Photosynthesis, water use efficiency and stable carbon isotope composition are associated with anatomical properties of leaf and xylem in six poplar species - Cao, X.; Jia, J. B.; Li, H.; Li, M. C.; Luo, J.; Liang, Z. S.; Liu, T. X.; Liu, W. G.; Peng, C. H.; Luo, Z. B. (Plant Biology, 2012)
Abstract Although fast‐growing Populus species consume a large amount of water for biomass production, there are considerable variations in water use efficiency (WUE) across different poplar species. To compare differences in growth, WUE and anatomical properties of leaf and xylem and to examine the relationship between photosynthesis/WUE and anatomical properties of leaf and xylem, cuttings of six poplar species were grown in a botanical garden. The growth performance, photosynthesis, intrinsic WUE (WUE i ), stable carbon isotope composition (δ 13 C) and anatomical properties of leaf and xylem were analysed in these poplar plants. Significant differences were found in growth, photosynthesis, WUE i and anatomical properties among the examined species. Populus cathayana was the clone with the fastest growth and the lowest WUE i /δ 13 C, whereas P. × euramericana had a considerable growth increment and the highest WUE i /δ 13 C. Among the analysed poplar species, the highest total stomatal density in P. cathayana was correlated with its highest stomatal conductance (g s ) and lowest WUE i /δ 13 C. Moreover, significant correlations were observed between WUE i and abaxial stomatal density and stem vessel lumen area. These data suggest that photosynthesis, WUE i and δ 13 C are associated with leaf and xylem anatomy and there are tradeoffs between growth and WUE i . It is anticipated that some poplar species, e.g. P. × euramericana , are better candidates for water‐limited regions and others, e.g. P. cathayana , may be better for water‐abundant areas.

The North American Carbon Program Multi-scale synthesis and Terrestrial Model Intercomparison Project – Part 1: Overview and experimental design - Huntzinger, D. N.; Schwalm, C.; Michalak, A. M.; Schaefer, K.; King, A. W.; Wei, Y.; Jacobson, A.; Liu, S.; Cook, R. B.; Post, W. M.; Berthier, G.; Hayes, D.; et al. (Biogeosciences, 2013)
Abstract. Terrestrial biosphere models (TBMs) have become an integral tool for extrapolating local observations and understanding of land-atmosphere carbon exchange to larger regions. The North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal model intercomparison and evaluation effort focused on improving the diagnosis and attribution of carbon exchange at regional and global scales. MsTMIP builds upon current and past synthesis activities, and has a unique framework designed to isolate, interpret, and inform understanding of how model structural differences impact estimates of carbon uptake and release. Here we provide an overview of the MsTMIP effort and describe how the MsTMIP experimental design enables the assessment and quantification of TBM structural uncertainty. Model structure refers to the types of processes considered (e.g. nutrient cycling, disturbance, lateral transport of carbon), and how these processes are represented (e.g. photosynthetic formulation, temperature sensitivity, respiration) in the models. By prescribing a common experimental protocol with standard spin-up procedures and driver data sets, we isolate any biases and variability in TBM estimates of regional and global carbon budgets resulting from differences in the models themselves (i.e. model structure) and model-specific parameter values. An initial intercomparison of model structural differences is represented using hierarchical cluster diagrams (a.k.a. dendrograms), which highlight similarities and differences in how models account for carbon cycle, vegetation, energy, and nitrogen cycle dynamics. We show that, despite the standardized protocol used to derive initial conditions, models show a high degree of variation for GPP, total living biomass, and total soil carbon, underscoring the influence of differences in model structure and parameterization on model estimates.