Quantifying Carbon and Nutrient Input From Litterfall in European Forests Using Field Observations and Modeling

Mathias Neumann, Liisa Ukonmaanaho, James Johnson, Sue Benham, Lars Vesterdal, Radek Novotný, Arne Verstraeten, Lars Lundin, Anne Thimonier, Panagiotis Michopoulos, Hubert Hasenauer

31 Citations (Scopus)

Abstract

Litterfall is a major, yet poorly studied, process within forest ecosystems globally. It is important for carbon dynamics, edaphic communities, and maintaining site fertility. Reliable information on the carbon and nutrient input from litterfall, provided by litter traps, is relevant to a wide audience including policy makers and soil scientists. We used litterfall observations of 320 plots from the pan-European forest monitoring network of the “International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests” to quantify litterfall fluxes. Eight litterfall models were evaluated (four using climate information and four using biomass abundance). We scaled up our results to the total European forest area and quantified the contribution of litterfall to the forest carbon cycle using net primary production aggregated by bioregions (north, central, and south) and by forest types (conifers and broadleaves). The 1,604 analyzed annual litterfall observations indicated an average carbon input of 224 g C · m−2 · year−1 (annual nutrient inputs 4.49 g N, 0.32 g P, and 1.05 g K · m−2), representing a substantial percentage of net primary production from 36% in north Europe to 32% in central Europe. The annual turnover of carbon and nutrient in broadleaf canopies was larger than for conifers. The evaluated models provide large-scale litterfall predictions with a bias less than 10%. Each year litterfall in European forests transfers 351 Tg C, 8.2 Tg N, 0.6 Tg P, and 1.9 Tg K to the forest floor. The performance of litterfall models may be improved by including foliage biomass and proxies for forest management.

Original languageEnglish
JournalGlobal Biogeochemical Cycles
Volume32
Issue number5
Pages (from-to)784-798
Number of pages15
ISSN0886-6236
DOIs
Publication statusPublished - May 2018

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