Deepdive: Estimating Global Biodiversity Patterns Through Time Using Deep Learning

A new framework called DeepDive uses a combination of simulation and deep learning to accurately estimate biodiversity through time from fossil data. Extensive simulations demonstrate its ability to account for sampling biases and accurately predict diversity trajectories. This framework was used to estimate global biodiversity dynamics for two animal groups, marine animals and the mammalian clade Proboscidea.