Machine Learning for Classification of Satellite Geolocation Data: Addressing the Path Segmentation Challenge in Animal Movement Ecology

Knowledge of global animal movement offers insight into our changing planet, and direct observation of patterns from space is an ideal vantage point. Due to hardware miniaturization of animal trackers and satellites (CubeSats), increasing numbers of geolocation records are becoming available. Ecologists, biologists, and conservationists apply this data in their research and initiatives, but robust methods for automated classification of the data are lacking. In order to quantify behavioral changes at scale for the study and stewardship of nature, a system is needed that can automatically segment and label movement states. Such a system can benefit science by reducing the setup time for research, thereby improving resource allocation of people, time, and funding. This manuscript explores the viability of machine learning models to address the challenge of segmenting active migration from summer or winter range residency. Recurrent neural network (RNN) and long short-term memory (LSTM) architectures are both evaluated and compared. Results show encouraging accuracy with F1-scores exceeding 80%, and work is scoped for future optimizations and feature inclusion.NASA Internet of Animals15. Life on lan

Effect of Membrane Microheterogeneity and Domain Size on Fluorescence Resonance Energy Transfer

Studies of multicomponent membranes suggest lateral inhomogeneity in the form of membrane domains, but the size of small (nanoscale) domains in situ cannot be determined with current techniques. In this article, we present a model that enables extraction of membrane domain size from time-resolved fluorescence resonance energy transfer (FRET) data. We expand upon a classic approach to the infinite phase separation limit and formulate a model that accounts for the presence of disklike domains of finite dimensions within a two-dimensional infinite planar bilayer. The model was tested against off-lattice Monte Carlo calculations of a model membrane in the liquid-disordered (ld) and liquid-ordered (lo) coexistence regime. Simulated domain size was varied from 5 to 50 nm, and two fluorophores, preferentially partitioning into opposite phases, were randomly mixed to obtain the simulated time-resolved FRET data. The Monte Carlo data show clear differences in the efficiency of energy transfer as a function of domain size. The model fit of the data yielded good agreement for the domain size, especially in cases where the domain diameter is <20 nm. Thus, data analysis using the proposed model enables measurement of nanoscale membrane domains using time-resolved FRET