McGree, J. | Search bioRxiv

bioRxiv2019

Using virtual reality and thermal imagery to improve statistical modelling of vulnerable and protected species

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 observing a koala at 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.

ecology↗

bioRxiv2019

Process noise distinguishes between indistinguishable population dynamics

Model selection is becoming increasingly important in mathematical biology. Model selection often involves comparing a set of observations with predictions from a suite of continuum mathematical models and selecting the model that provides the best explanation of the data. In this work we consider the more challenging problem of model selection in a stochastic setting. We consider five different stochastic models describing population growth. Through simulation we show that all five stochastic models gives rise to classical logistic growth in the limit where we consider a large number of identically prepared realisations. Therefore, comparing mean data from each of the models gives indistinguishable predictions and model selection based on population-level information is impossible. To overcome this challenge we extract process noise from individual realisations of each model and identify properties in the process noise that differ between the various stochastic models. Using a Bayesian framework, we show how process noise can be used successfully to make a probabilistic distinction between the various stochastic models. The relative success of this approach depends upon the identification of appropriate summary statistics and we illustrate how increasingly sophisticated summary statistics can lead to improved model selection, but this improvement comes at the cost of requiring more detailed summary statistics.

synthetic biology↗