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Journal of Animal Ecology 2010, 79, 693–700
doi: 10.1111/j.1365-2656.2010.01662.x
The interacting effects of temperature and food chain
length on trophic abundance and ecosystem function
Oliver S. Beveridge1*, Stuart Humphries2 and Owen L. Petchey1
1
Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield, S10 2TN, UK; and
Department of Biological Sciences, University of Hull, Cottingham Road, Kingston-upon-Hull, HU6 7RX, UK
2
Summary
1. While much is known about the independent effects of trophic structure and temperature on
density and ecosystem processes, less is known about the interaction(s) between the two.
2. We manipulated the temperature of laboratory-based bacteria-protist communities that
contained communities with one, two, or three trophic levels, and recorded species’ densities and
bacterial decomposition.
3. Temperature, food chain length and their interaction produced significant responses in microbial density and bacterial decomposition. Prey and resource density expressed different patterns of
temperature dependency during different phases of population dynamics. The addition of a predator altered the temperature-density relationship of prey, from a unimodal trend to a negative one.
Bacterial decomposition was greatest in the presence of consumers at higher temperatures.
4. These results are qualitatively consistent with a recent model of direct and indirect temperature
effects on resource-consumer population dynamics. Results highlight and reinforce the importance
of indirect effects of temperature mediated through trophic interactions. Understanding and predicting the consequences of environmental change will require that indirect effects, trophic structure, and individual species’ tolerances be incorporated into theory and models.
Key-words: Colpidium striatum, decomposition, Didinium nasutum, ecosystem function, indirect
and direct temperature effects, trophic interactions
Introduction
The temperature of aquatic systems is highly variable, both
spatially (Botte & Kay 2000; Coats et al. 2006) and temporally (Moralesbaquero & Cruzpizarro 1995; Balistrieri et al.
2006). The direct effects of temperature on organisms are well
documented, and include effects on: metabolic rate (Clarke
1991; Gillooly et al. 2001; Brown et al. 2004; Savage et al.
2004; Apple, Del Giorgi & Kemp 2006), nerve conduction
(Clarke 1991; Moran & Melani 2001), digestion ⁄ gut passage
time (Yee & Murray 2004) and cilial or flagellar activity
(Sleigh 1956). Consequently, individual-level processes such
as swimming (Winet 1976; Podolsky & Emlet 1993; Wilson,
James & Johnston 2000; Wilson 2005), feeding (Bolton &
Havenhand 1998; Yee & Murray 2004) and growth (Rose &
Caron 2007) are directly affected by temperature.
Temperature can affect populations indirectly via impacts
on trophic interactions (Ives 1995). Populations can be con*Correspondence author. E-mail: [email protected]
sidered to interact when actions of one population affect the
characteristics of a second (Abrams 1987). Prey density may
be indirectly affected by temperature via changes in predation rate, for example. In polychaetes a 10 C reduction in
temperature is correlated with a 34–67% decrease in ingestion rate (Bolton & Havenhand 1998, 2005; Loiterton, Sundbom & Vrede 2004). Similarly bacterivore grazing rates
decline with decreasing temperature (Delaney 2003).
Indirect effects of temperature mediated by trophic interactions imply that the ecological effects of temperature will
depend on trophic structure. Beisner, McCauley & Wrona
(1996) investigated the impact of two vs. three trophic levels
at 18 and 25 C on Daphnia pulex and phytoplankton population dynamics. At 18 C, the third trophic level (predator,
Mesostoma ehrenbergii) had little impact on Daphnia density,
but at 25 C the presence of the predator reduced the persistence of Daphnia and increased phytoplankton abundance.
These observations suggest an interaction between trophic
structure and temperature and thus the presence of indirect
temperature effects.
2010 The Authors. Journal compilation 2010 British Ecological Society
694 O. S. Beveridge, S. Humphries & O. L. Petchey
Predictions of mechanistic models of resource-consumer population dynamics support the intuitive importance of indirect effects (Vasseur & McCann 2005). A
negative prey density-temperature trend is due to the
positive effect of temperature on the consumer’s ingestion
rate, which counteracts the direct positive influence of
temperature on resource growth rate and density.
However, there is little quantitative data to verify the
presence or importance of indirect temperature effects via
alterations in trophic structure. Temperature may have
indirect effects by altering the strength of competitive
interactions (Jiang & Morin 2004). Here, we focus on
the temperature dependence of predator–prey interactions
in non-omnivorous food chains.
Decomposition is a particularly important function in allochthonous systems where organisms are dependent on energy
from the breakdown of material entering the system (Ribblett, Palmer & Coats 2005). An increase in temperature
results in an increase in bacterial decomposition because of a
net increase in bacterial metabolism (Abrams & Myron
1980). Variation in food chain length might also indirectly
affect decomposition rates. For example, the addition of bacterivorous grazers can increase the rate of bacterial decomposition (Ribblett et al. 2005; Jiang & Krumins 2006a; Krumins
et al. 2006; Jiang 2007).
Less well documented are possible interactive effects of
temperature and trophic structure on bacterial decomposition. Petchey et al. (1999) investigated the response of both
high and low complexity microbial communities to warming
environments (+0Æ1–0Æ2 C per generation). Petchey et al.
(1999) found a significant and positive effect of warming,
which interacted with community complexity, on decomposition rate. Microbial activity (ammonium production) has
also been observed to be dependent on the interaction
between warming and species richness (Newsham & Garstecki 2007).
Here we investigate indirect temperature effects via a factorial manipulation of environmental temperature and food
chain length. We measured the impact of temperature on
density within each trophic level of communities containing
different number of trophic levels. Analyses were repeated
for two phases of population dynamics, as microbial community density can be variable and cyclic (Holyoak & Lawler
1996). Consequences of community response were evaluated
by estimating their ability to decompose wheat grains.
Microbial microcosms form an ideal system to test the
interactive impacts of temperature and food chain length.
The systems are readily replicable and straight forward to
manipulate. Microcosms are closed systems with most environmental factors easily controlled, thus allowing responses
to be confidently attributed to experimentally manipulated
variables. Bacteria and protists have short generation times
allowing several generations to be investigated within a matter of weeks. Effectively long term effects of temperature and
community structure can be realized in a relatively short
experimental duration. Decomposition within protists microcosms can be readily assayed (Petchey et al. 1999; Jiang &
Krumins 2006a), allowing treatment effects on ecosystem
function to be realized.
Materials and methods
STUDY ORGANISMS AND CULTURING
Experiments were carried out using microbial microcosms consisting
of a 200-mL sterile jar, 100-mL media and foil lid allowing exchange
of gases but reducing the risk of contamination. Microcosm media
was Chalkley’s solution (Tompkins et al. 1995) with 0Æ55-g L)1 protist pellet (Carolina T.M. Protozoan pellets, Burlington, NC, USA).
To allow the assessment of decomposition provide a long term energy
source, each microcosm also contained two wheat grains. All work
was conducted using standard aseptic techniques. The total duration
of experimentation was 6 weeks.
Communities consisted of a resource bacterial fauna (Bacillus cereus Frankland and Frankland, Bacillus subtilis Ehrenberg and Serratia marcescens Bizio), consumer bacterivorous protist (Colpidium
striatum Stokes), and a predatory protist (Didinium nasutum Muller),
that readily consumes Colpidium. All organisms were obtained from
stock cultures stored at experimental temperatures to eliminate shock
effects. Bacterial inoculation took place on day 0, Colpidium inoculation on day 1 and Didinium inoculation on day 14. This inoculation
protocol ensured that consumers and predators had sufficient
resources available for successful establishment.
There were two experimental treatments: temperature and food
chain length. Food chain length had four levels: zero (sterile to assay
abiotic mass loss of wheat grains); one, bacteria alone; two, bacteria
+ Colpidium; and three, bacteria + Colpidium + Didinium. Temperature had six levels: 5, 9, 13, 17, 21 and 25 C (temperatures were chosen, so as to encompass a natural range, without risking cell
freezing). Microcosm temperature was controlled via immersion in a
water bath. The experiment was factorial, with each treatment combination replicated five times.
SAMPLING
Population density of each species was estimated every 7 days, via
the removal of aliquots (microcosms were not removed from water
baths). Microcosms were agitated prior to sampling to homogenize
population density and reduce sampling error. Decomposition was
assayed via dry mass loss of wheat grains during the experiment, converted to proportion of initial mass lost. Protist densities were estimated via direct counting under a dissection microscope (Nikon
SMZ1000). Only active motile cells were counted. Bacteria were
assayed via serial dilution (·50, ·2500, ·125 000) and agar plating
(Mikrobiolgie Nutrient Agar). Plates were incubated at room temperature for 48 h, and total bacterial density estimated via colony
counts. Bacterial species identification was not possible.
DATA AND ANALYSES
Mean population densities were calculated for two periods for each
population in each microcosm. The first period was over weeks 3 and
4, during which population densities were relatively constant through
time. The second period was over weeks 5 and 6, during which population densities were beginning to decline. Data before day 14 were
excluded as protist populations were in their growth phase or effectively absent. Species density was modelled (via a linear model) as a
function of temperature, food chain length, temperature2, and all
two-way interactions (following Crawley 2007). A separate analysis
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Animal Ecology, 79, 693–700
Temperature and food chains 695
was conducted for each species in each of the two time periods. Temperature was as a continuous explanatory variable, and food chain
length a categorical variable. The squared term was required to
model the observed non-linear (unimodal) effects of temperature
(Crawley 2007). For Didinium the food chain length parameter was
omitted from the model as Didinium was only present in the three trophic level system. A similar model was fitted to proportional wheat
seed mass loss. All analyses were conducted using the statistical package R (R Development Core Team 2007). The data were normally
distributed and conformed to assumptions of parametric analysis.
Results
POPULATION DENSITY
All populations increased in density from inoculation densities. In the final sampling event (day 42), several microcosms
had zero counts of Didinium and Colpidium (despite previously having positive counts). Further inspection of these
microcosms indicated that active Colpidium and Didinium
were present, but at densities too low to be detected by the
assay method. Aliquots of sufficient size to sample protists at
very low densities would have been large enough to constitute
a disturbance. Although reliable bacterial species identification was not possible via plating, both pink (Serratia) and
white (Baccilus) colonies were observed throughout the
experiment. No extinctions of any species were observed,
despite some data containing estimates of zero density. A
summary of population density statistics is given in Table 1.
Bacterial densities showed a non-linear response to temperature that depended on the presence or absence of higher
trophic levels (Fig. 1a,d and Table. 1). In the absence of
other species bacterial densities were highest at intermediate
temperatures, for weeks 3–4 (Fig. 1a). In weeks 5–6, this
trend was a simple negative function of temperature
(Fig. 1d). The presence of Colpidium resulted in an overall
lower bacterial density in all temperatures (except 5 C) in
data from weeks 3–4. As with bacteria alone, this trend was
reversed in data from weeks 5–6, where the addition of
Colpidium resulted in a positive relationship between bacterial density and temperature (Fig. 1d). In the three trophic
level system, in both periods of data, bacterial density
exhibited a negative unimodal trend, with lowest density at
intermediate temperatures (Fig. 1a,d). The overall density of
bacteria in cold treatments was higher in the three trophic
level system, than the one trophic level system.
Colpidium density also exhibited a non-linear response to
temperature that depended on the presence or absence of
Didinium (Fig. 1b,e and Table 1). In the absence of Didinium,
Colpidium density was a positive and saturating function of
temperature (Fig. 1b,e). In the three trophic level system,
Colpidium density exhibited a unimodal trend to increasing
temperature. As with bacterial density, Colpidium densities
were increased by the presence of Didinium in cold environments, but only during weeks 3–4. The opposite was observed
at the highest temperature (25 C), where the addition of
Didinium reduced the density of Colpidium. This reduction in
density was greater during weeks 5–6 (Fig. 1e) than during
weeks 3–4 (Fig. 1b).
Didinium population density was only significantly
affected by temperature, in weeks 5–6 (Table 1 and Fig. 1c,f).
In weeks 5–6, Didinium density exhibited a unimodal trend to
increasing temperature, with maximum density occurring
around 15 C.
DECOMPOSITION
Decomposition rate had a non-linear response to temperature that depended on food chain length (Fig. 2 and Table 2).
Abiotic mass loss was small ( 7%) with little temperature
response. The addition of bacteria resulted in a small increase
in mass loss compared with sterile microcosms, indicating
that biotic decomposition was occurring. The addition of a
second trophic level caused a significant increase in decomposition with a strong temperature interaction effect. In the
presence of Colpidium, at 25 C, the rate of decomposition
was c. 3Æ5 times higher than in the bacteria alone treatment.
The further addition of the third trophic level had little
impact on decomposition.
Discussion
Here we demonstrate how the density of organisms and the
functioning of an ecosystem depends on both biotic (community structure) and abiotic (temperature) components and
their interaction. The strong interaction of temperature and
food chain length on decomposition highlights the importance of indirect effects on ecosystem functioning. Thus, in
order to understand or predict the response of an organism
to a change in temperature, we must consider both the organism’s position within a community, and the possible effects of
temperature on species interactions.
The temperature dependency of microbial metabolic rate
is well documented (Fenchel & Finlay 1983). Energy or
resources entering this microbial system do so via the activity
of bacteria, and their decomposition of wheat seeds. The
positive trend between bacterial decomposition and temperature make our experiments similar to studies of the direct and
indirect effects of resource availability. Kaunzinger & Morin
(1998) manipulated microbial food chain length across a
nutrient gradient. Their results indicate that an organism’s
response to a change in nutrient concentration depends on
the number of trophic levels present in the community. Similarly, our results suggest that the response of an organism to
temperature depends on the number of trophic levels present
in an ecosystem.
Our results for bacterial (Fig. 1a) and Colpidium density
(Fig. 1b;), from the two trophic level system, resemble current model predictions (Vasseur & McCann 2005, their fig.
4). Vasseur & McCann (2005) modelled the equilibrium densities of an invertebrate consumer and a unicell resource
along a temperature gradient (0–40 C) in a two tropic level
system. Temperature dependence was model via impacts on
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Animal Ecology, 79, 693–700
696 O. S. Beveridge, S. Humphries & O. L. Petchey
Table 1. Summary of statistical models analysing species’ population densities (log10 cell per mL). Model structure and interpretation are
detailed in the text
Effect
d.f.
F-value
Temperature
Chain length
Temperature2
Temperature : chain length
Temperature2 : chain length
Error
Model coefficients
Chain length = 1
Chain length = 2
Chain length = 3
1
2
1
2
2
81
Intercept
5Æ2
6Æ7
6Æ7
16
6Æ7
0Æ19
4
18
0Æ023
<0Æ001
Temperature
0Æ2
)0Æ068
)0Æ16
Temperature2
)0Æ0074
0Æ0011
0Æ0056
Effect
d.f.
F-value
P-value
Temperature
Chain length
Temperature2
Temperature : chain length
Temperature2 : chain length
Error
Model coefficients
Chain length = 1
Chain length = 2
Chain length = 3
1
2
1
2
2
81
Intercept
6Æ5
5
6Æ5
0Æ45
7Æ43
5Æ1
20
3Æ7
<0Æ001
0Æ03
Temperature
)0Æ046
0Æ043
)0Æ15
Temperature2
0Æ00034
)0Æ00022
0Æ00039
Effect
d.f.
F-value
P-value
Temperature
Chain length
Temperature2
Temperature : chain length
Temperature 2 : chain length
Error
Model coefficients
Chain length = 2
Chain length = 3
1
1
1
1
1
54
Intercept
0Æ63
0Æ32
2Æ2
7Æ8
9Æ4
75
12
<0Æ001
<0Æ001
Temperature
0Æ26
0Æ25
Temperature2
)0Æ0067
0Æ00095
Effect
d.f.
F-value
P-value
Temperature
Chain length
Temperature2
Temperature : chain length
Temperature2 : chain length
Error
Model coefficients
Chain length = 2
Chain length = 3
1
1
1
1
1
52
Intercept
2
1Æ7
10
67
14
71
7Æ2
<0Æ001
0Æ0096
Temperature
0Æ11
0Æ13
Temperature2
)0Æ0022
)0Æ011
Effect
d.f.
F-value
P-value
Temperature
Temperature2
Error
Model coefficients
Chain length = 3
1
1
21
Intercept
0Æ045
1Æ3
0Æ52
0Æ28
0Æ48
Temperature
)0Æ042
Temperature2
0Æ002
Effect
d.f.
F-value
P-value
1
2Æ2
0Æ15
Temperature
P-value
Data set
Species
Weeks 3–4
Weeks 3–4
Weeks 3–4
Weeks 3–4
Weeks 3–4
Weeks 3–4
Bacteria
Bacteria
Bacteria
Bacteria
Bacteria
Bacteria
Weeks 3–4
Weeks 3–4
Weeks 3–4
Bacteria
Bacteria
Bacteria
Weeks 5–6
Weeks 5–6
Weeks 5–6
Weeks 5–6
Weeks 5–6
Weeks 5–6
Bacteria
Bacteria
Bacteria
Bacteria
Bacteria
Bacteria
Weeks 5–6
Weeks 5–6
Weeks 5–6
Bacteria
Bacteria
Bacteria
Weeks 3–4
Weeks 3–4
Weeks 3–4
Weeks 3–4
Weeks 3–4
Weeks 3–4
Colpidium
Colpidium
Colpidium
Colpidium
Colpidium
Colpidium
Weeks 3–4
Weeks 3–4
Colpidium
Colpidium
Weeks 5–6
Weeks 5–6
Weeks 5–6
Weeks 5–6
Weeks 5–6
Weeks 5–6
Colpidium
Colpidium
Colpidium
Colpidium
Colpidium
Colpidium
Weeks 5–6
Weeks 5–6
Colpidium
Colpidium
Weeks 3–4
Weeks 3–4
Weeks 3–4
Didinium
Didinium
Didinium
Didinium
Weeks 5–6
Didinium
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Animal Ecology, 79, 693–700
Temperature and food chains 697
Table 1. (Continued)
Effect
d.f.
F-value
P-value
Data set
Species
Temperature2
Error
Model coefficients
Chain length = 3
1
21
Intercept
)1Æ3
18
<0Æ001
Weeks 5–6
Weeks 5–6
Didinium
Didinium
Temperature
0Æ22
Temperature2
)0Æ0068
Weeks 5–6
Didinium
metabolic rates and consequently growth and ingestion rates.
One scenario modelled resulted in a linear negative resource
density–temperature relationship, and an increasing saturating relationship between consumer density and temperature.
Our findings of bacterial density-temperature trend in the
one and two food chain systems, lends empirical support to
the prediction of the Vasseur & McCann model.
Our experiment allowed us to extend the temperature
dependence of resource–community dynamics from a two to
a three trophic level system. This enabled us to test for the
presence of trophic cascades caused by the addition of a top
predator, and whether they were temperature dependent.
Theory on trophic level cascades would predict an increase in
the density of resource species in the presence of a predator
which reduced the density of consumers (Carpenter, Kitchell
& Hodgson 1985). Our data suggests the presence of a classical trophic cascade only at temperatures above 17 C.
Surprisingly, the density of Colpidium was greater in the
three trophic level system, compared with the two trophic
level system, below 9 C in weeks 3–4. Additionally the
density of bacteria was greatest in the three trophic level system (despite the presence of Colpidium) compared with the
two or one trophic level system, below 9 C (in both periods
of data). These observations suggest that in cold environments the overall effect of predation is to increase the density
of prey and resources. This seems counter intuitive as the
effect of predation on prey is to increase mortality. Abrams
(2009) discusses three scenarios where an increase in mortality can lead to greater density (the ‘Hydra effect’): (1) A population with fluctuating dynamics may experience a greater
average density because predation reduces the amplitude in
population cycles. (2) Complex organisms with multiple life
stages may experience an increase in average density with an
increase in mortality at one life stage resulting in greater success at another life stage. (3) An increase in consumer mortality in a resource-limited population can lead to more prudent
resource exploitation (i.e. reduced attack rate of consumers
upon resources), which in turn reduces competition for
resources and thus increases population density. Our experimental protocol was designed to highlight the effects rather
Fig. 1. Mean density of (a,d) bacteria, (b,e)
Colpidium and (c,f) Didinium across temperature treatments. Left hand column (a–c) are
data collected from weeks 3 to 4. Right hand
column (d–e) data collected from weeks 5 to
6. Light grey points and lines indicate data
collected from bacteria alone microcosms;
dark grey, bacteria–Colpidium microcosms;
and black, bacteria–Colpidium–Didinium
microcosms. Lines are the output of the
statistically significant model predictions
discussed in the text. An artificial horizontal
offset has been applied to data points to aide
interpretation.
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Animal Ecology, 79, 693–700
698 O. S. Beveridge, S. Humphries & O. L. Petchey
Fig. 2. Decomposition response to temperature and food chain
length, measured as proportion dry mass loss (both wheat grains
within microcosm). Light grey points and lines indicate data collected
from bacteria alone microcosms; dark grey, bacteria–Colpidium
microcosms; black, bacteria–Colpidium–Didinium microcosms; white
circles with dashed black line, sterile microcosms (abiotic mass loss).
Lines are the output of the statistically significant model predictions
discussed in the text. An artificial horizontal offset has been applied
to data points to aide interpretation.
than the mechanisms of responses, thus the exact mechanism
underlying the hydra effect cannot be tested. However, our
microbial organisms are simple, with short generation times,
thus differing mortality at various life stages will not be a
mechanism for the hydra effect in our communities. Future
studies may wish to employ the following modifications to
our methods: (1) greater temporal resolution in population
density, and increased experimental duration. This will allow
observations of population density dynamics and the impact
of predation on population cycling and crashing. (2) Decomposition rate should be assayed through time, in conjunction
with bacterial density. Therefore, an accurate measurement
of per capita bacterial decomposition rate is enabled. This
will highlight whether or not consumer predation does cause
a more prudent wheat seed exploitation by bacteria.
Decomposition was significantly lower in sterile microcosms than those containing bacteria. This indicates that bio-
logical decomposition took place in all microcosms
containing bacteria. As expected, decomposition was positively related to temperature (Abrams & Myron 1980). The
presence of bacterivores increased decomposition 3Æ5-fold
with a strong, positive and non-linear response to temperature. A 1Æ5- to 3-fold increase in bacterial decomposition
(wheat grain dry mass loss) has previously been observed
with the addition of bacterivores to a bacterial fauna (Jiang
& Krumins 2006a; Krumins et al. 2006; Jiang 2007). Similarly Ribblett et al. (2005) observed a 3Æ5- to 4-fold increase
in the bacterial decomposition of leaf disks because of the
addition of bacterivores.
Our experimental design allowed us to uniquely test the
impact on decomposition of food chain length, rather than
total protist species diversity or absence and presence of other
bacterivore species. Only the presence of a bacterial grazer,
not total food chain length was important in determining
decomposition rate in this system. Previous work has identified a positive correlation between Eukaryotic (including
third trophic levels) species richness and decomposition
(Krumins et al. 2006). Further work is required to investigate
the importance of species richness at the second trophic level
and independently at the third trophic level. It is important
to note that these previous studies (Ribblett et al. 2005; Jiang
& Krumins 2006a; Krumins et al. 2006; Jiang 2007) were
conducted at a single environmental temperature. Evidence
provided here suggests that the impact a bacterivore has on
decomposition is greatest at higher temperatures.
Various hypotheses have been proposed to explain how
the presence of bacterivores facilitates bacterial decomposition. (1) Bacterivores may alter the composition of the bacterial fauna, and result in a fauna more favourable to
decomposition (Krumins et al. 2006). (2) The addition of
bacterivores may increase the recycling of minerals and nutrients, which would otherwise limit bacteria activity (Ratsak,
Maarsen & Kooijman 1996). (3) The consumption of bacteria will likely maintain a bacterial population density below
carrying capacity, within the more active exponential growth
phase (Jurgens & Sala 2000).
An alteration in the bacterial fauna is unlikely to explain
the positive effects of bacterivores on decomposition in our
experiments. Our bacterial fauna was simple from the outset
and although reliable species identification was not possible,
Table 2. Summary of the statistical model analysing wheat seed mass loss. Model structure and interpretation are detailed in the text
Effect
d.f.
F-value
P-value
Temperature
Chain length
Temperature2
Temperature : chain length
Temperature2 : chain length
Error
Model coefficients
Sterile
Chain length = 1
Chain length = 2
Chain length = 3
1
3
1
3
3
12
Intercept
0Æ074
0Æ11
0Æ20
0Æ33
170
40
55
)35
9Æ9
0Æ00040
0Æ0312
Temperature
)0Æ0034
)0Æ012
)0Æ050
)0Æ13
Temperature2
0Æ00014
0Æ00057
0Æ0021
0Æ0023
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Animal Ecology, 79, 693–700
Temperature and food chains 699
both white (Bacillus) and pink (Serratia) colonies were
observed throughout the experiment. We were unable to
ascertain the limiting factors for bacterial activity in this
experiment, and so cannot draw conclusions on the impact of
bacterivores on resources limiting bacterial activity. Our data
best supports the hypothesis that bacterivores lowered the
density of the bacterial fauna and maintained an active bacterial population in exponential growth phase. Furthermore,
the impact of Colpidium on bacterial density increased with
temperature (Fig. 1), and may explain the strong food chain
length-temperature interaction observed for decomposition.
A fourth mechanism may also exist for the positive impact of
bacterivores on bacterial decomposition: Rozen et al. (2009)
observed that bacterial consumption of lysed bacterial debris
facilitates bacterial growth. As predation is unlikely to be
100% efficient, it seems plausible that Colpidium could
increase the concentration of bacterial debris, thus increasing
bacterial growth. However, we are unaware of any reported
link between Colpidium grazing and bacterial debris concentration in the literature.
The strength of predator ⁄ consumer impacts on prey ⁄ resources were positively temperature dependent. The addition
of Colpidium altered the bacterial temperature–density relationship from one of a unimodal nature, to a negative one.
This suggests that the direct positive influence of temperature
on bacterial density is counteracted by the indirect negative
impact of temperature on bacteria via its interaction with
Colpidium. A similar relationship was observed with Colpidium density and Didinium. For Colpidium this overall increase
in predator impact was most pronounced at later stages of
population dynamics, suggesting that population dynamic
phase may alter the importance of predator impact on prey
numbers.
A reduction in Colpidium density would be predicted to
increase the density of bacteria because of the reduction in
Colpidium impact on bacteria. However, the reduction in
Colpidium density did not correspond with an increase in bacterial density (Fig 1a & d). This implies that grazing by Colpidium is not directly dependent on Colpidium population
density. This suggests the presence of interference between
individual Colpidium. Interference has been proposed for
Colpidium and a competitor Paramecium caudatum (Jiang &
Krumins 2006b). Further work is required to investigate
whether there is intraspecies interference for Colpidium consumption of bacteria.
Our results reveal an important role for temperature and
community structure in controlling population size and
decomposition rate. We also clearly show a strong interaction between temperature and community structure, affecting both populations and their ecosystem function. The
importance of interactive effects between food chain length
(or vertical diversity as discussed in Duffy et al. 2007) and
temperature are made more compelling when one considers
that in addition to climate change biodiversity is also rapidly
declining (Ehrlich & Pringle 2008). Independently modelling
either the direct impacts of temperature or extinctions on ecosystems may lead to incorrect predictions. Our work demon-
strates that the biotic and abiotic components of an ecosystem
interact strongly, with impacts observed at all levels.
Acknowledgements
OSB was funded by a NERC studentship, OLP is a Royal Society
University Research Fellow. SH was supported by a NERC Advanced
Fellowship (NE ⁄ B500690 ⁄ 3). We also thank two anonymous reviewers for
their constructive and helpful comments.
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Received 10 August 2009; accepted 15 December 2009
Handling Editor: Tom Webb
2010 The Authors. Journal compilation 2010 British Ecological Society, Journal of Animal Ecology, 79, 693–700