Incidence-data-based species richness estimation via a Beta-Binomial model
Journal
Methods in Ecology and Evolution
Journal Volume
13
Journal Issue
11
Pages
2546
Date Issued
2022-11-01
Author(s)
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.
Subjects
beta-binomial model; doubletons; sample-based incidence data; singletons; tripletons
Publisher
WILEY
Type
journal article