2020 ESA Annual Meeting (August 3 - 6)

COS 149 Abstract - Community trait and functional diversity predictions on forest productivity change over time and among species pools

Franca Bongers1, Bernhard Schmid2, Helge Bruelheide3, Shan Li1, Keping Ma1 and Xiaojuan Liu1, (1)State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China, (2)RSL, Department of Geography, University of Zurich, Zurich, Switzerland, (3)Institute of Biology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
Background/Question/Methods

Experimental species rich tree plantations are useful to study the mechanisms that drive positive ecosystem productivity with increased species richness. In addition, it provides opportunities to predict productivity for reforestation purposes. Using community weighted functional trait values to predict community productivity is common practice due to the associated link of functional traits with ecological functions. Although the available species pool and the time of forest development are highly relevant for the functionality of diverse ecosystems and reforestation purposes, these factors are rarely considered. In this study we assessed the ability of community weighted mean (CWM) values and functional diversity (FD) indices of six common functional traits to predict forest productivity and determined how their predictions change with time and depend on species pool. We used 478 plots from a large tree biodiversity experiment in subtropic China (BEF-China) that has 1, 2, 4, 8, 16 or 24 species per plot that belong to 40 species, and runs since 2009.

Results/Conclusions

Across all plots, we found that CWM values explained more of the variation in plot productivity than FD indices. However, over time, the amount of variation explained by CWM decreased while the amount explained by FD increased. We conclude that early in forest development productivity will be mostly predicted by CWM and that later in development the ability of FD to predict productivity will increase. In addition, we found that considering different species pools substantially increased the ability to predict productivity by some FD indices. This shows that complementarity processes are different per species combination and thus cannot be represented by one and the same functional trait.

Overall, out results show that using CWM values can be a promising tool to predict productivity of plantation forests at early stage, but FD at later stage. However, inference about which functional trait characteristic the community should have to yield the highest productivity depends on the available species and the time that the plantation forest will grow. Thus exploring multiple-traits and functional diversity indices is necessary to detect the true relationship between diversity and ecosystem functioning.