Integrated stock assessments depend on reliable catch and observational data to produce unbiased estimates of fish stock biomass and productivity. However, biases in catch data due to unreported catch are common among many of the world’s fisheries, especially those that are high value and therefore more vulnerable to illegal fishing. Some of these fisheries rely on tag-recapture data to support the estimation of biomass in integrated assessments and set precautionary catch limits intended to safeguard the fish stocks from overexploitation. Tag-recapture data is generally considered to be a more powerful indicator of stock abundance than survey data if the underlying assumptions are met. The effect of unreported catches that include tagged fish on biomass estimates in tag-based integrated assessments is unclear. We used a simulation analysis to determine the impact of under-reported catch on a hypothetical Patagonian toothfish (Dissostichus eleginoides) stock. Several catch scenarios are presented that cover different magnitudes and trend types of under-reported catch. We used the CASAL model (commonly employed to assess toothfish stocks) as both the operating model and the estimation model to evaluate how the various scenarios affected estimation performance of biomass depletion. Biomass depletion was increasingly underestimated as the magnitude of under-reporting increased, regardless of trend in catch. When unreported catch exceeds twice the amount reported combined with unreported tag returns, the estimation model will fail to detect biomass has been depleted below a target reference point of 50 % within 20 years of fishing. The lack of detection will falsely indicate current catch limits are sustainable when in reality they are not. Importantly, these estimates remained largely unchanged among different trends in under-reported catch. For assessments reliant on tag-recapture data, this suggests that it is more critical to estimate the overall quantity of unreported catch and the number of unreported tagged fish than the trend.

综合种群评估依赖于可靠的捕获量和观测数据，以对鱼类种群生物量和生产力进行无偏估计。然而，由于未报告渔获量而导致的渔获量数据偏差在世界许多渔业中很常见，尤其是那些价值高、因此更容易受到非法捕捞的渔业。其中一些渔业依靠标签重新捕获数据来支持综合评估中的生物量估计，并设置预防性捕获限制，旨在保护鱼类种群免受过度开发。如果基本假设得到满足，标记重新捕获数据通常被认为是比调查数据更强大的种群丰度指标。在基于标签的综合评估中，包括标签鱼在内的未报告渔获量对生物量估计的影响尚不清楚。铁线莲） 股票。介绍了几种捕捞方案，涵盖了低报捕捞量的不同幅度和趋势类型。我们使用 CASAL 模型（通常用于评估牙鱼种群）作为操作模型和估计模型来评估各种情景如何影响生物量消耗的估计性能。无论产量趋势如何，随着漏报程度的增加，生物量消耗越来越被低估。当未报告的渔获量超过报告数量的两倍加上未报告的标签返回时，估计模型将无法检测到捕捞 20 年内生物量已消耗到 50% 的目标参考点以下。缺乏检测将错误地表明当前的捕捞限制是可持续的，而实际上并非如此。重要的，这些估计数在漏报渔获量的不同趋势中基本保持不变。对于依赖于标签重新捕获数据的评估，这表明估计未报告的捕捞总量和未报告的标签鱼的数量比趋势更为重要。