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Biogeochemical controls on iron speciation and cycling across upland to shoreline gradients in freshwater and estuarine coastal soils (Lake Erie and Chesapeake Bay, United States)
Coastal environments are dynamic interfaces that mediate carbon and nutrient exchanges between terrestrial landscapes and open waters, and understanding the biogeochemical factors controlling these exchanges, particularly iron (Fe) redox transformations, is crucial for predicting coastal ecosystem functions. Here, we investigated the mechanisms controlling Fe speciation changes across upland-to-shoreline gradients in freshwater and estuarine soils using Fe K-edge X-ray absorption spectroscopy, solid and porewater composition analysis, and 16S rRNA sequencing analysis. We show that Fe transformations depend primarily on inundation patterns. In unsaturated uplands, Fe occurs as Fe(III) oxyhydroxides, mainly goethite (9–35 %), Fe(II,III)-phyllosilicates (39–89 %), and Fe(III)-organic species (0–61 %). Soils influenced by estuarine waters exhibit porewater sulfide concentrations reaching up to 221 μM, Fe- and S-cycling bacteria, and up to 81 % pyrite (FeS 2 ), indicating that sulfur-driven redox dynamics control Fe transformations. In lacustrine wetlands, Fe(III) reduction is indicated by porewater Fe(II) concentrations increasing to 1.0–2.1 mM, and ~10–15 % of Fe as Fe(II,III)-(hydr)oxides (green rust), vivianite (Fe 3 (PO 4 ) 2 ·8H 2 O), and/or adsorbed Fe(II) species. EXAFS data also indicate reduction of structural Fe(III) to Fe(II) in phyllosilicates. The presence of Fe- and S-cycling bacteria, as well as sulfide (0–10 μM), suggests that Fe-cycling is microbially driven and potentially coupled with cryptic S-cycling. Fe(II) oxidation was indicated above/near the water table by the presence of Fe(III) oxyhydroxides (ferrihydrite, lepidocrocite). Furthermore, negligible Fe(III) or sulfate reduction was observed at some water-saturated sites located at the upland-wetland transition, likely due to oxic (sub-)surface water inputs. Overall, our results highlight the importance of considering both Fe-speciation and hydro-biogeochemical dynamics when predicting Fe-cycling at coastal interfaces.
Geographically aware estimates of remotely sensed water properties for Chesapeake Bay
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Sensitivity Analysis of Sediment Coagulations in the Chesapeake Bay
Hydrodynamic models are utilized for fast response to environmental hazards. However, limitations within the models may inhibit their accuracy. A three-dimensional aqueous transport model, ALGE, was developed at the Savannah River National Laboratory (SRNL) as a tool for pollutant dispersion. Recently at SRNL, the capability to account for coagulation and break-up of particles to better account for the fate and transport of materials. A sensitivity analysis was performed to determine a threshold for specified particle parameters that still yield accurate results. Dictating these thresholds advances the development of ALGE to become a product for efficient emergency response.
Uncultivated Viral Populations Dominate Estuarine Viromes on the Spatiotemporal Scale
Viruses are ubiquitous and abundant in the oceans, and viral metagenomes (viromes) have been investigated extensively via several large-scale ocean sequencing projects. However, there have not been any systematic viromic studies in estuaries. Here, we investigated the viromes of the Delaware Bay and Chesapeake Bay, two Mid-Atlantic estuaries. Deep sequencing generated a total of 48,190 assembled viral sequences (>5 kb) and 26,487 viral populations (9,204 virus clusters and 17,845 singletons), including 319 circular viral contigs between 7.5 kb and 161.8 kb. Unknown viruses represented the vast majority of the dominant populations, while the composition of known viruses, such as pelagiphage and cyanophage, appeared to be relatively consistent across a wide range of salinity gradients and in different seasons. A difference between estuarine and ocean viromes was reflected by the proportions of Myoviridae, Podoviridae, Siphoviridae, Phycodnaviridae, and a few well-studied virus representatives. The difference in viral community between the Delaware Bay and Chesapeake Bay is significantly more pronounced than the difference caused by temperature or salinity, indicating strong local profiles caused by the unique ecology of each estuary. Interestingly, a viral contig similar to phages infecting Acinetobacter baumannii (“Iraqibacter”) was found to be highly abundant in the Delaware Bay but not in the Chesapeake Bay, the source of which is yet to be identified. Highly abundant viruses in both estuaries have close hits to viral sequences derived from the marine single-cell genomes or long-read single-molecule sequencing, suggesting that important viruses are still waiting to be discovered in the estuarine environment.
Improving tide-estuary representation in MPAS-Ocean (Final Report)
The overarching goal of this research is to produce a skill-assessed tide-estuary representation, including BGC (biogeochemistry) components of the ecosystem, into the MPAS-O global climate modeling system. Specifically, in the original proposal we proposed one-way nesting, into global MPAS-O, of a high-resolution model for TES (tidal and estuary systems) including the coupling of the BGC components. We have completed the proposed tasks during the past 4 years. Specifically, after extensive consultation with the MPAS-O group during the first year, we have decided to go beyond the original proposal and to two-way nest SCHISM into MPAS-O. As a result, SCHISM-CoSiNE (with CoSiNE being the BGC model) is now fully integrated into MPAS-O as a subroutine (more precisely, an analysis member) and can be invoked as MPAS-O is running with two-way exchange of information between the TES and global models (with proper spatial interpolation done on the fly). As originally proposed, we have also developed the new finite-volume (FV) model MPAS-OI (aka ELCIRC-sub) for TES. We have conducted several tests using the coupled MPAS-SCHISM- CoSiNE model and also MPAS-OI for Columbia River, San Francisco Bay and Chesapeake Bay. Four journal publications have been produced so far as the result of the project and 1 is under review (cf. Publication section). With these achievements, we will be ready to conduct fully coupled simulations of TES and global ocean anywhere, as soon as some missing capabilities in MPAS-O (e.g. tides, improved ability to handle the boundary conditions, and streamlining of the global BGC model) are worked out. The significance of the outcome of this project cannot be overstated. It marks the first time, as far as we know, that a global climate model is tightly coupled to a bona fide nearshore and terrestrial model (SCHISM system); in fact SCHISM is the first bona fide compound flooding model that seamlessly covers the coastal ocean, estuaries, rivers and watersheds (Ye et al. 2020; Zhang et al. 2020). The coupled model will reduce the errors and uncertainties of the MPAS-O global climate model system. It also complements and extends on-going E3SM project, as the latter seeks in the long-term to include components required to simulate impacts of sea-level change and storm surge on coastal regions, including wave models and focusing resolution in coastal and storm-track regions, as an important component of the Cryosphere-ocean system. Note that a spectral wave model (the unstructured-grid version of Wave Watch III, aka Wind Wave Model) is already incorporated inside the SCHISM system and is therefore directly accessible in the coupled system.
Tracking animal movements via collaborative acoustic telemetry networks: Multiscale habitat use, phenology, and management insights
Abstract Estuaries support diverse fish and invertebrate communities, including resident species that rely on estuarine habitats year‐round and transient migratory species. The unique movement patterns of these animals connect habitats within and far beyond the estuary and are integrally linked to fisheries management objectives. With a focus on Chesapeake Bay, this study leveraged data from collaborative acoustic telemetry networks in the northwest Atlantic to assess habitat use and phenology of movements for seven species of fish (cownose rays, dusky sharks, smooth dogfish, alewife, striped bass, common carp, and blue catfish) and one invertebrate (horseshoe crabs). A total of 288 acoustically tagged individuals were detected >3.2 million times (6,743 to 2,095,717 detections per species) on receivers across ~20.5 degrees of latitude spanning the North American Atlantic seaboard from Florida, USA, to New Brunswick, Canada. Common metrics of movement and phenology grouped these species as resident (common carp, blue catfish, horseshoe crabs), primarily resident in estuaries (juvenile striped bass), and coastal migrant (cownose rays, dusky sharks, smooth dogfish, alewife); maximum distance traveled varied by three orders of magnitude among these species. Further analysis of phenology for coastal migrants elucidated the timing and duration of these species' use of Chesapeake Bay. Collectively, movements linked habitats within Chesapeake Bay and connected the estuary to coastal ecosystems both to the north (e.g., alewife) and south (e.g., cownose rays), creating networks of fisheries management jurisdictions that varied in complexity and identified opportunities for enhancement to current management or co‐management of some species. Our results elucidate the importance of estuaries to species with diverse movement behaviors, identify scales and pathways of habitat connectivity via animal movements, and highlight the utility of collaborative acoustic telemetry networks for quantifying movements relevant to both ecological research and fisheries management.
ALGE Contaminant Transport Model for Emergency Response
Model Description • ALGE is a 3-dimensional hydrodynamic code to predict the movement of a dissolved tracer throughout a water body. • It has been recoded as an emergency response tool. • Can model large-scale bays and estuaries (Chesapeake Bay, Lake Michigan, etc.) • Model includes suspended sediment transport, dissolved and particulate chemical transport, radioactive decay, and dynamic salinity.
COMPASS-FME Synoptic Sites Level 1 Sensor Data v1-2
This is the version 1-2 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems.L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds and out-of-service 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 up to 12 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, 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 v1-2 Synoptic L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning.
COMPASS-FME Synoptic Sites 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 synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems. 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 up to 12 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, 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 v2-0 Synoptic L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. 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”).
COMPASS-FME Synoptic Sites Level 2 Sensor Data v2-1
This is the version 2-1 Level 2 (L2) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems. 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. 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. Please see v2-1 L2 Sensor Package QStart.pdf for detailed information on data package structure, temporal coverage, and versioning.
Soil microbial ecology and microbiome-metabolite linkages improve understanding of ecosystem states along terrestrial-aquatic interfaces
These data are from Bandopadhyay et al., "Soil microbial ecology and microbiome-metabolite linkages improve understanding of ecosystem states along terrestrial-aquatic interfaces". This study aims to understand the soil microbial ecology along terrestrial-aquatic interfaces of a freshwater and estuarine region and how it relates to organic matter. We analyzed soil microbial (16S rRNA gene) and organic matter (Fourier-transform ion cyclotron resonance mass spectrometry, FTICR-MS) composition from upland (forested), transition (stressed forest), and wetland positions at three sites in each of the Lake Erie (freshwater) and Chesapeake Bay (estuarine) regions. This dataset includes 16S rRNA gene amplicon data (only processed file types included here) and organic matter composition from FTICR-MS data (raw and processed files included here) from upland (forested), transition (stressed forest), and wetland positions at three sites in each of the Lake Erie and Chesapeake Bay regions. These sites are part of the COMPASS-FME project (https://compass.pnnl.gov/FME/COMPASSFME). File formats and software needed to access files: 16S rRNA gene amplicon data: These files follow the format reported here https://ess-dive.gitbook.io/amplicon-sequencing-reporting-format#updates-in-v1.0.1. As per this format, there are four file types reported: 1. Taxon tables (also called sequence-by-sample or OTU (operational taxonomic unit)/ESV (exact sequence variant) tables) : available in a .txt file format and accessible using TextEdit or MS Excel. 2. Representative sequences (also called consensus sequences) : available in a .fasta format and accessible using TextEdit. 3. Sequencing metadata : available in a MS Excel workbook file format and CSV file format 4. Bioinformatic metadata : available in a MS Excel workbook file format and CSV file format FTICR-MS data: 1. Raw data converted to a processed file with intensities of the peaks in the given samples : available in a MS Excel CSV file format 2. Processed file used in analyses and visualizations (appended as icr_long_) : available in a MS Excel CSV file format 3. Metadata file for ICR features (appended as icr_meta) : available in a MS Excel CSV file format
Data for Kim et al., "Variations in the optical and molecular composition of dissolved organic matter exported from coastal wetlands"
Knowledge about sources and composition of marsh-derived dissolved organic matter (DOM) is critical for understanding the role of marshes in coastal biogeochemical cycling and the fate of marsh-derived DOM in the ocean. To investigate tidal variability in composition of marsh-derived DOM, Kim et al. examined the optical and molecular characteristics of hourly surface water samples at three tidal creeks in the Chesapeake Bay. Groundwater samples along the terrestrial landscape gradient as well as estuarine water from the adjacent estuary at each site were also collected to help resolve sources of surface water DOM. Samples were collected in summer 2024 at three sites – SWH: Sweet Hall Marsh, GCW: Kirkpatrick Marsh, and GWI: Goodwin Islands – which are part of synoptic sites in the Chesapeake Bay region of the COMPASS-FME (Coastal Observations, Mechanisms, and Predictions Across Systems and Scales - Field, Measurements, and Experiments) project. Surface water samples were collected hourly over a 48-hour period at each site. Groundwater and estuarine water samples were collected once. This dataset includes- Surface water depth and salinity- Dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) concentrations- Optical indices and relative composition of parallel factor analysis (PARAFAC) components- High resolution mass spectrometry data.
CoURAGE KAZR b1 Processing: Corrections, Calibrations, and Processing Report
The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility supports atmospheric and earth system research through a comprehensive network of fixed and mobile observatories. These facilities provide long-term and intensive campaign-based observations of clouds, aerosols, precipitation, radiation, and meteorological state variables. ARM observations are designed to improve the physical understanding and numerical representation of atmospheric processes in earth system models, with particular emphasis on cloud-radiation interactions and precipitation processes. The Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) deploys one of the ARM Mobile Facilities (AMF) to the Mid-Atlantic region surrounding Baltimore, Maryland, for the period 1 December 2024 through 30 November 2025. This deployment focuses on characterizing atmospheric structure, cloud properties, and precipitation processes across strong land-use and surface heterogeneity gradients associated with urban, rural, and coastal (Chesapeake Bay) environments. The CoURAGE deployment complements the Baltimore Social-Environmental Collaborative (BSEC), a DOE Urban Integrated Field Laboratory (UIFL), by providing high-quality atmospheric observations needed to connect urban surface processes, emissions, and meteorology to cloud and precipitation responses. In addition to the central urban site, ancillary observing sites were deployed to rural Maryland northwest of Baltimore and to an island site in Chesapeake Bay. These measurements further complement a long-term atmospheric observatory operated in Beltsville, Maryland, by Howard University in collaboration with the Maryland Department of the Environment. Together, these assets form a four-node regional atmospheric observatory network representing Baltimore and its three primary surrounding environments—urban, rural, and coastal/bay. This coordinated observational strategy enables investigation of spatial gradients in boundary-layer structure, cloud occurrence, precipitation evolution, and aerosol-cloud interactions across complex surface regimes. Within this network, vertically pointing cloud radars play a critical role by providing continuous, high-resolution measurements of cloud and precipitation vertical structure.
On the Sensitivity of Coastal Hypoxia to Its External Physical Forcings
Abstract The development of low‐oxygen zones threatening marine life (hypoxia) occurs annually in multiple coastal regions of the world. The largest estuary of the continental United States, the Chesapeake Bay, typically has ≈10 km 3 of water with dioxygen concentrations <3 mg L −1 in July. As numerical methods for refining model resolutions in targeted areas are becoming common, there is interest in assessing the feasibility of simulating coastal hazards such as hypoxia in Earth System Models (ESMs). These coupled models are typically not constrained by observations and thus likely to feature systematic biases in their land, atmosphere, or ocean components. This study relies on four numerical experiments to evaluate the sensitivity of Chesapeake Bay hypoxia to changes or biases in its external physical forcings. Hypoxia exhibits only a minor decrease (−1.6%) after reducing the Bay's terrestrial freshwater discharge by 9.5% (but keeping terrestrial nutrient loadings the same). Changes in freshwater discharges have their largest impact on hypoxia during one extreme event (−37% during 2011 tropical storm Lee). Similarly, changing oceanic conditions on the shelf or their temporal frequency impact hypoxia by only 5%–6%, indicating that the latter is predominantly dictated by local conditions. Although these results are promising from the perspective of ESMs, additional components of ESMs will need to be evaluated before general conclusions can be reached. We notably speculate that the Bay's hypoxia would exhibit higher sensitivity to other forcings not examined here, notably air temperatures and nutrient loadings.
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.
Projected Changes to Cool‐Season Storm Tides in the 21st Century Along the Northeastern United States Coast
Abstract This study investigates changes and uncertainties to cool‐season (November‐March) storm tides along the U.S. northeast coast in the 21st century under the high RCP8.5 emission scenario compared to late 20th century. A high‐fidelity (50‐m coastal resolution) hydrodynamic storm tide model is forced with three dynamically downscaled regional climate models (RCMs) over three decadal periods (historical, mid‐21st century, and late‐21st century) to project future changes in peak storm tide elevations at coastal counties in the region. While there is no absolute consensus on future changes to storm tides, for any one future decade two out of the three RCMs project an increase at counties along the Hudson River, Delaware River, and northern Chesapeake Bay due to more intense cyclones that track inland of these locations leading to favorable surge generating conditions. The same RCMs also project a decrease at counties facing the open ocean in the mid‐Atlantic Bight as cyclone densities just offshore of the coastline decrease, particularly by late‐century. The greater tidal range in northern areas of the region leads to uncertainty in the magnitude and sign of the projected changes to storm tide levels due to the arbitrary timing of the local tidal stage and the surge‐producing weather event. This random tide‐weather timing is less important in the Chesapeake Bay and unimportant in Albemarle Sound and Pamlico Sound. Similar to other recent studies, we highlight that sea level rise is likely to be more critical than storm climatology for future changes to the cool‐season coastal flooding potential.
Machine learning of factors for improving oyster hatchery production
Oyster aquaculture and restoration in the Chesapeake Bay are vital, yet hatcheries frequently struggle with inconsistent larval growth and sudden mass mortality events. Unpredictable disruptions in larval production cause large economic losses, represent a perceived risk to growers, and impede industry expansion. To better understand associations between production yield and its potential predictors, we applied machine learning (random forest, and neural network) and statistical (generalized additive model) models to a comprehensive dataset of environmental, water quality, and operational parameters from a Maryland oyster hatchery, aiming to identify key yield predictors and develop a robust forecasting tool. We used recursive Boruta algorithm for variable selection, pinpointing critical predictors, and employed cross-validation to fine-tune model settings. Shapley value analysis offered crucial insights into model interpretations, highlighting week number, Normalized Difference Vegetation Index, salinity, turbidity, and fecundity as primary drivers of yield variability. For low-yield cases, salinity-related variables were particularly important. Our findings provide an early warning system for potential production downturns, empowering hatchery operators to make data-driven decisions for optimizing water conditions, feeding schedules, and broodstock management. By boosting predictability and efficiency, this research directly supports economic stability of the oyster industry and ecological health of the Chesapeake Bay.