Quantifying the relative performance of two undetected-extinction models.

Extinctions of undiscovered species (undetected extinctions) constitute a portion of biodiversity loss that is often ignored. We compared the performance of 2 models of undetected extinctions - Tedesco and SEUX - when estimating undetected extinctions with both simulated and real-world data. We generated simulated data by considering a birth-death process in which less abundant species were more likely to go extinct. When detection rates were higher for common species, the 2 models underestimated the true number of undetected extinctions by up to 88.7%, and when detection rates were independent of abundance, the 2 models performed better; the SEUX model had an average bias of +3.1% and the Tedesco model had an average bias of -62.3%. We applied the models to 8 real-world data sets (e.g., Australian amphibians, Australian birds, North American bivalves) and found that true extinctions may be from 15% to 180% higher than observed values. For 6 of the 8 data sets, the SEUX model yielded absolute estimates that were 5.7-66.8% lower than those of the Tedesco model. We mainly attributed this difference to the SEUX model's assumption that there are no undetected extant species currently. We assessed the accuracy of the models' estimates with a logistic regression to test whether detection and extinction rates were uncorrelated across species. Rates were correlated for 3 of the 8 data sets; species discovered later had a higher probability of being extinct, suggesting that extinction numbers could be even higher for these groups. Despite caveats associated with the models, the evidence from both show biodiversity loss in these groups may be more severe than what has been documented.