Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
Google Scholar
Di Marco, M., Venter, O., Possingham, H. P. & Watson, J. E. M. Changes in human footprint drive changes in species extinctions risk. Nat. Commun. 9, 4621 (2018).
Google Scholar
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
Google Scholar
Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Lefcheck, J. S. et al. Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats. Nat. Commun. 6, 6936 (2015).
Google Scholar
Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).
Google Scholar
Schuldt, A. et al. Biodiversity across trophic levels drive multifunctionality in highly diverse forests. Nat. Commun. 9, 2989 (2018).
Google Scholar
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 211–220 (2020).
Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).
Google Scholar
Allan, E. et al. Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition. Ecol. Lett. 18, 834–843 (2015).
Google Scholar
Jing, X. et al. The links between ecosystem multifunctionality and above- and belowground biodiversity are mediated by climate. Nat. Commun. 6, 8159 (2015).
Google Scholar
Fanin, N. et al. Consistent effects of biodiversity loss on multifunctionality across contrasting ecosystems. Nat. Ecol. Evol. 2, 269–278 (2018).
Google Scholar
Hautier, Y. et al. Local loss and spatial homogenization of plant diversity reduce ecosystem multifunctionality. Nat. Ecol. Evol. 2, 50–56 (2018).
Google Scholar
Venter, O. et al. Global terrestrial human footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).
Google Scholar
Allan, E. et al. Interannual variation in land-use intensity enhances grassland multidiversity. Proc. Natl Acad. Sci. USA 111, 308–313 (2014).
Google Scholar
Moi, D. A. et al. Regime shifts in a shallow lake over 12 years: consequences for taxonomic and functional diversities, and ecosystem multifunctionality. J. Anim. Ecol. 91, 551–565 (2022).
Google Scholar
Moi, D. A. et al. Multitrophic richness enhances ecosystem multifunctionality of tropical shallow lakes. Funct. Ecol. 35, 942–954 (2021).
Google Scholar
Byrnes, J. E. K. et al. Investigating the relationship between biodiversity and ecosystem multifunctionality: challenges and solutions. Methods Ecol. Evol. 5, 111–124 (2014).
Li, F. et al. Human activitiesʼ fingerprint on multitrophic biodiversity and ecosystem functions across a major river catchment in China. Glob. Change Biol. 26, 6867–6879 (2020).
Enquist, B. J. et al. The megabiota are disproportionately importante for biosphere functioning. Nat. Commun. 11, 699 (2020).
Google Scholar
Eisenhauer, N. et al. A multitrophic perspective on biodiversity–ecosystem functioning research. Adv. Ecol. Res. 61, 1–54 (2019).
Google Scholar
Agostinho, A. A., Thomaz, S. M. & Gomes, L. C. Threats for biodiversity in the floodplain of the Upper Paraná River: effects of hydrological regulation by dams. Ecohydrol. Hydrobiol. 4, 255–268 (2004).
Chiaravalloti, R. M., Homewood, K. & Erikson, K. Sustainability and land tenure: who owns the floodplain in the Pantanal, Brazil? Land Use Policy 64, 511–524 (2017).
Pelicice, F. M. et al. Large-scale degradation of the Tocantins–Araguaia River Basin. Environ. Manag. 68, 445–452 (2021).
Malekmohammadi, B. & Jahanishakib, F. Vulnerability assessment of wetland landscape ecosystem services using driver-pressure-state-impact-response (DPSIR) model. Ecol. Indic. 82, 293–303 (2017).
McIntyre, P. B. et al. Fish extinctions alter nutrient recycling in tropical freshwaters. Proc. Natl Acad. Sci. USA 104, 4461–4466 (2006).
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).
Google Scholar
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).
Heino, J. et al. Lakes in the era of global change: moving beyond single-lake thinking in maintaining biodiversity and ecosystem services. Biol. Rev. 96, 89–106 (2020).
Google Scholar
Bridgewater, P. & Kim, R. E. The Ramsar conservation on wetlands at 50. Nat. Ecol. Evol. 5, 268–270 (2020).
Romero, G. Q. et al. Pervasive decline of subtropical aquatic insects over 20 years driven by water transparency, non-native fish and stoichiometric imbalance. Biol. Lett. 17, 20210137 (2021).
Google Scholar
Lansac-Tôha, F. M. et al. Scale-depedent patterns of metacommunity structuring in aquatic organisms across floodplain systems. J. Biogeogr. 48, 872–885 (2021).
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Weiss, K. C. B. & Ray, C. A. Unifying functional trait approaches to understand the assemblage of ecological communities: synthesizing taxonomic divides. Ecography 42, 2012–2020 (2019).
Laliberté, E. & Legendre, R. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).
Google Scholar
Mackereth, F. J. H, Heron, J & Talling, J. F. Water Analysis: Some Revised Methods for Limnologists. Publication No. 36 (Freshwater Biological Association, 1978).
Golterman, H. L., Clymo, R. S. & Ohnstad, M. A. M. Methods for Physical and Chemical Analysis of Freshwaters (Blackwell Scientific Publications, 1978).
Bernhardt, E. S. et al. The metabolic regimes of flowing waters. Limnol. Oceanogr. 63, S99–S118 (2018).
Sun, J. & Liu, D. Geometric models for calculating cell biovolume and surface area for phytoplankton. J. Plankt. Res. 25, 1331–1346 (2003).
Froese, R. & Pauly, D. FishBase (2018); www.fishbase.org
Porter, K. G. & Feig, Y. S. The use of DAPI for identifying and counting aquatic microflora1. Limnol. Oceanogr. 25, 943–948 (1980).
Manning, P. et al. Redifining ecosystem multifunctionality. Nat. Ecol. Evol. 2, 427–436 (2018).
Google Scholar
Hijmans, R. J. & van Etten, J. raster: Geographic analysis and modeling with raster data. R version 2.0–12 https://rspatial.org/raster (2012).
World Urbanization Prospects: The 2020 Revision: Highlights (United Nations, 2020).
Junk, W. J. et al. Brazilian wetlands: their definition, delineation, and classification for research, sustainable management, and protection. Aquat. Conserv. Mar. Freshwater Ecosyst. 24, 5–22 (2013).
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and nonlinear mixed effects models. R version 3.1.137 https://CRAN.Rproject.org/package=nlme (2018).
K. Barton, MuMIn: Model selection and model averaging based on information criteria (AICc and alike). R version 1–1 https://CRAN.R-project.org/package=MuMIn (2014).
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).
Schielzeth, H. Simple means to improve the interpretability ofregression coefficients. Meth. Ecol. Evol. 1, 103–113 (2010).
Aiken, L. S. & West, S. G. Multiple Regression: Testing and Interpreting Interactions (Sage Publications, 1991).
Rosseel, Y. lavaan: an R package for structural equation modeling. J. Stat. Softw. 48, 1–36 (2015).
Grace, J. B. & Bollen, K. A. Representing general theoretical concepts in structural equation models: the role of composite variables. Environ. Ecol. Stat. 15, 191–213 (2008).
R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
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