نبذة مختصرة : Elevated carbon dioxide (CO 2 ) can increase plant growth, but the magnitude of this CO 2 fertilization effect is modified by soil nutrient availability. Predicting how nutrient availability affects plant responses to elevated CO 2 is a key consideration for ecosystem models, and many modeling groups have moved to, or are moving towards, incorporating nutrient limitation in their models. The choice of assumptions to represent nutrient cycling processes has a major impact on model predictions, but it can be difficult to attribute outcomes to specific assumptions in complex ecosystem simulation models. Here we revisit the quasi-equilibrium analytical framework introduced by Comins and McMurtrie (1993) and explore the consequences of specific model assumptions for ecosystem net primary productivity (NPP). We review the literature applying this framework to plant-soil models and then analyze the effect of several new assumptions on predicted plant responses to elevated CO 2 . Examination of alternative assumptions for plant nitrogen uptake showed that a linear function of the mineral nitrogen pool or a linear function of the mineral nitrogen pool with an additional saturating function of root biomass yield similar CO 2 responses at longer timescales (>5 years), suggesting that the added complexity may not be needed when these are the timescales of interest. In contrast, a saturating function of the mineral nitrogen pool with linear dependency on root biomass yields no soil nutrient feedback on the very-long-term (>500 years), near-equilibrium timescale, meaning that one should expect the model to predict a full CO 2 fertilization effect on production. Secondly, we show that incorporating a priming effect on slow soil organic matter decomposition attenuates the nutrient feedback effect on production, leading to a strong medium-term (5-50 years) CO 2 response. Models incorporating this priming effect should thus predict a strong and persistent CO 2 fertilization effect over time. Thirdly, we demonstrate that ...
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