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Abstract A significant warming effect on arctic tundra is greening. Although this increase in predominantly woody vegetation has been linked to increases in gross primary productivity, increasing temperatures also stimulate ecosystem respiration. We present a novel analysis from small-scale plot measurements showing that the shape of the temperature- and light-dependent sink-to-source threshold (where net ecosystem exchange (NEE) equals zero) differs between two tussock tundra ecosystems differing in leaf area index (LAI). At the higher LAI site, the threshold is exceeded (i.e the ecosystem becomes a source) at relatively higher temperatures under low light but at lower temperatures under high light. At the lower LAI site, the threshold is exceeded at relatively lower temperatures under low light but at higher temperatures under high light. We confirmed this response at a single site where LAI was experimentally increased. This suggests the carbon balance of the tundra may be sensitive to small increases in temperature under low light, but that this effect may be significantly offset by increases in LAI. Importantly, we found that this LAI effect is reversed under high light, and so in a warming tundra, greater vegetation cover could have a progressively negative effect on net carbon uptake. 

Abstract Whole‐ecosystem interactions and feedbacks constrain ecosystem responses to environmental change. The effects of these constraints on responses to climate trends and extreme weather events have been well studied. Here we examine how these constraints respond to changes in day‐to‐day weather variability without changing the long‐term mean weather. Although environmental variability is recognized as a critical factor affecting ecological function, the effects of climate change on day‐to‐day weather variability and the resultant impacts on ecosystem function are still poorly understood. Changes in weather variability can alter the mean rates of individual ecological processes because many processes respond non‐linearly to environmental drivers. We assessed how these individual‐process responses to changes in day‐to‐day weather variability interact with one another at an ecosystem level. We examine responses of arctic tundra to changes in weather variability using stochastic simulations of daily temperature, precipitation, and light to drive a biogeochemical model. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates in our model. However, responses of some processes (e.g., respiration) were inconsistent with expectations because ecosystem feedbacks can moderate, or even reverse, direct process responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere; less variability led to higher carbon sequestration on land. The magnitude of modeled ecosystem response to weather variability was comparable to that predicted for the effects of climate mean trends by the end of the century. 

We use a simple model of coupled carbon and nitrogen cycles in terrestrial ecosystems to examine how explicitly representing grazers versus having grazer effects implicitly aggregated in with other biogeochemical processes in the model alters predicted responses to elevated carbon dioxide and warming. The aggregated approach can affect model predictions because grazer-mediated processes can respond differently to changes in climate from the processes with which they are typically aggregated. We use small-mammal grazers in arctic tundra as an example and find that the typical three-to-four-year cycling frequency is too fast for the effects of cycle peaks and troughs to be fully manifested in the ecosystem biogeochemistry. We conclude that implicitly aggregating the effects of small-mammal grazers with other processes results in an underestimation of ecosystem response to climate change relative to estimations in which the grazer effects are explicitly represented. The magnitude of this underestimation increases with grazer density. We therefore recommend that grazing effects be incorporated explicitly when applying models of ecosystem response to global change. 

We use the Multiple Element Limitation (MEL) model to examine the responses of twelve ecosystems - from the arctic to the tropics and from grasslands to forests - to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in annual precipitation. The ecosystems respond synergistically to elevated CO2, warming, and decreased precipitation combined because higher water use efficiency with elevated CO2 and higher fertility with warming compensate for the responses to drought. The response to elevated CO2, warming, and increased precipitation combined is additive. We analyze changes in ecosystem carbon (C) sequestration based on four nitrogen (N) and four phosphorus (P) attribution factors of the ecosystem: (1) changes in total N and P in the ecosystem, (2) changes in the distribution of N and P between vegetation and soil, (3) changes in vegetation C:N and C:P ratios, and (4) changes in soil C:N and C:P ratios. In the combined CO2 and climate change simulations, all ecosystems gain C. The relative contribution of changes in these four N and P attribution factors to changes in ecosystem C storage varies among ecosystems because of differences in the initial distributions of N and P between vegetation and soil and the openness of the ecosystem N and P cycles. The net transfer of N and P from soil (low C:N and C:P) to vegetation (high C:N and C:P) dominates the C response of forests. For tundra and grassland ecosystems, the C gain is also associated with an increase in soil C:N and C:P. In ecosystems with symbiotic N fixation, gains in C resulted from the accumulation of N and sometimes P. Because of differences in the openness of the N versus P cycles and the distribution of organic matter between vegetation and soil, changes in the N attribution factors do not always parallel changes in the P attribution factors. These findings highlight how differences among ecosystems in C-nutrient interactions and the amount of woody biomass interact to shape ecosystem C sequestration under simulated global change. By using a single model framework across multiple ecosystems, we suggest that a better understanding of the factors influencing the openness of the N and P cycles, controls on N and P distribution within ecosystems, and controls on ecosystem stoichiometry is needed to improve the representation of nutrient effects on C sequestration in ecosystems and their responses to elevated CO2 and climate change. 

We present a framework for assessing biogeochemical recovery of terrestrial ecosystems from disturbance. We identify three recovery phases. In Phase 1, nitrogen is redistributed from soil organic matter to vegetation, but the ecosystem continues to lose nitrogen because the recovering vegetation cannot take up nitrogen as fast as it is released from soil. In Phase 2, the ecosystem begins re-accumulating nitrogen and converges on a quasi-steady state in which vegetation and soil-microbial processes are in balance. In Phase 3, vegetation and soil-microbial processes remain in balance and the ecosystem slowly re-accumulates the remaining nitrogen. Phase 3 follows a balanced-accumulation trajectory along a continuum of quasi-steady states that approaches the true steady state asymptotically. We examine the effects of three ecosystem properties on recovery: openness of the nitrogen cycle, nitrogen distribution in and turnover between vegetation and soils, and the proportion of nitrogen losses that are in a refractory form. Openness exacerbates Phase 1 nitrogen losses but speeds recovery in Phases 2 and 3. A high fraction of ecosystem nitrogen in vegetation, resulting from nitrogen turnover that is slow in vegetation but fast in soil, exacerbates Phase 1 nitrogen losses but speeds recovery in Phases 2 and 3. A high proportion of nitrogen loss in refractory form mitigates Phase 1 nitrogen losses and speeds recovery in Phases 2 and 3. Application of our conceptual framework requires empirical recognition of the continuum of quasi-steady states constituting the balanced-accumulation trajectory and a distinction between the balanced-accumulation trajectory and the true steady state.