Transect-based monitoring has long been a valuable tool in ecosystem monitoring to measure multiple ecosystem attributes. The line-point intercept (LPI), vegetation height, and canopy gap intercept methods comprise a set of core methods, which provide indicators of ecosystem condition. However, users often struggle to design a sampling strategy that optimizes the ability to detect ecological change using transect-based methods. We assessed the sensitivity of each of these core methods to transect length, number, and sampling interval in 1-ha plots to determine: (1) minimum sampling required to describe ecosystem characteristics and detect change; and (2) optimal transect length and number to make recommendations for future analyses and monitoring efforts. We used data from 13 National Wind Erosion Research Network locations, including five LTAR sites, spanning the western United States, which included 151 plot sampling events over time across five biomes. We found that longer and increased replicates of transects were more important for reducing sampling error than increased sample intensity along fewer transects per plot. For all methods and indicators across biomes plots, three 100-m transects reduced sampling error such that indicator estimates fell within a 95% confidence interval of ±5% for canopy gap intercept and LPI-total foliar cover, ±5 cm for height, and ±2 species for LPI-species counts. For the same criteria at 80% confidence intervals, two 100-m transects are needed. Site-scale inference was strongly affected by sample design, consequently our understanding of ecological dynamics may be influenced by sampling decisions.
","language":"English","publisher":"American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America","doi":"10.1002/jeq2.20678","usgsCitation":"McCord, S.E., Webb, N.P., Van Zee, J.W., Courtright, E.M., Billings, B., Duniway, M.C., Edwards, B.L., Kachergis, E., Moriasi, D.N., Morra, B., Nafus, A., Newingham, B.A., Scott, D.A., and Toledo, D., 2025, Optimizing sampling across transect-based methods improves the power of agroecological monitoring data: Journal of Environmental Quality, v. 54, no. 3, p. 706-719, https://doi.org/10.1002/jeq2.20678.","productDescription":"14 p.","startPage":"706","endPage":"719","ipdsId":"IP-170564","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":498293,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jeq2.20678","text":"Publisher Index Page"},{"id":498101,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.0289560089564,\n 48.97409076449233\n ],\n [\n -121.35700711516505,\n 48.97409076449233\n ],\n [\n -121.35700711516505,\n 31.451111657107248\n ],\n [\n -97.0289560089564,\n 31.451111657107248\n ],\n [\n -97.0289560089564,\n 48.97409076449233\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"54","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"McCord, Sarah E.","contributorId":364571,"corporation":false,"usgs":false,"family":"McCord","given":"Sarah","middleInitial":"E.","affiliations":[{"id":79445,"text":"USDA-ARS Jornada Experimental Range, PO Box 30003, MSC 3JER, Las Cruces, NM, 88003, USA","active":true,"usgs":false}],"preferred":false,"id":952900,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Nicholas P.","contributorId":364574,"corporation":false,"usgs":false,"family":"Webb","given":"Nicholas","middleInitial":"P.","affiliations":[{"id":79445,"text":"USDA-ARS Jornada Experimental Range, PO Box 30003, MSC 3JER, Las Cruces, NM, 88003, USA","active":true,"usgs":false}],"preferred":false,"id":952901,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Zee, Justin W.","contributorId":364577,"corporation":false,"usgs":false,"family":"Van Zee","given":"Justin","middleInitial":"W.","affiliations":[{"id":79445,"text":"USDA-ARS Jornada Experimental Range, PO Box 30003, MSC 3JER, Las Cruces, NM, 88003, USA","active":true,"usgs":false}],"preferred":false,"id":952902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Courtright, Ericha M.","contributorId":364580,"corporation":false,"usgs":false,"family":"Courtright","given":"Ericha","middleInitial":"M.","affiliations":[{"id":79445,"text":"USDA-ARS Jornada Experimental Range, PO Box 30003, MSC 3JER, Las Cruces, NM, 88003, USA","active":true,"usgs":false}],"preferred":false,"id":952903,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Billings, Benjamin J","contributorId":169763,"corporation":false,"usgs":false,"family":"Billings","given":"Benjamin J","affiliations":[{"id":25582,"text":"Bureau of Land Management, San Luis Valley Field Office, Monte Vista, CO 81144","active":true,"usgs":false}],"preferred":false,"id":952904,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":952905,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Edwards, Brandon L.","contributorId":364583,"corporation":false,"usgs":false,"family":"Edwards","given":"Brandon","middleInitial":"L.","affiliations":[{"id":86850,"text":"USDA-ARS Jornada Experimental Range, PO Box 30003, MSC 3JER, Las Cruces, NM, 88003, USA; New Mexico State University, Jornada Experimental Range, PO Box 30003, MSC 3JER, Las Cruces, NM, 88003, USA","active":true,"usgs":false}],"preferred":false,"id":952906,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kachergis, Emily","contributorId":195930,"corporation":false,"usgs":false,"family":"Kachergis","given":"Emily","affiliations":[],"preferred":false,"id":952907,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moriasi, Daniel N","contributorId":270209,"corporation":false,"usgs":false,"family":"Moriasi","given":"Daniel","email":"","middleInitial":"N","affiliations":[{"id":56110,"text":"USDA-ARS USDA-ARS Grazinglands Research Laboratory, El Reno, OK 73036","active":true,"usgs":false}],"preferred":false,"id":952908,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Morra, Brian","contributorId":364584,"corporation":false,"usgs":false,"family":"Morra","given":"Brian","affiliations":[{"id":86853,"text":"USDA-ARS, Great Basin Rangelands Research Unit, 920 Valley Road, Reno, NV 89512, USA","active":true,"usgs":false}],"preferred":false,"id":952909,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nafus, Aleta","contributorId":167781,"corporation":false,"usgs":false,"family":"Nafus","given":"Aleta","email":"","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":true,"id":952910,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Newingham, Beth A.","contributorId":364585,"corporation":false,"usgs":false,"family":"Newingham","given":"Beth","middleInitial":"A.","affiliations":[{"id":86853,"text":"USDA-ARS, Great Basin Rangelands Research Unit, 920 Valley Road, Reno, NV 89512, USA","active":true,"usgs":false}],"preferred":false,"id":952911,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Scott, Drew A.","contributorId":364586,"corporation":false,"usgs":false,"family":"Scott","given":"Drew","middleInitial":"A.","affiliations":[{"id":86854,"text":"USDA-ARS Northern Great Plains Research Laboratory, 1701 10th Av. SW Mandan, ND 58454, USA","active":true,"usgs":false}],"preferred":false,"id":952912,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Toledo, David","contributorId":195936,"corporation":false,"usgs":false,"family":"Toledo","given":"David","email":"","affiliations":[],"preferred":false,"id":952913,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70267458,"text":"70267458 - 2025 - Stratigraphy, structure, and geomorphology of the central Appalachians across the North Mountain fault zone near Harrisonburg, Virginia, USA","interactions":[],"lastModifiedDate":"2025-05-23T14:22:38.733654","indexId":"70267458","displayToPublicDate":"2025-03-17T09:16:47","publicationYear":"2025","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Stratigraphy, structure, and geomorphology of the central Appalachians across the North Mountain fault zone near Harrisonburg, Virginia, USA","docAbstract":"This field trip focuses on the geology of the central Appalachian Valley and Ridge province near Harrisonburg, Virginia, USA. Recent geologic mapping utilizing 1-m resolution lidar data has revealed new insights into the Paleozoic stratigraphy, structural geology, and Neogene landscape evolution of the region. The detailed mapping reveals the presence of the Big Spring Station Member and multiple thrombolite zones in the Cambrian Conococheague Formation extending as far south as the Briery Branch 7.5 min quadrangle, providing insights into Late Cambrian sea-level fluctuations. Multiple outcrop exposures in the study area of this guidebook confirm recent work in Pennsylvania, USA, showing that the Ordovician Reedsville Shale overlies the Martinsburg Formation and that the two are distinct and mappable as separate formations rather than laterally equivalent units as previously interpreted. Our work extends the Silurian Williamsport Sandstone into Shenandoah County, Virginia, and describes its facies relationships with the Bloomsburg Formation along strike and across the Adams Run anticline. Mapping within the thick Devonian siliciclastic sequence reveals the presence of the Mahantango Formation on the western limb of Supin Lick syncline and illustrates its complex facies relationship with the Millboro Shale. In addition, we highlight new mapping criteria for the Brallier and Foreknobs Formations and demonstrate how the specific changes to the placement of the contact between them addresses previous challenges in their differentiation. We present cosmogenic burial ages of broad alluvial fan sediments in the Shenandoah Valley near Timberville and Briery Branch, Virginia, and erosion rates estimated for the Briery Branch stream basin. Both analyses provide new constraints on the timing of landscape evolution and karst development since the middle Pliocene. This field guide also highlights some significant structural features within the North Mountain fault zone, such as evidence of imbricated thrust sheets cut by cross-strike faults that have been exploited by Eocene igneous intrusions. Map-scale horses of Silurian and Ordovician rocks hold up ridges that are oblique to the regional strike. Deformation internal to one of these horse blocks is shown to be non-coaxial with respect to the main regional northwest directed transport.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"From the Ozark Plateaus and Arkansas River Valley to the Shenandoah Valley: Field guides for the 2025 GSA south-central and southeastern section meetings","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2025.0072(06)","usgsCitation":"Doctor, D.H., Gray, A., and Odom, W.E., 2025, Stratigraphy, structure, and geomorphology of the central Appalachians across the North Mountain fault zone near Harrisonburg, Virginia, USA, chap. of From the Ozark Plateaus and Arkansas River Valley to the Shenandoah Valley: Field guides for the 2025 GSA south-central and southeastern section meetings, v. 72, p. 93-142, https://doi.org/10.1130/2025.0072(06).","productDescription":"50 p.","startPage":"93","endPage":"142","ipdsId":"IP-175153","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":486501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","city":"Harrisonburg","otherGeospatial":"North Mountain fault zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.5,\n 39\n ],\n [\n -79.5,\n 38\n ],\n [\n -78.5,\n 38\n ],\n [\n -78.5,\n 39\n ],\n [\n -79.5,\n 39\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"72","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":938298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gray, Alexander Addison 0009-0008-3071-2179","orcid":"https://orcid.org/0009-0008-3071-2179","contributorId":350945,"corporation":false,"usgs":true,"family":"Gray","given":"Alexander Addison","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":938299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Odom, William Elijah 0000-0001-8577-5056","orcid":"https://orcid.org/0000-0001-8577-5056","contributorId":292616,"corporation":false,"usgs":true,"family":"Odom","given":"William","email":"","middleInitial":"Elijah","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":938300,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70274725,"text":"70274725 - 2025 - Ordovician stratigraphy, structure, and karst of the Falling Spring Valley, Alleghany County, Virginia, USA","interactions":[],"lastModifiedDate":"2026-04-08T14:35:10.261331","indexId":"70274725","displayToPublicDate":"2025-03-17T09:14:01","publicationYear":"2025","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Ordovician stratigraphy, structure, and karst of the Falling Spring Valley, Alleghany County, Virginia, USA","docAbstract":"This one-day trip highlights new findings on a preliminary bedrock geologic map that shows results from ongoing geologic mapping in the Falling Spring Valley of Alleghany County, Virginia, USA, which is the southern end of the larger Warm Springs Valley, an elongated anticlinal valley rimmed by Ordovician and Silurian siliciclastic rocks, and which is famous for its thermal springs. This mapping includes stratigraphic, structural, and karst field and lab research focused on the Ordovician strata exposed in the area, the oldest of which is the dolomitic upper part of the Beekmantown Formation (Lower Ordovician, Darriwilian), and the youngest of which is the Juniata Formation (Upper Ordovician, Katian), a sequence of siliciclastic redbeds. Warm Springs Valley is the location of the only known caves in the eastern United States—three at present—with thermal waters flowing in some of their passages. Stops on the trip will highlight key details from mapping efforts, primarily within the structurally deformed Ordovician carbonate sequence that is exposed in the core and limbs of the anticline, as well as the associated karst features that are developed in those carbonate rocks, including results of recent dye traces and water temperature monitoring that have improved our understanding of the karst hydrogeologic systems developed in these strata.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"From the Ozark Plateaus and Arkansas River Valley to the Shenandoah Valley: Field guides for the 2025 Southeastern and South-Central Section Meetings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2025.0072(05)","usgsCitation":"Haynes, J.T., Lambert, R.A., Martin, D.C., Orndorff, R.C., and Parker, M., 2025, Ordovician stratigraphy, structure, and karst of the Falling Spring Valley, Alleghany County, Virginia, USA, chap. of From the Ozark Plateaus and Arkansas River Valley to the Shenandoah Valley: Field guides for the 2025 Southeastern and South-Central Section Meetings, v. 72, p. 69-91, https://doi.org/10.1130/2025.0072(05).","productDescription":"23 p.","startPage":"69","endPage":"91","ipdsId":"IP-175668","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":502268,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","county":"Alleghany County","otherGeospatial":"Falling Spring Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.875,\n 38\n ],\n [\n -80,\n 38\n ],\n [\n -80,\n 37.75\n ],\n [\n -79.875,\n 37.75\n ],\n [\n -79.875,\n 38\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"72","noUsgsAuthors":false,"publicationDate":"2025-03-17","publicationStatus":"PW","contributors":{"editors":[{"text":"Admassu, Yonathan","contributorId":369433,"corporation":false,"usgs":false,"family":"Admassu","given":"Yonathan","affiliations":[],"preferred":false,"id":958958,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Garcia, Ángel","contributorId":369434,"corporation":false,"usgs":false,"family":"Garcia","given":"Ángel","affiliations":[],"preferred":false,"id":958959,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Hutto, Richard","contributorId":369435,"corporation":false,"usgs":false,"family":"Hutto","given":"Richard","affiliations":[],"preferred":false,"id":958960,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Haynes, John T.","contributorId":369314,"corporation":false,"usgs":false,"family":"Haynes","given":"John","middleInitial":"T.","affiliations":[{"id":16809,"text":"James Madison University","active":true,"usgs":false}],"preferred":false,"id":958859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lambert, Richard A.","contributorId":369315,"corporation":false,"usgs":false,"family":"Lambert","given":"Richard","middleInitial":"A.","affiliations":[{"id":87760,"text":"Warm Springs Anticline Cave Survey","active":true,"usgs":false}],"preferred":false,"id":958860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Delbert C.","contributorId":369316,"corporation":false,"usgs":false,"family":"Martin","given":"Delbert","middleInitial":"C.","affiliations":[{"id":87760,"text":"Warm Springs Anticline Cave Survey","active":true,"usgs":false}],"preferred":false,"id":958861,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Orndorff, Randall C. 0000-0002-8956-5803 rorndorf@usgs.gov","orcid":"https://orcid.org/0000-0002-8956-5803","contributorId":2739,"corporation":false,"usgs":true,"family":"Orndorff","given":"Randall","email":"rorndorf@usgs.gov","middleInitial":"C.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":958862,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Parker, Mercer 0000-0001-6683-6458 mercerparker@usgs.gov","orcid":"https://orcid.org/0000-0001-6683-6458","contributorId":203174,"corporation":false,"usgs":true,"family":"Parker","given":"Mercer","email":"mercerparker@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":958863,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70270732,"text":"70270732 - 2025 - Being loud to find a quiet bird: Surveying a secretive tropical avian species","interactions":[],"lastModifiedDate":"2025-08-22T15:49:41.151091","indexId":"70270732","displayToPublicDate":"2025-03-17T08:40:30","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1185,"text":"Caribbean Journal of Science","active":true,"publicationSubtype":{"id":10}},"title":"Being loud to find a quiet bird: Surveying a secretive tropical avian species","docAbstract":"Secretive birds are hard to detect, and thus, likely underestimated when surveyed, potentially preventing measures to protect them. We identified a sampling period and method that would yield the most reliable estimates of population numbers of the secretive Puerto Rican Lizard-Cuckoo (Coccyzus vieilloti). We addressed these objectives by comparing point counts (passive) and playback broadcast (active) survey methods from January to December, 2022 at Cambalache State Forest, Puerto Rico. We surveyed 20 stations (radius = 50 m each) between sunrise and 10 am, employing the time-of-detection method. We recorded strong responses from C. vieilloti after birds had been stimulated by playback broadcasts (recapture probabilities), corresponding to a behavioral response (Mb). For example, average capture (p) and recapture (c) probabilities within the peak breeding season for active surveys were 0.23 ± 0.10 and 0.49 ±0.08, respectively. Active surveys yielded higher estimates of C. vieilloti compared to passive surveys. For example, active survey expected population numbers (15.6 ha or area sampled) during peak breeding season were 42.87 ± 12.08 compared to 19.60 ± 2.08 individuals from passive surveys. Coefficients of variation population estimates of active surveys ranged between 10 and 16%, with one exception (28%), well within acceptable levels for wildlife studies. We show that secretive species like C. vieilloti can be reliably surveyed using playback broadcasts. Surveys should be conducted during the peak breeding season when estimates are higher and have better precision. Conducting similar tests on other secretive species could ensure that their status is not mischaracterized and that they receive the benefits of appropriate conservation measures, if warranted.
","language":"English","publisher":"BioOne","doi":"10.18475/cjos.v55i1.a7","usgsCitation":"Rodriquez-Rivera, K.X., Puente Rolon, A.R., and Collazo, J.A., 2025, Being loud to find a quiet bird: Surveying a secretive tropical avian species: Caribbean Journal of Science, v. 55, no. 1, p. 54-64, https://doi.org/10.18475/cjos.v55i1.a7.","productDescription":"11 p.","startPage":"54","endPage":"64","ipdsId":"IP-170207","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494529,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Cambalache State Forest, Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -66.675152143636,\n 18.47801185830444\n ],\n [\n -66.675152143636,\n 18.457140105969017\n ],\n [\n -66.61164345711724,\n 18.457140105969017\n ],\n [\n -66.61164345711724,\n 18.47801185830444\n ],\n [\n -66.675152143636,\n 18.47801185830444\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"55","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rodriquez-Rivera, Kenneth X.","contributorId":360269,"corporation":false,"usgs":false,"family":"Rodriquez-Rivera","given":"Kenneth","middleInitial":"X.","affiliations":[{"id":38462,"text":"University of Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":946923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Puente Rolon, Alberto R.","contributorId":360272,"corporation":false,"usgs":false,"family":"Puente Rolon","given":"Alberto","middleInitial":"R.","affiliations":[{"id":38462,"text":"University of Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":946924,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":946925,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70264626,"text":"70264626 - 2025 - Evaluating the potential to quantify salmon habitat via UAS-based particle image velocimetry","interactions":[],"lastModifiedDate":"2025-03-18T16:47:13.564948","indexId":"70264626","displayToPublicDate":"2025-03-16T11:34:04","publicationYear":"2025","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":"Evaluating the potential to quantify salmon habitat via UAS-based particle image velocimetry","docAbstract":"Continuous, high-resolution data for characterizing freshwater habitat conditions can support successful management of endangered salmonids. Uncrewed aircraft systems (UAS) make acquiring such fine-scale data along river channels more feasible, but workflows for quantifying reach-scale salmon habitats are lacking. We evaluated the potential for UAS-based mapping of hydraulic habitats using spectrally based depth retrieval and particle image velocimetry (PIV) by comparing these methods to a more well-established flow modeling approach. Our results indicated that estimates of water depth, depth-averaged velocity, and flow direction derived via remote sensing and modeling techniques were comparable and in good agreement with field measurements. Predictions of spring-run Chinook salmon (Oncorhynchus tshawytscha) juvenile rearing habitat produced from PIV and model output were similar, with small errors relative to direct field observations. Estimates of hydraulic heterogeneity based on kinetic energy gradients in the flow field were generally consistent between PIV and flow modeling, but errors relative to field measurements were larger. PIV results were sensitive to the velocity index (α) used to convert surface velocities to depth-averaged velocities. Sun glint precluded PIV analysis along the margins of some images and a large degree of overlap between frames was thus required to obtain continuous coverage of the reach. Similarly, shadows cast by riparian vegetation caused gaps in spectrally based bathymetric maps. Despite these limitations, our results suggest that for sites with sufficient water surface texture, UAS-based PIV can provide detailed hydraulic habitat information at the reach scale, with accuracies comparable to traditional field methods and multidimensional flow modeling.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR038045","usgsCitation":"Harrison, L.R., Legleiter, C.J., Overstreet, B., and White, J., 2025, Evaluating the potential to quantify salmon habitat via UAS-based particle image velocimetry: Water Resources Research, v. 3, no. 61, e2024WR038045, 21 p., https://doi.org/10.1029/2024WR038045.","productDescription":"e2024WR038045, 21 p.","ipdsId":"IP-163184","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":488333,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr038045","text":"Publisher Index Page"},{"id":483481,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"North Santiam River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.01923518970605,\n 44.68226153883313\n ],\n [\n -122.36818028867839,\n 44.68226153883313\n ],\n [\n -122.36818028867839,\n 44.857748774184074\n ],\n [\n -123.01923518970605,\n 44.857748774184074\n ],\n [\n -123.01923518970605,\n 44.68226153883313\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"61","noUsgsAuthors":false,"publicationDate":"2025-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Harrison, Lee R.","contributorId":174322,"corporation":false,"usgs":false,"family":"Harrison","given":"Lee","email":"","middleInitial":"R.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":930994,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":930995,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overstreet, Brandon 0000-0001-7845-6671 boverstreet@usgs.gov","orcid":"https://orcid.org/0000-0001-7845-6671","contributorId":169201,"corporation":false,"usgs":true,"family":"Overstreet","given":"Brandon","email":"boverstreet@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930996,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, James 0000-0002-7255-3785 jameswhite@usgs.gov","orcid":"https://orcid.org/0000-0002-7255-3785","contributorId":193492,"corporation":false,"usgs":true,"family":"White","given":"James","email":"jameswhite@usgs.gov","affiliations":[],"preferred":true,"id":930997,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70264651,"text":"70264651 - 2025 - Dynamic baseflow storage estimates and the role of topography, geology and evapotranspiration on streamflow recession characteristics in the Neversink Reservoir Watershed, New York","interactions":[],"lastModifiedDate":"2025-03-18T16:31:27.341468","indexId":"70264651","displayToPublicDate":"2025-03-15T11:10:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic baseflow storage estimates and the role of topography, geology and evapotranspiration on streamflow recession characteristics in the Neversink Reservoir Watershed, New York","docAbstract":"Estimates of dynamic groundwater volumes supplying baseflow to streams are important for water availability projections during extended periods of drought. The primary goals of this study were to provide dynamic storage volume estimates, inferred from streamflow recession analysis, for baseflow regimes within seven gaged catchments within the Neversink Reservoir Watershed (NRW), a critical municipal water source for New York City. Additionally, geomorphological properties, surficial geology and hydro-meteorological processes were quantified and described in relation to time and spatially variable recession behaviour and storage estimates across the NRW. To explore these relationships, we (1) evaluated seasonal trends in streamflow recession behaviour in relation to modelled potential evapotranspiration (PET) and catchment runoff rates, (2) derived empirical streamflow models for cool-season runoff using both linear and nonlinear reservoir assumptions for baseflow and (3) calculated metrics related to the geology and geomorphology of each catchment and compared these metrics to area normalised baseflow dynamic storage estimates. Results show that baseflow recession behaves as a nonlinear reservoir, and applying linear groundwater reservoir assumptions may underestimate the total dynamic storage volumes compared to what would be predicted for a nonlinear reservoir. Increases in PET caused decreases in storage conditions that resulted in increased recession rates and nonlinearity in streamflow recession during the growing season. Additionally, we found that while no single physical catchment characteristic solely predicted catchment storage dynamics, sediment volume and stream gradients were stronger predictors of normalised storage volumes than catchment surface area or surface topography alone. Within the NRW, catchments with the highest sediment volume exhibited the lowest recession rates and higher dynamic storage volumes, while the smallest catchment, mostly devoid of sediment, had the fastest recession rate and lowest dynamic storage volume.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70106","usgsCitation":"Benton, J., and Doctor, D.H., 2025, Dynamic baseflow storage estimates and the role of topography, geology and evapotranspiration on streamflow recession characteristics in the Neversink Reservoir Watershed, New York: Hydrological Processes, v. 39, no. 3, e70106, 17 p., https://doi.org/10.1002/hyp.70106.","productDescription":"e70106, 17 p.","ipdsId":"IP-163356","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":483480,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Neversink Reservoir Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.95860919620127,\n 42.2147005922842\n ],\n [\n -74.56712312527952,\n 42.2147005922842\n ],\n [\n -74.56712312527952,\n 41.956641367777735\n ],\n [\n -73.95860919620127,\n 41.956641367777735\n ],\n [\n -73.95860919620127,\n 42.2147005922842\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Benton, Joshua R. 0000-0002-1698-6455","orcid":"https://orcid.org/0000-0002-1698-6455","contributorId":352387,"corporation":false,"usgs":false,"family":"Benton","given":"Joshua R.","affiliations":[{"id":84197,"text":"**please fill in","active":true,"usgs":false},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":false,"id":931072,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":931073,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70264474,"text":"sir20255011 - 2025 - Comparison of hydrologic data and water budgets between 2003–08 and 2018–23 for the eastern part of the Arbuckle-Simpson aquifer, south-central Oklahoma","interactions":[],"lastModifiedDate":"2025-07-23T17:07:13.510916","indexId":"sir20255011","displayToPublicDate":"2025-03-14T15:26:55","publicationYear":"2025","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":"2025-5011","displayTitle":"Comparison of Hydrologic Data and Water Budgets Between 2003–08 and 2018–23 for the Eastern Part of the Arbuckle-Simpson Aquifer, South-Central Oklahoma","title":"Comparison of hydrologic data and water budgets between 2003–08 and 2018–23 for the eastern part of the Arbuckle-Simpson aquifer, south-central Oklahoma","docAbstract":"The Arbuckle-Simpson aquifer is divided spatially into three parts (eastern, central, and western). The largest groundwater withdrawals are from the eastern part of the Arbuckle-Simpson aquifer, which provides water to approximately 39,000 people in Ada and Sulphur, Oklahoma, and surrounding areas. The Arbuckle-Simpson aquifer, including the eastern part, is designated a sole source aquifer for its service area. Based primarily on data collected between 2003 and 2008, a series of comprehensive hydrologic studies of the Arbuckle-Simpson aquifer was published to provide the information necessary to perform groundwater-flow model simulations so that the Oklahoma Water Resources Board could determine how much water could be withdrawn from the aquifer while maintaining flow to springs and streams. As part of the Phase 1 studies, an aquifer water budget was developed from a numerical model for the period 2003–08. For this report, Phase 1 refers to the 2003–08 data collection period, although for some of the analyses, data collected prior to 2003 were used to inform model development work. Allocation of water from this aquifer was then established by the Oklahoma Water Resources Board in 2013. Additional well-spacing rules were also established by the Oklahoma Water Resources Board for sensitive sole source groundwater basins. To determine how the water budget for the eastern part of the Arbuckle-Simpson aquifer has changed over time, recently collected hydrologic data (2018–23) were compared to data collected during 2003–08. The analysis of changes in the aquifer water budget from 2003–08 to 2018–23 could help resource managers better understand changes in the overall balance of water in storage and the potential effects on streamflow, changes in groundwater levels, and the effects of different water uses in the aquifer area on available water in the eastern part of the Arbuckle-Simpson aquifer and streams overlying the eastern part of the Arbuckle-Simpson aquifer.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"The U.S. Geological Survey, in cooperation with the Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and the Wyoming Water Development Office, has developed standard methods of peak-flow frequency analysis for studies in Montana, North Dakota, South Dakota, and Wyoming. These methods describe the implementation of national flood frequency guidelines described in Bulletin 17C (https://doi.org/10.3133/tm4B5) for the four States and deviations from Bulletin 17C standard procedures to accommodate unusual hydrologic conditions. A U.S. Geological Survey data release accompanying this report (https://doi.org/10.5066/P1WHRK8H) provides example peak-flow frequency analyses for selected streamgages in the study area. The methods described in this report can be used to publish similar data releases for other streamgages in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255019","collaboration":"Prepared in cooperation with the Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and Wyoming Water Development Office","usgsCitation":"Siefken, S.A., Williams-Sether, T., Barth, N.A., Chase, K.J., and Cedar Face, M.A., 2025, Methods for peak-flow frequency analysis for streamgages in or near Montana, North Dakota, South Dakota, and Wyoming: U.S. Geological Survey Scientific Investigations Report 2025–5019, 19 p., https://doi.org/10.3133/sir20255019.","productDescription":"Report: vii, 19 p.; Data Release; Dataset","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-148119","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":483328,"rank":4,"type":{"id":34,"text":"Image 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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225-0046
The U.S. Geological Survey assessed the Quaternary evolution of Seven Mile Island, New Jersey, to quantify coastal sediment availability, which is crucial for establishing sediment budgets, understanding sediment dispersal, and managing coastlines. This report presents preliminary interpretations of seismic profiles, maps of Holocene sand thickness from the shoreline to 2 kilometers offshore, and tables quantifying the volume of available sediment along the coastal margin based on data collected during 2021 and 2022. The results reveal spatial variability in the thickness and cross-shore extent of Holocene sand. The study area was separated into northern, central, and southern zones by using underlying stratigraphy and geomorphic features. The characteristics and spatial extent of the Holocene sand deposit indicate that hydrodynamic processes contribute to its spatial variability. Northern Seven Mile Island contains the thickest deposits of Holocene sand that were formed by sediment bypass around the Townsends Inlet ebb-tidal delta. Specifically, swash bars have welded to the updrift end of Seven Mile Island and have formed thick deposits of Holocene sand that thicken landward and taper seaward. Despite their thickness, these deposits have the smallest cross-shore extent; therefore, northern Seven Mile Island has the smallest volume of Holocene sand of the three geomorphic zones. Central Seven Mile Island has the thinnest Holocene sand deposits because this section of the barrier island is outside the influence of ebb-tidal deltas. Southern Seven Mile Island has the greatest volumes of Holocene sand because of increased accommodation and deposition adjacent to the Hereford Inlet ebb-tidal delta. Even though tidal inlets exert variable influence on the three geomorphic zones, sediment is distributed fairly uniformly within each geomorphic zone; each of the three zones contains 31.05–36.48 percent of the volume of available Holocene sand.
Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Contact Us- USGS Publications Warehouse
","tableOfContents":"The U.S. Geological Survey, in cooperation with the Suffolk County Water Authority, evaluated the aquifer transmissivity and storage properties at the Alvahs Lane well field north of the village of Cutchogue, New York. This analysis of aquifer properties provides the Suffolk County Water Authority with hydrogeologic information needed to develop water supplies to meet the increasing water demands of the residents of Suffolk County, New York.
An aquifer test was conducted at the Alvahs Lane well field from October 18 through October 21, 2022, when a production well was pumped at 550 gallons per minute for about 24 hours, and groundwater-level drawdown and recovery were measured in two monitoring wells. The three wells are screened in a glaciofluvial aquifer under unconfined (water table) conditions. Drawdown and recovery data were analyzed with an analytical solution for partial penetration and delayed yield in an unconfined aquifer to provide estimates of the glaciofluvial aquifer properties. Inclusion of lateral aquifer boundaries was not necessary for the analysis to result in satisfactory matches with the observed water-level responses. Aquifer transmissivity was estimated at 32,000 feet squared per day. Assuming a saturated aquifer thickness of 120 feet, this result is equivalent to a horizontal hydraulic conductivity value of 270 feet per day. Specific yield was estimated at 0.15 (dimensionless). The estimated properties are consistent with those of a highly transmissive unconfined aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245128","collaboration":"Prepared in cooperation with the Suffolk County Water Authority","usgsCitation":"Misut, P.E., 2025, Analysis of aquifer framework and properties, Alvahs Lane well field, Cutchogue, New York: U.S. Geological Survey Scientific Investigations Report 2024–5128, 9 p., https://doi.org/10.3133/sir20245128.","productDescription":"iv, 9 p.","numberOfPages":"9","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154731","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":492784,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118489.htm","linkFileType":{"id":5,"text":"html"}},{"id":483275,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5128/images/"},{"id":483274,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5128/sir20245128.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5128 XML"},{"id":483273,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/preview/sir20245128/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5128 HTML"},{"id":483272,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5128/sir20245128.pdf","text":"Report","size":"2.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5128 PDF"},{"id":483271,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5128/coverthb.jpg"}],"country":"United States","state":"New York","city":"Cutchogue","otherGeospatial":"Alvahs Lane well field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.5463729228096,\n 41.03435851242847\n ],\n [\n -72.5463729228096,\n 40.987047126294755\n ],\n [\n -72.46023151369637,\n 40.987047126294755\n ],\n [\n -72.46023151369637,\n 41.03435851242847\n ],\n [\n -72.5463729228096,\n 41.03435851242847\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Excessive nitrogen discharge is a major concern for the Long Island Sound. Programs have been implemented to reduce point sources of nitrogen to the sound, but little is known about the nonpoint sources. This study aims to better understand the current groundwater contributions of nitrogen from nonpoint sources in the Long Island Sound watershed.
During the spring and summer of 2022, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, collected water-quality samples to analyze nutrients (nitrogen and phosphorus), chloride, and bromide at 45 stations in the Long Island Sound watershed in Connecticut, New York, and Rhode Island. The stations were in small drainage watersheds (5 to 30 square kilometers) in the southern part of the Long Island Sound watershed. During two separate synoptic sampling events, water-quality samples and instantaneous streamflow measurements were collected under base-flow conditions (where the streamflow is dominated by groundwater inputs rather than overland flow or runoff flow). One sampling event was in the nongrowing season (April 24–25, 2022), and the other was in the growing season (June 30–July 1, 2022). To calculate instantaneous nitrogen loads and yields, streamflow was measured at the time of sample collection.
Nitrogen concentrations, loads, and yields varied among sampling stations and by season. Total filtered nitrogen concentrations were generally lower in the nongrowing season (from less than 0.14 to 1.9 milligrams per liter) than in the growing season (from less than 0.23 to 3.0 milligrams per liter). Nitrate plus nitrite concentrations showed little variation between the nongrowing and growing seasons. Unfiltered ammonia plus organic nitrogen concentrations were generally lower in the nongrowing season (from less than 0.07 to 0.83 milligram per liter) than in the growing season (from 0.11 to 0.98 milligram per liter). In contrast, total filtered and unfiltered nitrogen loads and yields were higher in the nongrowing season than during the growing season, likely because streamflows were higher during the nongrowing season. Total unfiltered nitrogen yields during the nongrowing season ranged from less than 0.15 to 5.0 kilograms per square kilometer per day. Total unfiltered nitrogen yields during the growing season ranged from less than 0.12 to 2.5 kilograms per square kilometer per day. Total filtered nitrogen yields during the nongrowing season ranged from less than 0.13 to 5.2 kilograms per square kilometer per day. Total filtered nitrogen yields during the growing season ranged from less than 0.06 to 2.5 kilograms per square kilometer per day.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1206","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Laabs, K.L., Barclay, J.R., and Mullaney, J.R., 2025, Base-flow sampling to enhance understanding of the groundwater flow component of nitrogen loading in small watersheds draining into Long Island Sound: U.S. Geological Survey Data Report 1206, 23 p., https://doi.org/10.3133/dr1206.","productDescription":"Report: v, 23 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-161925","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":492783,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118484.htm","linkFileType":{"id":5,"text":"html"}},{"id":483188,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1206/full","linkFileType":{"id":5,"text":"html"},"description":"DR 1206 HTML"},{"id":483186,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1206/coverthb.jpg"},{"id":483187,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1206/dr1206.pdf","text":"Report","size":"7.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1206 PDF"},{"id":483189,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1206/dr1206.XML","linkFileType":{"id":8,"text":"xml"},"description":"DR 1206 XML"},{"id":483190,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1206/images/"},{"id":483191,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99IAXUI","text":"USGS data release","linkHelpText":"Nitrogen loads, yields, and associated field data collected during baseflow conditions and site attributes for small basins draining to Long Island Sound"}],"country":"United States","state":"Connecticut, New York, Rhode Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.91277546117404,\n 40.875888202775144\n ],\n [\n -73.5833363685162,\n 40.98693216297761\n ],\n [\n -72.40741106161018,\n 41.2504046305547\n ],\n [\n -71.4646381344502,\n 41.35702020040523\n ],\n [\n -71.40893145316154,\n 41.57350712794573\n ],\n [\n -71.49014822027934,\n 41.724945464467424\n ],\n [\n -72.43277552970126,\n 41.78157938825299\n ],\n [\n -72.757106016586,\n 41.53934120640511\n ],\n [\n -73.08115837726446,\n 41.93608782758082\n ],\n [\n -73.30909534441503,\n 41.8870827192506\n ],\n [\n -73.51694453574096,\n 41.73992349657644\n ],\n [\n -73.50192428145502,\n 41.280885844515296\n ],\n [\n -73.88228764759546,\n 40.963994488494365\n ],\n [\n -73.91277546117404,\n 40.875888202775144\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean conventional resources of 47 million barrels of oil and 876 billion cubic feet of gas in upper Paleozoic reservoirs of the Wind River Basin, Bighorn Basin, and Powder River Basin Provinces.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20243049","programNote":"National and Global Petroleum Assessment","usgsCitation":"Schenk, C.J., Mercier, T.J., Le, P.A., Cicero, A.D., Drake, R.M., II, Gelman, S.E., Hearon, J.S., Johnson, B.G., Lagesse, J.H., Leathers-Miller, H.M., and Timm, K.K., 2025, Assessment of undiscovered conventional oil and gas resources in upper Paleozoic reservoirs of the Wind River Basin, Bighorn Basin, and Powder River Basin Provinces, 2024: U.S. Geological Survey Fact Sheet 2024–3049, 4 p., https://doi.org/10.3133/fs20243049.","productDescription":"Report: 4 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-155711","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":492782,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118483.htm","linkFileType":{"id":5,"text":"html"}},{"id":482979,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95BL3S4","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project—Paleozoic Reservoirs of the Wind River Basin, Big Horn Basin, and Powder River Basin Provinces: Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables"},{"id":482978,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3049/fs20243049.pdf","text":"Report","size":"796 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3049"},{"id":482977,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3049/coverthb.jpg"},{"id":483263,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3049/fs20243049.xml"},{"id":483262,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3049/images"},{"id":483287,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243049/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Montana, Nebraska, South Dakota, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.58694947776809,\n 45.44307137234259\n ],\n [\n -110.58694947776809,\n 42.18474590335302\n ],\n [\n -103.24001465931977,\n 42.18474590335302\n ],\n [\n -103.24001465931977,\n 45.44307137234259\n ],\n [\n -110.58694947776809,\n 45.44307137234259\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
In June 2022, a historic flood event occurred in the headwaters of the Yellowstone River Basin. The flood resulted in millions of dollars in damages and substantial interruptions to Yellowstone National Park. The 2022 flood event was substantially higher in magnitude than other high-peak flow events over the last 30 years. The high discharge was primarily due to the combination of hydrologic mechanisms initiated by rain-on-snow, including a high-elevation snowpack that peaked later than average. However, the contributions of each hydrologic driver, rain and snow, have not been quantified and could be important for understanding future flood events in the region. The contribution of snowmelt to the total terrestrial water input (TWI) varied throughout the area, yet was concentrated in the headwaters of the Yellowstone, Stillwater, and Boulder rivers, along with the headwaters of Rock Creek in Wyoming and Montana. The primary atmospheric contributions to the TWI during the 2022 event were precipitation from moisture transported from the Pacific Ocean that converged over the Greater Yellowstone Area (GYA) and snowmelt from residual snowpack in the northeast part of Yellowstone National Park.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70099","usgsCitation":"Giovando, J., Reis, W., Zhang, W., and Barth, N.A., 2025, Hydrologic mechanisms for 2022 Yellowstone River flood and comparisons to recent historic floods: Hydrological Processes, v. 39, no. 3, e70099, 10 p., https://doi.org/10.1002/hyp.70099.","productDescription":"e70099, 10 p.","ipdsId":"IP-164578","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":483479,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Greater Yellowstone area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.8645863474894,\n 45.53668572000356\n ],\n [\n -111.8645863474894,\n 43.909029192960446\n ],\n [\n -108.86233230449172,\n 43.909029192960446\n ],\n [\n -108.86233230449172,\n 45.53668572000356\n ],\n [\n -111.8645863474894,\n 45.53668572000356\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Giovando, Jeremy","contributorId":352388,"corporation":false,"usgs":false,"family":"Giovando","given":"Jeremy","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":931074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reis, Wyatt","contributorId":352389,"corporation":false,"usgs":false,"family":"Reis","given":"Wyatt","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":931075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Wei","contributorId":352390,"corporation":false,"usgs":false,"family":"Zhang","given":"Wei","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":931076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barth, Nancy A. 0000-0002-7060-8244 nabarth@usgs.gov","orcid":"https://orcid.org/0000-0002-7060-8244","contributorId":298020,"corporation":false,"usgs":true,"family":"Barth","given":"Nancy","email":"nabarth@usgs.gov","middleInitial":"A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":931077,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273052,"text":"70273052 - 2025 - Evidence of red fox (Vulpes vulpes) depredating a Saltmarsh Sparrow (Ammospiza caudacuta) nest","interactions":[],"lastModifiedDate":"2025-12-15T14:48:17.221433","indexId":"70273052","displayToPublicDate":"2025-03-13T10:58:35","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7509,"text":"The Wilson Journal of Ornithology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evidence of red fox (Vulpes vulpes) depredating a Saltmarsh Sparrow (Ammospiza caudacuta) nest","title":"Evidence of red fox (Vulpes vulpes) depredating a Saltmarsh Sparrow (Ammospiza caudacuta) nest","docAbstract":"Saltmarsh Sparrows (Ammospiza caudacuta), a tidal-marsh specialist, face severe population declines due to habitat loss, sea-level rise, and predation. While previous research suggests that predation pressure increases at the southern extent of the species’ breeding range, data on local predator communities remain limited. To address this, we deployed game cameras at 16 Saltmarsh Sparrow nests across four salt marshes on Virginia’s eastern shore, the southern-most extent of their breeding range. Our study provides camera-documented evidence of red fox (Vulpes vulpes) predation on Saltmarsh Sparrow nests. We detected a suspected predation event by white-tailed deer (Odocoileus virginianus) and four other potential nest predators. Additionally, we detected a Willet (Tringa semipalmata) aggressively displacing a nesting female, suggesting interspecific interactions may contribute to nest failure.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15594491.2025.2535832","usgsCitation":"Re, B., Karpanty, S.M., and Hunter, E.A., 2025, Evidence of red fox (Vulpes vulpes) depredating a Saltmarsh Sparrow (Ammospiza caudacuta) nest: The Wilson Journal of Ornithology, v. 137, no. 4, p. 647-654, https://doi.org/10.1080/15594491.2025.2535832.","productDescription":"8 p.","startPage":"647","endPage":"654","ipdsId":"IP-176752","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":497494,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.22610873430784,\n 38.20480113736551\n ],\n [\n -77.22610873430784,\n 36.57433812738637\n ],\n [\n -76.01620307129923,\n 36.57433812738637\n ],\n [\n -76.01620307129923,\n 38.20480113736551\n ],\n [\n -77.22610873430784,\n 38.20480113736551\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"137","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Re, Bridget","contributorId":364008,"corporation":false,"usgs":false,"family":"Re","given":"Bridget","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":952164,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karpanty, Sarah M.","contributorId":364009,"corporation":false,"usgs":false,"family":"Karpanty","given":"Sarah","middleInitial":"M.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":952165,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":952166,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70264582,"text":"70264582 - 2025 - Overwinter survival of an estuarine resident fish (Fundulus heteroclitus) in North Carolina salt marsh creeks","interactions":[],"lastModifiedDate":"2025-08-19T15:28:24.757921","indexId":"70264582","displayToPublicDate":"2025-03-13T10:03:11","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20503,"text":"Journal of Fish of Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Overwinter survival of an estuarine resident fish (Fundulus heteroclitus) in North Carolina salt marsh creeks","title":"Overwinter survival of an estuarine resident fish (Fundulus heteroclitus) in North Carolina salt marsh creeks","docAbstract":"The mummichog Fundulus heteroclitus is a trophically important fish inhabiting Atlantic coastal salt marshes, with few in situ estimates of overwinter survival throughout the species range. We estimated overwinter apparent survival rates of F. heteroclitus at the approximate mid-latitudinal species range [coastal North Carolina (USA)] in four tidal creeks that experience variable winter water temperatures. To estimate apparent survival, we fitted a Cormack-Jolly-Seber model to daily mark-resight data autonomously obtained from fish marked with passive integrated transponder tags. Creek, year, mean daily water temperature, change in mean daily temperature, fish length and fish condition were considered for effects on the modelled parameters: apparent survival (Φ) (product of true survival and site fidelity) and detection probability (p). Modelling showed that water temperature and fish metrics were not related to Φ. Water temperature was directly related to p, indicating reduced fish activity and thus reduced detection probability or poor antenna detection performance at low temperatures. Creek was related to Φ and p, and the creek most open to its downstream estuary (lacking a culvert) had lower rates than the others. Greater loss (fish mortality plus emigration) in this one creek may more effectively transfer production of F. heteroclitus to larger waterbodies via emigration or predation. Conversely, lower Φ may reflect reduced detection efficiency. The results suggest that F. heteroclitus survival is insensitive to variable winter water temperatures typical of thermal dynamics in shallow estuaries in this region of its range. Median creek-specific overwinter Φ rates (range of median values, 2 × 10−8, 0.04) were roughly equal to previously published rates for these creeks during the growing season (April–October). At these latitudes and with increasingly moderate winters, the results indicate that natural mortality could arise equally or more so from predation during the growing season than mechanisms such as starvation, direct mortality, thermal morbidity and stress-related susceptibility to predation resulting from intermittently low water temperatures during the overwinter season.
","language":"English","publisher":"Wiley","doi":"10.1111/jfb.70020","usgsCitation":"Rudershausen, P.J., and O'Donnell, M.J., 2025, Overwinter survival of an estuarine resident fish (Fundulus heteroclitus) in North Carolina salt marsh creeks: Journal of Fish of Biology, v. 107, no. 1, p. 188-200, https://doi.org/10.1111/jfb.70020.","productDescription":"13 p.","startPage":"188","endPage":"200","ipdsId":"IP-168105","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":488323,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jfb.70020","text":"Publisher Index Page"},{"id":483454,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.61589538108844,\n 34.739737828981234\n ],\n [\n -76.80342045072118,\n 34.74057409854757\n ],\n [\n -76.80219234343787,\n 34.68681714274115\n ],\n [\n -76.61593931817359,\n 34.68548980814643\n ],\n [\n -76.61589538108844,\n 34.739737828981234\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"107","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Rudershausen, P. J.","contributorId":352331,"corporation":false,"usgs":false,"family":"Rudershausen","given":"P.","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":930816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O'Donnell, Matthew J. 0000-0002-9089-2377","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":295467,"corporation":false,"usgs":true,"family":"O'Donnell","given":"Matthew","middleInitial":"J.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":930817,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70264664,"text":"70264664 - 2025 - Exposure of wild mammals inhabiting Alaska to influenza A(H5N1) virus","interactions":[],"lastModifiedDate":"2025-03-26T16:09:33.212626","indexId":"70264664","displayToPublicDate":"2025-03-13T09:40:29","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1493,"text":"Emerging Infectious Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Exposure of wild mammals inhabiting Alaska to influenza A(H5N1) virus","docAbstract":"Serum samples from wild mammals inhabiting Alaska, USA, showed that 4 species, including Ursus arctos bears and Vulpes vulpes foxes, were exposed to influenza A(H5N1) viruses. Results indicated some mammals in Alaska survived H5N1 virus infection. Surveillance efforts may be improved by incorporating information on susceptibility and detectable immune responses among wild mammals.
","language":"English","publisher":"U.S. Centers for Disease Control and Prevention","doi":"10.3201/eid3104.241002","usgsCitation":"Ramey, A.M., Beckmen, K., Saafeld, D., Nicholson, K., Mangipane, B.A., Scott, L.C., Stallknecht, D., and Poulson, R., 2025, Exposure of wild mammals inhabiting Alaska to influenza A(H5N1) virus: Emerging Infectious Diseases, v. 31, no. 4, p. 804-808, https://doi.org/10.3201/eid3104.241002.","productDescription":"5 p.","startPage":"804","endPage":"808","ipdsId":"IP-167599","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":488689,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3201/eid3104.241002","text":"Publisher Index Page"},{"id":483521,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":931165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beckmen, Kimberlee B. 0000-0003-0747-7883","orcid":"https://orcid.org/0000-0003-0747-7883","contributorId":347502,"corporation":false,"usgs":false,"family":"Beckmen","given":"Kimberlee B.","affiliations":[],"preferred":false,"id":931166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saafeld, David T.","contributorId":347504,"corporation":false,"usgs":false,"family":"Saafeld","given":"David T.","affiliations":[],"preferred":false,"id":931167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nicholson, Kerry 0000-0001-9951-9897","orcid":"https://orcid.org/0000-0001-9951-9897","contributorId":329033,"corporation":false,"usgs":false,"family":"Nicholson","given":"Kerry","email":"","affiliations":[{"id":50377,"text":"Alaska Dept of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":931168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mangipane, Buck A.","contributorId":288781,"corporation":false,"usgs":false,"family":"Mangipane","given":"Buck","email":"","middleInitial":"A.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":931169,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scott, Laura Celeste 0000-0003-0303-5340","orcid":"https://orcid.org/0000-0003-0303-5340","contributorId":306143,"corporation":false,"usgs":true,"family":"Scott","given":"Laura","email":"","middleInitial":"Celeste","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":931170,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stallknecht, David E.","contributorId":225107,"corporation":false,"usgs":false,"family":"Stallknecht","given":"David E.","affiliations":[{"id":36701,"text":"Southeastern Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":931171,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Poulson, Rebecca L.","contributorId":198807,"corporation":false,"usgs":false,"family":"Poulson","given":"Rebecca L.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":931172,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70269914,"text":"70269914 - 2025 - Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change","interactions":[],"lastModifiedDate":"2025-08-07T17:05:49.242218","indexId":"70269914","displayToPublicDate":"2025-03-13T09:31:49","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change","docAbstract":"1. Population ecology and biogeography applications often necessitate the transfer of models across spatial and/or temporal dimensions to make predictions outside the bounds of the data used for model fitting. However, ecological data are often spatiotemporally unbalanced such that the spatial or the temporal dimension tends to contain more data than the other. This unbalance frequently leads model transfers to become substitutions, which are predictions to a different dimension than the predictive model was built on. Despite the prevalence of substitutions in ecology, studies validating their performance and their underlying assumptions are scarce.
2. Here, we present a successful case study demonstrating both space-for-time and time-for-space substitutions using emperor penguins (Aptenodytes forsteri) as the focal species. Using abundance-based species distribution models (aSDM) of adult emperor penguins in attendance during spring across 50 colonies, we predict long-term annual fluctuations in fledgling abundance and breeding success at a single colony, Pointe Géologie. Subsequently, we construct statistical models from time series of extended counts on Pointe Géologie to predict average fledgling abundance across 50 colonies.
3. Our analysis reveals that distance to nearest open water (NOW) exhibits the strongest association with both temporal and spatial data. aSDM’s space-for-time substitution performance, as measured by Pearson correlation coefficient was 0.63 and 0.56 when predicting breeding success and fledgling abundance time series, respectively. Linear regression of fledgling abundance on NOW yields similar time-for-space substitution performance when predicting abundance distribution of emperor penguin colonies with a correlation coefficient of 0.58.
4. We posit that such space-time equivalence arises because: 1) emperor penguins colonies conform to their existing fundamental niche; 2) there is not yet any environmental novelty when comparing the spatial vs temporal variation of distance to nearest open water; and 3) models of more specific components of life histories, such as fledgling abundance, rather than occurrence or total population abundance, are more transferable. Identifying these conditions empirically can enhance the qualitative validation of substitutions in cases where direct validation data are lacking.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.70025","usgsCitation":"Şen, B., Che-Castaldo, C., LaRue, M., Krumhardt, K., Landrum, L., Holland, M., Lynch, H., Delord, K., Barbraud, C., and Jenouvrier, S., 2025, Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change: Journal of Animal Ecology, v. 94, no. 5, p. 932-942, https://doi.org/10.1111/1365-2656.70025.","productDescription":"11 p.","startPage":"932","endPage":"942","ipdsId":"IP-170330","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":496436,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.70025","text":"Publisher Index Page"},{"id":493730,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Pointe Géologie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 154.44140675588028,\n -67.88310899484881\n ],\n [\n 154.44140675588028,\n -70.08242540623479\n ],\n [\n 161.81002372233837,\n -70.08242540623479\n ],\n [\n 161.81002372233837,\n -67.88310899484881\n ],\n [\n 154.44140675588028,\n -67.88310899484881\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"94","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Şen, Bilgecan","contributorId":359058,"corporation":false,"usgs":false,"family":"Şen","given":"Bilgecan","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":944928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Che-Castaldo, Christian Joseph 0000-0002-7670-2178","orcid":"https://orcid.org/0000-0002-7670-2178","contributorId":347906,"corporation":false,"usgs":true,"family":"Che-Castaldo","given":"Christian Joseph","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":944929,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaRue, Michelle A.","contributorId":348627,"corporation":false,"usgs":false,"family":"LaRue","given":"Michelle A.","affiliations":[{"id":37172,"text":"University of Canterbury","active":true,"usgs":false}],"preferred":false,"id":944930,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krumhardt, Kristen M.","contributorId":359059,"corporation":false,"usgs":false,"family":"Krumhardt","given":"Kristen M.","affiliations":[{"id":85742,"text":"NSF National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":944931,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Landrum, Laura","contributorId":359060,"corporation":false,"usgs":false,"family":"Landrum","given":"Laura","affiliations":[{"id":85742,"text":"NSF National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":944932,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holland, Marika M.","contributorId":359062,"corporation":false,"usgs":false,"family":"Holland","given":"Marika M.","affiliations":[{"id":85742,"text":"NSF National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":944933,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lynch, Heather J.","contributorId":347911,"corporation":false,"usgs":false,"family":"Lynch","given":"Heather J.","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":944934,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Delord, Karine 0000-0001-6720-951X","orcid":"https://orcid.org/0000-0001-6720-951X","contributorId":197702,"corporation":false,"usgs":false,"family":"Delord","given":"Karine","email":"","affiliations":[],"preferred":false,"id":944935,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Barbraud, Christophe","contributorId":197701,"corporation":false,"usgs":false,"family":"Barbraud","given":"Christophe","email":"","affiliations":[],"preferred":false,"id":944936,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jenouvrier, Stéphanie","contributorId":359063,"corporation":false,"usgs":false,"family":"Jenouvrier","given":"Stéphanie","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":944937,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70264400,"text":"70264400 - 2025 - MTAB 111, March 2025","interactions":[],"lastModifiedDate":"2025-03-14T14:08:10.828956","indexId":"70264400","displayToPublicDate":"2025-03-13T09:05:42","publicationYear":"2025","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":13451,"text":"Memo to All Banders (MTAB)","active":true,"publicationSubtype":{"id":30}},"title":"MTAB 111, March 2025","docAbstract":"This Memo to All Banders (MTAB 111) was released in March 2025. Subjects in this this memo are 1. The Chief’s Chirp; 2. Alerts – Highly Pathogenic Avian Influenza; 3. Staff updates – celebrating Karen Jone’s remarkable career and retirement, meeting reports and a field trip; 4. News – BandIt end of life! (starting February 1st, 2025 the BBL will no longer be accepting BandIt files), Notes From the Field: Black-bellied Whistling Ducks, Longevity records update, ABA Bird of the Year the Common Loon, EESC signs partnership with Audubon Society, and what 100 years of USGS bird monitoring data tells us about hummingbirds; 5. A note from the permitting shelves – changes to the application process for new master personal or station permits and don’t wait to submit authorization requests; 6. A note from the supply room – best practices for band supply; 7. Data management – banding data submission for birds released from rehabilitation; 8. Frequently asked questions – I had to replace a federal metal band or auxiliary marker, how should I submit this data to the BBL? 9. Banding and encounter highlights; 10. Message to the Flyways; 11. Recent literature; 12. Moments in history; 13. Upcoming events; and 14. Request for information.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Harvey, K., and McKay, J.L., 2025, MTAB 111, March 2025: Memo to All Banders (MTAB), 14 p.","productDescription":"14 p.","ipdsId":"IP-176859","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":483335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":483311,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/media/files/mtab-111-march-2025"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harvey, Kyra 0000-0003-4781-1874","orcid":"https://orcid.org/0000-0003-4781-1874","contributorId":296250,"corporation":false,"usgs":true,"family":"Harvey","given":"Kyra","email":"","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":930646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKay, Jennifer L. 0000-0002-8893-0231","orcid":"https://orcid.org/0000-0002-8893-0231","contributorId":296562,"corporation":false,"usgs":true,"family":"McKay","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":930751,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70266046,"text":"70266046 - 2025 - Movements and habitat use of Silver Carp in the Arkansas and White rivers","interactions":[],"lastModifiedDate":"2025-04-24T14:57:58.143549","indexId":"70266046","displayToPublicDate":"2025-03-13T00:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Movements and habitat use of Silver Carp in the Arkansas and White rivers","docAbstract":"Silver Carp Hypophthalmichthys molitrix is an invasive species found throughout the Mississippi River basin. Efforts have been made to control Silver Carp populations through removal programs and movement barrier implementation. Up to date information on diel, seasonal, and annual movements and habitat use by Silver Carp will benefit these efforts. Studies of Silver Carp movement are prevalent in the upper Mississippi River, Ohio River, and tributaries, but rare in tributaries of the lower Mississippi River. Between June 2021 and May 2022, we quantified average movement rates and residency periods of 48 Silver Carp in the free-flowing lower White River and lock-and-dam fragmented lower Arkansas River using passive acoustic telemetry arrays and internal implant acoustic transmitters. We also manually tracked Silver Carp in the two rivers during the four seasons to estimate diel movement rates and use of different habitats. On an annual scale, Silver Carp in the White River moved at faster rates than Silver Carp in the Arkansas River and were recorded more times by acoustic receivers. Diel movement rates varied by season in both rivers but were low overall. Silver Carp used lentic habitats more often than lotic habitats. Overall, results suggest the numerous locks and dams of the McClellan-Kerr Arkansas River Navigation System may limit large-scale, annual movement of Silver Carp in the Arkansas River compared to the White River. Low hourly diel movement rates and high occupancy of lentic habitats also should enable effective harvest of Silver Carp using active gears in those lentic habitats.
","language":"English","publisher":"Allen Press","doi":"10.3996/jfwm-23-066","usgsCitation":"Althoff, A., Kindschuh, J., Lochmann, S., Owens, D., Spurgeon, J.J., and Stevens, J., 2025, Movements and habitat use of Silver Carp in the Arkansas and White rivers: Journal of Fish and Wildlife Management, v. 15, no. 2, p. 493-509, https://doi.org/10.3996/jfwm-23-066.","productDescription":"17 p.","startPage":"493","endPage":"509","ipdsId":"IP-155044","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":490100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-23-066","text":"Publisher Index Page"},{"id":484978,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Arkansas River, White 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of climate change on fish and wildlife species in the United States","indexId":"ofr20211104","publicationYear":"2022","noYear":false,"title":"Effects of climate change on fish and wildlife species in the United States"},"id":1}],"isPartOf":{"id":70228323,"text":"ofr20211104 - 2022 - Effects of climate change on fish and wildlife species in the United States","indexId":"ofr20211104","publicationYear":"2022","noYear":false,"title":"Effects of climate change on fish and wildlife species in the United States"},"lastModifiedDate":"2025-07-23T16:56:40.604898","indexId":"ofr20211104F","displayToPublicDate":"2025-03-12T15:03:12","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1104","chapter":"F","displayTitle":"Potential Effects of Sea Level Rise and High Tide Flooding on Laterallus jamaicensis jamaicensis (Eastern Black Rail) Coastal Breeding Areas","title":"Potential effects of sea level rise and high tide flooding on Laterallus jamaicensis jamaicensis (eastern black rail) coastal breeding areas","docAbstract":"Laterallus jamaicensis jamaicensis (eastern black rails; Gmelin, 1789) are facing increasing risk from flooding in coastal breeding habitats because of rising sea levels combined with standard high tide flooding. In this report, we examine regional differences in relative rates of sea level rise, days in the breeding season above historical high tide flooding thresholds, future inundation of current (2021) emergent wetlands, and potential marsh resiliency for the breeding distribution of the eastern black rail across the Atlantic and U.S. Gulf coasts. By midcentury (2050), two sea level rise scenarios (intermediate low and intermediate) indicate that areas analyzed in Texas and the Mid-Atlantic will experience at least minor flood levels for more than half of the breeding season. By the end of the century (2100), all tidal gages in the Atlantic and U.S. Gulf coasts are projected to experience at least moderate flood levels for most of the current (April–September) eastern black rail breeding season. In some areas like New Jersey, this translates to inundation for most of the emergent wetlands in the representative parishes and counties analyzed in this report. In other parts of the coastal distribution, estimates of increases in inundation are lower or more variable, stemming from differences in the elevation of existing emergent marsh, especially at the herbaceous wetland/woody wetland transition zone. Sea level rise and tidal flooding are not projected to pose an equal risk across the coastal distribution of the eastern black rail, leading to variation in risk of nest loss because of flooding. The degree to which these wetlands and birds will adapt to changing sea level and salinity depends on a range of factors including future expansion of developed areas and the ability of marsh areas to move inland. Restoration and active management of coastal wetland areas may be necessary to maintain appropriate breeding habitat.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211104F","usgsCitation":"Nikiel, C.A., and Lyons, M.P., 2025, Potential effects of sea level rise and high tide flooding on Laterallus jamaicensis jamaicensis (eastern black rail) coastal breeding areas: U.S. Geological Survey Open-File Report 2021–1104–F, 40 p., https://doi.org/10.3133/ofr20211104F.","productDescription":"vii, 40 p.","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-172341","costCenters":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":483246,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211104F/full"},{"id":483244,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1104/f/ofr20211104f.XML"},{"id":483243,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1104/f/ofr20211104f.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1104-F"},{"id":483242,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1104/f/coverthb.jpg"},{"id":483245,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1104/f/images/"},{"id":492780,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118482.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","otherGeospatial":"Atlantic Coast, Gulf Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -66.73369411465748,\n 44.87861692762931\n ],\n [\n -67.9395715369188,\n 45.91302389167819\n ],\n [\n -68.52891205677287,\n 46.14233382856975\n ],\n [\n -69.66609739851674,\n 44.904840367756236\n ],\n [\n -71.8455845133013,\n 43.01632555370077\n ],\n [\n -75.76042364090121,\n 40.73258218965182\n ],\n [\n -77.41422284991526,\n 39.32302567933269\n ],\n [\n -77.11953264135055,\n 36.58955309284522\n ],\n [\n -79.0153316043617,\n 34.430624116007294\n ],\n [\n -82.3380440342791,\n 31.342877305678\n ],\n [\n -87.14213042595998,\n 31.473505392669267\n ],\n [\n -87.74548189802837,\n 32.39195075385649\n ],\n [\n -89.00813665013129,\n 32.354083649747\n ],\n [\n -89.30755056658234,\n 31.271920212222028\n ],\n [\n -92.00443786768639,\n 31.222304379727817\n ],\n [\n -95.98918412828937,\n 30.505582509882984\n ],\n [\n -98.19210772787893,\n 28.060885389466677\n ],\n [\n -98.10578570219339,\n 26.094479941263742\n ],\n [\n -97.05626247225148,\n 25.880720528127156\n ],\n [\n -97.21103649173618,\n 27.232424454623654\n ],\n [\n -95.67794712232511,\n 28.598718054492764\n ],\n [\n -93.37262411904577,\n 29.53246193068931\n ],\n [\n -90.09201823404798,\n 28.868540112379208\n ],\n [\n -88.84955647397418,\n 28.787680093703898\n ],\n [\n -88.58294319870708,\n 29.755309328858473\n ],\n [\n -86.26626920949336,\n 30.023219777079703\n ],\n [\n -84.98796116952035,\n 29.29748111241001\n ],\n [\n -83.97817016398578,\n 29.677155423717906\n ],\n [\n -82.92684471060598,\n 28.711279338050616\n ],\n [\n -83.33453111961292,\n 28.007674975959418\n ],\n [\n -81.38398690858428,\n 24.942184191892167\n ],\n [\n -81.48743115456318,\n 24.146104924998653\n ],\n [\n -79.34128084180263,\n 25.993764942081356\n ],\n [\n -81.14405051637162,\n 30.851402909345737\n ],\n [\n -74.95193417969506,\n 35.431852837245344\n ],\n [\n -75.10789103469754,\n 37.38614632113877\n ],\n [\n -73.3440560833726,\n 40.10806605773732\n ],\n [\n -69.15888557248354,\n 41.691566830140545\n ],\n [\n -70.25945555475435,\n 43.01474979449651\n ],\n [\n -66.73369411465748,\n 44.87861692762931\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Midwest Climate Adaptation Science Center
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Drought and its effect on streamflow are important to understand because of the potential to adversely affect water supply, agricultural production, and ecological conditions. The Red River of the North Basin in north-central United States and central Canada is susceptible to dry conditions. During an extended drought, streamflow conditions in the Red River of the North may become inadequate to support existing water supply needs in the basin for agriculture, industry, human use, and aquatic life. To understand potential future low-streamflow conditions in the Red River of the North Basin, the U.S. Geological Survey, in cooperation with the International Joint Commission, North Dakota Department of Water Resources, Red River Joint Water Resource District, and Red River Watershed Management Board, developed a water-balance model of the Red River of the North Basin upstream from Emerson, Manitoba, Canada, and coupled the model with stochastic weather inputs to simulate possible future low-streamflow conditions.
Historical changes in low-streamflow conditions were characterized across the Red River of the North Basin using multiple change-point analysis for 12 streamgages. Across these stations, significant change-point years in 1943 and 1994 marked increases in the magnitude of low-streamflow conditions. During 1920–2015, conversion of primary land (not affected by human use) to agricultural and secondary land was followed by a conversion from smalls grains to corn and soybeans as the dominant crop type. From land-use analysis, 1940–2000 was determined to have relatively stable land use and therefore was used as the calibration period for the water-balance model.
A deterministic water-balance model was developed for the Red River of the North Basin upstream from Emerson, Manitoba. The water-balance model was calibrated with data from 37 U.S. Geological Survey streamgages for 1940–2000 and verified using data for 2001–15. The calibrated water-balance model simulated streamflow distributions that mirrored the seasonal patterns of the observed mean monthly streamflow and the standard deviation of the monthly streamflow data, especially during the fall and winter months when streamflow was lowest. For the verification period, during the low-streamflow months of December through January, the difference between simulated and observed data was similar to the calibration comparison and successfully reproduced seasonal trends in the distribution of streamflow, even when using weather data that were outside the calibration period.
To determine the future risk of low-streamflow conditions in the Red River of the North Basin, a block-bootstrap method was used to generate multiple possible future climates. These stochastically generated weather time series were then input to a water-balance model to simulate a distribution of possible streamflows. Three sets of experiments were performed, with each experiment containing a set of scenarios. The first set of experiments from the stochastic streamflow model were designed to investigate how changes in reservoir management would affect the distribution of low streamflow. Relative to scenario 1 (present-day [2023] reservoir operation), scenario 2 (no reservoir operation) shifted the low-streamflow frequency curves downward, reducing the annual minimum monthly streamflow for the Emerson subbasin. Subbasins were defined by the contributing area upstream from a selected streamgage station. Relative to scenario 1, scenario 3 (regulated streamflow with an increased reservoir capacity of 10 percent) shifted the low-streamflow frequency curves upward for the Emerson subbasin. The magnitude of this upward shift, caused by increased reservoir capacity, was lower than the magnitude of the shift caused by the absence of the reservoirs, which indicates that the streamflow was most affected when the reservoirs were first constructed.
The second set of experiments from the stochastic streamflow model included two scenarios that were performed to better understand how the Red River of the North Basin responds to long periods of low or high precipitation. The results indicate that the model consistently overestimated streamflow, but the relative change between a wet and dry climate state of simulated streamflow distribution reasonably matched the relative change of historical streamflow. Across the subbasins, the model was most accurate for low-streamflow conditions associated with nonexceedance probabilities between 20 and 40 percent.
The third set of experiments from the stochastic streamflow model were done to investigate low-streamflow response across the basin to several drought events. Low-end streamflow was reduced when the basin was exposed to a drought, and the magnitude of the reduction increased with longer or more intense droughts. Compared to the low-intensity drought scenarios, the range of percent reductions (as indicated by the interquartile range) was larger for the high-intensity drought scenarios for all subbasins, and the subbasins of Grand Forks and Emerson had a smaller range of reductions compared to the other three subbasins. The larger drainage area—combined with the large contribution of the Red Lake River and several other Minnesota tributaries that generally experience wetter climate conditions—upstream from the Emerson and Grand Forks subbasins may contribute to the smaller range in reductions under the high intensity scenarios. Comparison of the percent reduction in low-end streamflow among subbasins also indicated that the effects of drought duration and intensity could be cumulative. Combining factors of time and intensity produced a larger reduction in streamflow than when each effect was isolated. The array of drought scenarios can be used to determine how a subbasin would respond to multiple possible future conditions. Based on climate predictions, the drought scenario that best matches a future anticipated drought scenario can be used to estimate a low streamflow response for a given subbasin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255002","collaboration":"Prepared in cooperation with the International Joint Commission, North Dakota Department of Water Resources, Red River Joint Water Resource District, and Red River Watershed Management Board","usgsCitation":"Redoloza, F.S., Glas, R.L., Nustad, R.A., and Ryberg, K.R., 2025, Evaluating drought risk of the Red River of the North Basin using historical and stochastic streamflow upstream from Emerson, Manitoba: U.S. Geological Survey Scientific Investigations Report 2025–5002, 58 p., https://doi.org/10.3133/sir20255002.","productDescription":"Report: viii, 58 p.; Data Release; Dataset","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-160450","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":492779,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118481.htm","linkFileType":{"id":5,"text":"html"}},{"id":483206,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13XEN8U","text":"USGS data release","linkHelpText":"Red River of the North low flow water-balance model and supporting data"},{"id":483203,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5002/sir20255002.XML"},{"id":483202,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5002/sir20255002.pdf","text":"Report","size":"9.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5002"},{"id":483201,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5002/coverthb.jpg"},{"id":483204,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5002/images/"},{"id":483207,"rank":7,"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":483205,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255002/full"}],"country":"Canada, United States","state":"Manitoba, Minnesota, North Dakota","otherGeospatial":"Red River of the North Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.08881710576394,\n 49.037592614274644\n ],\n [\n -97.61189514667993,\n 49.237246321777064\n ],\n [\n -97.98430773681044,\n 49.25792746144549\n ],\n [\n -98.47156466708684,\n 49.30311814027007\n ],\n [\n -98.18869515177104,\n 48.806510092341696\n ],\n [\n -98.92510212635796,\n 48.563256003263746\n ],\n [\n -98.41426650704827,\n 48.10818045779425\n ],\n [\n -97.49283622534884,\n 48.05240253638604\n ],\n [\n -97.3523048621935,\n 47.017146908752025\n ],\n [\n -97.10429716720503,\n 45.72316825195222\n ],\n [\n -95.52291599413297,\n 45.59412827523792\n ],\n [\n -94.77240339220764,\n 46.995377381657505\n ],\n [\n -94.76454577568065,\n 47.45566932791883\n ],\n [\n -93.68493211560182,\n 47.83688445747143\n ],\n [\n -94.11770692662546,\n 48.33322840096503\n ],\n [\n -95.73784376997673,\n 48.92757937919353\n ],\n [\n -97.08881710576394,\n 49.037592614274644\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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This report addresses system characterization of the Environmental Mapping and Analysis Program hyperspectral sensor by the DLR (German Aerospace Center, ground segment project management), GFZ (Deutsches Geoforschungszentrum, science lead) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the EnMAP hyperspectral sensor; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), and radiometric performances of the EnMAP hyperspectral sensor. Results of these analyses indicate that the Environmental Mapping and Analysis Program has a band-to-band geometric performance in the range of −0.135 to 0.15 pixel, geometric performance relative to the Operational Land Imager in the range of −27.716 meters (−0.92 pixel) to 32.892 meters (1.09 pixels) offset in comparison to Landsat 8 Operational Land Imager, offset of a radiometric comparison in the range of −0.012 to 0.020, slope of a radiometric comparison in the range of 0.947 to 1.031.
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1. Patterns of migratory connectivity are increasingly used to understand and manage threats throughout the annual cycle of migratory species. Strong migratory connectivity refers to when individuals from different populations remain spatially separated across the annual cycle, which may expose populations to unique sets of threats and conditions that cause differential population trends. However, the populations or groups used for species’ management are often defined a priori based on expert knowledge and/or management units, which may mask important population segregation and obscure differential population trends and their drivers.
2. We compared three approaches to defining management groups of a declining shorebird, the long-billed curlew (Numenius americanus), for annual cycle management: by expert-opinion, according to management flyways, and with unsupervised clustering of satellite tracking data that maximizes the strength of migratory connectivity.
3. Despite the curlews having a continuous breeding range and a pattern of parallel migration, all three approaches identified groups with different population trends, movement behaviours and habitat selection across the annual cycle, suggesting these are meaningful ecological groups. The expert and clustering approaches resulted in similar group structure, strong estimates of migratory connectivity (measured as MC = 0.64 across seasons), movement behaviour and habitat selection; however, the expert approach identified an additional divide between the easternmost grouping, which revealed strongly negative population trends in the group occupying the Chihuahuan desert during the stationary nonbreeding season. In contrast, the flyway delineation resulted in weaker estimates of migratory connectivity, marginal differences in population trends and less between-group differences in movement behaviour and habitat selection.
4. Synthesis and applications. Using measurements of migratory connectivity in concert with expert opinion can define ecologically distinct groups for wildlife management that differ in the environmental conditions they experience across seasons of the annual cycle, which is a key component for understanding and reversing declines of migratory species.
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The U.S. Geological Survey has a new decadal-scale coastal change assessment product that synthesizes nearly two dozen coastal datasets. A supervised machine-learning framework is used to combine existing datasets that describe the landscape and the hazards that affect it to determine the coastal change likelihood (CCL) in the coming decade at a resolution of 10 m per pixel for the NE United States from Maine to Virginia. Here, results from a series of statistical tests conducted on source data, the supervised classification, and the CCL outcomes as compared with historical land-cover change are presented. The overall accuracy of the aggregated land-cover dataset that serves as the foundation to which other source datasets are appended is 94%. The supervised learning classification that determines the final CCL output has an overall accuracy of 92%. The CCL predictions of high expected coastal change were consistent with 95% of the coastal and low-elevation landscape change in the last 20 years, as recorded by the Coastal Change Analysis Program land-cover change atlas. Results suggest that CCL provides accurate estimates of coastal landscape change in the next decade that are consistent with recent observed change. Additionally, best practices for applying CCL for planning purposes are outlined, and citing limitations, knowledge gaps, and opportunities for improved accuracy and further investigation are considered.
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