Designing forest biodiversity experiments: general considerations illustrated by a new large experiment in subtropical China

By Helge Bruelheide, Karin Nadrowski, T. Assmann, Jürgen Bauhus, S. Both, F. Buscot, Xiao‐Yong Chen, B. Ding, W. Durka, A. Erfmeier and 28 more. 2014

282 citations recorded by the source corpus.

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.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/70115987c41469a8f6f03ff6cfba7129df68502a

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Citations in the corpus

Identifying the tree species compositions that maximize ecosystem functioning in European forests (2018, background): “Compositions can be replicated within forest types under different soil conditions and levels of water supply, but also across different forest types to cover regional-scale gradients such as climate (see Bruelheide et al. 2014 for a diversity-oriented example).”
Biodiversity–ecosystem function experiments reveal the mechanisms underlying the consequences of biodiversity change in real world ecosystems (2016, result): “Other experiments have implemented a non-random biodiversity loss scenario and can compare the results with randomly assembled communities (e.g., Schl€apfer et al. 2005; Bruelheide et al. 2014).”
Globally, functional traits are weak predictors of juvenile tree growth, and we do not know why (2015, background): “sites were complemented by other TreeDivNet sites (Bruelheide et al. 2014), reforestation trials and studies designed to test specific ecological hypotheses.”