The history of species immigration can dictate how species interact in local communities, thereby causing historical contingency in community assembly. Since immigration history is rarely known, these historical influences, or priority effects, pose a major challenge in predicting community assembly. Here, we provide a graph-based, non-parametric, theoretical framework for understanding the predictability of community assembly as affected by priority effects. To develop this framework, we first show that the diversity of possible priority effects increases super-exponentially with the number of species. We then point out that, despite this diversity, the consequences of priority effects for multispecies communities can be classified into four basic types, each of which reduces community predictability: alternative stable states, alternative transient paths, compositional cycles and the lack of escapes from compositional cycles to stable states. Using a neural network, we show that this classification of priority effects enables accurate explanation of community predictability, particularly when each species immigrates repeatedly. We also demonstrate the empirical utility of our theoretical framework by applying it to two experimentally derived assembly graphs of algal and ciliate communities. Based on these analyses, we discuss how the framework proposed here can help guide experimental investigation of the predictability of history-dependent community assembly.

中文翻译：

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解开多物种群落中优先效应的复杂性

物种移民的历史可以决定物种如何在当地社区中相互作用，从而导致社区集会的历史偶然性。由于移民历史鲜为人知，这些历史影响或优先影响对预测社区集会构成重大挑战。在这里，我们提供了一个基于图的、非参数的理论框架，用于理解受优先效应影响的社区组装的可预测性。为了开发这个框架，我们首先表明可能的优先效应的多样性随着物种数量的增加而呈超指数增长。然后我们指出，尽管存在这种多样性，但优先效应对多物种群落的影响可以分为四种基本类型，每一种都会降低群落的可预测性：替代稳定状态，替代的瞬态路径、组成周期以及缺乏从组成周期到稳定状态的逃逸。使用神经网络，我们表明这种优先效应的分类能够准确解释群落的可预测性，特别是当每个物种反复移民时。我们还通过将我们的理论框架应用于藻类和纤毛虫群落的两个实验得出的组装图来证明其经验效用。基于这些分析，我们讨论了这里提出的框架如何帮助指导对历史相关社区集会可预测性的实验研究。我们表明，这种优先效应的分类能够准确解释群落的可预测性，特别是当每个物种反复移民时。我们还通过将我们的理论框架应用于藻类和纤毛虫群落的两个实验得出的组装图来证明其经验效用。基于这些分析，我们讨论了这里提出的框架如何帮助指导对历史相关社区集会可预测性的实验研究。我们表明，这种优先效应的分类能够准确解释群落的可预测性，特别是当每个物种反复移民时。我们还通过将我们的理论框架应用于藻类和纤毛虫群落的两个实验得出的组装图来证明其经验效用。基于这些分析，我们讨论了这里提出的框架如何帮助指导对历史相关社区集会可预测性的实验研究。