{"pageNumber":"49","pageRowStart":"1200","pageSize":"25","recordCount":68805,"records":[{"id":70261991,"text":"70261991 - 2024 - Warmwater fish in rivers","interactions":[],"lastModifiedDate":"2025-01-31T14:27:01.025151","indexId":"70261991","displayToPublicDate":"2024-11-01T09:45:48","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"5","title":"Warmwater fish in rivers","docAbstract":"<div class=\"page-content\"><div class=\"page-content\"><div class=\"page-content\"><p>In addition to the gears described in the previous version, this edition includes an updated water body definition that is inclusive of Mexico and Canada as well as standard methods for the use of cast nets in warmwater rivers. There were organizational changes in the trawling and hoop-net sections to make them consistent with the format for this edition, but the methods themselves have not been changed and no standardized gears (e.g., small-mesh and large-mesh trawls are still both present) have been removed.</p><p>The diversity of warmwater rivers of North America owes to the mosaic of precipitation and geology spanning the continent from the arid systems of the Sonoran Desert to the humid forests of the Appalachian Mountains. The types of rivers discussed in this chapter are highly variable in size from headwaters to mouth but will include parts of rivers that are nonwadeable and larger. Here, we use this flexible definition because we found that regardless of how we classified a river as a whole, whether through basin area, discharge, or stream order, there is sufficient diversity across North America such that major rivers of some regions would be left out. We, therefore, chose to use site-level characterization because characteristics like target fish species and communities and habitat characteristics like water depth, velocity, and channel geomorphology drive or constrain our decisions in the field about what sampling gear to use rather than overall river or river basin characteristics.</p></div></div></div>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Standard methods for sampling North American freshwater fishes, second edition","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"American Fisheries Society","doi":"10.47886/9781934874769.ch5","usgsCitation":"Pracheil, B., Braaten, P., Macias, E., Guy, C.S., Herzog, D., Hamel, M.J., Justice, J., Loeppky, A., Mollish, J., Simmons, J., and Tripp, S.J., 2024, Warmwater fish in rivers, chap. 5 <i>of</i> Standard methods for sampling North American freshwater fishes, second edition, p. 85-110, https://doi.org/10.47886/9781934874769.ch5.","productDescription":"26 p.","startPage":"85","endPage":"110","ipdsId":"IP-136997","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":481514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pracheil, Brenda M.","contributorId":280027,"corporation":false,"usgs":false,"family":"Pracheil","given":"Brenda M.","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":922576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Braaten, Patrick 0000-0003-3362-420X pbraaten@usgs.gov","orcid":"https://orcid.org/0000-0003-3362-420X","contributorId":152682,"corporation":false,"usgs":true,"family":"Braaten","given":"Patrick","email":"pbraaten@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":922575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Macias, Everardo Barba","contributorId":347841,"corporation":false,"usgs":false,"family":"Macias","given":"Everardo Barba","affiliations":[{"id":83259,"text":"ECOSUR-Tabasco","active":true,"usgs":false}],"preferred":false,"id":922577,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":922578,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Herzog, David P","contributorId":347842,"corporation":false,"usgs":false,"family":"Herzog","given":"David P","affiliations":[{"id":83260,"text":"Missouri Dept. Of Conservation","active":true,"usgs":false}],"preferred":false,"id":922579,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hamel, Martin J.","contributorId":171901,"corporation":false,"usgs":false,"family":"Hamel","given":"Martin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":922580,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Justice, John C","contributorId":347843,"corporation":false,"usgs":false,"family":"Justice","given":"John C","affiliations":[{"id":13217,"text":"Tennessee Valley Authority","active":true,"usgs":false}],"preferred":false,"id":922581,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Loeppky, Alison R","contributorId":347844,"corporation":false,"usgs":false,"family":"Loeppky","given":"Alison R","affiliations":[{"id":16603,"text":"University of Manitoba","active":true,"usgs":false}],"preferred":false,"id":922582,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mollish, Jon M","contributorId":347845,"corporation":false,"usgs":false,"family":"Mollish","given":"Jon M","affiliations":[{"id":13217,"text":"Tennessee Valley Authority","active":true,"usgs":false}],"preferred":false,"id":922583,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Simmons, Jeffrey W","contributorId":347846,"corporation":false,"usgs":false,"family":"Simmons","given":"Jeffrey W","affiliations":[{"id":13217,"text":"Tennessee Valley Authority","active":true,"usgs":false}],"preferred":false,"id":922584,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Tripp, Sara J.","contributorId":253122,"corporation":false,"usgs":false,"family":"Tripp","given":"Sara","email":"","middleInitial":"J.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":922585,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70261136,"text":"70261136 - 2024 - Machine learning and new-generation spaceborne hyperspectral data advance crop type mapping","interactions":[],"lastModifiedDate":"2024-11-26T15:30:10.726606","indexId":"70261136","displayToPublicDate":"2024-11-01T08:31:26","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning and new-generation spaceborne hyperspectral data advance crop type mapping","docAbstract":"<p><span>Hyperspectral sensors provide near-continuous spectral data that can facilitate advancements in agricultural crop classification and characterization, which are important for addressing global food and water security issues. We investigated two new-generation hyperspectral sensors, Germany’s Deutsches Zentrum für Luft‐ und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) and Italy’s PRecursore IperSpettrale della Missione Applicativa (PRISMA), within California's Central Valley in August 2021 focusing on five irrigated agricultural crops (alfalfa, almonds, corn, grapes, and pistachios). With reference data from the U.S. Department of Agriculture Cropland Data Layer, we developed a spectral library of the crops and classified them using three machine learning algorithms (support vector machines [SVM], random forest [RF], and spectral angle mapper [SAM]) and two philosophies: 1. Full spectral analysis (FSA) and 2. Optimal hyperspectral narrowband (OHNB) analysis. For FSA, we used 59 DESIS four-bin product bands and 207 of 238 PRISMA bands. For OHNB analysis, 9 DESIS and 16 PRISMA nonredundant OHNBs for studying crops were selected. FSA achieved only 1% to 3% higher accuracies relative to OHNB analysis in most cases. SVM provided the best results, closely followed by RF. Using both DESIS and PRISMA image OHNBs in SVM for classification led to higher accuracy than using either image alone, with an overall accuracy of 99%, producer’s accuracies of 94% to 100%, and user's accuracies of 95% to 100%.</span></p>","language":"English","publisher":"Ingenta","doi":"10.14358/PERS.24-00026R2","usgsCitation":"Aneece, I.P., Thenkabail, P., McCormick, R.L., Haireti, A., Foley, D., Oliphant, A., and Teluguntla, P., 2024, Machine learning and new-generation spaceborne hyperspectral data advance crop type mapping: Photogrammetric Engineering and Remote Sensing, v. 90, no. 11, p. 687-698, https://doi.org/10.14358/PERS.24-00026R2.","productDescription":"12 p.","startPage":"687","endPage":"698","ipdsId":"IP-163096","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":498261,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.24-00026r2","text":"Publisher Index Page"},{"id":464465,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -120.00224368510789,\n              37.29219185284313\n            ],\n            [\n              -120.00224368510789,\n              36.59828829039613\n            ],\n            [\n              -118.67344220549262,\n              36.59828829039613\n            ],\n            [\n              -118.67344220549262,\n              37.29219185284313\n            ],\n            [\n              -120.00224368510789,\n              37.29219185284313\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"90","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Aneece, Itiya P. 0000-0002-1201-5459","orcid":"https://orcid.org/0000-0002-1201-5459","contributorId":208265,"corporation":false,"usgs":true,"family":"Aneece","given":"Itiya","middleInitial":"P.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":919395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad 0000-0002-2182-8822","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":220239,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":919396,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCormick, Richard L. 0009-0002-8208-2136","orcid":"https://orcid.org/0009-0002-8208-2136","contributorId":346504,"corporation":false,"usgs":true,"family":"McCormick","given":"Richard","email":"","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":919400,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haireti, Alifu","contributorId":346506,"corporation":false,"usgs":false,"family":"Haireti","given":"Alifu","email":"","affiliations":[],"preferred":false,"id":919401,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Foley, Daniel 0000-0002-2051-6325","orcid":"https://orcid.org/0000-0002-2051-6325","contributorId":208266,"corporation":false,"usgs":true,"family":"Foley","given":"Daniel","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":919399,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oliphant, Adam 0000-0001-8622-7932 aoliphant@usgs.gov","orcid":"https://orcid.org/0000-0001-8622-7932","contributorId":192325,"corporation":false,"usgs":true,"family":"Oliphant","given":"Adam","email":"aoliphant@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":919398,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Teluguntla, Pardhasaradhi 0000-0001-8060-9841","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":211780,"corporation":false,"usgs":true,"family":"Teluguntla","given":"Pardhasaradhi","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":919397,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70274739,"text":"70274739 - 2024 - Heart of the West: Wyoming’s commitment to conservation of migratory ungulates","interactions":[],"lastModifiedDate":"2026-04-09T15:24:19.392049","indexId":"70274739","displayToPublicDate":"2024-10-31T10:18:46","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Heart of the West: Wyoming’s commitment to conservation of migratory ungulates","docAbstract":"<p><span>The small town of Superior, Wyoming, used to be a booming coal town. Pictures from the 1920s reveal sparkling new cars, a bowling alley, and other amenities supported by the wealth of the coal mines. Today, those prosperous days are nowhere to be seen. Superior doesn’t have a grocery store or a gas station, and the local bar is only open occasionally. Aside from the low-slung, modest houses built into the hills around town, the most prominent structure is the county road maintenance shop.</span></p><p><span>But those hills are also dotted with mule deer—lots of them. Superior represents&nbsp;the southern terminus of the world’s longest-recorded mule deer migration. The study of these deer has shaped how wildlife biologists think about migration, and the conservation of their corridor illustrates how science informs the management of iconic Western wildlife populations. These deer, and their story, may also represent what is possible when we recognize the habitat needs of wildlife that move across the same landscapes where we live and work.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"A watershed moment: The American West in the age of limits","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"University of Utah Press","usgsCitation":"Reed, E., and Kauffman, M.J., 2024, Heart of the West: Wyoming’s commitment to conservation of migratory ungulates, chap. <i>of</i> A watershed moment: The American West in the age of limits, p. 248-262.","productDescription":"15 p.","startPage":"248","endPage":"262","ipdsId":"IP-166746","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":502355,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -111.02791442700837,\n              45.00491516402994\n            ],\n            [\n              -111.02791442700837,\n              40.995252415428496\n            ],\n            [\n              -104.0279534875161,\n              40.995252415428496\n            ],\n            [\n              -104.0279534875161,\n              45.00491516402994\n            ],\n            [\n              -111.02791442700837,\n              45.00491516402994\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Emily","contributorId":299809,"corporation":false,"usgs":false,"family":"Reed","given":"Emily","affiliations":[{"id":63974,"text":"Wyoming Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":958899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":210786,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":484,"text":"Northwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":958900,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70260921,"text":"70260921 - 2024 - Best practices for incorporating climate change science into Department of the Interior analyses, consultations, and decision making","interactions":[],"lastModifiedDate":"2024-11-15T14:01:02.523252","indexId":"70260921","displayToPublicDate":"2024-10-31T09:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Best practices for incorporating climate change science into Department of the Interior analyses, consultations, and decision making","docAbstract":"<p>The purpose of this document is to provide technical guidance, practical application examples, and resource lists for those who conduct, manage, and/or interpret technical workflows within the Department of the Interior. This document is intended to support implementation of Department of the Interior policy 526 DM 1 and establish best practices for using climate change science to inform analysis, consultation, and decision making.</p><p>The Earth’s climate is an interconnected system that distributes energy, heat, and water around the planet. Due to human-driven increases in long-lived greenhouse gases, the Earth’s climate is now changing. For Departmental decision-making purposes, assuming a static, unchanging baseline climate is no longer consistent with current knowledge about the climate system.</p><p>There are uncertainties about future climate and how resources or assets (RoAs) will respond to new conditions. To depict the possibilities, the global climate science community develops scenarios and models to explore how future climate may respond to socioeconomic and technological development in the world.</p><p>Principles for informing policy development, planning and decisions, and regulatory processes using climate change science must: 1) consider the effects of future climate change, 2) characterize the risks, and 3) characterize the uncertainties.</p><p>Best practices include:</p><p><strong>Use multiple scenarios</strong> to assess risks from a range of plausible societal pathways. When constraints prevent the use of multiple scenarios or if decision makers are risk averse, ensure that the chosen scenario considers higher risk outcomes. This is particularly important for large investments or irreversible decisions and reduces the chances of overconfident decision making.</p><p><strong>Use multiple climate models within each scenario</strong> to account for the range of outcomes due to model uncertainty. Do not rely solely on a single model or an ensemble average.</p><p><strong>Use relevant climate data</strong>. Use a time-period for model projections of the future climate change consistent with the relevant timeframe of the policy, action, or decision being considered. Historical observations are useful for understanding past conditions and climate trends for the next several years, but not beyond the next decade. Consult with climate data and modeling experts to assess which data and model resources are most appropriate for any given application.</p><p><strong>Clearly describe key analysis uncertainties</strong> (including with any climate observations, models, and scenarios used), <strong>and how they were addressed</strong> in the analysis and/or decision process. This ensures transparency and learning among analysts and decision makers.</p>","language":"English","publisher":"Department of the Interior","doi":"10.21429/hjgj-j073","usgsCitation":"Terando, A.J., Tucker, A.M., Runyon, A.N., Miller, J., Perkins, J.L., Kimbrel, S.W., Cross, A.S., and Boyles, R.P., 2024, Best practices for incorporating climate change science into Department of the Interior analyses, consultations, and decision making, iv, 72 p., https://doi.org/10.21429/hjgj-j073.","productDescription":"iv, 72 p.","ipdsId":"IP-166512","costCenters":[],"links":[{"id":464070,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/unnumbered/70260921/coverthb.jpg"},{"id":464071,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/unnumbered/70260921/70260921.pdf","linkFileType":{"id":1,"text":"pdf"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Terando, Adam J. 0000-0002-9280-043X aterando@usgs.gov","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":173447,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","email":"aterando@usgs.gov","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":918516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tucker, Anna Maureen 0000-0002-1473-2048 amtucker@usgs.gov","orcid":"https://orcid.org/0000-0002-1473-2048","contributorId":257906,"corporation":false,"usgs":true,"family":"Tucker","given":"Anna","email":"amtucker@usgs.gov","middleInitial":"Maureen","affiliations":[],"preferred":true,"id":918517,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Runyon, Amber N. 0000-0002-7282-1217","orcid":"https://orcid.org/0000-0002-7282-1217","contributorId":346252,"corporation":false,"usgs":false,"family":"Runyon","given":"Amber","email":"","middleInitial":"N.","affiliations":[{"id":36976,"text":"U.S. National Park Service","active":true,"usgs":false}],"preferred":false,"id":918518,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, James A.","contributorId":346253,"corporation":false,"usgs":false,"family":"Miller","given":"James A.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":918519,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Perkins, Judy L.","contributorId":266176,"corporation":false,"usgs":false,"family":"Perkins","given":"Judy","email":"","middleInitial":"L.","affiliations":[{"id":54938,"text":"U.S. Bureau of Land Management, California State Office, 2800 Cottage Way, Sacramento, CA 95825","active":true,"usgs":false}],"preferred":false,"id":918520,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kimbrel, Sean W.","contributorId":346255,"corporation":false,"usgs":false,"family":"Kimbrel","given":"Sean","email":"","middleInitial":"W.","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":918521,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cross, Amanda S.","contributorId":346256,"corporation":false,"usgs":false,"family":"Cross","given":"Amanda","email":"","middleInitial":"S.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":918522,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Boyles, Ryan P. 0000-0001-9272-867X rboyles@usgs.gov","orcid":"https://orcid.org/0000-0001-9272-867X","contributorId":197670,"corporation":false,"usgs":true,"family":"Boyles","given":"Ryan","email":"rboyles@usgs.gov","middleInitial":"P.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":918523,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70260440,"text":"70260440 - 2024 - Deep-ocean macrofaunal assemblages on ferromanganese and phosphorite-rich substrates in the Southern California Borderland","interactions":[],"lastModifiedDate":"2024-11-01T13:42:47.123347","indexId":"70260440","displayToPublicDate":"2024-10-31T08:35:59","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Deep-ocean macrofaunal assemblages on ferromanganese and phosphorite-rich substrates in the Southern California Borderland","docAbstract":"<p><span>Mineral-rich hardgrounds, such as ferromanganese (FeMn) crusts and phosphorites, occur on seamounts and continental margins, gaining attention for their resource potential due to their enrichment in valuable metals in some regions. This study focuses on the Southern California Borderland (SCB), an area characterized by uneven and heterogeneous topography featuring FeMn crusts, phosphorites, basalt, and sedimentary rocks that occur at varying depths and are exposed to a range of oxygen concentrations. Due to its heterogeneity, this region serves as an optimal setting for investigating the relationship between mineral-rich hardgrounds and benthic fauna. This study characterizes the density, diversity, and community composition of macrofauna (&gt;300 μm) on hardgrounds as a function of substrate type and environment (depth and oxygen ranges). Rocks and their macrofauna were sampled quantitatively using remotely operated vehicles (ROVs) during expeditions in 2020 and 2021 at depths above, within, and below the oxygen minimum zone (OMZ). A total of 3,555 macrofauna individuals were counted and 416 different morphospecies (excluding encrusting bryozoans and hydrozoans) were identified from 82 rocks at depths between 231 and 2,688 m. Average density for SCB macrofauna was 11.08 ± 0.87 ind. 200 cm</span><sup>−2</sup><span>&nbsp;and mean Shannon-Wiener diversity per rock (H′</span><sub>[loge]</sub><span>) was 2.22 ± 0.07. A relationship was found between substrate type and macrofaunal communities. Phosphorite rocks had the highest H′ of the four substrates compared on a per-rock basis. However, when samples were pooled by substrate, FeMn crusts had the highest H′ and rarefaction diversity. Of all the environmental variables examined, water depth explained the largest variance in macrofaunal community composition. Macrofaunal density and diversity values were similar at sites within and outside the OMZ. This study is the first to analyze the macrofaunal communities of mineral-rich hardgrounds in the SCB, which support deep-ocean biodiversity by acting as specialized substrates for macrofaunal communities. Understanding the intricate relationships between macrofaunal assemblages and mineral-rich substrates may inform effects from environmental disruptions associated with deep-seabed mining or climate change. The findings contribute baseline information useful for effective conservation and management of the SCB and will support scientists in monitoring changes in these communities due to environmental disturbance or human impact in the future.</span></p>","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.18290","usgsCitation":"Guraieb, M., Mendoza, G., Mizell, K., Rouse, G.W., McCarthy, R., Pereira, O.S., and Levin, L.A., 2024, Deep-ocean macrofaunal assemblages on ferromanganese and phosphorite-rich substrates in the Southern California Borderland: PeerJ, v. 12, e18290, 33 p., https://doi.org/10.7717/peerj.18290.","productDescription":"e18290, 33 p.","ipdsId":"IP-166431","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466792,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.18290","text":"Publisher Index Page"},{"id":463531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Southern California Borderlands","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -121.5,\n              34\n            ],\n            [\n              -121.5,\n              31.5\n            ],\n            [\n              -117,\n              31.5\n            ],\n            [\n              -117,\n              34\n            ],\n            [\n              -121.5,\n              34\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-10-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Guraieb, Michelle","contributorId":345846,"corporation":false,"usgs":false,"family":"Guraieb","given":"Michelle","email":"","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":917695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mendoza, Guillermo F","contributorId":156382,"corporation":false,"usgs":false,"family":"Mendoza","given":"Guillermo F","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":917696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mizell, Kira 0000-0002-5066-787X kmizell@usgs.gov","orcid":"https://orcid.org/0000-0002-5066-787X","contributorId":4914,"corporation":false,"usgs":true,"family":"Mizell","given":"Kira","email":"kmizell@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":917697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rouse, Gregory W.","contributorId":345848,"corporation":false,"usgs":false,"family":"Rouse","given":"Gregory","email":"","middleInitial":"W.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":917698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCarthy, R.A.","contributorId":345849,"corporation":false,"usgs":false,"family":"McCarthy","given":"R.A.","email":"","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":917699,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pereira, Olivia S.","contributorId":340132,"corporation":false,"usgs":false,"family":"Pereira","given":"Olivia","email":"","middleInitial":"S.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":917700,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Levin, Lisa A.","contributorId":330607,"corporation":false,"usgs":false,"family":"Levin","given":"Lisa","email":"","middleInitial":"A.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":917701,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70260810,"text":"70260810 - 2024 - Detecting trajectories of regime shifts and loss of resilience in coastal wetlands using remote sensing","interactions":[],"lastModifiedDate":"2024-12-10T15:33:45.478559","indexId":"70260810","displayToPublicDate":"2024-10-31T06:56:03","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Detecting trajectories of regime shifts and loss of resilience in coastal wetlands using remote sensing","docAbstract":"<p><span>Many freshwater forested wetlands along the southeastern United States coastline are rapidly transitioning from forest to marsh or open water, due to climate change-related disturbances. Recent studies have found early warning signals (EWS) of regime shifts in other ecosystems, but it is unclear if these can be detected for coastal wetlands. In this study, we examined the ability to detect EWS of regime shifts in coastal wetlands within the Albemarle Pamlico peninsula (APP), North Carolina, U.S.A. We used the Landsat record (1985–2021) to examine trends of normalized difference vegetation index (NDVI) time series for selected areas known to have undergone regime shifts. We found that while 77% of the APP was either stable or revegetating, 22% of the landscape underwent a decrease in NDVI that would indicate a transition from forest to marsh or open water. Of the areas that transitioned, about half (11%) experienced an abrupt decrease in NDVI and 10% experienced a gradual decline. Increasing standard deviation and skewness of time series could serve as EWS of abrupt transitions, but can also provide false negative and positives. Our results suggest that ecosystem transitions from a forest to a marsh or open water can occur both rapidly and slowly, and remote sensing of NDVI time series can help identify EWS for some areas, but not all. Our results allow for prioritization of conservation/restoration of coastlines which will become important in the face of climate change and sea level rise.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10021-024-00938-5","usgsCitation":"Martinez, M., Ardon, M.L., and Gray, J., 2024, Detecting trajectories of regime shifts and loss of resilience in coastal wetlands using remote sensing: Ecosystems, v. 27, p. 1060-1075, https://doi.org/10.1007/s10021-024-00938-5.","productDescription":"16 p.","startPage":"1060","endPage":"1075","ipdsId":"IP-133828","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":463847,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Albemarle Pamlico Peninsula","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -75.63541246515713,\n              36.06271479689754\n            ],\n            [\n              -77.19335586830498,\n              36.06271479689754\n            ],\n            [\n              -77.19335586830498,\n              35.278844140439915\n            ],\n            [\n              -75.63541246515713,\n              35.278844140439915\n            ],\n            [\n              -75.63541246515713,\n              36.06271479689754\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"27","noUsgsAuthors":false,"publicationDate":"2024-10-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Martinez, Melinda 0000-0001-6652-9220","orcid":"https://orcid.org/0000-0001-6652-9220","contributorId":290467,"corporation":false,"usgs":true,"family":"Martinez","given":"Melinda","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":918159,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ardon, Marcelo L","contributorId":346120,"corporation":false,"usgs":false,"family":"Ardon","given":"Marcelo","email":"","middleInitial":"L","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":918160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gray, Joshua","contributorId":346121,"corporation":false,"usgs":false,"family":"Gray","given":"Joshua","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":918161,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70259877,"text":"ofr20241055 - 2024 - Sand supply to San Francisco Bay from the Sacramento and San Joaquin Rivers of the Central Valley, California","interactions":[],"lastModifiedDate":"2025-12-22T20:25:53.918489","indexId":"ofr20241055","displayToPublicDate":"2024-10-30T13:10:08","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":"2024-1055","displayTitle":"Sand Supply to San Francisco Bay from the Sacramento and San Joaquin Rivers of the Central Valley, California","title":"Sand supply to San Francisco Bay from the Sacramento and San Joaquin Rivers of the Central Valley, California","docAbstract":"<p>Sediment from the Central Valley via the Sacramento-San Joaquin Delta (Delta) and Suisun Bay is a primary source of sand to San Francisco Bay, California. Sand is mined from San Francisco Bay for commercial purposes, such as for use in concrete for construction. To better understand the supply of sand to Suisun Bay and San Francisco Bay, the U.S. Geological Survey (USGS), in cooperation with the San Francisco Bay Estuary Institute (SFEI) and the San Francisco Bay Conservation Development Commission (BCDC), initiated this study to compile and synthesize historical data and estimate the total sediment and sand portion of sediment exiting the Delta to Suisun Bay for a 20-year period between water years 2001 and 2020.</p><p>Sediment exiting the Delta is a combination of suspended sediment and bedload sediment. Seaward bedload transport was estimated using bedload transport equations and available hydraulic data at the two downstream-most streamgages in the Delta (where velocity is measured). Those two streamgages are about 25 kilometers upstream from the “exit” of the Delta at Mallard Island. The combined average annual net (seaward) bedload at these two streamgages was estimated to be 0.102 million cubic meters per year (Mm<sup>3</sup>/yr) for the study period. This volume of bedload is equivalent to 0.155 million metric tons per year (Mt/yr), assuming a bulk density of 1.517 metric tons per cubic meter (t/m<sup>3</sup>). The bedload composition was estimated to be 88 percent sand.</p><p>Between the two streamgages and Mallard Island, an annual average of 0.076 Mm<sup>3</sup>/yr of material was removed through mining during the study period, of which 97.5 percent was sand. In addition, 0.053 Mm<sup>3</sup>/yr was removed through dredging to support shipping and navigation, of which 76 percent was sand. The total volume of mined and dredged sediment material was approximately 0.128 Mm<sup>3</sup>/yr, equivalent to 0.194 Mt/yr, assuming a bulk density of 1.517 t/m<sup>3</sup>.</p><p>Assuming the estimated bedload reaching Mallard Island was reduced by mining and dredging, a mean bedload flux of −0.009 Mm<sup>3</sup>/yr was computed (using a bulk density of 1.517 t/m<sup>3</sup>), suggesting a deficit or landward transport of bedload. However, the total suspended-sediment and suspended-sand flux was in the seaward direction. The average total suspended flux of sediment to Suisun Bay through the cross section at the Mallard Island streamgage was estimated to be 0.482 million metric tons per year (Mt/yr; 0.015 Mt/yr sand) in the seaward direction. The results indicate a net flux out of the Delta of 0.469 Mt/yr of total sediment and 0.003 Mt/yr of sand.</p><p>The primary limitation of the study was the lack of physical bedload measurements to validate the bedload estimates. To better refine the estimates of bedload, physical measurements of bedload or repeat bathymetry would be necessary for a range of flow conditions. Such measurements could be used to calibrate transport equations and quantify the uncertainty in such estimates.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241055","collaboration":"Prepared in cooperation with the San Francisco Estuary Institute Aquatic Science Center, the California State Coastal Conservancy, and the San Francisco Bay Conservation and Development Commission","programNote":"Water Availability and Use Science Program","usgsCitation":"Marineau, M.D., Hart, D., Ely, C.P., and McKee, L., 2024, Sand supply to San Francisco Bay from the Sacramento and San Joaquin Rivers of the Central Valley, California: U.S. Geological Survey Open-File Report 2024–1055, 18 p., https://doi.org/10.3133/ofr20241055.","productDescription":"Report: viii, 18 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-157560","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":463205,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1055/images"},{"id":463204,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1055/ofr20241055.xml"},{"id":463203,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1055/ofr20241055.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":463201,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9I18RGG","text":"USGS Data Release","description":"Ely, C.P., and Marineau, M.D., 2023, Estimated bedload transport rates at Rio Vista and Jersey Point, California, 2011–2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9I18RGG.","linkHelpText":"Estimated bedload transport rates at Rio Vista and Jersey Point, California, 2011–2020"},{"id":497888,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117739.htm","linkFileType":{"id":5,"text":"html"}},{"id":463206,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/preview/ofr20241055/full"},{"id":463202,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1055/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.29538442038356,\n              38.56577858557708\n            ],\n            [\n              -122.29538442038356,\n              37.65383277017135\n            ],\n            [\n              -121.19683028697757,\n              37.65383277017135\n            ],\n            [\n              -121.19683028697757,\n              38.56577858557708\n            ],\n            [\n              -122.29538442038356,\n              38.56577858557708\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_ca@usgs.gov\" data-mce-href=\"mailto:dc_ca@usgs.gov\">Director</a>,<br><a href=\"https://ca.water.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://ca.water.usgs.gov\">California Water Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a><br>6000 J Street, Placer Hall<br>Sacramento, California 95819</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Data Collection and Analysis</li><li>Results</li><li>Discussion</li><li>References Cited</li><li>Appendix 1</li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2024-10-30","noUsgsAuthors":false,"publicationDate":"2024-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Marineau, Mathieu D. 0000-0002-6568-0743 mmarineau@usgs.gov","orcid":"https://orcid.org/0000-0002-6568-0743","contributorId":4954,"corporation":false,"usgs":true,"family":"Marineau","given":"Mathieu","email":"mmarineau@usgs.gov","middleInitial":"D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hart, David 0000-0002-1700-5524","orcid":"https://orcid.org/0000-0002-1700-5524","contributorId":345512,"corporation":false,"usgs":true,"family":"Hart","given":"David","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ely, Christopher P. 0000-0001-5276-5046","orcid":"https://orcid.org/0000-0001-5276-5046","contributorId":219282,"corporation":false,"usgs":true,"family":"Ely","given":"Christopher P.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKee, Lester","contributorId":205882,"corporation":false,"usgs":false,"family":"McKee","given":"Lester","email":"","affiliations":[{"id":37186,"text":"SFEI","active":true,"usgs":false}],"preferred":false,"id":916828,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70261886,"text":"70261886 - 2024 - Patterns and drivers of cottonwood mortality in the middle Rio Grande, New Mexico, USA","interactions":[],"lastModifiedDate":"2024-12-31T16:05:59.249922","indexId":"70261886","displayToPublicDate":"2024-10-30T11:05:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Patterns and drivers of cottonwood mortality in the middle Rio Grande, New Mexico, USA","docAbstract":"<p>Riparian ecosystems are some of the most valuable and vulnerable on the planet. Riparian tree mortality is increasing in the western United States, where altered streamflows are combining with warming climate. Between 2011 and 2013, one third of an extensive stand of <i>Populus deltoides</i> var. <i>wislizeni</i> (Rio Grande cottonwood) died along the middle Rio Grande on the Pueblo of Santa Ana in New Mexico. Mortality coincided with a severe drought that followed a decade of decreasing streamflow, but it was heterogeneous, with adjacent patches of dead and live trees. The goal of this research was to determine the drivers of mortality to provide insights into future risks of die-off and potential management interventions. We compared tree age, competition, tree-ring widths, sediment particle size and climate influences between live and dead forest patches in a nested plot design. Live and dead trees had similar age, stand density and particle sizes of shallow sediments. Tree-ring widths had the highest correlations with July–September streamflow (1932–2013). All trees had declining ring growth since 1992, coinciding with declining late summer streamflow. An accelerated decline in growth began in 2002, corresponding to recent warmer droughts. Trees that died had lower ring growth 3 years prior to death and in the mid-1900s. Dead trees also had coarser deep sediments 2.4–3.7 m below ground, suggesting that reduced water holding capacity was an important factor for mortality. Water management to increase streamflow during the late summer, especially during times of extended drought, could reduce mortality risk in the face of projected increasingly warm droughts.</p>","language":"English","publisher":"Wiley","doi":"10.1002/eco.2692","usgsCitation":"Varani, H., Margolis, E.Q., Muldavin, E., and Pockman, W.T., 2024, Patterns and drivers of cottonwood mortality in the middle Rio Grande, New Mexico, USA: Ecohydrology, v. 17, no. 8, e2692, 13 p., https://doi.org/10.1002/eco.2692.","productDescription":"e2692, 13 p.","ipdsId":"IP-164113","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":466793,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/eco.2692","text":"External Repository"},{"id":465568,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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,{"id":70260103,"text":"sir20245097 - 2024 - Use of continuous water-quality time-series data to compute total phosphorus concentrations and loads for the Missouri River at St. Joseph and Hermann, Missouri, 2007–22","interactions":[],"lastModifiedDate":"2025-12-22T20:23:35.597848","indexId":"sir20245097","displayToPublicDate":"2024-10-30T10:46:09","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":"2024-5097","displayTitle":"Use of Continuous Water-Quality Time-Series Data to Compute Total Phosphorus Concentrations and Loads for the Missouri River at St. Joseph and Hermann, Missouri, 2007–22","title":"Use of continuous water-quality time-series data to compute total phosphorus concentrations and loads for the Missouri River at St. Joseph and Hermann, Missouri, 2007–22","docAbstract":"<p>In support of Missouri’s Nutrient Loss Reduction Strategy, which was created to reduce the nutrient contamination of Missouri’s waterways from point and nonpoint sources, total phosphorus concentrations and loads were computed for the Missouri River at St. Joseph, Missouri, streamgage (U.S. Geological Survey station 06818000) and the Missouri River at Hermann, Mo., streamgage (U.S. Geological Survey station 06934500) for October 2007 to September 2022 using surrogate models and continuous turbidity sensor data. To obtain a more complete total phosphorus record for the study period, LOAD ESTimator (LOADEST) regression models using flow were used when turbidity sensor data were unavailable to estimate daily total phosphorus loads. This report presents the methods and results for the computed total phosphorus concentrations, loads, and yields for the two study sites on the Missouri River. With continued data collection and ongoing model evaluation and maintenance, the surrogate models may be useful into the future for computing total phosphorus concentrations and loads.</p><p>Daily mean total phosphorus concentrations calculated using a surrogate model at the Missouri River at St. Joseph, Mo., streamgage during the 15-year study period (water years 2008 through 2022) ranged from 0.104 to 4.56 milligrams per liter (mg/L; median of 0.272 mg/L), and computed total phosphorus daily loads (with gaps in the daily record filled using the LOADEST regression model) ranged from 5.19 to 1,760 tons per day (tons/d; median of 36.5 tons/d). Annual loads ranged from 9,570 tons in water year 2022 to 50,500 tons in water year 2019. The total load for the study period was 437,000 tons.</p><p>For the Missouri River at Hermann, Mo., streamgage during the same 15-year study period, daily mean total phosphorus concentrations, calculated using surrogate models applied to low and high turbidity values, ranged from 0.183 to 1.97 mg/L (median of 0.319 mg/L), and computed total phosphorus daily loads (with gaps in the daily record filled using the LOADEST regression model) ranged from 12.7 to 1,970 tons/d (median of 76.8 tons/d). Annual loads ranged from 22,600 tons in water year 2022 to 101,000 tons in water year 2019. The total load for the study period was 833,000 tons, which is nearly twice that at the Missouri River at St. Joseph, Mo., streamgage.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245097","collaboration":"Prepared in cooperation with Missouri Department of Natural Resources","usgsCitation":"Markland, K.M., 2024, Use of continuous water-quality time-series data to compute total phosphorus concentrations and loads for the Missouri River at St. Joseph and Hermann, Missouri, 2007–22: U.S. Geological Survey Scientific Investigations Report 2024–5097, 26 p., https://doi.org/10.3133/sir20245097.","productDescription":"Report: vii, 26 p.; Data Release; Dataset","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-161927","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":463254,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":463253,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245097/full"},{"id":463252,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5097/images/"},{"id":463251,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5097/sir20245097.XML"},{"id":463250,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5097/sir20245097.pdf","text":"Report","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5097"},{"id":463249,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5097/coverthb.jpg"},{"id":497886,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117740.htm","linkFileType":{"id":5,"text":"html"}},{"id":463255,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P17PHYDZ","text":"USGS data release","linkHelpText":"Data and model archive summaries to support use of continuous water-quality time-series data to compute total phosphorus concentrations and loads for the Missouri River at St. Joseph and Hermann, Missouri, 2007–22"}],"country":"United States","state":"Missouri","city":"Hermann, St. Joseph","otherGeospatial":"Missouri River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -91.48454431267173,\n              38.7389466373896\n            ],\n            [\n              -91.48454431267173,\n              38.678901791033724\n            ],\n            [\n              -91.40123726792416,\n              38.678901791033724\n            ],\n            [\n              -91.40123726792416,\n              38.7389466373896\n            ],\n            [\n              -91.48454431267173,\n              38.7389466373896\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -95.002636709889,\n              39.854445017011784\n            ],\n            [\n              -95.002636709889,\n              39.62967769348404\n            ],\n            [\n              -94.65186218929263,\n              39.62967769348404\n            ],\n            [\n              -94.65186218929263,\n              39.854445017011784\n            ],\n            [\n              -95.002636709889,\n              39.854445017011784\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/cm-water\" data-mce-href=\"https://www.usgs.gov/centers/cm-water\">Central Midwest Water Science Center</a><br>U.S. Geological Survey<br>400 South Clinton Street, Suite 269<br>Iowa City, IA 52240</p><p><a href=\"https://pubs.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Water-Quality Sample and Sensor Data</li><li>Surrogate Models</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Supplemental Figures</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2024-10-30","noUsgsAuthors":false,"publicationDate":"2024-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Markland, Kendra M. 0000-0002-0276-8684 kmarkland@usgs.gov","orcid":"https://orcid.org/0000-0002-0276-8684","contributorId":306212,"corporation":false,"usgs":true,"family":"Markland","given":"Kendra","email":"kmarkland@usgs.gov","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916997,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70261689,"text":"70261689 - 2024 - Field geology under the sea with a remotely operated vehicle: Mona Rift, Puerto Rico","interactions":[],"lastModifiedDate":"2024-12-18T16:38:53.741748","indexId":"70261689","displayToPublicDate":"2024-10-30T10:29:26","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Field geology under the sea with a remotely operated vehicle: Mona Rift, Puerto Rico","docAbstract":"<p><span>We implemented concepts of field geology at great ocean depths by constructing virtual outcrops from a string of overlapping video frames collected by remotely operated vehicles (ROVs). This lower-cost alternative to drilling boreholes allows stratigraphic extension into the offshore and regional interpretation of marine seismic profiles. The imagery was collected along a dive transect on the western wall of Mona Rift, a deep and narrow rift northwest of Puerto Rico, between water depths of 1560 m and 3927 m. The northern coast of Puerto Rico and its large offshore area are underlain by a mid-Eocene and younger forearc basin topped by a thick carbonate platform. There are no drill holes offshore, and tying seismic lines across the shoreline there is problematic. We describe our virtual outcrop and constrain its age and stratigraphy using seven rock samples collected by ROV and compare the outcrop's stratigraphy to deep boreholes and outcrops on land. Our formation descriptions and ages agree, for the most part, with those on land, but we identified a 100-m-thick section that is represented on land by an unconformity. Our stratigraphic interpretation indicates lateral variations in formation thicknesses and establishes a cross-section for additional sampling of the Eocene–Pliocene geology. It also suggests that Mona Rift has formed since the mid-Pliocene. The presence or absence of ferromanganese (Fe-Mn) crust on rocks along the transect may be correlated with the smoothness of the rock surface.</span></p>","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02762.1","usgsCitation":"ten Brink, U.S., Bialik, O.M., Chaytor, J., Flores, C., and Purkey Phillips, M., 2024, Field geology under the sea with a remotely operated vehicle: Mona Rift, Puerto Rico: Geosphere, v. 20, no. 6, p. 1575-1597, https://doi.org/10.1130/GES02762.1.","productDescription":"23 p.","startPage":"1575","endPage":"1597","ipdsId":"IP-163240","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466794,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02762.1","text":"Publisher Index Page"},{"id":465282,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mona Rift, Puerto Rico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -67.6333,\n              18.8333\n            ],\n            [\n              -67.6333,\n              18.6833\n            ],\n            [\n              -67.4583,\n              18.6833\n            ],\n            [\n              -67.4583,\n              18.8333\n            ],\n            [\n              -67.6333,\n              18.8333\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"20","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"ten Brink, Uri S. 0000-0001-6858-3001","orcid":"https://orcid.org/0000-0001-6858-3001","contributorId":201741,"corporation":false,"usgs":true,"family":"ten Brink","given":"Uri","email":"","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":921435,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bialik, Or M.","contributorId":347344,"corporation":false,"usgs":false,"family":"Bialik","given":"Or","email":"","middleInitial":"M.","affiliations":[{"id":25445,"text":"University of Münster","active":true,"usgs":false}],"preferred":false,"id":921436,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chaytor, Jason 0000-0001-8135-8677 jchaytor@usgs.gov","orcid":"https://orcid.org/0000-0001-8135-8677","contributorId":140095,"corporation":false,"usgs":true,"family":"Chaytor","given":"Jason","email":"jchaytor@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":921437,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flores, Claudia 0000-0003-0676-7061 cflores@usgs.gov","orcid":"https://orcid.org/0000-0003-0676-7061","contributorId":304396,"corporation":false,"usgs":true,"family":"Flores","given":"Claudia","email":"cflores@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":921438,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Purkey Phillips, Marcie","contributorId":346790,"corporation":false,"usgs":false,"family":"Purkey Phillips","given":"Marcie","affiliations":[{"id":29861,"text":"The University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":921439,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70262800,"text":"70262800 - 2024 - Spatial distribution patterns of invasive silver carp can inform removal efforts in an oxbow lake of the Mississippi River","interactions":[],"lastModifiedDate":"2025-01-23T15:46:28.226512","indexId":"70262800","displayToPublicDate":"2024-10-30T09:41:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Spatial distribution patterns of invasive silver carp can inform removal efforts in an oxbow lake of the Mississippi River","docAbstract":"<p>Oxbow lakes are highly productive waterbodies that host multiple life stages of many freshwater aquatic species. These lakes also provide foraging and rearing habitat to the invasive silver carp (<i>Hypophthalmichthys molitrix</i>) enabling populations to grow in biomass and abundance that can add propagule pressure to connected waterways and oxbows within the Mississippi River Basin. Ecologically these fish are undesirable because they overlap in diet and may compete for resources with native fishes and negatively impact recreational fisheries. Our goal was to evaluate silver carp distribution patterns in a major Mississippi River oxbow lake to inform removal programs and precision harvesting. We implanted 35 adult silver carp with acoustic tags and released them into the lake. Periodic tracking over 365 d revealed that fish were predominantly found in lake areas with water depths ranging from 2.0 to 5.9 m during all seasons, despite the availability of shallower and deeper water. Silver carp tended to aggregate in the wintertime (December–February) relative to other seasons. This information about lake area uses and seasonal aggregations could inform removal efforts in invaded waterbodies by exploiting natural behavioral and temporal vulnerabilities of this highly invasive and difficult-to-capture fish. </p>","language":"English","publisher":"REABIC","doi":"10.3391/mbi.2024.15.4.03","usgsCitation":"Besson, J., Miranda, L.E., Colvin, M.E., Dunn, C.G., and Riecke, D., 2024, Spatial distribution patterns of invasive silver carp can inform removal efforts in an oxbow lake of the Mississippi River: Management of Biological Invasions, v. 15, no. 4, p. 505-518, https://doi.org/10.3391/mbi.2024.15.4.03.","productDescription":"14 p.","startPage":"505","endPage":"518","ipdsId":"IP-161587","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":481053,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2024.15.4.03","text":"Publisher Index Page"},{"id":480994,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Moon Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -90.56421475590851,\n              34.466966058199986\n            ],\n            [\n              -90.56421475590851,\n              34.393119426569285\n            ],\n            [\n              -90.49028391694011,\n              34.393119426569285\n            ],\n            [\n              -90.49028391694011,\n              34.466966058199986\n            ],\n            [\n              -90.56421475590851,\n              34.466966058199986\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"15","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Besson, Jordan C.","contributorId":349791,"corporation":false,"usgs":false,"family":"Besson","given":"Jordan C.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":924815,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":924816,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Colvin, Michael E. 0000-0002-6581-4764","orcid":"https://orcid.org/0000-0002-6581-4764","contributorId":331490,"corporation":false,"usgs":true,"family":"Colvin","given":"Michael","email":"","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":924817,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":924818,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Riecke, Dennis K.","contributorId":349837,"corporation":false,"usgs":false,"family":"Riecke","given":"Dennis K.","affiliations":[],"preferred":false,"id":924911,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70261121,"text":"70261121 - 2024 - Examining inter-regional and intra-seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways","interactions":[{"subject":{"id":70261121,"text":"70261121 - 2024 - Examining inter-regional and intra-seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways","indexId":"70261121","publicationYear":"2024","noYear":false,"title":"Examining inter-regional and intra-seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways"},"predicate":"SUPERSEDED_BY","object":{"id":70261880,"text":"70261880 - 2025 - Examining inter-regional and intra-seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways","indexId":"70261880","publicationYear":"2025","noYear":false,"title":"Examining inter-regional and intra-seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways"},"id":1}],"supersededBy":{"id":70261880,"text":"70261880 - 2025 - Examining inter-regional and intra-seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways","indexId":"70261880","publicationYear":"2025","noYear":false,"title":"Examining inter-regional and intra-seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways"},"lastModifiedDate":"2025-01-27T17:18:30.964189","indexId":"70261121","displayToPublicDate":"2024-10-30T08:30:41","publicationYear":"2024","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":19836,"text":"Authorea","active":true,"publicationSubtype":{"id":32}},"title":"Examining inter-regional and intra-seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways","docAbstract":"The Central Valley of California (CVC) and Mid-Atlantic (MA) in the U.S. are both critical sites for nationwide food security (California Poultry Federation 2016, Prosser et al. 2017), and many waterfowl species annually, especially during the winter, providing feeding and roosting locations for a variety of species. Mapping waterfowl distributions, using NEXRAD, may aid in the adaptive management of important waterfowl habitat and allow various government agencies to better understand the interface between wild and domestic birds and commercial agricultural practices. We used 9 years (2014–2023) of data from the US NEXRAD network to model winter waterfowl relative abundance in the CVC and MA as a function of weather, temporal period, environmental conditions, and landcover characteristics using Boosted Regression Tree modelling. We were able to quantify the variability in effect size of 28 different covariates across space and time within two geographic regions which are critical to nationwide waterfowl management and host a high density of nationally important commercial agriculture. In general, weather, geographic (distance to features), and landcover condition (wetness index) predictors had the strongest relative effect on predicting wintering waterfowl relative abundance in both regions, while effects of land cover composition were more regionally and temporally specific. Increased daily mean temperature was a major predictor of increasing relative waterfowl abundance in both regions throughout the winter. Increasing precipitation had differing effects within regions, increasing relative waterfowl abundance in the MA, while decreasing in general within the CVC. Increasing relative waterfowl abundance in the CVC are strongly tied to the flooding of the landscape and rice availability, whereas waterfowl in the MA, where water is less limiting, are generally governed by waste grain availability and emergent wetland on the landscape. Waterfowl relative abundance in the MA was generally higher nearer to the Atlantic coast and lakes, while in the CVC they were higher nearer to lakes. Our findings promote a better understanding of spatial associations of waterfowl to landscape features and may aid in conservation and biosecurity management protocols.","language":"English","publisher":"Authorea","doi":"10.22541/au.173030440.00154170/v1","usgsCitation":"Hardy, M., Williams, C.K., Ladman, B.S., Pitesky, M.E., Overton, C.T., Casazza, M.L., Matchett, E., Prosser, D.J., and Buler, J.J., 2024, Examining inter-regional and intra-seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways: Authorea, https://doi.org/10.22541/au.173030440.00154170/v1.","productDescription":"51 p.","ipdsId":"IP-172616","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":466797,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.22541/au.173030440.00154170/v1","text":"External Repository"},{"id":466796,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.22541/au.173030440.00154170/v1","text":"External Repository"},{"id":464459,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hardy, Matthew J.","contributorId":343392,"corporation":false,"usgs":false,"family":"Hardy","given":"Matthew J.","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":919360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Christopher K.","contributorId":202263,"corporation":false,"usgs":false,"family":"Williams","given":"Christopher","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":919361,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ladman, Brian S.","contributorId":337102,"corporation":false,"usgs":false,"family":"Ladman","given":"Brian","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":919362,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pitesky, Maurice E.","contributorId":176920,"corporation":false,"usgs":false,"family":"Pitesky","given":"Maurice","email":"","middleInitial":"E.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":919363,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Overton, Cory T. 0000-0002-5060-7447 coverton@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-7447","contributorId":3262,"corporation":false,"usgs":true,"family":"Overton","given":"Cory","email":"coverton@usgs.gov","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":919364,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":919365,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Matchett, Elliott 0000-0001-5095-2884 ematchett@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-2884","contributorId":5541,"corporation":false,"usgs":true,"family":"Matchett","given":"Elliott","email":"ematchett@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":919366,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Prosser, Diann J. 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":221167,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":919367,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Buler, Jeffrey J.","contributorId":194648,"corporation":false,"usgs":false,"family":"Buler","given":"Jeffrey","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":919368,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70260403,"text":"70260403 - 2024 - Predictive modeling reveals elevated conductivity relative to background levels in freshwater tributaries within the Chesapeake Bay watershed, USA","interactions":[],"lastModifiedDate":"2024-11-27T15:58:32.944541","indexId":"70260403","displayToPublicDate":"2024-10-30T07:03:12","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19118,"text":"ES&T Water","active":true,"publicationSubtype":{"id":10}},"title":"Predictive modeling reveals elevated conductivity relative to background levels in freshwater tributaries within the Chesapeake Bay watershed, USA","docAbstract":"<div id=\"abstractBox\" class=\"article_abstract-content hlFld-Abstract\"><p class=\"articleBody_abstractText\">Elevated conductivity (i.e., specific conductance or SC) causes osmotic stress in freshwater aquatic organisms and may increase the toxicity of some contaminants. Indices of benthic macroinvertebrate integrity have declined in urban areas across the Chesapeake Bay watershed (CBW), and more information is needed about whether these declines may be due to elevated conductivity. A predictive SC model for the CBW was developed using monitoring data from the National Water Quality Portal. Predictor variables representing SC sources were compiled for nontidal reaches across the CBW. Random forests modeling was conducted to predict SC at four time periods (1999–2001, 2004–2006, 2009–2011, and 2014–2016), which were then compared to a national data set of background SC to quantify departures from background SC. Carbonate geology, impervious cover, forest cover, and snow depth were the most important variables for predicting SC. Observations and modeled results showed snow depth amplified the effect of impervious cover on SC. Elevated SC was predicted in two-thirds of reaches in the CBW, and these elevated conditions persisted over time in many areas. These results can be used in stressor identification assessments to prioritize future monitoring and to determine where management activities could be implemented to reduce salinization.</p></div>","language":"English","publisher":"American Chemical Society","doi":"10.1021/acsestwater.4c00589","usgsCitation":"Fanelli, R.M., Moore, J., Stillwell, C.C., Sekellick, A.J., and Walker, R., 2024, Predictive modeling reveals elevated conductivity relative to background levels in freshwater tributaries within the Chesapeake Bay watershed, USA: ES&T Water, v. 4, no. 11, p. 4978-4989, https://doi.org/10.1021/acsestwater.4c00589.","productDescription":"12 p.","startPage":"4978","endPage":"4989","ipdsId":"IP-164875","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":466799,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index 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Center","active":true,"usgs":true}],"preferred":true,"id":917551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sekellick, Andrew J. 0000-0002-0440-7655","orcid":"https://orcid.org/0000-0002-0440-7655","contributorId":215462,"corporation":false,"usgs":true,"family":"Sekellick","given":"Andrew","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":917552,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walker, Richard","contributorId":345806,"corporation":false,"usgs":false,"family":"Walker","given":"Richard","affiliations":[{"id":82718,"text":"University of Tennessee at Chattanooga","active":true,"usgs":false}],"preferred":false,"id":917553,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70260396,"text":"70260396 - 2024 - A systematic review of laboratory investigations into the pathogenesis of avian influenza viruses in wild avifauna of North America","interactions":[],"lastModifiedDate":"2024-10-31T11:38:08.097758","indexId":"70260396","displayToPublicDate":"2024-10-30T06:35:09","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19115,"text":"Proceeding of the Royal Society B","active":true,"publicationSubtype":{"id":10}},"title":"A systematic review of laboratory investigations into the pathogenesis of avian influenza viruses in wild avifauna of North America","docAbstract":"<div class=\"hlFld-Abstract\"><div class=\"abstractSection abstractInFull\"><p>The lack of consolidated information regarding the response of wild bird species to infection with avian influenza virus (AIV) is a challenge to both conservation managers and researchers alike, with related sectors also impacted, such as public health and commercial poultry. Using two independent searches, we reviewed published literature for studies describing wild bird species experimentally infected with avian influenza to assess host species’ relative susceptibility to AIVs. Additionally, we summarize broad-scale parameters for elements such as shedding duration and minimum infectious dose that can be used in transmission modelling efforts. Our synthesis shows that waterfowl (i.e. Anatidae) compose the vast majority of published AIV pathobiology studies, whereas gulls and passerines are less represented in research despite evidence that they also are susceptible and contribute to highly pathogenic avian influenza disease dynamics. This study represents the first comprehensive effort to compile available literature regarding the pathobiology of AIVs in all wild birds in over a decade. This database can now serve as a tool to all researchers, providing generalized estimates of pathobiology parameters for a variety of wild avian families and an opportunity to critically examine and assess what is known and identify where further insight is needed.</p></div></div>","language":"English","publisher":"The Royal Society of Publishing","doi":"10.1098/rspb.2024.1845","usgsCitation":"Gonnerman, M.B., Leyson, C., Sullivan, J.D., Pantin-Jackwood, M.J., Spackman, E., Mullinax, J.M., and Prosser, D., 2024, A systematic review of laboratory investigations into the pathogenesis of avian influenza viruses in wild avifauna of North America: Proceeding of the Royal Society B, v. 291, no. 2033, 9 p., https://doi.org/10.1098/rspb.2024.1845.","productDescription":"9 p.","ipdsId":"IP-163580","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":466800,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rspb.2024.1845","text":"Publisher Index Page"},{"id":463474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"291","issue":"2033","noUsgsAuthors":false,"publicationDate":"2024-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Gonnerman, Matthew Brandon 0000-0002-0791-9218","orcid":"https://orcid.org/0000-0002-0791-9218","contributorId":345802,"corporation":false,"usgs":true,"family":"Gonnerman","given":"Matthew","email":"","middleInitial":"Brandon","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":917532,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leyson, Christina","contributorId":224384,"corporation":false,"usgs":false,"family":"Leyson","given":"Christina","email":"","affiliations":[],"preferred":false,"id":917533,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":917534,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pantin-Jackwood, Mary J.","contributorId":197094,"corporation":false,"usgs":false,"family":"Pantin-Jackwood","given":"Mary","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":917535,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spackman, Erica","contributorId":82126,"corporation":false,"usgs":false,"family":"Spackman","given":"Erica","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":917536,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":917537,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":917538,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70268261,"text":"70268261 - 2024 - Inventorying ponds through novel size-adaptive object mapping using Sentinel-1/2 time series","interactions":[],"lastModifiedDate":"2025-06-18T15:03:04.772207","indexId":"70268261","displayToPublicDate":"2024-10-30T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Inventorying ponds through novel size-adaptive object mapping using Sentinel-1/2 time series","docAbstract":"<p><span>Ponds are an important source of greenhouse gases (GHGs) to the atmosphere, yet evaluating their role in global biogeochemical cycling is currently hampered by limitations in quantifying their global distribution. Existing satellite-derived estimates of lake distributions have difficulty identifying small lakes (5–10&nbsp;ha) and ponds (&lt;5&nbsp;ha) due to limitations in satellite resolution and challenges extracting individual small waterbodies from low-albedo surfaces, vegetated water, and lotic water systems including rivers and streams. In this study, we developed generalizable pond mapping strategies based on their spatial-temporal-spectral characteristics to fully exploit accessible medium-resolution optical and synthetic aperture radar (SAR) time series to identify ponds. Our novel approach entails: (1) making full use of ponds' characteristics from an object-based perspective; (2) extracting pond objects using seeds of prominent water pixels defined by the SAR VH signal; (3) constructing training samples of ponds with high representativeness; and (4) improving inter-class discrimination by combining features from optical and SAR data. We designed a novel Optical-SAR Pond Object Mapper (OptiSAR-POM) to achieve an improved estimate of pond size distribution by incorporating mapping strategies into the object-based image analysis framework. We generated landscape objects through an elaborate water-focused segmentation approach, which adaptively aligned the segmentation parameters with the size and distribution patterns of ponds to identify small waterbodies and increase inter-class variability. We further introduced an interactive learning process to construct random forests for object-based classification, which incorporated adaptive empirical thresholds to identify potential pond objects and select representative training samples of varying sizes. We tested the OptiSAR-POM framework using Sentinel-1/2 time series at three county-level study sites and three supplementary watershed-level study sites in the United States and China. Our approach yielded high overall accuracy (&gt;95&nbsp;%) for all sites and highlighted the ability of Sentinel-1/2 imagery to accurately detect small ponds (0.1–1&nbsp;ha) across diverse landscapes. The average producer's accuracy for small ponds at county-level sites improved by ∼45&nbsp;% compared to that of all other products with a 10-m or higher spatial resolution, addressing the absence of such information in existing regional and global datasets. The generated county-level pond maps revealed the numerical dominance of ponds in lentic waters, their substantial area contribution in human-impacted regions, and the relevance of studying biogeochemical processes in smaller waterbodies.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2024.114484","usgsCitation":"Liu, D., Zhu, X., Holgerson, M., Bansal, S., and Xu, X., 2024, Inventorying ponds through novel size-adaptive object mapping using Sentinel-1/2 time series: Remote Sensing of Environment, v. 315, 114484, 21 p., https://doi.org/10.1016/j.rse.2024.114484.","productDescription":"114484, 21 p.","ipdsId":"IP-167730","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":490911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"315","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Denghong","contributorId":357052,"corporation":false,"usgs":false,"family":"Liu","given":"Denghong","affiliations":[{"id":37969,"text":"Hong Kong Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":940631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Xioalin","contributorId":357055,"corporation":false,"usgs":false,"family":"Zhu","given":"Xioalin","affiliations":[{"id":37969,"text":"Hong Kong Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":940632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holgerson, Meredith","contributorId":218790,"corporation":false,"usgs":false,"family":"Holgerson","given":"Meredith","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":940633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":940634,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xu, Xiangtao","contributorId":348758,"corporation":false,"usgs":false,"family":"Xu","given":"Xiangtao","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":940635,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70260828,"text":"70260828 - 2024 - Adaptable plasmonic membrane sensors for fast and reliable detection of trace low micrometer microplastics in lake water","interactions":[],"lastModifiedDate":"2024-11-12T15:09:09.782218","indexId":"70260828","displayToPublicDate":"2024-10-29T09:05:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Adaptable plasmonic membrane sensors for fast and reliable detection of trace low micrometer microplastics in lake water","docAbstract":"<p><span>In freshwater environments, low-micrometer microplastics (LMMPs) have captured significant attention due to their prevalence and toxicity. Yet, rapid detection of LMMPs (1–10 μm) at the single-particle level within complex freshwater matrices remains a hurdle. We developed an adaptable plasmonic membrane sensor for fast detection of individual LMMPs in eutrophic lake waters. The plasmonic membrane sensor functions both as a membrane filter and as a sensor for LMMP collection and analysis. Among the four types of membrane sensors, polycarbonate track-etch (PCTE) membrane sensors exhibit superior imaging quality for LMMPs due to their flat and homogeneous surfaces. Besides the significantly improved imaging contrast and reduced background interferences, the Raman intensity of LMMPs is enhanced by 48% ± 25% on PCTE membrane sensors compared to unmodified membranes. The increased Raman intensities of a chemical probe with an increasing gold layer thickness and a decreasing membrane pore size suggest a surface-enhanced Raman scattering effect from the membrane sensors. The membrane sensors achieve a detection limit of 1 μg/L and an ultrafast scanning time of 0.01 s for individual LMMPs across natural eutrophic lake water. The developed membrane sensors offer an adaptable tool for the swift and reliable detection of individual LMMPs in complex environmental matrices.</span></p>","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.4c06503","usgsCitation":"Wu, Z., Janssen, S., Tate, M., Wei, H., and Qin, M., 2024, Adaptable plasmonic membrane sensors for fast and reliable detection of trace low micrometer microplastics in lake water: Environmental Science and Technology, v. 58, no. 45, p. 20172-20180, https://doi.org/10.1021/acs.est.4c06503.","productDescription":"9 p.","startPage":"20172","endPage":"20180","ipdsId":"IP-171215","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":466803,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.4c06503","text":"Publisher Index Page"},{"id":463868,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"45","noUsgsAuthors":false,"publicationDate":"2024-10-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Wu, Ziyan","contributorId":346132,"corporation":false,"usgs":false,"family":"Wu","given":"Ziyan","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":918229,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918230,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tate, Michael T. 0000-0003-1525-1219 mttate@usgs.gov","orcid":"https://orcid.org/0000-0003-1525-1219","contributorId":3144,"corporation":false,"usgs":true,"family":"Tate","given":"Michael T.","email":"mttate@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918231,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wei, Hoaran","contributorId":346133,"corporation":false,"usgs":false,"family":"Wei","given":"Hoaran","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":918232,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Qin, Mohan","contributorId":346134,"corporation":false,"usgs":false,"family":"Qin","given":"Mohan","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":918233,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70260375,"text":"70260375 - 2024 - Population structure of Desmophyllum pertusum found along the United States eastern continental margin","interactions":[],"lastModifiedDate":"2024-11-01T14:13:20.307568","indexId":"70260375","displayToPublicDate":"2024-10-29T08:29:55","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":958,"text":"BMC Research Notes","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Population structure of <i>Desmophyllum pertusum</i> found along the United States eastern continental margin","title":"Population structure of Desmophyllum pertusum found along the United States eastern continental margin","docAbstract":"<h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Objective</h3><p>The connectivity and genetic structuring of populations throughout a region influence a species’ resilience and probability of recovery from anthropogenic impacts. By gaining a comprehensive understanding of population connectivity, more effective management can be prioritized. To assess the connectivity and population genetic structure of a common cold-water coral species,<span>&nbsp;</span><i>Desmophyllum pertusum</i><span>&nbsp;</span>(<i>Lophelia pertusa</i>), we performed Restriction-site Associated DNA Sequencing (RADseq) on individuals from nine sites ranging from submarine canyons off New England to the southeastern coast of the United States (SEUS) and the Gulf of Mexico (GOM). Fifty-seven individuals and 3,180 single-nucleotide polymorphisms (SNPs) were used to assess genetic differentiation.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Results</h3><p>High connectivity exists among populations along the SEUS, yet these populations were differentiated from those to the north off New England and in Norfolk Canyon along the North Atlantic coast of the United States, as well as those in the GOM. Interestingly, Norfolk Canyon, located just north of North Carolina, and GOM populations exhibited low levels of genetic differentiation, corroborating previous microsatellite analyses and signifying gene flow between these populations. Increasing sample sizes from existing populations and including additional sampling sites over a larger geographic range would help define potential source populations and reveal fine-scale connectivity patterns among<span>&nbsp;</span><i>D. pertusum</i><span>&nbsp;</span>populations.</p>","language":"English","publisher":"Springer Nature","doi":"10.1186/s13104-024-06977-4","usgsCitation":"Weinnig, A.M., Aunins, A.W., Salamone, V.J., Quattrini, A., Nizinski, M.S., and Morrison, C., 2024, Population structure of Desmophyllum pertusum found along the United States eastern continental margin: BMC Research Notes, v. 17, no. 1, 326, 7 p., https://doi.org/10.1186/s13104-024-06977-4.","productDescription":"326, 7 p.","ipdsId":"IP-157145","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":466805,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13104-024-06977-4","text":"Publisher Index Page"},{"id":463535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Unites States","otherGeospatial":"Atlantic coastal margin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -85.71142930127395,\n              28.923532210772052\n            ],\n            [\n              -87.20770794927337,\n              29.778273658658676\n            ],\n            [\n              -88.12002541768051,\n              29.69258193898787\n            ],\n            [\n              -89.23879681191622,\n              28.44134714528691\n            ],\n            [\n              -86.44254662826377,\n              27.912389547448754\n            ],\n            [\n              -85.71142930127395,\n              28.923532210772052\n            ]\n    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Center","active":true,"usgs":true}],"preferred":true,"id":917476,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aunins, Aaron W. 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","middleInitial":"W.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":917477,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Salamone, Veronica J. 0000-0002-6274-6401","orcid":"https://orcid.org/0000-0002-6274-6401","contributorId":293174,"corporation":false,"usgs":true,"family":"Salamone","given":"Veronica","email":"","middleInitial":"J.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":917478,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Quattrini, Andrea M.","contributorId":333886,"corporation":false,"usgs":false,"family":"Quattrini","given":"Andrea M.","affiliations":[{"id":80003,"text":"Department of Invertebrate Zoology, Smithsonian Institution, Washington DC, United States of America","active":true,"usgs":false}],"preferred":false,"id":917479,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nizinski, Martha S.","contributorId":174770,"corporation":false,"usgs":false,"family":"Nizinski","given":"Martha","email":"","middleInitial":"S.","affiliations":[{"id":27510,"text":"NMFS National Systematics Laboratory, Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":917480,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morrison, Cheryl 0000-0001-9425-691X cmorrison@usgs.gov","orcid":"https://orcid.org/0000-0001-9425-691X","contributorId":202644,"corporation":false,"usgs":true,"family":"Morrison","given":"Cheryl","email":"cmorrison@usgs.gov","affiliations":[{"id":365,"text":"Leetown 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,{"id":70263169,"text":"70263169 - 2024 - Multi-decadal trophic shifts in Lake Erie yellow perch Perca flavescens","interactions":[],"lastModifiedDate":"2025-01-30T16:14:56.157932","indexId":"70263169","displayToPublicDate":"2024-10-28T10:08:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Multi-decadal trophic shifts in Lake Erie yellow perch <i>Perca flavescens</i>","title":"Multi-decadal trophic shifts in Lake Erie yellow perch Perca flavescens","docAbstract":"<p><span>In Lake Erie, yellow perch&nbsp;</span><i>Perca flavescens</i><span>&nbsp;support vast commercial and recreational fisheries, yet populations have recently declined. Using&nbsp;</span><i>N</i><span>&nbsp;=&nbsp;5889 yellow perch stomachs collected from 1997 to 2021, we explored trends in the feeding ecology and trophic level of yellow perch with generalized additive models. Models revealed a significant decrease in yellow perch trophic level (−0.15 trophic levels in the last decade), and significant dietary shifts. Yellow perch have shifted away from feeding on piscine prey and round goby&nbsp;</span><i>Neogobius melanostomus</i><span>&nbsp;over the 25-year period, and now feed on invertebrates more frequently—including invasive waterfleas (</span><i>Bythotrephes longimanus</i><span>&nbsp;and&nbsp;</span><i>Cercopagis pengoi</i><span>) and chironomids. Dietary patterns appear to reflect broad ecological changes—invasive waterfleas have proliferated while populations of forage fish and round goby have declined. Furthermore, hypoxia events have increased in duration and severity, which may explain observed increases in chironomid consumption, which are hypoxia tolerant. This study demonstrates trophic adaptability in yellow perch, which have changed feeding behavior and trophic position in response to novel invaders and changing environmental conditions.</span></p>","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2023-0348","usgsCitation":"Schmitt, J., Gorman, A., Knight, C., Dufour, M.R., Roberts, J., and Hartman, T., 2024, Multi-decadal trophic shifts in Lake Erie yellow perch Perca flavescens: Canadian Journal of Fisheries and Aquatic Sciences, v. 81, no. 11, p. 1560-1580, https://doi.org/10.1139/cjfas-2023-0348.","productDescription":"21 p.","startPage":"1560","endPage":"1580","ipdsId":"IP-140880","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":489855,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2023-0348","text":"Publisher Index Page"},{"id":481508,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -80.56325919457086,\n              41.96883002032561\n            ],\n            [\n              -80.71352325852209,\n              42.13778067147925\n            ],\n            [\n              -81.1801985471471,\n              42.12256709658425\n            ],\n            [\n              -82.40972750390958,\n              41.63184115572025\n            ],\n            [\n              -82.53711113456517,\n              41.3996082733955\n            ],\n            [\n              -82.45403485370326,\n              41.382988331945285\n            ],\n            [\n              -82.0829607991845,\n              41.49923867953902\n            ],\n            [\n              -81.86223827452686,\n              41.48020592339866\n            ],\n            [\n              -81.73401076664092,\n              41.482825773508495\n            ],\n            [\n              -81.3851200399411,\n              41.68976914941442\n            ],\n            [\n              -80.56325919457086,\n              41.96883002032561\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"81","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":925739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gorman, Ann Marie","contributorId":350334,"corporation":false,"usgs":false,"family":"Gorman","given":"Ann Marie","affiliations":[],"preferred":false,"id":925740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knight, Carey","contributorId":214230,"corporation":false,"usgs":false,"family":"Knight","given":"Carey","affiliations":[{"id":16232,"text":"Ohio Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":925741,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dufour, Mark Richard 0000-0001-6930-7666","orcid":"https://orcid.org/0000-0001-6930-7666","contributorId":291450,"corporation":false,"usgs":true,"family":"Dufour","given":"Mark","email":"","middleInitial":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":925742,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":925743,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hartman, Travis","contributorId":220316,"corporation":false,"usgs":false,"family":"Hartman","given":"Travis","email":"","affiliations":[{"id":37332,"text":"Ohio Department of Natural Resources, Division of Wildlife","active":true,"usgs":false}],"preferred":false,"id":925744,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70266311,"text":"70266311 - 2024 - Predator-specific mortality of sage-grouse nests based on predator DNA on eggshells","interactions":[],"lastModifiedDate":"2025-05-05T14:44:40.283853","indexId":"70266311","displayToPublicDate":"2024-10-28T09:41:24","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Predator-specific mortality of sage-grouse nests based on predator DNA on eggshells","docAbstract":"<p><span>Greater sage-grouse (hereafter sage-grouse;&nbsp;</span><i>Centrocercus urophasianus</i><span>) populations have declined across their range. Increased nest predation as a result of anthropogenic land use is one mechanism proposed to explain these declines. However, sage-grouse contend with a diverse suite of nest predators that vary in functional traits (e.g., search tactics or hunting mode) and abundance. Consequently, generalizing about factors influencing nest fate is challenging. Identifying the explicit predator species responsible for nest predation events is, therefore, critical to understanding causal mechanisms linking land use to patterns of sage-grouse nest success. Cattle grazing is often assumed to adversely affect sage-grouse recruitment by reducing grass height (and hence cover), thereby facilitating nest detection by predators. However, recent evidence found little support for the hypothesized effect of grazing on nest fate at the pasture scale. Rather, nest success appears to be similar on pastures grazed at varying intensities. One possible explanation for the lack of observed effect involves a localized response by one or more nest predators. The presence of cattle may cause a temporary reduction in predator density and/or use within a pasture (the cattle avoidance hypothesis). The cattle avoidance hypothesis predicts a decreased probability of at least one sage-grouse nest predator predating sage-grouse nests in pastures with livestock relative to pastures without livestock present during the nesting season. To test the cattle avoidance hypothesis, we collected predator DNA from eggshells from predated nests and used genetic methods to identify the sage-grouse nest predator(s) responsible for the predation event. We evaluated the influence of habitat and grazing on predator-specific nest predation. We evaluated the efficacy of our genetic method by deploying artificial nests with trail cameras and compared the results of our genetic method to the species captured via trail camera. Our molecular methods identified at least one nest predator captured predating artificial nests via trail camera for 33 of 35 (94%) artificial nests. We detected nest predators via our molecular analysis at 76 of 114 (67%) predated sage-grouse nests. The primary predators detected at sage-grouse nests were coyotes (</span><i>Canis latrans</i><span>) and corvids (</span><i>Corvidea</i><span>). Grazing did not influence the probability of nest predation by either coyotes or corvids. Sagebrush canopy cover was negatively associated with the probability a coyote predated a nest, distance to water was positively associated with the probability a corvid predated a nest, and average minimum temperature was negatively associated with the probability that either a coyote or a corvid predated a nest. Our study provides a framework for implementing an effective, non-invasive method for identifying sage-grouse nest predators that can be used to better understand how management actions at local and regional scales may impact an important component of sage-grouse recruitment.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.70213","usgsCitation":"Helmstetter, N.A., Conway, C.J., Roberts, S., Adams, J., Makela, P., and Waits, L., 2024, Predator-specific mortality of sage-grouse nests based on predator DNA on eggshells: Ecology and Evolution, v. 14, no. 10, e70213, 19 p., https://doi.org/10.1002/ece3.70213.","productDescription":"e70213, 19 p.","ipdsId":"IP-166147","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487947,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.70213","text":"Publisher Index Page"},{"id":485377,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -112.84878592693147,\n              44.720752546676636\n            ],\n            [\n              -116.22953206733101,\n              44.720752546676636\n            ],\n            [\n              -116.22953206733101,\n              41.995370325478774\n            ],\n            [\n              -112.84878592693147,\n              41.995370325478774\n            ],\n            [\n              -112.84878592693147,\n              44.720752546676636\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"14","issue":"10","noUsgsAuthors":false,"publicationDate":"2024-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Helmstetter, Nolan A.","contributorId":287004,"corporation":false,"usgs":false,"family":"Helmstetter","given":"Nolan","email":"","middleInitial":"A.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":935533,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935534,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roberts, Shane","contributorId":279606,"corporation":false,"usgs":false,"family":"Roberts","given":"Shane","affiliations":[{"id":56023,"text":"idfg","active":true,"usgs":false}],"preferred":false,"id":935535,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adams, Jennifer R.","contributorId":341225,"corporation":false,"usgs":false,"family":"Adams","given":"Jennifer R.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":935536,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Makela, Paul D.","contributorId":354380,"corporation":false,"usgs":false,"family":"Makela","given":"Paul D.","affiliations":[{"id":37086,"text":"U.S. Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":935537,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waits, Lisette P.","contributorId":338452,"corporation":false,"usgs":false,"family":"Waits","given":"Lisette P.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":935538,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70260180,"text":"70260180 - 2024 - Tissue distribution and temporal and spatial assessment of per- and polyfluoroalkyl substances (PFAS) in smallmouth bass (Micropterus dolomieu) in the mid-Atlantic United States","interactions":[],"lastModifiedDate":"2024-10-30T13:35:44.534951","indexId":"70260180","displayToPublicDate":"2024-10-28T08:27:57","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1564,"text":"Environmental Science and Pollution Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Tissue distribution and temporal and spatial assessment of per- and polyfluoroalkyl substances (PFAS) in smallmouth bass (<i<Micropterus dolomieu</i>) in the mid-Atlantic United States","title":"Tissue distribution and temporal and spatial assessment of per- and polyfluoroalkyl substances (PFAS) in smallmouth bass (Micropterus dolomieu) in the mid-Atlantic United States","docAbstract":"<p><span>Per- and polyfluoroalkyl substances (PFAS) have become an environmental issue worldwide. A first step to assessing potential adverse effects on fish populations is to determine if concentrations of concern are present in a region and if so, in which watersheds. Hence, plasma from adult smallmouth bass&nbsp;</span><i>Micropterus dolomieu</i><span>&nbsp;collected at 10 sites within 4 river systems in the mid-Atlantic region of the United States, from 2014 to 2019, was analyzed for 13 PFAS. These analyses were directed at better understanding the presence and associations with land use attributes in an important sportfish. Four substances, PFOS, PFDA, PFUnA, and PFDoA, were detected in every plasma sample, with PFOS having the highest concentrations. Sites with mean plasma concentrations of PFOS below 100&nbsp;ng/ml had the lowest percentage of developed landcover in the upstream catchments. Sites with moderate plasma concentrations (mean PFOS concentrations between 220 and 240&nbsp;ng/ml) had low (&lt; 7.0) percentages of developed land use but high (&gt; 30) percentages of agricultural land use. Sites with mean plasma concentrations of PFOS &gt; 350&nbsp;ng/ml had the highest percentage of developed land use and the highest number PFAS facilities that included military installations and airports. Four of the sites were part of a long-term monitoring project, and PFAS concentrations of samples collected in spring 2017, 2018, and 2019 were compared. Significant annual differences in plasma concentrations were noted that may relate to sources and climatic factors. Samples were also collected at two sites for tissue (plasma, whole blood, liver, gonad, muscle) distribution analyses with an expanded analyte list of 28 PFAS. Relative tissue distributions were not consistent even within one species of similar ages. Although the long-chained legacy PFAS were generally detected more frequently and at higher concentrations, emerging compounds such as 6:2 FTS and GEN X were detected in a variety of tissues.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s11356-024-35097-6","usgsCitation":"Blazer, V., Walsh, H.L., Smith, C.R., Gordon, S.E., Keplinger, B.J., and Wertz, T., 2024, Tissue distribution and temporal and spatial assessment of per- and polyfluoroalkyl substances (PFAS) in smallmouth bass (Micropterus dolomieu) in the mid-Atlantic United States: Environmental Science and Pollution Research, v. p., no. 31, p. 59302-59319, https://doi.org/10.1007/s11356-024-35097-6.","productDescription":"18","startPage":"59302","endPage":"59319","ipdsId":"IP-164838","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":466808,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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0000-0002-7226-1774","orcid":"https://orcid.org/0000-0002-7226-1774","contributorId":219236,"corporation":false,"usgs":true,"family":"Smith","given":"Cheyenne","email":"","middleInitial":"R.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":917327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gordon, Stephanie E. 0000-0002-6292-2612 sgordon@usgs.gov","orcid":"https://orcid.org/0000-0002-6292-2612","contributorId":200931,"corporation":false,"usgs":true,"family":"Gordon","given":"Stephanie","email":"sgordon@usgs.gov","middleInitial":"E.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":917328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Keplinger, Brandon J.","contributorId":204644,"corporation":false,"usgs":false,"family":"Keplinger","given":"Brandon","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":917329,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wertz, Timothy","contributorId":274363,"corporation":false,"usgs":false,"family":"Wertz","given":"Timothy","affiliations":[{"id":56607,"text":"Pennsylvania Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":917330,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70261313,"text":"70261313 - 2024 - Self-potential tomography preconditioned by particle swarm optimization— Application to monitoring hyporheic exchange in a bedrock river","interactions":[],"lastModifiedDate":"2024-12-06T14:15:01.195444","indexId":"70261313","displayToPublicDate":"2024-10-27T09:42:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Self-potential tomography preconditioned by particle swarm optimization— Application to monitoring hyporheic exchange in a bedrock river","docAbstract":"<p><span>A self-potential (SP) data-inversion algorithm was developed and tested on an analytical model of electrical-potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP-monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock-lined riverbed. The algorithm combined stochastic source-localization by particle-swarm-optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least-squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical-model data to evaluate the algorithm performance. Results indicated that true parameters of single-source models were inverted and approximated with small residual error, whereas inversion of analytical-model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non-uniqueness of the inverse-model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point-scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP-monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR037549","usgsCitation":"Ikard, S., Carroll, K.C., Brooks, S.C., Rucker, D.F., Smith-Vega, G., and Elwes, A., 2024, Self-potential tomography preconditioned by particle swarm optimization— Application to monitoring hyporheic exchange in a bedrock river: Water Resources Research, v. 60, no. 10, e2024WR037549, 25 p., https://doi.org/10.1029/2024WR037549.","productDescription":"e2024WR037549, 25 p.","ipdsId":"IP-160252","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":466810,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr037549","text":"Publisher Index Page"},{"id":464806,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","city":"Oak Ridge","otherGeospatial":"East Fork Poplar Creek","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -84.23096147469687,\n              36.034094349042874\n            ],\n            [\n              -84.40092348796415,\n              36.034094349042874\n            ],\n            [\n              -84.40092348796415,\n              35.91942637548165\n            ],\n            [\n              -84.23096147469687,\n              35.91942637548165\n            ],\n            [\n              -84.23096147469687,\n              36.034094349042874\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"60","issue":"10","noUsgsAuthors":false,"publicationDate":"2024-10-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Ikard, Scott 0000-0002-8304-4935","orcid":"https://orcid.org/0000-0002-8304-4935","contributorId":201775,"corporation":false,"usgs":true,"family":"Ikard","given":"Scott","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":920340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carroll, Kenneth C. 0000-0003-2097-9589","orcid":"https://orcid.org/0000-0003-2097-9589","contributorId":247827,"corporation":false,"usgs":false,"family":"Carroll","given":"Kenneth","email":"","middleInitial":"C.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":920341,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brooks, Scott C. 0000-0002-8437-9788","orcid":"https://orcid.org/0000-0002-8437-9788","contributorId":294464,"corporation":false,"usgs":false,"family":"Brooks","given":"Scott","email":"","middleInitial":"C.","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":920343,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rucker, Dale F. 0000-0002-8930-2747","orcid":"https://orcid.org/0000-0002-8930-2747","contributorId":294463,"corporation":false,"usgs":false,"family":"Rucker","given":"Dale","email":"","middleInitial":"F.","affiliations":[{"id":63573,"text":"hydroGEOPHYSICS, Inc.","active":true,"usgs":false}],"preferred":false,"id":920342,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith-Vega, Gladisol 0009-0001-1597-7944","orcid":"https://orcid.org/0009-0001-1597-7944","contributorId":346951,"corporation":false,"usgs":false,"family":"Smith-Vega","given":"Gladisol","email":"","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":920344,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Elwes, Aubrey 0009-0000-4058-8126","orcid":"https://orcid.org/0009-0000-4058-8126","contributorId":346952,"corporation":false,"usgs":false,"family":"Elwes","given":"Aubrey","email":"","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":920345,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70260185,"text":"70260185 - 2024 - Radiogenic strontium- and uranium-isotope tracers of water-rock interactions and hydrothermal flow in the Upper Geyser Basin, Yellowstone Plateau Volcanic Field, USA","interactions":[],"lastModifiedDate":"2024-10-30T12:08:08.229599","indexId":"70260185","displayToPublicDate":"2024-10-26T07:06:24","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18959,"text":"Geochemistry Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Radiogenic strontium- and uranium-isotope tracers of water-rock interactions and hydrothermal flow in the Upper Geyser Basin, Yellowstone Plateau Volcanic Field, USA","docAbstract":"<div class=\"article-section__content en main\"><p>Natural radiogenic isotopes (primarily<span>&nbsp;</span><sup>87</sup>Sr/<sup>86</sup>Sr) from hot springs in the Upper Geyser Basin of the Yellowstone Plateau volcanic field and associated rocks were used to evaluate groundwater flow patterns, water-rock reactions, and the extent of mixing between various groundwater sources. Thermal waters have very low uranium concentrations and<span>&nbsp;</span><sup>234</sup>U/<sup>238</sup>U activity ratios near 1.0, which limit their utility as tracers in this reducing setting. Thermal waters have higher Sr concentrations (&lt;22&nbsp;ng/g) and a wide range of<span>&nbsp;</span><sup>87</sup>Sr/<sup>86</sup>Sr values that vary both temporally at individual discharge sites and between adjacent springs, indicating that conduits tap different subsurface reservoirs to varying degrees. Sr from local rhyolites have<span>&nbsp;</span><sup>87</sup>Sr/<sup>86</sup>Sr compositions that bound the range of values observed in groundwater throughout the basin. Non-boiling springs on the west flank of the basin discharge water with low<span>&nbsp;</span><sup>87</sup>Sr/<sup>86</sup>Sr consistent with flow through young volcanic rocks exposed at the surface. Boiling springs in the central basin have higher<span>&nbsp;</span><sup>87</sup>Sr/<sup>86</sup>Sr values reflecting interactions with older, more radiogenic volcanic rocks. Variability in upwelling thermal waters requires mixing with a low<span>&nbsp;</span><sup>87</sup>Sr/<sup>86</sup>Sr component derived from young lava or glacial sediments, or more likely, from deeper sources of hot groundwater circulating through buried Lava Creek Tuff having intermediate<span>&nbsp;</span><sup>87</sup>Sr/<sup>86</sup>Sr. Isotope data constrain basin-wide output of thermal water to 110–140&nbsp;kg·s<sup>−1</sup>. Results underscore the utility of radiogenic Sr isotopes as valuable tracers of hydrothermal flow patterns and improve the understanding of temperature-dependent water-rock reactions in one of the largest continental hydrothermal systems on Earth.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GC011729","usgsCitation":"Paces, J., Hurwitz, S., Harrison, L.N., Lowenstern, J.B., and McCleskey, R., 2024, Radiogenic strontium- and uranium-isotope tracers of water-rock interactions and hydrothermal flow in the Upper Geyser Basin, Yellowstone Plateau Volcanic Field, USA: Geochemistry Geophysics, Geosystems, v. 25, no. 10, e2024GC011729, 29 p., https://doi.org/10.1029/2024GC011729.","productDescription":"e2024GC011729, 29 p.","ipdsId":"IP-167560","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":466813,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gc011729","text":"Publisher Index Page"},{"id":463416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone Plateau Volcanic Field","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -111.66965749255424,\n              45.18227303867559\n            ],\n            [\n              -111.66965749255424,\n              43.373355771361986\n            ],\n            [\n              -108.59348561755453,\n              43.373355771361986\n            ],\n            [\n              -108.59348561755453,\n              45.18227303867559\n            ],\n            [\n              -111.66965749255424,\n              45.18227303867559\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"25","issue":"10","noUsgsAuthors":false,"publicationDate":"2024-10-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Paces, James B. 0000-0002-9809-8493","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":118216,"corporation":false,"usgs":true,"family":"Paces","given":"James B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":917352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":917353,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harrison, Lauren N 0000-0002-6621-5958","orcid":"https://orcid.org/0000-0002-6621-5958","contributorId":300066,"corporation":false,"usgs":true,"family":"Harrison","given":"Lauren","email":"","middleInitial":"N","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917354,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lowenstern, Jacob B. 0000-0003-0464-7779 jlwnstrn@usgs.gov","orcid":"https://orcid.org/0000-0003-0464-7779","contributorId":2755,"corporation":false,"usgs":true,"family":"Lowenstern","given":"Jacob","email":"jlwnstrn@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917355,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. Blaine","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":917356,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70259674,"text":"sir20245082 - 2024 - Use of a numerical groundwater-flow model and projected climate scenarios to simulate the effects of future climate conditions on base flow for reach 1 of the Washita River alluvial aquifer and Foss Reservoir storage, western Oklahoma","interactions":[],"lastModifiedDate":"2025-12-22T20:17:19.51282","indexId":"sir20245082","displayToPublicDate":"2024-10-25T10:23:33","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":"2024-5082","displayTitle":"Use of a Numerical Groundwater-Flow Model and Projected Climate Scenarios To Simulate the Effects of Future Climate Conditions on Base Flow for Reach 1 of the Washita River Alluvial Aquifer and Foss Reservoir Storage, Western Oklahoma","title":"Use of a numerical groundwater-flow model and projected climate scenarios to simulate the effects of future climate conditions on base flow for reach 1 of the Washita River alluvial aquifer and Foss Reservoir storage, western Oklahoma","docAbstract":"<p>To better understand the relation between climate variability and future groundwater resources in reach 1 of the Washita River alluvial aquifer and Foss Reservoir in western Oklahoma, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation, used a previously published numerical groundwater-flow model and climate-model data to investigate changes in base flow and reservoir storage by evaluating three scenarios. The three projected climate scenarios were (1) a central-tendency scenario, (2) a warmer/drier scenario, and (3)&nbsp;a less-warm/wetter scenario. To estimate future base flow and groundwater availability in western Oklahoma, specifically in reach 1 of the Washita River alluvial aquifer, downscaled climate-model data from 231&nbsp;Coupled Model Intercomparison Project phase 5 (CMIP5) projections coupled with a previously published numerical groundwater-flow model were used to compare the effects of different climate scenarios on the aquifer. Changes in base flow and groundwater-level elevations during a 30-year baseline scenario (1985–2014) and the three 30-year projected climate scenarios (2050–79) under central-tendency, warmer/drier, and less-warm/wetter climatic conditions were assessed by using the calibrated model. In the simulations, the amount of base flow and reservoir storage declined in the central-tendency and warmer/drier scenarios compared to the amount of base flow and reservoir storage under historical climatic conditions (baseline scenario). Mean annual change in reservoir storage decreased from the baseline scenario the most in the warmer/drier scenario, followed by the central-tendency scenario, but increased in the less-warm/wetter scenario compared to the baseline scenario. At the end of the simulation period (2079), the largest magnitude differences in groundwater-level elevations in all three projected climate scenarios relative to the baseline scenario occurred upstream from Foss Reservoir. Results from incorporating downscaled climate projections into localized numerical groundwater-flow models can highlight potential future changes in and implications for groundwater resources and availability.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245082","issn":"2328-0328","collaboration":"Prepared in cooperation with Bureau of Reclamation","usgsCitation":"Labriola, L.G., Ellis, J.H., Gangopadhyay, S., Kirstetter, P.E., and Hong, Y., 2024, Use of a numerical groundwater-flow model and projected climate scenarios to simulate the effects of future climate conditions on base flow for reach 1 of the Washita River alluvial aquifer and Foss Reservoir storage, western Oklahoma: U.S. Geological Survey Scientific Investigations Report 2024–5082, 20 p., https://doi.org/10.3133/sir20245082.","productDescription":"Report: viii, 20 p.; 2 Datasets, Data Release","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-140254","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":497883,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117736.htm","linkFileType":{"id":5,"text":"html"}},{"id":463125,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245082/full","description":"SIR 2024-5082 HTML"},{"id":463003,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5082/images"},{"id":463001,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5082/coverthb.jpg"},{"id":463000,"rank":1,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5082/images"},{"id":463006,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://waterdata.usgs.gov/ok/nwis/","text":"USGS Water Data for Oklahoma","linkHelpText":"- USGS NWIS water data for Oklahoma"},{"id":463066,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS Water Data for the Nation","linkHelpText":"- USGS NWIS database"},{"id":463005,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XFE87Q","text":"USGS Data Release","linkHelpText":"- MODFLOW-NWT model data used to simulate base flow and groundwater availability under different future climatic conditions for reach 1 of the Washita River alluvial aquifer and Foss Reservoir, western Oklahoma"},{"id":463124,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5082/sir20245082.XML","description":"SIR 2024-5082 XML"},{"id":463002,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5082/sir20245082.pdf","size":"1.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5082"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Washita River alluvial aquifer and Foss Reservoir storage","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -99.99793544098728,\n              35.9\n            ],\n            [\n              -99.99793544098728,\n              35.458335525604184\n            ],\n            [\n              -98.75,\n              35.458335525604184\n            ],\n            [\n              -98.75,\n              35.9\n            ],\n            [\n              -99.99793544098728,\n              35.9\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/ot-water\" href=\"https://www.usgs.gov/centers/ot-water\">Oklahoma-Texas Water Science Center</a><br>U.S. Geological Survey<br>1505 Ferguson Lane<br>Austin, TX 78754–4501</p><p><a id=\"LPlnkOWA15180ebd-b368-51d6-d4d0-3194b6e2a465\" class=\"OWAAutoLink\" title=\"https://pubs.usgs.gov/contact\" href=\"https://pubs.usgs.gov/contact\" data-auth=\"NotApplicable\" data-olk-copy-source=\"MailCompose\" data-mce-href=\"../contact\">Contact Us- USGS Publications Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Climate Projections and the Numerical Groundwater-Flow Model for Reach 1 of the Washita River Alluvial Aquifer</li><li>Simulated Effects of Future Climate Conditions on Base Flow and Reservoir Storage</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2024-10-25","noUsgsAuthors":false,"publicationDate":"2024-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Labriola, Laura G. 0000-0002-5096-2940","orcid":"https://orcid.org/0000-0002-5096-2940","contributorId":345289,"corporation":false,"usgs":true,"family":"Labriola","given":"Laura G.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916209,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ellis, John H. 0000-0001-7161-3136","orcid":"https://orcid.org/0000-0001-7161-3136","contributorId":345290,"corporation":false,"usgs":true,"family":"Ellis","given":"John H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916210,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gangopadhyay, Subhrendu","contributorId":345291,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","email":"","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":true,"id":916211,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kirstetter, Pierre-Emmanuel 0000-0002-7381-0229","orcid":"https://orcid.org/0000-0002-7381-0229","contributorId":345292,"corporation":false,"usgs":false,"family":"Kirstetter","given":"Pierre-Emmanuel","email":"","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":true,"id":916212,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hong, Yang","contributorId":345293,"corporation":false,"usgs":false,"family":"Hong","given":"Yang","email":"","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":true,"id":916213,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70264291,"text":"70264291 - 2024 - Power analysis of water quality of standing water bodies in the Pacific Island Network, 2009–2017","interactions":[],"lastModifiedDate":"2025-03-10T15:15:57.285322","indexId":"70264291","displayToPublicDate":"2024-10-25T10:03:38","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Power analysis of water quality of standing water bodies in the Pacific Island Network, 2009–2017","docAbstract":"<div class=\"item-page-field\"><div class=\"simple-view-element\"><div class=\"simple-view-element-body\"><span class=\"dont-break-out preserve-line-breaks ng-star-inserted\">The National Park Service (NPS) Inventory and Monitoring Division (IMD) aims to provide data on park ecosystems' health to guide management decisions. Since 2007, NPS IMD has monitored water quality in marine areas, streams, anchialine pools, wetlands, and lakes in the Pacific Island Network (PACN) national parks. To maintain long-term monitoring program efficiency, protocols are reviewed and revised every 10 years based on trend analyses, including new power analyses for significant sampling regime changes. This report focuses on standing water bodies, evaluating statistical power across different sampling intensities to detect water quality trends and anomalies. It covers 10 areas with a varying number of sample stations. Data from 2009–2017 for nine water quality parameters were examined, and statistical power was assessed by using linear regression and Wilcoxon two-sample tests with 80% power and a Type I error rate of 0.05. Results show that higher sampling effort and larger effect sizes increase the power to detect changes, although power varies by parameter and site due to differences in mean and variance. The analysis results may be used to devise optimal sampling strategies, including balancing the number of sample sites and sampling frequency. Periodic evaluations and adaptive strategies are essential for maintaining statistical power and for the long-term management of the PACN water quality monitoring program, especially in the context of climate change.</span></div></div></div>","language":"English","publisher":"University of Hawai‘i at Hilo","usgsCitation":"Gorresen, P., Camp, R.J., and Raikow, D., 2024, Power analysis of water quality of standing water bodies in the Pacific Island Network, 2009–2017, 103 p.","productDescription":"103 p.","ipdsId":"IP-171336","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":483149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":483124,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10790/5397","linkFileType":{"id":5,"text":"html"}}],"country":"Commonwealth of the Northern Marianas Islands, United States","state":"Hawaii","otherGeospatial":"Island of Hawaii, Saipan","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              145.7686692256949,\n              15.077864559212983\n            ],\n            [\n              145.86832925410147,\n              15.283302926362126\n            ],\n            [\n              145.79451166955317,\n              15.297492590932833\n            ],\n            [\n              145.71452825998722,\n              15.224504910817288\n            ],\n            [\n              145.6616253302456,\n              15.127755732344568\n            ],\n            [\n              145.7686692256949,\n              15.077864559212983\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -156.3292278077463,\n              20.28209820847451\n            ],\n            [\n              -156.3292278077463,\n              18.8998780443399\n            ],\n            [\n              -154.62242382756705,\n              18.8998780443399\n            ],\n            [\n              -154.62242382756705,\n              20.28209820847451\n            ],\n            [\n              -156.3292278077463,\n              20.28209820847451\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gorresen, P. Marcos 0000-0002-0707-9212","orcid":"https://orcid.org/0000-0002-0707-9212","contributorId":196628,"corporation":false,"usgs":false,"family":"Gorresen","given":"P. 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,{"id":70260999,"text":"70260999 - 2024 - Silver carp experience metabolic and behavioral changes when exposed to water from the Chicago Area Waterway","interactions":[],"lastModifiedDate":"2024-11-21T14:29:08.569902","indexId":"70260999","displayToPublicDate":"2024-10-25T09:37:24","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Silver carp experience metabolic and behavioral changes when exposed to water from the Chicago Area Waterway","docAbstract":"<p>One of the hallmarks of invasive species is their propensity to spread. Removing an invasive species after establishment is virtually impossible, and so considerable effort is invested in preventing the range expansion of invaders. Silver carp (<i>Hypophthalmichthys</i> molitrix) were discovered in the Mississippi River in 1981 and have spread throughout the basin. Despite their propensity to expand, the ‘leading edge’ in the Illinois River has stalled south of Chicago and has remained stable for a decade. Studies have indicated that contaminants in the Chicago Area Waterway System (CAWS) may be contributing to the lack of upstream movement, but this hypothesis has not been tested. This study used a laboratory setting to quantify the role of contaminants in deterring upstream movement of silver carp within the CAWS. For this, water was collected from the CAWS near the upstream edge of the distribution and transported to a fish culture facility. Silver carp and one native species were exposed to CAWS water, and activity, behavior, avoidance, and metabolic rates were quantified. Results showed that silver carp experience an elevated metabolic cost in CAWS water, along with reductions in swimming behavior. Together, results indicate a role for components of CAWS water at deterring range expansion.</p>","language":"English","publisher":"SpringerNature","doi":"10.1038/s41598-024-71442-y","usgsCitation":"Schneider, A.E., Esbaugh, A.J., Cupp, A.R., and Suski, C., 2024, Silver carp experience metabolic and behavioral changes when exposed to water from the Chicago Area Waterway: Scientific Reports, v. 14, 24689, 11 p., https://doi.org/10.1038/s41598-024-71442-y.","productDescription":"24689, 11 p.","ipdsId":"IP-155110","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":466817,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-71442-y","text":"Publisher Index Page"},{"id":464358,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2024-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Schneider, Amy E. 0009-0000-3486-2934","orcid":"https://orcid.org/0009-0000-3486-2934","contributorId":346389,"corporation":false,"usgs":false,"family":"Schneider","given":"Amy","email":"","middleInitial":"E.","affiliations":[{"id":36403,"text":"University of Illinois","active":true,"usgs":false}],"preferred":false,"id":918863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Esbaugh, Andrew J.","contributorId":267780,"corporation":false,"usgs":false,"family":"Esbaugh","given":"Andrew","email":"","middleInitial":"J.","affiliations":[{"id":55496,"text":"The University of Texas at Austin, Marine Science Institute, Port Aransas, TX","active":true,"usgs":false}],"preferred":false,"id":918864,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cupp, Aaron R. 0000-0001-5995-2100 acupp@usgs.gov","orcid":"https://orcid.org/0000-0001-5995-2100","contributorId":5162,"corporation":false,"usgs":true,"family":"Cupp","given":"Aaron","email":"acupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":918865,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Suski, C. D.","contributorId":190151,"corporation":false,"usgs":false,"family":"Suski","given":"C.","middleInitial":"D.","affiliations":[],"preferred":false,"id":918866,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
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