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Drought-driven change in wildlife distribution and numbers:
a case study of koalas in south west Queensland
Leonie Seabrook
A,E
, Clive McAlpine
A,B
, Greg Baxter
A,B
, Jonathan Rhodes
A,B
,
Adrian Bradley
C
and Daniel Lunney
D
A
The University of Queensland, Landscape Ecology and Conservation Group, School of Geography,
Planning & Environmental Management, Brisbane, Queensland 4072, Australia.
B
The University of Queensland, The Ecology Centre, Brisbane, Queensland 4072, Australia.
C
The University of Queensland, School of Biomedical Sciences, Brisbane, Queensland 4072, Australia.
D
Office of Environment and Heritage NSW, PO Box 1967, Hurstville, New South Wales 2220, Australia and School
of Biological Sciences and Biotechnology, Murdoch University, Murdoch, Western Australia 6150, Australia.
E
Corresponding author. Email: l.seabrook@uq.edu.au
Abstract
Context. Global climate change will lead to increased climate variability, including more frequent drought and heatwaves,
in many areas of the world. This will affect the distribution and numbers of wildlife populations. In south-west Queensland,
anecdotal reports indicated that a low density but significant koala population had been impacted by drought from
2001–2009, in accord with the predicted effects of climate change.
Aims. The study aimed to compare koala distribution and numbers in south-west Queensland in 2009 with pre-drought
estimates from 1995–1997.
Methods. Community surveys and faecal pellet surveys were used to assess koala distribution. Population densities were
estimated using the Faecal Standing Crop Method. From these densities, koala abundance in 10 habitat units was interpolated
across the study region. Bootstrapping was used to estimate standard error. Climate data and land clearing were examined as
possible explanations for changes in koala distribution and numbers between the two time periods.
Key results. Although there was only a minor change in distribution, there was an 80% decline in koala numbers across the
study region, from a mean population of 59 000 in 1995 to 11 600 in 2009. Most summers between 2002 and 2007 were hotter
and drier than average. Vegetation clearance was greatest in the eastern third of the study region, with the majority of clearing
being in mixed eucalypt/acacia ecosystems and vegetation on elevated residuals.
Conclusions. Changes in the area of occupancy and numbers of koalas allowed us to conclude that drought significantly
reduced koala populations and that they contracted to critical riparian habitats. Land clearing in the eastern part of the region
may reduce the ability of koalas to move between habitats.
Implications. The increase in hotter and drier conditions expected with climate change will adversely affect koala
populations in south-west Queensland and may be similar in other wildlife species in arid and semiarid regions. The effect of
climate change on trailing edge populations may interact with habitat loss and fragmentation to increase extinction risks.
Monitoring wildlife population dynamics at the margins of their geographic ranges will help to manage the impacts of climate
change.
Additional keywords: climate variability, distribution, faecal standing crop method, habitat loss, Phascolarctos cinereus.
Received 1 April 2011, accepted 16 August 2011, published online 11 November 2011
Introduction
The geographic range of a species is broadly governed by the
presence of environmental conditions suitable for that species and
its ability to disperse to new areas (Gaston 2009). However,
within a geographic range, there is a complex internal structure of
distribution, often termed the area of occupancy, which reflects
finer scale variations in habitat suitability, local and meta-
population dynamics and legacies of past events (Brown et al.
1996; Gaston and Fuller 2009). This fine scale structure leads
to considerable variations in the density and numbers of
local populations within the area of occupancy (Gaston and
Fuller 2009). Changes in environmental conditions, such as
climate variability and loss of habitat, affect population
dynamics, including dispersal ability, breeding success and
mortality rates, and alter population density either temporarily
or permanently (Fahrig 2003; Hughes 2003; Parmesan 2006).
CSIRO PUBLISHING
Wildlife Research,2011, 38, 509–524
http://dx.doi.org/10.1071/WR11064
Journal compilation ÓCSIRO 2011 www.publish.csiro.au/journals/wr
These changes can occur cyclically, with populations expanding
or contracting as environmental conditions fluctuate, but if
conditions alter too much, there may be more permanent
effects on populations (Thomas et al.2006; Piessens et al.2009).
Climate is one of the major determinants of the distribution
of species (Caughley et al.1988). Climate change is affecting
species distributions, leading to range shifts and changes in
density and numbers within geographic ranges (Parmesan
and Yohe 2003; Wilson et al.2004; Thomas et al.2006).
Species will survive climate change either by adapting or
moving to remain within suitable environmental conditions.
The physiological capacity of a species to adapt is based on
its phenotypic plasticity (the ability of an individual to change
its physiological properties or behaviour in response to
environmental conditions) or microevolutionary changes over
short time spans (Fuller et al.2010). There is evidence that
evolutionary and physiological adaptations are occurring in
several species in response to climate change, although there is
little evidence that these adaptations will occur rapidly enough
to allow occupation of previously unsuitable climatic regions
(Parmesan 2006). During the process of climate-induced range
shifts, trailing edge populations become increasingly fragmented,
while leading edge populations tend to expand along a broad
front (Thomas 2010). Some trailing edge populations will
survive in refugia where local and regional variations in
climate provide favourable conditions (Hampe and Petit 2005).
However, many will be vulnerable to local declines arising from
climate variability and weather extremes, particularly drought
and heatwaves, which may affect a fauna species directly or
change habitat quality and resource availability (Adams 2010;
Albright et al.2010). Monitoring changes in distribution and
numbers at the limit of a species’range during conditions that
mimic those predicted under climate change scenarios will help
identify and manage the effects of climate change (Adams 2010).
Habitat loss and fragmentation are also important drivers
of changes in species distribution and numbers (Fahrig 2003;
Wiegand et al.2005). Habitat fragmentation has a range of effects
on forest-dependent mammals, including: decreases in habitat
amount and quality; lower breeding and dispersal success; and
increased risk of mortality due to predation or collisions with
motor vehicles (Andrén 1994; Cogger et al.2003; Fahrig 2003;
McAlpine et al.2006a). Changes in distribution and numbers
may lag behind habitat clearance, resulting in an extinction debt
(Tilman et al.1994; Mouquet et al.2011). The 2006 State
of the Environment report for Australia identified that loss of
native vegetation continues to be one of the greatest threats to
biodiversity (Beeton et al.2006). The combination of climate
change and habitat fragmentation may affect the long-term
viability of many species (Travis 2003).
Koalas (Phascolarctos cinereus) are tree-dwelling marsupial
folivores, endemic to Australia, which feed almost exclusively
on a limited variety of Eucalyptus,Corymbia and Angophora
species. They are found across a broad geographic range in
eastern and south-east Australia, occurring in the states of
Queensland, New South Wales, Victoria and South Australia
(Martin and Handasyde 1999). The distribution of the koala is
limited by climatic factors, the presence of suitable habitat and
the ability to colonise new areas (Martin and Handasyde 1999;
Adams-Hosking et al.2011). Since the European settlement of
Australia, changes in distribution have occurred mainly from
broad-scale habitat loss, but historical events such as pelt hunting,
bushfires and disease have also contributed to local and regional
population extinctions over time (Melzer et al.2000; Phillips
2000). In Queensland, a series of state-wide community surveys
indicate that, between 1928 and 1987 when the first and last state-
wide surveys were conducted, the range of the koala has
progressively contracted to the south and east, with a reduction
of ~27% in their extent of occurrence and 31% in the area of
occupancy (Gordon et al.2006). The survey results also show
changes in the densities of local koala populations.
The first koala survey in Queensland was carried out in 1928
by the Nature Lovers League to assess the impact of an open
hunting season in 1927 for koala pelts (Gordon et al.2006).
Nearly all replies from local councils and Dingo Boards
contained the same message, namely that koalas had become
scarce or absent in their districts since the open season. In 1967,
the Wildlife Preservation Society of Queensland carried out
a survey through primary schools across Queensland, asking
about current and historic sightings of koalas (Kikkawa and
Walter 1968). Results pointed to a range contraction from the
north and west. Koala deaths were attributed mainly to bushfires
and disease, with some mention of past hunting (Kikkawa and
Walter 1968). Campbell et al.(1979) reported on a second
survey carried out in 1977 by the Wildlife Preservation
Society of Queensland. This survey found that although the
distribution did not appear to have changed significantly since
1967, many districts reported lower numbers of koalas. Patterson
(1996) compared koala distribution from a 1986–1988 survey
with the 1967 and 1977 surveys. Koala sightings showed a
marked range contraction from the west in the southern half of
Queensland and some contraction to the south (Patterson 1996).
The contraction was attributed primarily to habitat loss,
fragmentation and the associated decline in habitat quality.
Within the koalas’broad range, their area of occupation and
densities are patchy and depend on the presence of favoured tree
species and fertile soils with higher levels of soil nutrients and
soil moisture (Bryan 1997; Sullivan et al.2003a). Population
densities vary from 9 per ha in parts of Victoria to less than 0.001
per ha in inland Queensland (Melzer et al.2000). In Queensland,
densities vary from 0.02–2 koalas per ha in Springsure (Gordon
et al.1990; Melzer and Lamb 1994), to 0.6–1.6 per ha in Oakey
(Gordon et al.1990), and 0.4 koala per ha in coastal Redlands
Shire (White and Kunst 1990).
Local koala populations face several threats, including loss
and fragmentation of habitat (Melzer et al.2000; McAlpine et al.
2006a; McAlpine et al.2006b), car strikes and dog attacks (Dique
et al.2003; Lunney et al.2007), and disease, which can lead either
to death or infertility (Gordon et al.1990; Hanger and Loader
2009). Koalas are susceptible to climatic extremes, particularly
heatwaves and droughts, which also affect the quality of nutrients
and moisture available in their diet (Cork and Braithwaite 1996;
Moore and Foley 2000). There is evidence that koalas can modify
their behaviour and physiology to increase water intake if
necessary (Krockenberger 2003; Ellis et al.2010), but climatic
stress on trees can lead to widespread leaf fall and subsequent
crashes in koala populations (Gordon et al.1988; Gordon et al.
1990). For example, Gordon et al.(1990) found, in northern and
western Queensland, that the variability in rainfall and its effect on
510 Wildlife Research L. Seabrook et al.
food sources was probably the most important factor affecting
koala populations, although in the eastern region it was high
infertility and prevalence of cystitis.
In the mid 1990s, Sullivan et al.(2003a,2004) conducted
the first broad-scale study of koala distribution and abundance
in the Mulgalands bioregion of south-west Queensland.
They estimated that the total population ranged from 44 593
to 77 567. Densities varied across the region from 0.0007 to
2.5 per ha, with the highest densities occurring in riverine and
residual habitats in the eastern Mulgalands. Since that study
was completed, south-west Queensland suffered a severe and
prolonged drought (Hughes 2003; Beeton et al.2006) and
anecdotal evidence suggested that regional koala numbers
had declined.
This study aimed to update our understanding of the
distribution and numbers of koalas in south-west Queensland
and compare the results with those of the study by Sullivan et al.
(2003a,2004) Several years of severe drought provided
conditions similar to those predicted by climate change
scenarios, and an opportunity to assess the likely impacts of
future climate change on koala populations. We identified koala
distribution using a community survey and faecal pellet survey. A
Faecal Standing Crop Method was used to estimate population
densities. From these densities, the numbers of koalas in 10
habitat units were interpolated across the study region. We
examined climate data and the area of vegetation clearing as
possible explanations for changes in koala distribution and
numbers between the two studies.
Materials and methods
Study area
The study area was the South West Natural Resource
Management (SWNRM) region, covering 187 000 km
2
of
southern inland Queensland, Australia (Fig. 1a). Average
annual rainfall ranges between 250 mm in the south-west and
550 mm in the north-east (Fig. 1b). The mean daily maximum and
minimum temperatures for Charleville, the region’s largest town,
are 28C and 13.5C respectively. Elevation varies from 580 m
above sea level in the north-east to 100 m above sea level in the
south. Regional drainage patterns are from north to south, with the
Warrego and Paroo Rivers along with the Nebine, Wallam and
Mungallala Creeks forming part of the Murray–Darling Basin
(Sattler and Williams 1999). The Bulloo River in the west of the
region has an internal drainage basin. The dominant landforms are
plains with generally sandy, infertile soils. The major plains
vegetation communities comprise mulga (Acacia aneura) with
poplar box (Eucalyptus populnea) and other eucalypt species co-
dominant in the higher rainfall areas to the east and north (Sattler
and Williams 1999). Separating the plains are low residual ranges
and plateaus, with shallow red earth soils. Residual vegetation has
some eucalypt communities of Thozet’s box (E. thozetiana)or
grey box (E. microcarpa), but it is dominated by Acacia species
(Table 1). There are significant floodplains, dominated by poplar
box and coolabah (E. coolabah). Riparian vegetation, comprising
river red gum (E. camaldulensis) and coolabah occurs as narrow
strips along waterways. Land use in the region is predominantly
cattle grazing. Although the SWNRM region does not correspond
exactly with the Mulgalands bioregion used in Sullivan’s study
(Sullivan et al.2002), the area common to both (the overlap
zone) covers 145 537 km
2
, or 78% of the Mulgalands bioregion
(Fig. 1a).
Koala distribution
We used a combination of two independent survey designs –a
community survey and faecal pellet survey –to assess the
distribution and numbers of koalas in south-west Queensland.
Community surveys
Community surveys are an effective way to obtain presence or
absence data relatively cheaply and to provide access to private
land (Lunney et al.2009). Community surveys have been used to
obtain locations across wide areas for several easily recognisable
species, including spotted-tailed quolls (Dasyurus maculatus)
(Lunney and Matthews 2001), platypus (Ornithorhynchus
anatinus) (Otley 2001), and bandicoots (Isoodon macrourus)
(FitzGibbon and Jones 2006). National and state-wide
assessments of koala distribution have all used surveys
targeting sections of the community (Kikkawa and Walter
1968; Campbell et al.1979; Phillips 1990; Reed et al.1990;
Patterson 1996; Lunney et al.2009). Surveys are useful at
identifying outlying records where animals are found on a
transient basis (Lunney and Matthews 2001). Koalas are
difficult to spot in the wild, but there is no confusion with
other species, and koalas are popular with people in the local
community, making community surveys a valuable and
repeatable source of information about this iconic species.
A community survey was distributed as unaddressed mail to
680 rural landholders in the SWNRM region. Respondents were
asked whether koalas occurred in their area, when koalas had last
been sighted, and whether they thought koala numbers were
changing. They were provided with a map of the region and asked
to mark their sightings. Questions were also included on other
species including common brushtail possums (Trichosurus
vulpecula) which use similar habitat, dingos or wild dogs
(Canis lupus) and foxes (Vulpes vulpes). Commonly sighted
species have been used in previous surveys to assess where
koalas were either genuinely not present or were not noticed
(Lunney et al.2009). We did not follow up the mail survey to
increase the rate of return. Although the survey was not sent to
people living in towns, there was media coverage of the research
project including contact details of the research team.
Faecal pellet surveys
Identifying species presence can be done through direct
observation, but in many cases indirect signs such as scats or
tracks are used (Wilson and Delahay 2001; Telfer et al.2006).
Surveys that count koalas through direct observation take many
person hours and, although they are used in the more densely
populated east coast koala populations and in smaller study areas
(Gordon et al.1990; Melzer and Lamb 1996; Dique et al.2004),
they are not practical for large areas with low-density populations
(Sullivan et al.2002). Faecal pellet surveys are a well-established
method of determining koala presence, and thus distribution and
habitat selection (Phillips and Callaghan 2000; McAlpine et al.
2006b). Koala faecal pellets are relatively long-lasting and easily
Drought-driven change in distribution and numbers Wildlife Research 511
distinguishable from the pellets of other mammals in the region,
such as goats (Capra hircus) or common brushtail possums, either
by external and internal appearance, or smell. There are
consequences in using indirect signs for detecting species
occupancy of sites, including false absences if signs decay
rapidly or false positives if pellets last many months (Rhodes
et al.2011). The issue of false absences was addressed through
field-ageing trials of pellets during the survey period. The issue of
false positives could not be determined during one-off surveys,
but in many survey sites there had been a significant rain and
flooding event at the start of 2009 (five months before the start of
field surveys), which would have washed away older pellets. We
acknowledge that the absence of pellets does not necessarily
equate to the absence of koalas. However, both Sullivan (2000)
and Munks et al.(1996) found that direct observation was not
practical for the low-density and patchily distributed koalas of
western Queensland because of the length of time needed to carry
out reliable counts. In addition, Sullivan (2000) found that there
(a)
(b)
Fig. 1. (a) the Mulgalands Bioregion (white), South West Natural Resource Management (SWNRM) region
(grey), and the overlap zone between the SWNRM and Mulgalands (hatched); (b) sample zones (SZ), stratified by
latitude and rainfall zones (RFZ).
512 Wildlife Research L. Seabrook et al.
was only a slight underestimate of koala density when using
faecal pellet surveys rather than direct observation.
The presence of fresh faecal pellets allowed an estimate to
be made of koala densities and numbers using a Faecal Standing
Crop Method (FSCM) (Sullivan et al.2002; Sullivan et al.
2004). FSCM has been used widely for estimating the density
and numbers of species such as deer (Cervus elephus and
Capreolus capreolus), elephant (Loxodonta africana) and
kangaroos (Macropodid spp.) (Johnson and Jarman 1987;
Latham et al.1996; Mayle 1996; Barnes 2001; Bennett et al.
2005). It was adapted for use with koalas in the Mulgalands
by Sullivan et al.(2002). Provided that pellets can be reliably
aged, and the daily production rate is known, the FSCM is a useful
tool for assessing the density of a cryptic and wide spread
species, such as the koala (Sullivan et al.2004).
We followed a similar sampling strategy to Sullivan et al.
(2002,2003a,2003b). Vegetation communities were classified
into 10 habitat units based on landform and dominant vegetation
(Table 1). We used the Regional Ecosystem mapping Version 5
supplied by the Queensland Herbarium (2005). The region was
also stratified into eight sampling zones based on differences
in rainfall on an east–west gradient, and in latitude on the north–
south gradient, following Sullivan et al.(2003b) (Fig. 1b). The
difference in study regions between this work and that of Sullivan
meant that none of Sullivan’s sample zone 1 overlapped with the
SWNRM region. We did not survey sites in the extreme south-
west of the region because both Sullivan et al.(2004) and the
community survey indicated that no koalas had been present for
many decades. In addition, surveys in the southern part of Sample
Zone 8 (SZ 8) (Fig. 1b), close to the New South Wales border,
were prevented by heatwaves in December 2009 followed by
flooding in January–March 2010. Evidence from the community
survey and personal communication with Queensland Parks and
Wildlife rangers in Charleville and Culgoa Flood Plain National
Park indicate that koalas are seldom present in this area, and
the lack of faecal pellet surveys did not, in our view, make a
significant difference to the population estimates.
Based on the findings of Gordon et al.(1988) and Munks et al.
(1996), koalas in semiarid regions are more commonly found in
riverine habitat (habitat unit 1), although they do utilise other
habitat for both feeding and resting (Witt and Pahl 1994; Melzer
and Lamb 1996; Ellis et al.2002). In the Mulgalands, Sullivan
et al.(2004) found the highest densities to be in riverine habitats,
although residual landforms also formed important habitat in the
north (Sullivan et al.2003a; Sullivan et al.2003b). Furthermore,
Rhodes et al.(2006) recommended surveying in high-quality
habitat if declines in population are suspected because the higher
densities of individuals living there allow a greater chance of
detecting declines. For these reasons, we sampled more sites in
riverine than in other habitats. Within each habitat unit, between
30 and 50 field sites, at least 3 km apart, were randomly generated
using Hawth’s Analysis Tools 3.26 (http://www.spatialecology.
com/htools/) in ArcGIS 9.3 (www.esri.com). In habitat unit 1
(riverine habitat with river red gums present), 100 sites were
generated. The distribution of sites was examined and where
possible two to three sites were chosen in a locality or on a single
property to maximise the efficiency of the time taken on field
surveys. In total, we surveyed 200 sites in the SWNRM region in
south-west Queensland for faecal pellets between May and
November 2009.
The faecal-pellet survey method incorporated elements from
the spot-assessment technique of Phillips and Callaghan (2000)
and from Sullivan et al.(2002). A central tree closest to the GIS-
generated point was flagged and it, plus the closest 29 Eucalyptus,
Corymbia or Angophora trees with a DBH (diameter at breast
height over bark) greater than 10 cm, formed a site. Site
characteristics, including tree species, height and DBH were
recorded. The area of the site was calculated by measuring the
radius of the circle of trees forming the site. Following the
experience of Sullivan et al.(2002), searches for faecal pellets
were carried out using two methods. First, under all species except
river red gums, a basal search in a radius of 1 m around the trunk of
the tree was conducted. If koala pellets were detected in the basal
search, then a second extended search using 1 m quadrats was
Table 1. Regional habitat units based on landforms and dominant vegetation species (modified from Sullivan 2000)
Landform Habitat
Unit
Dominant tree species Percentage
of region
A
Riverine
Eucalypt dominated 1 Eucalyptus camaldulensis, E. coolabah, E. populnea 2.5
Floodplain
Eucalypt dominated 2 E. coolabah,E. ochrophloia,E. largiflorens,E. populnea,E. melanophloia 5.4
Acacia dominated 3 Acacia cambagei,E. ochrophloia,E. coolabah 3.2
Plains
Eucalypt dominated 4 E. melanophloia,E. populnea 1.4
5E. populnea,E. melanophloia,E. intertexta 5.6
Acacia dominated 6 A. aneura,E. populnea,E. melanophloia,E. intertexta,Corymbia terminalis 24.6
7A. harpophylla,A. aneura,E. cambageana,C. terminalis,C. papuana 4.3
Angophora dominated 8 Angophora melanoxylon,Callitris columellaris,E. melanophloia,E. populnea,C. tesselaris,C. polycarpa 1.6
Residual/High Plains
Eucalypt dominated 9 E. thozetiana,E. cambageana,E. microcarpa 1.3
Acacia dominated 10 A. catenulata,A. stowardii,E. exserta,E. populnea,E. melanophloia,E. thozetiana,C. terminalis,
E. cambageana
10.7
A
Remaining area (39.4%) is either not potential habitat or has been cleared.
Drought-driven change in distribution and numbers Wildlife Research 513
carried out under the canopy in order that a density could be
calculated. During quadrat searches, the area of one-quarter of the
canopy was searched, using up to six quadrats. For example, if
one-quarter of the canopy was estimated at 3 m
2
, then three
quadrats were randomly placed under the canopy. For river red
gums, all pellet searches were done using quadrats. The growth
form of riverine trees, particularly river red gum, was often
multi-stemmed with spreading branches and an asymmetrical
canopy, and there was a greater chance of false absences under
these trees if only a basal search was used (Sullivan et al.2002).
Since riverine habitats support the greatest densities of koalas, it
was important to reduce the possibility of under-estimating
densities in these habitats by falsely recording pellets as absent
under a tree.
Koala density calculations
For the calculation of density, and hence koala abundance, we
used the Faecal Standing Crop Method (Sullivan et al.2002;
Sullivan et al.2004). The FSCM requires three types of data: the
number of pellets present, how many pellets are produced daily,
and the age of the pellets. The number of pellets was estimated by
counting the fresh pellets found in the quadrat search, adjusting
this by a visibility correction factor taken from Sullivan et al.
(2004), which was based on the percentage of ground cover in the
quadrats, and multiplying the adjusted number of pellets by the
canopy area of the tree to give an estimated total pellet count.
Daily pellet production was based on the findings of Sullivan
et al.(2004), and ranged from 138.2–163.3 pellets. The maximum
pellet age was estimated from field trials and was based
on external appearance and internal odour. Fresh koala pellets
have a characteristic sheen and a greenish colour, which
disappears after 1–3 days depending on weather conditions.
They also have a strong smell of eucalyptus oils when broken
open. Over a period of days or weeks, depending on weather
conditions, the characteristic eucalyptus smell fades. To assess
the age at which pellets lost their internal odour of eucalyptus oils,
fresh pellets were stored in a domestic refrigerator and three to
four were placed outside at the base of a eucalypt trunk at 3-day
intervals by an employee of SWNRM in Charleville (Sullivan
et al.2002). The age at which the smell faded was used to assign an
age range to the fresh pellets found in field surveys. This included
a margin for error of 4–5 days, so a pellet that lost its internal
odour at 10 days might be allocated a minimum age of 7 days and
a maximum age of 12 days.
Koala density was calculated for each tree at a site by applying
the equation:
Density ¼P=da
Where P = estimated total pellet count, d = daily pellet
production and a = maximum pellet age (Sullivan et al.2004).
Site densities were calculated by summing the pellet count under
all trees in the site.
Estimating koala numbers
The number of koalas was calculated for each habitat unit within
each of the eight sampling zones where sites with fresh pellets
were located, using an area weighted average interpolated from
the site densities. To enable a more accurate comparison with the
population estimates of Sullivan et al.(2004), the calculation of
koala numbers was limited to the area of the overlap zone between
the SWNRM region and the Mulgalands Bioregion (Fig. 1a).
Following Sullivan et al.(2004), we used an inverse weighted
distance (IWD) method in the ArcGIS Spatial Analyst tool to
interpolate a density surface from the sites, using a power of 2,
which gives greater weight to nearby sites. If only one site in a
habitat unit and sample zone contained fresh pellets, then the IWD
surface was generated for a distance of 50 km around that site.
We generated a minimum, mean and maximum IDW surface for
the habitat units in each sample zone. The density surfaces were
then reclassified into 20 classes using Jenks Natural Breaks
option to give an attribute table that could be multiplied by the
area of the habitat units (Sullivan et al.2004). Each habitat unit
was converted from polygons into a raster grid, with a cell size of
50 m, based on the percentage cover of the different regional
ecosystems making up the habitat unit. This step gave the
most accurate measure of the area covered by each habitat unit
because one regional ecosystem could contain a mixture of two
to three different habitat units. We then calculated the area of
each reclassified density surface in the habitat unit in which the
sites occurred. Finally, the reclassified IDW surface was then
converted back to density classes and multiplied by the tabulated
area of the habitat unit to estimate the number of koalas for that
habitat unit within a particular sample zone.
To obtain a measure of the accuracy of our parameter
estimates, we used a bootstrap method to estimate a standard
error of koala abundance (Efron and Tibshirani 1986; Sullivan
et al.2004). For each site with fresh pellets, 500 bootstrap
estimates were generated for the total pellet count after
adjusting for visibility. Fifty estimates were selected at random
for each site and the equation to calculate site densities
shown above was applied. Minimum, mean and maximum
IWD surfaces were generated, values reclassified, and areas
tabulated as described above and in Sullivan et al.(2004). In
two habitat units, our estimate of koala numbers was less than 100
koalas, so we did not generate bootstrap estimates because the
difference in total numbers would have been minimal. Once
numbers had been calculated for the 50 bootstrap estimates,
the standard error of the means were calculated together with
the 95% confidence interval.
Weather conditions and land clearing
We examined historical climate data for the region and estimated
the area of land clearing to assess whether either or both of these
factors might have contributed to a change in koala populations
between 1995 and 2009. Although we cannot unequivocally link
climatic conditions or land clearing with changes in koala
populations, there is evidence from previous work that allows
us to assign their effects to our findings (Gordon et al.1988;
Cogger et al.2003; McAlpine et al.2006b; Ellis et al.2010).
Anecdotal evidence from several landholders mentioned
the local disappearance of koalas during the severe drought of
2002–2007, with 2002 and 2006 being mentioned in particular.
Evidence from previous studies indicates that hot days and low
rainfall can have severe impacts upon koala populations, either
directly by causing physiological stress or indirectly by affecting
the nutrient and water content in eucalypt leaves (Gordon et al.
514 Wildlife Research L. Seabrook et al.
1988; Clifton et al.2007; Ellis et al.2010). We examined climate
data from the Bureau of Meteorology for the major towns across
the region from 1990 to 2009 to see if there had been significant
changes in climatic conditions. We totalled the amount of summer
rainfall in the six hottest months (1 October to 31 March), and
counted the number of days over 40C over the same period. This
temperature threshold was chosen for several reasons. In their
study of koala metabolism and heat balance, Degabriele and
Dawson (1979) stated that the body temperature, respiratory
rate and evaporative water loss of koalas rose rapidly when
air temperatures were above 30C. Bioclimatic modelling
conducted by Adams-Hosking et al.(2011) identified that
koalas occur in areas with maximum summer temperatures of
under 37.7C. However, Ellis et al.(2010) found that, in central
Queensland, the temperatures in trees used by koalas for shelter
in the summer could be around 2C cooler than ambient air
temperature, thereby moderating the effect on thermoregulation.
We accordingly picked 40C as an air temperature that would
result in significant physiological stress for koalas.
Another major cause of koala population decline is land
clearing and habitat fragmentation (Cogger et al.2003;
Sullivan et al.2003b; McAlpine et al.2006b). The Statewide
Landcover and Trees Study (SLATS) uses Landsat Thematic
Mapper satellite images to assess changes in woody vegetation
over a given time period (for methods see DNRW 2008). We used
data from SLATS for the years from 1995–2008 to quantify
woody vegetation clearance in the different habitat units. The data
were in the form of a GIS point file, with each point showing
the rate of clearing per annum for the 25 m
2
Landsat pixel where
woody vegetation had been cleared during the relevant time
period. A spatial join was used to combine data from the
SLATS point files with the habitat unit polygons derived from
remnant regional ecosystem mapping (Queensland Herbarium
2005). Where SLATS points occurred in areas that were classified
as cleared in the remnant regional ecosystem mapping, the pre-
clearing regional ecosystem data were used to assign a habitat
unit to that point. The area cleared in hectares was multiplied
by the percentage of each habitat unit of that point, and the totals
were summed to get the total area of clearing by habitat unit
since 1995.
Results
Distribution of koalas from community surveys
and field surveys
A total of 63 community surveys were returned (9.2%). The
distribution of records of koala occurrence was evenly spread
across the study area (Fig. 2a) and broadly agreed with the results
of the faecal pellet surveys. Due to the low number of returns, the
community survey was used only to map distribution according to
the reported sightings, but not analysed further.
The distribution of koalas shown by the field survey was
similar to the community survey (Fig. 2b). The most densely
occupied area was between Charleville and Tambo, where all the
sites had some signs of koala presence. There was a patchier
occurrence elsewhere in the north and eastern sections of the
region. Koala pellets were found at only one site in the south-west
of the region (Fig. 2b).
Nearly half of the field sites in habitat units 1, 2 and 9 had faecal
pellets present, with fresh pellets present at 70% of these sites
(Table 2). No pellets were found in habitat units 3, 4, and 8,
although the number of sites surveyed in these habitat units were
limited due to difficulties accessing them in the field or because
there were not enough trees of the genera Eucalyptus,Corymbia
or Angophora present at the site. The difference between the
number of sites with fresh pellets in the SWNRM region (48) and
those in the overlap region (27) (Table 2) was due to the densely
occupied sites between Charleville and Tambo on the Ward River
falling outside the overlap zone with the Mulgalands Bioregion
(Fig. 2b).
Estimated koala density and numbers
In 2009, koala density estimates varied from 0.002 to 1.85 koalas
per ha. Average densities in 2009 in Rainfall Zone 3 in the north-
eastern part of the region were 1.3 0.03 and in Rainfall Zone 2
were 0.04 0.02 (see Fig. 1bfor Rainfall Zone locations).
Sullivan et al.(2004) found that koala densities varied across
the region from 0.0007–2.5 per ha with average densities in
Rainfall Zone 3 being 1.8 0.06 and in Rainfall Zone 2 being
0.02 0.01
The bootstrap estimates of total koala numbers in the overlap
zone ranged from 9843 to 13 430 (95% confidence interval),
with the mean of 11 634 (904 standard error) (Table 3). The
overall decline compared with the numbers estimated by Sullivan
et al.(2004) in 1995 was 80% (Table 3).
Weather conditions and area of land cleared
Weather records collected by the Bureau of Meteorology
show that, between 1989 and 2009, the average number
of days over 40C over the summer months (October–March)
was only slightly higher than the long-term average since 1950.
However, between 2002 and 2007, Charleville, Bollon and
Tambo had more than double the average number of hot days,
with a peak across most of the region in 2006 (Fig. 3). There was
also a much lower-than-average annual rainfall, particularly in
2002–2003 (Fig. 3). The combination of an above-average
number of hot days and low summer rainfall was most evident
in Charleville and Bollon between 2002 and 2006 (see Fig. 1afor
locations). In Quilpie and Cunnamulla, the climate is both hotter
and drier than further east (Fig. 3). These conditions are probably
close to the climatic limits of the koala, and would contribute to
determining the western boundary of its range. Koalas were
uncommon near these towns, although there was one site with
old faecal pellets south-west of Quilpie (Fig. 2b).
The greatest amount of clearing had occurred in the eastern
third of the region, with a trend of increasing clearing towards the
south. Across all habitat units, 11.72% of clearing was in SZ 3,
18.17% in SZ 6 and 22.6% in SZ 9 (Table 4and see Fig. 1for zone
locations). The habitat units with the greatest percentage of
clearing were HU 5 (20.2%) (poplar box and silver-leaved
ironbark: E. melanophloia) and HU 9 (16%) (Thozet’s box
and Dawson gum: E. cambageana). Sullivan et al.(2003a)
found Thozet’s box and poplar box formed an important part
of the diet of koalas in south-west Queensland, and these levels of
clearing may have contributed to the decline in koala population
Drought-driven change in distribution and numbers Wildlife Research 515
size between 1995 and 2009 (Table 3) through loss of habitat and
by limiting the ability of koalas to disperse safely away from creek
lines.
Discussion
The effect of climate change on the trailing edge of species
distribution is predicted to lead to increasingly fragmented
(a)
(b)
Fig. 2. Distribution of koalas across the South West Natural Resource Management (SWNRM) region in 2009 from (a)
the community survey; and (b) the faecal pellet survey.
516 Wildlife Research L. Seabrook et al.
populations, some of which may survive in small refugia due to
local and regional variations in climate but many of which will
face local extinction from extreme events (Parmesan 2006;
Thomas et al.2006; Gaston and Fuller 2009). Trailing-edge
populations have been shown to be critical to the long-term
survival of species because they may contain individuals that
can adapt to changing climatic conditions (Wilson et al.
2004; Hampe and Petit 2005; Thomas et al.2006). This
project assessed the distribution and numbers of koalas in
south-west Queensland following a severe drought from 2001
to 2007 and compared the results with a previous study carried
out in the mid 1990s. Koalas in this region are at the western
limits of their geographic range, and form a trailing-edge
population. There was a marked decline in the area of
occupancy and in population size since the mid 1990s,
although their extent of occurrence, e.g. the geographic range,
contracted only slightly. Their area of occupancy contracted
mainly to the most optimal habitat along creeks and rivers,
with little use of habitat away from water courses. The decline
coincided with a period of 4–5 years where there was a
combination of low summer rainfall and an above average
number of very hot days, which mimic the predicted direction
of climate change in the region and are known to affect koala
survival (Gordon et al.1988; Gordon et al.1990; Ellis et al.2010).
The changes in distribution (both the extent of occurrence and
the area of occupancy) reflect the koalas’response to alterations
in their environment and ability to disperse in south-west
Queensland.
Extreme events, such as drought and increased temperature,
can affect mortality rates, breeding success and dispersal of
Table 2. Number of sites with koala faecal pellets surveyed within
each habitat unit in the South West Natural Resource Management
(SWNRM) region and the overlap zone with the Mulgalands bioregion
(the overlap zone)
Habitat
unit
Number
of sites
Number of
sites with
any pellets
Number of
sites in SWNRM
with fresh pellets
Number of sites
in overlap zone
with fresh pellets
17336 24 20
25023 17 5
340 0 0
410 0 0
5154 3 0
6365 2 1
700 0 0
820 0 0
973 2 1
10 12 1 0 0
Total 200 72 48 27
Table 3. Comparison of bootstrap estimates of koala numbers by rainfall zone, sample zone and habitat unit
Minimum and maximum figures are the mean 95% confidence interval
Abundance estimate 1995
A
Abundance estimate 2009
Minimum Mean Maximum Minimum Mean Maximum
Rainfall zone Rainfall zone
1zr
B
zr zr 1zr zr zr
214 530 18 490 22 930 22308 3152 3999
330 060 41 050 52 700 37535 8482 9431
Sample zone Sample zone
1zr zr zr 1Not sampled Not sampled Not sampled
210 849 13 822 17 234 2620 836 1052
34990 6823 8422 32597 3116 3635
4zr zr zr 4zr zr zr
53648 4633 5650 5442 896 1351
623 997 32 258 41 257 64511 5030 5303
7zr zr zr 7zr zr zr
833 42 48 81212 1377 1541
91076 1977 3026 9438 459 481
Habitat Unit Habitat Unit
120 312 26 980 34 260 16391 7459 8528
2597 682 850 22692 3230 3773
3714 819 1080 3zr zr zr
4zr zr zr 4zr zr zr
5635 1295 2269 5zr zr zr
6zr zr zr 6280 383 486
7353 456 543 7zr zr zr
861 99 130 8zr zr zr
96949 9998 13 447 9481 562 644
10 15 298 18 769 22 358 10 zr zr zr
Total 44 953 59 555 77 567 9843 11 634 13 430
A
Taken from Sullivan et al.(2004).
B
zr = no fresh pellets were found so koala numbers could not be estimated.
Drought-driven change in distribution and numbers Wildlife Research 517
50
40
30
20
10
0
50
40
30
20
10
0
50
40
30
20
10
0
50
40
30
20
10
0
50
Summer days >40°C Summer days >40°C Summer days >40°C Summer days >40°C Summer days >40°C
Summer rain (mm)Summer rain (mm)
Summer rain (mm)
Summer rain (mm)Summer rain (mm)
700
600
500
400
300
200
100
0
700
600
500
400
300
200
100
0
700
600
500
400
300
200
100
0
700
600
500
400
300
200
100
0
800
600
400
200
0
40
30
20
10
0
89...
90...
91...
92...
93...
94...
95...
96...
97...
98...
99...
00...
01...
02...
03...
04...
05...
06...
07...
08...
Fig. 3. The number of days over 40C (black, left axis) and total rainfall (grey, right axis) over the hottest months
(October–March) in major towns in south-west Queensland between 1990 and 2009 (see Fig. 1for town locations).
The black brackets mark the period of interest in summer weather conditions. The black dotted line is the average
number of days over 40C since 1950, and the grey dashed line is the average summer rainfall since 1950.
518 Wildlife Research L. Seabrook et al.
wildlife. For example, Adams (2010) found that higher
temperatures, and particularly severe drought, significantly
reduced reproductive output in bats in western North America.
In Australia, one day over 42C killed thousands of flying foxes
(Pteropus spp), with greater mortality among young and adult
females (Welbergen et al.2008). Bolger et al.(2005) found that
drought years resulted in low food availability for insectivorous
passerine birds in California, leading to very low breeding success
rates. Albright et al.(2010) modelled the impacts of drought on
North American birds and found that abundance, richness and
species composition of avian communities were affected in
different ways and at different magnitudes, with the greatest
negative impacts on species in the semiarid Great Plains. They
also suggested that there could be stronger effects for prolonged
or more frequent droughts (Albright et al.2010). Our study
supports these effects. We found the most likely causes of the
decline in numbers and area of occupancy of the koala in
south-west Queensland were climate extremes, particularly the
combination of low summer rainfall and very hot days, i.e.
heatwaves. Reports from landholders, based on the community
survey and subsequent discussions, allowed the view to be formed
that local disappearances of koalas occurred around 2002–2003
across most of the region, and around 2006 in the north-east of the
region. Although we cannot unequivocally state that the drought
caused the koala population declines, increased mortality due to
low rainfall and/or heatwaves has been found in other koala
research. In the very hot, dry summer of 1979–1980, 63% of
koalas died along on the Mungallala Creek in the eastern part
of the Mulgalands (Gordon et al.1988). Over this period, the
nearby town of Bollon recorded 31 days over 40C and summer
rainfall of 154 mm (see Fig. 3for comparison with recent weather
records). A resurvey of sites in Springsure, central Queensland, in
2009 found that, since 1995, koalas had disappeared at two out of
four sites, and densities were much decreased in the other two
(Ellis et al.2010). Similarly, Lunney et al. (in press) estimated that
~25% of koala population around Gunnedah in north-west New
South Wales perished in November–December 2009 during the
heatwaves in a long-running drought. At a larger scale, Gordon
et al.(1990) found that in northern and western Queensland
variability in rainfall, and its effect on food sources, was probably
the most important factor affecting koala populations. Adams-
Hosking et al.(2011) modelled a significant eastward contraction
in the koala’s distribution in Queensland and New South Wales
under projected climate change scenarios. They concluded that in
arid and semiarid regions, such as south-west Queensland,
climate change is likely to compound the impacts of habitat
loss, resulting in significant contractions in the distribution of
this species. Our study provides empirical evidence supporting
this prediction.
Understanding the impacts of climate change on species
will be of great importance in biodiversity conservation over
the coming decades (Parmesan 2006). The ability of species to
adapt to climate change will depend on several factors, including:
(i) dispersal ability; (ii) phenotypic plasticity; (iii) evolutionary
adaptability; and (iv) physiological tolerance (Williams et al.
2008). Range shifts in response to higher temperatures have
been seen in several species, generally either polewards or to
higher elevations (Tidemann 1999; Hughes 2003; Thomas et al.
2006; Thuiller et al.2008; Gibson et al.2009; Thomas 2010).
Some species are showing signs of physiological adaptation
to climate change, either through phenotypic plasticity or
behavioural change (Parmesan 2006; Fuller et al.2010). For
example, migratory species are changing their departure and
arrival times (Green and Pickering 2002; Gordo et al.2005)
and breeding seasons are commencing earlier (Crick et al.1997).
Thermal tolerance has been identified as one of the key
limitations for physiological adaptation to climate change
(Fuller et al.2010). There is evidence that koalas can adapt
their behaviour and physiology to deal with heat to some extent
(Krockenberger 2003; Ellis et al.2010). However, Clifton et al.
(2007) found that in coastal communities, the physiological
tolerance of koalas seemed be affected by hot night-time
temperatures and humidity, which reduced evaporative heat
loss. Bioclimatic modelling carried out by Adams-Hosking
et al.(2011) indicates that the maximum summer temperatures
that are physiologically acceptable for koalas are ~37.7C. This is
supported by Degabriele and Dawson (1979) who found that
evaporative water loss in koalas more than doubled and the
respiratory rate rose from a mean of 25.9 to 231.3 breaths per
minute when air temperatures increased from 30Cto40
C. If
these temperatures are exceeded on a regular basis, physiological
stress will lead to increased koala mortality. Thus, this study raises
significant implications for the effect of climate change for koalas
in south-west Queensland, and for other wildlife in the region. In
Table 4. Percentage of land cleared as a proportion of the pre-clearing extent in each sample zone (SZ) and each habitat unit
(HU) between 1995 and 2008
The column and row of totals show the percentage of all clearing within the SZ or HU (Source: SLATS)
HU1 HU2 HU3 HU4 HU5 HU6 HU7 HU8 HU9 HU10 Clearing
by SZ
all zones
SZ2 0.00 0.11 0.63 0.00 0.00 2.59 2.62 0.00 0.00 0.58 2.81
SZ3 3.12 1.20 22.28 9.98 10.15 17.93 17.33 0.00 11.76 6.07 11.72
SZ5 0.42 5.71 4.27 0.00 4.91 3.86 8.80 0.00 10.89 2.00 3.47
SZ6 7.19 8.02 13.84 0.04 21.50 23.00 5.45 14.43 18.61 10.71 18.17
SZ8 0.47 1.66 2.17 0.00 12.54 6.66 2.97 4.19 35.20 4.09 4.23
SZ9 15.40 12.64 13.18 4.30 24.66 24.24 13.30 22.59 0.00 0.00 22.60
Clearing by HU all zones 3.4 2.9 5.3 2.5 20.2 13.1 5.8 10.5 16.0 4.1
Drought-driven change in distribution and numbers Wildlife Research 519
Australia, the mean annual temperature has increased by 0.9C
since 1901 (Hennessy et al.2008). Since 1950, Queensland has
the greatest rise in mean annual temperature in Australia (1.2C)
and the greatest decline in total annual rainfall (–107 mm). By
2040, exceptionally hot years are predicted to occur every
2 years, affecting 60% of the state (Hennessy et al.2008).
Climate change predictions from the Queensland Government
report that, in Charleville, the number of days over 35C will
nearly double to 130 days per annum by 2070, and while there is
not a consensus on whether average annual rainfall will increase
or decrease, the majority of predictions point to a decrease in
rainfall, with increased variability and the likelihood of more
frequent droughts (DERM 2009). The drought and exceptionally
hot years that occurred between 2001 and 2007 mimicked these
climate change predictions.
Climate change will also affect terrestrial vegetation and
thus habitat quality and resource availability for many species
(Hughes 2003). Changes in foliar chemistry due to elevated CO
2
are predicted to significantly affect arboreal folivores, including
koalas (Kanowski 2001; Hughes 2003; Moore et al.2004;
Lunney et al. in press). For example, DeGabriel et al.(2009)
found that the reproductive success of common brushtail
possums was reduced when leaves contained a high tannin
content, which lowers the nitrogen available to folivores.
Lawler et al.(1997) found higher CO
2
levels affected the
carbon : nitrogen ratio in forest red gum (Eucalyptus
tereticornis), which in turn increased levels of plant secondary
metabolites (PSMs). Herbivorous beetle larvae feeding on these
leaves showed increased mortality rates and reduced body
size. Higher CO
2
levels also decreased the amount of nitrogen
allocated to leaf protein and increased that allocated to the
production of toxic plant defences, reducing the nutritional
quality of leaves (Gleadow et al.1998). Both studies found the
effects enhanced when nitrogen availability was limited. In
addition to changes in leaf chemistry, if drought conditions
are severe enough, particularly when combined with extremely
hot days, leaf fall and tree die back can lead to population crashes,
such as those seen at Mungallala Creek and Springsure (Gordon
et al.1988; Ellis et al.2010).
Habitat loss and fragmentation are also important drivers
of changes in species distribution and numbers (Fahrig 2003;
Wiegand et al.2005). Habitat loss and fragmentation were
identified as one of the main causes of extinction of woodland
birds in Australia, related to poor dispersal to isolated patches,
increasing nest predation, changes in habitat quality and
resource availability, and increasing interspecific completion
from aggressive bird species (Ford 2011). Brown et al.(2008)
found that reptiles in Victoria, Australia, had declined
significantly with loss of habitat and changes in woodland
structure, with declines in species richness even in remaining
large patches. For howler monkeys in the Central and South
American Neotropics, loss of habitat was more important than
fragmentation on distribution and abundance, probably because
howler monkeys are highly mobile and resistant to initial
habitat disturbance (Arroyo-Rodriguez and Dias 2010). Loss
of habitat has been identified as one of the most significant
threats to koalas in New South Wales (Reed and Lunney
1990) and south-east Queensland (Seabrook et al.2003;
McAlpine et al.2006b). Although in Queensland, broad-scale
clearing of remnant vegetation has been regulated since 2006,
some clearing is still permitted for drought fodder, infrastructure
and urban development, and non-remnant vegetation continues
to be cleared (McGrath 2004;/2005). In a study of koalas in
south-east Queensland, the total area of forest, larger forest
patches and smaller distances between patches were important
predictors of koala presence (McAlpine et al.2006b). Land
clearance and habitat fragmentation are likely to have affected
koala distribution and numbers, particularly in Sample Zone (SZ)
6 and 9 in the east of the region (Table 3and see Fig. 1for
locations). Between 1995 and 2008, SZ 9 lost 28% of high quality
river red gum and coolabah habitats to clearing, and nearly
25% of poplar box habitat, while SZ 6 lost 15% of riverine
and coolabah vegetation and 21% of poplar box habitat. The
contraction of koalas across the region to riverine habitats
suggests that animals had vacated less-favourable poplar box
habitats over the drought, but the clearing of key riverine habitat
would almost certainly have affected the number of koalas.
Management of trailing-edge populations during climate
change will be challenging because, even if greenhouse gases
are significantly reduced, there are likely to be lag effects on
species. There are already delays between habitat clearance and
loss of species, leading to an extinction debt (Tilman et al.1994;
Mouquet et al.2011), and the extinction debt may contribute to
the finding of Anderson et al.(2009) that changes in trailing-edge
populations happen more slowly than those in leading-edge
expansion. The interaction of habitat loss, fragmentation, and
climate extremes will increase pressure on some populations and
species, particularly in isolated trailing-edge populations where
dispersal ability is limited (Travis 2003; Thomas et al.2004). For
instance, Piessens et al.(2009) found that for populations of
Cupido minimus, a specialist herbivore butterfly, extinction
probabilities were significantly higher for small populations in
fragmented habitat during and after the hot summer in 2003 in
Europe. Williams et al.(2008) suggested that the first step towards
conserving species in a changing climate is to understand the
relative vulnerability of the species and implement known
conservation techniques in potential refuge areas, including
increasing habitat amount and connectivity. However, we also
need to adopt a dynamic and proactive approach to conservation
to deal with spatial changes in species distribution and ecosystem
components (Williams et al.2008). For many trailing-edge
populations, variations in local microclimates may make the
difference between survival and extinction (Fuller et al.2010).
For example, research on European mussels indicates that
populations with low genetic diversity at the southern or
trailing edge of their range are less adapted to heat stress than
central populations (Pearson et al.2009). However, cold
upwelling explained the presence of relict populations of
barnacles in Europe, indicating that microclimatic influences
can override broad climate change (Wethey and Woodin
2008). Local microclimatic variations may explain occasional
koala sightings in the Grey Ranges in the south-west of the study
area. Here, the local climate and habitat could be sufficiently
different from those along the Bulloo River to allow koalas to
persist. Further surveys would be required in this region to offer
a more definitive explanation.
For koalas in south-west Queensland, the identification of
potential refugia through continued monitoring of population
520 Wildlife Research L. Seabrook et al.
change at the edges of the range will allow us to assess which local
populations are resilient to climate change, and therefore where to
concentrate management actions. In 2009, koalas were found
almost exclusively along creek lines where river red gum and
coolabah were present. There was little evidence of koalas at high
densities in residual landscapes, as found by Sullivan et al.(2004)
in the mid 1990s. This demonstrates that riverine habitats are
critical refugia in the region and their role as core habitat will
become more important as climate change leads to a greater
incidence of hot, dry conditions. This finding agrees with other
research in semiarid western Queensland. Along Mungallala
Creek in the south-east of the SWNRM region, Gordon et al.
(1988) found that koalas survived in riparian vegetation and
died in more marginal habitats away from the creek during a
very hot dry summer. In the Desert Uplands bioregion north of
our study area, Munks et al.(1996) found that the greatest
densities of koalas were along creeks, with strong relationships
with proximity to water and leaf moisture. However, in
Springsure, in central Queensland, the areas worst affected by
drought were along creeks, where forest red gums and carbeen
(Corymbia tessellaris) suffered extensive tree death, while
koalas survived off creeks (Ellis et al.2010). Ellis et al.
(2010) reported similar results at Blair Athol in central
Queensland, where plains vegetation comprising popular box
and narrow-leaved ironbark (E. crebra) have continued to support
koala populations and are the preferred food trees at the site. It
appears that the key refuge habitats where koalas survive in
times of environmental stress vary among locations, and this is
provisionally linked to both moisture availability and soil
nutrients, which in turn affect the balance between leaf
nutrients and leaf toxins (Creagh 1992; Cork and Braithwaite
1996; Bryan 1997; Moore and Foley 2000; Moore et al.2004).
Although there will continue to be years when numbers will
expand and koalas will disperse into less optimal habitat, in south-
west Queensland the maintenance of core habitat along creek
lines with permanent waterholes will become increasingly critical
in the coming decades.
Approach and limitations
There are some differences between this study and that by
Sullivan that affect direct comparison in some sample zones
and in habitat units 9 and 10. First, the region surveyed by
Sullivan and the region of this study are not identical and
although estimates of koala abundance in 2009 are limited to
the overlap zone, Sullivan’s koala estimates are for a larger area.
The area covered by SZ 2 in 2009 is smaller than in 1995, and
did not include the region around Idalia National Park, where
Sullivan et al.(2004) found koalas in HU9 and HU10 vegetation.
We found no fresh pellets in HU9 or HU10 in SZ 2, but we
sampled a much smaller area of these two habitat units and this
reduced our chances of finding fresh pellets. Overall, we had
proportionally fewer sites in HU9 and 10 than in Sullivan et al.
(2004). This may affect the comparison between the two studies,
as their population estimates were high for these two habitats
(Table 3). We found no fresh pellets in HU10, so no density
estimates could be calculated, and we estimated low densities
from fresh pellets in HU9. Our conclusion is that the koala
population in these habitats markedly decreased during the
drought, despite the difference in sampling effort. The area
covered by SZ 6 and SZ 9 was slightly larger in Sullivan’s
study (Fig. 1). Fresh pellets were found by Sullivan at two
sites on one property in SZ 6 that was not in the overlap zone.
The sites with fresh pellets in SZ 8 were close to the boundary with
SZ 9 and the different estimates in these two zones may reflect a
slightly different boundary position between the two survey
periods, rather than a significant change in numbers. Finally,
there appears to be a discrepancy in koala abundances in HU1
and HU2 between the 1995 and 2009 figures, with our HU2
showing higher numbers. This may reflect a difference in the
habitat unit assigned to each site. Notwithstanding these minor
differences in the two survey methods, the field data and analysis
reflect a real decline in koala numbers since 1995 in south-west
Queensland. Our results show that climate change and land
clearing are key concerns for the long-term survival of koalas
in this region.
Acknowledgements
This project is funded by the Australian Research Council, the Australian
Koala Foundation and South West NRM through LP0882090. Data on land
clearing were supplied by the Remote Sensing Centre of the Queensland
Department of Environment and Resource Management. Field surveys were
carried out by Rebecca Condon, Virginia Seymour and Nicole Davies, with
help from Leonie Seabrook and several volunteers. We would like to thank
the staff of South West NRM and all landholders who participated in the
community survey and who allow us to carry out faecal pellet surveys on
their land. Their knowledge, help and hospitality were much appreciated.
The approved protocol for this project was obtained from the Animal
Ethics Committee at The University of Queensland (GPA/603/08/ARC)
and the Scientific Purposes Permit WISP05343008 was obtained from the
Environmental Protection Agency (now the Department of Environment and
Resource Management).
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