There is a large array of methods to extract knowledge and perform ecological forecasting from ecological time‐series. However, in spite of its importance for data‐mining, pattern‐matching and ecological synthesis, methods to assess their similarity are scarce. We introduce distantia (v1.0.1), an R package providing general toolset to quantify dissimilarity between ecological time‐series, independently of their regularity and number of samples. The functions in distantia provide the means to compute dissimilarity scores by time and by shape and assess their significance, evaluate the partial contribution of each variable to dissimilarity, and align or combine sequences by similarity. We evaluate the sensitivity of the dissimilarity metrics implemented in distantia, describe its structure and functionality, and showcase its applications with two examples. Particularly, we evaluate how geographic factors drive the dissimilarity between nine pollen sequences dated to the Last Interglacial, and compare the temporal dynamics of climate and enhanced vegetation index of three stands across the range of the European beech. We expect this package may enhance the capabilities of researchers from different fields to explore dissimilarity patterns between multivariate ecological time‐series, and aid in generating and testing new hypotheses on why the temporal dynamics of complex‐systems changes over space and time.

中文翻译：

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远缘：一个开源工具集，用于量化多元生态时间序列之间的差异

有大量方法可以从生态时间序列中提取知识并进行生态预测。然而，尽管它对数据挖掘、模式匹配和生态综合很重要，但评估它们相似性的方法却很少。我们引入了 fararia (v1.0.1)，这是一个 R 包，提供通用工具集来量化生态时间序列之间的差异，独立于它们的规律性和样本数量。远距离中的函数提供了按时间和形状计算相异性分数并评估其重要性的方法，评估每个变量对相异性的部分贡献，并通过相似性对齐或组合序列。我们评估了在 fararia 中实现的不同度量的敏感性，描述了它的结构和功能，并通过两个示例展示其应用。特别是，我们评估了地理因素如何导致可追溯到最后一次间冰期的九个花粉序列之间的差异，并比较了欧洲山毛榉范围内三个林分的气候时间动态和增强的植被指数。我们希望这个包可以增强来自不同领域的研究人员探索多元生态时间序列之间不同模式的能力，并有助于产生和测试关于复杂系统的时间动态为什么会随空间和时间变化的新假设。并比较欧洲山毛榉范围内三个林分的气候时间动态和增强的植被指数。我们希望这个包可以增强来自不同领域的研究人员探索多元生态时间序列之间不同模式的能力，并有助于产生和测试关于复杂系统的时间动态为什么会随空间和时间变化的新假设。并比较欧洲山毛榉范围内三个林分的气候时间动态和增强的植被指数。我们希望这个包可以增强来自不同领域的研究人员探索多元生态时间序列之间不同模式的能力，并有助于产生和测试关于复杂系统的时间动态为什么会随空间和时间变化的新假设。