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Abstract
Biodiversity loss and sparse observational data mean that critical conservation decisions may be based on little to no information. Emerging technologies, such as airborne thermal imaging and virtual reality, may facilitate species monitoring and improve predictions of species distribution. Here we combined these two technologies to predict the distribution of koalas, specialized arboreal foliovores facing population declines in many parts of eastern Australia. For a study area in southeast Australia, we complemented ground-survey records with presence and absence observations from thermal-imagery obtained using Remotely-Piloted Aircraft Systems. These field observations were further complemented with information elicited from koala experts, who were immersed in 360-degree images of the study area. The experts were asked to state the probability of habitat suitability and koala presence at the sites they viewed and to assign each probability a confidence rating. We fit logistic regression models to the ground survey data and the ground plus thermal-imagery survey data and a Beta regression model to the expert elicitation data. We then combined parameter estimates from the expert-elicitation model with those from each of the survey models to predict koala presence and absence in the study area. The model that combined the ground, thermal-imagery and expert-elicitation data substantially reduced the uncertainty around parameter estimates and increased the accuracy of classifications (koala presence vs absence), relative to the model based on ground-survey data alone. Our findings suggest that data elicited from experts using virtual reality technology can be combined with data from other emerging technologies, such as airborne thermal-imagery, using traditional statistical models, to increase the information available for species distribution modelling and the conservation of vulnerable and protected species.
Highlights
In the face of unprecedented biodiversity loss, critical decisions are needed on the conservation of vulnerable and protected species [1,2]
Accuracy of the base G model increased by 75% and root mean-square prediction error (RMSPE) decreased by 26% when groundsurvey observations were combined with data from the emerging technologies (GT_E model; Table 3)
The specificity of the GT_E model was relatively high given there was no negative impact on the sensitivity of this model (0.937)
Summary
In the face of unprecedented biodiversity loss, critical decisions are needed on the conservation of vulnerable and protected species [1,2]. Monitoring large areas using traditional ground-survey methods is logistically and financially infeasible; professional monitoring can be time consuming and expensive, and while volunteer data are a valuable source of lower-cost information [7], the data may be biased and range widely in quality [8,9,10]. These issues all contribute towards the sparse data problem
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