Sensitivity analysis as an aid in modelling and control of (poorly-defined) ecological systems

A literature review of the use of sensitivity analyses in modelling nonlinear, ill-defined systems, such as ecological interactions is presented. Discussions of previous work, and a proposed scheme for generalized sensitivity analysis applicable to ill-defined systems are included. This scheme considers classes of mathematical models, problem-defining behavior, analysis procedures (especially the use of Monte-Carlo methods), sensitivity ranking of parameters, and extension to control system design

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

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

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

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

Potential net primary productivity in South America: application of a global model

We use a mechanistically based ecosystem simulation model to describe and analyze the spatial and temporal patterns of terrestrial net primary productivity (NPP) in South America. The Terrestrial Ecosystem Model (TEM) is designed to predict major carbon and nitrogen fluxes and pool sizes in terrestrial ecosystems at continental to global scales. Information from intensively studies field sites is used in combination with continental—scale information on climate, soils, and vegetation to estimate NPP in each of 5888 non—wetland, 0.5° latitude °0.5° longitude grid cells in South America, at monthly time steps. Preliminary analyses are presented for the scenario of natural vegetation throughout the continent, as a prelude to evaluating human impacts on terrestrial NPP. The potential annual NPP of South America is estimated to be 12.5 Pg/yr of carbon (26.3 Pg/yr of organic matter) in a non—wetland area of 17.0 ° 106 km2. More than 50% of this production occurs in the tropical and subtropical evergreen forest region. Six independent model runs, each based on an independently derived set of model parameters, generated mean annual NPP estimates for the tropical evergreen forest region ranging from 900 to 1510 g°m—2°yr—1 of carbon, with an overall mean of 1170 g°m—2°yr—1. Coefficients of variation in estimated annual NPP averaged 20% for any specific location in the evergreen forests, which is probably within the confidence limits of extant NPP measurements. Predicted rates of mean annual NPP in other types of vegetation ranged from 95 g°m—2°yr—1 in arid shrublands to 930 g°m@?yr—1 in savannas, and were within the ranges measured in empirical studies. The spatial distribution of predicted NPP was directly compared with estimates made using the Miami mode of Lieth (1975). Overall, TEM predictions were °10% lower than those of the Miami model, but the two models agreed closely on the spatial patterns of NPP in south America. Unlike previous models, however, TEM estimates NPP monthly, allowing for the evaluation of seasonal phenomena. This is an important step toward integration of ecosystem models with remotely sensed information, global climate models, and atmospheric transport models, all of which are evaluated at comparable spatial and temporal scales. Seasonal patterns of NPP in South America are correlated with moisture availability in most vegetation types, but are strongly influenced by seasonal differences in cloudiness in the tropical evergreen forests. On an annual basis, moisture availability was the factor that was correlated most strongly with annual NPP in South America, but differences were again observed among vegetation types. These results allow for the investigation and analysis of climatic controls over NPP at continental scales, within and among vegetation types, and within years. Further model validation is needed. Nevertheless, the ability to investigate NPP—environment interactions with a high spatial and temporal resolution at continental scales should prove useful if not essential for rigorous analysis of the potential effects of global climate changes on terrestrial ecosystems

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