Designing forest biodiversity experiments: general considerations illustrated by a new large experiment in subtropical China. Issue 1 (22nd November 2013) - Bruelheide, Helge; Nadrowski, Karin; Assmann, Thorsten; Bauhus, Jürgen; Both, Sabine; Buscot, François; Chen, Xiao‐Yong; Ding, Bingyang; Durka, Walter; Erfmeier, Alexandra; Gutknecht, Jessica L. M.; Guo, Dali; et al.
Summary Biodiversity–ecosystem functioning (BEF) experiments address ecosystem‐level consequences of species loss by comparing communities of high species richness with communities from which species have been gradually eliminated. BEF experiments originally started with microcosms in the laboratory and with grassland ecosystems. A new frontier in experimental BEF research is manipulating tree diversity in forest ecosystems, compelling researchers to think big and comprehensively. We present and discuss some of the major issues to be considered in the design of BEF experiments with trees and illustrate these with a new forest biodiversity experiment established in subtropical China (Xingangshan, Jiangxi Province) in 2009/2010. Using a pool of 40 tree species, extinction scenarios were simulated with tree richness levels of 1, 2, 4, 8 and 16 species on a total of 566 plots of 25·8 × 25·8 m each. The goal of this experiment is to estimate effects of tree and shrub species richness on carbon storage and soil erosion; therefore, the experiment was established on sloped terrain. The following important design choices were made: (i) establishing many small rather than fewer larger plots, (ii) using high planting density and random mixing of species rather than lower planting density and patchwise mixing of species, (iii) establishing a map of the initial 'ecoscape' to characterize site heterogeneity before the onset of biodiversity effects and (iv) manipulating tree species richness not only in random but also in trait‐oriented extinction scenarios. Data management and analysis are particularly challenging in BEF experiments with their hierarchical designs nesting individuals within‐species populations within plots within‐species compositions. Statistical analysis best proceeds by partitioning these random terms into fixed‐term contrasts, for example, species composition into contrasts for species richness and the presence of particular functional groups, which can then be tested against the remaining random variation among compositions. We conclude that forest BEF experiments provide exciting and timely research options. They especially require careful thinking to allow multiple disciplines to measure and analyse data jointly and effectively. Achieving specific research goals and synergy with previous experiments involves trade‐offs between different designs and requires manifold design decisions.