A cautionary note on Bayesian estimation of population size by removal sampling with diffuse priors

2018 ◽  
Vol 60 (3) ◽  
pp. 450-462 ◽  
Author(s):  
Séverine Bord ◽  
Christèle Bioche ◽  
Pierre Druilhet
Keyword(s):  
Bayesian Estimation ◽  
Population Size ◽  
Cautionary Note ◽  
Removal Sampling

scholarly journals Bayesian Estimation of Population Size Changes by Sampling Tajima’s Trees

2019 ◽  
Author(s):  
Julia A. Palacios ◽  
Amandine Véber ◽  
Lorenzo Cappello ◽  
Zhangyuan Wang ◽  
John Wakeley ◽  
...  
Keyword(s):  
State Space ◽  
Bayesian Estimation ◽  
Population Size ◽  
Simulated Data ◽  
Effective Population ◽  
Large State Space ◽  
Population Sizes ◽  
Efficient Alternative ◽  
Inference Methods ◽  
Kingman’S Coalescent

AbstractThe large state space of gene genealogies is a major hurdle for inference methods based on Kingman’s coalescent. Here, we present a new Bayesian approach for inferring past population sizes which relies on a lower resolution coalescent process we refer to as “Tajima’s coalescent”. Tajima’s coalescent has a drastically smaller state space, and hence it is a computationally more efficient model, than the standard Kingman coalescent. We provide a new algorithm for efficient and exact likelihood calculations for data without recombination, which exploits a directed acyclic graph and a correspondingly tailored Markov Chain Monte Carlo method. We compare the performance of our Bayesian Estimation of population size changes by Sampling Tajima’s Trees (BESTT) with a popular implementation of coalescent-based inference in BEAST using simulated data and human data. We empirically demonstrate that BESTT can accurately infer effective population sizes, and it further provides an efficient alternative to the Kingman’s coalescent. The algorithms described here are implemented in the R package phylodyn, which is available for download at https://github.com/JuliaPalacios/phylodyn.

Bayesian removal estimation of a population size under unequal catchability

2005 ◽  
Vol 62 (2) ◽  
pp. 291-300 ◽  
Author(s):  
Samu Mäntyniemi ◽  
Atso Romakkaniemi ◽  
Elja Arjas
Keyword(s):  
Population Size ◽  
Probability Model ◽  
Bayesian Probability ◽  
Published Data ◽  
Data Set ◽  
Removal Experiments ◽  
Realistic Description ◽  
The Mean ◽  
Removal Sampling ◽  
Very High

We introduce a Bayesian probability model for the estimation of the size of an animal population from removal data. The model is based on the assumption that in the removal sampling, catchability may vary between individuals, which appears to be necessary for a realistic description of many biological populations. Heterogeneous catchability among individuals leads to a situation where the mean catchability in the population gradually decreases as the number of removals increases. Under this assumption, the model can be fitted to any removal data, i.e., there are no limitations regarding the total catch, the number of removals, or the decline of the catch. Using a published data set from removal experiments of a known population size, the model is shown to be able to estimate the population size appropriately in all cases considered. It is also shown that regardless of the statistical approach, a model that assumes equal catchability of individuals generally leads to an underestimation of the population. The example indicates that if there is only vague prior information about the variation of catchability among individuals, a very high number of successive removals may be needed to correctly estimate the population size.

scholarly journals ThetaMater: Bayesian estimation of population size parameter θ from genomic data

2017 ◽  
Vol 34 (6) ◽  
pp. 1072-1073 ◽  
Author(s):  
Richard H Adams ◽  
Drew R Schield ◽  
Daren C Card ◽  
Andrew Corbin ◽  
Todd A Castoe
Keyword(s):  
Bayesian Estimation ◽  
Population Size ◽  
Genomic Data ◽  
Size Parameter

Estimating riverine fish population size from single- and multiple-pass removal sampling using a hierarchical model

2002 ◽  
Vol 59 (4) ◽  
pp. 695-706 ◽  
Author(s):  
Robin J Wyatt
Keyword(s):  
Population Size ◽  
Hierarchical Model ◽  
Bayesian Approach ◽  
Salmo Trutta ◽  
Target Area ◽  
Single Pass ◽  
Multiple Pass ◽  
Individual Site ◽  
Removal Sampling ◽  
Riverine Fish

A hierarchical model is described for estimating population size from single- and multiple-pass removal sampling. The model is appropriate for two-stage sampling schemes, typified by surveys of riverine fish populations, in which multiple sites are surveyed, but a low number of passes are undertaken at each site. The model estimates the average population size within the target area from the raw catch data, and thus allows for differences in the sampling procedure at each site, such as including single-pass sampling. The model also uses the data from all sites to estimate the population size at each individual site. This results in generally improved precision for multiple-pass sites and provides comparable estimates from single-pass sites. A Bayesian approach is described for estimating the parameters of the hierarchical model using sampling importance resampling (SIR). An empirical Bayesian approach, which ignores prior uncertainty but is simpler to implement, is also described. Application of the hierarchical model is illustrated with electrofishing data for 0+ trout (Salmo trutta) in the River Inny, U.K.

Matrix heterogeneity affects population size of the harvest mice: Bayesian estimation of matrix resistance and model validation

Oikos ◽  
2010 ◽  
Vol 120 (2) ◽  
pp. 271-279 ◽  
Author(s):  
Misako Kuroe ◽  
Noriyuki Yamaguchi ◽  
Taku Kadoya ◽  
Tadashi Miyashita
Keyword(s):  
Bayesian Estimation ◽  
Population Size ◽  
Model Validation ◽  
Matrix Resistance ◽  
Matrix Heterogeneity

Relationships Between Surface Area and Population Size: A Cautionary Note

1979 ◽  
Vol 44 (4) ◽  
pp. 803-807 ◽  
Author(s):  
Richard W. Casteel
Keyword(s):  
Population Size ◽  
Surface Area ◽  
Regression Models ◽  
Significant Positive Correlation ◽  
Cautionary Note ◽  
Positive Correlation

Two models relating various measures of camp surface area to population size among !Kung Bushmen are examined. It is shown that though there does exist a significant, positive correlation between some areal measures and population size, neither of the previously published models correctly represents these relationships. Alternative regression models are presented for the !Kung Bushmen which describe surface area as a function of population size in an acceptably accurate and reasonably concise fashion.

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