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

Raymond L. Tremblay, Andrew J. Tyre, Maria Eglée Pérez, James D. Ackerman

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number109526
JournalEcological Modelling
StatePublished - May 1 2021


  • Bayesian model
  • Beta distribution
  • Demography
  • Multinomial Dirichlet model
  • Parameter estimates
  • Rare events

ASJC Scopus subject areas

  • Ecological Modeling


Dive into the research topics of 'Population projections from holey matrices: Using prior information to estimate rare transition events'. Together they form a unique fingerprint.

Cite this