Richness Estimation with the Presence of Species Identity Error

• Main Authors: Jai-Hua Yen, 顏嘉華
• Other Authors: 邱春火
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/9nbbah

碩士 === 國立臺灣大學 === 農藝學研究所 === 107 === Estimation of species richness in an area is always challenging statisticians regard to small sample units or the presence of species identity error. In the literatures, most richness estimators were only proposed to deal with the underestimation of the size-limited sample. However, species identity error almost exists in species surveys and seriously causes the inaccuracy of richness estimation. Therefore, the biased collected data due to species identity error should be adjusted to estimate the true richness. In the manuscript, we proposed a method to adjust the richness estimation with the existence of species identity error for single or multiple investigators. We choose Chao2 and first-order Jackknife richness estimator as the theoretical foundation of deriving the adjusted method. First, census of a subplot should be done by investigators in order to get the information of species identity error among investigators, so we can use the information of species identity error to adjust the observed, singleton, and doubleton richness in order to get the corrected Chao2 estimator. Nonetheless, the estimation will be inaccurate due to the increased variance of adjusted singleton, and doubleton richness. Then the adjusted estimator is proposed to tackle with the problem mentioned above. To investigate the performance of the adjusted estimator, we do several simulation studies and find out the estimation has the smallest root mean square error (RMSE) in most cases. In the end, we demonstrate an estimation of species richness by a weed survey data from Soft Bridge County in Taiwan for single investigator and a plant cover survey data from the Grand St. Bernard Pass in Switzerland for multiple investigators.