ANOVATS：一种基于子采样的检验方法，用于检测海洋研究中短时间序列之间的差异
ANOVATS: A subsampling-based test to detect differences among short time series in marine studies
AI总结：
针对海洋研究中短时间序列数据难以检测区域差异的问题，提出基于子采样的ANOVATS方法，可在小样本下检测区域差异并分组，无需专家知识或谱密度估计，通过北海浮游动物数据验证了有效性。
摘要：
评估海洋生态系统对于理解气候变化和人类活动的影响、维护健康的海洋和生态系统至关重要。在海洋科学中，生物学家和地质学家常基于专家知识（通常通过数据可视化）识别区域差异。但海洋研究中通过调查收集的时间序列数据通常仅涵盖几十年，限制了经典时间序列方法的适用性；且缺乏专家知识时，检测显著差异变得具有挑战性。为此，我们提出ANOVATS（小样本时间序列数据的方差分析），这是一种基于子采样的方法，用于检测固定组数的小样本时间序列数据中的区域差异。该方法无需进行谱密度估计（此步骤需要数据包含大量时间点）。此外，在使用ANOVATS程序检测所有区域的同质性差异后，我们设计了一个简单的ANOVATS事后程序来对区域进行分组。最后，我们通过分析北海不同层位收集的浮游动物生物量数据证明了该方法的有效性，结果显示其无需依赖先验生物或地理知识即可量化地理区域间的物种差异。
英文摘要：
Assessing marine ecosystems is important for understanding the impacts of climate change and human activity, as well as for maintaining healthy oceans and ecosystems. In marine science, it is common for biologists and geologists to identify regional differences based on expert knowledge, frequently through data visualization. However, time series data collected through surveys in marine studies typically span only a few decades, limiting the applicability of classical time series methods. Additionally, without expert knowledge, detecting significant differences becomes challenging. To address these issues, we introduce ANOVATS (ANOVA for small-sample time series data), a subsampling-based method to detect regional differences in small-sample time series data with a fixed number of groups. This method bypasses the need for spectral density estimation, which requires a large number of time points in the data. Furthermore, after detecting differences in homogeneity across all areas using the ANOVATS procedure, we devised a simple ANOVATS post hoc procedure to group the areas. Finally, we demonstrate the effectiveness of our method by analyzing zooplankton biomass data collected in different strata of the North Sea, showing its ability to quantify differences in species between geographical areas without relying on prior biological or geographical knowledge.
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