Uncertainty Quantification for NEON and Other Biodiversity Network Data with Hierarchical Bayes

Modeling the distribution and abundance of multiple species, so-called joint species distribution models (JSDMs), where species and other attributes are recorded on different scales brings many challenges. Because species are not independent, they must be modeled together. But how does one combine species recorded on different scales? Data may be continuous, discrete, censored, composition, nominal, and ordinal–combinations of observations are not described by standard distributions. Equally challenging is the fact that most of the values in species-abundance data sets are typically zero. Finally, application of non-linear link functions as used in GLMs make it hard to interpret estimates from a model. Generalized joint attribute modeling (GJAM) provides a common framework for synthesis of ecological attribute and abundance data, both for estimating responses to the environment and for prediction. In this workshop, the basic ideas needed for Bayesian analysis will be introduced, followed by examples showing how GJAM is used integrate biodiversity data from networks.

In addition, Jim will be presenting a talk October 11, LIfe Sciences 038, 3:45 - 5:00pm.  

Title of his E&E department talk is "Sensitivity and velocity of community reorganization across NEON: plants to insects to mammals responding jointly to climate change and each other."