Inferring extinction year using a Bayesian approach

Species sighting records are combined with statistical models to infer whether an endangered species might have become extinct, or instead has just gone unobserved for a lengthy period of time. The challenging part of developing these models lies in dealing with uncertain sightings. We propose a Bayesian hierarchical approach to infer the extinction time of a species based on historical sighting records which may be either certain or uncertain. The posterior distribution for extinction time is evaluated using the likelihood of sighting data and non-informative priors for model parameters. All the models discussed in this paper are implemented in JAGS, a program for analysing Bayesian models using Markov Chain Monte Carlo (MCMC) simulation. A general methodology is presented and then applied to the sighting record of the ivory-billed woodpecker (IBW)Campephilus principalis. It was found that the IBW most likely went extinct between 1940 and 1945, a little after the date of the last certain sighting. The methods developed were also applied to other species sighting records as well as some artificial sighting records. Through the results, it was found that the inferred time of extinction is significantly influenced by the last certain sighting if the sighting record consists of only certain sightings. In the presence of uncertain sightings, the inferred extinction time is influenced by either the last certain sighting or the time when the uncertain sighting rate drops.