Improving the understanding of temperate forest carbon dynamics

Abstract

The soil organic carbon (C) pool is estimated to contain at least three times as much organic C as is stored in vegetation. However, the processes controlling below-ground C dynamics are poorly understood, representing a key uncertainty in ecosystem models. Soil respiration rate (Rs) is a large component of the forest carbon cycle, however the factors that control it are still poorly understood, and those affecting autotrophic (Ra) and heterotrophic (Rh) respiration rates differ and vary in space and time. A variety of direct (i.e. soil and ingrowth cores) and indirect (i.e. rhizotron and minirhizotron) methods exist for obtaining estimates of fine root (< 2 mm diameter) production, with the consequence that there is a high variability in root biomass estimates between root studies. In this thesis I aim to contribute towards a better understanding of processes governing below-ground C dynamics. In particular I focus on: 1) the spatial and seasonal variability of Rs and drivers; 2) the uncertainty on fine root C pool measurement methods; 3) comparing novel datasets of Rs, fine root biomass and girth increment, with outputs from the SPA v2 model. To determine the dominant controls and spatial heterogeneity of Rs, I measured Rs and key biotic and abiotic drivers seasonally, in a Quercus robur forest in southern England. Measurements were made quarterly in three plots, each with measurement points arranged according to a spatial sampling design, enabling any spatial autocorrelation to be detected. Rs drivers were categorised into plant (i.e. leaf area index, weighted tree proximity (i.e. mean dbh within 4 m of a point), and fine root biomass), physical (i.e. soil moisture, soil temperature and soil bulk density) and substrate (i.e. litter depth and organic layer depth) factors. I explore: 1) what the dominant controls of Rs are and whether they change during the growing season; 2) whether micro-topography and stand structure are correlated with drivers, and influence the spatial variability of Rs, thereby simplifying up-scaling processes; 3) if physical drivers of Rs are spatially more homogeneous than plant drivers and the availability of substrate. I found no clear seasonal difference in drivers, with Rs consistently responding to litter depth, bulk density and soil moisture. The only significant response of Rs to micro-topography and tree factor was in August and September respectively and physical factors were found to be the most spatially homogeneous. Rs measurements were non-normally distributed, with ‘hotspots’ of particularly high fluxes found that remained stable throughout the measurement campaign. These findings suggest that the seasonal and spatial variability and distribution of Rs and its main drivers should be considered at the sampling design stage, to avoid bias for up-scaling non-linear processes. To address the uncertainties associated with determining fine root biomass change, we compared the measurement error for five methodologies (four indirect and one direct) in a Pinus contorta and Quercus robur forest during 2010. Rhizotron and ingrowth measurements were taken during 2010 and fine root standing crop was measured in 2009. Root length against the rhizotron screens was measured using novel software (ORIDIS), developed as part of a collaboration here in Edinburgh. The software was developed to increase precision and reduce the cost and processing time of rhizotron measurements. Differences in final cumulative root ingrowth for each conversion method ranged between 20.7g-2 - 245.0 g m-2 in the oak forest and 89.7 g m-2 - 273.0 g m-2 in the pine forest. The study found that indirect measurements of root length had less operator error than indirect measurements of root diameter. Direct methods of determining root growth using ingrowth cores also showed a seasonal trend; however artefacts may have been introduced into the method, from the affect of severing roots and changing soil conditions. To test the representation of below-ground processes in an ecosystem model, I validate modelled dynamics using default SPA v2 parameters, against independent CO2 flux and C pool datasets. The flux data were of eddy covariance and automatic chamber measurements, partitioned into root (Rroot), mycorhizal (Rmyc) and microbial heterotrophic (Rh) components. The biometric measurements were of foliage, fine root biomass and woody biomass increment. The key findings of this study were that: 1) SPA outputs compare well to ecosystem scale measurements of NEE and GPP. However, model-data mismatch occurs for fine root and wood C allocation; 2) the timing of fine root C allocation is 53 days too late and the turnover rate of fine roots 17 times too high; 3) the timing of modelled below-ground Rh and Ra could be improved by separating above and below-ground Ra and including individual root, mychorrizae and microbial C pools. The thesis concludes by discussing the implications of each chapter for our understanding and capability to model below-ground C dynamics. I find that the key challenge for measuring individual below-ground C pools and fluxes is ensuring that the measurements are spatially representative and avoid bias. The key challenge for modelling below-ground C dynamics is ensuring processes sufficiently reflect reality, when the sparse data that exist for corroboration, capture multiple processes. I explore the possibilities of further research that could be conducted, as a result of this work.