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From pots to plots: hierarchical trait‐based prediction of plant performance in a mesic grassland
- Schroeder‐Georgi, Thomas, Wirth, Christian, Nadrowski, Karin, Meyer, Sebastian T., Mommer, Liesje, Weigelt, Alexandra, Gibson, David
- The journal of ecology 2016 v.104 no.1 pp. 206-218
- biomass, ecosystems, equations, grasslands, leaves, models, nitrogen content, nutrient uptake, photosynthesis, prediction, regression analysis, trophic relationships, variance
- Traits are powerful predictors of ecosystem functions pointing to underlying physiological and ecological processes. Plant individual performance results from the coordinated operation of many processes, ranging from nutrient uptake over organ turnover to photosynthesis, thus requiring a large set of traits for its prediction. For plant performance on higher hierarchical levels, e.g. populations, additional traits important for plant‐plant and trophic interactions may be required which should even enlarge the spectrum of relevant predictor traits. The goal of this study was to assess the importance of plant functional traits to predict individual and population performance of grassland species with particular focus on the significance of root traits. We tested this for 59 grassland species using 35 traits divided into three trait clusters: leaf traits (16), stature traits (8) and root traits (11), using individual biomass of mesocosm plants as a measure of individual performance and population biomass of monocultures as a measure of population performance. We applied structural equation models to disentangle direct effects of single traits on population biomass and indirect effects via individual plant biomass or shoot density.We tested multivariate trait effects on individual and population biomass to analyse whether the importance of different trait clusters shifts with increasing hierarchical integration from individuals to populations. Traits of all three clusters significantly correlated with individual and population biomass. However, in spite of a number of significant correlations, above‐below‐ground linkages were generally weak, with few exceptions like N content. Stature traits exclusively affected population biomass indirectly via their effect on individual biomass, whereas root and leaf traits showed also direct effects and partly indirect effects via density. The inclusion of root traits in multiple regression models improved the prediction of individual biomass when compared with models with only above‐ground information only slightly (95% vs. 93% of variance prediction with and without root traits, respectively) but was crucial for the prediction of population biomass (77% and 49%, respectively). Root traits were more important for plant performance than leaf traits and were even the most important predictors at the population level Synthesis. Upscaling from the individual to the population level reflects an increasing number of processes requiring traits from different trait clusters for their prediction. Our results emphasize the importance of root traits for trait‐based studies especially at higher organizational levels. Our approach provides a comprehensive framework acknowledging the hierarchical nature of trait influences. This is one step towards a more process‐oriented assessment of trait‐based approaches.