Correctly classifying a species as extinct or extant is of critical importance if current rates of biodiversity loss are to be accurately quantified. Observing an extinction event is rare, so in many cases extinction status is inferred using methods based on the analysis of records of historic sighting events. The accuracy of such methods is difficult to test. However, results of recent experiments with microcosm communities suggest that the rate at which a population declines to extinction, potentially driven by varying environmental conditions, may alter one's ability accurately to infer extinction status. We tested how the rate of population decline, driven by historic environmental change, alters the accuracy of 6 commonly applied sighting-based methods used to infer extinction. We used data from small-scale experimental communities and recorded wild population extirpations. We assessed how accuracy of the different methods was affected by rate of population decline, search effort, and number of sighting events recorded. Rate of population decline and historic population size of the species affected the accuracy of inferred extinction dates; however, faster declines produced more accurate inferred dates of extinction, but only when population sizes were higher. Optimal linear estimation (OLE) offered the most reliable and robust estimates, though no single method performed best in all situations, and it may be appropriate to use a different method if information regarding historic search efforts is available. OLE provided the most accurate estimates of extinction when the number of sighting events used was >10, and future use of this method should take this into account. Data from experimental populations provide added insight into testing techniques to discern wild extirpation events. Care should be taken designing such experiments so that they mirror closely the abundance dynamics of populations affected by real-world extirpation events.
Bibliographical note© 2014 Society for Conservation Biology.
- Conservation of Natural Resources
- Extinction, Biological
- Linear Models
- Population Density
- Population Dynamics