论文标题
植物社区中的竞争优势：建模方法和理论预测
Competitive dominance in plant communities: Modeling approaches and theoretical predictions
论文作者
论文摘要
关于促进物种共存过程的定量预测是生态学积极研究的主题。尤其是，已知竞争性相互作用会塑造和维护生态群落，而某些物种在竞争中或在其他某些物种上占主导地位的情况是描述自然生态系统的关键。在这里，我们使用植物社区组装的随机，合成框架来开发生态理论，从而导致可预测经验测试的预测。我们提出了两个随机连续的马尔可夫模型，这些模型通过物种高度的层次结构结合了竞争优势。第一个模型在空间上是隐式的，它既可以预测生存的物种的预期数量，也可以预测高度聚集在实现模型群落中的条件。第二种允许在空间上进行个人的相互作用和可以帮助较短的植物克服高度驱动竞争的替代机制，并且表明聚类模式不仅在本地，而且在增加的空间尺度上也是如此。此外，尽管实际上在空间解释的模型中占地植物，但它允许植物的丰度不一定偏向较高的植物。
Quantitative predictions about the processes that promote species coexistence are a subject of active research in ecology. In particular, competitive interactions are known to shape and maintain ecological communities, and situations where some species out-compete or dominate over some others are key to describe natural ecosystems. Here we develop ecological theory using a stochastic, synthetic framework for plant community assembly that leads to predictions amenable to empirical testing. We propose two stochastic continuous-time Markov models that incorporate competitive dominance through a hierarchy of species heights. The first model, which is spatially implicit, predicts both the expected number of species that survive and the conditions under which heights are clustered in realized model communities. The second one allows spatially-explicit interactions of individuals and alternative mechanisms that can help shorter plants overcome height-driven competition, and it demonstrates that clustering patterns remain not only locally but also across increasing spatial scales. Moreover, although plants are actually height-clustered in the spatially-explicit model, it allows for plant species abundances not necessarily skewed to taller plants.
