Wolfgang Traylor - Bottom-up mechanistic ecosystem models framed Bayesian
Channel: Bayes@Lund
Date: 2024-03-19

Highly mechanistic dynamic ecosystem models are typically complex and parameter-rich, but thanks to being constrained by ecological mechanisms they are able to predict properties of past or future ecosystems with no contemporary analogs. However, they usually rely on manual tuning and lack quantitative uncertainty analyses. On the other hand, probabilistic models are typically simpler but less reliable outside their training domain. How can we combine the strengths of mechanistic detail and probabilistic predictions? I will present a case study of using a dynamic vegetation–herbivore model to predict woolly mammoth population densities in the long-vanished glacial steppe of Siberia. The model is bottom-up: Population densities are an emergent property, and most parameters are observable quantities. One model run by itself is a possibilistic prediction, but a customized likelihood function—based on mammoth occurrence only—allowed me to derive posterior probability distributions from Bayesian updating. The approach demonstrates how mechanistic detail, bottom-up model development, Bayesian uncertainty propagation, and preregistration mark the cornerstones of open-ended predictions under no-analog conditions.

License: CC-BY-4.0 (c) Wolfgang Traylor