The Mississippi Alluvial Plain (MAP) in the United States (US).
Understanding local-scale groundwater use, a critical component of the water budget, is necessary for implementing sustainable water management practices. The MAP is one of the most productive agricultural regions in the US and extracts more than 11 km3/year for irrigation activities. Consequently, groundwater-level declines in the MAP region pose a substantial challenge to water sustainability, and hence, we need reliable groundwater pumping monitoring solutions to manage this resource appropriately.
We incorporate remote sensing datasets and machine learning to improve an existing lookup table-based model of groundwater use previously developed by the U.S. Geological Survey (USGS). Here, we employ Distributed Random Forests, an ensemble machine learning algorithm to predict annual and monthly groundwater use (2014–2020) throughout this region at 1-km resolution, using pumping data from existing flowmeters in the Mississippi Delta. Our model compares favorably with the existing USGS model, with higher R2 (0.51 compared to 0.42 in the previous model), and lower root mean square error (RMSE) and mean absolute error (MAE)— 0.14 m and 0.09 m, respectively in our model, compared to 0.15 m and 0.1 m in the previous model. Therefore, this work advances our ability to predict groundwater use in regions with scarce or limited in-situ groundwater withdrawal data availability.
Light detection and ranging (lidar) has emerged as a valuable tool for examining the fine-scale characteristics of vegetation. However, lidar is rarely used to examine coastal wetland vegetation or the habitat selection of small mammals. Extensive anthropogenic modification has threatened the endemic species in the estuarine wetlands of the California coast, such as the endangered salt marsh harvest mouse (Reithrodontomys raviventris; SMHM). A better understanding of SMHM habitat selection could help managers better protect this species. We assessed the ability of airborne topographic lidar imagery in measuring the vegetation structure of SMHM habitats in a coastal wetland with a narrow range of vegetation heights. We also aimed to better understand the role of vegetation structure in habitat selection at different spatial scales. Habitat selection was modeled from data compiled from 15 small mammal trapping grids collected in the highly urbanized San Francisco Estuary in California, USA. Analyses were conducted at three spatial scales: microhabitat (25 m2), mesohabitat (2025 m2), and macrohabitat (~10,000 m2). A suite of structural covariates was derived from raw lidar data to examine vegetation complexity. We found that adding structural covariates to conventional habitat selection variables significantly improved our models. At the microhabitat scale in managed wetlands, SMHM preferred areas with denser and shorter vegetation and selected for proximity to levees and taller vegetation in tidal wetlands. At the mesohabitat scale, SMHM were associated with a lower percentage of bare ground and with pickleweed (Salicornia pacifica) presence. All covariates were insignificant at the macrohabitat scale. Our results suggest that SMHM preferentially selected microhabitats with access to tidal refugia and mesohabitats with consistent food sources. Our findings showed that lidar can contribute to improving our understanding of habitat selection of wildlife in coastal wetlands and help to guide future conservation of an endangered species.
There is growing interest in incorporating higher-resolution groundwater modeling within the framework of large-scale land surface models (LSMs), including processes such as three-dimensional flow, variable soil saturation, and surface water/groundwater interactions. Conversely, complex groundwater models (e.g., the U.S. Geological Survey Groundwater-Flow Model, MODFLOW) often use simpler representations of land surface dynamics (e.g., surface vegetation, evapotranspiration, recharge) and may benefit from higher process fidelity and temporal resolutions in these inputs. This study investigates the potential of improving groundwater representation in LSMs and land surface dynamics in MODFLOW through forcing MODFLOW with recharge from LSMs. Groundwater simulations build on an existing and well-calibrated MODFLOW model of the U.S. Northern High Plains aquifer, a hydrologically complex basin under the dual impacts of conversion of native vegetation to intense irrigated agricultural fields and climate change. Simulated groundwater recharge from four different land models are used to drive MODFLOW groundwater simulations. Results show relatively large discrepancies between recharge estimates among simulations. Forcing MODFLOW using recharge simulated by some of the LSMs in place of a simple water balance model marginally improves MODFLOW groundwater simulation. Further, our results support the efficacy of coupling LSMs to a sophisticated groundwater model such as MODFLOW. The coupling results in notable improvements in matching the historical groundwater levels through reduction of the skewness coefficient in percent bias histogram (from 1.50 and 1.41 in original LSMs to 0.44 and 0.27, respectively, when MODFLOW is forced by groundwater recharge from LSMs) and reduction of bias. This modeling effort seeks to identify the best compromise between comprehensive land surface processes from global LSMs and advanced representation of groundwater from regional models.
Landsat orthorectified products use Ground Control Points (GCPs) and Digital Elevation Models (DEM) to improve the geolocation accuracy and temporal consistency, and to account for the relief displacements due to the sensor-target geometry. In Collection-2, to improve the geometric harmonization between Landsat and Sentinel-2 (S2) orthorectified products, the Landsat GCP's absolute and relative accuracies were improved using the S2 Global Reference Image (GRI) dataset through a continent-level bundle adjustment method. The GRI is a highly accurate global image dataset that was developed by the European Space Agency (ESA) to improve the S2 multi-temporal geolocation accuracy. Since late August 2021, ESA has been using the GRI dataset in the geometric refinement process to generate S2 terrain-corrected (L1C) products. This paper presents the co-registration accuracy between the Landsat-8 (L8) Collection-2 terrain-corrected products and the S2 L1C products that were processed with and without the use of the GRI dataset. The image-to-image registration (I2I) analysis performed between the L8 and S2 data products over a set of globally distributed tiles shows a significant improvement in their co-registration accuracy when GRI is used in the S2 L1C product generation. The co-registration error is estimated to be <6 m circular error at 90% probability (CE90) when GRI is used, and >12 m CE90 when GRI is not used in the S2 product generation process. A similar I2I analysis was conducted between S2 L1C products, L8 L1TP products, and L8 and Landsat 9 (L9) L1TP products. The analysis shows that the S2 L1C products are co-registered with each other temporally to better than 5.1 m CE90 when GRI is used. The L8 L1TP products and L8 versus L9 L1TP products are both co-registered temporally to better than 3 m CE90.
Large-scale disturbances such as wildfire can have profound impacts on the composition, structure, and functioning of ecosystems. Bees are critical pollinators in natural settings and often respond positively to wildfires, particularly in forests where wildfire leads to more open conditions and increased floral resources. The use of Light Detection and Ranging (LiDAR) provides opportunities for quantifying habitat features across large spatial scales and is increasingly available to scientists and land managers for post-fire habitat assessment. We evaluated the extent to which LiDAR-derived forest structure measurements can predict forest bee communities after a large, mixed-severity fire. We hypothesized that LiDAR measurements linked to post-fire forest structure would improve our ability to predict bee abundance and species richness when compared to satellite-based maps of burn severity. To test this hypothesis, we sampled wild bee communities within the Douglas Fire Complex in southwestern Oregon, USA. We then used LiDAR and Landsat data to quantify forest structure and burn severity, respectively, across bee sampling locations. We found that the LiDAR forest structure model was the best predictor of abundance, whereas the Landsat burn severity model had better predictive ability for species richness. Furthermore, the Landsat burn severity model was better at predicting the presence and species richness of bumble bees (Bombus spp.), an ecologically distinct and economically important group within the Pacific Northwest. We posit that the divergent responses of the two modeling approaches are due to distinct responses by bee taxa to variation in forest structure as mediated by wildfire, with bumble bees in particular depending on closed-canopy forest for some portions of their life cycle. Our study demonstrates that LiDAR data can provide information regarding the drivers of bee abundance in post-wildfire conifer forest, and that both remote sensing approaches are useful for predicting components of wild bee diversity after large-scale wildfire.
","language":"English","doi":"10.1002/rse2.354","usgsCitation":"Galbraith, S.M., Valente, J., Dunn, C.J., and Rivers, J.W., 2024, Both Landsat- and LiDAR-derived measures predict forest bee response to large-scale wildfire: Remote Sensing in Ecology and Conservation, v. 10, no. 1, p. 24-38, https://doi.org/10.1002/rse2.354.","productDescription":"15 p.","startPage":"24","endPage":"38","ipdsId":"IP-144537","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":440572,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.354","text":"Publisher Index Page"},{"id":432888,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Douglas Fire Complex, southwestern Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.576158064366,\n 43.53770092737449\n ],\n [\n -123.576158064366,\n 42.89081714418663\n ],\n [\n -123.04040779515724,\n 42.89081714418663\n ],\n [\n -123.04040779515724,\n 43.53770092737449\n ],\n [\n -123.576158064366,\n 43.53770092737449\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Galbraith, Sara M.","contributorId":340887,"corporation":false,"usgs":false,"family":"Galbraith","given":"Sara","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":907638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Valente, Jonathon Joseph 0000-0002-6519-3523","orcid":"https://orcid.org/0000-0002-6519-3523","contributorId":340615,"corporation":false,"usgs":true,"family":"Valente","given":"Jonathon Joseph","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":910913,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunn, Christopher J.","contributorId":340888,"corporation":false,"usgs":false,"family":"Dunn","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":907640,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rivers, James W.","contributorId":23072,"corporation":false,"usgs":false,"family":"Rivers","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":7005,"text":"Department of Forest Ecosystems and Society, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":907641,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70251344,"text":"70251344 - 2024 - Response of corvid nest predators to thinning: implications for balancing short- and long-term goals for restoration of forest habitat","interactions":[],"lastModifiedDate":"2024-02-07T01:06:36.630253","indexId":"70251344","displayToPublicDate":"2024-01-31T19:04:35","publicationYear":"2024","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":"Response of corvid nest predators to thinning: implications for balancing short- and long-term goals for restoration of forest habitat","docAbstract":"Forest thinning on public lands in the Pacific Northwest USA is an important tool for restoring diversity in forest stands with a legacy of simplified structure from decades of intensive management for timber production. A primary application of thinning in young (< 50-year-old) stands is to accelerate forest development to mitigate loss of late-seral habitat to decades of logging. However, thinning may have short-term negative effects for some species associated with mature forest that are expected to benefit from the practice over the long term. An increased risk of nest predation is a primary concern to managers charged with stewardship of habitat for the federally threatened Marbled Murrelet (Brachyramphus marmoratus), a species that nests in older forests. Predation by corvids is the greatest cause of nest failure for the Marbled Murrelet, and corvids are known to respond positively to forest disturbance, but quantitative information is lacking on the potential impacts of thinning on risk of nest predation. We investigated the response of two common corvid nest predators, Steller’s Jay (Cyanocitta stelleri) and Canada Jay (Perisoreus canadensis), to variation in thinning intensity in young forest (< 50 years old) using data from a long-term silviculture experiment. We used a Before-After-Control-Impact (BACI) design, linear mixed modeling, and occupancy modeling to quantify differences in corvid observation rates among varying levels of thinning intensity, and to assess changes in jay response over more than a decade following thinning. We found an increase in observation rates of both species in the heavily thinned treatment during the first 5 to 7 years following thinning, and some evidence of a short-term increase in Steller’s Jay activity in the thinning-with-gaps treatment. Neither jay species responded to the least intensive thinning treatment, which reduced average canopy cover by < 30%. By approximately a decade after thinning, observation rates of jays did not differ between unthinned controls and any of the thinning treatments. Incorporating our quantitative information into landscape-level planning can help managers balance short- and long-term conservation goals.
","language":"English","publisher":"Avian Conservation and Ecology","doi":"10.5751/ACE-02578-190103","usgsCitation":"Hagar, J., Owen, T.K., Stevens, T.K., and Waianuhea, L.K., 2024, Response of corvid nest predators to thinning: implications for balancing short- and long-term goals for restoration of forest habitat: Avian Conservation and Ecology, v. 19, no. 1, 3, 11 p., https://doi.org/10.5751/ACE-02578-190103.","productDescription":"3, 11 p.","ipdsId":"IP-110816","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":440576,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-02578-190103","text":"Publisher Index Page"},{"id":425446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.31669804329832,\n 43.72071322185553\n ],\n [\n -122.22828499277165,\n 43.72071322185553\n ],\n [\n -122.22828499277165,\n 46.34467865412955\n ],\n [\n -124.31669804329832,\n 46.34467865412955\n ],\n [\n -124.31669804329832,\n 43.72071322185553\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hagar, Joan 0000-0002-3044-6607 joan_hagar@usgs.gov","orcid":"https://orcid.org/0000-0002-3044-6607","contributorId":3369,"corporation":false,"usgs":true,"family":"Hagar","given":"Joan","email":"joan_hagar@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":894180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Owen, Theodore K","contributorId":333872,"corporation":false,"usgs":false,"family":"Owen","given":"Theodore","email":"","middleInitial":"K","affiliations":[{"id":38051,"text":"Western EcoSystems Technology, Inc.","active":true,"usgs":false}],"preferred":false,"id":894181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stevens, Thomas K.","contributorId":333873,"corporation":false,"usgs":false,"family":"Stevens","given":"Thomas","email":"","middleInitial":"K.","affiliations":[{"id":38051,"text":"Western EcoSystems Technology, Inc.","active":true,"usgs":false}],"preferred":false,"id":894182,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waianuhea, Lorraine K 0000-0001-9697-6857","orcid":"https://orcid.org/0000-0001-9697-6857","contributorId":333874,"corporation":false,"usgs":false,"family":"Waianuhea","given":"Lorraine","email":"","middleInitial":"K","affiliations":[{"id":79996,"text":"Pacific Biosciences Research Center, University of Hawai’i at Mānoa","active":true,"usgs":false}],"preferred":false,"id":894183,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70251234,"text":"tm5A12 - 2024 - Methods of analysis—Determination of pesticides in filtered water and suspended sediment using liquid chromatography- and gas chromatography-tandem mass spectrometry","interactions":[],"lastModifiedDate":"2024-02-14T19:41:29.706991","indexId":"tm5A12","displayToPublicDate":"2024-01-31T12:53:26","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"5-A12","displayTitle":"Methods of Analysis—Determination of Pesticides in Filtered Water and Suspended Sediment using Liquid Chromatography- and Gas Chromatography-Tandem Mass Spectrometry","title":"Methods of analysis—Determination of pesticides in filtered water and suspended sediment using liquid chromatography- and gas chromatography-tandem mass spectrometry","docAbstract":"The widespread application of pesticides in agricultural and urban areas leads to their presence in surface waters. Presence of these biologically active chemicals in environmental waters potentially has adverse effects on nontarget organisms. To better understand the environmental fate of these contaminants, a robust method to capture chemicals with wide-ranging physicochemical properties has been developed. The method was developed by the U.S. Geological Survey’s Organic Chemistry Research Laboratory to monitor pesticides, pesticide degradates, and other agrochemicals in environmental surface waters throughout the country. The analysis involves a multiresidue method to determine 183 pesticides and pesticide degradates in filtered water samples and 178 pesticides and pesticide degradates in paired suspended sediment samples. After the filtration of whole water, contaminants are individually measured in the filtered water and the collected suspended sediment. Filtered water is extracted via solid-phase extraction, whereas suspended sediment is extracted using an ultrasonication, solid-liquid extraction. Samples are analyzed by liquid chromatography-tandem mass spectrometry using an electrospray ionization source in positive and negative modes and analyzed by gas chromatography-tandem mass spectrometry using an advanced electron ionization source in positive mode. Instrument parameters were optimized for the highest sensitivity, and at least two transitions (quantifier and qualifier) were monitored for each analyte.
Recoveries in test filtered water (n=9; 183 analytes) from the American River, California, and suspended sediment (n=9; 178 analytes) samples fortified at 15 nanograms per liter (ng/L) ranged from 70.1 to 121.0 and 71.1 to 117.0 percent in water and suspended sediment filter samples, respectively. Method detection limits of pesticides and pesticide degradates ranged from 0.5 to 10.6 ng/L in water and 0.7 to 11.8 ng/L in suspended sediment filters. Reporting limits were 1.1–21.1 ng/L and 1.5–23.7 ng/L in water and filter samples, respectively. The developed method is applied to surface-water samples for the analysis of pesticides, pesticide degradates, and other agrochemicals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm5A12","programNote":"Water Resources Mission Area—Water Availability and Use Science Program","usgsCitation":"Gross, M.S., Sanders, C.J., De Parsia, M.D., and Hladik, M.L., 2024, Methods of analysis—Determination of pesticides in filtered water and suspended sediment using liquid chromatography- and gas chromatography-tandem mass spectrometry: U.S. Geological Survey Techniques and Methods, book 5, chap. A12, 33 p., https://doi.org/10.3133/tm5A12.","productDescription":"Report: vi, 33 p.; Data Release","numberOfPages":"33","onlineOnly":"Y","ipdsId":"IP-139193","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":425118,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J8E544","text":"USGS Data Release","description":"Gross, M.S., Sanders, C.J., De Parsia, M.D., and Hladik, M.L., 2023, A multiresidue method for the analysis of pesticides in water using solid-phase extraction with gas and liquid chromatography-tandem mass spectrometry (ver. 2.0, April 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9J8E544.","linkHelpText":"A multiresidue method for the analysis of pesticides in water using solid-phase extraction with gas and liquid chromatography-tandem mass spectrometry (ver. 2.0, April 2023)"},{"id":425662,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm5A12/full"},{"id":425113,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/05/a12/tm5a12.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":425114,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/05/a12/tm5a12.xml"},{"id":425112,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/05/a12/covrthb.jpg"},{"id":425661,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/05/a12/images/"}],"contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Millions of small (< 10 ha) waterbodies embedded in grassland and agroecosystems in midcontinental North America provide breeding habitat to an estimated 50–80% of North America’s migratory ducks. Tens of millions of dollars are invested annually to conserve and enhance upland and wetland habitats for breeding ducks by prioritizing locations predicted to have high densities of breeding pairs under average precipitation conditions. An implicit assumption of this approach is that the distribution of breeding habitat remains relatively static. Climate change is an identified risk to this strategy. To assess this assumption and plan for potential forthcoming conditions, we estimated changes in potential breeding duck pairs under different climate scenarios by combining results of 1) a mechanistic hydrology model that simulates ecosystem processes for a subset of wetlands distributed across the U.S. Prairie Pothole Region (USPPR); 2) four downscaled climate model projections at mid- and late-century time horizons; and 3) U.S. Fish and Wildlife Service multi-decadal datasets and predictive breeding waterfowl pair statistical models. We conducted virtual and in-person informational sessions with partners to inform them on the best practices of using downscaled global circulation models and approaches for climate scenario planning. This close coordination led to a joint presentation at a monthly North Central Climate Adaptation Science Center seminar. We are also co-developing simulated wetland- waterfowl responses under different climate futures for wetlands. Information from these robust predictions of waterfowl habitat and settling patterns in this region provides land-management agencies insights in prioritizing current conservation actions given uncertainty. In addition, understanding how many breeding pairs the USPPR might support in coming decades will likely influence overall breeding population sizes and sustainable harvest objectives across North America.
","language":"English","publisher":"North Central Climate Adaptation Science Center","usgsCitation":"McKenna, O.P., and Rangwala, I., 2024, The impact of future changes in climate on breeding waterfowl pairs in the US Prairie Pothole Region: Final Report, 12 p.","productDescription":"12 p.","ipdsId":"IP-160169","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":501397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501396,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f83509de4b0e84f60868124/65c3d314d34ef4b119cae715"}],"country":"United States","state":"Iowa, Minnesota, Nebraska, North Dakota, South Dakota","otherGeospatial":"Prairie Pothole region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.05393441199925,\n 48.93957527305318\n ],\n [\n -108.27686560641123,\n 49.13252538326782\n ],\n [\n -108.40976144212098,\n 47.91192273687099\n ],\n [\n -106.07828064784712,\n 47.88578029277039\n ],\n [\n -101.59819304168207,\n 47.10144151067459\n ],\n [\n -100.41457783182686,\n 42.307798494527646\n ],\n [\n -96.89275640038099,\n 41.01374950027804\n ],\n [\n -96.60513562002711,\n 43.75532782507912\n ],\n [\n -94.98321580753591,\n 41.5817220442664\n ],\n [\n -94.20767561860225,\n 41.328376780271384\n ],\n [\n -93.63954575827131,\n 42.51490897209834\n ],\n [\n -93.78977025276507,\n 43.36437770770755\n ],\n [\n -94.24637551611141,\n 47.33263848178828\n ],\n [\n -95.05393441199925,\n 48.93957527305318\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":893736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rangwala, Imtiaz 0000-0002-4313-9374","orcid":"https://orcid.org/0000-0002-4313-9374","contributorId":148973,"corporation":false,"usgs":false,"family":"Rangwala","given":"Imtiaz","email":"","affiliations":[{"id":34534,"text":"Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado","active":true,"usgs":false}],"preferred":true,"id":957215,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70251310,"text":"70251310 - 2024 - Predicting the spatial distribution of wintering golden eagles to inform full annual cycle conservation in western North America","interactions":[],"lastModifiedDate":"2024-02-03T15:25:37.454332","indexId":"70251310","displayToPublicDate":"2024-01-31T09:21:43","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Predicting the spatial distribution of wintering golden eagles to inform full annual cycle conservation in western North America","docAbstract":"Wildlife conservation strategies focused on one season or population segment may fail to adequately protect populations, especially when a species’ habitat preferences vary among seasons, age-classes, geographic regions, or other factors. Conservation of golden eagles (Aquila chrysaetos) is an example of such a complex scenario, in which the distribution, habitat use, and migratory strategies of this species of conservation concern vary by age-class, reproductive status, region, and season. Nonetheless, research aimed at mapping priority use areas to inform management of golden eagles in western North America has typically focused on territory-holding adults during the breeding period, largely to the exclusion of other seasons and life-history groups. To support population-wide conservation planning across the full annual cycle for golden eagles, we developed a distribution model for individuals in a season not typically evaluated–winter–and in an area of the interior western U.S. that is a high priority for conservation of the species. We used a large GPS-telemetry dataset and library of environmental variables to develop a machine-learning model to predict spatial variation in the relative intensity of use by golden eagles during winter in Wyoming, USA, and surrounding ecoregions. Based on a rigorous series of evaluations including cross-validation, withheld and independent data, our winter-season model accurately predicted spatial variation in intensity of use by multiple age- and life-history groups of eagles not associated with nesting territories (i.e., all age classes of long-distance migrants, and resident non-adults and adult “floaters”, and movements of adult territory holders and their offspring outside their breeding territories). Important predictors in the model were wind and uplift (40.2% contribution), vegetation and landcover (27.9%), topography (14%), climate and weather (9.4%), and ecoregion (8.7%). Predicted areas of high-use winter habitat had relatively low spatial overlap with nesting habitat, suggesting a conservation strategy targeting high-use areas for one season would capture as much as half and as little as one quarter of high-use areas for the other season. The majority of predicted high-use habitat (top 10% quantile) occurred on private lands (55%); lands managed by states and the Bureau of Land Management (BLM) had a lower amount (33%), but higher concentration of high-use habitat than expected for their area (1.5–1.6x). These results will enable those involved in conservation and management of golden eagles in our study region to incorporate spatial prioritization of wintering habitat into their existing regulatory processes, land-use planning tasks, and conservation actions.
As part of the U.S. Geological Survey’s 2023 50‐State National Seismic Hazard Model (NSHM), we make modest revisions and additions to the central and eastern U.S. (CEUS) fault‐based seismic source model that result in locally substantial hazard changes. The CEUS fault‐based source model was last updated as part of the 2014 NSHM and considered new information from the Seismic Source Characterization for Nuclear Facilities (CEUS‐SSCn) Project. Since then, new geologic investigations have led to revised fault and fault‐zone inputs, and the release of databases of fault‐based sources in the CEUS. We have reviewed these databases and made minor revisions to six of the current fault‐based sources in the NSHM, as well as added five new fault‐based sources. Implementation of these sources follows the current NSHM methodology for CEUS fault‐based sources, as well as the incorporation of a new magnitude–area relationship and updated maximum magnitude and recurrence rate estimates following the methods used by the CEUS‐SSCn Project. Seismic hazard sensitivity calculations show some substantial local changes in hazard (−0.4g to 1.1g) due to some of these revisions and additions, especially from the addition of the central Virginia, Joiner ridge, and Saline River sources and revisions made to the Meers and New Madrid sources.
In 2012, the Secretary of the U.S. Department of the Interior placed a 20-year limit on mineral extraction on Federal lands in the Grand Canyon watershed to permit further study of the environmental effects of uranium mining. Tribal concerns were also noted by the U.S. Department of the Interior and included in the rationale for the decision stating Tribal resource impacts could not be mitigated and cultural degradation may result should mining occur within sacred and traditional places of Tribal peoples. The U.S. Geological Survey previously developed a conceptual framework for a uranium mine in the region that defined contaminant sources and physical, chemical, and biological processes that affect contaminant transport to ecological receptors. However, published risk models have largely ignored exposure pathways relevant to Tribal communities in terms of traditional uses and existential values of the resources included. This report presents an updated conceptual risk framework for uranium mining that includes indigenous knowledge components informed by the Havasupai Tribe perspective.
The expansion of the framework relied on connecting to the foundations of the Havasupai ceremonial wheel—food, environment, belief system, and ceremony. The framework is applied to uranium development near Red Butte, an important gathering place for multiple federally recognized Tribes including the Havasupai, Hopi, Navajo, and Zuni. Plants and animals important to the Havasupai for subsistence, ceremonial, and medicinal practices and how mining affects these practices are described. The final framework is presented in English and Havasupai to aid Tribal members in understanding how the framework relates to their community and to help preserve the language and historical cultural practices for future generations. New or expanded exposure pathways include inhalation, ingestion, and absorption from traditional food and medicines as well as ceremonial practices. The updated framework has allowed the U.S. Geological Survey to take first steps in understanding resources important to the Havasupai and to build relationships to improve co-production in our research. Ideally, the framework and other research can be used, along with indigenous knowledge, in Federal research and decision making for mining in the Grand Canyon region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231092","usgsCitation":"Tilousi, C., and Hinck, J.E., 2024, Expanded conceptual risk framework for uranium mining in Grand Canyon watershed—Inclusion of the Havasupai Tribe perspective (ver. 1.1, February 2024): U.S. Geological Survey Open-File Report 2023–1092, 25 p., https://doi.org/10.3133/ofr20231092.","productDescription":"vi, 25 p.","numberOfPages":"36","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-157227","costCenters":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"links":[{"id":499197,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116005.htm","linkFileType":{"id":5,"text":"html"}},{"id":425864,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip241","text":"General Information Product 241"},{"id":425233,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2023/1092/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":424862,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231092/full"},{"id":424859,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1092/images/"},{"id":424858,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1092/ofr20231092.XML"},{"id":424857,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1092/ofr20231092.pdf","text":"Report","size":"7.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1092"},{"id":424856,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1092/coverthb2.jpg"},{"id":425791,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip240","text":"General Information Product 240"},{"id":425790,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip239","text":"General Information Product 239"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.19147841939832,\n 38.32491175913418\n ],\n [\n -117.19147841939832,\n 32.88011313999995\n ],\n [\n -110.33600966939846,\n 32.88011313999995\n ],\n [\n -110.33600966939846,\n 38.32491175913418\n ],\n [\n -117.19147841939832,\n 38.32491175913418\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: January 30, 2024; Version 1.1: February 1, 2024","contact":"Associate Director, Natural Hazards Mission Area
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Many studies use landscape form to determine spatial patterns of tectonic deformation, and these are particularly effective when paired with independent measures of rock uplift and erosion. Here, we use morphometric analyses and 10Be catchment-averaged erosion rates, together with reverse slip rates from the Sierra Madre−Cucamonga fault zone, to reveal patterns in uplift, erosion, and fault activity in the range front of the San Gabriel Mountains in southern California, USA. Our analysis tests two prevailing hypotheses: (1) the range front of the San Gabriel Mountains is at steady state, in which rock uplift balances erosion and topographic elevations are stable throughout time, and (2) that west-to-east increases in elevation, relief, erosion rate, and stream-channel steepness across the interior of the massif reflect a parallel reverse-slip rate gradient on the range-bounding Sierra Madre−Cucamonga fault zone. We show that although deviations from steady state occur, the range-front hillslopes and stream channels are typically both well-connected and adjusted to patterns in Quaternary uplift driven by motion on the range-front fault network. Accordingly, landscape morphometrics, 10Be erosion rates, and model erosion rates effectively image spatial and temporal patterns in uplift. Interpreted jointly, these data reveal comparable peak slip rates on the Sierra Madre−Cucamonga fault zone and show that they do not monotonically increase from west to east. Thus, the eastward-increasing gradients developed within the interior of the massif are not solely related to reverse slip on the range-front faults. Evaluated on shorter length scales (<10 km), morphometric data corroborate earlier descriptions of the Sierra Madre−Cucamonga fault zone as multiple individual faults or fault sections, with slip rates tapering toward fault tips. We infer that these patterns imply the predominance of independent fault or fault section ruptures throughout the Quaternary, though data cannot rule out the possibility of large, connected Sierra Madre−Cucamonga fault zone ruptures. Deeper in the hanging wall of the Sierra Madre−Cucamonga fault zone, secondary faults accommodate range-front uplift. Motion on these faults may contribute to active uplift of the highest topography within the massif, in addition to partly reconciling differences between geologic and geodetic Sierra Madre−Cucamonga fault zone reverse-slip rates. This study provides a new, unified perspective on tectonics and landscape evolution in the San Gabriel Mountains.
The fracture of Earth materials occurs over a wide range of time and length scales. Physical conditions, particularly the stress field and Earth material properties, may condition rupture in a specific fracture regime. In nature, fast and slow fractures occur concurrently: tectonic tremor events are fast enough to emit seismic waves and frequently accompany slow earthquakes, which are too slow to emit seismic waves and are referred to as aseismic slip events. In this study, we generate simultaneous seismic and aseismic processes in a laboratory setting by driving a penny-shaped crack in a transparent sample with pressurized fluid. We leverage synchronized high-speed imaging and high-frequency acoustic emission (AE) sensing to visualize and listen to the various sequences of propagation (breaks) and arrest (sticks) of a fracture undergoing stick-break instabilities. Slow radial crack propagation is facilitated by fast tangential fractures. Fluid viscosity and pressure regulate the fracture dynamics of slow and fast events, and control the inter-event time and the energy released during individual fast events. These AE signals share behaviors with observations of episodic tremors in Cascadia, United States; these include: (a) bursty or intermittent slow propagation, and (b) nearly linear scaling of radiated energy with area. Our laboratory experiments provide a plausible model of tectonic tremor as an indicative of hydraulic fracturing facilitating shear slip during slow earthquakes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023AV001002","usgsCitation":"Yuan, C., Cochard, T., Denolle, M.A., Gomberg, J.S., Wech, A., Lizhi, X., and Weitz, D., 2024, Laboratory hydrofractures as analogs to tectonic tremors: AGU Advances, v. 5, no. 1, e2023AV001002, 15 p., https://doi.org/10.1029/2023AV001002.","productDescription":"e2023AV001002, 15 p.","ipdsId":"IP-155902","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":481064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023av001002","text":"Publisher Index Page"},{"id":480848,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Yuan, Congcong","contributorId":349711,"corporation":false,"usgs":false,"family":"Yuan","given":"Congcong","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":924628,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cochard, Thomas","contributorId":349712,"corporation":false,"usgs":false,"family":"Cochard","given":"Thomas","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":924629,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Denolle, Marine A.","contributorId":345689,"corporation":false,"usgs":false,"family":"Denolle","given":"Marine","email":"","middleInitial":"A.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":924630,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924631,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wech, Aaron 0000-0003-4983-1991","orcid":"https://orcid.org/0000-0003-4983-1991","contributorId":202561,"corporation":false,"usgs":true,"family":"Wech","given":"Aaron","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":924632,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lizhi, Xiao","contributorId":349713,"corporation":false,"usgs":false,"family":"Lizhi","given":"Xiao","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":924633,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Weitz, David","contributorId":349714,"corporation":false,"usgs":false,"family":"Weitz","given":"David","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":924634,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256146,"text":"70256146 - 2024 - Earth’s free surface complicates inference of absolute stress from earthquake-Induced stress rotations","interactions":[],"lastModifiedDate":"2024-07-25T16:24:54.322227","indexId":"70256146","displayToPublicDate":"2024-01-29T11:19:51","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Earth’s free surface complicates inference of absolute stress from earthquake-Induced stress rotations","docAbstract":"The stress redistribution from an earthquake can produce localized measurable rotations of the principal stress axes if the absolute level of differential stress in the crust in on the order of the earthquake stress drop. Two simple analytic solutions have been developed to estimate the differential stress from an observed stress rotation. However, each has assumptions that may not be accurate near Earth’s free surface. I model synthetic earthquakes in an elastic half-space, and show that the assumptions of the methods are accurate for strike-slip earthquakes, and for deep dip-slip earthquakes. However, they are incorrect for shallow dip-slip earthquakes. I introduce a free surface correction for one of the methods for dip-slip earthquakes. I revise an analysis of stress rotations due to great subduction zone earthquakes, including this correction. The results support the original conclusion of near complete stress drop for many shallow subduction zone earthquakes.","language":"English","publisher":"AGU","doi":"10.1029/2023GL106574","usgsCitation":"Hardebeck, J.L., 2024, Earth’s free surface complicates inference of absolute stress from earthquake-Induced stress rotations: Geophysical Research Letters, v. 51, no. 4, e2023GL106574, 8 p., https://doi.org/10.1029/2023GL106574.","productDescription":"e2023GL106574, 8 p.","ipdsId":"IP-146476","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":440598,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gl106574","text":"Publisher Index Page"},{"id":431448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Hardebeck, Jeanne L. 0000-0002-6737-7780","orcid":"https://orcid.org/0000-0002-6737-7780","contributorId":254964,"corporation":false,"usgs":true,"family":"Hardebeck","given":"Jeanne","email":"","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":906909,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70251681,"text":"70251681 - 2024 - Resource-driven pattern formation in consumer-resource systems with asymmetric dispersal on a plane","interactions":[],"lastModifiedDate":"2024-02-23T13:07:52.231971","indexId":"70251681","displayToPublicDate":"2024-01-28T07:07:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5826,"text":"SIAM Journal on Applied Mathematics","active":true,"publicationSubtype":{"id":10}},"title":"Resource-driven pattern formation in consumer-resource systems with asymmetric dispersal on a plane","docAbstract":"Close-kin mark–recapture (CKMR) is a method analogous to traditional mark–recapture but without requiring recapture of individuals. Instead, multilocus genotypes (genetic marks) are used to identify related individuals in one or more sampling occasions, which enables the opportunistic use of samples from harvested wildlife. To apply the method accurately, it is important to build appropriate CKMR models that do not violate assumptions linked to the species’ and population's biology and sampling methods. In this study, we evaluated the implications of fitting overly simplistic CKMR models to populations with complex reproductive success dynamics or selective sampling. We used forward-in-time, individual-based simulations to evaluate the accuracy and precision of CKMR abundance and survival estimates in species with different longevities, mating systems, and sampling strategies. Simulated populations approximated a range of life histories among game species of North America with lethal sampling to evaluate the potential of using harvested samples to estimate population size. Our simulations show that CKMR can yield nontrivial biases in both survival and abundance estimates, unless influential life history traits and selective sampling are explicitly accounted for in the modeling framework. The number of kin pairs observed in the sample, in combination with the type of kinship used in the model (parent–offspring pairs and/or half-sibling pairs), can affect the precision and/or accuracy of the estimates. CKMR is a promising method that will likely see an increasing number of applications in the field as costs of genetic analysis continue to decline. Our work highlights the importance of applying population-specific CKMR models that consider relevant demographic parameters, individual covariates, and the protocol through which individuals were sampled.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.4244","usgsCitation":"Seveque, A., Lonsinger, R.C., Waits, L.P., Brzeski, K.E., Komoroske, L., Ott-Conn, C.N., Mayhew, S.L., Norton, D.C., Petroelje, T., Swenson, J.D., and Morin, D.J., 2024, Sources of bias in applying close-kin mark–recapture to terrestrial game species with different life histories: Ecology, v. 105, no. 3, e4244, 16 p., https://doi.org/10.1002/ecy.4244.","productDescription":"e4244, 16 p.","ipdsId":"IP-147891","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":440616,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.4244","text":"Publisher Index Page"},{"id":432149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"105","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-01-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Seveque, Anthony","contributorId":340602,"corporation":false,"usgs":false,"family":"Seveque","given":"Anthony","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":907390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waits, Lisette P","contributorId":261163,"corporation":false,"usgs":false,"family":"Waits","given":"Lisette","email":"","middleInitial":"P","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":907392,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brzeski, Kristin E.","contributorId":340606,"corporation":false,"usgs":false,"family":"Brzeski","given":"Kristin","email":"","middleInitial":"E.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":907393,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Komoroske, Lisa M","contributorId":152475,"corporation":false,"usgs":false,"family":"Komoroske","given":"Lisa M","affiliations":[{"id":18933,"text":"NOAA Southwest Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":907394,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ott-Conn, Caitlin N.","contributorId":212132,"corporation":false,"usgs":false,"family":"Ott-Conn","given":"Caitlin","email":"","middleInitial":"N.","affiliations":[{"id":38429,"text":"Michigan Department of Natural Resources, Wildlife Disease Laboratory, Lansing, MI, USA","active":true,"usgs":false}],"preferred":false,"id":907395,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mayhew, Sarah L.","contributorId":340610,"corporation":false,"usgs":false,"family":"Mayhew","given":"Sarah","email":"","middleInitial":"L.","affiliations":[{"id":81637,"text":"Michigan Department of Natural Resource","active":true,"usgs":false}],"preferred":false,"id":907396,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Norton, D. Cody","contributorId":340611,"corporation":false,"usgs":false,"family":"Norton","given":"D.","email":"","middleInitial":"Cody","affiliations":[{"id":81637,"text":"Michigan Department of Natural Resource","active":true,"usgs":false}],"preferred":false,"id":907397,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Petroelje, Tyler R.","contributorId":340612,"corporation":false,"usgs":false,"family":"Petroelje","given":"Tyler R.","affiliations":[{"id":81637,"text":"Michigan Department of Natural Resource","active":true,"usgs":false}],"preferred":false,"id":907398,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Swenson, John D.","contributorId":340613,"corporation":false,"usgs":false,"family":"Swenson","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":907399,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Morin, Dana J.","contributorId":340614,"corporation":false,"usgs":false,"family":"Morin","given":"Dana","email":"","middleInitial":"J.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":907400,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70251210,"text":"70251210 - 2024 - U.S. Geological Survey Grand Canyon Monitoring and Research Center: Proceedings of the fiscal year 2023 annual reporting meeting to the Glen Canyon Dam Adaptive Management Program","interactions":[],"lastModifiedDate":"2024-02-01T19:12:39.98564","indexId":"70251210","displayToPublicDate":"2024-01-25T09:58:53","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"U.S. Geological Survey Grand Canyon Monitoring and Research Center: Proceedings of the fiscal year 2023 annual reporting meeting to the Glen Canyon Dam Adaptive Management Program","docAbstract":"This proceedings is prepared for the USBR and Glen Canyon Dam Adaptive Management Program (GCDAMP) to account for work conducted and products delivered in FY 2023 by SBSC's Grand Canyon Monitoring and Research Center (GCMRC) and to inform the Technical Work Group of science conducted by GCMRC and its cooperators in support of the GCDAMP. It includes a summary of accomplishments, modifications to work plans, and results related to projects included in GCMRC’s FY 2021-23 Triennial Work Plan. This work was done to support the 11 resource goals identified in the Glen Canyon Dam Long-Term Experimental and Management Plan (LTEMP) Environmental Impact Statement and Record of Decision.
","language":"English","publisher":"U.S. Department of the Interior, Bureau of Reclamation","usgsCitation":"Schultz, A.A., Anderson, G.M., Topping, D.J., Griffiths, R.E., Dean, D.J., Grams, P.E., Kohl, K., Salter, G.L., Kaplinski, M.A., Chapman, K., Mueller, E., Palmquist, E.C., Butterfield, B.J., Sankey, J., Deemer, B., Yackulic, C., Hansen, L.E., Eppehimer, D.E., Kennedy, T., Metcalfe, A., Muehlbauer, J., Ford, M., Dodrill, M., Dzul, M.C., Rinker, P., Pillow, M.J., Ward, D., Korman, J., Webb, M.A., Crossman, J.A., Frye, E.J., Rogowski, D.L., Dibble, K., Bair, L., Abbott, J., Gushue, T., Byerley, E.P., Thomas, J.E., Sabol, T.A., and Mihalevich, B.A., 2024, U.S. Geological Survey Grand Canyon Monitoring and Research Center: Proceedings of the fiscal year 2023 annual reporting meeting to the Glen Canyon Dam Adaptive Management Program, 189 p.","productDescription":"189 p.","ipdsId":"IP-159732","costCenters":[{"id":568,"text":"Southwest Biological Science 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Deposits of this type in New England formed in diverse tectonic settings including volcanic arcs and backarcs, a supra–subduction zone arc, a rifted forearc foreland basin, and a rifted continental margin. Following VMS mineralization on or near the seafloor, components of the tectonostratigraphic assemblages—volcanic ± sedimentary rocks, coeval intrusions, sulfide deposits, and underlying basements—were diachronously accreted to the Laurentian margin during the Paleozoic. Lead isotope data for galena show relatively large ranges for 206Pb/204Pb, 207Pb/204Pb, and 208Pb/204Pb. Evaluation of potential lead sources, using for comparison Pb-isotope data from modern and ancient settings, suggests that principal sources include the mantle, volcanic ± sedimentary rocks, and deeper basement rocks. Integration of the Pb-isotope values with published data such as Nd isotopes for the volcanic rocks and from deep seismic reflection profiles points to the involvement of several basements, including those of Grenvillian, Ganderian, Avalonian, and West African (and (or) Amazonian) affinity. Clustering of Pb-isotope data for VMS deposits within individual Cambrian and Ordovician volcanic and volcanosedimentary settings, delineated by differences in 206Pb/204Pb and µ (238U/204Pb) values, are consistent with lead derivation from at least four and possibly five different tectonostratigraphic assemblages with isotopically distinct basements. Collectively, our Pb-isotope data for New England VMS deposits provide a novel window into the nature of subarc basement rocks during pre-accretionary sulfide mineralization outboard of Laurentia during early Paleozoic time.
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Measuring the survival and growth of individual plants plays a key role in current efforts to monitor restoration success. However, the scale of modern restoration (e.g., >10,000 ha) challenges measurements of demographic rates with field data. In this study, we demonstrate how unoccupied aerial system (UAS) flights can provide an efficient solution to the tradeoff of precision and spatial extent in detecting demographic rates from the air. We flew two, sequential UAS flights at two sagebrush (Artemisia tridentata) common gardens to measure the survival and growth of individual plants. The accuracy of Bayesian-optimized segmentation of individual shrub canopies was high (73–95%, depending on the year and site), and remotely sensed survival estimates were within 10% of ground-truthed survival censuses. Stand age structure affected remotely sensed estimates of growth; growth was overestimated relative to field-based estimates by 57% in the first garden with older stands, but agreement was high in the second garden with younger stands. Further, younger stands (similar to those just after disturbance) with shorter, smaller plants were sometimes confused with other shrub species and bunchgrasses, demonstrating a need for integrating spectral classification approaches that are increasingly available on affordable UAS platforms. The older stand had several merged canopies, which led to an underestimation of abundance but did not bias remotely sensed survival estimates. Advances in segmentation and UAS structure from motion photogrammetry will enable demographic rate measurements at management-relevant extents.
Invasive sea lamprey (Petromyzon marinus) are controlled in the Great Lakes with 4-nitro-3-(trifluoromethyl)phenol (commonly 3-trifluoromethyl-4-nitrophenol or TFM). The proper amount of TFM must be applied during treatments to effectively kill larval sea lamprey while minimizing impacts to non-target species. In this study, bioassay tests were conducted in May, July, and September in a portable test trailer at six larval sea lamprey infested rivers in Michigan to determine potential seasonal changes in sensitivity of larval sea lamprey to TFM. Larvae greater than 60 mm were collected from each stream and exposed for 12 h in TFM-treated stream water using two independent continuous-flow diluter systems. A suite of water chemistries and larval physiological parameters were collected during the tests and modeled as potential predictors of seasonal changes in the sensitivity of larval sea lamprey to TFM. The observed minimum lethal concentrations to larval sea lamprey were 0–40% lower (May), 8% lower–59% higher (July), and 49–117% higher (September) than sea lamprey control personnel treatment prediction charts. Water temperature, liver glycogen content, and time of year were strongly associated with seasonal differences in TFM sensitivity, offering sea lamprey control personnel more exact predictions to limit potential residual lamprey surviving future treatments.
Regional mapping of actively deforming landslides, including measurements of landslide velocity, is integral for hazard assessments in paraglacial environments. These inventories are also critical for describing the potential impacts that the warming effects of climate change have on slope instability in mountainous and cryospheric terrain. The objective of this study is to identify slow-moving landslides in the Prince William Sound region, southcentral Alaska, United States, which has had rapid deglaciation since the mid-1800s, and assess their tsunamigenic plausibility. We use an automated time series persistent scatterer interferometric synthetic aperture radar processing method with 7 years of Sentinel-1 data (2016–22) to identify 43 slow-moving slopes with average velocities ranging from approximately 0.2 to 21 millimeters per year. Landslide presence is confirmed using aerial imagery and previous landslide inventory records. We assess the tsunamigenic plausibility of the landslides using empirically derived estimates of landslide mobility based on modeled landslide volumes. Of the identified landslides, our preliminary analysis suggests that 11 have tsunamigenic potential if they were to fail rapidly and catastrophically. Although our estimate of tsunamigenic plausibility is preliminary and can be refined with additional observations and analyses, it can be used to prioritize ongoing and future hazard assessment, surveillance, and research efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231099","collaboration":"Prepared in collaboration with Southern Methodist University","programNote":"Landslide Hazards Program","usgsCitation":"Schaefer, L.N., Kim, J., Staley, D.M., Lu, Z., and Barnhart, K.R., 2024, Satellite interferometry landslide detection and preliminary tsunamigenic plausibility assessment in Prince William Sound, southcentral Alaska: U.S. Geological Survey Open-File Report 2023–1099, 22 p., https://doi.org/10.3133/ofr20231099.","productDescription":"v, 22 p.","onlineOnly":"Y","ipdsId":"IP-155368","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":499202,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115975.htm","linkFileType":{"id":5,"text":"html"}},{"id":424451,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1099/coverthb.jpg"},{"id":424866,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1099/ofr20231099.xml"},{"id":424865,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1099/images"},{"id":424452,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1099/ofr20231099.pdf","text":"Report","size":"11.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1099"},{"id":424970,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231099/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1099"}],"country":"United States","state":"Alaska","otherGeospatial":"Prince William Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -149.54873591535403,\n 61.68671968753719\n ],\n [\n -149.54873591535403,\n 59.52701043286805\n ],\n [\n -143.89688082106878,\n 59.52701043286805\n ],\n [\n -143.89688082106878,\n 61.68671968753719\n ],\n [\n -149.54873591535403,\n 61.68671968753719\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, Mail Stop 966
Denver, CO 80225
Conservation prioritization frameworks are used worldwide to identify species at greatest risk of extinction and to allocate limited resources across regions, species, and populations. Conservation prioritization can be impeded by ecological knowledge gaps and data deficiency, especially in freshwater species inhabiting highly complex aquatic ecosystems. Therefore, we developed a flexible approach that calculates a species' imperilment risk based on the conservation principles of resiliency, redundancy, and representation (i.e., the “three R's”). Our approach organizes data on species traits, distributions, population connectivity, and threats within a Bayesian belief network capable of predicting resiliency and redundancy within representative ecological settings. Empirical data and expert judgment inform the model to provide robust and repeatable risk assessments for rare and data-deficient species. The model calculates resiliency at hierarchical spatial scales from distributional trends and population strength. Redundancy is estimated from the connectivity and quantities of extant populations. Resiliency, redundancy, and species' inherent vulnerability based on species traits collectively estimate extirpation risk within each unique ecological setting. Extirpation risks across ecological settings characterize representation and are aggregated to estimate global imperilment risk. We demonstrate the model's utility with Piebald Madtom (Noturus gladiator), a species petitioned for listing under the U.S. Endangered Species Act. Our results revealed that resiliency, redundancy, and extirpation risks can vary spatially across the species' range while identifying populations where additional sampling could disproportionally reduce uncertainty in estimated global imperilment risk. Our approach could standardize and expedite conservation status assessments, identify opportunities for early management intervention of at-risk species and populations, and strategically reduce uncertainty by focusing monitoring and research on priority information gaps.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4738","usgsCitation":"Dunn, C.G., Schumann, D.A., Colvin, M., Sleezer, L.J., Wagner, M., Jones-Farrand, D., Rivenbark, E., McRae, S., and Evans, K., 2024, Using resiliency, redundancy, and representation in a Bayesian belief network to assess imperilment of riverine fishes: Ecosphere, v. 15, e4738, 21 p., https://doi.org/10.1002/ecs2.4738.","productDescription":"e4738, 21 p.","ipdsId":"IP-137173","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":440628,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4738","text":"Publisher Index Page"},{"id":432153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2024-01-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Dunn, Corey Garland 0000-0002-7102-2165","orcid":"https://orcid.org/0000-0002-7102-2165","contributorId":288691,"corporation":false,"usgs":true,"family":"Dunn","given":"Corey","email":"","middleInitial":"Garland","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907429,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schumann, David A.","contributorId":267261,"corporation":false,"usgs":false,"family":"Schumann","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":907430,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Colvin, Michael E.","contributorId":264842,"corporation":false,"usgs":false,"family":"Colvin","given":"Michael E.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":907431,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sleezer, Logan John 0000-0002-5787-8629","orcid":"https://orcid.org/0000-0002-5787-8629","contributorId":331489,"corporation":false,"usgs":true,"family":"Sleezer","given":"Logan","email":"","middleInitial":"John","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":907432,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wagner, Matthew 0000-0002-3987-072X","orcid":"https://orcid.org/0000-0002-3987-072X","contributorId":221861,"corporation":false,"usgs":false,"family":"Wagner","given":"Matthew","affiliations":[{"id":40445,"text":"Student contractor to the U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":907435,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones-Farrand, D. Todd","contributorId":54713,"corporation":false,"usgs":true,"family":"Jones-Farrand","given":"D. Todd","affiliations":[],"preferred":false,"id":907433,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rivenbark, Erin","contributorId":340546,"corporation":false,"usgs":false,"family":"Rivenbark","given":"Erin","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907437,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McRae, Sarah","contributorId":340663,"corporation":false,"usgs":false,"family":"McRae","given":"Sarah","affiliations":[{"id":81646,"text":"South Atlantic-Gulf and Mississippi-Basin Unified Interior Regions","active":true,"usgs":false}],"preferred":false,"id":907436,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Evans, Kristine","contributorId":217902,"corporation":false,"usgs":false,"family":"Evans","given":"Kristine","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":907434,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70251088,"text":"sir20235139 - 2024 - Water-quality indicators of surface-water-influenced groundwater supplies in the Ohio River alluvial aquifer of West Virginia","interactions":[],"lastModifiedDate":"2024-01-24T17:05:22.407091","indexId":"sir20235139","displayToPublicDate":"2024-01-24T10:30:00","publicationYear":"2024","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":"2023-5139","displayTitle":"Water-Quality Indicators of Surface-Water-Influenced Groundwater Supplies in the Ohio River Alluvial Aquifer of West Virginia","title":"Water-quality indicators of surface-water-influenced groundwater supplies in the Ohio River alluvial aquifer of West Virginia","docAbstract":"The U.S. Geological Survey, in cooperation with the West Virginia Department of Health and Human Resources, studied surface-water-influenced groundwater supplies in the Ohio River alluvial aquifer of West Virginia for the purpose of understanding the influence of surface water on groundwater chemistry. Public groundwater supplies obtained from these aquifers receive substantial recharge from surface-water sources and are highly susceptible to degradation from water-soluble contaminants. Water samples were collected from 4 sites in the Ohio River and 23 groundwater wells in the alluvial aquifer from June 2019 to January 2020. Surface-water influence was assessed through characterization of groundwater quality, determination of recharge sources, estimation of groundwater age, and estimation of the fraction of Ohio River water entering groundwater pumped by wells in the alluvial aquifers.
Hydrogeochemical processes controlling solute concentrations in the Ohio River alluvial aquifer were evaluated with multivariate statistical analysis and identified to be primarily controlled by redox processes, input from sources of salinity, and carbonate dissolution. Meteoric recharge from the Ohio River and precipitation on the alluvium are the main sources of water entering the aquifer. The age of groundwater in the system was determined to be primarily from a modern source. Groundwater samples from every well included in this study had detections for at least one geochemical indicator of surface-water influence, and every well was determined to be susceptible and vulnerable to contamination from surface sources.
Results from binary mixing models and inverse geochemical models showed that sulfate, silica, and bicarbonate concentrations predict the fraction of Ohio River water entering alluvial wells for preliminary determination of surface-water influence when compared to fractions predicted using a numerical groundwater-flow model. In the absence of extensive analytes and geochemical or groundwater modeling capabilities, preliminary assessment of the fraction of Ohio River water entering groundwater wells in the Ohio River alluvium can be estimated for most sites using a linear relation between the equivalent ratio of bicarbonate to sulfate and the fraction of water computed by the average of the three geochemical models presented in this report. This approximation of the fraction of Ohio River water, coupled with information on the hydrogeological framework and geochemical indicators of surface-water influence, may be sufficient for preliminary assessment of surface-water influence in the absence of more detailed site information or reaction-transport models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235139","collaboration":"Prepared in cooperation with the West Virginia Department of Health and Human Resources, Bureau for Public Health","usgsCitation":"McAdoo, M.A., and Connock, G.T., 2024, Water-quality indicators of surface-water-influenced groundwater supplies in the Ohio River alluvial aquifer of West Virginia: U.S. Geological Survey Scientific Investigations Report 2023–5139, 42 p., https://doi.org/10.3133/sir20235139.","productDescription":"Report: x, 42 p.; Data Release","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-153944","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":424695,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13TPVMI","text":"USGS data release","linkHelpText":"PHREEQC files for geochemical simulations in 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Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228