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USGS2020

Are elevation and open-water conversion of salt marshes connected?

Salt marsh assessments focus on vertical metrics such as accretion or lateral metrics such as open-water conversion, without exploration of how the dimensions are related. We exploited a novel geospatial dataset to explore how elevation is related to the unvegetated-vegetated marsh ratio (UVVR), a lateral metric, across individual marsh “units” within four estuarine-marsh systems. We find that elevation scales consistently with the UVVR across systems, with lower elevation units demonstrating more open-water conversion and higher UVVRs. A normalized elevation-UVVR relationship converges across systems near the system-mean elevation and a UVVR of 0.1, a critical threshold identified by prior studies. This indicates that open-water conversion becomes a dominant lateral instability process at a relatively conservative elevation threshold. We then integrate the UVVR and elevation to yield lifespan estimates, which demonstrate that higher elevation marshes are more resilient to internal deterioration, with an order-of-magnitude longer lifespan than predicted for lower elevation marshes.

Massachusetts, New York, Maryland, Virginia↗

USGS2009

EAARL Coastal Topography-Assateague Island National Seashore, 2008: First Surface

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the Assateague Island National Seashore in Maryland and Virginia, acquired March 24-25, 2008. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multi-spectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for pre-survey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations.

Data Series↗

USGS2009

EAARL Coastal Topography-Assateague Island National Seashore, 2008: Bare Earth

These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the Assateague Island National Seashore in Maryland and Virginia, acquired March 24-25, 2008. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL) was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multi-spectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for pre-survey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations.

Data Series↗

USGS2011

An Analysis of the Published Mineral Resource Estimates of the Haji-Gak Iron Deposit, Afghanistan

The Haji-Gak iron deposit of eastern Bamyan Province, eastern Afghanistan, was studied extensively and resource calculations were made in the 1960s by Afghan and Russian geologists. Recalculation of the resource estimates verifies the original estimates for categories A (in-place resources known in detail), B (in-place resources known in moderate detail), and C 1 (in-place resources estimated on sparse data), totaling 110.8 Mt, or about 6% of the resources as being supportable for the methods used in the 1960s. C 2 (based on a loose exploration grid with little data) resources are based on one ore grade from one drill hole, and P 2 (prognosis) resources are based on field observations, field measurements, and an ore grade derived from averaging grades from three better sampled ore bodies. C 2 and P 2 resources are 1,659.1 Mt or about 94% of the total resources in the deposit. The vast P 2 resources have not been drilled or sampled to confirm their extent or quality. The purpose of this article is to independently evaluate the resources of the Haji-Gak iron deposit by using the available geologic and mineral resource information including geologic maps and cross sections, sampling data, and the analog-estimating techniques of the 1960s to determine the size and tenor of the deposit.

Haji-Gak iron deposit in Eastern Afghanistan↗

USGS2008

Soil sedimentology at Gusev Crater from Columbia Memorial Station to Winter Haven

A total of 3140 individual particles were examined in 31 soils along Spirit's traverse. Their size, shape, and texture were quantified and classified. They represent a unique record of 3 years of sedimentologic exploration from landing to sol 1085 covering the Plains Unit to Winter Haven where Spirit spent the Martian winter of 2006. Samples in the Plains Unit and Columbia Hills appear as reflecting contrasting textural domains. One is heterogeneous, with a continuum of angular-to-round particles of fine sand to pebble sizes that are generally dust covered and locally cemented in place. The second shows the effect of a dominant and ongoing dynamic aeolian process that redistributes a uniform population of medium-size sand. The texture of particles observed in the samples at Gusev Crater results from volcanic, aeolian, impact, and water-related processes.

Journal of Geophysical Research E: Planets↗

USGS2023

Exploring the influence of input feature space on CNN-based geomorphic feature extraction from digital terrain data

Many studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective way to consistently map landforms over large spatial extents. However, there is no consensus on the optimal input feature space for such analyses. We explore the impact of input feature space for extracting geomorphic features from land surface parameters (LSPs) derived from digital terrain models (DTMs) using convolutional neural network (CNN)-based semantic segmentation deep learning. We compare four input feature space configurations: (a) a three-layer composite consisting of a topographic position index (TPI) calculated using a 50 m radius circular window, square root of topographic slope, and TPI calculated using an annulus with a 2 m inner radius and 10 m outer radius, (b) a single illuminating position hillshade, (c) a multidirectional hillshade, and (d) a slopeshade. We test each feature space input using three deep learning algorithms and four use cases: two with natural features and two with anthropogenic features. The three-layer composite generally provided lower overall losses for the training samples, a higher F1-score for the withheld validation data, and better performance for generalizing to withheld testing data from a new geographic extent. Results suggest that CNN-based deep learning for mapping geomorphic features or landforms from LSPs is sensitive to input feature space. Given the large number of LSPs that can be derived from DTM data and the variety of geomorphic mapping tasks that can be undertaken using CNN-based methods, we argue that additional research focused on feature space considerations is needed and suggest future research directions. We also suggest that the three-layer composite implemented here can offer better performance in comparison to using hillshades or other common terrain visualization surfaces and is, thus, worth considering for different mapping and feature extraction tasks.

Earth and Space Science↗

USGS2024

Assisted migration of coho salmon: Influences of passage and habitat availability on population dynamics

Assisted migration is a means of introducing a species into a previously unoccupied area. Although this idea is relatively new for many species, there are many extant examples involving fish that can be instructive. We studied a case of assisted migration where upstream access of migrating adult coho salmon Oncorhynchus kisutch over a naturally impassible barrier was established through construction of fish ladders. Although these passage structures have successfully allowed coho salmon to colonize upstream locations, managers had concerns regarding how efficiently these structures passed fish, as well as questions regarding access to specific upstream habitats, and passage barriers further upstream. To address these concerns, we developed a stage-based population model to explore: (1) influences of passage over structures, (2) rearing habitats upstream of the structures, and (3) consequences of additional barriers to passage in the system. Model simulations suggest high fish passage at the ladders was associated with the highest smolt and adult abundance of coho salmon. The importance of passage was strongly influenced by juveniles rearing in a lake, where increased lake rearing at each passage scenario increased abundance of smolts and adults. Opening habitat further upstream was estimated to increase adult and smolt abundance up to 12%. Results of model simulations also helped to identify uncertainties that could be evaluated further (e.g., juvenile rearing in the lake). In general, our findings point to the importance of considering a full range of processes that can drive expected outcomes for assisted migration.

Oregon↗

USGS1979

Catalog of earthquakes in southern Alaska: January-March 1978

The National Center for Earthquake Research of the U.S. Geological Survey (USGS) began a program of telemetered seismic recording in south-central Alaska in 1971. The principal objectives of this program have been to use data recorded by this network to precisely locate earthquakes in the active seismic zones of southern Alaska, to delineate seismically active faults, to assess seismic risk, to document potential premonitory earthquake phenomena, to investigate current tectonic deformation, and to study the structure and physical properties of the crust and upper mantle. A task fundamental to all of these goals is the routine cataloging of earthquake parameters for earthquakes located within and adjacent to the seismograph network. The initial network of 10 stations, 7 around Cook Inlet and 3 near Valdez, was installed in 1971. Each summer since then additions or modifications to the network have been made. By the Fall of 1973, 26 stations extended from western Cook Inlet to eastern Prince William Sound, and 4 stations were located between Cordova and Yakutat. A year later 20 additional stations were installed. Thirteen of these were placed along the eastern Gulf of Alaska with support from the National Oceanic and Atmospheric Administration (NOAA) under the Outer Continental Shelf Environmental Program to investigate the seismicity of the outer continental shelf (OCS) region of interest for oil exploration. During the subsequent years the region covered by the network has remained relatively fixed while effort has been made to improve the instrumentation and installation of the stations in order to make them more reliable. This earthquake catalog presents origin times, focal coordinates and magnitudes for 384 shocks occurring in the first quarter of 1978. Readings from a total of 62 stations were used to locate the shocks, including 11 stations operated by the NOAA Alaska Tsunami Warning Center (formerly Palmer Observatory), and 5 stations operated by the Geophysical Institute of the University of Alaska (U. of A.). Earthquakes in south-central Alaska as small as magnitude 3.0 have been routinely located by the National Earthquake Information Service of the USGS and its predecessor since the great Alaska earthquake of 1964 and published in the reports "Preliminary Determination of Epicenters" (PDE). In contrast the shocks included in this catalog are as small as magnitude 1.0 and most are smaller than magnitude 3.0. Data for the larger historic earthquakes in south-central Alaska have been tabulated by Meyers (1976). The locations of the stations of the USGS seismograph network are plotted in Figure 1 and listed in Table 1 along with the additional stations from which readings were obtained. The USGS stations have single, vertical-component seismometers except for GLB, PNL, RDT, SKN, and VLZ which also have two horizontal seismometers.

Alaska↗

USGS2025

Hyperspectral imaging of river bathymetry using an ensemble of regression trees

Remote sensing has emerged as an effective tool for characterizing river systems, and machine learning (ML) techniques could make this approach even more powerful. To explore this possibility, we developed an ML-based workflow for hyperspectral imaging of river bathymetry using an ensemble of regression trees (HIRBERT). This approach involves using paired observations of depth and reflectance to select wavelength bands as predictors and then train a depth retrieval model; applying the model to the image yields a spatially continuous bathymetric map. We used data from five rivers with diverse morphologies and optical characteristics to assess whether HIRBERT can (1) provide more accurate depth estimates than a band ratio-based algorithm and (2) extend the range of depths detectable via remote sensing. Relative to single band combinations identified via optimal band ratio analysis (OBRA), regression tree ensembles improved depth retrieval performance, with observed versus predicted (OP) regression R 2 values increasing for all five sites. Similarly, HIRBERT provided more reliable depth estimates than OBRA over the full range of depths present along each river. These results suggest that by incorporating additional spectral information from multiple wavelength bands, ML could enhance bathymetric mapping across a range of river environments. In addition, we show how graphical tools can facilitate interpretation of ML-based depth retrieval models and yield insight regarding relationships between depth and reflectance. The HIRBERT workflow is packaged in free, standalone software developed to support applications in river research and management. Although ML can enhance remote sensing of river bathymetry, the limitations of this approach must also be acknowledged: Field measurements of water depth are required to train a depth retrieval model and the resulting model should only be applied to the image from which the training data were derived. The inherently image-specific nature of this approach implies that developing generalized regression tree ensembles that could be applied at larger scales would require additional research.

California, Idaho, Nebraska, Oregon, Wyoming↗

USGS2007

Mineralogic constraints on sulfur-rich soils from Pancam spectra at Gusev crater, Mars

The Mars Exploration Rover (MER) Spirit excavated sulfur-rich soils exhibiting high albedo and relatively white to yellow colors at three main locations on and south of Husband Hill in Gusev crater, Mars. The multispectral visible/near-infrared properties of these disturbed soils revealed by the Pancam stereo color camera vary appreciably over small spatial scales, but exhibit spectral features suggestive of ferric sulfates. Spectral mixture models constrain the mineralogy of these soils to include ferric sulfates in various states of hydration, such as ferricopiapite [Fe2/32+Fe43+ (SO4)6(OH)2??20(H2O)], hydronium jarosite [(H3O)Fe33+ (SO4)2(OH)6], fibroferrite [Fe3+(SO4)(OH)??5(H2O)], rhomboclase [HFe3+(SO4)2??4 (H2O)], and paracoquimbite [Fe23+ (SO4)3.9(H2O)]. Copyright 2007 by the American Geophysical Union.

Geophysical Research Letters↗

USGS1975

Linkage effects between deposit discovery and postdiscovery exploratory drilling

For the 1950-71 period of petroleum exploration in the Powder River Basin, northeastern Wyoming and southeastern Montana, three specific topics were investigated. First, the wildcat wells drilled during the ambient phases of exploration are estimated to have discovered 2.80 times as much petroleum per well as the wildcat wells drilled during the cyclical phases of exploration, periods when exploration plays were active. Second, the hypothesis was tested and verified that during ambient phases of exploration the discovery of deposits could be anticipated by a small but statistically significant rise in the ambient drilling rate during the year prior to the year of discovery. Closer examination of the data suggests that this anticipation effect decreases through time. Third, a regression model utilizing the two independent variables of (1) the volume of petroleum contained in each deposit discovered in a cell and the directly adjacent cells and (2) the respective depths of these deposits was constructed to predict the expected yearly cyclical wildcat drilling rate in four 30 by 30 min (approximately 860 mi 2 ) sized cells. In two of these cells relatively large volumes of petroleum were discovered, whereas in the other two cells smaller volumes were discovered. The predicted and actual rates of wildcat drilling which occurred in each cell agreed rather closely.

Montana, Wyoming↗

USGS2006

Reserve Growth in Oil Fields of West Siberian Basin, Russia

Although reserve (or field) growth has proven to be an important factor contributing to new reserves in mature petroleum basins, it is still a poorly understood phenomenon. Limited studies show that the magnitude of reserve growth is controlled by several major factors, including (1) the reserve booking and reporting requirements in each country, (2) improvements in reservoir characterization and simulation, (3) application of enhanced oil recovery techniques, and (4) the discovery of new and extensions of known pools in discovered fields. Various combinations of these factors can affect the estimates of proven reserves in particular fields and may dictate repeated estimations of reserves during a field's life. This study explores the reserve growth in the 42 largest oil fields in the West Siberian Basin, which contain about 55 percent of the basin's total oil reserves. The West Siberian Basin occupies a vast swampy plain between the Ural Mountains and the Yenisey River, and extends offshore into the Kara Sea; it is the richest petroleum province in Russia. About 600 oil and gas fields with original reserves of 144 billion barrels of oil (BBO) and more than 1,200 trillion cubic feet of gas (TCFG) have been discovered. The principal oil reserves and most of the oil fields are in the southern half of the basin, whereas the northern half contains mainly gas reserves. Sedimentary strata in the basin consist of Upper Triassic through Tertiary clastic rocks. Most oil is produced from Neocomian (Lower Cretaceous) marine to deltaic sandstone reservoirs, although substantial oil reserves are also in the marine Upper Jurassic and continental to paralic Lower to Middle Jurassic sequences. The majority of oil fields are in structural traps, which are gentle, platform-type anticlines with closures ranging from several tens of meters to as much as 150 meters (490 feet). Fields producing from stratigraphic traps are generally smaller except for the giant Talin field which contains oil in Jurassic river-valley sandstones. Principal source rocks are organic-rich marine shales of the Volgian (uppermost Jurassic) Bazhenov Formation, which is 30-50 m (98- 164 feet) thick. Bazhenov-derived oils are mostly of medium gravity, and contain 0.8-1.3 percent sulfur and 2-5 percent paraffin. Oils in the Lower to Middle Jurassic clastics were sourced from lacustrine and estuarine shales of the Toarcian Togur Bed. These oils are medium to low gravity, with low sulfur (less than 0.25 percent) and high paraffin (commonly to 10 percent) contents. Among the 42 fields analyzed for reserve growth, 30 fields are located in the Middle Ob region, which includes the Samotlor field with reserves of more than 25 BBO and the Fedorov field with reserves of about 5 BBO. Data used in the study include year of discovery, year of first production, annual and cumulative production, and remaining reserves reported by Russian reserve categories (A+B+C1 and C2) in January of each year. Correlation of these Russian resource categories to U.S. categories of the Society of Petroleum Engineers classification is complex and somewhat uncertain. Reserve growth in oil fields of West Siberia was calculated using a newly developed Group Growth method, which requires that the total reserve (proven reserve plus cumulative production) of individual fields with an equal length of reserve record be added together starting with discovery year or the first production year. Then the annual growth factor (AGF), which is the ratio of total reserves of two consecutive years, is calculated for all years. Once AGFs have been calculated, the cumulative growth factor (CGF) is calculated by multiplying the AGFs of all the previous years. The CGF data are used to develop reserve growth models. The West Siberian oil fields show a 13-fold reserve growth 20 years after the discovery year and only about a 2-fold growth after the first production year. This difference is attributed to extensive exploration and field delineation activities between the discovery and the first production years. Because of uncertainty in the length of evaluation time and in reported reserves during this initial period, reserve growth based on the first production year is more reliable for model development. However, reserve growth models based both on discovery year and first production year show rapid growth in the first few years and slower growth in the following years. In contrast, the reserve growth patterns for the conterminous United States and offshore Gulf of Mexico show a steady reserve increase throughout the productive lives of the fields. The different reserve booking requirements and the lack of capital investment for improved reservoir management and production technologies in West Siberian fields relative to U.S. fields are the probable causes for the difference in growth patterns. Reserve growth models based on the first production year predict that the reserve growth potential in the 42 largest oil fields of West Siberia over a five-year period (1998-2003) ranges from 270-330 million barrels or 0.34-0.42 percent per year. For a similar five-year period (1996-2001), models for the conterminous United States predict a growth of 0.54-0.75 percent per year. This abstract presents the contents of a poster prepared for the AAPG Hedberg Research Conference on Understanding World Oil Resources, November 12-17, 2006 - Colorado Springs, Colorado. A paper 'Reserve Growth in Oil Fields of West Siberian Basin, Russia' was published in Natural Resources Research, v. 12, no. 2, June, 2003.

Open-File Report↗

USGS2018

Post-eruption legacy effects and their implications for long-term recovery of the vegetation on Kasatochi Island, Alaska

We studied the vegetation of Kasatochi Island, central Aleutian Islands, to provide a general field assessment regarding the survival of plants, lichens, and fungi following a destructive volcanic eruption that occurred in 2008. Plant community data were analyzed using multivariate methods to explore the relationship between pre- and post-eruption plant cover; 5 major vegetation types were identified: Honckenya peploides beach, Festuca rubra cliff shelf, Lupinus nootkatensisFestuca rubra meadow, Leymus mollis bluff ridge (and beach), and Aleuria aurantia lower slope barrens. Our study provided a very unusual glimpse into the early stages of plant primary succession on a remote island where most of the vegetation was destroyed. Plants that apparently survived the eruption dominated early plant communities. Not surprisingly, the most diverse post-eruption community most closely resembled a widespread pre-eruption type. Microhabitats where early plant communities were found were distinct and apparently crucial in determining plant survival. Comparison with volcanic events in related boreal regions indicated some post-eruption pattern similarities. ?? 2010 Regents of the University of Colorado.

Arctic, Antarctic, and Alpine Research↗

USGS2016

Exploring climate niches of ponderosa pine (Pinus ponderosa Douglas ex Lawson) haplotypes in the western United States: Implications for evolutionary history and conservation

Ponderosa pine ( Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific ( P . p . var. ponderosa ) and Rocky Mountain ( P . p . var. scopulorum ) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P . p . vars. ponderosa and scopulorum , respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate.

PLoS ONE↗

USGS2023

Skinks of Oceania, New Guinea, and Eastern Wallacea: An underexplored biodiversity hotspot

Context: Skinks comprise the dominant component of the terrestrial vertebrate fauna in Oceania, New Guinea, and Eastern Wallacea (ONGEW). However, knowledge of their diversity is incomplete, and their conservation needs are poorly understood. Aims: To explore the diversity and threat status of the skinks of ONGEW and identify knowledge gaps and conservation needs. Methods: We compiled a list of all skink species occurring in the region and their threat categories designated by the International Union for Conservation of Nature. We used available genetic sequences deposited in the National Center for Biotechnology Information’s GenBank to generate a phylogeny of the region’s skinks. We then assessed their diversity within geographical sub-divisions and compared to other reptile taxa in the region. Key results: Approximately 300 species of skinks occur in ONGEW, making it the second largest global hotspot of skink diversity following Australia. Many phylogenetic relationships remain unresolved, and many species and genera are in need of taxonomic revision. One in five species are threatened with extinction, a higher proportion than almost all reptile families in the region. Conclusions: ONGEW contain a large proportion of global skink diversity on <1% of the Earth’s landmass. Many are endemic and face risks such as habitat loss and invasive predators. Yet, little is known about them, and many species require taxonomic revision and threat level re-assessment. Implications: The skinks of ONGEW are a diverse yet underexplored group of terrestrial vertebrates, with many species likely facing extreme risks in the near future. Further research is needed to understand the threats they face and how to protect them.

Pacific Conservation Biology↗

USGS2025

Cell penetrating peptide-mediated delivery of gene-silencing nucleic acids to the invasive common reed Phragmites australis via foliar application

As a popular tool for gene function characterization and gene therapy, RNA interference (RNAi)-based gene silencing has been increasingly explored for potential applications to control invasive species. At least two major hurdles exist when applying this approach to invasive plants: (1) the design and screening of species- and gene-specific biomacromolecules (i.e., gene-silencing agents or GSAs) made of DNA, RNA, or peptides that can suppress the expression of target genes efficiently, and (2) the delivery vehicle needed to penetrate plant cell walls and other physical barriers (e.g., leaf cuticles). In this study, we investigated the cell-penetrating peptide (CPP)-mediated delivery of multiple types of GSAs (e.g., double-stranded RNA (dsRNA), artificial microRNA (amiRNA), and antisense oligonucleotide (ASO)) to knock down a putative phytoene desaturase ( PDS ) gene in the invasive common reed ( Phragmites australis spp. australis ). Both microscopic and quantitative gene expression evidence demonstrated the CPP-mediated internalization of GSA cargos and transient suppression of PDS expression in both treated and systemic leaves up to 7 days post foliar application. Although various GSA combinations and application rates and frequencies were tested, we observed limitations, including low gene-silencing efficiency and a lack of physiological trait alteration, likely owing to low CPP payload capacity and the incomplete characterization of the PDS-coding genes (e.g., the recent discovery of two PDS paralogs) in P. australis . Our work lays a foundation to support further research toward the development of convenient, cost-effective, field-deployable, and environmentally benign gene-silencing technologies for invasive P. australis management.

Mississippi, Ohio↗

USGS1985

Environmental systems and management activities on the Kennedy Space Center, Merritt Island, Florida: results of a modeling workshop

In the early 1960's, the National Aeronautics and Space Administration (NASA) began purchasing 140,000 acres on Merritt Island, Florida, in order to develop a center for space exploration. Most of this land was acquired to provide a safety and security buffer around NASA facilities. NASA, as the managing agency for the Kennedy Space Center (KSC), is responsible for preventing or controlling environmental pollution from the Federal facilities and activities at the Space Center and is committed to use all practicable means to protect and enhance the quality of the surrounding environment. The Merritt Island National Wildlife Refuge was established in 1963 when management authority for undeveloped lands at KSC was transferred to the U.S. Fish and Wildlife Service. In addition to manage for 11 Federally-listed threatened and endangered species and other resident and migratory fish and wildlife populations, the Refuge has comanagement responsibility for 19,000 acres of mosquito control impoundments and 2,500 acres of citrus groves. The Canaveral National Seashore was developed in 1975 when management of a portion of the coastal lands was transferred from NASA to the National Park Service. This multiagency jurisdiction on Merritt Island has resulted in a complex management environment. The modeling workshop described in this report was conducted May 21-25, 1984, at the Kennedy Space Center to: (1) enhance communication among the agencies with management responsibilities on Merritt Island; (2) integrate available information concerning the development, management, and ecology of Merritt Island; and (3) identify key research and monitoring needs associated with the management and use of the island's resources. The workshop was structured around the formulation of a model that would simulate primary management and use activities on Merritt Island and their effects on upland, impoundment, and estuarine vegetation and associated wildlife. The simulation model is composed of four connected submodels. The Uplands submodel calculates changes in acres and structural components of vegetation communities resulting from succession, fire, facilities development, and shuttle launch depositions, as well as the quantity and quality of surface runoff and aquifer input to an impoundment and an estuary. The Impoundment submodel next determines water quality and quantity and changes in vegetation resulting from water level manipulation and prescribed burning. The Estuary submodel than determines water quality parameters and acres of seagrass beds. Finally, the Wildlife submodel calculates habitat suitability indices for key species of interest, based on vegetation conditions in the uplands and impoundments and on several hydrologic parameters. The model represents a hypothetical management unit with 2,500 acres of uplands, a 600-acre impoundment, and a 1,500-acre section of estuary. Two management scenarios were run to analyze model behavior. The scenarios differ in the frequency of shuttle launches and prescribed burning, the extent of facilities development, the amount of land disposed waste material applied, and the nature and timing of impoundment water level control. Early in a model development project, the process of building the model is usually of greater benefit than the model itself. The model building process stimulates interaction among agencies, assists in integrating existing information, and helps identify research needs. These benefits usually accrue even in the absence of real predictive power in the resulting model. Open communication occurs among the Federal, State, and local agencies involved with activities on Merritt Island and the agencies have a cooperative working relationship. The workshop provided an opportunity for all of these agencies to meet at one time and have focused discussions on the key environmental and multiagency resource management issues. The workshop framework helped to integrate information and assumptions from a number of disciplines and agencies. This integration occurred in the computer simulation model and among workshop participants as submodel linkages were developed and scenario results discussed. A number of research needs were identified at the workshop during the model building and testing exercises and associated discussions. These needs were based on the informed judgement of researchers and managers familiar with Merritt Island or similar areas, rather than on a comprehensive literature review of sensitivity analysis of the preliminary model developed at the workshop. Some of the needs can be addressed by interpreting the results of completed studies from similar geographic areas as they relate to Merritt Island, while other will require additional research studies on Merritt Island. Major research needs associated with the Upland submodel include behavior of the near-surface aquifer, factors limiting slash pine regeneration, frequency and effects of natural fire on various cover types, cumulative effects of shuttle launches, and fate in upland soils of nitrogen and phosphorous from land applied waste material. Key Impoundment submodel needs include documentation of vegetation changes in response to altered water depth, salinity, and nutrient concentrations and better specification of the functional characteristics of impoundments as chemical filters. Important information gaps identified in the Estuary submodel include a more complete analysis of factors contributing to phytoplankton abundance, evaluation of sources of turbidity other than phytoplankton, and identification and quantification of factors limiting seagrass distribution. Primary research needs associated with the Wildlife submodel include a survey of breeding habitat, production data, and harvest data for mottled ducks; data on the emigration and immigration of juvenile mullet (and other transient fish) in the impoundment; the contribution of various seagrasses to habitat requirements of sea trout; and the effects of dissolved oxygen on survival of juvenile sea trout. Ideally, the modeling workshop process is iterative in nature. Periods between workshops are used for research, data collection, and model refinement. Each workshop integrates information collected since the last workshop and produces a more credible model that is more useful in evaluating management alternatives. Participants felt that continued application of this process would help provide ongoing integration and communication among agencies and would allow each agency's planning and management activities to be viewed within the context of an overall assessment.

Report↗

USGS2021

Characterizing physical properties of streambed interface sediments using in situ complex electrical conductivity measurements

Streambed sediment physical properties such as surface area, are difficult to quantify in situ but exert a high‐level control on a wide range of biogeochemical processes and sorption of contaminants. We introduce the use of complex electrical conductivity (CC) methods (also known as spectral induced polarization (SIP)) that measure both real and imaginary conductivity to non‐invasively and efficiently characterize shallow streambed sediments. We explore the method through synthetic modeling, laboratory, and field measurements to demonstrate the sensitivity of imaginary conductivity to sediment surface area, controlled in part by fine‐grained iron oxides produced by anoxic groundwater discharge. Laboratory measurements verify expected relationships between CC parameters and sediment properties. Synthetic modeling using a 1D analytical model illustrates the influence of water layer depth and conductivity on the field CC measurements made at the streambed‐stream water interface. Specifically, the inverted sediment imaginary conductivity is less impacted by uncertainty in the water layer depth and conductivity relative to the real conductivity and phase shift. Field CC measurements along a landfill‐impacted river reveal discrete streambed zones with enhanced bulk surface area generally corresponding to anoxic groundwater discharges zones with high concentrations of fine‐grained iron oxide precipitates.

Water Resources Research↗