Population projections from holey matrices: Using prior information to estimate rare transition events

Population projection matrices are a common means for predicting short- and long-term population persistence for rare, threatened and endangered species. Data from such species can suffer from small sample sizes and consequently miss rare demographic events resulting in incomplete or biologically unrealistic life cycle trajectories. Matrices with missing values (zeros; e.g., no observation of seeds transitioning to seedlings) are often patched using prior information from the literature, other populations, time periods, other species, best guess estimates, or are sometimes even ignored. To alleviate this problem, we propose using a multinomial-Dirichlet model for parameterizing transitions and a Gamma for reproduction to patch missing values in these holey matrices. This formally integrates prior information within a Bayesian framework and explicitly includes the weight of the prior information on the posterior distributions. We show using two real data sets that the weight assigned to the prior information mainly influences the dispersion of the posteriors, the inclusion of priors results in irreducible and ergodic matrices, and more biologically realistic inferences can be made on the transition probabilities. Because the priors are explicitly stated, the results are reproducible and can be re-evaluated if alternative priors are available in the future.

Automated summary

Population projection matrices are commonly used to predict population persistence for rare species, but data from these species can suffer from small sample sizes and miss rare demographic events, leading to incomplete or unrealistic trajectories. To address this issue, a multinomial-Dirichlet model for transitions and a Gamma model for reproduction are proposed to patch missing values in the matrices, integrating prior information within a Bayesian framework and improving the realism of inferences on transition probabilities.