This study focuses on understanding the association of rare earth elements (REE; lanthanides + yttrium + scandium) with organic matter from the Middle Devonian black shales of the Appalachian Basin. Developing a better understanding of the role of organic matter (OM) and thermal maturity in REE partitioning may help improve current geochemical models of REE enrichment in a wide range of black shales. We studied relationships between whole rock REE content and total organic carbon (TOC) and compared the correlations with a suite of global oil shales that contain TOC as high as 60 wt.%. The sequential leaching of the Appalachian shale samples was conducted to evaluate the REE content associated with carbonates, Fe–Mn oxyhydroxides, sulfides, and organics. Finally, the residue from the leaching experiment was analyzed to assess the mineralogical changes and REE extraction efficiency. Our results show that heavier REE (HREE) have a positive correlation with TOC in our Appalachian core samples. However, data from the global oil shales display an opposite trend. We propose that although TOC controls REE enrichment, thermal maturation likely plays a critical role in HREE partitioning into refractory organic phases, such as pyrobitumen. The REE inventory from a core in the Appalachian Basin shows that (1) the total REE ranges between 180 and 270 ppm and the OM-rich samples tend to contain more REE than the calcareous shales; (2) there is a relatively higher abundance of middle REE (MREE) to HREE than lighter REE (LREE); (3) there is a disproportionate increase in Y and Tb with TOC likely due to the rocks being over-mature; and (4) the REE extraction demonstrates that although the OM has higher HREE concentration, the organic leachates contain more LREE, suggesting it is more challenging to extract HREE from OM than using traditional leaching techniques.
","language":"English","publisher":"MDPI","doi":"10.3390/en17092107","usgsCitation":"Bhattacharya, S., Sharma, S., Agrawal, V., Dix, M.C., Zanoni, G., Birdwell, J.E., Wylie, A.S., and Wagner, T., 2024, Influence of organic matter thermal maturity on rare earth element distribution: A study of Middle Devonian black shales from the Appalachian Basin, USA: Energies, v. 17, no. 9, 2107, 23 p., https://doi.org/10.3390/en17092107.","productDescription":"2107, 23 p.","ipdsId":"IP-160281","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":439736,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/en17092107","text":"Publisher Index Page"},{"id":428357,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Middle Devonian Appalachian Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.019722212101,\n 35.09354117626262\n ],\n [\n -80.0893868493873,\n 35.83616426236574\n ],\n [\n -75.48876301910367,\n 41.23650512855389\n ],\n [\n -74.6944038739024,\n 43.48785346597265\n ],\n [\n -79.3043542917768,\n 42.997887934674736\n ],\n [\n -83.72355980871455,\n 37.83971543304291\n ],\n [\n -85.019722212101,\n 35.09354117626262\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"9","noUsgsAuthors":false,"publicationDate":"2024-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Bhattacharya, Shailee","contributorId":336153,"corporation":false,"usgs":false,"family":"Bhattacharya","given":"Shailee","email":"","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":900057,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sharma, Shikha","contributorId":336154,"corporation":false,"usgs":false,"family":"Sharma","given":"Shikha","email":"","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":900058,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Agrawal, Vikas","contributorId":336156,"corporation":false,"usgs":false,"family":"Agrawal","given":"Vikas","email":"","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":900059,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dix, Michael C.","contributorId":336159,"corporation":false,"usgs":false,"family":"Dix","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":80761,"text":"Consultant (formerly with PremierCorex)","active":true,"usgs":false}],"preferred":false,"id":900060,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zanoni, Giovanni","contributorId":336160,"corporation":false,"usgs":false,"family":"Zanoni","given":"Giovanni","email":"","affiliations":[{"id":80763,"text":"RohmTek, Houston, TX","active":true,"usgs":false}],"preferred":false,"id":900061,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":900062,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wylie, Albert S. Jr.","contributorId":336282,"corporation":false,"usgs":false,"family":"Wylie","given":"Albert","suffix":"Jr.","email":"","middleInitial":"S.","affiliations":[{"id":80764,"text":"Independent researcher, Mohawk, MI","active":true,"usgs":false}],"preferred":false,"id":900063,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wagner, Tom","contributorId":336283,"corporation":false,"usgs":false,"family":"Wagner","given":"Tom","email":"","affiliations":[],"preferred":false,"id":900064,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70253052,"text":"ofr20241021 - 2024 - Special Contributing Area Loading Program user’s manual","interactions":[],"lastModifiedDate":"2024-04-26T16:36:34.348917","indexId":"ofr20241021","displayToPublicDate":"2024-04-26T11:23:22","publicationYear":"2024","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":"2024-1021","displayTitle":"Special Contributing Area Loading Program User’s Manual","title":"Special Contributing Area Loading Program user’s manual","docAbstract":"The Special Contributing Area Loading Program (SCALP) is a hydrologic routing program that simulates reservoir routing through a linear-reservoir-in-series method. The Java version of SCALP was developed to replicate and replace the functionality of an older version of the program written in Fortran. SCALP models flow through three reservoirs in series using an input runoff depth time series and information describing the hydrologic characteristics and sanitary flow for one or more land areas within a basin, supplied by the user. Each basin is herein referred to as a “Special Contributing Area” (SCA); the SCAs are a central concept in SCALP. Although flow through each SCA is routed separately, the user may simulate multiple SCAs in a batch simulation. The outputs of SCALP include information about flows through and overflows from the three reservoirs in the series.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241021","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Doyle, H.F., and Domanski, M.M., 2024, Special Contributing Area Loading Program user’s manual: U.S. Geological Survey Open-File Report 2024–1021, 15 p., https://doi.org/10.3133/ofr20241021.","productDescription":"Report: vi, 15 p.; Software Release","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-137188","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":427858,"rank":6,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9EE0614","text":"USGS software release","linkHelpText":"—SCALP (Special Contributing Area Loading Program, ver. 1.0.0)"},{"id":427857,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241021/full"},{"id":427856,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1021/images/"},{"id":427855,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1021/ofr20241021.XML"},{"id":427854,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1021/ofr20241021.pdf","text":"Report","size":"4.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1021"},{"id":427853,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1021/coverthb.jpg"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
An increase in volcanic thermal emissions can indicate subsurface and surface processes that precede, or coincide with, volcanic eruptions. Space-borne infrared sensors can detect hotspots—defined here as localized volcanic thermal emissions—in near-real-time. However, automatic hotspot detection systems are needed to efficiently analyze the large quantities of data produced. While hotspots have been automatically detected for over 20 years with simple thresholding algorithms, new computer vision technologies, such as convolutional neural networks (CNNs), can enable improved detection capabilities. Here we introduce HotLINK: the Hotspot Learning and Identification Network, a CNN trained to detect hotspots with a dataset of −3,800 satellite-based, Visible Infrared Imaging Radiometer Suite (VIIRS) images from Mount Veniaminof and Mount Cleveland volcanoes, Alaska. We find that our model achieves an accuracy of 96% (F1-score 0.92) when evaluated on −1,700 unseen images from the same volcanoes, and 95% (F1-score 0.67) when evaluated on −3,000 images from six additional Alaska volcanoes (Augustine Volcano, Bogoslof Island, Okmok Caldera, Pavlof Volcano, Redoubt Volcano, Shishaldin Volcano). In comparison with an existing threshold-based hotspot detection algorithm, MIROVA (Coppola et al., Geological Society, London, Special Publications, 2016, 426, 181–205), our model detects 22% more hotspots and produces 12% fewer false positives. Additional testing on −700 labeled Moderate Resolution Imaging Spectroradiometer (MODIS) images from Mount Veniaminof demonstrates that our model is applicable to this sensor’s data as well, achieving an accuracy of 98% (F1-score 0.95). We apply HotLINK to 10 years of VIIRS data and 22 years of MODIS data for the eight aforementioned Alaska volcanoes and calculate the radiative power of detected hotspots. From these time series we find that HotLINK accurately characterizes background and eruptive periods, similar to MIROVA, but also detects more subtle warming signals, potentially related to volcanic unrest. We identify three advantages to our model over its predecessors: 1) the ability to detect more subtle volcanic hotspots and produce fewer false positives, especially in daytime images; 2) probabilistic predictions provide a measure of detection confidence; and 3) its transferability, i.e., the successful application to multiple sensors and multiple volcanoes without the need for threshold tuning, suggesting the potential for global application.
The California Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP) investigated water quality of groundwater resources used for drinking-water supplies in the Madera-Chowchilla, Kings, Kaweah, Tule, and Tulare Lake groundwater subbasins of the southeastern San Joaquin Valley during 2013–15. The study focused primarily on groundwater resources used for domestic-supply wells in the southeastern San Joaquin Valley (SESJV-D), which correspond mostly to shallower parts of aquifer systems, compared to the groundwater resources used for public-supply wells in the southeastern San Joaquin Valley (SESJV-P). The investigation had three components: (1) characterization of the status of water quality in the SESJV-D, (2) comparison between water quality in the SESJV-D and SESJV-P, and (3) identification of natural and anthropogenic factors that potentially could affect water quality in these resources.
The characterization of water quality in the SESJV-D was based on data collected from 198 domestic wells sampled during 2013–15 by the U.S. Geological Survey (USGS); characterization of water quality in the SESJV-P was based on data collected from 124 wells sampled by the USGS during 2005–18 and an additional 1,577 wells with publicly available data reported to the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW). Measured concentrations were compared to regulatory and non-regulatory drinking-water quality benchmarks. A grid-based method was used to estimate the areal proportions of each study area and the whole southeastern San Joaquin Valley with high (greater than benchmark concentration), moderate (greater than half of the benchmark for inorganic and one-tenth of the benchmark for organic), and low concentrations relative to those benchmarks.
Natural and anthropogenic factors that could affect groundwater quality for the SESJV-D were identified in the context of the hydrogeologic setting of the southeastern San Joaquin Valley. The considered factors represented hydrologic conditions and position in the groundwater flow system (well depth, lateral position, presence of hydric soils, percentage of coarse-grained sediment, and aridity index), land-use characteristics (percentages of agricultural, urban, and natural land use, percentage of orchard or vineyard land use, and densities of septic tanks and underground storage tanks near the wells), and geochemical conditions (groundwater age class, oxidation-reduction class, pH, and dissolved oxygen and bicarbonate concentrations). Factors are compared between SESJV-D and SESJV-P at the scale of the five study areas.
One or more inorganic constituents with U.S. Environmental Protection Agency (EPA) or California maximum contaminant levels (MCLs) were detected at high concentrations in 47 percent of the SESJV-D and in 32 percent of the SESJV-P. The inorganic constituents most commonly present at high concentrations in the SESJV-D were nitrate, uranium, and arsenic. Within the SESJV-D, the proportion of the study area with high concentrations of inorganic constituents ranged from 19 percent in Madera-Chowchilla to 60 percent in Kings and Tulare Lake. One or more inorganic constituents with California State Water Resources Control Board Division of Drinking Water secondary maximum contaminant levels (SMCL-CAs) were detected at high concentrations in 14 percent of the SESJV-D and in 19 percent of the SESJV-P. The constituents most commonly present at high concentrations were manganese, iron, and total dissolved solids (TDS). Although the proportion of SESJV-D and SESJV-P with high concentrations of TDS greater than the upper SMCL were similar at 4 percent, the proportion of the SESJV-D with moderate concentrations (between the recommended and upper SMCL-CA), 30 percent, was greater than the proportion of the SESJV-P with moderate concentrations, 12 percent.
One or more organic constituents with MCLs were present at high concentrations in 19 percent of the SESJV-D and in 12 percent of the SESJV-P. All the constituents detected at high concentrations in the SESJV-D were fumigants, primarily 1,2,3-trichloropropane (1,2,3-TCP) and 1,2-dibromo-3-chloropropane (DBCP). Fumigants also were the constituents most commonly detected at high concentrations in the SESJV-P, although high concentrations of solvents also were detected. The SESJV-D dataset included analysis of many organic constituents without MCL benchmarks and with detection levels far below drinking water benchmark concentrations; detections at these low concentrations can be used as tracers of anthropogenic influence on groundwater. Pesticides and degradates of pesticides were detected in 60 percent of the SESJV-D; the most frequently detected pesticides were the herbicides simazine, didealkylatrazine (CAAT, a degradate of simazine and atrazine), diuron, and bromacil.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245009","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","programNote":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","usgsCitation":"Burow, K.R., Shelton, J.L., and Fram, M.S., 2024, Status of water quality in groundwater resources used for drinking-water supply in the southeastern San Joaquin Valley, 2013–15—California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2024–5009, 135 p., https://doi.org/10.3133/sir20245009.","productDescription":"Report: xiii, 135 p.; Data Release","numberOfPages":"136","onlineOnly":"Y","ipdsId":"IP-094434","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":428122,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245009/full"},{"id":493742,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116370.htm","linkFileType":{"id":5,"text":"html"}},{"id":428123,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DCTLXV","text":"USGS Data Release","description":"Balkan, M., Burow, K.R., and Shelton, J.L., and Fram, M.S., 2024, Data sets for: Status of water quality in groundwater resources used for drinking water supply in the southeast San Joaquin Valley, 2013–2015—California GAMA Priority Basin Project: U.S. Geological Survey data release, accessed January, 22, 2024, at https://doi.org/10.5066/P9DCTLXV","linkHelpText":"Data sets for: Status of water quality in groundwater resources used for drinking water supply in the southeast San Joaquin Valley, 2013–2015—California GAMA Priority Basin Project"},{"id":428120,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5009/sir20245009.xml"},{"id":428118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5009/covrthb.jpg"},{"id":428121,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5009/images"},{"id":428119,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5009/sir20245009.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.36728753741212,\n 37.719936264455484\n ],\n [\n -121.36728753741212,\n 35.78355104851377\n ],\n [\n -118.20322503741215,\n 35.78355104851377\n ],\n [\n -118.20322503741215,\n 37.719936264455484\n ],\n [\n -121.36728753741212,\n 37.719936264455484\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
A need exists for additional methods to examine cnidaria at the cellular level to aid our understanding of health, anatomy, and physiology of this important group of organisms. This need is particularly acute given that disease is emerging as a major factor in declines of ecologically important functional groups such as corals. Here we describe a simple method to process cnidarian cells for microscopic examination using the model organism Exaiptasia. We show that this organism has at least 18 cell types or structures that can be readily distinguished based on defined morphological features. Some of these cells can be related back to anatomic features of the animal both at the light microscope and ultrastructural level. The cnidome of Exaiptasia may be more complex than what is currently understood. Moreover, cnidarian cells, including some types of cnidocytes, phagocytize cells other than endosymbionts. Finally, our findings shed light on morphologic complexity of cell-associated microbial aggregates and their intimate intracellular associations. The tools described here could be useful for other cnidaria.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/dao03781","usgsCitation":"Work, T.M., Singarkhan, C., and Weatherby, T., 2024, Cytology in cnidaria using Exaiptasia as a model: Diseases of Aquatic Organisms, v. 158, p. 37-53, https://doi.org/10.3354/dao03781.","productDescription":"17 p.","startPage":"37","endPage":"53","ipdsId":"IP-159594","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":487639,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/dao03781","text":"Publisher Index Page"},{"id":481970,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"158","noUsgsAuthors":false,"publicationDate":"2024-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Work, Thierry M. 0000-0002-4426-9090 thierry_work@usgs.gov","orcid":"https://orcid.org/0000-0002-4426-9090","contributorId":1187,"corporation":false,"usgs":true,"family":"Work","given":"Thierry","email":"thierry_work@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":927130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Singarkhan, Chutimon","contributorId":335063,"corporation":false,"usgs":false,"family":"Singarkhan","given":"Chutimon","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":927131,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weatherby, Tina","contributorId":193516,"corporation":false,"usgs":false,"family":"Weatherby","given":"Tina","affiliations":[],"preferred":false,"id":927132,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70253192,"text":"70253192 - 2024 - Atmospheric river activity during the late Holocene exceeds modern range of variability in California","interactions":[],"lastModifiedDate":"2024-04-26T12:03:49.92142","indexId":"70253192","displayToPublicDate":"2024-04-25T06:59:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13795,"text":"Nature Communications Earth and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Atmospheric river activity during the late Holocene exceeds modern range of variability in California","docAbstract":"Atmospheric rivers are associated with some of the largest flood-producing precipitation events in western North America, particularly California. Insight into past extreme precipitation can be reconstructed from sedimentary archives on millennial timescales. Here we document atmospheric river activity near Leonard Lake, California, over 3,200 years, using a key metric of atmospheric river intensity, that is silicon/aluminum enriched layers that are highly correlated with modern records of integrated vapor transport. The late twentieth century had the highest median integrated vapor transport since the onset of the Medieval Climate Anomaly, with integrated vapor transport increasing during the Little Ice Age. The reconstruction suggests California has experienced pluvial episodes that exceeded any in the meteorologic instrumental era, with the largest episodes occurring two and three millennia ago. These results provide critical data to help avoid underestimation of potential risks and aid future planning scenarios.
Wildlife species confront threats from climate and land use change, exacerbating the influence of extreme climatic events on populations and biodiversity. Migratory waterbirds are especially vulnerable to hydrological drought via reduced availability of surface water habitats. We assessed how whooping cranes (Grus americana) modified habitat use and migration strategies during drought to evaluate their resilience to changing conditions and adaptive capacity. We categorized >8000 night-roost sites used by 146 cranes from 2010 to 2022 and examined relative use during non-drought, moderate drought, and extreme drought conditions. We found cultivated and uncultivated palustrine and lacustrine wetlands were generally used less during droughts than non-drought conditions. Conversely, impounded palustrine and lacustrine systems and rivers served more frequently as drought refugia (i.e., used more during drought than non-drought conditions). Night roosts occurred primarily on private lands (86% overall); public land use decreased with latitude and increased with drought severity, with greatest use (56%) occurring during severe autumn drought in the southern Great Plains. Quantifying use of identified critical habitats in the United States indicated that Cheyenne Bottoms State Waterfowl Management Area and Quivira National Wildlife Refuge were used less during drought, and the Central Platte River and Salt Plains National Wildlife Refuge received similar use during drought compared to non-drought conditions. Our findings provide insights into compensatory use of habitats, where impounded surface water may function in a complementary fashion with natural wetlands. Collectively, these and other types of wetlands distributed across the migration corridor provided a reliable network of habitat available across the Great Plains. A diversity of wetlands available during variable environmental conditions would be useful in supporting continued recovery of whooping cranes and likely have benefits for a wide array of migratory birds.
Bighead carp (Hypophthalmichthys nobilis), silver carp (H. molitrix), black carp (Mylopharyngodon piceus), and grass carp (Ctenopharyngodon idella), are invasive species in North America. However, they hold significant economic importance as food sources in China. The drifting stage of carp eggs has received great attention because egg survival rate is strongly affected by river hydrodynamics. In this study, we explored egg-drift dynamics using computational fluid dynamics (CFD) models to infer potential egg settling zones based on mechanistic criteria from simulated turbulence in the Lower Missouri River. Using an 8-km reach, we simulated flow characteristics with four different discharges, representing 45–3% daily flow exceedance. The CFD results elucidate the highly heterogeneous spatial distribution of flow velocity, flow depth, turbulence kinetic energy (TKE), and the dissipation rate of TKE. The river hydrodynamics were used to determine potential egg settling zones using criteria based on shear velocity, vertical turbulence intensity, and Rouse number. Importantly, we examined the difference between hydrodynamic-inferred settling zones and settling zones predicted using an egg-drift transport model. The results indicate that hydrodynamic inference is useful in determining the ‘potential’ of egg settling, however, egg drifting paths should be taken into account to improve prediction. Our simulation results also indicate that the river turbulence does not surpass the laboratory-identified threshold to pose a threat to carp eggs.
Environmental DNA (eDNA) sampling is an increasingly important tool for answering ecological questions and informing aquatic species management; however, several factors currently limit the reliability of ecological inference from eDNA sampling. Two particular challenges are 1) determining species source location(s) and 2) accurately and precisely measuring low concentration eDNA samples in the presence of multiple sources of ecological and measurement variability. The recently introduced eDNA Integrating Transport and Hydrology (eDITH) model provides a framework for relating eDNA measurements to source locations in riverine networks, but little empirical work has been done to test and refine model assumptions or accommodate low concentration samples, that can be systematically undermeasured. To better understand eDNA fate and transport dynamics and our ability to reliably quantify low concentration samples, we developed a hierarchical model and used it to evaluate a fate and transport experiment. Our model addresses several low concentration challenges by modeling the number of copies in each PCR replicate as a latent variable with a count distribution and conditioning detection and quantification on replicate copy number. We provide evidence that the eDNA removal rate declined through time, estimating that over 80% of eDNA was removed over the first 10 meters, traversed in 41 seconds. After this initial period of rapid decay, eDNA decayed slowly with consistent detection through our farthest site 1km from the release location, traversed in 250 seconds. Our model further allowed us to detect extra-Poisson variation in the allocation of copies to replicates. We extended our hierarchical model to accommodate a continuous effect of inhibitors and used our model to provide evidence for the inhibitor hypothesis and explore the potential implications. While our model is not a panacea for all challenges faced when quantifying low-concentration eDNA samples, it provides a framework for a more complete accounting of uncertainty.
","language":"English","publisher":"BiorXiv","doi":"10.1101/2024.03.27.586987","usgsCitation":"Augustine, B., Hutchins, P., Jones-Slobodian, D.N., Williams, J., Leinonen, E., and Sepulveda, A., 2024, A hierarchical model for eDNA fate and transport dynamics accommodating low concentration samples: BioRxiv, https://doi.org/10.1101/2024.03.27.586987.","productDescription":"61 p.","ipdsId":"IP-161502","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":459969,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/2024.03.27.586987","text":"External Repository"},{"id":465483,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Augustine, Ben 0000-0001-6935-6361","orcid":"https://orcid.org/0000-0001-6935-6361","contributorId":245736,"corporation":false,"usgs":true,"family":"Augustine","given":"Ben","email":"","affiliations":[{"id":49304,"text":"Department of Natural Resources, Cornell University","active":true,"usgs":false}],"preferred":false,"id":912510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hutchins, Patrick Ross 0000-0001-5232-0821","orcid":"https://orcid.org/0000-0001-5232-0821","contributorId":256658,"corporation":false,"usgs":true,"family":"Hutchins","given":"Patrick Ross","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":912511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones-Slobodian, Devin Nicole 0000-0001-9215-2930","orcid":"https://orcid.org/0000-0001-9215-2930","contributorId":305357,"corporation":false,"usgs":true,"family":"Jones-Slobodian","given":"Devin","middleInitial":"Nicole","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":912512,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Jacob R.","contributorId":343977,"corporation":false,"usgs":false,"family":"Williams","given":"Jacob R.","affiliations":[{"id":82269,"text":"Turner Institute of Ecoagriculture","active":true,"usgs":false}],"preferred":false,"id":912513,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leinonen, Eric","contributorId":343978,"corporation":false,"usgs":false,"family":"Leinonen","given":"Eric","affiliations":[{"id":82269,"text":"Turner Institute of Ecoagriculture","active":true,"usgs":false}],"preferred":false,"id":912514,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":912515,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70253898,"text":"70253898 - 2024 - Genetic structure of restored Brook Trout populations in the Southern Appalachian Mountains indicates successful reintroductions","interactions":[],"lastModifiedDate":"2024-07-15T15:03:38.550959","indexId":"70253898","displayToPublicDate":"2024-04-24T08:48:15","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Genetic structure of restored Brook Trout populations in the Southern Appalachian Mountains indicates successful reintroductions","docAbstract":"Wildlife reintroduction is an important conservation tool for threatened species, yet identifying appropriate source populations poses a challenge. In particular, the possibility of outbreeding depression is cited as a constraint limiting the range of candidate source populations for translocation. When multiple source lineages are mixed during reintroduction, genetic monitoring is necessary to evaluate whether sources contribute equally to subsequent generations and whether they are interbreeding as expected. Moreover, statistical analysis of genetic data should account for complex life histories that might affect the timescale of admixture and genetic drift. Here, we use samples collected over a 23-year period and a stochastic age-structured model to analyze the genetic mixing process in reintroduced Brook Trout (Salvelinus fontinalis) populations in the Southern Appalachians. Each restored population was seeded with two to three source populations. Previous research inferred reproductive isolation between source populations leading to a proposal of splitting the species into multiple taxa. In contrast, we found patterns of ancestry that were consistent with random mating and no advantage for one source lineage over any other. Brook Trout from different source streams are mixing as expected in the restoration sites. This result does not support the hypothesis that Brook Trout in the Southern Appalachian Mountains includes several distinct species. Mixing different sources from the same watershed seems to be an effective way to increase genetic diversity of reintroduced populations while minimizing risk to source populations.
","language":"English","publisher":"Springer","doi":"10.1007/s10592-024-01620-y","usgsCitation":"Smith, R.J., Kazyak, D.C., Kulp, M.A., Lubinski, B.A., and Fitzpatrick, B.M., 2024, Genetic structure of restored Brook Trout populations in the Southern Appalachian Mountains indicates successful reintroductions: Conservation Genetics, v. 25, p. 1007-1020, https://doi.org/10.1007/s10592-024-01620-y.","productDescription":"14 p.","startPage":"1007","endPage":"1020","ipdsId":"IP-158952","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":428350,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Tennessee","otherGeospatial":"Great Smoky Mountains National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.90629478474587,\n 35.85116809750147\n ],\n [\n -84.21639200734252,\n 35.85116809750147\n ],\n [\n -84.21639200734252,\n 35.2665417042201\n ],\n [\n -82.90629478474587,\n 35.2665417042201\n ],\n [\n -82.90629478474587,\n 35.85116809750147\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2024-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Rebecca J.","contributorId":229064,"corporation":false,"usgs":false,"family":"Smith","given":"Rebecca","email":"","middleInitial":"J.","affiliations":[{"id":41574,"text":"National Park Service, Yellowstone National Park, PO Box 168, 22 Stable Street, Yellowstone National Park, WY, 82190, USA","active":true,"usgs":false}],"preferred":false,"id":900031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":900032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kulp, Matt A.","contributorId":196801,"corporation":false,"usgs":false,"family":"Kulp","given":"Matt","email":"","middleInitial":"A.","affiliations":[{"id":35484,"text":"National Park Service, Great Smoky Mountains National Park","active":true,"usgs":false}],"preferred":false,"id":900033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":900034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fitzpatrick, Benjamin M.","contributorId":336140,"corporation":false,"usgs":false,"family":"Fitzpatrick","given":"Benjamin","email":"","middleInitial":"M.","affiliations":[{"id":80760,"text":"1. Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee","active":true,"usgs":false}],"preferred":false,"id":900035,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70264784,"text":"70264784 - 2024 - Environmental DNA dynamics of three species of unionid freshwater mussels","interactions":[],"lastModifiedDate":"2025-03-24T15:21:21.274494","indexId":"70264784","displayToPublicDate":"2024-04-24T08:17:43","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5840,"text":"Environmental DNA","active":true,"publicationSubtype":{"id":10}},"title":"Environmental DNA dynamics of three species of unionid freshwater mussels","docAbstract":"North American freshwater mussels are of special conservation concern due to their high endemism and the multiple anthropogenic stressors affecting them. Of the over 300 species in North America, nearly one third of these species are federally listed as threatened or endangered. Environmental DNA (eDNA) analysis has been successful in detecting freshwater mussels and could aid in monitoring their populations. Production and degradation rates of eDNA for the species of interest are needed to inform interpretation of eDNA detections, allow possible modeling of relative abundance and population location, and aid in mussel conservation through population identification. Here, we designed and tested qPCR assays for three freshwater mussel species, mucket (Ortmanniana ligamentina), fatmucket (Lampsilis siliquoidea), and the federally endangered spectaclecase (Cumberlandia monodonta). We performed laboratory experiments under controlled conditions to measure eDNA shedding and degradation rates for each species. Different biomasses, temperatures, and food regimens were tested independently to determine if these factors influence the amount of DNA produced by the mussels. Degradation rates of eDNA were measured from experimental tank water after mussels were removed. Overall, we observed low eDNA shedding rates for freshwater mussels compared to previous studies of fish eDNA shedding rates. Furthermore, temperature and feeding showed limited or no significant effects in the species studied. Environmental DNA degradation rates were consistent with those reported in the literature for other taxa. Collectively, our results will be useful for designing eDNA monitoring studies, modeling eDNA dispersal, and interpreting eDNA results to help inform freshwater mussel conservation efforts.
","language":"English","publisher":"Wiley","doi":"10.1002/edn3.543","usgsCitation":"Ruiz-Ramos, D., Thompson, N., Richter, C.A., Voshage, M., Schreier, T.M., Merkes, C.M., and Klymus, K.E., 2024, Environmental DNA dynamics of three species of unionid freshwater mussels: Environmental DNA, v. 6, no. 2, e543, 15 p., https://doi.org/10.1002/edn3.543.","productDescription":"e543, 15 p.","ipdsId":"IP-157659","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":488376,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/edn3.543","text":"Publisher Index Page"},{"id":483719,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruiz-Ramos, Dannise","contributorId":332474,"corporation":false,"usgs":false,"family":"Ruiz-Ramos","given":"Dannise","affiliations":[{"id":78382,"text":"formerly Columbia Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":931669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thompson, Nathan 0000-0002-1372-6340 nthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-1372-6340","contributorId":196133,"corporation":false,"usgs":true,"family":"Thompson","given":"Nathan","email":"nthompson@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":931670,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richter, Catherine A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":138994,"corporation":false,"usgs":true,"family":"Richter","given":"Catherine","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":931671,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Voshage, Megan C.","contributorId":332475,"corporation":false,"usgs":false,"family":"Voshage","given":"Megan C.","affiliations":[{"id":78382,"text":"formerly Columbia Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":931672,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schreier, Theresa M. 0000-0001-7722-6292 tschreier@usgs.gov","orcid":"https://orcid.org/0000-0001-7722-6292","contributorId":3344,"corporation":false,"usgs":true,"family":"Schreier","given":"Theresa","email":"tschreier@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":931673,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Merkes, Christopher M. 0000-0001-8191-627X cmerkes@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-627X","contributorId":139516,"corporation":false,"usgs":true,"family":"Merkes","given":"Christopher","email":"cmerkes@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":931674,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Klymus, Katy E. 0000-0002-8843-6241 kklymus@usgs.gov","orcid":"https://orcid.org/0000-0002-8843-6241","contributorId":5043,"corporation":false,"usgs":true,"family":"Klymus","given":"Katy","email":"kklymus@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":931675,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70257757,"text":"70257757 - 2024 - Characteristics of debris-flow-prone watersheds and debris-flow-triggering rainstorms following the Tadpole Fire, New Mexico, USA","interactions":[],"lastModifiedDate":"2024-09-09T16:49:25.320965","indexId":"70257757","displayToPublicDate":"2024-04-24T07:02:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2824,"text":"Natural Hazards and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Characteristics of debris-flow-prone watersheds and debris-flow-triggering rainstorms following the Tadpole Fire, New Mexico, USA","docAbstract":"Moderate- or high-severity fires promote increases in runoff and erosion, leading to a greater likelihood of extreme geomorphic responses, including debris flows. In the first several years following fire, the majority of debris flows initiate when runoff rapidly entrains sediment on steep slopes. From a hazard perspective, it is important to be able to anticipate when and where watershed responses will be dominated by debris flows rather than flood flows. Rainfall intensity averaged over a 15 min duration, I15, in particular, has been identified as a key predictor of debris flow likelihood. Developing effective warning systems and predictive models for post-fire debris flow hazards therefore relies on high-temporal resolution rainfall data at the time debris flows initiate. In this study, we documented the geomorphic response of a series of watersheds following a wildfire in western New Mexico, USA, with an emphasis on constraining debris flow timing within rainstorms to better characterize debris-flow-triggering rainfall intensities. We estimated temporal changes in soil hydraulic properties and ground cover in areas burned at different severities over >2 years to offer explanations for observed differences in spatial and temporal patterns in debris flow activity. We observed 16 debris flows, all of which initiated during the first several months following the fire. The average recurrence interval of the debris-flow-triggering I15 is 1.3 years, which highlights the susceptibility of recently burned watersheds to runoff-generated debris flows in this region. All but one of the debris flows initiated in watersheds burned primarily at moderate or high soil burn severity. Since soil hydraulic properties appeared to be relatively resilient to burning, we attribute reduced debris flow activity at later times to decreases in the fraction of bare ground. Results provide additional constraints on the rainfall characteristics that promote post-fire debris flow initiation in a region where fire size and severity have been increasing.
This study uses a one-dimensional steady-state hydraulic model and the Fluvial Egg Drift Simulator (FluEgg) to model the drift and dispersion of grass carp eggs and larvae in the Maumee River, Ohio, for 180 scenarios representing different combinations of 10 river flows, 6 water temperatures, and 3 spawning locations. The FluEgg simulations were used to quantify in-river suspended hatching rates (the percentage of eggs that hatch within the river and in suspension) and in-river larval retention rates (the percentage of larvae that reach the gas bladder inflation stage within the river after hatching in suspension), and identify which scenarios produce the highest likelihood of recruitment. The simulations indicate that at low flows, in-river suspended hatching and larval retention rates in the Maumee River are limited by the capacity of the flow to keep fertilized eggs in suspension, whereas at high flows, the limiting factor is the distance available for the eggs/larvae to drift in the river. A wide range of scenarios result in eggs hatching within the river, but all larvae drift into Maumee Bay prior to the gas bladder inflation stage when flows exceed the mean annual flow. The simulations were assessed in the context of the hydraulic conditions that trigger spawning and maximize egg fertilization and the nursery habitat requirements for larval grass carp. The results indicate that the Maumee River, although suitable for grass carp spawning, may not be an ideal setting for recruitment unless Maumee Bay provides adequate nursery habitat for larvae.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102345","usgsCitation":"LeRoy, J.Z., Doyle, H.F., Jackson, P.R., and Cigrand, C.V., 2024, Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 2—Optimal river conditions for egg and larval drift: Journal of Great Lakes Research, v. 50, 102345, 18 p., https://doi.org/10.1016/j.jglr.2024.102345.","productDescription":"102345, 18 p.","ipdsId":"IP-137490","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":439771,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1016/j.jglr.2024.102345","text":"Publisher Index Page"},{"id":428354,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Maumee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.38518414361491,\n 41.68593497191691\n ],\n [\n -83.45979759899954,\n 41.74305978003929\n ],\n [\n -83.66454217332985,\n 41.60155527334871\n ],\n [\n -83.8481872826162,\n 41.471249401310274\n ],\n [\n -84.12554495228478,\n 41.444009750259625\n ],\n [\n -84.20397403397705,\n 41.37227829616401\n ],\n [\n -84.41243215532246,\n 41.2918512649257\n ],\n [\n -84.35316138453301,\n 41.241526198376704\n ],\n [\n -84.09303055462573,\n 41.30190322738565\n ],\n [\n -84.060513011197,\n 41.379455129599705\n ],\n [\n -83.7812377285593,\n 41.39524150503729\n ],\n [\n -83.38518414361491,\n 41.68593497191691\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"LeRoy, Jessica Z. 0000-0003-4035-6872 jzinger@usgs.gov","orcid":"https://orcid.org/0000-0003-4035-6872","contributorId":174534,"corporation":false,"usgs":true,"family":"LeRoy","given":"Jessica","email":"jzinger@usgs.gov","middleInitial":"Z.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doyle, Henry F. 0000-0001-9942-8602 hfdoyle@usgs.gov","orcid":"https://orcid.org/0000-0001-9942-8602","contributorId":243432,"corporation":false,"usgs":true,"family":"Doyle","given":"Henry","email":"hfdoyle@usgs.gov","middleInitial":"F.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900028,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900029,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cigrand, Charles V. 0000-0002-4177-7583","orcid":"https://orcid.org/0000-0002-4177-7583","contributorId":201575,"corporation":false,"usgs":true,"family":"Cigrand","given":"Charles","email":"","middleInitial":"V.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900030,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70253134,"text":"dr1193 - 2024 - Calculation of a suspended-sediment concentration-turbidity regression model and flood-ebb suspended-sediment concentration differentials from marshes near Stone Harbor and Thompsons Beach, New Jersey, 2018–19 and 2022–23","interactions":[],"lastModifiedDate":"2026-01-27T17:29:12.572398","indexId":"dr1193","displayToPublicDate":"2024-04-22T12:15:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1193","displayTitle":"Calculation of a Suspended-Sediment Concentration-Turbidity Regression Model and Flood-Ebb Suspended-Sediment Concentration Differentials From Marshes Near Stone Harbor and Thompsons Beach, New Jersey, 2018–19 and 2022–23","title":"Calculation of a suspended-sediment concentration-turbidity regression model and flood-ebb suspended-sediment concentration differentials from marshes near Stone Harbor and Thompsons Beach, New Jersey, 2018–19 and 2022–23","docAbstract":"The U.S. Geological Survey collected water velocity and water quality data from salt marshes in Great Channel, southwest of Stone Harbor, New Jersey, and near Thompsons Beach, New Jersey, to evaluate restoration effectiveness after Hurricane Sandy and monitor postrestoration marsh health. Time series data of turbidity and water velocity were collected from 2018 to 2019 and 2022 to 2023 at both sites. Water samples were collected and analyzed for suspended-sediment concentration (SSC), which was used to derive a regression model to estimate a time series of SSC data from turbidity data. The SSC time series data were then combined with the water velocity data to calculate the flood-ebb SSC differential. This report presents the data collection methods, the repeated median regression model used to estimate SSC from turbidity, and the flood-ebb SSC differential calculations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1193","programNote":"Coastal/Marine Hazards and Resources Program","usgsCitation":"De Meo, O.A., Bales, R.D., Ganju, N.K., Marsjanik, E.D., and Suttles, S.E., 2024, Calculation of a suspended-sediment concentration-turbidity regression model and flood-ebb suspended-sediment concentration differentials from marshes near Stone Harbor and Thompsons Beach, New Jersey, 2018–19 and 2022–23: U.S. Geological Survey Data Report 1193, 12 p., https://doi.org/10.3133/dr1193.","productDescription":"Report: iv, 12 p.; 2 Data Releases","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-162873","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":499107,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116368.htm","linkFileType":{"id":5,"text":"html"}},{"id":427955,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1193/dr1193.XML"},{"id":427958,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CS5U6N","text":"USGS data release","linkHelpText":"Supplementary data in support of oceanographic and water quality times-series measurements made at Thompsons Beach and Stone Harbor, NJ from September 2018 to February 2023"},{"id":427957,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z0Z8DM","text":"USGS data release","linkHelpText":"Time-series measurements of oceanographic and water quality data collected at Thompsons Beach and Stone Harbor, New Jersey, USA, September 2018 to September 2019 and March 2022 to May 2023"},{"id":427956,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1193/images/"},{"id":427954,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1193/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DR 1193"},{"id":427953,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1193/dr1193.pdf","text":"Report","size":"3.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1193"},{"id":427952,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1193/coverthb.jpg"}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.65514977013649,\n 39.123992018486376\n ],\n [\n -74.84847491860745,\n 39.123992018486376\n ],\n [\n -74.84847491860745,\n 39.01856524124548\n ],\n [\n -74.65514977013649,\n 39.01856524124548\n ],\n [\n -74.65514977013649,\n 39.123992018486376\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Woods Hole Coastal and Marine Science Center
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Quissett Campus
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Longitudinal dispersal of migratory fish species can be interrupted by factors that fragment rivers, such as dams and reservoirs with incompatible habitats, and indirect alterations to variables, such as water temperature or turbidity. The endangered pallid sturgeon (Scaphirhynchus albus) population in the Upper Missouri River Basin in North Dakota and Montana is an example of such fragmentation and alteration due to the construction of dams. We applied a high-resolution, 2+-dimensional modelling framework composed of hydrodynamic and Lagrangian particle tracking components to simulate pallid sturgeon larval drift and dispersal along a 33-km section of the Upper Missouri River to evaluate three main issues: a comparison between multidimensional models and traditional 1-dimensional models, the sensitivity of hydrodynamics to channel morphology, and the implications of channel morphology on retention and transport-time metrics for larval fish. The results indicate that multidimensional models better represent breakthrough curves of transporting larvae compared to 1-dimensional models, especially for the long tail of slow drifters in the population. Results also indicate that channel morphology and hydraulic complexity play significant roles in larval dispersal with certain flow conditions and channel features increasing larval retention and providing potential management options to increase survival rates by adjusting flow conditions during spawning events. For example, modelling indicates increased retention times at discharges 23–38% daily flow exceedance, coincident with emergence of mid-channel sandbars. Findings additionally emphasize the need for improved understanding of biological factors that affect larval drift and dispersal.
As carbon storage technologies advance globally, methods to understand and mitigate induced earthquakes become increasingly important. Although the physical processes that relate increased subsurface pore pressure changes to induced earthquakes have long been known, reliable methods to forecast and control induced seismic sequences remain elusive. Suggested reservoir engineering scenarios for mitigating induced earthquakes typically involve modulation of the injection rate. Some operators have implemented periodic shutdowns (i.e., effective cycling of injection rates) to allow reservoir pressures to equilibrate (e.g., Paradox Valley) or shut‐in wells after the occurrence of an event of concern (e.g., Basel, Switzerland). Other proposed scenarios include altering injection rates, actively managing pressures through coproduction of fluids, and preinjection brine extraction. In this work, we use 3D physics‐based earthquake simulations to understand the effects of different injection scenarios on induced earthquake rates, maximum event magnitudes, and postinjection seismicity. For comparability, the modeled injection considers the same cumulative volume over the project’s operational life but varies the schedule and rates of fluid injected. Simulation results show that cyclic injection leads to more frequent and larger events than constant injection. Furthermore, with intermittent injection scenario, a significant number of events are shown to occur during pauses in injection, and the seismicity rate remains elevated for longer into the postinjection phase compared to the constant injection scenario.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220230330","usgsCitation":"Kroll, K.A., and Cochran, E.S., 2024, Cyclic injection leads to larger and more frequent induced earthquakes under volume-controlled conditions: Seismological Research Letters, v. 95, p. 2105-2117, https://doi.org/10.1785/0220230330.","productDescription":"13 p.","startPage":"2105","endPage":"2117","ipdsId":"IP-157897","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489102,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/2377896","text":"External Repository"},{"id":432863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"95","noUsgsAuthors":false,"publicationDate":"2024-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Kroll, Kayla A.","contributorId":146335,"corporation":false,"usgs":false,"family":"Kroll","given":"Kayla","email":"","middleInitial":"A.","affiliations":[{"id":6984,"text":"UC Riverside","active":true,"usgs":false}],"preferred":false,"id":910519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":910520,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70253174,"text":"70253174 - 2024 - Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States","interactions":[],"lastModifiedDate":"2024-04-23T11:58:30.217736","indexId":"70253174","displayToPublicDate":"2024-04-19T06:55:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17467,"text":"Machine Learning with Applications","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States","docAbstract":"The application of Long Short-Term Memory (LSTM) models for streamflow predictions has been an area of rapid development, supported by advancements in computing technology, increasing availability of spatiotemporal data, and availability of historical data that allows for training data-driven LSTM models. Several studies have focused on improving the performance of LSTM models; however, few studies have assessed the applicability of these LSTM models across different hydroclimate regions. This study investigated the single-basin trained local (one model for each basin), multi-basin trained regional (one model for one region), and grand (one model for several regions) models for predicting daily streamflow in water-limited Great Basin (18 basins) and energy-limited New England (27 basins) regions in the United States using the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) data set. The results show a general pattern of higher accuracy in daily streamflow predictions from the regional model when compared to local or grand models for most basins in the New England region. For the Great Basin region, local models provided smaller errors for most basins and substantially lower for those basins with relatively larger errors from the regional and grand models. The evaluation of one-layer and three-layer LSTM network architectures trained with 1-day lag information indicates that the addition of model complexity by increasing the number of layers may not necessarily increase the model skill for improving streamflow predictions. Findings from our study highlight the strengths and limitations of LSTM models across contrasting hydroclimate regions in the United States, which could be useful for local and regional scale decisions using standalone or potential integration of data-driven LSTM models with physics-based hydrological models.
To better understand the potential for amplified ground shaking at sites that house critical infrastructure, the U.S. Geological Survey (USGS) evaluated shear-wave velocities (VS) at six strong-motion recording stations in Southern California Edison facilities in southern California. We calculated VS30 (time-averaged shear-wave velocity in the upper 30 meters [m]), which is a parameter used in ground-motion prediction equations (GMPEs) to account for site amplification (Building Safety Seismic Council, 2003; Holtzer and others, 2005; Baltay and Boatwright, 2015). Previous site-characterization studies using multiple methods in Alameda, Napa, and Sonoma Counties, Calif., and in British Columbia (Catchings and others, 2017, 2019; Chan and others, 2018a, 2018b) show that some sites have significant lateral variability; thus, a single measurement of VS30 nearest to the strong-motion recording station may not accurately account for the actual subsurface velocity variations. In the summer of 2017, we recorded body and surface waves along linear profiles (118–174 m long) using active-source seismic methods (226-kilogram [kg] accelerated weight-drop and 3.5-kg sledgehammer impacts) near strong-motion recording stations. We used S-wave refraction tomography and a multichannel analysis of surface waves (MASW) method (using common midpoint cross-correlation; CMPCC) to evaluate two-dimensional (2-D) VS from body and surface waves, respectively. We evaluated VS from both Rayleigh- and Love-waves.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241016","usgsCitation":"Chan, J.H., Catchings, R.D., Goldman, M.R., Criley, C.J., and Sickler, R.R., 2024, Evaluation of 2-D shear-wave velocity models and VS30 at six strong-motion recording stations in southern California using multichannel analysis of surface waves and refraction tomography: U.S. Geological Survey Open-File Report 2024–1016, 58 p., https://doi.org/10.3133/ofr20241016.","productDescription":"Report: vii, 58 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","ipdsId":"IP-132062","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":499241,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116364.htm","linkFileType":{"id":5,"text":"html"}},{"id":427913,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P990O55F","text":"USGS Data Release","description":"Chan, J.H., Catchings, R.D., Goldman, M.R, Criley, C.J., and Sickler, R.R., 2021, High-resolution seismic data acquired at six Southern California seismic network (SCSN) recording stations in 2017: U.S. Geological Survey data release, https://doi.org/10.5066/P990O55F.","linkHelpText":"High-resolution seismic data acquired at six Southern California seismic network (SCSN) recording stations in 2017"},{"id":427918,"rank":6,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1016/coverthb.jpg"},{"id":427915,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1016/images"},{"id":427914,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1016/ofr20241016.pdf","text":"Report","size":"20 MB"},{"id":427917,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241016/full"},{"id":427916,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1016/ofr20241016.xml"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.02522723790126,\n 35.189340133329225\n ],\n [\n -121.02522723790126,\n 33.05031372839122\n ],\n [\n -115.5979811441514,\n 33.05031372839122\n ],\n [\n -115.5979811441514,\n 35.189340133329225\n ],\n [\n -121.02522723790126,\n 35.189340133329225\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Earthquake Science Center
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Climate change and climate variability impacts such as rising sea levels have the potential to exacerbate seawater intrusion and the strain on coastal freshwater resources in already stressed groundwater basins such as those in the Pajaro Valley groundwater basin, California. Regional hydrologic models are often coupled with climate projections to forecast future hydrologic conditions and inform adaptive resources management strategies. However, there is high uncertainty in the future projections of water resources due to uncertainties from downscaling global general circulation models (GCMs) to local scale climate change projections, future land use changes, and the inherent uncertainty of developed hydrologic models. Future climate projections and the magnitude of their influence on modeled hydrologic drivers are highly variable. Therefore, to develop a forecast model, an ensemble of different projections can be used to capture a wider range of basin responses and the associated uncertainties in the modeled forecasts. Understanding the reliability and uncertainty of forecasts is important for developing climate adaptation strategies such as developing protective thresholds, particularly at the basin scale where the impacts are felt, and adaptation is implemented. To demonstrate this, an uncertainty analysis of groundwater level and seawater intrusion forecasts for the Pajaro Valley groundwater basin was performed using an ensemble of three future climate projections with the Pajaro Valley Integrated Hydrologic Model (PVIHM) and the first-order second moment (FOSM) method. FOSM uncertainty analysis of hydrologic forecasts across a multi-GCM climate ensemble provides an upper and lower bound of potential impacts of climate change on sustainability targets related to mitigating seawater intrusion. The groundwater level forecasts’ narrow range of variability can help policymakers in adaptation planning by constraining possible outcomes to a focused range for risk-management decisions. However, less than one-third of groundwater level forecasts met the current protection thresholds to prevent chronic lowering of groundwater. Therefore, sustainability targets may need to be reassessed. Relative to groundwater level changes, the seawater intrusion forecasts had larger uncertainty due to the GCM climate projections and the simulated hydrologic response that were compounded by the propagation of scaling and bias from the GCMs and model simplifications in simulating the coastal boundary.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2024.131226","usgsCitation":"Earll, M.M., Henson, W.R., Lockwood, B., and Boyce, S.E., 2024, Evaluating seawater intrusion forecast uncertainty under climate change in the Pajaro Valley, California: Journal of Hydrology, v. 636, 131226, 17 p., https://doi.org/10.1016/j.jhydrol.2024.131226.","productDescription":"131226, 17 p.","ipdsId":"IP-118873","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":484763,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Pajaro Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.92596725165258,\n 37.07637156314877\n ],\n [\n -121.92596725165258,\n 36.716849299804664\n ],\n [\n -121.42957626930755,\n 36.716849299804664\n ],\n [\n -121.42957626930755,\n 37.07637156314877\n ],\n [\n -121.92596725165258,\n 37.07637156314877\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"636","noUsgsAuthors":false,"publicationDate":"2024-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Earll, Marisa M. 0000-0002-4367-2013 mearll@usgs.gov","orcid":"https://orcid.org/0000-0002-4367-2013","contributorId":223723,"corporation":false,"usgs":true,"family":"Earll","given":"Marisa","email":"mearll@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henson, Wesley R. 0000-0003-4962-5565 whenson@usgs.gov","orcid":"https://orcid.org/0000-0003-4962-5565","contributorId":384,"corporation":false,"usgs":true,"family":"Henson","given":"Wesley","email":"whenson@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lockwood, Brian","contributorId":80202,"corporation":false,"usgs":true,"family":"Lockwood","given":"Brian","email":"","affiliations":[],"preferred":false,"id":933960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyce, Scott 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933820,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70253895,"text":"70253895 - 2024 - Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 1—Spawning area identification using bidirectional drift modeling","interactions":[],"lastModifiedDate":"2024-05-20T15:42:40.394048","indexId":"70253895","displayToPublicDate":"2024-04-18T09:07:55","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 1—Spawning area identification using bidirectional drift modeling","title":"Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 1—Spawning area identification using bidirectional drift modeling","docAbstract":"Control of invasive grass carp (Ctenopharyngodon idella) populations in the Western Lake Erie Basin merits adaptive management guided by the best available science. Presently (2024), capture of mature grass carp in rivers during spawning season is most efficient, so knowing when and where grass carp are spawning is essential information for natural resource agencies. Using bidirectional drift modeling and grass carp ichthyoplankton samples captured in the Maumee River during the 2017–2019 spawning seasons, this study identified 12 probable grass carp spawning areas in the lower 96.5-kilometers of the Maumee River. These spawning areas were located both above and below the Grand Rapids/Providence low-head dams. Three areas showed evidence of multiyear use, while nine had multi-event use. Spawning activity had no definitive diel variation and occurred at an average photoperiod of 15.15 h. The maturation metric ADD15, or annual degree days above 15 degrees Celsius, generally exceeded the 655 threshold for spawning; however, some spawning occurred when ADD15 ≤235, indicating spawners likely matured in a warmwater discharge. The probable spawning areas were generally characterized by mean velocities between 0.4 and 2.1 m per second (with locally higher velocities possible), areas of high turbulence produced by dam spillways or bedrock outcroppings, channel constrictions, confluences, islands, and bridges with piers in the water. Spawning suitability indices (SSI), based on velocity, varied considerably between spawning areas and SSI models. These results could be used to inform control efforts and predict potential grass carp spawning locations in other rivers under threat of invasion.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102347","usgsCitation":"Jackson, P.R., Cigrand, C.V., Kocovsky, P.M., King, N.R., Kasprak, A., Lindroth, E., Doyle, H.F., Qian, S.S., and Mayer, C.M., 2024, Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 1—Spawning area identification using bidirectional drift modeling: Journal of Great Lakes Research, v. 50, 102347, 17 p., https://doi.org/10.1016/j.jglr.2024.102347.","productDescription":"102347, 17 p.","ipdsId":"IP-142521","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":439799,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102347","text":"Publisher Index Page"},{"id":434983,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CQ6FYX","text":"USGS data release","linkHelpText":"Hydraulic Model Archive and Fluvial Egg Drift Simulator (FluEgg) Results for Simulations of Invasive Carp Egg and Larval Drift in the Maumee River, Ohio (ver. 1.1, July 2023)"},{"id":428353,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Maumee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.38518414361491,\n 41.68593497191691\n ],\n [\n -83.45979759899954,\n 41.74305978003929\n ],\n [\n -83.66454217332985,\n 41.60155527334871\n ],\n [\n -83.8481872826162,\n 41.471249401310274\n ],\n [\n -84.12554495228478,\n 41.444009750259625\n ],\n [\n -84.20397403397705,\n 41.37227829616401\n ],\n [\n -84.41243215532246,\n 41.2918512649257\n ],\n [\n -84.35316138453301,\n 41.241526198376704\n ],\n [\n -84.09303055462573,\n 41.30190322738565\n ],\n [\n -84.060513011197,\n 41.379455129599705\n ],\n [\n -83.7812377285593,\n 41.39524150503729\n ],\n [\n -83.38518414361491,\n 41.68593497191691\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900018,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cigrand, Charles V. 0000-0002-4177-7583","orcid":"https://orcid.org/0000-0002-4177-7583","contributorId":201575,"corporation":false,"usgs":true,"family":"Cigrand","given":"Charles","email":"","middleInitial":"V.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900019,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":900020,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"King, Nicole R.","contributorId":239495,"corporation":false,"usgs":false,"family":"King","given":"Nicole","email":"","middleInitial":"R.","affiliations":[{"id":47892,"text":"University of Toledo Lake Erie Center, 6200 Bay Shore Road, Oregon, OH","active":true,"usgs":false}],"preferred":false,"id":900021,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":245742,"corporation":false,"usgs":false,"family":"Kasprak","given":"Alan","affiliations":[{"id":49307,"text":"Current: Utah State University. Former: Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, U.S. Geological Survey, Flagstaff, AZ 86001, USA","active":true,"usgs":false}],"preferred":false,"id":900022,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lindroth, Evan M. 0000-0002-9746-4359","orcid":"https://orcid.org/0000-0002-9746-4359","contributorId":336138,"corporation":false,"usgs":false,"family":"Lindroth","given":"Evan M.","affiliations":[{"id":80757,"text":"Maricopa County Flood Control District","active":true,"usgs":false}],"preferred":false,"id":900023,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Doyle, Henry F. 0000-0001-9942-8602 hfdoyle@usgs.gov","orcid":"https://orcid.org/0000-0001-9942-8602","contributorId":243432,"corporation":false,"usgs":true,"family":"Doyle","given":"Henry","email":"hfdoyle@usgs.gov","middleInitial":"F.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900024,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Qian, Song S. 0000-0002-2346-4903","orcid":"https://orcid.org/0000-0002-2346-4903","contributorId":306033,"corporation":false,"usgs":false,"family":"Qian","given":"Song","email":"","middleInitial":"S.","affiliations":[{"id":62440,"text":"Department of Environmental Sciences, University of Toledo, Toledo, OH 43606","active":true,"usgs":false}],"preferred":false,"id":900025,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mayer, Christine M.","contributorId":203271,"corporation":false,"usgs":false,"family":"Mayer","given":"Christine","email":"","middleInitial":"M.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":900026,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70253125,"text":"70253125 - 2024 - Network connectivity contributes to native small-bodied fish assemblages in the upper Mississippi River system","interactions":[],"lastModifiedDate":"2024-06-03T15:03:49.975123","indexId":"70253125","displayToPublicDate":"2024-04-18T07:15:24","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17465,"text":"Journal of Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Network connectivity contributes to native small-bodied fish assemblages in the upper Mississippi River system","docAbstract":"In a conservation context, identifying key habitats suitable for reproduction, foraging, or survival is a useful tool, yet challenging for species with large geographic distributions and/or living in remote regions.
The objective of this study is to identify selected habitats at multiple levels and scales of the threatened eastern North American population of golden eagles (Aquila chrysaetos). We studied habitat selection at three levels: landscape (second order of selection), foraging (third order of selection), and nesting (fourth order of selection).
Using tracking data from 30 adults and 366 nest coordinates spanning over a 1.5 million km2 area in remote boreal and Arctic regions, we modelled the three levels of habitat selection with resource selection functions using seven environmental features (aerial, topographical, and land cover). We then calculated the relative probability of selection in the study area to identify regions with higher probabilities of selection.
Eagles selected more for terrain ruggedness index and relative elevation than land cover (i.e., forest cover, distance to water; mean difference in relative selection strength: 1.2 [0.71; 1.69], 95% CI) at all three levels. We also found that the relative probability of selection at all three levels was ~ 25% higher in the Arctic than in the boreal regions. Eagles breeding in the Arctic travelled shorter foraging distances with greater access to habitat with a high probability of selection than boreal eagles.
Here we found which aerial and topographical features were important for several of the eagles’ life cycle needs. We also identified important areas to monitor and preserve this threatened population. The next step is to quantify the quality of habitat by linking our multi-level, multi-scale approach to population demography and performance such as reproductive success.