This document summarizes a short-term laboratory experiment that investigated the effects of temperature and prey density on trophic interactions between the predator Chaoborus obscuripes and the prey Daphnia magna. Functional response experiments were conducted at two temperatures (16°C and 20°C) across a range of Daphnia densities. Metabolic rates of Chaoborus were also measured at each temperature. Results were used to model the effects on short-term predator-prey interaction strength and long-term predator energetic efficiency.

1. Effects of temperature and prey density on trophic
interactions in aquatic food webs.
A short-term laboratory experiment with Chaoborus obscuripes and Daphnia
magna.
By Baptiste JAUGEON
Master READ, UFR SVE, Université Rennes 1, 35042 Rennes Cedex, France
Dates : du 1/04/2014 au 15/07/2014
Correspondante universitaire : Joan Van Baaren
Soutenu à Rennes le 16 juin 2014
Sous la direction de: BOUKAL David and SENTIS Arnaud
Department of Ecosystem Biology, Faculty of Science, University of South Bohemia,
Ceske Budejovice, Czech Republic
Picture : Baptiste Jaugeon

3. Acknowledgement:
I am very grateful to David Boukal for giving me the opportunity of this internship and make
us discover Czech food. As well I thank Arnaud for his guidance, his advices, his patience, last
moment reviews and climbing sessions. I am grateful to the French team, Charlene Gemard and
Noémie Pichon, for their support and I wish them all the best for their report and for the future.
I also thank the famous forward-thinking plant ecologist Tom Lachaise for printing this report
and daily communications. I don’t forget my girlfriend and our love letters. Finally I thank the
Hammond’s coffee and all the musicians, I hope Francesco de Bello et al. (2013) were right.

4. Contents
Introduction .............................................................................................................................. 1
Methods..................................................................................................................................... 4
Sampling and rearing........................................................................................................... 4
Functional response experiments........................................................................................ 4
Metabolic rates...................................................................................................................... 5
Statistical analyses and modelling....................................................................................... 5
Functional response .......................................................................................................... 5
Energetic efficiency........................................................................................................... 6
Short-term interaction strength....................................................................................... 6
Long-term interaction strength ....................................................................................... 7
Results ....................................................................................................................................... 9
Functional response experiment.......................................................................................... 9
Metabolic rates.................................................................................................................... 10
Energetic efficiency............................................................................................................. 11
Short-term interaction strength ........................................................................................ 11
Long-term interaction strength......................................................................................... 12
Discussion................................................................................................................................ 12
Effect of temperature on functional response.................................................................. 13
Effect of temperature on metabolic rate and energetic efficiency ................................. 13
Effect of temperature on short- and long-term interactions strengths and implications
for predator-prey stability ................................................................................................. 14
Conclusion............................................................................................................................... 14
References ................................................................................................................................. 1

5. 1
Introduction
One of the most important challenges of the 21st century for ecologists is to predict how
ecosystems, communities and trophic interactions may respond to the upcoming global change.
Understanding the interplay between global change and ecosystem functionality is crucial to
prevent irreversible shifts of ecosystem functioning and structure. Global warming and
eutrophication (nutrient enrichment) have been identified as two main factors causing
biodiversity changes and affecting ecosystem services and human well-being (Vitousek et al.,
1997; Parmesan et al., 2006). The objective of this study is to understand and predict how
temperature and prey density (as a proxy for nutrient enrichment) could influence predator-prey
interactions and long-term population dynamics and stability.
Two main reasons underlie this choice of the topic. First, most ecosystems are complex
and involve many interacting species. Previous studies have mainly dealt with direct effects of
global warming on single species (Bale, 2002; Deutsch et al., 2008; Dillon et al., 2010).
However each species can be affected both direct and indirectly through altered interactions
with other species. The effects of changing temperature on these interactions can be even
stronger than the direct effects on a given species (Sentis et al., 2013). Taking into account the
effects of temperature on species interactions is thereby crucial to understand and predict
population dynamics and ecosystem stability (Traill, et al., 2010).
Second, interactions between predators and their prey are among the most important
ones for the stability and functioning of ecosystems (Holt and Polis, 1997; Sih et al., 1998;
Schmitz, 2007). Predicting the consequences of warming on ecological communities thereby
requires understanding how temperature affects the strength of predator-prey interactions.
Recent studies indicate that warming can stabilize or destabilize food webs by affecting
predator feeding and metabolic rates and thereby altering the strengths of predator-prey
interactions (Rall et al., 2010). However, few studies have investigated the effects of
temperature on predator-prey interactions and population dynamics (Vucic-Pestic et al., 2011;
Sentis et al., 2012; Fussmann et al., 2014). Moreover, simultaneous impacts of temperature
changes on predation and metabolic rates in aquatic food webs remain unexplored. Thermal
properties of water and gases are quite different. For example, water has much higher thermal
capacity and temperature fluctuations in aquatic habitats are therefore much lower than in
terrestrial ones. In addition, many aquatic organisms rely on dissolved oxygen for respiration;
warmer water contains less dissolved oxygen and the same increase in temperature may

6. 2
therefore impose larger stress on aquatic than on terrestrial animals (Denny, 1993). As a
consequence, one may expect that aquatic organisms could respond to warming differently than
terrestrial organisms. It is therefore important to verify if the findings of previous studies
focused on the effects of temperature on food-web interactions, based almost exclusively on
terrestrial systems, also apply to aquatic food-webs.
The effects of temperature and enrichment on predator-prey interactions can be studied
on shorter or longer timescales. Over short timescales, predation strength can be characterized
by the functional response that describes the relationship between prey density and predator per
capita feeding rate (Holling, 1959). Two processes characterize the functional response:
handling time, which also includes digestion and primarily limits the ability of predators to
ingest prey at high densities, and search rate, which characterizes the ability of predators to
locate and capture prey and primarily limits the ability of predators to kill prey at low densities
at which handing and digestion are not limiting. Recent studies showed that temperature can
have a strong effect on the functional response of ectotherms (Rall et al., 2010; Vucic-Pestic et
al., 2011; Sentis et al., 2012). This effect is mainly driven by the impact of temperature on
metabolic rates which increase exponentially with temperature (Brown et al., 2004). This
causes handling time to decrease because it is mainly driven by digestion (Sentis et al. 2013),
which depends strongly on metabolic rate (Englund, 2011). Whereas the search rate tend to
increase, nonetheless this relationship is not always found because search rate depends mainly
on predator behaviour, which is more complex and less dependent on temperature than
metabolic or digestive rates. Nevertheless, in most cases, the resulting effect of warming is to
increase predation rate (Thompson, 1978; Gresens et al., 1982; Vucic-Pestic et al., 2011), which
strengthens predator-prey coupling.
Long-term effects of temperature on predator-prey interactions are driven mainly by the
relative effects of temperature on predator feeding and metabolic rates. The ratio between per
capita feeding rate and metabolic rate (hereafter, predator energetic efficiency) determines the
amount of energy available for activity, growth and reproduction and thus strongly influences
predator fitness. Empirical studies show that predator energetic efficiency generally decreases
with temperature (Rall et al., 2010; Vucic-Pestic et al., 2011; Sentis et al., 2012; Vasseur and
McCann 2005). If metabolism increase faster than feeding rate with temperature, energetic
efficiency would decrease. This would translate into lower average predator biomass and
suggests that warming weakens predator-prey interactions in the long run and hence increases
food-web stability. (Vasseur and McCann 2005; Rall et al., 2010; Binzer et al., 2012).

7. 3
The effect of temperature on predator-prey interactions strength thereby depends on the
timescales of interest: current theory and empirical observations support the idea that warming
respectively strengthens and weakens short- and long-term interactions. This highlights the
importance of considering different timescales and taking into account the relative effect of
temperature on feeding and metabolic rates (i.e. predator energetic efficiency) to be able to
understand and predict the consequences of climate change on population dynamics and food-
web stability.
The purpose of this study was to (1) investigate the effects of warming and prey density
on (1) predator feeding rate, (2) predator energetic efficiency and (3) short- and long-term
interactions between predators and their prey. I expected that search rate would increase and
handling time decrease with warming, which would strengthen the short-term interactions.
Moreover, metabolism should also increase with temperature. Energetic efficiency could be
lower at high temperatures and then decrease the long-term interactions strength.
The study is focused on small fishless water bodies, in which food webs are highly
interconnected and dominated by ectotherms (e.g. Klecka & Boukal, 2012). I combined
modelling and experimental approaches and used the phantom midge Chaoborus obscuripes
larvae (Diptera: Chaoboridae) as predators and Daphnia magna (Cladocera: Daphniidae) as
prey. Both species are common inhabitants of fishless ponds and lakes. Chaoborus larvae are
transparent ambush predators that remain stationary in the water column by the use of buoyancy
bladders. They usually do not move until they detect a prey and then start an attack. D. magna
is a planktonic filter-feeding crustacean and an important resource for predatory insects and
fishes. Daphnia are important for ecosystem functioning as they regulate primary production
(i.e. algae). Chaoborus larvae are among its main predators (Swift and Fedorenko, 1975).
I measured C. obscuripes functional response to quantify the effects of temperature and
prey density on its predation pressure. I carried out the experiment at two different temperatures,
one ambient and one raised by +4°C, which corresponds to the prediction for the year 2100
(IPCC 2013). I then used empirical measures of feeding and metabolic rates to quantify the
effects of temperature change on short- and long-term interactions. The findings have several
implications for population dynamics and food-web stability.

8. 4
Methods
Sampling and rearing
A colony of Daphnia magna, established from individuals collected in a pond near České
Budějovice, Czech Republic, was maintained on green algae Chlorella vulgaris (Chlorellacea:
Chlorellales) at 20 ± 1°C (value ± SE) under a 17L:7D photoperiod. Last instar Chaoborus
obscuripes larvae (11-13mm in length; mean weight ± SE: 8.826 ± 0. 0021 mg) were collected
in May 2013 from a small pond near the village of Munice (49°05'00.1"N; 14°23'29.6"E),
Czech Republic. They were kept separately in plastic jars and maintained at 16 ± 2°C, 17L:7D
photoperiod. In order to standardize their feeding status, all the individuals were fed ad libitum
with Daphnia sp.
Functional response experiments
I tested the functional response of C. obscuripes last instar larvae at two distinct temperatures
[(mean ± SE) 16.0 ± 0. 05°C and 20.0 ± 0.5°C]. The lower value corresponds to the ambient
water temperature at the locality when the larvae were collected and 20°C matches the increase
of 4°C predicted for 2100 (IPCC 2013). The experiments were performed in rectangular plastic
jars (8 cm in width, 5 cm length and 10 cm in height for a surface area of 40 cm2
) filled with 1
l of aged tap water and fine crystalline sand (grain size <1 mm) as substrate.
Juvenile D. magna (mean weight ± SE: 0.17 ± 0.0025 mg, approx. 1 mm long) obtained
from synchronous cohorts of D. magna were used as prey throughout the experiment. Prey
densities were 5, 15, 30, 50, 75, 110, 150 and 200 ind.l-1
, which covers the range of Daphnia
densities in Czech water bodies from less productive to eutrophic ones. Each predator was
starved for 24 hours and acclimated at the experimental temperature for 2 hours before the
experiment. D. magna prey were introduced in the experimental arena 1 hour before the
experiment, which started with the introduction of a single Chaoborus larva. After 6 hours, the
number of remaining D. magna was recorded to establish the predation rate. For each D. magna
density and temperature, the experiment was repeated eight times with a predator and four times
without it (control treatment to assess natural mortality of the prey).

9. 5
Metabolic rates
I estimated metabolic rates at both temperatures from respiration measurement using an O2
Microsensor (Unisense©) probe coupled to SensorTrace Basic v3.2.3 (Unisense©) software.
Predators were first starved for 24 hours and then individually transferred into sealed glass
chambers filled with distilled water and purified salt (0.14 g.l-1
). Salt was added to obtain a
similar conductivity as aged tap water used for the functional response experiment. I measured
oxygen concentration in the glass chambers just before the introduction of a single larva and
then after 24 hours. I calculated the metabolic rate by converting the oxygen consumption (ml.l-
1
) into J.h-1
(1 ml O2 = 20.1 J, Peters, 1986). I performed 32 replicates at each temperature, 16
with Chaoborus larva and 16 without the larva as controls to determine background oxygen
depletion.
Statistical analyses and modelling
All data were analysed in R (version 3.9.1; R Development Core Team, 2013). Only 3.8 ± 1.1%
(mean ± SE) of the prey died in control treatments (without predator), and mortality did not
differ significantly among temperatures (unpaired Wilcoxon test: W = 340, P = 0.76). I thereby
did not correct data for natural D. magna mortality in subsequent analyses.
Functional response
One simple mechanistic model to study predator-prey interactions is the non-linear functional
response model which describes the relationship between prey density and predator per capita
feeding rate (Holling 1959). Three principal types of functional response have been described:
type I response is characterized by a linear increase in the number of prey eaten, type II response
by a monotonic decelerating increase, and type III response by a sigmoidal increase in host
numbers attacked.
To discriminate between type II and type III functional responses, both models were fitted
to the data by maximum likelihood estimate, and compared using Akaike’s information
criterion corrected for small sample size (AICc). The best model is the one with the lower AICc
value (Burnham and Anderson, 2002). As these preliminary analyses indicated a type II
functional response, I used the type II Rogers’s random predator equation (Rogers, 1972) to
take into account prey depletion:
(1) 𝑵𝒆 = 𝑵 𝟎(𝟏 − 𝒆−𝒂(𝒕−𝒉𝑵 𝒆)
)

10. 6
where Ne is the number of prey eaten, N0 the initial prey density (units: ind.arena-1
), t the total
experimental time (day), h the prey handling time (day.ind-1
), and a the attack rate (arena.day-
1
.ind-1
).
To determine the effects of temperature on functional response parameters (a and h), I
considered different functional-response models covering all possible combinations of the
dependence of parameters on temperature: a and/or h may or may not depend on temperature.
This yields a total of 4 candidate models that were fitted to the data using a maximum likelihood
method and the package “BBMLE” (Bolker 2008). I then evaluated models according to their
∆AICc and determined the best-fitting model. I used parameter estimates from the best-fitting
model to calculate predator energetic efficiency and interaction strength as described below.
Energetic efficiency
To evaluate the energetic efficiency of C. obscuripes preying on D. magna for each
temperature, I used the following equation (Vasseur and McCann, 2005; Rall et al., 2010,
Vucic-Pestic et al., 2011; Sentis et al., 2012):
(2)
where y is the dimensionless energetic efficiency of the predator, F is the per capita energy
feeding rate (J.h-1
) equal to the per capita feeding rate (ind.h-1
) multiplied by the weight of one
D. magna prey (that gives the per capita biomass feeding rate (mg.h-1
)) multiplied a weight-
energy conversion factor (1 mg wet mass = 7 J; (Peters, 1983)), ω is a temperature-independent
assimilation efficiency (0.85 for carnivores, Peters, 1986), λ is a constant converting standard
metabolic rate into field metabolic rate (λ = 3 Savage et al., 2004), and I is the standard
metabolic rate (J.h-1
). Below the value of y=1, the predator is starving because its metabolism
is higher than the energy it gets from the prey. Only if the per capita energy feeding rate exceeds
the field metabolic rate (y>1), the predator has a positive energy budget and can grow and
reproduce.
Short-term interaction strength
I used the log-response ratio to evaluate the per capita short-term interaction strength (Berlow
et al., 1999, Emmerson and Raffaelli, 2004) given by the following equation:
I
F
y



0 0ln(( ) / )
_ _ eN N N
Short Term IS
t



11. 7
(3)
where Ne is the number of prey eaten, N0 the initial number of prey and t the total experimental
time. This assumes that the prey abundance in the absence of the predator is equal to the initial
prey density and that the prey mortality is negligible as was the case was in this study.
Long-term interaction strength
For both temperatures, I calculated long-term interaction strength using the following
population-dynamical model (Yodzis and Innes, 1992; Vasseur and McCann, 2005; Rall et al.,
2010):
𝒅𝑵
𝒅𝒕
= 𝒓 × 𝑵 (𝟏 −
𝑵
𝑲
) −
𝒂×𝑵
𝟏+𝒂×𝒉×𝑵
× 𝑷 (4)
𝒅𝑷
𝒅𝒕
=
𝒂×𝑵
𝟏+𝒂×𝒉×𝑵
× 𝒆 𝒄 𝑷 − 𝒄 × 𝒎 𝒑 × 𝑷 (5)
where t is time (day), N and P are prey and predator densities (ind.l-1
), K is the carrying capacity
of the prey species (ind.l-1
), mp (J.h-1
) is the metabolic rate of the predator, and c is a correction
factor that converts metabolism from J.h-1
into predator individuals per day (J-1
.h.ind.day-1
). The
feeding interaction follows a type II functional response, in which the parameters a and h
correspond to my empirical estimates. Moreover, the prey intrinsic growth rate r is related to
temperature and prey mass as (Savage et al., 2004):
(6)
where r0 is a normalisation constant independent of body size and temperature (11.661013
;
Savage et al., 2004a), M the prey mass (µg), br an allometric exponent (-0.25), Er the activation
energy for arthropods (-0.84 eV; Savage et al., 2004), k the Boltzmann’s constant (8.6210-5
eV.K-1
) and T the environmental temperature (K).
I then calculated the long-term predator and prey population densities, P*
, N*
, assuming
that the system is at equilibrium (i.e., dN/dt = 0; dP/dt = 0):
𝑵∗
=
𝑪×𝒎 𝒑
𝒆 𝒄×𝒂−𝑪×𝒎 𝒑×𝒂×𝒉
(7a)
𝑷∗
= 𝒓 × (𝟏 −
𝑵
𝑲
) ×
𝟏+𝒂×𝒉×𝑵∗
𝒂×𝑵∗ (7b)
/
0
r rb E kT
r r M e


12. 8
Subsequently, I calculated the long-term per capita interaction strength using a log-ratio
interaction strength (Berlow et al., 1999; Emmerson and Raffaelli 2004; Rall et al., 2010):
(8)
where P is the predator abundance, N+
is the prey abundance with predators and N-
is the prey
abundance without predators. Following Rall et al., (2010). Considering the system to be at
equilibrium, one can replace N+
by N*, P by P* and N-
by K (i.e. prey population reaches
carrying capacity in the absence of predators). I used prey carrying capacity values that
correspond to the experimental densities (from 0 to 200 ind.l-1
).
ln( / )
_ _
N N
Long Term IS
P
 


13. 9
Results
Functional response experiment
The relationship between prey density and the number of prey eaten is best described by a type
II functional response for both temperature (Fig. 1). The number of prey eaten first increases
linearly as prey density increases due to the increasing encounter rate, and subsequently reaches
a plateau due to the handling time constraints. The maximum feeding rate increases with
temperature.
Fig. 1. Type II functional response curves fit for fourth-instar Chaoborus feeding on Daphnia
16°C (left panel) and 20°C (right panel). ∆AICc= 29.4 compared with type III model at 16°C;
dAICc= 29.6 with compared type III model at 20°C.
According to ∆AICc values, the best fitting model is the one for which only the handling
time depend on temperature. In other words, temperature significantly influenced handling time
whereas acute test temperature only affected handling time. Handling time was lower at 20°C
compared to 16°C, whereas the attack rate was not influenced by temperature (Table 2).
Model assumptions ∆AICc df Akaiike weight
Only h depending on temperature 0 3 0.496
Only a depending on temperature 1.3 3 0.265
Both a and h depending on temperature 1.8 4 0.204
a and h temperature independent 5.3 2 0.036
Prey density
(Prey/Liter)
Numberofpreyeaten
Table 1. ∆AICc values and Akaike weights obtained when comparing different models.

14. 10
Table 2. Estimate of handling time, h,and attack rate, a (day.ind-1
), and their standard errors
(SE) and 95% CI obtained by fitting type II functional response model with h depending on
temperature to the data.
Temperature
(°C)
N
h
(day.ind-1
)
SE 95% CI
a
(arena.day-1
.ind-1
)
SE 95% CI
16 72 0.021 0.0017 0.016--0.024
1.203 0.135 0.78--1.58
20 72 0.016 0.0014 0.013--0.019
N = number of replicates.
Metabolic rates
Oxygen depletion in controls without larvae was positive. I thereby corrected data by
subtracting mean control oxygen consumption from mean predator oxygen consumption.
Metabolic rate was significantly higher at 20°C than at 16°C (W = 56, P = 0.02, Fig. 2).
Fig. 2. Temperature dependence of metabolic rate,
shown as mean and SE (N=16 and N=14 for 16°C
and 20°C, respectively).

15. 11
Energetic efficiency
Predator energetic efficiency (feeding
rate relative to metabolic rate)
increases with prey density at both
temperature (Fig. 3). It reaches a
plateau when feeding rate (i.e.
ingestion) is maximal, corresponding
to the asymptotic feeding rate given
by the functional response. The
energetic efficiency is lower at 20°C
compared to 16°C. The difference is
less pronounced at low prey densities
because the efficiency must be equal
to zero in the absence of prey
irrespective of temperature.
Short-term interaction strength
Short-term interaction strength decreases asymptotically with prey density and is always higher
at 20°C than at 16°C (Fig. 4).
0 50 100 150 200
0
10
20
30
40
Prey density (prey/liter)
Energeticefficiency
16°C
20°C
0 50 100 150 200
0.00.51.01.5
Prey density (ind/L)
Short-terminteractionstrength
16°C
20°C
Fig 3. Effect of prey density (ind.l-1
) and
temperature on the energetic efficiency of
Chaoborus obscuripes.
Fig 4. Effects of prey density and test temperature on short-term interaction
strength.

16. 12
Long-term interaction strength
For both temperature regimes, the relationship between long-term interaction strength and prey
density is characterized by a monotonically decelerating increase (Fig. 5). Long-term
interaction strength is lower at 20°C than at 16°C.
Discussion
Taking into account biotic interactions across trophic levels is essential to understand and
predict how enrichment (i.e. prey density) and warming influence predator-prey interactions,
population dynamics and food-web stability (Tylianakis et al., 2008; Putten et al., 2010). Recent
modelling studies showed that warming could strongly affect the strength of trophic interactions
and thereby modify the dynamics and stability of simple food webs (Vasseur and McCann,
2005; Rall et al., 2010; Binzer et al., 2012). In the present study, I also found important effects
of temperature on predator feeding and metabolic rates which should translate into changes to
population dynamics.
Fig. 5. Effect of prey density and temperature on long-term interaction strength.
0 50 100 150 200
0.40.60.81.01.21.4
Prey density
LongtermIS
16°C
20°C
Long-terminteractionstrength
Prey density (ind /L)

17. 13
Effect of temperature on functional response
As reported by Spitze et al (1985), C. obscuripes preying on Daphnia showed a type II
functional response. In addition, I found that the maximum feeding rate increases with
temperature.
As in other studies on arthropod predators (Englund et al., 2011, Rall et al., 2012, Sentis
et al., 2012), handling time decreased with temperature indicating that these predators are more
efficient to handle prey in warmer environments. This relationship between handling time and
temperature is comprehensible from the fact this parameter combines handling and digestive
processes involving biochemical process mobilizing temperature-dependent enzymes.
For the search rate, I did not find any effect of temperature, whereas most studies have
reported that predators are more efficient at searching prey in warmer conditions (Englund et
al., 2011; Rall et al., 2012; Sentis et al., 2012). Temperature can have two effects on attack
rates: it increases predators and prey velocities and encounter rates, resulting in higher attack
rates, but it also increases the prey escape efficiency, resulting in lower attack rates. This could
explain my results because Chaoborus larvae are ambush predators (Spitze et al., 1985), so
their attack rate depends mostly on prey behaviour than their own motility.
Effect of temperature on metabolic rate and energetic efficiency
In accordance with the metabolic theory of ecology (MTE, Brown et al., 2004), I found that
predator metabolic rate increases with temperature. Interestingly, the energetic efficiency
decreased with temperature. I thereby conclude that the increase in metabolism caused by
warming imposes energetic restriction on Chaoborus. In other words even if Chaoborus feeding
rate increases with temperature, less energy is available for its growth because of
disproportionately higher metabolic rate. This should increase the risk of predator starvation
with warming as reported in recent empirical studies (Fussman et al. 2014). At the same time,
energetic efficiency of Chaoborus increased with prey density, which suggests that the negative
impact of warming on predators may be dampened by an increase in resource availability. This
would confirm the predictions of theoretical models looking at the effects of temperature and
enrichment on food-chain dynamics (Binzer et al., 2012).

18. 14
Effect of temperature on short- and long-term interactions strengths and
implications for predator-prey stability
Short-term interaction strength decreased with prey density. This pattern is explained by the
density dependence of the predator feeding rate, i.e., type II functional response (Holling,
1959). As reported in previous studies (Sanford, 1999; Vucic-Pestic et al., 2011, Rall et al.,
2012), I found that warming increases short-term interaction strength, which may decrease
food-web stability (McCann et al., 1998, Rall et al., 2010). As interaction strength decreases
with prey density, this suggests that less productive ecosystems (with low prey densities) are
more likely to be unstable (i.e. have higher population fluctuations) with increasing
temperatures. Nevertheless, including predator’s metabolism in the predator-prey bioenergetic
model showed the opposite effect: long-term interaction strength decreased with temperature
and increased with prey density. These results thereby suggest that warming may weaken long-
term interactions and increase the stability of the system. Increasing prey density lead to the
opposite effect, which suggests that temperature may decrease the destabilising effects of
enrichment, as reported by Binzer et al. (2012). These contradicting conclusions based on short-
and long-term interaction strengths highlight the importance of metabolic rates and their
temperature dependence in predictions of long-term consequences of warming for predator-
prey dynamics.
Conclusions
I showed that warming and prey-density have important effects on the functional response and
the energetic efficiency of an aquatic predator. These findings confirm the results of previous
studies on terrestrial systems showing that the predator feeding rate increase with temperature
strengthening short-term interactions. However the energetic efficiency tended to decrease,
weakening long-term interactions stabilizing population dynamics. Nevertheless, the long-term
interaction strength tended to increase with prey-density destabilizing population dynamic.
Thus it is important for further studies to take into considerations the interaction strength
including the predator metabolism on longer time interval to predict the consequences of
warming and prey-density for ecosystems dynamic

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22. 4
Abstract — One of the most important challenges of the 21st century for ecologists is to
predict how ecosystems, communities and trophic interactions may respond to the
upcoming global change. Warming can stabilize or destabilize food webs by affecting
predator feeding and metabolic rates and thereby altering the strengths of predator-prey
interactions. The objective of this study was to understand and predict how temperature
and prey density (as a proxy for nutrient enrichment) could influence predator-prey
interactions and long-term population dynamics and stability. For this we need to
understand how temperature affects feeding rate and metabolism. In this report I discuss
the effects of global warming and prey density on prey-predator interactions and
ecosystems stability. I studied the dependence of Chaoborus obscuripes functional
response and metabolism on temperature. From these data I calculated the energetic
efficiency and the interaction strength on both short and long term at different temperature
and prey densities. Metabolic rate and feeding rate both increased with warming whereas
the energetic efficiency (ratio of feeding rate to metabolism) decreases, leading to a
decrease in long-term interaction strength. On the other hand, the long-term interaction
strength tended to increase with prey density. In summary, warming tended to increase
ecosystem stability whereas higher prey density tended to destabilize communities in my
experimental system.
Keywords: Climate change; prey-predator interactions; interaction strength; functional
response; Energetic efficiency; metabolism.
Résumé—Prédire comment les écosystèmes, les communautés et les interactions
trophiques vont répondre aux changements globaux reste un défi pour les écologues.
Le réchauffement peut fortement stabiliser ou déstabiliser les réseaux trophiques en
modifiant la force des interactions entre proies et prédateurs. Cela nécessite de
comprendre comment la température affecte le taux de d’ingestion (gain d’énergie) et le
métabolisme (perte d’énergie). J’ai étudié l’effet de la température et de la densité de
proies sur la réponse fonctionnelle et le métabolisme de Chaoborus obscuripes. A partir
de ces données j’ai calculé l’efficacité énergétique et la force des interactions à court
terme et à long terme. Le métabolisme et le taux d’ingestion du prédateur augmentent
tous les deux avec la température tandis que l’efficacité énergétique (ratio taux
d’ingestion sur le métabolisme) diminue, cela se traduit en une diminution de la force des
interactions à long terme. Cependant la force des interactions tend à augmenter avec la
densité de proies. Ainsi du point de la stabilité des écosystèmes le réchauffement tend à
stabiliser les communautés tandis que la densité de proie tend à les déstabiliser. Ces
résultats suggèrent que le réchauffement et l’enrichissement à des effets non négligeables
sur les interactions proies prédateur et la stabilité des écosystèmes.
Keywords: Réchauffement climatique; Interactions proies-prédateurs; Réponse
fonctionnelle; Efficacité énergétique ; métabolisme.