The Effects of Habitat Fragmentation on The

THE EFFECTS OF HABITAT FRAGMENTATION ON THE

DEMOGRAPHY AND POPULATION GENETIC STRUCTURE

OF UROMYS CAUDIMACULATUS.

Craig Streatfeild BSc. (Hons)

School of Natural Resource Sciences, Queensland University of Technology Brisbane, Queensland, Australia

This dissertation is submitted in fulfilment of requirements for the degree of Doctor of Philosophy 2009

The Giant white-tailed rat, Uromys caudimaculatus (top; photo courtesy Chris Chafer). View of the Atherton Tablelands showing the highly fragmented nature of the remaining rainforest (bottom).

ABSTRACT

Habitat fragmentation can have an impact on a wide variety of biological processes including abundance, life history strategies, mating system, inbreeding and genetic diversity levels of individual species. Although fragmented populations have received much attention, ecological and genetic responses of species to fragmentation have still not been fully resolved. The current study investigated the ecological factors that may influence the demographic and genetic structure of the giant white-tailed rat (Uromys caudimaculatus) within fragmented tropical rainforests. It is the first study to examine relationships between food resources, vegetation attributes and Uromys demography in a quantitative manner.

Giant white-tailed rat densities were strongly correlated with specific suites of food resources rather than forest structure or other factors linked to fragmentation (i.e. fragment size). Several demographic parameters including the density of resident adults and juvenile recruitment showed similar patterns. Although data were limited, high quality food resources appear to initiate breeding in female Uromys. Where data were sufficient, influx of juveniles was significantly related to the density of high quality food resources that had fallen in the previous three months. Thus, availability of high quality food resources appear to be more important than either vegetation structure or fragment size in influencing giant white- tailed rat demography. These results support the suggestion that a species’ response to fragmentation can be related to their specific habitat requirements and can vary in response to local ecological conditions.

In contrast to demographic data, genetic data revealed a significant negative effect of habitat fragmentation on genetic diversity and effective population size in U. caudimaculatus. All three fragments showed lower levels of allelic richness, number of private alleles and expected heterozygosity compared with the unfragmented continuous rainforest site. Populations at all sites were significantly differentiated, suggesting restricted among population gene flow. The combined effects of reduced genetic diversity, lower effective population size and restricted gene flow suggest that long-term viability of small fragmented populations may be at risk, unless effective management is employed in the future.

iii

A diverse range of genetic reproductive behaviours and sex-biased dispersal patterns were evident within U. caudimaculatus populations. Genetic paternity analyses revealed that the major mating system in U. caudimaculatus appeared to be polygyny at sites P1, P3 and C1. Evidence of genetic monogamy, however, was also found in the three fragmented sites, and was the dominant mating system in the remaining low density, small fragment (P2). High variability in reproductive skew and reproductive success was also found but was less pronounced when only resident Uromys were considered. Male body condition predicted which males sired offspring, however, neither body condition nor heterozygosity levels were accurate predictors of the number of offspring assigned to individual males or females. Genetic spatial autocorrelation analyses provided evidence for increased philopatry among females at site P1, but increased philopatry among males at site P3. This suggests that male-biased dispersal occurs at site P1 and female-biased dispersal at site P3, implying that in addition to mating systems, Uromys may also be able to adjust their dispersal behaviour to suit local ecological conditions.

This study highlights the importance of examining the mechanisms that underlie population-level responses to habitat fragmentation using a combined ecological and genetic approach. The ecological data suggested that habitat quality (i.e. high quality food resources) rather than habitat quantity (i.e. fragment size) was relatively more important in influencing giant white-tailed rat demographics, at least for the populations studied here . Conversely, genetic data showed strong evidence that Uromys populations were affected adversely by habitat fragmentation and that management of isolated populations may be required for long-term viability of populations within isolated rainforest fragments.

KEYWORDS: fragmentation, Uromys caudimaculatus, demography, microsatellite, genetic differentiation, assignment tests, genetic diversity, bottlenecks, effective population size, genetic autocorrelation, sex-biased dispersal, mating system.

TABLE OF CONTENTS

Abstract……………………………………………………………………………………….i Table of Contents…………………………………………………………………………..iii List of Figures…………………………………………………………………………...... viii List of Tables……………………………………………………………………...……...... xi List of Appendices………………………………………………………………………...xiii Acknowledgements……………………………………………………………………….xiv Statement of Original Authorship…………………………………………………….....xvi

1. General Introduction ______- 1 -

1.1 Habitat fragmentation______- 1 -

1.2 Genetic consequences of fragmentation ______- 5 -

1.3 The model system ______- 7 -

1.4 The highly fragmented landscape of the Atherton Tableland ______- 8 - 1.4.1 Significance of the study region ______- 8 -

1.5 Study species______- 9 -

1.6 The aims and objectives of the present study ______- 11 -

2. Assessment of the model system ______- 15 -

2.1 Introduction ______- 15 -

2.2 Methods ______- 16 - 2.2.1 Study sites ______- 16 -

2.2.2 Vegetation structure and floristic diversity ______- 20 -

2.2.3 Food resources diversity and density ______- 22 -

2.2.4 Mammal trapping ______- 26 -

2.2.5 Data analysis ______- 28 -

2.2.5.1. Vegetation structure ______- 28 -

2.2.5.2. Floristic diversity ______- 28 -

2.2.5.3. Food resource diversity______- 29 -

2.2.5.4. ‘Trappability’ of U. caudimaculatus and small mammal diversity _____- 30 -

2.3 Results ______- 30 - 2.3.1 Vegetation structure and floristic diversity ______- 30 -

2.3.2 Food resource diversity ______- 32 -

2.3.3 Mammal trapping intensity and trappability ______- 34 -

2.3.4 Small mammal species richness ______- 36 -

2.4 Discussion ______- 37 -

3. Uromys caudimaculatus demography within fragmented habitats: effects of habitat quantity versus quality. ______- 41 -

3.1 Introduction ______- 41 -

3.2 Methods ______- 43 - 3.2.1 Mammal trapping______- 43 -

3.2.2 Food resource density ______- 44 -

3.2.3 Vegetation structure ______- 44 -

3.2.4 Demographic variables ______- 44 -

3.2.5 Data analysis ______- 45 -

3.2.5.1. Food resources and vegetation structure______- 45 -

3.2.5.2. Demographic variables ______- 46 -

3.2.5.3. Demography, vegetation structure and food resources ______- 46 -

3.3 Results ______- 47 - 3.3.1 Vegetation structure and food resources______- 47 -

3.3.2 Giant white-tailed rat demographic parameters______- 50 -

3.3.2.1. Sex ratios ______- 55 -

3.3.2.2. Persistence ______- 57 -

3.3.3 Uromys demography, vegetation structure and food resource density ____ - 57 -

3.3.3.1. Timing of reproduction ______- 60 -

3.4 Discussion ______- 61 - 3.4.1 Temporal variation in food resource production ______- 61 -

3.4.2 U. caudimaculatus demographic parameters ______- 63 -

3.4.3 Food resources ______- 66 -

3.4.4 Competition, predation, vegetation structure, dispersal and den sites _____- 69 -

3.5 Conclusion ______- 72 -

4. Effects of habitat fragmentation on genetic diversity and population genetic structure in a rainforest rodent, Uromys caudimaculatus._- 73 -

4.1 Introduction ______- 73 -

4.2 Methods ______- 76 - 4.2.1 Sample collection and sample storage ______- 76 -

4.2.2 DNA extraction ______- 76 -

4.2.3 Microsatellites ______- 77 -

4.2.4 Microsatellite isolation, amplification and scoring ______- 77 -

4.2.5 Statistical analysis ______- 79 -

4.2.5.1. Hardy-Weinberg equilibrium, linkage disequilibrium and null alleles___- 79 -

4.2.5.2. Within-population patterns of genetic diversity ______- 80 -

4.2.5.3. Population bottlenecks ______- 80 -

4.2.5.4. Effective population size ______- 81 -

4.2.5.5. Population genetic structure ______- 82 -

4.2.5.6. Individual population assignments ______- 82 -

4.3 Results______- 83 - 4.3.1 Hardy-Weinberg equilibrium, linkage disequilibrium and null alleles ______- 83 -

4.3.2 Genetic diversity ______- 84 -

4.3.3 Population bottlenecks ______- 84 -

4.3.4 Effective population size ______- 87 -

4.3.5 Genetic structure ______- 88 -

4.3.6 Population assignments ______- 88 -

4.4 Discussion ______- 89 - 4.4.1 Genetic diversity within U. caudimaculatus populations ______- 90 -

4.4.2 Genetic bottlenecks ______- 91 -

4.4.3 Effective population size ______- 93 -

vii

4.4.4 Genetic differentiation among populations and assignment rates ______- 95 -

4.5 Conclusion ______- 97 -

5. Within population fine-scale genetic structure, parentage and reproductive success in U. caudimaculatus populations. ______- 98 -

5.1 Introduction ______- 98 -

5.2 Methods ______- 100 - 5.2.1 Sample collection, DNA extraction and microsatellite screening______- 100 -

5.2.2 Parentage analysis ______- 101 -

5.2.3 Effects of heterozygosity, body condition and relatedness on reproduction - 103 -

5.2.4 Sex specific relatedness and genetic autocorrelation ______- 104 -

5.3 Results ______- 105 - 5.3.1 Parentage, mating system and reproductive success ______- 105 -

5.3.2 Relatedness and genetic autocorrelation ______- 108 -

5.4 Discussion ______- 114 - 5.4.1 U. caudimaculatus mating system, reproductive success and mate choice - 114 -

5.4.2 Genetic autocorrelation and relatedness______- 119 -

5.4.3 Sex-biased dispersal ______- 120 -

5.5 Conclusion ______- 122 -

6. Overview and general discussion ______- 123 -

6.1 Chapter overviews ______- 124 - 6.1.1 Effects of fragmentation, food and vegetation on Uromys demography __ - 124 -

6.1.2 Genetic diversity ______- 125 -

6.1.3 Genetic mating system and sex-biased dispersal ______- 125 -

6.2 Demography versus genetics______- 126 - 6.2.1 Adaptive potential of small populations and functional genes ______- 128 -

6.3 Conservation biology and management recommendations______- 129 -

viii

6.4 Future directions ______- 132 -

6.5 Conclusion ______- 133 -

References - 134 - Appendices - 187 -

LIST OF FIGURES

Figure 2.1: Site P2 showing the complete isolation of the fragment and the surrounding cattle pasture matrix...... - 17 -

Figure 2.2: Regional map of the Atherton Tablelands showing the three isolated rainforest patches and the non-fragmented continuous forest...... - 18 -

Figure 2.3: Typical complex vegetation structure within each of the study sites.- 19 -

Figure 2.4: Representation of the closed canopy found within all study sites. ... - 19 -

Figure 2.5: Orientation, configuration and size of the trapping grids within each study site...... - 21 -

Figure 2.6: Structure and location of a seed catcher used to collect rainforest fruit, nuts and seeds...... - 23 -

Figure 2.7: Representative samples of the rainforest food resources from each of the six food resource categories ...... - 25 -

Figure 2.8: Floristic diversity among the four study sites...... - 32 -

Figure 2.9: Food resource diversity among the four study sites...... - 33 -

Figure 3.1: Fruit fall in the combined FLH + NLH + PL food resource categories for all sites and sampling periods ...... - 50 -

Figure 3.2: Density of adults and resident adults in each site and each sampling period ...... - 51 -

Figure 3.3: Density of juvenile Uromys that were captured for the first time for all sites and sampling periods...... - 54 -

Figure 3.4: Probability of persistence of adult males and females in all sites .... - 57 -

Figure 3.5: Uromys mean annual adult density as a function of the density of large fruits within the FLH food resource category...... - 59 -

Figure 3.6: Mean annual density of resident adult Uromys as a function of the density of large fruits within the FLH food resource category...... - 59 -

Figure 3.7: Densities of juveniles that were captured for the first time in site P1 as a function of food resource density from the combined FLH + NLH + PL food categories in the previous three months...... - 61 -

Figure 4.1: Mean allelic richness (RS) corrected for sample sizes of 21 individuals within the four populations...... - 85 -

Figure 4.2: Mean number of private alleles (P) corrected for sample sizes of 21 individuals within the four populations...... Error! Bookmark not defined.

Figure 5.1: Genetic autocorrelation of adult females and adult males within each population relative to a random subset of individuals ...... - 108 -

Figure 5.2: Genetic autocorrelation of adult female and male residents within each population relative to a random subset of individuals ...... - 109 -

Figure 5.3: Genetic autocorrelation in site P1 showing the relationship between the genetic correlation coefficient (r) as a function of distance afor (a) all adults, (b) adult males and adult females, (c) all adult residents and (d) adult male residents and adult female residents ...... - 110 -

Figure 5.4: Genetic autocorrelation in site P2 showing the relationship between the genetic correlation coefficient (r) as a function of distance for (a) all adults, (b) adult males and adult females and (c) all adult residents ...... - 111 -

Figure 5.5: Genetic autocorrelation in site P3 showing the relationship between the genetic correlation coefficient (r) as a function of distance afor (a) all adults, (b) adult males and adult females, (c) all adult residents and (d) adult male residents and adult female residents ...... - 112 -

Figure 5.6: Genetic autocorrelation in site C1 showing the relationship between the genetic correlation coefficient (r) as a function of distance for (a) all adults, (b) adult males and adult females, (c) all adult residents and (d) adult male residents and adult female residents...... - 113 -

xii

LIST OF TABLES

Table 2.1: Trap effort expressed as the total number of trap nights for each site and each trapping period ...... - 27 -

Table 2.2: Site means of the seven variables describing forest structure ...... - 31 -

Table 2.3: Proportion of new individuals captured on the final night of trapping - 34 -

Table 2.4: Trap availability at each site for each trapping period expressed as the proportion of available traps...... - 35 -

Table 2.5: Trappability at each site for each trapping period...... - 36 -

Table 2.6: Captures of small mammal species per site expressed as the percentage of total mammal captures per site...... - 37 -

Table 3.1: Mean density of all food resources, known to be eaten (KTBE) food resources and KTBE food resources within each resource category ...... - 48 -

Table 3.2: Density of adults, adult males and females and resident adult males and females expressed as mean annual MNKA densities...... - 52 -

Table 3.3: Proportion of adult Uromys that were residents in each site...... - 53 -

Table 3.4: The proportion of new juveniles that subsequently became residents within their site of first capture...... - 55 -

Table 3.5: Sex ratios of adult residents expressed as the proportion of males in each population...... - 56 -

Table 3.6: Sex ratio’s of newly captured juveniles expressed as the proportion of males in each population ...... - 56 -

xiii

Table 4.1: Sample sizes by sex and age structure (adults or juveniles) of individuals at the time of first capture...... - 76 -

Table 4.2: Microsatellite repeat motifs and primer sequences for all 11 loci ...... - 79 -

Table 4.3: Indices of genetic diversity within each populations...... - 86 -

Table 4.4: Heterozygosity excess, M ratio and mode-shift tests for evidence of recent population bottlenecks within each population...... - 87 -

Table 4.5: Long-term and contemporary effective population sizes for each population...... - 88 -

Table 4.6: Pair-wise genetic differentiation among each Uromys population ..... - 88 -

Table 4.7: Genetic assignment tests for all samples Uromys within each of the four populations...... - 89 -

Table 5.1: Sample sizes and sex of juveniles for parentage analysis and the number of candidate parents that potentially could have sired offspring...... - 102 -

Table 5.2: Description of the genetic mating system of U. caudimaculatus...... - 107 -

xiv

LIST OF APPENDICES

Appendix 1: Family, species and number of individual trees species identified within each study site...... - 187 -

Appendix 2: Species name, density/ha and category of all food resources collected in the first year (May 2002 to February 2003) ...... - 191 -

Appendix 3: Species name, density/ha and category of all food resources collected in the second year (May 2003 to February 2004) ...... - 192 -

Appendix 4: Isolation and characterisation of 11 polymorphic microsatellite loci in the giant white-tailed rat (Uromys caudimaculatus) primer note published in Molecular Ecology Notes 2005, 5 (2): 352-354...... - 194 -

Appendix 5: Linkage disequilibrium for each locus pair for all populations...... - 200 -

Appendix 6: Allele frequencies at 11 loci in all populations ...... - 202 -

Appendix 7: Genetic parents of juvenile Uromys from the four populations and the sampling period in which they were first captured in...... - 206 -

Appendix 8: Effects of food resources on space use and social mating system of U. caudimaculatus. Unpublished manuscript...... - 209 -

ACKNOWLEDGEMENTS

This thesis represents the culmination of several years of field work, data analysis and writing during which time I lived and breathed all manner of the life of a giant white-tailed rat. The journey has been a long one and at times very difficult, but a very rewarding and satisfying journey that I will long remember. I sincerely hope that my efforts to further understand how species respond to habitat fragmentation, and the general behavioural ecology of Uromys, will not go unnoticed.

Undertaking a PhD often involves the collaboration and assistance of numerous people. First and foremost amongst these are my supervisors – Associate Professor Peter Mather and the late Dr John Wilson. As my primary supervisor, John allowed me the freedom to pursue and develop several of my own research ideas while providing valuable feedback when sought. John was a never ending source of inspiration and a provider of stimulating discussions. You are indeed sorely missed.

Similarly, I would like to thank Peter for introducing me to the world of molecular ecology and fielding my rather obtuse questions, particularly in the early years. Although Peter already had a full complement of PhD students, he willingly took on the added responsibility of primary supervisor following the death of John. Despite his enormous workload, Peter was always willing to read chapter drafts and promptly provide invaluable comments. Thanks for your encouragement and patience Peter.

A number of people from the School of Natural Resource Sciences have provided valuable help and advice over the years. Thanks go firstly to the Ecological Genetics Group (Peter Prentis, Natalie Baker, Angela Duffy, Mark De Bruyn, Terrence Damanagoda, Kerrilee Horskins and Dave Hurwood) for stimulating discussions on all manner of molecular genetics. I thank Tony Clarke for statistical advice and for valuable feedback on a number of chapters and Ian Williamson and Susan Fuller, whose comments on the penultimate draft greatly improved this thesis. I also thank Dave Hurwood, Grant Hamilton and Alicia Toon for much needed advice on genetic analyses and Vincent Chand for developing the microsatellite markers. To Liz Dunlop, Geoff De Zylva, Danny Ward, Jo Chambers and Dave Elmouttie, thanks for the discussions, support and the memories.

xvi

This project would not have been possible without the many people who assisted with field work. They are too numerous to mention by name, however thanks go to Kerrilee Horksins, Andrew Hayes, Sam Maynard, Barry Jenkins, Liz Dunlop, Nathan Jensen, Martine Adriaansen and to the students in the 2000 – 2004 Ecological Applications classes. I’d especially like to thank David Elmouttie for his extensive help with field work and his friendship over the years. To Nigel Tucker, thank you for your extensive help in identifying all the plant species, for helping identify numerous half-eaten fruits, nuts and seeds, for your hospitality and for your willingness to share your far-reaching knowledge on rainforest ecology, revegetation and rehabilitation. I would also like to especially thank the staff at the Lake Eacham and Townsville offices of Queensland Parks and Wildlife Service.

All field work was conducted on private land. Hence, this project would not have been possible without the generous support of numerous land holders including the Beattie (site P1), Williams (site P2) and Walker (site P3) families. Special thanks are extended to Sinan Ogden and Moni Carlisle (site C1) for their very generous hospitality, particularly the freshly made cinnamon scrolls and freshly brewed coffee.

This research was supported by a grant from the Australian Geographic Society and I was funded by an Australian Postgraduate Award Scholarship. Ethics and permit approval for all aspects of this project were granted by the Queensland University of Technology Ethics Committee and the Queensland Environmental Protection Agency, respectively.

Special thanks go to my parents, Stan and Judy for their love and support (both emotional and financial) over the years. Finally, to my wife Heather, you have been an amazing strength, provided a good kick up the backside when needed and helped me keep a sense of perspective in the final stages of this long and arduous journey. We finally got there.

xvii

STATEMENT OF ORIGINAL AUTHORSHIP

This work has not been previously submitted for a degree or diploma at any other educational institution. To the best of my knowledge and belief, this thesis contains no material from another source except where due reference is made in the thesis itself.

Craig Streatfeild March 2009

Chapter 1: General Introduction - 1 -

1. General Introduction

1.1 Habitat fragmentation Expansion and increase in human land use over recent decades has resulted in extensive habitat loss, modification and fragmentation of many natural habitats worldwide, and these factors are considered a major threat to maintenance of biological and genetic diversity (Harris, 1984; Saunders et al. 1991; Hobbs, 1993; Frankham et al. 2002; Stutchbury, 2007; Aguilar et al. 2008; Sherry, 2008). Habitat fragmentation, the process where once continuous natural habitat is broken into smaller remnant habitat patches, occurs in a diverse range of habitats including but not limited to, grasslands, coral reefs and topical rainforests. In 1990 there was an estimated 1116 x 106 ha of tropical forest globally but has decreased by an average of 5.8 x 106 ha per year in the seven years to 1997 due to habitat destruction and fragmentation (Achard et al. 2002). In the last 25 years, fragmentation of tropical forests in Asia and the Amazon have increased from approximately 1.8 x 106 ha per year in the 1980s to over 2.5 x 106 ha per year during the 1990s and 2000s (Fearnside and Barbosa, 2004; Hansen and DeFries, 2004). This corresponds to an area of fragmentation larger than Canada and France combined (Wright, 2005). Further, rainforest in Cˆote d’Ivoire in west Africa has decreased by more than 80% over the last 40 years (Chatelain et al. 1996). Consequently, fragmentation and resulting loss of habitat have become issues of immense ecological interest (Soulé, 1986; Wiegand et al. 2005).

The process of habitat fragmentation leads to a reduction in the total amount of natural remnant habitat, resulting in highly heterogeneous landscapes composed of isolated fragments of suitable habitat that are embedded in a matrix of often unsuitable habitat that generally differs in structure, complexity and resource availability to that originally present (Kozakiewicz, 1993; Noss and Csuti, 1997; Reino et al.2009). Consequences of fragmentation also include creation of habitat patches of varying size, shape and degree of isolation (Andren, 1994). Furthermore, remaining remnant habitat patches often constitute the only remaining stands of original native vegetation.

Fragmentation not only leads to a decrease in total amount of original habitat, but also provides a catalyst for secondary effects. Reduction in remnant patch size

Chapter 1: General Introduction - 2 - following fragmentation can result in fragments of a size that are insufficient to sustain viable populations and increased isolation of patches can pose serious risks to overall long-term population stability (Nupp and Swhihart, 2000; Smith and Hellmann, 2002; Feeley and Terborgh, 2008). An increased probability of local extinction in isolated populations can arise as a result of three main stochastic factors:

1. Environmental stochasticity (unpredictable variation in population growth due to abiotic and biotic environmental factors including rainfall, temperature, food resources etc.: the entire population is affected in similar ways).

2. Demographic stochasticity (random fluctuations in birth rates, survival rates, sex- ratios etc.: individuals are affected independently).

3. Genetic stochasticity (increased inbreeding, loss of genetic diversity, accumulation of deleterious mutations etc: affects both individuals and entire populations: Andren, 1994; Wolff et al. 1997; Frankham et al. 2002; Keller and Waller, 2002).

There is a strong held belief that fragmenting continuous habitat is harmful to both flora and fauna (Laurance, 1997; Beier and Noss, 1998; Harrison and Bruna, 1999). Such harm includes the loss and/or decline of entire species and populations, changes in community compositions, alteration of ecosystem processes (Saunders et al. 1991) and decreased survival and reproduction in remnant patches (Litvaitis, 1993; Robinson et al. 1995). Some populations that inhabit small fragments, however, often perform better or at least as well as populations that within larger areas (Zanette, 2000; Marchesan & Carthew, 2004; Martin and Handasyde, 2007).

That numerous studies have documented negative, positive or no effect of fragmentation on density, demography, population dynamics and/or genetic structure in animal populations suggests that an individual species’ response to habitat fragmentation is unlikely to be straightforward or predictable and will depend strongly on species-specific properties and local ecological conditions (Robinson et al. 1992; Bowers et al. 1996; Wiegand et al. 2005; Debuse et al. 2007). Hence, an individual species’ response to fragmentation depends on its degree of specialisation, individual habitat requirements and dispersal abilities as well as

Chapter 1: General Introduction - 3 - interspecific competitive interactions and stochastic events (Kitchener et al. 1980; Laurance, 1990, 1994; Robinson et al. 1992; Harrison and Bruna, 1999; Bentley et al. 2000; Carvalho et al. 2008).

Long term persistence of populations within fragmented landscapes will depend on several factors including fragment size, fragment quality and degree of connectivity or isolation (Cushman et al. 2006; Lindsay et al. 2008; Segelbacher et al. 2008). For instance, MacArthur and Wilson’s (1963) equilibrium theory of island biogeography suggested that species richness is a function of island size and degree of isolation. When applied to individual species, however, intraspecific densities decline in larger areas (Schoener, 1986; Connor et. al. 2000) or alternatively, population density of a given species should be similar in fragments of similar size. This prediction has received mixed support suggesting that the association between population density and patch size are taxa and/or habitat context specific (Bowers and Matter, 1997; Dooley and Bowers, 1998; Connor et al. 2000; Debinski and Holt, 2000).

The extent to which fragment size is likely to be a critical factor in long-term population persistence will depend on individual space utilisation (Bentley, 2008) and the social organisation of a species (Ostfeld, 1992; Shepard and Swihart, 1995; Ims and Andreassen, 1999). This is likely to be confounded, however, by the quality of the fragmented remnant habitat. Because smaller patches have greater edge to area ratios (Laurance and Yensen, 1991), the quality of habitat fragments are expected to be affected by edge effects via three main processes (Murcia, 1995):

1. Abiotic factors, primarily involving environmental changes resulting from proximity to the dissimilar adjacent matrix that can lead to changes in nutrients, solar radiation, wind and water (Saunders, et al. 1991). This process often results in decreased soil moisture, increased temperatures and an increase in primary successional plant species.

2. Direct biological effects that involve changes in the abundance and distribution of species or individuals that inhabit edge habitats, leading to individuals with altered space use and behavioural patterns in small relative to large fragments (Andreassen et al. 1998).

Chapter 1: General Introduction - 4 -

3. Indirect biological effects involving changes in the interactions among species including elevated levels of predation and competition (Hausmann, 2004).

Because each fragment is unique in size, shape, isolation and composition of the surrounding matrix, these factors may affect fragments and the populations that inhabit them, differently.

Edge effects can also alter fragment quality via changes in vegetation structure and vegetation composition (Laurance, 1994, 1997), as well as floristic and food resource diversity and abundance (Adler, 1994; Zanette et al. 2000). Furthermore, because the social mating systems of some species are correlated with the spatial distribution of resources (Emlen and Oring, 1977; Clutton-Brock, 1989), changes in vegetation structure and composition via habitat fragmentation may lead to different levels of social organisation among populations that inhabit fragments of varying size and also between fragments and continuous habitats.

Metapopulation dynamics theory suggests that populations that remain in remnant patches depend more on immigration for long-term persistence than do populations in continuous habitats (Hanski, 1994; Theodorou et al. 2009). Degree of isolation of remnant patches has been shown to be a major influence on the long-term persistence of populations (Smith and Hellmann, 2002) and their demography (Eisto et al. 2000). Furthermore, the structure of the surrounding matrix (Stow et al. 2001) and the relative dispersal potential of the species in question (Driscoll, 2004), can influence immigration among patches within fragmented landscapes. Species that perceive the surrounding matrix as hostile are likely to exhibit decreased dispersal that can lead to increased population densities; a phenomenon referred to as the ‘fence effect’ (Krebs et al. 1969; Adler and Levins, 1994). Fence effects can also lead to systematic changes in demography, morphology, reproductive patterns and social organisation (Adler and Levins, 1994). Decreased dispersal in fragmented systems has also led to decreased (non-dispersive) movement patterns and reduced home range sizes in many small mammal species (Diffendorfer et al. 1995; Wolff, 1997) that in turn can alter population densities and mating systems.

Chapter 1: General Introduction - 5 -

1.2 Genetic consequences of fragmentation

Until recently, demographic and environmental factors were viewed as more important contributors to the persistence of populations inhabiting fragmented landscapes than were genetic factors (Lande, 1988; Frankham et al. 2002). The role of genetic factors in contributing to the persistence of fragmented populations, however, is now generally widely accepted (Frankham et al. 2002; Sarre and Georges, 2009) and can provide insights into the effects of fragmentation on fitness of individuals that inhabit fragmented habitats (Frankham 1995a, 1996; Lacy, 1997). In the absence of immigration, the mean fitness of individuals that reside in small isolated populations can be a concern because the size of the population is essentially limited to the number of animals that inhabit the fragment at the time of isolation. Hence for fragmented populations, genetic diversity is a key issue that can influence their long-term potential for persistence.

Two main genetic threats affect the mean fitness of individuals in small isolated populations (Frankham et al. 2002; Keller and Waller, 2002; Reed and Frankham, 2003):

1. Declines in genetic diversity levels and declines in heterozygosity that result from the loss of alleles associated with the effects of genetic drift (Lande, 1995; Sumner et al. 2004; Janečka et al. 2008), bottlenecks (Bouzat et al. 1998) and non-random mating (Frankham et al. 2002). Decreased genetic diversity can lead to a reduction in the long term evolutionary potential of populations to adapt to changing environmental conditions, (Lande, 1988, 1995; Frankham, 1996; Lacy, 1997; Frankham et al. 2002; Keller and Waller, 2002; Reed and Frankham, 2003; Sumner et al. 2004; Benedick et al. 2007). Furthermore, because the effects of selection against deleterious mutations may be negated in small populations by increased pressures of drift and/or deleterious mutations, deleterious recessive alleles will tend to accumulate more rapidly in smaller, isolated populations relative to larger, continuous populations (Lynch et al. 1995; Amos and Harwood, 1998; Keller and Waller, 2002). The above processes (in isolation) tend to be gradual and have limited effects on fragmented populations in the short term.

2. Inbreeding and inbreeding depression can also increase extinction probabilities via a reduction in individual fitness (Lande, 1988; Mills and Smouse, 1994; Keller

Chapter 1: General Introduction - 6 -

and Waller, 2002; Wright et al. 2008). In contrast to loss of genetic diversity, effects of inbreeding can rapidly accumulate in small isolated populations through restricted mating opportunities that can lead to reproduction between close relatives (Keller and Waller, 2002).

Loss of genetic diversity and effects of inbreeding can be further exacerbated by a reduction in effective population size (Ne, defined as the number of individuals in an idealised population that would show the same effect of random genetic drift as the population under consideration (Wright 1931)). Small populations lose genetic diversity in proportion to their effective population size (Wright, 1978; Lande, 1988), a situation far less pronounced in larger, more stable populations. Numerous factors associated with fragmentation and isolation contribute to reduced effective population size including unequal sex ratios (Raska-Jurgiel, 1992; Banks et al. 2005c), altered dispersal patterns (Stow, et al. 2001; Sumner, 2005) and variation in genetic mating systems and individual reproductive success (Frankham, 1995b). All these factors can differ between fragmented and unfragmented habitats.

As well as genetic changes associated with isolation and small population size, fragmentation can influence a population’s genetic structure via its impact on genetic mating systems. Remnant habitat patches often show altered canopy cover and structural complexity (Laurance, 1994), that can lead to variable spatial distributions of resources including nest sites, food and mates. Because the spatial distribution of resources are often linked to a species mating system (Emlen and Oring, 1977; Clutton-Brock, 1989), individuals that inhabit fragments with highly clumped or aggregated resources are more likely to develop polygynous mating systems compared with habitats where resources are more uniform in distribution (Emlen and Oring, 1977; Clutton-Brock, 1989). Under these conditions, if the resources can be defended actively and competition for mates is high, reproductive skew may be so great that genetic diversity is reduced compared with populations that employ monogamous genetic mating systems (Storz et al. 2001). Therefore, due in part to a reduction in effective population size, populations with unequal sex ratios and skewed reproduction are likely to lose genetic diversity and heterozygosity faster than similar sized populations with more uniformly distributed sex ratios and reproduction.

Chapter 1: General Introduction - 7 -

Prior to the application of genetic markers, mating behaviours were difficult to quantify accurately, especially for nocturnal or highly cryptic species. Over the last decade however, use of highly variable genetic markers (i.e. microsatellites; Jarne and Lagoda, 1996; Sunnucks, 2000) have provided a highly effective means for assessing genetic mating systems, reproductive success (Marshall et al. 1998; Coltman et al. 1999a; Banks et al. 2002), sex-biased dispersal and reproductive skew (Stow et al. 2001). This in turn has allowed effective population size and genetic diversity to be quantified accurately. Understanding behaviours that influence effective population size and levels of inbreeding can assist in effective management of fragmented populations (Sutherland, 1998).

1.3 The model system

In order to examine a species response to fragmentation effectively, a number of criteria must first be met before selecting a suitable study region and study animal:

1. The landscape must be highly fragmented and contain both isolated remnant habitat patches of varying sizes, as well as a large stand of unfragmented continuous habitat. Isolated habitat patches are imperative so that any confounding effects of emigration and immigration can be minimised, while an unfragmented habitat is required as a control. This allows comparisons to be made between fragments of similar size, fragments of differing size and between fragmented and unfragmented habitats.

2. Suitable habitat patches within the landscape must be spaced far enough apart to minimise dispersal among habitat patches, but close enough so that effects of climate and altitude are minimised as these variables can influence floristic diversity significantly, which in turn will determine habitat classification type.

3. Ideally, prior knowledge should suggest that the study species is likely to be adversely affected by fragmentation and show variation in density among remnant habitat patches. Furthermore, the candidate species should be reasonably abundant so that adequate samples sizes can be obtained.

Chapter 1: General Introduction - 8 -

1.4 The highly fragmented landscape of the Atherton Tableland

The Atherton uplands in the wet tropics region of north Queensland, Australia has undergone extensive habitat alteration and fragmentation since the early 1900s leading to a highly fragmented landscape (Winter et al. 1987). Historically, the rainforests of tropical north Queensland have undergone expansions and contractions during periods of intense climate change (Kershaw, 1985; Winter, 1997). During the most recent glacial period approximately 18,000 years ago, dramatic climatic changes resulted in contraction of once continuous rainforest in the wet tropics bioregion into two discrete refugia centered on the Thornton Peak uplands and the Atherton uplands (Nix and Switzer, 1991; Winter et al.1984; Winter, 1997).

Following the most recent contractions, rainforest areas expanded to their present day distributions (see Figure 1. in Winter, 1997). The distributions of many native vertebrate species were affected by these changes, with species colonising expanded rainforest habitat from main source populations around the Thornton Peak uplands in the northern region and the Atherton uplands in the southern region of the wet tropics bioregion (Winter, 1997).

The Atherton upland subregion proposed by Winter (1997) was later classified by Goosem et al. (1999), based on a system that included geology, climate and landform, as province four of the wet tropics bioregion. Elevation in the region from 600-900m (Law, 2001) results in a subtropical rather than tropical climate. In the wetter and lower elevations, complex mesophyll rainforests dominate, but vegetation changes to complex notophyll rainforest as altitude increases or rainfall decreases (Goosem et al. 1999).

1.4.1 Significance of the study region

The wet tropics bioregion comprises only 0.01% of the total land surface of Australia and yet has the highest vertebrate diversity. Over 30% of Australia’s marsupial species, 58% of bat species and 25% of rodent species occur within the wet tropics (Goosem et al. 1999). Furthermore, the Atherton uplands (province four of the wet tropics bioregion) sustain the highest diversity of non-volant mammals in Australia (Laurance, 1997) and support more rainforest mammals, including a number of endemics (Laurance, 1997; Williams, 1997), than any other province within the wet tropics.

Chapter 1: General Introduction - 9 -

Rainforest areas in the wet tropics have undergone extensive logging in the past resulting in a highly modified and fragmented system (Laurance, 1997). The Atherton uplands in particular, have lost over ~76,000ha of rainforest since logging began in 1909 (Winter et al. 1984; Laurance, 1997). This has left only a few large tracts of rainforest (>3000ha) but numerous smaller fragments ranging in size from one to 600ha surrounded by a matrix of agricultural crops and cattle pastures (Laurance, 1997). Some of the smaller fragments on the southern side of the Atherton Tableland - categorised as complex notophyll vine forest type 5b - are currently listed as a “Critically Endangered Ecological Community” (Environment Protection and Biodiversity Conservation Act, 1999).

Due to the high level of species diversity, coupled with extensive habitat modification and fragmentation, the Atherton uplands has become an area of high conservation significance and is a popular natural laboratory for studies on fragmented systems (Laurance, 1991, 1994, 1997), species diversity-habitat associations (Williams and Marsh, 1998; Williams et al. 1996), plant-animal interactions (Harrington et al. 1997, 2001; Law and Lean, 1999; Law, 2001), edge effects (Goosem and Marsh, 1997; Goosem, 2000), genetic variation studies (Campbell, 1996) and research on fine scale genetic structure of fragmented populations (Sumner et al. 2004; Sumner, 2005).

1.5 Study species

The giant white-tailed rat, Uromys caudimaculatus, is one of Australia’s largest rodents with adults weighing 665-1000g, a total length of ~700mm and a tail length of 310-360mm (Moore, 1995; Watts and Aslin, 1981). This species is endemic to the north-east coast of Queensland, Australia, inhabiting primarily rainforests (Williams and Marsh, 1998) and to a lesser extent wet sclerophyll (Moore, 1995) and dry sclerophyll forests (Vernes, 2003). One of only two members of the genus occurring in Australia, U. caudimaculatus is thought to have evolved in Papua New Guinea and is postulated to have first arrived on the Australian mainland 6-5 million years ago (Alpin, 2006) with the final arrival across land bridges during the Pleistocene ~7,000 years ago (Winter, 1997). The current Australian geographical range of this species extends from Townsville in the south to Cape York in the north. While large areas of unfragmented continuous rainforest habitat still exist within its natural geographical range, a large proportion of critical habitat is present within

Chapter 1: General Introduction - 10 - highly fragmented landscapes, including rainforest habitats within the wet tropics region of north Queensland.

Giant white-tailed rats feed primarily on rainforest fruits and seeds (Watts, 1977; Dennis, 2003), but they also feed opportunistically on fungi, insects and small vertebrates (Moore, 1995). Nesting habits are unclear but it has been suggested that tree holes and tree tops are favoured (Watts and Aslin, 1981; Moore, 1995). This species is also known to use sheltered rocky outcrops (Tad Theimer pers. comm.) and riverbank burrows (Craig Streatfeild pers. obs.). Previous studies suggest that U. caudimaculatus is territorial and has a solitary social system with individuals only coming together for breeding opportunities (Wellesley-Whitehouse, 1981; Moore, 1995). In contrast, anecdotal evidence suggests that U. caudimaculatus may occur in highly structured family groups, displaying high levels of parental care (Andrew Dennis pers. comm.). Little is known of the home- range size of this species but it has been suggested that they require a minimum of four hectares to persist and one individual has been observed to travel at least 500m in one night (Wellesley-Whitehouse, 1981; Moore, 1995).

From data on individuals bred in captivity, littler size is one to three and initial weight gain appears rapid, reaching 100g at two weeks of age. At six weeks of age, juveniles weigh approximately 150-200g and will start to accept solid food (Les Moore, pers. comm.; Watts and Aslin, 1981). Age at independence is largely unknown but it has been suggested that juveniles stay with their mother until 8-12 weeks of age, or until they weigh approximately 250-300g (Moore, 1995; Les Moore, pers. comm.). Young reach adult size and sexual maturity at approximately eight - 10 months of age by which time their weight has increased to around 500g. Length of oestrus cycle is seven days with a gestation period of 36-41 days (Watts and Aslin, 1981; Moore, 1995).

In a study that examined the genetic diversity of U. caudimaculatus populations in fragmented rainforest patches versus unfragmented continuous rainforest, Campbell (1996) found that fragment populations exhibited lower genetic diversity compared with populations from unfragmented continuous forest. Furthermore, Campbell estimated that the minimum distance over which giant white-tailed rats showed significant mtDNA haplotype diversity within the continuous rainforest, was 0.9km (referred to as a management or demographic unit), suggesting the presence of

Chapter 1: General Introduction - 11 - either philopatric female groups or complete family groups. Because mtDNA is maternally inherited and only provides information on female genetic history, the presence of family groups could not be confirmed. Nonetheless, the genetic consequences of habitat fragmentation suggest, at least for U. caudimaculatus, that local extinctions in remnant habitat patches, “could ultimately lead to the global extinction of Giant white-tailed rats in the fragmented landscape of the Atherton Tableland” (Campbell, 1996).

1.6 The aims and objectives of the present study

Understanding the ecological processes that underlie the demographic and genetic structure of fragmented populations is fundamental to their management and continued persistence (Caughley and Gunn, 1996). Furthermore, information on density, survival, sex-biased dispersal and mating systems are vital components of effective management practices for species that inhabit highly disturbed and/or endangered ecosystems. Over the last decade, numerous studies have reported the effects of habitat fragmentation on social and genetic mating systems (Ims and Andreassen, 1999; Walters et al. 1999; Banks et al. 2005b), sex-biased dispersal (Wolff et al. 1997; Sumner, 2005), population density (Laurance, 1994; Dooley and Bowers, 1998; Harrington et al. 2001), reproductive success (Breininger, 1999; Walters et al. 1999), space use (Ostfeld, 1992; Lima and Zollner, 1996), fine scale population genetic structure (Banks, et al. 2005a; Stow and Briscoe, 2005; Sumner, 2005) and a range of other social behaviours (Bjørnstad et al. 1998; Ims & Andreassen 1999; Stow and Sunnucks 2004).

In spite of a wealth of interest in fragmented populations, how species respond to fragmentation and the ecological factors associated with their response have not been fully resolved (Wiens, 1995; Lima and Zollner, 1996; Nupp and Swihart, 1996; Krohne and Hoche, 1999). This is particularly so in tropical regions where relative to temperate regions, species responses to habitat fragmentation are poorly studied (Adler, 1998, 2000). A major unresolved question concerns the effect habitat fragmentation has on the demographic and genetic performance of fragmented populations (Ims and Andreassen, 1999). While relative impacts of genetic versus demographic factors have long been debated (Lande, 1988; Frankham et al. 2002), empirical evidence suggests that each can affect persistence and long-term viability of fragmented populations (Saccheri et al. 1998; Frankham et al. 2002). This

Chapter 1: General Introduction - 12 - emphasises the importance of an integrated approach that combines both ecological and genetic approaches.

Although demographic and genetic factors can affect fragmented populations, few studies have combined data from rigorous demographic and genetic analyses (Tallmon et al. 2002). This is rather surprising considering that only via a combined genetic and demographic approach can long term viability of fragmented populations be adequately assessed. This understanding is essential for species such as U. caudimaculatus where populations may be especially prone to local extinction in fragmented habitats (Campbell, 1996). This thesis aims to address the shortcomings identified above by investigating intensively the ecological factors that influence demographic, spatial, social and fine scale genetic structuring of the giant white-tailed rat in a fragmented tropical rainforest landscape.

This thesis has the following broad aims:

1. To better understand the ecological mechanisms that underlie population-level processes and demography in fragmented habitats;

2. To assess the among and within population genetic consequences of fragmentation; and

3. To make recommendations for future management of U. caudimaculatus populations and restoration of the fragmented landscapes where they occur.

The study can provide important information on mechanisms that underlie the response of U. caudimaculatus populations to habitat fragmentation and provide landscape managers with specific tools to not only assess the dynamics of fragmented populations, but also to implement management strategies aimed at minimising or alleviating extinction probabilities. It can also provide much needed general biological information on U. caudimaculatus.

The structure of the thesis presentation is as follows. Following a general introduction to the problem (chapter 1), I outline the general biological methodology in chapter 2 and examine factors that may confound the demographic and genetic results. This includes assessing factors such as structural complexity of vegetation

Chapter 1: General Introduction - 13 - at the study sites, floristic diversity, trapping intensity, trappability of U. caudimaculatus and the suite of small mammal assemblages (potential interspecific competitors) within each site.

Chapter 3 investigates U. caudimaculatus demographics including but not limited to, densities of adults, resident adults and juveniles, sex ratios, persistence, timing of reproduction and juvenile recruitment. Densities of adults, resident adults and juveniles, juvenile recruitment and the timing of reproduction are then related to local ecological conditions including structural complexity of the vegetation and food resource abundance.

Chapter 4 describes the genetic methodology and investigates the levels of genetic differentiation and genetic diversity present in U. caudimaculatus populations as well as comparing among fragments and unfragmented continuous forest. I assess whether genetic diversity is significantly lower in the fragmented patches relative to continuous forest and examine the effective population size of U. caudimaculatus populations. Due to the spatial isolation of the study populations, dispersal between sites should be rare. I therefore also assessed the level of genetic differentiation among populations resulting from restricted gene flow and assignment tests were used to infer the likely population of origin of individual males and females.

In chapter 5, I use genetic paternity assignments to determine paternity of all sampled juveniles to examine the relative reproductive success and reproductive skew of individual males and females as well as adult residents. I determine the genetic relatedness between breeding partners to examine if U. caudimaculatus individuals exhibit inbreeding avoidance. Genetic spatial autocorrelation techniques were also used to examine the levels of relatedness among neighboring adults in an attempt to infer likely patterns of sex-biased dispersal.

Finally, in chapter 6, I summarise the major findings and discuss the impacts of habitat fragmentation on population density, demography and behaviour and evaluate mechanisms that underlie these processes. I also examine the interplay between demography and genetics and discuss the implications of these processes on the long-term demographic and genetic persistence of fragmented populations. Based on the results, I then provide suggestions for management guidelines for

Chapter 1: General Introduction - 14 -

U. caudimaculatus directed at ensuring the long-term persistence of this species in the Atherton Tableland region and highlight future research areas.

Chapter 2: Model system assessment - 15 -

2. Assessment of the model system

2.1 Introduction An essential requirement for many ecological studies is an accurate assessment of abundance and/or population density (Parmenter et al. 2003). For small mammals, trapping is one of the most widely used field techniques (Cunningham et al. 2005) and for cryptic and/or nocturnal species that are difficult to observe, mark-recapture methods are often the only feasible method available for estimating density (Slade and Blair, 2000; Parmenter et al. 2003). Accuracy of mark-recapture studies and the inferences that can be drawn from these methods, however, will strongly depend on an effective trapping protocol (Adler and Lambert, 1997; Ylönen et al. 2003). Trapping protocols should be designed therefore to maximise trappability (probability of capturing individuals given they are present in the population (Krebs and Boonstra, 1984)).

Trappability can be influenced by the type of trap chosen (Ylönen et al. 2003), trap placement (Laurance, 1992), trap density (Jones et al. 1996) and trap effort or intensity (Read et al. 1988; Adler and Lambert, 1997). For example, studies of Australian small mammals generally use grid based trap configurations with traps spaced at 20-25m intervals (Read et al. 1988) that should result in at least one trap within each individual’s home range (Jones et al. 1996). While studies in temperate regions generally trap for only 2-3 days per sampling period (Adler and Lambert, 1997), Rudd (1979) suggests that because tropical small mammals are often more difficult to capture, more intensive sampling strategies are necessary otherwise estimates of population densities may be biased and inaccurate.

Vegetation structure (Catling and Burt, 1995), relationships between species comprising small mammal assemblages (Dickman, 1991) and predator-prey interactions can also influence the accuracy of density estimates through altered capture probabilities. This is likely to be exacerbated in tropical areas following habitat fragmentation because the structure of remaining patches of remnant habitat can be altered significantly (Pardini et al. 2005). This in turn can directly affect animal assemblages, suites of interspecific competitors and predators (Saunders et al. 1991; Laurance, 1997).

Chapter 2: Model system assessment - 16 -

The main objectives of this chapter were to outline the general non-genetic methodology used in later chapters and to examine among site similarities and differences in vegetation characteristics, floristic diversity and small mammal assemblages that may affect accurate density estimates via altered trapping probabilities. Unequal trappabilities, if found could potentially confound results in later chapters that examine U. caudimaculatus demography, genetic diversity and paternity assignment.

2.2 Methods

2.2.1 Study sites

The study was conducted in the southern region of the Atherton uplands near the township of Malanda (17o 22’S, 145o 35’E) over a two year period from February 2002 to February 2004. In the first year of the study, two isolated rainforest fragments approximately equal in size (5.5ha and 6.5ha) were live trapped intensively (the 5.5ha patch is shown in Figure 2.1). Both patches have been completely isolated for at least 40 years (Harrington et al. 1997) and were chosen to minimise any potential effects of among patch movement by giant white-tailed rats. Both patches were surrounded on three sides by cattle pasture, varying in height from a few centimetres to 50-60cm, and a main road on the other. To determine if the observed animal-environment interactions within these small isolated patches were representative of the Atherton Tableland in general, two additional sites were added to the study in the second and final year. One site was within the non- fragmented 94,000ha Wooroonooran National Park, while the 80ha isolated rainforest patch was located within the Fur ‘n” Feathers rainforest tree-house wildlife sanctuary.

Chapter 2: Model system assessment - 17 -

Figure 2.1: Site P2 (5.5ha) showing the complete isolation of the fragment and the surrounding cattle pasture matrix.

Despite the highly fragmented system of the Atherton Tableland, very few completely isolated rainforest patches were found. More commonly, rainforest patches that first appeared to be discrete were often connected extensively via vegetated gullies or were very large. Therefore, the three sites that were chosen for use as isolated patches (P1, P2 and P3) represent some of the only suitable rainforest fragments that remain on the Atherton uplands, although other fragments do exist such as rainforest surrounding Lake Eacham (Figure 2.2)

The area surrounding Malanda has an elevation ranging from 720m to 750m and a mean average temperature of ~200C (Warburton, 1997). Rainfall is strongly seasonal with the wettest months occurring from December to April (Dennis and Marsh, 1997) and the uplands are characterised by a rainfall gradient decreasing from the southeast (annual mean = 2625mm) to the northwest (annual mean = 1275mm). Average annual rainfall at Malanda is 1671mm, with large year to year variation (max 2178mm in 1921; min 864mm in 2002). Natural vegetation of the uplands is predominantly Complex Mesophyll Vine Forest Type 1b (Tracey and Webb, 1975), later classified as Regional Ecosystem 7.8.2 (Goosem et al. 1999). As local rainfall decreases, Complex Notophyll Vine Forest Type 5b (Tracey and Webb, 1975) and Regional Ecosystem 7.8.3 (Goosem et al. 1999) begin to

Chapter 2: Model system assessment - 18 - dominate. Both rainforest types are characterised by high tree species diversity and structural complexity (Harrington et al. 2001) with canopy heights of ~30-40m (Dennis and Marsh, 1997). Figures 2.3 and 2.4 represent the structural complexity and the closed canopy typically found within the four study sites, respectively.

Of the four study sites, three (P2, P3, C1) were classified as Complex Mesophyll Vine Forest Type 1b (Tracey and Webb, 1975). The remaining isolated patch (P1) was located immediately adjacent to the 1b/5b isocline border. While this patch was a combination of both forest types, 1b was the predominant forest type.

Figure 2.2: Regional map of the Atherton Tablelands (Australian Map Grid Zone 55, WGD’66) showing the three isolated rainforest patches and the non-fragmented continuous forest. = dry sclerophyll forest, = rainforest. The star represents the location of the trapping grid within the continuous forest – site C1.

Chapter 2: Model system assessment - 19 -

Figure 2.3: Typical complexity of the vegetation structure within each of the study sites.

Figure 2.4: Representation of the closed canopy found within all four study sites.

Chapter 2: Model system assessment - 20 -

At each site, a permanent 25m x 25m trapping grid was established using measuring tapes and a sighting compass and all grids were oriented at random in relation to patch dimensions. Cross checks between transects were taken every 50m to ensure that grid points did not converge and remained within ± 2m of the desired 25m spacing. Each study site varied in area, configuration and topography resulting in different grid sizes and different numbers of grid points (Figure 2.5). Trapping grids encompassed the entire area of the smaller isolated patches (P1 and P2), while trapping grids in the larger isolated patch (P3) and the continuous forest (C1) were each 6ha in size.

Sampling open populations in the large fragment and continuous forest can inflate density estimates significantly by including individuals with home ranges that extend outside of the trapped area (Boutin, 1990). Therefore, the total area effectively sampled was estimated by adding to each trapping grid a boundary strip equal to ½ the mean maximum distance moved for all adults trapped in that grid (Ransome and Sullivan, 1997). This resulted in effective trapping grid areas of 8.8ha and 8.7ha for P3 and C1, respectively.

2.2.2 Vegetation structure and floristic diversity Vegetation structure and floristic diversity were assessed within a 50m x 50m grid (utilising every second grid point on every second transect of the 25m x 25m mammal trapping grid) that resulted in 28, 26, 23 and 23 data points within P1, P3, P2 and C1, respectively. Similarity of vegetation structure among sites was assessed using seven microhabitat variables: percent foliage cover (PFC), stem density (per m2) of shrubs/trees1-4m in height (1-4m stem density), 4-10m stem density (per m2), >10m stem density (per m2), 1-4m stem basal area (per m2), 4-10m stem basal area (per m2) and >10m stem basal area (per m2). Percent foliage cover was determined from a vertical digital photo taken at breast height using constant zoom and resolution settings at three random locations within 12.5m of each grid point. The image was then overlaid with a 10 x 10 grid and the percent canopy cover calculated by scoring the number of grid intersects covered with vegetation.

Chapter 2: Model system assessment - 21 -

Figure 2.5: Orientation, configuration and size of the trapping grids within each study site. Adj. area (adjusted grid area) in the large fragment and the unfragmented continuous forests is the actual grid size plus the addition of a boundary strip. P1 = Patch 1; P2 = Patch 2; P3 = Patch 3; C1 = Continuous.

Stem density, basal area and floristic diversity were determined at each 50m x 50m grid point using the point-centre quarter method following the methodology of Krebs (1999). The area around each point was divided into four 900 quadrats and the distance to, and the diameter at breast height of, the first tree in three height classes (1-4m, 4-10m and >10m), were measured resulting in 12 trees within each quadrat (four trees in each height class). To determine stem density within each height stratum at each grid point, Pollard’s (1971) unbiased estimate of density was used (from Krebs, 1999): n − )14(4 N P = 2 πΣ rij )( where:

NP = Point quarter estimate of population density n = Number of random points

Chapter 2: Model system assessment - 22 -

π = 3.14

rij = Distance from random point I to the nearest organism in quadrant j.

Stem basal area was calculated as stem density multiplied by basal area. Floristic diversity at each point was determined by identifying to species level each of the 12 trees used to calculate stem density and basal area. All values were converted to per hectare.

2.2.3 Food resources diversity and density The diversity and abundance of rainforest fruits and nuts (hereafter referred to as food resources) were determined by dividing the area around each 50m x 50m grid point into two 1800 quadrats, and then erecting two permanent 1m2 seed catchers within each 50m x 50m grid. Catchers were erected on galvanised steel poles approximately 1.5m above ground level and were positioned in areas free of overhanging vegetation to minimise access to the fallen fruit by animals climbing into the catchers (Figure 2.6). Although birds were able to access fruit within the seed catchers, it is unlikely they removed a significant portion considering the seed catchers only contained a relatively small proportion of the overall food resources within each site.

Chapter 2: Model system assessment - 23 -

Figure 2.6: Structure and location of one of the seed catchers used to collect rainforest fruit, nuts and seeds, in relation to the surrounding vegetation.

Food resources were censused from May 2002 to February 2004 in sites P1 and P2 and from May 2003 through to February 2004 in sites P3 and C1. Food collection coincided with mammal trapping periods at three monthly intervals. Seed catchers were cleared of all food resources at the start of each trapping period and cleared again 28 days later at the end of the trapping period. These sampling periods gave total food resource fall during the trapping period as well as the two months prior to each trapping period. Because fruit fall between trapping periods is likely to be subject to varying levels of decay, only fruit fall during the 28-day mammal trapping periods were used in subsequent analyses. To avoid possible biases in fruit decay during the 28-day sampling period, seed catchers were regularly checked and any fruits that began to decay were removed and recorded.

All collected food resources were identified to species level where possible, and categorised as “known-to-be-eaten” (KTBE) by U. caudimaculatus from a combination of published data (Cooper and Cooper, 1994; Dennis, 1997; Harrington et al. 1997; Finkelstein and Grubb, 2002), unpublished data (Andrew Dennis and David Westcott unpubl. data) and from distinct teeth marks left in fruits (on the forest

Chapter 2: Model system assessment - 24 - floor) by U. caudimaculatus. KTBE food resources were classified further into six discrete categories based on fruit morphology and the proportion of the total fruit (endocarp, mesocarp and exocarp) eaten by U. caudimaculatus as follows:

Category 1: NLH – Nuts with a thick endocarp and/or thick mesocarp and thin exocarp, with both mesocarp and/or endocarp eaten. Example: Aleurites rockinghamensis (Figure 2.7a).

Category 2: FSH – Small fruits with thick soft mesocarp. Example: Acronychia acidula (Figure 2.7b).

Category 3: FSL – Small fruits with thin pericarp and a large hard endocarp which isn’t eaten. Example: Elaeocarpus angustifolius (Figure 2.7c).

Category 4: FLH – Fruits larger than 2cm in diameter with a thick mesocarp and/or thick hard endocarp, both of which are eaten. Example: Ficus watkinsiana (Figure 2.7d).

Category 5: FLL – Fruits larger than 2cm in diameter with a thick hard endocarp which isn’t eaten. Example: Beilschmiedia recurva (Figure 2.7e).

Category 6: PL – Large pods with wind dispersed seeds and an eaten pericarp. Example: Flindersia brayleyana (Figure 2.7f).

Finkelstein and Grubb (2002) suggested that giant white-tailed rats should prefer food resources that have high lipid concentrations and calculated the lipid content of a range of rainforest foods that are known to be eaten by giant white-tailed rats. Food resources included in Finkelstein and Grubb (2002) were assigned to relevant categories that allowed an index of food resource quality to be calculated based on lipid content for each of the six food categories.

Chapter 2: Model system assessment - 25 -

a) b)

c) d)

e) f)

Figure 2.7: Representative samples of the rainforest food resources from each of the six food resource categories. Scale bar equates to 2cm (note the different scales for each fruit/nut). All images courtesy of Cooper & Cooper, 1994.

Chapter 2: Model system assessment - 26 -

2.2.4 Mammal trapping

Giant white-tailed rats were censused by live trapping every three months between February 2002 and February 2004, for a maximum of nine trapping periods. The number of trapping periods varied among sites, ranging from five to nine: the small fragmented sites (P1 and P2) were trapped from February 2002 through to February 2004 (9 trapping periods at each site), the continuous forest (C1) was trapped between November 2002 and February 2004 (6 trapping periods), while trapping in the largest fragment (P3) was undertaken between February 2003 and February 2004 (5 trapping periods). Trapping periods were chosen to coincide with the known reproductive biology of U. caudimaculatus (Moore, 1995; Watts and Aslin, 1981) as follows:

February – End of the breeding season and juveniles from reproduction that occurred at the start of the breeding season begin to enter the population.

May – Juveniles from the end of the breeding season begin to enter the population.

August – No reproductive activity.

November – Beginning of the breeding season.

During each trapping period, a single cage trap (20cm x 20cm x 56cm; Mascot Wire Works, Sydney, Australia), baited with a combination of cardboard soaked in linseed oil and a mixture of rolled oats with honey, vanilla essence and peanut butter was placed on the ground at each 25m x 25m grid point and checked each morning. Each site was trapped for eight to 13 nights per trapping period to maximise the probability of catching an individual, given it was present in the population. Trapping ceased when cumulative catch curves of newly captured individuals reached asymptote. An intense trapping regime was required because accurate density estimation and genetic paternity assessment requires each population to be censused as completely as possible. Trap effort across each trapping period is shown in Table 2.1.

Chapter 2: Model system assessment - 27 -

Table 2.1: Trap effort expressed as the total number of trap nights for each site and each trapping period. One trap equates to one trap night; *denotes trapping was not undertaken. Trapping period P1 P2 P3 C1 Feb. 2002 824 696 * * May 2002 824 696 * * Aug. 2002 824 792 * * Nov. 2002 929 844 * 768 Feb. 2003 1030 696 768 768 May 2003 1339 696 768 768 Aug. 2003 1236 957 768 768 Nov. 2003 1236 1044 768 768 Feb. 2004 1236 1044 768 768 Total for each site 9478 7465 3840 4608 Total across study 25391

After initial capture, all U. caudimaculatus individuals were given a unique numbered non-migratory microchip implanted into the nape of the neck (Compliance No. ISO 11784, Veterinary Marketing Network) and the following data were recorded: trap location, weight (to the nearest 2g using digital scales), tail length (to the nearest millimetre), sex and sexual condition of the individual (recorded as mature/reproductive female {perforate, extended nipples or other visible signs of pregnancy}, immature female {imperforate}, mature male {scrotal testes} or immature male {abdominal testes}). An ear biopsy was also collected from all individuals and tissue stored in 70% ethanol for later genetic analysis (chapters 4 and 5). Incidental captures of other species (including non-mammalian species) and closed (sprung) traps were also recorded.

After recapture within the same trapping period, all individuals were re-scanned (Pocket Reader EX, Destron Fearing) and the microchip number re-recorded. The sexual condition of each individual was also re-recorded. After recapture in subsequent trapping periods, all data were collected again so that temporal changes in body weight, tail length, sexual condition and spatial movements could be determined. Six of the 230 U. caudimaculatus chipped were found to have lost microchips on subsequent recapture (identifiable from the ear biopsy taken at initial capture). These individuals were given a new microchip and a new ear biopsy was taken to ensure congruence between microchip number and tissue sample.

Chapter 2: Model system assessment - 28 -

Captures of all other mammal species were recorded. All trapping was conducted under Queensland University of Technology animal ethics number 2007A and QPWS scientific purposes permit number F1/000372/00/SAA.

Population size of U. caudimaculatus on each trapping grid during each sampling period (presented in later chapters) was estimated by direct enumeration of the ‘Minimum-Known-To-Be-Alive’ method (MNKA: Krebs, 1966), defined as the number of animals captured at time t, plus those animals trapped before and after t but not during t. While population sizes are commonly estimated using the Jolly-Seber (Jolly and Dickson, 1983) model (Slade and Blair, 2000), the reliability of Jolly-Seber estimates decrease dramatically when population sizes are small (Krebs and Boonstra, 1984; Krebs et al. 1986) and accurate estimates require >10 animals per sampling period (Pollock et al. 1990). In a number of instances <10 marked animals were recaptured in the current study, particularly within P2, hence population size estimates using the Jolly-Seber model were not applicable here. Estimates of population size were converted to estimates of population density (number of animals per hectare (ha)) by dividing MNKA estimates by the effective trapping area of each site.

2.2.5 Data analysis

2.2.5.1. Vegetation structure Means of the seven variables describing vegetation structure were calculated for each site and compared among sites using multivariate analysis of variance (MANOVA). Each grid point within a site represented a single observation. All variables that violated the MANOVA assumptions were Log10 transformed except for PFC, which was arcsine transformed.

2.2.5.2. Floristic diversity Measurements of among site floristic diversity entailed two components: species richness (the number of species at each site) and species evenness (the variability in species abundance). Species richness among sites was described qualitatively. Species evenness among sites was compared using the Renyi diversity index (Hill, 1973; Rennolls and Laumonier, 2000; Bruhl et al. 2003) that takes into consideration richness and evenness in a combined statistic (Magurran, 2004).

Chapter 2: Model system assessment - 29 -

The Renyi index has the desired quality of encompassing several well known diversity measures including Simpson’s index (α = 1) that gives greater weight to more common species (Krebs, 1999; Magurran, 1988, 2004; Rennolls and Laumonier, 2000) and the Shannon-Weiner index (α = 2) that gives greater weight to rarer species (Magurran, 1988, 2004; Krebs, 1999). Because the Renyi index uses proportional abundances, the slope of the α diversity curve measures the departure from evenness. Species evenness is interpreted visually after plotting the diversity index against an alpha scaling value. The slopes of each curve represent species evenness at that site, hence slopes ranging from one to zero correspond with domination of the sample by one or a few abundant species through to complete evenness. Furthermore, curves that do not intersect over a range of scaling values can be described as having either higher or lower relative diversity.

The Renyi index is calculated as (after Rennolls and Laumonier, 2000):

s α log Σ pi i=1 H α= 1−α where:

pi = is the observed proportional abundance of the ith of s species log = is to the base of choice (in this case, e) α = is the order or scale parameter (α >0, ≠1).

2.2.5.3. Food resource diversity All food resource data were recorded initially as the total number of fruits per m2 per sampling period and later extrapolated to per hectare. Not all fruit and nuts collected are known to be consumed by U. caudimaculatus. Therefore, total food resource load was first sub-divided into those food resources that are known to be eaten (KTBE). Species richness of total food resources and KTBE food resources were described qualitatively. Among site species evenness (as outlined in section 2.2.5.2) was compared using the Renyi diversity index for 1) the total number of food resources for each species and 2) the total number of KTBE food resources for each species. Analyses of species evenness only used data from the second year to allow for the standardisation of sampling periods.

Chapter 2: Model system assessment - 30 -

2.2.5.4. ‘Trappability’ of U. caudimaculatus and composition of small mammal diversity ‘Trappability’ (Jolly and Dickson, 1983; Krebs and Boonstra, 1984) is the probability that an individual present in the population will be captured in a particular sampling period (Efford, 1992; Ransome and Sullivan, 1997). Trappability was estimated as Jolly trappability (Jolly and Dickson, 1983; Krebs and Boonstra, 1984), modified by substituting the ‘Minimum-Known-To-Be-Alive’ (MNKA: Krebs, 1966) marked population size at time i for the estimated marked population size at time i as estimated with the Jolly-Seber model (Jolly and Dickson, 1983).

S ⎛ total markedofnumber itimeatcaughtsindividual ⎞ ⎜ ⎟ ()100 ∑⎜ estimated MNKA population itimeatsize ⎟ Jolly tytrappabili ()% = i=1 ⎝ ⎠ S where: S = the number of sampling periods

Qualitative descriptions were used to describe the composition of small mammal assemblages among sites that were incidentally captured during the trapping periods. Because the Cape-York rat (Rattus leucopus) and the bush rat (R. fuscipes) are morphologically very similar and difficult to differentiate, they were combined into a single category (Rattus spp.).

Prior to analysis, all variables were tested for normality using Shapiro-Wilk’s W test and for equal variances using Levene’s test. All statistical tests followed Sokal and Rohlf (1995) or Quinn and Keough (2002) and were performed using Statistica 7.0 (StatSoft, 2004).

2.3 Results

2.3.1 Vegetation structure and floristic diversity. Vegetation structure among sites varied significantly for all variables except percent foliage cover and stem density of trees larger than 10m (Table 2.2). Despite the differences, no apparent trend was obvious among the small isolated fragments compared with either the larger fragment or the continuous forest that would indicate gross structural differences. Densities and basal area in the larger 4-10m and >10m

Chapter 2: Model system assessment - 31 - height classes were highest in P3 compared with other sites. Continuous forest (C1) showed intermediate densities and basal areas compared with all other sites. P2 was the only site that had not been subject to selective logging previously, but apart from slightly lower mid canopy density and basal area, showed no other structural attributes that could distinguish it from other previously logged sites. Overall it appears that no single site stands out as being structurally different, indicating that structural variation among sites reflects local ecological processes rather than any apparent affect of fragmentation.

Table 2.2: Site means (± SE) of the seven variables describing forest structure. Univariate ANOVA F ratios and MANOVA results are presented at the bottom of the table. * expressed in m2. 1-4m stem 1-4m stem 4-10m stem 4-10m stem >10m stem >10m stem PFC % Site density* basal area* density* basal area* density* basal area*

P1 94.0 ± 0.5 0.6 ± 0.1 0.02 ± 0.003 0.2 ± 0.03 0.04 ± 0.006 0.08 ± 0.01 0.3 ± 0.04

P2 94.9 ± 0.3 0.8 ± 0.1 0.02 ± 0.003 0.1 ± 0.01 0.02 ± 0.003 0.08 ± 0.007 0.6 ± 0.1

P3 94.0 ± 0.5 0.7 ± 0.09 0.008 ± 0.001 0.3 ± 0.04 0.06 ± 0.009 0.1 ± 0.01 0.7 ± 0.2

C1 94.9 ± 0.2 0.8 ± 0.1 0.01 ± 0.002 0.2 ± 0.02 0.05 ± 0.006 0.07 ± 0.01 0.5 ± 0.2

F3, 96 = 1.72 3.83 3.36 12.20 8.21 2.54 4.27

P = 0.168 0.012 0.022 <0.001 <0.001 0.061 0.007

MANOVA Wilks’ λ = 0.40, F = 4.66, P = <0.001

A total of 1188 individual trees belonging to 160 species and 43 families were marked (Appendix 1). Species richness varied from 64 species in C1 to 78 in P3. On a per hectare basis, however, species richness was higher in P3 (12 species) and P2 (11 species) followed by C1 and P1 with 10 species each, respectively. Individual tree species were distributed patchily among sites with only 14 species (Aglaia tomentose, Austromyrtus dallachiana, Calamus moti, Castanospora alphandii, Cryptocarya hypospodia, Cryptocarya mackinnoniana, Cryptocarya murrayi, Flindersia brayleyana, Haplostichanthus johnsonii, Helicia nortoniana, Macaranga subdentata, Neolitsea dealbata, Synima cordierorum and Tetrasynandra laxiflora) found at all four sites, whereas 90 species were found at only a single site. P1 had the most number of species found only at that site (31), while C1 had the fewest unique species (12). The five most common species (Argyrodendron peralatum, Castanospora alphandii, Castanosperma australe, Macaranga subdentata and Neolitsea dealbata) comprised 24% of total tree diversity, while 56 species were represented by only single individuals.

Chapter 2: Model system assessment - 32 -

Renyi index diversity among sites diverged as α (order of scale parameter) increased (as greater weight was given to more abundant species) and was highest in P3 followed by P2 = P1 and C1 (Figure 2.8). The most abundant species was represented by 45 individual trees in P1 (Castanosperma australe) and C1 (Macaranga subdentata) compared with 33 in P2 (Argyrodendron peralatum) and 32 in P3 (Neolitsea dealbata), respectively. When these species were removed from the analysis, diversity was uniform across the four sites. Site P1, that comprised both 1b and 5b rainforest types, is intermediate between P2 and C1 that are exclusively 1b rainforest, indicating that diversity did not change dramatically with changing forest type. Hence, differences in floristic diversity among sites appear to be a reflection of local environmental conditions.

Figure 2.8: Floristic diversity among the four study sites. ▲ = P1, ■ = P2, ● = P3, + = C1.

2.3.2 Food resource diversity In the first year of the study, food resources were collected from sites P1 and P2 only, where a total of 43 species in total were identified (Appendix 2). The total number of species found in P1 and P2 were 30 and 32, respectively and equated to 4.7 species per hectare in P1 and 5.9 in P2. Nineteen species (44%) were common to both sites. Twenty three species were utilised (KTBE) by U. caudimaculatus, of

Chapter 2: Model system assessment - 33 - which 11 (48%) were common to both sites (Appendix 2). On a per hectare basis, 2.7 KTBE species were found in P1 compared with 3.1 in P2.

During the second year of the study, 64 food resource species were collected in the four sites, ranging from 6 species per hectare in P3 to 3.9 per hectare in P1 (Appendix 3). Twenty-six species were found in more than a single site (41%) and only five species (Calamus moti, Castanospora alphandii, Elaeocarpus ruminatus, Ficus pleurocarpa and Flindersia brayleyana) were found in all four sites (7.8%). Of the food resources that are KTBE by U. caudimaculatus, 31 species were collected from all four sites and 5 species (Calamus moti, Castanospora alphandii, Elaeocarpus ruminatus, Ficus pleurocarpa and Flindersia brayleyana) were found in all sites (16%; Appendix 3). On a per hectare basis, KTBE resource diversity ranged from 3.2 species in P3 to 1.8 in C1, respectively.

In a similar fashion to floristic diversity, the Renyi index of diversity for total food resources diverged as α (order of scale parameter) increased (as greater weight was given to more abundant species) and was highest in P1 followed by P2, P3 and C1 (Figure 2.9a). The steeper slopes of P3 and C1 suggest that fruit fall in these sites was dominated by fewer species relative to the two small fragments. Although known to be eaten food resource diversity among sites also diverged as α increased, the results were opposite to total food resource diversity with diversity highest in P3 followed by C1, P1 and P2 (Figure 2.9b). Similarly to floristic diversity, site P1 that contains both 1b and 5b rainforest types, lay between P2 and C1 that are exclusively 1b rainforest, indicating that KTBE resource diversity did not change dramatically with changing forest type.

(a) (b)

Figure 2.9: Total (a) and KTBE (b) food resource diversity among the four study sites. ▲ = P1, ■ = P2, ● = P3, + = C1.

Chapter 2: Model system assessment - 34 -

2.3.3 Mammal trapping intensity and trappability

To obtain accurate density estimates, trap effort (the number of traps available) needs to be sufficiently high so that the probability of capturing individuals, given they are present in the population, is maximised. When all trapping periods for all sites were considered, 66% (19 of 29 periods) yielded no new individuals on the last night of trapping. This equates to an average final night increase of new individuals of 7.2% (Table 2.3). In eight trapping periods only a single new individual was caught on the final night of trapping. The average final night increase of new individuals decreased to 6.2% after including those trapping sessions when only one or less new individuals were trapped on the final night. In P2 during the August 2002 trapping period, only one individual was captured and this occurred on the final night leading to a proportion of new individual captured on the final night of trapping equalling 1. When this trapping period was removed, the final night increase of new individuals decreased to 2.7%, indicating that the probability of capturing individuals that were residing in the populations was high. Even though the trapping grids in P3 and C1 did not cover the entire extent of the available habitat, these sites had similar capture rates compared with isolated patches where the trapping grid covered the entire patch.

Table 2.3: Proportion of new individuals captured on the final night of trapping; numbers in parentheses denote the total number of individuals captured and the number of new individuals trapped on the final night of trapping; *denotes trapping was not undertaken.. Site Trapping period P1 P2 P3 C1 Feb. 2002 0.13 (8, 1) 0.00 (5, 0) * * May 2002 0.00 (8, 0) 0.25 (4, 1) * * Aug. 2002 0.00 (6, 0) 1.00 (1, 1) * * Nov. 2002 0.00 (4, 0) 0.00 (1, 0) * 0.06 (18, 1) Feb. 2003 0.00 (11, 0) 0.00 (1, 0) 0.03 (32, 1) 0.00 (3, 0) May 2003 0.03 (31, 1) 0.00 (1, 0) 0.00 (12, 0) 0.20 (5, 1) Aug. 2003 0.07 (29, 2) 0.00 (1, 0) 0.10 (10, 1) 0.00 (2, 0) Nov. 2003 0.00 (9, 0) 0.00 (2, 0) 0.21 (14, 3) 0.00 (2, 0) Feb. 2004 0.00 (2, 0) 0.00 (4, 0) 0.00 (1, 0) 0.00 (3, 0) Mean increase 0.03 0.14 0.07 0.07

Overall trap availability (i.e. the number of traps remaining open each morning) at all sites was high. Approximately 80% of all traps at P1 and P2 remained available for use by U. caudimaculatus (Table 2.4). Trap availability decreased to around 30% in

Chapter 2: Model system assessment - 35 -

P3 and C1, respectively due to activity by Australian brush-turkeys (Alectura lathami), a diurnal and medium sized predominantly non-flightless bird that regularly disturbs traps in search of food. Because A. lathami is diurnal, the majority of trap closures occurred at dawn after most U. caudimaculatus had ceased foraging. Therefore, trap availability shown in Table 2.4 is likely to be a conservative estimate.

Table 2.4: Trap availability at each site for each trapping period expressed as the proportion of available traps. *denotes trapping was not undertaken. Site Trapping period P1 P2 P3 C1 Feb. 2002 0.79 0.85 * * May 2002 0.66 0.72 * * Aug. 2002 0.80 0.78 * * Nov. 2002 0.86 0.85 * 0.28 Feb. 2003 0.84 0.93 0.27 0.31 May 2003 0.77 0.88 0.26 0.32 Aug. 2003 0.74 0.79 0.29 0.35 Nov. 2003 0.72 0.75 0.44 0.37 Feb. 2004 0.69 0.82 0.36 0.48 Mean 0.76 0.82 0.32 0.35

Trappability was very high across all four populations, trapping periods and years (Table 2.5). Trappability for each trapping period ranged from 0.62 to 0.96 in P1, 0.33 to 1.0 in P2, 0.77 to 1.0 in P3 and 0.86 to 1.0 in C1. There was no apparent effect of season or year, although trappability was lower in the first year in P1 and P2. Trappability can be lower where trapping grids do not cover an entire population, however, trappabilities in sites P3 and C1 were similar to the smaller isolated populations.

Chapter 2: Model system assessment - 36 -

Table 2.5: Mean trappability of U. caudimaculatus at each site for each trapping period. *denotes trapping was not undertaken. Totals at the bottom of the table equal mean ± SE. Site Trapping period P1 P2 P3 C1 Feb. 2002 0.62 1.0 * * May 2002 0.75 1.0 * * Aug. 2002 0.74 0.75 * * Nov. 2002 0.94 1.0 * 1.0 Feb. 2003 0.77 0.33 1.0 0.86 May 2003 0.94 0.75 0.77 0.94 Aug. 2003 0.94 0.75 0.77 0.92 Nov. 2003 0.96 1.0 0.88 0.80 Feb. 2004 0.88 1.0 1.0 1.0 Overall 0.84 ± 0.04 0.84 ± 0.08 0.88 ± 0.05 0.92 ± 0.03 Yr1 (May 02 – Feb 03) 0.80 ± 0.05 0.77 ± 0.16 - - Yr2 (May 03 – Feb 04) 0.93 ± 0.02 0.86 ± 0.07 0.86 ± 0.06 0.92 ± 0.04

2.3.4 Small mammal species richness Species richness of small mammal across all four sites was similar, (P1 = 7, P2 = 6, P3 = 6, C1 = 5; Table 2.6). The three most abundant species (U. caudimaculatus, M. cervinipes and Rattus spp.) accounted for 75% - 90% of all captures. Of the eight species captured, three species were only rarely encountered (Hydromys chyrsogaster, Isoodon macrourus and Hypsiprymnodon moschatus) and were the only species not to be captured at all four sites. Only a single species (H. moschatus) showed a clear difference in capture pattern between fragments and continuous forest sites and was never captured in any of the three fragments.

Chapter 2: Model system assessment - 37 -

Table 2.6: Captures of small mammal species per site expressed as the percentage of total mammal captures per site. Site Species P1 P2 P3 C1 U. caudimaculatus 29.8 7.7 33.4 15.0 (Giant white-tailed rat) Melomys cervinipes 28.9 35.4 19.5 15.1 (Fawn-footed Melomys) Rattus spp. 30.0 32.0 26.4 60.7 (Bush rat and CapeYork rat) Hydromys chyrsogaster 0.3 0 0.1 0 (Water rat) Perameles nasuta 8.4 16.9 9.9 7.1 (Long-nosed bandicoot) Isoodon macrourus 0.6 2.5 0 0 (Northern-brown bandicoot) Trichosurus vulpecular johnstonii 2.1 5.4 10.9 0 (Coppery brush-tailed possum) Hypsiprymnodon moschatus 0 0 0 2.1 (Musky-rat kangaroo)

2.4 Discussion Tropical rainforests are characterised by their high levels of structural complexity (Kricher, 1999) and plant diversity (Terborgh et al. 1996; Adler, 2000). This complexity and diversity provides abundant variation in forest architecture across a range of strata. In this study, forest structure and floristic diversity showed considerable variation among sites for all variables excluding percent foliage cover and stem density of larger canopy trees. Remaining structural variables representing the lower and mid canopy height strata varied significantly among sites but failed to show any distinct trend associated with habitat fragmentation. The small isolated fragments with higher edge to area ratios are often considered to be structurally depauperate (Laurance, 1994) and to have lower densities of large canopy trees compared with larger fragments and unfragmented forests (Laurance et al. 2000). Thus the two small isolated sites are expected to show reduced densities and basal areas of large canopy trees as well as reduced canopy cover compared with sites with larger edge to area ratios. This, however, was not the case here. The structural attributes of the small isolated patches were generally either higher than or intermediate to, sites P3 and C1.

Tree species diversity in fragmented systems is influenced by time since fragmentation, the level of structural complexity and the size of the remaining habitat fragments (Cannon et al. 1998). In fragments with decreased canopy structure and lower densities and basal areas of large canopy trees, increased diversity can result

Chapter 2: Model system assessment - 38 - from higher light penetration leading to greater diversity of primary successional plant species (Laurance et al. 2000). No apparent pattern, however, was found here for either species richness or species evenness and decreased structural complexity. Site P3 did, however, show higher floristic and KTBE food resource diversity compared with other sites. When both vegetation structure and diversity are considered, no single site stands out as being consistently different from the others, indicating that variation among sites was probably due to local ecological processes rather than gross habitat differences that have the potential to seriously confound estimates of U. caudimaculatus densities. This is especially important for site P1 that comprised both type 1b and 5b rainforest.

Although interspecific rates of competition and predation were not specifically examined here, several factors suggest that accurate estimates of giant white-tailed rat densities were not affected adversely by differences in predators or interspecific competitors. Warburton (1997) showed that the lesser sooty owl (Tyto multipunctata) and the rufous owl (Ninox rufa), both known predators of U. caudimaculatus, were essentially absent from most fragments and Laurance (1997) observed that all major predators of U. caudimaculatus, including the spotted-tailed quoll (Dasyurus maculatus), carpet pythons (Morelia spilota) and rufous owls (N. rufa) were either absent or rare in fragments. Ironically, within fragmented remnants, U. caudimaculatus is considered to be a major predator of small vertebrates (Laurance, 1997) and is thought to have few predators and few if any, interspecific competitors.

While U. caudimaculatus appears to have no obvious interspecific competitors, the combined densities of smaller rodent species may act collectively as significant competitors. Site C1 showed very high capture rates for Rattus spp. compared with the other three sites and was the only site where H. moschatus was captured. Neither Rattus spp. nor H. moschatus, however, are expected to exert strong competitive influences that would confound U. caudimaculatus density estimates for two main reasons. Rattus spp. feed on different suites of food resources including insects and smaller fruits (Laurance, 1994) and they also forage on the ground, while Uromys are scansorial, feeding primarily in the rainforest canopy on fruits and nuts. Secondly, while H. moschatus utilise similar resources to Uromys, they forage diurnally and on the ground, whereas Uromys are nocturnal and can access food resources preferentially from the canopy.

Chapter 2: Model system assessment - 39 -

For any ecological and/or genetic paternity study, accurate density estimates of the target species are essential and several factors can confound accurate estimates. For instance, inflated densities can occur on trapping grids that cover only a proportion of the habitat (P3 and C1) compared with trapping grids that cover the entire area of available habitat (P1 and P2). Boutin (1990) reported that inflated densities can occur when non-resident individuals migrate into the trapping grid. This potential problem was alleviated by adding a boundary strip equal to approximately ½ the original grid size at sites P3 and C1.

In addition, sampling intensity, or the distance between traps (Read et al. 1988) may not be sufficient to allow at least one trap in each individuals’ home range. This would likely result in trap saturation and only a fraction of the population being captured. It is unlikely that this was a problem here because the number or traps that still remained open following each night of trapping was high. In addition, at least 30% of traps remained empty and available for capture after giant white-tailed rats had ceased foraging for the night. This was substantially higher than Southern’s (1973) recommendation that sufficient traps be used so that at least 20% remain empty at the end of each trap day. These results suggest that the sampling intensity employed here was more than adequate so that trap numbers were not limiting.

Furthermore, the intensive trapping effort undertaken (eight – 13 nights per trapping period) and the use of cumulative catch curves to determine when trapping should cease, yielded very few new captures on final trapping nights. This suggests that sampling duration provided sufficiently high capture probabilities for animals given that individuals were still alive and resided at a site. The use of cumulative catch curves can be problematic however, for individuals that become ‘trap happy’, but this problem can be minimised by increasing sampling intensity and does not appear to be a problem for U. caudimaculatus.

Trappability has been shown previously to be affected adversely by a number of factors including vegetation structure (Catling and Burt, 1995), competitive interactions among species (Dickman, 1991) and predator-prey interactions (Sutherland and Predavec, 1999). While some differences in vegetation structure, floristic diversity and small mammal communities were evident, these differences were small and were not characteristic of individual sites. Hence, it is not surprising

Chapter 2: Model system assessment - 40 - that trappability estimates were similar among sites and remained consistently high because factors that influence trappability were not dissimilar. It is also encouraging that estimates of trappability in this study were similar and often higher than in other studies of tropical rodents (e.g. Adler and Lambert, 1997; Vieira et al. 2004).

Thus, the combination of similar vegetation structure, floristic diversity, small mammal assemblages, high trap availability and sampling intensity, and an intensive sampling strategy all contribute to very high trappability probability estimates, resulting in high probabilities of capturing individuals given they are present within sampled populations. Therefore, the estimated densities of U. caudimaculatus are likely to be real and not influenced adversely by potential confounding factors or poor experimental design.

Chapter 3: Demographic parameters of Uromys caudimaculatus - 41 -

3. Uromys caudimaculatus demography within fragmented habitats: effects of habitat quantity versus quality.

3.1 Introduction Fragmentation of forests is widespread in both tropical and temperate regions of the world (Diaz, et al. 1999; Benedick et al. 2007; Sherry, 2008) and has been particularly prevalent in the wet tropics region of north Queensland, Australia (Winter et al. 1987; Laurance, 1997). Fragmentation can lead to the overall loss of specific habitat (Nupp and Swihart, 1996), create habitat fragments of various sizes (Andren, 1994; Klok and de Roos, 1998; Piessens et al. 2009) and can change the overall quality of remaining habitat (Nupp and Swihart, 1996; Schmidt-Holmes and Drickhamer, 2001). Because severe alterations to the landscape often follow fragmentation, the distribution and density of animal populations can also vary dramatically (Wiens, 1995; Andren, 1997; Keuroghlian and Eaton, 2008a).

Sensitivity to fragmentation appears to be taxon specific. Insects and birds generally have large positive density-fragment size relationships (Bowers and Matter, 1997, Connor et al. 2001), whereas mammals show either positive, negative or no relationship between fragment size and density (Laurance, 1994; Nupp and Swihart, 1996; Bowers and Matter, 1997, Connor et al. 2001; Harrington et al. 2001; Lambert et al. 2003; Anderson et al. 2003). These conflicting outcomes suggest that individual specific habitat requirements are likely to be important factors that govern each species’ response to fragmentation (Saunders et al. 1991; Schmidt- Holmes and Drickhamer, 2001; Vergara and Armesto, 2009). For example, generalist species that can utilise the surrounding matrix may actually do well in fragmented habitats while habitat specialists may do relatively poorly (Laurance, 1991; Nupp and Swihart, 1996). This pattern is evident in some generalist rodent species including the white-footed mouse (Peromyscus leucopus) and the wood mouse (Apodemus sylvaticus) that usually show inverse relationships between population density and fragment size (Yahner, 1992; Nupp and Swihart, 1996; Krohne and Hoche, 1999).

Even where densities are similar among fragmented habitats, other aspects of demography including age and sex composition, breeding activity and the timing of reproduction may vary widely (Adler and Wilson, 1987). For instance, high

Chapter 3: Demographic parameters of Uromys caudimaculatus - 42 - demographic variability in deer mice (Peromyscus maniculatus) has been shown even at small geographic scales (Van Horne, 1981).

Although population densities of small mammals in fragmented habitats have been studied widely, factors that affect their density and demography are still poorly understood (Nupp and Swihart, 1996; Anderson et al. 2003: Koprowski, 2005; Brito and da Fonseca, 2008). This suggests that extrinsic and intrinsic factors other than simply habitat quantity (fragment size) may underlie each species’ individual demographic response to habitat fragmentation. Several factors may, however, affect small mammal density and demography. These include: interspecific competition (Laurance, 1994; Anderson et al. 2003), predation (Adler and Levins, 1994) dispersal (Krebs et al. 1969; Nupp and Swihart, 1996; Krohne and Hoch, 1999), availability of nest sites (Johnson et al. 2001), vegetation structure (Laurance, 1994; Seamon and Adler, 1996; Ostfeld et al. 1997; Manson et al. 1999) and food resources (Boutin, 1990; Adler, 1998, 2000; Ramsey et al. 2002; Schnurr et al. 2002; Ghizoni et al. 2005).

In tropical habitats, populations fluctuate largely in response to local environmental conditions (Adler, 1994) and problems associated with fragmentation including increased edge habitat can lead to altered tree species diversity (Lovejoy et al. 1986; Laurance et al. 2002), vegetation structure, microclimatic conditions (Saunders et al. 1991; Laurance et al. 1998; Tabarelli et al. 1999) and plant fruiting phenology patterns (Laurance et al. 2003). Thus, for tropical mammals that track seasonal food resources, differences in local environmental conditions among isolated populations, including the timing and abundance of fruit fall, may affect the timing and magnitude of reproductive activity and hence, their densities and demography. For instance, spiny rats (Proechimys semispinosus) show marked differences in reproductive activity, density and demography that appear to depend on several environmental variables including vegetation structure and food resources.

The giant white-tailed rat (Uromys caudimaculatus) is Australia’s largest tropical rainforest rodent, (Williams and Marsh, 1998) and feeds primarily on rainforest fruits and nuts (Watts, 1977; Watts and Aslin, 1981). Laurance (1994) and Harrington et al. (1997, 2001) noted that Uromys were less common in small habitat fragments, but anecdotal evidence (John Wilson pers. comm.) suggests that extreme density

Chapter 3: Demographic parameters of Uromys caudimaculatus - 43 - and demographic variation can occur among similar sized fragments. Furthermore, Hausmann (2004) reported no difference in the relative abundance of giant white- tailed rats between fragments and continuous rainforest as well as across a range of habitat fragment sizes. With the exception of the study by Laurance (1994), however, microhabitat characteristics have not been examined to date, and no study has examined how availability of food resources affects Uromys density and demographic parameters.

The main objectives of this chapter were therefore, to compare demographic parameters (density, persistence, recruitment body condition, sex ratios, breeding activity and timing of reproduction) among fragmented populations of U. caudimaculatus and to examine if these parameters were co-related with fragment size. Specifically, I aimed to determine the most important demographic parameters among the different sites, to relate demography to vegetation structure and food resources and ultimately to describe the demography of an Australian tropical forest rodent.

3.2 Methods

3.2.1 Mammal trapping Giant white-tailed rats were censused by live trapping at three monthly intervals between February 2002 and February 2004 (section 2.2.4). Briefly, sites P1 and P2 were trapped from February 2002 through to February 2004 (9 trapping periods in each site), C1 was trapped between November 2002 and February 2004 (6 trapping periods in total) and trapping in P3 began in February 2003 through to February 2004 (5 trapping periods in total). Population sizes of giant white-tailed rats were estimated using the ‘Minimum-Known-To-Be-Alive’ (MNKA) method and converted to mean density per hectare (section 2.2.4.).

Individuals weighing ≤ 450g were classified as juveniles and those > 450g were classified as adults. The lowest weights recorded for reproductively mature (scrotal testes for males or perforated/lactating for females) males and females were 492g and 470g, respectively. Because males of most small mammal species generally disperse before they reach sexual maturity (Lambin, 1994), assigning animals ≤ 450g as juveniles is likely to include animals born on the capture grid as opposed to those animals that migrated into a site from elsewhere.

Chapter 3: Demographic parameters of Uromys caudimaculatus - 44 -

3.2.2 Food resource density Rainforest food resources were censused at three monthly intervals from May 2002 to February 2004 (section 2.2.3). Briefly, food resources were collected from sites P1 and P2 between May 2002 and February 2004 and from sites P3 and C1 between May 2003 and February 2004. Food items were identified to species and classified into six “known-to-be-eaten” (KTBE) resource categories. Fruit catchers were cleared of all potential food resources at the start of each trapping period and cleared again 28 days later (approximating the end of each trapping period). This gave an estimation of total fruit fall during the trapping period as well as the two months prior to each trapping period.

3.2.3 Vegetation structure Seven microhabitat variables (percent foliage cover (PFC), stem density (per m2) of shrubs/trees1-4m in height (1-4m stem density), 4-10m stem density (per m2), >10m stem density (per m2), 1-4m stem basal area (per m2), 4-10m stem basal area (per m2) and >10m stem basal area were measured at each site as described in section 2.2.2.

3.2.4 Demographic variables Data on seven demographic variables were recorded (densities of adults, resident adults (captured in 3 or more trapping periods) and juveniles (≤450g), proportion of adults that were residents, juvenile recruitment, sex ratios, persistence and breeding activity). Adult densities included all individuals that reached adulthood during the trapping year (this included individuals first captured as juveniles that reached maturity subsequently, and individuals first caught as adults). High resource loads are expected to lead to higher resident densities, however, the proportion of residents within a population may still remain at similar levels among sites. Therefore the proportion of adult males and females that were residents in each population were calculated. Juvenile recruitment was derived by calculating the proportion of individuals first caught as juveniles that reached maturity subsequently, within each population. Sex ratios (proportion males divided by the total number of animals) were calculated separately for adult residents and juveniles.

In its strict sense, absolute survival could not be estimated because disappearance from trapping grids could be the result of either mortality or emigration, neither of

Chapter 3: Demographic parameters of Uromys caudimaculatus - 45 - which could be distinguished here. Therefore, persistence, which is often referred to as survival in the literature, was calculated separately for each population and for adult males and females as the proportion of individuals that persisted from trapping period t to trapping period t+1. Mean persistence probabilities were derived from the first three trapping periods in each year only, as persistence could not be calculated for the last trapping period.

3.2.5 Data analysis Prior to analysis, all variables were tested for normality using Shapiro-Wilk’s W test and for equal variances using Levene’s test. For linear regression analyses, all tests were first examined for departures from linearity using runs tests implemented in PRISM 5.00 (GraphPad Software, San Diego California USA, www.graphpad.com). All variables that failed normality or had unequal variances

were log10 transformed, except for proportion data that was Arcsin transformed. If after transformation, data still failed to meet parametric test assumptions, equivalent non-parametric tests were used. All statistical tests followed Sokal and Rohlf (1995) or Quinn and Keough (2002) and were performed using Statistica 7.0 (StatSoft, 2004).

3.2.5.1. Food resources and vegetation structure It was not possible to discern when fruit items fell in the two months preceding each trapping period (e.g. majority of fruit fall could have fallen at the start, the end or spread evenly across the two month period) or to determine decay rates during the two month period. Therefore to avoid any biases, only those food resources collected during the 28-day mammal trapping periods were included in analyses. Because examining among sampling period variation in fruit fall for all six food resource categories was considered unnecessary, variations in density were only described for the combined total of KTBE food resources identified by Finkelstein and Grubb (2002) as being preferred by Uromys (i.e. FLH, NLH and PL as described in section 2.2.3). Mean annual densities (averaged over the four sampling periods for each year) of all food resource categories, however, were tabulated and described qualitatively.

Principal Components Analysis (PCA; Quinn and Keough, 2002) was performed on the seven vegetation structure variables after standardisation using the correlation matrix method (centred about the means and scaled by standard deviations).

Chapter 3: Demographic parameters of Uromys caudimaculatus - 46 -

Regression analysis was then used to investigate the relationship between rat densities and the first two axes of the PCA – only the first two axes had eigen values >1. Because vegetation structure was only recorded once during the study, mammal data from the second year only were used, when all sites were trapped simultaneously.

3.2.5.2. Demographic variables Within and among population temporal variation for adult density, resident adult density and juvenile density were examined qualitatively for each sampling period by plotting estimates of these variables against time. Proportion of adult males and females that were residents, juvenile recruitment, sex ratios and persistence of adult males and females were tabulated and within and among population variation described.

Mean annual differences (averaged over four sampling periods in each year) for densities of all adults, adult males and females, resident male and female adults, proportions of adult males and females that were residents and persistence probabilities of adult males and females were examined for each year and site separately, using two separate analyses. In the first year when only P1 and P2 were sampled, a dependent t-test, or the nonparametric equivalent, a sign test, was used. Both tests are analogous to a two sample repeated measures one-way ANOVA. In the second year when all four sites were sampled, either one-way repeated measures ANOVAs followed by Tukey’s HSD multiple comparisons were used or Friedman ANOVAs, a nonparametric alternative to one-way repeated measures ANOVAs. Where samples sizes were adequate, variation in adult resident and newly captured juvenile sex ratios (expressed as the proportion of males in the population), per site were examined using Chi-squared tests.

3.2.5.3. Relationships between demographic variables, vegetation structure and food resources Regression analyses were used to examine the relationships between mean annual adult density, resident adult density, juvenile density and juvenile recruitment and (i) vegetation structure summarised in axis one of the PCA (ii) vegetation structure summarised in axis two of the PCA (iii) total food resource density and (iv) KTBE resources within each of the six categories. Combinations of the three resource categories (FLH + NLH, FLH + PL and FLH + NLH + PL - as described in section

Chapter 3: Demographic parameters of Uromys caudimaculatus - 47 -

2.2.3) that are considered important for Uromys (Finkelstein and Grubb, 2002) were also regressed against the three demographic variables. ANCOVA was used with resource category as the covariate to determine if the slopes of regressions lines differed significantly among food resource categories that were associated with U. caudimaculatus demography.

Linear regressions were used to examine the relationship between density of newly captured juveniles, proportion of new juveniles that became residents and food resource levels. Depending on the energetic costs of conception, gestation and lactation, juveniles could either be conceived during periods of peak fruit fall or born and weaned during periods of high fruit fall. Therefore, influx of newly captured juveniles (≤450g) was regressed against food resources at the time of their first capture as well as food resources in the previous sampling period, three months prior to their first capture. Resource density in the preceding sampling period was used because gestation and weaning periods for giant white-tailed rats are approximately 6 weeks for each or 12 weeks from conception until first capture. Because U. caudimaculatus densities are related to the density of food resources in three resource categories (FLH, FLH + NLH and FLH + NLH + PL - as shown in section 3.3.3 below), only combined densities of these three food categories were used in the analyses.

There were several sampling periods for mammals in all sites when resources were not collected (February 2002 for P1 and P2, November 2002 and February 2003 for C1 and February 2003 for P3). Hence, only data from sampling periods where both giant white-tailed demography and food resources were simultaneously estimated were used in the above analyses.

3.3 Results

3.3.1 Vegetation structure and food resources The first two principal components with eigen values >1 accounted for 72% of the variation in the original seven vegetation structure variables - PCA1 and PCA2 explained 50% and 22% of the variation, respectively. PCA1 represented higher density and basal area of trees in the 4-10m height class, while PCA2 represented higher density and basal area of trees in the lower 1-4m height class.

Chapter 3: Demographic parameters of Uromys caudimaculatus - 48 -

In the first year, mean densities of food resources varied considerably between sites P1 and P2 (Table 3.1). Total fruit fall was almost three times higher at site P2 (275,116 individual food resources per ha) compared with P1 (103,646). When only KTBE resources were examined, however, site P1 contained substantially more food resources than P2 and mean densities of food resources were higher in four of the six resource categories in P1 compared with P2 (Table 3.1). In the second year when fruits were collected from all sites, mean densities of total fruit fall were substantially less than the previous year at sites P1 and P2 and among site variability was lower, ranging from a high of 67,361 individual food resources/ha in site P3 to 32,396 in site C1.

KTBE resources also varied considerably among sites and in the case of P1 and P2 among years, and were highest in those fragments compared with the remaining two sites (P3 and C1). Of the six KTBE resource categories, site P1 had the highest mean densities for four of the six categories. In contrast, site C1 was extremely resource poor and recorded the lowest number of fruits in four of the six resource categories.

Table 3.1: Mean density (± 1 SE) of total food resources, known to be eaten (KTBE) food resources and KTBE food resources within each of the food resource categories for all sites and years. Resources are expressed as individual resources per hectare. KTBE food resource categories Sites Year Total KTBE NLH^ FSH^ FSL^ FLH^ FLL^ PL^ 103646 25260 2116 8952 521 8073 4036 1563 P1 1 ± ± ± ± ± ± ± ± 31647 7716 1816 6623 447 3695 2120 1029 275116 17670 39 11458 1813 1620 1775 965 P2 1 ± ± ± ± ± ± ± ± 102763 6096 0 5243 1767 817 1319 619 50553 30859 1107 13151 3874 10677 0 2051 P1 2 ± ± ± ± ± ± ± ± 15480 11658 779 11867 1773 5652 0 496 44174 21721 77 14506 2917 1427 2122 77 P2 2 ± ± ± ± ± ± ± ± 18119 7693 0 5602 1598 667 1943 49 67361 15729 556 764 2465 7917 3438 347 P3 2 ± ± ± ± ± ± ± ± 20448 2589 226 379 1102 1632 1274 158 32396 5035 0 521 764 1146 799 1806 C1 2 ± ± ± ± ± ± ± ± 9112 1505 0 192 529 0 292 627 ^ after the category descriptions in section 2.2.3.

Chapter 3: Demographic parameters of Uromys caudimaculatus - 49 -

Combined fruit fall in the three food resource categories (FLH + NLH + PL) fluctuated markedly among sampling periods with the timing of fruit fall peaks and lows different among sites, and among years for P1 and P2 (Figure 3.1). Peak fruit fall occurred from August to February depending on which site was examined. Overall, the highest peak occurred in P1 with ~16,000 fruits/ha followed by P3 and C1, both with ~12,000 fruits/ha. Site P2, which was very similar in size to P1 recorded a peak fruit fall density of only 4000 fruits/ha.

One of the most striking results was the sustained period of high fruit fall at the P1 site that lasted from November 2002 to May 2003 with estimates in these sampling periods ranging from approximately 11,000 fruits/ha to 16,000 fruits/ha (Figure 3.1). This site also showed a smaller period of sustained fruit fall in November 2003 and February 2004 with fruit fall ~30-50% that of the previous year’s level. In contrast to P1, the second isolated patch, P2, showed only two small fruit fall peaks, one in August 2002, the other in February 2003, and then a gradual increase beginning in August 2003 and lasting through to February 2004. Site P2 also showed much smaller fruit fall levels compared with site P1, with the highest peak ~75% lower than that highest peak in P1.

The largest fragment, P3, showed two peaks in fruit fall representing ~12,000 fruits/ha in August 2003 and February 2004 with a slightly lower density of approximately 9,000 fruit/ha in May 2003 (Figure 3.1). In contrast, November 2003 showed very low densities of fruit representing only 20% of the fruit fall in the three months immediately before or after. The continuous forest site showed only a single fruit fall peak in February 2004, reaching similar densities to that observed in P3 (~12,000 fruits/ha). The preceding nine months had very low fruit falls, however, that ranged from 3,500 fruits/ha in May 2003 to less than 1,000 fruits/ha in November 2003.

Chapter 3: Demographic parameters of Uromys caudimaculatus - 50 -

Figure 3.1: Density of fruit fall in the combined FLH + NLH + PL food resource categories for all sites and sampling periods. Note for sites P1 and P2, food resource sampling began in May 2002 whereas sampling began in May 2003 for sites P3 and C1. ▲ = P1, ■ = P2, ● = P3, + = C1.

3.3.2 Giant white-tailed rat demographic parameters Two hundred and thirty individuals were captured 1146 times over 25391 trap nights during the two year study. Only sites P1 and P3 showed any discernable peaks in adult density (Figure 3.2a). Densities of adults in P1 increased steadily from approximately 1 individual/ha (6 individuals over the entire 6.5ha patch) in February 2002 to 2.5 individuals/ha in February 2003 before showing a marked increased leading to a peak of 7.5 individuals/ha in August 2003. Adult numbers in both P1 and P3 exhibited density peaks three to six months following peaks in fruit fall. Adult densities at sites P2 and C1, however, remained very low and fluctuated around 0.5 and 1 individual/ha, respectively. This translates to between 5% and 50% of the adult population sizes at sites P1 and P3.

Resident adult densities mirrored that of total adult densities (Figure 3.2b). Only sites P1 and P3 showed any peaks in resident densities and this was particularly evident in P1 where resident densities reached a high of ~7 individuals/ha in August

Chapter 3: Demographic parameters of Uromys caudimaculatus - 51 -

2003). Both P2 and C1 had very low densities of resident adults that fluctuated around 0.5 and 1 individual/ha, respectively.

(a) (b)

Figure 3.2: Density of (a) all adults and (b) resident adult giant white-tailed rats in each site and each sampling period. ▲ = P1, ■ = P2, ● = P3, + = C1.

Mean annual adult density varied considerably both within and among populations and among years (Table 3.2). In the first year of the study, mean adult density per hectare was significantly higher in P1 (2.27) than in P2 (0.64) (t3 = 4.54, p = 0.02).

In the second year, annual densities varied significantly at all four sites (F3,9 = 26.13, p <0.001), with Tukey post-hoc comparisons identifying P1 > P3 > C1 = P2. Over both years, P1 consistently showed higher annual densities than P2, despite the similar size of these fragments.

Sixty-one adult female and 89 adult male individuals were captured during the study. For both sexes, adult density varied substantially among sites and among years (Table 3.2). In the first year, both adult male and adult female densities were

significantly higher in P1 compared with P2 (t3 = 4.22, p = 0.02 and t3 = 11.9, p = 0.001), respectively. In the second year, density of adult males and adult females were highest in P1 followed by P3, C1 and P2 (Friedman ANOVA, 2 2 χ (3) = 11.00, p = 0.011 and χ (3) = 12.00, p = 0.007), respectively. Examining among year density, adult males and adult females at site P1 showed marked density increases (1.12 individuals/ha to 3.27 and 1.15 individuals/ha to 2.31, respectively) between the first and second year. Female density at site P2 showed a similar trend increasing from 0.09 individuals/ha in year 1 to 0.32 in year 2, however, male density showed a slight decrease (Table 3.2).

Chapter 3: Demographic parameters of Uromys caudimaculatus - 52 -

Densities of adult male residents were similar at sites P1 and P2 in the first year (sign test; Z = 1.54, p = 0.25), but were significantly higher at site P1, followed by 2 sites P3, C1 and P2 in the second year (Friedman ANOVA, χ (3) = 11.00, p = 0.011; Table 3.2). No female residents were found at site P2 in the first year compared with a mean density of 1 individual/ha in P1. Density of resident females mirrored 2 that of males in year 2 with densities in P1>P3>C1>P2 (Friedman ANOVA, χ (3) = 12.00, p = 0.007). Among year density of resident males and females increased at site P1 with much higher densities in year 2 compared with year 1. While female residents at site P2 increased in the second year compared with year 1, there were fewer male residents in year 2 compared with year 1 (Table 3.2).

Table 3.2: Density of adults, adult males and females and resident adult males and females expressed as mean annual MNKA densities (± SE) per hectare. Mean adult Resident Resident Adult male Adult female Sites Year annual adult male adult female density density density density density P1 1 2.27 ± 0.19 1.12 ± 0.13 1.15 ± 0.10 1.00 ± 0.16 1.12 ± 0.19 P2 1 0.64 ± 0.05 0.55 ± 0.07 0.09 ± 0.05 0.50 ± 0.05 0.00 ± 0.00 P1 2 5.58 ± 0.78 3.27± 0.50 2.31 ± 0.33 2.77 ± 0.38 2.19 ± 0.10 P2 2 0.73 ± 0.07 0.41 ± 0.05 0.32 ± 0.05 0.36 ± 0.00 0.27 ± 0.05 P3 2 2.76 ± 0.28 1.51 ± 0.21 1.25 ± 0.26 1.34 ± 0.13 1.05 ± 0.07 C1 2 1.32 ± 0.07 0.63 ± 0.07 0.69 ± 0.00 0.46 ± 0.05 0.60 ± 0.03

The proportion of adults at each site that were classified as residents, although fluctuating across the study, remained quite high regardless of sex (Table 3.3). With the exception of only six sampling periods spread among all four sites, residents comprised at least two-thirds of the adult population and no discernable trend was evident in relation to habitat type (i.e. fragment or continuous). Although the mean annual proportion of adult males that were residents was higher in P1 compared with P2 during the first year, these differences were not significant

(t3 = 0.48, p = 0.66). The mean annual proportions of adult residents for both sexes 2 2 were similar in year 2 (Friedman ANOVA, χ (3) = 6.71, p = 0.8, χ (3) = 4.5, p = 0.21) for males and females, respectively.

Chapter 3: Demographic parameters of Uromys caudimaculatus - 53 -

Table 3.3: Proportion of adult Uromys that were residents in each site.

Males Site Trapping period P1 P2 P3 C1 Feb. 2002 1.00 (1, 1) 0.00 (1, 0) - - May 2002 0.80 (5, 4) 1.00 (3, 3) - - Aug. 2002 0.75 (8, 6) 0.75 (4, 3) - - Nov. 2002 1.00 (9, 9) 1.00 (3, 3) - 0.56 (9, 5) Feb. 2003 1.00 (7, 7) 1.00 (2, 2) 0.47 (15, 7) 0.71 (7, 5) May 2003 0.92 (13, 12) 1.00 (2, 2) 0.71 (14, 10) 0.71 (7, 5) Aug. 2003 0.89 (27, 24) 1.00 (2, 2) 0.92 (12, 11) 0.67, (6, 4) Nov. 2003 0.67 (27, 18) 0.67 (3, 2) 1.00 (15, 15) 1.00 (4, 4) Feb. 2004 0.95 (19, 18) 1.00 (2, 2) 0.92 (12, 11) 1.00 (3, 3)

Females P1 P2 P3 C1 Feb. 2002 0.75 (4, 3) * - - May 2002 0.86 (7, 6) * - - Aug. 2002 0.88 (8, 7) * - - Nov. 2002 1.00 (7, 7) 0.00 (1, 0) - 0.83 (6, 5) Feb. 2003 1.00 (9, 9) 1.00 (1, 1) 0.63 (8, 5) 1.00 (5, 5) May 2003 0.85 (13, 11) 0.50 (2, 1) 0.89 (9, 8) 0.86 (7, 6) Aug. 2003 0.79 (19, 15) 0.50 (2, 1) 0.64 (14, 9) 0.83 (6, 5) Nov. 2003 0.89 (19, 17) 1.00 (2, 2) 0.92 (12, 11) 1.00 (5, 5) Feb. 2004 0.74 (19, 14) 1.00 (2, 2) 1.00 (9, 9) 1.00 (5, 5) Numbers in parentheses denote the total number of adults followed by the number of residents. Residents were defined as animals that were captured in 3 or more sampling periods. * signifies that no resident adults were present. - indicates that trapping was not undertaken.

Although captures of newly weaned juveniles occurred at all sites, timing and magnitude of juvenile influxes varied considerably, both within and among sites (Figure. 3.3). In the first year when P1 and P2 were the only sites studied, very few juveniles were captured, with February and May being the only periods when juveniles were captured. During the second year, a small number of juveniles were captured at P2 with only one and four juveniles captured in August 2003 and February 2004, respectively. In contrast, site P1 was by far the most productive site with juveniles captured in six of the nine (67%) sampling periods including a sustained period that lasted through 2003. Juvenile captures peaked at four juveniles/ha, (equating to 25 juveniles) in May 2003 (Figure 3.3). Both the timing and magnitude of juvenile captures at P3 and C1 differed considerably, with juveniles captured in all sampling periods in 2003 at site P3, while none were captured after May 2003 at the continuous site (C1).

Chapter 3: Demographic parameters of Uromys caudimaculatus - 54 -

Figure 3.3: Density of juvenile (<450g) Uromys that were captured for the first time for all sites and sampling periods. ▲ = P1, ■ = P2, ● = P3, + = C1.

Annual juvenile density varied dramatically among sites ranging from 8 individuals/ha in P1 during the second year, to 0.2 in C1 and P2 in the first year. In the two sites trapped in both years, juvenile density increased appreciably at both sites, but especially so at site P1 where density increased from 1.6 juveniles/ha in year 1 to 8.0 in year 2.

The proportion of juveniles that reached maturity in their assumed population of birth (i.e. recruitment) was higher at site P1 followed by sites C1 and P2 (Table 3.4). The maximum proportion of juveniles that reached maturity was 0.45 and 0.42 in site P1 for year 1 and year 2, respectively. This equates to approximately 55% of all juveniles either dispersing from, or dying in the trapping grid. The low number at site P2 is likely to be an underestimate because four of the juveniles were captured in the last trapping period (February 2004). Because no subsequent trapping periods were undertaken, it was not possible to determine if any of these juveniles subsequently became residents.

Chapter 3: Demographic parameters of Uromys caudimaculatus - 55 -

Table 3.4: The proportion of new juveniles at the time of first capture that subsequently became residents within that site.

Site Trapping period P1 P2 P3 C1 Feb. 2002 0.33 (3, 1) 0.00 (4, 0) - - May 2002 0.00 (2, 0) 0.00 (1, 0) - - Aug. 2002 * * - - Nov. 2002 * * - 0.00 (3, 0) Feb. 2003 0.44 (9, 4) * 0.67 (9, 6) 0.00 (2, 0) May 2003 0.69 (26, 18) * 0.13 (8, 1) 0.00 (2, 0) Aug. 2003 0.05 (20, 1) 0.00 (1, 0) 0.25 (4, 1) * Nov. 2003 0.00 (7, 0) * 0.00 (7, 0) * Feb. 2004 * 0.00 (4, 0) * * Numbers in parentheses denote the total number of juveniles captured followed by the number that became residents. * signifies that no adults were captured. - indicates that trapping was not undertaken.

3.3.2.1. Sex ratios No consistent pattern was evident in adult resident sex ratios within or among sites (Table 3.5). Adult sex ratios were generally male-biased in P2 during both years, P1 in the second year and in the largest fragment (P3). Only one sampling period showed significant male-biased sex ratios, during August 2003 at site P1. In contrast, adult sex ratios were generally female-biased at site P1 in the first year and in the continuous population (C1).

Compared with adult residents, the sex ratios of newly weaned juveniles showed much greater within site variation with the exception of site C1 (Table 3.6) Generally, juvenile populations in sites P1 and P2 showed periods when sex ratios were either strongly or completely male or female-biased whereas the population in P3 was either uniform or completely female-biased.

Chapter 3: Demographic parameters of Uromys caudimaculatus - 56 -

Table 3.5: Sex ratios of adult residents (>450g) expressed as the proportion of males in each population and the corresponding Chi-squared p value where analyses were undertaken.

Site Trapping period P1 P2 P3 C1 Feb. 2002 0.25 (1, 3) * - -

May 2002 0.40 (4, 6) 1.00 (3, 0) - -

Aug. 2002 0.46 (6, 7) 1.00 (3, 0) - - (p = 0.78) Nov. 2002 0.56 (9, 7) 1.00 (3, 0) - 0.50 (5, 5) (p = 0.62) Feb. 2003 0.44 (7, 9) 0.67 (2, 1) 0.58 (7, 5) 0.50 (5, 5) (p = 0.62) (p = 0.56) May 2003 0.52 (12, 11) 0.67 (2, 1) 0.56 (10, 8) 0.45 (5, 6) (p = 0.83) (p = 0.64) (p = 0.76) Aug. 2003 0.62 (24, 15) 0.67 (2, 1) 0.55 (11, 9) 0.44 (4, 5) (p = 0.01) (p = 0.65) Nov. 2003 0.51 (18, 17) 0.50 (2, 2) 0.58 (15, 11) 0.44 (4, 5) (p = 0.87) (p = 0.43) Feb. 2004 0.56 (18, 14) 0.50 (2, 2) 0.55 (11, 9) 0.38 (3, 5) (p = 0.48) (p = 0.65) Numbers in parentheses denote the total numbers of males and females, respectively. Italics indicate significant differences.* signifies that no resident adults were present. - indicates that trapping was not undertaken.

Table 3.6: Sex ratios of newly captured juveniles (≤450g) expressed as the proportion of males in each population and the corresponding Chi-squared p value where analyses were undertaken.

Site Trapping period P1 P2 P3 C1 Feb. 2002 0.33 (1, 2) 0.25 (1, 3) - - May 2002 1.00 (1, 0) 0.00 (0, 1) - - Aug. 2002 * * - - Nov. 2002 0.67 (2, 1) * * - Feb. 2003 0.33 (3, 6) 0.56 (5, 4) 0.50 (1, 1) * May 2003 0.60 (15, 10) 0.50 (4, 4) 0.50 (1, 1) (p = 0.32) * Aug. 2003 0.30 (6, 14) 0.00 (0, 1) 0.00 (0, 4) (p = 0.07) * Nov. 2003 0.57 (4, 3) 0.00 (0, 7) * * Feb. 2004 1.00 (4, 0) * * * Numbers in parentheses denote the total numbers of males and females, respectively. * signifies that no new juveniles were captured. - indicates that trapping was not undertaken.

Chapter 3: Demographic parameters of Uromys caudimaculatus - 57 -

3.3.2.2. Persistence The mean probabilities of persistence for adult males and females were very similar among sites and years (Figure 3.4). No significant differences in persistence were found between P1 and P2 in the first year for adult males (t3 = 0.93, p = 0.42). Because no adult females were captured in more than one trapping period in P2 in the first year, no comparisons among P1 and P2 could be made. For adult males and females, persistence did not differ among sites in the second year (Friedman 2 2 ANOVA, χ (3) = 1.00, p = 0.80 and χ (3) = 2.20, p = 0.53), respectively. Persistence probabilities were very similar at sites P1 and P2 for adult males among years.

Figure 3.4: Probability of persistence (± 1 SE) of adult males and females in all sites and years. Y1 = year 1; Y2 = year 2. ■ = males; ▲= females. Numbers above error bars indicate the number of trapping period’s persistence probabilities were estimated for. Note: persistence for females from P2-Y1 could not be estimated.

3.3.3 Uromys demography, vegetation structure and food resource density Because vegetation structure was only measured once during the study, only demographic data from the second year was included in the analyses that examined relationships with the PCA derived variables. Of the demographic variables examined, none showed any significant relationship with either PCA1 or PCA2 (Table 3.7).

Chapter 3: Demographic parameters of Uromys caudimaculatus - 58 -

For both years, none of the demographic variables were related to total food resource density or KTBE resource density. Of the six KTBE food resource categories, only large fruits with a thick mesocarp and/or thick hard endocarp (FLH) were related to any variable, and showed positive relationships with mean adult 2 density (F1,4 = 16.07, p = 0.02, R = 0.80; Figure 3.5) and density of resident adults 2 (F1,4 = 19.04, p = 0.01, R = 0.83; Figure 3.6). These two variables were also significantly positively related to the density of FLH in combination with the high lipid content foods (NLH and PL; Table 3.7). FLH, when added to NLH and PL was 2 significantly related to juvenile recruitment (F1,4 = 12.7, p = 0.02, R = 0.66). ANCOVA revealed that the regression slopes of the different categories were not significantly different (Table 3.7).

Table 3.7: Regression models showing the relationship between the mean annual density of food resources, two Principal Components Analysis factors (PCA 1 and PCA 2) and Uromys demographic parameters. ANCOVA results are shown where multiple independent resource categories were significant. * indicate significant regression results. Dependent variable Statistics – both years (n=6) Independent variables Adult density: 2 FLH F1,4 = 16.07, p = 0.02, R = 0.80* 2 FLH + NLH F1,4 = 11.40, p = 0.03, R = 0.74* 2 FLH + NLH + PL F1,4 = 14.13, p = 0.02, R = 0.78* ANCOVA: F3,19 = 0.43, P = 0.73 PCA 1 F1,2 = 0.14, P = 0.72 PCA 2 F1,2 = 2.77, P = 0.17 Resident adult density: 2 FLH F1,4 = 19.04, P = 0.01, R = 0.83* 2 FLH + NLH F1,4 = 13.41, P = 0.02, R = 0.77* 2 FLH + NLH + PL F1,4 = 16.65, P = 0.02, R = 0.81* ANCOVA: F3,19 = 0.35, P = 0.79 PCA 1 F1,2 = 0.20, P = 0.68 PCA 2 F1,2 = 2.78, P = 0.18 Juvenile density: FLH F1,4 = 6.96, P = 0.06 FLH + NLH F1,4 = 5.33, P = 0.08 FLH + NLH + PL F1,4 = 6.02, P = 0.07 PCA 1 F1,2 = 0.52, P = 0.51 PCA 2 F1,2 = 0.85, P = 0.41 Juvenile recruitment: FLH F1,4 = 4.22, P = 0.11 FLH + NLH F1,4 = 5.02, P = 0.09 2 FLH + NLH + PL F1,4 = 12.7, P = 0.02, R = 0.66* PCA 1 F1,2 = 0.13, P = 0.75 PCA 2 F1,2 = 8.31, P = 0.10

Chapter 3: Demographic parameters of Uromys caudimaculatus - 59 -

Figure 3.5: Uromys mean annual adult density as a function of the density of large fruits with a thick mesocarp and/or thick hard endocarp (food category FLH).

Figure 3.6: Mean annual density of resident adult Uromys as a function of the density of large fruits with a thick mesocarp and/or thick hard endocarp (food category FLH).

Chapter 3: Demographic parameters of Uromys caudimaculatus - 60 -

3.3.3.1. Timing of reproduction If females are able to time reproduction so that cessation of weaning coincides with fruit fall peaks, there should be a positive relationship between captures of newly weaned juveniles and peaks in fruit fall. Alternatively, if females initiate reproduction during high food resource periods, then there should be a positive relationship between juvenile captures and the density of fruit fall in the previous three months (one sampling period). Insufficient data were available from sites P2 and C1 to examine these relationships. Data analyses were conducted, however, within sites P1 and P3, even though at site P3, data were only available for four sampling periods for current fruit fall and three sampling periods when a time lag of three months was used.

Timing of juvenile captures was not related to fruit fall at the time of capture in either P1 (p = 0.33) or P3 (p = 0.33). Juvenile density was, however, significantly positively related to food resources in the previous sampling period within P1 2 (F1,4 = 19.67, p = 0.007, R = 0.79; Figure 3.7). While food resources in the previous sampling period explained 97% of the variance in the density of newly captured juveniles at site P3, this relationship was, however, not significant due to the very 2 small sample size (F1,1 = 39.73, p = 0.100, R = 0.97).

Chapter 3: Demographic parameters of Uromys caudimaculatus - 61 -

3.4 Discussion

3.4.1 Temporal variation in food resource production In the tropical rainforests of north-eastern Australia, ~75% of annual rainfall occurs in the summer months (November to March), a period that generally corresponds with peaks in native fruit production (Hopkins and Graham, 1989). Several earlier studies have shown that peaks in fruit fall generally occur from late in the dry season through to the end of the wet season (i.e. September to March), and are generally year and site specific (Stocker et al. 1995; Dennis and Marsh, 1997; Boulter et al. 2006). The results shown here support earlier findings that reported among site and among year variability in fruit fall.

No single site showed any consistent pattern for any food resource category. For example, total annual fruit fall at site P2 in year 1 was almost 3 times that of site P1 in year 1 and was almost 10 times that of site C1, which was the most resource

Chapter 3: Demographic parameters of Uromys caudimaculatus - 62 - poor site. In contrast, site P2 was generally relatively resource poor in food resources that are known to be eaten by U. caudimaculatus. Interestingly, site P1 had the highest annual density in four of the six food resource categories and in stark contrast, the continuous forest site, C1 recorded the lowest densities in four of the six categories. The large fruits, nuts and pods (food categories FLH, NLH and PL, respectively) that are important food resources for Uromys (Finkelstein and Grubb, 2002) were consistently higher in the smaller fragment, P1, relative to all other sites, in particular P2 and C1. Indeed, one of the most striking results was evident among fragments of similar size where a fruit fall peak of approximately 16,000 individual fruits/ha in February 2003 at site P1 was four times that of site P2. This suggests that fragmentation does not impact peak fruit abundance directly as similar size fragments (P1 and P2) had the highest and lowest peaks in fruit fall, respectively, among the four sites. These data highlight that extreme within and among site variability in fruit fall can occur even over relatively small spatial scales (i.e. only a few kilometres).

Plausible explanations for variability in fruiting phenology and the variation in density of all food resources here include altered microclimates within the habitat patches due to edge effects and the presence of a rainfall gradient that decreases from an annual mean of ~2600mm at the south-eastern edge of the Tableland to ~1200mm at the north-western edge. Singh and Kushwaha (2005, 2006) noted that tropical trees respond in different ways to altered rainfall and microclimatic conditions and Chapman et al. (2005) noted altered (delayed or advanced) fruiting phenology in relation to changes in rainfall and microclimate. These results agree with the prediction by Laurance et al. (2003) that altered microclimates can influence fruiting phenology.

While some species of rainforest plants produce very large quantities of fruit that can last for only a few days to weeks (Adler, 1994), prolonged periods of resource abundance is not uncommon (Frith and Frith, 1985; Adler, 1994, 1998; Fredriksson et al. 2006; Adler and Lambert, 2008; Keuroghlian and Eaton, 2008b). Previous findings by Stocker et al. (1995) and Dennis and Marsh (1997) showed that on the Atherton Tableland, prolonged periods of high fruit abundance can last for several months at a time. High fruit fall at site P1 was consistent with earlier findings by Stocker et al. (1995) where fruit fall in other areas of the Atherton Tableland lasted for a six month period from November 2002 to May 2003. Stocker et al. (1995)

Chapter 3: Demographic parameters of Uromys caudimaculatus - 63 - suggested sustained fruit fall in their study resulted from continuous fruiting by candlenuts (Aleurites rockinghamensis) that lasted from October to August. The only site in the current study where candlenuts were found was at P1, where fruiting occurred continuously from November 2002 to August 2003. Brown tamarinds (Castanospora alphandii) also showed sustained fruit fall at site P1 and was one of the few species to contribute to peak fruit fall in C1 in February 2004.

3.4.2 U. caudimaculatus demographic parameters Tropical rodents typically show seasonality in reproduction and population structure (Gliwicz, 1984; Friend, 1987; Adler, 1994, 1998, 2000; Adler and Beatty, 1997; Harris and Macdonald, 2007; Shine and Brown, 2008; Šumbera et al. 2008). Within the constraints of the annual cycle, reproductive output and the timing of population fluctuations may vary among populations as a function of local resource conditions (Adler, 1994). For instance, although tropical spiny rats (Preochimys semispinosus) showed distinct fluctuations in population size, the timing and magnitude of fluctuations varied among populations (Adler, 1994). Results here generally show support for this pattern with each population showing strikingly discordant fluctuations that appeared independent of fragment size.

Each of the four sites showed distinct periods of reproductive quiescence, but the length and timing of the quiescent period varied widely among sites. At site P2, 63% of sampling periods failed to result in a single captured juvenile, while juveniles were captured in 67% and 80% of the sampling periods at P1 and P3, respectively. Interestingly, during the February 2004 sampling period when juvenile Uromys should be abundant, P2 was the only site where juveniles were captured. These results are broadly similar to, but show less annual cycling, compared with other Uromys studies. Horskins (2005) reported captures of juvenile Uromys throughout the year with peak numbers occurring in February. Les Moore (unpubl. data) observed that in the same 80ha fragment as used here (P3), densities of juvenile Uromys peaked in the wet season but numbers decreased significantly during the dry season. In contrast, Comport (1999) reported that juvenile densities reached their peak in June and August (the dry season) in rainforest and wet sclerophyll forests, respectively. Lastly, densities of black-footed tree rats (Mesembriomys gouldii), a tropical Australian rodent similar in size to Uromys, showed peaks in juvenile density during the early dry and early wet seasons (Friend, 1987).

Chapter 3: Demographic parameters of Uromys caudimaculatus - 64 -

Fragmented populations are predicted to show higher densities and higher relative survival (persistence) rates, reduced reproductive output and altered sex ratios compared with larger, continuous populations (Gliwicz, 1980; Adler and Levins, 1994; Banks et al. 2005c; Sorvari and Hakkarainen, 2007). Despite significant variation in demography among sites and years, the predicted differences between isolated and continuous populations suggested by Gliwicz (1980) and termed the “island syndrome” by Adler and Levins (1994) were not found here. In the second year of the study when all sites experienced similar environmental conditions, mean annual density, density of adults and resident adults and juvenile density were highest at the small fragmented P1 site, but were correspondingly lowest in the other similar sized small fragment (P2). Furthermore, the two largest sites, P3 and C1 that are effectively continuous habitat for U. caudimaculatus, showed vastly different demographic patterns. The 80ha fragment (P3) exhibited higher density for all individuals, adults, resident adults and juveniles compared with the continuous forest (C1).

Of interest was the finding that adult persistence and the proportion of the adult population that were residents did not differ among the four sites. Taken together, these results suggest that each site has a base resident population size that may be at or near the carry capacity governed by base food resource loads. Any increases in food availability can lead therefore, to increased reproduction by the resident population, rather than increased adult persistence. As density increases as a result of reproduction, a subset of the new weaned juveniles are likely to be recruited into the adult population - if sufficient food resources allow - leading to a proportion of the adult population comprised of residents that is relatively stable over time.

The sex ratios of adult residents were generally male-biased and broadly similar at all sites with the exception of the continuous forest (C1) and the small isolated fragment (P1) in the first year. In contrast, juvenile sex ratios generally fluctuated widely, both within and among sites. This pattern was particularly evident at site P3 where two successive sampling periods yielded only female juveniles whereas at site P1, biased juvenile sex ratios alternated between the sexes in three consecutive sampling periods. Despite the lack of a consistent pattern in sex ratios among sites, fragmentation could still influence sex ratios in Uromys in a number of ways.

Chapter 3: Demographic parameters of Uromys caudimaculatus - 65 -

A lower frequency of adult males in the unfragmented continuous habitat (C1) may suggest that fragmentation had affected adult sex ratios, possibly via altered dispersal patterns or relative mortality. Biased sex ratios associated with relative dispersal and/or mortality in fragmented systems have been reported in birds where sex ratios were male-biased due to female-biased dispersal (Dale, 2001) and in rodents where female-biased sex ratios result from male-biased dispersal (Raska- Jurgiel, 1992; Banks et al. 2005c). If Uromys exhibit male-biased dispersal, a common trait in mammals (Greenwood, 1980), the results of Raska-Jurgiel (1992) and Banks et al. (2000a,c) would suggest female-biased sex ratios in fragmented habitats and male-biased sex ratios in unfragmented habitat. This is essentially the opposite pattern to that observed here. In contrast, elevated proportions of males were observed in populations that were farther away from permanently inhabited forest in bank voles (Clethrionomys glareolus) that show male-biased dispersal (Van Apeldoorn et al. 1992). While sex ratios may be biased towards the more philopatric sex in fragmented systems due to relative dispersal or mortality, remnant fragments with a continual influx of immigrants should show sex ratios biased towards the dispersing sex (Banks et al. 2005c).

The variable pattern of juvenile sex ratios shown among sites may suggest that either offspring sex ratios or mortality is sex-biased. Although it has been argued that the capacity for adaptive control of sex ratios in vertebrates is probably limited (Packer et al. 2000; Palmer, 2000), Cockburn (1990) noted that offspring sex ratios in some marsupials can depart significantly from parity. This is particularly evident at the population level (Cockburn et al. 1985; Dickman, 1988; Johnson et al. 2001; Johnson and Ritchie, 2002) with male-biased sex ratios evident in Antechinus and common brushtail possums (Trichosurus vulpecula) where resource competition between mothers and daughters is high. This suggests that females may have the capacity to adjust offspring sex ratios in response to local ecological conditions. Biases in sex ratios could originate in several ways: 1) sex-biased mortality between birth and weaning; 2) maternal bias towards one sex at birth; 3) sex-selective abortion between conception and birth or 4) sex-biased conception. Alternatively, biased sex ratios could simply be due to demographic stochasticity. Although it is not possible to determine the potential mechanism(s) that may underlie the observed variation in juvenile sex ratios shown here, the result does provide avenues for future, experimental research.

Chapter 3: Demographic parameters of Uromys caudimaculatus - 66 -

Failure to observe a distinct relationship between demography and fragment size is reflected in other recent published studies (Harper et al. 1993; Johannesen and Ims, 1996; Adler and Lambert, 1997; Adler, 1998, 2000; but see Nupp and Swihart, 1996 and Anderson et al. 2003). In addition, these results contradict previous studies that have examined fragmentation effects on U. caudimaculatus densities. Harrington et al. (2001), used two of the same sites that were used here (P2 and P3), and while they found similar population fluctuations among sites, they also found that abundance of U. caudimaculatus was correlated positively with fragment size, reaching a peak at sites within the continuous forest. In contrast, Laurance (1994) found U. caudimaculatus were more abundant in fragments compared with unfragmented control sites, but abundances in fragments declined with fragment size. Neither Harrington et al. (2001) nor Laurance (1994), however, examined explicitly the casual factors that underlie relationships between rodent abundance and fragment size.

The variation in demography reported here suggests that Uromys can respond in complex ways to fragmentation, at least in fragments larger than ~six hectares. These results raise the question that if U. caudimaculatus demography is not responding to fragmentation in predictable ways, what factors can affect demographic parameters within different sized fragments? Various factors have been shown to influence demography including food resource availability, interspecific competition, predation, vegetation structure, altered dispersal patterns and relative availability of den or nest sites (Adler, 1994, 1998, 2000; Nupp and Swihart, 1996; Gerber et al. 2003)

3.4.3 Food resources Since Lack’s (1954) suggestion that most vertebrate populations are food limited, many studies have examined the effect of food resources on population processes. Most have reported positive relationships (Johns, 1989; Fitzgibbon, 1997; Bergallo and Magnusson, 1999; Johnson and Sherry, 2001; López-Bao et al. 2008), and rodent species often respond to higher food resource levels with increased densities and higher reproductive rates (Doonan and Slade, 1995; Nupp and Swihart, 1996). In a review of food provisioning studies, Boutin (1990) concluded that population densities generally increase with increased food availability (see also Yunger, 2002). How particular foods affect an individual species’ demography, however, is still unclear and management efforts are unlikely to be effective unless the types of

Chapter 3: Demographic parameters of Uromys caudimaculatus - 67 - food resources that do, and do not, limit populations are determined (Reynolds- Hogland et al. 2007).

Surprisingly, in the current study no relationship was evident between Uromys demography and total food resource availability or food resources that are ‘known to be eaten’ by Uromys. Variation in quantity, quality and handling times of food resources however, are likely to vary considerably leading to certain suites of food resources being preferred over others. Accordingly, strong relationships were found between certain Uromys demographic parameters and the density of large rainforest fruits (>2 centimetres in diameter) that have a high proportion of their volume eaten (category FLH). Several of these rainforest fruits including Beilschmiedia bancroftii, are exploited heavily by Uromys and in some cases, achieve close to a 100% predation rate (Harrington et al. 1997, 2001; Moore, 1995; Theimer, 2001). In addition, three Ficus spp. (F. crassipes, F. pleurocarpa and F. watkinsiana) that are included in the FLH food resource category, are important food staples for U. caudimaculatus (Nigel Tucker pers. comm.) Ficus are also important food resources for other tropical native frugivorous mammals (Foster, 1982; Fleming, 1992; Adler, 2000).

The results shown here are consistent with a number of other studies that have shown strong positive relationships between animal density and availability of preferred food resources (Balcomb et al. 2000; Johnson et al. 2001; Ramsey et al. 2002; Hanya et al. 2004; Ghizoni et al. 2005; Harris and Macdonald, 2007; Reynolds-Hogland et al. 2007; Keuroghlian and Eaton, 2008b; Marcello et al. 2008). For example, Neotropical populations of Central American spiny rats increased on islands with high levels of heavily exploited rainforest fruits (Adler, 1998) and Ficus spp. (Adler, 2000) and red-backed vole densities were strongly associated with densities of red maple seeds and deer and white-footed mice densities increased with high availabilities of red oak seeds (Schnurr et al. 2002). Further, Marcello (et al, 2008) found that reproductive output and relative density of white-footed mice was positively related to the periodic emergence of cicadas.

Finkelstein and Grubb (2002) noted that foods consumed preferentially by U. caudimaculatus in the Elaeocarpaceae, Euphorbiaceae, Proteaceae and Sapotaceae families (nuts (NLH) and pods (PL)), have significantly higher lipid concentrations than non-consumed foods. Further, preferred foods in the

Chapter 3: Demographic parameters of Uromys caudimaculatus - 68 -

Lauraceae had lower lipid concentrations, suggesting that nutrients other than lipids or proteins may be supplied by these foods (Finkelstein and Grubb, 2002). This suggestion was supported in the current study because food resources categorised as FLH have lower lipid concentrations compared with either of the categories containing large nuts and pods (Finkelstein and Grubb, 2002). These later two food resource categories in isolation, showed no statistically significant relationships with U. caudimaculatus density.

Increased population densities have been attributed to not only the quality of the food resource but also the quantity. In general, nuts were much less abundant than pods, and both were less abundant than large fruits. Nuts however, contain the highest lipid concentrations followed by pods and large fruits. Therefore, highly abundant foods with medium lipid concentrations may be more important in determining overall Uromys population density and the density of resident adults compared with foods that have high lipid concentrations but which are relatively scarce. High energy foods in conjunction with preferred staple foods, however, may be required for successful reproduction, weaning and juvenile recruitment.

While FLH food resources were related to juvenile recruitment, the only variable related significantly to juvenile recruitment was the combined density of FLH foods in conjunction with lipid rich nuts (NLH) and pods (PL). This suggests that juveniles may require higher energy food resources to establish themselves within a population, however, the mechanisms underlying this relationship are unknown. Intraspecific competitive interactions among Uromys and territorial behaviour are also unknown, but individuals are believed to live solitary lives (Moore, 1995). If Uromys are indeed solitary, it is plausible that high lipid food resources provide the necessary energy for rapid growth that would enable newly weaned juveniles to acquire, and then subsequently defend, a suitable territory.

The positive correlations within site P1 between fruit fall, juvenile recruitment and captures of newly weaned juveniles strongly suggests that Uromys respond to temporal variation in fruit fall by initiating reproduction during periods of abundant high quality food resources. Although this pattern was only statistically significant in P1, several lines of evidence suggest similar patterns may also occur in the other sites. Firstly, the lack of statistically significant results between the occurrence of juveniles and food resources in the largest fragment (P3) is likely due to very small

Chapter 3: Demographic parameters of Uromys caudimaculatus - 69 - sample sizes rather than a lack of a true relationship. The r squared value indicated that ~95% of the variation was explained by food resources suggesting a real effect of food resources and indicates that larger sample sizes could produce statistically significant results. While no analyses were possible for either site P2 or C1, incidental trapping data suggest that food resources can influence reproduction and that reproduction is a function of local ecological conditions. The continuous forest population received significant fruit falls in the final trapping period of February 2004 and in the following 1-2 months, possibly as a result of very high rainfall in February and March 2004 (Sinan Ogden pers. comm.). Coincidentally, high numbers of juvenile Uromys were observed foraging in and around the homestead directly adjacent to the trapping grid during May and June (Sinan Ogden pers. comm.). This suggests that food resources resulted in higher juvenile abundance in this population. Similarly, sustained periods of higher fruit fall in site P2 began in November 2003 and continued across the wet season and into April, 2004. Supplementary trapping for genetic studies in early August 2004 resulted in the capture of four juvenile males. Judging by their weight (200-250g) these juveniles were likely conceived approximately three to four months previously (March-May) that would have coincided with peak periods of fruit fall. The timing of juvenile captures at site P1 suggests that Uromys may reproduce opportunistically in response to periods of high food abundance. Similar relationships between reproduction, juvenile density and food resources have been found for a range of small mammals including Santiago Galapagos Mice (Nesoryzomys swarthy; Harris and Macdonald, 2007), long-tailed pocket mice (Chaetodipus formosus; Orland and Kelt, 2007) and white-footed mice (Peromyscus leucopus; Marcello et al. 2008).

While it is evident that numerous demographic parameters were related to specific suites of food resources available, other factors may also act in concert with food resources to explain the demographic results reported here.

3.4.4 Competition, predation, vegetation structure, dispersal and den sites Although this study was not designed to test for differences in rates of interspecific competition or predation explicitly, several lines of evidence suggest that neither are likely to explain differences in U. caudimaculatus demographic patterns (as discussed in chapter 2 - page 38).

Chapter 3: Demographic parameters of Uromys caudimaculatus - 70 -

Results also suggest that vegetation structure was not important in explaining Uromys density. These variables, however, primarily described density, height and basal area. However, other structural variables including ground cover and the presence of vines and rattans (which were not measured) may influence Uromys demography. These variables are unlikely however, to exert as strong an influence as availability of critical food resources.

Two possible factors that may explain the lack of a relationship between vegetation structure and Uromys are levels of predation and competition. Small rodent species are highly susceptible to both predation and interspecific competition and may rely more on vegetation structure for shelter and protection than larger rodent species. For example, vegetation structure has been linked to variation in density and demography in several species of small mammal including southern red-backed voles (Clethrionomys gapperi; Nordyke and Buskirk, 1991), white-footed mice (Peromyscus leucopus; Adler and Wilson, 1987; Anderson et al. 2003) and agile antechinus (Antechinus agilis; Banks et al. 2005c) but these relationships were not evident for the larger Central American spiny rat (Proechimys semispinosus; Adler, 2000). Furthermore, although Laurance (1994) did not examine the relationships between U. caudimaculatus and forest structure explicitly, he found smaller fragments had reduced levels of structural complexity resulting from reduced canopy and sub-canopy cover and higher disturbance levels compared with larger fragments or unfragmented control sites. When considered in parallel with findings of lower U. caudimaculatus abundance in smaller fragments, this implies that a positive association may exist between habitat structural complexity and Uromys abundance.

When individuals are confined to isolated populations, such as remnant habitat patches surrounded by an inhospitable matrix, dispersal may become frustrated or inhibited (Lidicker and Stenseth, 1992), resulting in higher population densities (Krebs et al. 1969; Adler and Levins, 1994; Nupp and Swihart, 1996; Krohne and Hoche, 1999). Whether or not dispersal is likely to be constrained will depend on several factors including the degree of habitat specialisation (Laurance, 1991), the nature of the surrounding matrix (Mesquita et al. 1999) and the degree of isolation or distance from other suitable habitats (Bowers and Matter, 1997).

Chapter 3: Demographic parameters of Uromys caudimaculatus - 71 -

U. caudimaculatus is generally regarded as a closed forest specialist and recent evidence suggests that even though individuals make foraging forays into the surrounding matrix (Craig Streatfeild unpubl. data), they may perceive the surrounding matrix as unfavourable and hence a barrier to dispersal (Harrington et al. 2001; Horskins, 2005). The three fragments used here were all surrounded by similar matrix habitat that could act as a barrier to dispersal for U. caudimaculatus (i.e. roads and cattle pasture: see Figure 2.1 for example), potentially resulting in frustrated dispersal and increased densities within fragments. This, however, does not explain the much higher densities in P1 compared with P2.

Another factor that may explain variation in demography among U. caudimaculatus populations is availability of suitable nest or den sites. Suitable dens sites have been shown to limit populations of woodrats (Neotoma fuscipes; Gerber et al. 2003), common brushtail possums (Trichosurus vulpecula; Johnson and Ritchie, 2002) and northern flying squirrels (Glaucomys sabrinus; Ransome and Sullivan, 2004). While the precise den requirements for U. caudimaculatus have not been characterised, individuals are known to utilise a variety of den sites including tree holes and tree tops (Moore, 1995; Watts and Aslin, 1981), sheltered rocky outcrops (Tad Theimer pers. comm.), hollow logs, buttress roots and even burrows constructed in riverbanks (Craig Streatfeild pers. obs.).

The broad diversity of den sites used by U. caudimaculatus suggests that they may not, in isolation, limit population density. However, high quality and safe den sites may be required to wean offspring successfully, thus limiting reproduction and U. caudimaculatus demography indirectly. If den sites are a limiting factor, as density increases competition for high quality and safe den sites should also increase, which in turn could lead to a lower proportion of the population reproducing. This has been found for Central American spiny rats (Proechimys semispinosus; Adler and Beatty, 1997).

Although U. caudimaculatus demography was strongly related to availability of food resources, several points relating to experimental design, the small number of sampling sites and potential statistical artefacts need to be addressed. While the numbers of sites sampled in this study were quite low and the duration of the study lasted only two years, the strong relationship between U. caudimaculatus density and food resources is unlikely to be a study artefact, or an effect of stochastic

Chapter 3: Demographic parameters of Uromys caudimaculatus - 72 - events for a number of reasons. Firstly, when effect sizes are questionable or small, statistically significant results can only be determined if the sample size is correspondingly large. However, small sample sizes will only produce highly significant results when effect sizes are very large, indicating that food resources are most probably affecting U. caudimaculatus demographics as shown here. Secondly, as part of a wider study, sites P1 and P2 have been sampled since 2000 using identical methodology, and P1 has consistently shown higher densities of both Uromys and high quality food resources compared with P2 (John Wilson unpubl. data). Thirdly, in 1990 and 1991, Uromys densities were consistently higher in site P3 compared with P2 (Les Moore pers. comm.). Fourth, in a study that examined the effectiveness of wildlife corridors on the Atherton Tableland, Horskins (2005) observed consistently higher densities of U. caudimaculatus in populations with large densities of FLH and NLH food resources, compared with those populations with lower food resource density.

3.5 Conclusion

The current study is one of the first to provide a quantitative comparison between food resources, fragment size and vegetation attributes with Uromys density. The study showed that specific suites of rainforest food resources, at least in part, are strongly associated with Uromys demographic parameters, a trend that is evident across habitat patches varying in size, shape, structural complexity and small mammal diversity. Although the association between Uromys demography and food resources was strongly positive, caution in interpreting these results is warranted due to the small number of sample sites. Previous studies that have examined the response of species’ to fragmentation have generally failed to examine potential mechanisms associated with population level patterns. If results here can be generalised to other species that inhabit fragmented habitats, future studies need to consider microhabitat characteristics and food resources in particular, if management efforts are to be effective.

Chapter 4: Genetic diversity and genetic differentiation - 73 -

4. Effects of habitat fragmentation on genetic diversity and population genetic structure in a rainforest rodent, Uromys caudimaculatus.

4.1 Introduction Loss and alteration of native habitat continues at an unprecedented rate worldwide. This is in spite of the World Conservation Monitoring Centre (WCMC, 1992) arguing that fragmentation of once-continuous habitat into smaller, discrete habitat patches is one of the greatest threats to worldwide biological diversity (WCMC, 1992). Additionally, genetic variation is one of the three biodiversity levels that the World Conservation Union (IUCN) has recommended for conservation (McNeeley et al. 1990; Reed and Frankham, 2003). Because genetic variation is the raw material underlying evolutionary change and adaptation (Amos and Harwood, 1998; Frankham, et al. 2002; Garcia de Leaniz et al. 2007) and is a major factor that can limit long-term adaptive response by individuals to environmental change (Reed and Frankham, 2003; Hedrick, 2005), conservation of genetic variation can contribute to long-term viability of fragmented and endangered populations (Allendorf and Leary, 1986; Frankham, 2005; Stow and Briscoe, 2005; Benedick et al. 2007; Mitrovski et al. 2008).

When once-continuous populations become fragmented, the impacts of fragmentation on genetic variation will depend on the level of ongoing gene flow among fragments. In turn, this will depend on their relative degree of isolation, the nature of intervening habitat between fragments and relative dispersal abilities of species utilising fragments. Restricted gene flow will tend to decrease genetic (allelic) diversity over time when alleles are lost via drift and/or selection at a faster rate than new alleles can be replaced via migration and/or mutation (Amos and Harwood, 1998: Allendorf and Seeb, 2000; Stow and Briscoe, 2005). In the presence of restricted gene flow and random loss of alleles, among population genetic differentiation is likely to increase and genetic diversity will decrease within remaining the habitat fragments (Frankham et al. 2002: Mossman and Waser, 2001).

Chapter 4: Genetic diversity and genetic differentiation - 74 -

In small isolated populations, rare alleles will be lost preferentially via drift and this can lead to allelic diversity decreasing faster than heterozygosity. Levels of heterozygosity are often correlated with population fitness and declines may result in inbreeding depression (Saccheri et al. 1998; Coltman et al. 1999b; Amos et al. 2001; Reed and Frankham, 2003; Wright et al. 2008). Over time, however, heterozygosity will also decrease as a function of reduced effective population size (Nei, 1987; Frankham, 1996; Jaquiéry et al. 2009), the number of generations since fragmentation (Steinberg and Jordan, 1997; Frankham, 1998) and exposure to genetic bottlenecks (Luikart et al. 1998a). Effective population size (Ne) can be defined as the size of an ideal population that experiences the same magnitude and rate of genetic change as an actual population of size N (Wright, 1931; Crow and Kimura, 1970). This is because in populations that are effectively closed with limited or no migration, average heterozygosity (that generally declines more slowly than allelic richness) decreases at a rate of 1/(2Ne) per generation (Wright, 1978; Sugg et al. 1996). Therefore, all else being equal, small isolated populations will tend to lose heterozygosity faster and have lower levels of genetic diversity, compared with larger more recently fragmented populations (Amos and Harwood, 1998; Frankham et al. 2002). This leaves small populations susceptible to inbreeding depression and possible extinction as a consequence of reduced heterozygosity (Frankham, 1995a; Ralls et al. 1988; Saccheri et al. 1998; Westemeier et al. 1998) particularly in populations that have suffered a recent bottleneck (Luikart et al. 1998b).

The impacts of fragmentation on population structure are likely to vary among taxa, populations and the specific life-history characteristics and behaviour of organisms in question (Lacy and Lindenmayer, 1995; Peacock and Smith, 1997a). Loss of genetic diversity in fragmented populations has been reported, however, across a wide range of taxa including prairie chickens (Tumpanuchus cupido; Westemeier et al. 1998), forest rats (Maxomys surifer; Srikwan and Woodruff, 2000), black grouse (Tetrao tetrix; Caizergues et al. 2003), white-spotted charr (Salvelinus leucomaenis; Yamamoto et al. 2004), desert bighorn sheep (Ovis canadensis nelsoni; Epps et al. 2005), Cunningham’s skink (Egernia cunninghami; Stow and Briscoe, 2005), Arctic foxes (Alopex lagopus; Nyström et al. 2006) and mountain pygmy possums (Burramys parvus; Mitrovski et al. 2008).

Chapter 4: Genetic diversity and genetic differentiation - 75 -

In the current study genetic differentiation among populations of the giant white- tailed rat were examined and related to effective population sizes. Although U. caudimaculatus primarily inhabits closed rainforests, they are also known to undertake forays into nearby buildings and campgrounds (Strahan, 1983; Menkhorst and Knight, 2004) as well as into the matrix that surrounds remnant habitat fragments (Craig Streatfeild unpubl. data). Although little is known about individual dispersal in U. caudimaculatus, a single individual was recorded to have moved 500m in a single night and Comport (1999) reported home ranges of approximately eight hectares but with very small core areas of only 0.5 hectares.

Two previous studies have examined genetic differentiation and genetic diversity in fragmented U. caudimaculatus populations with mixed results. Campbell (1996) employed mitochondrial DNA (mtDNA) to show extremely high levels of genetic structure among populations within continuous forests that were separated by only 0.9km. In contrast, Horskins (2005) examined variation at both mtDNA and microsatellite markers, but reported mixed evidence for genetic structure over several kilometres within a continuous habitat. When among population differentiation was examined, however, Horskins (2005) found high levels of genetic structure over distances as small as a few hundred metres. Lastly, both Campbell (1996) and Horskins (2005) reported lower levels of within population genetic diversity in fragments compared with populations from a continuous habitat.

Here I have used a more limited number of sampling sites that were sampled more intensively and used a more extensive array of genetic analyses compared with the studies of Campbell (1996) and Horskins (2005). The study was designed to capture and sample the majority of individuals present in each sampled population. My aims were to assess levels of genetic differentiation and to estimate effective population sizes among U. caudimaculatus populations separated by a maximum of several kilometres and to assess if populations within remnant habitat patches had significantly lower levels of genetic diversity compared with a population sampled from continuous habitat (‘control’).

Chapter 4: Genetic diversity and genetic differentiation - 76 -

4.2 Methods

4.2.1 Sample collection and sample storage DNA samples were collected from the same study sites and during the sampling periods outlined in section 2.2.1. After capture, ear tissue was collected from individuals and stored in 70% ethanol at -20oC until DNA was extracted. A concerted attempt was made to collect ear tissue from every individual captured, however, several individuals escaped before tissue could be collected. A total of 244 U. caudimaculatus DNA samples were collected during the study. DNA sample size varied from the demographic sample size outlined in section 3.3.2 because supplemental sampling was conducted in sites P1 and P2 in August 2001 and August 2004. Samples sizes, sex and age (age classifications are outlined in section 3.2.1) at first capture of all sampled U. caudimaculatus are outlined in Table 4.1.

Table 4.1: Sample sizes by sex and age structure (adults or juveniles) of individuals at the time of first capture. Unknown refers to individuals whose sex was not recorded. Adults (>450g) Juveniles (≤450g)

Site Males Females Unknown Males Females Unknown Total

P1 23 28 1 32 36 - 120 P2 9 5 1 5 7 - 27 P3 21 15 - 12 20 - 68 C1 13 7 - 4 5 - 29 Total 66 55 2 53 68 0 244

4.2.2 DNA extraction Approximately 100mg of ear tissue was rehydrated in 500µl of GTE buffer (100mM glycine, 10mM Tris, 1mM EDTA) for 30mins at room temperature to remove excess ethanol. Rehydrated tissue was then transferred to a 1.5mL sample tube containing 500μL extraction buffer (100mM NaCl, 50mM Tris, 10mM EDTA, 0.5% SDS) and 20μL proteinase K (20mg/mL). Each tube was lightly agitated and incubated at 55oC in a water bath. Following digestion, DNA was extracted using standard phenol-chloroform extraction techniques (Sambrook et al. 1989; Keane et al. 1994). Following extraction, DNA concentration and purity were quantified using an Eppendorf BioPhotometer (Eppendorf, North Ryde, New South Wales, Australia) and samples diluted to a final template concentration of 100ng/µL and stored at - 20oC when not in use.

Chapter 4: Genetic diversity and genetic differentiation - 77 -

4.2.3 Microsatellites Microsatellites, also know as short sequence repeats (SSRs) are short stretches or DNA sequences where motifs of 2-6 base pairs are tandemly repeated (Schlötterer, 1998; Sainudiin et al. 2004; Selkoe and Toonen, 2006). They are in general, distributed randomly over the eukaryotic genome (Schlötterer, 2000), are highly abundant (Queller et al. 1993; Jarne and Lagoda, 1996; Chambers and MacAvoy, 2000), and have been found within the genome of every organism examined to date (Li et al. 2002; Chambers and MacAvoy, 2000). Microsatellite loci are generally considered to evolve neutrally (with the exception of several implicated in diseases in humans (Queller et al. 1993) and social behaviour in rodents (Hammock and Young, 2004)). They possess high mutation rates ranging from 10-6 to 10-2 events per generation, that lead to high levels of polymorphism (Jarne and Lagoda, 1996; Li et al. 2002). While microsatellite mutational mechanisms are still not well understood, strand-slippage (Levinson and Gutman, 1987; Eisen, 1999) appears to be the dominant process (Ellegren, 2000; Amos et al. 2008). This results in alteration in number of motif repeats (Schlötterer, 1998) via addition of new alleles that are separated by a single repeat unit (Ellegren, 2000). Together, these characteristics make microsatellites ideal for estimating genetic diversity, assessing relative differentiation, population assignment, identifying migrants, estimating effective population size, assigning paternity and kinship and for estimating relatedness.

4.2.4 Microsatellite isolation, amplification and scoring For this study, microsatellite primers were not available for U. caudimaculatus or any other Uromys species1. Consequently, a species-specific genomic library was developed de novo using samples collected from site P1 in May 2000 (Chand et al. 2005; Appendix 4). Originally, 11 U. caudimaculatus specific primer sets were documented in Chand et al. (2005), however, two (UVC233 and UVC446) could not be scored confidently in all individuals due to the presence of numerous stutter bands so these loci were discarded subsequently. The remaining nine loci, with the addition of one isolated from Hydromys chrysogaster (W2A1; Hinds et al. 2002) and one isolated from Melomys cervinipes (MC2E; David Paetkau pers. comm.) were employed here. Primer sequences are presented in Table 4.2 and PCR

1 In her study corridor study using Uromys - as mentioned in the introduction - Horskins (2005) used microsatellites developed for this study.

Chapter 4: Genetic diversity and genetic differentiation - 78 - amplification, screening and scoring procedures are available elsewhere (Chand et al. 2005; Appendix 4).

To ensure consistent scoring across gels, a single reference individual was run on all gels in conjunction with known size standards and all automatic scoring was checked manually for errors. Additionally, when stutter bands are present, scoring errors can result from the mis-scoring of homozygotes and heterozygotes when alleles differ by a single repeat unit (Fernando et al. 2003; Hoffman and Amos, 2005). Often, this problem can be addressed by altering PCR conditions, or by comparing the banding pattern to a known homozygote. Because most loci used here were dinucleotide repeats, the potential mis-scoring of homo- and heterozygotes can be problematic. Altering the PCR conditions and comparing banding patterns with known homozygotes did not completely reduce the problem and several individuals could not be scored reliably. In such cases, individuals were not scored at the locus in question. To further minimise scoring errors, approximately one third of all individuals were re-genotyped at all loci. When mismatches were evident among gels, individuals with unresolved genotypes were re-genotyped until consistent scoring was achieved. This method yielded a mis- scoring rate of 0.019 (17 of 858 samples did not produce duplicate genotypes).

Chapter 4: Genetic diversity and genetic differentiation - 79 -

Table 4.2: Microsatellite repeat motifs and primer sequences for all 11 loci. Forward primers (F) were HEX labelled. Locus Repeat sequence Primer sequence Accession #

F: TACATTTAGTTGCATTTTCTG UVC21 (CA) AY821827 18 R: CCAGCAGTATTGTGTGTTTAA F: ACATGGCCTTTTCCAATA UVC202 (GA) (AA)(GA) AY821828 25 5 R: GTACCGTGTCTCTTATAATA F: GATGTATGGCTCCCGAAGAA UVC213 (CA) AY821829 22 R: TTCATTGAAGCAATAGAAAATTTAAC F: TTGGTTTGGTGTCAGGGTTAT UVC219 (GA) AY821830 20 R: ACCAATACTCTTCAAGCTAT F: CTCTGAACTCTGTAAATTAGC UVC232 (CA) AY821831 13 R: GGTTTTCACTGTTGTTGTTGC F: TTGGACTGATGCAGAAAGATACA UVC238 (CA) AY821833 18 R: TCAGACCAGCTGACAACACTTC F: CCTAAACCCAACGACAAGTGT UVC245 (GT) (CTGT) AY821834 12 8 R: GTAACTCAAAATTCTGCCTGC F: ATGTTCTCCAACCCTTCC UVC432 (GT) AY821835 27 R: GTTTTTCCTCCCATCTCC F: TTAACTATACATATCGGCAGGG UVC452 (CA) AY821837 21 R: CATGTGAGGAAGCGGAAAGACG # F: GGCTATGTCATGGGAAGGC W2A1 (TG) AF380122 29 R: AGAGGATCCCATAAGCTGACAC * F: ATCAACATTCCCTCCGA MC2E N/A R: ATCTTTTTCACAGCAGGACT N/A # Locus developed for Hydromys chrysogaster, (Hinds et al. 2002). * Locus developed for M elomys cervinipes, (David Paetkau pers. comm.) 4.2.5 Statistical analysis With the exception of assignment tests, all analyses here included only individuals first captured as adults.

4.2.5.1. Hardy-Weinberg equilibrium, linkage disequilibrium and null allele estimation Observed allele frequencies were tested for conformation to Hardy-Weinberg expectations and linkage disequilibrium in GENEPOP v3.4 (Raymond and Rousset, 1995). Significant per-locus departures from Hardy-Weinberg expectations were assessed using the global exact test of Guo and Thompson (1992) after correcting for multiple tests using the sequential Bonferroni correction (Rice, 1989). Linkage disequilibrium was examined between each pair of loci using the unbiased Fisher’s exact test with p values estimated using MCMC. The Markov chain algorithm was run for 10,000 permutations following 10,000 dememorization steps in both analyses.

Genotyping errors resulting from the presence of null alleles and small allele dominance can potentially bias population genetic (Van Oosterhout et al. 2004) and

Chapter 4: Genetic diversity and genetic differentiation - 80 - parentage analyses (Dakin and Avise, 2004). Therefore, prior to all analyses, null alleles and small allele dominance was assessed using MICROCHECKER v2.2.3 (Van Oosterhout et al. 2004).

4.2.5.2. Within-population patterns of genetic diversity Measures of within population genetic diversity were assessed by comparing the number of private alleles (P), allelic richness (RS), observed heterozygosity (HO), expected heterozygosity (HE) and fixation index (FIS). FIS values close to zero are indicative of random mating, whereas positive values indicate inbreeding or homozygous excess and negative values indicate a heterozygote excess, negative assortative mating or selection (Peakall and Smouse, 2006). Because the number of alleles and the number of private alleles in a sample are strongly dependent on sample size (Kalinowski, 2004), P and RS were calculated using the rarefaction procedure implemented in HP-RARE (Kalinowski, 2005) and estimated based on samples of 21 individuals (i.e. the smallest number of individuals genotyped at a locus within a population; Goudet, 1995). HO, HE and F were estimated using GENALEX v6 (Peakall & Smouse, 2006). Significant among population differences in P, RS, HO, HE, and FIS were assessed with one-way ANOVAs in STATISTICA 7.0 (StatSoft, 2004).

4.2.5.3. Population bottlenecks To assess whether fragmented U. caudimaculatus populations may have experienced a recent bottleneck relative to the control population in the continuous habitat, two complementary methods were used. For populations that have experienced a significant reduction in effective population size, number of alleles will decrease faster than will heterozygosity (Luikart et al. 1998b). Heterozygote excess relative to that expected at mutation-drift equilibrium for the number of alleles present can indicate a genetic bottleneck (Cornuet and Luikart, 1996). Excess heterozygosity (HE>Heq) was examined using BOTTLENECK v1.2.02 (Piry et al. 1999) using two microsatellite mutation models; the step-wide mutation model (SMM) and the two-phase model (TPM) with 90% single step mutations, 10% mutli- step mutations and 10% variance as recommended by Luikart et al. (1998a). SMM and TPM were used because they are considered to better approximate the mutation process in microsatellite loci relative to the infinite allele model (IAM) (Weber and Wong, 1993; Di-Rienzo et al. 1994; Primmer et al. 1998) and they are less likely to detect heterozygosity incorrectly in non-bottlenecked populations

Chapter 4: Genetic diversity and genetic differentiation - 81 - compared with the IAM (Luikart and Cornuet, 1998). Significance of heterozygote excess was assessed using a Wilcoxon signed rank test implemented in BOTTLENECK with 10,000 iterations. Distributions of allele frequencies were also investigated using the mode shift test implemented in BOTTLENECK. Allelic distributions under mutation-drift equilibrium will be approximately L-shaped, whereas a recent bottleneck will invoke a mode shift in the distribution (Luikart et al. 1998b).

Secondly, the M ratio test of Garza and Williamson (2001) uses the ratio of the number of alleles (k) to the allele size range (r). The M ratio test was used in conjunction with the heterozygote excess method because it exploits a different characteristic of microsatellites in the presence of population reduction (Pearse et al. 2006). For instance, because microsatellites do not always conform to a SMM, rare alleles may be intermediate in size rather than only present among the largest or shortest size classes. Hence, during a bottleneck when rare alleles are lost preferentially, the number of allele size classes will be reduced to a greater extent compared with the range in allele sizes (Garza and Williamson, 2001). M ratios close to 1 indicate stable population sizes whereas small ratios are indicative of a recent population bottleneck. The M ratio method was implemented in ARLEQUIN v3.11 (Excoffier et al. 2005) and significant among population differences were assessed using a one-way ANOVA in STATISTICA 7.0.

4.2.5.4. Effective population size

Long-term effective population size (Ne) was calculated based on expected heterozygosity at each locus, and then averaged for each population. Estimates were based on the SMM and IAM using the equations presented in Nei (1987) and Lehmann et al. (1998). These models were used because they are at opposite ends of the microsatellite mutation spectrum and have been shown by (Lehmann et

al. 1998) to provide robust estimates of long-term Ne. Average mutation rate (µ) was assumed to be 10-3 which is near the average mutation rates reported for microsatellite loci (Weber and Wong, 1993; Di-Rienzo et al. 1994; Jarne and Jagoda, 1996).

Contemporary Ne was estimated in NEESTIMATOR v1.3 (Peel et al. 2004) using the linkage disequilibrium (LD) method of Hill (1981). The LD method provides a

contemporary estimate of the effective number of breeders, Neb, in each population that produced the progeny from which the sample was drawn (i.e. the effective

Chapter 4: Genetic diversity and genetic differentiation - 82 - numbers of breeders that produced the individuals sampled from 2001 to 2004; Schwartz et al. 1998; Waples, 2005). The rationale behind this method is outlined in Waples (2005) and Wang (2005) and references therein. The LD method was used here because it does not assume random mating (Leberg, 2005), it avoids the underestimation of Neb inherit in temporal based methods that are 1) separated by only a single generation (Waples, 1989; Tallmon et al. 2004) and 2) affected by strong selection if present (Araki et al. 2007). Further, the method does not appear to be overly sensitive to reductions in population size (Waples, 2005). In addition,

Neb was also estimated using the heterozygote excess (HE) method of Pudovkin et al. (1996) that includes only the genotypes of offspring or juveniles. Based on the expected excess of heterozygosity in a cohort of offspring produced by a limited number of adults, this method calculates Neb in the parental generation that produced the sample of offspring. Contemporary Neb/N ratios were calculated by dividing the Neb estimate by the adult sample size shown in Table 4.1.

4.2.5.5. Population genetic structure

Pairwise estimates of population differentiation were assessed with FST according to Weir and Cockerham (1984) in GENALEX v6 and with significance tests generated using 9999 random permutations (Peakall & Smouse, 2006). Population differentiation was analysed using FST rather than Slatkin’s (1985) microsatellite specific RST, because FST-based estimates are more reliable when sample sizes are small and less than 20 loci are used (Gaggiotti et al. 1999; Balloux and Lugon- Moulin, 2002). In addition, analysis of molecular variance (AMOVA) was employed to examine within and among population variation. AMOVA was implemented in GENALEX v6 (Peakall & Smouse, 2006) following the procedures of Excoffier et al. (1992) and Michalakis and Excoffier (1996). Significance was assessed using 9999 random permutations.

4.2.5.6. Individual population assignments

Traditional population genetic models such as FST, attempt to characterise long term genetic patterns among populations (Manel et al. 2005). In contrast, assignment and exclusion methods can be used to characterise real-time genetic processes at the level of the individual (Paetkau et al. 1995; Paetkau et al. 2004; Manel et al. 2005) by examining the likelihood of an individuals’ multilocus genotype originating from its source population. Assignment and exclusion tests were conducted in the program GENECLASS v2.0 (Piry et al. 2004). Initially, the Bayesian method of

Chapter 4: Genetic diversity and genetic differentiation - 83 -

Rannala and Mountain (1997) showed by Cornuet at al. (1999) to be the most accurate of the three methods available in GENECLASS was used to assign individuals to populations because it provides the best assignment results over a range of loci, population sizes and mutation models (Cornuet et al. 1999).

A complimentary approach to population assignment involves performing an exclusion test that calculates the probability, within a given threshold, that an individual has originated from a specific population. The probabilities of individual genotypes coming from each population were calculated by comparing individual genotypes to 10,000 simulated individuals and using a threshold value of 0.01. The simulation method of (Paetkau et al. (2004) was used as it is more representative of real population processes relative to other available methods (i.e. Rannala & Mountain 1997; Cornuet et al. 1999). While the assignment tests only indicate the “most likely origin” of an individual, the exclusion test allows statistical rejection of all populations if the real population of origin was not sampled. In addition, the exclusion test allows identification of populations that cannot be rejected statistically as the population of origin, even if they are not the most likely population of origin (Abdelkrim et al. 2005a,b).

Finally, the detection of first generation migrants (i.e. individuals born in a population other than the one in which they were sampled) was also assessed. Lh, the likelihood of finding a given individual in the population in which it was sampled, was used as the likelihood method because it is the most appropriate statistic to use when all potential source populations have not been sampled (Paetkau et al. 2004; Piry et al. 2004). The Bayesian method of Rannala & Mountain (1997) in combination with the simulation method of Paetkau et al. (2004) were used to determine the critical value of the test statistic using 10,000 simulations at an α level of 0.01.

4.3 Results

4.3.1 Hardy-Weinberg equilibrium, linkage disequilibrium and null allele estimation After sequential Bonferroni correction, all loci in each population conformed to Hardy-Weinberg equilibrium, so null alleles were unlikely to be a problem. Further, tests for null alleles using MICROCHECKER were not significant for any of the 11 loci screened in any population. In contrast, significant linkage disequilibrium was

Chapter 4: Genetic diversity and genetic differentiation - 84 - found in 20 of 220 tests (Appendix 5). With the exception of the continuous forest population (C1), all populations showed significant disequilibrium to some degree, with site P1 showing the highest departure with 16 tests, or 80% of all significant departures. Despite the high number of significant results, no consistent pattern was evident for any locus pair across all populations, suggesting that physical linkage was unlikely. Hence, all loci were retained for subsequent analyses.

4.3.2 Genetic diversity

Microsatellite loci across all populations were generally highly polymorphic. Number of alleles per locus ranged from four to 12 with the highest mean number of alleles present in population C1, the only continuous population sampled (Table 4.3; Appendix 6) and was significantly higher in the continuous site relative to all fragmented sites (F3,40 = 5.13, p = 0.004). After adjusting for variation in population sizes using the rarefaction method of Kalinowski (2004), allelic richness (RS) was still significantly higher in C1 (F3,40 = 6.88, p <0.001) while the three fragmented sites did not differ from each other (Figure 4.1). Similarly, the number of private alleles after rarefaction adjustments were significantly higher in C1

(F3,40 = 20.31, p <0.0001), but no difference was observed between the three fragmented sites (Figure 4.2). Expected and observed heterozygosity were high at all four sites (Table 4.3), but expected heterozygosity was significantly higher in C1 compared with the fragmented sites (F3,40 = 4.69, p = 0.007) and approached significance for observed heterozygosity (F3,40 = 2.47, p = 0.076). Fixation index values (FIS), when averaged across all loci, were negative at all sites and did not differ significantly among the continuous and fragmented sites (F3,40 = 0.61, p = 0.611).

4.3.3 Population bottlenecks Despite all populations under the SMM and the populations from P1 and P3 under the TPM showing heterozygote excesses, Wilcoxon tests for recent population bottlenecks did not reveal a significant heterozygote excess for any site (Table 4.4).

Furthermore, no site differed significantly for M ratio values (F3,40 = 0.50, p = 0.684) and no population exhibited M ratios less than the 0.68 critical value that is characteristic of populations that have undergone a recent bottleneck. Similarly, allele frequencies in any population did not depart from an L-shaped distribution in the mode-shift test (Table 4.4).

Chapter 4: Genetic diversity and genetic differentiation - 85 -

Figure 4.1: Mean (± 1 SE) allelic richness (RS) corrected for sample sizes of 21 individuals (the smallest sample size at a locus) within the four populations.

Figure 4.2: Mean (± 1 SE) number of private alleles (P) corrected for sample sizes of 21 individuals (the smallest sample size at a locus) within the four populations.

Chapter 4: Genetic diversity and genetic differentiation - 86 -

Table 4.3: Indices of genetic diversity within each of the four populations. P1 P2 P3 C1

Locus N Na HO HE FIS N Na HO HE FIS N Na HO HE FIS N Na HO HE FIS

120 10 0.79 0.86 0.07 27 7 0.96 0.80 -0.20 68 7 0.82 0.80 -0.03 29 10 0.79 0.86 0.07 UVC21 115 9 0.96 0.85 -0.14 26 7 0.85 0.76 -0.11 62 10 0.84 0.81 -0.04 27 12 0.96 0.85 -0.14 UVC202 103 5 0.90 0.77 -0.16 27 4 0.93 0.74 -0.25 67 5 0.70 0.71 0.01 29 8 0.90 0.77 -0.16 UVC213 117 8 0.79 0.82 0.04 27 7 0.81 0.79 -0.03 65 8 0.72 0.75 0.04 28 7 0.79 0.82 0.04 UVC219 120 7 0.86 0.77 -0.12 27 5 0.78 0.73 -0.07 63 6 0.59 0.61 0.03 29 10 0.86 0.77 -0.12 UVC232 120 7 0.76 0.82 0.08 27 6 0.89 0.72 -0.23 68 6 0.66 0.64 -0.03 29 11 0.76 0.82 0.08 UVC238 119 7 0.93 0.78 -0.20 27 5 0.70 0.65 -0.09 68 6 0.51 0.52 0.02 29 6 0.93 0.78 -0.20 UVC245 118 9 0.82 0.83 0.01 27 9 0.78 0.80 0.03 58 9 0.88 0.85 -0.03 28 8 0.82 0.83 0.01 UVC432 119 9 0.79 0.75 -0.06 27 6 0.56 0.64 0.13 68 7 0.78 0.71 -0.10 29 9 0.79 0.75 -0.06 UVC452 103 9 0.75 0.80 0.06 25 7 0.64 0.66 0.03 67 8 0.70 0.69 -0.02 28 9 0.75 0.80 0.06 #W2A1 96 5 0.76 0.82 0.07 26 5 0.62 0.70 0.11 56 5 0.73 0.70 -0.05 21 7 0.76 0.82 0.07 MC2E

113 7.7 0.8 0.8 -0.03 26 6.2 0.8 0.7 -0.06 64 7.0 0.7 0.7 -0.02 28 8.8 0.8 0.8 -0.03 Mean

N = sample size, Na = uncorrected number of alleles, HO = observed heterozygosity, HE = expected heterozygosity, FIS = fixation index.

Chapter 4: Genetic diversity and differentiation - 87 -

Table 4.4: Heterozygosity excess, M ratio and mode-shift tests for evidence of recent population bottlenecks within each population. P1 P2 P3 C1

HE>Heq 4 7 4 5 TPM P(He>Heq) 0.86 0.26 0.68 0.45

HE>Heq 2 5 4 5 SMM P(He>Heq) 0.99 0.62 0.97 0.82

M ratio k/r 0.84 ± 0.05 0.84 ± 0.04 0.87 ± 0.05 0.90 ± 0.03

Mode-shift - No No No No

HE>Heq: number of loci showing a heterozygote excess. SMM: stepwise mutation model, TPM: two-phased mutation model, IAM: infinite alleles model. P(He>Heq): P-values of one- tailed Wilcoxon signed-ranks tests for heterozygote excess. k/r: ratio of the number of alleles to the allele range size. One-way ANOVA for M ratio values are shown in significance calculated using STATISTICA at α = 0.05. No = no departure from the L-shaped allele frequency distribution expected after a recent bottleneck.

4.3.4 Effective population size

Estimates of long-term Ne were much higher using the SMM compared with the IAM across all sites, ranging from 349 in site C1 to 170 in site P1 and 107 in site C1 to 68 in P1, respectively (Table 4.5). Regardless of the mutation mode employed, long-term Ne was significantly higher at the continuous site (C1) compared with all fragmented sites (SMM: F3,40 = 5.66, p = 0.003; IAM: F3,40 = 5.77, p = 0.002).

Contemporary estimates of Neb based on LD were lower in the two small fragments relative to the larger fragment and the continuous forest, respectively. Neb estimates ranged from 26.4 in the continuous forest (C1) to 8.2 recorded in the smallest fragment (P2; Table 4.5). The HE method revealed much smaller Ne estimates relative to the LD method and ranged from 13.1 in P3 to 3.9 in P2. With the exception of P2, all estimates were very similar across all sites (Table 4.5). Neb/N ratios derived from the contemporary estimates of Neb varied considerably for the two methods and among the four sites. Neb/N ratio calculated from the LD method ranged from 0.31 in P1 to 1.32 in C1, whereas the Neb/N ratio for the HE method ranged from a low of 0.19 in P1 to 0.54 in C1.

Chapter 4: Genetic diversity and differentiation - 88 -

Table 4.5: Long-term and contemporary effective population sizes for each population. Values in parentheses indicate the 95% CI around the mean. Numbers in parentheses adjacent to the Neb estimates represent Neb/c ratios. Sample sizes for N are outlined in Table 4.1. Method P1 P2 P3 C1

SMM 170 178 190 349 Long-term Ne IAM 68 70 70 107

Linkage 16.3 (0.31) 8.2 (0.51) 23.3 (0.65) 26.4 (1.32) Contempory Neb disequilibrium (14.8 - 18.0) (6.9 - 9.8) (20.0 - 27.5) (21.3 – 32.9)

Heterozygote Contempory Neb 10.0 (0.19) 3.9 (0.24) 13.1 (0.36) 10.7 (0.54) excess

4.3.5 Genetic structure

Pairwise FST estimates revealed highly significant (p <0.001) and moderate genetic structuring among the four sampled sites (Table 4.6). Pairwise FST values ranged from 0.049 between P2 and P3, the two geographically closest populations (see Figure 2.2 for the spatial proximity of these two study sites), to 0.115 between P1 and P3. AMOVA results further confirmed the high degree of structure evident among sites (FST, 3, 484 = 0.092, p < 0.001) with 91% of the genetic variance present within sites and only 9% of the variance being attributed to among population differentiation.

Table 4.6: Pair-wise genetic differentiation among each Uromys population. FST values are below the diagonal. * indicates significant pair-wise differentiation (p <0.001) calculated using 9999 permutations. P1 P2 P3 C1 P1 - P2 0.106* - P3 0.115* 0.049* - C1 0.076* 0.081* 0.107* -

4.3.6 Population assignments Based on a likelihood approach, two hundred and sixteen individuals (88%) were correctly assigned to the population from which they had been sampled (i.e. the highest log-likelihood value; Table 4.7). With the exception of site P2 where only 49% of individuals could be assigned correctly, population level assignments were

Chapter 4: Genetic diversity and differentiation - 89 - relatively high ranging from 97% in the continuous forest (C1) to 85% in the 80ha fragment (P3). Interestingly, the most likely population of origin for un-assigned individuals was not necessarily the closest site geographically, with only 36% of un- assigned individuals assigned to the closest geographical site. In the majority of cases (54%), the most likely population of origin for un-assigned individuals was the continuous forest (C1).

The complimentary exclusion method can be used to reject statistically all populations as being the source for un-assigned individuals if the real population of origin had not been sampled (i.e. if all populations had zero likelihood values). Further, populations that cannot be rejected unambiguously as the population of origin regardless of whether they are not the most likely, can also be identified. At a threshold of 0.01, all four sampled sites were excluded as being the source for two individuals in P1, suggesting that they came from a location that had not been sampled. In addition, three individuals from P2 (one adult male and two adult females), were excluded statistically (p <0.01) as having originated in that site and were most likely to have originated from the continuous site in one case and the largest fragment, P3, in the other two cases.

Table 4.7: Genetic assignment tests for all samples Uromys within each of the four populations. Values indicate the number of individuals correctly assigned to the population they were sampled in. Numbers in bold represent the number of correct assignments. Assigned population Proportion Sampled P1 P2 P3 C1 Unassigned correctly population (n) (n) (n) (n) (n) assigned (%)

P1 (n = 120) 115 0 1 3 1 96

P2 (n = 27) 1 13 6 7 0 48

P3 (n = 68) 0 3 59 6 0 85

C1 (n = 29) 0 0 0 28 1 97

Overall 88

4.4 Discussion Genetic theory and empirical evidence suggests that anthropogenic habitat alteration can adversely affect the persistence of populations and reduce genetic diversity in populations that inhabit fragments (Amos and Harwood, 1998;

Chapter 4: Genetic diversity and differentiation - 90 -

Lindenmayer and Peakall 2000; Young and Clarke, 2000; Frankham et al. 2002; Johansson et al. 2007; Mhemmed et al. 2008). This is particularly evident in populations with restricted gene flow where loss of genetic diversity via genetic drift is likely to outweigh addition of new alleles via migration or mutation. Numerous studies have shown measurable impacts of habitat fragmentation on levels of genetic variation (Knutsen et al. 2000; Johansson et al. 2005; Bergl et al. 2008) and this was also evident for the fragmented populations examined here.

4.4.1 Genetic diversity within U. caudimaculatus populations

Levels of genetic diversity detected in all four sampled sites here were quite high. Despite high diversity levels, the three fragmented sites showed significantly lower levels of allelic richness, number of private alleles and expected heterozygosity levels compared with the continuous site. In the case of allelic richness and the number of private alleles, significant reductions were found between the continuous and three fragmented sites even after controlling for variation in individual sample sizes. The continuous site (C1) had on average approximately two extra alleles per locus compared with fragmented sites and possessed approximately five times the average number of private alleles. Surprisingly, genetic diversity was not greater in the largest fragmented site (P3) compared with the two smaller fragmented sites (P1 and P2). This outcome may result from similar effective population sizes, and the fact that they were exposed to similar levels of isolation and time since they were first fragmented.

Results for U. caudimaculatus populations in fragments shown here are consistent with findings for a large number of mammals that have shown reduced genetic diversity as a result of habitat fragmentation and/or population isolation including the black-footed rock wallaby (Petrogale lateralis; Eldridge et al. 1998), forest rats, tree mice and tree shrews (Maxomys surifer; Chiropodomys gliroides and Tupaia glis; respectively, Srikman and Woodruff, 2000), Australian bush rats (Rattus fuscipes greyii; Hinten, et al. 2003), Tasmanian devils (Sarcophilus laniarius; Jones et al. 2004), Neotropical water rats (Nectomys squamipes; Almeida et al. 2005), agile antechinus (Antechinus agilis; Banks et al. 2005c), desert bighorn sheep (Ovis Canadensis nelsoni; Epps et al. 2005) and Seba’s short-tailed bat (Carollia perspicillata; Meyer et al. 2009). Further, recognition that genetic diversity has declined in habitat fragments concur with previous studies of the same species that employed mtDNA (Campbell, 1996) and microsatellite markers (Horskins, 2005),

Chapter 4: Genetic diversity and differentiation - 91 - respectively. Both studies reported reduced genetic diversity in remnant habitat fragments relative to populations within the continuous forest. Congruence with the results of these studies indicate that genetic variation in fragments is not simply an artefact of sampling from only a single population within the continuous forest that may have by chance, had higher mean levels of genetic diversity relative to other areas within the forest.

The mean fixation index FIS was negative for all four populations, suggesting that inbreeding, at least at the population level, is not yet a problem for fragmented populations of U. caudimaculatus. The degree to which reduced genetic diversity and lower heterozygosity however, could translate into increased probabilities of extinction due to, among other things, inbreeding depression, remains unclear. Lande (1988) argued that demographic parameters may be of more immediate importance than declines in genetic variation levels when determining population extinction risk. In a review of 170 species that examined the importance of genetic factors in determining species extinction risk in threatened taxa, Spielman et al. (2004) showed that low levels of heterozygosity correlated with lower evolutionary potential and a decline in reproductive fitness can lead to elevated extinction risk. They concluded that most species are not driven to extinction before genetic factors, including loss of genetic variation, impact on the process.

Even though fragmented populations of U. caudimaculatus are apparently yet to show any signs of inbreeding depression linked to reduced levels of genetic variation, the significant reduction in genetic diversity observed, nevertheless indicates that fragmentation has had a negative impact and highlight concerns for maintenance of genetic diversity in this species within the fragmented landscape of the Atherton Tableland. Furthermore, in the absence of ongoing migration from the continuous forest, loss of genetic variation due to drift is likely to be a growing problem, particularly in populations that inhabit fragments that have smaller effective population sizes and that are prone to repeated population bottlenecks.

4.4.2 Genetic bottlenecks The observed low levels of allelic diversity and heterozygosity could occur if the populations had experienced a recent genetic bottleneck. No genetic signatures of recent population bottlenecks were detected, however, in any of the sampled populations. However, lack of statistical evidence does not negate the possibility

Chapter 4: Genetic diversity and differentiation - 92 - that U. caudimaculatus populations could have been exposed to a recent bottleneck because past bottlenecks can go undetected (Luikart et al. 1998a; Luikart and Cornuet, 1998; Busch et al. 2007). For instance, life history traits including mating systems, generation length and post bottleneck population growth rate may all contribute to obscure effects of past bottlenecks (Lippé et al. 2006). Further, Williamson-Natesan (2005) showed via simulations, that estimation of heterozygote excess and M ratio methods vary in their ability to detect bottlenecks. They based this on a suite of parameters including how recent the bottleneck occurred, the severity of the bottleneck, the duration of the bottleneck and the pre bottleneck mutation rate. The heterozygote method was more likely to detect a bottleneck if it had only occurred recently, had not been very severe and the pre bottleneck mutation rate was small. Bottlenecks were more likely to be detected using the M ratio test when they were of longer duration and when the population had been sampled a number of generations after the bottleneck.

Even considering the limitations of tests used to detect bottlenecks, based on the recent demographic history of U. caudimaculatus it is unlikely that the results constitute a statistical artefact. For instance, adult densities across this study were relatively stable, particularly at sites P2 and P3 and to a lesser extent at P1. Further, no population showed severe population size fluctuations that occur in some mammals including snowshoe hares (Lepus americanus, Burton et al. 2002) and fossorial water voles (Arvicola terrestris, Berthier et al. 2006). Also, density estimates for adult U. caudimaculatus at the P1 site in each of the three years preceding this study (Kerrilee Horksins and John Wilson, unpubl. data), and in P2 and P3 during 1990-1991 (Les Moore, unpubl. data) were very similar to the densities observed here, suggesting that densities among years for adult U. caudimaculatus may be relatively stable. Even if recent population fluctuations, however, did occur but went undetected, heterozygote excess and deviations in allele frequencies from those expected under equilibrium conditions can be restored in as few as three generations with low levels of gene flow (Keller et al. 2001), an outcome that would generally mask past bottleneck signatures.

Results here are consistent with several other studies that did not detect evidence for recent bottlenecks. No evidence was found for a mode-shift or a heterozygote excess in cyclic vole and snowshoe hare populations (Burton et al. 2002; Berthier et al. 2006). Similarly, Trizio et al. (2005) failed to detect bottlenecks in red squirrel populations following fragmentation of their forest habitats. Bottlenecks are unlikely

Chapter 4: Genetic diversity and differentiation - 93 - to be detected in small mammal, and rodent populations in particular, unless populations that have experienced recent bottlenecks are effectively closed (Busch et al. 2007).

4.4.3 Effective population size

Results here also suggest that fragmentation has had a negative affect on Ne with estimates derived from both the SMM and IAM showing lower long-term effective population sizes in all three fragments compared with the continuous forest site.

Ne estimates based on SMM and IAM models were very similar among all three fragmented sites and were ~1.5 times lower compared with the continuous site.

Because long-term Ne will depend on the mutation mechanism and mutation rate, the true long-term Ne likely lies somewhere between the SMM and IAM estimates.

Larger Ne in the continuous site and similar estimates among the three fragmented sites were not unexpected considering long-term Ne is a function of expected heterozygosity that was significantly higher in C1, whereas no differences were found among the fragmented sites. While the time frame associated with long-term

Ne cannot be estimated in absolute terms, the generally high mutation rates of microsatellite loci mean that long-term Ne will likely represent recent history (Leberg, 2005) such as post-fragmentation, which on the Atherton Tableland has occurred within the last 100 years (Campbell, 1996). Furthermore, Schwartz et al. (1999) suggest caution when interpreting long-term Ne estimates as they are likely to be biased upwards due to higher pre-fragmentation levels of gene flow. Thus the estimates may actually represent Ne of the species as a whole (i.e. Ne of Uromys across the Atherton Tableland) rather than local populations (Leberg, 2005).

Accuracy of long-term Ne size estimates are only as precise as mutation rate estimates (Waples, 1991) that was assumed to be 10-3 for these analyses and was based on an estimated average of reported microsatellite mutation rates (Weber and Wong, 1993; Di-Rienzo et al. 1994; Jarne and Jagoda, 1996). An accurate estimation of the mutation rate becomes less important, however, when relative among population Ne estimates are of interest and identical loci are used (Lehmann et al. 1998). This is because varying the mutation rate would lead to a change in the absolute value of Ne, but not in the relative differences in Ne among populations.

While achieving an absolute estimate of Ne is appealing, the issue here was relative differences in Ne among populations. Ne estimates in this study were lower in fragmented sites compared with the continuous site.

Chapter 4: Genetic diversity and differentiation - 94 -

In contrast to long-term estimates of Ne, contemporary estimates of Ne actually reflect Neb, or the effective numbers of breeders that produced the populations that were actually sampled (Leberg, 2005; Waples, 2005). Examining the contemporary estimates of Neb using the LD method revealed quite small population sizes. Interestingly, the populations within the continuous forest and the largest fragment had the highest estimates of Neb, whereas the two small fragments showed the lowest Neb estimates. In the case of site P2, the estimated Neb was approximately three times lower than site C1. Neb estimated from cohorts of juvenile U. caudimaculatus using the HE method, however, revealed the Neb producing the juveniles was very small, and ~50% lower than estimates derived using the LD method.

LD and HE methods estimate Neb over different time scales. The LD method estimates Neb over the short to intermediate term, while HE methods estimate Neb from the parental population that the offspring had been sampled from (Wang,

2005). Hence, differences in the Neb estimates between these two methods may be indicative of Neb that is in decline due small population sizes. Additionally, Neb/N ratios in all four populations were much smaller when estimated using the HE method and ranged from 0.21 in P1 to 0.54 in C1 compared with the LD method that ranged from 0.35 in P1 to 1.32 in C1. Although these ratios are similar to those observed in a range of other taxa (Nunney and Elam, 1994; Boutellier and Perrin, 2000; Mactocq, 2004; Cutrera et al. 2006) and considering genetic diversity is lost at a rate of 1/2Ne per generation, they indicate that 1) the already low levels of genetic variation in fragmented sites compared with the continuous site are likely to decline further without significant immigration, and 2) the smaller fragmented sites are likely to suffer relatively larger decreases in genetic diversity relative to sites P3 and C1, due to lower Neb and Neb/N ratios.

Accurate estimates of Neb/N ratios depend on accurate estimates of Neb and N, hence Neb estimates derived from the LD and HE methods should be treated with some caution due to probable violations of underlying assumptions and problems associated with small census sizes (Pudovkin et al. 1996; Schwartz et al. 1998; Luikart and Cornuet, 1999; Leberg, 2005; Wang, 2005; Waples, 2005). For instance, one of the major drawbacks of the LD method is that >6 loci are needed and reasonably large census population sizes (~90 individuals) may be required to obtain precise estimates (Bartley et al. 1992; Schwartz et al. 1998). While the number of loci used in the current study was well above the minimum

Chapter 4: Genetic diversity and differentiation - 95 - recommended, none of the census population sizes were large. This could have biased Neb estimates downward. Waples (2006) suggests, however, that under realistic scenarios for natural populations, the LD method can provide fairly accurate

Neb estimates.

The major problem with the HE method lies in the assumption of random mating that is likely to be violated in most natural populations. Numerous authors have suggested caution with interpreting estimates of Neb based on this method (Luikart and Cornuet, 1998; Schwartz et al. 1998; Wang, 2005; Waples, 2005). Luikart and Cornuet (1998) used simulations over a range of mating systems, sample sizes and loci and found that while the HE method provided unbiased estimates for a range of mating systems, this method was most useful in populations with polygynous or polygamous mating systems when the true Neb was <10. Otherwise, large census samples and a large number of loci are required.

Lastly, differences between census and effective population sizes are also due to variation in reproductive success within the breeding population and deviations from equal sex ratios (Frankham, 1995b; Frankham et al. 2002). Both these factors can lead to reductions in Neb and bias Neb/N ratios downward. The magnitude of the biases are, however, unclear. While unequal sex ratios were evident in all four sampled sites, the magnitude of the skew was similar among sites that suggest the relative effect of unequal sex ratios on Neb was minimal. Additionally, reproductive skew was evident in all populations (chapter 5) and was greatest in P1 for both males and females. Hence higher levels of reproductive skew at this site may have contributed to lower estimates of Neb.

4.4.4 Genetic differentiation among populations and assignment rates

Results from the genetic differentiation analysis revealed that U. caudimaculatus populations showed significant genetic differentiation among sites with very low levels of gene flow at relatively fine spatial scales. All pairwise comparisons showed significant differentiation, even at scales of ~2km that separated the P2 and P3 fragments. Even though small sample sizes precluded isolation by distance analysis, an assessment of FST estimates between sites in relation to their estimated geographical distance (see Figure 2.2) revealed no isolation by distance effect. This is consistent with results of Van Staaden at al. (1996) and Mossman and Waser (2001) who, despite significant differentiation found no isolation by distance effect for Richardson’s ground squirrels (Spermophilus richardsonii) or white-footed

Chapter 4: Genetic diversity and differentiation - 96 - mice (Peromyscous leucopus), respectively. Significant genetic structure between geographically proximate U. caudimaculatus populations is also consistent with the findings of Campbell (1996) and Horskins (2005) who showed moderate levels of differentiation among populations at similar distances. Similar levels of differentiation have also been found for a range of small mammals that inhabit fragmented habitats including the Australian bush rat (Rattus fuscipes; Lindenmayer and Peakall, 2000), wood mice (Apodemus sylvaticus; Berckmoes et al. 2005), the fawn footed melomys (Melomys cervinipes; Horskins, 2005), Danish bank voles (Clethrionomys glareolus; Redecker et al. 2006) and bush rats (Rattus fuscipes; MacQueen et al. 2008).

AMOVA and genetic assignment test results were highly congruent. Within population variation explained 91% (FST = 0.092) of the total variation and 88% of individuals were correctly assigned to their source population. A surprising result from the assignment tests was that only 36% of individuals that were not assigned to their source populations could be assigned to a sampled population, whereas 54% were assigned to the continuous site. This was despite the closest population to the continuous forest being ~13km distant (P1) and P2 and P3 being ~15km and 17km away, respectively. This raises the issue of why some individuals may be moving out of the larger continuous forest habitat.

The area in the continuous forest where samples were taken possessed very low quantities of food resources consumed by U. caudimaculatus (chapter 3). If low levels of food resources within the continuous forest were widespread, then Uromys may actively disperse from the forest to avoid adverse ecological conditions. Alternatively, individuals may be displaced by conspecifics of superior quality. It would be interesting to determine if individuals that were not assigned to a source population contributed genes to the source population via siring offspring.

It is well documented that U. caudimaculatus can move relatively large distances, in the order of hundreds of metres in a single night (Wellesley-Whitehouse, 1981). Radio-tracking data has shown that it is not uncommon for U. caudimaculatus to make nightly forays from fragmented habitat patches into the surrounding matrix, potentially to forage for insects during periods of resource scarcity (Craig Streatfeild unpubl. data). The matrix surrounding remnant fragments does not appear, therefore, to be inhospitable for this rodent, at least for short-term foraging

Chapter 4: Genetic diversity and differentiation - 97 - excursions. Goosem and Marsh (1997) and Goosem (2000) showed, however, that movements of U. caudimaculatus could be inhibited by habitat modification including clearing for power lines and roads. Indeed, previous studies on other small rodent species have shown that roads can inhibit movement leading to genetic subdivision (Gerlach and Musolf, 2000; McDonald and St. Clair, 2004; Berckmoes et al. 2005) but see (Gauffre et al. 2008).

Previous studies examining genetic differentiation in fragmented habitats, have shown that habitat generalists that are able to use the matrix surrounding remnant habitat patches are less likely to be isolated by habitat fragmentation compared with habitat specialists (Mech and Hallett, 2001; Brouat et al. 2003). Since U. caudimaculatus is predominantly a rainforest habitat specialist, the extent of deforestation on the Atherton Tableland has left very few remaining suitable habitat patches, and those that remain are separated by distances of several kilometres. This may render dispersal across inhospitable agricultural land improbable for all but a few individuals.

4.5 Conclusion

Results of the study revealed a significant negative effect of habitat loss and fragmentation on genetic diversity and effective population sizes in U. caudimaculatus. At present, however, the remaining high levels of genetic variation and heterozygosity in populations that inhabit the fragments and the negligible levels of inbreeding suggests that populations may not yet be at risk from factors associated with declining levels of genetic variation such as reduced fitness as a consequence of inbreeding and associated loss of evolutionary potential. However, because migration, and hence gene flow from the larger continuous forest into the remaining habitat fragments is likely to be minimal for U. caudimaculatus, genetic variation within these fragments is likely to continue to decline and the problem will be exacerbated by already low Ne sizes. Hence, the long-term viability of U. caudimaculatus populations that reside in remnant habitat fragments may be at risk.

Chapter 5: Genetic mating system and fine scale genetic structure - 98 -

5. Within population fine-scale genetic structure, parentage and reproductive success in U. caudimaculatus populations.

5.1 Introduction It is now widely recognised based on theoretical and empirical studies that habitat fragmentation can affect demographic processes, population persistence and levels of genetic variation in species and populations that reside in fragmented habitats (Young and Clarke, 2000; Lindenmayer and Fisher, 2006). How fragmentation affects within-population genetic processes, including genetic mating systems and social genetic structure, however, is not as well understood (Peacock and Smith, 1997b; Amos and Balmford, 2001; Stow and Sunnucks, 2004; Banks et al. 2005b), but these issues are of fundamental importance to mate choice, inbreeding and individual fitness within isolated fragmented populations.

The extent to which habitat fragmentation affects species (and populations) is determined in part, by their degree of specialisation, relative dispersal potential and individual behavioural responses to fragmentation (Sumner et al. 2004). For example, where isolated populations are separated by inhospitable habitats, individuals may be unable or reluctant to disperse, resulting in elevated philopatry, increased relatedness, increased competition and disruption of social structures and breeding opportunities (Peacock and Smith, 1997a,b; Bjørnstad et al. 1998; Stow et al. 2001; Stow and Sunnucks, 2004) and disruption of inbreeding avoidance mechanisms (Sumner, 2005; Banks et al. 2007) including sex-biased dispersal and mate choice (Pusey, 1987; Clutton-Brock, 1989; Pusey and Wolf, 1996). All of these factors can lead to reduced effective populations sizes (Ne), loss of genetic diversity, increased inbreeding rates and lower mean fitness of individuals, that in combination, can increase extinction risk of fragmented populations (Frankham et al. 2002).

Sex-biased dispersal, where individuals of one sex tend to disperse, or disperse greater distances, than members of the opposite sex, is generally male-biased in mammals and female-biased in birds (Greenwood, 1980). Greenwood also noted that dispersal bias was strongly linked to the predominant social mating system,

Chapter 5: Genetic mating system and fine scale genetic structure - 99 - with polygyny (defined as single males that mate with more than one individual in a single breeding period) which is common in mammals, linked to male-biased dispersal and monogamy (defined as individuals that mate with only a single individual in a single breeding period) which is more common in birds, leading to female-biased dispersal. However, patterns of dispersal are less clear in species that exhibit promiscuous mating systems (defined as males and females that mate with multiple partners in a single breeding season).

Several hypotheses have been suggested to explain differences in dispersal behaviour among the sexes: 1) resource competition (Greenwood, 1980), 2) local mate competition (Dobson, 1982) and 3) inbreeding avoidance (Pusey, 1987; Pusey and Wolf, 1996). Each hypothesis predicts male-biased dispersal where polygyny predominates. In monogamous species, however, female-biased dispersal is predicted under the ‘resource competition’ hypothesis whereas no bias is expected under the remaining two hypotheses (Favre et al. 1997; Chapple and Keogh, 2005). Further, competition for mates and resources is likely influenced by local ecological conditions including density of individuals and resources, spatial distribution of resources, home range size and home range overlap, implying that genetic mating systems can vary in response to changing ecological conditions (McEachern et al. 2009).

One issue that potentially confounds understanding sex-biased dispersal patterns is the observation that mammals, as well as some other vertebrates, often exhibit flexibility in their social and genetic mating systems in relation to variation in local ecological conditions (Lott, 1991; Travis et al. 1995; Schradin and Pillay, 2005). Surprisingly however, studies that have examined intraspecific flexibility in sex- biased dispersal patterns are extremely rare, despite it being widely understood that ecological conditions can influence mating systems that in turn affect sex-biased dispersal patterns.

Little is known about the social organisation and genetic mating system of the giant white-tailed rat. Previous studies have suggested that giant white-tailed rats are territorial and solitary, with individuals only coming together to breed (Moore, 1995). Conversely, anecdotal evidence has suggested that Uromys can occur in highly structured family groups that display high levels of parental care (Andrew Dennis, pers. comm.). Further, Uromys may potentially exhibit flexibility in their social

Chapter 5: Genetic mating system and fine scale genetic structure - 100 - mating system based on morphology and space use patterns (Appendix 8). The assertion that mating systems can be influenced by local ecological conditions, including density and resource availability, suggests that Uromys may exhibit a range of mating systems depending on differences in food resource availability and conspecific densities among populations. In addition, extreme genetic structuring of female U. caudimaculatus reported by Campbell (1996) and high levels of among population genetic differentiation shown in the current study add weight to the idea that the genetic mating system, population genetic structure and sex-biased dispersal patterns in U. caudimaculatus may be adversely affected by habitat fragmentation.

As is the case for most mammalian taxa, the mating system of Uromys has been originally inferred from field studies that employed trapping to examine the spatial relationships of males and females. The nocturnal nature of Uromys, however, renders behavioural observations in isolation inadequate to examine their mating system effectively and genetic data provide a more powerful approach to address this issue. Therefore, microsatellite genetic markers were used, to infer the genetic mating system, paternity, local genetic structure, sex-biased dispersal and relatedness. Specifically, the following issues were examined: 1) the genetic mating system of Uromys, 2) if Uromys exhibits plasticity in their genetic mating system, 3) patterns of sex-biased dispersal, 4) whether Uromys within fragmented populations are more closely related compared with individuals in the continuous population due, for instance, to restricted dispersal and, 5) if Uromys show variation in their genetic mating system, does this translate into differences in sex-biased dispersal patterns.

5.2 Methods

5.2.1 Sample collection, DNA extraction and microsatellite screening DNA from ear tissue biopsies were collected from August 2001 to August 2004 as outlined in section 4.2.1 and sample sizes including sex and age classes are shown in Table 4.1. DNA was extracted using the standard phenol-chloroform method (Sambrook et al. 1989; Keane et al. 1994). PCR amplification, screening and scoring procedures are described in Chand et al. (2005).

Chapter 5: Genetic mating system and fine scale genetic structure - 101 -

5.2.2 Parentage analysis Parentage was assessed for all juveniles (≤450g) using the computer program CERVUS v3.0 (Marshall et al. 1998; Kalinowski et al. 2007). CERVUS uses a likelihood based approach to test statistically the probability that a particular parental genotype is most likely to be the true parent of a particular offspring. For each offspring, CERVUS calculates the alternate hypotheses that 1) the candidate parent is the true parent, and 2) the candidate parent is not the true parent; the likelihood ratio (LOD score) is the likelihood that the candidate parent is in fact the true parent divided by the likelihood that the candidate parent is not the true parent. The difference between the LOD scores of the two most likely parents (Δ) is compared with a critical Δ value calculated from a simulation that takes into account the total number of candidate parents sampled, the proportion of candidate parents sampled and an estimate of genotyping error.

The numbers of candidate mothers and fathers were the total number of adults captured (>450g) regardless of reproductive condition. All adults that were ‘known to be alive’ at the estimated time of conception (i.e. 3 months preceding an individual juveniles first capture) were classed as candidate parents for that juvenile. For the proportion of candidate parents sampled, an estimate of the population size needs to be calculated first. Estimated population size was calculated in ECOLOGICAL METHODOLOGY v6.1 (Krebs, 1999) using the frequency of capture model (Caughley, 1977). This model calculates a single population estimate based on the number of individuals not captured (zero frequency class) by fitting a variety of statistical distributions (negative binomial, geometric and Poisson) to the observed data (the frequency of individuals captured once, twice, three times etc.). The distribution that best fit the data was determined using STATISTICA 7.0 (StatSoft, 2004). Although Caughley’s method assumes a closed population, the use of this method was justified because parentage in each population was assessed over the entire study period that constituted a single discrete time period ranging effectively from one year to three years depending on the population. The proportion of candidate parents sampled was then calculated by dividing the number of adults that were actually sampled by the estimated population size and is shown in Table 5.1.

Chapter 5: Genetic mating system and fine scale genetic structure - 102 -

Table 5.1: Sample sizes and sex of juveniles for parentage analysis and the number of candidate parents (males and females) that potentially could have sired offspring. The proportion of male and females sampled is the number of candidate parents divided by the estimated population size for males and females, separately. Candidate Prop. males Candidate Prop. females Site Males Females Total Males sampled Females sampled P1 30 33 63 32 0.90 34 0.92

P2 5 6 11 10 0.89 8 0.90

P3 14 18 32 28 0.84 15 0.86

C1 4 5 9 13 0.83 7 0.81

All simulations were performed for each population separately using 10,000 simulated offspring genotypes with a genotyping error rate of 0.019 (section 4.2.4). Allele frequencies were generated from all individuals including potential offspring, as recommended by Marshall (pers. comm.). Potential parents of offspring were those adults that were known to be alive at the estimated time of conception that was assumed to be the sampling period prior to a juveniles’ first capture date and was based on the gestation and lactation period of giant white-tailed rats as referred to in section 1.5. Parentage was assigned using the “Parent Pair” analysis option which determines the most likely parent pair for each offspring. A single male and female were accepted as the parents of an offspring if CERVUS assigned the most likely ‘Parent Pair’ a confidence level of > 80% and with a maximum of one mismatched locus between the trio. This criterion is widely accepted in the relevant literature to accurately assign paternity (Marshall et al. 1998; Coltman et al. 1999a; Slate et al. 2000).

Failure to assign paternity and/or maternity can be due to two main factors: either the parental genotypes were not sampled or CERVUS cannot distinguish among multiple potential fathers and/or mothers. In the latter case, Shurtliff et al. (2005) showed that combining genotypic data of the putative parents with the spatial relationships of putative parents in relation to the offspring, allowed a greater number of paternity assignments to be made than would have been possible if CERVUS was used in isolation. Hence, when parentage could not be assigned unambiguously to a single mother or father, capture locations of the putative parents were compared with the location of first capture of the offspring. If one of the candidate parents was captured in the trap immediately adjacent to the offspring whereas the other candidate parent was captured several transects away,

Chapter 5: Genetic mating system and fine scale genetic structure - 103 - parentage was assigned to the adjacent individual, otherwise parentage was left unassigned.

5.2.3 Effects of heterozygosity, body condition and relatedness on reproduction Direct estimates of male and female reproductive skew and relative reproductive success were obtained from the analysis of parentage using CERVUS. Individual standardized heterozygosity (Hs), where Hs = the proportion of heterozygous typed loci for each individual / the mean heterozygosity of typed loci for the population from which the individual was sampled (Coltman et al. 1999b) and individual body condition were examined to determine if males and females that sired offspring had

higher HS and were in better body condition compared with adults that were not

know to have sired offspring. HS is an improvement over individual heterozygosity (i.e. the proportion of heterozygous loci) when individuals are typed at different

numbers of loci because HS is weighted by the heterozygosity at the locus, hence heterozygosity for each individual is measured on the same scale (Coltman et al. 1999b; Amos et al. 2001).

An index of body condition was estimated for all adult males and females based on the residuals of a linear regression of body mass on a morphological measurement associated with body length (Krebs and Singleton, 1993). This residual-based method for assessing individual body condition has been recommended because it does not vary with body size (Jakob et al. 1996; Schulte-Hostedde et al. 2001). Using this approach, the body mass and tail length (the surrogate for body length) of all individuals within each population were ln-transformed and the resulting regression equation was then used to predict an expected body mass from the observed body length measurement of each individual relative to all individuals from that population. The index of body condition was calculated for each individual from the ratio of the observed body mass to the expected body mass. With this method, an average individual will have a body condition index of one. Individuals with values greater than one are considered to be “above average” in body condition while individuals with values less than one are considered to be “below average” in body condition. Multiple body weight and tail length measurements from each individual are not independent data points. Therefore, independence was ensured by taking the mean body weight and tail length for each individual.

Chapter 5: Genetic mating system and fine scale genetic structure - 104 -

For each individual that sired offspring, HS and body condition were calculated and compared with a random sample of the same number of adults that did not sire any offspring. A multiple linear regression was used to examine whether HS and/or body condition were accurate predictors of relative reproductive success. In addition, logistic regressions were used to examine if HS and/or body condition could predict those animals that sired offspring versus those that did not. For logistic regression analysis, individuals that sired at least one offspring successfully were coded, ‘one’ and a random assortment of the same number of adults that failed to sire any offspring were coded, ‘zero’. Regressions were conducted in STATISTICA 7.0.

To examine whether females were selecting less-related males as fathers of their offspring preferentially, pairwise relatedness values between all adult giant white- tailed rat pairs were calculated using the relatedness estimator, rqg, of Queller & Goodnight (1989) in the program GENALEX v6 (Peakall and Smouse, 2006). For this analysis, pairwise relatedness values were calculated using only allele frequencies from the population in question. For each female, relatedness to her breeding partner was compared with a random distribution of relatedness values from male-female pairs that did not mate using a two-sample randomisation test with 10,000 randomisations in RUNDOM v3.0b (Jadwiszczak, 2003).

5.2.4 Sex specific relatedness and genetic autocorrelation Sex-biased dispersal patterns result in the philopatric sex often having higher levels of relatedness and stronger degrees of spatial autocorrelation relative to the dispersing sex (Temple et al. 2006). The autocorrelation coefficient (r) of Smouse and Peakall (1999) calculated in GENALEX v6 (Peakall and Smouse, 2006) was used to examine whether r values among males and females within each population were significantly different from a random sample of all adults sampled in the four populations. Significance of the difference between the r values for males and females in each population was assessed by comparing the 95% confidence intervals (generated from 1,000 permutations and bootstraps) around the mean values of r for each sex (as outlined in Peakall et al. 2003).

The above analysis was used to determine if sex-specific dispersal differences were evident among populations. To examine within population sex-specific dispersal patterns, spatial genetic autocorrelations (Smouse and Peakall, 1999; Peakall et al. 2003) were conducted using the ‘Multiple Dclass’ function in GENALEX v6 (Peakall

Chapter 5: Genetic mating system and fine scale genetic structure - 105 - and Smouse, 2006). Significance tests and bootstrap estimates of the 95% confidence intervals for the autocorrelation coefficient r were run using 999 permutations. Significant genetic autocorrelation is evident when the r value falls outside the upper (U) and lower (L) 95% confidence intervals and the bootstrapped 95% errors about r do not intercept the x axis (after Peakall et al. 2003; Temple et al. 2006).

The autocorrelation coefficient provides a measure of the genetic similarity between pairs of individuals at a priori specified distance classes. If sex-specific dispersal is evident, positive autocorrelation (higher r values) at shorter distance classes should be evident in the more philopatric sex (Peakall et al. 2003; Piggott et al. 2006). Genetic autocorrelations were conducted for all adults, adult males, adult females, all adult residents, adult male residents and adult female residents for each population separately using distance classes separated by 25m (the spacing distance between adjacent trap positions). Due to insufficient data, genetic autocorrelation could not be calculated for adult male and female residents in P2. The linear pairwise geographical matrix for the respective analyses were calculated from the mean capture locations of individuals (mean grid coordinates of all captures for each individual).

5.3 Results All 11 loci outlined in section 4.2.4 were used in the parentage analyses. The probability of parental exclusion across the 11 loci for the first parent ranged from 0.984 in P1 to 0.997 in C1 and for the second parent was 0.999 in all four populations.

5.3.1 Parentage, mating system and reproductive success A total of 115 juveniles were sampled from the four populations with sample sizes varying widely among sites (section 5.2.2, Table 5.1). Parentage was assigned to a total of 101 juveniles with assignment rates ranging from 91% in P2 to 67% in C1 for mothers of offspring, 84% in P1 to 67% in C1 for fathers of offspring and 64% in P2 to 79% in P1 for both parents. Interestingly, at all sites except for P2, over 50% of cases where juveniles could not be assigned a mother or father occurred within a single sampling period. This suggests that the true mother or father had not been sampled (sampling period 7 for P1, period 5 for P3 and period 6 for C1).

Chapter 5: Genetic mating system and fine scale genetic structure - 106 -

High levels of reproductive skew were evident at all sites. In P1, 57 juveniles were assigned parentage to 15 of the 34 sampled females (44%) and 11 of the 32 sampled males (34%; Appendix 7). Eleven juveniles were assigned paternity to four of the eight (50%) sampled females and five of the 10 (50%) sampled males in P2. Twenty-seven juveniles were assigned paternity to eight of 15 (53%) sampled females and 10 of 28 (36%) sampled males in P3 and in C1, six juveniles were assigned paternity to three of six (50%) and three of 13 (23%) sampled females and males, respectively (Appendix 7). Among residents, reproductive skew was not as pronounced with 83% of females and 48% of males reproducing in P1, 100% of males and females reproducing in P2, 60% of females and 40% of males reproducing in P3 and in C1, 50% of both male and female residents reproducing.

Of those individuals that were assigned parentage, reproductive success was extremely high. The mean number of offspring assigned per individual ranged from 3.6 (± 0.8 SE) at site P1 to 2.0 (± 0.8 SE) at site C1 for females and from 4.1 (± 2.3 SE) at site P1 to 1.6 (± 0.6 SE) at site P2 for males (Table 5.2). At the extreme, a single male sired 26 offspring over four sampling periods in P1 and two females, one each in P1 and P3 gave birth to nine offspring over three sampling periods (Table 5.2). Of the adults that were assigned parentage, the majority were residents. For females that sired offspring, 93% at site P1, 50% at P2, 75% at P3 and 100% at site C1 were residents. Very similar values were found for males with residents comprising 91% (P1), 60% (P2), 60% (P3) and 100% (C1) of males that fathered offspring, respectively.

The degree of multiple mating was very similar among sites and for males and females (Table 5.2). Approximately 25 to 50% of females and 20 to 33% of males had multiple breeding partners, with an average of between one and 1.6 mates depending on the site (Table 5.2). Although classifying individuals as ‘monogamous’ or ‘polygamous’ can be problematic, Uromys did exhibit mating systems that were consistent with monogamy (single males and a single females that mate exclusively during a breeding season) and polygamy (both males and females mate with multiple partners; Appendix 7). Of females that were assigned maternity of ≥ two juveniles within a single sampling period (that corresponds to a litter based on gestation and weaning period of U. caudimaculatus), three females at sites P1 and P3 and two females at P2 mated with a single male only, and three

Chapter 5: Genetic mating system and fine scale genetic structure - 107 - females, one each at sites P1, P2 and P3 had only one mate over multiple litters (Appendix 7).

Standardised heterozygosity (Hs) and body condition were assessed as predictors of male and female reproductive skew and individual reproductive success. For females, Hs and body condition did not predict individuals that produced offspring 2 (χ (1) = 1.98, p = 0.37). For males, however, the overall multiple logistic regression 2 was significant (χ (1) = 8.82, p = 0.01) with a strong positive relationship between 2 2 body condition and offspring sired (χ (1) = 6.90, p = 0.008), but not Hs (χ (1) = 0.54, p = 0.46). Neither Hs nor body condition predicted reproductive success (the number of offspring sired) for females (F2,25 = 0.19, p = 0.82) or males (F2,25 = 0.73, p = 0.49).

Table 5.2: Description of the genetic mating system of U. caudimaculatus. Mean ± SE values are presented. Except for multiple partners, values in parentheses represent ranges. No. with No. of offspring No. of litters No. of partners multiple assigned/individual assigned/individual Sex partners Females 3.6 ± 0.8 (1 – 9) 1.5 ± 0.2 (1 – 3) 4 (27%) 1.7 ± 0.3 (1 – 4) P1 Males 4.1 ± 2.3 (1 – 26) 1.6 ± 0.5 (1 – 6) 3 (27%) 1.5 ± 0.3 (1 – 4) Females 2.3 ± 0.8 (1 – 4) 1.0 ± 0.0 (1 – 1) - 1.5 ± 0.3 (1 – 2) P2 Males 1.6 ± 0.6 (1 – 4) 1.0 ± 0.0 (1 – 1) - 1.2 ± 0.2 (1 – 2) Females 3.0 ± 0.6 (1 – 6) 1.6 ± 0.3 (1 – 3) 4 (50%) 1.5 ± 0.3 (1 – 3) P3 Males 2.5 ± 0.8 (1 – 9) 1.3 ± 0.2 (1 – 3) 2 (20%) 1.5 ± 0.2 (1 – 3) Females 2.0 ± 0.8 (1 – 4) 1.3 ± 0.3 (1 – 2) 1 (33%) 1.3 ± 0.3 (1 – 2) C1 Males 2.0 ± 0.8 (1 – 4) 1.3 ± 0.2 (1 – 2) 1 (33%) 1.7 ± 0.5 (1 – 3)

While mean relatedness between parents of offspring was slightly higher than expected at random, no significant differences were found for site P1 (mean r between pairs = 0.06 ± 0.05; mean random r = -0.02 ± 0.06; p = 0.66) or site P2 (mean r between pairs = 0.04 ± 0.29; mean random r = -0.11 ± 0.13; p = 0.67). In the larger fragment (P3) and the continuous forest (C1), however, there was a suggestion that breeding pairs had lower relatedness values than expected at random (P3 mean r between pairs = -0.11 ± 0.04; mean random r = 0.03 ± 0.06; p = 0.06; C1 mean r between pairs = -0.20 ± 0.01; mean random r = -0.07 ± 0.13; p = 0.06). Despite breeding pairs having mean relatedness coefficients not different from random, mating between close relatives was identified in six instances: four cases at site P1 and one case each at sites P2 and P3. At site P1, seven offspring

Chapter 5: Genetic mating system and fine scale genetic structure - 108 - were sired by parents with a relatedness coefficient of 0.40 and parents of three offspring showed relatedness values consistent with second order relatives (> 0.25). At P2 and P3, a single offspring was produced by parents with relatedness values of 0.5 and 0.2, respectively.

5.3.2 Relatedness and genetic autocorrelation

Genetic autocorrelation coefficients between all adult males and females within each population showed a significant positive relationship for males and females at sites P1 and P3, and for males only at P2 (Figure 5.1). For the single population within the continuous forest, no autocorrelation was evident for males or females. Bootstrap 95% confidence intervals around the r estimates for males and females were not significant at any site.

* * * * *

Figure 5.1: Genetic autocorrelation of adult females (F) and adult males (M) within each population relative to a random subset of individuals from all four populations and the associated 95% CI error bars determined by bootstrapping. U and L correspond to the 95% CI for random spatial genetic structure. * denotes significantly more related than random.

When considering only adult male and female residents, a similar pattern emerged with significant positive genetic autocorrelation evident for males and females at sites P1 and P3, but not for either sex at C1 (Figure 5.2). Further, the positive autocorrelation shown by males at site P2 when all adults were included was not evident when only residents were considered. Female residents at P2 could not be examined because of insufficient numbers.

Chapter 5: Genetic mating system and fine scale genetic structure - 109 -

* * * *

Figure 5.2: Genetic autocorrelation of adult female (F) residents and adult male (M) residents within each population relative to a random subset of individuals from all four populations and the associated 95% CI error bars determined by bootstrapping. U and L correspond to the 95% CI for random spatial genetic structure. * denotes significantly more related than random.

Spatial autocorrelation among adult males and females at site P1 showed a significant positive autocorrelation to 75m (Figure 5.3a). When males and females were examined separately, females within 75m were significantly positively autocorrelated, whereas no correlation was found among males (Figure 5.3b). Interestingly, when all adult residents were examined regardless of sex, no genetic autocorrelation was found (Figure 5.3c). Separating the sexes, however, revealed significant positive autocorrelation for adult female residents up to 75m, with r values between 0.25 and 0.3 (Figure 5.3d). In contrast to P1, no significant genetic structure was found for the population at P2 at any distance or for any subset of the adult population (Figure 5.4a-c). In the largest fragment (P3), significant genetic structure was found between 75m and 100m structure for all adults combined (Figure 5.5a). This result was in contrast to site P1 and could be explained by significant genetic structure for males, not females (Figure 5.5b). When resident adults were examined in isolation, only males showed significant genetic structuring up to 75m, whereas no genetic autocorrelation was evident for adult female residents (Figure 5.5c-d). At the continuous forest site (C1), adults were significantly positively autocorrelated at 25m and 75m (Figure 5.6a). Analysing females and males separately revealed significant spatial genetic structure for females up to 75m and approached significance up to 125m but no spatial structure was evident for males (Figure 5.6b). The spatial genetic structure for adult residents, however, revealed no significant autocorrelation when all adult residents were combined for males and females separately (Figure 5.6c-d).

Chapter 5: Genetic mating system and fine scale genetic structure - 110 -

* * *

* *

Figure 5.3: Genetic autocorrelation in site P1 showing the relationship between the genetic correlation coefficient (r) as a function of distance and the associated 95% CI error bars determined by bootstrapping. U and L correspond to the 95% CI for random spatial genetic structure. (a) All adults, (b) adult males (M) and adult females (F), (c) all adult residents, and (d) adult male residents (M) and adult female residents (F). * denotes significantly more related than random at that distance class.

Chapter 5: Genetic mating system and fine scale genetic structure - 111 -

Figure 5.4: Genetic autocorrelation in site P2 showing the relationship between the genetic correlation coefficient (r) as a function of distance and the associated 95% CI error bars determined by bootstrapping. U and L correspond to the 95% CI for random spatial genetic structure. (a) All adults, (b) adult males (M) and adult females (F) and (c) all adult residents. * denotes significantly more related than random at that distance class.

Chapter 5: Genetic mating system and fine scale genetic structure - 112 -

* *

* * *

Figure 5.5: Genetic autocorrelation in site P3 showing the relationship between the genetic correlation coefficient (r) as a function of distance and the associated 95% CI error bars determined by bootstrapping. U and L correspond to the 95% CI for random spatial genetic structure. (a) All adults, (b) adult males (M) and adult females (F), (c) all adult residents, and (d) adult male residents (M) and adult female residents (F). * denotes significantly more related than random at that distance class.

Chapter 5: Genetic mating system and fine scale genetic structure - 113 -

* * *

Figure 5.6: Genetic autocorrelation in site C1 showing the relationship between the genetic correlation coefficient (r) as a function of distance and the associated 95% CI error bars determined by bootstrapping. U and L correspond to the 95% CI for random spatial genetic structure. (a) All adults, (b) adult males (M) and adult females (F), (c) all adult residents, and (d) adult male residents (M) and adult female residents (F). * denotes significantly more related than random at that distance class.

Chapter 5: Genetic mating system and fine scale genetic structure - 114 -

5.4 Discussion

5.4.1 U. caudimaculatus genetic mating system, individual reproductive success and mate choice Molecular genetic data have provided significant insight into the genetic mating system of U. caudimaculatus and indicate that Uromys display a range of mating behaviours: monogamy, polygyny and polyandry. Flexible reproductive behaviour in terms of genetic mating systems has been reported in a diverse range of taxa including reptiles (Chapple and Keogh, 2005), birds (Martinez et al. 1998) and mammals (Bouteiller and Perrin, 2000; Kyle et al. 2007; Bryja et al. 2009; McEachern et al. 2009). While polygamy (polygyny and polyandry) was the most common mating system observed, some individuals were monogamous, with monogamous parings within a single sampling period accounting for a significant proportion of litters in sites P1, P2 and P3 (50%, 100% and 67%, respectively). In addition, single females each from P1, P2 and P3 produced litters with a single male over multiple sampling periods.

Evidence for monogamous reproductive behaviour in Uromys within and among populations could be a significant finding considering Kleiman (1977) estimated that only 3% of mammals were socially monogamous but incidence of genetic monogamy is likely to be much rarer (Sun, 2003). These results support anecdotal evidence for apparent monogamous behaviour in Uromys (Andrew Dennis pers. comm.) and the finding that Uromys generally exhibit space use patterns that are consistent with social monogamy (Appendix 8)2. Genetic monogamy has been reported in several other small mammal species including the white-toothed shrew (Crocidura russula; Favre et al. 1997), the Malagasy giant jumping rat (Hypogeomys antimena; Sommer and Tichy, 1999; Sommer, 2003) and the California mouse (Peromyscus californicus; Ribble, 1991).

Reproductive behaviour is influenced by a number of factors including, but not limited to 1) size and exclusive use of female home range size (Komers and Brotherton, 1997), 2) availability of mates (Whiteman and Cote, 2004) and 3) the

2 Space use patterns and the social mating system of Uromys were examined and have been written up for publication. The theme of the manuscript, however, did not fit with the overall theme of the thesis but it is included in Appendix 8.

Chapter 5: Genetic mating system and fine scale genetic structure - 115 - costs associated with defending resources required for reproduction (Emlen and Oring, 1977). These factors are likely to vary both within and among populations and variations in mating behaviour can be simply a response to changing ecological and environmental conditions (Rubenstein 1980; Clutton-Brock, 1989; Reynolds, 1996). For example, when resources critical to reproduction vary in abundance, space and time, mating behaviour and group size are expected to co-vary (Emlen and Oring, 1977; Macdonald, 1983; Carr and Macdonald, 1986; Johnson et al. 2002; Przybylski et al. 2007). Uniformly or highly dispersed resources that are low in abundance should be relatively easy to defend by single females, and in turn single males can then monopolise the female. In contrast, when resources are spatially aggregated and abundant, the degree of resource dispersion and abundance will influence the number of females that are required for resource defence and also whether a single male or multiple males can monopolise those females.

Female space use patterns and mate availability may also contribute to a range of within and among site mating behaviour in Uromys. At site P2 for example, availability of females was very low (only 2 adult females were residents) and hence, it would be advantageous for males to consort with and defend females due to the low probability of finding another mate within that fragment. Higher mate availability (density) and mean home range overlap was evident at sites P1 and P3 compared with site P2 and this would likely promote polygamy (Appendix 8). The degree of variability in individual home range size and home range overlap at sites P1 and P3, however, suggest that at least for some individuals, mate availability may be limited which may also explain the presence of monogamous behaviour at these sites. For example, mixed mating reproductive behaviour in woodrats (Neotoma macrotis) is thought to arise as a consequence of heterogeneity in density within populations, with monogamy more prevalent in areas with lower population densities (Matocq, 2004). Even though the spatial distribution of females showed no distinct spatial structure (Appendix 8), these results were based on yearly averages and it is possible that within population heterogeneity in density and relative home range overlap may be evident when analysed at finer spatial and temporal scales.

All populations showed relatively high levels of reproductive skew (the proportion of individuals that breed) with ~50% of females and only ~30% of males producing

Chapter 5: Genetic mating system and fine scale genetic structure - 116 - offspring. Among residents, reproductive skew was somewhat less pronounced with ~60% of females and 50% of males producing offspring. This was particularly evident at site P2 where all resident males and females produced offspring and at P1 where 83% of females produced offspring. Thus, resources required for reproduction may limit female reproduction and access to sexually receptive females in turn, may limit male reproduction.

Resources likely to limit reproduction in females include food and nest sites. Foods high in protein and lipids are important resources that can limit reproduction in Uromys (Finkelstein and Grubb, 2002; chapter 3) and while the spatial distribution of adult females does not appear to be associated with these foods on a seasonal basis (Appendix 8), the spatial distribution of high quality food at a temporal scale linked to the reproductive cycle (i.e. every three months) may determine which individuals breed and which ones do not.

Extensive radio tracking conducted at site P1 (Craig Streatfeild unpubl. data) showed that females resident in areas of high resource loads were generally the ones that reproduced and produced the highest number of offspring. This implies that certain females may have differential access to the food resources necessary for reproduction. Areas with high densities of food resources also corresponded with locations of nests sites. Six resident females at site P1 that produced the most offspring consistently resided in the same nest sites that remained stable over multiple sampling periods, trhat in some cases lasted over 12 months. While the location and construction of nest sites were highly diverse, the fact that they were used exclusively and consistently imply that they were probably in secure locations that afforded protection from predators and/or competitors and/or provided thermoregulatory benefits during temperature extremes.

It is currently unknown if female Uromys in the other populations behave in a similar fashion but the results from P1 suggest that high quality food and nest sites may be important resources that influence successful reproduction for giant white-tailed rats. Such behaviour has been shown in Stephens’ woodrats (Neotoma stephensi) where successful reproduction by females was associated with the location of available den sites and access to high quality food (Vaughan and Czaplewski, 1985).

Chapter 5: Genetic mating system and fine scale genetic structure - 117 -

In the majority of mammalian species, females are a resource that limits male reproduction (Ostfeld, 1985; Clutton-Brock, 1989; Ostner et al. 2008; Lopuch and Radwan, 2009). If sexually receptive females are a limiting resource for male Uromys, some males should fail to reproduce. This was the case for all sites here except P2 where only two adult females resided in the population. In addition, relative reproductive success (the number of offspring assigned to each individual) in all populations was quite variable with certain individuals dominating reproduction. At one extreme, a single male (# 462705) at P1 was assigned as the father of 26 of the 57 juveniles that could be assigned paternity. This extreme case should, however, be treated with some caution due to the possible presence of unsampled males. For instance, if individual 462705 had a male sibling who remained unsampled, it is possible that paternity could be assigned incorrectly to this male, thus leading to inflated paternities, particularly if other candidate males had dissimilar genotypes. Nevertheless, the relatively high levels of reproductive skew and reproductive success shown here are intriguing because the general absence of sexual dimorphism in body size (Appendix 8) and lack of any evidence for intrasexual aggregation (Craig Streatfeild unpubl. data) argue against extreme levels of reproductive skew. However, several alternative explanations could account for the patterns of reproductive skew and reproductive success observed here for Uromys.

Firstly, in many species, individuals that sire offspring are generally larger compared with non-reproducing individuals (Loughry et al. 1998) because either increased size facilitates access to mates (Le Boeuf and Reiter, 1988) or larger individuals have more energy to invest in production of offspring (Clutton-Brock, 1991). Results here suggest that the males that reproduced successfully (assigned paternity) were generally in better body condition compared with a random subset of individuals that were not assigned paternity (i.e. did not reproduce). Differential reproductive success (the number of offspring assigned to individuals that did reproduce), however, was not related to body condition. Previous studies on mammals have found that males can experience considerable loss in body condition due to the energetic demands associated with reproduction (Clutton-Brock et al. 1982; Deutsch et al. 1990). If Uromys exhibit similar behaviour, a positive relationship between reproductive success and body condition would not be expected. Similar results were reported for the bushy-tailed woodrat (Neotoma cinerea) where body size did not influence the number of offspring produced (Topping and Millar, 1998).

Chapter 5: Genetic mating system and fine scale genetic structure - 118 -

Contrasting results in other studies found that body size (a surrogate for condition) is often correlated with reproductive success (Ribble, 1992; Solomon, 1993). The fact that body condition predicted which males reproduced, but failed to predict the number of offspring sired per male suggests that additional factors may also influence the number of offspring sired.

Sperm competition has been shown previously to influence reproductive success in males (Schwagmeyer et al. 1987), particularly in rodents (Stockley and Preston, 2004; Ramm and Stockley, 2007) through the combined effects of copulation frequency and the duration of copulations. Sperm competition may be an important selective pressure on reproductive success for Uromys considering the main prerequisite for sperm competition - multiple mating by females so that ejaculates of two or more males compete for fertilisation (Parker, 1998) - does occur with all sites except P2 showing evidence of multiple paternity. Whether or not sperm competition occurs in Uromys is however, unknown and requires further investigation.

Secondly, because reductions in fitness are often expected in offspring born to closely related parents (Saccheri et al. 1998; Keller, 1998), avoidance of inbreeding may be a strong selective force in the evolution of breeding behaviour (Keane, 1990a; Goossens et al. 2001). At all sites, however, Uromys bred with partners with a range of relatedness values when compared with potential available mates In the larger 80ha fragment (P3) and the continuous site (C1) individuals tended to breed with partners that had lower levels of relatedness than expected by chance, whereas relatedness between Uromys breeding pairs within the two small fragments were not different from random. Interestingly, there were several occasions in all three fragments when offspring were sired by closely related parents (>0.25) and the parents of seven offspring within P1 had relatedness values approaching ~0.5. These results imply that some Uromys may not actively seek unrelated individuals as breeding partners.

Evidence has been reported to suggest that in certain species, individuals may choose mates of intermediate relatedness rather than closely related or random partners. Female white-footed mice (Peromyscus leucopus; Keane, 1990b) and Japanese quail (Coturnix coturnix; Bateson, 1983) apparently prefer to mate with males of intermediate relatedness compared with non-relatives or siblings. In an

Chapter 5: Genetic mating system and fine scale genetic structure - 119 - open population of North American pikas (Ochotona princeps), Peacock and Smith (1997b) found regular breeding between individuals of intermediate relatedness and Lane et al (2007) found no relationship between genetic relatedness and paternity or reproductive skew in North American red squirrels (Tamiasciurus hudsonicus). Mating between close relatives, however, can still occur in randomly mating populations and has been shown in meadow voles (Microtus pennsylvanicus; Pugh and Tamarin, 1988) and black-tailed prairie dogs (Cynomys ludovicianus; Hoogland, 1992).

Mate choice has far reaching implications, however, for maintenance of genetic polymorphisms (Bleay and Sinervo, 2007). If mating between close relatives was widespread and ongoing, given enough time, the negative effects of inbreeding may manifest in reduced levels of heterozygosity in adults leading to declines in individual reproductive success. No relationships were found, however, between standardised heterozygosity Hs and either the degree of reproductive skew or reproductive success for either male or female Uromys.

The major histocompatibility complex (MHC) are genes that code for polymorphic molecules involved in pathogen defence (Klein et al. 1993; Carrington et al. 1999) and have been shown to play a role in mate choice in some species (Bonneaud et al. 2006). For example, previous studies have shown that individuals with either high MHC diversity (heterozygote advantage) and/or specific MHC alleles (frequency-dependent selection) are preferred under certain pathogenic conditions (Landry et al. 2001; Penn et al. 2002). Hence, a female may actively choose males with certain MHC profiles(that may be similar to her own) so pathogen resistant genes are maximised in her offspring. The role MHC gene complexes play in female mate choice in Uromys (if any), however, is currently unknown. If previous studies are any indication (e.g. Jordan and Bruford, 1998), examination of MHC profiles as a factor that affects mate choice, would be an exciting area for future research.

5.4.2 Genetic autocorrelation and relatedness Dispersal in individuals that utilise fragmented habitats is likely to be constrained by the inhospitable habitat that surrounds remnant habitat patches (Hansson et al. 2002). In cases where dispersal is constrained, average within population levels of genetic relatedness are expected to be higher relative to average relatedness within

Chapter 5: Genetic mating system and fine scale genetic structure - 120 - continuous populations (Sumner, 2005; Vignieri, 2007). As expected, giant white- tailed rats within the fragmented rainforest sites, particularly sites P1 and P3, were genetically more closely related compared with individuals from the continuous site. The finding of high levels of relatedness is also supported by estimates of genetic differentiation (chapter 4) that indicate infrequent among population dispersal. At P1 and P3, this pattern was independent of sex and residency status but this was not the case at P2, a result that was possibly due to the very small sample size for this population that contributed to the large confidence intervals about the mean. Nevertheless, similar results have been found in Danish bank voles (Clethrionomys glareolus; Redeker et al. 2005) and southern hairy-nosed wombats (Lasiorhinus latifrons; Walker et al. 2008), where lower levels of dispersal were evident from fragmented populations leading to higher levels of relatedness.

5.4.3 Sex-biased dispersal Sex-biased dispersal can lead to divergent patterns of genetic structure between the sexes (Chesser, 1991; Peakall et al. 2003) with proximate members of the more philopatric sex expected to show higher levels of relatedness and more genetic structure resulting from family or kin groups (Taylor et al. 1997; Stow et al. 2001). Models of sex-biased dispersal generally predict male-biased dispersal in polygynous species and either female or no bias in dispersal in monogamous species (Favre et al. 1997; Hammond et al. 2006). These predictions are generally supported with male-biased dispersal occuring in the majority of mammal species where polygyny predominates and female-biased dispersal more common in monogamous birds that defend resources (Greenwood, 1980; Dobson, 1982; Clarke et al. 1997).

In this study, male-biased dispersal and female philopatry were most evident at site P1. Within this site, adult females showed strong positive genetic structure up to 75m, regardless of whether or not they were residents of the population. These results were concordant with Campbell (1996) who reported fine scale mtDNA genetic structure in females that suggested extreme female philopatry in Uromys and that female dispersal away from their natal site is either uncommon or does not translate into successful reproduction. Initial evidence for female philopatry was also shown in the continuous rainforest site, C1, but was only evident when all females were included in the analysis but not when resident females were examined in isolation. This suggests that some females show natal philopatry but possibly

Chapter 5: Genetic mating system and fine scale genetic structure - 121 - disperse as adults rather than becoming residents. C1 was depauperate in food resources that may limit reproduction, and hence may explain why some adult females apparently choose dispersal over philopatry. Only a single years data were available from this population so it would be interesting to see if the pattern remained over multiple years when resource levels may vary.

Males in the largest fragment (P3) also showed strong genetic structure up to ~100m regardless of whether or not they were residents. This may suggest that male Uromys may be the more philopatric sex and that female-biased dispersal is more common at this site. Female-biased dispersal is an uncommon pattern in mammals (Nagy et al. 2007), however, with only ~5% of mammal species exhibiting female-biased dispersal patterns (Dobson, 1982). Furthermore, growing numbers of genetic studies over the last few decades have reported female-biased dispersal in a handful of mammals including the Californian mouse (Peromyscus californicus; Ribble, 1992), greater white-toothed shrew (Crocidura russula; Favre et al. 1997), North American porcupines (Erethizon dorsatum; Sweitzer and Berger, 1998), common wombats (Vombatus urusinus; Banks et al. 2002), hamadryas baboons (Papio hamadryas; Hammond et al. 2006), Siberian flying squirrels (Pteromys volans; Hanski and Selonen, 2008) and greater sac-winged bats (Saccopteryx bilineata; Nagy et al. 2007).

Male-biased dispersal may be promoted when competition for mates is high, however, if competition for resources is greater than local mate competition, female- biased dispersal can be favoured. This may be occurring at P3 where only 60% of resident females produced litters but is complicated by the observation that only 50% of females in C1 produced litters where there was no conclusive evidence for biased dispersal towards either sex. Nevertheless, several studies have shown female-biased dispersal under limited resource conditions such as dens sites (Johnson and Crossman, 1991; Price and Boutin, 1993; Berteaux and Boutin, 2000). In addition, Waser et al. (1986) suggested that female-biased dispersal could occur if the costs of inbreeding are greater for females than males - female dispersal is likely where polygyny predominates. If inbreeding avoidance causes female-biased dispersal in P3, we would also expect to see evidence for other inbreeding avoidance strategies, such as higher levels of multiple mating relative to populations with male-biased dispersal or no sex-biased dispersal. Indeed, half the females that reproduced successfully in P3 reproduced with multiple males. These

Chapter 5: Genetic mating system and fine scale genetic structure - 122 - data suggest that inbreeding avoidance strategies may be promoting female-biased dispersal in this population and is further supported by the observation that only ~35% of juvenile females reached adulthood and became residents compared with ~70% of males (Craig Streatfeild, unpubl. data).

This observation that Uromys populations may exhibit variation in sex-biased dispersal behaviour is an exciting result because very few studies have documented such behaviour in any species previously. In a recent review of mammalian sex- biased dispersal patterns, Lawson Handley and Perrin (2007) listed only one non- human mammal that has reported among population variation in sex-biased dispersal behaviours (shrews; Fontanillas et al. 2004). The lack of documented evidence for variation in sex-biased dispersal is intriguing because of the well established link between mating behaviours and sex-biased dispersal patterns and the large number of studies that report intraspecific variation in mating behaviours (reviewed in Lott, 1991). Thus, the mating system - sex-biased dispersal link implies that variation in mating behaviour should result in variation in sex-biased dispersal and will be an exciting area of future research.

5.5 Conclusion

This study indicates that a diverse range of reproductive and sex-biased dispersal behaviours may be evident in U. caudimaculatus. While the predominant mating system appeared to be polygyny in sites P1, P3 and C1, genetic evidence for monogamy was also evident in all three fragmented populations and was most prevalent in the low density, small fragment. Taken together, these data indicate that U. caudimaculatus exhibits several reproductive behaviours that do not appear to be caused by fragmentation, but rather are likely the result of individual responses to varying local ecological conditions. This variation highlights the suggestion by Reynolds (1996) that it may be better to describe variation among individuals rather than attempting to categorize species within the confines of a defined mating system. High variability in reproductive skew and individual reproductive success was also found but was less pronounced when only resident Uromys were considered. In addition and unexpectedly, a range of sex-biased dispersal behaviours was also found with evidence for male-biased dispersal at site P1 and female-biased dispersal at site P3. This suggests that along with mating systems, sex-biased dispersal behaviour may also be linked to changing local ecological conditions.

Chapter 6: Overview and general discussion - 123 -

6. Overview and general discussion

Fragmentation of once continuous areas of habitat results in a reduction in the total amount of suitable habitat available and the creation of isolated fragments that differ in size, shape and degree of connectivity (Saunders et al. 1991; Andren, 1994; Debinski and Holt, 2000; Ewers and Didham, 2006; Lindsay et al. 2008). As landscapes become progressively more fragmented, the remaining fragments of remnant vegetation become more isolated, a process that can impact a wide variety of biological processes in individual species including abundance, life history strategies, social structure, mating system, inbreeding and genetic diversity levels (Laurance, 1997; Frankham et al. 2002; Laurance et al. 2002; Banks et al. 2007). The effects of fragmentation on individual species, however, are not always negative and will likely depend on their degree of specialisation, individual habitat requirements, relative dispersal abilities and inbreeding avoidance mechanisms (Laurance 1990, 1994; Frankham et al. 2002; Ewers and Didham, 2006; Keyghobadi, 2007). The precise processes that determine which species are likely to be adversely affected by fragmentation, however, are still not well understood (Banks et al. 2007).

This aim of this thesis was to investigate how fragmentation of a once continuous rainforest habitat affected population level processes of the giant white-tailed rat, Uromys caudimaculatus and to investigate the mechanisms underlying Uromys population level responses to habitat fragmentation. The specific objectives were to examine if and how fragmentation, vegetation structure and food resources affect demography, among population genetic differentiation and within population genetic diversity and genetic structure of the giant white-tailed rat. The results of this study will not only contribute to the understanding of what processes influence a species’ response to fragmentation, but also add significantly to the lack of general biological information on U. caudimaculatus and will provide specific directions to resource managers on how best to manage fragmented Uromys populations.

Chapter 6: Overview and general discussion - 124 -

6.1 Chapter overviews

6.1.1 Effects of fragmentation, food and vegetation on Uromys demography and population density Isolated populations of small mammals are expected to show higher densities, increased survival (persistence), reduced dispersal and reduced reproductive rates (Adler and Levins, 1994). In contrast to these predictions, small mammal populations have shown a diversity of responses to habitat fragmentation, suggesting that factors other than the simple effect of habitat isolation are important in determining individual species’ response to fragmentation.

Perhaps one of the most significant results of this study was the finding that Uromys annual density and demography were strongly related to specific food resources that contained high lipid and protein levels (chapter 3) as opposed to factors associated with habitat fragmentation (i.e. fragment size) as previously shown by Laurance (1994) and Harrington et al. (2001). This was particularly evident in the two small habitat fragments, P1 and P2, where density was ~eight fold greater in the second year.

For organisms that track seasonal food resources, such as tropical rodents, differences in resource availability among isolated remnant fragments may affect the magnitude and timing of population fluctuations and reproduction. Interestingly, the results found here showed that not only did the magnitude and timing of within year fruit fall differ among sites, but so did Uromys demographic parameters. In addition, peaks in fruit fall of high quality food resources appear to initiate reproduction (chapter 3).

These results support the assertion that difficulties in predicting a species’ response to habitat fragmentation are often due to the specific habitat requirements of species that can vary in response to local ecological conditions (Bowers et al. 1996; Wiegand et al. 2005). In addition, these data suggest that habitat quality rather than absolute habitat quantity may be more critical for Uromys populations and that population size is determined primarily by local ecological conditions rather than absolute fragment size.

Chapter 6: Overview and general discussion - 125 -

6.1.2 Genetic diversity Species that experience population declines and increased isolation are particularly susceptible to erosion of within population genetic diversity and increased among population genetic differentiation (Frankham, 1996; Frankham et al. 2002). Over time, this can lead to reduced mean individual fitness and potentially, extinction (Reed and Frankham, 2003). The most important findings of this chapter were two fold. Firstly, gene flow was restricted among the four sampled sites (chapter 4). Sites P2 and P3 that were separated by only a few kilometres were genetically differentiated which is surprising considering that Uromys have the potential to move up to 500m in a single night (Moore, 1995). Secondly, all three fragmented populations showed significantly less genetic diversity (allelic diversity and expected heterozygosity), significantly fewer private alleles and lower historical effective population sizes compared with the single continuous population (chapter 4). The contemporary effective number of breeders was highest, however, in the largest 80ha fragment and the continuous forest relative to the two small fragments. This indicates that fragmentation may have had a negative effect on the effective number of breeders and in fragments of similar size, density can affect the effective number of breeders.

In the absence of gene flow, small populations will lose genetic diversity in proportion to their effective population size (Lande, 1988). Via the combined effects of reduced gene flow, erosion of genetic diversity and lower effective population sizes in fragments, the fragmented sites and particularly the smaller fragments, are potentially on an extinction trajectory unless effective management practices can be implemented.

6.1.3 Genetic mating system and sex-biased dispersal

Individuals in isolated populations that are separated by inhospitable habitat may be reluctant to disperse, leading to elevated levels of philopatry, altered social structures and reduced breeding opportunities leading to increased relatedness, (Peacock and Smith, 1997a,b; Bjørnstad et al. 1998; Stow et al. 2001; Stow and Sunnucks, 2004) and disruption of sex-biased dispersal and mate choice patterns (Sumner, 2005; Banks et al. 2007). For instance, in combination with limited dispersal, reduced densities in small fragments are likely to reduce an individual’s probability of encountering potential mates (Banks et al. 2007), a result that may lead to monogamous reproductive behaviour. In contrast, in high quality fragments,

Chapter 6: Overview and general discussion - 126 - reduced dispersal may lead to increased densities and home range overlap that can increase access to potential mates, leading to polygamous behavioural responses. Genetic analyses suggest that Uromys show a range of reproductive behaviours both within and among populations (chapter 5). The finding that some Uromys appear to exhibit monogamous reproductive behaviour is significant considering incidences of monogamy within mammals are rare (Kleiman, 1977; Sun, 2003).

While most giant white-tailed rats exhibited monogamous reproductive behaviour in P2 (low densities and negligible home range overlap), individuals in P1 and P3 showed both monogamous and polygamous reproductive behaviours: results that have been found in other small mammal species (e.g. Travis et al. 1995; Bouteiller and Perrin, 2000; Kyle et al. 2007) suggesting that some individuals may be able to monopolise resources critical to reproduction. This may explain why males in better body condition were more likely to sire offspring. While no direct evidence related unequal access to resources and reproductive skew, several lines of evidence suggest that resources may limit reproduction in Uromys.

Another major finding of this study was the significant fine-scale genetic structure in all sites except P2 (chapter 5). At site P1, females showed significant genetic structure over distances as small as 75m, which is consistent with the previous finding of Campbell (1996). In contrast, males at site P3 showed significant genetic structuring. While sex-biased dispersal is generally male-biased in mammals (Greenwood, 1980), several incidences of female-biased dispersal in mammals have been found previously (Ribble, 1992; Favre et al. 1997; Banks et al. 2002; Hammond et al. 2006). Few studies, however, have reported among population variability in sex-biased dispersal behaviours as shown here. These results suggest that the behavioural responses of Uromys interact in complex ways with local ecological conditions that vary in food, nest sites and mate availability.

6.2 Demography versus genetics

Debate about the relative importance of genetic and demographic factors began with Lande’s (1988) suggestion that demography may be of more immediate importance than population genetics in determining a species’ minimum viable population size. Over the last 15 years, the comments made by Lande (1988) were interpreted as small populations would likely suffer extinction at the hands of environmental and demographic stochastic factors before genetic factors have had

Chapter 6: Overview and general discussion - 127 - time to impact on them (Spielman et al. 2004). Recent empirical evidence has shown, however, that both demographic and population genetic factors can affect a species’ relative vulnerability to extinction (Groom, 1998; Saccheri et al. 1998; Tallmon et al. 2002; Dalén et al. 2006) and that both factors need to be considered when evaluating the relative viability of small populations (Srikwan and Woodruff, 2000).

Small or decreasing population size is often used as a measure of the demographic effects of fragmentation (Srikwan and Woodruff, 2000; Tallmon et al. 2002; Dalén et al. 2006). The number of juveniles and juvenile recruitment, however, may also be important demographic measures of fragmentation effects. Despite previous studies suggesting that U. caudimaculatus exhibit demographic effects of fragmentation (e.g. Laurance, 1994; Harrington et al. 2001), no consistent effects of fragmentation on demography were found here (chapter 3). For instance, the most divergent outcomes were evident among the two small fragments (i.e. overall density, density of residents, number of juveniles, recruits). Annual densities, recruitment and reproduction appeared to respond to local ecological conditions and varied significantly among the four sites (chapter 3). Examination of demographic parameters in isolation would suggest that populations within sites P1 and P3 may not be subject to the same level of risk by chance demographic and environmental factors compared with individuals in site P2.

Population density and demography alone, however, can be misleading (Smith and Nichols, 2004). Examination of the genetic data (chapter 4) strongly suggests that all three fragmented populations have been affected by fragmentation as shown by significant reductions in allelic diversity, relative number of private alleles and heterozygosity estimates relative to the single continuous population. The question of how much genetic diversity can be lost before genetic effects become evident and need to be managed directly is difficult to answer and depends on the inbreeding coefficient, FIS, effective population size and time since isolation (Spielman et al. 2004). The three habitat fragments have been isolated for approximately 100 years (Winter et al. 1987) and rodent populations show similar low levels of genetic variation. It is encouraging, however, that FIS values showed no significant differences across any of the four sites (chapter 4), suggesting that no apparent population level effect of inbreeding was evident yet within the fragmented populations. This is an important result considering significant inbreeding can

Chapter 6: Overview and general discussion - 128 - reduce reproduction and declines in population heterozygosity have been associated with reduced mean within-population individual fitness (Frankham et al. 2002). If individuals inhabiting fragmented habitats, however, display constrained dispersal as suggested here, average within population levels of relatedness are expected to be higher compared with average relatedness in the continuous habitats (Sumner, 2005; Vignieri, 2007). In addition, mating among close relatives was apparent within all fragmented populations. If this pattern of reproductive behaviour continues to occur, it is likely that over time populations will show higher inbreeding levels and associated fitness reductions. Furthermore, these effects will likely become evident earlier in the smaller fragments that already have lower effective population sizes compared with the larger 80ha fragment. These results highlight the importance of an integrated approach using demographic and genetic studies to better assess the viability of fragmented populations and to provide directions for future management.

6.2.1 Adaptive potential of small populations and functional genes Theoretical predictions and a multitude of empirical studies have shown that isolated populations lose genetic variation compared with their continuous counterparts (Frankham et al. 2002; Stow and Briscoe, 2005). When populations lose genetic diversity this can reduce their ability to adapt to future environmental conditions (Frankham et al. 2002; Willi et al. 2006) and this phenomenon is causing increasing concern in light of recent extinction predictions associated with global warming (Thomas et al. 2004), particularly within the Australian wet tropics (Williams et al. 2003). While neutral genetic markers (i.e. microsatellites) are the marker of choice for examining genetic diversity and genetic differentiation, they generally have limited function in understanding the effects of habitat fragmentation on long-term adaptive potential of fragmented populations.

Among vertebrates, mounting evidence suggests that genetic diversity is particularly important in highly variable, functionally important major histocompatibility complex (MHC) genes (Sommer, 2005). MHC genes play an essential role in immune and parasite defence (Potts and Wakeland, 1990; Kalbe et al. 2009). MHC diversity is assumed to improve population viability (Schad et al. 2004) and in contrast to neutral markers, variability in MHC genes reflects evolutionary relevant processes and so they are ideal genes to investigate the likely adaptive potential of fragmented populations (Sommer, 2005).

Chapter 6: Overview and general discussion - 129 -

The MHC set of genes are also particularly well suited to the study of mate choice. In addition to coding for immune and parasite defence, MHC has also been implicated as a possible source of individual specific body odours in mice and humans, and provides the basis for individual MHC profile recognition (Penn and Potts, 1999). Thus individuals have the potential ability to assess the genetic compatibility of potential mates and ‘select’ mates based on their MHC profiles that in combination with their own MHC genes can provide their offspring with the best immune response (Gillingham et al. 2009). This can enhance offspring survival and in turn, maximise their own reproductive success (Milinski, 2006 and references there in).

Thus analysis of MHC gene diversity would be a logical next step to examine mate choice in Uromys in more detail. This would be particularly interesting in light of the suggestion that Uromys may show behavioural flexibility for mating systems (Appendix 8).

6.3 Conservation biology and management recommendations

The giant white-tailed rat is a useful model organism for conservation biology and conservation genetics, especially with regard to the effects of habitat fragmentation. This species is not presently threatened in Queensland, however, over the last 100 years its rainforest habitat has undergone extensive clearing to the extent that the rainforest type in which Uromys predominantly inhabits, Regional Ecosystem 7.8.3, is currently listed as an endangered ecosystem (Queensland Environmental Protection Agency, 2007). Importantly, much of the remnant rainforest fragments utilised by U. caudimaculatus are currently on private land and under certain circumstances, can continue to be cleared (Queensland Vegetation Management Act, 1999). In addition to U. caudimaculatus, only one other species of Uromys is found on the Australian mainland, the masked white-tailed rat (U. hadrourus) that is locally rare and thought to only inhabit large continuous stretches of rainforest habitat (Moore, 1995). Interestingly, U. hadrourus, is thought to consume similar rainforest foods resources as U. caudimaculatus, thus the mechanisms that limit population size in U. caudimaculatus may also impact U. hadrourus populations. If this were the case, management recommendations from this study may also be relevant for management of masked white-tailed rat populations.

Chapter 6: Overview and general discussion - 130 -

The most immediate management priorities for the long-term viability of giant white- tailed rat populations are to stem the rate of loss of genetic diversity in fragmented populations by increasing connectivity of existing populations to facilitate gene flow, increase local population sizes where possible to mitigate potential environmental and demographic chance events and to increase the quantity of available high quality habitat. The latter objective will also serve the additional purpose of increasing the extent of the endangered complex notophyll vine forest (regional ecosystem 7.8.3).

Ameliorating the effects of habitat fragmentation may be achieved via restoration of original habitat by linking existing isolated fragments using corridors or by expanding the size of the existing fragments (Hobbs, 1993; Tucker, 2000a; Jansen, 2005), but will likely depend on the specific habitat requirements and behaviour of individual species (e.g. Horksins, 2005). Regardless of whether linkage of isolated fragments or increasing fragment size is the goal, the same basic principles apply - the ultimate goal being to restore degraded habitats to a ‘self-maintaining state’ (Tucker, 2000b). Achieving a self-maintaining state can be accelerated by introducing a suite of food resources including fleshy fruits, from a range of successional stages aimed at attracting seed dispersing birds and mammals (Tucker and Murphy, 1997).

Within the tropical rainforests of north-eastern Australia, several tree species produce seeds that are too large to be dispersed by species other than musky rat kangaroos (Hypsiprimondon moschatus), southern cassowaries (Casuarinus casuarius) and giant white-tailed rats (Dennis, 2003; Theimer, 2001, 2003). Uromys, however, appear less affected by fragmentation than are cassowaries or musky rat kangaroos, both of which are rare or completely absent in most remnant rainforest fragments (Laurance, 1994; Harrington et al. 1997; Theimer, 2003). Consequently, U. caudimaculatus are the most important and widespread seed predators and dispersal agents in Australian tropical rainforests (Goldberg, 1994; Harrington et al. 2001; Theimer, 2001; Dennis, 2003). Attracting giant white-tailed rats into revegetated areas, therefore, is of paramount importance and may be enhanced by planting rainforest plant species that are known to influence Uromys demography and reproduction, such as the suite of food resource within the three FLH, NLH and PL food categories (chapter 3). In particular, one of the main food resources for U. caudimaculatus are candlenuts (Aleurites rockinghamensis), which

Chapter 6: Overview and general discussion - 131 - are high energy yielding medium sized nuts (Finkelstein and Grubb, 2002) that have the capacity to produce large fruit crops over many months (Stocker et al. 1995). In addition, because candlenuts are a primary successional tree, they are fast growing and are able to produce fruit crops within a few years. Consequently, they are currently one of the major tree species used to restore rainforest areas in the wet tropics (Nigel Tucker pers. comm.).

While the utility of habitat corridors to provide additional habitat is widely recognised (Sutcliffe and Thomas 1996; Downes et al. 1997) their functionality in terms of facilitating connectivity among remnant fragments remains controversial (Beier and Noss 1998, Bennett, 1999; Horskins, 2005). For instance, Horskins (2005) found that a semi-natural riparian corridor linking two existing rainforests fragments provided habitat for U. caudimaculatus but failed to function as a conduit for gene flow and proposed that social factors may inhibit gene flow. As Horskins (2005) points out, the construction of wildlife corridors has focused predominantly on creating habitat structure, thus it may be possible that the functionality of this corridor for Uromys may have resulted from insufficient high quality habitat (i.e. food resources) or nest sites. Providing high quality habitat was one of the major strategies employed in the design of a 1.5km long corridor aimed at linking an isolated rainforest fragment to a large continuous tract of rainforest on the Atherton Tableland (Tucker, 2000a,b).

In matrix intolerant species, revegetating the matrix may help reduce the effects of fragmentation and isolation (Laurance et al. 2002). For instance, Wiegand et al. (2005) used mathematical models to show that increasing the amount of poor- quality habitat within the landscape (in place of the generally hostile matrix separating habitat patches) was a successful conservation strategy to improve matrix quality. By increasing matrix quality, survival of dispersers was enhanced because it provided shelter from predators and/or increased food sources and facilitated dispersal among fragmented patches. Whether or not this would be a successful long-term strategy for Uromys is unclear, however, one property on the Atherton Tableland was actively allowing previously cleared pasture habitat that bordered an existing semi-isolated fragment to revert to secondary forest. Within as little as one year, U. caudimaculatus were regularly observed feeding on fruits of a primary successional plant, wild tobacco (Solanum spp.) during periods of resource scarcity within the adjacent rainforest fragment (Mr Soley pers. comm.). While this

Chapter 6: Overview and general discussion - 132 - process may not result in successful linking of fragments, the mere fact that Uromys are present in this area will lead to the dispersal of plant species consumed by Uromys, and help to speed up the rehabilitation process with minimal human intervention.

Achieving positive conservation outcomes with limited input of time and money is particularly important due to increasing competition for limited conservation dollars. Conservation and revegetation projects will more and more be required to demonstrate rigorous methodology so that resource managers can justify expenditure to funding sources (Christensen, 2003; Freeman, 2004). This is nowhere more evident than in the wet tropics bioregion where restoring rainforest habitat on previously cleared land was estimated to cost $15,000 - $20,000 per ha in 2000 (Erskine, 2002). Furthermore, community rainforest revegetation projects on the Atherton Tableland received ~$500,000 of Federal funding between 1997 and 2002 (Freeman, 2004). Thus, it is now vital that researchers and resource managers consider and understand the mechanisms driving population level responses to fragmentation and design conservation projects, including revegetation of disturbed habitats that successfully achieve the specified goals.

6.4 Future directions

From this study, several important areas of future research have been identified including:

1) The use of MHC genes would be an exciting and extremely important avenue for future research to elucidate the affects of fragmentation on the genetic diversity of U. caudimaculatus populations.

2) Comparisons of genetic diversity in museum samples (provided adequate sample sizes were available) with modern populations would allow direct assessment of changes in genetic diversity to be tracked through time. This would contribute significantly to the understanding of temporal changes in genetic composition over time. For instance, this would address the issue of whether the majority of genetic diversity is lost within a few years following fragmentation or if genetic diversity tends to be lost linearly over time.

Chapter 6: Overview and general discussion - 133 -

3) Manipulative experiments, such as food supplementation should be conducted in resource depauperate sites (P2 and C1) and alteration of the spatial distribution of food resources within all populations, would provide definitive results on how high quality food resources affect Uromys demography and social structure.

4) Determining the social structure of cryptic, nocturnal species is notoriously difficult, and while rigorous trapping protocols were undertaken here, the approach adopted provided limited outcomes. New methods including recently developed proximity data loggers (www.sirtrack.com), would allow data on the spatial proximity of individuals to be gathered year round, not just during trapping periods. In addition, fluorescent dies can be used on adult and juveniles alike, to examine the size of social groups and allow quantification of nest sites.

5) Chapter 3 highlighted extreme biases in offspring sex ratios. In order to gain a better understanding of the mechanism(s) underlying the observed variation in juvenile sex-ratios shown here, manipulative experiments could be undertaken to investigate further whether: a. Females can actively bias offspring sex ratios via sex-selective abortion between conception and birth or sex-biased conception b. Sex-biased mortality occurs between birth and weaning.

6.5 Conclusion

This study has shown that a combination of ecological and genetic data is fundamental to understanding the response of Uromys populations to habitat fragmentation. Ecological data suggested that habitat quality (i.e. high quality food resources) rather than absolute habitat quantity (i.e. fragment size) may be more critical for determining Uromys demography and population persistence. Genetic data showed strong evidence that Uromys populations were impacted by habitat fragmentation and that management of isolated populations may be required for long-term viability of populations within isolated rainforest fragments.

References - 134 -

REFERENCES

Abdelkrim, J., Pascal, M., Clamet, C., & Samadi, S. (2005a) Importance of Assessing Population Genetic Structure before Eradication of Invasive Species: Examples from Insular Norway Rat Populations. Conservation Biology, 19, 1509-1518.

Abdelkrim, J., Pascal, M., & Samadi, S. (2005b) Island colonization and founder effects: the invasion of the Guadeloupe islands by ship rats (Rattus rattus). Molecular Ecology, 14.

Achard, F., Eva, H.D., Stibig, H.-J., Mayaux, P., Gallego, J., Richards, T., & Malingreau, J.-P. (2002) Determination of Deforestation Rates of the World's Humid Tropical Forests. Science, 297, 999-1002.

Adler, G.H. (1994) Tropical forest fragmentation and isolation promote asynchrony among populations of a frugivorous rodent. Journal of Animal Ecology, 63, 903-911.

Adler, G.H. (1998) Impacts of resource abundance on populations of a tropical forest rodent. Ecology, 79, 242-254.

Adler, G.H. (2000) Tropical tree diversity, forest structure and the demography of a frugivorous rodent, the spiny rat (Proechimys semispinosus). Journal of Zoology, 250, 57-74.

Adler, G.H. & Beatty, R.P. (1997) Changing reproductive rates in a Neotropical forest rodent, Proechimys semispinosus. Journal of Animal Ecology, 66, 472-480.

Adler, G.H. & Lambert, T.D. (1997) Ecological correlates of trap response of a Neotropical forest rodent, Proechimys semispinosus Journal of Tropical Ecology, 13, 59-68.

Adler, G.H. & Levins, R. (1994) The Island Syndrome in Rodent Populations. Quarterly Review of Biology, 69, 473-490.

References - 135 -

Adler, G.H. & Wilson, M.L. (1987) Demography of a habitat generalist, the white- footed mouse, in a heterogeneous environment. Ecology, 68, 1785-1796.

Adler, G.H. & Lambert, T.D. (2008) Spatial and temporal variation in the fruiting phenology of palms in isolated stands. Plant Species Biology, 23, 9-17.

Aguilar, R., Quesada, M., Ashworth, L., Herrerias-diego, Y., & Lobo, J. (2008) Genetic consequences of habitat fragmentation in plant populations: susceptible signals in plant traits and methodological approaches. Molecular Ecology, 17, 5177-5188.

Allendorf, F.W. & Leary, R.F. (1986) Heterozygosity and fitness in natural populations of animals In Conservation Biology: the science of scarcity and diversity (ed M.E. Soulé). Sinauer Associates, Sunderland, MS, U.S.A.

Allendorf, F.W. & Seeb., L.W. (2000) Concordance of genetic divergence among sockeye salmon populations at allozyme, nuclear DNA, and mtDNA markers. Evolution, 54, 640-651.

Almeida, F.C., Maroja, L.S., Moreira, M.A.M., Seuanez, H.N., & Cerqueira, R. (2005) Population structure and genetic variability of mainland and insular populations of the Neotropical water rat, Nectomys squamipes (Rodentia, Sigmodontinae). Genetics and Molecular Biology, 28, 693-699.

Alpin, K. (2006) Ten million years of rodent evolution in Australasia: phylogenetic evidence and a speculative historical biogeography. In Evolution and Biogeography of Australasian Vertebrates (eds J.R. Merrick, M. Archer, G.M. Hickey & M.S.Y Lee). Auscipub Pty Ltd.

Amos, W. & Balmford, A. (2001) When does conservation genetics matter? Heredity, 87, 257-265.

Amos, W. & Harwood, J. (1998) Factors affecting levels of genetic diversity in natural populations. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 353, 177-186.

References - 136 -

Amos, W., Worthington Wilmer, J., Fullard, K., Burg, T.M., Croxall, J.P., Bloch, D., & Coulson, T. (2001) The influence of parental relatedness on reproductive success. Proceedings of The Royal Society of London B., 268, 2021-2027.

Amos, W., Flint, J., & Xu, X. (2008) Heterozygosity increases microsatellite mutation rate, linking it to demographic history. BMC Genetics, 9, 72.

Anderson, C.S., Cady, A.B., & Meikle, D.B. (2003) Effects of vegetation structure and edge habitat on the density and distribution of white-footed mice (Peromyscus leucopus) in small and large forest patches. Canadian Journal of Zoology, 81, 897-904.

Andreassen, H.P., Hertzberg, K., & Ims, R.A. (1998) Space-use responses to habitat fragmentation and connectivity in the root vole, Microtus oeconomus. Ecology, 79, 1223-1235.

Andren, H. (1994) Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos, 71, 355-366.

Andren, H. (1997) Habitat fragmentation and changes in biodiversity. Ecological Bulletin, 46, 171-181.

Araki, H., Waples, R.S., & Blouin, M.S. (2007) A potential bias in the temporal

method for estimating Ne in admixed populations under natural selection. Molecular Ecology, 16, 2261-2271.

Balloux, F. & Lugon-Moulin, N. (2002) The estimation of population differentiation with microsatellite markers. Molecular Ecology, 11, 155-165.

Banks, S.C., Finlayson, G.R., Lawson, S.J., Lindenmayer, D.B., Paetkau, D., Ward, S.J., & Taylor, A.C. (2005c) The effects of habitat fragmentation due to forestry plantation establishment on demography and genetic variation in a marsupial carnivore, Antechinus agilis. Biological Conservation, 122, 581- 597.

References - 137 -

Banks, S.C., Lindenmeyer, D.B., Ward, S.J., & Taylor, A.C. (2005a) The effects of habitat fragmentation via forestry plantation establishment on spatial genotypic structure in the small marsupial carnivore, Antechinus agilis. Molecular Ecology, 14, 1667-1680.

Banks, S.C., Piggott, M.P., Stow, A.J., & Taylor, A.C. (2007) Sex and sociality in a disconnected world: a review of the impacts of habitat fragmentation on animal social interactions. Canadian Journal of Zoology, 85, 1065-1079.

Banks, S.C., Skerratt, L.F., & Taylor, A.C. (2002) Female dispersal and relatedness structure in common wombats (Vombatus ursinus). Journal of Zoology, 256, 389-399.

Banks, S.C., Ward, S.J., Lindenmayer, D.B., Finlayson, G.R., Lawson, S.J., & Taylor, A.C. (2005b) The effects of habitat fragmentation on the social kin structure and mating system of the agile antechinus, Antechinus agilis. Molecular Ecology, 14, 1789-1801.

Bartley, D., Bagley, M., Gall, G., & Bentley, B. (1992) Use of Linkage Disequilibrium Data to Estimate Effective Size of Hatchery and Natural Fish Populations. Conservation Biology, 6, 365-375.

Bateson, P. (1983). Optimal outbreeding. In Mate Choice (ed P. Bateson). Cambridge University Press, Cambridge, U.K.

Beier, P. & Noss, R.F. (1998) Do habitat corridors really provide connectivity? Conservation Biology, 12, 1241-1252.

Benedick, S., White, T.A., Searle, J.B., Hamer, K.C., Mustaffa, N., Vun Khen, C., Mohamed, M., Schilthuizen, M., & Jane K. Hill (2007) Impacts of habitat fragmentation on genetic diversity in a tropical forest butterfly on Borneo. Journal of Tropical Ecology, 23, 623-634.

Bennett, A.F. (1999) Linkages in the Landscape: the role of corridors and connectivity in wildlife conservation World Conservation Union, Gland, Switzerland and Cambridge, U.K.

References - 138 -

Bentley, J.M., Catterall, C.P., & Smith, G.C. (2000) Effects of fragmentation of Araucarian vine forest on small mammal communities. Conservation Biology, 14, 1075-1087.

Bentley, J.M. (2008) Role of movement, inter-remnant dispersal and edge effects in determining sensitivity to habitat fragmentation in two forest-dependent rodents. Austral Ecology, 33, 184-196.

Berckmoes, V., Sacheirs, J., Jordaens, K., Blust, R., Backeljau, T., & Verhagen, R. (2005) Effects of environmental pollution on microsatellite diversity in wood mouse (Apodemus sylvaticus) populations. Environmental Toxicology and Chemistry, 24, 2898-2907.

Bergallo, H.G. & Magnusson, W.E. (1999) Effects of climate and food availability on four rodent species in Southeastern Brazil. Journal of Mammalogy, 80, 472- 486.

Bergl, R.A., Bradley, B.J., Nsubuga, A., & Vigilant, L. (2008) Effects of Habitat Fragmentation, Population Size and Demographic History on Genetic Diversity: The Cross River Gorilla in a Comparative Context. American Journal of Primatology, 70, 848-859.

Berteaux, D. & Boutin, S. (2000) Breeding dispersal in female North American red squirrels. Ecology, 81, 1311-1326.

Berthier, K., Charbonnel, N., Galan, M., Chaval, Y., & Cosson, J.F. (2006) Migration and recovery of the genetic diversity during the increasing density phase in cyclic vole populations. Molecular Ecology, 15, 2665-2676.

Bjørnstad, O.N., Andreassen, H.P., & Ims, R.A. (1998) Effects of habitat patchiness and connectivity on the spatial ecology of the root vole Microtus oeconomus. Journal of Animal Ecology, 67, 127-140.

Bleay, C. & Sinervo, B. (2007) Discrete genetic variation in mate choice and a condition-dependent preference function in the side-blotched lizard:

References - 139 -

implications for the formation and maintenance of co-adapted gene complexes. 18, 2, 304-310.

Bonneaud, C., Chastel, O., Federici, P., Westerdahl, H., & Sorci, G. (2006) Complex Mhc-based mate choice in a wild passerine. Proceedings of The Royal Society of London B., 273, 1111-1116.

Boulter, S.L., Kitching, R.L., & Howlett, B.G. (2006) Family, visitors and the weather: patterns of flowering in tropical rain forests of northern Australia. Journal of Ecology, 94, 369-382.

Bouteiller, C. & Perrin, N. (2000) Individual reproductive success and effective population size in the greater white-footed shrew Crocidura russula. Proceedings of The Royal Society of London B., 267, 701-705.

Boutin, S. (1990) Food supplementation experiments with terrestrial vertebrates: patterns, problems, and the future. Canadian Journal of Zoology, 68, 203- 220.

Bouzat, J.L., Lewin, H.A., & Paige, K.N. (1998) The ghost of genetic diversity past: historical DNA analysis of the greater prairie chicken. American Naturalist, 152, 1-6.

Bowers, M.A., Gregario, K., Brame, C.J., Matter, S.F., & Dooley James L, J. (1996) Use of space and habitats by meadow voles at the home range, patch and landscape scales. Oecologia, 105, 107-115.

Bowers, M.A. & Matter, S.F. (1997) Landscape ecology of mammals: relationships betwen density and patch size. Journal of Mammalogy, 78, 999-1013.

Breininger, D.R. (1999) Florida Scrub-Jay demography and dispersal in a fragmented landscape. Auk, 116, 520-527.

Brito, D. & da Fonseca, G.A.B. (2007) Demographic consequences of population subdivision on the long-furred woolly mouse opossum (Micoureus paraguayanus) from the Atlantic forest. Acta Oecologica, 31, 60-68.

References - 140 -

Brouat, C., Sennedot, F., Audiot, P., Leblois, R., & Rasplus, J.-Y. (2003) Fine-scale genetic structure of two carabid species with contrasted levels of habitat specialization. Molecular Ecology, 12, 1731-1745.

Bruhl, C.A., Eltz, T., & Linsenmair, K.E. (2003) Size does matter - effects of tropical forest fragmentation on the leaf litter community in Sabah, Malaysia. Biodiversity and Conservation, 12, 1371-1389.

Bryja, J., Patzenhauerová, H., Albrecht, T., Mošanský, L., Stanko, M., & Stopka, P. (2008) Varying levels of female promiscuity in four Apodemus mice species. Behavioral Ecology and Sociobiology, 63, 251-260.

Burton, C., Krebs, C.J., & Taylor, E.B. (2002) Population genetic structure of the cyclic snowshoe hare (Lepus americanus) in southwestern Yukon, Canada. Molecular Ecology, 11, 1689-1701.

Busch, J.D., Waser, P.M., & DeWoody, J.A. (2007) Recent demographic bottlenecks are not accompanied by a genetic signature in banner-tailed kangaroo rats (Dipodomys spectabilis). Molecular Ecology, 16, 2450-2462.

Caizergues, A., Rätti, O., Helle, P., Rotelli, L., Ellison, L., & Rasplus, J.-Y. (2003) Population genetic structure of male black grouse (Tetrao tetrix L.) in fragmented vs. continuous landscapes. Molecular Ecology, 12, 2297-2305.

Campbell, N.J.H. (1996) Mitochondrial Control Region variation in two genera of Australian rodents; Melomys and Uromys. Application to phylogenetics, phylogeography and conservation. PhD, Southern Cross University, Lismore.

Cannon, C.H., Peart, D.R., & Leighton, M. (1998) Tree Species Diversity in Commercially Logged Bornean Rainforest. Science, 281, 1366-1368.

Carr, G.M. & Macdonald, D.W. (1986) The sociality of solitary foragers: a model based on resources dispersion. Animal Behaviour, 34, 1540-1549.

References - 141 -

Carrington, M., Nelson, G.W., Martin, M.P., Kissner, T., Vlahov, D., Goedert, J.J., Kaslow, R., Buchbinder, S., Hoots, K., & O'Brien, S.J. (1999) HLA and HIV- 1: Heterozygote Advantage and B*35-Cw*04 Disadvantage. Science, 283, 1748-1752.

Carvalho Alves Ribeiro, E., Jr., Pimentel Lima, A., Ernest Magnusson, W., & Luisa Kerti Mangabeira Albernaz, A. (2008) Long-term effect of forest fragmentation on the Amazonian gekkonid lizards, Coleodactylus amazonicus and Gonatodes humeralis. Austral Ecology, 33, 723-729.

Catling, P.C. & Burt, R.J. (1995) Studies of ground dwelling mammals of the eucalypt forests of south-eastern New South Wales - the effect of habitat variables on distribution and abundance. Wildlife Research, 22, 271-288.

Caughley, G. (1977) Analysis of Vertebrate Populations John Wiley and Sons, London, U.K.

Caughley, G. & Gunn, A. (1996) Conservation biology in theory and practice. Blackwell Science, Cambridge, MS, U.S.A.

Chambers, G.K. & MacAvoy, E.S. (2000) Microsatellites: consesus and controversy. Comparative Biochemistry and Physiology - Part B: Biochemistry and Molecular Biology, 126, 455-476.

Chand, V., Streatfeild, C.A., Wilson, J.C., & Mather, P.B. (2005) Isolation and characterization of 11 polymorphic microsatellite loci in the giant white-tailed rat (Uromys caudimaculatus). Molecular Ecology Notes, 5, 352-354.

Chapman, C.A., Chapman, L.J., Struhsaker, T.T., Zanne, A.E., Clark, C.J., & Poulson, J.R. (2005) A long-term evaluation of fruiting phenology: importance of climate change. Journal of Tropical Ecology, 21, 31-45.

Chapple, D.G. & Keogh, J.S. (2005) Complex mating system and dispersal patterns in a social lizard, Egernia whitii. Molecular Ecology, 14, 1215-1227.

References - 142 -

Chatelain, C., Gautier, L., & Spichiger, R. (1996) A recent history of forest fragmentation in southwestern Ivory Coast. Biodiversity and Conservation, 5, 783-791.

Chesser, R.K. (1991) Gene diversity and female philopatry. Genetics, 127, 437-447.

Christensen, J. (2003) Auditing conservation in an age of accountability. Conservation in Practice, 4, 12-19.

Clarke, A.L., Saether, B.-E., & Roskaft, E. (1997) Sex biases in avian dispersal: a reappraisal. Oikos, 79, 429-438.

Clutton-Brock, T.H. (1989) Mammalian mating systems. Proceedings of The Royal Society of London B., 236, 339-372.

Clutton-Brock, T.H. (1991) The Evolution of Parental Care Princeton University Press, Princeton, N.J., U.S.A.

Clutton-Brock, T.H., Guinness, F.E., & Albon., S.D. (1982) Red deer: behavior and ecology of two sexes. University of Chicago Press, Chicago, IL, U.S.A.

Cockburn, A. (1990) Sex ratio variation in marsupials. Australian Journal of Zoology, 37, 467-479.

Cockburn, A., Scott, M.P., & Dickman, C.R. (1985) Sex ratio and intrasexual kin competition in mammals. Oecelogia, 66, 427-429.

Coltman, D.W., Bancoft, D.R., Robertson, A., Smith, J.A., Clutton-Brock, T.H., & Pemberton, J.M. (1999a) Male reproductive success in a promiscuous mammal: behavioural estimates compared with genetic paternity. Molecular Ecology, 8, 1199-1209.

Coltman, D.W., Pilkington, J.G., Smith, J.A., & Pemberton, J.M. (1999b) Parasite- mediated selection against inbred Soay sheep in a free-living, island population. Evolution, 53, 1259-1267.

References - 143 -

Comport, S.S. (1999) Habitat utilisation and dispersal of fungal spores by the White- tailed rat on the rainforest-wet sclerophyll boundary in the Wet Tropics. MSc, James Cook University, Townsville.

Connor, E.F., Courtney, A.C., & Yoder, J.M. (2000) Individuals-area relationships: the relationship between animal population density and area. Ecology, 81, 734-748.

Cooper, W. & Cooper, W.T. (1994) Fruits of the rain forest. A guide to fruits in Australian tropical rain forests R. D. Press, Surry Hills, N.S.W., Australia.

Cornuet, J.-M. & Luikart, G.L. (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics, 144, 2001-2014.

Cornuet, J.-M., Piry, S., Luikart, G., Estoup, A., & Solignac, M. (1999) New methods for employing multilocus genotypes to select or exclude populations as origins of individuals. Genetics, 153, 1989-2000.

Crow, J.F. & Kimura, M. (1970) An Introduction to Population Genetics Theory Harper & Row, New York, N.Y., U.S.A.

Cunningham, R.B., Lindenmayer, D.B., MacGregor, C., Barry, S., & Welsh, A. (2005) Effects of trap position, trap history, microhabitat and season on capture probabilities of small mammals in a wet eucalypt forest. Wildlife Research, 32, 657-671.

Cushman, S.A., McKelvey, K.S., Hayden, J., & Schwartz, M.K. (2006) Gene flow in complex landscapes: testing multiple hypotheses with causal modelling. American Naturalist, 168.

Cutrera, A.P., Lacey, E.A., & Busch, C. (2006) Intraspecific variation in effective population size in talar tuco-tucos (Ctenomys talarum): the role of demography. Journal of Mammalogy, 87, 108-116.

References - 144 -

Dakin, E.E. & Avise, J.C. (2004) Microsatellite null alleles in parentage analysis. Heredity, 93, 504-509.

Dale, S. (2001) Female biased dispersal, low female recruitment, unpaired males, and the extinction of small isolated bird populations Oikos, 92, 344-356.

Dalén, L., Kvaløy, K., Linnell, J.D., Elmhagen, B., Strand, O., Tannerfeldt, M., Henttonen, H., Fuglei, E., Landa, A., & Angerbjörn, A. (2006) Population structure in a critically endangered arctic fox population: does genetics matter? Molecular Ecology, 15, 2809-2819.

Debinski, D.M. & Holt, R.D. (2000) A Survey and Overview of Habitat Fragmentation Experiments. Conservation Biology, 14, 342-355.

Debuse, V.J., King, J., & House, A.P.N. (2007) Effect of fragmentation, habitat loss and within-patch habitat characteristics on ant assemblages in semi-arid woodlands of eastern Australia. Landscape Ecology, 22, 731-745.

Dennis, A.J. (1997) Musky rat-kangaroos, Hypsiprymnodon moschatus, cursorial frugivores in Australia's wet-tropical rain-forests. PhD, James Cook University, Townsville.

Dennis, A.J. (2003) Scatter-hoarding by musky rat-kangaroos, Hypsiprymndon moschatus, a tropical rain-forest marsupial from Australia: implications for seed dispersal. Journal of Tropical Ecology, 19, 619-627.

Dennis, A.J. & Marsh, H. (1997) Seasonal reproduction in musky rat-kangaroos, Hysiprymnodon moschatus, a response to changes in resource availability. Wildlife Research, 24, 561-578.

Deutsch, C.J., Haley, M.P., & Le Boeuf, B.J. (1990) Reproductive effort of male northern elephant seals: estimates from mass loss. Canadian Journal of Zoology, 68, 2580-2593.

Di-Rienzo, A., Peterson, A.C., Garza, J.C., Valdes, A.M., Slatkin, M., & Freimer, N.B. (1994) Mutational processes of simple-sequence repeat loci in human

References - 145 -

populations. Proceedings of the National Academy of Sciences, USA, 91, 3166-3170.

Dickman, C.R. (1988) Sex ratio variation in response to interspecific competition. American Naturalist, 132, 289-297.

Dickman, C.R. (1991) Mechanisms of competition among insectivorous mammals. Ocelogia, 85, 464-471.

Diffendorfer, J.E., Gaines, M.S., & Holt, R.D. (1995) Habitat fragmentation and movements of three small mammals (Sigmodon, Microtus and Peromyscus). Ecology, 76, 827-839.

Dobson, F.S. (1982) Competition for mates and predominant male dispersal in mammals. Animal Behaviour, 30, 1183-1192.

Dooley, J.L., Jr. & Bowers, M.A. (1998) Demographic responses to habitat fragmentation: experimental tests at the landscape and patch scale. Ecology, 79, 969-980.

Doonan, T.J. & Slade, N.A. (1995) Effects of supplemental food on population dynamics of cotton rats, Sigmodon hispidus. Ecology, 76, 814-826.

Downes, S.J., Handasyde, K.A., & Elgar, M.A. (1997) The Use of Corridors by Mammals in Fragmented Australian Eucalypt Forests. Conservation Biology, 11, 718-726.

Driscoll, D.A. (2004) Extinction and outbreaks accompany fragmentation of a reptile community. Ecological Applications, 14, 220-240.

Efford, M. (1992) Comment-Revised estimates of the bias in the 'minimum number alive' estimator. Canadian Journal of Zoology, 70, 628-631.

Eisen, J.A. (1999). Mechanistic basis for microsatellite instability. In Microsatellites: Evolution and Applications (eds D.B. Goldstein & C. Schlötterer), pp. 34-48. Oxford University Press, Oxford, U.K.

References - 146 -

Eisto, A.K., Kuitunen, M., Lammi, A., Saari, V., Suhonen, J., Syrjasuo, S., & Tikka, P.M. (2000) Population persistence and offspring fitness in the rare bellflower Campanula cervicaria in relation to population size and habitat quality. Conservation Biology, 14, 1413-1421.

Eldridge, M.D.B., King, J.M., Loupis, A.K., Spencer, P.B.S., Taylor, A.C., Pope, L.C., & Hall, G.P. (1998) Unprecedented Low Levels of Genetic Variation and Inbreeding Depression in an Island Population of the Black-Footed Rock Wallaby. Conservation Biology, 13, 531-541.

Ellegren, H. (2000) Microsatellite mutations in the germline: implications for evolutionary inference. Trends in Genetics, 16, 551-558.

Emlen, S.T. & Oring, L.W. (1977) Ecology, Sexual Selection, and the Evolution of Mating Systems. Science, 197, 215-223.

Epps, C.W., Palsbøll, P.J., Wehausen, J.D., Roderick, G.K., Rob R. Ramey, I., & McCullough, D.R. (2005) Highways block gene flow and cause a rapid decline in genetic diversity of desert bighorn sheep. Ecology Letters, 8, 1029-1038.

Erskine, P.D. (2002) Land clearing and forest rehabilitation in the wet tropics of north Queensland, Australia. Ecological Management and Restoration, 3, 136-138.

Ewers, R.M. & Didham, R.K. (2006) Confounding factors in the detection of species responses to habitat fragmentation. Biological Reviews, 81, 117-142.

Excoffier, L., Laval, G., & Schneider, S. (2005) Arlequin (version 3.0): An integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online, 1, 47-50.

Excoffier, L., Smouse, P.E., & Quattro, J. (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics, 131, 479-491.

References - 147 -

Favre, L., Balloux, F., Goudet, J., & Perrin, N. (1997) Female-biased dispersal in the monogamous mammal Crocidura russula: evidence from field data and microsatellite patterns. Proceedings of The Royal Society of London B., 264, 127-132.

Fearnside, P.M. & Barbosa, R.I. (2004) Accelerating deforestation in Braziliam Amazonia: towards answering open questions. Environmental Conservation, 31, 7-10.

Feeley, K.J. & Terborgh, J.W. (2008) Direct versus indirect effects of habitat reduction on the loss of avian species from tropical forest fragments. Animal Conservation, 11, 353-360.

Fernando, P., Vidya, T.N.C., Rajapakse, C., Dangolla, A., & Melnick, D.J. (2003) Reliable noninvasive genotyping: fantasy or reality. Journal of Heredity, 94, 115-123.

Finkelstein, S. & Grubb, P.J. (2002) Lipid concentration in the embryo–endosperm fraction of seeds of Australian tropical lowland rainforest trees: relevance to defence and dispersal. Seed Science Research, 12, 173-180.

Fitzgibbon, C.D. (1997) Small mammals in farm woodlands: the effects of habitat, isolation and surrounding land-use patterns. Journal of Animal Ecology, 34, 530-539.

Fleming, T.H. (1992). How do fruit- and nectar-feeding birds and mammals track their food resources? In Effects of resource distribution on animal-plant interactions (eds M. Hunter, T. Ohgushi & P. Price), pp. 355-391. Academic Press, London, U.K.

Fontanillas, P., Petit, E., & Perrin, N. (2004) Estimating sex-specific dispersal rates with autosomal markers in hierarchically structured populations. Evolution, 58, 886-894.

Foster, R.B. (1982). The seasonal rhythm of fruitfall on Barro Colarado Island. In The ecology of a tropical forest: seasonal rhythms and long-term changes

References - 148 -

(eds E.G. Leigh, Jr., A.S. Rand & D.M. Windsor). Smithsonian Institution Press, Washington, D.C., U.S.A.

Frankham, R. (1995a) Conservation genetics. Annual Review of Genetics, 29, 305- 327.

Frankham, R. (1995b) Effective population size/adult populations size ratios in wildlife: a review. Genetic Research, 66, 95-107.

Frankham, R. (1996) Relationship of genetic variation to population size in wildlife. Conservation Biology, 10, 1500-1508.

Frankham, R. (1998) Inbreeding and Extinction: Island Populations. Conservation Biology, 13, 665-675.

Frankham, R., Ballou, J.D., & Briscoe, D.A. (2002) Introduction to Conservation Genetics Cambridge University Press, Cambridge, U.K.

Fredriksson, G.M., Wich, S.A., & Trisno (2006) Frugivory in sun bears (Helarctos malayanus) is linked to El Niño-related fluctuations in fruiting phenology, East Kalimantan, Indonesia. Biological Journal of the Linnean Society, 89, 489-508.

Freeman, A.N.D. (2004) Constraints to community groups monitoring plants and animals in rainforest revegetation sites on the Atherton Tablelands of far north Queensland. Ecological Management and Restoration, 5, 199-204.

Friend, G., R. (1987) Population Ecology of Mesembriomys gouldii (Rodentia: Muridae) in the Wet-Dry Tropics of the Northern Territory. Australian Wildlife Research, 14, 293-303.

Frith, C.B. & Frith, D.W. (1985) Seasonality of insect abundance in an Australian upland tropical rainforest. Australian Journal of Ecology, 10, 237-248.

References - 149 -

Gaggiotti, O.E., Lange, O., Rassmann, K., & Gliddon, C. (1999) A comparison of two methods for estimating average levels of gene flow using microsatellites data. Molecular Ecology, 8, 1513-1520.

Garcia de Leaniz, C., Fleming, I.A., Einum, S., Verspoor, E., Jordan, W.C., Consuegra, S., Aubin-Horth, N., Lajus, D., Letcher, B.H., Youngson, A.F., Webb, J.H., Vøllestad, L.A., Villanueva, B., Ferguson, A., & Quinn, T.P. (2007) A critical review of adaptive genetic variation in Atlantic salmon: implications for conservation. Biological Reviews, 82, 173-211.

Garza, J.C. & Williamson, E.G. (2001) Detection of reduction in population size using data from microsatellite loci. Molecular Ecology, 10, 305-318.

Gauffre, B., Estoup, A., Bretagnolle, V., & Cosson, J.F. (2008) Spatial genetic structure of a small rodent in a heterogeneous landscape. Molecular Ecology, 17, 4619-4629.

Gerber, L.R., Seabloom, E.W., Burton, R.S., & Reichman, O.J. (2003) Translocation of an imperilled woodrat population: integrating spatial and habitat patterns. Animal Conservation, 6, 309-316.

Gerlach, G. & Musolf, K. (2000) Fragmentation of landscape as a cause for genetic segregation of bank vole populations. Conservation Biology, 14, 1-10.

Ghizoni, I.R., Jr., Layme, V.M.G., Lima, A.P., & Magnusson, W.E. (2005) Spatially explicit population dynamics in a declining population of the tropical rodent, Bolomys lasiurus. Journal of Mammalogy, 86, 677-682.

Gillingham, M.A.F., Richardson, D.S., Løvlie, H., Moynihan, A., Worley, K., & Pizzari, T. (2009) Cryptic preference for MHC-dissimilar females in male red junglefowl, Gallus gallus. Proceedings of the Royal Society of London B., 276, 1083-1092.

Gliwicz, J. (1980) Island populations of rodents: their organization and functioning. . Biological Reviews, 55, 109-138.

References - 150 -

Gliwicz, J. (1984) Population dynamics of the spiny rat Proechimys semispinosus on Orchid Island (Panama). Biotropica, 16, 73-78.

Goldberg, J. (1994) An investigation into seed predation and caching in a tropical rain-forest rodent, Uromys caudimaculatus, the white-tailed rat. Honours, James Cook University, Townsville.

Goosem, M. (2000) Effects of tropical rainforest roads on small mammals: edge changes in community composition. Wildlife Research, 27, 151-163.

Goosem, M. & Marsh, H. (1997) Fragmentation of Small-mammal Community by a Powerline Corridor through Tropical Rainforest. Wildlife Research, 24, 613- 627.

Goosem, S.P., Morgan, G., & Kemp, J.E. (1999). Regional ecosystems of the wet tropics. In The Conservation Status of Queensland’s Bioregional Ecosystems (eds P.S. Sattler & R.D. Williams), pp. 1-72. Environmental Protection Agency, Brisbane, QLD, Australia.

Goossens, B., Chikhi, L., Taberlet, P., Waits, L.P., & Allaine, D. (2001) Microsatellite analysis of genetic variation among and within Alpine marmot populations in the French Alps. Molecular Ecology, 10, 41-52.

Goudet, J. (1995) FSTAT (vers. 1.2): a computer program to calculate F-statistics. Journal of Heredity, 86, 485-486.

Greenwood, P.J. (1980) Mating systems, philopatry and dispersal in birds and mammals. Animal Behaviour, 28, 1140-1162.

Guo, S.W. & Thompson., E.A. (1992) Performing the exact test of Hardy Weinberg proportions for multiple alleles. Biometrics, 48, 361-372.

Hammock, E.A.D. & Young, L.J. (2004) Functional Microsatellite Polymorphism Associated with Divergent Social Structure in Vole Species. Molecular Biology and Evolution, 21, 1057-1063.

References - 151 -

Hammond, R.L., Lawson Handley, L.J., Winney, B.J., Bruford, M.W., & Perrin, N. (2006) Genetic evidence for female biased dispersal and gene flow in a polygynous primate. Proceedings of The Royal Society of London B., 273, 479-484.

Hansen, M.C. & DeFries, R.S. (2004) Detecting long-term global forest change using continuous fields of tree-cover maps from 8-km Advanced Very High Resolution Radiometer (AVHRR) data for the years 1982-1999. Ecosystems, 7, 695-716.

Hanski, I. (1994) Patch-occupancy dynamics in fragmented landscapes. Trends in Ecology and Evolution, 9, 131-135.

Hanski, I.K. & Selonen, V. (2008) Female-biased natal dispersal in the Siberian flying squirrel Behavioral Ecology, 20, 60-67.

Hansson, B., Bensch, S., Hasselquist, D., & Nielsen, B. (2002) Restricted dispersal in a long-distance migrant bird with patchy distribution, the great reed warbler. Oecelogia, 130, 536-542.

Hanya, G., Yoshihiro, S., Zamma, K., Matsubara, H., Ohtake, M., Kubo, R., Noma, N., Agetsuma, N., & Takahata, Y. (2004) Environmental determinants of the altitudinal variations in relative group densities of Japanese macaques on Yakushima. Ecological Research, 19, 485-493.

Harper, S.J., Bollinger, E.K., & Barrett, G.W. (1993) Effects of habitat patch shape on population dynamics of meadow voles (Microtus pennsylvanicus). Journal of Mammalogy, 74, 1045-1055.

Harrington, G.N., Freeman, A.N.D., & Crowe, F.H.J. (2001) The effects of fragmentation of an Australian tropical rain forest on populations and assemblages of small mammals. Journal of Tropical Ecology, 17, 225-240.

Harrington, G.N., Irvine, A.K., Crome, F.H.J., & Moore, L.A. (1997). Regeneration of Large-Seeded Trees in Australian Rainforest Fragments: A Study of Higher- Order Interactions. In Tropical Forest Remnants. Ecology, Management, and

References - 152 -

Conservation of Fragmented Communities (eds W.F. Laurance & R.O. Bierregaard), pp. 292-303. The University of Chicago Press, Chicago, IL, U.S.A.

Harris, L.D. (1984) The Fragmented Forest University of Chicago Press, Chicago, IL., U.S.A.

Harris, D.B. & Macdonald, D.W. (2007) Population Ecology of the Endemic Rodent Nesoryzomys Swarthi in the Tropical Desert of the Galápagos Islands. Journal of Mammalogy, 88, 208-219.

Harrison, S. & Bruna, E. (1999) Habitat fragmentation and large-scale conservation: what do we know for sure? Ecography, 22, 225-232.

Hausmann, F. (2004) The utility of linear riparian rainforest for vertebrates on the Atherton and Evelyn Tablelands, north Queensland. MSc, Griffith University, Brisbane.

Hedrick, P.W. (2005) Large Variance in Reproductive Success and the Ne/N Ratio. Evolution, 59, 1596-1599.

Hill, M.O. (1973) Diversity and evenness: a unifying notation and its consequences. Ecology, 54, 427-431.

Hill, W.G. (1981) Estimation of effective population size from data on linkage disequilibrium. Genetical Research, 38, 209-216.

Hinds, F.E., Close, R.L., Campbell, M.T., & Spencer, P.B.S. (2002) Characterization of polymorphic microsatellite markers in the water rat (Hydromys chrysogaster). Molecular Ecology Notes, 2, 42-44.

Hinten, G., Harriss, F., Rossetto, M., & Baverstock, P.R. (2003) Genetic variation and island biogeography: Microsatellite and mitochondrial DNA variation in island populations of the Australian bush rat, Rattus fuscipes greyii. Conservation Genetics, 4, 759-778.

References - 153 -

Hobbs, R.J. (1993) Effects of landscape fragmentation on ecosystem processes in the Western Australian wheatbelt. Biological Conservation, 64, 193-201.

Hoffman, J.I. & Amos, W. (2005) Microsatellite genotyping errors: detection approaches, common sources and consequences for paternal exclusion. Molecular Ecology, 14, 599-612.

Hofmann, J.E., Getz, L.L., & Gavish, L. (1984) Home Range Overlap and Nest Cohabitation of Male and Female Prairie Voles. American Midland Naturalist, 112, 314-319.

Hoogland, J.L. (1992) Levels of inbreeding among prairie dogs. American Naturalist, 139, 591-602.

Hopkins, M.S. & Graham, A.W. (1989) Community phenological patterns of a lowland tropical rainforest in north-eastern Australia. Australian Journal of Ecology, 14, 399-413.

Horskins, K. (2005) The effectiveness of wildlife corridors in facilitating connectivity: assessment of a model system from the Australian Wet Tropics. PhD, Queensland University of Technology, Brisbane.

Ims, R.A. & Andreassen, H.P. (1999) Effects of experimental habitat fragmentation and connectivity on root vole demography. Journal of Animal Ecology, 68.

Jadwiszczak, P. (2003) RUNDOM PRO v3.01. Available at: http://pjadw.tripod.com/.

Jakob, E.M., Marshall, S.D., & Uetz, G.W. (1996) Estimating fitness: a comparison of body condition indices. Oikos, 77, 61-67.

Janečka, J.E., Tewes, M.E., Laack, L.L., Grassman, L.I., Jr., Haines, A.M., & Honeycutt, R.L. (2008) Small effective population sizes of two remnant ocelot populations (Leopardus pardalis albescens) in the United States. Conservation Genetics, 9, 869-878.

References - 154 -

Jansen, A. (2005) Avian Use of Restoration Plantings along a Creek Linking Rainforest Patches on the Atherton Tablelands, North Queensland. Restoration Ecology, 13, 275-283.

Jaquiéry, J., Guillaume, F., & Perrin, N. (2008) Predicting the Deleterious Effects of Mutation Load in Fragmented Populations. Conservation Biology, 23, 207- 218.

Jarne, P. & Lagoda, P.J.L. (1996) Microsatellites, from molecules to populations and back. Trends in Ecology and Evolution, 11, 424-429.

Johannesen, E. & Ims, R.A. (1996) Modelling survival rates: habitat fragmentation and destruction in root vole experimental populations. Ecology, 77, 1196- 1209.

Johansson, M., Primmer, C.R., & Merilä, J. (2007) Does habitat fragmentation reduce fitness and adaptability? A case study of the common frog (Rana temporaria). Molecular Ecology, 16, 2693-2700.

Johansson, M., Primmer, C.R., Sahlsten, J., & Merilä, J. (2005) The influence of landscape structure on occurrence, abundance and genetic diversity of the common frog, Rana temporaria. Global Change Biology, 11, 1664-1679.

Johns, A.D. (1989) Recovery of a peninsular Malaysian rain forest avifauna following selective timber logging: the first twelve years. Forktail, 4, 89-105.

Johnson, C.N., Clinchy, M., Taylor, A.C., Krebs, C.J., Jarman, P.J., Payne, A., & Ritchie, E.G. (2001) Adjustment of offspring sex ratios in relation to the availability of resources for philopatric offspring in the common brushtail possum. Proceedings of the Royal Society of London, 268, 2001-2005.

Johnson, C.N. & Crossman, D.G. (1991) Dispersal and social organization of the Northern hairy-nosed wombat Lasiorhinus krefftii. Journal of Zoology, 225, 605-613.

References - 155 -

Johnson, C.N. & Ritchie, E.G. (2002) Adaptive biases in offspring sex ratios established before birth in a marsupial, the common brushtail possum Trichosurus vulpecula. Behavioral Ecology, 13, 653-656.

Johnson, D.D.P., Kays, R., Blackwell, P., & Macdonald, D.W. (2002) Does the resource dispersion hypothesis explain group living? Trends in Ecology and Evolution, 17, 563-570.

Johnson, M.D. & Sherry, T.W. (2001) Effects of food availability on the distribution of migratory warblers among habitats in Jamaica. Journal of Animal Ecology, 70, 546-560.

Jolly, G.M. & Dickson, J.M. (1983) The problem of unequal catchability in mark- recapture estimation of small mammal populations. Canadian Journal of Zoology, 61, 922-927.

Jones, C., McShea, W., Conroy, M., & Kunz., T. (1996). Capturing Mammals. In Measuring and Monitoring Biological Diversity: Standard Methods for Mammals. (eds D. Wilson, F. Cole, J. Nichols, R. Rudran & M. Foster.), pp. 409. Smithsonian Institution Press, Washington, D.C., U.S.A.

Jones, M.E., Paetkau, D., Geffen, E., & Moritz, C. (2004) Genetic diversity and population structure of Tasmanian devils, the largest marsupial carnivore. Molecular Ecology, 13, 2197-2209.

Jordan, W.C. & Bruford, M.W. (1998) New perspectives on mate choice and the MHC. Heredity, 81, 239-245.

Kalbe, M., Eizaguirre, C., Dankert, I., Reusch, T.B.H., Sommerfeld, R.D., Wegner, K.M., & Milinski, M. (2009) Lifetime reproductive success is maximized with optimal major histocompatibility complex diversity. Proceedings of the Royal Society of London B., 276, 925-934.

Kalinowski, S.T. (2004) Counting alleles with rarefaction: private alleles and hierarchical sampling designs. Conservation Genetics, 5, 539-543.

References - 156 -

Kalinowski, S.T. (2005) HP-Rare: A computer program for performing rarefaction on measures of allelic diversity. Molecular Ecology Notes, 5, 187-189.

Kalinowski, S.T., Taper, M.L., & Marshall, T.C. (2007) Revising how the computer program CERVUS accommodates genotyping error increases confidence in paternity. Molecular Ecology, 16, 1099-1106.

Keane, B. (1990a) Dispersal and inbreeding avoidance in the white-footed mouse, Peromyscus leucopus. Animal Behaviour, 40, 143-152.

Keane, B. (1990b) The effect of relatedness on reproductive success and mate choice in the white-footed mouse, Peromyscus leucopus. Animal Behaviour, 39, 264-273.

Keane, B., Waser, P., Creel, S., Creel, N., Elliot, L., & Minchilla, D. (1994) Subordinate reproduction in dwarf mongooses. Animal Behaviour, 47, 289- 294.

Keller, K.F. & Waller, D.M. (2002) Inbreeding effects in wild populations. Trends in Ecology & Evolution, 17, 230-241.

Keller, L.F. (1998) Inbreeding and its fitness effects in an insular population of Song Sparrows (Melospiza melodia). Evolution, 52, 240-250.

Keller, L.F., Jeffery, K.J., Arcese, P., Beaumont, M.A., Hochachka, W.M., Smith, J.N.M., & Bruford, M.W. (2001) Immigration and the Ephemerality of a Natural Population Bottleneck: Evidence from Molecular Markers. Proceedings of The Royal Society of London B., 268, 1387-1394.

Kershaw, A.P. (1985) An extended late Quaternary vegetation record from north- east Queensland and its implications for the seasonal tropics in Australia. Proceedings of the Ecological Society of Australia, 13, 179-189.

Keuroghlian, A. & Eaton, D.P. (2008a) Importance of rare habitats and riparian zones in a tropical forest fragment: preferential use by Tayassu pecari, a wide-ranging frugivore. Journal of Zoology, 275, 283-293.

References - 157 -

Keuroghlian, A. & Eaton, D.P. (2008b) Fruit Availability and Peccary Frugivory in an Isolated Atlantic Forest Fragment: Effects on Peccary Ranging Behavior and Habitat Use. Biotropica, 40, 62-70.

Keyghobadi, N. (2007) The genetic implications of habitat fragmentation for animals. Canadian Journal of Zoology, 85, 1049-1064.

Kitchener, D.J., Chapman, A., Muir, B.G., & Palmer, M. (1980) The conservation value for mammals of reserves in the Western Australian wheatbelt. Biological Conservation, 18, 179-207.

Kleiman, D.G. (1977) Monogamy in mammals. Quarterly Review of Biology, 52, 39- 69.

Klein, J., Satta, Y., O’huigin, C., & Takahata, N. (1993) The molecular descent of the major histocompatibility complex. Annual review of Immunology, 11, 269-295.

Klok, C. & A.M. de Roos (1998) Effects of habitat size and quality on equilibrium density and extinction time of Sorex araneus populations. Journal of Animal Ecology, 76, 195-209.

Knutsen, H., Rukke, B.A., Jorde, P.E., & Ims, R.A. ( 2000) Genetic differentiation among populations of the beetle Bolitophagus reticulatus (Coleoptera: Tenebrionidae) in a fragmented and a continuous landscape. Heredity, 84, 667-676.

Komers, P.E. & Brotherton, P.N.M. (1997) Female space use is the best predictor of monogamy in mammals. Proceedings of the Royal Society of London. B, 264, 1261-1270.

Koprowski, J.L. (2005) The response of tree squirrels to fragmentation: a review and synthesis. Animal Conservation, 8, 369-376.

Kozakiewicz, M. (1993) Habitat isolation and ecological barriers - the effect on small mammal populations and communities. Acta Theriologica, 38, 1-30.

References - 158 -

Krebs, C.J. (1966) Demographic changes in fluctuating populations of Microtus californicus. Ecological Monographs, 36, 239-273.

Krebs, C.J. (1999) Ecological Methodology, 2nd edn. Addison-Welsey, Menlo Park, CA., U.S.A.

Krebs, C.J. & Boonstra, R. (1984) Trappability estimates for mark-recapture data. Canadian Journal of Zoology, 62, 2440-2444.

Krebs, C.J., Gilbert, B.S., Boutin, S., Sinclair, A.R.E., & Smith., J.N.M. (1986) Population biology of snowshoe hares: Demography of food supplemented populations in the southern Yukon. Journal of Animal Ecology, 55, 963-982.

Krebs, C.J., Keller, B.L., & Tamarin, R.H. (1969) Microtus population biology: demographic changes in fluctuating populations of M. ochrogaster and M. pennsylvanicus in southern Indiana. Ecology, 50, 587-607.

Krebs, C.J. & Singleton, G.R. (1993) Indices of Condition for Small Mammals. Australian Journal of Zoology, 41, 317-323.

Kricher, J. (1999) A Neotropical Companion: An Introduction to the Animals, Plants, and Ecosystems of the New World Tropics, Second Edition edn. Princeton University Press.

Krohne, D.T. & Hoch, G.A. (1999) Demography of Peromyscus leucopus populations on habitat patches: the role of dispersal. Canadian Journal of Zoology, 77, 1247-1253.

Kyle, C.J., Karels, T.J., Davis, C.S., Mebs, S., Clark, B., Strobeck, C., & Hik, D.S. (2007) Social structure and facultative mating systems of hoary marmots (Marmota caligata). Molecular Ecology, 16, 1245-1255.

Lack, D. (1954) Food as a limiting factor in birds. The natural regulation of animal numbers Oxford University Press, Oxford, U.K.

References - 159 -

Lacy, R.C. (1997) Importance of genetic variation to the viability of mammalian populations. Journal of Mammalogy, 78, 320-335.

Lacy, R.C. & Lindenmeyer, D.B. (1995) A simulation study of the impacts of population subdivision on the mountain brushtail possum Trichosurus caninus ogilby (Phalangeridae: Marsupialia) in south-eastern Australia II. Loss of genetic variation within and between subpopulations. Biological Conservation, 73, 131-142.

Lambert, T.D., Adler, G.H., Riveros, C.M., Lopez, L., Ascanio, R., & Terborgh, J. (2003) Rodents on tropical land-bridge islands. Journal of Zoology, 260, 179-187.

Lambin, X. (1994) Natal Philopatry, Competition for Resources, and Inbreeding Avoidance in Townsend's Voles. Ecology, 75, 224-235.

Lande, R. (1988) Genetics and Demography in Biological Conservation. Science, 241, 1455-1460.

Lande, R. (1995) Mutation and conservation. Conservation Biology, 9, 782-791.

Landry, C., Garant, D., Duchesne, P., & Bernatchez, L. (2001) ‘Good genes as heterozygosity’: the major histocompatibility complex and mate choice in Atlantic salmon (Salmo salar). Proceedings of The Royal Society of London B., 268, 1279-1285.

Lane, J.E., Boutin, S., Gunn, M.R., Slate, J., & Coltman, D.W. (2007) Genetic relatedness of mates does not predict patterns of parentage in North American red squirrels. Animal Behaviour, 74, 611-619.

Laurance, W.F. (1990) Comparative responses of five arboreal marsupials to tropical forest fragmentation. Journal of Mammalogy, 71, 641-653.

Laurance, W.F. (1991) Ecological Correlates of Extinction Proneness in Australian Tropical Rain Forest Mammals. Conservation Biology, 5, 79-89.

References - 160 -

Laurance, W.F. (1992) Abundance estimates of small mammals in Australian tropical rainforests: a comparison of four trapping methods. Wildlife Research, 19, 651-655.

Laurance, W.F. (1994) Rainforest fragmentation and the structure of small mammal communities in tropical Queensland. Biological Conservation, 69, 23-32.

Laurence, W.F. (1997) Responses of Mammals to Rainforest Fragmentation in Tropical Queensland: a Review and Synthesis. Wildlife Research, 24, 603- 612.

Laurance, W.F., Delamônica, P., Laurance, S.G., Vasconcelos, H.L., & Lovejoy, T.E. (2000) Rainforest fragmentation kills big trees. Nature, 404, 836.

Laurance, W.F., Ferreira, L.V., Rankin-de Merona, J., M., & Laurance, S.G. (1998) Rain Forest Fragmentation and the Dynamics of Amazonian Tree Communities. Ecology, 79, 2032-2040.

Laurance, W.F., Lovejoy, T.E., Vasconcelos, H.L., Bruna, E.M., Didham, R.K., Stouffer, P.C., Gascon, C., Bierregaard, R.O., Laurance, S.G., & Sampaio, E.M. (2002) Ecosystem decay of Amazonian forest fragments: A 22-year investigation. Conservation Biology, 16, 605-618.

Laurance, W.F., Rankin-de Merona, J., M., Andrade, A., Laurance, S.G., D'Angelo, S., Lovejoy, T.E., & Vasconcelos, H.L. (2003) Rain-forest fragmentation and the phenology of Amazonian tree communities. Journal of Tropical Ecology, 19,343-347.

Laurance, W.F. & Yensen, E. (1991) Predicting the impacts of edge effects in fragmented habitats. Biological Conservation, 55, 77-92.

Law, B., S. (2001) The diet of the common blossom bat (Syconycteris australis) in upland tropical rainforest and the importance of riparian areas. Wildlife Research, 28, 619-626.

References - 161 -

Law, B.S. & Lean, M. (1999) Common blossom bats Syconycteris australis as pollinators in fragmented Australian tropical rainforest. Biological Conservation, 91, 201-212.

Lawson Handley, L.J. & Perrin, N. (2007) Advances in our understanding of mammalian sex-biased dispersal. Molecular Ecology, 16, 1559-1578.

Le Boeuf, B.J. & Reiter, J. (1988). Lifetime reproductive success in northern elephant seals. In Reproductive success (ed T.H. Clutton-Brock). University of Chicago Press, Chicago, IL, U.S.A.

Leberg, P. (2005) Genetic approaches for estimating the effective size of populations. Journal of Wildlife Management, 69, 1385-1399.

Lehmann, T., Hawley, W.A., Grebert, H., & Collins, F.H. (1998) The effective population size of Anopheles gambiae in Kenya: implications for population structure. Molecular Biology and Evolution, 115, 264-276.

Levinson, G. & Gutman, G.A. (1987) Strand slippage mis-pairing: a major mechanism for DNA sequence evolution. Molecular Biology and Evolution, 4, 203-221.

Li, Y.C., Korol, A.B., Fahima, T., Beiles, A., & Nevo, E. (2002) Microsatellites: genomic distribution, putative functions and mutational mechanisms: a review. Molecular Ecology, 11, 2453-2465.

Lidicker, W.Z., Jr. & Stenseth, N.C. (1992). To disperse or not to disperse: who does it and why? In Animal dispersal: small mammals as a model (eds N.C. Stenseth & W.Z. Lidicker, Jr. ), pp. 5-18. Chapman and Hall, London, U.K.

Lima, S.L. & Zollner, P.A. (1996) Towards a behavioral ecology of ecological landscapes. Trends in Ecology & Evolution, 11, 131-135.

Lindenmayer, D.B. & Fischer, J. (2006) Habitat Fragmentation and Landscape Change: An Ecological and Conservation Synthesis CSIRO Publishing, Collingwood, VIC., Australia.

References - 162 -

Lindenmayer, D.B. & Peakall, R. (2000). The Tumut experiment - integrating demographic and genetic studies to unravel fragmentation effects: a case study of the native bush rat. In Genetics, Demography and Viability of Fragmented Populations (eds Andrew G. Young & Geoffrey M. Clarke), Cambridge University Press, Cambridge, U.K.

Lindsay, D.L., Barr, K.R., Lance, R.F., Tweddale, S.A., Hayden, T.J., & Leberg, P.L. (2008) Habitat fragmentation and genetic diversity of an endangered, migratory songbird, the golden-cheeked warbler (Dendroica chrysoparia). Molecular Ecology, 17, 2122-2133.

Lippé, C., Dumont, P., & Bernatchez, L. (2006) High genetic diversity and no inbreeding in the endangered copper redhorse, Moxostoma hubbsi (Catostomidae, Pisces): the positive sides of a long generation time. Molecular Ecology, 15, 1769-1780.

Litvaitis, J.A. (1993) Response of early successional vertebrates to historic changes in land use. Conservation Biology, 7, 866-873.

López-Bao, J.V., Rodríguez, A., & Palomares, F. (2008) Behavioural response of a trophic specialist, the Iberian lynx, to supplementary food: patterns of food use and implications for conservation. Biological Conservation, 141, 1857- 1867.

Lopuch, S. & Radwan, J. (2009) Condition dependence of sexual attractiveness in the bank vole. Behavioral Ecology and Sociobiology, 63, 339-344.

Lott, D.F. (1991) Intraspecific Variation in the Social Systems of Wild Vertebrates Cambridge University Press, Cambridge, U.K.

Loughry, W.J., Paulo, A.P., McDonough, C.M., Nelson, W.S., & Avise, J.C. (1998) Correlates of reproductive success in a population of nine-banded armadillos. Canadian Journal of Zoology, 76, 1815-1821.

Lovejoy, T.E., Bierregaard, R.O., Rylands, A.B., Malcon, J.R., Quintela, C.E., Harpe, L.H., Brown, K.S., Jr., Powell, A.H., Powell, G.V.N., Schubart,

References - 163 -

H.O.R., & Hays, M.B. (1986). Edge and other effects of isolation on Amazon forest fragments. In Conservation Biology: The science of scarcity and diversity (ed M.E. Soulé), pp. 257-285. Sinauer Associates, Sunderland, MS, U.S.A.

Luikart, G. & Cornuet, J.-M. (1999) Estimating the Effective Number of Breeders From Heterozygote Excess in Progeny. Genetics, 151, 1211-1216.

Luikart, G., Sherwin, W.B., Steele, B.M., & Allendorf, F.W. (1998a) Usefulness of molecular markers for detecting population bottlenecks via monitoring genetic change. Molecular Ecology, 7, 963-974.

Luikart, G.L. & Cornuet, J.-M. (1998) Empirical evaluation of a test for identifying recently bottlenecked populations from allele frequency data. Conservation Biology, 12, 228-237.

Luikart, G.L., Sherwin, W.B., Steele, B.M., & Allendorf, F.W. (1998b) Distortion of allele frequency distributions provides a test for recent population bottlenecks. Journal of Heredity, 89, 238-247.

Lynch, M., Conery, J., & Bürger, R. (1995) Mutation accumulation and the extinction of small populations. American Naturalist, 146, 489-518.

MacArthur, R. & Wilson, E.O. (1963) An equilibrium theory of Insular Biogeography. Evolution, 17, 373-383.

Macdonald, D.W. (1983) The ecology of carnivore social behaviour. Nature, 301, 379-384.

MacQueen, P.E., Nicholls, J.A., Hazlitt, S.L., & Goldizen, A.W. (2008) Gene flow among native bush rat, Rattus fuscipes (Rodentia: Muridae), populations in the fragmented subtropical forest of south-east Queensland. Austral Ecology, 33, 585-593.

Magurran, A.E. (1988) Ecological diversity and its measurement Princeton University Press, Princeton, N.J., U.S.A.

References - 164 -

Magurran, A.E. (2004) Measuring Biological Diversity Blackwell Science, Oxford, U.K.

Manel, S., Gaggiotti, O.E., & Waples, R.S. (2005) Assignment methods: matching biological questions with appropriate techniques. Trends in Ecology & Evolution, 20, 136-142.

Manson, R.H., Ostfeld, R.S., & Canham, C.D. (1999) Responses of a small mammal community to heterogeneity along forest-old-field edges. Landscape Ecology, 14, 355-367.

Marcello, G.J., Wilder, S.M., & Meikle, D.B. (2008) Population dynamics of a generalist rodent in relation to variability in pulsed food resources in a fragmented landscape. Journal of Animal Ecology, 77, 41-46.

Marchesan, D. & Carthew, S.M. (2004) Autecology of the yellow-footed antechinus (Antechinus flavipes) in a fragmented landscape in southern Australia. Wildlife Research, 31, 273-282.

Marshall, T.C., Slate, J., Kruuk, L.E.B., & Pemberton, J.M. (1998) Statistical confidence for likelihood-based paternity inference in natural populations. Molecular Ecology, 7, 639-655.

Martin, J.K. & Handasyde, K.A. (2007) Comparison of bobuck (Trichosurus cunninghami) demography in two habitat types in the Strathbogie Ranges, Australia. Journal of Zoology, 271, 375-385.

Martinez, J.G., Burke, T., Dawson, D., Soler, J.J., Soler, M., & Moller, A.P. (1998) Microsatellite typing reveals mating patterns in the brood parasitic great spotted cuckoo (Clamator glandarius) Molecular Ecology, 7, 289-297.

Matocq, M.D. (2004) Reproductive success and effective population size in woodrats (Neotoma macrotis). Molecular Ecology, 13, 1635-1642.

References - 165 -

McDonald, W.R. & St. Clair, C.C. (2004) The effect of artificial and natural barriers on the movement of small mammals in Banff National Park. Oikos, 105, 397- 407.

McEachern, M.B., McElreath, R.L., Van Vuren, D.H., & Eadie, J.M. (2009) Another genetically promiscuous ‘polygynous’ mammal: mating system variation in Neotoma fuscipes. Animal Behaviour, 77, 449-455.

McNeeley, J.A., Miller, K.R., Reid, W.V., Mittermeier, R.A., & Werner, T.B. (1990) Conserving the world's biological diversity. World Conservation Union, World Resources Institute, Conservation International, World Wildlife Fund-US and the World Bank, Washington, D.C., U.S.A.

Mech, S.H. & Hallett, J.G. (2001) Evaluating the effectiveness of corridors: a genetic approach. Conservation Biology, 15, 467-474.

Menkhorst, P. & Knight, F. (2004) A Field Guide to the Mammals of Australia Oxford University Press, South Melbourne, VIC. Australia.

Mesquita, R.C.G., Delamônicaa, P., & Laurance, W.F. (1999) Effect of surrounding vegetation on edge-related tree mortality in Amazonian forest fragments. Biological Conservation, 91, 129-134.

Meyer, C.F.J., Kalko, E.K.V., & Kerth, G. (2009) Small-Scale Fragmentation Effects on Local Genetic Diversity in Two Phyllostomid Bats with Different Dispersal Abilities in Panama. Biotropica, 41, 95-102.

Mhemmed, G., Kamel, H., & Chedly, A. (2008) Does habitat fragmentation reduce genetic diversity and subpopulation connectivity? Ecography, 31, 751-756.

Michalakis, Y. & Excoffier, L. (1996) A Generic Estimation of Population Subdivision Using Distances Between Alleles With Special Reference for Microsatellite Loci. Genetics, 142, 1061-1064.

Milinski, M. (2006) The Major Histocompatibility Complex, Sexual Selection and Mate Choice. Annual Review of Ecology and Systematics, 37, 159-186.

References - 166 -

Mills, L.S. & Smouse, P.E. (1994) Demographic consequences of inbreeding in remnant populations. The American Naturalist, 144, 412-431.

Mitrovski, P., Hoffmann, A.A., Heinze, D.A., & Weeks, A.R. (2008) Rapid loss of genetic variation in an endangered possum. Biology Letters, 4, 134-138.

Moore, L.A. (1995). Giant white-tailed rat. In The Mammals of Australia (eds R. Strahan), pp. 638-640. Reed Books, Sydney, N.S.W., Australia.

Mossman, C.A. & Waser, P.M. (2001) Effects of habitat fragmentation on population genetic structure in the white-footed mouse (Peromyscus leucopus). Canadian Journal of Zoology, 79, 285-295.

Murcia, C. (1995) Edge effects in fragmented forests: implications for conservation. Trends in Ecology & Evolution, 10, 58-62.

Nagy, M., Heckel, G., Voigt, C.C., & Mayer, F. (2007) Female-biased dispersal and patrilocal kin groups in a mammal with resource-defence polygyny. Proceedings of the Royal Society of London B., 274, 3019-3025.

Nei, M. (1987) Molecular Evolutionary Genetics Columbia University Press, New York, N.Y., U.S.A.

Nix, H.A. & Switzer, M.A. (1991) Rainforest Animals: Atlas of Vertebrates Endemic to Australia’s Wet Tropics. Kowari 1. Australian National Parks and Wildlife Service, Canberra. A.C.T., Australia.

Nordyke, K.A. & Buskirk, S.W. (1991) Southern Red-backed Vole, Clethrionomys gapperi, populations in relation to stand succession and old-growth character in the central Rocky Mountains. Canadian Field-Naturalist, 105, 330-334.

Noss, R.F. & Csuti, B. (1997). Habitat fragmentation. In Principles of conservation biology (eds G.K. Meffe & C.R. Carroll), pp. 269-304. Sinauer, Sunderland, MS. U.S.A.

References - 167 -

Nunney, L. & Elam, D.R. (1994) Estimating the Effective Population Size of Conserved Populations. Conservation Biology, 8, 175-184.

Nupp, T.E. & Swhihart, R.K. (2000) Landscape-level correlates of small mammal assemblages in forest fragments of farmland. Journal of Mammalogy, 81, 512-526.

Nupp, T.E. & Swihart, R.K. (1996) Effect of forest patch area on population attributes of white-footed mice (Peromyscus leucopus) in fragmented landscapes. Canadian Journal of Zoology, 74, 467-472.

Nyström, V., Angerbjörn, A., & Dalén, L. (2006) Genetic consequences of a demographic bottleneck in the Scandinavian arctic fox. Oikos, 114, 84-94.

Orland, M.C., Kelt, D.A. (2007) Responses of a Heteromyid Rodent Community to Large- and Small-Scale Resource Pulses: Diversity, Abundance and Home- range Dynamics. Journal of Mammalogy, 88, 1280-1287.

Ostfeld, R.S. (1985) Limiting Resources and Territoriality in Microtine Rodents. The American Naturalist, 126, 1-15.

Ostfeld, R.S. (1992). Small mammal herbivores in a patchy environment: individual strategies and population responses. In Effect of Resource Distrubution on Animal Plant Interactions (eds M.D. Hunter, T. Oghushi & P.M. Price), pp. 43-74. Academic Press, San Diego, CA, U.S.A.

Ostfeld, R.S., Manson, R.H., & Canham, C.D. (1997) Effects of rodents on survival of tree seeds and seedlings invading old fields. Ecology, 78, 1531-1542.

Ostner, J., Nunn, C.L., & Schülke, O. (2008) Female reproductive synchrony predicts skewed paternity across primates. Behavioral Ecology, 19, 1150- 1158.

Packer, C., Collins, D.A., & Eberly, L.E. (2000) Problems with primate sex ratios. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 355, 1627-1635.

References - 168 -

Paetkau, D., Calvert, W., Stirling, I., & Strobeck, C. (1995) Micrsatellite analysis of population structure in Canadian polar bears. Molecular Ecology, 7, 1283- 1292.

Paetkau, D., Slade, R., Burden, M., & Estoup, A. (2004) Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation- based exploration of accuracy and power. Molecular Ecology, 13, 55-65.

Palmer, A.R. (2000) Quasireplication and the contract of error: lessons from sex ratios, heritabilities and fluctuating asymmetry. Annual Review of Ecology and Systematics, 31, 441-480.

Pardini, R., Marques de Souza, S., Braga-Neto, R., & Metzger, J.P. (2005) The role of forest structure, fragment size and corridors in maintaining small mammal abundance and diversity in an Atlantic forest landscape. Biological Conservation, 24, 253-266.

Parker, G.A. (1998). Sperm competition and the evolution of ejaculates: towards a theory base. In Sperm competition and sexual selection (eds T.R. Birkhead & A.P. Møller), pp. 3–54. Academic Press, London, U.K.

Parmenter, R.R., Yates, T.L., Anderson, D.R., Burnham, K.P., Dunnum, J.L., Franklin, A.B., Friggens, M.T., Lubow, B.C., Miller, M., Olson, G.S., Parmenter, C.A., Pollard, J., Rexstad, E., Shenk, T.M., Stanley, T.R., & White, G.C. (2003) Small-mammal density estimation: A field comparison of grid-based vs. web-based density estimators. Ecological Monographs, 73, 1- 26.

Peacock, M.M. & Smith, A.T. (1997a) The effect of habitat fragmentation on dispersal patterns, mating behavior, and genetic variation in a pika (Ochotona princeps) metapopulation. Oecelogia, 112, 524-533.

Peacock, M.M. & Smith, A.T. (1997b) Nonrandom mating in pikas Ochotona princeps: evidence for inbreeding between individuals of intermediate relatedness. Molecular Ecology, 6, 801-811.

References - 169 -

Peakall, R., Ruibal, M., & Lindenmeyer, D.B. (2003) Spatial autocorrelation analysis offers new insights into gene flow in the Australian bush rat, Rattus fuscipes. Evolution, 57, 1182-1195.

Peakall, R. & Smouse, P.E. (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes, 6, 288-295.

Pearse, D.E., Arndt, A.D., Valenzuela, N., Miller, B.A., Cantarelli, V., & Sites, J.W., Jr (2006) Estimating population structure under nonequilibrium conditions in a conservation context: continent-wide population genetics of the giant Amazon river turtle, Podocnemis expansa (Chelonia; Podocnemididae). Molecular Ecology, 15, 985-1006.

Peel, D., Ovenden, J.R., & Peel, S.L. (2004) NEESTIMATOR: software for estimating effective population size, Version 1.3. Queensland Government, Department of Primary Industries and Fisheries, St Lucia, QLD, Australia.

Penn, D.J., Damjanovich, K., & Potts, W.K. (2002) MHC heterozygosity confers a selective advantage against multiple-strain infections. Proceedings of the National Academy of Sciences USA, 99, 11260-11264.

Penn, D.J. & Potts, W.K. (1999) The Evolution of Mating Preferences and Major Histocompatibility Complex Genes. American Naturalist, 153, 145-164.

Piessens, K., Adriaens, D., Jacquemyn, H., & Honnay, O. (2009) Synergistic effects of an extreme weather event and habitat fragmentation on a specialised insect herbivore. Oecologia, 159, 117-126.

Piggott, M.P., Banks, S.C., & Taylor, A.C. (2006) Population structure of brush- tailed rock-wallaby (Petrogale pencillata) colonies inferred from analysis of faecal DNA. Molecular Ecology, 15, 93-105.

Piry, S., Alapetite, A., Cornuet, J.-M., Paetkau, D., Baudouin, L., & Estoup, A. (2004) GENECLASS2: A Software for Genetic Assignment and First- Generation Migrant Detection. Journal of Heredity, 95, 536-539.

References - 170 -

Piry, S., Luikart, G.L., & Cornuet, J.-M. (1999) BOTTLENECK: A Computer Program for Detecting Recent Reductions in the Effective Population Size from Allele Frequency Data. Journal of Heredity, 90, 502-503.

Pollard, J.H. (1971) On distance estimators of density in randomly distributed forests. Biometrics, 27, 991-1002.

Pollock, K.H., Nichols, J.D., Brownie, C., & Hines., J.E. (1990) Statistical inference for capture-recapture experiments. Wildlife Monograph, 107, 1-97.

Potts, W.K. & Wakeland, E.K. (1990) Evolution of Diversity at the Major Histocompatibility Complex. Trends in Ecology & Evolution, 5, 181-187.

Price, K. & Boutin., S. (1993) Territorial bequeathal by red squirrel mothers. Behavioral Ecology, 4, 144-150.

Primmer, C.R., Saino, N., Møller, A.P., & Ellegren, H. (1998) Unraveling the process of microsatellite evolution through analysis of germ line mutations in barn swallows Hirundo rustica. Molecular Biology and Evolution, 15, 1047-1054.

Przybylski, M., Reichard, M., Spence, R., & Smith, C. (2007) Spatial distribution of oviposition sites determines variance in the reproductive rate of European bitterling (Rhodeus amarus). Behaviour, 144, 1403-1417.

Pudovkin, A.I., Zaykin, D.V., & Hedgecock, D. (1996) On the potential for estimating the effective number of breeders from heterozygote excess in progeny. Genetics, 383-387.

Pugh, S.R. & Tamarin, R.H. (1988) Inbreeding in a population of meadow voles, Microtus pennsylvanicus. Canadian Journal of Zoology, 66, 1831-1934.

Pusey, A. & Wolf, M. (1996) Inbreeding avoidance in animals. Trends in Ecology & Evolution, 11, 201-206.

Pusey, A.E. (1987) Sex-biased dispersal and inbreeding avoidance in birds and mammals. Trends in Ecology and Evolution, 2, 295-299.

References - 171 -

Queller, D.C. & Goodnight, K.F. (1989) Estimating Relatedness Using Genetic Markers. Evolution, 43, 258-275.

Queller, D.C., Strassman, J.E., & Hughes, C.R. (1993) Microsatellites and Kinship. Trends in Ecology & Evolution, 8, 285-288.

Quinn, G.P. & Keough, M.J. (2002) Experimental Design and Data Analysis for Biologists Cambridge University Press, Cambridge, U.K.

Ralls, K., Ballou, J.D., & Templeton, A. (1988) Estimates of lethal equivalents and the cost of inbreeding in mammals. Conservation Biology, 2, 185-193.

Ramm, S.A. & Stockley, P. (2007) Ejaculate allocation under varying sperm competition risk in the house mouse, Mus musculus domesticus. Behavioral Ecology, 18, 491-495.

Ramsey, D., Efford, M., Cowan, P., & Coleman, J. (2002) Factors influencing annual variation in breeding by common brushtail possums (Trichosurus vulpecula) in New Zealand. Wildlife Research, 29, 39-50.

Ransome, D.B. & Sullivan, T.P. (1997) Food limitation and habitat preference of Glaucomys sabrinus and Tamiasciurus hudsonicus. Journal of Mammalogy, 78, 538-549.

Ransome, D.B. & Sullivan, T.P. (2004) Effects of food and den-site supplementation on populations of Glaucomys sabrinus and Tamiasciurus douglasii. Journal of Mammalogy, 85, 206-215.

Raska-Jurgiel, E. (1992) Demography of woodland rodents in fragmented habitat. Acta Theriologica, 37, 73-90.

Raymond, M. & Rousset., F. (1995) Population genetics software for exact test and eumenicism. Journal of Heredity, 86, 248-249.

References - 172 -

Read, V.T., Malafani, K.W.J., & Myers, K. (1988) A Comparison of Grid and Index- line Trapping Methods for Small Mammals Surveys. Australian Wildlife Research, 15, 673-687.

Redeker, S., Andersen, L.W., Pertoldi, C., Madsen, A.B., Jensen, T.S., & Jørgensen, J.M. (2006) Genetic structure, habitat fragmentation and bottlenecks in Danish bank voles (Clethrionomys glareolus). Mammalian Biology, 71, 144-158.

Reed, D.H. & Frankham, R. (2003) Correlation between fitness and genetic diversity. Conservation Biology, 17, 230-237.

Reino, L., Bejab, P., Osbornec, P.E., Morgadoe, R., Fabiá, A., & Rotenberry, J.T. (2009) Distance to edges, edge contrast and landscape fragmentation: Interactions affecting farmland birds around forest plantations. Biological Conservation, 142, 824-838.

Rennolls, K. & Laumonier, Y. (2000) Species diversity structure analysis at two sites in the tropical rain forest of Sumatra. Journal of Tropical Ecology, 16, 253- 270.

Reynolds-Hogland, M.J., Pacifici, L.B., & Mitchell, M.S. (2007) Linking resources with demography to understand resource limitation for bears. Journal of Applied Ecology, 44, 1166-1175.

Reynolds, J.D. (1996) Animal breeding systems. Trends in Ecology & Evolution, 11, 68-72.

Ribble, D.O. (1991) The monogamous mating system of Peromyscus californicus as revealed by DNA fingerprinting. Behavioral Ecology and Sociobiology, 29, 161-166.

Ribble, D.O. (1992) Lifetime reproductive success and its correlates in the monogamous rodent, Peromyscus californicus. Journal of Animal Ecology, 61, 457-468.

References - 173 -

Rice, W.R. (1989) Analyzing table of statistical tests. Evolution, 43, 223-225.

Robinson, G.R., Holt, R.D., Gaines, M.S., Hamburg, S.P., Johnson, M.L., Fitch, H.S., & Martinko, E.A. (1992) Diverse and contrasting effects of habitat fragmentation. Science, 257, 524-526.

Robinson, S.K., Thompson, P.R., III, Donovan, T.M., Whilchcad, D.R., & Faaborg, J. (1995) Regional forest fragmentation and the nesting success of migratory birds. Science, 267, 1987-1990.

Rubenstein, D.I. (1980). On the evolution of alternative mating strategies. In Limits to Action: The Allocation of Individual Behavior (ed E.R. Staddon), pp. 65- 100. Academic Press, New York, N.Y., U.S.A.

Rudd, R.L. (1979) Is the tropical small mammal collector a fruiting tree. Malayan Nature Journal, 33, 71-74.

Saccheri, I., Kuussaari, M., Kankare, M., Vikman, P., Fortelius, W., & Hanski, I. (1998) Inbreeding and extinction in a butterfly metapopulation. Nature, 392, 491-494.

Sainudiin, R., Durrett, R.T., Aquadro, C.F., & Nielsen, R. (2004) Microsatellite Mutation Models: Insights from a Comparison of Humans and Chimpanzees. Genetics, 168, 383-395.

Sambrook, J., Fritsch, E.F., & Maniatis, T. (1989) Molecular Cloning: a laboratory manual, 2nd edn. Cold Spring Harbor Laboratory Press, New York, N.Y., U.S.A.

Sarre, S.D. & Georges, A. (2009) Genetics in conservation and wildlife management: a revolution since Caughley. Wildlife Research, 36, 70-80.

Saunders, D.A., Hobbs, R.J., & Margules, C.R. (1991) Biological consequences of ecosystem fragmentation; a review. Conservation Biology, 5, 18-32.

References - 174 -

Schad, J., Sommer, S., & Ganzhorn, J.U. (2004) MHC Variability of a Small Lemur in the Littoral Forest Fragments of Southeastern Madagascar. Conservation Genetics, 5, 299-309.

Schlötterer, C. (1998). Microsatellites. In Molecular Genetic Analysis of Populations: A practical approach (ed A.R. Hoelzel), pp. 237-261. Oxford University Press, Oxford, U.K.

Schlötterer, C. (2000) Evolutionary dynamics of microsatellite DNA. Chromosoma, 109, 365-371.

Schmid-Holmes, S. & Drickamer, L.C. (2001) Impact of forest patch characteristics on small mammal communities: a multivariate approach. Biological Conservation, 99, 293-305.

Schnurr, J.L., Ostfeld, R.S., & Canham, C.D. (2002) Direct and indirect effects of masting on rodent populations and tree seed survival. Oikos, 96, 402-410.

Schradin, C. & Pillay, N. (2005) Intraspecific variation in the spatial and social organization of the african striped mouse. Journal of Mammaology, 86, 99- 107.

Schoener, T.W. (1986). Patterns in terrestrial vertebrate versus arthropod communities: do systematic differences in regularity exist? In Community Ecology (eds J. Diamond & T.J. Crase), pp. 556-586. Harper & Row, New York, N.Y., U.S.A.

Schulte-Hostedde, A.I., Millar, J.S., & Hickling, G.J. (2001) Evaluating body condition in small mammals. Canadian Journal of Zoology, 79 1021-1029.

Schwagmeyer, P.L., Coogins, K.A., & Lamey, T.C. (1987) The effects of sperm competition on variability in male reproductive success: some preliminary analyses. American Naturalist, 130, 485-492.

Schwartz, M.K., Tallmon, D.A., & Luikart, G.L. (1998) Review of DNA-based census and effective population size estimators. Animal Conservation, 1, 293-299.

References - 175 -

Schwartz, M.K., Tallmon, D.A., & Luikart, G.L. (1999) Using genetics to estimate the size of wild populations: many methods, much potential, uncertain utility. Animal Conservation, 2, 321-323.

Seamon, J.O. & Adler, G.H. (1996) Population performance of generalist and specialist rodents along habitat gradients. Canadian Journal of Zoology, 74, 1130-1139.

Segelbacher, G., Manel, S., & Tomiuk, J. (2008) Temporal and spatial analyses disclose consequences of habitat fragmentation on the genetic diversity in capercaillie (Tetrao urogallus). Molecular Ecology, 17, 2356-2367.

Selkoe, K.A. & Toonen, R.J. (2006) Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecology Letters, 9, 615-629.

Shepard, B.F. & Swihart, R.K. (1995) Spatial dynamics of fox squirrels (Sciurus niger) in fragmented landscapes. Canadian Journal of Zoology, 73, 2098- 2105.

Sherry, T.W. (2008) Community approach to tropical forest fragmentation gets us closer to understanding mechanisms by revealing strong indirect effects. Animal Conservation, 11, 361-363.

Shine, R. & Brown, G.P. (2008) Adapting to the unpredictable: reproductive biology of vertebrates in the Australian wet–dry tropics. Philosophical Transactions of the Royal Society B., 363, 363-373.

Shurtliff, Q.R., Pearse, D.E., & Rogers, D.S. (2005) Parentage Analysis Of The Canyon Mouse (Peromyscus crinitus): Evidence For Multiple Paternity Journal of Mammalogy, 86, 531-540.

Singh, K.P. & Kushwaha, C.P. (2005) Emerging paradigms of tree phenology in dry tropics. Current Science, 89, 964-975.

References - 176 -

Singh, K.P. & Kushwaha, C.P. (2006) Diversity of Flowering and Fruiting Phenology of Trees in a Tropical Deciduous Forest in India. Annals of Botany, 97, 265- 276.

Slade, N.A. & Blair, S. (2000) An emperical test of using counts of individuals captured as indicies of population size. Journal of Mammalogy, 81, 1035- 1045.

Slate, J., Marshall, T.C., & Pemberton, J.M. (2000) A retrospective assessment of the accuracy of the paternity inference program CERVUS. Molecular Ecology, 9, 801-808.

Slatkin, M. (1985) Gene flow in natural populations. Annual Review of Ecology and Systematics, 16, 393-340.

Smith, J.N.M. & Hellmann, J.J. (2002) Population persistence in fragmented landscapes. Trends in Ecology & Evolution, 17, 397-399.

Smith, W.P. & Nichols, J.V. (2004) Demography of two endemic forest-floor mammals of southeastern Alaska temperate rainforest. Journal of Mammalogy, 85, 540-551.

Smouse, P.E. & Peakall, R. (1999) Spatial autocorrelation analysis of multiallele and multi-locus genetic micro-structure. Heredity, 82, 561-573.

Sokal, R.R. & Rohlf, F.J. (1995) Biometry, Third Edition edn. W. H. Freeman and Company, New York, N.Y., U.S.A.

Solomon, N.G. (1993) Body size and social preferences of male and female prairie voles, Microtus ochrogaster. Animal Behaviour, 45, 1031-1033.

Sommer, S. (2003). Social and reproductive monogamy in rodents: the case of the Malagasy giant jumping rat (hypogeomys antimena). In Monogamy: Mating Strategies and Partnerships in Brids, Humans and other Mammals (eds U.H. Reichard & C. Boesch), pp. 109-124. Cambridge University Press, Cambridge, U.K.

References - 177 -

Sommer, S. (2005) Major histocompatibility complex and mate choice in a monogamous rodent. Behavioral Ecology & Sociobiology, 58, 181-189.

Sommer, S. & Tichy, H. (1999) Major histocompatibility complex (MHC) class II polymorphism and paternity in the monogamous Hypogeomys antimena, the endangered, largest endemic Malagasy rodent. Molecular Ecology, 8, 1259- 1272.

Sorvari, J. & Hakkarainen, H. (2007) Forest clearing and sex ratio in forest-dwelling wood ant Formica aquilonia. Naturwissenschaften, 94, 392-395.

Soulé, M.E. (1986) Conservation biology: the science of scarcity and diversity. Sinauer, Sunderland, Sunderland, MS, U.S.A.

Southern, H.N. (1973) A yardstick for measuring populations of small rodents. Mammal Review, 3, 1-10.

Spielman, D., Brook, B.W., & Frankham, R. (2004) Most species are not driven to extinction before genetic factors impact them. Proceedings of the National Academy of Sciences, 101, 15261-15264.

Srikwan, S. & Woodruff, D.S. (2000). Genetic erosion in isolated small-mammal populations following rainforest fragmentation. In Genetics, Demography and Viability of Fragmented Populations (eds A.G. Young & G.M. Clarke). Cambridge University Press, Cambridge, U.K.

StatSoft (2004) STATISTICA data analysis software system. Version 7. www.statsoft.com.

Steinberg, E.K. & Jordan., C.E. (1997). Using molecular genetics to learn about the ecology of threatened species: The allure and illusion of measuring genetic structure in natural populations. In Conservation Biology. For the Coming Decade (eds P.L. Fiedler & P.M. Kareiva), pp. 440-460. Chapman and Hall, New York, N.Y., U.S.A.

References - 178 -

Stocker, G.C., Thompson, W.A., Irvine, A.K., Fitzsimon, J.D., & Thomas, P.R. (1995) Annual Patterns of Litterfall in a Lowland and Tableland Rainforest in Tropical Australia. Biotropica, 27, 412-420.

Stockley, P. & Preston, B.T. (2004) Sperm competition and diversity in rodent copulatory behaviour. Journal of Evolutionary Biology, 17, 1048-1057.

Storz, J.F., Bhat, H.R., & Kunz, T.H. (2001) Genetic consequences of polygyny and social structure in an Indian fruit bat, Cynopterus sphinx. II. Variance in male mating success and effective population size. Evolution, 55, 1224-1232.

Stow, A.J. & Briscoe, D.A. (2005) Impact of habitat fragmentation on alleleic diversity at microsatellite loci in Cunningham's skink (Egernia cunninghhamii); a preliminary study. Conservation Genetics, 6, 455-459.

Stow, A.J. & Sunnucks, P. (2004) Inbreeding avoidance in Cunningham’s skinks (Egernia cunninghami) in natural and fragmented habitat. Molecular Ecology, 13, 443-447.

Stow, A.J., Sunnucks, P., Briscoe, D.A., & Gardner, M.G. (2001) The impact of habitat fragmentation on dispersal of Cunningham's skink (Egernia cunninghammii): evidence from allelic and genotypic analyses of microsatellites. Molecular Ecology, 10, 867-878.

Strahan, R. (1983) Complete book of Australian mammals Angus and Robertson, Sydney, N.S.W., Australia.

Stutchbury, B.J.M. (2007) The effects of habitat fragmentation on animals: gaps in our knowledge and new approaches. Canadian Journal of Zoology, 85, 1015-1016.

Sugg, D.W., Chesser, R.K., Dobson, F.S., & Hoogland, J.L. (1996) Population genetics meets behavioural ecology. Trends in Ecology & Evolution, 11, 338-342.

References - 179 -

Sumner, J. (2005) Decreased relatedness between male prickly forest skinks (Gnypetoscincus queenslandiae) in habitat fragments. Conservation Genetics, 6, 333-340.

Sumner, J., Jessop, T., Paetkau, D., & Mortiz, C. (2004) Limited effect of anthropogeneic habitat fragmentation on molecular diversity in a rain forest skink, Gnypetoscincus queenslandiae. Molecular Ecology, 13, 259-269.

Sun, L. (2003). Monogamy correlates, socioecological factors, and mating systems in beavers. In Monogamy: Mating Strategies and Partnerships in Brids, Humans and Other Mammals (eds U.H. Reichard & C. Boesch), pp. 138- 146. Cambridge Univerity Press, Cambridge, U.K.

Sunnucks, P. (2000) Efficient genetic markers for population biology. Trends in Ecology and Evolution, 15, 199-203.

Sutcliffe, O.L. & Thomas, C.D. (1996) Open Corridors Appear to Facilitate Dispersal by Ringlet Butterflies (Aphantopus hyperantus) between Woodland Clearings. Conservation Biology, 10, 1359-1365.

Sutherland, D.R. & Predavec, M. (1999) The effects of moonlight on microhabitat use by Antechinus agilis (Marsupialia: Dasyuridae). Australian Journal of Zoology, 47, 1-17.

Sutherland, W.J. (1998) The importance of behavioural studies in conservation biology. Animal Behaviour, 56, 801-809.

Sweitzer, R.A. & Berger, J. (1998) Evidence for female-biased dispersal in North American porcupines (Erethizon dorsatum). Journal of Zoology, 244, 159- 166.

Tabarelli, M., Mantovani, W., & Peres, C.A. (1999) Effects of habitat fragmentation on plant guild structure in the montane Atlantic forest of southeastern Brazil. Biological Conservation, 91, 1999.

References - 180 -

Tallmon, D.A., Draheim, H.M., Mills, S., & Allendorf, F.W. (2002) Insights into recently fragmented vole populations from combined genetic and demographic data. Molecular Ecology, 11, 699-709.

Tallmon, D.A., Luikart, G., & Beaumont, M.A. (2004) Comparative Evaluation of a New Effective Population Size Estimator Based on Approximate Bayesian Computation. Genetics, 167, 977-988.

Taylor, A.C., Horsup, A., Johnson, C.N., Sunnucks, P., & Sherwin, B. (1997) Relatedness structure detected by microsatellite analysis and attempted pedigree reconstruction in an endangered marsupial, the northern hairy- nosed wombat Lasiorhinus krefftii. Molecular Ecology, 6, 9-19.

Temple, H.J., Hoffman, J.I., & Amos, W. (2006) Dispersal, philopatry and intergroup relatedness: fine-scale genetic structure in the white-breasted thrasher, Ramphocinclus brachyurus. Molecular Ecology, 15, 3449-3458.

Theimer, T.C. (2001) Seed scatterhoarding by white-tailed rats: consequences for seedling recruitment by an Australian rain forest tree. Journal of Tropical Ecology, 17, 177-189.

Theimer, T.C. (2003) Intraspecific variation in seed size affects scatterhoarding behaviour of an Australian tropical rain-forest rodent. Journal of Tropical Ecology, 19, 95-98.

Theodorou, K., Souan, H., & Couvet, D. (2009) Metapopulation persistence in fragmented landscapes: significant interactions between genetic and demographic processes. Journal of Evolutionary Biology, 22, 152-162.

Thomas, C.D., Cameron, A., Green, R.E., Bakkenes, M., Beaumont, L.J., Collingham, Y.C., Erasmus, B.F.N., de-Sequeira, M.F., Grainger, A., Hannah, L., Hughes, L., Huntley, B., Jaarsveld, A.S.v., Midgley, G.F., Miles, L., Ortega-Huerta, M.A., Peterson, A.T., Phillips, O.L., & Williams, S.E. (2004) Extinction risk from Climate change. Nature, 427, 145-148.

References - 181 -

Topping, M.G. & Millar, J.S. (1998) Mating patterns and reproductive success in the bushy-tailed woodrat ( Neotoma cinerea ), as revealed by DNA fingerprinting. Behavioral Ecology and Sociobiology, 43, 115-124.

Tracey, J.G. & Webb, L.J. (1975) Key to the vegetation of the humid tropical region of North Queensland, with 15 maps at 1:100,000 scale CSIRO Long Pocket Laboratories, Indooroopilly, QLD, Australia.

Travis, S.E., Slobodchikoff, C.N., & Keim, P. (1995) Ecological and demographic effects on intraspecific variation in the social system of prairie dogs. Ecology, 76, 1794-1803.

Terborgh, J., Foster, R.B., & Nunez, V.P. (1996) Tropical tree communities: a test of the non equilibrium hypothesis. Ecology, 77, 561-567.

Trizio, I., Crestanello, B., Galbusera, P., Wauters, L.A., Tosi, G., Matthysen, E., & Hauffe, H.C. (2005) Geographical distance and physical barriers shape the genetic structure of Eurasian red squirrels (Sciurus vulgaris) in the Italian Alps. Molecular Ecology, 14, 469-481.

Tucker, N.I.J. (2000a) Linkage restoration: Interpreting fragmentation theory for the design of a rainforest linkage in the humid Wet Tropics of north-eastern Queensland. Ecological Management and Restoration, 1, 35-41.

Tucker, N.I.J. (2000b) Evaluating effectiveness of restored habitat linkages at Donaghy's corridor: Measures used and questions asked. Ecological Management and Restoration, 1, 61-62.

Tucker, N.I.J. & Murphy, T.M. (1997) The effects of ecological rehabilitation on vegetation recruitment: some observations from the Wet Tropics of North Queensland. Forest Ecology and Management, 99, 133-152.

Van Apeldoorn, R.C., Oostenbrink, W.T., Van Winden, A., & Van Der Zee, F.F. (1992) Effects of Habitat Fragmentation on the Bank Vole, Clethrionomys glareolus, in an Agricultural Landscape. Oikos, 65, 265-274.

References - 182 -

Van Horne, B. (1981) Demography of Peromyscus maniculatus populations in seral stages of coastal coniferous forest in southeast Alaska. Canadian Journal of Zoology, 59, 1045-1061.

Van Oosterhout, C., Hutchinson, W.F., Wills, D.P.M., & Shipley, P. (2004) MICRO- CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes, 4, 535-538.

Van Staden, M.J., Michener, G.R., & Chesser, R.K. (1996) Spatial analysis of microgeographic structure in Richardson's ground squirrels. Canadian Journal of Zoology, 74, 1187-1195.

Vaughan, T.A. & Czaplewski, N.J. (1985) Reproduction in Stephens' Woodrat: the wages of folivory. Journal of Mammalogy, 66, 429-443.

Vergara, P.M. & Armesto, J.J. (2009) Responses of Chilean forest birds to anthropogenic habitat fragmentation across spatial scales. Landscape Ecology, 24, 25-38.

Vernes, K. (2003) Fine-scale habitat preferences and habitat partitioning by three mycophagous mammals in tropical wet sclerophyll forest, northeastern Australia. Austral Ecology, 28, 471-479.

Vieira, M.V., Grelle, C.E.V., & Gentile, R. (2004) Differential trappability of small mammals in three habitats of southeastern Brazil. Brazilian Journal of Biology, 64, 895-900.

Vignieri, S.N. (2007) Cryptic behaviours, inverse genetic landscapes, and spatial avoidance of inbreeding in the Pacific jumping mouse. Molecular Ecology, 16, 853-866.

Walker, F.M., Sunnucks, P., & Taylor, A.C. (2008) Evidence for habitat fragmentation altering within-population processes in wombats. Molecular Ecology, 17, 1674-1684.

References - 183 -

Walters, J.R., Ford, H.A., & Cooper, C.B. (1999) The ecological basis of sensitivity of brown treecreepers to habitat fragmentation: a preliminary assessment. Biological Conservation, 90, 13-20.

Wang, J. (2005) Estimation of effective population sizes from data on genetic markers. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 360, 1395-1409.

Waples, R.S. (1989) A generalized approach for estimating effective population sixe from temporal changes in allele frequency. Genetics, 121, 379-391.

Waples, R.S. (1991). Genetic methods for estimating the effective size of cetacean populations. In Genetic Ecology of Whales and Dolphins (eds A.R. Hoelzel), pp. 279-300. International Whaling Commission, Special Issue 13, Cambridge, U.K.

Waples, R.S. (2005) Genetic estimates of contemporary effective population size: to what time periods do the estimates apply? Molecular Ecology, 14, 3335- 3352.

Waples, R.S. (2006) A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conservation Genetics, 7, 167-184.

Warburton, N.H. (1997). Remnants in tropical Australia. In Tropical Forest Remnants: Ecology, Management and Conservation of Fragmented Communities (eds W.F. Laurance & R.O. Bierregaard). University of Chicago Press, Chicago, IL, U.S.A.

Waser, P.M., Austad, S.N., & Keane, B. (1986) When should animals tolerate inbreeding? American Naturalist, 128, 529-537.

Watts, C.H.S. (1977) The Foods Eaten by some Australian Rodents (Muridae). Australian Wildlife Research, 4, 151-157.

References - 184 -

Watts, C.H.S. & Aslin, H.J. (1981) The rodents of Australia Angus and Robertson, Sydney, N.S.W., Australia.

Weber, J.L. & Wong., C. (1993) Mutation of human short tandem repeats. Human Molecular Genetics, 2, 1123-1128.

Weir, B.S. & Cockerham, C.C. (1984) Estimating F-statistics for the analysis of population structure. Evolution, 38, 1358-1370.

Wellesley-Whitehouse, H.L. (1981) A study on the comparative ecologies of two rainforest murid rodents, Uromys caudimaculatus and Rattus fuscipes coriaius in North Queensland. Honours thesis, James Cook University, Townsville.

Westemeier, R.L., Brawn, J.D., Simpson, S.A., Esker, T.L., Jansen, R.W., Walk, J.W., Kershner, E.L., Bouzat, J.L., & Paige, K.N. (1998) Tracking the Long- Term Decline and Recovery of an Isolated Population. Science, 282, 1695- 1698.

Whiteman, E.A. & Côté, I.M. (2004) Dominance hierarchies in group-living cleaning gobies: causes and foraging consequences. Animal Behaviour, 67, 239-247.

Wiegand, T., Revilla, E., & Moloney, K.A. (2005) Effects of Habitat Loss and Fragmentation on Population Dynamics. Conservation Biology, 19, 108-121.

Wiens, J.A. (1995) Habitat fragmentation: island versus landscape perspectives on bird conservation. Ibis, 137, S97-S104.

Willi, Y., Van Buskirk, J., & Hoffmann, A.A. (2006) Limits to the Adaptive Potential of Small Populations. Annual Review of Ecology and Systematics, 37, 433- 458.

Williams, S.E. (1997) Patterns of Mammalian Species Richness in the Australian Tropical Rainforests: Are Extinctions during Historical Contractions of the Rainforest the Primary Determinants of Current Regional Patterns in Biodiversity? Wildlife Research, 24, 513-530.

References - 185 -

Williams, S.E., Bolitho, E.E., & Fox, S. (2003) Climate change in Australian tropical rainforests: an impending environmental catastrophe. Proceedings of the Royal Society of London, 270, 1887-1892.

Williams, S.E. & Marsh, H. (1998) Changes in small mammal assemblage structure across a rainforest/open forest ecotone. Journal of Tropical Ecology, 14, 187-198.

Williams, S.E., Pearson, R.G., & Walsh, P.J. (1996) Distributions and biodiversity of the terrestrial vertebrates of Australia's Wet Tropics: a review of current knowledge. Pacific Conservation Biology, 2, 327-362.

Williamson-Natesan, E.G. (2005) Comparison of methods for detecting bottlenecks from microsatellite loci. Conservation Genetics, 6, 551-562.

Winter, J.W. (1997) Responses of Non-volant Mammals to Late Quaternary Climatic Changes in the Wet Tropics Region of North-eastern Australia. Wildlife Research, 24, 493-511.

Winter, J.W., Bell, F.C., Pahl, L.I., & Atherton, G.G. (1987) Rainforest clearfelling in northeastern Australia. Proceedings of the Royal Society of Queensland, 98, 41-57.

Winter, J.W., Bell, F.C., Pahl, L.I., & Atherton, R.G. (1984). The Specific Habitats of Selected northeastern Australian Rainforest Mammals. In. Report to World Wildlife Fund, Sydney, N.S.W., Australia.

Wolff, J.O. (1997) Population Regulation in Mammals: An Evolutionary Perspective. Journal of Animal Ecology, 66, 1-13.

Wolff, J.O., Schauber, E.M., & Edge, W.D. (1997) Effects of Habitat Loss and Fragmentation on the Behaviour and Demography of Gray-Tailed Voles. Conservation Biology, 11, 945-956.

World Conservation Monitoring Centre, W.C.M.C. (1992) Global Biodiversity: Status of the Earth’s Living Resources Chapman & Hall, London, U.K.

References - 186 -

Wright, S. (1931) Evolution in Menfdelian populations. Genetics, 16, 97-158.

Wright, S. (1978) Evolution and the Genetics of Populations University of Chicago Press, Chicago, IL., U.S.A.

Wright, S.J. (2005) Tropical forests in a changing environment. Trends in Ecology & Evolution, 20, 553-560.

Wright, L.I., Tregenza, T., & Hosken, D.J. (2008) Inbreeding, inbreeding depression and extinction. Conservation Genetics, 9, 833-843.

Ylönen, H., Jacob, J., & Kotler, B.P. (2003) Trappability of rodents in single-capture and multiple capture traps in arid and open environments: Why don't Ugglan traps work? Ann. Zool. Fennici, 40, 537-541.

Young, A.G. & Clarke, G.M. (2000). Introduction: genetics, demography and the conservation of fragmented populations. In Genetics, Demography and Viability of Fragmented Populations (eds A.G. Young & G.M. Clarke). Cambridge University Press, Cambridge, U.K.

Yunger, J.A. (2002) Response of two low-density populations of Peromyscus leucopus to increased food availability. Journal of Mammalogy, 83, 267-279.

Zanette, L. (2000) Fragment size and the demography of an area-sensitive songbird. Journal of Animal Ecology, 69, 458-470.

Zanette, L., Doyle, P., & Tremont, S.M. (2000) Food shortage in small fragments: evidence from an area-sensitive passerine. Ecology, 81, 1654-1666.

Appendices - 187 -

Appendix 1: Family, species and number of individual trees species identified within each study site. Sites FAMILY Species P1 P2 P3 C1 Anacardiaceae Blepharocarya involucrigera - - - 8 Annonaceae Haplostichanthus johnsonii 1 4 4 2 Annonaceae Melodorum leichhardtii 1 - - - Annonaceae Polyalthia australis - - 2 4 Apocynaceae Melodinus australis - - - 2 Apocynaceae Neisosperma poweri - 2 3 1 Apocynaceae Cerbera inflata - - 1 - Araliaceae Polyscias elegans - 3 - - Arecaeae Calamus caryotoides 6 - - 1 Arecaeae Calamus moti 2 12 2 7 Corynocarpaceae Corynocarpus cribbianus - 4 - - Cunoniaceae Ceratopetalum succirubrum - - 4 1 Cunoniaceae Caldcluvia australiensis - - 1 - Cunoniaceae Geissois biagiana - - 1 - Cunoniaceae Gillbeea adenopetela - - 1 - Davidsoniaceae Davidsonia pruriens 1 - - 2 Dichapetalaceae Dichapetalum papuanum 6 4 - 1 Ebenaceae Diospyros cupulosa - 1 - - Elaegnaceae Elaeagnus triflora 1 1 - 1 Elaeocarpaceae Elaeocarpus angustifolius - 1 - - Elaeocarpaceae Elaeocarpus foveolatus - - - 1 Elaeocarpaceae Elaeocarpus ruminatus 1 1 - - Elaeocarpaceae Sloanea australis - 1 4 3 Euphorbiaceae Codiaeum varigatum - - 1 - Euphorbiaceae Glochidion harveyanum 1 1 - - Euphorbiaceae Glochidion hylandii - - 1 - Euphorbiaceae Macaranga subdentata 12 14 13 45 Euphorbiaceae Mallotus philippensis 4 - - - Euphorbiaceae Mallotus polyadenos 6 - - - Euphorbiaceae Mallotus sp. 1 - - - Euphorbiaceae Rockinghamia angustifolia - 1 - - Euphorbiaceae Aleurites rockinghamensis 1 - - - Eupomatiaceae Eupomatia laurina - 1 1 1 Fabaceae Castanosperma australe 45 1 - - Lauraceae Cryptocarya oblata - - 5 - Lauraceae Endiandra palmerstonii - - 1 - Lauraceae Beilschmiedia recurva - - 1 - Lauraceae Beilschmiedia tooram - 6 22 1 Lauraceae Bielschmedia sp.* - 1 - - Lauraceae Endiandra acuminata - 6 2 - Lauraceae Endiandra insignis - 13 5 1 Lauraceae Endiandra monothyra - 1 4 - Lauraceae Endiandra sankeyana - 5 12 - Lauraceae Cryptocarya hypospodia 6 1 1 3 Lauraceae Cryptocarya mackinnoniana 8 9 6 10 Lauraceae Cryptocarya murrayi 2 4 4 3 Lauraceae Cryptocarya triplinervis 4 6 - - Lauraceae Cryptocarya glaucocarpa - 2 - - Lauraceae Cryptocarya onoprienkoana - 1 4 - Lauraceae Cryptocarya sp. 1 3 - 2 Lauraceae Endiandra wolfei - - 6 - Lauraceae Litsea leefeana 4 9 16 -

Appendices - 188 -

Appendix 1 cont.: Sites FAMILY Species P1 P2 P3 C1 Lauraceae Neolitsea dealbata 5 6 32 5 Lecythidaceae Planchonia careya - - 1 - Meliaceae Dysoxylum klanderi - 1 - - Meliaceae Dysoxylum mollissimum 2 1 - - Meliaceae Dysoxylum parasiticum - - 23 - Meliaceae Dysoxylum rufum - 2 - - Meliaceae Dysoxylum oppositifolium - 11 2 - Meliaceae Dysoxylum pettigrewianum - - - 1 Meliaceae Aglaia sapindina 5 - - - Meliaceae Aglaia tomentosa 6 5 3 7 Meliaceae Anthocarpa nitidula* 31 - - - Meliaceae Melia azedarach 1 - - - Meliaceae Synoum glandulosum - 1 - - Menispermaceae Carronia protensa 3 2 - - Mimosaceae Acacia celsa - - - 3 Mimosaceae Archidendron hendersonii 1 - - - Monimiaceae Tetrasynandra laxiflora 1 3 2 1 Monimiaceae Tetrasynandra longipes 1 - - - Monimiaceae Wilkea angustifolia - - 7 - Monimiaceae Wilkea sp. 4 2 - 1 Monimiaceae Daphnandra repandula - - 1 - Monimiaceae Doryphora aromatica - 10 8 15 Moraceae Ficus hispida - 1 - - Myristicaceae Myristica insipida - 4 2 3 Myrsinaceae Rapanea porosa - - 2 - Myrtaceae Rhodomyrtus macrocarpa 3 - - - Myrtaceae Syzygium cormiflorum 1 - 1 1 Myrtaceae Syzygium gustavoides - - 7 - Myrtaceae Syzygium kuranda 4 - 2 2 Myrtaceae Austromyrtus dallachiana 6 3 4 1 Myrtaceae Austromyrtus minutiflora* 12 - - - Myrtaceae Austromyrtus sp.* 1 - 1 - Myrtaceae Decaspermum humile 1 - - - Myrtaceae Pilidiostigma tropicum 3 - 9 2 Myrtaceae Rhodamnia sessiliflora 2 - 6 3 Myrtaceae Rhodomyrtus pervagata 1 2 - 2 Myrtaceae Syzygium johnsonii - - - 2 Myrtaceae Syzygium luehmannii 1 - - - Oleaceae Cinnamomum oliveri - - 1 - Passifloraceae Parsonsia straminea 1 - - - Proteaceae Triunia sp. - - 1 - Proteaceae Helicia nortoniana 2 3 4 3 Proteaceae Athertonia diversifolia - - 2 - Proteaceae Opisthiolepis heterophylla - - 1 - Proteaceae Cardwellia sublimis - 2 - 1 Proteaceae Darlingia darlingiana 1 - - 5 Proteaceae Stenocarpus sinatus 7 - - - Rhamnaceae Alphitonia petriei - - 1 1 Rhamnaceae Alphitonia whitei - 4 - 2 Rhamnaceae Rhamnus napalensis - - 1 - Rosaceae Prunus turneriana - - - 1 Rubiaceae Gardenia ovularis - - 3 1 Rubiaceae Hodgkinsonia frutescens 8 - - - Rubiaceae Psychotria sp. 1 - 2 2 Rutaceae Acronychia acidula 4 21 - 8

Appendices - 189 -

Appendices - 190 -

Appendix 1 cont.: Sites FAMILY Species P1 P2 P3 C1 Rutaceae Acronychia sp. Hair* 3 - - - Rutaceae Acronychia vestita - - 1 14 Rutaceae Euodia bon sp. - 1 - - Rutaceae Euodia sp. 10 - - - Rutaceae Melicope elleryana - 3 1 - Rutaceae Sarcomelicope simplicifolia 1 - - - Rutaceae Flindersia acuminata - 5 - 2 Rutaceae Flindersia bourjotiana - - 1 - Rutaceae Flindersia brayleyana 3 5 3 3 Rutaceae Flindersia schottiana 1 - - - Sapindaceae Castanospora alphandii 34 3 6 1 Sapindaceae Diploglottis bracteata - 1 - - Sapindaceae Diploglottis diphyllostegia 2 - - - Sapindaceae Arytera pauciflora - 1 1 4 Sapindaceae Cupaniopsis flagelliformis 1 1 2 - Sapindaceae Lepid sp. - - - 1 Sapindaceae Mischarytera lautereriana 1 - - - Sapindaceae Mischocarpus sp. 3 - - - Sapindaceae Mischocarpus grandissimus - 1 - - Sapindaceae Mischocarpus lachnocarpus - 1 - 1 Sapindaceae Mischocarpus macrocarpus - 1 3 2 Sapindaceae Sacrotoechia serrata - - - 1 Sapindaceae Synima cordierorum 10 1 2 2 Sapindaceae Synima macrophylla 4 7 4 - Sapindaceae Toechima erythrocarpum - - 1 7 Sapindaceae Guioa acutifolia - - 1 - Sapindaceae Guioa lasioneura - - 2 - Sapotaceae Pouteria obovata - - 1 2 Sapotaceae Plancheonella sp. 1 - - - Solanaceae Solanum discolor 1 - 1 - Sterculiaceae Argyrodendron peralatum 6 33 - 23 Sterculiaceae Argyrodendron sp. Boonjee - - 3 - Sterculiaceae Brachychiton acerifolius 1 - - - Sterculiaceae Franciscodendron laurifolium - - 7 2 Sterculiaceae Argyrodendron trifoliolatum* - - 5 34 Symplocaceae Symplocos cochinchinensis - - 2 - Symplocaceae Symplocos sp. 1 1 - - Symplocaceae Symplocus hayesii - - - 1 Theaceae Ternstroemia cherryi 6 - - - Urticaceae Dendrocnide moroides 1 - - - Winteraceae Zygogynum semecarpoides - - 4 - Xanthophyllaceae Xanthophyllum octandrum - 1 2 1 Zamiaceae Bowenia spectabilis - - 1 - Zingiberaceae Alpinia caerulea 1 - - - Zingiberaceae Alpinia modesta* - - 1 - - Unknown 1 - - - 1 - Unknown 2 1 - - - - Unknown 3 1 - - - - Unknown 4 - 1 - - - Unknown 5 - 1 - - - Unknown 6 - 1 - - - Unknown 7 - 2 - - - Unknown 8 - - - 1

Appendices - 191 -

Appendix 2: Species name, density/ha and category of all food resources collected in the first year (May 2002 to February 2003). KTBE = fruits that are eaten by U. caudimaculatus; RC = Resource Category using the criteria outlined on page 11. Sites Species P1 P2 KTBE RC Acronychia acidula 391 11574 Y 2 Acronychia vestita 6250 93750 Y 2 Aglaia tomentosa - 463 N 3 Aleurites rockinghamensis 12695 - Y 1 Athertonia diversifolia - 232 Y 1 Beilschmiedia recurva - 7639 Y 5 Calamus caryotoides 20117 - Y 3 Calamus moti 4297 3935 Y 3 Castanosperma australe 1758 - N 6 Castanospora alphandii 43359 1157 Y 4 Connarus conchocarpus 781 232 N 6 Cryptocarya clarksoniana 175391 5787 N 3 Cryptocarya hypospodia - 232 N 3 Cryptocarya mackinnoniana 6445 463 Y 3 Cryptocarya melanocarpa 233008 51157 N 3 Cryptocarya oblata - 3935 Y 4 Dysoxylum oppositifolium - 13657 N 2 Dysoxylum rufum 13867 - N 5 Elaeagnus triflora 9375 38657 N 2 Elaeocarpus angustifolius 8984 2546 Y 5 Elaeocarpus ruminatus 2734 10648 Y 3 Endiandra hypotephra 8594 - N 5 Endiandra monothyra - 45833 N 5 Endiandra sankeyana - 7407 N 5 Endiandra sideroxylon - 7639 N 5 Ficus crassipes 2148 - Y 4 Ficus obliqua 22656 53241 Y 2 Ficus pleurocarpa 195 - Y 4 Ficus watkinsiana 586 694 Y 4 Flindersia acuminata - 926 Y 6 Flindersia brayleyana 9375 4861 Y 6 Fontainea picrosperma 6250 - Y 2 Irvingbaileya australis - 2778 Y 2 Litsea leefeana 12891 1263889 N 3 Melia azedarach 391 232 Y 3 Melodinus australis 195 232 N 4 Myristica insipida - 232 Y 6 Neisosperma poweri 2148 926 Y 4 Neolitsea dealbata - 232 N 3 Pilidiostigma tropicum 1172 13426 N 3 Plancheonella sp. 5469 - N 3 Syzygium cormiflorum 1563 - N 5 Syzygium kuranda 8789 - Y 5

Appendices - 192 -

Appendix 3: Species name, density/ha and category of all food resources collected in the second year (May 2003 to February 2004). KTBE = fruits that are eaten by U. caudimaculatus; RC = Resource Category using the criteria outlined on page 11. Sites Species P1 P2 P3 C1 KTBE RC Acronychia acidula - 52778 417 - Y 2 Acronychia vestita 21094 103009 4792 - N 2 Aleurites rockinghamensis 6641 - - - Y 1 Alphitonia petriei - 232 1875 6667 N 3 Argyrodendron peralatum - - - 3125 N 6 Argyrodendron sp. Boonjee - - 417 - N 6 Athertonia diversifolia - 463 3333 - Y 1 Beilschmiedia bancroftii - - 31667 - Y 4 Beilschmiedia recurva - 232 - - Y 5 Calamus caryotoides 3125 - - - Y 2 Calamus moti 3125 2546 3542 1667 Y 2 Cardwellia sublimis - - 208 417 Y 6 Castanosperma australe - 232 - - N 6 Castanospora alphandii 19141 3241 1875 6667 Y 4 Cerbera inflata - - - 208 N 5 Connarus conchocarpus 1367 - - - N 6 Cryptocarya melanocarpa 8203 232 - - N 3 Cryptocarya oblata - - 417 - Y 3 Cryptocarya clarksoniana 43555 2315 - - N 3 Cryptocarya pleurosperma - - - 208 Y 2 Cupaniopsis flagelliformis 36328 - - - N 3 Davidsonia pruriens 195 - - - Y 4 Desmos goezeanus - 2778 - - N 6 Diploglottis diphyllostegia 24414 - - - Y 4 Dysoxylum oppositifolium - 232 - - N 2 Dysoxylum rufum 4688 - 208 - N 5 Elaeagnus triflora 2930 694 - 208 N 2 Elaeocarpus angustifolius - 2083 14167 3542 Y 3 Elaeocarpus ruminatus 7813 31250 16250 4583 Y 3 Endiandra cowleyana - - - 208 N 3 Endiandra monothyra - - 1042 - N 5 Endiandra sankeyana - - 625 - N 5 Endiandra wolfei - - 9375 208 N 3 Ficus obliqua 72461 31944 - 208 Y 2 Ficus pleurocarpa 195 926 1250 208 Y 4 Ficus watkinsiana 3320 - - - Y 4 Flindersia acuminata - 232 - - Y 6 Flindersia bourjotiana 1758 - - 1875 Y 6 Flindersia brayleyana 10547 232 1042 9792 Y 6 Fontainea picrosperma 195 - 625 1042 Y 2 Gonioyhalamus australis - - 833 - Y 6 Hylandia dockrillii - 694 10000 - Y 4 Litsea leefeana - 23380 7500 - N 3 Melia azedarach 15430 - - - Y 3

Appendices - 193 -

Appendix 3 cont.: Sites Species P1 P2 P3 C1 KTBE RC Neisosperma poweri 16797 231 833 - Y 4 Pilidiostigma tropicum - 1852 266042 139375 N 3 Pouteria castanosperma - 2546 1458 - Y 4 Prunus turneriana - 926 - - Y 4 Siphonodon membranaceus - - 208 - Y 4 Stenocarpus reticulatus - - 3125 208 N 6 Syzygium gustavoides - - 5833 - Y 5 Syzygium Kuranda - - 625 - Y 5 Triunia erythocarpa - - - 3750 N 5 Unknown 9 - - 3333 - N - Unknown 10 - - 1250 - N - Unknown 11 - - 208 - N - Unknown 12 - - 1458 - N - Unknown 13 - - - 208 N - Unknown 14 - - - 417 N - Unknown 15 - - - 625 N - Unknown 16 - - 4583 7708 N - Unknown 17 - - - 1250 N - Unknown 18 - - 3542 - N - Unknown 19 - - 208 - N -

Appendices - 194 -

Chand V, Streatfeild CA, Wilson JC, Mather PB

School of Natural Resource Sciences Queensland University of Technology GPO Box 2434 Brisbane, Queensland, 4001 Australia

Keywords: Uromys, paternity, mating system, microsatellite.

Corresponding author: Craig Streatfeild School of Natural Resource Sciences Queensland University of Technology GPO Box 2434 Brisbane, Queensland, 4001 Australia

Appendices - 195 -

Abstract Microsatellite loci were developed for the giant white-tailed rat (Uromys caudimaculatus) to aid in assigning paternity and to subsequently investigate their mating system and sex- biased dispersal characteristics. Twenty two primer sets were originally developed and of these, 11 were polymorphic with between five and 10 alleles per locus. In addition, two primer sets designed for Hydromys chyrsogaster also amplified in this species despite an evolutionary divergence of 15 million years.

Appendices - 196 -

Introduction The giant white-tailed rat (Uromys caudimaculatus), is one of Australia’s largest rodents whose primary habitat includes the rainforests of far north Queensland, Australia (Moore, 1995; Watts & Aslin, 1981). Previous studies suggest that this rodent is territorial and exhibits a solitary existence with individuals only coming together for breeding opportunities (Moore, 1995). Conversely, anecdotal evidence suggests that U. caudimaculatus can occur in highly structured family groups, displaying high levels of parental care (Dennis, pers comm.). Furthermore, it is widely recognised that some rodents may adjust their behaviour in response to local ecological conditions (Travis et al. 1995) but it is not known if the giant white-tailed rat is capable of behavioural plasticity.

The nocturnal nature of this animal renders behavioural observations in isolation, inadequate to effectively examine their social organisation. Therefore, we developed microsatellite loci to aid in assigning paternity and to subsequently investigate the mating system and sex-biased dispersal characteristics in giant white-tailed rat populations in isolated and continuous habitats.

Microsatellite libraries for U. caudimaculatus were constructed using approximately 10μg of high molecular weight DNA isolated from ear tissue using a standard proteinase K phenol:chloroform extraction protocol (Fukatsu, 1999) and digested with DpnII restriction enzyme for 3 hours before separation on a 1.5% agarose gel. Fragments of size 300-700 bp were excised, purified and ligated to an equal volume of plasmid vector pUC18 (Amersham- Pharmacia). The plasmids came digested with BamHI and dephosphorylated to allow the overhanging ends to match those from the DpnII digest. Recombinant plasmids were heat shocked into competent E. coli cells (JM109 strain, Promega) and incubated for one hour at 37oC. Cells were spread onto agar plates containing LB-ampicillin, KGAC and IPTG and incubated overnight at 37oC to promote selective growth of transformed colonies. A total of 2500 recombinant colonies were chosen from plates and incubated overnight in a grid formation on new LB-ampicillin agar plates and later stored at 4oC. Recombinant colonies were blotted from the plates onto filter membranes (Hybond-N, Amersham) and screened for microsatellite repeats with oligonucleotides probes [(ACC)8, (AAC)8, (AAG)8, (AGC)8, 32 (ACG)8, (ACT)8, (CA)15, (AG)12] end labelled with [γ ]-dATP (Perkin Elmer). Cross-linked single stranded DNA was hybridized with probes overnight before being exposed onto X-ray film for 12 hours. Autoradiographs revealed sixty one positive clones that hybridized with probed repeats. Nucleotide sequencing was performed on the plasmid inserts from all positive colonies using the BigDye Terminator kit (Perkin Elmer) and universal plasmid primers (M13 F & R, Amersham Pharmacia Biotech). DNA sequencing was performed on an ABI 3730 automated sequencer (Australian Genome Research Facility, University of Queensland, Brisbane, Australia). A total of 22 Primers were designed using the primer

Appendices - 197 - design software ‘Primer3’ (Rozen and Skaletsky, 2000) with the forward primer of each set fluorescently HEX labelled (GeneWorks).

Microsatellite PCR was performed for all loci as follows: 5-10ng of DNA was used as template in 20μL PCR reactions also containing 1 X PCR buffer (100mM Tris-HCl pH 8.8, 500mM KCl, 1% Triton X-100; Fisher Biotech), 125μM dNTPs, 0.4μM of each primer,

1.5mM-2mM MgCl2, 0.2U Taq Tth plus (Fisher Biotech) and ddH2O to 20µL volume. Amplification was performed in a Mastercycler (Eppendorf) under the following conditions: Initial denaturation of 3 min at 94oC, 30-35 cycles of 30-45 s at 94oC, 30-45 s at annealing temperature, 30-45 s at 72oC followed by a final extension of 10 min at 72oC before holding at 4oC. Amplified PCR products were resolved on a 5% acrylamide gel accompanied by a TAMAR T350 size marker (Genescan) using a Gel-Scan 2000 (Corbett Research) and analyzed using ONE-Dscan software (v2.03; Scanalytics, Inc.).

A sample of 22-29 giant white-tailed rat individuals from a single population were typed for a total of 22 microsatellite loci. Seven loci failed to amplify consistently, three were monomorphic and 11 amplified in all individuals typed, showing between five and 10 alleles per locus (Table 1). In addition, two loci developed for Hydromys chyrsogaster (Hinds et al. 2002) amplified in all U. caudimaculatus individuals despite an evolutionary divergence of 15 million years (Watts & Aslin, 1981). However, only W2A1 amplified sufficiently well for allele scoring to be unambiguous. All loci were screened for deviations from Hardy-Weinberg equilibrium (HWE) and genotypic disequilibrium using GENEPOP (version 3.4; Raymond & Rousset, 1995). Polymorphic information content (PIC) and total exclusionary power were calculated using CERVUS (version 2; Marshall et al. 1998).

Mean values for observed and expected heterozygosity were 0.76 and 0.77 respectively with no loci showing significant deviations from HWE. Two loci (213/233) departed from genotypic equilibrium after Bonferroni corrections for multiple comparisons. The mean PIC value was 0.72 and the combined values for all loci for first parent and second parent total exclusionary power were 0.997 and 0.999 respectively. The high levels of polymorphism, PIC values and the total exclusionary power of these loci will aid in assigning paternity in this species which is currently under investigation in isolated and continuous habitats.

Acknowledgments Funding for this project was provided by an Australian Postgraduate Award Scholarship to Craig Streatfeild. Further funding was supplied by a grant from the Australian Geographic Society. Tissue samples were collected under the approval of QUT animal ethics number 2007A and QPWS scientific purposes permit number F1/000372/00/SAA. We thank the various people who assisted in obtaining ear tissue for this study and who provided advice during the optimization process.

Appendices - 198 -

References Fukatsu, T (1999) Acetone preservation: a practical technique for molecular analysis. Molecular Ecology. 8, 1935-1945. Hinds FE, Close RL, Campbell MT & Spencer PBS (2002) Characterization of polymorphic microsatellite markers in the water rat (Hydromys chrysogaster). Molecular Ecology Notes. 2, 42-44. Marshall TC, Slate J, Kruuk LEB & Pemberton JM (1998) Statistical confidence for likelihood-based paternity inference in natural populations. Molecular Ecology 7: 639-655. Moore LA (1995) Giant white-tailed rat. In .The Mammals of Australia. (ed. R. Strahan.) pp. 638-640. Reed Books, Sydney, Australia

Raymond M & Rousset F (1995) GENEPOP (Version 3.4): A population genetics software for exact tests and ecumenicism. Journal of Heredity, 86, 248-249. Rozen S &. Skaletsky H. J (2000) Primer3 on the WWW for general users and for biologist programmers. In: Krawetz S, Misener S (eds) Bioinformatics Methods and Protocols: Methods in Molecular Biology. Humana Press, Totowa, NJ, pp 365-386; Source code available at http://fokker.wi.mit.edu/primer3/. Travis SE, Slobodchikoff CN & Keim P (1995) Ecological and demographic effects on intraspecific variation in the social system of prairie dogs. Ecology 76, 1794-1803. Watts CHS & Aslin HJ (1981) The Rodents of Australia. Angus and Robertson. Sydney, Australia.

Appendices - 199 -

Table 1: Characteristics of 11 microsatellite loci for the giant white-tailed rat (Uromys caudimaculatus). N, Number of individuals typed; A, number of alleles; HO, observed heterozygosity; HE, expected heterozygosity; PIC, polymorphic information content; Ta, annealing temperature; Pr(1), first parent exclusionary power; Pr(2), second parent exclusionary power. # Locus developed for Hydromys chrysogaster, (Hinds et al. 2002).

Locus Repeat Motif Primer sequence (5’ to 3’) Size (bp) N A HO HE PIC Pr(1) Pr(2) Ta GenBank Accession no. UVC21.1 (CA)18 F:TACATTTAGTTGCATTTTCTG 146-172 29 9 0.75 0.78 0.73 0.37 0.55 58 AY821827 R:CCAGCAGTATTGTGTGTTTAA

UVC202 GA)25(AA)(GA)5 F:ACATGGCCTTTTCCAATA 130-158 22 8 0.64 0.79 0.75 0.41 0.59 50 AY821828 R:GTACCGTGTCTCTTATAATA

UVC213 (CA)22 F:GATGTATGGCTCCCGAAGAA 140-148 28 5 0.71 0.75 0.69 0.32 0.49 50 AY821829 R:TTCATTGAAGCAATAGAAAATTTAAC

UVC219 (GA)20 F:TTGGTTTGGTGTCAGGGTTAT 136-148 26 6 0.58 0.65 0.56 0.22 0.37 58 AY821830 R:ACCAATACTCTTCAAGCTAT

UVC232 (CA)13 F:CTCTGAACTCTGTAAATTAGC 142-158 28 7 0.57 0.70 0.65 0.29 0.46 50 AY821831 R:GGTTTTCACTGTTGTTGTTGC UVC233 (CA)16 F:AAGCAGGTCTGCCTCTGTGT 196-206 24 7 0.92 0.81 0.76 0.43 0.61 66 AY821832 R:ACTTTGGCTCGCAGCTCTAC

UVC238 (CA)18 F:TTGGACTGATGCAGAAAGATACA 98-122 22 7 0.82 0.76 0.70 0.34 0.51 56 AY821833 R:TCAGACCAGCTGACAACACTTC

UVC245 (GT)12(CTGT)8 F:CCTAAACCCAACGACAAGTGT 144-156 29 5 0.86 0.71 0.64 0.28 0.45 50 AY821834 R:GTAACTCAAAATTCTGCCTGC

UVC432 (GT)27 F:ATGTTCTCCAACCCTTCC 130-158 24 10 0.79 0.88 0.85 0.56 0.72 50 AY821835 R:GTTTTTCCTCCCATCTCC

UVC446 (CA)20 F:TCTCTAGGCACAGTGGTTTGG 170-198 28 8 0.93 0.85 0.81 0.50 0.67 58 AY821836 R:CAGCTTCTTCTTATGCGTCCC

UVC452 (CA)21 F:TTAACTATACATATCGGCAGGG 136-146 28 6 0.79 0.76 0.71 0.34 0.52 50 AY821837 R:CATGTGAGGAAGCGGAAAGACG # W2A1 (TG)29 180-200 27 10 0.82 0.85 0.81 0.50 0.67 52 Mean 7.33 0.77 0.77 0.72 0.997 0.999

Appendices - 200 -

Appendix 5: Linkage disequilibrium for each locus pair for all populations. * denotes significant departures after sequential Bonferroni corrections (α = 0.0002). Population Locus Pair P1 P2 P3 C1 21/202 * 21/213 21/219 * 21/232 21/238 21/245 * 21/432 * 21/452 * 21/ W2A1 21/MC2E 202/213 * 202/219 * * 202/232 * 202/238 202/245 202/432 * 202/452 * 202/W2A1 * 202/MC2E 213/219 213/232 * 213/238 213/245 213/432 213/452 213/W2A1 213/MC2E 219/232 219/238 * 219/245 219/432 * * 219/452 * 219/W2A1 219/MC2E 232/238 232/245 232/432 * 232/452 * * 232/W2A1 232/MC2E 238/245 238/432 238/452 238/W2A1 238/MC2E 245/432 245/452 245/W2A1 245/MC2E 432/452

Appendices - 201 -

Appendix 5 cont.: Population Locus Pair P1 P2 P3 C1 432/W2A1 432/MC2E 452/W2A1 452/MC2E WA21/MC2E

Appendices - 202 -

Appendix 6: Allele frequencies at 11 loci in all populations. n denotes the population size at that loci. Population Locus/alleles P1 P2 P3 C1 UVC21 n 120 27 68 29 138 0.004 - - - 144 0.358 0.093 0.081 0.086 146 0.021 - - 0.241 148 0.021 - - 0.138 150 0.042 0.130 0.044 - 158 0.004 - - - 162 - 0.019 0.059 - 164 - - - 0.086 166 0.038 0.278 0.213 0.052 168 0.267 0.074 0.294 0.172 170 0.229 0.259 0.228 0.121 172 0.017 - - 0.034 174 - 0.148 0.081 0.017 176 - - - 0.052

UVC202 n 115 26 62 27 101 - - - 0.019 103 0.004 - - 0.019 125 - - - 0.019 127 0.009 0.115 0.097 0.037 129 0.161 0.327 0.169 0.167 131 0.078 0.308 0.331 0.185 133 0.017 0.135 0.016 0.130 135 0.204 - 0.177 0.019 137 0.487 0.038 0.032 0.204 139 0.030 0.058 0.008 0.019 141 0.009 0.019 0.097 0.167 143 - - 0.040 - 145 - - - 0.019 151 - - 0.032 -

UVC213 n 103 27 67 29 122 - - - 0.138 124 - - - 0.017 140 0.301 0.204 0.269 0.034 142 0.223 0.296 0.291 0.241 144 0.359 0.204 0.075 0.224 146 0.039 0.296 0.358 0.310 148 0.078 - 0.007 0.017 150 - - - 0.017

Appendices - 203 -

Appendix 6 cont.: Population Locus/alleles P1 P2 P3 C1 UVC219 n 117 27 65 28 132 0.009 - - - 134 0.316 0.056 0.054 - 136 0.038 0.037 0.115 0.036 138 0.487 0.241 0.123 0.232 140 0.034 0.019 0.115 0.125 142 0.017 0.259 0.438 0.179 144 0.060 0.167 0.062 0.125 146 0.038 0.222 0.038 0.250 148 - - 0.054 0.054

UVC232 n 120 27 63 29 142 0.054 - 0.079 0.017 146 0.246 0.167 0.183 0.293 148 0.013 0.389 0.127 0.069 150 0.529 0.259 0.579 0.328 152 0.108 0.167 0.016 0.155 154 - - - 0.034 156 - 0.019 0.016 0.052 158 0.046 - - 0.017 166 - - - 0.017 170 - - - 0.017 174 0.004 - - -

UVC238 n 120 27 68 29 100 0.242 0.019 0.125 0.017 104 - - - 0.017 106 0.029 0.148 0.029 - 112 - - - 0.034 114 - - - 0.069 116 0.425 0.352 0.353 0.241 118 0.221 0.352 0.463 0.172 120 - - - 0.121 122 0.075 0.093 0.022 0.259 124 - 0.037 0.007 0.034 130 - - - 0.017 132 0.004 - - - 136 0.004 - - 0.017

Appendices - 204 -

Appendix 6 cont.: Population Locus/alleles P1 P2 P3 C1 UVC245 n 119 27 68 29 144 0.176 0.093 0.007 0.052 146 - - 0.007 - 148 0.218 0.130 0.272 0.310 152 0.172 0.519 0.632 0.190 154 0.038 0.019 0.037 - 156 0.387 0.241 0.044 0.259 160 - - - 0.052 162 0.004 - - 0.138 164 0.004 - - -

UVC432 n 118 27 58 28 122 - - - 0.018 130 0.030 0.167 0.147 0.179 132 0.305 0.056 0.233 - 140 - 0.093 0.164 0.036 142 0.153 0.093 0.060 0.143 144 - 0.370 0.060 0.071 145 0.004 - - - 146 0.110 0.056 0.155 0.214 148 0.191 0.093 0.095 0.125 150 0.081 0.019 0.026 0.214 152 0.085 0.056 0.060 - 156 0.042 - - -

UVC452 n 119 27 68 29 104 0.004 - - - 132 - - - 0.017 136 0.038 0.056 0.243 0.069 138 0.017 0.111 0.022 0.103 140 0.244 - 0.007 0.034 142 0.151 0.185 0.132 0.379 144 0.273 0.037 0.096 0.293 146 0.265 0.556 0.449 0.069 148 - 0.056 0.051 - 154 0.004 - - 0.017 156 - - - 0.017 158 0.004 - - -

Appendices - 205 -

Appendix 6 cont.: Population Locus/alleles P1 P2 P3 C1 W2A1 n 103 25 67 28 168 - - - 0.018 178 0.024 0.180 0.187 0.161 182 0.058 0.080 0.134 - 184 0.262 0.060 0.142 0.304 186 0.005 0.040 0.007 0.107 188 0.267 0.540 0.485 0.250 190 0.029 0.020 0.015 0.036 192 0.015 - 0.007 0.018 194 0.155 0.080 - 0.089 196 0.184 - 0.022 0.018

MC2E n 96 26 56 21 202 - - - 0.024 208 0.536 0.327 0.259 0.190 210 0.083 0.038 0.134 0.143 212 0.005 - - 0.095 214 0.005 0.019 0.036 0.238 216 0.370 0.365 0.446 0.238 218 - 0.250 0.125 0.071

Appendices - 206 -

Appendix 7: Genetic parents of juvenile Uromys from the four populations and the sampling period in which they were first captured in. The dash (-) indicates that parentage could not be assigned. Population Sampling period Juveniles Females Males P1 1 457868 164087 - P1 1 454030 - - P1 2 479492 00061C0269 301732 P1 5 480795 456441 462705 P1 5 476226 457868 462705 P1 5 478610 457868 462705 P1 5 482480 457868 462705 P1 5 481099 479219 477204 P1 5 478639 479219 - P1 5 481531 479219 - P1 5 484693 - - P1 5 477663 965-26969 462705 P1 6 472175 456441 50300F3900 P1 6 473973 456441 50300F3900 P1 6 476729 457868 462705 P1 6 477495 457868 462705 P1 6 479400 457868 462705 P1 6 481457 457868 462705 P1 6 473726 462600 462705 P1 6 483268 462600 462705 P1 6 483040 462600 463136 P1 6 468739 462600 50300F3900 P1 6 471042 479002 313376 P1 6 474071 479219 462705 P1 6 480929 479219 462705 P1 6 467921 479219 477204 P1 6 479681 479219 40503D2000 P1 6 485149 479219 40503D2000 P1 6 473215 - 40503D2000 P1 6 467535 - 50300F3900 P1 6 470571 20337C6F42 462705 P1 6 470673 20337C6F42 462705 P1 6 470993 20337C6F42 462705 P1 6 479907 20337C6F42 478611 P1 6 465338 965-26969 462705 P1 6 476430 965-26969 462705 P1 6 484706 965-26969 462705 P1 7 466832 456441 462705 P1 7 467546 471215 480586 P1 7 472851 479002 313376 P1 7 474368 479002 - P1 7 466048 482445 313376 P1 7 468341 482445 313376 P1 7 470187 482445 313376 P1 7 473600 482445 313376 P1 7 472638 - 313376 P1 7 470845 - - P1 7 473407 - - P1 7 474274 - - P1 7 473353 - -

Appendices - 207 -

Appendix 7 cont.: Population Sampling period Juveniles Females Males P1 7 467607 20337C6F42 462705 P1 7 465610 20337C6F42 478611 P1 7 472065 965-26969 462705 P1 7 472496 965-26969 462705 P1 7 474180 965-26969 462705 P1 7 468686 965-26969 - P1 8 469851 467486 472343 P1 8 472546 472734 478611 P1 8 468576 474472 480795 P1 8 473119 482480 477663 P1 8 469249 20337C6F42 462705 P1 8 470932 20337C6F42 480929 P1 8 464512 965-26969 462705 P2 1 2026585F03 178607 314608 P2 1 456017 178607 - P2 1 457537 475448 - P2 1 202361421A 475448 ET1452 P2 2 967-69687 178607 - P2 5 484898 - 965-79788 P2 7 474832 484898 381026 P2 9 474718 466406 307935 P2 9 471566 466406 307935 P2 9 473135 484898 381026 P2 9 473618 484898 381026 P3 5 477623 477541 481428 P3 5 475863 480528 482043 P3 5 476895 480528 482043 P3 5 475611 482826 485240 P3 5 480705 482826 485240 P3 5 484053 482826 485240 P3 5 484288 482826 485240 P3 5 475782 - 482488 P3 5 480872 - 485240 P3 5 477926 - - P3 5 481822 - - P3 5 483824 - - P3 5 482501 - 485240 P3 6 468637 480528 476551 P3 6 472233 480528 482043 P3 6 465845 482431 481428 P3 6 474682 482431 481428 P3 6 464733 482826 485240 P3 6 469859 482826 485240 P3 6 467191 484145 481428 P3 6 470457 484145 481428 P3 7 471837 474116 476895 P3 7 466529 474116 485240 P3 7 472535 484145 476551 P3 7 469517 - - P3 7 466084/474530 - - P3 8 464846 474030 464606 P3 8 471434 474030 -

Appendices - 208 -

Appendix 7 cont.: Population Sampling period Juveniles Females Males P3 8 466293 474630 469635 P3 8 472026 474630 480705 P3 8 472020 484145 476188 P3 8 470284 484145 476551 C1 4 479792 479569 480834 C1 4 477491 479569 484047 C1 4 481319 479569 484047 C1 5 478551 480147 484047 C1 5 478321 481369 483110 C1 6 465156 479569 484047 C1 6 466829 - - C1 6 470077 - - C1 6 474036 - -

Appendices - 209 -

Appendix 8: Effects of food resources on space use and social mating system of the giant white-tailed rat (Uromys caudimaculatus). Unpublished manuscript.

C. A. Streatfeild1* and P. B. Mather1

1School of Natural Resource Sciences, Queensland University of Technology GPO Box 2434, Brisbane, Queensland, 4001, Australia

Keywords: Uromys, rainforest, food resources, space use, social mating system

*Corresponding author: Craig Streatfeild Kellogg Brown and Root Pty Ltd GPO Box 2434 Brisbane, Queensland, 4001 Australia

Appendices - 210 -

Abstract The distribution and abundance of resources including food, shelter, nest sites and reproductive mates can influence individual space utilisation patterns, mate availability and inter-sexual competitive interactions, that in turn can lead to differences in social mating systems. Furthermore, fragmentation can alter the spatial distribution and quality of available resources implying that social mating systems may differ in fragments compared with continuous populations. In contrast to predictions, ecological factors including food quality and vegetation structure failed to predict the spatial distribution of giant white-tailed rat females, and females in turn failed to predict the spatial distribution of males. Attributes conducive to social monogamy including body mass and home range size differed little among sites. Home range overlap, however, varied significantly among sites and was conducive to social monogamy in the smallest fragment but was more conducive to polygamy/promiscuity in the remaining three sites. None of the indices of social mating systems that are conducive to either monogamy or polygamy were found consistently at any single site, suggesting that Uromys can potentially adjust their reproductive behaviour in response to local ecological conditions.

Appendices - 211 -

Introduction The socioecological model is one of the main theoretical frameworks used to understand animal social mating systems (Emlen and Oring, 1977) and aims to link social and ecological factors in functional explanations for the distribution of, and association between, reproductive males and females (Eberle and Kappeler, 2002). Mammals often exhibit marked intersexual differences in spatial patterns and social behaviour (Wilson, 2000) and these differences result from different selection pressures acting on males and females to maximise their individual reproductive success (Davies and Lundberg, 1984; Gehrt and Fritzell, 1998). For example, reproductive success of females is closely linked with their ability to access and exploit resources required for reproduction, while male reproductive success is primarily determined by their ability to find and mate with females (Trivers, 1972; Emlen and Oring, 1977; Davies and Lundberg, 1984; Clutton-Brock and Parker, 1992; Reynolds, 1996). The spatial distribution of females, therefore, should be influenced more by the distribution of resources including food, shelter and nest sites while the spatial distribution of males are likely to be determined primarily by the distribution of reproductively active females (Emlen and Oring, 1977; Lindstrom and Seppa, 1996; Carranza and Valencia, 1999).

Variation in the distribution and abundance of available resources is likely to influence the degree of monogamy or polygamy practiced via the combined effects of population density, home-range size and home-range overlap, all of which determine the number of potential mates individuals in practice, have access to (Clutton-Brock, 1989). For example, when resources are spatially clumped, the spatial distribution of females’ home ranges should be highly aggregated, potentially leading to females being monopolised by larger and competitively superior males that should result in patterns of space use that are conducive with social polygyny (Emlen and Oring, 1977; Lindstrom and Seppa, 1996). In this case, sexual selection should favour larger male body size relative to females (Boonstra et al. 1993) and males should have large home ranges and lower same-sex overlap compared with females (Topping and Millar, 1996). In contrast, social monogamy is most likely when resources are evenly spaced, leading to female home ranges that are less aggregated and so more easily defended by single males only. Socially monogamous species are expected to show smaller differences in home range size and overlap (Topping and Millar, 1996) and sexual dimorphism in body size (Boonstra et al. 1993).

Appendices - 212 -

Additionally, the ability of males to monopolise access to females will depend on the degree of competition among males that in turn is influenced by the operational sex ratio (OSR; i.e. the ratio of sexually receptive males to females (Emlen and Oring, 1977; Davies and Lundberg, 1984)). When the OSR is male-biased, males may be unable to monopolise females exclusively due to high levels of intruder pressure (Eberle and Kappeler, 2002), leading to higher levels of polygamy compared with OSR at parity (Emlen and Oring, 1977; Hoogland, 2003).

Social monogamy is extremely rare in mammals and has only been reported in about 3% of species studied, (Kleiman, 1977). Anecdotal evidence suggests that the giant white-tailed rat (U. caudimaculatus) can occur in highly structured family groups that display high levels of parental care (Andrew Dennis, pers. comm.). Conversely, previous studies have suggested however, that U. caudimaculatus is territorial and solitary with individuals only coming together for breeding opportunities (Moore, 1995). One possible explanation for these contrasting observations would be that giant white-tailed rats may exhibit a flexible social mating system.

To date, no previous studies have investigated the social mating system of U. caudimaculatus to 1) determine whether this species exhibits intraspecific variability, 2) examine the ecological factors that underlie this variation if the social mating system is flexible or 3) whether fragmentation affects the type of social mating system present at a site. Therefore, we examined the relationship between ecological conditions within fragmented and continuous habitats and behavioural and morphological indicators of Uromys social mating system.

Methods Study sites The study was conducted in the southern region of the Atherton uplands near the township of Malanda (17o 22’S, 145o 35’E) over a two year period from February 2002 to February 2004. In the first year of the study, two isolated rainforest fragments approximately equal in size (5.5ha and 6.5ha) were live trapped intensively. Both patches have been completely isolated for at least 40 years (Harrington et al. 1997) and were chosen to minimise any potential effects of among patch movement by giant white-tailed rats. Both patches were surrounded on three

Appendices - 213 - sides by cattle pasture, varying in height from a few centimetres to 50-60cm, and a main road on the other. To determine if the observed animal – environment interactions within these small isolated patches were representative of the Atherton Tableland in general, two additional sites were added to the study in the second and final years. One site was within the non-fragmented 94,000ha Wooroonooran National Park, while the 80ha isolated rainforest patch was located within the Fur ‘n” Feathers rainforest tree-house wildlife sanctuary.

Elevation surrounding Malanda ranged from 720m to 750m, with a mean average temperature of ~200C (Warburton, 1997). Rainfall is strongly seasonal with the wettest months occurring from December to April (Dennis and Marsh, 1997) and the uplands are characterised by a rainfall gradient decreasing from the southeast (annual mean = 2625mm) to the northwest (annual mean = 1275mm). Average annual rainfall at Malanda is 1671mm, with large year to year variation (max 2178mm in 1921; min 864mm in 2002). Natural vegetation of the uplands is predominantly Complex Mesophyll Vine Forest Type 1b (Tracey and Webb, 1975), later classified as Regional Ecosystem 7.8.2 (Goosem et al. 1999). As local rainfall decreases, Complex Notophyll Vine Forest Type 5b (Tracey and Webb, 1975) and Regional Ecosystem 7.8.3 (Goosem et al. 1999) begin to dominate. Both rainforest types are characterised by high tree species diversity and structural complexity (Harrington et al. 2001) with canopy heights of ~30-40m (Dennis and Marsh, 1997).

At each site, a permanent 25m x 25m trapping grid was established using measuring tapes and a sighting compass, and all grids were oriented at random in relation to patch dimensions. Each study site varied in area, configuration and topography resulting in different grid sizes and a different numbers of grid points per site. Trapping grids encompassed the entire area of the smaller isolated patches (P1 and P2), while trapping grids in the larger isolated patch (P3) and the continuous forest (C1) were each 6ha in size.

Appendices - 214 -

Sullivan, 1997). This resulted in effective trapping grid areas of 8.8ha and 8.7ha for P3 and C1, respectively.

Mammal trapping Giant white-tailed rats were censused by live trapping every three months between May 2002 and February 2004. Sites P1 and P2 were trapped from May 2002 through to February 2004 (8 trapping periods in total at each site), C1 was trapped between May 2003 and February 2004 (4 trapping periods in total) and trapping in P3 began in May 2003 through to February 2004 (4 trapping periods in total). During each trapping period, a single cage trap (20cm x 20cm x 56cm; Mascot Wire Works, Sydney, Australia), baited with a combination of cardboard soaked in linseed oil and a mixture of rolled oats with honey, vanilla essence and peanut butter, was placed on the ground at each 25m x 25m grid point and checked each morning. Each site was trapped for eight to 13 nights per trapping period to maximise the probability of catching an individual, given it was present in the population. Trapping ceased when cumulative catch curves of newly captured individuals reached asymptote.

After initial capture, all U. caudimaculatus individuals were given a unique numbered non-migratory microchip implanted into the nape of the neck (Compliance No. ISO 11784, Veterinary Marketing Network) and the following data were recorded: trap location, weight (to the nearest 2g using digital scales), tail length (to the nearest millimetre), sex and sexual condition of the individual (recorded as mature/reproductive female {perforate, extended nipples or other visible signs of pregnancy}, immature female {imperforate}, mature male {scrotal testes} or immature male {abdominal testes}). After recapture within the same trapping period, all individuals were re-scanned (Pocket Reader EX, Destron Fearing) and the microchip number re-recorded. The sexual condition of each individual was also re-recorded. All trapping was conducted under Queensland University of Technology animal ethics number 2007A and QPWS scientific purposes permit number F1/000372/00/SAA.

Population size of U. caudimaculatus on each trapping grid during each sampling period was estimated by direct enumeration of the ‘Minimum-Known-To-Be-Alive’ method (MNKA: Krebs, 1966), defined as the number of animals captured at time t, plus those animals trapped before and after t but not during t. While population

Appendices - 215 - sizes are commonly estimated using the Jolly-Seber (Jolly and Dickson, 1983) model (Slade and Blair, 2000), the reliability of Jolly-Seber estimates decrease dramatically when population sizes are small (Krebs and Boonstra, 1984; Krebs et al. 1986) and accurate estimates require >10 animals per sampling period (Pollock et al. 1990). In a number of instances <10 marked animals were recaptured in the current study, particularly within P1, hence population size estimates using the Jolly-Seber model were not applicable here. Estimates of population size were converted to estimates of population density (number of animals per hectare (ha)) by dividing MNKA estimates by the effective trapping area of each site.

Food resources Abundance of rainforest fruits and nuts (hereafter referred to as food resources) were determined by dividing the area around each 50m x 50m grid point into two 1800 quadrats, and then erecting two permanent 1m2 seed catchers within each 50m x 50m grid. Catchers were erected on galvanised steel poles approximately 1.5m above ground level and were positioned in areas free of overhanging vegetation to minimise access to the fallen fruit by animals climbing into the catchers. Food resources were collected at three monthly intervals coinciding with mammal trapping from May 2002 to February 2004 in sites P1 and P2, and from May 2003 through to February 2004 in sites P3 and C1. Seed catchers were cleared of all food resources at the start of each trapping period and cleared again 28 days later at the end of the trapping period. This gave total food resource fall during the trapping period as well as the two months prior to each trapping period.

Appendices - 216 - et al. 1997; Finkelstein and Grubb, 2002), unpublished data (Andrew Dennis and David Westcott unpubl. data) and from distinct teeth marks left in fruits (on the forest floor) by U. caudimaculatus. Food resources were classified further into six discrete categories based on fruit morphology and the proportion of the total fruit (endocarp, mesocarp and exocarp) eaten by U. caudimaculatus.

Vegetation structure Vegetation structure and floristic diversity were assessed within a 50m x 50m grid (utilising every second grid point on every second transect of the 25m x 25m mammal trapping grid) that resulted in 28, 26, 23 and 23 data points within P1, P3, P2 and C1, respectively. Similarity of vegetation structure among sites was assessed using seven microhabitat variables: percent foliage cover (PFC), stem density (m2) of shrubs/trees1-4m in height (1-4m stem density), 4-10m stem density (m2), >10m stem density (m2), 1-4m stem basal area (m2), 4-10m stem basal area (m2) and >10m stem basal area (m2). Percent foliage cover was determined from a vertical digital photo that was taken at breast height using constant zoom and resolution settings at three random locations within 12.5m of each grid point. The image was then overlaid with a 10 x 10 grid and the percent canopy cover calculated by scoring the number of grid intersects covered with vegetation.

Stem density, basal area and floristic diversity were determined at each 50m x 50m grid point using the point-centre quarter method following the methodology of Krebs (1999). The area around each point was divided into four 900 quadrats and the distance to, and the diameter at breast height of the first tree in three height classes (1-4m, 4-10m and >10m) were measured resulting in 12 trees within each quadrat (four trees in each height class). Pollard’s (1971) unbiased estimate of density was used to determine stem density within each height strata at each grid point.

Data analysis Different grid sizes were used for mammal trapping (25m x 25m) compared with the grid size used for assessing food resources and vegetation structure (50m x 50m). Therefore, prior to analyses that examined relationships between Uromys capture locations, food resources and vegetation structure, the original capture locations of all Uromys were assigned to the 50m x 50m grid used for food resources and vegetation, using a method similar to Lacher and Mares (1996). First, when giant white-tailed rats were captured at one of the 25m x 25m grid points that did not

Appendices - 217 - correspond with one of the grid points used to collect food resources and vegetation data, it was assumed that the animals had an equal probability of being captured at each 50m x 50m grid point immediately adjacent to where food and vegetation data were collected. Hence, the single capture was apportioned equally among immediately adjacent 50m x 50m grid points. For example, if an animals’ capture location was in the centre of the grid and adjacent to four 50m x 50m grid points, then the single capture was divided by four and ¼ of a capture assigned to each immediately adjacent 50m x 50m grid points. Further, if an animal was captured on the periphery of the grid and was immediately adjacent to only two 50m x 50m grid points, then ½ of that capture was assigned to each adjacent grid point. Secondly, if a giant white-tailed rat was captured at one of the 50m x50m grid points, then 100% of that capture was assigned to the original capture location. Using this method, all Uromys captures were re-apportioned so that the original number of captures remained the same.

To summarise the variability in vegetation structure among sites, Principal Components Analysis (PCA; Quinn and Keough, 2002) was performed on the seven vegetation structure variables after standardisation using the correlation matrix method (centred about the means and scaled by the standard deviation). Regression analysis was then used to investigate the relationship between rat densities and the first two axes of the PCA. Streatfeild (2008) showed that certain suites of food resources with high lipid contents are important for U. caudimaculatus reproduction: large fruits with a thick mesocarp and/or thick hard endocarp, large nuts with a thick endocarp and/or thick mesocarp and thin exocarp, and large pods with wind dispersed seeds. Therefore, only the combined mean densities per hectare of food resources in these categories were used here.

Moran’s I coefficient was used to quantify the spatial distribution of food resources, vegetation structure (using the first two PCA derived variables outlined in section 3.3.1), adult males and females and resident adult males and females. This is a common statistic used for autocorrelation analysis (Legendre and Fortin, 1989; Diniz-Filho et al. 2003; Fortin and Dale, 2005; Rangel et al. 2006). Two dimensional spatial correlograms were constructed for food resources, vegetation structure and for the mean centre of activity (mean grid coordinates of all captures for each individual) for adult males and adult females for each year and site separately, using the computer program SAM 2.0 (www.ecoevol.ufg.br/sam; Rangel et al.

Appendices - 218 -

2006). The statistical significance of Moran’s I for each lag length was assessed using α = 0.05 corrected for multiple tests (Benjamini and Hochberg, 1995). This procedure has greater statistical power than the sequential Bonferroni correction (Rice, 1989) especially for a large number of comparisons (Benjamini and Hochberg, 1995). A lag distance of 50m was used, equivalent to the Euclidean distance between adjacent grid points. Upper limits of lag distances at all four sites were set a priori at 300m, and were truncated based on distances that correspond to less than ¾ of the total grid size. Prior to autocorrelation analysis, all variables were log10 transformed to improve normality and homogeneity of variances.

To examine associations between food resources, PCA derived vegetation structural variables and capture locations of adult females, the centre of activity of each adult male and female were calculated and assigned to the nearest grid point. Multiple regression (Quinn and Keough, 2002) conducted in SAM 2.0 (www.ecoevol.ufg.br/sam; Rangel et al. 2006) was used to examine if capture locations of adult males, adult females, resident adult males and resident adult females were predicted by 1) food resources, or 2) vegetation structure.

Linear regression was used to test if capture locations of resident adult females could predict capture locations of resident adult males. Prior to analysis, we tested for spatial autocorrelation in the residuals of the response variables from the linear regressions and for multicollinearity between multiple independent variables. Multicollinearity was assessed by the magnitude of the variance inflation factor (VIF) from multiple ordinary least squares regressions using SPSS 14.0 (SPSS Inc., Chicago IL) following the recommendation of Allison (1999). Correlograms and VIFs revealed no spatial autocorrelation or multicollinearity, respectively, in any of the regression models.

Various correlates of social mating systems have been proposed including home range size (Gaulin and Fitzgerald, 1988; Luque-Larena et al. 2004), home range overlap (Luque-Larena et al. 2004; Topping and Millar, 1996), sexual size dimorphism (Heske and Ostfeld, 1990; Boonstra et al. 1993; Ostfeld and Heske, 1993) and OSR (Emlen and Oring, 1977; Hoogland, 2003). To assess space use by Uromys, traditional home range estimators and overlap methods were considered unsuitable because most animals were captured only a few times. Hence, mean squared distance (MSD; Diffendorfer et al. 1995; Slade and Swihart

Appendices - 219 -

1983) was calculated as an index of home range size for adult males and females and the number of same and opposite-sex adult conspecifics that shared capture locations was used as an index of home range overlap (Roger Powell pers. comm.).

MSD was calculated following Slade and Swhihart (1983) where

n _ n _ 2 2 ∑(xi − x) + ∑(yi − y) MSD = i=1 i=1 n −1

_ _ xi and yi are locations of individual animals, x and y are the mean of the x and y coordinates, and n is the number of captures for each individual. MSD is the sum of the two-dimensional variances, is unbiased with regard to sample size and can be used with as few as 3 captures (Slade and Russell, 1998). Slade and Russell (1998) found that MSD of prairie voles showed better correlations with other methods for estimating home range size, including minimum convex polygon, harmonic mean and kernel densities, compared with other measurements of distance moved (mean distance from the centre of activity and mean distance between successive capture sites). Within-population differences for MSD were analysed using t-tests for all adults and adult residents with greater than 3 captures. The number of same and opposite-sex adults and resident adults that shared capture locations were analysed using a Mann-Whitney U test using only the second year of data to allow for standardisation of trapping periods. Additionally, all adults, irrespective of whether they were first captured as adults or first captured as juveniles that reached adulthood subsequently were used in MSD and overlap analyses.

Sexual size dimorphism of adult males and adult females was examined by calculating the maximum body mass of each adult individual at each site separately, irrespective of year or whether individuals were juveniles that reached adulthood within the population of first capture. Because pregnancy can bias female body mass upwards, visibly pregnant females were excluded from the analysis. In addition, biases caused by transient individuals were minimised by including only those animals captured > 3 times (i.e. the same data set used for MSD). Within- population differences in body mass of adult males, adult females, adult male residents and adult female residents were analysed using Mann-Whitney U tests because the statistical distribution differed from normal.

Appendices - 220 -

It is often difficult to determine exactly which individuals in a population are physiologically sexually receptive. Because all adult Uromys and very few non- adults were in breeding condition, adult sex ratios were used as an index of OSR. Biases in adult sex ratios and resident adult sex ratios were examined using a Chi- squared test for each year and site separately.

Results Spatial patterns of vegetation and Uromys distributions Spatial autocorrelograms for the vegetation variables revealed varying degrees of spatial dependence across all four sites. In the first year of the study, food resources showed significant (P < 0.05) levels of positive autocorrelation at a lag length of 50m (Moran’s I = 0.329), whereas adult males, adult females, resident adult males, resident adult females and the two principle components describing vegetation structure showed no spatial autocorrelation at any lag distances in site P1. In site P2 during the first year, the distributions of adult males and resident adult males showed significant spatial autocorrelation at 50m (Moran’s I = 0.367 and 0.395), while food resources, adult females and variables representing vegetation structure showed no significant spatial structuring. No adult females were residents in P2 in the first year.

In the second year, adult males and resident adult males in P1 were positively spatially dependent at 300m (Moran’s I = 0.333 and 0.332), whereas none of the other five variables showed any degree of spatial dependence. At site P2 in the second year, food resources showed significant positive autocorrelation at 50m (Moran’s I = 0.302) and significant negative autocorrelation at 300m (Moran’s I = -0.468). The distributions, however, of vegetation structure, adult males, adult females, adult male residents and adult female residents were not spatially autocorrelated. At site P3, the first principle component describing vegetation structure showed significant negative spatial dependence at 200m (Moran’s I = -0.259), whereas food resources, the second principle component, adult males, adult females, adult male residents and adult female residents showed no spatial structuring. Adult females and resident adult females in C1 were spatially dependent at 50m. However, none of the other five variables showed any significant positive or negative spatial autocorrelation.

Appendices - 221 -

The spatial distribution of food resources and the vegetation structure summarised in PCA1 and PCA2, representing higher density and basal area of trees in the 4-10m height class and higher density and basal area of trees in the lower 1-4m height class, respectively did not predict the spatial distribution of adult males, adult females, resident adult males or resident adult females at sites P1, P2 and C1. Furthermore, food resources and vegetation structure showed no relationship with adult males and resident adult males at site P3. A significant multiple regression for 2 adult females at P3 (F3,22 = 3.99, p = 0.02, R = 0.35), however, indicated that adult females were negatively associated with PCA1 (t = - 3.15, p = 0.005, R2 = 0.28) and positively associated with PCA2 (t = 2.28, p = 0.03, R2 = 0.15). In addition, a significant multiple regression between resident adult females and food resources, 2 PCA1 and PCA 2 (F3,22 = 3.14, p = 0.04, R = 0.29) showed resident adult females were significantly negatively related to PCA1 (t = - 2.79, p = 0.011, R2 = 0.28). No relationship was found between the spatial distribution of adult resident females and adult resident males at any site.

Space use Within-site analyses revealed that adult males and females showed no significant differences in MSD at either site P1 (t53 = 1.05, p = 0.30), P3 (t24= 0.96, p = 0.35) or

C1 (t15 = -0.35, p = 0.73; Figure 1.). In contrast, adult males had significantly larger areas of space use compared with adult females at site P2 (t4 = 3.52, p = 0.02; Figure 1.). The very small sample size at P2, however, means that this result should be treated with caution. As with adults, MSD of resident males did not differ significantly from resident females at sites P1 (t48 = 1.20, p = 0.23), P3 (t21 = 0.69, p = 0.50) or C1 (t11 = -0.14, p = 0.89; Figure 2.). Due to small sample sizes, no statistical analysis was conducted at P2, but male space use patterns were approximately four times that of females (Figure 2.).

There was little difference in the number of same-sex conspecifics that shared capture locations among sites (Figure 3.). No significant differences were observed in the number of same-sex conspecifics that shared capture locations between adult males and adult females at sites P1 (Mann-Whitney U: z = 1.67, p = 0.09), P2 (z = 0.65, p = 0.67) or C1 (z = 0.70, p = 0.48). At site P3, however, adult males overlapped twice as many same-sex conspecifics compared with adult females (z = 2.29, p = 0.02).

Appendices - 222 -

The number of opposite-sex conspecifics that shared capture locations did not differ between males and females at sites P1 (Mann-Whitney U: z = -1.45, p = 0.15), P2 (z = 0.00, p = 1.00) or P3 (z = -0.93, p = 0.35; Figure. 4.). Adult males in C1, however, overlapped significantly more opposite-sex individuals than adult females (z = -1.97, p = 0.05; Figure 4.).

The number of same-sex adult residents that shared capture locations showed marked differences among sites (Figure 5.). Significant differences between resident adult males and adult females in the number of same-sex conspecifics that shared capture locations were evident at sites P3 (Mann-Whitney U: z = 2.50, p = 0.01) and P1 (z = 1.91, p = 0.05). In contrast, adult male residents at site C1 (z = -0.07, p = 0.94) did not overlap more same-sex conspecifics than female residents (Figure 5.).

The number of opposite-sex adult residents sharing capture locations did not differ between resident adult males and females at sites P1 (z = -1.22, p = 0.22), P3 (z = -0.14, p = 0.89) or C1 (z = -0.79, p = 0.43; Figure 6.). No statistical analyses were conducted at P2 due to small sample sizes.

Body mass and sex ratios While body mass of adult males was greater than adult females, within site analysis showed no significant differences in maximum body mass between males and females at sites P1 (Mann-Whitney U: z = 1.78, p = 0.08), P3 (z = 1.69, p = 0.09) or C1 (z = 1.69, p = 0.09). In contrast, males weighed significantly more than females at P2 (z = 1.96, p = 0.05; Figure 7.). The significant result at site P2, however, should be treated with caution due to the small sample size.

Body mass of residents, while greater for males than females, were not significantly greater at P1 (z = 1.68, p = 0.09), or C1 (z = 1.57, p = 0.11). At site P3, however, resident males were significantly heavier than resident females (z = 2.27, p = 0.02; Figure 8.). Small sample sizes in P2 precluded statistical analysis.

Adult sex ratios were not significantly male-biased at any site: P1 (♂ = 36, ♀ = 38; χ2 = 0.54, p = 0.82), P2 (♂ = 9, ♀ = 6; χ2 = 0.6, p = 0.44), P3 (♂ = 29, ♀ = 21; χ2 = 1.28, p = 0.26) and C1 (♂ = 13, ♀ = 9; χ2 = 0.73, p = 0.39). Similarly, resident adult sex ratios failed to show any significant bias towards males in any of the four

Appendices - 223 - sites: P1 (♂ = 29, ♀ = 24; χ2 = 0.47, p = 0.49), P2 (♂ = 2, ♀ = 3), P3 (♂ = 16, ♀ = 12; χ2 = 0.57, p = 0.45) and C1 (♂ = 6, ♀ = 7; χ2 = 0.07, p = 0.78).

Discussion Spatial distribution patterns These results show that the spatial distribution of food resources and vegetation variables generally did not differ greatly among sites. Food resources that provide giant white-tailed rats with high quality food for reproduction (Haken and Batzli, 1996) and that influence population size (Streatfeild, 2008 - chapter 3), showed greater spatial structuring in P1 during the first year and P2 during the second year than for either P3 or C1. Limited spatial structuring of food resources and a lack of structure observed in the vegetation variables was not unexpected for a number of reasons. Many tropical rainforest trees produce food resources seasonally and/or periodically (Adler and Beatty, 1997) with fruits and nuts of some species often lasting for only very short periods of time (Adler et al. 1997). If the spatial location of fruit producing trees differed among sampling periods, such that over a yearly cycle most of the entire trapping grid received similar quantities of total fruit fall, then there should be minimal spatial autocorrelation among neighbouring grid points. Due to low sample sizes of the preferred food resources, spatial autocorrelation analyses were not possible for all sites on a sampling trip basis. The spatial location of fruit producing trees at sites P1 and P3, however, did appear to vary within each fragment with certain areas producing fruit at different times of the year.

The spatial distribution of females is likely to show a similar degree of spatial structuring as resources. This prediction was supported in general, with correlograms showing no significant spatial autocorrelation for adult or resident females at all sites except C1, where females showed spatial structuring over 50m. For abundant and highly aggregated resources, the costs of resource defence by a single female would likely outweigh any benefits, resulting in considerable home range overlap among neighbouring females (Wauters and Dondt, 1992; Quinn and Whisson, 2005) and spatial structuring (Kraus et al. 2003). For less abundant and less aggregated resources, however, the benefits of resource defence may outweigh any costs leading to female home ranges that do not overlap (Kraus et al. 2003), and limited spatial structure, as observed here. Interestingly, in a review of vertebrate social systems, Lott (1991) suggested that social monogamy most likely

Appendices - 224 - evolves in response to habitats with resources that show little spatial structure (see also Emlen and Oring, 1977; Sun, 2003), a pattern also seen here.

Spatial associations between Uromys and food resources Uromys prefer certain suites of high quality food resources (Finkelstein and Grubb, 2002; Moore, 1995) and have higher population densities and reproductive rates in habitats with high quality food resources (Streatfeild, 2008 - chapter 3). Although population density of giant white-tailed rats was not associated with vegetation structure, areas of higher vegetation structure and cover may provide more protection from predators and influence their spatial structure. Hence, the spatial distribution of food resources and vegetation structure may predict the spatial distribution of adult and resident adult females. In contrast to this prediction, however, vegetation structure and food resources failed to predict the capture locations of adult females or resident females in any of the four sites, with the exception of females at P3. In addition, females failed to predict the capture locations of males at all four sites. These data in general, therefore, do not support the prediction that spatial structuring of resources influences the spatial distribution of female Uromys, and in turn, the distribution of male Uromys.

The finding that food resources and vegetation structure were not concordant with the spatial location of females was surprising. Small mammals, however, may make decisions on which area they utilise based on availability of not only food and shelter, but also the availability of nest sites, water and potentially, several other parameters (Lacher and Mares, 1996). For example, the spatial distribution of female raccoons (Procyon lotor) is determined primarily by standing water (Gehrt and Fritzell, 1998) and the spatial distribution of riparian woodrats (Neotoma fuscipes riparia) is primarily determined by the availability of den sites as well as a combination of several other microhabitat characteristics (Gerber et al. 2003). Uromys are known to occupy a range of nest sites including rocky outcrops, tree crowns, fallen logs, buttress roots and river bank burrows (Craig Streatfeild, unpubl. data; Moore, 1995; Tad Theimer, pers. comm.).

Uromys generally reside in nests that are either underground or in enclosed structures, hence the need for predator protection afforded by vegetative cover may be reduced allowing Uromys to occupy microhabitats with lower vegetation cover as found in prairie voles (Klatt and Getz, 1987). Suitable conditions for nest sites,

Appendices - 225 - therefore, may be more important than vegetation structure or food resources for predicting the spatial distribution of U. caudimaculatus individuals. This is consistent with radio tracking data from sites P1 and P2 (Craig Streatfeild unpubl. data) showing that some females utilise nest locations outside of the remnant fragment – an outcome that suggests limited nest availability. This was particularly evident at P1, where, for approximately two years, two adult females occupied nest sites in trees that were located in the surrounding matrix. The two females returned to the remnant fragment on a nightly basis to forage. Further, Solomon et al. (2005) also found that prairie vole (Microtus ochrogaster) nests were not associated with high vegetative cover or areas of high quality food and Rhodes and Richmond (1985) found that the spatial distribution of pine voles (M. pinetorum) was determined by the location of nest sites that were constructed preferentially in areas with cool soil temperatures and intermediate levels of soil moisture.

Capture locations of U. caudimaculatus were assumed to occur at foraging locations. Several lines of evidence suggest, however, that this may not necessarily be the case. First, radio tracking from P1 (Craig Streatfeild unpubl. data) showed that Uromys generally entered traps in the last few hours of the nocturnal activity period, and after foraging had been completed for the night. Hence, individuals may have been captured, not at their foraging location, but on their way back to their nest sites. In addition, incidental trapping that was conducted over several 24 hour periods confirmed that Uromys, as well as Melomys cervinipes and Rattus spp. generally entered traps towards the end of foraging activities (John Wilson unpubl. data). Finally, similar to several other tropical rainforest rodent species, Uromys are known to cache food resources (Goldberg, 1994; Theimer, 2001) and appear to select a variety of microhabitats including areas at the base of trees, saplings and fallen logs for this purpose. Hence, if Uromys were captured near cache locations, associations between areas of high density food resources or structurally complex vegetation would not be evident.

Space use patterns, morphology and OSR as correlates of mating systems In a review of female space use in relation to social mating systems, Komers and Brotherton (1997) stated that female space use is the best predictor of mammalian social mating systems with socially monogamous females occupying small and exclusive home ranges that can be defended by only a single male (but see Swihart and Slade, 1989). These results show that adult male Uromys had larger home

Appendices - 226 - ranges relative to adult females at site P2 but similar sized home ranges in the remaining three populations. The findings from P2, however, may simply be the result of a statistical artefact due to small sample sizes and should be treated with caution.

Similar home range sizes for males and females shown here are supported by data from Comport (1999) who found no differences in home range size between males and female Uromys using radio tracking, and Horskins (2005) who found no differences in distances moved for male and female Uromys on the Atherton Tableland. These data are also concordant with several other studies that showed no differences in home range size between males and females (e.g. Gaulin and Fitzgerald, 1986, 1988; Ribble and Salvoni, 1990; Dos Santos Pires and Dos Santos Fernandez, 1999; Seamon and Adler, 1999; Puckey et al. 2004), but contrast numerous studies showing males moved greater distances compared with females (Swihart and Slade, 1989; Bubela and Happold, 1993; Diffendorfer et al. 1995; Slade et al. 1997; Shier and Randall, 2004; Wu and Yu, 2000). If the data for P2 is representative of what occurs in larger populations, Uromys at P2 may exhibit space use patterns conducive to polygyny, whereas patterns of space use at sites P1, P3 and C1 are more similar to that expected from a socially monogamous mammal (Gaulin and Fitzgerald, 1986, 1988).

Inferring social mating systems from space use patterns can, however, be more complex than outlined by Komers and Brotherton (1997) and is a function of not only female space use but also male space use and intrasexual and intersexual overlap. For instance, social monogamy is predicted when males and females exhibit low levels of intra- and intersexual overlap. However, higher degrees of intra- and intersexual overlap, particularly for males, may promote social polygyny (Luque-Larena et al. 2004). These data show varying levels of intra- and intersexual overlap for males and females as well as resident males and females across the four populations.

The small number of opposite-sex individuals that overlapped at P2 are consistent with expected patterns for social monogamy, and are similar to that seen in Microtus ochrogaster (Hofmann et al. 1984), Peromyscus californicus (Ribble and Salvioni, 1990) and Cynomys gunnisoni (when resources were uniformly distributed; Travis et al. 1995). In particular, Hofmann et al. (1984) showed that socially

Appendices - 227 - monogamous prairie voles had extensive home range overlap with their mates but little or no overlap with other conspecifics. In contrast, the pattern of higher levels of intra- and intersexual overlap shown at sites P1, P3 and C1 are consistent with socially polygamous/promiscuous mammal species including M. pennsylvanicus (Madison, 1980), M. californicus (Ostfeld, 1986), Mastacomys fuscus (Bubela and Happold, 1993), Neotoma cinerea (Topping and Millar, 1996), Proechimys semispinosus (Seamon and Adler, 1999), Niviventer coxingi (Wu and Yu, 2000) and Chionomys nivalis (Luque-Larena et al. 2004). Interestingly, Comport (1999) found extensive home range overlap among males, males and females but not among female Uromys, suggesting territoriality among females but not males, suggesting a polygamy/promiscuity social mating system. Based on MSD and intra- and intersexual overlap, Uromys exhibit characteristics associated with social monogamy as well as social polygamy/promiscuity but this appears to be independent of site and degree of habitat fragmentation.

Moreover, the degree of overlap may be relatively more important than MSD as an indicator of the social mating system because home range overlap is directly related to the number of mates to which an individual has access (Banks et al. 2007). Thus, home range overlap implies that Uromys exhibit intraspecific variability in their social mating system with overlap patterns suggesting social monogamy is the predominant mating system in P2 while social polygamy/promiscuity predominates in P1, P3 and C1.

Space use has been hypothesised to differ in fragmented relative to unfragmented populations due to higher densities in fragments resulting from decreased dispersal. In such circumstances, space use is expected to decrease and individual overlap to increase (Ims et al. 1993). Qualitative comparisons between MSD and overlap in the four populations did not reveal, however, any apparent affect of fragmentation per se. Rather, there is some evidence to suggest that density may influence opposite-sex overlap. Population P1 showed much higher densities compared with the remaining three populations and an examination of figures 4 and 6 shows that Uromys within P1 also overlapped more often with opposite-sex conspecifics compared with Uromys at sites P2, P3 and C1. Uromys at site P3, however, often overlapped with fewer individuals compared with Uromys within C1, where densities where significantly lower, suggesting that a complex relationship may exist between density and home range overlap.

Appendices - 228 -

Although male Uromys were generally heavier than females, only for adult males at P2 and resident adult males at P3 was this significant. Sexual selection theory predicts positive correlations between sexual size dimorphism and the opportunity for intrasexual competition for mates (Darwin 1871; Alexander et al. 1979). For example, in arvicoline rodents, polygyny is characterized by more intense intrasexual competition among males relative to that seen for monogamous species, leading to more pronounced sexual dimorphism in the former relative to the latter mating system (Heske and Ostfeld, 1990; Boonstra et al. 1993; Ostfeld and Heske, 1993). Comparisons with the social mating systems of small mammals show that the degree of sexual dimorphism found for adults at P2 and resident adults in P3 more closely resembles that of a polygynous mating system, while a lack of sexual dimorphism in sites P1 and C1 more closely resembles that found in monogamous social mating systems (Boonstra et al. 1993; Luque-Larena et al. 2004, but see Lambin and Krebs, 1991). These results are partly consistent with Horskins (2005) who found no difference in body mass between adult male and female Uromys. In Uromys, however, body weight generally increases with age (Les Moore, pers. comm.) implying that the observed differences in size dimorphism among males and females could simply be due to age related differences, rather than sex related differences.

OSR may indirectly affect social mating systems as a result of variation in the intensity of sexual selection (Emlen and Oring, 1977). When OSR are male-biased, sexually receptive females are a limiting resource and the intensity of competition and aggression among males for mates is likely to be high. This, in turn, should promote larger body size in males compared with females and result in a positive correlation between male body size and individual reproductive success (Yoccoz and Mesnager, 1998). As the OSR becomes increasingly male-biased, however, the high costs associated with increased intruder pressure may limit the ability of the most competitive males to monopolize females. This can lead to opportunities for females to mate with multiple males (Emlen and Oring, 1977). In contrast, an OSR approaching unity is more consistent with social monogamy (Hoogland, 2003).

Although adult sex ratios in the four sites examined here are generally consistent with the degree of sexual dimorphism in body size as predicted from sexual selection theory, it is unknown whether OSR has influenced patterns of space use

Appendices - 229 - in male and female Uromys. Previous studies do not show a consistent effect of OSR on social mating systems. For example, Hoogland (2003) found no relationship between the OSR and social mating system in five species of Cynomys, whereas Luque-Larena et al. (2004) found a balanced OSR for Chionomys nivalis and suggested that this was consistent with a promiscuous social mating system. In addition, Michener and Mclean (1996) reported that conflict between sexually receptive male Spermophilus richardsonii for access to females was highest when the OSR was not biased. Experimental manipulations would be needed to separate the effects of space use, morphology and OSR on social mating systems in Uromys. In addition, these interactions may be complicated by the use of adult sex ratios as a surrogate for OSR.

Conclusions Taken together, these data do not provide clear evidence that Uromys exhibit intraspecific differences in social mating systems. The results show that certain indices of social mating system, including space use (sites P1, P3, and C1), same and opposite-sex overlap in (site P2) and body mass (sites P1 and P3), are in general, conducive to a monogamous social mating system. None of the indices of social mating systems that are conducive to either monogamy or polygyny were found consistently in any single population. Even though no consistent relationships were found between the distribution of resources and the distribution of adult and resident adult females, it is possible that within population mating system flexibility occurs as a result of differential use of resources by some individuals. This would be evident in statistical analyses at finer levels of spatial and temporal scales than possible here. Experimental manipulations and analyses on a finer spatial and temporal scale are needed to investigate further the social mating systems of Uromys and the ecological conditions that influence them. Further, genetic paternity analysis is required to better understand the mating system of giant white-tailed rats.

Appendices - 230 -

Acknowledgements We thank David Elmouttie, David Hurwood, Kerrilee Horksins, Andrew Hayes, Sam Maynard, Barry Jenkins, Liz Dunlop, Nathan Jensen, Martine Adriaansen and the students in the 2000 – 2004 QUT Ecological Applications classes for providing valuable help in the field during this study. We also thank Nigel Ticker for identifying the plant species and help with identifying food resources and the staff at the Lake Eacham and Townsville offices of Queensland Parks and Wildlife Service. All filed work was conducted on private land, therefore, we also thank the Beattie (site P1), Williams (site P2) and Walker (site P3) families and Sinan Ogden and Moni Carlisle (site C1). Norm Slade, Roger Powell, Tom Christ, Nancy Solomon and Brian Keane are thanked for their valuable advice concerning indices of spaces use and overlap, and Lance Waller for discussions on the use of spatial statistics. Funding for this study was provided by a grant from the Australian Geographic Society to CAS and an Australian Postgraduate Award Scholarship to CAS.

Appendices - 231 -

References Adler, G.H. & Beatty, R.P. (1997) Changing reproductive rates in a Neotropical forest rodent, Proechimys semispinosus. Journal of Animal Ecology, 66, 472-480. Adler, G.H., Endries, M., & Piotter, S. (1997) Spacing patterns within populations of a tropical forest rodent, Proechimys semispinosus, on five Panamanian islands. Journal of Zoology, 240, 43-53. Alexander, R.D., Hoogland, J.L., Howard, R.D., Noonan, K.M., & Sherman, P.W. (1979). Sexual dimorphism and breeding systems in pinnipeds, ungulates, primates and humans. In Evolutionary biology and human social behavior : an anthropological perspective (eds N.A. Chagnon & W. Irons). Duxbury Press, North Scituate, MS, U.S.A. Allison, P.D. (1999) Logistic Regression Using the SAS System – Theory and Application SAS Institute, Inc., Cary, N.C., U.S.A. Benjamini, Y. & Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. B, 57, 289-300. Boonstra, R., Gilbert, B.S., & Krebs, C.J. (1993) Mating systems and sexual dimorphism in mass in microtines. Journal of Mammalogy, 74, 224-229. Boutin, S. (1990) Food supplementation experiments with terrestrial vertebrates: patterns, problems, and the future. Canadian Journal of Zoology, 68, 203- 220. Bubela, T.M. & Happold, D.C.D. (1993) The social organisation and mating system of an Australian subalpine rodent, the broad-toothed rat, Mastacomys fuscus, in subalpine Australia. Wildlife Research, 20, 405-417. Carranza, J. & Valencia, J. (1999) Red deer females collect on male clumps at mating areas. Behavioral Ecology, 10, 525-532. Clutton-Brock, T.H. (1989) Mammalian mating systems. Proceedings of The Royal Society of London B., 236, 339-372. Clutton-Brock, T.H. & Parker, G.A. (1992) Potential Reproductive Rates and the Operation of Sexual Selection. The Quarterly Review of Biology, 67, 437- 456. Comport, S.S. (1999) Habitat utilisation and dispersal of fungal spores by the White- tailed rat on the rainforest-wet sclerophyll boundary in the Wet Tropics. MSc, James Cook University, Townsville.

Appendices - 232 -

Cooper, W. & Cooper, W.T. (1994) Fruits of the rain forest. A guide to fruits in Australian tropical rain forests R. D. Press, Surry Hills, N.S.W., Australia. Darwin, C.R. (1871) The descent of man and selection in relation to sex John Murray, London, U.K. Davies, N.B. & Lundberg, A. (1984) Food distribution and a variable mating system in the dunnock, Prunella modularis. Journal of Animal Ecology, 53, 895-912. Dennis, A.J. (1997) Musky rat-kangaroos, Hypsiprymnodon moschatus, cursorial frugivores in Australia's wet-tropical rain-forests. PhD, James Cook University, Townsville. Dennis, A.J. & Marsh, H. (1997) Seasonal reproduction in musky rat-kangaroos, Hysiprymnodon moschatus, a response to changes in resource availability. Wildlife Research, 24, 561-578. Diffendorfer, J.E., Gaines, M.S., & Holt, R.D. (1995) Habitat fragmentation and movements of three small mammals (Sigmodon, Microtus and Peromyscus). Ecology, 76, 827-839. Diniz-Filho, J.A.F., Bini, L.M., & Hawkins, B.A. (2003) Spatial autocorrelation and red herrings in geographical ecology. Global Ecology and Biogeography, 12, 53-64. Dos Santos Pires, A. & Antonio Dos Santos Fernandez, F. (1999) Use of space by the marsupial Micoureus demerarae in small Atlantic Forest fragments in south-eastern Brazil. Journal of Tropical Ecology, 15, 279-290. Eberle, M. & Kappeler, P.M. (2002) Mouse Lemurs in space and time: a test of the socioecological model. Behavioral Ecology and Sociobiology, 51, 131-139. Emlen, S.T. & Oring, L.W. (1977) Ecology, Sexual Selection, and the Evolution of Mating Systems. Science, 197, 215-223. Finkelstein, S. & Grubb, P.J. (2002) Lipid concentration in the embryo–endosperm fraction of seeds of Australian tropical lowland rainforest trees: relevance to defence and dispersal. Seed Science Research, 12, 173-180. Fortin, M.-J. & Dale, M.R.T. (2005) Spatial analysis: a guide for ecologists Cambridge University Press, Cambridge. U.K. Gaulin, S.J.C. & Fitzgerald, R.W. (1986) Sex differences in spatial ability: an evolutionary hypothesis and test. American Naturalist, 127, 74-88. Gaulin, S.J.C. & Fitzgerald, R.W. (1988) Home-range as a predictor of mating systems in Microtus. Journal of Mammalogy, 69, 311-319.

Appendices - 233 -

Gehrt, S.D. & Fritzell, E.K. (1998) Resource distribution, female home range dispersion and male spatial interactions: group structure in a solitary carnivore. Animal Behaviour, 55, 1211-1227. Gerber, L.R., Seabloom, E.W., Burton, R.S., & Reichman, O.J. (2003) Translocation of an imperilled woodrat population: integrating spatial and habitat patterns. Animal Conservation, 6, 309-316. Goldberg, J. (1994) An investigation into seed predation and caching in a tropical rain-forest rodent, Uromys caudimaculatus, the white-tailed rat. Honours, James Cook University, Townsville. Goosem, S.P., Morgan, G., & Kemp, J.E. (1999). Regional ecosystems of the wet tropics. In The Conservation Status of Queensland’s Bioregional Ecosystems (eds P.S. Sattler & R.D. Williams), pp. 1-72. Environmental Protection Agency, Brisbane, QLD, Australia. Haken, A.E. & Batzli, G.O. (1996) Effects of Availability of Food and Interspecific Competition on Diets of Prairie Voles (Microtus ochrogaster). Journal of Mammalogy, 77, 315-324. Harrington, G.N., Irvine, A.K., Crome, F.H.J., & Moore, L.A. (1997). Regeneration of Large-Seeded Trees in Australian Rainforest Fragments: A Study of Higher- Order Interactions. In Tropical Forest Remnants. Ecology, Management, and Conservation of Fragmented Communities (eds W.F. Laurance & R.O. Bierregaard), pp. 292-303. The University of Chicago Press, Chicago, IL, U.S.A. Heske, E.J. & Ostfeld, R.S. (1990) Sexual dimorphism in size, relative size of testes, and mating systems in North American voles. Journal of Mammalogy, 71, 510-519. Hoogland, J.L. (2003) Sexual dimorphism in prairie dogs. Journal of Mammalogy, 84, 1254-1266. Horskins, K. (2005) The effectiveness of wildlife corridors in facilitating connectivity: assessment of a model system from the Australian Wet Tropics. PhD, Queensland University of Technology, Brisbane. Ims, R.A., Rolstad, J., & Wegge, P. (1993) Predicting space use responses to habitat fragmentation: can voles Microtus oeconomus serve as an experimental model system (EMS) for capercaillie grouse Tetrao urogallus in boreal forest? Biological Conservation, 67, 261-268.

Appendices - 234 -

Klatt, B.J. & Getz, L.L. (1987) Vegetation characteristics of Microtus ochrogaster and M. pennsylvanicus habitats in east-central Illinois. Journal of Mammalogy, 68, 569-577. Kleiman, D.G. (1977) Monogamy in mammals. Quarterly Review of Biology, 52, 39- 69. Komers, P.E. & Brotherton, P.N.M. (1997) Female space use is the best predictor of monogamy in mammals. Proceedings of the Royal Society of London. B, 264, 1261-1270. Kraus, C., Kunkele, J., & Trillmich, F. (2003) Spacing behaviour and its implication for the mating system of a precocial small mammal: an almost asocial cavy Cavia magna? Animal Behaviour, 66, 225-238. Krebs, C.J. (1999) Ecological Methodology, 2nd edn. Addison-Welsey, Menlo Park, CA., U.S.A. Lacher, T.E., Jr. & Mares, M.A. (1996) Availability of resources and use of space in eastern chipmunks, Tamias striatus. Journal of Mammalogy, 77, 833-849. Lambin, X. & Krebs, C.J. (1991) Spatial organization and mating system of Microtus townsendii. Behavioral Ecology and Sociobiology, 28, 353-363. Legendre, P. & Fortin, M.-J. (1989) Spatial pattern and ecological analysis. Plant Ecology, 80, 107-138. Lindstrom, K. & Seppa, T. (1996) The environmental potential for polygyny and sexual selection in the sand goby, Pomatoschistus minutus. Proceedings of the Royal Society of London. B, 163, 1319-1323. Lott, D.F. (1991) Intraspecific Variation in the Social Systems of Wild Vertebrates Cambridge University Press, Cambridge, U.K. Luque-Larena, J.J., Lopez, P., & Gosalbez, J. (2004) Spacing behavior and morphology predict promiscuous mating strategies in the rock-dwelling snow vole Chionomys nivalis. Canadian Journal of Zoology, 82, 1051-1060. Madison, D.M. (1980) Space use and social structure in meadow voles, Microtus pennsylvanicus. Behavioral Ecology and Sociobiology, 7, 65-71. Michener, G.R. & Mclean, I.G. (1996) Reproductive behaviour and operational sex ratio in Richardson's ground squirrels. Animal Behaviour, 52, 743-758. Moore, L.A. (1995). Giant white-tailed rat. In The Mammals of Australia (eds R. Strahan), pp. 638-640. Reed Books, Sydney, N.S.W., Australia. Ostfeld, R.S. (1986) Territoriality and Mating System of California Voles Journal of Animal Ecology, 55, 691-706.

Appendices - 235 -

Ostfeld, R.S. & Heske, E.J. (1993) Sexual dimorphism and mating systems in voles. Journal of Mammalogy, 74, 230-233. Pollard, J.H. (1971) On distance estimators of density in randomly distributed forests. Biometrics, 27, 991-1002. Puckey, H., Lewis, M., Hooper, D., & Michell, C. (2004) Home range, movement and habitat utilisation of the Carpentarian rock-rat (Zyzomys palatalis) in an isolated habitat patch. Wildlife Research, 31, 327-337. Quinn, G.P. & Keough, M.J. (2002) Experimental Design and Data Analysis for Biologists Cambridge University Press, Cambridge, U.K. Quinn, J.H. & Whisson, D.A. (2005) The effects of anthropogentic food on the spatial behaviour of small Indian mongooses (Herpestes javanicus) in a subtropical rainforest. Journal of Zoology, 267, 339-350. Rangel, T.F.L.V.B., Diniz-Filho, J.A.F., & Bini, L.M. (2006) Towards an integrated computational tool for spatial analysis in macroecology and biogeography. Global Ecology and Biogeography, 15, 321-327. Ransome, D.B. & Sullivan, T.P. (1997) Food limitation and habitat preference of Glaucomys sabrinus and Tamiasciurus hudsonicus. Journal of Mammalogy, 78, 538-549. Reynolds, J.D. (1996) Animal breeding systems. Trends in Ecology & Evolution, 11, 68-72. Rhodes, D.H. & Richmond, M.E. (1985) Influence of soil texture, moisture and temperature on nest-site selection and burrowing by the pine vole, Microtus pinetorum. American Midland Naturalist, 113, 102-108. Rice, W.R. (1989) Analyzing table of statistical tests. Evolution, 43, 223-225. Ribble, D.O. & Salvioni, M. (1990) Social organization and nest co-occupancy in Peromyscus californicus, a monogamous rodent. Behavioral Ecology and Sociobiology, 26, 9-15. Seamon, J.O. & Adler, G.H. (1999) Short-term use of space by a Neotropical forest rodent, Proechimys semispinosus. Journal of Mammalogy, 80, 899-904. Shier, D.M. & Randall, J.A. (2004) Spacing as a Predictor of Social Organization in Kangaroo Rats (Dipodomys heermanni arenae). Journal of Mammalogy, 85, 1002-1008. Slade, N.A. & Russell, L.A. (1998) Distances as indices to movements and home- range size from trapping records of small mammals. Journal of Mammalogy, 79, 346-351.

Appendices - 236 -

Slade, N.A., Russell, L.A., & Doonan, T.J. (1997) The impact of supplemental food on movements of prairie voles (Microtus ochrogaster). Journal of Mammalogy, 78, 1149-1155. Slade, N.A. & Swihart, R.K. (1983) Home range indices for the hispid cotton rat (Sigmodon hispidus) in northeastern Kansas. Journal of Mammalogy, 64, 580-590. Solomon, N.G., Christiansen, A.M., Lin, Y.K., & Hayes, L.D. (2005) Factors affecting nest site location of prairie voles (Microtus ochrogaster). Journal of Mammalogy, 86, 555-560. Streatfeild, C.A., (2008) The effects of habitat fragmentation on the demography and population genetic structure of Uromys caudimaculatus. PhD, Queensland University of Technology, Brisbane. Sun, L. (2003). Monogamy correlates, socioecological factors, and mating systems in beavers. In Monogamy: Mating Strategies and Partnerships in Brids, Humans and Other Mammals (eds U.H. Reichard & C. Boesch), pp. 138- 146. Cambridge Univerity Press, Cambridge, U.K. Swihart, R.K. & Slade., N.A. (1989) Differences in home-range size between sexes of Microtus ochrogaster. Journal of Mammalogy, 70, 816-820. Theimer, T.C. (2001) Seed scatterhoarding by white-tailed rats: consequences for seedling recruitment by an Australian rain forest tree. Journal of Tropical Ecology, 17, 177-189. Topping, M.G. & Millar, J.S. (1996) Spatial distribution in the bushy-tailed woodrat (Neotoma cinerea) and its implications for the mating system. Canadian Journal of Zoology, 74, 565-569. Tracey, J.G. & Webb, L.J. (1975) Key to the vegetation of the humid tropical region of North Queensland, with 15 maps at 1:100,000 scale CSIRO Long Pocket Laboratories, Indooroopilly, QLD, Australia. Travis, S.E., Slobodchikoff, C.N., & Keim, P. (1995) Ecological and demographic effects on intraspecific variation in the social system of prairie dogs. Ecology, 76, 1794-1803. Trivers, R.L. (1972). Parental investment and sexual selection. In Sexual Selection and the Descent of Man 1871–1971. (ed B. Campbell), pp. 136–179. Aldine, Chicago, IL, U.S.A. Warburton, N.H. (1997). Remnants in tropical Australia. In Tropical Forest Remnants: Ecology, Management and Conservation of Fragmented

Appendices - 237 -

Communities (eds W.F. Laurance & R.O. Bierregaard). University of Chicago Press, Chicago, IL, U.S.A. Wauters, L. & Dondt, A.A. (1992) Spacing behaviour of red squirrels, Sciurus vulgaris: variation between habitats and the sexes. Animal Behaviour, 43, 297-311. Wu, H.-Y. & Yu, H.-T. (2000) Population dynamics of the spiny country rat (Niviventer coxingi) in a subtropical montane forest in central Taiwan. Journal of Zoology, 250, 339-346. Wilson, E.O. (2000) Sociobiology: the new synthesis, 25th anniversary edn. Belknap Press of Harvard University Press, Cambridge, U.K. Yoccoz, N.G. & Mesnager, S. (1998) Are alpine bank voles larger and more sexually dimorphic because adults survive better? Oikos, 82, 85-98.

Appendices - 238 -

Figure Legends

Figure 1. Mean squared distance moved (± 1 SE) for all adult male (M) and adult female (F) Uromys for all sites. Numbers above error bars represent sample sizes.

Figure 2. Mean squared distance moved (± 1 SE) for all resident adult males (M) and resident adult females (F) for all sites. Numbers above error bars represent sample sizes.

Figure 3. Mean number of adult males (M) and adult females (F) that shared capture locations with same-sex adult conspecifics (± 1 SE) for all sites in the second year only. Numbers above error bars represent sample sizes.

Figure 4. Mean number of adult males (M) and adult females (F) that shared capture locations with opposite-sex adult conspecifics (± 1 SE) for all sites in the second year only. Numbers above error bars represent sample sizes.

Figure 5. Mean number of resident adult males (M) and resident adult females (F) that shared capture locations with same-sex adult conspecifics (± 1 SE) for all sites in the second year only. Numbers above error bars represent sample sizes.

Figure 6. Mean number of resident adult males (M) and resident adult females (F) that shared capture locations with same-sex adult conspecifics (± 1 SE) for all sites in the second year only. Numbers above error bars represent sample sizes.

Figure 7. Maximum body mass (mean ± 1 SE) of adult males and females. Numbers above error bars represent sample sizes.

Figure 8. Maximum body mass (mean ± 1 SE) of resident adult males and resident adult females. Numbers above error bars represent sample sizes.

Appendices - 239 -

Fig. 1.

Appendices - 240 -

Fig. 2.

Appendices - 241 -

Fig. 3.

Appendices - 242 -

Fig. 4.

Appendices - 243 -

Fig. 5

Appendices - 244 -

Fig. 6.

Appendices - 245 -

Fig. 7.

Appendices - 246 -

Fig. 8.