NDLI: A nonparametric lower bound for the number of species shared by multiple communities

Content Provider	Springer Nature Link
Author	Pan, H. Y. Chao, Anne Foissner, Wilhelm
Copyright Year	2009
Abstract	In biological and ecological statistical inference, it is practically useful to provide a lower bound for species richness in a community. Chao (1984, 1989) derived a non-parametric lower bound for species richness in a single community. However, there have been no lower bounds proposed in the literature for the number of species shared by multiple communities. Based on sample species abundance or replicated incidence records from each of the N communities, we derive in this article a nonparametric approach to constructing a lower bound for the number of species shared by N (N≥2) communities. The approach is valid for all types of species abundance distributions (for abundance data) or species detection probabilities (for replicated incidence data). Variance estimators for the proposed lower bounds are obtained by using typical asymptotic theory. Simulation results are reported to examine the performance of the lower bounds. Replicated incidence data of ciliate species collected in three areas from Namibia, southwest Africa, are used for illustration. We also briefly discuss the application of the proposed method to estimate the size of a shared population (i.e., the number of individuals in the intersection of multiple populations) based on capture-recapture data from each population.
Starting Page	452
Ending Page	468
Page Count	17
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	14
Issue Number	4
e-ISSN	15372693
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Abundance data Community overlap Diversity indices Replicated incidence data Shared species Similarity Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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