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CPS2007 Jai-Hua Yen et al.
Table 6.
Species richness adjustment for data set of weed species from Soft Bridge
county in the North of Taiwan in farmland, with = 12, ̂ = 0.82, and ̂ = 0.14.
Estimated
Method ̂
s.e.
Observed 74.0 19.0 9.0 92.4 11.27
Adjusted 83.6 24.1 10.6 105.4 18.68

4. Discussion and Conclusion
Species richness is the simplest and most popular measure of biodiversity.
The approach of estimating species richness is widely discussed due to its
application in many ecological or agricultural issues mentioned by Carvalheiro
et al. (2011) and Garibaldi et al. (2013). In the manuscript, we demonstrated
the effect of species identity error while sampling in estimating species
richness. When the mean probability that a species is misidentified into
another species which belongs to the sampling plot is high, the observed
richness and singleton richness will be seriously negative biased which
implying most richness estimators’ serious underestimation even though
increasing sampling units. Our simulations show that the adjusted richness
estimator removes a large proportion of the negative bias under different
settings of sampling units, species identity error, and species detection model.
We suggest that the adjusted richness estimator for incidence data should be
applied to estimate species richness of the target region since species identity
error occurs almost in every investigation of species.

Acknowledgements
The research was supported by the Taiwan National Science Council under
Project 107-2118-M-002001-MY2 and Council of Agriculture under Project
107AS-1.2.7-ST-a6.

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