Evaluation of detection methods and sampling
designs used to determine the abundance of
feral cats
A. Robley, D. Ramsey, L. Woodford, M. Lindeman,
M. Johnston and D. Forsyth
2008
Arthur Rylah Institute for Environmental Research
Technical Report Series No. 181
Arthur Rylah Institute for Environmental Research Technical Series No. 181
Evaluation of detection methods and sampling
designs used to determine the abundance of feral cats
Prepared by: Alan Robley, Dave Ramsey, Luke Woodford, Michael Lindeman,
Michael Johnston and, David Forsyth
Arthur Rylah Institute for Environmental Research
123 Brown Street, Heidelberg, Victoria 3084
June 2008
In partnership with:
Arthur Rylah Institute for Environmental Research
Department of Sustainability and Environment
Heidelberg, Victoria
Report produced by:
Arthur Rylah Institute for Environmental Research
Department of Sustainability and Environment
PO Box 137
Heidelberg, Victoria 3084
Phone (03) 9450 8600
Website: www.dse.vic.gov.au/ari
© State of Victoria, Department of Sustainability and Environment 2008
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or email customer.service@dse.vic.gov.au
Citation: Robley, A., Ramsey, D., Woodford, L., Lindeman, M., Johnston, M. and Forsyth, D. (2008). Evaluation of
detection methods and sampling designs used to determine the abundance of feral cats. Arthur Rylah Institute for
Environmental Research Technical Report Series No. 181. Department of Sustainability and Environment, Heidelberg,
Victoria
ISSN 1835-3827 (print)
ISSN 1835-3835 (online)
ISBN 978-1-74208-858-7 (print)
Disclaimer: This publication may be of assistance to you but the State of Victoria and its employees do not guarantee
that the publication is without flaw of any kind or is wholly appropriate for your particular purposes and therefore
disclaims all liability for any error, loss or other consequence which may arise from you relying on any information in
this publication.
Front cover photo: Feral cat with GPS-Satellite collar near cage trap (Alan Robley).
Authorised by: Victorian Government, Melbourne
Printed by: PRINTROOM 77 St Georges Rd, Preston 3072
Contents
List of tables and figures ..................................................................................................................iv
Acknowledgements ............................................................................................................................v
Summary ............................................................................................................................................1
1
1.1
Introduction .............................................................................................................................2
Objectives..................................................................................................................................2
2
2.1
Methods ....................................................................................................................................3
Study area..................................................................................................................................3
2.2
Sampling design ........................................................................................................................3
2.3
2.2.1
Capturing feral cats ....................................................................................................3
2.2.2
Determining the area of use .......................................................................................4
2.2.3
Placement of detection devices ..................................................................................5
Data analysis .............................................................................................................................7
2.3.1
Simulation modelling .................................................................................................8
3
3.1
Results ....................................................................................................................................10
Captures ..................................................................................................................................10
3.2
Area of use and home range ....................................................................................................11
3.3
Detection .................................................................................................................................11
3.3.1
Utilisation distribution .............................................................................................12
3.4
Detection probability estimates ...............................................................................................13
3.5
Simulation modelling ..............................................................................................................15
4
Discussion...............................................................................................................................16
References ........................................................................................................................................18
3
List of tables and figures
List of tables
Table 1. Details of captured feral cats ............................................................................................... 10
Table 2. AIC values for two models of the utilisation distribution (UD) fitted to the GPS locations
of 5 cats. circular – circular bivariate normal model; ellipse – bivariate ellipse model;
difference – difference in AIC between the circular and ellipse models ................................. 12
Table 3. Maximum likelihood estimates of parameters for the bivariate normal ellipse model of the
UD fitted to the GPS locations of five cats. X – Easting coordinate of range centre (m); Y –
northing coordinate of range centre (m); σx – spatial scale in the easting direction (m); σy spatial scale in the northing direction; ρ – covariance between easting and northing. HR –
area of the 95% isopleth (ha) .................................................................................................. 12
Table 4. Maximum likelihood estimates of the parameter g(0), the probability of detection for a
infrared camera placed at the centre of the cats home range, per night. L95%, U95% - lower
and upper 95% profile likelihood confidence intervals .......................................................... 14
List of figures
Figure 1. Anglesea study site .............................................................................................................. 3
Figure 2. VHF / GPS data logger collar with PIT tag for identification by data logger. 1 – VHF
aerial. 2 – GPS aerial. 3 – timed release. 4 – PIT tag ............................................................... 4
Figure 3. Data logger activated by the passive integrated transponder ............................................... 5
Figure 4. Locations of cage traps, DNA sampler, heat-in-motion cameras and leg-hold traps.
Devices were placed at the same location in sequence ............................................................. 6
Figure 5. Locations of sand plots ........................................................................................................ 6
Figure 6. Detection devices assessed during Phase 1. cage trap, Victor Soft-Catch 1.5 trap, (heatin-motion activated camera and sand plots ............................................................................... 7
Figure 7. Feral cat with VHF/GPS collar .......................................................................................... 10
Figure 8. Core and peripheral area of use. Black dots are the radio-tracking locations for the six
feral cats that were used to determine area of use. Contours are the 50% (core) and 90%
(peripheral) isopleths .............................................................................................................. 11
Figure 9. Feral cat captured by a heat-in-motion camera set at a leg-hold data logger ..................... 12
Figure 10. The 95% contour of the bivariate normal ellipse model of the UD (dashed line) fitted to
the GPS locations for cat number 2000 (black circles). Grey squares show the locations of
devices .................................................................................................................................... 13
Figure 11. Spatial detection function derived from the bivariate normal ellipse model of the UD
(equation 4). Contours give the per night probability of detection for a device placed at
various distances (dx and dy) from the range centre. Parameters used for this model were
g(0) = 0.0125, x = 1000 m, y = 1000 m, = 0.5. ................................................................. 14
Figure 12. (A) The relative bias in the estimate of K, and (B) the probability that K was greater
than 1.0 (i.e. incorrectly indicated a cat increase). Based on 1000 simulated indices of cat
abundance for varying monitoring intensities where the true value of K was 0.5................... 15
4
Acknowledgements
We thank Elise Jeffery (Alcoa Australia), Dale Fuller, Emma Danby, Lachie Davies, Darren
Balderis, Aaron Ledden and Katrina Lovett (Parks Victoria), Steve Coulson, Amy Douglas
(Wildlife Unlimited), Mike Stevens (Parks Victoria) and Peter Barker for their valuable assistance
during this project.
5
6
Monitoring changes in feral cat populations
Summary
Feral cats are believed to be responsible for the extinction or decline of native marsupials and birds
in Australia, and are listed as a known or perceived threatening process for 58 native species under
the Commonwealth Environment Protection and Biodiversity Conservation Act 1999. Although
many agencies and organisations commit significant resources to managing feral cats, there is little
reliable information on the impacts of feral cats, nor on the benefits of controlling feral cats. This
situation is at least partly a result of the uncertainty about our ability to accurately and precisely
estimate the relative or absolute abundance of feral cats, and the kill rates of control operations.
In 2007 the Commonwealth Department of Environment, Water, Heritage and the Arts
commissioned the Arthur Rylah Institute for Environmental Research to undertake research to
evaluate detection methods and sampling designs used to determine the abundance of feral cats
that have established wild populations in Australia.
Ten feral cats (8 males and 2 females) were captured and fitted with GPS / VHF collars at
Anglesea, southwest Victoria. Radio-tracking data was obtained for each cat; however, as a result
of few locations per cat being available we pooled locations to calculate the core and peripheral
area of use.
Forty cage traps, a DNA sampler and heat-in-motion cameras were deployed in September 2007
for 20 nights. Thirty-seven leg-hold traps were deployed in October 2007 for 28 nights, along with
a second round of 40 heat-in-motion cameras for 15 nights. At each leg-hold and cage trap/DNA
sampler we placed a passive integrated transponder data logger and a heat-in-motion camera to
record cats and their interaction with devices. Thirty-six sand plot monitoring stations were
established in October 2007 for three nights, each with a heat-in-motion camera located nearby.
Overall the detection rate for feral cats was low. Five cats (four collared and one unidentifiable)
were detected by the cameras; none were detected by the loggers at the cage trap/DNA sampler,
despite several photos being taken of cats investigating the device. Two cats were detected by the
loggers at the leg-hold devices, and photographs of four collared cats and one unidentified cat
were taken at the leg-hold devices. As a result we were able to determine detection probabilities
and simulate outcomes for a control program using heat-in-motion cameras only.
The detection probability of a single camera placed in the centre of a cats home range for one night
ranged between 0.007 and 0.012. Incorporating this into a spatial detection function, we were able
to estimate the per night probability of detection for a camera placed at various distances from the
centre of a cats home range. This ranged from 0.012 at the centre to 0.002 up to 2000 m out from
the centre.
We used this information to simulate various monitoring intensities and their ability to correctly
identify a 50% decrease in cat abundance, i.e. the efficacy of control. Of the nine monitoring
intensities we modelled, all indicated a post-control decrease in cat abundance. However, the
potential to wrongly conclude that the cat population had actually increased was highest for the
lowest level of monitoring (9 cameras over 5 nights), which was wrong 49% of the time compared
to the highest intensity (49 cameras for 20 nights) which was wrong only 15% of the time.
On average, 230 usable GPS locations were recorded for each cat over a 90-day period. This level
of detail for the resource use of feral cats has not been previously collected. Current work is
investigating movement and habitat use, which will be useful for land managers in understanding
more about where feral cats reside and what habitat features may influence movement paths.
Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
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Monitoring changes in feral cat populations
1 Introduction
Feral cats (Felis catus) probably became established in Australia soon after the arrival of the first
Europeans. Feral populations now occupy most parts of the mainland, Tasmania and some
offshore islands (Abbott 2002). Cats eat a wide range of native wildlife (reviewed in Robley et al.
2004), and for this reason are thought to reduce the distribution and abundance of many native
species, especially on islands.
Feral cats are listed as a known or perceived threatening process for 58 native species under the
Commonwealth’s Environment Protection and Biodiversity Conservation Act 1999. Although
many agencies and organisations commit resources to managing feral cats (Reddiex et al. 2004),
there is little reliable information on the impacts of feral cats (e.g., Abbott 2002), or on the benefits
of controlling feral cats (Dickman 1996; Reddiex et al. 2004; Robley et al. 2004). This lack of
information is at least partly due to uncertainty about our ability to accurately and precisely
estimate the relative or absolute abundance of feral cats, or the kill rates obtained in control
operations.
Detecting changes in animal abundance is one of the most frequent problems facing wildlife
managers. In this situation indices of animal abundance are frequently used to estimate relative
changes in animal abundance, in preference to the more time-consuming and expensive methods
involving estimation of absolute abundance. However, the effectiveness of various devices and
methods for detecting animals is often unknown, and this has hampered our ability to optimally
design monitoring programs based on indices.
Our aim was to determine the probability of detecting cats with various monitoring devices and
methods, notably traps (cage and leg-hold), DNA collection for analysis of individuals, cameras
and sand plots. These can all be used to index cat abundance, but their relative sensitivity for
indexing cats is unknown.
Here we define detection probability as the probability of a particular device detecting an
individual cat, per night (e.g. Ball et al. 2005). This quantity can be estimated by placing
individual devices within an individual cat’s home range and recording interactions by the cat with
the device. The central idea is that the encounter rate by a cat with a single device is directly
related to the utilisation of the cat’s home range. Hence a device placed in an area with relatively
high utilisation (e.g. range centre) is more likely to be encountered than if it is placed in an area of
low utilisation (e.g. periphery of range). We can then define a spatial detection function, which
models how the detection probability declines as the distance between the device and the centre of
the animal’s range increases, similar to distance or point sampling (Buckland et al. 1993; Buckland
et al. 2006). Once estimated, the detection function (or functions) can be used to determine the
appropriate monitoring regime required to correctly identify a prescribed change in cat abundance.
1.1 Objectives
The objectives of this study were as follows:
1. Estimate the probability of detecting feral cats with various detection devices and methods (e.g.
track counts, cage traps, leg-hold traps, camera based techniques and DNA based techniques).
2. Evaluate the ability of these detection devices in various sampling designs to estimate a
reduction in absolute and relative abundance of feral cats.
3. Field-test the best sampling design(s) and determine their ability to estimate the absolute and
relative abundance of feral cats.
4. Provide recommendations for using the best sampling designs as the basis for developing
national monitoring protocols to estimate the absolute and relative abundance of feral cats.
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Monitoring changes in feral cat populations
2 Methods
2.1 Study area
The trial was conducted at the Alcoa lease area (6600 ha) adjacent to the town of Anglesea in
south-western Victoria. The site is jointly managed by Parks Victoria and Alcoa Australia and is
listed on the Register of the National Estate. The study site lies within the Alcoa lease area, but
outside the actual mine area (Figure 1). The vegetation is eucalypt woodland and heath, and the
area experiences warm summers and cold winters and a moderate annual average rainfall of 150
mm.
This site was selected on the following basis: (1) Parks Victoria were able to provide historical
information indicating the presence of feral cats, consisting of several years of sand plot data, cat
trapping data and observational sightings of feral cats, (2) there was extensive all-weather vehicle
access, (3) the site supported several different vegetation types — riparian open eucalypt forest,
heath-open eucalypt forest, heath- eucalypt woodlands, (4) there was permanent water, (5) the
terrain was undulating (important for VHF radio reception), and (6) services such as
accommodation were nearby.
Anglesea
#
Aloca
Lease
Alcoa
Lease
Anglesea
Bass Strait
0
1
N
2
3 km
Figure 1. Anglesea study site.
2.2 Sampling design
2.2.1
Capturing feral cats
Feral cats were trapped using a combination of cage traps and soft-jawed leg-hold traps placed on
roads and tracks throughout the study area. Captured cats were sedated with 4 mg/kg of Zoletil 100
their weight and sex recorded, a hair sample collected for DNA analysis, and a Sirtrack VHF/GPS
data-logging collar attached. These collars include a mortality sensor, a timed release mechanism,
colour-coded aerials and a passive integrated transponder (PIT) (Figure 2).
Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
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Monitoring changes in feral cat populations
Figure 2. VHF / GPS data logger collar with PIT tag for identification by data logger. 1 – VHF
aerial. 2 – GPS aerial. 3 – timed release. 4 – PIT tag.
The GPS units were programmed to collect positional information every two hours for 90 days, at
which time the units automatically released. This information was used to determine the area of
use of each feral cat.
2.2.2
Determining the area of use
In order to determine the most effective sampling design, we first needed to determine what areas
in the landscape cats were using. We attempted to determine the location of collared feral cats
twice a day using two six-metre vehicle-mounted aerial towers, which allowed us to determine an
accurate location using the null-peak method. Data were loaded into the software program ‘Locate
III’ (Nams 2006) to determine the location of each cat.
The maximum likelihood estimator method developed by Lenth (1981) was used to determine the
location of cats from bearings taken within 20 minutes of each other. The method produces an
estimate of the most likely true location of animals based on intersecting bearings. It also produces
an error ellipse around each location, similar to the 95% confidence interval in univariate statistics.
This interval can be used to determine the accuracy of each estimated location.
The calculated locations were then used to determine an area of use. A kernel density method was
used to determine how intensively different parts of study area were used by cats and allowed for
the determination of centres of activity (Worton 1989). Kernel analysis is a nonparametric
statistical method for estimating probability densities from a set of points. In the context of home
range analysis, these methods describe the probability of finding an animal in any one place. A
regular grid is superimposed on the data and a density estimate is calculated at each grid
intersection based on a probability density function for each location (the kernel). The density
estimate at each intersection is essentially the sum of the kernel densities at that point. A bivariate
kernel density surface (i.e., a utilisation distribution, UD) is then calculated over the entire grid
using the density estimates at each grid intersection. The resulting kernel density surface will have
relatively large values in areas with many observations, and low values in areas with few. Home
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Monitoring changes in feral cat populations
range estimates are derived by drawing contour lines (i.e. isopleths) encompassing various
cumulative volumes of the surface (e.g. 95% isopleth encompasses 95% of the surface volume).
We defined the ‘core area’ of activity as the area completely enclosed by the 50% isopleth and the
‘peripheral area’ as that between the 90% and 50% isopleth. While there are a number of
alternative methods for determining ‘core areas’ (Harris et al. 1990) we chose this approach as it
has been widely applied elsewhere (Piran and White 1994; White et al. 1996; Howell and
Chapman 1990; Cimino and Lovari 2003).
2.2.3
Placement of detection devices
The study area was divided into 500 m2 grids, and two-thirds of the detection devices were
allocated to the central point of the grids inside the 50% isopleth and the remaining third to the
central point of the grids in the 90% isopleth. The actual grids with a device located within them
were selected using a random number generator.
A PIT data logger was placed underneath each of the detection devices. This logger recorded
individual cat interactions with a device (Figure 3).
Figure 3. Data logger activated by the passive integrated transponder.
Forty cage traps, DNA sampler and heat-in-motion cameras were deployed in September 2007 for
20 nights (800 detection nights). Thirty seven leg-hold traps were deployed in October 2007 for 28
nights, along with a second round of 40 heat-in-motion cameras for 15 nights (1036 leg-hold
detection nights, 600 camera trap nights; Fig 4.), and 36 sand plot monitoring stations were
installed in October 2007 for 3 nights (108 detection nights; Fig 5.).
Cage and leg-hold traps were wired open so that cats could freely interact with each device a
number of times. This also allowed more than one cat to encounter and interact with each device.
Cage traps, DNA hair sampler and heat-in-motion cameras were tested simultaneously, as the
DNA sampler is a modified cage trap and the cameras did not interfere with the cats interacting
with the cage/DNA device. Leg-hold traps and finally sand plots were tested in sequence (Figure
6).
We set 78 cage traps and 42 leg-hold traps for 40 nights (4800 trap nights). Cage traps were baited
with a combination of chicken and tuna oil, with chicken pieces replaced every 4–5 days, and a
Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
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Monitoring changes in feral cat populations
Feline Attracting Phonic (FAP) audio lure was placed behind each cage. Cages were partially
concealed under vegetation and wrapped in black plastic. Leg-hold traps had a lure of tuna oil, cat
urine, ‘Bobcat’ anal gland scent, or FAP. All traps were checked for captures twice a day.
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Bass Strait
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Figure 4. Locations of cage traps, DNA sampler, heat-in-motion cameras and leg-hold traps.
Devices were placed at the same location in sequence.
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Figure 5. Locations of sand plots.
6
Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
Monitoring changes in feral cat populations
Figure 6. Detection devices assessed during Phase 1. (top left) cage trap (Photo: A. Robley),
(bottom left) Victor Soft-Catch 1.5 trap, (top right) heat-in-motion activated camera (Photo: J
Nelson), (bottom right) sand plots (Photo: A. Robley).
2.3 Data analysis
The number of encounters by radio-collared cats with detection devices in their home range was
used to estimate the chance of a cat finding a single device per day (the detection probability), and
how this varied according to where the device was in an individual’s home range.
The probability of encountering a device was modelled as a function of home range use, i.e. the
closer to the centre of a home range a device is the greater the chance of it being encountered, and
the farther away the less chance of it being encountered. The encounter probability as well as
information on home range size was then used to estimate a spatial detection function (SDF) that
describes how capture probability varies over space.
Once the SDF was estimated, simulation techniques were used to estimate the overall detection
probability for an arbitrary number of devices placed in an area with a known number of cats. This
allowed predictions to be made about the optimal number of devices required to estimate cat
abundance with a specified level of confidence. For example, we could estimate how many
devices would need to be placed in an area to give the most accurate index of cat abundance or to
produce a precise index for statistical comparisons for before and after a control operation.
The SDF is a parametric model of the relationship between the animal’s utilisation distribution
(UD) and the probability of the animal detecting a device located somewhere within that utilisation
distribution. Because the SDF is a parametric model, we require a parametric model of the
underlying UD. We estimated the fit of two parametric models of the UD for each of the GPS
collared cats. The simplest model for the UD is the circular bivariate normal distribution with the
second model being the bivariate ellipse. The detection function for the circular bivarate normal
home range model is given by
P g(0)e(d
2
/ 2 )
Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
equation 1
7
Monitoring changes in feral cat populations
where P is the probability of detection, d is the distance between the device and the centre of the
home range, g(0) is the probability of detection when the device is placed at the centre of the range
(i.e. when d = 0) and is the spatial scale of the home range.
Each model was fitted to the GPS data for each cat using maximum likelihood methods. The fit of
each model was assessed for each cat by comparing Akaike’s information criterion (AIC) with the
model with the lowest AIC value indicating the best fit (Burnham and Anderson 1998). In general,
a difference in AIC of five or more indicates a substantially better fit (Burnham and Anderson
1998).
Once the best-fitting model for the UD was found, the value of the UD at the location of each
device could then be calculated. Because both models of the UD were parametric models the
resulting UDs were probability densities, so the volume under each UD was equal to one. Using
the maximum likelihood estimates of the parameters of the best-fitting model, values for the UD
were calculated over an 1140 m2 grid using a cell size of 10 m for each cat. The value of the UD at
each device location was then interpolated from this grid using 2D linear interpolation. These
values were considered to be estimates of the relative encounter or utilisation probability of each
device.
We modelled the detection (or not) by cats of the devices present in its UD as an increasing
function of the utilisation probability of the device. Specifically, we modelled the odds of
detection as a constant multiplied by the utilisation probability of the device. That is,
Pij
1Pij
jUDij
equation 2
where Pij is the probability of detection of device i by cat j, UDij is the utilisation probability of
device i by cat j and j are the cat-specific parameters to be estimated.
However, due to the low number of encounters by cats with devices, we pooled encounters over
the five cats used in the analyses. Hence only a single value was able to be estimated from this
analysis. Note that equation 2 stipulates an intercept through the origin which states that the odds
of detection for a UD equal to zero must also be zero. Maximum likelihood was used to estimate
the parameter by maximising the joint likelihood
 UD yi 
UD i 1yi
i
L 
1

i11UD   1UD 
i
i
n
equation 3
Where yi = 1 indicates a detection and yi = 0 indicates a non-detection for the i th observation
(cat/device combination). Once estimated, the value of was then inserted back into equation 2 to
estimate the probability of detection at the point of highest UD probability (i.e. home range
centre). This is equivalent to the parameter g(0) in equation 1. Profile likelihood methods were
used to estimate 95% confidence intervals for and hence for Pj.
2.3.1
Simulation modelling
The estimated detection function, parameterised in terms of g(0) and spatial scale (equation 1)
was then used to simulate various monitoring intensities and their ability to correctly identify a
50% decrease in cat abundance (i.e. true control efficacy). The model is a spatially explicit,
individual based model that simulates cat home ranges and associated monitoring in twodimensional space. The ‘detection’ algorithm allows for ‘competition’ between cats for devices by
8
Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
Monitoring changes in feral cat populations
simulating detection occurrences in continuous time according to a competing Poisson process
(Ramsey et al. 2005). We simulated monitoring of various intensities and duration within a
nominal area of cat habitat of 100 km2. Cats were initially set at 0.5 cats/km2 for the pre-control
monitoring before being reduced to 0.25 cats/km2 for the post-control monitoring. Monitoring
consisted of grids of devices as follows:
 number of devices — 9, 25, 49 (i.e., 3 3; 5 5, 7 7) using a spacing of 2, 1.5 and
1.0 km respectively
 days exposure — 5, 10 and 20 consecutive days.
Each combination of devices and days exposure was simulated at each cat density (i.e. pre- and
post-control). A total of 1000 simulations were undertaken for each combination. For each
combination of monitoring intensities, an index of cat abundance was calculated as the mean
number of cats detected per device night. We then calculated a measure of control efficacy (K) as
the ratio of the pre and post-control indices. We then compared the estimated value of K with the
true value (0.5) to determine the relative bias as
Rbias (K ) 
E(Kö) K
K
equation 4
Where E(Kö) is the estimated value of K based on the mean of the 1000 simulated estimates and K
is the true value of K (0.5).
We also estimated the number of times the wrong conclusion would be made (i.e. the index
indicated a cat increase post control) by calculating the proportion of the 1000 simulated monitors
where the estimate of K was greater than 1.
Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
9
Monitoring changes in feral cat populations
3 Results
3.1 Captures
Ten feral cats were captured — eight males (3.5 – 5.0 kg) and two females (3.1 – 3.5 kg; Figure 7;
Table 1).
Figure 7. Feral cat with VHF/GPS collar.
Table 1. Details of captured feral cats
Date
Captured
ID
Sex
Weight
(kg)
9/08/2007
3400a
M
4.9
10/08/2007
0400b
F
12/08/2007
1400
12/08/2007
No. GPS
Locations
Home range
(km2)
Colour
–
–
Tabby
3.5
–
–
Tortoise shell
M
5.0
226
10
Tabby
3800c
M
3.5
242
1
Black
16/08/2007
4200
M
3.5
446
9
Black, white socks and chest
17/08/2007
0200
M
5.3
190
16
Black
19/08/2007
0800
M
4.9
200
60
Black
26/08/2007
1600
M
4.1
191
9
Tabby
4/09/2007
2000
F
3.1
161
8
Tortoise shell
7/09/2007
1200d
M
5.7
–
–
Tabby, white back feet
a – collar not retrieved, b – died several days after capture, c – outside study area, not used in detection analysis, d – too
few locations to determine home range.
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Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
Monitoring changes in feral cat populations
3.2 Area of use and home range
Radio-tracking results indicated that cats 4200, 0800 and 1600 were spending the majority of their
time away from the remaining cats, and therefore were not usable in calculating detection
probabilities. Cat number 0400 died several days after being collared.
Because of the low number of radio-tracking locations obtained from the remaining six cats we
pooled the data to determine the core (50% isopleth) and peripheral (90% isopleth) areas. These
were later used to determine the spatial allocation of the detection devices (Figure 8).
The VHF/GPS data-logging collars automatically released 90 days after being attached. All but
one collar was retrieved and data downloaded for seven cats (0400 had died, 3400 was not
retrieved and 1200 had too few locations to be usable). On average 230 usable GPS locations were
recorded for each cat over a 90 day period. We used this data to estimate home range for each of
the collared cats using kernel analysis (Table 1). Cat home ranges were between 1 km2 and
60 km2, with a median of 9 km2.
#
#
#
#
#
#
#
##
#
#
#
#
# #
#
# #
#
#
##
##
## #
#
# # #
# ## #
#
##
#
##
Aloca
Alcoa
LeaseLease
#
#
Anglesea
Bass Strait
0
1
N
2
3 km
Figure 8. Core and peripheral area of use. Black dots are the radio-tracking locations for the six
feral cats that were used to determine area of use. Contours are the 50% (core) and 90%
(peripheral) isopleths.
3.3 Detection
The overall detection of feral cats was low. Five cats (four collared and one unidentifiable) were
detected by the cameras, but only one was detected by the loggers at the cage trap/DNA sampler
despite obtaining several photos of cats investigating the trap.
Two cats were detected by the loggers at the leg-hold devices, and photographs of four collared
cats (Figure 9) and one unidentified cat were taken at the leg-hold devices.
Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
11
Monitoring changes in feral cat populations
Figure 9. Feral cat captured by a heat-in-motion camera set at a leg-hold data logger.
3.3.1
Utilisation distribution
The fits for both the circular normal and elliptical ranges to the GPS data for each of the five cats
is given in Table 2. In all cases the bivariate ellipse model was a better fit to the GPS locations
than the circular normal model as judged by the lower AIC values for the elliptical fits. The
estimates of the parameters for the elliptical fits for each of the 5 cats are given in Table 3. A
graphical representation of the ellipse model is given in Figure 10.
Table 2. AIC values for two models of the utilisation distribution (UD) fitted to the GPS locations
of 5 cats. circular – circular bivariate normal model; ellipse – bivariate ellipse model; difference
– difference in AIC between the circular and ellipse models.
Cat ID
Circular (ha)
Ellipse (ha)
Difference
0200
3658
3647
11
0800
4743
4637
106
1400
4145
4089
56
2000
3589
3575
14
4200
8205
8064
141
Table 3. Maximum likelihood estimates of parameters for the bivariate normal ellipse model of
the UD fitted to the GPS locations of five cats. X – Easting coordinate of range centre (m);
Y – northing coordinate of range centre (m); σx – spatial scale in the easting direction (m); σy spatial scale in the northing direction; ρ – covariance between easting and northing. HR – area
of the 95% isopleth (ha).
12
Cat ID
X
Y
σx
σy
ρ
HR(ha)
0200
253254
5750563
1299
945
–0.209
2261
0800
252825
5749753
3147
1674
–0.617
7799
1400
253234
5747424
1165
674
0.415
1344
2000
253408
5748624
949
695
0.256
1201
4200
254474
5748970
869
1006
–0.657
1241
Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
Monitoring changes in feral cat populations
%
%
%
%
% %
%
%
#
#### # ##
# #
%
%
# %
###
%
## #
% % %## %### %
##
# ## #
##### ##
## #
# ##
# #
# # ##
#% # %
%# % %## % %
## # #
## #
# #
##
#
#%
%
#%
# % %
###
## %##
# #%
## # ##
########## ## #
#####
### %
%
%
#
% % %
# ## #### #
# # ##
#### ###
% %
Alcoa Lease
%
Anglesea
Bass Strait
0
1
N
2
3km
Figure 10. The 95% contour of the bivariate normal ellipse model of the UD (dashed line) fitted
to the GPS locations for cat number 2000 (black circles). Grey squares show the locations of
devices.
3.4 Detection probability estimates
From the 40 devices, four infrared cameras failed and were therefore, not used in further analyses.
Most of the cage/camera devices were exposed for 19 to 22 consecutive nights, although two were
only exposed for 10 and 12 nights respectively. In all, devices were exposed for a total of 711
exposure nights. Examination of the PIT tag readers found that only a single cat was detected
during the exposure period at a cage trap/DNA sampler. However, infrared cameras recorded nine
detections involving four cats and eight different cameras. For any cat that was detected by the
same camera twice, only the first detection was used in analysis as subsequent detections of the cat
by the same camera were not considered to be independent events. As there were insufficient
detections by cages/DNA sampler for analyses, we focus here on the detections by cameras only.
Maximum likelihood estimates of the parameter g(0) for each cat using equations 2 and 3 are
given in Table 4. These estimates show that the probability of a cat being detected by an infrared
camera placed at the centre of its range varied between 0.007 and 0.013 per day. Combining these
values with the estimates of the parameters for the ellipse model of the UD gives the spatial
detection function of the form

 2
2

1
P g(0)e
dy
dx dy 
2 dx 2
equation 4
 2(1 p )   2  2 xy 
 x
y 


Where P is the detection probability, dx and dy are the distances from the home range centre to the
device in the easting (x) and northing (y) direction respectively and x, y and are the parameters
of the ellipse model of the UD. A graphical depiction of this spatial detection function is given in
Figure 11.
Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
13
Monitoring changes in feral cat populations
Table 4. Maximum likelihood estimates of the parameter g(0), the probability of detection for a
infrared camera placed at the centre of the cats home range, per night. L95%, U95% - lower
and upper 95% profile likelihood confidence intervals.
Cat ID g(0)
L95%
U95%
0200
0.007 0.0033 0.0124
0800
0.002 0.0010 0.0036
1400
0.012 0.0056 0.0206
2000
0.013 0.0062 0.0230
4200
0.012 0.0060 0.0223
Figure 11. Spatial detection function derived from the bivariate normal ellipse model of the UD
(equation 4). Contours give the per night probability of detection for a device placed at various
distances (dx and dy) from the range centre. Parameters used for this model were g(0) =
0.0125, x = 1000 m, y = 1000 m, = 0.5.
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Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
Monitoring changes in feral cat populations
3.5 Simulation modelling
All of the nine monitoring intensities modelled indicated a decrease in the post-control cat
population on average. However, the relative bias in the estimate of K was much higher for the
lowest monitoring intensity (9 devices for 5 nights) with a 77% negative bias in the estimate of K
compared with an 8% bias for the highest monitoring intensity (49 devices for 20 nights) (Figure
12). The lowest monitoring intensity also incorrectly indicated an increase in the cat population
post-control 49% of the time compared with 15% of the time for the highest monitoring intensity
(Figure 12).
Figure 12. (A) The relative bias in the estimate of K, and (B) the probability that K was greater
than 1.0 (i.e. incorrectly indicated a cat increase). Based on 1000 simulated indices of cat
abundance for varying monitoring intensities where the true value of K was 0.5.
Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
15
Monitoring changes in feral cat populations
4 Discussion
The requirement for this project to succeed was to capture 15–20 feral cats at two sites, attach
VHF/GPS collars, track their locations and determine an area of use, place multiple detection
devices in this area, determine the probability of encounter, design a monitoring protocol, and test
this in the field. We completed all these tasks to a limited degree with the exception of field
validation of the suggested protocol.
All of the 10 captured cats from Anglesea had VHF/GPS collars successfully fitted. One of these
cats died shortly after being collared, and three spent the majority of their time away from the core
study area. The collar of another cat failed, or the cat moved well away from the study site. This
meant that only five cats remained available for use in the study. We recorded all five of these cats
at detection devices, plus two unidentified cats. This is the first time such complex collars have
been made small enough to fit feral cats. In fact we were restricted to feral cats of 1.75 kg or above
because of ethical considerations about the weight of the collars, although this did not prove to be
an issue as all captured cats were heavier than this.
We have proven that our analytical approach can provide robust direction in setting protocols for
the assessment of changes in abundance of feral cats, and that the suggested protocol could be
implemented in the field, i.e., it is practical for land managers to undertake.
While all the indicators were that the habitat at Anglesea was suitable, and previous Parks Victoria
records indicated a strong level of cat activity, the results indicate that underlying feral cat density
was low. This resulted in the small sample size and the subsequent low levels of detection, so we
were unable to assess most devices. However, based on the simulation models, 49 cameras placed
in a 6 km 6 km grid spaced at 1 km intervals and left in situ for 20 days would be a reasonable
protocol to use for assessing changes in feral cat abundance. This approach has a number of
advantages, including the ease of establishment and operation of cameras and lower labour costs
compared to cage and leg-hold trapping. All other devices (although not assessed) have
significantly more complexity in operation. For example, leg-hold traps and DNA analysis require
technical experts, and DNA samplers, cage traps and leg-hold traps have to be checked each day,
which increases the cost of monitoring. Cameras are essentially set-and-forget devices requiring
minimal servicing in the field, but they have a substantial set-up cost, require maintenance, and
can be stolen.
Caution should be used in applying the suggested protocol of 49 cameras set for 20 days, as there
is a degree of uncertainty in the derived estimates due to the low level of detections and the small
sample size. Further work is required to either test cage traps and DNA detection devices (as was
originally planned) or field-trial the suggested protocol to increase our confidence in its precision.
It is recommended that at least one further trial be undertaken with a larger sample size of cats to
validate the suggested protocol and further assess the current techniques. Ideally this would be
undertaken before and after a feral cat control operation.
The second site was to be in south-western Victoria, where local land managers indicated that feral
cat activity suggested a reasonable chance of capturing sufficient cats. The advantages of this site
were similar to those of the Anglesea site, i.e. all weather roads, temperate eucalypt forest, and
moderate to high annual rainfall and close to facilities. In order for the project to succeed it was
imperative that we could radio-track a sufficient number of cats with a high degree of confidence.
Problems with the reception range of the VHF beacon on the VHF/GPS collars, flat terrain, and
dense vegetation that hampered reception prevented us from using this site, even though the
manufacturer redesigned the collar to improve reception range by 75%. This issue was unforseen
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Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
Monitoring changes in feral cat populations
and resulted in significant delays in field trials, ultimately leading to attempts to capture cats at the
Grampians and again at Anglesea in an attempt to keep the project on track.
An attempt to validate this approach was made at the Grampians National Park; again both habitat
and previous local knowledge indicated that cat abundance was sufficient to anticipate the capture
of sufficient feral cats. However, despite considerable effort (945 trap nights), no feral cats were
captured. A second attempt to validate the approach was made at Anglesea, but was also
disappointing, as only one feral cat was captured. However, a considerable number of feral cats
were detected using heat-in-motion cameras as part of a spot-tailed quoll survey in tall wet forest
in the Otway Ranges National Park (J. Nelson pers. comm.) approximately 50 km southwest of
Anglesea. Although cats are arid adapted, allowing them to survive with little water and in high
temperatures, their staple food supply in temperate south-eats Australia is not. It is possible that
feral cats have been affected by the prolonged period of below average rainfall in south-eastern
Australia, and that places like the Otway Ranges are an important drought refuge.
If further trials are to be undertaken, pre-site surveys should be carried out to establish the likely
presence of a high population of feral cats.
Initial estimates of home ranges for the seven feral cats at Anglesea varied considerably. The
smallest (1 km2) was for a cat that was, in the main, a town cat. GPS data indicate that this cat
spent 90% of its time within 1 km of the Anglesea golf course. This collar was retrieved from the
front deck of a holiday rental house in the town. The majority of cats occupied areas between 8
and 10 km2. These are generally larger than those reported in the literature. For example in open
woodland in NSW cats were reported to occupy a mean area of 4.23 km2 (n = 15, MCP 100;
Molsher et al. 2005), and in semi-arid Victoria 6.2 km2 (n = 6, MCP 100; Jones and Coman 1982).
The remaining two cats at Anglesea had very large ranges, i.e., 16 km2 and 60 km2. The larger
areas occupied here may reflect differences in food resources and analytical techniques, in
particular the ability of GPS locations to be collected throughout a 24 hour period in all weather
and terrain conditions. Previous studies have been able to use only ground-based radio-tracking to
collect location data. GPS location data from the recovered collars will allow us to investigate
habitat use, home range and movement patterns of feral cats and provide valuable information to
Parks Victoria and Alcoa on the management of feral cats in the Anglesea area.
Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
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Monitoring changes in feral cat populations
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Arthur Rylah Institute for Environmental Research Technical Report Series No. 181
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ISSN 1835-3827 (print)
ISSN 1835-3835 (online)
ISBN 978-1-74208-858-7 (print)
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