Bias-Adjusting Observer Species Composition Estimates of Tuna Caught by Purse-Seiners Using Port-Sampling Data: A Mixed-Effects Modeling Approach Based on Paired Well-Level Data

Abstract

For large-scale tropical tuna purse-seine fisheries, it is prohibitively costly to obtain adequate sampling coverage to estimate fleet-level catch composition solely from sample data. Logbook or observer data, with complete fleet coverage, are often available but may be considered unreliable for species composition. Previous studies have developed models, trained with sample data, to predict set-level species compositions based on environmental and operational covariates. Here, models were developed to predict well-level species composition from uncorrected observer data and covariates affecting the observers’ view of the catch during loading, with port-sampling data as the response variable. The analysis used paired, well-level data from sets made on floating objects by the Eastern Pacific Ocean tuna purse-seine fleet during 2023–2024. Results indicated that, overall, observer data proportions of bigeye (BET) and yellowfin tunas tended to be greater than the model-estimated proportions, with the opposite occurring for skipjack tuna (SKJ). However, vessel effects sometimes modified these tendencies. Model complexity was greatest for BET and least for SKJ. For BET, observer data proportions and model-estimated proportions were more similar when the vessel had a hopper. They were also more similar in 2023 as compared to 2024, suggesting sample data for bias adjustments should be collected annually. The approach shows potential for predicting the species composition of unsampled wells.

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