The Importance of Flowering Resources to the Ecology of the Western Pygmy Possum, Cercartetus Concinnus

The importance of flowering resources to the ecology of the Western Pygmy possum, Cercartetus concinnus

Briony Ruth Horner School of Earth and Environmental Sciences The University of Adelaide

June 2012

Contents

Contents ...... ii

Figures ...... iv

Tables ...... vi

Plates ...... xi

Abstract ...... xii

Statement of Originality ...... xiv

Acknowledgements ...... xv

Chapter 1 : Introduction...... 1

1.1 Factors that shape populations ...... 1

1.2 Flowering Plants ...... 6

1.3 Pollination ...... 9

1.4 Cercartetus species ...... 10

1.5 Cercartetus concinnus ...... 13

1.6 Aims ...... 14

Chapter 2 : Floral resources and the demographic patterns of Cercartetus concinnus ...... 16

2.1 Introduction ...... 17

2.2 Methods ...... 20

2.3 Results ...... 26

2.4 Discussion ...... 43

Chapter 3 : Foraging on spatially and temporally heterogeneous food resources ...... 49

3.1 Introduction ...... 50

3.2 Methods ...... 52

3.3 Results ...... 58

3.4 Discussion ...... 69

Chapter 4 : Feeding preferences of a nectarivorous marsupial; choices made between flowering species ...... 76

4.1 Introduction ...... 77

4.2 Methods ...... 80

4.3 Results ...... 88

4.4 Discussion ...... 103

Chapter 5 : Discussion...... 110

Chapter 6 : Appendices ...... 118

Chapter 7 : References ...... 164

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Figures

Figure 2.1: Total monthly rainfall in millimetres over the two years of the study (Bureau of

Meteorology 2000)...... 20

Figure 2.2: Monthly captures of individual male (dark) and female (open) C. concinnus. Each month represents 1800 trap nights...... 27

Figure 2.3: The capture frequency of individual male (dark) and female (open) C. concinnus.

...... 28

Figure 2.4: The percentage of new (dark) and recaptured (open) individuals per month...... 28

Figure 2.5: Monthly captures of adults (dark), sub-adults (open) and juveniles (shaded) expressed as a percentage...... 30

Figure 2.6: Monthly captures of female individuals with pouch young (dark) and suckling young (open)...... 30

Figure 2.7: Predictions of the number of females giving birth each month, based on females with pouch young and suckling young...... 31

Figure 2.8: Monthly mean weights (± s.e.) for adults. a) Male, b) Females without pouch young...... 33

Figure 2.9: The flowering densities per month of the six dominant species at Newland Head

Conservation Park, expressed as the number of survey plots with the species flowering...... 34

Figure 2.10: The presence of flowering over seasons and habitat types for each of the key flowering species, portrayed using a decision tree ...... 35

Figure 2.11: Mean (± s.e.) captures of C. concinnus for each season and grid within a habitat type, with mean (± s.e.) flowering densities per season and grid for the two dominant flowering species in each season...... 38

Figure 2.12: Mean (± s.e.) captures of adult a) male and b) female C. concinnus over seasons

(1, 2 and 3) and habitats A (dark), B (open) and C (shaded)...... 39

Figure 2.13: The proportion of females to males captured per month over two years as a function of the number of grids containing plants in flower for all of the 6 species combined. n=24, R2=0.22 ...... 41

Figure 4.1: The number of times a species was visited first by C. concinnus, when exposed to flowers of two different species...... 88

Figure 4.2: Feeding of C. concinnus at pairs of flowering species during experimental preference trials...... 91

Figure 4.3: Feeding of a) male and b) female C. concinnus at pairs of flowering species during experimental preference trials...... 94

Figure 6.1: Example of the output of a package created by Alltraders Pty Ltd to determine the availability of each habitat component to the animal in each tracking event...... 127

Figure 6.2: Example of the output of a package created by Alltraders Pty Ltd to determine the availability of each habitat component to the animal in each tracking event...... 128

Tables

Table 2.1: The duration of reproductive activities for C. concinnus based on observations made by Bowley (1939) and Casanova (1958)...... 23

Table 2.2: Numbers of C. concinnus captured, marked and recaptured over 24 months of trapping at Newland Head Conservation Park...... 26

Table 2.3: Movements and use of habitats by recaptured animals within and between trips.

Short distances ranged from 10 to 100m, long distances were over 100m...... 29

Table 2.5: The logistic regression models used to predict the influence of space (Grids - G), time (Season - S and Year - Y) and flowering species (F) on the presence or absence of captures of C. concinnus...... 36

Table 2.6: The estimated odds of the presence of C. concinnus increasing in response to an increase in the presence of flowering species...... 37

Table 2.7: AIC model selection for captures of male and female C. concinnus using space

(Grids - G), time (Season - S) and flowering species (FS). Females are divided into those with and without pouch young (PY)...... 40

Table 2.8: The odds of capturing male and female C. concinnus when particular plant species were flowering. PY (pouch young)...... 40

Table 2.9: Models selected using logistic regression with a binomial distribution and logit link function to determine the influence of flowering species and the amount of flowering present on sex ratios (the proportion of females to males) and reproductive activity (the proportion of females with pouch young)...... 42

Table 3.1: AIC selection of fixed effects models for each activity using season (S), sex and habitat component (HC) as variables...... 62

Table 3.2: The probability of C. concinnus spending time in each habitat component for each activity...... 63

Table 3.3: Relative ranking of preference for habitat components by C. concinnus derived from compositional analysis...... 67

Table 4.1: The number of male and female C. concinnus used in trials for each plant species pair...... 80

Table 4.2: The influence of sex and species pair (SP) on the proportion of times C. concinnus visited a species first...... 89

Table 4.3: The odds of a species being visited first by C. concinnus, calculated using the null model (for eucalypt v non-eucalypt) and species pair model (for species pair)...... 89

Table 4.5: Odds ratios calculated for a) the proportion of feeding bouts and b) the proportion of time spent feeding by male and female C. concinnus, calculated using the model selected by

AIC...... 95

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Table 4.7: The influence of floral characteristics on the feeding behaviours of C. concinnus.

Determined using Poisson regression with a log link function. The strongest models were selected using AIC ...... 99

Table 4.8: The effect of a unit change in particular floral characteristics on each feeding behaviour...... 100

Table 4.9: The percentage of time C. concinnus spent on each activity during feeding trials (n

= number of trials)...... 101

Table 4.10: The grooming behaviour of C. concinnus during captive feeding trials of 10 minute duration...... 102

Table 5.1: A summary of the most preferred flowering species for C. concinnus identified by each measure across the three flowering seasons...... 111

Table 6.1: The Coefficients for the logistic regression model selected in Table 2.5...... 118

Table 6.2: Logistic regression was used to determine the influence of space (trapping grid), time (flowering season and year) and flowering species on captures of adult males (Table

2.7)...... 119

Table 6.5: Model selection and coefficients using logistic regression with a binomial distribution and logit link function to determine the influence of flowering species and the amount of flowering present on the proportion of male to female C. concinnus captured (Table

2.9)...... 125

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Table 6.6: Models selection and coefficients using logistic regression with a binomial distribution and logit link function to determine the influence of flowering species and the amount of flowering present on the proportion of females with young C. concinnus captured

(Table 2.9)...... 126

Table 6.7: Logistic regression with a binomial distribution and logit link function were used to determine the influence of the variables: sex, flowering season and activity on the proportion of foraging time spent over each tracking event (Chapter 3)...... 129

Table 6.8: Poisson regression was used to determine which predictor variables; sex, season

(S), release habitat (RH – A,B,C), the availability of flowering (F) and the availability of each flowering species (FS), influenced the distance travelled per minute (Chapter 3) ...... 131

Table 6.9: Fixed effects models with a binomial distribution were used to determine the influence of sex, season (S), habitat component (H) and flowering (F) on the distribution of time spent feeding on flowers for each tracking event (Chapter 3)...... 133

Table 6.10: Fixed effects models with a binomial distribution were used to determine the influence of sex, season (S), habitat component (H) and flowering (F) on the distribution of time spent feeding on substrates other than flowers (e.g. leaves and bark) for each tracking event (Chapter 3)...... 138

Table 6.11: Fixed effects models with a binomial distribution were used to determine the influence of sex, season (S), habitat component (H), flowering (F), heavy rain (HR) and strong wind (SW) on the distribution of time spent travelling for each tracking event (Chapter 3)..

...... 142

Table 6.12: Fixed effects models with a binomial distribution were used to determine the influence of sex, season (S), habitat component (H), heavy rain (HR) and strong wind (SW) on the distribution of time spent grooming for each tracking event (Chapter 3)...... 147

Table 6.13: Fixed effects models with a binomial distribution were used to determine the influence of sex, season (S), habitat component (H), heavy rain (HR) and strong wind (SW) on the distribution of time spent resting for each tracking event (Chapter 3)...... 150

Table 6.14: The Coefficients for the Poisson regression model selected for the length of feeding bouts in Table 4.4. The model selected showed that species pair influenced the proportion of feeding bouts...... 155

Table 6.15: The influence of floral characteristics on the feeding behaviours of C. concinnus was determined using Poisson regression with a logit link function. (Table 4.7) ...... 156

Table 6.16: The influence of floral characteristics on the feeding behaviours of male C. concinnus was determined using poisson regression with a logit link function. (Table 4.7) . 159

Table 6.17: The influence of floral characteristics on the length of feeding bouts for female C. concinnus was determined using Poisson regression with a logit link function. (Table 4.7) . 163

Plates

Plate 2.1 A Pitfall trap with C. concinnus, sand and leaf litter on the base and a PVC pipe (6cm diameter) for shelter...... 21

Plate 2.2: Female C. concinnus with pouch young and with suckling young...... 22

Plate 3.1: C. concinnus with chemi-tag glued on its back feeding on a B. ornata inflorescence.

...... 52

Plate 3.2: C. concinnus sleeping between old B. ornata inflorescences...... 61

Plate 4.1: A B. ornata with about 2/3 of the flowers open...... 81

Plate 4.2: Callistemon rugulosus inflorescence ...... 82

Plate 4.3: Eucalyptus cosmophylla flower...... 82

Plate 4.4: Perspex tank set up for a feeding trial...... 83

Plate 4.5: C. concinnus feeding on a B. ornata inflorescence...... 84

Plate 5.1: C. concinnus interrupted while feeding on E. baxteri flowers...... 116

*Pictures taken by Gavin Hedrick, Briony Horner, Leanne Schneider and other field helpers.

Abstract

The basic requirements of a species are food, space and shelter. The presence of these resources and their availability and quality will influence population patterns. This relationship is not a simple one as both the resources being used and the consumers’ needs are variable. Resources are generally heterogeneous rather than homogeneous, in that their availability is subject to spatio-temporal variation. Consumers’ needs are driven by their resource requirements which may vary over time, for example, the needs of each animal will change with age and reproductive patterns. Species that depend on heterogeneous resources will often reflect this in their ecological patterns including abundance, distribution, reproductive success and timing, physiology, movements, use of space, territorial behaviour, foraging behaviour and diet.

The aim of this study was to describe the influence of a spatially and temporally heterogeneous food resource on the ecological patterns of an animal species that uses them. This relationship was considered in the demographic patterns, foraging behaviour, diet and feeding preferences of the western pygmy possum, Cercartetus concinnus. The study was based on 261 individuals captured 452 times over 43,200 trap nights and 24 trapping periods between July 1997 and June 1999. This species is a small marsupial that is known to use pollen and nectar in its diet. Nectarivorous animals are particularly vulnerable to the fluctuations of their highly variable food resource and provide a good case for the study of the effects of heterogeneous resources. Newland Head Conservation Park in southern South Australia was chosen as the study site since it provides a year round cycle of flowering resources and the opportunity to monitor a population as it responded to spatial and temporal resource heterogeneity without the extreme fluctuations in population density that can occur in arid areas.

The influence of heterogeneous food resources was evident in most aspects of this species’ ecology, from dietary choices to their distribution and abundance. Floral resources were the primary dietary resource for this species although, as is often found with species that rely on heterogeneous resources, they did use other food resources. The timing and distribution of flowering of a few key species could be used to predict the distribution and abundance of C. concinnus. This indicates that C. concinnus can and does track floral resources, moving

xii spatially to follow particular flowering species as they become available. While the distribution of the species could be predicted, the low recapture rates found during trapping and the distribution pattern of those animals that were recaptured may indicate a nomadic movement pattern in which animals move randomly when foraging to increase their potential for encountering an unpredictable food resource. At the population level, reproductive timing was associated with peaks in flowering of two dominant flowering species, suggesting a dependence on these resources for reproduction.

Cercartetus concinnus also showed a capacity to switch from one food resource to the next as the flowering of one species finished and the next commenced. Both sex and seasonal patterns were evident in foraging behaviour and appeared to relate to the reproductive requirements of each sex. As the key floral resources tended to be available seasonally in pairs, paired dietary preference trials were used to determine whether C. concinnus made choices between the species and what floral characteristics might be driving those choices. Some preference was shown for eucalypt over non-eucalypt (Banksia and Callistemon) species where flowering periods overlapped. The strength of the preferences displayed depended on the species pair combination, the sex of the animal and floral characteristics. The preference of male C. concinnus for eucalypt species over non-eucalypt was clear, with the strength of this preference being defined by the species pairing. In contrast females showed a preference for eucalypts unless they were paired with B. ornata, at which point their preference appeared to shift to the non-eucalypt species. This preference appears driven by the females’ reproductive requirements with B. ornata flowering at the same time as the larger peaks in reproductive activity and the densities of flowers produced being greater than the other species. The relationship with floral characteristics is much stronger for males than females with males being associated with plants containing low nectar volumes and high sugar concentrations.

This study has revealed that a species thought to use an opportunistic dietary pattern can show a preference for a heterogeneous food resource as it becomes available and that the availability of this resource can be used to predict demographic patterns such as distribution, abundance and reproductive timing. These results also emphasise the importance of considering the influence of spatio-temporal patterns of both the resources and consumers when studying their interactions.

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Statement of Originality

This work contains no material which has been accepted for the award of any other degree or diploma in any university or other tertiary institution to Briony Horner and, to the best of my knowledge and belief, contains no material previously published or written by another person, except where due reference has been made in the text.

I give consent to this copy of my thesis, when deposited in the University Library, being made available for loan and photocopying, subject to the provisions of the Copyright Act 1968.

I also give permission for the digital version of my thesis to be made available on the web, via the University’s digital research repository, the Library catalogue, the Australasian Digital Theses Program (ADTP) and also through web search engines, unless permission has been granted by the University to restrict access for a period of time.

Signed: Date: 04/06/2012

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Acknowledgements

Thanks to Sue Carthew for her patience in sticking with this project over such a long time. For her advice and constant encouragement to keep going.

For statistical help and support I thank Steve Delean.

Thankyou to all of the people who helped dig 300 traps into the ground, take off lids, put on lids, spent hours checking traps, followed possums around in the middle of the night and pulled traps out. Janine, John and Ben Horner, Melanie and Matthew Timmis, Darrin Smith, Leanne Schneider, Laila Demailovich, James Mau and lots of others.

Thanks to everyone in the lab and the zoo for sharing the ups and downs of data analysis and writing.

Thanks to my Mum and Dad, for their continued love and support without their help this would not have been possible to complete.

Thanks to my beautiful Inara for your love and cuddles even when I had to work a lot.

Thanks to Ben, Nonni, Mel, Matt, Bailey, Finn, Oliver and Elanore for being an amazing family and supporting me and my study for so many years.

Thanks to Glenn for helping me over the final hurdles.

This research was supported financially by Dpt. Earth and Environmental Sciences, University of Adelaide and the Wildlife Conservation Fund.

The work was carried out under a scientific permit (No. Z23440) issued by South Australia’s Dept. of Environment, Heritage and Aboriginal Affairs and with approval from The University of Adelaide’s Animal Ethics Committee (No. W/30/96A).

Chapter 1 : Introduction

1.1 Factors that shape populations

The basic requirements of species are food, shelter and mates. These requirements are not often independent of other factors. For example, the availability of shelter can influence the distribution and abundance of a species (Harper, McCarthy et al. 2008; Rosalino, Macdonald et al. 2005). The search for mates can drive space requirements (Ellis, Melzer et al. 2009; Horne, Garton et al. 2008; Leiner and Silva 2007a; Ostfeld 1990; Pasch and Koprowski 2006) and habitat preferences can be driven by food requirements (Bos, Carthew et al. 2002; Cockburn 1981; Eyre and Smith 1997; Rosario, Cardoso et al. 2008). Some of these factors will be more important to a species than others. For example, the arboreal behaviour of chimpanzees in Guinea, West Africa, is influenced by microclimate, where the hunt for food resources is secondary to the avoidance of wet ground (Takemoto 2004). As a further example, populations of badgers (Meles meles) in mediterranean cork-oak woodlands in Portugal, are typically limited by one or a combination of food, surface water and sett (den) site availability (Rosalino, Macdonald et al. 2005). A study by Rosalino et al (2005) found that sites suitable for setts (dens) were the most limiting factor for badger distribution, over and above food and surface water availability.

The presence of resources and their abundance, distribution and quality will influence population patterns, with some species particularly influenced by one resource (Scarlett and Woolley 1980; Takemoto 2004; Tallmon and Mills 1994) and others by a combination of resources (Batzli and Henttonen 1993; Bertolino 2007; Eyre and Smith 1997; Hanya 2004; Thibault, Ernest et al. 2010). This form of influence on population patterns is called bottom- up control (Krebs 2009) and can affect one or more of the four main processes of population dynamics; births, deaths, immigration and emigration. Thus the effects of resources may be evident in species abundance (Marcello, Wilder et al. 2008; Mendel, Vieira et al. 2008; White 2008), distribution (Chamaille-Jammes, Fritz et al. 2008; Penalba, Molina-Freaner et al. 2006; Rosalino, Macdonald et al. 2005), population structure (Martins, Bonato et al. 2006b), reproduction (Bieber and Ruf 2009; Di Bitetti and Janson 2000; Genin 2007; Marcello, Wilder et al. 2008; Scarlett and Woolley 1980), physiology (Fietz, Pflug et al. 2005; Genin 2007; Smith

1 and Broome 1992; Wauters, Vermeulen et al. 2007), movements (Dickman, Predavec et al. 1995; Raboy and Dietz 2004; Sayers and Norconk 2008), use of space (Bertolino 2007; Pinto, Azevedo-Ramos et al. 2003; Taylor 1993a; Taylor 1993b), territorial behaviour (Ostfeld 1985; Ostfeld 1990) and foraging behaviour and diet (Diaz and Kitzberger 2006; Hanya 2004; Loureiro, Bissonette et al. 2009; Smith and Broome 1992).

The relationship between population patterns and resources however, is not simple, as there are factors influencing both the resources being used and the consumer. Resource availability and distribution are often subject to spatial and or temporal variation. This results in a range of behavioural responses within each population pattern. For example, a migratory pattern may occur in response to predictable spatial and temporal shifts in food, habitat and climatic resources, and a nomadic movement pattern may be a response to unpredictable food resources (Mueller and Fagan 2008). Heterogeneity in the availability of food resources can also bring about a range of dietary patterns. For example, animals may follow an opportunistic diet, where they use resources proportional to their availability, switching from one to the next as they become available (Boyes and Perrin 2009; Stone 2007) or they may follow a specific diet where they track the food resources they prefer by shifting their distribution (Bertolino 2007; Diaz and Kitzberger 2006; Ford 1979; Morton 1982; Pinto, Azevedo-Ramos et al. 2003; Taylor 1993a; Taylor 1993b). The advantage of an opportunistic diet is that animals can stay in one place and satisfy their resource requirements without the energy expenditure required to change location. A specific diet should have its benefits in the rewards provided by the food resources that are being tracked. These rewards should be enough to counter the energy expended in tracking.

In addition to spatial and temporal variation in resource availability and distribution there are variations in the resource requirements of consumers. The types and quantities of resources required can vary with sex and over time, driven by activities such as reproduction, hibernation or migration. Animals may need to build up energy supplies to survive these activities (Fietz, Pflug et al. 2005) or use particular microhabitat features (Bowers and Smith 1979). The timing of particular resource requirements in some species will follow seasonal patterns (Bertolino 2007) or can be very specific, with reproduction only occurring when the right food resources are available at the right time (Bieber 1998). Sex differences in habitat use (Bowers and Smith 1979; Johnson, Knouft et al. 2007) and energy requirements (Genin 2007) are particularly evident and often relate to reproduction. Males generally require more

2 energy in their search for mates, while females require energy and shelter for producing and raising young (Ostfeld 1985; Smith and Broome 1992). Sexual dimorphism will also drive sex based differences in energy requirements, with larger animals needing more energy to survive (Rosalino, Santos et al. 2009).

The most obvious display of a relationship between an animal and the resources it depends on is in distribution and abundance. Many studies have shown a relationship between resource availability and species abundance (Adamik and Kral 2008; Fox, Read et al. 1994; Harper, McCarthy et al. 2008; Magnusdottir, Wilson et al. 2008; Predavec and Dickman 1994; Sharpe 2004; Thibault, Ernest et al. 2010; Wang, Wolff et al. 2009) and/or distribution (Kumar, Mudappa et al. 2010; Raboy and Dietz 2004; Tallmon and Mills 1994). For example, Harper et al. (2008) found that the abundance of common brushtail possums (Trichosurus vulpecula) and common ringtail possums (Pseudocheirus peregrinus) within remnants surrounding Melbourne, Australia, was related to the density of potential den sites within the remnant and food availability within the surrounding landscape. In addition, ringtail possum density within the remnant further increased with den availability within the surrounding landscape. In another example, Kumar et al. (2010) showed the influence of water availability on the distribution of the Asian elephant (Elephas maximus) in the Anamalai Hills, India. During the dry season, animals congregated around water sources when water was scarce and dispersed when it became abundant. This pattern is particularly evident in arid areas, with the boom and bust nature of particular resources requiring the species that use them to have shifting home-ranges or nomadic movement patterns (Magnusdottir, Wilson et al. 2008; Morton 1982; Mueller and Fagan 2008; Read 1984).

Movements towards areas of greater food resource availability are not just limited to those of animals in arid areas where resources are boom and bust. In more temperate environments where resources are readily available animals may still track resource availability, but in this situation they are likely to be doing so to seek out preferred resources. Animals may track such resources by changing location (Telleria, Ramirez et al. 2008), expanding foraging or home ranges (Lurz, Garson et al. 2000; Schwemmer and Garthe 2008; Taylor 1993a; Taylor 1993b), or spending more time foraging (Hanya 2004). This was demonstrated in a study by Taylor (1983 a; b) of the Tasmanian bettong (Bettongia gaimardi) in northern Tasmania, Australia, where home range size was dictated by the distribution of its food resources, fungal sporocarps. Similarly, a study of the howler monkey (Alouatta belzebul) in eastern Amazonia

3 showed that the distribution of fruits was a key element in explaining spatial use patterns, with the expansion of home range area positively correlated with the search for new fruit sources (Pinto, Azevedo-Ramos et al. 2003).

Some species will shift their distribution completely to follow the resources they prefer, increasing their foraging effort, changing their home range completely or by not having a home range at all (Mueller and Fagan 2008). For example, the lesser hairy-footed dunnart (Sminthopsis youngsoni) in the Simpson Desert, Australia, often travels long distances, shifting its distribution regularly to follow food resources (Haythornthwaite and Dickman 2006b). Optimal foraging theory suggests that to maximise the amount of energy gained when searching for food resources, animals should match movement patterns and energy expenditure to the food reward received (Pyke 1984). Tracking food resources by shifting spatial location or increasing time spent foraging is indicative of an increase in effort that appears less effective than the strategy of following a purely opportunistic diet. However, it may be that the rewards provided by the resources being tracked confer a particular benefit energetically and or nutritionally.

Those species capable of following a more opportunistic diet appear to have adapted more effectively to heterogeneous food resources and should exhibit less dramatic fluctuations in abundance and distribution. However, they may track resource shifts over time by switching from one resource to the next as they become available (Bertolino, Mazzoglio et al. 2004; Boyes and Perrin 2009; Loureiro, Bissonette et al. 2009; Martins, Bonato et al. 2006a; Murdoch 1969; Rosalino, Loureiro et al. 2005; Sabbatini, Staininati et al. 2008). For example the Meyer parrot (Poicephalus meyeri) in the Okavango Delta, Botswana is an opportunistic generalist that tracks resource availability across a wide suite of potential insects and unripe and ripe seeds. It shows a preference for unripe seeds when they are seasonally available (Boyes and Perrin 2009). Finlayson’s squirrel (Callosciurus finlaysonii), introduced into Italy, also switches between resources as they become available, consuming flowers when they are available and falling back to the staples of bark and buds otherwise (Bertolino, Mazzoglio et al. 2004).

Not all shifts in animal abundance will be related to shifts in distribution, they may also be a function of demographic perturbations. For example, Sharpe (2004) found a drop in population density of squirrel gliders in north-east New South Wales, Australia, in response to the failure of a key flowering resource over winter and spring. Reproductive failure was

4 proposed as a reason rather than migration because of the widespread decline in glider densities. A number of small mammal species rely particularly heavily on a production peak (masting) of a particular food resource to get them through their reproductive effort (Bieber 1998; Fietz, Pflug et al. 2005; Yang, Bastow et al. 2008). A study of the fat dormouse (Glis glis) in central Germany found a lack of reproduction in 1993 coincided with a failed mast of fruit from oaks and beeches in autumn of that year (Bieber 1998). Other species such as the honey possum (Tarsipes rostratus), in Western Australia, can reproduce almost year round following one flowering resource after the other throughout the year (Scarlett and Woolley 1980).

One of the most interesting examples of the relationship between an animal and its resources is that of the mountain pygmy-possum (Burramys parvus) found in Kosciuszko National Park, Australia (Broome 2001a; Broome 2001b; Smith and Broome 1992). It is influenced by a combination of food and microhabitat resources and shows season and sex based differences in their use. The population patterns affected by these resources include diet, distribution, movement patterns, reproductive timing and body weight. The species has a diet dominated by arthropods (mostly the bogong moth), seeds and berries. The availability of these food resources fluctuates both spatially and temporally, with moths being available at high elevations and seeds and berries at lower elevations. The quantity of each food resource consumed influences the condition of animals, with those at higher elevations putting on more weight due to the high protein diet of bogong moths. This diet is important for females as they require the energy to reproduce immediately after their winter hibernation and ensure their young can reach a sufficient weight to survive their first winter. As a result, the populations are sexually segregated with females at high elevation and males and juveniles at low elevations. Males and juveniles receive adequate nutrition from seeds and berries, with some arthropod material. They will exploit moths during peak densities, making nightly or seasonal excursions to high elevations.

Territorial behaviour also emphasises the influence of resource availability. Such behaviour is evident across a range of species including nectar feeding birds (Armstrong, Gass et al. 1987; Telleria, Ramirez et al. 2008), primates (Raboy and Dietz 2004) and small mammals (Lurz, Garson et al. 2000; Ostfeld 1985). A review of territorial behaviour in small mammals by Ostfeld (1990) attributes this activity to a drive for reproductive success, with males defending access to females and females defending the food resources they need for

5 reproduction. This behaviour is evident particularly in species that rely on heterogeneous food resources (Ostfeld 1990).

While resource availability and distribution can influence a great range of population patterns, it is particularly those food resources displaying spatio-temporal variation that have the strongest influence (Abrams 2010; Buskirk and Millspaugh 2006; Nonaka and Holme 2007; Yang, Bastow et al. 2008). Food resources such as plant produced material or prey species are all driven by climatic shifts and, as a result, so are the population patterns of the species that are influenced by them (White 2008). A review by White (2008) presented evidence to support the case for climate and food resources as the foundation for the limitation of population abundance or bottom up control.

1.2 Flowering Plants

Plant produced resources tend to be spatially and temporally variable. Their distribution is often patchy with the timing and success of flowering and fruit set varying over time and with climatic conditions. Pollen and nectar from flowers provide a significant food resource for a broad range of insect, bird and mammal species (Cartar 2009; Chamberlain and Holland 2008; Diaz and Kitzberger 2006; Dobson , Goldingay et al. 2005; Fleming and Nicolson 2002; Ford 1979; Holland, Bennett et al. 2007; Horner 1994; Huang, Ward et al. 1987; Penalba, Molina-Freaner et al. 2006; Pestell and Petit 2007a; Raboy and Dietz 2004; Scarlett and Woolley 1980; Turner 1984a; van Tets and Whelan 1997). Pollen offers protein, nitrogen, amino acids, starch, sterols and lipids. Protein makes up over 60% of this and provides long term energy (Roulston and Cane 2000; van Tets 1998). The consumption of pollen by mammals visiting flowers was for some time thought to be by chance (while nectar feeding) rather than intentional and of little benefit to the mammals that consumed it (Turner 1984a). Mammals are now understood to be capable of digesting pollen grains effectively by extracting the protoplasts, and pollen is considered an important food resource (Turner 1984a; Turner 1984b; van Tets and Whelan 1997). Studies have shown that small mammals would need to digest pollen from very few inflorescences to satisfy their minimum daily protein requirements (Turner 1984a; van Tets 1998).

In contrast to pollen, nectar is a rich easily digestible, carbohydrate solution and offers sugars that provide a short term energy gain (Baker, Baker et al. 1998). It is composed of a range of different sugar and non-sugar components, in differing combinations and concentrations for

6 different species (Baker and Baker 1982; Baker, Baker et al. 1998; Heyneman 1983; Hiebert and Calder 1983; Inouye, Favre et al. 1980; Nicolson and Van Wyk 1998; Van Wyk and Nicolson 1995). The production of nectar varies within and among plant species, both spatially and temporally (Horner 1994; Keasar, Sadeh et al. 2008; Law and Chidel 2008). For example, a study by Keasar et al. (2008) found variability in nectar production rates and standing crops of Rosmarius officinalis and identified rainfall and temperature as influencing factors. This pattern was also identified by Law and Chidel (2008) in spotted gum (Corymbia maculate) forests in New South Wales. In addition Horner (1994) found that two species of Banksia in south-eastern South Australia, produced more nectar overnight than during the day. The composition of nectar has also been shown to change over time and with flower age (Morrant, Petit et al. 2010; Nicolson and Van Wyk 1998)

Some mammal species rely on floral resources to satisfy their daily energy requirements at specific times (Fleming and Nicolson 2002; Fleming and Muchhala 2008; van Tets 1997; van Tets 1998; van Tets, Hutchings et al. 2000). The extent to which these mammals rely on floral resources varies. The honey possum (Tarsipes rostratus), for example, uses floral resources year round (Richardson, Wooller et al. 1986), while bats (Baker, Baker et al. 1998; Fleming and Muchhala 2008; Penalba, Molina-Freaner et al. 2006; Rodriguez-Pena, Stoner et al. 2007; Winter and Stich 2005), pygmy possums (Cadzow and Carthew 2004; Evans and Bunce 2000; Horner 1994; Pestell and Petit 2007a; Tulloch and Dickman 2007; Turner 1984a; van Tets and Hulbert 1999), feather-tail gliders (Huang, Ward et al. 1987; Turner 1984b), sugar gliders (Carthew 1993; Carthew 1994; Goldingay, Carthew et al. 1987; van Tets and Whelan 1997), squirrel gliders (Dobson, Goldingay et al. 2005; Sharpe 2009) and yellow bellied gliders (Carthew, Goldingay et al. 1999; Goldingay 1990) will use them opportunistically and often show a preference for floral resources when available. Other species, such as rodents (Cocucci and Sersic 1998; Goldingay, Carthew et al. 1987), dasyurids (Carthew 1994; Goldingay 2000; Goldingay, Carthew et al. 1987; van Tets and Whelan 1997), opossums (Sperr, Fronhofer et al. 2009) and primates (Poulsen, Clark et al. 2001; Stone 2007; Sussman and Raven 1978) use these resources occasionally or by chance.

The distribution and availability of floral food resources can influence aspects of the ecology of the species using them. Foraging behaviour and space use (Carthew 1994; Goldingay 1990; Holland, Bennett et al. 2007; Rothenwöhrer, Becker et al. 2010), shifts in distribution (Davey 1984; Diaz and Kitzberger 2006), movement patterns (Ford 1979), reproductive timing

(Fleming and Nicolson 2002; Scarlett and Woolley 1980; Ward 1990a), and dietary preferences (Baker, Baker et al. 1998; Fleming, Xie et al. 2008; Johnson, van Tets et al. 1999; Landwehr, Richardson et al. 1990; Leseigneur and Nicolson 2009; Lotz and Nicolson 1996; Morrant, Petit et al. 2010; Rodriguez-Pena, Stoner et al. 2007; Schondube and del Rio 2003) have all been shown to influence flowering patterns. As plants do not tend to flower in isolation and often overlap in flowering with other species, the animals that consume floral resources may show preferences for one species over another (Horner 1994; Morrant, Petit et al. 2010). These preferences may be driven not just by nectar volume, concentrations and constituents but by pollen rewards, flowering timing and density, which species are flowering concurrently, and possibly territorial behaviour of other animals.

A number of studies have considered the relationship between nectarivorous animals and floral characteristics (Baker, Baker et al. 1998; Carthew 1993; Carthew 1994; Fleming, Xie et al. 2008; Johnson, van Tets et al. 1999; Landwehr, Richardson et al. 1990; Leseigneur and Nicolson 2009; Lotz and Nicolson 1996; Majetic, Raguso et al. 2009; Morrant, Petit et al. 2010; Rodriguez-Pena, Stoner et al. 2007; Schondube and del Rio 2003; Turner 1984a; van Tets and Nicolson 2000). These studies have found patterns of preference for particular sugars in nectar, particular nectar concentrations and floral scents. While this research has provided valuable insights into the relationship between floral characteristics and animal species, it has not necessarily drawn a complete picture of this relationship and the influence that floral resources can have on the ecology of the animals that use them.

A range of studies have addressed the influence of spatio-temporal variability in seed and insect abundance on the demographic patterns of small mammals (Bieber 1998; Bieber and Ruf 2005; Bieber and Ruf 2009; Fietz, Pflug et al. 2005; Sailer and Fietz 2009; Schmidt and Ostfeld 2008; Smith and Broome 1992; Vandegrift and Hudson 2009). However, very few have looked in detail at the influence of floral resources on these animals. There has been some speculation on the influence of floral resources on capture rates of species such as the eastern pygmy possum (Cercartetus nanus) in relationship to Banksia and Eucalyptus species (Bladon, Dickman et al. 2002; Harris, Goldingay et al. 2007), cape spiny mouse (Acomys subspinosus) in relationship to Protea humiflora (Fleming and Nicolson 2002) and the squirrel glider (Petaurus norfolcensis) in relationship to flower failure in Eucalyptus species (Sharpe 2004). However, only two studies appear to have confirmed such a relationship; Bradshaw et al. (2007) positively correlated captures of the honey possum (Tarsipes rostratus) in Western

Australia with the flowering of Banksia ilicifolia (Bradshaw, Phillips et al. 2007) and Penalba et al. (2006) showed that seasonal densities of the southern long nosed bat (Leptonycteris curasoae) in Guaymas, Sonora, Mexico were determined by the availability of flower and fruit resources provided by columnar cacti. In a similar vein, the influence of floral resources on foraging behaviour in the yellow-bellied glider (Petaurus australis) was considered in southeastern Australia (Goldingay 1990). The amount of time a glider spent in a tree was related to the number of flowers in a tree when flower abundance was at an intermediate level. The influence of floral resources on reproductive timing was identified for the honey possum (T. rostratus) as females with pouch-young were recorded more frequently in winter when nectar was most abundant and less frequently in autumn when food was scarce (Wooller, Richardson et al. 2000). A field experiment in woodland sites in New South Wales by Tulloch and Dickman (2007) showed increased movement in C. nanus in response to food supplementation, indicating the species had the capacity to monitor and respond to changes in resource availability. While these studies have touched on some aspect of the relationship of these species with floral resources they have not considered fully the influence of floral resources on the demographic patterns of a species.

1.3 Pollination

The relationship of animals with the flowering plants that they feed upon is not one sided. As a consequence of their visiting flowering plants, animals often provide a service to the plant in the form of pollen transfer (Carthew 1993; Carthew 1994; Carthew and Goldingay 1997; Fleming and Nicolson 2002; Goldingay, Carthew et al. 1987; Goldingay, Carthew et al. 1991; Letten and Midgley 2009; Turner 1981; Wooller and Wooller 2003). Some species of animal are more effective in this role than others, transferring more pollen on their bodies than other species and moving it more effectively to the appropriate destination (Carthew 1993; Carthew 1994; Carthew and Goldingay 1997; Evans and Bunce 2000; Goldingay 2000; Goldingay, Carthew et al. 1987; Goldingay, Carthew et al. 1991; Hackett and Goldingay 2001; Hopper and Burbidge 1982). An animals’ ability to transfer pollen will be limited by the extent to which they groom or preen, the number of flowers or inflorescences they visit per plant, the distances they travel between plants and the number of plants visited in a foraging period.

Over the past 30 years non-flying mammals have been recognised for their role in pollination (Carthew and Goldingay 1997; Cocucci and Sersic 1998; Fleming and Nicolson 2002;

Goldingay 2000; Johnson, Pauw et al. 2001; Sperr, Fronhofer et al. 2009). The relationship between some non-flying mammal species and the plants they pollinate is thought to be closely evolved (Carthew and Goldingay 1997). For example, some species of Banksia and Protea produce much of their nectar overnight and a scent apparently attractive to mammal visitors. Some also have anthesis (flower opening) triggered by touch (Carpenter 1978; Carthew and Goldingay 1997; Horner 1994; Rourke and Wiens 1977; Turner 1982). In these species the density of inflorescences available per plant tends to be low encouraging animals to move from plant to plant regularly and promoting out-crossing. Other species visited by non-flying mammals have attributes suited to a wide range of pollinators. Eucalypts, for example, tend to provide a large flowering crop within one tree (Goldingay 1990) such that a non-flying mammal will not have to move from one plant to the next very often to satisfy its dietary requirements. This may limit pollination for this genus to self-pollination with limited outcrossing depending on the grooming habits and dietary requirements of the pollinator.

1.4 Cercartetus species

The genus Cercartetus consists of four species of small nocturnal marsupials. Species are patchily distributed over tropical, temperate and semi-arid parts of Australia. The western pygmy possum (C. concinnus) is the most broadly distributed being found from southern Western Australia, through southern South Australia, Victoria and in some parts of New South Wales. The little pygmy possum (C. lepidus) is found in Tasmania, southern South Australia and into parts of Victoria. The eastern pygmy possum (C. nanus) is found in Tasmania, Victoria, New South Wales and in a small part of South Australia. The long-tailed pygmy possum (C. caudatus) is found in only a few patches in Queensland, Indonesia and New Guinea. There is some overlap in distribution of C. lepidus and C. concinnus, C. concinnus and C. nanus and C. nanus and C. lepidus. They range in weight from 8-40 g, producing up to six young but carrying to weaning an average of two young. Studies have for some time speculated on the association of captures with the presence of flowering plants (Bladon, Dickman et al. 2002; Bowen and Goldingay 2000; Harris, Goldingay et al. 2007; Morrant, Petit et al. 2010; Tulloch and Dickman 2007; Ward 1990a; Ward 1992). While Cercartetus are considered to be omnivorous, feeding on nectar, pollen, invertebrates and plant material, pollen and nectar appear to the make up a large percentage of their food intake when it is available (Cadzow and Carthew 2004; Horner 1994; Huang, Ward et al. 1987; Morrant, Petit et al. 2010; Pestell and Petit 2007a; Turner 1984a; van Tets and Hulbert 1999). Reviews of

10 three of these Cercartetus species have been conducted relatively recently (Harris 2008; Harris 2009a; Harris 2009b).

Members of the genus have gained some notice in the area of pollination biology, having been observed as pollinators for a number of Proteaceae and Myrtaceae species (Cadzow and Carthew 2004; Carthew 1993; Carthew 1994; Evans and Bunce 2000; Goldingay, Carthew et al. 1987; Goldingay, Carthew et al. 1991; Horner 1994; Turner 1982; Turner 1984a; Turner 1985; van Tets and Whelan 1997; Wooller, Russell et al. 1983). This discovery has led to a number of studies concerning the capacity of this genus to use and digest pollen (Cadzow and Carthew 2004; Horner 1994; Huang, Ward et al. 1987; Morrant, Petit et al. 2010; Turner 1984a; van Tets 1998; van Tets and Hulbert 1999; van Tets and Whelan 1997). These studies have revealed that Cercartetus species are able, without any specific adaptation, to digest pollen grains and satisfy their daily nitrogen requirements.

As floral resources are spatially and temporally heterogeneous, the demographic patterns of Cercartetus should reveal dietary flexibility, a capacity for mobility to allow spatial shifts from one resource to the next, opportunistic reproductive patterns, territorial behaviour in the form of resource defence and weight fluctuations in response to resource shifts. The first of these population patterns is evident, as Cercartetus are known to use a range of food resources in their diet (Cadzow and Carthew 2004; Horner 1994; Huang, Ward et al. 1987; Morrant, Petit et al. 2010; Ward 1990a; Ward 1992). However, the extent of selection for particular food resources has not been studied in great detail. Low recapture rates and average movements of 50m to 100m between trapping periods (24 hours) and up to 80m overnight with radio-tracking supports the suggestion that the species is quite mobile (Harris, Goldingay et al. 2007; Horner 1994; Morrant, Petit et al. 2010; Pestell and Petit 2007b; Tulloch and Dickman 2006; Tulloch and Dickman 2007; Ward 1992). While home ranges have been estimated for C. nanus, these were calculated over an 11 day period for two individuals and do not support the existence of a permanent home range for this species (Harris, Goldingay et al. 2007; Laidlaw and Wilson 1996). The low residency rate indicated by low recaptures suggests that Cercartetus species may have shifting home ranges or no home range at all (Dickman, Predavec et al. 1995; Morton 1982; Read 1984), and that their distribution and use of space may instead be nomadic (Mueller and Fagan 2008). This mobility in response to food resources is normally more evident in small mammals in arid regions where they move large distances to make maximum use of resources during both

11 good seasons and drought (Dickman, Predavec et al. 1995; Haythornthwaite and Dickman 2006b).

The lack of a stable home range is also suggested by the selection of nest locations which appears to be rather haphazard, with animals found in hollows, knot holes, burrows, bird nests, leaf litter, between inflorescences, grass tussocks and in less sheltered positions, depending on the habitat (Hickman and Hickman 1960; Kemp and Carthew 2004; Wakefield 1963; Ward 1990a; Ward 1992). Both C. nanus and C. concinnus have been observed to change nests frequently (Kemp and Carthew 2004; Ward 1990a), with the only exception being lactating females that have tended to return to the same nest site (Horner 1994; Ward 1990a). This casual use of nesting sites may be an indication that populations are influenced more by food availability than shelter availability, although data are still somewhat limited.

Signs of aggression from lactating female C. concinnus and C. lepidus (Horner 1994) and the partial exclusion of male C. nanus from prime habitat by females (Turner 1985) may indicate that female dominance is also characteristic of Cercartetus species. This is true for two related species, honey possum (T. rostratus) in Western Australia (Russell 1986; Wooller, Renfree et al. 1981) and mountain pygmy possum (Burramys parvus) in Victoria and New South Wales (Kerle 1984). In these, females are behaviourally dominant, excluding males from prime habitat. This territorial behaviour might be expected for females that depend on patchy resources during reproduction (Ostfeld 1990). Without these behaviours and the resulting increased availability of prime food resources, females may not be able to raise young successfully.

The influence of floral resources on Cercartetus species may also be seen in the timing of reproduction. Although data are limited, studies have suggested that the number of litters produced per year and the timing of those litters may be linked with the presence of and peaks in flowering for C. nanus, C. concinnus and C. lepidus (Horner 1994; Ward 1990a; Ward 1992). Ward (1990a) found that breeding patterns of C. nanus were related to food resource availability, influencing both the timing and duration of the breeding season. In particular the presence or absence of Banksia inflorescences appeared to influence when reproduction occurred at this site (Ward 1990a). The average number of litters in a year for C. nanus was 2.5 but Ward (1990a) found that the presence of a Banksia species with a long flowering season resulted in some females producing three litters in a year. Ward (1992) suggested that births for C. lepidus could occur throughout the year and attributed this to the floristic

12 diversity. Horner (1994) found that reproduction for both C. concinnus and C. lepidus occurred following peaks in B. marginata and B. ornata flowering.

Another demographic attribute that may be influenced by the availability of floral resource is weight fluctuations. A study by Ward (1990a) found that the body weight of some individual C. nanus fluctuated and appeared to be influenced by changes in food availability. In addition, weight measurements from individual C. concinnus and C. lepidus also appeared to be influenced by high flowering densities, with some increases in weight during maximum flowering periods (Horner 1994; Ward 1992). This capacity of the species to store fat allows them to survive through periods with lower resource availability. A further adaptation to heterogeneous food resources is the capacity of this genus to use torpor. Animals can enter torpor for lengths of time extending from one day to a week to assist survival during periods of food shortage and harsh weather (Geiser 1987; Geiser 1993).

In general, there has been considerable speculation on the association of particular demographic patterns in species from this genus and the influence of spatial and temporal patterns of floral resources. However, the extent of this relationship has not yet been studied in detail for this genus.

1.5 Cercartetus concinnus

Cercartetus concinnus has the broadest distribution of any member of the genus. It is found in habitats ranging from coastal heath through temperate woodland to mallee woodland. In South Australia it has a patchy distribution of isolated populations primarily due to habitat fragmentation. To date no published studies exist that present a thorough study of demographic patterns, reproductive timing and weight fluctuations over time for this species. However, particular ecological patterns of this species suggest that floral resources have an influence on their ecology (Cadzow and Carthew 2004; Horner 1994; Huang, Ward et al. 1987; Ward 1990b; Ward 1992). Fluctuation in the size of populations of C. concinnus in more arid areas also suggests a strong link with food resource availability (Carthew and Cadzow 2001; Pearson, King et al. 1999; Robertson 2001). In arid areas, the heterogeneity of flowering resources is even more exaggerated by extreme climatic conditions, with flowering being absent in some years (White 2008).

In recent years some understanding of the ecology of C. concinnus has been gained with studies focusing on dietary patterns and revealing a preference for pollen and nectar in the

13 diet of an animal previously thought to be strongly omnivorous (Cadzow and Carthew 2004; Horner 1994; Morrant, Petit et al. 2010; Pestell and Petit 2007a). Dietary studies have also focused specifically on dietary preferences with a study by Landwehr et al. (1990) finding a preference for fructose over sucrose and glucose. More recently, Morrant et al. (2010) noted a preference for E. rugosa over other Eucalyptus species and that this species had a much higher proportion of fructose and glucose to sucrose.

C. concinnus is particularly suited to a study considering the influence of spatially and temporally variable resources on the demographic patterns of a small nectarivorous marsupial. The species is known to use floral food resources and previous studies suggest that links between its demographic patterns and the spatial and temporal patterns of floral resources do exist (Cadzow and Carthew 2004; Horner 1994; Landwehr, Richardson et al. 1990; Morrant, Petit et al. 2010; Pestell and Petit 2007a; Ward 1990b; Ward 1992).

1.6 Aims

The aim of this study was to consider the response of a small mammal species to the availability and distribution of a heterogeneous food resource (flowering plants). Newland Head Conservation Park provided the perfect opportunity to study this interaction as it contained a readily trappable population of C. concinnus and a year round cycle of flowering from six shrub and tree species (B. marginata, B. ornata, Callistemon rugulosus, E. baxteri, E. cosmophylla and E. diversifolia) that were patchily distributed across the site. This gave a framework for observing the demographic patterns of C. concinnus in response to both spatial and temporal shifts in floral resource abundance. The relationship between C. concinnus and floral resources was considered at three scales; 1) broad demographic patterns, 2) foraging behaviour and diet and 3) choices between food resources.

As a foundation for understanding the influence of temporal and spatial shifts in floral resource availability on the demographic patterns of C. concinnus, this study determined whether they tracked these shifts in floral resources as measured via their abundance, distribution, recapture rates, movements, reproductive timing and success, and physiology (Chapter 2). To further understand this relationship, an observational study of their foraging behaviour was conducted (Chapter 3). This was used to determine dietary patterns, foraging activity and use of microhabitat features. At least two of the six main flowering species on the site overlapped their flowering timing, providing the chance to assess whether C. concinnus

14 displayed preferences for one flowering species over the other. Pair-wise feeding trials were conducted to determine these preferences (Chapter 4). These results allowed a comparison with floral characteristics to ascertain what was driving the preferences displayed. As males and females often have quite different resource requirements throughout a year, the role of sex across each aspect of this study was considered to determine whether animals were displaying and meeting those requirements differently. Finally the foraging behaviour and dietary preferences displayed by C. concinnus were used to consider their capacity as pollinators.

Chapter 2 : Floral resources and the demographic patterns of Cercartetus concinnus

2.1 Introduction

The demographic patterns of species are regularly linked with the resources they use (Abrams 2010; Mueller and Fagan 2008; Wiens 1976). The presence of these resources and their abundance will not only influence a species abundance (Marcello, Wilder et al. 2008; Mendel, Vieira et al. 2008; White 2008) and distribution (Chamaille-Jammes, Fritz et al. 2008; Penalba, Molina-Freaner et al. 2006; Rosalino, Macdonald et al. 2005) but may also shape population structure (Martins, Bonato et al. 2006b), reproductive patterns (Bieber and Ruf 2009; Di Bitetti and Janson 2000; Genin 2007), physiological patterns (Fietz, Pflug et al. 2005; Wauters, Vermeulen et al. 2007) and animal movement (Dickman, Predavec et al. 1995; Sayers and Norconk 2008). The basic resource requirements of species are food, shelter and mates. These can range from geological features such as the geological faults important for badger (Meles meles) setts in Portugal (Rosalino, Macdonald et al. 2005), hollows in mature trees for nests (Beyer, Goldingay et al. 2008; Harper, McCarthy et al. 2008), investing time in the search for mates (Garavanta, Wooller et al. 2000), mate guarding (Genin 2008; Schubert, Schradin et al. 2009), space to meet foraging requirements (Horne, Garton et al. 2008) to particular food resources such as wild rabbits for polecats (Mustela putorius) in Portugal (Santos, Matos et al. 2009).

Most species depend on resources that are spatially and temporally heterogeneous. The population dynamics of these species often follow peaks and troughs that correlate with spatio-temporal changes in resource availability (Cotton 2007; Fleming 1992; Genin 2007; Santos, Matos et al. 2009; Wauters, Githiru et al. 2008). These patterns are particularly evident in species that use plant produced food such as flowers, fruit or seed (Cotton 2007; McConkey and Drake 2007; Telleria, Ramirez et al. 2008; Wauters, Githiru et al. 2008). Such resources are limited as they will only be present at particular times of the year and the extent of the resource supplied in any given year can fluctuate, as availability is often driven by climatic factors (Adamik and Kral 2008; Law and Chidel 2008; Magnusdottir, Wilson et al. 2008; Previtali, Meserve et al. 2010; White 2008). Many species will forage opportunistically, switching between resources when they are available (Boyes and Perrin 2009; Hampe 2008; Loureiro, Bissonette et al. 2009; Stone 2007). These species will tend to track their food resources temporally using what is available to them. In fact, some species, such as the edible dormouse (Glis glis) in Germany (Bieber 1998; Fietz, Pflug et al. 2005) will take further

17 advantage of temporal shifts in resources by using resource pulses to boost their energy intake for reproduction. Other species will track resources both spatially and temporally, shifting their home range (Stradiotto, Cagnacci et al. 2009), changing their foraging patterns (Diaz and Kitzberger 2006; Masi, Cipolletta et al. 2009; Twinomugisha and Chapman 2008) or even migrating (Bladon, Dickman et al. 2002; Lurz, Garson et al. 1997; McConkey and Drake 2007; Penalba, Molina-Freaner et al. 2006; Predavec and Dickman 1994; Schwemmer and Garthe 2008; Smith and Broome 1992; Telleria, Ramirez et al. 2008) as the resources they require become available in other areas.

Plant produced food resources are particularly prone to spatio-temporal heterogeneity (Fleming 1992). They provide an important food resource for a broad range of species from birds and bats to primates and small mammals (Fleming 1992). Considering the patchy distribution and often unreliable production of floral resources, animals that cannot fly between patches must have specific adaptations or display particular demographic patterns to make effective use of these resources. A number of studies have noted the capacity of small mammals inhabiting arid environments to use long distance movements between patches to survive (Dickman, Predavec et al. 1995; Haythornthwaite and Dickman 2006b). Other studies have commented on the extreme fluctuations in density of these populations (Haythornthwaite and Dickman 2006a; Magnusdottir, Wilson et al. 2008; Morton 1982; Thibault, Ernest et al. 2010; White 2008). Mueller and Fagan (2008) also presented an outline of population patterns suggesting nomadism as an adaptation to spatio-temporally heterogeneous food resources.

Marsupial pygmy-possums from the genus Cercartetus are known to use pollen and nectar as a food resource (Cadzow and Carthew 2004; Evans and Bunce 2000; Huang, Ward et al. 1987; Pestell and Petit 2007a; Turner 1984a; van Tets and Hulbert 1999; van Tets and Whelan 1997). A number of studies have also speculated that they may track flowering resources (Bladon, Dickman et al. 2002; Cadzow and Carthew 2004; Carthew and Cadzow 2001; Horner 1994; Tulloch and Dickman 2006; Tulloch and Dickman 2007; Ward 1990a; Ward 1992). However, the extent of the influence of floral resources on this genus is not yet understood. A population of the western pygmy possum (Cercartetus concinnus) was studied here to determine the influence of spatio-temporal shifts in flowering resources on demographic patterns. In particular, this study considered the influence of flowering resources on the abundance, distribution, reproductive timing, population structure, physiology and

18 movement patterns of C. concinnus. This relationship was then used to make predictions about the demographic patterns of C. concinnus in relationship to flowering availability and individual flowering species.

2.2 Methods

2.2.1 Site Selection

This study was conducted at Newland Head Conservation Park (990 ha), on the Fleurieu Peninsula in South Australia (lat. -35.609441, long. 138.520432). The site contains a suite of plants that offer flowering throughout the year. The vegetation is coastal mallee heath dominated by Banksia marginata, B. ornata, Callistemon rugulosus, Eucalyptus baxteri, E. cosmophylla and E. diversifolia. The distribution and density of these species varied across the site creating three distinct habitat types dominated by different species; A: B. ornata and E. diversifolia, B: B. marginata and E. baxteri and C: C. rugulosus and E. cosmophylla.

The climate is Mediterranean with an average yearly rainfall of 535mm and an average maximum temperature of 20.2 0C. This temperate climate is relatively predictable, with the region rarely experiencing the severe drought conditions suffered elsewhere in Australia. Total rainfall for the site was 546 mm in year 1 and 480 mm in year 2. Monthly rainfall totals (Figure 2.1) show three falls of over 80mm in the first year of the study and only one at the beginning of the second year of the study.

Figure 2.1: Total monthly rainfall in millimetres over the two years of the study (Bureau of Meteorology 2000).

A NOTE: This figure/table/image has been removed to comply with copyright regulations. It is included in the print copy of the thesis held by the University of Adelaide Library.

2.2.2 Trapping

Trapping was conducted for six nights per month over 24 months between July 1997 and June 1999. A total of 300 permanently installed, un-baited, pitfall traps (20L plastic buckets with a depth of 40cm) (Plate 2.1) were configured as 12 grids of 25, spaced at 10m intervals in five rows of five. These grids were distributed over an area of 21 hectares (600m x 350m). Grids were distributed evenly across the three habitat types with an average distance of 70m between grids. Drift fence, as is often used to help funnel animals into traps and increase capture rates (Moseby and Read 2001), was not used in this study as trials indicated that it did not increase the capture rate. The base of each trap was covered in sand and litter, with a 10cm length (6cm diameter) of PVC tube containing grass to provide shelter. Traps were checked in the early morning and late afternoon and closed between trapping sessions using plastic lids covered with sand and leaf litter.

Plate 2.1 A Pitfall trap with C. concinnus, sand and leaf litter on the base and a PVC pipe (6cm diameter) for shelter.

All capture locations were recorded on a site map so that they could be linked to trap grids and habitat types. Maps were kept for each recaptured individual to monitor use of space and distances travelled over time. Each Cercartetus captured was weighed, measured (skull, body, tail and pes lengths) and individually marked using a system of ear notches. Notes on the condition and age class of each animal and reproductive activity of females were recorded. When pouch young were observed they were counted and a crown–rump measurement was recorded. Females were considered to be suckling young that were out of the pouch when their pouch was distended and nipples enlarged. Predictions of birth month were made for females captured with pouch and suckling young (Plate 2.2) based on observations of pouch young development for C. concinnus (Bowley 1939; Casanova 1958) (Table 2.1). Animals were classified as juvenile while they were out of the pouch but still suckling from their mothers, weighed less than 7g and still had a full coat of soft guard hairs. Sub-adults had separated from their mother, weighed less than 8.9g and did not have a full coat of adult hair. Adults had a full coat of adult hair and generally weighed more than 9g. These distinctions were made for the purposes of this study using the recapture data and growth curves that were generated from these and visual assessment over time.

Plate 2.2: Female C. concinnus with pouch young and with suckling young.

Table 2.1: The duration of reproductive activities for C. concinnus based on observations made by Bowley (1939) and Casanova (1958) who both published notes on the timing of pouch young development and weaning for captive C. concinnus. Birth to nest: the young were in the pouch. Nest to eyes open: the young were absent from the mothers pouch and had their eyes closed they were observed in a nest created by the mother. Eyes open to out of nest: the young had their eyes open but had not left the nest.

Birth to nest Nest to Eyes open to out of Total eyes open nest

~ 27 days ~ 6 days ~ 13 days ~ 46 days

Distances moved between recaptures were recorded and categorised as short (10-100m) or long (>100m). These categories were selected rather than using within and between grid movements as the distances between grids varied. Movements between the three habitat types were also recorded.

2.2.3 Flowering

A measure of flower availability in each trapping grid was recorded for the species known to be used by C. concinnus, which included Banksia marginata, B. ornata, Callistemon rugulosus, Eucalyptus baxteri, E. cosmophylla and E. diversifolia (Chapter 3). The presence or absence of flowering for each species was recorded monthly (24 months) in 432 plots of 10m2 that neighboured or joined each of the 300 traps (36 plots at each of the 12 trapping grids). This was used to develop monthly and seasonal estimates of the availability of flowering resources in each habitat type and in relationship to individuals captured. Monthly flowering density was expressed for each species as the number of plots with flowering present (n=432). Flowering densities were also divided by flowering season (see section 2.3.2) and habitat type for comparison with captures.

2.2.4 Analysis

Captures datum were used to determine the population patterns of C. concinnus. Since the number of trap nights per month was the same for each trapping period, captures were compared as totals per month rather than mean captures per trap night. To identify sex based differences in capture rates, time between first and last captures and physical traits independent t-tests (Mann-Whitney test for non-parametric data) were used. Differences in movements and use of habitats by recaptured animals were assessed using χ2 tests. These tests identified differences both within and between sexes for the percentage time spent travelling short and long distances and the percentage of time they changed or did not change habitat.

To identify variation in the occurrence of six dominant plant species, we modelled the presence/absence of flowering in each of the 432 plots as a function of temporal (flowering seasons) and spatial (habitat groups) predictor variables using regression trees assuming binomially-distributed errors (De'ath 2002; De'ath and Fabricius 2000). The data was split by these two variables, with each split minimising the sum of squares within each group. The proportion of the total sum of squares explained by each split was represented by the relative lengths of the vertical lines in the tree plots at each split (De'ath 2002; De'ath and Fabricius 2000).

To compare the effect of spatial and temporal differences in flowering patterns and flowering species on captures of C. concinnus, logistic regression with a binomial distribution and logit link function was used. This analysis was carried out for a series of binomial response variables describing the presence or absence of a capture at each trap location. A separate analysis was conducted for all captures and male adults, females with pouch young and females without pouch young. The predictor variables were flowering season (a categorical variable representing the season in which the animal was captured as defined by flowering timing), grid (a categorical variable representing capture location as a measure of spatial distribution), flowering species (a series of binary variables representing the presence or absence of each individual flowering species) and year (a categorical variable representing each year of data collection). I defined a priori candidate model sets where these predictor variables were modelled individually (with the exception of year) and in selected combinations. I then ranked these models based on Akaike’s information criteria (AIC) (Akaike 1973) and the highest-ranked model was used for inference.

The highest-ranked model was used to determine the odds ratio for each term in the model, which is a measure of effect size. This describes the strength of association between the two binary variables. This value was expressed as a percentage change in odds (with 95% confidence interval) providing a measure of the likelihood (odds) of an animal being captured in relation to an increase in the availability of each flowering species.

In order to determine the influence of flowering on population sex ratios and the timing of reproduction, logistic regression with a binomial distribution and logit link function was used. Sex ratios (the proportion of females to males captured at any trap location) and the timing of reproduction (the proportion of females with pouch young) were the binomial response variables. The predictor variables were flowering season (a categorical variable representing the season in which the animal was captured as defined by flowering timing), grid (a categorical variable representing capture location as a measure of spatial distribution), flowering species (a series of binary variables representing the presence or absence of each individual flowering species) and flowering (all six flowering species were combined to create a binary variable representing the presence and absence of flowering at each trap location). Again, I defined a priori candidate model sets where these predictor variables were modelled individually and in selected combinations. I then ranked these models based on Akaike’s information criteria (AIC) (Akaike 1973) and the highest-ranked model was used for inference. The highest-ranked model was used to determine the odds ratio (a measure of effect size) for each term in the model. This value was expressed as a percentage change in odds (with 95% confidence interval) providing a measure of the likelihood (odds) of an animal being captured in relation to an increase in the availability of each flowering species. All analysis was carried out using R version 2.11.1 (R Core Development Team 2010).

2.3 Results

2.3.1 Captures

A total of 452 captures of 261 individual C. concinnus were recorded during 24 sampling periods between July 1997 and June 1999 from a total of 43,200 trap nights, giving a trap success of 1.05%. Males made up 59% and females 41% of marked individuals (Table 2.2), giving an overall sex ratio of 1.4:1. The capture rate was 0.6 (0.36 male and 0.24 female) individuals per 100 trap nights. The number of individuals caught varied monthly, ranging from 2 to 33 (Figure 2.2). This variability was apparent in both sexes with captures of individuals ranging from 2 to 23 for males and 0 to 16 for females. Monthly sex ratios were quite variable with the most notable feature being the ratio of males to females during March of 1998 and 1999 (over 5:1 in contrast to the average of 1.4:1). Fewer males than females were caught in November of both years (sex ratios of 0.9:1 and 0.7:1 respectively) and July (0.5:1) of 1998 and May (0.9:1) of 1999.

Table 2.2: Numbers of C. concinnus captured, marked and recaptured over 24 months of trapping at Newland Head Conservation Park. Independent t-tests were used to determine whether there was a significant difference between males and females. *P>0.05, **P>0.001 .

Male Female Total No. captures 291 161 452 No. individuals 154 107 261 Mean no. captures per individual ± s.e. * 1.9 ± 0.1 1.5 ± 0.1 1.7 ± 0.1 (range) (1 – 14) (1 - 6) % individuals recaptured 37 (n=57) 32 (n=34) 35 (n=91) Mean months between first & last recapture ± s.e. ** 3.2 0.6 1.7 0.4 2.6 0.4 (range) (0 – 20) (0 – 12)

Figure 2.2: Monthly captures of individual male (dark) and female (open) C. concinnus. Each month represents 1800 trap nights.

No. individuals 15

0 J A S O N D J F M A M J J A S O N D J F M A M J

1997 1998 1999

Over half of the marked population (65.1%) was caught only once during the study (Table 2.2). This pattern was evident for both males and females, with 63% of males and 68% of females being captured only once (Figure 2.3). Of those animal recaptured, capture frequency was significantly greater for males than females, with 2 males being recaptured 13 & 14 times respectively (Table 2.2). The average length of time between first and last capture was significantly longer for males (3.2 months) than females (1.7 months) (Table 2.2). Months known to be alive was not estimated for this population as recapture rates were low. Captures of new individuals fluctuated throughout the year, with the lowest point being in April of both years (Figure 2.4). However, they made up 50% or more of the captures for 19 of the 24 sampling periods. Increases in captures of new individuals did not necessarily coincide with increases in the abundance of juveniles and sub-adults.

Figure 2.3: The capture frequency of individual male (dark) and female (open) C. concinnus.

100 90 80 70 60 50 40 30 No. Individuals 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

No. times caught

Figure 2.4: The percentage of new (dark) and recaptured (open) individuals per month.

100

40 % Captures % Captures 20

0 J A S O N D J F M A M J J A S O N D J F M A M J

1997 1998 1999

Distances travelled between recaptures were significantly shorter (t=4.34, P<0.001) within a trip (67m ± 4, n=69) than between trips (139m ± 6, n=93). The distances travelled by males and females within and between trips were not significantly different. However males travelled longer distances more frequently than females between recaptures and were more

28 likely to change habitat (Table 2.3). Interestingly, animals recaptured between trips changed habitat at the same rate as those recaptured within a trip (Male, X2=1.9079, df=1, Female, X2=0.2632, df=1). Only eight individuals were recaptured over five or more months, with only one of these being female. The males all moved between habitats A and B or used all three habitats. The female was the only animal recaptured in the same grid each time it was caught.

Table 2.3: Movements and use of habitats by recaptured animals within and between trips. Short distances ranged from 10 to 100m, long distances were over 100m. X2 tests were used to determine whether there were differences in movements and use of habitats by recaptured animals. These tests identified differences both within and between sexes for the percentage time spent travelling short and long distances and the percentage of time they changed or did not change habitat.

Male Female P Distances travelled Within a trip Short 71% P<0.05 Short 91% P<0.01 <0.001 Between trips Long 85% P<0.05 Long 63% ns <0.05 Habitat changes Within a trip Not changed 71% P<0.05 Not changed 82% P<0.05 <0.005 Between trips Changed 66% ns Changed 58% ns

Adults made up 81.4% of all captures, sub-adults 12.8% and juveniles 5.8%. The proportion of adults to sub-adults and juveniles was similar for males (82.4%) and females (79.7%). Males were captured more frequently than females in all but the juvenile age class. The percentage of sub-adults and juveniles captured were very similar for the first (19.1%) and second year (17.9%). In the 97-98 trapping year, of 204 individuals captured, 12 were juveniles (5.9% of captures). In the 98-99 trapping year, of 162 individuals captured, 9 were juveniles (5.6% of captures). While sub-adults were captured in 10 out of the 12 months of each trapping year, juveniles were captured during only 5 months of each trapping year. Juveniles were most likely to be captured in July, August, November and February (Figure 2.5).

Figure 2.5: Monthly captures of adults (dark), sub-adults (open) and juveniles (shaded) expressed as a percentage.

100

40 % Captures 20

0 J A S O N D J F M A M J J A S O N D J F M A M J 1997 1998 1999

Signs of pouch activity were observed in 31.7% (31.6% yr1 and 31.9% yr2) of adult females captured, with a total of 34 pouch young recorded in the first year and 26 in the second. The average litter size was 2.6 ± 0.17 young with a range of 1-4. Only one female was recorded having more than one litter. This animal had one litter in July 97 and another in May 98, giving a 10 month interval between successive litters. Females with young were captured in seven out of 12 months of each year, with the largest numbers observed in May (Figure 2.6). These captures were distributed between females suckling young and those with pouch young.

Figure 2.6: Monthly captures of female individuals with pouch young (dark) and suckling young (open).

No. Individuals 2

0 J A S O N D J F M A M J J A S O N D J F M A M J 1997 1998 1999

A record of breeding in captive C. concinnus was used to calculate the length of time between birth and the point that young would emerge from the pouch (Bowley 1939; Casanova 1958). This was used to predict the birth month for each of the females captured with pouch young or suckling young. It was apparent from this that reproductive activity occurred throughout most months of the year, with the exceptions being November to January (Figure 2.7). Peaks in births occurred in September 1997, May 1998 and to a lesser extent February and May 1999. The number of juvenile captured made up only 35% of the total number of young that were counted in the pouch (Figure 2.6) and 27% of predicted births (Figure 2.7). This fits with the recapture rate of 35% (Table 2.1).

Figure 2.7: Predictions of the number of females giving birth each month, based on females with pouch young and suckling young.

7 6 5 4 3

No. Individuals 2 1 0 J A S O N D J F M A M J J A S O N D J F M A M J

1997 1998 1999

The mean adult weight was 11.1g with a range from 8.1 to 20.6g. Females with pouch young were significantly larger than adult males in snout/vent and tail length measurements (Table 2.4). Adult females without pouch young were significantly heavier than males. This difference between males and females was not evident in juveniles and sub-adults. With the exception of measurements for females with pouch young, other physical measures showed very little difference between males and females. Females with pouch young were significantly larger than females without pouch young in snout-vent measurement. Adult weights for each sex fluctuated throughout the trapping period (Figure 2.8). Fluctuations were greater in the second year, particularly for females. Males maintained a more constant weight than females, with maximum weights recorded in February of the second trapping year.

Table 2.4: Mean ± s.e. of body measurements (mm) for: a) All captures divided by age class and b) Adults divided into sex and adult females divided into those with pouch young (PY) and without PY, (n) = sample size. Independent t-tests were used to determine whether there was a significant difference between male adult and both female adult categories (*) and lactating female and non-lactating female categories (^), P>0.001. a) All captures divided by age class

n Weight Snout/vent Skull Tail Pes

Juvenile 21 5.9 ± 0.2 60.2 ± 1.2 16.7 ± 0.4 68.7 ± 1.0 11.2 ± 0.1

Sub adult 47 8 ± 0.1 69 ± 0.6 18.5 ± 0.2 76.1 ± 0.8 11.6 ± 0.1

Adult 298 11.8 ± 0.1 78.2 ± 0.3 19.9 ± 0.1 82.8 ± 0.3 12.2 ± 0.03

b) Adults by sex and reproductive status

n Weight Snout/vent Skull Tail Pes

Adult Male 192 11.1 ± 0.1 77.6 ± 0.4 19.9 ± 0.1 82 ± 0.3 12.2 ± 0.04

Females with PY 33 14.4 ± 0.4*^ 82 ± 1*^ 20.2 ± 0.1 86.3 ± 1.1* 12.4 ± 0.1

Females without PY 73 12.3 ± 0.3* 77.8 ± 0.7 19.8 ± 0.1 83.2 ± 0.7 12.3 ± 0.1

Figure 2.8: Monthly mean weights (± s.e.) for adults. a) Male, b) Females without pouch young (gaps in this data represents months when females without pouch young were not captured). a)

Weight (g) Weight 10 b) 5

Weight (g) Weight 10

5 J A S O N D J F M A M J J A S O N D J F M A M J

1997 1998 199

2.3.2 Flowering

Flowering of the six dominant species occurred throughout the year at Newland Head Conservation Park (Figure 2.9). Flowering was divided into three seasons based on the flowering peaks recorded. The plants with flowering peaks in each of the seasons were: Season 1; (January to March) B. marginata and E. baxteri, Season 2; (April to August), B. ornata and E. cosmophylla, Season 3; (September to December), C. rugulosus and E. diversifolia. The distribution of the flowering species over the site also varied by habitat type, with two species being dominant in each of the three habitat types in the study area; Habitat A: B. ornata and E. diversifolia, Habitat B: B. marginata and E. baxteri and Habitat C: C.

33 rugulosus and E. cosmophylla. Thus flowering could be represented over the trapping area both spatially and temporally, as described in the regression tree (Figure 2.10). In each of the seasons the two species that flowered dominantly were available in particular habitats. In Season 1, B. marginata and E. baxteri both occurred in Habitat B with Habitats A and C containing very little flowering. In contrast, during Seasons 2 and 3 one of the species flowered in Habitat C with the other flowering in Habitats A and B. During these seasons flowering densities were higher in grids of Habitat A than grids of Habitat B.

Figure 2.9: The flowering densities per month of the six dominant species at Newland Head Conservation Park, expressed as the number of survey plots with the species flowering (432 plots surveyed each month). Seasons: 1 , 2 , 3 . Flowering species: B. marginata (∆), B. ornata (□), C. rugulosus (◊), E. baxteri (▲), E. cosmophylla (■), and E. diversifolia (♦).

200

180

160

140

120

100 No. Plots 80

0 J A S O N D J F M A M J J A S O N D J F M A M J

Figure 2.10: The presence of flowering over seasons and habitat types for each of the key flowering species, portrayed using a decision tree (De'ath 2002; De'ath and Fabricius 2000). The histogram shows the distribution of flowering split first by season and then by habitat type with the length of the branches being proportional to the variance represented by each split. Bar plots are displayed at each node showing the distribution of flowering presence across species. The values at the x-axis represent the maximum height of the y axis (the number of the 432 survey plots with flowering present for all of the 24 survey months) and n (the number of grids - 12 per month for 24 months). Flowering species are B. marginata , B. ornata , C. rugulosus , E. baxteri , E. cosmophylla , E. diversifolia .

Seasons 1 & 3 Season 2 (Apr – Aug)

Season 1 Season 3 (Jan-March) (Sept – Dec) C B & A

C & A B B & A C

66 : n = 48 110 : n = 24 234 : n = 64 179 : n = 32 129 : n = 40 298 : n = 80

2.2.4 Captures and Flowering

The relationship between captures of C. concinnus and flowering was modelled using a logistic regression with a binomial distribution and logit link function. This analysis was carried out for a series of binomial response variables describing the presence or absence of a capture at each trap. Time (season and year), space (trapping grid) and flowering species were used as predictor variables. The model selected using AIC indicated that this relationship was best represented by a combination of flowering species, grid and season, with an interaction between grid and season (Table 2.5 and Appendices Table 6.1). This model explained 46.5% of the variance in the model, with a weight of 0.67. While year was included as a variable in the model selection process, the model selected indicated that clumping data across years into season increased the strength of the model, although not the % of variation explained.

Model Log Lik df AICc Weight % DE

F + G + S + G x S -435.846 42 970.435 0.671 46.47

F + G + S -464.358 20 971.861 0.329 39.66

F + G + S x Y + G x S x Y -386.216 90 1035.580 0.000 58.32

F -520.573 7 1055.546 0.000 26.23

G + S + G x S -487.128 36 1056.869 0.000 34.22

G + S -529.120 14 1087.779 0.000 24.19

G + S x Y + G x S x Y -424.728 84 1087.801 0.000 49.12

G -538.332 12 1101.798 0.0000 21.99

S -621.406 3 1248.897 0.0000 2.14

Null -630.369 1 1262.753 0.0000 0.00

Within each season, there was a tendency for the distribution of captures of C. concinnus to be higher in one or other of the three habitats (Figure 2.11). So, in Season 1 captures were highest in Habitat B followed by A then C, in Seasons 2 and 3 captures were highest in Habitat A followed by B then C. When related to flowering densities, captures were higher in areas containing B. marginata and E. baxteri in Season 1, B. ornata in Season 2 and E. diversifolia in Season 3 (Figure 2.11). Indeed, these four flowering plant species were found to be significantly associated with the abundance and distribution of captures of C. concinnus (Table 2.6). The model selected using AIC (Table 2.5) was used to determine the odds of capturing C. concinnus in relationship to each of the six flowering plant species while taking into account the effect of season and grid location (Table 2.6). So, for example, with each 10% increase in the proportion of plots with B. marginata flowering the estimated odds of capturing C. concinnus increased by 16.3%.

Table 2.6: The estimated odds of the presence of C. concinnus increasing in response to an increase in the presence of flowering species. Odds values were represented as a percentage probability. Values calculated using the model selected by AIC and displayed as odds, 95% CIs, parameter estimates, standard errors (SE) and significance (P).

Species %Odds 95% CI Estimate SE P

B. marginata 16.3 11, 21.8 0.1509 0.02381 <0.001

B. ornata 5.1 2.5, 7.7 0.0495 0.01276 <0.001

C. rugulosus 4.5 0, 9.4 0.0442 0.02323 0.057

E. baxteri 5.5 1.4, 9.7 0.0531 0.02021 0.008

E. cosmophylla 1.3 0,6.6 -0.0125 0.02618 0.631

E. diversifolia 10.5 7.4, 13.7 0.0996 0.01448 <0.001

Season 1 Season 2 Season 3 Captures 8 8 8

6 6 6

4 4 4

2 2 2 Mean Captures Mean Captures Mean Captures Mean Captures Mean Captures Mean Captures 0 0 0 A B C A B C A B C B. marginata B. ornata C. rugulosus 30 30 30

20 20 20

10 10 10 Mean No. Plots Mean No. Plots 0 Mean No. Plots 0 0 A B C A B C A B C

E. baxteri E. cosmophylla E. diversifolia 30 30 30

20 20 20

10 10 10 Mean No. Plots Mean No. Plots 0 Mean No. Plots 0 0 A B C A B C A B C

When considering each sex separately, captures of males and females varied with season and habitat type (Figure 2.12). The mean number of males captured was highest in Season 1, while mean captures for females was relatively consistent across all seasons. Logistic regression models were generated to determine the influence of space (trapping grid), time (flowering season) and flowering species on the presence or absence of captures of adults grouped by sex, with females divided into those with and without young in pouch (PY). Each of the models selected using AIC included trapping grid, season and flowering species as variables (Table 2.7 and Appendices Tables 6.2, 6.3 and 6.4). These models were used to determine the odds (or probability) of capturing male and female C. concinnus as a function of increases in flowering over the study site for each of the six dominant flowering plant species, while taking into account the effects of season and grid location (Table 2.8). Flowering plant species associated with captures and the strength of this association varied between the sexes and in relationship to female reproductive status. The probability of capturing males was significantly associated with only two plant species, while females were associated with four. Females with PY and without were associated with both Banksia species and different Eucalyptus species. The probability of an increase in captures of females was greater with an increase in flowering of B. marginata and E. baxteri than with an increase in B. ornata and E. diversifolia. For every 10% increase in B. marginata flowering there was an increase in the probability of capturing adult males by 11.2%, females with PY by 15.5% and females without PY by 12.8% (Table 2.8).

Figure 2.12: Mean (± s.e.) captures of adult a) male and b) female C. concinnus over seasons (1, 2 and 3) and habitats A (dark), B (open) and C (shaded). a) b)

4 4

3 3

2 2 Mean Captures Mean Captures Mean 1 1

0 0 1 2 3 1 2 3

Table 2.7: AIC model selection for captures of male and female C. concinnus using space (Grids - G), time (Season - S) and flowering species (FS). Females are divided into those with and without pouch young (PY). (value) = number of individuals.

Log Lik df AICc Weight % DE

Males (178) G + S + FS -341.887 20 726.920 0.999 32.55

Females with PY (33) G + S + FS -94.795 20 371.916 0.954 29.78

Females without PY (71) G + S + FS -164.385 20 232.736 0.945 37.03

Table 2.8: The odds of capturing male and female C. concinnus when particular plant species were flowering. PY (pouch young). Odds values represent the percentage probability of captures increasing as the presence of flowering increases. Values calculated using models selected by AIC and displayed as odds, 95% CIs, parameter estimates, standard errors (SE) and significance (P).

Species %Odds 95% CI Estimate SE P

Male B. marginata 11.2 7, 15.5 0.1057 0.01949 <0.001

E. diversifolia 7.3 4, 10.7 0.0704 0.01596 <0.001

Female with PY B. marginata 15.5 3.8, 28.5 0.1440 0.05443 0.008

B. ornata 11.2 5, 17.7 0.1058 0.02909 <0.001

E. baxteri 18.7 1.3, 39.1 0.1715 0.08081 0.034

Female no PY B. marginata 12.8 5.7, 20.4 0.1206 0.03317 <0.001

B. ornata 5.3 0.2, 10.6 0.0514 0.02525 0.042

E. diversifolia 7.3 2.6, 12.3 0.0708 0.02323 0.002

The availability of flowering also influenced other related population patterns in C. concinnus, such as sex ratios and the timing of reproduction. Logistic regression with a binomial distribution and logit link function was used to determine the influence of flowering species and the amount of flowering present on these population patterns. For example, as the presence of flowering increased, the proportion of females relative to males increased (Figure 2.13). This proportion changed with flowering season, with the ratio of males to females in Season 1 (3.2:1) being over twice that in Seasons 2 and 3 (1.5:1 and 1.3:1 respectively). The strongest model selected was the amount of flowering present (Table 2.9 and Appendix, Table 6.5). This model represented 23.71% of the variance in the proportion of females to males. The proportion of females with pouch young was influenced by flowering species with the model selected representing 54.4% of the variance in the data (Table 2.9 and Appendix, Table 6.6). For every 10% increase in flowering availability for B. ornata and E. baxteri, the likelihood that a female was lactating increased by 3.2 times and 9.8 times respectively (Table 2.10).

Figure 2.13: The proportion of females to males captured per month over two years as a function of the number of grids containing plants in flower for all of the 6 species combined. n=24, R2=0.22

300

250

200 No Plots

150

100

0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Proportion of Captures

Model Log Lik df AICc Weight % DE

Proportion of females Flowering -45.089 2 94.749 0.88 23.71 present

Proportion of females with Flowering -23.023 7 67.047 0.6594 54.42 pouch young species

Table 2.10: The odds of particular demographic patterns of C. concinnus (a) Proportion of females to males and b) Proportion of females carrying young, being influenced by different flowering variables (Number of plots with flowering present and flowering species). Odds values represent the percentage probability of the response variable (population pattern) increasing with changes in the variable (flowering present or flowering species). Values calculated using models selected by AIC and displayed as odds, 95% CIs, parameter estimates, standard errors (SE) and significance (P).

Species %Odds 95% CI Estimate SE P

Proportion of females Flowering 1.23 1,1.5 -2.0546 0.8082 0.01 present

Proportion of females B. ornata 3.2 1.1,9.3 11.8176 5.0728 0.01 with pouch young

E. baxteri 9.8 1.7,57 22.8541 8.5421 0.007

The influence of other variables such as rainfall, cloud cover, wind and temperature were also tested to determine the influence of other variables on the probability of capture. None of these gave any reportable results.

2.4 Discussion

This study is the first to show that the distribution and abundance of C. concinnus will shift in response to the timing and abundance of specific flowering species. These results confirm what other studies have speculated, that C. concinnus tracks flowering resources (Cadzow and Carthew 2004; Carthew and Cadzow 2001; Horner 1994; Robertson 2001). In addition this study supports the predictions of Fleming (1992) concerning resource variability which suggest that to maximise fitness animals should closely track resources over a range of demographic patterns. The demographic patterns of C. concinnus influenced by resource variability were abundance, distribution, sex ratios, and reproductive timing. In fact, this relationship with the spatio-temporal shifts in the availability of flowering resources could be used to predict their abundance and distribution. This link between the abundance and distribution of food resource and the abundance and distribution of the species that use them has been observed in a number of studies (Harper, McCarthy et al. 2008; Haythornthwaite and Dickman 2006a; Penalba, Molina-Freaner et al. 2006; Telleria, Ramirez et al. 2008; Wauters, Githiru et al. 2008). For example, in Melbourne, Australia, Harper et al. (2008) found a relationship between the abundance of common brushtail possums (Trichosurus vulpecula) and common ringtail possums (Pseudocheirus peregrinus), and den and food availability.

While C. concinnus fed upon seven different flowering species (Chapter 3), only six of these were dominant on the site and only four were important in influencing measures of population demography. The spatial and temporal patterns of these flowering plant species differed and resulted in shifts in the distribution of C. concinnus to follow these resources. This form of resource tracking involving regular spatial shifts in distribution is evident in highly mobile species such as birds and bats (Rothenwöhrer, Becker et al. 2010; Schwemmer and Garthe 2008) but has been less frequently observed in small mammals. Studies of species in arid conditions appear to provide the majority of the examples for this form of resource tracking (Dickman, Predavec et al. 1995; Haythornthwaite and Dickman 2006b), with animals moving long distances in search of food resources and rarely being recaptured in the same location.

Captures of C. concinnus increased with flowering availability in this study, however, the already low recapture rate did not. Low recapture rates could be attributed to a range of drivers, including the use of vegetation for movement (and therefore reduced trapability), short lifespan, trap avoidance and animal mobility in response to resources (Carthew and Keynes 2000; Dickman, Predavec et al. 1995; Haythornthwaite and Dickman 2006b; Klinger 2007). Short lifespan does not appear to be true as I have recaptured animals after 2 and 2.5 years (beyond this study) suggesting that they will live in the wild for some time (pers. obs.). Further, C. concinnus does not appear to learn to avoid traps as, in this study, some animals were caught on up to three consecutive nights. Here, it appears related to a combination of the use of vegetation for 87% of their travel time (Chapter 3) and animal mobility.

Mueller and Fagan (2008) presented a review of animal movement and distribution patterns and their relationship to resource distribution. Under their model, the variability in spatial and temporal distribution of the flowering resources available to C. concinnus should result in non-oriented, or random movement mechanisms and a nomadic population distribution. This appears to fit with the temporal shifts in captures of C. concinnus, the low recapture rates, the distances moved between recaptures, the lack of recaptures in the same area and the broad shifts in distribution to follow flowering species. In addition to appearing to fit a nomadic population pattern, male and female C. concinnus may fit different types of nomadism (Mueller and Fagan 2008). Males are more mobile than females, being more likely to travel further and change habitat between recaptures, fitting type I nomadism. Females, in contrast, will travel shorter distances between recaptures and are not as likely to change habitat between recaptures, fitting type II nomadism (Mueller and Fagan 2008). A nomadic movement pattern allows species to make effective use of spatially and temporally variable food resources. Random movements, rather than directed movements, mean that animals will not miss food resources that may only be available every other year and will not be disappointed by the lack of resources in a place they had previously fed. In this study, for example, flowering was almost completely absent in September 1998, while in the previous year flowering was available in relatively high densities at this time. If animals were returning to the same patches to find resources rather than moving randomly to monitor food availability they would have missed the available resources elsewhere in the Conservation Park. As it was, targeted trapping revealed that animals were present where flowering resources were available. A study of the eastern pygmy possum (C. nanus), in New South Wales, Australia, by Tulloch and Dickman (2007) showed that C. nanus had the capacity to

44 monitor and respond to changes in resource availability. Without a random movement pattern these species could not locate new food resources. This adaptation is particularly important for species in arid environments where resources are subject to more extremes in variation due to climatic conditions (Carthew and Cadzow 2001; Dickman, Predavec et al. 1995; Haythornthwaite and Dickman 2006b; Robertson 2001). Further study to explore the movement patterns of this species using radio-telemetry and observation based tracking could provide valuable insights into the use of space by small mammal species and the use of directed and random movement,

The strength of the relationship with particular flowering species here differed between the sexes and for females with pouch young. The spatial and temporal patterns of both B. marginata and E. diversifolia were linked with the abundance and distribution of both sexes while B. ornata and E. baxteri were only linked with captures of females. This differing response of males and females to resource availability has been observed in other small mammal species and has been associated with to the different resource requirements of each sex (Broome 2001a; Smith and Broome 1992). The most common explanation for this pattern is that females track food resources and that males track females (Ostfeld 1985; Ostfeld 1990). Further, during times of maximum reproductive activity females will often defend resources aggressively, leaving males to forage in less resource rich areas (Bayart and Simmen 2005; Ostfeld 1985; Ostfeld 1990). Species such as the mountain pygmy possum (Burramys parvus), in New South Wales, Australia, display clearly the use of a better quality resource by females with males being restricted to a lower quality resource (Broome 2001a; Smith and Broome 1992). This division of resources allows for reproductive success in females who need the protein rich Bogong moth to ensure survival of their young. It is interesting to note in the present study that an increase in flowering abundance in an area resulted in an increase in the proportion of female to male C. concinnus. This may indicate that like B. parvus, female C. concinnus are able to exclude or reduce the number of males in an area with abundant food resources. In addition, considering the higher capture rate of males, the increase in proportion of females to males with an increase in flowering availability confirms that females track food resources more closely than males.

The defence of food resources may also be supported by female C. concinnus being larger than males, indicating that they physically have the capacity to defend an area that is resource rich, excluding males. While this study did not consider female aggression specifically, it was

45 observed in a previous study in which a female (just released) was observed chasing a male (captured and identified) out of a nest site (Horner 1994). It is interesting to note that this type of female dominance has been observed in another nectarivorous marsupial, the honey possum (Tarsipes rostratus) in Western Australia (Wooller, Renfree et al. 1981). This suggests that female dominance may be a particularly important behaviour for reproductive success in species that depend on spatially and temporally variable food resources. As such, further research to understand the role of resource defence in shaping the distribution patterns of male and female C. concinnus would be valuable.

In the present study, the highest density of captures of male individuals occurred in Season 1 when the lowest density of flowering was available. Interestingly, towards the end of this season females began producing young. In fitting with the proposed response of males to mating opportunities with females (Ostfeld 1985; Ostfeld 1990), increased capture densities of males then may be a result of increased activity as males hunt for mating opportunities. In this season captures of both males and females were significantly linked to the presence of B. marginata and females with E. baxteri. Flowering of these species is restricted to only one habitat type so it could be expected that captures would be higher as the animals appear to be spending most of their time in a smaller area and moving between more limited resources. The combined effect could be that females are there for the food resources required for reproduction and males are there to find females.

Heterogeneity in resources should lead to an opportunistic reproductive pattern that follows or synchronises with resource pulses (Fleming 1992; Pyke 1981). This use of food resource peaks for reproductive success is quite common in a range of species, with some studies reporting a failure in reproduction with a failure in food resource (Bieber 1998; Sharpe 2004). A study by Bieber (1998) recorded a year of reproductive failure for the fat dormouse (Glis glis), in Germany, that coincided with a lack of food resources in autumn of that year. They suggested that in years with low food availability males do not invest energy in reproduction. In the system studied here, reproductive activity in C. concinnus was most closely linked with flowering of the Banksia species and E. baxteri, with the larger reproductive peaks occurring during the time that B. marginata and E. baxteri were flowering. As a result, young would be in the pouch at the time that B. ornata is flowering, allowing lactating females the opportunity to take advantage of this species which has the longest flowering peak of the flowering species. Lactating females were captured in 7 out of 12

46 months of both years, with an absence of reproductive activity over the summer period. This may be a reflection of lower flowering resource availability at this time or the species available (mostly E. diversifolia and C. rugulosus). Other studies on C. concinnus have reported lactating females throughout all months of the year (Bowley 1939; Casanova 1958; Clark 1967; Horner 1994; Robertson 2001; Ward 1990b). This along with the results of the present study suggest that C. concinnus is an opportunistic breeder using the resources available to support reproductive activity at any time of the year. The timing of reproduction or reproductive peaks may also be influenced by the pollen and nectar resources provided by each plant species. These will vary between flowering species and may have some influence on the timing of reproductive peaks (Baker and Baker 1982; Morrant, Petit et al. 2010; Nicolson and Van Wyk 1998; van Tets and Whelan 1997).

As is typical with many small mammal species (Carthew and Keynes 2000; Pestell and Petit 2007b) the proportion of male C. concinnus captured in this study was higher than females. This is sometimes attributed to males being more mobile than females and therefore more trappable (Carthew and Keynes 2000). In this study the greater distances travelled between recaptures and higher recapture rates for males support this suggestion. Studies of the eastern pygmy possum (C. nanus), honey possum (T. rostratus) and kaluta (Dasykaluta rosamondae) have also found that males travel further than females and use more space (Bladon, Dickman et al. 2002; Bradshaw and Bradshaw 2002; Harris, Goldingay et al. 2007; Kortner, Rojas et al. 2010).

The time between first and last capture for males was longer than for females, whether this indicates longer survivorship is questionable, however, due to the increased trapability of males. In both this study and one at Mount Scott (Horner 1994) the proportion of female juveniles captured was higher than male juveniles. However, by sub-adult in both studies this ratio had shifted towards males and by adulthood males were captured in much higher densities. The proportions of adults to sub-adults and juveniles was slightly lower at Mt Scott Conservation Park (Horner 1994) possibly indicating a lower recruitment rate at Newland head. Despite the monthly fluctuations in capture rate and the different capture densities over each year it is interesting to note that the population structure at Newland head remained the same. These results along with the shifts in sex ratios across different trapping periods suggest that higher densities of females are present but not trappable during all of the year.

A number of factors may influence the extent to which a flowering plant species is able to shape the demographic patterns of C. concinnus. For example, particular plant species may be preferred food resources and able to meet particular dietary needs, a plant species may be the most abundant resource in the habitat or may be associated with particular habitat structures. It is interesting to note that a study of C. nanus found its abundance more closely linked with floristic structure and the abundance of particular Banksia and Myrtaceae species than vegetation structure characteristics (Tulloch and Dickman 2006). However, in the present study the species strongly linked with the demographic patterns of C. concinnus were located on sandy soil, in areas with thick understory and scattered patches of open ground. In contrast, the two species that showed no significant relationship to the demographic patterns of C. concinnus (C. rugulosus and E. cosmophylla) were found on clay soils with less understory and more expanses of open ground. This possible association with particular habitat features requires more research and may explain further the variability in the abundance and distribution of this species.

This study has highlighted the influence that spatially and temporally variable food resources can have on the demographic patterns of a species that rely upon them. In the case of C. concinnus their demographic patterns are closely linked with the spatio-temporal availability of flowering resources. This is evident in their tracking of changes in flowering abundance over space and time. A sex based difference in their response to these resources is apparent with females tracking flowering resources more closely than males. To further explore this relationship a more detailed study of individual movement patterns using field observations and radio telemetry would provide insights into their use of space in response to flowering resource availability and possibly nomadic population pattern. In addition, further research into resource defence by females using field observation and captive trials could shed light on fluctuations of male and female distribution patterns.

Chapter 3 : Foraging on spatially and temporally heterogeneous food resources

3.1 Introduction

Understanding foraging behaviour can provide insight into the habitat and food resources used by animals and help to identify social dynamics and the major drivers in an animals’ behaviour. At the centre of most foraging behaviours is choice (Abrams 2010; MacArthur and Pianka 1966). Animals must make choices about the habitat components and food resources they use and the social interactions they prioritise. Even if these choices are not specific to particular resources they are still choices, in that an omnivorous diet is a choice to consume a broad range of food resources rather than a specific few.

These decisions are all influenced by the need for survival and reproductive success. The way in which an animal uses habitat can reveal for example, whether shelter for predator avoidance and nest sites (Bertolino 2007; Di Stefano, York et al. 2009; Harris, Goldingay et al. 2007; Massey, Bowen et al. 2009) or the distribution of food resources (Abensperg-Traun and De Boer 1992; Ellis, Melzer et al. 2009; Lurz, Garson et al. 2000; Rosario, Cardoso et al. 2008) or in the case of chimpanzees in Guinea, microclimate (Takemoto 2004) are more important. Food resource use can highlight sex based differences in nutritional requirements (Castro, Masero et al. 2009; Lodé 1999; Rosalino, Santos et al. 2009; Walker, Parker et al. 2006) and whether particular foods are targeted for the peak energy requirements of reproduction (Marcello, Wilder et al. 2008; Sailer and Fietz 2009; Smith and Broome 1992). The use of space and distances travelled can reveal the amount of time invested in the search for food (Bakaloudis 2010; Holland, Bennett et al. 2007; Mueller and Fagan 2008; Nonaka and Holme 2007; Pyke 1984) and whether social interactions such as hunting for mates (Loretto and Vieira 2005; Taylor 1993b) or the defence of food or mate resources (Ford and Paton 1982; Ostfeld 1990; Raboy and Dietz 2004) are more important than the quest for food resources. Foraging behaviour can also reveal which food and habitat resources are preferred and which are avoided and the extent to which particular resources are used (Bos, Carthew et al. 2002; Chesson 1978; Cockburn 1981; Eyre and Smith 1997; Ganas, Ortmann et al. 2008).

Spatio-temporal variability in habitat and food resources will of course play a major role in shaping foraging behaviour. If resources are readily available and do not fluctuate spatially and temporally, animals should be able to focus their priority on other behaviours such as

50 seeking out or defending mates. However, when resources are spatially and temporally heterogeneous animals should invest more of their time searching for and making choices between resources as their availability fluctuates (Abrams 2010; MacArthur and Pianka 1966). In addition, when making choices about resources there is the aspect of quality which adds a further dimension (Cartar 2004; Cole, Hainsworth et al. 1982; Hanya 2004; Keasar, Sadeh et al. 2008; Lin and Batzli 2001; Lurz, Garson et al. 1997; Lurz, Garson et al. 2000; Winter and Stich 2005). The relationship between animals and their food resources has often been described in terms of optimal foraging theory (Pyke, Pulliam et al. 1977). This suggests that feeding choices and the time spent searching for a food resource should match the reward received. From this perspective, foraging animals should select the food resource that takes the least effort to attain and satisfies their energy requirements.

Plant produced food resources, like flowers and fruit, are temporally and spatially heterogeneous, and evidence of their influence should be seen in an animal’s foraging behaviour. The use of floral resources requires their consumers to be able to discern and track temporal changes in availability and spatial distribution of flowering (Fleming 1992; Pyke 1981). In fact, many animals that use plant produced food resources are considered to track these resources. This can be seen in their behaviour through shifts in diet, foraging area, foraging method, distribution or their use of particular habitat components (Cartar 2009; Cotton 2007; Fleming 1992; Goldingay 1990; Loureiro, Bissonette et al. 2009; McConkey and Drake 2007; Sabbatini, Staininati et al. 2008; Sharpe and Goldingay 1998; Telleria, Ramirez et al. 2008)

Here, I examined the foraging behaviour of C. concinnus to identify the factors that might influence the habitat and food resource choices of these nectarivores. In particular, I was interested in the role of the floral resources they are known to consume (Cadzow and Carthew 2004; Horner 1994; Morrant, Petit et al. 2010; Pestell and Petit 2007a) so their responses to the temporal and spatial heterogeneity of this food resource could be identified. This study will build on the findings of previous dietary studies (Cadzow and Carthew 2004; Morrant, Petit et al. 2010; Pestell and Petit 2007a) by identifying the strength of selection for particular food resources, whether choices for particular flowering plant species are apparent and the activity patterns and distances travelled in the pursuit of these resources.

3.2 Methods

3.2.1 Site selection

This study was conducted at Newland Head Conservation Park (Chapter 2). The vegetation is coastal mallee heath dominated by Banksia marginata, B. ornata, Callistemon rugulosus, Eucalyptus baxteri, E. cosmophylla and E. diversifolia. The distribution and density of these species varied across the site creating three distinct habitat types dominated by different species; A: B. ornata and E. diversifolia, B: B. marginata and E. baxteri and C: C. rugulosus and E. cosmophylla.

3.2.2 Tracking

In order to assess the foraging behaviour, habitat use and diet of C. concinnus the foraging pathways of up to nine animals were tracked each month over 19 months, from November 1997 to May 1999. Animals were tracked using small chemi-luminescent tags (‘Starlight SL-5’ mini-chemical lights), glued to the fur on their back (Plate 3.1). This method was selected over other techniques such as radio tracking because of the small size of the animals and the detailed and immediate observation of foraging activity it allowed (Bos and Carthew 2003). Adult male and females were used with the exception of females with young. This limitation meant that it was not always possible to track females each month. In addition, some animals were tracked more than once due to low capture rates and the recapture of individuals over time.

Plate 3.1: C. concinnus with chemi-tag glued on its back feeding on a B. ornata inflorescence.

A trial was conducted to determine whether foraging activity would change during the night (30 individuals). No observable difference in animal behaviour was found whether animals were released at dusk or at any hour of the night through until 1 hour before dawn. As a result, animals were released after dusk at the initial point of capture, and carefully followed at a distance of ~ 3 m for at least 30 minutes. An initial 10 minutes before data points were collected was used to allow the animal to resume normal activities. However, this time was not necessary for many of the animals with 82% of individuals commencing foraging or travelling in the first 5 minutes after release. The tags provided enough light that the animal was visible at distances of up to 10m depending on the density of the vegetation. Two observers were used when the vegetation was very thick as it was not always possible for one person to maintain visual contact with the animal. A small red light was used when the animal’s activity type was difficult to see, with the light held on the animal for only a few seconds. All efforts were made to minimise disturbance of the animal. In some cases the thickness of the bush meant that observers had to stand in one spot as the animals moved around them to forage. The presence of observers appeared to have little impact with some animals approaching and climbing over stationary observers as they would a bush. If animals were disturbed by a noise made by the observers they would stop where they were and remain very still for a short time (this was very different to resting behaviour as the animal would remain very still and rigid in its posture). At this point data recording would be stopped until the animal resumed normal activity.

Activity type and the habitat component occupied were recorded during tracking by use of a tape recorder, with the time at which an activity or habitat changed noted. This method required minimal movement from the observer and minimal noise, reducing the impact on the animal being observed. Observations were then transcribed to data sheets with a data point being recorded every minute the animal was monitored. Data points were recorded as the activity being observed and habitat type being used at that minute rather than an average of the behaviour over each minute. Activities were divided into feeding, travelling (including climbing, running and walking), grooming and resting. Feeding was recorded when an animal was licking or chewing at a food substrate and was divided into two categories: flowers and other (leaves and branches). Grooming was recorded when an animal was licking itself. Resting was recorded when an animal was stationary. The habitat components recorded were sand, leaf litter, grass/shrubs and the individual species (Banksia marginata, B. ornata, Callistemon rugulosus, Eucalyptus baxteri, E. cosmophylla, E. diversifolia, E. fasciculosa and E.

53 incrassata). The grass and shrubs category included Xanthorrhoea, Leptospermum, Calytrix and all of the other shrub, sedge and grass species. Individual plant species were separated out as they were considered potential food sources. As the length of each tracking event was different for each individual the data recorded was converted to a relative proportion of the total time tracked.

With the focus of this study being foraging behaviour, each tracking event was stopped if the animals went to sleep. This point was identified when the animal curled up in a ball in a sheltered but visible site or when an animal crawled into a hole in a tree or clump of grass or leaf litter and did not move for at least 30 minutes. At the end of the tracking period the animal was recaptured (if it was within reach) and the tag removed by gently cutting it off the fur it was glued to. On those few occasions when the animal could not be recaptured the tag fell off within one to two days (pers. obs. from recaptured animals).

The path taken by each animal was plotted by marking the point of each direction change with flagging tape during tracking. The following day a map was constructed using compass bearings and distance measurements between these flags. The trap where the animal was released was used as an anchor for drawing the map. If the animal passed near any other traps, measurements were taken of distance and direction to the trap, so that any errors in measurements could be corrected.

3.2.3 Resource availability

The availability of each of the habitat components was measured within each of the three main habitat types (Chapter 2) to determine whether C. concinnus used each component more or less than they were available. Habitat components were recorded as present or absent at one metre intervals along three randomly selected transects of 50 metres length in each habitat type. Point samples were taken using a two metre pole, with each habitat component recorded as present if it touched anywhere along the length of the pole. The habitat components recorded were: sand, leaf litter, grass, shrubs, Banksia species, C. rugulosus and Eucalyptus species. The availability of each habitat component within habitat types was estimated as a proportion of the total number of points measured (150 points per habitat type). To determine the availability of each habitat component to the animal in each tracking event a computer program was written by Alltraders Pty Ltd using a 3D graphics package (open GL) and C++ with the data stored in Microsoft Access (See examples of output in

Appendices Figures 6.1 and 6.2) . This program used a map of the study site defining the boundaries of each habitat type and overlayed a map of each tracking event. The proportion of each habitat type (A, B and C) available was calculated using the total distance travelled during a tracking event and extending this distance as a straight line in a circle from the release point. The availability of habitat components for each individual was then estimated as the proportions of each habitat type it could have reached multiplied by the proportion of each habitat component available in each habitat type.

3.2.4 Flowering season Rather than using traditional seasons for this analysis, season was classified according to the three flowering seasons identified in Chapter 2. These seasons were based on the flowering peaks of the six key flowering species on the site. Season 1 included January to March when B. marginata and E. baxteri had flowering peaks, Season 2 included April to August when B. ornata and E. cosmophylla had flowering peaks and Season 3 included September to December when C. rugulosus and E. diversifolia had flowering peaks.

3.2.5 Analysis To determine the influence of flowering season, sex and activity on the proportion of foraging time recorded for a tracking event I used logistic regression with a binomial distribution and logit link function. Since tracking time varied between individuals, observations were converted to proportions of time spent on each activity type. The binomial response variable was the proportion of foraging time spent weighted by the total number of observations for each individual. The predictor variables were sex (a categorical variable), flowering season (a categorical variable representing the season in which the animal was captured as defined by flowering timing) and activity (a categorical variable representing each of the different activities observed). I defined a priori candidate model sets where these predictor variables were modelled individually and in selected combinations. I note that in the summary tables presented (for example, Tables 6.9) the weights are expressed after rounding to 4 decimal places. As such, the model with a weight of 1 should actually be ≥0.999 and the other models <0.0001.

To determine the influence of flowering, sex, flowering season and release habitat on the distance travelled during each tracking event, Poisson regression with a log link was used. As the time each animal was tracked varied, the distance travelled was converted to a relative

55 metres per minute for each tracking event and used as the response variable. The predictor variables were sex (a categorical variable), flowering season (a categorical variable representing the season in which the animal was captured as defined by flowering timing), flowering species (a series of binary variables representing the presence or absence of each individual flowering species), flowering count (a binary variable representing the presence or absence of flowering) and release habitat (a categorical variable representing the habitat type that the animal was released in). Again I defined a priori candidate model sets where these predictor variables were modelled individually and in selected combinations. I then ranked these models based on Akaike’s information criteria (AIC) (Akaike 1973) and the highest- ranked model was used for inference.

In order to assess the influence of habitat components on each activity we assessed each activity using a series of fixed effects models with a binomial error distribution. The response variable for each activity type was the proportion of time spent, measured as the relative proportion of observations for each individual within each activity type. The predictor variables used were sex (a categorical variable), flowering season (a categorical variable representing the season in which the animal was captured as defined by flowering timing) and habitat component (a categorical variable representing each of the different habitat components used). I defined a priori candidate model sets where these predictor variables were modelled individually and in selected combinations. I then ranked these models based on Akaike’s information criteria (AIC) (Akaike 1973). This model was used to predict mean proportions of time spent with 95% confidence intervals for each cell of the interaction between categorical predictors. These values were used to give each habitat component a relative ranking based on probability of use by each sex over each flowering season. In addition χ2 tests were used to determine the difference in distribution of time spent on feeding and non-feeding activities across the food plant species.

Proportional habitat component use and availability for each tracking event were used to identify selection for particular habitat components and the strength of that selection relative to availability (Manly, McDonald et al. 2002). Habitat component use and availability were measured for design III data, where use and availability were measured for each tracking event as a proportion of time spent on each habitat component and the proportion of each habitat component available (Manly, McDonald et al. 2002; Manly, Miller et al. 1972). Compositional analysis was used to test whether habitat use varied from random. This

56 analysis estimated use relative to availability and then ranked the relative use of each habitat component, providing a rank of the habitat components from most to least preferred (Aebischer, Robertson et al. 1993). In addition, the strength of selection for each habitat component was calculated using Manly’s selectivity ratio (wi). Selectivity ratios (wi) values >1 indicate selection for a habitat component and <1 avoidance of a habitat component. Habitat selection was tested using a χ2 log likelihood statistic (Manly, McDonald et al. 2002). These analyses were carried out using the “adehabitat” package (Calenge 2006) in R version 2.11.1 (R Core Development Team 2010). All analysis was carried out using R version 2.11.1 (R Core Development Team 2010).

3.3 Results

Seventy three individual C. concinnus were tracked on 93 occasions between November 1997 and May 1999. This included 48 males (tracked 61 times) and 25 females (tracked 32 times). Tracking time averaged 42 minutes and ranged from 30 to 92 minutes, giving a total of 3948 minutes of observations.

3.3.1 Foraging behaviour

Overall C. concinnus spent 33.4% of its active time feeding, 41.9% travelling, 20.5% resting and 4.2% grooming. The majority of the time spent feeding was on flowers (85.3%) for pollen and nectar, with the remainder feeding on insects, and incising branches for sap (9.6%) and lerps on leaves (5.1%). Climbing on anything from grass to trees made up 86.6% of travelling time, while travel on the ground took up only 13.4%. Logistic regression was used to determine the influence of the sex, season and activity on the proportion of time spent foraging (Appendices, Table 6.7). The model selected using AIC found that an interaction between sex, season and activity represented 45.26% of the variability in the data (logLik = - 2040.84, df =24, weight = 1). Both male and female C. concinnus spent more time travelling and feeding than resting and grooming (Figure 3.1). Males showed no strong seasonal patterns when feeding and while females spent less time feeding in season three this was not a strong seasonal pattern. Females travelled less and spent more time resting in season 2 than in seasons 1 and 3. Males fed more than females in all but Season 1, while females travelled more than males in all but Season 2. Grooming occurred most frequently immediately after feeding and both sexes groomed the most in Season 2.

Figure 3.1: Mean proportion of time spent each season on each activity for a) male (n=61 tracking events) and b) female (n=32 tracking events) C. concinnus. Season 1; dark, Season 2; light and Season 3; medium. a)

0.6

0.4

Time 0.2 b) 0

0.6

0.4

Time 0.2

0 Feeding Traveling Grooming Resting

3.3.2 Distances Travelled

To understand further the influence of flowering resources on the foraging behaviour of C. concinnus I considered the distance travelled during each tracking event and the influence of sex, season, release habitat and flowering availability. C. concinnus travelled an average of 1.45m (± 0.16) per minute during tracking events. A poisson regression model with a log link function was used to determine which variables; sex, season, release habitat and the availability of each flowering species at the release site, influenced the distance travelled per minute (Appendices, Table 6.8 a and b). This could be predicted using a combination of all variables and represented 36.6% of the variability in the data (Log Likelihood = 781.827, df = 18, AIC = 1603, weight = 1). Males travelled further than females, both sexes travelled further per minute in Seasons 1 and 2 and the distance travelled increased with the absence of flowering at the release site (Figure 3.2). Of the three habitat types available to C. concinnus, animals tended to travel shorter distances in Habitats A (containing B. ornata and E. diversifolia) and B (containing B. marginata and E. baxteri), than in Habitat C (with C.

59 rugulosus and E. cosmophylla). This was also supported by their use of these habitat types during the trials, with Habitat A (47%) and B (37%) being used more frequently than Habitat C (20%). Only 10% of females switched habitat type during foraging while 32% of males switched habitat, supporting the suggestion that males travel further than females.

Figure 3.2: The mean (±se) distance travelled per minute during foraging by C. concinnus for a) sex, b) season, c) release habitat and d) flowering availability in release habitat. a) b)

2 2

1.5 1.5

1 1 Meters per per min. Meters 0.5 per min.Meters 0.5

0 0 M F 1 2 3 Sex Season c) d)

2.5 3

2 2.5 2 1.5 1.5 1 1 Meters per per min Meters per min Meters

0.5 0.5

0 0 A B C Present Absent Habitat type Flowering

3.3.3 Activity in habitat components

C. concinnus spent most of their active time (81%) in the tree and tall shrub species, including those that provided food resources (Banksia, Callistemon and Eucalyptus species). Of their remaining activity time 6% was spent on the ground (open ground and leaf litter) and 13% in grass and shrubs (ranging from small grasses to waist high shrubs and Xanthorrhoea). Feeding was observed only in B. marginata, B. ornata, Callistemon rugulosus, E. baxteri, E. cosmophylla, E. diversifolia and E. incrassata. Travelling occurred in all habitat components with the majority of time spent in some form of vegetation cover. Grooming and resting was conducted mostly in trees but also in grass and shrubs and occasionally on open ground. The species that provided food resources were the most commonly used habitat component for all activities irrespective of the presence of flowering. Sleeping was observed on eight occasions with the majority occurring in B. ornata (five), twice in grass and once in E. fasciculosa (Plate 3.2).

Plate 3.2: C. concinnus sleeping between old B. ornata inflorescences.

Fixed effects models were used to determine the influence of sex, season and habitat component on the distribution of time spent within each activity type (Table 3.1 and Appendices, Tables 6.9 to 6.13). All models included season and all but the model for grooming included sex as a variable. These models were used to predict the probability of an animal using a particular habitat component in any given season for each of five behaviour categories. Habitat components were ranked in order from highest to lowest probability of an animal spending time in them (Table 3.2 ).

Table 3.1: AIC selection of fixed effects models for each activity using season (S), sex and habitat component (HC) as variables. (See Appendices, Tables 6.9 to 6.13)

Activity Model selected LogLik df AICc Weight %DE

Feeding on flowers S x HC x Sex -1354.857 61 2840.428 1 63.86

Feeding on other S + HC x Sex -227.844 23 502.907 1 70.38

Travelling S x HC x Sex -1344.830 61 2820.374 1 42.01

Grooming S x HC -285.979 31 636.168 0.9 34.26

Resting S x HC x Sex -958.115 61 2046.945 1 40.69

The species that C. concinnus was most likely to be recorded feeding on followed the pattern expected, based on the availability of flowering in each of the three flowering seasons (Chapter 2). In Season 1, male and female C. concinnus were more likely to feed on flowers of B. marginata and some E. baxteri (Table 3.2). In Season 2, both sexes were most likely to feed on B. ornata and some E. cosmophylla. Interestingly, in Season 2 males were also likely to feed on E. diversifolia and E. incrassata, with a higher probability of using E. diversifolia than E. cosmophylla even though the latter was more readily available over the site. In Season 3, both sexes were likely to feed on a combination of E. diversifolia and C. rugulosus, with males more likely to feed on C. rugulosus and females on E. diversifolia. C. concinnus was more likely to feed on insects and sap from leaves and branches during Season 1 when floral resources were most limited (Chapter 2). When feeding on leaves and branches both sexes were most likely to use E. diversifolia and females used B. ornata while males did not (Table 3.2). Only 4.3% of individuals fed on more than one species within an individual tracking event.

Table 3.2: The probability of C. concinnus spending time in each habitat component for each activity. These were ranked using values predicted with terms derived from the models selected for each activity (Appendices Tables 6.9 to 6.13). The probability of an animal spending time decreases from left to right across the table for each flowering season. Components with no probability of being used were not included in the table. G: ground, GS: grass and shrubs, Bm: B. marginata, Bo: B. ornata, Cr: C. rugulosus, Ed: E. diversifolia, Eb: E. baxteri, Ec: E. cosmophylla, Ei: E. incrassata, Ef: E. fasciculosa. (>) means the component to the left has a greater probability of use and (=) means an equal probability of use.

Activity Sex Season 1 Season 2 Season 3

Feeding on flowers M Bm>Eb>Bo>Ec>Ei Bo>Ed>Ec>Ei Cr>Ed

F Bm>Eb Bo>Ec>Bm Ed>Cr

Feeding on other M Ed>Eb Ed

F Bo>Ed>Eb Ed>Bo Ed>Cr

Travel M GS>Bo>Ed>G>Bm>Eb>Ec>Cr=Ef Bo>GS>G>Ed>Ec>Eb>Ei>Bm>Ef GS>Bo>G>Ed>Eb>Ec>Cr>Ef

F Bo>Ed>GS>G>Bm>Eb>Ec Bo>G>GS>Ec>Ed>Eb>Ef>Bm=Cr G>Ed>Bo=GS>Ec>Cr>Eb>Bm>Ef

Grooming All Bo>Eb=GS>Ed=Ec>Bm Bo> Ed=Eb>GS>Bm GS>Ed=Ec>Eb

Resting M Bo>GS>Ec>Eb>Ed=Bm>G>Ef Bo>Eb>GS> Ed>Ec Ec>GS>Bo>Ed>Eb=Ef

F Eb>Bo>GS>G>Ec Bo>Eb>Ed>GS>Bm=G Ec>GS>Ed>Bo>Eb>Cr>G

While feeding did not occur in the open ground and grass and shrub components, the other activities did. C. concinnus spent more time overall travelling on the tree and shrub species such as Banksia, Eucalypt and Callistemon, however, when considered as individual habitat components, the probability of an animal using grass and shrubs or ground for travel was high (Table 3.2). Interestingly the probability of males using grass and shrubs was higher than any other habitat component in Seasons 1 and 3 and the probability of females using the ground higher than any other habitat component in Season 3. Females were more likely to travel through B. ornata in Seasons 1 and 2 while males were more likely to travel through it in Season 2. The less active behaviours such as resting and grooming were most likely to occur in habitat components providing shelter (Table 3.2). Grooming was most likely to occur in B. ornata, E. diversifolia, E. baxteri and grass and shrubs. Males were likely to sit in B. ornata in Seasons 1 and 2 and E. cosmophylla in Season 3 and likely to use grass and shrubs for resting across all seasons. Females were most likely to rest in E. baxteri in Season 1, B. ornata in Season 2 and E. cosmophylla in Season 3, unlike males they made very little use of grass and shrubs.

The proportions of time spent in each of the food plant species were different for feeding and non-feeding activities (χ2= 26, df = 7, P > 0.001). For example, E. baxteri and E. cosmophylla were used for non-feeding activities more than for feeding in all seasons combined (Figure 3.3). Banksia ornata and E. diversifolia were used almost equally for both activities. Banksia marginata and C. rugulosus were used less often overall, but when used they were more likely to be for feeding. The proportion of feeding and non-feeding time spent on each feed plant species remained significantly different despite changes in use during Season 1 (χ2= 78.6, df = 7, P > 0.001), Season 2 (χ2 = 35.3, df = 7, P > 0.001) and Season 3 (χ2= 105, df = 7, P > 0.001) (Figure 3.3). Cercartetus concinnus used B. ornata to move from one point to the next more than any other species in both Seasons 1 and 2. This use of B. ornata coincided with the flowering of species in the same and neighbouring habitat types in Season 1 and then with B. ornata flowering in Season 2. In Season 3 however, it was not used for transport as much as E. cosmophylla. Callistemon rugulosus and E. cosmophylla generally occurred in the same habitat and often grew close together. The increase in use of E. cosmophylla for non-feeding activities occurred at the same time as the increased use of C. rugulosus for feeding activities in Season 3. Cercartetus concinnus spent more time on feeding than on non-feeding activities when visiting B. marginata in Season 1, B. ornata in Season 2 and E. diversifolia and C. rugulosus in Season 3.

Figure 3.3: Percentage of time spent on each flowering species by C. concinnus as a proportion of time spent on feeding (dark) and non-feeding (light) activities. a) Seasons combined n=93 tracking events, b) Season 1 n=29, c) Season 2 n=40, d) Season 3 n=24. Bm: Banksia marginata, Bo: B. ornata, Cr: Callistemon rugulosus, Ed: Eucalyptus diversifolia, Eb: E. baxteri, Ec: E. cosmophylla. a)

% Time % Time 20 b) 0

% Time % Time 20 c) 0

% Time % Time 20 d) 0

% Time % Time 20

0 Bm Bo Cr Eb Ec Ed

3.3.3 Availability and use of habitat components

In order to assess the strength of selection for particular habitat components by C. concinnus the availability of each was measured. As expected there was considerable difference in availability between the sand, leaf litter and grass/shrubs components in comparison with the individually recorded tree species (Figure 3.4). Leaf litter and grass/shrubs were by far the most abundant habitat components available to C. concinnus, with E. diversifolia and B. ornata being the most abundant of the food plant species. Species such as E. fasciculosa and B. marginata were least abundant. For most habitat components there was a difference in use compared to availability with some being used more than available (eg. B. ornata) and some used less than available (eg. leaf litter) (Figure 3.4).

Figure 3.4: The percentage of time spent in each habitat component by C. concinnus during tracking (bars) and the percentage availability of each component (dashes). (n=83) LL: Leaf Litter, GS: grass and shrubs, Bm: Banksia marginata, Bo: B. ornata, Cr: Callistemon rugulosus, Ed: Eucalyptus diversifolia, Eb: E. baxteri, Ec: E. cosmophylla, Ef: E. fasciculosa.

35 30 25 20 15 10 Percentage Percentage 5 0 Sand LL G/S Bm Bo Cr Eb Ec Ed Ef

Compositional analysis was used to assess use relative to availability and then rank the relative use of each habitat component. It showed that the use of habitat components by C. concinnus was non-random (Wilks’ Lambda=0.325, p=0.002, Table 3.3) with animals using particular habitat components more than others. The ranking of habitat components listed the components in order of use relative to other habitat components as B. ornata < Grass/shrubs < B. marginata < E. diversifolia < sand = E. baxteri = E. cosmophylla < E. fasciculosa < C. rugulosus < Leaf litter. Banksia ornata was used significantly more than all habitat components other than the grass and shrubs category, while leaf litter and sand were not used more than any of the vegetation. The Banksia species and grass and shrub components were all used more than the Eucalyptus species.

Table 3.3: Relative ranking of preference for habitat components by C. concinnus derived from compositional analysis. Symbols represent relative use of habitat components with ‘+’ meaning it is used more and ‘-‘ used less. When the difference is significant at p<0.05 the sign is tripled. The rank is the count of the number of habitat types used (+).

Sand LL G/S Bm Bo Cr Eb Ec Ed Ef Rank Sand 0 +++ ------+ + --- - + 4 LL --- 0 ------0 G/S +++ +++ 0 +++ - +++ +++ + +++ +++ 8 Bm + +++ --- 0 --- +++ + + + +++ 7 Bo +++ +++ + +++ 0 +++ +++ +++ +++ +++ 9 Cr - +++ ------0 ------1 Eb - + ------+ 0 + - + 4 Ec + +++ ------+++ - 0 - +++ 4 Ed + +++ ------+ + + 0 +++ 6 Ef - +++ ------+ ------0 2

Manly’s selectivity index was used to determine the strength of selection for habitat components by all individuals independent of activity (Figure 3.5). For all C. concinnus combined across sexes and season a χ2 log likelihood statistic showed no significant selection for one habitat component over another. However, selectivity ratios (wi) indicated a trend of selection for some habitat components and the avoidance of others, with as expected, food tree species being selected for and non-food components being avoided. As the key food plant species tended to flower in pairs through each of the three flowering seasons (Season 1: B. marginata and E. baxteri, Season 2: B. ornata and E. cosmophylla, Season 3: C. rugulosus and E. diversifolia), it was expected that the strength of selection might shift with each season. When considered by flowering season, the dominant flowering species were the most strongly selected for in both Seasons 1 and 3 (Figure 3.5). However, in Season 2 while B. ornata was selected for most strongly, E. baxteri was selected as strongly as E. cosmophylla. There was a change in the strength of selection between the seasons with B. marginata being selected for in Season 1 more strongly than any of the other species in any of the other seasons, while in Seasons 2 and 3 it was avoided.

Figure 3.5: Manly selectivity measure for C. concinnus, selection ratios (± CI). a) All Seasons, b) Season 1, c) Season 2, d) Season 3. Selectivity ratios >1 indicate preference while values <1 indicate avoidance. LL: Leaf Litter, G/S: grass and shrubs, Bm: Banksia marginata, Bo: B. ornata, Cr: Callistemon rugulosus, Ed: Eucalyptus diversifolia, Eb: E. baxteri, Ec: E. cosmophylla, Ef: E. fasciculosa. a)

2 Selection ratios Selection 0 b) Bm Bo Eb Ec Cr Ed G/S Ef Sand LL

Selection ratios Selection 0 Bm Eb Bo Ec Ed G/S Sand Ef LL Cr c)

2 Selection ratios Selection 0 Bo Eb Ec Ed Ef Bm G/S Sand LL Cr d)

2 Selection ratios Selection 0 Cr Ed Eb Ec Bo Ef Bm G/S Sand LL

3.4 Discussion

Detailed observations of behaviour in the natural environment showed that the small marsupial C. concinnus spent the majority of its foraging time searching for and feeding on flowers. Individuals showed a preference for the flowering species that provided food resources by using them more than they were available in the habitat. Their foraging behaviour varied with sex, flowering season and habitat component. Distances travelled also varied across sex, flowering season, habitat type and flower availability at the point of release. The influence of spatial and temporal variability in floral resources is evident particularly in both the sex and seasonal variation in foraging behaviour.

Sex and season were identified as factors that influenced variability in the time spent on foraging activities and the use of habitat components. These sex and seasonal differences in activity patterns and habitat use have been regularly observed across a range of species including insects (Torres, Osorio-Beristain et al. 2009) birds (Castro, Masero et al. 2009), frogs (Johnson, Knouft et al. 2007) and terrestrial mammals (Hanya 2004; Lodé 1999; Lurz, Garson et al. 2000; Masi, Cipolletta et al. 2009). Different behaviour and habitat use across both sex and season are generally attributed to reproductive needs such as searching for mates or building energy stores (Bakaloudis 2010; Kortner, Rojas et al. 2010; Loretto and Vieira 2005; Ostfeld 1985), different body sizes (Bowers and Smith 1979; Pipia, Ciuti et al. 2008), changes in resource availability (Leiner, Setz et al. 2008; Pavelka and Knopff 2004;

Rothenwöhrer, Becker et al. 2010; Schmidt and Ostfeld 2008), food quality (Hanya 2004) and climatic conditions (Hanya 2004; Takemoto 2004). A relatively common example of the differences in sex and season is found in the use of space by black-eared opossums (Didelphis aurita) in Brazil. Here, males are influenced by reproductive season and females by resource availability (Loretto and Vieira 2005). As a further example, the activity budget of the european pole cat (Mustela putorius), in France, is shaped by a combination of sex and resource availability, with females and males investing differing amounts of time into travel and foraging based on the dispersion of prey (Lodé 1999).

In the case of C. concinnus the behaviour and habitat component use of males and females followed a pattern that could be attributed to their differing resource requirements for reproductive activity (Ostfeld 1985). Both sexes were more active during Season 1 with

69 females feeding more in this and Season 2. This may fit with the males hunting for mates, although no observations were made of male female interactions during tracking, and females feeding to build up energy stores for raising young. Females were less active in Season 2 than in other seasons when they were most likely to be carrying young or have young in the nest (Chapter 2). Males travelled less and rested more in Season 3 which would provide a suitable build up of energy stores for searching for mating opportunities in Season 1. During Season 3 males fed more on C. rugulosus than on E. diversifolia. Callistemon rugulosus has a much higher energy equivalent than E. diversifolia (Chapter 4, Figure 4.5), and may indicate that males are building up energy in this season.

A further explanation for the increased activity levels for both sexes during Season 1 is the availability of floral resources. These were not in high abundance (Chapter 2 Section 2.3.2) and were distributed along a very small narrow band across the site. This would have increased the travel time required to find these resources and is evident in the longer distances travelled in this season when compared with Season 3. Increases in travel time with decreases in food supply have been observed in a number of species including the mouse opossum (Marmosops paulensis), in Brazil, which traveled further when using fruits in a clumped distribution (Leiner and Silva 2007a). Similar distances were travelled in Season 2 as in Season 1. This may be a response to resource patchiness in season 2 or resource quality. This may indicate the importance of considering more than just the spatial and temporal patterns of food resource availability. In fact the quality of particular floral resources may well influence the time spent searching and feeding. For example, if the number of inflorescences per trees was low or the weather was dry, reducing nectar supply (Wooller, Richardson et al. 1998), then the available nectar would be reduced and as a result animals would have to work harder to find and harvest food to satisfy their energy requirements. The differing lengths of time spent foraging in each season may therefore relate to the ease with which the food source, in this case nectar and pollen, could be harvested. In addition, there may be a variation in foraging effort required based on the species of plant used. For example, Cercartetus concinnus may spend less time feeding on Eucalyptus species where the nectar can be reached easily and more time feeding on Banksia and Callistemon where a greater physical effort is required to reach nectar as the animal has to pull its head in among the flowers of the inflorescence (pers. obs.). When considering the energetic balance proposed by optimal foraging theory (Pyke 1984), this effort may be balanced by the large quantity of nectar supplied by the Banksia and Callistemon inflorescences (Chapter 4, Figure 4.5).

Cercartetus concinnus consumed pollen and nectar more than any other food source at Newland Head Conservation Park. The six dominant flowering species on the site (Banksia marginata, B. ornata, Callistemon rugulosus, Eucalyptus baxteri, E. cosmophylla, and E. diversifolia) were used the most frequently and C. concinnus shifted their diet from one species to another as they became available (flowering availability is detailed in Chapter 2, Figure 2.8). This form of tracking resource availability is common with many species that rely on plant produced food resources (Boyes and Perrin 2009; Hampe 2008; Leiner and Silva 2007a; Loureiro, Bissonette et al. 2009; Pavelka and Knopff 2004; Silver, Ostro et al. 1998; Telleria, Ramirez et al. 2008) or resources that fluctuate in availability over time (Martinoli, Preatoni et al. 2001; Schwemmer and Garthe 2008). Even when flowers were not readily available in the ecosystem C. concinnus would seek them out as a food source. This was particularly evident for males. They foraged more broadly than females, using the species that were abundant in each season and also those that were less abundant. For example in Season 1 both sexes used B. marginata and E. baxteri and males also used B. ornata, E. cosmophylla and E. incrassata. In Season 2 both sexes used B. ornata and E. cosmophylla and males also used E. diversifolia and E. incrassata.

Cercartetus concinnus was also observed consuming small insects, licking at lerps and incising trees to lick sap. This however, was irregular, occurring mostly in Season 1 when floral resource availability was lowest. These results indicate that while this species is capable of living on a generalist diet using a range of dietary material, it has a preference for pollen and nectar. Consumption of less preferred food resources is a very common feature of animals that track food resources that fluctuate in availability (Leiner and Silva 2007b; Loureiro, Bissonette et al. 2009; Martinoli, Preatoni et al. 2001; Raboy and Dietz 2004; Sharpe and Goldingay 1998). Golden headed lion tamarins (Leontopithecus chrysomelas), in Brazil, for example will consume less preferred food resources such as gum or fungi when fruit, flowers and nectar are scare. In areas where their preferred food resources are abundant they are rarely observed using the lesser preferred food sources (Raboy and Dietz 2004). This is also the case for stoats (Mustela erminea), in Europe, when rodents became scarce they switched to fruit as a secondary food source (Martinoli, Preatoni et al. 2001). In such species use of resources is usually related to the availability of their preferred resource rather than the availability of their secondary food resource. This certainly appears to be the case for C. concinnus with their use of insect and sap material occurring at a time of lower floral resource availability.

Cercartetus concinnus spent a large portion of their active time (42%) travelling. The majority of this time appeared to be spent in the search for floral resources as indicated by the large proportion of their time spent feeding on flowers (36%) and in the absence of observed social encounters. This large investment of travel time in the search for food resources is not uncommon and evident in other species that use high energy food resources such as nectar and fruit. For example, the black howler monkey (Alouatta pigra), in Belize, spent more time travelling during the season that they were using fruit expending more energy in pursuit of this preferred food resource and less time when feeding on foliage, a less preferred resource (Pavelka and Knopff 2004) In this case, as with C. concinnus, the rewards offered by the preferred food resources, in the form of energy and protein, may increase the profitability of searching for them. This, perhaps provides the required trade off for the extra travel time that is put into searching for them (Pyke 1981).

In addition to showing a preference for floral resources over insects and sap, C. concinnus showed a seasonal preference for particular plant species that flowered concurrently. Animals fed more on B. marginata than E. baxteri in Season 1 and more on B. ornata than E. cosmophylla in Season 2. In Season 3 males fed more on C. rugulosus than E. diversifolia and females more on E. diversifolia than C. rugulosus. The profitability of a food source may play a role in shaping the preference of C. concinnus and influencing the switch from one resource to the next. Differing use of flowers may relate to nectar volumes, concentration and flower morphology and the ease with which nectar is accessed. In all seasons males showed a preference for the species with the highest energy equivalents (Chapter 4, Table 4.5). The different preference by males and females in Season 3 may relate again to the differing energy requirements of each sex with females not having a need for high energy food in Season 3 as that was the end of the breeding season for most individuals (Chapter 2, Figure 2.6).

The use of habitat components by C. concinnus during foraging activity also showed some selectivity with some species being used more than others and habitat component use not matching availability. In fact, the key food plants were used more than available and the habitat components that did not relate to food were used less than available. When considered in relationship to availability, compositional analysis indicated that habitat use was significantly different to random and that animals showed a preference for B. ornata and grass/shrubs over other habitat components. Interestingly these were the two components used the most for travel. Manly’s selectivity index showed that the habitat components used

72 for feeding, while not necessarily used more frequently, were more strongly selected for. Banksia marginata was selected for more strongly than any other habitat component and those components that did not provide food resources were avoided. The seasonal patterns of selection showed that some food plant species were also avoided when they were not flowering. The only Eucalyptus species that was not strongly selected for (E. fasciculosa) was not fed upon by C. concinnus.

Predator avoidance appeared to play a role in the choice of substrate for travel, with C. concinnus spending only 6% of their time in open ground (sand, leaf litter). The Banksia species and the combination of grass and shrubs were the most heavily used habitat components. These particular components provided thick cover for movement and were well connected across the whole site, allowing animals to move easily while remaining out of sight of predators such as owls, foxes and cats that occur at the site (pers. obs.). Banksia ornata was used more commonly than all other species by C. concinnus and covered a large proportion of the study site, providing a thick connection between patches of other species. The use of cover for movement is a fairly typical defence from predators (Bertolino 2007; Bos, Carthew et al. 2002; Gray, Hurst et al. 1998) and is also more practical for foraging within flowering plants. In the present study on particularly still nights animals were observed to freeze whenever a noise was made, while on windy nights they moved much more readily and continuously (pers. obs.).

The use of vegetation for 94% of activity is interesting to note in reference to the cryptic nature of C. concinnus and provides some explanation as to the difficulty in catching them. Similarly, a study that tracked 45 eastern pygmy possums (C. nanus) individuals, in New South Wales, Australia, using the spool and line method observed the regular use of vegetation for travel by this species, although they did not quantify the proportion of time spent in this habitat component (Evans and Bunce 2000). In this study, there were sex based differences in the selection of substrate for travel, with males using grass and shrubs more than females in all seasons and females using B. ornata and E. diversifolia more than males in most seasons. The different use of substrates for travel may be the result of differing behaviour for each sex. This may be evidenced by males travelling more frequently and further than females. In this behaviour males may shift between patches of flowering plants more frequently using a greater diversity of habitat components, while females may stay in a patch using less habitat components as a result. This may also be reflected in the broader diet

73 of males, indicating that they roam more widely than females. All of these behaviours by males may be a result of the more random, nomadic behaviour perhaps required to find females.

Foraging observations at Newland Head Conservation Park revealed that C. concinnus was capable of moving long distances (over 100m) between scattered patches of flowering resources (pers. obs.). One individual, in this study, was recorded moving an average of 5m per minute. On some occasions when there were not any obvious flowering plants in the area, the animal being tracked would travel well over 100m in a direct line towards the only tree in the area that was flowering (pers. obs). This suggests that they have some capacity for spatial memory, as observed in a range of other species from insects such as bees (Dyer 1996) to nectar feeding bats and birds (Sutherland and Gass 1995; Winter and Stich 2005) and seed and fruit feeding primates such as mangabeys (Janmaat, Byrne et al. 2006) and baboons (Noser and Byrne 2007). Spatial memory is considered an important behavioural trait for animals that feed on temporally variable food resource such as nectar (Armstrong, Gass et al. 1987; Cole, Hainsworth et al. 1982). Cole et al (1982) suggest that the ecology of food resource distribution in space and time generates important evolutionary influences on learning. For example, humming birds have adapted to changes in resource availability by remembering the flowers they have visited so that they don’t return to a depleted nectar source (Cole, Hainsworth et al. 1982). It would be interesting to assess the capacity of C. concinnus to use spatial memory in more detail using a combination of observation based tracking to determine the flowering resources approached and avoided and captive trials to investigate their capacity to return to a previously used food resource.

Another factor that may contribute to the ability of C. concinnus to find floral resources when they are sparsely distributed are floral volatiles. Particular components of nectar will omit very strong scent that could be part of the mechanism for attracting pollinators (Majetic, Raguso et al. 2009). Very few studies have been conducted on the role of floral volatiles in attracting mammal pollinators (Johnson, McQuillan et al. 2011; Johnson, Burgoyne et al. 2011). Further study could provide some insight into the use of floral volatiles by C. concinnus for detecting flowers over long distances.

The capacity of C. concinnus to track the heterogeneous pattern of their food resources is highlighted by seasonal dietary shifts and apparent spatial memory. It is also supported by what appears a relatively haphazard movement pattern to find available food resources,

74 described by Mueller anf Fagan (2008) as a nomadic population pattern. While this appears to contradict the use of spatial memory by C. concinnus, they can actually be complimentary, with the random movements allowing the discovery of other resources when a known food source is depleted. This nomadic movement pattern is also reflected in the absence of permanent nesting sites for all but females with young (Horner 1994; Kemp and Carthew 2004) and the low recapture rates, movement patterns and distances between recapture locations (Chapter 2).

The fact that the distances travelled by C. concinnus changed with season and food resource availability indicates the adaptive nature of this species to change its behaviour to meet its resource requirements. The difference in movement patterns and use of space across sex and season is common across a broad range of species from small mammals such as Ningauis to Squirrels (Bos and Carthew 2007a; Pasch and Koprowski 2006). In each case these spatio- temporal shifts are considered to relate to reproductive requirements and resource availability. Carthew (1994) found that the movement distances of sugar gliders (Petaurus breviceps) in New South Wales, Australia, increased when there were fewer plants and fewer inflorescences per plant available, emphasising the influence of resource availability on the movement patterns of animals that consume floral resources. This relationship between daily movement and food supply was also observed for the mouse opossum (Marmosops paulensis) in Brazil (Leiner and Silva 2007a), a species very similar to C. concinnus.

In conclusion, the heterogeneity of floral resources and differing seasonal requirements of male and female C. concinnus shape their foraging behaviour. The preferences displayed for particular floral resources suggest a preference for the larger inflorescences and higher energy reward of the Banksia species. Food resources and predator avoidance appear to influence its use of space not just horizontally but vertically, as the species uses vegetation for travel and selects strongly for the vegetation currently flowering or surrounding flowering plants. The questions raised by this study relate particularly to animal movement patterns, their use of space and spatial memory. Radio-tracking and captive trials (Winter and Stich 2005) with this species may provide further insight into what drives choice of movement direction and determine whether animals are moving randomly or in a directional pattern or using a combination of these.

Chapter 4 : Feeding preferences of a nectarivorous marsupial; choices made between flowering species

4.1 Introduction

Many animals rely on food resources that shift both spatially and temporally in availability and quality (Chamaille-Jammes, Fritz et al. 2008; Cotton 2007; Harper, McCarthy et al. 2008; Marcello, Wilder et al. 2008; Penalba, Molina-Freaner et al. 2006; Rothenwöhrer, Becker et al. 2010; Stone 2007; Wauters, Vermeulen et al. 2007; Worman and Chapman 2006; Zhou, Zhang et al. 2008). The resources used by animals will impact on their reproductive success and survival. As a result the choices made by animals for one food resource over another are not often a simple picture of preference (Abrams 2010). There will always be a range of factors influencing them including the availability and nutritional value of the resource and the specific requirements of the species involved. Nectarivorous animals in particular, are vulnerable to these factors as the availability of flowers varies both spatially and temporally (Bradshaw, Phillips et al. 2007; Diaz and Kitzberger 2006; Goldingay, Sharpe et al. 2006; Morrant, Petit et al. 2010; Penalba, Molina-Freaner et al. 2006). Nectar and pollen tend to be seasonal and species specific, with flower densities, pollen loads and nectar volumes, compositions and concentrations all varying between species. Pollen offers a protein reward, providing long term energy. Nectar offers sugar, providing a high short term energy reward.

As few plants flower at times when no others are flowering, floral rewards are often available from a suite of concurrently flowering plant species. As a result, animals that use floral resources are, in many cases, able to make choices. The foraging choices of these animals could be driven by flowering timing and density, flower size and structure, pollen load and composition and nectar volume, concentration and composition or features that are harder to quantify like smell, taste and the animals’ nutritional and energy requirements (Carpenter 1978; Fleming, Xie et al. 2008; Johnson, van Tets et al. 1999; Keasar, Sadeh et al. 2008; Majetic, Raguso et al. 2009; Morrant, Petit et al. 2010; Rodriguez-Pena, Stoner et al. 2007; Schlumpberger, Cocucci et al. 2009; Wooller, Richardson et al. 1993). In addition, optimal foraging theory suggests that maximum reward for minimum effort would be the best strategy (Pyke 1981; Pyke 1984). Thus we should see animals targeting large quantities of pollen and nectar in high volumes and concentrations. However this is not true of all animals as nectar composition also appears to play a role with some species showing preferences for different sugars at different compositions (Fleming, Xie et al. 2008; Landwehr, Richardson et

77 al. 1990; Leseigneur and Nicolson 2009; Lotz and Nicolson 1996; Rodriguez-Pena, Stoner et al. 2007; Schondube and del Rio 2003).

Over the past 30 years there has been increasing recognition of the use of pollen and nectar by non-flying mammals, particularly in regards to their role in pollination (Carthew and Goldingay 1997; Cocucci and Sersic 1998; Fleming and Nicolson 2002; Goldingay 2000; Goldingay, Carthew et al. 1991; Johnson, Pauw et al. 2001; Sperr, Fronhofer et al. 2009). The extent to which non-flying mammalian flower visitors rely on floral resources appears to vary, with at least 59 species known to contribute to pollination (Carthew and Goldingay 1997). The honey possum (Tarsipes rostratus), in Western Australia, for example uses floral resources year round for all of their dietary intake (Richardson, Wooller et al. 1986), while species such as pygmy possums and gliders use these resources when available or over particular seasons (Cadzow and Carthew 2004; Dobson , Goldingay et al. 2005; Goldingay 1990; Holland, Bennett et al. 2007; Morrant, Petit et al. 2010; Pestell and Petit 2007a; Sharpe and Goldingay 1998; Turner 1984a; Turner 1984b). Other species, such as rodents (Cocucci and Sersic 1998; Hackett and Goldingay 2001; Johnson, Pauw et al. 2001; Letten and Midgley 2009), antechinus (Goldingay 2000), and opossums (Sperr, Fronhofer et al. 2009) appear to use these rewards much more opportunistically. Pollen has been found to satisfy the daily nitrogen requirements of species such as pygmy possums and rodents (van Tets 1997; van Tets 1998; van Tets, Hutchings et al. 2000). As many nectarivorous animals play a role in pollination, the choices they make to feed on or avoid a particular flowering plant species could impact significantly on that species’ reproductive success. It is important therefore to understand the choices animals might make between plants that are flowering concurrently, and to elucidate what are the floral rewards and nutritional requirements that might be driving those choices.

This study focused on the western pygmy possum (Cercartetus concinnus) as it is known to feed regularly and extensively on pollen and nectar (Cadzow and Carthew 2004; Horner 1994; Landwehr, Richardson et al. 1990; Morrant, Petit et al. 2010; Pestell and Petit 2007a). Newland Head Conservation Park provided the perfect opportunity to monitor the response of this species to the availability of floral rewards. The suite of flowering species found there has a year round cycle of flowering with six main plant species that are known food sources of C. concinnus (Chapter 2). The flowering timing of these species overlapped in pairs of eucalypt

78 and non-eucalypt (Banksias and Callistemon) providing the chance to compare whether preferences were displayed for particular flowering plant species over others.

A study by Landwehr et al. (1990) found that C. concinnus, in Western Australia, showed a sugar preference for fructose over glucose. The results of Morrant et al. (2010) provide some support for this preference for fructose in the diet of C. concinnus. They found an apparent sugar preference for C. concinnus in a study at Innes Conservation Park in South Australia, where the species consumed E. rugosa more than any of the other Eucalyptus species. Eucalyptus rugosa had a higher proportion of fructose and glucose to sucrose than the other eucalypts in the park although this was not significant. However, sugar preferences do not draw a complete picture of the factors that could drive preference in this species. In a field based situation the resources available may not include the most preferred sugar or may include a range of different sugars, requiring C. concinnus to make choices. A range of characteristics beyond the sugar composition could influence preferences, including pollen volume, nectar volume, composition, concentration and energy equivalents and inflorescence size.

In this study I used an experimental approach to determine whether C. concinnus displayed preferences for particular flowering plant species over others. I further aimed to determine whether floral rewards influenced these preferences and whether there were sex differences in the choices made. While there has been some previous consideration of nectar and pollen concentrations and composition and their role in driving foraging choices, very little has been done to consider the role of other floral characteristics as attractants. This study will build knowledge on the feeding behaviours of nectarivorous species and the resource features that drive these choices.

4.2 Methods

4.2.1 Paired preference trials Temporarily captive C. concinnus were used to conduct 100 pair-wise feeding preference trials on five plant species pairs over a period of four years. The animals used in these trials were captured as part of an ongoing trapping program at Newland Head Conservation park (see Chapter 2.2 for details). They were kept overnight so they could be used for the trials conducted in the evening between 8pm and 10pm when the animals were awake. After the trials the animals were kept in separate cages and provided with flowers to feed on during the night. Animals were released at the point of capture just before dawn on the morning following the feeding trial. Inflorescences from two flowering plant species were offered concurrently to each animal to determine whether they showed a preference for one species over the other. 20 trials were conducted for each plant species pair using both male and female adult possums. The number of male and female individuals used varied with each plant species pair as this was dependent on captures for that month. On occasions when capture densities were low some individuals were used more than once for a plant species pair (Table 4.1). A trial was considered to have failed (approximately 1 in 10 trials failed) if the animal did not feed and was not included in the data set. The plant species used in these experiments were known food sources for C. concinnus (Chapter 3) and were the dominant flowering plant species in the area at the time (Chapter 2, Figure 2.7). The species used included Banksia marginata, B. ornata, Callistemon rugulosus, Eucalyptus baxteri, E. cosmophylla and E. diversifolia.

Table 4.1: The number of male and female C. concinnus used in trials for each plant species pair. The number of individuals in brackets.

Species pair Male (ind.) Female (ind.) E. baxteri v B. marginata 11 (6) 9 (5) E. cosmophylla v B. marginata 16 (13) 4 (3) E. cosmophylla v B. ornata 12 (7) 8 (4) E. diversifolia v B. ornata 13 (9) 7 (3) E. diversifolia v C. rugulosus 9 (6) 11 (7)

For the purposes of this study the flowering plants have been divided into eucalypt and non- eucalypt categories. The non-eucalypt species were Banksia and Callistemon. Flowers of these species differ from the Eucalyptus species due to their characteristic flower spike. This is an elongated inflorescence consisting of a woody axis covered in tightly-packed flowers attached at right angles. Banksias can have well over 1,000 flowers in a single flower spike (Horner 1994; Turner 1984a). The style is much longer than the perianth, and is initially trapped by the upper perianth parts. These are gradually released over a period of days, from bottom to top (Plate 4.1). The flowers of the Callistemon are attached as small capsules on the side of the axis. The petals are inconspicuous, with the most obvious part of the flower being the stamen with the pollen at the tip of the filament. Callistemon tend to open from the side that receives the most sunlight and warmth to the other (per. obs) (Plate 4.2). The flowers of the Eucalyptus species are very distinctive as hard woody cups with no petals, instead they have numerous fluffy stamens circling the perimeter of the cup. These flowers are clustered together on stems in differing densities depending on the species (Plate 4.3).

Plate 4.1: A B. ornata with about 2/3 of the flowers open.

Plate 4.2: Callistemon rugulosus inflorescence

Plate 4.3: Eucalyptus cosmophylla flower.

A large perspex tank (50cm2 x 40cm in height) in a dimly light room was used to conduct the trials. The floor was covered in sand and leaf litter and a piece of PVC tubing was placed in the middle to provide shelter. Each animal was allowed 5 to 10 minutes in the tank to acclimatise before the plant samples were introduced. Two plant samples, each consisting of either a branch with flowers or a single inflorescence, were placed on the ground one at each end of the tank (Plate 4.4). To prevent any bias by directional movement of the animal, the positioning of each sample type was alternated for each trial, either on the left or right of the tank. Each plant sample presented to an animal displayed an equivalent surface area of flowers (approximately 20cm x 10cm, 1 non-eucalypt inflorescence to 1 cluster of eucalypt flowers). The Banksia and Callistemon inflorescences used had ½ to ¾ of their flowers open as this is when maximum nectar production occurs (Armstrong and Paton 1990; Horner 1994; Turner 1985). To ensure that each sample had a full nectar and pollen load branches of flowers (eucalypts) and inflorescences (non-eucalypts) were covered with a mesh bag the day before the trial was conducted. Branches were removed from the plant up to 3 hours before use and stored in a cool place with their stems wrapped in damp paper.

Plate 4.4: Perspex tank set up for a feeding trial.

Observations were recorded for 10 minutes per animal as the number of seconds an animal spent on particular activities. Time was selected as a unit of measure rather than the weight of food consumed, as the amounts consumed were considered too small to measure accurately. Preference was assessed using a number of variables; the plant species visited first, the time spent feeding at each species and the number and length of feeding bouts. If an animal was in the middle of a feeding bout at the 10 minute mark the trial was continued until it had finished. In addition to recording feeding behaviour the time spent grooming and on other non-feeding activities was recorded. Because of the restriction of the small cage and the animals interest in escaping it was not considered relevant to include categories such as travel and resting.

The foraging behaviour of C. concinnus in these trials involved licking nectar from the base of the flower or around the nectar front on inflorescences and licking pollen from pollen presenters. When foraging for pollen they would lick the anthers of the flower or inflorescence they were feeding upon. Nectar foraging was more physical, each animal would grab handfuls of stamens and pulling its face in to the nectar (Plate 4.5). All of the foraging observed was very hurried and quite frantic in pace. Grooming after feeding involved a clean of the whole body not just the face. As foraging involved a lot of physical contact with inflorescences and flowers it would have lead to pollen being deposited across the stomach and may have also stimulated anthesis.

Plate 4.5: C. concinnus feeding on a B. ornata inflorescence.

4.2.4 Floral characteristics As a result of the foraging behaviours observed both nectar and pollen were measured to quantify the value of each plant species as a resource. Nectar was quantified in terms of the volume, concentration and energy equivalents available. Pollen was quantified as the volume of pollen available rather than a pollen grain count as the size of grains varied amongst the species. A minimum of 20 inflorescences and flowers of the six flowering species used in the trials were bagged overnight to measure the nectar available during the period from dusk (6pm) until dawn (6am). Samples were then collected and assessed using the methods outlined below.

As Banksia and Callistemon flowers are clustered together on an inflorescence the nectar of these flowers will often drip onto the lower flowers and central rachis. As a result it is easier to measure the nectar load of the inflorescence as a whole (Armstrong and Paton 1990). Nectar volume was measured using a method from Armstrong and Paton (1990), where inflorescences were centrifuged by placing in a plastic bag and swinging it around by hand. This method has been found to remove around 70% of the nectar from an inflorescence (Armstrong and Paton 1990) and for field based comparisons was considered as efficient as the methods used with the other species. Inflorescences sampled had 1/2 - 3/4 of their flowers open so that they were equivalent with those used for all trials. A syringe was used to draw the nectar out of the bag after centrifuging and measure the sample. Nectar loads of Eucalyptus were measured for individual flowers using micro-capillary tubes. As it was not possible to collect all of the nectar available on either of the suites of species the measures of nectar volume was considered equivalent for field analysis purposes. Pollen load was measured for the portion of the flower and inflorescence displaying pollen rather than taken as an estimate over the whole of an inflorescence and flower. Counts of the number of flowers per inflorescence and measurements of the size of inflorescences and flowers were also taken for all species.

Sugar concentration and energy per unit volume were assessed by placing nectar samples onto a hand held refractometer (Bolten, Feinsinger et al. 1979; Hiebert and Calder 1983; Inouye, Favre et al. 1980). Concentrations were obtained as g sugar per 100g nectar. To calculate energy available per inflorescence or flower the mean g sugar per 100g nectar (refractometer reading) for each species was converted to g sugar per 100ml nectar (Kearns and Inouye 1993). This value was then used to calculate the total mg of sugar per

85 inflorescence or flower using the mean nectar volume (Bolten, Feinsinger et al. 1979) and then converted to energy available using the assumption that 1 mg of sucrose is equivalent to 16.74J (Collins and Briffa 1983). Pollen was quantified as the average volume of pollen per pollen presenter. This was measured under a microscope as the height and widths in cross section of the pollen bundle on the presenter, giving a final measure in mm3. This was then converted to volume of pollen per flower or inflorescence. For Banksias this involved counting the number of pollen presenters (stigmas – flowers are protandrous and pollen is first presented on the stigmatic surface) per inflorescence and multiplying 1/3 (the flowering front) of the average number of stigmas by the average pollen volume. For the Eucalyptus and Callistemon pollen volume was calculated as the volume of pollen on each stamen multiplied by the average number per flower. As inflorescences and flowers were so different in size both pollen and nectar volume were converted to a relative measure per cm2 of flowering surface area. To do this inflorescences and flowers were measured to gain a total flowering surface area.

4.2.3 Analysis To determine whether the feeding behaviour of C. concinnus indicated a preference for one species over another (eucalypt over non-eucalypt) within each species pair I used a series of logistic regressions with a binomial distribution and logit link function. The binomial response variables for each model included; the species visited first (the proportion times visited for each species within a pair), feeding bouts (the proportion of feeding bouts on each species within a pair) and time spent feeding (the proportion of time spent on each species within a pair). The predictor variables were sex (a categorical variable) and species combination (a categorical variable representing each species combination). I defined a priori candidate model sets where these predictor variables were modelled as species combination and species combination with sex. I then ranked these models based on Akaike’s information criteria (AIC) (Akaike 1973). AIC was selected over the Bradley-Terry model as it was not possible to run all combinations of species pairs since all species did not flower at the same time (Firth 2005). The highest-ranked model was used to determine the odds ratio for each term in the model, which is a measure of effect size. This describes the strength of association between the response and predictor variables. This value was expressed as a percentage change in odds (with 95% confidence interval) providing a measure of the likelihood (odds) of a particular species being visited more frequently or for longer.

As a further measure of preference the influence of sex and species combination on bout length (the number of seconds spent per feeding bout) were assessed using Poisson regression with a log link function. As each animal carried out multiple feeding bouts on each species these were converted to a mean bout length per individual and used as a continuous response variable. The predictor variables were sex (a categorical variable) and species combination (a categorical variable representing each species combination). I again defined a priori candidate model sets where these predictor variables were modelled as species combination and species combination with sex. I then ranked these models based on Akaike’s information criteria (AIC) (Akaike 1973). Wilcoxon signed rank tests were used to compare bout length for each species pair and activity patterns for each sex (Zar 1999).

The floral characteristic were compared using Kruskal-Wallis tests to determine whether there were any significant differences between plant species (Zar 1999). To determine which of these floral characteristics influenced the use of each the six dominant flowering species I used Poisson regression with a log link function. This analysis was done for both sexes combined and males and females separately. The continuous response variables for each model included; the number of times each species was visited first, the number of feeding bouts, the time spent feeding (seconds) and the length of feeding bouts (seconds). The predictor variables used were pollen volume (actual and relative- converted to amount per cm2), nectar volume (actual and relative- converted to amount per cm2), nectar concentration (g sugar per 100g nectar) and energy equivalents (joules). For the response variable length of feeding bouts a further predictor variable, number of feeding bouts, was added. I defined a priori candidate model sets where these predictor variables were modelled individually and in selected combinations (see Appendices Tables 6.16 to 6.18). I then ranked these models based on Akaike’s information criteria (AIC) (Akaike 1973). The highest-ranked model was used to determine the effect size of the predictor variable. This value was expressed as a measure (with 95% confidence interval) of the response to a particular floral characteristic. All analysis was carried out using R version 2.11.1 (R Core Development Team 2010).

4.3 Results

4.3.1 Diet Choices Paired preference trials showed that eucalypts were consistently visited first more often by C. concinnus than non-eucalypt species (Figure 4.1). To determine whether these visits were influenced by sex or species pair combination we used a logistic regression with a binomial distribution and logit link function. Model selection using AIC indicated that, with the selection of the null model, the eucalypt species were visited first irrespective of the species pair combination or the sex of the animal (Table 4.2). The effect size generated from the null model was used to determine that the odds of a eucalypt being visited first were 3.8 times greater than a non-eucalypt being visited first (Table 4.3). While the model selected was not the species pair the effect of species pair on the species visited first was measured using this model and the odds of a species being visited was calculated (Table 4.3). This choice to feed on eucalypts first over non-eucalypt species was significant for four out of five species pairs. Eucalyptus baxteri and E. cosmophylla were visited first 5.7 times more frequently than B. marginata. Eucalyptus diversifolia was visited first 3 times more than B. ornata and 4 times more than C. rugulosus. While not significant E. cosmophylla was visited first 2.3 times more frequently than B. ornata.

Figure 4.1: The number of times a species was visited first by C. concinnus, when exposed to flowers of two different species. n= 20.

Banksia marginata (Bm) ⎕ B. ornata (Bo) ⎕ Callistemon rugulosus (Cr) ⎕ Eucalyptus baxteri (Eb) ⎕ E. cosmophylla (Ec) ⎕ E. diversifolia (Ed) ⎕

0 No. times visited first first visited times No. Eb v Bm Ec v Bm Ec v Bo Ed v Bo Ed v Cr

Table 4.2: The influence of sex and species pair (SP) on the proportion of times C. concinnus visited a species first. This was determined using logistic regression with a binomial distribution and logit link function. Models ranked using AIC.

Model Log Likelihood df AICc weight %DE

null -51.396 1 104.832 0.8155 0.00

SP x sex -42.848 10 108.167 0.1539 16.63

SP -50.380 5 111.399 0.0306 1.98

Table 4.3: The odds of a species being visited first by C. concinnus, calculated using the null model (for eucalypt v non-eucalypt) and species pair model (for species pair). Odds values represent the likelihood of the eucalypt species being visited first over the non-eucalypt species it is paired with. Displayed as; odds, 95% CIs, parameter estimates, standard errors (SE), z values and significance (P).

Species Pair Odds 95% CI Estimate SE z value P

Eucalypt v non-Eucalypt 3.8 (2.3,6.1) 1.32 0.25 5.397 <0.001

E. baxteri v B. marginata 5.7 (1.6, 19.6) 1.73 0.63 2.770 0.005

E. cosmophylla v B. marginata 5.7 (1.6, 19.6) 1.73 0.63 2.770 0.005

E. cosmophylla v B. ornata 2.3 (0.9, 6.1) 0.85 0.49 1.736 0.082

E. diversifolia v B. ornata 3.0 (1.1, 8.4) 1.1 0.52 2.127 0.033

E. diversifolia v C. rugulosus 4.0 (1.3, 12.1) 1.39 0.56 2.480 0.013

The number and length of feeding bouts and how they varied between species and the combination of these in the time spent feeding were also assessed. As with the species visited first measure, for all of the trials the eucalypt sample was visited more frequently (ie. had a greater number of feeding bouts) and this was significant for all but E. cosmophylla v B. ornata (Figure 4.2). Eucalypt samples were visited an average of 2.4 ± 0.4 times during each trial with a maximum of 8 visits, while the non-eucalypt samples were visited and average of 0.9 ± 0.2 during each trial with a maximum of 4 visits. In some trials the eucalypt sample was visited to the exclusion of the non-eucalypt. Time spent feeding ranged from 2 to 517 seconds during a trial with animals spending an average of 126±27 seconds feeding on eucalypts and 70±22 seconds feeding on non-eucalypts. The mean time spent feeding overall followed a similar pattern to the number of feeding bouts with animals spending more time on eucalypts than non-eucalypts, although this was only significant for E. cosmophylla v B. marginata and E. diversifolia v C. rugulosus (Figure 4.2).

Variability in feeding bout length was high with bouts ranging from 1 to 377 seconds. As a result E. diversifolia v B. ornata was the only trial in which the difference between eucalypt and non-eucalypt species was significant, with animals spending significantly longer feeding on B. ornata during a bout (Figure 4.2). Although not significant there was also a strong trend for B. marginata to be visited for longer periods than either of the two eucalypts it was paired with. A strong linear relationship between the number of feeding bouts and bout length was apparent (R2=0.39, n=10), with bout lengths being shorter for the species with more feeding bouts in three out of the five trials. This influence of the number of feeding bouts and a series of floral characteristics on bout length was assessed using Poisson regression (see results section 4.3.2 and Appendices 6.15). Modelling of this relationship showed that 39.1% of the variability in feeding bout length (weight=0.3, df=2, P=0.053) was explained by the number of feeding bouts. The odds ratios calculated using this model showed that bout length decreased by 5.9% (95% CI 0.1, 12.1) with each additional feeding bout.

Figure 4.2: Feeding of C. concinnus at pairs of flowering species during experimental preference trials. Values expressed as the mean of each measure per species ± s.e. 20 trials were conducted per species pair. z values P≤ 0.05*, 0.01**, 0.001***, t values P≤ 0.05 #

Banksia marginata (Bm) ⎕ B. ornata (Bo) ⎕ Callistemon rugulosus (Cr) ⎕

Eucalyptus baxteri (Eb) ⎕ E. cosmophylla (Ec) ⎕ E. diversifolia (Ed) ⎕

1 No. feeding bouts

0 ** *** *** *** 140 120 100 80

(sec.) (sec.) 60 40

Feeding bout length length bout Feeding 20 0 #

160 140 120 100 80 (sec.) (sec.) 60 40 Time Time spent feeding 20 0 * *** Eb v Bm Ec v Bm Ec v Bo Ed v Bo Ed v Cr

The influence of predictor variables plant species pair and sex on the number of feeding bouts and the time spent feeding was assessed using logistic regression with a binomial distribution and logit link function. The influence of the same predictor variables on the length of feeding bouts was determined using Poisson regression with a log link function. The strongest models were selected using AIC and showed that feeding bout length was influenced by species pair only, and that the number of feeding bouts and total time spent feeding were influenced by the combination of species pair and sex (Table 4.4). The influence of species pair was particularly apparent for E. cosmophylla and B. ornata, with the number and length of bouts varying depending on the species they were paired with (Figure 4.2). Eucalyptus cosmophylla was visited more frequently and in shorter bouts when paired with B. marginata than with B. ornata and B. ornata was visited more frequently and in shorter bouts when paired with E. cosmophylla than with E. diversifolia. The response of C. concinnus to B. marginata and E. diversifolia was consistent for both the number and lengths of bouts regardless of the species they were paired with. Interestingly, when considering the total time spent feeding, it was only B. ornata that varied markedly. The time spent feeding on the other species did not appear to be influenced strongly by the species they were paired with.

Table 4.4: The influence of sex of the animals and plant species pair (SP) on the strength of preference displayed by C. concinnus when exposed to flowers of two different plant species during feeding trials. Determined using logistic regression (number of bouts and time spent) and Poisson regression (bout length), with the strongest model selected using AIC . (See Appendices 6.14 for coefficients for bout length).

Feeding behaviour Model Log Likelihood df AICc weight %DE Proportion bouts SP x sex * -105.727 10 233.926 0.966 19.13 SP -115.045 5 240.728 0.032 8.42 null -122.374 1 246.789 0.002 0.00 Bout length SP * -548.147 10 1119.140 0.522 5.69 null -557.640 1 1119.317 0.478 0.00 SP x sex -544.348 20 1133.756 0.000 7.88 Proportion time spent SP x sex * -142.111 10 306.695 1 16.87 SP -158.936 5 328.511 0 4.81 null -165.225 1 332.490 0 0.00

Males visited eucalypts more often (feeding bouts) than non-eucalypts in all of the trials (Figure 4.3), with this relationship being significant in four out of five species pairs. While there was some influence of species pairing for B. ornata which was visited less frequently when paired with E. diversifolia, visit frequency to both E. cosmophylla and E. diversifolia was consistent despite the species they were paired with (Figure 4.3). Odds ratios showed that visit frequency was highest for E. diversifolia and that non-eucalypt species were visited more frequently when paired with E. baxteri and E. cosmophylla (Table 4.5). As with the number of feeding bouts, more time was spent feeding on eucalypts than their non-eucalypt pairs (Figure 4.3). The time spent on each species showed that male C. concinnus displayed their strongest preference for E. diversifolia, then E. cosmophylla and E. baxteri, then B. marginata, B. ornata and C. rugulosus. The influence of the number of feeding bouts and a series of floral characteristics on feeding bout length for males was assessed using Poisson regression (see results section 4.3.2 and Appendices 6.16). The model selected using AIC showed that 38.5% of the variability in feeding bout length (weight=0.36, df=2, P=0.056) was explained by the number of feeding bouts. The odds ratios calculated using this model showed that bout length decreased by 4% (95% CI 0.02, 8) with each additional feeding bout.

Interestingly, while males were quite consistent in their response to both eucalypts and non- eucalypts, females were not (Figure 4.3). The preferences displayed by females were strongly influenced by the non-eucalypt in the species pair. This was particularly evident for E. diversifolia and E. cosmophylla, where the frequency of visits to these species was higher when paired with B. marginata and C. rugulosus than when paired with B. ornata. Eucalyptus cosmophylla was visited less frequently than B. ornata and while E. diversifolia was visited more frequently than B. ornata it was not significant. Females did however spend more time feeding on B. ornata when paired with E. diversifolia and on eucalypts when not paired with B. ornata. While the odds of spending time feeding on B. ornata was 1.8 times greater than E. cosmophylla it was not significant (Table 4.5). Females showed their strongest preference for B. ornata, then E. cosmophylla, E. diversifolia and E. baxteri, then B. marginata and C. rugulosus. Bout lengths for females were shorter for eucalypts than non-eucalypts in four of the 5 trials but only significantly so for E. baxteri v B. marginata and E. diversifolia v B. ornata (Figure 4.3). In contrast to males, no relationship existed for females between feeding bout length and the number of feeding bouts (see results section 4.3.2 and Appendices 6.17).

Figure 4.3: Feeding of a) male and b) female C. concinnus at pairs of flowering species during experimental preference trials. Values expressed as the mean of each measure per species, ± s.e. 20 trials were conducted per species pair. z values (Table 4.5) P≤ 0.05*, 0.01**, 0.001***, t values P≤ 0.05 #

Banksia marginata (Bm) ⎕ B. ornata (Bo) ⎕ Callistemon rugulosus (Cr) ⎕ Eucalyptus baxteri (Eb) ⎕ E. cosmophylla (Ec) ⎕ E. diversifolia (Ed) ⎕

a) b)

No. feeding bouts 1

0 ** * *** *** ** * *** 160 140 120 100 80

(sec.) (sec.) 60 40 20 Feeding bout lenght lenght bout Feeding 0 # # 275 250 225 200 175 150

(sec.) (sec.) 125 100 75

Time Time spent feeding 50 25 0 ** *** ** * * * ** Eb v Bm Ec v Bm Ec v Bo Ed v Bo Ed v Cr Eb v Bm Ec v Bm Ec v Bo Ed v Bo Ed v Cr

Table 4.5: Odds ratios calculated for a) the proportion of feeding bouts and b) the proportion of time spent feeding by male and female C. concinnus, calculated using the model selected by AIC. Odds values represent the likelihood of a) the proportion of feeding bouts being more for a species and b) the proportion of time spent feeding being greater. The relevant plant species is indicated by the estimate (Est.), with a positive estimate indicating the eucalypt and a negative estimate indicating the non-eucalypt. Displayed as; odds, 95% CIs, parameter estimates, standard errors (SE) and significance (P).

a) Proportion of feeding bouts Male Female

Species Pair Odds 95% CI Est. SE z values P Odds 95% CI Est. SE z values P

E. baxteri v B. marginata 1.46 (0.7, 3) 0.37 0.36 1.054 0.292 5.75 (2.0, 16.9) 1.75 0.54 3.229 0.0012 E. cosmophylla v B. marginata 2.19 (1.2, 4) 0.78 0.3 2.594 0.009 5.0 (1.4, 17.6) 1.61 0.63 2.545 0.011

E. cosmophylla v B. ornata 2.36 (1.2, 4.9) 0.86 0.36 2.392 0.017 1.64 (0.7, 3.5) -0.49 0.38 -1.287 0.198

E. diversifolia v B. ornata 6.0 (2.7, 13.5) 1.79 0.41 4.389 <0.001 1.43 (0.5, 3.8) 0.36 0.49 0.724 0.469 E. diversifolia v C. rugulosus 4.83 (2, 11.8) 1.58 0.45 3.513 <0.001 6.25 (2.1, 18.2) 1.83 0.54 3.403 <0.001 b) Proportion of time spent feeding E. baxteri v B. marginata 1.27 (0.7, 2.4) 0.24 0.31 0.775 0.438 2.94 (1.1, 7.8) 1.08 0.49 2.182 0.029 E. cosmophylla v B. marginata 1.5 (0.9, 1.3) 0.41 0.28 1.441 0.149 3.59 (1, 12.4) 1.28 0.62 2.051 0.040

E. cosmophylla v B. ornata 2.4 (1.3, 4.6) 0.87 0.33 2.682 0.007 1.75 (0.85, 3.6) -0.56 0.36 -1.550 0.121

E. diversifolia v B. ornata 3.28 (1.7, 6.4) 1.19 0.33 3.553 <0.001 4.0 (1.2, 13) -1.39 0.59 -2.342 0.019

E. diversifolia v C. rugulosus 3.97 (1.6, 10.1) 1.38 0.47 2.914 0.004 12.26 (2.3, 65) 2.51 0.84 2.986 0.003

When comparing the strengths of preference displayed by males and females it is interesting to note that females displayed a much stronger preference for the eucalypts when paired with B. marginata and C. rugulosus than did the males (Tables 4.5). The reverse was true when eucalypts were paired with B. ornata, with the males showing a stronger preference for the eucalypts and the females a preference for B. ornata.

4.3.2 Floral Characteristics The floral characteristics of the six species used in the feeding trials were quantified as floral display (the size and density of flowers and inflorescences per m2), nectar characteristics (volume per flower or inflorescence and concentration) and pollen load (volume per flower or inflorescence) available over the period from dusk to dawn (Table 4.6). Nectar concentrations were significantly heterogeneous (X2=57.45, df=5, P<0.0001), but showed no consistent pattern for eucalypts and non-eucalypts. E. baxteri had the lowest concentration with E. cosmophylla and E. diversifolia measuring well above that and being comparable to B. ornata. C. rugulosus had the most concentrated nectar with B. marginata having a lower concentration than all of the species but E. baxteri. The nectar and pollen volume per flower (Eucalyptus species) or inflorescence (Banksia and Callistemon species) was, as expected, higher for the larger inflorescences and flowers. However, when converted to volume per cm2 of flowering surface area, nectar volume was not significantly different (X2=10.09, df=5, P=0.07254) (Table 4.6). Conversely, even when converted to volume per cm2 of flowering surface area, pollen volume was higher for the Banksia and Callistemon species and this difference between the species was significant (X2=108.33, df=5, P<0.0001). The energy equivalents calculated using nectar volume and concentration ranked the plant species from highest to lowest as C. rugulosus, E. diversifolia, B. ornata, E. cosmophylla, B. marginata and E. baxteri with the difference between the species being significant (X2= 96.0764, df = 5, P<0.0001).

Table 4.6: The floral display, nectar characteristics and pollen loads available from dusk to dawn of Eucalyptus (per flower), Banksia (per inflorescence) and Callistemon (per inflorescence) species. Values presented as means with standard errors and the number of samples (n). Kruskal-Wallis tests were used to determine whether there was a significant difference between plant species for each floral characteristic P<0.0001***

Plant Species Number on Flowering Nectar Nectar volume Nectar volume Energy Pollen volume Pollen volume shrub or surface area concentration (ul) per cm2 equivalents (cm3) per cm2 tree per m2 (FSA) (g sugar per *** of FSA (ul) per cm2 *** FSA (cm3) (cm2) 100gm) of FSA (J) *** *** *** Measurements per inflorescences B. marginata 1.4 124.11 31.16 242.22 5.91 34.76 543.62 13.27 ±0.21 (20) ±7.75 (20) ±0.92 (36) ±103.88 (20) ±2.54 (20) ±26.01 (20) ±0.64 (20) B. ornata 2.1 269.74 39.98 583.56 6.56 54.36 1335.63 15 ±0.28 (20) ±10.89 (20) ±2.34 (30) ±192.82 (29) ±2.17 (29) ±76.25 (20) ±0.86 (20) C. rugulosus 13.8 98.37 48.23 243.12 7.49 73.31 254.54 7.84 ±1.26 (40) ±4.67 (20) ±2.33 (40) ±11.75 (95) ±0.36 (95) ±11.34 (42) ±0.35(42) Measurement per flower E. baxteri 39.8 1.96 16.18 12.72 6.49 18.49 3.8 1.94 ±14.99 (20) ±0.11 (20) ±1.12 (23) ±1.51 (25) ±0.77 (25) ±0.28 (20) ±0.14 (20) E. cosmophylla 97.1 9.73 35.42 59.01 6.06 41.5 42.96 4.42 ±6.85 (20) ±0.43 (20) ±1.21 (34) ±9.77 (40) ±1 (40) ±1.47 (20) ±0.15 (20) E. diversifolia 33.43 1.95 38.23 14.8 7.59 56.29 9.43 4.83 ±5.07 (20) ±0.11 (20) ±2.86 (12) ±1.6 (32) ±0.82 (32) ±0.27 (20) ±0.14 (20)

To determine which of these floral characteristics influenced each of the feeding behaviours a series of Poisson regressions were used (Table 4.7 and Appendices 6.15 to 6.17). An additional predictor variable (number of feeding bouts) was added for the response variable feeding bout length. For each of the feeding behaviours the strongest model was selected using AIC. Nectar volume had the strongest influence on the number of times a species was visited first, the number of feeding bouts at each species and the time spent feeding for both sexes combined. These relationships represented over 60% of the variability in the data and followed the pattern of preference for eucalypts over non-eucalypts. The number of times a species was visited first, the number of feeding bouts and the time spent feeding decreased as nectar volume increased (Table 4.8). The effect size measured showed that for every ul decrease in nectar volume the number of times a species was visited first, the number of feeding bouts increased by a count of one and time spent feeding increased by one second. In contrast, feeding bout length was influenced by relative pollen volume, with this relationship representing 59% of the variability in the data. For this, feeding bout length increased by 1.4 seconds for every unit increase in relative pollen volume (Table 4.8).

While all of the feeding behaviours of males could be related to one or a combination of floral characteristics, the only relationship evident for females was between feeding bout length and relative pollen volume (Table 4.7). Relative pollen volume influenced 65% of the variability in feeding bout length for females and 49% for males, suggesting a stronger influence of pollen volume for females. Feeding bout length increased by 1.5 seconds for females and 1.2 seconds for males with a unit increase in relative pollen volume (Table 4.8). The feeding behaviour of males was also influenced by a combination of nectar volume and concentration. As nectar volume decreased the number of times a species was visited first, number of feeding bouts and time spent feeding increased. As nectar concentration increased the number of feeding bouts and time spent feeding increased (Table 4.8).

Table 4.7: The influence of floral characteristics on the feeding behaviours of C. concinnus. Determined using Poisson regression with a log link function. The strongest models were selected using AIC . (See Appendices 6.15 to 6.17 for all of the models tested.)

Feeding behaviours Model Selected Log Df AICc Weight %DE Likelihood All Times visited first Nectar Volume -27.080 2 64.160 0.5663 62.37 No. feeding bouts Nectar Volume -40.503 2 91.006 0.7463 67.89 Feeding bout length Relative Pollen Volume -40.875 2 91.750 0.4193 59.35 Time spent feeding Nectar Volume -38.393 2 86.786 0.7339 64.13 Male Times visited first Nectar Volume -22.212 2 54.423 0.5174 73.94 No. feeding bouts Nectar Volume & Concentration -33.587 3 83.174 0.7337 92.44 Feeding bout length Relative Pollen Volume -38.396 2 86.791 0.2901 49.37 Time spent feeding Nectar Volume & Concentration -29.689 3 75.379 0.8678 94.86 Female Feeding bout length Relative Pollen Volume -42.491 2 94.982 0.5637 64.65

Table 4.8: The effect of a unit change in particular floral characteristics on each feeding behaviour. The effect size represent the probability of an increase in each feeding behaviour with a unit increase (positive estimate) or decrease (negative estimate) in each floral characteristic. Values calculated using the strongest models selected by AIC and displayed as Effect size, 95% CIs, parameter estimates, standard errors (SE), t values and significance (P).

Feeding behaviours Floral characteristics Effect size 95% CI Estimate SE t values P All Times visited first (count) Nectar Volume (ul) 1.002 1, 1.003 -0.0188 0.0052 -3.641 0.007 No. feeding bouts (count) Nectar Volume (ul) 1.008 1, 1.01 -0.0814 0.0198 -4.113 0.003 Feeding bout length (sec.) Relative Pollen Volume (cm3) 1.4 1.1, 1.7 3.2081 0.9387 3.418 0.009 Time spent feeding (sec.) Nectar Volume (ul) 1.006 1, 1.009 -0.0607 0.0160 -3.782 0.005 Male Times visited first (count) Nectar Volume (ul) 1 1, 1.002 -0.0152 0.0032 -4.764 0.001 No. feeding bouts (count) Nectar Volume (ul) 1 1, 1.01 -0.1153 0.0129 -8.875 <0.001 Nectar Concentration (g sugar 1.13 1.03, 1.2 1.2180 0.4066 2.996 0.020 per 100gm) Feeding bout length (sec.) Relative Pollen Volume (cm3) 1.2 1, 1.4 2.0459 0.7326 2.793 0.024 Time spent feeding (sec.) Nectar Volume (ul) 1 1, 1.01 -0.0953 0.0088 -10.827 <0.001 Nectar Concentration (g sugar 1.1 1.03, 1.2 0.9481 0.2754 3.443 0.011 per 100gm) Female Feeding bout length (sec.) Relative Pollen Volume (cm3) 1.5 1.2, 1.9 4.221 1.103 3.825 0.005

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4.3.3 Activity patterns The average trial length out of 109 trials was 606.03 ± 1.78 seconds. During the trials animals spent 32.4% of the time feeding, 0.8% of the time grooming and 66.8% of the time on other activities (running around, sitting and sleeping) (Table 4.9). The time spent on feeding and other activities varied significantly between males and females with males spending more time feeding (t(107) = 2.14, P < 0.05) and females spending more time on other activities (t(107) = 2.22, P < 0.05). Time spent grooming during trials was small and not significantly different for males and females.

Table 4.9: The percentage of time C. concinnus spent on each activity during feeding trials (n = number of trials). The number of individuals used is different to the number of trials.

Total Male Female P (n = 109) (n = 67) (n = 42)

No. individuals 57 38 19

Feeding 32.4 35.8 26.9 0.05

Grooming 0.8 0.7 0.9 n.s.

Other 66.8 63.4 72.2 0.05

Only 26% of C. concinnus groomed during the feeding trials, with a greater proportion of all females (38%) grooming than males (18%). Mean grooming bout length was 20.2 seconds (Table 4.10) and was higher for males (27.1 seconds) than for females (15.1 seconds). This difference was not significant however, as variability in grooming bout length was high, ranging from one to 75 seconds. C. concinnus fed for an average of 2.5 bouts before grooming, taking an average of 6.5 minutes to start. Only four individuals groomed more than once. Fewer animals groomed in the trials with B. ornata and more with B. marginata and C. rugulosus. However, when they did groom more time was spent grooming and more repeat grooming bouts occurred in the trials with B. ornata. In addition, they took longer to start grooming in the trials with B. marginata. No grooming patterns were apparent for the Eucalyptus species, although grooming was more likely to occur after a feeding bout on a eucalypt (76.9% of grooming bouts) than non-eucalypt (11.5%).

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Table 4.10: The grooming behaviour of C. concinnus during captive feeding trials of 10 minute duration. n = number of individuals

Trial n Grooming Time before No. feeding No. time grooming bouts before Times (seconds) (seconds) grooming groomed E. baxteri v B. marginata 5 17.4 ± 5.8 511 ± 35.6 3.4 ± 1.2 1.2 ± 0.2 E. cosmophylla v B. marginata 6 14.5 ± 4.4 431.5 ± 57.5 2.2 ± 0.4 1 ± 0 E. cosmophylla v B. ornata 3 22 ± 10.5 285.3 ± 54.2 2 ± 1.5 1.3 ± 0.3 E. diversifolia v B. ornata 3 30.3 ± 18.9 366.6 ± 93.3 2 ± 1 1.7 ± 0.7 E. diversifolia v C. rugulosus 9 21.4 ± 7.1 336.6 ± 49.2 2.7 ± 0.4 1.1 ± 0.1 All 26 20.2 ± 3.6 390 ± 28 2.5 ± 0.3 1.2 ± 0.1

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4.4 Discussion

When offered a choice between flowers of eucalypt and non-eucalypt species, and considering a range of foraging behaviours, C. concinnus consistently showed a preference for the eucalypt species. However, this evident preference does not give a complete picture and also requires consideration of sex differences, species pair combinations and floral characteristics. These three factors represent aspects of the animals’ requirements and the timing and quality of the food resources available to them. Differing use of food resources by each sex has been observed for a number of species and is generally attributed to the resource requirements of each sex and the nutritional value of each food resource (Bos and Carthew 2007b; Hemingway 1999; Martins, Bonato et al. 2006a; Rosalino, Santos et al. 2009; Smith and Broome 1992).

Of the feeding behaviours examined, all but feeding bout length supported the pattern of preference for eucalypts. When considered independently of sex and species pair, bout length decreased with an increase in the number of feeding bouts. The other feeding behaviours were influenced by one or a combination of species pairing and sex with these influences evident in the different species preferred by each sex and the extent to which particular non- eucalypts were avoided. For example, males showed a clear preference for eucalypts over non-eucalypts, with the strongest choice being for E. diversifolia, followed by E. cosmophylla, then E. baxteri. The number of feeding bouts was influenced by the eucalypt species, while the time spent feeding on each eucalypt was influenced by the non-eucalypt they were paired with. In fact, the response of males to non-eucalypts suggested a secondary preference for B. marginata and C. rugulosus over B. ornata. Males fed for longer on E. cosmophylla when paired with B. ornata than with B. marginata and for longer on E. diversifolia when paired with B. ornata than C. rugulosus. This apparent secondary preference for a food resource is evident in species such as the badger (Meles meles), in Portugal, that will preferentially consume olives when they are available over any other food resource (Loureiro, Bissonette et al. 2009). A study by Pavelka et al. (2004) describes black howling monkey (Alouatta pigra), in Belize, as being as frugivorous as possible and as folivorous as necessary. These examples and this present study show the capacity of species to respond to temporally variable in food resources by using what is available to them.

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The pattern of preference displayed by females did not suggest a clear choice for eucalypts over non-eucalypts, as there were no consistent responses evident to either suite of species. The most interesting shift from selection of eucalypts over non-eucalypts was in the females’ use of B. ornata. They showed a clear preference for B. ornata over E. cosmophylla and, in some measures of feeding, for B. ornata over E. diversifolia. The preference for B. ornata over E. diversifolia was not as clear cut as the eucalypt was visited first and more frequently. However, the overall time spent feeding on B. ornata was much greater than on E. diversifolia. Females showed a stronger preference for E. cosmophylla when paired with B. marginata than with B. ornata and for E. diversifolia when paired with C. rugulosus than B. ornata. This suggests that they would use the eucalypts less with B. ornata available than with the other two non-eucalypts available. It would appear that the eucalypt species may be the secondary preference for females.

Foraging choices of nectarivores can be driven by a range of floral characteristics including flowering timing and density, flower size and structure, pollen load and composition and nectar volume, concentration and composition (Carpenter 1978; Fleming, Xie et al. 2008; Johnson, van Tets et al. 1999; Keasar, Sadeh et al. 2008; Majetic, Raguso et al. 2009; Morrant, Petit et al. 2010; Rodriguez-Pena, Stoner et al. 2007; Schlumpberger, Cocucci et al. 2009; Wooller, Richardson et al. 1993). In the setting of the feeding trials conducted here, the focus was particularly on pollen and nectar as food resources and their influence on feeding preferences as the other possible variables such as flowering timing and density were controlled for in the experimental design. While relative nectar volume per cm2 of flowering surface area was not significantly different across the species, the other floral characteristics were different, with relative pollen volume being higher for the non-eucalypts and nectar concentration and energy equivalents showing no clear pattern between eucalypts and non- eucalypts. The species with the highest concentration and energy equivalents was C. rugulosus followed by B. ornata and E. diversifolia.

The strongest relationships found between floral characteristics and feeding behaviour was with nectar volume. Three of the four measures of feeding (all but bout length) were influenced by nectar volume. As the eucalypt species had a significantly lower nectar volume than the non-eucalypt species this influence reflected the same preference for eucalypts over non-eucalypts. When males were considered independently of females the variability in some foraging behaviours were best represented by a combination of nectar volume and

104 concentration. The number of feeding bouts and time spent feeding by males increased as nectar concentration increased and volume decreased. Females, when analysed separately showed no relationship with nectar volume or concentration in any of their feeding behaviours.

In contrast to the other feeding behaviours, bout length was strongly influenced by the amount of pollen per cm2 of flowering surface area, as pollen volume increased, feeding bout length increased. This relationship was evident for both males and females. While these results might suggest that females in particular target pollen, their avoidance of B. marginata and C. rugulosus and secondary preference for the eucalypts they were paired with appears to counter this. The reasons for these two species being used less by females are unclear. The importance of a protein rich diet, particularly for females, as a resource during the reproductive process is evident for a range of small mammal species (Smith and Broome 1992; Vandegrift and Hudson 2009). In fact, studies have found that protein rather than the energy content of food can dictate preference and influence the timing of breeding (Wasserman and Chapman 2003; White 2008). In this study, both sexes of C. concinnus likely maximised their use of pollen with long feeding bouts on larger inflorescences. In addition males made use of the high energy rewards found in nectar with high concentrations. This is consistent with the requirements of most species, in that males require increased energy as they pursue reproductive opportunities with females and females require protein for reproductive success (Bieber and Ruf 2009; Fietz, Pflug et al. 2005; Haythornthwaite and Dickman 2006b; Ostfeld 1990; Sailer and Fietz 2009; Smith and Broome 1992). The preference of females for B. ornata also appears closely linked with the timing of reproduction as its flowering peak coincides with the peak time that young are born (Chapter 2). Another possible explanation for this relationship with pollen volume may be found if there is a link between fresh nectar production and increased pollen loads. Further study is required to investigate this relationship with pollen volume, and the possible links with pollen composition and nectar production.

Another floral characteristic that may play a role in the preferences displayed by C. concinnus for the plant species it forages on is nectar composition. A recent study of C. concinnus in Innes National Park, South Australia, found that glucose and fructose were higher in the species used most frequently by C. concinnus than in other species on the study site (Morrant, Petit et al. 2010). Landwehr et al. (1990) also found, in a captive trial, that C. concinnus

105 showed a preference for fructose over glucose but did not display any strong preference for other sugars or combinations of sugars. They suggested that rather than having a clear sugar preference, C. concinnus was an opportunistic forager that would feed on whatever they came across irrespective of the types and quantities of sugar present. While this current study does show that C. concinnus will feed on all of the food resources offered to them, in the pattern of an opportunist, they do show a preference for particular species and were influenced by the combination of species available.

Landwehr et al. (1990) also studied the diet of the honey possum (Tarsipes rostratus), in Western Australia, a small mammal species with daily nitrogen requirements similar to C. concinnus (Wooller, Richardson et al. 1999). They suggested that it was more likely that concentration and smell rather than sugar type influence the foraging patterns of T. rostratus and other specialised nectarivores and sighted the strong smell of the plant species visited by T. rostratus. Landwehr et al. (1990) concluded that it was improbable that sugar preferences played a major role in determining plant visitation patterns of T. rostratus. While this study has not considered floral scent it is also interesting to note that at least some of the species visited by C. concinnus at Newland Head did exude a strong smell. However, concentration was identified as a floral characteristic that did influence the preferences displayed by male C. concinnus. The influence of nectar concentration was also evident in a study on the foraging behaviour of T. rostratus with the amount of nectar consumed adjusted based on concentration (Richardson, Wooller et al. 1986). Another small nectarivorous mammal, the Queensland blossom bat (Syconycteris australis) showed no preference for sugar other than an avoidance of high concentrations of fructose (Law 1993). Instead it responded to variations in sugar concentration, preferring higher concentrations of sugar, suggesting that its floral choices were also driven by nectar concentrations. This influence of nectar concentration on preference is also evident in other nectarivorous bird and bat species (Rodriguez-Pena, Stoner et al. 2007; Schondube and del Rio 2003). As perhaps a further complication, a study on three nectarivorous bird species found that sugar preferences can also change with nectar concentration (Fleming, Xie et al. 2008).

The work of Morrant et al. (2010), at Innes National Park in South Australia, indicated that C. concinnus showed a preference for E. rugosa over other species that were flowering at the same time. Pestell et al. (2007) also showed that this species featured prominently in the diet of C. concinnus at the same site. It is important to note that Banksia species were absent from

106 this study site. While E. diversifolia was used frequently by C. concinnus at Newland Head Conservation Park, the study by Morrant et al. (2010) found that it was absent from the diet despite being available (Morrant, Petit et al. 2010). However, the earlier study by Pestell et al. (2007) did find E. diversifolia present in the diet of C. concinnus along with E. rugosa and commented on the difficulty of telling these grains apart. As the studies by Morrant et al. (2010) and Pestell et al. (2007) used only swabs and or scats as a measure of diet composition it is difficult to make a satisfactory comparison to this current study. However, if there is a difference between sites it emphasises the role of preference in the diet of C. concinnus and that these preferences can change with the resources available. Morrant et al. (2010) linked the choice of E. rugosa over E. diversifolia to sugar composition, finding that E. diversifolia was lower in fructose and glucose than E. rugosa and the other species used by C. concinnus. Alternatively, the difference in foraging choices may also relate to additional floral characteristics such as nectar concentration and pollen volume as found in this current study.

The pattern of selection of a food resource based on the availability of another is seen across a range of species (Brown and Morgan 1995; Lurz, Garson et al. 2000; Rosalino, Loureiro et al. 2005). The european badger (Meles meles), in Portugal, for example will use a range of food resources but will target olives when they are available, showing a preference for this food resource over others (Rosalino, Loureiro et al. 2005). Similarly the red squirrel (Sciurus vulgaris), in England, will avoid using the smaller sitka spruce seeds when larger norway spruce seeds are available (Lurz, Garson et al. 2000). In this case the squirrels appeared to be maximising their energy and nitrogen intake by targeting the larger seed. When considered in the light of optimal foraging theory, flower visiting animals should show a preference for the species that provides the greatest rewards for the least effort, maximising their energy gain (Pyke 1981). It may be that for small mammals the most effective strategy for maximizing the reward they receive is to change the length of their feeding bout to suit the resource they are harvesting and use the food resources available rather than investing large amounts of time looking for or harvesting a resource that is not readily available. It appears, in the case of a species like C. concinnus, that while they show a preference between species, they will, like most opportunistic foragers, switch to the food resource that maximises their energy intake. The increase in feeding bout length and related decrease in the number of feeding bouts observed for males shows that their feeding behaviour is about maximising energy gain with time on an inflorescence or flower.

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The foraging behaviour and preferences of C. concinnus have implications for the flowering species they are visiting. Grooming behaviour of C. concinnus within trials indicated that only a small percentage of animals groomed in a 10 minute foraging period and that animals did not start grooming for an average of 6 minutes after commencing feeding or at least 2 feeding bouts. Animals travelled over the flowers they were feeding on, touching them with their underbelly, feet and then their head as they reached into the flowers to find nectar. As a result they would most certainly come into contact with pollen, pick it up on their fur and carry it with them. Their foraging for pollen was not thorough, leaving pollen available to be picked up on their fur. Samples taken from the fur of C. concinnus after capture in pitfall traps found at least three pollen grains on 75% of the individuals captured (Pestell and Petit 2007a). While this number of pollen grains is very low, the animals had a lot of time to groom when in the trap. Other studies of pollen loads on mammal species have found animals carrying between 200 and 3000 pollen grains (Carthew 1993; Goldingay, Carthew et al. 1987; Goldingay, Carthew et al. 1991; Hackett and Goldingay 2001). These studies also noted a significant drop in the number of grains carried if the animals were left in traps overnight before samples were taken. Goldingay (1987) highlighted that many studies had underestimated pollen loads on non-flying mammals. These results suggest that C. concinnus do carry pollen between flowers and inflorescences and are not removing pollen from their fur completely with grooming over short periods. As such, C. concinnus is able to play a role as a pollinator and although not specifically studied here, will likely transfer pollen from one flower or inflorescence to another. In addition the preferences displayed by C. concinnus were not to the exclusion of the other flowering species indicating that all of the species have C. concinnus as a potential pollinator. This, however, also means that there is the possibility of pollen transfer to the wrong species. Although as discovered in field tracking observations only a small number of animals changed the species they were foraging on within the time they were tracked (Chapter 3).

There are a number of further directions for research that are worth exploring to extend our understanding of the relationship between C. concinnus and the floral resources it uses. For example, the role of floral scent has not been taken into account in these preference trials but would be worth considering. As mentioned above, a number of the flowering species on the site had a particularly strong odour (eg. B. ornata). Interestingly, one of the species that C. concinnus avoided completely, despite the fact that it flowered prolifically (E. fasciculosa), smelt strongly of rotting flesh. A study by Landwehr (1990) also mentioned the possibility of

108 odour as an attractant for a similar nectarivorous marsupial, T. rostratus. In addition, while the results from this and a number of other studies (Landwehr, Richardson et al. 1990; Law 1993; Richardson, Wooller et al. 1986; Rodriguez-Pena, Stoner et al. 2007; Schondube and del Rio 2003) have shown that nectar concentration and volume play an important role in floral preference and foraging choices, this does not exclude the role of nectar composition in the preferences displayed by C. concinnus. With the development of a washing method for sampling nectar from species with low nectar volume it would be beneficial to apply these techniques to the species used in these trials (Morrant, Schumann et al. 2009).

In conclusion, C. concinnus do make choices between plants species that flower at the same time. These choices were reflected not only in the number of times an animal visited the flowers presented to them but also by the amount of time animals spent feeding at a particular flower. The preferences displayed were influenced by sex, species pair and floral traits indicating that the resource requirements of C. concinnus and the availability and nutritional value of a food resource play an important role in shaping preferences. In addition, while a preference was shown for one plant species over another, this was not shown to the exclusion of the other species. This indicates that C. concinnus is capable of using whatever food resource is available to them, even if it is not preferred, supporting the suggestion that this species is an opportunistic feeder (Landwehr, Richardson et al. 1990). This study also confirms the role of nectar concentration in dietary choices of nectarivores and adds the influence of pollen volume to be considered as a further contributor to preference and feeding behaviour.

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Chapter 5 : Discussion

Cercartetus concinnus is a nectarivore that feeds on a resource that varies both spatially and temporally. Its reliance on nectar and pollen from a range of species has a significant influence on a number of aspects of its ecology including its abundance and distribution, movements, reproduction, foraging patterns, diet and territorial behaviour. This relationship is shaped by the differing resource requirements of each sex across different seasons.

At Newland Head Conservation Park C. concinnus consumes pollen and nectar from eight flowering species, belonging to the Proteaceae and Myrtaceae families; Banksia marginata, B. ornata, Callistemon rugulosus, Eucalyptus baxteri, E. cosmophylla, E. diversifolia, E. incrassata and E. leptophylla. Of these species, six were particularly abundant in the study site (B. marginata, B. ornata, C. rugulosus, E. baxteri, E. cosmophylla and E. diversifolia). The spatial distribution and capture densities of C. concinnus related closely to the distribution and abundance of four of these species; B. marginata, B. ornata, E. baxteri and E. diversifolia. I was able to show that the probability of capturing an animal increased with an increase in flowering abundance of each of these plant species and that this relationship was significant. This relationship, while not addressed here, is likely to be influenced by the structural complexity of these habitats as well as the flowering species present. This potential influence of habitat structure was suggested by the higher capture densities in Habitats A and B which both had thicker layers of heath, Xanthorrhoea and B. ornata and less open ground in comparison to Habitat C. However, habitat structure alone was not the driving force, as their distribution across habitats shifted with flowering season. Sex based differences were also evident in this relationship with captures of males increasing in response to only two of the flowering plant species that influenced captures of females (B. marginata and E. diversifolia).

The relationship of C. concinnus with particular flowering species and the preferences displayed varied depending on the measure used and sex (Table 5.1). While females were relatively consistent in preferences displayed across captures, foraging and feeding trials, males showed more variation. In Season1, both sexes showed a preference for E. baxteri over B. marginata in feeding trials while B. marginata was associated with increased capture densities and fed on more frequently in foraging observations. Captures of females with young in the pouch were also associated with E. baxteri. In Season 2, captures of males were

110 not related to the availability of a flowering plant species. Their foraging patterns revealed that they fed on four different species, using B. ornata most frequently during field observation. However, E. cosmophylla was preferred in feeding trials. In Season 3, captures of males were associated with E. diversifolia and they displayed a strong preference for this species in feeding trials. However, foraging observations in the field showed that males fed more on C. rugulosus more than E. diversifolia.

Table 5.1: A summary of the most preferred flowering species for C. concinnus identified by each measure across the three flowering seasons.

Season 1 2 3 Female Captures B. marginata B. ornata E. diversifolia E. baxteri Foraging B. marginata B. ornata E. diversifolia Feeding Trials E. baxteri B. ornata E. diversifolia Male Captures B. marginata - E. diversifolia Foraging B. marginata B. ornata C. rugulosus Feeding Trials E. baxteri E. cosmophylla E. diversifolia

The different preferences displayed by C. concinnus for the different measures used in this study could be the result of a number of factors. The most obvious of these is the availability of food resources in the study site. While an animal may show a preference in a feeding trial where different resources are equally readily available and easily accessible, the same preference may not be possible in a natural situation where resources may be patchily distributed in time and space and animals perhaps also compete for them with other species such as birds and bats. In fact, a study of common voles (Microtus arvalis) found a disparity when comparing preference trials with stomach contents (Lantova and Lanta 2009). It suggested, as appears to be the case in this current study, that preferences in the field may be strongly influenced by availability. These results emphasise the need for a range of techniques to be applied in order to gain a full understanding of diet and food preference. It is interesting to note that the preferences of females in Seasons 2 and 3 actually coincided with the most abundant flowering species on the study site.

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The profitability of a food source may also play a role in shaping the preference of C. concinnus and influencing the seasonal switch from one resource to the next. The preferences observed in the feeding trials were sex specific, with males showing a clear preference for the eucalypts over the non-eucalypts while females showed a preference for B. ornata over the eucalypts but eucalypts over B. marginata and C. rugulosus. Males showed a preference for species with low nectar volume and high concentrations and low pollen volume, while females fed for longer in response to a high pollen volume. When considering the different preferences revealed via the different measures used in the light of optimal foraging theory, flower visiting animals should minimize the effort they invest to gain maximum energy and nutrition (Pyke 1981). Feeding on Eucalyptus species in a natural situation may be less profitable than feeding on species that produce on average higher energetic rewards in terms of pollen loads and energy equivalents (Chapter 4) like Banksia and Callistemon. It may also require more energy to reach the flowers of the eucalypts as they are much higher up in trees and the nectar is presented in separate cups. The eucalypts may be less reliably full of nectar as, in contrast to the Banksia, they attract other foragers at night such as bats and insects that may compete for night time nectar resources (pers. obs.).

This study has emphasised the importance of considering the difference between males and females when assessing food preferences. In fact, the foraging activity of C. concinnus suggests that the energy requirements of this species changes with season and for each sex. Theory on the resource requirements of animals suggest that males will track females to maximise mating opportunities and females will track the food resources they require for reproductive success (Ostfeld 1990). This behaviour has been noted in other studies of animal resource use with males investing energy in the search for and defence of mating opportunities and females investing energy in the search for or defence of food resources (Bayart and Simmen 2005; Kortner, Rojas et al. 2010; Lurz, Garson et al. 1997).

The preference of females for B. ornata appears closely linked with the timing of reproduction as its flowering peak coincides with the peak time that young are born. Reproduction occurred from the end of flowering Season 1 through Season 2 and at the beginning of Season 3, with the centre of each reproductive peak coinciding with flowering peaks in either B. ornata or E. diversifolia and once with E. baxteri. Both B. ornata and E. diversifolia are the most reliable flowering species on the site producing flowers in both years of the study in greater densities than any of the other flowering species (Chapter 2). In the

112 case of the foraging activity of C. concinnus, males rested the most in season 3 and travelled the most in Season 1. Females travelled similar amounts in Seasons 1 and 3 and less in Season 2 and fed the least in Season 3. When considered in the light of reproductive activity, these patterns suggest that males may be building up energy in Season 3 in preparation for the search for mating opportunities in Seasons 1 and 2. In Season 3 males also showed a preference for C. rugulosus over E. diversifolia, targeting the species with the highest nectar concentration and energy equivalents. Females fed the most in Seasons 1 and 2 with reproduction commencing at the end of Season 1 and into Season 2, travelling less in Season 2 when they may have been carrying and raising young. The food resource provided by B. ornata in Season 2 may have been such that females did not need to travel very far. The reduced activity level for females was also seen in a higher capture rate in this season with captures dominated by females with pouch young. It is interesting to note that the proportion of males to females was higher in Season 1 (3.2:1) than in the other two seasons (1.5:1 and 1.3:1). This increased proportion of males to females not only coincides with the season before females start producing young, it is also the point when flowering is restricted to just one habitat type.

The suggestion the females may aggressively defend their food resources is not out of keeping with the behaviours displayed by female C. concinnus. They were observed displaying very aggressive behaviour towards males when released at the same locations, caught in the same traps or held in neighbouring cages (pers. obs.). Territoriality by females has been strongly linked with food resource availability and is thought to be particularly evident when food resources are patchy or in limited abundance (Ostfeld 1985; Ostfeld 1990). Behavioural dominance has been observed in other small mammals such as honey possum (Tarsipes rostratus) and mountain pygmy possum (Burramys parvus) with observations made of females restricting male access to food resources and captures revealing the limited distribution of males (Garavanta, Wooller et al. 2000; Russell 1986). In T. rostratus the males are more active than females and have significantly larger home ranges (Bradshaw and Bradshaw 2002).

During season 2, at the time when more females are reproducing than in the other seasons (chapter 2) the distribution of males was not associated with any flowering species. Field tracking revealed that their diet used a broader range of flowering plant species than that of females (Chapter 3). If males are being excluded to some extent from the most profitable food

113 resources they may need to use a more opportunistic diet. In fact, their higher mobility, indicated during field tracking (Chapter 3), may be a need to cover more ground to find the food resources they need.

A further indication of the influence of floral resources on population patterns is evident in the low recapture rates for C. concinnus (45%). Of those animals recaptured, the average time between first and last recapture was under 2 months for females and just over 3 months for males. Only eight animals were recaptured over five months or more, of these only one remained in the same area and this was the only female recaptured over this many months. The males that were recaptured moved between habitats and did not give any strong indication of selection for particular sites. A possible explanation for the low recapture rates could be a short life span. This does not appear to be the case however, as I have recaptured animals after 2 and 2.5 years in a separate study, suggesting that they will live in the wild for some time (pers. obs.). Further, C. concinnus does not appear to learn to avoid traps as, in this study, some animals were caught on up to three consecutive nights. Rather, these low residency rates and lack of recaptures of animals in the same area, along with the relationship between captures and flowering species availability, suggest that C. concinnus, like many species, has adapted to the use of a heterogeneous food resource by tracking its spatial and temporal shifts.

The movements of C. concinnus between recaptures indicated a fairly haphazard pattern of movement rather than one where animals returned regularly to the same sites. This seems to contradict the idea of a general shift of the population as they follow the flowering of particular plant species. However, this directed approach on the broad scale with a more haphazard approach on the fine scale may allow individual C. concinnus to locate flowering resources that they might otherwise miss with more specific movements. To add a further complication, tracking behaviour revealed the possibility of spatial memory, with individual C. concinnus moving in a very direct fashion between very isolated patches of flowering resources at a time when flowering abundance was particularly low. While this complicates an understanding of their movement patterns it does fit with the use of spatially and temporally heterogeneous food resources. I suggest that C. concinnus will make broad shifts in distribution to follow flowering resources and that while using those resource will move haphazardly to increase their probability of finding food, if a large food supply is found they

114 will return to this resource when other resources are not readily available. More detailed tracking over longer periods in the future should help corroborate this.

A review of movement patterns by Mueller and Fagan (2008) has described this type of movement pattern as nomadic. They suggest that this pattern is most likely in species that use heterogeneous food resources. Unlike a migratory pattern, where a species moves from one patch of predictable resources to another, this movement pattern involves a degree of random spatial movement to increase the likelihood of encounters with a variable food resource. This random use of space is particularly effective in relationship to plant produced food resources as they will not reliably produce a large crop of flowers, fruit or seed each year (Bieber 1998; Sharpe 2004). This was demonstrated by the flowering of E. baxteri and E. cosmophylla at Newland Head Conservation Park (Chapter 2). These species did not flower consistently over the study with E. baxteri only flowering in the second year of the study and E. cosmophylla flowering heavily at the beginning and end of the study but having a very small flowering peak in the middle of the study.

A difference in the movement of males and females between recaptures again suggests that they may be responding to differing resource requirements. Males travelled long distances more frequently than females and were more likely to change habitat (Chapter 2). They also travelled further per minute than females when foraging (Chapter 3). Males may need to travel further, not just in the hunt for food resources but also to increase their probability of finding mating opportunities, a more elusive resource even than flowers. Females, on the other hand may not need to travel so far to find food resources and may, as suggested by the increase in the proportion of females with increased flower availability, defend these resources, restricting the access of males to them.

The spatial and temporal patterns of flowering plants are evident in the ecology of C. concinnus. In return it appears that C. concinnus is capable of contributing effectively to pollination. The foraging behaviour of C. concinnus means that a large proportion of its body will come into contact with pollen during the process of searching for nectar giving a large surface area for pollen transfer (Plate 5.1). While C. concinnus will forage intentionally for pollen (Chapter 4) as well as nectar by licking pollen from the pollen presenter, observations suggest it is not thorough in this process and will not spend the time to remove all of the pollen from all of the flowers or inflorescence surface. The observations of grooming behaviour and plant visitation made during tracking and feeding preference trials show that

115 grooming made up only a small proportion of foraging time and this did not differ across sex. This short proportion of time grooming suggests that not all of the pollen would have been removed from animals and that pollen would be effectively transferred. Studies of other non- flying mammal species have found that the pollen loads they carry are generally very high (Carthew 1993; Goldingay, Carthew et al. 1991; Hackett and Goldingay 2001) and that over 70% of movements from one inflorescence to another were to different plants (Carthew 1994). In this study, field observations revealed that only 4.3% of individuals fed on more than one species within the time that they were monitored, suggesting that pollen would be effectively transferred to the correct species. They also swapped regularly from one plant to the next such that outcrossing would likely have occurred (pers. obs.). These results show that C. concinnus will effectively contribute to the pollination of the flowering species it visits. Further study on deliberate pollen harvesting and patterns of pollen transfer by this species could provide more insight into the effectiveness of this species as a pollinator.

Plate 5.1: C. concinnus interrupted while feeding on E. baxteri flowers.

116

In conclusion the role of flowering resources in shaping the ecology of Cercartetus concinnus is a significant one. Floral resources are the primary dietary component for this species at Newland Head Conservation Park. The spatial and temporal patterns of availability of this resource influence the ecological patterns of C. concinnus. The distribution of flowering species at Newland Head Conservation Park has resulted in C. concinnus tracking these changes with a pattern of shifts in their distribution over each year. In addition, changes in the availability of flowering from one year to the next have influenced movement patterns, revealing the possibility of nomadism and the use of spatial memory. These adaptations allow this species to locate floral resources that might otherwise be missed. The reproductive timing of C. concinnus follows the commencement of flowering of three of the dominant species on the site, with the main peak coinciding with the flowering of B. ornata. The resource requirements of this species are driven by reproductive success and are evident in their different use of floral resources and differing activity levels over time. Foraging behaviour, diet and distribution patterns suggest that females may be territorial in their defence of food resources. Males appear to be more opportunistic in their diet using a broader range of species and spending more time travelling. The pattern of response to floral resources evident in the ecological patterns of C. concinnus fits with the theories presented on the response of animals to heterogeneity in resources (Fleming 1992; MacArthur and Pianka 1966; Mueller and Fagan 2008; Ostfeld 1990; Wiens 1976).

This study has highlighted the need for further research into the movement patterns of small mammal species influenced by spatially and temporally variable food resources. A study of spatial memory and movement patterns could provide further insights into the way C. concinnus uses space and the methods it uses for finding food resources. Field tracking could also be used to consider in more detail the use of or rejection of flowering plant species along the foraging path taken by C. concinnus. This would provide more insight into selection for particular flowering resources and the possible cues used by this species to find food resources. The potential of floral scent as an attractant also deserves some consideration as this may play a role in animals locating food resources along a foraging path. This would need to include testing of scent at different stages of flowering as there does appear to be a change in the smell coming from inflorescences at different stages of anthesis (per. obs.). While not considered within the framework of this study the role of habitat in the use of space should be considered as there does appear to be a preference for habitats with more understory.

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Chapter 6 : Appendices

Table 6.1: The Coefficients for the logistic regression model selected in Table 2.5. The model showed that the presence of captures of C. concinnus was influenced by a combination of flowering species, grid and season, with an interaction between grid and season. *P>0.05, **P>0.01, ***P0.001.

Estimate Std. Error z value Pr(>|z|) Grid B1 Season 1 -3.53625 0.39294 -8.999 < 2e-16 *** Grid B2 Season 1 0.37021 0.36569 1.012 0.311365 Grid B3 Season 1 0.15724 0.39626 0.397 0.691517 Grid B4 Season 1 -0.74438 0.33304 -2.235 0.025409 * Grid C1 Season 1 0.24112 0.53369 0.452 0.651410 Grid C2 Season 1 -1.62397 1.07423 -1.512 0.130598 Grid C3 Season 1 0.12359 0.60090 0.206 0.837041 Grid C4 Season 1 0.71604 0.51756 1.384 0.166512 Grid A1 Season 1 1.58462 0.46204 3.430 0.000604 *** Grid A2 Season 1 0.52348 0.55146 0.949 0.342486 Grid A3 Season 1 0.36029 0.57264 0.629 0.529238 Grid A4 Season 1 0.91088 0.46032 1.979 0.047841 * Grid B1 Season 2 0.04708 0.36388 0.129 0.897049 Grid B2:season2 -0.82962 0.52326 -1.585 0.112859 Grid B3:season2 -0.58322 0.55990 -1.042 0.297573 Grid B4:season2 0.46608 0.57752 0.807 0.419644 Grid C1:season2 -0.83675 0.74237 -1.127 0.259682 Grid C2:season2 -1.48040 0.82726 -0.018 0.985722 Grid C3:season2 0.47752 0.67731 0.705 0.480794 Grid C4:season2 -1.96564 0.87938 -2.235 0.025400 * Grid A1:season2 -1.01732 0.50884 -1.999 0.045576 * Grid A2:season2 -1.82021 0.69504 -2.619 0.008822 ** Grid A3:season2 -1.19072 0.64963 -1.833 0.066813 . Grid A4:season2 -0.21097 0.48271 -0.437 0.662074 Grid B1 Season 3 -0.55305 0.54646 -1.012 0.311509 Grid B2:season3 0.31728 0.58409 0.543 0.586989 Grid B3:season3 1.13906 0.62427 1.825 0.068060 . Grid B4:season3 0.75550 0.59263 1.275 0.202370

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Grid C1:season3 -0.16619 0.83214 -0.200 0.841702 Grid C2:season3 0.66161 1.37687 0.481 0.630858 Grid C3:season3 0.23309 0.82736 0.282 0.778152 Grid C4:season3 -0.05081 0.74705 -0.068 0.945770 Grid A1:season3 -1.67958 0.65631 -2.559 0.010494 * Grid A2:season3 -0.18303 0.71667 -0.255 0.798427 Grid A3:season3 -1.02095 0.79054 -1.291 0.196544 Grid A4:season3 0.62707 0.60728 1.033 0.301797 Bm 0.15088 0.02381 6.336 2.36e-10 *** Bo 0.04947 0.01276 3.878 0.000105 *** Cr 0.04421 0.02323 1.903 0.057017 Eb 0.05309 0.02021 2.627 0.008614 ** Ec -0.01259 0.02618 -0.481 0.630595 Ed 0.09961 0.01448 6.879 6.03e-12 ***

Table 6.2: Logistic regression was used to determine the influence of space (trapping grid), time (flowering season and year) and flowering species on captures of adult males (Table 2.7). a) Model selection for males against predictor variables space (Grids - G), time (Season - S and Year - Y) and flowering species (F)

logLik df AICc dAICc weight dBIC %DE G + S + F -341.887 20 726.920 0.000 0.9998 0.000 32.55 G x S + F -322.869 42 744.481 17.561 0.0002 86.550 39.23 G + S -368.684 14 766.906 39.986 0.0000 19.616 23.14 F -382.578 7 779.556 52.636 0.0000 7.763 18.26 G x S -349.523 36 781.659 54.739 0.0000 105.879 29.87 G -388.317 12 801.769 74.849 0.0000 47.557 16.24 G x S x Y + F -276.907 90 816.962 90.042 0.0000 266.448 55.38 G x S x Y -297.986 84 834.316 107.396 0.0000 274.627 47.97 S -415.153 3 836.390 109.470 0.0000 50.261 6.82 S x Y -411.285 7 836.971 110.051 0.0000 65.179 8.17 Null -434.556 1 871.125 144.206 0.0000 77.741 0.00

119 b) The Coefficients for the logistic regression model selected for Male C. concinnus in Table 2.7. *P>0.05, **P>0.01, ***P0.001.

Estimate Std. Error z value Pr(>|z|) intercept -3.456242 0.296379 -11.662 < 2e-16 *** Grid B2 0.494217 0.288039 1.716 0.086199 Grid B3 0.262356 0.318487 0.824 0.410078 Grid B4 -0.250865 0.284494 -0.882 0.377889 Grid C1 -0.120828 0.414047 -0.292 0.770422 Grid C2 -17.190348 853.551436 -0.020 0.983932 Grid C3 0.069937 0.396746 0.176 0.860077 Grid C4 0.266998 0.376354 0.709 0.478057 Grid D1 0.798169 0.309191 2.581 0.009838 ** Grid D2 -0.433089 0.403049 -1.075 0.282585 Grid D3 -0.047636 0.357422 -0.133 0.893974 Grid D4 0.830303 0.295352 2.811 0.004935 ** Season2 -0.599477 0.249342 -2.404 0.016206 * Season3 -0.976197 0.262817 -3.714 0.000204 *** Bm 0.105748 0.019491 5.426 5.78e-08 *** Bo 0.020229 0.012458 1.624 0.104420 Cr 0.040938 0.027877 1.468 0.141971 Eb 0.020091 0.022130 0.908 0.363950 Ec -0.008546 0.027612 -0.310 0.756937 Ed 0.070439 0.015962 4.413 1.02e-05 ***

120

Table 6.3: Logistic regression was used to determine the influence of space (trapping grid), time (flowering season and year) and flowering species on captures of adult females with pouch young (Table 2.7). a) Model selection for adult females with pouch young against predictor variables space (Grids - G), time (Season - S and Year - Y) and flowering species (F).

logLik df AICc dAICc weight dBIC %DE G + S + F -164.385 20 371.916 0.000 0.9543 38.788 29.78 F -181.800 7 378.000 6.085 0.0455 0.000 17.16 G + S -180.283 14 390.105 18.189 0.0001 36.607 18.26 G -186.363 12 397.861 25.945 0.0000 37.441 13.86 S -199.444 3 404.972 33.056 0.0000 12.635 4.38 G x S + F -154.154 42 407.051 35.136 0.0000 142.912 37.20 S x Y -197.381 7 409.162 37.246 0.0000 31.162 5.87 null -205.488 1 412.990 41.074 0.0000 13.398 0.00 G x S -167.526 36 417.665 45.749 0.0000 135.677 27.51 G x S x Y + F -129.860 90 522.867 150.951 0.0000 366.145 54.80 G x S x Y -143.992 84 526.329 154.414 0.0000 360.432 44.56

121 b) The Coefficients for the logistic regression model selected for adult females with pouch young C. concinnus in Table 2.7. *P>0.05, **P>0.01, ***P0.001.

Estimate Std. Error z value Pr(>|z|) (Intercept) -4.29406 0.46706 -9.194 < 2e-16 *** gridB2 -0.48646 0.50917 -0.955 0.339376 gridB3 -0.02205 0.49574 -0.044 0.964515 gridB4 -1.26163 0.50780 -2.485 0.012973 * gridC1 -1.43174 0.86496 -1.655 0.097869 gridC2 -1. 35232 0.91511 -1.478 0.139470 gridC3 -0.30060 0.61896 -0.486 0.627218 gridC4 -0.82920 0.72142 -1.149 0.250391 gridD1 -0.36075 0.50663 -0.712 0.476429 gridD2 -0.81250 0.60752 -1.337 0.181091 gridD3 -0.95099 0.61306 -1.551 0.120853 gridD4 0.70394 0.40915 1.720 0.085345 season2 -1.08599 0.51776 -2.097 0.035951 * season3 -0.20511 0.40814 -0.503 0.615275 Bm 0.12061 0.03317 3.636 0.000277 *** Bo 0.05136 0.02525 2.034 0.041974 * Cr 0.06385 0.03266 1.955 0.050563 Eb -0.04767 0.05945 -0.802 0.422649 Ec -0.06705 0.09075 -0.739 0.460043 Ed 0.07080 0.02323 3.048 0.002305 **

122

Table 6.4: Logistic regression was used to determine the influence of space (trapping grid), time (flowering season and year) and flowering species on captures of adult females without pouch young (Table 2.7). a) Model selection for adult females without pouch young against predictor variables space (Grids - G), time (Season - S and Year - Y) and flowering species (F).

logLik df AICc dAICc weight dBIC %DE G + S + F -94.795 20 232.736 0.000 0.9450 39.076 37.03 F -112.066 7 238.533 5.797 0.0521 0.000 19.92 G + S -107.468 14 244.475 11.739 0.0027 30.445 24.48 G -112.286 12 249.706 16.970 0.0002 28.754 19.71 S + Y -119.217 7 252.835 20.099 0.0000 14.302 12.84 G x S + F -79.493 42 257.729 24.993 0.0000 133.057 52.18 S -127.394 3 260.872 28.137 0.0000 8.004 4.75 G x S -90.855 36 264.324 31.588 0.0000 121.804 40.93 null -132.188 1 266.390 33.654 0.0000 6.265 0.00 G x S x Y -66.314 84 370.972 138.236 0.0000 344.543 65.23 G x S x Y + F -55.788 90 374.724 141.988 0.0000 357.470 75.65

123 b) The Coefficients for the logistic regression model selected for adult females without pouch young C. concinnus . Model selected was Grid + Season + Flowering Species. *P>0.05, **P>0.01, ***P0.001.

Estimate Std. Error z value Pr(>|z|) (Intercept) -7.21521 1.01958 -7.077 1.48e-12 *** gridB2 -1.41232 1.11980 -1.261 0.207229 gridB3 -1.50403 1.18415 -1.270 0.204037 gridB4 -0.33131 0.71857 -0.461 0.644746 gridC1 -16.62058 2238.37876 -0.007 0.994076 gridC2 -16.46171 2206.16299 -0.007 0.994046 gridC3 1.19342 0.79994 1.492 0.135730 gridC4 -0.26737 1.17965 -0.227 0.820692 gridD1 -1.05524 0.73773 -1.430 0.152608 gridD2 0.23380 0.59113 0.396 0.692459 gridD3 -2.17188 1.12588 -1.929 0.053724 gridD4 0.62239 0.54923 1.133 0.257130 season2 1.09069 0.91576 1.191 0.233645 season3 1.96912 0.96507 2.040 0.041311 * Bm 0.14403 0.05443 2.646 0.008146 ** Bo 0.10582 0.02909 3.637 0.000276 *** Cr -0.62275 0.72578 -0.858 0.390863 Eb 0.17146 0.08081 2.122 0.033847 * Ec 0.01642 0.06471 0.254 0.799757 Ed 0.04918 0.04064 1.210 0.226240

124

logLik df AICc dAICc weight dBIC %DE Flowering Present -45.089 2 94.749 0.000 0.8801 0.000 23.71 Null -48.353 1 98.888 4.139 0.1111 3.350 0.00 Flowering Species -41.483 7 103.965 9.216 0.0088 8.678 49.91

b) Coefficients:

Estimate Std. Error z value Pr(>|z|) (Intercept) 1.1304 0.2574 4.391 1.13e-05 *** Flowering Count -2.0546 0.8082 -2.542 0.0110 *

125

logLik df AICc dAICc weight dBIC %DE Flowering Species -23.023 7 67.047 0.000 0.6594 0.000 54.42 Flowering Count -31.920 2 68.411 1.365 0.3333 1.903 19.60 Null -36.929 1 76.039 8.993 0.0074 8.743 0.00

b) Coefficients:

Estimate Std. Error z value Pr(>|z|) (Intercept) -3.4411 1.7453 -1.972 0.04865 * Bm -0.4636 17.9322 -0.026 0.97938 Bo 11.8176 5.0728 2.330 0.01983 * Cr -4.8275 4.3405 -1.112 0.26605 Eb 22.8541 8.5421 2.675 0.00746 ** Ec -2.1921 5.3018 -0.413 0.67926 Ed 13.2322 8.2965 1.595 0.11073

126

Figure 6.1: Example of the output of a package created by Alltraders Pty Ltd to determine the availability of each habitat component to the animal in each tracking event.

F046 T2 January 1999 Season: 1 Grid :D2 Trap 36/30 Grid ref 490/350

Dist Travelled: 226 Area: 3515.978 Length: 191.66 Width: 26.315

Habitats used: 2 (m) A: 226 B: 0 C: 0 (%)D: 100 B: 0 C: 0

Habitat available (%) D: 82.96 B: 16.86 C: 0.18

127

Figure 6.2: Example of the output of a package created by Alltraders Pty Ltd to determine the availability of each habitat component to the animal in each tracking event.

M056 T1 February 1998 Season: 1

Grid B2 Trap 41/45 Grid ref 440/200

Dist Travelled: 188.29* Area: 1640.442

Length: 7160.585 Width: 15.393

Habitats used: 2

(m) D: 150.26 B: 40.38 C: 0

(%) D: 78.82 B: 21.18 C: 0

Habitat available (%) D: 63.08 B: 29.06 C: 7.86

128

logLik df AICc dAICc weight dBIC %DE S x Sex x A -2040.837 24 4133.131 0.000 1 1.433 45.26 S x A -2075.633 12 4176.136 43.005 0 0.000 44.01 S x A + Sex -2075.633 13 4178.284 45.152 0 5.919 44.01 Sex x A -2132.815 8 4282.026 148.895 0 90.687 41.96 S + Sex x A -2132.815 10 4286.239 153.108 0 102.525 41.96 A -2145.585 4 4299.278 166.147 0 92.551 41.50 Sex + A -2145.585 5 4301.333 168.202 0 98.470 41.50 S + A -2145.585 6 4303.399 170.268 0 104.389 41.50 S + Sex + A -2145.585 7 4305.477 172.346 0 110.308 41.50 S x Sex + A -2145.585 9 4309.666 176.535 0 122.146 41.50 Null -3303.781 1 6609.572 2476.441 0 2391.187 0.00 Sex -3303.781 2 6611.594 2478.463 0 2397.106 0.00 S -3303.781 3 6613.627 2480.495 0 2403.024 0.00 S + Sex -3303.781 4 6615.670 2482.539 0 2408.943 0.00 S x Sex -3303.781 6 6619.792 2486.660 0 2420.781 0.00

129 b) Coefficients:

Estimate Std. Error z value Pr(>|z|) F Season1 Feeding -0.50246 0.10558 -4.759 1.94e-06 *** F Season1 Travel 0.58628 0.14709 3.986 6.73e-05 *** F Season1 Grooming -3.22190 0.35347 -9.115 < 2e-16 *** F Season1 Resting -1.95998 0.21753 -9.010 < 2e-16 *** M Season 1 Feeding 0.07370 0.12376 0.595 0.551517 M Season1 Travel -0.32524 0.17268 -1.883 0.059634 M Season1 Grooming 0.09940 0.40724 0.244 0.807169 M Season 1 Resting 0.39177 0.24689 1.587 0.112552 F Season2 Feeding -0.12242 0.13762 -0.890 0.373705 F Season2 Travel -0.61760 0.19313 -3.198 0.001384 ** F Season2 Grooming 0.92994 0.41119 2.262 0.023725 * F Season2 Resting 1.53059 0.25367 6.034 1.60e-09 *** M Season2 Feeding 0.07444 0.16519 0.451 0.652266 M Season2 Travel 0.32722 0.23209 1.410 0.158573 M Season2 Grooming 0.09903 0.47866 0.207 0.836097 M Season2 Resting -1.07274 0.29860 -3.593 0.000328 *** F Season3 Feeding -0.26480 0.14329 -1.848 0.064608 F Season3 Travel 0.16472 0.19787 0.832 0.405152 F Season3 Grooming -0.24909 0.52843 -0.471 0.637376 F Season3 Resting 1.16107 0.26632 4.360 1.30e-05 *** M Season3 Feeding 0.48688 0.18427 2.642 0.008236 ** M Season3 Travel -0.81756 0.25804 -3.168 0.001533 ** M Season3 Grooming -0.69608 0.70596 -0.986 0.324132 M Season3 Resting -0.88189 0.33033 -2.670 0.007591 **

130

Table 6.8: Poisson regression was used to determine which predictor variables; sex, season (S), release habitat (RH – A,B,C), the availability of flowering (F) and the availability of each flowering species (FS), influenced the distance travelled per minute (Chapter 3). *P>0.05, **P>0.01, ***P0.001. Banksia marginata (Bm), B. ornata (Bo) Callistemon rugulosus (Cr), Eucalyptus diversifolia (Ed), E. baxteri (Eb), E. cosmophylla (Ec).

a) Model selection:

logLik df AICc dAICc weight %DE FS + S x RH x Sex -781.827 18 1603.008 0.000 1 36.57 FS + S x RH -800.929 12 1628.500 25.491 0 32.26 F + S x Sex x RH -820.900 13 1670.543 67.534 0 27.44 F + S x RH -839.612 7 1695.475 92.466 0 22.61 FS -839.635 7 1695.522 92.514 0 22.61 F + S + RH -857.602 5 1727.351 124.343 0 17.67 F + S -861.475 3 1731.020 128.011 0 16.57 F + S x Sex -859.874 5 1731.895 128.887 0 17.02 F x S -860.898 4 1731.901 128.893 0 16.73 F + S + Sex -861.410 4 1732.924 129.916 0 16.58 F -865.292 2 1736.625 133.617 0 15.46 S -907.861 2 1821.763 218.755 0 2.12 RH -908.190 3 1824.449 221.440 0 2.01 Sex -910.010 2 1826.061 223.053 0 1.39 Null -914.087 1 1832.195 229.187 0 0.00

131 b) Coefficients:

Estimate Std. Error t value Pr(>|t|) Bm -2.54903 0.43554 -5.853 8.23e-09 *** Bo -1.66361 0.25984 -6.402 3.23e-10 *** Cr -0.35905 0.46310 -0.775 0.438479 Ed -1.93239 0.62237 -3.105 0.001999 ** Ec -0.06766 0.67086 -0.101 0.919695 Eb -5.57580 0.53577 -10.407 < 2e-16 *** RH B, Female 3.89846 0.52185 7.470 3.07e-13 *** RH C, Female 2.77446 1.35568 2.047 0.041166 * RH A, Female -2.36542 0.55917 -4.230 2.73e-05 *** RH B, Male -0.18253 0.40879 -0.447 0.655391 RH C, Male -2.49310 1.32413 -1.883 0.060240 RH A, Male 0.50045 0.57618 0.869 0.385451 S, RH B, Female -0.91877 0.24923 -3.686 0.000249 *** S, RH C, Female -1.04162 0.50885 -2.047 0.041119 * S, RH A, Female 1.27472 0.24990 5.101 4.63e-07 *** S, RH B, Male 0.19797 0.21373 0.926 0.354708 S, RH C, Male 0.89349 0.50096 1.784 0.075035 S, RH A, Male -0.74899 0.29940 -2.502 0.012645 *

132

logLik df AICc dAICc weight dBIC %DE S x H x sex -1354.857 61 2840.428 0.000 1 16.151 63.86 S x H + sex -1448.064 32 2962.482 122.054 0 4.345 61.00 S x H -1449.309 31 2962.827 122.400 0 0.000 61.00 S + H x sex -2592.340 23 5231.899 2391.471 0 2231.381 26.03 H -2626.489 11 5275.266 2434.838 0 2217.657 25.05 S + H + sex -2624.088 14 5276.635 2436.208 0 2233.360 25.06 S + H -2625.338 13 5277.073 2436.646 0 2229.025 25.06 null -3446.671 2 6897.355 4056.928 0 3796.504 0.00 sex -3445.700 3 6897.425 4056.997 0 3801.396 0.00 S + sex -3444.265 5 6898.595 4058.168 0 3812.198 0.00 F -3446.395 3 6898.817 4058.389 0 3802.788 0.00 S -3445.518 4 6899.079 4058.651 0 3807.868 0.00 S x sex -3444.115 7 6902.352 4061.924 0 3825.568 0.00 F x S x sex -3440.227 13 6906.852 4066.424 0 3858.803 0.02

133 b) Fixed effects - Habitat components (H); ground (G), grass and shrubs (GS), Banksia marginata (Bm), B. ornata (Bo) Callistemon rugulosus (Cr), Eucalyptus diversifolia (Ed), E. baxteri (Eb), E. cosmophylla (Ec), Eucalyptus fasciculosa (Ei), Eucalyptus incrassata (Ei).

Estimate Std. Error z value Pr(>|z|) H G, S 1, Female -2.409e+01 2.827e+03 -0.009 0.993 H G, S 2, Female -2.328e+01 2.103e+03 -0.011 0.991 H G, S 3, Female -2.302e+01 2.213e+03 -0.010 0.992 H GS, S 1, Female -2.110e-05 3.998e+03 0.000 1.000 H Bm, S 1, Female 2.023e+01 2.827e+03 0.007 0.994 H Bo, S 1, Female -3.189e-01 4.357e+03 -0.000073 1.000 H Ed, S 1, Female -3.310e-01 4.372e+03 -0.000076 1.000 H Eb, S 1, Female 1.945e+01 2.827e+03 0.007 0.995 H Ec, S 1, Female -4.790e-01 4.571e+03 -0.000105 1.000 H Ei, S 1, Female -2.093e-01 4.224e+03 -0.000050 1.000 H Cr, S 1, Female -3.461e-01 4.392e+03 -0.000079 1.000 H Ef, S 1, Female -2.110e-05 3.998e+03 0.000 1.000 H G, S 1, Male 5.042e-01 3.416e+03 0.000148 1.000 H GS, S 2, Female -8.174e-06 4.983e+03 0.000 1.000 H GS, S 3, Female -1.106e-05 5.077e+03 0.000 1.000 H Bm, S 2, Female -3.847e+00 3.523e+03 -0.001 0.999 H Bm, S 3, Female -2.079e+01 4.630e+03 -0.004 0.996 H Bo, S 2, Female 2.129e+01 4.838e+03 0.004 0.996 H Bo, S 3, Female -5.353e-06 5.533e+03 0.000 1.000 H Ed, S 2, Female -2.548e-02 5.464e+03 -0.000005 1.000 H Ed, S 3, Female 2.063e+01 4.900e+03 0.004 0.997 H Eb, S 2, Female -2.014e+01 4.601e+03 -0. 004 0.997 H Eb, S 3, Female -2.014e+01 4.752e+03 -0.004 0.997 H Ec, S 2, Female 1.872e+01 5.031e+03 0.004 0.997 H Ec, S 3, Female -5.581e-06 5.804e+03 0.000 1.000 H Ei, S 2, Female -6.075e-02 5.297e+03 -0.000011 1.000 H Ei, S 3, Female -6.158e-06 5.364e+03 0.000 1.000 H Cr, S 2, Female -5.604e-06 5.474e+03 0.000 1.000 H Cr, S 3, Female 2.007e+01 4.917e+03 0.004 0.997 H Ef, S 2, Female -8.175e-06 4.983e+03 0.000 1.000 H Ef, S 3, Female 9.578e-07 5.077e+03 0.000 1.000

134

H G, S 2, Male 1.944e-01 4.349e+03 0.000045 1.000 H G, S 3, Male -2.179e-01 4.532e+03 -0.000048 1.000 H GS, S 1, Male -1.310e-05 4.831e+03 0.000 1.000 H Bm, S 1, Male 8.152e-01 3.416e+03 0.000239 1.000 H Bo, S 1, Male 1.823e+01 4.760e+03 0.004 0.997 H Ed, S 1, Male -1.075e-01 5.338e+03 -0.000020 1.000 H Eb, S 1, Male -5.765e-01 3.416e+03 -0.000169 1.000 H Ec, S 1, Male 1.817e+01 4.957e+03 0.004 0.997 H Ei, S 1, Male 1.782e+01 4.639e+03 0.004 0.997 H Cr, S 1, Male -3.967e-02 5.326e+03 -0.000007 1.000 H Ef, S 1, Male -1.374e-05 4.831e+03 0.000 1.000 H GS, S 2, Male 3.926e-08 6.150e+03 0.000 1.000 H GS, S 3, Male -1.782e-05 6.410e+03 0.000 1.000 H Bm, S 2, Male -1.764e+01 4.825e+03 -0.004 0.997 H Bm, S 3, Male -4.099e-01 5.807e+03 -0.000071 1.000 H Bo, S 2, Male -1.863e+01 5.468e+03 -0.003 0.997 H Bo, S 3, Male -1.832e+01 6.655e+03 -0.003 0.998 H Ed, S 2, Male 2.011e+01 6.485e+03 0.003 0.998 H Ed, S 3, Male -1.553e-01 6.113e+03 -0.000025 1.000 H Eb, S 2, Male 8.733e-01 5.641e+03 0.000155 1.000 H Eb, S 3, Male 8.733e-01 6.008e+03 0.000145 1.000 H Ec, S 2, Male -1.845e+01 5.640e+03 -0.003 0.997 H Ec, S 3, Male -1.811e+01 6.884e+03 -0.003 0.998 H Ei, S 2, Male 3.636e-01 5.878e+03 0.000062 1.000 H Ei, S 3, Male -1.803e+01 6.518e+03 -0.003 0.998 H Cr, S 2, Male -4.865e-07 6.783e+03 0.000 1.000 H Cr, S 3, Male 6.941e-01 6.103e+03 0.000114 1.000 H Ef, S 2, Male -5.824e-06 6.150e+03 0.000 1.000 H Ef, S 3, Male 4.353e-06 6.410e+03 0.000 1.000

135 c) Predictions using the selected model. These probabilities represented the influence of the combined variable on time spent.

hat se lower upper prob Prob Prob lower upper G, S 1, F -23.920201 2592.6468978 -5105.508121 5057.667719 0.0000 0.0000 1.0000 GS, S 1, F -23.920200 2592.6466195 -5105.507575 5057.667174 0.0000 0.0000 1.0000 Bm, S 1, F -3.861263 0.9064449 -5.637895 -2.084631 0.0206 0.0035 0.1106 Bo, S 1, F -23.920202 2592.6490380 -5105.512317 5057.671912 0.0000 0.0000 1.0000 Ed, S 1, F -23.920200 2592.6466195 -5105.507575 5057.667174 0.0000 0.0000 1.0000 Eb, S 1, F -4.638598 0.9177783 -6.437443 -2.839752 0.0096 0.0016 0.0552 Ec, S 1, F -23.920200 2592.6466195 -5105.507575 5057.667174 0.0000 0.0000 1.0000 Ei, S 1, F -23.920200 2592.6466195 -5105.507575 5057.667174 0.0000 0.0000 1.0000 Cr, S 1, F -23.920200 2592.6466195 -5105.507575 5057.667174 0.0000 0.0000 1.0000 Ef, S 1, F -23.920200 2592.6466195 -5105.507575 5057.667174 0.0000 0.0000 1.0000 G, S 2, F -23.744341 2657.8030156 -5233.038251 5185.549570 0.0000 0.0000 1.0000 GS, S 2, F -23.744341 2657.8030156 -5233.038251 5185.549570 0.0000 0.0000 1.0000 Bm., S 2, F -6.891304 0.8510864 -8.559433 -5.223175 0.0010 0.0002 0.0054 Bo, S 2, F -2.301552 0.6184430 -3.513700 -1.089403 0.0910 0.0289 0.2517 Ed, S 2, F -23.744341 2657.8034663 -5233.039135 5185.550453 0.0000 0.0000 1.0000 Eb, S 2, F -23.744341 2657.8034652 -5233.039133 5185.550451 0.0000 0.0000 1.0000 Ec, S 2, F -5.039469 0.6620590 -6.337105 -3.741834 0.0064 0.0018 0.0232 Ei, S2, F -23.744341 2657.8034657 -5233.039134 5185.550452 0.0000 0.0000 1.0000 Cr, S 2, F -23.744351 2657.8167526 -5233.065186 5185.576484 0.0000 0.0000 1.0000 Ef, S 2. F -23.744351 2657.8167526 -5233.065186 5185.576484 0.0000 0.0000 1.0000 G, S 3, F -23.651337 3035.0138073 -5972.278399 5924.975726 0.0000 0.0000 1.0000 GS, S 3, F -23.651337 3035.0138073 -5972.278399 5924.975726 0.0000 0.0000 1.0000 Bm, S 3, F -23.651337 3035.0138073 -5972.278399 5924.975726 0.0000 0.0000 1.0000 Bo, S 3, F -23.651336 3035.0133876 -5972.277576 5924.974903 0.0000 0.0000 1.0000 Ed, S 3, F -2.718156 0.6528399 -3.997722 -1.438590 0.0619 0.0180 0.1918 Eb, S 3, F -23.651347 3035.0292951 -5972.308765 5925.006071 0.0000 0.0000 1.0000 Ec, S 3, F -23.651347 3035.0292951 -5972.308765 5925.006071 0.0000 0.0000 1.0000 Ei, S 3, F -23.651347 3035.0292951 -5972.308765 5925.006071 0.0000 0.0000 1.0000 Cr, S 3, F -3.298498 0.6581881 -4.588547 -2.008450 0.0356 0.0101 0.1183 Ef, S 3, F -23.651337 3035.0138073 -5972.278399 5924.975726 0.0000 0.0000 1.0000 G, S 1, M -23.729896 2057.6556305 -4056.734932 4009.275140 0.0000 0.0000 1.0000 GS, S 1, M -23.729896 2057.6556305 -4056.734932 4009.275140 0.0000 0.0000 1.0000 Bm, S 1, M -2.541889 0.4801339 -3.482951 -1.600826 0.0730 0.0298 0.1679 Bo, S 1, M -5.679445 0.5406016 -6.739024 -4.619865 0.0034 0.0012 0.0098 Ed, S 1, M -23.729899 2057.6589880 -4056.741516 4009.281717 0.0000 0.0000 1.0000

136

Eb, S 1, M -4.710935 0.5038549 -5.698491 -3.723380 0.0089 0.0033 0.0236 Ec, S 1, M -5.893552 0.5540144 -6.979420 -4.807684 0.0027 0.0009 0.0081 Ei, S 1, M -5.975733 0.5598675 -7.073073 -4.878392 0.0025 0.0008 0.0076 Cr, S 1, M -23.729896 2057.6556301 -4056.734931 4009.275139 0.0000 0.0000 1.0000 Ef, S 1, M -23.729896 2057.6556301 -4056.734931 4009.275139 0.0000 0.0000 1.0000 G, S 2, M -23.594584 2792.1576351 -5496.223549 5449.034380 0.0000 0.0000 1.0000 GS, S 2, M -23.594584 2792.1576351 -5496.223549 5449.034380 0.0000 0.0000 1.0000 Bm, S 2, M -23.594584 2792.1576351 -5496.223549 5449.034380 0.0000 0.0000 1.0000 Bo, S 2, M -2.000432 0.4175034 -2.818739 -1.182125 0.1192 0.0563 0.2347 Ed, S 2, M -2.934368 0.4241682 -3.765738 -2.102999 0.0505 0.0226 0.1088 Eb, S 2, M -23.594583 2792.1556006 -5496.219560 5449.030394 0.0000 0.0000 1.0000 Ec, S 2, M -4.613370 0.4654101 -5.525574 -3.701167 0.0098 0.0040 0.0241 Ei, S 2, M -4.661511 0.4677693 -5.578339 -3.744683 0.0094 0.0038 0.0231 Cr, S 2, M -23.594603 2792.1839580 -5496.275161 5449.085954 0.0000 0.0000 1.0000 Ef, S 2, M -23.594603 2792.1839579 -5496.275161 5449.085954 0.0000 0.0000 1.0000 G, S 3, M -23.556126 3011.1628468 -5925.435305 5878.323054 0.0000 0.0000 1.0000 GS, S 3, M -23.556126 3011.1628468 -5925.435305 5878.323054 0.0000 0.0000 1.0000 Bm, S 3, M -23.556126 3011.1628461 -5925.435304 5878.323053 0.0000 0.0000 1.0000 Bo, S 3, M -23.556126 3011.1628463 -5925.435304 5878.323053 0.0000 0.0000 1.0000 Ed, S 3, M -2.694691 0.6437400 -3.956421 -1.432961 0.0633 0.0188 0.1926 Eb, S 3, M -23.556122 3011.1580315 -5925.425864 5878.313619 0.0000 0.0000 1.0000 Ec, S 3, M -23.556135 3011.1766298 -5925.462329 5878.350060 0.0000 0.0000 1.0000 Ei, S 3, M -23.556122 3011.1580315 -5925.425864 5878.313619 0.0000 0.0000 1.0000 Cr, S 3, M -2.357832 0.6412851 -3.614751 -1.100914 0.0864 0.0262 0.2496 Ef, S 3, M -23.557219 3012.8096660 -5928.664165 5881.549726 0.0000 0.0000 1.0000

137

a) Model Selection using AIC

logLik df AICc dAICc weight dBIC %DE S + H x sex -227.844 23 502.907 0.000 1 23.933 70.38 H -256.889 11 536.065 33.159 0 0.000 65.28 S x H x sex -203.083 61 536.881 33.974 0 234.148 74.85 S + H -255.398 13 537.193 34.286 0 10.688 65.18 S + H + sex -254.917 14 538.292 35.386 0 16.561 65.15 S x H -241.986 31 548.182 45.275 0 106.898 67.78 S x H + sex -241.511 32 549.377 46.470 0 112.783 67.74 null -594.956 2 1193.925 691.018 0 614.617 0.00 F -594.226 3 1194.479 691.572 0 619.994 -0.04 S -593.324 4 1194.691 691.784 0 625.024 -0.13 S + sex -592.811 5 1195.687 692.780 0 630.833 -0.17 sex -594.886 3 1195.799 692.892 0 621.313 0.00 S x sex -591.493 7 1197.107 694.201 0 641.867 -0.27 F x S x sex -588.601 13 1203.599 700.692 0 677.094 -0.47

138 b)Fixed effects - Habitat components (H); ground (G), grass and shrubs (GS), Banksia marginata (Bm), B. ornata (Bo) Callistemon rugulosus (Cr), Eucalyptus diversifolia (Ed), E. baxteri (Eb), E. cosmophylla (Ec), Eucalyptus fasciculosa (Ei), Eucalyptus incrassata (Ei).

Estimate Std. Error z value Pr(>|z|) S1 -2.441e+01 1.035e+03 -0.024 0.981 S2 -2.597e+01 1.035e+03 -0.025 0.980 S3 -2.782e+01 1.035e+03 -0.027 0.979 GS , F -8.993e-05 1.464e+03 0.000 1.000 Bm, F -8.993e-05 1.464e+03 0.000 1.000 Bo, F 1.708e+01 1.035e+03 0.017 0.987 Ed, F 1.824e+01 1.035e+03 0.018 0.986 Eb, F 1.597e+01 1.035e+03 0.015 0.988 Ec, F -8.996e-05 1.464e+03 0.000 1.000 Ei, F -8.996e-05 1.464e+03 0.000 1.000 Cr, F -8.996e-05 1.464e+03 0.000 1.000 Ef, F -8.996e-05 1.464e+03 0.000 1.000 M -6.253e-01 1.225e+03 -0.001 1.000 GS, M -6.405e-05 1.732e+03 0.000 1.000 Bm, M -6.405e-05 1.732e+03 0.000 1.000 Bo, M -1.918e+01 2.232e+03 -0.009 0.993 Ed, M -4.837e-01 1.225e+03 -0.000395 1.000 Eb, M 6.990e-01 1.225e+03 0.001 1.000 Ec, M -6.459e-05 1.732e+03 0.000 1.000 Ei, M -6.459e-05 1.732e+03 0.000 1.000 Cr, M -6.459e-05 1.732e+03 0.000 1.000 Ef, M -5.097e-04 1.733e+03 0.000 1.000

139 c) Predictions using the selected model. These probabilities represented the influence of the combined variable on time spent.

hat se lower upper prob Prob Prob lower upper G, 1, F -25.974645 2265.021245 -4465.416285 4413.466995 0.0000 0e+00 1.0000 GS, 1, F -25.974633 2265.007590 -4465.389510 4413.440244 0.0000 0e+00 1.0000 Bm, 1, F -25.974633 2265.007590 -4465.389510 4413.440244 0.0000 0e+00 1.0000 Bo, 1, F -7.325338 1.618936 -10.498453 -4.152224 0.0007 0e+00 0.0155 Ed, 1, F -6.170462 1.609303 -9.324696 -3.016228 0.0021 1e-04 0.0467 Eb, 1, F -8.440366 1.644869 -11.664310 -5.216422 0.0002 0e+00 0.0054 Ec, 1, F -25.974650 2265.026538 -4465.426664 4413.477365 0.0000 0e+00 1.0000 Ei, 1, F -25.974650 2265.026542 -4465.426671 4413.477372 0.0000 0e+00 1.0000 Cr, 1, F -25.974650 2265.026542 -4465.426672 4413.477372 0.0000 0e+00 1.0000 Ef, 1, F -25.974650 2265.026542 -4465.426672 4413.477373 0.0000 0e+00 1.0000 G, 2, F -27.534697 2265.021173 -4466.976196 4411.906802 0.0000 0e+00 1.0000 GS, 2, F -27.534685 2265.007518 -4466.949420 4411.880051 0.0000 0e+00 1.0000 Bm, 2, F -27.534685 2265.007518 -4466.949420 4411.880051 0.0000 0e+00 1.0000 Bo, 2, F -8.885390 1.514579 -11.853966 -5.916815 0.0001 0e+00 0.0027 Ed, 2, F -7.730514 1.503823 -10.678007 -4.783021 0.0004 0e+00 0.0083 Eb, 2, F -10.000418 1.542425 -13.023571 -6.977265 0.0000 0e+00 0.0009 Ec, 2, F -27.534702 2265.026466 -4466.986575 4411.917171 0.0000 0e+00 1.0000 Ei, 2, F -27.534702 2265.026469 -4466.986582 4411.917179 0.0000 0e+00 1.0000 Cr, 2, F -27.534702 2265.026470 -4466.986582 4411.917179 0.0000 0e+00 1.0000 Ef, 2, F -27.534702 2265.026470 -4466.986583 4411.917179 0.0000 0e+00 1.0000 G, 3, F -29.387726 2265.022594 -4468.832010 4410.056558 0.0000 0e+00 1.0000 GS, 3, F -29.387714 2265.008939 -4468.805234 4410.029807 0.0000 0e+00 1.0000 Bm, 3, F -29.387714 2265.008939 -4468.805234 4410.029807 0.0000 0e+00 1.0000 Bo, 3, F -10.738419 2.954863 -16.529950 -4.946888 0.0000 0e+00 0.0071 Ed, 3, F -9.583543 2.949309 -15.364189 -3.802897 0.0001 0e+00 0.0218 Eb, 3, F -11.853447 2.969238 -17.673153 -6.033741 0.0000 0e+00 0.0024 Ec, 3, F -29.387730 2265.027887 -4468.842389 4410.066928 0.0000 0e+00 1.0000 Ei, 3, F -29.387730 2265.027891 -4468.842396 4410.066935 0.0000 0e+00 1.0000 Cr, 3, F -29.387730 2265.027891 -4468.842396 4410.066936 0.0000 0e+00 1.0000 Ef, 3, F -29.387730 2265.027891 -4468.842397 4410.066936 0.0000 0e+00 1.0000 G, 1, M -26.217986 1184.672148 -2348.175397 2295.739425 0.0000 0e+00 1.0000 GS, 1, M -26.217986 1184.672148 -2348.175397 2295.739425 0.0000 0e+00 1.0000 Bm, 1, M -26.217986 1184.672148 -2348.175397 2295.739425 0.0000 0e+00 1.0000 Bo, 1, M -6.217986 1184.672148 -2348.175396 2295.739425 0.0000 0e+00 1.0000 Ed, 1, M -7.279400 1.249532 -9.728483 -4.830316 0.0007 1e-04 0.0079

140

Eb, 1, M -8.366642 1.258167 -10.832649 -5.900634 0.0002 0e+00 0.0027 Ec, 1, M -26.217986 1184.672148 -2348.175396 2295.739424 0.0000 0e+00 1.0000 Ei, 1, M -26.217986 1184.672148 -2348.175396 2295.739424 0.0000 0e+00 1.0000 Cr, 1, M -26.217986 1184.672148 -2348.175396 2295.739424 0.0000 0e+00 1.0000 Ef, 1, M -26.217986 1184.672148 -2348.175396 2295.739424 0.0000 0e+00 1.0000 G, 2, M -27.778038 1184.672476 -2349.736091 2294.180015 0.0000 0e+00 1.0000 GS, 2, M -27.778038 1184.672476 -2349.736091 2294.180015 0.0000 0e+00 1.0000 Bm, 2, M -27.778038 1184.672476 -2349.736091 2294.180015 0.0000 0e+00 1.0000 Bo, 2, M -27.778038 1184.672476 -2349.736091 2294.180015 0.0000 0e+00 1.0000 Ed, 2, M -8.839452 1.529018 -11.836326 -5.842578 0.0001 0e+00 0.0029 Eb, 2, M -9.926694 1.536165 -12.937576 -6.915811 0.0000 0e+00 0.0010 Ec, 2, M -27.778038 1184.672476 -2349.736091 2294.180015 0.0000 0e+00 1.0000 Ei, 2, M -27.778038 1184.672476 -2349.736090 2294.180015 0.0000 0e+00 1.0000 Cr, 2, M -27.778038 1184.672476 -2349.736091 2294.180015 0.0000 0e+00 1.0000 Ef, 2, M -27.778038 1184.672476 -2349.736091 2294.180015 0.0000 0e+00 1.0000 G, 3, M -29.631067 1184.674670 -2351.593420 2292.331287 0.0000 0e+00 1.0000 GS, 3, M -29.631067 1184.674670 -2351.593420 2292.331287 0.0000 0e+00 1.0000 Bm, 3, M -29.631067 1184.674670 -2351.593420 2292.331287 0.0000 0e+00 1.0000 Bo. 3, M -29.631067 1184.674670 -2351.593420 2292.331287 0.0000 0e+00 1.0000 Ed, 3, M -10.692481 2.744776 -16.072242 -5.312719 0.0000 0e+00 0.0049 Eb, 3, M -11.779722 2.749054 -17.167868 -6.391577 0.0000 0e+00 0.0017 Ec, 3, M -29.631067 1184.674670 -2351.593420 2292.331287 0.0000 0e+00 1.0000 Ei, 3, M -29.631067 1184.674670 -2351.593419 2292.331286 0.0000 0e+00 1.0000 Cr, 3, M -29.631067 1184.674670 -2351.593420 2292.331286 0.0000 0e+00 1.0000 Ef, 3, M -29.631067 1184.674670 -2351.593420 2292.331286 0.0000 0e+00 1.0000

141

logLik df AICc dAICc weight dBIC %DE S x H x sex -1344.830 61 2820.374 0.000 1 83.900 42.01 S x H -1405.408 31 2875.024 54.651 0 0.000 39.30 H -1545.036 11 3112.361 291.987 0 142.554 32.93 S + H -1543.183 13 3112.763 292.389 0 152.517 32.89 S + H + sex -1542.995 14 3114.449 294.076 0 158.977 32.88 H + S x sex -1541.211 16 3115.017 294.644 0 169.078 32.86 H + HR + SW -1544.602 13 3115.601 295.227 0 155.355 32.92 F x S x sex -2239.692 13 4505.782 1685.408 0 1545.536 -0.13 1 -2251.538 2 4507.090 1686.716 0 1494.042 0.00 S -2249.672 4 4507.388 1687.015 0 1503.980 -0.04 F -2251.385 3 4508.796 1688.422 0 1500.569 -0.01 sex -2251.436 3 4508.898 1688.524 0 1500.672 -0.01 S + sex -2249.483 5 4509.031 1688.657 0 1510.436 -0.05 S x sex -2247.700 7 4509.522 1689.148 0 1520.541 -0.07

142 b) Fixed effects - Habitat components (H); ground (G), grass and shrubs (GS), Banksia marginata (Bm), B. ornata (Bo) Callistemon rugulosus (Cr), Eucalyptus diversifolia (Ed), E. baxteri (Eb), E. cosmophylla (Ec), Eucalyptus fasciculosa (Ei), Eucalyptus incrassata (Ei).

Estimate Std. Error z value Pr(>|z|) G, 1, F -3.11062 0.30020 -10.362 < 2e-16 *** G, 2, F -0.02165 0.38442 -0.056 0.955082 G, 3, F 0.92506 0.35991 2.570 0.010162 * GS, 1, F 0.26087 0.32495 0.803 0.422085 Bm, 1, F -0.42419 0.38158 -1.112 0.266279 Bo, 1, F 1.58499 0.27593 5.744 9.24e-09 *** Ed, 1, F 1.17202 0.28542 4.106 4.02e-05 *** Eb, 1, F -0.42418 0.38158 -1.112 0.266292 Ec, 1, F -0.72110 0.41657 -1.731 0.083440 Ei, 1, F -17.63256 1550.65381 -0.011 0.990927 Cr, 1, F -17.63692 1554.03542 -0.011 0.990945 Ef, 1, F -17.63877 1555.47315 -0.011 0.990952 G, 1, M 0.12021 0.34734 0.346 0.729265 GS, 2, F -0.39014 0.43866 -0.889 0.373793 GS, 3, F -0.74335 0.39726 -1.871 0.061318 Bm, 2, F -2.88853 1.09480 -2.638 0.008330 ** Bm, 3, F -2.07764 0.60854 -3.414 0.000640 *** Bo, 2, F -0.34999 0.36344 -0.963 0.335564 Bo, 3, F -2.06747 0.35828 -5.771 7.90e-09 *** Ed, 2, F -1.39669 0.41530 -3.363 0.000771 *** Ed, 3, F -1.50829 0.36072 -4.181 2.90e-05 *** Eb, 2, F -0.22139 0.51053 -0.434 0.664547 Eb, 3, F -1.18541 0.50286 -2.357 0.018406 * Ec, 2, F 0.54516 0.51221 1.064 0.287176 Ec, 3, F 0.17476 0.47695 0.366 0.714062 Ei, 2, F 0.19840 1942.48756 0.000 0.999919 Ei, 3, F -0.64470 1953.83321 0.000 0.999737 Cr, 2, F 14.32411 1554.03576 0.009 0.992646

143

Cr, 3, F 16.87494 1554.03544 0.011 0.991336 Ef, 2, F 16.12851 1555.47321 0.010 0.991727 Ef, 3, F 14.62140 1555.47326 0.009 0.992500 G, 2, M 0.11608 0.45354 0.256 0.797993 G, 3, M -1.08068 0.48158 -2.244 0.024830 * GS, 1, M 0.72425 0.36768 1.970 0.048865 * Bm, 1, M 0.31720 0.43428 0.730 0.465140 Bo, 1, M -0.71434 0.32642 -2.188 0.028638 * Ed, 1, M -1.09316 0.34789 -3.142 0.001676 ** Eb, 1, M 0.24768 0.43595 0.568 0.569939 Ec, 1, M -0.43770 0. 50429 -0.868 0.385419 Ei, 1, M 0.08852 1778.64719 0.000 0.999960 Cr, 1, M 13.62670 1554.03575 0.009 0.993004 Ef, 1, M 13.62857 1555.47348 0.009 0.993009 GS, 2, M -0.18940 0.50527 -0.375 0.707772 GS, 3, M 0.51393 0.51862 0.991 0.321706 Bm, 2, M -0.38199 1.32967 -0.287 0.773898 Bm, 3, M -15.06547 1214.96576 -0.012 0.990107 Bo, 2, M 0.62777 0.43571 1.441 0.149643 Bo, 3, M 1.73798 0.49561 3.507 0.000454 *** Ed, 2, M 1.17318 0.50368 2.329 0.019849 * Ed, 3, M 1.25773 0.53410 2.355 0.018530 * Eb, 2, M -0.88371 0.62360 -1.417 0.156452 Eb, 3, M 1.12652 0.64546 1.745 0.080930 . Ec, 2, M -0.29675 0.63850 -0.465 0.642097 Ec, 3, M 0.44883 0.66977 0.670 0.502777 Ei, 2, M 14.37480 2128.92387 0.007 0.994613 Ei, 3, M 0.94847 2457.28007 0.000 0.999692 Cr, 2, M -27.77724 1753.97062 -0.016 0.987365 Cr, 3, M -13.69667 1554.03582 -0.009 0.992968 Ef, 2, M -16.19010 1555.47388 -0.010 0.991695 Ef, 3, M -11.56431 1555.47365 -0.007 0.994068

144 c) Predictions using the selected model. These probabilities represented the influence of the combined variable on time spent.

hat se lower upper prob Prob Prob lower upper G.1.F -3.110611 0.3002027 -3.699008 -2.522213 0.0427 0.0242 0.0743 GS.1.F -2.849740 0.2792379 -3.397047 -2.302434 0.0547 0.0324 0.0909 Bm.1.F -3.534779 0.3436119 -4.208258 -2.861300 0.0283 0.0147 0.0541 Bo.1.F -1.525624 0.2194321 -1.955711 -1.095537 0.1786 0.1239 0.2506 Ed.1.F -1.938595 0.2315807 -2.392494 -1.484697 0.1258 0.0837 0.1847 Eb.1.F -3.534779 0.3436119 -4.208258 -2.861300 0.0283 0.0147 0.0541 Ec.1.F -3.831699 0.3821192 -4.580653 -3.082745 0.0212 0.0101 0.0438 Ei.1.F -20.740832 1548.8416988 -3056.470562 3014.988898 0.0000 0.0000 1.0000 Cr.1.F -20.740832 1548.8416991 -3056.470562 3014.988898 0.0000 0.0000 1.0000 Ef.1.F -20.740832 1548.8416990 -3056.470562 3014.988898 0.0000 0.0000 1.0000 G.2.F -3.132260 0.2401196 -3.602895 -2.661626 0.0418 0.0265 0.0653 GS.2.F -3.261542 0.2504537 -3.752432 -2.770653 0.0369 0.0229 0.0589 Bm.2.F -6.445107 1.0144384 -8.433406 -4.456808 0.0016 0.0002 0.0115 Bo.2.F -1.897274 0.1777705 -2.245704 -1.548844 0.1304 0.0957 0.1753 Ed.2.F -3.356947 0.2586721 -3.863944 -2.849949 0.0337 0.0206 0.0547 Eb.2.F -3.777834 0.3016118 -4.368993 -3.186675 0.0224 0.0125 0.0397 Ec.2.F -3.308215 0.2544095 -3.806858 -2.809572 0.0353 0.0217 0.0568 Ei.2.F -20.564974 1169.0725819 -2311.947234 2270.817287 0.0000 0.0000 1.0000 Cr.2.F -6.445107 1.0144384 -8.433406 -4.456808 0.0016 0.0002 0.0115 Ef.2.F -4.642555 0.4326561 -5.490561 -3.794549 0.0095 0.0041 0.0220 G.3.F -2.185569 0.1985252 -2.574678 -1.796459 0.1011 0.0708 0.1423 GS.3.F -2.668047 0.2224483 -3.104045 -2.232048 0.0649 0.0429 0.0969 Bm.3.F -4.687364 0.4712191 -5.610954 -3.763774 0.0091 0.0036 0.0227 Bo.3.F -2.668030 0.2224473 -3.104027 -2.232034 0.0649 0.0429 0.0969 Ed.3.F -2.521842 0.2142417 -2.941756 -2.101928 0.0743 0.0501 0.1089 Eb.3.F -3.795152 0.3233841 -4.428985 -3.161319 0.0220 0.0118 0.0406 Ec.3.F -2.731913 0.2263224 -3.175505 -2.288321 0.0611 0.0401 0.0921 Ei.3.F -20.471973 1194.1173864 -2360.942050 2319.998105 0.0000 0.0000 1.0000 Cr.3.F -2.947562 0.2408088 -3.419547 -2.475577 0.0499 0.0317 0.0776 Ef.3.F -5.202939 0.5969196 -6.372901 -4.032977 0.0055 0.0017 0.0174 G.1.M -2.990407 0.1747024 -3.332824 -2.647991 0.0479 0.0345 0.0661 GS.1.M -2.005290 0.1382948 -2.276348 -1.734232 0.1186 0.0931 0.1500 Bm.1.M -3.097407 0.1804971 -3.451182 -2.743633 0.0432 0.0307 0.0604 Bo.1.M -2.119759 0.1412257 -2.396562 -1.842957 0.1072 0.0834 0.1367 Ed.1.M -2.911552 0.1707021 -3.246128 -2.576976 0.0516 0.0375 0.0706

145

Eb.1.M -3.166896 0.1844966 -3.528509 -2.805282 0.0404 0.0285 0.0570 Ec.1.M 0.2652992 -4.149200 -4.669187 -3.629214 0.0155 0.0093 0.0259 Ei.1.M -20.550540 878.2800205 -1741.979380 1700.878300 0.0000 0.0000 1.0000 Cr. 1.M -7.000630 1.0091132 -8.978492 -5.022768 0.0009 0.0001 0.0065 Ef.1.M -7.000630 1.0091132 -8.978492 -5.022768 0.0009 0.0001 0.0065 G.2.M -2.895975 0.1655358 -3.220425 -2.571524 0.0524 0.0384 0.0710 GS.2.M -2.490393 0.1472082 -2.778921 -2.201865 0.0765 0.0585 0.0996 Bm.2.M -6.273452 0.7175416 -7.679833 -4.867070 0.0019 0.0005 0.0076 Bo.2.M -1.747552 0.1253026 -1.993146 -1.501959 0.1484 0.1199 0.1821 Ed.2.M -3.040637 0.1734378 -3.380575 -2.700699 0.0456 0.0329 0.0629 Eb.2.M -4.177569 0.2687394 -4.704298 -3.650840 0.0151 0.0090 0.0253 Ec.2.M -3.806370 0.2301707 -4.257505 -3.355236 0.0217 0.0140 0.0337 Ei.2.M -5.866814 0.5884178 -7.020112 -4.713515 0.0028 0.0009 0.0089 Cr.2.M -20.415232 836.3130028 -1659.588718 1618.758253 0.0000 0.0000 1.0000 Ef.2.M -6.967761 1.0103204 -8.947989 -4.987533 0.0009 0.0001 0.0068 G.3.M -3.146039 0.2680781 -3.671472 -2.620606 0.0412 0.0248 0.0678 GS.3.M -2.390329 0.2157178 -2.813136 -1.967522 0.0839 0.0566 0.1227 Bm.3.M -20.376765 1203.2665121 -2378.779128 2338.025599 0.0000 0.0000 1.0000 Bo.3.M -2.604876 0.2278406 -3.051443 -2.158308 0.0688 0.0452 0.1036 Ed.3.M -3.317750 0.2842507 -3.874881 -2.760618 0.0350 0.0203 0.0595 Eb.3.M -3.381424 0.2907222 -3.951240 -2.811609 0.0329 0.0189 0.0567 Ec.3.M -3.681251 0.3249596 -4.318172 -3.044330 0.0246 0.0131 0.0455 Ei.3.M -20.376765 1203.2665126 -2378.779129 2338.025600 0.0000 0.0000 1.0000 Cr.3.M -3.977972 0.3656418 -4.694630 -3.261314 0.0184 0.0091 0.0369 Ef.3.M -4.098767 0.3843702 -4.852133 -3.345401 0.0163 0.0078 0.0340

146

logLik df AICc dAICc weight dBIC %DE S x H -285.979 31 636.168 0.000 0.8557 80.899 34.26 S + H -307.046 13 640.490 4.322 0.0986 0.000 28.20 S + H + sex -306.920 14 642.299 6.131 0.0399 6.583 28.13 H + S x sex -306.820 16 646.235 10.067 0.0056 20.052 28.12 H -315.612 11 653.512 17.345 0.0001 3.462 29.15 H + HR + SW -315.220 13 656.837 20.669 0.0000 16.347 29.27 S x H x sex -267.812 61 666.339 30.171 0.0000 249.621 39.36 S -408.305 4 824.653 188.485 0.0000 141.001 -0.96 S + sex -408.178 5 826.421 190.253 0.0000 147.583 -1.02 S x sex -408.078 7 830.278 194.110 0.0000 161.053 -1.04 1 -416.878 2 837.768 201.600 0.0000 144.476 0.00 sex -416.581 3 839.189 203.021 0.0000 150.719 -0.08

147 b) Fixed effects - Habitat components (H); ground (G), grass and shrubs (GS), Banksia marginata (Bm), B. ornata (Bo) Callistemon rugulosus (Cr), Eucalyptus diversifolia (Ed), E. baxteri (Eb), E. cosmophylla (Ec), Eucalyptus fasciculosa (Ei), Eucalyptus incrassata (Ei).

Estimate Std. Error z value Pr(>|z|) G, S1 -2.386e+01 3.439e+03 -0.007 0.994 G, S2 1.714e-01 4.540e+03 0.000038 1.000 G, S3 2.048e-01 5.329e+03 0.000038 1.000 GS, S1 1.800e+01 3.439e+03 0.005 0.996 Bm, S1 1.689e+01 3.439e+03 0.005 0.996 Bo, S1 1.886e+01 3.439e+03 0.005 0.996 Ed, S1 1.759e+01 3.439e+03 0.005 0.996 Eb, S1 1.800e+01 3.439e+03 0.005 0.996 Ec, S1 1.781e+01 3.439e+03 0.005 0.996 Ei, S1 -2.578e-06 4.863e+03 0.000 1.000 Cr, S1 -2.578e-06 4.863e+03 0.000 1.000 Ef, S1 -2.661e-02 4.896e+03 -0.000005 1.000 GS, S2 4.435e-01 4.540e+03 0.000098 1.000 Bm, S2 3.954e-01 5.329e+03 0.000074 1.000 Bo, S2 1.005e+00 4.540e+03 0.000221 1.000 Ed, S2 -1.695e+01 6.784e+03 -0.002 0.998 Eb, S2 8.178e-01 4.540e+03 0.000180 1.000 Ec, S2 -1.891e+01 6.766e+03 -0.003 0.998 Ei, S2 1.423e+00 4.540e+03 0.000313 1.000 Cr, S2 -4.575e-01 5.329e+03 -0.000086 1.000 Ef, S2 9.636e-01 4.540e+03 0.000212 1.000 GS, S3 -1.562e+00 5.329e+03 -0.000293 1.000 Bm, S3 -1.787e+01 5.471e+03 -0.003 0.997 Bo, S3 -2.752e-01 5.329e+03 -0.000052 1.000 Ed, S3 -1.056e-06 6.421e+03 0.000 1.000 Eb, S3 5.417e-07 7.536e+03 0.000 1.000 Ec, S3 -2.296e-06 6.421e+03 0.000 1.000 Ei, S3 5.419e-07 7.536e+03 0.000 1.000

148

Cr, S3 1.597e+01 5.723e+03 0.003 0.998 Ef, S3 3.063e-07 7.587e+03 0.000 1.000

c) Predictions using the selected model. These probabilities represented the influence of the combined variable on time spent.

hat se lower upper prob Prob Prob lower upper G, S1 -23.864149 3446.6245829 -6779.248332 6731.520034 0.0000 0.0000 1.0000 GS, S1 -5.861631 0.4772866 -6.797112 -4.926149 0.0028 0.0011 0.0072 Bm, S1 -6.967816 0.7701366 -8.477284 -5.458348 0.0009 0.0002 0.0042 Bo, S1 -4.999418 0.3507736 -5.686934 -4.311902 0.0067 0.0034 0.0132 Ed, S1 -6.270881 0.5649701 -7.378222 -5.163539 0.0019 0.0006 0.0057 Eb, S1 -5.861631 0.4772866 -6.797112 -4.926149 0.0028 0.0011 0.0072 Ec, S1 -6.045858 0.5141724 -7.053636 -5.038081 0.0024 0.0009 0.0064 Ei, S1 -23.864149 3446.6242409 -6779.247661 6731.519363 0.0000 0.0000 1.0000 Cr, S1 -23.864149 3446.6242409 -6779.247661 6731.519363 0.0000 0.0000 1.0000 Ef, S1 -23.864150 3446.6265351 -6779.252159 6731.523858 0.0000 0.0000 1.0000 G, S2 -23.731589 3029.7512758 -5962.044090 5914.580911 0.0000 0.0000 1.0000 GS, S2 -5.246695 0.3402727 -5.913630 -4.579761 0.0052 0.0027 0.0102 Bm, S2 -5.791469 0.4221208 -6.618826 -4.964112 0.0030 0.0013 0.0069 Bo, S2 -4.010182 0.2332311 -4.467315 -3.553049 0.0178 0.0113 0.0278 Ed, S2 -4.676631 0.2797878 -5.225016 -4.128247 0.0092 0.0054 0.0159 Eb, S2 -4.726585 0.2842282 -5.283672 -4.169498 0.0088 0.0050 0.0152 Ec, S2 -23.731592 3029.7559263 -5962.053208 5914.590023 0.0000 0.0000 1.0000 Ei, S2 -23.731592 3029.7559843 -5962.053321 5914.590137 0.0000 0.0000 1.0000 Cr, S2 -23.731592 3029.7559266 -5962.053208 5914.590024 0.0000 0.0000 1.0000 Ef, S2 -7.744330 1.0373406 -9.777518 -5.711142 0.0004 0.0001 0.0033 G, S3 -23.691896 4147.4554256 -8152.704530 8105.320738 0.0000 0.0000 1.0000 GS, S3 -5.261387 0.4715557 -6.185637 -4.337138 0.0052 0.0021 0.0129 Bm, S3 -23.691894 4147.4508091 -8152.695480 8105.311692 0.0000 0.0000 1.0000 Bo, S3 -23.691894 4147.4508088 -8152.695479 8105.311691 0.0000 0.0000 1.0000 Ed, S3 -6.523548 0.8086905 -8.108582 -4.938515 0.0015 0.0003 0.0071 Eb, S3 -7.218580 1.1216379 -9.416990 -5.020169 0.0007 0.0001 0.0066 Ec, S3 -6.116221 0.6727596 -7.434829 -4.797612 0.0022 0.0006 0.0082 Ei, S3 -23.691894 4147.4508088 -8152.695479 8105.311691 0.0000 0.0000 1.0000 Cr, S3 -23.691894 4147.4508087 -8152.695479 8105.311691 0.0000 0.0000 1.0000 Ef, S3 -23.691894 4147.4508086 -8152.695479 8105.311691 0.0000 0.0000 1.0000

149

logLik df AICc dAICc weight dBIC %DE S x H x sex -958.115 61 2046.945 0.000 1 89.099 40.69 S x H -1016.093 31 2096.396 49.451 0 0.000 36.50 H -1135.849 11 2293.986 247.041 0 102.808 27.88 H + HR + SW -1134.296 13 2294.989 248.044 0 113.371 27.87 S + H -1134.846 13 2296.090 249.145 0 114.472 27.84 S + H + sex -1134.776 14 2298.011 251.066 0 121.167 27.84 H + S x sex -1134.276 16 2301.148 254.203 0 133.838 27.84 1 -1518.410 2 3040.832 993.887 0 806.412 0.00 sex -1518.236 3 3042.497 995.552 0 812.899 0.00 S -1517.391 4 3042.826 995.881 0 818.046 -0.03 S + sex -1517.322 5 3044.709 997.764 0 824.742 -0.03 S x sex -1516.822 7 3047.766 1000.821 0 837.414 -0.04

150 b) Fixed effects - Habitat components (H); ground (G), grass and shrubs (GS), Banksia marginata (Bm), B. ornata (Bo) Callistemon rugulosus (Cr), Eucalyptus diversifolia (Ed), E. baxteri (Eb), E. cosmophylla (Ec), Eucalyptus fasciculosa (Ei), Eucalyptus incrassata (Ei).

Estimate Std. Error z value Pr(>|z|)

G, 1, F -5.49167 0.85546 -6.420 1.37e-10 *** G, 2, F -0.91487 1.19883 -0.763 0.44538 G, 3, F -1.12206 1.39840 -0.802 0.42233 GS, 1, F 0.29993 0.78665 0.381 0.70300 Bm, 1, F -17.79543 4199.67128 -0.004 0.99662 Bo, 1, F 0.53516 0.75333 0.710 0.47746 Ed, 1, F -17.78253 4172.66934 -0.004 0.99660

Eb, 1, F 1.89487 0.65134 2.909 0.00362 **

Ec, 1, F -1.12339 1.17846 -0.953 0.34045 Ei, 1, F -17.79810 4205.28392 -0.004 0.99662 Cr, 1, F -17.79408 4196.84467 -0.004 0.99662 Ef, 1, F -17.80078 4210.92841 -0.004 0.99663 G, 1, M -0.93122 1.09206 -0.853 0.39381 GS, 2, F 1.23304 1.11594 1.105 0.26919 GS, 3, F 2.40052 1.30899 1.834 0.06667 Bm, 2, F 17.79542 4199.67140 0.004 0.99662 Bm, 3, F 1.62638 5316.04069 0.000 0.99976 Bo, 2, F 3.36617 1.04716 3.215 0.00131 ** Bo, 3, F 1.80993 1.30041 1.392 0.16398 Ed, 2, F 19.98472 4172.66941 0.005 0.99618 Ed, 3, F 20.40413 4172.66947 0.005 0.99610 Eb, 2, F 1.34565 0.98094 1.372 0.17013 Eb, 3, F 0.07944 1.26145 0.063 0.94979 Ec, 2, F -15.70806 3202.05049 -0.005 0.99609 Ec, 3, F 4.87211 1.56193 3.119 0.00181 ** Ei, 2, F 0.97706 5275.54318 0.000 0.99985

151

Ei, 3, F 1.62638 5323.14522 0.000 0.99976 Cr, 2, F 0.97706 5264.95618 0.000 0.99985 Cr, 3, F 19.19451 4196.84482 0.005 0.99635 Ef, 2, F 0.97706 5282.62410 0.000 0.99985 Ef, 3, F 1.61154 5345.24236 0.000 0.99976 G, 2, M -15.69530 2255.50616 -0.007 0.99445 G, 3, M -15.42329 3202.12102 -0.005 0.99616 GS, 1, M 2.33745 0.99629 2.346 0.01897 * Bm, 1, M 18.49965 4199.67134 0.004 0.99649 Bo, 1, M 2.40256 0.96703 2.484 0.01297 * Ed, 1, M 18.30081 4172.66940 0.004 0.99650 Eb, 1, M -0.89555 0.94741 -0.945 0.34453 Ec, 1, M 2.54290 1.34896 1.885 0.05942 Ei, 1, M 1.16151 4829.73794 0.000 0.99981 Cr, 1, M 1.16151 4820.04561 0.000 0.99981 Ef, 1, M 16.69476 4210.92857 0.004 0.99684 GS, 2, M 15.41979 2255.50608 0.007 0.99455 GS, 3, M 14.16386 3202.12094 0.004 0.99647 Bm, 2, M -18.49423 5271.09119 -0.004 0.99720 Bm, 3, M -2.32254 6977.47500 0.000 0.99973 Bo, 2, M 13.13108 2255.50604 0.006 0.99535 Bo, 3, M 13.94567 3202.12094 0.004 0.99653 Ed, 2, M -1.56834 4743.25588 0.000 0.99974 Ed, 3, M -2.42521 5259.72884 0.000 0.99963 Eb, 2, M 16.99541 2255.50604 0.008 0.99399 Eb, 3, M 17.04122 3202.12095 0.005 0.99575 Ec, 2, M 31.11183 3916.68662 0.008 0.99366 Ec, 3, M 13.22739 3202.12106 0.004 0.99670 Ei, 2, M 15.65947 6606.68261 0.002 0.99811 Ei, 3, M 15.01016 7381.43423 0.002 0.99838

152

Cr, 2, M 15.65947 6594.97109 0.002 0.99811 Cr, 3, M -2.55395 6607.45923 0.000 0.99969 Ef, 2, M 0.12353 6173.20269 0.000 0.99998 Ef, 3, M 17.61443 6230.98658 0.003 0.99774

c) Predictions using the selected model. These probabilities represented the influence of the combined variable on time spent.

hat se lower upper prob Prob Prob lower upper G.1.F -5.491448 0.8554391 -7.168108 -3.814787 0.0041 0.0008 0.0216 GS.1.F -5.191619 0.8033521 -6.766189 -3.617049 0.0055 0.0012 0.0262 Bm.1.F -23.289523 4204.9904410 -8265.070787 8218.491741 0.0000 0.0000 1.0000 Bo.1.F -4.956367 0.7701860 -6.465932 -3.446803 0.0070 0.0016 0.0309 Ed.1.F -23.289523 4204.9905391 -8265.070980 8218.491934 0.0000 0.0000 1.0000 Eb.1.F -3.596711 0.6632700 -4.896721 -2.296702 0.0267 0.0074 0.0914 Ec.1.F -6.614936 1.1907661 -8.948837 -4.281034 0.0013 0.0001 0.0136 Ei.1.F -23.289523 4204.9906241 -8265.071146 8218.492100 0.0000 0.0000 1.0000 Cr.1.F -23.289523 4204.9904834 -8265.070871 8218.491825 0.0000 0.0000 1.0000 Ef.1.F -23.289523 4204.9905773 -8265.071054 8218.492008 0.0000 0.0000 1.0000 G.2.F -6.406578 0.8398584 -8.052701 -4.760456 0.0016 0.0003 0.0085 GS.2.F -4.873637 0.5577261 -5.966781 -3.780494 0.0076 0.0026 0.0223 Bm.2.F -6.406578 0.8398584 -8.052701 -4.760456 0.0016 0.0003 0.0085 Bo.2.F -2.505283 0.4578038 -3.402578 -1.607988 0.0755 0.0322 0.1669 Ed.2.F -4.204389 0.5076498 -5.199383 -3.209395 0.0147 0.0055 0.0388 Eb.2.F -3.166094 0.4695741 -4.086459 -2.245729 0.0405 0.0165 0.0957 Ec.2.F -23.113662 3008.9580688 -5920.671477 5874.444153 0.0000 0.0000 1.0000 Ei.2.F -23.113662 3008.9580678 -5920.671475 5874.444151 0.0000 0.0000 1.0000 Cr.2.F -23.113662 3008.9580688 -5920.671477 5874.444153 0.0000 0.0000 1.0000 Ef.2.F -23.113662 3008.9582258 -5920.671785 5874.444460 0.0000 0.0000 1.0000 G.3.F -6.613584 1.1062070 -8.781750 -4.445418 0.0013 0.0002 0.0116 GS.3.F -3.913153 0.5301771 -4.952300 -2.874006 0.0196 0.0070 0.0535 Bm.3.F -23.020660 3671.1908028 -7218.554633 7172.513313 0.0000 0.0000 1.0000 Bo.3.F -4.268500 0.5570358 -5.360290 -3.176710 0.0138 0.0047 0.0401 Ed.3.F -3.991979 0.5354578 -5.041476 -2.942482 0.0181 0.0064 0.0501 Eb.3.F -4.639349 0.5949234 -5.805399 -3.473299 0.0096 0.0030 0.0301 Ec.3.F -2.864862 0.4855508 -3.816541 -1.913182 0.0539 0.0215 0.1286 Ei.3.F -23.020660 3671.1908024 -7218.554633 7172.513313 0.0000 0.0000 1.0000

153

Cr.3.F -5.213133 0.6802870 -6.546495 -3.879770 0.0054 0.0014 0.0202 Ef.3.F -23.020660 3671.1908010 -7218.554630 7172.513310 0.0000 0.0000 1.0000 G.1.M -6.422847 0.6788044 -7.753304 -5.092391 0.0016 0.0004 0.0061 GS.1.M -3.785482 0.3830467 -4.536253 -3.034710 0.0222 0.0106 0.0459 Bm.1.M -5.718608 0.5392232 -6.775486 -4.661731 0.0033 0.0011 0.0094 Bo.1.M -3.485157 0.3739603 -4.218119 -2.752195 0.0297 0.0145 0.0600 Ed.1.M -5.904632 0.5699027 -7.021641 -4.787623 0.0027 0.0009 0.0083 Eb.1.M -5.423543 0.4981826 -6.399981 -4.447106 0.0044 0.0017 0.0116 Ec.1.M -5.003309 0.4533136 -5.891804 -4.114815 0.0067 0.0028 0.0161 Ei.1.M -23.099222 2422.9763781 -4772.132923 4725.934479 0.0000 0.0000 1.0000 Cr. 1.M -23.099222 2422.9763792 -4772.132925 4725.934481 0.0000 0.0000 1.0000 Ef.1.M -7.528861 1.0678218 -9.621792 -5.435930 0.0005 0.0001 0.0043 G.2.M -22.963914 2178.8455123 -4293.501118 4247.573290 0.0000 0.0000 1.0000 GS.2.M -3.742869 0.3474026 -4.423778 -3.061960 0.0231 0.0118 0.0447 Bm.2.M -22.963914 2178.8455109 -4293.501115 4247.573287 0.0000 0.0000 1.0000 Bo.2.M -3.598117 0.3430240 -4.270444 -2.925790 0.0266 0.0138 0.0509 Ed.2.M -4.098374 0.3606988 -4.805344 -3.391404 0.0163 0.0081 0.0326 Eb.2.M -3.692701 0.3458250 -4.370519 -3.014884 0.0243 0.0125 0.0468 Ec.2.M -6.209819 0.5931090 -7.372313 -5.047325 0.0020 0.0006 0.0064 Ei.2.M -22.963914 2178.8455270 -4293.501147 4247.573319 0.0000 0.0000 1.0000 Cr.2.M -22.963914 2178.8455275 -4293.501148 4247.573320 0.0000 0.0000 1.0000 Ef.2.M -22.963914 2178.8455124 -4293.501118 4247.573290 0.0000 0.0000 1.0000 G.3.M -22.925453 3134.2012901 -6165.959982 6120.109075 0.0000 0.0000 1.0000 GS.3.M -3.766528 0.5256214 -4.796746 -2.736310 0.0226 0.0082 0.0609 Bm.3.M -22.925453 3134.2012907 -6165.959983 6120.109076 0.0000 0.0000 1.0000 Bo.3.M -4.274993 0.5552050 -5.363194 -3.186791 0.0137 0.0047 0.0397 Ed.3.M -4.471101 0.5704414 -5.589166 -3.353036 0.0113 0.0037 0.0338 Eb.3.M -4.848270 0.6076738 -6.039311 -3.657230 0.0078 0.0024 0.0252 Ec.3.M -3.449288 0.5125850 -4.453955 -2.444622 0.0308 0.0115 0.0798 Ei.3.M -22.925453 3134.2013138 -6165.960028 6120.109122 0.0000 0.0000 1.0000 Cr.3.M -22.925453 3134.2013136 -6165.960028 6120.109121 0.0000 0.0000 1.0000 Ef.3.M -4.848778 0.6077319 -6.039932 -3.657623 0.0078 0.0024 0.0251

154

Estimate Std. Error z value Pr(>|z|) spp.in.pairBmEb.Bm 0.1238 0.3237 0.382 0.70237 spp.in.pairBmEc.Bm -0.3328 0.3062 -1.087 0.27781 spp.in.pairBoEc.Bo -0.4686 0.2478 -1.891 0.05954 . spp.in.pairBoEd.Bo 0.1536 0.3567 0.431 0.66702 spp.in.pairCrEd.Cr -0.7024 0.4220 -1.664 0.09704 . spp.in.pairBmEb.Eb -0.9306 0.2059 -4.519 8.79e-06 *** spp.in.pairBmEc.Ec -0.9340 0.1887 -4.949 1.22e-06 *** spp.in.pairBoEc.Ec -0.7162 0.2194 -3.264 0.00122 ** spp.in.pairBoEd.Ed -0.7769 0.1851 -4.198 3.51e-05 *** spp.in.pairCrEd.Ed -0.9200 0.1816 -5.066 6.94e-07 ***

155

Table 6.15: The influence of floral characteristics on the feeding behaviours of C. concinnus was determined using Poisson regression with a logit link function. The strongest models were selected using AIC. (Table 4.7) a) Times visited first

Model Selected LogLik df AICc dAICc Weight dBIC %DE

Nectar volume -27.080 2 64.160 0.000 0.5663 0.000 62.37

Energy equivalents -28.594 2 67.188 3.028 0.1246 3.028 49.06

Pollen -28.720 2 67.439 3.280 0.1099 3.280 47.76

Relative pollen volume -29.277 2 68.554 4.395 0.0629 4.395 41.60

Null -31.966 1 69.647 5.487 0.0364 7.470 0.00

Nectar volume & concentration -26.829 3 69.657 5.497 0.0362 1.800 64.21

Pollen & nectar volume -26.903 3 69.807 5.647 0.0336 1.950 63.67

Relative nectar volume -30.976 2 71.952 7.792 0.0115 7.792 17.96

Nectar concentration -31.330 2 72.660 8.501 0.0081 8.501 11.94 Relative pollen & nectar volume -28.719 3 73.437 9.278 0.0055 5.580 47.77

Relative energy equivalents -31.816 2 73.632 9.473 0.0050 9.473 2.96

156 b) Number of feeding bouts

Model Selected LogLik df AICc dAICc Weight dBIC %DE

Nectar volume -40.503 2 91.006 0.000 0.7463 0.000 67.89

Energy equivalents -42.912 2 95.823 4.818 0.0671 4.818 48.02

Nectar volume & concentration -39.987 3 95.974 4.968 0.0622 1.271 71.04

Pollen & nectar volume -40.485 3 96.970 5.965 0.0378 2.267 68.01

Pollen volume -43.692 2 97.385 6.379 0.0307 6.379 39.24

Null -46.184 1 98.081 7.076 0.0217 9.059 0.00

Relative nectar volume -44.381 2 98.763 7.757 0.0154 7.757 30.26

Relative pollen volume -44.867 2 99.735 8.729 0.0095 8.729 23.14

Nectar concentration -45.602 2 101.203 10.198 0.0046 10.198 10.99

Relative energy equivalents -46.107 2 102.215 11.209 0.0027 11.209 1.51

Relative pollen & nectar volume -43.494 3 102.988 11.982 0.0019 8.285 41.60

c) Feeding bout length

Model Selected LogLik df AICc dAICc Weight dBIC %DE

Pollen volume -41.462 2 92.923 1.174 0.2331 1.926 54.29

Relative pollen & nectar volume -39.347 3 94.695 2.945 0.0962 0.000 70.05

Number feeding bouts -42.896 2 95.792 4.043 0.0555 4.795 39.10

Energy equivalents -42.931 2 95.862 4.112 0.0536 4.865 38.67

Null -45.376 1 96.465 4.716 0.0397 7.451 0.00

Pollen volume & energy -40.551 3 97.101 5.352 0.0289 2.407 61.90 equivalents Relative nectar volume -43.687 2 97.374 5.624 0.0252 6.377 28.66

Pollen & nectar volume -40.857 3 97.714 5.965 0.0212 3.020 59.49

Nectar volume -44.313 2 98.625 6.876 0.0135 7.628 19.15

Relative energy equivalents -45.034 2 100.068 8.318 0.0065 9.071 6.60

Nectar concentration -45.333 2 100.665 8.916 0.0049 9.668 0.86

Nectar volume & concentration -43.059 3 102.119 10.369 0.0023 7.424 37.08

157 d) Time spent feeding

Model Selected LogLik df AICc dAICc Weight dBIC %DE

Pollen & nectar volume -37.568 3 91.137 4.350 0.0834 0.653 69.59

Nectar volume & concentration -38.132 3 92.263 5.477 0.0475 1.779 65.96

Energy equivalents -41.276 2 92.552 5.766 0.0411 5.766 36.16

Null -43.520 1 92.754 5.968 0.0371 7.951 0.00

Relative nectar volume -42.027 2 94.053 7.267 0.0194 7.267 25.82

Pollen volume -42.182 2 94.364 7.577 0.0166 7.577 23.48

Nectar concentration -42.857 2 95.713 8.927 0.0085 8.927 12.43

Relative pollen volume -43.104 2 96.207 9.421 0.0066 9.421 7.99

Relative energy equivalents -43.393 2 96.785 9.999 0.0049 9.999 2.52

Relative pollen & nectar volume -41.888 3 99.777 12.990 0.0011 9.293 27.84

158

Table 6.16: The influence of floral characteristics on the feeding behaviours of male C. concinnus was determined using poisson regression with a logit link function. The strongest models were selected using AIC. (Table 4.7) a) Times visited first

Model Selected LogLik df AICc dAICc Weight dBIC %DE

Nectar volume -22.212 2 54.423 0.000 0.6525 0.728 73.94

Nectar volume & concentration -20.696 3 57.393 2.970 0.1478 0.000 80.75

Energy equivalents -24.163 2 58.326 3.902 0.0927 4.630 61.50

Pollen -24.878 2 59.756 5.333 0.0453 6.061 55.58

Pollen & nectar volume -21.960 3 59.920 5.496 0.0418 2.527 75.22

Null -28.935 1 63.585 9.161 0.0067 11.872 0.00

Relative pollen volume -27.047 2 64.094 9.670 0.0052 10.398 31.46

Relative pollen & energy -24.161 3 64.322 9.899 0.0046 6.929 61.51 equivalents Nectar concentration -28.515 2 67.030 12.606 0.0012 13.334 8.06

Relative nectar volume -28.722 2 67.444 13.021 0.0010 13.749 4.17 Relative energy equivalents -28.741 2 67.482 13.059 0.0010 13.787 3.81

Relative pollen & nectar volume -27.032 3 70.065 15.641 0.0003 12.672 31.65

159 b) Number of feeding bouts

Model Selected LogLik df AICc dAICc Weight dBIC %DE

Nectar volume & concentration -33.587 3 83.174 0.000 0.7337 0.000 92.44

Nectar volume -37.713 2 85.426 2.252 0.2380 5.949 82.75

Pollen & nectar volume -37.590 3 91.180 8.006 0.0134 8.006 83.17

Energy equivalents -40.721 2 91.442 8.267 0.0118 11.965 68.51

Pollen volume -42.285 2 94.570 11.396 0.0025 15.093 56.95

Null -46.499 1 98.712 15.538 0.0003 21.219 0.00

Relative pollen volume -44.999 2 99.998 16.823 0.0002 20.521 25.92

Relative nectar volume -45.239 2 100.478 17.304 0.0001 21.001 22.28

Nectar concentration -46.116 2 102.231 19.057 0.0001 22.754 7.38

Relative energy equivalents -46.450 2 102.900 19.726 0.0000 23.423 0.98

Relative pollen & nectar volume -44.153 3 104.305 21.131 0.0000 21.131 37.45

160 c) Length of feeding bout

Model Selected LogLik df AICc dAICc Weight dBIC %DE

Relative pollen volume -38.396 2 86.791 0.000 0.2901 0.371 49.37

Pollen volume -38.662 2 87.324 0.532 0.2223 0.903 46.60

Number feeding bouts -39.365 2 88.729 1.938 0.1101 2.309 38.54

Null -41.798 1 89.311 2.520 0.0823 4.874 0.00

Energy equivalents -39.876 2 89.752 2.960 0.0660 3.331 31.92

Relative pollen & nectar volume -37.059 3 90.118 3.326 0.0550 0.000 61.24

Relative nectar volume -40.130 2 90.261 3.469 0.0512 3.840 28.37

Pollen volume & energy -37.784 3 91.567 4.776 0.0266 1.450 55.20 equivalents Nectar volume -40.902 2 91.804 5.012 0.0237 5.383 16.42

Relative energy equivalents -40.960 2 91.919 5.128 0.0223 5.499 15.44

Nectar volume & concentration -38.081 3 92.161 5.370 0.0198 2.043 52.46

Pollen & nectar volume -38.223 3 92.446 5.655 0.0172 2.328 51.08

Nectar concentration -41.460 2 92.920 6.129 0.0135 6.500 6.54

161 d) Time spent feeding

Model Selected LogLik df AICc dAICc Weight dBIC %DE

Nectar volume & concentration -29.689 3 75.379 0.000 0.8678 0.000 94.86

Nectar volume -34.643 2 79.287 3.908 0.1230 7.606 86.15

Pollen & nectar volume -34.630 3 85.259 9.880 0.0062 9.880 86.19

Energy equivalents -38.514 2 87.028 11.650 0.0026 15.347 69.96

Pollen volume -40.541 2 91.081 15.703 0.0003 19.400 54.94

Null -44.527 1 94.768 19.390 0.0001 25.070 0.00

Relative pollen volume -43.389 2 96.779 21.400 0.0000 25.097 20.35

Relative nectar volume -43.432 2 96.865 21.486 0.0000 25.183 19.66

Nectar concentration -44.070 2 98.140 22.761 0.0000 26.458 8.74

Relative energy equivalents -44.442 2 98.884 23.505 0.0000 27.203 1.69

Relative pollen & nectar volume -42.667 3 101.335 25.956 0.0000 25.956 31.06

162

Table 6.17: The influence of floral characteristics on the length of feeding bouts for female C. concinnus was determined using Poisson regression with a logit link function. The strongest model was selected using AIC. (Table 4.7)

Model Selected LogLik df AICc dAICc Weight dBIC %DE

Relative pollen volume -42.491 2 94.982 0.000 0.5637 0.000 64.65

Pollen volume -43.463 2 96.925 1.943 0.2134 1.943 57.07

Relative pollen & nectar volume -41.990 3 99.980 4.998 0.0463 1.301 68.02

Pollen & nectar volume -42.034 3 100.068 5.086 0.0443 1.389 67.74

Pollen volume & energy -42.069 3 100.138 5.156 0.0428 1.458 67.51 equivalents Energy equivalents -45.207 2 100.414 5.432 0.0373 5.432 39.15

Null -47.691 1 101.096 6.113 0.0265 8.096 0.00

Relative nectar volume -46.782 2 103.564 8.582 0.0077 8.582 16.62

Nectar volume -46.860 2 103.719 8.737 0.0071 8.737 15.31

Relative energy equivalents -47.551 2 105.102 10.119 0.0036 10.119 2.76

Number feeding bouts -47.584 2 105.168 10.186 0.0035 10.186 2.11

Nectar concentration -47.686 2 105.372 10.390 0.0031 10.390 0.09

Nectar volume & concentration -46.239 3 108.478 13.496 0.0007 9.798 25.20

163

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What I hope to do now that this is finished!

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