Estimating koala density from incidental koala sightings in South‐East Queensland, Australia (1997–2013), using a self‐exciting spatio‐temporal point process model

• Published in: Ecology and evolution Vol. 11; no. 20; pp. 13805 - 13814
• Main Authors: Dissanayake, Ravi Bandara, Giorgi, Emanuele, Stevenson, Mark, Allavena, Rachel, Henning, Joerg
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.10.2021; John Wiley and Sons Inc; Wiley
• Subjects: citizen science; Clustering; Data collection; Datasets; Environmental protection; Geography; koala; modeling; Original Research; Phascolarctos cinereus; Polls & surveys; population; Population decline; Population density; Populations; Public concern; Public participation; Queensland; Wildlife; Wildlife conservation; Wildlife habitats; Queensland Australia; Australia; citizen science; modeling; koala; Queensland; population
• ISSN: 2045-7758; 2045-7758
• DOI: 10.1002/ece3.8082

The koala, Phascolarctos cinereus, is an iconic Australian wildlife species facing a rapid decline in South‐East Queensland (SEQLD). For conservation planning, the ability to estimate the size of koala populations is crucial. Systematic surveys are the most common approach to estimate koala populations but because of their cost they are often restricted to small geographic areas and are conducted infrequently. Public interest and participation in the collection of koala sighting data is increasing in popularity, but such data are generally not used for population estimation. We modeled monthly sightings of koalas reported by members of the public from 1997 to 2013 in SEQLD by developing a self‐exciting spatio‐temporal point process model. This allowed us to account for characteristics that are associated with koala presence (which vary over both space and time) while accounting for detection bias in the koala sighting process and addressing spatial clustering of observations. The density of koalas varied spatially due to the heterogeneous nature of koala habitat in SEQLD, with a mean density of 0.0019 koalas per km2 over the study period. The percentage of land areas with very low densities (0–0.0005 koalas per km2) remained similar throughout the study period representing, on average, 66% of the total study area. The approach described in this paper provides a useful starting point to allow greater use to be made of incidental koala sighting data. We propose that the model presented here could be used to combine systematic koala survey data (which is spatially restricted, but more precise) with koala sighting data (which is incidental and often biased by nature, but often collected over large geographical areas). Our approach could also be adopted for modeling the density of other wildlife species where data is collected in the same manner. A self‐exciting spatio‐temporal point process model was developed to estimate koala density from observed koala sightings data while accounting for spatio‐temporal detection bias and clustering of observations. An example of koala densities between 1997 and 2006, estimated from koala sightings data is shown below.