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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

HCMM hydrological analysis in Utah

Data reduction and preliminary comparisons and correlations of Heat Capacity Mapping Mission data to ground truth measurements were made. The data covered Utah Lake and the surrounding area. Output modes include a digital hard copy record of the intensity value for each pixel and color graphics. Analyses of non-diatom net plankton (algae), turbidity, nitrogen, phosphorus, and temperatures were made. In addition, infrared data for the agricultural area around Utah lake were also preliminarily examined and compared to depth to groundwater data.

Source record↗
DOE OSTI2025

Data for "A 13-year record indicates differences in the duration and depth of soil carbon accrual among potential bioenergy crops"

Data sets for material included in "A 13-year record indicates differences in the duration and depth of soil carbon accrual among potential bioenergy crops" by Kantola et al., 2025, in Global Change Biology Bioenergy. Data include soil organic carbon (SOC), carbon stable isotope ratios, annual belowground biomass, and annual post-harvest litter for four crops, maize/soybean, miscanthus, switchgrass, and prairie, between 2008 and 2021.

bioenergy crops↗
DOE OSTI2024

Interpreting strain tensor data to characterize and monitor reservoirs for CO2 storage and other applications

Recent advances in instrumentation have made it feasible to measure the transient strain tensor caused by small changes in fluid volume or pressure in the subsurface and this has opened the door to new opportunities for characterization and monitoring. We have deployed strainmeters and then conducted injection well tests in an underlying reservoir at 530m depth. The resulting data indicated that the horizontal strain at shallow strainmeters (30 to 40m depth) was tensile and the vertical strain was compressive. The radial strain was less than the horizontal strain, and the strain rates decreased from 100 nanostrain/day to roughly 10 n/d over a few days. The signal at two strainmeters at shallow depth were consistent, although the magnitude of the horizontal strains were different reflecting the different radial directions from the well. The signal at a deep strainmeter deployed at reservoir depth was much different, with tensile vertical strains and compressive horizontal strains. These data can be interpreted by inverting poroelastic forward models developed using numerical and analytical methods. The average horizontal strain in the caprock resembles the transient pressure in the underlying reservoir and classic type-curve methods from transient well testing can be used for preliminary interpretations of strain data. We have developed closed-form analytical solutions to a pressurized poroelastic inclusion and inhomogeneity in a half-space. This model is fast and can be inverted to estimate reservoir stiffness and geometry. Numerical models developed using finite element methods allow more details of the subsurface to be included in the inversion, but they require much longer run times and this makes inversion cumbersome using standard methods. We have developed an inversion approach that uses a proxy model created using machine learning to do most of the forward calculations. The proxy model is periodically updated and refined using the finite element model to ensure accuracy. This approach significantly reduces the computational requirements and makes it feasible to use Bayesian inversion with large numerical models. We have shown that the strain tensor in the caprock is sensitive to pressure in the reservoir, boundaries in the reservoir, and pressure in the caprock caused by leaks. These results indicate that coupling strain tensor data with inversion has the potential to help evaluate reservoirs during initial characterization, and to monitor them during the CO2 injection and storage process.

Murdoch, Larry↗
NASA NTRS1986

Low-g measurements by NASA

Low-g accelerometers were used by NASA on a variety of flights for over 12 years. These flights have included aircraft parabolas, suborbital trajectories, and orbital missions. This large quantity of data has undergone only limited in-depth analyses. Examples of data in many forms and results of analyses such as power spectral densities and shock spectra are given for a wide variety of missions. Accelerometers used to acquire the data are also identified.

Chassay, Roger P.↗
NASA NTRS1987

Analysis of airborne imaging spectrometer data for the Ruby Mountains, Montana, by use of absorption-band-depth images

Airborne Imaging Spectrometer-1 (AIS-1) data were obtained for an area of amphibolite grade metamorphic rocks that have moderate rangeland vegetation cover. Although rock exposures are sparse and patchy at this site, soils are visible through the vegetation and typically comprise 20 to 30 percent of the surface area. Channel averaged low band depth images for diagnostic soil rock absorption bands. Sets of three such images were combined to produce color composite band depth images. This relative simple approach did not require extensive calibration efforts and was effective for discerning a number of spectrally distinctive rocks and soils, including soils having high talc concentrations. The results show that the high spectral and spatial resolution of AIS-1 and future sensors hold considerable promise for mapping mineral variations in soil, even in moderately vegetated areas.

Brickey, David W.↗
DOE OSTI2025

Videos, photos, and AI-derived grain size data associated with “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization” under review. This data package includes five data types: 1) raw photos and videos from drone survey and walking smartphone surveys; 2) images derived from raw videos; 3) manual labeling of reference scales; 4) metadata for all images and photo resolution derived from artificial intelligence (AI) models or manual labels, 5) grain size data obtained from AI models for all photos, 6) metadata and grain size data after quality control, 7) summaries of sample efficiency for all data, and 8) computational fluid dynamics (CFD) data used to support hydro-biogeochemical (HBGC) parameter estimation. Such data is used to 1) demonstrate significant improvements in accuracy, efficiency, and quality control for grain size data collection with the help of AI models, 2) study the spatial heterogeneity of grain size and observation reproducibility based on tens of thousands of data points generated by the AI models, and 3) evaluate the impacts of grain size heterogeneity on key HBGC parameters across sediment-to-reach and hourly-to-yearly scales. In particular, the data package contains 116 folders and 179696 files. The files include 41 videos in .mov format, 64047 photos in .jpg format, 13541 video-derived photos in .png format, 12747 segmentation mask data in .tif format, 12747 segmentation data in .json format, 24771 .csv files that with metadata and grain size for each individual photo as well as water depth and velocity data from CFD and observation, 51791 .txt files of raw AI predicted labels, and 11 flight record data in .srt format. The summary for all metadata and grain size statistics information is included in “Scales_V3_NG.csv” and “Statistics_V3_NG.csv”. The summary for data that pass data quality control (QC) level 0-2 is included in “QCStatistics_V3_NG.csv”. The QC level 0 represents photos whose photo resolution is positive, excluding photos that miss reference scale. The QC level 1 means reference scale circularity uncertainty is less than 5% for smartphone images while representing photo resolution is larger than 0.44 mm/pixel for drone images. The QC level 2 means excluding photos whose grain number is less than 100, a minimum number of grains recommended by classic literature. The summary for each video’s name, length, frame rates, survey area, grain number, survey efficiency, etc. can be found in “QCSummary_V3_NG.csv”. The summary for site name, GPS coordinates, and number of images at each site can be found in “SitesSummary_V3_*.csv” files. Overall computational efficiency summary is reported in Table 4 of accompanying manuscript. Additionally, the nitrate concentration data used in this work was downloaded from an existing dataset published on ESS-DIVE (Boat-Dragged Sensor Hanford Reach.csv; Conner A. et al., 2020). We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Port of Benton, and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the data were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate data collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Examining Lake Michigan Spring Euphotic Depth (Zeu) Anomalies: Utilizing 10 Years of MODIS-Aqua Data at 4 Kilometer Resolution

Examination of ten years of euphotic depth anomalies in Lake Michigan during the months of March-June indicates the following: The well-known and frequently observed occurrence of a turbidity feature in the southern part of Lake Michigan during the spring season has become less common during the period 2003-2012. Overall, the clarity of Lake Michigan water in the southern end of the lake appears to have increased spring season over the period 2003-2012. Euphotic depth can be used as a primary indicator of changes in Lake Michigan lacustrine optics, and for other large lakes. Unique events, such as the heavy rains in June 2008, can have a distinct signature in the euphotic depth anomaly distribution in Lake Michigan.

Acker, James G.↗
DOE OSTI2024

Frost Tables, NGEE Areas B, C and D, Utqiagvik (Barrow), Alaska, 2012-2014

This dataset represents spatially intensive thaw depth surveys with individual point measurements spaced ~0.5 m apart. The three ~10x10m grids cover an ice wedge and a portion of its two neighboring polygons. The data contain thaw depth, frost table elevation, ground surface elevation, active layer depth and surface water inundation across three seasons (2012, 2013 and 2014) at Utqiagvik (Barrow) NGEE Areas B, C and D. There are a total of 69 extracted files separated by month, year, and area. The data were originally provided in excel workbooks, these files have been preserved within the zip "Original_Files." The files were transformed for archiving with images pulled out into JPEGs, data preserved as CSV files and some methodological information preserved as TXT files. Some functions, features, and formatting were lost during the transformation. Missing data were defined with blank cells. Additional documentation can be found in files labelled with "information.txt" file endings.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).[2024-07-15] Renamed BEO_Thaw_Depth_Frost_Table_Area_*_*_*.csv, BEO_Thaw_Depth_Frost_Table_Area_*_*_*_Plot_1.JPEG, and BEO_Thaw_Depth_Frost_Table_Area_*_*_*_Plot_2.JPEG names that had E or L to be Early or Late. Renamed BEO_Thaw_Depth_Frost_Table_Area_*_*_*_Plot_1.JPEG, BEO_Thaw_Depth_Frost_Table_Area_*_*_*_Plot_2.JPEG, and BEO_Thaw_Depth_Frost_Table_Area_*_information_Plot_1.JPEG to BEO_Thaw_Depth_Frost_Table_Area_*_*_*_Gridpoints.JPEG, BEO_Thaw_Depth_Frost_Table_Area_*_*_*_3Dgraphic.JPEG, and BEO_Thaw_Depth_Frost_Table_Area_*_information_LiDAR_DEM.JPEG, respectively. Updated BEO_Thaw_Depth_Frost_Table_flmd.csv file to reflect above updates.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1979

Alternate methods of applying diffusants to silicon solar cells

Low-melting phosphate and borate glasses were screen printed on silicon wafers and heated to form n and p junctions. Data on surface appearance, sheet resistance and junction depth are presented. Similar data are reported for vapor phase transport from sintered aluminum metaphosphate and boron-containing glass-ceramic solid sources. Simultaneous diffusion of an N(+) layer with screen-printed glass and a p(+) layer with screen-printed Al alloy paste was attempted. No p(+) back surface field formation was achieved. Some good cells were produced but the heating in an endless-belt furnace caused a large scatter in sheet resistance and junction depth for three separate lots of wafers.

Brock, T. W.↗
DOE OSTI2024

Continuous snow depth, ground interface temperature and shallow soil temperature measurements from 2021-10-1 to 2022-6-14, Seward Peninsula, Alaska

The dataset contains co-located snow depth, ground interface temperature, and shallow soil temperature measured at 98 discrete locations in a watershed located along the Nome-Teller road at mile marker 27 (referred to as T27), and at 53 discrete locations on a hillslope located along the Kougarok road at mile marker 64 (referred to as K64), in Seward Peninsula, Alaska. The dataset aims to understand the local heterogeneity of snow depth, snow temperature, and soil temperature dynamics and their interactions in a discontinuous permafrost region. The dataset is also valuable to train and evaluate machine learning and physical models to predict snow depth or the impact of snow depth on ground surface temperature. At each location, temperatures above and below the ground surface were measured by a pair of vertically deployed distributed temperature profiling probes designed based on Dafflon et al (2022). The probes have high precision temperature sensors spaced at 5 or 10 cm. The mean daily snow depth was estimated by identifying the pair of consecutive sensors with maximum drop of daily temperature high-frequency fluctuations. The ground interface temperature was measured by the sensor located 1-5 cm above the ground surface at 15-minute intervals. The shallow soil temperature was measured by the sensor located 1-5 cm below the ground surface at 15-minute intervals. The dataset includes a description of the probe locations in the "Probe_locations_*.csv" file and the 3 data files (Snow_depths_*.csv, Ground_interface_temperatures_*.csv, Shallow_soil_temperatures_*.csv). * is either T27 or K64, which are the two study sites. In each data file, each column corresponds to a measurement location. Metadata files include data descriptions (_dd.csv) for tabular data. All included files are listed and described in xxxx_flmd.csv. A more detailed description of data processing, along with an updated dataset incorporating multiple seasons and improved snow depth estimation is available at https://doi.org/10.15485/2480365 (Wang et al., 2025a, Wang et al., 2025b).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Pluto’s Sputnik Planitia: Composition of geological units from infrared spectroscopy

We have compared spectroscopic data of Sputnik Planitia on Pluto, as acquired by New Horizons’ Linear Etalon Imaging Spectral Array (LEISA) instrument, to the geomorphology as mapped by White et al. (2017) using visible and panchromatic imaging acquired by the LOng-Range Reconnaissance Imager (LORRI) and the Multi-spectral Visible Imaging Camera (MVIC). We have focused on 13 of the geologic units identified by White et al. (2017), which include the plains and mountain units contained within the Sputnik basin. We divided the map of Sputnik Planitia into 15 provinces, each containing one or more geologic units, and we use LEISA to calculate the average spectra of the units inside the 15 provinces. Hapke-based modeling was then applied to the average spectra of the units to infer their surface composition, and to determine if the composition resulting from the modeling of LEISA spectra reflects the geomorphologic analyses of LORRI data, and if areas classified as being the same geologically, but which are geographically separated, share a similar composition. We investigated the spatial distribution of the most abundant ices on Pluto’s surface - CH4, N2, CO, H2O, and a non-ice component presumed to be a macromolecular carbon-rich material, termed a tholin, that imparts a positive spectral slope in the visible spectral region and a negative spectral slope longward of ~1.1 μm. Because the exact nature of the non-ice component is still debated and because the negative spectral slope of the available tholins in the near infrared does not perfectly match the Pluto data, for spectral modeling purposes we reference it generically as the negative spectral slope endmember (NSS endmember). We created maps of variations in the integrated band depth (from LEISA data) and areal mass fraction (from the modeling) of the components. The analysis of correlations between the occurrences of the endmembers in the geologic units led to the observation of an anomalous suppression of the strong CH4 absorption bands in units with compositions that are dominated by H2O ice and the NSS endmember. Exploring the mutual variation of the CH4 and N2 integrated band depths with the abundance of crystalline H2O and NSS endmember revealed that the NSS endmember is primarily responsible for the suppression of CH4 absorptions in mountainous units located along the western edge of Sputnik Planitia. Our spectroscopic analyses have provided additional insight into the geological processes that have shaped Sputnik Planitia. A general increase in volatile abundance from the north to the south of Sputnik Planitia is observed. Such an increase first observed and interpreted by Protopapa et al., 2017 and later confirmed by climate modeling (Bertrand et al., 2018) is expressed geomorphologically in the form of preferential deposition of N2 ice in the upland and mountainous regions bordering the plains of southern Sputnik Planitia. Relatively high amounts of pure CH4 are seen at the southern Tenzing Montes, which are a natural site for CH4 deposition owing to their great elevation and the lower insolation they are presently receiving. The NSS endmember correlates the existence of tholins within certain units, mostly those coating the low-latitude mountain ranges that are co-latitudinal with the tholin-covered Cthulhu Macula. The spectral analysis has also revealed compositional differences between the handful of occurrences of northern non-cellular plains and the surrounding cellular plains, all of which are located within the portion of Sputnik Planitia that is presently experiencing net sublimation of volatiles, and which do not therefore exhibit a surface layer of bright, freshly-deposited N2 ice. The compositional differences between the cellular and non-cellular plains here hint at the effectiveness of convection in entraining and trapping tholins within the body of the cellular plains, while preventing the spread of such tholins to abutting non-cellular plains.

Pluto's Sputnik Planitia↗
NASA NTRS2001

Arctic Stratus Cloud Properties and Their Effect on the Surface Radiation Budget: Selected Cases from FIRE ACE

To study Arctic stratus cloud properties and their effect on the surface radiation balance during the spring transition season, analyses are performed using data taken during three cloudy and two clear days in May 1998 as part of the First ISCCP Regional Experiment (FIRE) Arctic Cloud Experiment (ACE). Radiative transfer models are used in conjunction with surface- and satellite-based measurements to retrieve the layer-averaged microphysical and shortwave radiative properties. The surface-retrieved cloud properties in Cases 1 and 2 agree well with the in situ and satellite retrievals. Discrepancies in Case 3 are due to spatial mismatches between the aircraft and the surface measurements in a highly variable cloud field. Also, the vertical structure in the cloud layer is not fully characterized by the aircraft measurements. Satellite data are critical for understanding some of the observed discrepancies. The satellite-derived particle sizes agree well with the coincident surface retrievals and with the aircraft data when they were collocated. Optical depths derived from visible-channel data over snow backgrounds were overestimated in all three cases, suggesting that methods currently used in satellite cloud climatologies derive optical depths that are too large. Use of a near-infrared channel with a solar infrared channel to simultaneously derive optical depth and particle size appears to alleviate this overestimation problem. Further study of the optical depth retrieval is needed. The surface-based radiometer data reveal that the Arctic stratus clouds produce a net warming of 20 W m(exp -2) in the surface layer during the transition season suggesting that these clouds may accelerate the spring time melting of the ice pack. This surface warming contrasts with the net cooling at the top of the atmosphere (TOA) during the same period. All analysis of the complete FIRE ACE data sets will be valuable for understanding the role of clouds during the entire melting and refreezing process that occurs annually in the Arctic.

Doug, Xiquan↗
NASA NTRS2013

Data Compression Algorithm Architecture for Large Depth-of-Field Particle Image Velocimeters

A large depth-of-field particle image velocimeter (PIV) is designed to characterize dynamic dust environments on planetary surfaces. This instrument detects lofted dust particles, and senses the number of particles per unit volume, measuring their sizes, velocities (both speed and direction), and shape factors when the particles are large. To measure these particle characteristics in-flight, the instrument gathers two-dimensional image data at a high frame rate, typically >4,000 Hz, generating large amounts of data for every second of operation, approximately 6 GB/s. To characterize a planetary dust environment that is dynamic, the instrument would have to operate for at least several minutes during an observation period, easily producing more than a terabyte of data per observation. Given current technology, this amount of data would be very difficult to store onboard a spacecraft, and downlink to Earth. Since 2007, innovators have been developing an autonomous image analysis algorithm architecture for the PIV instrument to greatly reduce the amount of data that it has to store and downlink. The algorithm analyzes PIV images and automatically reduces the image information down to only the particle measurement data that is of interest, reducing the amount of data that is handled by more than 10(exp 3). The state of development for this innovation is now fairly mature, with a functional algorithm architecture, along with several key pieces of algorithm logic, that has been proven through field test data acquired with a proof-of-concept PIV instrument.

Bos, Brent↗
NASA NTRS1994

Cycle life versus depth of discharge update on modeling studies

The topics are presented in viewgraph form and cycle life vs. depth of discharge data for the following are presented: data as of three years ago; Air Force/Crane-Fuhr-Smithrick; Ken Fuhr's Data; Air Force/Crane Data; Eagle-Pitcher Data; Steve Schiffer's Data; John Smithrick's Data; temperature effects; and E-P, Yardney, and Hughes 26% Data. Other topics covered include the following: LeRC cycling tests of Yardney Space Station Cells; general statements; general observations; two different models of cycle life vs. depth of discharge; and other degradation modes.

Thaller, Lawrence H.↗
DOE OSTI2025

Ubiquity and Causes of Soil Water Preferential Flow Across 17 Ecoregions

Abstract Preferential flow (PF) in soil causes the rapid transport of water, nutrients, and contaminants into the subsurface, influencing groundwater recharge and streamflow. Data scarcity has hindered the quantification of PF occurrence and the identification of its drivers across diverse ecoregions. We address this gap by analyzing high‐frequency, multi‐depth soil moisture data across 17 ecoregions in the USA, using ∼1,500 sensors at 40 sites. We discovered that PF is widespread, with sites experiencing PF in up to 60% of rainfall events ≥2 mm. Multiple approaches consistently show that PF is more likely to occur with increased peak rainfall intensity, finer textured material, low soil moisture variability, humid climate, and higher net primary productivity. This suggests that PF patterns could shift with projected climate changes, increasing uncertainty in predictions of groundwater recharge, water quality, and streamflow generation. Plain Language Summary Water can bypass part of the soil's matrix through a process called preferential flow (PF). This quick transport of water through the soil brings with it nutrients and contaminants and eventually makes it to groundwater and streams. To ensure ample amounts of good quality groundwater and surface water we need to understand when and where PF occurs. We inferred when PF occurred at 40 different sites across 17 ecoregions in the USA using soil moisture and rainfall data. We found that PF happened at all sites and in up to 60% of rainfall events ≥2 mm. Preferential flow was most likely at sites with high rainfall intensities, high clay content in soils, low variability in soil moisture, and high vegetation productivity. As rainfall intensities are predicted to increase due to climate change and vegetation becomes more productive, PF occurrence becomes more important for predicting groundwater recharge, water quality, and streamflow generation. Key Points Preferential flow (PF) is ubiquitous across the USA and occurs in up to 60% of all rainfall events ≥2 mm Rainfall intensity, soil texture, and antecedent soil moisture emerge as critical in generating PF across diverse ecoregions Two different PF detection approaches show similar relationships between key drivers and occurrence of PF

Li, Bonan↗
NASA NTRS1994

Design document for the MOODS Data Management System (MDMS), version 1.0

The MOODS Data Management System (MDMS) provides access to the Master Oceanographic Observation Data Set (MOODS) which is maintained by the Naval Oceanographic Office (NAVOCEANO). The MDMS incorporates database technology in providing seamless access to parameter (temperature, salinity, soundspeed) vs. depth observational profile data. The MDMS is an interactive software application with a graphical user interface (GUI) that supports user control of MDMS functional capabilities. The purpose of this document is to define and describe the structural framework and logical design of the software components/units which are integrated into the major computer software configuration item (CSCI) identified as MDMS, Version 1.0. The preliminary design is based on functional specifications and requirements identified in the governing Statement of Work prepared by the Naval Oceanographic Office (NAVOCEANO) and distributed as a request for proposal by the National Aeronautics and Space Administration (NASA).

Source record↗
DOE OSTI2024

Benefits and Limits of Phasing Alleles for Network Inference of Allopolyploid Complexes

Abstract Accurately reconstructing the reticulate histories of polyploids remains a central challenge for understanding plant evolution. Although phylogenetic networks can provide insights into relationships among polyploid lineages, inferring networks may be hindered by the complexities of homology determination in polyploid taxa. We use simulations to show that phasing alleles from allopolyploid individuals can improve phylogenetic network inference under the multispecies coalescent by obtaining the true network with fewer loci compared with haplotype consensus sequences or sequences with heterozygous bases represented as ambiguity codes. Phased allelic data can also improve divergence time estimates for networks, which is helpful for evaluating allopolyploid speciation hypotheses and proposing mechanisms of speciation. To achieve these outcomes in empirical data, we present a novel pipeline that leverages a recently developed phasing algorithm to reliably phase alleles from polyploids. This pipeline is especially appropriate for target enrichment data, where the depth of coverage is typically high enough to phase entire loci. We provide an empirical example in the North American Dryopteris fern complex that demonstrates insights from phased data as well as the challenges of network inference. We establish that our pipeline (PATÉ: Phased Alleles from Target Enrichment data) is capable of recovering a high proportion of phased loci from both diploids and polyploids. These data may improve network estimates compared with using haplotype consensus assemblies by accurately inferring the direction of gene flow, but statistical nonidentifiability of phylogenetic networks poses a barrier to inferring the evolutionary history of reticulate complexes.

Evolutionary Biology↗
NASA NTRS1984

Anisotropy and shear-velocity heterogeneities in the upper mantle

Long-period surface waves are used to map lateral heterogeneities of velocity and anisotropy in the upper mantle. The dispersion curves are expanded in spherical harmonics up to degree 6 and inverted to find the depth structure. The data are corrected for the effect of surface layers and both Love and Rayleigh waves are used. Shear wave velocity and shear polarization anisotropy can be resolved down to a depth of about 450 km. The shear wave velocity distribution to 200 km depth correlates with surface tectonics, except in a few anomalous regions. Below that depth the correlation vanishes. Cold subducted material shows up weakly at 350 km as fast S-wave anomalies. In the transition region a large scale pattern appears with fast mantle in the South-Atlantic. S-anisotropy at 200 km can resolve uprising or downwelling currents under some ridges and subduction zones. The Pacific shows a NW-SE fabric.

Nataf, H.-C.↗