- Hierarchical trait-based prediction of plant performance

Hierarchical trait-based prediction of plant performance

Plant functional traits are powerful tools when it comes to the prediction of plant performance and ecosystem functioning. For a long time ecologists focused on aboveground traits but widely ignored root traits because their sampling and measurement is expensive and labour-intensive. A number of newer studies have shown that root traits link not only to belowground (e.g. N cycling) but also to aboveground functions (e.g. biomass production). So far it has never been tested, how the importance of root traits for plant performance depends on the hierarchical level (individual vs. population).

We hypothesized that root traits are more important on the population level than on the individual level, due to an increase in process complexity.

Methods:

The experiment is conducted in the frame of the Jena Experiment in Jena, Germany. The plots are of 6 plant species richness levels (1, 2, 4, 8, 16, 60 species) combined with 4 functional group richness levels (1, 2, 3, 4 functional groups). We used univariate and multiple regression analyses to test the importance of 35 root-, leaf- and stature traits for the prediction of individual and population biomass production of 59 European grassland species.

Results:

We found that traits of all three clusters (root, leaf and stature) correlate with both individual and monoculture biomass. The importance of root traits as predictors significantly increased when upscaling from the individual plant to the population level. Root traits were more important predictors of individual plant performance than leaf traits and were even the most important predictors at the population level.

Conclusions:

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.

This work is performed by Thomas Schröder-Georgi

letzte Änderung: 03.09.2015