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USGS2025

Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning

Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km 2 (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications.

California↗

USGS2026

Miocene evolution of the Humboldt Current

Diatom records from the East Pisco Basin (EPB) of southern Peru and offshore Ocean Drilling Program (ODP) Hole 682 A reveal stepwise increases in the primary productivity of the Humboldt Current during the middle and late Miocene. Although diatoms are present back through the late middle Eocene, successively enhanced diatom production occurs during the Miocene in four steps. The first step between 14.2 and 13.8 Ma marks the onset of diatom deposition in the Pisco-0 sequence. A second step at ∼12.9 Ma coincides with a major drop in global sea level during which diatom deposition ceased in the EPB but continued in ODP 682 A as well as in Chile, Ecuador, and Colombia. Beginning at 10.4 to 10.0 Ma, a major increase in diatom sedimentation rates in both the EPB and in ODP 682 A signals an abrupt intensification of the Humboldt Current productivity, which may be related to the closure of the Central American Seaway to deeper water circulation. A fourth step coincides with the transgressive deposition of the diatom-rich Pisco-2 sequence at 8.4 Ma and is enhanced by the onset of the global Late Miocene Biogenic Bloom (8.0–4.5 Ma). Between 7 and 6 Ma, common subtropical diatoms in ODP 682 A contrast with high diatom deposition rates in the EPB, suggesting variable El Niño-La Niña conditions. During this time, fossil vegetation evidence from southern Peru supports the presence of seasonal periods of enhanced rainfall typical of El Niño conditions. The Miocene trend of increasing sea mammal diversity in the EPB parallels these steps, further supporting stepwise enhancement of primary productivity in the Humboldt Current during the Miocene.

Humboldt Current↗

USGS2026

Diverging mineral chemistry of iron and nickel throughout Earth’s changing redox conditions reveals foundation for their evolution as protein cofactors

Iron (Fe) and nickel (Ni) were both foundational to early metabolism, yet their biological trajectories diverged as Earth’s surface redox state changed. Here, we integrate mineral chemistry network analysis, protein metal-site coordination-sphere analysis, and curated redox comparisons to test how geochemistry and metalloprotein architecture co-evolved. Mineral network analyses show broader electronegativity variation and network diversity for Fe-bearing minerals through time relative to Ni-bearing minerals. In structural analyses of protein metal centers in a combined Fe/Ni protein structure set, it is shown that Fe- and Ni-associated environments differ in amino-acid composition, hydropathy structure, and cysteine representation. The greater chemical diversity and electronegativity variation in Fe minerals mirror the higher redox and structural versatility of Fe-binding proteins. The presence of Fe in a broader range of mineral and protein environments demonstrates the chemical adaptability of the metal, from the anoxic Archean to oxidative Earth surface conditions following the Great Oxidation Event. Iron, with its broad redox potential range in Fe-oxidoreductases, has a central role in both anaerobic and aerobic metabolisms. Nickel, by contrast, is less widespread in biology. Today, Ni is predominantly employed in deeply branching anaerobic pathways and by proteins with narrower redox potential ranges. Our results show that evolutionary processes, constrained by metal chemistry, habitually utilize Fe as a redox generalist while retaining Ni in specialized roles. The divergent paths of Ni and Fe, from rocks to proteins, demonstrate the intimate relationship between planetary geochemistry and metabolic origins on Earth and suggest that Fe/Ni geochemistry may inform habitability assessments in extraterrestrial environments when interpreted within specific planetary environmental contexts.

Life↗

USGS2026

Patterns and drivers of cliff erosion in Big Sur, California, USA using repeat photogrammetry, 2017–2023

Seacliff erosion in steep terrain poses major risks to transportation and critical infrastructure. In Big Sur, California, USA, seacliff erosion threatens the sustainability of the central coast stretch of California State Route 1, a transportation corridor that is critical to the region's economy. Published cliff retreat rates for the region range from 1 to 40 cm yr −1 , highlighting that high-resolution, process-based studies could enhance understanding of the causes of spatial and temporal variability. We quantified cliff erosion and investigated its drivers along ∼13 km of the Big Sur coastline at week–month timescales during the late fall to early spring wet seasons between January 2017 and June 2023 by analyzing 3D point clouds developed from aerial imagery using four-dimensional structure-from-motion (4D SfM) photogrammetry techniques. We calculated cliff face retreat rates of 2.23 ± 3.06 cm yr −1 (mean ±1 σ ), an order of magnitude lower than long-term estimated rates for the region (which included large deep-seated landslides), but in line with short-term rates reported across California. Change detection imagery comparison, cliff profiles through time, and statistical analysis reveal a cyclical cliff evolution process in which erosion by wave action at the cliff base destabilizes the cliff and primes it for subsequent failure during precipitation events. Although more erosion by volume could be attributed to precipitation-induced increases in soil moisture (784 m 3 km −1 yr −1 ) compared with erosion attributed to wave power (282 m 3 km −1 yr −1 ), our observations underscore the coupled nature of these processes in driving cliff evolution, consistent with established theory and observations.

California↗

USGS2026

Earthquake ground-motion model adjustments for the San Francisco Bay area

We develop adjustments to ergodic ground‐motion models (GMMs) to improve their performance in the San Francisco Bay Area (SFBA). GMMs are widely used in hazard assessments to estimate characteristics of ground shaking based on known properties of the source, path, and site. Such models are often developed using datasets containing records from various regions, resulting in models that represent median ground‐motion behavior, which may not adequately represent ground motions within subregions. This is true for the SFBA, where ground motions attenuate more rapidly with distance than in many other parts of California that dominate GMM databases. To support improved seismic hazard estimates in the SFBA, we calculate regional constants and anelastic attenuation coefficient adjustments relative to two commonly used ergodic GMMs: BSSA14 ( Boore et al. , 2014 ) and ASK14 ( Abrahamson et al. , 2014 ). These adjustments are obtained for a suite of ground‐motion intensity measures (peak ground acceleration, peak ground velocity, and 5%‐damped pseudospectral acceleration at oscillator periods ranging from 0.075 to 10 s) using mixed‐effects regression. Use of the regionally adjusted models reduces the overall bias by up to 0.5 natural log units for BSSA14 and up to 0.6 natural log units for ASK14. We demonstrate one application of our attenuation adjustments and their implications in an earthquake early warning case study of the 2014 M 6.0 South Napa earthquake. The predicted extent of shaking using the adjusted models better matches observed shaking at large source‐to‐site distances, especially for lower shaking intensities, thus potentially reducing overalerting. We encourage the use of our model adjustments when ergodic models are considered for seismic hazard studies in the SFBA.

California↗

USGS2026

A roadmap for identifying and interpreting physical processes and national water model prediction bias associated with baseflow index regimes across the contiguous United States

Understanding how groundwater–surface water interactions shape streamflow variability is critical for diagnosing low flow behavior and prediction bias in continental scale hydrologic models. We present a process informed framework that links observed baseflow (BF) dynamics, watershed attributes, and National Water Model (NWM) performance across the contiguous United States. Using daily observed streamflow from 797 reference quality streamgages, we developed monthly baseflow index (BFI) signatures using a streamgage specific, calibrated digital filter. Hierarchical clustering of these signatures identified seven distinct BFI regimes capturing regional and seasonal variability. We evaluated NWM v3.0 retrospective streamflow performance within each regime using multiple hydrograph and flow duration curve-based metrics. Model skill varied systematically across regimes: mixed flow systems were simulated most accurately, while predominantly BF dominated and quickflow dominated regimes exhibited substantially poorer performance. Across nearly all regimes, the NWM underestimated observed BFI magnitude and frequently failed to reproduce seasonal BF patterns, indicating systematic biases in simulated low flow contributions. To relate these regimes to potential process controls, we trained a Random Forest classifier using static watershed attributes and applied Shapley Additive Explanations to identify features most strongly associated with each regime. Results highlight regionally varying influences, including the dominant role of snow fraction and seasonal runoff timing in snow dominated basins and the importance of evapotranspiration and aridity in quickflow dominated systems. Collectively, these findings demonstrate how hydrologic signatures combined with interpretable machine learning can diagnose regime specific model biases and generate process-based hypotheses about limitations in large scale hydrologic prediction systems.

contiguous United States↗

USGS2024

Remote sensing evapotranspiration in ensemble-based framework to enhance cascade routing and re-infiltration concept in integrated hydrological model applied to support decision making

Integrated hydrological models (IHMs) help characterize the complexity of surface–groundwater interactions. The cascade routing and re-infiltration (CRR) concept, recently applied to a MODFLOW 6 IHM, improved conceptualization and simulation of overland flow processes. The CRR controls the transfer of rejected infiltration and groundwater exfiltration from upslope areas to adjacent downslope areas where that water can be evaporated, re-infiltrated back to subsurface, or discharged to streams as direct runoff. The partitioning between these three components is controlled by uncertain parameters that must be estimated. Thus, by quantifying and reducing those uncertainties, next to uncertainties of the other model parameters (e.g. hydraulic and storage parameters), the reliability of the CRR is improved and the IHM is better suited for decision support modelling, the two key objectives of this work. To this end, the remotely sensed MODIS-ET product was incorporated into the calibration process for complementing traditional hydraulic head and streamflow observations. A total of approximately 150,000 observations guided the calibration of a 13-year MODFLOW 6 IHM simulation of the Sardon catchment (Spain) with daily stress periods. The model input uncertainty was represented by grid-cell-scale parameterization, yielding approximately 500,000 unknown input parameters to be conditioned. The calibration was carried out through an iterative ensemble smoother. Incorporating the MODIS-ET data improved the CRR implementation, and reduced uncertainties associated with other model parameters. Additionally, it significantly reduced the uncertainty associated with net recharge, a critical flux for water management that cannot be directly measured and rather is commonly estimated by IHM simulations.

Sardon catchment↗

USGS2025

To heal or not to heal?: 2. The moment-recurrence time behavior of repeating earthquakes in the 2011 Prague, Oklahoma aftershock sequence is consistent with laboratory healing rates

The timing and failure conditions of an earthquake are governed by the interplay between fault reloading and restrengthening. The moment-recurrence time behavior of repeating earthquakes can give observational estimates of fault healing rates; however, it is difficult to link these observed healing rates to laboratory studies of frictional healing in part because of uncertainty in lithology. Here, we study the 2011 Prague earthquake sequence, which includes repeating earthquakes in the Arbuckle group and the granitic basement, and compare them to laboratory experiments on samples of the Arbuckle and Troy granite (representative of the basement rock) (Okamoto et al., 2025, https://doi.org/10.1029/2024JB030573 ). We find three spatially distinct groups of repeating earthquakes with different moment-recurrence behavior: (a) constant moment-recurrence time in the Arbuckle group, (b) scattered moment-recurrence time at the intersection of the foreshock-mainshock fault in the granitic basement, and (c) moment-predictable behavior outside of the foreshock-mainshock fault intersection also in the granitic basement. Our observation of stagnant healing for repeating sequences in the Arbuckle group is consistent with laboratory observations of low healing rates for moderately high pore fluid pressures in Arbuckle samples. For the moment-predictable group, the source radius that is required in order to match healing rates is consistent with source radius estimations when taking into account reasonable attenuation of the P- pulse width. Overall, we observe diverse healing behaviors in the seismic families that are consistent with laboratory healing rates, providing seismic evidence that contact-scale frictional mechanisms are relevant to large-scale earthquake dynamics.

Oklahoma↗

USGS2025

Megafaunal community structure on ferromanganese and phosphorite hardgrounds in the Southern California Borderland

The Southern California Borderland (SCB) is a topographically complex region on the active continental margin that hosts varied hardground habitats, including ferromanganese (FeMn) crusts and phosphorites, marine minerals being considered for resource extraction. The SCB is influenced by seasonal upwelling and terrestrial inputs, and has a well-defined oxygen-minimum zone (OMZ). We analyzed megafaunal community composition, density, and diversity across 41 ROV video transects at 10 SCB hard substrate sites spanning depth (378–2765 m), temperature (1.79–7.96 °C), and oxygen (3.91–105.67 μM) gradients, with varying rock types (FeMn crusts, phosphorites, other rocks). We counted 32,426 individuals representing 146 unique taxa over a total area of 21,935 m 2 . Echinodermata contributed 48 % of the total abundance; Cnidaria 24 %; Porifera 13 %; Annelida 6 %; Arthropoda 6 %; Chordata 2 %; Mollusca, Foraminifera, and Hemichordata <1 %. Megafauna communities showed clear heterogeneity, with density, diversity, and community composition varying among transects within sites and across sites. Rock type recorded no influence on megafaunal density, but habitats that included FeMn crusts had higher diversity and distinct taxonomic composition. Location, depth, and oxygen were the most important variables explaining variance among megafaunal communities, with distinct communities formed at deeper depths and within the OMZ. This study provides baseline information about the megafauna inhabiting SCB hardgrounds, particularly those associated with minerals considered for their resource potential. It extends existing documentation of such communities to greater depths, reveals increased representation of sponges and corals below 1000 m, and highlights the variability within and among deep-sea megafaunal communities, offering context for informed policy decisions.

Southern California borderland↗

USGS2026

Localization of spatiotemporally heterogeneous subsurface flows using autoencoder-based deep learning framework for time-lapse self-potential tomography

Self-potential (SP) monitoring has emerged as a valuable method for characterizing subsurface hydrogeological features and processes due to its sensitivity to fluid-induced electrokinetic effects. Despite advancements in SP inversion, challenges remain in imaging groundwater dynamics from SP activities due to complex hydrological settings and transient noise. In this study, a deep learning autoencoder (AE)-based framework is proposed for the spatiotemporal localization of subsurface fluid movement from time-lapse SP tomography. Temporal segments of time-lapse numerical inversions were first derived from long-term SP monitoring conducted from a floodplain site in Oak Ridge, Tennessee, known for active hyporheic exchange. Subsequently, AE models based on vision transformer (ViT), convolutional long short-term memory (ConvLSTM), convolutional neural network, and temporal convolutional network were individually trained and compared on the SP tomography segments for reconstruction performance. Finally, the reconstruction error over time serves as an anomaly score to identify moments of active SP variation, whereas spatial distributions of errors within these moments are analyzed to image and localize regions associated with anomalous subsurface fluid movement. The results demonstrate that ConvLSTM- and ViT-AE are most capable for the localization task with contrasting error distributions and consistent delineation of anomalies. Applying the method to both SP arrays parallel and perpendicular to the stream produced consistent anomaly zones near a fault or karst feature, validating the robustness and generalization of the approach. These results demonstrate the potential of the proposed framework as a scalable and interpretable tool for spatiotemporal analysis of subsurface flow dynamics in complex hydrogeological systems.

Tennessee↗

USGS2026

Legacy of the fumigant 1,2-dibromo-3-chloropropane (DBCP) in California groundwater

The fumigant pesticide 1,2-dibromo-3-chloropropane (DBCP) was widely used in California agriculture during the 1960s and 1970s before being banned in 1979. Despite this ban, DBCP continues to contaminate groundwater due to its persistence and mobility. This study evaluates the distribution, historical trends, and projected persistence of DBCP in California using data from over 13,000 public supply wells and additional domestic, irrigation, and observation wells (1980-2022). Since 2010, DBCP has been detected in 9% of public supply wells statewide, with higher frequencies in the San Joaquin Valley (21%) and upper Santa Ana River watershed (13%), where DBCP use was most prevalent. Approximately 70% of wells had decreasing concentration trends, whereas increases were more common in deeper wells, indicating downward vertical migration of the DBCP front. Groundwater age estimates show that recharge timing aligns with the 1960s–1970s loading period, enabling reconstruction of peak inputs and providing a basis for age based modeling. To estimate future persistence, we applied a one dimensional advection–dispersion model that simulates long term declines in peak concentrations based on groundwater age, historical loading, and a 38 year degradation half life. Model projections suggest that concentrations above the maximum contaminant level may persist in a declining number of wells until approximately 2080 (range: 2048–2109), with longer persistence in the San Joaquin Valley. The simplified modeling framework, based on age distributions typical of wells capturing peak concentrations, can provide practical regional scale assessment of non-point source contaminants where long-term monitoring exists. This study highlights how the legacy of DBCP contamination will likely affect California's groundwater resources throughout the 21st century.

California↗

USGS2022

U-Pb scheelite ages of tungsten and antimony mineralization in the Stibnite-Yellow Pine district, central Idaho

The Stibnite-Yellow Pine district contains the largest antimony resource in the United States, as well as significant gold, and is a historic producer of tungsten. Application of in situ laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) direct dating of scheelite from two Au-Sb-W ore deposits, Yellow Pine and Hangar Flats, yielded an older group of U-Pb ages in the range of 60.0 ± 2.8 to 57.0 ± 1.1 Ma and a younger U-Pb age for scheelite intergrown with stibnite of 47.4 ± 1.1 Ma. These in situ analyses were calibrated by isotope dilution-thermal ionization mass spectrometry (ID-TIMS) U-Pb lower intercept ages of two coarsely crystalline scheelite samples that yielded ages of 57.52 ± 0.22 and 56.62 ± 0.16 Ma. Scheelite of the latter age is of sufficient quality to serve as a primary reference material for LA-ICP-MS scheelite U-Pb geochronology. The group of older U-Pb scheelite ages agrees with 40 Ar/ 39 Ar ages of 56.9 ± 1.2 to 56.38 ± 0.54 Ma on adularia from Yellow Pine and Hangar Flats, whereas the younger U-Pb scheelite age is similar to an 40 Ar/ 39 Ar age of 46.00 ± 0.40 Ma on adularia from an epithermal gold-silver deposit in the adjacent Thunder Mountain caldera. Our results indicate that the main stage of tungsten mineralization occurred at ca. 57 Ma, whereas the main stage of antimony mineralization occurred at ca. 47 Ma—thereby providing first-time age constraints for antimony and tungsten mineralization in the Stibnite-Yellow Pine district.

Idaho↗

USGS2024

Mafic alkaline magmatism and rare earth element mineralization in the Mojave Desert, California: The Bobcat Hills connection to Mountain Pass

Occurrences of alkaline and carbonatite rocks with high concentrations of rare earth elements (REE) are a defining feature of Precambrian geology in the Mojave Desert of southeastern California. The most economically important occurrence is the carbonatite stock at Mountain Pass, which constitutes the largest REE deposit in the United States. A central scientific goal is to understand the genesis of the carbonatite ore body in the context of widespread REE-rich igneous activity. A swarm of mafic alkaline (shonkinite) dikes has been mapped and sampled at Bobcat Hills, 65 km southeast of the Mountain Pass mine. Whole-rock geochemistry and zircon geochronology demonstrate a clear affinity to the ca. 1.4 Ga Mountain Pass intrusive system. Bobcat Hills dikes have comparably high REE concentrations (La ∼1,000× chondritic) and an error-weighted mean 207 Pb/ 206 Pb zircon crystallization age of 1,426 ± 2 Ma (2 σ ). Unlike the alkaline intrusions at Mountain Pass, which have abundant inherited zircon from Paleoproterozoic basement rocks and crustally influenced oxygen isotope compositions (δ 18 O zircon = 6.5–7.5‰), the Bobcat Hills dikes lack any evidence of crustal assimilation and have oxygen isotope values that overlap a mantle range (Bobcat Hills average δ 18 O zircon = 5.6 ± 0.3‰). The dikes were a high-temperature, early center of mafic alkaline magmatism in the Mojave Desert that serve as a snapshot of melt generation from a spatially extensive, metasomatized mantle source. We propose that modification of the crust over many tens of Myr at Mountain Pass created an environment that favored crustal assimilation and enabled ascent of late-stage, REE-rich carbonatite magmas.

California↗

USGS2024

Earthquake-triggered ground-failure inventory associated with the M7.1 2018 Southcentral Alaska earthquake

The 30 November 2018, magnitude (Mw) 7.1 earthquake in Southcentral Alaska triggered substantial landslides, liquefaction, and ground cracking throughout the region, resulting in widespread geotechnical damage to buildings and infrastructure. Despite a challenging reconnaissance and remote-sensing environment, we constructed a detailed digital inventory of ground failure associated with the event from several sources. Sources included information derived from remotely sensed data, and data compiled from literature, social media postings, and earthquake damage information compiled by local, state, and federal agencies. Each instance of ground failure within the inventory contains information on the location and type of observed ground failure, and the methods and data used to document the occurrence. Where high-quality data, such as LIDAR or satellite imagery, were available and showed the ground-failure instance clearly, the extent is mapped as a polygon or polyline. All other locations are mapped as points. There are a total of 886 ground-failure instances documented within the inventory (400 landslides, 286 liquefaction features, and 200 features unattributed to specific processes). A semi-quantitative confidence scheme is used to describe mapping certainty associated with each ground-failure feature. This inventory represents a relatively moderate ground-failure-triggering event that occurred in a subarctic environment. This data paper describes the content within the inventory, the inventory data collection procedures, and limitations of the data. Events of this type are not often documented in detail; thus, adding the inventory data to the US Geological Survey Open Repository of Earthquake-Triggered Ground-Failure Inventories further diversifies the datasets available to the scientific community to be used to better understand and model earthquake-triggered ground failure.

Alaska↗

USGS2025

Comparative assessment of a restored and natural wetland using 13C-DNA SIP reveals a higher potential for methane production in the restored wetland

Wetlands are the largest natural source of methane (CH 4 ), a potent greenhouse gas produced by methanogens. Methanogenesis rates are controlled by environmental factors such as redox potential, temperature, and carbon and electron acceptor availability and are presumably dependent on the composition of the active methanogen community. We collected intact soil cores from a restored and natural freshwater depressional wetland on Maryland’s Delmarva Peninsula (USA) to assess the effects of wetland restoration and redox shifts on microbial processes. Intact soil cores were incubated under either saturated (anoxic) or unsaturated (oxic) conditions and amended with 13 C-acetate for quantitative stable isotope probing (qSIP) of the 16S rRNA gene. Restored wetland cores supported a distinct community of methanogens compared to natural cores, and acetoclastic methanogens putatively identified in the genus Methanosarcina were among the most abundant taxa in restored anoxic and oxic cores. The active microbial communities in the restored wetland cores were also distinguished by the unique presence of facultatively anaerobic bacteria belonging to the orders Firmicutes and Bacteroidetes . In natural wetland incubations, methanogen populations were not among the most abundant taxa, and these communities were instead distinguished by the unique presence of aerobic bacteria in the phyla Acidobacteria , Actinobacteria , and class Alphaproteobacteria . Iron-reducing bacteria, in the genus Geobacter , were active across all redox conditions in both the restored and the natural cores, except the natural oxic–anoxic condition. These findings suggest an overall higher potential for methanogenesis in the restored wetland site compared to the natural wetland site, even when there is evidence of Fe reduction.

Maryland↗

USGS2025

New paleomagnetic constraints on the eruption timing, stratigraphy, and post-emplacement deformation of the Picture Gorge Basalt within the Columbia River Basalt Group

The Picture Gorge Basalt (PGB) is part of the Miocene Columbia River Basalt Group (CRBG). The PGB, which outcrops in eastern Oregon, is considered coincident in time with the voluminous Grande Ronde Basalt. New radiometric ages have expanded the age‐range of the PGB, suggesting it began erupting prior to the Steens Basalt to its south and continued until after cessation of the Grande Ronde Basalt eruptions, an interval of 1.5 Ma. However, the existing paleomagnetism of the PGB implies this eruption timeline is an overestimate. To reconcile the radiometric and paleomagnetic timescales for the PGB, we conducted a paleomagnetic study on sections of the PGB to construct a detailed, high‐quality magnetostratigraphy. Our data indicate the stratigraphically lowest lava flows in the PGB are of reversed polarity, revealing a new paleomagnetic transition with the PGB and a reversed (R)–normal (N)–reversed (R) sequence. This suggests one of two timeline possibilities for PGB volcanism: (a) eruptions began and during through CRBG polarity chrons R0–N0–R1, penecontemporaneous with Steens Basalt, or (b) eruptions began and persisted during CRBG polarity chrons R1–N1–R2. Our work supports a longer interval of PGB volcanism than was suggested by previous paleomagnetic data but is at odds with the suggestion that PGB eruptions lasted through the entire main CRBG. We favor a scenario wherein PGB eruptions begin with R0 and continue into the R1 paleomagnetic interval. The paleomagnetic results also record a ∼18° vertical‐axis rotation of east‐central Oregon after ∼16 Ma with respect to stable North America.

Oregon↗

USGS2025

Landsliding follows signatures of wildfire history and vegetative regrowth in a steep coastal shrubland

Five years after the deadly and destructive 9 January 2018 Montecito debris flows (Santa Barbara County, California, USA), an atmospheric river storm on 9 January 2023 triggered widespread landsliding that affected many of the same drainages in the Santa Ynez Mountains. Using high-resolution aerial imagery, we identified >10,000 landslides over an ∼160 km 2 area. Most of the landslides were shallow (<1 m in depth) translational debris slides that initiated on steep (∼40°), south-facing hillslopes, with the highest incidence of landsliding in a sandstone-dominated bedrock unit. The landslides mobilized into debris flows and delivered substantial quantities of sediment downstream, which contributed to costly infrastructure impairments. We detected order-of-magnitude differences in landslide density across the study area that could not be attributed to variations in geomorphology (topographic aspect and slope), geology (bedrock type), or hydrology (seasonal antecedent rainfall, peak hourly storm rainfall intensity, total storm rainfall), which are usually considered relevant factors for shallow landsliding. Rather, we found that vegetation regrowth following wildfire was likely a relevant factor associated with the highly variable landslide densities. Hillslopes with less and different types of vegetation regrowth after fire appear to have been more susceptible to shallow landslides. We identify a possible vegetation control on postfire landsliding, which highlights an opportunity for hypothesis testing using more advanced techniques to track the evolution of vegetation cover and vegetation type in steep shrubland environments following wildfire.

California↗

USGS2026

Satellite tracking of Galapagos Petrel Pterodroma phaeopygia reveals distribution and movements during chick rearing

We tracked 19 adult Galapagos Petrels Pterodroma phaeopygia during the chick-rearing seasons in 2009 and 2010 (Santa Cruz Island [ n = 16] and Floreana Island [ n = 3]) in the Galápagos Islands, Ecuador. Eight petrels performed 27 complete foraging trips lasting 0.6 to 18.8 days. Short trips (3.2 ± 2.1 days; 785 km; max displacement 671 km) and long trips (10.8 ± 3.9 days; 2,856 km; max displacement 1,034 km) resulted in concentrated use of waters off southern and western Isabela Island and within the Galápagos Marine Reserve (GMR). Less concentrated time extended farther southwest and eastward, in that case toward mainland Ecuador. Total distance covered among all completed trips, independent of duration, was strongly correlated with trip duration ( R ² = 0.92), indicating a strategy favoring active searching and foraging over commuting. Petrels ranged across Ecuador's exclusive economic zone (EEZ), as well as other countries' (Colombia, Costa Rica, Perú), and waters beyond; they spent 46%, 27%, and 34% of their time in the GMR during short, long, and apparent (incomplete) trips, respectively. However, overlap with EEZs or marine protected areas (MPAs) does not necessarily confer protection, because commercial tuna fishing, including legal fishing historically permitted inside the GMR, occurs within these waters. Including all complete and incomplete trips, petrels spent 37% of their time in high-seas waters without formal protection, outside both MPAs and EEZs. While some hot spots overlapped Galápagos MPAs, the far-ranging nature of chick-provisioning petrels underscores the importance for this species of also having coordinated, multinational protection of the high seas.

Floreana Island, Galápagos Islands, Santa Cruz Isl↗