Patterns and driving factors of litter decomposition across Chinese terrestrial ecosystems

Abstract

Litter decomposition is a vital component of carbon and nutrients cycling and energy flow within terrestrial ecosystems. Understanding the litter decomposition rate with a temperature change of 1 °C (kDD, degree days−1) is critical for quantifying the climate-carbon, nutrients, and energy cycle feedback and predicting the response of ecosystems to climate change. However, the spatial pattern of kDD is uncertain, given differences in litter quality and soil property responses to temperature. Therefore, our goal was to explore the spatial patterns and driving factors of the kDD with a synthesized dataset, which included 1370 individual studies from 253 publications and accumulated daily temperature of each site from 389 meteorological stations. The dataset covered forest, grassland, and cropland ecosystems. The kDD significantly decreased with increasing latitude and varied with climate variables, litter quality, soil properties, and experimental duration under different ecosystems. The averaged kDD in the forest, grassland, and cropland ecosystems were 1.30, 2.23, and 3.35 × 10−4 degree days−1, respectively. The kDD markedly decreased with the increase of experimental years. Climate variables [mean annual temperature (MAT) and precipitation (MAP)] accounted for 36.8% of the total variance in the kDD, followed by soil properties (e.g., soil sand content and pH; 28.1%); experimental duration (23.0%); and litter properties (e.g., nitrogen and lignin; 12.1%). MAT and MAP had substantially indirect effects on kDD via regulating soil and litter properties. Lignin directly and negatively affected kDD. Higher correlations between kDD and litter properties were maintained if the effects of climate, soil properties, and experimental duration were removed. Overall, our findings highlight that intrinsic (litter properties) and extrinsic (climatic and soil properties) variables directly and indirectly regulated the kDD at a regional scale, respectively, providing a framework for optimizing the ecosystem process model to global warming scenarios. Meantime, these results clarify the crucial role of environmental variables in regulating the kDD, potentially affecting the terrestrial carbon, nutrients, and energy-climate feedback.