Page 413 - Contributed Paper Session (CPS) - Volume 6

CPS2007 Jai-Hua Yen et al.

Richness estimation with species identity error
Jai-Hua Yen, Chun-Huo Chiu
Division of Biometry, Department of Agronomy, National Taiwan University, Taiwan

Abstract
Richness estimation of an interesting area is always a challenge statistical work
due to small sample size or species identity error. In the literatures, most
richness estimators were only proposed to tackle the underestimation of the
size-limited sample. However, species identity error almost occurs in each
species survey and seriously reduces the accuracy of observed, singleton, and
doubleton richness in turns to influence the behavior of richness estimator.
Therefore, to estimate the true richness, the biased collected data due to
species identity error should be modified before processing the richness
estimation work. In the manuscript, we propose a new approach to correct the
bias of richness estimation due to species identity error. First, a species list
inventory from a subplot obtained by the investigator was used to estimate
the species identity error rate. Then, we can correct the biased observed,
singleton, and doubleton richness of the raw sampling data from the
interesting area. Finally, the rich-ness estimators proposed in the literatures
could be supplied to get the more correct estimates based on adjusted
observed data. To investigate the behavior of the proposed method, we
performed simulations by generating data sets from various species models
with different species identity error rates. For the purpose of illustration, the
real data was supplied to demonstrate our proposed approach. A
presence/absence weeds species was surveyed in the organic farmland
located at Soft Bridge County in the North of Taiwan.

Keywords
Biodiversity; Singleton; Doubleton; Sampling error

1. Introduction
Long-term biodiversity monitoring is the basis for ecological research and
promotion of organic agriculture. In recent years, more and more non-
professional citizen scientists have participated in the projects of monitoring
diversity, so the possibility of species identity errors may increase dramatically
in the collected data. Therefore, correcting the impact of species identification
errors becomes an important statistical issue.
Species richness is the most intuitive and widely used as biodiversity index
due to its ecological intuitive concept and simplest form. However, due to the

402 | I S I W S C 2 0 1 9