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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The protist Aurantiochytrium has universal subtelomeric rDNAs and is a host for mirusviruses

Viruses are the most abundant biological entities in the world's oceans, where they play important ecological and biogeochemical roles. Metagenomics is revealing new groups of eukaryotic viruses, although disconnected from known hosts. Among these are the recently described mirusviruses, which share some similarities with herpesviruses.50 years ago, "herpes-type" viral particles were found in a thraustochytrid member of the labyrinthulomycetes, a diverse group of abundant and ecologically important marine eukaryotes, but could not be further characterized by methods then available. Long-read sequencing has allowed us to connect the biology of mirusviruses and thraustochytrids. We sequenced the genome of the genetically tractable model thraustochytrid Aurantiochytrium limacinum ATCC MYA-1381 and found that its 26 linear chromosomes have an extraordinary configuration. Subtelomeric ribosomal DNAs (rDNAs) found at all chromosome ends are interspersed with long repeated sequence elements denoted as long repeated-telomere and rDNA spacers (LORE-TEARS). We identified two genomic elements that are related to mirusvirus genomes. The first is a ~300-kbp episome (circular element 1 [CE1]) present at a high copy number. Strikingly, the second, distinct, mirusvirus-like element is integrated between two sets of rDNAs and LORE-TEARS at the left end of chromosome 15 (LE-Chr15). Similar to metagenomically derived mirusviruses, these putative A. limacinum mirusviruses have a virion module related to that of herpesviruses along with an informational module related to nucleocytoplasmic large DNA viruses (NCLDVs). CE1 and LE-Chr15 bear striking similarities to episomal and endogenous latent forms of herpesviruses, respectively, and open new avenues of research into marine virus-host interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Fungal endophytes

Organisms are commonly grouped into ecological guilds that reflect their shared resource use and similar ecological roles. The guild concept has been used to categorize the vast diversity of the fungal kingdom (estimated at 2.2–5 million species) into groups of fungi with common lifestyles, such as mycorrhizal symbionts, pathogens of animals and plants, and the group that is the focus of this primer — fungal endophytes of plants. Fungal endophytes have resisted scientists’ attempts to silo organisms into neat assemblages. In conclusion, scattered across the fungal tree of life and lacking few diagnostic characteristics, they are defined less by what they are than by what they are not.

Cosner, Julian [Oak Ridge National Laboratory (ORN↗
DOE OSTI2020

A dataset of eco-evidence tools to inform early-stage environmental impact assessments of hydropower development

The datasets described herein provide the foundation for a decision support prototype (DSP) toolkit aimed at assisting stakeholders in determining evidence of which aspects of river ecosystems have been impacted by hydropower. The DSP toolkit and its application are presented and described in the article “Evidence-based indicator approach to guide preliminary environmental impact assessments of hydropower development” [1]. Development of the DSP and the output for decision support centralize around 42 river function indicators describing the dimensionality of river ecosystems through six main categories: biota and biodiversity, water quality, hydrology, geomorphology, land cover, and river connectivity. Three main tools are represented in the DSP: A science-based questionnaire (SBQ), an environmental envelope model (EEM), and a river function linkage assessment tool (RFLAT). The SBQ is a structured survey-style questionnaire whose objective is to provide evidence of which indicators have been impacted by hydropower. Based on a global literature review, 140 questions were developed from general hypotheses regarding the impacts of dams on rivers. The EEM is a model to predict the likelihood of hydropower impacting indicators based on a several variables. The intended use of the EEM is for situations of new hydropower development where results of the SBQ are incomplete or highly uncertain. The EEM was developed through the compilation of a dataset containing attributes of dams, reservoirs, and geospatial information on environmental concerns, which was combined with data on ecological indicators documented at those sites through literature review. The model operates through 247 “envelopes” and weighting factors, representing the individual effect of each variable on each indicator, all available through spreadsheets. Finally, the RFLAT is a tool to examine causal relationships amongst indicators. Inter-indicator relationships were hypothesized based on literature review and summarized into node and edge datasets to represent the structure of a graphical network. Bayes theorem was used estimate conditional probabilities of inter-indicator relationships based on the output of the SBQ. Nodes and edges were imported into R programming environment to visualize ecological indicator networks. The datasets can be expanded upon and enriched with more detailed questions for the SBQ, building upon the EEM with to develop more sophisticated models, and identifying new relationships for the RFALT. Additionally, once the tools are applied to numerous hydropower developments, the output of the tools (e.g. evidence of impacted indicators) becomes a very useful dataset for meta-analyses of hydropower impacts.

13 HYDRO ENERGY↗
DOE OSTI2026

From observation to replication: machine-learning-driven quantification and replication of fine-scale fish kinematics and behavior

Long-term quantification of fish behavior is essential for aquatic ecology, wildlife telemetry, and biomechanical device development. However, the observation duration required to obtain reliable behavioral and kinematic metrics remains unclear, and few tools exist to physically reproduce natural swimming motion for controlled experimentation. We address these challenges by developing a generalizable framework that models behavioral reliability (Spearman–Brown reliability index) as a function of observation duration and derives metric-specific monitoring thresholds. Using juvenile white sturgeon (Acipenser transmontanus) as a case study, we demonstrate that the minimum duration needed for reliable estimates varies substantially across kinematic features: to exceed a reliability of 0.8, total distance traveled requires 12 days, average curvature (mm?¹) 15 days, tail-beat frequency (Hz) 8 days, and average speed (body length/s) 17 days. We further bridge digital analysis and physical testing by developing a hardware-in-the-loop simulator that reconstructs machine-learning-derived swimming kinematics with high fidelity (correlation coefficient 0.98–0.99, RMSE 1.22–1.27 mm over a 5-minute segment). This platform enables realistic, repeatable motion stimuli for evaluating aquatic sensing technologies and bio-integrated devices under controlled conditions. Together, these contributions provide a scalable approach for designing long-term behavioral studies and a data-driven connection between ecological observation and robotic experimentation.

Hwang, SungJoo↗
DOE OSTI2026

A spatiotemporally explicit and scalable indicator of intact lands across the conterminous United States, 1986–2023

Globally, ecologically intact areas are increasingly scarce. Agricultural expansion into previously uncultivated areas drives the loss of intact lands that might otherwise exhibit high levels of ecological integrity. Thus, the absence of cultivation can be an indicator of intact lands as measured from remote sensing data and thematic maps. Our objective for this study was to develop and compare tractable approaches based on remotely sensed satellite data to map spatial patterns of potentially intact lands across the conterminous U.S. (CONUS). Using annual cultivation probabilities derived from satellite observations, we classified and mapped potentially intact lands across CONUS from 1986 to 2023 at 30 m resolution. We created three maps, first by applying a constant cultivation probability threshold across CONUS, second by varying the threshold state-by-state to maximize state-level overall accuracies, and third by equalizing the state-level user's and producer's accuracies to minimize classification bias. Validation against 800,000+ independent ground samples resulted in CONUS-level overall accuracies ≥85% for the roughly 660 million ha of potentially intact land. Map accuracy varied with the proportion of potentially intact lands across regions, with the Pacific-Mountain and Great Plains regions exhibiting the highest accuracies, while Eastern CONUS exhibited a greater mix of potentially intact and non-intact lands and more moderate map accuracies. These novel maps and approaches can be adapted to different spatiotemporal extents to support conservation and production decisions ranging from species and ecosystems protection to reducing land conversion and climate mitigation.

agriculture↗
DOE OSTI2023

Land-use history, fire regime, and large-mammal herbivory affect deer-preferred plant diversity in longleaf pine woodlands

Since ecological systems often experience multiple disturbances, understanding changes in important ecological interactions, such as plant-herbivore interactions, may require studies capable of disentangling the unique and interactive effects of multiple forms of disturbance. For example, understanding how mammalian herbivores affect plant communities may require understanding how widespread past disturbances, such as agricultural land use, interact with contemporary disturbances, such as prescribed fire. Here, we tested if past agricultural land use and contemporary fire regime modified the effects of mammalian herbivory on focal plant communities by measuring the richness of plant species preferred by deer at 26 longleaf pine woodlands containing paired open and large-mammal exclusion plots. Land-use history significantly affected the community composition of deer-preferred species, and focal species were 8.19 times more likely to be present in post-agricultural sites than non-agricultural sites. Large-mammal herbivory only affected plant species richness in woodlands with low fire frequencies wherein focal species were on average 2.23 times more likely to be present in exclusion plots than open plots. These results suggest that past and present disturbances can mediate contemporary plant-animal interactions and may explain spatial patterns in the intensity of large-mammal herbivory. Our findings also suggest that common management practices, such as use of prescribed burns, may indirectly promote plant species richness by reducing deer herbivory.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Belowground cross-trophic networks impact CH4 and CO2 emissions in degraded alpine peatlands

Belowground organisms forming complex cross-trophic ecological networks are essential for maintaining peatland carbon stability and energy flow. However, how peatland degradation affects the biodiversity and cross-trophic ecological networks of soil communities remains poorly understood. Here, we examined the degradation effects on soil prokaryotes (i.e., bacteria, archaea), fungi and nematodes in alpine peatlands on the eastern Tibetan Plateau, characterized by varying water table depths (indicating degradation levels). We found that peatland degradation, accompanied by significant shifts in soil moisture and pH (P < 0.05), reduced the taxonomic richness and phylogenetic diversity of prokaryotes, fungi, and nematodes, particularly in deeper soil layers (20–50 cm). Crucially, peatland degradation weakened potential cross-trophic interactions within bipartite networks of prokaryotes-nematodes and fungi-nematodes, resulting in less than 6.5 %–28.8 % of unchanged modules. Degradation-induced changes in soil moisture and pH were identified as primary drivers of biodiversity loss and network restructuring. Furthermore, such changes of belowground cross-trophic networks (particularly prokaryote-nematode) were significantly correlated with greenhouse gas emissions, such as decreased CO2 emissions, maintained CH4 emissions (leading to a higher CH4/CO2 ratio in deep layers), and reduced temperature sensitivity (Q10) of soil respiration. These findings underscore the critical need to protect soil biodiversity and cross-trophic networks in peatlands, particularly under the threat of climate change, to preserve peatland carbon stocks and maintain ecosystem stability. Our findings highlight that belowground cross-trophic networks are pivotal to decipher soil carbon dynamics of degraded peatlands and project the fate of peatland carbon stocks under future climate change scenarios.

Cross-trophic networks↗
DOE OSTI2023

The influence of submarine canyons-related processes on recent benthic foraminiferal distribution, Espírito Santo Basin, Southeastern Brazil

The complex topography of submarine canyons may result in different composition of benthic foraminifera assemblages. To understand how trophic, hydrological and sedimentological conditions in submarine canyons can influence the distribution of benthic foraminifera, and to use this information to corroborate paleoenvironmental interpretations for the Holocene, we investigated recent benthic foraminiferal assemblages (total fauna >63 μm) and sedimentological data in two canyons (CANWN and CAND) in the Espírito Santo Basin (ESB) between 18°20' and 21°20' S. Surface sediment samples (0–2 cm) were collected inside the canyons (150 to 1300 m water depth) and in shelf-slope adjacent transects (50 to 1300 m water depth). The density, taxonomic diversity, and assemblage composition of benthic foraminifera change with depth and location. The distinct ecological preferences of the most abundant taxa allowed us to recognize five benthic foraminiferal groups. Three groups (V, III, and I) are present in different bathymetric sectors; Group V: outer shelf (50 m), Group III: upper, and middle – lower slope (150 to 400 m), and Group I: middle – lower slope (1000 to 1300 m). Groups II and IV show no characteristic bathymetric distribution and are present only in CAND and in CANWN, respectively. Group V consists of Hanzawaia boueana, Peneroplis planatus, and Quinqueloculina lamarckiana; Group III is dominated by Globocassidulina rossensis and Trifarina spp.; Group I consists of Globocassidulina crassa, Bolivina lowmani, Gavelinopsis versiformis, Alabaminella weddellensis, and Epistominella exigua. The main species in Group II (CAND, 150, 1000 to 1300 m) are Trifarina angulosa, Globocassidulina subglobosa, and Discorbis vilardeboanus. Group IV (middle – lower CANWN, 1000 to 1300 m), consists mainly of agglutinated species Glomospira charoides, Rhabdammina abyssorum, and Psammosphaera fusca. Further, our data suggest that the quantity (and quality) of food supply, hydrodynamic conditions and sediment properties are the main drivers controlling the bathymetric distribution of benthic foraminiferal assemblages in both canyons. The middle – lower CANNW revealed unstable trophic conditions, related to terrigenous sediment input due to turbidity currents. In CAND, the foraminiferal assemblages ecology indicated sufficient organic matter supply that favors species establishment and diversity, indicating a more productive and less unstable environment than in CANWN.

58 GEOSCIENCES↗
DOE OSTI2021

Is a Prescribed Fire Sufficient to Slow the Spread of Woody Plants in an Infrequently Burned Grassland? A Case Study in Tallgrass Prairie

In many mesic grasslands, such as the central Great Plains in North America, frequent fire is a key regulator of ecological processes. Long periods of infrequent fire facilitate the conversion of herbaceous-dominated grassland to woody-dominated shrubland or woodland. At the Konza Prairie Biological Station in northeast Kansas, one infrequently burned portion of the landscape has undergone transformation from grassland to woodland after nearly 30 yr without fire. In Spring 2017, a prescribed burn was implemented to assess fire effectiveness on woody plant mortality. A postfire census of 3 000+ individual woody plants identified the distribution of species by size (height), topographic position, and slope on the landscape. Mortality and canopy fire damage were calculated for each individual. In lowland locations with near-continuous shrub cover (30.7% of the landscape), woody plants were unaffected by fire. However, in upland and slope locations, where shrubs and trees were sparser, survival probability varied by topographic position and species. In these locations 68% of all woody individuals experienced 90% or greater fire damage to the canopy, with 56% of these individuals exhibiting new canopy regrowth within 2 mo after the fire. Here, the two most abundant woody shrubs, Cornus drummondii and Rhus aromatica, showed high survival at all height classes and landscape positions. The two abundant tree species, Gleditsia triacanthos and Juniperus virginiana, showed increased survival probability with tree height that varied by landscape position. Survival of J. virginiana also varied according to proximity and size of neighboring clonal shrubs, providing a mechanism for persistence of this fire-sensitive tree species even at small height classes. The probability survival curves developed here are useful for managers assessing when to prescribe fire to maximize mortality for J. virginiana and provide insight relevant for broader ecological understanding of woody encroachment within grasslands throughout the world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions

Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R 2 values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4% to 8.4% for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. In conclusion, these findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.

Environmental drivers↗
DOE OSTI2021

Predicting measures of soil health using the microbiome and supervised machine learning

Soil health encompasses a range of biological, chemical, and physical soil properties that sustain the commercial and ecological value of agroecosystems. Monitoring soil health requires a comprehensive set of diagnostics that can be cost-prohibitive for routine analyses. The soil microbiome provides a rich source of information about soil properties, which can be assayed in a high-throughput, cost-effective way. We evaluated the accuracy of random forest (RF) and support vector machine (SVM) regression and classification models in predicting 12 measures of soil health, tillage status, and soil texture from 16S rRNA gene amplicon data with an operationally relevant sample set. We validated the efficacy of the best performing models against independent datasets and also tested best practices for processing microbiome data for use in machine learning. Soil health metrics could be predicted from microbiome data with the best models achieving a Kappa value of ~0.65, for categorical assessments, and a R2 value of ~0.8, for numerical scores. Biological health ratings were better predicted than chemical or physical ratings. Validation with independent datasets revealed that models had general predictive value for soil properties, including yield. The ecological profiles of several taxa important for model accuracy matched the observed relationships with soil health, including Pyrinomonadaceae, Nitrososphaeraceae, and Candidatus Udeaobacter. Models trained at the highest taxonomic resolution proved most accurate, with losses in accuracy resulting from rarefying, sparsity filtering, and aggregating at higher taxonomic ranks. Furthermore, our study provides the groundwork for developing scalable technology to use microbiome-based diagnostics for the assessment of soil health.

16S rRNA gene↗
DOE OSTI2022

The nitrogen gap in soil health concepts and fertility measurements

Soil nitrogen (N) often limits productivity in agroecosystems, prompting fertilizer applications that increase crop yields but can degrade the environment. Nitrogen's dual role in both productivity and environmental quality should center it in soil health frameworks. We use recent evidence to argue that N availability is an emergent property of the integrated soil biogeochemical system and is strongly influenced by plant traits and their interactions with microbes and minerals. Building upon this, we theorize that the sources of plant and microbial N shift across soil health gradients, from inorganic N dependence in ecologically simple systems with poor soil health to a highly networked supply of organic N in healthy soils; ergo, investments in soil health should increase ecological complexity and the pathways by which plants can access N, leading to more resilient nutrient supplies and yields in a variable climate. However, current N assessment methods derive from a historical emphasis on inorganic N pool sizes and are unable to capture the shifting drivers of N availability across soil health gradients. We highlight the need to better understand the plant-microbial-mineral interactions that regulate bioavailable N as a first step to improving our ability to measure it. We conclude it will be necessary to harness agroecosystem complexity, account for plant and microbial drivers, and carefully integrate external N inputs into soils' internal N network to expand the routes by which N from organic pools can be made bioavailable. Finally, by emphasizing N in soil health concepts, we argue that researchers can accelerate advances in N use efficiency and resiliency.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Microbial and Environmental Processes Shape the Link between Organic Matter Functional Traits and Composition

Dissolved organic matter (DOM) is a large and complex mixture of molecules that fuels biogeochemical reaction in virtually all ecosystems on Earth. However, the relative importance of deterministic and stochastic processes in structuring DOM composition remains poorly characterized. Here we develop a framework for partitioning molecular composition based on key molecular traits, including lability vs. recalcitrance and activity vs. inactivity. Within this framework, we examine the ecological processes governing the assembly of DOM fractions by deploying aquatic microcosms on mountainsides that span gradients of temperature and nutrient loading in subtropical and subarctic ecosystems. Across study regions, deterministic and stochastic processes primarily structure active and inactive fractions, respectively. However, recalcitrant molecules are more deterministically assembled than labile molecules in the inactive fraction. Deterministic processes leading to variable selection generally exhibit more variation across the energy supply gradient for inactive fractions, and their importance increases with energy supply for recalcitrant molecules in both active and inactive fractions. Together, our results indicate that active and inactive fractions of DOM assemblages are structured by contrasting ecological processes, and their recalcitrant components are sensitive to global change. In conclusion, our framework opens new avenues to understand the assembly and turnover of DOM in a changing world, which can be used to predict carbon cycling at local to global scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Impact of Changing Winds on the Mauna Loa CO 2 Seasonal Cycle in Relation to the Pacific Decadal Oscillation

Abstract Long‐term measurements at the Mauna Loa Observatory (MLO) show that the CO 2 seasonal cycle amplitude (SCA) increased from 1959 to 2019 at an overall rate of 0.22 0.034 ppm decade −1 while also varying on interannual to decadal time scales. These SCA changes are a signature of changes in land ecological CO 2 fluxes as well as shifting winds. Simulations with the TM3 tracer transport model and CO 2 fluxes from the Jena CarboScope CO 2 Inversion suggest that shifting winds alone have contributed to a decrease in SCA of −0.10 0.022 ppm decade −1 from 1959 to 2019, partly offsetting the observed long‐term SCA increase associated with enhanced ecosystem net primary production. According to these simulations and MIROC‐ACTM simulations, the shorter‐term variability of MLO SCA is nearly equally driven by varying ecological CO 2 fluxes (49%) and varying winds (51%). We also show that the MLO SCA is strongly correlated with the Pacific Decadal Oscillation (PDO) due to varying winds, as well as with a closely related wind index (U‐PDO). Since 1980, 44% of the wind‐driven SCA decrease has been tied to a secular trend in the U‐PDO, which is associated with a progressive weakening of westerly winds at 700 mbar over the central Pacific from 20°N to 40°N. Similar impacts of varying winds on the SCA are seen in simulations at other low‐latitude Pacific stations, illustrating the difficulty of constraining trend and variability of land CO 2 fluxes using observations from low latitudes due to the complexity of circulation changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Conceptualizing Biogeochemical Reactions With an Ohm's Law Analogy

Abstract In studying problems like plant‐soil‐microbe interactions in environmental biogeochemistry and ecology, one usually has to quantify and model how substrates control the growth of, and interaction among, biological organisms (and abiotic factors, e.g., adsorptive mineral soil surfaces). To address these substrate‐consumer relationships, many substrate kinetics and growth rules have been developed, including the famous Monod kinetics for single‐substrate‐based growth and Liebig's law of the minimum for multiple‐nutrient‐colimited growth. However, the mechanistic basis that leads to these various concepts and mathematical formulations and the implications of their parameters are often quite uncertain. Here, we show that an analogy based on Ohm's law in electric circuit theory is able to unify many of these different concepts and mathematical formulations. In this Ohm's law analogy, a resistor is defined by a combination of consumers’ and substrates’ kinetic traits. In particular, the resistance is equal to the mean first passage time that has been used to derive the Michaelis‐Menten kinetics under substrate replete conditions for a single substrate as well as the predation rate of individual organisms. We further show that this analogy leads to important insights on various biogeochemical problems, such as (a) multiple‐nutrient‐colimited biological growth, (b) denitrification, (c) fermentation under aerobic conditions, (d) metabolic temperature sensitivity, and (e) the legitimacy of Monod kinetics for describing bacterial growth. We expect that our approach will help both modelers and nonmodelers to better understand and formulate hypotheses when studying certain aspects of environmental biogeochemistry and ecology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Organizational Principles of Hyporheic Exchange Flow and Biogeochemical Cycling in River Networks Across Scales

Hyporheic zones increase freshwater ecosystem resilience to hydrological extremes and global environmental change. However, current conceptualizations of hyporheic exchange, residence time distributions, and the associated biogeochemical cycling in streambed sediments do not always accurately explain the hydrological and biogeochemical complexity observed in streams and rivers. Specifically, existing conceptual models insufficiently represent the coupled transport and reactivity along groundwater and surface water flow paths, the role of autochthonous organic matter in streambed biogeochemical functioning, and the feedbacks between surface-subsurface ecological processes, both within and across spatial and temporal scales. While simplified approaches to these issues are justifiable and necessary for transferability, the exclusion of important hyporheic processes from our conceptualizations can lead to erroneous conclusions and inadequate understanding and management of interconnected surface water and groundwater environments. This is particularly true at the landscape scale, where the organizational principles of spatio-temporal dynamics of hyporheic exchange flow (HEF) and biogeochemical processes remain largely uncharacterized. This article seeks to identify the most important drivers and controls of HEF and biogeochemical cycling based on a comprehensive synthesis of findings from a wide range of river systems. We use these observations to test current paradigms and conceptual models, discussing the interactions of local-to-regional hydrological, geomorphological, and ecological controls of hyporheic zone functioning. This improved conceptualization of the landscape organizational principles of drivers of HEF and biogeochemical processes from reach to catchment scales will inform future river research directions and watershed management strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Hydrological Control on Soil Redox Condition and Carbon Loss of Coastal Wetland Under Sea-Level Rise

Coastal wetlands are critical carbon sinks with their biogeochemical and ecological functioning shaped by dynamic hydrological conditions that are increasingly influenced by climate change. A key unresolved question is how hydrologic flow, vegetation response, and rising sea levels interact to regulate soil redox condition and carbon loss in coastal wetlands. Using a field-tested hydrological–biogeochemical–ecological modeling framework, we reveal how the interplay between terrestrial groundwater discharge and tidal fluctuations generates complex groundwater flow patterns at the terrestrial–aquatic interface, and how these patterns modulate soil redox conditions, in turn influencing soil organic matter decomposition and carbon loss. Notably, rising sea levels suppress soil CO2 emissions while reducing lateral dissolved carbon losses, thereby enhancing litter carbon sequestration under anoxic conditions. As vegetation responds to sea-level rise and carbon inputs diminish, litter carbon subsequently declines. These findings underscore a critical hydrological control on carbon cycling, advancing our understanding of coastal ecosystem resilience in a warming world.

Chen, Kewei [ORNL] (ORCID:000000032580514X)↗
DOE OSTI2021

Phenological shifts in lake stratification under climate change

One of the most important physical characteristics driving lifecycle events in lakes is stratification. Already subtle variations in the timing of stratification onset and break-up (phenology) are known to have major ecological effects, mainly by determining the availability of light, nutrients, carbon and oxygen to organisms. Despite its ecological importance, historic and future global changes in stratification phenology are unknown. Here, we used a lake-climate model ensemble and long-term observational data, to investigate changes in lake stratification phenology across the Northern Hemisphere from 1901 to 2099. Under the high-greenhouse-gas-emission scenario, stratification will begin 22.0 ± 7.0 days earlier and end 11.3 ± 4.7 days later by the end of this century. It is very likely that this 33.3 ± 11.7 day prolongation in stratification will accelerate lake deoxygenation with subsequent effects on nutrient mineralization and phosphorus release from lake sediments. Further misalignment of lifecycle events, with possible irreversible changes for lake ecosystems, is also likely.

54 ENVIRONMENTAL SCIENCES↗