Multiple responses of fine root resource uptake strategies to gravel content in a subtropical plantation

Most forest soils contain substantial amounts of gravel. However, unlike the more widely known root resource uptake behaviors which respond to resource patches in substrate without gravels, how roots respond to substrate containing different gravel levels is poorly understood. We grew roots in substrates with five gravel levels (0, 10, 20, 30, and 40% of volume) in a subtropical Schima superba plantation, determined fine root dynamics and turnover rate with minirhizotrons, measured fine root morphological, architectural, mycorrhizal colonization, chemistry, and mass allocation. The presence of gravel in the substrate delayed the timing of peak root growth. In the substrate with higher gravel content, plants produced more in roots in autumn, but there were fewer roots in summer and the roots tended to exhibit lower fine root turnover rate and mycorrhizal colonization, but higher root biomass allocation. The higher root biomass in the substrate with higher gravel content was associated with higher root carbon/nitrogen ratio. Our findings emphasize the complexity of root resource uptake behavior in response to gravel content and suggest that incorporating substrate gravel content into root studies may help to improve the prediction of patch exploitation and nutrient acquisition in stony soils.

Linking Foliar Traits to Belowground Processes

Above- and belowground systems are linked via plant chemistry. In forested systems, leaf litter chemistry and quality mirror that of green foliage and have important afterlife effects. In systems where belowground inputs dominate, such as grasslands, or in ecosystems where aboveground biomass is frequently removed by burning or harvesting, foliar traits may provide important information regarding belowground inputs via exudates and fine-root turnover. Many, if not most, of the plant traits that drive variation in belowground processes are also measurable via remote sensing technologies. The ability of remote sensing techniques to measure fine-scale biodiversity and plant chemistry over large spatial scales can help researchers address ecological questions that were previously prohibitively expensive to address. Key to these potential advances is the idea that remotely sensed vegetation spectra and plant chemistry can provide detailed information about the function of belowground processes beyond what traditional field sampling can provide.

Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output

Parameters of a process-based forest growth simulator are difficult or impossible to obtain from field observations. Reliable estimates can be obtained using calibration against observations of output and state variables. In this study, we present a Bayesian framework to calibrate the widely used process-based simulator Biome-BGC against estimates of gross primary production (GPP) data. We used GPP partitioned from flux tower measurements of a net ecosystem exchange over a 55-year-old Douglas fir stand as an example. The uncertainties of both the Biome-BGC parameters and the simulated GPP values were estimated. The calibrated parameters leaf and fine root turnover (LFRT), ratio of fine root carbon to leaf carbon (FRC : LC), ratio of carbon to nitrogen in leaf (C : Nleaf), canopy water interception coefficient (Wint), fraction of leaf nitrogen in RuBisCO (FLNR), and effective soil rooting depth (SD) characterize the photosynthesis and carbon and nitrogen allocation in the forest. The calibration improved the root mean square error and enhanced Nash–Sutcliffe efficiency between simulated and flux tower daily GPP compared to the uncalibrated Biome-BGC. Nevertheless, the seasonal cycle for flux tower GPP was not reproduced exactly and some overestimation in spring and underestimation in summer remained after calibration. We hypothesized that the phenology exhibited a seasonal cycle that was not accurately reproduced by the simulator. We investigated this by calibrating the Biome-BGC to each month's flux tower GPP separately. As expected, the simulated GPP improved, but the calibrated parameter values suggested that the seasonal cycle of state variables in the simulator could be improved. It was concluded that the Bayesian framework for calibration can reveal features of the modelled physical processes and identify aspects of the process simulator that are too rigid.