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

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.

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

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

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).

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.

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).

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 = 𝑵𝟎(𝟏 − 𝒆−𝒂(𝒕−𝒉𝑵𝒆))

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

Fy

0 0ln(( ) / )_ _ eN N N

Short Term ISt

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× 𝒆𝒄𝑷 − 𝒄 ×𝒎𝒑 × 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 (11.661013;

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 (8.6210-5

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×𝒎𝒑

𝒆𝒄×𝒂−𝑪×𝒎𝒑×𝒂×𝒉 (7a)

𝑷∗ = 𝒓 × (𝟏 −𝑵

𝑲) ×

𝟏+𝒂×𝒉×𝑵∗

𝒂×𝑵∗ (7b)

/

0r rb E kT

r r M e

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 NLong Term IS

P

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)

Nu

mb

er

of

pre

y e

ate

n

Table 1. ∆AICc values and Akaike weights obtained when comparing different models.

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).

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)

E

nerg

etic e

ffic

iency

16°C

20°C

0 50 100 150 200

0.0

0.5

1.0

1.5

Prey density (ind/L)

Short

-term

inte

raction s

tre

ngth

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.

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

0.4

0.6

0.8

1.0

1.2

1.4

Prey density

Long

term

IS

16°C

20°C

Long-t

erm

inte

raction

str

en

gth

Prey density (ind /L)

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).

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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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.