Improving the understanding of temperate forest carbon dynamics
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Date
28/11/2013Item status
Restricted AccessEmbargo end date
31/12/2100Author
Meacham, Theresa Marie
Metadata
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.