AbstractThe biogenic structures produced by termites, ants and earthworms provide key functions across global ecosystems1,2. However, little is known about the drivers of the soil engineering effects caused by these small but important invertebrates3 at the global scale. Here we show, on the basis of a meta-analysis of 12,975 observations from 1,047 studies on six continents, that all three taxa increase soil macronutrient content, soil respiration and soil microbial and plant biomass compared with reference soils. The effect of termites on soil respiration and plant biomass, and the effect of earthworms on soil nitrogen and phosphorus content, increase with mean annual temperature and peak in the tropics. By contrast, the effects of ants on soil nitrogen, soil phosphorus, plant biomass and survival rate peak at mid-latitude ecosystems that have the lowest primary productivity. Notably, termites and ants increase plant growth by alleviating plant phosphorus limitation in the tropics and nitrogen limitation in temperate regions, respectively. Our study highlights the important roles of these invertebrate taxa in global biogeochemical cycles and ecosystem functions. Given the importance of these soil-engineering invertebrates, biogeochemical models should better integrate their effects, especially on carbon fluxes and nutrient cycles.
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Fig. 1: Map of collated data on soil engineering effects.Fig. 2: Effects of soil engineering on 47 ecosystem properties.Fig. 3: Latitudinal trends of variance-weighted soil engineering effects (lnRR).Fig. 4: Environment-mediated unit changes of soil engineering effects on 17 ecosystem properties.Fig. 5: Correlative relationship of soil engineering effects between soil nutrients and C dynamics.
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Data availability
The datasets used in this study are available on Figshare84 (https://doi.org/10.6084/m9.figshare.27823221). The map in Fig. 1a was created using the software R version 4.2.3 (ref. 83).
Code availability
We used R for analysis and visualization, and this is available on Figshare84 (https://doi.org/10.6084/m9.figshare.27823221).
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R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, 2021).Wu, D., Du, E., Eisenhauer, N., Mathieu, J. & Chu, C. Global engineering effects of soil invertebrates on ecosystem functions. Figshare https://doi.org/10.6084/m9.figshare.27823221 (2024).Download referencesAcknowledgementsWe thank M. Farfan for proofreading the manuscript. We acknowledge funding from the National Natural Science Foundation of China (31925027 and 32330064) and the China National Key Research Development Program (2022YFF0802300) to C.C., and the Open Project of the State Key Laboratory of Biocontrol (2021SKLBC-KF02) to D.W. E.D was supported by the National Natural Science Foundation of China (42373078) and the Fundamental Research Funds for the Central Universities. N.E. acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG; German Centre for Integrative Biodiversity Research, FZT118, Ei 862/29-1 and Ei 862/31-1). J.M. was supported by the FaunaServices group, funded by the French Foundation for Research on Biodiversity through its synthesis centre CESAB and by the SoilFauna group funded by the sDiv synthesis centre.Author informationAuthors and AffiliationsState Key Laboratory of Biocontrol, School of Ecology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaDonghao Wu & Chengjin ChuState Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaEnzai DuSchool of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaEnzai DuGerman Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, GermanyNico EisenhauerInstitute of Biology, Leipzig University, Leipzig, GermanyNico EisenhauerSorbonne Université, CNRS, IRD, INRAE, Université Paris Est Créteil, Université de Paris Cité, Institute of Ecology and Environmental Sciences of Paris (iEES-Paris), Paris, FranceJérome MathieuAuthorsDonghao WuView author publicationsYou can also search for this author inPubMed Google ScholarEnzai DuView author publicationsYou can also search for this author inPubMed Google ScholarNico EisenhauerView author publicationsYou can also search for this author inPubMed Google ScholarJérome MathieuView author publicationsYou can also search for this author inPubMed Google ScholarChengjin ChuView author publicationsYou can also search for this author inPubMed Google ScholarContributionsD.W. and C.C. conceived the idea for this study. E.D. provided the global map of terrestrial nitrogen and phosphorus limitation. D.W. compiled the datasets of soil engineering effects and environmental predictors. D.W. analysed the data. D.W. wrote the first manuscript draft with input from N.E., J.M., E.D. and C.C., and finalized the manuscript. All authors interpreted the data and revised the manuscript.Corresponding authorCorrespondence to
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Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended data figures and tablesExtended Data Fig. 1 The overall soil engineering effects on multiple ecosystem functions simultaneously.a Overall effects for termites; b for ants; c for earthworms. By grouping similar functions from the same pair of bioturbated vs. reference soil, we tested if invertebrate engineering can simultaneously enhance multiple ecosystem functions. By using the data subsets that reported full statistics including the average, standard deviation, and sample size, we grouped ecosystem functions into three classes, including soil, microbial, and plant properties (with the respective functions and number of observations listed in the figure). The multilevel linear mixed-effect model is used to fit the intercept-only models for the log response ratio (LnRR) of ecosystem functions between bioturbated and reference soil. The mean values and 95% confidence intervals of the predicted LnRR are reported as dots and range of error bars, respectively. Significant (p < 0.05) effects are printed in filled dots, while nonsignificant in empty dots. Sample size (n) and p-value are denoted in a-c.Extended Data Fig. 2 The overall soil engineering effects on 47 ecosystem properties by combining all three taxa.The multilevel linear mixed-effect model is used to fit the intercept-only models for the log response ratio (LnRR) of ecosystem properties between bioturbated and reference soil. The mean value and 95% confidence interval of the predicted LnRR are reported as the symbol and range of the error bars, respectively. Positive values indicate increased functions in bioturbated soil than in reference soil. Shaded areas show the summarized effects of soil engineers on each of the four categories of ecosystem properties. Blue circles and lines denote engineering effects weighted by sample size, while orange triangles and lines denote engineering effects weighted by sampling variance. Filled and empty symbols refer to significant (p < 0.05) and nonsignificant engineering effects, respectively. Sample size (n) and p-value are denoted per ecosystem property for both replication-weighted (left) and variance-weighted (right) models.Extended Data Fig. 3 The nonlinear pattern of net primary productivity (NPP) along the absolute latitude gradients.The adjusted r-squared (R2), sample size (n), p-value and AIC of the nonlinear model are reported, with the quadratic regression fitted for the upper panel (a-c), and segmented regression with one break point (see the vertical dashed line for the mean and see the purple area for the 95% confidence interval) for the lower panel (d-f). For comparison, the AIC of the linear model with absolute latitude as predictor is also reported. The left (a,d), middle (b,e) and right (c,f) panels show the data from all study sites, study sites of ants only, and the study sites of ants that reports engineering effects on soil N, respectively. Significant trends (p < 0.05) are denoted by solid lines (mean values of NPP) with shaded areas (95% confidence interval), while nonsignificant trends are denoted by dashed lines. In d, the second slope in dashed line is not significantly different from 0, with the 95% confidence interval ranging between -0.052 and 0.001.Extended Data Fig. 4 The relationship between body size and soil engineering effects (LnRR).Body size is represented by the maximum head width (mm) of the soldier caste for termites, maximum head width (mm) of the worker caste for ants, and maximum body mass (g per individual) for earthworms. Soil engineering effects on six ecosystem functions for termites (top), ants (middle), and earthworms (bottom) include: a soil C content; b soil N content; c soil P content; d plant biomass; e microbial biomass; and f soil respiration. The multilevel linear mixed-effect model is used to fit the multiple regression models for the log response ratio (LnRR) of ecosystem properties between bioturbated and reference soil, with body size and four environmental predictors (Wetness, MAT, NPP, and soil depth) as the fixed effects. To compare the relative effects between body size and environmental predictors, we scaled and centered all predictors before analyses. Positive and negative effects of body size are depicted by blue and red lines, respectively. Patterns shown are based on the variance-weighted models, with dot size scaling with the sampling variance of LnRR (see Methods for details). Similar effects are detected from the replication-weighted models (see Supplementary Tables 19–21). Significant linear relationships (p < 0.05) are indicated by solid lines (mean values of LnRR) with shaded areas (95% confidence interval), while nonsignificant trends are depicted by dashed lines. Sample size (n) and p-value are denoted in a-f.Extended Data Fig. 5Correlative relationship of soil engineering effects (effect size) between a soil respiration and soil moisture content; b plant biomass and soil clay content. Linear regression models were used to visualize the correlative relationships between functions, while two-sided Pearson’s correlation coefficients were reported to describe the correlation strength. Data are presented as the mean values (lines) and 95% confidence interval (shaded areas) of the predicted effect size on soil respiration (left panel) and plant biomass (right panel). Only regression models reaching significance level (p < 0.05) are printed in solid lines while the others in dashed lines. Colors and symbols for each taxon are specified in legends, with the Pearson’s correlation coefficients (r), sample size (n) and p-value reported in the parentheses after the taxon name.Supplementary informationSupplementary InformationThis file contains the Supplementary Discussion, Supplementary Figs. 1 and 2, Supplementary Tables 1–25 and the 2020 PRISMA checklist.Reporting SummaryRights and permissionsSpringer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Reprints and permissionsAbout this articleCite this articleWu, D., Du, E., Eisenhauer, N. et al. Global engineering effects of soil invertebrates on ecosystem functions.
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