Theory and application of an improved species richness estimator

Species richness is an essential biodiversity variable indicative of ecosystem states and rates of invasion, speciation, and extinction both contemporarily and in fossil records. However, limited sampling effort and spatial aggregation of organisms mean that biodiversity surveys rarely observe every species in the survey area, which introduces bias to the estimated richness and inaccuracy to comparisons of communities across space and time. Here we present a nonparametric, asymptotic, and bias-corrected richness estimator, Ω T , by modelling how spatial abundance characteristics affect observation of species richness. We conduct simulation tests and applied Ω T to a tree census and a seaweed survey. Ω T consistently outperforms common estimators in balancing bias, precision, and difference detection accuracy. Our results provide theoretical insights into how natural and observer-induced variation affects species observation and support Ω T as a promising and application-ready richness estimator for a wide variety of data.