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USGS2024

The U.S. Geological Survey Ohio Water Microbiology Laboratory

The U.S. Geological Survey Ohio Water Microbiology Laboratory is a part of the Ohio-Kentucky-Indiana Water Science Center. The mission of the laboratory is to provide microbiological data of public health significance from surface waters, groundwaters, and sediments for a variety of study objectives. The laboratory conducts internal projects, works with external cooperators, and assists U.S. Geological Survey offices and National programs. The laboratory offers guidance, study design, and data interpretation expertise to collaborators, all following rigorous quality control and quality assurance procedures.

Fact Sheet↗

USGS2024

Shallow faulting and folding beneath south‐central Seattle, Washington State, from land‐based high‐resolution seismic‐reflection imaging

The geologic framework of the Seattle fault zone (SFZ) has been extensively studied, but the structure and fault strand locations in the central portion of the fault zone through the city of Seattle have remained controversial. Much of what is known about the SFZ has come from light detection and ranging (lidar)‐topographic surveys and paleoseismic investigations of fault scarps primarily west of Puget Sound, regional gravity and aeromagnetic modeling, and multiscale marine seismic imaging in waters both west and east of Seattle. We analyze ∼24 km of land‐based P ‐wave seismic‐reflection data that fill in a critical gap in our understanding of the SFZ beneath the urban areas of West Seattle, south‐central Seattle, and Mercer Island. These data image deformed strata in the upper 1 km, including upwarped Tertiary rock and younger sediments. Collectively, these data provide evidence for multiple Quaternary‐active thrust faults, back thrusts, and sub‐basins within the SFZ beneath the city of Seattle. The results indicate that multiple and potentially active back thrusts in the upper ∼500 m extend across the length of the SFZ and the entire urban corridor that may be analogous to those on Bainbridge Island west of Puget Sound.

Washington↗

USGS2026

Subsurface water ice mapping on Mars: A probabilistic approach

Subsurface water ice deposits on Mars are an important resource for potential future human exploration. They are also an indicator of the planet’s past climate. However, the distribution of subsurface water ice in Mars’s midlatitudes is uncertain because spacecraft imagery cannot directly observe subsurface ice in most cases. Various spacecraft remote sensing instruments are sensitive to subsurface water ice, including thermal imaging spectrometers, radar sounders, and neutron spectrometers. Geomorphic analyses of images can also implicate subsurface ice. Building upon the data products from the Mars Subsurface Water Ice Mapping project, we provide a probabilistic framework to jointly interpret existing data and estimate the likelihood of subsurface water ice in the Martian midlatitudes between 60 ∘ S and 60 ∘ N with uncertainty. Broadly, we find that near-surface ice is likely present poleward of ∼45 ∘ in both the northern and southern hemispheres. However, closer to the equator, existing remote sensing data cannot uniquely constrain the presence of subsurface water ice. Our probabilistic results provide a framework for quantifying the abundance of ice on Mars, and our uncertainty estimates allow future analysis and exploration to target regions of high uncertainty.

Planetary Science Journal↗

USGS2025

Aeromagnetic and magnetotelluric imaging of west-central Idaho and the Stibnite-Yellow Pine mining district: A regional to district perspective

Aeromagnetic and magnetotelluric (MT) data are used to better understand the geology and mineral resources near the Stibnite-Yellow Pine mining district in central Idaho. The reduced-to-pole (RTP) transformation of regional-scale aeromagnetic data shows that allochthonous island-arc rocks west of the Salmon River suture are significantly more magnetic than the Laurentian continental rocks east of the suture and that the granitoids of the Idaho batholith have moderate to low magnetization in both early, metaluminous, and late, peraluminous phases. Application of tilt derivative to aeromagnetic data highlights major crustal-scale structures. The 5-km upward continued magnetic data indicate island-arc rocks have deep magnetic sources. The 110-km-long MT profile images resistivity structure to depths around 30 km. At shallow depths, resistivity corresponds to mapped geologic units, with moderate resistivities underlying volcanic and roof-pendant metasedimentary rocks and moderate to high resistivities occurring beneath the Idaho batholith. Crustal-scale moderate resistivities beneath the suture image the results of tectonomagmatic processes that accompanied suturing and translating allochthonous terranes. Low resistivity values beneath and fringing the batholith are derived from metasedimentary rocks that may have served as a melt source and reductant during melt generation and provided metals during later ore formation. In the Stibnite-Yellow Pine mining district, a high-resolution aeromagnetic compilation is shown to correlate with mapped lithologies and mineral deposit-related structures. The RTP transform distinguishes magnetic and nonmagnetic granitoid phases of the Idaho batholith. The tilt derivative highlights metasedimentary rocks, some of which are favorable ore hosts. The Meadow Creek fault hosts the Stibnite and Hangar Flats deposits and is imaged as a magnetic low due to hydrothermal alteration. Reconstructions of magnetic anomaly offsets and orebodies indicate around 3 km of post-95 Ma dextral separation, with some or all of the offset inferred to postdate the main Au mineralization episode (61–66 Ma).

Idaho↗

USGS2025

System characterization report on the Environmental Mapping and Analysis Program (EnMAP)

This report addresses system characterization of the Environmental Mapping and Analysis Program hyperspectral sensor by the DLR (German Aerospace Center, ground segment project management), GFZ (Deutsches Geoforschungszentrum, science lead) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the EnMAP hyperspectral sensor; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), and radiometric performances of the EnMAP hyperspectral sensor. Results of these analyses indicate that the Environmental Mapping and Analysis Program has a band-to-band geometric performance in the range of −0.135 to 0.15 pixel, geometric performance relative to the Operational Land Imager in the range of −27.716 meters (−0.92 pixel) to 32.892 meters (1.09 pixels) offset in comparison to Landsat 8 Operational Land Imager, offset of a radiometric comparison in the range of −0.012 to 0.020, slope of a radiometric comparison in the range of 0.947 to 1.031.

Open-File Report↗

USGS2025

Estimating agricultural irrigation water consumption for the High Plains aquifer region with integrated energy- and water-balance evapotranspiration modeling approaches

Estimation of irrigation water use provides essential information for the management and conservation of agricultural water resources. Conventionally, water use data are created based on reports and surveys from water users, whereas manual records may not be complete due to lacking flow meters, measurement gaps, inconsistent methods across regions, and time- and cost-consuming data processing. Alternatively, spatially explicit estimation of irrigation water use can be conducted efficiently using remote sensing evapotranspiration (ET) modeling approaches. In this study, we created a gridded blue water evapotranspiration (BWET) dataset to estimate historical irrigation water consumption (1986 – 2020) in the croplands across the United States High Plains aquifer region. The BWET data were generated by integrating an energy-balance ET model [Operational Simplified Surface Energy Balance model (SSEBop)] and a water-balance ET model [Vegetation ET model (VegET)]. BWET in croplands indicates crop consumptive use of irrigation water extracted from surface water and groundwater resources. The BWET estimates were compared with reported irrigation water use data for all counties within the aquifer region. The results revealed high agreement between growing season (May – September) BWET and annual water withdrawal at county level. Specifically, correlation coefficients of volumetric BWET and water withdrawal were 0.90 and 0.96, respectively, for the entire aquifer region and western Kansas. The timeseries of BWET and water withdrawal showed similar temporal trends and high covariations. The BWET estimates were systematically lower than the water withdrawal measurements, which was primarily attributed to blue water losses in the irrigation system. The irrigation efficiency, calculated as the ratio of BWET to water withdrawal depth, was 0.57 and 0.74 for the entire aquifer region and western Kansas, respectively. This study demonstrates the capability of using satellite-based ET models (e.g., SSEBop and VegET) to efficiently estimate crop water consumption and evaluate irrigation efficiency at landscape, county, and regional scales.

Colorado, Kansas, Nebraska, New Mexico, Oklahoma, ↗

USGS2025

ARCHI: A new R package for automated imputation of regionally correlated hydrologic records

Missing data in hydrological records can limit resource assessment, process understanding, and predictive modeling. Here, we present ARCHI (Automated Regional Correlation Analysis for Hydrologic Record Imputation), a new, open-source software package in R designed to aggregate, impute, cluster, and visualize regionally correlated hydrologic records. ARCHI imputes missing data in “target” records by linear regression using more complete “reference” records as predictors. Automated imputation is implemented using a novel, iterative algorithm that allows each site to be considered a target or reference for regression, growing the pool of complete references with each imputed record until viable gap-filling ceases. Users can limit artifacts from spurious correlations by specifying model-acceptance criteria and applying geospatial, correlation, and group-based filters to control reference selection. ARCHI provides additional functions for visualizing results, clustering records with similar correlation structures, evaluating holdout data, and interactive parameterization with an accessible and intuitive graphical user interface (GUI). This methods brief provides an overview of the ARCHI package, modeling guidelines, and benchmarking on two regional groundwater-level datasets from the Central Valley, CA and Long Island, NY. We evaluate ARCHI alongside widely used multivariate imputation software to highlight and contextualize its computational efficiency, imputation accuracy, and model transparency when applied to large, groundwater-level datasets.

California, New York↗

USGS2026

Tectonic controls on volcanism and associated hydrothermal activity in a sediment-dominated mid-ocean ridge; Escanaba Trough

Mid-ocean ridges, the Earth's most extensive volcanic system, exhibit unique characteristics in sediment-dominant environments. Thick sediment cover insulates the crust and channels fluid along pathways that can lead to the formation of distinct crustal alteration patterns, exceptionally large mineral deposits, and specialized chemosynthetic ecosystems. This study presents an interdisciplinary investigation into the tectonics of the Escanaba Trough, a heavily sedimented axial valley at the southern Gorda Ridge in the Northeast Pacific Ocean. A primary challenge in such environments is overcoming the masking effect of thick sediments on basement structures that control magmatic and hydrothermal activity. We address this by employing three-dimensional (3D) magnetic modeling of high-resolution near-seafloor magnetic data collected by an autonomous underwater vehicle (AUV). The 2022 surveys with AUV Sentry provided data for 3D magnetic susceptibility models, refining our understanding of the geometry of sub-sediment laccoliths/saucer-shaped sills and hydrothermal alteration. In conjunction with a new 1:100,000 scale lithostratigraphic map, we outline the tectonic controls on the emplacement of Escanaba Trough's three main volcanic centers, characterize the geometry of its spreading segments, and provide volumetric data on the distribution of sub-sediment volcanism in the southern Gorda Ridge.

Escanaba Trough, Pacific Ocean↗

USGS2026

Distributed faulting of the northern West Napa Fault Zone in Napa Valley, California

Mapped surface ruptures from the 24 August 2014 M w 6.0 South Napa earthquake in the Napa Valley, California, show a 2‐km‐wide zone of distributed faulting in the southern and central West Napa fault zone (WNFZ). In the northern WNFZ at Hendry Winery (HW), however, the mapped 2014 surface ruptures encompass an ∼100‐m‐wide zone, implying significant narrowing of the near‐surface fault zone to the north. We present a tomographic shear‐wave velocity ( ⁠⁠ V S ) model and guided‐wave data that indicate the northern WNFZ is at least 400‐m wide, with multiple near‐surface fault traces. Our V S model shows that the 2014 surface ruptures are underlain by discrete low‐velocity zones (LVZs), and coincident guided‐wave data show that the LVZs carry fault‐zone guided waves. If nearby (<500 m) mapped faults to the east of HW are part of the WNFZ, the entire WNFZ is more than 1 km wide in the northern Napa Valley. WNFZ guided waves travel up to 38% slower than S body waves, and low‐strain guided‐wave shaking is up to five times stronger than the associated body‐wave shaking. Our data suggest that guided waves, traveling along distributed faults, may result in an increased shaking hazard over a 1‐km‐wide area of the northern Napa Valley during future significant earthquakes. In places, the 2014 surface ruptures were difficult to find one year after the earthquake, and paleoseismic trenching showed only weak evidence for faulting, which may not have been identified in trenches if the locations of the 2014 surface ruptures had not been previously mapped ( Prentice et al. , 2015 ). Guided‐wave and V S tomography data, however, show strong evidence for faulting beneath the 2014 surface ruptures and at locations to the east. Although paleoseismic trenching is the gold standard for identifying near‐surface faulting, methods such as peak ground velocities of guided waves may better identify immature near‐surface fault traces.

California↗

USGS2025

Global maps of critical mineral production in 2023

Introduction The global production of many mineral commodities, especially critical minerals, is concentrated in a few countries that have mineral resources and the infrastructure necessary to mine and process those resources. For this reason, the type and amount of mineral production differ by country. For example, many countries produce such metallic ores as gold and silver, whereas only a few countries produce magnesium, niobium, platinum-group metals, and rare earths. The concentration of mining and processing in certain countries necessitates the existence of a global supply chain. A mineral supply chain is the sequence of mining and processing of minerals and manufacturing of products. Mineral supply chains are global in scale, complex, and dynamic. Supply chain data can be used to understand how a country’s mineral resources and various economic, technical, and environmental factors affect the complexity of global supply chains. This fact sheet summarizes the world’s leading countries (those accounting for 5 percent or more of a commodity’s global production in 2023) for production of select mineral commodities (mainly critical minerals) in the mining and processing stages. These countries and the minerals they produce are synthesized on global maps to communicate the status of, and potential risk to, mineral commodity supply chains from geographic production concentration. Trade data from United Nations Statistics Division (2025) is used to support assessments of the observed production data.

Fact Sheet↗

USGS2024

System characterization report on the Earth Surface Mineral Dust Source Investigation (EMIT) sensor

Executive Summary This report addresses system characterization of the Earth Surface Mineral Dust Source Investigation (EMIT) sensor, an imaging spectrometer developed by the National Aeronautics and Space Administration. This report is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior) and radiometric performances. Results of these analyses indicate that the EMIT sensor has a band-to-band geometric performance in the range of −0.355 to 0.210 pixel with a few exceptions of shortwave infrared channels. Geometric offset relative to the Landsat 8 Operational Land Imager ranged from −15.966 meters (−0.266 pixel) to 43.844 meters (0.731 pixel). Offset of a radiometric comparison ranged from −0.016 to 0.025, and slope of a radiometric comparison ranged from 0.837 to 0.985. EMIT agreed with Radiometric Calibration Network measurements within 5 percent across most of the spectral channels.

Open-File Report↗

USGS1999

The effects of the Chesapeake Bay impact crater on the geological framework and correlation of hydrogeologic units of the lower York-James Peninsula, Virginia

This report documents the highly variable structure, stratigraphy, and buried topography of the outer rim of the Chesapeake Bay impact crater created by its impact and burial. Lithologies of cores are correlated with borehole geophysical logs to characterize the physical properties of the stratigraphic units and their geophysical signatures. The correlation between cores, well cuttings, and borehole geophysical logs is augmented with seismic-reflection data, and these data are compiled into a lithostratigraphic cross section that illustrates the geological framework of the lower York-James Peninsula and immediate surrounding areas.

Virginia↗

USGS2025

The effects of line simplification on planform geometry

Data on maps should retain accuracy regardless of scale. Yet, as cartographic lines are generalized, there can be impacts on properties such as topology, density, and planform geometry. Here, we investigate the use of the Scale Specific Sinuosity (S3) metric (Stanislawski et al., 2023) to evaluate the effects of line simplification on planform geometry, which is the bends of streams in map view. We employ an open-source Python S3 workflow to characterize the geometry of five diverse stream channels in the United States. The original data are extracted from the U.S. Geological Survey National Hydrography Dataset 1:24,000-scale vector data (U.S. Geological Survey, 2000) (Table 1), and the simplification is done using the Visvalingam and Whyatt method (2017) with a simplification tolerance of 0.5, 1.0, 1.5, and 2.0 km. The S3 analysis is calculated at each level of simplification and S3 derivatives are generated. Derivatives include measures of sinuosity, fractal dimension, and the dominant bend wavelength. The findings show that the change in planform geometry is scale-dependent, though simplification will have little effect on straighter lines. The change becomes more apparent in complex lines as the degree of simplification aligns with the scale of the dominant bend geometries. These logical conclusions are evidence that the S3 is a useful metric for automated characterization of bend geometry regardless of line complexity.

Conference Paper↗

USGS2026

Uncertainty quantification of geologic energy storage in depleted gas reservoirs using material balance equations embedded in a hierarchical errors-in-variables model

The storage potential for gas in geologic settings, such as depleted hydrocarbon reservoirs and solution-mined salt caverns, is becoming salient to future energy infrastructure planning. Technologies such as carbon capture, utilization, and storage, carbon dioxide-enhanced oil recovery, and natural gas and hydrogen storage help meet growing energy demands, reduce carbon emissions to meet climate goals, and provide energy security amid geopolitical uncertainties. Therefore, estimates of underground gas storage capacity could be useful for efficiently navigating the energy transitions. Material balance is a fundamental method in reservoir engineering for estimating original gas in place and potential storage capacity at the scale necessary for national assessments of subsurface pore space resources. However, the deterministic method cannot accommodate multiple data sources or quantify uncertainty in predictions. In this study, a method that embeds material balance equations within a hierarchical errors-in-variables model is proposed which allows the estimation of the distributions of reservoir properties needed for assessments. Uncertainties associated with these reservoir properties have traditionally been expert-elicited, whereas the uncertainty estimates from the proposed models are data-driven. Capacity and uncertainty estimates can be used in a probabilistic resource assessment, supplementing information traditionally used by assessors or even replacing this expert elicitation step when data are unavailable. Various regression models are compared in a case study of the Michigan Basin, a large contributor to the United States’ current natural gas storage capacity. In particular, errors-in-variables models help ameliorate regression dilution and can quantify uncertainty in predictions of pressure in addition to storage capacity. Overfitting is addressed by quantifying generalization error and model averaging in simple and stratified cross-validation against reported working gas capacity, representing the varying quality and quantity of available data. Incorporating a statistical framework into existing numerical methods in reservoir engineering can improve the quality of estimation, and in particular, this method brings rigor to uncertainty quantification as part of a larger effort by the U.S. Geological Survey to assess domestic energy gas storage resources in depleted hydrocarbon reservoirs.

Mathematical Geosciences↗

USGS2026

High‐resolution surface deformation and slip distribution observations for the 2023 Kahramanmaraş, Türkiye, earthquake sequence help constrain the rupture process

Splay, or branch, faults are a common geometric feature of earthquake surface ruptures and may provide constraints on the rupture behavior of an earthquake. The 2023 M w 7.8 Pazarcık and M w 7.5 Elbistan, Türkiye, earthquakes are examples of ruptures with multiple small splays, and the Pazarcık earthquake nucleated on a splay fault, the Narlı fault, before rupturing bilaterally on the East Anatolian fault (EAF). Here, we present 3‐m‐resolution surface displacement from subpixel correlation of Planet Dove optical images for the entirety of both ruptures with corresponding surface slip distributions. For a 30‐km‐long study region spanning the Narlı‐EAF intersection, we compare surface slip derived from five data sets with different resolutions (on‐the‐ground, WorldView, Planet Dove, Sentinel‐2, and Sentinel‐1) to elucidate complementary information. In addition, we integrate information from the surface expression of faulting with published dynamic rupture simulations and rupture process studies to constrain a rupture evolution for the Pazarcık earthquake that is consistent across data sets. This work highlights the complementary nature of disparate surface slip data sets and the role that high‐resolution surface displacement information, including from fault splays, can play in constraining nonunique rupture models and refining understanding of the earthquake rupture process.

Bulletin of the Seismological Society of America↗

USGS2024

The spatially adaptable filter for error reduction (SAFER) process: Remote sensing-based LANDFIRE disturbance mapping updates

LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel treatment activity (i.e., events) submissions from various agencies, and disturbances detected by the U.S. Geological Survey Earth Resources Observation and Science (EROS)-based Remote Sensing of Landscape Change (RSLC) process. The RSLC process applies a bi-temporal change detection algorithm to Landsat satellite-based seasonal composites to generate the interim disturbances that are subsequently reviewed by analysts to reduce omission and commission errors before ingestion them into LF’s disturbance products. The latency of the disturbance product is contingent on timely data availability and analyst review. This work describes the development and integration of the Spatially Adaptable Filter for Error Reduction (SAFER) process and other error and latency reduction improvements to the RSLC process. SAFER is a random forest-based supervised classifier and uses predictor variables that are derived from multiple years of pre- and post-disturbance Landsat band observations. Predictor variables include reflectance, indices, and spatial contextual information. Spatial contextual information that is unique to each contiguous disturbance region is parameterized as Z scores using differential observations of the disturbed regions with its undisturbed neighbors. The SAFER process was prototyped for inclusion in the RSLC process over five regions within the conterminous United States (CONUS) and regional model performance, evaluated using 2016 data. Results show that the inclusion of the SAFER process increased the accuracies of the interim disturbance detections and thus has potential to reduce the time needed for analyst review. LF does not track the time taken by each analyst for each tile, and hence, the relative effort saved was parameterized as the percentage of 30 m pixels that are correctly classified in the SAFER outputs to the total number of pixels that are incorrectly classified in the interim disturbance and are presented. The SAFER prototype outputs showed that the relative analysts’ effort saved could be over 95%. The regional model performance evaluation showed that SAFER’s performance depended on the nature of disturbances and availability of cloud-free images relative to the time of disturbances. The accuracy estimates for CONUS were inferred by comparing the 2017 SAFER outputs to the 2017 analyst-reviewed data. As expected, the SAFER outputs had higher accuracies compared to the interim disturbances, and CONUS-wide relative effort saved was over 92%. The regional variation in the accuracies and effort saved are discussed in relation to the vegetation and disturbance type in each region. SAFER is now operationally integrated into the RSLC process, and LANDFIRE is well poised for annual updates, contingent on the availability of data.

Fire↗

USGS2025

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

Forecasting water levels in complex ecosystems like wetlands can support effective water resource management, ecological conservation, and understanding surface and groundwater hydrology. Predictive models can be used to simulate the complex interactions among natural processes, hydrometeorological factors, and human activities. The Greater Everglades in the USA is a well-known example of an ecosystem where complexity has motivated adoption of machine learning algorithms in water level prediction studies. This paper aims to contribute to extending existing machine learning algorithms by integrating spatiotemporal data with deep-learning algorithms in the forecasting process. In this study, a deep-learning model is developed to predict water levels on a regional scale, covering a large area of approximately 9,138 square kilometers in the Everglades ecosystem. This model has the architecture of Convolutional Long Short-Term Memory which can deal with spatiotemporal data by capturing both spatial and temporal dependencies in the training data. The forecasting capabilities of this model (referred to as the global model) are assessed by comparing the global model to two Artificial Neural Networks developed at two different gaging stations, referred to here as local models. One local model is developed at a gaging station directly influenced by nearby water control structures, whereas the other is developed at a gaging station located farther away from these structures. By leveraging data from the Everglades Depth Estimation Network spanning from January 2002 to May 2023, the global and local models were trained to forecast water levels with a two-day lead time. Our findings suggest that both the global and local models perform with approximately the same level of accuracy, with Mean Absolute Relative Error values ranging from 0.38% to 1.4% at the selected stations. The developed global model has demonstrated strong potential as a standalone forecasting tool for the entire study area in the Everglades and could eliminate the need for developing multiple local models. This finding also highlights how machine learning can capture complex spatial and temporal relationships to generate accurate water level predictions on a regional scale.

Florida↗

USGS2024

Integrating Sr isotopes, microchemistry, and genetics to reconstruct Salmonidae species and life history

Recent approaches to fisheries research emphasize the importance of the coproduction of knowledge in building resilient and culturally mindful fisheries management frameworks. Despite widespread recognition of the need for Indigenous knowledge and historical reference points as baseline data, archaeological data are rarely included in conservation biology research designs. Here we propose a novel multiproxy method to learn from former fisheries stewards by generating archaeological data on past salmonid population parameters. We used a newly developed, high throughput qPCR (HT-qPCR) chip, originally designed for environmental DNA (eDNA), for species identification of archaeological salmonid vertebrae. We combine this with the laser ablation split-stream (LASS) approach to identify ocean-migration versus freshwater residency. We test this multidisciplinary approach using both contemporary and archaeological salmonid samples and new radiocarbon dates from the Tronsdal Site on the Skagit River, Washington State, USA. This is a useful approach for extracting information about Salmonidae species and life history diversity from archaeological remains to reconstruct historic baselines for several population parameters in anadromous species with long periods of freshwater residency. The approach outlined in this paper may be particularly useful for research investigating past fisheries dynamics, offering hundreds to thousands of years of temporal depth for modern fisheries management, harvest policies, restoration ecology, and conservation biology.

Idaho, Oregon, Washington↗