Root hemiparasitic plants both compete with and extract resources from host plants. By reducing the abundance of dominant plants and releasing subordinates from competitive exclusion, they can have an outsized impact on plant communities. Most research on the ecological role of hemiparasites is manipulative and focuses on a small number of hemiparasitic taxa. Here, we ask whether patterns in natural plant communities match the expectation that hemiparasites affect the structure of plant communities. Our data were collected on 129 national park units spanning the continental United States. The most common hemiparasite genera were Pedicularis, Castilleja, Krameria, and Comandra. We used null models and linear mixed models to determine whether hemiparasites were associated with changes in community richness and evenness. Hemiparasite presence did not affect community metrics. Hemiparasite abundance was positively associated with increasing evenness of herbaceous species, but not with species richness. The associations that we observed on a continental scale are consistent with evidence that the impacts of root hemiparasitic plants on evenness can be substantial and abundance dependent but that effects on richness are less pronounced. Hemiparasites mediate competitive exclusion in communities to facilitate species coexistence and merit consideration of inclusion in ecological theories of coexistence.
Surface and subsurface moored buoy, ship-based, remotely sensed, and reanalysis datasets are used to investigate thermal variability of northern Gulf of Alaska (NGA) nearshore, coastal, and offshore waters over synoptic to century-long time scales. NGA sea surface temperature (SST) showed a larger positive trend of 0.22 ± 0.10 °C per decade over 1970–2021 compared to 0.10 ± 0.03 °C per decade over 1900–2021. Over synoptic time scales, SST covariance between two stations is small (<10%) when separation exceeds 100 km, while stations separated by 500 km retain 50% of their co-variability for seasonal and longer fluctuations. Relative to in situ sensor data, remotely sensed SST data has limited accuracy in some NGA settings, capturing 60–70% of the daily SST anomaly in coastal and offshore waters, but often <25% nearshore. North Pacific and NGA leading modes of SST variability leave 25–50% of monthly variance unresolved. Analysis of the 2014–2016 Pacific marine heatwave shows that NGA coastal surface temperatures warmed contemporaneously with offshore waters through 2013, but deep inner shelf waters (200–250 m) exhibited delayed warming. Offshore surface waters cooled from 2014 to 2016, while shelf waters continued to warm from the combined effects of local air-sea and advective heat fluxes. We find that annually averaged Sitka air temperature is a leading predictor (r2 = 0.37, p < 0.05) for following-year NGA coastal water column temperature. Our results can inform future environmental monitoring designs, assist forward-looking projections of marine conditions, and show the importance of in situ measurements for nearshore studies that require knowledge of thermal conditions over time scales of days and weeks.
The Landsat program has a long history of providing remotely sensed data to the user community. This history is being extended with the addition of the Landsat 9 satellite, which closely mimics the Landsat 8 satellite and its instruments. These satellites contain two instruments, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). OLI is a push-broom sensor that collects visible and near-infrared (VNIR) and short-wave infrared (SWIR) wavelengths at 30 m ground sample distance, along with a panchromatic 15 m band. The TIRS sensor contains two long-wave thermal spectral channels centered at 10.9 and 12 µm. The data from these two instruments, on both satellites, are combined into a single Landsat product. The Landsat 5–9 satellites follow a 16 day repeat cycle designated as the Worldwide Reference System (WRS-2), which provides a global notional gridded mapping for identifying individual Landsat scenes. The Landsat 8 and 9 satellites are flown such that their orbital tracks are separated by 8 days in this 16 day cycle. During the commissioning period of Landsat 9, and during its ascent to its operational WRS-2 orbit, the Landsat 9 satellite’s orbital track went under and crossed over the orbital track of the Landsat 8 satellite. This produced a unique situation where nearly time-coincident imagery could be obtained from the instruments of the two spacecrafts. From a radiometric standpoint, this allowed for near-time cross-calibration between the instruments to be performed. From a geometry perspective, calibration is achieved through high-resolution reference imagery over specific ground locations, thus ensuring calibration of the instruments and for the instruments to be well cross-calibrated geometrically. Although these underfly data do not provide calibration of the instruments between the platforms from a geometric perspective, they allow for the verification of the calibration steps involving the instruments and spacecraft. This paper discusses the co-registration of this unique set of data while also discussing other geometric aspects of these data by looking at and comparing the differences in sensor viewing and sun angles associated with the collections from the two platforms for imagery obtained over common geographic locations. The image-to-image comparisons between Landsat 8 and 9 coincident pairs, where both datasets are precision terrain products, are registered to within 2.2 m with respect to their root-mean-squared radial error (RMSEr). The 2.2 m represents less than 0.1 of a 30 m multispectral pixel in misregistration between the L9 and L8 underfly products that will be available to the user community. This unique dataset will provide well-registered, near-coincident image acquisitions between the two platforms that can be a key to any calibration or application comparisons. The paper also presents that, for images for which one of the image pairs failed precision corrections and became a terrain-corrected only product type, a range of 8–14 m RMSEr could be expected in co-registration, while, in cases where both image pairs failed the precision correction step and both images became a terrain-corrected only product type, a 14 m RMSEr could be expected for co-registration.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14153781","usgsCitation":"Choate, M.J., Rengarajan, R., Storey, J., and Lubke, M., 2022, Landsat 9 geometric characteristics using underfly data: Remote Sensing, v. 14, no. 15, 3781, 18 p., https://doi.org/10.3390/rs14153781.","productDescription":"3781, 18 p.","ipdsId":"IP-141262","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":446884,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14153781","text":"Publisher Index Page"},{"id":406670,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"15","noUsgsAuthors":false,"publicationDate":"2022-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Choate, Michael J. 0000-0002-8101-4994","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":216866,"corporation":false,"usgs":true,"family":"Choate","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":851559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengarajan, Rajagopalan 0000-0003-1860-7110","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":242014,"corporation":false,"usgs":false,"family":"Rengarajan","given":"Rajagopalan","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":851560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storey, James C. 0000-0002-6664-7232","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":242015,"corporation":false,"usgs":false,"family":"Storey","given":"James C.","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":851561,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lubke, Mark 0000-0002-7257-2337","orcid":"https://orcid.org/0000-0002-7257-2337","contributorId":261911,"corporation":false,"usgs":false,"family":"Lubke","given":"Mark","email":"","affiliations":[{"id":53079,"text":"KBR, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":851562,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70239382,"text":"70239382 - 2022 - A geospatial knowledge graph prototype for national topographic mapping","interactions":[],"lastModifiedDate":"2023-01-11T15:03:10.21326","indexId":"70239382","displayToPublicDate":"2022-08-06T08:58:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12997,"text":"International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","active":true,"publicationSubtype":{"id":10}},"title":"A geospatial knowledge graph prototype for national topographic mapping","docAbstract":"Knowledge graphs are a form of database representation and handling that show the potential to better meet the challenges of data interoperability, semi-automated information reasoning, and information retrieval. Geospatial knowledge graphs (GKG) have at their core specialized forms of applied ontology that provide coherent spatial context to a domain of information including non-spatial attributes. This paper discusses research toward the development of a prototype GKG based on national topographic databases of geospatial feature instances, attributes, properties, metadata, and annotations. The challenges are to capture and represent geographic semantics inherent in the source data, to align such graph models with standards where possible, to test logical computations, and to visualize the data using a cartographic user interface. Data integration from outside sources was tested through SPARQL and GeoSPARQL queries. Called the MapKB, the approaches applied in this prototype use a number of software components to build a system architecture aligned with those objectives and are composed entirely of free and open-source software. The system and ontology design were validated through reasoning and competency questions. Technical aspects of the prototype software succeeded, but customization was found to be needed for user-based design.
","language":"English","publisher":"International Society of Photogrammetry and Remote Sensing","doi":"10.5194/isprs-archives-XLVIII-4-W1-2022-511-2022","usgsCitation":"Varanka, D.E., 2022, A geospatial knowledge graph prototype for national topographic mapping: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. XLVIII-4/W1-2022, p. 511-516, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-511-2022.","productDescription":"6 p.","startPage":"511","endPage":"516","ipdsId":"IP-120273","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":446887,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-511-2022","text":"Publisher Index Page"},{"id":411719,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"XLVIII-4/W1-2022","noUsgsAuthors":false,"publicationDate":"2022-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Varanka, Dalia E. 0000-0003-2857-9600 dvaranka@usgs.gov","orcid":"https://orcid.org/0000-0003-2857-9600","contributorId":1296,"corporation":false,"usgs":true,"family":"Varanka","given":"Dalia","email":"dvaranka@usgs.gov","middleInitial":"E.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":861370,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70233932,"text":"70233932 - 2022 - Trends analysis of Rangeland Condition Monitoring Assessment and Projection (RCMAP) fractional component time series (1985–2020)","interactions":[],"lastModifiedDate":"2024-01-19T15:18:40.15331","indexId":"70233932","displayToPublicDate":"2022-08-05T11:36:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8118,"text":"GIScience & Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Trends analysis of Rangeland Condition Monitoring Assessment and Projection (RCMAP) fractional component time series (1985–2020)","docAbstract":"Rangelands have a dynamic response to climate change, fire, and other anthropogenic disturbances. The Rangeland Condition, Monitoring, Assessment, and Projection (RCMAP) product aims to capture this response by quantifying the percent cover of eight rangeland components, associated error, and trends across the western United States using Landsat from 1985 to 2020. The current generation of RCMAP has been improved with more training data, regional-scale Landsat composites, and more robust change detection. We assess the temporal patterns in each component with a linear model and a structural change method that determines break points using an 8-year temporal moving window. The linear and structural change methods generally agreed on patterns of change, but the latter found breaks more often, with at least one break point in most pixels. The structural change model provides more robust statistics on the significant minority of pixels with non-monotonic trends, while detrending some interannual signal potentially superfluous from a long-term perspective. Although break point density within one year of fire and vegetation treatments was ~10× and ~4× that of unburned areas, respectively, break point detection in the correct year of fire was only moderately accurate. Climate responses in break points proved more robust, with strong spatiotemporal relation in break point density with both aridity index values and aridity index change. Break point density strongly responds to both increased and decreased aridity and is reflective of ecosystem resilience. Data provide spatiotemporal information on the occurrence of breaks, but even more importantly, attribute those change events to specific component(s).
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2022.2104786","usgsCitation":"Shi, H., Rigge, M.B., Postma, K., and Bunde, B., 2022, Trends analysis of Rangeland Condition Monitoring Assessment and Projection (RCMAP) fractional component time series (1985–2020): GIScience & Remote Sensing, v. 59, no. 1, p. 1243-1265, https://doi.org/10.1080/15481603.2022.2104786.","productDescription":"23 p.","startPage":"1243","endPage":"1265","ipdsId":"IP-135462","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":446897,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15481603.2022.2104786","text":"Publisher Index Page"},{"id":424623,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SJXUI1","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Rangeland Condition Monitoring Assessment 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(Geography)","active":false,"usgs":true}],"preferred":true,"id":847707,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Postma, Kory 0000-0001-8058-498X","orcid":"https://orcid.org/0000-0001-8058-498X","contributorId":293879,"corporation":false,"usgs":false,"family":"Postma","given":"Kory","affiliations":[{"id":63548,"text":"KBRwyle, under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":847708,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bunde, Brett 0000-0003-0228-779X","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":288364,"corporation":false,"usgs":false,"family":"Bunde","given":"Brett","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":847709,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70234258,"text":"70234258 - 2022 - Understanding impacts of sea-level rise and land management on critical coastal marsh habitat","interactions":[],"lastModifiedDate":"2022-10-21T15:45:16.955892","indexId":"70234258","displayToPublicDate":"2022-08-05T10:39:33","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":7504,"text":"Final Report","active":true,"publicationSubtype":{"id":1}},"title":"Understanding impacts of sea-level rise and land management on critical coastal marsh habitat","docAbstract":"Coastal wetlands in the Louisiana Mississippi River Deltaic Plain (MRDP) experience some of the highest rates of relative sea-level rise (SLR) in the world, leading to elevated surface water salinity and prolonged flooding. Elevated salinity causes a shift toward more salt-tolerant vegetation communities, associated with changes in ecosystem function and services. As sea level continues to rise, even salt-tolerant plant communities succumb to impacts of excessive flooding through submergence and conversion to open water. To better characterize the impacts of SLR on coastal wetland health and sustainability, we focused on two key landscape transitions in this project: 1) freshwater marsh transition to saltwater marsh, and 2) saltwater marsh transition to open water. We investigated these transitions using data with greater spatial and temporal resolution than previous studies in this region, allowing us to identify the mechanisms underlying widely observed landscape changes.
","language":"English","publisher":"South Central Climate Adaptation Science Center","usgsCitation":"Stagg, C., 2022, Understanding impacts of sea-level rise and land management on critical coastal marsh habitat: Final Report, 20 p.","productDescription":"20 p.","ipdsId":"IP-143747","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":408614,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":404866,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f8c652fe4b0546c0c397b4a/5f315e4482ceae4cb3ca5195"}],"country":"United States","state":"Louisiana","otherGeospatial":"Mississippi River deltaic plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.50590005310218,\n 29.42101094649145\n ],\n [\n -89.50590005310218,\n 30.0568363860655\n ],\n [\n -90.26633984845733,\n 30.0568363860655\n ],\n [\n -90.26633984845733,\n 29.42101094649145\n ],\n [\n -89.50590005310218,\n 29.42101094649145\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stagg, Camille 0000-0002-1125-7253","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":222386,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":848355,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70234246,"text":"70234246 - 2022 - Bedrock depth influences spatial patterns of summer baseflow, temperature and flow disconnection for mountainous headwater streams","interactions":[],"lastModifiedDate":"2022-08-05T13:15:34.056536","indexId":"70234246","displayToPublicDate":"2022-08-05T08:08:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Bedrock depth influences spatial patterns of summer baseflow, temperature and flow disconnection for mountainous headwater streams","docAbstract":"In mountain headwater streams, the quality and resilience of summer cold-water habitat is generally regulated by stream discharge, longitudinal stream channel connectivity and groundwater exchange. These critical hydrologic processes are thought to be influenced by the stream corridor bedrock contact depth (sediment thickness), a parameter often inferred from sparse hillslope borehole information, piezometer refusal and remotely sensed data. To investigate how local bedrock depth might control summer stream temperature and channel disconnection (dewatering) patterns, we measured stream corridor bedrock depth by collecting and interpreting 191 passive seismic datasets along eight headwater streams in Shenandoah National Park (Virginia, USA). In addition, we used multi-year stream temperature and streamflow records to calculate several baseflow-related metrics along and among the study streams. Finally, comprehensive visual surveys of stream channel dewatering were conducted in 2016, 2019 and 2021 during summer low flow conditions (124 total km of stream length). We found that measured bedrock depths along the study streams were not well-characterized by soils maps or an existing global-scale geologic dataset where the latter overpredicted measured depths by 12.2 m (mean) or approximately four times the average bedrock depth of 2.9 m. Half of the eight study stream corridors had an average bedrock depth of less than 2 m. Of the eight study streams, Staunton River had the deepest average bedrock depth (3.4 m), the coldest summer temperature profiles and substantially higher summer baseflow indices compared to the other study steams. Staunton River also exhibited paired air and water annual temperature signals suggesting deeper groundwater influence, and the stream channel did not dewater in lower sections during any baseflow survey. In contrast, Paine Run and Piney River did show pronounced, patchy channel dewatering, with Paine Run having dozens of discrete dry channel sections ranging from 1 to greater than 300 m in length. Stream dewatering patterns were apparently influenced by a combination of discrete deep bedrock (20+ m) features and more subtle sediment thickness variation (1–4 m) depending on local stream valley hydrogeology. In combination, these unique datasets show the first large-scale empirical support for existing conceptual models of headwater stream disconnection based on spatially variable underflow capacity and shallow groundwater supply.","language":"English","publisher":"Copernicus","doi":"10.5194/hess-26-3989-2022","usgsCitation":"Briggs, M., Goodling, P.J., Johnson, Z., Rogers, K., Hitt, N.P., Fair, J.H., and Snyder, C.D., 2022, Bedrock depth influences spatial patterns of summer baseflow, temperature and flow disconnection for mountainous headwater streams: Hydrology and Earth System Sciences, v. 26, no. 15, p. 3989-4011, https://doi.org/10.5194/hess-26-3989-2022.","productDescription":"23 p.","startPage":"3989","endPage":"4011","ipdsId":"IP-132407","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - 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2022 - Status of water-level altitudes and long-term water-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2021","interactions":[],"lastModifiedDate":"2022-08-19T14:18:51.913895","indexId":"sir20225065","displayToPublicDate":"2022-08-05T07:27:23","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5065","displayTitle":"Status of Water-Level Altitudes and Long-Term Water-Level Changes in the Chicot and Evangeline (Undifferentiated) and Jasper Aquifers, Greater Houston Area, Texas, 2021","title":"Status of water-level altitudes and long-term water-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2021","docAbstract":"Since the early 1900s, groundwater withdrawn from the primary aquifers that compose the Gulf Coast aquifer system—the Chicot and Evangeline (undifferentiated) and Jasper aquifers—has been the primary source of water in the greater Houston area, Texas. This report, prepared by the U.S. Geological Survey in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District, is one in an annual series of reports depicting the status of water-level altitudes and water-level changes in aquifers in the greater Houston area.
In contrast to previous reports, the Chicot and Evangeline aquifers are treated as a single hydrogeologic unit in this report. In 2021, shaded depictions of water-level altitudes for the Chicot and Evangeline aquifers (undifferentiated) ranged from 300 feet (ft) below the North American Vertical Datum of 1988 (NAVD 88) to 300 ft above NAVD 88. The largest decline in water-level altitudes indicated by the 1977–2021 long-term water-level-change map for the Chicot and Evangeline aquifers (undifferentiated) was in the north-central part of The Woodlands, Tex., whereas the 1990–2021 long-term water-level-change map for the Chicot and Evangeline aquifers (undifferentiated) depicts a large area of decline in water-level altitudes in northwestern Harris County, northwest of Jersey Village, Tex. The largest rise in water-level altitudes in the Chicot and Evangeline aquifers (undifferentiated) was observed in a relatively large area in southeastern Harris County for 1977–2021, whereas the largest rise in water-level altitudes for 1990–2021 was in a relatively large area in central Harris County.
In 2021, shaded depictions of water-level altitudes for the Jasper aquifer ranged from 250 ft below NAVD 88 to 300 ft above NAVD 88. The 2000–21 long-term water-level-change map for the Jasper aquifer depicts water-level declines throughout most of the study area where water-level-altitude data from the Jasper aquifer were collected, with the largest decline in northern Harris County southwest of The Woodlands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225065","collaboration":"Prepared in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District","usgsCitation":"Braun, C.L., and Ramage, J.K., 2022, Status of water-level altitudes and long-term water-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2021 (ver. 1.1, August 19, 2022): U.S. Geological Survey Scientific Investigations Report 2022–5065, 25 p., https://doi.org/10.3133/sir20225065.","productDescription":"Report: iv, 25 p.; Data Releases; Dataset","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-127697","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":405340,"rank":9,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2022/5065/versionHist.txt","text":"Version History","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":405339,"rank":8,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5065/sir20225065.pdf","text":"Report","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5065"},{"id":405338,"rank":7,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5065/coverthb2.jpg"},{"id":404816,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T8FJWO","text":"USGS data release","linkHelpText":"Groundwater-level altitudes and long-term groundwater-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2021"},{"id":404817,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":404815,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R6CX2T","text":"USGS data release","linkHelpText":"Depth to groundwater measured from wells completed in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2021"},{"id":404813,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5065/images"},{"id":404812,"rank":1,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5065/sir20225065.XML"},{"id":404820,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225064","text":"Scientific Investigations Report 2022–5064","linkHelpText":"—Treatment of the Chicot and Evangeline aquifers as a single hydrogeologic unit and use of geostatistical interpolation methods to develop gridded surfaces of water-level altitudes and water-level changes in the Chicot and Evangeline aquifers (undifferentiated) and Jasper aquifer, greater Houston area, Texas, 2021"}],"country":"United States","state":"Texas","otherGeospatial":"Greater Houston area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.185302734375,\n 28.82061274169944\n ],\n [\n -94.757080078125,\n 28.82061274169944\n ],\n [\n -94.757080078125,\n 30.590637026892917\n ],\n [\n -96.185302734375,\n 30.590637026892917\n ],\n [\n -96.185302734375,\n 28.82061274169944\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: August 5, 2022; Version 1.1: August 19, 2022","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
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The greater Houston area of Texas includes approximately 11,000 square miles and encompasses all or part of 11 counties (Harris, Galveston, Fort Bend, Montgomery, Brazoria, Chambers, Grimes, Liberty, San Jacinto, Walker, and Waller). From the early 1900s until the mid-1970s, groundwater withdrawn from the three primary aquifers that compose the Gulf Coast aquifer system—the Chicot, Evangeline, and Jasper aquifers—had been the primary source of water for the greater Houston area. The withdrawal of groundwater was unregulated prior to 1975, resulting in land-surface subsidence caused by large water-level declines in the greater Houston area.
This report, prepared by the U.S. Geological Survey in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District, describes updates to the ways in which water-level altitudes and water-level changes in the greater Houston area are presented relative to previous U.S. Geological Survey reports. The first update involves presenting water-level altitudes and water-level changes as a combined (undifferentiated) representation of the Chicot and Evangeline aquifers. The second update concerns the methods used to depict water-level altitudes and water-level changes in the greater Houston area in interpretive reports, with geostatistical interpolation methods replacing manual contouring methods.
The Chicot and Evangeline aquifers have historically been described as distinct hydrogeologic units for the purpose of water-level mapping. A confining unit does not separate these two aquifers in the study area, and water-level data from colocated wells screened in these aquifers indicate that there is likely a substantial degree of hydrogeologic connection. From a groundwater-flow perspective, these two aquifer units predominantly function as a single unit. Hence, the decision was made to combine the Chicot and Evangeline aquifers into a single, undifferentiated hydrogeologic unit for the purposes of assessing water-level altitudes and water-level changes over time. The 2020 water-level altitudes for the Chicot, Evangeline, and Jasper aquifers were re-created in this report from computer algorithms of the contoured datasets as gridded surfaces to demonstrate the similarity of results from geostatistical interpolation methods to those from manual contouring methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225064","collaboration":"Prepared in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District","usgsCitation":"Ramage, J.K., Braun, C.L., and Ellis, J.H., 2022, Treatment of the Chicot and Evangeline aquifers as a single hydrogeologic unit and use of geostatistical interpolation methods to develop gridded surfaces of water-level altitudes and water-level changes in the Chicot and Evangeline aquifers (undifferentiated) and Jasper aquifer, greater Houston area, Texas, 2021: U.S. Geological Survey Scientific Investigations Report 2022–5064, 51 p., https://doi.org/10.3133/sir20225064.","productDescription":"Report: vi, 51 p.; Data Release; Dataset","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-134432","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":404777,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":404775,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5064/images"},{"id":404774,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5064/sir20225064.XML"},{"id":404821,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225065","text":"Scientific Investigations Report 2022–5065","linkHelpText":"—Status of water-level altitudes and long-term water-level changes in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2021"},{"id":404776,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R6CX2T","text":"USGS data release","linkHelpText":"Depth to groundwater measured from wells completed in the Chicot and Evangeline (undifferentiated) and Jasper aquifers, greater Houston area, Texas, 2021"},{"id":404771,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5064/coverthb.jpg"},{"id":404772,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5064/sir20225064.pdf","text":"Report","size":"23.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5064"}],"country":"United States","state":"Texas","otherGeospatial":"Greater Houston area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.185302734375,\n 28.82061274169944\n ],\n [\n -94.757080078125,\n 28.82061274169944\n ],\n [\n -94.757080078125,\n 30.590637026892917\n ],\n [\n -96.185302734375,\n 30.590637026892917\n ],\n [\n -96.185302734375,\n 28.82061274169944\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
As marine ecosystems respond to climate change and other stressors, it is necessary to evaluate current and past hybridization events to gain insight on the outcomes and drivers of such events. Ancestral introgression within the gadids has been suggested to allow cod to inhabit a variety of habitats. Little attention has been given to contemporary hybridization, especially within cold-water-adapted cod (Boreogadus saida Lepechin, 1774 and Arctogadus glacialis Peters, 1872). We used whole-genome, restriction-site associated, and mitochondrial sequence data to explore the degree and direction of hybridization between these species where previous hybridization had not been reported. Although nearly identical morphologically at certain life stages, we detected very distinct nuclear and mitochondrial lineages. We detected one potential hybrid with a Arctogadus mitochondrial haplotype and Boreogadus nuclear genotype, but no early generational hybrids. The presence of a late generation hybrid suggests that at least some hybrids survive to maturity and reproduce. However, a historical introgression event could not be excluded. Contemporary gene flow appears asymmetrical from Arctogadus into Boreogadus, which may be due to overlap in timing of spawning, environmental heterogeneity, or differences in population size. This study provides important baseline information for the degree of potential hybridization between these species within Alaska marine environments.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/as-2021-0030","usgsCitation":"Wilson, R.E., Sonsthagen, S.A., Lavretsky, P., Majewski, A., Arnason, E., Halldorsdottir, K., Einarsson, A., Wedemeyr, K., and Talbot, S.L., 2022, Low levels of hybridization between sympatric cold-water-adapted Arctic cod and Polar cod in the Beaufort Sea confirm genetic distinctiveness: Arctic Science, v. 8, no. 4, p. 1082-1093, https://doi.org/10.1139/as-2021-0030.","productDescription":"12 p.","startPage":"1082","endPage":"1093","ipdsId":"IP-130423","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446914,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/as-2021-0030","text":"Publisher Index Page"},{"id":433449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Beaufort Sea, Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -174.72007918044827,\n 75.06986491260304\n ],\n [\n -174.76506022895288,\n 69.14800723197729\n ],\n [\n -166.15259714556063,\n 69.52600683358969\n ],\n [\n -156.95456159243057,\n 71.39604084905038\n ],\n [\n -136.81825817727585,\n 69.54036567664357\n ],\n [\n -129.58194373827234,\n 70.33510131179872\n ],\n [\n -126.37318701117965,\n 74.99060622515827\n ],\n [\n -174.72007918044827,\n 75.06986491260304\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Robert E.","contributorId":293234,"corporation":false,"usgs":false,"family":"Wilson","given":"Robert","email":"","middleInitial":"E.","affiliations":[{"id":63255,"text":"Nebraska State Museum","active":true,"usgs":false}],"preferred":false,"id":908846,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":908847,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lavretsky, P.","contributorId":341733,"corporation":false,"usgs":false,"family":"Lavretsky","given":"P.","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":908848,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Majewski, A.","contributorId":341734,"corporation":false,"usgs":false,"family":"Majewski","given":"A.","email":"","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":908849,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arnason, E.","contributorId":341736,"corporation":false,"usgs":false,"family":"Arnason","given":"E.","email":"","affiliations":[{"id":36649,"text":"University of Iceland","active":true,"usgs":false}],"preferred":false,"id":908850,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Halldorsdottir, K.","contributorId":341738,"corporation":false,"usgs":false,"family":"Halldorsdottir","given":"K.","email":"","affiliations":[{"id":36649,"text":"University of Iceland","active":true,"usgs":false}],"preferred":false,"id":908851,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Einarsson, A.W.","contributorId":341742,"corporation":false,"usgs":false,"family":"Einarsson","given":"A.W.","email":"","affiliations":[{"id":36649,"text":"University of Iceland","active":true,"usgs":false}],"preferred":false,"id":908852,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wedemeyr, K.","contributorId":341745,"corporation":false,"usgs":false,"family":"Wedemeyr","given":"K.","email":"","affiliations":[{"id":20318,"text":"Bureau of Ocean Energy Management","active":true,"usgs":false}],"preferred":false,"id":908853,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":908854,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70234236,"text":"70234236 - 2022 - Understory plant communities show resistance to drought, hurricanes, and experimental warming in a wet tropical forest","interactions":[],"lastModifiedDate":"2022-08-04T14:33:52.831258","indexId":"70234236","displayToPublicDate":"2022-08-04T09:23:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5860,"text":"Frontiers in Forests and Global Change","active":true,"publicationSubtype":{"id":10}},"title":"Understory plant communities show resistance to drought, hurricanes, and experimental warming in a wet tropical forest","docAbstract":"Global climate change has led to rising temperatures and to more frequent and intense climatic events, such as storms and droughts. Changes in climate and disturbance regimes can have non-additive effects on plant communities and result in complicated legacies we have yet to understand. This is especially true for tropical forests, which play a significant role in regulating global climate. We used understory vegetation data from the Tropical Responses to Altered Climate Experiment (TRACE) in Puerto Rico to evaluate how plant communities responded to climate warming and disturbance. The TRACE understory vegetation was exposed to a severe drought (2015), 2 years of experimental warming (4°C above ambient in half of the plots, 2016–2017 and 2018–2019), and two major hurricanes (Irma and María, September 2017). Woody seedlings and saplings were censused yearly from 2015 to 2019, with an additional census in 2015 after the drought ended. We evaluated disturbance-driven changes in species richness, diversity, and composition across ontogeny. We then used Bayesian predictive trait modeling to assess how species responded to disturbance and how this might influence the functional structure of the plant community. Our results show decreased seedling richness after hurricane disturbance, as well as increased sapling richness and diversity after warming. We found a shift in species composition through time for both seedlings and saplings, yet the individual effects of each disturbance were not significant. At both ontogenetic stages, we observed about twice as many species responding to experimental warming as those responding to drought and hurricanes. Predicted changes in functional structure point to disturbance-driven functional shifts toward a mixture of fast-growing and drought-tolerant species. Our findings demonstrate that the tropical forest understory community is more resistant to climatic stressors than expected, especially at the sapling stage. However, early signs of changes in species composition suggest that, in a warming climate with frequent droughts and hurricanes, plant communities might shift over time toward fast-growing or drought-tolerant species.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/ffgc.2022.733967","usgsCitation":"Alonso-Rodriguez, A.M., Wood, T.E., Torres-Diaz, J., Cavaleri, M.A., Reed, S., and Bachelot, B., 2022, Understory plant communities show resistance to drought, hurricanes, and experimental warming in a wet tropical forest: Frontiers in Forests and Global Change, v. 5, 733967, 16 p., https://doi.org/10.3389/ffgc.2022.733967.","productDescription":"733967, 16 p.","ipdsId":"IP-133340","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":446918,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffgc.2022.733967","text":"Publisher Index Page"},{"id":404824,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Puerto Rico","otherGeospatial":"Bosque experimental de Luquillo, Luquillo Experimental Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.8681869506836,\n 18.240764185529784\n ],\n [\n -65.70304870605469,\n 18.240764185529784\n ],\n [\n -65.70304870605469,\n 18.34800827349917\n ],\n [\n -65.8681869506836,\n 18.34800827349917\n ],\n [\n -65.8681869506836,\n 18.240764185529784\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2022-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Alonso-Rodriguez, Aura M.","contributorId":206281,"corporation":false,"usgs":false,"family":"Alonso-Rodriguez","given":"Aura","email":"","middleInitial":"M.","affiliations":[{"id":37300,"text":"International Institute of Tropical Forestry, USDA Forest Service, Sabana Field Research Station, Luquillo, Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":848288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Tana E.","contributorId":33193,"corporation":false,"usgs":true,"family":"Wood","given":"Tana","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":848289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Torres-Diaz, Jamarys","contributorId":294541,"corporation":false,"usgs":false,"family":"Torres-Diaz","given":"Jamarys","email":"","affiliations":[{"id":63595,"text":"USDA Forest Service International Institute of Tropical Forestry, Jardín Botánico Sur, Río Piedras, Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":848290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cavaleri, Molly A.","contributorId":206282,"corporation":false,"usgs":false,"family":"Cavaleri","given":"Molly","email":"","middleInitial":"A.","affiliations":[{"id":34284,"text":"School of Forest Resources and Environmental Science, Michigan Technological University","active":true,"usgs":false}],"preferred":false,"id":848291,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":848292,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bachelot, Benedicte","contributorId":294542,"corporation":false,"usgs":false,"family":"Bachelot","given":"Benedicte","email":"","affiliations":[{"id":63597,"text":"Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, USA","active":true,"usgs":false}],"preferred":false,"id":848293,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70234229,"text":"70234229 - 2022 - A comprehensive assessment of mangrove species and carbon stock on Pohnpei, Micronesia","interactions":[],"lastModifiedDate":"2023-04-14T17:00:52.620563","indexId":"70234229","displayToPublicDate":"2022-08-04T09:07:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"A comprehensive assessment of mangrove species and carbon stock on Pohnpei, Micronesia","docAbstract":"Mangrove forests are the most important ecosystems on Pohnpei Island, Federated States of Micronesia, as the island communities of the central Pacific rely on the forests for many essential services including protection from sea-level rise that is occurring at a greater pace than the global average. As part of a multi-component assessment to evaluate vulnerabilities of mangrove forests on Pohnpei, mangrove forests were mapped at two points in time: 1983 and 2018. In 2018, the island had 6,426 ha of mangrove forest. Change analysis indicated a slight (0.76%) increase of mangrove area between 1983 and 2018, contrasting with global mangrove area declines. Forest structure and aboveground carbon (AGC) stocks were inventoried using a systematic sampling of field survey plots and extrapolated to the island using k-nearest neighbor and random forest species models. A gridded or wall to wall approach is suggested when possible for defining carbon stocks of a large area due to high variability seen in our data. The k-nearest neighbor model performed better than random forest models to map species dominance in these forests. Mean AGC was 167 ± 11 MgC ha-1, which is greater than the global average of mangroves (115 ± 7 MgC ha-1) but within their global range (37–255 MgC ha-1) Kauffman et al. (2020). In 2018, Pohnpei mangroves contained over 1.07 million MgC in AGC pools. By assigning the mean AGC stock per species per area to the map, carbon stock distributions were visualized spatially, allowing future conservation efforts to be directed to carbon dense stands.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0271589","usgsCitation":"Woltz, V., Peneva-Reed, E., Zhu, Z., Bullock, E.L., MacKenzie, R.A., Apwong, M., Krauss, K., and Gesch, D.B., 2022, A comprehensive assessment of mangrove species and carbon stock on Pohnpei, Micronesia: PLoS ONE, v. 17, no. 7, e0271589, 19 p.; Data Release, https://doi.org/10.1371/journal.pone.0271589.","productDescription":"e0271589, 19 p.; Data Release","ipdsId":"IP-120770","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":446921,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0271589","text":"Publisher Index 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zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":848253,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bullock, Eric L. 0000-0003-3279-6771","orcid":"https://orcid.org/0000-0003-3279-6771","contributorId":224710,"corporation":false,"usgs":false,"family":"Bullock","given":"Eric","email":"","middleInitial":"L.","affiliations":[{"id":40922,"text":"Department of Earth & Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":848254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"MacKenzie, Richard A.","contributorId":169073,"corporation":false,"usgs":false,"family":"MacKenzie","given":"Richard","email":"","middleInitial":"A.","affiliations":[{"id":25408,"text":"Institute of Pacific Islands Forestry, Pacific Southwest Research Station, Hilo, HI, USA","active":true,"usgs":false}],"preferred":false,"id":848255,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Apwong, Maybeleen","contributorId":251804,"corporation":false,"usgs":false,"family":"Apwong","given":"Maybeleen","email":"","affiliations":[{"id":25408,"text":"Institute of Pacific Islands Forestry, Pacific Southwest Research Station, Hilo, HI, USA","active":true,"usgs":false}],"preferred":true,"id":848256,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":223022,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":848257,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":848258,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70234221,"text":"sir20225030 - 2022 - Sediment and nutrient retention on a reconnected floodplain of an Upper Mississippi River tributary, 2013–2018","interactions":[],"lastModifiedDate":"2026-04-09T17:11:54.43681","indexId":"sir20225030","displayToPublicDate":"2022-08-04T07:15:52","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5030","displayTitle":"Sediment and Nutrient Retention on a Reconnected Floodplain of an Upper Mississippi River Tributary, 2013–2018","title":"Sediment and nutrient retention on a reconnected floodplain of an Upper Mississippi River tributary, 2013–2018","docAbstract":"The connection of rivers with their floodplains has been greatly reduced in agricultural drainage basins, especially in the Upper Mississippi River Basin. The restriction of the Mississippi River from its floodplain has reduced the sediment trapping and nutrient deposition capabilities of the floodplain, exacerbating water quality problems in the river and in downstream waterbodies. A small part of the Maquoketa River, a tributary to the Upper Mississippi River, was permanently reconnected to its floodplain in 2010 when a levee failure resulted in breaches in two locations. This study quantified the water quality benefits of that reconnection from October 2013 through September 2018. As part of the study, data from groundwater monitoring wells were used to determined hydraulic connectivity and surface-water/groundwater mixing; soil samples were collected in the floodplain to quantify floodplain sediment and nutrient retention potential during postflood and dry, interflood periods; and sensors were placed in the Maquoketa River to quantify total suspended solids, nitrogen, and phosphorus concentrations and loads.
The floodplain aquifer in the study area had low hydraulic gradients toward the Maquoketa (mean of 0.017) and Mississippi Rivers (mean of 0.0029) and reducing water-quality conditions (dissolved oxygen less than 1.0 milligram per liter [mg/L] and nitrate less than 0.04 mg/L as nitrogen) capable of denitrification. A specific conductance-based mixing indicated precipitation was the predominate source of groundwater; however, specific conductance-based mixing analysis was unable to distinguish between the river or direct precipitation as the source.
The floodplain was fully inundated five times during the study: in June–July 2014, March 2015, January 2017, February 2018, and September 2018. During the March 2015 flood (the only inundation event with sufficient duration to leave quantifiable sediment deposition in the study area), the equivalent of 0.91 percent of the nitrate load and 3.8 percent of the phosphorus load was deposited as sediment on the floodplain. Potential nitrogen losses on the floodplain because of denitrification ranged from 250 kilograms per day (kg/d) as nitrogen in March 2015 to 668 kg/d as nitrogen in October 2014. Potential denitrification rates indicate that when the soil is inundated, inorganic nitrogen present in the soil and in the water column is rapidly denitrified. Soil phosphorus measurements indicated that floodplain soils contain a mean of 365 milligrams per kilogram as phosphorus but still have the capacity to remove phosphorus from flood waters of the Maquoketa River depending on the surface water phosphorus concentration. Results from this study indicate that restoration of even small river-floodplain connections can improve water quality in the Upper Mississippi River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225030","collaboration":"Prepared in cooperation with the Eastern Tallgrass Prairie & Big Rivers Landscape Conservation Cooperative","usgsCitation":"Bartsch, L.A., Kreiling, R.M., Gruhn, L.R., Garrett, J.D., Richardson, W.B., and Nalley, G.M., 2022, Sediment and nutrient retention on a reconnected floodplain of an Upper Mississippi River Tributary, 2013–2018: U.S. Geological Survey Scientific Investigations Report 2022–5030, 27 p., https://doi.org/10.3133/sir20225030.","productDescription":"Report: viii, 27 p.; Data Releases; Datasets","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-121775","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":608,"text":"Upper Mississippi Science Center","active":false,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":404760,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A12PVX","text":"USGS data release","linkHelpText":"Maquoketa River floodplain-river connectivity 2014-2016 data"},{"id":404761,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":404762,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://www.umesc.usgs.gov/data_library/water_quality/water_quality_data_page.html","text":"USGS Long Term Resource Monitoring Program database","linkHelpText":"—Long Term Resource Monitoring Program—Water Quality"},{"id":404757,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5030/coverthb.jpg"},{"id":502387,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113356.htm","linkFileType":{"id":5,"text":"html"}},{"id":404758,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5030/sir20225030.pdf","text":"Report","size":"7.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5030"}],"country":"United States","state":"Iowa","otherGeospatial":"Maquoketa River, Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.35,\n 42.1333\n ],\n [\n -90.2667,\n 42.1333\n ],\n [\n -90.2667,\n 42.1833\n ],\n [\n -90.35,\n 42.1833\n ],\n [\n -90.35,\n 42.1333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602
Less than 30% of all species reintroductions have been successful and it is important that factors associated with success or failure be identified. Officials experimentally translocated 14 adult female American black bears (Ursus americanus) from Great Smoky Mountains National Park, North Carolina and Tennessee, USA, to Big South Fork National River and Recreation Area in the Cumberland Plateau of Kentucky and Tennessee, USA, in 1996–1997. Since that time, the reintroduced bear population has continued to expand in size and range so our study objective was to use spatially explicit capture-recapture methods across a wide spatial extent to estimate bear population abundance and growth. We constructed 440 (223 in KY, 217 in TN) hair traps in our primary sampling area in 2019 arranged in clusters of 4–9 traps/cluster, which we augmented with data from 138 hair traps in a secondary sampling area in Tennessee collected in 2018. We extracted and genotyped DNA from hair samples to construct spatially explicit capture histories, using spatial covariates to model inhomogeneous densities. Population abundance estimates across our 36,035-km2 study area were 411 males and 406 females excluding cubs. Based on an initial standing population of 18 adult and subadult bears, the mean annual growth rate (λ) from 1998 to 2019 was 1.199. The mean annual harvest rate in Kentucky from 2013 to 2019 was 5.1% and in Tennessee from 2014 to 2019 was 13.2%. Based on simulations, the hunting seasons reduced mean λ from 1.217 to 1.199, but growth was rapid despite harvest. Genetic diversity was retained, with similar expected heterozygosity as in the source population. The lack of conspecifics, highly productive habitat, and an initial age and sex distribution that was skewed toward the most fecund members of the population likely contributed to the rapid growth and high levels of gene retention in this bear population.
The U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC) routinely produces finite‐fault models following significant earthquakes. These models are spatiotemporal estimates of coseismic slip critical to constraining downstream response products such as ShakeMap ground motion estimates, Prompt Assessment of Global Earthquake for Response loss estimates, and ground failure assessments. Because large earthquakes can involve slip over tens to hundreds of kilometers, point‐source approximations are insufficient, and it is vital to rapidly assess the amount, timing, and location of slip along the fault. Initially, the USGS finite‐fault products were computed in the first several hours after a significant earthquake, using teleseismic body wave and surface wave observations. With only teleseismic waveforms, it is generally possible to obtain a reliable model for earthquakes of magnitude 7 and larger. Here, we detail newly implemented updates to NEIC’s modeling capabilities, specifically to allow joint modeling of local‐to‐regional strong‐motion accelerometer, Global Navigation Satellite System (GNSS), and Interferometric Synthetic Aperture Radar (InSAR) observations in addition to teleseismic waveforms. We present joint inversion results for the 2015
Across coastal areas of the northern Gulf of Mexico, the Deepwater Horizon oil spill resulted in significant ecological injury, and over 8 billion USD directed to restoration activities. Oyster restoration projects were implemented with regional goals of restoring oyster abundance, spawning stock, and population resilience. Measuring regional or large-scale ecosystem restoration outcomes challenges traditional project-specific monitoring and outcome reporting. We examine the outcomes of oyster restoration at the project-level and discuss potential pathways to measure progress toward region-level goals. An estimated 15 km2 of oyster habitat was restored across 11 different estuaries with 62 individual reef footprints created, ranging in size from ~0.2 to 1.45 km2. Individual sites were distributed across the salinity gradient, and all reefs were subtidal. One-year post-restoration, mean total oyster density across all sites was 53.0 ± 60.7 ind m−2 of which 38.4 ± 42.2 ind m−2 were adult (>25 mm shell height) oysters. Recent data (2018/2019) available for all sites indicates reduced densities of total oysters (44.6 ± 70.9 ind m−2) and adult oysters (14.6 ± 21.6 ind m−2). These data provide insight into project specific outcomes, suggesting an overall enhancement in oyster abundance compared to pre-restoration, but fall short of informing outcomes at the regional-level that incorporate cumulative effects on adjacent and connected reef populations, or inform overall resiliency of the regional oyster resource. Developing regional outcome benchmarks that enable assessment of cumulative and synergistic impacts of individual projects may benefit from broader spatial and temporal monitoring requirements that can better inform development of regional tools or models. Such tools would enable cumulative effects analyses examining net resource change, resilience and assess impacts of restoration activities on regional resource status.
","language":"English","publisher":"Springer","doi":"10.1007/s00267-022-01691-y","usgsCitation":"La Peyre, M., Marshall, D.A., Buie, S.C., Hijuelos, A., and Steyer, G., 2022, Are we falling short on restoring oysters at a regional scale?: Environmental Management, v. 70, p. 581-592, https://doi.org/10.1007/s00267-022-01691-y.","productDescription":"12 p.","startPage":"581","endPage":"592","ipdsId":"IP-138828","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433315,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.36958808470514,\n 24.1126519217827\n ],\n [\n -80.85940900302997,\n 24.714635678707992\n ],\n [\n -81.10116830711196,\n 25.46706673170469\n ],\n [\n -82.39537707405206,\n 27.091011945078975\n ],\n [\n -82.65786309241751,\n 28.308364041898628\n ],\n [\n -82.5772062119935,\n 28.999162563752748\n ],\n [\n -83.9904926193391,\n 30.19347675768212\n ],\n [\n -85.07008045187222,\n 29.677396598341033\n ],\n [\n -86.43938452544526,\n 30.518697342417497\n ],\n [\n -87.63633030899686,\n 30.340763113128546\n ],\n [\n -88.02876199353206,\n 30.727492702231586\n ],\n [\n -88.68357561069969,\n 30.436884709045927\n ],\n [\n -89.8845804986809,\n 30.130244087006545\n ],\n [\n -89.88022780179678,\n 29.65775338151731\n ],\n [\n -94.0525922925487,\n 29.622956144774975\n ],\n [\n -96.92033720263936,\n 28.066156015973654\n ],\n [\n -97.46297846111713,\n 26.978138281620108\n ],\n [\n -97.21131853158765,\n 25.94538903066173\n ],\n [\n -83.36958808470514,\n 24.1126519217827\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"70","noUsgsAuthors":false,"publicationDate":"2022-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marshall, Danielle Aguilar","contributorId":341509,"corporation":false,"usgs":false,"family":"Marshall","given":"Danielle","email":"","middleInitial":"Aguilar","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":908524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buie, Sarah Catherine Leblanc","contributorId":341510,"corporation":false,"usgs":false,"family":"Buie","given":"Sarah","email":"","middleInitial":"Catherine Leblanc","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":908525,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hijuelos, Ann","contributorId":341511,"corporation":false,"usgs":false,"family":"Hijuelos","given":"Ann","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":908526,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Steyer, Gregory 0000-0001-7231-0110","orcid":"https://orcid.org/0000-0001-7231-0110","contributorId":218813,"corporation":false,"usgs":true,"family":"Steyer","given":"Gregory","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":908527,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70236526,"text":"70236526 - 2022 - Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA","interactions":[],"lastModifiedDate":"2022-11-16T17:03:55.108551","indexId":"70236526","displayToPublicDate":"2022-08-03T07:21:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA","docAbstract":"Building continental-scale hydrologic models in data-sparse regions requires an understanding of spatial variation in hydrologic processes. Extending these models to ungaged locations requires techniques to group ungaged locations with gaged ones to make process importance and model parameter transfer decisions to ungaged locations. This analysis (1) tested the utility of fundamental streamflow statistics (FDSS) in defining hydrologic regions across Alaska, USA; (2) evaluated if the hydrologic regions represented different hydrologic processes; and (3) tested the ability of random forest and direct assignment techniques, informed by statistically estimated FDSS (FDSSest) and basin characteristics (BCs), to correctly assign ungaged locations to hydrologic regions. Six hydrologic regions were identified across the domain using FDSS. Differences in mean flow, phase shift of the seasonal cycle, and skewness were the primary characteristics defining each region. Two regions represented arctic and continental climates, generally in the northern portion of the domain; four regions represented the southern, maritime portion of the domain. Random forest modeling with BCs (67% success rate) outperformed FDSSest (58% success rate) suggesting that no statistically estimated streamflow was needed to assign ungaged locations to a region. For regions with many sites, most region assignment techniques performed similarly. Random forest modeling performance declined when BCs and FDSSest were both used to predict region membership, suggesting FDSSest had little information in addition to BCs. This analysis demonstrated that FDSS-based hydrologic regions discern process differences across a data-sparse and hydrologically diverse landscape. Process importance rankings from random forest-derived BCs provided model-independent information for making modeling decisions.
As sustainable development practitioners have worked to “ensure healthy lives and promote well-being for all” and “conserve life on land and below water”, what progress has been made with win–win interventions that reduce human infectious disease burdens while advancing conservation goals? Using a systematic literature review, we identified 46 proposed solutions, which we then investigated individually using targeted literature reviews. The proposed solutions addressed diverse conservation threats and human infectious diseases, and thus, the proposed interventions varied in scale, costs, and impacts. Some potential solutions had medium-quality to high-quality evidence for previous success in achieving proposed impacts in one or both sectors. However, there were notable evidence gaps within and among solutions, highlighting opportunities for further research and adaptive implementation. Stakeholders seeking win–win interventions can explore this Review and an online database to find and tailor a relevant solution or brainstorm new solutions.
We investigate the shallow plumbing system of the Deccan Traps Large Igneous Province using rock and mineral data from Giant Plagioclase Basalt (GPB) lava flows from around the entire province, but with a focus on the Saurashtra Peninsula, the Malwa Plateau, and the base and top of the Western Ghats (WG) lava pile. GPB lavas in the WG typically occur at the transition between chemically distinct basalt formations. Most GPB samples are evolved basalts, with high Fe and Ti contents, and show major and trace elements and Sr-Nd-Pb isotopic compositions generally similar to those of previously studied Deccan basalts. Major element modeling suggests that high-Fe, evolved melts typical of GPB basalts may derive from less evolved Deccan basalts by low-pressure fractional crystallization in a generally dry magmatic plumbing system. The basalts are strongly porphyritic, with 6–25% of mm- to cm-sized plagioclase megacrysts, frequently occurring as crystal clots, plus relatively rare olivine and clinopyroxene. The plagioclase crystals are mostly labradoritic, but some show bytownitic cores (general range of anorthite mol%: 78–55). A common feature is a strong Fe enrichment at the plagioclase rims, indicating interaction with an Fe-rich melt similar to that represented by the matrix compositions (FeOt up to 16–17 wt%). Plagioclase minor and trace elements and Sr isotopic compositions analyzed by laser ablation inductively coupled plasma mass spectrometry show evidence of a hybrid and magma mixing origin. In particular, several plagioclase crystals show variable 87Sr/86Sri, which only partially overlaps with the 87Sr/86Sri of the surrounding matrix. Diffusion modeling suggests residence times of decades to centuries for most plagioclase megacrysts. Notably, some plagioclase crystal clots show textural evidence of deformation as recorded by electron back-scatter diffraction analyses and chemical maps, which suggest that the plagioclase megacrysts were deformed in a crystal-rich environment in the presence of melt. We interpret the plagioclase megacrysts as remnants of a crystal mush originally formed in the shallow plumbing system of the Deccan basalts. In this environment, plagioclase acquired a zoned composition due to the arrival of chemically distinct basaltic magmas. Prior to eruption, a rapidly rising but dense Fe-rich magma was capable of disrupting the shallow level crystal mush, remobilizing part of it and carrying a cargo of buoyant plagioclase megacrysts. Our findings suggest that basaltic magmas from the Deccan Traps, and possibly from LIPs in general, are produced within complex transcrustal magmatic plumbing systems with widespread crystal mushes developed in the shallow crust.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/petrology/egac075","usgsCitation":"Marzoli, A., Renne, P.R., Andreasen, R., Spiess, R., Chiaradia, M., Ruth, D.C., Tholt, A., Pande, K., and Costa, F.J., 2022, The Shallow Magmatic Plumbing System of the Deccan Traps, Evidence from Plagioclase Megacrysts and Their Host Lavas: Journal of Petrology, v. 63, no. 9, egac075, https://doi.org/10.1093/petrology/egac075.","productDescription":"egac075","ipdsId":"IP-137938","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":489006,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pure.au.dk/portal/en/publications/28790e6d-08a1-4f02-b893-195df971663e","text":"External Repository"},{"id":463238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"63","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Marzoli, A.","contributorId":167328,"corporation":false,"usgs":false,"family":"Marzoli","given":"A.","email":"","affiliations":[{"id":24687,"text":"Universitá Degli Studi di Padova, Padova, Italy","active":true,"usgs":false}],"preferred":false,"id":916902,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Renne, Paul R. 0000-0003-1769-5235","orcid":"https://orcid.org/0000-0003-1769-5235","contributorId":229577,"corporation":false,"usgs":false,"family":"Renne","given":"Paul","email":"","middleInitial":"R.","affiliations":[{"id":37390,"text":"Department of Earth and Planetary Science, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":916903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andreasen, R","contributorId":345557,"corporation":false,"usgs":false,"family":"Andreasen","given":"R","email":"","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":916904,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spiess, R","contributorId":345558,"corporation":false,"usgs":false,"family":"Spiess","given":"R","email":"","affiliations":[{"id":82629,"text":"Universita di Padova","active":true,"usgs":false}],"preferred":false,"id":916905,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chiaradia, M","contributorId":345561,"corporation":false,"usgs":false,"family":"Chiaradia","given":"M","email":"","affiliations":[{"id":82630,"text":"Universite de Geneve","active":true,"usgs":false}],"preferred":false,"id":916906,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ruth, Dawn Catherine Sweeney 0000-0001-9369-9364","orcid":"https://orcid.org/0000-0001-9369-9364","contributorId":334908,"corporation":false,"usgs":true,"family":"Ruth","given":"Dawn","email":"","middleInitial":"Catherine Sweeney","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":916907,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tholt, A.J.","contributorId":345562,"corporation":false,"usgs":false,"family":"Tholt","given":"A.J.","email":"","affiliations":[{"id":38176,"text":"Berkeley Geochronology Center","active":true,"usgs":false}],"preferred":false,"id":916908,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pande, K","contributorId":345563,"corporation":false,"usgs":false,"family":"Pande","given":"K","email":"","affiliations":[{"id":7210,"text":"Indian Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916909,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Costa, Fabio J. V.","contributorId":289278,"corporation":false,"usgs":false,"family":"Costa","given":"Fabio","email":"","middleInitial":"J. V.","affiliations":[{"id":62093,"text":"Policia Federal","active":true,"usgs":false}],"preferred":false,"id":916910,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70263067,"text":"70263067 - 2022 - Juvenile salmon habitat use drives variation in growth and highlights vulnerability to river fragmentation","interactions":[],"lastModifiedDate":"2025-01-29T15:37:00.8291","indexId":"70263067","displayToPublicDate":"2022-08-03T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Juvenile salmon habitat use drives variation in growth and highlights vulnerability to river fragmentation","docAbstract":"Widespread stream network fragmentation from dams and culverts has altered habitat connectivity in river ecosystems and presents an acute threat to migratory fish. To support watershed management for an iconic migratory fish group, we assessed juvenile salmon growth outcomes across habitat use strategies and characterized how these life histories may be impacted by stream connectivity loss. Juvenile coho salmon (Oncorhynchus kisutch) in the Big Lake drainage, Alaska, USA, were individually tracked over 2012–2013 and categorized into habitat use behaviors, with fish either remaining in streams throughout freshwater residency or migrating seasonally to overwinter in lake habitats. Size, growth rate, and body condition of smolts (n = 1113) were compared across habitat use strategies. Juvenile coho salmon that moved seasonally to lake overwintering habitats, the most frequently observed strategy, grew faster and were significantly larger as smolts compared to their counterparts who remained in streams exclusively (spring Age 1 fish: 18% larger by weight, 9% faster growth rate; spring Age 2+ fish: 26% heavier, 11% faster growth). Environmental data from a subset of overwinter lakes indicate that greater foraging opportunity and lower energy costs may be implicated in growth advantages conferred by lentic overwintering strategies. Habitat use strategies requiring seasonal migrations, however, increased exposure to stream connectivity loss, and fish blocked from accessing a potential overwinter headwater lake by a culvert and dam had lowest body condition among study groups. Stream network fragmentation restricts access to preferred overwinter habitats, and our findings suggest this may constrain freshwater rearing strategies associated with strong juvenile coho salmon growth. As size at smolt has been implicated as a driver of salmon survival through ocean residency, reduced freshwater habitat connectivity during juvenile stages may have deleterious impacts on later marine life stages. Consequently, conservation of stream connectivity across lentic and lotic habitats represents an important watershed management priority for juvenile salmon.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4192","usgsCitation":"Sethi, S., Carey, M.P., Gerken, J., Harris, B., Wolf, N., Cunningham, C., Restrepo, F., and Ashline, J., 2022, Juvenile salmon habitat use drives variation in growth and highlights vulnerability to river fragmentation: Ecosphere, v. 13, no. 8, e4192, 14 p., https://doi.org/10.1002/ecs2.4192.","productDescription":"e4192, 14 p.","ipdsId":"IP-132334","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":489905,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4192","text":"Publisher Index Page"},{"id":481452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Big Lake watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -149.98177488959556,\n 61.59244437152677\n ],\n [\n -149.98177488959556,\n 61.46764452271171\n ],\n [\n -149.59522554516565,\n 61.46764452271171\n ],\n [\n -149.59522554516565,\n 61.59244437152677\n ],\n [\n -149.98177488959556,\n 61.59244437152677\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":925435,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carey, Michael P. 0000-0002-3327-8995 mcarey@usgs.gov","orcid":"https://orcid.org/0000-0002-3327-8995","contributorId":5397,"corporation":false,"usgs":true,"family":"Carey","given":"Michael","email":"mcarey@usgs.gov","middleInitial":"P.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":925436,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gerken, Jonathon","contributorId":350130,"corporation":false,"usgs":false,"family":"Gerken","given":"Jonathon","affiliations":[{"id":12428,"text":"U. 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Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":925437,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harris, Bradley P.","contributorId":350131,"corporation":false,"usgs":false,"family":"Harris","given":"Bradley P.","affiliations":[{"id":12915,"text":"Alaska Pacific University","active":true,"usgs":false}],"preferred":false,"id":925438,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wolf, Nathan","contributorId":350132,"corporation":false,"usgs":false,"family":"Wolf","given":"Nathan","affiliations":[{"id":12915,"text":"Alaska Pacific University","active":true,"usgs":false}],"preferred":false,"id":925439,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cunningham, Curry","contributorId":350133,"corporation":false,"usgs":false,"family":"Cunningham","given":"Curry","affiliations":[{"id":12915,"text":"Alaska Pacific University","active":true,"usgs":false}],"preferred":false,"id":925440,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Restrepo, Felipe","contributorId":350134,"corporation":false,"usgs":false,"family":"Restrepo","given":"Felipe","affiliations":[{"id":12915,"text":"Alaska Pacific University","active":true,"usgs":false}],"preferred":false,"id":925441,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ashline, Josh","contributorId":350135,"corporation":false,"usgs":false,"family":"Ashline","given":"Josh","affiliations":[{"id":12915,"text":"Alaska Pacific University","active":true,"usgs":false}],"preferred":false,"id":925442,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70234212,"text":"70234212 - 2022 - Sclerochronological records of environmental variability and bivalve growth in the Pacific Arctic","interactions":[],"lastModifiedDate":"2022-08-15T14:03:40.905011","indexId":"70234212","displayToPublicDate":"2022-08-02T06:54:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3194,"text":"Progress in Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Sclerochronological records of environmental variability and bivalve growth in the Pacific Arctic","docAbstract":"The Pacific Arctic region has experienced, and is projected to continue experiencing, rapid climate change. Large uncertainties exist in our understanding of the impact these physical changes have on the region’s ecology. This is, in part, due to the lack of long-term data. Here we investigate bivalve mollusc growth increment width chronologies (sclerochronologies) to develop a long-term biological data series in an Arctic species and address the hypothesis that benthic production in the Pacific Arctic region is in decline with implications for predators (e.g., walrus, whales, seals, and sea ducks). Growth increments formed in the shells of two bivalve mollusc species, Astarte borealis and Liocyma fluctuosa, were examined using conventional sclerochronological techniques. The A. borealis and L. fluctuosa samples exhibited measured longevities of >148 and >18 years, respectively, in the coastal waters of Alaska’s Chukchi Sea. Dendrochronology crossdating techniques facilitated the development of two robust (expressed population signal >0.85) independent growth increment width chronologies. These chronologies provide evidence of the growth conditions through time for each species (1985-2015 for A. borealis and 1997-2014 for L. fluctuosa). Linear regression analyses identified that both species grew more rapidly in years with warmer sea surface temperature and lower sea ice concentration. The results provide evidence that benthic ecosystems are benefiting from the warmer conditions and reduced sea ice that have accompanied recent Arctic climate trends. This result is encouraging for benthic predators in the eastern Chukchi Sea as it alleviates the concern that their benthic prey has already become food limited by weakened pelagic-benthic coupling. More broadly, this initial A. borealis chronology is among the longest biological data series for any Arctic species and highlights the feasibility of multicentennial biological data for the Arctic.
This paper proposes a graph-based meta learning approach to separately predict water quantity and quality variables for river segments in stream networks. Given the heterogeneous water dynamic patterns in large-scale basins, we introduce an additional meta-learning condition based on physical characteristics of stream segments, which allows learning different sets of initial parameters for different stream segments. Specifically, we develop a representation learning method that leverages physical simulations to embed the physical characteristics of each segment. The obtained embeddings are then used to cluster river segments and add the condition for the meta-learning process. We have tested the performance of the proposed method for predicting daily water temperature and streamflow for the Delaware River Basin (DRB) over a 14 year period. The results confirm the effectiveness of our method in predicting target variables even using sparse training samples. We also show that our method can achieve robust performance with different numbers of clusterings.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","conferenceDate":"August 14-18, 2022","conferenceLocation":"Washington DC","language":"English","publisher":"ACM Digital Library","doi":"10.1145/3534678.3539115","usgsCitation":"Chen, S., Zwart, J.A., and Jia, X., 2022, Physics-guided graph meta learning for predicting water temperature and streamflow in stream networks, in Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, Washington DC, August 14-18, 2022, p. 2752-2761, https://doi.org/10.1145/3534678.3539115.","productDescription":"10 p.","startPage":"2752","endPage":"2761","ipdsId":"IP-138051","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":420911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Chen, Shengyu","contributorId":297452,"corporation":false,"usgs":false,"family":"Chen","given":"Shengyu","email":"","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":883313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":883314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":883315,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237209,"text":"70237209 - 2022 - Chapter 1: General conceptual model for climate change in the Upper San Francisco Estuary","interactions":[],"lastModifiedDate":"2022-10-05T20:04:24.970765","indexId":"70237209","displayToPublicDate":"2022-08-01T11:35:25","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":12617,"text":"IEP Technical Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"99","chapter":"1","title":"Chapter 1: General conceptual model for climate change in the Upper San Francisco Estuary","docAbstract":"This report is a collaboration by many state and federal agencies working in the Upper San Francisco Estuary to analyze the potential impacts of climate change to different ecosystems found here. Management stategies for ecological values in the face of climate change require reliable and focused information. In this technical report, our focus is on the Upper San Francisco Estuary (SFE), which contains the Sacramento-San Joaquin Delta and Suisun Bay. This area is home to three interconnected ecosystems: open water, floodplain, and tidal marsh. For this geographical area, we have decades of in-depth monitoring information and scientific investigations that have been successfully used to address a number of management needs. In 2019, the Interagency Ecological Program established a diverse work team to improve our ability to anticipate and respond to climate change impacts. The charge to the group was to:
• synthesize science relevant to climate change,
• determine important knowledge gaps, and
• identify ecosystem metrics for climate change.
We focus our analyses on the likely impacts of climate change on interconnected aquatic habitats. We illustrate how changes in habitats are likely to affect diverse species.
In this report we describe ecological trends attributable to climate change and likely future impacts. We address four principal questions:
1. How have the habitats and biotic communities changed due to climatic trends and events?
2. How are estuarine habitats, flora, and fauna likely to change as climate change trends continue?
3. What are key metrics to document ecosystem change as a result of climate change?
4. How should our monitoring change to improve information value?
Our work builds on the similar work of the San Francisco Baylands Goals Project (Goals Project 2015), which addressed climate change impacts to wetlands downstream of the confluence of the Sacramento and San Joaquin Rivers. We aim to contribute to an integrated baseline understanding of climate change impacts for the entire San Francisco Estuary.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Synthesis of data and studies related to the effect of climate change on the ecosystems and biota of the Upper San Francisco Estuary Year 2022","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"Interagency Ecological Program","usgsCitation":"Bush, E., Herbold, B., and Brown, L.R., 2022, Chapter 1: General conceptual model for climate change in the Upper San Francisco Estuary: IEP Technical Report 99, 63 p.","productDescription":"63 p.","startPage":"8","endPage":"70","ipdsId":"IP-133000","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":407970,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":407858,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://iep.ca.gov/Publications/Library"}],"country":"United States","state":"California","otherGeospatial":"Upper San Francisco Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.54699707031249,\n 37.896530447543\n ],\n [\n -121.30004882812499,\n 37.896530447543\n ],\n [\n -121.30004882812499,\n 38.46219172306828\n ],\n [\n -122.54699707031249,\n 38.46219172306828\n ],\n [\n -122.54699707031249,\n 37.896530447543\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Bashevkin, Samuel M.","contributorId":267859,"corporation":false,"usgs":false,"family":"Bashevkin","given":"Samuel","email":"","middleInitial":"M.","affiliations":[{"id":24727,"text":"Delta Stewardship Council","active":true,"usgs":false}],"preferred":false,"id":853906,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853917,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Bush, Eva","contributorId":297190,"corporation":false,"usgs":false,"family":"Bush","given":"Eva","email":"","affiliations":[{"id":64315,"text":"Delta Stewardship Council Delta Science Program","active":true,"usgs":false}],"preferred":false,"id":853918,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Castillo, Gonzalo","contributorId":269408,"corporation":false,"usgs":false,"family":"Castillo","given":"Gonzalo","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":853907,"contributorType":{"id":2,"text":"Editors"},"rank":4},{"text":"Colombano, Denise","contributorId":297365,"corporation":false,"usgs":false,"family":"Colombano","given":"Denise","affiliations":[],"preferred":false,"id":853908,"contributorType":{"id":2,"text":"Editors"},"rank":5},{"text":"Hartman, Rosemary","contributorId":200388,"corporation":false,"usgs":false,"family":"Hartman","given":"Rosemary","email":"","affiliations":[],"preferred":false,"id":853909,"contributorType":{"id":2,"text":"Editors"},"rank":6},{"text":"Herbold, Bruce","contributorId":51223,"corporation":false,"usgs":false,"family":"Herbold","given":"Bruce","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":853919,"contributorType":{"id":2,"text":"Editors"},"rank":7},{"text":"Khanna, Shruti","contributorId":205167,"corporation":false,"usgs":false,"family":"Khanna","given":"Shruti","email":"","affiliations":[{"id":37041,"text":"Department of Land, Air, and Water Resources, University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":853636,"contributorType":{"id":2,"text":"Editors"},"rank":8},{"text":"Keeley, Annika","contributorId":297191,"corporation":false,"usgs":false,"family":"Keeley","given":"Annika","email":"","affiliations":[{"id":64315,"text":"Delta Stewardship Council Delta Science Program","active":true,"usgs":false}],"preferred":false,"id":853635,"contributorType":{"id":2,"text":"Editors"},"rank":9},{"text":"Kwan, Nicole","contributorId":297192,"corporation":false,"usgs":false,"family":"Kwan","given":"Nicole","email":"","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":853637,"contributorType":{"id":2,"text":"Editors"},"rank":10},{"text":"Lehman, Peggy W.","contributorId":96168,"corporation":false,"usgs":false,"family":"Lehman","given":"Peggy","email":"","middleInitial":"W.","affiliations":[{"id":7101,"text":"California Department of Water Resources, Geodetic Branch","active":true,"usgs":false}],"preferred":false,"id":853910,"contributorType":{"id":2,"text":"Editors"},"rank":13},{"text":"Mahardja, Brian","contributorId":174645,"corporation":false,"usgs":false,"family":"Mahardja","given":"Brian","email":"","affiliations":[{"id":13461,"text":"U.C. Davis","active":true,"usgs":false}],"preferred":false,"id":853911,"contributorType":{"id":2,"text":"Editors"},"rank":14},{"text":"Matthias, Bryan G.","contributorId":240763,"corporation":false,"usgs":false,"family":"Matthias","given":"Bryan","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":853638,"contributorType":{"id":2,"text":"Editors"},"rank":15},{"text":"Pien, Catarina","contributorId":297193,"corporation":false,"usgs":false,"family":"Pien","given":"Catarina","email":"","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":853639,"contributorType":{"id":2,"text":"Editors"},"rank":16},{"text":"Wulff, Marissa L. 0000-0003-0121-9066","orcid":"https://orcid.org/0000-0003-0121-9066","contributorId":229534,"corporation":false,"usgs":true,"family":"Wulff","given":"Marissa","email":"","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853640,"contributorType":{"id":2,"text":"Editors"},"rank":17},{"text":"Malinich, Timothy D.","contributorId":7583,"corporation":false,"usgs":true,"family":"Malinich","given":"Timothy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":853912,"contributorType":{"id":2,"text":"Editors"},"rank":17},{"text":"McKenzie, Ryan","contributorId":297366,"corporation":false,"usgs":false,"family":"McKenzie","given":"Ryan","email":"","affiliations":[],"preferred":false,"id":853913,"contributorType":{"id":2,"text":"Editors"},"rank":17}],"authors":[{"text":"Bush, Eva","contributorId":297190,"corporation":false,"usgs":false,"family":"Bush","given":"Eva","email":"","affiliations":[{"id":64315,"text":"Delta Stewardship Council Delta Science Program","active":true,"usgs":false}],"preferred":false,"id":853632,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herbold, Bruce","contributorId":51223,"corporation":false,"usgs":false,"family":"Herbold","given":"Bruce","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":853633,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853634,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237658,"text":"70237658 - 2022 - Examining industry vulnerability: A focus on mineral commodities used in the automotive and electronics industries","interactions":[],"lastModifiedDate":"2022-10-18T15:51:22.56241","indexId":"70237658","displayToPublicDate":"2022-08-01T10:45:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3266,"text":"Resources Policy","active":true,"publicationSubtype":{"id":10}},"title":"Examining industry vulnerability: A focus on mineral commodities used in the automotive and electronics industries","docAbstract":"Automotive manufacturing is material-intensive and dependent on a broad range of mineral commodities. Moreover, the automotive manufacturing industries are reliant on complex and sometimes opaque multi-tiered global supply chains. Among the many industries on which automotive supply chains depend are the electronics and semiconductor industries, which are themselves material-intensive and reliant on opaque global supply chains. A linear programming model built on mineral end-use data and input-output tables provides a tool for investigating inter-industry relationships between the two sets of industry sectors and industrial vulnerability to mineral commodity supply disruptions. Supply disruptions in aluminum, magnesium metal, and zinc—metals used in the body-in-white, wheels and other parts—have significant potential to disrupt the automotive industries. On the other hand, supply disruptions in gallium, tellurium, and indium for example—semiconductor elements used in power electronics, screen coatings and other parts—have significant potential to impact the electronics and computer industries. More interestingly, case studies of the automotive and electronics industries show how supply disruptions in mineral commodities that are generally considered semiconductor materials, such as gallium, can significantly impact the automotive sector.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.resourpol.2022.102894","usgsCitation":"Manley, R., Alonso, E., and Nassar, N.T., 2022, Examining industry vulnerability: A focus on mineral commodities used in the automotive and electronics industries: Resources Policy, v. 78, 102894, 8 p., https://doi.org/10.1016/j.resourpol.2022.102894.","productDescription":"102894, 8 p.","ipdsId":"IP-135107","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":487793,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.resourpol.2022.102894","text":"Publisher Index Page"},{"id":408492,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"78","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Manley, Ross 0000-0002-3341-4766","orcid":"https://orcid.org/0000-0002-3341-4766","contributorId":223012,"corporation":false,"usgs":true,"family":"Manley","given":"Ross","email":"","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854894,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alonso, Elisa 0000-0002-0090-8284","orcid":"https://orcid.org/0000-0002-0090-8284","contributorId":223015,"corporation":false,"usgs":true,"family":"Alonso","given":"Elisa","email":"","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854895,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nassar, Nedal T. 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":197864,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal","email":"nnassar@usgs.gov","middleInitial":"T.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854896,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70234118,"text":"70234118 - 2022 - Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs","interactions":[],"lastModifiedDate":"2022-08-01T14:36:06.939046","indexId":"70234118","displayToPublicDate":"2022-08-01T09:25:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1333,"text":"Continental Shelf Research","active":true,"publicationSubtype":{"id":10}},"title":"Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs","docAbstract":"Structure from motion (SfM) photogrammetry is an increasingly common technique for measuring landscape change over time by deriving 3D point clouds and surface models from overlapping photographs. Traditional change detection approaches require photos that are geotagged with a differential GPS (DGPS) location, which requires expensive equipment that can limit the ability of communities and researchers to perform frequent (i.e. daily, weekly, and/or monthly) surveys. Crowd-sourced photos can lower the barrier to entry and substantially increase the frequency of surveys, although such photos often lack accurate location information and can vary in quality. This paper presents a SfM approach for monitoring environmental change in high relief coastal environments that does not require all photos have DGPS location information and does not require field survey data. A 1.5 km section of coastal bluffs near the Elwha River Delta (Washington state) is used to demonstrate the efficacy of this approach. Photos of the bluff were collected with a digital SLR camera or phone camera while either on foot along the beach or from a boat as part of monitoring following removal of two large dams along the Elwha River during 2011–2013. Only 33% of photos had DGPS location information, whereas most photos had no location information or locations that were accurate to a couple of meters. All photos were processed using 3D, 4D, and fixed-floating (FF) SfM alignment methods and the resulting dense point clouds are used to compare the different alignment approaches with crowd-sourced photo sets. Results demonstrate that 4D and FF approaches are more likely to reconstruct and are more accurate than the 3D approach. While the 4D and FF have comparable accuracies, the FF approach is several orders of magnitude more efficient, as this method can leverage camera location information from relatively few photos to improve the accuracy of all aligned and derived products. Effectively utilizing crowd-sourced photos in SfM change detection can improve the frequency of surveying a landscape in a more cost-effective approach that also has potential for citizen-science engagement and communication. This is especially important for data-poor environments such as high-relief coastal cliffs and bluffs, where near-nadir imagery and LIDAR may fail to accurately capture near-vertical cliffs or bluff faces. Based on the analysis of different photo alignment and filtering approaches, we present suggested best practices for engaging citizen scientists in coastal cliff and bluff monitoring efforts through collecting photos amenable for SfM reconstruction.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.csr.2022.104799","usgsCitation":"Wernette, P., Miller, I.M., Ritchie, A.C., and Warrick, J.A., 2022, Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs: Continental Shelf Research, v. 245, 104799, 12 p., https://doi.org/10.1016/j.csr.2022.104799.","productDescription":"104799, 12 p.","ipdsId":"IP-129225","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":446968,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.5061/dryad.63xsj3v4s","text":"External Repository"},{"id":404570,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Elwha River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.54572296142578,\n 48.146846885734256\n ],\n [\n -123.52460861206055,\n 48.12805945422104\n ],\n [\n -123.51877212524414,\n 48.13413175409871\n ],\n [\n -123.52632522583006,\n 48.13963057588326\n ],\n [\n -123.53284835815428,\n 48.146846885734256\n ],\n [\n -123.54537963867186,\n 48.150970035875766\n ],\n [\n -123.54572296142578,\n 48.146846885734256\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"245","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wernette, Phillipe Alan 0000-0002-8902-5575","orcid":"https://orcid.org/0000-0002-8902-5575","contributorId":259274,"corporation":false,"usgs":true,"family":"Wernette","given":"Phillipe Alan","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":847869,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Ian M. 0000-0002-3289-6337","orcid":"https://orcid.org/0000-0002-3289-6337","contributorId":41951,"corporation":false,"usgs":false,"family":"Miller","given":"Ian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":847870,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ritchie, Andrew C. aritchie@usgs.gov","contributorId":4984,"corporation":false,"usgs":true,"family":"Ritchie","given":"Andrew","email":"aritchie@usgs.gov","middleInitial":"C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":847871,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":847872,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}