Spatial heterogeneity of soil respiration in a 64-year-old longleaf pine forest at Fort Benning, Georgia

Background/Question/Methods

The spatial heterogeneity of soil respiration (Rs) can be affected by numerous abiotic and biotic environmental variables. Although soil temperature is traditionally used to model temporal variation in Rs, soil temperature has been found to be less important in controlling spatial variability in Rs. The objective of this study was to characterize spatial patterns in Rs in a 64-year-old longleaf pine (Pinus palustris Mill.) forest at Fort Benning, Georgia. The experimental design consisted of three subplots with 25 permanent Rs collars (10 cm diameter, 4.5 cm height) placed 6 m apart in a 5x5 grid. Soil respiration, temperature, and moisture were measured simultaneously and systematically on all 75 collars morning (830-1100 hrs EST), midday (1130-1400 hrs), and afternoon (1330-1700 hrs) on six days in July and August 2012. Vegetative cover, litterfall, root biomass, soil characteristics, and soil chemistry were measured at study conclusion. Root biomass included living and dead very fine, fine, coarse, and very coarse root categories and soil analysis included measuring bulk density, buried coarse woody debris, charcoal, C, N, OM, pH, and micronutrients. The distance and diameter (DBH) of trees within 8 m of each collar was also measured. 

Results/Conclusions

Data were averaged across the six days and by time period and Rs collar. Depending on the time period, the coefficient of variation ranged from 34-42% for Rs, 41-66% for soil moisture, and 2-6% for soil temperature. In all time periods, spatial variability in Rs was significantly related to mean DBH of trees within 1 m of the Rs collars (partial R²= 0.10-0.15) and significantly but weakly related to soil nitrogen and woody vegetative cover. In addition, soil moisture explained 3-10% of the variation in Rs in the morning and midday periods, and soil temperature explained only 4% of the variation in Rs and only in the afternoon time period. Principal components analysis indicated that over 45% of the variation in environmental variables was captured with five principal components, corresponding with overall forest density; local tree effects; soil carbon and nitrogen; soil micronutrients; and understory vegetative cover and root biomass. These results indicate that soil temperature is less important than other abiotic and biotic variables in controlling the spatial heterogeneity in Rs. Future analyses will include first and second order spatial analysis, including geographically weighted regression, which will also be presented.