© 2007 Heron Publishing—Victoria, Canada
Temporal variation and high-resolution spatial heterogeneity in soil CO2 efflux in a short-rotation tree plantation
Inge Vande Walle (1,2), Roeland Samson (1,3), Brecht Looman (1), Kris Verheyen (4) and Raoul Lemeur (1)
1. Laboratory of Plant Ecology, Ghent University, Coupure links 653, 9000 Ghent, Belgium / 2. Corresponding author (inge.vandewalle@ugent.be) / 3. Department of Applied Biological Sciences, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium / 4. Laboratory of Forestry, Ghent University, Geraardsbergse Steenweg 267, 9090 Gontrode, Belgium / Received May 22, 2006; accepted September 5, 2006; published online March 1, 2007
Summary
Soil CO2 efflux (SR) is the second largest carbon flux on earth. We investigated the driving factors of the seasonal change and short-distance
spatial variation in SR in a short-rotation plantation of willow (Salix viminalis Orm). Total annual SR ranged from 723 to 1149 g C m–2 year–1. Both an exponential and a logistic model were fitted to the data, with soil temperature at a depth of 5 cm as the independent
variable. The R2 values for individual sampling points ranged from 0.83 to 0.95 and from 0.85 to 0.93 for the exponential and logistic models,
respectively, indicating that soil temperature largely determined the seasonal variation in SR. Modeled soil SR at 10 °C ranged
from 1.22 to 1.95 µmol m–2 s–1, whereas modeled annual Q10 values were between 3.31 and 6.13. These high Q10 values were attributed to the absence of drought during the study in 2005. When the coefficients of the general SR models
were replaced by linear dependencies on soil and vegetation-related characteristics, the resulting spatially explicit exponential
and logistic SR models explained 85 and 86%, respectively, of the variability within the dataset. The analysis indicated that
soil carbon concentration, leaf area index, soil pH and root biomass caused differences in SR at the short distances considered
in this study. However, incorporating information on variables considered to account for spatial variability in the model
did not result in a higher R2 compared with a simple temperature function. When the general SR models were applied to independent datasets from the same
plantation, the logistic model provided a better fit than the exponential model when drought occurred. Drought greatly reduced
the annual Q10 values of SR.
Keywords:
exponential model, forest ecosystem, logistic function, Q
10 value, seasonality, spatial variability, willow.
