Spatio-temporal model reduces species misidentification bias of spawning eggs in stock assessment of spotted mackerel in the western North Pacific
Spatio-temporal model reduces species misidentification bias of spawning eggs in stock assessment of spotted mackerel in the western North Pacific
复制标题
时空模型减少了北太平洋西部斑点鲭鱼种群评估中产卵卵的物种错误识别偏差
DOI：
10.1016/j.fishres.2020.105825
复制
发表时间：
2021
影响因子：
2.4
通讯作者：
Takasuka Akinori
中科院分区：
文献类型：
--
作者：
Kanamori Yuki;Nishijima Shota;Okamura Hiroshi;Yukami Ryuji;Watai Mikio;Takasuka Akinori
基于形态特征的物种识别包括对物种的误识别，导致种群评估中的估计偏差，并带来难以解决的挑战。斑马鱼的产卵：澳大利亚斑马鱼和小马鱼的产卵。西北太平洋的日本鱼用于种群评估，作为产卵生物量的指数，并根据过去的证据根据卵直径进行分类。然而，这两个物种在卵直径分布上的差异已经变得如此模糊，以至于产卵的鲱鱼可能被归类为斑点鳕鱼。这可以用种群丰度越大，卵直径分布越大，与斑点鳗卵直径分布重叠来解释。这导致了对物种的错误识别和对斑点鳕鱼丰度的有偏见的估计。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误认的影响。在这里，我们使用15年的产卵数据，证明了最近开发的时空模型可以容易而有效地减少对斑点鳗卵密度和种群丰度的估计偏差。我们在时空模型中加入了物种识别误差，即鲱鱼的卵密度对其捕获率的影响。根据模型估计的指数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，对斑点鳕鱼丰度估计的回顾偏差减少了约一半。这些结果表明，纳入物种错误识别偏差是改进种群评估的一个重要过程。
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 mackerelScomber australicusand chub mackerelS. japonicusin 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.