Species identification based on morphological characteristics includes species misidentification, leading to estimation bias in stock assessment and posing challenges difficult to be resolved. The spawning eggs of spotted mackerel Scomber australicus and chub mackerel S. japonicus in the western North Pacific are used for stock assessment as an index of spawning biomass and are classified based on egg diameter by past evidence. However, the difference in the distribution of egg diameters between the two species has become so ambiguous that the spawning eggs of chub mackerel may be classified as spotted mackerel. This can be explained by the larger distribution of egg diameters in chub mackerel with increasing stock abundance, resulting in overlap with the distribution of egg diameters in spotted mackerel. This leads to species misidentification and biased estimates of spotted mackerel abundance. To overcome this bias, it is necessary to develop a standardization method to remove the effect of species misidentification. Here, we demonstrate that a recently-developed spatio-temporal model can easily and efficiently reduce estimation bias for egg density and stock abundance in the spotted mackerel, using 15 years data for spawning eggs. We incorporated species identification error as the effect of the egg density of chub mackerel on the catchability of spotted mackerel in the spatio-temporal model. The index estimated from the model decreased temporal fluctuation substantially. When using the index accounting for species misidentification, the retrospective bias of abundance estimates for spotted mackerel decreased by about half compared with using the indices that ignored species misidentification. These results suggest that incorporating species misidentification bias is an essential process for improving stock assessment.

基于形态特征的物种识别包括物种识别错误，导致种群评估中的估计偏差，并带来难以解决的挑战。斑鲭鱼Scomber australicus和鲭鱼S. japonicus的产卵在北太平洋西部，作为产卵生物量的一种指标，用于种群评估，并根据以往的证据根据卵径进行分类。但是，两个物种之间的卵径分布差异变得如此模棱两可，以至于mac鲭鱼的产卵可归类为斑点鲭鱼。这可以用鱼的蛋直径更大的分布和增加的种群丰度来解释，从而导致与斑鲭鱼的蛋直径的分布重叠。这会导致物种误判和斑点鲭鱼丰度的估计偏差。为了克服这种偏见，有必要开发一种标准化方法来消除物种识别错误的影响。这里，我们证明，使用15年产卵数据，最近开发的时空模型可以轻松有效地降低斑点鲭鱼的卵密度和种群丰度的估计偏差。在时空模型中，我们将物种识别错误纳入了mac鲭鱼卵密度对斑点鲭鱼捕获能力的影响。从模型估计的指标大大减少了时间波动。当使用考虑物种错误识别的指数时，与使用忽略物种错误识别的指数相比，斑点鲭鱼的丰度估计值的回顾性偏差减少了大约一半。这些结果表明，纳入物种错误识别偏见是改善种群评估的重要过程。