Benayas, J. M. R., Newton, A. C., Diaz, A. & Bullock, J. M. Enhancement of biodiversity and ecosystem services by ecological restoration: a meta-analysis. Science 325, 1121–1124 (2009).
Google Scholar
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
Google Scholar
Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).
Google Scholar
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).
Google Scholar
Chen, C., Xiao, W. & Chen, H. Y. H. Meta-analysis reveals global variations in plant diversity effects on productivity. Nature 638, 435–440 (2025).
Google Scholar
Eisenhauer, N. et al. A multitrophic perspective on biodiversity–ecosystem functioning research. Adv. Ecol. Res. 61, 1–54 (2019).
Google Scholar
Thompson, R. M., Hemberg, M., Starzomski, B. M. & Shurin, J. B. Trophic Levels and trophic tangles: the prevalence of omnivory in real food webs. Ecology 88, 612–617 (2007).
Google Scholar
Barnes, A. D. et al. Energy flux: the link between multitrophic biodiversity and ecosystem functioning. Trends Ecol. Evol. 33, 186–197 (2018).
Google Scholar
Wang, S. & Brose, U. Biodiversity and ecosystem functioning in food webs: the vertical diversity hypothesis. Ecol. Lett. 21, 9–20 (2018).
Google Scholar
Poisot, T., Mouquet, N. & Gravel, D. Trophic complementarity drives the biodiversity-ecosystem functioning relationship in food webs. Ecol. Lett. 16, 853–861 (2013).
Google Scholar
Albert, G., Gauzens, B., Loreau, M., Wang, S. & Brose, U. The hidden role of multi-trophic interactions in driving diversity–productivity relationships. Ecol. Lett. 25, 405–415 (2022).
Google Scholar
Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).
Google Scholar
Tye, S. P., Fey, S. B., Gibert, J. P. & Siepielski, A. M. Predator mass mortality events restructure food webs through trophic decoupling. Nature 626, 335–340 (2024).
Google Scholar
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).
Google Scholar
Hirt, M. R. et al. Environmental and anthropogenic constraints on animal space use drive extinction risk worldwide. Ecol. Lett. 24, 2576–2585 (2021).
Google Scholar
Ceballos, G. et al. Accelerated modern human–induced species losses: entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).
Google Scholar
Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).
Google Scholar
Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).
Google Scholar
Duffy, J. E. Why biodiversity is important to the functioning of real-world ecosystems. Front. Ecol. Environ. 7, 437–444 (2009).
Google Scholar
Jochum, M. et al. The results of biodiversity–ecosystem functioning experiments are realistic. Nat. Ecol. Evol. 4, 1485–1494 (2020).
Google Scholar
Manning, P. et al. in Advances in Ecological Research vol. 61 (eds Eisenhauer, N. et al.) Ch. 3, 323–356 (Academic, 2019).
Duffy, J. E. et al. The functional role of biodiversity in ecosystems: incorporating trophic complexity. Ecol. Lett. 10, 522–538 (2007).
Google Scholar
Hines, J. et al. in Advances in Ecological Research vol. 53 (eds Woodward, G. & Bohan, D. A.) Ch. 4, 161–199 (Academic, 2015).
Reiss, J., Bridle, J. R., Montoya, J. M. & Woodward, G. Emerging horizons in biodiversity and ecosystem functioning research. Trends Ecol. Evol. 24, 505–514 (2009).
Google Scholar
Thompson, R. M. et al. Food webs: reconciling the structure and function of biodiversity. Trends Ecol. Evol. 27, 689–697 (2012).
Google Scholar
Thébault, E. & Loreau, M. The relationship between biodiversity and ecosystem functioning in food webs. Ecol. Res. 21, 17–25 (2006).
Google Scholar
Maureaud, A., Andersen, K. H., Zhang, L. & Lindegren, M. Trait-based food web model reveals the underlying mechanisms of biodiversity–ecosystem functioning relationships. J. Anim. Ecol. 89, 1497–1510 (2020).
Google Scholar
Montoya, J. M., Rodríguez, M. A. & Hawkins, B. A. Food web complexity and higher-level ecosystem services. Ecol. Lett. 6, 587–593 (2003).
Google Scholar
Srivastava, D. S. & Bell, T. Reducing horizontal and vertical diversity in a foodweb triggers extinctions and impacts functions. Ecol. Lett. 12, 1016–1028 (2009).
Google Scholar
Wu, D., Xu, C., Wang, S., Zhang, L. & Kortsch, S. Why are biodiversity—ecosystem functioning relationships so elusive? Trophic interactions may amplify ecosystem function variability. J. Anim. Ecol. 92, 367–376 (2023).
Google Scholar
Zhao, Q. et al. Relationships of temperature and biodiversity with stability of natural aquatic food webs. Nat. Commun. 14, 3507 (2023).
Google Scholar
Schneider, F. D., Brose, U., Rall, B. C. & Guill, C. Animal diversity and ecosystem functioning in dynamic food webs. Nat. Commun. 7, 12718 (2016).
Google Scholar
Ward, C. L. & McCann, K. S. A mechanistic theory for aquatic food chain length. Nat. Commun. 8, 2028 (2017).
Google Scholar
Post, D. M. The long and short of food-chain length. Trends Ecol. Evol. 17, 269–277 (2002).
Google Scholar
Oksanen, L., Fretwell, S. D., Arruda, J. & Niemela, P. Exploitation ecosystems in gradients of primary productivity. Am. Nat. 118, 240–261 (1981).
Google Scholar
Yodzis, P. Energy flow and the vertical structure of real ecosystems. Oecologia 65, 86–88 (1984).
Google Scholar
Beauchesne, D., Cazelles, K., Archambault, P., Dee, L. E. & Gravel, D. On the sensitivity of food webs to multiple stressors. Ecol. Lett. 24, 2219–2237 (2021).
Google Scholar
Laigle, I. et al. Species traits as drivers of food web structure. Oikos 127, 316–326 (2018).
Google Scholar
Tylianakis, J. M. et al. Resource heterogeneity moderates the biodiversity-function relationship in real world ecosystems. PLoS Biol. 6, e122 (2008).
Google Scholar
Brun, P. et al. The productivity-biodiversity relationship varies across diversity dimensions. Nat. Commun. 10, 5691 (2019).
Google Scholar
Fine, P. V. A. Ecological and evolutionary drivers of geographic variation in species diversity. Annu. Rev. Ecol. Evol. Syst. 46, 369–392 (2015).
Google Scholar
Barnes, A. D. et al. Consequences of tropical land use for multitrophic biodiversity and ecosystem functioning. Nat. Commun. 5, 5351 (2014).
Google Scholar
Gauzens, B. et al. Fluxweb: an R package to easily estimate energy fluxes in food webs. Methods Ecol. Evol. 10, 270–279 (2019).
Google Scholar
Burdon, F. J., McIntosh, A. R. & Harding, J. S. Mechanisms of trophic niche compression: evidence from landscape disturbance. J. Anim. Ecol. 89, 730–744 (2020).
Google Scholar
Strona, G. & Bradshaw, C. J. A. Coextinctions dominate future vertebrate losses from climate and land use change. Sci. Adv. 8, eabn4345 (2022).
Google Scholar
Barnes, A. D. et al. Biodiversity enhances the multitrophic control of arthropod herbivory. Sci. Adv. 6, eabb6603 (2020).
Google Scholar
Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 14, eaax0121 (2019).
Google Scholar
Glasser, J. W. The role of predation in shaping and maintaining the structure of communities. Am. Nat. 113, 631–641 (1979).
Google Scholar
Zheng, J. et al. Nematode predation modulates the energetic dynamics of soil micro-food webs with consequences for soil multifunctionality. Soil Biol. Biochem. 212, 110019 (2026).
Google Scholar
Rooney, N., McCann, K., Gellner, G. & Moore, J. C. Structural asymmetry and the stability of diverse food webs. Nature 442, 265–269 (2006).
Google Scholar
Neutel, A.-M., Heesterbeek, J. A. P. & de Ruiter, P. C. Stability in real food webs: weak links in long loops. Science 296, 1120–1123 (2002).
Google Scholar
Polis, G. A. & Strong, D. R. Food web complexity and community dynamics. Am. Nat. 147, 813–846 (1996).
Google Scholar
Shurin, J. B. et al. A cross-ecosystem comparison of the strength of trophic cascades. Ecol. Lett. 5, 785–791 (2002).
Google Scholar
Elmhagen, B., Ludwig, G., Rushton, S. P., Helle, P. & Lindén, H. Top predators, mesopredators and their prey: interference ecosystems along bioclimatic productivity gradients. J. Anim. Ecol. 79, 785–794 (2010).
Google Scholar
Gordon, C. E., Feit, A., Grüber, J. & Letnic, M. Mesopredator suppression by an apex predator alleviates the risk of predation perceived by small prey. Proc. R. Soc. B 282, 20142870 (2015).
Google Scholar
Digel, C., Curtsdotter, A., Riede, J., Klarner, B. & Brose, U. Unravelling the complex structure of forest soil food webs: higher omnivory and more trophic levels. Oikos 123, 1157–1172 (2014).
Google Scholar
Perkins, D. M. et al. Consistent predator-prey biomass scaling in complex food webs. Nat. Commun. 13, 4990 (2022).
Google Scholar
Gutgesell, M. K. et al. On the dynamic nature of omnivory in a changing world. BioScience 72, 416–430 (2022).
Google Scholar
De Laender, F. et al. Reintroducing environmental change drivers in biodiversity–ecosystem functioning research. Trends Ecol. Evol. 31, 905–915 (2016).
Google Scholar
Brimacombe, C. et al. Publication-driven consistency in food web structures: implications for comparative ecology. Ecology 106, e4467 (2025).
Google Scholar
Havens, K. E. Pelagic food web structure in Adirondack Mountain, USA, lakes of varying acidity. Can. J. Fish. Aquat. Sci. 50, 149–155 (1993).
Google Scholar
Caroni, R., Piscia, R. & Manca, M. Indicators of climate-driven change in long-term zooplankton composition: insights from Lake Maggiore (Italy). Water 17, 511 (2025).
Google Scholar
Brauns, M., Kneis, D., Brabender, M. & Weitere, M. Habitat availability determines food chain length and interaction strength in food webs of a large lowland river. River Res. Appl. 38, 323–333 (2022).
Google Scholar
Hall, R. O. Jr., Wallace, J. B. & Eggert, S. L. Organic matter flow in stream food webs with reduced detrital resource base. Ecology 81, 3445–3463 (2000).
Google Scholar
Saito, V. S. et al. Untangling the complex food webs of tropical rainforest streams. J. Anim. Ecol. 93, 1022–1035 (2024).
Google Scholar
Perkins, D. M. et al. Bending the rules: exploitation of allochthonous resources by a top-predator modifies size-abundance scaling in stream food webs. Ecol. Lett. 21, 1771–1780 (2018).
Google Scholar
O’Gorman, E. J. et al. Unexpected changes in community size structure in a natural warming experiment. Nat. Clim. Change 7, 659–663 (2017).
Google Scholar
Gauzens, B., Rall, B. C., Mendonça, V., Vinagre, C. & Brose, U. Biodiversity of intertidal food webs in response to warming across latitudes. Nat. Clim. Change 10, 264–269 (2020).
Google Scholar
Kortsch, S. et al. Disentangling temporal food web dynamics facilitates understanding of ecosystem functioning. J. Anim. Ecol. 90, 1205–1216 (2021).
Google Scholar
Mor, J.-R. et al. Dam regulation and riverine food-web structure in a Mediterranean river. Sci. Total Environ. 625, 301–310 (2018).
Google Scholar
Brose, U. et al. Predator traits determine food-web architecture across ecosystems. Nat. Ecol. Evol. 3, 919–927 (2019).
Google Scholar
Peters, R. H. The Ecological Implications of Body Size (Cambridge Univ. Press, 1983).
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Google Scholar
Lang, B., Ehnes, R. B., Brose, U. & Rall, B. C. Temperature and consumer type dependencies of energy flows in natural communities. Oikos 126, 1717–1725 (2017).
Google Scholar
Gauzens, B. et al. Fluxweb: estimate energy fluxes in food webs. Methods Ecol. Evol. 10, 270–279 (2019).
Google Scholar
Moore, J. C. & de Ruiter, P. C. Energetic Food Webs: an Analysis of Real and Model Ecosystems (Oxford Univ. Press, 2012).
Pinheiro, J., Bates, D. & R Core Team. Nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1.164 (2023).
Pinheiro, J. & Bates, D. Mixed-Effects Models in S and S-PLUS (Springer Science & Business Media, 2006).
Lefcheck, J. S. PiecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).
Google Scholar
Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 90, 363–368 (2009).
Google Scholar
Henseler, J., Fassott, G., Dijkstra, T. K. & Wilson, B. Analysing quadratic effects of formative constructs by means of variance-based structural equation modelling. Eur. J. Inf. Syst. 21, 99–112 (2012).
Google Scholar
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2024).
Barnes, A., gauzens & B. E. ecodivlab/BEF-in-Food-Webs: BEF in Food Webs code and data. Zenodo https://doi.org/10.5281/zenodo.17728782 (2026).
Source:
www.nature.com
