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

Ravi Bandara Dissanayake,Joerg Henning,Emanuele Giorgi,Mark Stevenson,Rachel Allavena

doi:10.1002/ece3.8082

Ravi Bandara Dissanayake, Joerg Henning + Show 3 more

Open Access

https://doi.org/10.1002/ece3.8082

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Abstract

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.

Highlights

Since the time of first European settlement in Australia, koalas have faced threats from humans
We use South-East Queensland (SEQLD) incidental koala sighting data collected over a period of 17 years to develop a modeling approach to estimate koala density, accounting for spatio-temporal detection biases and biases arising from geographic clustering of observations
We present here the results of spatio-temporal point process model where koala population density was estimated from citizen science koala sighting data by adjusting for spatio-temporal detection bias

Summary

| INTRODUCTION

Since the time of first European settlement in Australia, koalas have faced threats from humans. Systematic sampling methods are used to count koalas and estimate koala population densities These methods typically follow a defined approach to collect data from areas that are thought to be representative of the entire geographic area of interest. These include transect and distance sampling methods (Crowther et al, 2020; Dique et al, 2004; Wilmott et al, 2019) which require skilled observers to identify and count koalas (Thomas et al, 2010). We use SEQLD incidental koala sighting data collected over a period of 17 years to develop a modeling approach to estimate koala density, accounting for spatio-temporal detection biases and biases arising from geographic clustering of observations

| MATERIALS AND METHODS

Findings

| DISCUSSION