Employing Gaussian process priors for studying spatial variation in the parameters of a cardiac action potential model

Cardiac cells exhibit variability in the shape and duration of their action potentials in space within a single individual. To create a mathematical model of cardiac action potentials (AP) which captures this spatial variability and also allows for rigorous uncertainty quantification regarding within-tissue spatial correlation structure, we developed a novel hierarchical Bayesian model making use of a latent Gaussian process prior on the parameters of a simplified cardiac AP model which is used to map forcing behavior to observed voltage signals. This model allows for prediction of cardiac electrophysiological dynamics at new points in space and also allows for reconstruction of surface electrical dynamics with a relatively small number of spatial observation points. Furthermore, we make use of Markov chain Monte Carlo methods via the Stan modeling framework for parameter estimation. We employ a synthetic data case study oriented around the reconstruction of a sparsely-observed spatial parameter surface to highlight how this approach can be used for spatial or spatiotemporal analyses of cardiac electrophysiology

Fluid hunter motivation in Central Africa: Effects on behaviour, bushmeat and income

Individual motivation for the rural use of common‐pool resources (CPRs) can be fluid, with the line between subsistence and commercial often unclear and in flux. Implications of fluid motivation are understudied yet important for social–ecological systems (SESs), such as bushmeat hunting throughout Central Africa that is essential to local protein/nutrition, income and culture. Making locally informative predictions of multiple SESs nested within a landscape‐scale SES has been historically difficult, but community‐driven participatory approaches provide new kinds and quantities of data, opening previously inaccessible doors for research and governance. We apply hierarchical Bayesian structural equation modelling to a novel dataset of 910 hunts from 111 gun and trap hunters across nine villages in Gabon, generated in a participatory process whereby hunters conducted GPS self‐follows in conjunction with paraecologist surveys of their motivation, behaviour and offtake. We (i) establish the human behaviour driving gun‐hunting and trapping success and predict its effect on offtake across villages and (ii) link fluid motivation of gun hunters to their behaviour, number of animals hunted, biomass yielded and income earned. Gun hunts across villages yielded more animals during the night than the day, and when hunters brought high amounts of ammunition and walked far distances from villages. Gun hunts were less successful when coupled with trapping while per‐hunt success of trapping itself was generally low and difficult to predict. Fluid gun hunters hunted fewer animals when motivated strictly by subsistence, despite no reduction in ammunition brought or distance walked, while offtake from strictly commercial versus mixed motivation was the same. Numbers of animals hunted, biomass and income were tightly linked. We discuss the implications of these results for the ecological sustainability of hunting and participatory forecasting in bushmeat research and policy. Read the free Plain Language Summary for this article on the Journal blog