Abstract
The koala, Phascolarctos cinereus, is an iconic Australian
wildlife species, but faces rapid decline in South-East Queensland
(SEQLD). For conservation planning, estimating koala populations is
crucial. Systematic surveys are the most common approach to estimate
koala populations, but such surveys are restricted to small geographic
areas, they are costly and conducted infrequently. Public interest and
participation in the collection of koala sightings is increasing in
popularity, but such data is generally not used for population
estimation. We used incidental sightings of koalas reported by members
of the public from 1997-2013 in SEQLD to estimate the yearly
spatio-temporal koala sightings density. For this, a spatio-temporal
point process model was developed accounting for observed koala density,
spatio-temporal detection bias and clustering. The density of koalas
varied throughout the study period due to the heterogeneous nature of
koala habitat in SEQLD, with density estimates ranging between 0.005 to
8.9 koalas per km2.
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. However, land areas with more koalas per km2showed larger annual variations, 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.
We did find that clustering of koala sightings was not prominently
different between the mating and non-mating seasons of koalas. While
acknowledging the limitations associated sightings data, 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.
In future research, the proposed model proposed here could be used for
systematic survey data and ultimately for combining koala survey
data with koala sightings data and remove the spatial bias more
reliably.
Key words: Koala, modelling, population, citizen science, Queensland
Introduction
Direct observations and counting of koalas in the field faces many
challenges, because koalas are difficult to detect in their natural
habitat and they are widely dispersed (Ellis, et al. 2013, Masters, et
al. 2004, McGregor, et al. 2013). Various methods are used to count
koalas and estimate koala population density: systematic field surveys
(David S. Dique, et al. 2003), distance sampling (D. S. Dique, et al.
2003), counting the number of faecal pellets under trees (Seabrook, et
al. 2011, Sullivan, et al. 2002, Sullivan, et al. 2003),
capture-mark-recapture methods (Masters, Duka, Berris and Moss 2004) and
community surveys (Hollow 2015). Counts of vocalisations heard and
spotlight surveys are also sometimes used (Smith and Andrews 1997).
Systematic surveys of strip transects (Dique, et al. 2004) and distance
sampling using line transects are the most common methods to estimate
koala density, but distance sampling techniques are only suitable for
small areas because they are labour intensive and therefore expensive
(Kjeldsen, et al. 2015). Research has described the relationship between
koala’s tree preference and the presence of koala scat (Ellis,
FitzGibbon, Melzer, Wilson, Johnston, Bercovitch, Dique and Carrick
2013) and koala scat prevalence has been shown to correlate well with
koala density (Ellis, et al. 1998, Lunney, et al. 2009). Scat surveys
are less expensive than systematic surveys and have been therefore been
used to estimate koala population density (McAlpine, et al. 2006,
Rhodes, et al. 2008). Postal surveys of targeted communities and
incidental sightings of koalas by members of the public have also been
used to estimate population counts (Cork, et al. 2000, Lunney, Crowther,
Shannon and Bryant 2009, Lunney, et al. 2016, Predavec, et al. 2016,
Sequeira, et al. 2014). These methods are suitable for smaller
geographical areas with varying success and require statistical analysis
to estimate population counts based on reported koala numbers (Santika,
et al. 2014).
To review koala density estimates for conservation purposes, it is
important to generate long-term datasets of koala populations over large
geographical areas rather than to generate population counts at
infrequent intervals (Ellis, Sullivan, Lisle and Carrick 1998, Lunney,
et al. 2014, Lunney, Predavec, Miller, Shannon, Fisher, Moon, Matthews,
Turbill and Rhodes 2016). Long-term survey data have been used to
predict koala populations. A recent study in South-East Queensland
(Santika, et al. 2015) attempted to estimate the geographic distribution
of koala populations across a wide geographical area using spatial
modelling techniques informed by long term (Rhodes, et al. 2015) line
and strip transect survey data (1996 and 2015) collected by distance
sampling. Ecological modelling techniques can also provide an
alternative to active, longitudinal data collection (Schmolke, et al.
2010), although their validity is questionable in the absence of
standardised methods for estimating wildlife density and distribution
(Dique, Preece, Thompson and Villiers 2004, McGregor, Kerr and
Krockenberger 2013).
With the advancement of communication technologies and the widespread
availability of dedicated mobile applications, public participation in
the collection of wildlife data is increasing in popularity. Attempts
have been made to estimate wildlife populations using incidental
sighting data alone, and/or in combination with survey data (Dorazio
2014, Sequeira, Roetman, Daniels, Baker and Bradshaw 2014). For
instance, member of the public were invited to take part in collecting
data on koala sightings as part of a program titled the ‘Great Koala
Count’ in the Australian states of New South Wales and South Australia
in 2012 (Sequeira, Roetman, Daniels, Baker and Bradshaw 2014). The
‘Great Koala Count’ has generated large number of incidental koala
sighting using specific guidelines for data collection in pre-identified
geographical areas in those two states. In South-East Queensland
incidental koala sightings are collected since 1997, although no formal
field protocols are provided to members of the public for the data
collection (Dissanayake, et al. 2019). While these data have been used
to describe koala population trends and to describe spatial biases
identified (Dissanayake, Stevenson, Allavena and Henning 2019), it has
not yet been used to estimate long-term koala density. In this study, we
have developed a modelling approach to estimate koala density from
observed sightings data over a period of 17 years, while addressing
spatio-temporal detection biases and potentially clustering of
observations.
Materials and methods