Marine turbidite paleoseismology relies on the assumption of synchronous triggering of turbidity currents by earthquake shaking to infer rupture extent and recurrence. Such inference commonly depends on age dating and correlation of the physical stratigraphy of deposits carried by turbidity currents (i.e., turbidites) across great distances. Along the Cascadia subduction zone, which lies offshore the Pacific Northwest, USA, turbidite facies in core photographs, X-ray computed tomography images, and magnetic susceptibility (MS) data exhibit differences in character over relatively short distances, which implies that not all deposits can be correlated with confidence. Thus, subjective correlation based on expected similarity over great distances and weak age constraints does not independently support paleoseismic models. We present a new method for correlating turbidites along the Cascadia margin that can yield a more objective and repeatable stratigraphic framework to underpin earthquake recurrence. We use dynamic time warping to correlate MS logs and measure correlation coefficients of core pairs to evaluate correlation strength. We then compare these measures to a distribution of correlation coefficients of randomly generated turbidite sequences and find that only a small number of core pairs can be correlated more confidently than randomly stacked turbidites. This methodology promises a more robust correlation strategy for future stratigraphic studies.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B37343.1","usgsCitation":"Nieminski, N.M., Sylvester, Z., Covault, J., Gomberg, J.S., Staisch, L.M., and McBrearty, I., 2025, Turbidite correlation for paleoseismology: Geological Society of America Bulletin, v. 137, no. 1-2, p. 29-40, https://doi.org/10.1130/B37343.1.","productDescription":"12 p.","startPage":"29","endPage":"40","ipdsId":"IP-155495","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":481038,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/b37343.1","text":"Publisher Index Page"},{"id":480831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.26453789074651,\n 48.071035359833985\n ],\n [\n -127.39602874177069,\n 48.071035359833985\n ],\n [\n -127.39602874177069,\n 41.99234863119753\n ],\n [\n -123.26453789074651,\n 41.99234863119753\n ],\n [\n -123.26453789074651,\n 48.071035359833985\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"137","issue":"1-2","noUsgsAuthors":false,"publicationDate":"2024-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Nieminski, Nora M.","contributorId":216510,"corporation":false,"usgs":false,"family":"Nieminski","given":"Nora","email":"","middleInitial":"M.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":924622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sylvester, Zoltan","contributorId":349708,"corporation":false,"usgs":false,"family":"Sylvester","given":"Zoltan","affiliations":[{"id":13603,"text":"University of Texas, Austin","active":true,"usgs":false}],"preferred":false,"id":924623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Covault, Jake","contributorId":349709,"corporation":false,"usgs":false,"family":"Covault","given":"Jake","affiliations":[{"id":13603,"text":"University of Texas, Austin","active":true,"usgs":false}],"preferred":false,"id":924624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924625,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Staisch, Lydia M. 0000-0002-1414-5994 lstaisch@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-5994","contributorId":167068,"corporation":false,"usgs":true,"family":"Staisch","given":"Lydia","email":"lstaisch@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924626,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McBrearty, Ian","contributorId":242933,"corporation":false,"usgs":false,"family":"McBrearty","given":"Ian","email":"","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":924627,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271395,"text":"70271395 - 2025 - 3D Dynamic rupture modeling of the 6 February 2023, Kahramanmaraş, Turkey Mw 7.8 and 7.7 earthquake doublet using early observations","interactions":[],"lastModifiedDate":"2025-09-11T14:30:00.058998","indexId":"70271395","displayToPublicDate":"2023-12-01T09:22:28","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"displayTitle":"3D Dynamic rupture modeling of the 6 February 2023, Kahramanmaraş, Turkey Mw 7.8 and 7.7 earthquake doublet using early observations","title":"3D Dynamic rupture modeling of the 6 February 2023, Kahramanmaraş, Turkey Mw 7.8 and 7.7 earthquake doublet using early observations","docAbstract":"The 2023 Turkey earthquake sequence involved unexpected ruptures across numerous fault segments. We present 3D dynamic rupture simulations to illuminate the complex dynamics of the earthquake doublet. Our models are constrained by observations available within days of the sequence and deliver timely, mechanically consistent explanations of the unforeseen rupture paths, diverse rupture speeds, multiple slip episodes, heterogeneous fault offsets, locally strong shaking, and fault system interactions. Our simulations link both earthquakes, matching geodetic and seismic observations and reconciling regional seismotectonics, rupture dynamics, and ground motions of a fault system represented by 10 curved dipping segments and embedded in a heterogeneous stress field. The Mw 7.8 earthquake features delayed backward branching from a steeply branching splay fault, not requiring supershear speeds. The asymmetrical dynamics of the distinct, bilateral Mw 7.7 earthquake are explained by heterogeneous fault strength, prestress orientation, fracture energy, and static stress changes from the previous earthquake. Our models explain the northward deviation of its eastern rupture and the minimal slip observed on the Sürgü fault. 3D dynamic rupture scenarios can elucidate unexpected observations shortly after major earthquakes, providing timely insights for data‐driven analysis and hazard assessment toward a comprehensive, physically consistent understanding of the mechanics of multifault systems.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230028","collaboration":"Scripps Institution of Oceanography at UCSD; LMU Munich","usgsCitation":"Gabriel, A., Ulrich, T., Marchandon, M., Biemiller, J.B., and Rekoske, J., 2025, 3D Dynamic rupture modeling of the 6 February 2023, Kahramanmaraş, Turkey Mw 7.8 and 7.7 earthquake doublet using early observations: The Seismic Record, v. 3, no. 4, p. 342-356, https://doi.org/10.1785/0320230028.","productDescription":"15 p.","startPage":"342","endPage":"356","ipdsId":"IP-156921","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":495364,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230028","text":"Publisher Index Page"},{"id":495309,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Turkey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 25.222044480407874,\n 41.70234913656259\n ],\n [\n 25.222044480407874,\n 35.876326576331095\n ],\n [\n 38.29831189096615,\n 35.876326576331095\n ],\n [\n 38.29831189096615,\n 41.70234913656259\n ],\n [\n 25.222044480407874,\n 41.70234913656259\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Gabriel, Alice-Agnes","contributorId":204611,"corporation":false,"usgs":false,"family":"Gabriel","given":"Alice-Agnes","email":"","affiliations":[{"id":36958,"text":"LMU Munich, Germany","active":true,"usgs":false}],"preferred":false,"id":948365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ulrich, Thomas","contributorId":204613,"corporation":false,"usgs":false,"family":"Ulrich","given":"Thomas","email":"","affiliations":[{"id":36958,"text":"LMU Munich, Germany","active":true,"usgs":false}],"preferred":false,"id":948366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marchandon, Mathilde","contributorId":361195,"corporation":false,"usgs":false,"family":"Marchandon","given":"Mathilde","affiliations":[{"id":78422,"text":"LMU Munich","active":true,"usgs":false}],"preferred":false,"id":948367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Biemiller, James Burkhardt 0000-0001-6663-7811","orcid":"https://orcid.org/0000-0001-6663-7811","contributorId":343684,"corporation":false,"usgs":true,"family":"Biemiller","given":"James","email":"","middleInitial":"Burkhardt","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":948368,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rekoske, John","contributorId":361197,"corporation":false,"usgs":false,"family":"Rekoske","given":"John","affiliations":[{"id":39679,"text":"Scripps Institution of Oceanography, UCSD","active":true,"usgs":false}],"preferred":false,"id":948369,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247925,"text":"70247925 - 2025 - A new genomic resource to enable standardized surveys of SNPs across the native range of brook trout (Salvelinus fontinalis)","interactions":[],"lastModifiedDate":"2025-06-12T15:16:30.011322","indexId":"70247925","displayToPublicDate":"2023-08-16T07:14:53","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"A new genomic resource to enable standardized surveys of SNPs across the native range of brook trout (Salvelinus fontinalis)","docAbstract":"Understanding how genetic diversity is distributed across spatiotemporal scales in species of conservation or management concern is critical for identifying large-scale mechanisms affecting local conservation status and implementing large-scale biodiversity monitoring programmes. However, cross-scale surveys of genetic diversity are often impractical within single studies, and combining datasets to increase spatiotemporal coverage is frequently impeded by using different sets of molecular markers. Recently developed molecular tools make surveys based on standardized single-nucleotide polymorphism (SNP) panels more feasible than ever, but require existing genomic information. Here, we conduct the first survey of genome-wide SNPs across the native range of brook trout (Salvelinus fontinalis), a cold-adapted species that has been the focus of considerable conservation and management effort across eastern North America. Our dataset can be leveraged to easily design SNP panels that allow datasets to be combined for large-scale analyses. We performed restriction site-associated DNA sequencing for wild brook trout from 82 locations spanning much of the native range and domestic brook trout from 24 hatchery strains used in stocking efforts. We identified over 24,000 SNPs distributed throughout the brook trout genome. We explored the ability of these SNPs to resolve relationships across spatial scales, including population structure and hatchery admixture. Our dataset captures a wide spectrum of genetic diversity in native brook trout, offering a valuable resource for developing SNP panels. We highlight potential applications of this resource with the goal of increasing the integration of genomic information into decision-making for brook trout and other species of conservation or management concern.
","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.13853","usgsCitation":"Mamoozadeh, N., Whiteley, A., Letcher, B., Kazyak, D.C., Tarsa, C., and Meek, M.H., 2025, A new genomic resource to enable standardized surveys of SNPs across the native range of brook trout (Salvelinus fontinalis): Molecular Ecology Resources, v. 25, no. 5, e13853, 20 p., https://doi.org/10.1111/1755-0998.13853.","productDescription":"e13853, 20 p.","ipdsId":"IP-131140","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":420112,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":442416,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1755-0998.13853","text":"Publisher Index Page"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.6786836933223,\n 56.166976346152666\n ],\n [\n -94.14468702070491,\n 47.08307817034964\n ],\n [\n -92.51056863816888,\n 42.91191021947495\n ],\n [\n -87.00436086872412,\n 44.654738247987865\n ],\n [\n -84.32559114929909,\n 45.085175130714475\n ],\n [\n -82.36615994752714,\n 43.52800793410151\n ],\n [\n -83.10427467833833,\n 41.587627550354966\n ],\n [\n -80.69647601966305,\n 36.368563831439815\n ],\n [\n -73.71195238176415,\n 38.28746421536982\n ],\n [\n -67.66146011605736,\n 42.24542777289187\n ],\n [\n -59.367465848527615,\n 45.80532589653012\n ],\n [\n -51.975361569808285,\n 46.90758264561123\n ],\n [\n -55.588343031317294,\n 53.42772716335526\n ],\n [\n -63.510725698877096,\n 59.815051369668424\n ],\n [\n -67.53203549408073,\n 58.2637040362616\n ],\n [\n -70.11875061971323,\n 60.92641233501996\n ],\n [\n -77.6736876145174,\n 60.191241702301085\n ],\n [\n -78.6577747653739,\n 58.72581664807592\n ],\n [\n -76.67490428706276,\n 57.39981043053075\n ],\n [\n -76.47345786072836,\n 56.25669451722865\n ],\n [\n -79.46472819774651,\n 54.55825161693332\n ],\n [\n -78.4979588526761,\n 51.970677119789684\n ],\n [\n -80.3159706886334,\n 51.28964766864186\n ],\n [\n -82.11007249574554,\n 53.0826991618579\n ],\n [\n -82.48644825178549,\n 55.11790376823541\n ],\n [\n -87.6786836933223,\n 56.166976346152666\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Mamoozadeh, Nadya","contributorId":328674,"corporation":false,"usgs":false,"family":"Mamoozadeh","given":"Nadya","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":881018,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whiteley, Andrew R.","contributorId":286853,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew R.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":881019,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Letcher, Benjamin 0000-0003-0191-5678","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":242666,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":881020,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":140409,"corporation":false,"usgs":true,"family":"Kazyak","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":881021,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tarsa, Charlene","contributorId":270644,"corporation":false,"usgs":false,"family":"Tarsa","given":"Charlene","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":881022,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meek, Mariah H.","contributorId":289676,"corporation":false,"usgs":false,"family":"Meek","given":"Mariah","email":"","middleInitial":"H.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":881023,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70262170,"text":"70262170 - 2025 - Estimating recruitment rate and population dynamics at a migratory stopover site using an integrated population model","interactions":[],"lastModifiedDate":"2025-01-15T16:49:46.046539","indexId":"70262170","displayToPublicDate":"2023-02-21T10:44:06","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Estimating recruitment rate and population dynamics at a migratory stopover site using an integrated population model","docAbstract":"Consideration of the full annual cycle population dynamics can provide useful insight for conservation efforts, but collecting data needed to estimate demographic parameters is often logistically difficult. For species that breed in remote areas, monitoring is often conducted during migratory stopover or at nonbreeding sites, and the recruitment rate of new breeding adults can be difficult to estimate directly. Here, we present an integrated population model that uses mark-resight and count data to estimate survival probability, population growth rate, and recruitment rate for an Arctic-breeding shorebird of conservation concern, the red knot (Calidris canutus rufa), from data collected during spring stopover in Delaware Bay, USA, from 2005 to 2018. At this site, red knots feed primarily on the eggs of spawning horseshoe crabs (Limulus polyphemus), a legally harvested species. We used this model to estimate the relationship between horseshoe crab abundance and red knot demographics, which informed a recent revision to the framework used to establish horseshoe crab harvest regulations. Our analysis indicates that the red knot population was most likely stable from 2005 to 2018 (average λ = 1.03, 95% credible interval [CRI]: 0.961, 1.15) despite low recruitment rates (average ρ = 0.088, 95% CRI: 0.012, 0.18). Adult survival probability was positively associated with horseshoe crab abundance in the same year (β = 0.35, 95% CRI: 0.09, 0.63), but we found no effect of horseshoe crab abundance two years previously on recruitment of new adults (β = −0.08, 95% CRI: −0.41, 0.38). Our approach demonstrates the utility of integrated population models for understanding population dynamics, even when data are only available from migratory stopover monitoring.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4439","usgsCitation":"Tucker, A.M., McGowan, C., Nuse, B., Lyons, J.E., Moore, C.T., Smith, D.R., Sweka, J., Anstead, K., DeRose-Wilson, A., and Clark, N., 2025, Estimating recruitment rate and population dynamics at a migratory stopover site using an integrated population model: Ecosphere, v. 14, no. 2, e4439, 16 p., https://doi.org/10.1002/ecs2.4439.","productDescription":"e4439, 16 p.","ipdsId":"IP-139218","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":466693,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4439","text":"Publisher Index Page"},{"id":466429,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.68003613362366,\n 39.75828902455723\n ],\n [\n -75.68003613362366,\n 38.72868626218502\n ],\n [\n -74.8091737193643,\n 38.72868626218502\n ],\n [\n -74.8091737193643,\n 39.75828902455723\n ],\n [\n -75.68003613362366,\n 39.75828902455723\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Tucker, Anna Maureen 0000-0002-1473-2048 amtucker@usgs.gov","orcid":"https://orcid.org/0000-0002-1473-2048","contributorId":257906,"corporation":false,"usgs":true,"family":"Tucker","given":"Anna","email":"amtucker@usgs.gov","middleInitial":"Maureen","affiliations":[],"preferred":true,"id":923340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":3381,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor P.","email":"cmcgowan@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":923341,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nuse, Bryan L.","contributorId":348305,"corporation":false,"usgs":false,"family":"Nuse","given":"Bryan L.","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":923342,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":923343,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moore, Clinton T. 0000-0002-6053-2880 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-6053-2880","contributorId":3643,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","middleInitial":"T.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":923344,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":923345,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sweka, John A.","contributorId":348306,"corporation":false,"usgs":false,"family":"Sweka","given":"John A.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":923346,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Anstead, Kristen A.","contributorId":348307,"corporation":false,"usgs":false,"family":"Anstead","given":"Kristen A.","affiliations":[{"id":83332,"text":"Atlantic States Marine Fisheries Commission","active":true,"usgs":false}],"preferred":false,"id":923347,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"DeRose-Wilson, Audrey","contributorId":348308,"corporation":false,"usgs":false,"family":"DeRose-Wilson","given":"Audrey","affiliations":[{"id":36379,"text":"Delaware Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":923348,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Clark, Nigel A.","contributorId":348310,"corporation":false,"usgs":false,"family":"Clark","given":"Nigel A.","affiliations":[{"id":38864,"text":"British Trust for Ornithology","active":true,"usgs":false}],"preferred":false,"id":923349,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70239251,"text":"70239251 - 2025 - Haploid gynogens facilitate disomic marker development in paleotetraploid sturgeons","interactions":[],"lastModifiedDate":"2025-06-12T15:08:25.025471","indexId":"70239251","displayToPublicDate":"2022-12-01T06:46:56","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"Haploid gynogens facilitate disomic marker development in paleotetraploid sturgeons","docAbstract":"Acipenseriformes (sturgeons and paddlefishes) are of substantial conservation concern, and development of genomic resources for these species is difficult due to past whole genome duplication. Development of disomic markers for polyploid organisms can be challenging due to difficulty in resolving alleles at a single locus from those among duplicated loci. In this study, we detail the development of disomic markers for the endangered pallid sturgeon (Scaphirhynchus albus) found in North America. One of the strategies for pallid sturgeon conservation is to stock U.S. rivers with offspring of pure pallid sturgeon, but introgression with the sympatric shovelnose sturgeon (S. platorynchus) threatens pallid sturgeon genetic integrity. Currently, 19 microsatellite loci are used to differentiate between both species and their hybrids, but the markers are insufficient to robustly identify backcrosses. We performed double digest restriction site-associated DNA sequencing (ddRADseq) on shovelnose sturgeon haploid gynogens to produce a reduced-representation genomic reference. Contiguous sequences that were heterozygous within a haploid individual were flagged as potentially encompassing multiple loci. Approximately 60 individuals of each species from two management units were sequenced, and reads were mapped to the haploid reference to identify single nucleotide polymorphisms (SNPs) at individual loci. The final data set contained 11,082 microhaplotyped loci which offer at least an order of magnitude greater resolution for species discrimination than the current panel of 19 microsatellites. These markers will be used to examine a larger sample of Scaphirhynchus individuals throughout their ranges to determine the extent and trajectory of hybridization.
This report presents results from a four-year project (2018–2022) to document the effects of small, run-of-river dams and dam removal on water quality (stream temperature and dissolved oxygen (DO)), aquatic macroinvertebrates, and fishes. Temperature and DO are critical water quality parameters that shape biogeochemical processes and biotic assemblages in streams. Macroinvertebrate and fish assemblages can be reflective of habitat and water quality due to their diversity and sensitivity to high temperatures and low DO and are often used as indicators of ecosystem health (e.g., Clean Water Act Section 401). This study aimed to better explain the responses of these important ecological parameters to small dam removals, which may support a more comprehensive understanding of the benefits of restoration to aquatic ecosystems.
We collected pre- and post-restoration water quality data and macroinvertebrate samples at 16 small dams in Massachusetts that have been removed (10 sites) or are currently being considered for removal (6 sites). General results from these monitoring efforts indicate that:
● 15 of 16 small dams increased impoundment water temperatures and warming persisted downstream at 11 of those sites, relative to upstream. Dam removal reduced summer impoundment warming at 7 of 10 removal sites and reduced downstream warming at 5 of 10 sites. These in-stream temperature improvements occurred within 5 years after dam removal.
● 13 of 16 small dams negatively impacted dissolved oxygen (DO) concentrations within the impoundments, but the magnitude of impact varied across sites. Negative impoundment DO impacts did not consistently translate downstream, and downstream responses to dam removal were generally minimal and variable across sites. Dam removal significantly reduced negative impoundment DO impacts within 1 year after removal at 7 of 10 sites, and sites with greater pre-removal impacts experienced the greatest magnitude of DO recovery after dam removal.
● Interannual variability in dam impacts on water quality across sites suggests periods of extreme weather (i.e., droughts or high precipitation) due to climate change may exacerbate adverse impacts from run-of-river dams.
● Macroinvertebrate assemblages within dam impoundments differed from assemblages in adjacent un-impounded stream sections and exhibited a loss of sensitive organisms (an average of 17% fewer). Dam removal led to more similar macroinvertebrate assemblages throughout most stream sections, and recovery of sensitive taxa occurred relatively quickly (1-3 years).
● Fish species richness increased upstream at 2 of 10 removal sites, suggesting potential increases in fish passage from downstream reaches. However, particular species, such as American Eel (Anguilla rostrata), exhibited both positive and negative responses to dam removal across study sites. Incorporating more sites with pre-and post-dam removal fish data could allow for better understanding factors explaining site-specific differences.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/css92498424","usgsCitation":"Abbott, K., Roy, A.H., and Nislow, K., 2025, Restoring aquatic habitats through dam removal: Cooperator Science Series CSS-148-2022, ii, 161 p., https://doi.org/10.3996/css92498424.","productDescription":"ii, 161 p.","ipdsId":"IP-142787","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494912,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","noUsgsAuthors":false,"publicationDate":"2022-11-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Abbott, Katherine M.","contributorId":347949,"corporation":false,"usgs":false,"family":"Abbott","given":"Katherine M.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":947078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":947079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nislow, Keith","contributorId":201434,"corporation":false,"usgs":false,"family":"Nislow","given":"Keith","affiliations":[{"id":27110,"text":"U.S. Dept of Agriculture, Forest Service","active":true,"usgs":false}],"preferred":false,"id":947080,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70267308,"text":"70267308 - 2025 - Strontium isotopes reveal diverse life history variations, migration patterns, and habitat use for Broad Whitefish (Coregonus nasus) in Arctic, Alaska","interactions":[],"lastModifiedDate":"2025-05-21T13:39:20.268062","indexId":"70267308","displayToPublicDate":"2022-05-02T00:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Strontium isotopes reveal diverse life history variations, migration patterns, and habitat use for Broad Whitefish (Coregonus nasus) in Arctic, Alaska","docAbstract":"Conservation of Arctic fish species is challenging partly due to our limited ability to track fish through time and space, which constrains our understanding of life history diversity and lifelong habitat use. Broad Whitefish (Coregonus nasus) is an important subsistence species for Alaska’s Arctic Indigenous communities, yet little is known about life history diversity, migration patterns, and freshwater habitat use. Using laser ablation Sr isotope otolith microchemistry, we analyzed Colville River Broad Whitefish 87Sr/86Sr chronologies (n = 61) to reconstruct movements and habitat use across the lives of individual fish. We found evidence of at least six life history types, including three anadromous types, one semi-anadromous type, and two nonanadromous types. Anadromous life history types comprised a large proportion of individuals sampled (collectively, 59%) and most of these (59%) migrated to sea between ages 0–2 and spent varying durations at sea. The semi-anadromous life history type comprised 28% of samples and entered marine habitat as larvae. Nonanadromous life history types comprised the remainder (collectively, 13%). Otolith 87Sr/86Sr data from juvenile and adult freshwater stages suggest that habitat use changed in association with age, seasons, and life history strategies. This information on Broad Whitefish life histories and habitat use across time and space will help managers and conservation planners better understand the risks of anthropogenic impacts and help conserve this vital subsistence resource.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0259921","usgsCitation":"Leppi, J., Rinella, D., Wipfli, M.S., Brown, R., Spaleta, K., and Whitman, M., 2025, Strontium isotopes reveal diverse life history variations, migration patterns, and habitat use for Broad Whitefish (Coregonus nasus) in Arctic, Alaska: PLoS ONE, v. 17, no. 5, e0259921, 23 p., https://doi.org/10.1371/journal.pone.0259921.","productDescription":"e0259921, 23 p.","ipdsId":"IP-130266","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489729,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0259921","text":"Publisher Index Page"},{"id":486220,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Central Beaufort Sea region study area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -165.4844861317618,\n 71.33547921153408\n ],\n [\n -165.4844861317618,\n 67.76058936865724\n ],\n [\n -141.04895393727227,\n 67.76058936865724\n ],\n [\n -141.04895393727227,\n 71.33547921153408\n ],\n [\n -165.4844861317618,\n 71.33547921153408\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-05-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Leppi, Jason C.","contributorId":355578,"corporation":false,"usgs":false,"family":"Leppi","given":"Jason C.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":937682,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rinella, Daniel J.","contributorId":355579,"corporation":false,"usgs":false,"family":"Rinella","given":"Daniel J.","affiliations":[{"id":81169,"text":"Fish and Wildlife Field Conservation Office","active":true,"usgs":false}],"preferred":false,"id":937683,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wipfli, Mark S. 0000-0002-4856-6068 mwipfli@usgs.gov","orcid":"https://orcid.org/0000-0002-4856-6068","contributorId":1425,"corporation":false,"usgs":true,"family":"Wipfli","given":"Mark","email":"mwipfli@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Randy J.","contributorId":355580,"corporation":false,"usgs":false,"family":"Brown","given":"Randy J.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":937685,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spaleta, Karen J.","contributorId":355581,"corporation":false,"usgs":false,"family":"Spaleta","given":"Karen J.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":937686,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whitman, Matthew S.","contributorId":355582,"corporation":false,"usgs":false,"family":"Whitman","given":"Matthew S.","affiliations":[{"id":84781,"text":"Arctic District Office","active":true,"usgs":false}],"preferred":false,"id":937687,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263078,"text":"70263078 - 2025 - Mid-Atlantic big brown and eastern red bats: Relationships between acoustic activity and reproductive phenology","interactions":[],"lastModifiedDate":"2025-01-29T15:21:35.716674","indexId":"70263078","displayToPublicDate":"2022-04-21T09:11:58","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1398,"text":"Diversity","active":true,"publicationSubtype":{"id":10}},"title":"Mid-Atlantic big brown and eastern red bats: Relationships between acoustic activity and reproductive phenology","docAbstract":"Acoustic data are often used to describe bat activity, including habitat use within the summer reproductive period. These data inform management activities that potentially impact bats, currently a taxa of high conservation concern. To understand the relationship between acoustic and reproductive timing, we sampled big brown bats (Eptesicus fuscus) and eastern red bats (Lasiurus borealis) on 482 mist-netting and 35,410 passive acoustic sampling nights within the District of Columbia, Maryland, Pennsylvania, Virginia, and West Virginia, 2015–2018. We documented the proportion of female, pregnant, lactating, and juvenile big brown and eastern red bats within each mist-net sampling event and calculated locally estimated non-parametric scatterplot smoothing (LOESS) lines for each reproductive and acoustic dataset. We compared the peak in acoustic activity with the peaks of each reproductive condition. We determined that the highest levels of acoustic activity within the maternity season were most associated with the period wherein we captured the highest proportions of lactating bats, not juvenile bats, as often assumed.
","language":"English","publisher":"MDPI","doi":"10.3390/d14050319","usgsCitation":"Deeley, S., Ford, W., Kalen, N., Freeze, S.R., St. Germain, M., Muthersbaugh, M., Barr, E., Kniowski, A., Silvis, A., and De La Cruz, J., 2025, Mid-Atlantic big brown and eastern red bats: Relationships between acoustic activity and reproductive phenology: Diversity, v. 14, no. 5, 319, 10 p., https://doi.org/10.3390/d14050319.","productDescription":"319, 10 p.","ipdsId":"IP-121293","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":489901,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/d14050319","text":"Publisher Index Page"},{"id":481450,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Pennsylvania, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.25530015178774,\n 36.70653171645171\n ],\n [\n -75.9946912821696,\n 37.10180526805351\n ],\n [\n -75.19504161789322,\n 38.36836358051259\n ],\n [\n -76.48701828029624,\n 38.818266151495564\n ],\n [\n -76.2031303760896,\n 39.73786499844381\n ],\n [\n -77.69708409926074,\n 40.137693355107004\n ],\n [\n -79.44015181682143,\n 39.526196534839116\n ],\n [\n -79.80681382582783,\n 38.159439528815355\n ],\n [\n -80.83452329797606,\n 37.36299701483496\n ],\n [\n -82.04438843121486,\n 37.43818246197527\n ],\n [\n -83.63369764586643,\n 36.61527852888986\n ],\n [\n -82.51056571837813,\n 36.620894905645116\n ],\n [\n -81.33158688038374,\n 36.697047413041055\n ],\n [\n -79.25530015178774,\n 36.70653171645171\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-04-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Deeley, Sabrina","contributorId":350177,"corporation":false,"usgs":false,"family":"Deeley","given":"Sabrina","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":925467,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. 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However, the current network of grassland conservation areas may not be adequate for halting population declines and biodiversity loss. Here, we evaluate a network of grassland conservation areas within Wisconsin, U.S.A., that includes both large Focal Landscapes and smaller targeted conservation areas (e.g., Grassland Bird Conservation Areas, GBCAs) established within them. To date, this conservation network has lacked baseline information to assess whether the current placement of these conservation areas aligns with population hot spots of grassland-dependent taxa. To do so, we fitted data from thousands of avian point-count surveys collected by citizen scientists as part of Wisconsin's Breeding Bird Atlas II with multinomial N-mixture models to estimate habitat–abundance relationships, develop spatially explicit predictions of abundance, and establish ecological baselines within priority conservation areas for a suite of obligate grassland songbirds. Next, we developed spatial randomization tests to evaluate the placement of this conservation network relative to randomly placed conservation networks. Overall, less than 20% of species statewide populations were found within the current grassland conservation network. Spatial tests demonstrated a high representation of this bird assemblage within the entire conservation network, but with a bias toward birds associated with moderately tallgrasses relative to those associated with shortgrasses or tallgrasses. We also found that GBCAs had higher representation at Focal Landscape rather than statewide scales. Here, we demonstrated how combining citizen science data with hierarchical modeling is a powerful tool for estimating ecological baselines and conducting large-scale evaluations of an existing conservation network for multiple grassland birds. Our flexible spatial randomization approach offers the potential to be applied to other protected area networks and serves as a complementary tool for conservation planning efforts globally.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8270","usgsCitation":"Nunes, L.A., Ribic, C., and Zuckerberg, B., 2025, Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization: Ecology and Evolution, v. 11, no. 22, p. 16006-16020, https://doi.org/10.1002/ece3.8270.","productDescription":"15 p.","startPage":"16006","endPage":"16020","ipdsId":"IP-126101","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":497717,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.8270","text":"Publisher Index Page"},{"id":497474,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Central Wisconsin Grasslands Conservation Area, Southwest Grasslands and Stream Conservation Area, Western Prairie Habitat Restoration Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.79195210637957,\n 44.827258718630105\n ],\n [\n -91.41144360585636,\n 43.85420962467394\n ],\n [\n -91.08303881583507,\n 42.83204929697988\n ],\n [\n -90.58076409826324,\n 42.48980507075734\n ],\n [\n -89.31258381269711,\n 42.531068987157965\n ],\n [\n -89.424469717728,\n 44.894480414879325\n ],\n [\n -92.79195210637957,\n 44.827258718630105\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","issue":"22","noUsgsAuthors":false,"publicationDate":"2021-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Nunes, Laura A.","contributorId":363905,"corporation":false,"usgs":false,"family":"Nunes","given":"Laura","middleInitial":"A.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":952097,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ribic, Christine 0000-0003-2583-1778 caribic@usgs.gov","orcid":"https://orcid.org/0000-0003-2583-1778","contributorId":147952,"corporation":false,"usgs":true,"family":"Ribic","given":"Christine","email":"caribic@usgs.gov","affiliations":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":952096,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zuckerberg, Benjamin","contributorId":363908,"corporation":false,"usgs":false,"family":"Zuckerberg","given":"Benjamin","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":952098,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70269697,"text":"70269697 - 2025 - Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System","interactions":[],"lastModifiedDate":"2025-07-30T14:37:59.419554","indexId":"70269697","displayToPublicDate":"2021-02-01T09:32:01","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8912,"text":"International Journal of Applied Earth Observations and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System","docAbstract":"The large groundwater reserves of the Urucuia Aquifer System (UAS) enabled agricultural development and economic growth in the western Bahia State, in northeastern Brazil. Over the last several years, concern has grown around the aquifer’s diminishing water levels, and water balance (WB) studies are in demand. Considering the lack of measured actual evapotranspiration (ETa), a major component of the water cycle, this work uses the Operational Simplified Surface Energy Balance (SSEBop) model to estimate ETa, and compares it to basin-scale estimates from the Soil Moisture Accounting Procedure (SMAP) monthly model and from an annual WB closure method, based on gridded meteorological data and the Gravity Recovery and Climate Experiment (GRACE) product. Additionally, a comparative assessment of different versions of the SSEBop parameterization was performed. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used to implement eight different versions of the SSEBop algorithm over the UAS between 2000 and 2013. SSEBop and SMAP ETa yielded similar seasonal patterns, with correlation coefficient (r) up to 0.65, mean difference (MD) of 0.8 mm/month and mean absolute difference (MAD) of 18.5 mm/month. Comparison of SSEBop annual ETa estimates to annual SMAP and WB closure estimates yielded low MD (12.1 and −7.3 mm/year, respectively) and MAD (82.5 and 82.8 mm/year, respectively), but also low r values (0.00 and 0.37, respectively). The comparison of the different SSEBop versions indicated the need to incorporate a calibration step of the aerodynamic heat resistance (rah) parameter. SSEBop results were also used for land cover and drought monitoring. Analysis indicates that agriculture, associated with an increasing trend of atmospheric evaporative demand, is responsible for the decrease in groundwater levels and streamflow in the studied time period.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2021.102298","usgsCitation":"Comini de Andrade, B., de Andrade Pinto, E., Ruhoff, A., and Senay, G.B., 2025, Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System: International Journal of Applied Earth Observations and Geoinformation, v. 98, 102298, 16 p., https://doi.org/10.1016/j.jag.2021.102298.","productDescription":"102298, 16 p.","ipdsId":"IP-125399","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":493300,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jag.2021.102298","text":"Publisher Index Page"},{"id":493182,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Urucuia Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -43,\n -9.75\n ],\n [\n -47,\n -9.75\n ],\n [\n -47,\n -16\n ],\n [\n -43,\n -16\n ],\n [\n -43,\n -9.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"98","noUsgsAuthors":false,"publicationDate":"2021-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Comini de Andrade, Bruno César","contributorId":358913,"corporation":false,"usgs":false,"family":"Comini de Andrade","given":"Bruno César","affiliations":[{"id":85711,"text":"Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.","active":true,"usgs":false}],"preferred":false,"id":944465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Andrade Pinto, Eber José","contributorId":358914,"corporation":false,"usgs":false,"family":"de Andrade Pinto","given":"Eber José","affiliations":[{"id":85714,"text":"Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil, and Serviço Geológico do Brasil-CPRM, Belo Horizonte, Brazil","active":true,"usgs":false}],"preferred":false,"id":944466,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ruhoff, Anderson","contributorId":269919,"corporation":false,"usgs":false,"family":"Ruhoff","given":"Anderson","email":"","affiliations":[{"id":56044,"text":"Universidade Federal do Rio Grande do Sul","active":true,"usgs":false}],"preferred":false,"id":944467,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944468,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266307,"text":"70266307 - 2025 - Thicknesses of lava flows in satellite images: Comparison of layered mare units with terrestrial analogs","interactions":[],"lastModifiedDate":"2025-05-02T15:34:19.960507","indexId":"70266307","displayToPublicDate":"2020-05-20T10:30:55","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"Thicknesses of lava flows in satellite images: Comparison of layered mare units with terrestrial analogs","docAbstract":"New data including measured reflectance (%Ro), programmed open-system pyrolysis data, and kerogen elemental analyses obtained on the Mississippian Barnett Shale in the Fort Worth Basin, Texas, indicate that secondary-gas generation starts at 1.5% Ro and not at the previously prescribed 1.1% Ro. Oil-cracking kinetic parameters derived from pyrolysis experiments in the presence and absence of water indicate that secondary-gas generation will not occur at a thermal maturity as low as 1.1% Ro and requires a minimum thermal maturity of 1.5% Ro. This difference is especially important in using the Barnett Shale as an analog for evaluating other possible shale-gas plays. The new reflectance measurements have a good relationship with hydrogen indices (HIs) and compare well with other published data sets. However, the relationship does not compare well with the previously published data used to prescribe 1.1% Ro as the start of secondary-gas generation in the Barnett Shale. This discrepancy is attributed to differences in measured %Ro values and not attributed to differences in the HI values. Lack of publicly available information on the previously reported %Ro values makes it difficult to ascertain the reason for their lower values. These lower %Ro values also have impact on the previously prescribed relationship for estimating %Ro from the temperature at maximum yield by programmed open-system pyrolysis (Tmax). As a result, the new data do not agree with a previously described relationship, and the considerable scatter makes the new relationship unreliable. However, the relationship between the HI and %Ro has less scatter, which indicates that HI offers a better proxy in calculating %Ro than Tmax for the Barnett Shale. Comparison of various programmed open-system pyrolysis methods (i.e., Rock-Eval II, Rock-Eval 6, Source Rock Analyzer, and Hawk) indicates that variations in HI are within ±10% of one another. An HI of at least 44 mg/g total organic carbon is prescribed as a more certain limit for the start of secondary-gas generation and prospective in situ gas-shale accumulations.
","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/01251716053","usgsCitation":"Lewan, M., and Pawlewicz, M., 2025, Reevaluation of thermal maturity and stages of petroleum formation of the Mississippian Barnett Shale, Fort Worth Basin, Texas: AAPG Bulletin, v. 101, no. 12, p. 1945-1970, https://doi.org/10.1306/01251716053.","productDescription":"26 p.","startPage":"1945","endPage":"1970","ipdsId":"IP-074437","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":485708,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Fort Worth basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100,\n 34\n ],\n [\n -100,\n 30.5\n ],\n [\n -96.9,\n 30.5\n ],\n [\n -96.9,\n 34\n ],\n [\n -100,\n 34\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"101","issue":"12","noUsgsAuthors":false,"publicationDate":"2017-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Lewan, Michael 0000-0001-6347-1553 mlewan@usgs.gov","orcid":"https://orcid.org/0000-0001-6347-1553","contributorId":173938,"corporation":false,"usgs":true,"family":"Lewan","given":"Michael","email":"mlewan@usgs.gov","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":936687,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pawlewicz, M.J.","contributorId":354949,"corporation":false,"usgs":false,"family":"Pawlewicz","given":"M.J.","affiliations":[{"id":6605,"text":"USGS","active":true,"usgs":false}],"preferred":false,"id":936688,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70268678,"text":"70268678 - 2024 - U.S.-Mexico Borderland & vegetation community map","interactions":[],"lastModifiedDate":"2026-01-16T16:14:40.748506","indexId":"70268678","displayToPublicDate":"2025-06-01T10:10:41","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"U.S.-Mexico Borderland & vegetation community map","docAbstract":"People on both sides of the United States-Mexico border need a high-resolution, binational vegetation community map that spans the entire United States-Mexico borderlands. Traditionally, mapping efforts in this region were impeded by complex logistics related to the international border, differing national needs and plans, and resource allocations and priorities. To address this need, scientists from the U.S. Geological Survey (USGS) Southwest Biological Science Center partnered with the Sonoran Joint Venture, the U.S. Fish and Wildlife Service (FWS) Migratory Bird Program, data engineers from the Department of Biosystems Engineering at the University of Arizona, and collaborators from the Wildlands Network, the Borderlands Program to produce the first prototype land cover map within the overlapping Mojave Desert, Sonoran Desert, and the North American Bird Conservation Initiative’s Bird Conservation Region 33 (BCR33) using Landsat satellite data. BCR33 is an area of high biodiversity, providing habitat for bird species of concern and other wildlife. The land cover map supports FWS recovery plan efforts related to conservation planning activities for many species, including Yellow-billed Cuckoo (Coccyzus americanus), Cactus Ferruginous Pygmy-Owl (Glaucidium brasilianum cactorum), Southwestern Willow Flycatcher (Empidonax traillii extimus), Yuma Ridgway’s Rail (Rallus obsoletus yumanensis), Bendire’s thrasher (Toxostoma bendirei), LeConte’s thrasher (Toxostoma lecontei), Masked Bobwhite (Colinus virginianus ridgwayi), jaguar (Panthera onca), and endangered plants such as Bartram’s stonecrop (Graptopetalum bartramii) and the Pima pineapple cactus (Coryphantha robustispina ssp. robustispina). In 2024, a Phase-II map for the full BCR33 region was completed, increasing the understanding of the binational nature of natural communities. The published map and associated paper can be found here.
","language":"English","publisher":"Department of the Interior","usgsCitation":"Nagler, P.L., 2024, U.S.-Mexico Borderland & vegetation community map, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-169494","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":491564,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://eros.usgs.gov/doi-remote-sensing-activities/2024/usgs/us-mexico-borderland-and-vegetation-community-map"},{"id":498746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Borderland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.10014888935096,\n 34.16115185091701\n ],\n [\n -116.10014888935096,\n 28.217554494522687\n ],\n [\n -109.77287116074022,\n 28.217554494522687\n ],\n [\n -109.77287116074022,\n 34.16115185091701\n ],\n [\n -116.10014888935096,\n 34.16115185091701\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":941624,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70262445,"text":"70262445 - 2024 - Mesocarnivores in residential yards: Influence of yard features on the occupancy, relative abundance, and overlap of coyotes, grey fox, and red fox","interactions":[],"lastModifiedDate":"2025-01-17T16:24:57.018933","indexId":"70262445","displayToPublicDate":"2025-01-17T10:15:37","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3777,"text":"Wildlife Research","active":true,"publicationSubtype":{"id":10}},"title":"Mesocarnivores in residential yards: Influence of yard features on the occupancy, relative abundance, and overlap of coyotes, grey fox, and red fox","docAbstract":"As conversion of natural areas to human development continues, there is a lack of information about how developed areas can sustainably support wildlife. While large predators are often extirpated from areas of human development, some medium-bodied mammalian predators (hereafter, mesocarnivores) have adapted to co-exist in human-dominated areas.
How human-dominated areas such as residential yards are used by mesocarnivores is not well understood. Our study aimed to identify yard and landscape features that influence occupancy, relative abundance and spatial-temporal overlap of three widespread mesocarnivores, namely, coyote (Canis latrans), grey fox (Urocyon cineroargenteus) and red fox (Vulpes vulpes).
Over the summers of 2021 and 2022, we deployed camera-traps in 46 and 96 residential yards, spanning from low-density rural areas (<1 home per km2) to more urban areas (589 homes per km2) in north-western Arkansas, USA.
We found that mesocarnivore occupancy was marginally influenced by yard-level features as opposed to landscape composition. Fences reduced the occupancy probability of coyotes, although they were positively associated with the total area of potential shelter sites in a yard. We found that relative abundance of grey fox was highest in yards with poultry, highlighting a likely source of conflict with homeowners. We found that all three species were primarily nocturnal and activity overlap between the species pairs was high.
Thus, these species may be using spatio-temporal partitioning to avoid antagonistic encounters and our data supported this, with few examples of species occurring in the same yards during the same 24-h period.
As the number of residential yards continues to grow, our results suggested that there are ways in which our yards can provide resources to mesocarnivores and that homeowners also have agency to mitigate overlap with mesocarnivores through management of their yard features.
The increasing threat of post-wildfire hazards creates an imperative for improved post-wildfire flooding and debris flow prediction capabilities. Because rainfall is a primary driver of predictive hydrology and debris flow initiation and inundation models, recent efforts have emphasized the need for interdisciplinary collaboration between meteorology and post-wildfire hazard science that develops more accurate rainfall estimates with longer lead times. In this work, we identified critical knowledge gaps for developing rainfall estimates and filled those gaps by reviewing recent literature and synthesizing pre-existing datasets. Gap areas were organized into the following general topics: a) rainfall intensity-duration-frequency relations, b) time-varying rainfall, c) spatially varying rainfall, and d) rainfall regimes.
Recent key research advances include the increasing availability of gridded quantitative rainfall estimates, the expanded use of distributed hydrologic and erosion models that incorporate spatial and temporal variability in rainfall, and the linking of concepts and modeling from the atmospheric and climate sciences with post-wildfire hazard science. We prototype a rainfall regime regionalization schema that captures self-similar properties of rainfall intensity (k, the maximum rainfall intensity) and temporal scaling (n, the decay rate). Our k-n relations schema could serve as a framework for organizing, interpreting, and predicting post-wildfire hydrologic and erosional responses. Finally, we summarize salient gaps for implementing spatiotemporally varying rainfall as the driver of post-wildfire hydrologic models designed to improve the prediction of flooding and debris flow hazards to the built environment for emergency managers.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2024.104990","usgsCitation":"Collar, N.M., Moody, J.A., and Ebel, B., 2024, Rainfall as a driver of post-wildfire flooding and debris flows: A review and synthesis: Earth-Science Reviews, v. 260, 104990, 32 p., https://doi.org/10.1016/j.earscirev.2024.104990.","productDescription":"104990, 32 p.","ipdsId":"IP-164156","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":464885,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"260","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Collar, Natalie M. 0000-0003-4711-0090","orcid":"https://orcid.org/0000-0003-4711-0090","contributorId":306155,"corporation":false,"usgs":false,"family":"Collar","given":"Natalie","email":"","middleInitial":"M.","affiliations":[{"id":66376,"text":"Colorado School of Mines, Department of Civil and Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":920406,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moody, John A. 0000-0003-2609-364X jamoody@usgs.gov","orcid":"https://orcid.org/0000-0003-2609-364X","contributorId":771,"corporation":false,"usgs":true,"family":"Moody","given":"John","email":"jamoody@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":920407,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":920408,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70261548,"text":"70261548 - 2024 - Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales","interactions":[],"lastModifiedDate":"2024-12-13T15:13:03.033614","indexId":"70261548","displayToPublicDate":"2025-01-01T07:58:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales","docAbstract":"Decision makers are often confronted with inadequate information to predict nutrient loads and yields in freshwater ecosystems at large spatial scales. We evaluate the potential of using data mapped at large spatial scales (regional to global) and often coarse resolution to predict nitrogen yields at varying smaller scales (e.g., at the catchment and stream reach level). We applied the SPAtially Referenced Regression On Watershed attributes (SPARROW) model in three regions: the Upper Midwest part of the United States, New Zealand, and the Grande River Basin in southeastern Brazil. For each region, we compared predictions of nitrogen delivery between models developed using novel large-scale datasets and those developed using local-scale datasets. Large-scale models tended to underperform the local-scale models in poorly monitored areas. Despite this, large-scale models are well suited to generate hypotheses about relative effects of different nutrient source categories (point and urban, agricultural, native vegetation) and to identify knowledge gaps across spatial scales when data are scarce. Regardless of the spatial resolution of the predictors used in the models, a representative network of water quality monitoring stations is key to improve the performance of large-scale models used to estimate loads and yields. We discuss avenues of research to understand how this large-scale modelling approach can improve decision making for managing catchments at local scales, particularly in data poor regions.","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2024.122520","usgsCitation":"Suarez-Castro, A.F., Robertson, D., Lehner, B., de Souza, M.L., Kittridge, M., Saad, D., Linke, S., McDowell, R.W., Ranjbar, M.H., Ausseil, O., and Hamilton, D.P., 2024, Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales: Water Research, v. 268, no. 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Bernhard","contributorId":347156,"corporation":false,"usgs":false,"family":"Lehner","given":"Bernhard","affiliations":[{"id":36802,"text":"McGill University, Canada","active":true,"usgs":false}],"preferred":false,"id":920986,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"de Souza, Marcelo L.","contributorId":347157,"corporation":false,"usgs":false,"family":"de Souza","given":"Marcelo","email":"","middleInitial":"L.","affiliations":[{"id":48748,"text":"Brazilian National Water and Sanitation Agency","active":true,"usgs":false}],"preferred":false,"id":920987,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kittridge, Michael","contributorId":347158,"corporation":false,"usgs":false,"family":"Kittridge","given":"Michael","email":"","affiliations":[{"id":83088,"text":"Headwaters Hydrology, NZ","active":true,"usgs":false}],"preferred":false,"id":920988,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Saad, David A. 0000-0001-6559-6181","orcid":"https://orcid.org/0000-0001-6559-6181","contributorId":217251,"corporation":false,"usgs":true,"family":"Saad","given":"David A.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":920989,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Linke, Simon 0000-0002-1797-3947","orcid":"https://orcid.org/0000-0002-1797-3947","contributorId":347159,"corporation":false,"usgs":false,"family":"Linke","given":"Simon","email":"","affiliations":[{"id":36909,"text":"CSIRO","active":true,"usgs":false}],"preferred":false,"id":920990,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McDowell, Rich W.","contributorId":347160,"corporation":false,"usgs":false,"family":"McDowell","given":"Rich","email":"","middleInitial":"W.","affiliations":[{"id":83090,"text":"Lincoln University, NZ","active":true,"usgs":false}],"preferred":false,"id":920991,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ranjbar, Mohammad H.","contributorId":347161,"corporation":false,"usgs":false,"family":"Ranjbar","given":"Mohammad","email":"","middleInitial":"H.","affiliations":[{"id":80337,"text":"Griffith University, Australia","active":true,"usgs":false}],"preferred":false,"id":920992,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ausseil, Olivier","contributorId":347162,"corporation":false,"usgs":false,"family":"Ausseil","given":"Olivier","email":"","affiliations":[{"id":83091,"text":"Girffith University, Australia","active":true,"usgs":false}],"preferred":false,"id":920993,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hamilton, David P. 0000-0002-9341-8777","orcid":"https://orcid.org/0000-0002-9341-8777","contributorId":347163,"corporation":false,"usgs":false,"family":"Hamilton","given":"David","email":"","middleInitial":"P.","affiliations":[{"id":83086,"text":"Griffith University, Brisbane, Australia","active":true,"usgs":false}],"preferred":false,"id":920994,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70261890,"text":"sir20245126 - 2024 - Simulating present and future groundwater/surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska","interactions":[],"lastModifiedDate":"2025-07-10T15:28:29.176134","indexId":"sir20245126","displayToPublicDate":"2024-12-31T15:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5126","displayTitle":"Simulating Present and Future Groundwater/Surface-Water Interactions and Stream Temperatures in Beaver Creek, Kenai Peninsula, Alaska","title":"Simulating present and future groundwater/surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska","docAbstract":"In many places, coldwater ecosystems are facing increasing pressure from anthropogenic warming. This study examined stream temperatures and the water balance in the Beaver Creek watershed on the Kenai Peninsula in south-central Alaska—an area that is experiencing rapid warming. Low-gradient streams near the Kenai coast provide important spawning and rearing habitat for salmon but may be especially vulnerable to rising temperatures, because of long residence times, inflows from abundant riparian wetlands, and reliance on groundwater discharge that may also warm, or decrease in volume with rising evapotranspiration. In recent decades, observed maximum 7-day temperatures have consistently exceeded statistical (regression-based) projections. Here we simulate total streamflows and temperatures with a physics-based model that links the Soil Water Balance, MODFLOW 6 and SNTEMP simulation codes on a 7-day timestep. The model is based on existing data and groundwater levels, instream flows, and stream temperatures collected during 2019–23. Future climate scenarios were developed for 2023–50 from downscaled climate projections.
Results indicate that groundwater discharge is about 64 percent of the total streamflow during the months of May through September. Total streamflow and groundwater discharge are expected to remain similar to current conditions through 2050. Stream temperatures are expected to rise; by midcentury, near the Beaver Creek mouth the model predicts 34 to 63 additional days per year with average weekly temperatures above 13 degrees Celsius, 14 to 81 additional days with average weekly temperatures above 15 degrees Celsius, and routine exceedances of 20 degrees Celsius during the warmest periods. Projected stream temperatures vary spatially. Areas of high groundwater inflows in the lower main stem and some tributaries may be most resilient to warming air temperatures during dry conditions. During storm events, groundwater-dominated tributaries may have the coolest stream temperatures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20245126","usgsCitation":"Leaf, A.T., Haserodt, M.J., Meyer, B.E., Westenbroek, S.M., and Koch, J.C., 2024, Simulating present and future groundwater/surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska: U.S. Geological Survey Scientific Investigations Report 2024–5126, 111 p., https://doi.org/10.3133/sir20245126.","productDescription":"Report: ix, 111 p.; 2 Data Releases; Dataset","numberOfPages":"126","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167012","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":465606,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5126/sir20245126.XML"},{"id":465583,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14UAWGB","text":"USGS data release","linkHelpText":"Surface water and groundwater hydrology and temperature, Beaver Creek, Kenai Peninsula, Alaska, 2022–2023"},{"id":465585,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/national-hydrography/access-national-hydrography-products","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":465584,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"http://doi.org/10.5066/P9K30VAP","text":"USGS data release","linkHelpText":"Soil water balance, groundwater flow, and stream temperature models for Beaver Creek, Alaska, 2019 to 2050"},{"id":492014,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118271.htm","linkFileType":{"id":5,"text":"html"}},{"id":465607,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5126/images/"},{"id":465605,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245126/full"},{"id":465582,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5126/sir20245126.pdf","text":"Report","size":"34.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5126"},{"id":465581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5126/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaver Creek, Kenai Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -152.3330540056542,\n 60.957367731319806\n ],\n [\n -152.38912986860709,\n 59.25155522317334\n ],\n [\n -148.50867874659667,\n 59.25563148250791\n ],\n [\n -148.50867874659667,\n 60.95766646209441\n ],\n [\n -152.3330540056542,\n 60.957367731319806\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Wolf harvest season setting is complicated and controversial. State law requires Montana Fish, Wildlife and Parks (MFWP) to both reduce the wolf population and avoid federal relisting under the Endangered Species Act (Montana Fish, Wildlife and Parks, 2002). Disparate stakeholder groups each have different objectives for wolf management. For instance, big game advocates want to see improved big game populations and hunting opportunities in northwest Montana, while wolf advocates want to see regulations that minimize wolf mortality. Decision making about season setting tries to balance these objectives. Wolf hunting and trapping season decisions are made by the Montana Fish and Wildlife Commission and are informed by annual wolf abundance estimates from an integrated patch occupancy model (iPOM, Sells et al., 2022c) as well as the predictions of wolf abundance into the future under potential constant harvest levels. Parametric uncertainty (uncertainty surrounding the value of a parameter) from the iPOM estimates is propagated through to future projections, providing the Commission with plausible and worst-case outcomes of different levels of public harvest over the short term, i.e., five years into the future, on the wolf population in Montana (Parks et al., 2024).
An alternative approach to inform wolf management and harvest decisions is through adaptive management. Adaptative management is appropriate for decisions that are made iteratively and when monitoring data are collected to learn about the outcomes from decisions, where monitoring data help to reduce critical uncertainties regarding ecosystem function or management outcomes (Walters, 1986; Williams, 2011). Management strategy evaluation (MSE) is one way to develop an adaptive management framework. MSE was developed by fisheries managers and scientists to more accurately and fully incorporate various forms of uncertainty, consider long-term time horizons, and add more transparency in a fisheries context (Punt et al., 2016). It has been used routinely and has become a standard approach for complicated and contentious marine fisheries management situations, yet it has been underutilized in wildlife management (but see Bunnefeld et al., 2013, 2011).
MSE is a forward simulation approach for testing prospective management options or strategies over a wide range of possible states (Punt et al., 2016). A MSE framework captures the ‘truth’ or what is happening in the system (termed the ‘the operating model’) and the information available to the decision makers (termed ‘the estimation model’ or ‘management strategy’). More precisely, there are four main processes modeled. First, models are constructed based on current understanding and data to represent ‘truth’. Second, the collection of monitoring data is simulated from the ‘truth’ model. Third, the simulated monitoring data are fit to an estimation model and the next time step’s population metrics are predicted from the estimated parameters. Fourth, based on the estimation model results and the predictions, the decision-making process is simulated following a management strategy, whereby a decision is made and the implementation of this decision feeds back into the ‘truth’ model (Figure 1). This process continues through time. Additionally, each simulation through time is repeated to capture the full range of stochasticity and uncertainty.
The U.S. Geological Survey (USGS) conducted hydrogeologic investigations, reviewed existing data, and developed a preliminary conceptual model of the groundwater system as part of technical support of the U.S. Environmental Protection Agency (EPA) at the North Penn Area 1 Superfund Site (hereafter, the NP1 Site) located within the Borough of Souderton in Montgomery County, Pennsylvania. Field work and monitoring took place during 2012–18. The area is underlain by sedimentary formations that form a fractured-rock aquifer used for drinking water and industrial supply. The EPA placed the Site on the National Priorities List in 1989, identifying tetrachloroethylene (PCE) and trichloroethylene (TCE) as contaminants of concern.
During 2012–18, the USGS conducted field activities that included drilling an 82-foot (ft)-deep monitoring well (MG 2220) in 2016, reconstructing a 208-ft-deep former industrial production well (MG 668 [Granite Knitting Mill]), and collecting borehole geophysical and video logs and water levels from those and five additional wells, which ranged in depth from about 50 to 200 ft below land surface. Continuous water levels were collected during 2014–17, and a synoptic set of water levels were measured in April 2018 in the seven wells.
The borehole geophysical logs (caliper, acoustic televiewer, natural gamma, single-point resistance, vertical flow, and fluid temperature and resistivity) and borehole video logs in the seven wells were evaluated to assess potential for lithologic correlation and to identify and describe water-bearing features, which included both low- and high-angle fractures and other openings oriented along dipping bedding planes, joints, or possible faults. Borehole geophysical logs collected by USGS in 1992 in a 300-ft-deep former production well near the Site were also evaluated. Few to no distinctive features were identified on geophysical logs (natural gamma and single-point resistance) that could be used for correlation, thus limiting this approach to determining local geologic structure. Extensive fracturing in the upper 62 ft of monitoring well MG 2220 indicates that the well was likely drilled through a zone of faulting, and other evidence of faulting is present in the area near the Site. Assessment of continuous water levels showed hydraulic connections among some wells as indicated by rising or falling water levels in response to changes in pumping rates at nearby wells. A map of water levels measured in April 2018 indicates potential for groundwater flow generally toward the stream to the south and southwest of the Site, but the limited water-level data are insufficient to describe vertical groundwater gradients or lateral gradients in any detail.
Review of 1999–2022 volatile organic compound (VOC) monitoring data collected by the Pennsylvania Department of Environmental Protection for five monitoring wells indicates that the highest groundwater concentrations of PCE and TCE were found in samples from extraction well MG 2201 (S-1) downgradient from, and nearest to, the previously identified Site contaminant source area, and these concentrations fluctuated through time. PCE concentrations were higher than TCE concentrations in samples from all five monitoring wells and were much higher than TCE concentrations in samples from extraction well MG 2201 (S-1). Temporally variable recharge is a possible factor affecting observed fluctuations in PCE concentrations in groundwater samples from well extraction MG 2201 (S-1), as indicated by a general inverse relation between PCE concentrations and water levels in a nearby long-term observation well. The PCE concentration of 1,830 micrograms per liter (μg/L) in a May 2018 water sample from monitoring well MG 2220 was more than four times the PCE concentration of 444 μg/L in a December 2017 sample from the nearby extraction well MG 2201 (S-1), which is open to fewer fractures. Low concentrations of VOCs were measured in surface water at two stream sites downgradient from wells with the highest groundwater VOC concentrations at the Site, indicating that discharge of contaminated groundwater to the stream is likely.
Development of a conceptual model of the groundwater system was constrained by limited data. In areas with no pumping, groundwater-flow directions generally are thought to be controlled by topography and geologic structure (bedding orientation) and likely to the south and southwest of the Site, with local flow directions affected by orientations of fractures, joints, and local faults. Additional investigations that could help improve the conceptual model of the groundwater system and help delineate the extent of groundwater contamination and its transport are discussed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241080","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Senior, L.A., Risser, D.W., Goode, D.J., and Bird, P.H., 2024, Hydrologic investigations and a preliminary conceptual model of the groundwater system at North Penn Area 1 Superfund Site, Souderton, Montgomery County, Pennsylvania: U.S. Geological Survey Open-File Report 2024–1080, 78 p., https://doi.org/10.3133/ofr20241080.","productDescription":"xi, 78 p.","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-151018","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":494216,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118273.htm","linkFileType":{"id":5,"text":"html"}},{"id":465486,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1080/ofr20241080.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2024-1080 XML"},{"id":465485,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241080/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1080 HTML"},{"id":465479,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1080/images/"},{"id":465476,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1080/ofr20241080.pdf","text":"Report","size":"18.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1080 PDF"},{"id":465475,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1080/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Montgomery County","city":"Souderton","otherGeospatial":"North Penn Area 1 Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.33380565402877,\n 40.30337215850042\n ],\n [\n -75.33067431094733,\n 40.30297414782885\n ],\n [\n -75.32310689850118,\n 40.30864557850933\n ],\n [\n -75.32121504538941,\n 40.31133187946756\n ],\n [\n -75.32415067952832,\n 40.31496319053656\n ],\n [\n -75.33002194780529,\n 40.3133714069823\n ],\n [\n -75.33432754454195,\n 40.307053646040714\n ],\n [\n -75.33380565402877,\n 40.30337215850042\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, Pennsylvania 17070
The U.S. Geological Survey investigated land cover at subannual time steps within six wetland areas in Dixie Valley, Churchill County, Nevada, from October 2015 to January 2022. As requested by the U.S. Fish and Wildlife Service, we used aerial photography and satellite remote sensing data to map surface water and other land cover types within the wetland complexes. We identified five land cover classes using the green normalized difference vegetation index (gNDVI) and its inverse relationship to the normalized difference water index (NDWI) within three U.S. Department of Agriculture National Agriculture Imagery Program aerial images (acquired in 2015, 2017, and 2019) and 110 European Space Agency Sentinel-2 satellite images (acquired 2015–2022). The relative wetness of soil conditions within each land cover class is estimated by comparison to previously published observations of relative conductivity measured by 79 field-based sensors within the wetlands from 2019 to 2021. We mapped the areal coverage of the five land cover classes for approximately 385 acres (1,559,000 square meters [m²]) comprising six individual wetland complexes as well as a larger 1,298- acre (5,254,000-m2) area of interest inclusive of the wetland complexes and adjacent landscape. Land cover of open water (Class 5) primarily within ponds at one of the wetland complexes comprised 8,333 m2, on average, of the wetland complexes. Land cover of mixed shallow surface water, saturated soil, and vegetation (Class 4) comprised 111,723 m2 on average of the wetland complexes. Land cover of dense green vegetation canopy cover (Class 3) that often (46 percent of observations) had underlying surface water or saturated soil conditions comprised 592,522 m2 on average of the wetland complexes. The remaining areas of the wetland complexes not mapped as these three land cover types (Classes 2 and 1) had sparse vegetation or bare soil cover and commonly (greater than or equal to 67 percent of observations) had dry soil conditions. The investigation of land cover detailed in this report could inform future efforts to map land cover more precisely via higher resolution remote sensing or ground-based surveying or could be incorporated with other environmental monitoring data to characterize habitat and hydrology of the wetland complexes at Dixie Meadows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241029","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service","usgsCitation":"Sankey, J.B., Bransky, N.D., and Caster, J.J., 2024, Investigation of land cover within wetland complexes at Dixie Meadows, Churchill County, Nevada, from October 2015 to January 2022: U.S. Geological Survey Open-File Report 2024–1029, 10 p., https://doi.org/10.3133/ofr20241029.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-150955","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":494217,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118272.htm","linkFileType":{"id":5,"text":"html"}},{"id":465474,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1029/images/"},{"id":465473,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1029/ofr20241029.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2024-1029 XML"},{"id":465466,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90U1VAM","text":"USGS data release","linkHelpText":"Land cover classification data for wetland complexes at Dixie Meadows, Nevada from October 2015 to January 2022"},{"id":465472,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241029/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1029 HTML"},{"id":465465,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1029/ofr20241029.pdf","text":"Report","size":"5.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1029 PDF"},{"id":465464,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1029/coverthb.jpg"}],"country":"United States","state":"Nevada","county":"Churchill County","otherGeospatial":"Dixie Meadows","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.0333,\n 39.808333\n ],\n [\n -118.091667,\n 39.808333\n ],\n [\n -118.091667,\n 39.75\n ],\n [\n -118.0333,\n 39.75\n ],\n [\n -118.0333,\n 39.808333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001