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Abstract Landslides pose a significant hazard worldwide. Despite advances in landslide monitoring, predicting their size, timing, and location remains a major challenge. We revisit the 2017 Mud Creek landslide in California using radar interferometry, pixel tracking, and elevation change measurements from satellite and airborne radar, lidar, and optical data. Our analysis shows that pixel tracking of optical imagery captured the transition from slow motion to runaway acceleration starting ~ 1 month before catastrophic failure—an acceleration undetected by satellite InSAR alone. Strain rate maps revealed a new slip surface formed within the landslide body during acceleration, likely a key weakening mechanism. Failure forecast analysis indicates the acceleration followed a hyperbolic trend, suggesting failure time could have been predicted at least 6 days in advance. We also inverted for the landslide thickness during the slow-moving phase and found variations from < 1 to 36 m. While thickness inversions provide important first-order information on landslide size, more work is needed to better understand how landslide subsurface properties and deforming volumes may evolve during the transition from slow-to-fast motion. Our findings underscore the need for integrated remote sensing techniques to improve landslide monitoring and forecasting. Future advancements in operational monitoring systems and big data analysis will be critical for tracking slope instability and improving regional-scale failure predictions. 

IntroductionThe ‘social brain hypothesis’ proposes that brain development (particularly primates) is driven by social complexity, more than group size. Yet, small insects with minute brains are capable of the most complex social organization in animals - which warrants further attention. Research has focused on highly eusocial hymenopterans with extreme caste specialization and very large colony sizes that have passed social evolutionary points of no return. However, facultatively social insects that form small colonies (< 20 individuals) are likely to provide greater insight on brain selection at the origin-point of social group living. MethodsWe undertake the first neurobiological investigation of the facultatively social allodapine bees (Apidae: Xylocopinae: Allodapini), an exploratory study comparing single- and multi-female colonies ofExoneura angophorae. Using volume as a proxy for neural investment, we measured mushroom body calyces, optic lobes, antennal lobes and whole brains of queens, workers, and single-females to test three theories associating brain development with behavior: social brain hypothesis; distributed cognition hypothesis; sensory environment hypothesis. ResultsMushroom bodies were reduced in subordinate workers, but did not differ between queens and single-females. Workers had larger optic lobes than queens, but did not differ from single-females. There were no differences in antennal lobes or whole brain volume. DiscussionSocial caste, rather than multi-female versus single-female nesting, influenced mushroom body volume in this allodapine bee – counter to both social brain and distributed cognition theories and in alignment with halictine and ceratinine bees that also form small facultatively social colonies. Optic lobe enhancement is likely a response to dietary niche requirements for extra-nidal foraging behavior – which may be a highly plastic trait capable of rapid transition among allodapine and ceratinine bees that conforms with ecological intelligence hypotheses. These broad volumetric trends require further investigations on the functional neural circuitry involved in the aforementioned environmental contexts. 

Abstract The importance of lithium for emerging industrial, aerospace, defense, and most significantly, lithium-ion battery technologies, is leading to a rapid increase in the demand for this critical resource. Although current global production of lithium is confined to historically exploited lithium-bearing pegmatites and closed-basin saline brines, new occurrences of these and several nascent types of lithium deposits are under varying stages of active exploration, development, and construction. This includes lithium resources associated with volcano-sedimentary deposits, continental and geothermal brines, and rare element granites. This paper presents an overview of lithium uses, production trends, the different types of lithium deposits, and their sizes, grades, and global distribution, as well as introducing the 24 papers in these two Special Issues of Economic Geology that review these lithium mineral systems and deposits in detail. These contributions include reviews and overviews of major deposit types, regional assessments of lithium provinces, deposit-specific research, and exploration techniques for finding additional resources. It is our hope that the scientific compilation and new insights presented in these two Special Issues of Economic Geology spur innovative thought and research in lithium deposit genesis and exploration to support the sustainable extraction of this critical element. 

Resistive switching devices are promising candidates for the next generation of nonvolatile memory and neuromorphic computing applications. Despite the advantages in retention and on/off ratio, filamentary-based memristors still suffer from challenges, particularly endurance (flash being a benchmark system showing 10⁴to 10⁶ cycles) and uniformity. Here, we use WO₃as a complementary metal-oxide semiconductor–compatible switching oxide and demonstrate a proof-of-concept materials design approach to enhance endurance and device-to-device uniformity in WO₃-based memristive devices while preserving other performance metrics. These devices show stable resistive switching behavior with >10⁶ cycles, >10⁵-second retention, >10 on/off ratio, and good device-to-device uniformity, without using current compliance. All these metrics are achieved using a one-step pulsed laser deposition process to create self-assembled nanocomposite thin films that have regular guided filaments of ≈100-nanometer pitch, preformed between WO₃grains and interspersed smaller Ce₂O₃grains. 

Locomotion is a complex process involving specific interactions between the central neural controller and the mechanical components of the system. The basic rhythmic activity generated by locomotor circuits in the spinal cord defines rhythmic limb movements and their central coordination. The operation of these circuits is modulated by sensory feedback from the limbs providing information about the state of the limbs and the body. However, the specific role and contribution of central interactions and sensory feedback in the control of locomotor gait and posture remain poorly understood. We use biomechanical data on quadrupedal locomotion in mice and recent findings on the organization of neural interactions within the spinal locomotor circuitry to create and analyse a tractable mathematical model of mouse locomotion. The model includes a simplified mechanical model of the mouse body with four limbs and a central controller composed of four rhythm generators, each operating as a state machine controlling the state of one limb. Feedback signals characterize the load and extension of each limb as well as postural stability (balance). We systematically investigate and compare several model versions and compare their behaviour to existing experimental data on mouse locomotion. Our results highlight the specific roles of sensory feedback and some central propriospinal interactions between circuits controlling fore and hind limbs for speed-dependent gait expression. Our models suggest that postural imbalance feedback may be critically involved in the control of swing-to-stance transitions in each limb and the stabilization of walking direction. 

Abstract Climate change is expected to induce shifts in the composition, structure and functioning of Arctic tundra ecosystems. Increases in the frequency and severity of tundra fires have the potential to catalyse vegetation transitions with far‐reaching local, regional and global consequences.We propose that post‐fire tundra recovery, coupled with climate change, may not necessarily lead to pre‐fire conditions. Our hypothesis, based on surveys and literature, suggests two climate–fire driven trajectories. One trajectory results in increased woody vegetation under low fire frequency; the other results in grass dominance under high frequency.Future research should address uncertainties regarding possible tundra ecosystem shifts linked to fires, using methods that encompass greater temporal and spatial scales than previously addressed. More case studies, especially in underrepresented regions and ecosystem types, are essential to broaden the empirical basis for forecasts and potential fire management strategies.Synthesis. Our review synthesises current knowledge on post‐fire vegetation trajectories in Arctic tundra ecosystems, highlighting potential transitions and alternative ecosystem states and their implications. We discuss challenges in defining and predicting these trajectories as well as future directions.