Recent advancements in species sampling methods and bioinformatic tools have led to the collection of many species occurrence datasets, motivating the development of statistical tools to extract valuable insights and enhance our understanding of biodiversityṪhere is a rich literature in ecology on so-called joint species distribution models (JSDMs), which usually take the form of multivariate probit latent factor regression models. However, such models cannot deal with the fact that we regularly discover many new species as the sampling is being conducted. In this paper, we analyze tropical tree occurrence data leveraging an infinite latent features model. This approach does not require specifying species identity in advance and allows a growing number of binary outcomes.