Pinyon–juniper (PJ) woodlands are an important component of dryland ecosystems across the US West and are potentially susceptible to ecological transformation. However, predicting woodland futures is complicated by species-specific strategies for persisting and reproducing under drought conditions, uncertainty in future climate, and limitations to inferring demographic rates from forest inventory data. Here, we leverage new demographic models to quantify how climate change is expected to alter population demographics in five PJ tree species in the US West and place our results in the context of a climate adaptation framework to resist, accept, or direct ecological transformation. Two of five study species, Pinus edulis and Juniperus monosperma, are projected to experience population declines, driven by both rising mortality and decreasing recruitment rates. These declines are reasonably consistent across various climate futures, and the magnitude of uncertainty in population growth due to future climate is less than uncertainty due to how demographic rates will respond to changing climate. We assess the effectiveness of management to reduce tree density and mitigate competition, and use the results to classify southwest woodlands into areas where transformation is (a) unlikely and can be passively resisted, (b) likely but may be resisted by active management, and (c) likely unavoidable, requiring managers to accept or direct the trajectory. Population declines are projected to promote ecological transformation in the warmer and drier PJ communities of the southwest, encompassing 37.1%–81.1% of our sites, depending on future climate scenarios. Less than 20% of sites expected to transform away from PJ have potential to retain existing tree composition by density reduction. Our results inform where this adaptation strategy could successfully resist ecological transformation in coming decades and allow for a portfolio design approach across the geographic range of PJ woodlands.
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\"}}]}","volume":"29","issue":"15","noUsgsAuthors":false,"publicationDate":"2023-05-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Noel, Adam Roy 0000-0002-0891-4005","orcid":"https://orcid.org/0000-0002-0891-4005","contributorId":294761,"corporation":false,"usgs":true,"family":"Noel","given":"Adam","email":"","middleInitial":"Roy","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":906155,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shriver, Robert K.","contributorId":297511,"corporation":false,"usgs":false,"family":"Shriver","given":"Robert K.","affiliations":[{"id":64419,"text":"Department of Natural Resources and Environmental Science, University of Nevada, Reno; Ecology, Evolution, and Conservation Biology Graduate Program, University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":906156,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crausbay, Shelley D.","contributorId":197220,"corporation":false,"usgs":false,"family":"Crausbay","given":"Shelley","email":"","middleInitial":"D.","affiliations":[{"id":54831,"text":"Conservation Science Partners, Inc","active":true,"usgs":false}],"preferred":false,"id":906157,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":906158,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70244024,"text":"70244024 - 2023 - Integrated water resources trend assessments: State of the science, challenges, and opportunities for advancement","interactions":[],"lastModifiedDate":"2023-12-20T17:45:16.768153","indexId":"70244024","displayToPublicDate":"2023-05-29T08:15:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10956,"text":"Journal of the American Water Resource Association (JAWRA)","active":true,"publicationSubtype":{"id":10}},"title":"Integrated water resources trend assessments: State of the science, challenges, and opportunities for advancement","docAbstract":"Water is vital to human life and healthy ecosystems. Here we outline the current state of national-scale water resources trend assessments, identify key gaps, and suggest advancements to better address critical issues related to changes in water resources that may threaten human development or the environment. Questions like, “Do we have less suitable drinking water now than we had 20 years ago?” or “Are flood events more common now than they were in the past?” prompted improvements in data, trend estimation methods, and modeling frameworks to track changes in, and better understand how land use and climate influence four water resources domains: surface and groundwater quantity and quality. However, continued advancement in trend assessments to better address issues related to changes in water availability is needed. Areas of need include more timely and efficient delivery of water resources trend results and improved capacity to estimate trends at unmonitored locations. Additional integration pieces include increased understanding of groundwater–surface water interactions, incorporation of both quantity and quality trends into water availability estimates, and the refinement of trend metrics to account for the competing needs of society and ecological integrity. Coupled with improved driver attribution studies, these components will better inform current and future water resources management.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.13137","usgsCitation":"Stackpoole, S.M., Oelsner, G.P., Stets, E.G., Hecht, J.S., Johnson, Z., Tesoriero, A.J., Walvoord, M.A., Chanat, J.G., Dunne, K., Goodling, P.J., Lindsey, B.D., Meador, M.R., and Spaulding, S., 2023, Integrated water resources trend assessments: State of the science, challenges, and opportunities for advancement: Journal of the American Water Resource Association (JAWRA), v. 59, no. 6, p. 1181-1197, https://doi.org/10.1111/1752-1688.13137.","productDescription":"17 p.","startPage":"1181","endPage":"1197","ipdsId":"IP-145969","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":443310,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.13137","text":"Publisher Index Page"},{"id":417572,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-05-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Stackpoole, Sarah M. 0000-0002-5876-4922 sstackpoole@usgs.gov","orcid":"https://orcid.org/0000-0002-5876-4922","contributorId":3784,"corporation":false,"usgs":true,"family":"Stackpoole","given":"Sarah","email":"sstackpoole@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - 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2023 - Landscape-scale drivers of tayra abundance in the Ecuadorian Andes","interactions":[],"lastModifiedDate":"2024-08-16T11:15:56.850755","indexId":"70256557","displayToPublicDate":"2023-05-29T06:13:32","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1006,"text":"Biodiversity and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Landscape-scale drivers of tayra abundance in the Ecuadorian Andes","docAbstract":"Habitat conversion to agriculture and overexploitation of wildlife are the two largest drivers of biodiversity loss globally. Biodiversity loss is especially prevalent in areas undergoing rapid economic development at the expense of natural land cover as is the case across much of South America. Despite expected declines in wildlife populations associated with ongoing large-scale land conversions, for many species we lack sufficient data on key threats and drivers of abundance in order to inform appropriate management and conservation. Collecting data to estimate critical state variables such as abundance can be expensive, logistically challenging, and even implausible on spatial scales relevant to species management, especially for carnivores which are elusive and difficult to monitor. Here, we use detection-non-detection data collected using a structured camera trap survey repeated over two years in the Ecuadorian Andes to produce insights into the habitat associations and potential threats faced by the tayra, a medium-sized carnivore that despite being perceived to be relatively common in South America, remains largely understudied. We use hierarchical modelling to estimate an index of abundance for the tayra while accounting for imperfect detection. We demonstrate the tayra to be a lowland habitat generalist, with conversion of land to agriculture potentially benefitting this species in the short term, with increasing proportion of core agricultural land being associated with higher indices of abundance. However, we highlight that this state could potentially serve as an ecological trap in the long term. We provide evidence for negative impacts of human population density on tayra abundance. We hypothesize this relationship could be underpinned by conflict and subsequent persecution by humans, which is likely to be exacerbated in agricultural landscapes. These findings suggest that like many carnivores, the tayra may be able to adapt and even thrive in human modified landscapes given societal acceptance, thus, conservation strategies for the tayra that focus on fostering co-existence between humans and this medium-sized carnivore may contribute to its future.
Alluvial plain landscapes are some of the most agriculturally productive lands in the world but often have modified stream ecosystems due to cultivation history. This context requires consideration when establishing water quality management goals. We analyzed state water quality databases to demonstrate that Mississippi Alluvial Plain (MAP) ecoregion streams have elevated specific conductivity (SC) and nutrients and lower macroinvertebrate local and regional taxa pools compared to streams in other ecoregions, potentially reducing the efficacy of traditional biomonitoring approaches within the region. To overcome these challenges, we used threshold indicator taxa analysis (TITAN) to compare macroinvertebrate assemblage responses to water quality gradients among ecoregions in Mississippi. We identified individual taxa and assemblage-level responses to increasing water quality degradation in MAP streams. Observed responses occurred at higher concentrations for SC, total organic carbon (TOC) and total phosphorus (TP), but not total nitrogen (TN) relative to other ecoregions. These responses appeared to be driven by a large proportion of indicator taxa considered tolerant or unresponsive in other ecoregions, responding negatively to increasing water quality stressors in MAP streams. Our observed assemblage-level stressor responses to WQ gradients in MAP streams demonstrate shifting tolerance in highly altered ecosystems may require adjustments to recovery expectations but also provide useful measures for monitoring improvements in regional water quality. For example, our observed macroinvertebrate assemblage response to increasing TP identified a management goal similar to guidance based on distributional analysis of water quality data within the MAP ecoregion (0.11 vs 0.128 mg L−1) and thus provide some biological basis for previously identified nutrient goals for the region. Our approach can guide and monitor success of nutrient reduction efforts in MAP watersheds and other alluvial plain agroecosystems where reference conditions do not exist, and local and regional taxa pools are less diverse and may not support full recovery of ecological assemblages. While our results are promising, they should also be compared with more sensitive and less habitat-limited biological assemblages (e.g., algae or bacteria) to better understand complex ecological responses to best management practices designed to increase sustainability of high production agricultural regions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2023.110377","usgsCitation":"Taylor, J.M., DeVilbiss, S.E., and Hicks, M.B., 2023, Using taxa-based approaches to delineate stream macroinvertebrate assemblage responses to stressor gradients in modified alluvial agroecosystems: Ecological Indicators, v. 153, 110377, 13 p., https://doi.org/10.1016/j.ecolind.2023.110377.","productDescription":"110377, 13 p.","ipdsId":"IP-140788","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":443328,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2023.110377","text":"Publisher Index Page"},{"id":418852,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"153","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Jason M.","contributorId":212809,"corporation":false,"usgs":false,"family":"Taylor","given":"Jason","email":"","middleInitial":"M.","affiliations":[{"id":38685,"text":"USDA, ARS Sedimentation Lab","active":true,"usgs":false}],"preferred":false,"id":877317,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeVilbiss, Stephen E.","contributorId":316291,"corporation":false,"usgs":false,"family":"DeVilbiss","given":"Stephen","email":"","middleInitial":"E.","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":877318,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hicks, Matthew B. 0000-0001-5516-0296 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correction using in situ observations and climatology isohyets for the MENA region","docAbstract":"The availability of reliable gridded precipitation datasets is limited around the world, especially in arid regions. In this study, we utilized observations from satellite-based precipitation data and in situ rain gauge observations to determine a suitable precipitation dataset in the Middle East & North Africa (MENA) region. First, we evaluated seven different precipitation products using rain gauge observations. The validation was conducted at the daily, monthly, and annual time scales. Results indicated a weaker correlation between in situ rain gauge observation and satellite precipitation data at the daily time step (r: 0.02 to 0.44), mainly due to the lack of range in precipitation distribution. However, the agreement between precipitation estimates and in situ gauge observations improved at monthly (r: 0.02 to 0.66) and annual time scales (r: −0.22 to 0.57), indicating greater reliability of satellite-based precipitation at monthly and annual time scales. Based on the results and dataset availability, the Multi-Source Weighted-Ensemble Precipitation (MSWEP) was deemed suitable to create a bias-corrected new precipitation dataset for the MENA region. This study highlights the benefits of an adjusted regional precipitation product for hydrologic applications in the MENA region, such as streamflow or runoff estimation, to improve the reliability of the model outputs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2023.105010","usgsCitation":"Kagone, S., Velpuri, N., Khand, K., Senay, G.B., Van der Valk, M.R., Goode, D.J., Hantash, S.A., Al-Momani, T.M., Momejian, N., and Eggleston, J., 2023, Satellite precipitation bias estimation and correction using in situ observations and climatology isohyets for the MENA region: Journal of Arid Environments, v. 215, 105010, 14 p., https://doi.org/10.1016/j.jaridenv.2023.105010.","productDescription":"105010, 14 p.","ipdsId":"IP-126383","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":443334,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jaridenv.2023.105010","text":"Publisher Index Page"},{"id":417535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Jordan, Lebanon","otherGeospatial":"West Bank","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 39.30649160058408,\n 35.36856869578705\n ],\n [\n 32.084784344491425,\n 35.36856869578705\n ],\n [\n 32.084784344491425,\n 28.71772323486526\n ],\n [\n 39.30649160058408,\n 28.71772323486526\n ],\n [\n 39.30649160058408,\n 35.36856869578705\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"215","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":216913,"corporation":false,"usgs":true,"family":"Kagone","given":"Stefanie","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":873972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Velpuri, Naga Manohar 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Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":873975,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Van der Valk, Michael R.","contributorId":305834,"corporation":false,"usgs":false,"family":"Van der Valk","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":66311,"text":"HYDROLOGY.NL","active":true,"usgs":false}],"preferred":false,"id":873976,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goode, Daniel J. 0000-0002-8527-2456","orcid":"https://orcid.org/0000-0002-8527-2456","contributorId":216750,"corporation":false,"usgs":true,"family":"Goode","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873977,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hantash, Salam Abu","contributorId":305835,"corporation":false,"usgs":false,"family":"Hantash","given":"Salam","email":"","middleInitial":"Abu","affiliations":[{"id":66313,"text":"Palestinian Water Authority, West Bank","active":true,"usgs":false}],"preferred":false,"id":873978,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Al-Momani, Thair M.","contributorId":305836,"corporation":false,"usgs":false,"family":"Al-Momani","given":"Thair","email":"","middleInitial":"M.","affiliations":[{"id":66314,"text":"Ministry of Water and Irrigation, Amman, Jordan","active":true,"usgs":false}],"preferred":false,"id":873979,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Momejian, Nanor","contributorId":305837,"corporation":false,"usgs":false,"family":"Momejian","given":"Nanor","email":"","affiliations":[{"id":66315,"text":"Queens University, Kingston, Canada","active":true,"usgs":false}],"preferred":false,"id":873980,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Eggleston, Jack R. 0000-0001-6633-3041","orcid":"https://orcid.org/0000-0001-6633-3041","contributorId":204628,"corporation":false,"usgs":true,"family":"Eggleston","given":"Jack R.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873981,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70260121,"text":"70260121 - 2023 - Operationalizing crop model data assimilation for improved on-farm situational awareness","interactions":[],"lastModifiedDate":"2024-10-29T12:16:04.993327","indexId":"70260121","displayToPublicDate":"2023-05-26T07:12:10","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"Operationalizing crop model data assimilation for improved on-farm situational awareness","docAbstract":"Modelling and analysis of helicopter electromagnetic data result in resistivity and susceptibility models and derivatives of magnetic data that characterise shallow parts of the Stillwater Complex, critical for aiding exploration and expansion of globally scarce critical and battery mineral resources that include platinum group elements, nickel, copper and chromium. The magnetic susceptibly models derived from the electromagnetic data and the tilt derivative of the magnetic data image layering, mafic dikes, banded iron formation, and serpentinised peridotite. Known areas with contact-type mineralisation are generally characterised by low resistivities and susceptibilities where the volume of mineralised rock is large and/or the depth is shallow. We use iso-cluster and edge detection analysis of both resistivities and susceptibilities to identify potential mineralisation in poorly characterised regions as well as faults. Low resistivity layers beneath large landslides reflect water saturated porous slip surfaces which can interfere with drilling. This uncommon approach of tightly linking the resistivity and susceptibility models and magnetic anomaly data to rock property, surficial geologic, drill hole and soil geochemistry data to image the geology in the upper ∼100 m, aids identification of prospective mineralised regions as well landslides and faults that can impact mineral exploration and local hazards.
Mineral resource assessments performed by the U.S. Geological Survey provide a synthesis of available information about the location of known and suspected mineral deposits. This study focuses on skarn-hosted tungsten resources in the northern Rocky Mountain region of east-central Idaho and western Montana which have seen moderate tungsten trioxide production in the past from a variety of mineralization styles including skarn, vein and replacement, and wolframite-quartz veins. The area’s geology is dominated by large Cretaceous and Tertiary plutons that are emplaced into a belt of Mesoproterozoic to Permian sedimentary rock and affected by tectonism related to the Sevier and later Laramide orogenies. Known tungsten skarn mineral sites are associated with contacts between Cretaceous plutons and calcareous and argillaceous sedimentary or metasedimentary rocks, including two skarn deposits in Montana (Calvert and Browns Lake) that are consistent with an updated grade and tonnage model.
This study (1) delineates permissive tracts where undiscovered tungsten skarn deposits may occur within 1 kilometer of the surface; (2) presents a tungsten mineral site dataset from a variety of public sources; (3) evaluates currently available geochemical, geophysical, and radiometric age data in support of tract delineation; (4) provides probabilistic estimates of the amount of tungsten and tungsten-mineralized rock that could be contained in undiscovered deposits within one major tract; (5) estimates the value of total undiscovered deposits using economic filter analysis; and (6) provides a synthesis of metallogenic controls on regional tungsten skarn and granitoid-related mineral deposits.
Two permissive tracts were delineated: the Great Falls tectonic zone (GFTZ)-Cretaceous tract, for which a quantitative assessment was performed, and the Bitterroot tract, which was assessed in a qualitative manner. The quantitative three-part assessment, conducted in August 2019, indicates that undiscovered tungsten resources might exist in skarn-type deposits within the study area. Using a negative binomial function, a mean of 4 undiscovered deposits was calculated from panel estimates. Simulation results that combine an updated grade and tonnage model with estimates of undiscovered deposits include the amounts of ore and contained tungsten trioxide at different levels of uncertainty. A mean of 250,000 metric tons and median of 200,000 metric tons contained tungsten trioxide was calculated for the undiscovered deposits within the GFTZ-Cretaceous tract. The value of undiscovered deposits was estimated using a new economic filter that considers factors such as mine type, deposit depth, deposit geometry, metallurgical recovery rate, cutoff grade, and tract area.
A review of the regional Archean to Paleogene geology suggests that ore metal (copper, molybdenum, and tungsten) variations in intrusion-related deposits of Montana and Idaho may be controlled by a number of factors including the age and composition of underlying basement terranes, depth of emplacement, pluton chemistry and degree of fractionation, redox conditions, and aqueous fluid-melt partition coefficients.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235012","programNote":"Mineral Resources Program","usgsCitation":"Andersen, A.K., Goldman, M.A., Bennett, M.M., Dicken, C.L., Brown, P.J., and Parks, H.L., 2023, Tungsten resources of the northern Rocky Mountains, Montana and Idaho— A synthesis and quantitative assessment of skarn-hosted resources: U.S. Geological Survey Scientific Investigations Report 2023-5012, 87 p., https://doi.org/10.3133/sir20235012.","productDescription":"Report: viii, 87 p.; Data Release","numberOfPages":"87","onlineOnly":"Y","ipdsId":"IP-122167","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":500706,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114742.htm","linkFileType":{"id":5,"text":"html"}},{"id":417441,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9094RVV","text":"Spatial data associated with tungsten skarn resource assessment of the northern Rocky Mountains, Montana and Idaho","description":"Goldman, M.A., Dicken, C.L., Brown, P.J., Andersen, A.K., Bennett, M.M., and Parks, H.L., 2022, Spatial data associated with tungsten skarn resource assessment of the northern Rocky Mountains, Montana and Idaho: U.S. Geological Survey data release, https://doi.org/10.5066/P9094RVV."},{"id":417443,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5012/sir20235012.pdf","text":"Report","size":"55 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":417442,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5012/covrthb.jpg"}],"country":"United States","state":"Idaho, Montana","otherGeospatial":"Northern Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.93058150555078,\n 48.9704513230287\n ],\n [\n -116.93058150555078,\n 43.38357304109826\n ],\n [\n -111.08762543219147,\n 43.38357304109826\n ],\n [\n -111.08762543219147,\n 48.9704513230287\n ],\n [\n -116.93058150555078,\n 48.9704513230287\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035
There is a paucity of data evaluating water use by raptors. Although raptors are believed to satisfy their water requirements through metabolic processes, they are known to experience reduced reproductive success during periods of drought, and there is evidence of water being important for site occupancy in arid landscapes. Several raptor species have a seasonal or year-round presence in west Texas, a drought-prone, semi-arid region of the Southern Great Plains. We examined species-specific timing of free water use by common raptors in this region, and examined environmental conditions associated with water use. We collected 4549 camera trap-days of data across 4 yr at ten human-made water sources placed for cattle. We recorded 14 species of raptors among the 1177 detections of raptors visiting water sources; of these, 1084 raptors (92.1%) perched at tanks, and 93 (7.1%) flew by tanks. Of the raptors that perched at tanks, 63.5% drank and 20.8% both bathed and drank. Barn Owls (Tyto alba; 35.6%), Swainson's Hawks (Buteo swainsoni; 32.0%), and Northern Harriers (Circus hudsonius; 21.0%) were the predominate species detected. Visits by Northern Harriers and Swainson's Hawks increased with increasing temperature and decreasing precipitation. Visits by Barn Owls increased with increasing drought severity. Further, detections per 100 trap-days increased substantively across our 4-yr study period during which the region experienced one of the worst droughts on record. Although our data do not demonstrate these raptors require free water, they do reveal an increasing use of free water in relation to hotter and drier conditions. How this influences survival and reproduction remains unknown, but may become a pressing question because current climate models predict the study area will experience increases in heat and decreases in precipitation.
","language":"English","publisher":"BioOne","doi":"10.3356/JRR-21-70","usgsCitation":"Boal, C.W., Bibles, B.D., and Gicklhorn, T., 2023, Patterns of water use by raptors in the southern Great Plains: Journal of Raptor Research, v. 57, no. 3, p. 444-455, https://doi.org/10.3356/JRR-21-70.","productDescription":"12 p.","startPage":"444","endPage":"455","ipdsId":"IP-134224","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908580,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bibles, Brent D.","contributorId":341539,"corporation":false,"usgs":false,"family":"Bibles","given":"Brent","email":"","middleInitial":"D.","affiliations":[{"id":81739,"text":"Unity College","active":true,"usgs":false}],"preferred":false,"id":908581,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gicklhorn, Trevor S.","contributorId":341540,"corporation":false,"usgs":false,"family":"Gicklhorn","given":"Trevor S.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908582,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243941,"text":"sir20235050 - 2023 - Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, August 3–10, 2020","interactions":[],"lastModifiedDate":"2026-03-09T16:14:58.563672","indexId":"sir20235050","displayToPublicDate":"2023-05-25T14:04:57","publicationYear":"2023","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":"2023-5050","displayTitle":"Bathymetric and Velocimetric Surveys at Highway Bridges Crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, August 3–10, 2020","title":"Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, August 3–10, 2020","docAbstract":"Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 15 bridges at 10 highway crossings of the Missouri and Mississippi Rivers near Washington, Louisiana, and St. Louis, Missouri, on August 3–10, 2020. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches about 1,640 to 1,970 feet longitudinally and generally extending laterally across the active channel from bank to bank during moderate flood-flow conditions. These surveys provided channel geometry and hydraulic conditions at the time of the surveys and provided characteristics of scour holes that may be useful in developing predictive guidelines or equations for computing potential scour depth. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood-flow assessment of the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every in-channel pier. Scour holes were present at most piers for which bathymetry could be obtained, except those on banks or surrounded by riprap. All the bridge sites in this study were previously surveyed and documented in previous studies, including the two new bridge structures at Louisiana and Washington (structures A8141 and A8504, sites 22 and 32, respectively). Comparisons between bathymetric surfaces from the previous surveys and those of the current (2020) study do not indicate any consistent correlation in channel-bed elevations with streamflow conditions. The comparisons of the 2020 surveys to two previous surveys at the new bridge structure A8141 at Washington (site 22) resulted in net erosion of the channel bed in both comparisons, despite the 2020 streamflow being less than either previous survey. Alternatively, there was a net gain of sediment at new bridge structure A8504 at Louisiana (site 32) between 2014 and 2020, which was the most substantial increase in the surveys detailed in this report; substantially less flow in 2020 than in 2014 or changes to the channel and spur dikes near the bridge may have contributed to the observed sediment gain.
Pier size, nose shape, and skew to approach flow had a substantial effect on the size of the scour hole observed at a given pier. Larger and deeper scour holes were present at piers with wide or blunt noses caused by exposed footings, seal courses, or caissons. When a pier was skewed to primary approach flow, the scour hole was generally deeper and larger than at a similar pier without skew; however, the shape of the scour hole near skewed piers in this study generally was longer and deeper on the leeward side, contrary to the general shape of scour holes for skewed piers. However, this phenomenon has been observed historically at these sites, and likely is exacerbated by debris rafts or other turbulence-inducing features near the atypical scour holes. A substantial scour hole was observed near pier 11 of structure A6500 (site 33), which was deeper than in the 2016 survey. The scour holes observed at pier 17 of structure L0561 (site 25) and piers 3 and 4 of structure A1500 (site 34) also were slightly deeper and wider in 2020 than in 2016. At new bridge structures A8141 at Washington (site 22) and A8504 at Louisiana (site 32), the smaller cross-sectional area and configuration of the piers of the new bridges resulted in substantially less scour than with the wider old piers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235050","collaboration":"Prepared in cooperation with Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2023, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, August 3–10, 2020 (ver. 1.1, June 2023): U.S. Geological Survey Scientific Investigations Report 2023–5050, 129 p., https://doi.org/10.3133/sir20235050.","productDescription":"Report: xii, 129 p.; 4 Data Releases","numberOfPages":"146","onlineOnly":"Y","ipdsId":"IP-137672","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":500928,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114743.htm","linkFileType":{"id":5,"text":"html"}},{"id":417650,"rank":9,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2023/5050/versionHist.txt","text":"Version History","size":"1.19 kB","linkFileType":{"id":2,"text":"txt"}},{"id":417532,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5050/images"},{"id":417434,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F04JC5","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, August 3–10, 2020"},{"id":417432,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WDI9YF","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, December 2008 through August 2018"},{"id":417431,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94M4US7","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri River between Kansas City and St. Louis, Missouri, January 2010 through May 2017"},{"id":417429,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71C1VCC","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, October 2008 through May 2016"},{"id":417428,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5050/sir20235050.XML","linkFileType":{"id":8,"text":"xml"}},{"id":417427,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5050/sir20235050.pdf","text":"Report","size":"26.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5050"},{"id":417426,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5050/coverthb2.jpg"},{"id":417651,"rank":10,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235050/full"}],"country":"United States","state":"Illinois, Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.7316541640954,\n 39.115075665885314\n ],\n [\n -90.7316541640954,\n 38.326846254515004\n ],\n [\n -90.02883149031791,\n 38.326846254515004\n ],\n [\n -90.02883149031791,\n 39.115075665885314\n ],\n [\n -90.7316541640954,\n 39.115075665885314\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: May 25, 2023; Version 1.1: June 1, 2023","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Digital maps of bedrock elevation and overburden thickness (depth to bedrock) were constructed for the five boroughs of New York City by the U.S. Geological Survey, in cooperation with the New York City Department of Design and Construction, from a compilation of historical and newly acquired data. Raster surfaces were interpolated from a point database containing data from more than 14,000 locations collected from a variety of sources. These data were collected between 1905 and 2021. These maps were constructed to supplement existing tools for the evaluation of potential construction of geothermal heat pump technology for buildings in New York City.
The bedrock underlying the study area ranges from easily weathered to very resistant to weathering. This differential susceptibility to erosion, along with numerous north-northwest-trending faults, is believed to control the shape of the bedrock surface. Glacial scouring of the bedrock during the Pleistocene Epoch is the most recent control on the topography of bedrock surfaces. Overburden thickness is an important consideration for evaluation and construction of geothermal systems.
Bedrock-surface elevation ranges from about 360 feet above sea level (in central Staten Island and northern Bronx) to 1,200 feet below sea level (in southern Queens) (North American Vertical Datum of 1988). The overburden thickness ranges from 0 foot thick at surface outcrops on Staten Island, Manhattan, and the Bronx, to 1,280 feet thick in southeastern Queens.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1176","collaboration":"Prepared in cooperation with the New York City Department of Design and Construction","usgsCitation":"DeMott, L.M., Stumm, F., and Finkelstein, J., 2023, Bedrock-surface elevation and overburden thickness maps of the five boroughs, New York City, New York: U.S. Geological Survey Data Report 1176, 22 p., https://doi.org/10.3133/dr1176.","productDescription":"Report: vi, 22 p.; 3 Data releases","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-138256","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":417351,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S7Q68D","text":"USGS data release","linkHelpText":"Horizontal-to-vertical spectral ratio soundings and depth-to-bedrock data for bedrock surface elevation of the five boroughs, New York City, New York"},{"id":417346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1176/coverthb.jpg"},{"id":417347,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1176/dr1176.pdf","text":"Report","size":"9.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1176"},{"id":417348,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/dr1176/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DR 1176"},{"id":417349,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1176/dr1176.XML"},{"id":417350,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1176/images/"},{"id":417352,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P911CSI3","text":"USGS data release","linkHelpText":"Geospatial data for bedrock surface elevation of the five boroughs, New York City, New York"},{"id":417353,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O24NP7","text":"USGS data release","linkHelpText":"Continuous marine seismic-reflection surveys and derived depth-to-bedrock point data from the East River, New York City, New York"},{"id":424286,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://ny.water.usgs.gov/maps/nycbedrock/","text":"NYC Bedrock and Groundwater Mapper"}],"contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The U.S. Geological Survey (USGS), in cooperation with the New Jersey Department of Environmental Protection (NJDEP) and the New Jersey Office of Emergency Management (NJOEM), created digital flood-inundation maps for approximately 1,430 square miles of the New Jersey coast and tidewaters through 10 coastal counties stretching from Cumberland County through Bergen County, New Jersey. The maps depict extent and depth estimates of coastal flooding corresponding to selected tidal elevations recorded by 25 real-time USGS tide gages located within the study area. The flood-inundation maps can be accessed through the USGS Interagency Flood Risk Management (InFRM) Flood Decision Support Toolbox (FDST).
Previously published modeled data were utilized from the coupled ADvanced CIRCulation Model (ADCIRC) and Simulating Waves Nearshore (SWAN) model. Simulated tropical storm events were selected based on parameters including landfall location or closest approach location, maximum wind speed, central pressure, and radii of winds. Two storm events were selected per tide gage providing two “scenarios” and accompanying inundation-map libraries for each gage. Flood-inundation maps reflect between 9 to 30 stages (elevations) at each tide gage that correspond to areal extents and depths for ADCIRC-SWAN storm time steps extracted from modeled hydrographs at the gage locations. Water-surface elevations from ADCIRC-SWAN node points extending through each tide gage station extent were used to interpolate a water surface. Combining these surfaces with a geographic information system (GIS) topobathymetric digital elevation model (TBDEM) delineated the area flooded by coastal waters at each tide gage elevation.
The availability of these maps to visualize potential inundation for selected water levels along with real-time water level data available online from USGS tide gages, coastal impact statements, and forecasted tide elevations from the National Weather Service (NWS) will provide emergency management personnel and residents with a link between numeric and text warning information and images of estimated inundation extents in their community. User selected display of inundation allows early response activities to NWS forecasted water level elevations or mitigation planning by selecting targeted water levels and planning critical pre-flood activities such as building elevations, early traffic pattern changes because of neighborhood building inundation levels, improved understanding about when major road access is affected, as well as for post-flood recovery efforts.
A subsequent analysis of several community metrics including total structures, structure density, percent of buildings inundated, and roads and bridges affected by flooding was used to evaluate moderate flooding impacts among the mapped station extents. Initial comparisons are presented to show the variability of these characteristics within each mapped station extent then extended to evaluate impacts from moderate flooding on these same areas. The analysis used simulated inundation layers at the moderate flood stage to investigate the magnitude of inundation on building structures and major roads among the mapped station extents. Experimental equations were developed to begin testing if a mathematical equation could help identify communities that were disproportionately impacted at moderate flood stage. The community analysis of impacts to moderate flooding based on these inundation scenario maps should provide community leaders and local and state planning officials with tools to better visualize and understand how flooding begins to disrupt and damage building structures and major roads as a surrogate for direct increased risk to human life and property.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235005","collaboration":"Prepared in cooperation with New Jersey Department of Environmental Protection","usgsCitation":"Suro, T.P., Niemoczynski, M.J., Boetsma, A., and Niemoczynski, L.M., 2023, Moderate flood level scenarios—Synthetic storm-driven flood-inundation maps for coastal communities in 10 New Jersey counties: U.S. Geological Survey Scientific Investigations Report 2023–5005, 49 p., https://doi.org/10.3133/sir20235005.","productDescription":"Report: viii, 49 p.; Data Release","numberOfPages":"49","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-136043","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":417140,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5005/images/"},{"id":500685,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114740.htm","linkFileType":{"id":5,"text":"html"}},{"id":417141,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RVF9P8","text":"USGS data release","linkHelpText":"Synthetic storm-driven flood-inundation grids for coastal communities in 10 New Jersey counties"},{"id":417139,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5005/sir20235005.XML"},{"id":417138,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235005/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5005"},{"id":417137,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5005/sir20235005.pdf","text":"Report","size":"21.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5005"},{"id":417136,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5005/coverthb.jpg"}],"country":"United States","state":"New Jersey","county":"Atlantic County, Bergen County, Cape May County, Cumberland County, Essex County, Middlesex County, Monmouth County, Ocean County, Salem County, Union County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.1277080228522,\n 40.638935692142724\n ],\n [\n -74.41159623268268,\n 40.50386433220055\n ],\n [\n -74.13706697482502,\n 40.31620957676867\n ],\n [\n -74.07155431101805,\n 40.27813984647429\n ],\n [\n -74.2088189399465,\n 39.95129023758125\n ],\n [\n -74.58317622656678,\n 39.43518753081048\n ],\n [\n -74.78283386864535,\n 39.20591467723193\n ],\n [\n -75.42236225342704,\n 39.6781159291034\n ],\n [\n -75.58146443695806,\n 39.64689563243181\n ],\n [\n -75.51907142380868,\n 39.57479497276381\n ],\n [\n -75.55650723169862,\n 39.449642684385594\n ],\n [\n -75.2258242620061,\n 39.23250052123328\n ],\n [\n -75.13535439293969,\n 39.15996996348281\n ],\n [\n -75.03864522255803,\n 39.19382687463093\n ],\n [\n -74.92009849757379,\n 39.14303539458916\n ],\n [\n -75.00120941466811,\n 38.92494240360199\n ],\n [\n -74.90138059362884,\n 38.89338447021902\n ],\n [\n -74.52078321341743,\n 39.249413543217884\n ],\n [\n -74.25873255818952,\n 39.44482463320827\n ],\n [\n -74.05907491611174,\n 39.72851891657447\n ],\n [\n -73.93740900673615,\n 40.3827789234833\n ],\n [\n -73.99044306791338,\n 40.49911897812299\n ],\n [\n -74.17762210736144,\n 40.48488407551238\n ],\n [\n -74.0372378277754,\n 40.579726378448925\n ],\n [\n -74.06219503303558,\n 40.65787019904499\n ],\n [\n -74.1277080228522,\n 40.638935692142724\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
We describe the development of a custom 37 K Affymetrix Axiom myDesign single nucleotide polymorphism (SNP) array for a culturally and ecologically important apex predator, the golden eagle (Aquila chrysaetos). Using this SNP array, we performed population genomic analysis on 154 individuals of known natal localities and detected three genetic clusters that we designated as Taiga/High Arctic, Great Basin, and Rocky Mountains/Great Plains. Each of these clusters appears to display clinal variation within these geographic regions. After determining genetic structure, we performed an assignment test of 32 individuals, five of which were siblings of individuals used in the assessment of genetic structure, three had associated telemetry data, and the remaining individuals were of unknown natal locations. Using this array, four siblings were correctly assigned to the same geographic region as their sibling and the genetic assignment of the radio telemetered birds agreed with the expected movement patterns displayed by these individuals. For the remaining individuals, we were able to assign all but five individuals to one of the three genetic clusters. Our genetic assignments illustrates the utility of this SNP array to accurately assign most individuals to predesignated geographical regions. While further compiling genetic and other data types, we can increase the power of this tool for identifying those breeding populations that may need assistance due to anthropogenic stressors that negatively impact their population viability. The use of this genetic resource will help substantiate decisions by multiple conservation groups that seek to preserve the natural population structure of the golden eagle.
","language":"English","publisher":"Springer","doi":"10.1007/s10592-023-01508-3","usgsCitation":"Judkins, M.E., Roemer, G., Millsap, B., Barnes, J.G., Bedrosian, B.E., Clarke, S.L., Domenech, R., Herring, G., Lamont, M., Smith, B.W., Stahlecker, D.W., Stuber, M.J., Warren, W.C., and Van Den Bussche, R., 2023, A 37 K SNP array for the management and conservation of Golden Eagles (Aquila chrysaetos): Conservation Genetics, v. 24, p. 391-404, https://doi.org/10.1007/s10592-023-01508-3.","productDescription":"14 p.","startPage":"391","endPage":"404","ipdsId":"IP-125275","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":417435,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","noUsgsAuthors":false,"publicationDate":"2023-03-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Judkins, Megan 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Wildlife","active":true,"usgs":false}],"preferred":false,"id":873769,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bedrosian, Bryan E.","contributorId":305745,"corporation":false,"usgs":false,"family":"Bedrosian","given":"Bryan","email":"","middleInitial":"E.","affiliations":[{"id":35591,"text":"Teton Raptor Center","active":true,"usgs":false}],"preferred":false,"id":873770,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clarke, Stephen L.","contributorId":305746,"corporation":false,"usgs":false,"family":"Clarke","given":"Stephen","email":"","middleInitial":"L.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":873771,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Domenech, Robert","contributorId":199743,"corporation":false,"usgs":false,"family":"Domenech","given":"Robert","email":"","affiliations":[{"id":35594,"text":"Raptor View Research 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setting","interactions":[],"lastModifiedDate":"2023-05-25T12:35:05.358711","indexId":"70243912","displayToPublicDate":"2023-05-25T07:06:35","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"The truth is in the stream: Use of tracer techniques and synoptic sampling to evaluate metal loading and remedial options in a hydrologically complex setting","docAbstract":"Two synoptic sampling campaigns were conducted to quantify metal loading to Illinois Gulch, a\nsmall stream affected by historical mining activities. The first campaign was designed to\ndetermine the degree to which Illinois Gulch loses water to the underlying mine workings, and to\ndetermine the effect of these losses on observed metal loads. The second campaign was designed to evaluate metal loading within Iron Springs, a subwatershed that was responsible for the majority of the metal loading observed during the first campaign. A continuous, constant-rate\ninjection of a conservative tracer was initiated prior to both sampling campaigns and maintained\nthroughout the duration of each study. Tracer concentrations were subsequently used to determine streamflow in gaining stream reaches using the tracer-dilution method, and as an indicator of hydrologic connections between Illinois Gulch and subsurface mine workings. Streamflow losses to the mine workings were quantified during the first campaign using a series of slug additions in which specific conductivity readings were used as a surrogate for tracer concentration. Data from the continuous injections and slug additions were combined to develop spatial streamflow profiles along each study reach. Streamflow estimates were multiplied by observed metal concentrations to yield spatial profiles of metal load that were in turn used to quantify and rank metal sources. Study results indicate that Illinois Gulch loses water to subsurface mine workings and suggest that remedial measures that reduce flow loss (e.g. channel lining) could lessen metal loading from the Iron Springs area. The primary sources of metals to Illinois Gulch include diffuse springs and groundwater, and a draining mine adit. Diffuse sources were determined to have a much larger effect on water quality than other sources that had been the subject of previous investigations due to their visual appearance, supporting the idea that “the truth is in the stream”. The overall approach of combining spatially intensive sampling with a rigorous hydrological characterization is applicable to non-mining constituents such as nutrients and pesticides.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2023.162458","usgsCitation":"Runkel, R.L., Verplanck, P., Walton-Day, K., McCleskey, R., and Byrne, P., 2023, The truth is in the stream: Use of tracer techniques and synoptic sampling to evaluate metal loading and remedial options in a hydrologically complex setting: Science of the Total Environment, v. 876, 162458, 15 p., https://doi.org/10.1016/j.scitotenv.2023.162458.","productDescription":"162458, 15 p.","ipdsId":"IP-147276","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":498440,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://orcid.org/0000-0002-2699-052X","text":"External Repository"},{"id":417422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Breckenridge District, Illinois Gulch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.03588092628337,\n 39.47184696761008\n ],\n [\n -106.03586531996221,\n 39.47158193097033\n ],\n [\n -106.0351162165624,\n 39.47134098769499\n ],\n [\n -106.03369604136695,\n 39.47138917641641\n ],\n [\n -106.03197934607566,\n 39.470702483978386\n ],\n [\n -106.02790609633927,\n 39.47112413803276\n ],\n [\n -106.02444149311532,\n 39.4696423139238\n ],\n [\n -106.02359875179056,\n 39.46769059499084\n ],\n [\n -106.02229234667409,\n 39.465750869468195\n ],\n [\n -106.02031034392866,\n 39.4654135203798\n ],\n [\n -106.01873410552521,\n 39.4654376167972\n ],\n [\n -106.01717347344193,\n 39.46540147216794\n ],\n [\n -106.01756363146276,\n 39.46585930275603\n ],\n [\n -106.01898380665817,\n 39.46601592831908\n ],\n [\n -106.02031034392866,\n 39.46594363964175\n ],\n [\n -106.0215588495954,\n 39.466268938097954\n ],\n [\n -106.02276053629916,\n 39.46722072855263\n ],\n [\n -106.02361888394458,\n 39.46970255129088\n ],\n [\n -106.02521072866917,\n 39.47078681499352\n ],\n [\n -106.02773895264343,\n 39.47161807238939\n ],\n [\n -106.03145325700082,\n 39.471124138032025\n ],\n [\n -106.03348867411313,\n 39.47181082630925\n ],\n [\n -106.03588092628337,\n 39.47184696761008\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"876","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873716,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Verplanck, Philip L. 0000-0002-3653-6419","orcid":"https://orcid.org/0000-0002-3653-6419","contributorId":212813,"corporation":false,"usgs":true,"family":"Verplanck","given":"Philip","middleInitial":"L.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":873717,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873718,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCleskey, R. 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Three programs to survey wetland-breeding birds covering (respectively) Great Lakes coastal wetlands, inland Great Lakes wetlands, and the Prairie Pothole Region offer an opportunity to test whether regionally specific models of habitat use by wetland-obligate breeding birds are transferrable across regions. We first developed independent, regional population density models for four species of wetland-obligate birds: Pied-billed Grebe (Podilymbus podiceps), Virginia Rail (Rallus limicola), Sora (Porzana carolina), and American Bittern (Botaurus lentiginosus). We then used adjusted pseudo-R2 values to compare the amount of variation explained by each model when applied to data collected in each of the three regions. Although certain habitat characteristics, such as emergent vegetation and wetland area, were consistently important across regions, models for each species differed by region—both in variables selected for inclusion and often in the directionality of relationships for common variables—indicating that habitat associations for these species are regionally specific. When we applied a model developed in one region to data collected in another region, we found that explanatory power was reduced in most (71%) models. Therefore, we suggest that ecological analyses should emphasize regionally specific habitat association models whenever possible. Nonetheless, models created from inland Great Lakes wetland data had higher median explanatory power when applied to other regions, and the amount of explanatory power lost by other transferred models was relatively small. Thus, while regionally specific habitat association models are preferable, in the absence of reliable regional data, habitat association models developed in one region may be applied to another region, but the results need to be cautiously interpreted. Additionally, we found that median explanatory power was higher when local-scale habitat characteristics were included in the models, indicating that regionally specific models should ideally be based on a combination of local- and landscape-scale habitat characteristics. Conservation practitioners can leverage such regionally specific models and associated monitoring data to help prioritize areas for management activities that contribute to regional conservation efforts.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4499","usgsCitation":"Elliott, L.H., Bracey, A.M., Niemi, G.J., Johnson, D., Gehring, T.M., Gnass Giese, E.E., Fiorino, G.E., Howe, R.W., Lawrence, G.A., Norment, C.J., Tozer, D.C., and Igl, L., 2023, Application of habitat association models across regions: Useful explanatory power retained in wetland bird case study: Ecosphere, v. 14, no. 5, e4499, 19 p., https://doi.org/10.1002/ecs2.4499.","productDescription":"e4499, 19 p.","ipdsId":"IP-109710","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":443363,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4499","text":"Publisher Index Page"},{"id":417421,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Illinois, Indiana, Michigan, Minnesota, New York, North Dakota, 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0000-0001-9516-876X","orcid":"https://orcid.org/0000-0001-9516-876X","contributorId":305779,"corporation":false,"usgs":false,"family":"Tozer","given":"Douglas","email":"","middleInitial":"C.","affiliations":[{"id":66293,"text":"Long Point Waterfowl and Wetlands Research Program, Birds Canada","active":true,"usgs":false}],"preferred":false,"id":873814,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Igl, Lawrence 0000-0003-0530-7266","orcid":"https://orcid.org/0000-0003-0530-7266","contributorId":221711,"corporation":false,"usgs":true,"family":"Igl","given":"Lawrence","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":873815,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70244170,"text":"70244170 - 2023 - Introduction to the digitization of seismic data: A user’s guide","interactions":[],"lastModifiedDate":"2023-06-28T15:26:29.061119","indexId":"70244170","displayToPublicDate":"2023-05-25T06:46:08","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Introduction to the digitization of seismic data: A user’s guide","docAbstract":"Modern seismic data are collected, distributed, and analyzed using digital formats, and this has become a standard for the field. Although most modern seismometers still make use of analog electronic circuits, their data are converted from an analog voltage output to time‐tagged counts by way of digitization. Although much of the digitization process is not complicated to conceptualize, there is a fair bit of jargon in digitizer specifications, and a few pitfalls that can arise in the processes of recording and analyzing ground‐motion data. In this article, we review some of the fundamental physical properties of data acquisition systems and the basic steps in digitizing data from an analog instrument (specifically a seismometer). We then briefly discuss the digitization process and some of the key properties needed to make these data useful for seismological applications. Finally, we discuss some of the filtering processes that naturally arise from digitization and how it can affect the processing workflow. The end goal is to provide a user guide that will enable seismologists to have a working knowledge of the digitization process. We focus on aspects central to seismological applications and have tried to avoid getting bogged down in signal processing formalism.
We evaluated wildlife population health from the perspective of statistical means vs. variances. We outlined the choices necessary to provide the framework for our study. These consisted of spatial and temporal boundaries (e.g., choice of sentinel species, populations, time frame), measurement techniques (molecular to population level), and appropriate statistical analyses. We chose to assess the health of 19 sea otter populations, located in the north Pacific from the Aleutian Islands, AK, to Santa Barbara, CA, and varying in population growth rates and length of occupancy. Our focal metric was gene expression (i.e., mRNA transcripts) data that we had previously generated across sea otter populations as a measure of population health. We used statistical methods with different approaches (i.e., means vs. variances) and examined the subsequent interpretive outcomes and how these influence our assessment of “health.” Interpretations based on analyses using variances versus means overlapped to some degree. In general, sea otter populations with low variation in gene expression were limited by food resources and at or near carrying capacity. In populations where the variation in gene expression was moderate or high, four out of five populations were increasing in abundance, or had been recently increasing. Where we had additional information on sources of stressors at the level of the population, we were able to draw inferences from those stressors to specific gene expression results. For example, gene expression patterns of sea otters from Western Prince William Sound were consistent with long term exposure to petroleum hydrocarbons, whereas in Kachemak Bay, patterns were consistent with exposure to algal toxins. Ultimately, determination of population or ecosystem health will be most informative when multiple metrics are examined across disciplines in the context of specific scenarios and goals.
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The sensitivity of the ASCE standardized ETo equation to meteorological variables from GOES-PRWEB dataset was evaluated for the island of Puerto Rico. Island wide, ETo is most sensitive to daily mean relative humidity (RHmean), followed by solar radiation, daily maximum (Tmax) and minimum (Tmin) air temperatures, and wind speed with average absolute relative sensitivity coefficients (SCs) of 0.98, 0.57, 0.50, 0.27, and 0.12, respectively. The derived SCs guided the prioritization of bias correction of meteorological data for ETo estimation from two downscaled climate models (CNRM and CESM). The SCs were applied to evaluate how meteorological variables contribute to model errors and projected future changes in ETo from 1985–2005 to 2040–2060 at irrigated farms in the south. Both models project a 5.6% average increase in annual ETo due to projected increases in Tmax and Tmin and a decrease in RHmean. Despite ETo being most sensitive to relative changes in RHmean, the contributions from RHmean, Tmax, and Tmin to future changes in ETo are similar. CESM projects increases in ETo in March, November, and December, increasing the potential for crop water stress. Study limitations are discussed.
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Rico","active":true,"usgs":false}],"preferred":false,"id":873577,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70243904,"text":"70243904 - 2023 - Assessing individual movement, habitat use, and behavior of non-breeding marine birds in relation to prey availability in the US Atlantic","interactions":[],"lastModifiedDate":"2023-05-24T18:53:49.243988","indexId":"70243904","displayToPublicDate":"2023-05-24T13:24:43","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Assessing individual movement, habitat use, and behavior of non-breeding marine birds in relation to prey availability in the US Atlantic","docAbstract":"Resource availability is a key factor driving marine bird movements and distributions, but direct information on prey availability is difficult to obtain at relevant scales. We present novel methods for describing multi-scale trophic associations, combining movement analyses of marine birds with estimates of forage fish surface aggregations from digital aerial survey data and species occupancy from bottom trawl survey data. We analyzed satellite telemetry data from northern gannets Morus bassanus, red-throated loons Gavia stellata, and long-tailed ducks Clangula hyemalis in the US Atlantic during the non-breeding period. Using discrete-time hidden Markov models to distinguish area-restricted (i.e. putative foraging) from transit movements, we examined how environmental factors influence movement, and how forage fish species distributions and surface aggregations influence habitat use by gannets and loons that have greater dietary reliance. Our results suggest that chlorophyll a concentration significantly affected movement behavior across species, highlighting the importance of higher-productivity areas around estuaries during colder months when regional productivity is low. Though variable across species and seasons, spatial cross-correlation analysis revealed that herring species (Family Clupeidae), including Atlantic menhaden Brevoortia tyrannus, may be important resources; it also showed positive spatial correlations with forage fish aggregations. This suggests that prey patch dynamics and factors driving aggregation formation may be as important as species composition. However, spatial patterns were generally low (<0.3), suggesting a mismatch in spatiotemporal resolution, exemplifying the challenges in quantifying trophic relationships in marine systems. Disentangling predator-prey relationships is critical to understanding the mechanisms driving marine bird behavior in rapidly changing marine systems.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps14316","usgsCitation":"Gulka, J., Berlin, A., Friedland, K., Gilbert, A., Goetsch, C., Montevecchi, W., Perry, M., Stenhouse, I., Williams, K.A., and Adams, E.A., 2023, Assessing individual movement, habitat use, and behavior of non-breeding marine birds in relation to prey availability in the US Atlantic: Marine Ecology Progress Series, v. 711, p. 77-99, https://doi.org/10.3354/meps14316.","productDescription":"23 p.","startPage":"77","endPage":"99","ipdsId":"IP-150930","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":443380,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps14316","text":"Publisher Index Page"},{"id":417406,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Atlantic Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.25987342428122,\n 25.49083684996951\n ],\n [\n -60.361707727220605,\n 25.49083684996951\n ],\n [\n -60.361707727220605,\n 45.94936874492856\n ],\n [\n -81.25987342428122,\n 45.94936874492856\n ],\n [\n -81.25987342428122,\n 25.49083684996951\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"711","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gulka, Julia","contributorId":303827,"corporation":false,"usgs":false,"family":"Gulka","given":"Julia","email":"","affiliations":[],"preferred":false,"id":873676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berlin, Alicia 0000-0002-5275-3077","orcid":"https://orcid.org/0000-0002-5275-3077","contributorId":216023,"corporation":false,"usgs":true,"family":"Berlin","given":"Alicia","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":873677,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedland, Kevin","contributorId":292483,"corporation":false,"usgs":false,"family":"Friedland","given":"Kevin","affiliations":[],"preferred":false,"id":873678,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gilbert, Andrew","contributorId":194560,"corporation":false,"usgs":false,"family":"Gilbert","given":"Andrew","affiliations":[],"preferred":false,"id":873679,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goetsch, Chandra","contributorId":214868,"corporation":false,"usgs":false,"family":"Goetsch","given":"Chandra","email":"","affiliations":[],"preferred":false,"id":873680,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Montevecchi, William","contributorId":171895,"corporation":false,"usgs":false,"family":"Montevecchi","given":"William","affiliations":[{"id":26965,"text":"Memorial University of Newfoundland","active":true,"usgs":false}],"preferred":false,"id":873681,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Perry, Matthew","contributorId":194761,"corporation":false,"usgs":false,"family":"Perry","given":"Matthew","affiliations":[],"preferred":false,"id":873682,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stenhouse, Iain","contributorId":194567,"corporation":false,"usgs":false,"family":"Stenhouse","given":"Iain","affiliations":[],"preferred":false,"id":873683,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Williams, Kate A.","contributorId":219079,"corporation":false,"usgs":false,"family":"Williams","given":"Kate","email":"","middleInitial":"A.","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":873684,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Adams, Evan A.","contributorId":204599,"corporation":false,"usgs":false,"family":"Adams","given":"Evan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":873685,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70243860,"text":"ofr20231044 - 2023 - ECCOE Landsat quarterly Calibration and Validation report—Quarter 4, 2022","interactions":[],"lastModifiedDate":"2023-05-25T13:29:39.490622","indexId":"ofr20231044","displayToPublicDate":"2023-05-24T11:48:52","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1044","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 4, 2022","title":"ECCOE Landsat quarterly Calibration and Validation report—Quarter 4, 2022","docAbstract":"The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 4 (October–December) of 2022. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the ECCOE Landsat Cal/Val Team closely monitored was the lowering of the Landsat 7 orbit. On April 6, 2022, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor was placed into standby mode, and a series of spacecraft burns was completed through the month of April to lower the satellite’s orbit by 8 kilometers. Imaging resumed at a lower orbit of 697 kilometers on May 5, 2022, extending the science mission. Additional information about the Landsat 7 orbit lowering is here: https://www.usgs.gov/centers/eros/news/landsat-7-lowered-standard-landsat-orbit#:~:text=The%20satellite's%20primary%20science%20mission%20has%20ended&text=On%20April%206%2C%202022%2C%20the,satellite's%20orbit%20by%208%20kilometers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231044","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Shrestha, A., Tuli, F.T.Z., Shaw, J.L., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Thome, K., Kaita, E., Barsi, J., Levy, R., and Miller, J., 2023, ECCOE Landsat quarterly Calibration and Validation report—Quarter 4, 2022: U.S. Geological Survey Open-File Report 2023–1044, 39 p., https://doi.org/10.3133/ofr20231044.","productDescription":"Report: vii, 39 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-149560","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":417400,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231044/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":417365,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":417362,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1044/ofr20231044.pdf","text":"Report","size":"4.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1044"},{"id":417361,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1044/coverthb.jpg"},{"id":417363,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1044/ofr20231044.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":417364,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1044/images"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The northern spotted owl (Strix occidentalis caurina; hereinafter NSO) was listed as “threatened” under the Endangered Species Act in 1990 and population declines have continued since that listing. Given the species’ protected status, any proposed activities on Federal lands that might impact NSO require consultation with U.S. Fish and Wildlife Service and part of that consultation often includes surveys to determine presence and occupancy status of the species in the proposed activity area. The objective of this report is to present study-area specific estimates of the probability of detection for NSO pairs from twelve 2-week seasonal survey periods using data from a recent range-wide meta-analysis. These estimates were a by-product of pair occupancy modeling but might provide insight into potential changes in the effect of the invasive barred owl on NSO detection rates. We used two-species multi-season occupancy models to estimate the probability of detection for NSOs on each of 11 study areas for each 2-week survey period and relative to the range-wide effect of barred owl presence or absence. Detection probabilities within the season generally increased from the earliest surveys in March through mid-season, decreasing again in the late season on five study areas. For three other study areas, detection rates were highest during the earliest survey periods in late March or early April. Estimates of cumulative seasonal detection of NSO (across a maximum of six within-season surveys) were less than 0.90 when barred owls (BO) were present on all but one study area, regardless of when surveys were conducted within a season. However, despite low detection rates, the probability that a territory was occupied when an NSO pair was not detected over six within-season surveys was also very low. When BO are not present on a territory, a six-survey protocol had a high probability of detecting an NSO pair at least once during the season on all study areas, except for the very lowest per-survey estimates. Conducting most surveys earlier in the season, when the probability of detecting pairs is highest (through May on most areas) could improve seasonal detection rates. However, alternative methods of population monitoring—such as the use of passive acoustic recorders—may be needed to continue monitoring NSO for research and management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231012","collaboration":"Prepared in cooperation with Oregon State University","usgsCitation":"Dugger, K.M., Franklin, A.B., Lesmeister, D.B., Davis, R.J., Wiens, J.D., White, G.C., Nichols, J.D., Hines, J.E., Yackulic, C.B., Schwarz, C.J., Ackers, S.H., Andrews, L.S., Bailey, L.L., Bown, R., Burgher, J., Burnham, K.P., Carlson, P.C., Chestnut, T., Conner, M.M., Dilione, K.E., Forsman, E.D., Gremel, S.A., Hamm, K.A., Herter, D.R., Higley, J.M., Horn, R.B., Jenkins, J.M., Kendall, W.L., Lamphear, D.W., McCafferty, C., McDonald, T.L., Reid, J.A., Rockweit, J.T., Simon, D.C., Sovern, S.G., Swingle, J.K., and Wise, H., 2023, Estimating northern spotted owl (Strix occidentalis caurina) pair detection probabilities based on call-back surveys associated with long-term mark-recapture studies, 1993–2018: U.S. Geological Survey Open-File Report 2023–1012, 25 p., https://doi.org/10.3133/ofr20231012.","productDescription":"vii, 25 p.","onlineOnly":"Y","ipdsId":"IP-133003","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":417167,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231012/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1012"},{"id":417166,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1012/ofr20231012.pdf","text":"Report","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1012"},{"id":417169,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1012/ofr20231012.XML"},{"id":417168,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1012/images"},{"id":417165,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1012/coverthb.jpg"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.82419135781177,\n 37.90972622681076\n ],\n [\n -122.3277779102568,\n 38.08861577320431\n ],\n [\n -120.925210233316,\n 42.44504507790097\n ],\n [\n -120.0290356856878,\n 48.995370896990295\n ],\n [\n -123.35793848445479,\n 48.94274756685837\n ],\n [\n -123.44660126094462,\n 48.247927989659445\n ],\n [\n -125.06690973675748,\n 48.590141468479914\n ],\n [\n -124.10299464056828,\n 46.115944811333236\n ],\n [\n -124.32184346408252,\n 43.795550298890845\n ],\n [\n -124.71979791193871,\n 42.67568668540227\n ],\n [\n -124.10920825253021,\n 41.43162232110191\n ],\n [\n -124.53261803772267,\n 40.21134172936314\n ],\n [\n -123.80095675617886,\n 38.77121416707598\n ],\n [\n -122.82419135781177,\n 37.90972622681076\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
777 NW 9th Street
Suite 400
Corvallis, OR 97330
Throughout the COVID-19 pandemic, veterinary diagnostic laboratories have tested diagnostic samples for SARS-CoV-2 both in animals and over 6 million human samples. An evaluation of the performance of those laboratories is needed using blinded test samples to ensure that laboratories report reliable data to the public. This interlaboratory comparison exercise (ILC3) builds on 2 prior exercises to assess whether veterinary diagnostic laboratories can detect Delta and Omicron variants spiked in canine nasal matrix or viral transport medium.
The ILC organizer was an independent laboratory that prepared inactivated Delta variant at levels of 25 to 1000 copies per 50 µL of nasal matrix for blinded analysis. Omicron variant at 1000 copies per 50 µL of transport medium was also included. Feline infectious peritonitis virus (FIPV) RNA was used as a confounder for specificity assessment. Fourteen test samples were prepared for each participant. Participants used their routine diagnostic procedures for RNA extraction and real-time reverse transcriptase-PCR. Results were analyzed according to International Organization for Standardization (ISO) 16140–2:2016.
Overall, laboratories demonstrated 93% detection for Delta and 97% for Omicron at 1000 copies per 50 µL. Specificity was 97% for blank samples and 100% for blank samples with FIPV. No differences in Cycle Threshold (Ct) values were significant for samples with the same virus levels between N1 and N2 markers, nor between the 2 variants.
The results indicated that all ILC3 participants were able to detect both Delta and Omicron variants. The canine nasal matrix did not significantly affect SARS-CoV-2 detection.
Development in natural areas is a leading threat to biodiversity. Global conservationists have called for the expansion of protected areas to preserve wildlands that are free from buildings, and in the U.S., the ‘America the Beautiful’ initiative aims to protect 30% of land and water areas by 2030 (known as the ‘30x30’ target). Here, we determined opportunities and limitations for conservation in the conterminous U.S. by assessing the extent of buildings in wildland vegetation. We focused specifically on National Forest lands, as these contain numerous private inholdings where development may occur. Using a newly available building footprint dataset, we determined 1) whether buildings were present and 2) numbers of buildings within three distances of wildland vegetation (100, 250, and 500 m), representing varying magnitudes of ecological impact. Our findings revealed that 29% of wildland vegetation nationwide was within 500 m of a building, 15% was within 250 m, and 5% was within 100 m. National Forest lands were less affected by building disturbance, but a substantial proportion (12%) of wildland vegetation area was within 500 m of a building. Of National Forest lands that were within 500 m of an inholding, 76% was not yet in proximity to a building; consequently, ∼10% of National Forest lands (143,474 km2) are susceptible to impacts from future development on inholdings. We conclude that National Forest inholdings are therefore important opportunity areas for 30x30 conservation goals. Our assessments can inform where conservation efforts can limit impacts from present and future development on biodiversity.
The U.S. Geological Survey Science Application for Risk Reduction (SAFRR) Project has created four major hazard scenarios—ShakeOut, ARkStorm, Tsunami Scenario, and HayWired—with multidisciplinary teams of scientists, academics, and practitioners. By presenting a clear and highly detailed narrative of potential damage from earthquakes, tsunamis, and winter storms, the scenarios are intended to foster science-based preparedness strategies and disaster risk reduction innovations.
This evaluation explores the presence of these scenarios in cultures of preparedness and their role in disaster risk reduction, and reports barriers and enablers to creating and using these scenarios. To do this, the evaluation team developed a mixed-methods study that includes background research for each scenario, qualitative interviews, data collection of media and academic engagement, and examples of SAFRR scenario use in hazard planning. The data collection led to the development of a hazard scenario evaluation tool that combines theories from multiple disciplines to create a best practice set of categories that aid in scenario use and efficacy. The evaluation tool categories—actionable, longitudinal, educational, relevant, and thorough—are organized as a series of checklists that are used to determine how the scenario planners prioritized different aspects of the scenarios to achieve their goals. The tool could also be used to aid scenario planning for other regional disaster risk reduction scenarios of a similar scope.
Findings from this evaluation include detailed narratives of scenario use over time, demonstrating that the scenarios have continued to be useful in hazard planning and preparedness across the globe. Examples of use include using the scenarios to advocate for resilient building and development policy, to promote hazard response exercises, and as source data for the development of new hazard models and science. The scenarios themselves are innovative, both in the hazard science created for scenario development and in their branding and public engagement as U.S. Geological Survey products. This SAFFR retrospective is a descriptive evaluation and does not formally address the effects of the scenarios. Nevertheless, this report does include evidence of scenario affects as discovered through qualitative interviews and research, which is presented to explore how the SAFRR scenarios have been received by cultures of preparedness.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20235011","usgsCitation":"Smithhisler, N., and Burkardt, N., 2023, The Science Application for Risk Reduction (SAFRR) Scenario Retrospective 2006–21: U.S. Geological Survey Scientific Investigations Report 2023–5011, 116 p., https://doi.org/10.3133/sir20235011.","productDescription":"Report: viii, 116 p.; Program Agenda","onlineOnly":"Y","ipdsId":"IP-129947","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":417356,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5011/sir20235011.xml"},{"id":417355,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5011/images"},{"id":417314,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5011/agenda.pdf","text":"ARkSTORM Summit Program","size":"3.10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"ARkSTORM Summit Program"},{"id":417313,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5011/sir20235011.pdf","text":"Report","size":"15.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5011"},{"id":417312,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5011/coverthb.jpg"},{"id":500692,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114738.htm","linkFileType":{"id":5,"text":"html"}}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118