenberg preying on Colpidium) and controls containing only prey. We grew replicates
of each species combination in two temperature regimes (constant or slowly increasing
temperature). Independent of these experiments, we quantified the temperature
dependence of r (intrinsic rate of increase) for each species.
4. Comparison of effects of temperature on r to effects on mean density revealed that
intraspecific competition buffered species densities against temperature changes that
increased r by over 200%. Interspecific interactions did not affect species responses to
environmental change. Temperature change had weak effects on species densities,
whether or not other species were present.
5. The results suggest that natural populations, regulated by direct density dependence,
may be buffered against directional environmental change. Environmental change will
have large effects when populations experience little density dependence, and when
environmental change has different effects on the vital rates of different species.
Key-words: density dependence, protists, microcosms, temperature change
Journal of Animal Ecology (2001) 70, 80–90
Introduction
The importance of density dependence is a central
question in ecology (e.g. Nicholson 1933; Andrewartha
& Birch 1954; Dennis & Taper 1994; Murdoch 1994;
Turchin 1995). Understanding density dependence
takes on added importance from expected directional
changes in global climate in the next century (IPCC
© 2001 British
Ecological Society
Correspondence: Jeremy Fox.
*Present address and correspondence: Jeremy Fox, NERC
Centre for Population Biology, Imperial College at Silwood
Park, Ascot, Berkshire, SL5 7PY, United Kingdom, Tel.:. + 44
(0)20 75942475, Fax: + 44 (0)1344 873173.
1996). Responses of individuals, species, and communities to climate change will depend on how per-capita
growth rates vary with population densities, but we
lack a general understanding of how density dependence
will affect species responses to climate change (Ives
1995; Sæther et al. 2000).
Predicted effects of climate change derive largely
from individual- or species-level responses (Bazzaz
1990; Oechel et al. 1994; Vitousek 1994; Barry et al.
1995). These predictions assume that individuals or
species do not interact, so that individual- or specieslevel responses can be aggregated to predict responses
of multispecies communities and ecosystems (Bolker
et al. 1995; Pacala & Deutschman 1996; Weiner 1996;
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Davis et al. 1998a,b). This assumption fails if populations are regulated by density dependence generated
by intra- and interspecific interactions (Davis et al.
1998a,b). Recent theoretical and empirical work
indicates that intra- and interspecific interactions
significantly affect responses of species and communities to environmental change (Ives & Gilchrist 1993;
González & Frost 1994; Bolker et al. 1995; Lawton
1995; Körner & Bazzaz 1996; Pacala & Deutschman
1996; Weiner 1996; Brown, Valone & Curtin 1997;
Davis et al. 1998a,b; Díaz et al. 1998; Navas 1998;
Whittaker & Tribe 1998). However, there are few general theoretical treatments of such effects (but see e.g.
Hassell, Godfray & Comins 1993). Many models
generate predictions from taxon-specific physiological
considerations, e.g. indirect effects of CO2 increase
on herbivorous insects mediated by changes in leaf
chemistry (Lindroth 1996; Kinney et al. 1997).
General, qualitative predictions may be possible by
focusing on net effects of the environment on population growth rates, and of species on one another.
Consider a linear autoregression model of singlespecies population growth,
n(t + 1) = b0 + ae(t) + n(t) + b1n(t) + ε(t, n)
eqn 1
where n(t) is abundance at time t. The parameter b0 is
the environment-independent component of densityindependent growth rate (intrinsic rate of increase).
Density-independent growth rate is also a linear function (with slope a) of an environmental factor, represented by the random variable e(t). The environmental
factor may be a single key factor like temperature
or pH, or a composite measure summarizing the
magnitudes of several different environmental variables. Directional environmental change is represented
by a change in the long-term mean of e. Abundance at
time t + 1 is also a linear function ( parameterized by
b1) of abundance at time t. The ‘error’ term ε includes
both environmental variability not captured by e(t)
and residual nonlinear effects of n(t) (for more details
see Ives & Gilchrist 1993; Ives 1995).
Long-term mean abundance (denoted by N ) equals
−(aE + b0 )
-------------------------b1
eqn 2
where E denotes long-term mean environmental
conditions. Equation 2 is the negative of the ratio of
density-independent growth rate to the strength of
density dependence. The change in N with a change
in E is given by
-------------∆N = −a∆E
b1
© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
80–90
eqn 3
when residual nonlinear effects of n(t) are small (Ives
1995). The numerator in eqn 2 is the change in densityindependent growth rate produced by change in
E. Equation 2, and its multispecies extension, allow three
general, qualitative predictions of the effect of community
structure on species responses to environmental change.
First, strong intraspecific competition (b1 < < 0)
generates direct density dependence (sensu Turchin
1995) that will buffer N against changes in E (Ives &
Gilchrist 1993; Ives 1995). Second, predator–prey
interactions also generate direct density dependence
and should also buffer N against changes in E (Royama
1981; Turchin & Taylor 1992; Ives & Gilchrist 1993).
Third, inverse density dependence (Turchin 1995)
generated by interspecific competition will increase the
effect of changes in E on N (Ives & Gilchrist 1993; Frost
et al. 1995). These predictions assume that the strength
of density dependence is independent of e. Otherwise,
predicting effects of environmental change on abundance
will require detailed knowledge of how the strength of
density dependence varies with e.
Testing these predictions in most natural communities would be difficult (but see Ives, Carpenter & Dennis
1999; Sæther et al. 2000). Most species potentially
interact directly or indirectly with many others, and
intrinsic rates of increase (r values) of different species
will depend on e in different ways. We tested these three
predictions by examining the effects of directional
change in a key environmental factor (temperature) on
simple communities of protists in laboratory microcosms. Much of the world is expected to warm during
the next century (IPCC 1996). Protists are useful for
temperature-change experiments because temperature
strongly affects protist growth rates ( Norland & Gojdics
1967). Protist microcosms permit collection of longterm (dozens to hundreds of generations) population
dynamic data in a reasonable amount of time, using
communities with known composition and species
interactions (Lawler & Morin 1993). Microcosms
allow the experimental control and replication necessary to separate effects of temperature from effects of
species interactions. Environmental manipulations can
be maintained over many generations, and can involve
gradually changing conditions as well as the static,
contrasting conditions typical of field experiments on
environmental change. Like field experiments, microcosm experiments advance understanding by revealing
the effects of environmental change that would occur
under a particular set of circumstances.
We first measured density-independent growth rates
(intrinsic rates of increase, r) at various temperatures.
We compared mean densities in constant environments
to densities in gradually warming environments to
test whether direct density dependence generated by
intraspecific competition affected responses to temperature change. We expected that mean densities in
increasing temperature environments would change
in the same direction as r (e.g. if r increased at higher
temperatures, mean density would increase), but that
changes in mean density would be small if intraspecific
competition was strong. We grew two pairs of competing protists, and two predator–prey pairs, in both
constant- and increasing-temperature environments to
test whether direct density dependence generated by
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predation, or inverse density dependence generated
by interspecific competition, affected responses to
temperature change. Predator–prey interactions, like
intraspecific competition, should prevent effects of
environmental change (e.g. if increasing temperatures
decreased mean prey density, prey density would
decrease less with predators present than without
predators). Competitive interactions should enhance
effects of environmental change (e.g. if increasing
temperatures increased the density of a competing
species, its density would increase more with a competitor present than without).
Methods
  
Microcosms were loosely capped 240 mL glass bottles
containing 100 mL of growth medium (0·28 or 0·56 g
of Carolina Biological Supply Protozoan Pellets /L
of well water, depending on the experiment [Table 1]) and
two wheat seeds to provide additional, slow-release
carbon and nutrients. We autoclaved these materials
before use. We inoculated bacteria (Serratia marcescens
Bizio, Bacillus cereus Frankland & Frankland, and
Bacillus subtilis [Ehrenberg] Cohn) 24 h before
addition of protists to standardize food for the
bacterivores. Bacterivorous protists (Colpidium striatum,
Tetrahymena thermophila [mating type VII], Blepharisma
americanum, and Paramecium tetraurelia [mating
type VII]) served as competitors, and Colpidium
served as the prey for the predators (Didinium nasutum
or Euplotes patella, depending on the experiment). We
added predators 3 –5 days after Colpidium. Colpidium,
Blepharisma, Didinium, and Euplotes were obtained
from Carolina Biological Supply (Burlington, NC).
Tetrahymena and Paramecium were obtained from the
American Type Culture Collection (Rockville, MD,
ATCC No. 30307 and No. 30568, respectively). We
added protists as ~2 mL volumes drawn from agitated
stock cultures. Weekly addition of one sterile wheat
seed to each culture and replacement of 10% of the
(agitated) culture medium with fresh, sterile medium
ensured that bacteria did not exhaust the carbon or
nutrient supplies.
Sampling occurred every 2–3 days using standard
procedures (Lawler & Morin 1993). We agitated microcosms and withdrew 10 drops of medium (~0·3 mL)
with a sterile Pasteur pipette. Sample volume was
determined by weight. We counted protists using a
Nikon SMZ-U stereoscopic microscope. If protists
were numerous, we diluted the sample by weight (~10fold to ~40-fold dilution, as necessary), subsampled
from the dilution, and back-calculated to obtain density in an undiluted sample. We converted counts to
log10[(n per mL) + 1] and calculated geometric mean
density over time for each species in each replicate.
Use of geometric means reduced heteroscedasticity.
For populations that became extinct, we excluded
post-extinction zero counts from the calculations. We
included intermittent zero counts resulting from
sampling low (but non-zero) densities.
    
  
Measurement of r at various temperatures (2 – 4 replicates per species per temperature) quantified the direct
effect of temperature on density-independent growth
rate. We measured r at the lowest and highest temperatures each species experienced during subsequent
density-dependence experiments (see next subsection);
measurements of r were separate from and independent
of these experiments. We measured r for Tetrahymena
and Colpidium at several other temperatures as well.
An incubator (accurate to ±1 °C) controlled temperature. We added low numbers of protists (< 20, the
number in one Pasteur pipette drop [~0·03 mL] ) from
Table 1. Designs of the density dependence experiments. Each experiment is a factorial design repeating each protist treatment
in two temperature treatments. Protists are listed by genus. Temperature treatments are constant (at the indicated temperature),
or increasing stepwise by 1 °C once every 3 –5 days. Interspecific interactions were either competitive, or predator–prey; NA = not
applicable. Each experiment used Protozoan Pellet (PP) medium at a concentration of 0·56 g PP L –1, except for the Colpidium–
Tetrahymena experiment (0·28 g PP L–1). A lower concentration was used in the Colpidium–Tetrahymena experiment because
Colpidium rapidly excludes Tetrahymena in more concentrated medium (J. W. Fox, unpublished data). Due to logistical
constraints, the increasing temperature treatment in the Colpidium–Tetrahymena experiment began 1 °C higher than the constant
temperature treatment. In the Colpidium–Didinium experiment, we did not increase the temperature beyond 27 °C
© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
80–90
Protist treatments
Temp. treatments
Intersp. interaction
Blepharisma alone
Paramecium alone
Both spp. together
Colpidium alone
Tetrahymena alone
Both spp. together
Colpidium (prey) alone
Colpidium + Didinium
Colpidium (prey) alone
Colpidium + Euplotes
15 °C, 15 °C + 1 °C/5 days
15 °C, 15 °C + 1 °C/5 days
15 °C, 15 °C + 1 °C/5 days
22 °C, 23 °C + 1 °C/3 – 4 days
22 °C, 23 °C + 1 °C/3 – 4 days
22 °C, 23 °C + 1 °C/3 – 4 days
22 °C, 22 °C + 1 °C/5 days
22 °C, 22 °C + 1 °C/5 days
15 °C, 15 °C + 1 °C/5 days
15 °C, 15 °C + 1 °C/5 days
NA
NA
Competition
NA
NA
Competition
NA
Predator–prey
NA
Predator–prey
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stock cultures acclimated for several days at each temperature, and sampled 2–3 times per day throughout
the initial period of log-linear (exponential) growth (i.e.
before protists became dense enough to experience any
intraspecific competition). This period lasted 36 –
96 h depending on the species and temperature. The
slope of a linear regression of ln[(n per mL) + 1] vs.
time (h) estimated r in each replicate. All r-values were
measured in 0·56 g PP/L cultures.
 
© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
80–90
We conducted two competition experiments, and two
predator–prey experiments, to assess how densitydependent intra- and interspecific interactions affected
the response of mean protist densities to temperature
change (Table 1). Treatments were randomly assigned
to bottles. For each competing species, experiments
were complete factorial designs, crossing the presence
or absence of a competing species with constant or
slowly increasing temperature. For prey, experiments
used complete factorial designs, crossing the presence
or absence of a predator with constant or slowly
increasing temperature. For predators, experiments
used single-factor designs with temperature as the
factor, since predators cannot grow without prey.
Competition experiments used four replicates per
treatment combination; predator–prey experiments
used five. Protist stock cultures acclimated to initial
temperature regimes for several days.
We used s and post-hoc tests to examine
predicted effects of intra and interspecific interactions
on responses to temperature change. We analysed geometric mean density of each prey or competitor species
in each experiment using a 2-way  for the effects
of the other species, temperature regime, and their
interaction. We also compared means with a Tukey’s
post-hoc test. We tested for effects of intraspecific
competition on each species by comparing mean
density in the ‘constant temperature/no competitors or
predators’ treatment to mean density in the ‘increasing
temperature / no competitors or predators’ treatment.
Lack of a significant difference between these two
treatments (according to the Tukey’s test) indicates
direct density dependence generated by intraspecific
competition buffered species against temperature
change. A significant difference between these two
treatments indicates weak intraspecific competition
allowed mean density to respond to temperature
change. If intraspecific competition is weak, mean
densities in the increasing temperature treatment
should be significantly higher or lower than constanttemperature densities, depending on how temperature
change affected r (e.g. if r increased at higher temperatures, mean density should be higher in the increasing
temperature treatment).
We tested for effects of interspecific interactions on
the responses of prey and competitors to temperature
change using the interaction terms in the 2-way
s. Significant interaction terms indicate that
responses to temperature change varied depending on
whether or not another species was present. Significant
interaction terms suggest an important influence of
community structure (density dependence generated
by interspecific interactions) on species responses to
environmental change. We expected the form of the
interaction to vary depending on whether the other
species was a predator or competitor. If the other species was a predator, the  interaction term should
reveal a smaller effect of temperature change with the
predator than without it. For example, if increasing
temperatures decreased mean prey density, prey density would decrease less with predators present than
without predators, generating a significant predator ×
temperature interaction term in the . If the other
species was a competitor, the interaction term should
reveal a larger effect of temperature change with the
competitor than without it.
The s are conservative tests for the effects
of interspecific interactions.  might not reveal
important effects of interspecific interactions, depending on the way that per-capita growth rates vary with
densities. For instance, if a predator strongly suppresses its prey, and prey compete intraspecifically only
at high density (i.e. without the predator), temperature
change will have little effect on mean prey density.
 will not reveal an interactive effect of community structure and temperature change in this case,
even though community structure (a predator–prey
interaction) regulates prey with the predator present.
We tested the effect of temperature change on mean
predator densities using a 1-way . We expected
predators to be well-buffered against temperature
change since both intraspecific competition and
predator–prey interactions generate direct density
dependence. We could not separate effects of intraspecific competition and interspecific (predator–prey)
interactions on predator densities because predators
cannot grow without prey.
Results
    r
Temperature strongly affected r for most species (Fig. 1,
Table 2). Colpidium and Tetrahymena exhibited Q10
values of about 2–3 (i.e. a 10 °C increase in temperature
doubled or tripled r) from 10 °C up to approximately
22 – 27 °C (Fig. 1a). At higher temperatures, r began to
level off (Fig. 1a, Table 2). Effects of temperature on r
for Colpidium and Tetrahymena resemble those observed
for other protists (Fig. 1a, Fenchel 1987).
 
In the Blepharisma–Paramecium competition experiment, Paramecium attained a relatively constant density in all treatments after ~15 days (Fig. 2b,c,e,f).
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Table 2. Temperature dependence of r. (a) Results of 1-way
s for effect of temperature on r. No statistical test is
possible for Didinium because Didinium failed to grow in one
of two replicates at 27 °C. (b) For Colpidium and Tetrahymena,
temperatures sharing a letter produce r-values which do not
differ significantly in a Fisher’s protected LSD test (P > 0·05)
Species
d.f.
F
P
(a) 
Colpidium
Tetrahymena
Blepharisma
Euplotes
Paramecium
9, 10
8, 9
1, 1
1, 1
1, 1
34·472
54·351
18·857
18·529
48·352
< 0·0001
< 0·0001
0·0122
0·0077
0·0022
Species
Temperature (°C)
(b) Fisher’s protected LSD tests
Colpidium
10 12 16 18 20 22 25 27 29 31
a b c
cd e
ef de de e
f
Tetrahymena 10 12 16 20 22 25 27 29 31
a a b
b c d d c
c
Fig. 1. Temperature dependence of r for the protists used in
this study. Symbols give means (± SE). Some error bars are
smaller than the symbols. (a) All species. (b) A portion of (a),
rescaled to better display species with low r-values. Symbols in
(b) are slightly offset horizontally to enable clear display of
error bars.
Blepharisma growing alone increased slowly, reaching a constant density only after ~40 days (Fig. 2a,d).
No extinctions occurred. Interspecific competition
significantly reduced the geometric mean density of
Blepharisma (2-way , main effect of interspecific
interaction, F1,12 = 269·282, P < 0·0001; Table 3a).
Competition reduced Blepharisma density less in
© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
80–90
increasing temperatures ( interaction term,
F1,12 = 9·959, P = 0·0083), although a Tukey’s post-hoc
test lacks the power to detect this difference (Table 3a;
Tukey’s test detects only the main effect of interspecific
competition).
In the Colpidium–Tetrahymena competition experiment, Colpidium and Tetrahymena each grew quickly
to high density in both constant and warming environments, and then declined during the second half of the
experiment (Fig. 3a,b,d,e). Tetrahymena went extinct
in one constant-temperature replicate containing
Colpidium. Increasing temperature significantly decreased
mean Colpidium density (2-way , main effect
of temperature, F1,12 = 9·789, P = 0·0087; Table 3b).
Fig. 2. Population dynamics from the Blepharisma–Paramecium competition experiment. Each panel shows dynamics from a
representative replicate.
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Table 3. Results of the density dependence experiments. Treatments are the presence or absence of an interspecific interaction,
and constant or increasing temperature. For each prey or competitor species in each experiment, mean densities sharing a letter
do not differ significantly in a Tukey’s test at the P = 0·05 level. Densities are not compared across species or experiments
Intersp. int.
Temp.
Geometric mean density ± 1 SE
(a) Blepharisma–Paramecium experiment
Absent
con.
Blepharisma: 1·937 ± 0·053
Absent
incr.
Blepharisma: 2·079 ± 0·022
Present
con.
Blepharisma: 1·194 ± 0·048
Present
incr.
Blepharisma: 1·582 ± 0·007
a
a
b
b
Paramecium: 2·126 ± 0·095
Paramecium: 2·134 ± 0·044
Paramecium: 2·156 ± 0·028
Paramecium: 2·089 ± 0·020
a
a
a
a
(b) Colpidium–Tetrahymena experiment
Absent
con.
Colpidium: 2·857 ± 0·056
Absent
incr.
Colpidium: 2·666 ± 0·054
Present
con.
Colpidium: 2·826 ± 0·029
Present
incr.
Colpidium: 2·664 ± 0·076
a
b
a
b
Tetrahymena: 2·393 ± 0·021
Tetrahymena: 2·294 ± 0·038
Tetrahymena: 1·035 ± 0·204
Tetrahymena: 0·969 ± 0·122
a
a
b
b
(c) Colpidium–Didinium experiment
Absent
con.
Absent
incr.
Present
con.
Present
incr.
Colpidium: 3·245 ± 0·031
Colpidium: 3·145 ± 0·073
Colpidium: 1·780 ± 0·123
Colpidium: 1·873 ± 0·075
a
a
b
b
Didinium: 0·772 ± 0·018
Didinium: 0·878 ± 0·028
(d) Colpidium–Euplotes experiment
Absent
con.
Absent
incr.
Present
con.
Present
incr.
Colpidium: 2·970 ± 0·033
Colpidium: 3·007 ± 0·022
Colpidium: 1·866 ± 0·193
Colpidium: 2·304 ± 0·111
a
a
b
b
Euplotes: 0·918 ± 0·046
Euplotes: 0·788 ± 0·058
Fig. 3. Population dynamics from Colpidium–Tetrahymena competition experiment. Each panel corresponds to dynamics from
a representative replicate.
© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
80–90
Competition from Colpidium significantly decreased
mean Tetrahymena density (2-way , main effect
of interspecific interaction, F1,12 = 123·268, P < 0·0001;
Table 3b).
In the Didinium–Colpidium predator–prey experiment, Colpidium without Didinium grew quickly to
high density in both constant and changing environments (Fig. 4a,c). Didinium generated fluctuating
predator–prey dynamics in both environments (Fig.
4b,d). Didinium became extinct in one replicate of the
increasing temperature treatment soon after being
added. We excluded this replicate from all analyses.
Didinium became extinct in all remaining increasing
temperature replicates by Day 33, but persisted in all
constant temperature replicates until the experiment
ended (Fig. 4b,d). Didinium significantly decreased
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Fig. 4. Population dynamics from the Didinium–Colpidium predator–prey experiment. Each panel shows dynamics from a
representative replicate.
Fig. 5. Population dynamics from the Euplotes–Colpidium predator–prey experiment. Each panel shows dynamics from a
representative replicate.
© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
80–90
mean Colpidium density (2-way , main effect of
interspecific interaction, F1,15 = 266·791, P < 0·0001;
Table 3c). Increasing temperature significantly increased
mean Didinium density (1-way , F1,7 = 10·531,
P = 0·0142; Table 3c).
In the Euplotes-Colpidium predator–prey experiment, Colpidium without Euplotes grew quickly to high
density in both constant and changing environments
(Fig. 5a,c). Euplotes reduced Colpidium density and, in
some replicates, apparently generated cyclic predator–
prey dynamics with a period approximately equal to the
length of the experiment (Fig. 5b,d). In one constant
temperature replicate, Euplotes drove Colpidium extinct
by Day 25 and became extinct itself by Day 40. We
included this replicate in all analyses; deleting it from
the analyses did not affect the results. Euplotes significantly reduced mean Colpidium density (2-way ,
main effect of interspecific interaction, F1,16 = 64·299,
P < 0·0001; Table 3d). Increasing temperatures reduced
mean Colpidium density, although the effect was not
quite significant (2-way , main effect of temperature, F1,16 = 4·433, P = 0·0514; Table 3d). Temperature
change did not affect mean Euplotes density (1-way
, F1,8 = 3·107, P = 0·1160; Table 3d).
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© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
80–90
Discussion
  
The main conclusion of these experiments is that
strong intraspecific density dependence appeared to
buffer densities against temperature changes that
substantially altered intrinsic growth rates (Fig. 1,
Table 3). Increasing temperatures raised r by as much
as 200% but failed to increase mean densities, even
in the absence of the complications of predators and
competitors. Natural populations may respond similarly
to directional environmental change (see Whittaker
& Tribe 1998 for a possible example). Vital rates of
natural populations are frequently density-dependent
(e.g. Alvarez-Buylla 1994; Wills et al. 1997; Fryxell
et al. 1998; Jenkins et al. 1999; Silva Matos, Freckleton
& Watkinson 1999; Webb & Peart 1999), and natural
population dynamics often are well-described by
single-species models containing intraspecific density
dependence (Woiwod & Hanski 1992; Turchin &
Taylor 1992; Dennis & Taper 1994; Turchin 1995; Zeng
et al. 1998). Species interactions may simplify rather
than complicate the task of predicting species responses
to directional environmental change if species interactions typically generate direct density dependence
in population growth rates.
An alternative interpretation of these results is that
the protists may have stopped dividing early in the
experiments, due to stress from increasing temperatures or ageing medium. Non-dividing populations would not change with time or vary in density
between temperature treatments. This explanation
seems unlikely. The highest temperatures experienced
were not stressful, since temperature increase generally increased r (Fig. 1). Prey grew rapidly following overexploitation by predators, and Blepharisma
increased throughout the experiment, indicating that
reproduction continued in old cultures (Figs 2, 4).
A non-reproducing population should decline by
10% per week as a consequence of medium replacement,
but many populations did not decline (Figs 2, 3a,c).
More likely, protist densities stabilized when densitydependent division rate balanced mortality (see below).
These patterns are not an artefact of ignoring bacterial dynamics. We ignored bacterial dynamics in
order to focus sampling effort on the protists. Bacteria
can be ignored because bacterial dynamics are fast
relative to protist dynamics (Schaffer 1981).
Interspecific interactions failed to modify responses
to environmental change, except for Blepharisma
(Table 3). Strong intraspecific competition may have
prevented any effect of interspecific interactions on
species responses to environmental change. A population
dense enough to experience strong intraspecific competition might not be affected by temperature change,
regardless of predation or interspecific competition.
Blepharisma growing alone exhibited extended population growth (Fig. 2a,d), suggesting that Blepharisma
experienced relatively weak intraspecific competition
during the experiment. However, the s are
probably conservative tests for effects of community
structure on species responses to temperature change. More
powerful tests would require quantifying the strengths
of intra- and interspecific interactions with time-series
analysis (Ives 1995; Sæther et al. 2000). Non-stationary
population dynamics, the limited length of each time
series, and long, irregular intervals between samples
(relative to the generation times of the organisms),
make time-series analysis difficult (Figs 2–5; Ives 1995;
Turchin 1995; Lewellen & Vessey 1998; Ives et al. 1999).
The results are broadly consistent with the model of
Ives (1995; see also Ives & Gilchrist 1993), which predicts small changes in abundance when direct density
dependence is strong (eqn 3). But eqn 3 predicts that
temperature changes that increased r by as much as
200% (Fig. 1) should have produced similar increases
in mean density. Lack of evidence for such large
increases in density suggests the results are more consistent with a logistic-type model where intrinsic rate of
increase (r), but not carrying capacity (K), depends
on environment. Mean densities of bacterivores that
spent most of an experiment at or near a temperatureindependent carrying capacity (i.e. all bacterivores
growing alone, except Blepharisma) would not change
with temperature. Growth of bacterivorous protists in
laboratory microcosms is well-described by the
logistic equation (Gause 1934; Vandermeer 1969).
Consideration of the mechanisms determining carrying
capacity provides insight into when phenomenological
models like eqn 1 or the logistic equation will correctly
predict species responses to environmental change.
Consideration of mechanisms suggests the assumption
that the strength of density dependence is independent
of environment (eqn 1) was violated in our experiments,
and only appeared to hold because temperature change
affected all species in the same way.

Simple consumer–resource models predict, and experiments confirm, that bacterivore carrying capacity is an
outcome of the interaction between bacterivores and
their bacterial resource (Tilman 1982; Kaunzinger
& Morin 1998). Equilibrial consumer and resource
densities represent a balance between resource growth,
consumption, and consumer mortality. Lack of an
effect of temperature on equilibrium consumer abundance (carrying capacity) suggests that increasing temperature affected protist consumers and their bacterial
resources in a similar manner, so that increased percapita bacterial productivity at higher temperatures
matched increased protist per-capita feeding rate, which
matched increased per-capita metabolic demands.
Limited data indicate that bacterial production and
specific growth rates are about as temperature-sensitive
as protist growth rates (Fenchel 1987; White et al. 1991).
A similar argument may apply to protist predators and
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P.J. Morin
their prey. Predators often did not maintain stable
densities (Figs 4, 5), but might be viewed as oscillating
around a temperature-independent mean (Table 3d).
Different results might obtain in an experiment with
herbivorous protists and algal prey, because photosynthesis is less temperature-sensitive than respiration
(Eppley 1972; Goldman & Carpenter 1974; Lefèvre et al.
1994). Comparison with other studies also suggests
that the results we observed are unlikely to generalize
to more physiologically diverse communities.
   
In contrast to this study, other recent studies indicate
that environmental change can alter mean densities
even when intra- and interspecific interactions are
strong (González & Frost 1994; Brown et al. 1997;
Davis et al. 1998a,b; Jones et al. 1998; Navas 1998; for
a classical example see Park 1954). Navas (1998)
reviewed 20 studies comparing effects of elevated CO2
on single-plant or monoculture biomass to effects on
species biomasses in mixtures and found that 39% of
species in monoculture and 60% of isolated plants
responded to CO2 in a qualitatively different fashion
when grown in mixtures. González & Frost (1994)
found that laboratory bioassays for the effect of food
and pH on reproduction and survival of two rotifer
species failed to predict rotifer responses to lake
acidification. The authors ascribed this failure in part
to reduced predation at low pH. Brown et al. (1997)
attributed long-term changes in rodent and ant assemblages in south-eastern Arizona to increased shrub
cover caused by increased winter rainfall. In terrestrial
microcosms, Jones et al. (1998) found indirect effects of
increased CO2 on soil fungi and their collembolan
predators, probably mediated through effects on plant
photosynthesis and carbon allocation. Davis et al.
(1998a,b) found complicated interactive effects of
temperature, competitors, parasitoids and dispersal on
Drosophila spp. abundances along a temperature
gradient in laboratory microcosms. Sæther et al. (2000)
predicted a significant increase in the carrying capacity
of a songbird population with increasing mean winter
temperature. Environmental change probably had varied
effects on species’ vital rates in these other studies,
changing the strength of intra- and interspecific interactions and explaining the contrasting outcomes between
these other studies and the present work. These other
studies also involved more speciose communities, and
so provided more opportunity for interactive effects of
environmental change and community structure. Our
experiments traded off realistic levels of species richness
for the ability to clearly separate effects of environment
from effects of intra and interspecific interactions.
© 2001 British
Ecological Society,
Journal of Animal
Ecology, 70,
80–90
 
In another protist microcosm study, Petchey et al.
(1999) found dramatic effects of gradually increasing
temperature on community structure and ecosystem
function. Gradually increasing temperature led to
increased extinction rates in multispecies communities.
The contrast between the present study and that of
Petchey et al. (1999) probably traces to the higher
temperatures used by Petchey et al. (up to 34 °C).
Temperatures > 30 °C approach or exceed the
physiological tolerances of many mid-latitude protists
(Norland & Gojdics 1967). Petchey et al. (1999) used
physiologically challenging high temperatures because
they wanted to cause extinctions and examine the
consequences of extinction for ecosystem function. We
used lower temperatures to focus on the interactive
effects of community structure and environmental
change on density, which extinctions might have obscured.
This study addresses only the effect of changes in
mean environmental conditions on mean density. Natural
environmental change is also likely to involve changes
in environmental variability (IPCC 1996). Theoretical
and experimental results indicate that changes in the
frequency and magnitude of environmental variability
can affect mean density and other population properties
(e.g. May 1973; Kaitala et al. 1997).
Directional environmental change may affect
population properties besides mean density. Didinium
went extinct in the increasing temperature treatment
by Day 33, but persisted until the end of the experiment
(42 days) in the constant temperature treatment.
Increasing temperatures may have produced higheramplitude predator–prey oscillations, increasing extinction risk (Fig. 4b,d). Temperature also may have affected
Blepharisma feeding behaviour. Some Blepharisma
became omnivorous in cultures containing Paramecium.
Omnivorous Blepharisma first appeared in samples
on Day 21 in the increasing temperature treatment,
and on Day 29 in the constant temperature treatment.
Low bacterial levels induce omnivory in Blepharisma
(Giese 1973). Bacterial scarcity induces a size increase
in some individuals, allowing these individuals to
consume ciliates at the cost of greatly reduced efficiency in bacterivory (Giese 1973; Fenchel 1980; Morin
1999). Visual inspection of food vacuoles confirmed
that some Blepharisma consumed both Paramecium
and smaller, bacterivorous Blepharisma. The appearance of omnivorous Blepharisma possibly prevented
populations in the constant temperature treatment
from going extinct (Fig. 2c; note initial decline in
Blepharisma, followed by gradual increase beginning
on Day 27), although the net effect of Paramecium
on Blepharisma mean density was a negative, competitive effect (Table 3a).
Conclusion
This study suggests that populations experiencing
direct density dependence will be among those least
affected by directional environmental change. Populations lacking direct density dependence include rare
and invading species not subject to strong predation or
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dense enough to experience strong intraspecific competition. Environmental change is also more likely to
alter species densities when it has varied effects on the
vital rates of different species. When environmental
change alters the density-dependent vital rates that
regulate population size, predicting the effects of
change often will require detailed knowledge of how
population growth rates vary with both density and
environmental conditions (e.g. Sæther et al. 2000).
The task of incorporating density dependence into
predicted species responses to environmental change
remains a challenge for ecologists.
Acknowledgements
We thank Jill McGrady-Steed, Christina Kaunzinger,
Yoko Kato, Tim Casey, Owen Petchey, Marlene Cole,
Timon McPhearson, Lin Jiang, Jennifer Johnson, Pat
Harris and Henry Stevens for helpful comments on
earlier versions of the manuscript. The suggestions of
two anonymous referees significantly improved the
work. Owen Petchey and Tony Ives provided advice
on time-series analysis. This work was supported by
NSF grants DEB-9424494 and DEB-9806427 to Peter
J. Morin and Tim Casey, and by a Rutgers University
Hoffman-LaRoche Fellowship to Jeremy W. Fox.
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Received 22 March 2000; revision received 5 June 2000