The seismic potential of the Lesser Antilles megathrust remains poorly known, despite the potential hazard it poses to numerous island populations and its proximity to the Americas. As it has not produced any large earthquakes in the instrumental era, the megathrust is often assumed to be aseismic. However, historical records of great earthquakes in the 19th century and earlier, which were most likely megathrust ruptures, demonstrate that the subduction is not entirely aseismic. Recent occurrences of giant earthquakes in areas where such events were previously thought to be improbable have illustrated the importance of critically evaluating the seismic potential of other “low-hazard” subduction zones, such as the Lesser Antilles.
Using the method of coral microatoll paleogeodesy developed in Sumatra, we examine 20th-century vertical deformation on the forearc islands of the Lesser Antilles and model the underlying strain accumulation on the megathrust. Our data indicate that the eastern coasts of the forearc islands have been subsiding by up to ∼8 mm/yr relative to sites closer to the arc, suggesting that on the time scale of the 20th century, a portion of the megathrust just east of the forearc islands has been locked. Our findings are in contrast to recent models based on satellite geodesy that suggest little or no strain accumulation anywhere along the Lesser Antilles megathrust. This discrepancy is potentially explained by the different time scales of measurement, as recent studies elsewhere have indicated that interseismic coupling patterns may vary on decadal time scales and that century-scale or longer records are required to fully assess seismic potential. The accumulated strain we have detected will likely be released in future megathrust earthquakes, uplifting previously subsiding areas and potentially causing widespread damage from strong ground motion and tsunami waves.
Benthic diatom assemblages are known to be indicative of water quality but have yet to be widely adopted in biological assessments in the United States due to several limitations. Our goal was to address some of these limitations by developing regional multi-metric indices (MMIs) that are robust to inter-laboratory taxonomic inconsistency, adjusted for natural covariates, and sensitive to a wide range of anthropogenic stressors. We aggregated bioassessment data from two national-scale federal programs and used a data-driven analysis in which all-possible combinations of 2–7 metrics were compared for three measures of performance. After ranking the best-performing MMIs, we selected the final MMIs by evaluating stress-response relations in independent regional datasets of diatom samples paired with measures of several water-quality stressors, including herbicides and streamflow flashiness. Each regional MMI performed well at calibration sites and represented diverse aspects of the structure and function of diatom communities. Most metrics included in the best MMIs were modeled to account for natural variation including climate, topography, soil characteristics, lithology, and groundwater influence on streamflow. MMI performance improved with higher numbers of component metrics, but this effect diminished beyond six metrics. Component metrics of MMIs were associated with a broad suite of measured stressors in every region, including salinity, nutrients, herbicides, and streamflow flashiness. We provide a web-based software application that allows users in the conterminous United States to apply our MMIs to their own datasets and compare MMI scores from their sites to a broader regional context.
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dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":854301,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spaulding, Sarah A. 0000-0002-9787-7743","orcid":"https://orcid.org/0000-0002-9787-7743","contributorId":223186,"corporation":false,"usgs":true,"family":"Spaulding","given":"Sarah","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":854302,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tyree, Meredith","contributorId":207506,"corporation":false,"usgs":false,"family":"Tyree","given":"Meredith","email":"","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":854303,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schulte, Nicholas O. 0000-0001-6284-4987","orcid":"https://orcid.org/0000-0001-6284-4987","contributorId":290510,"corporation":false,"usgs":false,"family":"Schulte","given":"Nicholas","email":"","middleInitial":"O.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":854304,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lee, Sylvia S","contributorId":245621,"corporation":false,"usgs":false,"family":"Lee","given":"Sylvia","email":"","middleInitial":"S","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":854305,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mitchell, Richard M.","contributorId":215406,"corporation":false,"usgs":false,"family":"Mitchell","given":"Richard M.","affiliations":[{"id":39239,"text":"USEPA, Washington D.C.","active":true,"usgs":false}],"preferred":false,"id":854306,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pollard, Amina A.","contributorId":297613,"corporation":false,"usgs":false,"family":"Pollard","given":"Amina","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":854307,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70249307,"text":"70249307 - 2022 - Active‐source interferometry in marine and terrestrial environments: Importance of directionality and stationary phase","interactions":[],"lastModifiedDate":"2023-10-04T12:28:40.614882","indexId":"70249307","displayToPublicDate":"2022-01-04T07:27:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Active‐source interferometry in marine and terrestrial environments: Importance of directionality and stationary phase","docAbstract":"We utilize active‐source seismic interferometry with dense seismic arrays both offshore and onland to explore the utility of this method to create virtual sources and reveal body‐wave reflections in these two different environments. We first utilize data from an ocean‐bottom cable (OBC) array in the Gulf of Mexico with equal numbers of sources (160 airgun shots) and receivers (160 ocean‐bottom four‐component sensors). We next use data from a geophone array across the Bighorn Mountains of Wyoming with many receivers (1300 vertical‐component geophones) but a small number of sources (14 borehole active‐source shots). We find that the OBC virtual source results, which produce strong reflections from sub‐seafloor structures, are far superior to the onland results which lack usable reflections, and we explore reasons for these differences through a set of selective stacking approaches. We present techniques to account for the direction the seismic waves travel (directionality) and stationary phase and show that improvements can be made when incorporating these corrections. Although interferometric methods are based on assumptions of large numbers of widely distributed actual sources, we find that selective exclusion of potentially problematic source–receiver pairs can yield improved results. These geometric adjustments to active‐source interferometry methods have utility for dense‐nodal‐array surveys that are now common in academic studies, but that often suffer from sparse source geometry.
Harpacticoid copepods can be a substantial component of the meiobenthic community in lakes and serve an ecological role as detritivores. Here we present the first species-level lake-wide quantitative assessment of the harpacticoid assemblage of Lake Ontario with emphasis on the status of nonindigenous species. Additionally, we provide COI-5P sequences of harpacticoid taxa through Barcode of Life Data System (BOLD). Harpacticoids were collected at depths from 0.1 to 184 m and from a range of substrates from August to September 2018 as part of the Cooperative Science and Monitoring Initiative (CSMI) offshore benthic survey. Twenty-six meiobenthic samples were analyzed using microscopy for community composition analysis of harpacticoids. We found thirteen indigenous and three nonindigenous species of harpacticoid, with the introduced species dominating at shallow depths. The community transitioned from nonindigenous to indigenous species dominance as depth increased. Nonindigenous species accounted for 79% of the community (by abundance) at depths <20 m, 55% from 20 to 40 m, and only 24% at depths >40 m. The nonindigenous species encountered included the first detections of Schizopera borutzkyi (Monchenko, 1967) and Heteropsyllus nunni (Coull, 1975) from Lake Ontario. S. borutzkyi was the most abundant harpacticoid species in the lake, approaching a maximum density of 50,000/m2 and a lake-wide average density of 7,900/m2. Numerically important indigenous species included Bryocamptus nivalis (Willey, 1925), Canthocamptus robertcokeri (Wilson, 1958), Canthocamptus staphylinoides (Pearse, 1905), and Moraria cristata (Chappuis, 1929). The prevalence of nonindigenous harpacticoids in the meiobenthos of Lake Ontario suggests further investigations of Great Lakes meiofauna communities are warranted.
Globally, sea turtle research and conservation efforts are underway to identify important high-use areas where these imperiled individuals may be resident for weeks to months to years. In the southeastern Gulf of Mexico, recent telemetry studies highlighted post-nesting foraging sites for federally endangered green turtles (Chelonia mydas) around the Florida Keys. In order to delineate additional areas that may serve as inter-nesting, migratory, and foraging hotspots for reproductively active females nesting in peninsular southwest Florida, we satellite-tagged 14 green turtles that nested at two sites along the southeast Gulf of Mexico coastline between 2017 and 2019: Sanibel and Keewaydin Islands. Prior to this study, green turtles nesting in southwest Florida had not previously been tracked and their movements were unknown. We used switching state space modeling to show that an area off Cape Sable (Everglades), Florida Bay, and the Marquesas Keys are important foraging areas that support individuals that nest on southwest Florida mainland beaches. Turtles were tracked for 39–383 days, migrated for a mean of 4 days, and arrived at their respective foraging grounds in the months of July through September. Turtles remained resident in their respective foraging sites until tags failed, typically after several months, where they established mean home ranges (50% kernel density estimate) of 296 km2. Centroid locations for turtles at common foraging sites were 1.2–36.5 km apart. The area off southwest Florida Everglades appears to be a hotspot for these turtles during both inter-nesting and foraging; this location was also used by turtles that were previously satellite tagged in the Dry Tortugas after nesting. Further evaluation of this important habitat is warranted. Understanding where and when imperiled yet recovering green turtles forage and remain resident is key information for designing surveys of foraging resources and developing additional protection strategies intended to enhance population recovery trajectories.
The Jerusalem cricket subfamily Stenopelmatinae is distributed from south-western Canada through the western half of the United States to as far south as Ecuador. Recently, the generic classification of this subfamily was updated to contain two genera, the western North American Ammopelmatus, and the Mexican, and central and northern South American Stenopelmatus. The taxonomy of the latter genus was also revised, with 5, 13 and 14 species being respectively validated, declared as nomen dubium and described as new. Despite this effort, the systematics of Stenopelmatus is still far from complete. Here, we generated sequences of the mitochondrial DNA barcoding locus and performed two distinct DNA sequence-based approaches to assess the species’ limits among several populations of Stenopelmatus, with emphasis on populations from central and south-east Mexico. We reconstructed the phylogenetic relationships among representative species of the main clades within the genus using nuclear 3RAD data and carried out a molecular clock analysis to investigate its biogeographic history. The two DNA sequence-based approaches consistently recovered 34 putative species, several of which are apparently undescribed. Our estimates of phylogeny confirmed the recent generic update of Stenopelmatinae and revealed a marked phylogeographic structure within Stenopelmatus. Based on our results, we propose the existence of four species-groups within the genus (the faulkneri, talpa, Central America and piceiventris species-groups). The geographic distribution of these species-groups and our molecular clock estimates are congruent with the geological processes that took place in mountain ranges along central and southern Mexico, particularly since the Neogene. Our study emphasises the necessity to continue performing more taxonomic and phylogenetic studies on Stenopelmatus to clarify its actual species richness and evolutionary history in Mesoamerica.
Walleye Sander vitreus and Yellow Perch Perca flavescens are culturally, economically, and ecologically significant fish species in North America that are affected by drivers of global change. Here, we review and synthesize the published literature documenting the effects of ecosystem changes on Walleye and Yellow Perch. We focus on four drivers: climate (including temperature and precipitation), aquatic invasive species, land use and nutrient loading, and water clarity. We identified 1,232 tests from 370 papers, split evenly between Walleye (n = 613) and Yellow Perch (n = 619). Climate was the most frequently studied driver (n = 572), and growth or condition was the most frequently studied response (n = 297). The most commonly reported relationship was “no effect” (42% of analyses), usually because multiple variables were tested and only a few were found to be significant. Overall responses varied among studies for most species-response–driver combinations. For example, the influence of invasive species on growth of both Walleye and Yellow Perch was approximately equally likely to be positive, negative, or have no effect. Even when results were variable, important patterns emerged; for example, growth responses of both species to temperature were variable, but very few negative responses were observed. A few relationships were relatively consistent across studies. Invasive species were negatively associated with Walleye recruitment and abundance, and higher water clarity was negatively associated with Walleye abundance, biomass, and production. Some variability in responses may be due to differences in methodology or the range of variables studied; others represent true context dependence, where the effect of a driver depends on the influence of other variables. Using common metrics of impact, publishing negative results, and robust analytical approaches could facilitate comparisons among systems and provide a more comprehensive understanding of the responses of Walleye and Yellow Perch to ecosystem change.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10741","usgsCitation":"Hansen, G., Ruzich, J., Krabbenhoft, C., Kundel, H., Mahlum, S., Rounds, C., Van Pelt, A., Eslinger, L., Logsdon, D., and Isermann, D.A., 2022, It’s complicated and it depends: A review of the effects of ecosystem changes on Walleye and Yellow Perch populations in North America: North American Journal of Fisheries Management, v. 42, no. 3, p. 484-506, https://doi.org/10.1002/nafm.10741.","productDescription":"23 p.","startPage":"484","endPage":"506","ipdsId":"IP-133285","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480945,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United 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O.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":924332,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Eslinger, Lawrence D.","contributorId":349476,"corporation":false,"usgs":false,"family":"Eslinger","given":"Lawrence D.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":924333,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Logsdon, Dale E.","contributorId":349478,"corporation":false,"usgs":false,"family":"Logsdon","given":"Dale E.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":924334,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 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populations of generalist predators such as common ravens (ravens; Corvus corax), which in turn may depress populations of many types of species at lower-trophic levels, including desert tortoises (Gopherus agassizii) or greater sage-grouse (Centrocercus urophasianus). Management of subsidized ravens often has targeted local breeding populations that are presumed to affect species of concern and ignored “urban” populations of ravens. However, little is known about how ravens move, especially in response to the presence of anthropogenic subsidies. Therefore, subsidized ravens from distant populations that are not managed may influence local prey. To better understand this issue, we deployed global positioning system – global system for mobile communications transmitters to track movements of 19 ravens from September to December 2020 relative to 2 land cover types that provide subsidies: developed areas and cultivated crops. On average, ravens moved 41.5 km (±30.5) per day, although daily movement distances ranged from 0.13– 206.1 km. Raven movement among cover types during the non-breeding season varied widely, with 100% of individuals each using land cover types that provide subsidy and other types at least once in the season. On 100% of days ravens used areas that did not provide subsidy, on 86.7% of days they used developed areas, and on 20.5% of days they used cultivated crops. Although on some days a raven would stay exclusively in areas that did not provide subsidy, there were no days in which a single raven ever stayed exclusively in developed or cultivated crops. Ravens moved shorter distances on days when they used subsidies more frequently. Further, time spent in developed areas and cultivated crops increased when ravens roosted closer to them, although this effect was greater for developed areas than for cultivated crops. Individual ravens were not associated exclusively with either of the subsidy-providing landscapes we considered, but instead all birds used both subsidized and other landscapes. Our research suggests that management of ravens during the non-breeding season and possibly during the breeding season, intended to reduce risk of predation on desert tortoises, will be most effective if conducted on a broad scale because of distances the birds travel and the lack of separation between putative “urban” and “natural” populations of ravens.
","language":"English","publisher":"Berryman Institute","doi":"10.15142/whgt-5q40","usgsCitation":"Duerr, A.E., Bloom, P.H., Ross, K., Miller, T., Braham, M., Fesnock, A., and Katzner, T., 2022, Influence of anthropogenic subsidies on movements of common ravens: Human-Wildlife Interactions, v. 15, no. 3, p. 1-13, https://doi.org/10.15142/whgt-5q40.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-128213","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":402801,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.4873046875,\n 32.62087018318113\n ],\n [\n -114.2138671875,\n 32.62087018318113\n ],\n [\n -114.2138671875,\n 35.496456056584165\n ],\n [\n -119.4873046875,\n 35.496456056584165\n ],\n [\n -119.4873046875,\n 32.62087018318113\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Duerr, Adam E.","contributorId":190590,"corporation":false,"usgs":false,"family":"Duerr","given":"Adam","email":"","middleInitial":"E.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":845467,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bloom, Peter H.","contributorId":242659,"corporation":false,"usgs":true,"family":"Bloom","given":"Peter","email":"","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":845468,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ross, Kerry","contributorId":292685,"corporation":false,"usgs":false,"family":"Ross","given":"Kerry","email":"","affiliations":[{"id":38830,"text":"Bloom Research Inc.","active":true,"usgs":false}],"preferred":false,"id":845472,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Tricia A.","contributorId":64790,"corporation":false,"usgs":true,"family":"Miller","given":"Tricia A.","affiliations":[],"preferred":false,"id":845469,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Braham, Melissa A.","contributorId":140127,"corporation":false,"usgs":false,"family":"Braham","given":"Melissa A.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":845470,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fesnock, Amy L","contributorId":290517,"corporation":false,"usgs":false,"family":"Fesnock","given":"Amy L","affiliations":[{"id":6696,"text":"BLM","active":true,"usgs":false}],"preferred":false,"id":845471,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":845456,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227172,"text":"ofr20211030L - 2022 - System characterization report on the Satellogic NewSat multispectral sensor","interactions":[{"subject":{"id":70227172,"text":"ofr20211030L - 2022 - System characterization report on the Satellogic NewSat multispectral sensor","indexId":"ofr20211030L","publicationYear":"2022","noYear":false,"chapter":"L","displayTitle":"System Characterization Report on the Satellogic NewSat Multispectral Sensor","title":"System characterization report on the Satellogic NewSat multispectral sensor"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2024-11-27T14:21:36.54492","indexId":"ofr20211030L","displayToPublicDate":"2022-01-03T13:35:00","publicationYear":"2022","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":"2021-1030","chapter":"L","displayTitle":"System Characterization Report on the Satellogic NewSat Multispectral Sensor","title":"System characterization report on the Satellogic NewSat multispectral sensor","docAbstract":"This report addresses system characterization of Satellogic’s NewSat satellite (also known as ÑuSat) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Since 2016, Satellogic has launched 17 NewSat satellites. All NewSat satellites have four-band imagers with a 1-meter (m) ground sample distance, and values in pixels are identical to values in meters. All NewSats have been launched into Sun-synchronous orbits of about 475 kilometers, with inclinations of about 97.5 degrees. The satellites have expected lifetimes of about 3 years. More information on the Satellogic satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://satellogic.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that the NewSat satellites have an interior geometric performance in the range of −0.119 (−0.119 pixel) to 0.020 m (0.020 pixel) in easting and −0.148 (−0.148 pixel) to 0.014 m (0.014 pixel) in northing in band-to-band registration, an exterior geometric performance of −9.04 (−9.04 pixels) to −5.84 m (−5.84 pixels) in easting and 1.25 (1.25 pixels) to 3.11 m (3.11 pixels) in northing offset in comparison to Sentinel-2, an exterior geometric performance using ground control points of a 6.5-m circular error (95 percent), a radiometric performance in the range of 0.034 to 0.081 in offset and 0.652 to 0.808 in slope, and a spatial performance in the range of 1.61 to 1.76 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.081 to 0.138.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"System characterization of Earth observation sensors","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030L","usgsCitation":"Vrabel, J.C., Bresnahan, P., Stensaas, G.L., Anderson, C., Christopherson, J., Kim, M., and Park, S., 2022, System characterization report on the Satellogic NewSat multispectral sensor (ver. 1.1, April 2022), chap. L of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 28 p., https://doi.org/10.3133/ofr20211030L.","productDescription":"v, 28 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-135435","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":393738,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/l/ofr20211030l.pdf","text":"Report","size":"7.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-L"},{"id":393737,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/l/coverthb2.jpg"},{"id":399739,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/l/versionHist.txt","size":"1 kB"}],"edition":"Version 1.0: January 3, 2022; Version 1.1: April 28, 2022","contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The revision of federal safety regulations for integrity management of gas transmission pipelines to require explicit consideration of seismicity increases the importance for operators to be actively identifying high-consequence areas (HCAs), evaluating seismic-related threats, and choosing a risk model to support risk management decisions. To ensure equal access to information by both operators and inspectors, the authors have compiled publicly available data and tools for practical seismic risk assessments, such as Microsoft building footprints, the USGS National Seismic Hazard Models, and the USGS Ground Failure product.
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Simon 0000-0003-3017-9585","orcid":"https://orcid.org/0000-0003-3017-9585","contributorId":241863,"corporation":false,"usgs":true,"family":"Kwong","given":"N.","email":"","middleInitial":"Simon","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":830280,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":830281,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baker, J. W. 0000-0003-2744-9599","orcid":"https://orcid.org/0000-0003-2744-9599","contributorId":198187,"corporation":false,"usgs":false,"family":"Baker","given":"J.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":830282,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Luco, Nico 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":145730,"corporation":false,"usgs":true,"family":"Luco","given":"Nico","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":830283,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ludwig, K. A. 0000-0002-0935-9410 kaludwig@usgs.gov","orcid":"https://orcid.org/0000-0002-0935-9410","contributorId":596,"corporation":false,"usgs":true,"family":"Ludwig","given":"K.","email":"kaludwig@usgs.gov","middleInitial":"A.","affiliations":[{"id":5059,"text":"Office of the Chief Scientist for National Hazards","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":830284,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stephens, Vasey J. 0000-0003-2661-7861","orcid":"https://orcid.org/0000-0003-2661-7861","contributorId":269838,"corporation":false,"usgs":false,"family":"Stephens","given":"Vasey","email":"","middleInitial":"J.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":false,"id":830285,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228442,"text":"70228442 - 2022 - Watershed-scale risk to aquatic organisms from complex chemical mixtures in the Shenandoah River","interactions":[],"lastModifiedDate":"2022-02-10T12:58:26.050876","indexId":"70228442","displayToPublicDate":"2022-01-03T06:53:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Watershed-scale risk to aquatic organisms from complex chemical mixtures in the Shenandoah River","docAbstract":"River waters contain complex chemical mixtures derived from natural and anthropogenic sources. Aquatic organisms are exposed to the entire chemical composition of the water, resulting in potential effects at the organismal through ecosystem level. This study applied a holistic approach to assess landscape, hydrological, chemical, and biological variables. On-site mobile laboratory experiments were conducted to evaluate biological effects of exposure to chemical mixtures in the Shenandoah River Watershed. A suite of 534 inorganic and organic constituents were analyzed, of which 273 were detected. A watershed-scale accumulated wastewater model was developed to predict environmental concentrations of chemicals derived from wastewater treatment plants (WWTPs) to assess potential aquatic organism exposure for all stream reaches in the watershed. Measured and modeled concentrations generally were within a factor of 2. Ecotoxicological effects from exposure to individual components of the chemical mixture were evaluated using risk quotients (RQs) based on measured or predicted environmental concentrations and no effect concentrations or chronic toxicity threshold values. Seventy-two percent of the compounds had RQ values <0.1, indicating limited risk from individual chemicals. However, when individual RQs were aggregated into a risk index, most stream reaches receiving WWTP effluent posed potential risk to aquatic organisms from exposure to complex chemical mixtures.
The size of an ecosystem affects ecological interactions, but less is known about how ecosystem size may affect social interactions. We posit that ecosystem size could serve as a basis for understanding and contextualizing social interactions, connecting how ecosystem size influences natural resource investment decisions and the use of ecosystem services. We leverage international (Canada, Czech Republic, Germany, United States of America) inland recreational fishery data to explore whether certain ecosystem sizes receive a disproportionate amount of fish stocked (a measure of resource investment) and attract more angler effort – our measure of an ecosystem service. We find that smaller lentic waterbodies receive a disproportionate amount of fish stocked per area and also attract more angler effort per area consistently in all four countries. Therefore, we find that resource use and resource investment is matched by ecosystem size. We conclude that small waterbodies are prioritized by both managers and users and contribute more (per area) to recreational fisheries compared to large and more visible waterbodies on the landscape. An increasing focus on smaller-sized lakes and rivers, also those anthropogenically created, in science, assessment, and management is warranted.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2022.106388","usgsCitation":"Kaemingk, M., Arlinghaus, R., Birdsong, M., Chizinski, C., Lyach, R., Wilson, K., and Pope, K.L., 2022, Matching of resource use and investment according to waterbody size in recreational fisheries: Fisheries Research, v. 254, 106388, 6 p., https://doi.org/10.1016/j.fishres.2022.106388.","productDescription":"106388, 6 p.","ipdsId":"IP-124536","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433561,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"254","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kaemingk, M.A.","contributorId":340850,"corporation":false,"usgs":false,"family":"Kaemingk","given":"M.A.","email":"","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":908908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arlinghaus, R.","contributorId":268274,"corporation":false,"usgs":false,"family":"Arlinghaus","given":"R.","affiliations":[{"id":55610,"text":"IGB Leibniz-Institute of Freshwater Ecology and Inland Fisheries","active":true,"usgs":false}],"preferred":false,"id":908909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Birdsong, M.H.","contributorId":341801,"corporation":false,"usgs":false,"family":"Birdsong","given":"M.H.","email":"","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":908910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chizinski, C.J.","contributorId":340849,"corporation":false,"usgs":false,"family":"Chizinski","given":"C.J.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":908911,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lyach, R.","contributorId":341802,"corporation":false,"usgs":false,"family":"Lyach","given":"R.","affiliations":[{"id":81789,"text":"Institute for Evaluations and Social Analyses,","active":true,"usgs":false}],"preferred":false,"id":908912,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilson, K.L.","contributorId":341803,"corporation":false,"usgs":false,"family":"Wilson","given":"K.L.","email":"","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":908913,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pope, Kevin L. 0000-0003-1876-1687","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":270762,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"","middleInitial":"L.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908914,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228318,"text":"70228318 - 2022 - Pathways for avian influenza virus spread: GPS reveals wild waterfowl in commercial livestock facilities and connectivity with the natural wetland landscape","interactions":[],"lastModifiedDate":"2022-09-27T16:42:47.008718","indexId":"70228318","displayToPublicDate":"2022-01-02T06:47:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3849,"text":"Transboundary and Emerging Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Pathways for avian influenza virus spread: GPS reveals wild waterfowl in commercial livestock facilities and connectivity with the natural wetland landscape","docAbstract":"Zoonotic diseases are of considerable concern to the human population and viruses such as avian influenza (AIV) threaten food security, wildlife conservation and human health. Wild waterfowl and the natural wetlands they use are known AIV reservoirs, with birds capable of virus transmission to domestic poultry populations. While infection risk models have linked migration routes and AIV outbreaks, there is a limited understanding of wild waterfowl presence on commercial livestock facilities, and movement patterns linked to natural wetlands. We documented 11 wild waterfowl (three Anatidae species) in or near eight commercial livestock facilities in Washington and California with GPS telemetry data. Wild ducks used dairy and beef cattle feed lots and facility retention ponds during both day and night suggesting use for roosting and foraging. Two individuals (single locations) were observed inside poultry facility boundaries while using nearby wetlands. Ducks demonstrated high site fidelity, returning to the same areas of habitats (at livestock facilities and nearby wetlands), across months or years, showed strong connectivity with surrounding wetlands, and arrived from wetlands up to 1251 km away in the week prior. Telemetry data provides substantial advantages over observational data, allowing assessment of individual movement behaviour and wetland connectivity that has significant implications for outbreak management. Telemetry improves our understanding of risk factors for waterfowl–livestock virus transmission and helps identify factors associated with coincident space use at the wild waterfowl–domestic livestock interface. Our research suggests that even relatively small or isolated natural and artificial water or food sources in/near facilities increases the likelihood of attracting waterfowl, which has important consequences for managers attempting to minimize or prevent AIV outbreaks. Use and interpretation of telemetry data, especially in near-real-time, could provide key information for reducing virus transmission risk between waterfowl and livestock, improving protective barriers between wild and domestic species, and abating outbreaks.
Lake wide acoustic (AC) and bottom trawl (BT) surveys are conducted annually to generate indices of pelagic and benthic prey fish densities in Lake Michigan. The BT survey has been conducted each fall since 1973 using 12-m trawls at depths ranging from 9 to 110 m and includes 70 fixed locations distributed across seven transects; this survey estimates densities of seven prey fish species [i.e., alewife (Alosa pseudoharengus), bloater (Coregonus hoyi), rainbow smelt (Osmerus mordax), deepwater sculpin (Myoxocephalus thompsonii), slimy sculpin (Cottus cognatus), round goby (Neogobius melanostomus), ninespine stickleback (Pungitius pungitius)] as well as for age-0 yellow perch (Perca flavescens) and large (> 350 mm) burbot (Lota lota). The AC survey has been conducted each late summer/early fall since 2004, and the 2021 survey consisted of 25 transects [507 km total (315 miles)] covering bottom depths ranging from 15 to 235 m and 42 midwater trawl tows covering bottom depths ranging 13 to 215 m; this survey estimates densities of three prey fish species (i.e., alewife, bloater, and rainbow smelt). The data generated from these surveys are used to estimate various population parameters that are, in turn, used by state and tribal agencies in managing Lake Michigan fish stocks.
For the BT survey, total biomass density of prey fish equaled only 2.4 kg/ha, the 5th lowest estimate of the time series and well below the long-term average total biomass of 34.28 kg/ha. For the AC survey, total biomass density of prey fish equaled 6.61 kg/ha, 50% higher than the longterm average total biomass of 4.28 kg/ha.
The AC survey reported bloater to be the dominant species (by biomass) among prey fishes, while the BT survey reported co-dominance of alewife, bloater, and round goby. Mean biomass of yearling and older (YAO) alewives in 2021 was 1.71 kg/ha in the AC survey and 0.504 kg/ha in the BT survey. Catchability of YAO alewives continues to be substantially lower for the BT survey since 2014.
Comparing the acoustic estimate to previous years, YAO alewife biomass was 10% higher than the 2019 estimate and less than the average from 2004-2019. Numeric density of age-0 alewife from the AC survey was 352 fish/ha in 2021, which is 71% of the long-term mean of 499 fish/ha. The alewife age distribution remained truncated, with age-0 fish and age-1 fish dominating the population. Biomass density of YAO bloater was 3.7 kg/ha in the AC survey and 0.43 kg/ha in the BT survey- each at least an order of magnitude lower than what was estimated by the BT survey between 1981 and 1998. Numeric density of age-0 bloater was the highest ever measured for the AC survey at 1,037 fish/ha while for the BT survey, it was 20 fish/ha. Biomass density of YAO rainbow smelt was 0.13 kg/ha in the AC survey and 0.005 kg/ha in the BT survey, continuing the trend of low rainbow smelt biomass that has been observed since 2001. Numeric density of age-0 rainbow smelt was 84 fish/ha in the AC survey and 1.9 fish/ha in the BT survey, indicating a weak year-class. All four prey fish species sampled only by the BT survey indicated below average biomass densities. Deepwater sculpin was estimated at 0.45 kg/ha, which makes 11 of the past 12 years when biomass was <1 kg/ha. Slimy sculpin was estimated at 0.05 kg/ha, the sixth lowest density ever measured. Round goby was estimated at 0.63 kg/ha, which was below the average biomass of 0.84 kg/ha since 2008. Ninespine stickleback density was < 1 fish/ha. Burbot biomass remained near record low levels, and only three age-0 yellow perch were caught in all trawls, indicating a weak yellow perch year-class in 2021.
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III 0000-0002-1689-2133","orcid":"https://orcid.org/0000-0002-1689-2133","contributorId":189812,"corporation":false,"usgs":true,"family":"Tingley","given":"Ralph","suffix":"III","email":"","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":851149,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":851150,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Turschak, Benjamin A.","contributorId":150497,"corporation":false,"usgs":false,"family":"Turschak","given":"Benjamin","email":"","middleInitial":"A.","affiliations":[{"id":18038,"text":"University of Wisconsin, Milwaukee","active":true,"usgs":false}],"preferred":true,"id":851151,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hanson, Dale","contributorId":190498,"corporation":false,"usgs":false,"family":"Hanson","given":"Dale","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":851152,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228830,"text":"70228830 - 2022 - Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix A: Statistical hydrology","interactions":[],"lastModifiedDate":"2024-03-26T17:00:56.038518","indexId":"70228830","displayToPublicDate":"2022-01-01T11:53:47","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17147,"text":"Interagency Flood Risk Management Report","active":true,"publicationSubtype":{"id":1}},"title":"Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix A: Statistical hydrology","docAbstract":"Statistical analysis of the observational record from U.S. Geological Survey (USGS) streamgaging stations and other historical information provides an informative means of estimating flood flow frequency. Flood flow frequency is defined by values or quantiles of discharge for selected annual exceedance probabilities (AEPs) (England and others, 2018). The annual peak discharge data as part of systematic operation of a streamgaging station provides the foundation for a detailed analysis of peak discharge, but additional historical information pertaining to peak discharges also can be used. An annual peak discharge is defined as the maximum instantaneous discharge for a streamgaging station for a given water year, and annual peak discharge data for USGS streamgaging stations can be acquired through the USGS National Water Information System (NWIS) database (USGS, 2018). The statistical analyses are based on water-year increments. A water year is the 12-month period from October 1 of a given year through September 30 of the following year designated by the calendar year in which it ends.
For the statistical hydrology portion of the multi-layered analysis, InFRM team members from the USGS analyzed annual peak discharge records for the 15 USGS streamgaging stations (gages) shown on Figure A.1. Information on the period of record data for those USGS gages are listed in Table A.1.
","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"U.S. Army Corps of Engineers, Federal Emergency Management Agency","usgsCitation":"Wallace, D., 2022, Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix A: Statistical hydrology: Interagency Flood Risk Management Report, 64 p.","productDescription":"64 p.","ipdsId":"IP-101867","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":427112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396304,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/#ha"}],"country":"United States","state":"Texas","otherGeospatial":"Neches River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96,\n 32\n ],\n [\n -96,\n 30\n ],\n [\n -94,\n 30\n ],\n [\n -94,\n 32\n ],\n [\n -96,\n 32\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David S. 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":205198,"corporation":false,"usgs":true,"family":"Wallace","given":"David S.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835668,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70262186,"text":"70262186 - 2022 - Broadscale population structure and hatchery introgression of Midwestern brook trout: Midwestern brook trout population genetics","interactions":[],"lastModifiedDate":"2025-01-15T17:52:54.665605","indexId":"70262186","displayToPublicDate":"2022-01-01T11:45:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Broadscale population structure and hatchery introgression of Midwestern brook trout: Midwestern brook trout population genetics","docAbstract":"Brook Trout Salvelinus fontinalis have faced significant declines throughout their native range and have been stocked in Midwestern waters since the late 1800s to offset such losses. Several studies have investigated the genetic effects of these stockings, but these efforts have been confined to relatively small spatial scales. In this study, we compiled 8,454 Brook Trout microsatellite genotypes from 188 wild Midwestern populations and 26 hatchery strains to provide novel insights of broadscale population structure, regional patterns of genetic diversity, and estimates of hatchery introgression for inland Wisconsin populations. Our results indicate high levels of differentiation among our study populations, a lack of hydrological population structuring, lower estimates of genetic diversity in the Driftless Area, and that hatchery introgression has been largely confined to regions of inland Wisconsin that have been heavily affected by anthropogenic disturbances (i.e., the Driftless Area). We also provide evidence that populations may be able to purge hatchery‐derived alleles, discuss possible mechanisms behind this phenomenon, and consider their relevance to accurate estimation of hatchery introgression. Collectively, these results summarize the genetic effects of over a century of anthropogenic disturbance on native Brook Trout populations and emphasize the importance of integrating historical data into contemporary genetic research of intensively managed species.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/tafs.10333","usgsCitation":"Bradley Erdman, Matthew G. Mitro, Joanna D.T. Griffin, David Rowe, Kazyak, D.C., Keith Turnquist, Michael Siepker, Loren Miller, Stott, W., Hughes, M., Sloss, B., Kinnison, M.T., and Larson, W., 2022, Broadscale population structure and hatchery introgression of Midwestern brook trout: Midwestern brook trout population genetics: Transactions of the American Fisheries Society, v. 151, no. 1, p. 81-99, https://doi.org/10.1002/tafs.10333.","productDescription":"19 p.","startPage":"81","endPage":"99","ipdsId":"IP-132403","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Minnesota, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.62198592078825,\n 47.17494781150509\n ],\n [\n -95.43596059373883,\n 47.28169312893286\n ],\n [\n -95.46739097764518,\n 42.15136704567587\n ],\n [\n -86.50823833580571,\n 42.15136704567587\n ],\n [\n -86.62198592078825,\n 47.17494781150509\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"151","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Bradley Erdman","contributorId":348382,"corporation":false,"usgs":false,"family":"Bradley Erdman","affiliations":[{"id":83360,"text":"University of Maine School of Biology and Ecology","active":true,"usgs":false}],"preferred":false,"id":923419,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matthew G. Mitro","contributorId":348383,"corporation":false,"usgs":false,"family":"Matthew G. Mitro","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923420,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Joanna D.T. Griffin","contributorId":348384,"corporation":false,"usgs":false,"family":"Joanna D.T. Griffin","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923421,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"David Rowe","contributorId":348385,"corporation":false,"usgs":false,"family":"David Rowe","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923422,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":140409,"corporation":false,"usgs":true,"family":"Kazyak","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":923423,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keith Turnquist","contributorId":348386,"corporation":false,"usgs":false,"family":"Keith Turnquist","affiliations":[{"id":83363,"text":"College of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923424,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Michael Siepker","contributorId":348387,"corporation":false,"usgs":false,"family":"Michael Siepker","affiliations":[{"id":24495,"text":"Iowa Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923425,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Loren Miller","contributorId":348388,"corporation":false,"usgs":false,"family":"Loren Miller","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923426,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stott, Wendylee 0000-0002-5252-4901 wstott@usgs.gov","orcid":"https://orcid.org/0000-0002-5252-4901","contributorId":191249,"corporation":false,"usgs":true,"family":"Stott","given":"Wendylee","email":"wstott@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":923427,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hughes, Michael","contributorId":348579,"corporation":false,"usgs":false,"family":"Hughes","given":"Michael","affiliations":[],"preferred":false,"id":923611,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sloss, Brian","contributorId":191462,"corporation":false,"usgs":false,"family":"Sloss","given":"Brian","affiliations":[],"preferred":false,"id":923612,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kinnison, Michael T.","contributorId":169617,"corporation":false,"usgs":false,"family":"Kinnison","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":923613,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Larson, Wesley 0000-0003-4473-3401 wlarson@usgs.gov","orcid":"https://orcid.org/0000-0003-4473-3401","contributorId":199509,"corporation":false,"usgs":true,"family":"Larson","given":"Wesley","email":"wlarson@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923418,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70230312,"text":"70230312 - 2022 - Response in the water level of Anvil Lake, Wisconsin, to changes in meteorological and climatic changes, Wisconsin","interactions":[],"lastModifiedDate":"2022-09-13T16:42:30.131021","indexId":"70230312","displayToPublicDate":"2022-01-01T11:39:31","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Response in the water level of Anvil Lake, Wisconsin, to changes in meteorological and climatic changes, Wisconsin","docAbstract":"Anvil Lake, a relatively shallow seepage lake in northern Wisconsin, USA, has experienced dramatic changes in water level since elevation records began in 1938 in response to changes in meteorological and climatic conditions (Figure 1. Robertson et al., 2018). Anvil Lake’s water level record shows a pronounced 10–15-yr cycle, with recurring highs and lows with a typical swing of over 1 m. Although experiencing large cycles in water levels, the long-term average levels were relatively stable until about 1987, when water level dropped dramatically by an additional 1 m (in 2016). Water levels then rebounded dramatically, reaching near “normal” water levels in 2020. At its lowest level, the lake had a maximum depth of 8.2 m (mean depth of 4.7 m) and an area of 128 ha.\nLike most long-term records, Anvil Lake’s water level record has been measured by several observers using various techniques. To verify the consistency of the various datums used throughout this period, historical photographs with the water’s edge identified were obtained, tied to NAVD 1988 using a Real Time Kinematic satellite global positioning system, and compared with the measured water levels (See Figure 1).\nTo determine the causes of the changes in water level, a complete water budget was estimated for Anvil Lake from 1980 to 2014. Water levels in Anvil Lake were simulated (Figure 1) using a hydrodynamic model (General Lake Model, GLM), with daily lake evaporation estimated by\nGLM, monthly lake/groundwater exchange estimated with a groundwater model (MODFLOW), daily precipitation from the North American Land Data Assimilation System (NLDAS), and stream inflow and outflow were set as zero because the lake has no inlets or outlet. Atmospheric fluxes (precipitation minus evaporation) primarily drove the lake-level fluctuations and trends, but sub-decadal fluctuations in net groundwater exchange (groundwater inflow minus lake seepage) either enhanced or reduced the lake level response to the atmospheric drivers.\nThe changes in water levels were shown to affect the extent of stratification and water quality in the lake (Robertson et al., 2018). During periods of lower precipitation and lower water levels, Anvil Lake was a polymictic lake, whereas during periods of higher precipitation and higher water levels the lake was a dimictic lake with stratification lasting throughout summer. During periods with higher water levels, the water quality in the lake was shown to improve slightly as a result of the nutrients being diluted in a larger volume of water. If precipitation increases in the future, as results from many General Circulation Models (GCMs) suggest (Robertson et al., 2016), and if that outweighs the effects of increased evaporation caused by increased air temperatures, water levels in Anvil Lake may be expected to fluctuate at a higher level. Higher water levels in Anvil Lake are expected to result in the lake becoming more strongly stratified and have slightly improved water quality (lower nutrient and algal concentrations and increased water clarity) (Robertson et al., 2018).","language":"English","publisher":"Wisconsin Department of Natural Resources","usgsCitation":"Robertson, D., 2022, Response in the water level of Anvil Lake, Wisconsin, to changes in meteorological and climatic changes, Wisconsin, 2 p.","productDescription":"2 p.","ipdsId":"IP-130734","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":406607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398290,"type":{"id":15,"text":"Index Page"},"url":"https://wicci.wisc.edu/water-resources-working-group/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wisconsin","otherGeospatial":"Anvil Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.07800674438477,\n 45.93515431167519\n ],\n [\n -89.05139923095703,\n 45.93515431167519\n ],\n [\n -89.05139923095703,\n 45.9536560062781\n ],\n [\n -89.07800674438477,\n 45.9536560062781\n ],\n [\n -89.07800674438477,\n 45.93515431167519\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":839932,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70262314,"text":"70262314 - 2022 - Kirtland’s Warbler breeding productivity and habitat use in red pine-dominated habitat in Wisconsin, USA","interactions":[],"lastModifiedDate":"2025-01-23T17:39:20.813095","indexId":"70262314","displayToPublicDate":"2022-01-01T11:26:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":947,"text":"Avian Conservation and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Kirtland’s Warbler breeding productivity and habitat use in red pine-dominated habitat in Wisconsin, USA","docAbstract":"During the breeding season, Kirtland’s Warblers (Setophaga kirtlandii) are strongly associated with young jack pine (Pinus banksiana) forests in northern Lower Michigan, USA. Since 2007, the species has been breeding in unusual habitat, red pine (Pinus resinosa) dominated plantations, in central Wisconsin, USA. Kirtland’s Warbler productivity and habitat use in red pine is not well understood, and the central Wisconsin population is at a range edge, a situation often associated with lower productivity. To compare range-edge and range-core populations, we estimated reproductive success and characterized habitat use of Kirtland’s Warblers in central Wisconsin red pine-dominated plantations during 2015–2017 using logistic regression models. We also monitored nests and fledgling success, and estimated nest survival using logistic exposure models. Trees were closer together and herbaceous vegetation was taller and denser within territories than at randomly located points outside of territories. Females selected nest sites with deeper dead ground vegetation and live vegetation that was taller and denser than was available at randomly located points within male territories. Nest success was not strongly influenced by within-patch habitat factors. Nest daily survival rate was 0.97 (95% CI = 0.94–0.98). The average number of young fledged per nest was between 2.5 and 2.8. Nest parasitism by Brown-headed Cowbirds (Molothrus ater) was 22.7%. Overall, reproductive success in the peripheral central Wisconsin breeding population of Kirtland’s Warblers that used red pine-dominated plantations was similar to that of Kirtland’s Warblers breeding in typical jack pine habitat in the range core. Young red pine-dominated habitat appears to approximate young jack pine in habitat quality for Kirtland’s Warblers, and this may provide managers some flexibility in habitat maintenance for this conservation-reliant species.
","language":"English","publisher":"Resilience Alliance","doi":"10.5751/ace-02009-170103","usgsCitation":"Olah, A., Ribic, C., Grveles, K., Warner, S., Lopez, D., and Pidgeon, A., 2022, Kirtland’s Warbler breeding productivity and habitat use in red pine-dominated habitat in Wisconsin, USA: Avian Conservation and Ecology, v. 17, no. 1, 3, 23 p., https://doi.org/10.5751/ace-02009-170103.","productDescription":"3, 23 p.","ipdsId":"IP-107637","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481096,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-02009-170103","text":"Publisher Index Page"},{"id":481018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Adams County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-89.8982,44.2493],[-89.8376,44.249],[-89.7247,44.2479],[-89.717,44.2475],[-89.606,44.2458],[-89.5977,44.2458],[-89.5976,44.156],[-89.5981,44.0685],[-89.598,43.9824],[-89.5972,43.8945],[-89.5978,43.818],[-89.5995,43.7306],[-89.6,43.6427],[-89.7195,43.643],[-89.779,43.6411],[-89.7866,43.6411],[-89.7891,43.6433],[-89.786,43.6506],[-89.7892,43.6597],[-89.7892,43.6647],[-89.7924,43.667],[-89.7974,43.6693],[-89.8012,43.6697],[-89.8114,43.6788],[-89.8178,43.6915],[-89.8273,43.7034],[-89.828,43.7065],[-89.828,43.7106],[-89.828,43.7165],[-89.8318,43.722],[-89.8376,43.727],[-89.842,43.7288],[-89.8433,43.7338],[-89.8446,43.7411],[-89.8447,43.7497],[-89.8466,43.7566],[-89.8504,43.762],[-89.8549,43.7661],[-89.8612,43.772],[-89.8613,43.7766],[-89.8695,43.7834],[-89.8785,43.7924],[-89.8855,43.8002],[-89.8912,43.8052],[-89.9033,43.8133],[-89.9084,43.8174],[-89.9135,43.8242],[-89.9174,43.832],[-89.9231,43.8397],[-89.9327,43.8469],[-89.9384,43.8515],[-89.9479,43.8546],[-89.9524,43.8596],[-89.9633,43.8723],[-89.9659,43.8805],[-89.9672,43.8914],[-89.9705,43.901],[-89.9731,43.9101],[-89.9731,43.9178],[-89.9719,43.9206],[-89.9674,43.922],[-89.956,43.922],[-89.9522,43.9238],[-89.9503,43.9252],[-89.9484,43.9311],[-89.9504,43.9425],[-89.9492,43.9457],[-89.9524,43.9539],[-89.9582,43.9603],[-89.964,43.9785],[-89.9685,43.9875],[-89.973,43.9944],[-89.9769,43.9985],[-89.98,43.9998],[-89.9915,44.0029],[-89.9979,44.0047],[-90.0036,44.0075],[-90.0062,44.0102],[-90.0049,44.0129],[-89.9999,44.0166],[-89.9974,44.0184],[-89.9955,44.0221],[-89.9993,44.028],[-90.0032,44.0316],[-90.0108,44.0384],[-90.0154,44.0443],[-90.0179,44.0498],[-90.0186,44.0561],[-90.018,44.0598],[-90.0206,44.0616],[-90.0238,44.0675],[-90.0264,44.0748],[-90.0265,44.083],[-90.0253,44.0903],[-90.0234,44.0953],[-90.0209,44.0985],[-90.0139,44.1013],[-90.0107,44.104],[-90.0088,44.1072],[-90.0108,44.1109],[-90.0114,44.1172],[-90.0076,44.1204],[-90.0019,44.1223],[-89.9949,44.1232],[-89.9886,44.1241],[-89.9797,44.1287],[-89.9753,44.1338],[-89.9715,44.1415],[-89.9715,44.1497],[-89.971,44.1598],[-89.9697,44.162],[-89.9685,44.163],[-89.964,44.1634],[-89.957,44.1644],[-89.9519,44.1648],[-89.9474,44.1653],[-89.943,44.1644],[-89.9385,44.1622],[-89.9353,44.1581],[-89.9308,44.1521],[-89.9289,44.1517],[-89.9263,44.1526],[-89.9219,44.1554],[-89.9181,44.1618],[-89.9188,44.1668],[-89.9226,44.1722],[-89.9227,44.174],[-89.9207,44.175],[-89.9195,44.175],[-89.9163,44.1741],[-89.9118,44.1732],[-89.9074,44.1736],[-89.901,44.1764],[-89.8972,44.1805],[-89.8979,44.1842],[-89.903,44.1864],[-89.9106,44.1878],[-89.9151,44.1914],[-89.9164,44.1932],[-89.9145,44.1955],[-89.9056,44.1969],[-89.9024,44.1992],[-89.9043,44.2014],[-89.9114,44.2051],[-89.9165,44.2092],[-89.9191,44.2142],[-89.9185,44.2183],[-89.9127,44.221],[-89.9051,44.2224],[-89.9,44.2279],[-89.893,44.2284],[-89.8924,44.2325],[-89.8944,44.2398],[-89.8982,44.2493]]]},\"properties\":{\"name\":\"Adams\",\"state\":\"WI\"}}]}","volume":"17","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Olah, Ashley","contributorId":348827,"corporation":false,"usgs":false,"family":"Olah","given":"Ashley","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":923813,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ribic, Christine 0000-0003-2583-1778 caribic@usgs.gov","orcid":"https://orcid.org/0000-0003-2583-1778","contributorId":147952,"corporation":false,"usgs":true,"family":"Ribic","given":"Christine","email":"caribic@usgs.gov","affiliations":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923812,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grveles, Kim","contributorId":348828,"corporation":false,"usgs":false,"family":"Grveles","given":"Kim","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":923814,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, Sarah","contributorId":348829,"corporation":false,"usgs":false,"family":"Warner","given":"Sarah","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":923815,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lopez, Davin","contributorId":348830,"corporation":false,"usgs":false,"family":"Lopez","given":"Davin","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":923816,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pidgeon, Anna","contributorId":348831,"corporation":false,"usgs":false,"family":"Pidgeon","given":"Anna","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":923817,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230227,"text":"70230227 - 2022 - A shared vision for enhancing ecological resilience in the U.S. - Mexico borderlands: The Sky Island Restoration Collaborative","interactions":[],"lastModifiedDate":"2022-04-06T15:14:23.160741","indexId":"70230227","displayToPublicDate":"2022-01-01T11:24:23","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":10538,"text":"SERNews","active":true,"publicationSubtype":{"id":30}},"title":"A shared vision for enhancing ecological resilience in the U.S. - Mexico borderlands: The Sky Island Restoration Collaborative","docAbstract":"No abstract available.
","language":"English","publisher":"Society for Ecological Restoration","usgsCitation":"Norman, L., Girard, M., Pulliam, H.R., Villarreal, M.L., Clark, V.A., Flesch, A.D., Petrakis, R., Leibowitz, J., Tosline, D., Vaughn, K., Wagner, T., Weaver, C., Hare, T., Perez, J.M., Lopez Bujanda, O.E., Austin, J.T., Funicelli Campbell, C., Callegary, J.B., Wilson, N.R., Conn, J., Sisk, T., and Nabhan, G.L., 2022, A shared vision for enhancing ecological resilience in the U.S. - Mexico borderlands: The Sky Island Restoration Collaborative: SERNews, v. 36, no. 1, p. 19-27.","productDescription":"9 p.","startPage":"19","endPage":"27","ipdsId":"IP-136655","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":398122,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398222,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.ser.org/page/SERNews"}],"country":"Mexico, United States","state":"Arizona, Chihuahua, New Mexico, Sonora","otherGeospatial":"Madrean Archipelago Ecoregion, Madrean Sky Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.884765625,\n 27.819644755099446\n ],\n [\n -106.50146484374999,\n 27.819644755099446\n ],\n [\n -106.50146484374999,\n 33.63291573870479\n ],\n [\n -111.884765625,\n 33.63291573870479\n ],\n [\n -111.884765625,\n 27.819644755099446\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":839609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Girard, Michele","contributorId":257996,"corporation":false,"usgs":false,"family":"Girard","given":"Michele","email":"","affiliations":[{"id":52204,"text":"U.S. Forest Service (Ret.) and Cuenca Los Ojos","active":true,"usgs":false}],"preferred":false,"id":839610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pulliam, H. Ron","contributorId":289680,"corporation":false,"usgs":false,"family":"Pulliam","given":"H.","email":"","middleInitial":"Ron","affiliations":[{"id":52202,"text":"Borderlands Restoration Network","active":true,"usgs":false}],"preferred":false,"id":839611,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":839612,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clark, Valer 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nrwilson@usgs.gov","orcid":"https://orcid.org/0000-0001-5145-1221","contributorId":5770,"corporation":false,"usgs":true,"family":"Wilson","given":"Natalie","email":"nrwilson@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":839656,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Conn, Jeff","contributorId":258002,"corporation":false,"usgs":false,"family":"Conn","given":"Jeff","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":839657,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Sisk, Tom","contributorId":289711,"corporation":false,"usgs":false,"family":"Sisk","given":"Tom","email":"","affiliations":[],"preferred":false,"id":839658,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Nabhan, Gary 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Assessments are based on a bottom trawl survey conducted in October and an acoustics-midwater trawl survey conducted in September-October. In 2020, USGS-GLSC vessels were not permitted to cross into Canada due to the COVID-19 pandemic, so prey fish surveys sampled only sites in U.S. (Michigan) waters of Lake Huron. This prevented USGS from providing information about the current status and trends of prey fish communities in Georgian Bay and the North Channel. Prey fish biomass in U.S. waters of Lake Huron in 2020 remained below levels observed prior to community-wide declines that began in the early to mid-1990s. Fish community biomass was dominated by two species, Bloater (Coregonus hoyi) and Rainbow Smelt (Osmerus mordax). While both surveys found Bloater biomass in the main basin had declined from levels observed in 2019, Bloater still comprised over three-quarters of prey fish biomass in Lake Huron in 2020. Biomass and abundance for other prey fish species were within the range observed over the past five years. Current low biomass of invasive species like Alewife (Alosa pseudoharengus) and Rainbow Smelt is consistent with fish community objectives focused on restoration of native fish communities. Reduced lake productivity, predation by a recovering piscivore community, and shifts in food web dynamics that favor fish production in nearshore environments may prevent prey fish biomass in offshore areas from returning to levels observed prior to the early 1990’s. However, the dominance of Bloater in bottom trawl catches and acoustic surveys suggests that current lake conditions are conducive to the recovery of some native species.","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Hondorp, D.W., O’Brien, T.P., Esselman, P., and Roseman, E., 2022, Status and trends of the Lake Huron prey fish community, 1976-2020, 31 p.","productDescription":"31 p.","ipdsId":"IP-134682","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":398391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398390,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org"}],"country":"Canada, United States","otherGeospatial":"Lake Huron","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.7705078125,\n 45.81348649679973\n ],\n [\n -84.4189453125,\n 45.5679096098613\n ],\n [\n -83.81469726562499,\n 45.390735154248894\n ],\n [\n -83.507080078125,\n 45.166547157856016\n ],\n [\n -83.38623046875,\n 44.73892994307368\n ],\n [\n -83.507080078125,\n 44.315987905196906\n ],\n [\n -83.9794921875,\n 44.02442151965934\n ],\n [\n -83.95751953125,\n 43.59630591596548\n ],\n [\n -83.60595703125,\n 43.61221676817573\n ],\n [\n -82.891845703125,\n 44.05601169578525\n ],\n [\n -82.46337890625,\n 42.94838139765314\n ],\n [\n -81.705322265625,\n 43.37311218382002\n ],\n [\n -81.683349609375,\n 44.11125397357155\n ],\n [\n -81.134033203125,\n 44.61393394730626\n ],\n [\n -81.49658203125,\n 45.205263456162385\n ],\n [\n -81.024169921875,\n 44.629573191951046\n ],\n [\n -79.95849609375,\n 44.41024041296011\n ],\n [\n -79.903564453125,\n 44.77793589631623\n ],\n [\n -79.56298828125,\n 44.72332018895825\n ],\n [\n -80.145263671875,\n 45.56021795715051\n ],\n [\n -80.804443359375,\n 46.03510927947334\n ],\n [\n -81.58447265624999,\n 46.18743678432541\n ],\n [\n -82.63916015625,\n 46.255846818480315\n ],\n [\n -84.122314453125,\n 46.39998810407942\n ],\n [\n -83.81469726562499,\n 46.17983040759436\n ],\n [\n -83.8916015625,\n 46.126556302418514\n ],\n [\n -83.968505859375,\n 45.98169518512228\n ],\n [\n -84.6826171875,\n 46.11132565729796\n ],\n [\n -84.7705078125,\n 45.81348649679973\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":828709,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Brien, Timothy P. 0000-0003-4502-5204 tiobrien@usgs.gov","orcid":"https://orcid.org/0000-0003-4502-5204","contributorId":2662,"corporation":false,"usgs":true,"family":"O’Brien","given":"Timothy","email":"tiobrien@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":828710,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":828711,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":828712,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256646,"text":"70256646 - 2022 - Agent-based modeling of movements and habitat selection by mid-continent mallards","interactions":[],"lastModifiedDate":"2024-09-09T16:11:44.295133","indexId":"70256646","displayToPublicDate":"2022-01-01T11:07:39","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"FWS/CSS-143-2022","title":"Agent-based modeling of movements and habitat selection by mid-continent mallards","docAbstract":"We found that the absence of existing conservation measures would reduce wintering mallard population size by ~70-80%, underlining the importance of current wetland easements for waterfowl foraging. Under standard conditions, the partial active flooding of easements later in the season and the upgrading of unmanaged wetlands to managed status resulted in greatest mallard populations, indicating that active flooding (stored water release) was able to considerably increase carrying capacity under strong drought conditions.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Weller, F., Webb, E.B., Beatty, W., Fogenburg, S., Kesler, D., Blenk, R., Eadie, J., Ringelman, K., and Miller, M.L., 2022, Agent-based modeling of movements and habitat selection by mid-continent mallards: Cooperator Science Series FWS/CSS-143-2022, ii, 102 p.","productDescription":"ii, 102 p.","ipdsId":"IP-138825","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":431977,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/agent-based-modeling-movements-and-habitat-selection-mid-continent-mallards"},{"id":433631,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Weller, Florian G.","contributorId":341462,"corporation":false,"usgs":false,"family":"Weller","given":"Florian G.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":908463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":908464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beatty, William S. 0000-0003-0013-3113","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":224795,"corporation":false,"usgs":true,"family":"Beatty","given":"William S.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":908465,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fogenburg, Sean","contributorId":341463,"corporation":false,"usgs":false,"family":"Fogenburg","given":"Sean","affiliations":[],"preferred":false,"id":908466,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kesler, Dylan","contributorId":341464,"corporation":false,"usgs":false,"family":"Kesler","given":"Dylan","affiliations":[{"id":37290,"text":"The Institute for Bird Populations","active":true,"usgs":false}],"preferred":false,"id":908467,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Blenk, Robert H.","contributorId":341465,"corporation":false,"usgs":false,"family":"Blenk","given":"Robert H.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":908468,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Eadie, John M.","contributorId":341466,"corporation":false,"usgs":false,"family":"Eadie","given":"John M.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":908469,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ringelman, Kevin","contributorId":341467,"corporation":false,"usgs":false,"family":"Ringelman","given":"Kevin","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":908470,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Miller, Matt L.","contributorId":341468,"corporation":false,"usgs":false,"family":"Miller","given":"Matt","email":"","middleInitial":"L.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":908471,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70227426,"text":"70227426 - 2022 - Gas hydrates on Alaskan marine margins","interactions":[],"lastModifiedDate":"2022-01-14T16:38:17.536274","indexId":"70227426","displayToPublicDate":"2022-01-01T10:32:18","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Gas hydrates on Alaskan marine margins","docAbstract":"Gas hydrate distributions on the marine margins of the U.S. state of Alaska are more poorly known than those on other U.S. margins, where bottom simulating reflections have been systematically mapped on marine seismic data to support modern, quantitative assessments of gas-in-place in gas hydrates. The extent of bottom simulating reflections in the U.S. Beaufort Sea has been known since the late 1970s, and researchers have investigated the possibility that remnant gas hydrate persists in association with decaying subsea permafrost on both the U.S. and Canadian Beaufort continental shelves. In the Bering Sea, possible gas hydrate-related features have been widely mapped, revealing zones of free gas and concentrated gas hydrate within the hydrate stability zone in features called velocity amplitude anomalies (VAMPs). However, there are few reports on bottom simulating reflections along the more than 2500 km of the Aleutian arc and along the transform plate margin in southeast Alaska. Here we examine selected seismic profiles from southeast Alaska, along the Aleutian margin, and on the Bering continental slope, emphasizing surveys acquired with large airgun arrays, and review the results obtained from Bering Sea’s Aleutian Basin and from the U.S. Beaufort Sea. In the new analyses, we detect hydrate-related bottom simulating reflections in southeastern Alaska and the eastern and central parts of the Aleutian arc, but not in the western Aleutian arc or beneath the continental slope from the island arc north into the Aleutian Basin. In the Bering Sea, recognition of hydrate-related bottom simulating reflections is complicated by the widespread existence of a bottom simulating reflector associated with a diagenetic transition (opal CT). Our detection of continental slope hydrate-related bottom simulating reflections in southeast Alaska and the eastern and central Aleutian arcs expands the area of potential gas hydrate distribution on Alaskan margins and underscores the need for more systematic analysis of existing seismic data to inform quantitative evaluation of gas-in-place.
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