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FERAL MAMMALS IN AUSTRALIA’S RANGELANDS: FUTURE THREAT,
MONITORING AND MANAGEMENT
G. P. Edwards, A. Pople, P. Caley and K. Saalfeld
Abstract
In this paper we provide an initial brief overview of past changes in the biodiversity of
Australia’s rangelands. Following on, we focus on current and future threats to biodiversity
posed by feral mammals (predators and herbivores) inhabiting the rangelands, exploring
trends in populations and options for management. Notably, rabbits have declined in recent
years in the wake of Rabbit Haemorrhagic Disease, populations of camels have increased
dramatically, while foxes appear to have moved northwards thereby threatening native fauna
within their expanded range. Finally we examine how to monitor the impacts of feral
mammals so that management can be applied at the correct time and scale. Factors which
need to be considered when designing a monitoring programme are discussed. While it is pest
impact that should ideally be monitored, this is rarely achieved in practice. Rather, monitoring
usually involves population assessments, the untested assumption being that higher densities
equate to higher impacts. Current ‘best practice’ methods of monitoring populations of feral
mammals in the rangelands are discussed briefly in the closing section.
AN OVERVIEW OF CHANGES IN THE BIODIVERSITY OF AUSTRALIA’S
RANGELANDS
There have been dramatic changes in the biodiversity of Australia’s rangelands over the last
200 years (Morton 1990; Short & Smith 1994; Smith & Quin 1996; Woinarski 2001). Patterns
of biodiversity loss vary according to taxa and depending on geographic locality. Mammals of
the arid and semi-arid zones have suffered most with 14 species now extinct on the mainland
while the ranges of a similar number have declined markedly in extent (Morton 1990). In the
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more mesic northern rangelands, which include the Kimberley and the tropical savannas, the
situation is not as catastrophic- several declines but no extinctions in the mammalian fauna
(Woinarski 2001). Rangeland birds have fared better than the mammals. Although a
significant number of bird species have declined, some to the point of regional extinction,
none has totally disappeared (Morton 1990; Reid & Fleming 1992; Woinarski 2001). The
same appears to be true of reptiles and amphibians (Morton 1990; Woinarski 2001). Nine
plant species which formerly occurred in the rangelands are now considered to be extinct on
the mainland (Leigh & Briggs 1992) while a further 22 rangeland species are considered
threatened (Woinarski 2001).
Decline in the biodiversity of Australia’s rangelands is ongoing (Woinarski 2001). The rufous
hare-wallaby (Lagorchestes hirsutus) became extinct on the mainland as recently as 1991
(Langford & Burbidge 2001). The ranges of other arid zone mammals like the greater bilby
(Macrotis lagotis) (Paltridge & Southgate 2001) and the black- footed rock wallaby (Petrogale
lateralis) (Gibson 2000; Copley pers. comm.) have continued to decline since that time.
Recent declines have also been noted in mammals of the northern rangelands including the
northern quoll (Dasyurus hallucatus) (Braithwaite & Griffiths 1996), the pale field rat (Rattus
tunneyi) and the black- footed tree-rat (Mesembriomys gouldii) (Woinarski 2000). Recher and
Lim (1990) and Recher (1999) have argued that, within a few decades, the loss of bird
biodiversity in the rangelands will rival that currently ascribed to mammals.
The processes which have wrought change to biodiversity in the rangelands are purported to
include droughts and climatic change, predation by feral animals, grazing by domestic stock
and feral animals, and altered patterns of fire (Morton 1990; Woinarski 2001). Changes
cannot be attributed directly to any single process and are likely due to a complex interaction
between two or more of these or other factors (Woinarski 2001). In this paper we focus on
feral mammals (predators and herbivores) in the rangelands. We outline the threats posed by
feral mammals to biodiversity now and in the future. We also examine ways in which the
level of threat posed by feral mammals can be accurately gauged so that appropriate
management can be applied at the correct time and scale.
THREATS TO BIODIVERSITY POSED BY FERAL MAMMALS: CURRENT
STATUS, FUTURE TRENDS AND MANAGEMENT
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Habitat degradation, competition and other impacts of introduced herbivores
Rabbit (Oryctolagus cuniculus)
European rabbits are widespread in the arid and semi-arid rangelands where they have had a
profound effect on the vegetation (Williams et al. 1995). In many areas, perennial pasture
species have been replaced by annuals largely as a result of intensive rabbit grazing (Hall et
al. 1964), and until recently, the recruitment of palatable shrubs and trees was suppressed by
rabbits over vast expanses of the arid rangelands (Lange & Graham 1983; Foran et al. 1985;
Cooke 1987). The Commonwealth Environment Protection and Biodiversity Protection Act
(EPBC) (1999) identifies key processes which threaten the survival, abundance and/or
evolutionary development of native species. Accordingly, Threat Abatement Plans must be
prepared and implemented. Competition and land degradation by European rabbits is
currently listed as a key threatening process under this act. The impacts which rabbits have, or
might have had, on native mammals is open to conjecture. Several authors (Morton 1990;
Williams et al. 1995; Woinarski 2001) suggest that rabbits have played a key role in the
demise of arid zone mammals whether directly through competition and habitat degradation
or by supporting high populations of introduced predators. A recent study (Robley et al. in
press) indicates that the latter of these mechanisms has had by far the greater impact on native
mammals. Australia wide, biological control has had a significant impact on rabbit
populations. Myxomatosis, which was deliberately introduced in the early 1950s, had a
marked initial impact on rabbit populations but did not solve the rabbit problem in the longterm (Coman 1999). Rabbit Haemorrhagic Disease (RHD), which became established in
1995, has had a more profound effect. It has reduced rabbit numbers across much of the arid
and semi-arid rangelands by over 80% (Cooke 1999; Neave 1999). This has been enough to
allow the regeneration of many perennial shrubs and trees (Sandell & Start 1999) that were
threatened with extinction across the arid rangelands (Woinarski 2001). Rabbit Haemorrhagic
Disease is endemic in rangeland rabbit populations and at this point in time there is no
evidence of any reduction in its impact (Cooke 1999).
Goat (Capra hircus)
Feral goats are common in the arid and semi-arid rangelands of New South Wales,
Queensland, South Australia and Western Australia- particularly where stock water has been
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provided and dingoes (Canis lupus dingo) are controlled (Parkes et al. 1996). In 1993 the
population of feral goats was estimated at 2.6 million with most of these in the rangelands
(Parkes et al. 1996). Left unchecked, feral goat populations have the potential to reach much
higher densities than currently exist (Parkes et al. 1996). Although the environmental impacts
of feral goats are not well documented, competition and land degradation by feral goats is
listed as a key threatening process under the EPBC Act. Goats are believed to have a
deleterious effect on perennial vegetation, contribute to soil erosion and compete with native
mammals for forage (Parkes et al. 1996; Woinarski 2001). Goats are widely harvested for
commercial purposes (Parkes et al. 1996). Harvesting is usually accomplished by trapping
and mustering. Alternative control techniques are ground-based and aerial shooting (Parkes et
al. 1996; Edwards et al. 1997). In South Australia, yellow- footed rock-wallaby (Petrogale
xanthopus) populations have responded positively to a combination of fox and goat control in
recent years (de Preu et al. 2001).
Horse (Equus caballus) and donkey (E. asinus)
Feral horses are patchily distributed within the rangelands (Dobbie et al. 1993) whereas the
major concentration of donkeys is in northern Australia (Wilson et al. 1992). The most
reliable population estimates are from the Northern Territory where recent aerial surveys
show that there are about 265,000 horses and 165,000 donkeys (K. Saalfeld, Parks and
Wildlife Service of the Northern Territory, unpublished data, 1986-2001). Although the
environmental impacts of feral horses are not well documented, it is believed that they
contribute to erosion, damage vegetation and disperse weeds (Dobbie et al. 1993). While
unequivocal data on the competitive impacts of horses on native animals are lacking, there
has been a striking recovery in populations of the black- footed rock-wallaby on Finke Gorge
National Park near Alice Springs since large numbers of feral horses were removed during the
1990s (Matthews et al. 2001). Trapping, mustering and aerial shooting are the best methods
for humanely controlling feral horses and donkeys over large areas (Dobbie et al. 1993).
Camel (Camelus dromedarius)
Feral camels are widely distributed in the arid and semi-arid rangelands of Western Australia,
South Australia and the Northern Territory. The most reliable population estimates are from
the Northern Territory where recent aerial surveys show that there about 200,000 camels (K.
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Saalfeld & G. Edwards, Parks and Wildlife Service of the Northern Territory, unpublished
data, 2001). This extrapolates to a population of about 740,000 camels Australia wide (Short
et al. 1988). The Northern Territory population more than doubled between 1993 and 2001
(Wurst & Saalfeld 1993; K. Saalfeld & G. Edwards, Parks and Wildlife Service of the
Northern Territory, unpublished data, 2001) and, left unchecked, will double again in about 8
years. Although the environmental impacts of feral camels are not well documented,
anecdotal evidence indicates that they contribute to erosion, damage vegetation and foul
waterholes (P. Latz, pers. comm.). Like goats, camels are viewed as a resource and are liveharvested in central Australia. However, current offtake (approximately 5,000 p.a., P. Seidel
pers. comm.) is unable to contain population growth even within the Northern Territory.
Trapping and mustering are the main methods used to harvest camels for commercial
purposes. In remote areas where it is not economically feasible to harvest camels, the only
option available for reducing camel numbers and impacts is aerial shooting.
Water buffalo (Bubalus bubalis)
Feral water buffalo are confined to the wetter parts of the northern rangelands. Prior to the
national Brucellosis and Tuberculosis Eradication Campaign, which saw widespread
elimination of populations, there were approximately 340,000 water buffalo in northern
Australia (Bayliss & Yeomans 1989a,b). The current population is about 73,000 (K. Saalfeld,
Parks and Wildlife Service of the Northern Territory, unpublished data, 1997-2000).
Obviously the population has enormous growth potential. Disturbance by water buffalo
facilitates salt-water intrusion in wetland areas leading to wholesale vegetation change
(Whitehead et al. 1990). Water buffalo have also been linked to declines in fauna populations
(Braithwaite et al. 1984). Given the nature of the habitat which they occupy, aerial shooting is
the most cost effective option for reducing water buffalo populations.
Pig (Sus scrofa)
There are estimated to be between 3.5 and 23.5 million feral pigs in Australia with most of
these in the rangelands (Choquenot et al. 1996). Feral pigs occur patchily around wetland
areas of the dryer rangelands but are widespread and common in the wetter northern
rangelands. Pigs are omnivorous and consume a wide range of plants and animals (Choquenot
et al. 1996). Pigs also root up ground contributing to soil erosion and bank destabilisation, and
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locally threaten some plant species (Russell-Smith & Bowman 1992; Choquenot et al. 1996).
Accordingly, predation, habitat degradation, competition and disease transmission by feral
pigs is listed as a key threatening process under the EPBC Act. Aerial shooting, ground
shooting, trapping and poisoning are among the methods used to control feral pigs
(Choquenot et al. 1996).
Predation
Cat (Felis catus)
Feral cats are distributed throughout the rangelands (Dickman 1996). Although densities as
high as 6.3 km-2 have been recorded in the Mitchell grass downs during an eruption of the
long- haired rat (Rattus villosissimus) (G. Edwards, Parks and Wildlife Service of the Northern
Territory, unpublished data, 1994), densities in the order of 0.1-0.6 km-2 are more typical of
arid rangelands (Jones & Coman 1982; Edwards et al. 2001). Contrary to the predictions of
Newsome et al. (1997), feral cats have not declined in abundance in concert with rabbits over
large parts of the arid rangelands in the wake of RHD (Edwards et al. in press). Predation by
feral cats is listed as a key threatening process under the EPBC Act. The clearest evidence that
cat predation can have a serious impact on populations of native animals comes from recent
attempts to reconstruct rangeland mammal assemblages. A reintroduction programme for the
rufous hare-wallaby in central Australia failed due to predation by feral cats (Gibson et al.
1994). More recently, predation by feral cats has hampered attempts to reintroduce the
burrowing bettong (Bettongia lesueur) and numbat (Myrmecobius fasciatus) in Western
Australia (Christensen & Burrows 1995; Friend & Thomas 1995) and the brush-tailed bettong
(Bettongia penicillata) in New South Wales (D. Priddel, NSW National Parks and Wildlife
Service, unpublished data, 2002). Although feral cats have been eradicated from islands using
a combination of techniques (Veitch 1985; Berruti 1986; van Rensburg et al. 1987; van
Rensburg & Bester 1988; Bloomer & Bester 1992), broadscale control on the Australian
mainland has proved problematical (Christensen & Burrows 1995). Because feral cats rarely
scavenge (Bayly 1978; Paltridge et al. 1997), it appears that successful control can only be
achieved by baiting at times of low prey abundance (Short et al. 1997).
Red fox (Vulpes vulpes)
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Foxes are distributed throughout the arid and semi-arid range lands (Saunders et al. 1995).
Reported densities are in the range 0.6-2 km-2 (Marlow 1992; Saunders et al. 1995) with
densities being higher in areas with rabbits but without dingoes (Saunders et al. 1995). Foxes
appear to be expanding their range in a northerly direction. They were rarely encountered in
the Tanami Desert northwest of Alice Springs in the 1970s and early 1980s (Bolton & Latz
1978; Gibson 1986). Now they are relatively common there as far north as Tennant Creek
(Paltridge & Southgate 2001). Foxes have not declined in abundance in concert with rabbits
over large parts of the arid rangelands in the wake of RHD (Sandell & Start 1999; Edwards et
al. in press). There is abundant evidence that predation by foxes is a major threat to many
species of native fauna (Burbidge & McKenzie 1989). Studies in Western Australia have
shown that foxes have a major impact on existing populations of black-footed rock-wallabies
(Kinnear et al. 1988, 1998), brush-tailed bettongs (Saunders et al. 1995), numbats (Friend
1990) and tammar wallabies (Macropus eugenii: Saunders et al. 1995). Populations of all
these animals increased following intensive fox control. Similarly, in New South Wales,
malleefowl (Leipoa ocellata) survival has been shown to increase following fo x removal
(Priddel 1989; Priddel & Wheeler 1990). In the Northern Territory, one of the two last known
wild populations of the rufous hare-wallaby was exterminated by a fox (Lundie-Jenkins et al.
1993). Predation by foxes is appropriately listed as a key threatening process under the EPBC
Act. Remaining wild populations of the bilby may be at risk of extinction if foxes continue to
push northwards into the bilby’s remaining stronghold (Newsome et al. 1997). Foxes readily
scavenge and can be effectively controlled over large areas using baits containing compound
1080 (sodium mono- fluroacetate) (Christensen & Burrows 1995; Saunders et al. 1995;
Thomson et al. 2000).
MONITORING THE IMPACTS OF FERAL MAMMALS
Designing a monitoring program
Monitoring and management action
Population monitoring serves two purposes in vertebrate pest management. Firstly, it provides
necessary information, such as population size or trend, which underpins management-based
decision making (Elzinga et al. 2001). Secondly, it provides ‘research and performance
evaluation’ (Possingham 2001), by indicating whether a control strategy is working and
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allowing that strategy to be refined. This latter role forms the basis of adaptive management,
where management learns about the system through monitoring responses to management
actions (Shea et al. 1998). Ideally, adaptive management should be structured like an
experiment, with controls and a range of treatments, in order to maximise what can be learnt
from manipulating the system.
Ideally, it is pest damage that should be monitored (Hone 1994). However, if it is impractical
to monitor impacts or if the relationship between damage and absolute population size (N) of
the pest is known, then one can monitor N and use it to indicate damage. Possible
relationships between population size and damage are shown in Fig. 1. Figure 1 is an
oversimplification, particularly as it assumes that damage (e.g. overgrazing) will be a function
of pest density only, and ignores temporal and spatial variation in environmental conditions
and variation in the density of other species. For example, in the rangelands, the potential for
land degradation through overgrazing is likely to be heightened during drought. Drought
would shift the impact curve from C to A in Fig. 1. The cost of controlling large herbivores is
also likely to be cheaper during drought as animals are concentrated around persistent feed
and water. Whether or not there is a linear relationship between pest abundance and impact (B
in Fig. 1) will vary case-by-case (Hone 1994). Nonlinearities may signify the existence of
stable states that can provide a level of resilience to a change. This may be vegetation
resisting further degradation or rehabilitation (Friedel 1991), or a population of endangered
prey remaining trapped within a ‘predator pit’ despite some effort expended in control (Pech
et al. 1995).
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Cost ($)
Impact (A)
Impact (B)
Control
Impact (C)
Pest density
Figure 1. Relationships between pest density and the per capita cost of control (dashed line) and the
per capita cost of damage or impact (solid lines). A target density could be identified at the point of
intersection. After Braysher (1993).
Dealing with uncertainty
Uncertainty (or error) in our understanding of how a rangeland system functions, and how it
will respond to management intervention (e.g. predator control), arises from a number of
sources. Process uncertainty results from natural variation in the environment through time
and can be further broken down into demographic and environmental stochasticity (Hilborn &
Mangel 1997). Observation uncertainty results from measurement or sampling error (Hilborn
& Mangel 1997). Finally, where mathematical models are used, there is model uncertainty,
which arises from ignorance of how a system works, and includes uncertainty in parameters
and in the actual model structure (Parma et al. 1998).
There are a range of ways in which observation uncertainty can be reduced including
increasing sample size and stratification. The appropriate level of precision should be
identified before a survey is undertaken and will depend on factors that include the size of the
population change that needs to be detected (sensitivity sensu Eberhardt 1978) and/or the
proximity to some threshold that will trigger a management action.
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Process uncertainty introduces complications when interpreting a time series of data, as
important trends may be obscured by inherent variability in the ecological processes involved.
Detecting trends in the face of process uncertainty requires an understanding of how
populations respond to environmental change through time, primarily food supply driven by
rainfall. Estimates of precision from a regression of ln(N) against time will be misleading
because data are likely to be autocorrelated (McCallum 2000).
An important problem is determining how much monitoring should be done. Should more or
fewer resources be invested in estimating vertebrate pest numbers? In wildlife management,
little use is made of standard errors in broad-scale population estimates beyond assessing
whether there has been a significant year-to-year change in population size. This uncertainty
in population size (observation error) can also be used to determine the risk of an undesirable
management outcome. This may be excessive control, leading to wasted effort, or too little
control, leading to less effective damage mitigation. The uncertainty in future population size
due to environmental stochasticity (process error) and model uncertainty would also need to
be incorporated into this risk assessment. This approach is well established in conservation
biology (e.g. Burgman et al. 1993), but can be applied to other areas of wildlife management
(e.g. McCarthy 1996). This framework could then be used to determine the adequacy of
present monitoring. Surveys could obviously be increased in frequency or intensity to reduce
risk, or reduced in frequency if the resulting increased risks were acceptable.
Indices of abundance and bias
An index I of population size (or density) is some attribute that changes in a predictable
manner with changes in absolute population size N (or density) (Caughley & Sinclair 1994). I
may be a count of sign (e.g. the density of tracks), which indicates that the species of interest
is present, or a direct count of individuals of the species (e.g. the number of kangaroos seen
per km along a road). Strip-based direct counts of animals are best viewed as indices of
population size as they typically underestimate N because observers do not see all of the
individuals (ie. the counts are negatively biased) (Jolly 1969). Indices are useful for tracking
relative abundance only if the ‘proportionality constant’ β connecting them to N (i.e. I=βN)
remains more or less constant among surveys (Lancia et al. 1996, McCallum 2000). This is
often assumed but rarely tested. Factors affecting counts (e.g. weather, observer experience,
velocity of travel) will therefore need to be either standardised or corrected for to ensure β
10
does not vary. Procedures for determining bias in direct counts are described in the next
section.
For incomplete counts, observation error comprises sampling variation and error in β. Using
the delta method (Seber 1982), an approximation of the variance of N is (Lancia et al. 1996):
 var( x )
var( β ) 
var( N ) = N 2  2 (1 − α ) +

β2 
 x
where x is the raw count of animals, α is the proportion of the study area sampled (i.e. 1-α is
the finite population correction), and β is the proportion of animals counted in the sample
unit, such as an aerial survey strip (i.e. N =
x
).
αβ
Current ‘best practices’
Across the rangela nds, little attempt is made to monitor the actual impacts of feral mammals.
There may be several reasons for this. First, the impacts of many of the feral mammals that
inhabit the rangelands are poorly understood (see above). It is unfortunate that many of the
studies which have addressed this issue lack scientific rigour (Hone 1994). Second, most feral
mammal species are widely distributed and it is neither practical nor efficient to monitor
impacts at the appropriate scales. Third, grazing impacts in particular are often confounded
due to the presence of other grazing species. As a result, monitoring, where it occurs at all,
usually involves population assessments. These are used as the basis for management
decisions, the untested assumption being that higher densities equate to higher impacts.
Population estimates for larger herbivores tend to be undertaken at irregular intervals and it is
important to consider the intervening environmental conditions (ie. process error), in addition
to observation error, before invoking long-term trend.
For large feral herbivores (horses, donkeys, camels, goats, buffalo), broad-scale monitoring
can be achieved efficiently through aerial survey, using either fixed wing aircraft or
helicopters and either strip or line transects (Bayliss & Yeomans 1989a,b; Wurst & Saalfeld
1994; Southwell 1996; Pople et al. 1998). Aerial survey data incorporate two types of biasavailability bias and perception bias. Perception bias is a result of observers missing animals
that are potentially visible, while availability bias arises because some animals are concealed
from the observers (Marsh & Sinclair 1989). Survey parameters (e.g. height, velocity, strip
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width) have varied among surveys although comparisons can usually be made over time and
between areas when β, and therefore N, has been estimated.
In aerial strip transect sampling, factors affecting bias (e.g. observer, group size and habitat)
can be identified using double counting and log- linear modelling (Marsh & Sinclair 1989;
Choquenot 1995b; Pople et al. 1998). An assumption of double counting that is potentially
violated in aerial surveys is equal ‘catchability’ of sightings. Caughley & Grice (1982)
suggested through simulation that violation of this assumption only introduces significant bias
when sighting probabilities are <0.5. Unequal catchability, or capture heterogeneity, is often a
problem in mark-recapture studies, and can be partly addressed in aerial surveys by modelling
the heterogeneity. For example, the relationship between sighting probability and group size
can be quantified using regression methods (Graham & Bell 1989; Pople et al. 1998). The
potential overall bias can also be tested with independent data, such as index- manipulationindex (Bayliss & Yeomans 1989a).
Aerial line transect sampling offers survey-specific correction for perception bias (Buckland
et al. 1993). The method has generally been limited to helicopter surveys, because the
primary assumption of sighting probability on and near the line being 100% is more likely to
be met than in fixed-wing surveys. However, combining line transect sampling and double
counting (Borchers et al. 1998) can account for uncertain sighting probability on the line. This
may allow wider application of the technique to fixed-wing surveys, which are considerably
cheaper than surveys by helicopters.
Feral pigs are cryptic, and although changes in absolute density may be monitored by markrecapture (e.g. Caley 1993), over large areas pigs are most effectively monitored by aerial
survey. Due to the dense cover typically occupied, surveys are conducted at low altitude
(30.5–46 m) and low velocity (80–93 km h-1 ), necessitating the use of helicopters (Hone
1990a,b; Choquenot 1995a). Surveys are generally conducted in the cool of early morning
and evening, when a greater proportion of pigs occur in more open habitats.
Spotlight transect counts are the most popular method of monitoring rabbit populations, the
data serving as indices of relative abundance. Line transect sampling (Buckland et al. 1993)
could potentially give estimates of absolute abundance, providing the proportion of rabbits
underground (=availability bias) is estimated. In practice, spotlight counts appear to reliably
12
detect changes in rabbit abundance arising from active management such as poisoning
(Robinson & Wheeler 1983), predator removal (Newsome et al. 1989), or introduction of
disease (Mutze et al. 1998). Although there have been concerns expressed as to the precision
and accuracy of spotlight counts (Fletcher et al. 1999), Caley & Morley (2002) demonstrated
that properly conducted spotlight transect counts are highly precise.
For cats and foxes, broad-scale monitoring is likely to be limited to presence-absence or
abundance rank. Track-based methods appear most appropriate as they offer reasonable
precision and are time efficient (Edwards et al. 2000). There are two classes of track-based
techniques- active and passive. Active methods use attractants (e.g. Linhart & Knowlton
1975; Roughton & Sweeney 1982; Conner et al. 1983; Smith et al. 1994; Thompson &
Fleming 1994); passive methods do not (e.g. Newsome et al. 1972; Thompson et al. 1989;
Allen et al. 1996; Mahon et al. 1998). Such indices have a non- linear relationship with
density, with the index becoming less sensitive as density increases (Edwards et al. 2000).
This may be acceptable if changes in the lower density range need to be detected, as is often
the case in the arid rangelands (Edwards et al. 2000).
New approaches
Thermal imaging has been used in the survey of a number of large vertebrate species since the
late 1960’s, primarily in temperate habitats (Croon et al. 1968; Graves et al. 1972; Parker &
Driscoll 1972; Wride & Barker 1977; Wyatt et al. 1980, 1985; Best et al. 1982; Trivedi et al.
1982; Wiggers & Beckerman 1993; Boonstra et al. 1994; Garner et al. 1995; Naugle et al.
1996; Gill et al. 1997; Havens & Sharp 1998; Focardi et al. 2001). Early surveys had limited
success (Wiggers & Beckerman 1993), due primarily to poor resolution and temperature
differentiation of the imaging equipment. Interference from canopy cover, in particular, led to
difficulty in distinguishing between animal and background (Croon et al. 1968; Graves et al.
1972; Parker & Driscoll 1972). Additionally, the cost of thermal imaging equipment was a
significant impediment to the broad-scale application of the technique (Croon et al. 1968;
Graves et al. 1972; Parker & Driscoll 1972).
Recent advances have resulted in substantial improvements in the accuracy of thermal
imagery for wildlife survey. It is now generally accepted that the technique detects a greater
proportion of the population under investigation than other methods (Gill et al. 1997; Havens
13
& Sharp 1998; Focardi et al. 2001). For example, on Cobourg Peninsula, Northern Territory,
approximately four times as many Bali cattle (Bos javanicus) were detected with thermal
imagery than with standard aerial survey (K. Saalfeld & D. Lawson, Parks and Wildlife
Service of the Northern Territory, unpublished data, 2001). However, thermal imaging
remains expensive. Depending upon the intended use (e.g. ground verses aerial survey) and
the desired level of resolution, thermal imaging equipment can cost from $100,000 to
$500,000 (FLIR Systems Inc, Sierra Pacific InfraRed). In addition, expensive survey
platforms (e.g. helicopter) are required as surveys need to be conducted at relatively low
velocities. Currently, thermal imaging appears best suited to either fine-scale surveys over
small areas or the assessment of bias in broad-scale aerial surveys of large vertebrates. In the
latter context, two species for which the technique may be particularly appropriate are feral
pigs in tropical habitats and camels in arid areas.
14
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