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The Impact of Phytophthora Cinnamomi on Distribution of The

The Impact of Phytophthora Cinnamomi on Distribution of The

The impact of cinnamomi on the yellow-footed

antechinus (mardo) (Antechinus flavipes leucogaster)

(Marsupialia: Dasyuridae)

This thesis is presented for the degree of Doctor of Philosophy of

Murdoch University.

2008

Submitted by

Rodney Armistead

BSc Honours (Deakin University)

i

I declare that this thesis is my own account of my research and contains work which has not previously been submitted for a degree at any tertiary education institution.

………………………………

Rodney Armistead

ii

ABSTRACT This is the first study to investigate and provide definitive evidence that the plant pathogen

Phytophthora cinnamomi is a significant threat to the mammal fauna of Western Australia.

This study investigated the impact of P. cinnamomi-induced habitat disturbance and degradation on Antechinus flavipes leucogaster (yellow-footed antechinus) or mardo.

Phytophthora cinnamomi is an introduced and invasive soil-borne plant pathogen that kills many common and structurally important plant species, which results in significant changes to the structural characteristics of affected areas. An evaluation of P. cinnamomi affected and unaffected areas of the northern jarrah () forest revealed significant declines in the structure, composition and complexity of all areas affected by P. cinnamomi. Dieback Expression Score values ranged from a mean value of 1.88 ± 1.01 to

3.8 ± 0.41 at the P. cinnamomi affected sites, indicating a high degree of disturbance. A non-metric multidimensional scaling (MDS) analysis using 16 habitat variables identified significant (ANISOM: R=0.343, P<0.003) separation among affected and unaffected sites.

A SIMPER analysis revealed that ground and shrub cover vegetation, small and total log densities, percentage leaf litter cover, and the densities of small, medium, tall single crowned and total preissii were the greatest contributors separating affected and unaffected areas.

iii Presently, our understanding of how P. cinnamomi affects the fauna of Western Australia is limited. This providing a unique opportunity to examine how P. cinnamomi-induced disturbance impacts upon the mardo. The mardo is a small insectivorous marsupial that is regarded as being common and a habitat generalist that occupies a broad range of forest and woodland habitats throughout the south-west of Western Australia. Until the present study, the specific habitat requirements, and therefore the factors limiting the present distribution of the mardo have received little attention. Therefore, in addition to being the first study to evaluate the impact of P. cinnamomi on Western Australian fauna, this study also provides important information about the present distribution of the mardo.

Detection-nondetection mark-release surveys conducted in P. cinnamomi affected and unaffected regions of the northern jarrah forest, revealed that although, mardos were recorded at most sites, the number of mardo individuals, captures and detections were considerably lower at P. cinnamomi affected areas. Patch Occupancy analysis, using an information theoretic approach, revealed that the probability of a mardo occupying a region of the northern jarrah forest affected by P. cinnamomi ranged from a likelihood of 0.0 to

25.0%, while in contrast there was a 41.0 to 51.0% likelihood of a mardo occurring among unaffected regions. This discovery supports the hypothesis that P. cinnamomi-induced habitat disturbance impacts upon the distribution of the mardo.

An evaluation of the micro-habitat features important to the mardo using Patch Occupancy modelling using an information theoretic approach identified large logs and X. preissii densities as positive contributors to the present distribution of the mardo in the northern jarrah forest. Indeed, the likelihood of a mardo occupying an area with large logs and dense patches of X. preissii ranged from 62.2% to 85.0%. In contrast, in the P. cinnamomi iv affected sites with lower X. preissii densities the patch occupancy probabilities ranged from

0.0% to 45.7%. Logs and X. preissii strongly contribute to the understorey and may increase nest locations and cover while offering protection from predators. Mardos may avoid P. cinnamomi affected areas because of lower X. preissii densities, which may result in fewer nest locations, reduced cover and an increased likelihood of predation. However, the results of the study must be treated as preliminary findings, therefore there may be additional environmental related or unrelated to P. cinnamomi factors that may also contribute to the occupancy rates of the mardo. Therefore, further studies and research on the ecology and biology of the mardo is strongly encouraged. Until this research is conducted, P. cinnamomi most be considered as significant threat to the conservation of the mardo. Therefore, the conservation of the mardo in the northern jarrah forest depends on limiting the spread and impact of P. cinnamomi, as well as the retention of large logs and tall X. preissii. Given that large logs and tall X. preissii contribute to the distribution of the mardo, strong consideration must be given to using these natural elements to rehabilitate the most severely disturbed areas of the northern jarrah forest.

Consideration must be given to the conservation of other small and threatened mammal species that inhabit susceptible plant communities in the south-west of Western Australia.

An understanding of how P. cinnamomi impacts on the mardo and other native mammals will contribute to our ability to control, protect and manage vulnerable communities and ecosystems in Western Australia. If the spread and impact of this pathogen is left unchecked, the ultimate consequence to the conservation of many small to medium native mammals that are dependant on structurally complex habitat may be devastating.

v ACKNOWLEDGEMENTS

I wish to thank the following people for help and support. Alcoa World Alumina,

Department of Environment and Conservation and Murdoch University, especially Ian

Colquhoun and Sam (Ian) Freeman from Alcoa and the Department of Environment and

Conservation for providing funding, field equipment and field sites.

My patient and helpful supervisors, Trish Fleming, Giles Hardy, Bernie Dell and Mark

Garkaklis. For those people who helped with the field work and writing including, Duncan

Sutherland, Lien Sim, Lesley Gibson, Peter Spencer, Mike Craig, Bill Dunstan, Trudy

Paap, Kobus Wentzel, Marie Murphy, Damien Cancilla, Todd Bell, Owen Nichols, Mike

Calver, Barbara Wilson and Kylie Arnett. Jodie Wood, Corrine Gaskin and Maggie Lilith for setting up the sites and undertaking the initial surveys.

Alcoa staff who helped organise field equipment, maps, field trips and provided work when the coppers were empty, Alex Rushmann, Kyle Walmsley, Rowen Beale, Naomi Kerp,

Peter San Mugal, Mel Norman, Allison Steele, Andrew Grigg and Rod McGregor.

My family, Nanna A, Mum, Dad, Donna, Ed, Woody, Helen, Peter, Lauren, Britt, Jack,

Pip, Emerald, Sally and Max. To Grandma Neale, you encouraged me to turn over rocks and logs, because if you don’t look you will never know. I thank Hanno, Bec and Shaun for fun distractions, surfs, beers and BBQ’s.

I would also like to thank the staff at the numerous coffee shops that I visited while writing my thesis. The Gellibrand River Hotel, a place that reminded me of the good things in life.

Yanchep, Trigg Beach, Johanna and Thirteenth Beach for keeping it real and where many constructive thoughts were produced. And especially to Lucy Lee you provided continued support, patience and impatience. As well as being a reliable source of encouragement and confidence throughout this journey. vi TABLE OF CONTENTS ABSTRACT ...... iii ACKNOWLEDGEMENTS ...... vi CHAPTER 1. GENERAL INTRODUCTION ...... 1 1.1. The impact of Phytophthora cinnamomi in the northern jarrah forest...... 3 1.1.1. History of Phytophthora cinnamomi in the jarrah forest ...... 5 1.2. The study animal; Antechinus leucogaster flavipes (Marsupialia: Dasyuridae) ...... 8 1.2.1. General description ...... 8 1.2.2. Habitat preference and distribution ...... 10 1.2.3. Life history and reproduction ...... 11 CHAPTER 2. THE IMPACT OF PHYTOPHTHORA CINNAMOMI ON THE VEGETATION, COMPOSITION, COMPLEXITY, AND STRUCTURE IN JARRAH PLANT COMMUNITIES ...... 15 2.1. Introduction ...... 15 2.2. Methods and Materials ...... 16 2.2.1. General features of the survey region; location and climate ...... 16 2.2.2. Geology and soil types ...... 16 2.2.3. Description of the jarrah forest and vegetation communities ...... 16 2.2.4. Survey site selection ...... 17 2.2.5. Evaluation of Phytophthora cinnamomi-induced disturbance ...... 22 2.2.6. Measurement and quantification of habitat variables ...... 24 2.2.7. Data analysis ...... 26 2.3. Results ...... 31 2.3.2. Multivariate analysis of Phytophthora cinnamomi-induced disturbance...... 31 2.4. Discussion ...... 36 2.4.1. The impact of Phytophthora cinnamomi on the vegetation structure, composition and complexity of the northern jarrah forest ...... 36 2.4.2. Interpreting the impact of Phytophthora cinnamomi using the Dieback Expression Score...... 38 2.4.3. The impact of Phytophthora cinnamomi on the forest floor litter ...... 39 2.4.4. The impact of Phytophthora cinnamomi on the structure of the understorey vegetation ...... 39 2.4.5. The impact of Phytophthora cinnamomi on fallen log densities ...... 40 2.5. Concluding remarks ...... 40

vii CHAPTER 3. THE IMPACT OF PHYTOPHTHORA CINNAMOMI ON THE DISTRIBUTION OF THE MARDO IN THE NORTHERN EUCALYPTUS MARGINATA (JARRAH) FOREST, WESTERN AUSTRALIA ...... 42 3.1. Introduction ...... 42 3.2.2. Mardo trapping procedures ...... 43 3.2.3. Data analysis and model development ...... 45 3.3. Results ...... 49 3.3.1. Trapping data ...... 49

3.3.2. QAICC model selection ...... 51 3.4. Discussion ...... 55 3.4.1. Patch occupancy assumptions ...... 55 3.4.2. The threat and impact of Phytophthora cinnamomi in the northern jarrah forest...... 56 3.4.3. Factors affecting the distribution of the mardo in the northern jarrah forest ...... 57 3.4.4. The threat of Phytophthora cinnamomi to other northern jarrah forest fauna ...... 58 3.4.5. The impact of Phytophthora cinnamomi on native mammals from eastern Australia ...... 59 3.5. Concluding remarks and management implications ...... 60 CHAPTER 4. MARDO HABITAT PREFERENCES: IDENTIFYING KEY HABITAT ELEMENTS AND MARDO SUSCEPTIBILITY TO PHYTOPHTHORA CINNAMOMI ...... 62 4.1. Introduction ...... 62 4.2. Methods ...... 64 4.2.1. Study site ...... 64 4.2.2. Live mardo trapping procedures ...... 64 4.2.3. Habitat variables ...... 64 4.2.4. Data analysis and model development ...... 66 4.2.5. Candidate models, fitting and selection ...... 67 4.3. Results ...... 68 4.3.1. Trapping results...... 68 4.3.2. Naïve model selection ...... 70 4.3.3. Model selection: habitat affecting mardo detectability (p) ...... 71 4.3.4. Model selection: habitat characteristics affecting mardo patch occupancy (ψ) ...... 72 4.3.5. Model selection for combined detection and patch occupancy (ψ) model ...... 74 4.3.6. Habitat variation between Phytophthora cinnamomi affected and unaffected trap stations ...... 74

viii 4.4. Discussion ...... 80 4.4.1. The importance of logs to the mardo and other small native mammal fauna ...... 80 4.4.2. The importance of Xanthorrhoea species to the mardo and other small native mammal fauna ...... 81 4.4.3. The impact of Phytophthora cinnamomi on the habitat requirements of the mardo...... 85 4.5. Concluding remarks ...... 85 5. GENERAL DISCUSSION ...... 88 5.1. Impact of Phytophthora cinnamomi on the mardo in the northern jarrah forest ...... 88 5.2.1. An improved understanding of how the plant pathogen Phytophthora cinnamomi affects the habitat requirements of the mardo in the northern jarrah forest ...... 88 5.2.2. An understanding of the habitat requirements of the mardo ...... 90 5.2.3. Contributing information vital for management measures required for the conservation of mardo and other native mammal species that inhabit plant communities susceptible to Phytophthora cinnamomi ...... 91 5.2. Other fauna species and the threat of Phytophthora cinnamomi: an integrated approach to managing P. cinnamomi and the conservation of native mammal species ...... 93 5.3. Developing and implementing strategies for the rehabilitation of affected and disturbed areas ...... 94 5.4. Concluding remarks and management implications ...... 96 REFERENCES ...... 98 APPENDIX 1. Complete model selection results fitting Antechinus flavipes (mardo) detectability (p) and patch occupancy (ψ) model to the mardo trapping data...... 110 APPENDIX 2. Complete model selection results fitting detectability (p) and patch occupancy (ψ) model of MacKenzie et al. (2002) to the mardo trapping data...... 113

ix LIST OF FIGURES Figure 1.1. Present (dark areas) and former (shaded areas) distribution of Antechinus flavipes or the yellow-footed antechinus (Crowther 2008; Menkhorst and Knight 2001)...... 9 Figure 1.2. Antechinus flavipes leucogaster (yellow footed antechinus) or mardo on the trunk of grandis at a Phytophthora cinnamomi free location in the northern jarrah (Eucalyptus marginata) forest...... 10 Figure 2.2. The mean (± 1 SD) monthly rainfall (A) and monthly temperature (± 1 SD) (B) recorded by the Bureau of Metrology at Dwellingup for the period of January 1993 to December 2004...... 20 Figure 2.3. Survey site 1: Contrast between open areas and dense patch of . There is a lack of leaf litter in the foreground. The small logs may be remnants of salvage logging conducted during 1970-1980 or trees killed by Phytophthora cinnamomi. An isolated Xanthorrhoea preissii in foreground...... 27 Figure 2.4. Survey site 2: There is a lack of canopy and understorey vegetation. Scattered semi-mature Eucalyptus marginata (jarrah) and Corymbia calophylla (marri) trees are present. Logs in background may be remnants from salvage logging operations conducted during 1970-1980 or trees killed by Phytophthora cinnamomi...... 27 Figure 2.5. Survey site 3: There is a lack of understorey and canopy vegetation at this site. The leaf litter is thin and several dead Eucalyptus marginata (jarrah) trees are present at this site. Logs in background may be remnants of salvage logging operations conducted during 1970-1980 or trees killed by Phytophthora cinnamomi. 28 Figure 2.6. Survey site 4: There is a lack of understorey and canopy vegetation and little litter and woody debris at this site. There were many dead Eucalyptus marginata (jarrah), and Xanthorrhoea preissii at this site. Phytophthora cinnamomi was still active killing susceptible plants at this site during the survey period. Logs in background may be remnant of salvage logging operations conducted during 1970-1980 or trees killed by Phytophthora cinnamomi. 28 Figure 2.7. Survey site 5: Disease free region of this survey site with dense understorey and canopy vegetation consisting of Eucalyptus marginata (jarrah), Corymbia calophylla (marri), Bossiaea aquifolium, Pteridium esculentum (bracken ) and Xanthorrhoea preissii. 29 Figure 2.8. Survey site 6: This site has been described as being disease free and long time undisturbed. The understorey and canopy vegetation was dense and structurally rich at this site. The site was dominated by Xanthorrhoea preissii, Eucalyptus marginata (jarrah), Corymbia calophylla (marri) and Eucalyptus patens (black butt). The thick litter layer can be seen along with a large Macrozamia riedlei in foreground of this photograph. 29 Figure 2.9. Mean (± 1SD) Dieback Expression Score recorded at each survey site. 33 Figure 2.10. Non-metric multi-dimensional scaling (nMDS) ordination created from Bray-Curtis similarity analysis of the 16 habitat variables for each survey x site. Survey site are represented by their individual site number. Survey sites 1, 2, 3 are severely infested (DES 3-4), 4, 5 and 6 represent moderately (DES 2-3) affected, intermediately or subtly affected (DES 1-2) healthy or unaffected (DES 0) Eucalyptus marginata (jarrah) forest, respectively. ANOSIM full Global R comparison between survey sites R= 0.334 P<0.001 (Stress <0.01). 33 Figure 4.1. Total number of Antechinus flavipes (mardo) resident individuals (A) and captures (B) recorded at each survey site according to gender. ). Survey sites 1, 2, 3, are severely infested (DES 3-4), 4, 5 and 6 represent intermediately or subtly affected (DES 1-2) healthy or unaffected (DES 0) Eucalyptus marginata (jarrah) forest, respectively. 69 Figure 4.3. The probability of Antechinus flavipes (mardo) patch occupancy (ψ) after model averaging from the 23 top ranked models (bars represent confidence intervals). Survey sites 1, 2, 3, are severely infested (DES 3-4), 4, 5 and 6 represent intermediately or subtly affected (DES 1-2) healthy or unaffected (DES 0) Eucalyptus marginata (jarrah) forest, respectively. 73 Figure 4.4. The mean (± SE) values for the habitat characteristics total log (A), large log (B), total (C) and tall multiple-crowned Xanthorrhoea preissii (D) densities, which were identified as being critical to the detectability (p) and patch occupancy (ψ) of mardos at “successful” trap stations for detecting resident Antechinus flavipes (mardo) and “unsuccessful” trap stations that did not detect resident A. flavipes (mardos). The mean (± SE) value for the 150 trap stations is also given (overall). 77 Figure 4.5. The mean (± SE) values for single crowned (A) and medium/small Xanthorrhoea preissii (B) densities identified and ground cover vegetation structure (C) which were identified as being critical to the detectability (p) and patch occupancy (ψ) of mardos at “successful” trap stations for detecting resident Antechinus flavipes (mardo) and “unsuccessful” trap stations that did not detect resident A. flavipes (mardos). The mean (± SE) value for the 150 trap stations is also given (overall). 78

xi LIST OF TABLES Table 2.1. Site description including GPS location, aspect, topography, soil type and vegetation group recorded at each survey site...... 21 Table 2.2. The susceptible and resistant plant species and their degree of susceptibility to dieback and expected status and density in Phytophthora cinnamomi-affected areas according to Shearer and Dillon (1995)...... 23 Table 2.3. Disease Expression Scores (DES) and description of Phytophthora cinnamomi symptoms and habitat variables used to create the symptoms rating...... 23 Table 2.4. Habitat variables and brief explanation of the techniques used to measure them. Each habitat characteristic was evaluated within a 12.5 m radius of each of the 25 trap stations at each survey site...... 25 Table 2.5. The disease and degradation status as well as approximate timing of the initial Phytophthora cinnamomi infestation. The approximate timing of timber harvest, fire frequency and last time each survey was burnt, are also given...... 30 Table 2.6. Results of the SIMPER analysis following a Bray-Curtis similarity analysis to determine which of the 16 habitat variables contribute to variation separating each survey site. The term “%” represents the percentage contribution each variable contributed to the separation between each survey site...... 34 Table. 2.7. Mean (±SD) habitat variables recorded at each survey site...... 35 Table 3.2. An explanation of the terms, model parameters and covariates used to model the impact Phytophthora cinnamomi has on Antechinus flavipes (mardo)...... 49 Table 3.3. The number of Antechinus flavipes (mardo) individuals and captures recorded at trap stations where multiple mardo detections were recorded. The number of trap stations at each survey site that recorded multiple mardo detections, and the total number of captures recorded for the entire study are given. The level of Phytophthora cinnamomi disturbance at each site is indicated as the mean ± 1SD Dieback Expression Score (DES)...... 50 Table 3.4. Summary of model selection results fitting Antechinus flavipes (mardo) detectability (p) and patch occupancy (ψ) model to the mardo trapping data. The term “and” represents the main and interactive affects of the parameters (site, time and gender), whilst “+” indicates the additive affect of a habitat covariate. DES = Dieback Expression Score...... 53 Table 4.1 Summary of the trapping effort undertaken during each survey period and timing each site was surveyed. Trapping surveys were conducted over four nights each month from January 2003 to August 2003. The timing of the survey was limited due to the presence of adult male Antechinus flavipes (mardo) (all adult males die after mating, new cohorts do not enter populations until four months old)...... 66 Table 4.2. Successful captures of Antechinus flavipes (mardo) residents recorded at each survey sites. ). Survey sites 1, 2, 3 are severely infested (DES 3-4), 4, 5 and 6 represent subtly and moderately (DES 1-2) affected, and healthy or unaffected (DES 0) Eucalyptus marginata (jarrah) forest, respectively. The “total successful

xii trap stations” are not cumulative totals because males and females were detected at the same trap stations...... 70 Table 4.3. Summary of QAICc model selection results fitting the resident Antechinus flavipes (mardo) encounter history to detectability (p) and patch occupancy (ψ) naïve models. Model notation “*” represents the main and interactive affects of site, gender and time...... 71 Table 4.4. Summary of model selection results fitting the resident Antechinus flavipes (mardo) encounter history and habitat variables to the detectability (p). The notation terms used in the following models include (ψ) which representing patch occupancy, ‘*’ representing the main and interactive affects of site, gender and time, whilst “+” indicates the additive affect of the habitat covariates. Over- dispersion factor (ĉ) = 2.457...... 75 Table 4.5. Summary of model selection results fitting the resident Antechinus flavipes (mardo) encounter history and habitat variables to the patch occupancy (ψ). The notation terms used in the following models includes (p) for detectability, ‘*’ representing the main and interactive affects of site, gender and time, whilst “+” indicates the additive affect of the habitat covariates. Over-dispersion factor (ĉ) = 2.457...... 76 Table. 4.6. Mean data from all habitat variables and standard deviation values recorded for each trap station (625 m2). Success and unsuccessful trap stations indicate that resident Antechinus flavipes (mardo) were detected or not detected respectively. Affected represents the trap stations located in areas disturbed by Phytophthora cinnamomi (DES > 1) and unaffected represents the trap stations located in disease free areas (DES <0) of the Eucalyptus marginata (jarrah) forest. 78

xiii CHAPTER 1. GENERAL INTRODUCTION

Australia has the worst record for recent mammal extinction of any country, since European settlement, 27 native marsupials and rodent species have been listed as extinct under the

Environment Protection and Biodiversity Act (EPBC 1999). In addition, a further 4 species are listed critically endangered, 33 species as endangered and 55 as vulnerable of becoming extinct (Burbidge and McKenzie 1989; EPBC 1999). The reasons for these extinctions and declines include habitat loss and modification due to agriculture, urbanisation, logging and mining. In addition, considerable pressure from human persecution, hunting, poisoning, disease and predation from introduced cats and foxes have also contributed to these declines and extinctions (Lunney and Leary 1988; Short and Smith 1994; Smith and Quinn

1996; Wilson and Friend 1999; Morris 2000). Presently, many native mammal populations persist in small, highly fragmented and isolated remnant patches of native vegetation and as a consequence require some form of management to insure their persistence (Short and

Smith 1994). However, the integrity of these patches of natural habitat is threatened from the devastating impact of the introduced plant pathogen Phytophthora cinnamomi.

Phytophthora cinnamomi is a soil-borne plant pathogen that causes disease that kills many native plant species and consequently degrades a wide range of vegetation communities in the south-west of Western Australia (Shearer and Tippett 1989; Shearer et al. 2004, 2007).

Phytophthora cinnamomi is a microscopic water mould from the class Oomycota (Shearer

1994). The pathogen invades and kills susceptible plant species by entering the root and effectively rotting root and collar tissue, this results in restricted water and nutrient uptake causing severe water stress, which eventually kills the infected plant. The greatest impact has occurred in dry sclerophyll and heathland communities throughout eastern and southern

1 Australia, especially those dominated by plant species from the families ,

Epacridaceae, Xanthorrhoeaceae and Fabaceae, which are generally highly susceptible. The impact of P. cinnamomi has been particularly severe in the northern jarrah (Eucalyptus marginata) forest which is the area of interest for this study, due to a large number of structurally dominant plant species susceptible to P. cinnamomi.

Once an area becomes infested the subsequent affect is often dramatic and devastating, with the death of susceptible plant (often on mass), foliage collapse and decomposition resulting in significant reductions in projected canopy cover, coarse woody debris and leaf litter

(Weste and Marks 1974; Shearer and Tippett 1989; Wardell-Johnson and Nichols 1991).

These changes prompted concern for small mammal species that inhabit susceptible plant communities such as those in the northern jarrah forest. Moreover, severe disturbances resulting from P. cinnamomi infestation may cause permanent habitat loss and fragmentation, potentially resulting in reduced gene flow, genetic drift, reduced individual fitness and may affect the long term persistence of some populations (Weste and Marks

1987; Shearer and Tippett 1989; Lacy 1997). To date, there has been no concerted and long term research directed at understanding how P. cinnamomi-induced disturbance and habitat degradation affects small (average adult body mass <500 g) to medium (average adult body mass 500–5000 g) sized mammal populations in Western Australia. Several minor studies and literature reviews make reference to the potential impact P. cinnamomi may have on small mammal populations in the south-west of Western Australia (Wardell-Johnson and

Nichols 1991; Wills 1993; Garkaklis et al. 2004; Gaskin 2002; Lilith 2002; Majer et al.

2002).

2 A lack of data on this subject is alarming, considering that the jarrah forest has been the last bastion for a number of mammals close to extinction including the chudtich (Dasyurus geoffroii) and woylie (Bettongia penicillata) (Morris et al. 2004). Therefore, it is essential that the threat of P. cinnamomi-induced disturbance on small to medium sized mammals be acknowledged and included as an integral component of any recovery and management plan. This is especially the case for species listed as Threatened or Priority Listed Species under the Department of Environment and Conservation Wildlife Conservation (Specially

Protected Fauna) Notice 2006.

In contrast to Western Australia, a number of studies evaluating the impact of P. cinnamomi on native fauna have been undertaken in the south-west of Victoria (Newell and

Wilson 1993; Newell 1994; Laidlaw 1997; Laidlaw and Wilson 2006). These Victorian studies clearly show that P. cinnamomi-induced plant deaths affect the abundance and distribution of Antechinus agilis (agile antechinus) [formally, Antechinus stuartii (brown antechinus) Dickman et al. 1998], Sminthopsis leucopus (white footed dunnart), Rattus fuscipes (bush rat) and R. lutreolus (swamp rat) populations (Wilson et al. 1990; Newell and Wilson 1993; Laidlaw and Wilson 2006). In the present study, the aim was to investigate the impact of P. cinnamomi-induced plant deaths on the biology, habitat selection and distribution of Antechinus flavipes leucogaster (yellow-footed antechinus) or mardo.

1.1. The impact of Phytophthora cinnamomi in the northern jarrah forest The first indication that Phytophthora cinnamomi has infested a jarrah forest plant community is sudden chlorosis and death among understorey woody shrubs (Shearer and

Tippett 1989; Shearer and Dillon 1995). The most noticeable plant species to perish early

3 are the structurally important Banksia grandis (bull banksia), longifolia, P. elliptica (snottygobbles) and (hairy tube flower). Banksia grandis is highly susceptible and is often used as an indicator species defining the distribution of infestation (Shearer and Tippett 1989). Other important understorey species, such as

Xanthorrhoea preissii (grasstrees) and Macrozamia riedlei (zamia palm), although highly susceptible appear to vary in levels of resistance and timing of death (Shearer and Tippett

1989; McDougall 1997). Eucalyptus marginata (jarrah) also varies in resistance to the pathogen, and in some areas take up to ten years to succumb (Shearer and Tippett 1989).

The expression of the disease may vary from total patch death to gradual crown decline and foliage dieback, depending on soil type, topography, hydrological cycles and presence of susceptible plant species (Shea 1977; Davison 1994; Wilson et al. 2003). In the jarrah forest, the most susceptible sites are those areas associated with water courses or poorly drained sites with sandy soils and low nutrient content (Dell and Malajczuk 1989). The impact of disease is particularly devastating in areas where the dominant soils are laterite, which are widespread throughout the jarrah forest (Shearer and Tippett 1989). In contrast, in deep river valleys that dissect the jarrah forest with deep red-brown loamy soil, the vegetation are largely unaffected (Dell and Malajczuk 1989; Shearer and Tippett 1989).

Soils of these areas are unfavourable to P. cinnamomi because of antagonistic bacteria, unsuitable soil structure and other microclimatic factors (Dell and Malajczuk 1989).

Therefore, the forest exists as a patchy mosaic of unaffected forest, diseased forest with dying and recently dead plants and areas with highly degraded forest. In addition, there are extensive regions of forest that have been impacted by logging and mining operations

(Colquhoun and Hardy 2000). However, the impacts of these disturbances differ spatially and temporally due to long term impact of P. cinnamomi.

4 1.1.1. History of Phytophthora cinnamomi in the jarrah forest There is strong evidence that Phytophthora cinnamomi was introduced into Western

Australia, possibly in infested soil surrounding exotic fruit trees and garden plants planted in orchards and European style gardens of the timber towns that were established south-east of Perth in the late 1800’s (Shearer and Tippett 1989; Colquhoun and Hardy 2000).

Another possible source was infected soil on road construction machinery brought into

Western Australia from Asia (Podger 1972; Shearer and Tippett 1989). However, there is no definitive explanation detailing exactly how or when P. cinnamomi entered Western

Australia.

The first evidence of disease symptoms in the jarrah forest was reported as unusual patches of dead and dying E. marginata trees near Karragullen, 35 km south-east of Perth in 1921

(Podger 1972). These initial infections appeared to be of recent origin, but were regarded as unimportant because they were restricted to small and discrete patches of forest (Podger

1972; Dell and Malajczuk 1989). Over the next ten years, however, reports describing similar patches of dead and dying E. marginata trees became increasingly common. Some of these plant deaths were approximately 80 km from the original tree deaths (Shearer and

Tippett 1989). Despite these findings, concern for these new areas of diseased forest remained minimal.

During the 1930s and 1940s, these plant deaths became increasingly widespread throughout the jarrah forest (Shearer and Tippett 1989). During this period the timber industry commenced transporting timber by road instead of rail, which required a major undertaking of road construction throughout the jarrah forest and it appears that gravel and soil from infested areas with significant tree deaths was often used. However, at the time the casual

5 agent (P. cinnamomi) remained unknown and the movement of infected soil resulted in the extent of dead and dying trees becoming widespread and increasingly common (Podger

1972; Shea 1977; Dell and Malajczuk 1989; Shearer and Tippett 1989). During this period, the pathogen may have been dispersed through as much as 20, 000 hectares of the jarrah forest (Dell and Malajczuk 1989). By the end of the 1940’s, a dramatic increase in diseased areas became significant enough to prompt concern, particularly to the economically- important timber industry (Shearer and Tippett 1989). It was during this period the term

‘jarrah dieback’ was introduced to describe the sudden and widespread E. marginata deaths. Since the Western Australian colony was established in 1829 until present, E. marginata formed the bulk of timber used by the Western Australian timber industry

(Podger 1972).

Concern for the dramatic plant deaths throughout the jarrah forest was sufficient to establish a State and Commonwealth government funded research initiative at Dwellingup in 1948. The main aim of this research was to determine the casual agents and strategies to limit the impact (Podger et al. 1965; Shearer and Tippett 1989). It was not until 1965 that

P. cinnamomi was identified as the causal agent of ‘jarrah dieback’ (Podger et al. 1965).

The identification of P. cinnamomi as the organism causing jarrah dieback was extremely significant and allowed for an aggressive undertaking of research and implementation of quarantine and hygiene strategies (Podger et al. 1965; Shearer and Tippett 1989). Because

E. marginata was valuable as a timber export to Western Australia the majority of the attention was focused on viability of future E. marginata harvests. Initial research programs, therefore concentrated on forestry as opposed to ecological impacts of P. cinnamomi. It was not until the late 1990’s that the emphasis of concern shifted from the jarrah forest to other unique plant communities, including Banksia woodlands and 6 heathlands of the Swan Coastal Plain surrounding Perth, Stirling Ranges and Fitzgerald

River National Park (Wills 1993; Shearer and Dillon 1995). However, it was not until 2002, when Dr. Mark Garkaklis of Murdoch University observed the impact of P. cinnamomi on fauna in Victoria did research commence on how P. cinnamomi may affect the native mammal fauna of Western Australia.

1.1.2. The susceptibility of the jarrah forest plant species and potential impacts on fauna In the south-west of Western Australia, approximately 41% of the recognised plant species exhibit some susceptibility to death from P. cinnamomi (Shearer et al. 2004). Many of these susceptible species are woody perennials, which form integral components of the vegetation structure and complexity. Shearer and Dillon (1995) surveyed vegetation in highly disturbed, actively diseased and healthy areas of jarrah forest and placed common jarrah forest plant species into 5 groups depending on their degree of susceptibility to P. cinnamomi. These groups were arranged by the presence or absence of each plant species in old and active diseased areas and were used as a measure of resistance or susceptibility to the pathogen. Species that were common in highly disturbed sites were regarded as resistant

(Group 1), those that had generally died were regarded as highly susceptible (Groups 4 and

5). Those species within moderate groups (Groups 2 and 3) were found to have only died in one third or less of the sites surveyed (Shearer and Dillon 1995; Shearer et al. 2004). The most susceptible species are from the family Proteaceae, with 92% of the species found in

Western Australia being susceptible to the pathogen (Wills 1993). A range of other highly susceptible species occurs in the Epacridaceae, Fabaceae and Dilleniaceae (Wills 1993;

Weste and Ashton 1994). There are also numerous species from the Myrtaceae,

Goodeniaceae, Mimosaceae, Xanthorrhoeaceae, Cyperaceae and Apiaceae that are also

7 susceptible to P. cinnamomi (Shearer and Tippett 1989; Wills 1993; McDougall et al. 2001;

Shearer et al. 2004, 2007).

The loss of the structurally important plant species is a major concern for the ecology and biology of those mammal species, populations and communities that depend on dense cover

(Podger 1972). Furthermore, because P. cinnamomi kills plants from all strata levels, arboreal, semi-arboreal and terrestrial ground dwelling mammals are threatened. This accounts for most mammal guilds occupying jarrah forest plant communities, the exception being the semi-aquatic Hydromys chrysogaster (water rat) (Wardell-Johnson and Nichols

1991). In addition, many of plant species from groups 4 and 5 are known to support nesting substrates, refuge, and act as nutrient reservoirs for small and medium sized mammals

(Whittell 1954; Wooller et al. 1982; Laidlaw and Wilson 1996; Goldingay 2000; Hackett and Goldingay 2001; Kemp and Carthew 2004).

1.2. The study animal; Antechinus leucogaster flavipes (Marsupialia: Dasyuridae) 1.2.1. General description Antechinus flavipes leucogaster (yellow-footed antechinus) is a small, semi-arboreal, insectivorous dasyurid marsupial. Antechinus flavipes is one of the 10 Australian species from the Antechinus genus and is the only Antechinus species in Western Australia. This species has a vast distribution and consequently three sub-species are recognised;

Antechinus flavipes flavipes has a wide distribution extending through Queensland, New

South Wales, Victoria and South Australia, A. f. rubeculus is restricted to regions along the east coast of northern Queensland. The third subspecies, A. f. leucogaster which is known locally as the mardo, has a wide distribution throughout the south-west of Western

Australia (Christensen and Kimber 1975; Crowther et al. 2002; How et al. 2002; Crowther

8 2008) (Figure 1.1). The three sub-species are characterised by grizzled slate grey to yellow pelage and russet flanks, rump, belly, feet and legs (Crowther 2008) (Figure 1.2). Sexual dimorphism exists in this species with males achieving a mass of 75g (average 56 g) and females 52 g (average 34 g) (Menkhorst and Knight 2001; Crowther 2008). It is a very active animal, when released it has been observed to leap and bound vertically up trees and

Xanthorrhoea species and hang upside down under logs and rocky outcrops (Crowther

2008). The conservation status of all three subspecies is low risk of becoming extinct according to IUCN (Maxwell et al. 1996).

A. f. rubeculus

A. f. leucogaster A. f. flavipes

Figure 1.1. Present (dark areas) and former (shaded areas) distribution of Antechinus flavipes or the yellow-footed antechinus (Crowther 2008; Menkhorst and Knight 2001).

9

Figure 1.2. Antechinus flavipes leucogaster (yellow footed antechinus) or mardo on the trunk of Banksia grandis at a Phytophthora cinnamomi free location in the northern jarrah (Eucalyptus marginata) forest.

1.2.2. Habitat preference and distribution Crowther (2002) used BIOCLIME models to predict the distribution of several Antechinus species in eastern Australia. He suggests that mean annual rainfall, high average temperatures, increased annual evaporation and solar radiation contribute to the distribution of A. flavipes. This species appears to be restricted to inland forests of New South Wales,

Queensland, Victoria, South Australia and the south-west of Western Australia where the mean average rainfall ranges from 282 to 1663 mm (Watt 1997; Crowther 2002).

10 Because A. flavipes has an extensive distribution it has been associated with a range of habitat communities. In south-eastern Queensland, Dwyer et al. (1979) recorded A. f. flavipes in forests with E. sclerophylla (scribbly gum), Tristania conferta (brush box), E. pilularis (black butt) and E. gummifera (blood wood) forests with heath and swamp associations. In Victoria, A. flavipes inhabit a dry forest and woodland areas, dominated by

E. camaldulensis (river red gum), E. microcarpa (grey box), E. leucoxylon (yellow gum),

E. melliodora (yellow box), E. baxteri (brown stringy bark) and E. viminalis (manna gum) with an understorey varying from tall dense to sparse shrub and tussock grass (Menkhorst

1995). In northern Queensland the sub-species A. f. rubeculus inhabits notophyll vine-forest and adjacent rainforest (Van Dyck 1982). While in Western Australia, A. f. leucogaster

(mardo) is confined to the jarrah and E. diversicolor (karri) forests and associated open woodlands of the south-west region (How et al. 2002). In Western Australia, Christensen and Kimber (1975) found mardos exists in higher densities among jarrah forest areas unburnt for more than 40 years. In contrast, lower densities were recorded in areas 2 to 30 years post fire. Previous studies focusing on habitat selection by A. flavipes identified a preference for structurally rich and complex microhabitats, characterised by hollow bearing logs, fine woody debris, Xanthorrhoea species (grasstrees) and rock crevices (Mac Nally et al. 2001; Mac Nally and Horrocks 2002; Marchesan and Carthew 2004; Stokes et al. 2004;

Korodaj 2007; Swinburn et al. 2007; Kelly and Bennett 2008).

1.2.3. Life history and reproduction Smith (1984) characterised the life history and reproductive behaviour of A. flavipes as typical to dasyurid life history strategy “1” (Lee et al. 1982). This life history is defined as semelparous and is categorised by monestrous females with short oestrus periods lasting only 2-3 days. Gestation occurs over 23-30 days. Mating in the Antechinus genus is long

11 and exhaustive, often lasting up to 12 hours, which has been considered a form of mate guarding (Shimmin et al. 2000). Several weeks following mating all males die (complete male die-off) (Lee and Cockburn 1985; Lee et al. 1982). This phenomenon has been well studied among both Antechinus and Phascogale genera and the cause of death has been associated with corticosteroid-induced gastric haemorrhage and complications of immunosuppression (Lee et al. 1977; Lee et al. 1982; Wilson and Bourne 1984). There are also a number of factors contributing to the demise of the male Antechinus, including increased internal and ecto-parasites loads, intestinal bacteria and severe haemorrhaging within the intestine.

The timing of mating, male die-off and birth varies dramatically between geographically isolated populations of the same species. This geographic variation in the timing of life history aspects often varies with altitude and latitude (McAllan and Dickman 1986). The timing of mating has also been associated with photoperiod and its effect on flushes of invertebrate abundance (Dickman 1991a). This life history reflects the predictable climate in which these animals inhabit and suggests that a second litter would be produced during the dry and cold extremes of the year, when food availability is low and daily temperatures are unpredictable.

1.3. Thesis aims Mardos are widely distribution throughout the jarrah and karri forests and associated woodlands, where they are considered common. They are easily captured using conventional trapping methods, and their distribution overlaps the distribution of

P. cinnamomi. Therefore, because of these reasons mardos were considered to be an excellent model to evaluate potential impacts of P. cinnamomi on the ecology and biology 12 of a Western Australian native mammal (Christensen and Kimber 1975; Noss 1989, 1999;

How et al. 2002). Moreover, a broad range of research conducted in eastern Australia successfully employed similar Antechinus species to model small mammal habitat preferences, impact of fire, mining, logging, grazing and P. cinnamomi (Dickman 1980;

Fox 1982; Newell and Wilson 1993; Tasker and Dickman 2004).

The plant pathogen P. cinnamomi can devastate the habitat requirements of many native mammal species by killing many common and structurally important plant species that provide nest sites, cover, food and protection from predation (Wardell-Johnson and Nichols

1991; Wills 1993; Wilson et al. 1994; Garkaklis et al. 2004). The death and collapse of these susceptible plants has potentially serious consequences on the distribution of many native fauna species from south-west of Western Australia, a region, recognised for its diverse and unique biodiversity (Wardell-Johnson and Horwitz 1996; Wardell-Johnson et al. 2004). However, the impact of P. cinnamomi on small native mammals in the south- west of Western Australia is poorly understood.

The general aim of this study was to quantify the impact that P. cinnamomi-induced disturbance and forest degradation have on the distribution of the yellow-footed antechinus or mardo (Antechinus flavipes leucogaster). In order to fully understand the dynamics associated with the likely impact that P. cinnamomi has on the distribution of the mardo, the following sub-aims were:

1) Identify changes to the vegetation composition, complexity and structure resulting

from P. cinnamomi induced plants deaths in a high rainfall area of the northern

jarrah forest (Chapter 2). 13

2) Identify which habitat characteristics are most affected by P. cinnamomi induced

plant deaths in a high rainfall area of the northern jarrah forest (Chapter 2).

3) Determine by trapping surveys the impact P. cinnamomi induced plant deaths have

on detectability and patch occupancy rates of the mardo in a high rainfall area of

the northern jarrah forest (Chapter 3).

4) Determine by trapping surveys the habitat preferences of mardo in the northern

jarrah forest and evaluate how these habitat characteristics are affected by P.

cinnamomi (Chapter 4).

14 CHAPTER 2. THE IMPACT OF PHYTOPHTHORA CINNAMOMI ON THE VEGETATION, COMPOSITION, COMPLEXITY, AND STRUCTURE IN JARRAH PLANT COMMUNITIES

2.1. Introduction The introduced plant pathogen Phytophthora cinnamomi is a major threatening process to the conservation of the unique and diverse flora and fauna of the South-west Botanical

Province of Western Australia (Garkaklis et al. 2004; Shearer et al. 2007). Recently the susceptibility of 5710 plant species from this region was assessed and it was concluded that

2285 species can be regarded as susceptible and 800 as highly susceptible (Shearer et al.

2004, 2007). Many susceptible plant species are common and consequentially are important contributors to the composition and structure of the vegetation. In the northern jarrah

(Eucalyptus marginata) forest, jarrah, Banksia grandis, Persoonia longifolia and

Xanthorrhoea preissii are all highly susceptible to P. cinnamomi (Shearer and Dillon 1995;

Shearer et al. 2004, 2007). In addition, many plant species that contribute to the ground cover vegetation layer are perennial woody shrubs from the families Proteaceae,

Dilleniaceae, Fabaceae, Liliaceae, Mimosaceae and Myrtaceae are susceptible to P. cinnamomi (Heddle et al. 1980b; Bell and Heddle 1989; Shearer and Tippett 1989; Shearer et al. 2007). Consequently the death and collapse of these and other susceptible plant species can significantly degrade the habitat composition, complexity and structure of affected regions. However, with the exception of McDougall et al. (2002b) few studies have directly measured and quantified the impact P. cinnamomi has on vegetation structure and complexity of affected areas of the northern jarrah forest. Therefore, the aims of the present chapter are to (1) measure, quantify and compare the vegetation structure,

15 composition and complexity in P. cinnamomi affected areas and un-affected areas and (2) determine which jarrah forest habitat elements are most effected by the pathogen.

2.2. Methods and Materials 2.2.1. General features of the survey region; location and climate The survey sites were located in an area of high rainfall, 16 km south of Dwellingup in the

Darling Range (32º42’37”S, 116º 03’34”E). This area is 120 km south-east of Perth (Figure

2.1). The climate of the region is Mediterranean, with predictable long, hot dry summers and cool, wet winters (Gentilli 1989). The region receives high annual rainfall ranging between 1200-1300 mm (Figure 2.2A). The mean monthly temperature at Dwellingup ranges from 14.9º C during August to 29.6 º C in January (Figure 2.2B). Rainfall is highly seasonal with the majority falling during the winter months of June and July and just 5 % falling during the summer months (Shearer and Tippett 1989).

2.2.2. Geology and soil types The geology of the area is dominated by granite and granite gneisses, which have been intruded with older mafic rocks representing metamorphic belts (Churchward and

Dimmock 1989). In some areas of the jarrah forest, granites have been intruded by dolerite dykes and both are mantled with deep laterite profiles (Shearer and Tippett 1989). The soils are derived from highly weathered lateritic profile and are severely nutrient depleted

(Churchward and Dimmock 1989; Havel 1975a).

2.2.3. Description of the jarrah forest and vegetation communities The jarrah forest is unique to the south-west of Western Australia and is categorised as a dry, open sclerophyll forest (Shearer and Tippett 1989). The dominant species throughout the forest are E. marginata and Corymbia calophylla (marri). In the creek lines and deep

16 dissecting valleys, jarrah shares the overstorey with E. megacarpa (bullich) and E. patens

(black-butt) (Shearer and Tippett 1989). The understorey is generally open and dominated by Xanthorrhoea preissii, Banksia grandis, Allocasaurina fraseriana, Persoonia longifolia and P. elliptica (Podger 1972; Wills 1993; Shearer and Dillon 1995; Shearer et al. 2004). A diverse range of plant species from the families Proteaceae, Dilleniaceae, Fabaceae,

Liliaceae, Mimosaceae and Myrtaceae contribute to the ground cover vegetation layer

(Heddle et al. 1980a; Bell and Heddle 1989). Although E. marginata, X. preissii and B. grandis are common to many of the jarrah forest plant communities, great variation exists among understorey species. This variation has been attributed to differences in soil types, aspect, rainfall and topography (Havel 1975a; b).

2.2.4. Survey site selection Survey sites were selected to compare affected and unaffected areas of the northern jarrah forest. Sites were selected with consultation with Alcoa World Alumina and Department of

Environment and Conservation (DEC) staff with considerable experience in the northern jarrah forest and P. cinnamomi related issues in Western Australia. Six sites large enough to establish permanent hectare plots containing 25 trap stations or survey points were sought.

Six large sites were selected so to hypothetically overlap the home range of several mardos.

During the site selection process, aerial photography and maps showing the distribution of

P. cinnamomi were consulted. Once a region with highly degraded and un-disturbed forest was selected, visual assessments were undertaken to ensure that the forest degradation observed in the aerial photographs had occurred as a result of P. cinnamomi and not because of logging or fire. Following visual assessment, six survey sites were selected with degrees of P. cinnamomi-induced disturbance ranging from severely degraded (survey sites

17 1 and 2), moderately disturbed (survey sites 3 and 4) and un-disturbed (survey sites 5 and

6). The disturbance categories given to each survey site were selected by Alcoa, Murdoch and DEC staff prior to the commencement of the current survey. The aspect, soil type and dominant vegetation type according to Havel (1975a, b) at each survey site described Table

2.1. Six 1 hectare survey sites were selected. At each survey site, smaller sampling points recognised as “trap stations” were established for small mammal and vegetation surveys.

The trap stations were arranged in a 5 x 5 grid with 25 m spacing (1 hectare). The location of each trap station was marked with flagging tape, given a unique identification number and spatial coordinates were recorded (Garmon GPS unit) (Table 2.1).

18 Perth

Dwellingup

Study area

Figure 2.1. Location of survey area, 120 kilometres from Perth sites near Dwellingup, Western Australia.

19

A

300

250

200

150

100

Average rainfall (mm) Average rainfall 50

0

April May June July March August October JanuaryFebruary September NovemberDecember Month

B

35 30 25 20 15 10

(Degrees Celsius) 5 Average temperature 0

April May June July March August JanuaryFebruary October September NovemberDecember Month

Figure 2.2. The mean (± 1 SD) monthly rainfall (A) and monthly temperature (± 1 SD) (B) recorded by the Bureau of Metrology at Dwellingup for the period of January 1993 to December 2004.

20 Table 2.1. Site description including GPS location, aspect, topography, soil type and vegetation group recorded at each survey site. Site 1 Site 2 Site 3 Site 4 Site 5 Site 6

Figure 2.3 Figure 2.4. Figure 2.5. Figure 2.6. Figure 2.7. Figure 2.8. 1. Site location and description

E 412935.90 E 412505.63 E 412520.70 E 413184.26 E 414644.00 E 415340.28 1.1. Latitude and longitude N 6372043.38 N 6371488.26 N 6372363.54 N 6372007.95 N 6328402.80 N 6370364.83

1.2. Aspect North east, slightly East, slight incline North, mid slope with North, mid slope with slight West, in the lower part of a Southerly, in the lower incline slight incline incline steep gully regions of a steep gully

1.3. Topographical position Upland site, no Upland, 150 m from a Mid-slope, approximately Mid-lower slope and Mid-lower slope, a bush track Lower slope in dissecting permanent water fresh water spring 100 m from a permanent approximately 150 m from separates site from permanent deep valley. A small swamp permanent swamp stream. Stream is 40 m from permanent stream is site present alongside the site

1.4. Soil type Black gravel Black gravel Laterite Laterite Laterite and clay Clay and dolerite

2. Vegetation communities Type PS. Type PS. Type PS. Type PS. Type TS. Type Q

2.1. Havel site-vegetation Eucalyptus marginata Eucalyptus marginata Eucalyptus marginata Eucalyptus marginata Eucalyptus marginata Eucalyptus marginata types and associated Corymbia calophylla Corymbia calophylla Corymbia calophylla Corymbia calophylla Corymbia calophylla Corymbia calophylla indicator species (Bell and Acacia browniana Acacia browniana Acacia browniana Acacia browniana Acacia urophylla Heddle 1989; Havel 1975a; Adenanthos barbiger Adenanthos barbiger Adenanthos barbiger Adenanthos barbiger Banksia grandis Bossiaea aquifolium b) Allocasaurina Allocasaurina fraseriana Allocasaurina fraseriana Allocasaurina fraseriana Bossiaea aquifolium Clematis pubescens fraseriana Banksia grandis Banksia grandis Banksia grandis Hypocalymma angustifolium Eucalyptus patens Banksia grandis Daviesia decurrens Daviesia decurrens Daviesia decurrens Hovea chorizemifolia Hypocalymma Daviesia decurrens Hovea chorizemifolia Hovea chorizemifolia Hovea chorizemifolia Lasiopetalum floribundum angustifolium Hovea chorizemifolia Lasiopetalum floribundum Lasiopetalum floribundum Lasiopetalum floribundum capitellatus Phyllanthus calycinus Lasiopetalum Macrozamia riedlei Macrozamia riedlei Macrozamia riedlei Leucopogon verticillatus Leucopogon capitellatus floribundum Persoonia longifolia Persoonia longifolia Persoonia longifolia Macrozamia riedlei Leucopogon propinquus Macrozamia riedlei Phyllanthus calycinus Phyllanthus calycinus Phyllanthus calycinus Phyllanthus calycinus Macrozamia riedlei Persoonia longifolia Trymalium ledifolium Trymalium ledifolium Trymalium ledifolium Pteridium esculentum Phyllanthus calycinus Phyllanthus calycinus Pteridium esculentum Trymelium ledifolium

21 2.2.5. Evaluation of Phytophthora cinnamomi-induced disturbance At each trap station (n=150) a Dieback Expression Score (DES) was given after a visual assessment of disease status, structure, complexity and health of the local vegetation.

The DES is based on the state of four components; (1) presence or absence of susceptible and resistant “indicator species” (Table 2.2), (2) canopy cover, (3) understorey vegetation cover and (4) the depth and extent of ground covered by leaf litter and fine woody debris (woody material with a diameter less than <5 cm diameter and length >10 cm).

Each DES component was selected because they have been previously used to evaluate the disease status and distribution of P. cinnamomi (Shearer and Tippett 1989; Weste and Marks 1974). The DES values were used in this study to avoid difficulties in interpreting older P. cinnamomi infestations. Older infestations are difficult to interpret because of an absence of highly susceptible plant species and the re-colonisation by some susceptible plants (McDougall 1997). For example, the susceptible Banksia sessilis (parrot bush) rapidly and aggressively invades highly degraded regions of the northern jarrah forest (Rockel et al. 1982). Therefore, this explains why several indicator plant species were used during the present study, including the susceptible B. grandis, P. longifolia, X. preissii, B. sessilis and A. fraseriana (Table 2.2). These species are considered good indicator species because they are large, common and obvious plants that either die rapidly or strongly exhibit disease symptoms (yellow and chlorotic leaves) when infected by P. cinnamomi (Shearer and Tippett 1989).

Allocasuarina fraseriana and E. marginata vary in susceptibility and were only used when other species where absent or disease status was unclear. Typical symptoms in A. fraseriana and E. marginata are leaf senescence and canopies dominated by epicormic growth. In order to be certain that such disturbance being assessed is caused by P. cinnamomi, more than one indicator species was evaluated during each survey. Several 22 resistant species that tend to colonise areas disturbed by P. cinnamomi were also used as

indicators of post-infestation and disturbance (Table 2.2).

Table 2.2. The susceptible and resistant plant species and their degree of susceptibility to dieback and expected status and density in Phytophthora cinnamomi-affected areas according to Shearer and Dillon (1995). Plant species Dieback Degree of Expected health if Group susceptibility pathogen is present and density Banksia grandis 5 Very high Dead / Low Persoonia longifolia 5 Very high Dead / Low Xanthorrhoea preissii 5 Very high Dead / Low Banksia sessilis* 5 Very high Dead / low Allocasuarina fraseriana 4 High Dead / Low Eucalyptus marginata 4 High Dead / Low Conostylis setosa 2 Low Alive / frequent Lechenaultia biloba 2 Low Alive / frequent Corymbia calophylla 1 Low Alive / frequent * Although Banksia sessilis is highly susceptible to P. cinnamomi, it is able to invade highly degraded areas. Because of this, B. sessilis is an effective indicator of older infestations.

Table 2.3. Disease Expression Scores (DES) and description of Phytophthora cinnamomi symptoms and habitat variables used to create the symptoms rating. Dieback Expression Status of susceptible plant Vegetation Canopy Leaf litter Score and P. cinnamomi species structure cover symptoms rating 0 Absent No disease symptoms Closed Closed Thick, apparent, dense, healthy 1. Subtly or locally Dense, infrequent and Moderately Moderate Thick, but impacted localised deaths in understorey closed closed sparse 2. Moderately or Infrequent, Eucalyptus Open Open Sparse Intermediatly impacted marginata still present, but absence of susceptible understorey species 3. Severely impacted Infrequent, unhealthy in all Very open Very open Very Sparse strata layers 4. Extremely severe Absent Very open, Very open, Very open, impacted in some in some in some areas absent areas absent areas absent

The three remaining DES components include a visual analysis of canopy cover,

understorey vegetation structure as well as presence and depth of leaf litter. These

variables were included in the DES analysis because they represent changes to 23 vegetation structure and complexity following the death and subsequent foliage collapse of susceptible plant species. A DES evaluation was limited to 5 minutes per trap station.

The DES was ranked between 0 and 4, with scores of 0 representing no evidence of disease symptoms with dense vegetation, 1 subtle or localised disease symptoms, 2 moderate or intermediate impact, 3 severe impact and 4 extremely severe impact (Table

2.3).

2.2.6. Measurement and quantification of habitat variables At each trap station (n=150) 16 habitat variables were measured within a 25 m square block with the trap station positioned in the centre (total area of 625 m2). Each habitat variable was selected a priori after a comprehensive literature review of habitat use and selection by an array of small native mammal species from temperate regions of south- eastern Australia (Fox 1982; Catling and Burt 1995; Knight and Fox 2000; Monamy and Fox 2000; Catling et al. 2001; Wilson et al. 2001; Tasker and Dickman 2004; Fox and Monamy 2007; Frazer and Petit 2007). The selection of the habitat features were validated following a comprehensive literature review of the impact of P. cinnamomi in southern Australia (Weste and Marks 1974; Shea 1977; Weste and Marks 1987; Dell and Malajczuk 1989; Shearer and Tippett 1989; Shearer and Dillon 1995) (see Chapter

1). A description of each habitat variable and the technique used to measure its status is given in Table 2.4.

24 Table 2.4. Habitat variables and brief explanation of the techniques used to measure them. Each habitat characteristic was evaluated within a 12.5 m radius of each of the 25 trap stations at each survey site. 1. Tree health rating: The two nearest neighbour trees (diameter at breast height > 10 cm) to the centre of each trap station were visually assessed for twig dieback, leaf density and height of the crown. Together these data provide information for a health rating categorised as:

1. Dead or close to being dead, with few brown leaves and high leaf senescence. Open crown, therefore providing little cover 2. Dying, some leaf senescence. Open crown, mostly epicormic growth and providing little cover. Evidence of high levels of stress, 3. Moderate health, some leaf senescence and epicormic growth. Evidence of stress. 4. Good health, dense canopy with no evidence or little evidence of stress. 5. Excellent health, dense canopy.

2. Vegetation structure, composition and complexity: Phytophthora cinnamomi kills many structurally important plant species, often resulting in a significant change in the vegetation structure, composition and complexity. To measure if these changes occurred and to what extent, the following variables were measured.

2.1 Crown cover: was assessed using a densitometer. Four readings were taken at each trap station (north, east, south and west), from which an average was calculated and multiplied by 1.04 to give percentage canopy cover.

2.2. Ground cover: Direct counts of all living leaf and twig that contacted the 1 cm thick ranging pole between 0-80 cm above ground level (Cockburn 1981).

2.3. Shrub cover: Direct counts of all living leaf and twig that contacted the 1 cm thick ranging pole between 80-180 cm above ground level (Cockburn 1981).

2.4. Fine woody debris: (woody material with a diameter <5 cm and length >10 cm). Fine woody debris includes twigs and other debris that potentially provides cover for fauna was visually evaluated and categorised as:

1. Thin. None or very sparse, 2. Moderate. Scattered piles (<20 cm diameter) of fine woody debris, moderate contribution to the understorey complexity, 3. Thick. Scattered piles (>20 cm diameter) or continuous cover by fine woody debris, high contribution to the understorey complexity.

2.5. Leaf litter: The depth of the litter was visually assessed and categorised as: 1. Thin. None, or estimated depth of 0-0.5 cm, 2. Moderate. Estimated depth of 0.5-2 cm, 3. Thick. Estimated depth > 2 cm.

2.5. Percentage litter cover (or bare ground): The percentage area covered by leaf litter and fine woody debris was visually assessed.

25 3. Xanthorrhoea preissii density Xanthorrhoea preissii are highly susceptible to Phytophthora cinnamomi. They are also a dominant structural species in the understorey and provide habitat for a range of fauna. In order to evaluate the impact P. cinnamomi on X. preissii the densities of the following size and structural classes were counted. The densities of living X. preissii were derived from direct counts of individual plants within 12.5 m of each trap station. Counts were recorded for different size categories as following:

3.1. Small. Bole with crown <0.5 m tall, 3.2. Small to medium. Bole with crown 0.5 - 1.0 m tall, 3.3. Medium. Bole with crown 1.0- 1.5 m tall, 3.4. Tall with single crown. Bole with crown >1.5 m tall with single crown. 3.5. Tall with multiple crowns. Bole with crown >1.5 m tall with two or more crowns 3.6. Total X. preissii density. The cumulative total of all X. preissii categories

4. Densities and size of fallen logs and standing trees: Fallen logs are important structurally and as fauna habitat. The diameter of all fallen logs with a diameter >20 cm (at the widest section) were counted. All logs were assessed for the degree of cover and presence of hollows that could provide habitat for Antechinus flavipes (mardo). Logs were excluded if >80% of the log was outside the trap station boundary. Densities were recorded separately for two log categories: To evaluate log densities and fauna habitat potential, the following log categories were evaluated and counted:

4.1. Small to medium sized logs, with no to moderate habitat potential for mardos. Fallen logs with a diameter of 20-80 cm at the widest section. Logs with little cover and no or few hollows. 4.2 Medium to large logs with high habitat potential for mardos. Fallen logs greater than 80 cm at the widest section with plentiful cover and hollows for refuge and nesting. 4.3. Total log densities. The cumulative total of both log categories.

4.4. Trunk diameter at breast height (DBH) (cm). The diameter of the nearest tree (with a DBH >10 cm) from the trap station was measured at breast height using a standard DBH measuring tape.

2.2.7. Data analysis A Kruskall Wallis non-parametric Analysis of Variance and Tukey type a posteriori test using SPSS (SPSS Inc 2004) was used to determine if the mean Disease Expression

Score recorded at each trap station varied between survey sites (Kinnear and Gray 1994;

Zar 1999). Differences in vegetation composition and structure at each survey site were tested using Bray-Curtis similarity analysis and configured using non-metric multi- dimensional scaling analysis (nMDS). An analysis of similarity (ANOSIM 500 permutations) was also conducted. In order to evaluate which habitat variable contributed to the variance between survey sites a SIMPER (similarity percentages) analysis was conducted using PRIMER 6. A square-root transformation was applied to all habitat variables prior to analysis (Kinnear and Gray 1994; Zar 1999). The nMDS,

ANOSIM and Simper analyses were carried out using the computer package PRIMER 6

(Plymouth Routines in Multivariate Ecological Research) (Clarke and Gorley 2001).

26

Figure 2.3. Survey site 1: Contrast between open areas and dense patch of Banksia sessilis. There is a lack of leaf litter in the foreground. The small logs may be remnants of salvage logging conducted during 1970-1980 or trees killed by Phytophthora cinnamomi. An isolated Xanthorrhoea preissii in foreground.

Figure 2.4. Survey site 2: There is a lack of canopy and understorey vegetation. Scattered semi-mature Eucalyptus marginata (jarrah) and Corymbia calophylla (marri) trees are present. Logs in background may be remnants from salvage logging operations conducted during 1970-1980 or trees killed by Phytophthora cinnamomi.

27

Figure 2.5. Survey site 3: There is a lack of understorey and canopy vegetation at this site. The leaf litter is thin and several dead Eucalyptus marginata (jarrah) trees are present at this site. Logs in background may be remnants of salvage logging operations conducted during 1970-1980 or trees killed by Phytophthora cinnamomi.

Figure 2.6. Survey site 4: There is a lack of understorey and canopy vegetation and little litter and woody debris at this site. There were many dead Eucalyptus marginata (jarrah), Banksia grandis and Xanthorrhoea preissii at this site. Phytophthora cinnamomi was still active killing susceptible plants at this site during the survey period. Logs in background may be remnant of salvage logging operations conducted during 1970-1980 or trees killed by Phytophthora cinnamomi.

28

Figure 2.7. Survey site 5: Disease free region of this survey site with dense understorey and canopy vegetation consisting of Eucalyptus marginata (jarrah), Corymbia calophylla (marri), Bossiaea aquifolium, Pteridium esculentum (bracken fern) and Xanthorrhoea preissii.

Figure 2.8. Survey site 6: This site has been described as being disease free and long time undisturbed. The understorey and canopy vegetation was dense and structurally rich at this site. The site was dominated by Xanthorrhoea preissii, Eucalyptus marginata (jarrah), Corymbia calophylla (marri) and Eucalyptus patens (black butt). The thick litter layer can be seen along with a large Macrozamia riedlei in foreground of this photograph.

29 Table 2.5. The disease and degradation status as well as approximate timing of the initial Phytophthora cinnamomi infestation. The approximate timing of timber harvest, fire frequency and last time each survey was burnt, are also given. Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Figure 2.3 Figure 2.4. Figure 2.5. Figure 2.6. Figure 2.7. Figure 2.8. 1. Phytophthora cinnamomi background and current status

1.1. Approximate date of Late 1950’s to mid 1960’s Late 1950’s to mid Late 1960’s to mid 1970’s Late 1960’s to mid 1970’s Uncertain Not affected initial infestation 1960’s

1.2. Current disease status Post infestation. Old Post-infestation. Old Post infestation, 30-40 years Post infestation, 30-40 Majority of site is disease free. No disease expression, healthy. infestation, 50-60 years ago. infestation, 50-60 years ago. Some recent deaths, years ago. Plants, dying Visual evidence of old This site has not been disturbed Some colonisation by the ago suggest pathogen is still during survey period, infestation at 11 trap stations. for a long time. susceptible species, Banksia active suggests pathogen is still Difficult to estimate date when sessilis active infested 1.3. Trap stations affected 25 25 22 25 11 0 by P. cinnamomi

1.4. Trap stations not 0 0 3 0 14 25 affected by P. cinnamomi

1.5. Disturbance status (as Severely affected Severely affected Moderately affected Moderately affected Healthy forest Healthy forest determined by senior Alcoa, DEC and Murdoch staff)

2. Timber harvesting 2.1. First timber harvested 1930-1940 1930-1940 1920-1930 1920-1930 1940-1950 Prior to 1920

2.2. Last timber harvest 1970-1980 1970-1980 1970-1980 1970-1980 1940-1950 1940-1950 (decade) 2.3. Last timber harvest Pole cutting and Salvage Salvage Pole cutting and Salvage Pole cutting and Salvage Associated with Nanga Brook Associated with Nanga Brook method used timber mill. Harvest method is timber mill. Harvest method is unknown. unknown. 2.4. Number of timber 2 2 1 1 1 2 harvested

3. Fire regimes 3.1. Last burnt 1993-1994 1993-1994 1987-1988 1987-1988 1990-1991 1982-1983 3.2. Number of times burnt 9 9 9 9 7 6 (since 1920)

30 2.3. Results 2.3.1. Analysis of Dieback Expression Scores and the impact of Phytophthora Of the 150 trap stations, 108 (72.0%) were located in areas affected by Phytophthora cinnamomi. The 108 affected trap stations were located at five (Site 1, 2, 3, 4 and 5) of the 6 survey sites (Table 2.5). The number of infested trap stations varied between sites

(25 at survey sites 1, 2, 4; 22 at survey site 3 and 11 at survey site 5). The disturbance history of each survey site is presented in Table 2.5.

The Dieback Expression Score (DES) differed across sites. The overall mean (± 1 standard deviation) DES value was 2.3 ± 1.4 (n = 150), whilst between survey sites,

DES values ranged from 0.0 ± 0.0 at site 6 where no P. cinnamomi symptoms were evident, to 3.8 ± 0.4 at Site 2, reflecting the severity of the disturbance (Figure 2.9). Of the 100 trap stations set at infested sites 1, 2, 3 and 4, 79 scored a DES score of either 3 or 4. A Kruskall Wallace analysis of variance test identified a significant (χ2=100.63,

P<0.0001) difference in Dieback Expression Score between survey sites. A Tukey type a posteriori test indicated that the level of P. cinnamomi-induced degradation at sites 1,

2, 3 and 4 was similar, but markedly different from sites 5 and 6.

2.3.2. Multivariate analysis of Phytophthora cinnamomi-induced disturbance Multivariate analysis of habitat data shows clear differences in habitat structure and vegetation health across survey sites, as evident from the nMDS ordination graph

(Figure 2.10). The nMDS ordination graph shows a clear separation between sites 1, 2,

3 and 4 from sites 5 and 6. A close relationship exists between sites 1, 2, 3 and 4, which cluster together, whilst sites 5 and 6 separate from each other and the remaining four survey sites (Figure 2.10). Analysis of similarity (ANOSIM) identified a significant

(R=0.343, P<0.003) difference separating the survey sites in relation to vegetation structure and health. These results support the hypothesis that there is a significant

31 difference in habitat structure and health between sites exhibiting symptoms of P. cinnamomi and those with no disease symptoms.

The SIMPER analysis shows that 9 of the 16 habitat variables tested contributed to explaining the variance between the survey sites (Table 2.6). These variables include ground and shrub cover vegetation, percentage leaf litter cover, small and total log densities, as well as total, small, medium sized and large single crowned X. preissii density (Table 2.6). The most important variables are total X. preissii densities, ground and shrub vegetation cover, which explain between 9.79% and 62.95% of the variance that exists between the sites 1, 2, 3 and 4 from sites 5 and 6 (Table 2.6).

The mean values for each habitat variable are shown in Table 2.7. Of the 9 main explanatory habitat variables, the structure of the shrub vegetation, percentage litter cover, small, medium, tall single crowned and total X. preissii densities were all greater at sites 5 and 6 (Table 2.7). The structure of the ground vegetation was greater at sites 2,

3, 4. Small and total log densities were greater at sites 1, 2, 3 and 4 (Table 2.7).

Vegetation within the shrub layer (vegetation structure 80-180 cm above the ground) was dramatically denser at sites 5 and 6 when compared to the affected sites (1-4). In contrast, ground cover (vegetation structure 0-80 cm above the ground) was greater at survey sites 1-4 compared to sites 5 and 6. However, X. preissii densities differed dramatically between sites 1-4 and sites 5 and 6, with considerably lower densities occurring at sites 1-4. The differences in X. preissii densities between survey sites were greatest for the tall single crowned and tall, multi-crowned individuals (Table 2.7). The percentage litter cover also differed between those locations affected by P. cinnamomi

(sites 1-5) compared to site 6 which was unaffected by P. cinnamomi (Table 2.7).

32 Furthermore, ground cover vegetation, total and small log densities were greater at the severely affected sites 1-4, compared to sites 5 and 6 (Table 2.7).

5

4

3

2 Dieback Expression Score 1

0 1 2 3 4 5 6 Survey site

Figure 2.9. Mean (± 1SD) Dieback Expression Score recorded at each survey site.

Figure 2.10. Non-metric multi-dimensional scaling (nMDS) ordination created from Bray-Curtis similarity analysis of the 16 habitat variables for each survey site. Survey site are represented by their individual site number. Survey sites 1, 2, 3 are severely infested (DES 3-4), 4, 5 and 6 represent moderately (DES 2-3) affected, intermediately or subtly affected (DES 1-2) healthy or unaffected (DES 0) Eucalyptus marginata (jarrah) forest, respectively. ANOSIM full Global R comparison between survey sites R= 0.334 P<0.001 (Stress <0.01). 33 Table 2.6. Results of the SIMPER analysis following a Bray-Curtis similarity analysis to determine which of the 16 habitat variables contribute to variation separating each survey site. The term “%” represents the percentage contribution each variable contributed to the separation between each survey site. Site 1 2 3 4 5 6 Habitat variables % Habitat variables % Habitat variables % Habitat variables % Habitat variables % Habitat % variables 1 Ground cover 40.70 2 Shrub cover 20.78 Total log densities 10.74 Small -Medium log densities 8.40 Total X. preissii densities 5.16

3 Shrub cover 28.84 Ground Cover 48.25 Ground cover 24.51 Total X. preissii densities 8.55 Total log densities 11.47 % Litter cover 8.01 Small log densities 8.99 Small X. preissii densities 6.35 % litter cover 6.72 Total log densities 5.91

4 Ground cover 42.64 Ground cover 50.00 Ground cover 48.82 Shrub cover 23.06 Shrub cover 12.39 Shrub cover 13.49 Total log densities 9.61 Total X. preissii densities 9.09 Total X. preissii densities 8.40 Small log densities 7.38 Total log densities 5.72 % Litter cover 7.33 Total X. preissii densities 5.20 Small -Medium log densities 5.07 Total log densities 4.07

5 Shrub cover 49.29 Shrub cover 53.60 Shrub cover 62.95 Shrub Cover 53.86 Total X. preissii densities 14.42 Ground cover 16.57 Total X. preissii densities 13.58 Ground cover 17.98 Ground cover 11.58 Total X. preissii 11.74 Ground cover 9.79 Total X. preissii densities 12.47 Total log densities 5.04 Litter cover 2.54 Small X. preissii densities 2.62 % Litter cover 2.46 Small log densities 3.91 Total log densities 2.49 Medium X. preissii densities 1.89 Small X. preissii densities 2.45

6 Total X. preissii densities 28.12 Total X. preissii densities 24.92 Total X. preissii densities 31.80 Total X. preissii densities 26.28 Shrub cover 50.05 Shrub cover 22.25 Shrub cover 22..23 Shrub cover 28.86 Shrub cover 23.35 Total X. preissii densities 20.48 Ground cover 11.85 Ground cover 21.16 Ground cover 12.07 Ground cover 22.65 Ground cover 11.67 Total log densities 7.22 Total log densities 4.29 Medium X. preissii densities 5.02 Medium X. preissii densities 4.44 Medium X. preissii densities 3.34 Small-medium log densities 5.84 Medium X. preissii densities 4.11 Tall, single crown X. preissii densities 3.83 % Litter cover 3.14 Small X. preissii densities 3.02

34 Table. 2.7. Mean (±SD) habitat variables recorded at each survey site. Habitat variables Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Overall mean 1.Tree health rating 2.1 ± 0.1 2.68 ± 0.1 3.2 ± 0.1 2.0 ± 0.1 3.6 ± 2 4.4 ± 0.2 3.0 ± 0.09 (Ranked: 1= unhealthy, little cover to 5= dense cover) 2. Vegetation structure, composition and complexity 2.1. Percentage projected canopy cover (%) 46.7 ± 6.7 42.1 ± 5.8 75.4 ± 4.1 67.4 ± 3.1 87.7 ± 1.6 90.1 ± 1.0 68.2 ± 2.3 2.2. Vertical structure of the ground cover vegetation 6.0 ± 1.7 13.3 ± 1.8 8.0 ± 1.2 12.5 ± 1.8 7.2 ± 1.5 7.4 ± 1.1 9.1 ± 0.6 (counts of live vegetation touches between 0-80 cm) 2.3. Vertical structure of the shrub cover vegetation 6.9 ± 1.3 1.7 ± 0.6 0.9 ± 0.4 3.4 ± 1.1 21.1 ± 2.4 11.3 ± 1.6 7.2 ± 0.8 (counts of live vegetation touches between 1-180 cm) 2.4. Cover provided by fine woody debris 1.0 ± 0.4 1.1 ± 0.1 1.5 ± 0.1 1.1 ± 0.1 1.9 ± 0.1 1.9 ± 0.2 1.4 ± 0.1 (Ranked: 1=no cover, 2=moderate cover, 3=dense cover) 2.5. Depth of leafy material 1.3 ± 0.1 1.4 ± 0.1 1.7 ± 0.2 1.2 ± 0.1 2.0 ± 0.1 1.9 ± 0.2 1.6 ± 0.1 (Ranked: 1=no cover, 2=moderate cover, 3=dense cover) 2.6. Percentage litter cover (%) 39.6 ± 4.6 38.9 ± 0.6 64.0 ± 6.0 41.2 ± 5.1 77.6 ± 3.0 71.6 ± 3.9 55.5 ± 0.2 3. Xanthorrhoea preissii densities 3.1. Small X. preissii densities (counts per 625 m2) 0.2 ± 0.1 0.6 ± 0.2 0.7 ± 0.3 0.5 ± 0.2 2.3 ± 0.6 0.8 ± 0.3 0.8 ± 0.2 3.2. Small/medium X. preissii densities (counts per 625 m2) 1.0 ± 0.3 1.4 ± 0.6 1.0 ± 0.4 1.3 ± 0.4 3.2 ± 0.5 1.6 ± 0.4 1.6 ± 0.2 3.3. Medium X. preissii densities (counts per 625 m2) 0.8 ± 0.2 0.6 ± 0.5 0.6 ± 0.1 0.7 ± 0.3 3.1 ± 0.6 4.0 ± 0.8 1.2 ± 0.2 3.4. Tall, single crowned X. preissii densities (counts per 625 m2) 0.5 ± 0.1 0.1 ± 0.4 0.2 ± 0.1 0.3 ± 0.1 0.4 ± 0.1 2.6 ± 0.6 0.7 ± 0.1 3.5. Tall, multiple crowned X. preissii densities (counts per 625 m2) 0.1 ± 0.4 0.3 ± 0.1 0.2 ± 0.1 0.3 ± 0.1 0.4 ± 0.2 2.3 ± 0.6 0.6 ± 0.6 3.6. Total X. preissii densities (counts per 625 m2) 2.7 ± 0.5 3.4 ± 0.9 2.8 ± 0.7 3.5 ± 0.9 10.9 ± 2.2 11.8 ± 1.8 5.8 ± 0.6 4. Densities and size of fallen logs and standing trees 4.1. Small to medium log densities (counts per 625 m2) 3.7 ± 0.9 4.2 ± 0.7 3.2 ± 0.4 3.6 ± 0.5 1.2 ± 0.3 0.4 ± 0.2 2.7 ± 0.3 4.2. Large log densities (counts per 625 m2) 0.8 ± 0.3 0.6 ± 0.2 0.4 ± 0.1 0.6 ± 0.1 0.1 ± 0.8 0.9 ± 0.5 0.6 ± 0.1 4.3. Total log densities (counts per 625 m2) 4.2 ± 1.1 4.8 ± 0.7 3.4 ± 0.4 4.2. ± 0.5 1.7 ± 0.3 0.9 ± 0.3 3.2 ± 0.2 4.4. Diameter of trunk at breast hight (DBH) (cm) 50.4 ± 4.2 56.8 ± 5.9 55.6 ± 5.9 55.6 ± 3.5 63.2 ± 2.7 72.0 ± 6.7 58.9 ± 2.2

35 2.4. Discussion 2.4.1. The impact of Phytophthora cinnamomi on the vegetation structure, composition and complexity of the northern jarrah forest The vegetation structure, composition and complexity clearly differed between

Phytophthora cinnamomi affected and unaffected survey sites. Elevated Dieback

Expression scores recorded at sites 1 – 4 reflected greater levels of disturbance and degradation. In addition, a multivariate analysis of the habitat variables identified a clear separation between P. cinnamomi affected sites 1 – 4 from moderately affected and unaffected sites 5 and 6, respectively. Subsequent SIMPER analysis revealed that major variables contributing to the separation of affected and unaffected survey sites included ground and shrub cover vegetation, percentage litter cover, small and total log densities, small, medium, tall single crowned and total X. preissii densities. Variation among these habitat variables and the survey sites can be directly attributed to P. cinnamomi.

Phytophthora cinnamomi is considered a major threatening process to the native flora and fauna of southern and eastern Australia (Garkaklis et al. 2004; Shearer et al. 2004,

2007). Indeed, in recognition of the seriousness of the threat P. cinnamomi presents, it has been listed as a Key Threatening Process to Australia’s biodiversity according to the provisions of the Commonwealth’s Environment Protection and Biodiversity

Conservation Act 1999 (Environment Australia 2001). The impact of P. cinnamomi has been severe among many plant communities in the south-west of Western Australia

(Shearer and Dillon 1995; McDougall et al. 2002a). For example, in the northern jarrah forest, many frequently occurring and structurally dominant species, such as the E. marginata, B. grandis, P. longifolia and X. preissii are all highly susceptible to P. cinnamomi. The death and subsequent collapse of these and other susceptible plant species explains the variation in vegetation structure and complexity separating the P. cinnamomi affected and unaffected survey sites.

36 The impact of P. cinnamomi is often irreversible and long lasting because it alters successional patterns and continues to indirectly influence the health and survival of resistant plant species (Shearer and Tippett 1989). The factors contributing to the longevity of P. cinnamomi is its ability to persist during harsh conditions as dormant chlamydospores or within the tissue of resistant plant species (Shearer and Tippett

1989). During periods of optimal environmental and climatic conditions, zoospore production and dispersal will occur resulting in further disease outbreaks (Shearer and

Tippett 1989; Garkaklis et al. 2004). These outbreaks can limit seedling regeneration and kill mature plants that survived the initial infestation (Shearer and Tippett 1989;

Weste and Kennedy 1997; Weste et al. 1999). Indeed, during the course of the present study, several large E. marginata, A. fraseriana and X. preissii exhibited diseased symptoms, died and collapsed. This resulted in further degradation of the canopy and understorey vegetation. Although E. marginata, A. fraseriana and X. preissii are all susceptible to P. cinnamomi, declines in their health and eventual death may occur over a ten year period (McDougall 1997; Shearer et al. 2004). Therefore, constant monitoring of areas infested by the pathogen is highly recommended because it may take several years before the full extent of the impact of P. cinnamomi becomes evident

(Shea 1977). Evidence symptomatic of an old P. cinnamomi infestation was discovered at 11 of 25 trap stations at site 5. These symptoms were not apparent during the initial assessment, but a dramatic senescence during the 2002/2003 summer by the Bossiaea aquifolium (water bush) revealed the decaying remains of B. grandis and X. preissii.

Because of the continuing collapse at sites 3 and 4 and the unexpected discovery of P. cinnamomi symptoms at site 5, the initial dieback categories were relinquished for a more quantitative assessment using Dieback Expression Scores (DES).

37 2.4.2. Interpreting the impact of Phytophthora cinnamomi using the Dieback Expression Score. A clear difference in the mean Dieback Expression Scores was evident between survey sites. The DES score was created to provide a suitable measure that is low cost, reliable and easy to replicate that can be used to quantify the impact of P. cinnamomi. Previous studies have identified some difficulties in measuring the impact of P. cinnamomi, especially in older infestations (McDougall 1997; Shearer et al. 2004, 2007). Older infestations are difficult to interpret because of an absence of highly susceptible indicator species including B. grandis and P. longifolia. In contrast, re-colonisation by susceptible plants species may occur if P. cinnamomi is completely absent from previously diseased areas. For example, both B. sessilis (parrot bush) and X. preissii were recorded at severely affected survey sites. Banksia sessilis rapidly and aggressively invades highly disturbed and degraded jarrah forest, where it becomes a principle structural component. Indeed, B. sessilis has successfully colonised a section of site 1, forming a dense, structurally rich patch of vegetation that overlaps 10 of the 25 trap stations. The factors contributing to the occurrence of B. sessilis in highly degraded areas are unclear and require investigation. Regeneration of the highly susceptible X. australis has been recorded in the Brisbane Ranges, Grampians and Otway National

Parks (Weste and Kennedy 1997; Weste et al. 1999; Cahill et al. 2002). Weste and

Kennedy (1997) suggest that the regeneration of susceptible species may depend on the susceptibility of individual species, propagule type (seed or root stock), presence of pollinators, seasonal conditions and the presence of the pathogen. The ability of B. sessilis to colonise highly degraded areas is possible due to its rapid growth and profuse production of viable seed (Rockel et al. 1982). Other contributing factors include variations in soil type, structure of underlying cap rock, aspect, topography and life cycle of individual plant species are likely contributors to the survival of these plant species. Indeed, the top soil where the dense patch of B. sessilis occurred at site 1 was

38 found to be considerably deeper (>30 cm) in comparison to the rest of site 1 (<10 cm to cap rock). In contrast to these observations, a recent study discovered that depressions in the cap rock allowed water to pool beneath the top soil, which appeared to influence the expression of disease symptoms among jarrah forest vegetation (Gleeson 2002). An understanding of the relationship between P. cinnamomi and structure of the underlying cap rock is required and may contribute to slowing or restricting the spread of

P. cinnamomi.

2.4.3. The impact of Phytophthora cinnamomi on the forest floor litter The presence of living healthy plants generally encourages a thick leaf litter layer cover on the forest floor. Therefore, the percentage litter cover was found to be greater at the

P. cinnamomi free locations. In contrast, the death and collapse of susceptible plant species results in a dramatic but short term increase in forest floor litter, which rapidly disappears following fire, natural decomposition, wind and water movement (Podger

1972). Generally, litter is not replenished, leaving large areas of bare ground. The susceptible plants E. marginata, B. grandis and resistant C. calophylla contribute 80 –

90% of the litter biomass in unaffected areas of the northern jarrah forest (McDougall

1997). The removal of the susceptible E. marginata and B. grandis has serious implications on the litter biomass, moisture content in the soil and nutrient cycles

(Postle et al. 1986).

2.4.4. The impact of Phytophthora cinnamomi on the structure of the understorey vegetation At the P. cinnamomi affected areas, there was a very slight increase in the structure of the ground level plant species (Figure 2.3-2.6). This increase was due to the presence of resistant grasses, sedges, small resistant shrubs including, Conostylis species, Hibbertia hypericoides, Trymalium ledifolium and Lechenaultia biloba. The opening of canopy

39 and shrub layer vegetation appears to contribute to the survival and growth of the resistant grasses, sedges and shrubs. By contrast, in those areas not affected by P. cinnamomi, reduced sunlight and increased litter may limit the growth and survival of ground cover species.

2.4.5. The impact of Phytophthora cinnamomi on fallen log densities An increase in the small-medium log densities was evident at all of the P. cinnamomi affected survey sites. This was possibly a result of the salvage logging operations undertaken during the 1970’s and 1980’s. Salvage logging operations were conducted in the area after Mackay and Campbell (1973) discovered that the timber cut from dead and P. cinnamomi affected E. marginata were more prone to shrink and warp while curing when compared to unaffected trees. This resulted in the removal of the larger and more productive E. marginata from areas under threat of becoming infested. The small logs at the infested sites are possibly the waste timber from the crowns left behind after the useful and larger sections of the tree were removed for timber. Indeed, each survey site has historically been affected by fire and the activities associated with timber extraction. However, the capacity to interpret the impact of these disturbances is impeded by a scarcity of written records, aerial photography and published literature. In addition, the impact of logging and fire has been obscured by the irreversible and permanent degradation caused by P. cinnamomi.

2.5. Concluding remarks Phytophthora cinnamomi kills a broad range of common and structurally important jarrah forest plant species. After these susceptible plant species die, collapse and decompose, dramatic changes occur within the vegetation structure and composition.

Currently, P. cinnamomi has a wide distribution throughout the northern jarrah forest,

Swan Coastal Plain and south coastal areas of Western Australia. The impact on

40 structure of the vegetation in these areas is also significant and warrants investigation.

Understanding the impact P. cinnamomi has on the vegetation structure will contribute to our understanding and management of resistant plant species and fauna communities.

In addition, understanding the processes and the role of B. sessilis among highly degraded dieback areas of the jarrah forest may contribute to rehabilitation strategies and protocols. This is because many invertebrate and vertebrate species depend on structurally rich areas for nesting, refuge cover, food and protection from predation.

Although P. cinnamomi infestation is wide spread throughout the northern jarrah forest, its impact has been patchy, varying with topography, aspect, soil type and densities of susceptible plant species. Consequently, the forest currently exists as a patchy mosaic of disease-free, long time undisturbed forest (site 6), subtly affected (site 5), severely degraded (Site 2, 3 and 4) regions and significantly degraded regions that have been colonised by B. sessilis (Site 1). Investigation into how fauna respond to disturbance caused by P. cinnamomi is of high importance and form the aim of the next part of this thesis.

41 CHAPTER 3. THE IMPACT OF PHYTOPHTHORA CINNAMOMI ON THE DISTRIBUTION OF THE MARDO IN THE NORTHERN EUCALYPTUS MARGINATA (JARRAH) FOREST, WESTERN AUSTRALIA

3.1. Introduction The introduced plant pathogen Phytophthora cinnamomi is recognised as one of 13 key

Threatening Processes to the Australian environment (Commonwealth Environment protection and Biodiversity Conservation (EPBC) Act 1999) (Environment Australia

2001), because of its lethality to a wide range of threatened flora and its capacity to change community floristic richness and structure throughout southern Australia

(Shearer et al. 2004, 2007). In the south-west of Western Australia, 41% of native plant species are susceptible and few northern jarrah forest over-storey and understorey plant species are resistant (Shearer et al. 2007). The structurally dominant species Eucalyptus marginata (jarrah), Banksia grandis and Xanthorrhoea preissii are all highly susceptible

(Shearer and Dillon 1995; Shearer et al. 2007). The death and collapse of these and other susceptible plant species degrades habitat structure and quality for fauna.

Our understanding of how P. cinnamomi affects native plant communities is comprehensive (Podger 1972; Shea 1977; Shearer and Tippett 1989; Shearer et al. 2004,

2007; Cahill et al. 2008), but its impact on fauna communities is poorly understood

(Garkaklis et al. 2004). Following disturbance from P. cinnamomi there is often very little or no regeneration of susceptible plant species, and therefore the effect upon the vegetation composition is permanent (Shearer and Tippett 1989). A more open forest results with increased exposure of fauna to exotic predators, while many important habitat and food plants are killed (Wilson et al. 1994). Similar reductions in habitat quality have contributed to declines among the diversity and density of small mammals in eastern Australia (Knight and Fox 2000; Monamy and Fox 2000; Masters et al. 2003;

42 Garkaklis et al. 2004; Tasker and Dickman 2004). However, similar studies for south- western Australia are lacking.

The present study evaluates the effect of P. cinnamomi-induced plant death and habitat disturbance on the Antechinus flavipes leucogaster or mardo, a small, highly active, nocturnal, carnivorous marsupial common in a range of jarrah forest, karri forest and associated woodland plant communities (Wardell-Johnson 1986; Menkhorst and Knight

2001; How et al. 2002; Crowther 2008). The mardo is a habitat generalist and is routinely captured using conventional trapping methods, therefore we considered it to be an excellent indicator species according to criteria suggested by Noss (1989; 1999).

The specific aim of the present study was to determine the relationship between P. cinnamomi-induced disturbance and the distribution of the mardo in the northern jarrah forest using the patch occupancy models developed by MacKenzie et al. (2002). It was hypothesised that mardos would be negatively impacted on by habitat modification caused by P. cinnamomi and therefore would be absent or less likely to occur in infected areas. These investigations will provide insights into the preferred habitat of the mardo and factors influencing their distribution in the northern jarrah forest.

3.2. Methods 3.2.1. Study site The study locality, major regional features and general trapping procedures are described in Chapter 2, Section 2.2. More specific detail relevant to analyses carried out in this chapter is explained below.

3.2.2. Mardo trapping procedures Mardo trapping surveys were conducted from May 2002 to April 2004. Each survey was carried out over 4 consecutive nights, except during May-August 2002 (preliminary

43 surveys), and December 2002 and November 2003, when only 3 nights were surveyed

(Table 3.1). Trapping surveys during October 2003 were conducted over two consecutive nights to reduce stress to the pouch young-bearing adult females (Table

3.1). The trapping surveys scheduled for December 2003, February and March 2004 were cancelled due to inclement weather conditions.

Six survey sites were assessed. At the commencement of the study, these sites were selected as two severely degraded sites (sites 1 and 2), two moderately disturbed sites

(sites 3 and 4) and two healthy forest sites (sites 5 and 6). However, upon closer inspection during the course of the study, the impact of P. cinnamomi was determined to be greater than suggested by first impression at sites 3 and 4, and an old, localised infestation was discovered at the 'healthy' forest site 5. The infestation at site 5 covered

11 of the 25 trap stations and was not noticeable until summer of 2003 following a significant senescence by Bossiaea aquifolium (water bush), which revealed several dead P. cinnamomi susceptible plants. Rather than discounting and relocating the survey site an ‘adaptive management’ approach was undertaken. McCarthy and Possingham

(2006) describe adaptive management as a ’balance of the requirements of management with the need to learn about the systems being managed, which leads to better decisions’. As previously stated, an absence of this knowledge will result in a failure to design and implement appropriate management and conservation protocols. Therefore, the 'forest health' categories were relinquished in favour of more quantitative assessment of P. cinnamomi impact in the analyses using the mean DES values recorded at each survey site (Chapter 2). This resulted in survey sites 1, 2, 3, 4 becoming recognised as being severely affected (DES, 3 – 4), site 5 as intermediately affected (DES, 1 – 2) and site 6 as being unaffected (DES, 0).

44 At each survey site, 25 trap stations were established in a grid formation at 25 metre intervals. During each survey period a medium-sized aluminium Elliott type b trap (33 x

11 x 10 cm) was set at each trap station. Traps were baited with a mixture of rolled oats, peanut butter, honey and vanilla essence. Each trap was covered with a calico bag and then a plastic bag during the winter months for additional water protection. Shredded paper was placed inside the trap to provide nesting material. Traps were checked and emptied between 0500 and 1000 hours. Traps were closed for the day during hot summer (December-February) months, and were reopened and rebaited in the late afternoon. Before release at the point of capture, each animal was sexed, weighed

(nearest 0.5 g) and head, tail and short pes were measured (nearest mm). Each new individual was given an identifiable ear notch (kept for DNA analysis). Trapping was conducted in accordance with Wildlife Conservation Act 1950 and appropriate

Department of Environment and Conservation (DEC) permits to take fauna for scientific purposes (permit numbers SF003985 and SF004310; Murdoch University

Ethics Approval Number. 910R/02).

3.2.3. Data analysis and model development Patch occupancy models developed by MacKenzie et al. (2002) were used to estimate the probability of a survey site or trap station being occupied by mardos. These methods use detection-nondetection data and multinomial likelihood methods to estimate patch occupancy parameters (MacKenzie et al. 2002; 2006). To model detectability (p) and mardo occupancy (ψ), a set of candidate models were developed. Patch occupancy models use data collected from the trap stations, the dynamics of individual animals is not incorporated into this analysis. The data sets contain data (1) representing the presence of mardos or (0) representing absence of mardos. For example, this hypothetical data ‘0101’ represents four monthly trapping sessions for a single trap

45 station. This data set suggests that mardos were absent for the first month and third months, but present for the second and forth month.

The data used to confirm occupancy were encounter histories from only those trap stations where multiple mardo detections were recorded. If a trap station recorded a single detection during the entire survey period (May 2002 – April 2004), this single detection was removed from the encounter history and modified to represent non- detection. This multiple detection criterion was applied since MacKenzie and Royle

(2005) define a particular survey site or area as being “occupied” only if the target species is physically present on more than one survey occasion. Multiple detections suggest several important qualities including: 1) mardos are regularly present, 2) the trap station lies within the home range of one or more mardos and therefore is regularly visited during normal foraging activities, and (3) the surrounding habitat offers suitable characteristics that encourage the presence of mardos. By contrast, single detections were deemed “random use” as opposed to true occupancy of a patch (MacKenzie 2005;

MacKenzie and Royle 2005). This approach for determining detection-nondetection data was implemented because Antechinus males dramatically increase their foraging activities and home range just prior to the mating period, which often results in the use of inhospitable habitat (Carter 2003). Therefore, these males are likely to encounter trap stations they would not normally encounter, and their presence may therefore reflect social factors rather than habitat characteristics. Supporting this temporal nature of males utilising inhospitable habitat, the majority of the single detections were identified during the breeding period in July and August.

Parameters and covariates included in the patch occupancy models were selected a priori after a comprehensive literature review of the impact of P. cinnamomi-induced disturbance and habitat degradation in southern Australia (Podger 1972; Podger et al. 46 1965; Wilson et al. 1990; Wills 1993; Wilson et al. 1994; Shearer and Dillon 1995;

Shearer et al. 2004; Laidlaw and Wilson 2006) (See Chapter 1). The parameters and covariates used during the current study are explained in Table 3.2.

Model fitting and selection was performed using the Patch Occupancy function in

Program MARK Version 4.3 which utilises the Akaike Information-Criteria (AIC) theoretic approach (Burnham and Anderson 2002). Because the ratio of sample size to the number of variables was less than 40, AICC was used as the basis for model selection, which is a second order bias-corrected form of AIC (Burnham and Anderson

2002). Akaike weights (wi) (MacKenzie and Bailey 2004) were calculated for each model using Program MARK, to measure the relative likelihood of each model given the data and the candidate model set. A goodness-of-fit test (MacKenzie and Bailey

2004) for the global model found evidence of over-dispersion (χ2= 81.073; P= 0.05; over-dispersion factor ĉ = 1.733) within the data. In order to compensate for this, quasi- likelihood adjustments were made, so that all final analyses were carried out on QAICC

(quasi-AICC) values as suggested by Burnham and Anderson (2002).

47 Table 3.1. Summary of Antechinus flavipes (mardo) trapping effort undertaken during each survey period at each trapping site, including the expected trap nights and the actual number of trap nights taking account Corvus coronoides perplexus (Australian raven) trap interference. Trapping Date Sites surveyed Expected Actual trap occasion trap nights nights 1. May 1 07/05 - 09/05/2002 1, 2, 3, 4 300 300 27/05 - 29/05/2002 5 75 75 2. June 1 03/06 - 05/06/2002 1, 2, 3, 4 300 300 24/06 - 26/06/2002 5 75 75 3. July 1 08/07 - 10/07/2002 1, 2, 3, 4 300 147 29/07 - 31/07/2002 5 75 75 4. August 1 04/08 - 06/08/2002 1, 2, 3, 4 300 185 26/08 - 28/08/2002 5 75 75 5. October 27/10 – 30/10/2002 1, 2, 3, 4 400 400 6. November 17/11 – 20/11/2002 5, 6 200 200 7. December 2 16/12 – 18/12/2002 1, 3, 4 225 225 8. January 14/01 – 17/01/2003 5, 6 200 200 9. February 02/02 – 05/02/2003 1, 2, 4, 5 400 400 10. March 02/03 – 05/02/2003 1, 2, 3, 6 400 374 11. April 07/04 – 10/04/2003 1,2, 5, 6 400 358 12. May 08/05 – 11/05/2003 5, 6 200 200 13. June 16//06 – 19/06/2003 1, 2, 3, 4 400 335 14. July 07/07 – 10/07/2003 3, 4, 5, 6 400 335 15. August 16/08 – 19/08/2003 1, 2, 4, 6 400 372 16. October3 13/10 – 14/10/2003 1, 3, 5, 6 200 200 17. November 2 28/11 – 30/11/2003 5, 6 150 150 18. January 26/01 – 29/01/2004 1, 5, 6 300 300 19. March/April 29/03 – 01/04/2004 1, 6 200 200 Total 5975 5481 1. Preliminary trapping surveys, conducted by Honours students M. Lilith, C. Gaskin and J. Wood; all other trapping conducted by R. Armistead. 2. Survey shortened to 3 nights because of extreme weather conditions 3. Survey period shortened 2 nights to reduce stress on mothers with pouch young

48 Table 3.2. An explanation of the terms, model parameters and covariates used to model the impact Phytophthora cinnamomi has on Antechinus flavipes (mardo). Parameter Description

Time 1. No effect of time (.); or 2. Differences in detectability across the 20 survey periods (time); or 3. Account for typical Antechinus life history and show expected trend among male activity (life history). Covariate Dieback 1. Covariate was not included in the analyses (a 'naïve' model); Expression or Score (DES) 2. The degree of disturbance caused by P. cinnamomi (assessed using the methods described in Chapter 2 and Table 2.2) was included. The mean DES from the 25 trap stations at each survey site was included as a covariate in the analyses; termed '(DES)'.

Model The term “and” represents the main and interactive affects, whilst notation “+” represents the additive affect only.

3.3. Results 3.3.1. Trapping data Multiple mardo captures were recorded at 51 of the 150 trap stations (34% naïve occupancy estimate) (Table 3.3). At the 51 trap stations, 73 mardo individuals were captured 310 times (Table 3.3). The number of individuals recorded at each study site varied, ranging from 4 individuals at the severely infested site 4, to 37 recorded at the healthy forest site 6 (Table 3.3). Of the 73 individuals recorded, 47 were male and 26 female (Table 3.3). More males than females were recorded at four of the six sites

(exceptions were sites 3 and 5). There were no female mardos recorded at sites 2, 3 and

4. The greatest number of females (14 females captured 67 times) and males (23 males captured 99 times) were recorded at sites 5 and 6 respectively (Table 3.3).

Of the 51 successful trap stations, only 16 were located in areas identified as effected by

P. cinnamomi (DES ≥ 1) whilst 35 were in unaffected areas (DES = 0). The greatest number of successful trap stations was 21 trap stations recorded at the healthy forest site

6. By contrast, there were no multiple captures recorded at the severely-infested sites 2

49 and 3 (Table 3.3). There was little overall difference in the numbers of trap stations recording multiple detections, with 36 trap stations recording multiple detections of females and 39 for males. However, at each site there were generally more trap stations recorded multiple male detections (the exception being site 5, where more trap stations recorded female captures) (Table 3.3).

Table 3.3. The number of Antechinus flavipes (mardo) individuals and captures recorded at trap stations where multiple mardo detections were recorded. The number of trap stations at each survey site that recorded multiple mardo detections, and the total number of captures recorded for the entire study are given. The level of Phytophthora cinnamomi disturbance at each site is indicated as the mean ± 1SD Dieback Expression Score (DES). Site DES Total Number of individuals and Number of Total (mean ± 1SD) trap captures recorded at trap trap stations captures nights stations successful for successful for (♂,♀) multiple detections multiple detections Individuals Captures (♂,♀) (♂,♀) 1 3.20 ± 0.71 1068 10 (6, 4) 45 (30, 15) 81 50 (34,16) Severely affected 2 3.80 ± 0.41 629 0 (0,0) 0 (0.0) 01 1(1,0) Severely affected 3 3.08 ± 1.09 761 0 (0,0) 0 (0,0) 0 7 (2, 5) Severely affected 4 2.76 ± 1.01 819 4 (4,0) 4 (4,0) 21 4 (4,0) Moderately affected 5 1.88 ± 1.01 1190 22 (14, 8) 95 (26, 69) 202 113 (28, 85) Subtly affected 6 0.00 ± 0.00 1014 37 (23, 14) 166 (99, 67) 21 180 (110, 70) No affect Total 3.29 ± 1.62 5481 73 (47, 26) 310 (159, 151) 51 355 (179, 176) 1 All successful trap stations were located at P. cinnamomi disturbed locations. 2 Six of the successful trap stations at site 5 were located at P. cinnamomi disturbed locations.

50 3.3.2. QAICC model selection

The five top QAICC ranked models suggest that the most useful predictors of mardo detectability and patch occupancy are site, the level of P. cinnamomi impact, as determined by the Dieback Expression Score, and gender (Table 3.4). The five top ranking models were extremely competitive with low ∆QAICc values (0.00 – 2.23) and moderate to high Akaike weights (wi) (0.103 - 0.316) (Table 3.4). The cumulative model Akaike weights of the 5 top ranked models suggest that these models explain

98.2% of the variance within the data, while the remaining models only explain 1.2%.

Indeed, according to Burnham and Anderson (2002), inferences should be based only on those models that contribute 90% or more of the Akaike weights; in this study, the five top-ranked models fit this criterion. Therefore, the remaining site and habitat parameters either singularly or in combination, were deemed unlikely.

The five top-ranked models had similar site and habitat parameter structure, with no effect from any of the parameters on mardo detectability (p), whilst site, gender and the covariate DES were identified as the most important predictors of mardo patch occupancy (ψ) (Table 3.4). This may be due to the strong correlation between sites and the DES score, with the more degraded sites 1, 2, 3 and 4 having greater DES scores when compared to lesser disturbed sites 5 and 6 (Table 3.3). Only a slight difference in detectability rates exists between male and female mardos (Figure 3.1).

The difference in mean DES score between the severely affected sites (1, 2, 3 and 4) and subtly affected site 5 and healthy forest site 6 was clearly dramatic, as indicated by the mean Dieback Expression Scores (Table 3.3). The effect DES has on mardo detectability (p) is evident in both the third and fourth-ranked models. The effect DES has on mardo patch occupancy probability (ψ) was revealed in a negative relationship between DES and mardo occupancy for the top-ranked model:

51 {p (.) ψ (site + DES); slope on the logit scale: - 0.50 [0.338SE]} as well as a lesser degree for the third-ranked model:

{p ( . + DES) ψ (site + DES); slope on the logit scale: -0.06 [0.09SE]}

The inclusion of Dieback Expression Score in these models therefore provides strong evidence linking P. cinnamomi-induced disturbance to reduced mardo occupancy.

Because the five top-ranked QAICC models had similar explanatory ability, parameter estimates for detectability and patch occupancy were model-averaged (Burnham and

Anderson 2002). The model-averaged data indicate that there were significant differences in patch occupancy probability (ψ) across the 6 survey sites (Figure 3.2).

The greatest model-averaged occupancy rates were recorded at the subtly affected site 5 and the healthy forest site 6 (Figure 3.2). The lowest occupancy probability rates were recorded at the severely-infested sites 2, 3 and 4 (Figure 3.2). This suggests that there is a 51.50% and 40.71% likelihood of a mardo occupying a subtly affected and healthy forest site, respectively, compared to a zero to 25.24% likelihood that a mardo would be recorded at a trap station set at a survey site in an area affected by P. cinnamomi. It is noteworthy that the probability of occupancy (25.24%) recorded at site 1, is considerable greater than that recorded at the other severely-infested sites (Figure 3.2).

52 Table 3.4. Summary of model selection results fitting Antechinus flavipes (mardo) detectability (p) and patch occupancy (ψ) model to the mardo trapping data. The term “and” represents the main and interactive affects of the parameters (site, time and gender), whilst “+” indicates the additive affect of a habitat covariate. DES = Dieback Expression Score. Rank Model QAICC ∆ QAICC Model weight Likelihood # Parameters in Deviance (wi) model 1 p (.) ψ (site + DES) 539.89 0.00 0.316 1.000 8 523.35 2 p (.) ψ (site) 540.00 0.11 0.298 0.946 7 525.59 3 p (. + DES) ψ (site + DES) 541.52 1.63 0.139 0.443 9 522.84 4 p (. + DES) ψ (site) 541.73 1.83 0.126 0.399 8 525.18 5 p (gender) ψ (site) 542.13 2.23 0.103 0.328 8 525.59 6 p (site) ψ (site) 546.87 6.98 0.009 0.031 12 521.68 7 p (.) ψ (site and gender ) 549.72 9.82 0.002 0.008 15 522.32 8 p (site) ψ (site and gender) 550.47 10.58 0.002 0.005 6 518.62 9 p (site) ψ (gender) 550.92 11.02 0.001 0.004 14 538.60 10 p (gender) ψ (site and gender) 551.93 12.04 0.001 0.002 30 522.32 11 p (time) ψ (site) 552.24 12.35 0.001 0.002 7 484.59 12 p (site) ψ (.) 553.67 13.78 0.000 0.001 30 53926 13 p (time and gender) ψ (gender) 558.84 19.85 0.000 0.000 18 491.19 14 p (site and gender) ψ (site) 559.07 19.18 0.000 0.000 13 520.39 15 p (site and gender) ψ (.) 563.61 23.71 0.000 0.000 14 536.21 Top 15 models are shown, the remaining models are shown in Appendix 1.

53 0.5

0.4

0.3

0.2

0.1 Probability of detection

Female Male

Gender

Figure 3.1. The probability (±SE) of detecting male and female Antechinus flavipes (mardo) according to the estimates given in the fifth ranked model.

1 0.9

0.8 0.7 0.6 0.5 0.4 0.3 0.2

Probability occupancy of 0.1 0 0 1 2 3 4 5 6 Survey sites

Figure 3.2. The probability estimates (±SE) of Antechinus flavipes (mardo) patch occupancy (ψ) by model averaging from the 4 top ranked models (bars represent confidence intervals). Survey sites 1, 2, 3, are severely infested (DES 3-4), 4, 5 and 6 represent intermediately or subtly affected (DES 1-2) healthy or unaffected (DES 0) Eucalyptus marginata (jarrah) forest, respectively.

54

3.4. Discussion The disturbance and habitat degradation caused by P. cinnamomi clearly impacts upon the distribution of mardos in the northern jarrah forest. Patch occupancy models identified site and Dieback Expression Score as representative surrogates to the habitat degradation caused by P. cinnamomi and as important predictors of mardo patch occupancy. Phytophthora cinnamomi is a significant pathogen to many common and structurally-important jarrah forest plant species (Shearer and Dillon 1995; Shearer et al. 2007). Consequently, following the death, collapse and decomposition of these structural important plant species, the subsequent degradation to the vegetation structure and complexity is often severe (Chapter 2). The Patch Occupancy models strongly suggest that mardos are less likely to occupy areas degraded by P. cinnamomi in favour of the structurally diverse, disease-free forest. Mardos possibly avoid areas degraded by

P. cinnamomi because of declines to vegetation structure and complexity.

3.4.1. Patch occupancy assumptions Before these results can be accepted with confidence, the assumptions and performance of the habitat model must be assessed. There are three underlying assumptions governing the patch occupancy estimates (MacKenzie et al. 2003). Every attempt was made to avoid violating these assumptions in this study. The first assumption is closure of all survey sites during the survey period. To ensure closure of the populations, only the encounter histories from trap stations that recorded multiple captures were used for analyses. The second patch occupancy assumption requires that the target species (i.e. the mardo) is not misidentified. Mardos are the only Antechinus species in the northern jarrah forest and have a distinct morphology from the other small mammal species encountered during this survey, including Sminthopsis dolichura (little long tailed dunnart) and Cercartetus concinnus (). Neither of these species

55 were encountered in Elliott traps used to capture mardos. Therefore, it is very unlikely that this assumption had been violated. The final assumption requires each survey site to be independent. This assumption was not violated because each survey site was separate by at least 1000 m, and a mardo home range is possibly no larger than 500 m (Carati

1982). Although all attempts were made to avoid violating these assumptions, some over-dispersion was identified within the encounter history data, which resulted in a modification of the variance inflation factor (ĉ).

3.4.2. The threat and impact of Phytophthora cinnamomi in the northern jarrah forest Many structurally important jarrah forest plant species are susceptible to P. cinnamomi including the E. marginata, B. grandis and X. preissii. As shown in Chapter 2, the death, collapse and the decomposition of these susceptible plant species results in substantial changes within the understorey vegetation structure, leaf litter cover, log and

X. preissii densities. In addition, previous studies also identified significant changes within the canopy vegetation and coarse woody debris following infestation from P. cinnamomi (Weste and Marks 1974; Shearer and Tippett 1989; Wardell-Johnson and

Nichols 1991). As mentioned in Chapter 2, the impact resulting from P. cinnamomi infestation can often be permanent, since the pathogen can persist in the soil for long periods effectively restricting the growth and survival of any highly susceptible plant species (Shearer and Tippett 1989). Banksia sessilis may be an important exception to this general rule, since these plants can invade highly degraded areas. For example, at site 1, a very dense patch of B. sessilis was present, which may explain the increased probability of mardo patch occupancy recorded at this site, compared to the other severely affected survey sites. Therefore, the increase in vegetation structure that B. sessilis contribute may play an important role in maintaining mardos in these highly degraded areas of the jarrah forest.

56

3.4.3. Factors affecting the distribution of the mardo in the northern jarrah forest This study examined the affect P. cinnamomi has on forest structure and distribution of the mardo, but other factors may influence the distribution of the mardo, including predation, competition, aspect, topography and climatic variability. Indeed, previously conducted studies have identified significant reductions in nest and refuge site availability, prey and food resources, while increasing the likelihood of predation

(Wilson et al. 1994; Rhind 1998; Sutherland and Predavec 1999; Knight and Fox 2000;

Garkaklis et al. 2004; Tasker and Dickman 2004; Laidlaw and Wilson 2006; Tulloch and Dickman 2006; Frazer and Petit 2007; Holland and Bennett 2007). In terms of exposure to predation, the mardo is vulnerable to other native marsupials (quoll or

Dasyurus species), birds of prey, as well as introduced cats and foxes (Kinnear et al.

1988; 1998; Risby et al. 1999; 2000). Certainly, likely predators of the mardo were recorded on or within close proximity to the study sites. Chuditch (Dasyurus geoffroii) were regularly captured during simultaneous trapping surveys, while the tawny frogmouth (Podargus strigoides), Australian owlet-nightjar (Caprimulgus cristatus) and black kite (Milvus migrans) were recorded in surrounding areas. In addition, feral cats and foxes are also likely to occur in these areas (although none were recorded during the study period). Feral foxes and cats are known to have a devastating impact on the native fauna (Kinnear et al. 1988; 1998; Risby et al. 1999; 2000).

Another important factor possibly influencing mardo occupancy rates is a decline in their major food resources. The diet of the mardo mostly consists of invertebrates and to a lesser degree birds and reptiles (Hindmarsh and Majer 1977; Majer 1978). Previous studies in the karri forest and southern and northern jarrah forest indicate that mardos consume invertebrates from Araneae (spiders), Diplopoda (millipedes), Blattodea

(cockroaches), Coleoptera (adult and larval beetles) Dermaptera (earwigs), Homoptera 57 (plant feeding bugs), Heteroptera (predatory bugs) and Hymenoptera (ants, wasps and bees) (Hindmarsh and Majer 1977; Majer 1978; Sawle 1979). Studies have linked declines in invertebrate abundance and diversity to the presence of P. cinnamomi, especially among leaf litter inhabiting species (Nichols and Burrows 1985; Postle et al.

1986). Following the decline in canopy and understorey vegetation due to P. cinnamomi, there is a reduction in the standing biomass of forest floor litter, which results in the microclimate of the soil and litter becoming unfavourable to litter- inhabiting invertebrate taxa such as Blattodea, Dermaptera, and Araneae (Postle et al.

1986; Majer and Abbott 1989).

3.4.4. The threat of Phytophthora cinnamomi to other northern jarrah forest fauna Other jarrah forest fauna, and potential prey for mardos, are affected by P. cinnamomi include birds, reptiles and frogs (Hindmarsh and Majer 1977; Majer 1978; Nichols and

Watkins 1984; Nichols and Bamford 1985; Armstrong and Nichols 2000). Nichols and

Bamford (1985) observed a dramatic decline in the abundance and diversity of the most common jarrah forest reptiles with the exception of the Pogona minor (dwarf bearded dragon) and the Cryptoblepharus plagiocephalus (callose-palmed fence skink). These two species prefer open forest areas and therefore appear to benefit from the opening of vegetation structure following P. cinnamomi infestation. Other reptile species (including

Ctenotus labillardieri, Egernia napoleonsis, Lerista distinguenda, Menetia greyii and

Morethia obscura) apparently prefer areas of structurally-rich vegetation, and are either absent or observed in significantly reduced densities in P. cinnamomi disturbed areas

(Nichols and Bamford 1985). Although P. cinnamomi has been implicated as a factor affecting frog abundance and diversity in the jarrah forest, Nichols and Bamford (1985) offer no data or information linking frog declines to the impact of P. cinnamomi. Of the six frog species detected during their study, only two species, Pseudophryne guentheri and Heleioporus eyrei, were recorded in insufficient densities to associate them with 58 particular habitat features and to the impact of P. cinnamomi. In terms of birds,

Armstrong and Nichols (2000) observed relatively high densities of species that prefer open or cleared forest areas in P. cinnamomi affected areas, including Lalage suerii

(white winged trillers), Calyptorhynchus magnificus (red-tailed cockatoos), Cracticus tibicen (Australian magpies) and Rhipidura leucophrys (willie wagtails). By contrast, bird species that prefer dense forest, including the Eopsaltria griseogularis (western yellow robin), Colluricincla harmonica (grey shrike thrush) and Climacteris rufa

(rufous tree-creeper), were less abundant in P. cinnamomi effected areas. Therefore, the loss of vegetation following P. cinnamomi infestation can have severe implications for animal species that prefer dense areas of vegetation. In turn, such absences can affect the abundance of their natural predators.

3.4.5. The impact of Phytophthora cinnamomi on native mammals from eastern Australia Declines in mardo occupancy due to the impact of P. cinnamomi is consistent with the results of similar studies conducted in the Brisbane Ranges and the Anglesea coastal heath, south-west of Melbourne. These studies record that reductions in vegetation structure following P. cinnamomi infestation also contribute to declines in small mammal abundance and diversity (Newell and Wilson 1993; Newell 1994; Laidlaw

1997; Laidlaw and Wilson 2006). Indeed, a previously conducted study in P. cinnamomi affected areas in the south–west of Victoria, found that a congener to the mardo, A. agilis (agile antechinus) demonstrated a significant decline in abundance in P. cinnamomi infested areas (Newell and Wilson 1993; Newell 1994). A reduction in habitat suitability due to the death and collapse of susceptible plant species was identified as a major influence in these declines. The loss the highly sensitive grasstree,

Xanthorrhoea australis, which dies rapidly after infestation, appeared to have a strong negative affect on A. agilis abundance (Newell and Wilson 1993; Newell 1994).

59 Similarly, species such as the Rattus fuscipes (bush rat), R. lutreolus (swamp rat) and

Sminthopsis leucopus (white-footed dunnart) were less abundant in areas of the

Anglesea heath infested by P. cinnamomi (Laidlaw 1997; Laidlaw and Wilson 2006).

Similar to the mardo, the bush rat, swamp rat and agile antechinus require dense patches of understorey vegetation and leaf litter as cover to forage, protection from predators and to encourage invertebrates and fungi for consumption (Knight and Fox 2000;

Monamy and Fox 2000; Tasker and Dickman 2004; Fox and Monamy 2007).

3.5. Concluding remarks and management implications Patch occupancy models identified that mardos are less likely to occur in areas affected by P. cinnamomi in favour of areas with structurally complex, disease-free vegetation.

During the present study, the most complex vegetation was located at the B. sessilis dominated region of site 1 and the unaffected regions of sites 5 and 6. These are also the regions where the greatest number of mardo individuals, captures and subsequent patch occupancy rates were recorded, therefore, confirming the hypothesis that mardos prefer structurally complex vegetation and as a consequence are affected by P. cinnamomi.

These results have significant consequences for other structure-dependant, small to medium sized mammal species that inhabit plant susceptible communities in the south- west of Western Australia.

A consequence of P. cinnamomi disturbance is that mardos are patchily distributed throughout this region of the northern jarrah forest. Contributing to this patchy distribution is the patchy mosaic of disease-free forest, subtly disturbed forest, highly degraded forest and highly degraded forest colonised by the tall shrub, B. sessilis (parrot bush). However, the data presented in this study is not definitive and further investigation is required. It is recommended that broad scale trapping surveys be undertaken across the jarrah forest. This information will add to that already presented 60 in this thesis and will further contribute to our understanding of the mardo, its habitat preferences and the multitude of factors influencing its present distribution.

Understanding the mardos preferred habitat and how it is influenced by P. cinnamomi is extremely important to understand the full potential of this pathogen and the threat it presents to Australia’s faunal biodiversity. Therefore, the main aim of the following chapter is to gain a better understanding of the microhabitat factors that influence the distribution of the mardo.

61 CHAPTER 4. MARDO HABITAT PREFERENCES: IDENTIFYING KEY HABITAT ELEMENTS AND MARDO SUSCEPTIBILITY TO PHYTOPHTHORA CINNAMOMI

4.1. Introduction The ecology, biology and habitat preferences of the eastern Australian Antechinus species have been well studied (Lee et al. 1977; Fox 1982; Lee et al. 1982; Van Dyck

1982; Smith 1984; Wilson and Bourne 1984; Watt 1997), while similar traits among

Antechinus flavipes leucogaster (yellow-footed antechinus) or mardo populations remain poorly understood. This species is a small, cryptic, crepuscular, insectivore from the marsupial family Dasyuridae. Indeed, previous studies conducted in the northern jarrah forest have examined the immediate impact and long-term effects of fire

(Schmidt and Mason 1973; Christensen and Kimber 1975; Swinburn et al. 2007), diet

(Hindmarsh and Majer 1977; Majer 1978), nesting and a brief study on population dynamics (Carati 1982). However, to date, there have been few studies evaluating mardo habitat selection and their responses to habitat disturbance in the northern jarrah forest, especially with regard to the impact of P. cinnamomi. The few exceptions include, unpublished dissertations from Carati (1982), Gaskin (2002), Lilith (2002),

Wood (2002) and Carter (2003).

Declines in habitat structure and quality are major concerns for the conservation of

Australia’s small native mammal species (Monamy and Fox 2000; Tasker and Dickman

2004). Previous studies show that changes to vegetation structure, complexity and floristic richness cause dramatic declines in small mammal richness, distribution and abundance (Knight and Fox 2000; Monamy and Fox 2000; Tasker and Dickman 2004).

Changes to the vegetation structure alter foraging effectiveness, food availability and number of nesting and refuge substrates, whilst increasing the likelihood of predation

62 (Catling and Burt 1995; Knight and Fox 2000; Monamy and Fox 2000; Wilson et al.

1990).

Phytophthora cinnamomi kills a wide variety of structurally important plant species, which often has a devastating impact on the vegetation structure, complexity and floristic richness (Wills 1993; Shearer et al. 2004, 2007). Therefore, understanding the habitat requirements of a particular mammal species will assist with effective management programs in areas susceptible to disturbance from logging, mining and P. cinnamomi (Burgman and Lindenmayer 1998; Kroll and Haufler 2006). In this chapter, patch occupancy modelling using presence-absence methodology developed by

MacKenzie et al. (2002) and MacKenzie and Royle (2005) were used to evaluate habitat preferences among mardo populations in areas affected by P. cinnamomi-induced disturbance and habitat degradation. Presence-absence surveys are useful when attempting to determine relative abundance and density of mammals, especially with small, nocturnal and cryptic species such as the mardo that are difficult to detect. Like many small cryptic mammal species, individual mardo are not readily observed and consequently not all individuals will be detected during a survey. Therefore, the aims of this study were to:

1) Conduct presence-absence surveys to evaluate mardo patch occupancy and

detectability.

2) Determine habitat characteristics that influence mardo detectability and patch

occupancy.

3) Evaluate if the preferred habitat of the mardo is affected by P. cinnamomi.

63 4.2. Methods

4.2.1. Study site The study locality, geology and weather patterns are described in Chapter 2 (Section

2.1).

4.2.2. Live mardo trapping procedures Mardo detection-nondetection surveys were conducted monthly over four consecutive nights from January 2003 to August 2003 (Table 4.1). The detection-nondetection survey methods are explained in Chapter 3 (Section 3.2.2). The data for the current study were collected during a growth and reproductive period for the mardo, which began immediately after postnatal juvenile dispersal and territory establishment

(January 2003) and were completed prior to male die-off in August (2003). Male mardos die soon after mating and new juvenile male cohorts do not enter the trappable populations until January. Mardos were trapped using medium sized aluminium Elliott

Type b traps baited with a mixture of rolled oats, peanut butter, honey and vanilla essence. At each survey site, 25 trap stations were established in a grid formation at 25 m intervals. Each trap was covered with a calico bag and then a plastic bag during winter months for additional water protection. Shredded paper was placed inside the trap to provide nesting material. Traps were checked and emptied between 0500 and

1000 hours. Trapping was conducted in accordance with Wildlife Conservation Act

1950 and appropriate Department of Environment and Conservation (DEC) permits to take fauna for scientific purposes (permit numbers SF003985 and SF004310; Murdoch

University Ethics Approval Number. 910R/02).

4.2.3. Habitat variables The habitat covariates tested were selected after a comprehensive literature review of habitat use and selection by an array of native small native mammal species from temperate regions of southern Australia (Fox 1982; Catling and Burt 1995; Catling et al.

64 2001; Wilson et al. 2001; Knight and Fox 2000; Monamy and Fox 2000; Tasker and

Dickman 2004; Fox and Monamy 2007; Frazer and Petit 2007; Kelly and Bennett 2008)

(See Chapter 1). All habitat variables and the Dieback Expression Score (P. cinnamomi- induced disturbance) were gathered from within a 12.5 m radius (625 m2) surrounding each trap station. A description of each habitat variable and the methods used to collect them is provided in Chapter 2 (Table 2.4). A Dieback Expression Score (DES) was recorded for each trap station at each survey site (n=25) based on the presence or absence of susceptible and resistant plant species (Table 2.3), complexity of litter layer, canopy cover and understorey vegetation structure. Dieback Expression Scores were ranked from 0 (healthy forest or least disturbed) to 4 (extremely severely disturbed)

(Table 2.3). The DES values recorded at sites 1, 2, 3 and 4 (3.20 ± 0.70, 3.8 ± 0.41, 3.2

± 0.71, 2.76 ± 1.09 respectively) were sufficient to be categorised as severely degraded.

The DES values at sites 5 (1.88 ± 1.01) and 6 (0.00 ± 0.00) were sufficient to rank these sites as subtly disturbed and healthy respectively. Trap stations and survey sites were deemed “unaffected” if a DES value of 0 – 0.9 was recorded. In contrast, trap stations and survey sites with DES values greater than 1 were deemed to be affected by P. cinnamomi.

65 Table 4.1 Summary of the trapping effort undertaken during each survey period and timing each site was surveyed. Trapping surveys were conducted over four nights each month from January 2003 to August 2003. The timing of the survey was limited due to the presence of adult male Antechinus flavipes (mardo) (all adult males die after mating, new cohorts do not enter populations until four months old). Trapping occasion Date Sites surveyed Total trap nights 1. January 14/01 – 17/01/2003 5, 6 200

2. February 02/02 – 05/02/2003 1, 2, 4, 5 400

3. March 02/03 – 05/02/2003 1, 2, 3, 6 374

4. April 07/04 – 10/04/2003 1, 2, 5, 6 358

5. May 08/05 – 11/05/2003 5, 6 200

6. June 16/06 – 19/06/2003 1, 2, 3, 4 335

7. July 07/07 – 10/07/2003 3, 4, 5, 6 325

8. August 16/08 – 19/08/2003 1, 2, 4, 6 372

Total 2564

4.2.4. Data analysis and model development The patch occupancy models used to analyse the detection-nondetection and habitat data are explained in Chapter 3 (Section 3.2.3) and are based on the methods of

MacKenzie et al. (2002, 2006) and MacKenzie and Royle (2005). Mardo detectability

(p) and patch occupancy (ψ) candidate models were developed from encounter histories using those trap stations that successfully detected resident mardos. Resident mardos were characterised as those individuals captured at a survey site during two or more distinct survey periods. Resident mardos were used because of the assumed familiarity these individuals have with the surrounding habitat. By developing the habitat selection model using the encounter histories developed from the resident detections differentiate between “used” (no or low resident detections) trap stations from those areas that are truly “occupied” (multiple resident detections). Resident individuals were used to distinguish between a site being seasonally “used” rather than “occupied”. MacKenzie et al (2002) states that if a species is physically present within a unit only at random

66 points in time during the season, then that could be defined as “use” rather than occupancy.

4.2.5. Candidate models, fitting and selection Model fitting and selection was performed using the Patch Occupancy function in

Program MARK Version 4.3 (Burnham and Anderson 2002) which utilizes the Akaike

Information-Criteria (AIC) theoretic approach (Burnham and Anderson 2002). Because the ratio of sample size to variables was less than 40, AICC was used as the basis for model selection, which is a second order bias-corrected form of AIC (Burnham and

Anderson 2002). Mardo habitat preference models were developed using a two-stage selection process. Initially, the naïve models were developed and tested. Naïve models were constructed a priori (as described in Section 3.2.3) without the effect of habitat or environmental variables (hence the use of the term “naïve”). After the most informative and parsimonious naïve model was identified, the habitat and environmental parameters were then introduced to develop the habitat selection models. The affect each habitat covariate had on mardo detectability (p) and patch occupancy (ψ) were modelled using logistic-link function offered in Program MARK. The contribution each habitat covariate had to the models was tested in a stepwise manner: variables were adding to the models singularly and then in combination. If a covariate or group of covariates failed to improve the QAIC ranking, they were excluded from further involvement within the modelling process. Akaike weights (Burnham and Anderson 2002) were calculated for each model set using Program MARK, which was used to measure the relative likelihood of each model given the data.

A goodness-of-fit test (MacKenzie and Bailey 2004) for the global model {p (site and time and gender) ψ (site and gender)} found evidence of over-dispersion (χ2=115.23; p<

0.05, over-dispersion factor = 2.457) within the data. In order to compensate for this, 67 quasi-likelihood adjustments were made, so that all final analyses were carried out on

QAICC (quasi-AICC) values as suggested by Burnham and Anderson (2002).

4.3. Results 4.3.1. Trapping results Thirty-two resident mardos were captured 188 times over 2564 trap nights (7.33% total trap success). Twelve resident males and 6 females where captured 69 and 47 times respectively at the healthy forest site 6, which was the greatest number of resident individuals and captures recorded (Figure 4.1). There was little difference in the number of resident mardo captures recorded at sites 1 and 5 with 38 captures recorded from 8 individuals and 34 captures from 6 individuals, respectively (Figure 4.1). There were no resident mardos recorded at the severely affected sites 2, 3 and 4 (Figure 4.1). Resident mardos were detected at 52 (34.67% naïve occupancy) of the 150 trap stations. Of the

52 successful trap stations, 19 stations were at locations affected by P. cinnamomi, the remaining 33 stations were at locations unaffected by P. cinnamomi. The proportion of trap stations which recorded resident mardos varied greatly between the survey sites.

The healthy forest site (site 6) had the greatest number of successful trap stations with

23 (92.0% naïve occupancy rate), whilst the subtly affected site (site 5) had 17 (68.0% naïve occupancy rate) successful trap stations (Table 4.2).

68

20 18 16 14 12 10 8 6 Resident mardos Resident 4 2 0 Total Male Male Male Male Male Male Male Male Female Female Female Female Female Female

1 2 3 4 5 6 Survey sites and gender

A

80 70 60 50 40 30 20 10 Resident mardo captures mardo Resident 0 Total Male Male Male Male Male Male Male Female Female Female Female Female Female

1 2 3 4 5 6 Surveys sites and sex

B Figure 4.1. Total number of Antechinus flavipes (mardo) resident individuals (A) and captures (B) recorded at each survey site according to gender. Survey sites 1, 2, 3 are severely infested (DES 3-4), 4, 5 and 6 represent intermediately or subtly affected (DES 1-2) healthy or unaffected (DES 0) Eucalyptus marginata (jarrah) forest, respectively.

69 Table 4.2. Successful captures of Antechinus flavipes (mardo) residents recorded at each survey sites. ). Survey sites 1, 2, 3 are severely infested (DES 3-4), 4, 5 and 6 represent intermediately or subtly affected (DES 1-2) healthy or unaffected (DES 0) Eucalyptus marginata (jarrah) forest, respectively. The “total successful trap stations” are not cumulative totals because males and females were detected at the same trap stations. Site Gender Affected by Unaffected by P. cinnamomi P. cinnamomi 1 Female 5 (20.0%) 0 (0.0%) Male 12 (48.0%) 0 (0.0%) Total successful trap stations 12 (48.0%) 0 (0.0%) 2 Female 0 (0.0%) 0 (0.0%) Male 0 (0.0%) 0 (0.0%) Total successful trap stations 0 (0.0%) 0 (0.0%) 3 Female 0 (0.0%) 0 (0.0%) Male 0 (0.0%) 0 (0.0%) Total successful trap stations 0 (0.0%) 0 (0.0%) 4 Female 0 (0.0%) 0 (0.0%) Male 0 (0.0%) 0 (0.0%) Total successful trap stations 0 (0.0%) 0 (0.0%) 5 Female 7 (28.0%) 9 (36.0%.) Male 3 (12.0%) 3 (12.0%) Total successful trap stations 7 (28.0%) 10 (40.0%) 6 Female 0 (0.0%) 15 (60.0%) Male 0 (0.0%) 23 (92.0%) Total successful trap stations 0 (0.0%) 23 (92.0%)

4.3.2. Naïve model selection The top naïve ranked model had a 73.50% likelihood of being the most appropriate model to explain the data set, which was considerably greater than the second ranked model which had a likelihood of 13.40% (Table 4.3). The structure of the top ranked model included constant mardo detectability (p) and site as important predictors of mardo patch occupancy (ψ) (Table 4.3). The remaining naïve models were not considered useful for analysing the habitat variables because of high∆QAIC C values

(∆QAICC > 2) and low Akaike weights (wi) (Table 4.3).

70 Table 4.3. Summary of QAICc model selection results fitting the resident Antechinus flavipes (mardo) encounter history to detectability (p) and patch occupancy (ψ) naïv e models. Model notation “*” represents the main and interactive affects of site, gender and time. Rank Model QAICC ∆QAICC Akaike Likelihood # Parameters Deviance model in model weight (wi) 1 p (.) ψ (site) 320.09 0.00 0.735 1.000 7 305.71 2 p (site) ψ (.) 323.48 3.39 0.134 0.183 7 309.10 3 p (time) ψ (site) 324.62 4.35 0.076 0.104 14 295.14 4 p (site*gender) ψ (.) 326.72 6.62 0.027 0.037 13 299.44 5 p (.) ψ (site* gender) 327.31 7.22 0.019 0.027 13 300.04 6 p (gender) ψ (site) 328.85 8.76 0.005 0.007 14 299.38 7 p (site) ψ (site) 330.10 10.01 0.002 0.002 12 305.02 8 p (time) ψ (site* gender) 332.49 12.40 0.000 0.000 20 289.47 9 p (site*sex) ψ (site) 336.06 15.96 0.000 0.000 18 297.62 10 p (site) ψ (site* gender) 337.78 17.69 0.000 0.000 18 299.35 11 p (site*sex) ψ (site* gender) 346.76 26.67 0.000 0.000 24 294.39 12 p (gender*time) ψ 348.24 28.14 0.000 0.000 28 286.24 (site*gender) 13 P (.) ψ (.) 370.93 50.84 0.000 0.000 28 286.24 14 p (gender) ψ (gender) 374.13 54.04 0.000 0.000 4 365.29 15 p (time) ψ (.) 374.92 54.82 0.000 0.000 2 352.34 16 p (site*time) ψ 374.13 116.92 0.000 0.000 4 365.99 (site*gender) 17 p (site* gender *time.) ψ (.) 569.01 248.92 0.000 0.000 97 280.88 18 p (site* gender *time) ψ 614.35 294.26 0.000 0.000 108 275.08 (site* gender.) (.) no effect from site, gender or time on mardo detectability and patch occupancy.

4.3.3. Model selection: habitat affecting mardo detectability (p) The model selection based on site habitat characteristics resulted in three top ranked

detectability candidate models, that were extremely competitive, with similar model

weights (wi) and low delta Akaike values ∆QAIC( C ≤ 2) (Table 4.4). The structure of

the three top ranked detectability models were similar and included a constant p, while

large log densities, tall multiple-crowned X. preissii densities and ground cover

vegetation were key habitat variables affecting mardo patch occupancy (Table 4.4).

Large log densities were included among the three top ranked model and as a

consequence contributed 54.00% of the confidence set (Table 4.4). The remaining

detectability candidate models were considered as less likely based on their high

∆QAICC values (∆QAICC > 2) and low Akaike weights (wi) (Table 4.4) (Burnham and

Anderson 2002).

71 According to coefficients recorded from the three top ranked models, a weak to strong positive relationship exists between mardo detectability and large logs (slope on logit scale ranged from 0.28 (0.13SE) - 0.31 {0.13SE}), tall multiple crowned X. preissii densities (slope on logit scale: 0.08 {0.06SE}) (Table 4.4). A weak negative relationship exists between ground cover vegetation (slope on logit scale: -0.03 {0.03SE}) and mardo detectability (Table 4.4). Because the three top-ranked QAICC models had similar explanatory ability, parameter estimates for detectability were model-averaged.

The probability of mardo detectability according to the model averaged data was 0.28 ±

0.20. This suggests that when sampling in areas with high densities of large logs and tall multiple-crowned X. preissii, with little ground cover vegetation there was a 28.00% likelihood of detecting a mardo.

4.3.4. Model selection: habitat characteristics affecting mardo patch occupancy (ψ) The ten top ranked patch occupancy candidate models were extremely competitive with similar model weights (wi) ranging from 0.04 to 0.10 and low delta Akaike values

(∆QAICC ≤ 2) (Table 4.5). The structure of the ten top ranked models were similar, consisting of constant detectability (p), while site and total and large log densities, tall single-crowned, tall multiple-crowned, total and medium sized X. preissii densities where shown to affect mardo patch occupancy (ψ) (Table 4.5). Large logs, tall single and multiple-crowned X. preissii densities, either individually or in combination contributed to six of the ten top ranked models, which comprised 70.0% of the confidence within all models (Table 4.5). The remaining patch occupancy candidate models were deemed less likely based on their∆QAIC C values (∆QAICC > 2) and low

Akaike weights (wi) (Table 4.5) (Burnham and Anderson 2002).

According to coefficients from the seven top ranked models, a strong positive relationship exists between mardo patch occupancy and large log densities (slope on

72 logit scale: 0.57 {0.49SE} - 0.83 {0.69SE}), tall multiple-crowned X. preissii densities

(slope on logit scale: 0.70 {1.06SE} - 1.02 {0.54SE}) and tall single-crowned X. preissii densities (slope on logit scale 0.57 {0.54SE} - 0.65 {0.53SE}) (Table 4.5). Weak positive relationships were identified between mardo patch occupancy and densities of medium sized X. preissii, total X. preissii and total logs (Table 4.5). Because the ten top-ranked QAICC models had similar explanatory ability it was considered necessary to model-average, which resulted in the greatest patch occupancy rates being recorded at the subtly affected site 5 (0.62 ± 0.15) and healthy forest site 6 (0.83 ± 0.13), whilst lowest patch occupancy rates of zero were recorded at the severely-infested sites 2, 3 and 4 (Figure 4.3). From these results, the likelihood that a mardo will be present within areas of the jarrah forest with high densities of large logs, total logs and X. preissii, especially the taller individuals at sites 1, 5 and 6 is 45.70%, 62.21% and 85.04%, respectively (Figure 4.3).

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2

Probability of occupancy of Probability 0.1 0 0 1 2 3 4 5 6 Survey sites

Figure 4.3. The probability of Antechinus flavipes (mardo) patch occupancy (ψ) after model averaging from the 23 top ranked models (bars represent confidence intervals). Survey sites 1, 2, 3, are severely infested (DES 3-4), 4, 5 and 6 represent intermediately or subtly affected (DES 1-2) healthy or unaffected (DES 0) Eucalyptus marginata (jarrah) forest, respectively.

73 4.3.5. Model selection for combined detection and patch occupancy (ψ) model Models testing the effects of the habitat variables on mardo detectability (p) and patch occupancy (ψ) combined are shown in Appendix 2. These candidate models also identified large logs, tall multi-crowned X. preissii densities and tall single-crowned X. preissii densities as important habitat characteristics to mardo detectability and patch occupancy. Because of this, the more concise and parsimonious candidate model sets were selected to explain the affect habitat characteristics on mardo.

4.3.6. Habitat variation between Phytophthora cinnamomi affected and unaffected trap stations Total and large log densities did not vary greatly between successful and unsuccessful trap stations (Figure 4.4A and B). However, there are dramatic differences among total, tall single and multiple-crowned X. preissii densities at the successful compared to the unsuccessful trap stations (Figure 4.4C, D and Figure 4.5 E, F). The structure of the ground cover was greater at the unsuccessful trap stations than at the successful trap stations (Figure 4.5G). The mean and standard deviation for all habitat variables tested during this study are shown in Table 4.6. These values are categorised depending on their success for mardo detections and whether they are located at an area effected (DES

>1) or unaffected (DES <0) by P. cinnamomi. In addition to the previously mentioned results, shrub cover structure, percentage canopy cover, percentage litter cover and

Dieback Expression Score all dramatically differed between successful unaffected and unsuccessful affected trap stations (Table 4.6). However, these variables did not contribute to the structure of the top ranked QAICc models (Table 4.4 and 4.5).

74 Table 4.4. Summary of model selection results fitting the resident Antechinus flavipes (mardo) encounter history and habitat variables to the detectability (p). The notation terms used in the following models include (ψ) which representing patch occupancy, ‘*’ representing the main and interactive affects of site, gender and time, whilst “+” indicates the additive affect of the habitat covariates. Over-dispersion factor (ĉ) = 2.457. Rank Model and co variables QAICC ∆QAICC Akaike model Likelihood # Parameters Deviance Coefficient of habitat variables weight (wi) in model 1 p (.) + Large log densities ψ (site) 317.57 0.00 0.22 1.00 8 301.08 0.31 ± 0.13 2 p (.) + Large log and Tall multi-crowned X. preissii densities ψ (site) 318.11 0.53 0.17 0.77 9 299.48 0.29 ± 0.14 / 0.08 ± 0.06 3 p (.) + Large logs densities and Vertical structure of ground vegetation cover ψ (site) 318.59 1.02 0.13 0.60 9 299.97 0.28 ± 0.13 / -0.03 ± 0.03 4 p (.) + Tall multi-crowned X. preissii densities ψ (site) 320.58 3.01 0.05 0.22 8 304.09 0.09 ± 0.06 5 p (.) + Vertical structure of ground cover vegetation ψ (site) 320.93 3.35 0.04 0.19 8 304.43 -0.04 ± 0.03 6 p (.) ψ (site) 320.97 3.39 0.04 0.18 7 306.58 Model did not include habitat variables 7 p (.) + Tall single crowned-X. preissii densities ψ (site) 321.05 3.47 0.04 0.17 8 304.43 0.09 ± 0.06 8 p (.) + Tall multi-crowned-X. preissii densities and ground vegetation cover ψ (site) 321.37 3.80 0.03 0.15 9 302.76 0.08 ± 0.06 / -0.03 ± 0.03 9 p (.) + Depth of leafy material ψ (site) 321.45 3.87 0.03 0.13 8 305.19 0.28 ± 0.26 10 p (.) + Cover provided by fine woody debris cover ψ (site) 321.68 4.11 0.03 0.13 8 305.21 0.26 ± 0.23 11 p (.) + Diameter of trunk at breast height (DBH) ψ (site) 321.70 4.12 0.03 0.11 8 305.48 0.06 ± 0.06 12 p (.) + Total log densities ψ (site) 322.98 4.41 0.02 0.10 8 305.70 0.04 ± 0.05 13 p (.) + Medium X. preissii densities ψ (site) 322.19 4.62 0.02 0.10 8 305.74 0.01 ± 0.02 14 p (.) + Total X. preissii densities ψ (site) 322.24 4.66 0.02 0.09 8 306.00 - 0.05 ± 0.07 15 p (.) + Percentage litter cover ψ (site) 322.85 5.27 0.02 0.07 8 306.36 0.01 ± 0.015 16 p (.) + Vertical structure of shrub vegetation cover ψ (site) 322.86 5.29 0.02 0.07 8 306.37 -0.03 ± 0.071 17 p (.) + Medium/small X preissii densities ψ (site) 322.89 5,32 0.02 0.07 8 306.39 -0.02 ± 0.05 18 p (.) + Small X. preissii densities ψ (site) 322.91 5.33 0.02 0.07 8 306.42 - 0.03 ± 0.09 19 p (.) + Percentage projected canopy cover ψ (site) 322.92 5.34 0.02 0.07 8 306.43 -0.05 ± 0.13 20 p (.) + Dieback Expression Score (DES) ψ (site) 323.06 5.41 0.02 0.07 8 306.49 0.05 ± 0.16 21 p (.) + Tree health rating ψ (site) 323.58 5.48 0.02 0.06 8 306.56 0.01 ± 0.05 22 p (.) + Small log densities ψ (site) 324.37 6.00 0.01 0.05 9 304.96 -0.28 ± 0.22 23 p (site ) ψ (.) 325.46 6.79 0.00 0.03 7 309.98 Model did not include habitat variables 24 p (time) ψ (site) 327.57 7.89 0.00 0.02 14 295.98 Model did not include habitat variables 25 p (site *gender) ψ (.) 328.17 9.99 0.00 0.01 13 300.29 Model did not include habitat varaibles 26 p (.) ψ (site *gender) 330.97 10.60 0.00 0.01 13 300.89 Model did not include habitat variables 27 p (site) ψ (site) 330.97 13.40 0.00 0.00 12 305.89 Model did not include habitat variables 28 p (time) ψ (site*gender) 333.31 15.74 0.00 0.00 20 290.30 Model did not include habitat variables 29 p (site*gender) ψ (site) 336.90 19.33 0.00 0.00 18 298.47 Model did not include habitat variables 30 p (.) ψ (site) 338.64 21.06 0.00 0.00 18 300.20 Model did not include habitat variables 31 p (site*gender) ψ (site*gender) 347.60 30.03 0.00 0.00 24 295.24 Model did not include habitat variables 32 p (.) ψ (.) 371.98 54.41 0.00 0.00 2 367.94 Model did not include habitat variables 33 p (time) ψ (.) 375.93 55.86 0.00 0.00 9 357.31 Model did not include habitat variables 34 p (site*gender) ψ (.) 569.81 249.73 0.00 0.00 97 281.69 Model did not include habitat variables 35 p (site*gender*time) ψ (site*gender) 605.40 285.40 0.00 0.00 108 275.87 Model did not include habitat variables (.) no effect from site, gender or time on mardo detectability (p)and patch occupancy (ψ).

75 Table 4.5. Summary of model selection results fitting the resident Antechinus flavipes (mardo) encounter history and habitat variables to the patch occupancy (ψ). The notation terms used in the following models include (p) for detectability, ‘*’ representing the main and interactive affects of site, gender and time, whilst “+” indicates the additive affect of the habitat covariates. Over-dispersion factor (ĉ) = 2.457. Rank Model and co variables QAICC ∆QAICC Akaike model Likelihood # Parameters Deviance Coefficient of habitat variables weight (wi) in model

1 p (.) ψ (site) + Large log and tall multi-crowned X. preissii densities 320.07 0.00 0.10 1.00 9 301.45 0.83 ± 0.69 / 1.02 ± 0.54 2 p (.) ψ (site) + Tall single-crowned X. preissii densities 320.09 0.02 0.10 0.99 8 303.59 0.65 ± 0.53 3 p (.) ψ (site) + Large log and tall single-crowned X. preissii densities 320.39 0.31 0.09 0.86 9 310.77 0.57 ± 0.49 / 0.57 ± 0.54 4 p (.) ψ (site) + Large log densities 320.76 0.55 0.08 0.76 8 304.13 0.74 ± 0.67 5 p (.) ψ (site) + Tall multi-crowned X. preissii densities 320.76 0.68 0.07 0.71 8 304.26 0.81 ± 1.04 6 p (.) ψ (site) 320.96 0.89 0.06 0.64 7 306.59 Model did not include habitat variables 7 p (.) ψ (site) + Tall single and multi-crowned X. preissii densities 321.17 1.09 0.06 0.58 9 302.54 0.62 ± 0.57 / 0.79 ± 1.06 8 p (.) ψ (site) + Total X. preissii densities 321.24 1.16 0.06 0.56 8 304.74 0.07 ± 0.06 9 p (.) ψ (site) + Total log densities 321.82 1.75 0.04 0.42 8 305.33 0.12 ± 0.13 10 p (.) ψ (site) + Medium/small X. preissii densities 321.95 1.87 0.04 0.39 8 305.46 0.17 ± 0.17 11 p (.) ψ (site) + Small log densities 322.34 2.27 0.03 0.32 8 305.85 0.10 ± 0.14 12 p (.) ψ (site) + Percentage litter cover (%) 322.47 2.40 0.03 0.30 8 305.97 - 0.13 ± 0.17 13 p (.) ψ (site) + Vertical structure of ground cover vegetation 322..69 2.62 0.03 0.27 8 306.20 - 0.03 ± - 0.05 14 p (.) ψ (site) + Tree health rating 322.74 2.66 0.03 0.26 8 306.24 0.28 ± 0.49 15 p (.) ψ (site) + Medium X. preissii densities 322.76 2.69 0.03 0.26 8 306.27 0.08 ± 0.14 16 p (.) ψ (site) + Cover provided by fine woody debris 322.78 2.71 0.03 0.26 8 306.29 0.34 ± 0.63 17 p (.) ψ (site) + Small X. preissii densities 322.84 2.77 0.03 0.25 8 306.35 0.06 ± 0.15 18 p (.) ψ (site) + Diameter of trunk at breast height (DBH) 322.89 2.81 0.02 0.24 8 306.39 - 0.06 ± 0.15 19 p (.) ψ (site) + Dieback Expression Score (DES) 322.93 2.86 0.02 0.24 8 306.44 0.16 ± 0.43 20 p (.) ψ (site) + Percentage projected canopy cover 323.08 3.00 0.02 0.22 8 306.58 0.01 ± 0.21 21 p (.) ψ (site) + Vertical structure of shrub cover vegetation 323.08 3.00 0.02 0.22 8 306.58 0.00 ± 0.03 22 p (site) ψ (.) 323.36 4.29 0.01 0.12 8 306.98 Model did not include habitat variables 23 p (.) ψ (site) + Depth of leafy material 323.03 4.95 0.01 0.08 9 306.41 0.23 ± 0.59 24 p (time) ψ (site) 325.46 5.39 0.01 0.07 14 295.98 Model did not include habitat variables 25 p (site*gender) ψ (.) 327.57 7.49 0.00 0.02 13 300.29 Model did not include habitat variables 26 p (.) ψ (site*gender) 328.17 8.10 0.00 0.02 12 300.89 Model did not include habitat variables 27 p (site) ψ (site) 330.98 10.90 0.00 0.01 12 3005.89 Model did not include habitat variables 28 p (time) ψ (site*gender) 333.31 13.24 0.00 0.00 20 290.30 Model did not include habitat variables 29 p (site*gender) ψ (site) 336.90 16.83 0.00 0.00 18 230.20 Model did not include habitat variables 30 p (.) ψ (site) 338.64 18.56 0.00 0.00 18 300.20 Model did not include habitat variables 31 p (site*gender) ψ (site*gender) 347.60 27.53 0.00 0.00 24 295.24 Model did not include habitat variables 32 p (.) ψ (.) 371.98 51.91 0.00 0.00 2 367.94 Model did not include habitat variables 33 p (time) ψ (.) 375.93 55.86 0.00 0.00 9 357.31 Model did not include habitat variables 34 p (site*gender*time) ψ (.) 569.81 249.73 0.00 0.00 97 281.69 Model did not include habitat variables 35 p (site*gender*time) ψ (site*gender) 605.40 285.40 0.00 0.00 108 275.87 Model did not include habitat variables (.) no effect from site, gender or time on mardo detectability and patch occupancy.

76 10 5

8 4

6 3

4 2

Mean total log density log total Mean 2 1

0 station density/trap log large Mean 0 Successful Unsuccessful Overall Successful Unsuccessful Overall P. cinnamomi status P. cinnamomi status A Mean total log densities B. Mean large log densities

25 5

20 4

15 3 density/trap station density/trap 10 density/trap station density/trap 2

5 1 X. preissii Mean tall multiple crowned X. preissii

Total Total 0 0 Successful Unsuccessful Overall Successful Unsuccessful Overall P. cinnamomi status P. cinnamomi status

C. Mean total Xanthorrhoea preissii densities D. Mean tall multiple-crowned Xanthorrhoea preissii densities

Figure 4.4. The mean (± SE) values for the habitat characteristics total log (A), large log (B), total (C) and tall multiple-crowned Xanthorrhoea preissii (D) densities, which were identified as being critical to the detectability (p) and patch occupancy (ψ) of mardos at “successful” trap stations for detecting resident Antechinus flavipes (mardo) and “unsuccessful” trap stations that did not detect resident A. flavipes (mardos). The mean (± SE) value for the 150 trap stations is also given (overall).

77 5 5

4 4

3 3

2 station density/trap 2 density/trap station density/trap 1 X. medium/small Mean 1 preissii

Mean tall single crowned X. preissii preissii X. crowned single tall Mean 0 0 Successful Unsuccessful Overall Successful Unsuccessful Overall P. cinnamomi status P. cinnamomi status

A. Mean single-crowned Xanthorrhoea preissii densities B. Mean medium/small Xanthorrhoea preissii densities

20

15

10

5 structure/trap station structure/trap Ground cover vegetation cover Ground

0 Successful Unsuccessful Overall P. cinnamomi status

C. Mean ground cover vegetation structure.

Figure 4.5. The mean (± SE) values for single crowned (A) and medium/small Xanthorrhoea preissii (B) densities identified and ground cover vegetation structure (C) which were identified as being critical to the detectability (p) and patch occupancy (ψ) of mardos at “successful” trap stations for detecting resident Antechinus flavipes (mardo) and “unsuccessful” trap stations that did not detect resident A. flavipes (mardos). The mean (± SE) value for the 150 trap stations is also given (overall).

78 Table. 4.6. Mean data from all the habitat variables and standard deviation values recorded for each trap station (625 m2). Success and unsuccessful trap stations indicate that resident Antechinus flavipes (mardo) were detected or not detected respectively. Affected represents the trap stations located in areas disturbed by Phytophthora cinnamomi (DES > 1) and unaffected represents the trap stations located in disease free areas (DES <0) of the Eucalyptus marginata (jarrah) forest. Habitat variables Successful trap stations Unsuccessful trap stations Total Unaffected Affected Total Unaffected Affected Overall mean (n=52) (n=33) (n=19) (n=98) (n=7) (n=91) (n=150) 1.Tree health rating (Ranked between; 1= unhealthy, little cover to 5= dense cover) 3.7 ± 1.1 4.1 ± 0.9 2.9 ± 1.0 2.9 ± 1.0 2.9± 1.1 2.5 ± 0.7 3.0 ± 0.1 2. Vegetation structure and complexity 2.1 Percentage projected canopy cover (%) 78.8 ± 20.7 87.6 ± 4.3 62.3 ± 27.9 53.7 ± 22.2 62.3 ± 2.8 56.5 ± 1.9 68. 2 ± 2.3 2.2. Vertical structure of the ground cover vegetation 6.5 ± 5.9 7.3 ± 6.1 5.1 ± 5.4 10.2 ± 8.0 5.1 ± 5.4 10.8 ± 8.2 9.1 ± 0.6 (counts of live vegetation touches between 0-80 cm) 2.3. Vertical structure of the shrub cover vegetation 12.2 ± 11.2 12.8± 10.9 11.1± 12.2 2.9 ± 5.0 11.1 ± 12.2 3.2 ± 5.2 7.2 ± 0.8 (counts of live vegetation touches between 1-180 cm) 2.4. Cover provided by fine woody debris 1.8 ± 0.7 1.9 ± 0.8 1.5 ± 0.7 1.2 ± 0.5 1.5 ± 0.7 1.1 ± 0.4 1.43 ± 0.1 (Ranked: 1=no cover, 2=moderate cover, 3=dense cover) 2.5. Depth of leafy material 1.9 ± 0.8 2.1 ± 0.8 1.5 ± 0.7 1.5 ± 0.7 1.5 ± 0.7 1.3 ± 0.6 1.6 ± 0.0 (Ranked: 1=no cover, 2=moderate cover, 3=dense cover) 2.6. Percentage litter cover (%) 65.7 ± 24.8 73.5 ± 19.2 51.6 ± 27.7 50.5 ± 40.0 5.1 ± 2.8 45.1 ± 27.8 55.5 ± 0.2 3. Xanthorrhoea preissii densities 3.1. Small X. preissii densities (counts per 625 m2) 1.3 ± 3.1 1.8 ± 3.7 0.5 ± 1.2 0.5 ± 1.2 1.0 ± 1.5 0.6 ± 1.2 0.8 ± 0.2 3.2. Small/medium X. preissii densities (counts per 625 m2) 1.0 ± 0.3 1.4 ± 0.6 1.0 ± 0.3 1.6 ± 2.8 2.1 ± 2.7 1.6 ± 0.4 1.6 ± 0.2 3.3. Medium X. preissii densities (counts per 625 m2) 3.0 ± 3.4 4.2 ± 3.8 1.0 ± 1.1 0.6 ± 1.2 1.1 ± 1.7 0.7± 1.2 1.2 ± 0.2 3.4. Tall, single-crowned X. preissii densities (counts per 625 m2) 1.5 ± 2.4 2.0 ± 2.8 0.6 ± 0.7 0.4 ± 1.1 0.0 ± 0.0 0.2 ± 0.5 0.7 ± 0.1 3.5. Tall, multiple-crowned X. preissii densities (counts per 625 m2) 1.2 ± 2.4 1.8 ± 2.8 0.2 ± 0.5 0.2± 0.5 0.3 ± 0.8 0.2 ± 0.5 0.6 ± `0.6 3.6.Total X. preissii densities (counts per 625 m2) 9.1 ± 8.7 12.2 ± 9.3 3.5 ± 3.6 2.5 ± 3.3 4.6 ± 4.9 2.7 ± 3.4 5.8 ± 0.6 4. Densities and size of fallen logs and standing trees 4.1. Small to medium log densities (counts per 625 m2) 1.8 ± 3.5 0.5 ± 0.8 4.1 ± 5.1 3.7 ± 2.8 3.0 ± 2.0 3.4 ± 2.7 2.7 ± 0.3 4.2. Large log densities (counts per 625 m2) 0.6 ± 1.2 0.4 ± 1.1 1.0 ± 1.4 0.6 ± 1.0 0.0 ± 0.0 0.5 ± 0.8 0.6 ± 0.1 4.3. Total log densities (counts per 625 m2) 2.5 ± 4.0 1.2± 1.5 4.6 ± 5.9 4.2. ± 2.9 3.3 ± 1.8 3.9 ± 2.9 3.2 ± 0.3 4.4 Diameter of trunk at breast hight (DBH) (cm) 66.0 ± 28.7 68.2 ± 30.0 62.1 ± 25.8 66.3 ± 25.4 62.9 ± 30.9 54.9 ± 25.7 58.9 ± 22.4 5. Impact of Phytophthora cinnamomi 5.1. Dieback Expression score 2.1 ± 1.4 1.0 ± 0.0 3.8 ± 0.9 4.0 ± 1.2 2.1 ± 1.1 4.2 ± 1.1 3.3 ± 1.6 (Ranked: 1 least affected to 5 most affected)

79 4.4. Discussion Mardos exhibit a strong non-random preference for areas with large logs and dense patches of X. preissii in the northern jarrah forest. Patch occupancy models developed using resident mardo encounter histories revealed a strong relationship between large logs, tall single and multiple-crowned X. preissii densities with mardo detectability and site occupancy. The selection of habitat types among small mammal species is often made on the basis of its suitability for nesting and foraging (Braithwaite 1979). Previous research conducted elsewhere in Australia on the ecological importance of large logs and Xanthorrhoea species suggest that when present they strongly contribute to the structure of understorey vegetation and provide potential nest sites, food resources and cover allowing mardos to forage, find potential mates, avoiding intra and inter-species competition and predation (Braithwaite 1979; Stokes et al. 2004; Laidlaw and Wilson

1996; Mac Nally et al. 2001; Borsboom 2005; Tulloch and Dickman 2006; Frazer and

Petit 2007; Holland and Bennett 2007; Kelly and Bennett 2008).

4.4.1. The importance of logs to the mardo and other small native mammal fauna The importance of large logs has been identified in other Antechinus populations occupying a variety of habitats throughout eastern Australia (Braithwaite 1979; Settle and Croft 1982; Statham and Harden 1982; Mac Nally et al. 2001; Korodaj 2007).

Indeed, two recent studies identified the importance of logs to the habitat utilization, behaviour and occurrence of the eastern yellow-footed antechinus sub-species A. f. flavipes (Mac Nally et al. 2001; Korodaj 2007). Large logs contribute to the composition and complexity within the understorey while increasing abundance of nests and foraging opportunities required by small mammal species (Dickman 1991b; Kelly and Bennett 2008). Large logs serve as habitat for invertebrates, which are considered the main dietary requirement of the mardo and other Antechinus species (Braithwaite

1979). Research conducted by Braithwaite (1979) observed A. stuartii concentrating in 80 areas with high log densities to exploit log-inhabiting invertebrates when seasonal declines occurred elsewhere in the landscape. The use of logs for cover while foraging has been identified in Peromyscus gossypinus (cotton mice) in America using fluorescence powder tracking (McCay 2000). McCay (2000) suggests that cotton mice traverse under logs to reduce the risk of predation. In the current study and a recent study by Swinburn et al. (2007), mardos were recorded traversing distances up to twenty metres underneath fallen logs, these activates may be a mechanism to avoid predators while foraging. As previously mentioned, potential predators of the mardo

[Dasyurus geoffroii (Chuditch), Podargus strigoides (tawny frogmouth), Caprimulgus cristatus (Australian owlet-nightjar) and Milvus migrans (black kite)] were recorded in close proximity of each survey site, while feral cats and foxes are also likely to occur in the area.

4.4.2. The importance of Xanthorrhoea species to the mardo and other small native mammal fauna The Xanthorrhoea species are important to the ecology of a diverse array of Australian invertebrate, mammal and bird species (Borsboom 2005). There are 28 species of

Xanthorrhoea in Australia, which are all characterised by a single or multiple-crowns of long narrow leaves and blackened leaf-based covered trunk (Lamont et al. 2004;

Borsboom 2005). Species from the Xanthorrhoea genus are widespread and abundant where they occur, therefore as a consequence they strongly contribute to the structural composition and complexity of the understorey and midstorey vegetation (Lamont et al.

2004; Borsboom 2005). They are slow growing and long lived and therefore persist and contribute to the composition of the landscape for several hundred years. For these reasons, it should be no surprise that a recent report on the ecology of Xanthorrhoea species identified 315 invertebrate and 85 vertebrate species, including 19 native mammal species that are known to utilise Xanthorrhoea species (Borsboom 2005). This

81 list includes mammal species that occur in the south-west of Western Australia that have been identified using Xanthorrhoea species, including Cercartetus concinnus

(western pygmy possum), Sminthopsis species (dunnarts), Parantechinus apicalis

(dibbler), Isoodon obesulus (southern brown bandicoot), Tarsipes rostratus () and the Phascogale calura (red tailed phascogale). However, several Western

Australia species were absent from Borsboom’s (Borsboom 2005) list, including

Pseudocheirus occidentalis (western ring tail possum), Bettongia penicillata (woylie) and mardo (Whittell 1954; Wayne et al. 2005).

Mardos nest underground, within logs, large cut E. marginata stumps, and in the trunk and “upper regions of the grassy section of Xanthorrhoea” (Whittell 1954; Wardell-

Johnson 1986; Swinburn et al. 2007). Radio, fluorescent dye, spool and thread tracking surveys conducted by Swinburn et al. (2007) during the current study showed that mardos nest in the trunks of dead X. preissii and among the dense grassy skirts of large multiple-crowned individuals. Research conducted on A. f. flavipes, the South

Australian yellow-footed antechinus subspecies, found 10 out of 15 maternal females nesting among the grassy skirts of large X. semiplana teneata ranging from 0.95 to 3.00 m in height (Marchesan and Carthew 2004). No nests were observed during the current study because of the destructive means required to locate and extract nests. The dense grassy skirts of Xanthorrhoea species offer attractive nesting possibilities because of their high insulation properties to the extremes of temperature, wind, rain, fire and predators (Lamont et al. 2004; Moir et al. 2006; Frazer and Petit 2007; Swinburn et al.

2007). Multi-crowned X. preissii may be more desirable than single crowned or small individuals because of the potential ability to buffer invertebrates and vertebrates from these extremes (Lamont et al. 2004).

82 Swinburn et al. (2007) observed considerable temperature and humidity differences between the inside and outside of X. preissii grassy skirts. Research conducted on how fires affect X. preissii demonstrate that the temperature 100 mm below the apex can remain below 60°C, while the combusting dead leaves on the outside reach temperatures in excess 1000 °C (Lamont et al. 2004).

Tall multiple-crowned Xanthorrhoea individuals possibly offer considerable insulation and protection to mardos because of the increased skirt volume. Indeed, a relationship exists between multiple-crowned X. preissii densities, patch occupancy rates, numbers of resident mardos individuals and capture rates. Indeed, the increased densities of multiple-crowned X. preissii observed at site 6 could offer multiple nest locations, which may be an important measure of predator avoidance. Mardos often defecate within or at the entrance of their nests which may attract predators (Wardell-Johnson

1986; Croft 2004). This was observed during the current study with mardo scats regularly found and removed from a lid of a nest box positioned within a X. preissii skirt, suggesting that mardo were using this X. preissii for nesting. Although the nest box (surveyed every three months during the study period) itself has been in a X. preissii for four years, there was no evidence that it has ever been occupied.

It must also be noted that controlled ecological burning has not occurred at any of the survey sites for at least ten years and as a consequence the majority of X. preissii individuals counted had long (often >1 m in length), thick, dense grassy skirts.

Christensen and Kimber (1975) recorded high mardo trap success rates in the northern jarrah forest Amphion Block 6, which at that time had not been burnt for 40 years. The

Amphion Block 6 remains unburnt, and there are plentiful tall X. preissii individuals scattered throughout this region with considerable grassy skirts that regularly reach 2

83 metres in height (pers. obs.). It is possible that time since fire is not the only factor contributing to the high mardo capture rates obtained by Christensen and Kimber

(1975), but the densities of tall X. preissii with thick grassy skirts. This statement is supported by Monamy and Fox (2000) who suggest that time since disturbance is not necessarily governing the successional response of swamp rats (Rattus lutreolus) and eastern mouse (Pseudomys gracilicaudatus) following fire. Instead, the successional response of swamp rats and eastern chestnut mice is relative to regenerative capabilities of the local vegetation to replenish its structural and cover attributes, which may not be governed by time. Swinburn et al. (2007) discovered that logs and X. preissii contribute to the occurrence of mardos in burnt areas, but further investigations are required.

Xanthorrhoea species were also identified as potential contributors to foraging activities and as escape refuge for mardo. In the current study, upon release, mardos would often flee into the grassy skirts of nearby X. preissii. This response appeared to be well developed and may be a commonly used response when mardos are in the presence of predators and other threats. Research conducted on other small mammal species identified the importance of Xanthorrhoea species to foraging activities, nesting and refuge. For example, during mark, release and recapture monitoring in the jarrah forest, southern brown bandicoots (Isoodon obesulus) were observed seeking refuge beneath the skirts of large X. preissii when released from capture (Kirsch 1968). On closer inspection, Kirsch (1968) found numerous bandicoot diggings beneath the skirts of the

Xanthorrhoea species. Research conducted on bush rats (Rattus fuscipes) found that they preferentially use areas where the canopies of X. johnsonii form dense tight clumps

(Frazer and Petit 2007).

84 4.4.3. The impact of Phytophthora cinnamomi on the habitat requirements of the mardo At a landscape level, mardos favoured areas not affected by P. cinnamomi (Chapter 3).

This may be a consequence of lower densities of large logs and X. preissii in P. cinnamomi affected areas. Xanthorrhoea preissii is very susceptible to P. cinnamomi

(Shearer and Tippett 1989; Shearer and Dillon 1995; Shearer et al. 2007) and during the current study their densities were consistently greater at unaffected survey sites than at the affected survey sites (See Chapter 2 for details of X. preissii densities). These results are identical to previously published results, which show significant declines in the densities of X. preissii in the presence of P. cinnamomi (McDougall 1997). Mardo detections were considerably lower at the P. cinnamomi affected trap stations, suggesting that mardos avoid using these areas, possibly because of reduced vegetation composition and complexity that follows the death and collapse of the plant species susceptible to P. cinnamomi. Absence of large logs is possibly a direct and indirect consequence of P. cinnamomi. Directly, E. marginata (jarrah) trees are killed by P. cinnamomi, while in contrast salvage logging has indirectly resulted in the removal of mature and productive jarrah from the study region (Podger et al. 1965; Shearer and

Tippett 1989). Therefore, it is unlikely that many trees survive to an age when hollow production begins. Studies have shown that jarrah trees are unlikely to produce useful hollows until approximately 200 years old (Abbott and Loneragan 1986). This is supported by McComb (1994) who also found a decrease in hollow bearing trees in areas affected by P. cinnamomi.

4.5. Concluding remarks Patch occupancy models identified large logs and X. preissii densities as important habitat factors for the distribution of the mardo in the northern jarrah forest. In the northern jarrah forest, large logs and X. preissii strongly contribute to the composition

85 and complexity of the understorey and have been identified as potential nest locations, cover and protection for mardos from predators while foraging for food and potential mates. However, P. cinnamomi kills many structurally important jarrah forest species including X. preissii. The death and collapse of X. preissii and other susceptible plants alters the structural composition and complexity of the forest. Mardos possibly avoid these areas because of the altered structure, decrease in the availability of nest locations and a perceived increased risk of predation. These outcomes identify P. cinnamomi as a major threat to the preferred habitat mardo and as a consequence to its distribution.

In areas affected by P. cinnamomi, it is possible that large fallen logs provide the nest sites and cover that is lost when X. preissii die and collapse. This was evident at the severely affected site 1, where the trap stations that consistently detected mardos being those with high densities of large fallen logs. Therefore, these results suggest that P. cinnamomi indirectly contributes to decline in the distribution of the mardo by killing and reducing the densities of X. preissii. Although, the death and collapse of X. preissii appear to have a significant impact on occurance of mardos, this may not be the only

P. cinnamomi-induced factor contributing to the mardo declines observed in the current study. Therefore, further research is required to further understand the biology and ecology of the mardo in other regions of its distribution. For example, the impact of topography, aspect, different Havel vegetation groups, P. cinnamomi and other significant disturbances on mardos requires attention. This requires extensive trapping surveys broader range of jarrah forest habitats than those surveyed in the present study.

This will allow for a greater understanding of the impact and/or threat posed by P. cinnamomi on the mardo and other small native mammal species through the south-west of Western Australia. In order to effectively manage all small mammal populations, especially in the presence of P. cinnamomi and other significant disturbances we require

86 a broad understanding of both the pathogen and each species present distribution and habitat preference. This will require a great deal of effort and cost. However, considering the threat posed by P. cinnamomi and the presence of conservation dependant fauna in Western Australia, this effort and cost is warranted. Furthermore, until additional research is conducted, all management and conservation programs should recognised P. cinnamomi as a significant threatening process to the fauna of the south-west of Western Australia.

87 5. GENERAL DISCUSSION

5.1. Impact of Phytophthora cinnamomi on the mardo in the northern jarrah forest This is the first study to link the impact of Phytophthora cinnamomi on the structural complexity of the understorey vegetation and the occurrence of a small native mammal in the south-west of Western Australia. Phytophthora cinnamomi kills many common and structurally important plant species in the jarrah forest including jarrah (Eucalyptus marginata), bull banksia (Banksia grandis) and grass tree (Xanthorrhoea preissii)

(Shearer and Tippett 1989; McDougal 1997; McDougal et al. 2002b). Surveys of the northern jarrah forest altered by P. cinnamomi found that the death, collapse and consequential decomposition of these and other susceptible plant species resulted in dramatic and long-term reductions to vegetation structure and complexity, litter layers and X. preissii densities. Many of these habitat variables were identified as being important to the mardo (Antechinus flavipes leucogaster). Subsequently the number of mardo individuals, captures rates, detectability and patch occupancy rates were considerably lower in areas affected by P. cinnamomi compared to those recorded in unaffected areas. Mardos may avoid areas affected by P. cinnamomi because of a lack of cover, food and nesting resources resulting from the decline or absence of vegetation structure and complexity, litter layer and X. preissii densities.

5.2. The three key findings and contributions of this study are as follows.

5.2.1. An improved understanding of how the plant pathogen Phytophthora cinnamomi affects the habitat requirements of the mardo in the northern jarrah forest The most significant contribution of this thesis is the provision of definitive evidence identifying P. cinnamomi as a significant threatening process to a fauna species in

Western Australia. The structure and complexity of the vegetation in P. cinnamomi- affected areas differ dramatically from unaffected areas with dramatic declines in the

88 structure of the shrub layer, percentage leaf litter cover and in the densities of small, medium, tall and total X. preissii. When present, the highly susceptible X. preissii often occurs in high densities and strongly contributes towards the structure and complexity of the understorey vegetation; however, in areas affected by P. cinnamomi, this important structural component is lost. In conclusion, Phytophthora cinnamomi is a virulent pathogen that kills a wide range of common and structurally important plant species that occur in the northern jarrah forest.

Mardo detectability and patch occupancy rates were considerably lower in areas disturbed and degraded by P. cinnamomi, supporting the hypothesis that P. cinnamomi impacts on the distribution of mardo in the northern jarrah forest. Mardos apparently avoid areas infested with P. cinnamomi which exhibit reduced understorey vegetation structure, leaf litter cover and lower fallen log and X. preissii densities. Reductions in vegetation complexity and structure lower the availability of nest and refuge sites, and food resources, while significantly increasing the likelihood of predation from native and exotic predators (Nichols and Burrows 1985; Nichols and Bamford 1985; Nichols and Watkins 1984; Postle et al. 1986). Stokes et al. (2004) recently highlighted the importance of vegetation cover for predator avoidance by A. flavipes in New South

Wales.

The diet of the mardo consists mostly of invertebrates with a minor contribution from small reptiles, birds and mammals (Crowther 2008; Hindmarsh and Majer 1977; Majer

1978). Previous studies have identified significant associations between declining invertebrate, reptile and bird abundance and diversity in the presence of P. cinnamomi

(Armstrong and Nichols 2000; Nichols and Burrows 1985; Nichols and Bamford 1985;

Nichols and Watkins 1984). Declines in main dietary items may contribute to lower

89 rates of patch occupancy. In conclusion, declines in vegetation structure and preferred dietary items may be sufficient to discourage mardos from using areas affected by P. cinnamomi.

5.2.2. An understanding of the habitat requirements of the mardo Detection-nondetection trapping surveys were conducted for P. cinnamomi-affected and unaffected sites within the northern jarrah forest. These Patch Occupancy models identified a strong positive relationship between mardo detectability and patch occupancy and the following vegetation parameters: structurally rich and complex understorey vegetation, thick litter, large fallen logs, tall single and multiple-crowned X. preissii, and total X. preissii densities. Previous studies have also identified the importance of Xanthorrhoea species for cover, food and nesting for the mardo, A. f. flavipes, Parantechinus apicalis, Isoodon obesulus, Cercartetus concinnus, C. nanus,

Rattus fuscipes and various Sminthopsis species in Western Australia (Frazer and Petit

2007; Laidlaw and Wilson 1996; Marchesan and Carthew 2004; Swinburn et al. 2007;

Whittell 1954).

The importance of large logs for mardo presence was also identified during the present study. Indeed, at site 1, a severely impacted site, it was the presence of several fallen logs and a dense patch of Banksia sessilis (parrot bush) that may have contributed to the moderate mardo occupancy probability recorded at this site. Logs may become an important habitat component for nesting and cover to the mardo in the absence of dense patches of X. preissii. The importance of hollow-bearing trees, large logs and coarse woody debris have been linked to A. flavipes populations in New South Wales and central Victoria (Dickman 1991b; Kelly and Bennett 2008; Mac Nally and Horrocks

2002; Mac Nally et al. 2001).

90 The mardo may use areas with greater X. preissii and large log densities because they increase the availability of dietary items such as invertebrates as well as nest and refuge sites while offering protective cover from predation (Braithwaite 1979; Tasker and

Dickman 2004; Swinburn et al. 2007). These results concur with those of previously conducted studies that have identified the importance of large logs and Xanthorrhoea species to a broad range of native mammal species (Whittell 1954; Settle and Croft

1982; Statham and Harden 1982; Braithwaite 1979; Laidlaw and Wilson 1996;

Marchesan and Carthew 2004; Wayne et al. 2005; Frazer and Petit 2007; Korodaj 2007;

Swinburn et al. 2007).

5.2.3. Contributing information vital for management measures required for the conservation of mardo and other native mammal species that inhabit plant communities susceptible to Phytophthora cinnamomi Effective management of mardo populations in the presence of P. cinnamomi requires a broad understanding of both the pathogen and the habitat preferences of the mardo.

Mardos are habitat generalists with a wide distribution throughout the south-west of

Western Australia, inhabiting a number of plant communities that are susceptible to P. cinnamomi (How et al. 2002). Therefore, further research is required to (1) evaluate the habitat requirements of the mardo in other regions of its distribution, and (2) assess the impact and/or threat posed by P. cinnamomi more broadly within the region. The current distribution of the mardo significantly overlaps the distribution of P. cinnamomi in this region. The mardo can therefore play an important role as an indicator species to monitor the impact of P. cinnamomi as well as other significant disturbances (e.g. mining and wild fire) on the small native mammals of the south-west of Western

Australia.

The key to the conservation management of mardo requires an approach that integrates

(1) the ecology, biology and genetics of P. cinnamomi, and (2) the further development 91 of methods that limit the future spread and impact of P. cinnamomi. Present strategies for conserving susceptible plant communities include integrated hygiene, quarantine and ex situ fungicide control (Shearer et al. 2007). Hygiene and quarantine controls include road closures and wash down (clean on entry) principles when entering unaffected regions from those that are P. cinnamomi affected (Garkaklis et al. 2004;

Shearer et al. 2004, 2007). These strategies have proven to be successful in limiting the spread of P. cinnamomi. Long-term management of P. cinnamomi is being addressed, including determining susceptibility among individual plant species, effects of hydrology, and mechanisms to eradicate P. cinnamomi from diseased soil (Shearer et al.

2004, 2007).

In addition, there remains the need to further develop our understanding of the limiting or contributing factors affecting the distribution and abundance of the mardo and other native mammal species that inhabit P. cinnamomi-susceptible plant communities in

Western Australia. Therefore, similar research to that conducted during the present study is required and should explore the biology, ecology and habitat preferences of small mammal populations in order to predict the potential impact of P. cinnamomi.

Furthermore, an understanding of the impact of other threatening processes such as mining, timber harvesting, exotic predators (foxes and cats), competitors (rabbits, cattle, horses and goats), and native predators (chuditch, birds of prey, and goannas) is essential for any conservation program to be successful (Wilson et al. 2004). An improved understanding of these four points can assist in the prioritisation of funding for researching conservation programs and the development of rehabilitation techniques within areas highly degraded by P. cinnamomi.

92 5.2. Other fauna species and the threat of Phytophthora cinnamomi: an integrated approach to managing P. cinnamomi and the conservation of native mammal species The long-term management of Western Australia’s native mammals already addresses the impacts of introduced predators (foxes and cats) and competitors (rabbits, hares, horses, goats and camels) in areas of conservation value (Burbidge and McKenzie 1989;

Smith and Short 1994; Wilson et al. 2004; Jones et al. 2004). In regard to P. cinnamomi, the present study highlights the importance of acknowledging this pathogen as a considerable threat to the conservation of Western Australia’s fauna. For example, there are several Western Australian marsupial taxa whose current range overlaps regions with susceptible plant communities which may consequently be threatened by

P. cinnamomi-induced habitat degradation (Garkaklis et al. 2004). These include the critically endangered Potorus gilberti (Gilbert’s potoroo), the endangered

Parantechinus apiculus (dibbler), the vulnerable Dasyurus geoffroii (chuditch),

Myrmecobius fasciatus (numbat), Setonix brachyurus (quokka), Pseudocheirus occidentalis (western ring-tailed possum) and Bettongia penicillata ogilbyi (woylie). A further three species presently regarded as being low risk, near threatened species,

Phascogale tapoatafa tapoatafa (brush-tailed phascogale), Isoodon obesulus fusciventor

(quenda or southern brown bandicoot) and Macropus irma (western brush wallaby), also occur in susceptible plant communities (Garkaklis et al. 2004).

It is not just the threatened or endangered species that are vulnerable to the effects of P. cinnamomi. A number of small, common species may also be threatened by detrimental habitat changes brought about by infestation with P. cinnamomi. Species such as Rattus fuscipes (southern bush rat) and Pseudomys shortridgei (heath mouse) are dependant on structurally rich environments (Quinlan et al. 2004; Frazer and Petit 2007; Whelan

2003). In addition, Tarsipes rostratus (honey possum) and the western pygmy possum

(Cercartetus concinnus) depend on the pollen and nectar from many highly susceptible 93 proteaceous plant species, and may, therefore, be particularly vulnerable to the impact of P. cinnamomi (Wooller et al. 1982; 1984; Cadzow and Carthew 2004; Pestall and

Petit 2007). Many of these species are limited to remnant patches of natural habitat within reserves and National Parks such as the Stirling Ranges and Fitzgerald River

National Parks. However, these parks, along with many other areas contain plant communities that are highly susceptible to P. cinnamomi. Therefore, until sufficient surveys are conducted, all mammal species inhabiting susceptible plant communities should be considered vulnerable to the impact of the pathogen.

The threat P. cinnamomi presents to the mammal fauna of the southern Western

Australia is highlighted by data collected for southern Victoria which has identified the pathogen as a significant threat to abundance and distribution of a number of small mammal species. The abundance of the common A. agilis (agile antechinus) declined in infested regions of the Brisbane Ranges, in southern Victoria, and this decline was attributed to the death of X. australis, which is highly susceptible to the pathogen

(Newell and Wilson 1993; Newell 1994). Similarly, a study conducted in the Angelsea heathlands, southern Victoria, showed that A. agilis, R. fuscipes, R. lutreolus (swamp rat) and Sminthopsis leucopus (white-footed dunnart) were less abundant in areas infested by P. cinnamomi compared to unaffected areas (Laidlaw 1997; Laidlaw and

Wilson 2006).

5.3. Developing and implementing strategies for the rehabilitation of affected and disturbed areas Presently, Alcoa World Alumina and the Department of Environment and Conservation

(DEC) are developing and implementing techniques to rehabilitate regions of the northern jarrah forest severely affected by P. cinnamomi. These rehabilitation techniques, known as Dieback Forest Rehabilitation (DFR) embrace some of the

94 findings and recommendations of the present study. For example, during the present study large fallen logs were identified as important habitat components to the mardo.

Current DFR techniques now ensure that large logs are no longer piled and burnt, but instead they are retained and pushed into strategically located habitat piles. Preliminary trapping surveys show that female mardo are using the log piles for nesting

(unpublished report to DEC 2005). Further research and long term monitoring is required. In addition, infested areas are being rehabilitated with P. cinnamomi resistant jarrah and marri as well as other species in an attempt to recreate the vegetation structure and complexity herein identified as important to the mardo. When, present in highly impacted areas, B. sessilis is also being retained. As shown during the present study, dense patches of B. sessilis appear to have a positive influence on mardo patch occupancy.

The conservation of nesting and refuge sites is critical to many small mammal species

(van der Ree et al. 2006). Xanthorrhoea preissii is highly susceptible to P. cinnamomi and was identified as a major contributing factor to the presence of mardos. In addition, mardos have been found using X. preissii for nesting (Swinburn et al. 2007). Therefore, a major recommendation from the current study is that further research be conducted to

(1) understand why some X. preissii individuals remain in long term degraded areas, (2) determine if there are variations in susceptibility to P. cinnamomi among X. preissii populations and (3) evaluate the feasibility of transplanting and maintaining mature X. preissii individuals to rehabilitate severely degraded sites. Any research conducted to evaluate the transplantation of X. preissii into highly disturbed locations should include phosphite applications or other fungicides to ensure that the plants are not killed by the pathogen.

95 5.4. Concluding remarks and management implications Phytophthora cinnamomi has devastated many native plant communities throughout southern Australia. In Western Australia, the magnitude of the impact has encouraged

40 years of funding, research and policy both at the university and Government level in an attempt to understand the pathogen. However, until the present study, the indirect impact of P. cinnamomi on habitat quality for native fauna has been largely ignored.

The present study has been the first to provide definitive evidence that P. cinnamomi is a significant threatening process to a fauna species in Western Australia. As a consequence of this evidence, it is an urgent recommendation that research is continued to further our understanding about the threat P. cinnamomi presents to other Western

Australian mammal fauna. Given inevitable funding restrictions, a way forward is to identify key fauna species from different guilds and model their responses to P. cinnamomi. Until such studies have been completed, P. cinnamomi should be considered by all environmental managers and planners as a significant threatening agent to the conservation of native mammal fauna in the south-west of Western

Australia.

96

97 REFERENCES

Abbott I. & Loneragan I. (1986) Ecology of jarrah (Eucalyptus marginata) in the northern Jarrah forest of Western Australia. In: Bulletin No 1. Department of Conservation and Land Management, Perth.

Armstrong K. N. & Nichols O. G. (2000) Long-term trends in avifaunal recolonisation of rehabilitated bauxite mines in the jarrah forest of south-western Australia. Forest Ecology and Management 126, 213-25.

Bell D. T. & Heddle E. M. (1989) Floristic, morphologic and vegetational diversity. In: The Jarrah forest: A complex Mediterranean ecosystem (eds B. Dell, J. J. Havel and N. Malajczuk) pp. 53-66. Kluwer Academic Publishers, Dordrecht, The Netherlands.

Borsboom A. C. (2005) Xanthorrhoea: A review of current knowledge with a focus on X. johnsonii and X. latifolia, two Queensland protected plants-in-trade. Environmental Protection Agency, Queensland.

Braithwaite R. W. (1979) Social dominance and habitat utilization in Antechinus stuartii (Marsupialia). Australian Journal of Zoology 27, 517-28.

Burbidge A. A. & McKenzie M. L. (1989) Patterns in modern decline of Western Australia’s vertebrate fauna: causes and conservation implications. Biological Conservation 50, 143-98.

Burgman M. A. & Lindenmayer D. B. (1998) Conservation Biology for the Australian Environment. Surrey Beatty & Sons Pty Ltd, Sydney.

Burnham K. P. & Anderson D. R. (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer-Verlag, New York, New York, USA.

Cadzow B. & Carthew S. M. (2004) The importance of two species of Banksia in the diet of the western pygmy possum Cercartetus concinnus and the little pygmy possum Cercartetus lepidus in South Australia. In: The Biology of Possums and Gliders (eds R. L. Goldingay and S. M. Jackson) pp. 246-253. Surrey Beatty, Sydney.

Cahill D. M., Wilson B. A. & Armistead R. J. (2002) Phytophthora Dieback: Assessment in the Otway National Park, Victoria. A report conducted by Deakin University for Parks Victoria, Geelong.

Carati C. J. (1982) Ecological strategies of A. flavipes leucogaster (Waterhouse 1838) in the jarrah forest (Eucalyptus marginata) of Western Australia. Honours thesis. School of Environmental and Life Sciences. Murdoch University, Perth, Western Australia.

Carter J. (2003) Vegetation attributes that influence the mardo (Antechinus flavipes leucogaster) in healthy, dieback affected and rehabilitated forest. Honours thesis. Conservation Biology, Murdoch University, Perth.

Catling P. C. & Burt R. J. (1995) Studies of the ground-dwelling mammals of Eucalypt forests in south-eastern New South Wales: the effect of habitat variables on distribution and abundance. Wildlife Research 22, 271-88.

98 Catling P. C., Coops N. C. & Burt R. J. (2001) The distribution and abundance of ground-dwelling mammals in relation to time since wildfire and vegetation structure in south-eastern Australia. Wildlife Research 28, 555-64.

Christensen P. E. & Kimber P. C. (1975) Effect of prescribed burning on the flora and fauna of south-west Australian forests. In: Managing Terrestrial Ecosystems (eds J. Kikkawa and H. A. Nix) pp. 85-106. Proceedings of the Ecological Society of Australia.

Churchward H. M. & Dimmock G. M. (1989) The soils and landforms of the northern jarrah forest. In: The Jarrah forest: A complex Mediterranean ecosystem (eds B. Dell, J. J. Havel and N. Malajczuk) pp. 13-21. Kluwer Academic Publishers, Dordrecht, The Netherlands.

Clarke K. R. & Gorley R. N. (2001) Primer V5: User Manual/Tutorial. Primer-E, Plymouth.

Colquhoun I. J. & Hardy G. E. S. J. (2000) Managing the risk of Phytophthora root and collar rot during bauxite mining in the Eucalyptus marginata (jarrah) forest of Western Australia. Plant Disease 84, 116-27.

Croft D. B. (2004) Behaviour of Carnivorous marsupials. In: Predators With Pouches: The Biology of Carnivorous Marsupials (eds M. E. Jones, C. R. Dickman and M. Archer). CSIRO Publishing, Collingwood.

Crowther M. S. (2002) Distribution of species of the Antechinus stuartii - A. flavipes complex as predicted by bioclimatic models. Australian Journal of Zoology 50, 77-91.

Crowther, M. S., Spencer P. B. S., Alpers D. & Dickman C. R. (2002) Taxonomic status of the mardo, Antechinus flavipes leucogaster (Marsupialia : Dasyuridae): a morphological, molecular, reproductive and bioclimatic approach. Australian Journal of Zoology 50, 627-47.

Crowther, M. S. (2008) Yellow-footed Antechinus, Antechinus flavipes (Waterhouse, 1838). In. The Mammals of Australia. Third Edition. (Eds S. V. Dyck and R. Strahan). Reed New Holland, Sydney.

Davison E. M. (1994) Role of environment in dieback of jarrah: Effects of waterlogging on jarrah and Phytophthora cinnamomi, and the infection of jarrah by P. cinnamomi. Journal of the Royal Society of Western Australia 77, 123-6.

Dell B. & Malajczuk N. (1989) Jarrah dieback - A disease caused by Phytophthora cinnamomi. In: The Jarrah forest: A complex Mediterranean ecosystem (eds B. Dell, J. J. Havel and N. Malajczuk) pp. 67-87. Kluwer Academic Publishers, Dordrecht, The Netherlands.

Dickman C. R. (1980) Ecological studies of Antechinus stuartii and Antechinus flavipes (Marsupialia: Dasyuridae) in open-forest and woodland habitats. Australian Zoologist 20, 433-46.

Dickman C. R. (1991a) Mechanisms of competition among insectivorous mammals. Oecologia 85, 464-71.

99 Dickman C. R. (1991b) Use of trees by ground-dwelling mammals: implications for management. In: Conservation of Australia's Forest Fauna (ed D. Lunney) pp. 125-36. The Royal Zoological Society of New South Wales, Sydney.

Dickman C. R., Parnaby H. E., Crowther M. S. & King D. H. (1998) Antechinus agilis (Marsupialia: Dasyuridae), a new species from the A. stuartii complex in south-eastern Australia. Australian Journal of Zoology 46, 1-26.

Dwyer P. D., Hockings M. & Wilmer J. (1979) Mammals of Cooloola and Beerwah. Proceedings of the Royal Society of Queensland 90, 65-84.

Environment Australia (2001) Threat Abatement Plan for the Dieback Caused by the Root-rot Fungus Phytophthora cinnamomi. Biodiversity Group, Canberra.

Fox B. J. (1982) Fire and mammalian secondary succession in an Australian coastal heath. Ecology 63, 1332-41.

Fox B. J. & Monamy V. (2007) A review of habitat selection by the swamp rat, Rattus lutreolus (Rodentia: Muridae). Austral Ecology 32, 837-49.

Frazer D. S. & Petit S. (2007) Use of Xanthorrhoea semiplana (grass-trees) for refuge by Rattus fuscipes (southern bush rat). Wildlife Research 34, 379-86.

Garkaklis M. J., Calver M. C., Wilson B. A. & Hardy G. E. S. J. (2004) Habitat alteration caused by an introduced plant disease, Phytophthora cinnamomi: a potential threat to the conservation of Australian forest fauna. In: Conservation of Australia's Forest Fauna (Second Edition) (ed D. Lunney). Royal Society of New South Wales, Mosman.

Gaskin C. (2002) Fungal utilisation by mammals: The effects of Phytophthora cinnamomi degradation on mycophagy in the Darling Range, W.A. Honours thesis in: Conservation Biology, Murdoch University, Perth.

Gentilli J. (1989) Climate of the jarrah forest. In: The Jarrah forest: A complex Mediterranean ecosystem (eds B. Dell, J. J. Havel and N. Malajczuk) pp. 23-40. Kluwer Academic Publishers, Dordrecht, The Netherlands.

Gleeson A. (2002) The influence of soil characteristics on the spread of Phytophthora cinnamomi in the northern jarrah forest (Eucalyptus marginata) of south-western Australia. Honours thesis. University of Western Australia, Perth.

Goldingay R. L. (2000) Small dasyurid marsupials - are they effective pollinators? Australian Journal of Zoology 48, 597-606.

Hackett D. J. & Goldingay R. L. (2001) Pollination of Banksia spp. by non-flying mammals in north-eastern New South Wales. Australian Journal of Botany 49, 637-44.

Havel J. J. (1975a) Site-vegetation mapping in the northern jarrah forest (Darling Range). 1. Definition of site-vegetation types. In: Forests Department Bulletin No. 86, Perth, Western Australia.

Havel J. J. (1975b) Site-vegetation mapping in the northern jarrah forest (Darling Range). 2. Location and mapping of site-vegetation types. In: Forests Department Bulletin No. 87, Perth, Western Australia. 100 Heddle E. M., Havel J. J. & Loneragan O. W. (1980a) Focus on the northern jarrah forest. Conservation and recreation areas. In: Forest Focus pp. 1-27. Department of Conservation, Perth.

Heddle E. M., Loneragan O. W. & Havel J. J. (1980b) Vegetation complexes of the Darling System, Western Australian. In: Atlas of natural resources, Darling System, Western Australia. Department of Conservation and Environment, Perth.

Hindmarsh R. & Majer J. D. (1977) Food requirements of mardo (Antechinus flavipes (Waterhouse) and the effect of fire on mardo abundance. In: Research Paper. Forests Department of Western Australia.

Holland G. J. & Bennett A. F. (2007) Occurrence of small mammals in a fragmented landscape: the role of vegetation heterogeneity. Wildlife Research 34, 387-97.

How R. A., Cooper N. K., Girardi L. & Bow B. G. (2002) The mardo: an examination of geographic variation in morphology and reproductive potential in Antechinus flavipes in south-western Australia. Records of the Western Australian Museum 20, 441-7.

Jones M. E., Oakwood M., Belcher C. A., Morris K., Murray A. J., Woolley P. A., Firestone K. B., Johnson B. & Burnett S. (2004) Carnivore concerns: Problems, issues and solutions for conserving Australasia's marsupial carnivores. In: Predators with Pouches: The Biology of Carnivorous Marsupials (eds M. Jones, C. Dickman and M. Archer) pp. 422-34. CSIRO Publishing, Melbourne.

Kelly L. T. & Bennett A. F. (2008) Habitat requirements of the yellow-footed antechinus (Antechinus flavipes) in box-ironbark forest, Victoria, Australia. Wildlife Reseach, 128-33.

Kemp L. F. & Carthew S. M. (2004) Nest site selection by the western pygmy-possum Cercartetus concinnus. In: The Biology of Australian Possums and Gliders (eds R. L. Goldingay and S. M. Jackson). Surrey Beatty and Sons, Chipping Norton.

Kinnear J. E., Onus, M. L., and Bromilow, R.N. (1988) Fox control and rock wallaby population dynamics. Australian Wildlife Research 15, 435-50.

Kinnear J. E., Onus, M. L., And Summer, N. R. (1998) Fox control and rock wallaby population dynamics. Australian Wildlife Research 25, 81-8.

Kinnear P. R. & Gray C. D. (1994) SPSS for Windows Made Simple. Lawrence Erlbaum Associates, Hove.

Kirsch J. A. W. (1968) Burrowing by the quenda. Western Australian Naturalist 10, 178-80.

Knight E. H. & Fox B. J. (2000) Does habitat structure mediate the effects of forest fragmentation and human-induced disturbances on the abundance of Antechinus stuartii? Australian Journal of Zoology 48, 577-95.

Korodaj T. (2007) Determinants of Antechinus occurrence in a fragmented landscape: Dead wood matters. Honours thesis. In: School of Environmental and Information Sciences. Charles Sturt University, Albury.

101 Kroll A. J. & Haufler J. B. (2006) Development and evaluation of habitat models at multiple scales: A case study with the dusky flycatcher. Journal of Wildlife Management 71, 14-22.

Lacy R. C. (1997) Importance of genetic variation to the viability of mammalian populations. Journal of Mammalogy 78, 320-35.

Laidlaw W. S. & Wilson B. A. (1996) The home range and habitat utilisation of Cercartetus nanus (Marsupialia: Burramyidae) in coastal heathland, Anglesea, Victoria. Australian Mammalogy 19, 63-8.

Laidlaw W. S. (1997) The effects of Phytophthora cinnamomi on the flora and fauna of the Eastern Otways. Ph.D., Deakin University, Geelong.

Laidlaw W. S. & Wilson B. A. (2006) Habitat utilisation by small mammals in a coastal heathland exhibiting symptoms of Phytophthora cinnamomi infestation. Wildlife Research 33, 639-49.

Lamont B. B., Wittkulm R. & Korczynskyj D. (2004) Turner Review No. 8. Ecology and ecophysiology of grasstrees. Australian Journal of Botany, 561-82.

Lee A. K., Bradley A. J. & Braithwaite R. W. (1977) Corticosteroid levels and males mortality in Antechinus stuartii. . In: The Biology of Marsupials (eds B. Stonehouse and D. Gilmore) pp. 209-20. MacMillan, London.

Lee A. K., Woolley P. A. & Braithwaite R. W. (1982) Life history strategies of dasyurid marsupials. In: Carnivorous Marsupials (ed M. Archer) pp. 1-11. Royal Zoological Society of New South Wales, Mosman.

Lee A. K. & Cockburn A. (1985) Evolutionary Ecology of Marsupials. Cambridge University Press, Cambridge.

Lilith M. (2002) The effect of plant disease on fauna and validation of trapping methodology in the Darling Range forest ecosystems. Honours thesis in: Conservation Biology, Murdoch University, Perth.

Lunney D. & Leary T. (1988) The impact on native mammals from land-use changes and exotic species in the Bega District, New South Wales, since settlement. Australian Journal of Ecology 13, 67-92.

Mac Nally R., Parkinson, A., Horrocks, G., Conole, L., and Tzaros, C. (2001) Relationships between terrestrial vertebrate diversity, abundance and availability of coarse woody debris on south-eastern Australian floodplains. Biological Conservation 99, 191-205.

Mac Nally R. & Horrocks G. (2002) Habitat change and restoration: responses of a forest-floor mammal species to manipulations of fallen timber in floodplain forests. Animal Biodiversity and Conservation 25, 1-12.

Mackay J. F. G. & Campbell G. S. (1973) Seasonal behaviour and properties of timber from jarrah trees killed or affected by dieback disease. Australian Forestry Research 2, 1-8.

102 MacKenzie D. I., Nichols J. D., Lachman G. B., Droege S., Royle J. A. & Langtimm C. A. (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248-55.

MacKenzie D. I., Nichols J. D., Hines J. E., Knutson M. G. & Franklin A. B. (2003) Estimating site occupancy, colonization and local extinction when a species is detected imperfectly. Ecology 84, 2200-7.

MacKenzie D. I. & Bailey L. L. (2004) Assessing the fit of site-occupancy models. Journal of Agricultural, biological, and environmental statistics 9, 300-18.

MacKenzie D. I. (2005) What are the issues with presence-absence data for wildlife managers. Journal of Wildlife Management 69, 849-60.

MacKenzie D. I. & Royle J. A. (2005) Designing occupancy studies: general advice and allocating survey effort. Journal of Applied Ecology 42, 1105-14.

MacKenzie D. I., Nichols J. D., Royle J. A., Pollock K. H., Bailey L. L. & Hines J. E. (2006) Occupancy Estimation and Modelling: Inferring patterns and dynamics of species occurrence. Elseveir, Amsterdam.

Majer J. D. (1978) Further notes on the food requirements of the mardo (Antechinus flavipes (Waterhouse). In: Research Paper 49. Forests Department of Western Australia.

Majer J. D. & Abbott I. (1989) Invertebrates of the jarrah forest. In: The Jarrah Forest: a Complex Mediterranean Ecosystem (eds B. Dell, J. J. Havel and N. Malajczuk) pp. 111-22. Kluwer, Dordrecht.

Majer J. D., Recher H. F., Heterick B. E. & Postle A. C. (2002) The canopy, bark, soil and litter invertebrate fauna of the Darling Plateau and adjacent woodland near Perth, Western Australia, with reference to the diversity of forest and woodland invertebrates. 7.

Marchesan D. & Carthew S. M. (2004) Autoecology of the yellow-footed antechinus (Antechinus flavipes) in a fragmented landscape in southern Australia. Wildlife Research 31, 273-82.

Masters P., Dickman C. R. & Crowther M. S. (2003) Effects of cover reduction on mulgara Dasycercus cristicauda (Marsupialia : Dasyuridae), rodent and invertebrate populations in central Australia: Implications for land management. Austral Ecology 28, 658-65.

Maxwell S., Burbidge A. & Morris K. (1996) The 1996 Action Plan for Australian Marsupials and Monotremes. Wildlife Australia, Canberra.

McAllan B. M. & Dickman C. R. (1986) The role of photoperiod in the timing of reproduction in the dasyurid marsupial Antechinus stuartii. Oecologia 68, 259-64.

McCay T. S. (2000) Use of woody debris by cotton mice (Peromyscus gossypinus) in a southeastern pine forest. Journal of Mammalogy 2, 527-35.

103 McComb B. (1994) Mapping potential habitat for vertebrates in forests of Western Australia. In: A final report. Report for the Department of Conservation and Land Management, Western Australia.

McDougall K. L. (1997) Vegetation patterns in the northern jarrah forest of Western Australia in relation to dieback history and the current distribution of Phytophthora cinnamomi. Ph.D. Murdoch University, Perth.

McDougall K. L., St J. Hardy G. E. & J. H. R. (2001) Additions to the host range of Phytophthora cinnamomi in the jarrah (Eucalyptus marginata) forest of Western Australia. Australian journal of Botany 49, 193-8.

McDougall K. L., Hardy G. E. S. J. & Hobbs R. J. (2002a) Distribution of Phytophthora cinnamomi in the northern jarrah (Eucalyptus marginata) forest of Western Australia in relation to dieback age and topography. Australian Journal of Botany 50, 107-14.

McDougall K. L., Hobbs R. J. & Hardy G. E. S. J. (2002b) Floristic and structural differences between Phytophthora infested and adjoining un-infested vegetation in the northern jarrah (Eucalyptus marginata) forest of Western Australia. Australian Journal of Botany 50, 277-88.

Menkhorst P. W. (1995) Mammals of Victoria. Oxford University Press and Department of Conservation and Natural Resources, Melbourne.

Menkhorst P. & Knight F. (2001) A Field Guide to the Mammals of Australia. Oxford University Press, Oxford, U.K.

Moir M., Brennan K. E. C. & Wittkuhn R. S. (2006) Fire refugia: The mechanism governing animal survivorship in a highly flammable understorey plant. Forest Ecology and Management 234S, S166.

Monamy V. & Fox B. J. (2000) Small mammal succession is determined by vegetation density rather than time elapsed since disturbance. Austral Ecology 25, 580-7.

Morris K. D. (2000) The status and conservation of native rodents in Western Australia. Wildlife Research 27, 405-419.

Morris K., Johnson B., Orell P., Gaikhorst G., Wayne A. & Moro D. (2004) Recovery of the threatened Chuditch (Dasyurus geoffroii): A case study. In: Predators with Pouches: The Biology of Carnivorous Marsupials (eds M. E. Jones, C. R. Dickman and M. Archer). CSIRO Publishing, Collingwood.

Newell G. R. & Wilson B. A. (1993) The relationship between cinnamon fungus (Phytophthora cinnamomi) and the abundance of Antechinus stuartii (Dasyuridae : Marsupialia) in the Brisbane Ranges, Victoria. Wildlife Research 20, 251-9.

Newell G. R. (1994) The effects of Phytophthora cinnamomi on the habitat utilization of Antechinus stuartii in a Victorian forest. Ph.D. Deakin University,

Nichols O. G. & Watkins D. (1984) Bird utilization of rehabilitated bauxite minesites in Western Australia. Biological Conservation 30, 109-31.

104 Nichols O. G. & Bamford M. J. (1985) Reptile and frog utilisation of rehabilitated bauxite minesites and dieback-affected sites in Western Australia's jarrah Eucalyptus marginata forest. Biological Conservation 34, 227-50.

Nichols A. O. & Burrows R. (1985) Recolonisation of revegetated bauxite mines by predatory invertebrates. Forest Ecology and Management 1049-64.

Noss R. F. (1989) Indicators for monitoring Biodiversity: A hierarchical approach. Conservation Biology 4, 355-64.

Noss R. F. (1999) Assessing and monitoring forest biodiversity: A suggested framework and indicators. Forest Ecology and Management 115, 135-46.

Pestall A. J. L & Petit S. (2007) Diet of the western pygmy possum, Cercartetus concinnus Gould (Marsupialia: Burramyidae), at Innes National Park, South Australia, and evaluation of diet sampling methods. Australian Journal of Zoology 55, 275-284.

Podger F. D., Doepel R. F. & Zentmyer G. A. (1965) Association of Phytophthora cinnamomi with a disease of Eucalyptus marginata forest in Western Australia. Plant Disease Reporter 49, 943-7.

Podger F. D. (1972) Phytophthora cinnamomi, a cause of lethal disease in indigenous plant communities in Western Australia. Phytopathology 62, 972-81.

Postle A. C., Majer J. D. & Bell D. T. (1986) Soil and litter invertebrates and litter decomposition in jarrah (Eucalyptus marginata) forest affected by jarrah dieback fungus (Phytophthora cinnamomi). Pedobiologia 29, 47-69.

Quinlan K., Moro D. & Lund M. (2004) Improving trapping success for rare species by targeting habitat types using remotely sensed data: a case study of the heath mouse (Pseudomys shortridgei) in Western Australia. Wildlife Research 31, 219-227.

Rhind S. (1998) Ecology of the brush-tailed phascogale in jarrah forest of south-western Australia. In: Conservation Biology. Murdoch University, Perth.

Risby D., Calver, M.C., and Short, J. C. (1999) The impact of cats and foxes on small vertebrate fauna of Heirison Prong, Western Australia. I. Exploring the potential impact using diet analysis. Wildlife Research 26, 621-30.

Risby D., Calver, M.C., and Short, J. C. (2000) The impact of cats and foxes on small vertebrate fauna of Heirison Prong, Western Australia. II. Field experiments. Wildlife Research 27, 223-36.

Rockel B. A., McGann L. R. & Murray D. I. L. (1982) Phytophthora cinnamomi Rands causing death in Dryandra sessilis on old dieback sites in the Jarrah forest. Australasian Plant Pathology 11, 49-50.

Sawle M. (1979) Habitat components of Antechinus flavipes in the Karri forest southwest of Western Australia. Honours thesis. Murdoch University, Perth.

Schmidt W. & Mason M. (1973) Fire and fauna in the northern jarrah forest of Western Australia. Western Australian Naturalist 12, 162-4.

105 Settle G. A. & Croft D. B. (1982) Maternal behaviour of Antechinus stuartii (Dasyuridae: Marsupialia) in captivity. In: Carnivorous Marsupials (ed M. Archer) pp. 365-81. Royal Zoological Society of New South Wales, Sydney.

Shea S. R. (1977) Environmental factors of the northern Jarrah forest in relation to pathogenicity and survival of Phytophthora cinnamomi. In: Bulletin 85. Forests Department, Perth, Western Australia.

Shearer B. L. & Tippett J. T. (1989) Jarrah Dieback: The dynamics and management of Phytophthora cinnamomi in the Jarrah (Eucalyptus marginata) forest of south-Western Australia. In: Department Conservation and Land Management Research Bulletin No. 3, Como, Western Australia.

Shearer B. L. (1994) The major plant pathogens occurring in native ecosystems of south Western Australia. Journal of the Royal Society of Western Australia 77, 113-22.

Shearer B. L. & Dillon M. (1995) Susceptibility of plant species in Eucalyptus marginata forest to infection by Phytophthora cinnamomi. Australian Journal of Botany 43, 113-34.

Shearer B. L., Crane C. E. & Cochrane A. (2004) Quantification of the susceptibility of the native flora of the South-West Botanical Province, Western Australia, to Phytophthora cinnamomi. Australian Journal of Botany 52, 435-43.

Shearer B. L., Crane C. E., Barret S. & Cochrane A. (2007) Phytophthora cinnamomi invasion, a major threatening process to conservation of flora diversity in the South- west Botanical Province of Western Australia. Australian Journal of Botany 55, 225-38.

Shimmon G. A., Taggert D. A. & Temple-Smith P. D. (2000) Mating behaviour in the agile antechinus (Antechinus agilis, Marsupialia: Dasyuridae). Journal of Zoology, London 258, 39-48.

Short J. & Smith A. P. (1994) Mammal decline and recovery in Australia. Journal of Mammalogy 75, 288-97.

Smith G. C. (1984) The biology of the yellow-footed antechinus, Antechinus flavipes (Marsupialia: Dasyuridae), in a swamp forest on Kinaba Island, Cooloola, Queensland. Australian Wildlife Research 11, 465-80.

Smith A. P. & Quinn D. G. (1996) Patterns and causes of extinction and decline in Australian conilurine rodents. Biological Conservation 77, 243-267.

Statham H. L. & Harden R. H. (1982) Habitat utilization of Antechinus stuartii (Marsupialia) at Petroi, Northern New South Wales. In: Carnivorous Marsupials (ed M. Archer) pp. 165-85. Royal Zoological Society of New South Wales, Sydney.

Stokes V. L., Pech, R. P., Banks, P. B., and Arther, A. D. (2004) Foraging behaviour and habitat use by Antechinus flavipes and Sminthopsis murina (Marsupialia : Dasyuridae) in response to predation risk in eucalyptus woodland. Biological Conservation 117, 331-42.

Sutherland D. R. & Predavec M. (1999) The effects of moonlight on microhabitat use by Antechinus agilis (Marsupialia : Dasyuridae). Australian Journal of Zoology 47, 1- 17. 106 Swinburn M., Fleming P. A., Craig M. D., Grigg A. H., Garkaklis M. J., Hardy G. E. S. & Hobbs R. J. (2007) Selection of grasstrees (Xanthorrhoea preissii) by mardo (Antechinus flavipes leucogaster): relationship with time since fire in jarrah forest of Western Australia. Wildlife Research 35, 640-51.

Tasker E. M. & Dickman C. R. (2004) Small mammal community composition in relation to cattle grazing and associated burning in eucalyptus forest of the Northern Tablelands of New South Wales. In: Conservation of Australia's Forest Fauna (ed D. Lunney). Royal Society of New South Wales, Mosman.

Tulloch A. I. & Dickman C. R. (2006) Floristic and structural components of habitat use by the eastern pygmy-possum (Cercartetus nanus) in burnt and unburnt habitats. Wildlife Research 33, 627-37. van der Ree R., Bennett A. F. & R. S. T. (2006) Nest-site selection by the threatened brush-tailed phascogale (Phascogale tapoatafa) (Marsupialia : Dasyuridae) in a highly fragmented agricultural landscape. Wildlife Reseach 33, 113-9.

Van Dyck S. (1982) The relationship of Antechinus stuartii and A. flavipes (Dasyuridae. Marsupialia) with special reference to Queensland. In: Carnivorous Marsupials (ed M. Archer) pp. 723-66. Royal Zoological Society of New South Wales, Sydney.

Van Dyck, S., & Strahan, R. (2008) The Mammals of Australia. Reed New Holland. Sydney.

Wardell-Johnson G. (1986) Use of nest boxes by Mardo, Antechinus flavipes leucogaster, in regenerating Karri forest in south-western Australia. Australian Wildlife Research 13, 407-17.

Wardell-Johnson G. & Nichols O. G. (1991) Forest wildlife and habitat management in south-western Australia: knowledge, research and direction. In: Conservation of Australia’s fauna (ed D. Lunney) pp. 161-92. Royal Zoological Society of New South Wales, Mosman, New South Wales.

Wardell-Johnson G. & Horwitz, P. (1996) Conserving biodiversity and the recognition of heterogeneity in ancient landscapes: a case study from south-western Australia. Forest Ecology and Management 85, 219-238.

Wardell-Johnson G. Calver, M., Saunders, D., Conroy, S.,& Jones, B. (2004) Why the integration of demographic and site-based studies of disturbance is essential for the conservation of jarrah forest fauna. In: Conservation of Australia's Forest Fauna (Second Edition) (ed D. Lunney). Royal Society of New South Wales, Mosman.

Watt A. (1997) Population ecology and reproductive seasonality in three species of Antechinus (Marsupialia : Dasyuridae) in the wet tropics of Queensland. Wildlife Research 24, 531-47.

Wayne A. F., Rooney J. F., Ward C. G., Vellios C. V. & Lindenmayer D. B. (2005) The life history of Pseudocheirus occidentalis (Pseudocheiridae) in the jarrah forest of south-western Australia. Australian Journal of Zoology 53, 325-37.

Weste G. & Marks G. C. (1974) The distribution of Phytophthora cinnamomi in Victoria. Transactions of the British Mycological Society 63, 559-72.

107 Weste G., and Marks, G. C. (1987) The biology of Phytophthora cinnamomi in Australian forests. Annnual Review of Phytopathology 25, 207-29.

Weste G. & Ashton D. H. (1994) Regeneration and survival of indigenous dry sclerophyll species in the Brisbane ranges, Victoria, after a Phytophthora cinnamomi epidemic. Australian Journal of Botany 42, 239-53.

Weste G. & Kennedy J. (1997) Regeneration of susceptible native species following the decline of Phytophthora cinnamomi over a period of 20 years on defined plots in hte Grampians, Western Victoria. Australian Journal of Botany 45, 167-90.

Weste G., Walchhuetter T. & Walshe T. (1999) Regeneration of Xanthorrhea australis following epidemic disease due to Phytophthora cinnamomi in the Brisbane Ranges, Victoria. Australian Journal of Botany 48, 162-9.

Whelan, J. K. (2003) The impact of Phytophthora cinnamomi on the abundance and mycophagous diet of the bush rat (Rattus fuscipes). Honours thesis. Conservation Biology, Murdoch University, Perth.

Whittell M. H. M. (1954) John Gilbert's notebook on marsupials. Western Australian Naturalist 5, 104-14.

Wills R. T. (1993) The ecological impact of Phytophthora cinnamomi in the Stirling Range National Park, Western Australia. Australian Journal of Ecology 18, 145-59.

Wilson B. A. & Bourne M. (1984) Reproduction on male swamp antechinus Antechinus minimus maritimus. Australian Journal of Zoology 32, 311-8.

Wilson B. A., Robertson D., Moloney D. J., Newell G. R. & Laidlaw W. S. (1990) Factors effecting small mammal distribution and abundance in the eastern Otway Ranges, Victoria In: Australian Ecosystems: 200 Years of Utilization, Degradation and Reconstruction (eds D. A. Saunders, A. J. M. Hopkins and R. A. How) pp. 379-96. Surry Beatty & Sons, Chipping Norton.

108 Wilson B. A., Newell G., Laidlaw W. S. & Friend G. (1994) Impact of plant diseases on faunal communities. Journal of the Royal Society of Western Australia 77, 139-44.

Wilson B. A. & Friend G. R. (1999) Responses of Australian mammals to disturbance: A review. Australian Mammalogy 21, 87-105.

Wilson B. A., Aberton J. G. &, Reichl, T. R. (2001) Effects of fragmented habitat and fire on the distribution and ecology of the swamp antechinus (Antechinus minimus martimus) in the eastern Otways, Victoria. Wildlife Research 28, 527-36.

Wilson B. A., Lewis A. & Aberton J. (2003) Spatial model for predicting the presence of cinnamon fungus (Phytophthora cinnamomi) in sclerophyll vegetation communities in south-eastern Australia. Austral Ecology 28, 108-15.

Wilson B. A., Dickman C. R. & Fletcher T. P. (2004) Dasyurid dilemmas: Problems and solutions for conserving Australia's small carnivorous marsupials. In: Predators with Pouches: The Biology of Carnivorous Marsupials (eds M. Jones., C. Dickman. and M. Archer) pp. 407-21. CSIRO Publishing, Melbourne.

Wood J. (2002) Flower visitation by mammals in healthy and diseased Jarrah forest ecosystems. Honours thesis. Conservation Biology, Murdoch University, Perth.

Wooller R. D., Russell E. M., Renfree M. B. & Towers P. A. (1982) A comparison of seasonal changes in the pollen loads of nectarivorous marsupials and birds. Australian Wildlife Research 10, 311-7.

Wooller R. D., Russell E. M. & Renfree M. B. (1984) Honey possums and their food plants. In: Possums and Gliders (eds A. P. Smith and I. D. Hume) pp. 439-43. Australian Mammal Society, Sydney.

Zar J. H. (1999) Biostatistical analysis. Prentice-Hall, Upper Saddle River.

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APPENDIX 1. Complete model selection results fitting Antechinus flavipes (mardo) detectability (p) and patch occupancy (ψ) model to the mardo trapping data.

The term “and” represents the main and interactive affects of the parameters (site, time and gender), whilst “+” indicates the additive affect of a habitat covariate. DES = Dieback Expression Score

110 Rank Model QAICC ∆ QAICC Model weight Likelihood # Parameters Deviance (wi) in model 1 p (.) ψ (site + DES) 539.89 0.00 0.316 1.000 8 523.35 2 p (.) ψ (site) 540.00 0.11 0.298 0.946 7 525.59 3 p (. + DES) ψ (site + DES) 541.52 1.63 0.139 0.443 9 522.84 4 p (. + DES) ψ (site) 541.73 1.83 0.126 0.399 8 525.18 5 p (gender) ψ (site) 542.13 2.23 0.103 0.328 8 525.59 6 p (site) ψ (site) 546.87 6.98 0.009 0.031 12 521.68 7 p (.) ψ (site and gender ) 549.72 9.82 0.002 0.008 15 522.32 8 p (site) ψ (site and gender) 550.47 10.58 0.002 0.005 6 518.62 9 p (site) ψ (gender) 550.92 11.02 0.001 0.004 14 538.60 10 p (gender) ψ (site and gender) 551.93 12.04 0.001 0.002 30 522.32 11 p (time) ψ (site) 552.24 12.35 0.001 0.002 7 484.59 12 p (site) ψ (.) 553.67 13.78 0.000 0.001 30 53926 13 p (time and gender) ψ (gender) 558.84 19.85 0.000 0.000 18 491.19 14 p (site and gender) ψ (site) 559.07 19.18 0.000 0.000 13 520.39 15 p (site and gender) ψ (gender) 563.61 23.71 0.000 0.000 14 536.21 16 p (time) ψ (site and gender) 564.57 24.68 0.000 0.000 36 481.33 17 p ( site and gender) ψ (site and gender) 571.19 31.30 0.000 0.000 24 518.38 18 p (.) ψ (.) 589.33 49.43 0.000 0.000 2 585.28 19 p (.) ψ (gender) 591.33 49.79 0.000 0.000 3 583.59 20 p (gender) ψ (.) 591.75 51.44 0.000 0.000 3 583.24 21 p (gender) ψ (gender) 600.91 51.85 0.000 0.000 4 583.59 22 p (time) ψ (.) 623.26 61.02 0.000 0.000 25 545.67

111 Rank Model QAICC ∆ QAICC Model weight Likelihood # Parameters Deviance (wi) in model 23 p (time and time.) ψ (site) 623.73 83.83 0.000 0.000 60 469.36 24 p (site and gender) ψ (.) 653.26 113.36 0.000 0.000 49 533.38 25 p (time and gender) ψ (.) 653.26 113.36 0.000 0.000 49 533.38 26 p (time and site) ψ (.) 1119.61 579.72 0.000 0.000 145 498.84 27 p (time and site) ψ (site) 1136.07 596.17 0.000 0.000 150 467.78 28 p (time and site) ψ (site and gender) 1195.23 655.33 0.000 0.000 156 464.56 29 p (time and site and gender) ψ (.) 5839.48 5299.50 0.000 0.000 289 436.99 30 p (time and site and gender) ψ (site and gender) 6416.05 5876.10 0.000 0.000 300 459.53

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APPENDIX 2. Complete model selection results fitting detectability (p) and patch occupancy (ψ) model of MacKenzie et al. (2002) to the mardo trapping data.

The notation terms used in the following models is “*” represents the main and interactive (site, time and gender) affects of the parameters, whilst “+” indicates the additive affect of the habitat covariates. Over-dispersion factor or ĉ= 2.457.

113 Rank Model structure QAICC ∆QAICC Model Likelihood Parameters Deviance weight (wi) in model 1 p (.) + Large log densities ψ (site) 317.57 0.00 0.041 1.000 8 301.08 2 p (.) + Large log densities ψ (site) + Tall single crown X. preissii densities 317.83 0.26 0.035 0.877 9 299.22 3 p (.) + Large log densities ψ (site) + Tall multiple X. preissii densities 318.03 0.45 0.032 0.797 9 299.41 4 p (.) + Large log and Tall multiple crown X. preissii densities ψ (site) 318.11 0.53 0.031 0.767 9 299.48 5 p (.) + Large log densities ψ (site) + large log densities 318.34 0.76 0.028 0.683 9 299.72 6 p (.) + Large log densities ψ (site) + Medium sized X. preissii densities 318.46 0.88 0.026 0.644 10 297.69 7 p (.) + Large log densities ψ (site) + Tall single and multiple crown X. preissii densities 318.48 0.91 0.026 0.636 9 299.86 8 p (.) + Large log densities and ground cover vegetation structure ψ (site) 318.59 1.02 0.024 0.600 9 299.98 9 p (.) + Total large logs densities ψ (site) + small/medium sized X. preissii densities 318.65 1.08 0.024 0.583 9 300.03 10 p (.) + Total large logs densities and ground cover vegetation structure ψ (site) + Tall single crown X. preissii densities 318.89 1.32 0.021 0.516 10 298.14 11 p (.) + Total large logs densities ψ (site) + total log densities 318.96 1.39 0.020 0.499 9 300.34 12 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + Tall multiple crown X. preissii densities 318.98 1.41 0.020 0.495 10 298.22 13 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + Total X. preissii densities 319.14 1.56 0.019 0.457 10 298.38 14 p (.) + large log ψ (site) + DBH 319.19 1.61 0.018 0.447 9 300.56 15 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + small/medium sized X. preissii densities 319.23 1.65 0.018 0.438 10 298.47 16 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + large log densities 319.24 1.67 0.018 0.435 10 298.48 17 p (.) + large log densities ψ (site) + litter cover 319.26 1.69 0.018 0.429 9 300.64 18 p (.) + large log densities ψ (site) + ground cover vegetation structure 319.27 1.70 0.017 0.427 9 300.66 19 p (.) + large log densities ψ (site) + small log densities 319.28 1.71 0.017 0.426 9 300.66 20 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + Tall single crown X. preissii densities 319.38 1.81 0.017 0.406 10 298.62 21 p (.) + large log densities and ground cover vegetation structure ψ (site) + large log densities 319.42 1.84 0.016 0.399 10 298.66 22 p (.) + large log densities and ground cover vegetation structure ψ (site) + total X. preissii densities 319.43 1.85 0.016 0.396 10 298.67 23 p (.) + large log densities ψ (site) + Tree health 319.44 1.86 0.016 0.394 9 300.82 24 p (.) + large log densities ψ (site) + small X. preissii densities 319.44 1.87 0.016 0.393 9 300.83 25 p (.) + large log densities ψ (site) + DES 319.54 1.96 0.015 0.375 9 300.92 26 p (.) + large log densities ψ (site) + medium sized X. preissii densities 319.55 1.98 0.015 0.372 9 300.94 27 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + medium sized X. preissii densities 319.56 1.99 0.015 0.370 10 298.80 28 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + DBH 319.57 1.99 0.015 0.369 10 298.81 29 p (.) + large log densities ψ (site) Debris depth 319.63 2.05 0.015 0.358 9 301.00 30 p (.) + large log densities ψ (site) + small/medium X. preissii densities 319.65 2.07 0.014 0.354 10 298.88 31 p (.) + large log densities ψ (site) + shrub cover vegetation structure 319.69 2.12 0.014 0.346 9 301.08

32 p (.) + large log and Tall multiple crown X. preissii densities ψ (site) + percentage canopy cover 319.70 2.12 0.014 0.346 9 301.08 33 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + large log densities 319.79 2.22 0.013 0.330 10 299.03 34 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + ground cover vegetation structure 319.85 2.27 0.013 0.321 10 299.08 35 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + percentage litter cover 319.85 2.28 0.013 0.317 10 299.09 36 P (.) + large log densities and ground cover vegetation structure ψ (site) + small log densities 319.87 2.30 0.013 0.302 10 299.11 37 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + small X. preissii densities 319.97 2.40 0.012 0.299 10 299.21

114 Rank Model structure QAICC ∆QAICC Model Likelihood Parameters Deviance weight (wi) in model 38 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + tree health 319.99 2.42 0.012 0.2948 10 299.23 39 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + debris cover 320..02 2.44 0.012 0.295 10 299.25 40 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + DES 320.07 2.50 0.012 0.274 10 299.31 41 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + medium X. preissii densities 320.17 2.59 0.011 0.287 10 299.41 42 p (.) + large log densities and ground cover vegetation ψ (site) + ground cover vegetation 320.17 2.59 0.011 0.274 10 299.41 43 p (.) + large log and tall multiple crown X. preissii densities (site) + small/medium sized X. preissii densities 320.19 2.61 0.011 0.273 10 299.42 44 p (.) + large log densities and ground cover vegetation ψ (site) + DBH 320.21 2.63 0.011 0.271 10 299.47 45 p (.) + large log densities and ground cover vegetation ψ (site) + small log densities 320.23 2.66 0.011 0.268 10 299.47 46 p (.) + large log and tall multiple crown X. preissii densities (site) + ground cover vegetation 320.23 2.66 0.011 0.265 10 299.48 47 p (.) + large log and tall multiple crown X. preissii densities ψ (site) + canopy cover 320. 2.67 0.011 0.264 10 299.66 48 p (.) + large log densities and ground cover vegetation ψ (site) + Debris depth 320.43 2.85 0.009 0.240 10 299.67 49 p (.) + large log densities and ground cover vegetation ψ (site) + small X. preissii densities 320.54 2.96 0.009 0.227 10 299.78 50 p (.) + large log densities and ground cover vegetation ψ (site) + Tree health 320.55 2.98 0.009 0.225 10 299.79 51 p (.) + Tall multiple crown X. preissii densities ψ (site) 320.59 3.01 0.00 0.222 8 304.09 52 p (.) + large log densities and ground cover vegetation ψ (site) + Litter depth 320.60 3.02 0.00 0.222 10 299.83 53 p (.) + large log densities and ground cover vegetation ψ (site) +Medium X. preissii densities 320.60 3.03 0.00 0.220 10 299.84 54 p (.) + large log densities and ground cover vegetation ψ (site) + DES 320.67 3.09 0.00 0.213 10 299.91 55 p (.) + large log densities and ground cover vegetation ψ (site) +Ground cover vegetation 320.69 3.12 0.00 0.210 10 299.93 56 p (.) + large log densities and ground cover vegetation ψ (site) + Shrub cover vegetation 320.72 3.15 0.00 0.207 10 299.96

57 p (.) + large log densities and ground cover vegetation ψ (site) + Percentage canopy cover 320.73 3.15 0.00 0.206 10 299.96 58 p (.) + ground cover vegetation ψ (site) 320.92 3.35 0.007 0.187 8 304.43 59 p (.) ψ (site) 320.96 3.39 0.007 0.183 7 306.58 60 p (.) + Tall single crown X. preissii densities ψ (site) 321.05 3.47 0.007 0.176 8 300.79 61 p (.) + large log densities ψ (site) + Tree health 321.55 3.98 0.005 0.137 10 305.70 62 p (.) Tree health ψ (site) 321.69 4.11 0.005 0.128 8 305.21 63 p (.)+ DBH ψ (site) 321.70 4.13 0.005 0.127 8 305.48 64 p (.)+ total log densities ψ (site) 321.98 4.40 0.004 0.111 8 305.70 65 p (.) + medium X. preissii densities ψ (site) 322.19 4.62 0.003 0.097 8 305.74 66 p (.) + Total X. preissii densities ψ (site) 322.24 4.66 0.003 0.085 8 306.00 67 p (.)+ percentage litter cover ψ (site) 322.49 4.92 0.002 0.072 8 306.36 64 p (.)+ total log densities ψ (site) 321.98 4.40 0.004 0.111 8 305.70 65 p (.) + medium X. preissii densities ψ (site) 322.19 4.62 0.003 0.097 8 305.74 66 p (.) + Total X. preissii densities ψ (site) 322.24 4.66 0.003 0.085 8 306.00 67 p (.)+ percentage litter cover ψ (site) 322.49 4.92 0.002 0.072 8 306.36

115 Rank Model structure QAICC ∆QAICC Model Likelihood Parameters Deviance weight (wi) in model 68 p (.) +shrub cover vegetation ψ (site) 322.85 4.27 0.002 0.071 8 306.37 69 p (.)+ small/medium X. preissi densities ψ (site) 322.86 5.29 0.002 0.070 8 306.39 70 p (.) + small X. preissii densities ψ (site) 322.89 5.32 0.002 0.069 8 306.42 71 p (.) + canopy cover ψ (site) 322.91 5.33 0.002 0.069 8 306.43 72 p (.) DES ψ (site) 322.92 5.34 0.002 0.067 8 306.488 73 p (.) Tree health ψ (site) 322.98 5.41 0.002 0.0645 8 306.56 74 p (.) ψ small log densities (site) 323.06 5.48 0.001 0.049 8 304.96 75 p (.) litter depth ψ (site) 323.58 6.00 0.001 0.034 9 309.98 76 p (site) ψ (.) 324.36 6.79 0.000 0.034 7 309.98 77 p (time) ψ (site) 325.46 7.89 0.000 0.019 14 295.29 78 p (site*gender) ψ (.) 327.57 9.99 0.000 0.006 13 300.29 79 p (.) ψ (site*gender) 330.17 10.60 0.000 0.005 13 300.89 80 p (site) ψ (site) 330.98 13.40 0.000 0.001 12 305.88 81 p (time) ψ (site*gender) 333.31 15.75 0.000 0.000 20 290.30 82 p (site*gender) ψ (site) 336.91 19.33 0.000 0.000 18 298.47 83 p (.) ψ (site) 338.63 21.06 0.000 0.000 18 300.20 84 p (site*gender) ψ (site*gender) 347.60 30.03 0.000 0.000 24 295.24 85 p (.) ψ (.) 371.94 54.41 0.000 0.000 2 367.94 86 p (time) ψ (.) 375.93 58.36 0.000 0.000 9 357.31 87 p (site*gender*time) ψ (.) 569.81 249.73 0.000 0.000 97 281.69 88 p (site*gender*time) ψ (site*gender) 605.40 285.40 0.000 0.000 108 275.87

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