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Open Access Publications from the University of California

Faculty Publications

The Department of Earth System Science (ESS) focuses on how the atmosphere, land, and oceans interact as a system, and how the Earth will change over a human lifetime.

Cover page of H<sub>2</sub> in Antarctic firn air: Atmospheric reconstructions and implications for anthropogenic emissions.

H2 in Antarctic firn air: Atmospheric reconstructions and implications for anthropogenic emissions.

(2021)

The atmospheric history of molecular hydrogen (H2) from 1852 to 2003 was reconstructed from measurements of firn air collected at Megadunes, Antarctica. The reconstruction shows that H2 levels in the southern hemisphere were roughly constant near 330 parts per billion (ppb; nmol H2 mol-1 air) during the mid to late 1800s. Over the twentieth century, H2 levels rose by about 70% to 550 ppb. The reconstruction shows good agreement with the H2 atmospheric history based on firn air measurements from the South Pole. The broad trends in atmospheric H2 over the twentieth century can be explained by increased methane oxidation and anthropogenic emissions. The H2 rise shows no evidence of deceleration during the last quarter of the twentieth century despite an expected reduction in automotive emissions following more stringent regulations. During the late twentieth century, atmospheric CO levels decreased due to a reduction in automotive emissions. It is surprising that atmospheric H2 did not respond similarly as automotive exhaust is thought to be the dominant source of anthropogenic H2. The monotonic late twentieth century rise in H2 levels is consistent with late twentieth-century flask air measurements from high southern latitudes. An additional unknown source of H2 is needed to explain twentieth-century trends in atmospheric H2 and to resolve the discrepancy between bottom-up and top-down estimates of the anthropogenic source term. The firn air-based atmospheric history of H2 provides a baseline from which to assess human impact on the H2 cycle over the last 150 y and validate models that will be used to project future trends in atmospheric composition as H2 becomes a more common energy source.

A Bayesian Approach to Evaluation of Soil Biogeochemical Models

(2021)

Abstract. To make predictions about the effect of rising global surface temperatures, we rely on mathematical soil biogeochemical models (SBMs). However, it is not clear which models have better predictive accuracy, and a rigorous quantitative approach for comparing and validating the predictions has yet to be established. In this study, we present a Bayesian approach to SBM comparison that can be incorporated into a statistical model selection framework. We compared the fits of a linear and non-linear SBM to soil respiration CO2 flux data compiled in a recent meta-analysis of soil warming field experiments. Fit quality was quantified using two Bayesian goodness-of-fit metrics, the Widely Applicable information criterion (WAIC) and Leave-one-out cross-validation (LOO). We found that the linear model generally out-performed the non-linear model at fitting the meta-analysis data set. Both WAIC and LOO computed a higher overfitting penalty for the non-linear model than the linear model, conditional on the data set. Fits for both models generally improved when they were initialized with lower and more realistic steady state soil organic carbon densities. Testing whether linear models offer definitively superior predictive performance over non-linear models on a global scale will require comparisons with additional site-specific data sets of suitable size and dimensionality. Such comparisons can build upon the approach defined in this study to make more rigorous statistical determinations about model accuracy while leveraging emerging data sets, such as those from long-term ecological research experiments.

Linking microbial communities to soil carbon cycling under anthropogenic change using a trait-based framework

(2021)

Microbial physiology may be critical for projecting future changes in soil carbon. Still, predicting the ecosystem implications of microbial processes remains a challenge. We argue that this challenge can be met by identifying microbial life history strategies based on their phenotypic characteristics, or traits, and representing these strategies in models simulating different environmental conditions. By adapting several theories from macroecology, we define microbial high yield (Y), resource acquisition (A), and stress tolerance (S) strategies. Using multi-omics and carbon stable isotope probing tools, we empirically validated our Y-A-S framework by studying variations in community traits along gradients of resource availability and abiotic conditions arising from anthropogenic change. Across a Britain-wide land use intensity gradient, we used isotope tracing and metaproteomics to show that microbial resource acquisition and stress tolerance traits trade off with growth yield measured as carbon use efficiency. Reduced community growth yield with intensification was linked to decreased microbial biomass and increased biomass-specific respiration which subsequently translated into lower organic carbon storage in such soil systems. We concluded that less-intensive management practices have more potential for carbon storage through increased microbial growth yield by greater channelling of substrates into biomass synthesis. In Californian grass and shrub ecosystems, we used metatranscriptomics and metabolomics to infer traits of in situ microbial communities on plant leaf litter in response to long-term drought. This experimental set-up provided gradients of resource availability and water stress. We observed that drought causes greater microbial allocation to stress tolerance. The most discernable physiological adaptations to drought in litter communities were production or uptake of compatible solutes like trehalose and ectoine as well as inorganic ions to maintain cellular osmotic balance. Grass communities also increased expression of genes for synthesis of capsular and extracellular polymeric substances possibly as a mechanism to retain water. These results showed a clear functional response to drought in grass litter communities with greater allocation to survival relative to growth that reduced decomposition under drought. In contrast, communities on chemically complex shrub litter had smaller differences in gene expression and metabolite profiles in response to drought, suggesting that the drought stress response is constrained by litter chemistry which also reduces decomposition rates. Overall, our findings suggest trade-offs between drought stress tolerance, resource acquisition and growth yield in communities across different ecosystems. These empirical studies demonstrate how trade-offs in key microbial traits can have consequences on soil carbon decomposition and storage. We recommend the use of our Y-A-S framework in experimental and modelling studies to mechanistically link microbial communities to system-level processes.

Cover page of Carbon Cycle Implications of Soil Microbial Interactions

Carbon Cycle Implications of Soil Microbial Interactions

(2021)

The soil environment contains the largest pool of carbon on Earth, with controls on soil carbon residency and flux being an emergent property of microbial metabolism. Despite the fact that microbial interactions have metabolic implications, the contribution of interactions are often overlooked regarding the carbon cycle. Here, we hypothesize that microbial interactions are intrinsically coupled to carbon cycling through eco-evolutionary principles. Interactions drive phenotypic responses that result in allocation pattern shifts and changes in carbon use efficiency. These changes promote alterations in resource availability and community structure, thereby creating selective pressures that contribute to diffuse evolutionary mechanisms. The outcomes then feed back into microbial metabolic operations with consequences for carbon turnover, continuing a feedback loop of microbial interactions, evolutionary processes, and the carbon cycle.

Cover page of Dynamic simulation of carbonate fuel cell-gas turbine hybrid systems

Dynamic simulation of carbonate fuel cell-gas turbine hybrid systems

(2021)

Hybrid fuel cell/gas turbine systems provide an efficient means of producing electricity from fossil fuels with ultra low emissions. However, there are many significant challenges involved in integrating the fuel cell with the gas turbine and other components of this type of system. The fuel cell and the gas turbine must maintain efficient operation and electricity production while protecting equipment during perturbations that may occur when the system is connected to the utility grid or in stand-alone mode. This paper presents recent dynamic simulation results from two laboratories focused on developing tools to aid in the design and dynamic analyses of hybrid fuel cell systems. The simulation results present the response of a carbonate fuel cell/gas turbine, or molten carbonate fuel cell/gas turbine, (MCFC/GT) hybrid system to a load demand perturbation. Initial results suggest that creative control strategies will be needed to ensure a flexible system with wide turndown and robust dynamic operation. Paper No. GT2004-53653,

Cover page of The role of fire in global forest loss dynamics.

The role of fire in global forest loss dynamics.

(2021)

Fires, among other forms of natural and anthropogenic disturbance, play a central role in regulating the location, composition and biomass of forests. Understanding the role of fire in global forest loss is crucial in constraining land-use change emissions and the global carbon cycle. We analysed the relationship between forest loss and fire at 500 m resolution based on satellite-derived data for the 2003-2018 period. Satellite fire data included burned area and active fire detections, to best account for large and small fires, respectively. We found that, on average, 38 ± 9% (± range) of global forest loss was associated with fire, and this fraction remained relatively stable throughout the study period. However, the fraction of fire-related forest loss varied substantially on a regional basis, and showed statistically significant trends in key tropical forest areas. Decreases in the fraction of fire-related forest loss were found where deforestation peaked early in our study period, including the Amazon and Indonesia while increases were found for tropical forests in Africa. The inclusion of active fire detections accounted for 41%, on average, of the total fire-related forest loss, with larger contributions in small clearings in interior tropical forests and human-dominated landscapes. Comparison to higher-resolution fire data with resolutions of 375 and 20 m indicated that commission errors due to coarse resolution fire data largely balanced out omission errors due to missed small fire detections for regional to continental-scale estimates of fire-related forest loss. Besides an improved understanding of forest dynamics, these findings may help to refine and separate fire-related and non-fire-related land-use change emissions in forested ecosystems.

Cover page of The Latent Dirichlet Allocation model with covariates (LDAcov): A case study on the effect of fire on species composition in Amazonian forests.

The Latent Dirichlet Allocation model with covariates (LDAcov): A case study on the effect of fire on species composition in Amazonian forests.

(2021)

Understanding and predicting the effect of global change phenomena on biodiversity is challenging given that biodiversity data are highly multivariate, containing information from tens to hundreds of species in any given location and time. The Latent Dirichlet Allocation (LDA) model has been recently proposed to decompose biodiversity data into latent communities. While LDA is a very useful exploratory tool and overcomes several limitations of earlier methods, it has limited inferential and predictive skill given that covariates cannot be included in the model. We introduce a modified LDA model (called LDAcov) which allows the incorporation of covariates, enabling inference on the drivers of change of latent communities, spatial interpolation of results, and prediction based on future environmental change scenarios. We show with simulated data that our approach to fitting LDAcov is able to estimate well the number of groups and all model parameters. We illustrate LDAcov using data from two experimental studies on the long-term effects of fire on southeastern Amazonian forests in Brazil. Our results reveal that repeated fires can have a strong impact on plant assemblages, particularly if fuel is allowed to build up between consecutive fires. The effect of fire is exacerbated as distance to the edge of the forest decreases, with small-sized species and species with thin bark being impacted the most. These results highlight the compounding impacts of multiple fire events and fragmentation, a scenario commonly found across the southern edge of Amazon. We believe that LDAcov will be of wide interest to scientists studying the effect of global change phenomena on biodiversity using high-dimensional datasets. Thus, we developed the R package LDAcov to enable the straightforward use of this model.

Drought legacies mediated by trait trade-offs in soil microbiomes

(2021)

Soil microbiomes play a key role in driving biogeochemical cycles of the Earth system. As drought frequency and intensity increase due to climate change, soil microbes and the processes they control will be impacted. Even after a drought ends, microbiomes and other systems take time to recover and may display a memory of previous climate conditions. Still, the mechanisms involved in these legacy effects remain unclear, making it difficult to predict climate and biogeochemical rates in the future. Here, we used a trait-based microbiome model (DEMENTpy) to implement trade-off-mediated mechanisms that may lead to drought legacy effects on litter decomposition. Trade-offs were assumed to follow the Y-A-S framework that defines three primary life-history strategies of microorganisms: high growth Yield, resource Acquisition, and Stress tolerance. We represented cellular trade-offs between osmolytes required for drought tolerance and investment in enzymes involved in litter decomposition. Simulations were run under varying levels of drought severity and dispersal. With high levels of dispersal, no legacy effects were predicted by DEMENTpy following drought. With limited dispersal, severe drought resulted in a persistent legacy of altered community-level traits and reduced litter decomposition. Moderate drought resulted in a transient legacy that disappeared after two years, consistent with recent empirical observations in Southern California ecosystems. These results imply that greater movement along the trade-off between enzyme investment and osmolyte production resulted in stronger legacy effects. More generally, factors that shift the position of a microbiome in YAS space may alter the legacy outcome following drought. Our trait-based modeling study motivates additional empirical measurements to quantify YAS traits and trade-offs that are needed to make accurate predictions of soil microbiome resilience and functioning. Also, our study illustrates an emerging approach for representing trait trade-offs in microbiomes and vegetation that dictate ecosystem responses to drought and other environmental perturbations.