Spatial heterogeneity of soil CO2 efflux after harvest and prescribed fire in a California mixed conifer forest
Introduction
Soil-surface CO2 efflux, commonly referred to as soil respiration, is one of the main carbon fluxes in the global carbon cycle (IPCC, 1996), and the largest carbon source to the atmosphere (60–90% of total ecosystem respiration; Liang et al., 2004) in forest ecosystems. Forest ecosystems act generally as sink of carbon, however disturbances can switch forests from carbon sinks to sources (Amiro et al., 2010, Dore et al., 2012, Thornton et al., 2002).
Disturbances can vary in type and intensity. They can range from high intensity events, such as land-use changes, stand replacing fires or clear-cut harvest, to low intensity events, such as thinning and low intensity fires. Thus forest management practices, that include removal of biomass by mechanical methods or by prescribed fire, represent a disturbance to forest ecosystems. The strength and persistence of disturbance effects are greatly determined by their impacts on soil CO2 efflux, because even if vegetation recovers promptly after the disturbance, the carbon lost in decomposition of old and new material can exceed the newly restored carbon sink (Restaino and Peterson, 2013).
High spatial and temporal variability in soil CO2 efflux has been found in numerous studies (Han et al., 2007, Ngao et al., 2012, Tedeschi et al., 2006, Vincent et al., 2006) and control of soil CO2 efflux has been attributed mainly to soil temperature and soil water content (Hanson et al., 1993, Raich and Schlesinger, 1992). While these variables can explain most of the temporal variability of soil CO2 efflux, they are often unable to explain its spatial variability (Tedeschi et al., 2006, Xu and Qi, 2001, Yim et al., 2003). Temporal variation of soil CO2 efflux is relatively easy to quantify, especially with the development of techniques based on continuous measurements made using chambers (Edwards and Riggs, 2003), below canopy eddy covariance (Law et al., 1999) and CO2 soil profiles (Jassal et al., 2005). However, spatial variability is still understudied and mostly uncertain (Ngao et al., 2012). Spatial variability of soil CO2 efflux has been linked to several biotic and abiotic factors, such as species composition, leaf area, fine root biomass, litter depth, soil bulk density, and soil carbon content (Ngao et al., 2012). Nonetheless, the contribution of these factors is highly variable across ecosystems, sites, and seasonally within the same sites. This is because soil CO2 efflux is the result of both heterotrophic and autotrophic processes, and these processes are controlled independently by different factors (Tang and Baldocchi, 2005).
Improved understanding of the mechanisms and quantification of the spatial heterogeneity of soil CO2 efflux is essential to scale up from point measurements to the stand and all the way to the global scale, or to verify and/or integrate flux measurements obtained using different techniques (Dore et al., 2003). For example, when using eddy covariance, efflux of CO2 from soil (though scaled up to the same spatial scale of eddy covariance) can replace low quality night ecosystem respiration measurements (Wohlfahrt et al., 2005), explain heterogeneity in the footprint of measured ecosystem fluxes (Ngao et al., 2012), and help partition the respiratory flux between aboveground and soil fluxes (Baldocchi, 2003). Still, determination of spatial variability is difficult and methods are limited (Tang and Baldocchi, 2005). High spatial variability requires a high number of samples to be taken to obtain meaningful results, especially when studies are aimed to detect differences among ecosystems or treatments. Common use of a relatively small number of measurements could explain in part the lack of agreement among studies quantifying effects of disturbances on soil CO2 efflux (Kobziar and Stephens, 2006).
In addition to the high number of replicates needed, results are confounded by practical difficulties in the measurement techniques, such as the disturbance of the soil–air interface, effect of leakage, effect of pressure on diffusivity of CO2 from soil, and the time and labor needed to take measurements in several locations. Also, because most of the factors controlling soil CO2 efflux lie underground and are difficult to quantify, it is not possible to visually estimate spatial variability and easily evaluate the number and position of soil CO2 efflux measurement locations.
In our study we analyzed the effect of forest management practices on spatial variability of soil CO2 efflux. We compared four different treatment types: un-manipulated control, prescribed fire, clear cut harvest, and clear cut harvest followed by mechanical soil ripping (sub-soiling). Clear cut harvests intensely disturbed the ecosystem by totally removing aboveground biomass and affecting the soil during mechanical operations, prescribed fire was a less intense disturbance. Our aim was to: (1) quantify and characterize spatial variability of soil CO2 efflux and its temporal changes in a mixed conifer forest in the central Sierra Nevada of California; (2) determine if spatial variability of soil CO2 efflux was affected by the most commonly used fuel treatments and commercial harvesting methods used in these mixed conifer forests; (3) and to analyze the effects of samples size on soil CO2 efflux estimates. To explore this we compared uncertainty of soil CO2 efflux estimates using a different number of random subsamples compared to the full measured dataset. Second, we developed a protocol aimed to select a smallest number of soil CO2 efflux measurement locations among the locations measured initially. Even if our protocol implies an a priori assessment of the spatial variability of soil CO2 efflux, it could be useful for long term monitoring of fewer selected locations to complement/validate continuous soil CO2 efflux or eddy covariance. Alternatively, it could be used when selecting the location for a permanent, continuous soil CO2 efflux system, because these systems usually have a low number of chambers and thus a high risk of measuring locations that do not represent a larger area.
We had the opportunity to characterize the spatial and temporal variability of soil CO2 efflux in an area subject to a range of disturbance severity but with generally the same climate, vegetation, management history, and soil type. In addition, the treatments created conditions ranging from the high autotrophic contribution of an undisturbed, dense forest, to the single heterotrophic contribution of a clear cut harvested stand.
Section snippets
Methods
This study was conducted at Blodgett Forest (38°54′N, 120°39′W), a University of California Research Station in the central Sierra Nevada near Georgetown, California, that is actively managed as a commercial timberland. Total annual precipitation in the mixed-conifer forest located between 1100 and 1410 m a.s.l. averages about 1600 mm, falling between September and May, and almost absent in the summer (Stephens and Collins, 2004). The average minimum daily temperature in January is 0.6 °C and the
Results
Soil CO2 efflux varied in space, and this spatial variability differed among treatments (p < 0.001) and in time. Coefficient of variation (CV) was lowest at the UND site, was slightly higher at the FIRE site and highest at the HARV sites, mirroring the intensity of disturbance (Fig. 3). CV in soil CO2 efflux was highest at the beginning of both spring and fall.
We can be confident that the higher CV at the HARV site was not caused by a difference in the site characteristics, such as a different
Discussion
In this study we quantified the effects of silvicultural practices (prescribed fire and harvesting) on spatial variability of soil CO2 efflux in a mixed conifer forest in the central Sierra Nevada. Spatial variability increased after disturbance, particularly after harvest, despite the harvested site being less complex and heterogeneous than the undisturbed stand. The harvested areas had minimal vegetation cover, consisting of one year old conifer seedling planted on a 2.5 m grid, and thus
Acknowledgements
This project was funded by The US Joint Fire Sciences Program. We thank Rob York, Jen York, and Blodgett staff for collecting data and maintaining instruments during this project.
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