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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Design-to-Deployment Continuum Platform for Microscopes and Computing Ecosystems

Science ecosystems with networked computing systems and physical instruments are increasingly being deployed with a goal to achieve the productivity promised by AI-supported remote automation. In support of these efforts, the virtual infrastructure twins (VITs) have been successfully utilized to develop the orchestration codes for these ecosystems without requiring physical access to expensive instruments, such as electron microscopes. Currently, the utility of such a VIT is severely limited by the computing capacity and capability of the computing system used as its host. Furthermore, codes developed on the VIT typically need to be transferred and refactored for production use, particularly, on high-performance systems with accelerators. In response, we develop a design-to-deployment continuum platform wherein a VIT runs natively on the ecosystem's own computing system, and thereby facilitates the continual in-situ testing and transition of codes for production use. Here, we describe the development and testing of software for remote microscope steering and GPU-based image reconstruction using this platform on a multi-GPU computing system networked to Nion microscopes. We demonstrate a continual transition of steering and reconstruction codes developed under VIT platform to production ecosystem deployment.

Al-Najjar, Anees [Oak Ridge National Laboratory (O↗
DOE OSTI2025

Fine‐root dynamics in deeper soils: a critical but overlooked component of ecosystem responses to climate warming

Climate warming is predicted to strongly affect the functioning of terrestrial ecosystems. The plant root system is a critical component of these ecosystems, with fine roots, in particular, playing a key role in plant water and nutrient uptake and transport. In addition, root litter and exudation represent the dominant plant carbon inputs into soil. Consequently, understanding fine‐root responses to warming is essential for predicting how the growth, resilience, and carbon storage of terrestrial ecosystems will respond to future climate change. Despite the growing literature on fine‐root responses to warming, most studies have focused on topsoil (0–30 cm). However, a significant portion of the fine‐root mass occurs below this depth. For instance, c. 40% of fine‐root mass is found below 30 cm in temperate and tropical ecosystems. Due to the importance of fine roots for plants and belowground carbon cycling, focusing solely on surface soils overlooks the critical need for insights into how roots in deeper soil layers (e.g. below 30 cm) respond to warming. We argue that studying the entire soil profile is necessary to comprehensively understand fine‐root dynamics under climate warming and the implications for soil organic carbon cycling, water, and nutrient uptake.

climate warming↗
DOE OSTI2026

Multi-resolution Arctic Shrub Cover Dataset Derived from UAS and Airborne SfM and LiDAR (2013-2025)

We synthesized 177 unoccupied aerial system flights and 77 airborne flights across the Arctic and created a multi-resolution benchmark data of low-to-tall shrub fractional cover leveraging Structure-from-Motion and Light Detection and Ranging. The resulting dataset covered a total of 1899 km2 across Alaska, Western Canada, Sweden, and Siberian Arctic, including key sites from the Oro Arctic to the High Arctic. The dataset is organized into 6 primary data collection directories (“Abisko,” “AWI,” “ERE,” “Fairbanks,” “NGEE,” “Toolik”), each containing site and flight subdirectories. Flight directories include shrub cover rasters (*.tifs) at 1 m, 5 m, and 30 m resolution, the canopy height model at 1 m resolution (*.tifs), and a bounding box *.kml file. For the AWI, Abisko, NGEE, and Fairbanks collections, we also include the GCC raster at 1 m resolution (*.tif). Files are organized by Collection > Site > Flight Name > Data Files. Flight rasters are in the local UTM zone and the .kml files are in the geographic coordinate system EPSG 4326. We also include a .csv file that details the source datasets for every flight. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

canopy height model↗
DOE OSTI2024

Environmental DNA Detection in Marine Macrophyte Ecosystems as a Potential Blue Carbon Source in Sediments

“Blue carbon” refers to the carbon sequestered by the world’s oceanic and coastal ecosystems, particularly through coastal vegetation such as mangroves, salt marshes, seagrasses, and marine macroalgae. These ecosystems play a crucial role in the global carbon cycle by serving as significant carbon sinks, absorbing carbon dioxide from the atmosphere and storing it in biomass and sediments over long periods. This study explores the use of environmental DNA (eDNA) to detect marine macrophytes and microalgae assemblages contributing to blue carbon in sediments across various coastal ecosystems. The research addresses the challenges of traditional monitoring methods by utilizing high-throughput sequencing of the 18S-V9 region amplified using eDNA from sediment samples collected at eight locations in the United States and South Korea. The results reveal a diverse array of taxa, underscoring the variability in community composition across different conditions. Notably, sites with seagrass beds and Ulva blooms showed distinct patterns in microalgal community structure. This study underscores the potential of eDNA analysis in providing comprehensive insights into the biodiversity of marine macrophyte ecosystems, thus informing conservation efforts and enhancing the understanding of marine ecological dynamics.

Xing, Qikun (ORCID:0000000206179027)↗
DOE OSTI2025

Modeling the mechanisms of coastal vegetation dynamics and ecosystem responses to changing water levels

Coastal forests are increasingly experiencing mortality due to inundation by fresh- and seawater, leading to their replacement by marshes. These shifts alter vegetation composition, biogeochemical cycling, carbon storage, and hydrology. Using a hydraulically enabled ecosystem demography model (FATES-Hydro), we conducted numerical experiments to investigate the mechanisms behind inundation-driven forest loss and the ecosystem-scale consequences of forest-to-marsh transitions. We compared mortality processes and their effects across broadleaf and conifer trees at two coastal sites – Lake Erie (freshwater) and Chesapeake Bay (saline). Our simulations show that hydraulic failure, driven by root loss under prolonged flooding, is the primary mortality mechanism across both tree types and sites. Forest replacement by marsh reduced ecosystem-scale leaf area index (LAI), gross primary production (GPP), transpiration, and deep soil water uptake in conifer forests, while broadleaf forests experienced smaller changes due to lower initial LAI and greater marsh compensation. Marsh invasion occurred following canopy thinning driven by tree mortality. These findings suggest that, under similar root loss, hydraulic failure dominates coastal tree mortality regardless of species or water type, with denser forests experiencing stronger ecosystem impacts. Our study identifies key mortality mechanisms and offers testable hypotheses for future empirical studies on coastal vegetation change.

Ding, Junyan [Occidental College, Los Angeles, CA ↗
DOE OSTI2026

Implementing belowground controls on nutrient uptake in ELMv2-SPRUCE improves representation of a boreal peatland ecosystem

Boreal peatlands store 13 %–32 % of the global soil carbon (C) stock, a service dependent on plant-mycorrhizal fungi associations. In these nutrient poor systems, ectomycorrhizal and ericoid mycorrhizal fungi supply up to >80 % of the nutrient requirements of their plant hosts, partly with mined nitrogen (N) and phosphorus (P) from soil organic matter that are otherwise inaccessible to plants. Despite the ecological significance, mycorrhizal associations are only represented in a few land surface or ecosystem models. We modify the peatland branch of version 2 of the Energy Exascale Earth System Land Model (ELMv2-SPRUCE) to replace the default photosynthesis-driven inorganic N and P (NP) uptake process with a more realistic representation of the process via three pathways: (1) direct inorganic NP uptake by uncolonized fine roots, (2) indirect inorganic NP acquisition and (3) indirect NP acquisition from organic sources by mycorrhizal roots. We systematically evaluated the performance of the default and modified models with field observations from a whole ecosystem warming and carbon dioxide fertilization experimental site: Spruce and Peatland Responses Under Changing Environment (SPRUCE), in northern Minnesota, USA. The modified model reduces the underestimation of the growth response of shrubs in the default model to warming from 40 %–80 % to 17 %–35 % and reduces the overall relative absolute error on C fluxes from 1.61 to 1.54 in calibration. Improvements on modeled shrub growths and shrub-moss community net ecosystem exchanges are also seen in validation. The improved growth response of shrubs to warming is accompanied by several-fold increase in direct inorganic NP uptake and decrease in fungal colonization rate. The modified model simulates a smaller magnitude of transition of the ecosystem from C sink to C source under warming due to alleviation of plant nutrient limitation. Equifinality analysis shows the newly added parameters in the modified model can be constrained by the observed C fluxes. Sensitivity analysis shows the newly added parameters have stronger statistical interactions than the preexisting parameters in the default model. Overall, the modified model is an improvement over the default ELMv2-SPRUCE and will be a useful tool for understanding boreal peatland change.

Wang, Yaoping [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2025

DeepLynx Ecosystem 2025

Poor data integration and governance continue to plague complex engineering projects, resulting in missed cost, schedule, and performance targets. Departments operate in isolated systems with manual data exchange, creating fragmented information that compounds errors and leads to significant delays and cost overruns. The DeepLynx ecosystem addresses these challenges through an open-source, modular data management platform that transforms fragmented project data into an integrated digital thread. Built on a federated microservice architecture, the ecosystem comprises seven specialized tools centered around DeepLynx Nexus, a unified data catalog with hierarchical organization and graph-based navigation capabilities. The ecosystem includes: DeepLynx Stream for real-time timeseries data ingestion from industrial sources; DeepLynx Ingest for governed data uploads with formal review workflows; DeepLynx Lattice for ontology-based entity and relationship extraction; DeepLynx Run for workflow orchestration and secure AI/ML compute; DeepLynx Visualize for 3D digital twin visualization; and DeepLynx Insight for AI-assisted document analysis with traceable, grounded responses. Deployable in cloud, on-premise, or hybrid environments using containerized Docker applications and Helm charts, the DeepLynx ecosystem provides flexible infrastructure that adapts to organizational requirements. By consolidating project data into a unified data lake with role-based access controls and OAuth2 authentication, DeepLynx enables digital thread and digital twin capabilities that improve decision-making, reduce risk, and support complex engineering workflows throughout the project lifecycle.

42 - ENGINEERING↗
DOE OSTI2025

Better practices for inferring ecosystem water use strategy from eddy covariance data

Eddy covariance data are critical for inferring ecosystem water use strategies. Yet, such inferences are sensitive to a range of assumptions applied across studies, hindering our understanding of water use strategies within and across eddy covariance sites. A recent analysis across 151 FLUXNET2015 and AmeriFlux-FLUXNET datasets found that poor model performance was the key driver of non-robust inferences of ecosystem water use strategies. Here, we leverage this previous analysis to (i) identify the specific assumptions that improve inference model performance across most sites, (ii) explain the mechanisms behind the performance improvements, and (iii) check whether better performance improves water use inference. We find that the common practice of fitting a model to canopy conductance (G c ) derived from the evapotranspiration (ET) observations, rather than to observed ET itself, artificially amplifies data errors and degrades the model performance. Next, accounting for vegetation dynamics by applying a growing season filter or incorporating satellite LAI data improves performance, but the former practice may remove soil water stress periods. Lastly, using the leaf-to-air vapor pressure deficit (VPD l ) derived from ET observations as a model input may artificially inflate performance. Based on these results, we recommend selecting observed ET (rather than derived G c ) as the response variable, carefully accounting for vegetation dynamics, and avoiding derived VPD l as a model input; these best practices improve model performance by c. 20% and robustness by c. 80% across all eddy covariance sites. Nevertheless, the performance improvements do not always correspond to more robust inference of water use strategies, as model parameter selection and surface energy budget closure corrections still strongly influence the ecosystem water use parameter estimation in a site-specific manner.

AmeriFlux↗
DOE OSTI2025

Experimental Soil Warming Impacts Soil Moisture and Plant Water Stress and Thereby Ecosystem Carbon Dynamics

Experimental soil heating experiments have found a consistent increase in soil-surface CO 2 emissions ( F s ), but inconsistent soil organic carbon (SOC) responses. Interpretation of heating effects is complicated by spatial heterogeneity and soil moisture, nitrogen availability, and microbial and plant responses. Here we applied a mechanistic ecosystem model to interpret heating impacts on a California forest subjected to 1 m deep, 4°C heating. The model accurately simulated control-plot CO 2 fluxes, SOC stocks, fine root biomass, soil moisture, and soil temperature, and the observed increases in F s and decreases in fine root biomass. We show that a complex suite of interactions can lead to a consistent increase in F s (~17%) over the 5-year study period, with very small changes in SOC stocks (<1%). Modeled increases in leaf water stress from soil drying reduced GPP and NPP. The resulting reduction in leaf and fine root allocation increased fine root litter inputs to the soil and reduced root exudation. Soil heating led to about a 50% larger increase in root autotrophic respiration than in heterotrophic respiration, with the heating effect on both these fluxes decreasing over the simulation period. Increased heterotrophic respiration led to increased soil N availability and plant N uptake. These heating responses are mechanistically linked, of magnitudes that can affect ecosystem dynamics, and long-term observations of them are rarely made. Therefore, we conclude that a coupled observational and mechanistic modeling framework is needed to interpret manipulation experiments, and to improve projections of climate change impacts on terrestrial ecosystem carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Simulating Marine Ecosystem Dynamics and Biogeochemical Cycling With Multiple Plankton Functional Types

Current representations of marine ecosystems in Earth System Models are greatly simplified, neglecting key interactions between dynamic food webs, biogeochemistry, and climate change. We use the Marine Biogeochemistry Library code base within the Community Earth System Model 2.2.2 to create an expanded ecosystem model with eight phytoplankton groups and four zooplankton size classes (MARBL-8P4Z). Incorporating more specific plankton types and size classes has the potential to capture a wider range of possible behaviors of the ecosystem, its complex interactions with biogeochemistry, and its feedback to climate change. It also permits stronger observational constraints, including in situ group-specific biomass and various observational estimates of plankton community composition. MARBL-8P4Z broadly captures observed global-scale patterns in biomass and community composition for both phytoplankton and zooplankton, with a good performance in simulating broad biogeochemistry fields. The model shows comparable spatial patterns and magnitudes to the observed picophytoplankton biomass (Prochlorococcus, Synechococcus, picoeukaryotes), and captures the seasonal cycle of mesozooplankton biomass. Picophytoplankton groups and microzooplankton dominate biomass and production in oligotrophic, subtropical regions, while nano-phytoplankton, diatoms and the larger zooplankton groups prevail at higher latitudes and within upwelling zones. The model simulates reasonable energy transfer efficiency through the food web, with tight linkages between the phytoplankton community composition, zooplankton grazing, and carbon export, with the potential to link to fisheries models. Thus, MARBL-8P4Z has the potential to account for key climate-driven ecological shifts in the plankton that will modify ocean biogeochemistry in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Gap-filled methane and carbon dioxide fluxes across two ecosystem states at the US-OWC AmeriFlux site (2015−2016, 2020−2022)

This dataset contains gap-filled measurements of methane flux (FCH4), net ecosystem CO2 exchange (NEE) partitioned into gross primary productivity (GPP) and ecosystem respiration (RE), as well as latent heat flux (LE) from a Great Lakes coastal freshwater wetland at the US-OWC AmeriFlux site. The dataset covers the peak growing seasons (June−September) of 2015−2016, dominated by Typha spp., and 2020−2022, characterized by floating-leaved species (lotus and water lily). These data were generated to investigate how rising water levels and vegetation shifts influence CH4 and CO2 fluxes across two distinct ecosystem states in this wetland. The dataset, provided in CSV format, includes half-hourly gap-filled flux data from June to September for 2015, 2016, 2020, 2021, and 2022. The gap-filled data refers to measurements where missing values due to instrument issues or quality control were filled using artificial neural networks (ANNs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Tree Inventory

This is the tree inventory (diameter, species, and live/dead status) data from the Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment is part of the COMPASS-FME (Coastal Observations, Mechanisms, and Predictions Across Systems and Scales: Field Measurements and Experiments; see https://compass.pnnl.gov/FME/COMPASSFME) project. It addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in eastern Maryland, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments.This dataset includes:- An overall dataset README file.- The tree inventory data in both "wide" and "long" forms. These contain the same information but are structured differently, with the former more useful for human viewers and the latter more amenable for programmatic analyses.- A key to the species/genus codes used, which follow the U.S. Department of Agriculture's PLANTS schema (https://plants.usda.gov/).- A copy of the R code used to generate the wide- and long-form data files.All files are comma-separated value (CSV) and no special software is required to read them.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 1 Sensor Data v2-1

This is the version 2-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds, out-of-service, and outlier flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project** This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v2-1 TEMPEST L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024 This dataset was updated 2026-03-12: (i) data now go through 2025-12-31 (previous end was 2025-06-30) and (ii) dataset and file names updated to “…v2-1” (previously was “v2-0”).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 2 Sensor Data v2-1

This is the version v2-1 Level 2 (L2) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Level 2 (L2) data consist of sensor observations from the COMPASS-FME synoptic sites, TEMPEST, and DELUGE. Compared to the L1 data, these are more consistent (always 15-minute timestamps for the entire year); better QA/QC’d (out of bounds, out of service, and extreme outlier values are removed); and more complete, with a gap-filled time series available alongside the main observations, and additional derived (calculated) variables. L2 data are intended to be rapidly and easily usable in analyses and simulations. However, algorithmic outlier identification always carries the risk of removing valid data, and Level 1 data may be more suitable for analyses that focus on variability or extreme events. This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific Parquet (a high performance, space efficient format; see https://parquet.apache.org) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are reported every 15 minutes. Please see v2-1 TEMPEST L2 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. Data files are in Apache Parquet, a high performance, space efficient format for tabular data. These files can be read using R's `arrow` package (https://arrow.apache.org/docs/r/), with similar tools available in other languages. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗
DOE OSTI2024

Native bee Pollination Ecosystem Services in Agricultural Wetlands and Riparian Protected Lands

Abstract Many freshwater wetlands and riparian systems are protected within agricultural landscapes. Yet, pollinator ecosystem services are seldom considered key ecosystem services provided by these conservation easements. The purpose of this study is to explore the extent of protected aquatic lands to provide pollination ecosystem services by assessing pollinator abundances, crop yield changes, and value estimations of increased soybean yields from a subset of common native solitary bees. We created a novel geodatabase of United States Department of Agriculture (USDA) conservation easements and used this database in the InVEST crop pollination model to model wild solitary bee pollination. We then estimated the monetary value of yield increases provided by pollinators. We found that wetland uplands provided the greatest potential for pollination services for ground nesting bees, followed by herbaceous and forested riparian respectively. Stem nesters preferred forested riparian, then upland habitats. In soybeans fields, we found wild pollinators can provide up to 5.5% yield response from current private aquatic conservation lands. The current landscape is not optimized to use wetlands and riparian conservation lands as pollinator habitat, but these results suggest protected aquatic lands can sustainably increase wild pollination services to agricultural crops if landscapes are managed, protected, and optimized with pollinator services as co-benefit.

Hinson, Audra L. (ORCID:0000000242314820)↗
DOE OSTI2024

Refining water and carbon fluxes modeling in terrestrial ecosystems via plant hydraulics integration

Plant hydraulics substantially affects terrestrial water and carbon cycles by modulating water transport and carbon assimilation. Despite improved drought simulations in certain ecosystems through their integration into land surface models (LSMs), the broader application of plant hydraulics in diverse ecosystems and hydroclimates is still underexplored. Here, in this study, we implemented the recently developed Noah-Multiparameterization Land Surface Model (Noah-MP LSM) equipped with a plant hydraulics scheme (Noah-MP-PHS) across 40 FLUXNET sites globally. Employing the Shuffled Complex Evolution-University of Arizona (SCE-UA) auto-calibration algorithm, we optimized key plant hydraulics parameters for these sites spanning eight vegetation types in both arid and humid climates. Noah-MP-PHS significantly improves the simulation of evapotranspiration (ET) and gross primary production (GPP) by better representing atmospheric and soil water stress compared to traditional soil hydraulic schemes (SHSs, such as Noah and CLM). The augmented Noah-MP-PHS models reduce surface flux overestimation and underestimation, exhibiting an average increase of 0.14 and 0.15 in Kling-Gupta Efficiency (KGE) compared to Noah and CLM, respectively. The explicit consideration of plant capacitance in PHS reveals substantial deep-layer and nocturnal root water uptake especially under dry conditions. We employed eXplainable Machine learning (XML) to quantify the model’s relative sensitivity to newly introduced leaf-, stem and root-related parameters in PHS. The sensitivity analysis reveals a rise in root parameter importance and a decline in leaf and stem parameters as conditions shift from humid to arid. These findings indicate that as aridity states vary, the most influential parameters affecting surface fluxes variation may change in parameter calibration for PHS applications. Our findings underscore the importance of incorporating plant hydraulics into LSMs to enhance simulations of terrestrial water and carbon dynamics. These findings are crucial for understanding ecosystem responses to global climate changes and guide the broader application of PHS at larger scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Freshwater Biogeochemical Hotspots: High Primary Production and Ecosystem Respiration in Shallow Waterbodies

Abstract Ponds, wetlands, and shallow lakes (collectively “shallow waterbodies”) are among the most biogeochemically active freshwater ecosystems. Measurements of gross primary production (GPP), respiration (R), and net ecosystem production (NEP) are rare in shallow waterbodies compared to larger and deeper lakes, which can bias our understanding of lentic ecosystem processes. In this study, we calculated GPP, R, and NEP in 26 small, shallow waterbodies across temperate North America and Europe. We observed high rates of GPP (mean 8.4 g O 2 m −3 d −1 ) and R (mean −9.1 g O 2 m −3 d −1 ), while NEP varied from net heterotrophic to autotrophic. Metabolism rates were affected by depth and aquatic vegetation cover, and the shallowest waterbodies had the highest GPP, R, and the most variable NEP. The shallow waterbodies from this study had considerably higher metabolism rates compared to deeper lakes, stressing the importance of these systems as highly productive biogeochemical hotspots.

Geology↗
DOE OSTI2026

Investigating Coastal Vegetation Dynamics and Ecosystem Impacts Under Elevated CO 2 and Temperature: A Process‐Based Approach

Coastal forests are increasingly vulnerable to climate change and sea-level rise, with flooding and salinity driving transitions to marsh-dominated ecosystems. Using the coastal version of FATES-Hydro, we conducted 30-year simulations at two coastal forest sites—a broadleaf swamp white oak stand at Lake Erie and a conifer loblolly pine stand at Chesapeake Bay—under historical climate and elevated CO 2 (+100 ppm) and temperature (+1.5°C) scenarios. Elevated CO 2 increased net primary productivity at both sites, while warming alone intensified hydraulic stress and accelerated mortality, particularly in the conifer stand. Simulations show that elevated temperatures intensify vapor pressure deficit and hydraulic stress on trees already experiencing salinity- and submersion-driven water stress, increasing tree mortality beyond what would be expected in a non-water-limited environment. Marsh expansion partially compensated for tree loss at the Lake Erie site but reduced ecosystem productivity in the conifer forest at Chesapeake Bay. In conclusion, our results highlight how differences in stand structure, phenology, and local hydrology modulate ecosystem trajectories under climate change, emphasizing the importance of demographic and community-level processes for predicting the fate of coastal forests.

Ding, Junyan [Barcelona Supercomputing Center (BSC↗