Many experiments have shown that loss of biodiversity reduces the capacity of ecosystems to provide the multiple services on which humans depend1,2. However, experiments necessarily simplify the complexity of natural ecosystems and will normally control for other important drivers of ecosystem functioning, such as the environment or land use. In addition, existing studies typically focus on the diversity of single trophic groups, neglecting the fact that biodiversity loss occurs across many taxa3,4 and that the functional effects of any trophic group may depend on the abundance and diversity of others5,6. Here we report analysis of the relationships between the species richness and abundance of nine trophic groups, including 4,600 above- and below-ground taxa, and 14 ecosystem services and functions and with their simultaneous provision (or multifunctionality) in 150 grasslands. We show that high species richness in multiple trophic groups (multitrophic richness) had stronger positive effects on ecosystem services than richness in any individual trophic group; this includes plant species richness, the most widely used measure of biodiversity. On average, three trophic groups influenced each ecosystem service, with each trophic group influencing at least one service. Multitrophic richness was particularly beneficial for ‘regulating’ and ‘cultural’ services, and for multifunctionality, whereas a change in the total abundance of species or biomass in multiple trophic groups (the multitrophic abundance) positively affected supporting services. Multitrophic richness and abundance drove ecosystem functioning as strongly as abiotic conditions and land-use intensity, extending previous experimental results7,8 to real-world ecosystems. Primary producers, herbivorous insects and microbial decomposers seem to be particularly important drivers of ecosystem functioning, as shown by the strong and frequent positive associations of their richness or abundance with multiple ecosystem services. Our results show that multitrophic richness and abundance support ecosystem functioning, and demonstrate that a focus on single groups has led to researchers to greatly underestimate the functional importance of biodiversity.
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We thank B. Schmid, F. T. Maestre and S. Kéfi for comments that helped improve this manuscript. W. Ulrich and N. J. Gotelli provided statistical advice. We thank the people who maintain the Biodiversity Exploratories program: A. Hemp, K. Wells, S. Gockel, K. Wiesner and M. Gorke (local management team); S. Pfeiffer and C. Fischer (central office), B. König-Ries and M. Owonibi (central database management); and E. Linsenmair, D. Hessenmöller, J. Nieschulze, E.-D. Schulze and the late E. Kalko for their role in setting up the project. This work was funded by the Deutsche Forschungsgemeinschaft Priority Program 1374 ‘Infrastructure-Biodiversity Exploratories’. Fieldwork permits were given by the responsible state environmental offices of Baden-Württemberg, Thüringen and Brandenburg (according to §72 BbgNatSchG). Figure icons were created by R. D. Manzanedo.
The authors declare no competing financial interests.
Nature thanks Y. Hautier, F. Isbell and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Extended data figures and tables
Extended Data Figure 1 Functional effects of multitrophic richness and abundance on 14 grassland ecosystem services.
a, Standardized coefficients (mean ± s.e.m.) of the abundances (triangles) and richness (circles) of those trophic groups that significantly affect a given function are shown. b, The net effect (that is, the sum of significant standardized effects). c, Difference in adjusted R2 between the final multitrophic models and those models using the abundance and richness of the best performing individual trophic group (unitrophic) or plant species richness (plant richness). Ecosystem services are organized by the main four types of services they associate with (provisioning, supporting, regulating and cultural). The number of trophic groups included in the most parsimonious model is given next to their adjusted R2. Multifunctionality results at 25%, 50%, 75% and 90% thresholds are also shown (see Methods).
Extended Data Figure 2 Functional effects of environmental factors and multitrophic richness and abundance on 14 grassland ecosystem functions.
a, Standardized slope estimates (mean ± s.e.m.) for each significant predictor are shown, with the exception of study region and soil type, which were retained in all models. b, Net effect (sum of significant standardized effects) for multitrophic richness and abundance. c, The total amount of variance explained by either environmental + plant species richness, environmental + the abundance and richness of the best individual trophic predictor, or by environmental + multitrophic diversity and abundance are shown for each function (adjusted R2, to control for the high number of predictors included). The number of trophic groups included in the best models (2.15 ± 1.2 across functions, and 1.94 ± 1.2 across functions and multifunctionality indices) is given next to the adjusted R2 value. The increase in the adjusted R2 values in models with plant-species-richness averaged 0.07 ± 0.12 (across functions) and 0.06 ± 0.11 (across functions and multifunctionality indices). Ecosystem services are organized by the main four types of services they associate with (top–bottom: provisioning, supporting, regulating and cultural). TWI, topographic wetness index, based on the aspect and position in the slope, and the inclination of the slope. Multicollinearity between the predictors introduced is unlikely (Extended Data Table 3).
Extended Data Figure 3 Number of trophic groups necessary to predict multifunctionality measures calculated with all possible combinations of 1–9 services, and their net effects.
The number of predictors selected in the best models (left) and their overall effects (sum of standardized coefficients; right) across all possible combinations of 1–9 services (N = 501) are shown. Error bars show the 95% confidence intervals, estimated for all possible combinations of n (1 to 9) functions in both cases. Only the 9 services with fewer than 20 data gaps were considered in these analyses (see details in Methods). Multifunctionality for these combinations was calculated at the 25% (upper panel), 50%, 75% and 90% (bottom panel) thresholds. Services removed were flower cover, arbuscular mycorrhizal colonization, soil aggregate stability, phosphorous retention index and pest control.
Extended Data Figure 4 Functional effect of the different trophic groups on contrasting multifunctionality scenarios.
Overall functional effects (significant standardized coefficients; mean ± s.e.m.) from the most parsimonious model) of the richness (open bars) and abundance (hatched bars) of each group are shown according to ref. 34.
Extended Data Figure 5 Functional importance of species richness and abundance compared to environmental drivers.
Venn diagrams showing the variance partition for the four components of our statistical models (environment: climate, soil and land-use intensity; species richness of the nine trophic groups, abundance of primary producers, above- and below-ground predators, below-ground herbivores and soil microbial decomposers). The variance not explained by the model (the residual) is also shown. The variance explained by richness, abundance and their overlap is summed up as Biota. Each panel represents an individual function or multifunctionality metric.
Overall functional effects (mean ± s.e.m. of the standardized slopes obtained from the model; with the exception of a, where error could not be estimated) of the richness (open bars) and abundance (hatched bars) of each group. a, The values were calculated after weighting each standardized coefficient (those in Extended Data Fig. 1) by the adjusted R2 of the model to account for differences in model performance. b, c, The values were calculated as the standardized coefficients in a general model fitted to all services at once, including ‘service identity’ as an extra predictor and ‘plot’ as random factor to control for pseudo-replication (reduced models (b); the ones presented in the main text), or full models (c) and, d, calculated as multi-model average parameters from a model fitted to all services at once. Correlations (Spearman’s rank correlation coefficients) between the different approaches are given.
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Soliveres, S., van der Plas, F., Manning, P. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016). https://doi.org/10.1038/nature19092
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