Speaker
Description
Populations globally are experiencing undue stress due to climate change, habitat destruction and overharvesting. Predicting impending dynamical change or collapse is challenging due to the complex spatiotemporal dynamics natural populations display and the difficulty in obtaining ecological time series. When we explicitly consider a population’s distribution over space, we can quantify population spatial distribution patterns and how they change during a dynamical transition (such as collapse). Here, we quantify population distribution patterns with computational topology and use this coarse grain information from a very limited number of time steps in a population time series to predict the future state. Using supervised machine learning methods, we find that coarse grain (in space and time) topological information has a high success rate of classifying populations at risk of collapse.
