Laboratoire Evolution et Diversité Biologique, CNRS, Université Paul Sabatier, Bâtiment 4R3, 31062 Toulouse cedex 4, France.
J Theor Biol. 2010 Feb 21;262(4):650-61. doi: 10.1016/j.jtbi.2009.11.004. Epub 2009 Nov 12.
I present a model of stochastic community dynamics in which death occurs randomly in the community, propagules disperse randomly from a regional pool, and recruitment of new individuals of a species is proportional to the species local abundance multiplied by its local competitive ability. The competitive ability of a species is assumed to be determined by a function of one trait of the species, and I call this function the environmental filtering function. I show that information on local species abundances in a network of plots, together with trait data for each species, enables the inference of both the immigration rate and the environmental filtering function in each plot. I further study how the diversity patterns produced by this model deviate from the neutral predictions, and how this deviation depends on the characteristics of the environmental filtering function. I show that this inference framework is more powerful at detecting trait-based environmental filtering than existing statistical approaches based on trait distributions, and discuss how the predictions of this model could be used to assess environmental heterogeneity in a plot, to detect functionally meaningful trade-offs among species traits, and to test the assumption that there exists a simple relationship between species traits and local competitive ability.
我提出了一个随机社区动态模型，其中社区中的死亡是随机发生的，繁殖体从区域库中随机扩散，物种的新个体的补充与物种的局部丰度成正比，乘以其局部竞争力。假设一个物种的竞争力由该物种的一个特征的函数决定，我将这个函数称为环境过滤函数。我表明，在一个小区网络中，关于本地物种丰度的信息，以及每个物种的特征数据，能够推断出每个小区的移民率和环境过滤函数。我进一步研究了该模型产生的多样性模式与中性预测的偏差，以及这种偏差如何取决于环境过滤函数的特征。我表明，与基于特征分布的现有统计方法相比，这种推断框架更能检测基于特征的环境过滤，并且讨论了如何使用该模型的预测来评估小区中的环境异质性，检测物种特征之间功能上有意义的权衡，以及检验物种特征与局部竞争力之间存在简单关系的假设。
