
Abstract The seasonal litterfall plays an important role in the process of forest carbon and nutrient cycles. The current dynamic vegetation models use a simplified method to simulate seasonal patterns of litterfall, and assume that litterfall inputs distributed evenly through the year for deciduous trees or occur once during the start of year for evergreen trees. In this study, we collected more than 400 litterfall measurements for different forest ecosystems from existing literature and monographs, and analyzed the seasonal patterns of litterfall over the various forest types. The results showed that the total annual litterfall varied significantly by forest types in the range of 3–11 Mg ha−1 y−1. The seasonal litterfall patterns had diverse forms and varied obviously among the forest types. For tropical forests, the litter peaks occurred mostly in spring or winter, corresponding to the drought season; for temperate broadleaved and needle-leaved evergreen forests, litter peaks could occur at various seasons; and for temperate deciduous broadleaved and boreal evergreen needle-leaved forests, litter peaks were observed in autumn. Global analyses showed that seasonal patterns of litterfall were determined by both the physiological mechanism and environmental variables.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 216 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
