A Bayesian-weighted approach to predicting the number of newly discovered rare species

doi:10.1111/cobi.13253

CORC > 成都生物研究所 > 中国科学院成都生物研究所 > 生物多样性与生态系统服务领域 > 中国科学院山地生态恢复与生物资源利用重点实验室

	A Bayesian-weighted approach to predicting the number of newly discovered rare species
	Shen, Tsung-Jen 1; Chen, Youhua 2
刊名	CONSERVATION BIOLOGY
	2019
卷号	33 期号:2 页码:444-455
关键词	Bayesian statistics biodiversity survey diversity estimation sampling theory species rarity censo de biodiversidad estadistica bayesiana estimacion de la diversidad rareza de especies teoria del muestreo
ISSN号	0888-8892
DOI	10.1111/cobi.13253
产权排序	2
文献子类	Article
英文摘要	In natural ecological communities, most species are rare and thus susceptible to extinction. Consequently, the prediction and identification of rare species are of enormous value for conservation purposes. How many newly found species will be rare in the next field survey? We took a Bayesian viewpoint and used observed species abundance information in an ecological sample to develop an accurate way to estimate the number of new rare species (e.g., singletons, doubletons, and tripletons) in an additional unknown sample. A similar method has been developed for incidence-based data sets. Five seminumerical tests (3 abundance cases and 2 incidence cases) showed that our proposed Bayesian-weight estimator accurately predicted the number of new rare species with low relative bias and low relative root mean squared error and, accordingly, high accuracy. Finally, we applied the proposed estimator to 6 conservation-directed empirical data sets (3 abundance cases and 3 incidence cases) and found the prediction of new rare species was quite accurate; the 95% CI covered the true observed value very well in most cases. Our estimator performed similarly to or better than an unweighted estimator derived from Chao et al. and performed consistently better than the naive unweighted estimator. We recommend our Bayesian-weight estimator for conservation applications, although the unweighted estimator of Chao et al. may be better under some circumstances. We provide an R package RSE (rare species estimation) at for implementation of the estimators.
学科主题	Environment/ecology
URL标识	查看原文
WOS关键词	ABUNDANCE DISTRIBUTION ; SAMPLE ; RICHNESS ; COVERAGE ; FRAGMENTATION ; COMPLETENESS ; ESTIMATOR ; PRECISION ; RARITY ; FOREST
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
语种	英语
出版者	WILEY
WOS记录号	WOS:000460664300021
内容类型	期刊论文
源URL	[http://210.75.237.14/handle/351003/30955]
专题	生物多样性与生态系统服务领域_中国科学院山地生态恢复与生物资源利用重点实验室
通讯作者	Chen, Youhua
作者单位	1.Natl Chung Hsing Univ, Inst Statm & Dept Appl Math, 250 Kuo Kuang Rd, Taichung 40227, Taiwan; 2.Chinese Acad Sci, CAS Key Lab Mt Ecol Restorat & Bioresource Utiliz, Ecol Restorat & Biodivers Conservat Key Lab Sichu, Chengdu Inst Biol, Chengdu 610041, Peoples R China
推荐引用方式 GB/T 7714	Shen, Tsung-Jen,Chen, Youhua. A Bayesian-weighted approach to predicting the number of newly discovered rare species[J]. CONSERVATION BIOLOGY,2019,33(2):444-455.
APA	Shen, Tsung-Jen,&Chen, Youhua.(2019).A Bayesian-weighted approach to predicting the number of newly discovered rare species.CONSERVATION BIOLOGY,33(2),444-455.
MLA	Shen, Tsung-Jen,et al."A Bayesian-weighted approach to predicting the number of newly discovered rare species".CONSERVATION BIOLOGY 33.2(2019):444-455.