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UNIVERSIDADE DE LISBOA

FACULDADE DE CIÊNCIAS

DEPARTAMENTO DE BIOLOGIA ANIMAL

Regulation of body weight in small rodents: a trade‐off

between starvation and predation?

Rita Isabel Caetano Monarca

Tese orientada pela Professora Maria da Luz Mathias e pelo Professor John

R. Speakman, especialmente elaborada para a obtenção do grau de doutor

em Biologia (Ecologia).

2015

This study was funded by Fundação para a Ciência e a Tecnologia (FCT) through a PhD

fellowship – SFRH/BD/47333/2008.

This thesis should be cited as follows:

Monarca, R.I., 2015. Regulation of body weight in small rodents: a trade‐off between

starvation and predation?. Ph.D. Thesis. University of Lisbon. Portugal.

PRELIMINARY NOTES

According to the Post‐graduate Studies Regulation (Diário da República, 2ª série, nº 57, 23 March

2015) this dissertation includes papers published or submitted for publication and the candidate,

as co‐author. As doctoral candidate I was responsible for the scientific planning, sampling design,

data collection, statistical analyses and writing of all manuscripts. The advisors of the thesis were

deeply involved in the conceptual framework and in all the stages of the studies.

Papers format was made uniform to improve text flow.

Lisboa, Junho de 2015

Rita I. Monarca

NOTA PRÉVIA

Na elaboração desta dissertação foram usados artigos publicados, ou submetidos para publicação,

em revistas científicas indexadas. De acordo com o previsto no Regulamento de Estudos Pós‐

Graduados da Universidade de Lisboa, publicado no Diário da República, 2.ª série, n.º 57, de 23

de Março de 2015, a candidata esclarece que participou na concepção, obtenção dos dados,

análise e discussão dos resultados de todos os trabalhos, bem como na redacção dos respectivos

manuscritos.

A dissertação, por ser uma compilação de publicações internacionais, está redigida em Inglês.

A formatação do texto foi uniformizada de modo a tornar a leitura mais fluída.

Lisboa, Junho de 2015

Rita I. Monarca

Index

ACKNOWLEDGMENTS .................................................................................................... I

RESUMO ...................................................................................................................... III

ABSTRACT .................................................................................................................... IX

CHAPTER 1 .................................................................................................................... 1

GENERAL INTRODUCTION

1.1. SCIENTIFIC FRAMEWORK ............................................................................................. 3

1.1.1. ENERGY – GENERAL PRINCIPLES ....................................................................................... 3

1.1.2. INTERACTION BETWEEN ENERGY COMPONENTS ................................................................... 7

� MODELS EXPLAINING ENERGY HOMEOSTASIS ............................................................................. 7

1.1.3. SIGNALLING OF ENERGY BALANCE ................................................................................... 11

1.1.4. FACTORS INFLUENCING ENERGY BALANCE ......................................................................... 13

� PREDATION ....................................................................................................................... 14

� LIMITED FOOD RESOURCES ‐ STARVATION ............................................................................... 16

1.1.5. DYSREGULATION OF ENERGY BALANCE – OBESITY .............................................................. 17

1.2. AIMS AND APPROACH .............................................................................................. 23

1.3. THESIS ORGANISATION ............................................................................................. 25

1.4. REFERENCES .......................................................................................................... 26

CHAPTER 2 .................................................................................................................. 35

PREDATION RISK AND STARVATION

2.1 ABSTRACT .............................................................................................................. 37

2.2. INTRODUCTION ...................................................................................................... 38

2.3. MATERIAL AND METHODS ........................................................................................ 40

2.4. RESULTS ............................................................................................................... 49

2.5. DISCUSSION .......................................................................................................... 59

2.6. CONCLUSIONS ........................................................................................................ 66

2.7. ACKNOWLEDGEMENTS ............................................................................................. 66

2.8. REFERENCES .......................................................................................................... 66

CHAPTER 3 .................................................................................................................. 71

GROWTH AND DEVELOPMENT

3.1. ABSTRACT ............................................................................................................. 73

3.2. INTRODUCTION ...................................................................................................... 74

3.3. METHODS ............................................................................................................. 76

3.4. RESULTS ............................................................................................................... 80

3.5. DISCUSSION .......................................................................................................... 86

3.6. CONCLUSION ......................................................................................................... 92

3.7. REFERENCES .......................................................................................................... 93

CHAPTER 4 .................................................................................................................. 99

PHYSICAL ACTIVITY AND BEHAVIOUR

4.1. ABSTRACT ............................................................................................................101

4.2. INTRODUCTION ............................................................................................................ 102

4.3. METHODS .................................................................................................................. 103

4.4. RESULTS ..................................................................................................................... 108

4.5. DISCUSSION ................................................................................................................ 115

4.6. REFERENCES ................................................................................................................ 118

CHAPTER 5 ................................................................................................................ 123

GENERAL DISCUSSION

1) DO ANIMALS UNDER PERCEIVED RISK OF PREDATION ADJUST THEIR BODY WEIGHT? ...................... 126

2) WHICH PHYSIOLOGICAL AND BEHAVIOUR FACTORS ARE CHANGED TO PRODUCE SUCH ADJUSTMENTS? ..... 127

3) WHAT SIGNAL THE ENERGY BALANCE AND IMBALANCE TO PRODUCE SUCH ADJUSTMENTS? ............ 130

IMPLICATIONS AND FUTURE DIRECTIONS ..................................................................................... 131

REFERENCES ......................................................................................................................... 134

I

ACKNOWLEDGMENTS

Taking the final steps of long journey, there are several people who crossed my way and

whom I am greatly indebted.

At institutional level, I would like to thank “Fundação para a Ciência e Tecnologia” for

funding my work through a PhD grant.

To my supervisors for accepting the challenge of accompanying along this journey. To

Professor Maria da Luz Mathias, who many years ago put some faith in me by accepting

me into the “small mammal group” and who across the years have been getting used to

my simple ways of seeing the world, sometimes called minimalistic. My sincere gratitude

to Professor John Speakman, for all his guidance, helping cross several bumps along the

way and for making all these energetics related topics much more familiar.

In Lisbon, to my colleagues Joaquim, Sofia, Ana Cerveira, Cristiane, Isabel, Flávio,

Margarida and Ana Quina, for their constant help, words of support and scientific input.

Ana Cerveira, Joaquim and Sofia, many thanks for the labour hours helping me on the field

work and maintaining the animals when I was away. I hope that in a near future our out

of the box ideas for research projects will see the light of day. Patricia Napoleão, thank

you for helping me with the first steps into the biochemistry lab.

In Aberdeen, to the energetics research group, Aqeel, Mia, Rachel, Madu, Quinn, Sharon,

David, Catherine, and Lobke, many thanks for your support. A special word for Lobke, for

all the help on the constant attempts of getting the study up and running.

In Beijing, to the lab colleagues, Teresa, Li‐Li, Deng Bao, Yanchao, Xinyu, Mengqin,

Chaoqun, Guanlin, I would like to thank you all for the way I was welcome into the group,

you were never refused me any help dealing with lab issues or every day issues of a non‐

speaker of the local language. To Professor De‐Hua Wang, thank you very much for

II

proving part of the equipment to perform the experiments. To Teresa and Panqian, thanks

for the really fun moments we shared during our city adventures.

A special thanks to Paula Lopes for the amazing help on putting together the first

foundations of this project, and for every now and then quick word of advice.

To worldwide spread but always close friends Andreia, Sofia, Guida, Hugo, Paulo, Raul e

Inês(es), feels good to know I can count on you.

To my mother who always supported my choices and to my core family thank you for the

encouragement along this journey.

Tiago, thank you so much for all the encouragement, support, patience and love, to make

it through the times apart and always be there for me, I hope that finally we can spend

more time together.

III

RESUMO

Os mecanismos envolvidos na regulação do peso do corpo, assim como as pressões

evolutivas que controlam estes mecanismos, têm sido largamente discutidos. Diversas

teorias têm proposto uma panóplia de modelos explicativos, que incluem diversas

componentes, como sobrevivência em períodos de escassez de recursos, sucesso

reprodutor, alterações de comportamento ou ausência de co‐adaptação às actuais dietas.

No entanto, o funcionamento destes mecanismos e a interacção dos factores que os

influenciam permanece em grande parte desconhecida.

A desregulação do balanço energético pode originar situações de excesso de peso e em

casos extremos conduzir à obesidade. Durante o século XX, a incidência de obesidade

aumentou nas sociedades ocidentais de forma epidémica. Na base desta desregulação

existe uma forte componente genética, mas também uma forte influência ambiental. A

capacidade de armazenar energia na forma de gordura, pode representar uma vantagem

adaptativa visto conferir um grau de protecção contra períodos em que os recursos

alimentares são escassos e que podem incorrer em eventos de fome.

O modelo clássico de regulação de peso propõe que o peso corporal seja pré‐determinado

internamente, dependendo exclusivamente de factores individuais. Sempre que o peso

do corpo varia acima ou a abaixo deste valor pré‐estabelecido são desencadeados

mecanismos que actuam de modo a restabelecer o peso corporal inicial. Recentemente,

outros modelos têm surgido, nomeadamente o modelo de duplo ponto de intervenção,

que propõe não a existência de um valor pré‐determinado, mas a existência de um

IV

intervalo, com um limite inferior e um limite superior, entre os quais o peso pode variar

livremente. Quando o peso atinge o limite superior, ou o limite inferior do intervalo, são

activados mecanismos que restabelecem o peso do corpo para o intervalo pré‐

estabelecido.

Tem sido demonstrado que os pequenos mamíferos possuem mecanismos de regulação

do peso do corpo muito eficientes. O acesso permanente a alimento ou a modificação da

alimentação para dietas muito ricas em gordura raramente resultam em aumentos de

peso que possam ser classificados como obesidade. No entanto, os factores que

condicionam estes mecanismos são ainda desconhecidos. O risco de predação, assim

como o risco de fome têm sido indicados como dois factores que condicionam os limites

de variação do peso do corpo, mantendo‐o dentro do intervalo pré‐determinado.

Neste trabalho, utilizando pequenos mamíferos como modelos de estudo, analisou‐se a

possível existência de um equilíbrio entre o risco de predação e o risco de fome, testando‐

se experimentalmente a hipótese segundo a qual o desaparecimento da pressão de

predação conduzirá à proliferação do fenótipo associado à acumulação de gordura.

Segundo esta hipótese, os genes responsáveis pela eficiente acumulação de reservas de

gordura terão sido mantidos no pool genético por um fenómeno de deriva genética, em

humanos, depois da eliminação do risco de fome e predação, das populações ancestrais,

devido ao desenvolvimento da agricultura e armas, estes genes deveriam ter sido

eliminados, por terem perdido o seu valor adaptativo.

Para a avaliação dos efeitos do risco de predação, pequenos mamíferos foram submetidos

a estímulos sonoros que simulam a presença de um predador, e as suas respostas

V

fisiológicas e comportamentais foram analisadas. Esta metodologia foi aplicada a duas

espécies de pequenos roedores, o rato‐do‐campo Apodemus sylvaticus e à estirpe

C57BL/6 de ratinhos de laboratório. Pretendeu‐se investigar se o risco de predação afecta

o peso do corpo dos animais e de que modo se processa este efeito, nomeadamente em

termos de consumo de comida, alterações de padrões de comportamento, metabolismo,

termorregulação e actividade física. Pretendeu‐se também analisar se os mecanismos

envolvidos na regulação do balanço energético são sinalizados através de mensagens

hormonais que actuam a nível do sistema nervoso central.

Os resultados obtidos mostram uma significativa influência do risco de predação sobre o

peso do corpo, por outras palavras, os animais submetidos a um tratamento que simula a

presença de um predador reduziram o seu peso corporal ou a taxa de ganho de peso

quando submetidos a dietas com elevado teor de gordura. Esta alteração é feita através

da variação tanto da quantidade de energia ingerida, como da quantidade de energia

gasta, processos que podem funcionar de forma independente ou em simultâneo. A

redução da quantidade de energia ingerida baseia‐se principalmente na diminuição da

quantidade de comida consumida e não na alteração das taxas de digestibilidade e

absorção. O aumento da energia despendida ocorre através da alteração dos padrões de

atividade física e comportamento, ou seja, os animais favorecem comportamentos que

aumentam a actividade física e que são mais dispendiosos do ponto de vista energético,

como moveram‐se dentro da caixa, em oposição a manterem‐se imóveis ou em descanso

como método de poupança de energia. A taxa de metabolismo basal, que muitas vezes é

associada a poupança de energia, em animais que estão adaptados a ambientes extremos

VI

como desertos ou ambientes subterrâneos, não é alterada em consequência do risco de

predação.

A exposição estocástica a períodos de fome induziu reduções acentuadas do peso do

corpo, que foram recuperadas assim que a disponibilidade de comida foi restabelecida. A

compensação do peso perdido foi feita através do aumento da comida ingerida, e da

alteração de comportamento, sendo adoptados comportamentos que reduzem o gasto

energético. Ao contrário do previsto, após a exposição a vários períodos de fome, os

animais não sobrecompensaram a massa corporal perdida, ou seja, o peso corporal é

restabelecido para valores semelhantes ao período de referência, anterior aos eventos de

fome, não sendo estabelecido um novo limite de regulação. Segundo o previsto pelo

modelo de duplo ponto de intervenção, esperava‐se que o aumento da frequência de

períodos de fome reestabelecesse o nível mínimo de reservas para um limite superior, ao

de referência, de modo a assegurar a sobrevivência em próximos eventos de escassez.

Os dados revelaram também algumas diferenças no que se refere à resposta de machos

e fêmeas, tendo as fêmeas exibido uma resposta evidente ao elevado risco de predação,

principalmente quando expostas a dietas com alto teor de gordura. A resposta à

exposição a uma dieta rica em gordura, após um período inicial de dieta pouco rica em

gordura, também é diferente entre machos e fêmeas, tendo as fêmeas demonstrado

maior resistência ao aumento de peso que os machos. Estas diferenças poderão estar

relacionadas com a influência do tamanho do corpo em questões de dominância e

agressividade, e com o investimento na reprodução.

VII

Os níveis de corticosterona foram alterados em resposta ao aumento do risco de

predação, mas os níveis de leptina circulante são explicados principalmente pelas reservas

de gordura acumuladas. No entanto, os animais submetidos a períodos de fome

registaram elevados níveis de leptina circulante, o que é uma resposta inesperada face ao

observado em humanos após dietas de redução de peso, em que o abandono das dietas

para perda de peso é atribuído à manutenção de baixos níveis de leptina, após a dieta.

Estes resultados suportam a importância do papel da pressão de predação sobre a

regulação do peso do corpo, e demonstram que estas alterações podem ser alcançadas

através do recurso a uma combinação de processos fisiológicos e comportamentais, que

resultam em redução do consumo de energia ou aumento do seu gasto.

A investigação dos mecanismos que regulam as variações de peso, nomeadamente a sua

componente ambiental é fundamental para a compreensão de disfunções como a

obesidade e doenças associadas, nomeadamente na forma como vários fenótipos são

expressos sob diferentes condições ambientais. No entanto, a conhecimento da base

molecular e genética, destes mecanismos, é essencial para o desenvolvimento de novos

medicamentos e formas de terapia que permitam prevenir e controlar estas disfunções.

Palavras‐chave: Regulação de peso; Obesidade; Risco de predação; Fome.

IX

ABSTRACT

The mechanisms and evolutionary background of body weight regulation have been

largely discussed in the last years. Several models were put forward to explain the

energetic imbalances that lead to disorders as obesity, linking environmental and genetic

factors, and their effects over body weight. Unlike humans, small wild mammals have a

strong body weight regulation system, being the risk of predation one of the factors

suggested to explain the non‐prevalence of overweight animals within natural

populations. Accordingly, carrying large fat reserves influences the ability to escape

predators, being the risk of predation the main factor influencing their upper limit for

body weight regulation. On the other hand the risk of starving, due to carrying reduce fat

storages during famine periods is likely to guide the lower boundary for body weight

regulation, a trade‐off between these two pressure factors is suggested to modulate body

weight in small mammals. The predications of this model were tested using wood mice

(Apodemus sylvaticus) and C57BL/6 mice by experimentally manipulating the risks of

starvation and predation. Physiological and behavioural responses were tested to

investigate the response of mice to elevated risks of starvation and predation. Results

showed that reductions in body weight, and body weight gain, can be induced by

manipulating the predation risk. The variations were mostly explained by reduction of

food intake, and increase in energy expenditure through alteration of physical activity and

behaviour. Resting metabolic rate and thermogenic capacity were not affected. Males and

females responded differently to high fat diets, females showed stronger homeostasis

mechanisms. Starvation periods were compensated by overfeeding and reduction of

activity during the recovery period but did not induce the increase of the fat storages

above pre‐starvation. Corticosterone levels signed the perceived risks of predation and

circulating leptin was correlated with the levels of fat storage. These observation strongly

support the role of predation risk over the regulation of body weight, showing the

influence of environmental components over the setting of body weight regulation limits.

Keywords: Predation‐Starvation trade‐off; Weight regulation; Body weight; Obesity

CHAPTER 1

General Introduction

Chapter 1 – General Introduction

3

General Introduction

1.1. Scientific framework

1.1.1. Energy – General principles

Energy is considered a key currency for all the biological processes. Ultimately, energy

is one of the most essential resource for animals life, therefore acquiring the amount of

food to supply their daily need of energy is a basic goal of every animal.

The first law of thermodynamics states that energy can be transferred and transformed,

but cannot be created or destroyed (Blaxter 1989). When applied to animal physiology,

this process can be summarised by the following equation, all terms expressed as energy

per unit of time:

Energy Intake = Energy Expenditure + Energy Storage

Energy Intake primarily refers to the chemical energy obtained through consumed food

and fluids; Energy Expenditure refers to performed work, and energy lost through radiant,

conductive, and convective heat, and also evaporation. Energy Storage is the rate of

change in body’s macronutrient stores (Hall et al. 2012). The balance between energy

intake, energy expenditure and energy storage can be referred as energy homeostasis.

The Energy Intake components are macronutrients (proteins, carbohydrates and fat) and

micronutrients (vitamins, minerals and trace elements). Micronutrients are part of the

diet, but their contribution to the total energy ingested is not significant. Once ingested,


4

the absorption of macronutrients is variable and incomplete, typically urine accounts for

2‐3% (Grodzínski & Wunder 1975) of total energy input, faecal loss is variable, depending

on the individual variance, diet quality and intestinal factors, red pandas can lose 80% of

energy input through faeces (Wei et al. 2000) and grey seals about 8% (Ronald & Keiver

1984). The metabolized energy is given by the difference between the total energy

content of the consumed food and the energy content of faeces and urine. The

transformed macronutrient can then be used in the biological processes or be stored.

The rate of Energy Expenditure is variable across the day and during the life span.

Accordingly, maintenance, growth, reproduction, lactation, running, and other processes,

all require energy but the amounts of fuel may be variable.

The energy expenditure budget can be divided in three main components: i) Resting/Basal

metabolism, ii) Thermal effect of food, iii) Thermo regulatory metabolism and iv) Activity

metabolism.

i) Resting metabolic rate represents approximately two‐thirds of total human energy

expenditure (Cunningham 1991), and 40% of small mammals total energy expenditure

(reviewed by Speakman 2000). Basal metabolic rate can be defined as the minimal rate

necessary to sustain basic physiological processes, as respiration, heart rate and cellular

repair, measured in an inactive, post‐absorptive, non‐reproductive animal, at an

environmentally neutral temperature. This standardised estimation of metabolic rate

requires several criteria to be accomplished, however in many cases these assumptions

are not easy to achieve (McNab 1997).


5

Basal metabolic rate requires animals to be inactive at thermoneutrality, thus is not

measured on many studies, due to the difficulties of certifying that animals are post

absorptive. Thus measurements of resting metabolic rate are often used. They may be

slightly higher than basal metabolic rate, due to the thermal effect of food. According with

Johnstone et al. (2005), about 63% of resting metabolic rate variation can be explained by

the fat free mass, 2% by the individual variation of subjects, 6% by the body fat content,

2% by age, 0.5% by analytic error and 26,6% of the variation is unexplained.

ii) The Thermal effect of food also commonly referred as Diet induced thermogenesis,

and specific dynamic action (review by Secor 2009), is the energy associated to the

digestion and processing of food, in other words, it is the energetic cost of digesting,

assimilating and transporting nutrients from ingested food to cells and tissues. It is

assumed to be a fixed percentage of total energy intake, but may be affected by the meal

size and composition (Jonge & Bray 1997). Although, a small part of the thermal effect of

food consists of regulated heat production to dissipate excessive energy intake (Lowell &

Spiegelman 2000).

iii) In homeotherms the maintenance of a stable body temperature depends upon the

balance between heat production and heat loss ‐ Thermal regulation. The heat loss

through the body surface is proportional to the difference between ambient temperature

and surface temperature. Therefore the energy allocated to the maintenance of body

temperature is highly influenced by the ambient temperature. As the temperature falls

below core temperature, the heat demands for thermoregulation increases. Basal

metabolic rate, thermal effect of food and physical activity, all contribute to supply this


6

energy demand. If these sources do not supply the required energy, specific extra heat is

generated to maintain a stable body temperature.

Humans, mainly in developed countries, were able to modify their environmental

conditions to permanently balance the ambient temperature and their heat production

(Garland et al. 2011; Speakman & Keijer 2012). These conditions do not necessarily apply

to wild animals, hence the energy allocated to balance the body temperature significantly

increases. The production of heat necessary to increase the body temperature may

require shivering or non‐shivering. The non‐shivering thermogenesis includes the heat

generation using the brown adipose tissue, not involving muscular contractions. Non‐

shivering thermogenesis and brown adipose tissue are commonly the focus of studies

regarding seasonal acclimatisation of small mammals, and recently also human physiology

due to its possible anti‐obesity role (Cannon & Nedergaard 2004; Seale, Kajimura &

Spiegelman 2009).

iv) Physical activity can be defined as the thermic effect of any bodily movement that

goes beyond the basal metabolic rate (Caspersen, Powell & Christenson 1985). It

comprises the energy expenditure required for skeletal muscles to produce movement,

including minimal movement, physical exercise and powerful muscular work. Physical

activity and exercise are not synonymous. Physical activity includes exercise as well as

other activities which involve corporal movement. In humans, there is often a partition

between exercise and non‐exercise activity (Levine, Schleusner & Jensen 2000; Hollowell

et al. 2009). Energy costs with physical activity are correlated with body weight and resting

metabolic rate (Schoeller & Jefford 2002), but is one of the most variable energy

expenditure components.


7

1.1.2. Interaction between energy components

The three components of the energy equation interact through a series of processes

that favours homeostasis, responding to the energy available. As so, if the energy

expenditure increases, animals must increase their food intake to compensate the

discrepancy or use the energy stored. Consequently, energy is stored if the food intake

exceeds the energy expenditure.

The main components of the energy balance equation may vary across time, accordingly

with the stage of development; typically energy storage is positive during growth and

development resulting in the increase of body weight. During adult life, body weight is

usually stable, and energy storage approaches zero.

Models explaining energy homeostasis

The regulation system underpinning energy input and expenditure has not been fully

established. Multiple theories have been put forward to explain energy homeostasis; each

model is the product of a different context and therefore they focus on different aspects

of body weight regulation: the set point model is mostly based on physiological and

genetic determination, whereas the settling point regulation model is built around the

effects of social, nutritional environment. The dual point intervention model is suggested

as an alternative model including weak physiological regulation within two boundaries


8

that may vary across individuals and accommodates both environmental and molecular‐

physiological views points.

a) Set point model (Lipostatic model)

Kennedy (1953) was among the first to propose that the regulation of body fat involves

an internal “lipostat” that maintains body fat hence overall weight at a predetermined

level. It was then suggested that fat would produce a signal that once sensed by the brain

would inform about the levels of fat. If these levels were above or below the target body

fat, the brain would activate mechanisms to increase or reduce the energy expenditure

and bring the fat levels to the target point (Figure 1). The discovery of leptin many years

later (Zhang et al. 1994) gave strong molecular support to the model suggested by

Kennedy, by exposing the presence of a hormonal signal that informs the brain about the

fat status of the body, as reviewed by Friedman and Halaas (1998). Leptin is a hormone

primarily produced by the adipose tissue and interacts with brain areas known to be

related with energy homeostasis (Mercer & Speakman 2001; Bellinger & Bernardis 2002).

Figure 1 – Classic set‐point model of body weight regulation: Compensatory

mechanisms are applied when body mass is below or above the set point of

regulation.


9

b) Settling point model

One of the statements of the set‐point model is that it denies the role of environmental,

behaviour and economic factors, resuming the mechanism is strictly due to physiological

components. According to the settling point model (Wirtshafter & Davis 1977), the fat

storage in a body can be compared to the water levels in a water reservoir, in analogy like

a lake (Speakman et al. 2011) (Figure 2).

Figure 2 ‐ Body weight regulation according to the settling point model, here

illustrated as a lake. Input of water is rain falling in the hill, the output of water

is proportional to the depth of water in the outflow, as indicated by the size of

the arrows. A ‐ As the volume of inflow rain increases, the depth of outflow

raises; B ‐Rain fall represents energy input, energy expenditure is compared to

the outflow; depth of outflow represents the settling point, and the energy

storage is illustrated by the amount of water in the reservoir (lake) (adapted

from Speakman et al. (2011)).


10

A natural equilibrium in maintained in the lake, due to the extra rain inflow, the water

levels rises until the outflow equals the inflow. Regarding body weight, fat storages

represent the lake, energy intake is represented by the rain and energy expenditure by

the water outflow. In such system, there is little active regulation in order to predefine

body weight, the interaction of several environmental factors (e.g. improved housing,

food abundance) results in a dynamic equilibrium (“settling point”) dependent on the

individual constitution and interaction between energy input and expenditure (Dubois et

al. 2012). The settling point model denies any role for the physiological regulation of body

fatness and weight.

c) Dual intervention point model

Contrarily to the suggested by the classic “lipostat” model of Kennedy (1953), the dual

intervention point model argues that body weight is set within a range limited by an upper

limit point and a lower limit point (Levitsky 2002; Speakman 2007). If body weight rises

above the upper limit or decreases below the lower limit, the individual enables

physiological mechanisms that change energy balance to maintain the body weight within

the pre‐set range. Between these boundaries the physiological regulation is poor (Figure

3). The nature of the intervention points is still unclear, they might depend on a

combination of genetic and environmental factors acting together. A strong evolutionary

background has supported this model, the upper intervention point defined by the risk of

predation and the lower intervention point established by the risk of starvation

(Speakman 2007, 2008). The upper limit point of intervention is mostly supported by data


11

obtained in animal studies, given that since 2 million year ago modern humans have

developed tools, weapons and strategies to avoid predation.

Figure 3 ‐ The dual intervention point model. Over time body weight varies,

when the intervention points are reached physiological feedback controls that

operate in order to reduce or increase the body mass.

1.1.3. Signalling of energy balance

Insulin was the first hormonal signal to be related with the regulation of energy

balance. It is secreted by beta cells in the pancreas and enters in central nervous system

via the circulation. Insulin is considered an “adiposity signal” that supresses food intake,

and promotes lipid storage (Assimacopoulos‐Jeannet et al. 1995). The discovery of leptin,

a hormone secreted by the adipose tissue, provided additional information about the

signals sent to brain (Zhang et al. 1994). Both insulin and leptin have been involved in the

regulation of body weight (Figure 4) (Varela & Horvath 2012), as their levels are

proportional to the amount of body fat (Bagdade, Bierman & Porte 1967; Considine et al.

1996), and their delivery on the brain is directly related to their circulating levels (Schwartz

et al. 1996).


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Two neuronal populations NPY/AGRP and POMC/CART, located in the arcuate area of the

hypothalamus (Hahn et al. 1998), express specific neuropeptides known to be involved in

energy homeostasis and related with food intake. Both leptin and insulin receptors are

expressed at the NPY and POMC neurons at the hypothalamus (Cowley et al. 2001; Könner

et al. 2007). NPY/AGRP are inhibited by leptin, hence activated by low levels of leptin,

food intake is stimulated by fasting due to the activation of NPY. On the other hand, POMC

neurons are stimulated by leptin and inhibited by NPY (Cowley et al. 2001). Insulin

signalling in the brain is essential for glucose homeostasis, POMC insulin sensitive neurons

may be involved in response to positive energy balance through promoting fat storage in

white adipose tissue (Hill et al. 2010).

Figure 4 ‐ Leptin and insulin secreted by white adipose tissue (WAT) and the

pancreas, respectively, inform the hypothalamus about the energy status of

the organism, regulating several peripheral functions. Arc‐ arcuate nucleus;

AgRP‐ agouti‐related protein; InsR‐ insulin receptor; LepR‐b‐ leptin receptor b;

POMC‐ proopiomelanocortin; VMH‐ ventromedial hypothalamus. (adapted

from Varela & Horvath (2012)).


13

1.1.4. Factors influencing energy balance

Factors affecting the energy balance are linked to two main pillars, genetics and

environment. Both factors interact at different levels, linking and affecting several

components of energy expenditure and intake and the overall energetic balance, as

represented in Figure 5.

Figure 5 ‐ Linkages among genetics and environmental effects over energy

balance, through several co‐existent mechanisms (adapted from Speakman

2004).

Several loci have been identified as related with energy balance. Studies with identical

twin suggest that 60 to 70% of body mass index variance is due to genetic differences

(Allison et al. 1996; Segal et al. 2009). It has also been demonstrated that genes can

influence the food intake (Rankinen & Bouchard 2006), weight gain (Bouchard et al. 1990),

physical activity (Mustelin et al. 2012) and basal metabolic rate (Rønning et al. 2007).


14

The influence of environment over energy balance components are mostly due to

alteration of behaviour, and its interaction with other components. Two main factors

causing imbalance of energy homeostasis are the availability of food and physical activity.

In humans, the constant food availability, their dense energetic content and portion size

are likely to influence energy intake, whilst the required physical activity is reduced mostly

by the characteristics of the neighbourhood namely existence of paths for walking and

cycling, traffic and street safety (Lee et al. 2012), television viewing (Hernández et al.

1999) and also car use (Wen et al. 2006).

Several small mammal species, as mice and voles, use environmental cues, such as day

length and temperature to trigger seasonal variations on their body mass and adiposity,

making them attractive models for the study of body weight variation as these conditions

can easily be simulated in laboratory (Bartness, Demas & Song 2002; Zhao, Chen & Wang

2010). Even though had been identified that small mammals have a strong body weight

regulation (El‐bakry, Plunkett & Bartness 1999; Peacock & Speakman 2001), keeping their

weights in a dynamic equilibrium (Levitsky 2002).

Predation

When focusing on small wild animals, the environmental components incorporates

factors, such as the risk of being predated, that have lost adaptive value to humans since

the development of defensive tools and discovery of fire, and evolution of social

behaviour.


15

Predation represents an important element of the overall interaction between species

and their survival demands, being a selective key force on the evolutionary and adaptive

processes involved (Abrams 2000). Nevertheless, the effects of perceived risk of predation

have been demonstrated to play a more important role shaping the preys traits than

direct consumption (Preisser, Bolnick & Benard 2005; Creel & Christianson 2008) (Figure

6).

The risk of predation in known to affect morphology (McCollum & Leimberger 1997;

Brönmark, Lakowitz & Hollander 2011), physiology (Slos & Stoks 2008) and behaviour

(Abramsky et al. 2004; Verdolin 2006) including aggression, movement patterns (Sih &

McCarthy 2002) and foraging (Brown et al. 1988; Vásquez 1994). Responses to predation

risk, i.e. activation of defence mechanisms are usually assessed by a reduction in other

fitness components such as reproduction or growth (Harvell 1990; Ylönen & Ronkainen

1994).


16

Figure 6 – Pathways of predation effect over prey population dynamics

(adapted from Creel & Christianson 2008).

Limited food resources ‐ Starvation

All animals at some time during their life may face events of limited food resources that

ultimately could lead to mortality. Starvation refers to the biological state wherein a

postabsorptive animal, otherwise willing is unable to eat, as the result of extrinsic

limitations on food resources (McCue 2010).

The frequency and duration of starvation events are highly variable, the predictability of

such events is also variable, it occurs seasonally or randomly according with unpredictable


17

factors. Animals are constantly spending energy to maintain their basic functions,

however they are not constantly acquiring energy, meaning that at any point they must

rely on physiological processes to survive based on the energy internally stored, resulting

in a disrupted energy balance during starvation periods. The ability to tolerate starvation

varies across different animal groups, small birds and mammals may tolerate only one day

of starvation (Mosin 1984; Cherel, Robin & Maho 1988), in contrast to fish (Olivereau &

Olivereau 1997), amphibians (Grably & Piery 1981), and large mammals as polar bears

(Atkinson & Ramsay 1995) that are often able to endure several months without feeding.

Large energy reserves may extend the toleration to starvation, likewise small animals are

more susceptible to food deprivation than larger animals.

Reduction of body mass associated with starvation requires prioritizing given the high

demands of maintaining tissues with high turnover rates, which takes some species to

catabolize tissues to save resources during starving periods, like the gastrointestinal track

(Battley et al. 2000; Ferraris & Carey 2000). Liver and adipose tissue are equally mobilized

(Merkle & Hanke 1988; Lamosova, Mácajová & Zeman 2004)but usually at slower rates.

Skeletal muscles is less likely to be catabolize given its role in locomotion. Brain tissue,

heart and reproductive organs are seldom catabolized during starving periods.

1.1.5. Dysregulation of energy balance – Obesity

Obesity can be explained as a prolonged imbalance between energy intake and energy

expenditure, where the food input exceeds the energy output and the difference is stored

as fat. The accumulation of energy for future needs, in the form of fat reserves, can be


18

found across almost all animal species, from Caenorhabditis elegans (Jones & Ashrafi

2009), to crustaceans, fish (Brockerhoff & Hoyle 1967) and birds (Meissner 2009).

In recent years, obesity has become a major public health problem in modern human

societies (James 2008), revealing ineffective mechanisms of body weight regulation. In

the mid 1970s, obesity became an issue, and has been recognized by the world health

organization as a disease, since the 1990s. It started by affecting mostly developed

societies, but has extended now to developing countries, its recognition as public health

issue is unquestionably accepted.

The origins of obesity have been associated with a combination of genetic (Aguilera, Olza

& Gil 2013) and environmental factors (Hill & Peters 1998), but the prevalence of obesity

is still being debated.

Theories explaining Obesity

Obesity is associated with a series of metabolic dysfunctions that ultimately will reduce

the fitness. Given this scenario is expected that the genes responsible for the overweight

phenotype to be quickly eliminated from the genetic pool, reducing the prevalence of

obesity. However, this has not been the case, suggesting that this apparently

unfavourable phenotype may uncover some selective advantages.

Several theories have emerged to explain the prevalence of obesity at the light of human

evolution, attempting to integrate genetic and environmental causes:

The Thrifty gene hypothesis was first suggested by Neel (1962), as a response to the

prevalence of diabetes medical condition, and later on expanded to the overweight


19

disorder. This hypothesis relies on the fact that genes promoting an efficient food process

and fat deposition (Prentice, Hennig & Fulford 2008), during periods of abundant

resources, would be advantageous during periods of reduced availability of resources,

allowing their holders to survive possible famine periods. A second advantage of these

genes is that they would sustain fertility during famine (Norgan 1997), given the role of

fat on reproductive function. Neel’s theory can be easily applied to ancestral times when

hominids needed to actively hunt and forage for food, however after the development of

agriculture, the advantage provided by the “thrifty genes” were lost because food became

always available. The periods of famine became improbable; therefore the phenotype

favouring large fat reserves has a negative impact on the body condition, being difficult

to understand how it was favoured by natural selection.

Arising from the challenges of the thrifty genes hypothesis, the Thrifty phenotype

hypothesis has been discussed by several authors coping the mismatch between fetal

development and the environment on childhood/adulthood. According with Hales and

Barker (1992), early poor nutrition imposes allocation of resources, thus some tissues like

the pancreas have reduced investment. Another viewpoint (Wells 2007) is that the

intrauterine environment signs the nutritional environment of the mother, forecasting the

environment of the childhood, however the mismatch of these predictions may take to

adverse effects of adult phenotype.

The discussion around the prevalence obesity is still open. Recently Sellayah et al. (2014)

suggested a theory based on ancestral exposure to environmental conditions.

Accordingly, modern obesity results from a differential exposure of hominids to

environmental factors. The susceptibility to obesity is variable across ethnicity (Caprio et


20

al. 2008; Albrecht & Gordon‐Larsen 2013), would be explained by the differential

exposure of ancestrals to distinct climate conditions. Therefore, descents of migrants

(who left Africa 50000 years ago) to colonize cold climates carry genes involved in

thermoregulation and cold adaptation, providing high metabolic rates and resistance to

obesity.

In particular, Baschetti (1998) has argued that metabolic dysfunction on population like

Native American and Pacific Islanders can be explained by the exposure to diets that were

unknown. These genetically unknown foods were introduced to the populations recently,

therefore they have not adapted to them.

In modern societies, diets can be deficient in some critical macro and micronutrients. Diet

characteristics can therefore stimulate the overconsumption of food to compensate their

nutritional content, and eventually lead to weight gain. The protein leverage hypothesis

(Simpson & Raubenheimer 2005; Sørensen et al. 2008) suggests that the consume of

proteins is prioritised over lipids and carbohydrates, hence feeding on a diet with deficient

protein content may require increased food intake to reach the whole protein

requirement.

In a social perspective, Belsare et al. (2010) have suggested a theory based on the

aggression control. The two major causes of aggression are the need of food and

reproduction. Aggression itself is energetically costly, therefore satiated individuals are

likely to reduce their levels of aggression. Hence, a full stomach, high levels of blood

glucose or fat stored, should cue for aggression control. Reduction of aggression is


21

followed by a disinvestment in muscle and bone structure, especially in human urban

lifestyle.

Contrarily to the “Thrifty” genotype hypothesis, the “Fertility first hypothesis” (Corbett,

McMichael & Prentice 2009) states that the thrifty phenotypes have prevailed due to

effects over fertility and not due to starvation. The increased adiposity, insulin insensitivity

and increased anabolic steroid levels, when is exposed to unlimited food supplies as

modern sedentary life style, is maladaptive and through several mechanisms conferred

Polycystic Ovary Syndrome (Holte 1998), causing elevated levels of infertility.

More recently, Speakman (2007) suggests that contrarily to what is argued by the thrifty

gene hypothesis, obesity does not have an adaptive role in the evolutionary process.

Accordingly, the Drifty phenotype hypothesis argues that during famine periods,

mortality affects mostly very young and elder people, due to disease effect and not

actually starvation (Mokyr & Cormac 2002). Therefore the age pattern of mortality does

not compromise the reproductive success of the individuals of breeding age, thus genes

predisposing to obesity were maintained in the genetic pool by genetic drift, or absence

of selection and not because they were positively selected (Speakman 2008). The random

mutations that allowed body weight increase were not removed from the genetic pool,

but were maintained due to genetic drift, explaining why some people are able to keep

their body weight normal despite the environmental conditions. Moreover, if the thrifty

gene hypothesis was correct early hominids were expected to gain weight and grow fat,

in periods of resource abundance, which has not been reported in any study.


22

A particular scenario of the drifty phenotype hypothesis is the Predation release

hypothesis, it takes into account the pressure of predation risk, and suggests that early

hominids have been subjected to stabilizing selection for body fatness, with obesity

selected against the risk of predation. As other animals, Humans were once under severe

risk of predation (Berger 2006), nevertheless since 2 million years, predation has been

released due to the increase in body size, intelligence and development of tools

(Speakman 2007).

However, the predation pressure has not been released in other mammals, like small wild

mammals who are known to have strong body weight regulation mechanisms (El‐bakry et

al. 1999; Peacock & Speakman 2001; Hambly & Speakman 2005). Small mammals carrying

large fat depots would survive longer without food, but the carrying of fat may increase

the risk of mortality by reducing its ability to escape from predators because they are less

able to run away, while lean animals can escape into refugees that predators cannot

access (Sundell & Norrdahl 2002), besides the maintenance of such fat reserves requires

longer time foraging which increase the exposure to predators.

Such regulatory system is primarily mediated by the photoperiod (Bartness et al. 2002),

but individuals adjust their responses to local variations, as temperature, resource levels

and predatory risk. Animals should trade‐off their foraging efforts and vigilance status in

relation to the temporal variation of the predatory risk (Lima & Bednekoff 1999). A strong

regulatory system for body weight have been described on small wild mammals. Studies

modifying their diets did not cause them to gain weight (Peacock & Speakman 2001), as

animals are able to keep their weights in a dynamic equilibrium (Levitsky 2002). They can


23

overfeed after a restriction period, or increase physical activity and reduce energy intake

when they have free access to food (Hambly & Speakman 2005).

1.2. Aims and approach

The main aim of this thesis is to experimentally test the predictions of the “predation

release hypothesis”, accordingly to analyse whether the body weight of small mammals

is modulated by the risk of predation and by the risk of starvation.

In this context, I aim to give an overall view of the energy balance of small rodents in a

scenario of increased risk of predation, analysing energy intake, expenditure and storage,

in order to identify physiological and behavioural mechanisms activated to cope with the

alterations of body weight induced by environmental factors, such as predation risk, food

composition and availability, as well as the influence of other factors as sex and growth

rate.

Therefore, the key questions for this thesis are:

1) Do animals under perceived risk of predation adjust their body weight?

2) Which physiological and behaviour factors are changed to produce such

adjustments: food intake, digestibility, metabolic rate, physical activity, behaviour

or combinations of the above?

3) What signals the energy balance and imbalance to produce such adjustments?


24

The overall approach developed for this thesis involved the use of wild rodents, as well as

a laboratory model, to analyse different traits and several components of energy balance.

The Wood mouse Apodemus sylvaticus was selected as a model wild rodent, given that it

is a common and widespread species, categorised as least concern at the IUCN red list

(Schlitter et al. 2008) and a common prey species for several predator species such as

carnivores and birds (Bontzorlos, Peris & Vlachos 2005; Lanszki, Zalewski & Horvath 2007).

Animal captures were performed in Portalegre – Portugal (N39º17' W 7º26'), where a

total of 38 individuals where trapped during a 6 weeks fieldwork campaign. The animals

captured where used to test the use of sound cues as method to simulate predation risk,

and also as founders of a laboratory colony in the animal facilities in the Faculty of

Sciences ‐ University of Lisbon.

The wood mouse colony was established and maintained between June 2011 and July

2012, producing 163 animals from 34 litters. Animals born in the laboratory colony were

used on experiments to test the effects of predation risk and fat enriched diets over

growth rates and also for exposure to stochastic periods of starvation.

Animal capture was authorized by the Institute of Nature Conservation and Biodiversity,

Lisbon, Portugal and experimental procedures followed the European guidelines for the

use of animals for scientific purposes (86/609/EEC).

Influence of predation risk over physical activity and behaviour were tested between

January and April 2013, at the Institute of Genetics and Developmental Biology of the

Chinese Academy of Sciences – Beijing. The experimental procedure included


25

implantation of telemetry devices in laboratory mice, C57BL/6 strain, to permanently

monitor their physical activity and body temperature, and to assess behavioural changes

through the use of a set a behaviour tests. The C57BL/6 mouse is widely use in obesity

research, it is known to respond to high fat diets by developing diet induced obesity and

some associated disorders (Lin et al. 2000; Collins et al. 2004). This study was given ethical

approval by the ethical review board of the Institute of Genetics and Developmental

Biology, Chinese Academy of Sciences.

1.3. Thesis organisation

The dissertation is organised in five chapters. Chapter 1 includes a general introduction

that sets the state of art and the scientific framework for the addressed questions. The

main objectives are outlined, followed by a brief description of the approach used to

achieve such aims and organisation of the thesis. Chapter 2 and 3 are composed by

scientific papers submitted to international peer reviewed journals, and Chapter 4

includes a manuscript to be submitted.

Chapter 2 ‐ Behavioural and physiological responses of wood mice (Apodemus sylvaticus)

to experimental manipulations of predation and starvation risk (published in Physiology

and Behavior) ‐ focuses on the role of perceived risk of predation and starvation on the

determination of upper and lower limits of body weight, using the analysis of energy

intake and assimilation, energy expenditure through resting metabolic rate and physical

activity, and the chemical signs involved in theses mechanisms. This chapter approaches

the main question by two opposite sides, giving a complete overview of the main problem.


26

First it analyses the effect of predation risk, as a factor setting the upper limit of body

weight, and secondly it tests the role of starvation defining the lower intervention point

of body weight.

In Chapter 3 – Effects of predation risk and high fat diet on body weight regulation of

growing Wood mouse (Apodemus sylvaticus) ‐ Investigated the influenced of perceived

risk of predation on the growth and development of sexual dimorphism of weaned pups,

whose body weight and food intake were monitored over a period of 100 days. The

influence of exposure to fat enriched diet at different life stages was also investigated.

Chapter 4 ‐ Predation risk modulates diet induced obesity in male C57BL/6 mice (Accepted

for publication in Obesity) – analyses the role of behaviour and physical activity

components on the energy homeostasis thought the use of implanted telemetric devices

and behavioural assessment tests.

In Chapter 5 the main results are summarized and discussed, integrating the different

approaches and main findings of the previous chapters. The implications and prospects

for future research are also discussed.

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CHAPTER 2

Predation risk and Starvation

Monarca, R.I., Mathias, M.L. & Speakman, J.R. (2015) Behavioural and physiological

responses of wood mice (Apodemus sylvaticus) to experimental manipulations of

predation and starvation risk. Physiology & Behavior, 149, 331–339.

Chapter 2 – Predation and Starvation

37

2. Behavioural and physiological responses of wood mice

(Apodemus sylvaticus) to experimental manipulations of

predation and starvation risk

2.1 Abstract

Body weight and the levels of stored body fat have fitness consequences. Greater levels

of fat may provide protection against catastrophic failures in the food supply, but they

may also increase the risk of predation. Animals may therefore regulate their fatness

according to their perceived risks of predation and starvation: the starvation‐–predation

trade‐off model. We tested the predictions of this model in wood mice (Apodemus

sylvaticus) by experimentally manipulating predation risk and starvation risk. We

predicted that under increased predation risk individuals would lose weight and under

increased starvation risk they would gain it. We simulated increased predation risk by

playing the calls made by predatory birds (owls: Tyto alba and Bubo bubo) to the mice.

Control groups included exposure to calls of a non‐predatory bird (blackbird: Turdus

merula) or silence. Mice exposed to owl calls at night lost weight relative to the silence

group, mediated via reduced food intake, but exposure to owl calls in the day had no

significant effect. Exposure to blackbird calls at night also resulted in weight loss, but

blackbird calls in the day had no effect. Mice seemed to have a generalised response to

bird calls at night irrespective of their actual source. This could be because in the wild any

bird calling at night will be a predation risk, and any bird calling in the day would not be,

because at that time the mice would normally be resting, and hence not exposed to avian


38

predators. Consequently, mice have not evolved to distinguish different types of call but

only to respond to the time of day that they occur. Mice exposed to stochastic 24h

starvation events altered their behaviour (reduced activity) during the refeeding days that

followed the deprivation periods to regain the lost mass. However, they only marginally

elevated their food intake and consequently had reduced body weight/fat storage

compared to that of the control unstarved group. This response may have been

constrained by physiological factors (alimentary tract absorption capacity) or behavioural

factors (perceived risk of predation). Overall the responses of the mice appeared to

provide limited support for the starvation‐predation trade‐off model, and suggest that

wood mice are much more sensitive to predation risk than they are to starvation risk.

2.2. Introduction

Obesity has become one of the major public health problems in western societies

(James 2008). This has led to a need for a better understanding of the mechanisms that

underpin the regulation of body weight and fatness (Speakman 2013, 2014a). A balance

between energy intake and energy expenditure is necessary to maintain a stable body

weight. It is well established that the central nervous system (CNS) regulates food intake

and energy expenditure in response to neuronal, hormonal and nutrient signals (Schwartz

et al. 2000; Morton et al. 2006). However, the exact mechanisms by which these signals

are integrated and hence regulate the overall levels of adiposity remain elusive.

There have been several theoretical models that have attempted to describe the

underlying mechanisms involved in the regulation of body fatness. Among the earliest of


39

these was the ‘lipostatic set point’ model. This model suggests that the size of body fat

depots is sensed by a ‘lipostat’, which adjusts food intake and energy metabolism to

maintain the body and fat masses at a set‐point (Kennedy 1953). This model requires a

signal from the body that indicates to the brain the levels of body fat and a ‘set point’ in

the brain that allows an appropriate response to these peripheral fat level – increasing

them if they are too low, by a stimulation of intake and inhibition of expenditure, and

decreasing them if they are too high by the reverse processes. Although the hormone

leptin, produced primarily in white adipose tissue (Zhang et al. 1994; Friedman & Halaas

1998b) has been often suggested to be the peripheral fat signal in this model, a central

location for the ‘set point’ has never been identified. Moreover, this model is in conflict

with observations of the patterns of changes in animal and human body weight

(Wirtshafter & Davis 1977; Berthoud 2006) not least of which is the obesity epidemic itself

(Speakman 2014a).

An alternative interpretation suggests that body weight and fat mass are not regulated by

a set‐point, but rather are free to vary dependent on environmental factors, but are

constrained to fall between upper and lower intervention points, above and below which

animals (and humans) intervene physiologically to bring their body weight and fatness

back into the acceptable range (Wirtshafter & Davis 1977; Levitsky 2002; Speakman 2007).

In humans the upper intervention point may be located at different positions in different

individuals, explaining why some individuals become obese when exposed to

environments with readily available food supplies, but others are able to regulate their

body weights at normal levels (Speakman 2008). Based on a wealth of data from small

mammals and birds e.g. (Lima 1986; Cuthill et al. 2000), it has been suggested that the


40

lower intervention point may be defined by the risk of starvation, while the upper

intervention point may be defined by the risk of predation (Speakman 2007, 2008).

The starvation‐predation trade‐off model predicts that an increased risk of predation

would decrease the upper intervention point, and animals would tend to regulate their

weight at lower levels. Conversely, an increase in starvation risk would increase the lower

intervention point, and animals would regulate their body weights and fat mass at higher

levels. We aimed in the present study to experimentally manipulate both predation risk

and starvation risk to test these predictions.

2.3. Material and Methods

Animal capture was authorized by the Institute of Nature Conservation and

Biodiversity, Lisbon, Portugal (licence ICNB 231/2010/CAPT); experimental procedures

were conducted in the University of Lisbon facilities by an expert in laboratory animal

science accredited by the Portuguese Veterinary Authority (1005/92, DGV‐Portugal,

following FELASA category C recommendations), according to the European guidelines

(86/609/EEC).

2.3.1. Predation risk

Adult wood mice Apodemus sylvaticus used in the predation experiment were captured

near Portalegre – Portugal (N 39º17' W 7º26'), using Sherman traps, and brought to the

lab in the University of Lisbon where they were housed in individual cages, under


41

controlled light (12D:12L) and temperature (20ºC), a small plastic box was provided for

nesting and shelter. Animals were fed ad libitum, with commercial pellets for laboratory

mice (Maintenance diet ‐ Scientific Animal Food Engineering, France), and had free access

to drinking water. After a three week acclimation period, animals were randomly assigned

into experimental groups. A total of 29 animals, 13 males and 16 females, were used in

the tests. Due to the limited number of individuals, each animal was included in 2 ̶ 3 of the

five experimental groups and the order of the treatments was randomly selected, as

follows: control (silence), blackbird sound during the night, owl sound during the night,

owl sound during the day and blackbird sound during the day. After each testing period

animals were allowed a recovery period of two weeks, to avoid interference of previous

treatment over the trials. During the recovery time, housing and maintenance conditions

were as described above and mice were not handled, except for the weekly bedding

change. Exposure to owl calls during the daytime was tested in 22 animals; exposure to

owl calls at night was tested in 18 animals; exposure to blackbird calls during the daytime

was tested on 18 individuals: and exposure to these calls in the day was tested on 10

animals. Finally 10 animals were measured without exposure to any bird calls.

Predation Risk Treatment

Elevated predation risk was simulated by exposing the animals to a playback call lasting 2

min from nocturnal predators observed at the location in the field where the mice had

been captured. The 2 minute call period started at 11 pm and was repeated every 2 h until

7 am the next day. Playback sounds from barn owl Tyto alba and eagle‐owl Bubo bubo

were obtained from commercial digital recordings of bird songs (European bird calls; Jean


42

C. Roché, Kosmos Verlag, Stuttgart, Germany). Speakers were placed 3 m away from the

mice, and the sound level was adjusted by human ear, to guarantee that sound was heard

by the animals. A second group was exposed to a 2 minute long playback of calls from the

blackbird Turdus merula, a non‐threatening bird species, also observed at the mouse

capture site. The exposure procedure and setup were the same as described above for

the predation risk group.

The third and fourth treatment groups were exposed to the broadcasting procedure

described above, using owl and blackbird calls, but the exposures were made during

daytime. The daytime playback calls were broadcasted during a 2 minute period, every

2h, starting at 11 am until 7 pm. Animals in a further control group were kept in silence

during the entire period of the experiments. The period of exposure to the treatment

lasted 5 days.

Body weight

Mice were weighed daily between 3pm and 6pm each day. Since mice were exposed

to repeated treatments they could not be dissected at the end of each experimental

period to obtain direct measurements of body fatness.

Food intake and Dry mass absorption efficiency

Food intake and digestibility were quantified in mice from the five experimental

groups. At the beginning of the test, each animal was weighed and placed in a clean


43

individual cage (30 cm x 20 cm). The cage floor was covered with absorbent paper, and

food was placed in excess in the food hopper. After 24h, the food left in the hopper was

weighed, as well as any food remains on the cage floor. Faeces were collected, weighed

and dried at 80ºC, until the weight was stabilized (72 h). The procedure was repeated

for the 5 consecutive days of the experiment, between 3pm and 6pm each day. Apparent

dry mass absorption efficiency was calculated as the difference between food intake and

faecal output divided by food intake e.g. (Mueller & Diamond 2001; Gebczyński et al.

2009).

Faecal corticosterone levels

On the sixth day, after the trials had finished, animals were placed in a clean cage and

fresh faeces were collected for measurement of corticosterone levels. Faeces were stored

in absolute ethanol at ‐30ºC, until being processed. The faecal concentration of

corticosterone peaks about 6 – 12 h after a stressful event (Ylönen et al. 2006), thus faeces

were collected between 10 am and 1 pm for animals in the control group and exposed to

the sound treatment during night time, and between 5 pm and 8 pm for animals exposed

to the sound treatments during daytime.

Hormone extraction was performed following a modified method of Goymann (Goymann

et al. 1999). Briefly, ~0.3g of faeces (Sartorius) was added to 4ml of methanol, and

pulverized using a small pallet knife. The mixture was then vortexed for 1 h at 500 rpm,

followed by 1 h of centrifugation at 6000 rpm. The supernatant was then transferred to

another tube and diluted with the buffer solution from an EIA Kit. Corticosterone levels


44

were determined using Enzyme Immunoassay (EIA) commercial kits (ADI‐900‐097, Assay

Designs).

Resting metabolic rate

Oxygen consumption was measured, after 6 days of experimental treatment, using an

open‐flow respirometry system (Servomex, series 4100) as previously described (Duarte

et al. 2010). Briefly mice were placed in a cylindrical chamber, and dried atmospheric air

was pumped into the chamber at a flow rate of 500ml/min. Ambient temperature was set

at 29ºC, in the thermoneutral zone (Haim, McDevitt & Speakman 1995). Each animal was

continuously monitored, during 2 h for two consecutive days, during the daytime when

wood mice are normally inactive (Wolton, 1983). No food or water was available inside

the chamber. Measurements of oxygen concentration were digitised approximately 35x

per second and the accumulated data averaged over 30s intervals (mean of approximately

1000 measurements). These 30s averages were then saved. Resting metabolic rate was

estimated as the average value of the five lowest consecutive readings (equivalent to 2min

and 30s in the chamber) (Hayes et al., 1992), and the average of the measurements made

on consecutive days was used for further analysis. VO2 was calculated after Depocas and

Hart (1957)as VO2=V2 (F1O2–F2O2)/(1–F1O2), where V2 is the flow rate measured after the

metabolic chamber, and F1O2 and F2O2 are the oxygen concentrations before and after

the metabolic chamber. All the values were corrected to standard temperature and

pressure (STPD). Baseline values of atmospheric oxygen were corrected by a 30 minute

measurement prior to each run.


45

2.3.2. Starvation risk

Animals and experimental design

The 30 mice used in this study were the first generation descendants of the mice used

in the experiment on predation risk. After weaning at 21 days of age, animals were

separated from their mothers, and housed in individual cages, with wood shavings for

bedding, a small plastic box and shredded paper for enrichment. Ambient temperature

was controlled at 20ºC and the light cycle was 12L:12D. Food and water were provided ad

libitum, and the animals were fed on commercial chow pellets (Scientific Animal Food

Engineering, France).

When the animals reached 12–14 weeks old, they were allocated into one of two groups:

the control group (6 males and 6 females) and the stochastic starvation group (9 males

and 9 females). Animals in the control group were fed daily with 50g of chow pellets. Every

day uneaten food on the feeder and cage floor was collected and weighed and the initial

amount was replaced into the feeder. The procedure was repeated for a total of 18 days.

Body weight was also monitored on a daily basis. At days 7 and 14 the faeces were

collected, dried for 48h at 80ºC and weighed. Animals in the starvation group were fed

following the procedure described above, the first 3 days were established as baseline,

and then they were submitted to a set of stochastic food deprivation days. The probability

of having a starvation day was pre‐established as 0.28 corresponding to a total of 4 days

within 15 remaining days of the study. Each starvation day was followed by a feeding day;

starvation days corresponded to days 6, 9, 13 and 15. In the starvation days all the food

was removed from the feeder and replaced 24 h later. For the starvation group, faeces

were collected at days 4, 7 and 14. After collection, faeces were dried for 24 h at 80ºC,


46

and dry mass was measured using a 4 figure balance (Sartorius). Dry mass absorption

efficiency was calculated as the difference between food intake and faecal output divided

by food intake e.g. (Gebczyński et al., 2009; Mueller and Diamond, 2001).

Plasma assays

After the 18 days, all the animals were starved for 4 h before the collection of

approximately 1ml of blood by cardiac puncture, followed by sacrifice through cervical

dislocation. Samples were collected between 4pm and 6pm, blood was immediately

centrifuged for 10 min at 20.000 rpm. Plasma was then stored at ‐80ºC until processing.

Plasma leptin levels were measured using the ELISA based method, with commercially

available kits (Millipore Corporation, USA ‐ mouse leptin kit). After blood collection,

animals were dissected, and full body fat was collected and weighed.

Behavioural observations

Animals were video recorded over an 8 hour period on multiple occasions (4 h of light and

4 h of dark). The 8 hour period coincided with two different times of day. In the afternoon

cameras were set 2 h before the dark phase, and allowed to operate for 4 h, while in the

morning cameras were set 2 h before the light phase, and allowed to operate for 4 h. As

mouse vision is limited at wave lengths of light below 630nm (Jacobs et al., 1999), dark

phase records were made under red light illumination (Phillips, Infrared PAR38).


47

The control group was observed three times, on days 2, 4 and 9. The starvation group was

observed 5 times, including free feeding days, starvation days, and refeeding days. Within

each of the recorded films, each animal was observed for 5 random periods of 10 min,

and the time spent on each of categorized behaviours was registered. Dominant

behaviours were classified into four categories: grooming, feeding, resting and general

activity, following the ethogram previously described for mice (see Speakman & Rossi

1999; Speakman et al. 2001 for details). General activity included walking, climbing the

cage and all general movements. Feeding included eating chow, drinking and occasional

coprophagia. Resting was considered when the animal was sleeping or sitting, was not

moving in the cage and was not grooming. Grooming behaviour was registered when the

animals were not moving and included licking the fur and tail and scratching with any limb.

2.3.3. Statistical Analyses

Predation risk

All the data are expressed as mean ± S.E. Mixed modelling followed by pairwise

comparisons was used to compare body weight, food intake and apparent absorption

efficiency variation across the 5 experimental days; treatment and day of measurement

were included in the model as fixed factors. Body weight was included as a covariate, for

the analysis of food intake variation. Individual ID was included in the models as a random

factor, to account for repeated measures. The influence of treatment on RMR and

cumulative food intake was analysed through mixed modelling, setting body weight as

covariate, and individual ID as a random factor due to the repeated measurement of

individuals across treatments. One‐way ANOVA was used to compare corticosterone


48

levels on the last day of treatment, using group as a fixed factor. Due to the wide range of

body weight across individual animals (14–62g), body weight variation was analysed as

change relative to day zero of the study. Given the unbalanced number of males and

females across the treatment groups, and the reduction of statistical power, sex was not

included in the models as an independent predictor.

Starvation risk

All the data were expressed as mean ± S.E. linear mixed modelling was used to test the

body weight and food intake differences between groups and sexes, over the 18 days of

the study, and individual ID was included as a random factor to account for the repeated

measures. On the analysis of food intake, body mass was included as a covariate in the

model. One‐way analysis of variance (ANOVA) with post hoc Tukey tests were used to test

group differences in body weight and food intake, between time points when group

differences were significant over the entire time course.

Differences in cumulative food intake and dry mass absorption efficiency were tested

using one‐way analysis of variance (ANOVA) with pos hoc Tukey tests. Analysis of

covariance (ANCOVA) was performed to test differences in plasma leptin levels and body

fat between treatment groups, and body fat mass and body weight were used as

covariates accordingly. Dominant behaviours were registered with the Etholog 2.2.5

software (Ottoni, 2000) and analysed using a GLM model followed by Tukey post hoc tests.

All data were analysed using SPSS 19.0 for Windows.


49

2.4. Results


Animals exposed to the sounds during the night, both owl and blackbird, significantly

reduced their body mass, when compared with the control ‘silence’ group (p<0.001). Owl

calls during the night caused an average of 8% (S.E=1.2; p<0.001) reduction in the body

weight, and the blackbird calls a 6% (S.E=1.6; p<0.001) reduction (Figure 1). Body weight

variation between silent controls, and animals exposed to either owl or blackbird calls

during the day, was not significantly different (p=0.659; p=0.293). The variation in the

body weight was mostly explained by time (day of experiment effect: F5,32=8.188;

p<0.001), treatment (F4,32=30.031; p<0.001), and the interaction between day of

measurement and treatment (F20,32=2.386; p=0.001).

Time (days)

0 1 2 3 4 5

Bo

dy

Ma

ss (

%)

80

85

90

95

100

105

ControlPredator day timePredator night timeNon Predator day timeNon Predator night time

Figure 1 – Changes in body mass (mean ± S.E.) relative to baseline in response

to exposure to predator and non‐predator sounds played during the day or at

night across the 5 days of the study. The control group was exposed to silence.


50

The average food intake on the first day of the study was 3.18g across all the groups.

Variations in food intake were mostly explained by day of measurement (F4,28=4.322;

p=0.002), treatment (F4,28=14.979; p<0.001), body mass (F1,28=10.077; p=0.002) and the

interaction between day of measurement and treatment (F1,28=2.157; p=0.006). Animals

in the control group (3.38±0.153g) and animals exposed to the treatment during the day

(predator: 3.49±0.129g; non predator: 3.730±0.133g) had higher food intake than mice

submitted to the treatment during night time (predator: 3.21±0.131g; non predator:

2.82±0.148g) (Figure 2).

Time (days)

1 2 3 4 5

Ad

just

ed

Fo

od

Inta

ke (

g)

0

1

2

3

4

5

ControlPredator day timePredator night timeNon Predator day timeNon Predator night time

Figure 2 – Variation of food intake (g) (adjusted for BM=28,29g; mean ± S.E.),

in response to exposure to predator and non‐predator sounds played during

the day or at night across the 5 days of the study. The control group was

exposed to silence.

Differences in cumulative food intake after the 5 days of treatment were explained by

different treatments between groups (F4,8=5.435; p=0.001), and the effect of body mass


51

(F1,66=14.059; p<0.001). Cumulative food intake was lower in mice submitted to the

treatment during the night (predator: 15.6±0.79g; non‐predator: 13.6±1.0g) and greater

in animals in the control group (16.7±1.0g) and exposed to the treatment during the day

(predator: 17.6±0.8g; non‐predator: 18.4±0.8g) (Adjusted for BM=28.4g).

Apparent dry mass absorption efficiency (control= 81.7±0.39; predator day= 80.5±0.24;

predator night = 80.4±0.63; non‐predator day = 81.3±0.32; non‐predator night=

81.7±0.61) showed no significant differences over the 5 days of the study (F4,27=1.171,

p=0.323), and no influence due to the treatment (F4,27=0.935, p=0.444). Resting metabolic

rate was predominantly influenced by body weight (F1,8=47,681, p<0.001), and was not

affected by the experimental treatment (F4,8=1.021, p=0.403) (Figure 3).

Body Mass (g)

10 20 30 40 50 60

RM

R (

VO

2 m

l/min

)

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

Control Predator day timePredator night time Non Predator day time Non Predator night time

Figure 3 – Effects of body mass (g) on resting metabolic rate (ml O2/min) for all

the measured individuals. Experimental treatment had no significant effect on

the resting metabolic rate (F4,8=1.021, p=0.403).


52

Corticosterone levels varied significantly with the treatment group (ANOVA: F5,49=7.692;

p<0.001). Levels were highest in the groups exposed to the playback during daytime, of

both owl (257.9±53.90 ng/ml) and blackbird (155.0±22.3 ng/ml) sounds (Tukey p=0.308).

In contrast, exposure to sounds at night (owl: 47.4±12.85 ng/ml; blackbird: 34.0±14.2

ng/ml) did not result in elevated corticosterone levels relative to the silent control group

(41.0±14.1 ng/ml) (Tukey p=0.166).


Body weight

The body weight variation was mostly explained by day of measurement (F18,78=2.659,

p<0.001) and the interaction between treatment and day of measurement (F18,78=9.204

p<0.001), and the effect of sex is not significant (F1,78=0.71, p=397). During the first six

days of the experiment, prior to the starvation periods, animals in the two groups did not

differ significantly in body weight (control, 29.1±0.55g; starvation, 29.5±0.45g; ANOVA

F1,26=0.198, p=0.657). Animals in the starving group significantly decreased their body

weight on the deprivation days (Table I). The first and second starvation days resulted in

a reduction of 8% in the body weight; on the third and fourth starvation days the animals

lost 12% of their body weight (Figure 4).


53

Table I – Mean ± S.E. of body mass (g) and ANOVA results, on starvation (day

6, 9, 13 and 15) and refeeding (day 7,10, 14 and 16) days of the study.

Control Starvation ANOVA

Starvation days

Day 6 28.52 ±1.87g 26.82±1.62g F1,26=42.905; p<0.001

Day 9 27.85 ±1.81g 26.21±1.55g F1,26=6.482; p=0.017

Day 13 28.25 ±1.74g 25.02±1.50g F1,26=110.353; p<0.001

Day 15 27.81 ±1.67g 24.20±1.45g F1,26=78.354; p<0.001

Refeeding days

Day 7 28.44±1.38g 27,27±1.69 F1,26=18.453; p<0.001

Day 10 27.77±1.39g 26,57±1.67 F1,26=2.833; p=0.104

Day 14 27,97±1.39g 26,91±1.53 F1,26=57.716; p<0.001

Day 16 27,78±1.42g 27,08±1.63 F1,26=126.303; p<0.001

Days of treatment

0 2 4 6 8 10 12 14 16 18 20

Bo

dy

Ma

ss (

%)

60

70

80

90

100

110

120

ControlStarvation

Figure 4 – Effects of stochastic exposure to starvation on the body mass

variation of the wood mice, across the 18 days of the study.


54

Except for the day following the second 24h starvation period (day 10: GLM F1,26=2.8,

p=0.104), the body weight on the first refeeding days was also significantly reduced

relative to the control animals. On average the body weight took 2 ̶ 3 days to recover to

control levels after the 24h starvation period. At the end of the experimental period, body

fat of the control group (1.7±0.23g) was higher than in the starved animals (1.58±0.198g).

These differences were mostly explained by total body weight (ANCOVA: F1,25=23.3,

p<0.001), and also by an effect of the treatment (F2,25=3.4, p=0.048).

Food intake

The analysis of food intake revealed that its variation is mostly explained but the

interaction of treatment across days of the study (F17,75=70.214, p<0.001), effects due to

sex (F1,75=0.798, p=0.377) and body mass (F1,75=3.602, p=0.061) did not significantly affect

the food intake. During the 6 initial days, when all animals were fed ad libitum, the two

groups did not differ in the average quantity of food consumed (control, 3.9 ±0.15g/day;

starvation, 4.0±0.12g/day; ANOVA F1,147=0.038, p=0.846) (Figure 5).


55

Days of treatment

0 2 4 6 8 10 12 14 16 18 20

Adj

uste

d F

ood

inta

ke (

g)

0

1

2

3

4

5

6

7

ControlStarvation

Figure 5 – Effects of stochastic exposure to starvation on the food intake (g)

variation over the 18 days of the study (adjusted for BM= 27.85g)

Over the first day of refeeding (day 6), after the first starving period, there were no

significant differences in the food intake between the control group and starvation group

(control, 4.3±0.28g; starvation, 4.4±0.23g; ANOVA F1,28=0.063, p=0.804). Food intake on

the first day of refeeding (day 10), after the second deprivation day (day), was also not

significantly different between the two groups (control, 4.1±0.29g; starvation, 4.9±0.24g;

ANOVA F1,28=3.921, p=0.058). However, on the first refeeding day, after the third (day 14)

and fourth (day 16) days with no access to food, food intake in the starvation group was

elevated compared with the control group (day 14: control, 4.5±0.24g; starvation,

5.4±0.20g; ANOVA F1,27=8.476, p=0.007: day 16: control, 4.1 ±0.23g; starvation,

5.8±0.197g; ANOVA F1,26=30.848, p<0.001).At the end of the baseline period of 5 days,

cumulative food intake (Figure 6) was not significantly different between the two groups

(control, 19.4 ±1.51g; starvation, 19.9±1.23g; ANOVA F1,28=0.043, p=0.836). Cumulative


56

food intake on day 12 (after 2 starvation events), was also not significantly different

between the two groups (control, 49.5±2.71g; starvation, 43.1 ± 2.213g; ANOVA

F1,28=2.214, p=0.079), but showed a trend (p >0.05 <.1) towards being greater in the

control group. On the last day of the study, cumulative food intake of the control group

was significantly higher than the starvation group (control, 77.0±3.67g; starvation, 62.8 ±

3.00g; ANOVA F1,28=8.94, p=0.006). The shortfall in intake of the starved group relative to

the controls over the 18 day study was 14.2g of food, almost equal to the daily intake

during baseline (3.94g/day) multiplied by the number of starvation days (=4) (3.94 x 4 =

15.8g). Therefore overall the intermittently starved mice did not compensate their food

intake during the days that they had food available for the days when food was absent

(Figure 6).

Figure 6 – Cumulative food intake (g) during the exposure to the stochastic

starvation treatment. Day 5 (feeding day), day 12 (after two starving events),

and day 18 (after four starving events).

Day 5 Day 12 Day 18

Cum

ula

tive

foo

d in

take

(g

)

0

20

40

60

80

100

Control GroupStarvation Group


57

Apparent dry mass absorption efficiency was not significantly different between the

groups (control=0.804±0.004; refeeding=0.815±0.005: F1,71=0.016; p=0.899). Moreover

within the starvation group, the starvation/refeeding treatment did not significant affect

apparent dry mass absorption efficiency (F1,71=1.680; p=0.199).

Behaviour and Activity

General activity was significantly reduced on the starvation day and the first refeeding

day in the starved animals, compared with the control data (p=0.001 and p=0.004

respectively). The difference in general activity levels between the starvation day and the

first refeeding day was not significant (p=0.202). Time spent resting was significantly

increased on starvation (p=0.004), and refeeding days (p=0.005) compared with control

days. Resting time did not differ between starving and refeeding days (p=0.999). The

differences in the time spent grooming were also significantly different between the

control period and starving days (lower when starving: p=0.016), but not different

between starving and refeeding days or between control and refeeding days. Time spent

feeding was not significantly increased on the refeeding days compared with control days

(p=0.41) (Figure 7).


58

Figure 7 – Effects of the stochastic starvation treatment on the time spent in

different behaviour categories (general activity, grooming, feeding and

resting), on the starvation days and refeeding days.

Circulating leptin

Circulating leptin levels were related to body weight (ANCOVA F1,11= 5.428; p<0.04),

body fat content (ANCOVA F1,11=14.171; p<0.003) and independently treatment (ANCOVA

F2,11=6.303; p<0.015) (Figure 8). Levels of circulating leptin for the control group (6.2±2.6

ng/ml) were significantly lower than for animals exposed to the starvation treatment

(10.5±1.8 ng/ml).

General Activity Grooming Feeding Resting

% T

ime

sp

ent

on

ea

ch b

eha

vio

ur (

%/8

h)

0

20

40

60

80

100

ControlFasting DayRefeeding


59

Figure 8 – Variation of plasma leptin levels (ng/ml) against body fat (g) for the

control group and starved animals, measured on the final day of the study.

2.5. Discussion


According to the predation‐starvation trade‐off hypothesis, under increased risk of

predation wood mice were predicted to decreasing their fat reserves (Speakman, 2007),

thus decrease their body weight. Consistent with this prediction, when mice were

exposed to owl calls at night they significantly reduced their body weight relative to mice

kept in silence. However, contrasting our expectations the mice also showed a significant

reduction in body weight when exposed at night to calls of a non‐predatory bird

(blackbird).

Moreover, when exposed to either owl or blackbird sounds in the daytime, they did not

reduce their body weight. The different effects of the sounds broadcasted during daytime


60

and nighttime, are possibly associated with the animal activity periods. Wood mice are

mostly active during the night (Corp et al., 1997; Wolton, 1983). Diurnal activity has been

documented rarely and usually associated with the breeding season. Birds which naturally

are active at night, where these mice live, are mostly potential predators. The mice may

therefore have evolved to respond to any bird calling at night, and in contrast have

similarly evolved to ignore any bird calling in daytime. An alternative explanation was that

during the day the mice were asleep and did not hear the calls, but at night they were

awake and were stressed by any noise causing them to reduce intake and lose weight.

However, this interpretation was at odds with the corticosterone data (see below) which

clearly indicated that the mice could hear the calls in the daytime and if anything were

more stressed by them that heard calls at night. They just did not translate this stress into

altered food intake or body weights.

These data suggest a generalisation of the response and the categorisation of the stimulus

(reviewed by Shettleworth (2001)). Large investments in predation avoidance may

compromise other fitness components, such as reproduction (Lima and Dill, 1990), and

the mechanism of discriminating traits may include an intensive training and learning

process (Kurt and Ehret, 2010; Lederle et al., 2011); therefore generalisation may be a

mechanism of saving resources. In addition, with respect to predation stimuli, the

opportunity for a learning process to occur may be limited, given that predator attacks

are often fatal (Vermeij, 1982). Hence most responses to predators may be innate, as has

been demonstrated for several taxa (Griffin et al., 2000). According to the ‘Predator

Recognition Continuum Hypothesis’ (Ferrari et al., 2007), a combination of innate and

learned recognition mechanisms is probably involved. Kindermann (2009) has


61

demonstrated that predator‐naïve rodents (mice, rats, and gerbils) do not discriminate

their respond towards auditory cues of predator and non‐predator birds. Since the mice

in this experiment were wild caught from sites where all three of the bird species that we

used are found, it is possible that they had been previously exposed to these calls in the

wild before the experiments began, this factor was not controlled, hence we cannot rule

out either a naïve or learned response to calls occurring at night.

The body weight responses of the mice suggested that mice were unable to discriminate

between the owl calls and the blackbird calls, and instead reacted only to the cue of

generalised bird calls, combined with the time of day that they were played. The data

from the corticosterone assays supports the hypothesis that the mice were unable to

distinguish between broadcasted sounds of different species, since the corticosterone

levels were equally elevated during the diurnal period, independent of the type of bird

call, and for exposure to calls at night were also not different between the sources of the

calls. In an earlier study, faecal corticosterone levels in bank voles (Myodes glareolus)

were elevated in response to weasel (Mustela nivalis) faeces and this was presumed to be

part of the mechanism underpinning the reduction in body weight of these small rodents

in response to predation risk (Tidhar et al., 2007). Similarly rats showed elevated

corticosterone in blood when exposed to a live cat (Felis catus) compared to a stuffed

model (Blanchard et al., 1998) rabbits (Oryctolagus cuniculus) showed elevated

corticosterone in blood when exposed to fox (Vulpes vulpes) as opposed to sheep (Ovis

aries) faeces (Monclús et al., 2005) and domestic mice had higher levels of cortisol when

exposed to owl sounds compared to silence or human voice sounds (Eilam et al., 1999).

Since the faecal corticosterone in the wood mice studied here was not elevated in the


62

groups that lost weight, relative to the control mice that did not lose weight, this suggests

that elevated corticosterone was not part of the mechanism underlying the altered body

weight changes reported here. Furthermore, lag time between hormonal levels in the

blood, and the signal on faeces is species‐specific and highly dependent on animal gut

function (Harper and Austad, 2000; Palme et al., 1996) which, as shown by Touma (2003)

is variable across the day. Therefore we consider that our method of perceived risk

assessment may have underestimated the stress induced by the predation treatments,

given that the levels of metabolites measured were highly affected by the circadian cycle.

It is unlikely that reduced corticosterone levels are due to acclimation to the testing

procedure, predation defense is highly repeatable (Dammhahn and Almeling, 2012;

Dosmann and Mateo, 2014), and habituation is not likely to cause fear extinction

(Takahashi et al., 2005).

The reduction in body weight was correlated with a reduction in food intake. There were

no differences in dry mass absorption efficiency and no change in resting metabolic rate

beyond that anticipated from the reduced body weight. Although the data that we

collected cannot rule out a contribution by elevated physical activity levels, the main

mechanism for enabling the negative energy balance, to effect the reduction in body

weight, was to reduce food intake. This strategy makes sense because it is probably during

foraging that wood mice are most susceptible to avian predation risks. Hence reducing

food intake probably has the dual benefit of directly reducing predation risk, at the same

time as forcing a negative energy balance which reduces body weight, thereby generating

secondary anti‐predation benefits. Indeed the main benefit of losing weight in terms of


63

predation risk may be the reduced energy demands (Figure 3) which reduce the required

foraging time.


Although the responses of the wood mice to predation risk were largely in line with the

predictions of the predation‐starvation trade‐off model the responses to experimental

elevations in starvation risk were largely at odds with the predictions. We anticipated that

in response to intermittent and unpredictable starvation events the mice would increase

their levels of stored fat, enabling them to cope with subsequent starvation events

without the need to enable behavioural or physiological responses, but simply by drawing

on stored fat reserves. In fact, when exposed to starvation the mice altered their

behaviour significantly reducing the costly components of their behaviour (activity and

grooming) and increasing the less costly elements (resting and sleeping). Once food

became available again they did not substantially elevate their food intake to offset the

shortfall in their intake but instead had only a modest or insignificant increase in intake,

matched with a continued suppression of activity to get themselves back into positive

energy balance and increase in weight. This strategy meant that it took 2‐3 days to recover

their body weight after the starvation events, and they did not show any elevation in body

weight above that of the controls. This was mirrored by the fact that at the end of the 18

day experiment the starvation group actually had lower fat reserves on average than the

control animals – the complete opposite of the predictions of the starvation‐predation

trade‐off hypothesis.


64

There have been many previous studies of the responses of other small mammals to

periods of calorie restriction (reviewed in Speakman & Mitchell 2011) or to every other

day feeding protocols. The responses of the animals to these previous manipulations are

not directly comparable to the data generated here, the key element of which was the

stochastic nature of the food deprivation events. Nevertheless, three previous studies

have concerned the responses of domestic strains of mice to stochastic food deprivation.

Swiss mice increased their food intake dramatically, and decreased their energy

expenditure, on days between 24h starvation events (Cao et al., 2009). However, overall

after 4 weeks, similar to the findings here, their overall body weight was decreased.

However, the same research group found over 4 weeks of treatment, with 3 starvation

and 4 non‐starvation days per week, that the elevated food intake on the feeding days

was sufficient to maintain the body weight both in the same mouse strain and in striped

hamsters (Zhao and Cao, 2009). In C57Bl/6 mice, in response to stochastic intermittent

starvation, there was also a significant increase in food intake on the days between

starvation events, such that over a 42 day experiment the overall intake was not reduced

in the intermittently starved group (Zhang et al., 2012). Since these latter mice also

enabled behavioural (reduced activity) and physiological (reduced body temperature)

responses to the manipulation, at some points during the experiment they elevated their

fatness above that of the control group, in line with the starvation‐predation model

predictions.

The responses of the wood mice studied here differ substantially from these previous

studies. There was virtually no compensatory response in food intake. Hence, body weight

was restored predominantly by reduced expenditure (reduced activity). Consequently


65

they ended up with lower, rather than elevated fat reserves. One potential reason for this

response is that the mice were constrained in their alimentary tract capacity and unable

to process greater levels of food intake (Drent and Daan, 1980; Koteja, 1996; Peterson et

al., 1990). We cannot eliminate this possibility that the absence of a food intake response

was due to a physiological constraint. A second reason, however, is that wood mice may

be acutely sensitive to predation risks, and choose not to increase their food intake levels

on the refeeding days, because this would expose them to elevated predation risk.

Separating between these explanations was beyond the scope of the current work.

Circulating leptin levels in intermittently starved C57BL/6 mice were dependent only on

the levels of body fatness (Zhang et al., 2012). This is consistent with many studies

showing a relation of leptin to stored fat (Friedman and Halaas, 1998). In wood mice the

levels of leptin also were dependent on fatness, but in contrast to previous work were

independently elevated among the mice that had been intermittently starved. This

response was unexpected because reductions in food intake, for example, during caloric

restriction, generally lead to reductions in circulating leptin levels which forms a primary

stimulus to overconsume when released from restriction (Friedman and Halaas, 1998;

Hambly et al., 2012; Morton et al., 2006; Rosenbaum and Leibel, 1998). The fact that wood

mice did not show this response to intermittent starvation in their leptin levels, is

consistent with the fact they also did not show post starvation hyperphagia.

Understanding the mechanisms by which these mice elevate leptin levels after food

restriction, and hence avoid post restriction hyperphagia, may be important because in

humans the reduced levels of leptin when dieting are presumed to be part of the primary

response causing people to break their diets (Hopkins et al., 2014; Keim et al., 1998).


66

2.6. Conclusions

The current data provide limited support for the starvation‐predation trade‐off model

for understanding levels of stored body fat/body weight. Wood mice responded in a

manner that was complex and not entirely as expected, but could be interpreted as

consistent with the model if it is assumed that their generalised response to calls at night

was a generalisation to the predation risk of any bird in the wild that calls at night.

However, when faced with elevated starvation they did not show the anticipated

responses. This may in part be because these mice are substantially more susceptible to

predation than to starvation, and their potential responses to starvation were

compromised by the need to maintain a low level of predation risk.

2.7. Acknowledgements

This work was supported by European Funds through COMPETE and by National Funds

through the Portuguese Science Foundation (FCT) within project PEst‐

C/MAR/LA0017/2013 and PhD fellowship (SFRH/BD/47333/2008).

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CHAPTER 3

Growth and Development

Chapter 3 – Growth and Development

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3. Effects of predation risk and high fat diet on body weight

regulation of growing Wood mouse (Apodemus sylvaticus)

3.1. Abstract

Nutrition in the early stages of life can alter organ function and thereby affect the

adult predisposal for disease, explaining in part why obese children are commonly also

obese adults. Obesity can be explained by a combination of both genetic and

environmental factors. In recent years models incorporating an environmental

component have gained relevance, compared with the classic lipostatic set‐point

approach. The risk of predation has been suggested as one of the factors explaining the

common absence of obese individuals among the populations of small mammals. Non‐

lethal effects of predation can induce phenotypic plasticity, allowing prey to cope with the

risks of being predated due to increased exposure during foraging, balance against the

risk of starving due to the insufficient fat reserves. In this study, weaned wood mice were

fed on high fat or control diet and submitted to high risk of predation (or not), simulated

by the broadcasting of owl calls during the night period. We hypothesized that the risk of

predation would play a role on the regulation of body weight by activating mechanisms

that would include modulation of energy intake and expenditure. Furthermore, we tested

the whether the risk of predation reduced the impact of exposure to fat enriched diets,

by comparing the growth trajectories of animals on two feeding regimes, and different

timings of exposure to the diet. Results showed that animals exposed to the predation

treatment gained less body mass, however variations in food intake and RMR did not

explain the observed differences. Feeding on high fat diet resulted in individuals with


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higher body weight, females reached their adult size earlier than males, and were more

responsive to the predation treatment, revealing stronger mechanisms of energy

homeostasis. This suggested that in males larger body size may be related with social

dominance and be involved in their reproductive success. Our data suggest that

predisposition to gain body fat due to the exposure to high fat diet in early stage of

growth, may be affected by the age of the initial exposure, partially supporting the

expected weight increase due to early over‐nutrition.

3.2. Introduction

While obesity is widely recognised to be a problem of energy imbalance (Hall et al

2012) the causes of such imbalance are still unclear. A combination of both genetic and

environmental factors have been used to explain its prevalence. In recent years models

incorporating environmental components, such as behaviour, have gained relevance

(Levitsky 2002; Speakman 2007), relative to the classic “lipostat” set‐point approach

where body weight regulation was presumed to be dependent only on individual

physiological attributes (Kennedy 1953). The risk of predation has been suggested as one

of the environmental factors influencing the regulation of body weight, explaining virtual

absence of overweight and obese animals among the populations of small mammals

(Speakman 2007). This is potentially because small mammals carrying large fat reserves

are unable to run as fast evading predation or to hide from predators in small burrows

(Sundell & Norrdahl 2002). Moreover the time required for foraging for resources, and


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exposure to predators, is higher for fatter individuals, potentially further increasing their

exposure to predators.

It has been suggested that the risk of predation induces changes in behaviour, foraging

activity (Jedrzejewski, Rychlich & Jedrzejewski 1993; Eilam 2005), morphology (Brönmark,

Lakowitz & Hollander 2011) and physiological reactions (Slos & Stoks 2008). Heikkila

(1993) demonstrated that predation risk can influence the development and maturity of

voles. Until sexual maturity, energy is mostly allocated to growth and development,

assuming scenarios of limited resources, and demanding survival effort. On the other

hand, over‐nutrition in early life represents an obesity risk factor, and can induce a series

of alterations such as hyperinsulinaemia, hyperphagia, hyperleptinaemia and

hypertension (López et al. 2007; Martins et al. 2008; Rodrigues et al. 2009), that overall

are linked with the obesity related diseases. Factors acting in the early stages of life

(Gluckman & Hanson 2004; Elahi et al. 2009), and personal history, are as risky for the

development of adult metabolic diseases, as adult environment and life‐style, which

explains in part why obese children are commonly obese adults (Serdula et al. 1993).

In this study we investigated the role of long term exposure to predation risk combined

with a high fat diet, on the regulation of post weaning body weight. The wood mouse

Apodemus sylvaticus (Linnaeus, 1758) was chosen as model given that it is a common

prey of several species of nocturnal raptors (Bontzorlos, Peris & Vlachos 2005), reptiles

(Santos et al. 2007) and carnivores (Lanszki, Zalewski & Horvath 2007). In this species

therefore coping with predation is an important fitness trait.


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We hypothesized that predator risk plays role setting the limits of body weight and

regulating the diet induced obesity. Moreover, we expected that the predation risk

treatment would trigger compensation mechanisms to maintain energy homeostasis,

through imbalance of energy intake and expenditure, that ultimately would be reflected

in body weight. Additionally we investigated the effects of high fat nutrition introduced at

different life stages, and whether risk of predation would compensate the effects of over‐

nutrition in early age, reversing the diet induced body weight increase.

3.3. Methods

All experimental procedures were conducted by an accredited expert in laboratory

animal science by the Portuguese Veterinary Authority (1005/92, DGV‐Portugal, following

FELASA category C recommendations), according to the European guidelines

(86/609/EEC).

Animal housing and Predation risk treatment

Wood mice (Apodemus sylvaticus) were first generation descendants from wild parents

caught in Portalegre – Portugal, in a cork oak forest. After birth, animals were kept with

their parents and siblings until they were 20 days old. On day 20 after birth (weaning),

each individual was separated from their mother and litter and placed in a clean individual

cage (30x20 cm). Each cage was supplied with absorbent paper covering the floor, and


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acting as nesting material, and a small plastic box for enrichment. Animals were kept in

rooms under controlled light (12L: 12D), and temperature conditions (20ºC). Throughout

the experimental period, food and water were provided ad libitum. The cages were

cleaned every 3 days, bedding material was renewed, uneaten food on the hopper and

cage floor was weighed and replaced by approximately 60g of the experimental diets.

The predation risk treatment involved the broadcasting of playback calls from nocturnal

raptors Barn Owl (Tyto alba) and Eurasian Eagle‐Owl (Bubo bubo) (European bird calls;

Jean C. Roché, Kosmos Verlag, Stuttgart, Germany). The owl calls were broadcasted for 2

minutes, every 2 hours, during the night time (9 pm to 7 am). To avoid litter effects,

animals from the same litter were allocated to a different combination of diet and

predatory risk (details below). Wood mice litters have typically 4‐6 pups (Zizkova & Frynta

1996), allowing us to separate the animals evenly for the groups.

Experiment 1 ‐ Long term effects of predation risk on diet induced obesity

Thirty seven animals (18 females and 19 males) were kept in a silent room, and 37

individuals (19 females and 18 males) were exposed to experimental conditions of

predation risk simulation (details above). In each room, animals were fed on one of two

diets: high fat (45% fat; 19.9 kJ/g ‐ D12451 ‐ Research Diets New Brunswick, NJ, USA) or

low fat (3% fat; 12.1 kJ/g ‐ A04 ‐ Scientific Animal Food Engineering, France). Animals were

weighed and food intake was determined based on uneaten food left in the feeder every

3 days. Animals were monitored over 100 days, until they were 120 days old. At the


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experimental end‐point resting metabolic rate was measured by open flow respirometry

(details below) and mice were subsequently killed and dissected.

Experiment 2 – Effects of early nutrition vs diet induced obesity

On the weaning day (day 20 since birth), 24 wood mouse were separated into two

groups (12 control + 12 predation risk) fed on low fat diet for 70 days. Animals in the

control group were maintained in a silent room, and animals in the predation risk group

were submitted to the predation risk treatment described above. After a 70 day period,

animals were started on a high fat diet. Following the procedure described before, body

mass, and food intake were measured for 30 days every 3 days until the study end‐point

at day 100. At the end of the trial, resting metabolic rate was measured (as above), and

the animals were then killed and dissected.

Resting Metabolic Rate

At the end of the 100 days period, resting metabolic rate of all the animals in the study

was measured using open‐circuit respirometry device. Briefly, Oxygen consumption was

measured, using an open‐flow respirometry system (Servomex, Xentra series 4100). Mice

were placed in a perspex chamber of approximately 1 dm3. After placing the animal in the

chamber, dried atmospheric air was pumped into the chamber at a flow rate of

500ml/min. Ambient temperature was set at 29ºC, within thermoneutral zone for

Apodemus sylvaticus (Haim, McDevitt & Speakman 1995). Each animal was monitored

twice, during 2 hours for two consecutive days, during the light period when wood mice


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are less active, the average of these two measurements was used for following analysis.

No food or water was available inside the chamber, and the animals were not starved

before entering in the chamber. Measurements of oxygen concentration in the excurrent

airstream were recorded at 30s intervals, CO2 was not removed from the air pumped into

the chamber to maximize the accuracy of the measures (Koteja 1996). Resting metabolic

rate was estimated as the average value of the five lowest consecutive readings

(equivalent to 2min30s in the chamber). VO2 was calculated after Depocas and Hart (1957)

as VO2=V2 (F1O2–F2O2)/(1‐F1O2), where V2 is the flow rate measured after the metabolic

chamber, and F1O2 and F2O2 are the oxygen concentrations before and after the metabolic

chamber. All the values were corrected to standard temperature and pressure (STPD).

Baseline values of atmospheric oxygen were corrected by a 30 minutes measurement

prior to each run.

Plasma assays

After the respirometry measurements (102‐105 days after the beginning of the

experiment) approximately 1ml of blood was collected, from each mouse, by cardiac

puncture and animals were sacrificed by cervical dislocation. All animals were killed

between 16h and 18h. Animals were dissected, subcutaneous, and visceral fat depots

were removed and wet weight was recorded. To account the effect of feeding into the

circulating levels of metabolites, animals were starved 4 hours prior to the blood

collection. After collection, blood was centrifuged at 10000 rpm for 10 minutes; plasma

was collected and kept at ‐80ºC until processed. Plasma leptin levels were measured using


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ELISA based method with commercially available kits (Millipore Corporation, USA ‐ Mouse

leptin kit).

Statistical Analysis

All the values are expressed as mean ± S.E. A mixed model was used to test the effects of

sex, treatment and day of measurement on the body mass and energy intake. Individual

ID was included in the model, nested within group, as a random factor to account for

repeated measures. When significant this ANOVA procedure was followed by pairwise

comparisons to test between‐group differences for the different time points. Effects on

RMR were tested using analysis of covariance (ANCOVA) considering treatment group as

a fixed factor, and body mass as covariate (following recommendations in Tschöp et al.

(2012)). To correct for the influence of body fatness in the plasma metabolites levels,

these were also tested using analysis of covariance, considering body fat as covariate. P

level <0.05 was taken as significant for all the statistic tests.

3.4. Results

Experiment 1

On the first day of the study, all the animals had approximately the same body mass

11.5±0.16g (Figure 1).


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Figure 1 ‐ Body mass variation (Mean ± S.E.) across the study. A – Females; B‐ Males.

The variation of body mass was mostly explained by the day of measurement

(F34,282=299.509; p<0.001), by the effect of sex (F1,282=57.354; p<0.001), and by the

interaction of sex, day of measurement and treatment (F139,282=7.780; p<0.001). Females

in the control group (no fat and no predation risk) increased their body weight to

26.3±2.29g, representing a gain of 128% when feeding on low fat diet, and to 28.3±1.89g

(gain of 145%) when feeding on high fat diet (no predation). In contrast the body weight

achieved by females feeding on a low fat diet but under predation risk was only 117%

(25.0±1.31g) when feeding on low fat diet and 135% (27.5±1.53g) when feeding on high

fat diet. Males in the control group feeding on low fat diet with no predation risk increased

their body mass to 219% to 36.7±2.70g and to 37.9±3.37g (230%) when feeding on high

fat diet. When submitted to the predation risk treatment, males feeding on low fat diet

gained 198% of their starting body mass (to 34.3±2.26g), and on high fat diet under

predation risk increased their body mass to 36.1±2.50g (237%).

The significant factors explaining the differences in the energy intake were the day of

measurement (F32,266=19.190; p<0.001), sex (F1,266=9.9904; p=0.003), treatment


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(F3,266=9.654; p=0.003), the interaction between treatment and day of measurement

(F96,266=1.324; p=0.02), and the interaction between treatment, sex and day of

measurement (F131,266=1.345; p=0.008). The energy intake was higher in animals feeding

on high fat diet (Control: 194.0±9.3 kJ; Predation: 216.7±10.2 kJ) than animals feeding on

low fat diet (Control: 158.3±6.4 kJ; Predation: 167.7± 6.4 kJ), accordingly animals on the

predation risk treatment also showed elevated energy intake compared with controls

(Figure 2).

Figure. 2 – Energy intake (Mean ± S.E.) across the study. A – Females; B‐ Males.

Analysis of the resting metabolic rate did not reveal any significant differences due to

treatment effect (F3,75=1.297; p=0.282), and the differences in RMR were mostly explained

by the body mass (F1,75=1.122; p=0.016) (Figure.3). Body fat was included as covariate to

test different differences in plasma leptin levels, and this was the main factor explaining

the variation of the plasma leptin (F1,22= 42.619; p<0.001) (Figure. 4). The differences

between treatment groups were not statistically significant (F3,22=0.991; p=0.415).


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Figure 3 – Resting metabolic rate (ml.O2‐min‐1) against body mass of animals

on feeding on low and high fat diet and on predation risk treatment.

Figure 4 – Plasma leptin levels (ng/ml) against body fat of animals on low fat

diet and high fat diet.


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Experiment 2

The body mass variation after the introduction of the high fat diet was explained by sex

(F1,90=69.665; p<0.001), day of measurement (F10,90=32.972; p<0.001), interaction of

treatment group and day of measurement (F30,90= 3.410; p<0.001), and the interaction of

day of measurement, sex and treatment (F43,90= 2.412; p<0.001). In females, the change

of treatment had no influence on body mass (F3,46=0.324: p=0.808) but in males the

variation of treatment across time significantly affected their body mass (F30,46=3.627;

p>0.001). Males increased their body mass in response to the high fat diet, but females

did not show a significant response (Figure 5).

Figure 5 ‐ Variation of body mass during 30 days, of females (A) and males (B)

introduced to high fat diet at weaning day and 70 days post‐weaning.

Differences in the energy intake were mostly due to the day of measurement

(F10,90=2.929; p=0.001), and the interaction of treatment and day of measurement

(F30,90=1.505;p=0.045). The energy intake was elevated on the first days after the

introduction of the high fat diet and individuals on the predation risk treatment had


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elevated energy intake when introduced to high fat diet (p=0.05), on controls this

alteration was not observed (p=0.40 (Figure 6).

Figure 6 ‐ Variation of energetic intake, during 30 days, of females (A) and

males (B) introduced to high fat diet at weaning and 70 days post‐weaning.

Figure 7 ‐ Resting metabolic rate (ml.O2‐min‐1) against body mass of animals

feeding on high fat diet since weaning and animals feeding on high fat diet

since day 70.


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Feeding on high fat diet since weaning did not modify the resting metabolic rate (Figure

7) when compared with animals started on this diet after 70 days (F3,56=1.887; p=0.142).

The variation in RMR was not significantly related to variations in body mass (F1,56=1.470;

p=0.230).

Circulating levels of leptin (Figure 8) were mostly associated with the amount of body fat

(F1,25=51.453; p<0.001). Variation due to the diet and predation treatment are not

statistically significant (F3,25=1.515; p=0.235).

Figure 8 – Plasma leptin levels (ng/ml) against body fat of animals on high fat

diet since weaning and started on the diet after 70 days.

3.5. Discussion

The wood mouse was used as a model, to study the combined effects of high fat diet

and predation risk on the determination of body weight, starting at weaning. Some


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previous studies have shown that over‐nutrition in early life increases obesity

susceptibility by reprogramming energy homeostasis (Rodrigues et al. 2009; Glavas et al.

2010). Moreover the allocation of energy in the early stages of life is more variable than

in adulthood and can be divided between growth and development of physical functions,

for instance to improve reproductive performance, or skeletal growth.

Sex biased growth

Our data showed a reduced influence of the predation risk over the variation of body

weight, compared with the effect of the high fat diet. It is usually accepted that being

small means being vulnerable (Arendt 1997), juveniles are commonly more vulnerable to

predation than adults (Meri et al. 2008) given that they lack the sensory skills and

apparatus to identify the threat and escape. In this study the shape of the growth curve

was different between males and females, independently of the diet or predation

treatment. Females reached their mature body size and virtually stop growing at day 30

(40 days after birth). In contrast in males, the growth rate was continuous during the study

span; such differences are also observed in rats (Slob & Van der Werff Ten Bosch 1975;

Cortright & Koves 2000).

These different growth patterns between males and females produce a clear sexual size

dimorphism, with males ultimately reaching higher body masses than females. Sexual

dimorphism has been reported in several rodent species (Lima, Bozinovic & Jaksic 1997;

Scharff et al. 1999; Jackson & Aarde 2003), and has been associated and explained by the

species mating system (Schulte‐Hostedde 2007). Our data suggests that in the wood

mouse the sexual dimorphism is established by males extending the time of post weaning


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growth, as described for Mastomys species (Jackson & Aarde 2003) and rats (Slob & Van

der Werff Ten Bosch 1975). In some species, males are bigger from birth (Smith & Leigh

1998), which is not the case of the wood mouse, or have faster growth rates compared

with females (Clair 1998; Badyaev 2002). Our data appear consistent with the latter from

6 days post‐weaning onwards. Wood mice adults size is reached when their body mass

rises above 20g (Fernandez, Evansa & Dunstone 1996; Rosário & Mathias 2004). The

effects of treatment and diet on the body weight are more notorious when animals

reached adult size (20g), possibly because during early stages of development individuals

are characterized by feeding drive and insensitivity to environmental cues, contrary to

later stages when homeostasis circuits are fully developed (Crespi & Unkefer 2014).

Sex differences in energy homeostasis and fat storage have been reported for several

species (Cortright & Koves 2000; Valle et al. 2005; Shi & Clegg 2009). Importantly females

are generally considered more efficient in the use of resources than males, being able to

activate mechanism to save energy during periods of scarce resources availability. The

energy allocation of males is directed towards reproductive success. In the wood mouse,

males with higher body mass, were described to produce larger offsprings (Bartmann &

Gerlach 2001). This association is not reported for females suggesting that in the wood

mouse the same sex‐biased resource allocation may occur as described for the house

mouse (Perrigo & Bronson 1985). In the house mouse, males allocate energy for traits that

will benefit their social status and dominance, improving the chances of mating, while

females have other motivations for the use of their resources. Such differences in energy

allocation may explain why males apparently have limited body weight regulation: large

size improves their fitness, contrarily to the assumptions of the predation release


89

hypothesis (Speakman 2007). Analysis of the relationship between predation risk and

body weight may require taking into consideration factors as the reproductive system,

given that in polygynous systems being big may be advantageous for males. Consistent

with these arguments females on the predation treatment had reduced body weight

compared with controls.

Diet effects

Contrary to the report by Díaz and Alonso (2003), the wood mice in our study

responded positively to the high energetic content of the food by increasing their body

weight and fat reserves, when compared with the animals fed on low fat diet. The high fat

diet enhanced the response to the predation risk in females, but not males. Sex biased

energy allocation is expected in small mammals due to the higher investment of females

in reproductive traits, such as lactation, more importantly females are considered more

efficient in the use of resources than males.

Unexpectedly, the body mass variation was not associated with a reduction on the food

intake, moreover, compared with controls the energy intake was increased in almost all

the animals under predation risk treatment (except for females on the low fat diet).

Enhanced energy consumption of the animals feeding on enriched fat diets may be

explained by a deficit in some essential aminoacids relative to other nutrients or minerals

(Illius & Jessop 1996), though given the formulation of these diets, such deficit it is unlikely

to occur. Imbalanced diets may take animals to increase their food intake in order to

obtain the same amount of nutrients. These differences often cause animals increase lipid


90

synthesis and gain weight due to the elevated energy input (Kyriazakis, Emmans &

Whittemore 2010). Proteins are commonly ignored when considering gains of body

weight, however Simpson & Raubenheimer (2005) suggested that the appetite for

proteins may drive to excessive energy intake, explaining overweight.

The energy balance equation states that energy input equals energy expenditure plus

storage. Accordingly, we looked into other energy expenditure components such as the

resting metabolic rate, as a candidate to explain the elevated energetic consumption at

the same time as a stable body mass. RMR represents about 37% of the total energy

expenditure of the wood mouse (Corp 1994), however we did not find variations in the

oxygen consumption.

In tadpoles of the Rana temporaria oxygen consumption increased during short term

exposure to predation risk but declined after long term exposure (Steiner & Buskirk 2009).

Data on rats (Huppertz‐kessler et al. 2011) shows that post natal stress induces changes

in brain tissue and a reduction in body weight. However, the referred study stresses

animals by a combining handling, cold and noise, as it is not possible to isolate the effect

of each stress factor, the accurate interpretation of the results may be compromised. The

short term exposure to predatory cues led to changes in heart and metabolic rates

(Hawkins, Armstrong & Magurran 2004; Steiner & Buskirk 2009). Our long term exposure

protocol showed that the predatory risk did not affect the resting metabolic rate of the

animals, however immediate effects after the exposure to the sound treatment cannot

be excluded. Moreover, given the inconclusive observations, other candidate factors

among both physiological and behavioural causes should be evaluated in future.


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Early nutrition

The influence of high fat nutrition in early life stages is taken as a risk factor for the

development of obesity, leptin resistance and associated diseases (Bowman et al. 2004;

Kahn, Hull & Utzschneider 2006; Van Gaal, Mertens & De Block 2006). The introduction of

high fat diet to males fed on low fat diet since weaning increased the gain of body mass,

reaching a new level similar to males that were fed the high fat diet since weaning. For

females, the introduction of high fat diet did not trigger a gain in body mass, which

supports the previous statement that females reach their adult body size earlier than

males. It also suggests that the body weight regulation mechanism of females was more

able to respond to the potential increase in energy intake by the high fat diet.

Several studies have focussed on the influence of high fat diet consumption by mothers,

during pregnancy and lactation, on the energy homeostasis and obesity predisposition of

their offspring when adult (e.g. Srinivasan et al. 2006; Bayol, Farrington & Stickland 2007;

Tsuduki et al. 2013). However, the effects of post weaning nutrition are poorly studied.

We address this question by introducing high fat diets to pups at different stages of their

development: immediately after weaning or 70 days post‐weaning. Our results suggested

that at these stages the high fat diet does not influence the overall predisposition for

weight gain, which was contrary to the expectation. Overall, the exposure to high fat diet

resulted in considerably more weight gain compared with the chow fed animals, and

therefore in significant body weight differences due to the diet effect. Early exposure to

over‐nutrition is likely to induce leptin resistance (Glavas et al. 2010) and consequence

susceptibility to develop obesity in adulthood. The timing of the exposure to the fat diet


92

may explain such influence. Some studies have demonstrated that maternal exposure to

high fat diet may cause alterations in the cognitive and behavioural dysfunctions in the

offspring (Niculescu & Lupu 2009; Peleg‐Raibstein, Luca & Wolfrum 2012; Mendes‐da‐

Silva et al. 2014). In particular Niculescu (2009) reported effects on hypothalamus

development, which is an important brain structure associated with energy homeostasis

(Schwartz et al. 2000). During gestation, and after birth the neuronal networks

responsible for the body weight regulation are still incomplete, the Neuropeptide Y and

Proopiomelanocortin expression are highly susceptible to environmental conditions,

which may induce an incorrect programming of regulation complex (Plagemann et al.

1999; Plagemann 2006). Our study focussed on post weaning, at this stage the

hypothalamus is less vulnerable, and it is also during this period that leptin enrols a

function on energy expenditure (Mistry, Swick & Romsos 1999), given that earlier, leptin

is unable to inhibit food intake and consumed energy is mostly allocated to growth and

development (Ahima & Hileman 2000). Nevertheless, high fat diet is likely to supress

neurogenesis of “energy‐balance neurons” (Mcnay et al. 2012), leading to premature

aging of the homeostasis system compromising its further function. However, these

differences are apparently sex‐specific (Lee et al. 2014), explaining why females were able

to maintain their energy balance when exposed to high fat diet 70 days after weaning.

3.6. Conclusion

Our data supports the hypothesis that the predation risk is involved in the regulation

of body weight and setting of its upper limits. However, we were unable to identify the

mechanism responsible for increased energy expenditure and consequent reduction of


93

body weight. We also suggest that other factors are likely to influence the limits of body

weight, such as the species social system, which implicates being highly species‐

dependent.

Moreover, in the light of the predation risk hypothesis large body size constrains the

species survival, however being big may provide social and reproductive advantages that

overall benefits fitness. Therefore, the positive impact of the high body weight may go

beyond the survival during starvation periods but also with increase the individual fitness

by improving the social status and dominance.

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CHAPTER 4

Physical Activity and Behaviour

Monarca, R.I., Mathias, M.L., Wang. D.H. & Speakman, J.R. (2015) Predation risk

modulates diet induced obesity in male C57BL/6 mice. Obesity.(in press)

Chapter 4 – Physical Activity and Behaviour

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4. Predation risk modulates diet induced obesity in male C57BL/6

mice

4.1. Abstract

Objective: In this study we aimed to examine the behavioural and physiological changes

induced by experimentally varying the risk of predation, in male mice fed on a high fat

diet. In particular, we aimed to assess if the risk of being predated modulates the body

weight gain, providing an ecological context for the obesity resistance observed in many

species of small mammals.

Methods: Body weight, food intake, physical activity and core body temperature of 35

male C57BL/6 mice were monitored for twenty days, whilst feeding on high fat diet. A

third of the animals were exposed to elevated risk of predation through exposure to the

sounds of nocturnal predatory birds and these were compared to animals exposed to a

neutral noise or silence.

Results: Male mice exposed to predation risk had significantly lower weight gain than the

neutral or silent groups. Reduced of food intake and increased physical activity were the

main proximal factors explaining this effect. The risk of predation also induced changes in

boldness.

Conclusions: Our study provides evidence supporting the role of predation risk on body

weight gain of small mammals.


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4.2. Introduction

Obesity has been explained as an imbalance between energy intake and expenditure,

influenced by a complex interplay of genetic, social and environmental factors (Prentice

2001; Song, Lee & Sung 2014). Observations on many wild small mammal species have

suggested that contrary to humans, they have a strong regulation mechanism that allows

them to avoid the risk of becoming obese (El‐bakry, Plunkett & Bartness 1999; Peacock &

Speakman 2001). One explanation for this strong regulatory system is that body weight

fluctuates according to a dual intervention point mechanism (Levitsky 2002; Speakman

2007; Speakman et al. 2011). The dual point intervention model suggests that there are

upper and lower limit points of regulation (Levitsky 2002; Speakman 2007, 2014).

Between these points mass may vary freely but when the intervention point is reached

animals enable physiological mechanisms to avoid further weight gain or weight loss. This

allows them to maintain their body weight within a limited range. The risk of predation

has been suggested as a factor setting the upper limit point of intervention, given that an

animal carrying large fat reserves may have an elevated risk of being predated (Sundell &

Norrdahl 2002). In contrast, the risk of starvation and the impact on reproduction

(Tataranni et al. 1997), due to insufficient energy reserves, have been proposed as

regulators of the lower limit point of intervention. One hypothesis for the diversity in

human adiposity in modern societies is that the virtual absence of predation risk for 2

million years has led to genetic drift in the genes that control the upper intervention point

(Speakman 2007).

Inbred C57BL/6 mice are sensitive to diet induced obesity when fed a high fat diet and

have been a popular model for the study of obesity (Zhang et al. 2012b; Yang et al. 2014).


103

In this study, we aimed to evaluate whether experimentally altering the risk of predation

modulates weight gain of C57BL/6 mice, induced by feeding on high fat diet. Second, we

investigated the behavioural and physiological adjustments in response to the exposure

to elevated risk of predation, that might underpin this effect, through monitoring multiple

parameters including the food intake, physical activity, body temperature and assessing

behavioural changes by observing their exploratory behaviour and boldness.

4.3. Methods

Animals and predation treatment

Thirty five male mice C57BL/6 were purchased from a commercial supplier (Vital River

Ltd, Beijing, China), aged 10‐12 weeks old and individually housed in standard mouse

cages (30 ×15 ×20 cm). Wood shavings and shredded paper were provided for bedding

and environmental enrichment. Light conditions in the housing room were set as 12L: 12D

(lights on at 7am). Animals had free access to food and water for the whole experimental

period. First, mice were maintained on a 10% kcal/fat diet (D12450B ‐ Research Diets,

New Brunswick, USA), for transmitter implantation and recovery period (details below),

lasting a total of two weeks. After this period mice were started on high fat diet, 45%

kcal/fat diet (D12451 ‐ Research Diets, New Brunswick, USA), and randomly assigned into

one of three groups: control, predation and neutral (respectively 13, 9 and 13 individuals

per group). Body weight and food intake were monitored on a daily basis, for all the

animals, between 6 and 7 p.m. Each group was monitored for 20 days; the differential

treatments started on the 8th day. Animals in the predation group were submitted to a


104

treatment simulating the presence of predators near the cages, and risk of being

predated. The treatment consisted of a 2 minute playback of owl calls (Tyto alba and Bubo

bubo) accessed from commercially available compilations of bird songs (European bird

calls; Jean C. Roché, Kosmos Verlag, Germany), broadcasted every 2 hours, for a total of 6

events per night, starting 1 hour after lights switched off. The neutral treatment was

composed of playback of the recorded sound of a human voice reading a scientific paper

(Neel, 1962 ‐ Diabetes Mellitus: A "Thrifty" Genotype Rendered Detrimental by

"Progress"?) in English, monotonically during 2 minutes, played every 2 hours, in a total

of 6 events per night. This neutral treatment exposed animals to a sound that did not

represent a threat of predation. All the sounds were played using a computer connected

to audio speakers. Speakers were placed 2 meters way from the cages, and sound volume

was regulated by human ear to guarantee that it was heard by the animals. Animals in the

control group were kept in a silent room for the entire period of the experiment.

Physical activity and core body temperature

Two weeks before the experiment a sub‐set of animals were implanted with telemetry

transmitters to monitor body temperature and physical activity (Model PDT‐4000 E‐

Mitter, Mini‐Mitter, Bend, OR, USA). The transmitter was inserted intraperitoneally.

Animals were anesthetized with a mixed flow of Isoflurane and Oxygen, allowing us to

make a small incision of approx. 1 cm in both the ventral skin and peritoneal wall. After

the insertion of the transmitter, the two layers were sutured independently. The total

surgical procedure took 15‐20 minutes, per mouse. After the surgical procedure, mice


105

were given a recovery period of one week. Receiver pads (ER‐4000 Receiver, Mini‐Mitter,

Bend, USA) were installed below the mouse cages, receiving the information from the

transmitters, data were them collected by a windows based software (VitalView™: Mini‐

Mitter, Bend, USA), at 15 seconds intervals. Due to the limited number of receiver pads

available, the number of monitored mice for these parameters was 7 per group.

Corticosterone levels

Corticosterone levels have been previously implicated as a mechanism linking elevated

predation risk to food intake and weight regulation in rodents (Eilam et al. 1999).

Corticosterone was measured on the last day of the experiment, after 12 days exposed to

the predation risk treatment. Given that faecal concentration of corticosterone peaks 6 –

12 hours after a stressful event (Ylönen et al. 2006), each animal was placed in a clean

cage, between 10am and 3pm, and fresh faeces were collected and stored in 100%

ethanol at ‐30ºC, until being processed. Hormone extraction was performed following a

modified method of Goymann (1999). Briefly, ~0.05g of faeces (Sartorius) were added to

1ml of 80% methanol, and pulverized using a small pallet knife. The blend was then

vortexed for one hour at 500 rpm, followed by 20 minutes of centrifugation at 2500g. The

supernatant was then transferred to another tube and diluted with a buffer solution.

Corticosterone levels were measured using Enzyme Immunoassay (EIA) commercial kits

(Cayman Chemical Item no. 500655).


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Behaviour analysis

Dominant behaviours

Animals were randomly video recorded over the 12 days experimental period. Each

mouse was monitored for a total period of 10 hours, starting 2 hours before dark phase,

cameras were set at 5pm, and left for 10 hours. Dark phase recording were made using

infrared night vision mode from the video cameras (JVC, GZ‐MG20). Within each recording

the animals were observed for 5 random periods of 10 minutes, and their dominant

behaviours were listed.

Dominant behaviours were classified into four categories: grooming, feeding, resting and

general activity, according with the ethogram previously described for this species

(Speakman & Rossi 1999). General activity included walking, climbing the cage and all

general movements; Feeding included eating the pellets provided, occasional coprophagy

and drinking. Resting behaviour was considered when the animal was sleeping or sitting

not moving in the cage or grooming. Grooming was registered when the animals were

seen licking the fur or tail, scratching with any limb and not moving in the cage. Time spent

on each behaviour was registered with ETHOWATCHER® software using Real‐time

ethography mode (Crispim Junior et al. 2012).

Exploration and Boldness

Individual reaction towards a novel situation, e.g. new and unknown environment, is

commonly used as an index of animals general behaviour and to unravel fitness related

traits (Walsh & Cummins 1976; Réale et al. 2007). The open field test was used to assess

these behavioural components. Trials were conducted after 12 days on the treatment.


107

The tests were performed after “dusk” during the normal circadian active period, between

8pm and 12pm. The dimensions of the open field arena were 50×50x40 cm, constructed

with plastic covered plywood. A video camera was positioned above the apparatus, to

record the behaviour of the animals using infrared sensitive night mode; the testing room

was illuminated by a red bulb. For the test, each animal was placed in the same corner of

the arena, using a plastic jar to transport each mouse for the home cage to the arena to

minimise handling. Each mouse was allowed to explore the apparatus freely for a 10

minutes trial period. During the tests the experimenter was not in the room. Between

trials, the apparatus was cleaned using 70% ethanol. Each animal was tested once.

The videos were analysed using ETHOWATCHER® in activity analysis mode (Crispim Junior

et al. 2012), and the following aspects were extracted: Total distance travelled; % of time

resting; % of time in the central area of arena (625 cm2, 12.5 cm away from the walls).

Statistics

All the data were expressed as means ± S.E. General Linear Modelling with repeated

measures was used to test the body weight variation and food intake across the days of

the study, including treatment as a fixed factor, and body‐weight as covariate when

testing food intake variation. The effect of treatment on concentrations of faecal

corticosterone were analysed using One‐way analysis of variance (ANOVA), followed by

post hoc Tukey test, setting treatment group as a fixed factor. Physical activity and core

body temperature data were averaged for each hour, and analysed during the baseline

and treatment periods, for variation over the 24h period and daily across the study. GLM


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modelling with repeated measures procedure was used, considering repeated measures

accordingly along 24 hours or days of study, setting treatment group as fixed factor,

followed by post hoc Tukey test.

Dominant behaviours were analysed through multivariate GLM model followed by Tukey

post hoc tests. Open‐field data were analysed through GLM model, including total

distance travelled, % of time resting and % of time in the central area of the arena as

variables and treatment group as factor. This procedure was followed by post‐hoc Tukey

tests to examine the differences between treatment groups. All data were analysed using

SPSS 22.0 for Windows. Statistical significance was set at p = 0.05.

4.4. Results

To analyse the effects of predation risk on the body weight, mice were first fed with

high fat diet for 8 days. During this baseline period, body weight increased in all three

groups (day effect: F7,224= 72.656; p<0.001) but did not differ significantly between the

groups (F2,32= 0.423; p<0.653). After starting the predation risk treatments the variation

in body weight was also mostly affected by the day of measurement (F12,384=100.403;

p<0.001), but there was also a significant effect of the treatment (F1,32= 5.194; p=0.011)

and the interaction between treatment and day of measurement (F24,384=3.800; p<0.001).

Over the 12 days of sound treatment, animals in both control group and neutral groups

increased their body weight by 10% (Tukey: p=0.902), consistent with the baseline rate of

increase. However, animals under the predation treatment increased their body weight

by only 4% (Tukey: predation vs control p=0.013; predation vs neutral p=0.032) (Figure 1).


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Figure 1 – Body weight variation (%) (Mean±S.E) in male C57BL/6 mice

throughout the baseline and predation treatment days.

The analysis of food intake (Figure 2) revealed that during the baseline period the

variation in food intake was mostly correlated with variations in the body weight

(F1,30=15.479; p<0.001). Day of measurement (F6,180=0.778; p<0.588) and treatment group

(F2,30=1.592; p=0.220) had no significant effects. Throughout the treatment period, the

food intake in the predation group was reduced (2.72±0.11g), when compared with

animals in the control group (3.36±0.08g) and neutral group (3.54±0.09g) (F2,30=16.034;

p<0.001). During this period body weight (F1,30=2.850; p=0.102), and day of measurement

(F12,360=0.709; p=0.743), were not significantly related to food intake.


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Figure 2 – Food intake (g) (Mean±S.E) variation in male C57BL/6 mice

throughout baseline and treatment periods.

Analysis of corticosterone levels showed a significant difference between the treatment

groups (F3,18=20.408; p<0.001). Post hoc tests revealed (p=0.005) that corticosterone

levels were elevated in both the neutral (544.1±51.1 ng/ml) and predation groups

(323.4±136.2 ng/ml) relative to the control animals (95.5± 55.9 ng/ml).

Physical activity and core body temperature

Animals exhibited similar patterns of circadian physical activity. Briefly, mice were

active during the dark phase and inactive during the light phase. Two peaks in physical

activity occurred during the light changes, at 7 pm “dusk” and 7 am “dawn”

(F23,391=39.193; p<0.001). Throughout the baseline period (Figure 3A) animals showed

inconsistent activity variation during the dark period which resulted in a small but


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statistically significant group effect, specifically between control group and neutral

treatment group (F2,17=6.185 p=0.01; Tukey p=0.007). During the treatment period (Figure

3B), the physical activity pattern slightly changed, resulting in significant effects due to

the treatment (F2,17=13.886 p<0.01), animals in the predation treatment group increased

their activity during the dark period compared with controls (Tukey p=0.001) and neutral

group (Tukey p=0.001).

Figure 3 ‐ Physical activity of male C57BL/6 over 24 hour periods (mean±S.E).

Light period: 7h to 19h; Dark period: 19h to 7h. A ‐ Physical activity during the

baseline period. B ‐ Physical activity during the predation risk treatment period.

Daily activity (Figure 4) was primarily affected by the day of measurement during the

treatment period (F12,204= 8.349; p<0.01) but not during the baseline (F6,102= 1.047; p<0.4).

During the treatment, daily activity was 42% higher in the group submitted to the

predation treatment (4.19±0.18 counts/h) compared to controls (2.94±0.19 counts/h) and

22% higher than the neutral group (3.46±0.2 counts/h). Between the baseline period and

treatment the animals in the predation group increased their activity by 23%, control and

neutral group reduced activity by 10% and 20% respectively.


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Figure 4 ‐ Daily activity (Mean ± S.E. for 24hours) of male C57BL/6 mice across

baseline and treatment periods.

Figure 5 ‐ Core body temperature of male C57BL/6 mice, over 24 hour period.

A – Core body temperature during the baseline period. B ‐ Core body

temperature during the predation risk treatment period.

Variation in core body temperature of all the animals followed a circadian pattern that

peaked between 6pm an 7pm, and between 5am and 6am, about one hour before dusk


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and dawn respectively (Figure 5). The core body temperature was generally about 1ºC

higher during the dark phase. The time of measurement was the main factor associated

with the variation of core body temperature during the baseline period (F23,391=72.685;

p<0.001) and the treatment period (F23,391=157.731; p<0.001). The treatment did not

affect the daily core body temperature variation (F2,17=0.085; p=0.919; Control:

37.0±0.11ºC; Neutral: 37.1±0.12 ºC; Predation: 37.1±0.11 ºC). During the predatory risk

treatment, the core body temperature was not affected by the treatment effect

(F2,17=0.060; p=0.942; control: 36.8±0.08 ºC; neutral: 36.8±0.08 ºC; predation: 36.6±0.08

ºC). Rather the variation was mostly correlated with the hour of measurement

(F23,391=157.731; p<0.001) (Figure 6).

Figure 6 ‐ Daily variation of core body temperature of male C57BL/6 mice

measured for 24 hour periods across the baseline period and 12 treatment

days.


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Behaviour observations

The time spent on each of the main behaviours categories (Figure 7) was not different

between treatment groups (F6,40=0.716; p=0.639). Animals spent 39.4± 3.1% of time on

general activity, 33.7± 4.0% resting, 10.3± 1.8% of time feeding and 16.6± 1.5% of time

grooming.

Figure 7 ‐ Effects of experimental treatment over the time spent on each

category of dominant behaviours (Mean± S.E. %time/10h) (General activity,

resting, feeding and grooming).

The analysis of boldness and exploration variables revealed that the treatment had a

significant association with the % time resting (F2,32=3.501, p<0.042) and the % of time in

the centre of the arena (F2,32=14.866, p<0.001), but not with the total distance travelled

(F2,32=1.627, p=0.212) (Figure 8). However, the apparent tendency was not validated by

the post‐hoc Tukey tests, when analysing the % of time resting (Tukey: Control vs

Predation p=0.076; Control vs Neutral p=0.085; Neutral vs Predation p=0.973). Animals in


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the group submitted to elevated predation risk spent significantly more time at the central

area of the arena (11.7±1.7%) than both control and neutral groups (Tukey: p<0.001 and

p=0.001; Control: 3.7±0.6%; Neutral: 5.3±0.9%).

Figure 8 – Boldness and exploration behaviour parameters (Mean ±S.E.) (Total

distance travelled, % of time immobile and % of time in the centre of the area).

4.5. Discussion

In this study we analysed the effect of the perceived risk of predation on energy

balance of male C57BL/6 mice, by inducing weight gain through feeding on high fat diet.

These animals are sensitive to diet induced obesity but the propensity to gain weight is

variable between individuals (Zhang et al. 2012b). Our data showed that the rate of body

weight gain was consistent between animals that were exposed to a neutral noise (human

speech) or to no noise at all. However, body weight gain was significantly reduced in


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animals submitted to the predation risk treatment, strongly supporting the original

hypothesis that predation risk is a modulating factor over weight control. Analysing the

parameters that influenced energy balance, the reduced rate of gain in body weight of

the predation treatment group was explained by a combination of lower food intake and

increased physical activity, particularly during the dark phase.

Since we only studied young males we do not know if this response would also be

observed in females, or in older individuals. In fact loss of hearing in older C57BL/6 mice

(Willott & Turner 1999) may render them unresponsive to sound cues, suggesting the

response may be specific to younger animals.

Previous studies have also found impacts of perceived predation risk on foraging

behaviour and food intake in several species (Brown 1988; Lima 1998; Gentle & Gosler

2001). Our study showed a clear reduction of food intake when mice were exposed to the

predator calls. Noise and other stress sources have been demonstrated to cause reduction

of body weight gained on rats (Alario et al. 1987) and mice (Finger, Dinan & Cryan 2011).

Because we used a neutral noise treatment, which did not cause a reduction in weight

gain relative to mice kept in silence, we can rule out the possibility that the retarded

weight gain was a generalised stress effect due to noise. Previous work has suggested that

a mediating mechanism by which predator risk may influence energy balance and hence

body weight is via an increase in stress hormones. Indeed we found that corticosterone

levels were elevated in the mice exposed to predator calls relative to the mice kept in

silence. However, the levels of corticosterone were also increased in mice exposed to the

neutral sounds, which did not have retarded weight gain. Hence it seems unlikely that

these increased levels of corticosterone mediated the weight reduction effect. Rather, it


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suggests that mice may be able to distinguish between sounds, and discriminate if they

represent a predation threat necessitating modulation of energy balance and body

weight.

These observations contradict the suggestion of a generalisation process made by

previous studies (Getschow et al. 2013), which suggest that animals may have a

generalized response towards auditory cues, not distinguishing if the source represents a

threat. Nevertheless, recognition of predation cues has been discussed to comprise both

innate and learnt components, being often dependent upon experience (Apfelbach et al.

2005).

The imbalance between energy intake and expenditure was reinforced by the variation in

activity patterns. The predation risk caused a general increase of the physical activity in

the animals, supported by the behaviour observations. This effect was unexpected

because freezing and reduced physical activity is a commonly used strategy by prey

species to avoid detection by predators, as reported for fish (Johansson & Andersson

2009), larval frogs (Anholt, Werner & Skelly 2000) and voles (Jedrzejewski, Rychlich &

Jedrzejewski 1993). In contrast, increased activity or ‘ fleeing’ is used mostly after being

spotted by the predator, to reduce the distance between predator and prey (Eilam 2005).

Moreover, this variation occurred during the naturally active period, meaning that the

sound disturbance did not disrupt the circadian cycle, but involved modification of the

level of activity within the active phase of the cycle. We should also take into

consideration the diet component given that fat enriched diets have been described to

cause alterations on the circadian clock (Kohsaka et al. 2007; Bravo et al. 2014) and induce

depressive and anxiety‐like behaviours (Mizunoya et al. 2013). The data obtained from


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the open field test also resulted in unexpected observations. According to Eilam (Eilam

2003), animals avoid the open area in the centre of the arena and seek the area close to

the walls, which provides a more secure environment, which reflects observations made

in natural patches (Abramsky et al. 2004). Our data suggests an increased boldness in the

animals under elevated risk of predation. This may appear counterintuitive as predation

avoidance strategy but boldness has been associated with the capacity to make quick

decisions (Mamuneas et al. 2015), therefore it may have adaptive role, being beneficial in

a high risk environment (Sih, Bell & Johnson 2004).

Another feature of energy balance that should not be neglected is the body temperature.

Lowering body temperature is among the mechanisms used by animals to save energy, as

exemplified in hibernation, torpor (Geiser 2004) and caloric restriction studies (Zhang et

al. 2012a). However in our study, we did not find significant variations in body

temperature associated with the experimental treatment and weight gain.

In summary, the current data supports the role of the predation risk in the regulation of

body weight, modulating obesity levels by reducing food intake and promoting energy

expenditure.

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CHAPTER 5

General Discussion

Chapter 5 – General Discussion

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5. General Discussion

The dysregulation of the balance between energy input and expenditure is the main cause

of obesity. A combination of environmental and genetic factors have been recognized to

influence such imbalance of energy homeostasis. However, the evolutionary factors

explaining the origin, prevalence and success of obesity phenotypes are still under

discussion.

It is generally accepted that body weight is regulated, as supported by several studies

(MacLean et al. 2006; Hambly et al. 2012). However the factors involved in setting body

weight limits are still unclear. According to the predation release hypothesis (Speakman

2007), small mammals are able to maintain their body weight within certain limits. It has

been suggested that the upper limit is set by the risk of predation, given that larger

individuals would require more energy to maintain their body, therefore spend more time

foraging and exposed to predators. Moreover, animals carrying larger fat depots may not

be able to hide in small burrows and may have their running and escaping capacities

compromised. On the other hand, the risk of starvation due to carrying reduced energy

stores has been suggested as the factor underpinning the lower limit of body weight.

Following the predictions of the predation release hypothesis, this thesis aims to test

whether the risk of being predated influences the body weight of small mammals (Wood

mouse Apodemus sylvaticus and Laboratory mice – C57BL/6), in addition to elucidate the

physiological and behaviour factors that are modified to support such alterations and


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balance the trade‐off between the risk of being predated and the risk of starvation, and

how these alterations are signalled. Accordingly, 3 key questions were considered:

1) Do animals under perceived risk of predation adjust their body weight?

Results shown that the perceived risk of predation is likely to cause reductions in body

weight in adult mice, reduction in growth rate of pups, and reduction of weight gain when

exposed to enriched fat diets, which strongly supports the proposed hypothesis. Both

animal models were equally affected by the risk of predation, showing 6% to 8% reduction

on the body weight when compared with controls. Moreover, the data on the wood

mouse also points to some sex‐specific responses. In humans, intentional weight loss is

highly sex‐influenced, females are often more concerned about their appearance than

males, and have different eating patterns (Rolls, Fedoroff & Guthrie 1991; Keski‐Rahkonen

et al. 2005). However, in rodents these differences may be associated with social

dominance and reproductive success, larger animals are usually higher in the hierarchy

rank (Huang, Wey & Blumstein 2011) and dominance has been correlated with

reproductive success (Horne & Ylönen 1996; Rolland et al. 2003), which may stimulate

individuals to drift from their upper intervention point.

The dual intervention point model suggests that predation risk is one of the main factors

setting the upper limit boundary for body weight, as the risk of starvation may support

the lower point of intervention (Speakman et al. 2011). The wood mouse response to the

stochastic starvation exposure were not in line with the predictions of the model. The dual

intervention point model suggests that the risk of starvation is responsible for setting the


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lower limit of intervention, therefore an increased risk of starvation should push the fat

storage above the lower boundary, re‐setting the lower limit above the initial limit, to

avoid starvation, during further famine events.

The complexions of these interventions points are unclear, however it is expected that

their genetic encoding comprises multiple genes, with different mutation rates, which

may explain the variability of physiological and behavioural responses encountered to

predation risk treatment.

2) Which physiological and behaviour factors are changed to produce such

adjustments?

The alterations in body weight were mostly explained by adjustments in the energy

intake, modification of the energy expenditure and a combination of both, suggesting that

multiple strategies can be involved in the energy homeostasis.

Energy intake was reduced through reducing the amount of ingested food and not by

alteration of the digestive efficiency, to extract or absorb less energy from the consumed

food, which is unexpected given that the digestive features are highly plastic and often

vary their function according with environmental conditions (McWilliams & Karasov 2001;

Naya, Karasov & Bozinovic 2007). However, these results are consistent with the

compensation mechanisms identified to cope with caloric restriction (Hambly &

Speakman 2005; Mitchell et al. 2015).


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Although animals’ metabolic rate have been linked with the wiliness to take risks (Careau

et al. 2008; Huntingford et al. 2010). A possible explanation for this relation is the fact that

animals with higher metabolic rates have elevated energetic requirements. To fulfil such

demands, animals may require more time foraging and may need to forage more often,

therefore increasing the time expose to predators and the overall risk taking. The energy

expenditure variation pointing to an increase in energy loss is not achieved by modifying

resting metabolic rate.

Physical activity and behaviour were revealed to be highly flexible traits. The adjustments

of physical activity and behaviour resulted into embracing activities less energetically

costly to save energy to cope with starvation, and optimize the regain of the lost weight.

On the other hand, predation risk induced animals to spend more time walking and

moving in the cage, than spending time resting, sleeping or ingesting food, resulting in an

overall increase of energy expenditure and reduction in body weight. These activities are

possibly related with the attempts to escape the risk.

The exposure to predation risk did not cause adjustments in the core body temperature.

Some bird species use facultative hypothermia as a strategy to save energy, and also rats

have altered their body temperature in response to caloric restriction (Severinsen &

Munch 1999). However, it has been suggested that hypothermia involves extra costs, as

being highly vulnerable to predators (Pravosudov & Lucas 2000). Therefore, under severe

risk of predation is likely that hypothermia is avoided, which is supported by the present

results.


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Results provided evidence that the risk of predation has a role modulating the body

weight of small mammals. Thus, is likely to be involved in the environmental components

affecting the upper limits of body weight. The role of starvation is not so clear. Exposure

to stochastic starvation did not provide strong evidence for the role of starvation on the

definition of the lower limits of weight. Animals were able to compensate the body mass

lost during the starvation periods by overfeeding and reducing physical activity, during

the recovery period, but did not overcompensate the food intake to load the fat stores,

as insurance measure against further starvation events.

However, the hypothesis of overstock food stores due to starvation should not be fully

excluded, given that some species, such as the Syrian hamster (Mesocricetus auratus),

respond to starvation by increasing hoarding and supplying their external food storages

(Buckley & Schneider 2003). The wood mouse is a scatter hoarder (Jensen & Nielsen

1986), therefore external energy storage may take part in the energy balance of the

species. Nevertheless, hoarding may have only a partial role, because contrary to the

Syrian hamster whose overfeeding capacity is nearly absent (Day & Bartness 2003), the

wood mice has been demonstrated to be capable of over feeding in energetic demanding

periods. Hoarding is overall considered advantageous (Mcnamara, Houston & Krebs 1990;

Wauters, Suhonen & Dhondt 1995), particularly if the metabolic costs of carrying reserves

are high, or if the intake rate is reduced. However, external storages are extremely

vulnerable to pilferage (Vander Wall & Jenkins 2003) and perishing (Hadj‐Chikh, Steele &

Smallwood 1996), therefore carrying internal storages can be a safer strategy.


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3) What signal the energy balance and imbalance to produce such

adjustments?

Transmission of information across the body is an essential factor to understand the

interaction between nutritional status of the animal and its environment, to respond the

need of acquiring energy through foraging or fat storages, and assess the risks of being

predated. The brain is the main controller that receives, transmits and integrates signals

from multiple sources, hormonal, neural, and sensory, modulating the foraging and

metabolism.

Corticosterone (the primary glucocorticoid in rodents) has been used to provide

information about the perceived risk of predation, and recently was related with obesity

increase and food intake (La Fleur et al. 2004; Sominsky & Spencer 2014). Accordingly, the

exposure to sounds simulating the presence of an avian predator, increased the levels of

faecal corticosterone. As response to the sound treatment animals generally increased

their levels of corticosterone, suggesting an elevated stress level due to the treatment.

However, corticosterone levels were also elevated in the animals under neutral sound

treatment, which may conceal a generalised response towards the sound might be

occurring. Moreover, even though the corticosterone levels were elevated, the effects

over body mass and food intake were different between the wood mouse and the

C57BL/6 mouse. The predation pressure has been eliminated from the C57BL/6 mice

many generations ago, therefore the successful response to predation cues no longer

compromises survival and no longer acts as a selective factor. On the contrary, on the

wood mice the response to the risk of being predated is still an important trait for their

survival.


131

The primarily role of leptin is to inform the brain about the nutritional status of the body

triggering the need to eat in case of energy deficiency or to stop eating if energy levels are

sated, working in a feedback loop to maintain constant energy stores. Results shown that

circulating levels of leptin are consistent with the levels of body fatness. However, animals

exposed to starvation revealed unexpected low levels of leptin explained by the absence

of post restriction hyperphagia (Schwartz et al. 2000; Hambly et al. 2012). The mechanism

underpinning the avoidance of hyperphagia after weight loss may be essential to help

humans coping with weight reduction diets, given that clinical trials have suggested that

the absence of leptin signalling after weight loss is responsible for the lack of satiation

(Kissileff et al. 2012).

Implications and future directions

To experimentally test the predation release hypothesis, the predator‐prey complex

dynamic was reduced to a simple one prey species‐one predator species interaction

system. However, multi‐predator environments have also received attention (Lima 1992;

Sih, Englund & Wooster 1998). Some of the multi‐predator models suggest that when

more than one predator is involved, the optimal strategy for survival, may actually be

investing in foraging rather than evade predation (Sih et al. 1998). Few animals in the wild

exist as part of single predator prey relationships and expanding the predation release

hypothesis in the light of a multi predator model may represent a future challenge.

The existence of a lower limit intervention point explains why generally losing weight is

not an easy process, and commonly the resistance to lose weight is higher than to gain


132

weight (Levin & Keesey 1998; Levin & Dunn‐Meynell 2002). Current data shows that mice

are able to lose weight either due to exposure to predation or to starvation, and to gain

weight due to diet alteration. These observations suggest that when having free access to

food, and a non‐enriched diet, their body weight is placed comfortably above the lower

limit intervention point.

Assuming that the upper and lower limits of body weight are genetically encoded by

multiple genes, the genetic basis of such regulation system is crucial for understanding

the molecules involved, as well as for the development of animal models for the study of

obesity.

Currently, several animal models are available for the study of obesity and obesity‐

associated human diseases (reviews by (Kanasaki & Koya 2011; Lutz & Woods 2012).

Rodents (mice and rats) are among the most popular models, however given the close

phylogenetic relationship to humans, Old World monkeys, such as macaques, rhesus

monkey, and baboons are also relevant models for the study of obesity (Comuzzie et al.

2003; Wagner et al. 2006).

The use of two animal models, the wood mouse Apodemus sylvaticus and the C57BL/6

strain of laboratory mice, provided a broader view when testing the initial hypothesis, and

helped identify that a very dynamic system can involve multiples strategies of response.

In humans, the framework supporting the predation release hypothesis, and the

expansion of the obese phenotypes is no longer viable at the light of the present

environmental and socio‐economic context. One of the assumptions of the predation

release hypothesis in that the increase of body weight is limited by an upper boundary,


133

such limit is in first hand dependent on the risk of predation. Taking this principle to the

edge, the predation risk will restrain the growth of an obesity epidemic. However, if the

population has been released from predation pressure, the body weight will theoretically

grow until the drifted upper point is attained. Even though, the results presented supports

the down regulation of body weight due to predation effect, the information provided

regarding the extent of a possible epidemic is very limited. The predation pressure has

been eliminated from human societies many years ago, however given that recent data

shows some apparent stability on the obesity prevalence (Ogden et al. 2014), the role of

predation may have been replaced by another environmental pressure likely to affect

modern humans.

The World Health Organization has recognised obesity as disease in 1948, and later, as an

epidemic (James 2008). Around the world, many efforts have been devoted to the

prevention of obesity, such as encouraging physical activity (Pratt et al. 2015), and healthy

diets (James, Thomas & Kerr 2007), however the success of these programmes is low, and

medical treatment is often pursued. Obesity can rarely be cured, and the current drugs

available either affect food intake and fat absorption, or affect thermogenesis increasing

energy expenditure (Bray & Tartaglia 2000). Thus one of the most important implications

of uncover the mechanisms underpinning energy homeostasis and body weight

modulation is the development of ways to successfully control it. In other words, once the

mechanisms involved in the body weight regulation are fully established, new

pharmaceuticals and therapies can be produced for the treatment and control of obesity.

Therefore, uncovering the genetic and molecular basis of the body weight regulation is a

fundamental step for the development new therapeutics, the genes related with obesity


134

control multiples aspects, such appetite, metabolism and energy homeostasis (Yang, Kelly

& He 2007). The way these phenotypes are expressed under different environmental

conditions, is an interesting approach to disclose the basis the regulation model.

Recent developments on the study of metabolomics (Rauschert et al. 2014) may also

provide good instruments to understand the genetic‐environment interaction of body

weight regulation, offering new analysis tools, which by the discovery of suitable

biomarkers can help identifying precursors of obesity phenotype, being an important

resource for the prevention of associated disorders.

Along with the molecular basis of the system, the way the molecular signals are received

and interpreted by the central nervous system also require further investigation. This will

provide the necessary framework, given that ultimately the brain is the main coordinator

of energy homeostasis, integrating peripheral signals and responding accordingly to fix

the energetic imbalance.

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