Natural systems contain more complexity than is accounted for in models of modern coexistence theory. Coexistence modelling often disregards variation arising from stochasticity in biological processes, heterogeneity among individuals and plasticity in trait values. However, these unaccounted-for sources of uncertainty are likely to be ecologically important and have the potential to impact estimates of coexistence. We applied a Bayesian modelling framework to data from an annual plant community in Western Australia to propagate uncertainty in coexistence outcomes using the invasion criterion and ratio of niche to fitness differences. We found accounting for this uncertainty altered predictions of coexistence versus competitive exclusion for 3 out of 14 species pairs and yielded a probability of priority effects for an additional species pair. The propagation of uncertainty arising from sources of biological complexity improves our ability to predict coexistence more accurately in natural systems.

中文翻译：

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考虑到人口统计不确定性增加了对物种共存的预测：以一年生植物为例

自然系统包含的复杂性比现代共存理论模型所解释的要复杂。共存建模通常忽略由生物过程中的随机性、个体间的异质性和性状值的可塑性引起的变化。然而，这些无法解释的不确定性来源可能在生态上很重要，并有可能影响共存的估计。我们将贝叶斯建模框架应用于西澳大利亚一年生植物群落的数据，以使用入侵标准和生态位与适应度差异的比率来传播共存结果的不确定性。我们发现考虑到这种不确定性改变了对 14 个物种对中的 3 个的共存与竞争排斥的预测，并产生了对额外物种对的优先影响的概率。