Azaele Sandro, Muneepeerakul Rachata, Maritan Amos, Rinaldo Andrea, Rodriguez-Iturbe Ignacio
Department of Civil and Environmental Engineering, E-Quad, Princeton University, Princeton, NJ 08544, USA.
Proc Natl Acad Sci U S A. 2009 Apr 28;106(17):7058-62. doi: 10.1073/pnas.0805845106. Epub 2009 Apr 9.
A major issue in modern ecology is to understand how ecological complexity at broad scales is regulated by mechanisms operating at the organismic level. What specific underlying processes are essential for a macroecological pattern to emerge? Here, we analyze the analytical predictions of a general model suitable for describing the spatial biodiversity similarity in river ecosystems, and benchmark them against the empirical occurrence data of freshwater fish species collected in the Mississippi-Missouri river system. Encapsulating immigration, emigration, and stochastic noise, and without resorting to species abundance data, the model is able to reproduce the observed probability distribution of the Jaccard similarity index at any given distance. In addition to providing an excellent agreement with the empirical data, this approach accounts for heterogeneities of different subbasins, suggesting a strong dependence of biodiversity similarity on their respective climates. Strikingly, the model can also predict the actual probability distribution of the Jaccard similarity index for any distance when considering just a relatively small sample. The proposed framework supports the notion that simplified macroecological models are capable of predicting fundamental patterns-a theme at the heart of modern community ecology.
现代生态学中的一个主要问题是了解广泛尺度上的生态复杂性是如何由有机体层面的机制调节的。对于宏观生态模式的出现，哪些具体的潜在过程至关重要？在这里，我们分析了一个适用于描述河流生态系统中空间生物多样性相似性的通用模型的分析预测，并将其与在密西西比 - 密苏里河水系收集的淡水鱼类物种的经验出现数据进行对比。该模型涵盖了迁入、迁出和随机噪声，且无需物种丰度数据，就能在任何给定距离上重现观察到的杰卡德相似性指数的概率分布。除了与经验数据高度吻合外，这种方法还考虑了不同子流域的异质性，表明生物多样性相似性对各自气候有很强的依赖性。引人注目的是，该模型在仅考虑相对较小样本时，也能预测任何距离下杰卡德相似性指数的实际概率分布。所提出的框架支持了这样一种观点，即简化的宏观生态模型能够预测基本模式——这是现代群落生态学的核心主题。
