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DOES BEHAVIOURAL PLASTICITY CONTRIBUTE TO DIFFERENCES IN POPULATION GENETIC

STRUCTURE IN WILD RABBIT POPULATIONS IN ARID AND SEMI-ARID AUSTRALIA?

Mr Geoffrey Anthony de Zylva – B. App. Sc., Hons. School of Natural Resource Sciences Queensland University of Technology

Submitted for the degree of Doctor of Philosophy (Science), in 2007.

Keywords

Oryctolagus cuniculus

European Rabbit

Australia

DNA

mtDNA

microsatellite

behaviour

flexible behaviour

genetic variability

metapopulations

genetic bottleneck

Abstract

The European rabbit, Oryctolagus cuniculus, was introduced to Australia in 1859

and quickly became a significant vertebrate pest species in the country across a wide

distribution. In arid and semi-arid environments, rabbit populations exist as

metapopulations – undergoing frequent extinction recolonisation cycles. Previous

studies identified population genetic structuring at the regional level between arid

and semi-arid environments, and habitat heterogeneity was suggested as a possible

causal factor. For the most part, rabbit behaviour has been overlooked as a factor

that could contribute to explaining population genetic structure in arid and semi-arid

environments.

This study utilised a combination of genetic sampling techniques and a simulated

territorial intrusion approach to observing wild rabbit behaviour in arid and semi-

arid environments. The genetic component of the study compared population

samples from each region using four polymorphic microsatellite loci. The

behavioural component examined variation in the level of territoriality exhibited by

three study populations in the arid region towards rabbits of known versus unknown

origins (resident vs transgressor (simulating dispersal)).

A difference was observed in population genetic structure determined from nuclear

markers between arid and semi-arid regions, which supports findings of previous

research using mitochondrial DNA data in the same area. Additionally, differences

in aggressive response to known vs unknown rabbits were identified in parts of the

arid region, which together with the effects of habitat heterogeneity and connectivity

may explain the observed differences in population genetic structure.

Knowledge of behavioural plasticity and its effect on relative dispersal success and

population genetic structure may contribute to improved management and control of

feral rabbit populations at the regional level within Australia; and may assist with

conservation efforts in the species’ natural range in Europe.

Table of Contents CH1 - INTRODUCTION .......................................................................................................................... 1

DISPERSAL, HABITAT VARIABILITY, AND GENE FLOW ............................................................ 1 MODELLING GENE FLOW ......................................................................................................... 3 METAPOPULATIONS.................................................................................................................. 5 BEHAVIOURAL DIVERSITY AND GENETIC DETERMINATION..................................................... 10 GROUP LIVING, COOPERATION, AND SOCIALITY ..................................................................... 10 RESOURCE DEFENCE ............................................................................................................... 13 BEHAVIOURAL FLEXIBILITY..................................................................................................... 15 THE EUROPEAN RABBIT ........................................................................................................... 16

CH2 – EXPERIMENTAL DESIGN AND METHODOLOGY ......................................................................... 24 DESCRIPTION OF STUDY SITES ................................................................................................. 25 POPULATION SAMPLING ........................................................................................................... 27 GENETIC METHODS ................................................................................................................. 28 BEHAVIOURAL METHODS ........................................................................................................ 29

CH3 – GENETIC ANALYSIS .................................................................................................................. 32 MATERIALS AND METHODS ..................................................................................................... 33 DNA EXTRACTION................................................................................................................... 34 POLYMERASE CHAIN REACTION (PCR).................................................................................... 35 RESULTS................................................................................................................................... 41 DISCUSSION.............................................................................................................................. 50

CH4 – RABBIT BEHAVIOUR.................................................................................................................. 57 MATERIALS AND METHODS ..................................................................................................... 57 ANALYSIS METHODS – HABITAT CONDITIONS ......................................................................... 60 ANALYSIS METHODS – BEHAVIOUR ........................................................................................ 61 RESULTS................................................................................................................................... 64 DISCUSSION………………………………………………………………………………… 103

CH5 - GENERAL DISCUSSION.............................................................................................................111 POPULATION GENETICS..........................................................................................................111 BEHAVIOURAL ECOLOGY .......................................................................................................114 PEST MANAGEMENT ISSUES ...................................................................................................119 FUTURE DIRECTIONS OF RESEARCH AND CONCLUSION..........................................................123

APPENDIX 1 – LIST OF ALL RABBIT BEHAVIOURS ...............................................................................125 BIBLIOGRAPHY ..................................................................................................................................127

List of Tables and Figures FIGURE 1.1 - TYPES OF METAPOPULATION ............................................................................................ 6 FIGURE 2.1 – AREAS OF STUDY ............................................................................................................ 27 TABLE 3.1 – MICROSATELLITE PRIMERS ............................................................................................... 35 TABLE 3.2 – PCR AND ELECTROPHORESIS CONDITIONS (TA = ANNEALING TEMPERATURE)................... 38 TABLE 3.3 – POPULATION SAMPLE SIZES AT EACH LOCUS ..................................................................... 39 TABLE 3.4 – NUMBER OF ALLELES PER LOCUS PER POPULATION ........................................................... 41 TABLE 3.5 – MEAN ALLELIC STATISTICS ACROSS ALL LOCI FOR EACH POPULATION.............................. 42 TABLE 3.6 – SIGNIFICANT GENIC DIFFERENTIATION FOR POPULATION PAIRS ACROSS ALL LOCI ............ 43 TABLE 3.7 – MATRIX OF SIGNIFICANT GENIC DIFFERENTIATION BETWEEN POPULATION PAIRS ............. 44 TABLE 3.8 – PAIRWISE POPULATION FST VALUES.................................................................................. 45 TABLE 3.9 – SIGNIFICANCE OF PAIRWISE POPULATION FST VALUES...................................................... 45 FIGURE 3.1 – SORTED MEAN FIS............................................................................................................ 46 TABLE 3.10 – AMOVA SUMMARY TABLE ............................................................................................ 47 FIGURE 3.2 – AMOVA SUMMARY PIE CHART...................................................................................... 47 FIGURE 3.3 – RANDOMISATION OF PHIPT ............................................................................................. 48 FIGURE 3.4 - UPGMA TREE FOR NEI SIMILARITY MATRIX .................................................................... 49 TABLE 3.11 – SPECIES WITH REDUCED GENETIC DIVERSITY .................................................................. 51 TABLE 4.1 – SITE LOCATIONS................................................................................................................ 58 FIGURE 4.1 – VISION FIELD OF VIDEO CAMERA ..................................................................................... 60 TABLE 4.2 – BEHAVIOUR OBSERVED ON VIDEO..................................................................................... 62 TABLE 4.3 - WARREN COUNT DATA ..................................................................................................... 64 FIGURE 4.2 – 2001 RABBIT WEIGHT V SEX (TOTAL CAPTURES) ............................................................ 65 FIGURE 4.3 – 2002 RABBIT WEIGHT V SEX (TOTAL CAPTURES) ............................................................ 66 FIGURE 4.4 – MEAN DECOY WEIGHT .................................................................................................... 67 TABLE 4.4 – MEAN PERCENTAGE COVER............................................................................................... 67 FIGURE 4.5 – MEAN PERCENT COVER COMPARISON BETWEEN YEARS SITE 1 ........................................ 68 FIGURE 4.6 – MEAN PERCENT COVER COMPARISON BETWEEN YEARS SITE 2 ........................................ 69 FIGURE 4.7 – MEAN PERCENT COVER COMPARISON BETWEEN YEARS SITE 3 ........................................ 70 FIGURE 4.8 – SCATTERPLOT OF TOTAL BEHAVIOUR VS NUMBER OF RABBITS ...................................... 72 FIGURE 4.9 – MEAN PLOT OF SUM BEHAVIOUR PER RABBIT PER HOUR................................................ 73 FIGURE 4.10 – AGGRESSIVE BEHAVIOUR SITE 1 CONTROL 2001........................................................... 75 FIGURE 4.11 – AGGRESSIVE BEHAVIOUR SITE 1 EXPERIMENTAL 2001.................................................. 76 FIGURE 4.12 – AGGRESSIVE BEHAVIOUR SITE 2 CONTROL 2001........................................................... 77 FIGURE 4.13 – AGGRESSIVE BEHAVIOUR SITE 2 EXPERIMENTAL 2001.................................................. 78 FIGURE 4.14 – AGGRESSIVE BEHAVIOUR SITE 3 CONTROL 2001........................................................... 79 FIGURE 4.15 – AGGRESSIVE BEHAVIOUR SITE 3 EXPERIMENTAL 2001.................................................. 80 FIGURE 4.16 – AGGRESSIVE BEHAVIOUR SITE 1 CONTROL 2002........................................................... 81 FIGURE 4.17 – AGGRESSIVE BEHAVIOUR SITE 1 EXPERIMENTAL 2002.................................................. 82 FIGURE 4.18 – AGGRESSIVE BEHAVIOUR SITE 2 EXPERIMENTAL 2002.................................................. 83 FIGURE 4.19 – AGGRESSIVE BEHAVIOUR SITE 1 CONTROL 2002........................................................... 84 FIGURE 4.20 – AGGRESSIVE BEHAVIOUR SITE 2 EXPERIMENTAL 2002.................................................. 85 FIGURE 4.21 – 10MIN INTERVAL PLOT SITE 1 2001 ............................................................................... 87 FIGURE 4.22 – 10MIN INTERVAL PLOT SITE 2 2001 ............................................................................... 88 FIGURE 4.23 – 10MIN INTERVAL PLOT SITE 3 2001 ............................................................................... 89 FIGURE 4.24 – 10MIN INTERVAL PLOT SITE 1 2002 ............................................................................... 90 FIGURE 4.25 – 10MIN INTERVAL PLOT SITE 2 2002 ............................................................................... 91 FIGURE 4.26 – 10MIN INTERVAL PLOT SITE 3 2001 ............................................................................... 92 TABLE 4.5 – T TEST SITE 1 CONTROL V EXPERIMENTAL 2001 .............................................................. 93 TABLE 4.6 – T TEST SITE 2 CONTROL V EXPERIMENTAL 2001 .............................................................. 93 TABLE 4.7 – T TEST SITE 3 CONTROL V EXPERIMENTAL 2001 .............................................................. 94 TABLE 4.8 – T TEST SITE 1 CONTROL V EXPERIMENTAL 2002 .............................................................. 94 TABLE 4.9 – T TEST SITE 3 CONTROL V EXPERIMENTAL 2002 .............................................................. 94 TABLE 4.10 – ANOVA ACROSS ALL SITES CONTROL DATA 2001 ......................................................... 95 TABLE 4.11 – ANOVA ACROSS ALL SITES EXPERIMENTAL DATA 2001 ................................................ 96 TABLE 4.12 – T TEST SITE 1 V SITE 3 CONTROL DATA 2002.................................................................. 96 TABLE 4.13 – T TEST SITE 1 V SITE 3 EXPERIMENTAL DATA 2002 ........................................................ 97 TABLE 4.14 – T TEST YEAR COMPARISON AT SITE 1 ............................................................................ 98 TABLE 4.15 – T TEST YEAR COMPARISON AT SITE 3 ............................................................................ 98 FIGURE 4.27 – GENERAL LINEAR MODELLING...................................................................................... 99 TABLE 4.16 – PERCENTAGE OF TOTAL BEHAVIOUR OCCURING IN FIRST 15MINS.................................101 TABLE 4.17 – PROPORTION OF AGGRESSIVE BEHAVIOUR IN FIRST 15 MINS ..........................................101 FIGURE 5.1 – BREAKDOWN OF SOCIAL SYSTEMS DUE TO VARIABLE RESOURCES..................................116 FIGURE 5.2 – RABBIT CALCI VIRUS RELEASE, MITCHELL, 1996............................................................122

STATEMENT OF ORIGINAL AUTHORSHIP

The work contained in this thesis has not been previously submitted for a degree or diploma at any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made. Signature:_______________ Date:___________________

ACKNOWLEDGEMENTS This thesis would not have been possible without the support of Peter Mather and John Wilson. Thankyou for your advice, encouragement, and inspirational enthusiasm for ecology. I also wish to recognise the financial support from The School of Natural Resource Sciences QUT, and the Federal Government. Many thanks also to Dave Berman and his team of “Bulloo Warriors” from the Queensland Department of Natural Resources, particularly Michael Brennan, Craig Hunter, Peter Elsworth, and John Conroy – I would be buried in the desert if it weren’t for you blokes. Thankyou to Stanbroke Pastoral Company for access to Bulloo Downs, and thanks to Geoff and Wendy Murrell and all the staff of Bulloo Downs for your hospitality during my field trips. To the various landholders in the Mitchell Region, thankyou for access to your properties during my various pilot trips – I hope the rabbits stay away for many years to come. I owe a huge debt of thanks to those who volunteered their time to drive to the middle of Australia and chase rabbits: Ben de Zylva (who enjoyed the first trip so much he came back for more), Alison Crawford, and Alex Wilson. Our trips to Bulloo Downs would not have been possible without the logistical support team: Jo Chambers, Peter Prentis, and Stephen Craig-Smith – thanks for driving to Cunnamulla. This project would not have been possible without the cast of thousands from QUT, from the admin support to the radiation lab. Nat and Juanita (thanks for the help in the lab), Craig and Danny (thanks for sharing an office with me), and everyone at the Campus Club (thanks for the 12 hour lunches). Special thanks to Grant Hamilton and his efforts to “Show me the bunnies!” Let this thesis serve as an example of “what not to do” – the external factors such as drought and disease necessitated much variation to the original experimental design, to the point that resulted in a fairly limp dataset, containing far too many assumptions. If future students read this, please make sure you have the ability to collect enough data to rigorously test your theory. Check your field sites early, and if it looks like you can’t get the data – find another way to test your theory – or even change your topic altogether. Finally, thankyou to Rebecca, without your help I wouldn’t have made it this far.

Chapter 1 Introduction

Introduction

In many animal species, social behaviour can influence many aspects of life history

characteristics. The interaction, however, is bi-directional in the sense that social

behaviour can be influenced by a species’ characteristics in addition to external

environmental factors. The idea that behaviour patterns are inflexible within species

has been challenged by new research into social systems and genetics. This review

aims to explore the idea that a species' social structure can be influenced by different

environmental factors that it experiences.

Dispersal, Habitat Variability, And Gene Flow

Organisms that live in groups must ultimately decide whether to stay in the natal

territory or to disperse into new areas. Many factors influence the potential for

individuals to disperse successfully, not the least of which, is social organisation. If

an individual is of low rank in a social hierarchy, then dispersal to a new territory

may be a good option if the cost of dispersing is offset by the benefits gained by

reaching a new territory. Dispersal is only effective however, if an individual is able

to survive and reproduce in the new habitat. Three main types of dispersal have been

described (Krebs 1994). Diffusion is the gradual movement of a population across

hospitable terrain that occurs over several generations. Jump dispersal occurs when

individuals move large distances in a single event, usually across areas of

unfavourable habitat. Species introduced to non native areas through human

intervention can be viewed as an assisted form of jump dispersal. Secular dispersal is

a diffusion event that occurs over geological time and usually involves an

evolutionary change in the species across a specified time period; it can also be

associated with continental drift.

Habitat quality has the potential to affect both the social organisation of a species, its

dispersal dynamics, and the interrelationships between the two. A habitat that is

temporally and spatially stable is likely to be used in a different manner to one that is

dynamic. In a study of the red squirrel, Sciurus vulgaris, Lurzs et al. (1997)

examined the effect of habitat variability (temporal and spatial) on dispersal. They

studied squirrel dispersal patterns in a stable habitat with a reliable food supply, and

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a variable habitat with temporal and spatial differences in food availability. In both

habitats, they observed male-biased dispersal in spring and female-biased dispersal in

autumn. More adults dispersed however, in the variable (66%) than in the stable

(31%) habitat. Large differences were also evident in the extent of site fidelity

between the two squirrel populations. Food availability was the main factor that

affected female dispersal. In contrast, male dispersal was influenced by the

distribution of females with male site fidelity high in the stable habitat, whereas

males tracked the movement of females in the variable habitat. This most likely

occurs because the stable habitat has sufficient resources to satisfy female needs.

Lurz's et al. (1997) data on squirrels suggest that female dispersal patterns are an

adaptive response to the spatial and temporal predictability of food resources.

Dispersal of individuals into potentially new habitat or territory that leads to effective

reproduction can result in gene flow. Different dispersal strategies can therefore lead

to different population genetic structures that are consequences of different

behaviour patterns. Dispersal or migration alone does not constitute gene flow - there

must also be an exchange or transfer of genetic material i.e. reproduction. Gene flow

(or a lack thereof) can lead to population structuring, which is defined as differences

in genetic variation among constituent parts of a species’ natural range, provided the

effect is not counteracted by other evolutionary processes such as a mutation, natural

selection or genetic drift. Gene flow is a major factor which influences population

structure because it determines the extent to which each local population of a species

acts as an independent evolutionary unit. If a large amount of gene flow occurs

among local populations, then the collection of populations evolve together; but if

there is little gene flow each population will tend to evolve independently (Slatkin

1994). A number of theoretical models have been developed which describe gene

flow and its potential effects on population genetic structure.

Modelling Gene Flow

The simplest models are based on the island model of migration which was first

proposed by Wright (1931). In this model, a species' distribution consists of discrete

populations that are geographically separated and are assumed to be large enough

such that genetic drift can be ignored as a population structuring process. Migration

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is assumed to occur between population islands as a process in which the allele

frequency of the migrants is equivalent to that of the total population and therefore

the amount of migration is measured as the probability that a randomly chosen allele

in any sub-population comes from a migrant (Hartl and Clark 1997).

Two alternative models were developed that address population structure in

continuous rather than discrete population systems: 'Isolation by distance' models,

and 'Stepping stone' models. Sewall Wright was also responsible for the early work

on isolation by distance. The theory is based on the premise that if, in the continuous

distribution of a population, migration of individuals and subsequent interbreeding is

restricted to short distances due to short range dispersal; then remote populations

may be differentiated because of the distance among them (Wright 1943).

The concept of a species' range being large enough such that colonies develop and

exchange genetic information through migration was developed by Kimura and

Weiss (1964). They proposed three types of stepping stone model with increasing

degrees of complexity referred to as 1, 2, and 3 dimensional models.

A one dimensional model is where the colonies are located in a linear fashion.

Migration can only occur between adjacent colonies, that is, for each generation an

individual can migrate 'one step' in either direction. For the other two models, the

array of colonies will increase. The two dimensional model assumes a rectangular

arrangement of colonies, therefore an individual can migrate in four directions. The

third dimensional model introduces a cubic system in which migration can occur in

six directions. It is important to note that the 3rd dimension does not necessarily have

to be of a spatial or habitat capacity, it may simply refer to an attribute of the species

that enables greater variety in life style. Social rank is an example of one factor that

may provide a third dimension to population structure.

The development of methods for estimating gene flow occurred as a corollary to the

theoretical work that developed the models. Direct estimation methods are based on

experiments or field observation which gather measurements of dispersal distances.

The distance estimates can be converted into estimated gene flow based on the

assumption that migrant individuals have the same probability of reproductive

3


success as do residents. Indirect methods, however, are based on mathematical

models which explain interactions of gene flow and other forces to predict how much

gene flow must have been occurring to explain the observed patterns (Slatkin 1994).

Wrights FST statistic is the best known of these methods; it is a measure of the

correlation between genes in a sub-population relative to the entire population

(Wright 1951). The model states that in an island model at equilibrium,

FST = 1/1 + 4Nm where N = the effective population size, and m = immigration rate.

There is no way to gain a separate estimate for the terms N and m, however, by

solving for Nm the formula is transformed to Nm = 1/4 (1/ FST - 1). FST can be

calculated easily from allele frequency data. By solving the equation, one gains an

estimate of gene flow for the population under study.

Distinct advantages and disadvantages are associated with the direct and indirect

methods of gene flow estimation, which are discussed by Slatkin (1994). Direct

estimates can reveal certain aspects of the dispersal mode such as the life stage most

common for dispersal, and the environmental conditions most conducive to dispersal.

The disadvantage of the direct methods is that they are limited by the size of the

project, and it can be difficult to gather information regarding any long distance

dispersal or dispersal under abnormal environmental conditions. Indirect methods are

able to incorporate any effects of variation in dispersal and average out the

differences over time. The major disadvantage however, is that the methods rely on

assumptions regarding allele frequencies, and these assumptions cannot always be

tested independently.

The use of indirect methods to measure dispersal, and in particular, to estimate gene

flow using Nm has been accepted practise for many years. More recently however,

conjecture has grown regarding the validity of the formula. Whitlock and McCauley

(1999) argue that in many cases FST does not equate to the formula 1/(4Nm + 1),

because the formula is based on several assumptions that are violated in most natural

systems. The five critical assumptions are:

1) The alleles at the loci are selectively neutral and are not linked to selected loci.

2) The rate of mutation is not high relative to the rate of migration.

3) All populations are created equal, with a constant number of individuals and

equal contributions to the migrant pool.

4


4) Migration is random (no spatial structure).

5) The system is in equilibrium between migration and genetic drift.

Measurement of genetic variation from genetic data is a valid use of FST, however, it

is clear that estimates of dispersal and gene flow based on F statistics should be

viewed with care. Estimates may be correct within a few orders of magnitude, and

should be performed only in situations when the biological question depends on

estimating migration rates among populations where 'errors' associated with the

estimate can be relatively large (Whitlock and McCauley 1999).

Metapopulations

Following on from the ideas on dispersal developed with Island and Stepping-Stone

models, came the concept of metapopulations. The term itself is used to define a set

of local populations that interact via individuals moving among populations (i.e.

dispersing). The characteristic feature of a metapopulation is that local populations

are dynamic and will undergo phases of extinction, and subsequent recolonisation

from other populations within the system; this is also referred to as turnover. Several

kinds of metapopulation were characterised and summarised in Harrison (1991)

(figure 1.1).

5


Closed circles represent habitat patches; filled = occupied, unfilled = vacant. Dashed lines show the boundaries of populations. Arrows indicate migration (colonisation). A. Levins-type metapopulation. B. Mainland-island/source-sink metapopulation. C. ‘Patchy population’. D. Non-equilibrium metapopulation. E. An intermediate combination of B and C.

Figure 1.1 Types of Metapopulations (reproduced from Harrison, 1991)

The Levin's (1969) model of metapopulations (figure 1.1A) was the first step in

developing theories behind newer models. It is based on the scenario where a set of

conspecific populations exists in a balance, at the regional level, between extinction

and colonisation. This model most closely resembles the island and stepping stone

models of migration. Mainland-island and Source-sink metapopulations (figure 1.1B)

6

halla

This figure is not available online. Please consult the hardcopy thesis available from the QUT Library


occur when there is one large central patch that is resistant to extinction, with

peripheral patches that undergo periods of extinction and subsequent recolonisation

by migrants from the main patch. There is a distinct difference however, between

these types of metapopulations with respect to the outlying patches. Island habitats

are simply smaller versions of mainland habitats, whereas sinks are qualitatively

different from sources, being unsuitable in some way for survival and reproduction

(Harrison 1991). This type of metapopulation has also been referred to as a 'Core-

satellite system' (Hanski and Gilpin, 1991).

The patchy population (figure 1.1C) describes systems where habitat patches exhibit

spatial and temporal variation, however, there are also large amounts of dispersal

among patches, which effectively makes the group of populations a single interacting

unit. There is little opportunity for extinction to occur in a system like this because of

the high rates of dispersal. A non-equilibrium population (figure 1.1D) is

diagrammatically similar to the basic Levin's model (figure 1.1A) except that the

recolonisation process does not occur. If there is a lack of migration (recolonisation),

then when a patch becomes extinct, it will remain so. It represents a population

system of species in regional decline.

The main factors affecting localised extinction rates are usually stochastic in nature,

and include demographic, genetic, environmental, and catastrophic processes/events

(Shaffer 1981; Harrison 1991). Random changes in birth and death rates represent

demographic factors. These are most likely to have the greatest effect on small

populations or those in regional decline that are below a population size threshold

(Ebenhard 1991). Obviously, threshold levels will vary among species. Genetic

stochasticity concerns the loss of heterozygosity through drift effects and inbreeding

– the net result being a reduction in fitness, and increased probability of extinction. A

genetic effect, like a demographic effect, is more likely to occur in small populations,

however, it will definitely be more pronounced in a population that is newly small,

and is not conditioned to undergoing periods of population flux.

Environmental stochasticity and catastrophes are probably the most important causes

of local extinction because they can affect populations of varying sizes (Harrison

1991). Variations in environmental characters such as food availability and weather

7


conditions may affect the entire range of patches in a region, yet not all populations

are likely to go extinct. This observation led to the idea that certain patches are

effectively refuges that enable survival through adverse environmental conditions;

either by providing basal nutritional requirements or by providing better quality

shelter sites that, in some species, will facilitate a period of torpor until conditions

are more conducive to reproduction and dispersal (Harrison, 1991). In some

instances, larger patches will be better suited for use as refugia simply due to size

and ability to ‘absorb’ adverse conditions better than smaller patches – this would be

commonly observed in mainland-island metapopulations. Catastrophic events such as

flood, drought, and fire usually cause widespread extinctions in metapopulations.

While survival may be higher in larger patches, this will depend to a large extent on

the species in question and the nature of the catastrophe.

A metapopulation can persist only when colonisation follows extinction events.

Colonisation can be defined as starting with the arrival of a propagule (the migrants)

and ending when the extinction probability of the population no longer depends on

the initial state of the propagule (Ebenhard 1991). While the process could be viewed

simply as dispersal from an occupied patch, the migrant individuals must move

through inhospitable habitat in order to colonise the extinct patch. This process will

present its own set of problems. The success of the propagule will depend on the

probability of finding a suitable patch, and effectively reproducing once there.

Differences in dispersal rates among sex and age classes are most common in

polygamous species and in long-lived species with many litters per female (Hansson

1991). Other important observations on dispersal made by Hansson (1991) are that

dispersal distances appear to be longer in poor environments and habitat specialists

are more affected by boundaries than habitat generalists. Thus the ability of a species

to survive the dispersal phase through harsh environments will enhance its ability to

function as a metapopulation. Individuals in the colonised patch will have a higher

probability of extinction in the new habitat, than if they dispersed within the natal

patch. Ebenhard (1991) presents data which suggests the best colonisers will be large

propagules with potential for rapid increase in variable habitats or with a low

mortality in stable habitats. Dispersing propagules may also reach patches with

extant populations, and while this is not considered a colonisation event in the

strictest terms, it does have some important ramifications for metapopulation

8


dynamics. The migrant individuals may offer the opportunity for gene flow to occur,

possibly reducing the chance of inbreeding and any associated deleterious effects

(Gilpin, 1987). Migrants arriving successfully in an occupied patch may also be of

benefit to the local population if it is in decline, for whatever reason, by boosting the

species abundance in the patch – an occurrence referred to as the 'rescue effect'

(Hanski, 1991). An alternative scenario, however is that the migrant individuals may

not integrate with the local population at all, and instead develop their own

independent breeding group which would be detrimental to the original declining

population.

Severe fluctuations in population size (where periods of small population sizes

occur) can reduce allelic diversity and heterozygosity levels in a population. It is an

effect commonly referred to as a genetic bottleneck (Hedrick 1999). Elephant seals

and African cheetahs are two examples of species which have low levels of genetic

diversity that can be explained by historical bottlenecks (Bonnell and Selander 1974;

O'Brien et al. 1987). Random genetic drift caused by migration of a few individuals

to a new patch from an established subpopulation can create a bottleneck known as a

founder effect (Hartl 1997). The classic examples of founder effects occur in

instances where species have been introduced (translocated) through human activities

into completely new habitats (eg. Bufo marinus, the cane toad, and Oryctolagus

cuniculus, the European rabbit in Australia).

9


Behavioural Diversity And Genetic Determination

Individual differences in behaviour can influence differences in dispersal strategy.

Dispersal, however, represents only one type of behaviour and there are a great

diversity of potential behaviours, many of which have a genetic component.

Evidence for the genetic determination of dispersal/movement behaviour is

widespread, one example occurs in fruit fly larvae. The larvae occur either as

‘rovers’, which move a long way to find food, or ‘sitters’, which forage in a more

restricted area; the polymorphism is determined by alleles at a cyclic GMP-

dependent protein kinase gene (Partridge and Sgro 1998). It is clear that selection is

able to act upon genes controlling behaviour; in the case of the fruit fly larvae, where

‘rovers’ may have an advantage in crowded populations, ‘sitters’ may have an

advantage in low density populations. Studies on the genetics of behaviour led

Alcock (1984) to the following generalisations:

1. Single allelic differences can influence behavioural differences among

individuals.

2. Artificial selection for certain behavioural traits can be highly effective in

altering the behaviour of a population over time.

3. Physiological effects that are determined by genetic differences among

individuals are responsible for their distinctive behavioural characteristics.

4. Differences in the genetic and physiological characteristics of populations of the

same species may be related to variation in ecological pressures operating in

different areas.

The fact that selection can act on genes which determine behaviour, therefore means

that behaviour can evolve through this process, like any other trait which is under

natural selection.

Group Living, Cooperation, And Sociality

While evolution of social behaviour has been studied extensively, much of the

earliest work focused on how evolution of behavioural strategies were of benefit to

the group. Tinbergen (1964) suggested that groups of 'capable' individuals survive,

while those containing inferior individuals do not, and therefore cannot reproduce

effectively. He was essentially arguing for group selection influencing the fitness of

10


individuals. This theory was opposed by Williams (1966) who suggested that clutch

size and many social interactions enhance individual fitness. Williams argued social

behaviours evolved for the benefit of the individual, not the group. Altruistic

behaviours however, which involve the act of sacrificing ones personal fitness for

the benefit of others, does not appear to fit his argument easily. Hamilton (1964a,b),

in discussing the evolution of altruism, raised the issue of inclusive fitness when he

suggested that individuals can pass copies of their genes to future generations by

assisting the reproduction of close relatives (indirect fitness) as well as via their own

reproductive efforts (direct fitness). Hamilton described a model that allowed for

interactions between relatives which affect fitness. Species which act 'altruistically'

may evolve behaviours so that individuals maximise their inclusive fitness and this

implies a limited restraint on selfish competitive and self-sacrificing behaviours

(Hamilton 1964a,b). Hamilton’s theory can more easily explain the evolution of

altruistic behavioural patterns such as cooperative breeding and coloniality.

The bell minor, Manorina melanophrys, is an example of a species that breeds

cooperatively. Individuals have never been observed breeding unassisted and

individual helpers, even breeders, often give aid to a number of breeding pairs within

a breeding season (Painter et al. 2000). The species has a multi-tiered behavioural

and social organisation, which was observed in studies by Clarke (1984, 1989) and

Clarke & Fitzgerald (1994). There are three levels of social organisation: colony,

coterie, and the nest contingent. The colony is a geographically discrete collection of

up to 200 individuals that communally defend an area against both inter- and

intraspecific avian competitors. The coterie is a group within the colony that contains

one or more breeding pairs. While helpers may aid more than one pair within a

coterie, they do not interact with members from other coteries except in territorial

defense. The third level of social organisation is the nest contingent, which consists

of individuals that assist at the nest as well as the breeding pair(s). Painter et al.

(2000) found (using microsatellite analysis) significant differences between coteries

in a high density colony, which resulted from related individuals associating

preferentially with each other. They also showed that individuals helping at the nest

were close relatives of the breeders, thus supporting models of kin selection for the

evolution of altruism in this bird. The classic examples of kin selection, however,

occur in eusocial insects including bees, wasps, and ants. In eusocial societies, a

11


queen produces all the offspring, and an army of sterile workers that share most of

their genes in common with their siblings. The evolution of eusocial systems is

complicated however, by ploidy differences between the two sexes. Females are

diploid and males haploid, a situation which changes the argument about altruism

when genetic relationships between offspring and parents are considered.

The main reason for the evolution of social behaviour is that natural selection has

influenced the frequencies of genes that give rise to such displays. Some species will

have certain evolutionary adaptations that favour the adoption of sociality while

others will not. Ultimately, it is natural selection or genetic drift acting on random

mutations that cause social behaviour to evolve in species, and consequently there

are several advantages and disadvantages. Through the selective process each

condition will affect individual fitness in a different manner for each species.

Costs and benefits of social behaviour (from Alcock, 1984)

Benefits:

Reduction in predator pressure by better detection and/or repulsion.

Improved foraging efficiency for large game or spatially and temporally clumped

resources.

Better defence of limited resources (space and food) against other groups of

conspecifics.

Enhanced care of offspring through communal feeding and protection.

Costs:

• Competition within the group for food, mates, nest sites and materials.

• Risk of infection by contagious diseases and parasites.

• Exploitation of parental care by conspecifics.

• Increased risk that conspecifics will kill progeny.

For species that have evolved solitary lifestyles, the costs may be greater than any

benefits gained from social living. Conversely, for species that live in social

communities the costs may be equalised or bettered by the benefits of the behaviour.

A good example occurs in two closely related species of freshwater fish, Lepomis

12


macrochris and Lepomis gibbosus (bluegill and pumpkinseed sunfish), studied by

(Gross and MacMillan 1981). The bluegill sunfish exhibits social behaviour during

the breeding season when males construct nests close together to form a colony.

Formation of the colony results in reduced pressure from the primary predators of

their eggs, which are catfish and snails, because males cooperate in colony defense.

The advantage of sociality to the bluegill is reduced by factors such as conspecific

interference and predation of eggs, as well as disease (fungi) that can spread through

dense colonies. The pumpkinseed sunfish however, lives a solitary life due in part to

the evolution of large, strong mouthparts designed for crushing snails and deterring

other potential predators. Colonial nesting is not advantageous to the pumpkinseed

sunfish because predation is not as great a problem as it is for the bluegill sunfish.

Resource Defence

The use of caches (food storing) usually occurs in species that exhibit territorial

behaviour and the act of creating the cache is a potentially costly exercise. Roberts

(1979) argued that adaptations are likely to arise that will reduce costs and/or

increase benefits - and that territoriality is one such adaptation in this sense because

it reduces the amount of competitors that are able to gain access to stored resources.

When food is clumped spatially, aggressiveness can be expected to increase because

the cost of defending an area is small compared to the benefit of access to a large

share of the resource (Grant 1993). If food is clumped temporally, aggression levels

may be expected to fall because any time spent defending is simultaneously time

away from resource utilisation (Trivers 1972; Wells 1977; Robb and Grant 1998).

The mountain lion (Puma concolor), is one organism in which intraspecific

aggression is known to occur (Pierce et al. 1998). In this instance the food resource is

not clumped temporally, but instead the social class of females with kittens utilise the

resource at an earlier time than other social classes. Adult females usually have

overlapping home ranges that also overlap within the range of one or more males.

Females tend to reduce confrontations through a system of mutual avoidance,

however, it is not uncommon for males to kill other males, females, juveniles and

kittens (Seindensticker et al. 1973). As mountain lions are known to cache food and

have overlapping home ranges, Pierce et. al. (1998) suggest that females with kittens

that visit the cached prey at different times to the other social classes, could obtain

13


fitness benefits by further minimising the probability of contact with other mountain

lions. In this example, one could argue that the resource is clumped spatially, in the

cache location, however, aggressiveness does not increase (with respect to the

suckling females) due to the differential timing of feeding events.

In some cases, resource clumping (spatially) is the result of an organism actively

caching food. Smith and Reichman (1984) in their review of caching by birds and

mammals limited their discussion to the movement of potential food from one

location to another for consumption at a later date. Not all species cache food, those

that do are found predominantly in temperate rather than tropical areas. This is most

likely because food resources are more predictable temporally in tropical areas which

probably negates the need to cache food. The high temperatures and humidity of a

tropical environment may also promote spoiling of cached food, which reduces the

efficiency of the method as a means of survival through unfavourable conditions

(Smith and Reichman 1984). Species that are known to cache food, will do so in one

of two ways. They can create a horde cache, or many scatter caches. The evolution of

caching behaviour is a method of resource defence. Horde caches can be effective

methods of food storage when the individual is able to defend the cache from

competitors. If an active defence is not feasible however, then scattering several

caches across a home range can be a viable alternative provided the organism is

capable of remembering all cache locations. Many species have been shown to

possess the ability to remember the location of caches and distinguish which are used

and unused (Wrazen and Wrazen 1982; Sherry 1984; Smith and Reichman 1984).

As mentioned above, resource defence through territoriality is one of the factors that

is considered to have led to the evolution of group living in many species. The cost

of sharing the resource within a territory with conspecifics is balanced by the benefits

gained from exclusive access to the resource, whether it is cached or distributed

naturally. Furthermore, the benefits in terms of fitness and selection are increased if

the members of the group belong to the same deme. If an individual is not dominant

or not producing offspring, then by participating in group behaviour, it may

contribute to the survival and breeding success of closely related individuals and thus

increase the likelihood of a small percentage of its own genes being passed to the

next generation through relatives (inclusive fitness).

14


Territory defence, however, is not the only contribution to group living that an

individual can make. Other activities that benefit the collective include predator

avoidance/warnings, collection of food, and rearing of young. If the group consists of

individuals that are not related, there may still be benefits associated with

participating in predator warnings and avoidance, as well as access to communal

food resources. Social hierarchies develop as a consequence of group living, and

therefore many species that live in groups (though not all) exhibit this structure to

varying degrees. In a study of the crane, Grus grus, foraging in cereal farmland

Alonso et al. (1995) found that birds left more resource-rich patches earlier than

expected and at higher intake rates than in poor patches, although they stayed longer

when in larger flocks. The results suggest that cranes may change their foraging

behaviour according to their expected energy balance. In this instance, cranes benefit

from group association by gaining greater food intake, and better avoidance of

predators.

Behavioural Flexibility

Behaviour can be modified by the environment, and clear-cut relationships between

energy requirements, resource distribution, and social systems can often be

demonstrated (Pough et al. 1989). An animal with a large mass will have much

greater energy requirements relative to a small animal. To obtain the necessary

resources, the animal may have to search widely across their home range - the area

in which they live to find their food and shelter. One might expect to find the size of

home range correlated with size/mass of an animal, but this does not take into

account the possibility of habitat patchiness. An animal may utilise a resource that

occurs in an uneven distribution, if so, then the size of the home range will be a

reflection of the quality of the habitat (in terms of the resource in question). Forest

duikers (small African antelope) have been shown to be more active in habitats of

high quality, although differences in home range have been observed between the

blue and red species (Bowland and Perrin 1995). Bowland (1995) found that core

areas in the home range of both duiker species were usually associated with bed sites.

Blue duiker home ranges and core areas however, were fixed year round with no

overlap between neighbours, while home ranges and core areas of red duikers

15


overlapped extensively. Temporal separation in red duikers is suggested between

some individuals and not others, which means there may be occasions where red

duiker individuals come in contact whilst using the overlapping home range. If

contact is occurring between red duikers, then passive or tolerance behaviour may

occur - which may manifest itself simply as non-recognition (ignoring). The fact that

red duiker home ranges overlap, suggests an absence of territoriality, however the

blue duiker appears to behave conversely with strictly defined home ranges.

Therefore, one might expect to observe aggressive, territory defence behaviour in

blue duikers.

The European rabbit (Oryctolagus cuniculus), is another species that exhibits

territorial behaviour patterns and group living attributes. The population

demographics and abundance of the rabbit make it a useful study species to further

examine theories of behavioural ecology and population genetics.

The European Rabbit

The European Rabbit (Oryctolagus cuniculus) is believed to have evolved in

southern France and Spain. While the species may have been widely distributed

throughout western Europe during pre-historic times including the Pliocene and

Pliestocene; glacial activity 3000 years ago confined rabbit populations to only

warmer southern refuge areas in Europe (Corbet 1986; Flux 1994). Thus populations

historically must have been exposed to large fluctuations in size and demography.

The European rabbit, however, is also a pest and game species, and natural

distributions are often in close association with humans (Flux 1994). Consequently,

rabbit populations were established by humans across much of the European

continent, South America, New Zealand, and Australia.

While domestic rabbits were present on the first fleet which arrived in Australia in

1788, the wild European rabbit was first introduced to the Australian mainland by

Thomas Austin, a keen sportsman and member of an acclimatisation society. The

role of the societies was to facilitate the emigration of settlers from the United

Kingdom to Australia; one method employed was to introduce game species. The

first wild rabbits were introduced at Geelong in 1859 and were maintained in

16


enclosures, but later some were deliberately released into the wild or escaped

(Williams et al. 1995).

Further deliberate releases were made in South Australia and New South Wales; and

by 1900, the rabbit “front” had entered parts of Western Australia, Queensland and

the Northern Territory. The spread of rabbit populations continued at various rates,

the result being the current distribution in which most areas south of the Tropic of

Capricorn are populated, and rabbit populations north of this line generally consist of

small, scattered populations in suitable habitats (Rolls 1984; Stodart and Parer 1988;

Myers et al. 1994).

The great success of rabbit colonisation in Australia can be attributed to a number of

factors:

• Lack of predators and parasites,

• favourable climate and soils,

• human activity, and

• efficient reproductive biology.

When the European rabbit was first released into Australia, few natural predators

were present in sufficient numbers or possessed the ability to significantly reduce

population growth, thus relaxing one of the ecological constraints present on rabbit

populations in their natural habitats in Europe. The Australian climate and landscape

also facilitated rabbit colonisation because the winter season is not as harsh as that of

Europe, indeed many areas of the Australian continent experience a Mediterranean

type climate all year round. In many parts of Australia, soils are composed of sandy

loams ideal for burrowing which also sustain the growth of suitable feed. The rabbit

also proved to be a better competitor in Australia than many native burrowing

herbivorous species such as the bilby (Macrotis lagotis), and thus found ready-made

burrows in many instances.

The single most important factor which led to the successful colonisation of

Australia by rabbits, however, was the actions of humans. Initially the rabbit spread

along riparian systems, following watercourses, but its spread was greatly aided by

17


the pastoral activities of the early European settlers. The clearing of forest for the

growth of grain crops and raising of cattle made vast areas of land available to rabbit

populations that were previously inaccessible and/or unsuitable. Thus, the rabbit

spread to a variety of different habitats, although the degree to which rabbit

populations utilise specific habitat types depends largely on the type of vegetation

present.

The vegetation suitable for rabbits can be classified into five categories (Williams et

al, 1995).

1. Shrub (scrub and bracken thickets) either with or without an overstorey of trees.

2. Patches of dense scrub interspersed with patches of grassland in various

proportions.

3. Savanna woodland with extensive grassland.

4. Grasslands of varying vegetation density

5. Short or sparse grass with varying extents of bare ground.

As ground cover levels decrease, accordingly there is an increase in the size and

structure of warren systems; in the most open of environments, the rabbit will rely

heavily on underground shelter. A rabbit that has colonised a new area, however, will

not dig a new warren, unless the area consists of sandy soils (Cowan 1987a).

Usually, the colonisers live in depressions under logs or rocks (termed a squat).

Females dig shallow burrows in the squat in order to raise a litter – called a stop –

they are usually well concealed to avoid detection from predators. If further tunnels

are excavated within the stop for successive generations of litters, the stop can be

referred to as a warren (Mykytowycz et al. 1960).

Generally, rabbits are largely nocturnal animals, and only emerge from warrens

between one to three hours before dusk, returning just before dawn. Typically, they

will engage in a period of grazing, followed by socialising, on or around the warren

until dark, at which point they will venture further a field (Williams et al. 1995).

Rabbits remain above ground for the duration of the night, although Fullager (1981)

reported that presence of predators will cause them to retreat to their warrens.

18


Group size varies from between two to ten individuals, and within the groups,

typically, independent dominance hierarchies exist for males and females (Williams

et al. 1995). Males compete to gain access to females, and females compete to gain

access to nesting sites. Consequently, male aggression occurs near females, and

female aggression occurs near nesting sites (Cowan, 1987a). A female living as the

sole female in a social group will have greater longevity and greater reproductive

success than will females competing in the same group (Cowan 1987b), which may

account for the evolution of female dominance hierarchies – and the fact that they

will attack individuals attempting to establish in their territory (Parer 1982).

Mykytowycz’s (1958, 1959, 1960) studies of an experimental rabbit population in

Canberra, Australia, provided extremely useful data on social behaviour and

dominance hierarchies, that expanded the work of Southern (1948) studying a

population in the United Kingdom; and provided the baseline of social behaviour that

many researchers have used in subsequent studies. The population was enclosed, but

all individuals were identified and marked prior to introduction to the study area,

therefore social interactions were able to be recorded at the individual level. When

the top ranked male was experimentally removed from the population, all remaining

males attempted to improve their position, however, the second ranked male always

succeeded in this contest. When the original top ranked male was returned to the

population, there was prolonged and severe fighting, with the loser downgraded to

the lowest rank in the group. Similar experiments with the female hierarchy did not

produce the same aggressive results (Mykytowycz, 1958).

In the first year, the study obtained evidence that dominance hierarchies, and

therefore social behaviour patterns, had a clear link to survival. The offspring of the

dominant pair had greater survival than those of subordinates; and the dominant pair

was also able to breed more frequently (Mykytowycz, 1959). During the second year,

the survivors of the first breeding season formed several groups each with a distinct

dominance hierarchies. Although the groups were of mixed parentage, the offspring

of the original dominant female were always dominant, and those of subordinate

females were generally also subordinate. Again, the offspring of dominant pairs had

greater survival rates because of breeding earlier in the season under better resource

(food and nests) conditions (Mykytowycz, 1960; Henderson, 1979).

19


While the dominant male in a group will have first choice and access to females, it is

not always possible for him to guard two females at the same time. Therefore, it is

not uncommon for the dominant male to sire only about 60% of the litters in a group

(Daly 1981) – the remainder of the litters being sired by subordinate males through

promiscuous matings. This occurs, in part, due to female synchrony of the oestrus

period (Parer and Fullagar 1986). The fact that populations generally live in groups

creates situations in which the dominance hierarchies (combined with environmental

factors) inhibit reproduction below the highest physiological capacity (Mykytowycz

and Fullagar, 1973).

Territory defence of the group is usually conducted by males, and the territorial

boundaries are a reflection of the size of the home range of the dominant male

(Williams et al. 1995). Mykytowycz and Gambale (1965) studied a 45 acre area

containing three populations, and found that dispersal between warrens only occurred

during non-breeding seasons; the study also reinforced the importance of warrens for

survival and the tendency for group living. Food resources (eg. grazing patches),

however, are typically spread over a large area, and therefore often cannot be

defended adequately. If population density is high, several social groups may occur

in large warrens, while at the other end of the scale, a single group may utilise

several warrens provided population density is low (Wood 1980; Fullagar 1981;

Williams et al. 1995).

Australian populations of the European rabbit, particularly those in arid

environments, have existed as metapopulations – frequently undergoing periods of

extinction and recolonisation (Parer and Fullager, 1986). Like many

metapopulations, the regional persistence of the rabbit in Australia has often relied

on certain patches acting as refuges during times of unfavourable conditions (eg.

drought). As a result of this pattern, rabbit population numbers have fluctuated

accordingly. Such a population dynamic effectively pushes the population through a

genetic bottleneck whenever a large population size fluctuation occurs. Similarly,

introductions of diseases such as myxomatosis and rabbit calici virus, whilst not

eradicating the species, have caused great perturbations to population size and hence

have probably resulted in significant genetic bottleneck effects.

20


In a study of rabbit populations in the East Anglia region of Britain, Surridge et al.

(1999) found that local populations were genetically distinct from one another and

had small effective population sizes. It is thought the distinction occurs due to the

combined effects of their natural social structure and random genetic drift acting on

bottlenecked populations after exposure to myxomatosis. She argued that the genetic

structure observed in East Anglia represented recent events rather than historical

influences (Surridge et al. 1999). On the other hand, Queney et al. (2000) and Zenger

(2003) found no evidence of genetic bottlenecks in rabbit populations in northern

France and Australia respectively. They argued that levels of genetic diversity in

rabbit populations in Europe may not have been affected by disease outbreaks

causing high mortality, and rapid population expansion following a population crash

can limit the effect of the crash on the population genetic structure. In another study

of rabbit populations in East Anglia, Webb et al. (1995) showed that population

genetic structure was influenced by social organisation. In particular, the natal

dispersal pattern where females exhibit philopatry, and males disperse to new social

groups before the start of the new breeding system results in detectable differences to

population genetic structures.

Small effective population sizes have also been observed in some wild rabbit

populations in Australia. Daly (1979) suggested this was influenced by a

combination of social structuring (i.e. dominant individuals providing the majority of

genetic information to subsequent generations) and habitat heterogeneity. Studies

conducted in Britain, focused on populations that exist in largely stable

environmental conditions, which facilitate the development of stable social groups.

In Australia, however, habitats where rabbits are found are not always of the best

quality in terms of resource availability, and therefore a significant amount of habitat

heterogeneity may occur. Rabbit population genetic structure in arid Australia differs

from that in semi-arid and mediterranean systems. Fuller et al. (1996) examined

rabbit populations in an arid region of south western Queensland (1600km2) and

reported that significant gene flow occurred across large geographic areas because no

significant genetic differences were observed among populations (panmixia). It was

suggested that environmental fluctuations had caused frequent localised

extinction/recolonisation events leading to homogenising gene flow. The study was

21


extended to an examination of a semi arid region 500km east of the arid region,

where a significant difference in population structuring was observed (Fuller et al.

1997). While populations in the western system (arid) essentially function as a

panmictic unit, the eastern system (semi-arid) exhibited distinct population

structuring. The structure was hypothesised to be related to the pattern of distribution

of good quality habitats, which can be described as more 'patchy' in the semi arid

compared with the arid regions. The fact that one system was essentially panmictic

while the other was genetically structured over small geographic distances, suggested

that rabbits may also be influenced by other factors that result in variations in

population gene flow. The cause of this dichotomy was hypothesised to be a

combination of spatial and temporal variation in three primary resources – food,

nests, and mates.

Hamilton (2003) examined long term connectivity levels among local rabbit

populations and found they are influenced by the spatial distribution of resources and

other habitat factors. Hamilton developed a habitat heterogeneity model using

specific population parameters representative of the eastern semi-arid region. The

validity of model assumptions was assessed by regressing model output against

independent population genetic data, which could explain over 80% of the variation

in the structured genetic data set (Hamilton, 2003).

Cowan and Garson (1985) studied the social structure of two wild rabbit populations

in England (Oxfordshire and Northumberland) that were exposed to different

environmental conditions. One population was located on a chalk hill and the other

was located on a sand hill. Discrete social groups were only evident at the chalk hill

site, where females competed for burrows, and male territorial behaviour was

observed due to clumped female dispersal (Cowan and Garson 1985). The sand hill

population had more rabbits than the chalk hill site and growth rates were negatively

correlated with density. The authors concluded that scramble competition for food

occurred at the sand hill site while contests occurred for nests and mates in the chalk

hills. In this instance it is clear that the sand hill habitat has more abundant resources

than the chalk hills, and thus was able to support larger population sizes; although

due to the large numbers and lack of resource clumping, there was no fitness

advantage to being 'social'. Variation in social structure due to habitat parameters has

22


also been observed in other species, such as the brushtail possum in New Zealand

(Jolly et al. 1999; Taylor et al. 2000), which, like the rabbit in Australia, is a

significant introduced pest.

While it is widely accepted that rabbit social organisation and dispersal potential can

influence the genetic structure and patterns observed, it is unclear whether the degree

of organisation in rabbit social systems in arid and semi-arid Australia is a response

to differences in the extent of habitat heterogeneity in the region. Models of dispersal

and gene flow in Australian populations of the European rabbit, based on habitat

characteristics, have been developed and can account for over 80% of genetic

variation (Hamilton, 2003). It is not known however, if rabbit behavioural flexibility

contributes to the remaining 20% of genetic variation, or what (if any) are the

potential social structure consequences of density effects in arid and semi-arid

environments. Fuller et al. (1996, 1997) completed the initial research of rabbit

population genetics in arid and semi-arid Australia; which was followed by

Hamilton’s (2003) research on connectivity. Therefore, the next step in an holistic

approach to understanding, and ultimately managing rabbit populations in Australia

is to study the relationship between what type of social systems are present and

variation in habitat characteristics. The specific questions the current project will

focus on in order to address the main objectives are listed below:

• Do patterns of genetic structuring vary with differences in major habitat

attributes?

• Do aggressive/territory defence behaviour patterns vary with differences in

availability of key resources?

The questions will be addressed using genetic marker studies in areas with different

habitat attributes and behavioural experiments under varied environmental conditions

to quantify difference in aggression patterns. The observation experiments will aim

to determine if rabbits behave differently when exposed to different habitat

conditions in Australia; while the genetic assessments will aim to present evidence

that variable social systems have distinct effects in terms of population gene flow.

23

Chapter 2 Experimental Design and Methodology

Experimental Design And Methodology

Recent population genetic studies in arid and semi-arid Queensland identified

regions where different genetic structures were present based on variation in the

mitochondrial DNA genome. Fuller et al. (1996, 1997) identified a region in far

western Queensland that showed high levels of gene flow among rabbit populations

resulting in effective panmixus over large areas (>1000 km2). The same study also

identified a region 600km east of the western panmictic zone that exhibited much

lower levels of gene flow, resulting in population genetic structuring at much smaller

spatial scales. The basis of the observed differences in genetic structure was

suggested to be variation in the levels of habitat heterogeneity in terms of vital

resources – food and nesting sites.

For the most part, the behaviour of rabbits has been overlooked as a component

which could contribute to an explanation for patterns of population genetic structure

observed in arid and semi-arid Australia. The purpose of the present study is to

investigate the potential that rabbits may be capable of flexible territorial behaviour

patterns depending on the amount and distribution of favourable habitat. If rabbits

adjust their aggressive behaviour in response to differences in habitat conditions in

arid Australia, then their well documented social/territorial defence may be relaxed

in times of abundant resources and result in subsequent population explosions and

consequent dispersal events.

The first part of this study will assess the genetic structure of O.cuniculus in arid and

semi-arid Queensland using highly variable nuclear DNA markers (microsatellites);

and compare the results with those from previous studies (Fuller et al. 1996, 1997)

that examined patterns in mitochondrial DNA and more conservative nuclear

allozyme markers (Fuller 1995). This comparison is necessary because the

mitochondrial genome is maternally inherited and the predominant dispersing sex in

rabbits is known to be the male, which could result in female only genetic

structuring. A solution to this problem is to examine variation in nuclear DNA in

combination with mitochondrial DNA, however, at present, the only use of nuclear

DNA in studies of rabbit populations in arid and semi-arid south west Queensland

has been via allozyme electrophoresis, where variation was limited as a result of

24


functional constraints on coding sequences and potential loss of genetic diversity

levels due to past bottlenecks.

The second part of the study will assess rabbit behavioural flexibility. The initial

design was to conduct field experiments at sites in the arid and semi-arid regions

identified by Fuller et al. (1996, 1997) that were used for assessing population

genetic structure to test the potential for differences in aggressive behaviour

associated with habitat difference. Due to the effect of recent releases of rabbit calici

virus (1996), however, the study sites (especially those in the eastern semi-arid zone)

no longer have rabbit populations large enough to study. Even in the western arid

zone, sites from which genetic material was sampled in the past now also have very

low rabbit numbers due to calici virus, extreme drought, and control efforts of

property owners. New study sites were located within the panmictic arid zone

identified by Fuller (1995) – and were used subsequently for the behaviour

component of this project.

In order to determine if differences exist in the relative levels of aggression and

territorial response depending on resource availability, it was originally planned to

conduct field experiments in both habitat types (arid and semi-arid); this was not

possible for the previously listed reasons. Therefore, the only remaining option for

the behavioural component of the project was to examine levels of aggression and

territoriality in the same arid sites under high and low resource availability

conditions.

Description of Study Sites

The study sites were located within two major regions of south western Queensland.

The regions were identified in the studies by Fuller (1995) and Fuller et al. (1996,

1997) and are broadly identified in Figure 2.1.

The arid zone (referred to as ‘western’) is located in far south western Queensland,

and is centered on a large cattle property called Bulloo Downs, 28° 31.62’ S 142°

57.63’ E (owned by Stanbroke Pastoral Co.). The property is 1,093,500 hectares in

size, located 120km south west of Thargomindah, and on the edge of the “Channel

25


Country”. Land in this area is susceptible to floods which result from rains either in

situ (average rainfall is about 200mm/year) or further upstream in the catchment

areas for the Bulloo River that runs through the middle of the property and drains

into the Bulloo Lakes in the south west corner. The property is large enough that

several landforms exist, however, the systems used in this study were confined to

sandy hills separated by claypans. Few trees occur on the property except in areas

adjacent to channels and waterholes and pasture growth is dependant on rainfall or

floodwaters especially in the winter months, therefore, rabbit numbers fluctuate

extensively depending on the frequency of rainfall.

The semi-arid zone (referred to as ‘eastern’) is located in a 200km radius around the

township of Mitchell in the Maranoa district (approximately 500km west of Brisbane

and 600km east of Bulloo Downs). Properties around Mitchell are much smaller than

in western Queensland therefore more were included for sampling in order to cover

the same geographic area. The region consists of cleared pastoral areas interspersed

with remnant dry sclerophyll forest which is known to be unfavourable for rabbits.

26


East

West

Figure 2.1 – Areas of study, West and East.

Population Sampling

An assessment of patterns of genetic variation between the regions required sampling

of DNA from target populations in both regions. During pilot trips in 1999 and 2000,

however, it became apparent that due to situations beyond the control of the project

(ie drought and disease), there were insufficient populations to allow appropriate

genetic sampling in both regions. While the best design for the project consisted of

collecting new samples under present day conditions, the next best option, was to use

historical tissue samples that were collected between 1993 and 1995 by Fuller (1995)

and were stored at -80°C. It is acknowledged that these samples were collected by

Fuller, under different environmental conditions, however, using the historical

samples was the only way to achieve any assessment of microsatellite genetic

variation in the regions of interest during the timeframe of this project.

27


The following describes the collection of tissue samples from the arid and semi-arid

zones (Fuller, 1995):

In the arid zone, approximately one hundred adult animals were sampled humanely

from 3 sites on Bulloo Downs; in the semi-arid zone, a minimum of thirty adult

rabbits were taken from eight sites centered around the township of Mitchell.

Animals were dissected within 30 minutes from time of death and a small piece of

liver tissue was collected from each animal and samples stored in cryoware vials

(Nalgene Co.) on liquid nitrogen. On return to the laboratory all samples were stored

at –80°C until used for genetic analysis.

In the arid zone, there are many water bores on Bulloo Downs and populations were

sampled at three of these sites named: Ponto, Thurloo, and Willala.

The semi arid region consists of smaller properties, and eight sample sites were

named after the properties on which they were found: Alice Downs, Bowann,

Claravale, Currawong, Glenalba, Glenlea, Polworth, and Verniew. A minimum

sample size of 30 was set as the target, though this was not always attained.

Consequently, sample sizes were uneven across the data set.

Genetic Methods

Genetic variation, in the form of multiple alleles at genetic loci exists in most natural

populations (Hartl 1988), but the methods used to sample variation will depend on

the species and the specific question to be answered. Allozyme electrophoresis was

used as the genetic analysis standard for many years due to its ease of use, speed,

cost, and results (distinguishable loci with codominant alleles that can be scored

unambiguously). The disadvantage of allozymes is that they are functional gene

products, and so are limited by past and present functional constraints on the gene.

Microsatellites consist of tandem repeats of very short nucleotide motifs; the repeat

array is usually 10-50 copies of a sequence that is 1-10bp long (more commonly 2-

5bp long). Unlike allozymes which are the product of coding DNA, microsatellite

loci are randomly distributed and present at high frequency in eukaryotic non-coding

28


regions and because most are not constrained, they usually show high levels of

genetic variation. The function (if any) and evolutionary significance of

microsatellites is unknown.

Variability at microsatellite loci is derived from the number of repeats of the motif

sequence; each variation is considered a different allele and the alleles can be

discriminated on the basis of size using electrophoretic methods. New alleles are

formed through any genetic mutation which results in a net loss or gain of repeats.

As microsatellite analysis is a method which relies on size discrimination, any

mutation that does not lead to a difference in the number of repeats will not be

identified. Microsatellites can be classified depending on the repeat motif length (di,

tri, tetra nucleotide) and continuity (perfect or interrupted) eg. (GT)18 is a

dinucleotide continuous repeat and (TG)3CG(TG)17 is interrupted. The type, length

and continuity appear to affect the rate of mutation and levels of allelic variation.

Interruption in the core sequence seems to stabilise the array, such that loci with pure

repeat sequences are more polymorphic than those with interruptions. Levels of

allelic diversity are correlated with repeat length - loci with longer repeats are more

polymorphic than loci with short repeats.

Ten different microsatellite loci were trialed throughout the course of this project,

however, only five were able to optimised for local laboratory conditions. One of the

five was found to be a nested repeat of another loci, therefore, microsatellite

variation was assayed using four polymorphic microsatellite loci developed

specifically for use on O.cuniculus in European populations; Sat3 and Sat5

developed by Mougel et al. (1997) and SOL28 and SOL30 developed by Rico et al.

(1994).

Behavioural Methods

Studies of behaviour patterns under laboratory conditions are abundant for insects

and many invertebrate species; a considerable amount of behavioural research has

also been conducted on vertebrate species with the emphasis generally on species of

commercial value or ecological significance. The exact design used in studies,

however, depends largely on the species in question and the nature of the research.

29


Historically, studies of behaviour have been made from direct observation of the

organism usually involving a code system and direct recording onto paper. More

recently, behaviour observation studies have utilised a variety of approaches

including video, night vision, infra-red and motion sensors. The great advantage with

video technologies, is that a permanent record of the behaviour in question is created.

The observation tape can be played many times which enables multiple analyses and

post hoc correction of interpretive mistakes.

Examples of video technology use in behavioural ecology are widespread. Bozinovic

and Vasquez (1999) used video cameras to study foraging behaviour of the Degu

Octodon degus, a diurnal rodent found in semi-arid Chile; and observed a time-

minimizing foraging behaviour. In this instance, video cameras were used to measure

the overall time budget of the animal, including frequency and duration of patch

visits, and food gathering events. Widowski and Duncan (1996) used overhead video

cameras to study the behaviour of laying hens in response to high and low frequency

flourescent light sources.

In some cases, a large amount of raw video footage is generated in order to observe

the species over a long period, however, it may not be necessary to use the

continuous footage. In a study that examined foraging behaviour of stabled horses,

the subjects were video recorded between 19:00 h and 12:00 h for the duration of the

experimental period (horses were allowed outside for 7 h per day for exercising and

grazing) and behavioural data were collected by time sampling the video tape every 2

min (Winskill et al. 1996).

Experiments which focus on a specific aspect of behaviour require the relevant

patterns to be defined prior to study and will vary accordingly. Greaves and

Wedderburn (1995) used the definitions of lying, standing and grazing when

studying the ability of goats and sheep to affect rates of soil erosion. In a study which

examined the behaviour of fish in response to the presence of a trolling line,

Akiyama et al. (1995) used the definitions of appearance into the camera view,

approach to the lure, attack on the lure, touching the lure, being hooked, and

captured. Behaviour of the European rabbit has been studied extensively in closed

populations and has resulted in the characterisation of many behaviour patterns in

30


great detail (Southern 1948; Lockley 1954; Cowan 1983; Fallows 1988; Webb

1988). A complete list of rabbit behaviours is provided in Appendix 1.

The focus of the present study was on the flexibility of rabbit aggressive/territorial

defence type behaviours. Therefore, the only behaviours considered for analysis

were: Tail Flagging, Displacement, Circling, and Aggressive Leaping. The four

behaviours were chosen because they had previously been correlated directly to

aggression and territory defence in rabbits, and were easily identified on the video

footage collected.

The specific details of the behaviour study will be discussed in full detail in chapter

4, however, a brief summary is contained herein. Data were collected at three

(replicate) sites on “Bulloo Downs” in arid western Queensland, located

approximately 10km south of the Willala site used in the genetic analysis.

Aggressive/territory defence behaviour was measured by presenting each study site

population with a rabbit of known (Control) and unknown (Experimental) origin.

Control rabbits were trapped within each study site, and experimental rabbits were

trapped from other sand hill populations separated by a minimum of 2km.

Behavioural responses of the study populations were recorded under red light on

Video8 film using SONY camcorders with “nightshot” capability. Camcorders were

set up on tripods located 40m into the adjacent claypan, and left unattended for 1hour

at each filming site. The video data was transcribed into ethograms scoring the

frequency of aggressive behaviour, which were then subjected to statistical analysis

using statistica (version 6).

31

Chapter 3 Genetic Analysis

Genetic Analysis

Studies which examine the population genetic structures of pest populations can be a

useful tool for developing control and or management strategies. Given the

significant pest status of the European rabbit in Australia and to a lesser extent, in

New Zealand and Britain; and also considering that in parts of its native range in

Spain it is considered endangered due to population declines resulting from predation

and disease – it is not surprising that many population studies have been completed

on rabbits using a variety of genetic markers in widespread environments

(Richardson 1980; Richardson et al. 1980; Daly 1981; Richardson 1981; Ross and

Sanders 1984; Loo et al. 1987; Hardy et al. 1994; Rico et al. 1994; Webb et al. 1995;

Fuller et al. 1996; van Haeringen et al. 1996; Fuller et al. 1997; Mougel et al. 1997;

Surridge et al. 1997; Surridge et al. 1999; Surridge et al. 1999; Surridge et al. 1999;

Queney et al. 2000; Queney et al. 2001; Richardson et al. 2002; Wilson et al. 2002;

Zenger 2003).

Recent population genetic studies in arid and semi-arid Queensland identified

geographic regions with different population genetic structures based on variation in

the mitochondrial DNA genome. Fuller et al. (1996, 1997) identified a region in far

western Queensland that had high levels of gene flow among populations resulting in

a large area of arid land showing effective panmixus. The same studies also

identified a region 600km to the east of the panmictic zone which, in contrast

exhibited restricted levels of gene flow, resulting in significant population genetic

structuring at the local scale. The basis of the differences observed in patterns of

genetic structures was suggested to be associated with variation in habitat

heterogeneity and to relate specifically to differences in food and nesting site

availability.

Frankham’s (1996) study into the relationship between genetic variation and

population size tested two predictions relevant to the study of rabbit population

genetics in arid and semi-arid Australia. He found that 1) Genetic variation within

species should be related to population size; and 2) genetic variation within species

should be related to island size. Given the patchy distribution of favourable habitat

and hence, populations of rabbits identified in the eastern region; it would be

32


expected that rabbit populations in the western region would show higher levels of

genetic variation. Rabbit populations in arid and semi-arid Australia constantly

endure periods of hardship that reduce population sizes and consequently push

populations through periodical genetic bottlenecks. The consequences of a genetic

bottleneck are usually reduced levels of genetic variation – specifically lower allelic

diversity and in some cases, heterozygosity. The critical period occurs when rabbit

populations expand after a bottleneck because it provides the opportunity for

increased levels of genetic variation in the western region as a consequence of high

dispersal. In the eastern system, however, habitat heterogeneity may promote higher

levels of inbreeding post bottleneck, which will not favour an increase in genetic

variation and may cause the loss of rare alleles and fixation of common alleles.

The purpose of the genetic component of this study was to assess the genetic

structure of rabbit populations in arid and semi-arid Queensland using a highly

variable nuclear DNA marker (microsatellites); and to compare the results with those

from earlier studies that examined variation in the same populations using markers

from the maternally inherited mitochondrial genome.

Materials And Methods

As mentioned previously, external influences beyond the control of the project

prevented the collection of fresh tissue samples from the study sites. The procedure

outlined herein records the collection of samples used by Fuller (1995) for allozyme

and mitochondrial DNA analysis – and subsequently used in this project for

microsatellite analysis. Only the raw tissue samples were re-used, DNA extraction

and all procedures thereafter were performed during the course of this project.

O.cuniculus liver tissue samples were collected from shot specimens in the arid and

semi arid regions of western Queensland during a number of field excursions

between 1992 and 1996. The arid region (“Bulloo Downs”), was sampled at the bore

sites named: Ponto, Thurloo, and Willala. These site are separated by a minimum

distance of 33km and a maximum distance of 50km. These sites were representative

of the greater arid region of panmixia identified by Fuller et al. (1996, 1997). The

semi arid region (around Mitchell) was sampled at the following properties: Alice

33


Downs, Bowann, Claravale, Currawong, Glenalba, Glenlea, Polworth, and Verniew.

These sites were separated by a minimum distance of 25km within the region and a

total of 150km across the entire region. A random sample of 30 individuals were

sought from all sites (both arid and semi arid), though this was not always attained.

Consequently, even sample sizes were not evident in the data set, however the more

important figure is the number of samples that were successfully used in DNA

extraction and microsatellite analysis, and these sample sizes are presented later in

table 3.3.

DNA Extraction

Centrifugation steps were conducted at 13000 rpm using an Heraeus Biofuge 13.

Gilson pipettes were used to transfer solutions. Gentle inversion (30 seconds) and

wide bore pipette tips were used to mix and pipette DNA solutions to prevent

shearing throughout the procedure.

Whole genomic DNA was extracted from approximately 100mg of liver tissue from

each sample by incubation at 55°C overnight in an extraction buffer (100mM Tris,

20mM EDTA, 100mM NaCl, 10% SDS, 2M DTT, 10mg/ml Proteinase K). The

supernatant was transferred to fresh tubes. An equal volume of phenol was added to

the supernatant. The solution was mixed and centrifuged for 15 minutes. The

supernatant was transferred to a new 1.5mL microfuge tube. The phenol extraction

was repeated until a clear interface was visible between the aqueous and phenol

phases. This was followed by two further 5 minute centrifugation/extraction steps

using phenol-chloroform-isoamyl alcohol (24:24:1) and chloroform-isoamyl alcohol

(24:1).

To the resulting supernatant was added 100µL of 3M sodium acetate and 400µL ice

cold isopropanol. The solution was mixed and incubated on ice for 10 minutes to

facilitate DNA precipitation, then centrifuged for 15 minutes. The supernatant was

discarded and the pellet washed with 70% ice cold ethanol and centrifuged for 5

minutes. Following this, the pellet was isolated, allowed to dry for 75 minutes and

34


resuspended in 50µL TE buffer. Samples were left at room temperature overnight to

facilitate DNA dispersion.

Presence of genomic DNA was confirmed using 3µL of DNA sample, 2µL of

bromophenol blue 6X loading dye and 7µL ddH2O, electrophoresed through a 1%

1X TBE agarose gel at 100 volts for 20 minutes in a Hoefer mini-submarine gel unit.

φX174 – HaeIII was loaded adjacent to genomic products as a DNA size marker.

DNA was visualised on a UV light transluminator by ethidium bromide flourescence.

Photographs of gels were taken using a Polaroid CU 5 land camera under consistent

lighting, aperture, and shutter speed settings. Extracted genomic DNA was quantified

using a Beckman UV spectrophotometer and stored at -80°C until required.

Polymerase Chain Reaction (PCR)

DNA samples were screened for variation at four polymorphic microsatellite loci:

Sol28, Sol30 (Rico, Rico et al. 1994), Sat3, and Sat5 (Mougel, Mounolou et al.

1997). The primer sequences, repetition code and product size for each locus are

listed in table 3.1. Visualisation of short length nucleic acids (microsatellites) is

difficult with conventional agarose electrophoresis, therefore autoradiography was

used.

Locus Genbank

Number

Primer Sequence Repeat Size (bp)

Sol30 X79215 F: 5’CCCGAGCCCCAGATATTGTTACCA3’

R: 5’TGCAGCACTTCATAGTCTCAGGTC3’ (TC)14A(T)4(TC)5 153-172

Sat3 J03744 F: 5’GGAGAGTGAATCAGTGGGTG3’

R: GAGGGAAAGAGAGAGACAGG3’ (TC)22 146-162

Sol28 X79216 F: 5’ATTGCGGCCCTGGGGAATGAACC3’

R: 5’TTGGGGGGATATCTTCAATTTCAGA3’ (TC)23(N)3(TC)4 164-176

Sat5 X99887 F: 5’GCTTCTGGCTTCAACCTGAC3’

R: 5’CTTAGGGTGCAGAATTATAAGAG3’ (TC)23TTT(CT)5 206-234

Table 3.1 – Microsatellite Primers used for PCR

35


For this procedure, sample DNA is labeled with a radioactive isotope during PCR

with substitution of dCTP with dCTP-P32, a radioactive labelled compound.

Geneworks dCTP-P32 (10mCi/mL in 10mM Tricine pH 7.6 stored at 4°C) was used

in all reactions. Microsatellite DNA is run through a 5% (denaturing) acrylamide-

urea gel, 0.1mm thick, 38x50cm and DNA banding patterns (alleles) visualised by

exposure of the gel to radiography film. Details of the procedure are presented as

follows.

Preparation Of DNA Samples

PCR was performed in sterile 500µL microtubes using a total reaction volume of

20µL. Approximately 50-100ng of genomic DNA was placed in each tube. A master

mix for up to 60 samples was created using: 10xBuffer, Taq polymerase

(Boehringer-Mannheim), dNTP (1.25mM for A, G, T and 0.125mM for C)

(Promega), dCTP-P32 (1mM), forward and reverse primers (120nM), MgCl2 (see

table 2 for concentrations), and water to adjust to the final volume. The master mix

was aliquoted into each sample, then placed in a thermal cycler (with heated lid)

according to the following reaction conditions.

1) 94°C for 3 minutes. 2) 94°C for 30 seconds. 3) Ta for 60 seconds. See Table 2. 4)

72°C for 60 seconds. 5) repeat steps 2-4 29 times. 6) 72°C for 8 minutes.

At the completion of PCR cycling and immediately prior to loading onto the gel, 7µL

of loading dye (containing formamide, bromophenol blue and Xylene cyanol FF

dyes) was placed in each tube. All tubes were heated at 95°C for 5 minutes in order

to denature the DNA, then placed on ice while loading took place. Internal reference

samples were run on each gel to enable comparisons among gels of unique alleles.

Preparing The Gel And Pre-Running

A Biorad Sequi-Gen GT rig was used to run microsatellite gels. A gel mix was

prepared consisting of 60mL sequencing mix (48mL 10X TBE + 60mL 40%

Acrylamide 19:1 + 220g Urea), 300µL APS (20% ammonium persulphate in H2O),

36


and 30µL TEMED. The solution was mixed carefully so as not to aerate and then

cast between the glass plates of the gel rig. A shark tooth comb was inserted at the

top of the gel to create sample wells. The gel was left undisturbed for at least 60

minutes.

The gel rig was set up according to the Biorad Sequi-Gen GT manual. In brief, the

gel plate sandwich was placed in the gel rig and the upper and lower baths were

filled with 1400mL and 350mL 1X TBE respectively. The combs were removed and

the gel front flushed using a syringe to remove precipitated urea and bubbles. The gel

was pre-run at 120W (constant), for 60 minutes until the gel was at a temperature of

40-50°C.

Loading And Running

The gel front was flushed again to remove excess urea. 5µL of each denatured

sample (heated to 95°C then placed on ice) was loaded into each well and reference

markers were loaded into the middle wells. The system was run for 1.5 to 2 hours

depending on the locus (table 2) at 100W.

At the conclusion of the run, the glass plates were removed from the gel rig and

separated. Blotting paper 35 x 43 cm was rolled onto the gel and lifted slowly to

separate the gel from the glass plate; any sticking of the gel to the plate was avoided

by flushing the gel with running buffer as the paper was lifted.

The gel was placed into a Biorad Model 583 Gel Dryer and covered with cling wrap,

and any air bubbles were removed. A rubber gasket was placed over the gel and the

vacuum turned on. The lid was closed and the program heat cycle set to 90°C for 10

minutes, then 80°C for 50 minutes.

Exposing And Developing

37


In the dark room, an x-ray film was placed in an auto-rad cassette with the gel. The

cassette was closed and left undisturbed for 3 to 48 hours depending on the state of

radioactive decay of the P32.

The film was removed from the cassette and placed in a AGFA Automated

Developer unit; after which, the film was dry and ready to handle.

Locus MgCl2 Ta Run time

Sol30 2mM 58°C 105min

Sat3 1mM 60°C 90min

Sol28 2mM 60°C 105min

Sat5 2mM 57°C 120min

Table 3.2 – PCR and electrophoresis conditions for each locus

(Ta = annealing temperature)

Films were scored by numbering the alleles present, where 1 is the smallest (fastest

migrating) allele. Each gel was capable of running up to 32 samples, therefore in

most instances complete populations could be screened using a single film. Addition

of internal reference samples enabled comparison among films where more than 32

samples were present.

Not all samples were amplified successfully at each locus, resulting in uneven

sample sizes within populations across loci (Table 3.3).

38


Locus Ponto

(W)

Thurloo

(W)

Willalla

(W)

Alice Downs

(E)

Bowann

(E)

Claravale

(E)

Sol30 46 85 48 16 28 18

Sat3 48 56 48 15 27 18

Sol28 47 64 46 15 28 18

Sat5 41 43 48 16 28 18

Locus Currawong

(E)

Glenalba

(E)

Glenlea

(E)

Polworth

(E)

Verniew

(E)

Sol30 25 28 30 13 34

Sat3 25 27 30 12 33

Sol28 31 31 32 14 34

Sat5 31 29 32 14 34

Table 3.3 – Differences in population sample size at each locus

(W) = Western/Arid population; (E) = Eastern/Semi-arid population

Data were analysed using Genepop, Fstat, and Genalex run on desktop PC.

Mean allelic statistics (number of alleles, number of expected alleles under random

mating, heterozygosity) were calculated using Genalex. These statistics provide an

overview of the genetic variation present in the test populations, and indicate whether

the mating patterns are deviating from random.

Populations were tested for confirmation to Hardy-Weinberg equilibrium, a formal

test of random mating. It should be noted that very few natural populations meet all

eight assumptions under which the Hardy-Weinberg model was developed. In this

study, one of the assumptions that may be violated is that of population size being

very large. The current populations in the semi-arid Eastern region are virtually non-

existent due to the effect of drought and calici virus; and the populations in the arid

Western region are also greatly reduced in size due to drought and integrated pest

management strategies. The population size problems, however, were the reason for

39


utilising previously collected tissue samples. The populations in both regions were

much larger when they were collected. If Hardy-Weinberg equilibrium is confirmed,

it allows further testing based on the assumption of random mating. These tests are

considered below.

Genotypic disequilibrium examines each locus pair across the study populations and

using the observed gene frequencies, tests whether the genes are linked or in random

association. Conversely, the test of genic differentiation is based on each population

pair across all loci and uses the observed gene frequencies to test if there are

significant genetic differences between each population pair – it is a test that may

show trends of a regional dichotomy, if one exists, between populations from the

western vs eastern regions.

FIS variation (inbreeding in individuals relative to subpopulation) variation was

calculated using Genepop. The statistics were calculated for all loci and averaged

over each population with standard error. The purpose of the test is identify if (based

on observed gene frequencies) any populations show evidence of higher levels of

individual inbreeding relative to the other populations.

Population pairwise FST was calculated using Fstat to measure the level of inbreeding

in the subpopulations relative to the total population. In this study, the main aim of

the test is to observe any differences in the amount of inbreeding in one population

relative to another. If there are differences between the regions based on behavioural

flexibility affecting population genetic structure, the comparison between eastern and

western populations should show significant differences. In other words, if eastern

populations are more isolated and suffer greater habitat heterogeneity than western

populations; one might expect to observe more inbreeding in the east vs the west.

A hierarchical Analysis of Molecular Variation (AMOVA) was conducted and

bootstrapped (1000 times) using Genalex. Bootstrapping is a particularly important

process as it entails the random resampling of the genetic data and provides a robust

comparison of the observed results with random gene frequencies. A population

similarity matrix (Nei) based on genotypic information was calculated within

40


Genalex and used to construct an UPGMA tree to pictorially represent which

populations are most similar based on their population genetic relationship.

Results

The number of alleles at loci Sat3 and Sol30 did not vary among regions, or among

populations within regions, with the exception of the Bowann population at the Sol30

locus. Table 3.4 lists the number of alleles at each population per locus.

Locus Ponto

(W)

Thurloo

(W)

Willalla

(W)

Alice Downs

(E)

Bowann

(E)

Claravale

(E)

Sol30 5 5 5 5 4 5

Sat3 5 5 5 5 5 5

Sol28 7 7 6 5 4 4

Sat5 8 6 9 6 4 5

Locus Currawong

(E)

Glenalba

(E)

Glenlea

(E)

Polworth

(E)

Verniew

(E)

Sol30 5 5 5 5 5

Sat3 5 5 5 5 5

Sol28 5 4 4 6 5

Sat5 6 6 5 4 6

Table 3.4 – Limited variation in the number of alleles at Sol30 and Sat3 loci per

population

The mean values for allelic statistics were calculated using Genalex across all

populations and are shown in Table 3.5. Where Na = Number of alleles, Ne = Number

of expected alleles under random mating, and He = Heterozygosity. All populations

had more alleles observed than expected under random mating. The western

populations of Ponto, Thurloo and Willala had more alleles and greater

heterozygosity values than did eastern populations.

41


Population Ponto

(W)

Thurloo

(W)

Willala

(W)

Alice Downs

(E)

Bowann

(E)

Claravale

(E)

Na 6.25 5.75 6.25 5.25 4.25 4.75

Na Freq. >= 5% 4.75 4.75 4.50 4.50 3.75 4.50

Ne 3.22 3.51 3.47 3.34 2.85 3.14

No. Private Alleles 0.00 0.00 0.25 0.00 0.00 0.00

He 0.68 0.70 0.70 0.66 0.58 0.64

Population Currawong

(E)

Glenalba

(E)

Glenlea

(E)

Polworth

(E)

Verniew

(E)

Na 5.25 5.00 4.75 5.00 5.25

Na Freq. >= 5% 3.25 4.25 3.75 4.00 3.75

Ne 2.96 3.13 2.87 3.10 2.70

No. Private Alleles 0.00 0.00 0.00 0.00 0.00

He 0.62 0.63 0.61 0.62 0.56

Table 3.5 – More alleles and greater heterozygosity in Western populations

Data were tested for Hardy-Weinberg equilibrium using Genepop. At the individual

locus level, three populations were not in equilibrium at a single locus only (p =

0.05). They were: Bowann and Currawong @ Sol28; and Glenalba @ Sat3

respectively. However, when calculated across all loci sampled, all populations

conformed to equilibrium (p = 0.05), thus allowing further analyses based on the

random mating assumptions of Hardy-Weinberg.

Genotypic disequilibrium was tested using Genepop. Each locus pair was tested

using data from all populations, and no significant results were observed (p = 0.05).

This implies that the genes comprising these populations are in random association

and do not show the effects of linkage. As mentioned in Ch. 2, one set of primers

trialed for the project was found to be nested within a larger repeat sequence already

being used – had the nesting not been discovered, the results would have skewed to

showing possible linkage effects.

42


Genic differentiation among all population pairs across all loci were tested using

Genepop. There was no distinct pattern consistently observed to depict an east/west

dichotomy. There were significant differences (p < 0.05) between some population

pairs and these values are displayed in Table 3.6. while Table 3.7 summarises the

data in matrix format to allow a better visualisation of the regional pattern (or lack

thereof). The seemingly random pattern observed is probably due to greater

individual variation within the population pairs.

Population Pair χ2 df p

Ponto & Thurloo 16.3 8 0.04

Ponto & Claravale Inf. 8 high sig.

Ponto & Glenalba 16.6 8 0.03

Ponto & Glenlea 28.4 8 high sig.

Ponto & Verniew 19.6 8 0.01

Thurloo & Bowann 33.0 8 high sig.

Thurloo & Currawong 16.2 8 0.04

Thurloo & Glenalba 23.5 8 high sig.

Thurloo & Glenlea 20.0 8 0.01

Thurloo & Verniew 18.7 8 0.02

Willala & Bowann 18.7 8 0.02

Willala & Glenlea 16.5 8 0.04

Willala & Verniew 18.7 8 0.02

Alice Downs & Bowann 27.9 8 high sig.

Alice Downs & Glenlea 21.2 8 0.01

Alice Downs & Verniew 15.7 8 0.05

Bowann & Claravale 22.9 8 high sig.

Bowann & Glenlea 27.9 8 high sig.

Claravale & Verniew 16.4 8 0.04

Glenalba & Glenlea 24.5 8 high sig.

Glenalba & Verniew 20.8 8 0.01

Glenlea & Verniew 19.0 8 0.01

Table 3.6 – Significant genic differentiation among some population pairs

43


A numeric code for population sites is used in further tables to assist presentation.

The numbers 1-11 correspond to the populations in the following order:

Western Region (arid) - 1) Ponto, 2) Thurloo, 3) Willala. (highlighted)

Eastern Region (semi-arid) – 4) Alice Downs, 5) Bowann, 6) Claravale, 7)

Currawong, 8) Glenalba, 9) Glenlea, 10) Polworth, 11) Verniew. (non-highlighted)

Pop 1 2 3 4 5 6 7 8 9 10 11

1

2 X

3 NS NS

4 NS NS NS

5 NS X X X

6 X NS NS NS X

7 NS X NS NS NS NS

8 X X X NS NS NS NS

9 X X X X X NS NS X

10 NS NS NS NS NS NS NS NS NS

11 X X X X NS X NS X X NS

Table 3.7 – Matrix of significant genic differentiation does not depict regional

dichotomy based on population pairs

NS = Not significant; X = Significant at p = 0.05.

Pairwise population Fst (Weir & Cockerham method), measuring inbreeding in the

subpopulation relative to the total population, was calculated using Genepop, and

tested for significance using the formula:

Significance = FST.2n.(a-1) and degrees of freedom = (a-1)(p-1);

where n = number of samples, a = number of alleles, and p = number of populations.

Values were calculated for each locus and summed for the value over all loci.

Western populations were not significantly different among the region, however,

44


they had significantly different levels of inbreeding compared with all eastern

populations (Alice Downs was the exception, and is discussed further in this

chapter). The FST data is shown in table 3.8 and the test of significance shown in

table 3.9. POP 1 2 3 4 5 6 7 8 9 10 11

1

2 0.0034

3 -0.0038 -0.0016

4 0.0149 0.0055 0.0130

5 0.0088 0.0218 0.0160 0.0198

6 0.0142 0.0081 0.0124 0.0039 0.0112

7 0.0042 0.0051 0.0057 0.0022 -0.0024 0.0063

8 0.0159 0.0215 0.0194 0.0061 -0.0023 0.0093 0.0076

9 0.0207 0.0108 0.0132 0.0154 0.0281 0.0134 0.0017 0.0311

10 0.0091 0.0078 0.0110 0.0093 0.0127 0.0077 -0.0112 0.0198 0.0019

11 0.0294 0.0212 0.0283 0.0140 0.0167 0.0278 0.0023 0.0376 0.0226 0.0196

Table 3.8 – Pairwise Population FST values

POP 1 2 3 4 5 6 7 8 9 10 11

1

2 NS

3 NS NS

4 X NS X

5 X X X NS

6 X X X NS NS

7 X X X NS NS NS

8 X X X NS NS NS NS

9 X X X NS X NS NS X

10 X X X NS NS NS NS X NS

11 X X X X NS X NS X X X

Table 3.9 – All Western populations have significantly different levels of inbreeding

relative to Eastern populations

NS = Not Significant; X= Significant at p = 0.05

45


FIS (inbreeding in individuals relative to subpopulation) variation was calculated for

all loci and averaged over each population with standard error. Figure 3.1 display the

values and graphic representation of the values sorted from smallest to largest to

determine any trends with respect to the eastern and western regions. Given that the

value for Thurloo rests well among the eastern populations, there is no obvious

pattern at the regional level. Indeed, variation at the individual level is most likely

causing the large standard errors observed (figure 3.1).

Sorted Mean Fis

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

Pont

Willa PW VN

BW GL AD GBThu

rCU CL

Site

Mea

n Fi

s

Mean Fis

Figure 3.1 – Individual inbreeding relative to subpopulation varies across the entire

data set with no regional differences observed.

A hierarchical AMOVA was performed using GENALEX. The regional grouping

used for this analysis was West V East (Arid v Semi-arid). Table 3.10 and Figure 3.2

display the results which show large amounts of variation present among individuals.

The genetic variation was shown to be significantly different from random (in terms

of genetic structuring) through a random data resampling process (bootstrapping

1000 times), the results of which are graphically presented in figure 3.3.

46


Source df SS MS Est. Var. Stat Value Prob

Among Regions 1 71.24 71.24 0.31 PhiRT 0.08 0.001

Among Pops./Regions 9 70.16 7.80 0.14 PhiPR 0.04 0.001

Indiv./Within Pops. 390 1257.50 3.22 3.22 PhiPT 0.12 0.001

Table 3.10 – AMOVA summary – significant variation at all levels of hierarchy

Analysis of Molecular Variance

Among Regions8%

Among Pops./Regions

4%

Indiv./Within Pops.88%

Figure 3.2 – AMOVA Summary – most variation is at the individual level

47


Freq. dist. of permuted PhiPT vs observed PhiPT

0

100

200

300

400

500

600

-1.00

0-0.

840-0.

680-0.

520-0.

360-0.

200-0.

0400.1

200.2

800.4

400.6

000.7

600.9

20

PhiPT

Freq

uenc

y

Permute PhiPTDataPhiPT

Figure 3.3 – Randomisation of PhiPT – Data is significantly different from random

A genetic similarity matrix (Nei) was calculated and used to construct an UPGMA

tree to assess the genetic distances between pairs of populations (figure 3.4).

GENALEX was used for all calculations. Closer similarity was evident between

western populations relative to eastern populations (ie. the west clusters out first,

followed by the east).

48


0.935 0.968 1.000

Bowann

Glenalba

Ponto

Willala

Currawong

Verniew

Glenlea

Eastern populations

Claravale

Polworth

Thurloo

Western populations

Alice Downs

Figure 3.4 - UPGMA tree for Nei similarity matrix – Western populations are more

similar to each other than Eastern populations.

49


Discussion

The population dynamics of O. cuniculus in arid and semi-arid Australia results from

“boom-bust” style fluctuations. During the initial natural spread, rabbits moved north

through South Australia’s arid country into Queensland. This was followed by later

colonisation of the semi-arid eastern parts of Queensland. In classical metapopulation

style, rabbits utilise arid habitats as widespread populations during periods of

favourable environmental conditions (food, water); but most likely retreat to specific

refuge areas during prolonged adverse conditions (e.g. drought). When favourable

conditions return, population explosions force expansion from refuges into

unoccupied habitat.

A similar system operates in semi arid areas of southern Queensland, with the

exception that favourable habitat is limited in extent and is patchy in distribution.

There are generally more forested areas (unsuitable for rabbit dispersal), and the

distribution of food and suitable sandy loam soils for nesting is less predictable than

in the west. When rabbits succeed in colonising a suitable patch during times of

dispersal under favourable conditions, they seed the new population with whatever

alleles they carry, and any increase in genetic variation will depend on subsequent

mutations. When conditions become unfavourable, populations must survive in situ

because few, if any, additional refuges are available unlike the situation in arid areas.

The genetic structure of rabbit populations in the two regions reflects the distribution

of favourable habitats and the relative ease of dispersal. In the arid system, the

emergence and spread of new alleles via mutation and dispersal will occur more

readily than in semi-arid environments. While new alleles should arise through

mutation at the same rate in both environments, the survival of new alleles will be

higher in a system where population sizes are larger, successful dispersal is more

common, and reproduction occurs at much higher rates. The three arid (or western)

populations, Ponto, Thurloo, and Willala, possess a greater mean number of alleles

compared with all populations from the semi-arid (eastern) system (Table 4). The

Sol28 and Sat5 loci showed the greatest variation in terms of the number of alleles (7

and 9 respectively). In both instances, for arid populations, a single common allele

dominated at frequencies of 60-70% with alternative alleles making up the

50


remainder. The alleles in low frequencies are effectively rare, however, they occur

across the entire region, providing evidence for a refuge-expansion metapopulation

model for the arid region. In contrast, in the semi-arid region, the Sol28 and Sat5 loci

show identical dominant alleles in all populations as in the arid region, but they occur

at much higher frequencies, approaching 90%. As dispersal is more difficult in the

semi-arid region (Hamilton, 2003), and populations are smaller, it is likely that rare

alleles will not persist at all sites because they will be lost due to drift effects and are

not replaced easily by dispersal.

Low microsatellite allelic diversity is observed in numerous species exhibiting

historical demographic fluctuations (Table 3.11).

Species Allelic Diversity Ref

Cervus elaphus 1.9-4.7 Polziehn et al. (2000)

Cervus nippon 1.8-5.1 Goodman et al. (2001)

Alces alces 2.6-5.2 Broders et al. (1999)

Table 3.11 – Examples of species with reduced allelic diversity due to population

demographics in the last 200 years.

Low allelic diversity, however, is not intrinsically linked to population fluctuation as

the Mississippi white tailed deer (Odocoileus virginianus) shows. Despite evidence

of past genetic bottlenecks in some areas, restored deer populations have high allelic

diversity (Deyoung et al. 2003).

While comparisons of genetic diversity between species are qualitative, it is possible

to compare allelic diversity of rabbit populations studied elsewhere to those found in

arid and semi-arid South Western Queensland. Allelic diversity in rabbit populations

sampled in this study are significantly lower than that in populations in Spain (9.62

alleles) (Queney et al. 2001), although populations in France (5.03) (Queney et al.

2001) and the United Kingdom (5.05) (Surridge et al. 1999) share a similar allelic

diversity to those found here. Australian populations are descended from a small

number of rabbits first introduced in the 19th

century, and such an event effectively

places a large demographic constraint on the colonising populations, and ensures a

51


limited genetic stock relative to the parent gene pool overseas. Zenger et al.(2003)

found however, no evidence of a genetic bottleneck in five rabbit populations (allelic

diversity 5.03) in Australia despite the well known demographic bottleneck that

occurred with the introduction of the species. Zenger et al. (2003) suggest the rapid

population expansion countered the bottleneck effects, which explains why

Australian allelic diversity is similar to that of source populations in the UK after a

limited amount of time since introduction.

Analysis of FIS

variation measures the reduction in heterozygosity of an individual

due to non random mating within a subpopulation, and ranges from –1 to 1 where 1

represents a subpopulation with no heterozygotes. FIS

was tested because previous

studies had identified population genetic structuring in the eastern semi-arid system

based on mtDNA (Fuller et al. 1996, 1997; Wilson et al. 2002). If population genetic

structure within the eastern system results from isolation as a consequence of habitat

heterogeneity, then a heterozygote deficiency would be expected in eastern

populations relative to western populations. A decrease in heterozygosity would most

likely result from increased levels of inbreeding and would make allele frequencies

move toward fixation through the sharing of genes by common descent. FIS

values

for each population (all loci averaged) were not large in magnitude and ranged from

–0.08 to 0.05; which may indicate there is no heterozygote deficiency. FIS

values

were sorted from smallest to largest to test for any patterns among regions. Ponto and

Willala have the smallest FIS

values, however, the other western population

(Thurloo) FIS

clustered with the eastern populations. Eastern populations may have

been exposed to more inbreeding relative to western populations, however, given the

magnitude of the standard errors, caution must be exercised when interpreting data in

this way.

A comparison of observed heterozygosity values between rabbit populations

examined here and those studied elsewhere shows that on average, western and

eastern populations had observed heterozygosity (He) of 0.69 and 0.62, which is very

similar to the values obtained by Zenger et al. (2003) for five additional populations

in Australia (He = 0.66). While eastern populations did show lower H

e than western

52


populations, values were so close to each other that they cannot be considered

significantly different. Furthermore, data collected by Queney et al. (2001) in Spain

and France showed He values of 0.66 and 0.63 respectively – these are regions that

have sustained rabbit populations far longer than the time that rabbits have been in

Australia, yet they exhibit very similar levels of heterozygosity. Surridge et al.

(1999) studied populations in the UK and reported He values of 0.44 – it is not known

why this value is lower than the values in other countries, one possibility is a

relatively recent demographic bottleneck in the study populations. While it is

unknown exactly where the “source” populations are located in the UK that provided

the stock translocated to Australia; heterozygosity in introduced populations does not

show a lasting effect of the historical bottleneck. The main reason is likely to be the

rapid growth rate of rabbit populations in Australia post introduction. Average

heterozygosity is influenced by population growth rate, assuming the population

eventually attains a large size (Nei et al. 1975, Hedrick 2000) – which is something

that rabbit populations in Australia most certainly did.

Analysis of genic differentiation among population pairs (Tables 3.5 and 3.6) shows

no distinct pattern, both within and between regions. The analysis is based on the

allelic diversity of each population pair and a more thorough analysis of this can be

achieved using FST

that takes into account allele frequencies in each population

(Tables 3.7 and 3.8).

The most important feature of Tables 3.8 and 3.9 is that no significant differences

were observed among arid (western) populations, while significant differences in

allelic frequencies were observed among some population pairs in the semi-arid

(eastern) region. Furthermore, semi-arid populations were significantly differentiated

from all arid populations (Alice Downs and Thurloo being the only pair wise

exception). Alice Downs is the most western population in the semi-arid region and

Thurloo is the most eastern population in the arid region; and represents the closest

(geographically) population pair potentially linking the arid and semi-arid systems –

although there is at least 500km distance separating the two sites. Even though

isolation by distance has been discounted through the work of Fuller et al. (1996,

1997) and Wilson et al. (2002); Alice Downs, which is the western most semi-arid

53


population could be viewed as being representative of an “historical gateway”

between west and east, which may account for the association with Thurloo and the

non-association with other arid populations.

Analysis of molecular variance (AMOVA) shows that most of the variation (88%)

observed was present at the individual within population level (Figure 1); and is

statistically significant. This result does not imply that no regional based differences

exist, rather it is more a reflection of the fact that this analysis examined within

species variation given only a relatively short amount of time since colonisation. In

essence, there does not appear to have been sufficient time for many new mutations

to arise in situ. Therefore any pattern observed in the semi-arid east will be result

largely from the genetic composition of the arid populations from which they were

derived. As stated previously, the limiting effect of an introduction based

demographic bottleneck can be offset by rapid population growth.

The significance of the AMOVA was tested through a process of 1000

randomisations. The result of randomly assigning alleles was compared with the

acutal pattern (Figure 3.3) to show the genetic patterns observed were significantly

different from random.

This is not the first instance of a population study that reported differences among

regional groups based on FST

but that also show limited geographic variation based

on AMOVA. Ryberg et al. (2002) studied the genetic relationships of American

alligator populations distributed across different ecological and geographic scales.

The authors studied six populations that were divided between inland and coastal

areas, in addition to four different river systems. Alligator habitats in south eastern

USA show variable degrees of continuity/suitability, such that coastal areas are more

homogeneous, while inland habitats are heterogeneous – a situation that is similar to

the difference between rabbit habitats in arid and semi-arid areas of south western

Queensland, Australia. Differences in habitat characteristics are likely to affect the

genetic structure of alligator populations in south-eastern USA. Ryberg et al. (2002)

measured FST

and found significant differentiation among all alligator populations,

however, AMOVA failed to demonstrate a geographic pattern relative to the

54


differentiation indicated by FST,

and found 90% of variation and could be attributed

to within population variability.

The construction of a UPGMA tree based on Nei’s similarity index shows that the

arid populations are the most closely related in terms of gene diversity and

frequency. All arid populations cluster together before any semi-arid populations join

the tree (Figure 3.4). The east-west dichotomy depicted does not illustrate a

phylogenetic or evolutionary lineage, but serves as pictorial evidence for a difference

between the two regions based on modern gene frequencies.

While the current study used microsatellite DNA to assess population genetic

structure of rabbit populations in arid and semi arid Australia, previous studies in this

region (Fuller et al. 1996, 1997; Wilson et al. 2002) identified patterns of population

genetic structure based solely on mitochondrial DNA, which is maternally inherited.

Due to innate sex-biased dispersal strategies in rabbits where males often move to a

new territory and females are more likely to remain within the natal territory, it was

unclear whether mtDNA could adequately represent the real genetic structure pattern.

The use of bi-parentally inherited markers, however, has provided confirmation that

the patterns of mtDNA genetic variation reported by Fuller et al. (1996, 1997) and

Wilson et al. (2002) do not result soley from female biased philopatry in the eastern

region.

Rabbits in arid and semi-arid Australia are constantly exposed continuously to

periods of fluctuating resources, and populations are therefore forced through

sequential demographic bottlenecks whenever a period of poor resources occurs. The

data in the current study suggests that eastern populations have a different level of

inbreeding relative to western populations, but, most genetic variation is still present

within populations. This outcome is a consequence of different rates of population

recovery (post demographic crash) within each region – which in turn reflects the

prevailing habitat conditions within each region. Following on from this, the data do

not indicate any genetic bottlenecks associated with demographic bottlenecks due to

the rapid “rebound” of populations in keeping with the cyclical nature of

metapopulation dynamics. While semi-arid populations do show different amounts of

55


inbreeding relative to arid populations (FST

), there is no evidence of a loss in

heterozygosity associated with inbreeding.

The identification of regional differences in genetic variation based on nuclear DNA

agree continuously to s with earlier studies based on mitochondrial DNA. Previous

studies (Fuller et al. 1996, 1997; Wilson et al. 2002) suggested that the mechanism

responsible for the observed patterns was associated with heterogeneity of essential

resources in the semi-arid environments. The role that rabbit behaviour plays in this

pattern has been largely ignored in understanding patterns of population genetic

structure. The European rabbit is a territorial organism with strict social hierarchies

and behaviour that can affect levels of gene flow (ie dispersal) – particularly in

habitats with a heterogeneous distribution of favourable resources. The observed

genetic differences between the eastern and western regions could result from a

combination of habitat heterogeneity and localised inbreeding due to “traditional”

territorial behaviour in the east, compared with relaxed territorial behaviour (less

inbreeding, more scramble competition) in the west.

This study has provided additional genetic evidence for the existence of two

‘population systems’ linked to differences in environmental attributes. The next part

of the current study examines the potential for rabbit behavioural plasticity linked to

variation in local environmental conditions. If behavioural plasticity is present, it

may contribute to observed genetic diversity patterns in arid and semi-arid

environments.

56

Chapter 4 Behaviour

Rabbit Behaviour

Rabbit behaviour has been studied in great detail with respect to social organisation

and dominance hierarchies (Southern, 1948; Mykytowycz, 1958, 1959, 1960;

Mykytowycz et al. 1960; Mykytowycz and Gambale, 1965; Mykytowycz and

Fullagar, 1973; Henderson, 1979; Wood, 1980; Daly, 1981; Fullagar, 1981; Cowan

and Garson, 1985; Parer and Fullagar, 1986; Cowan, 1987; Myers et al. 1994;

Richardson et al. 2002). Aggressive behaviour patterns have been characterised

extensively because they are an important component of the process that determines

dominance hierarchies in both sexes. Previous behavioural studies have been limited

however, to examining ‘wild’ populations that live in enclosures or small areas with

defined boundaries (eg. golf course). These studies have required, or have been able

to view rabbits at close range through the use of hides and/or audio-visual

equipment. Studying wild populations under natural field conditions in arid and semi

arid Australia is much more difficult because areas containing suitable habitat are

very large and wild rabbits are not familiar with humans. To date, the possibility that

innate rabbit behaviour may contribute to explaining observed patterns of population

genetic structure in arid and semi-arid Australia has not been fully tested. The work

of Hamilton (2003) suggested that rabbit behaviour contributed 20% to observed

population genetic structure; although the behaviour itself was not quantified or

qualified. Therefore, the purpose of this part of the study was to determine if

aggressive behaviour in the European rabbit varies with resource levels and if so,

could this affect dispersal rates and hence influence regional population genetic

structures.

Materials And Methods

Field experiments were conducted at Bulloo Downs in September 2001 and April

2002. Identical methodologies were employed during both field trips. Timing of the

field trips was such that behavioural data could be collected at the same sites under

different environmental conditions. Limited rainfall in June 2001 created enough

pasture (“green pick”) so that in September 2001 resource (food) availability was

moderate (only on the sand substrates). Lack of rain during the intervening six

months, combined with summer heat, meant that conditions in April 2002 were very

57

Chapter 4 Behaviour

dry (drought) and consequently resource availability was very low.

Three sand hill sites inhabited by rabbits were selected in the north west corner of the

Currawilla paddock (Table 4.1). This location is approximately 10km south of the

Willala site used in the genetics study. The area is characterised by white sand hills

(where rabbits burrow), separated by large clay pans (where rabbits feed and

socialise). Trees and shrubs are sparse and are mostly located on sand hills.

Experimental sites were separated by a minimum of 1km.

Site Number Location

1 28°43’43”S 142°52’15”E

2 28°43’28”S 142°52’40”E

3 28°43’15”S 142°53’09”E

Table 4.1 – Site locations for behaviour experiments

Within each site, four trap locations were selected along the edge of the sandhill. A

treadle operated cage trap was placed at each location. The trap locations represented

filming points for each site, and were also used to capture local rabbits for use as

control decoys. Additional traps were placed at various sand hills in the vicinity of

the sites (within 20km); and were used to capture experimental rabbits (individuals

unknown to those living at the study sites). While rabbits are extremely neophobic

towards objects placed within their territories, avoidance attenuates after a couple of

presentations of new stimuli (Sunnucks, 1998). Therefore, all traps were left open for

the first 7 days of each trip before trapping commenced.

All traps were set each night using chopped carrot as bait. Traps at film sites were

not set until after filming was completed each night. Traps were cleared in the

morning and any rabbits (up to a maximum of 3) suitable for experiments were

retained in cage traps with whole carrots for food under drop sheets in the shade.

Only juvenile male rabbits were used as “decoys” for the experiments because they

are the age/sex class that disperse most. The objective was to simulate the

behavioural response of resident rabbits to a foreign rabbit attempting to enter

58

Chapter 4 Behaviour

(disperse into) the local territory.

Three SONY video cameras were set up on tripods (1 per site each night) with red

light spot lights (lightforce) located 40m into the adjacent claypan. The use of video

cameras enabled the field work to be completed at a faster rate (i.e. 3 cameras could

be run almost simultaneously); and the film provided a permanent record that could

be viewed many times where necessary. Rabbits do not see red light, therefore the

use of red filters on the spotlights enabled filming of rabbits with natural behaviour

patterns. Video cameras were operated in “night shot” mode, which is not a true

night vision system, but does amplify any available light. The combined effect of

using red spot lights with nightshot produced video footage in black and white

(rather than red). This was much easier to view. Each filming night, cameras were

turned on at dusk and left unattended and uninterrupted for one hour. All cameras

were turned on within a 15 minute period which allowed for time spent adjusting the

cameras, transferring the decoy rabbit, and travelling between sites. Filming time

was limited to one hour because the 12 volt batteries running the spotlights only held

sufficient charge to illuminate the film area for one hour. Decoy rabbits were placed

in the cage at the film sites immediately prior to filming, after which, they were

marked by fur clipping and released at the point of capture. Video cameras were

zoomed in to allow observation of sufficient detail of activities around the cages. The

cage containing the decoy rabbit was the centre of focus, and the camera was zoomed

in to enable clear vision of 2m around the cage. More area was visible behind the

cage and less in front of the cage as indicated by figure 4.1. The exact distance

visible behind the cage varied at each film site depending on the contours of the land.

59

Chapter 4 Behaviour

2m

amera

rap Cage TVideo C

2m

40m

16.5m

Figure 4.1 – Vision field of video camera at each site

Resource levels at sampled sites were assessed by measuring vegetation cover. Eight

100m transects were studied per site, four located on sand substrate and four located

on clay substrate. The data were recorded as one of three classifications at each meter

mark along the transect: 1)Present Green, 2) Present Dry or 3) Absent. The

vegetation assessment was conducted during both (September 2001 and April 2002)

field trips using identical transects.

Analysis Methods – Habitat Conditions

Habitat conditions at the test sites were very dry in April 2002 compared with

September 2001. Generally, there was less grass (dry or green), and water levels

were greatly reduced to the point that many waterholes and dams were dry or very

near dry.

The number of active warren entrances were counted at each film site during

September 2001 and April 2002. Rabbit capture data (sex, weight) were recorded at

all trapping sites. The number of rabbits trapped and active warren counts were used

as an indicator of habitat conditions because they are directly affected by the

availability of food and water. The mean weight of decoys was calculated and

compared between seasons.

The best estimator of habitat conditions is a direct measure of food resources, which

60

Chapter 4 Behaviour

was achieved by calculating the percentage cover for each category (green, dry,

absent) on each transect. The values were averaged and a standard deviation

calculated for comparison between seasons.

An index measuring resource per individual was calculated for each season. It is

based on the percentage of green pick available on sand substrates and the total

number of unique rabbit captures (from trap data).

Analysis Methods - Behaviour

Video data were transferred from video8 tape to VHS, to enable viewing on standard

VCR units. The markings denoting date, site, and control or experimental decoy were

covered with black tape by an assistant, so that observers had no idea what treatment

was being examined in each replicate (blind).

All video data were viewed and converted to a paper format (“ethograph”) that

contained a column representing each 60 second time period of the video tape. In

each column, the number of rabbits present was recorded with a separate row used

for each individual. If the individual stayed for longer than one minute then the same

row was used in consecutive columns. Aggressive behaviours displayed by each

rabbit were counted and recorded on the ethograph using code symbols and tallies.

Wild rabbits could not be identified individually; so that if a rabbit was on screen

then went off screen, but came back later during the video, this was not known.

Therefore every rabbit that entered the viewing area was treated as a ‘new’

individual. A number was used as the designator code for a new rabbit (the number

changed for each rabbit according to how many had preceeded it on tape) and a code

symbol of two slashes was used to denote a rabbit leaving the screen.

The potential for activity (all behaviour inclusive) during the hour was measured by

counting the number of rabbits present within a 2m radius of the decoy. Counts were

performed for every 60 second time period during the one hour video resulting in 60

records per tape. Rabbit count data was averaged over each site and treatment

(control and experimental) and standard errors calculated.

61

Chapter 4 Behaviour

Not all of the behaviour patterns listed in appendix 1 were observed in this study

because quality of the video footage was limited partly by the distance from the cage,

power of the zoom, and lens quality. Behaviours observed on video and their

definitions are presented in table 4.2.

Behaviour Description

Grazing (up and down) Feeding with head lowered near vegetation; and head raised

away from vegetation but still chewing.

Resting alert Head up with ears erect, but not chewing.

Alert Sitting upright with front legs raised off ground, ears erect.

Resting Inactive with ears flattened, eyes partially or wholly closed.

Lying with legs tucked beneath, lying on side with white

belly fur exposed.

Grooming Licking or scratching the fur. Rabbits may flick the front

paws rapidly up and down (“air box”) before grooming the

head and ears.

Moving Either slow hops while feeding, or rapid running in response

to disturbance from people, predators etc.

Chasing One individual rapidly pursuing another. The chasing animal

may attempt to bite the fleeing animal if it gets close enough.

Displacement One individual moves toward another resulting in the latter

moving away. Sometimes accompanied by a threat with the

head thrust forward and ears flattened.

Circling Local animals hopping around the cage containing the decoy

animal.

Tail Flagging Individual hops with rather stiff looking hind legs and raises

tail to expose white underside. This behaviour is performed

by both sexes during aggressive interactions, although more

commonly by males. It is also seen when males are circling

females.

Paw scraping Rapid scratching of the ground with the fore paws. Can be

either to expose roots during foraging or it is seen before

males chin mark, defecate, or urinate during patrolling and

territorial marking.

62

Chapter 4 Behaviour

Aggressive leaping

(fighting)

Two individuals simultaneously jump towards each other.

They pass in the air, land, and then repeat the process in the

opposite direction. Usually these jumping fights are brief, 1-5

leaps. Mainly seen between males on territory boundaries,

occasionally between two females. Individuals from the same

group sometimes interact in this way if they have come into

close contact “unintentionally” eg. If one of them is engaged

in a rapid chase.

Table 4.2 – Behaviour of wild rabbits observed on video records from Bulloo Downs

Behaviour descriptions involving eyes open/closed and chewing could not be scored

due to video resolution. To assess differences in aggressive behaviour, it was

necessary to record the number of times each aggressive behaviour was observed

during the one hour period. Aggressive behaviour patterns observed, recorded and

used in subsequent analyses were: Tail Flagging, Displacement, Circling, and

Aggressive leaping.

Not all aggressive behaviours were used in the analysis. Chasing was included with

displacement behaviour because the two were closely linked. Paw scraping was

observed, but could not be attributed to either foraging or aggression due to video

quality. Similarly, aggressive/territorial behaviours involving urine spraying, bowing

and chin marking could not be easily distinguished. Fighting involving close contact

and parallel running was not observed at any stage.

The frequency of the four aggressive behaviours observed was recorded for the

duration of the video. Data for the hour was recorded each minute resulting in 60

data periods. The mean number of observations and standard error was calculated for

each decoy type (control and experimental) at each site.

A large difference (determined from fresh dung piles, active warren counts, and trap

data) was observed in the number of rabbits present at the sites in September 2001

compared with April 2002, due to significant differences in available resources. To

compare data from the two seasons, the number of aggressive displays per rabbit was

63

Chapter 4 Behaviour

calculated for each video tape, then averaged for each decoy type per site and

standard errors calculated. The calculation was performed by dividing the count of

behaviour in each minute period by the number of rabbits observed within 2m of the

cage for the corresponding period.

Results

Habitat Conditions

Active warren numbers were lower at both sites in 2002 compared with 2001. The

smallest reduction occurred at site 3, where the 2002 count was 55% of the 2001

count; the largest difference occurred at site 2, where the 2002 count was only 18%

of the 2001 count (table 4.3).

YEAR SITE ACTIVE INACTIVE

2001 1 61 9

2001 2 33 3

2001 3 138 12

2002 1 26 59

2002 2 6 1

2002 3 76 54

Table 4.3 – Active and Inactive Warren Count Data for 2001 and 2002

During each field trip, a total of 840 trap nights were used. In September 2001, a

total of 375 rabbits were trapped; 126 were male, 159 were female, and 90 were too

young to determine sex. Of the 375 individuals captured, 188 were recaptures. The

average weight of males captured was 892.5g (std error = 44.8g), the average weight

of females captured was 1135.7g (std error = 44.3g), and the average weight of

kittens captured that were able to be weighed was 340g (n=7; std error = 25.9g). A

scatterplot of rabbit weight v sex is shown in figure 4.2.

64

Chapter 4 Behaviour

2001 Capture Data Weight v Sex

g

500g

1000g

1500g

2000g

2500g

Sex

Wei

ght (

g)

Kitten Female Male

Figure 4.2 – 2001 Rabbit Weight v Sex (total captures). Favourable habitat

conditions prior to the trip induced a breeding cycle causing the variation in capture

weights.

In April 2002, a total of 169 rabbits were trapped; 99 were male, and 70 were female;

no kittens were trapped. The total number of 169 included 97 recaptures. The

average weight of male captures was 1527.8g (std error = 19.1g), and the average

weight of female captures was 1466.4g (std error = 23.7g). A scatterplot of rabbit

weight v sex is shown in figure 4.3. The effect of relatively poorer environmental

conditions in 2002 is evident in this data as fewer rabbits were trapped, no kittens

were trapped, and there was less variation in the weight of those rabbits that were

trapped.

65

Chapter 4 Behaviour

2002 Capture DataWeight v Sex

g

500g

1000g

1500g

2000g

2500g

Sex

Wei

ght (

g)

Male Female

Figure 4.3 – 2002 Rabbit Weight v Sex (total captures). Poor habitat conditions are

reflected in capture data where no kittens were observed and variation in weight was

reduced.

The average weight of rabbits retained as decoys was calculated for each

site/treatment/year and graphed with standard error (figure 4.4), where n denotes the

number of rabbits used as decoys at each site, and therefore denotes the number of

behaviour videos (samples) recorded at each site. Size of the decoys used in 2002

was significantly larger than 2001 due to lower resource availability (ie. tending

towards drought). Only the smallest, most immature males (no scrotal testes) were

retained as decoys during 2002.

66

Chapter 4 Behaviour

Mean Decoy Weight

0200400600800

10001200140016001800

1 2 3

Site

Wei

ght (

g) Control 2001Experimental 2001Control 2002Experimental 2002

n=1 n=8n=3 n=8 n=6 n=5 n=6 n=5 n=6 n=5 n=9

Figure 4.4 – Decoys used in 2002 were significantly larger than those used in 2001.

The mean percentage cover for each category (green, dry, absent) at each site on

each substrate during each year are presented in Table 4.4

Year Site Substrate Mean Percentage Cover std error

Absent Dry Green Absent Dry Green

Sep-01 1 Clay 34.0 50.3 15.7 8.4 4.7 4.0

Sep-01 1 Sand 27.3 13.7 59.0 4.6 4.8 3.8

Sep-01 2 Clay 60.3 22.6 17.1 14.6 9.2 5.5

Sep-01 2 Sand 39.5 5.6 54.9 4.3 0.9 4.0

Sep-01 3 Clay 76.5 13.5 10.0 4.7 3.1 3.1

Sep-01 3 Sand 46.4 14.9 38.7 4.3 6.3 4.3

Apr-02 1 Clay 91.7 8.3 0.0 2.0 2.0 0.0

Apr-02 1 Sand 79.7 15.3 5.0 3.4 2.1 1.7

Apr-02 2 Clay 80.0 19.0 1.0 3.3 3.5 0.6

Apr-02 2 Sand 80.3 15.7 4.0 3.0 2.4 1.1

Apr-02 3 Clay 89.0 10.1 0.9 2.0 1.6 0.4

Apr-02 3 Sand 56.2 42.5 1.3 10.0 9.4 0.6

Table 4.4 – Mean percentage cover across all sites/substrates/years.

67

Chapter 4 Behaviour

A comparison of percentage vegetation cover between years shows a large decrease

in the amount of available green vegetation in 2002 across all sites. While

environmental conditions were in general much worse in 2002 due to drought, micro-

differences were also apparent among sites within the season with respect to the total

amount of vegetation available (both green and dry). Vegetation data are better

displayed using the following graphs of mean percentage cover and 95% confidence

interval at each site (Fig 4.5 to 4.7).

Figure 4.5 – Mean percent cover (+/- 95%CI) of the different vegetation types on

sand and clay at Site 1 for September 2001 and April 2002

68

Chapter 4 Behaviour



69

Chapter 4 Behaviour



70

Chapter 4 Behaviour

Resource / Individual Index

The calculation of the resource/individual index (R/I) was based on the average

amount of green vegetation on sand substrates across the three sites; and the total

number of unique rabbit captures (ie. ignoring recaptures) collected during each

sampling trip. The index facilitates comparison between seasons when there were

observed differences in the distribution of food and the size of rabbit populations.

There was a much greater abundance of food in September (R/I = 0.27) relative to

April (R/I = 0.05).

Behaviour

A scatterplot of the rabbits observed vs total behaviour count (Fig 4.8) shows a clear

correlation in the raw data between these variables. It is for this reason, that all

subsequent analyses were performed on a transformed data set (behaviour per

rabbit), which removed any potential density effects interfering with analysis of

behaviour.

71

Chapter 4 Behaviour

Scatterplot - Both years data - All Behaviours (summed) vs Rabbit NumbersTotal Behaviours = -9.4059+3.8336*x

-10 0 10 20 30 40 50 60 70 80 90

Number of Rabbits

-50

0

50

100

150

200

250

300

350

400

Tota

l Beh

avio

ur C

ount

Rabbit Number:Total Behaviours: r 2 = 0.7951; r = 0.8917, p = 00.0000

Figure 4.8 – The amount of behaviour observed is correlated to the number of rabbits

present.

Totals of aggressive behaviour per rabbit were calculated for the one hour test

period. Values were tested using ANOVA within each year and treatment

combination to determine if data from all sites could be pooled for further analysis.

During September 2001, the mean summed control data at each site were not

significantly different among sites, F(2, 9) = 1.38, p = 0.30; and the mean summed

experimental data were not significantly different among sites, F(2, 16) = 0.98, p =

0.40.

During April 2002, the mean summed control data at sites 1 and 3 (site 2 was

excluded due to problems with dingos) were not significantly different t = -1.28

d.f.=6 p = 0.25; and the mean summed experimental data were significantly different

among sites 1 and 3, t = -2.43 d.f. = 15, p = 0.03. The differences in experimental

72

Chapter 4 Behaviour

data in April 2002 are probably due to the micro-differences in the resource levels at

each site, and therefore the data could not be pooled for further analysis. A plot of the

mean of each site and treatment with 95% confidence intervals follows (figure 4.9).

Figure 4.9 – Mean Plot of Sum Aggressive Behaviour per Rabbit per Hour for each

year shows no clear differences between treatments or years because of large error

margins

A students t-test was used to test for significant differences between the mean values

of control (pooled) and experimental (pooled) data in September 2001; and the

separate site means of control and experimental data in April 2002. There were no

significant differences observed in any instance. T tests were also used to test for

differences in control (pooled) data between years and experimental data (at each

site) between years. No significant differences were observed.

73

Chapter 4 Behaviour

74

Data were averaged for each year, site, and treatment combination. The graphs of

total aggressive behaviour per rabbit for each minute of observation (1hour total) are

presented in Figures 4.10 – 4.20 on the following pages; and show the distribution of

aggressive behaviour (per rabbit) throughout the data collection period (1hr).

75

18/09/01 Site1 t4 Control Total Behaviours per rabbit(total individuals = 11; total behav/rabbit = 19)

0

0.5

1

1.5

2

2.5

3

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Time (min)

Beh

avio

urs

BehavioursMean behav/min observation

Figure 4.10 – Aggressive behaviour per rabbit at Site 1 for control rabbits in September 2001 (n=1)

75

76

Sept 2001 Site1 Expt Avg Total Behaviours per Rabbit(avg tot ind. = 12.88; avg tot behav/rabbit = 20.44)

0

0.5

1

1.5

2

2.5

3

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Time (min)

avg

beha

viou

rs/ra

bbit

Avg behavioursmean behav/min observation

Figure 4.11 – Aggressive behaviour per rabbit at Site 1 for experimental rabbits in September 2001 (n=8)

76

77

Sept 2001 Site2 Control Avg Total Behaviours per rabbit(avg total ind. = 24; avg tot behav/rabbit = 28.16)

0

0.5

1

1.5

2

2.5

3

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Time (min)

Avg

beh

avio

urs/

rabb

it

Avg behavioursMean behav/min observation


77

78

Sept 2001 Site2 Expt Avg Total Behaviours per Rabbit(avg total ind. = 18.6; avg tot behav/rabbit = 27.19)

0

0.5

1

1.5

2

2.5

3

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Time (min)

Avg

beh

avio

urs/

Rab

bit

Avg behavioursMean behav/min observation


78

79

Sept 2001 Site3 Control Avg Total Behaviour per Rabbit(avg tot rabbits = 28.8; avg tot behav/rabbit = 40.91)

0

0.5

1

1.5

2

2.5

3

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Time (min)

Avg

Beh

avio

ur/R

abbi

t

Avg behaviourMean behav/min observation


79

80

Sept 2001 Site 3 Expt Avg Total Behaviour per Rabbit(avg total rabbits = 32.8; Avg Tot Behav/Rabbit = 33.6)

0

0.5

1

1.5

2

2.5

3

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Time (min)

Avg

Beh

avio

ur/R

abbi

t

Avg BehaviourMean behav/min observation


80

81

April 2002 Site 1 Control Avg Total Behaviours per rabbit(avg tot ind. = 10.66; avg tot behav/rabbit = 13.78)

0

0.5

1

1.5

2

2.5

3

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Time (min)

avg

beha

viou

rs/ra

bbit

avg behavioursmean behav/min observation

Figure 4.16 – Aggressive behaviour per rabbit at Site 1 for control rabbits in April 2002 (n=3)

81

82

April 2002 Site1 Expt Avg Total Behaviours per Rabbit(avg tot ind. = 12.62; avg tot behav/rabbit = 16.45)

0

0.5

1

1.5

2

2.5

3

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Time (min)

avg

beha

viou

rs/ra

bbit

Avg behavioursmean behav/min observation

Figure 4.17 – Aggressive behaviour per rabbit at Site 1 for experimental rabbits in April 2002 (n=8)

82

83


0

0.5

1

1.5

2

2.5

3

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Time (min)

avg

beha

viou

rs/ra

bbit


Figure 4.18 – Aggressive behaviour per rabbit at Site 2 for experimental rabbits in April 2002 (n=6). This site was severely affected by dingo activity.

83

84

April 2002 Site3 Control Avg Total Behaviours per Rabbit(avg tot ind. = 29.8; avg tot behav/rabbit = 35.35)

0

0.5

1

1.5

2

2.5

3

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Time (min)

Avg

beh

avio

urs/

rabb

it


Figure 4.19 – Aggressive behaviour per rabbit at Site 3 for control rabbits in April 2002 (n=5)

84

85

85


0

0.5

1

1.5

2

2.5

3

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

time (min)

avg

beha

viou

rs/ra

bbit

avg behaviourmean behav/min observation

Figure 4.20 – Aggressive behaviour per rabbit at Site 3 for experimental rabbits in April 2002 (n=9)

Chapter 4 Behaviour

86

The data for each hour was grouped into 6 intervals of 10 minutes duration, this was

done to remove most of the “zero” scores, while still obtaining an overview of any

trends during the filming period. It also allowed the following comparisons (of each

data period) via statistical methods:

Within year, site, and treatment

Within year and site, between treatments

Within year, between sites, within treatments

Between years, within site and treatments

Within year, site, and treatment (ANOVA).

During September 2001, at Site 1 and Site 2, both control and experimental

treatments were not significantly different for any time period.

Site 1 expt F(5, 42) = 1.07, p = 0.39; Site 2 cont F(5, 30) = 0.63, p = 0.68; Site 2 expt

F(5, 24) = 2.52, p = 0.06. Site 3 control data shows a significant difference, F(5, 24)

= 3.18, p = 0.02, the significant result occurs due to the first time period (0-10min)

having significantly more aggressive behaviour per rabbit than the last two time

periods (40-60min) as shown in the decreasing trend (figure 4.23). Site 3

experimental data shows that the first time period (0-10min) was significantly

different from the rest of the hour (10-60min) F (5, 30) = 7.17, p = 0.0002. This is

confirmed by the graph in figure 4.23 that shows the significantly higher mean.

Chapter 4 Behaviour

87

Figure 4.21 – September 2001 Site 1 Aggressive Behaviour per Rabbit in 10min

intervals is not significantly different due to large error margins

Chapter 4 Behaviour

88

Figure 4.22 – September 2001 Site 2 Aggressive Behaviour per Rabbit in 10min

intervals is not significantly different due to large error margins

Chapter 4 Behaviour

89

Figure 4.23 – September 2001 Site 3 Aggressive Behaviour per Rabbit is

significantly higher in the 0-10min phase

During April 2002, there were no significant differences in the control data for any

site (1 & 3 – note that no control data was collected at site 2, and the experimental

data at site 2 was severely affected by dingo activity.). Site 1 cont F(5, 12) = 1.03, p

= 0.45; Site 3 cont F(5, 24) = 2.31, p = 0.08. The experimental data at site 1 and 3

was significantly different within each hour. Site 1 experimental F(5, 42) = 4.21, p =

0.003 was significant due to the 10-20min period being significantly higher than all

other periods except the 20-30min. Site 3 experimental F(5, 48) = 3.60, p = 0.008

was significant due to the 0-10min period being significantly higher than the period

from 20-60min, the 10-20min period was part of the decreasing trend from the initial

period to the lower levels observed in the remainder of the hour. Figures 4.24 – 4.26

show the graphs mean values.

Chapter 4 Behaviour

90

Figure 4.24 – April 2002 Site 1 Aggressive Behaviour per Rabbit is significantly

higher in 10-20min period for experimental data

Chapter 4 Behaviour

91

Figure 4.25 – April 2002 Site 2 Aggressive Behaviour per Rabbit was severely

affected by the activity of dingos

Chapter 4 Behaviour

92

Figure 4.26 – April 2002 Site 3 Aggressive Behaviour per Rabbit is significantly

higher in 0-10min period for experimental data

Chapter 4 Behaviour

93

Within year and site, between treatments (t-test)

T tests were used to compare the mean number of aggressive behaviours per rabbit

during each time period. Results for each site are tabulated below (tables 4.5 – 4.9)

and significant results are highlighted. Across all tests, the only significant difference

between control and experimental data was observed in the 30-40min period at site 2

during September 2001; a result most likely caused by the average experimental

value being very close to zero.

Mean

Expt

Mean

Cont

t-value df p Valid N

expt

Valid N

Cont

Std.Dev.

Expt

Std.Dev.

Cont

F-ratio

variances

p variances

0-10 1.8 0.0 0.6 7 0.60 8 1 2.8 0.0 0.0 1.0

10-20 4.6 4.0 0.1 7 0.93 8 1 6.1 0.0 0.0 1.0

20-30 6.3 4.0 0.3 7 0.76 8 1 6.9 0.0 0.0 1.0

30-40 3.9 5.0 -0.2 7 0.83 8 1 4.9 0.0 0.0 1.0

40-50 2.0 6.0 -1.3 7 0.24 8 1 2.9 0.0 0.0 1.0

50-60 1.9 0.0 0.4 7 0.72 8 1 4.8 0.0 0.0 1.0

Table 4.5 - September 2001 Site 1 Control v Experimental

Mean

Expt

Mean

Cont


expt

Valid N

Cont

Std.Dev.

Expt

Std.Dev.

Cont

F-ratio

variances

p variances

0-10 4.3 4.8 -0.2 9 0.87 5 6 4.0 5.3 1.7 0.6

10-20 8.7 4.8 1.4 9 0.21 5 6 5.4 4.1 1.8 0.5

20-30 6.8 4.7 0.7 9 0.48 5 6 4.1 5.0 1.5 0.7

30-40 0.9 7.1 -2.8 9 0.02 5 6 1.3 4.8 12.6 0.0

40-50 3.0 3.4 -0.2 9 0.87 5 6 4.8 2.2 4.6 0.1

50-60 3.5 3.3 0.1 9 0.94 5 6 2.3 3.3 2.2 0.5

Table 4.6 - September 2001 Site 2 Control v Experimental is significant during 30-

40min period

Chapter 4 Behaviour

94

Mean

Expt

Mean

Cont


expt

Valid N

Cont

Std.Dev.

Expt

Std.Dev.

Cont

F-ratio

variances

p variances

0-10 15.2 12.6 0.8 9 0.43 6 5 3.5 6.9 3.8 0.2

10-20 4.8 8.8 -1.2 9 0.27 6 5 5.4 5.8 1.2 0.9

20-30 5.2 6.2 -0.3 9 0.79 6 5 6.4 5.9 1.2 0.9

30-40 3.4 8.6 -2.0 9 0.07 6 5 2.9 5.6 3.8 0.2

40-50 3.3 3.2 0.0 9 0.97 6 5 4.7 2.5 3.6 0.2

50-60 1.7 1.6 0.1 9 0.92 6 5 2.6 0.7 14.3 0.0

Table 4.7 - September 2001 Site 3 Control v Experimental

Mean

Expt

Mean

Cont


expt

Valid N

Cont

Std.Dev.

Expt

Std.Dev.

Cont

F-ratio

variances

p variances

0-10 1.8 2.3 -0.2 9 0.85 8 3 4.6 4.0 1.3 1.0

10-20 6.6 3.2 1.2 9 0.27 8 3 3.8 5.6 2.2 0.4

20-30 4.7 5.4 -0.3 9 0.79 8 3 4.3 0.8 29.5 0.1

30-40 1.4 1.0 0.2 9 0.81 8 3 2.2 1.7 1.7 0.9

40-50 1.8 1.5 0.2 9 0.85 8 3 2.4 2.6 1.2 0.7

50-60 0.3 0.3 -0.1 9 0.91 8 3 0.5 0.6 1.1 0.8

Table 4.8 - April 2002 Site 1 Control v Experimental

Mean

Expt

Mean

Cont


expt

Valid N

Cont

Std.Dev.

Expt

Std.Dev.

Cont

F-ratio

variances

p variances

0-10 10.5 11.5 -0.3 12 0.76 9 5 6.1 2.9 4.5 0.2

10-20 6.4 8.5 -0.5 12 0.62 9 5 6.08 9.4 2.4 0.3

20-30 3.2 8.3 -1.8 12 0.10 9 5 3.31 7.5 5.2 0.0

30-40 4.1 2.3 0.7 12 0.51 9 5 5.38 3.2 2.9 0.3

40-50 3.8 1.1 1.2 12 0.27 9 5 4.82 2.4 4.1 0.2

50-60 1.9 3.7 -0.7 12 0.50 9 5 2.28 7.2 10.0 0.0

Table 4.9 - April 2002 Site 3 Control v Experimental

Chapter 4 Behaviour

95

Within year, between sites, within treatments (ANOVA)

Variation between sites in aggressive behaviour per rabbit within season and

treatment was measured using one way ANOVA. An analysis was performed for

each time period. There were no significant differences in control data among sites

during September 2001 (table 4.10).

Time Period (min) Statistic

0-10 F(2, 9) = 3.1, p = 0.09

10-20 F(2, 9) = 1.0, p = 0.40

20-30 F(2, 9) = 0.1, p = 0.88

30-40 F(2, 9) = 0.3, p = 0.78

40-50 F(2, 9) = 0.6, p = 0.56

50-60 F(2, 9) = 1.1, p = 0.37

Table 4.10 - September 2001 Control data shows no difference among sites

A significant difference, however, was observed in experimental data among sites

during September 2001 only for the 0-10min period (table 4.11). This resulted from

the relative level of aggression per rabbit at site 3 being much greater than at sites 1

and 2. For all other periods there were no significant differences among sites. This

result highlights the behavioural variation in the one hour period between sites

during the same season, which may be caused by the micro-variation in resource

levels at each site during the season, however further research is required to prove

this conclusively.

Chapter 4 Behaviour

96

Time Period (min) Statistic

0-10 F(2, 16) = 28.9, p = 0.00

10-20 F(2, 16) = 0.9, p = 0.43

20-30 F(2, 16) = 0.1, p = 0.90

30-40 F(2, 16) = 1.1, p = 0.37

40-50 F(2, 16) = 0.2, p = 0.82

50-60 F(2, 16) = 0.4, p = 0.70

Table 4.11 - September 2001 Experimental data is significantly different for 0-10min

Site 2 was excluded from further analysis in April 2002 because of high activity of

dingos interfering with rabbit behaviour, therefore, t tests were used to compare data

only between sites 1 and 3. There was a significant difference in control data among

sites during April 2002 only for the 0-10min period. The result was caused by the

aggression per rabbit at site 3 being much greater than site 1. For all other periods

there were no significant differences among sites (table 4.12).

Mean

Site 1

Mean

Site 3


Site 1

Valid N

Site 3

Std.Dev.

Site 1

Std.Dev.

Site 3

F-ratio

variances

p variances

0-10 2.3 11.5 -3.8 6 0.01 3 5 4.0 2.9 1.9 0.5

10-20 3.2 8.5 -0.9 6 0.42 3 5 5.6 9.4 2.8 0.6

20-30 5.4 8.3 -0.7 6 0.54 3 5 0.8 7.5 90.7 0.0

30-40 1.0 2.3 -0.6 6 0.54 3 5 1.7 3.2 3.3 0.5

40-50 1.5 1.1 0.2 6 0.82 3 5 2.6 2.4 1.2 0.8

50-60 0.3 3.7 -0.8 6 0.46 3 5 0.6 7.2 155.9 0.0

Table 4.12 - April 2002 Control Data - Site 3 is significantly higher during 0-10min

than Site 1

Chapter 4 Behaviour

97

There was a significant difference in experimental data among sites during April

2002 only for the 0-10min period. The result was caused by the level of aggression

per rabbit at site 3 being much greater than site 1. For all other periods no significant

differences were observed among sites (table 4.13).

Mean

Site 1

Mean

Site 3


Site 1

Valid N

Site 3

Std.Dev.

Site 1

Std.Dev.

Site 3

F-ratio

variances

p variances

0-10 1.8 10.5 -3.3 15 0.01 8 9 4.6 6.1 1.8 0.4

10-20 6.6 6.4 0.1 15 0.95 8 9 3.8 6.1 2.6 0.2

20-30 4.7 3.2 0.8 15 0.45 8 9 4.3 3.3 1.7 0.5

30-40 1.4 4.1 -1.3 15 0.20 8 9 2.2 5.4 5.8 0.0

40-50 1.8 3.8 -1.0 15 0.32 8 9 2.4 4.8 4.1 0.1

50-60 0.3 1.9 -2.0 15 0.07 8 9 0.5 2.3 17.3 0.0

Table 4.13 - April 2002 Experimental Data - Site 3 is significantly higher during 0-

10min than Site 1

Between years, within site and treatments (t test)

The differences observed among sites prevented the pooling of all site data to test

between years, however, as there were no significant differences between control and

experimental treatments at each site – the data were combined (to give a larger data

set) for each site to allow a comparison of the aggressive behaviour per rabbit

between years for sites 1 and 3 (tables 4.14 – 4.15).

Chapter 4 Behaviour

98

Mean

Sept

Mean

April


Sept

Valid N

April

Std.Dev.

Sept

Std.Dev.

April

F-ratio

variances

p variances

0-10 1.6 1.9 -0.2 18 0.87 9 11 2.7 4.2 2.5 0.2

10-20 4.5 5.7 -0.5 18 0.62 9 11 5.8 4.3 1.8 0.4

20-30 6.0 4.9 0.5 18 0.62 9 11 6.5 3.6 3.2 0.1

30-40 4.0 1.3 1.8 18 0.09 9 11 4.6 2.0 5.1 0.0

40-50 2.4 1.7 0.6 18 0.57 9 11 3.1 2.3 1.8 0.4

50-60 1.7 0.3 1.0 18 0.32 9 11 4.6 0.5 75.6 0.0

Table 4.14 - Site 1 – September 2001 v April 2002 – no difference

Mean

Sept

Mean

April


Sept

Valid N

April

Std.Dev.

Sept

Std.Dev.

April

F-ratio

variances

p variances

0-10 14.0 10.9 1.5 23 0.14 11 14 5.2 5.1 1.0 0.9

10-20 6.6 7.1 -0.2 23 0.85 11 14 5.7 7.1 1.6 0.5

20-30 5.6 5.1 0.2 23 0.81 11 14 5.8 5.5 1.1 0.8

30-40 5.8 3.4 1.2 23 0.24 11 14 4.9 4.7 1.1 0.8

40-50 3.2 2.8 0.3 23 0.79 11 14 3.7 4.2 1.3 0.7

50-60 1.6 2.6 -0.6 23 0.53 11 14 1.9 4.5 5.5 0.0

Table 4.15 - Site 3 – September 2001 v April 2002 – no difference

No significant differences were observed between years in any time period, at any

site, though the data does show that site 1 consistently had a latency period for the

first 10 minutes, while site 3 had the highest levels of activity in the initial period.

A general linear model (Statistica) was used to investigate the general trends within

the data set. The process is an extension of the multiple regression / multivariate

regression models that allows for linear combinations or transformations of multiple

variables and also permits examination of repeated measure factors (StatSoft, 2006).

The model was applied across the entire data set to identify trends in the amount of

aggressive behaviour per rabbit with respect to the following variables: Year, Site,

Treatment, Time (within the hour). The process did not identify statistically

Chapter 4 Behaviour

99

significant effects of the previously mentioned variables. However, the ability to

examine and view all data on one graph did identify one trend that was not apparent

from other analyses. The graph of mean behaviour/rabbit at each site plotted against

Year, and Treatment (Figure 4.27) shows a consistent decrease in the amount of

rabbit behaviour at each site between years, for the treatment in question. Sites 1 and

3 (Site 2 is excluded due to high levels of dingo activity) exhibited greater amounts

of behaviour responses in 2001 compared with 2002. At each site, the degree of

reduced behaviour between years was virtually identical; and while the reduction was

less in the experimental data, the same pattern was observed in both the control and

experimental data sets. This result highlights a consistent effect of drier conditions in

2002 on the amount of aggressive behaviour per rabbit – that is, an overall reduction

in activity.

Figure 4.27 – General Linear Modelling identified a consistent reduction in

behaviour between years for each treatment at each site (Site 2 excluded due to dingo

activity).

Chapter 4 Behaviour

100

Sixty minutes of video data was collected each time based on power limitations of

the experiment. It enabled an examination to identify any behavioural trends that

may occur throughout the hour. However, previous studies of rabbit territorial

behaviour suggest that any aggressive response to a decoy (ie. intruder) is most likely

to occur quite soon after the decoy is introduced to the site (B.Cooke and D.Berman

pers comm). This response was most evident at Site 3. Therefore, data from only the

first 15 minutes of each tape were analysed and compared in further detail to

examine differences in aggressive response towards known (control) and unknown

(experimental) rabbits.

This analysis used a different time period (15 mins) than had been used previously.

A 10 minute time interval was used for overall analysis (ANOVA etc) of the entire

dataset; the grouping removed a lot of the “zeros” to facilitate statistical analysis,

whilst still enabling examination of trends during the hour as the data were split into

6 discrete periods. The 15min initial period was chosen for the analysis of the critical

period when the most aggressive activity occurs based on comments from field

experts (ie. if an aggressive territorial response were to occur in a natural system, it

would happen within 15mins of trespassing, or not at all). To split the entire data set

into 15min periods (for the previous analyses simply for the sake of consistent

numbers) would reduce the number of discrete time periods to 4 per hour and make it

harder to justify as an examination of trends over one hour. Hence two different time

periods were used in the two different analyses.

The average percentage aggressive behaviour (out of total aggressive behaviour per

rabbit observed) occurring in the first 15 minutes are presented below in table 4.16.

At all sites (where sufficient data exists), there was a higher percentage of aggressive

behaviour evident in the experimental treatments compared with control treatments

during the first 15 minutes of the test period.

Chapter 4 Behaviour

101

Year Site Treatment Percentage

Sept 2001 1 Control 10.5

Sept 2001 1 Experimental 21.9





April 2002 1 Control 20.6

April 2002 1 Experimental 29.0


April 2002 3 Control 45.3


Table 4.16 – Percentage of Total Aggressive Behaviour Occurring in first 15mins is

higher for experimental sites

The proportion of total aggressive behaviour in the experimental and control data at

15 minutes are presented in table 4.17 below. The values are calculated by dividing

the number of behaviours observed at 15 minutes (either experimental or control), by

the sum of the number of behaviours for each class, as defined by the following

formula:

Pexpt15 = Nexpt15 / (Nexpt15 + Ncont15) and Pcont15 = Ncont15 / (Nexpt15 + Ncont15)

Year Site Pexpt Pcont

September 1 0.70 0.30 September 2 0.59 0.41 September 3 0.63 0.37

April 1 0.76 0.24 April 3 0.46 0.54

Table 4.17 – Proportion of aggressive behaviour occurring in first 15 minutes

Chapter 4 Behaviour

102

If there was no difference in response to control or experimental rabbits, then the

proportion in each class in Table 4.17 should be 0.5. It is important to remember that

this is a test of the treatments only within the replicated design. The previously

identified effect of seasonal difference (however slight it may be), was not a factor

included in this analysis as the data were examined within season and site factors. A

two-tailed, one sample t-test with the test value = 0.5 was performed to test the

significance of the values. The difference between proportions for September was

significant (p = 0.05, df = 2), and the proportions of control to experimental

responses for April were not significantly different (p = 0.58, df = 1).

Chapter 4 Behaviour

103

Discussion

The purpose of the behaviour component of the study was to determine

quantitatively, if aggressive behaviour in the European rabbit varied under different

resource conditions. It therefore required tests to be conducted under different

environmental conditions. It was quite clear from personal observation that

environmental conditions were very different during the two sampling trips; and this

was confirmed by the analysis of warren counts, capture data, and vegetation

transects. A good reflection of the effect of the minor rainfall that occurred in June

2001 were the data relating to individual weights of rabbit captures (Figure 4.2).

Rainfall initiated a period of breeding, which is why weight of rabbits ranged from

200g to over 2000g. The break in the data lines in figure 4.2 indicates that the time

since rainfall was around 3 months because rabbits can be aged by weight up to

1000g at the rate of 10g per day (D.Berman pers comm). Weight of female rabbits

during September 2001 was more variable than male rabbits due to breeding cycle,

some may have been pregnant with late litters and others were in poor condition after

having produced their litters.

Rabbit capture data for April 2002 also demonstrate the change in resource quality

from September 2001. No kittens were captured in 2002, and the range in weight for

adult males and females was nearly identical. These differences are illustrated by the

graph of mean decoy weight (i.e. only those rabbits used in filming experiments were

considered) for both sampling trips (figure 4.4). An extensive search of literature

found no studies reporting the effect of decoy size on behavioural response, however,

given the large difference between mean decoy weights (between years) in this study,

the possibility that decoy size introduced an unwanted variable into the experiment

cannot be excluded.

One of the best ways to elicit a territorial response is through the use of a decoy, and

there are many examples of decoy use in the literature. Hau et al. (2004) used decoys

to conduct simulated territorial intrusions in populations of the spotted antbird

(Hylophylax n. naevioides) to examine hormonal control of territorial aggression.

Chapter 4 Behaviour

104

They found that antbirds can produce hormones (during the non-breeding season)

that may serve as a precursor of sex steroids for the regulation of year round

territorial behaviour (Hau et al. 2004). Wiklund and Village (1992) used caged

decoys to simulate territorial intrusion in the European Kestral (Falco tinnunculus),

and found that territorial behaviour varied depending on the stage of the breeding

cycle. The simulated territorial intrusion approach was a crucial aspect of the present

study.

Food availability is a key resource for wild rabbit populations, and quantification of

the differences in food availability between sampling trips was necessary in order to

test the hypothesis that wild rabbits may alter their social systems as a response to the

amount and distribution of favourable habitat. Transects were used to assess the

amount of green vegetation, representing edible food for rabbits. During September

2001, only sand substrates exhibited a moderate level of green cover, whereas clay

substrates had low amounts of green cover. This was a further indication of the poor

environmental conditions present and a reflection of the amount of rainfall from

June/July 2001 and the weather conditions during the intervening period. If a larger

amount of rainfall had occurred during the winter period, a higher level of green

cover would be expected on clay substrates as well as on sand substrates. The months

between September 2001 and April 2002 were very hot and no rainfall was recorded

at all, and the effect of this is clearly illustrated in the vegetation graphs. Extremely

low levels of green vegetation were present at all sites on all substrates (0-5%). As a

corollary, there were high levels of bare ground at all sites and substrates in April

2002, except at site 3 on the sand substrate, where there was a high level of dry

vegetation present (42%). This effect at site 3 may have been caused by the slightly

larger size of the sand hill and the presence of a few more established trees on the hill

that may have aided in local moisture retention.

The resource/individual index (RI) was five times larger in September 2001

compared with April 2002. It highlights a qualitative difference between the two

sample periods, ie. there was an actual difference in the availability of food to

individual rabbits, and not merely fewer individuals eating less food. The value of RI

Chapter 4 Behaviour

105

for April 2002 (0.05), is an additional indicator of the drought that affected the

region and the population systems within.

Statistical analysis of total rabbit aggressive behaviour data identified differences

solely in experimental data between sites 1 and 3 during April 2002. Aside from

preventing the pooling of data for further analysis, this result suggests that rabbit

behaviour differed at these sites. Differences may have been caused by the variance

of site 3 with respect to the amount of bare ground, dry and green vegetation;

however, if this was so, then a similar difference might also be expected in the

control data for those sites. The April 2002 control data of site 3 has a larger mean,

however, the variance around the mean is so large that a significant difference was

not evident.

A general trend was present (albeit masked by high variances) that suggests flexible

behaviour systems is an attribute of rabbit populations in arid environments. Table

4.16 shows that at every site during both years, where sufficient data exists, there

was a higher percentage of total aggressive response recorded during the first 15

minutes for all experimental data versus control data at all sites. The actual

percentages varied among sites, which most likely reflects the size of the

populations, the local environmental conditions, levels of resources present, and

relative activity levels of predators during the sampling trips. Table 4.17 shows the

proportion of control and experimental aggressive behaviour occurring in the first 15

minutes (in relation to the total amount of aggressive behaviour observed). As

discussed earlier, the initial time period appears critical in terms of territorial

defence; and it is evident that a difference in response occurred because the

proportions are not 0.5, which could be expected under identical response patterns.

The proportional difference in values was found to be statistically significant for

September 2001, however, it was non-significant in 2002 when environmental

conditions were most harsh. It is apparent, therefore, that under the most severe (ie.

drought) conditions it was not possible to detect a difference in aggressive response

to control and experimental rabbits.

Chapter 4 Behaviour

106

Control rabbits used in the experiments were ‘known’ to the test population and were

of low social rank. They therefore would likely generate less of an initial aggressive

response than will an experimental rabbit brought from outside the population.

Control rabbits, however, remained part of the social hierarchy within the territories

of capture, and therefore it is highly probable that any aggressive behaviour observed

against control rabbits were part of the normal social interactions among individuals

within a group. Evidence for this is provided by the lack of an initial burst of

aggressive behaviour as observed with experimental rabbits, and the general

accumulation of aggressive behaviour across the sampling hour. It is also unknown if

the released control rabbits were able to “learn” from their experience of being in the

cage, and what effect that might have on their response to a different rabbit being

presented at the same location on subsequent nights. Although fur marking prevented

re-use of the same individuals, there was evidence of recaptures at all sites (data not

presented) – particularly in 2002, when conditions were drier. It is possible that

recaptured rabbits became “trap happy” in order to utilise the food resource (chopped

carrot) within the traps. Recent example of other species exhibiting trap happy

behaviour can be found in Gibba turtles (Deforce et al. 2004), Australian fur seals

(Hume et al. 2002), and European badgers (Tuyttens et al. 1999).

Analysis of aggressive behaviour data was also conducted to assess differences in

behaviour over different time periods within the one hour sample in order to

investigate further the trend identified with analysis of sum total data.

During September 2001, within each site and treatment, the lack of significant

differences among time intervals at sites 1 and 2 indicates an even distribution of

aggressive behaviour across the sampling period. Site 3, however, exhibited a

decreasing trend that is consistent with the idea that most aggression occurs early

during the sampling time. A similar pattern was observed during April 2002 where

no significant difference was found among time intervals for the control data,

however, there were differences for the experimental data. During April at site 1,

there appeared to be a latency period of ten minutes before a burst of aggressive

activity followed by a gradual decline. At site 3 (April), the pattern matched

Chapter 4 Behaviour

107

September 2001 in that levels of aggression peaked in the first time interval and

gradually reduced. The observed trend of an early peak in aggressive behaviour

varied in strength among sites, which can be explained by the distribution of food

resources. Whilst the environmental conditions and food resources were different

between seasons, they were also not identical within a season. Therefore, variation in

behaviour patterns may be due to micro-variation in environmental and food

conditions among sites within each season. During September 2001, the collective

amount of vegetation (both dry and green) at each site on sand substrate was 73%,

61%, and 55% for sites 1-3 respectively, however, during April 2002 the vegetation

amount was reduced to approximately 20% at sites 1 and 2, and 44% at site 3. For

site 1, the large reduction in vegetation availability is a possible explanation for the

behaviour trend observed in April as opposed to the consistent level of behaviour

observed in September. For site 3, there was only minor change to the total amount

of vegetation available, which explains the similar patterns observed between

seasons at this site. It is probable that site 1 was in a much greater state of decline (in

a metapopulation sense) than site 3, due to a combination of the amount of vegetation

available and the general environmental conditions (ie the weather) – thus causing

the variation in the observed behavioural response between sites.

Effect of habitat variation on behaviour (in general) is extremely well studied,

however, as Brashares and Arcese (2002) comment, there are few species that vary

sufficiently in social behaviour to permit detailed intraspecific comparisons. (ie.

variable behaviour across different habitats). The vegetation component of this study

measured the variables of food quantity and quality; and counts of active warrens can

be used as an indicator of density – three habitat variables previously shown to

influence territoriality (Maher and Lott, 2000). Lombardi et al. (2003) examined the

ecological responses of the European rabbit to varied (3 types) habitats; scrubland

(dense cover, low food), grassland (little cover, high food), and ecotone between the

two. In the scrubland, rabbits were dispersed among cover, and in grassland, rabbits

were linked to aggregated burrows. Rabbits reached the highest abundance in

ecotone, whereas low food and refuge availability limited abundance in scrubland

and grassland respectively (Lombardi et al. 2003). Therefore, the study also showed

Chapter 4 Behaviour

108

that rabbits were able to modify behaviour patterns in order to adapt to specific

habitat conditions across a very small geographic area (2km separated all sites). The

significance of this is that while Lombardi et al. (2003) did not examine the genetic

structure of the rabbit populations, they would almost certainly have shared common

genes without significant structuring based on distances rabbits are known to

disperse (Williams et al. 1995). Taken together, this suggests that the rabbit

populations studied by Lombardi et al. (2003) were probably of the same genetic

stock, yet able to modify behaviour patterns across a small area.

There are several points of statistical relevance that require further comment:

1. Throughout the present study of rabbits in arid and semi-arid Australia,

statistically significant differences were not observed between control and

experimental (total) data due to the large variances present in the behavioural

data; and a posteriori one can infer that unless the trend had been extremely

strong, it would be highly unlikely to detect any underlying differences that

were present.

2. The use of correlation data does not necessarily imply cause and effect. Figure

4.8 illustrated a correlation between the total number of rabbits observed and

the total amount of behaviour, which was cause to use the calculation of

behaviour/rabbit to remove density effects between years. That is not to say

there is a cause and effect relationship between these variables; more likely,

the external factors influencing the number rabbits can be considered causal

for the effect of behaviour variability. This ideawill be explored further in

Chapter 5.

3. A type 1 error occurs when one statistically rejects a null hypothesis (based on

whatever level of α is set, usually 0.05) when it is fact true. It represents the

“false positive” scenario, which is particularly undesirable as it can result in

wasted efforts of further research that is unable to replicate the original

results (Statsoft, 2006). A type 2 error occurs when one fails to reject the null

hypothesis when the alternative is true; it effectively rejects the researcher’s

theory when it is actually correct. It is an unfortunate result when one

considers that valuable theories and technologies could be lost due to this

Chapter 4 Behaviour

109

type of error (Statsoft, 2006). When one encounters datasets like that

presented here (results affected by drought and disease) with a large amount

of statistically non-significant results – the question arises as to whether there

may be type 1 or 2 errors. Clearly, one cannot say without further research if

an error was committed. Based on the current data however, it would appear

that a type 2 error would more likely be committed in this instance. That is

not to say that a rigorous statistical approach has not been adhered to – where

the statistical results suggested the rejection of an hypothesis was necessary,

it was done. The only way to discount the effect of type 2 (and type 1) errors

on the results here (and the theory behind it regarding flexible behaviour) is

to replicate the experiments in times of greater resource abundance, thus

removing the potential impact of drought and disease from the data.

These data illustrate the differences between sites in terms of the size of local

populations and the manner in which each population behaved – the differences are

ultimately a reflection of the difficult environmental conditions that prevailed during

the study period. The fact that constant levels of aggression per rabbit were observed

across the hour test period in some instances does not suggest that rabbit aggression

will occur in this manner in non experimental situations. Additionally, the restraint of

the decoy rabbit within the study area precluded certain aggressive behaviour

patterns directed at the decoy from being observed and measured. Aggressive

fighting, chasing and displacement of territorial transgressors may occur very quickly

within the population systems used in this study under non-experimental conditions.

Site 3 was the only site during both September 2001 and April 2002 that had a

population of rabbits behaving with the largest number of aggressive responses in the

initial time interval. This is most likely due to the larger population size at site 3 as

indicated by warren count and personal observation. After initial aggressive displays

towards the decoy, which was restrained in a cage and hence could not escape the

site, resident rabbits may have been more concerned with social and or territorial

interactions within the population. This behavioural response is similar to that which

might be expected under non experimental conditions when a foreign rabbit enters a

Chapter 4 Behaviour

110

territory and is subsequently forced to leave after a short amount of time. Territorial

displacement is a strong behavioural indicator and (although manifest in different

ways) is observed in a variety of species such as American redstart (Marra, 2000),

woolly monkeys (White et al. 2000), common loons (Piper et al. 2000), trout

(Landergren, 1999), and gecko lizards (Petren, 1998).

While the cages used for trapping and restraining decoys were in place for a week

prior to video taping to avoid the neophobic effects described by Sunnucks (1998);

their presence in a natural system cannot be completely ignored in terms of their

effect upon the observed behaviour. It is assessed that sufficient attenuation was

achieved to minimize the effect on this study; however, one may consider it an

unavoidable facet of experimental field research.

Despite drought conditions, predators, and other associated problems, the results of

the behavioural component of this project support the potential for flexible social

patterns and variable territorial response in the European rabbit, which can be at least

in part explained by variability in resource availability. The potential was evident in

the variation in levels of aggressive behaviour response during the initial contact

period depending on the origin of the transgressor rabbit. The implications of the

potential for flexible social structures on general rabbit population dynamics and

their influence on population genetic structures are developed in the following

chapter.

Chapter 5 Discussion and Conclusions General Discussion

The ability to exhibit variable behaviour patterns is one mechanism that allows

animals to adapt to extant conditions. It is something that occurs across virtually all

animal species, with perhaps one of the most striking examples being carrion crows

in Japan that developed a behaviour pattern to use vehicles stopped at traffic lights to

crack walnuts (Nihei, 1995). For the European rabbit, however, survival in the arid

environments of western Queensland compared with that in more temperate climates

is very difficult without certain adaptations to lifestyle. This fact will hold true for

most species found in arid and non-arid climes. The adaptations required may take

several forms, and include patterns of feeding, methods for obtaining shelter, and

reproductive cycles; one or all of which may contribute to the formation of

metapopulations in arid environments (Hanski and Gilpin, 1991; Kutt, 2003).

The metapopulation dynamics of European rabbit populations in western Queensland

are in keeping with source-sink models as outlined earlier and supported by personal

observations and discussions with landholders. In dry times, the only rabbits that

survive live in proximity to permanent sources of water – the largest being the

Bulloo Lakes in the south-west of the Bulloo Downs property. When floodwaters

arrive to regenerate green pastures, they trigger a burst in reproduction that

subsequently initiates widespread dispersal to ‘sink’ type habitats.

Population Genetics

The cyclical nature of rabbit metapopulations in arid western Queensland (and to a

wider extent, central Australia) influences population genetic structures as

demographic bottlenecks erode genetic diversity, however, the rapid population

growth following the event ensures that any genetic effects are minimised (Zenger,

2003). The European rabbit was introduced to Australia less than 200 years ago,

from what was probably an already limited genetic stock in the United Kingdom (see

results of Surridge et al. 1999). Even though the evolution of new alleles can occur

very rapidly at microsatellite loci relative to mitochondrial and other nuclear genes

that code for functional products, the rate of evolution of new mutations that result in

novel allelles is not high enough to have a major effect on population genetic

111

Chapter 5 Discussion and Conclusions structures. This is mainly a result of the instability of many European rabbit

metapopulations in arid parts of the country. A new microsatellite allele produced

through mutation will be rare in the initial generations after its evolution. Given the

non-selective nature of microsatellite DNA, the maintenance of a new rare allele

within a population is virtually totally a result of stochastic processes.

It is possible that as a consequence of a particularly severe drought, as was

experienced in eastern Australia during the course of this project, a number of alleles

were lost, leaving only a limited number in high frequencies. Consequently, the

population genetic structure of a recently introduced species to arid Australian

environments may be very susceptible to severe climatic fluctuations that are

prolonged in duration.

Semi-arid environments should offer more stability to rabbit populations by way of

higher annual rainfall leading to greater availability of food resources. Shelter, in the

form of nesting sites, will vary among locations as it largely depends on soil type and

the scale of vegetation cover. In the semi-arid region studied during this project, the

vegetation consisted of patches of open ground separated by remnant dry

schlerophyll forest areas. The soil type varied from black clay to sandy loam. The

genetic diversity of semi-arid populations was lower than that present in arid

populations in spite of environmental conditions being more stable. There are several

possible explanations for this:

1. The evolution of new alleles occurs at the same rate as in arid populations but are

not retained in the population at the same rate as a consequence of population

size, dispersal rate, and behaviour.

2. The low sample size of semi-arid populations results in a low probability of

detecting rare alleles, even if they are present.

3. Semi-arid rabbit populations that were isolated due to intervening unfavourable

habitat patches (forests) were not experiencing large amounts of gene flow,

which was not identified in the genetic assessment due to the small number of

alleles present in the populations after bottleneck effects.

The genetic structuring observed through this project represents a sample from a

112

Chapter 5 Discussion and Conclusions single point in time. For species living in relatively stable environments, and

therefore maintaining stable populations, a single sample can be considered

representative of the overall population genetic structure. In environments with

flexible conditions, however, a single sample can only provide a representation of

conditions at the time of sampling. This is particularly important for a species such as

the European rabbit that has the ability to reproduce very quickly, and can experience

very large fluctuations in population size. To be truly representative, one would have

to collect samples during periods illustrative of each type of condition. As stated

previously, this was not possible during this project, and the genetic data are

representative of more favorable conditions than when this project was conducted.

One can only theorise as to the effect of prolonged drought on the population genetic

structure of rabbits in the arid region. Given that rabbit populations crash and only

survive in refuge areas near permanent water, its possible that genetic diversity could

reduce to levels similar to the semi-arid region (showing few rare alleles). If

unfavorable conditions persist, then it is also possible that genetic drift effects could

lead to genetic structuring, and temporarily breakdown the panmictic system

identified in previous research (of Fuller et al. 1996, 1997), until a boom population

cycle occurs in more favorable conditions.

Dobson (1998) states that characteristics of social systems (group composition,

mating preferences, and dispersal) may influence the gene dynamics of a population.

Additionally, a connection between population genetics and behaviour was shown by

Chesser et al. (1993) in that social and genetic traits of a population are likely to co-

evolve. It has also been shown that the genetic structuring caused by social breeding

groups can then limit the further evolution of social behaviours (Chesser, 1998).

The genetic component of this project suggests that different population genetic

structures may be present in arid and semi-arid zones identified previously (Fuller et

al., 1996, 1997). The environmental conditions, population fluctuations, and habitat

connectivity certainly play a role in determining the level of genetic structuring

observed as demonstrated by Hamilton (2003). However, results of the current

project suggest that the importance of the 20% contribution of behaviour towards

population genetic structuring (identified by Hamilton, 2003) can vary depending on

availability of favourable resources.

113

Chapter 5 Discussion and Conclusions

Behavioural Ecology

The behavioural ecology of the European Rabbit has been studied extensively given

its status as a significant pest species, and was reviewed previously as part of this

project. The main focus of this study, however, was to explore the possibility that

individual rabbits are able to modify their behaviour according to differences in

environmental conditions.

Under normal conditions in a temperate environment, if a single rabbit behaves in a

manner to maximise individual fitness, it needs to reach a dominant position within

the population social hierarchy. For males, this ensures greater access to females, and

for females, it ensures greater access to nesting sites. Membership of a social group,

also provides the best access to food resources. In arid and semi-arid environments,

when environmental conditions decline, individuals compete for scarce resources and

only the best competitors are likely to survive. In contrast, when conditions are

favourable population sizes can expand rapidly and competition is relaxed. Cowan

and Garson (1985) showed this flexibility of behavioural response in two English

populations of European rabbit, whereby excess food availability resulted in

scramble competition. A sudden increase in population size may force a breakdown

or relaxation of dominance hierarchy systems. Any individual that attempts to

maintain its social position within the territorial group during this time, may in fact

reduce its fitness by doing so because the time required to defend a social position

(be it access to females or nesting sites) increases with the size of the population.

Thus, in scenarios where rabbits are found at very large population sizes, any rabbit

attempting to defend social rank may not have the time to utilise the benefits of the

dominant position it is trying to defend (or attain). The individual may be

overwhelmed by many others making use of food resources and engaging in matings

wherever possible rather than wasting opportunities by trying to defend resources

that are plentiful.

The dominance hierarchy system present in European rabbits is well defined (as

outlined in Ch 2) and is functional under most environmental conditions that are

reasonably stable. The current project showed that rabbits may adjust their behaviour

114

Chapter 5 Discussion and Conclusions patterns where the distribution of favourable resources varied in time. If the

behaviour patterns of rabbit populations can vary according to resource distribution

and abundance, then it is possible that the normal social dominance system may

break down under intense population pressure when resources per individual (RI)

are abundant in “boom” times. This apparent flexibility may be more of a break

down or short circuit that occurs when RI causes population size to reach a threshold

level, and continues as such until population size decreases to a level where social

dominance becomes a better option for maintaining individual fitness advantages.

Therefore, the degree of efficacy of alternative social systems in a population, can be

expressed as function of resource availability (figure 5.1). This function represents

the cause and effect relationship between variable environmental conditions and the

effect it has on population density, and ultimately, social system efficacy. It is not

simply a correlation relationship between variables because the “resources”

component encompasses the main factors that determine rabbit survival and

abundance. The scramble competition that occurs in the boom period is one of the

main reasons why the arid region exhibits panmixia; the other reason is the fact that

the arid region has little impediment for dispersal (such as forest) during favorable

years.

115


Little advantage in defending social position

position

Figure 5.1 – Breakdown of social systems due to variable resources

The optimal level of RI for successful dominance hierarchies is represented by the

area between α + i and β – j. When RI is greater than β – j, the efficacy of social

system declines until RI reaches β, beyond which there is no benefit for maintaining

a dominance hierarchy social system. Similarly, when RI is less than α + i, the

benefits of dominance hierarchy are reduced until RI = α, which is the point where

insufficient benefit is gained from defending such poor quality resources. When RI =

α and below, the population system is experiencing the most harsh conditions, such

as drought. It is likely that the levels of RI during the two sample periods of this

study are located at the lower end of the scale. September 2001 was relatively higher

in terms of RI compared to April 2002. However, it is probable that on an absolute

scale, RIApril was equal to or less than α on Fig 5.1 due to the effects of drought; and

RISeptember was equal to a point between α and α + i. This assessment is based on the

results of the simulated territorial intrusion experiments reported in Ch 4.

Mating systems and social structures are known to be flexible, depending on relative

population density (and other ecological variables), in a variety of vertebrate species

(Maher and Lott, 2000). A specific example can be found in a study of Gunnison

prairie dogs (Travis et. al. 1995). The Gunnison prairie dog social system varies

Social System Efficacy

Resource / Individual

Favours dominance

α 0 α + i β - j β

116

Chapter 5 Discussion and Conclusions such that monogamy is favoured when resources are uniform regardless of

population density, but this gives way to polygyny when resources become

heterogeneous at intermediate densities, and to polyandry and polygyny at high

densities (Travis et. al., 1995). Thus resource availability affects the breeding

system directly. Additionally, Rodel et al. (2004) identified the proportion of first

time breeders (which have lower success) as another factor that may contribute to

density effects on average reproductive rates in rabbits.

The relationship between social structure and population genetic structure has been

studied extensively. The consensus in the literature is that social systems influence

gene dynamics. Dobson et al. (1998) examined breeding groups and gene dynamics

in a socially structured population of black tailed prairie dogs (Cynomys

ludovicianus). Using allozymes, pedigrees and demographic models they found that

prairie dog breeding groups showed significant genetic differentiation sub-

structuring. Further examples can be found in populations of red howling monkeys

(Alouatta seniculus) that exhibit population genetic structuring as a result of social

groupings as studied by Pope (1992, 1998); and even in humans where native

American populations of Navaho exhibit reduced genetic variability due to

inbreeding effects (Long et al. 1998).

The concept of interference competition in a territorial species was explained by

Lack (1966) as the possibility of an inverse relationship between density and

population growth rate. In the case of the European rabbit, it is likely that

interference competition also affects the breakdown of dominance hierarchies and

hence the social structure (which is effectively the mating system) in high density

populations in arid and semi-arid environments. So rabbit metapopulations are

effectively regulated as a response to habitat heterogeneity. While the European

rabbit has been shown to exhibit flexible behaviour in previous studies (such as

Cowan and Garson, 1985) and the present study to an extent; the influence of social

system variability on genetic structure was not as pronounced as in the above listed

examples. One may argue that the genetic differences between arid and semi-arid

rabbit populations are influenced by the differing social structure caused by habitat

heterogeneity. The environmental constraints on this project resulted in the conduct

of behaviour experiments in different resource periods within the arid region; and

117

Chapter 5 Discussion and Conclusions although differences were observed between control and experimental treatments,

there was not an observed effect on the population genetic structure (given panmictic

results).

These results offer evidence that the flexible behaviour observed in European rabbit

populations in arid Australia is due to phenotypic plasticity (single genotype able to

respond adaptively to local conditions) rather than genotypic polymorphism

(different genotypes adapted to local conditions). Via and Lande (1985) state that

when a population is subdivided, genetic differences may develop among the

subpopulations, perhaps resulting in different life history responses to environmental

changes. Although the arid region is a very large geographic area, the scramble

competition that occurs when resources (and consequently density) are high, ensures

that no genetic differences occur among subpopulations (Fuller et al., 1996, present

study). Clearly, the only way to conduct an absolute test of phenotypic plasticity is to

conduct translocation experiments (Alcock, 1998); which were originally planned as

part of this study (arid vs semi-arid), however, the environmental issues

(drought/disease) and logistics prevented such experiments. The next best thing,

therefore, is to demonstrate phenotypic plasticity by studying the behaviour and

genetics across a small geographic area. This was demonstrated in the present study

through the varied response to control and experimental rabbits.

The genes controlling rabbit behaviour are obviously a complex group rather than a

single gene-single product. Although the genetic portion of this study found evidence

supporting the existence of two ‘systems’ in arid and semi-arid Australia, it is

reasonable to assume that the functional genes controlling behaviour are well

constrained and therefore (statistically) have little chance of rapid mutation

producing a selective advantage. This means that while two discrete population

systems exist in arid and semi-arid Australia, behavioural plasticity, and to a certain

extent, the population genetic structure of rabbits are generally functions of local

environmental conditions (see Hamilton (2003), for further detail on quantifying the

causes of genetic structure in this system). The behaviour of rabbits can certainly

contribute to the population genetic structure, but one can argue that the primary

factor influencing the behaviour patterns themselves is the extant conditions for the

population in question. Therefore, if there is a change in the environmental

118

Chapter 5 Discussion and Conclusions conditions (including habitat connectivity) in either the arid or semi-arid regions, it is

plausible to expect a corresponding change in population genetic structure - only if

the change is severe enough to cause a change in the resources available per

individual, that then alters the benefits associated with defending social position as

described in Fig 5.1.

Brashares and Arcese (2002), demonstrated that Oribi antelope (Ourebia ourebi)

females exhibit variable behaviour in response to availability and quality of food

while males respond to variable distribution and range of females. By identifying

social behaviour variation among contiguous subpopulations in a small geographic

area, the authors established that phenotypic plasticity (rather than genotypic

polymorphism) was the primary mechanism at work (Brashares and Arcese, 2002).

Recent research into ‘candidate genes’ (a gene identified in one organism

hypothesised to influence a similar phenotype in another organism) has provided

further insights into behavioural flexibility – ie. changes in expression of candidate

genes can reveal their contribution to behaviour variation and/or phenotypic

plasticity (Fitzpatrick et al. 2005).

Management of Rabbit Populations in Australia

Although pest management is not the focus of this study, it would be remiss not to

consider the implications of this project on European rabbit management given its

status as a significant vertebrate pest in Australia; and for the same reason, it is not

surprising that much research has been devoted to improving control of this pest.

Early control efforts were aimed at preventing dispersal through the use of rabbit

proof fences, however, these were largely ineffectual given the size of the area of

land involved and the fact that often the “rabbit front” passed the fence before the

fence was even completed. Other methods involved the use of “rabbiters”, however,

as the meat and pelt industries grew, so did the conflict of interests for rabbiters –

and realistically, shooting, netting, ferreting, and trapping were never effective as

widespread management tools, much less for eradicating the rabbit in Australia.

It is accepted by most biologists that the rabbit has become a permanent inhabitant of

Australia, and therefore, the focus needs to be on effective population management,

119

Chapter 5 Discussion and Conclusions rather than complete eradication (Williams et al. 1995). Methods such as baiting,

mechanical ripping of warrens, and netted fencing, are particularly useful for

localised control, however, the success of any overall control program will depend

on the population levels in areas adjacent to those under treatment because it is

impossible to apply localised control everywhere. If there is a population “source” or

“refuge” located nearby, then localised management efforts are likely to be

undermined by dispersal from areas adjacent to areas where populations are being

controlled.

The introduction of biological control through myxomatosis was initially very

effective in Australia, as the disease spread via rabbit to rabbit contact (via fleas),

and required little human action other than release of the disease. The problem with

myxomatosis was that resistance to the disease gradually increased to the point that

very large populations of rabbits developed once more. In the mid 1990’s, the rabbit

calici virus was identified as a new form of biological control agent for rabbits and as

such, has been applied widely around Australia with many release sites across the

country (Kovalski, 1998). To date, the combined effect of the virus and significant

drought conditions have resulted in some of the lowest rabbit population densities

seen for many decades.

One of the problems associated however, with developing successful management

strategies, is the perception that arises from short term success. The general

population and government agencies, often view a significant reduction in population

size of a pest as the successful conclusion to a management program; whereas in

reality it is only the beginning and initial successes must be followed by funding and

efforts to sustain treatments. The most successful management efforts are a

combination of treatments in what amounts to an integrated pest management

approach across areas of land that can be viewed as effective management units

(Holder et al. 2004).

The mechanical ripping of warrens is highly effective in preventing recolonisation of

an area that has been cleared of rabbits (Brennan, pers comm), and is an ideal follow

up treatment in arid and semi-arid areas (Edwards et al. 2002). While the cost is

high, both environmentally and economically, it is efficient provided the terrain is

120

Chapter 5 Discussion and Conclusions conducive to the use of large earthmoving equipment. Areas with greater tree

coverage, however, cannot be treated in this way. Chemical treatment through the

use of baits, is also expensive and can cause death of non-target species (McIlroy,

1983; Choquenot et al. 1990), however, it may present as a viable control option to a

landholder potentially facing a plague of rabbits.

Biological controls represent potentially one of the most cost (and effort) effective

methods for managing any pest. This is possible if the biological control agent can

establish itself in the wild and spread through populations naturally after initial

introduction. Myxomatosis and rabbit calici virus were successful biological control

agents that have persisted in populations over many generations (Cooke et al. 2004).

Development of resistance is a major problem that often arises when diseases are

used as biological control agents. While myxomatosis is established in Australian

populations, there is also a high level of resistance that has evolved since it was first

released, resulting in declining rabbit mortality rates (Kerr and McFadden, 2002). It

also means that new viral strains must be developed constantly in order to maintain

control. Similarly, while the rabbit calci virus has been effective in arid and semi-

arid environments since its release in 1996, it is likely that resistance will evolve in

time, and when it does, rabbit population sizes will probably increase once more.

The importance of an integrated approach to management of rabbit populations is

highlighted by the way that the effects of drought conditions have combined with the

effects of rabbit calici virus to reduce the population sizes in arid and semi-arid

Australia (Story et al. 2004). In some cases, local control efforts have included

ripping of old warren systems to reduce the risk of recolonisation, and laying of baits

to target residual rabbits surviving in refugia.

Understanding the behavioural attributes of rabbit populations can also be of major

use in developing better pest management strategies in the future that have a

biological control component provided the vector depends on some degree of rabbit

to rabbit contact to enable effective transmission of the disease. The current study

has identified the potential for rabbits to exhibit differential amounts of aggressive

behaviour in arid environments as a response to regional environmental conditions,

which may impact the efficiency of disease vectors in certain situations. If population

121

Chapter 5 Discussion and Conclusions densities are very high, and the normal pattern of the rabbit territorial social system

are “short-circuited”, then it is possible the ensuing scramble competition would be

far more conducive to spreading diseases than if dispersing rabbits were continually

successfully excluded from colonising new patches. Lombardi et al. (2003) presented

evidence collected in Mediterranean environments to suggest that mortality by

disease may be linked to levels of rabbit aggression. Therefore, landholders and

management planners, may want to consider the behavioural status of their target

populations as part of their strategic plans. That is not to say that biological control

cannot be used under normal territorial conditions, however, it does mean a more

judicious approach to the application of disease agents may be required. Biological

control agents are likely to be more effective when released from multiple sites in

regions where territoriality is high and or dispersal is relatively low; a good example

of this method was observed in the Mitchell region in 1996 (eastern / semi-arid area

in this project). The Department of Natural Resources used the sites shown in figure

5.2 for the release of rabbit calici virus - the area was previously shown to have low

natural levels of rabbit dispersal (Fuller, 1995). If the virus was not released in such a

widespread manner, it is doubtful whether the effects would have been as dramatic.

Figure 5.2 – Rabbit calci virus release, Mitchell, 1996 (source: DNRM)

122

halla

This figure is not available online. Please consult the hardcopy thesis available from the QUT Library


Future Directions of Research and Conclusion

Although the European rabbit is well researched, there are several research

opportunities available to address the knowledge gaps that have been identified in

this project, which would assist management and conservation efforts in the rabbit’s

introduced and native ranges respectively.

The drought conditions and effectiveness of the calci virus program in the semi-arid

region forced the modification of the experimental design used here so that

behaviour experiments were conducted solely in arid conditions on population sizes

at low levels. An interesting study would be to conduct identical experiments in the

semi-arid and arid regions when population sizes are relatively large. However, this

is unlikely to be possible for at least ten to twenty years and possibly longer, given

the current effects of drought and successful implementation of management

programs targeting rabbit refuge areas (Berman, 2004).

Further research opportunities also include quantifying the population size (or

Resource / Individual level) at which dominance hierarchies may break down – i.e.

under what environmental conditions and population densities does the “short-

circuit” of normal rabbit social systems occur? The question may be answered

through the use of computer simulation modelling and direct population size

estimations in the field when threshold densities are achieved.

The aim of this research project was to assess whether wild rabbits adjust their

behavioural patterns as a response to variation in environmental factors, that leads to

observable differences in population genetic structure. Consequently there are two

major outcomes of this project:

1. A difference in population genetic structure was observed at the individual level

between arid and semi-arid regions which supports the findings of Fuller (1995)

and Fuller et. al. (1996, 1997) that identified regional differences using

maternally inherited markers.

2. Differences in the aggressive response to known vs unknown rabbits were

identified in parts of the arid region, which together with the effects of habitat

123


heterogeneity and connectivity (Hamilton, 2003) may explain the observed

differences in population genetic structure.

A major outcome of this study would be if the findings could be utilised to improve

management strategies, particularly those reliant on biological vectors, in countries

where Oryctolagus cuniculus is a significant pest species. Additionally, the outcomes

from this project may assist in better conservation practices in the native range of

southern Europe where the rabbit is an endangered species.

124

APPENDIX 1 Description of rabbit behaviours (modified from Webb, 1988) 1. Grazing Down: Feeding with head lowered near vegetation. 2. Grazing Up: Head raised away from vegetation but still chewing. 3. Resting Alert: Head up with ears erect, but not chewing. 4. Alert: Sitting upright with front legs raised off ground, ears erect. 5. Resting: Inactive with ears flattened, eyes partially or wholly closed. Lying with

legs tucked beneath, lying on side with white belly fur exposed. 6. Grooming: Licking or scratching the fur. Rabbits may flick the front paws

rapidly up and down (“air box”) before grooming the head and ears. 7. Moving: Either slow hops while feeding, or rapid running in response to

disturbance from people, predators etc. 8. Chasing: One individual rapidly pursuing another. The chasing animal may

attempt to bite the fleeing animal if it gets close enough. 9. Displacement: One individual moves toward another resulting in the latter

moving away. Sometimes accompanied by a threat with the head thrust forward and ears flattened.

10. Sexual Following: Male follows female at a slow pace, often stops to sniff the ground where the female been.

11. Circling: Male hops around female. Often accompanied by behaviours 12 and 13. (NOTE: Circling used in this report refers to local animals circling the cage in which the decoy animal was located.)

12. Urine Spray: Male sprays urine over another individual while leaping over or past it. Usually target animal is a female, occasionally a subordinate male.

13. Tail Flagging: Individual hops with rather stiff looking hind legs and raises tail to expose white underside. This behaviour is performed by both sexes during aggressive interactions, although more commonly by males. It is also seen when males are circling females.

14. Tail Wagging: Tail lowered so that black topside is visible and wagged rapidly from side to side. Performed by females towards courting males and towards their own young.

15. Bowing: One individual lowers head and flattens ears as another approaches. Usually performed by females toward males which then proceed to sniff, groom, and chin mark on the female’s head or move around behind the female and attempt to mount. Occasionally performed by a subordinate individual to a higher ranking animal of the same sex, and also by juveniles to adults.

16. Chin Marking: Rubbing the chin over an object, releasing a secretion from the chin (sub mandibular) gland (Myktowycz 1968)

17. Paw Scrapping: Rapid scratching of the ground with the fore paws. Can be either to expose roots during foraging or it is seen before males chin mark, defecate, or urinate during patrolling and territorial marking. Also performed by males during agnostic encounters (see below).

18. Parallel Running and Paw Scrapping: Males (and sometimes females) of neighbouring social groups run in parallel along the territory boundary, occasionally stopping to paw scrape.

19. Fighting (“Aggressive Leaping”): Two individuals simultaneously jump towards each other. They pass in the air, land, and then repeat the process in the opposite direction. Usually these jumping fights are brief, 1-5 leaps. Mainly seen

125

between males on territory boundaries, occasionally between two females. Individuals from the same group sometimes interact in this way if they have come into close contact “unintentionally” eg. If one of them is engaged in a rapid chase.

20. Fighting (involving close contact): Individuals locked together in combat comprising vigorous scratching with the hind legs and biting.

126

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