Mapping local and global variability in plant trait distributions.

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration-specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen ([Formula: see text]) and phosphorus ([Formula: see text]), we characterize how traits vary within and among over 50,000 [Formula: see text]-km cells across the entire vegetated land surface. We do this in several ways-without defining the PFT of each grid cell and using 4 or 14 PFTs; each model's predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.

Implications of improved representations of plant respiration in a changing climate.

Land-atmosphere exchanges influence atmospheric CO2. Emphasis has been on describing photosynthetic CO2 uptake, but less on respiration losses. New global datasets describe upper canopy dark respiration (R d) and temperature dependencies. This allows characterisation of baseline R d, instantaneous temperature responses and longer-term thermal acclimation effects. Here we show the global implications of these parameterisations with a global gridded land model. This model aggregates R d to whole-plant respiration R p, driven with meteorological forcings spanning uncertainty across climate change models. For pre-industrial estimates, new baseline R d increases R p and especially in the tropics. Compared to new baseline, revised instantaneous response decreases R p for mid-latitudes, while acclimation lowers this for the tropics with increases elsewhere. Under global warming, new R d estimates amplify modelled respiration increases, although partially lowered by acclimation. Future measurements will refine how R d aggregates to whole-plant respiration. Our analysis suggests R p could be around 30% higher than existing estimates.

Predicting leaf area index from scaling principles: corroboration and consequences.

Leaf area index (LAI) is a key biophysical variable in most process-based forest-ecosystem models. However, most such models require LAI as an input, typically obtained from empirical observations. We tested whether scaling principles based on trade-offs between single leaf and canopy properties could be effectively used to model LAI, thereby obviating the need for empirical observations. To do so, we used the process-oriented model, PnET, configured to estimate LAI from these same scaling principles. We derived biologically based LAI predictions (LAIPnET) for the Harvard Forest (Massachusetts, USA) eddy covariance tower site, a predominately mixed deciduous hardwood forest, using PnET, and compared these with a locally observed phenology record and with LAI estimates from both local (ground-based) photosynthetically active radiation transmittance (LAITRANS) and normalized difference vegetation index satellite data (LAINDVI). We generated the LAIPnET trajectory by running the PnET model with meteorological observations from the flux tower as model drivers. We derived LAITRANS from measurements of above- and below-canopy photosynthetically active radiation at the flux tower, and LAINDVI from observations from the Advanced Very High Resolution Radiometer (AVHRR) satellite-borne sensor of surface greenness for the 1 km2 cell containing the flux tower. Over a 5-year period, LAIPnET and LAITRANS values were comparable intra- and interannually, with maximum values differing by less than 0.1 to 0.2 LAI units (m2 m(-2)). Values of LAINDVI were similar to LAIPnET and LAITRANS in midsummer, but higher LAI values were predicted in the early and late portions of the growing season. In addition, we used the three alternative LAI trajectories in a modified version of the PnET model and compared the resulting outputs of gross primary production (GPP) with GPP estimates from the flux tower for 5 continuous years. The LAIPnET and LAITRANS inputs resulted in a difference of less than 3% in mean annual GPP from 1995 to 1999, and these were within 7 and 9%, respectively, of the annual eddy flux-based estimates over the same time period. The results indicate that biologically based LAI scaling approaches can closely track temporal changes in a deciduous forest and have potential for spatial and temporal scaling of LAI.