Results
The selected monthly sightings over the 17-year observation period were
merged into a single data set, reducing the number of sightings from
14,256 to 6,580. The total number of sightings and the subset of
sightings used for the data analysis are shown in Figures 3a and 3b.
Figure 3 . Maps of the study areas in South-East Queensland,
Australia showing: (a) point location of all incidental koala sightings
recorded in KoalaBASE for the period January 1997 to December 2013 (n =
14,256); and (b) the subset of koala sightings (n = 6,580) using the
data analysis to estimate koala density.
A line plot showing temporal pattern of the koala population by calendar
time estimated from the spatio-temporal point process model using koala
sightings (n = 6,580) is shown in Figure 4. The koala population was low
for the period 1997 to 2000 without prominent peaks, then fluctuated
with peaks from 2001 to 2008 (with biennial larger peaks), before
reaching large seasonal peaks in 2009 and 2010, declining again to peaks
similar to pre-2009 period, followed by another large peak in 2013.
Figure 4. Koala population by calendar time estimated from the
spatio-temporal point process model using koala sightings (n = 6,580)
recorded in South-East Queensland, 1997-2013.
Parameter estimates from a spatio-temporal point process model used to
estimate koala population densities in South-East Queensland between
1997 and 2013 are shown in Figure 1. The coefficients of land lot
density and mean temperature of the coldest month had positive
coefficients, while the coefficients of the other covariates included in
the model were negative (Table 1).
Table 1. Estimated parameters from a spatio-temporal point
process model used to estimate koala population densities in South-East
Queensland between 1997 and 2013.
The relative estimated koala population density in South-East
Queensland, 1997-2013 is shown in Figure 5 ranging from 0 to 6 or above
koalas per km2, with the spatial distribution of
sightings remaining more or less the same. Based on the model presented
in Equation 6, đ was > 0 and \(\varphi\) was small
suggesting that koalas aggregate in large areas. The estimates are
consistent with the observed sightings, with no koalas or very low koala
density in the western part of the study area and increasing density
towards the Eastern coast of South-East Queensland, with prominent
pockets of high koala density in known areas with good koala habitat.
Figure 5. Estimated relative koala population density (koalas
per km2) in South-East Queensland, 1997-2013.
Estimates were derived from a spatio-temporal point process model using
koala sightings data (n = 6,580).
The percentage of land area in South-East Queensland with varying koala
sightings density (koalas per km2) for each year of
the 1997-2013 period is shown in Table 2. The percentage of land areas
with very low sightings densities (0-0.25 koalas per
km2) remained similar throughout the study period
representing in average (SD) 68.3% (0.06) of the total study area
(Table 2). However, land areas with more koalas per
km2 showed larger variations over the years, with
koala mean (SD) densities of 0.25-0.5, 0.5-1, 1-2, 2-5 and
> 5 koalas per km2 representing 16.8%
(0.21), 13.8% (0.25), 0.7% (0.20), 0.3% (0.13), and 0.2% (0.1) of
the study area in South-East Queensland,
respectively.
Table 2. Percentage of land area in South-East Queensland,
1997-2013, with varying koala population density (koalas per
km2). Estimates were derived from a spatio-temporal
point process model using koala sightings data (n = 6,580).
Discussion
We present here the results of spatio-temporal point process model,
where relative koala population density was estimated considering
spatio-temporal detection bias, observed koala densities and potential
clustering effects. As partial likelihood estimation was used, the
intercept was not calculated and absolute koala sightings density could
not be estimated. However, relative koala population density estimates
were produced for each year of the 17-year observation period.
The density of the koala population in South-East Queensland varied
throughout the study region due to the heterogeneous nature of koala
habitat, with density estimates ranging from 0.005 to 8.9 koalas per
km2. Limited information of koala densities exist in
Australia, but Rhodes, Beyer, Preece and McAlpine (2015) estimated koala
densities varying between 0.001 and 11.0 koalas per ha in coastal
regions of South-East Queensland, with an average of 0.04 koalas per ha
(or 4 koalas per km2). However, the model developed by
Rhodes et al. (2015) utilized data collected through multiple systematic
surveys, which were implemented in small areas and did not predict koala
populations across large geographic areas due to uncertainties
associated with extrapolations. In fact, extrapolating koala densities
from statistical models for large geographical areas is questionable as
koala habitat is not continuously distributed. To avoid this fallacy,
densities should be predicted to strata of different habitat types
(Dique, Preece, Thompson and Villiers 2004).
Actual koala numbers are very difficult to estimate. In 2010, the
Department of Environmental Heritage and Protection (DEHP) predicted
that the Queensland koalaâs population was between 157,000 and 177,000
animals, while the Threatened Species Scientific Committee of Australia
estimated Queenslandâs koala population to be approximately 167,000
animals in 2010, representing as 43% decline from 1990 (Rhodes, Beyer,
Preece and McAlpine 2015). Another study estimated Queenslandâs koala
population to be about 79,300 in 2012 (Adams-Hosking, et al. 2016).
Using expert elicitation methods the koala population for the whole of
Australia was approximated to be 329,000 individuals (ranging from
144,000 to 605,000) (Adams-Hosking, McBride, Baxter, Burgman, de
Villiers, Kavanagh, Lawler, Lunney, Melzer, Menkhorst, Molsher, Moore,
Phalen, Rhodes, Todd, Whisson, McAlpine and Richardson 2016).
The results of the statistical model presented here provide estimates of
yearly koala population densities, which are informed and therefore
strongly influenced by observed sightings. Our model results showed
increased koala population densities in some years, which might simply
represent a higher observed fraction of koalas from the true koala
population. We could also show strong clustering of koalas in locations
in and around the Moreton Bay and Redland areas which is similar to the
high density areas identified by Rhodes, Beyer, Preece and McAlpine
(2015) using systematic field survey data. However, our model did
identify low densities of koalas in the western part of South-East
Queensland whereas Rhodes et al. (2015) predicted higher densities
there, although this was probably due to the uncertainty associated with
the model estimates for this region.
Importantly, we were able to estimate koala population density over time
and space while incorporating a range of covariates expected to be
associated with observed sighting densities or spatio-temporal detection
bias. For example, distance to primary roads was considered to be
covariate predominately influencing spatio-temporal bias, while foliage
protective cover was influencing an observerâs ability to sight a koala
and therefore impacting on observed sighting density. However, the
contribution of covariates to the two different components of the model
cannot be quantified as these components were included as additive
factors on a log-scale in the model. Considering that covariates with a
negative sign, would decrease estimated koala population density, our
model indicated that larger distance to primary roads, denser foliage,
higher altitude, but also increases precipitation would result in less
sightings being reported. In contrast, increased lot density and warmer
temperatures in the colder months were associated with increases
population densities.
Uncertainties in estimated koala densities can be further reduced if
additional data are collected at the time of each sighting event. This
data could then be used in the modelling approaches to estimate and
remove the effect of the bias on population density estimates. In our
study, no observer-related variables were collected at the time when
koalas were sighted. It has been shown that the probability of detection
of a koala by an observer varies with previous experience of detecting
koalas: an experienced observer can have a detection rate of around
70%, while an inexperienced observer might have a detection rate of
only 30% (Corcoran, et al. 2019). As a result, many koalas may go
undetected simply because of the lack of observer experience. The
situation is somewhat different in systematically conducted field
surveys by trained individuals where detection probabilities are
estimated (Rhodes, Beyer, Preece and McAlpine 2015). Thus, incidental
sightings reported by members of the public represent a biased sample of
the koala population at any given time, but the collection of data on
experience of observers at the time of the sighting, could provide value
information to address this bias.
Koala sightings vary between seasons of the year. Such seasonal
variations might be due to more frequent dispersal of koalas during
breeding periods, but also due to better visibility of animals and
better weather conditions that are more favorable for people to go
outdoors and spot koalas. Interestingly, the results of our model
indicated that clustering of koalas is not prominently different between
the mating (theta1 = 2.0056) and non-mating seasons of koalas (theta2 =
2.029). This might be explained by koalas being solitary animals and
although they travel over larger distances in the breeding season, their
greater mobility might not necessary be associated with clustering of
animals.
We included an average home range of koalas in our model, as we did not
have detailed koala home range information for different parts of our
study area. We realize that koala home ranges are not uniform and even
within the Redland Local Government Area, koala home ranges of koalas
vary between 0.05 and 0.55 km2 (de Oliveira, Murray,
de Villiers and Baxter 2014). The precisions of koala densities could be
improved, if home ranges appropriate for each habitat types are included
in the model.
It has been predicted that drier and warmer climatic conditions have an
undesirable impact on koala habitat and thereby negatively impacting
koala populations (Adams-Hosking, et al. 2011). Unfortunately, our study
was constrained by the non-availability of temporally varying
covariates. As a result, the temporal effect of covariates, such as the
impact of temperature changes over time on koala densities, could not be
quantified.
Overall, while acknowledging the limitations associated sightings data
collected by members of the public, we developed a statistical model
that addressed the spatio-temporal bias associated with observed koala
sightings and provided long-term relative koala density estimates for
one of the largest koala populations of Australia over a 17-year period.
In future research, the model proposed here could be used for systematic
survey data and ultimately for combining (spatially restricted, but more
precise) koala survey data with koala sightings data, that is incidental
and often biased by nature, but often collected over large geographical
areas.