Food web complexity underlies biodiversity effects on ecosystem functioning

Data availability

All data used in this study are available at Zenodo (https://doi.org/10.5281/zenodo.20130985)83. Source data are provided with this paper.

Code availability

Code for computing food web properties, energy fluxes, stability, NPP and statistical analyses is available at Zenodo (https://doi.org/10.5281/zenodo.20130985)83.

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Acknowledgements

This work was initiated from the Food web Structure, Ecosystem functioning and Diversity across ecosystems (FuSED) workshop at the German Centre for Integrative Biodiversity Research (iDiv) Halle–Jena–Leipzig, funded by the German Research Foundation (DFG–FZT 118, 202548816).

Funding

A.D.B. acknowledges support by the Marsden Fund Council from Government funding managed by Royal Society Te Apārangi (grant no. MFP-23-UOW-029). We acknowledge funding by the German Research Foundation (DFG-FOR 5000, Ei 862/29-1) and the Portuguese Foundation for Science and Technology (UIDB/04326/2020, UIDP/04326/2020, LA/P/0101/2020, UIDB/04292/2020, CEECINST/00146/2018/CP1493/CT0007). V.S.S. was supported by NSF/FAPESP grant 2022/01452-1. D.I.K. was supported by the Russian Science Foundation (project no. 25-24-00639). M.B. was funded through BiodivRestore ERA-NET Cofund (grant no. 101003777) and the Federal Ministry of Education and Research Germany (16LW0174K). P.K. was supported by the NERC Pushing the Frontiers grant (NE/Y001184/1). S.K. acknowledges support from (www.coastclim.org), and the Research Council of Finland (grant no. 361049). D.G.-C. was funded by the New Zealand Biological Heritage National Science Challenge and the Austrian Science Fund (grant reference FWF ESPRIT ESP-671). M.C.N. acknowledges the Research Council of Finland University Profiling funding for InterEarth (grant no. 353218). R.A.S. was funded by the Russian Science Foundation, grant no. 23-14-00201. S.L.E. was supported by NSF grants DEB-9207498, DEB-9629268, DEB-0212315 and the USDA Forest Service Northern Research Station. D.M.P. was supported by the British Ecological Society grants SR21\100750 & 4973-6013. A.J.T. was supported by the Canada Research Chairs Programme and a Natural Sciences and Engineering Research Council of Canada Discovery Grant (RGPIN-2023-03977).

Author information

Authors and Affiliations

Te Aka Mātuatua – School of Science, The University of Waikato, Hamilton, New Zealand

Andrew D. Barnes

German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

Ulrich Brose, Nico Eisenhauer, Emilio Berti, Jes Hines, Benjamin Rosenbaum & Benoit Gauzens

Institute of Biodiversity, Friedrich Schiller University Jena, Jena, Germany

Ulrich Brose, Emilio Berti, Benjamin Rosenbaum & Benoit Gauzens

Institute of Biology, Leipzig University, Leipzig, Germany

Nico Eisenhauer & Jes Hines

Department River Ecology, Helmholtz Centre for Environmental Research-UFZ, Magdeburg, Germany

Mario Brauns

USDA Forest Service Northern Research Station, Grand Rapids, MN, USA

Susan L. Eggert

Departament de Biologia Animal, Biologia Vegetal i Ecologia, Unitat d’Ecologia, Universitat Autonoma de Barcelona, Barcelona, Spain

David Garcia-Callejas

Institute of Biology, University of Graz, Graz, Austria

David Garcia-Callejas

CSIRO Environment, Albury, New South Wales, Australia

Darren P. Giling

Gulbali Institute, Charles Sturt University, Albury, New South Wales, Australia

Darren P. Giling

Flathead Lake Biological Station, University of Montana, Polson, MT, USA

Robert O. Hall Jr.

Department of Global Change Ecology, Biocenter, University of Würzburg, Würzburg, Germany

Malte Jochum

A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow, Russia

Daniil I. Korobushkin & Ruslan A. Saifutdinov

Tvärminne Zoological Station, University of Helsinki, Hanko, Finland

Susanne Kortsch

Department of Environmental Sciences, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland

Susanne Kortsch & Marie C. Nordström

Centre for Biodiversity and Sustainability, School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK

Pavel Kratina

Water Research Institute (IRSA), National Research Council (CNR), Verbania, Italy

Marina Manca & Jordi-René Mor

School of Life Sciences, University of Essex, Colchester, UK

Eoin J. O’Gorman

Centre for Biodiversity Monitoring and Conservation Science, Leibniz Institute for the Analysis of Biodiversity Change, Bonn, Germany

David Ott

Centre for Pollution Research and Policy, Brunel University of London, Uxbridge, UK

Daniel M. Perkins

Environmental Sciences Department, Federal University of São Carlos, São Carlos, Brazil

Victor S. Saito

Ecosystems and Global Change Group,School of the Environment, Trent University, Peterborough, Ontario, Canada

Andrew J. Tanentzap

CCMAR (CCMAR/CIMAR LA) – Centre of Marine Sciences, University of Algarve, Faro, Portugal

Catarina Vinagre

MARE—Marine and Environmental Sciences Centre/ARNET—Aquatic Research Network, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal

Catarina Vinagre

Authors

Andrew D. Barnes

Ulrich Brose

Nico Eisenhauer

Emilio Berti

Mario Brauns

Susan L. Eggert

David Garcia-Callejas

Darren P. Giling

Robert O. Hall Jr.

Jes Hines

Malte Jochum

Daniil I. Korobushkin

Susanne Kortsch

Pavel Kratina

Marina Manca

Jordi-René Mor

Marie C. Nordström

Eoin J. O’Gorman

David Ott

Daniel M. Perkins

Benjamin Rosenbaum

Ruslan A. Saifutdinov

Victor S. Saito

Andrew J. Tanentzap

Catarina Vinagre

Benoit Gauzens

Contributions

A.D.B., U.B., N.E., D.G.-C., D.P.G., J.H., M.J., E.J.O.ʼG., D.O., D.M.P., B.R., C.V. and B.G. designed the study. A.D.B. assembled the datasets and A.D.B., E.B., B.R. and B.G. conducted the analysis. U.B., M.B., S.L.E., D.P.G., R.O.H., D.I.K., S.K., P.K., M.M., J.-R.M., M.C.N., E.J.O.ʼG., D.O., D.M.P., R.A.S., V.S.S., A.J.T. and C.V. contributed data. All authors contributed to discussions on study design, analysis and interpretation. A.D.B. wrote the manuscript with contributions from all authors.

Corresponding author

Correspondence to

Andrew D. Barnes.

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Extended data figures and tables

Extended Data Fig. 1 Dependence of total energy flux through food webs on taxon richness and food web complexity.

(a) The relationship between taxon richness and total energy flux in food webs across ecosystem types (β = 1.24 ± SE 0.14, df = 305, p < 0.001). (b) Structural equation model (Fisher’s C8 = 4.3, P = 0.83) testing the indirect effect of taxon richness on total energy flux via maximum trophic level and total trophic complementarity across all organisms in food webs. Fitted trend line and associated 95% CI in (a) is taken from the linear mixed effects model with food web study (dataset) nested within ecosystem type set as a random intercept. Points are coloured according to ecosystem type purely as a visual aid to illustrate cross-ecosystem differences in taxon richness and total energy flux. Arrow widths in (b) are scaled to standardised path coefficients to indicate relative effect sizes (with path coefficient values given next to each arrow). Green arrows indicate positive relationships, while grey arrow denote quadratic relationships with arrows weighted according to Cohen’s f2 (see Methods). R2 values give the estimated proportion of variance explained, based on conditional pseudo-R2 from the linear mixed effects models. Figure created in BioRender; Barnes, A. https://biorender.com/3v93kyd (2026).

Source data

Extended Data Fig. 2 Little evidence for the importance of other dimensions of food web complexity for modulating BEF relationships in food webs.

Results from the cross-ecosystem structural equation model (SEM) (C32 = 38.13, P = 0.21) testing the effects of taxon richness on primary consumption and predation via trophic omnivory (‘Omnivory’), food web connectance (‘connectance’), maximum trophic level (‘Max TL’), trophic complementarity of primary consumers (‘1° consumer complementarity’), and trophic complementarity of predators (‘predator complementarity’). Arrow widths are scaled to standardised path coefficients (numbers adjacent to arrows) from the SEM to indicate relative effect sizes. Green and brown arrows indicate positive and negative relationships, respectively. Grey arrows indicate the absolute effect size of quadratic relationships whereby non-directional standardized path coefficients are derived from Cohen’s f2. Figure created in BioRender; Barnes, A. https://biorender.com/3v93kyd (2026).

Extended Data Fig. 3 Effects of food web taxon richness and complexity on per capita predation rates and food web stability.

(a) Marginal effects of taxon richness on per capita predation rates (log10 ratio of energy flux to predators per g of prey biomass) and local food web stability (inverse of the real part of the dominant eigenvalue of the Jacobian matrix). Solid trend lines and associated 95% CI denote statistically significant relationships (p < 0.05) derived from the LMM models. (b) Outcomes of the cross-ecosystem structural equation model (SEM) (C32 = 39.30, P = 0.18) testing the effects of taxon richness on per capita predation rates and local food web stability via maximum trophic level (‘Max TL’), trophic complementarity of primary consumers (‘1° consumer complementarity’), and trophic complementarity of predators (‘predator complementarity’), with (c) associated effect plot summarising direct effects (solid bars) derived from the SEM and indirect effects (hatched bars) mediated by other food web properties. Arrow widths are scaled to standardised path coefficients (numbers adjacent to arrows) from the SEM to indicate relative effect sizes. Green and brown arrows in (b) indicate positive and negative relationships, respectively. Grey arrows indicate the absolute effect size of quadratic relationships whereby non-directional standardized path coefficients are derived from Cohen’s f2. Teal and yellow bars in (c) denote standardised effects of food web properties on per capita predation and food web stability, respectively. ‘Trophic compl.’ = trophic complementarity; ‘Max TL’ = maximum trophic level. Figure created in BioRender; Barnes, A. https://biorender.com/0scvc75 (2026).

Source data

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Barnes, A.D., Brose, U., Eisenhauer, N. et al. Food web complexity underlies biodiversity effects on ecosystem functioning.

Nature (2026). https://doi.org/10.1038/s41586-026-10710-5

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Received: 25 March 2025

Accepted: 26 May 2026

Published: 01 July 2026

Version of record: 01 July 2026

DOI: https://doi.org/10.1038/s41586-026-10710-5