Methods to reliably identify jump discontinuities in biological time series and to assess the specific contribution of various covariates are rapidly progressing. Here, we took advantage of these statistical improvements as well as those seen in complementary, down-scaled climate and biogeochemical models to investigate causes of the substantial interannual variability observed in recruitment strength in hindcast analyses. This systematic meta-analysis included 23 data-rich, commercially valuable, warm- and cold-temperate stocks in the North, Norwegian and Barents Seas. Since this study focuses on recruitment strength variability, we have used the term “recruitment regime shift” to distinguish from the concept of ecosystem regime shift. The breakpoint analysis revealed that the former criterion applied to more than half of the time series, mainly with respect to North Sea stocks but also to those in the Norwegian Sea. The exploratory analysis using vcGAM indicated that 1–3 shifts per stock were real, when using five drivers spanning spawning stock biomass to large-scale climatic processes. Thus, non-stationary relationships were extensively prevalent, indicating that each stock is uniquely adapted to its locally varying conditions. Outputs from the stationary GAM resembled those from the vcGAM but not after the threshold year. In-depth case studies showed that the proxy of a given driver for the process which was to be included should be critically considered in a spatiotemporal context. Furthermore, the stock-specific uncertainty associated with the given recruitment figures as such should also be an in-built component of the model construct and thereby in the evaluation of the output.

中文翻译：

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在经历明显气候波动的可收获种群中，补充机制的转变和非平稳性是普遍现象

可靠地识别生物时间序列中的跳跃不连续性和评估各种协变量的具体贡献的方法正在迅速发展。在这里，我们利用这些统计改进以及互补的、缩小规模的气候和生物地球化学模型中所见的改进来调查后报分析中观察到的招募强度显着年际变化的原因。这项系统性荟萃分析包括北海、挪威海和巴伦支海的 23 种数据丰富、具有商业价值的暖温带和冷温带种群。由于本研究重点关注招募强度的变异性，因此我们使用“招募制度转变”一词来区别于生态系统制度转变的概念。断点分析显示，前一个标准适用于一半以上的时间序列，主要涉及北海股​​票，但也适用于挪威海股票。使用 vcGAM 进行的探索性分析表明，当使用涵盖产卵种群生物量和大规模气候过程的五个驱动因素时，每个种群确实存在 1-3 个变化。因此，非平稳关系广泛存在，表明每种股票都独特地适应其当地不同的条件。固定 GAM 的输出与 vcGAM 的输出相似，但在阈值年份之后不同。深入的案例研究表明，应在时空背景下严格考虑要纳入的流程的给定驱动因素的代理。此外，与给定招聘数据相关的特定股票的不确定性也应该是模型构建的内置组成部分，从而在输出评估中也是如此。