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Fine-root turnover rates of European forests revisited: an analysis of data from sequential coring and ingrowth cores

Artikel i vetenskaplig tidskrift
Författare I. Brunner
M. R. Bakker
Robert G. Björk
Y. Hirano
M. Lukac
X. Aranda
I. Børja
T. D. Eldhuset
H. S. Helmisaari
C. Jourdan
B. Konôpka
B. C. López
C. Miguel Pérez
H. Persson
I. Ostonen
Publicerad i Plant and Soil
Volym 362
Nummer/häfte 1-2
Sidor 357-372
ISSN 0032-079X
Förlag Springer Netherlands
Publiceringsår 2013
Publicerad vid Institutionen för geovetenskaper
Institutionen för biologi och miljövetenskap
Sidor 357-372
Språk en
Länkar dx.doi.org/10.1007/s11104-012-1313-...
Ämnesord Annual production, Decision Matrix, Fine-root turnover rates, Ingrowth cores, Maximum-Minimum formula, Sequential coring
Ämneskategorier Terrestrisk ekologi

Sammanfattning

Background and Aims Forest trees directly contribute to carbon cycling in forest soils through the turnover of their fine roots. In this study we aimed to calculate root turnover rates of common European forest tree species and to compare them with most frequently published values. Methods We compiled available European data and applied various turnover rate calculation methods to the resulting database. We used Decision Matrix and Maximum-Minimum formula as suggested in the literature. Results Mean turnover rates obtained by the combination of sequential coring and Decision Matrix were 0.86 yr−1 for Fagus sylvatica and 0.88 yr−1 for Picea abies when maximum biomass data were used for the calculation, and 1.11 yr−1 for both species when mean biomass data were used. Using mean biomass rather than maximum resulted in about 30 % higher values of root turnover. Using the Decision Matrix to calculate turnover rate doubled the rates when compared to the Maximum-Minimum formula. The Decision Matrix, however, makes use of more input information than the Maximum-Minimum formula. Conclusions We propose that calculations using the Decision Matrix with mean biomass give the most reliable estimates of root turnover rates in European forests and should preferentially be used in models and C reporting.

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