Chao, A., Colwell, R. K., Chiu, C. H., & Townsend, D. (2017). Seen once or more than once: applying Good–Turing theory to estimate species richness using only unique observations and a species list.
Chao, A., R. K. Colwell, Chun‐Huo Chiu, and D. Townsend. “Seen Once or More than Once: Applying Good–Turing Theory to Estimate Species Richness Using Only Unique Observations and a Species List” (2017).
Chao, A., et al. Seen Once or More than Once: Applying Good–Turing Theory to Estimate Species Richness Using Only Unique Observations and a Species List. 2017.
Summary Due to sampling limitations, almost every biodiversity survey misses species that are present, but not detected, so that empirical species counts underestimate species richness. A wide range of species richness estimators has been proposed in the literature to reduce undersampling bias. We focus on nonparametric estimators, which make no assumptions about the mathematical form of the underlying species abundance/incidence distributions. For replicated incidence data, in which only species presence/absence (or detection/non-detection) is recorded in multiple sampling units, most existing nonparametric estimators of the number of undetected species are based on the frequency counts of the uniques (species detected in only one sampling unit) and duplicates (species detected in exactly two sampling units). Some survey methods, however, record only uniques and super-duplicates (species observed in more than one sampling unit). Using the Good–Turing frequency formula, we developed a method to estimate the number of duplicates for such data, allowing estimation of true species richness, including undetected species. We test our estimators on several empirical datasets for which doubletons were recorded and on simulated sampling data, then apply them to an extensive, but previously unusable survey of coral reef fishes, for which only uniques and super-duplicates were recorded. We extend the method to abundance data and discuss other potential applications.