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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A model-independent data assimilation (MIDA) module and its applications in ecology

Abstract. Models are an important tool to predict Earth system dynamics. An accurate prediction of future states of ecosystems depends on not only model structures but also parameterizations. Model parameters can be constrained by data assimilation. However, applications of data assimilation to ecology are restricted by highly technical requirements such as model-dependent coding. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module. MIDA works in three steps including data preparation, execution of data assimilation, and visualization. The first step prepares prior ranges of parameter values, a defined number of iterations, and directory paths to access files of observations and models. The execution step calibrates parameter values to best fit the observations and estimates the parameter posterior distributions. The final step automatically visualizes the calibration performance and posterior distributions. MIDA is model independent, and modelers can use MIDA for an accurate and efficient data assimilation in a simple and interactive way without modification of their original models. We applied MIDA to four types of ecological models: the data assimilation linked ecosystem carbon (DALEC) model, a surrogate-based energy exascale earth system model: the land component (ELM), nine phenological models and a stand-alone biome ecological strategy simulator (BiomeE). The applications indicate that MIDA can effectively solve data assimilation problems for different ecological models. Additionally, the easy implementation and model-independent feature of MIDA breaks the technical barrier of applications of data–model fusion in ecology. MIDA facilitates the assimilation of various observations into models for uncertainty reduction in ecological modeling and forecasting.

58 GEOSCIENCES↗
DOE OSTI2023

A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)

Tropical forest dynamics play a crucial role in the global carbon, water, and energy cycles. However, realistically simulating the dynamics of competition and coexistence between different plant functional types (PFTs) in tropical forests remains a significant challenge. This study aims to improve the modeling of PFT coexistence in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a vegetation demography model implemented in the Energy Exascale Earth System Model (E3SM) land model (ELM), ELM-FATES. Specifically, we explore (1) whether plant trait relationships established from field measurements can constrain ELM-FATES simulations and (2) whether machine learning (ML)-based surrogate models can emulate the complex ELM-FATES model and optimize parameter selections to improve PFT coexistence modeling. We conducted three ensembles of ELM-FATES experiments at a tropical forest site near Manaus, Brazil. By comparing the ensemble experiments without (Exp-CTR) and with (Exp-OBS) consideration of observed trait relationships, we found that accounting for these relationships slightly improves the simulations of water, energy, and carbon variables when compared to observations but degrades the simulation of PFT coexistence. Using ML-based surrogate models trained on Exp-CTR, we optimized the trait parameters in ELM-FATES and conducted another ensemble of experiments (Exp-ML) with these optimized parameters. The proportion of PFT coexistence experiments significantly increased from 21 % in Exp-CTR to 73 % in Exp-ML. After filtering the experiments that allow for PFT coexistence to agree with observations (within 15 % tolerance), 33 % of the Exp-ML experiments were retained, which is a significant improvement compared to the 1.4 % in Exp-CTR. Exp-ML also accurately reproduces the annual means and seasonal variations in water, energy, and carbon fluxes and the field inventory of aboveground biomass. This study represents a reproducible method that utilizes machine learning to identify parameter values that improve model fidelity against observations and PFT coexistence in vegetation demography models for diverse ecosystems. Our study also suggests the need for new mechanisms to enhance the robust simulation of coexisting plants in ELM-FATES and has significant implications for modeling the response and feedbacks of ecosystem dynamics to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration

Climate change is reshaping vulnerable ecosystems, leading to uncertain effects on ecosystem dynamics, including evapotranspiration (ET) and ecosystem respiration (R eco ). However, accurate estimation of ET and R eco still remains challenging at sparsely monitored watersheds, where data and field instrumentation are limited. In this study, we developed a hybrid predictive modeling approach (HPM) that integrates eddy covariance measurements, physically based model simulation results, meteorological forcings, and remote-sensing datasets to estimate ET and R eco in high space–time resolution. HPM relies on a deep learning algorithm and long short-term memory (LSTM) and requires only air temperature, precipitation, radiation, normalized difference vegetation index (NDVI), and soil temperature (when available) as input variables. We tested and validated HPM estimation results in different climate regions and developed four use cases to demonstrate the applicability and variability of HPM at various FLUXNET sites and Rocky Mountain SNOTEL sites in Western North America. To test the limitations and performance of the HPM approach in mountainous watersheds, an expanded use case focused on the East River Watershed, Colorado, USA. The results indicate HPM is capable of identifying complicated interactions among meteorological forcings, ET, and R eco variables, as well as providing reliable estimation of ET and R eco across relevant spatiotemporal scales, even in challenging mountainous systems. The study documents that HPM increases our capability to estimate ET and R eco and enhances process understanding at sparsely monitored watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

ChatHPC: Building the Foundations for a Productive and Trustworthy AI-Assisted HPC Ecosystem

ChatHPC democratizes large language models for the high-performance computing (HPC) community by providing the infrastructure, ecosystem, and knowledge needed to apply modern generative AI technologies to rapidly create specific capabilities for critical HPC components while using relatively modest computational resources. Our divide-and-conquer approach focuses on creating a collection of reliable, highly specialized, and optimized AI assistants for HPC based on the cost-effective and fast Code Llama fine-tuning processes and expert supervision. We target major components of the HPC software stack, including programming models, runtimes, I/O, tooling, and math libraries. Thanks to AI, ChatHPC provides a more productive HPC ecosystem by boosting important tasks related to portability, parallelization, optimization, scalability, and instrumentation, among others. With relatively small datasets (on the order of KB), the AI assistants, which are created in a few minutes by using one node with two NVIDIA H100 GPUs and the ChatHPC library, can create new capabilities with Meta’s 7-billion parameter Code Llama base model to produce high-quality software with a level of trustworthiness of up to 90% higher than the 1.8-trillion parameter OpenAI ChatGPT-4o model for critical programming tasks in the HPC software stack.

Young, Aaron [ORNL] (ORCID:0000000254484667)↗
DOE OSTI2020

Modeling biological nitrogen fixation in global natural terrestrial ecosystems

Biological nitrogen fixation plays an important role in the global nitrogen cycle. However, the fixation rate has been usually measured or estimated at a particular observational site. To quantify the fixation amount at the global scale, process-based models are needed. This study develops a biological nitrogen fixation model to quantitatively estimate the nitrogen fixation rate by plants in a natural environment. The revised nitrogen module better simulates the nitrogen cycle in comparison with our previous model that has not considered the fixation effects. The new model estimates that tropical forests have the highest fixation rate among all ecosystem types, which decreases from the Equator to the polar region. The estimated nitrogen fixation in global terrestrial ecosystems is 61.5 Tg N yr-1 with a range of 19.8–107.9 Tg N yr-1 in the 1990s. Our estimates are relatively low compared to some early estimates using empirical approaches but comparable to more recent estimates that involve more detailed processes in their modeling. Furthermore, the contribution of nitrogen made by biological nitrogen fixation depends on ecosystem type and climatic conditions. This study highlights that there are relatively large effects of biological nitrogen fixation on ecosystem nitrogen cycling. and the large uncertainty of the estimation calls for more comprehensive understanding of biological nitrogen fixation. More direct observational data for different ecosystems are in need to improve future quantification of fixation and its impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Filling the Gaps: A Bayesian Mixture Model for Imputing Missing Soil Water Content Data

ABSTRACT Soil water content (SWC) data are central to evaluating how soil moisture varies over time and space and influences critical plant and ecosystem functions, especially in water‐limited drylands. However, sensors that record SWC at high frequencies often malfunction, leading to incomplete timeseries and limiting our understanding of dryland ecosystem dynamics. We developed an analytical approach to impute missing SWC data, which we tested at six eddy flux tower sites along an elevation gradient in the southwestern United States. We impute missing data as a mixture of linearly interpolated SWC between the observed endpoints of a missing data gap and SWC simulated by an ecosystem water balance model (SOILWAT2). Within a Bayesian framework, we allowed the relative utility (mixture weight) of each component (linearly interpolated vs. SOILWAT2) to vary by depth, site and gap characteristics. We explored “fixed” weights versus “dynamic” weights that vary as a function of cumulative precipitation, average temperature, and time since the start of the gap. Both models estimated missing SWC data well ( R 2 = 0.70–0.88 vs. 0.75–0.91 for fixed vs. dynamic weights, respectively), but the utility of linearly interpolated versus SOILWAT2 values depended on site and depth. SOILWAT2 was more useful for more arid sites, shallower depths, longer and warmer gaps and gaps that received greater precipitation. Overall, the mixture model reliably gap‐fills SWC, while lending insight into processes governing SWC dynamics. This approach to impute missing data could be adapted to accommodate more than two mixture components and other types of environmental timeseries.

Ogle, Kiona [School of Informatics, Computing, and↗
DOE OSTI2026

How can an ecosystem approach support integrated management of marine renewable energy? An initial assessment from an environmental point of view

With the increasing installation of marine renewable energy (MRE) devices in areas already subject to multiple anthropogenic activities and environmental changes, it is necessary to develop tools and methods for the integrated management of marine ecosystems. The ecosystem approach is a holistic environmental management method that considers all components of an ecosystem. The ecosystem approach has demonstrated utility in the application to various anthropogenic activities and is relevant for consideration within the context of MRE. Indeed, many of the effects observed on marine ecosystems from those other activities are also applicable to MRE development. This review is an initial assessment where we summarize the potential effects of MRE development on marine ecosystems and propose schematic frameworks for applying the ecosystem approach to MRE. We also provide a non-exhaustive list of commonly used models pertinent to the ecosystem approach and associated with several reference studies. An outline of core questions that can currently be answered using available modeling tools central to the ecosystem approach is provided, along with recommendations for the application of this approach to the MRE context. Further, we identify key knowledge gaps and areas that require additional investigation for meaningful application of the ecosystem approach to MRE development. Our recommendations mainly concern the current limitations of applying the ecosystem approach to concrete cases, such as consolidating knowledge of the effects of MRE on the local environment, the need to obtain fine-scale data, considering effects at different spatiotemporal scales, and, finally, the need for an interdisciplinary vision.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Next-Generation Ecosystem Experiments–Tropics Flyer

Tropical forests cycle more carbon and water than any other ecosystem, and they play critical roles in determining Earth’s energy balance. Additionally, intact tropical forests are estimated to be Earth’s largest carbon sink, yet the processes controlling tropical forest carbon cycling are not well established. Understanding of carbon and related water and energy exchanges between tropical forests and the atmosphere lags that of other ecosystems, and poor model representation of these processes is the most significant source of terrestrial uncertainty in climate projections. To address these challenges, the Terrestrial Ecosystem Science (TES) program within the Department of Energy’s (DOE) Office of Biological and Environmental Research (BER) is supporting a next-generation ecosystem experiments project in the tropics (NGEE–Tropics).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Methane and Carbon Dioxide Fluxes in a Temperate Tidal Salt Marsh: Comparisons Between Plot and Ecosystem Measurements

Tidal wetlands are comprised of complex interdependent pathways where measurements of carbon exchange are often scale dependent. Common data collection methods (i.e., chambers and eddy covariance) are inherently constrained to different spatial and temporal scales which could generate biased information for applications of carbon accounting, identifying functional relationships and predicting future responses to climate change. Consequently, it is needed to systematically evaluate measurements derived from multiple approaches to identify differences and how techniques complement each other to reconcile interpretations. For our study, to accomplish this, we tested ecosystem-scale eddy covariance with plot-scale chamber measurements within a temperate salt marsh. We found good agreement (R 2 = 0.71–0.95) when comparing measurements of CH 4 emissions and CO 2 exchange but this agreement was dependent upon canopy phenology with discrepancies mainly arising during senescence and dormancy phenophases. The environmental drivers for CH 4 and CO 2 fluxes were mostly preserved across different measurement techniques, but the number of drivers increases while their individual strength decreases at the ecosystem scale. Empirical upscaling models parameterized with chamber measurements overestimated annual net ecosystem exchange (NEE; 108%) and gross primary production (GPP; 12%) while underestimating ecosystem respiration (Reco; 14%) and CH 4 emissions (69%) compared to eddy covariance measurements. Our results suggest that the environmental complexity of CH 4 and CO 2 fluxes in salt marshes may be underestimated by chamber-based measurements, and highlights how different techniques are complementary while considering limitations at each level of measurement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Current and Future Global Lake Methane Emissions: A Process‐Based Modeling Analysis

Abstract Freshwater ecosystem contributions to the global methane budget remains the most uncertain among natural sources. With warming and accompanying carbon release from thawed permafrost and thermokarst lake expansion, the increase of methane emissions could be large. However, the impact and relative importance of various factors related to warming remain uncertain. Based on diverse lake characteristics incorporated in modeling and observational data, we calibrate and verify a lake biogeochemistry model. The model is then applied to estimate global lake methane emissions and examine the impacts of temperature increase for the first and the last decades of the 21st century under different climate scenarios. We find that current emissions are 24.0 ± 8.4 Tg CH 4 yr −1 from lakes larger than 0.1 km 2 , accounting for 11% of the global total natural source as estimated based on atmospheric inversion. Future projections under the RCP8.5 scenario suggest a 58%–86% growth in emissions from lakes. Our model sensitivity analysis indicates that additional carbon substrates from thawing permafrost may enhance methane production under warming in the Arctic. Warming enhanced methane oxidation in lake water can be an effective sink to reduce the net release from global lakes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Ecological acclimation: A framework to integrate fast and slow responses to climate change

Ecological responses to climate change occur across vastly different time-scales, from minutes for physiological plasticity to decades or centuries for community turnover and evolutionary adaptation. Accurately predicting the range of ecosystem trajectories will require models that incorporate both fast processes that may keep pace with climate change and slower ones likely to lag behind and generate disequilibrium dynamics. However, the knowledge necessary for this integration is currently fragmented across disciplines. We develop ‘ecological acclimation’ as a unifying framework to emphasize the similarity of dynamics driven by processes operating on dramatically different time-scales and levels of biological organization. The framework focuses on ecoclimate sensitivities, measured as the change in an ecological response variable per unit of climate change. Acclimation processes acting at different time-scales cause these sensitivities to shift in magnitude and even direction over time. We highlight shifting ecoclimate sensitivities in case studies from diverse ecosystems, including terrestrial plant communities, coral reefs and soil microbiomes. Models predicting future ecosystem states inevitably make assumptions about acclimation processes; these assumptions must be explicit for users to evaluate whether a model is appropriate for a given forecast horizon. Similarly, decision frameworks that clearly account for multiple acclimation processes and their distinct time-scales will help natural resource managers plan for ecological impacts of climate change from years to many decades into the future. We outline a synthetic research programme focused on the time-scales of ecological acclimation to reduce uncertainty in ecological forecasts.

climate adaptation↗
DOE OSTI2022

Data and scripts associated with the manuscript "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning"

This package contains the data and scripts used in "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning" (Jiang et al., 2022). The data.zip file contains the flux tower and automated chamber observations used for developing the deep learning model for modeling soil respiration. The scripts.zip file contains the Jupyter notebooks and python scripts for preprocessing the data, training the deep learning models, and postprocessing the results. The src.zip contains the source code for training the deep learning model, performing mutual information analysis, and plotting functions. The trained_models.zip contains multiple folders used for hosting the trained deep-learning models and the associated soil respiration predictions. The whole process is performed using python. We include the REAMD.md to document the python package requirements.Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of both Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Exploring the determinants of organic matter bioavailability through substrate-explicit thermodynamic modeling

Microbial decomposition of organic matter (OM) in river corridors is a major driver of nutrient and energy cycles in natural ecosystems. Recent advances in omics technologies enabled high-throughput generation of molecular data that could be used to inform biogeochemical models. With ultrahigh-resolution OM data becoming more readily available, in particular, the substrate-explicit thermodynamic modeling (SXTM) has emerged as a promising approach due to its ability to predict OM degradation and respiration rates from chemical formulae of compounds. This model implicitly assumes that all detected organic compounds are bioavailable, and that aerobic respiration is driven solely by thermodynamics. Despite promising demonstrations in previous studies, these assumptions may not be universally valid because OM degradation is a complex process governed by multiple factors. To identify key drivers of OM respiration, we performed a comprehensive analysis of diverse river systems using Fourier-transform ion cyclotron resonance mass spectrometry OM data and associated respiration measurements collected by the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium. In support of our argument, we found that the incorporation of all compounds detected in the samples into the SXTM resulted in a poor correlation between the predicted and measured respiration rates. The data-model consistency was significantly improved by the selective use of a small subset (i.e., only about 5%) of organic compounds identified using an optimization method. Through a subsequent comparative analysis of the subset of compounds (which we presume as bioavailable) against the full set of compounds, we identified three major traits that potentially determine OM bioavailability, including: (1) thermodynamic favorability of aerobic respiration, (2) the number of C atoms contained in compounds, and (2) carbon/nitrogen (C/N) ratio. We found that all three factors serve as “filters” in that the compounds with undesirable properties in any of these traits are strictly excluded from the bioavailable fraction. This work highlights the importance of accounting for the complex interplay among multiple key traits to increase the predictive power of biogeochemical and ecosystem models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Investigating the Global Biogeophysical Impact of Area and Mass Based Wood Harvest in a Vegetation Demography Model

Wood harvesting alters land surface properties and energy redistribution, but there is a lack of studies estimating these changes on a global scale. We coupled a vegetation demographic model, the Functionally Assembled Terrestrial Ecosystem Simulator, with the E3SM land model to perform offline model simulation to investigate the land biogeophysical responses, including canopy coverage, leaf area index, albedo, surface roughness length, and energy fluxes, to historical wood harvest on the global scale. In this study, we found 50% less harvested carbon (C) when choosing the area-based harvest rate as driving data that has not been spatially harmonized, compared to reharmonized mass-based harvesting. By considering the uncertainty from reconstruction of historical wood harvest time series and the choice of wood harvest approach in the model, continuous wood harvest (1850–2015) results in 5%–10% of canopy coverage loss, contributing 0.5%–1% increase of albedo over disturbed land, which is much stronger than a non-demographic land surface model. Changes in energy flux from the wood harvest are negligible (<1%), but the responses of land surface properties vary (up to 30%) due to differences in model structure between the single canopy, sun-shade leaf model and vegetation demographic model.

Shu, Shijie [Lawrence Berkeley National Laboratory↗
DOE OSTI2024

Implementing Ordinary Differential Equation Solvers in Rust Programming Language for Modeling Vehicle Powertrain Systems: Preprint

Efficient and accurate ordinary differential equation (ODE) solvers are necessary for powertrain and vehicle dynamics modeling. However, current commercial ODE solvers can be financially prohibitive, leading to a need for accessible, effective, open-source ODE solvers designed for powertrain modeling. Rust is a compiled programming language that has the potential to be used for fast and easy-to-use powertrain models, given its exceptional computational performance, robust package ecosystem, and short time required for modelers to become proficient. However, of the three commonly used (>3,000 downloads) packages in Rust with ODE solver capabilities, only one has more than four numerical methods implemented, and none are designed specifically for modeling physical systems. Therefore, the goal of the Differential Equation System Solver (DESS) was to implement accurate ODE solvers in Rust designed for the component-based problems often seen in powertrain modeling. DESS is a text-based software package that provides a flexible framework for building and solving systems of ODEs. This allows DESS to be included as a dependency for automotive powertrain models that require a variety of solvers and solver configurations. Seven explicit ODE solver methods have been implemented in DESS: Euler’s, Heun’s, midpoint, Ralston’s, classic Runge-Kutta, Bogacki-Shampine, and Cash-Karp. These represent five fixed-step methods and two adaptive-step methods. This paper shows that the solver implementations increase accuracy and computational efficiency compared to Euler's method when modeling a system of three thermal masses in Rust. DESS also includes features designed for modeling component-based physical systems. Users can define relationships between nodes in their system, which the package then translates into a system of equations, leading to simpler and more intuitive code. In the case of a three-thermal-mass system, the user can specify node thermal properties (e.g., thermal capacitance), how nodes are interconnected, and thermal conductance between nodes rather than providing a system of equations. The core contribution from this work is an open-source, text-based Rust package with ODE solvers for automotive powertrain modeling to support cost-free, fast, and accurate simulation.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Foliar functional traits from imaging spectroscopy across biomes in eastern North America

Summary Foliar functional traits are widely used to characterize leaf and canopy properties that drive ecosystem processes and to infer physiological processes in Earth system models. Imaging spectroscopy provides great potential to map foliar traits to characterize continuous functional variation and diversity, but few studies have demonstrated consistent methods for mapping multiple traits across biomes. With airborne imaging spectroscopy data and field data from 19 sites, we developed trait models using partial least squares regression, and mapped 26 foliar traits in seven NEON (National Ecological Observatory Network) ecoregions (domains) including temperate and subtropical forests and grasslands of eastern North America. Model validation accuracy varied among traits (normalized root mean squared error, 9.1–19.4%; coefficient of determination, 0.28–0.82), with phenolic concentration, leaf mass per area and equivalent water thickness performing best across domains. Across all trait maps, 90% of vegetated pixels had reasonable values for one trait, and 28–81% provided high confidence for multiple traits concurrently. Maps of 26 traits and their uncertainties for eastern US NEON sites are available for download, and are being expanded to the western United States and tundra/boreal zone. These data enable better understanding of trait variations and relationships over large areas, calibration of ecosystem models, and assessment of continental‐scale functional diversity.

Wang, Zhihui↗
DOE OSTI2020

Ectomycorrhizal Plant-Fungal Co-invasions as Natural Experiments for Connecting Plant and Fungal Traits to Their Ecosystem Consequences

Introductions and invasions by fungi, especially pathogens and mycorrhizal fungi, are widespread and potentially highly consequential for native ecosystems, but may also offer opportunities for linking microbial traits to their ecosystem functions. In particular, treating ectomycorrhizal (EM) invasions, i.e., co-invasions by EM fungi and their EM host plants, as natural experiments may offer a powerful approach for testing how microbial traits influence ecosystem functions. Forests dominated by EM symbiosis have unique biogeochemistry whereby the secretions of EM plants and fungi affect carbon (C) and nutrient cycling; moreover, particular lineages of EM fungi have unique functional traits. EM invasions may therefore alter the biogeochemistry of the native ecosystems they invade, especially nitrogen (N) and C cycling. By identifying “response traits” that favor the success of fungi in introductions and invasions (e.g., spore dispersal and germination) and their correlations with “effect traits” (e.g., nutrient-cycling enzymes) that can alter N and C cycling (and affect other coupled elemental cycles), one may be able to predict the functional consequences for ecosystems of fungal invasions using biogeochemistry models that incorporate fungal traits. Here, we review what is already known about how EM fungal community composition, traits, and ecosystem functions differ between native and exotic populations, focusing on the example of EM fungi associated with species of Pinus introduced from the Northern into the Southern Hemisphere. We develop hypotheses on how effects of introduced and invasive EM fungi may depend on interactions between soil N availability in the exotic range and EM fungal traits. We discuss how such hypotheses could be tested by utilizing Pinus introductions and invasions as a model system, especially when combined with controlled laboratory experiments. Finally, we illustrate how ecosystem modeling can be used to link fungal traits to their consequences for ecosystem N and C cycling in the context of biological invasions, and we highlight exciting avenues for future directions in understanding EM invasion.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Simulated plant-mediated oxygen input has strong impacts on fine-scale porewater biogeochemistry and weak impacts on integrated methane fluxes in coastal wetlands

Methane (CH 4 ) emissions from wetland ecosystems are controlled by redox conditions in the soil, which are currently underrepresented in Earth system models. Plant-mediated radial oxygen loss (ROL) can increase soil O 2 availability, affect local redox conditions, and cause heterogeneous distribution of redox-sensitive chemical species at the root scale, which would affect CH 4 emissions integrated over larger scales. In this study, we used a subsurface geochemical simulator (PFLOTRAN) to quantify the effects of incorporating either spatially homogeneous ROL or more complex heterogeneous ROL on model predictions of porewater solute concentration depth profiles (dissolved organic carbon, methane, sulfate, sulfide) and column integrated CH 4 fluxes for a tidal coastal wetland. From the heterogeneous ROL simulation, we obtained 18% higher column averaged CH 4 concentration at the rooting zone but 5% lower total CH 4 flux compared to simulations of the homogeneous ROL or without ROL. This difference is because lower CH 4 concentrations occurred in the same rhizosphere volume that was directly connected with plant-mediated transport of CH 4 from the rooting zone to the atmosphere. Sensitivity analysis indicated that the impacts of heterogeneous ROL on model predictions of porewater oxygen and sulfide concentrations will be more important under conditions of higher ROL fluxes or more heterogeneous root distribution (lower root densities). Despite the small impact on predicted CH 4 emissions, the simulated ROL drastically reduced porewater concentrations of sulfide, an effective phytotoxin, indicating that incorporating ROL combined with sulfur cycling into ecosystem models could potentially improve predictions of plant productivity in coastal wetland ecosystems.

54 ENVIRONMENTAL SCIENCES↗