Toward a methodical framework for comprehensively assessing forest multifunctionality

Abstract

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.

Keywords: BEF‐China; forest biodiversity experiments; high‐throughput methods; multitrophic interactions; standardized protocols.

Figure 1

Example of a large tree diversity experiment: (a) partial view of site A and (b) site B of the BEF‐China experiment seven and six years after planting, respectively. (c) Monoculture plot of Triadica cochinchinensis (site A) and (d) eight‐species tree mixture of Castanea henryi, Castanopsis sclerophylla, Choerospondias axillaris, Liquidambar formosana, Nyssa sinensis, Quercus serrata, Sapindus saponaria, and Triadica sebifera (site A). To increase generality of BEF relationships, the experiment was established at two sites (about 5 km apart) with only small overlap of species pools. Photographs: S. Trogisch

Figure 2

Range of methodical approaches applied in BEF‐China to study effects of tree diversity including leaf functional trait diversity (5) and genetic diversity (6) on plant biomass production and tree growth (1 + 2 = aboveground and belowground tree biomass and productivity, 3 = tree growth and canopy architecture, 4 = herb‐layer biomass and diversity), aboveground multitrophic interactions (7 = herbivory, 8 = plant‐fungal pathogens interactions, 9 = trophobiosis), belowground microbial interactions (10 = microbial diversity, 11 = microbial biomass and activity), nutrient cycling and soil erosion (12 + 13 = leaf litter and deadwood decomposition, 14 = soil fertility and C storage, 15 = soil erosion). Numbers in this figure reflect numbering of ecosystem functions and variables in Table 1

Figure 3

Identifying the links and underlying mechanisms between tree diversity and key ecosystem functions requires the coordinated assessment of forest multifunctionality across trophic levels and ecosystem subsystems. For example, consistent datasets of relevant ecosystem functions are needed to analyze the effect of tree diversity on tree biomass using structural equation modeling. Shown is a simplified conceptual structural equation model which links aboveground (herbivory, leaf pathogen infestation) and soil‐related processes (soil microbial biomass and diversity, decomposition of leaves and roots and deadwood decomposition) affecting tree biomass. Solid and dashed arrows show hypothetical significant and nonsignificant positive or negative effects, respectively. Increasing arrow width specifies hypothetical strength of causal relationship between variables. Positive and negative relationships are indicated by “+” and “−” signs, respectively