Effectiveness of thermal cameras compared to spotlights for
counts of arid zone mammals across a range of ambient
,Christopher N. Johnson
and Sarah Legge
National Environmental Science Program Threatened Species Recovery Hub, Centre for Biodiversity and
Conservation Science, University of Queensland, St Lucia, Qld 4075, Australia.
School of Natural Sciences, Private Bag 55, University of Tasmania, Hobart, Tas. 7001, Australia.
Arid Recovery, PO Box 147, Roxby Downs, SA 5725, Australia.
University of New South Wales, Sydney, NSW 2052, Australia.
Fenner School of Environment and Society, The Australian National University, Canberra, ACT 2601,
Corresponding author. Email: email@example.com
Abstract. Effective monitoring of mammal species is critical to their management. Thermal cameras may enable more
accurate detection of nocturnal mammals than visual observation with the aid of spotlights. We aimed to measure
improvements in detection provided by thermal cameras, and to determine how these improvements depended on ambient
temperatures and mammal species. We monitored small to medium sized mammals in central Australia, including small
rodents, bettongs, bilbies, European rabbits, and feral cats. We conducted 20 vehicle-based camera transects using both a
spotlight and thermal camera under ambient temperatures ranging from 108Cto358C. Thermal cameras resulted in more
detections of small rodents and medium sized mammals. There was no increased benefit for feral cats, likely due to their
prominent eyeshine. We found a strong relationship between increased detections using thermal cameras and
environmental temperature: thermal cameras detected 30% more animals than conventional spotlighting at approximately
158C, but produced few additional detections above 308C. Spotlighting may be more versatile as it can be used in a greater
range of ambient temperatures, but thermal cameras are more accurate than visual surveys at low temperatures, and can be
used to benchmark spotlight surveys.
Keywords: arid zone mammals, conservation, detectability, distance sampling, monitoring, spotlighting, thermal
Received 4 June 2020, accepted 6 February 2021, published online 12 March 2021
Conservation actions are best evaluated with effective moni-
toring (Legge et al. 2018). Monitoring of many mammal species
across Australia is limited by difficulties in detecting animals,
and by challenges in obtaining estimates of actual abundance
rather than indices of activity. This is especially relevant in cases
where it is difficult to conduct a trapping program, such as where
animals occupy habitats that are hard to sample (e.g. arboreal
animals living high up in tree canopies), or where some animals
are hyperabundant and cause trap saturation (Wayne et al. 2008;
Page et al. 2013). As an alternative to trapping, travelling at
night with a spotlight or torch and counting all animals seen
(spotlighting), while recording distance of animals from the
transect line to allow distance-sampling analysis, remains a
common technique to monitor animals when trapping is
impractical and abundance estimates are required.
Although spotlighting with distance sampling is an important
tool for monitoring wildlife in many situations, it has limitations.
One drawback is that some sources of detection error are not
measurable or able to be estimated. Although some sources of
error such as distance from transect (where detections close to
the observer are more likely than those further away), observer
experience, or wind can be measured and factored into analysis,
other major causes of error cannot be measured. For example,
many animals are principally detectable from eyeshine, but
some animals in a population might not have their face directed
towards the observer or their head may be obscured by vegeta-
tion (Lindenmayer et al. 2001;Goldingay and Sharpe 2004).
This might not change with distance to transect, and hence may
not be considered part of the estimated error. The head angles of
undetected animals cannot be measured, and so animals facing
away from the observer or with their heads hidden from view
Journal compilation Australian Mammal Society 2021 www.publish.csiro.au/journals/am
would constitute a ‘ghost’ subpopulation that could not be
integrated into any form of abundance analysis, resulting in
underestimates of actual density.
Thermal camera technology might be able to overcome errors
with detectability associated with spotlight surveys, creating a
more robust estimate of wildlife density (Gill et al. 1997;
Cilulko et al. 2013). These cameras see only heat, so detectability
is not dependent on whether an animal faces the observer, diplays
eyeshine, has visibly noticeable features (e.g. black fluffy tail), or
is moving. Instead, detection relies on whether the animal’s body
heat contrasts with the background. In addition to this, many
thermal camera modules have the ability to record video of the
feed, allowing independent verification.
There are limits to the effectiveness of thermal cameras. If
animals are to be detected their body temperature must contrast
with the environment (Kays et al. 2019). This condition would
not hold when ambient temperatures are similar to or greater
than average mammalian body temperatures (,358C: Morrison
1962). As most mammals have insulating fur and their exterior
temperature is lower than body temperature (Gerken 1996;
Simpson et al. 2016), even lower ambient temperatures might
be needed for thermal cameras to be effective. For example,
Vinson et al. (2020) found detectability of furred arboreal
mammals decreased when ambient temperatures were above
248C. Added to this, many microhabitats in the landscape retain
heat (e.g. under logs), making it difficult to discern the heat
generated by animals from background variability. As well as
environmental constraints, many animal species have beha-
vioural or physical traits that make them better suited to detec-
tion by spotlighting. For example, Focardi et al. (2001) did not
find thermal cameras to be better than spotlights to survey foxes,
due to their bright eyeshine and propensity to look at the
Our study aimed to assess the relative effectiveness of
adding thermal cameras to traditional visual spotlight surveys
in different survey contexts, and to understand in which
species and climates thermal cameras would provide the most
benefit. We monitored small rodents, medium sized native
mammals like bettongs and bilbies, European rabbits, feral cats
and kangaroos. We asked the following questions. First, are
detection rates from surveys using a thermal camera and
spotlight together different from those derived from just a
spotlight, and how do these differences vary among mammal
species? Second, does including thermal camera detections
alter detection functions and estimated strip widths in distance
sampling? Third, what temperatures are required for thermal
imaging to work optimally? Finally, can insights learnt from
using thermal cameras enable more accurate density estimates?
We had an opportunity to measure the accuracy of density
estimates derived from thermal imagery and spotlighting in
a situation where we had a relatively reliable benchmark
of the actual population of rabbits in a fenced paddock
(see McGregor et al. 2019).
The comparison of thermal cameras to spotlight surveys was
conducted at Arid Recovery, a conservation reserve in central
South Australia (308290S, 1368530E), in an arid landscape with
160 mm average rainfall per year (records from Olympic Dam
Aerodrome, www.bom.gov.au/climate/data, accessed 30 October
2019). The area is dominated by swales with scattered saltbush
,50 cm high and longitudinal dunes dominated by shrubs typi-
cally 1–5 m high. Arid Recovery is a fenced conservation reserve.
In over half of the fenced reserve (60 km
), all European rabbits
(Oryctolagus cuniculus), feral cats (Felis catus) and red foxes
(Vulpes vulpes) have been removed (Moseby and Read 2006),
whereas the other half (62 km
) is where populations of native and
invasive mammals coexist in manipulative experiments (Moseby
et al. 2012;West et al. 2018;McGregor et al. 2019;Ross et al.
2019). Numerous threatened native mammals have been rein-
troduced, including bilbies (Macrotis lagotis), burrowing bet-
tongs (Bettongia lesueur), and stick-nest rats (Leporillus
conditor). Small mammals including the spinifex hopping mouse
(Notomys alexis) and plains mouse (Pseudomys australis)are
abundant inside the reserve (Moseby et al. 2009). Outside the
fenced reserve, European rabbits and feral cats are abundant, and
there are occasional records of dingoes (Canis lupus)and
The comparison between detections using spotlighting and
thermal cameras was conducted on five different transects
through four areas of the Arid Recovery reserve and adjacent
land (Fig. 1), sampled a total of 20 times. Each transect took
between 2 and 5 h. Four samples each were taken from the
northern natives and feral coexistence paddocks (see Fig. 1),
four from outside the reserve on the boundary, and eight from
inside the natives-only feral-free paddocks. All transects were
between 12 km and 18 km long.
All vehicle-based spotlight/thermal camera surveys were
conducted between June 2016 and December 2018. Surveys
began at least 1 h after sunset. Before beginning a transect, we
would record time, wind (none, light, medium, strong), moon
phase, and whether each observer would be considered expe-
rienced (more than 50 h spotlighting for animals) or inexperi-
enced. The front passenger seat was occupied by someone
using a 100 W spotlight scanning to the left of the vehicle (not
the right-hand side), and a second surveyor used a thermal
camera from the rear passenger seat. The thermal camera
(FLIR scout III 640, FLIR_ Systems, Wilsonville, USA) had
a resolution of 640 512 pixels. This was the highest known
resolution of thermal cameras commercially available in
Australia for under AU$10 000 in 2016. The vehicle was
driven at a speed between 5 and 20 km h
. We used the
, where objects hotter than the background
would appear red.
Records were kept of all mammals seen, and the perpendicu-
lar distance from the road of each sighted animal was recorded.
Most distances were estimated, but at least once every five
detections the distance was measured with a rangefinder (800 m
Laser Rangefinder, Kogan, Melbourne, Australia) to avoid drift
of estimated distance. Animals observed on the road by the
driver were recorded as a distance of zero. As well as recording
animals seen, the user of the thermal camera also recorded
whether an animal seen by the spotlighter was not seen by
thermal camera and vice versa. In some cases, it was ambiguous
as to whether one would have seen it had the other not (e.g. the
spotlighter might look more intently for an animal if the
thermal camera user announced that they had spotted an animal).
BAustralian Mammalogy H. McGregor et al.
For the few ambiguous cases, we discussed the sighting as a
group and made a collective decision on how the detection
should be recorded. However, the vast majority of instances
(.98% of sightings) were unambiguous for numerous reasons.
First, neither the spotlighter or thermal camera user can see
where the other was looking (a spotlight beam is invisible
through a thermal camera). Second, transects were short enough
that fatigue was rarely an issue (i.e. people were looking intently
most of the time regardless of whether the other person detected
an animal). As the spotlighter was in the front seat typically
scanning forward-left, while the thermal camera user was in the
back scanning left to back-left, most error would be from the
thermal camera user alerted to animal presence by the spot-
lighter, not the other way around as the former is scanning
ground already scanned by the latter. Overall error associated
with this issue was deemed to be minimal in regard to our core
aims, given that we were comparing spotlighting versus a
spotlight/thermal camera combination. Therefore, our tally of
how many animals would have been seen were there no thermal
camera should be largely accurate.
Animals seen were identified where possible; however, all
small-mammal detections (i.e. animals less than 100 g) were
grouped together. In some cases where only the thermal camera
detected a medium-sized mammal (1–3 kg), we would assume the
identity based on which paddock we were in. For example, all
medium mammals in the dingo paddock were classed as rabbit,
and those in the core reserve counted as bettongs or bilbies.
To estimate the detection functions of each species group
(small mammals, bettongs/bilbies, rabbits, cats), we used distance
sampling in R ver. 3.5.1 (www.r-project.org) using library
‘Rdistance’ ver. 2.1.3 (McDonald et al. 2019). For each group,
we tested whether thermal cameras altered the detection function,
Arid Recovery fences
Fig. 1. Map of the Arid Recovery reserve in central South Australia, including the boundary fence (black line with
marks) and spotlighting transects where thermal cameras were also used (white dashed lines).
Thermal cameras and arid zone mammals Australian Mammalogy C
and the best formof assuming the detection function. For this, we
tested four models that included whether an animal was detected
using a thermal camera, a model without this variable, then a
version of both with either a half-normal pattern (representing a
gradual decline in detections and a slight ‘tail’ at greater
distances) or negative-exponential (represents a rapid decline
but low probabilities of detecting distant animals). The top model
was selected using Information Theory by calculating Akaike
weights corrected for small sample sizes (AICc) and selection of
the model with the lowest score (Burnham and Anderson 1998).
To understand how extra thermal camera sightings affect the
estimated strip width (the most critical parameter for estimating
density), we compared this from the top models above with the
same model run with only spotlight records.
Once we had estimated a density for each spotlight/thermal
camera survey, we then measuredthe effect of the thermal camera
in adding detections under different conditions. We calculated the
percentagedifference between animal detections for each transect
if thermal camera detections were added, where 0 represents no
extra benefit and 2 would represent a 200% increase in density
estimates derived from using thermal cameras. We did not use the
density estimates derived from distance sampling, as these reflect
both the count and estimated strip widths. For example, in a
transecta thermal cameramight add many sightings but these alter
the estimated strip width, and result in a lower density estimate.
However, we would have no way of knowing whether this lower
estimate was more accurate or not. Therefore, the portion of extra
sightings was a better and more tangible refection of the value of
thermal cameras. To ensure adequate sample size, we combined
the counts for all medium-sized mammals (bettongs, bilbies, and
rabbits). Then, we compared this ratio as our response variable
against nightly temperature and observer experience as predictor
variables. Temperature was recorded from the nearest Bureau of
Meteorology weather station at the start of each transect (Olympic
Dam Aerodrome, www.bom.gov.au/products/IDS60801/). We
used generalised linear models in R and compared four models
(one with each variable, one with both, and null).
Finally, we tested the accuracy of density estimates for rabbits
in an area with a known inferred density. This was a fenced
paddock of 37 km
, where a rabbit reduction experiment was
conducted (see McGregor et al. 2019). We removed 2215
individuals within one month.Two lines of evidence (track counts
and spotlight counts) agreed in suggesting that rabbit activity
declined by 80% as a result of thisremoval. We therefore inferred
that the population consisted of approximately 2769 rabbits
before the removal. Two transect counts were conducted in the
three months preceding this removal, and we compared density
estimated from spotlighting counts alone, and with a thermal-
camera derived correction factor, with the known density.
From 2016 to 2018, we conducted 20 spotlight/thermal camera
surveys in which mammals were recorded both by an observer
using a spotlight and by an observer using a thermal camera. On
average, transects were 14.5 km (minimum 8.7 km to maximum
18.6 km), covering a total 289.2 km. In total, 853 animals were
sighted: 258 rabbits, 185 burrowing bettongs, 66 cats, 269 small
mammals, 41 red kangaroos, 28 greater bilbies, 3 dingoes, 2
western barred bandicoots, and 1 fox. Thermal cameras resulted
in many additional sightings of animals compared with
spotlighting (Table 1).
The addition of thermal camera detections altered the
distance-detection curves for many species. Detections of Euro-
pean rabbits were best described by a half-normal function
(Table 2). Thermal cameras altered the form of the relationship
by showing relatively fewer detections at greater distance
(Fig. 2). Detections of feral cats were best described by a
Table 1. Detections of different species during spotlighting, along with the number of extra detections contributed by the thermal camera
Spotlight detections Additional thermal camera detections % increase in detections
European rabbits 221 31 14
Feral cats 66 0 0
Burrowing bettong 142 43 23
Small mammals 228 41 15
Greater bilby 25 3 11
Red kangaroo 6 3 33
Table 2. Details of the top models of distance functions for animal groups with enough detections
Top models are those with the lowest AICc scores for each group. ‘Funct’ refers to the form of the detection function (HN, half-normal; NE, negative-
exponential), ‘Thermal’ refers to whether the model included a variable relating to whether the animal was detected by thermal cameras, and ‘Prob. det’ is the
probability of detection
Species Funct Thermal Prob. det y PDAICc
European rabbits HN Yes 0.49 18.5 ,0.001 0.62
Feral cats HN No 0.42 10.6 ,0.001 0.97
Burrowing bettongs NE Yes 0.16 –17.3 ,0.001 0.89
Small mammals NE Yes 0.10 –15.6 ,0.001 1.00
DAustralian Mammalogy H. McGregor et al.
half-normal function (Table 2), and there were no extra thermal
camera detections. Detections of both burrowing bettongs and
small mammals were best described by a negative-exponential
function (Table 2), and thermal cameras increased detections at
greater distances. When the top models were then rerun with
only spotlight data, the effective strip widths were similar for
rabbits, bettongs and small mammals (Table 3).
We calculated the proportion of sightings that thermal
cameras added to each transect of medium-sized mammals
and small mammals, and found top models for both groups
included ambient temperature (i.e. had the lowest AICc values:
Table 4). User experience was present in the top model for small
mammals, but a model that did not include this variable also had
strong support, suggesting it is uninformative. Both top models
revealed a strong significant relationship between the proportion
of extra detections added by the thermal camera and ambient
temperature, for both medium-sized mammals (coef ¼–0.02,
t¼–4.89, d.f. ¼0.16,18, P¼0.001) (Fig. 3) and small mammals
Probability of detection
20 40 60
Feral cat European rabbit
Burrowing bettong Small mammal
Probability of detection
50 100 150 200 250 300
Probability of detection
20 40 60 80 100 120
Probability of detection
10 20 30 40
Fig. 2. Detection functions of the top models for each animal type, including the different forms from thermal (solid)
and spotlighted (dashed) detections. No extra feral cats were detected by thermal cameras, so there was no difference in
thermal prediction for this species.
Table 3. Differences in the estimated strip width and detection probability of the top models of detection functions (Table 2) when both thermal
camera and spotlight detections are included (Both), or when applied to only the spotlight detections (Spotlight)
Species Estimated strip width Detection probability
Both Spotlight Both Spotlight
European rabbits 61 61.8 0.49 0.5
Burrowing bettongs 11 8.9 0.13 0.09
Small mammals 4.4 3.7 0.1 0.08
Thermal cameras and arid zone mammals Australian Mammalogy E
(coef ¼–0.03, t¼–2.34, d.f. ¼1.18,14, P¼0.03) (Fig. 3). This
model suggested that when temperature was 108C that the
thermal camera detected 31% more medium-sized mammals
(95% CI ¼26–34%) and 48% more small mammals (95%
CI ¼34–59%) than standard spotlight counts, but this benefit
decreased by half by 208C, and there were no additional detec-
tions when temperatures were above 308C.
The accuracy of density estimates for rabbits in a fenced
paddock was tested. As transects were conducted during
temperatures above 258C, we were not able to incorporate
thermal camera detections. Using the top detection functions
for rabbits (Table 2,Fig. 2) based on spotlighting only,
the two spotlighting transects conducted before the rabbit
removal (November 2016 and January 2017) estimated 2118
Table 4. Information Theory selection table for comparing models of the portion of extra sightings that thermal cameras added, against ambient
temperature (Temp) and whether the thermal camera user was experienced (Exp)
Top models were considered as those with the lowest D; logLik is the log-likelihood of model
Model description d.f. logLik AICc DWeight
Small mammals Temp and Exp 4 0.44 10.8 0 0.54
Temp 3 –1.87 11.7 0.98 0.33
Null 2 –4.63 14.2 3.42 0.1
Exp 3 –3.99 16 5.23 0.04
Medium mammals Temp 3 19.43 –31.4 0 0.73
Temp and Exp 4 20.02 –29.4 1.98 0.27
Null 2 10.95 –17.2 14.15 0
Exp 3 12.23 –17 14.39 0
Maximum daily temperature (°C)
Maximum daily temperature (°C)
Portion extra detections
Portion extra detections
15 20 3025 35
10 15 20 3025 35
Fig. 3. Relationships between the proportion of extra detections provided by thermal cameras to spotlight surveys against maximum daily temperature for
medium sized mammals (0.5–3 kg) and small mammals (,0.5 kg). Grey shading represents 95% confidence intervals.
FAustralian Mammalogy H. McGregor et al.
(95% CI ¼2005–2232) and 2061 rabbits (95% CI ¼1951–
2172) inside the paddock. If we assume, based on Fig. 3,that
31% were missed in spotlighting, the population estimates
become 2775 and 2700 rabbits (95% CI ¼2430–3082) from
the two surveys, much closer to the independent estimate of
population size and approximately 2769.
We found that thermal cameras increased detection rates of
small mammals by approximately 50% and medium-sized
mammals by approximately 30%, when ambient temperatures
were 208C or lower. This result agrees with other studies that
have found a similar improvement in detection (Gill et al. 1997).
Although there were differences in detection functions between
thermal cameras and spotlighting, these would not alone account
for approximately 30–50% additional detections. We propose
that most additional detections were due to thermal cameras
accounting for animals that by positional circumstances would
not normally be detected (e.g. not facing the observer and cre-
ating no eyeshine). The approximately 30% improvement
reported here was almost identical to the increase in the detec-
tion rates of kangaroos reported when combining a thermal
camera with human observers in aerial censuses. However, the
improvement in rabbit detections found with thermal cameras
was lower than the 60% increase of Focardi et al. (2001). This
could be due to differences in habitat. Because we conducted
surveys in an area with fewer shrubs and less-dense ground
vegetation than Focardi et al. (2001), detection rates of rabbits
by spotlighting would have been higher.
We found that thermal cameras resulted in different detection
functions compared with spotlights, shifting the average detec-
tion distance for most species. Broadly, we found that thermal
cameras increased sightings of animals in the 5–50 m range,
which we suspect reflects the limit of thermal camera use for
animals of such sizes. With more detections added within this
distance band, this altered the detection functions and estimated
strip widths of species relative to where most of their spotlight
detections were located. Most spotlight detections of small
mammals and bettongs were close to the road (,10 m), so
thermal cameras increased detections at intermediate distances,
expanding the detection functions/estimated strip widths. Yet
most spotlight detections of rabbits were over a wider area with
many beyond 50 m, so the addition of thermal camera detections
inversely decreased the estimated strip widths. We recorded no
benefit of using thermal cameras to detect cats, most likely for
the same reason. Their eyeshine is so bright and can be seen from
distances of up to 310 m (in this study), so there was little
potential benefit of having extra sightings in the 5–50 m band.
This suggests that while thermal cameras can add extra sightings
for certain species, these are focused at intermediate distances
from the road (5–50 m) and thermal cameras provide little
benefit either at very close or long range or for species with
We were able to achieve our aims and provide an under-
standing of where and when thermal cameras might be of use;
however, we were not able to investigate the statistical implica-
tions within distance sampling. By necessity, we pooled detec-
tions from multiple transects to obtain detection functions.
These transects spanned areas and times with vastly different
animal assemblages, so we were not able to assume density as
stable and investigate the full implications of model parameters
on density estimates. However, our research does suggest
areas for consideration. Of particular concern would be the
interplay between increasing detection rates, temperature and
detection functions and estimated strip widths. We demonstrate
a substantial linear relationship between the former two, but had
insufficient data to examine the latter. As estimated strip width is
the most important variable affecting density estimates
(Buckland et al. 2001), further research for each species would
be required before accurate inferences of density can be made
(e.g. Augusteyn et al. 2020).
Our results might be biased by our study circumstances, and
not completely relevant elsewhere. The vast majority of the cat
detections reported here were from a fenced paddock that had a
high density of feral cats, with no major mammalian predators
(e.g. dingos, foxes) nor any active conservation hunting for at
least two years prior to our surveys. This period of safety could
have meant that the cats were less wary, less likely to avoid
vehicles and lights, and therefore more detectable by spotlight
survey. In situations where cats are more cryptic, thermal
cameras may have a larger effect on detectability of cats but
only at short to medium distances.
As expected, ambient temperature had a strong effect on the
performance of thermal cameras relative to standard spotlight
counts. At night-time temperatures above 308C, thermal cam-
eras provided almost no additional benefit, and would often
detect virtually no animals at all. Even at temperatures above
208C, there were substantially fewer detections. This agrees with
Vinson et al. (2020), who recorded similar results for Australian
arboreal mammals. This means that thermal cameras should not
be used exclusively for long-term monitoring programs that
include warmer months, or in areas where minimum nightly
temperatures regularly exceed 308C. In such instances, variabil-
ity in detection rates would more likely relate to temperature
than differences in mammal abundance or activity. Using
thermal cameras in dense forests should not have such limita-
tions as background temperatures are typically below 308C
(Burke et al. 2019;Kays et al. 2019), yet for many open habitats
there may be seasons through the year when thermal cameras are
not useful. Spotlighting has no such limitations and thus may be
a more temporally versatile method of monitoring.
Although thermal cameras were not ideal in warm tempera-
tures for small to medium sized mammals (20–5000 g) at our
study site, they were a valuable addition for vehicle-based
spotlighting counts in cooler temperatures. Not only did our
use of thermal cameras result in higher total detections, they also
provided a more accurate estimation of the absolute size of an
independently censused rabbit population. We suggest that other
studies could also use thermal cameras to provide a greater
understanding of whether spotlighting might be underestimating
detections of monitored mammal species. Thermal cameras
would be especially useful for studies where detecting a large
proportion of the population of a small to medium sized species
is critical, for example in surveys to locate populations of
threatened species where every detection is important or for
monitoring populations at low density where population esti-
mates have large confidence intervals.
Thermal cameras and arid zone mammals Australian Mammalogy G
Conflict of interest
The authors declare no conflicts of interest.
Arid Recovery hosted and supported this project. Arid Recovery is an
independent conservation and research initiative supported by BHP, The
University of Adelaide, Bush Heritage Australia and The South Australian
Department for Environment and Water. Samantha Bryson-Kirby, Guy
Nelson, Monica Griffiths, Nathan Beerkens, Emily Gregg, and Melissa
Jensen assisted with field data collection. Katherine Tuft and Jose´ Joaquı´n
Lahoz-Monfort assisted with project development. HMcG and SL are sup-
ported by the National Environmental Program’s Threatened Species
Recovery Hub. This research did not receive any specific funding.
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