I introduce a Bayesian nonparametric framework for modeling ordinal regression relationships which evolve in discrete time. The motivating application involves a key problem in fisheries research on estimating relationships between age, length and maturity, the latter recorded on an ordinal scale, across time. The methodology builds from nonparametric mixture modeling for the joint stochastic mechanism of covariates and latent continuous responses. This approach yields flexible inference for ordinal regression functions while at the same time avoiding challenges present in parametric models. A novel dependent Dirichlet process prior for time-dependent mixing distributions extends the model to the dynamic setting. The methodology is applied to study relationships between maturity, age, and length for Chilipepper rockfish, using data collected over 15 years along the coast of California. I will also outline related methodology for handling missing values in heterogeneous data, and for combining datasets from multiple sources that are not all jointly representative of the target population.