Modelling rhino presence with Bayesian networks

Abstract

Modelling complex systems such as how the white rhinoceros Ceratotherium simum simum uses a landscape requires innovative and multi-disciplinary approaches. Bayesian networks have been shown to provide a dynamic, easily interpretable framework to represent real-world problems. This, together with advances in remote sensor technology to easily quantify environmental variables, make non-intrusive techniques for understanding and inference of ecological processes more viable than ever. However, when modelling an animal’s use of a landscape we only have access to presence locations. These data are also extremely susceptible to both temporal and spatial sampling bias in that animal presence locations often originate from aerial surveys or from individual rhinos fitted with tracking collars. In modelling species’ presence, little recognition is given to finding quantifiable drivers and managing confounding variables. Here we use presence-unlabelled modelling to construct Bayesian networks for rhino presence with remotely sensed covariates and show how it can provide an understanding of a complex system in a temporal and spatial context. We find that strategic unlabelled data sampling is important to counter sampling biases and discretisation of covariate data needs to be well considered in the tradeoff between computational efficiency and data accuracy. We show how learned Bayesian networks can be used to not only reveal interesting relations between drivers of rhino presence, but also to perform inference. Having temporally aware environmental variables such as soil moisture and distance to fire, allowed us to infer rhino presences for the following time step with incomplete evidence. We confirmed that in general, white rhinos tend to be close to surface water, rivers and previously burned areas with a preference for warm slopes. These relationships between drivers shift notably when modelling for individuals. We anticipate our dissertation to be a starting point for more sophisticated models of complex systems specifically investigating its use to model individual behaviour.