Article
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A Richness Estimator Based on Integrated Data
Version 1
: Received: 9 July 2023 / Approved: 10 July 2023 / Online: 10 July 2023 (09:05:09 CEST)
A peer-reviewed article of this Preprint also exists.
Chiu, C.-H. A Richness Estimator Based on Integrated Data. Mathematics 2023, 11, 3775. Chiu, C.-H. A Richness Estimator Based on Integrated Data. Mathematics 2023, 11, 3775.
Abstract
Species richness is a widely used measure for assessing the diversity of a particular area. However, observed richness often underestimates the true richness due to resource limitations, particularly in the small-sized sample or highly heterogeneous assemblage. Estimating species richness in a large-scale region typically involves an integrated data set consisting of subsamples collected independently from different subregions. However, the pooled sample of integrated data is no longer a random sample from the entire region, and the use of different sampling schemes results in variations in data formats. Consequently, employing a single sampling distribution to model the pooled sample becomes impractical, rendering existing richness estimators inadequate. This study theoretically explains the applicability of Chao's lower bound estimators in estimating species richness for large-scale areas using the pooled sample. Additionally, a new nonparametric estimator is introduced, which adjusts the bias of Chao's lower bound estimator by leveraging the Good-Turing frequency formula. This proposed estimator only utilizes the pooled sample's singleton, doubleton, and tripleton richness. Simulated data sets across various models are employed to demonstrate the statistical performance of the estimator, showcasing its ability to reduce bias and provide accurate 95% confidence intervals. Real data sets are also utilized to illustrate the practical application of the proposed approach.
Keywords
Chao’s lower bound estimator; Good-Turing frequency formula; Integrated data; singleton; doubleton; tripleton
Subject
Environmental and Earth Sciences, Ecology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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