Data reconstruction can improve abundance index estimation: An example using Taiwanese longline data for Pacific bluefin tuna

Abstract

Catch-per-unit-effort (CPUE) is often the main piece of information used in fisheries stock assessment; however, the catch and effort data that are traditionally compiled from commercial logbooks can be incomplete or unreliable due to many reasons. Pacific bluefin tuna (PBF) is a seasonal target species in the Taiwanese longline fishery. Since 2010, detailed catch information for each PBF has been made available through a catch documentation scheme. However, previously, only market landing data with a low coverage of logbooks were available. Therefore, several nontraditional procedures were performed to reconstruct catch and effort data from many alternative data sources not directly obtained from fishers for 2001–2015: (1) Estimating the catch number from the landing weight for 2001–2003, for which the catch number information was incomplete, based on Monte Carlo simulation; (2) deriving fishing days for 2007–2009 from voyage data recorder data, based on a newly developed algorithm; and (3) deriving fishing days for 2001–2006 from vessel trip information, based on linear relationships between fishing and at-sea days. Subsequently, generalized linear mixed models were developed with the delta-lognormal assumption for standardizing the CPUE calculated from the reconstructed data, and three-stage model evaluation was performed using (1) Akaike and Bayesian information criteria to determine the most favorable variable composition of standardization models, (2) overall R2 via cross-validation to compare fitting performance between area-separated and area-combined standardizations, and (3) system-based testing to explore the consistency of the standardized CPUEs with auxiliary data in the PBF stock assessment model. The last stage of evaluation revealed high consistency among the data, thus demonstrating improvements in data reconstruction for estimating the abundance index, and consequently the stock assessment.

Deriving a Statistically Reliable Abundance Index from Landings Data: An Application to the Taiwanese Coastal Dolphinfish Fishery with a Multispecies Feature

“…The third category (system‐based testing) is based on the consistency of the estimates, with auxiliary information on the year effect that represents the annual relative levels of abundance (see Chang et al. for a demonstration). Currently, there is no integrated stock assessment model developed for the Dolphinfish stock in the Kuroshio Current, and few data on the stock are available; hence, the third method is not feasible in this case.…”

“…Therefore, we used AIC to decide the final variable combination of each model, but we used bootstrap‐ R 2 , which determines the overall correlation between the actual and predicted values while avoiding overfitting issues (Chang et al. ), to compare model performance. Pseudo‐ R 2 (Faraway ) was used only for single‐model discussion.…”

“…The final mean and SD of R 2 were obtained from 200 replications of the above procedures, and the result was then termed the bootstrap‐ R 2 (see details in Chang et al. ). Iterating 200 times was sufficient, as a larger number of iterations did not produce substantially different estimates.…”

“…The actual number of fishing days per trip (FDPT), however, may vary from 1 to 3 d depending on vessel size (FDPT for tuna fishing vessels may be greater than those for traditional Dolphinfish vessels; Chang et al. ). The assumption of a single fishing day per trip thus may underestimate the fishing effort by larger‐sized vessels.…”

“…This study considered two categories of methods introduced by Hinton and Maunder () for CPUE model evaluation: Akaike's information criterion (AIC) and pseudo‐ R 2 (Faraway ); and the cross‐validation and bootstrap (Zhang and Yang ) for estimating the “bootstrap‐ R 2 ” (referred to as “overall‐ R 2 ” in Chang et al. ).…”