The ecology of the quokka (Setonix brachyurus) in the southern

Transcription

The ecology of the quokka (Setonix brachyurus) in the
southern forests of Western Australia
Karlene Bain
BSc (Biology) PgD (Zoology)
This thesis is presented for the degree of Doctor of Philosophy of
The University of Western Australia
School of Animal Biology
Faculty of Science
June 2015
Dedicated to Jarrah
A little orphan who changed my life. You forced me to become nocturnal, spend every
waking minute in the bush and kept me company at foot for much of my field work. I gave
you the best start I could and now I hope the outcomes of this research will continue that
effort for your species.
Jarrah: a quokka joey orphaned in 2012 after his mother was killed on the road by a
passing motorist. After 12 months of care and focused bush preparation, he subsequently
became only the second surviving joey in my study site in six years of field work. All other
joeys bar one were predated by feral cats as they emerged from the pouch.
ii
Abstract
Effective management of threatened species is dependent on knowledge of habitat, spatial
requirements and threatening processes that affect populations, habitat quality, availability
and connectivity. This knowledge is not available for all species and focal species whose
ecological requirements encompass those of other species are often used as surrogates for
management. Due to its large geographic range and sympatric association with a diverse
range of endemic and threatened taxa, the quokka, Setonix brachyurus is such a species. It is
a threatened species endemic to south-western Australia and is patchily distributed across a
geographic range of approximately 18 000 km2. Ecological studies have been conducted for
this species in the northern-most areas of its distribution on the mainland. The results of
those studies have historically been used to guide management of habitat for the quokka in
other parts of its distribution, despite observed differences in climate, population size, habitat
characteristics and availability, and associated ecological communities. The goal of this
study was to quantify a rapid survey technique, and to investigate habitat requirements,
spatial ecology and response to fire for the quokka in the southern forests of south-western
Australia in an effort to inform in situ conservation and guide future management of this
species and others that share similar spatial, compositional, and functional requirements in
this region.
A rapid survey technique based on counts of fresh faecal pellets was developed and
evaluated against abundance estimates obtained from capture-mark-recapture (CMR)
methods. Relative abundances obtained from faecal pellet counts were highly correlated
with population estimates (R2 = 0.97) derived from more intensive CMR methods and
provided a rapid and inexpensive survey option, where variation in detection probability was
actively accounted for.
The features driving occupancy of habitat by quokkas in the southern forests were
investigated. Quokkas in this region occupied habitats with complex vegetation structure,
low densities of woody debris and fine scale habitat patchiness. Habitat features important
to quokkas in the south were subtly different from those in the north where dense
understorey and early seral stage vegetation were important. The subtle differences were
found to be important and extrapolation of knowledge between these disconnected and
ecologically diverse areas could result in inappropriate management and an increased risk of
local extinction. Despite contiguous and extensive vegetation, significant segregation of
subpopulations of quokkas was observed. Failure to consider anthropogenic processes that
affect favoured habitat variables could contribute to increased habitat fragmentation resulting
in intervening distances between habitat patches that are too great for successful dispersal,
immigration and recolonisation processes.
iii
Spatial use patterns and home range size of quokkas in the southern forests and the ability of
individuals to move between segregated habitat patches was investigated using radiotelemetry. Quokkas in this region had much larger home ranges (mean 71.4 ha) and moved
larger distances (up to 10 km per night) than previously reported for this species. Movement
of quokkas between occupied habitat patches that were up to 14 km apart but connected by a
linear riparian system was recorded. Emigrations across much larger distances are therefore
possible, where habitats are suitably connected. A diverse range of habitats were used in
addition to the riparian vegetation that has characteristically been considered primary habitat
for this species. This has implications for the current approach to habitat protection, which
focuses on protection of linear riparian systems. In particular, the broader habitat and spatial
requirements for the species in this region are unlikely to be met by the current approach
alone.
Fire has the potential to affect habitat connectivity and availability for the quokka in the
southern forests. Factors driving the use of areas by quokkas following fire and the refuge
value of unburnt vegetation were investigated. Retention of vertical vegetation structure,
more than 20% of the area unburnt, and multiple unburnt pockets were important for
quokkas to recolonise fire-affected areas rapidly. The application of fire to achieve these
outcomes was dependent on relatively high surface and soil moisture and day of burn
conditions that contributed to slow fire movement. Moisture differentials in riparian systems
and discontinuous vegetation in rocky outcrops contributed to unburnt refugia under these
conditions.
Intense homogenising wildfire resulted in the complete loss of vertical
vegetation structure, a lack of unburnt pockets and no re-colonisation of areas by quokkas for
the duration of the study.
This study provides explicit ecological criteria to guide survey, management of habitat and
fire planning in areas of the southern forest where quokkas are present.
Use of such
information will enable more effective management of critical habitat for this species and
other taxa occupying a similar ecological niche.
iv
Acknowledgements
Special thanks go to my supervisors, Associate Professor Roberta Bencini and Dr Adrian
Wayne for their support and critical evaluation of research design and draft manuscripts.
Their role was particularly challenging given my remote location and the part-time nature of
my research. Dr Jamie O’Shea, Dr Harriett Mills and Dr Brian Chambers also provided
valuable advice and support as panel members throughout this period. I would like to thank
Graeme (Tubby) Liddelow and Keith Morris, who played a pivotal role in initiating this
project and shared their considerable knowledge of the quokka at various stages throughout
the project. Tubby also spent a considerable amount of time in the field with me in the early
phases of the project, passing on his field survey skills. Thanks to Matthew Williams for his
feedback on statistical analyses and to Dr Elizabeth Edmonds for her critical review of my
draft thesis.
Particular mention goes to the Department of Parks and Wildlife’s (DPaW) nature
conservation team in Walpole. I was managing this team when I commenced the project and
received a high level of support from all team members.
Jason Fletcher, Nic Slatter,
Charlene Hordyk, Carol Ebbett, Roslyn Burnside and Janine Liddelow in particular kept me
company during all night tracking missions and early morning trapping sessions, provided
valuable sounding boards and helped me to stay sane during those inevitable times when my
part-time PhD was at odds with my professional commitments.
Particular mention also goes to the DPaW managers in Frankland District and Warren
Region.
Peter Bidwell (Former District Manager), Alison Donovan (Current District
Manager), Peter Keppel (Regional Manager) and Brad Barton (Regional Leader for Nature
Conservation) saw the value of this project in improving the department’s ability to manage
quokkas in the southern forests and enabled me to use departmental vehicles and equipment
for much of my field work. They also allowed some flexibility in fire roster duties to enable
me to complete my field work when I would otherwise have been required to be in phone
contact or within 15 minutes of the office. Brad Barton spent several nights in the field
assisting me with tracking and was also great to bounce ideas off.
Finally and most importantly, I am grateful to my fiancé Matthew Corlett for his support,
encouragement and patience throughout my candidature. It has been nearly eight years of
full time work coupled with part-time research, which has definitely provided us with many
challenges.
v
Foreword
This thesis is presented as a series of stand-alone manuscripts prepared for publication, with
the exception of Chapters 1 and 6, which provide an introduction and overall discussion. A
single reference list for citations in all chapters is provided at the end of the thesis. Because
core chapters of this thesis are intended as stand-alone pieces of work for publication in
scientific journals, some repetition between chapters was unavoidable. Chapters 2 to 5 are
written as scientific papers with multiple authors; therefore plural pronouns are used. In
addition, there are some stylistic differences according to target journals. For example for
publication in Wildlife Research, the abstract is formatted to include labelled sections:
Context; Aims; Methods; Key results; Conclusions; Implications as per journal requirements.
Chapter 2 has been published in Wildlife Research and Chapter 3 has been submitted to the
same journal and has now been accepted subject to review. Chapter 4 will be submitted to
Journal of Zoology, and Chapter 5 will be submitted to the International Journal of Wildland
Fire.
All procedures for sample and data collection in this thesis were approved by the Animal
Ethics Committee at the University of Western Australia (RA/3/100/693) and the DPaW
Animal Ethics Committee (CAEC/44/2006, DEC AEC 44/06, DEC AEC 09/24). All fauna
handling was conducted under a Licence to Take Fauna for Scientific Purposes (SC 00856),
issued by DPaW.
vi
Declaration of Candidate Contribution
All parts of this thesis have been written by Karlene Bain with advice and editorial input
from Associate Professor Roberta Bencini (UWA) and Dr Adrian Wayne (DPaW) and
editorial review by PhD advisory panel members: Dr Brian Chambers and Dr Harriet Mills.
Chapters 2 to 5 are co-authored manuscripts of which K. Bain was the primary author. The
publication status and contributions of the co-authors to each chapter are outlined below:
Chapter 2:
Bain K., Wayne A., and Bencini R. (2014). Overcoming the challenges of
measuring the abundance of a cryptic macropod: is a qualitative approach good enough?
Wildlife Research 41, 84-93. This manuscript has been published in Wildlife Research. K.
Bain developed the design of this study in consultation with R. Bencini and A. Wayne. K.
Bain carried out the field work, data analysis and manuscript preparation. R. Bencini and A.
Wayne contributed with editorial comments and discussions regarding interpretation of
results. Peer review of the manuscript was provided by Keith Morris (Manager, Biodiversity
Conservation Group, DPaW) and Dr Matthew Williams (Biometrician, DPaW). Professor
Ken Pollock reviewed the MARK Analysis.
Development of study design 95%, data collection 100%, data analysis 100%,
interpretation of results 95%, manuscript preparation 90%
Chapter 3:
Bain, K., Wayne, A., and Bencini, R. (2015). Risks in extrapolating habitat
preferences over the geographical range of threatened taxa: a case study of the quokka
(Setonix brachyurus) in the southern forests of Western Australia. This manuscript was
submitted to Wildlife Research in December 2014 and accepted on 23 March 2015, subject
to review. K. Bain developed the design of this study in consultation with R. Bencini and A.
Wayne. K. Bain carried out the field work, data analysis and manuscript preparation. R.
Bencini and A. Wayne contributed with editorial comments and discussions regarding
interpretation of results. Peer review of the manuscript was provided by Sarah Comer
(Regional Ecologist, DPaW) and Graeme Liddelow (Research Scientist, DPaW).
vii
Chapter 4:
Bain, K., Bencini, R., and Wayne, A. (2015). Spatial ecology of the quokka
(Setonix brachyurus) in the southern forests of Western Australia: implications for the
maintenance, or restoration, of functional metapopulations.
This manuscript will be
submitted to the Journal of Zoology in June 2015. K. Bain developed the design of this
study in consultation with R. Bencini and A. Wayne. K. Bain carried out the field work, data
analysis and manuscript preparation. R. Bencini and A. Wayne contributed with editorial
comments and discussions regarding interpretation of results. Peer review of the manuscript
was provided by Dr Brian Chambers (University of Western Australia) and Graeme
Liddelow (Research Scientist, DPaW).
Chapter 5:
Bain, K., Wayne, A., and Bencini, R. (2015). Prescribed burning as a
conservation tool for management of quokka habitat in the southern forests of Western
Australia. This manuscript will be submitted to the International Journal of Wildland Fire in
June 2015. K. Bain developed the design of this study in consultation with R. Bencini and
A. Wayne. K. Bain completed fire application and monitoring field work with assistance
from DPaW colleagues and volunteers. K. Bain completed data analysis and manuscript
preparation. R. Bencini and A. Wayne contributed with editorial comments and discussions
regarding interpretation of results. Peer review of the manuscript was provided by Dr
Lachlan McCaw (Research Scientist, DPaW) and Dr Matthew Williams (Biometrician,
DPaW).
__________________________________
Karlene Bain
__________________________________
Roberta Bencini (Coordinating Supervisor)
8/06/2015
viii
Table of Contents
Abstract ........................................................................................................................................ iii
Acknowledgements ........................................................................................................................ v
Foreword .......................................................................................................................................vi
Declaration of Candidate Contribution ....................................................................................... vii
Table of Contents ..........................................................................................................................ix
Chapter 1: General Introduction................................................................................................ 1
1.1
Research Context ........................................................................................................... 2
Mammal declines in Australia ............................................................................................... 2
South-western Australia: a priority for conservation ............................................................ 3
The southern forest: provision of moist and stable refugia ................................................... 4
The quokka ............................................................................................................................. 5
Filling the knowledge gaps .................................................................................................... 6
1.2
Aim and scope................................................................................................................ 9
Chapter 2: Overcoming the challenges of measuring the abundance of a cryptic
macropod: is a qualitative approach good enough? ............................................................... 13
2.1
Abstract ........................................................................................................................ 14
2.2
Introduction .................................................................................................................. 15
2.3
Methods........................................................................................................................ 17
Study sites............................................................................................................................. 17
Testing the Liddelow rapid-survey technique ...................................................................... 17
Mark-recapture .................................................................................................................... 19
Sign and sighting surveys..................................................................................................... 20
Analysis of data .................................................................................................................... 21
2.4
Results .......................................................................................................................... 23
Population abundance estimates using mark-recapture data .............................................. 23
Testing the Liddelow rapid-survey technique ...................................................................... 24
Testing individual components of the Liddelow rapid-survey technique ............................. 25
ix
Defining a quantitative survey technique............................................................................. 26
2.5
Discussion .................................................................................................................... 31
2.6
Acknowledgements ...................................................................................................... 33
Chapter 3:
Risks in extrapolating habitat preferences over the geographical range of
threatened taxa: a case study of the quokka (Setonix brachyurus) in the southern forests
of Western Australia .................................................................................................................. 35
3.1
Abstract ........................................................................................................................ 36
3.2
Introduction .................................................................................................................. 37
3.3
Methods........................................................................................................................ 39
Study area ............................................................................................................................ 39
Analysis of data .................................................................................................................... 40
3.4
Results .......................................................................................................................... 42
3.5
Discussion .................................................................................................................... 49
3.6
Chapter 4:
Spatial ecology of the quokka (Setonix brachyurus) in the southern forests
of Western Australia: implications for the maintenance, or restoration, of functional
metapopulations ......................................................................................................................... 55
4.1
Abstract ........................................................................................................................ 56
4.2
Introduction .................................................................................................................. 57
4.3
Methods........................................................................................................................ 58
Study area ............................................................................................................................ 58
Analysis of data .................................................................................................................... 59
4.4
Results .......................................................................................................................... 63
Home range .......................................................................................................................... 63
Home range overlap ............................................................................................................ 66
Distances travelled ............................................................................................................... 67
Dispersal and emigration..................................................................................................... 68
Habitat analysis ................................................................................................................... 69
x
4.5
Discussion .................................................................................................................... 71
Home range size ................................................................................................................... 71
Seasonal variation in home range size ................................................................................ 72
Home range overlap ............................................................................................................ 73
Habitat use ........................................................................................................................... 74
Conclusion ........................................................................................................................... 75
4.6
Chapter 5:
Prescribed burning as a conservation tool for management of quokka
habitat in the southern forests of Western Australia .............................................................. 77
5.1
Abstract ........................................................................................................................ 78
5.2
Introduction .................................................................................................................. 79
5.3
Methods........................................................................................................................ 80
Study area ............................................................................................................................ 80
Factors influencing occupancy and colonisation probability .............................................. 82
Fire predictor variables ....................................................................................................... 83
Analysis of data .................................................................................................................... 84
5.4
Results .......................................................................................................................... 85
Factors influencing occupancy and colonisation probability .............................................. 85
Refuge value of unburnt vegetation ..................................................................................... 86
Fire predictor variables ....................................................................................................... 86
5.5
Discussion .................................................................................................................... 90
Factors affecting recolonisation of habitat following fire ................................................... 90
Refuge value of unburnt vegetation ..................................................................................... 91
Prescribing for effective refuge within planned burns ......................................................... 92
5.6
xi
Chapter 6:
6.1
General Discussion ............................................................................................. 95
Insights into the ecology of quokkas in the southern forests ....................................... 96
Habitat preferences (see chapter 3) ..................................................................................... 96
Spatial ecology (see chapter 4) ............................................................................................ 97
Fire response (see chapter 5)............................................................................................... 99
6.2
Insights of broader conservation relevance ................................................................ 100
6.3
The quokka as a focal species in the southern forests................................................ 101
6.4
Management implications .......................................................................................... 103
Survey methodology ........................................................................................................... 103
Habitat management .......................................................................................................... 104
Fire management ............................................................................................................... 105
Management of feral pigs .................................................................................................. 106
Management of introduced predators ................................................................................ 106
Climate change .................................................................................................................. 107
Recovery planning ............................................................................................................. 108
Future research .................................................................................................................. 112
References ................................................................................................................................. 115
xii
Chapter 1: General Introduction
1
1.1
Research Context
Mammal declines in Australia
Over the past two centuries, the mammal fauna of Australia has collapsed to a greater degree
than any other plant or animal group (Woinarski et al. 2014). This degree of loss is far more
than that reported for mammal fauna in any other continent over the same period (McKenzie et
al. 2007, Woinarski et al. 2014). A total of 29 mammal species, of which 27 were endemic,
have become extinct in Australia in the last 200 years. An additional 94 species are currently at
risk of extinction, representing more than 20% of Australia’s mammal fauna (DOE 2015).
Many factors have contributed to these declines, such as habitat loss, inappropriate fire regimes,
and predation by introduced cats (Felis catus) and foxes (Vulpes vulpes) (Burbidge and
McKenzie 1989; Short 2004; McKenzie et al. 2007; Burbidge et al. 2009; Woinarski et al.
2014). In many cases species declines are likely to be a result of multiple and interactive causal
factors. The long-term recovery of such species is subsequently dependent on an integrated
approach to management of threatening processes and management or restoration of habitat
qualities relevant to the species at risk (McKenzie et al. 2007). However, the number of
threatened species far exceeds available conservation resources, and the extinction of Australian
mammals is showing no evidence of abating. Species continue to be lost at approximately one
species per decade, with the Christmas Island pipistrelle (Pipistrellus murrayi) the most recent
loss in 2009 (Woinarski et al. 2014).
2
South-western Australia: a priority for conservation
Key questions surrounding the identification of priorities, the concentration of resources on
areas where there is greatest need and benefit, and systematic responses to the challenges facing
threatened species are now at the forefront of conservation planning. Many of the proposed
solutions involve identification of priority areas that are important for conservation. Factors
driving the selection of priority areas include: the concentration of threatened or endemic
species; the presence of iconic or genetically important species; the presence of unique habitats;
the feasibility of long-term protection; the levels of threat; the net benefits of investment; and
socio-economic factors (e.g. Myers et al. 2000; Sanderson et al. 2002, Elith and Leathwick
2009, Pannell et al. 2012).
South-western Australia between Shark Bay and Esperance is an area that is currently
considered a priority, having been declared an international biodiversity hotspot due to its high
concentration of endemic species and historical loss of habitat (Myers et al. 2000). Here,
approximately 79 per cent of flora and 22 percent of vertebrate fauna are endemic. Only 10.8%
of the original extent of vegetation in south-western Australia remains intact and 100% of this is
now protected (Myers et al. 2000). The region is also recognised as an important area for
relictual taxa and provides habitat for 97 species of terrestrial fauna and 2500 species of flora
that are of conservation concern in Western Australia (Hopper and Gioia 2004; DPaW 2014a,
DPaW 2014b). Among the species of fauna most at risk here include high-profile mammals
such as the quokka (Setonix brachyurus), Gilbert’s potoroo (Potorous gilbertii), numbat
(Myrmecobius fasciatus), western ringtail possum (Pseudocheirus occidentalis) and dibbler
(Parantechinus apicalis), which are endemic to south-western Australia (DPaW 2014b).
3
The southern forest: provision of moist and stable refugia
The southern forest occurs between Nannup and Denmark in south-western Australia.
Compared to other areas of Australia, the southern forest has a relatively intact fauna, having
experienced unusually low rates of mammal extinction in the past 200 years (McKenzie et al.
2007). This is probably due to the size of the area, the large proportion of land that is reserved
as conservation estate, the wide range of habitats and corridors for movement, the diverse
landforms, the relatively stable climate and the occurrence of moist and stable habitats that have
remained virtually unchanged for millions of years (Main and Main 1991; Hopper 1992;
Wardell-Johnson and Coates 1996; Horwitz 1997).
The presence of plants of the genus
Gastrolobium (plants that are highly toxic to exotic species but not to native animals) in mesic
areas may also have protected native species from the impact of introduced predators (Short
2004). The annual rainfall of this region (800 mm to 1400 mm) is uniquely high for Western
Australia (Pink 2012) and the associated dense vegetation and landscape productivity have also
been used to explain the low extinction rate of mammals in this region (McKenzie et al. 2007).
The productivity and stability of habitats in the southern forest is of particular importance given
predictions for a drying and warming climate in south-western Australia, which is expected to
be associated with reduced vegetation health and an increase in disturbance regimes in many
ecosystems (CSIRO 2001; IPCC 2001; Howden et al. 2003; Hughes 2003). These habitats
support a diverse range of endemic, relictual and threatened species and offer a unique
opportunity to conserve and protect a large number of species through landscape level
management of threatening processes. An integrated approach towards the management of
threatening processes is likely to be instrumental for building the resilience of ecological
communities in this region, protecting key habitats and ecosystem function and facilitating
species conservation outcomes. However, poor knowledge of the ecological requirements and
threat response for many of the species and ecosystems within this region limits effective
management response.
4
The quokka
The quokka is a medium-sized mammal with a geographic range that has dramatically declined
since European settlement (Kitchener 1995, Hayward et al. 2003; Woinarski et al. 2014).
Habitat requirements of the quokka appear to have become more focused on dense riparian
valleys and swamps, where it is thought to be more protected from introduced predators
(Hayward et al. 2005a, 2007). This species is best known from Rottnest Island, where it is
abundant but genetically depauperate (Sinclair 2001; Alacs et al. 2011). However, the species
also occurs on the mainland of Western Australia where it was once common but is now
classified as vulnerable under the Australian Environment Protection and Biodiversity
Conservation Act 1999 and as ‘fauna that is rare or likely to become extinct’ under the State’s
Wildlife Conservation Act 1950.
The quokka occurs in many of the ecosystems in the southern forest that are considered refugia
in terms of their relatively stable climate, moisture and importance to species with ancient
lineages, poor dispersal mechanisms and high habitat specificity. Such ecosystems include
moist forests such as the karri and tingle forests that only occur in areas with greater than 1100
mm rainfall (Borg et al. 1988), riparian systems associated with deep creek lines, and peat based
wetlands. In comparison to many of the species supported by these ecosystems, the quokka is
relatively easy to monitor, is relatively widespread and shares a number of characteristics with
other species of interest in these systems, particularly other threatened and endemic small to
medium sized mammals such as: the western ringtail possum (Pseudocheirus occidentalis),
chuditch (Dasyurus geoffroii), brush tailed phascogale (Phascogale tapoatafa), western brush
wallaby (Macropus irma), quenda (Isoodon obesulus fusciventer), water rat (Hydromys
chrysogaster) and western false pipistrelle (Falsistrellus mackenziei).
The quokka is
consequently considered a focal species by land managers in this area and is one of the species
that is actively considered during planning processes for the protection of these unique habitats
(Burbidge et al. 1995; Burrows et al. 2004).
The quokka has been lost from over 50% of its former range on the mainland of Western
Australia since the time of European settlement (Woinarski et al. 2014) and the southern forest
now supports the most extensive remaining quokka population on the mainland, and the most
genetically diverse population of the species (P. Spencer unpublished data). Other populations
on the mainland include those in the northern jarrah forest between Perth and Collie, the Muddy
Lakes area on the Swan Coastal Plain, and disjunct reserves north and east of Albany on the
south coast (Sinclair 1998; de Tores et al. 2008; Sinclair and Hyder 2009; DEC 2013) (Figure
1.1).
5
Figure 1.1: Location of the study area and the known area of extent of quokkas in southwestern Australia. Known extent has been determined from DPaW records of quokka sightings
between 1990 and 2015.
Filling the knowledge gaps
Much work has been completed on the ecology of quokkas on Rottnest Island (e.g. Main et al.
1959; Barker 1961; Storr 1964a; Holsworth 1967; Nichols 1971; Kitchener 1973, 1981’ Poole
et al. 2014) and in the northern jarrah forest (Storr 1964b; Sinclair 1998; Hayward 2005;
Hayward et al. 2003, 2004, 2005, 2007). The outcomes of work in the northern jarrah forest
have been used to guide decision making processes for this species and its habitat in the
southern forest, despite geographical separation and substantial ecological differences. Prior to
this study, no formal research had been conducted on the population of quokkas in the southern
forest. The following paragraphs summarise key areas of knowledge of the ecology of quokkas
in the northern jarrah forest that are currently informing management of quokkas throughout
their distribution on the mainland of WA.
6
Quokkas in the northern jarrah forest are largely restricted to Taxandria swamps that occur
patchily throughout the region (Hayward et al. 2005a), with distances of up to 40 km separating
extant populations (Hayward et al. 2007). Population density in the region ranges between 0.07
and 4.3 individuals per hectare and the widely scattered populations rarely exceed 30
individuals (Hayward et al. 2003). The total adult quokka population in the northern jarrah
forest may be as few as 150 individuals (Hayward et al. 2003; DEC 2013).
The mean home-range size for quokkas in the northern jarrah forest has been estimated at
approximately 6 ha, with nocturnal ranges larger than diurnal ranges due to animals foraging
outside of the swamp at night (Hayward et al. 2004). Seasonal variation in the size of home
ranges is cyclical, peaking in autumn when water is restricted and fresh plant growth for forage
are low, and declining in spring at the end of the highest rainfall period when water and fresh
plant growth is abundant (Hayward et al. 2004).
Dispersal, emigration and immigration
processes have not been observed in this population, leading to speculation that fundamental
metapopulation processes are no longer occurring here (Hayward et al. 2003, 2004, 2005).
Genetic analysis also supports the notion that there have been no recent movements between
subpopulations in this region (Alacs et al. 2011) and that movement of quokkas between
swamps was historically more common (Sinclair 1998). The lack of movement between habitat
patches has been attributed to the inhibiting effect of introduced predators such as the fox
(Hayward et al. 2003, 2004, 2005) and an absence of suitable habitat patches within dispersal
distance (Christensen and Kimber 1975; Hayward et al. 2003, 2004).
Suitable habitat for quokkas in the northern jarrah forest contains a mosaic of younger seral
stage vegetation (<10 yrs) and older vegetation (>19 yrs) (Christensen and Kimber 1975;
Hayward et al. 2003, 2007). This mosaic meets the dietary requirements of quokkas in this
region by providing fresh regrowth following fire (Hayward 2005) and also provides denser
cover that allows quokkas to find refuge from introduced predators (Hayward et al. 2003, 2004,
2005, 2007). Hayward et al. (2007) also found that the intensity of control of introduced foxes
was an important factor in determining occupancy of habitats by quokkas in the northern jarrah
forest. Quokkas were more likely to be present in habitats with a more intensive baiting regime,
which at the time of their study equated to monthly baiting.
Despite their preference for areas with an intensive fox baiting regime, the size of populations of
quokka in the northern jarrah forest have reportedly not increased following fox control
programs (Hayward et al. 2003). The apparent lack of recovery of populations has been
attributed to low recruitment levels and the previously mentioned lack of dispersal between
habitat patches (Hayward et al. 2003).
7
Female quokkas in the northern jarrah forest average 2.2 births per year, with more than 50%
mortality of pouch young (Hayward et al. 2003). The gestation period for this species is 25-27
days, and one young is usually produced from a pregnancy (Shield 1964, 1968). The initial
emergence of pouch young from the pouch occurs at 180 days and final pouch exit occurs at
200 days; weaning of the young occurs at 300 days and sexual maturity is reached at 8 months
for females and 13 months for males (Shield 1964; Kitchener 1995; Miller et al. 2009). Pouch
young are considered most vulnerable to predation between initial emergence from the pouch
and weaning, when they are dependent on their mothers and have limited mobility.
While it is expected that quokkas in the southern forest will be somewhat similar to quokkas in
the northern jarrah forest, the two geographical areas are very different. The southern forest has
a higher average rainfall, higher productivity and density of resources, contains more mesic,
climatically buffered and stable ecosystems, has been exposed to different historical
applications of fire and fox baiting regimes, and the vegetation communities here often contain
a dense understorey that is more continuous than in the northern jarrah forest. In addition,
quokkas appear to be more abundant in the southern forests, occupying a more diverse range of
habitats over a wider geographical area in a seemingly functional metapopulation.
In the northern jarrah forest, quokkas occupy isolated habitat patches, have low population
abundance and seem to be in a state of metapopulation collapse.
The use of ecological
information from the northern jarrah forest seems a high risk strategy for the conservation of
this species in the southern forest. Such geographic extrapolation of ecological data is known to
have caused conservation concern for a range of other species (e.g. Constible et al. 2010;
Murray et al. 2011; Heinanen et al. 2012; Williams-Tripp et al. 2012; Young et al. 2012). In
order to adequately conserve and protect quokkas and associated faunal communities in the
southern forest, knowledge gaps relevant to the ecological requirements of the species within
this unique area need to be addressed.
This knowledge is intrinsically important for the
conservation of this threatened species, but is also critically important where quokkas are being
used as a surrogate species for the management of broader faunal communities sharing the same
habitat (e.g. Burrows et al. 2004).
8
1.2
Aim and scope
The aim of this study was to improve our understanding of the ecological requirements of the
quokka within the southern forest and to provide essential information for the conservation of
this species and for the conservation and protection of associated habitat and faunal
communities in this region. This thesis places particular emphasis on the quokka because of its
status as a threatened species, and because of the ecological niche it appears to occupy in the
southern forest, which could enable it to be effectively used as a focal species for management
of a range of other threatened and priority listed mammals that share the same habitat. This
study was confined to the forests between Nannup and Walpole in south-western Australia
(Figure 1.1) where the most abundant groups of quokkas occur.
Key research aims included:
•
To use conventional methods for population estimation to calibrate a rapid survey
technique for the quokka (Setonix brachyurus) in the southern forests of Western
Australia, with a view to providing quantitative outcomes (Chapter 2).
Mark-recapture studies at large spatial scales are impractical and expensive and a rapid
survey technique is an attractive option to provide a measure of relative abundance for
cryptic species, using indicators of activity. In 2003, a rapid survey technique was
devised by Graeme Liddelow (DPaW) to provide a qualitative measure of relative
abundance (i.e. high, medium, low) using the subjective assessment of indicators of
activity. Selected indicators include: faecal pellets, tracks, sightings and the presence of
intricate runway tunnels (called ‘runnels’) that the quokkas make through the
vegetation.
The technique has been widely adopted by land managers throughout
south-western Australia, but prior to this study, the relative abundance categories had
not been validated and so could not provide a quantitative estimate of population size.
In this study, the hypothesis tested was that indices of activity such as faecal pellets,
runnels, tracks and sightings can be used in a rapid-survey approach to quantify the
abundance of quokkas in the southern forests of Western Australia. Established field
techniques were used for the estimation of populations to evaluate the subjective
categorisation of quokka abundance through the Liddelow rapid-survey technique and
to devise a survey approach that could generate quantitative abundance estimates.
9
•
To develop a habitat suitability model (HSM) for the quokka in the southernmost
areas of its range and in doing so, investigate the risks associated with
geographical
extrapolation
of
ecological
information
for
species
across
environmental gradients (Chapter 3).
The quokka (Setonix brachyurus) occupies a large geographic range in south-western
Australia and information documented in the northern part of its range is currently used
to manage the species in southern parts of its range, despite observed differences in
climate, population size, habitat characteristics and availability, and associated
ecological communities. This study developed a HSM for quokkas in the southern
forest and assessed the transferability of models between regions, with particular
emphasis on implications for conservation of the species and landscape level
management of habitat.
The hypothesis being tested was that extrapolation of
knowledge for threatened taxa between parts of their range that are disconnected and/or
ecologically diverse can result in significant sources of error that undermine the
effectiveness of conservation efforts.
•
To assess the ability of quokkas to move between increasingly segregated habitat
patches and the implications of spatial use patterns for habitat management and
conservation (Chapter 4).
Perceptions of habitat fragmentation are changing to include an awareness of more
subtle segregation of habitat patches within a seemingly continuous landscape caused
by factors such as fire regimes and introduced animals. In the extensive and relatively
continuous southern forest between Nannup and Denmark, quokkas occur in discrete
patches with distances of up to 40 km separating occupied habitat.
The spatial
availability and connectivity of suitable habitat in this landscape is influenced by fire
regime and other disturbances that alter the habitat features identified as being
important for quokkas. Prior to this study, little information was available on the spatial
ecology of this species in the southern parts of its range, where more than 60% of the
mainland population occurs. In other parts of their range, quokkas are capable of
moving distances of only 2 km, which raises concerns that the segregation of habitat
patches occurring as a result of forest management practices may increase distances
between areas of suitable habitat to a point where individuals may no longer have the
ability to maintain the spatial distribution of the species in the southern forests. This
study investigated spatial use patterns and the home range size of quokkas in the
southern forest the implications of these for management of habitat and the
maintenance, or restoration, of a functional metapopulation.
10
•
To investigate factors driving recolonisation of fire-affected areas by quokkas, the
spatial arrangement and refuge value of unburnt vegetation and fire prediction
parameters that may help to guide fire planning in the southern forests of Western
Australia (Chapter 5).
The spatial and temporal characteristics of the fire mosaic needed to facilitate
biodiversity conservation in the southern forests are poorly understood and there is little
guidance available to managers regarding the characteristics of desirable ‘mosaics’ such
as patch size, connectivity or the timing of fires in relation to faunal population trends.
This study used the quokka as a model species and investigated elements of fire regimes
that are most important for predicting early recolonisation of burnt areas, with particular
focus on generating explicit ecological criteria and prediction parameters that can be
used for contemporary fire planning.
In Chapter 6, recommendations are provided from the above studies that are relevant to
monitoring of populations, management of habitat, management of fire and introduced animals,
implementation of the current recovery plan for quokkas, mitigation of climate change impacts,
and use of the quokka as a focal species for management. Findings derived from this research
will contribute to more proactive and effective management of the quokka and its habitat both
for conservation of the species and as a focal species for the conservation of co-occurring taxa
that occupy a similar ecological niche.
11
12
Chapter 2: Overcoming the challenges of measuring
the abundance of a cryptic macropod: is a qualitative
approach good enough?
Examples of techniques for measuring quokka abundance
This chapter was submitted to the journal of Wildlife Research on 8 April 2014, accepted 22
April 2014 and published online 22 May 2014
13
2.1
Abstract
Context. An understanding of population size and status is necessary for the implementation of
appropriate conservation measures to recover threatened taxa. Mark-recapture studies at large
spatial scales are impractical and expensive, so a rapid survey technique is an attractive option
to provide a measure of relative abundance for cryptic species, using indicators of activity.
Aims. The aim of our study was to use conventional methods for population estimation to
calibrate a rapid survey technique for the quokka (Setonix brachyurus) in the southern forests of
Western Australia, with a view to providing quantitative outcomes from this widely adopted
monitoring approach.
Methods. The accuracy of relative abundances obtained from the rapid survey technique were
evaluated by comparing them with abundance estimates obtained through established methods
for the estimation of populations, including web-based mark–recapture and transect-based
counts of activity indicators and sightings.
Key results. The rapid survey technique was effective at determining presence of quokkas but
resulted in an overestimation of population size due to inaccurate assumptions about occupancy
and relative abundance of animals. An alternative survey method based on counts of fresh
faecal-pellet groups was found to provide a more reliable and practical estimation of population
abundance (R2 = 0.97).
Conclusions. Activity indices can be used to quantify population abundance, but only for
indicators of activity that can be detected readily and for which freshness of activity can be
determined.
Implications. Our findings suggest that a rapid survey based on activity indices can be used to
evaluate quantitatively the population size of a species that is rare and potentially mobile at a
landscape scale. The attraction of these techniques is that they provide a rapid and inexpensive
survey option that is potentially applicable to any cryptic and/or threatened species and is
practical for resource-constrained land managers.
Keywords: faecal pellets, indirect survey method, population size estimates, quokka, rapid
survey, relative abundance, runnels, Setonix brachyurus, sightings, tracks.
14
2.2
Introduction
The ability to measure population abundance is critical for making informed management
decisions, particularly for threatened species. However, in some cases it is difficult to obtain
accurate estimates of abundance due to the investment of time and resources required to collect
the data and the assumptions of many statistical models, which require high detection
probabilities and large numbers of animals (Caughley 1977; Gardner and Mangel 1996; Bolen
and Robinson 1999; Anderson 2001; Defos du Rau 2003). These challenges are acute for
cryptic and/or threatened species, which often occur at low densities, display secretive
behaviour, and occupy relatively inaccessible habitat (Conroy 1996; McKelvey and Pearson
2001; Hamm et al. 2002).
Methods for estimating relative size of populations are often
considered as a more cost effective and practical alternative, despite weaknesses documented in
the literature relating to known biases, inconsistent detection and an inability to meet model
assumptions (Nichols and Pollock 1983; Montgomery 1987; Slade and Blair 2000; McKelvey
and Pearson 2001; Hopkins and Kennedy 2004). Monitoring techniques such as the use of
photographic capture (Karanth and Nichols 1998), driving transects (Caro 1999; Olson et al.
2005), walked line transects (Short and Turner 1991; le Mar et al. 2001; Poole et al. 2003;
Wayne et al. 2005) and dung-pellet counts (Johnson and Jarmin 1987; Allen et al. 1996;
Buckland et al. 2001; Hayward et al. 2003) have been widely used to provide a quantitative
estimate of population size for small and medium sized mammals. More recently, these have
been coupled with sophisticated models that can actively account for changing detection
probabilities (e.g. Anderson 2001; Buckland et al. 2001; Bailey et al. 2004, 2007). The result is
a more accurate estimate, but often there is still a requirement for a large sample size, which
remains challenging for cryptic species and impractical for land managers and practitioners
involved in rapid and responsive decision making for the management of threatened species.
The quokka (Setonix brachyurus) is a wallaby listed as vulnerable (IUCN 2014). It is restricted
to south-western Australia and two near-shore islands, Rottnest and Bald Island (White 1952;
Storr 1964a; Maxwell et al. 1996; Sinclair 1998). On the south-western Australian mainland,
quokkas occur in three areas, namely, the northern jarrah (Eucalyptus marginata) forest between
Perth and Collie, disjunct reserves around Albany, and the continuous southern forest between
Nannup and Denmark (Sinclair 1998; de Tores et al. 2008).
15
There is a greater level of habitat connectivity within the southern forest and preliminary DNA
analysis suggests that animals are more likely to move among habitat patches in a functioning
meta-population (P. Spencer, unpublished data). If this is the case, then the southern forest
population of quokkas is likely to be important in terms of genetic diversity and resilience to
disturbances and demographic fluctuations. Currently, little is known about the ecology or
conservation status of quokkas in the southern forests of Western Australia. A cost-effective but
reliable and practical technique for determining population size is fundamental to understanding
the ecology of the quokka in this area and for the implementation of effective conservation and
management actions.
Capture–mark–release methods can provide reliable population abundance estimates,
particularly when detection probability is accounted for through population modelling. This
technique is time consuming, inefficient and expensive for quokkas in the southern forest
because of the large area of potential habitat, much of which is remote and inaccessible, dense
vegetation, where quokkas move using intricate runway tunnels (called ‘runnels’) and the
seasonal movement patterns of quokkas, which make their direct observation, detection and
capture more challenging, particularly in the southern part of their range (see Chapter 4). In the
northern jarrah forest, transect counts of faecal-pellet groups have been used to estimate
population densities of quokkas in discrete swampy habitat (Hayward et al. 2005b). This
approach is potentially applicable to the southern forest population, but is likely to be more
challenging because of the higher density of vegetation and the movement of quokkas across the
landscape.
In 2003, a rapid survey technique was devised to provide a qualitative measure of relative
abundance (i.e. high, medium, low) using the subjective assessment of indicators of activity
including faecal pellets, runnels, tracks and sightings (G. Liddelow, pers. comm.). The
technique (hereafter called the ‘Liddelow rapid-survey technique’) has been widely adopted by
land managers throughout south-western Australia, but the relative abundance categories have
not been validated and cannot provide a quantitative estimate of population size. In the present
study, the hypothesis tested was that indices of activity such as faecal pellets, runnels, tracks
and sightings can be used in a rapid-survey approach to quantify the abundance of quokkas in
the southern forests of Western Australia. Established field techniques were used for the
estimation of populations to evaluate the subjective categorisation of quokka abundance through
the Liddelow rapid-survey technique, with a view to providing quantitative outcomes from this
widely adopted monitoring approach.
16
2.3
Methods
Study sites
The southern forests occur between Nannup and Denmark in the far south-west of Western
Australia and encompass the Southern Jarrah and Warren biogeographical subregions (IBRA
2004). This study was undertaken in a subset of these forests between Manjimup and Walpole.
About 85% of the 10 000-km2 region is primary native vegetation that is publicly vested and
managed by DPaW. The human population in the area is low (<8000) and concentrated in and
around four town centres. The region has a Mediterranean-type climate, with warm dry
summers and mild wet winters. Between 2001 and 2011, the average annual rainfall in the
southern part of the region was 1098 mm, of which only 12% fell in the four driest months
(December-March; Pink 2012). The vegetation consists of a mosaic of forest, woodland,
shrubland, wetland and coastal ecotypes, often with complex vegetation structure and dense
understorey. The tall forests in which this study was conducted are dominated by jarrah, karri
(Eucalyptus diversicolor), marri (Corymbia calophylla) and tingle (Eucalyptus jacksonii, E.
guilfoylei and E. brevistylis).
The native forest is largely contiguous, with large areas
inaccessible by road, such as the Walpole Wilderness area (325 116 ha). The dense understorey
makes the forest effectively impenetrable to humans in most places.
Testing the Liddelow rapid-survey technique
In total, 137 habitats between Manjimup and Walpole were surveyed for quokkas by using the
Liddelow rapid-survey technique. The technique consisted of field observations of the apparent
level of quokka activity, as evidenced by runnels, faecal pellets, tracks and incidental sightings.
Surveys targeted defined habitat most likely to be occupied by quokkas, such as creek lines and
swamps. Searches for quokka activity continued until the observer was satisfied that they had
obtained an adequate assessment of the site, typically in less than 30 min. Where quokka
activity was recorded, the observations of runnels, faecal pellets, tracks and incidental sightings
were then integrated to derive a qualitative and relative estimate of quokka abundance, which
was rated subjectively as low, medium or high.
A subset of 12 study sites was then selected on the basis of relative-abundance categories
obtained through the Liddelow rapid-survey technique, including four sites each for low,
medium and high abundance (Figure 2.1). At each site, a trapping web was established, with a
central axis following a creek line. Eight lines radiating from the centre were each 240 m long.
Population abundance estimates obtained through mark–recapture were used as an indicator of
true abundance and were compared with the relative abundances given to each of the 12 sites by
application of the Liddelow rapid-survey technique.
17
Figure 2.1: The 12 study sites representing four sites each at low, medium and high
abundance of quokkas (Setonix brachyurus) according to the Liddelow rapid-survey technique.
The components of the Liddelow rapid-survey technique (runnel counts, faecal-pellet counts,
track counts and incidental sightings) were then compared individually to the population
abundance estimates obtained through mark–recapture. Each of the methods is described in
detail in the following sections.
18
Mark-recapture
Quokkas in the northern jarrah forest are considered trap shy (Hayward et al. 2003) and it was
expected that the same would apply in the southern forests. Southern populations are also likely
to move between suitable habitat patches and abundance estimates may be confounded by
immigration and dispersal, particularly at extremely low densities and with sparse data. For the
purpose of the present study, the trapping regime was completed within one optimal time period
to minimise the effect of temporal variation and the potential effect of replacement owing to the
movement of animals between subpopulations. Autumn was selected as the optimal period for
trapping because of the moderate weather conditions and early sunrise, which allows traps to be
cleared earlier (first light) and reduces the amount of time animals spend in the traps. The
choice of autumn was also considered to be likely to maximise detection probability, because
the core habitat is drier, potential food resources are more restricted and quokkas are expected to
be food limited (Chapter 4).
Trapping webs were selected instead of trapping grids, to provide the capacity to relate
population estimates to the area of habitat the quokkas occupied (Anderson et al. 1983;
Buckland et al. 1993). These were established following recommendation of Anderson et al.
(1983) that trap spacing should result in at least 8–12 traps per home range in the centre of the
web. Because there are no reports on the home range of the quokka in the southern forest, the
spacing along each line was determined using the home range of 6 ha for quokkas in the
northern jarrah forest (Hayward et al. 2004). This resulted in an average density of 14.1 traps
ha–1 in the centre of the web. Each web consisted of 65 traps 30 m apart and trapped an
effective area of 18.1 ha. After a 3-day pre-feeding period, trapping was conducted for 10
consecutive nights by using ‘Thomas’ soft-wall traps, made with shade cloth stretched over a
steel frame (450x450x800 mm, Sheffield Wire Works, Welshpool, Western Australia) and
baited with apples. The main non-target species in this area was Rattus fuscipes. For this
reason, the Thomas traps were modified to include a reinforced rat-sized escape hole in the back
of the trap and the trigger plate was adjusted so that rats would not set off the trap.
Captured quokkas were removed from traps, weighed, ear tagged with unique identity codes and
released. Pes measurements, pouch condition and presence of pouch young were also recorded.
The establishment of trapping webs, trap set and trap checking took an average of 87 man hours
per site, factoring in the density of the vegetation, the intricacies of web establishment, the
labour associated with getting traps onsite, pre-feeding and the need for two people to check the
traps to ensure they were cleared within 3 h after sunrise, as per standard operating procedures
of the DPaW.
19
Sign and sighting surveys
Runnel counts, track counts and faecal-pellet counts were undertaken at each site 10 days before
trapping, to ensure that counts were not influenced by the presence of traps or bait (Wayne et al.
2005). Eight transects, each 240 m long, were established as a part of the trapping web and
were visited each morning for 10 consecutive days.
The transects were constructed by
‘bashing’ a path through the understorey and marking this with flagging tape. Care was taken
not to damage the runnel network during transect construction, but some damage was inevitable
due to the extensive nature of the runnels and the density of the understorey vegetation. Use of
the web arms as the focus for all measurements was necessary because of the density of the
habitat, the damage that constructing additional transects would have caused to the runnel
network and the possibility of creating access for feral predators through the habitat. Each of
the rapid-survey elements when applied at a transect level took an average of 3 h per site.
Trapping web arms were used as transects, and so, additional time would need to be allowed if
these rapid survey elements were applied in isolation.
Transect establishment could be
undertaken in conjunction with the counts, and a much lower standard of transect, and a lower
associated level of disturbance to the vegetation, would be needed. Transect establishment and
counts would be expected to take approximately 8 h per site.
Fresh faecal-pellet groups identified by their soft exterior and green colour when broken apart
(Hayward et al. 2003) were counted and collected daily to avoid repeat counting on subsequent
days along each transect.
All fresh pellets were also removed on the day before counts
commenced, to ensure that only fresh pellets were counted on the first day of survey.
Track counts were completed by clearing a small area of leaf litter at the entrance or within
selected runnels, to expose the sandy substrate underneath. The clearings were visited each
morning and those with quokka tracks present were recorded and the surface smoothed free of
tracks. There was no attempt to determine whether more than one animal had moved across a
single pad. Runnel counts were completed by counting active runnels, identified by compacted
leaf litter and an absence of fallen debris, crossing each transect. When animals were directly
observed during surveys, these were recorded as sightings.
20
Analysis of data
Population abundance estimates using mark–recapture
Program distance (Thomas et al. 2010) was considered for generation of density estimates from
the capture records. However, the assumption that all animals near the centre of the web are
captured with certainty (Buckland et al. 2001) could not be met, as evidenced by remote
cameras detecting untagged quokkas. Population size was calculated from capture records
within the trapping period, using MARK closed capture models with heterogeneity (White and
Burnham 1999). The use of Jolly–Seber models was considered, because these are open
population models allowing for immigration, emigration, recruitment and mortality during the
trapping period (Lefebvre et al. 1982). However, these models assume that capture probabilities
vary only by trapping occasion and do not allow for heterogeneity or behavioural response to
trapping, and, in our study, individual heterogeneity was considered to be an important potential
source of bias for capture probabilities. Mark–recapture was conducted for a short time period
within habitats that are geographically defined because of topography and/or landform. In
addition, the home range of the quokka has been shown to be stable at this time of the year, so
dispersal, immigration and migration are unlikely (Chapter 4). It was, therefore, considered that
the data met the closure criteria to enable the use of closed capture models with heterogeneity
(Lefebvre et al. 1982; Kendall 1999).
The effective sampling area was consistent across webs and this area is included in the methods
to enable the reader to convert the abundance to a density if they prefer; however, abundance
values are used throughout this article for ease of communication.
Model selection was based on the Akaike information criterion (Akaike 1973) corrected for
small sample size (AICc). AICc was interpreted as a measure of the lack of fit from the ‘true’
model and the model with the lowest AICc value was considered the most appropriate
(Burnham and Anderson 2002). The models included the effect of factors such as trap response,
time, group behaviour and individual heterogeneity on the probability of capture and recapture
(Table 2.1).
The population abundance estimates obtained through MARK were compared with the
Liddelow rapid survey technique by using ANOVA, with population abundance as the response
variable and relative abundance category as the group (Stata10, StataCorp. 2007). Bonferroni
multiple comparison tests were used to compare the population abundance estimates by relative
abundance to account for the detected variance between groups.
21
Table 2.1:
Combination of factors modelled using Program MARK (White and Burnham
1999).
Model name
Model description
Huggins closed population estimation
Mo p(.)=c(.),N
Probability of capture and recapture constant
Mo2 p(.) c(.), N
Presence of capture influence on recapture
Mt p(t)=p(t), N
Presence of an equal temporal effect on capture and
recapture
Mt2 p(t) c(t), N
Presence of temporal effect on capture and recapture
Mt3 p(t) c(.), N
Presence of a temporal effect on capture only
Mg p(g) c(g), N
Presence of a site effect on capture and recapture
Mb p(.) c(.) +constant (b), N
Presence of behavioural influence (e.g. gender) on
capture and recapture
Mtb p(t) c(.) +constant (b),N
Presence of temporal and behavioural influence on
capture
Closed captures with heterogeneity
Mh pa(.) pb(.),N
Presence of individual heterogeneity in capture
Mbh pa(.) pb(.) beh constant N
Presence
of
behavioural
heterogeneities in capture
and
individual
Testing individual components of the Liddelow rapid-survey technique
At each site, the number of runnels, tracks, faecal pellet groups and sightings of quokkas were
each divided by the number of visits to obtain average counts of runnels, tracks, faecal pellets
and sightings for each site to use as independent variables in linear regressions. These values
were used in a linear regression (Stata10, StataCorp. 2007) that modelled all possible
combinations of runnels, faecal pellets, tracks and sightings against population abundance
estimates obtained in MARK. Model selection was based on the AICc and the model with the
lowest AIC value was considered the best fit (Burnham and Anderson 2002). R2-values were
used to determine the level of model uncertainty and the potential need for model averaging.
Distance sampling was considered for estimating the abundance of quokkas from each of the
activity indices, but the replication required for model robustness and some of the model
assumptions, particularly those relating to randomly placed lines or points, could not be met
within the sensitive habitats being surveyed (Buckland et al. 2001).
22
2.4
Results
Population abundance estimates using mark-recapture data
Estimates of population size ( ) for each of the 12 sites were obtained using a family of models
generated in program MARK (White and Burnham 1999) that incorporated several sources of
variability in the probability of capture. AICc weights were computed for all candidate models
(Table 2.2). Temporal and behavioural heterogeneity, as well as individual heterogeneity,
contributed to capture success, as suggested by the AICc weights (Table 2.2); however, the
contribution was slight, as indicated by the relatively high capture probabilities generated for all
models.
The capture probability was high enough to justify the use of the derived population estimate
from the MARK modelling (hereafter referred to as ‘population abundance’) as a measure of the
actual population size, given that even the weakest model produced a capture probability of 1.0
by Day 4 of the 10-day trapping period.
Table 2.2:
Combinations of factors modelled with Program MARK Closed Captures with
Heterogeneity, their AICc weights and derived capture probabilities.
AICc
weight
Mean daily
No. of
Standard
capture
parameters
Error
probability
Mtb p(t) c(.) +constant b
0.48
7
0.51
0.338
Mt3 p(t) c(.)
0.17
2
0.58
0.033
Mo2 p(.) c(.)
0.10
2
0.35
0.056
Mb p(.) c(.) +constant
0.10
2
0.35
0.056
Mo p(.) =c(.)
0.10
1
0.42
0.027
Mt p(t)= c(t)
0.04
7
0.44
0.069
Mt2 p(t) c(t)
0.01
12
0.59
37.061
Mg p(g) c(g)
0.00
24
0.44
0.149
Mh pa(.) pb(.)
1.0000
1
0.73
0.013
Mbh pa(.) pb(.) beh constant
1.0000
1
0.71
6.026
Model name
Huggins closed population estimation
Closed Captures with Heterogeneity
23
There was a strong relationship between the relative abundance categories and the mean
population abundance estimates calculated for each site (Figure 2.2). The population
abundances for sites categorised as low or medium were significantly different (P = 0.025), with
1.25 and 4.75 individuals, respectively. There was also a significant (P = 0.037) difference in
the mean population abundance between sites with a medium and those with a high relative
abundance, with 4.75 and 8.5 individuals, respectively. The range of values, however
overlapped in sites categorised with medium and high abundance, showing that as the
abundance of animals increases, the categorisation of relative abundance becomes more difficult
(Figure 2.2, Table 2.3).
Population Abundance
Estimate (N)
14
12
10
8
6
4
2
0
Low
Medium
High
Relative Abundance Category obtained through
Rapid Survey
Figure 2.2: Variance in the actual abundance (mark-recapture) for sites categorized as having a
relative abundance of low (mean=1.25 individuals), medium (mean=4.75 individuals), and high
(mean=8.5 individuals). The mean population abundance is significantly different between the
sites categorized as having a relative abundance of low or medium (p=0.025) and those having a
relative abundance of medium and high (p=0.037).
24
Table 2.3:
Population abundance estimates, faecal pellet counts, runnel counts, track
counts and sightings for sites categorized as having a relative abundance of low,
medium, and high through the Liddelow rapid-survey technique.
Study
Relative
abundance
category
Population
Abundance
Estimate ( )
Faecal
pellet
count
Runnel
count
Track
count
Sighting
count
1
Low
1.0
1.1
12.0
0.0
0.0
2
Low
1.0
1.1
18.0
0.0
0.0
3
Low
1.0
1.3
9.0
0.1
0.0
4
Low
2.0
1.3
22.0
0.0
0.0
5
Medium
3.0
2.6
27.0
0.0
0.0
6
Medium
3.0
2.7
18.0
0.0
0.0
7
Medium
5.0
4.4
16.0
0.0
0.3
8
Medium
8.0
5.1
31.0
0.2
0.2
9
High
6.0
5.8
28.0
0.0
0.0
10
High
7.0
7.3
66.0
0.1
0.2
11
High
8.0
8.3
21.0
0.1
1.6
12
High
13.0
12.5
76.0
0.0
0.3
site
Testing individual components of the Liddelow rapid-survey technique
The strongest model for predicting population abundance was based solely on faecal-pellet
counts and a strong linear relationship was demonstrated between faecal-pellet counts and
population abundance (R2 = 0.966, AIC 24.04, Table 2.4, Figure 2.3). The models containing
pellet counts combined with other estimate techniques were also strong. There was a minimal
change in the AICc values at the removal of track counts and sightings from the model, and
where these were the only elements in the model, they demonstrated poor correlation with
population abundance (R2 = 0.18 and 0.025, respectively) and had high AICc values, which
suggests that they were not contributing to model strength.
There was a linear relationship between runnel counts and population abundance (R2 = 0.697),
but the data seemed more inclined toward a quadratic relationship (Table 2.4, Figure 2.3) and
the high delta AICc values for models containing runnel counts without pellet counts showed
that these are weak models for predicting population abundance.
25
Defining a quantitative survey technique
The linear relationship between faecal-pellet counts and population abundance estimates (N) can
be represented by the equation N = 1.09(x) – 0.21, where x = number of faecal pellet groups and
N = estimated population size. The mean number of faecal-pellet groups and the standard error
were calculated across all sites for each day when the counts were undertaken (Figure 2.4). The
point at which the standard error stabilised determined the adequate sample regime for
estimating abundance from pellet counts, with minimal variation around the calculated mean.
The standard error was stable from the first day of survey, suggesting that a single visit to each
site is adequate; however, all fresh-looking pellets were removed before counts commenced, to
ensure that fresh pellets were from the previous night.
Similarly, the mean number of faecal-pellet groups and the standard error were calculated at a
site level for each length of transect where counts were undertaken (Figure 2.5). The point at
which the standard error stabilised determined the minimum number and length of transects
required for an optimal sample regime. For three of the sites, the standard error of the mean was
stable following collection of samples from a single transect (240 m); for two sites, the standard
error was stable after two transects (480 m); for one site, the standard error was stable after three
transects (720 m) and, for six sites, the standard error of the mean was stable following
collection of samples from four transects (960 m).
26
Population Abundance (N)
14
12
10
8
6
4
2
0
0
2
4
6
8
10
12
Faecal Pellet Count
a)
Population Abundance (N)
12
10
8
6
4
2
0
0
20
40
60
80
Runnel Count
b)
Figure 2.3: a) Linear relationship between faecal pellet counts and population abundance (N)
and b) quadratic relationship between runnel counts and population abundance (N).
27
Table 2.4:
Modelled activity indices for the prediction of quokka abundance (N) in the
southern forests of Western Australia.
AICc, Akaike information criterion corrected for small sample size
Model (N, dependent variable)
R²
AICc
Delta AICc
N, Pellets
0.966
24.041
0.000
N, Pellets, runnels, tracks, sightings
0.976
25.698
1.657
N, Pellets, runnels
0.967
25.701
1.660
N, Pellets, runnels, tracks
0.967
27.448
3.660
N, Runnels, sightings
0.732
45.198
21.157
N, Runnels, tracks, sightings
0.736
48.875
24.834
N, Runnels
0.697
50.303
26.262
N, Runnels, tracks
0.702
52.137
28.096
N, Sightings
0.183
62.241
38.200
N, Tracks
0.025
64.353
40.312
N, Tracks, sightings
0.183
65.236
41.195
28
Mean Faecal Pellet Count
5
4
3
2
1
0
0
2
4
6
Number of Days
8
10
Standard Error of Mean Faecal
Pellet Count
a)
5
4
3
2
1
0
0
2
4
6
Number of Days
8
10
b)
Figure 2.4: a) Mean number of faecal pellets and b) the standard error recorded for all sites over
the 10 day survey period.
29
Standard Error of Mean Faecal
Pellet Count
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
1
2
3
4
5
6
7
8
No. Transects
Figure 2.5: Logarithmic trend lines for the standard error of mean faecal pellet counts across
eight transects at each of the 12 study sites.
30
2.5
Discussion
The present paper makes an important contribution to the problem of surveying cryptic and rare
mammals, and is a timely contribution, given that the Liddelow rapid-survey technique is being
widely implemented in quokka surveys in the southern forest. Faecal-pellet counts alone
performed better than the composite Liddelow rapid-survey technique because runnels, tracks
and sightings generated unreliable estimates and confounded the overall estimate of abundance.
Faecal-pellet counts correlated very strongly with the population abundance of quokkas,
demonstrating that the Liddelow rapid-survey technique can be refined back to a quantitative
method based on faecal-pellet counts that is simple, rapid and accurate.
To obtain a reliable rapid estimate of population abundance, a minimum of 960 m of transect
should be surveyed on two repeat visits, with the first used to familiarise the observer with
fresh-looking pellets already onsite such that the second visit can be used to record new pellets
(deposited overnight). Variation in detection probability should be considered by undertaking
surveys at the same time of the year with the same amount of moisture in the landscape (e.g. late
summer and early autumn before the opening autumn rains when quokkas are concentrated in
the moister parts of the landscape and food is limited), at the same time of the day (e.g.
morning, when fresh faecal pellets are easier to detect due to their moist exterior) and using
observers that are trained to identify fresh faecal pellets and differentiate the faecal pellets of
quokka from those of western brush wallaby (Macropus irma).
Discriminating fresh from older pellets is critical for using this method to estimate quokka
abundance. At all sites, faecal pellets were consistently deposited in the proximity of previously
recorded faecal-pellet deposits, despite these pellets having been removed. Capture patterns on
the trapping webs showed that sections of the web were routinely visited by the same group of
quokkas. Determining the age of quokka faecal pellets is challenging because of the high
variability of the rates of decay of pellets over space and time (K. Bain, pers. obs.). Failure to
do so can substantially overestimate population abundance owing to persistence of old pellets,
the accumulation of pellets in areas routinely visited by the same group of animals, and
variation in the temporal and spatial use of habitat by quokkas. Although daily removal of
faecal pellets can overcome this bias, it may also reduce the deposition rate in subsequent days
if animals share common latrine areas or are motivated to defecate where they encounter old
pellets (Vernes 1999). Differentiating faecal pellets produced by quokkas from those produced
by the western brush wallaby is also a potential source of error (Triggs 1996; Hayward et al.
2005b). Some moderate level of observer skill is therefore necessary to ensure that quality data
are collected.
31
There was a significant linear relationship between runnels and population abundance, but the
data appeared more inclined toward a non-linear relationship, such as a quadratic relationship.
This might be expected because quokkas use and share runnels to move through their habitat,
creating an access network through suitable habitat. At low animal densities, an approximate
linear relationship in the number of runnels would be expected. However, at higher densities it
is logical that the number of runnels within the network would plateau as access to all suitable
habitat is achieved. Incidentally, whereas the number of runnels may plateau at increasingly
higher quokka densities, the average frequency of use of runnels would be expected to continue
to increase. To verify this, more data would need to be collected at the sites of highest density.
Although runnels are the most conspicuous sign of quokka activity, determining currency of use
is more difficult because of their temporal persistence and their use by other animals. In dense
vegetation, runnels create an access network through and between suitable habitat, which is
used by quokkas as well as other species including the southern brown bandicoot (Isoodon
obesulus fusciventer) and common brushtail possum (Trichosurus vulpecula hypoleucus).
Therefore, runnels may be useful for determining that the habitat has been occupied by quokkas,
but they are less effective as a means for determining abundance or recentness of activity.
The use of tracks and sightings were unreliable because individuals and their tracks were
undetectable at most sites because of dense vegetation, a lack of exposed and suitable substrate,
the wind causing movement of vegetation that swept the sand of tracks and the presence of other
animals. The use of baited or unbaited sand pads described by Mawson and Orell (2001) to
assist with track detection may help overcome substrate limitations; however, practical
considerations such as sand free from the introduced plant pathogen, Phytopthora cinnamomi,
would need to be addressed; a certified disease-free sand supply was not available for the
present study.
Remote cameras were unavailable in sufficient numbers to be used for population-size estimates
in the present study, but are likely to be an efficient and useful quantitative technique for
monitoring in remote and difficult terrain, with minimal resources and minimal environmental
disturbance (Rowcliffe et al. 2008). Cameras are likely to provide more reliable and accurate
activity indices than faecal-pellet counts and other indirect measures, particularly in poor
weather conditions (Glen and Dickman 2003; Thomas et al. 2010; Hamilton and Rolfe 2011).
Indirect methods such as faecal-pellet counts and camera trapping rates can be practical and
reliable techniques for estimating population abundance. The attraction of these techniques is
that they provide a rapid and inexpensive survey option that is potentially applicable to any
cryptic and/or threatened species and is practical for resource-constrained land managers.
Although these may be rapid approaches to monitoring, there are several factors that are likely
to affect detection probability among years, areas or observers and an understanding of how this
variation can be accounted for is important, such as through a combination of systematic or
32
stratified sampling, tightening up standard monitoring protocols, measuring key covariates,
and/or estimation of detection probability using mark–recapture, distance or occupancy models,
where this is achievable and practical.
Quokkas have morphological features such as face dimensions, ear damage, body condition,
body size and shape that could be used to differentiate individuals on camera traps, within a
short period of time. Therefore, capture–recapture models to estimate abundance, based on the
re-trapping of recognisable individuals by cameras, may be possible for this species. Models of
occupancy (MacKenzie et al. 2002) that can estimate underlying detection probabilities from
faecal-pellet counts or camera trapping data could also be adopted, if the assumptions of the
models can be met and where the proportion of area used by the species can be used to evaluate
population trends reliably. Given the ready detectability of quokkas from faecal pellets and
runnels, occupancy modelling has the potential to become a valuable tool for monitoring
quokka population trends in the southern forest.
In the interim, the present study has provided a robust and defensible test of the Liddelow rapidsurvey technique, showing that it can be pared back to an alternative quantitative method based
on faecal-pellet counts.
Faecal pellet counts correlated very strongly with the population
abundance of quokkas in the southern forests, as they have in the northern jarrah forest
(Hayward et al. 2005). The method is useful because of its simplicity and ability to be applied
over large spatial scales in a relatively short period of time. Although the faecal-pellet method
is not as rapid as the Liddelow approach, it still takes less than 10% of the time that trap-based
mark–recapture surveys would take for this species. With a focus on standard monitoring
protocols, this technique could be rolled out across the southern forest, with minimal variation
in observer bias, as opposed to the widely used Liddelow rapid-survey technique, which is
purely subjective. If this was to be undertaken, it could generate the first accurate estimate of
abundance for quokkas in the southern forests and significantly improve the capacity of land
managers to make important conservation decisions for the management of this unique species
in this challenging landscape.
2.6
Acknowledgements
This research was funded in part by the Department of Environment and Conservation (DEC),
Western Australia and was undertaken as a part of the delivery of the Warren Region Nature
Conservation Service Plan.
This study was conducted under WA DEC Animal Ethics
Committee approval DECAEC 24/2009, UWA Animal Ethics Committee approval
RA/3/100/693 and scientific purposes license number SC000856. Thanks to Graeme Liddelow
for familiarisation with the Liddelow rapid-survey method, Dr Keith Morris and Matthew
Williams for their comments on our manuscript and Professor Ken Pollock for his assistance
with the Mark Analysis.
33
34
Chapter 3:
Risks in extrapolating habitat
preferences over the geographical range of threatened
taxa: a case study of the quokka (Setonix brachyurus)
in the southern forests of Western Australia
Example of ecotypes occupied by quokkas in the southern forests of Western Australia
This chapter was submitted to the journal of Wildlife Research on 2 December 2014, accepted 8
July 2015 and published online 19 August 2015.
35
3.1
Abstract
Context. Extrapolation of knowledge for threatened taxa between parts of their range that are
disconnected and/or ecologically diverse can result in significant sources of error that
undermine the effectiveness of conservation efforts.
Aims. We investigated the risks associated with extrapolation of ecological information across
environmental gradients, using the quokka (Setonix brachyurus) as a case study. Information
documented in the northern part of its range is currently used to manage this species across its
range in south-western Australia. We examined the suitability of this approach by developing a
habitat suitability model (HSM) for the quokka in the southernmost areas of its range and
comparing this with existing knowledge for the species.
Methods. A total of 327 sites, representative of a range of ecotypes, were surveyed for
presence/absence of quokkas. Occupancy models were applied to select a subset of habitat
variables that best predicted occupancy patterns.
Key results. Occupancy patterns were influenced by complex vegetation structure, low levels of
woody debris and habitat patchiness. HSMs developed for quokkas in the north could not
predict occupancy patterns in the south.
Significant fragmentation of subpopulations was
observed due to patchiness in the availability of suitable habitat.
Conclusions. The choice of predictor variables in HSMs that are not transferrable between
regions could contribute to inappropriate management of habitat for quokkas and an increased
risk of local extinctions. In addition, failure to consider processes that affect preferred habitat
variables could contribute to the segregation of habitat patches and intervening distances that
are too great for successful dispersal, immigration and recolonisation processes.
Implications. The extrapolation of HSMs between geographical areas can increase the risk of
outcomes that are detrimental to the conservation of threatened species.
Where such
extrapolation is necessary, actions guided by the HSMs should be implemented in a
management framework that can detect adverse effects, allow for inclusion of new ecological
information, and explicitly consider the limitations and assumptions of this approach.
In
addition, perceptions of habitat fragmentation need to include processes such as fire regimes and
feral animals that affect the availability and connectivity of habitat and have the potential to
adversely affect population viability.
Key words: habitat occupancy, fragmentation, metapopulation, threatened species, vegetation
structure, fire regime.
36
3.2
Introduction
Effective strategies for the conservation of threatened species require knowledge of factors
driving occupancy and survival (Moilanen and Hanski 1998). Species distribution models
(SDMs) and habitat suitability models (HSMs) are increasingly being used to determine the
ecological requirements of single species, to predict their occupancy and to support
conservation planning and impact evaluation processes (Guisan and Zimmerman 2000, Guisan
et al. 2006, Real et al. 2009, Acevedo et al. 2010). These models can provide useful ecological
insight and strong predictive capability where ecologically relevant data with high resolution are
used (e.g. Lindenmayer et al. 1999; Osborne et al. 2001). However, there are many examples
where the models fail, due to use of coarse resolution data or use of high resolution data from a
single region, which is applied broadly (e.g. Constible et al. 2010; Murray et al. 2011; Heinanen
et al. 2012; Williams-Tripp et al. 2012; Young et al. 2012). In these instances, geographical
extrapolation of the models results in poor predictive performance. Sources of error associated
with such extrapolation are particularly important for threatened species that occur in discrete
and highly variable locations.
Serious consequences arising from such extrapolation include an inability to adequately manage
habitat for threatened species due to the lack of transferability of HSMs between geographical
regions.
The management of the threatened and widely distributed Canadian toad Bufo
hemiophrys, (Constible et al. 2010) provides such an example. In the prairies, the species is
closely associated with aquatic habitats and so timber harvesting activities in this region are not
required to protect upland habitats. However, in the boreal forests in the northern parts of its
distribution, the species spends more than 30% of its time in upland habitats and here failure to
protect upland habitat from timber harvesting activities will result in loss of critical habitat and
an increased risk of local extinctions (Constible et al. 2010).
A species potentially affected by the risk of extrapolating knowledge over its geographical
range is the quokka (Setonix brachyurus), a monogeneric, monospecific wallaby, listed as
vulnerable by the IUCN (IUCN 2011). The quokka is restricted to south-western Australia and
two near-shore islands (White 1952; Storr 1964; Maxwell et al. 1996; Sinclair 1998). On the
mainland of Western Australia, quokkas occur in three areas: the northern jarrah (Eucalyptus
marginata) forest, which runs from north of Perth to Collie; disjunct reserves around Albany on
the south coast; and the southern forests between Nannup and Denmark (Sinclair 1998; de Tores
et al. 2008; DEC 2013).
37
The important habitat features driving occupancy of quokkas in the southern forest are largely
unknown. In the absence of knowledge, quokkas in the southern forest have been managed
using information documented for quokkas in the northern jarrah forest, where populations are
vulnerable to predation by feral animals and favour habitats with a dense understorey and a mix
of early seral stage and long unburnt vegetation (Christensen and Kimber 1975; Hayward et al.
2005a; Hayward et al. 2007). While these habitat features are expected to also apply to quokkas
in the southern forest there is little or no empirical evidence to substantiate this and it is unclear
how appropriate it is to extrapolate what is known of this species in the north to how it is
managed in the south. The southern forests are more mesic, the climate more temperate and the
near-ground vegetation generally denser and more extensive and contiguous than in the northern
forests. Preliminary DNA analysis of quokkas from Rottnest Island, the northern jarrah forest
and the southern forest, suggests that animals in the southern population are more likely to
move between habitat patches in a functioning metapopulation (P. Spencer unpublished data).
If this is the case, the southern forest quokkas may be vulnerable to processes that increase the
distance between suitable habitat patches.
In this study, we developed a HSM for the quokka in the southernmost areas of its range,
identifying specific habitat features that can be used to predict its occupancy across multiple
ecotypes in this region. We aimed to investigate the appropriateness of extrapolating what is
known from the northern jarrah forest to quokkas in the southern forests and the potential effect
of factors such as fire regimes and feral animals on the connectivity of suitable habitat.
38
3.3
Methods
Study area
This study was carried out in the forests between Nannup and Walpole in south-western
Australia (Figure 3.1), an area dominated by open and closed heaths, tall forests, wetlands and
creek systems. About 65% of the area is national park vested in the Conservation Commission
for the purpose of conservation, much of which has been designated as such since 2004 (Kile
2013).
A total of 327 sites were randomly selected from a map (approximate aggregated area of 1308
ha) and were representative of a range of forest, woodland, heath and wetland ecotypes. Each
site was surveyed for presence or absence of quokkas three times during a single summer/
autumn period (Figure 3.1). Presence was determined by walking two 1 km transects and
recording the occurrence of fresh faecal pellets (Hayward et al. 2005b; Bain et al. 2014).
Chapter 4 identifies that quokkas are capable of moving large distances; however, they are
doing so within a defined area (70 ha, the maximum diameter of a stable HR was 3.1 km). A
distance of at least 5 km separated survey sites to ensure site independence. Surveys were
undertaken at a time of the year when home ranges are known to be stable for the species in this
region, as shown in Chapter 4. All surveys were completed by a single trained observer to
reduce potential errors in differentiating faecal pellets produced by quokkas from those of the
western brush wallaby (Macropus irma) (Triggs 1996; Hayward et al. 2005b).
Figure 3.1: Location of survey sites for the quokka within the southern forest of Western
Australia.
39
A series of habitat attributes were recorded at 20 random points along each transect including:
landform, understorey, midstorey and overstorey height, canopy and understorey cover,
horizontal density, vegetation structure, vegetation type, understorey diversity, leaf litter depth,
height and density of woody debris, vegetation age (time since last fire), season of the last fire
event, distance to an alternative vegetation age, number of different vegetation ages within 1
km, adjacent vegetation age (yrs), distance to private land, distance to creek line, presence of
pigs, presence of predators, presence of competitors and dryness of habitat (see Table 3.1 for a
more detailed explanation of these habitat attributes and their measures). The multiple sample
points were averaged for each site to reduce variability caused by short-term changes in the
spatial distribution of individuals.
Analysis of data
Habitat models were developed to discriminate between locations where quokkas were present
and those where they were absent. In order to reduce the number of variables to a level not
likely to result in an over-fitted model, the procedure of Hosmer and Lemeshow (2000) was
used with a stricter cut-off of p>0.10, to enable a reduction in variables to nine or fewer. This
procedure included all of the variables considered likely to be important for site occupancy.
We maximized the model likelihood for the observed set of data to obtain parameter estimates
for detection probabilities (p) and occupancy rate (ψ) using program MARK. We modelled p as
either constant or as a function of sampling session (time). We modelled ψ as either constant, or
as a function of the remaining habitat variables. Temporally replicated transects were treated as
occasions. Models met the assumptions outlined in MacKenzie et al. (2002). Parametric
bootstrap and Pearson Chi-Square goodness-of-fit tests were used to assess the fit of the
occupancy models to our data (MacKenzie and Bailey 2004). We used Akaike's Information
Criterion (AIC) with a small sample size correction (AICc) for model selection and considered
models with delta AICc values <2 to have strong support (Burnham and Anderson 2002). In
addition, cumulative AICc weights were calculated to evaluate strength of evidence for each
model.
40
Table 3.1: Variables initially considered for use in a predictive model of habitat suitability for
the quokka, in the southern forests of Western Australia.
Variable
Landform
Understorey,
midstorey and
overstorey height
Canopy and
understory cover
Horizontal density
Vegetation structure
Vegetation type
Understorey diversity
Leaf litter depth
Depth of woody
debris
Density of woody
debris
Vegetation age
Season of fire
Distance to
alternative vegetation
age
Number of different
vegetation ages
within 1 km
Alternative vegetation
age
Distance to private
land
Distance to creek line
Presence of pigs
Presence of predators
Presence of
competitors
Description
Categorical classification as dry creek, wet creek, moist creek, heath, mid
slope or ridge.
Average height of vegetation from the ground to the lowest vegetation
layer, middle vegetation layer and tallest vegetation layer respectively (m).
Measured using a clinometer.
Average percentage of ground covered by the crown foliage and
understorey foliage (%) respectively. Measured using digital cover
estimation techniques (Macfarlane et al. 2000, Macfarlane et al. 2007).
Density of understorey vegetation measured using a modified digital cover
estimation technique, involving use of a white sheet 2 m from the
photographer and an image of the intervening vegetation taken horizontally.
Number of vegetation layers including ground cover, understorey, multiple
midstorey layers and overstorey.
A categorical classification of dominant vegetation type within the habitat.
Number of species contributing to the understorey.
Depth of the surface leaf litter layer (mm). Leaf litter includes dead leaves,
fine twigs and bark on the forest floor (Gould et al. 2007).
Depth of the near-surface woody debris on the forest floor above the leaf
litter (m). Consists of suspended leaves, twigs, branches and bark from the
understorey, midstorey and overstorey vegetation.
Density of the near-surface woody debris on the forest floor above the leaf
litter. Categorical judgment of density: none, sparse, medium or dense.
Time since the vegetation was last burnt in a prescribed burn or bush fire
(yrs).
Season in which the habitat was last burnt: spring, summer or autumn.
Shortest distance to nearest vegetation age different to that within the
habitat being surveyed (m); determined using GIS and DPAW vegetation
age mapping data.
Number of different vegetation ages within 1 km; determined using GIS
and DPAW vegetation age mapping data
Nearest vegetation age different to that within the habitat being surveyed
(yrs)
Average distance to nearest private property (km); measured using ArcGIS
9.1 mapping software (ESRI 2006)
Average distance to nearest creek line (m); measured using ArcGIS 9.1
mapping software (ESRI 2006)
Categorical assessment of current presence or absence based on surveys for
faecal material, diggings and detections by remote sensor camera traps
undertaken concurrently and using the same protocols as the occupancy
surveys for quokkas
Categorical assessment of the presence or absence of feral predators such as
foxes and cats based on surveys for faecal material, diggings and detections
by remote sensor camera traps undertaken concurrently and using the same
protocols as the occupancy surveys for quokkas
Categorical assessment of presence or absence of other herbivores such as
kangaroos, wallabies, bandicoots and rabbits based on surveys for faecal
pellets, diggings and shelters undertaken concurrently and using the same
protocols as the occupancy surveys for quokkas .
41
3.4
Results
Quokkas were detected at 88 of the 327 sites surveyed, yielding a model generated occupancy
of 0.22 (naïve occupancy 0.27), and occurred in discrete patches with distances between 12 and
40 km separating occupied habitat (Figure 3.2).
Occupied sites included a range of forest, woodland and wetland ecotypes but the most
commonly occupied sites consisted of jarrah, marri (Corymbia calophylla), karri (Eucalyptus
diversicolor) or tingle (Eucalyptus guilfoylei or Eucalyptus jacksonia) forest habitats with a
sedge-dominated understorey (Table 3.2).
Of the 24 measured habitat variables, 17 were excluded from further analysis on the basis of
univariate logistic regression models and included: vegetation age, height of woody debris,
distance to private land, number of different vegetation ages within 1 km, adjacent vegetation
age (yrs), distance to creek line, height of understorey, leaf litter depth, understorey diversity,
horizontal density, height of overstorey, canopy cover, presence of pigs, presence of predators,
season of fire, landform description and presence of competitors (Table 3.3).
Vegetation age was expected to be a significant driver of occupancy however, quokkas occupied
habitat with vegetation ages ranging between six months and 50 yrs and did not show any
preference for particular age categories. The high proportion of unoccupied sites associated
with 0.5-10 yrs (Figure 3.3) reflects the high proportion of this age category in the landscape.
Figure 3.2: Location of survey sites that were occupied by quokkas during this study.
42
Table 3.2:
Vegetation types occupied by the quokka in the southern forests of Western
Australia during summer and autumn.
Vegetation Description
No. sites
occupied
No. sites
surveyed
Jarrah (Eucalyptus marginata)/ bullich (Eucalyptus
megacarpa) with an understorey dominated by woody species
0
11
Jarrah/ bullich with an understorey dominated by sedge
species such as Ghania and Anarthria
6
14
Jarrah/ marri (Corymbia calophylla) / Casuarina forest with an
understorey dominated by woody species
12
37
Jarrah/ marri/ Casuarina forest with an understorey dominated
by Lepidosperma, Anarthria, Empodisma or Ghania
25
43
Karri (Eucalyptus diversicolor)/ jarrah with an understorey
dominated by woody species
1
21
Karri/ jarrah forest with a sedge dominated understorey
16
29
Tingle (Eucaluptus guilfoylei)/ karri forest with a woody
understorey
1
24
Tingle/ karri forest with a Lepidosperma dominated
understorey
12
30
Banksia or Casuarina woodland with understorey dominated
by woody species
1
16
Banksia or Casuarina woodland with understorey dominated
by Lepidosperma, Anarthria or Ghania
2
16
Taxandria woodland (sometimes with Karri) with a
Taxandria/ Callistachyus/ Melaleuca midstorey and well
developed understorey (often containing Ghania and
Lepidosperma)
2
19
Taxandria linearifolia or Melaleuca thicket with an
understorey dominated by Ghania, Empodisma or other sedges
(no midstorey)
6
25
Melaleuca woodland with Taxandria or Melaleuca midstorey
with and an understorey dominated by heath species
4
17
Open heath dominated by Anarthria, Dasypogon, Evandra
with no mid or overstorey or forest vegetation with no mid or
overstorey (e.g. low Taxandria thickets or regenerating forest)
0
25
43
Table 3.3:
Variable culling process (Hosmer and Lemeshow 2000).
Habitat variables were excluded from further analysis if the P-value was greater than 0.1
following univariate logistic regressions.
Mean (± SE)
Description
Univariate logistic
regression
Absent
Present
-2 Log
Likelihood
Sig.
Vegetation age (yrs)
11.5 (0.7)
11.9 (1.17)
-190.4
0.78
Height of woody debris (m)
0.18 (0.01)
0.17 (0.01)
-190.4
0.78
Height of understorey (m)
1.1 (0.04)
1.2 (0.04)
-190.3
0.54
Leaf litter height (m)
17.2 (1.1)
15.6 (1.3)
-190.1
0.41
Understorey diversity
4.9 (0.1)
5.2 (0.2)
-189.5
0.17
Horizontal Density (%)
76.4 (7.2)
64.6 (1.9)
-189.7
0.22
Height of overstorey
21.3 (0.8)
24.4 (1.1)
-188.8
0.14
Canopy cover (%)
80.7(12.4)
57.0 (2.1)
-189.3
0.13
Height of midstorey (m)
5.1 (0.3)
6.3 (0.3)
-185.5
0.07
Understorey cover (%)
69.4 (7.3)
52.2 (2.2)
-187.7
0.02
Vegetation structure
2.0 (0.04)
3.2 (0.05)
-143.1
<0.0
Number different vegetation ages
within 1 km
3.3 (0.1)
3.19 (0.2)
-190.4
0.73
Distance to alternative vegetation age
(m)
595.2(40.1)
169.4 (38.1)
-158.9
<0.0
Adjacent vegetation age (yrs)
11.8 (0.6)
12.1 (1.1)
-190.4
0.78
11.3 (0.8)
-190.2
0. 42
77.0 (9.5)
-190.3
0.56
Presence of pigs
Categorical
-190.4
0.97
Presence of predators
Categorical
-190.3
0.57
Season of fire
Categorical
-190.2
0.48
Landform description
Categorical
-189.7
0.24
Presence of competitors
Categorical
-189.3
0.14
Dryness of habitat
Categorical
-187.7
0.02
Vegetation type
Categorical
-187.0
0.01
Density of woody debris
Categorical
-179.9
<0.0
Distance to private land (km)
Distance to creek line (m)
10.4 (0.5)
84.4 (7.1)
44
Occupied
46-50
41-45
36-40
31-35
26-30
21-25
16-20
11-15
6-10
Unoccupied
0.5-5
Number of sites
100
90
80
70
60
50
40
30
20
10
0
Vegetation Age (years)
Figure 3.3: The relationship between vegetation age and the number of sites occupied by
quokkas. Vegetation age relates to time since the site was last burnt (yrs).
The remaining seven variables were used as covariates in occupancy models, to help explain the
differences in ψ between sites, and included: height of midstorey, understorey cover, vegetation
structure, distance to an alternative vegetation age, dryness of habitat, vegetation type and
density of woody debris (Table 3.4). Our analysis highlighted an AICc weight of 0.75 for
model P(.)ψ(DWD+VEGST+DISA), which included the density of woody debris, vegetation
structure and distance to an alternative vegetation age (Table 3.4). Detection probability was
constant across the three sampling periods for all sites and was 0.94 (SE=0.014).
The fit of the model was considered satisfactory because the Pearson Chi-Square statistic was
not significant (p=0.99) and the Chi-Square value for the most parameterized model fell within
in the 49th percentile of the bootstrapped values. The minimal change in the maximum log
likelihood ratio following removal of the variables height of midstorey, understorey cover,
vegetation type and dryness of habitat from the model indicated that these variables were not
contributing significantly to the model. The removal of the variables distance to adjacent
vegetation age, density of woody debris and vegetation structure from the model led to large
increases in the AICc value and resulted in a poorly fitting model.
The probability of occupancy of habitat by quokkas decreased with increasing density of woody
debris. Habitats with a dense woody debris layer were always unoccupied and sites with a
medium woody debris layer were unoccupied 75% of the time (Figure 3.4). The highest
occupancy rate occurred in habitats with sparse or no woody debris, with occupancy rates of
71% and 35% respectively.
45
The probability of a habitat being occupied by quokkas was also dependent on the structure of
the vegetation and, in particular, the number of vegetation layers. Habitats with three vegetation
layers or more were occupied with a probability of 86%, while habitats with two or fewer
vegetation layers had an occupancy rate of only 6% (Figure 3.5).
Habitats were occupied by quokkas if they were within 0 and 450 m of an alternative vegetation
age (Figure 3.6), but the age of the vegetation was not significant. Habitats greater than 450 m
from an alternative vegetation age were unoccupied 100% of the time. In habitats that were
occupied by quokkas, the average distance to an alternative vegetation age was 169 m
(SE=7.43).
Table 3.4:
Rankings of models using Akaike’s Information Criterion corrected for small
sample size (AICc) in program MARK to explain the occupancy rate (ψ) by
quokkas and detection probability (p) in the southern forests of Western
Australia.
DWD=density of woody debris, VEGST=vegetation structure, DISA=distance to an alternative
vegetation age, USCOV= understorey cover, VEGTY=vegetation type, DRY=dryness of
habitat, MIDHT= height of midstorey
Occupancy and detection probability were modelled as a constant (.), as a function of time (t) or
as a function of habitat variables.
The ∆AICc values are the difference in AICc values
standardised to the model with the lowest AICc. The AICc weights are the Akaike weights
associated with each model and provide the relative strength of evidence for each model. The
log likelihood (-2Log likelihood), k is the number of parameters and the Pearson Chi-Square
goodness of fit test statistics is also presented for each model.
Model
-2LnL
AICc
∆AICc
AICc
Weight
k
X²
p
P(.)ψ(DWD VEGST DISA)
198.57
206.70
0.00
0.75
5
0.41
0.99
P(.)ψ(DWD VEGST DISA USCOV)
199.09
209.28
2.58
0.21
6
1.06
0.99
P(.)ψ(DWD VEGST DISA USCOV
VEGTY)
182.02
213.56
6.86
0.02
19
0.6
0.99
VEGTY DRY)
183.40
214.95
8.25
0.01
20
0.62
0.99
VEGTY DRY MIDHT)
183.31
217.07
10.37
0.00
21
0.69
0.99
P(.)ψ(DWD VEGST)
264.71
270.78
64.08
0.00
4
51.77
<0.0
P(.)ψ(DWD DISA)
262.74
270.87
64.17
0.00
3
189.43
<0.0
P(.)ψ(VEGST DISA)
265.80
271.87
65.18
0.00
3
12.5
0.13
P(.)ψ(.)
496.01
500.05
293.35
0.00
2
1.60
0.66
P(.)ψ(t)
495.58
503.71
297.01
0.00
4
0.99
0.91
46
Number of sites N=327
120
100
80
60
Occupied
40
Unoccupied
20
0
None
Sparse
Medium
Dense
Density of woody debris
Figure 3.4: The relationship between density of woody debris and the number of sites occupied
by quokkas. Woody debris consists of suspended leaves, twigs, branches and bark fallen from
the understorey, midstorey and overstorey vegetation.
200
180
160
140
120
100
80
60
40
20
0
Occupied
Unoccupied
1
2
3
4
5
Vegetation Structure (Number of Vegetation Layers)
Figure 3.5: The relationship between vegetation structure (number of vegetation layers) and the
number of habitats occupied by quokkas.
47
120
100
80
60
40
Occupied
20
Unoccupied
501-2900
451-500
401-450
351-400
301-350
251-300
201-250
151-200
101-150
51-100
0-50
0
Distance to closest alternative vegetation age (m)
Figure 3.6: The relationship between the distance to an alternative vegetation age (m) and the
number of habitats occupied by quokkas.
48
3.5
Discussion
While HSMs developed for quokkas in the north may provide some predictive capability in the
south, the choice of predictor variables that are not transferrable between regions and subtle
differences in the habitats favoured by quokkas in this region could contribute to important
sources of error associated with geographical extrapolation. The HSM developed for quokkas
in the northern jarrah forest is based on dense understorey, early seral stage vegetation and a
variable intensity of baiting for introduced foxes (Hayward et al. 2007). A dense understorey is
characteristic across the landscape in the southern forest region and so is not a feature that can
easily differentiate habitat patches. The southern forest contains more mesic, climatically
buffered and stable ecosystems which have a very different response to fire and different
patterns of growth and senescence of vegetation. This makes the use of seral stages and
vegetation age challenging to apply as predictors here. In addition, baiting for foxes has been
applied throughout the southern forest at a consistent rate and frequency, since commencement
of baiting (Burbidge et al. 1995). While the vulnerability of quokkas to fox predation in this
region is likely to be comparable to that in the northern jarrah, the use of baiting intensity as a
predictor of occupancy is meaningless here.
Occupancy of habitat by quokkas in the south was strongly linked to a complex vegetation
structure (minimum of three layers), low densities of woody debris and habitat patchiness
(between 0 and 450 m to an alternative vegetation age). In the north, density of understorey,
time since fire (less than 10 yrs), adjacent vegetation age (greater than 25 yrs), and the presence
of feral predators were significant drivers of habitat preference by quokkas (Christensen and
Kimber 1975; Hayward et al. 2005a; Hayward et al. 2007). None of these variables were found
to be significant in the southern forests and if these variables were the only ones considered for
the management of the species, this could negatively affect the habitat qualities that quokkas
actually favour in this region.
The range of ecotypes occupied in the southern forest was more diverse than in the northern
jarrah forest and many are associated with sites that have high levels of permanent moisture,
stable productivity and relatively stable habitat features. The vegetation also tends to have a
higher complexity and density, due to the high rainfall in the region and the mesic nature of the
karri and tingle ecotypes, which provide an edaphic barrier to fire under some conditions
(Burrows and Wardell-Johnson 2003).
These systems burn relatively infrequently and
vegetation age can be quite old, while still maintaining the conditions favoured by quokkas in
this region. Quokkas need diurnal shelters that aid their thermoregulation (Kitchener 1981).
Multiple vegetation layers achieve this by providing insulation and protection from the
elements.
Burning more frequently to encourage early seral stage vegetation, as is
recommended for the northern forests, could reduce the structural complexity of the vegetation
in these areas by removing midstorey layers (Spencer and Baxter 2006).
49
Similarly, managing habitats to maximise density of understorey vegetation, as is recommended
for the north, could result in a dense woody debris layer in some southern forest ecotypes and
thus render the habitat unsuitable. A dense woody debris layer on the forest floor suppresses
new growth, smothers the understorey vegetation and substantially impedes the movement of
quokkas, thus affecting food availability and safe passage through their habitat. Dense woody
debris also provides poorer shelter and insulation to quokkas, due to lower levels of shading and
moisture than living vegetation.
One similarity between north and south is the need for a variety of seral stages to provide for
dietary requirements and refuge from predators. While this equates to a mosaic of young
vegetation and long unburnt habitat in the northern jarrah forest (Christensen and Kimber 1975;
Hayward et al. 2005a; Hayward et al. 2007), the mosaic seems more complex in the southern
forest and habitat patchiness is more important than the age of the vegetation. The average
distance to an alternative vegetation age in habitats that were occupied by quokkas was 169 m,
suggesting that a relatively fine scale mosaic is required to maximize the suitability of habitat.
Surprisingly, the presence of feral predators was not found to be a significant variable in the
south and this is likely to be due to patterns of occupancy reflecting selection of habitat with
qualities that permit persistence of quokkas in the presence of these predators. Foxes and feral
cats are most efficient at hunting in open vegetation or in vegetation where there is easy access,
such as walk trails or burnt ground (Saunders and McLeod 2007). The occurrence of quokkas
primarily in habitat with complex vegetation structure that reduces the hunting efficiency of
these predators may account for their non-significance in the models. However, both foxes and
feral cats have been observed to prey on immature quokkas in the southern forest and
recruitment levels have been low in sites where quokkas occur in the presence of these
predators. While occupancy of habitat may be unaffected by feral predators, recruitment and
population demography are likely to be affected, as would be the survivorship of animals
moving between habitat patches. Hayward et al. (2007) found that the persistence of quokka
populations in the northern jarrah forest was related to the intensity of control of introduced
predators (foxes). The broad scale and consistent nature of baiting in the southern forest makes
this variable irrelevant in this region.
Feral pigs were also not found to be significant in predicting quokka occupancy in this study
and this is likely to be due to the way this variable was measured. Measures that adequately
account for pig activity levels in the years preceding the survey are likely to be more strongly
related to the quality of quokka habitat than simple contemporaneous assessments of pig
presence, as was used in this study. In the southern forest, the digging, foraging and wallowing
behaviours of feral pigs have been observed to remove seedlings and lignotubers, to disturb the
soil profiles and substantially alter the density and structure of the vegetation, particularly in
areas that have been recently burnt (Burnside et al. 2012). While quokkas in this study
50
occupied habitat in the presence of feral pigs, sustained feral pig activity is likely to reduce the
suitability of habitat for quokkas as a result of altered vegetation structure, which was a variable
that was significant in the models. The alteration of vegetation structure as a result of feral pig
digging and wallowing has also been recorded in other areas of Australia (Choquenot et al.
1996; Hone 2002; Adams 2014).
Despite the contiguous and extensive natural vegetation system of the southern forest, quokkas
occupied discrete habitat patches separated by distances of up to 40 km. The spatial availability
and connectivity of suitable habitat in this landscape is influenced by fire regime and other
disturbances that alter the habitat features identified in this study as being important for
quokkas.
Anthropogenic processes may have already contributed to the loss of quokka
subpopulations from some areas and the apparent fragmentation of subpopulations challenges
our perceptions of fragmentation in natural ecosystems.
The potential for anthropogenic
fragmentation in this landscape associated with fire regimes, feral animals and other
disturbances such as timber harvesting, is less obvious than physical fragmentation through land
clearing, but it is potentially equally as detrimental.
In the southern forest, genetics (P. Spencer unpublished data) and movement patterns (Chapter
4) indicate that quokkas are still moving between habitat patches. Without active management
of processes that affect vegetation structure, diversity of vegetation ages, accumulation of
woody debris and feral animals (fox, cat, pig), suitable habitat patches are likely to become
more segregated and intervening distances too great for successful dispersal, immigration and
recolonisation processes that may be critical to the maintenance of a functioning
metapopulation.
In many areas, quokkas are explicitly considered when planning fire management programs and
feral animal control activities. The risks of local extinction will be significantly reduced if
future management of this species takes into account the size and patchiness of fire, particularly
within and adjacent to key riparian systems; the intensity of fire to maximize the retention of
vegetation structure; and the active management of fire to avoid senescence or death of
midstorey species and the associated accumulation of woody debris. Fire management regimes
should consider both potential habitat and intervening non-habitat areas to maximize the
availability and accessibility of suitable habitat and the functionality of the metapopulation.
Management of feral pigs following fire is also essential to reduce the effect that this species has
on regenerating vegetation and the subsequent structure of the vegetation. The control of feral
cats and foxes should also be considered to improve quokka recruitment within high priority
habitats.
51
This study has generated knowledge of habitat important to quokkas in the southern forests and
an understanding of the subtleties of fragmentation in a contiguous and natural system, which
will improve our ability to protect potential habitat, to predict the outcomes of disturbance
events and to minimize spatial segregation of habitat patches as a result of forest management
activities. Important differences between habitats in the north and south demonstrate the risks
of extrapolating knowledge between these areas. These differences, if overlooked, could lead to
inappropriate management actions and local extinctions.
In this instance, the failure of the HSM between the two ecologically diverse regions was due to
the selected predictors, and in particular those relating to fire and predator baiting regimes,
which were not transferrable between regions due to spatial differences in ecotypes, the
application of fire, the behaviour of fire and approaches to predator baiting.
The HSM
developed for quokkas in the north was not intended to apply to the species across its
distribution and so was regionally-specific in terms of its choice of predictors. The HSM
developed in this study was also intended to be regionally-specific in order to investigate the
appropriateness of extrapolation between the two regions.
This study has identified that
predictions by HSMs at a regional scale should only be transferred across geographical areas
where predictor variables have been selected that are relevant to both areas and where these
have been verified against local knowledge of the ecology of the study species. In addition,
models that explicitly account for imperfect detection when building HSMs such as the
occupancy models used in this study are expected to produce more accurate estimates of habitat
relationships and improve predictive performance relative to approaches that do not account for
detection bias, particularly for difficult-to-detect species (Rota et al. 2011).
Despite the potential consequences of extrapolation of ecological knowledge, in the absence of
complete information, practitioners are commonly seduced to do so regardless. This is likely to
continue despite the demonstration of risks and adverse outcomes. It is therefore important that
when unsubstantiated extrapolations are made they are duly acknowledged and the risks of
doing so identified.
It is also important that management actions influenced by these
extrapolations are implemented within an active adaptive management framework that can
detect any adverse effects, test the validity of extrapolations, help fill some of the knowledge
gaps, and contribute to an improved approach for the future.
Another important outcome of this study is the realization that our perception of fragmentation
in natural ecosystems needs to be adjusted to include consideration of processes such as fire.
While these processes may be less obvious than physical fragmentation through land clearing,
they have the potential to contribute to the segregation of suitable habitat patches for threatened
species and the creation of intervening distances that are too great for successful dispersal,
immigration and recolonisation processes critical for the maintenance of a viable
metapopulation.
52
3.6
Acknowledgements
This research was funded in part by DPaW, Western Australia and was undertaken as a part of
the delivery of the Warren Region Nature Conservation Service Plan. This study was conducted
under DPaW Animal Ethics Committee approval DECAEC 24/2009, UWA Animal Ethics
Committee approval RA/3/100/693 and scientific purposes license number SC000856. We
thank DPaW employees Roslyn Burnside, Jason Fletcher, Charlene Hordyk and Nicholas Slatter
for their assistance with field work; and Dr Lachlan McCaw, Dr Matthew Williams, Manda
Page and Graeme Liddelow for their critical review of our manuscript.
53
54
Chapter 4:
Spatial ecology of the quokka (Setonix
brachyurus) in the southern forests of Western
Australia: implications for the maintenance, or
restoration, of functional metapopulations
55
4.1
Abstract
Knowledge of spatial ecology for threatened species is necessary for the effective management
of habitat availability, connectivity and metapopulation processes. We used radio telemetry to
investigate the home range size and movement patterns of the quokka (Setonix brachyurus) in
the southern forests of Western Australia. We aimed to assess the ability of animals to move
between increasingly segregated habitat patches and to identify implications for metapopulation
function. We found that quokkas in this region have a much larger home range (71 ± 5.8 ha)
and move larger distances (up to 10 km per night) than previously reported for this species.
Temporal and sex variations in home range size, overlap and movement patterns provided
insights into social structure, reproductive strategies and resource availability for the species in
this part of its range. While riparian vegetation was used exclusively for movement between
habitat patches, quokkas spent only 40% of their time in this ecotype. The current management
paradigm of protecting linear riparian vegetation as habitat for quokkas is important for
maintaining habitat connectivity, but is unlikely to meet broader habitat and spatial
requirements. Management of preferred habitat as well as riparian corridors is necessary for
maintenance of a functional metapopulation.
Keywords: home range, movements, radio telemetry, riparian vegetation, habitat connectivity
56
4.2
Introduction
The effective management of habitat availability and connectivity for threatened species
requires knowledge of home range, movement patterns and of the factors driving them
(Moilanen and Hanski 1998; Beasley et al. 2007). For species that do not have their basic
habitat needs met or the ability to traverse the distance between suitable habitat patches, the
likelihood of local extinctions, genetic isolation and a collapsing metapopulation is significantly
increased (Saunders et al. 1991; Noss and Cooperrider 1994; Moilanen and Hanski 1998).
Habitat fragmentation is currently a significant threat to species worldwide (IUCN 2014).
Perceptions of habitat fragmentation are changing to include an awareness of more subtle
segregation of habitat patches within a seemingly continuous landscape caused by factors such
as fire regimes and feral animals (Lindenmayer et al. 2000; Bain et al. 2015).
A species potentially affected by these factors is the quokka (Setonix brachyurus), which once
occurred throughout south-western Australia, but it is now confined to a small number of
fragmented populations, and is therefore listed as vulnerable by the IUCN (IUCN 2014). The
distribution of this species is entirely within the only biodiversity hotspot on mainland Australia
(Myers et al. 2000). On the mainland of Western Australia, quokkas occur in three areas: the
northern jarrah (Eucalyptus marginata) forest, which runs from north of Perth to Collie; disjunct
reserves around Albany on the south coast; and the southern forests between Nannup and
Denmark (Sinclair 1998; de Tores et al. 2008; DEC 2013). In the southern forests, the quokka
occurs in discrete patches with distances of up to 40 km separating occupied habitat (Bain et al.
2015). Fire management and processes that alter vegetation structure, such as feral pigs, are key
process affecting the distance between suitable habitat patches in this region (Bain et al. 2015).
Little information is available on the spatial ecology of this species in the southern parts of its
range, where more than 60% of mainland populations occur (DEC 2013).
In other parts of their distribution, quokkas have an annual home range size of between 0.13 ha
and 7 ha (Holsworth 1967; Kitchener 1973; Hayward et al. 2004). Variations in the size of
home ranges and movement patterns within these studies have been attributed to the distribution
of shelter vegetation and seasonal changes in food and water resources (Nicholls 1971;
Hayward et al. 2004). In the northern jarrah (Eucalyptus marginata) forest, quokkas are
capable of moving distances of more than 2 km but there has been minimal detected movement
between habitat patches, which has been attributed to low population densities, a population that
is below the carrying capacity of the site, the inhibiting effect of introduced predators and an
absence of suitable microhabitat patches within dispersal distance (Christensen and Kimber
1975; Hayward et al. 2003; Hayward et al. 2004).
57
Quokkas in the southern forest were expected to have a spatial ecology similar to that recorded
for quokkas in the northern jarrah forest; however, the segregation of habitat patches occurring
as a result of forest management practices may increase distances between areas of suitable
habitat to a point where individuals may no longer have the ability to maintain the spatial
distribution of the species in this part of its range. In this study, we investigated the spatial use
patterns and home range size of quokkas in the southern forest and the implications that this has
for the management of their habitat and the maintenance, or restoration, of a functional
metapopulation.
4.3
Methods
Study area
The southern forests of Western Australia encompass the Southern Jarrah and Warren
biogeographical subregions (IBRA 2004, Figure 4.1).
The area has a Mediterranean-type
climate with warm dry summers (November to January) and mild wet winters (June to August).
The study site was located north of Walpole and covered an area of approximately 200 ha
comprised of a series of seasonally inundated creek lines surrounded by low ridgelines (Figure
4.1). The study site boundary was defined based on habitat that had been used by quokkas in
the area within the last ten years as ascertained by the presence of old and fresh faecal material
(Bain et al. 2014).
All quokkas detected during the first year of the study were fitted with radio transmitter collars
weighing 30.0 g (Sirtrack, New Zealand) and tracked for up to two years. It is unlikely that
there were animals present in the site and undetected in the first year of the study, due to regular
trapping and deployment of numerous camera traps for monitoring the condition of collared
animals. A total of 29 quokkas were collared, consisting of ten adult males, 16 adult females
and three sub adult males. Six new animals (two males and three females, one with a young at
foot) moved into the site in the second year of the study and were not collared. Seven of the 29
collared animals were excluded from home range estimates; two adult males and one adult
female permanently changed their home range during the study, one adult female did not have a
sufficient number of fixes to accurately estimate home range size and three sub adult males
were excluded to remove potential inaccuracies arising from the effect of age on home range.
Hourly locations were obtained for individuals during 12 hour nocturnal (from 18:00 to 06:00)
and 12 hour diurnal (from 07:00 to 19:00) tracking periods conducted up to four times each
month.
Locations were obtained using a R1000 receiver and a 6-element Yagi antenna
(Sirtrack, New Zealand). Signal range varied between 100 m and 800 m depending on terrain,
large trees and rock outcrops obscuring the signal trajectory. Triangulation was used to locate
radio-collared quokkas because dense vegetation prevented direct observation without
58
disturbing the natural behaviour of the animals. Telemetry stations were positioned using a
differential global position system (GPS) and compass bearings were taken from a minimum of
three telemetry stations in rapid succession (< 10 min) or using multiple trained observers
simultaneously. At the end of each tracking session, the mapping program Ozi Explorer (D and
L Software Pty Ltd Australia) was used to generate animal location coordinates from telemetry
station and azimuth data.
The accuracy of triangulation was assessed using two activated radio-collars (dummy collars)
randomly positioned at ground level within the study area prior to each telemetry session (Lee et
al. 1985). The locations of the dummy collars were not known to the person(s) undertaking
triangulation. The difference between the triangulation derived location and the known location
were used to determine accuracy for each tracking session. Where the difference was greater
than 50 m, all estimated locations for that session were discarded. The average distance
between true transmitter position and locations estimated by triangulation was 31.4 ± 0.19 m
(N=16,259).
Analysis of data
The program Animal Space Use 1.3 (Horne and Garton 2009) was used to identify the strongest
home range estimation model using the likelihood cross validation criterion (Horne and Garton
2006). In all cases, the Adaptive Kernel came out as the strongest model followed closely by
the Fixed Kernel. All home range sizes in this study were consequently estimated using
Adaptive Kernel Density.
Animal Space Use 1.3 was also used to select the smoothing
parameter (h) using the likelihood cross-validation (CVh) method. This was based on findings
from Horne and Garton (2006) that CVh produces estimates with better fit and less variability
than least squares cross-validation. Ranges 8 software (Kenward et al. 2008) was used to
generate home range estimates, incremental area plots, home range overlap analysis and to
calculate inter-location distances. The models for estimation of home range size assume that
locations of animals are independent however, if the successive locations are close in time, then
locations may not be independent and therefore subjected to auto-correlation (Swihart and Slade
1986). To minimize a probable auto-correlation of the data, based on the observed small
movement rate of quokkas during the day, we used only one location per day. For nocturnal
locations, we used a minimum interval of one hour between locations.
We combined locations for both years and calculated annual 50% and 95% isopleth Adaptive
Kernel home ranges (Worton 1989).
We determined the minimum number of telemetry
locations needed to estimate quokka home ranges by plotting home range size against the
number of locations for 95% fixed kernel estimates to obtain incremental area plots. Annual
and seasonal home ranges stabilized after an average of 60 (± 16.2) and 30 locations (± 4.6),
respectively. These sample sizes were used as the minima for annual and seasonal home range
calculations, respectively. Home range was defined as the area within which an individual was
59
found 95% of the time; core area was defined as the area within which an individual was found
50% of the time; nocturnal home range was defined as the area within which an individual was
found between dusk and dawn, and vice versa for diurnal home range; seasons were defined as
summer (December to February), autumn (March to May), winter (June to August) and spring
(September to November). A total of 15,840 locations were used in the home range analyses,
with an average total number of locations per quokka of 864 (± 48.3), and an average number of
seasonal locations per quokka of 216 (± 12.1).
To identify factors associated with variation in home range size, core area and distances
travelled, we used these as response variables in linear models using Stata 10 (Stata Corp.
2007). We used an information-theoretic approach (Burnham and Anderson 2002) to assess
seven candidate models that represented different hypotheses regarding the importance of sex,
diurnal or nocturnal periods and season on home range size, core area and distances travelled.
To avoid heterogeneous variance of residuals, response variables were natural-log-transformed
before model fitting (Zar 1999). We used Akaike's Information Criterion adjusted for small
sample size (AICc) to rank models and Akaike weights to evaluate model likelihood (Burnham
and Anderson 2002). For each model set, we assessed the fit of the most complex model
through inspection of residual plots and a goodness-of-fit test. We used two sample t-tests
(Stata Corp. 2007) for post hoc analysis of factors identified in the models as having the most
influence on the response variables.
The distance travelled by quokkas was measured as the sum of linear distances between
sequential locations sampled hourly in a 12 hour tracking period. Intermittently sampled
locations, such as those where several hours had lapsed between accurate locations, were not
used in this analysis as these were not considered to be reflective of continuous animal
movement. For animals tracked for two years, seasonal and monthly distances travelled were
the mean of the two years.
To determine microhabitat use patterns, we mapped habitat features using GPS in the field and
aerial photographs in Quantum GIS2.2 (QGIS development team 2014). The identified features
included: creek lines, jarrah (Eucalyptus marginata) forest, karri (Eucalyptus diversicolor)
forest, Taxandria (Taxandria linearifolia) thickets, Melaleuca (Melaleuca preissiana) heath,
open heath and gravel roads (Table 4.1, Figure 4.1). Telemetry locations were plotted for each
quokka on the habitat map. The mean proportion of time spent by quokkas in each habitat was
calculated by intersecting the locational point data and the mapped spatial habitat polygon data
using vector analysis in QGIS2.2. We used analysis of variance to test for the effect of sex,
diurnal/ nocturnal period, season and habitat features on the proportion of time spent by
quokkas at each location. We calculated the relative significance of habitat features for quokkas
in the study area by adjusting the mean proportion of time spent in each habitat feature by the
proportional availability of the habitat feature in this site (ranked habitat score).
The
60
proportional availability of each habitat type was calculated as the percentage that it contributed
to the total area of the study site.
Figure 4.1: Location of study site and arrangement of habitat features within the study site.
61
Table 4.1:
Microhabitat features available for use by quokkas in the study site.
Proportion was defined as the percentage of the total study area that was covered by each
individual habitat feature.
Habitat
Feature
Area
(ha)
Proportion
(%)
Creek line
16.7
8.3
Seasonally inundated bodies of water confined within creek
banks.
Taxandria
thickets
10.6
5.3
Riparian vegetation dominated by Taxandria linearifolia
with mid and under storey of Lepidosperma effusum,
Ghania decomposita, and Desmocladus flexuosa. These
areas are mossy and remain dark and moist during the heat
of the day.
Melaleuca
heath
35.6
17.6
Riparian vegetation dominated by Melaleuca
with a mid and understorey of Taxandria
Astartea fascicularis and Sphenotoma gracile.
cool during the heat of the day, but more
Taxandria thickets.
Jarrah
forest
98
48.6
Mid slope and low ridgelines dominated by Eucalyptus
marginata and Corymbia calophylla forest, with a
midstorey of Banksia grandis and Persoonia longifolia and
a dense understorey of Taxandria parviceps, Thomasia
paniculata, Boronia stricta and Bossiaea linophylla.
Karri
forest
19.8
9.8
Tall forest of Eucalyptus diversicolor and Corymbia
calophylla, well-developed midstorey of Allocasuarina
decussata, Trymalium floribundum and Acacia pentadenia,
and a dense understorey of Chorilaena quercifolia,
Leucopogon verticillatus and Hovea elliptica.
Open
heath
16.9
8.4
Low dense vegetation dominated by Taxandria parviceps,
Anarthria scabra, Evandra aristata, Meeboldina scariosa,
Astartea fascicularis and Acacia littorea.
Road
4
2
Gravel surface, width between six and eight metres. Low
traffic flow (<10 cars per week)
Description
preissiana,
parviceps,
Moist and
open than
62
4.4
Results
Home range
The mean annual home range size for quokkas in the southern forest was 71 ± 5.8 ha and the
mean core area was 18 ± 1.8 ha (n=22 animals). The strongest models identified season and
diurnal or nocturnal period as the most important factors for explaining variation in the size of
home range and core areas (Table 4.2). The removal of sex from the candidate models resulted
in a negligible change in the ∆AICc and model likelihood values, suggesting that this variable
was not contributing to an improved fit of the model to the data.
As expected from the models, the home range of male quokkas (78.2 ± 1.9 ha) and their core
area (19.8 ± 1.6 ha) were not significantly larger than those of female quokkas that had a home
range of 73.4 ± 3.7 ha (t=-1.58; p=0.14) and core area of 16.9 ± 1.0 ha (t=-1.27; p=0.22). For
both sexes, home range contracted significantly in winter (t=2.47, p=0.03). The home range
size for female quokkas was stable in spring, summer and autumn, ranging between 54.4 ± 7.9
and 56.7 ± 4.8 ha but contracted in winter to 38.2 ± 5.3 ha. The core area for females was stable
across all seasons, ranging between 13.9 ± 2.8 and 15.5 ± 3.1 ha (Figure 4.2). For male
quokkas, the size of their home range was largest in summer and autumn ranging between 90.1
± 9.5 and 92.8 ha ± 10.9 ha before contracting in winter to 35.8 ± 1.1 ha (Figure 4.2). The core
area for males followed a similar pattern to the home range, with the largest areas recorded in
summer and autumn and the smallest areas recorded in winter (Figure 4.2).
The nocturnal ranges for both sexes were similar to annual home ranges and were significantly
larger than the diurnal home ranges (t=-3.08, p=0.01). Female quokkas had a nocturnal home
range size of 64 ± 3.4 ha and a diurnal home range of 47 ± 6.6 ha. Male quokkas had a
nocturnal home range of 78 ± 8.6 ha and a diurnal home range of 47 ± 4.6 ha (Figure 4.2).
63
Table 4.2:
Results of candidate models investigating the effect of sex, season and diurnal
or nocturnal period (DON) on the home range, core area and distance travelled
by quokkas in the southern forests of WA
Response variable
Home Range size
Core area size
Distance travelled
Model parameters
Season, DON
Sex, Season, DON
Season
Gender, Season
DON
Sex
Sex, DON
Season, DON
Sex, Season, DON
Sex, Season
Season
Sex, DON
Sex
DON
Season, DON
Sex, Season, DON
Season
Sex, Season
DON
Sex, DON
Sex
∆AICc
0
1
2.35
3.41
8.01
8.83
19.06
0
0.74
2.51
3.09
5.71
6.1
6.19
0.00
2.00
2.11
4.11
99.56
101.54
106.52
AICc Wt
0.47
0.29
0.14
0.09
0.01
0.00
0.00
0.43
0.30
0.12
0.09
0.02
0.02
0.02
0.54
0.20
0.19
0.07
0.00
0.00
0.00
64
120
100
80
Male Home Range (ha)
60
Female Home Range (ha)
40
Male Core Area (ha)
20
Female Core Area (ha)
Autumn
Summer
Spring
**Winter
Diurnal
*Nocturnal
Annual
0
Figure 4.2: Home range estimates for 22 adult quokkas (Setonix brachyurus) in the southern
forest of Western Australia. * Nocturnal home ranges were significantly larger than diurnal
ranges (p=0.01); ** winter home ranges were significantly smaller than annual home ranges
(p=0.03).
65
Home range overlap
The home ranges of collared adult females overlapped by an average of 80 ± 2.5% and all home
ranges of adult females overlapped with every female that was radio collared during this study,
by a minimum of 54%. Core areas for females also overlapped by an average of 64 ± 3.8% and
females overlapped with at least 12 other females by a minimum of 39% (Table 4.3). Small
numbers of females were frequently located together during the day and had core areas and
nocturnal ranges that were almost identical.
Males were never located with other males during the day although their home ranges
overlapped by an average of 54 ± 0.9% and all males shared a minimum of 32% of their home
range with up to seven other males. The core areas of adult males overlapped only slightly with
three to four other adult males, with an average overlap of 8 ± 0.6% (Table 4.3).
The home range of male quokkas overlapped with those of females by an average of 72 ± 1.9%
and males shared their home range by a minimum of 38% with all of the radio-collared females
in the site, often overlapping completely with two or more females. Male core areas overlapped
with the core areas of at least 13 radio-collared females by an average of 49 ± 3.3% and in some
cases core areas almost completely overlapped with one or two females (Table 4.3).
Table 4.3:
The mean percentage of home range area (95% and 50% Kernel) that adult
quokkas shared with radio-collared conspecifics
Males on males
Females on females
Females on males
Mean 95% Kernel
Min,
Home Range Overlap % Max
(SE)
%
54 (0.9)
33, 58
80 (2.5)
54, 98
72 (1.9)
38, 100
Mean 50% Kernel
Home Range Overlap
% (SE)
8 (0.6)
64 (3.8)
49 (3.3)
Min,
Max
%
0, 12.5
39, 100
2, 100
66
N
16
30
22
Distances travelled
Quokkas in the southern forest travelled mean (straight-line) distances of between 475 m and
10,748 m during a 12 hour period (n=22 individuals over 84 sampling periods). Mean diurnal
movements (1,397 ± 199.7 m) were significantly smaller than mean nocturnal movements
(4,606 ± 372.7 m, p= 0.001). Within a 24 hour period, peak times of activity occurred between
17:00 and 10:00, with the largest hourly distances travelled between 03:00 and 06:00 h and a
smaller peak between 17:00 and 22:00 (Figure 4.3). Mean nocturnal distances travelled in
winter (2,198 ± 227.5 m) were significantly shorter than distances travelled in spring (3,317 ±
374.6 m), summer (3,554 ± 423.6 m) and autumn (3,749 ± 464.9 m) (p=0.02, Figure 4.4).
There was no significant effect of sex on mean distance travelled (p=0.50, Figure 4.4).
Mean hourly distance
travelled (m)
600
500
400
300
200
100
23:00
21:00
19:00
17:00
15:00
13:00
11:00
09:00
07:00
04:00
02:00
00:00
0
Figure 4.3: Mean hourly distances travelled in a 24 hour cycle by quokkas in the southern
forests of Western Australia (N=22 quokkas over 84 sampling periods).
67
Mean nocturnal
distance travelled (m)
6000
5000
4000
3000
2000
1000
Male
Female
Spring 2012
Winter 2012
Autumn 2012
Summer 2012
Spring 2011
Winter 2011
Autumn 2011
Summer 2011
0
Figure 4.4: Variation in mean nocturnal distances travelled between seasons, years and sexes;
N= 22 individuals (14 females and 8 males) and 36 nocturnal sampling periods. Nocturnal
distance travelled was calculated as the straight-line sum of hourly inter-location distances
recorded between 18:00 and 06:00.
Dispersal and emigration
Three adult quokkas completely shifted their home range during this study. One adult female
(2.2 kg body mass) moved 14 km to the south east of the site; one male (4.8 kg) moved 4 km to
the south of the site and one male (5.1 kg) moved 2 km to the south-west. All used riparian
systems as corridors during these movements. All animals emigrated in mid-late August and
established new home ranges in areas where other quokkas were present.
Prior to the two males emigrating, their core area had an overlap of 100% and 97% respectively
with two males that remained in the study area for the duration of the study. This overlap is
significantly higher than the average 8% overlap of stable core areas for other males. The two
males that emigrated were two of the largest animals collared in the study area in a system
where the weight of adult males ranged between 4.1 kg and 5.9 kg.
The female that emigrated had a larger core area and home range than all of the other females
and completely overlapped with all collared females in the site. This animal was one of the
smaller females in the site, in a system where the weight of adult females ranged between 2.2 kg
and 4.3 kg. She did not produce any pouch young during the period of this study, but had pouch
staining suggesting she had done so previously.
68
Six new animals (two males and three females, one with a young at foot) were trapped in the
second year of the study. They were first detected in late August and early September in the
second year of the study. Repeat trapping and camera images indicate that these animals
remained in the system at least until the end of the study, but as they were not collared, it is
unknown where they came from, whether they established stable home ranges and how they
interacted with the established individuals. It is unlikely that these animals were present in the
system and undetected in the first year of the study due to regular trapping and deployment of
numerous camera traps for monitoring the condition of collared animals.
Habitat analysis
Quokkas spent 16% of their time in the Taxandria thickets, 22% of their time in the Melaleuca
heath, 60% of their time in the jarrah and karri forest, and less than 2% of their time in the creek
line and open heath habitats (Table 4.4). There was no significant effect of sex (p=0.93),
diurnal/ nocturnal periods (p=0.31) or season (p=0.59) on the proportion of time spent in each of
the microhabitats. There were, however, significant differences in the amount of time spent in
the different habitat features (p=0.00), and following adjustment of the mean proportions to take
into account the relative availability of each habitat feature, Taxandria thickets were identified
as the most preferred feature of the habitat throughout the year for both genders, followed by
Melaleuca heath and jarrah forest (Table 4.4).
69
Table 4.4:
Proportion of time spent by quokkas in each of the seven identified habitat
features.
The ranked habitat score provides a measure of the proportion of time spent by quokkas in each
habitat feature in relation to the relative availability of each of these features within the site.
Creek
Line
Jarrah
Forest
Karri
Forest
Taxandria Melaleuca
Thickets
Heath
Open
Heath
Road
8.3
48.6
9.8
5.3
17.6
8.4
2.0
Proportion of
quokka locations
(%)
1.6
56.0
2.0
16.3
22.4
1.5
0.2
Ranked habitat
score
0.2
1.2
0.2
3.1
1.3
0.2
0.1
Male locations (%)
1.6
53.0
4.7
15.8
22.6
2.0
0.3
Female locations
(%)
1.7
57.9
0.3
16.6
22.3
1.3
0.1
Male nocturnal
locations (%)
2.1
60.2
3.1
11.8
19.9
2.4
0.5
Male diurnal
locations (%)
0.9
44.1
6.8
20.7
26.0
1.5
0.0
Female nocturnal
locations (%)
1.4
61.8
0.3
14.2
20.9
1.2
0.2
Female diurnal
locations (%)
2.1
52.9
0.2
19.5
24.0
1.3
0.0
Proportional habitat
availability (%)
70
4.5
Discussion
Home range size
The average home range size of 71.4 ha for quokkas in this study is much larger than previously
reported home range sizes of 0.13 ha (Kitchener 1973) and 7 ha (Holsworth 1967) on Rottnest
Island and 6.39 ha in the northern jarrah forest (Hayward et al. 2004). Of the home range
estimates available for this species, those reported by Hayward et al. (2004) are most
comparable, given their use of kernel density estimators. Their study was for a similar duration
and used similar field methods and analysis techniques. There was some variation in the
intensity of sampling between the two studies with Hayward et al. (2004) obtaining an average
of 35 fixes for 58 animals in comparison to the average of 864 fixes for 22 animals obtained in
this study. However, kernel estimates are known to produce unbiased home range estimates
that are robust to variation in sample size (Swihart and Slade 1985; Seaman and Powell 1996;
Powell 2000). Hayward et al. (2004) collected diurnal and nocturnal locations no more than
once per day and their sampling design may have contributed to an inability to detect large
movements occurring during nocturnal periods. While sampling design may help to explain
some of the variation, this is unlikely to be the only explanation given home ranges in the
southern forest were an order of magnitude larger than those estimated for quokkas in the
northern jarrah forest.
Likely additional explanations for the observed differences include the relative availability of
resources and the greater connectivity and availability of suitable habitat patches. The density
of quokkas in this study was low with average densities ranging between 0.08-0.14/ ha in
comparison with 5-15/ ha on Rottnest Island (DEC 2013) and 0.8-4.7/ ha in the northern jarrah
forest (Hayward et al. 2005a). Many studies have found that when animal densities are low,
individuals distribute themselves relative to the location and availability of high quality
resources (e.g. Rosenzweig 1991; Morris and MacEachern 2010; van Beest et al. 2014). The
low density of the quokka population in the southern forest, combined with the large home
ranges estimated for quokkas in this region may be a reflection of low resource availability.
This is contrary to what we expected, given the high rainfall of the region which is generally
associated with higher productivity and availability of resources (Coops et al. 1998). While the
southern forests are generally more productive than northern jarrah forests, the availability of
food resources at ground level may be more limited. Conditions such as dense canopy and
midstorey layers that increase shading and reduce temperature have been shown to limit the rate
of nutrient uptake and growth of plants on the forest floor (Coops et al. 1998; Schuerings et al.
2014). These conditions are common in habitats occupied by quokkas in the southern forest
(Bain et al. 2014) and are likely to limit resource availability and contribute to larger home
ranges here.
71
Movement and spatial use patterns of quokkas in the southern forest are also less constrained by
habitat availability and connectivity than those of quokkas in the northern jarrah forest. In the
southern forest, quokkas use a diverse range of ecotypes in addition to the riparian systems that
are occupied in other parts of their distribution on the mainland (Hayward et al. 2004). This
combined with the widespread and contiguous nature of vegetation in the southern forest, means
that individuals are able to range more freely to meet their energy demands than they are in
other parts of their range where habitats are more fragmented, such as in the northern jarrah
forest (DEC 2013).
Where population density is low, some species have also been shown to expand their
movements to maintain contact with neighbours that have become more widely dispersed (e.g.
Guyer et al. 2012). Quokkas in the southern forest travelled distances of up to 10 km within a
12 hour period, which is substantially greater than distances previously documented for this
species in other parts of its distribution. While the distances travelled are most likely to be
related to foraging activities, social structure may also play a role and this is discussed further in
subsequent sections.
Seasonal variation in home range size
There were strong seasonal influences on home range for both sexes. Larger home ranges for
both male and female quokkas in summer and autumn and smaller home ranges in winter are
likely to have been influenced by the relative availability of palatable and nutritional food.
Information on seasonal variation in the nutritive value of Australian flora is generally lacking
however, tannins are expected to be more important for diet selection in summer and autumn
when food is less abundant and plants are under increased stress (Stolter et al. 2013). At these
times, many plants are less palatable and digestible, resulting in animals foraging over wider
areas and needing to consume a greater biomass to meet their nutritional requirements (McIvor
1981, van Rees and Beard 1984). In winter and spring, plants are generally more palatable,
digestible and there are more food plants available due to the emergence of annuals and
germination of seedlings, which make a diverse diet over a smaller area more possible (Stolter
et al. 2013).
The water content of plants is also likely to be more important in summer. As there are no
permanent sources of water available within this area during the peak of summer and autumn,
quokkas rely on the water content of foliage and dew and might need to cover larger areas to
meet their hydration requirements. In support of this, Hayward et al. (2004) also observed that
quokkas in the northern jarrah forest were wider ranging during summer and autumn and they
inferred this was to meet hydration needs.
72
While female home range and movements were also largest in summer and autumn, their home
range in these seasons was substantially smaller than male home ranges. This is likely to have
been influenced by the seasonal emergence of pouch young, which occurred for all resident
females in late February 2011 and early March 2012. The presence of dependent young at foot
would be expected to reduce mobility at these times. Changes in predator activity levels may
also have influenced female home range, given the only detected occurrence of feral cats (Felis
catus) and foxes (Vulpes vulpes) in the site correlated with emergence of pouch young in
summer and autumn. The movement of a single fox into the system in late summer 2011
coincided with higher nutritional requirements for this species as a result of pre-breeding and
dispersal behaviours (Winstanley et al. 1999, Balogh et al. 2001). Similarly, the movement of
two feral cats into the system in autumn 2012 coincided with higher nutritional requirements for
feral cats as a result of breeding demands in autumn and food shortages in late summer/ autumn
(Read and Bowen 2001; Short et al. 2002).
Home range overlap
Females demonstrated a high shelter and group fidelity, with spatial overlap of up to 99.9%
common for small groups of females and shared diurnal shelters, to which they returned each
morning. This would limit the potential size of their home range and could help to explain their
relatively constant home range throughout the seasons, despite food limitations and the energy
demands of lactation (Miller et al. 2009; 2010). Compensatory benefits that could offset the
energy requirements of different seasons include a larger number of individuals to keep runnels
open and free from debris, predator vigilance and decreased heat loss during winter. Only one
female did not have pouch young for the duration of this study and her data was removed from
home range analysis due to her emigration from the site. Her home range and movement
patterns were most similar to that of a male quokka and she did not demonstrate any shelter or
group fidelity prior to emigration. This suggests that shelter and group fidelity may be related
to the need to protect the young.
Core area use for males was almost exclusive (mean 8.6% overlap), they were never located in a
shared shelter and did not return to the same shelter each morning.
While the distances
travelled by males and females were similar, males were not as constrained about returning to
their diurnal shelter, which allowed a greater area of habitat to be traversed in response to
changing resources. One such resource is mate availability. Exclusive use of core areas has not
been recorded for male quokkas in other studies and may reflect reproductive strategies such as
competition for mates. McLean et al. (2009) observed that on Rottnest Island, female quokkas
tolerated or encouraged the advances of only one or a few males, mating partnerships were
developed and maintained over many years, most females had one male as a primary consort
and males attempted to defend females with whom they had just copulated. If this is the case
73
for quokkas in the southern forests, the minimal overlap in core area of males could be
associated with male defence of females (Moss 1995; McLean et al. 2009).
Timing of breeding at this site is estimated to have occurred at least in July 2010 and August
2011, as inferred from estimated age of pouch young (Miller et al. 2009). The emigration of
two large adult males from the system occurred in August 2011. Both males had significantly
overlapping core areas with other males, which suggests that their movement was in response to
insufficient access to mates or competitive behaviour from the males with whom their core areas
overlapped. Both males presented with injuries that were consistent with male aggression,
including torn ears and patches of missing fur just prior to emigrating. An adult female also
emigrated from the site in August 2011. She had a much larger home range than any other
female in the site and was the only female with no pouch young for the duration of the study.
Pouch staining was indicative of previous young rearing by this quokka and her body condition
was comparable with all other females in the site. The reproductive inactivity of this female and
her emigration during a breeding period is likely to be linked to mate availability. Similar
behaviour has been recorded for female koalas (Phascolarctos cinereus) following hormonal
contraception. Sustained failure to produce dependent young resulted in female koalas moving
large distances outside of their normal home range, presumably in search of more suitable mates
(Hynes et al. 2011).
Habitat use
Quokkas in the southern forest utilised a diverse range of ecotypes well outside of the riparian
systems, which are considered their primary habitat in other parts of their distribution on the
mainland (Hayward et al. 2004). Quokkas in this study spent only 40% of their time in riparian
ecotypes such as creek lines, Melaleuca heath and Taxandria thickets. This is possibly due to
the structural complexity and density of other vegetation types contiguous with the riparian
systems that also provide habitat conditions suitable for quokkas in this region (Bain et al.
2015).
Geographical extrapolation of spatial movement data
The results from this study are based on data from a single subpopulation. As has already been
established in this thesis, the extrapolation of knowledge for threatened taxa between parts of
their range that are ecologically diverse can result in significant sources of error that undermine
the effectiveness of conservation efforts. Spatial replication of this study is required to validate
this data for broader extrapolation in the southern forest, given the high variability in ecotypes
that quokkas occupy in this region. The results of this study also provide an opportunity to
revise what is understood in the north and whether what has been observed in the north is
natural or an artifact of changes to the habitat and threatening processes.
74
Conclusion
In support of our hypothesis, the movement of three adult quokkas out of the system and six
animals (five adults, one young at foot) into the system during this study highlights that
movement is occurring between occupied habitats in the southern forests. The large distance
moved by animals in this subpopulation also provides some confidence that quokkas have the
ability to traverse substantial distances between occupied habitats. While quokkas in this study
spent only 40% of their time in riparian vegetation, they used this ecotype exclusively to
emigrate and reach nearby habitat patches.
Riparian vegetation provides a corridor with
complex vegetation structure, shelter, safety from predators, moisture and diverse feeding
opportunities, which have previously been identified as important for quokkas in the southern
forest (Bain et al. 2015).
The more expansive use of habitat combined with the large spatial requirements of this
subpopulation has significant management implications for conservation measures of the
quokka in the southern forests. In particular, disturbance activities such as logging and fire that
traditionally protect linear riparian systems with a buffer of 100 m are unlikely to be effectively
protecting all suitable and important quokka habitat in this region.
The management of
intervening habitat and connecting riparian vegetation are also important.
The spatial
availability and connectivity of suitable habitat continues to be of management concern for
quokkas in the southern forests, however, the mobility of quokkas in this landscape suggests
that gene flow can occur and that the metapopulation is likely to be functional where preferred
habitat and riparian corridors are managed effectively.
4.6
Acknowledgements
This research was supported by DPaW, Western Australia and was undertaken as a part of the
delivery of the Warren Region Nature Conservation Service Plan. The study was conducted
under DPAW Animal Ethics Committee approval DECAEC 24/2009, UWA Animal Ethics
thank DPAW employees Graeme Liddelow, Roslyn Burnside, Jason Fletcher, Nicholas Slatter,
Charlene Hordyk and Carol Ebbett for their assistance with field work and Dr Brian Chambers
and Graeme Liddelow for their critical review of our manuscript.
75
76
Chapter 5:
Prescribed burning as a conservation
tool for management of quokka habitat in the southern
forests of Western Australia
Example of mild fire behaviour, which resulted in a patchy mosaic of burnt and unburnt
vegetation and retention of vertical vegetation structure
Example of intense fire behaviour, which resulted in no unburnt pockets and a complete loss of
vertical vegetation structure
This chapter was submitted to the International Journal of Wildland Fire on 13 July 2015,
accepted 14 January 2016 and published online 22 March 2016.
77
5.1
Abstract
This study investigated the current application of fire for biodiversity conservation in the
southern forests of Western Australia.
In particular, we examined factors driving the
recolonisation of burnt areas by a model species, the quokka (Setonix brachyurus), the spatial
arrangement and refuge value of unburnt vegetation and fire prediction parameters that may
help to guide fire planning. Retention of vertical vegetation structure, > 20% of the total area as
unburnt vegetation, and multiple unburnt patches (> 36 ha and within 1 km of at least two other
patches) were found to be important for quokkas to recolonise fire-affected areas rapidly. The
application of fire to achieve these outcomes was dependent on high surface and soil moisture
and field conditions that contributed to a fire rate of spread of <50 m/h. Intense wildfire
resulted in the complete loss of vertical vegetation structure and a lack of unburnt patches,
which contributed to these areas remaining uncolonised for the duration of the study. Burning
with high moisture differentials, maximizing the effectiveness of edaphic barriers to fire,
retaining unburnt vegetation associated with mesic and rocky habitats, and maintaining
vegetation structure were found to be important elements of fire regimes in this region. This
study has important implications for the application of prescribed fire for biodiversity
conservation in fire-prone ecosystems.
Keywords: colonisation rates, fire regime, moisture differential, vegetation structure, mesic
habitats, patchiness.
78
5.2
Introduction
The use of prescribed fire to generate heterogeneous environments, often referred to as fire
mosaics, is a commonly advocated strategy for biodiversity conservation (Burrows 2008;
Penman et al. 2011; Di Stefano et al. 2013). However, the spatial and temporal characteristics
of the fire mosaic needed to facilitate biodiversity conservation in a given region are poorly
understood (Clarke 2008; Driscoll et al. 2010; Haslem et al. 2012; Di Stefano et al. 2013).
Fire management guidelines developed to promote heterogenous fire outcomes often encourage
the application of fire under conditions most likely to achieve patchiness of burnt and unburnt
vegetation, such as the regular application of low intensity fire under moist spring conditions
(e.g. Burrows et al. 2004; Burrows and McCaw 2013). Under these conditions, forests are
expected to burn mildly and vegetation in swamps and creek lines often remains mostly unburnt
due to higher levels of moisture than surrounding ecotypes. In this way, more mesic areas that
are important for threatened species are afforded more protection from fire (Burrows et al.
2004; Burrows and McCaw 2013). However, with an overall drying trend, the edaphic barrier
of moisture is becoming less effective and mesic areas are increasingly vulnerable to fire (IPCC
2007; Williams et al. 2009).
Active management of fire is important for the protection and maintenance of habitat for many
threatened species in south-western Australia (e.g. Friend and Wayne 2003; Hayward et al.
2005a; Brown et al. 2009; Valentine et al. 2014). Therefore, understanding the role of fire in
the ecology of threatened species and embracing opportunities to apply fire to generate genuine
conservation outcomes is increasingly important. This is particularly so given the expectations
of larger, more frequent and more severe wildfires with a warming and drying climate (IPCC
2007; Cary et al. 2012; Driscoll et al. 2012). The increasing risk of severe wildfires to human
populations has also increased political pressure to introduce prescribed fire for the proactive
protection of human life and property (e.g. Boer et al. 2009; Keelty 2012). This increases the
risk of inappropriate fire regimes threatening ecosystems and species for which ecological
knowledge may be lacking.
A species that is currently used as a focal species for management of fire in the southern forests
of Western Australia is the quokka Setonix brachyurus (Burrows et al. 2004). The quokka is a
medium-sized macropod that is declared ‘vulnerable’ according to the IUCN (2014), has a wide
geographical distribution on the Australian mainland, and is known to be sensitive to fireregimes. In the northern parts of their distribution, quokkas require mature but not senescent
riparian vegetation (5 to 12 yrs since fire) for diurnal refuge, they utilise recently burnt
vegetation for feeding, and infrequent fire is considered necessary to regenerate senescing
habitat (Christensen and Kimber 1975; Hayward et al. 2005a, 2007). In the southern parts of
their distribution quokkas use a diverse range of ecotypes outside of the riparian systems (Bain
et al. 2015) and favour habitats with complex vegetation structure, low densities of woody
79
debris and fine scale habitat patchiness (Bain et al. 2015). These habitat preferences can be
maintained or significantly altered by the fire regime. We hypothesized that fire regimes that
maintain or promote vegetation structure, result in patchiness of burnt and unburnt vegetation
and reduce woody debris on the forest floor would result in more rapid recolonisation of habitat
by quokkas.
Using the quokka as a model species, this study aims to investigate the current application of
fire for biodiversity in the southern forests of Western Australia, with particular emphasis on the
factors driving recolonisation of areas post fire, the spatial arrangement and refuge value of
unburnt vegetation and identification of fire prediction parameters that may help to guide fire
management for biodiversity conservation.
5.3
Methods
Study area
This study was carried out in the forests between Manjimup and Denmark in south-western
Australia (Figure 5.1). Vegetation cover in this region is relatively continuous, with tall forests
interspersed with diverse ecotypes such as woodlands, sedge lands, shrub lands, creeks, rivers,
wetlands and granite outcrops (Shepherd 2003). Forests in the region are dominated by jarrah
(Eucalyptus marginata), marri (Corymbia calophylla), karri (Eucalyptus diversicolor), red
tingle (Eucalyptus jacksonii) and yellow tingle (Eucalyptus guilfoyliei), with some species
growing up to 80 m tall. Ecotypes that are occupied by quokkas in this region often have a
sedge-dominated understorey and a complex vegetation structure with up to six layers of
vegetation (Bain et al. 2015).
The region has a Mediterranean-type climate with warm dry summers (November – January)
and mild wet winters (June – August). About 85% of the region supports native vegetation with
65% of the area vested as national park for the purpose of conservation (Kile 2013). Prescribed
fire is used extensively within this landscape to conserve and promote elements of biodiversity
and for fuel reduction to mitigate bushfires and protect human life and private lands (DPaW
2013). Between May 2009 and May 2011, 143,781 ha in the region were subject to prescribed
burns and an additional 34,115 ha burnt under wildfire conditions (DPaW 2013).
To evaluate the responses of quokkas to fire in this landscape, we measured habitat variables,
fire predictor parameters and estimated presence of quokkas in 14 treatment areas and in six
control areas for two years prior to fire and three years following fire. Fourteen areas within the
planned burn area and known to be occupied by quokkas were selected; 13 were burnt as
planned and covered an aggregated area of 50,965 ha (size range 1,114 ha to 9,987 ha), and the
remaining area was burnt in a wildfire before the planned prescribed burn, and covered an area
of 14,800 ha.
80
The treatment areas were selected based on: (i) being occupied by quokkas prior to the fire and
thus known to contain suitable habitat; (ii) being part of the prescribed burning program; (iii)
within relatively flat areas (slopes < 15 degrees); and (iv) within the same rainfall zone (9001200 mm annual average rainfall; Pink 2012). Many of the treatment areas were adjacent to
other treatment areas due to the temporal and spatial nature of the burn planning processes in
this region. This, combined with large recorded movements of quokkas in this region (up to 10
km per night, Chapter 4), meant that complete independence between areas could not be
guaranteed between sample periods.
However, independence within sample periods was
achieved by timing surveys to coincide with periods where dispersal and emigration/
immigration processes were unlikely or minimal (Chapter 4). Six control areas were selected
that were also occupied by quokkas at the beginning of the study and were matched to the
treatment areas in terms of time since last fire, topography and vegetation (Figure 5.1).
Figure 5.1: Location of the study area and study sites. Treatment areas were burnt in prescribed
burns between May 2009 and May 2011; the dark grey area was burnt by a wildfire in
December 2009 before the prescribed burns could be implemented.
81
Factors influencing occupancy and colonisation probability
Within each treatment and control area six, two km non-linear transects were walked to assess
the presence of quokkas using faecal pellets counts as described in Bain et al. (2014). Transects
were positioned at least 200 m from the edge of the treatment area boundary, a minimum of 1.0
km from other transects and targeted all main ecotypes within the area likely to be suitable for
quokkas (Bain et al. 2015). The presence or absence of fresh faecal pellets was recorded every
100 m along each transect. All areas were surveyed twice each autumn to reduce the probability
of failing to detect animals that were present. Data from transects were pooled for each
treatment area to obtain area-level encounter histories for occupancy modelling. Fresh faecal
pellet groups were geo-referenced using a GPS in the field and mapped in QGIS.
For each treatment area, post-fire variables were recorded that were most likely to affect the
habitat characteristics known to affect occupancy of quokkas: vegetation structure, density of
woody debris and proximity to vegetation of different age (Bain et al. 2015). These included:
the proportion of the area unburnt, the number, average size and spatial arrangement of unburnt
patches and the scorch height.
The spatial arrangement of unburnt patches within treatment areas was plotted from a fixed
wing aircraft, verified on the ground and then mapped in QGIS. The proportion of area unburnt
(%) and the number and average size of unburnt patches (ha) patches were calculated using
QGIS tools. The spatial arrangement of unburnt patches was categorized into one of four
categories: (i) none present; (ii) isolated patches separated from other unburnt vegetation by a
distance of > 5 km; (iii) patches separated from other unburnt vegetation by 1-5 km; (iv) two or
more clustered patches within 1 km of each other.
In the month following fire, scorch height, defined as the height of charring on tree trunks (m),
was measured every 100 m along the transects using a clinometer. Data from transects were
pooled and averaged for each treatment area. Prior to analysis, continuous variables were
standardised as z-scores following recommendations in McKenzie et al. (2006).
Additional transect-level data were collected to assess post-fire habitat characteristics associated
with use of habitat by quokkas. Distance to unburnt vegetation and distance to the edge of the
burnt area were calculated in QGIS for each point on transects. The number of unburnt patches
within 1 km and 5 km of each point and the size of the closest unburnt patches were recorded by
overlaying transect points on plots of unburnt patches in QGIS. These data were averaged to
obtain the post-fire habitat values for points where quokkas were present or absent.
82
Fire predictor variables
Fire predictor variables were recorded for each treatment area on the day of ignition and
included: surface moisture content, Soil Dryness Index, Fire Danger Index, the rate of spread,
time since last fire and the size of the fire. The surface moisture content (SMC) is a measure of
the moisture in the top 5-10 mm of leaf litter expressed as a percentage (Sneeuwjagt and Peet
1998) and was measured on the day of burn using a fine fuel moisture meter (Wiltronics
Research Pty Ltd, Victoria). Prescribed burning in the southern forest is usually planned when
SMCs are between 9 and 22% (Sneeuwjagt and Peet 1998).
Soil Dryness Index (SDI) is used to predict the dryness of soils, deep forest litter, logs and
living vegetation based on daily rainfall and an estimate of evapotranspiration derived from
maximum temperature. The index estimates the amount of effective rainfall required to restore
the soil moisture profile to full capacity and ranges from 0 when soils are saturated to 2000
when soils are extremely dry (Mount 1972; Burrows et al. 1987; Finkele et al. 2006). SDI is
calculated daily for a number of sites across the south-west forests (Australian Bureau of
Meteorology 2009-2011). For this study we used the SDI calculations for the Shannon, which
is 5 km west of the study area. The recommended SDI limits for prescribed burning in jarrah
forest are between 700-800 in spring and summer, with a fall by 400 units over more than four
days in autumn (Sneeuwjagt and Peet 1998). In burns that have a significant component of
mature karri or tingle forest, it is common for ignitions to be commenced up to an SDI of 1200
(McCaw pers.com.)
Fire Danger Index (FDI) is the predicted maximum rate of spread of a fire based on surface
moisture content and wind speed. Calculations assume level topography, 60% crown cover and
five years of leaf litter accumulation (Sneeuwjagt and Peet 1998) and are calculated daily for
jarrah and karri forest types throughout the fire season by the local DPaW office. An FDI range
is prescribed for each planned burn, which takes into account local variation in topography,
crown cover and leaf litter accumulation. Burns are planned for when the calculated FDI is
within the prescribed FDI range.
The rate of spread (ROS) is the forward rate of movement of the head fire, expressed in metres
per hour. This parameter is calculated in the field by measuring the distance travelled by a fire
in 15 minutes and multiplying this by four. A mild prescribed burn in standard forest fuels
typically has a rate of spread of between 5 and 50 m/h (W. Bailye pers. comm.). Rates of spread
were documented daily by the field officer in charge and averaged for the duration of the burn.
Time since last fire (TSF) is the inter-fire period for a particular area expressed in years. This
parameter was calculated from digital fire history records maintained by DPaW. The size of the
fire (FSz) was calculated in hectares from a map of each treatment area in QGIS.
83
Analysis of data
The multi-season occupancy model (MacKenzie et al. 2006) was used to estimate the detection
probability (p), occupancy rate (ψ) and colonization probability (γ) using Program MARK
(White and Burnham 1999).
This model assumes that habitats are closed to changes in
occupancy within a season, but allows for colonization and local extinction between seasons.
Temporally replicated transects were treated as occasions.
Three sets of models were developed: one that separated spatial variation, temporal variation,
and fire effects in the analysis by focusing on two covariates: treatment (control vs treatment)
and time (before vs. after the fire). Once we had confirmed greater levels of support for models
that estimate a fire effect, we developed two sets of models to identify the combination of post
fire habitat variables and fire predictor variables that best described occupancy and colonisation
parameters respectively. We used Akaike's Information Criterion with a small sample size
correction (AICc) for model selection and considered models with delta AICc values <2 to have
strong support, with preference being given to the most parsimonious model (Quinn and
Keough 2002). Akaike weights were calculated for each model to provide an indication of the
relative likelihood of the model (Burnham and Anderson 2002). Parametric bootstrap and
Pearson Chi-Square goodness-of-fit tests were used to assess the fit of the models to our data
(MacKenzie and Bailey 2004).
To assess patterns in the use of unburnt patches as refuge, analysis of variance was used to
evaluate the differences between areas within the treatment areas where quokkas were present
following fire and areas where they were not detected. Occupancy was used as the dependent
variable and this was considered acceptable given the consistently high estimates of detection
probability generated from the multi-season models (Table 5.1) and the low likelihood of false
absences.
84
5.4
Results
Factors influencing occupancy and colonisation probability
The probability of detection and the rate at which quokkas recolonised areas in this study were a
function of the interaction between treatment (control vs treatment) and time (pre vs post burn)
(Table 5.1, model set 1). Parameter estimates from the strongest model (model weight of 0.78)
indicate that the probability of colonisation was highest prior to fire and three years post fire and
that detection probability remained relatively constant, with a slight decrease in detection in the
first year following fire (Figure 5.2).
The probability of colonisation remained constant
throughout the study in the control areas and the detection probability followed the same pattern
as in the treatment areas, with a slight decrease in detection in the third year of the study (the
first year following fire) (Figure 5.2).
The strongest model (model weight of 0.83) described scorch height, the proportion of area
unburnt and the size of unburnt pockets as having the strongest influence on colonisation of
post-fire habitat by quokkas (Table 5.1, model set 2).
The recolonisation of post fire
environments by quokkas was most rapid for areas with scorch heights of less than ten metres
(mean 3.4 m ± SE 0.54), where more than 20% of the area was unburnt (mean 36.9% ± SE
3.55), and unburnt pockets were larger than 36 ha (mean 143.0 ha ± SE 23.94). These areas
were recolonised by quokkas within 12 months (mean 0.4 yrs ± SE 0.11), with some areas
occupied immediately following fire (Figure 5.3). Four areas were burnt with moderate
intensity, characterized by higher scorch heights (mean 12.6 m ± SE 0.61), a lower proportion
of area unburnt (mean 20.0% ± SE 1.71), and smaller unburnt pockets (mean 20.1 ha ± SE
2.14). Quokkas were detected in these areas within an average of 2.4 yrs (± SE 0.07; Figure
5.3). One treatment area was burnt with high intensity within a wildfire area and post fire
conditions included high scorch heights (mean 27.1 m ± SE 1.4) and no unburnt pockets.
Quokkas had still not been detected within this area at the end of this study, four years
following the fire event.
85
Refuge value of unburnt vegetation
A total of 87% of unburnt patches occurred in association with creek lines, swamps or granite
outcrops. The distance to unburnt vegetation within the fire boundary, the size of the closest
unburnt patch and the number of unburnt patches within 1 km had the strongest influence on the
activity patterns of quokkas following fire. In particular, for the first year post fire, all quokkas
were detected within 230 m (mean 68 m ± SE 10.54) of unburnt vegetation, were associated
with unburnt patches that were larger than 36 ha (mean 175.7 ha ± SE 28.49) and were within 1
km of at least two unburnt patches (mean 3.38 ± SE 0.19). For areas that were mildly burnt,
unburnt patches were less important in the second and third years post-fire as shown by greater
movements away from these refuges as the surrounding vegetation recovered. All quokkas
detected in the third year post-fire were still within 1 km of unburnt vegetation (mean 319.4 m ±
SE 25.29).
The fire predictor variables best able to predict colonisation probability included surface
moisture content, Soil Dryness Index and rate of spread (Table 5.1, model set 3). Models
containing these predictors had a combined model weight of 1.0, and the top model had a
weight of 0.79 (Table 5.1). Areas that at the time of fire had a surface moisture content greater
than 11%, a Soil Dryness Index lower than 800 and a rate of spread less than 40 m/h were
recolonised by quokkas within 12 months. Quokkas took more than four years to recolonise
areas with a surface moisture content of 8%, a Soil Dryness Index of 922 and an average rate of
spread of 130 m/h.
The surface moisture content, average rate of spread and Soil Dryness Index were all closely
correlated with the three post fire habitat variables found to be most influential on the time taken
by quokkas to recolonize areas following fire (Table 5.2).
Scorch height decreased with
increasing surface moisture and increased with increasing rate of spread and Soil Dryness Index.
The proportion of area unburnt increased with increasing surface moisture and decreased with
increasing rate of spread and Soil Dryness Index. The average size of unburnt patches increased
with increasing surface moisture and decreased with increasing rate of spread and Soil Dryness
Index (Table 5.2).
86
Table 5.1:
Comparison of fitted models for quokka occupancy, colonisation and detection
probability using multi season occupancy models
Top models presented. ∆ AICc is the difference in AICc from the top ranked model; w is the
model weight; k is the number of parameters in the model; -2L is twice the negative loglikelihood value; ψ is the estimated occupancy in the first year of the study; γ is the estimated
colonisation probability; p is the detection probability. Post fire habitat covariates modelled
include: treatment (TR), time (t), the proportion of area unburnt (UB), the average size of
unburnt patches (SP), average scorch height (SH), the number of unburnt patches (NP) and their
spatial arrangement or clustering (CP). Fire predictor covariates modelled include: Soil Dryness
Index (SDI), surface moisture content (SMC), Fire Danger Index (FDI), time since last burnt
(TSF), fire size (FSz) and rate of spread (ROS).
Model
∆ AICc
w
k
-2L
Model set #1 Interactive effects of fire treatment (control vs treatment) and time (pre vs post
burn) on detection probability
ψ(.),p(t, TR), γ(t, TR)
0.00
0.83
6
41.94
ψ(.), p(t), γ(t)
3.48
0.15
4
53.21
ψ(.), p(t),γ(t)
7.09
0.02
5
53.21
ψ(.),p(.), γ(.)
30.65
0.00
3
83.55
Model set #2 Combinations of fire effect covariates and time (number of years post fire) that
best describe recolonisation parameters
ψ(.), p(t), γ (SH, UB, SP, t)
0.00
0.73
7
6.67
ψ(.), p(t), γ (UB, SP, NP, t)
2.71
0.19
7
9.38
ψ(.), p(t), γ (SH,UB, NP, t)
4.55
0.07
7
11.22
ψ(.), p(t), γ (SP, NP, CP, t)
8.86
0.01
7
15.53
ψ(.), p(t), γ ( NP, CP, t)
14.12
0.00
6
29.46
ψ(.), p(t), γ (SH, UB, SP,NP, CP, t)
29.12
0.00
9
5.46
Model set #3 Combinations of fire predictor variables and time (number of years post fire) that
best describe recolonisation parameters
ψ(.), p(t), γ(SDI, SMC, ROS, t)
0.00
0.79
7
5.54
ψ(.), p(t), γ(SMC, ROS, t)
2.67
0.21
6
16.88
ψ(.), p(t), γ(SDI, FDI, TSF, SE, FSz, t)
22.15
0.00
8
15.56
ψ(.), p(t), γ(SMC, FDI, TSF, SE, FSz, t)
40.65
0.00
9
15.86
ψ(.), p(t), γ(SDI, SMC, FDI, TSF, SE, FSz ROS, t)
121.33
0.00
11
5.54
87
1.0
p
0.8
0.6
γ
0.4
0.2
0.0
Pre-fire
Year 1
(Post-fire)
Year 2
(Post-fire)
Year 3
(Post-fire)
Figure 5.2: Estimated detection probability (p) and colonisation rate (γ) for quokkas in
treatment areas (solid line) and control areas (dashed line) in the southern forests of Western
Australia.
Table 5.2:
Pearson’s correlations (R²) between significant fire predictor variables and postfire habitat conditions that have the highest influence on recolonisation of
habitat by quokkas.
Surface moisture
Average rate of
Soil Dryness
content (%)
spread (m/h)
Index
Scorch height
-0.78
0.82
0.60
Proportion of area unburnt (ha)
0.74
-0.80
-0.64
Average size of unburnt patches(ha)
0.60
-0.60
-0.48
88
R² = 0.88
Time (yrs) to recolonise post- fire
5
4
3
2
1
0
0
10
20
30
Fire Intensity (Scorch Height, m)
R² = 0.95
4
3
2
1
0
0
20
40
60
Proportion of area unburnt (%)
a)
5
5
b)
R² = 0.90
4
3
2
1
0
0
100
200
300
Average size of unburnt patches
(ha)
c)
Figure 5.3: The effect of fire response variables on the time taken by quokkas to recolonise
areas in the southern forest post fire: a) fire intensity (scorch height) with a linear trend line
fitted; b) proportion of area unburnt with a polynomial trend line fitted; c) size of unburnt
patches with a polynomial trend line fitted.
89
5.5
Discussion
Factors affecting recolonisation of habitat following fire
In support of our hypothesis, recolonisation of fire-affected areas by quokkas in the southern
forest was strongly affected by the intensity and patchiness of the fire, the presence of unburnt
patches that presumably provided refuge during and after the fire event, the structure of
vegetation remaining within burnt areas and associated implications for recovery of a complex
vegetation structure. In particular, scorch height, the proportion of area unburnt and the size of
unburnt patches were identified as measurable post burn habitat variables with the greatest
influence on recolonisation of areas by quokkas in this region. Quokkas recolonized post fire
habitats within 12 months where scorch heights were less than ten metres, more than 20% of the
area remained unburnt and unburnt patches were larger than 36 ha.
These findings are
consistent with known habitat requirements of quokkas in this region, including vegetation with
a complex structure (at least three layers) and proximity to areas of alternative vegetation age
(Bain et al. 2015).
Variation in fire intensity is an important source of heterogeneity in fire-affected ecosystems,
particularly in relation to the structural complexity of vegetation and the retention of unburnt
patches (Bradstock et al. 2010; Leonard et al. 2014; Robinson et al. 2014). Scorch height can
provide a measure of the average flame height during the fire, which can be an indirect measure
of fire intensity and the effect of fire on vertical vegetation structure (Burrows 1997; Gould et
al. 2007; Clarke 2008). In this scenario, midstorey and overstorey species often survive the fire
relatively intact, allowing them to continue to contribute to the ongoing structural diversity of
the vegetation (Burrows 1997; Gould et al. 2007; Clarke 2008). In addition, the edaphic
barriers to fire that are created by rock, large logs, discontinuous vegetation and moisture are
most effective under these conditions, resulting in a greater patchiness of burnt and unburnt
vegetation (Clarke 2002; Penman et al. 2007; Leonard et al. 2014). Unburnt patches in this
study were invariably associated with rocky outcrops or ecotypes with higher levels of moisture
such as creek lines and swamps.
In contrast, intense fire behaviour and associated high scorch heights can increase the loss of
vertical vegetation structure and increase the time taken for vegetation to return to a complex
structure. More intense fires can also overcome edaphic barriers and result in homogeneous fire
outcomes over broad spatial scales (Price and Bradstock 2012; Burrows 2013; O’Donnell et al.
2014). This was the case within the wildfire-affected area in this study, where intense fire
behaviour resulted in scorch heights greater than 27 m that affected all vegetation layers,
removed structural complexity and resulted in all components of the landscape burning,
including riparian systems and rock outcrops. Quokkas had yet to recolonise this area at the end
of the study, four years following the fire. The collapse of dead midstorey vegetation in the
third year following fire may have contributed to ongoing unsuitable habitat conditions for
90
quokkas in this area, due to rapid accumulation of woody debris on the forest floor (Bain et al.
2015).
Refuge value of unburnt vegetation
Unburnt patches clearly act as a refuge for animals to escape fire, persist in and subsequently
recolonise the post-fire landscape, or to assist with post-fire recolonisation from adjoining
unburnt areas. In this study, quokkas were recorded in unburnt patches well inside the fireaffected area within a month following fire, suggesting that at least some individuals remained
in situ during the fire, taking refuge within these patches. The size of these patches and their
spatial arrangement relative to other patches or other unburnt vegetation were important
determinants of whether they were occupied following fire. Quokkas occupied unburnt patches
greater than 36 ha and within 1 km of at least two other unburnt patches. For the first year
following fire, 100% of detections were within 230 m of unburnt vegetation. Quokkas were
detected further away from unburnt patches in the second and third years following fire,
however, all detections remained within 1 km of unburnt patches, which suggests that the
patches were still central to movement patterns as the surrounding vegetation recovered. All
unburnt patches occupied by quokkas were associated with creek lines, gullies or granite
outcrops.
The relative importance of unburnt patches as refuge areas is likely to depend on the degree to
which they provide resources that are otherwise unavailable within the surrounding burnt area
(Penman et al. 2007; Robinson et al. 2014). The preferential selection by quokkas for unburnt
patches greater than 36 ha and within 1 km of at least two other unburnt patches may be related
to predator pressure. Increased distance from unburnt vegetation has previously been associated
with an increased risk of predation for many small and medium sized herbivores (Banks 2001;
Le Mar and McArthur 2005; Styger et al. 2011). Therefore herbivores tend to forage in
proximity to a safe refuge where predators are present, but utilise the burnt areas to take
advantage of the greater abundance of forage following fire (Southwell and Jarman 1987;
Blumstein et al. 2002; Hayward 2002; Archibald and Bond 2004).
In addition, the home range size of quokkas in this region is large, with core areas of females
overlapping substantially but no overlap of core areas for males (Bain et al. 2015b). Group
fidelity among females and the lack of core range overlap between males are likely to influence
the suitability of unburnt patches in terms of their size and ability to meet the space
requirements of individuals. This is also likely to affect the viability of subpopulations of
quokkas persisting in unburnt patches while surrounding burnt areas are recovering.
91
The spatial arrangement of refuge patches and their context in the broader fire mosaic is
important for maintaining populations over time (Watson et al. 2012; Robinson et al. 2014).
The ability of quokkas and other species to either disperse through the burnt landscape or use
refuge patches as ‘stepping stones’ is important in maintaining habitat connectivity and
movement patterns at a landscape scale (Templeton et al. 2011; Driscoll et al. 2012). The
effective provision of refuge patches within burnt areas is particularly important where
adjoining areas are planned to be burnt within three years, which is often the case in this region
where recently burnt areas offer a low risk boundary for the implementation of future burns.
Cumulative impacts of multiple large areas burnt adjacent to each other and with limited
temporal separation are potentially significant where effective refuge areas have not been
achieved.
Prescribing for effective refuge within planned burns
The regime and the manner in which prescribed fires are undertaken is important in determining
the likely availability of temporary refuge, suitable habitat, and long-term persistence of species
in and surrounding fire-affected areas. This is of particular interest given the recent political
pressure to increase prescribed burning to protect human life and assets, which has arisen from a
number of large and intense wildfires that have resulted in loss of lives and properties (e.g.
Keelty 2012; Price and Bradstock 2012). While there is a genuine need to protect human life
and property from severe wildfires, the simultaneous achievement of ecological outcomes is
possible and should not be overlooked.
Prescribing to retain 20% or more of an area unburnt is a common occurrence in the southern
forest (e.g. Bain 2009) and this study has confirmed moisture parameters and field rates of
spread that can achieve this. Soil moisture contents greater than 11%, Soil Dryness Indices
lower than 800 and field rates of spread less than 40 m/h contributed to mild fire behaviour in
forested areas of this region that maximised the retention of vegetation structure, promoted
retention of unburnt vegetation due to active edpahic barriers, and resulted in rapid
recolonisation of burnt areas by quokkas.
The spatial arrangement of the 20% unburnt
vegetation is important from both an ecological perspective and from a burn security
perspective. Clustered but spatially separated patches are likely to provide the best refuge value
for fauna, as long as they meet the minimum size requirements, 36 ha for quokkas, and
contribute to habitat connectivity within and between burnt areas. From the perspective of burn
security, spatially separated pockets are also less likely to re-ignite under hot dry weather
conditions that might allow fire to escape from the secured boundary into adjacent unburnt
vegetation.
92
The proactive management of fire to protect taxa and ecosystems sensitive to fire regimes is of
increasing importance in the context of climate change, given the expected increase in large
scale and intense wildfires and the increasing challenges associated with protecting mesic
habitats and their ecological function. Current evidence indicates prescribed burns can create
conditions conducive to the persistence and recolonisation of habitat by quokkas in the short
term, but it also clear that some fire regimes can adversely affect quokkas in the short to
medium term and may well have long term implications for habitat connectivity and
metapopulation function. This is particularly the case where the spatial and temporal scale of
impact is such that animals can no longer safely move between suitable habitat patches.
The quokka has been used as the model species in this study because it is often used as a focal
species for the management of other taxa that occupy a similar ecological niche and are likely to
be sensitive to fire regime (Burrows et al. 2004). For example, other threatened and endemic
small-medium sized mammals and reptiles that co-occur with the quokka such as the western
ringtail possum (Pseudocheirus occidentalis), chuditch (Dasyurus geoffroii), brush tailed
phascogale (Phascogale tapoatafa), western brush wallaby (Macropus irma), quenda (Isoodon
obesulus fusciventer), water rat (Hydromys chrysogaster), western false pipistrelle (Falsistrellus
mackenziei),
short
nosed
snake
(Elapognathus
minor)
and
square
nosed
snake
(Rhinoplocephalus bicolor) are expected to benefit from a fire regime that suits the quokka.
Although the exact spatial arrangement of refugia may vary, the importance of moisture
differentials, maximizing the effectiveness of edaphic barriers to fire, retention of unburnt
vegetation associated with mesic and rocky habitats and retention of vegetation structure are all
likely to be common requirements from a fire regime for these species. Wayne et al. (2006)
provide support that these requirements from a fire regime are important for the western ringtail
possum.
In addition, short-range endemic taxa and Gondwanan relics that are restricted to mesic
vegetation within the karri/ tingle forests and riparian systems are also expected to benefit from
a regime that focuses on the retention of unburnt vegetation associated with these ecotypes.
Examples include the tingle spider (Bertmainius tingle), roly poly millipede (Cynotelopus
notabilis), the sunset frog (Spicospina flammocaerulea) and the Nornalup Frog (Geocrinia
lutea); all of which have a limited capacity for dispersal, slow growth and low fecundity, which
equate to poor capacity to recover from frequent disturbance events (Harvey 2002). The
protection of mesic habitat from intense wildfire is likely to be critical to the ongoing
persistence of these taxa in this region, given their historical protection from such fire regimes
by moisture differentials that are becoming increasingly compromised by drying climatic
conditions (IPCC 2007).
93
This study has improved our understanding of the factors driving recolonisation of burnt areas
by quokkas in the southern forests of Western Australia, the spatial arrangement and refuge
value of unburnt vegetation and the fire parameters that best predict quokka recolonisation of
fire-affected habitats in this region. This information will enable land managers to incorporate
these ecological requirements into fire planning where quokkas and similar species are present.
Use of such explicit ecological criteria during fire planning and implementation may help to
build ecosystem resilience and provide protection against the increase in homogenising
wildfires that are predicted under a drying climate scenario (Flannigan et al. 2009; Williams et
al. 2009; Wilson et al. 2010).
5.6
Acknowledgements
This research was supported by DPaW, Western Australia and was undertaken as a part of the
delivery of the Warren Region Nature Conservation Service Plan. The study was conducted
under DPAW Animal Ethics Committee approval DECAEC 24/2009, UWA Animal Ethics
thank the DPaW Frankland District fire team for their assistance with prescribed burning and
access to departmental datasets and calculations. We also thank Dr Lachlan McCaw and Dr
Matthew Williams for their critical review of our manuscript.
94
Chapter 6:
General Discussion
This chapter summarises the information presented in preceding chapters and concludes the
thesis. In particular, it presents some of the key findings of this thesis and discusses the
potential implications that these may have for the future conservation of threatened species both
in the southern forests of Western Australia and more broadly.
95
6.1
Insights into the ecology of quokkas in the southern forests
This study has generated a number of ecological insights, some that are specific to the quokka
and others that have broader relevance for the conservation of threatened species. Those that
are specific to the quokka are summarised below:
Habitat preferences (see chapter 3)
Occupancy of habitat by quokkas in the southern forest was strongly linked to a complex
vegetation structure (minimum of three layers), low densities of woody debris and habitat
patchiness (between 0 and 450 m to an alternative vegetation age). These habitat features
provide quokkas with insulation and protection from the elements, provide for dietary
requirements and refuge from predators, and allow safe passage through their habitat.
Quokkas occupied habitat in the presence of foxes and feral cats however, both species have
been observed to prey on immature quokkas in the southern forest (Bain et al. 2015). Marsupial
young are particularly vulnerable to these predators at the time of pouch emergence, when their
movements are unsteady and they are easily separated from their mother. Recruitment and
population demography are likely to be affected by introduced predators, as would be the
survivorship of animals moving between habitat patches.
Quokkas in this study also occupied habitat in the presence of feral pigs however, sustained
feral pig activity has been linked to modified vegetation structure in this region and in other
parts of Australia (Choquenot et al. 1996; Hone 2002; Burnside et al. 2012; Adams 2014).
Digging, foraging and wallowing behaviours of feral pigs have been observed to remove
seedlings and lignotubers, to disturb the soil profiles and substantially alter the density and
structure of the vegetation, particularly in areas that have been recently burnt (Choquenot et al.
1996; Hone 2002; Burnside et al. 2012). As a result, sustained feral pig activity is likely to
reduce the suitability of habitat for quokkas as a result of altered vegetation structure.
Despite the contiguous and extensive natural vegetation system of the southern forest, quokkas
occupied discrete habitat patches separated by distances of up to 40 km. The spatial availability
and connectivity of suitable habitat in this landscape is influenced by fire regime and other
disturbances that alter the habitat features identified in this study as being important for
quokkas. These anthropogenic processes may have already contributed to the loss of quokka
subpopulations from some areas and the apparent fragmentation of subpopulations challenges
our perceptions of fragmentation in natural ecosystems.
96
Spatial ecology (see chapter 4)
The average home range size of 71.4 ha for quokkas in the southern forest is much larger than
previously reported home range sizes of 0.13 ha (Kitchener 1973) and 7 ha (Holsworth 1967) on
Rottnest Island and 6.39 ha in the northern jarrah forest (Hayward et al. 2004). The variation
between regions can be explained by differences in the sampling design between studies, low
resource availability and greater availability and connectivity of suitable habitat.
Conditions such as dense canopy and midstorey layers that increase shading and reduce
temperature have been shown to limit the rate of nutrient uptake and growth of plants on the
forest floor (Coops et al. 1998; Schuerings et al. 2014). These conditions are likely to limit
resource availability and contribute to larger home ranges here. In addition, movement and
spatial use patterns of quokkas in the southern forest are less constrained by habitat availability
and connectivity than quokkas in the northern jarrah forest. This allows individuals to range
more freely to meet their energy demands than they can in other parts of their range where
habitats are more fragmented and confined.
Seasonal variation in the size of home range was strongly linked to the availability and
palatability of food and the availability of water. Quokkas had larger home ranges in summer
and autumn when water was less abundant and when tannins were expected to be more
important for diet selection (Stolter et al. 2013). At these times, many plants are less palatable
and indigestible, resulting in animals foraging wider and requiring higher biomass to meet their
nutritional needs. Home ranges were smaller in winter and spring when fresh growth of plants
was abundant and plants were generally more palatable, which makes diet mixing over a smaller
area more possible (Stolter et al. 2013).
Quokkas in this study travelled distances of up to 10 km within a 12 hour period, which is
substantially greater than distances previously documented for this species in other parts of its
distribution. They also utilised a diverse range of ecotypes well outside of the riparian systems,
which are considered their primary habitat in other parts of their distribution on the mainland
(Hayward et al. 2004).
Quokkas in this study spent only 40% of their time in riparian
vegetation and this is possibly due to the structural complexity and density of other vegetation
types contiguous with the riparian systems that also provide habitat conditions suitable for
quokkas in this region (Bain et al. 2015).
Female quokkas demonstrated a high shelter and group fidelity in comparison to males, with
spatial overlap of up to 100% common for small groups of females and shared diurnal shelters,
to which they returned each morning. Shelter and group fidelity seemed to be related to the
need to protect the young, as the non-reproductive female in this study showed no such fidelity
and had home range and movement patterns more similar to those of a male quokka.
97
Core area use for males was almost exclusive (mean 8% overlap), they were never located in a
shared shelter and did not return to the same shelter each morning. Exclusive use of core areas
may reflect reproductive strategies such as competition for mates. On Rottnest Island, mating
partnerships are developed and maintained over many years with most females having one male
as a primary consort. The male actively defends the female against other males and would be
expected to maintain a core area free of males for this purpose (Moss 1995; McLean et al.
2009). This behaviour seems to be occurring in the southern forest, given the emigration of two
large adult males coincided with breeding season, both males had significantly overlapping core
areas with other males, and both presented with injuries consistent with male aggression,
including torn ears and patches of missing fur just prior to emigrating.
The movement of three adult quokkas out of the system and six animals (five adults, one young
at foot) into the system during this study highlights that movement is occurring between
occupied habitats in the southern forests. These animals have the ability to traverse substantial
distances between occupied habitats and in all cases, individuals used riparian vegetation to
reach nearby habitat patches. The mobility of quokkas in this landscape suggests that the
metapopulation is likely to be functional, where preferred habitat and riparian corridors are
managed effectively. It also provides an indication of what might have been the case in the
north prior to excessive threats from introduced predators and changes to habitat due to
processes such as clearing, fire, timber harvesting and dieback.
Linear riparian systems have traditionally been protected to protect quokka habitat in the
southern forest. This is important for maintaining habitat connectivity, but is unlikely to meet
broader habitat and spatial requirements for the species, given the large home range, large
nocturnal distances travelled and variety of habitat used outside of the riparian systems in this
region.
98
Fire response (see chapter 5)
Retention of vertical vegetation structure, more than 20% of the area unburnt, and multiple
unburnt pockets larger than 36 ha and within 1 km of at least two other pockets were found to
be important for rapid recolonisation of fire-affected areas by quokkas. These fire outcomes
were associated with mild fire behaviour that was achieved from ignition under conditions
where soil moisture contents were greater than 11%, Soil Dryness Indices were lower than 800
and field rates of spread were less than 50 m/h.
In contrast, intense homogenising wildfire resulted in a complete loss of vertical vegetation
structure and a lack of unburnt pockets, which contributed to these areas remaining uncolonised
by quokkas for the duration of the study. These fire outcomes were associated with field
conditions where surface moisture content was less than 8%, Soil Dryness Index was greater
than 922 and an average field rates of spread were greater than 130 m/h. In addition, the
collapse of dead midstorey vegetation following fire may have contributed to ongoing
unsuitable habitat conditions for quokkas in this area, due to rapid accumulation of woody
debris on the forest floor (Bain et al. 2015).
Clustered but spatially separated pockets were found to be important for quokkas and
contributed to habitat connectivity within and between burnt areas. The preferential selection
by quokkas for unburnt pockets greater than 36 ha and within 1 km of at least two other unburnt
pockets may be related to predator pressure, given the increased risk of predation for many
small and medium sized mammals following fire (Banks 2001; Le Mar and McArthur 2005;
Styger et al. 2011). The tendency of individuals to forage in proximity to a safe refuge has been
previously demonstrated to be of importance to quokkas and other species where predators are
present (e.g. Blumstein et al. 2002; Hayward 2002; Bain et al. 2015).
The spatial arrangement of refuge patches and their context in the broader fire mosaic is also
important for maintaining populations over time (Watson et al. 2012; Robinson et al. 2014).
The ability of quokkas and other species to either disperse through the burnt landscape or use
refuge patches as ‘stepping stones’ is important in maintaining habitat connectivity and
movement patterns at a landscape scale (Templeton et al. 2011; Driscoll et al. 2012). This is of
particular importance where adjoining areas are planned to be burnt within three years, which is
often the case in this region.
99
6.2
Insights of broader conservation relevance
In this study, the failure of a HSM between two ecologically diverse regions was due to the
selected predictors, and in particular those relating to fire and predator baiting regimes, which
were not transferrable between regions due to spatial differences in ecotypes, the application of
fire, the behaviour of fire and approaches to predator baiting. This suggests that predictions by
HSMs at a regional scale should only be transferred across adjacent regions where predictor
variables have been selected that are relevant to both regions and where these have been verified
against local knowledge of the ecology of the study species.
In addition, models that explicitly account for imperfect detection are expected to produce more
accurate estimates of habitat relationships and improve predictive performance (Rota et al.
2011). This is particularly relevant for difficult-to-detect species.
Another outcome of this study is the realization that our perception of fragmentation in natural
ecosystems needs to be adjusted to include consideration of processes such as feral animals and
fire. While these processes may be less obvious than physical fragmentation through land
clearing, they have the potential to contribute to the segregation of suitable habitat patches for
threatened species and the creation of intervening distances that are too great for successful
dispersal, immigration and recolonisation processes.
The proactive management of fire to manage habitat connectivity, and to protect fire regime
sensitive taxa and ecosystems is of increasing importance in the context of climate change,
given the expected increase in large-scale and intense wildfires and the increasing challenges
associated with protecting mesic habitats and their ecological function. The regime and the
manner in which prescribed fires are undertaken is important in determining the likely
availability of temporary refuge, suitable habitat, and long-term persistence of species in and
surrounding fire-affected areas.
Burning with moisture differentials, maximizing the
effectiveness of edaphic barriers to fire, retention of unburnt vegetation associated with mesic
and rocky habitats and retention of vegetation structure were found to be important elements of
fire regimes for threatened, endemic and relictual taxa in this region.
100
6.3
The quokka as a focal species in the southern forests
The focal species approach has been successfully used to define conservation goals, focus threat
management efforts, manage habitat connectivity and metapopulation dynamics, identify effects
of habitat modification, and to assist with reserve selection (e.g. Margules and Pressey 2000;
Watson et al. 2001; McCarthy et al. 2006; Nicholson et al. 2013). The concept is based on the
assumption that conservation initiatives that incorporate the habitat needs and threat response
requirements of viable populations of these focal species will meet the needs of sympatric
species with less-demanding requirements (Lambeck 1997; Noss et al. 1999). However, three
common criticisms include concerns about the ability of a suite of focal species to legitimately
act as surrogates for other elements of the biota, the lack of sufficient data for decision making
processes, and the absence of conceptually valid methods for the selection of focal species in
most landscapes (Lindenmayer et al. 2002; Roberge and Angelstam 2004; Branton and
Richardson 2011).
Most authors agree that a robust method for systematically selecting focal species can increase
the effectiveness of conservation programs (Margules and Pressey 2000; Lindenmayer et al.
2002; Roberge and Angelstam 2004; Branton and Richardson 2011). Many criteria have been
proposed for identifying focal species, most of which are associated with rarity, population
status, biological and ecological traits, vulnerability of habitat or populations to threatening
processes, and knowledge available for decision making processes (e.g. Beazley and Cardinal
2004; Roberge and Angelstam 2004; Branton and Richardson 2011).
Qualities that make the quokka a potentially suitable focal species include:
•
Threatened status, which gives its management a higher level of political and social
importance.
•
Large geographic range with one of the most abundant and genetically important
populations occurring in this region.
•
Wide-ranging spatial requirements; which help to define minimum patch size, inter
patch distances and define corridor configurations.
•
Specific habitat requirements in relation to structural complexity, patch mosaics and
woody debris accumulation, which define the structural attributes of the habitat patches
and connecting vegetation.
•
Documented vulnerability to threatening processes such as inappropriate fire regime
and introduced predators and good knowledge of population response to these
processes.
•
Consistent occurrence in habitats that also contain a wide range of other threatened,
endemic and/or relictual taxa that are likely to benefit from similar management
regimes.
101
•
Availability of relevant ecological and biological knowledge that enables informed
decision making processes, although paradoxically this study has shown that ecological
knowledge was not as complete as previously thought.
The quokka as a focal species is likely to be a useful starting point for biodiversity conservation
in the southern forests of Western Australia and given the availability of ecologically relevant
information, there is an opportunity here for application of explicit recommendations rather than
general principles. However, inter-species differences relevant to ecological requirements and
threat response can be marked. A set of focal species with spatial, compositional and functional
requirements that encompass those of all other co-occurring species is likely to be more
effective in meeting the requirements of the ecosystems in which they occur (Lambeck 1997;
Lindenmayer et al. 2014). To ensure the contemporary suitability of all selected focal species, a
selection process that is logical, defendable and adaptable to changing knowledge is essential.
102
6.4
Management implications
The findings reported in this thesis can contribute to more proactive and effective management
of the quokka and its habitat both for conservation of the species and as a focal species for the
conservation of co-occurring taxa that occupy a similar ecological niche.
In particular, the following management actions are recommended:
Survey methodology
Application of the faecal pellet survey method across the southern forests of Western
Australia to generate an accurate estimate of abundance and occupancy rate for quokkas in
this region.
Use of the faecal pellet survey method can generate estimates of abundance and occupancy rates
for quokkas in the southern forest. Use of this survey technique can allow effective spatial and
temporal comparison of abundance and occupancy to assess population status and trends and
can contribute to the identification of critical habitat and habitat connectivity issues that require
management.
This could significantly improve the capacity of land managers to make
important conservation decisions in this region. This method can be used to survey beyond the
sites involved in this study to develop a more comprehensive understanding of the distribution
and abundance of this species within the region, which is important to inform management of
priority areas.
For longer term monitoring of trends, a survey approach that actively accounts for variation in
detection probability is recommended. Given the high detectability of quokkas from faecal
pellets, occupancy modelling has the potential to become a valuable tool for monitoring quokka
population trends in the southern forest. Collection of data for occupancy models can be readily
undertaken in conjunction with relative abundance survey work.
103
Habitat management
Development of management strategies for the identification and management of quokka
habitat that proactively maximize habitat connectivity and metapopulation function.
Management of habitat for quokkas needs to consider not only protection of occupied habitat,
but also the spatial and temporal availability of potential habitat and landscape level
connectivity of habitat patches. In many areas, quokkas are already explicitly considered when
planning fire management programs and control activities for introduced predators. The risks of
local extinction of quokkas in this region will be significantly reduced if future management of
this species takes into account the size and connectivity of habitat patches, promotion of a
multiple layered vegetation structure, promotion of a mosaic of vegetation ages and active
regeneration of habitats where senescing vegetation is likely to compromise vegetation structure
and contribute to dense layers of woody debris.
In addition, current approaches to protection of quokka habitat that focus on the linear
protection of riparian systems need to be revised to take into account the broader habitat and
spatial requirements for the species, given the large home range, large nocturnal distances
travelled and variety of habitat used outside of the riparian systems in this region.
104
Fire management
Proactive management of fire to protect high priority areas from intense and homogenising
wildfires and application of prescribed fire that promotes habitat conditions suitable for
quokkas.
The proactive management of fire to protect taxa and ecosystems sensitive to fire regimes is of
increasing importance in the context of climate change, given the expected increase in largescale and intense wildfires and the increasing challenges associated with protecting mesic
habitats and their ecological function. Current evidence indicates prescribed burns can create
conditions conducive to the persistence and recolonisation of habitat by quokkas in the short
term, but it is also clear that some fire regimes can adversely affect quokkas and may have long
term implications for habitat connectivity and metapopulation function. This is particularly the
case where the spatial and temporal scale of impact is such that animals can no longer safely
move between suitable habitat patches. Such outcomes are often associated with large-scale,
intense and homogenising fires or multiple adjacent fires where adequate refuge and connecting
vegetation has not been retained.
Post fire conditions important for quokkas in the southern forest include retention of vertical
vegetation structure, more than 20% of the area unburnt and multiple clustered but spatially
separated pockets, which act as refugia within burnt areas. This kind of fire regime also
minimises the rapid accumulation of woody debris following fire. Fire parameters required to
achieve this are provided in this study and should be incorporated into future fire management
guidelines for this species. In addition, master burn planning processes that plan the application
of prescribed fire in this region need to actively consider the habitat and refuge requirements of
quokkas and other focal species at a landscape scale, both spatially and temporally.
105
Management of feral pigs
Management of feral pigs in high priority areas, particularly following fire or other
disturbances that result in an open understorey vegetation and exposed soil substrate.
The proactive management of feral pigs in areas following disturbance such as fire are
important for the retention of a complex vegetation structure in these habitats. Sustained feral
pig activity has been observed to remove seedlings and lignotubers, to disturb the soil profiles
and substantially alter the density and structure of the vegetation in the southern forest,
particularly in areas that have been recently burnt. In the absence of feral pig control programs,
habitat modification occurring as a result of pig activity is likely to affect the availability and
connectivity of habitat for quokkas and other species in this region, and may affect
metapopulation function. In some cases, areas affected by feral pigs in this region are showing
no sign of recovery more than six years after their removal.
Management of introduced predators
Management of introduced predators in high priority areas to improve quokka recruitment
and survivorship.
While all populations of quokka are currently persisting in the presence of introduced predators,
both foxes and feral cats have been observed to prey on immature quokkas in the southern forest
and recruitment levels have been low in sites where quokkas occur in the presence of these
predators. While occupancy of habitat may be unaffected by introduced predators, recruitment
and population demography are likely to be affected, as would be the survivorship of animals
moving between habitat patches.
106
Climate change
Integrated management of threatening processes in areas of high priority to build ecosystem
resilience to the expected effects of climate change.
Species such as the quokka that have a restricted geographic range and a high degree of habitat
specialisation are at particular risk of extinction as a result of environmental changes that are
predicted under climate change scenarios (Isaac 2009; Gibson et al. 2010). These species are
expected to experience further range contractions and the resulting smaller, highly fragmented
and isolated populations are expected to be more susceptible to losses in genetic diversity and to
interactive threats such as fire and introduced animals (Pouliquen-Young and Newman 2000;
Isaac 2009; Williams et al. 2008; Gibson et al. 2010). Gibson et al. (2010) predict that under
the most extreme climate change scenario, almost all of the range of the quokka will be lost by
2070. Their prediction is based on an assumption that no dispersal between habitat patches is
occurring. This is not the case for the population in the southern forest and this population is
subsequently likely to become even more important for the future of the species in terms of its
abundance and genetic diversity. In addition, the higher stability of habitats occupied by
quokkas in this region may be somewhat buffered from the effects of climate change and so
provide refuge for species that are habitat specialists.
The dispersal of animals between habitat patches and the protection of refugia from interactive
threatening processes that could reduce the resilience of species to the effects of climate change
are critical.
Conservation strategies likely to be most effective include: identifying and
protecting refugia where quokkas are expected to persist over time (Gibson et al. 2010);
maximising the availability and connectivity of habitat patches; and mitigating interactive
threatening processes such as predation by introduced predators and inappropriate fire regimes,
which may undermine the resilience of species and ecological communities to climate change.
107
Recovery planning
This section summarises the existing recovery actions and tasks identified in the recovery plan for the quokka (DEC 2013) and provides an indication of
how new knowledge available from this study may contribute to these (Table 6.1). Recovery actions and tasks have been copied directly from the
recovery plan.
Table 6.1:
Contribution of this study to recovery actions and tasks identified in the quokka recovery plan (DEC 2013). Recovery actions and tasks
have been copied directly from this plan.
Recovery Action
Tasks that this project contributes to
Contribution of this study to recovery actions and tasks
Undertake survey and
Continue with a collaborative approach to research to develop
The faecal pellet survey method developed as a result of this
regular monitoring
a suitable and consistent assessment and monitoring
study can generate estimates of abundance and occupancy
methodology of populations across their geographic range,
rates for quokkas in the southern forest. Use of this survey
and different land tenures.
technique can allow effective spatial and temporal comparison
Develop protocols for monitoring and particularly for
detecting new quokka occurrences.
of abundance and occupancy to assess population trends in
this region and can contribute to the identification of critical
habitat and habitat connectivity issues that require
Establish population trends and occupancy rates.
management.
108
Table 6.1:
Continued
Recovery Action
Undertake research
Identify and implement the most effective predator
As a part of this study, research and monitoring has been undertaken in
and monitoring to
control techniques at priority sites where predator
an effort to improve our understanding of quokkas in the southern forest
improve
control will have the greatest conservation outcome
in relation to their habitat requirements, movement patterns and
understanding of
for quokka, and use findings to adapt existing
response to fire.
threats and
management practices to achieve better management
effectiveness of
outcomes.
mitigation programs
This study has produced a habitat suitability model for quokkas in the
southern forest and has highlighted issues with extrapolation of
Investigate the effects of activities associated with
information from the northern jarrah forest, which has previously been
clearing close to quokkas, and make
common practise for management of this species. The HSM may also
recommendations on acceptable activities to
contribute to an improved approach to the planning of clearing
eliminate or mitigate any detrimental effects
operations and habitat modelling for climate change mitigation for this
Commence habitat modelling studies to address
species across its geographical range.
climate change issues and identify the role of
This study has identified introduced predators as an issue requiring
translocation in this process.
ongoing management for the species in the southern forest, and this is
consistent with tasks identified in the recovery plan. This study has also
identified fire regime and habitat modification by feral pigs as
threatening processes that have the potential to significantly affect
quality and connectivity of habitat for quokkas in this region. While
both of these processes are identified in the management requirements
(RA4), neither has been identified as requiring research action within
the existing recovery plan for this species. In my opinion, this is a
significant deficiency of the plan.
109
Table 6.1:
Continued
Recovery Action
Protect and manage
Identify and implement feral pig control at priority
This study has identified predation by the introduced fox and cat and
key populations and
sites where control will have the greatest
habitat modification by feral pigs as issues requiring proactive
habitats.
conservation outcome for quokka.
management for the species in the southern forest. This is consistent
Implement the DEC Quokka Fire Management
Guideline No S5, and monitor success.
with tasks identified in the recovery plan. The management of feral pigs
is most critical following fire or other disturbances that expose the
understorey and soil substrate.
Identify areas of potential quokka habitat and apply
management regimes to maintain these habitats for
quokkas. Identify suitable areas of remnant
vegetation that can be protected or enhanced through
revegetation and hydrological management.
This study has also identified inappropriate fire regime as a threatening
process. Explicit ecological criteria and fire parameters required to
achieve these are provided in this study and should be incorporated into
future fire management guidelines for this species. In addition, master
burn planning processes that plan the application of prescribed fire in
Continue introduced predator control programs on
DEC land, and where possible, coordinate baiting
programs across different land tenures to maximise
this region need to actively consider the habitat and refuge requirements
of quokkas and other focal species at a landscape scale, both spatially
and temporally.
effectiveness
HSMs developed in this study can contribute to processes that identify
areas of potential habitat for quokkas to enable effective management of
these areas.
110
Table 6.1:
Continued
Recovery Action
Undertake
Evaluate the need for translocations and captive
This study has confirmed that quokkas in the southern forest are moving
translocations and
breeding to maintain the metapopulations.
between habitat patches in what seems to be a functioning
captive breeding as
required
Implement any translocations of quokkas into the
wild under the guidance of the DEC translocation
policy (CALM 1995), with due regard of the need to
maintain the genetic integrity of metapopulations,
and in particular the maintenance of genetic integrity
between subpopulations on the islands and the
mainland.
metapopulation. Genetic studies undertaken during this project, that
have not been reported on as a part of this project, also confirm that this
is occurring and that the southern forest population has the highest
genetic diversity of all known populations (P. Spencer unpublished
data). This information may contribute to decision making processes
relevant to the translocation of quokkas and the maintenance of genetic
integrity within the southern forest population.
Undertake education
Provide educational interpretive information on
Information in this study can be used for education and communication
and communication
quokkas and environmental management to
activities and provides a more complete understanding of quokka
activities
encourage conservation behaviour at sites where
ecology and the management issues relevant to this species across its
tourism and quokkas coincide.
distribution than what was previously available.
111
Future research
Some suggested priorities for research that are likely to directly contribute to essential
knowledge and operational improvements for the conservation and management of quokkas and
their habitat include:
•
Develop a GIS based habitat suitability model for quokkas that identifies habitat that is
currently important for quokkas, potential habitat and important connections that can be
used in planning processes such as master burn planning.
•
Develop a focal species approach to management of threatened fauna within the
southern forest that identifies a suite of species with spatial, compositional and
functional requirements that encompass those of all other co-occurring species and
follows a selection process that is logical, defendable and adaptable to changing
knowledge.
•
Incorporate population dynamics (e.g. dispersal), biotic interactions (e.g. predation) and
physiological tolerances (e.g. energy and water balance) into climate change modelling
for quokkas as per recommendations made in Gibson et al. (2010).
•
Develop management response options for species and ecological communities in the
southern forest that are at risk from climate change, including the quokka.
•
Investigate the effects of large, intense and homogenising wildfire events on population
trends for quokkas and co-occurring species on the mainland.
•
Develop an understanding of the influence of climate change on fire regimes and
ecosystem response in the southern forest as a basis for developing management
response options.
•
Quantify the interactive effects of fire and feral pigs on habitat quality for quokkas and
other threatened species in the southern forest and the factors driving feral pig foraging
activities in these areas.
•
Develop techniques for the effective control of feral pigs to enable protection of priority
habitats.
•
Identify the most effective predator control techniques at priority sites where predator
control will have the greatest conservation outcome for quokkas, as per recovery plan
recommendations (DEC 2013)
•
Develop techniques for the effective control of feral cats in the southern forest to enable
protection of priority habitats.
112
Research priorities continued:
•
Develop guidelines for habitat protection that take into account the broader habitat and
spatial requirements for quokkas in the southern forest and consider ecotypes outside of
the riparian system. Such guidelines should be applied during activities such as timber
harvesting and land use planning.
•
Investigate the historical and contemporary impacts of Phytophthora cinnamomi on
quokka distribution and habitat quality, as per recovery plan recommendations (DEC
2013).
•
Investigate the effects of hydrological change and in particular drying trends on mesic
habitats that are important for quokkas and other species that are likely to contract to
these refugia.
•
Develop an understanding of the ecology and genetic diversity of the population(s) of
quokkas in the south coast reserve system north and east of Albany and how this relates
to the quokkas in the southern forest and northern jarrah forest.
113
114
References
Acevedo, P., Ward, A. I., Real, R., and Smith, G. C. (2010). Assessing biogeographical
relationships of ecologically related species using favourability functions: a case study on
British deer. Diversity and Distributions 16, 515-528.
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In
‘International Symposium on Information Theory’. 2nd edn. (Eds B. N. Petran and F. Csaaki.)
pp. 267–281. (Akadeemiai Kiadi: Budapest, Hungary.)
Alacs, E. A., Spencer, P. B. S., Tores, P. J., and Krauss, S. L. (2011). Population genetic
structure of island and mainland populations of the quokka, Setonix brachyurus
(Macropodidae): A comparison of AFLP and microsatellite markers. Conservation Genetics 12,
297-309.
Allen, L., Engeman, R., and Krupa, H. (1996). Evaluation of three relative abundance indices
for assessing dingo populations. Wildlife Research 23, 197-206.
Anderson, D. R. (2001). The need to get the basics right in wildlife field studies. Wildlife
Society Bulletin 29, 1294-1297.
Anderson, D. R., Burnham, K. P., White, K. P., and Otis, D. L. (1983). Density estimation of
small mammal populations using a trapping web and distance sampling methods. Ecology 64,
674-680.
Archibald, S., and Bond, W. J. (2004). Grazer movements: spatial and temporal responses to
burning in a tall-grass African savanna. International Journal of Wildland Fire 13, 377-85.
Australian Bureau of Meteorology (2009-2011). Ground moisture calculations: SDI summary
for Shannon. www.bom.gov.au/ reguser/byuser/bomw0065/sdisummary.html. Accessed 20092011.
Bailey, L. L., Hines, J. E., Nichols, J. D., and MacKenzie, D. I. (2007). Sampling design tradeoffs in occupancy studies with imperfect detection: examples and software. Ecological
Applications 17, 281-290.
Bailey, L. L., Simons, T. R., and Pollock, K. H. (2004). Estimating detection probability
parameters for plethodon salamanders using the robust capture–recapture design. The Journal of
Wildlife Management 68, 1-13.
Bain, K. (2009). Prescribed fire plan for Denmark F101B, Spring 2009. Department of
Environment and Conservation, Walpole.
115
Bain, K., Wayne, A., and Bencini, R. (2014). Overcoming the challenges of measuring the
abundance of a cryptic macropod: is a qualitative approach good enough? Wildlife Research 41,
84-93.
Bain, K., Wayne, A., and Bencini, R. (2015). Risks in extrapolating habitat preferences over the
geographical range of threatened taxa: a case study of the quokka (Setonix brachyurus) in the
southern forests of Western Australia. Wildlife Research. 42, 334-343
Balogh, S., Gentle, M., and Brown, S. (2001). Outfox the fox: landholder surveys assisting with
implementing best practice fox baiting. In ‘Proceedings: 12th Australasian Vertebrate Pest
Control Conference.’ (Department of Natural Resources and Environment: Victoria.)
Banks, P. B. (2001). Predation-sensitive grouping and habitat use by eastern-grey kangaroos: a
field experiment. Animal Behaviour 61, 1013–21.
Barker, S. (1961). Studies on marsupial nutrition. III. The copper-molybdenum-inorganic
suplphate interaction in the Rottnest quokka, Setonix brachyurus (Quoy and Gaimard).
Australian Journal of Biological Sciences 14, 646-658.
Beasley, J. C., Devault, T. L., Retamosa, M. I., and Rhodes, Jr. O. E. (2007). A hierarchical
analysis of habitat selection by raccoons in northern Indiana.
The Journal of Wildlife
Management 71, 1125-1133.
Beazley, K., and Cardinal, N. (2004). A systematic approach for selecting focal species for
conservation in the forests of Nova Scotia and Maine Environmental Conservation 31, 91-101.
Blumstein, D. T., Daniel, J. C., Schnell., M. R., Ardron. J. G., and Evans, C. S. (2002)
Anitpredator behaviour of red-necked pademelons: a factor contributing to species survival?
Animal Conservation 5, 325-331.
Boer, M. M., Sadler, R. J., Wittkuhn, R. S., McCaw, L., and Grierson, P. F. (2009). Long-term
impacts of prescribed burning on regional extent and incidence of wildfires - Evidence from 50
years of active fire management in SW Australian forests. Forest Ecology and Management
259, 132-142.
Bolen, E. G., and Robinson, W. L. (1999). ‘Wildlife Ecology and Management.’ 4th edn.
(Prentice Hall: New Jersey.)
Borg, H., Stoneman, G. L., and Ward, C. G. (1988). The effect of logging and regeneration on
groundwater, streamflow and stream salinity in the southern forest of Western Australia.
Journal of Hydrology 99, 270-253.
Bradstock, R. A., Hammill, K. A., Collins, L., and Price, O. (2010). Effects of weather, fuel and
terrain on fire severity in topographically diverse landscapes of south-eastern Australia.
Landscape Ecology 25, 607-619.
116
Branton, M. and Richardson, J. S. (2011). Assessing the value of the umbrella-species concept
for conservation planning with meta-analysis. Conservation Biology 25, 9-20.
Brown, S.; Clarke, R.; and Clarke, M. (2009). Fire is a key element in the landscape-scale
habitat requirements and global population status of a threatened bird: The Mallee Emu-wren
(Stipiturus mallee). Biological Conservation 142, 432-445.
Buckland, S. T., Anderson, D. R., Burnham, K. P., and Laake, J. L. (1993). ‘Distance Sampling:
Estimating Abundance of Biological Populations.’ (Chapman and Hall: London.)
Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., and Thomas,
L. (2001). ‘Introduction to Distance Sampling – Estimating Abundance of Biological
Populations.’ (Oxford University Press: Oxford.)
Burbidge, A. A. and McKenzie, N. L. (1989). Patterns in the modern decline of Western
Australia’s vertebrate fauna: causes and conservation implications. Biological Conservation 50,
143-198.
Burbidge, A. A., McKenzie, N. L., Brennan, K. E. C., Woinarski, J. C. Z., Dickman, C. R.,
Baynes, A., Gordon, G., Menkhorst, P. W., and Robinson, A. C. (2009). Conservation status
and biogeography of Australia’s terrestrial mammals. Australian Journal of Zoology 56, 411422.
Burbidge, A. A., Start, A. N., Morris, K. D., and Armstrong, R. (1995). ‘Western ShieldBringing Back our Wildlife.’ (Department of Conservation and Land Management: Perth).
Burnham, K. P., and Anderson, D. R. (2002). ‘Model selection and multimodel inference: a
practical information-theoretic approach. Second edition’. (Springer: New York.)
Burnside, R., Bain, K., and Sawyer, M. (2012). Quantification of the impact of feral pigs (Sus
scrofa) on the sunset frog (Spicospina flammacaerulea), Reedia (Reedia spathacea), Walpole
burrowing crayfish (Engaewa walpolea) and quokka (Setonix brachyurus). Report for South
Coast Natural Resource Management. Department of Parks and Wildlife, Walpole.
Burrows, N. (1997). Predicting canopy scorch height in jarrah forests. CALMScience 2, 267–
274.
Burrows, N. D. (1987). The soil dryness index for use in fire control in the South-West of
Western Australia. Technical report no. 17. Department of Conservation and Land
Management, Perth.
Burrows, N. D. (2008). Linking fire ecology and fire management in south-west Australian
forest landscapes. Forest Ecology and Management 255, 2394–2406.
117
Burrows, N. D., Liddelow, G., and Sneeuwjagt, R. (2004). Adaptive Fire Management: Interim
Guidelines for Forest Populations of Quokka (Setonix brachyurus).
Fire Management
Guideline. Department Conservation and Land Management, Perth.
Burrows, N., and McCaw, L. (2013). Prescribed burning in southwestern Australian forests.
Frontiers in Ecology and the Environment 11, e25–e34.
Burrows, N. D. (2013). Fire dependency of a rock-outcrop plant Calothamnus rupestris
(Myrtaceae) and implications for managing fire in south-western Australian forests. Australian
Journal of Botany 61, 81-88.
Burrows, N., and Wardell-Johnson, G., (2003). Interactions of fire and plants in south-western
Australia’s forested ecosystems: a review. In: ‘Fire in South-Western Australian Ecosystems:
Impacts and Management’. (Eds. N. Burrows and I. Abbott.) pp. 225–268. (Backhuys: Leiden.)
Caro, T. M. (1999). Conservation monitoring: estimating mammal densities in woodland
habitats. Animal Conservation 2, 305-315.
Cary, G. J., Bradstock, R. A., Gill, A. M., and Williams, R. J. (2012). Global change and fire
regimes in Australia. In ‘Flammable Australia: Fire Regimes, Biodiversity and Ecosystems in a
Changing World’. (Eds R. A. Bradstock, R. J. Williams, A. M. Gill) pp. 149–170. (CSIRO
Publishing: Victoria).
Caughley, G. (1977). ‘Analysis of Vertebrate Populations.’ (John Wiley and Sons: Brisbane.)
Christensen, P. E. S., and Kimber, P. C. (1975). Effect of prescribed burning on the flora and
fauna of south-western Australian forests. Proceedings of the Ecological Society of Australia 9,
85-106.
Clarke, M. F. (2008). Catering for the needs of fauna in fire management: science or just
wishful thinking? Wildlife Research 35, 385–394.
Clarke, P. (2002). Habitat islands in fire-prone vegetation: do landscape features influence
community composition? Journal of Biogeography 29, 677–684.
Conroy, M. J. (1996). Designing a study to assess mammalian diversity. In ‘Measuring and
Monitoring Biological Diversity: Standard Methods for Mammals’. (Eds D. E. Wilson, E. R.
Cole, J. D. Nichols, R. Rudran and M. S. Foster.) pp. 177–234. (Smithsonian Institution Press:
Washington, DC.)
Constible, J. M.; Gregory, P. T.; and Larsen, K. W. (2010). The pitfalls of extrapolation in
conservation: movements and habitat use of a threatened toad are different in the boreal forest.
Animal Conservation 13, 43-52
118
Coops, N. C.; Waring, R. H.; and Landsberg, J. J. (1998). Assessing forest productivity in
Australia and New Zealand using a physiologically-based model driven with averaged monthly
weather data and satellite-derived estimates of canopy photosynthetic capacity. Forest Ecology
and Management 104, 113-127.
CSIRO (2001). ‘Climate Change: projections for Australia. CSIRO Climate Impact Group.’
(CSIRO Publications: Victoria.)
D and L Software Pty Ltd Australia. Ozi Explorer version 3.95.5. http://www.oziexplorer.com.
de Tores, P., Burbidge, A., Morris, K., and Friend, T. (2008). Setonix brachyurus. In ‘IUCN
2013.
IUCN
Red
List
of
Threatened
Species.
Version
2013.2’.
Available
at
http://www.iucnredlist.org.
Defos du Rau, P., Barbraud, C., and Mondain-Monval, J. (2003). Estimating breeding
population size of the red-crested pochard (Netta rufina) in the Camargue (southern France),
taking into account detection probability implications for conservation. Animal Conservation 6,
379-385.
Department of Environment and Conservation (2012). Fuel age records 1953-2012 for
conservation estate managed by the Department of Environment and Conservation, Western
Australia. Unpublished GIS data.
Department of Environment and Conservation (2013). ‘Quokka (Setonix brachyurus) Recovery
Plan. Western Australian Wildlife Management Program No. 56’. (Department of Environment
and Conservation: Western Australia).
Department of Parks and Wildlife (2014a). Wildlife Conservation (Rare Flora) Notice 2014.
Government Gazette, Western Australia.
Department of Parks and Wildlife (2014b). Wildlife Conservation (Specially Protected Fauna)
Notice 2014. Government Gazette, Western Australia.
Department of the Environment (2015). Environment Protection Biodiversity Conservation Act
1999
(EPBC
Act)
List
of
Threatened
Fauna.
http://www.environment.gov.au/cgi-
bin/sprat/public/publicthreatenedlist.pl?wanted=fauna. Accessed March 2015.
Di Stefano, J., McCarthy, M. A., York, A., Duff, T. J., Slingo, J. and Christie, F. (2013).
Defining vegetation age class distributions for multispecies conservation in fire-prone
landscapes. Biological Conservation 166, 111–117.
Driscoll, D. A., Felton, A., Gibbons, P., Felton, A. M., Munro, N. T., and Lindenmayer, D. B.
(2012). Priorities in policy and management when existing biodiversity stressors interact with
climate-change. Climatic Change 111, 533–557.
119
Driscoll, D. A., Lindenmayer, D. B., Bennett, A. F., Bode, M., Bradstock, R. A., Cary, G. J.,
Clarke, M. F., Dexter, N., Fensham, R., Friend, G., Gill, M., James, S., Kay, G., Keith, D. A.,
MacGregor, C., Possingham, H. P., Russel-Smith, J., Salt, D., Watson, J. E. M., Williams, D.,
and York, A. (2010). Resolving conflicts in fire management using decision theory: assetprotection versus biodiversity conservation. Conservation Letters 3, 215–223.
Elith, J., and Leathwick, J. (2009). ‘Conservation prioritisation using species distribution
modelling. Spatial conservation prioritization: quantitative methods and computational tools.’
(Oxford University Press: Oxford, UK.)
Environmental Systems Research Institute (2006). ArcGIS Desktop: Release 9.1. Redlands,
California.
Finkele, K., Mills, G. A., Beard, G., and Jones, D. A. (2006). National daily gridded soil
moisture deficit and drought factors for use in prediction of Forest Fire Danger Index in
Australia.
BMRC Research Report No. 119, Bureau of Meteorology Research Centre,
Melbourne.
Flannigan, M. D., Krawchuk, M. A., de Groot, W. J., Wotton, B. M., and Gowman, L. M.
(2009). Implications of changing climate for global wildland fire. International Journal of
Wildland Fire 18, 483–507.
Friend, G. R., and Wayne, A. F. (2003). Relationships between mammals and fire in southwest
Western Australian ecosystems: what we know and what we need to know. In: ‘Fire in
Ecosystems of South-West Western Australia: Impacts and Management.’ (Eds I. Abbott, N.
Burrows) pp. 363-380. (Backhuys Publishers: Leiden).
Gardner, S. N., and Mangel, M. (1996). Mark–resight population estimation with imperfect
observations. Ecology 77, 880-884.
Gibson, L., McNeill, A., de Tores, P., Wayne, A., and Yates, C. (2010). Will future climate
change threaten a range restricted endemic species, the quokka (Setonix brachyurus), in south
west Australia? Biological Conservation 143, 2453-2461.
Glen, A. S., and Dickman, C. R. (2003). Monitoring bait removal in vertebrate pest control: a
comparison using track identification and remote photography. Wildlife Research 30, 29-33.
Gould, J. S., McCaw, W. L., Cheney, N. P., Ellis, P. F., Knight, I. K., and Sullivan, A. L.
(2007). ‘Project Vesta - Fire in Dry Eucalypt Forest: Fuel Structure, Fuel Dynamics and Fire
Behaviour.’ (Ensis-CSIRO: Canberra and Department of Environment and Conservation:
Perth).
Guisan, A., and Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology.
Ecological Modelling 135, 147-186.
120
Guisan, A., Broennimann, O., Engler, R., Vust, M., Yoccoz, N. G., Lehmann, A., and
Zimmermann, N. E. (2006). Using niche-based models to improve the sampling of rare species.
Conservation Biology 20, 501-511.
Guyer, C.; Johnson, V. M.; and Hermann, S. M. (2012). Effects of population density on
patterns of movement and behaviour of gopher tortoises (Gopherus polyphemus).
Herpetological Monographs 26, 122-134.
Hamilton, N., and Rolfe, J. (2011). ‘Assessment of introduced predator presence within the
Perup Sanctuary, WA.’ (Department of Environment and Conservation: Woodvale, WA.)
Hamm, K. A., Diller, L. V., and Kitchen, D. W. (2002). Comparison of indices to estimate
abundance of dusky-footed woodrats. Wildlife Society Bulletin 30, 64-70.
Harvey M. S. (2002). Short-range endemism amongst the Australian fauna: some examples
from non-marine environments. Invertebrate Systematics 16, 555–570.
Haslem, A., Avitabile, S. C., Taylor, R. S., Kelly, L. T., Watson, S. J., Nimmo, D. G., Kenny,
S. A., Callister, K. E., Spence-Bailey, L. M., Bennett, A. F. and Clarke, M. F. (2012). Timesince-fire and inter-fire interval influence hollow availability for fauna in a fire-prone system.
Biological Conservation 152, 212–221.
Hayward, M. W. (2002). The ecology of the quokka (Setonix brachyurus) (Macropodidae:
Marsupialia) in the northern jarrah forest of Australia, PhD dissertation, University of New
South Wales, Sydney.
Hayward, M. W. (2005). Diet of the quokka (Setonix brachyurus) (Macropodidae: Marsupialia)
in the northern jarrah forest of Western Australia. Wildlife Research 32, 15-22.
Hayward, M. W., de Tores, P. J., and Banks, P. B. (2005a) Habitat use of the quokka Setonix
brachyurus (Macropodidae: Marsupialia) in the northern jarrah forest of Australia. Journal of
Mammalogy 86, 683-688.
Hayward, M. W., de Tores, P. J., Augee, M. L., Fox, B. J., and Banks, P. B. (2004). Home range
and movements of the quokka Setonix brachyurus (Macropodidae: Marsupialia), and its impact
on the viability of the metapopulation on the Australian mainland. Journal of Zoology 263, 219228.
Hayward, M. W., de Tores, P. J., Dillon, M. J., and Banks, P. B. (2007). Predicting the
occurrence of the quokka, Setonix brachyurus (Macropodidae: Marsupialia), in Western
Australia’s northern jarrah forest. Wildlife Research 34, 194-199.
Hayward, M. W., de Tores, P. J., Dillon, M. J., and Fox, B. J. (2003). Local population structure
of a naturally occurring metapopulation of the quokka (Setonix brachyurus Macropodidae:
Marsupialia). Biological Conservation, 110, 343-355.
121
Hayward, M. W., de Tores, P. J., Dillon, M. J., Fox, B. J., and Banks, P. B. (2005b). Using
faecal pellet counts along transects to estimate quokka (Setonix brachyurus) population density.
Wildlife Research 32, 503-507.
Heinanen, S., Erola, J., and Von Numers, M. (2012). High resolution species distribution
models of two nesting water bird species: a study of transferability and predictive performance.
Landscape Ecology 27, 545-555.
Holsworth, W. N. (1967). Population dynamics of the quokka, Setonix brachyurus, on the West
End of Rottnest I., Western Australia. I. Habitat and distribution of the quokka. Australian
Journal of Zoology 15, 29-46.
Hone, J. (2002). Feral pigs in Namadgi National Park, Australia: dynamics, impacts and
management. Biological Conservation 105, 231-242.
Hopkins, H. L., and Kennedy, M. L. (2004). An assessment of indices of relative and absolute
abundance for monitoring populations of small mammals. Wildlife Society Bulletin 32, 12891296.
Hopper, S. D. (1992). Patterns of plant diversity at the population and species levels in southwest Australian Mediterranean ecosystems. In ‘Biodiversity of Mediterranean Ecosystems in
Australia.’ (Ed R. J. Hobbs.) pp. 2746. (Surrey Beatty and Sons: Chipping Norton.)
Hopper, S. D., and Gioia, P. (2004). The southwest Australian floristic region: evolution and
conservation of a global hot spot of biodiversity. Annual Review of Ecology and Systematics 35,
623-650.
Horne,
J.
S.,
and
Garton,
E.
O.
(2009).
Animal
Space
Use
1.3.
http://www.cnr.uidaho.edu/populationecology/animal_space_use.
Horwitz, P. (1997). Comparative endemism and richness of the aquatic invertebrate fauna in
peatlands and shrublands of far south-western Australia. Memoirs of the Museum of Victoria 56,
313-321.
Hosmer D. W., and Lemeshow S. (2000). ‘Applied Logistic Regression’. (John Wiley and Sons:
New York.)
Howden, M., Hughes, L., Dunlop, M., Zethoven, I., Hilbert, D., and Chilcott, C. (2003).
Climate Change Impacts on Biodiversity in Australia. Outcomes of a workshop sponsored by
the Biological Diversity Advisory Committee, 1-2 October. Commonwealth of Australia,
Canberra.
Huggett, A. (2007). ‘A review of the focal species approach in Australia.’ (Land and Water
Australia: Canberra).
122
Hughes, L. (2003). Climate change and Australia: Trends, projections and impacts. Austral
Ecology 28, 423-443.
Hynes, E. F., Handasvde, K. A., Shaw, G. and Renfree, M. (2011). The effects of gestagen
implants on the behaviour of free-ranging female koalas. Applied Animal Behaviour Science
134, 209-216.
Intergovernmental Panel on Climate Change (2007). ‘Climate change 2007 - impacts,
adaptation and vulnerability.’ (Cambridge University Press: Cambridge).
Interim Biogeographical Regionalisation for Australia (IBRA) (2004). ‘IBRA Version 6.1 Subregions.’ (Australian Government Department of the Environment and Heritage: Canberra.)
Available at http://www.environment.gov.au.
International Union for the Conservation of Nature (IUCN) (2014). ‘IUCN Red List of
Threatened Species. Version 2014.3.’ Available at http://www.iucnredlist.org.
Isaac, J. L. (2009). Effects of climate change on life history: implications for extinction risk in
mammals. Endangered Species Research 7, 115-123.
Johnson, C. N., and Jarman, P. J. (1987). Macropod studies at Wallaby Creek VI. A validation
of the use of dung-pellet counts for measuring absolute densities of populations of
Macropodids. Australian Wildlife Research 14, 139-145.
Karanth, K. U., and Nichols, J. D. (1998). Estimation of tiger densities in India using
photographic captures and recaptures. Ecology 79, 2852-2862.
Keelty, M. (2012). Report on the Post Incident Analysis of the 2011 Margaret River and
Nannup bushfires. State Emergency Management Committee, Australian Government.
Kendall, W. L. (1999). Robustness of closed capture–recapture methods to violations of the
closure assumption. Ecology 80, 2517-2525.
Kenward, R. E., Walls, S. S., South, A. B., and Casey, N. M. (2008). ‘Ranges 8: for the
analysis of tracking and location data.’ (Anatrack Ltd:Wareham.)
Kile, G. (2013). Review of the Implementation of the Regional Forest Agreement for the SouthWest Forest Region of Western Australia for the Period 1999 – 2009, WA RFA Reviewer’s
Report for the State Government.
Kitchener, D. J. (1973). Notes on home range and movement in two small macropods, the
potoroo (Potorous apicalis) and the quokka (Setonix brachyurus). Mammalia 37, 231–240.
Kitchener, D. J. (1981). Factors influencing selection of shelter by individual quokkas, Setonix
brachyurus (Marsupialia), during hot summer days on Rottnest Island. Australian Journal of
Zoology 29, 875-84.
123
Kitchener, D. J. (1995). Quokka (Setonix brachyurus). In: ‘The Mammals of Australia, Second
Edition.’ (Ed R. Strahan.) pp 401–403. (Australian Museum and Reed New Holland: Sydney.)
Lambeck, R. J. (1997). Focal Species: A Multi-Species Umbrella for Nature Conservation.
Le Mar, K., and McArthur, C. (2005). Comparison of habitat selection by two sympatric
macropods, Thylogale billardierii and Macropus rufogriseus rufogriseus, in a patchy eucalyptforestry environment. Austral Ecology 30, 674–83.
Le Mar, K., Southwell, C., and McArthur, C. (2001). Evaluation of line transect sampling to
estimate nocturnal densities of macropods in open and closed habitats. Wildlife Research 28, 916.
Lee, J. E., White, G. C., Garrott, R. A., Bartmann, R. M., and Alldredge, A. W. (1985).
Accessing accuracy of a radiotelemetry system for estimating animal locations. Journal of
Wildlife Management 49, 658-663.
Lefebvre, L. W., Otis, D. L., and Holler, N. R. (1982). Comparison of open and closed models
for cotton rat population estimates. The Journal of Wildlife Management 46, 156–163.
Leonard, S. W. J., Bennett, A. F. and Clarke, M. F. (2014). Determinants of the occurrence of
unburnt forest patches: Potential biotic refuges within a large, intense wildfire in south-eastern
Australia. Forest Ecology and Management 314, 85–93.
Lindenmayer, D. B., Cunningham, R. B., and McCarthy, M. A. (1999). The conservation of
arboreal marsupials in the montane ash forests of the central highlands of Victoria, south-eastern
Australia. VIII. Landscape analysis of the occurrence of arboreal marsupials. Biological
Conservation 89, 83-92.
Lindenmayer, D. B., Lane, P. W., Westgate, M. J., Crane, M., Michael, D., Okada, S., and
Barton, P. S. (2014). An Empirical Assessment of the Focal Species Hypothesis. Conservation
Biology 28, 1594-1603.
Lindenmayer, D. B., Manning, A. D., Smith, P. L., Possingham, H. P., Fischer, J., Oliver, I., and
McCarthy, M. A. (2002). The Focal-Species Approach and Landscape Restoration: a Critique.
Lindenmayer, D. B., McCarthy, M. A., Parris, K. M. and Pope, M. L. (2000). Habitat
fragmentation, landscape context, and mammalian assemblages in southeastern Australia.
Journal of Mammalogy 81, 787-797.
Macfarlane, C., Coote, M., White, D. A., and Adams, M. A. (2000). Photographic exposure
affects indirect estimation of leaf area in plantations of Eucalyptus globulus Labill. Agricultural
and Forest Meteorology 100, 155-168.
124
Macfarlane, C., Hoffman, M., Eamus, D., Kerp, N., Higginson, S., McMurtrie, R., and Adams,
M. (2007). Estimation of leaf area index in eucalypt forest using digital photography.
Agricultural and Forest Meteorology 143, 176-188.
MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Royle, J. A., and Langtimm, C.
A. (2002). Estimating site occupancy rates when detection probabilities are less than one.
Ecology 83, 2248-2255.
MacKenzie, D. I., Nichols, J. D., Royle, J. A., Pollock, K. H., Bailey, L. L., and Hines, J. E.
(2006). ‘Inferring patterns and dynamics of species occurrence.’ (Elsevier Press: London.)
MacKenzie, D., and Bailey, L. (2004). Assessing fit of site occupancy models. Journal of
Agricultural, Biological and Ecological Statistics 9, 300-318.
Main, A. R and Main, B. Y. (1991). Report on the Southern Forest Region of Western Australia
re: Gondwanic Fauna Elements of Southern Forest Region - Western Australia. Australian
Heritage Commission, Canberra.
Main, A. R., Shield, J. W., and Waring, H. (1959). Recent studies on marsupial ecology. In
‘Biogeography and Ecology in Australia.’ (Eds A. Keast, R. L. Crocker and C. S. Christian.) pp.
315-331. (Dr W. Junk: The Hague.)
Margules, C. R., and Pressey, R. L. (2000). Systematic conservation planning. Nature 405, 243253.
Mattiske, E. M. and Havel, J. J. (1998). ‘Regional Forest Agreement vegetation complexes.’
(Department of Conservation and Land Management: Perth.)
Mawson, P., and Orell, P. (2001). Sand pads: using tracks to monitor fauna. Wildlife Notes 10,
1-2.
Maxwell, S., Burbidge, A. A., and Morris, K. (1996). ‘The 1996 Action Plan for Australian
Marsupials
and
Monotremes.’
(Wildlife
Australia,
Canberra.)
Available
at
http://www.environment.gov.au/resource/action-plan-australian-marsupials-and-monotremes.
McCarthy, M. A., Thompson, C. J., and Williams, N. S. G. (2006). Logic for designing nature
reserves for multiple species. American Naturalist 167, 717-727.
JG McIvor, J. G. (1981). Seasonal changes in the growth, dry matter distribution and herbage
quality of three native grasses in northern Queensland Australian Journal of Experimental
Agriculture and Animal Husbandry 21, 600-609.
McKelvey, K. S., and Pearson, D. E. (2001). Population estimation with sparse data: the role of
estimators versus indices revisited. Canadian Journal of Zoology 79, 1754-1765.
125
McLean, I. G., Cameron, E. Z., Linklater, W. L., Schmitt, N. T., and Pulskamp, K. S. M.
(2009). Partnerships in the social system of a small macropod marsupial, the quokka (Setonix
brachyurus). Behaviour 146, 89-112.
Miller, S., Bencini, R. and Hartmann, P. E. (2009). Composition of the milk of the quokka,
Setonix brachyurus. Australian Journal of Zoology 57, 11-21.
Miller, S., Bencini, R. and Hartmann, P. E. (2010). Consumption of milk by quokka (Setonix
brachyurus) young. Australian Journal of Zoology 58, 121-126.
Moilanen, A., and Hanski, I. (1998). Metapopulation dynamics: effects of habitat quality and
landscape structure. Ecology 79, 2503-2515
Montgomery, W. I. (1987). The application of capture–mark–recapture methods to the
enumeration of small mammal populations. Symposium of the Zoological Society of London 58,
25-57.
Morris, D. W., and MacEachern, J. T. (2010). Active density-dependent habitat selection in a
controlled population of small mammals. Ecology 91, 3131-3137.
Moss, G. L. (1995). Home range, grouping patterns and the mating system of the red kangaroo
(Macropus rufus) in the arid zone of New South Wales, Australia. Ph.D. Thesis, University of
New South Wales, Sydney.
Mount, A. B. (1972). The Derivation and Testing of a Soil Dryness Index using Run-off Data.
Tasmanian Forestry Commission Bulletin 4, 1-31.
Murray, J. V., Choy, S. L., McAlpine, C. A., Possingham, H. P., and Goldizen, A. W. (2011).
Evaluating model transferability for a threatened species to adjacent areas: Implications for
rock-wallaby conservation. Austral Ecology 36, 76-89.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B., and Kent, J. (2000).
Biodiversity hotspots for conservation priorities. Nature 403, 853-858.
Nichols ,D. G. (1971). Daily and seasonal movements of the quokka, Setonix brachyurus
(Marsupialia), on Rottnest Island. Australian Journal of Zoology 19, 215-26.
Nichols, J. D., and Pollock, K. H. (1983). Estimation methodology in contemporary small
mammal capture–recapture studies. Journal of Mammalogy 64, 253-260.
Nicholson, E., Lindenmayer, D. B., Frank, K., and Possingham, H. P. (2013). Testing the focal
species approach to making conservation decisions for species persistence Diversity and
Distributions 19, 530-540
Noss, R. F., and Cooperrider, A. Y. (1994). ‘Saving nature’s legacy: protecting and restoring
biodiversity.’ (Island Press: California.)
126
Noss, R., Dinerstein, E., Gilbert, B., Gilpin, M., Miller, B., Terborgh, J., and Trombulak, S.
(1999). Core areas: where nature reigns. In ‘Continental Conservation: Scientific Foundations of
Regional Reserve Networks.’ (Eds M. Soul´e and J. Terborgh.) pp. 99-128. (Island Press:
Washington DC.)
O’Donnell, A. J., Boer, M. M., McCaw, W. L., and Grierson, P. F. (2014). Scale-dependent
thresholds in the dominant controls of wildfire size in semi-arid southwest Australia. Ecosphere
5, 1-13.
Olson, K. A., Fuller, T. K., Schaller, G. B., Odonkhuu, D., and Murray, M. G. (2005).
Estimating the population density of Mongolian gazelles Procapra gutturosa by driving longdistance transects. Oryx 39, 164–169.
Osborne, P. E., Alonso, J. C., Bryant, R. G. (2001). Modelling landscape-scale habitat use using
GIS and remote sensing: a case study with great bustards. Journal of Applied Ecology 38, 458471.
Pannell, D. J., Roberts, A. M., Park, G., Alexander, J., Curatolo, A., and Marsh, S. (2012).
Integrated assessment of public investment in land-use change to protect environmental assets
in Australia. Land Use Policy 29, 377-387.
Penman, T. D., Christie, F. J., Andersen, A. N., Bradstock, R. A., Cary, G. J., Henderson, M. K.
Price, O., Tran, C., WardleI, G. M., Williams, R. J., and York, A. (2011). Prescribed burning:
how can it work to conserve the things we value? International Journal of Wildland Fire 20,
721–733.
Penman, T. D., Kavanagh, R. P., Binns, D. L., and Melick, D. R. (2007). Patchiness of
prescribed burns in dry sclerophyll eucalypt forests in south-eastern Australia. Forest Ecology
and Management 252, 24-32.
Pink, B. (2012). ‘Year Book Australia 2012, Number 92.’ (Australian Bureau of Statistics,
Commonwealth of Australia: Canberra.)
Poole, D. W., Cowan, D. P., and Smith, G. C. (2003). Developing a census method based on
sight counts to estimate rabbit (Oryctolagus cuniculus) numbers. Wildlife Research 30, 487-493.
Poole, H. L.; Mukaromah, L; Kobryn, H, T., and Fleming, P. A. (2014). Spatial analysis of
limiting resources on an island: diet and shelter use reveal sites of conservation importance for
the Rottnest Island quokka. Wildlife Research 41, 510-521.
Pouliquen-Young O. and Newman P. (2000). The implications of climate change for land-based
nature conservation strategies. Final report 96/1306. Australian Greenhouse Office,
Environment Australia, Canberra; Institute for Sustainability and Technology Policy, Murdoch
University, Perth.
127
Powell, R. A. (2000). Animal home-ranges and territories and home-range estimators. In
‘Research Techniques in Animal Ecology’. (Eds. L. Boitani and T. K. Fuller.) pp. 65-110.
(Columbia University Press: New York.)
Price, O., and Bradstock, R. (2012). The efficacy of fuel treatment in mitigating property loss
during wildfires: Insights from analysis of the severity of the catastrophic fires in 2009 in
Victoria, Australia. Journal of Environmental Management 113, 146-157.
QGIS Development Team (2014). QGIS Geographic Information System. Open Source
Geospatial Foundation Project. http://qgis.osgeo.org.
Quinn, G. P., and Keough, M. J. (2002). Experimental Design and Data Analysis for Biologists.
Cambridge University Press, Cambridge.
Read, R., and Bowen, Z. (2001). Population dynamics, diet and aspects of the biology of feral
cats and foxes in arid South Australia. Wildlife Research 28, 195-203.
Real, R., Barbosa, A. M., Rodriguez, A., Garcıa, F. J., Vargas, J. M., Palomo, L. J., and Delibes,
M. (2009). Conservation biogeography of ecologically interacting species: the case of the
Iberian lynx and the European Rabbit. Diversity and Distributions 15, 390-400.
Roberge, J. M., and Angelstam, P. (2004). Usefulness of the umbrella species concept as a
conservation tool. Conservation Biology 18, 76-85.
Robinson, N. M., Leonard, S. W. J., Bennett, A. F. and Clarke, M. F. (2014). Refuges for birds
in fire-prone landscapes: The influence of fire severity and fire history on the distribution of
forest birds. Forest Ecology and Management 318, 110-121.
Rosenzweig, M. L. (1991). Habitat selection and population interactions - the search for
mechanism. American Naturalist 137, 5-28.
Rota, C. T., Fletcher, R. J., Evans, J. M. and Hutto, R. L. (2011). Does accounting for imperfect
detection improve species distribution models? Ecography 34, 659-670.
Rowcliffe, J. M., Field, J., Turvey, S. T., and Carbone, C. (2008). Estimating animal density
using camera traps without the need for individual recognition. Journal of Applied Ecology 45,
1228-1236.
Sanderson, E., Redford, K., Chetkiewicz, C., Medellin, R., Rabinowitz, A., Robinson, J., and
Taber, A. (2002). Planning to Save a Species: the Jaguar as a Model. Conservation Biology 16,
58-72.
Saunders, D. A., Hobbs, R. J., and Margules. C. R. (1991). Biological consequences of
ecosystem fragmentation: a review. Conservation Biology 5, 18-32.
Saunders, G., and McLeod, L. (2007). ‘Improving fox management strategies in Australia’.
(Bureau of Rural Sciences: Canberra.)
128
Schuerings, J., Jentsch, A., Hammerl, V., Lenz, K., Henry, H. A. L., Malyshev, A. V., and
Kreyling, J. (2014). Increased winter soil temperature variability enhances nitrogen cycling and
soil biotic activity in temperate heathland and grassland mesocosms. Biogeosciences 11, 70517060.
Seaman, D. E., and Powell, R. W. (1996). An evaluation of the accuracy of kernel density
estimators for home-range analysis. Ecology 77, 2075-2085.
Shepherd, D. P. (2003). Implementation of the National Vegetation Information System model
in Western Australia. Report to the Bureau of Rural Sciences, Canberra.
Shield, J. W. (1968). Reproduction of the quokka, Setonix brachyurus, in captivity. Journal of
Zoology, London 155, 427-444.
Shield, J.W., 1964. A breeding season difference in two populations of the Australian macropod
marsupial (Setonix brachyurus). Journal of Mammalogy 45, 616-625.
Short, J. (2004). Mammal decline in southern Western Australia - Perspectives from
Shortridge's collections of mammals in 1904-07. Australian Zoologist 32, 605-624.
Short, J., and Turner, B. (1991). Distribution and abundance of spectacled hare-wallabies and
euros on Barrow Island, Western Australia. Wildlife Research 18, 421-429.
Short, J., Turner, B., and Risbey, D. (2002). Control of feral cats for nature conservation. III.
Trapping. Wildlife Research 29, 475-487.
Sinclair, E. A. (1998). Morphological variation among populations of the quokka, Setonix
brachyurus (Macropodidae: Marsupialia), in Western Australia. Australian Journal of Zoology
46, 439-449.
Sinclair, E. A. (2001). Phylogeographic variation in the quokka, Setonix brachyurus
(Marsupialia: Macropodidae): implications for conservation. Animal Conservation 4, 325-333.
Sinclair, E. A., and Hyder, B. M. (2009). Surviving quokka (Setonix brachyurus) population on
the Swan Coastal Plain, Western Australia. Australian Mammalogy 31, 67-69.
Slade, N. A., and Blair, S. M. (2000). An empirical test of using counts of individuals captured
as indices of population size. Journal of Mammalogy 81, 1035-1045.
Sneeuwjagt, R. J., and Peet, G. B. (1998). ‘Forest fire behaviour tables for Western Australia.’
(Department of Conservation and Land Management: Perth.)
Southwell, C. J., and Jarman, P. J. (1987). Macropod studies at Wallaby Creek III. The effect of
fire on pasture utilisation by macropodids and cattle. AustralianWildlife Research 14, 117-124.
Spencer, R., and Baxter, G. S. (2006). Effects of fire on the structure and composition of open
eucalypt forests. Austral Ecology 31, 638-646.
129
StataCorp. (2007). Stata Statistical Software: Release 10. College Station, Texas.
Stolter, C., Ball, J. P., and Julkunen-Tiitto, R. (2013). Seasonal differences in the relative
importance of specific phenolics and twig morphology result in contrasting patterns of foraging
by a generalist herbivore. Canadian Journal of Zoology 91, 338-347.
Storr, G. M. (1964a). Studies on marsupial nutrition-IV. Diet of the quokka, Setonix brachyurus
(Quoy and Gaimard), on Rottnest Island, Western Australia. Australian Journal of Biological
Sciences 17, 469-481.
Storr, G. M. (1964b). The environment of the quokka (Setonix brachyurus) in the Darling
Range, Western Australia. Journal of the Royal Society of Western Australia 47, 1-2.
Styger, J. K., Kirkpatrick, J. B., Marsden-Smedley, J. O. N., and Leonard, S. W. J. (2011). Fire
incidence, but not fire size, affects macropod densities. Austral Ecology 36, 679-686.
Swihart, R. K., and Slade, N. A. (1985). Influence of sampling interval on estimates of homerange size. Wildlife Management 49, 1019-1025.
Templeton, A. R., Brazeal, H., and Neuwald, J. L. (2011). The transition from isolated patches
to a metapopulation in the eastern collared lizard in response to prescribed fires. Ecology 92,
1736-1747.
Thomas, L., Buckland, S. T., Rexstad, E. A., Laake, J. L., Strindberg, S., Hedley, S. L., Bishop,
J. R. B., Marques, T. A., and Burnham, K. P. (2010). Distance software: design and analysis of
distance sampling surveys for estimation of population size. Journal of Applied Ecology 47, 514.
Triggs, B. E. (1996). ‘Tracks Scats and Other Traces.’ (Oxford University Press: Melbourne.)
Valentine, L. E.; Fisher, R.; Wilson, B. A.; Sonneman, T.; Stock, W. D.; Fleming, P. A.; and
Hobbs, R. J. (2014). Time since fire influences food resources for an endangered species,
Carnaby's cockatoo, in a fire-prone landscape. Biological Conservation 175, 1-9.
Van Beest, F, M., Uzal, A.; Vander Wal, E.; Laforge, M. P.; Contasti, A. L.; Colville, D.;
Mcloughlin, P. D. (2014). Increasing density leads to generalization in both coarse-grained
habitat selection and fine-grained resource selection in a large mammal. Journal of Animal
Ecology 83, 147-156.
Van Rees, H., and Beard, J. A. (1984). Seasonal variation in in vitro digestibility and chemical
composition of a range of alpine plants, from Victoria, Australia. Australian Rangeland Journal
6, 86-91.
Vernes, K. (1999). Pellet counts to estimate density of a rainforest kangaroo. Wildlife Society
Bulletin 27, 991–996.
130
Wardell-Johnson, G., and Coates, D. (1996). Links to the past: local endemism in four species
of forest eucalypts in south-western Australia. In ‘Gondwanan Heritage: Past, Present and
Future of the Western Australian Biota.’ (Eds S. Hopper, J. Chappill, M Harvey and A.
George.) pp 137-154. (Surrey Beatty and Sons: Chipping Norton.)
Watson, J., Freudenberger, D., and Paull, D. (2001). An assessment of the focal-species
approach for conserving birds in variegated landscapes in south eastern Australia. Conservation
Biology 15, 1364-1373.
Watson, S. J., Taylor, R. S., Nimmo, D. G., Kelly, L. T., Haslem, A., Clarke, M. F., and
Bennett, A. F. (2012). Effects of time since fire on birds: how informative are generalized fire
response curves for conservation management? Ecological Applications 22, 685-696.
Wayne, A. F., Cowling, A., Lindenmayer, D. B., Ward, C. G., Vellios, C. V., Donnelly, C. F.,
and Calver, M. C. (2006). The abundance of a threatened arboreal marsupial in relation to
anthropogenic disturbances at local and landscape scales in Mediterranean-type forests in southwestern Australia. Biological Conservation 127, 463-476.
Wayne, A. F., Cowling, A., Ward, C. G., Rooney, J. F., Vellios, C. V., Lindenmayer, D. B., and
Donnelly, C. F. (2005). A comparison of survey methods for arboreal possums in jarrah forest,
Western Australia. Wildlife Research 32, 701-714.
White, G. C., and Burnham, K. P. (1999). Program MARK: survival estimation from
populations of marked animals. Bird Study 46, 120-139.
White, S. R. (1952). The occurrence of the quokka in the south-west. Western Australian
Naturalist 3, 101-103.
Williams, R. J., Bradstock, R. A., Cary, G. J., Enright, N. J., Gill, A. M., Lucas, C., Whelan, R.
J., Andersen, A. N., Bowman, D. J. M. S., Clarke, P. J., Cook, G. D., Hennessy, K. J., Liedloff,
A., and York, A. (2009). ‘The Impact of Climate Change on Fire Regimes and Biodiversity in
Australia – a Preliminary National Assessment.’ (CSIRO: Darwin).
Williams, S. E., Shoo, L. P., Isaac, J. L., Hoffmann, A. A., and Langham, G. (2008). Towards
an integrated framework for assessing the vulnerability of species to climate change. PLoS
Biology 12, 2621-2626.
Williams-Tripp, M., D’Amico, F. J. N., Page, C., Bertrand, A., Nemoz, M., and Brown, J. A.
(2012). Modeling Rare Species Distribution at the Edge: The Case for the Vulnerable Endemic
Pyrenean Desman in France. The Scientific World Journal 12, 1-6.
Wilson, A. M., Latimer, A. M., Silander Jr., J. A., Gelfand, A. E., and De Klerk, H. (2010). A
hierarchical Bayesian model of wildfire in a Mediterranean biodiversity hotspot: implications of
weather variability and global circulation. Ecological Modelling 221, 106-112.
131
Winstanley, R., Buttemer, W. and Saunders, G. R. (1999). Fat deposition and seasonal variation
in body composition of red fox (Vulpes vulpes) in Australia Canadian Journal of Zoology 77,
406-412.
Woinarski, J., Burbidge, A., Harrison, P. (2014). ‘The Action Plan for Australian Mammals
2012.’ (CSIRO Publishing: Victoria.)
Woolnough, A. P., Gray, G. S., Lowe, T. J., Kirkpatrick, W. E., Rose, K., and Martin, G. R.
(2005). Distribution and abundance of pest animals in Western Australia: a survey of
institutional knowledge. Report for Department of Agriculture Vertebrate Pest Research
Section, Forrestfield.
Worton, B. J. (1989). Kernel methods for estimating the utilization distribution in home range
studies. Ecology 70, 164-168.
Young, N. E., Stohlgren, T. J., Evangelista, P. H., Kumar, S., Graham, J., and Newman, G.
(2012). Regional data refine local predictions: modelling the distribution of plant species
abundance on a portion of the central plains. Environmental Monitoring Assessment 184, 54395451.
Zar. J. H. (1999). ‘Biostatistical Analysis.’ (Prentice Hall: Upper Saddle River, New Jersey.)
132

The ecology of the quokka (Setonix brachyurus) in the southern

Transcription

Similar documents

Brochure - Habitat for Zoomanity A Life Science Webquest

2016 Hollywood Build Fact Sheet

The truth about… The truth about… habitats

Uchee Creek

Restoration methods and project results at Kis

Marina de Joyuda - staging.files.cms.plus.com

Salish Sucker Recovery and Management

Ron Reid Couchiching Conservancy

Activity 22