Dr. Christian Stratton (Dept. of Mathematical Sciences, MSU)

2/29/2024  3:10pm

Abstract: 

Assessment of similarity in species composition or abundance across sampled locations is a common goal in multi-species monitoring programs. Existing statistical techniques, known as ordination, provide a framework for clustering sample locations based on species composition by projecting high-dimensional community data into a low-dimensional, latent ecological gradient representing species composition. However, these techniques require specification of the number of distinct ecological communities present in the latent space, which can be difficult to determine in advance. We develop an ordination model capable of simultaneous clustering and ordination that allows for estimation of the number of clusters present in the latent ecological gradient. This model draws latent coordinates for each sample location from a Dirichlet process mixture model, affording researchers probabilistic statements about the number of clusters present in the latent ecological gradient. We apply this model to vegetation data collected in Craters of the Moon National Monument and Preserve (CRMO) in Idaho, USA, yielding ordination results that align with existing ecological gradients within the park.