Including community composition in biodiversity-productivity models

1. Studies on biodiversity and ecosystem functioning (BEF) have elicited debate over the interpretation of the positive relationship between species richness and plant productivity. Manipulating richness cannot be achieved without affecting composition; it is thus essential to consider the latter in statistical models. 2. We firstly review existing approaches that use species richness as an explanatory variable and propose modifications to improve their performance. We use an original dataset to illustrate the analyses. The classical method where composition is coded as a factor with a level for each different species mixture can be improved by defining the levels using clustering. Methods based on ordinations reduce the dimensionality of plant composition and use the new coordinates as fixed effects; they provide a much better fit to our observations. 3. Secondly, we develop a new method where composition is included as a similarity matrix affecting the residual variance-covariance. Si...

1. 有关生物多样性与生态系统功能（biodiversity and ecosystem functioning, BEF）的研究，围绕物种丰富度与植物生产力之间正相关关系的解读引发了学界争议。若仅操控物种丰富度，必然会对群落组成造成影响，无法做到仅改变丰富度而不波及组成，因此在统计模型中纳入群落组成这一因素至关重要。2. 本研究首先回顾了以物种丰富度作为解释变量的现有分析方法，并提出改进方案以提升其性能。我们采用一套原创数据集来演示相关分析流程。经典方法将群落组成编码为一个因子，且针对每一种不同的物种组合设置一个水平，该方法可通过聚类方法定义各水平以实现优化。基于群落排序的方法可降低植物群落组成的数据维度，并将降维后的新坐标作为固定效应纳入模型，此类方法对我们的观测数据拟合效果更佳。3. 其次，本研究开发了一种全新的分析方法，将群落组成以相似性矩阵的形式纳入模型，用于影响残差方差-协方差结构。Si...