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

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.

2010 ◽  
Vol 67 (12) ◽  
pp. 2032-2044 ◽  
Author(s):  
Philippe Ruiz ◽  
Christophe Laplanche

We present a Bayesian hierarchical model to estimate the abundance and the biomass of brown trout ( Salmo trutta fario ) by using removal sampling and biometric data collected at several stream sections. The model accounts for (i) variability of the abundance with fish length (as a distribution mixture), (ii) spatial variability of the abundance, (iii) variability of the catchability with fish length (as a logit regression model), (iv) spatial variability of the catchability, and (v) residual variability of the catchability with fish. Model measured variables are the areas of the stream sections as well as the length and the weight of the caught fish. We first test the model by using a simulated dataset before using a 3-location, 2-removal sampling dataset collected in the field. Fifteen model alternatives are compared with an index of complexity and fit by using the field dataset. The selected model accounts for variability of the abundance with fish length and stream section and variability of the catchability with fish length. By using the selected model, 95% credible interval estimates of the abundances at the three stream sections are (0.46,0.59), (0.90,1.07), and (0.56,0.69) fish/m2. Respective biomass estimates are (9.68, 13.58), (17.22, 22.71), and (12.69, 17.31) g/m2.


Author(s):  
A. H. Gandhi ◽  
H. K. Raval

As forming of the double or multiple curvature surfaces, includes roller forming at least once in the sequential process; its efficient performance is of great importance for controlling the final product dimensions. Most efficient and economical way to produce the cylinder is to roll the plate through the roller in single pass. Literature review revels that, most of the reported analytical models for the prediction of springback were developed with the assumption of zero initial strain. However, in practice multiple pass bending is recommended to work within the power limitation of the machine and to improve the accuracy of the final product. An attempt is made to develop the analytical model for estimation of top roller position as a function of desired radius of curvature, for multiple pass 3-roller forming of cylinders, considering real material behavior. Due to the change of Young's modulus of elasticity (E) under deformation, the springback is larger than the springback calculated with constant E. Developed analytical model was modified to include the effect of change of Young's modulus during the deformation. Developed multiple pass analytical models were compared with the single pass analytical model and experiments (on pyramid type 3-roller bending machine).


2014 ◽  
Vol 46 (4) ◽  
pp. 270-274 ◽  
Author(s):  
Bonnie Koo ◽  
Kaity Ball ◽  
Anne-Marie Tremaine ◽  
Christopher B. Zachary

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