Global patterns and drivers of decomposition of mixed-species litter

• Mixed-species litter decomposition rates are 4.4% higher than mono-species litter; • Mixing effects on litter decomposition are highly context-dependent and temporally dynamic; • Mixing effects of litter decomposition vary by ecosystem type, climate, and decomposition stage; • Phylogenetic distance and litter quality divergence shape mixing effects of litter decomposition. Mixed-species litter modifies decomposition rates through complex interplays driven by species composition and functional traits of litter. However, there remains no consensus on how ecosystem type, climate, species traits, and decomposition stage jointly influence the direction and magnitude of litter mixing effects. We conducted a global meta-analysis of 1,258 effect sizes from 91 field studies (1989–2024) to assess how ecosystem type, climate, and species traits influence mixed-species litter decomposition rates across different stages of decomposition. Decomposition rates of mixed-species litter were 4.4 % significantly higher than those of the mono-species litter. Synergistic effects were most pronounced in temperate and forest ecosystems. These effects were generally observed after 180 days, and peaked between 360 and 720 days, but they declined as decomposition progressed, often shifting to additive or antagonistic effects as recalcitrant compounds accumulated. The relationship between species diversity and mixing effects was not linear, depending on specific species combinations and proportions. Phylogenetic distance and litter quality divergence between species significantly affect the mixing effect of decomposition. The mixing effects of litter decomposition are highly context-dependent and temporally dynamic. Our results provide empirical support for a dynamic, stage-dependent theory of litter mixing effects, emphasizing that their strength and direction hinge on critical decomposition phases and trait-mediated interactions. Recognizing these temporal dynamics is essential for predicting biodiversity impacts on ecosystem carbon and nutrient cycling.