The impact of Phytophthora cinnamomi on …...i The impact of Phytophthora cinnamomi on the...

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The impact of Phytophthora 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)

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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

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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 (Eucalyptus marginata) 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 Xanthorrhoea preissii were the greatest contributors separating affected

and unaffected areas.

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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

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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.

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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.

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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

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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

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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

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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 Banksia 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 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. .................... 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), 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. 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

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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

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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

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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

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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

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

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).

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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

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are the structurally important Banksia grandis (bull banksia), Persoonia longifolia, P.

elliptica (snottygobbles) and Adenanthos barbiger (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.

5

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

6

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

7

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

8

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

9

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).

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).

A. f. leucogaster A. f. flavipes

A. f. rubeculus

10

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).

11

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

12

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

13

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).

14

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).

15

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,

16

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

17

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

18

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).

19

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

Perth

Dwellingup

Study area

20

A

0

50

100

150

200

250

300

January

February

March

April MayJune

July

August

September

October

November

December

Month

Aver

age r

ainfa

ll (m

m)

B

0

5

10

15

20

25

30

35

January

February

March AprilMay

JuneJu

lyAugu

st

September

October

November

December

Month

Aver

age t

empe

ratu

re

(Deg

rees

Cels

ius)

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.

21

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

1.1. Latitude and longitude E 412505.63 N 6371488.26

E 412520.70 N 6372363.54

E 412935.90 N 6372043.38

E 413184.26 N 6372007.95

E 414644.00 N 6328402.80

E 415340.28 N 6370364.83

1.2. Aspect North east, slightly incline

East, slight incline North, mid slope with slight incline

North, mid slope with slight incline

West, in the lower part of a steep gully

Southerly, in the lower regions of a steep gully

1.3. Topographical position Upland site, no permanent water

Upland, 150 m from a fresh water spring

Mid-slope, approximately 100 m from a permanent swamp

Mid-lower slope and approximately 150 m from permanent swamp

Mid-lower slope, a bush track separates site from permanent stream. Stream is 40 m from site

Lower slope in dissecting deep valley. A small permanent stream is 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 types and associated indicator species (Bell and Heddle 1989; Havel 1975a; b)

Eucalyptus marginata Corymbia calophylla Acacia browniana Adenanthos barbiger Allocasaurina fraseriana Banksia grandis Daviesia decurrens Hovea chorizemifolia Lasiopetalum floribundum Macrozamia riedlei Persoonia longifolia Phyllanthus calycinus Trymelium ledifolium

Eucalyptus marginata Corymbia calophylla Acacia browniana Adenanthos barbiger Allocasaurina fraseriana Banksia grandis Daviesia decurrens Hovea chorizemifolia Lasiopetalum floribundum Macrozamia riedlei Persoonia longifolia Phyllanthus calycinus Trymalium ledifolium



Eucalyptus marginata Corymbia calophylla Acacia urophylla Banksia grandis Bossiaea aquifolium Hypocalymma angustifolium Hovea chorizemifolia Lasiopetalum floribundum Leucopogon capitellatus Leucopogon verticillatus Macrozamia riedlei Phyllanthus calycinus Pteridium esculentum

Eucalyptus marginata Corymbia calophylla Acacia urophylla Bossiaea aquifolium Clematis pubescens Eucalyptus patens Hypocalymma angustifolium Phyllanthus calycinus Leucopogon capitellatus Leucopogon propinquus Macrozamia riedlei Phyllanthus calycinus Pteridium esculentum

22

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

23

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 Group

Degree of susceptibility

Expected health if 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 Score and P. cinnamomi symptoms rating

Status of susceptible plant species

Vegetation structure

Canopy cover

Leaf litter

0 Absent No disease symptoms apparent, dense, healthy

Closed Closed Thick,

1. Subtly or locally impacted

Dense, infrequent and localised deaths in understorey

Moderately closed

Moderate closed

Thick, but sparse

2. Moderately or Intermediatly impacted

Infrequent, Eucalyptus marginata still present, but absence of susceptible understorey species

Open Open Sparse

3. Severely impacted

Infrequent, unhealthy in all strata layers

Very open Very open Very Sparse

4. Extremely severe impacted

Absent Very open, in some areas absent

Very open, in some areas absent

Very open, in some 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

24

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.

25

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.

26

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).

27

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.

28

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.

29

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.

30

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 initial infestation

Late 1950’s to mid 1960’s




Uncertain

Not affected

1.2. Current disease status Post infestation. Old infestation, 50-60 years ago. Some colonisation by the susceptible species, Banksia sessilis

Post-infestation. Old infestation, 50-60 years ago

Post infestation, 30-40 years ago. Some recent deaths, suggest pathogen is still active

Post infestation, 30-40 years ago. Plants, dying during survey period, suggests pathogen is still active

Majority of site is disease free. Visual evidence of old infestation at 11 trap stations. Difficult to estimate date when infested

No disease expression, healthy. This site has not been disturbed for a long time.

1.3. Trap stations affected by P. cinnamomi

25 25 22 25 11 0

1.4. Trap stations not affected by P. cinnamomi

0 0 3 0 14 25

1.5. Disturbance status (as determined by senior Alcoa, DEC and Murdoch staff)

Severely affected Severely affected Moderately affected Moderately affected Healthy forest Healthy forest

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 (decade)

1970-1980 1970-1980 1970-1980 1970-1980 1940-1950 1940-1950

2.3. Last timber harvest method used

Pole cutting and Salvage Salvage Pole cutting and Salvage Pole cutting and Salvage Associated with Nanga Brook timber mill. Harvest method is unknown.

Associated with Nanga Brook timber mill. Harvest method is unknown.

2.4. Number of timber harvested

2 2 1 1 1 2

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 (since 1920)

9 9 9 9 7 6

31

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

32

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).

33

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).

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).

0

1

2

3

4

5

1 2 3 4 5 6

Survey site

Die

back

Exp

ress

ion

Scor

e

34

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

2

Ground cover Shrub cover Total log densities Small -Medium log densities Total X. preissii densities

40.70 20.78 10.74 8.40 5.16

3

Shrub cover Ground cover Total log densities Small log densities % litter cover

28.84 24.51 11.47 8.99 6.72

Ground Cover Total X. preissii densities % Litter cover Small X. preissii densities Total log densities

48.25 8.55 8.01 6.35 5.91

4

Ground cover Shrub cover Total log densities Small log densities Total X. preissii densities

42.64 23.06 9.61 7.38 5.20

Ground cover Shrub cover Total X. preissii densities Total log densities Small -Medium log densities

50.00 12.39 9.09 5.72 5.07

Ground cover Shrub cover Total X. preissii densities % Litter cover Total log densities

48.82 13.49 8.40 7.33 4.07

5

Shrub cover Total X. preissii densities Ground cover Total log densities Small log densities

49.29 14.42 11.58 5.04 3.91

Shrub cover Ground cover Total X. preissii Litter cover Total log densities

53.60 16.57 11.74 2.54 2.49

Shrub cover Total X. preissii densities Ground cover Small X. preissii densities Medium X. preissii densities

62.95 13.58 9.79 2.62 1.89

Shrub Cover Ground cover Total X. preissii densities % Litter cover Small X. preissii densities

53.86 17.98 12.47 2.46 2.45

6

Total X. preissii densities Shrub cover Ground cover Total log densities Small-medium log densities

28.12 22.25 11.85 7.22 5.84

Total X. preissii densities Shrub cover Ground cover Total log densities Medium X. preissii densities

24.92 22..23 21.16 4.29 4.11

Total X. preissii densities Shrub cover Ground cover Medium X. preissii densities Tall, single crown X. preissii densities

31.80 28.86 12.07 5.02 3.83

Total X. preissii densities Shrub cover Ground cover Medium X. preissii densities % Litter cover

26.28 23.35 22.65 4.44 3.14

Shrub cover Total X. preissii densities Ground cover Medium X. preissii densities Small X. preissii densities

50.05 20.48 11.67 3.34 3.02

35

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 (Ranked: 1= unhealthy, little cover to 5= dense cover)

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

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 (counts of live vegetation touches between 0-80 cm)

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

2.3. Vertical structure of the shrub cover vegetation (counts of live vegetation touches between 1-180 cm)

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

2.4. Cover provided by fine woody debris (Ranked: 1=no cover, 2=moderate cover, 3=dense cover)

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

2.5. Depth of leafy material (Ranked: 1=no cover, 2=moderate cover, 3=dense cover)

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

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

36

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.

37

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).

38

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

39

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

40

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

41

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.

42

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;

43

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

44

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).

45

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

46

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.

47

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).

48

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 occasion

Date Sites surveyed Expected trap nights

Actual trap nights

1. May 1 07/05 - 09/05/2002 27/05 - 29/05/2002

1, 2, 3, 4 5

300 75

300 75

2. June 1 03/06 - 05/06/2002 24/06 - 26/06/2002

1, 2, 3, 4 5

300 75

300 75

3. July 1 08/07 - 10/07/2002 29/07 - 31/07/2002

1, 2, 3, 4 5

300 75

147 75

4. August 1 04/08 - 06/08/2002 26/08 - 28/08/2002

1, 2, 3, 4 5

300 75

185 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

49

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 Expression Score (DES)

1. Covariate was not included in the analyses (a 'naïve' model); or

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 notation

The term “and” represents the main and interactive affects, whilst “+” 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

50

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 (mean ± 1SD)

Total trap

nights

Number of individuals and captures recorded at trap stations successful for multiple detections

Number of trap stations successful for multiple detections

Total captures

(♂,♀)

Individuals (♂,♀)

Captures (♂,♀)

1 3.20 ± 0.71 Severely affected

1068 10 (6, 4) 45 (30, 15) 81 50 (34,16)


629 0 (0,0) 0 (0.0) 01 1(1,0)


761 0 (0,0) 0 (0,0) 0 7 (2, 5)

4 2.76 ± 1.01 Moderately

affected

819 4 (4,0) 4 (4,0) 21 4 (4,0)

5 1.88 ± 1.01 Subtly affected

1190 22 (14, 8) 95 (26, 69) 202 113 (28, 85)

6 0.00 ± 0.00 No affect

1014 37 (23, 14) 166 (99, 67) 21 180 (110, 70)

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.

51

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:

52

{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).

53

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 (wi)

Likelihood # Parameters in model

Deviance

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.

54

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

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.

0.1

0.2

0.3

0.4

0.5

Female Male

Gender

Prob

abili

ty o

f det

ectio

n

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

0 1 2 3 4 5 6

Survey sites

Prob

abili

ty o

f occ

upan

cy

55

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 (western pygmy possum). Neither of these species

56

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.

57

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

58

(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

59

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).

60

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

61

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.

62

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

63

(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.

64

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.

65

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.

66

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

67

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,

68

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).

69

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Res

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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.

70

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 P. cinnamomi

Unaffected by 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%)


3 Female 0 (0.0%) 0 (0.0%)

Male 0 (0.0%) 0 (0.0%)


4 Female 0 (0.0%) 0 (0.0%)

Male 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%)


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).

71

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 model

weight (wi)


Deviance

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

(site*gender) 348.24 28.14 0.000 0.000 28 286.24

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

(site*gender) 374.13 116.92 0.000 0.000 4 365.99

17 p (site* gender *time.) ψ (.) 569.01 248.92 0.000 0.000 97 280.88 18 p (site* gender *time) ψ

(site* gender.) 614.35 294.26 0.000 0.000 108 275.08

(.) 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).

72

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

73

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).

00.10.20.30.40.50.60.70.80.9

1

0 1 2 3 4 5 6

Survey sites

Prob

abili

ty o

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upan

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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.

74

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).

75

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 weight (wi)


Deviance Coefficient of habitat variables

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).

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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 weight (wi)


Deviance Coefficient of habitat variables

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.

77

0

1

2

3

4

5

Successful Unsuccessful Overall

P. cinnamomi status

Mean

large

log d

ensit

y/trap

stati

on

0

2

4

6

8

10


P. cinnamomi status

Mean

tota

l log

dens

ity

A Mean total log densities B. Mean large log densities

0

5

10

15

20

25


P. cinnamomi status

Total

X. pr

eissii

dens

ity/tra

p stat

ion

0

1

2

3

4

5


P. cinnamomi status

Mean

tall mu

ltiple c

rowned

X. p

reissii

density

/trap s

tation

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).

78

0

1

2

3

4

5


P. cinnamomi status

Mean

tall s

ingle

crown

ed X.

preis

sii

dens

ity/tra

p stat

ion

0

1

2

3

4

5


P. cinnamomi status

Mean

med

ium/sm

all X.

preiss

ii den

sity/t

rap st

ation

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

0

5

10

15

20


P. cinnamomi status

Groun

d cove

r veget

ation

struct

ure/tra

p stati

on

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).

79

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

(n=52) Unaffected

(n=33) Affected (n=19)

Total (n=98)

Unaffected (n=7)

Affected (n=91)

Overall mean (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 (counts of live vegetation touches between 0-80 cm)

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

2.3. Vertical structure of the shrub cover vegetation (counts of live vegetation touches between 1-180 cm)

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

2.4. Cover provided by fine woody debris (Ranked: 1=no cover, 2=moderate cover, 3=dense cover)

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

2.5. Depth of leafy material (Ranked: 1=no cover, 2=moderate cover, 3=dense cover)

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

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 (Ranked: 1 least affected to 5 most affected)

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

80

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

81

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

82

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 (honey

possum) 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).

83

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

84

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 chestnut 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).

85

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

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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

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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.

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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

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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

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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).

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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

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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.

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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

94

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

95

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.

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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.

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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

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Deviance

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

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Deviance

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.

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Rank Model structure QAICC ∆QAICC Model weight (wi)

Likelihood Parameters in model

Deviance

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

115



Deviance

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

116



Deviance

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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