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At least 505 records · Page 28Linked to original sources
USGS2026

Predicting reach-scale macroinvertebrate community changes due to declining flows in an aridland river

Understanding the connection between streamflow and macroinvertebrate community structure is critical for managing river ecosystems under scenarios of change caused by human activities and drought. Streamflow influences physical habitat characteristics, including depth, water velocity, and substrate composition, which structure macroinvertebrate communities. Although strong empirical evidence has linked macroinvertebrates to these variables, most studies are correlative, and predictive approaches that link altered streamflow to changes in habitat availability and community structure remain limited. To address this gap, we coupled benthic macroinvertebrate surveys with reach-scale, 2-dimensional hydraulic models to quantify changes in community structure in response to altered streamflows. We characterized macroinvertebrate communities and physical habitat conditions in riffle, run, and pool mesohabitats across 3 reaches of a free-flowing section of the Verde River (Arizona, USA) over 3 y and developed hydraulic models using overflight data, ground surveys, and streamflow monitoring. Macroinvertebrate communities differed among mesohabitats, with riffles supporting higher macroinvertebrate densities and pools exhibiting higher taxonomic richness. The 2-dimensional hydraulic models indicated that declining streamflows could reduce the area of suitable habitat in riffles and runs relative to pools, potentially shifting the distribution of mesohabitats and altering community composition. These changes may lead to declines in overall macroinvertebrate diversity and abundance under reduced flow conditions. By explicitly linking streamflow to habitat availability and community structure, this study demonstrates how integrating hydraulic modeling with biological surveys can improve predictions of ecological response to flow alteration. This framework provides a basis for flow management strategies aimed at maintaining habitat heterogeneity and supporting biodiversity in river networks.

Arizona↗
USGS2026

Detecting earthquakes in noisy real-time GNSS data with deep learning for improved PGD magnitude estimation

To disseminate accurate and useful warnings, earthquake early warning (EEW) systems must quickly determine the size and location of an earthquake to estimate expected shaking. Traditional seismic‐based algorithms tend to underestimate the true magnitudes of large earthquakes, a phenomenon known as magnitude saturation. This limitation motivated the recent inclusion of Global Navigation Satellite Systems (GNSS) data into the U.S. Geological Survey’s ShakeAlert EEW system with the Geodetic First Approximation of Size and Time (GFAST) algorithm because GNSS data do not saturate with large ground motions. However, the noise levels of GNSS data are very high compared with traditional seismic data, which obscures P ‐wave arrivals and can result in less accurate magnitude estimations if displacement amplitudes are low, such as for lower magnitude earthquakes or large source–station distances. In this study, we develop a deep‐learning model that detects earthquakes in GNSS data and use the Ridgecrest, California, earthquake sequence as a case study to demonstrate how the model could act as a filter to reduce the amount of low‐quality data that enters an algorithm like GFAST. To preserve our limited real earthquake data for model inference, we generated a training dataset composed of >700,000 synthetic displacement waveforms. We combined the synthetic waveforms with real‐time GNSS noise to produce realistically noisy training waveforms and then tested our model on additional synthetic data and performed inference using the real data that were held back. We discuss the performance of our trained model on both the unseen synthetic data and real inference data. Our model can be used to selectively filter only high‐quality data where an earthquake signal is observed for input into an algorithm like GFAST (outperforming a simple signal‐to‐noise ratio–based filter) to reduce the error in GFAST’s real‐time earthquake magnitude estimations.

California↗
USGS2026

Coal beneath Federal lands in the United States—Mines, reserves, and resources

The U.S. Geological Survey (USGS) compiled a list of coal mines and tabulated the coal reserves and available coal resources beneath Federal lands in the conterminous United States. Coal resources beneath Federal lands in Alaska are also discussed in this report. In 2024, the 34 coal mines on Federal lands produced more than 261 million short tons of coal. Surface mining is used at 23 of the coal mines, and underground mining is used at 11. These 34 coal mines control more than 4.2 billion short tons of reported coal reserves. Most of the coal mines (31) and more than 98 percent of the reported coal reserves are on Federal lands west of the Mississippi River. Of all the States, Wyoming has the most coal mines on Federal lands (14) and produces the most coal from Federal lands. The Powder River Basin has the most coal mines per basin or coal field operating on Federal lands (12 in Wyoming, 2 in Montana). Most of the available coal resources in the conterminous United States are also west of the Mississippi River. There are five basins or coal fields in the West that each contain available coal resources of more than 25 billion short tons. The USGS estimates that more than 355 billion short tons of available coal resources remain beneath Federal lands in the conterminous United States. Alaska contains substantial quantities of coal resources. The USGS estimates that Alaska has at least 140 billion short tons of identified available coal resources but may ultimately have as much as 5.5 trillion short tons of coal resources.

Circular↗
USGS2026

Global performance of remote sensing-based and reanalysis-driven models to estimate open water evaporation

Evaporation plays an essential role in the water cycle, influencing local and regional climates while directly impacting water availability in lakes. However, directly measuring evaporation over water bodies remains challenging due to the high costs of installing and maintaining the required in situ instrumentation. Although several remote sensing algorithms have been providing evaporation estimates, the lack of a global validation hinders our understanding of their relative uncertainties and performances across different regions. Here, we analyze the performance of a suite of models that leverage satellite data and meteorological reanalysis to estimate evaporation over lakes worldwide. We compare 3 remote sensing-based models, 1 reanalysis-driven model and 1 ensemble approach, using in situ observations from 27 lakes representing a diverse range of geographic and climatic regions. Our results demonstrate that, overall, the ensemble outperformed any individual model in terms of accuracy, with a RMSE and a bias of 1.3 and 0.3 mm day −1 , respectively. These findings highlight the benefits of using an ensemble approach to estimate open water evaporation with satellite-based models at the global scale, leveraging the unique strengths of each model. For the individual models, differences in the representation of heat storage changes and advection effects led to lower values of RMSE and bias, depending on the location and depth of the lakes. This study sets the path for future improvement of open water evaporation algorithms globally, while remote sensing techniques are proven satisfactory to monitoring of water loss in lakes globally, an essential step toward effective large-scale water resources management.

Water Resources Research↗
USGS2026

Brown bullhead catfish melanoma represents a novel transmissible cancer

Since 2012, brown bullhead catfish ( Ameiurus nebulosus ) in a lake that spans Vermont, USA, and Quebec, Canada, have shown a high rate of melanomas, suggesting a causal contaminant or contagion 1 . We tested the hypothesis that this affliction represents a clonally transmissible cancer, a rare phenomenon in which cancer cells themselves spread between individuals, behaving more like parasites than conventional tumours 2 . Whole-genome sequencing of tumour and matched non-tumour host tissues revealed that tumour mitochondrial and nuclear genomes are more closely related to each other than to their hosts or unaffected fish. Hundreds of thousands of genetic variants are shared among tumour samples but are absent from host fish, vastly exceeding levels seen in conventional cancers 3 . These findings indicate that melanoma in these brown bullheads represents the fourth documented type of naturally occurring transmissible cancer in animals, after dogs 4 , Tasmanian devils 5 and several bivalve species 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 . This raises important questions about the cancer’s origin, the mode of transmission and the long-term impact on fish populations.

Quebec, Vermont↗
USGS2026

Predicting disease spread from host movement data: Chronic wasting disease in North America as a case study

1. Rare long-distance movements can increase the spatial spread of invasive species and shifts in species ranges. However, in the context of wildlife disease spread, seasonal migrations only matter if they lead to transmission beyond an individual’s initial set of contacts, and the importance of juvenile dispersal depends on whether those juveniles become infected before they disperse. 2. High resolution animal movement data are increasing, but it is unclear how to leverage these data to improve predictions future disease spread particularly in regions without pre-existing movement data. 3. We predicted the speed of chronic wasting disease (CWD) spread based on movement metrics of mule deer ( Odocoileus hemionus ) and white-tailed deer ( Odocoileus virginianus ) and compared our predictions to observed spreading rates in North America from 2005 to 2022. We hypothesized that CWD should spread faster in regions with long-distance migratory mule deer, but this hypothesis was only partially supported. 4. Observed rates of CWD spread were faster in western epicenters with more mobile mule deer than in epicenters with only white-tailed deer (14-18 km/yr compared to 5-7 km/yr, respectively). However, the observed speed of CWD spread did not align well with migration distances. Deer movements in the Canadian Prairie Provinces (CPP) were far shorter than those in Colorado and Wyoming, yet had faster CWD spread much of which was open prairies where deer movements tend to be along riverine corridors rather than the longer elevational migrations in more montane regions. 5. Generally, our predicted rates of CWD spread from three different models were far slower than the observed rates regardless of whether the model included dispersal or migration. Understanding of these discrepancies is needed before high-resolution animal movement data can be used to predict broad-scale patterns of disease invasion. 6. Anthropogenic movements likely explain longer-distance movements of CWD across North America, but it remains unclear whether human-mediated movements or alternative analytical methods and deer movement metrics can explain the faster than predicted local CWD spread.

Journal of Animal Ecology↗
USGS2026

Connecting and coordinating conservation action across landscapes through regional planning processes and syntheses

Modern threats to conservation are often global; however, relevant conservation decisions and actions typically occur within local jurisdictions. The socio-ecological complexity of these problems requires collaboration across large landscapes, diverse community interests, and geopolitical boundaries, yet a lack of interoperability of planning products among decision makers, jurisdictions, and objectives impedes collaboration. Here, we provide a working model for the collaborative, iterative development of a landscape conservation design (LCD) in the Midwest region of the United States, the Midwest Conservation Blueprint, and demonstrate its applicability to the coordination of voluntary conservation actions and investments across jurisdictional boundaries. We used spatial prioritization software to synthesize over 20 datasets to develop a basemap of priority lands and waters for conservation across a diverse set of societal and ecological values (e.g., water quality, biodiversity, recreation). The Midwest Conservation Blueprint is a living plan, updated annually based on improvements in underlying data, understanding of local conditions, and public input. Landscape-scale planning is not designed to replace local knowledge or supersede decision-making authorities. However, by synthesizing existing data that represent diverse socio-ecological values and planning at a broader scale, the Midwest Conservation Blueprint documents where there are shared priorities and opportunities for collaborative landscape conservation.

Midwest↗
USGS2026

Colocating artificial intelligence data centers with energy infrastructure on Federal public lands—A science synthesis and spatial analysis to inform decision making

Executive Summary Artificial intelligence (AI) is rapidly transforming industries and economies, creating an urgent need to strategically plan for the energy and infrastructure required to support increasing AI use. U.S. Federal agencies and bureaus have been directed to explore ways to accelerate permitting, development, and deployment of energy resources and AI technologies, including encouraging the colocation of energy infrastructure and data centers. To inform these initiatives, this report synthesizes relevant scientific information and presents a spatial analysis of existing energy infrastructure and data centers on or near U.S. Federal public lands managed by the Bureau of Land Management (BLM). The purpose of this science synthesis and spatial analysis is to provide the BLM with foundational information for considering potential colocation of data centers with energy infrastructure on Federal public lands to support evidence-based decisions. Additionally, this report provides insight into current (2025) and potential future energy demands by providing projections of a range of potential future environmental conditions relevant to maintaining industry-recommended cooling temperature standards necessary for efficient data center operations. As a part of this effort, a rapid response literature review was conducted of the best available science on the topic of data center development and energy infrastructure in July–August 2025, supplemented by additional resources recommended by U.S. Federal agency and bureau subject matter experts (hereafter experts; including the U.S. Department of Energy National Laboratory of the Rockies) and peer reviewers. To better understand current conditions relevant to AI data center development, a spatial analysis was conducted across Alaska and 11 States in the Western United States, Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming, all of which contain extensive BLM-managed surface lands (hereafter referred to as “BLM lands”) that could be considered for the colocation of energy infrastructure and AI data centers. This effort identified BLM lands within 10 miles of existing transmission lines, consistent with methods used in previous BLM programmatic environmental impact statements. This report describes the types of data centers operating within the United States, which vary in ownership, size, technology, and proximity to end users. This report then outlines the primary considerations of data center development, including reliable energy supply, natural resources (such as water availability to support cooling requirements), and relevant policy and regulatory considerations. Energy supply considerations are pivotal for data center operation. Between 2014 and 2018, data centers in the United States accounted for nearly 2 percent of the Nation’s total electricity consumption, and data center energy consumption is projected to increase from 2 to 6.7–12 percent of total U.S. electricity use by 2028. These energy requirements necessitate careful consideration of energy supply when considering potentially suitable locations for data center development. Experts anticipate that an increase in renewable energy generation will likely support most potential future power demand needs, including for data centers, followed by increases in natural gas, nuclear, and geothermal energy production. Additional capacity in the form of battery storage will likely not generate electricity, but may improve the reliability and flexibility of supply, helping to ensure that growing data center loads can be met. However, the U.S. Department of Energy estimates that the United States will need, on average, 57 percent more energy transmission infrastructure by 2035 to account for the growing power demand introduced by development such as data centers. Cooling server equipment in data centers requires large amounts of electricity and water, and this demand can be exacerbated by hot and humid conditions. Energy efficient water-based cooling technologies may reduce electricity consumption onsite but require more water consumption. This additional water demand has the potential to increase water stress and competition with other users. As such, developing data centers will likely need a thorough assessment of current and potential future water availability, as well as consideration of how water demand may change across other sectors. Data center development involves policy and regulatory considerations, as projects must undergo environmental review and authorization processes that can take 18–24 months. Coordinating these environmental reviews and authorizations with other energy development projects, such as building new transmission lines, may cause additional delays. Recent efforts by the U.S. Department of Energy and U.S. Department of the Interior aim to expedite environmental reviews and authorizations and improve coordination across agencies. The spatial analysis identified 771 existing AI data centers and more than 3,300 power plants. The spatial analysis found that 6 percent of AI data centers and 22 percent of power plants in the Western United States were on or within 1 mile of BLM lands, and California had the largest number of facilities. Most existing AI data centers were near high-voltage transmission lines and close to power plants, supporting efficient energy delivery. More than 90,000,000 acres of BLM lands were within 10 miles of existing high-voltage transmission lines, representing 38 percent of BLM lands in the study area. Available transmission infrastructure and the overlap with BLM lands varied by State, and Alaska had limited overlap compared to the rest of the Western United States. To operate most efficiently, data center temperatures must be at or below 80.6 degrees Fahrenheit. This analysis of future temperature and precipitation projections indicated increasing cooling demands for data centers, particularly in Arizona, California, and Nevada, where rising temperatures are expected to increase energy and operational costs while potentially stressing current regional electrical grid infrastructure. This report highlights relevant energy supply, natural resources, and regulatory considerations for data center development on BLM lands. This report does not provide a comprehensive ecological, regulatory, land suitability, or permitting analysis. The factors described here are contextual considerations only and are not intended to identify, rank, quantify, or recommend optimal areas for data center colocation. This spatial analysis focused solely on energy considerations relevant to data centers and did not consider water availability, critical habitats, BLM National Conservation Lands, areas of cultural or historical significance, and other sensitive resources. These topics are recognized as critical but were not within the scope of this science synthesis and spatial analysis.

Scientific Investigations Report↗
USGS2026

Modification of rod surface elevation tables for monitoring extreme elevation shifts in wetlands

Rod surface elevation tables (RSETs) are effective tools for measuring changes in ground surface height, but these are not designed to withstand large changes in height (e.g., ±50 cm). After extreme changes in elevation due to erosion or accretion, RSET sites may be deemed unusable; however, this equipment usually can function indefinitely with in-situ modifications. Here, we provide a methodological description for in-situ modifications to RSETs that can be invaluable for long-term monitoring. In a sampling site with a high rate of surface elevation loss, longer pins can be inserted into the RSET arm to continue measurements. In a site with a high rate of accretion, RSETs buried in sediment can be raised by adding rod extensions, which is a particularly effective approach if the surface elevation can be measured before and after the attachment. Any modification to an RSET requires a reconceptualization of the original equations used to estimate change in the surface elevation, and these revised equations are described in this paper. We tested the measurement variability of the RSET before and after modification in West Dongting Lake, China. Results indicated that modification did not compromise data quality or precision and stability of RSET measurements. The reconceptualized equations incorporate offset adjustments that maintain datum consistency, enabling continuous data collection across pre- and postmodification phases with millimeter-scale precision. These RSET modifications can be used to extend the life of long-term RSET monitoring sites if the surface elevation has changed beyond the measurement capacity of the original RSET.

Ecosystem Health and Sustainability↗
USGS2026

Predicted habitat use for reintroduced grizzly bears in the transboundary North Cascades ecosystem

Grizzly bears ( Ursus arctos ) were once numerous in the North Cascades transboundary region of Washington State (United States) and British Columbia (Canada); however, few remain today. To support ongoing reintroduction evaluations, we used simulations based on movement models developed in the Northern Rocky Mountains to predict habitat use by a small founding group of grizzly bears in the North Cascades during the early stages of reintroduction. These simulations represent movements across a spatially explicit landscape based on individual-specific selection and movement parameters. We first evaluated predictive performance in three nearby populations of grizzly bears in the Squamish-Lillooet, McGillvary Mountains, and North Stein-Nahatlatch regions of the Coast Mountains, British Columbia. After applying an elevation-based calibration, predicted habitat use showed strong agreement with GPS location data from 73 collared bears. Across population–sex subgroups, Spearman rank correlations were ≥0.95, with 69.2%–83.3% of locations occurring within the top five habitat classes (covering 50% of mapped habitat) and 17.1%–36.2% in the top class (representing 10%). Overall, 24.5% and 77.9% of locations fell within the top class and top five classes, respectively. Seasonal validation showed strongest predictive performance from May to late summer or fall. Model predictions for the North Cascades indicate that habitat use during early reintroduction is likely to be concentrated in the central and northern mountainous portions of the ecosystem along the US–Canada border. These maps can guide recovery planning when no local bear data are yet available in the North Cascades transboundary region.

British Columbia, Washington↗
USGS2026

The influence of tree canopy cover data choices on urban ecosystem accounting

Because urban landscapes are heterogeneous, the methods and spatial resolution used to depict the land surface greatly influence the representation of urban features. Land cover products such as tree canopy cover (TCC) are particularly sensitive to the methodology and resolution used in their creation. Differences in TCC mapping have implications on the outcomes of ecosystem service (ES) models, including those underlying natural capital accounting. Here, we quantify the sensitivity of physical rainfall interception and local climate regulation ES models for 189 U.S. cities to TCC inputs from four TCC products: a) National Land Cover Database (NLCD), b) Enhanced NLCD TCC, c) aggregated city-specific composite, and d) global tree canopy height dataset. We find both city-level and aggregate differences in TCC estimates, from a 38% decrease to a 3% increase relative to an aggregated high-resolution product. These differences result in up to 3% overestimations and 27% underestimation of rainfall interception and 2–56% underestimation of local climate regulation ES. City size, population, and greenness in addition to climatic variation drove differences between TCC products, and this variation requires users to carefully consider the choice of input data for any planned analysis. Though high-resolution data can offer greater nuance and accuracy, more limited spatiotemporal availability can hinder their usefulness for long-term monitoring applications such as natural capital accounting. The differences found in this study provide valuable insights for making informed decisions on data inputs for use in urban ecosystem research and for contextualizing model outcomes.

Contiguous United States↗
USGS2026

Habitat restoration efforts increase patch retention times in migratory mule deer (Odocoileus hemionus)

Mule deer ( Odocoileus hemionus ) populations have been declining across much of their range, often due to habitat alteration. Habitat alterations, including the introduction of non-native annual grasses and the expansion of pinyon–juniper (PJ) forests on sagebrush ecosystems, are changing resource availability compared to historical conditions. Extensive habitat restoration has been completed across the State of Utah to foster plant communities that better reflect historical conditions and optimal mule deer habitat. We investigated how patch retention times differ on eight different habitat restoration treatment types and random locations during summer and winter across the State of Utah, to evaluate the efficacy of restoration treatments for migratory mule deer, as retention time is expected to reflect resource availability. We developed seasonal moving-window Brownian Bridge Movement Models for individual mule deer ( n = 593 animals), and intersected occurrence distributions (OD) with habitat restoration treatments and randomly generated background landscape polygons. We quantified patch retention time as the number of days individuals’ OD overlapped treatment or random polygons during each season, using a Poisson regression. In winter sites treated to remove PJ, both with (56.5 days, 95% CI [29.3, 108.0]) and without seeding (55.3 days, 95% CI [29.1, 105.8]), as well as sagebrush restoration sites (59.0 days, 95% CI [30.6, 114.8]), had increased retention times when compared to the landscape average (41.9 days, 95% CI [20.7, 84.1]), with effects decreasing 5+ years post-treatment. Retention times in summer were longer across all treatment types in the 2–4-year post-treatment age class (37.5 days, 95% CI [26.3, 53.1]) compared to the landscape average (28.6 days, 95% CI [25.7, 31.8]). Our results demonstrate the positive effects of habitat restoration through increased patch retention times on mule deer winter and summer range and may suggest follow-up treatment 5+ years after initial treatment to maximize mule deer use.

Utah↗
USGS2026

Assessing working lands as habitat for disturbance-dependent birds using autonomous recording units

Introduction Working lands can provide habitat for declining disturbance-dependent birds, such as Bachman's Sparrow ( Peucaea aestivalis ) and Northern Bobwhite ( Colinus virginianus ), but it remains unclear whether restoration following intensive land use (e.g. surface mining) yields habitat comparable to less intensive land uses (e.g. timber production). Objectives We evaluated effects of land use both in working lands (recently replanted surface mine reclamation site and recently replanted timber production site) and reference sites explicitly managed for wildlife (recently restored young pine savanna and mature pine savanna) and understory vegetation characteristics on relative abundance of disturbance-dependent birds within upland pine ( Pinus spp.) communities in southeastern Georgia, United States. We hypothesized that the mine reclamation site would support similar bird species but in lower relative abundances than other land uses. Methods We conducted daily acoustic surveys using autonomous recording units (ARUs) and measured vegetation surveys once per point from May to June 2024. We used Royle-Nichols models, an adapted occupancy model, to estimate relative abundance for eight bird species. Results Land use influenced relative abundance for all species and explained more variation in relative abundance than measured understory vegetation characteristics. The mining reclamation site supported most disturbance-dependent species and, in some cases, equal or higher relative abundances, particularly for Northern Bobwhite and Common Nighthawk ( Chordeiles minor ). Responses across timber production and savanna sites varied by species. Vegetation effects were generally weaker and species-specific, suggesting that land use history and associated habitat legacies play an important role in structuring these bird communities. Conclusion Working lands, including reclaimed mines, can provide habitat for disturbance-dependent birds, and ARUs are effective for evaluating these patterns across land uses.

Restoration Ecology↗
USGS2026

Blanchard's cricket frogs (Acris blanchardi) have re-expanded northward since mid-2000s: Landscape predictors of occupancy

Blanchard's Cricket Frogs ( Acris blanchardi ) declined along their northern range edge in the upper Midwest in the mid-to late 1900s. The causes of these declines remain poorly understood, as does the current status of this species and their habitat requirements. In 2024, we revisited more than 300 locations previously sampled between 2003 and 2008 along this range edge in Minnesota, Wisconsin, Iowa, and Illinois. We conducted roadside anuran call surveys at each site and extracted land cover information from the National Land Cover Database and a global stream layer. We developed a multi-species occupancy model, which included imperfect detection, species traits, and species associations, to test for changes in occupancy through time and determine whether environmental characteristics were associated with cricket frog presence. Blanchard's Cricket Frogs occupied more sites and occurred farther north in 2024 relative to the mid-2000s, suggesting a partial recolonization in the zone of previous decline. Forest, croplands, and latitude were negatively associated with cricket frog occupancy, whereas wetlands, river size, and development did not have strongly supported effects. Species associations implied that wetland permanence could have been an important determinant of occupancy, but we did not have this covariate information for many of our sites due to private ownership of surrounding lands. Causes of earlier declines remain largely unknown, and many previously occupied regions apparently remain devoid of cricket frogs. However, some populations have been expanding northward over the past two decades in the upper Midwest.

Iowa, Minnesota, Wisconsin↗
USGS2026

Proposing sources for discrete groundwater discharges to patterned pools in three regional raised northern peat bogs

Raised northern peat bogs are generally assumed to be entirely precipitation-fed (ombrogenous), suggesting they are void of groundwater (minerogenous) inflow from underlying sediments. Patterned pools in raised bogs have often been attributed to surficial flow filling depressions along the peat surface that produces subtle differences in peat pore-water chemistry. However, we present evidence that certain patterned pools may be partially fed by localized upwelling of minerogenous groundwater from underlying glacial sediments in three northern peat bogs of Maine, USA. Underlying permeable glacial deposits, embedded in hydraulically confining glacio-marine clay deposits, were delineated using ground-penetrating radar and transient electromagnetic surveys. Paired point measurements of temperature and specific conductance (SpC) were surveyed around pools, and statistical relationships indicative of groundwater upwelling were established. Uncrewed aerial systems (UAS) thermal infrared (TIR) mapping was conducted, augmented by handheld TIR imaging, to examine characteristic groundwater temperatures in cold and warm seasons across pool surfaces. Surface water samples were acquired to assess the relationship between SpC / temperature signals and elevated iron and manganese concentrations that could be indicative of glacial aquifer sources. The combined datasets present evidence for localized upwelling in pools underlain by glacial structures, and the possibility of minerogenous groundwater contributions. Upwelling through the peat matrix is possibly partially facilitated by macropore, “peat pipe” features that serve as preferential flowpaths of varying lengths to the surface. Such upwelling could drive a positive feedback loop where elevated concentrations of ionic constituents repeatedly dilate the peat matrix and/or terminal electron acceptors enhance anaerobic respiration and microbial activity, accelerating the humification of peat around patterned pools and potentially magnifying carbon loss.

Maine↗
USGS2026

Velocity-independent dry friction on mica: A realization of ideal Amontons-Coulomb friction

The Amontons-Coulomb friction law assumes that the frictional force between materials is independent of sliding velocity. However, as Coulomb noted, this is a rough approximation, and a second-order dependence of friction on the logarithm of sliding velocity is incorporated in a commonly used ‘rate- and state-dependent’ friction representation. Here we conduct shear experiments on mica, a layer-structured mineral, at temperatures ranging from 25 to 200ºC and under normal stress of 100 MPa. The friction coefficient clearly depends on the logarithmic sliding velocity at 25ºC, but rate sensitivity decreases with increasing temperature until at 200ºC, the friction coefficient is independent of sliding velocity. Our findings could initiate the development of velocity-independent frictional materials, realizing the ideal Amontons-Coulomb friction.

Physical Review Letters↗
USGS2026

Restoration of Gavia immer (common loon) in Minnesota—2025 annual report

In cooperation with the Minnesota Department of Natural Resources, the U.S. Geological Survey monitored 98 Gavia immer (common loon) focal territories and 43 nonfocal territories in north-central Minnesota in 2025. Focal territories are those sampling units from which study inferences can be made. Nonfocal territories were adjacent to focal territories and were observed to monitor common loon dynamics across an entire waterbody. In collaboration with lake associations and private citizens, we deployed 44 artificial nesting platforms to 44 focal territories, called treatment territories. Territorial surveys to monitor occupancy, nesting, and chick survival were completed between April 27 and August 14, 2025. We attempted to visit each focal and nonfocal territory once per week. At least 1 nest attempt was observed in 40 of 54 control territories and 37 of 44 treatment territories. In treatment territories, 14 nests were on an artificial nesting platform, and the remaining nests were on natural substrates. Chicks or other evidence of hatching were observed in 20 of 54 control territories (37 percent) and 28 of 44 treatment territories (64 percent). Twelve of those successful nests in treatment territories were on artificial nesting platforms.

Minnesota↗