Incidence‐data‐based species richness estimation via a Beta‐Binomial model - Chun‐Huo Chiu (Methods in Ecology and Evolution)
Abstract Individual‐based abundance data and sample‐based incidence data are the two most widely used survey data formats to assess the species diversity in a target area, where the sample‐based incidence data are more available and efficient for estimating species richness. For species individual with spatial aggregation, individual‐unit‐based random sampling scheme is difficult to implement, and quadrat‐unit‐based sampling scheme is more available to implement and more likely to fit the model assumption of random sampling. In addition, sample‐based incidence data, without recording the number of individuals of a species and only recording the binary presence or absence of a species in the sampled unit, could considerably reduce the survey loading in the field. In this study, according to sample‐based incidence data and based on a beta‐binomial model assumption, instead of using the maximum likelihood method, I used the moment method to derive the richness estimator. The proposed richness estimation method provides a lower bound estimator of species richness for beta‐binomial models, in which the new method only uses the number of singletons, doubletons and tripletons in the sample to estimate undetected richness. I evaluated the proposed estimator using simulated datasets generated from various species abundance models. For highly heterogeneous communities, the simulation results indicate that the proposed estimator could provide a more stable, less biased estimate and a more accurate 95% confidence interval of true richness compared to other traditional parametric‐based estimators. I also applied the proposed approach to real datasets for assessment and comparison with traditional estimators. The newly proposed richness estimator provides different information and conclusions from other estimators.