NDLI: Sequential stopping rules for species accumulation

Content Provider	Springer Nature Link
Author	Christen, J. Andrés Nakamura, Miguel
Copyright Year	2003
Abstract	Identifying and counting the total number of biological species observed to date, and plotting versusa measure of the effort used to record them, gives rise to a species accumulation curve. Interest typically is concerned with estimating the total number of species in the area of study, having observed only the accumulation curve, having no information on species frequencies. This article considers the problem of optimally stopping the sampling process. We use a sequential procedure with a fixed maximum horizon for accumulation. A utility function based on the number of new species to be observed and the effort saved from the maximum horizon is adopted, and a workable algorithm based on backward induction is obtained. An example in accumulation of bat species is also presented.
Starting Page	184
Ending Page	195
Page Count	12
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	8
Issue Number	2
e-ISSN	15372693
Language	English
Publisher	Springer-Verlag
Publisher Date	2003-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Backward induction Bayesian methods Biodiversity Monte Carlo methods Utility functions. 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

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Sequential Stopping Rules for Species Accumulation

A Sequential Monte Carlo Approach for MLE in a Plant Growth Model

Bayesian estimation of cluster-level test accuracy based on different sampling schemes

Geostatistical hierarchical model for temporally integrated radon measurements

Simultaneous Confidence Inference on Species Accumulation Curves

Statistics as viewed by biologists

Bayesian multivariate process modeling for prediction of forest attributes

Bayesian estimation of survival from mark-recapture data

A Bayesian Approach to Spatial Prediction With Flexible Variogram Models

Sequential stopping rules for species accumulation

Similar Documents

Sequential Stopping Rules for Species Accumulation

A Sequential Monte Carlo Approach for MLE in a Plant Growth Model

Bayesian estimation of cluster-level test accuracy based on different sampling schemes

Geostatistical hierarchical model for temporally integrated radon measurements

Simultaneous Confidence Inference on Species Accumulation Curves

Statistics as viewed by biologists

Bayesian multivariate process modeling for prediction of forest attributes

Bayesian estimation of survival from mark-recapture data

A Bayesian Approach to Spatial Prediction With Flexible Variogram Models

Sequential stopping rules for species accumulation