Phantom Species: Adjusting Estimates Of Colonization And Extinction For Pseudo-Turnover

Ecologists rely on field surveys to monitor long-term ecological change but finite sampling and the prevalence of rare species mean that surveys inevitably miss some species present at a given location. These 'phantom species' produce pseudo-turnover by inflating observed rates of local colonization and extinction in resurvey studies, especially among rare species. In this paper, we quantify the probability that pseudo-turnover occurs due to imprecise plot relocation and/or shifts in where individuals are located. Using sampling models derived from the binomial distribution, we estimate probabilities of missing species as a function of local abundance and sampling intensity. Spatially explicit simulations confirm that our binomial model is robust to non-random sampling schemes and clumped species distributions. False absences decline predictably as species abundance and sampling intensity increase allowing us to statistically adjust naive estimates of colonization and extinction for expected rates of pseudo-turnover. To illustrate the model's real-world utility, we analyze apparent colonizations and extinctions for 331 species distributed over 83 sites in southern Wisconsin forests surveyed in both the 1950s and 2000s. Limited sampling in the 1950s means expected rates of pseudo-colonization are appreciable. Accounting for pseudo-turnover thus reduces estimated community-wide colonization rates by 51% compared to naive (observed) rates. More complete sampling in the 2000s limited overestimates of local extinction to 14%. We distinguish three zones of inference based on sampling intensity and species abundance where: 1) naive estimates approximate true values of turnover, 2) adjustments are important, and 3) species are too rare relative to sampling to reliably infer turnover. Accounting for phantom species substantially improves our ability to accurately estimate local colonization and extinction rates, enhancing our ability to infer community dynamics and monitor long-term ecological change.