Feeding the largest share of the global population, cereal production must enhance sustainability while ensuring food security under global change. Unfortunately, the number of sustainable practices needed to support production, ecosystem services and land conservation remains virtually unknown. We compiled a database of 1570 observations from 349 sites in 57 countries to assess how the number of sustainable practices influences multiple ecosystem services. Our findings reveal that a high number of sustainable practices is crucial for enhancing agroecosystem services such as soil carbon storage, fertility and microbial habitat while supporting yield. Sustainable practices such as crop rotation, limited tillage and incorporation of crop residues were especially important. North America, Eastern Europe and China were particularly dependent on the use of multiple sustainable practices to maintain ecosystem services. Findings underscore the need for integrative strategies employing multiple sustainable practices for mitigating global change, ensuring food security and sustaining ecosystems.
Statistical autocorrelation between sampling units violates independence assumptions in many analyses. Here, we used simulations and empirical analyses to demonstrate how shared evolutionary history between species and species overlap among communities leads to an insidious form of autocorrelation, termed compositional autocorrelation. We simulated compositionally autocorrelated ecosystem functions across communities and assessed the type I error, statistical power and accuracy of slope estimates from naïve linear regression models and mixed models accounting for compositional autocorrelation. Mixed models maintained lower type I error, similar or higher statistical power, and more accurate slope estimates compared to linear regression. Re-analysing an empirical dataset, we found linear regression underestimated uncertainty in species richness effects for eight of 10 ecosystem functions. As species overlap and shared evolutionary history are common features in community data, our results highlight the need to explicitly consider compositional autocorrelation in statistical analyses to ensure correct inferences.