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SUMMARY The thermal structure of the continental crust plays a critical role in understanding its elastic and rheologic properties as well as its dynamic processes. Thermal parameter data sets on continental scales have been used to constrain the crustal thermal structure, including both the direct (e.g. temperature, heat flux and heat conductivity measured at the surface) and indirect (e.g. seismically derived Mohorovičić discontinuity (Moho) temperature, geomagnetically derived Curie depth) observations. In this study, we present a new continental scale crustal heat generation model with additional information from seismologically inferred crustal composition. Together with previous direct and indirect thermal parameter data sets in the conterminous United States, we use the new crustal heat generation model to construct a 3-D crustal temperature model under a newly developed Bayesian framework. Specifically, we first derive profiles of crustal heat generation based on an empirical geochemical relationship at 1683 locations where seismologically derived crustal composition information is available. Then for each of these locations, the average heat generation values in the upper, middle and lower crust are combined with other thermal parameters through a Markov Chain Monte-Carlo inversion for a conductive, vertically smooth temperature profile. The results, posterior distributions of temperature profiles, are used to generate a 3-D crustal thermal model with the uncertainties systematically assessed. The new temperature model overall exhibits similar patterns to that from the U.S. Geological Survey National Crustal Model, but also reduces possible biases and the model's dependence on a single thermal parameter. 

Neat, densely packed, and highly aligned carbon nanotube fibers (CNTFs) have appealing room-temperature axial thermal conductivity (k) and thermal diffusivity (α) for applications in lightweight heat spreading, flexible thermal connections, and thermoelectric active cooling. Although CNTFs are regularly produced from different input carbon nanotubes (CNTs), prior work has not quantified how the CNT molecular aspect ratio r (i.e., molecular length-to-diameter ratio) influences k and α in well-aligned, packed CNTFs. Here, we perform self-heated steady-state and three-omega thermal measurements at room temperature on CNTF suspended in vacuum. Our results show that k increases from 150 to 380W/mK for viscosity-averaged molecular aspect ratios increasing from r=960 to 5600 and nanotube diameters of ∼2 nm, which we attribute to the effects of thermal resistances between CNT bundles. CNTFs made with varying volume fraction ϕ of constituent high-r and low-r CNT have properties that fall within or below the typical macroscopic rule-of-mixtures bounds. The thermal diffusivity α scales with k, leading to a sample-averaged volumetric heat capacity of 1.5±0.3MJ/m3K. This work's findings that fibers made from longer CNT have larger k and α at room temperature motivate further investigation into thermal transport in solution-spun CNTF. 

The performance of global ocean biogeochemical models can be quantified as the misfit between modeled tracer distributions and observations, which is sought to be minimized during parameter optimization. These models are computationally expensive due to the long spin‐up time required to reach equilibrium, and therefore optimization is often laborious. To reduce the required computational time, we investigate whether optimization of a biogeochemical model with shorter spin‐ups provides the same optimized parameters as one with a full‐length, equilibrated spin‐up over several millennia. We use the global ocean biogeochemical model MOPS with a range of lengths of model spin‐up and calibrate the model against synthetic observations derived from previous model runs using a derivative‐free optimization algorithm (DFO‐LS). When initiating the biogeochemical model with tracer distributions that differ from the synthetic observations used for calibration, a minimum spin‐up length of 2,000 years was required for successful optimization due to certain parameters which influence the transport of matter from the surface to the deeper ocean, where timescales are longer. However, preliminary results indicate that successful optimization may occur with an even shorter spin‐up by a judicious choice of initial condition, here the synthetic observations used for calibration, suggesting a fruitful avenue for future research. 

ABSTRACT The PRobe far-Infrared Mission for Astrophysics (PRIMA) concept aims to perform mapping with spectral coverage and sensitivities inaccessible to previous FIR space telescopes. PRIMA’s imaging instrument, PRIMAger, provides unique hyperspectral imaging simultaneously covering 25–235 µm. We synthesize images representing a deep, 1500 h deg−2 PRIMAger survey, with realistic instrumental and confusion noise. We demonstrate that we can construct catalogues of galaxies with a high purity (>95 per cent) at a source density of 42 k deg−2 using PRIMAger data alone. Using the XID+ deblending tool, we show that we measure fluxes with an accuracy better than 20 per cent to flux levels of 0.16, 0.80, 9.7, and 15 mJy at 47.4, 79.7, 172, and 235 µm, respectively. These are a factor of ∼2 and ∼3 fainter than the classical confusion limits for 72–96 and 126–235 µm, respectively. At $$1.5 \le z \le 2$$, we detect and accurately measure fluxes in 8–10 of the 10 channels covering 47–235 µm for sources with $$2 \lesssim \log ({\rm SFR}) \lesssim 2.5$$, a 0.5 dex improvement on what might be expected from the classical confusion limit. Recognizing that PRIMager will operate in a context where high-quality data will be available at other wavelengths, we investigate the benefits of introducing additional prior information. We show that by introducing even weak prior flux information when employing a higher source density catalogue (more than one source per beam), we can obtain accurate fluxes an order of magnitude below the classical confusion limit for 96–235 µm. 

Abstract Environmental DNA (eDNA) has revolutionized ecological research, particularly for biodiversity assessment in various environments, most notably aquatic media. Environmental DNA analysis allows for non‐invasive and rapid species detection across multiple taxonomic groups within a single sample, making it especially useful for identifying rare or invasive species. Due to dynamic hydrological processes, eDNA samples from running waters may represent biodiversity from broad contributing areas, which is convenient from a biomonitoring perspective but also challenging, as hydrological knowledge is required for meaningful biological interpretation. Hydrologists could also benefit from eDNA to address unsolved questions, particularly concerning water movement through catchments. While naturally occurring abiotic tracers have advanced our understanding of water age distribution in catchments, for example, current geochemical tracers cannot fully elucidate the timing and flow paths of water through landscapes. Conversely, biological tracers, owing to their immense diversity and interactions with the environment, could offer more detailed information on the sources and flow paths of water to the stream. The informational capacity of eDNA as a tracer, however, is determined by the ability to interpret the complex biological heterogeneity at a study site, which arguably requires both biological and hydrological expertise. As eDNA data has become increasingly available as part of biomonitoring campaigns, we argue that accompanying eDNA surveys with hydrological observations could enhance our understanding of both biological and hydrological processes; we identify opportunities, challenges, and needs for further interdisciplinary collaboration; and we highlight eDNA's potential as a bridge between hydrology and biology, which could foster both domains. This article is categorized under:Science of Water > Hydrological ProcessesScience of Water > MethodsWater and Life > Nature of Freshwater Ecosystems 

Abstract On 5 April 2024, 10:23 a.m. local time, a moment magnitude 4.8 earthquake struck Tewksbury Township, New Jersey, about 65 km west of New York City. Millions of people from Virginia to Maine and beyond felt the ground shaking, resulting in the largest number (>180,000) of U.S. Geological Survey (USGS) “Did You Feel It?” reports of any earthquake. A team deployed by the Geotechnical Extreme Events Reconnaissance Association and the National Institute of Standards and Technology documented structural and nonstructural damage, including substantial damage to a historic masonry building in Lebanon, New Jersey. The USGS National Earthquake Information Center reported a focal depth of about 5 km, consistent with a lack of signal in Interferometric Synthetic Aperture Radar data. The focal mechanism solution is strike slip with a substantial thrust component. Neither mechanism’s nodal plane is parallel to the primary northeast trend of geologic discontinuities and mapped faults in the region, including the Ramapo fault. However, many of the relocated aftershocks, for which locations were augmented by temporary seismic deployments, form a cluster that parallels the general northeast trend of the faults. The aftershocks lie near the Tewksbury fault, north of the Ramapo fault. 

ABSTRACT We present initial findings from the ongoing Community Stress Drop Validation Study to compare spectral stress-drop estimates for earthquakes in the 2019 Ridgecrest, California, sequence. This study uses a unified dataset to independently estimate earthquake source parameters through various methods. Stress drop, which denotes the change in average shear stress along a fault during earthquake rupture, is a critical parameter in earthquake science, impacting ground motion, rupture simulation, and source physics. Spectral stress drop is commonly derived by fitting the amplitude-spectrum shape, but estimates can vary substantially across studies for individual earthquakes. Sponsored jointly by the U.S. Geological Survey and the Statewide (previously, Southern) California Earthquake Center our community study aims to elucidate sources of variability and uncertainty in earthquake spectral stress-drop estimates through quantitative comparison of submitted results from independent analyses. The dataset includes nearly 13,000 earthquakes ranging from M 1 to 7 during a two-week period of the 2019 Ridgecrest sequence, recorded within a 1° radius. In this article, we report on 56 unique submissions received from 20 different groups, detailing spectral corner frequencies (or source durations), moment magnitudes, and estimated spectral stress drops. Methods employed encompass spectral ratio analysis, spectral decomposition and inversion, finite-fault modeling, ground-motion-based approaches, and combined methods. Initial analysis reveals significant scatter across submitted spectral stress drops spanning over six orders of magnitude. However, we can identify between-method trends and offsets within the data to mitigate this variability. Averaging submissions for a prioritized subset of 56 events shows reduced variability of spectral stress drop, indicating overall consistency in recovered spectral stress-drop values. 

The methane seeps on the Pacific margin of Costa Rica support extensive animal diversity and offer insights into deep-sea biogeography. During five expeditions between 2009 and 2019, we conducted intensive faunal sampling via 63 submersible dives to 11 localities at depths of 300–3600 m. Based on these expeditions and published literature, we compiled voucher specimens, images, and 274 newly published DNA sequences to present a taxonomic inventory of macrofaunal and megafaunal diversity with a focus on invertebrates. In total 488 morphospecies were identified, representing the highest number of distinct morphospecies published from a single seep or vent region to date. Of these, 131 are described species, at least 58 are undescribed species, and the remainder include some degree of taxonomic uncertainty, likely representing additional undescribed species. Of the described species, 38 are known only from the Costa Rica seeps and their vicinity. Fifteen range extensions are also reported for species known from Mexico, the Galápagos seamounts, Chile, and the western Pacific; as well as 16 new depth records and three new seep records for species known to occur at vents or organic falls. No single evolutionary narrative explains the patterns of biodiversity at these seeps, as even morphologically indistinguishable species can show different biogeographic affinities, biogeographic ranges, or depth ranges. The value of careful molecular taxonomy and comprehensive specimen-based regional inventories is emphasized for biodiversity research and monitoring.