State-space stock assessment models have become increasingly common in recent years due to their ability to estimate unobserved variables and explicitly model multiple sources of random error. Therefore, they may be able to better estimate unobserved processes such as misreported fishery catch. We examined whether a state-space assessment model was able to estimate misreported catch in a simulated fishery. We tested three formulations of the estimation model, which exhibit increasing complexity: (i) assuming no misreporting, (ii) assuming misreporting is constant over time, and (iii) assuming misreporting follows a random walk. We tested these three estimation models against simulations using each of the three assumptions and an additional fourth assumption of uniform random misreporting over time. Overall, the worst estimation errors occurred when misreporting was ignored while it was in fact occurring, while there was a relatively small cost for estimating misreporting when it was not occurring. Estimates of population scale and fishing mortality rate were particularly sensitive to misreporting assumptions. Furthermore, in the uniform random scenario, the relatively simple model that assumed misreporting was fixed across ages and time was more accurate than the more complicated random walk model, despite the increased flexibility of the latter.

中文翻译：

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用于评估状态空间库存评估模型中误报渔获量的模拟测试方法

由于状态空间估计模型能够估计未观察到的变量并显式地模拟随机误差的多种来源，因此近年来已变得越来越普遍。因此，他们也许能够更好地估计未观察到的过程，例如误报的渔业产量。我们检查了状态空间评估模型是否能够估计模拟渔业中误报的渔获量。我们测试了估计模型的三种公式，它们表现出越来越高的复杂性：（i）假设没有错误报告，（ii）假设错误报告随时间变化是恒定的，以及（iii）假设错误报告遵循随机游走。我们使用三个假设中的每一个以及随时间推移进行统一随机错误报告的另一个第四假设，针对仿真测试了这三个估计模型。总体，当错误报告实际上被忽略时，最严重的估计错误发生了，而当错误报告没有发生时，估计错误的成本相对较低。人口规模和捕鱼死亡率的估计值对错误报告的假设特别敏感。此外，在统一的随机场景中，尽管后者具有更大的灵活性，但假设较复杂的随机游走模型相对较简单的模型（假设错误报告在各个年龄和时间上都是固定的）更为准确。