Ecological forecasting with dynamic Generalized Additive Models (DGAMs)

Time series analysis and forecasting are standard goals in applied ecology. But ecological forecasting is difficult because ecology is complex. The abundances of species, for example, fluctuate for many reasons. Food and shelter availability limit survival. Biotic interactions affect colonization and vital rates. Severe weather events and climate variation alter habitat suitability. These sources of variation make it difficult to understand, let alone predict, ecosystem change. Moreover, most available time series software cannot handle features that dominate ecological data, including overdispersion, clustering, missingness, discreteness and nonlinear effects. In this talk, I will introduce Dynamic Generalized Additive Models (DGAMs) as one solution to meet this complexity. I illustrate a number of models that can be tackled with the mvgam R package, which builds Stan code to specify probabilistic Bayesian models that include nonlinear smooth functions, random effects and dynamic processes, all with a simple interface that is familiar to most R users.

November 2, 2023

2:15 PM – 2:45 PM

Online