31I.31II.32III.34IV.35V.37VI.38VII.3941References41 Summary The rhizosphere priming effect (RPE) is a mechanism by which plants interact with soil functions. The large impact of the RPE on soil organic matter decomposition rates (from 50% reduction to 380% increase) warrants similar attention to that being paid to climatic controls on ecosystem functions. Furthermore, global increases in atmospheric CO2 concentration and surface temperature can significantly alter the RPE. Our analysis using a game theoretic model suggests that the RPE may have resulted from an evolutionarily stable mutualistic association between plants and rhizosphere microbes. Through model simulations based on microbial physiology, we demonstrate that a shift in microbial metabolic response to different substrate inputs from plants is a plausible mechanism leading to positive or negative RPEs. In a case study of the Duke Free‐Air CO2 Enrichment experiment, performance of the PhotoCent model was significantly improved by including an RPE‐induced 40% increase in soil organic matter decomposition rate for the elevated CO2 treatment – demonstrating the value of incorporating the RPE into future ecosystem models. Overall, the RPE is emerging as a crucial mechanism in terrestrial ecosystems, which awaits substantial research and model development.

Elevated atmospheric CO2 concentration (eCO2) has the potential to increase vegetation carbon storage if increased net primary production causes increased long-lived biomass. Model predictions of eCO2 effects on vegetation carbon storage depend on how allocation and turnover processes are represented.We used data from two temperate forest free-air CO2 enrichment (FACE) experiments to evaluate representations of allocation and turnover in 11 ecosystem models.Observed eCO2 effects on allocation were dynamic. Allocation schemes based on functional relationships among biomass fractions that vary with resource availability were best able to capture the general features of the observations. Allocation schemes based on constant fractions or resource limitations performed less well, with some models having unintended outcomes. Few models represent turnover processes mechanistically and there was wide variation in predictions of tissue lifespan. Consequently, models did not perform well at predicting eCO2 effects on vegetation carbon storage.Our recommendations to reduce uncertainty include: use of allocation schemes constrained by biomass fractions; careful testing of allocation schemes; and synthesis of allocation and turnover data in terms of model parameters. Data from intensively studied ecosystem manipulation experiments are invaluable for constraining models and we recommend that such experiments should attempt to fully quantify carbon, water and nutrient budgets.

Non-structural carbohydrates (NSC) in plant tissue are frequently quantified to make inferences about plant responses to environmental conditions. Laboratories publishing estimates of NSC of woody plants use many different methods to evaluate NSC. We asked whether NSC estimates in the recent literature could be quantitatively compared among studies. We also asked whether any differences among laboratories were related to the extraction and quantification methods used to determine starch and sugar concentrations. These questions were addressed by sending sub-samples collected from five woody plant tissues, which varied in NSC content and chemical composition, to 29 laboratories. Each laboratory analyzed the samples with their laboratory-specific protocols, based on recent publications, to determine concentrations of soluble sugars, starch and their sum, total NSC. Laboratory estimates differed substantially for all samples. For example, estimates for Eucalyptus globulus leaves (EGL) varied from 23 to 116 (mean = 56) mg g(-1) for soluble sugars, 6-533 (mean = 94) mg g(-1) for starch and 53-649 (mean = 153) mg g(-1) for total NSC. Mixed model analysis of variance showed that much of the variability among laboratories was unrelated to the categories we used for extraction and quantification methods (method category R(2) = 0.05-0.12 for soluble sugars, 0.10-0.33 for starch and 0.01-0.09 for total NSC). For EGL, the difference between the highest and lowest least squares means for categories in the mixed model analysis was 33 mg g(-1) for total NSC, compared with the range of laboratory estimates of 596 mg g(-1). Laboratories were reasonably consistent in their ranks of estimates among tissues for starch (r = 0.41-0.91), but less so for total NSC (r = 0.45-0.84) and soluble sugars (r = 0.11-0.83). Our results show that NSC estimates for woody plant tissues cannot be compared among laboratories. The relative changes in NSC between treatments measured within a laboratory may be comparable within and between laboratories, especially for starch. To obtain comparable NSC estimates, we suggest that users can either adopt the reference method given in this publication, or report estimates for a portion of samples using the reference method, and report estimates for a standard reference material. Researchers interested in NSC estimates should work to identify and adopt standard methods.

Large uncertainty exists in model projections of the land carbon (C) sink response to increasing atmospheric CO 2 . Free-Air CO 2 Enrichment (FACE) experiments lasting a decade or more have investigated ecosystem responses to a step change in atmospheric CO 2 concentration. To interpret FACE results in the context of gradual increases in atmospheric CO 2 over decades to centuries, we used a suite of seven models to simulate the Duke and Oak Ridge FACE experiments extended for 300 years of CO 2 enrichment. We also determine key modeling assumptions that drive divergent projections of terrestrial C uptake and evaluate whether these assumptions can be constrained by experimental evidence. All models simulated increased terrestrial C pools resulting from CO 2 enrichment, though there was substantial variability in quasi-equilibrium C sequestration and rates of change. In two of two models that assume that plant nitrogen (N) uptake is solely a function of soil N supply, the net primary production response to elevated CO 2 became progressively N limited. In four of five models that assume that N uptake is a function of both soil N supply and plant N demand, elevated CO 2 led to reduced ecosystem N losses and thus progressively relaxed nitrogen limitation. Many allocation assumptions resulted in increased wood allocation relative to leaves and roots which reduced the vegetation turnover rate and increased C sequestration. In addition, self-thinning assumptions had a substantial impact on C sequestration in two models. Accurate representation of N process dynamics (in particular N uptake), allocation, and forest self-thinning is key to minimizing uncertainty in projections of future C sequestration in response to elevated atmospheric CO 2 .

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