Inferring extinction year using a Bayesian approach

Abstract

Species sighting records are combined with statistical models to infer whether an endangered species might have become extinct or instead it has just gone unobserved for a lengthy period of time. The challenging part of developing these models lies in the inclusion of 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 analyzing Bayesian models using Markov Chain Monte Carlo (MCMC) simulation. A general methodology is presented, and it is 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. Interestingly, for the IBW dataset, the inclusion of uncertain sightings did not significantly change the inference about the extinction date. We explore the important role of the last certain sighting when estimating the extinction date. When estimating the extinction date of a species it is important to understand the role of the last certain sighting. If there are no uncertain sightings in the sighting record then the species is highly likely to go extinct soon after the last certain sighting. Nevertheless, this can change when there are uncertain sightings. We find that extinction is likely to have occurred either close to the last certain sighting or at the time where the rate of uncertain sightings drops, as determined by our analysis.