Physiological Diversity, UFZ, Helmholtz Centre for Environmental Research, Permoserstrasse 15, 04318, Leipzig, Germany.
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103, Leipzig, Germany.
Ecology. 2018 May;99(5):1214-1226. doi: 10.1002/ecy.2216. Epub 2018 Apr 18.
Plant functional traits may explain the positive relationship between species richness and ecosystem functioning, but species-level trait variation in response to growth conditions is often ignored in trait-based predictions of community performance. In a large grassland biodiversity experiment (Jena Experiment), we measured traits on plants grown as solitary individuals, in monocultures or in mixtures. We calculated two measures of community-level trait composition, i.e., community-weighted mean traits (CWM) and trait diversity (Rao's quadratic entropy; FD) based on different contexts in which traits were measured (trait origins). CWM and FD values of the different measurement origins were then compared regarding their power to predict community biomass production and biodiversity effects quantified with the additive partitioning method. Irrespective of trait origin, models combining CWM and FD values as predictors best explained community biomass and biodiversity effects. CWM values based on monoculture, mixture-mean or community-specific trait data were similarly powerful predictors, but predictions became worse when trait values originated from solitary-grown individuals. FD values based on monoculture traits were the best predictors of community biomass and net biodiversity effects, while FD values based on community-specific traits were the best predictors for complementarity and selection effects. Traits chosen as best CWM predictors were not strongly affected by trait origin but traits chosen as best FD predictors varied strongly dependent on trait origin and altered the predictability of community performance. We conclude that by adjusting their functional traits to species richness and even specific community compositions, plants can change community-level trait compositions, thereby also changing community biomass production and biodiversity effects. Incorporation of these plastic trait adjustments of plants in trait-based ecology can improve its predictive power in explaining biodiversity-ecosystem functioning relationships.
植物功能特性可能解释了物种丰富度与生态系统功能之间的正相关关系，但在基于性状的群落表现预测中，往往忽略了物种水平上对生长条件的性状变化。在一个大型草原生物多样性实验（耶拿实验）中，我们测量了在单独个体、纯培养或混合物中生长的植物的性状。我们基于不同的性状测量背景（性状起源），计算了群落水平性状组成的两个度量值，即群落加权平均性状（CWM）和性状多样性（Rao 的二次熵；FD）。然后比较了不同测量起源的 CWM 和 FD 值在预测群落生物量生产和用附加分区方法量化的生物多样性效应方面的能力。无论性状起源如何，将 CWM 和 FD 值组合作为预测因子的模型都能很好地解释群落生物量和生物多样性效应。基于纯培养物、混合物平均值或群落特定性状数据的 CWM 值同样是强有力的预测因子，但当性状值来源于单独生长的个体时，预测效果会变差。基于纯培养物性状的 FD 值是群落生物量和净生物多样性效应的最佳预测因子，而基于群落特定性状的 FD 值是互补和选择效应的最佳预测因子。作为最佳 CWM 预测因子选择的性状不太受性状起源的影响，但作为最佳 FD 预测因子选择的性状强烈依赖于性状起源，并改变了群落表现的可预测性。我们得出的结论是，植物通过调整其功能性状以适应物种丰富度甚至特定的群落组成，可以改变群落水平的性状组成，从而改变群落生物量生产和生物多样性效应。在基于性状的生态学中纳入这些植物的可塑性性状调整可以提高其解释生物多样性-生态系统功能关系的预测能力。
