Covariates
The following spatial covariates were used as explanatory variables in the model:
The covariate Distance to primary roads was considered to only influence spatio-temporal bias associated with these sightings (Dissanayake, Stevenson, Allavena and Henning 2019), since it quantifies the easiness of access to koala habitat by an observer. The other covariates can be assumed to mostly influence observed koala sightings density. These covariates were obtained from online spatial databases (http://qldspatial.information.qld.gov.au, http://www.bom.gov.au) and included as raster maps with a 1 square kilometre resolution.

Spatio-temporal modelling of koala sighting density

The koala sightings were modelled as a realisation of a spatio-temporal point process model (STPP) model (Baddeley, et al. 2016). STPPs are a useful statistical tool that allow to model the spatial and temporal variation of sightings within a region and time window of interest. Within this modelling framework two main methods can be distinguished: (1) mechanistic models, where subject matter knowledge is used to inform the probability that a sighting will occur at a particular location and a particular time point; and (2) empirical models, where the objective is to use observational data to inform estimates of koala density. The approach used here is a combination of the two methods.
More specifically, we assumed that the koala density at a particular location and a particular time is dependent on the three groups of variables: (a) variables that relate to the spatio-temporal detection bias; (b) spatio-temporally referenced variables that are known to be associated with koala density; and (c) variables that reflect well-established knowledge on the home range of koalas. The main mechanistic component of the model is the third group of variables, while the first two are modelled as a log-linear regression of koala density. A partial likelihood approach was used to fit the model (Diggle, et al. 2010).
Three factors were considered in the spatio-temporal modelling of koala density: spatio-temporal detection bias \(b\left(x,t\right)\), observed koala density \(q(x,\ t)\), and clustering r(x,t|Ht). Let \(\mathcal{H}_{t}\) denote the full story of the process up to yeart. We assumed that conditionally on \(\mathcal{H}_{t}\), the intensity \(\lambda\) of the process is at a location x in yeart was given by: