ANNUAL SOBEL LECTURE

Biodiversity data tend to be extremely biased towards large and charismatic organisms that are relatively easy to observe and accessible to human observers. We seek to address this gap and fundamentally improve understanding of life on earth through (relatively) unbiased automated monitoring of insects, fungi, birds and mammals at sites across the earth. Each site contains audio monitors (to identify bird vocalizations), camera traps (to detect mammals and large birds), malaise traps (to capture insects) and cyclone samplers (to capture fungal spores). Taxonomic classification of the insect and fungi species is based on DNA barcoding applied to the collected samples. There is interest in applying joint species distribution modeling (JSDMs) to infer the impact of covariates (habitat, environmental disruption, climate, etc) on the biological communities being monitored, while also inferring interaction networks among the species. In addition, there is interest in the discovery of new species and the study of factors related to biodiversity. Our data contain large numbers of insects and fungi species that were previously unknown to science, and a fundamental aspect of the data is that most of the species being sampled are extremely rare. This talk will introduce our ERC-funded Lifeplan study and describe some of the exciting data being collected, while highlighting the important role of novel AI, machine learning and statistical methods in analyzing and interpreting the data. A particular focus is on a new paradigm for inference in Bayesian factor models for massive-dimensional and sparse data; we apply this approach to data on 10,000s of species of fungi.