Merging Ecology and Earth System Modeling: Biotic and Abiotic Controls Drive Soil Carbon and Nitrogen Cycling in Microbial-Explicit Soil Biogeochemistry Models

Ecological processes drive terrestrial biogeochemistry, yet the incorporation of ecology into the Earth system models that we use to understand and project global change remains. My dissertation focuses on expediting the incorporation of ecology into Earth system models, first by laying out a roadmap from initial assessment of ecological insights to eventual ESM incorporation, and then by demonstrating this roadmap using the example of microbially- controlled carbon and nitrogen cycling in soil. The paradigm around SOM formation and loss has shifted in recent decades away from a focus on the chemically recalcitrant leftovers of litter decomposition and towards a paradigm with microbial residues and mineral interactions at its heart. The MIcrobial-MIneral Carbon Stabilization model (MIMICS) was developed as a way of exploring this new paradigm and examining the relationships between environmental drivers, litter chemistry, microbial physiology, and physical and chemical stabilization mechanisms for SOM. In the first chapter of my dissertation, I document a systematic approach to improve ecological process representation in Earth system models, highlighting multiple points along the way where ecological observations and modeling iteratively strengthen one another. In the second chapter, I develop and validate a new version of MIMICS with coupled N cycling using a large litter decomposition dataset. In the final chapter, I examine MIMICS-CN’s representation of the drivers of SOM C:N ratios using a landscape-scale data synthesis and model-data comparison. Together, these chapters describe and demonstrate the process of improving biogeochemical models along the path to ESMs by introducing new process representations of ecological concepts

Microbial carbon use efficiency: accounting for population, community, and ecosystem-scale controls over the fate of metabolized organic matter

Microbial carbon use efficiency (CUE) is a critical regulator of soil organic matter dynamics and terrestrial carbon fluxes, with strong implications for soil biogeochemistry models. While ecologists increasingly appreciate the importance of CUE, its core concepts remain ambiguous: terminology is inconsistent and confusing, methods capture variable temporal and spatial scales, and the significance of many fundamental drivers remains inconclusive. Here we outline the processes underlying microbial efficiency and propose a conceptual framework that structures the definition of CUE according to increasingly broad temporal and spatial drivers where (1) CUEP reflects population-scale carbon use efficiency of microbes governed by species-specific metabolic and thermodynamic constraints, (2) CUEC defines community-scale microbial efficiency as gross biomass production per unit substrate taken up over short time scales, largely excluding recycling of microbial necromass and exudates, and (3) CUEE reflects the ecosystem-scale efficiency of net microbial biomass production (growth) per unit substrate taken up as iterative breakdown and recycling of microbial products occurs. CUEE integrates all internal and extracellular constraints on CUE and hence embodies an ecosystem perspective that fully captures all drivers of microbial biomass synthesis and decay. These three definitions are distinct yet complementary, capturing the capacity for carbon storage in microbial biomass across different ecological scales. By unifying the existing concepts and terminology underlying microbial efficiency, our framework enhances data interpretation and theoretical advances

Divergent controls of soil organic carbon between observations and process-based models

The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationships a priori. While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions

SoDaH: the SOils DAta Harmonization database, an open-source synthesis of soil data from research networks, version 1.0

Data collected from research networks present opportunities to test theories and develop models about factors responsible for the long-term persistence and vulnerability of soil organic matter (SOM). Synthesizing datasets collected by different research networks presents opportunities to expand the ecological gradients and scientific breadth of information available for inquiry. Synthesizing these data is challenging, especially considering the legacy of soil data that have already been collected and an expansion of new network science initiatives. To facilitate this effort, here we present the SOils DAta Harmonization database (SoDaH; https://lter.github.io/som-website, last access: 22 December 2020), a flexible database designed to harmonize diverse SOM datasets from multiple research networks. SoDaH is built on several network science efforts in the United States, but the tools built for SoDaH aim to provide an open-access resource to facilitate synthesis of soil carbon data. Moreover, SoDaH allows for individual locations to contribute results from experimental manipulations, repeated measurements from long-term studies, and local- to regional-scale gradients across ecosystems or landscapes. Finally, we also provide data visualization and analysis tools that can be used to query and analyze the aggregated database. The SoDaH v1.0 dataset is archived and available at https://doi.org/10.6073/pasta/9733f6b6d2ffd12bf126dc36a763e0b4 (Wieder et al., 2020)

Microbial carbon use efficiency: accounting for population, community, and ecosystem-scale controls over the fate of metabolized organic matter

Microbial carbon use efficiency (CUE) is a critical regulator of soil organic matter dynamics and terrestrial carbon fluxes, with strong implications for soil biogeochemistry models. While ecologists increasingly appreciate the importance of CUE, its core concepts remain ambiguous: terminology is inconsistent and confusing, methods capture variable temporal and spatial scales, and the significance of many fundamental drivers remains inconclusive. Here we outline the processes underlying microbial efficiency and propose a conceptual framework that structures the definition of CUE according to increasingly broad temporal and spatial drivers where (1) CUEP reflects population-scale carbon use efficiency of microbes governed by species-specific metabolic and thermodynamic constraints, (2) CUEC defines community-scale microbial efficiency as gross biomass production per unit substrate taken up over short time scales, largely excluding recycling of microbial necromass and exudates, and (3) CUEE reflects the ecosystem-scale efficiency of net microbial biomass production (growth) per unit substrate taken up as iterative breakdown and recycling of microbial products occurs. CUEE integrates all internal and extracellular constraints on CUE and hence embodies an ecosystem perspective that fully captures all drivers of microbial biomass synthesis and decay. These three definitions are distinct yet complementary, capturing the capacity for carbon storage in microbial biomass across different ecological scales. By unifying the existing concepts and terminology underlying microbial efficiency, our framework enhances data interpretation and theoretical advances

A click chemistry-based, free radical-initiated delivery system for the capture and release of payloads

AbstractClick chemistries are efficient and selective reactions that have been leveraged for multi-stage drug delivery. A multi-stage system allows independent delivery of targeting molecules and drug payloads, but targeting first-phase materials specifically to disease sites remains a challenge. Stimuli-responsive systems are an emerging strategy where common pathophysiological triggers are used to target payloads. Oxidative stress is widely implicated in disease, and we have previously demonstrated that reactive oxygen species (ROS) can crosslink and immobilize polyethylene glycol diacrylate (PEGDA) in tissue mimics. To build on these promising results, we present a two-step, catch-and-release system using azide-DBCO click chemistry and demonstrate the capture and eventual release of a fluorescent payload at defined times after the formation of a PEGDA capturing net. The azide component is included with radical-sensitive PEGDA, and the payload is conjugated to the DBCO group. In cell-free and cell-based tissue mimic models, azides were incorporated at 0–30% in the first-phase polymer net, and DBCO was delivered at 2.5–10 µM in the second phase to control payload delivery. The payload could be captured at multiple timepoints after initial net formation, yielding a flexible and versatile targeting system. Matrix metalloproteinase (MMP)-degradable peptides were incorporated into the polymer backbone to engineer fluorescent payload release by MMPs, which are broadly upregulated in diseases, through degradation of the capture net and directly from the DBCO. Taken together, this research demonstrates proof-of-principle for a responsive and clickable biomaterial to serve as a multi-potent agent for the treatment of diseases compounded by high free radicals

Increasing the spatial and temporal impact of ecological research: A roadmap for integrating a novel terrestrial process into an Earth system model

Terrestrial ecosystems regulate Earth’s climate through water, energy, and biogeochemical transformations. Despite a key role in regulating the Earth system, terrestrial ecology has historically been underrepresented in the Earth system models (ESMs) that are used to understand and project global environmental change. Ecology and Earth system modeling must be integrated for scientists to fully comprehend the role of ecological systems in driving and responding to global change. Ecological insights can improve ESM realism and reduce process uncertainty, while ESMs offer ecologists an opportunity to broadly test ecological theory and increase the impact of their work by scaling concepts through time and space. Despite this mutualism, meaningfully integrating the two remains a persistent challenge, in part because of logistical obstacles in translating processes into mathematical formulas and identifying ways to integrate new theories and code into large, complex model structures. To help overcome this interdisciplinary challenge, we present a framework consisting of a series of interconnected stages for integrating a new ecological process or insight into an ESM. First, we highlight the multiple ways that ecological observations and modeling iteratively strengthen one another, dispelling the illusion that the ecologist’s role ends with initial provision of data. Second, we show that many valuable insights, products, and theoretical developments are produced through sustained interdisciplinary collaborations between empiricists and modelers, regardless of eventual inclusion of a process in an ESM. Finally, we provide concrete actions and resources to facilitate learning and collaboration at every stage of data- model integration. This framework will create synergies that will transform our understanding of ecology within the Earth system, ultimately improving our understanding of global environmental change, and broadening the impact of ecological research.Terrestrial ecology has historically been underrepresented in the Earth system models (ESMs) that are used to understand and project global environmental change. The prevalent existing paradigm in ecology- ESM integration separates tasks along disciplinary lines. We recommend a new set of steps for ecology- ESM integration that shifts away from this historical paradigm toward a more collaborative one in which empiricists and modelers are involved in coproducing knowledge at every stage of data collection, theory development, and model integration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/171207/1/gcb15894_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/171207/2/gcb15894.pd