Malcolm S. Itter

This paper develops a Bayesian dynamical IPM framework that integrates population density and individual growth data to improve predictions of size-structured population dynamics under global change.

This paper develops a Bayesian hierarchical dynamical model to quantify uncertainty in forest dynamics predictions and applies it to a forest inventory dataset.

The paper uses LiDAR and audio recordings to identify forest structural factors influencing eastern whip-poor-will abundance in Pennsylvania.

The paper develops a Bayesian dynamical model to quantify and propagate uncertainty in forest growth predictions using continuous inventory data, aiding adaptive management decisions.