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, Dr. Clark will introduce Dynamic Generalized Additive Models (DGAMs) as one solution to meet this complexity. He will 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.
Dr. Nicholas Clark is an Australian Research Council Early Career Fellow in the School of Veterinary Science at the University of Queensland. He is broadly interested in exploring new ways to (1) understand how ecological communities are formed and (2) predict how they will change over time. Dr Clark’s research focuses on developing computational tools and adapting techniques from statistical forecasting to study how organisms and ecosystems respond to change, with applications across a variety of ecological systems. He is also the developer of {mvgam}, an R package for fitting and interrogating Bayesian Dynamic GAMs.
The slidedeck can be accessed as an html version or downloaded as a PDF