TY - JOUR
T1 - Comparative performance of species-richness estimators using data from a subtropical forest tree community
AU - Wei, Shi guang
AU - Li, Lin
AU - Walther, Bruno A.
AU - Ye, Wan hui
AU - Huang, Zhong liang
AU - Cao, Hong lin
AU - Lian, Ju Yu
AU - Wang, Zhi Gao
AU - Chen, Yu Yun
N1 - Funding Information:
Acknowledgments The authors acknowledge the Chinese Forest Biodiversity Monitoring Network, the Knowledge Innovation Project of The Chinese Academy of Sciences (KZCX2-YW-430), and the National Key Technology R&D Program (2008BAC39B02) for financial support of this research work. The Biodiversity Committee and the Bureau of Science and Technology for Resources and Environment of the Chinese Academy of Sciences facilitated the plot establishment through financial assistance. The authors also thank the assistance of a number of colleagues and hired helpers who assisted in field-data collection and species identification. Dr. Xinsheng Hu, Professor Shenglei Fu, and two anonymous referees reviewed this study and made valuable suggestions. Professor Fangliang He of the University of Alberta, Professor Yifang Sun of the TunHai University, Dr. Richard Condit of the CTFS, and Dr. Pierre Legendre of the University of Montreal assisted with data analysis training.
PY - 2010/1
Y1 - 2010/1
N2 - We used survey data collected from a large plot (20 ha) of sub-tropical forest in the Dinghushan Nature Reserve, Guangdong Province, southern China, in 2005 to test the comparative performance of nine species-richness estimators (number of observed species, three species-individual curve models, five nonparametric estimators). As the true species richness, we used the 210 free-standing shrub and tree species of >1 cm diameter at breast height recorded during the survey. This true species richness was then used to calculate performance measures of bias, accuracy, and precision for each estimator, whereby we distinguished performance for low, medium, and high sampling intensity. Unsurprisingly, all estimators performed better than the number of observed species in terms of bias and accuracy. Surprisingly, however, two curve models (logistic and logarithm) outperformed all other estimators in terms of bias, accuracy, and precision, which is in contrast to most other previous studies, in which nonparametric methods usually outperform curve models. Intriguingly, relative estimator performance changed between low, medium, and high sampling intensity, sometimes dramatically, reinforcing the assertion that the influence of sampling intensity on estimator performance is an important aspect to investigate and to consider when choosing estimators for ecological surveys. Because these results are based on only one dataset, the results should be treated with caution, both because (1) the generality of these results needs to be confirmed with simulated datasets and (2) more work is needed to establish what "true" species richness is extrapolated by each of the tested estimators in both the statistical and the practical sense. Nevertheless, the two curve estimators, namely Logistic and Logarithm, should be considered in future studies of comparative performance of species-richness estimators because of their outstanding performance in this study.
AB - We used survey data collected from a large plot (20 ha) of sub-tropical forest in the Dinghushan Nature Reserve, Guangdong Province, southern China, in 2005 to test the comparative performance of nine species-richness estimators (number of observed species, three species-individual curve models, five nonparametric estimators). As the true species richness, we used the 210 free-standing shrub and tree species of >1 cm diameter at breast height recorded during the survey. This true species richness was then used to calculate performance measures of bias, accuracy, and precision for each estimator, whereby we distinguished performance for low, medium, and high sampling intensity. Unsurprisingly, all estimators performed better than the number of observed species in terms of bias and accuracy. Surprisingly, however, two curve models (logistic and logarithm) outperformed all other estimators in terms of bias, accuracy, and precision, which is in contrast to most other previous studies, in which nonparametric methods usually outperform curve models. Intriguingly, relative estimator performance changed between low, medium, and high sampling intensity, sometimes dramatically, reinforcing the assertion that the influence of sampling intensity on estimator performance is an important aspect to investigate and to consider when choosing estimators for ecological surveys. Because these results are based on only one dataset, the results should be treated with caution, both because (1) the generality of these results needs to be confirmed with simulated datasets and (2) more work is needed to establish what "true" species richness is extrapolated by each of the tested estimators in both the statistical and the practical sense. Nevertheless, the two curve estimators, namely Logistic and Logarithm, should be considered in future studies of comparative performance of species-richness estimators because of their outstanding performance in this study.
KW - Bootstrap
KW - Chao1
KW - Chao3
KW - Jackknife
KW - Species-individual curves
KW - Species-richness estimation
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U2 - 10.1007/s11284-009-0633-2
DO - 10.1007/s11284-009-0633-2
M3 - Article
AN - SCOPUS:77951092223
SN - 0912-3814
VL - 25
SP - 93
EP - 101
JO - Ecological Research
JF - Ecological Research
IS - 1
ER -