Kwiatkowski
Bonnie L.
Kwiatkowski
Bonnie L.
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ArticleRecovery from disturbance requires resynchronization of ecosystem nutrient cycles(Ecological Society of America, 2013-04) Rastetter, Edward B. ; Yanai, Ruth D. ; Thomas, R. Q. ; Vadeboncoeur, Matthew A. ; Fahey, Timothy J. ; Fisk, M. C. ; Kwiatkowski, Bonnie L. ; Hamburg, S. P.Nitrogen (N) and phosphorus (P) are tightly cycled in most terrestrial ecosystems, with plant uptake more than 10 times higher than the rate of supply from deposition and weathering. This near-total dependence on recycled nutrients and the stoichiometric constraints on resource use by plants and microbes mean that the two cycles have to be synchronized such that the ratio of N:P in plant uptake, litterfall, and net mineralization are nearly the same. Disturbance can disrupt this synchronization if there is a disproportionate loss of one nutrient relative to the other. We model the resynchronization of N and P cycles following harvest of a northern hardwood forest. In our simulations, nutrient loss in the harvest is small relative to postharvest losses. The low N:P ratio of harvest residue results in a preferential release of P and retention of N. The P release is in excess of plant requirements and P is lost from the active ecosystem cycle through secondary mineral formation and leaching early in succession. Because external P inputs are small, the resynchronization of the N and P cycles later in succession is achieved by a commensurate loss of N. Through succession, the ecosystem undergoes alternating periods of N limitation, then P limitation, and eventually co-limitation as the two cycles resynchronize. However, our simulations indicate that the overall rate and extent of recovery is limited by P unless a mechanism exists either to prevent the P loss early in succession (e.g., P sequestration not stoichiometrically constrained by N) or to increase the P supply to the ecosystem later in succession (e.g., biologically enhanced weathering). Our model provides a heuristic perspective from which to assess the resynchronization among tightly cycled nutrients and the effect of that resynchronization on recovery of ecosystems from disturbance.
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ArticleProcessing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter(Ecological Society of America, 2010-07) Rastetter, Edward B. ; Williams, Mathew ; Griffin, Kevin L. ; Kwiatkowski, Bonnie L. ; Tomasky, Gabrielle ; Potosnak, Mark J. ; Stoy, Paul C. ; Shaver, Gaius R. ; Stieglitz, Marc ; Hobbie, John E. ; Kling, George W.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.
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ArticleSporadic P limitation constrains microbial growth and facilitates SOM accumulation in the stoichiometrically coupled, acclimating microbe-plant-soil model(Elsevier, 2021-11-29) Pold, Grace ; Kwiatkowski, Bonnie L. ; Rastetter, Edward B. ; Sistla, Seeta A.Requirements for biomass carbon (C), nitrogen (N), and phosphorus (P) constrain organism growth and are important agents for structuring ecosystems. Arctic tundra habitats are strongly nutrient limited as decomposition and recycling of nutrients are slowed by low temperature. Modeling interactions among these elemental cycles affords an opportunity to explore how disturbances such as climate change might differentially affect these nutrient cycles. Here we introduce a C–N–P-coupled version of the Stoichiometrically Coupled Acclimating Microbe-Plant-Soil (SCAMPS) model, “SCAMPS-CNP”, and a corresponding modified CN-only model, “SCAMPS-CN”. We compared how SCAMPS-CNP and the modified SCAMPS-CN models project a moderate (RCP 6.0) air warming scenario will impact tussock tundra nutrient availability and ecosystem C stocks. SCAMPS-CNP was characterized by larger SOM and smaller organism C stocks compared to SCAMPS-CN, and a greater reduction in ecosystem C stocks under warming. This difference can largely be attributed to a smaller microbial biomass in the CNP model, which, instead of being driven by direct costs of P acquisition, was driven by variable resource limitation due to asynchronous C, N, and P availability and demand. Warming facilitated a greater relative increase in plant and microbial biomass in SCAMPS-CNP, however, facilitated by increased extracellular enzyme pools and activity, which more than offset the metabolic costs associated with their production. Although the microbial community was able to flexibly adapt its stoichiometry and become more bacteria-like (N-rich) in both models, its stoichiometry deviated further from its target value in the CNP model because of the need to balance cellular NP ratio. Our results indicate that seasonality and asynchrony in resources affect predicted changes in ecosystem C storage under warming in these models, and therefore build on a growing body of literature indicating stoichiometry should be considered in carbon cycling projections.
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ArticleN and P constrain C in ecosystems under climate change: role of nutrient redistribution, accumulation, and stoichiometry(Ecological Society of America, 2022-05-28) Rastetter, Edward B. ; Kwiatkowski, Bonnie L. ; Kicklighter, David W. ; Barker Plotkin, Audrey ; Genet, Helene ; Nippert, Jesse B. ; O'Keefe, Kimberly ; Perakis, Steven S. ; Porder, Stephen ; Roley, Sarah S. ; Ruess, Roger W. ; Thompson, Jonathan R. ; Wieder, William R. ; Wilcox, Kevin R. ; Yanai, Ruth D.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.
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ArticleAn approach to modeling resource optimization for substitutable and interdependent resources(Elsevier, 2020-03-31) Rastetter, Edward B. ; Kwiatkowski, Bonnie L.We develop a hierarchical approach to modeling organism acclimation to changing availability of and requirements for substitutable and interdependent resources. Substitutable resources are resources that fill the same metabolic or stoichiometric need of the organism. Interdependent resources are resources whose acquisition or expenditure are tightly linked (e.g., light, CO2, and water in photosynthesis and associated transpiration). We illustrate the approach by simulating the development of vegetation with four substitutable sources of N that differ only in the cost of their uptake and assimilation. As the vegetation develops, it uses the least expensive N source first then uses progressively more expensive N sources as the less expensive sources are depleted. Transition among N sources is based on the marginal yield of N per unit effort expended, including effort expended to acquire C to cover the progressively higher uptake costs. We illustrate the approach to interdependent resources by simulating the expenditure of effort to acquire light energy, CO2, and water to drive photosynthesis in vegetation acclimated to different conditions of soil water, atmospheric vapor pressure deficit, CO2 concentration, and light levels. The approach is an improvement on the resource optimization used in the earlier Multiple Element Limitation (MEL) model.
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PreprintA stable isotope simulator that can be coupled to existing mass balance models( 2005-01-07) Rastetter, Edward B. ; Kwiatkowski, Bonnie L. ; McKane, Robert B.To facilitate the simulation of isotope dynamics in ecosystems, we developed software to model changes in the isotopic signatures of the stocks of an element using the output from any parent model that specifies the stocks and flux rates of that element based on a mass balance approach. The software alleviates the need to recode the parent model to incorporate isotopes. This parent model can be a simple mass balance spreadsheet of the system. The isotopic simulations use a linear, donor-controlled approximation of the fluxes in the parent model, which are updated for each time step. These approximations are based on the output of the parent model, so no modifications to the parent model are required. However, all fluxes provided to the simulator must be gross fluxes, and the user must provide the initial isotopic signature for all stocks, the fractionation associated with each flux, and the isotopic signature of any flux originating from outside the system. We illustrate the use of the simulator with two examples. The first is based on a model of the carbon and nitrogen mass balance in an eight-species food web. We examine the consequences of using the steady-state assumption implicit in multi-source mixing models often used to map food webs based on 13C and 15N. We also use the simulator to analyze a pulse chase 15N-labeling experiment based on a spreadsheet model of the nitrogen cycle at the Harvard Forest Long Term Ecological Research site. We examine the constraints on net vs. gross N mineralization that are necessary to match the observed changes in the isotopic signatures of the forest N stocks.
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ArticleEcosystem feedbacks constrain the effect of day‐to‐day weather variability on land–atmosphere carbon exchange(Wiley, 2023-08-30) Rastetter, Edward B. ; Griffin, Kevin L. ; Kwiatkowski, Bonnie L. ; Kling, George W.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.