Estimation of Fish Abundance Indices Based on Scientific Research Trawl Surveys

Abstract

Summary. The fish abundance index over an ocean region is defined here to be the integral of expected catch per unit effort (CPUE), approximated by the sum of expected CPUE over grid squares. When trawl surveys are done within grid squares selected according to a probability sampling design, several other sources of variation such as the fish population dynamics and the catching process are also involved. In such situations model-assisted methods for estimating abundance, assessed under both design and model perspectives, have some advantages over purely design-based methods such as the Horvitz-Thompson (HT) estimator or purely model-based prediction approaches. This article develops model-assisted empirical likelihood (EL) methods via loglinear regression and nonparametric smoothing. The methods are applied to grid surveys of the Grand Bank region carried out annually by Fishery Products International from 1996 through 2002. The HT and EL methods produce similar point estimates of abundance indices. Simulation results, however, indicate that the EL estimator under local linear smoothing is associated with smaller standard errors.

The effect of random and density‐dependent variation in sampling efficiency on variance of abundance estimates from fishery surveys

“…Alternatively, AIV and AI can be estimated using a statistical model‐based approach by modelling catch process (e.g. Cadigan, ; Chen et al, ) or spatiotemporal modelling (e.g. Shelton, Thorson, Ward, & Feist, ; Thorson et al, ), which have become the standards used in certain parts of the world (e.g.…”

“…In survey simulations, we followed the often‐postulated survey catch process that u s,i = q s,i A i , where u s,i is the catch per unit effort at station i , during survey s and A i is the “true” fish density at the station i (e.g. Chen et al, ; Schnute, ). For each simulated annual survey, u s,i was calculated using the A i value from the simulated pollock distribution and randomly drawing q s,i from a gamma distribution with mean and variance V(q) .…”

“…Strand, ). A common attribute of design‐based estimators is the assumption that sampling efficiency ( q , often also referred to as survey gear catchability) is constant across time and space (Chen, Thompson, & Wu, ). Sampling efficiency is defined here as the ratio of the survey estimate of abundance to the true abundance (e.g.…”

Potential application of baited remote underwater video to survey abundance of west coast rock lobster Jasus lalandii

“…The true abundance of rock lobster can only be estimated through the application of one of several types of surveys, all of which will imperfectly reflect true abundance (Chen et al . ; Watson & Jury ). Although one might think it is imperative that the accuracy be known before adopting a new survey type, neither nets nor traps were tested for their accuracy in reflecting west coast rock lobster abundance, despite the reliance on these gear types in surveys since 1934 (Von Bonde ).…”

Performance comparison of traditional sampling designs and adaptive sampling designs for fishery-independent surveys: A simulation study

“…Nevertheless, an efficient sampling design is also necessary for saving cost and reducing biases. Therefore the model-assisted optimal survey design was suggested for fishery surveys (Chen et al, 2004). In this study, we only considered the randomness induced by sampling designs in the simulations.…”