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At least 109 records · Page 6Linked to original sources

USGS2026

Spontaneous combustion of metal sulfide minerals and implications for seafloor massive sulfide mining

Metal sulfide minerals are abundant phases in many environments on earth, including seafloor massive sulfide (SMS) deposits, which occur at hydrothermal vents throughout the global ocean. These deep-sea settings are currently undergoing exploration for potential future mining. Here, we describe rocks from Escanaba Trough on the southern Gorda Ridge that underwent spontaneous combustion during laboratory processing. We compared the mineralogy, composition, and thermogravimetric behavior of the combusting samples to rocks from the same location that did not combust. We then used thermodynamic analyses to evaluate the exothermic nature of different metal sulfide oxidation reactions. The combusting rocks are primarily composed of nanocrystalline marcasite pseudomorphing blades of primary pyrrhotite. These altered, nanocrystalline rocks are less stable than their primary predecessors. Typically, recrystallization decreases reactivity as minerals trend towards steady state; however, both of the combusting samples we characterized are secondary, and demonstrate the instability of this metastable intermediate phase. Further, thermodynamic calculations indicate that metal sulfide oxidation is more exothermic when oxygen serves as the oxidant, as occurs in seawater or air. The distinct mineralogy, texture, and setting of SMS deposits in the modern oceans thus suggests a greater potential for self-heating in comparison to their ancient analogues.

Scientific Reports↗

USGS2026

Geospatial Agentic Services: A framework for interoperable geospatial intelligence

Autonomous GIS uses large language models as its reasoning core to automate spatial data collection, analysis, modeling, and visualization for solving spatial problems with minimal human intervention. Often implemented through autonomous geospatial agents that work independently or collaboratively, it represents a shift from GIS as a passive software environment requiring expert operation towards systems capable of task-oriented interaction, workflow generation and increasingly autonomous spatial analysis and decision support. However, current GIS agents are often isolated and tightly coupled with specific platforms, tools, or applications, which limits their reuse and interoperability across systems and domains. In this paper, we introduce Geospatial Agentic Services, or GAS, as a service-oriented framework for publishing, discovering, invoking, and composing GIS agents as interoperable geospatial services. GAS extends traditional geospatial interoperability beyond access to data and predefined operations towards geospatial intelligence interoperability, where GIS agents function as discoverable, reusable, and collaborative service entities that encapsulate spatial knowledge and reasoning. Building on service-oriented architecture, GAS supports structured geospatial skill declarations, agentic service registry and discovery, and distributed agent orchestration. It also integrates validation, provenance, reproducibility, and governance to support transparent and trustworthy geospatial analyses. To demonstrate the framework, we present a GAS implementation and illustrate its use through common invocation modes and representative AI-orchestrated workflows. By connecting traditional geospatial service architecture with emerging agentic interoperability, GAS provides a conceptual and architectural foundation for interoperable, reusable, and scalable agentic geospatial services and workflows in the era of autonomous GIS.

Annals of GIS↗

USGS2026

Solar energy development selectively filters avian taxonomic and functional diversity across a perennial agricultural landscape

Solar energy is rapidly expanding across agricultural landscapes, yet its consequences for avian communities remain poorly understood, particularly among heterogeneous land-use matrices. In 2024 and 2025, we surveyed birds across 107 sites in New York State, including conventional solar arrays with mowing or sheep grazing to manage vegetation, solar-adjacent working lands (e.g. hayfields, pastures) and reference working lands independently located from solar arrays. We integrated diversity metrics across four trait dimensions—taxonomic, morphological, ecological and conservation status—and examined interactions between species-level abundance and traits to understand avian community responses to solar energy development. Avian species in solar sites tended to be common, non-migratory (in behaviour and wing morphology) and associated with shrubland or forest. Compared to reference working lands, bird communities in solar and solar-adjacent sites were less taxonomically diverse and were composed of fewer declining and range-restricted species. However, solar and solar-adjacent sites retained comparable diversity of intrinsic traits, suggesting that ecological function may persist despite apparent species filtering and functional reshuffling. While solar energy development can support avian communities with unique functional signatures, our findings suggest that conventional solar arrays sited on agricultural working lands in New York maintain low bird diversity and are unlikely to support conservation-priority species—such as grassland birds characterized by declining populations, migratory tendencies and open-habitat dependence. Synthesis and applications . Our study provides a pathway to leverage functional diversity to quantitatively characterize solar-associated avian communities and the ecological mechanisms that shape them. Our results suggest that meeting biodiversity conservation goals while achieving solar energy targets requires ecologically informed siting and processes, particularly across regions in which co-prioritization of conservation, agriculture and energy presents sustainability trade-offs.

New York↗

USGS2026

Stability changes after weak layer collapse: Insights from an unexpected dataset

While conducting avalanche mitigation or traveling through the backcountry, avalanche professionals sometimes experience a "whumpf": a collapse-mode crack accompanied by surface cracking that does not produce an avalanche. These whumpfs are a clear warning sign of instability in the surrounding terrain. But assessing the collapsed slope itself, if it is steep enough to avalanche, carries much more uncertainty. Can slopes that whumpf and are steep enough to slide still avalanche? The answer to this question supports avalanche mitigators and ski guides in assessing whether to open such slopes. Previous research on collapsed slopes is limited to one study conducted before tests that estimate crack propagation propensity, such as the Propagation Saw Test (PST), were developed. This work showed that weak layer shear strength decreased immediately after collapsing and then increased over time. However, no research has examined crack propagation propensity after a whumpf. Studying propagation propensity is key for assessing how whumpfs immediately affect slope stability. To improve our understanding of changes in propagation propensity following weak layer collapse, we conducted PSTs on a test slope in the hours and days after an unexpected collapse occurred on a layer of surface hoar. Two PSTs at our study site three days prior to this collapse provided an estimate of weak layer propagation propensity. PST cut lengths decreased immediately following the collapse, suggesting that the collapse increased the propagation propensity. However, over the next 4-5 hours, PST cut lengths steadily increased at our site, likely due to ongoing sintering and strengthening in the collapsed weak layer. By the end of day two, we were no longer able to initiate propagation, suggesting substantial stabilization of the weak layer. Our results support prior findings and observations suggesting that slopes may remain capable of producing avalanches immediately after a collapse but likely stabilize over the next day or two. Although this research does not provide answers about precisely when a slope becomes stable after a collapse, it does highlight uncertainties in our understanding and interpretation of the stability of slopes that have whumped.

Montana↗

USGS2026

Revisiting avalanche destructive scale and associated mass estimates

Avalanche destructive (D) size classifications are essential for forecasting, communication, and risk management, yet size estimation remains one of the more subjective elements of avalanche assessment. Practitioners typically rely on visual impressions of destructive potential, path length, and estimated mass and volume. Recent work indicates that size is often underestimated even by experienced observers, especially when based on photographs or distant observations. Although mass is central to defining destructive size, the empirical foundations of mass–size relationships have rarely been revisited, and expert variability in size assignment could be decreased if more consistent operational tools were available. We evaluated whether a simple, practitioner-focused calculation tool using slab dimensions, snow density, and snow water equivalent can provide reliable mass estimates that correspond meaningfully with existing destructive size classes. Using this tool, we estimated avalanche mass and compared the results to observer-assigned D sizes. We also simulated mass outputs using a dense flow avalanche simulation module and compared observer estimates and simple-tool outputs to recent volume-based methods for D-size classification. Across 143 well-documented dry snow avalanches from multiple avalanche climates, the size estimation tool tends to classify avalanches larger than field-based observers. We found a mean bias of 0.47 D-size units and a typical (mean absolute) disagreement of 0.56. Our results show that 95% of events landed within one whole D-class of each other and 71% within a half-class. Agreement between the simple tool mass estimates and dense flow avalanche simulations was generally strong, though discrepancies emerged in cases with substantial entrainment or complex terrain. We compared the size tool to the simulation module with entrainment (n=4) and without entrainment (n=8). With entrainment, the two methods estimate the same D-size class in 4 of 4 comparable events and 7 of 8 events without entrainment. Also, the simulation module estimates larger sizes than the observer in 7 of 8 cases without entrainment and all 4 cases with entrainment. These findings support that development of practical, mass-based tools could improve D-size consistency and emphasize the value of revisiting historical mass–size relationships. The accompanying application offers practitioners an accessible and operationally useful resource for improving D-size estimation from field observations.

Conference Paper↗

USGS2026

Start-zone characteristics and timing of wet slab avalanches In Colorado

Wet slab avalanches (WSAs) release when liquid water reduces the strength of a buried weak layer or interface, and their timing is governed primarily by the snowpack energy balance and the movement of meltwater. We classified 717 WSAs from the Colorado Avalanche Information Center database (2011–2025) into five start-zone terrain categories and analysed the timing of naturally triggered, near- and above-treeline events using rank-based and Bayesian hierarchical methods. Start-zone terrain strongly governs the natural occurrence of WSAs: approximately 64% released in Rocky start zones (Cliffs/Rocks), while only ≈4% occurred on Open Slopes. After controlling for latitude and aspect, Rocky start zones released approximately five days before Open Slopes (90% CrI: 0.6–9.9 days). Latitude was the dominant timing predictor, shifting release by roughly 3–4 weeks from southern to northern Colorado. Cornice start zones concentrated WSA activity in early to mid-spring, with 40 of 41 releasing before mid-May. These findings support start-zone terrain classification as a low-cost addition to observation protocols for improving the spatial targeting of wet slab outlooks.

Colorado↗

USGS2026

Patterns of glide avalanche release: Insights from 23 years of observations along the Going-to-the-Sun Road, Montana, USA

Glide avalanches pose a notable hazard during annual spring snow removal operations along the Going-to-the-Sun Road (GTSR) in Glacier National Park (GNP), USA. Glide cracks consistently form on specific slopes near and above the GTSR. Some glide crack locations produce glide avalanches most years (known as repeater paths), whereas others are less predictable and either release or melt in place. Previous studies of glide avalanches have focused on different meteorological, snow-pack, and terrain parameters that influence glide release. The timing of glide avalanche release remains difficult to predict, which can be a challenge. To the authors' knowledge, no previous work has approached the issue of timing by comparing the chronological release order of glide paths within an operation. To address this, we examine the release order of repeater paths along the GTSR and investigate different topographical factors that may influence release timing. To investigate the timing of glide avalanche release, we analyzed a dataset of annually recorded glide avalanches from 2003 to 2025 along the GTSR from mid-March to June. We identified 359 glide avalanches with starting zones covering all aspects between 1,600 and 2,700 meters, and performed analysis on topographic factors including elevation, aspect, slope angle, and bed surface. Additionally, we assessed the likelihood and sequence of the release of seven repeater paths. Seven repeater paths along the GTSR generate glide avalanches nearly every year (65–100% of the study period), with two paths (Peak 7774 and Peak 7850) releasing first in over half of the years observed (p-value < 0.01). These paths have been informally recognized as "indicator paths," though this distinction has not been quantified until now. Identifying factors that drive the timing of glide avalanches using a days-scale operational signal can support the safeguarding of infrastructure and personnel. By examining release order and related variables, this study provides practical insights to inform operations managing glide avalanche hazards. Our methods may help forecasters pinpoint indicator paths and potentially recognize the onset of glide avalanche cycles, offering a framework for other operations.

Montana↗

USGS2026

Tracking water in snow: Bridging sensor and and model simulations

Forecasting wet slab avalanches remains one of the most demanding challenges in operational avalanche forecasting. A key obstacle is the difficulty of observing and quantifying liquid water content (LWC) and tracking its infiltration into the snowpack. Field measurements are labor-intensive, highly variable in space, and often influenced by user application or sensor design — making them impractical for forecasting operations. In this study, we evaluated LWC measurements obtained from two capacitive sensors, the SLF SnowPro-17 and -25, and compared them with simulations from the snow cover model SNOWPACK. We analyzed five selected days with pronounced melt events during spring 2025 at Bridger Bowl Ski Area, Montana. The SnowPro-17 measured LWC on the sidewall and the SnowPro-25 was inserted into the pit wall. We then analyzed measurements from the two sensors to assess consistency and potential bias. For each of the five days, we also analyzed liquid water infiltration by layer, comparing field measurements with model-simulated LWC. This allowed us to assess how well the model captured both the timing and magnitude of infiltration processes relative to in-situ observations. Our results show that, the absolute LWC values differ by approximately 1% by vol. on average between measurements from the capacitive sensors, but the timing of liquid water infiltration is largely consistent across both measurements and model simulations. Within our sample, the SnowPro-17 consistently reports higher mean LWC values than the SnowPro-25, and exhibits greater variability, indicated by a larger standard deviation. We found good agreement when we compared in-situ sensor measurements to LWC infiltration in snow cover model simulations. This suggests that, despite measurement challenges and sensor variability, SNOWPACK can provide valuable information on the timing of LWC infiltration. For practitioners, this work highlights that reliable forecasting of wet slab avalanches depends on understanding when and how liquid water reaches critical layers. Because direct LWC measurements are rarely feasible in operational settings, using physically based models may offer a practical pathway forward.

Montana↗

USGS2026

Applying automated avalanche terrain mapping to wildlife ecology: A case study of Dall's sheep in southcentral Alaska

Avalanche hazard has been studied extensively in the context of human risk, but its role in alpine wildlife ecology remains poorly understood. Long-term telemetry studies have revealed that avalanches are an important source of mortality for some mountain ungulates. Investigating the processes underlying these events is challenging because avalanche activity and snowpack conditions are rarely monitored across the spatial and temporal extent of wildlife studies. Recent advances in automated avalanche terrain mapping provide a new opportunity to address this knowledge gap by quantifying wildlife exposure to modeled avalanche terrain across large landscapes. We demonstrate this application using Dall's sheep (Ovis dalli dalli) in southcentral Alaska, where a 12-year study identified avalanches as the leading known cause of adult mortality. We used GPS locations from 134 adult Dall's sheep (385 sheep-years) collected between 2012 and 2024 across Chugach State Park and the Talkeetna Mountains, Alaska, USA. We characterized seasonal patterns of terrain exposure using the Automated Avalanche Terrain Exposure Scale and complementary metrics representing potential release areas, runout terrain, slope, and aspect. Guided by previous studies of Dall's sheep habitat selection, we developed a priori ecological expectations for seasonal exposure and used descriptive summaries of GPS locations to examine patterns of sheep use. Sheep locations occurred almost exclusively within avalanche terrain throughout the year, with Complex terrain accounting for 61–67% of locations across seasons. Winter and spring locations occurred on slightly steeper slopes while simultaneously having lower potential release area values and lower-value runout terrain than summer and autumn locations. Use of south-facing terrain increased from 64% of locations in summer to 70% in autumn and winter and 83% in spring—coinciding with the highest seasonal concentration of avalanche mortality. These descriptive patterns illustrate how complementary avalanche-terrain metrics can reveal patterns of wildlife exposure that would not be apparent from slope angle and aspect alone. Integrating automated avalanche terrain mapping with wildlife telemetry provides a new approach for identifying patterns of terrain exposure to guide further investigation into factors influencing wildlife avalanche risk.

Alaska↗

USGS2026

Climate-driven shifts in snowpack conditions favorable for wet snow avalanches: Insights from snow cover model simulations

Forecasting wet snow avalanches remains a major challenge due to their temporal and spatial variability. Long-term climate trends are producing more variable and extreme weather, creating conditions outside historical experience, and increasing forecasting uncertainties. To assess how climate change affects the frequency and timing of wet snow avalanches, we examined the long-term conditions that support wet snow formation. Specifically, we ask: Will the onset of wet snow avalanche conditions or season end shift toward the mid century? And will maximum snow height decrease in avalanche starting zones? To address these questions, we analyze long-term snow cover simulations across our study site in Glacier National Park, Montana, located in an intermountain snow climate. We examined trends in simulated snow depth, and timing of liquid water infiltration over the past four decades (1979–2023) using a physically based snow cover model. To force the model with meteorological data, we use the long-term regional hydroclimate reanalysis dataset U.S. Geological Survey CONUS404 with 4 km spatial resolution and 1h temporal resolution. The use of gridded meteorological inputs to drive a one-dimensional point model introduces inherent scale mismatches. To address this limitation, we use multiple grid points within the study area to simulate a more representative, generalized snowpack, and filter the grid points based on elevation to distinguish between upper- and mid-elevation starting zones. Finally, we analyzed shifts in snow depth, melt-out-day, and LWCindex (bulk liquid water content (LWC) normalized by the residual water content). We use snow depth as a proxy for changes in slab thickness, the LWCindexas an indicator of wet snow avalanche conditions, and melt-out date as a measure of shifts in season length. We ran simulations on flat terrain and on north- and south-facing virtual slopes (38°), across mid-elevation (1,600–1,900 m) and upper-elevation (≥1,900 m) sites for the historical dataset as baseline and a Pseudo-Global-Warming (PGW) dataset to identify a warming driven shift. Across all aspects and both elevation bands, PGW forcing produces a consistent reduction in annual maximum snow depth (median shift −0.15 to −0.26 m, p<0.001) over 44 seasons, with the largest reductions on north-facing slopes and a modest but significant strengthening of this reduction over the 44-year simulated record. Melt-out starts 10–14 days earlier under PGW across all aspect–elevation combinations (p<0.001), again most pronounced on north-facing, higher-elevation terrain. The LWCindex threshold-crossing dates likewise shift earlier under PGW, indicating an earlier onset of wet snow avalanche conditions across the domain. Together, these results point to a snowpack that is both thinner and wetter earlier in the season under future warming, with the strongest signal consistently on north-facing slopes at higher elevation. This work lays the groundwork for climate-informed forecasting and mitigation and offers historical context for avalanche operations in a changing climate. Understanding long-term trends can help avalanche professionals interpret current observations, improve wet snow avalanche forecasting, and develop more effective mitigation strategies in a warming climate.

Montana↗

USGS2026

Flood-inundation maps for Río Grande de Arecibo in and near Utuado Municipio, Puerto Rico, 2026

The U.S. Geological Survey (USGS) created digital flood-inundation maps for a 3.4-mile reach of Río Grande de Arecibo in the municipality of Utuado Municipio, Puerto Rico. Water-surface profiles were computed for the stream reach by using a one-dimensional, steady-state, step-backwater model. This model was calibrated to the current (2026) stage-streamflow relation (rating curve number 11.0) for the USGS streamgage 50024950, Río Grande de Arecibo below Utuado, Puerto Rico. The resulting hydraulic model was used to compute 11 water-surface profiles for water levels (flood stages) ranging from 14.00 to 34.00 feet at the streamgage and ranging from what are referred to as “minor flood stage” to above “major flood stage,” as reported by the National Weather Service. The 34.00-foot stage exceeds the peak stage (32.92 feet) recorded at this streamgage in 1998. The simulated water-surface profiles were used in combination with a digital elevation model derived from lidar data to map the inundated areas associated with each flood profile. The flood-inundation maps and the supporting hydraulic model produced by this study can be used by emergency managers and local officials to define flood hazard areas and assess flood mitigation strategies to help protect life and property, to coordinate flood response activities such as evacuations and road closures, and to aid post-flood recovery efforts. Information about map limitations, associated uncertainties, and how to view the maps and access the associated data online is included in this report.

Puerto Rico↗

USGS2026

Considerations for using autonomous recording units to monitor northern bobwhite occupancy at large spatial scales

Northern bobwhites ( Colinus virginianus ) have experienced widespread declines across their range, increasing the need for population monitoring across broad spatial and temporal scales. Yet large-scale monitoring of post-breeding bobwhites remains limited by the time-intensive nature of fall (i.e., autumn) covey counts. Passive acoustic monitoring with autonomous recording units (ARUs) and automatic detection software may help overcome these constraints but requires further study. From 2017 to 2021, we paired in-person observer point counts with ARU surveys during the fall in southern Iowa, USA to 1) evaluate how ARU detections compare to observer detections, 2) assess the accuracy of automatic classification software for covey calls, 3) gauge the influence of weather and ordinal date on detection, and 4) offer best practices for processing ARU acoustic data. We found high correspondence (89.2%) in detection and non-detection outcomes between paired ARU and observer surveys. Across manually processed recordings, 95.7% of covey call detections occurred within a 35-min window (50–15 min before sunrise). Increasing wind speed and cloud cover were associated with reduced detection probabilities. Wind speeds ≥5 m/s resulted in detection probabilities <0.5. The cumulative probability of detecting bobwhites at a site exceeded 0.995 (95% Credible Interval [CrI] = 0.987, 0.999) after 4 survey days when technicians processed the files. Manual processing had the highest mean detection probability (0.75, 95% CrI = 0.66, 0.82) compared with automatic detection software with 2 different confidence thresholds (0.46 [95% CrI = 0.39, 0.53] and 0.45 [95% CrI = 0.36, 0.55]). Automatic detection software allowed for substantially faster processing with relatively few false positives (false positive rate of 0.014 to 0.038). Autonomous recording units coupled with automatic detection software provided a promising alternative to in-person observer point counts of bobwhite covey calls for determining site occupancy and have the potential to greatly increase capacity for landscape-level bobwhite monitoring.

Iowa↗

USGS2026

First application of a coastal-dune model (AeoLiS) to simulate fluvial-aeolian morphodynamics

Quantifying aeolian processes is important to decision-making in river management and for optimizing strategies for ecosystem conservation, natural resource management, and recreation. We applied the AeoLiS aeolian transport model, originally developed for coastal environments, to a dryland river corridor to evaluate its ability to simulate sediment dynamics in a fluvial-aeolian system. The model reproduced spatial and temporal patterns of geomorphic change at a Grand Canyon sandbar, including aeolian dune formation, observed in field data. The model effectively captured trends in observed sediment movement in the spring and summer months. Thus, despite assuming a single grain-size, static vegetation, and not directly accounting for variable sediment supply from fluvial sources, the model performs well at capturing aeolian processes in a complex fluvial-aeolian system. AeoLiS demonstrates strong potential for dryland river applications and management, due to its flexibility and compatibility with coupled modeling frameworks.

Arizona↗

USGS2026

Niobium-bearing minerals from the Sheep Creek carbonatite-related deposits of southwest Montana, USA

The Sheep Creek area in southwest Montana contains a number of small, tabular, carbonatite-related deposits that are rich in Nb and REE minerals. Nb-rich minerals from Sheep Creek and nearby Woods Creek are herein described in detail. The carbonatite veins contain a mix of calcite and Fe-bearing dolomite, with locally abundant apatite, baryte, celsian, actinolite, phlogopite, monazite-(Ce), and allanite-(Ce). Nb-bearing minerals include columbite-(Fe), aeschynite-(Ce), baotite, niobobaotite, chevkinite, fergusonite-(Nd), and two varieties of Ti-Nb-Fe-oxide. An early oxide with Ti/Nb (atomic) near unity later broke down to a fine-grained mixture of columbite + rutile with distinctly lower Nb content. The earlier oxide is tentatively identified as a rare variety of Nb-rich brookite. In both oxide phases, pentavalent Nb entered the TiO 2 structure in coupled substitution with a mixture of Fe 3+ and Fe 2+ . A single zircon crystal intergrown with the Nb-REE minerals was dated by in situ U-Pb methods to 111.6 ± 2 Ma. This age is in agreement with previous dating of monazite (Th-Pb) and molybdenite (Re-Os), and supports the idea that the Sheep Creek deposits are Cretaceous in age.

Montana↗

USGS2026

Elemental and isotope geochemistry of Smackover Formation brines in Alabama and Arkansas: Implications for fluid mixing and water-rock interaction

This study investigates the geochemical characteristics of brines within the Upper Jurassic Smackover Formation in Alabama and Arkansas through elemental and isotopic analyses and geochemical modeling to reveal the extent and nature of fluid mixing and water-rock interactions. Smackover Formation brines exhibit Na-Cl-Ca hydrogeochemical facies and sulfate depletion relative to seawater. Lower sulfate concentrations, coupled with high H 2 S concentrations in some brines (0-364 mg/L) at temperatures >80°C, suggest that thermochemical sulfate reduction occurred. Cl/Br and Na/Br ratios indicate that salinity was controlled by the mixing of meteoric water with evaporated seawater that had reached halite saturation, rather than by halite dissolution. The δ 18 O and δ 2 H values of the brines also suggest mixing between meteoric water and evaporated seawater that had reached gypsum and halite saturation. Geochemical modeling of seawater evaporation predicts a Cl-Na or Cl-Mg facies after halite saturation, contrasting with the observed Cl-Ca dominance of the Smackover Formation brines. The Ca-enriched brines may have evolved from Cl-Mg-dominated evaporated seawater through dolomitization of limestone. The 87 Sr/ 86 Sr ratios range from 0.70740 to 0.71048, higher than the average of Jurassic seawater (0.707). The non-linear relationship between 87 Sr/ 86 Sr and 1000/Sr suggests alteration of brine geochemistry by fluid mixing or by inputs from Sr and 87 Sr-enriched rock sources. Lithium concentrations are positively correlated with 87 Sr/ 86 Sr ratios and TDS in brines enriched in these constituents. These correlations suggest deeper-seated geogenic sources and have implications for Li exploration in the northern Gulf Coast basin. Normal faults associated with the pre-Jurassic topography may have provided potential pathways for brine migration into the Smackover Formation under the influence of overpressure or thermohaline convection in the Gulf Coast.

Alabama↗

USGS2026

Predictability creates vulnerability: Long-term acoustic telemetry reveals hidden threat windows for endangered Atlantic sturgeon

1.Individual movement strategies govern access to important habitats but can expose certain animals to spatially and temporally variable threats, producing consistent, phenotype-specific differences in risk. For endangered Atlantic sturgeon ( Acipenser oxyrinchu s), migration timing shapes exposure risk to vessel strikes, dredging activity, and shifting estuarine conditions, yet management to minimize exposure to these threats is predominantly based on population-average patterns of river occupancy. 2. We analysed a decade of acoustic telemetry data from 195 Atlantic sturgeon in the Hudson River to quantify individual variation in migration timing. We clustered individuals into distinct behavioural phenotypes and evaluated the consistency of these phenotypes across years to determine whether certain individuals may be present in spatiotemporal areas that are not represented in current conservation frameworks. 3. Results identified two persistent phenotypes: the short-residency phenotype exhibiting synchronized occupancy typical of the prescribed Atlantic sturgeon spawning run and an extended-residency group that migrates into the river later, displays prolonged river occupancy and diffuse outmigration into the fall. Extended-residency individuals were last detected approximately 88 days after short-residency individuals and occupied habitats downriver of the presumed spawning grounds, extending the period of sturgeon presence into riverine habitats outside the current spatiotemporal area of conservation focus. 4. Individual phenotypic membership was repeatable across nearly a decade, indicating that these behaviours reflect stable, consistent phenotypes rather than stochastic variation. 5. Synthesis and applications . Our results reveal persistent intrapopulation movement diversity in the largest remaining Atlantic sturgeon population and highlight predictable periods during which a unique phenotype may experience disproportionate exposure to in-river threats. These findings indicate that environmental reviews, permitting decisions and seasonal work windows may benefit from incorporating phenotype-specific occupancy patterns and percentile-based estimates of river residency rather than relying solely on historical spawning dates or population averages. Expanding telemetry and monitoring designs to include broader spatial and seasonal sampling may further improve detection of overlooked movement strategies and reduce potential biases in demographic and habitat use assessments. More broadly, recognizing behavioural diversity in recovery planning may provide a more complete basis for evaluating habitat use, threat exposure, and conservation priorities across sturgeon populations.

New York↗

USGS2026

Does wind disrupt overwintering monarch butterfly clusters? An observational study of western monarchs

Wind protection is a central focus of western monarch butterfly ( Danaus plexippus ) overwintering habitat conservation. The Wind Disruption Hypothesis guiding management decisions predicts that winds at or above 2 m/s disrupt clusters and that cluster abundance decreases as wind speed increases. At two California groves during the 2023–2024 overwintering season, we paired time-lapse estimates of visible cluster size with nearby measurements of maximum wind gust. Stronger gusts were not consistently followed by decreases over 30-min or next-day response windows. In the 30-min analysis, the wind association depended on approximate local temperature and whether visible butterflies were in direct sunlight. Stronger gusts were associated with declines under cool, sunny conditions and increases under warm, sunny conditions. No clear wind association was detected when no visible butterflies were in direct sunlight. The next-day analysis likewise indicated that the wind association varied with sun exposure. Nearby maximum gusts did not measure sustained exposure at butterfly positions, so this was not an exact test of the historical threshold. The results did not support the predicted consistent decline with increasing wind under the monitored conditions. Physiological responses to interacting wind, ambient temperature, and solar exposure offer a plausible hypothesis for future research.

California↗

USGS2026

Inverse modeling for post-wildfire hydrologic process understanding over snow-dominated watersheds

Wildfire-induced changes to vegetation and soil properties can alter snowpack dynamics and hydrologic processes in ways that complicate predictions of post-wildfire water availability. To better understand these effects, we coupled the Parameter ESTimation in C++ (PEST++) software with the Weather Research & Forecasting Hydrologic model (WRF-Hydro) across three burned, snow-dominated watersheds in the western United States. We incorporated three burn-severity-based land cover categories into WRF-Hydro and used an iterative Ensemble Smoother (iES) within PEST++ to estimate model parameters controlling snow accumulation and melt, infiltration, and streamflow generation. This framework supported three complementary objectives: (1) assessing parameter sensitivities under pre- and post-wildfire conditions, (2) comparing pre- and post-wildfire parameter ensembles to identify wildfire-driven shifts in hydrologic processes, and (3) using counterfactual simulations to isolate the hydrologic response to wildfire from interannual climate variability. Our findings indicate that model parameter sensitivity varied by domain and shifted following wildfire. Maximum snow albedo and snow albedo decay were most influential in the higher-elevation East Troublesome domain, while the rain-snow partitioning temperature threshold dominated in the lower-elevation Chippy Creek and Las Conchas domains. Following wildfire, streamflow sensitivity shifted away from snowpack parameters toward soil hydraulic parameters, consistent with wildfire-driven reductions in evapotranspiration and increases in soil moisture. Incorporating wildfire effects led to complex changes in simulated snowpack accumulation and streamflow timing, generally leading to earlier snowmelt freshet initiation and cessation and reduced late season flows. Simulations were more sensitive to interannual climate variations than wildfire land cover changes. These results highlight both the potential and challenges of ensemble-based inverse modeling for understanding post-wildfire hydrologic change in snow-dominated watersheds.

Colorado, Montana, New Mexico↗