Ecosystem feedbacks constrain the effect of day-to-day weather variability on land-atmosphere carbon exchange.

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.

N and P constrain C in ecosystems under climate change: Role of nutrient redistribution, accumulation, and stoichiometry.

We use the Multiple Element Limitation (MEL) model to examine responses of 12 ecosystems to elevated carbon dioxide (CO2 ), warming, and 20% decreases or increases in precipitation. 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 responses to drought. Response to elevated CO2 , warming, and increased precipitation combined is additive. We analyze changes in ecosystem carbon (C) based on four nitrogen (N) and four phosphorus (P) attribution factors: (1) changes in total ecosystem N and P, (2) changes in N and P distribution 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 contributions of these four 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 to vegetation dominates the C response of forests. For tundra and grasslands, the C gain is also associated with increased soil C:N and C:P. In ecosystems with symbiotic N fixation, C gains resulted from N accumulation. Because of differences in N versus P cycle openness and the distribution of organic matter between vegetation and soil, changes in the N and P attribution factors do not always parallel one another. Differences among ecosystems in C-nutrient interactions and the amount of woody biomass interact to shape ecosystem C sequestration under simulated global change. We suggest that future studies quantify the openness of the N and P cycles and changes in the distribution of C, N, and P among ecosystem components, which currently limit understanding of nutrient effects on C sequestration and responses to elevated CO2 and climate change.

Model responses to CO2 and warming are underestimated without explicit representation of Arctic small-mammal grazing.

We use a simple model of coupled carbon and nitrogen cycles in terrestrial ecosystems to examine how "explicitly representing grazers" vs. "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 compared with the processes with which they are typically aggregated. We use small-mammal grazers in a 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.

Alleviation of nutrient co-limitation induces regime shifts in post-fire community composition and productivity in Arctic tundra.

Recent unprecedented fires in the Arctic during the past two decades have indicated a pressing need to understand the long-term ecological impacts of fire in this biome. Anecdotal evidence suggests that tundra fires can induce regime shifts that change tussock tundra to more shrub-dominated ecosystems. However, the ecological mechanisms regulating these shifts are poorly understood, but are hypothesized to involve changes to nutrient availability in this nutrient limited system. Here we conducted a 4-year two-factorial (control: C, nitrogen along: N+ , phosphorus alone: P+ , nitrogen and phosphorus combined: NP+ ) fertilization experiment in both unburned and burned tundra to test this hypothesis after a decade of post-fire recovery. A decade after fire, the burned site exhibited an increase in soil nitrogen and phosphorus availability and a transition toward taller, more productive, and more deciduous vegetation. This shift in vegetation structure, composition, and function was induced at the unburned site through the addition of both NP+ and the alleviation of their co-limitation. Both burned and unburned tundra responded similarly to fertilizer treatments by increasing leaf area index, greenness, and canopy height in NP+ treatments, and exhibited no significant response in individual N+ or P+ treatments. These results point to a greater need to understand coupled carbon, nitrogen, and phosphorus cycles in this system, and suggest that post-fire regime shifts are regulated by the alleviation of nitrogen and phosphorus co-limitation in Arctic tundra.

Nitrate is an important nitrogen source for Arctic tundra plants.

Plant nitrogen (N) use is a key component of the N cycle in terrestrial ecosystems. The supply of N to plants affects community species composition and ecosystem processes such as photosynthesis and carbon (C) accumulation. However, the availabilities and relative importance of different N forms to plants are not well understood. While nitrate (NO3-) is a major N form used by plants worldwide, it is discounted as a N source for Arctic tundra plants because of extremely low NO3- concentrations in Arctic tundra soils, undetectable soil nitrification, and plant-tissue NO3- that is typically below detection limits. Here we reexamine NO3- use by tundra plants using a sensitive denitrifier method to analyze plant-tissue NO3- Soil-derived NO3- was detected in tundra plant tissues, and tundra plants took up soil NO3- at comparable rates to plants from relatively NO3--rich ecosystems in other biomes. Nitrate assimilation determined by 15N enrichments of leaf NO3- relative to soil NO3- accounted for 4 to 52% (as estimated by a Bayesian isotope-mixing model) of species-specific total leaf N of Alaskan tundra plants. Our finding that in situ soil NO3- availability for tundra plants is high has important implications for Arctic ecosystems, not only in determining species compositions, but also in determining the loss of N from soils via leaching and denitrification. Plant N uptake and soil N losses can strongly influence C uptake and accumulation in tundra soils. Accordingly, this evidence of NO3- availability in tundra soils is crucial for predicting C storage in tundra.

Ecosystem responses to climate change at a Low Arctic and a High Arctic long-term research site.

Long-term measurements of ecological effects of warming are often not statistically significant because of annual variability or signal noise. These are reduced in indicators that filter or reduce the noise around the signal and allow effects of climate warming to emerge. In this way, certain indicators act as medium pass filters integrating the signal over years-to-decades. In the Alaskan Arctic, the 25-year record of warming of air temperature revealed no significant trend, yet environmental and ecological changes prove that warming is affecting the ecosystem. The useful indicators are deep permafrost temperatures, vegetation and shrub biomass, satellite measures of canopy reflectance (NDVI), and chemical measures of soil weathering. In contrast, the 18-year record in the Greenland Arctic revealed an extremely high summer air-warming of 1.3 °C/decade; the cover of some plant species increased while the cover of others decreased. Useful indicators of change are NDVI and the active layer thickness.

Nutrient limitation in tropical secondary forests following different management practices.

Secondary forests now make up more than one-half of all tropical forests, and constraints on their biomass accumulation will influence the strength of the terrestrial carbon (C) sink in the coming decades. However the variance in secondary tropical forest biomass for a given stand age and climate is high and our understanding of why is limited. We constructed a model of terrestrial C, nitrogen (N), and phosphorus (P) cycling to examine the influence of disturbance and management practices on nutrient limitation and biomass recovery in secondary tropical forests. The model predicted that N limited the rate of forest recovery in the first few decades following harvest, but that this limitation switched to P approximately 30-40 yr after abandonment, consistent with field data on N and P cycling from secondary tropical forest chronosequences. Simulated biomass recovery agreed well with field data of biomass accumulation following harvest (R2  = 0.80). Model results showed that if all biomass remained on site following a severe disturbance such as blowdown, regrowth approached pre-disturbance biomass in 80-90 yr, and recovery was faster following smaller disturbances such as selective logging. Field data from regrowth on abandoned pastures were consistent with simulated losses of nutrients in soil organic matter, particularly P. Following any forest disturbance that involved the removal of nutrients (i.e., except blowdown), forest regrowth produced reduced biomass relative to the initial state as a result of nutrient loss through harvest, leaching and/or sequestration by secondary minerals. Differences in nutrient availability accounted for 49-94% of the variance in secondary forest biomass C at a given stand age. Management lessons from this study are the importance of strategies that help retain nutrients on site, recognizing the role of coarse woody debris in immobilization and subsequent release of nutrients, and the potential for nutrient additions to enhance biomass growth and recovery in secondary tropical forests.

Modeling long-term changes in tundra carbon balance following wildfire, climate change, and potential nutrient addition.

To investigate the underlying mechanisms that control long-term recovery of tundra carbon (C) and nutrients after fire, we employed the Multiple Element Limitation (MEL) model to simulate 200-yr post-fire changes in the biogeochemistry of three sites along a burn severity gradient in response to increases in air temperature, CO2 concentration, nitrogen (N) deposition, and phosphorus (P) weathering rates. The simulations were conducted for severely burned, moderately burned, and unburned arctic tundra. Our simulations indicated that recovery of C balance after fire was mainly determined by the internal redistribution of nutrients among ecosystem components (controlled by air temperature), rather than the supply of nutrients from external sources (e.g., nitrogen deposition and fixation, phosphorus weathering). Increases in air temperature and atmospheric CO2 concentration resulted in (1) a net transfer of nutrient from soil organic matter to vegetation and (2) higher C : nutrient ratios in vegetation and soil organic matter. These changes led to gains in vegetation biomass C but net losses in soil organic C stocks. Under a warming climate, nutrients lost in wildfire were difficult to recover because the warming-induced acceleration in nutrient cycles caused further net nutrient loss from the system through leaching. In both burned and unburned tundra, the warming-caused acceleration in nutrient cycles and increases in ecosystem C stocks were eventually constrained by increases in soil C : nutrient ratios, which increased microbial retention of plant-available nutrients in the soil. Accelerated nutrient turnover, loss of C, and increasing soil temperatures will likely result in vegetation changes, which further regulate the long-term biogeochemical succession. Our analysis should help in the assessment of tundra C budgets and of the recovery of biogeochemical function following fire, which is in turn necessary for the maintenance of wildlife habitat and tundra vegetation.

Modeling carbon-nutrient interactions during the early recovery of tundra after fire.

Fire frequency has dramatically increased in the tundra of northern Alaska, USA, which has major implications for the carbon budget of the region and the functioning of these ecosystems, which support important wildlife species. We investigated the postfire succession of plant and soil carbon (C), nitrogen (N), and phosphorus (P) fluxes and stocks along a burn severity gradient in the 2007 Anaktuvuk River fire scar in northern Alaska. Modeling results indicated that the early regrowth of postfire tundra vegetation was limited primarily by its canopy photosynthetic potential, rather than nutrient availability, because of the initially low leaf area and relatively high inorganic N and P concentrations in soil. Our simulations indicated that the postfire recovery of tundra vegetation was sustained predominantly by the uptake of residual inorganic N (i.e., in the remaining ash), and the redistribution of N and P from soil organic matter to vegetation. Although residual nutrients in ash were higher in the severe burn than the moderate burn, the moderate burn recovered faster because of the higher remaining biomass and consequent photosynthetic potential. Residual nutrients in ash allowed both burn sites to recover and exceed the unburned site in both aboveground biomass and production five years after the fire. The investigation of interactions among postfire C, N, and P cycles has contributed to a mechanistic understanding of the response of tundra ecosystems to fire disturbance. Our study provided insight on how the trajectory of recovery of tundra from wildfire is regulated during early succession.

Arctic canopy photosynthetic efficiency enhanced under diffuse light, linked to a reduction in the fraction of the canopy in deep shade.

We investigated how radiation conditions within a tundra canopy were linked to canopy photosynthesis, and how this linkage explained photosynthetic sensitivity to sky conditions, that is total radiation and its diffuse fraction. We measured within canopy radiation at leaf scales and net CO2 exchanges at canopy scales, under varied total irradiance and diffuse fraction, in Alaskan shrub tundra. Normalised mean radiation profiles within canopies showed no significant differences with varied diffuse fractions. However, radiation density distribution was non-normal, being more unimodal under diffuse conditions and distinctly bimodal under direct sunlight. There was a nearly three-fold increase in the proportion of the canopy in deep shade under direct illumination, compared to diffuse conditions. Under diffuse conditions the canopy had higher light-use efficiency (LUE), resulting in up to 17% greater photosynthesis. The enhancement in LUE under diffuse illumination was not related to differences in the mean light profiles, but instead was due to significant shifts in the density distribution of light at leaf scales, in particular a reduced fraction of the canopy in deep shade under diffuse illumination. These results provide unique information for testing radiative transfer schemes in canopy models, and for better understanding canopy structure and trait variation within plant canopies.

Predicting ecosystem carbon balance in a warming Arctic: the importance of long-term thermal acclimation potential and inhibitory effects of light on respiration.

The carbon balance of Arctic ecosystems is particularly sensitive to global environmental change. Leaf respiration (R), a temperature-dependent key process in determining the carbon balance, is not well-understood in Arctic plants. The potential for plants to acclimate to warmer conditions could strongly impact future global carbon balance. Two key unanswered questions are (1) whether short-term temperature responses can predict long-term respiratory responses to growth in elevated temperatures and (2) to what extent the constant daylight conditions of the Arctic growing season inhibit leaf respiration. In two dominant Arctic species Eriophorum vaginatum (tussock grass) and Betula nana (woody shrub), we assessed the extent of respiratory inhibition in the light (RL/RD), respiratory response to short-term temperature change, and respiratory acclimation to long-term warming treatments. We found that R of both species is strongly inhibited by light (averaging 35% across all measurement temperatures). In E. vaginatum both RL and RD acclimated to the long-term warming treatment, reducing the magnitude of respiratory response relative to the short-term response to temperature increase. In B. nana, both RL and RD responded to short-term temperature increase but showed no acclimation to the long-term warming. The ability to predict plant respiratory response to global warming with short-term temperature responses will depend on species-specific acclimation potential and the differential response of RL and RD to temperature. With projected woody shrub encroachment in Arctic tundra and continued warming, changing species dominance between these two functional groups, may impact ecosystem respiratory response and carbon balance.

Interactions among resource partitioning, sampling effect, and facilitation on the biodiversity effect: a modeling approach.

Resource partitioning, facilitation, and sampling effect are the three mechanisms behind the biodiversity effect, which is depicted usually as the effect of plant-species richness on aboveground net primary production. These mechanisms operate simultaneously but their relative importance and interactions are difficult to unravel experimentally. Thus, niche differentiation and facilitation have been lumped together and separated from the sampling effect. Here, we propose three hypotheses about interactions among the three mechanisms and test them using a simulation model. The model simulated water movement through soil and vegetation, and net primary production mimicking the Patagonian steppe. Using the model, we created grass and shrub monocultures and mixtures, controlled root overlap and grass water-use efficiency (WUE) to simulate gradients of biodiversity, resource partitioning and facilitation. The presence of shrubs facilitated grass growth by increasing its WUE and in turn increased the sampling effect, whereas root overlap (resource partitioning) had, on average, no effect on sampling effect. Interestingly, resource partitioning and facilitation interacted so the effect of facilitation on sampling effect decreased as resource partitioning increased. Sampling effect was enhanced by the difference between the two functional groups in their efficiency in using resources. Morphological and physiological differences make one group outperform the other; once these differences were established further differences did not enhance the sampling effect. In addition, grass WUE and root overlap positively influence the biodiversity effect but showed no interactions.

Nitrogen dynamics in arctic tundra soils of varying age: differential responses to fertilization and warming.

In the foothills of the Brooks Range, Alaska, different glaciation histories have created landscapes with varying soil age. Productivity of most of these landscapes is generally N limited, but varies widely, as do plant species composition and soil properties (e.g., pH). We hypothesized that the projected changes in productivity and vegetation composition under a warmer climate might be mediated through differential changes in N availability across soil age. We compared readily available [water-soluble NH4 (+), NO3 (-), and amino acids (AA)], moderately available (soluble proteins), hydrolyzable, and total N pools across three tussock-tundra landscapes with soil ages ranging from 11.5k to 300k years. The effects of fertilization and warming on these N pools were also compared for the two younger sites. Readily available N was highest at the oldest site, and AA accounted for 80-89 % of this N. At the youngest site, inorganic N constituted the majority (80-97 %) of total readily available N. This variation reflected the large differences in plant functional group composition and soil chemical properties. Long-term (8-16 years) fertilization increased the soluble inorganic N by 20- to 100-fold at the intermediate-age site, but only by twofold to threefold at the youngest site. Warming caused small and inconsistent changes in the soil C:N ratio and AA, but only in soils beneath Eriophorum vaginatum, the dominant tussock-forming sedge. These differential responses suggest that the ecological consequences of warmer climates on these tundra ecosystems are more complex than simply elevated N-mineralization rates, and that the responses of landscapes might be impacted by soil age, or time since deglaciation.

Incident radiation and the allocation of nitrogen within Arctic plant canopies: implications for predicting gross primary productivity.

Arctic vegetation is characterized by high spatial variability in plant functional type (PFT) composition and gross primary productivity (P). Despite this variability, the two main drivers of P in sub-Arctic tundra are leaf area index (LT ) and total foliar nitrogen (NT ). LT and NT have been shown to be tightly coupled across PFTs in sub-Arctic tundra vegetation, which simplifies up-scaling by allowing quantification of the main drivers of P from remotely sensed LT . Our objective was to test the LT -NT relationship across multiple Arctic latitudes and to assess LT as a predictor of P for the pan-Arctic. Including PFT-specific parameters in models of LT -NT coupling provided only incremental improvements in model fit, but significant improvements were gained from including site-specific parameters. The degree of curvature in the LT -NT relationship, controlled by a fitted canopy nitrogen extinction co-efficient, was negatively related to average levels of diffuse radiation at a site. This is consistent with theoretical predictions of more uniform vertical canopy N distributions under diffuse light conditions. Higher latitude sites had higher average leaf N content by mass (NM ), and we show for the first time that LT -NT coupling is achieved across latitudes via canopy-scale trade-offs between NM and leaf mass per unit leaf area (LM ). Site-specific parameters provided small but significant improvements in models of P based on LT and moss cover. Our results suggest that differences in LT -NT coupling between sites could be used to improve pan-Arctic models of P and we provide unique evidence that prevailing radiation conditions can significantly affect N allocation over regional scales.

Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter.

Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions. We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data. We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified leaf-area trajectory and with the EnKF sequentially recalibrating leaf-area estimates to compensate for persistent model-data deviations. When used together, adaptive noise estimation and sequential recalibration substantially improved filter performance, but it did not improve performance when used individually. The EnKF estimates of leaf area followed the expected springtime canopy phenology. However, there were also diel fluctuations in the leaf-area estimates; these are a clear indication of a model deficiency possibly related to vapor pressure effects on canopy conductance.

Resource-based niches provide a basis for plant species diversity and dominance in arctic tundra.

Ecologists have long been intrigued by the ways co-occurring species divide limiting resources. Such resource partitioning, or niche differentiation, may promote species diversity by reducing competition. Although resource partitioning is an important determinant of species diversity and composition in animal communities, its importance in structuring plant communities has been difficult to resolve. This is due mainly to difficulties in studying how plants compete for below-ground resources. Here we provide evidence from a 15N-tracer field experiment showing that plant species in a nitrogen-limited, arctic tundra community were differentiated in timing, depth and chemical form of nitrogen uptake, and that species dominance was strongly correlated with uptake of the most available soil nitrogen forms. That is, the most productive species used the most abundant nitrogen forms, and less productive species used less abundant forms. To our knowledge, this is the first documentation that the composition of a plant community is related to partitioning of differentially available forms of a single limiting resource.

The controls on net ecosystem productivity along an Arctic transect: a model comparison with flux measurements.

Assessments of carbon (C) fluxes in the Arctic require detailed data on both how and why these fluxes vary across the landscape. Such assessments are complicated because tundra vegetation has diverse structure and function at both local and regional scales. To investigate this diversity, the Arctic Flux Study has used the eddy covariance technique to generate ecosystem CO2 -exchange data along a transect in northern Alaska. We use an extant process-based model of the soil-plant-atmosphere continuum to make independent predictions of gross photosynthesis and foliar respiration at 9 of the sites along the transect, using data on local canopy structure and meteorology. We make two key assumptions: (i) soil respiration is constant throughout the flux measurement period, so that the diurnal cycle in CO2 exchange is driven by canopy processes only (except at two sites where a soil respiration-temperature relationship was indicated in the data); and (ii) mosses and lichens play an insignificant role in ecosystem C exchange, even though in some locations their live biomass exceeds 300 g m-2 . We found that even with these assumptions the model could explain much of the dynamics of net ecosystem production (NEP) at sites with widely differing vegetation structure and moss/lichen cover. Errors were mostly associated with the predictions of maximum NEP; the likely cause of such discrepancies was (i) a mismatch between vegetation sampled for characterizing the canopy structure and that contained within the footprint of the eddy covariance flux measurements, or (ii) an increase in daytime soil and root respiration. Thus the model results tended to falsify our first assumption but not our second. We also note evidence for an actual reduction in NEP caused by water stress on warm, dry days at some sites. The model-flux comparison also suggests that photosynthesis may be less sensitive to low temperatures than leaf-level gas-exchange measurements have indicated.

Aggregating Fine-Scale Ecological Knowledge to Model Coarser-Scale Attributes of Ecosystems.

As regional and global scales become more important to ecologists, methods must be developed for the application of existing fine-scale knowledge to predict coarser-scale ecosystem properties. This generally involves some form of model in which fine-scale components are aggregated. This aggregation is necessary to avoid the cumulative error associated with the estimation of a large number of parameters. However, aggregation can itself produce errors that arise because of the variation among the aggregated components. The statistical expectation operator can be used as a rigorous method for translating fine-scale relationships to coarser scales without aggregation errors. Unfortunately this method is too cumbersome to be applied in most cases, and alternative methods must be used. These alternative methods are typically partial corrections for the variation in only a few of the fine-scale attributes. Therefore, for these methods to be effective, the attributes that are the most severe sources of error must be identified a priori. We present a procedure for making these identifications based on a Monte Carlo sampling of the fine-scale attributes. We then present four methods of translating fine-scale knowledge so it can be applied at coarser scales: (1) partial transformations using the expectation operator, (2) moment expansions, (3) partitioning, and (4) calibration. These methods should make it possible to apply the vast store of fine-scale ecological knowledge to model coarser-scale attributes of ecosystems.

A general biogeochemical model describing the responses of the C and N cycles in terrestrial ecosystems to changes in CO(2), climate, and N deposition.

A model that simulates carbon (C) and nitrogen (N) cycles in terrestrial ecosystems is developed. The model is based on the principle that the responses of terrestrial ecosystems to changes in CO(2), climate, and N deposition will encompass enzymatic responses, shifts in tissue stoichiometry, changes in biomass allocation among plant tissues, altered rates of soil organic matter turnover and N mineralization, and ultimately a redistribution of C and N between vegetation and soils. The model is a highly aggregated, process-based, biogeochemical model designed to examine changes in the fluxes and allocation of C and N among foliage, fine roots, stems, and soils in response to changes in atmospheric CO(2) concentration, temperature, soil water, irradiance, and inorganic nitrogen inputs. We use the model to explore how changes in CO(2) concentration, temperature, and N inputs affect carbon storage in two ecosystems: arctic tundra and temperate hardwood forest. The qualitative responses of the two ecosystems were similar. Quantitative differences are attributed to the initial distribution of C and N between vegetation and soils, to the amounts of woody tissue in the two ecosystems, and to their relative degree of N limitation. We conclude with a critical analysis of the model's strengths and weaknesses, and discuss possible future directions.