Identifying interspecific associations is very important for understanding the community assembly process. However, most methods provide only an average association and assume that the association strength does not vary along the environmental gradient or with time. The scale effects are generally ignored. We integrated the idea of wavelet and network topological analysis to provide a novel way to detect nonrandom species associations across scales and along gradients using continuous or presence–absence ecological data. We first used a simulated species distribution data set to illustrate how the wavelet correlation analysis builds an association matrix and demonstrates its statistical robustness. Then, we applied the wavelet correlation network to a presence–absence data set of soil invertebrates. We found that the associations of invertebrates varied along an altitudinal gradient. We conclude by discussing several possible extensions of this method, such as predicting community assembly, utility in the temporal dimension, and the shifting effects of highly connected species within a community. The combination of the multiscale decomposition of wavelet and network topology analysis has great potential for fostering an understanding of the assembly and succession of communities, as well as predicting their responses to future climate change across spatial or temporal scales.

中文翻译：

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使用小波相关网络识别沿多尺度梯度变化的种间关联

识别种间关联对于理解社区组装过程非常重要。然而，大多数方法仅提供平均关联并假设关联强度不随环境梯度或随时间变化。规模效应通常被忽略。我们整合了小波和网络拓扑分析的思想，提供了一种使用连续或存在-不存在生态数据检测跨尺度和梯度的非随机物种关联的新方法。我们首先使用模拟物种分布数据集来说明小波相关分析如何构建关联矩阵并证明其统计稳健性。然后，我们将小波相关网络应用于土壤无脊椎动物的存在-不存在数据集。我们发现无脊椎动物的关联沿着海拔梯度变化。我们最后讨论了这种方法的几种可能的扩展，例如预测群落组装、时间维度的效用以及群落内高度连接物种的转移效应。小波多尺度分解和网络拓扑分析的结合对于促进对群落的组装和演替的理解以及预测它们对未来跨空间或时间尺度的气候变化的反应具有巨大的潜力。