Contribution of Litter Layer to Greenhouse Gas Fluxes between Atmosphere and Soil Varies with Forest Succession

Autor: Jun Jiang, Ying-Ping Wang, Hao Zhang, Mengxiao Yu, Fengcai Liu, Shiting Xia, Junhua Yan
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Forests, Vol 13, Iss 4, p 544 (2022)
Druh dokumentu: article
ISSN: 1999-4907
DOI: 10.3390/f13040544
Popis: Surface litter layer strongly influences CO2, N2O, and CH4 fluxes (FCO2, FN2O, and FCH4) between the atmosphere and forest floor through litter decomposition (litter-internal, fL-L) or interactions between litter and mineral soil (litter-induced, fL-S). However, the relative contribution of fL-L or fL-S to these greenhouse gas (GHG) fluxes in forests at different succession stages remain unclear. We conducted a field experiment where surface litter was either removed (LR), left intact (CT), doubled (LD), or exchanged (LE) in a Masson pine forest (PF, early stage of succession) and an evergreen broadleaved forest (BF, climax of succession) at the Dinghushan Nature Reserve in southern China, and studied the responses of FCO2, FN2O, and FCH4 from August 2012 to July 2013. The results showed that both FCO2 and FN2O were increased by LD treatment with a greater increase in BF (41% for FCO2 and 30% for FN2O) and decreased by LR treatment with the greater decrease in PF (−61% for FCO2 and −58% for FN2O). LD treatment decreased FCH4 by 14% in PF and 6% in BF, and LR treatment increased FCH4 by 5% in PF and 18% in BF. fL-S contributed more to FCO2 (36%) and FN2O (45%) than fL-L in PF, whereas contributions of fL-L to FCO2 (41%) and FN2O (30%) were much bigger than fL-S in BF. The greater FCH4 in PF and BF resulted from the contributions of fL-L (−14%) and fL-S (−12%), respectively. Our results indicated that fL-L is the major source of GHG fluxes in BF, whereas fL-S dominates GHG fluxes in PF. The results provide a scientific reference for quantifying the contributions of fL-L and fL-S to GHG fluxes during the subtropical forest succession and should be considered in ecosystem models to predict global warming in the future.
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