arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 0802.3274q-bio.PEnlin.PS
收藏

高斯竞争如何导致亮度或均匀物种分布

How Gaussian competition leads to lumpy or uniform species distributions

Simone Pigolotti, Cristobal Lopez, Emilio Hernandez-Garcia, Ken Haste Andersen

摘要:

理论生态学中的一个中心模型考虑了一系列物种对广泛资源的竞争。最近的研究表明,基本上可能有两种不同的结果。在竞争中幸存下来的物种或多或少地均匀分布在资源谱上,或者它们的分布是“集中”(或“集群”)的,由具有相似资源使用的物种集群组成,这些物种被资源空间中的间隙分开。这些结果中的哪一个将发生关键取决于竞争内核,它反映了竞争物种的资源利用模式的形状。在文献中考虑的大多数模型假设一个高斯竞争内核。这是不幸的,因为基于这种高斯假设的预测并不稳健。事实上,高斯核是一个边界情况,与此函数的轻微偏差可能导致均匀或集中的物种分布。在这里,我们说明了高斯假设的非鲁棒性,通过模拟不同的实现标准的竞争模型与恒定的承载能力。在这种情况下,集中的物种分布可以通过次级生态或进化机制或模型的数值实现细节来实现。我们分析了这种敏感性的起源,并讨论了它的背景下,最近的应用程序的模型。

英文摘要:

A central model in theoretical ecology considers the competition of a range of species for a broad spectrum of resources. Recent studies have shown that essentially two different outcomes are possible. Either the species surviving competition are more or less uniformly distributed over the resource spectrum, or their distribution is 'lumped' (or 'clumped'), consisting of clusters of species with similar resource use that are separated by gaps in resource space. Which of these outcomes will occur crucially depends on the competition kernel, which reflects the shape of the resource utilization pattern of the competing species. Most models considered in the literature assume a Gaussian competition kernel. This is unfortunate, since predictions based on such a Gaussian assumption are not robust. In fact, Gaussian kernels are a border case scenario, and slight deviations from this function can lead to either uniform or lumped species distributions. Here we illustrate the non-robustness of the Gaussian assumption by simulating different implementations of the standard competition model with constant carrying capacity. In this scenario, lumped species distributions can come about by secondary ecological or evolutionary mechanisms or by details of the numerical implementation of the model. We analyze the origin of this sensitivity and discuss it in the context of recent applications of the model.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑