Cornell University, Department of Plant Biology, Ithaca, New York 14850 USA.
Am J Bot. 2009 Aug;96(8):1430-44. doi: 10.3732/ajb.0900063.
A spatially explicit, reiterative algorithm (SERA) is presented and used to predict multiple aspects of plant population and community dynamics. Using simple physical principles and empirically derived relationships, SERA provides an analytical venue to test alternative hypotheses about individual functional traits governing ecological or evolutionary processes at the population or community level of complexity. Our analyses show that, as a result of competition for light and space, individual-level features scale up to produce species ensemble properties such as the scaling of self-thinning, size-dependent mortality, realistic size-frequency distributions, and a broad spectrum of empirically observed relationships for the species examined. SERA also predicts the competitive exclusion of conifers by angiosperms and the age at which reproductive maturity is achieved by different species. SERA serves as a null hypothesis by demonstrating that biologically complex phenomena, including widely observed scaling relationships at the species-ensemble level, can emerge from the operation of simple and transparent "rules" governing competition for space and light.
本文提出了一种空间显式、迭代算法（SERA），并将其用于预测植物种群和群落动态的多个方面。该算法利用简单的物理原理和经验推导的关系，为测试关于控制种群或群落水平复杂性的个体功能特征的生态或进化过程的替代假设提供了一个分析场所。我们的分析表明，由于对光和空间的竞争，个体水平的特征会扩展，从而产生物种总体特征，例如自疏、与大小相关的死亡率、现实的大小频率分布以及广泛观察到的与所研究物种相关的关系。SERA 还预测了针叶树被被子植物竞争排斥，以及不同物种达到生殖成熟的年龄。SERA 通过证明包括物种总体水平上广泛观察到的尺度关系在内的复杂生物学现象可以从简单透明的“规则”的运作中产生，这些规则可以竞争空间和光，因此它可以作为一个零假设。
