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Supplementary material 2 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Brief description of the Field Experiment

Figure 1 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Figure 1 A. Conceptual diagram of the mechanistic approach of the planned Research Unit. B. Conceptual scheme of the proposed evolutionary niche shifts in plant monocultures and mixtures. This idea feeds into our understanding of how evolutionary history influences the ecological interactions of species that compete for growth factors, ultimately defining biotope space (gray rectangle; Hutchinson 1978). Graphically depicted, species (ellipses) in mixture will show increasing niche differentiation over time due to competition (niche overlap). Thus, history of selection in diverse communities is expected to result in greater interspecific differences (less overlap of ellipses) and more specialization (smaller ellipses) than a history of isolation (monocultures). In monocultures, species will experience strong selection pressure by accumulating soil-borne pathogens, and species may invest energy in chemical and morphological defense traits (depicted by ellipses shifting towards the same corner of the habitat space). Plants in mixtures together may exploit more available biotope space than single monocultures, causing increasing diversity effects on ecosystem functions over time. However, there is limited support for this assumption for traits related to light (e.g., Lipowsky et al. 2015, Roscher et al. 2015) and resource use (Jesch et al. 2018) so far.

Figure 4 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Figure 4 Experimental design and hypotheses of the Ecotron Experiment. Briefly, four treatments will be established based on monoliths from a selection of the 9-year old Trait-Based Experiment (TBE; Ebeling et al. 2014) and from bare ground plots of the Jena Experiment as well as two seed sources: the respective plots and the original seed material that was used for the set-up of the TBE. (1) With plot-specific plant history and with plot-specific soil history; (2) without plot-specific plant history and with plot-specific soil history; (3) with plot-specific plant history and without plot-specific soil history; and (4) without plot-specific plant history and without plot-specific soil history. We expect the biodiversity–ecosystem function relationships to differ among the four treatments (see main text for details).

Figure 3 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Figure 3 Hypothesized slope of BEF relationships in the different treatments of the Field Experiment (see main text for details). Note that the 'with plant history, with soil history' only serves as a control in the Field Experiment, and effects of plant history can only be tested in the planned Ecotron Experiment. Redrawn after Vogel et al. (2019). '+', with; '-', without.

Supplementary material 4 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Detailed design of the Ecotron Experiment

Supplementary material 5 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Brief description of the Ecotron Experiment

Figure 2 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Figure 2 Structure of the proposed Research Unit. Three complementary experimental approaches are envisaged to study long-term biodiversity-ecosystem function (BEF) relationships, and how these are influenced by plant history and soil history. BEF patterns are studied in the Field Experiment with long-term plant diversity plots and manipulations of soil-history effects. BEF mechanisms are studied in the Ecotron Experiment and in Microcosm Experiments. In the Ecotron Experiment, plant history and soil history are independently crossed and detailed process measurements are possible. The Microcosm Experiments zoom in on focal interactions. In the Field Experiment and in the Ecotron Experiment, studies are conducted at the community level as well as at the plant individual level (magnifier; see detailed design of studies in the Appendices). Subprojects' (SPs') participation in experiments are illustrated with lines. The SPs of the proposed Research Unit fall into two tightly linked main categories (in gray) with two research areas each that aim at exploring variation in community assembly processes, micro-evolutionary changes, and resulting differences in biotic interactions as determinants of the long-term BEF relationship. Subprojects under "Microbial community assembly" (blue) and "Assembly and functions of animal communities" (red) mostly focus on plant diversity effects on the assembly of communities and their feedback effects on biotic interactions and ecosystem functions, while subprojects under "Mediators of plant-biotic interactions" (orange) and "Intraspecific diversity and micro-evolutionary changes" (green) mostly focus on plant diversity effects on plant trait expression and micro-evolution. PIs with requested personnel are underlined.

Supplementary material 3 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Plant species lists of the Field Experiment and the Ecotron Experiment

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Data from: An a posteriori species clustering for quantifying the effects of species interactions on ecosystem functioning

1. Quantifying the effects of species interactions is key to understanding the relationships between biodiversity and ecosystem functioning but remains elusive due to combinatorics issues. Functional groups have been commonly used to capture the diversity of forms and functions and thus simplify the reality. However, the explicit incorporation of species interactions is still lacking in functional group-based approaches. Here we propose a new approach based on an a posteriori clustering of species to quantify the effects of species interactions on ecosystem functioning. 2. We first decompose the observed ecosystem function using null models, in which species diversity does not affect ecosystem function, to separate the effects of species interactions and species composition. This allows the identification of a posteriori functional groups that have contrasting diversity effects on ecosystem functioning. We then develop a formal combinatorial model of species interactions in which an ecosystem is described as a combination of co-occurring functional groups, which we call an assembly motif. Each assembly motif corresponds to a particular biotic environment. We demonstrate the relevance of our approach using datasets from a microbial experiment and the long-term Cedar Creek Biodiversity II experiment. 3. We show that our a posteriori approach is more accurate, more efficient and more parsimonious than a priori approaches. The discrepancy between a priori and a posteriori approaches results from the way each clustering is set up: a priori approaches are based on ecosystem or species properties, such as ecosystem size (number of species or functional groups) or species' functional traits, whereas our a posteriori approach is based only on the observed interaction and composition effects on ecosystem functioning. 4. Our findings demonstrate that an a posteriori approach is highly explanatory: it identifies who interacts with whom, and quantifies the effects of species interactions on ecosystem functioning. They also highlight that a combinatorial modelling of ecosystem functioning can predict the functioning of an ecosystem without any hypothesis about the biotic or environmental determinants or any information on species functional traits. It only requires the species composition of the ecosystem and the observed functioning of others that share the same assembly motif.