Quantification and uncertainty of root growth stimulation by elevated CO2 in a mature temperate deciduous forest.

Increasing CO2 levels are a major global challenge, and the potential mitigation of anthropogenic CO2 emissions by natural carbon sinks remains poorly understood. The uptake of elevated CO2 (eCO2) by the terrestrial biosphere, and subsequent sequestration as biomass in ecosystems, remain hard to quantify in natural ecosystems. Here, we combine field observations of fine root stocks and flows, derived from belowground imaging and soil cores, with image analysis, stochastic modelling, and statistical inference, to elucidate belowground root dynamics in a mature temperate deciduous forest under free-air eCO2 to 150 ppm above ambient levels. eCO2 led to relatively faster root production (a peak volume fold change of 4.52 ± 0.44 eCO2 versus 2.58 ± 0.21 control), with increased root elongation relative to decay the likely causal mechanism for this acceleration. Physical analysis of 552 root systems from soil cores support this picture, with lengths and widths of fine roots significantly increasing under eCO2. Estimated fine root contributions to belowground net primary productivity increase under eCO2 (mean annual 204 ± 93 g dw m-2 yr-1 eCO2 versus 140 ± 60 g dw m-2 yr-1 control). This multi-faceted approach thus sheds quantitative light on the challenging characterisation of the eCO2 response of root biomass in mature temperate forests.

Model selection and parameter estimation for root architecture models using likelihood-free inference.

Plant root systems play vital roles in the biosphere, environment and agriculture, but the quantitative principles governing their growth and architecture remain poorly understood. The 'forward problem' of what root forms can arise from given models and parameters has been well studied through modelling and simulation, but comparatively little attention has been given to the 'inverse problem': what models and parameters are responsible for producing an experimentally observed root system? Here, we propose the use of approximate Bayesian computation (ABC) to infer mechanistic parameters governing root growth and architecture, allowing us to learn and quantify uncertainty in parameters and model structures using observed root architectures. We demonstrate the use of this platform on synthetic and experimental root data and show how it may be used to identify growth mechanisms and characterize growth parameters in different mutants. Our highly adaptable framework can be used to gain mechanistic insight into the generation of observed root system architectures.