Temperature and Precipitation Diversely Control Seasonal and Annual Dynamics of Litterfall in a Temperate Mixed Mature Forest, Revealed by Long‐Term Data Analysis

Abstract Litterfall is a good indicator of overall forest functions in forest ecosystems. Globally, forest litterfall has been extensively investigated, however, there is a lack of long‐term data analysis to show the various litterfall components in relation to environmental factors on the monthly and yearly scales. Here, monthly (May–October) and annual (1981–2018) litterfall including leaves, twigs, bark, reproductive, and miscellaneous fractions were collected in a mixed mature Pinus koraiensis forest on Changbai Mountain in Northeast, China, across 30 years. Based on these long‐term litterfall data, we analyzed the seasonal and annual variations in different litterfall fractions and the internal/external drivers. We observed that both the leaf and total litterfall exhibited a strong, similar seasonal pattern, with the highest levels between September and October, and the annual litterfall had an “S‐shaped” increasing pattern from 1981 to 2018. The other litterfall fractions showed distinct monthly and yearly fluctuations across the 30 years. Mean monthly evapotranspiration and temperature (minimum and maximum) were the best predictors for monthly litterfall. By contrast, the models that best predicted the annual litterfall production included mean annual precipitation and mean monthly precipitation and temperature in May and October. Our study, using a unique dataset of detailed long‐term litterfall dynamics, has potentially major significance for enhancing our understanding of the role of climatic factors controlling forest litterfall amount and seasonality in temperate mixed mature forests. This insight is of paramount importance for modeling and estimating soil carbon sequestration and nutrient cycling of temperate forests under climate change.