Department of Biological Sciences, University of Waikato, Private Bag 3105, Hamilton, 3240, New Zealand.
Department of Statistics, University of Waikato, Private Bag 3105, Hamilton, 3240, New Zealand.
Ecol Lett. 2012 Nov;15(11):1291-1299. doi: 10.1111/j.1461-0248.2012.01852.x. Epub 2012 Aug 21.
Community assembly involves two antagonistic processes that select functional traits in opposite directions. Environmental filtering tends to increase the functional similarity of species within communities leading to trait convergence, whereas competition tends to limit the functional similarity of species within communities leading to trait divergence. Here, we introduce a new hierarchical Bayesian model that incorporates intraspecific trait variation into a predictive framework to unify classic coexistence theory and evolutionary biology with recent trait-based approaches. Model predictions exhibited a significant positive correlation (r = 0.66) with observed relative abundances along a 10 °C gradient in mean annual temperature. The model predicted the correct dominant species in half of the plots, and accurately reproduced species' temperature optimums. The framework is generalizable to any ecosystem as it can accommodate any species pool, any set of functional traits and multiple environmental gradients, and it eliminates some of the criticisms associated with recent trait-based community assembly models.
群落组装涉及两个对立的过程，它们朝着相反的方向选择功能特征。环境过滤倾向于增加群落中物种的功能相似性，导致特征趋同，而竞争则倾向于限制群落中物种的功能相似性，导致特征分歧。在这里，我们引入了一个新的层次贝叶斯模型，将种内特征变异纳入一个预测框架，将经典的共存理论和进化生物学与最近的基于特征的方法统一起来。模型预测与沿平均年温度 10°C 梯度观察到的相对丰度呈显著正相关(r = 0.66)。该模型预测了一半样地中的优势种，准确地再现了物种的最佳温度。该框架适用于任何生态系统，因为它可以容纳任何物种库、任何功能特征集和多个环境梯度，并且消除了与最近基于特征的群落组装模型相关的一些批评。
