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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Carbon cycling across ecosystem succession in a north temperate forest: Controls and management implications

Despite decades of progress, much remains unknown about successional trajectories of carbon (C) cycling in north temperate forests. Drivers and mechanisms of these changes, including the role of different types of disturbances, are particularly elusive. To address this gap, we synthesized decades of data from experimental chronosequences and long-term monitoring at a well-studied, regionally representative field site in northern Michigan, USA. Our study provides a comprehensive assessment of changes in above- and belowground ecosystem components over two centuries of succession, links temporal dynamics in C pools and fluxes with underlying drivers, and offers several conceptual insights to the field of forest ecology. Our first advance shows how temporal dynamics in some ecosystem components are consistent across severe disturbances that reset succession and partial disturbances that slightly modify it: both of these disturbance types increase soil N availability, alter fungal community composition, and alter growth and competitive interactions between short-lived pioneer and longer-lived tree taxa. Further, these changes in turn affect soil C stocks, respiratory emissions, and other belowground processes. Second, we show that some other ecosystem components have effects on C cycling that are not consistent over the course of succession. For example, canopy structure does not influence C uptake early in succession but becomes important as stands develop, and the importance of individual structural properties changes over the course of two centuries of stand development. Third, we show that in recent decades, climate change is masking or overriding the influence of community composition on C uptake, while respiratory emissions are sensitive to both climatic and compositional change. In synthesis, we emphasize that time is not a driver of C cycling; it is a dimension within which ecosystem drivers such as canopy structure, tree and microbial community composition change. Changes in those drivers, not in forest age, are what control forest C trajectories, and those changes can happen quickly or slowly, through natural processes or deliberate intervention. Stemming from this view and a whole-ecosystem perspective on forest succession, we offer management applications from this work and assess its broader relevance to understanding long-term change in other north temperate forest ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Soil resistance and recovery during neotropical forest succession

The recovery of soil conditions is crucial for successful ecosystem restoration and, hence, for achieving the goals of the UN Decade on Ecosystem Restoration. Here, we assess how soils resist forest conversion and agricultural land use, and how soils recover during subsequent tropical forest succession on abandoned agricultural fields. Our overarching question is how soil resistance and recovery depend on local conditions such as climate, soil type and land-use history. For 300 plots in 21 sites across the Neotropics, we used a chronosequence approach in which we sampled soils from two depths in old-growth forests, agricultural fields (i.e. crop fields and pastures), and secondary forests that differ in age (1–95 years) since abandonment. We measured six soil properties using a standardized sampling design and laboratory analyses. Soil resistance strongly depended on local conditions. Croplands and sites on high-activity clay (i.e. high fertility) show strong increases in bulk density and decreases in pH, carbon (C) and nitrogen (N) during deforestation and subsequent agricultural use. Resistance is lower in such sites probably because of a sharp decline in fine root biomass in croplands in the upper soil layers, and a decline in litter input from formerly productive old-growth forest (on high-activity clays). Soil recovery also strongly depended on local conditions. During forest succession, high-activity clays and croplands decreased most strongly in bulk density and increased in C and N, possibly because of strongly compacted soils with low C and N after cropland abandonment, and because of rapid vegetation recovery in high-activity clays leading to greater fine root growth and litter input. Furthermore, sites at low precipitation decreased in pH, whereas sites at high precipitation increased in N and decreased in C : N ratio. Extractable phosphorus (P) did not recover during succession, suggesting increased P limitation as forests age. These results indicate that no single solution exists for effective soil restoration and that local site conditions should determine the restoration strategies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Assessing impacts of selective logging on water, energy, and carbon budgets and ecosystem dynamics in Amazon forests using the Functionally Assembled Terrestrial Ecosystem Simulator

Abstract. Tropical forest degradation from logging, fire, and fragmentation not only alters carbon stocks and carbon fluxes, but also impacts physical land surface properties such as albedo and roughness length. Such impacts are poorly quantified to date due to difficulties in accessing and maintaining observational infrastructures, as well as the lack of proper modeling tools for capturing the interactions among biophysical properties, ecosystem demography, canopy structure, and biogeochemical cycling in tropical forests. As a first step to address these limitations, we implemented a selective logging module into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) by mimicking the ecological, biophysical, and biogeochemical processes following a logging event. The model can specify the timing and aerial extent of logging events, splitting the logged forest patch into disturbed and intact patches; determine the survivorship of cohorts in the disturbed patch; and modifying the biomass and necromass (total mass of coarse woody debris and litter) pools following logging. We parameterized the logging module to reproduce a selective logging experiment at the Tapajós National Forest in Brazil and benchmarked model outputs against available field measurements. Our results suggest that the model permits the coexistence of early and late successional functional types and realistically characterizes the seasonality of water and carbon fluxes and stocks, the forest structure and composition, and the ecosystem succession following disturbance. However, the current version of FATES overestimates water stress in the dry season and therefore fails to capture seasonal variation in latent and sensible heat fluxes. Moreover, we observed a bias towards low stem density and leaf area when compared to observations, suggesting that improvements are needed in both carbon allocation and establishment of trees. The effects of logging were assessed by different logging scenarios to represent reduced impact and conventional logging practices, both with high and low logging intensities. The model simulations suggest that in comparison to old-growth forests the logged forests rapidly recover water and energy fluxes in 1 to 3 years. In contrast, the recovery times for carbon stocks, forest structure, and composition are more than 30 years depending on logging practices and intensity. This study lays the foundation to simulate land use change and forest degradation in FATES, which will be an effective tool to directly represent forest management practices and regeneration in the context of Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Learning to Predict and Improve Build Successes in Package Ecosystems

Software has become increasingly complex, with a typical application depending on tens or hundreds of packages. Finding compatible versions and build configurations of these packages is challenging. This paper presents a method to learn the likelihood of software build success, and techniques for leveraging this information to guide dependency solvers to better software configurations. We leverage the heavily parameterized package recipes from the Spack package manager to produce a training data set of builds, and we use Graph Neural Networks to learn whether a given package configuration will build successfully or not. We apply our tool to the U.S. Exascale Computing Project’s software stack. We demonstrate its effectiveness in predicting whether a given package will build successfully. We show that our technique can be used to improve the solutions generated by dependency solvers, reducing the need for developers to find working builds by trial and error.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Influence of restored mussel reefs on denitrification in marine sediments

Globally we have lost the majority of shellfish reefs, with the concomitant loss of ecosystem services. Restoration provides opportunities to regain lost services and engage society in the wider ecosystem benefits of restoration. Previous research has demonstrated that the functional role of shellfish can be context dependent, and we therefore measured fluxes of nutrients and dissolved gases from four restored mussel reefs along a grain size gradient in New Zealand as a metric of success in ecosystem function. This globally novel research found that restored mussel reefs were successful in enhancing denitrification. Mussel reefs can therefore be important in reducing the risk of future eutrophication, but the importance of nutrient remineralisation varies in response to site conditions. These data show that mussel reef creation can quickly restore ecosystem function, but site selection to enhance specific services is important. Furthermore, this study provides evidence of a poorly realised ecosystem service provided by restored mussel beds that allows future restoration efforts to better target areas in order to have the largest impact on the removal of anthropogenic nitrogen.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A new bioenergy model that simulates the impacts of plant‐microbial interactions, soil carbon protection, and mechanistic tillage on soil carbon cycling

Abstract Advancing our predictive understanding of bioenergy systems is critical to design decision tools that can inform which feedstock to plant, where to plant it, and how to manage its production to provide both energy and ecosystem carbon (C) benefits. Here, we lay the foundation for that advancement by integrating recent developments in the science of belowground processes in shaping the C cycle into a new bioenergy model, FUN‐BioCROP (Fixation and Uptake of Nitrogen‐Bioenergy Carbon, Rhizosphere, Organisms, and Protection). We show that FUN‐BioCROP can approximate the historical trajectory of soil C dynamics as natural ecosystems were successively converted into intensive agriculture and bioenergy systems. This ability relies in part on a novel tillage representation that mechanistically models tillage as a process that increases microbial access to C. Importantly, the impacts of tillage and feedstock choice also influence FUN‐BioCROP simulations of warming responses with no‐till perennial feedstocks, miscanthus, and switchgrass, having more C that is unprotected and susceptible to warming than tilled annual feedstocks like corn–corn–soybean. However, this susceptibility to warming is balanced by a greater potential for increases in belowground C allocation to enhance soil C stocks in perennial systems. Collectively, our model results highlight the importance of belowground processes in evaluating the ecosystem C benefits of bioenergy production.

09 BIOMASS FUELS↗
DOE OSTI2021

Widespread co‐occurrence of Sebacinales and arbuscular mycorrhizal fungi in switchgrass roots and soils has limited dependence on soil carbon or nutrients

Societal Impact Statement This work addresses a novel group of Sebacinales mycorrhizal fungi being considered for development as inoculants in managed ecosystems because of their potential access to nutrients in soil organic matter. By comparing the diversity, distributions, and abundances of the Sebacinales with the more common arbuscular mycorrhizal fungi in switchgrass, a biofuel crop, we demonstrate that current suggestions for a Sebacinales revolution in agriculture should be tempered by their ecology. In particular, Sebacinales are rare compared to arbuscular mycorrhizal fungi, and are only weakly associated with soil carbon, suggesting that ideas about improved soil carbon cycling associated with Sebacinales need to be sufficiently studied across a range of environmental conditions prior to their consideration for broad‐spectrum soil inoculants. Summary Arbuscular mycorrhizal (AM) fungi are widespread and important root symbionts, but recent work suggests that Sebacinales fungi may play an equally important role in both plant success and ecosystem carbon and nutrient cycling based on their worldwide occurrence and putative access to organic matter. However, the ecological impacts of Sebacinales will depend on their abundance and distribution relative to AM fungi and environmental soil carbon and nutrient patterns, which remain unexplored. We characterized Glomeromycota and Sebacinales fungi in switchgrass ( Panicum virgatum L.) roots and soils across 14 sites with diverse soil conditions. We examined group richness differences, co‐occurrence patterns, and how the relative abundance of these fungi related to soil carbon, nutrient stoichiometry, and host size. Sebacinales were widespread, but less diverse, common, and abundant than Glomeromycota. Moreover, co‐occurrences were predominantly random, suggesting relatively few interactions between these groups. Sebacinales increased relative to Glomeromycota in soils with more carbon, but explanatory power was limited. Based on our findings, we suggest that Sebacinales are likely complementary to AM fungi in roots. Expectations that Sebacinales have large effects on soil carbon and nutrient cycling may need to be reconsidered, at least based on their limited abundances relative to AM fungi in switchgrass. This is an important consideration as Sebacinales are candidates for use as inoculants in managed ecosystems.

Lee, Marissa R.↗
DOE OSTI2020

2020 State of the Science Report, Chapter 6: Changes in Benthic and Pelagic Habitats Caused by Marine Renewable Energy Devices

Most marine renewable energy (MRE) devices must be attached to the seafloor in some way, either through gravity foundations, pilings, or anchors, and with mooring lines, transmission cables, and devices themselves in the water column. Physical changes in benthic and pelagic habitats have the potential to alter species occurrence or abundance at a localized scale, lead to some level of habitat loss, provide opportunities for colonization by non-native species, alter patterns of ecological succession, modify ecosystem functioning, and affect behavioral responses of marine organisms. The transformation of the seafloor and/or water column habitat to new hard substratum because of the presence of the MRE devices may also lead to artificial reef effects or changes in animal behavior. While there is no indication that MRE devices affect marine habitats differently than other structures currently and historically placed in the ocean, regulators and stakeholders may continue to have concerns. https://tethys.pnnl.gov/publications/state-of-the-science-2020-chapter-6-habitat-changes

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Near‐Surface Hydrology and Soil Properties Drive Heterogeneity in Permafrost Distribution, Vegetation Dynamics, and Carbon Cycling in a Sub‐Arctic Watershed

Abstract Discontinuous permafrost environments exhibit strong spatial heterogeneity at scales too small to be driven by weather forcing or captured by Earth System Models. Here we analyze effects of observed spatial heterogeneity in soil and vegetation properties, hydrology, and thermal dynamics on ecosystem carbon dynamics in a watershed on the Seward Peninsula in Alaska. We apply a Morris global sensitivity analysis to a process‐rich, successfully tested terrestrial ecosystem model (TEM), ecosys , varying soil properties, boundary conditions, and weather forcing. We show that landscape heterogeneity strongly impacts soil temperatures and vegetation composition. Snow depth, O‐horizon thickness, and near‐surface water content, which vary at scales of O(m), control the soil thermal regime more than an air temperature gradient corresponding to a 140 km north–south distance. High shrub productivity is simulated only in talik (perennially unfrozen) soils with high nitrogen availability. Through these effects on plant and permafrost dynamics, landscape heterogeneity impacts ecosystem productivity. Simulations with near‐surface taliks have higher microbial respiration (by 78.0 gC m −2 yr −1 ) and higher net primary productivity (by 104.9 gC m −2 yr −1 ) compared to runs with near‐surface permafrost, and simulations with high shrub productivity have outlying values of net carbon uptake. We explored the prediction uncertainty associated with ignoring observed landscape heterogeneity, and found that watershed net carbon uptake is 60% larger when heterogeneity is accounted for. Our results highlight the complexity inherent in discontinuous permafrost environments and demonstrate that missing representation of subgrid heterogeneity in TEMs could bias predictions of high‐latitude carbon budget.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Geothermal Entrepreneurship Organization (GEO) Accelerating Technology Transfer, Testing and Adoption of Cutting-edge Extreme Environment Drilling

The Geothermal Entrepreneurship Organization launched in 2019 with the goal of building a geothermal innovation ecosystem at the University of Texas at Austin (UT Austin), and in the State of Texas at large. The theses underlying the work of GEO were 1) that with targeted advocacy, recruitment, organization, and coalition building, research institutions with legacy excellence in petroleum and geosystems engineering could become engines of geothermal innovation, research and development; 2) that startups were the appropriate vehicle to speed these innovations from the lab into the field, and building a geothermal startup ecosystem in Texas would not only advance next generation geothermal concepts into the field, but also help spur oil and gas engagement in the space, and 3) that with targeted engagement, education, and recruitment across stakeholders in the oil and gas industry and the State of Texas generally, the oil and gas industry, and other legacy oil and gas entities in the State could become sources of large scale deployment of geothermal energy. The overall goal was to create a ‘snowball’ effect, where targeted impactful actions would catalyze self-sustaining, organic growth of a new geothermal ecosystem in the State of Texas. That goal was achieved through GEO’s work. To test its theses, GEO began work by interviewing and recruiting UT Austin faculty and alumni into geothermal. At the beginning of the GEO project, there was no geothermal activity ongoing within the UT Austin Petroleum and Geosystems Engineering Department, the Bureau of Economic Geology, or the Jackson School of Geoscience, and many faculty approached had not before considered how their skillsets might apply in the space. By the end of the project period, three major research consortia focused on geothermal were launched as a result of GEO’s work, one at the Bureau of Economic Geology, one within the Petroleum and Geosystems Engineering Department, and another organized by GEO across six research institutions across the State of Texas, called the Texas Geothermal Institute. Geothermal curricula was launched at UT Austin, and UT Austin began attracting new geothermal enthusiasts into its faculty, including Dr. Silviu Livescu, former Chief Scientist of Baker Hughes. Startups recruited and mentored by GEO launched, raised funding, and deployed (or are currently deploying) their concepts in the field. By the end of the project period, the GEO concept expanded to faculty beyond UT Austin to other institutions, like Texas A&M, the University of Houston, and Rice University, and geothermal engagement began at those institutions as well. Multiple faculty members and alumni across these institutions launched geothermal startup companies, launched geothermal research consortia, and/or began teaching geothermal courses. In 2020, GEO launched what was to become the largest geothermal conference in the world by its second year, ‘PIVOT – From Hydrocarbons to Heat’, and the resulting momentum catalyzed the Society of Petroleum Engineers to launch a geothermal technical section, drove more startups to launch out of the ecosystem, and drove actors in the State of Texas, NGOs, and stakeholders globally to become engaged. Riding this momentum, the Texas Geothermal Energy Alliance was launched, the first ever industry association dedicated to advancing geothermal energy in the State of Texas. The Texas geothermal ecosystem after only two years of building and support is now robust, quickly growing, and self-sustaining. By 2021, the Texas geothermal ecosystem had attracted the attention of philanthropists, funding entities, media, and influencers outside of Texas, and GEO’s executive director was invited to give a TED talk about oil and gas engagement in building the future of geothermal energy, which elevated the success of the ecosystem to a global audience.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Large ecosystem-scale effects of restoration fail to mitigate impacts of land-use legacies in longleaf pine savannas

Ecological restoration is a global priority, with potential to reverse biodiversity declines and promote ecosystem functioning. Yet, successful restoration is challenged by lingering legacies of past land-use activities, which are pervasive on lands available for restoration. Although legacies can persist for centuries following cessation of human land uses such as agriculture, we currently lack understanding of how land-use legacies affect entire ecosystems, how they influence restoration outcomes, or whether restoration can mitigate legacy effects. Using a large-scale experiment, we evaluated how restoration by tree thinning and land-use legacies from prior cultivation and subsequent conversion to pine plantations affect fire-suppressed longleaf pine savannas. In this work, we evaluated 45 ecological properties across four categories: 1) abiotic attributes, 2) organism abundances, 3) species diversity, and 4) species interactions. The effects of restoration and land-use legacies were pervasive, shaping all categories of properties, with restoration effects roughly twice the magnitude of legacy effects. Restoration effects were of comparable magnitude in savannas with and without a history of intensive human land use; however, restoration did not mitigate numerous legacy effects present prior to restoration. As a result, savannas with a history of intensive human land use supported altered properties, especially related to soils, even after restoration. The signature of past human land-use activities can be remarkably persistent in the face of intensive restoration, influencing the outcome of restoration across diverse ecological properties. Understanding and mitigating land-use legacies will maximize the potential to restore degraded ecosystems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Quantifying the drivers and predictability of seasonal changes in African fire

Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning condition and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Xopt and Badger: a machine learning ecosystem for real-time accelerator control and optimization

Machine learning (ML)-based black-box optimization algorithms have demonstrated significant improvements in accelerator optimization speed, often by orders of magnitude. However, deploying these algorithms in real-time facility control remains challenging due to the specialized expertise and infrastructure required. To bridge this gap, we introduce the Xopt ecosystem, a versatile suite of tools designed to make advanced ML-based optimization accessible to the broader accelerator community. This ecosystem includes Xopt, a modular Python framework that facilitates the integration of ML-based optimization algorithms with arbitrary control problems, and Badger, a graphical user interface built on top of Xopt, which enables seamless deployment of ML algorithms in real-time control systems. The Xopt ecosystem has been successfully applied towards solving challenging real-time control problems at leading international accelerator facilities, including SLAC, LBNL, Argonne, Fermilab, BNL, DESY, and ESRF, demonstrating its effectiveness in real-world optimization tasks. In this presentation, we provide an overview of Xopt’s capabilities and illustrate its impact through case studies from SLAC accelerator facilities including LCLS, LCLS-II, and FACET-II.

Roussel, Ryan [SLAC]↗
DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Unlocking the benefits of transparent and reusable science for climate-risk management

People around the world seek climate-risk information to guide their decisions. For instance, projections about future flood risk inform where households choose to live, how lenders manage credit risks, and which communities receive federal funding. Yet data limitations and fundamental validation challenges raise important concerns about the reliability of such projections. The principles of transparency and reusability help address these concerns by enabling scrutiny of assumptions and methods, development of foundational data and tools, and consistent application of evaluation standards. While there is ongoing debate about how much transparency commercial climate-risk services should provide, many expect non-commercial actors to lead the way on operationalizing transparency and reusability to fulfill their knowledge-building role in the climate-risk ecosystem. However, despite prominent success stories, we find a substantial gap between principles and practice: only four percent of the most-cited peer-reviewed climate-risk studies in recent years fully share their data and code despite this being a widely accepted minimum standard for transparency. We highlight low-cost measures that non-commercial researchers can take now to improve transparency and reusability. We also emphasize that transformative progress requires substantial investment, cross-sector collaboration, and careful consideration of tradeoffs, data rights, and multiple perspectives on equity. We hope this perspective accelerates both immediate actions and longer-term conversations to improve the ability of science to effectively support timely, evidence-based, and sound climate-risk management.

Open Science↗
DOE OSTI2024

Changes in microbial community and network structure precede shrub degradation in a desert ecosystem

Large-scale restoration is intended to promote ecological recovery. Improvements in plant and microbial conditions, however, may slow or even reverse in late succession. To better understand long-term restoration outcomes and underlying drivers of successional pathways, we tracked plant, bacterial and fungal, and soil conditions across a 40-year shrub plantation that was intended to stabilize desertified land in northern China. Here, we found that planted Haloxylon ammodendron shrubs developed and then subsequently became degraded after 30–40 years. Bacterial abundance and α-diversity were much higher than those of fungi, but no significant differences in composition and structure were found in different plantation ages. In contrast, the dominant taxa of fungal communities shifted from symbiotroph and saprotroph species towards pathotroph species with increased soil nutrients in the plantation chronosequence after two decades. The changes in fungal dominant species led to a transition in microbial network structure and function, with an increase in negative linkages among taxa that began in the middle stages of succession. Changes in fungal community structure had direct and indirect negative effects on shrub leaf physiology, root activity, and biomass. Our results highlight the preceding role of a breakdown in soil microbial community composition and network structure on the degradation of shrub performance in long-term desert succession. Our study emphasizes the importance of understanding soil-microbial-plant linkages on restoration outcomes, and mechanisms that can slow or reverse the recovery of ecosystems.

Bacterial and fungal community composition↗
DOE OSTI2023

Fusion of Multiple Models for Improving Gross Primary Production Estimation With Eddy Covariance Data Based on Machine Learning

Abstract Terrestrial gross primary production (GPP) represents the magnitude of CO 2 uptake through vegetation photosynthesis, and is a key variable for carbon cycles between the biosphere and atmosphere. Light use efficiency (LUE) models have been widely used to estimate GPP for its physiological mechanisms and availability of data acquisition and implementation, yet each individual GPP model has exhibited large uncertainties due to input errors and model structure, and further studies of systematic validation, comparison, and fusion of those models with eddy covariance (EC) site data across diverse ecosystem types are still needed in order to further improve GPP estimation. We here compared and fused five GPP models (VPM, EC‐LUE, GOL‐PEM, CHJ, and C‐Fix) across eight ecosystems based on FLUXNET2015 data set using the ensemble methods of Bayesian Model Averaging (BMA), Support Vector Machine (SVM), and Random Forest (RF) separately. Our results showed that for individual models, EC‐LUE gave a better performance to capture interannual variability of GPP than other models, followed by VPM and GLO‐PEM, while CHJ and C‐Fix were more limited in their estimation performance. We found RF and SVM were superior to BMA on merging individual models at various plant functional types (PFTs) and at the scale of individual sites. On the basis of individual models, the fusion methods of BMA, SVM, and RF were examined by a five‐fold cross validation for each ecosystem type, and each method successfully improved the average accuracy of estimation by 8%, 18%, and 19%, respectively.

Environmental Sciences & Ecology↗
DOE OSTI2021

Cryptic genetic structure and copy-number variation in the ubiquitous forest symbiotic fungus Cenococcum geophilum

Ectomycorrhizal (ECM) fungi associated with plants constitute one of the most successful symbiotic interactions in forest ecosystems. ECM support trophic exchanges with host plants and are important factors for the survival and stress resilience of trees. However, ECM clades often harbour morpho-species and cryptic lineages, with weak morphological differentiation. How this relates to intraspecific genome variability and ecological functioning is poorly known. Here, we analysed 16 European isolates of the ascomycete Cenococcum geophilum, an extremely ubiquitous forest symbiotic fungus with no known sexual or asexual spore-forming structures but with a massively enlarged genome. We carried out whole-genome sequencing to identify single-nucleotide polymorphisms. We found no geographic structure at the European scale but divergent lineages within sampling sites. Evidence for recombination was restricted to specific cryptic lineages. Lineage differentiation was supported by extensive copy-number variation. Finally, we confirmed heterothallism with a single MAT1 idiomorph per genome. Synteny analyses of the MAT1 locus revealed substantial rearrangements and a pseudogene of the opposite MAT1 idiomorph. Our study provides the first evidence for substantial genome-wide structural variation, lineage-specific recombination and low continent-wide genetic differentiation in C. geophilum. Our study provides a foundation for targeted analyses of intra-specific functional variation in this major symbiosis.

54 ENVIRONMENTAL SCIENCES↗