Five Confidence Intervals of the Closed Population Size in the Capture-recapture Problem under Inverse Sampling with Replacement

2004 ◽  
Vol 46 (4) ◽  
pp. 474-480 ◽  
Author(s):  
Kung-Jong Lui
Keyword(s):  
Population Size ◽  
Confidence Intervals ◽  
Inverse Sampling ◽  
Closed Population ◽  
Capture Recapture

scholarly journals Estimation of a closed population size of tadpoles in temporary pond

2017 ◽  
Vol 78 (2) ◽  
pp. 328-336
Author(s):  
M. S. C. S. Lima ◽  
J. Pederassi ◽  
C. A. S. Souza
Keyword(s):  
Population Size ◽  
Bayes Theorem ◽  
Venn Diagram ◽  
Temporary Pond ◽  
Larval Phase ◽  
Closed Population ◽  
Lotic Environment ◽  
Capture Recapture ◽  
Kolmogorov Smirnov ◽  
Smirnov Test

Abstract The practice of capture-recapture to estimate the diversity is well known to many animal groups, however this practice in the larval phase of anuran amphibians is incipient. We aimed at evaluating the Lincoln estimator, Venn diagram and Bayes theorem in the inference of population size of a larval phase anurocenose from lotic environment. The adherence of results was evaluated using the Kolmogorov-Smirnov test. The marking of tadpoles for later recapture and methods measurement was made with eosin methylene blue. When comparing the results of Lincoln-Petersen estimator corresponding to the Venn diagram and Bayes theorem, we detected percentage differences per sampling, i.e., the proportion of sampled anuran genera is kept among the three methods, although the values are numerically different. By submitting these results to the Kolmogorov-Smirnov test we have found no significant differences. Therefore, no matter the estimator, the measured value is adherent and estimates the total population. Together with the marking methodology, which did not change the behavior of tadpoles, the present study helps to fill the need of more studies on larval phase of amphibians in Brazil, especially in semi-arid northeast.

scholarly journals Marked Cards in the Pack: Using Playing Cards to Teach the Importance of Sample Size & Testing Assumptions in Capture–Recapture Estimations of Population Size

2020 ◽  
Vol 82 (6) ◽  
pp. 396-401
Author(s):  
Michael Calver ◽  
Timothy Blake
Keyword(s):  
Secondary School ◽  
Sample Size ◽  
Population Size ◽  
Confidence Intervals ◽  
Population Ecology ◽  
Simple Approach ◽  
Population Estimates ◽  
Index Method ◽  
Capture Recapture ◽  
Approach To Teaching

Estimating population size is essential for many applications in population ecology, so capture–recapture techniques to do this are often taught in secondary school classrooms and introductory university units. However, few classroom simulations of capture–recapture consider the sensitivity of results to sampling intensity, the important concept that the population size calculated is an estimate with error attached, or the consequences of violating assumptions underpinning particular capture–recapture models. We describe a simple approach to teaching the Lincoln index method of capture–recapture using packs of playing cards. Students can trial different sampling intensities, calculate 95% confidence intervals for population estimates, and explore the consequences of violating specific assumptions.

scholarly journals A Simulation Study on Increasing Capture Periods in Bayesian Closed Population Capture-Recapture Models with Heterogeneity

2020 ◽  
Vol 18 (1) ◽  
pp. 2-23
Author(s):  
Ross M. Gosky ◽  
Joel Sanqui
Keyword(s):  
Population Size ◽  
Simulation Study ◽  
Simulated Data ◽  
Population Models ◽  
Data Set ◽  
Closed Population ◽  
Population Sizes ◽  
Capture Recapture

Capture-Recapture models are useful in estimating unknown population sizes. A common modeling challenge for closed population models involves modeling unequal animal catchability in each capture period, referred to as animal heterogeneity. Inference about population size N is dependent on the assumed distribution of animal capture probabilities in the population, and that different models can fit a data set equally well but provide contradictory inferences about N. Three common Bayesian Capture-Recapture heterogeneity models are studied with simulated data to study the prevalence of contradictory inferences is in different population sizes with relatively low capture probabilities, specifically at different numbers of capture periods in the study.

scholarly journals Accuracy of Alternative Approaches to Capture-Recapture Estimates of Disease Frequency: Internal Validity Analysis of Data from Five Sources

2000 ◽  
Vol 152 (8) ◽  
pp. 771-779 ◽  
Author(s):  
Ernest B. Hook ◽  
Ronald R. Regal
Keyword(s):  
Population Size ◽  
Confidence Intervals ◽  
Internal Validity ◽  
Data Sets ◽  
Positive Dependence ◽  
Validity Analysis ◽  
Downward Bias ◽  
Capture Recapture ◽  
Alternative Approaches ◽  
Incomplete Lists

Abstract The authors used “internal validity analysis” to evaluate the performance of various capture-recapture methods. Data from studies with five overlapping, incomplete lists generated subgroups whose known sizes were compared with estimates derived from various four-source capture-recapture analyses. In 15 data sets unanalyzed previously (five subgroups of each of three new studies), the authors observed a trend toward mean underestimation of the known population size by 16–25%. (Coverage of the 90% confidence intervals associated with the method found to be optimal was acceptable (13/15), despite the downward bias.) The authors conjectured that (with the obvious exception of geographically disparate lists) most data sets used by epidemiologists tend to have a net positive dependence; that is, cases captured by one source are more likely to be captured by some other available source than are cases selected randomly from the population, and this trend results in a bias toward underestimation. Attempts to ensure that the underlying assumptions of the methods are met, such as minimizing (or adjusting adequately) for the possibility of loss due to death or migration, as was undertaken in one exceptional study, appear likely to improve the behavior of these methods. Am J Epidemiol 2000;152:771–9.

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