Modeling for understanding v. modeling for numbers

Author Posting. © The Author(s), 2016. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Ecosystems 20 (2017): 215-221, doi:10.1007/s10021-016-0067-y.I draw a distinction between Modeling for Numbers, which aims to address how much, when, and where questions, and Modeling for Understanding, which aims to address how and why questions. For-numbers models are often empirical, which can be more accurate than their mechanistic analogues as long as they are well calibrated and predictions are made within the domain of the calibration data. To extrapolate beyond the domain of available system-level data, for-numbers models should be mechanistic, relying on the ability to calibrate to the system components even if it is not possible to calibrate to the system itself. However, development of a mechanistic model that is reliable depends on an adequate understanding of the system. This understanding is best advanced using a for-understanding modeling approach. To address how and why questions, for-understanding models have to be mechanistic. The best of these for-understanding models are focused on specific questions, stripped of extraneous detail, and elegantly simple. Once the mechanisms are well understood, one can then decide if the benefits of incorporating the mechanism in a for-numbers model is worth the added complexity and the uncertainty associated with estimating the additional model parameters.This work has been supported in part by NSF grants 0949420, 1026843, 1065587, 1107707, and 1503781.2017-11-1

Modeling coupled biogeochemical cycles

Author Posting. © Ecological Society of America, 2011. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Frontiers in Ecology and the Environment 9 (2011): 68–73, doi:10.1890/090223.Organisms require about 30 essential elements to sustain life. The cycles of these elements are coupled to one another through the specific physiological requirements of the organisms. Here, I contrast several approaches to modeling coupled biogeochemical cycles using an example of carbon, nitrogen, and phosphorus accumulation in a Douglas-fir (Pseudotsuga menziesii) forest ecosystem and the response of that forest to elevated atmospheric carbon dioxide concentrations and global warming. Which of these approaches is most appropriate is subject to debate and probably depends on context; nevertheless, this question must be answered if scientists are to understand ecosystems and how they might respond to a changing global environment.This work was supported by National Science Foundation (NSF) grant #DEB-0716067

An approach to modeling resource optimization for substitutable and interdependent resources

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Rastetter, E. B., & Kwiatkowski, B. L. An approach to modeling resource optimization for substitutable and interdependent resources. Ecological Modelling, 425, (2020): 109033, doi:10.1016/j.ecolmodel.2020.109033.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.This work was supported in part by the National Science Foundation under NSF grants 1651722, 1637459, 1603560, 1556772, 1841608. Any Opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the National Science Foundation

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 those 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.Fil: Flombaum, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinacion Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; ArgentinaFil: Sala, Osvaldo Esteban. Arizona State University. School of Life Sciences and School of Sustainability; Estados UnidosFil: Rastetter, Edward B.. Marine Biological Laboratory. The Ecosystem Center; Estados Unido

Responses of a tundra system to warming using SCAMPS : a stoichiometrically coupled, acclimating microbe–plant–soil model

Author Posting. © Ecological Society of America, 2014. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecological Monographs 84 (2014): 151-170, doi:10.1890/12-2119.1.Soils, plants, and microbial communities respond to global change perturbations through coupled, nonlinear interactions. Dynamic ecological responses complicate projecting how global change disturbances will influence ecosystem processes, such as carbon (C) storage. We developed an ecosystem-scale model (Stoichiometrically Coupled, Acclimating Microbe–Plant–Soil model, SCAMPS) that simulates the dynamic feedbacks between aboveground and belowground communities that affect their shared soil environment. The belowground component of the model includes three classes of soil organic matter (SOM), three microbially synthesized extracellular enzyme classes specific to these SOM pools, and a microbial biomass pool with a variable C-to-N ratio (C:N). The plant biomass, which contributes to the SOM pools, flexibly allocates growth toward wood, root, and leaf biomass, based on nitrogen (N) uptake and shoot-to-root ratio. Unlike traditional ecosystem models, the microbial community can acclimate to changing soil resources by shifting its C:N between a lower C:N, faster turnover (bacteria-like) community, and a higher C:N, slower turnover (fungal-like) community. This stoichiometric flexibility allows for the microbial C and N use efficiency to vary, feeding back into system decomposition and productivity dynamics. These feedbacks regulate changes in extracellular enzyme synthesis, soil pool turnover rates, plant growth, and ecosystem C storage. We used SCAMPS to test the interactive effects of winter, summer, and year-round soil warming, in combination with microbial acclimation ability, on decomposition dynamics and plant growth in a tundra system. Over 50-year simulations, both the seasonality of warming and the ability of the microbial community to acclimate had strong effects on ecosystem C dynamics. Across all scenarios, warming increased plant biomass (and therefore litter inputs to the SOM), while the ability of the microbial community to acclimate increased soil C loss. Winter warming drove the largest ecosystem C losses when the microbial community could acclimate, and the largest ecosystem C gains when it could not acclimate. Similar to empirical studies of tundra warming, modeled summer warming had relatively negligible effects on soil C loss, regardless of acclimation ability. In contrast, winter and year-round warming drove marked soil C loss when decomposers could acclimate, despite also increasing plant biomass. These results suggest that incorporating dynamically interacting microbial and plant communities into ecosystem models might increase the ability to link ongoing global change field observations with macro-scale projections of ecosystem biogeochemical cycling in systems under change.This work was funded by a DOE Global Change Education Program Graduate Fellowship, the NOAA Climate and Global Change Postdoctoral Fellowship Program, and UCSB EEMB Block Grant to S. A. Sistla and NSF DEB 0919049 to E. B. Rastetter and J. P. Schimel, and Arctic LTER Project NSF-1026843

Controls on nitrogen cycling in terrestrial ecosystems : a synthetic analysis of literature data

Author Posting. © Ecological Society of America, 2005. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecological Monographs 75 (2005): 139–157, doi:10.1890/04-0988.Isotope pool dilution studies are increasingly reported in the soils and ecology literature as a means of measuring gross rates of nitrogen (N) mineralization, nitrification, and inorganic N assimilation in soils. We assembled data on soil characteristics and gross rates from 100 studies conducted in forest, shrubland, grassland, and agricultural systems to answer the following questions: What factors appear to be the major drivers for production and consumption of inorganic N as measured by isotope dilution studies? Do rates or the relationships between drivers and rates differ among ecosystem types? Across a wide range of ecosystems, gross N mineralization is positively correlated with microbial biomass and soil C and N concentrations, while soil C:N ratio exerts a negative effect on N mineralization only after adjusting for differences in soil C. Nitrification is a log-linear function of N mineralization, increasing rapidly at low mineralization rates but changing only slightly at high mineralization rates. In contrast, NH4+ assimilation by soil microbes increases nearly linearly over the full range of mineralization rates. As a result, nitrification is proportionately more important as a fate for NH4+ at low mineralization rates than at high mineralization rates. Gross nitrification rates show no relationship to soil pH, with some of the fastest nitrification rates occurring below pH 5 in soils with high N mineralization rates. Differences in soil organic matter (SOM) composition and concentration among ecosystem types influence the production and fate of mineralized N. Soil organic matter from grasslands appears to be inherently more productive of ammonium than SOM from wooded sites, and SOM from deciduous forests is more so than SOM in coniferous forests, but differences appear to result primarily from differing C:N ratios of organic matter. Because of the central importance of SOM characteristics and concentrations in regulating rates, soil organic matter depletion in agricultural systems appears to be an important determinant of gross process rates and the proportion of NH4+ that is nitrified. Addition of 15N appears to stimulate NH4+ consumption more than NO3− consumption processes; however, the magnitude of the stimulation may provide useful information regarding the factors limiting microbial N transformations.This research was supported by a grant from The Andrew W. Mellon Foundation to The Ecosystems Center of the Marine Biological Laboratory, Woods Hole, Massachusetts, and by a grant from the National Science Foundation to Utah State University, Logan, Utah

Sporadic P limitation constrains microbial growth and facilitates SOM accumulation in the stoichiometrically coupled, acclimating microbe-plant-soil model

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Pold, G., Kwiatkowski, B. L., Rastetter, E. B., & Sistla, S. A. Sporadic P limitation constrains microbial growth and facilitates SOM accumulation in the stoichiometrically coupled, acclimating microbe-plant-soil model. Soil Biology & Biochemistry, 165, (2022): 108489, https://doi.org/10.1016/j.soilbio.2021.108489.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.This work was funded by the National Science Foundation Signals in the Soil grant number 1841610 to SAS and EBR

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

Author Posting. © The Author(s), 2013. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Oecologia 173 (2013): 1575-1586, doi:10.1007/s00442-013-2733-5.In the northern foothills of the Brooks Range, Alaska, a series of glacial retreats has created a landscape that varies widely in time since deglaciation (= soil age), from ~10k years to more than 2M years. Productivity of the moist tundra that covers most of this landscape is generally N-limited, but varies widely, as do plant-species composition and key soil properties such as pH. These differences might be altered in the future because of the projected increase in N availability under a warmer climate. We hypothesized that future changes in productivity and vegetation composition across soil ages might be mediated through changes in N availability. To test this hypothesis, we compared readily available-N (water-soluble ammonium, nitrate, and amino acids), moderately-available N (soluble proteins), hydrolysable-N, and total-N pools across three tussock-tundra landscapes with soil ages ranging from 11.5k to 300k years. We also compared the effects of long-term fertilization and warming on these N pools for the two younger sites, in order to assess whether the impacts of warming and increased N availability differ by soil age. Readily available N was largest at the oldest site, and amino acids (AA) accounted for 80-89 % of this N. At the youngest site, however, 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 soluble inorganic N by 20-100 fold at the intermediate-age site, but only by 2-3 fold at the youngest-soil site. Warming caused small and inconsistent changes in the soil C:N ratio and soluble AA, but only in soils beneath Eriophorum vaginatum, the dominant tussock-forming sedge. These differential responses suggest that the impacts of warmer climates on these tundra ecosystems are more complex than simply elevated N mineralization, and that the response of the N cycling might differ strongly depending on the ecosystem’s soil age, initial soil properties, and plant-community composition.Primary financial support came from NSF grant #DEB-0444592 to the MBL, and additional logistical support from NSF-OPP

Recovery from disturbance requires resynchronization of ecosystem nutrient cycles

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

Model responses to CO(2) and warming are underestimated without explicit representation of Arctic small-mammal grazing

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Rastetter, E. B., Griffin, K. L., Rowe, R. J., Gough, L., McLaren, J. R., & Boelman, N. T. Model responses to CO(2) and warming are underestimated without explicit representation of Arctic small-mammal grazing. Ecological Applications, (2021): e02478, https://doi.org/10.1002/eap.2478.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.This work was supported in part by the National Science Foundation under NSF grants 1651722, 1637459, 1603560, 1556772, 1841608 to E.B.R.; 1603777 to N.T.B. and K.L.G.; 1603654 to R.J.R.; 1603760 to L.G.; and 1603677 to J.R.M