Toward a methodical framework for comprehensively assessing forest multifunctionality

Biodiversity–ecosystem functioning (BEF) research has extended its scope from communities that are short‐lived or reshape their structure annually to structurally complex forest ecosystems. The establishment of tree diversity experiments poses specific methodological challenges for assessing the multiple functions provided by forest ecosystems. In particular, methodological inconsistencies and nonstandardized protocols impede the analysis of multifunctionality within, and comparability across the increasing number of tree diversity experiments. By providing an overview on key methods currently applied in one of the largest forest biodiversity experiments, we show how methods differing in scale and simplicity can be combined to retrieve consistent data allowing novel insights into forest ecosystem functioning. Furthermore, we discuss and develop recommendations for the integration and transferability of diverse methodical approaches to present and future forest biodiversity experiments. We identified four principles that should guide basic decisions concerning method selection for tree diversity experiments and forest BEF research: (1) method selection should be directed toward maximizing data density to increase the number of measured variables in each plot. (2) Methods should cover all relevant scales of the experiment to consider scale dependencies of biodiversity effects. (3) The same variable should be evaluated with the same method across space and time for adequate larger‐scale and longer‐time data analysis and to reduce errors due to changing measurement protocols. (4) Standardized, practical and rapid methods for assessing biodiversity and ecosystem functions should be promoted to increase comparability among forest BEF experiments. We demonstrate that currently available methods provide us with a sophisticated toolbox to improve a synergistic understanding of forest multifunctionality. However, these methods require further adjustment to the specific requirements of structurally complex and long‐lived forest ecosystems. By applying methods connecting relevant scales, trophic levels, and above‐ and belowground ecosystem compartments, knowledge gain from large tree diversity experiments can be optimized.