From late-December 2024 to mid-March 2025, a 50-km-long dyke intrusion triggered over 300 earthquakes (magnitude 4 to 5.9) between Fentale and Dofen volcanoes along the Northern Main Ethiopian Rift. Dyke intrusions periodically occur along the Fentale–Dofen magmatic segment and are an expression of ongoing rift extension. Preliminary analyses using interferometric synthetic aperture radar revealed extensive ground deformation (up to 60 cm), which closely matched the temporal and spatial evolution of surface manifestations and earthquake locations from global catalogues. While global catalogues are critical for real-time monitoring, the precision of locations in remote and or sparsely instrumented regions can be low. In this investigation, we present surface-wave relocation results of the dyking episode that began near Fentale volcano in December 2024. We estimate relative locations using differential traveltimes measured from regional-to-teleseismic distance surface-wave observations of earthquakes reported by the U.S. Geological Survey. Relative relocations reduce the initial region of diffuse seismicity to a 50-km-long narrow band bounding the strike of surface manifestations and the zone of maximum surface deformation. We demonstrate the precision of surface-wave relocations over incremental time periods, capturing the progression of dyking from seismic onset through seismic migration and caldera subsidence. Results showcase the utility of surface-wave relocations in the characterization of dyking episodes and provide complementary insights into the current understanding of the Fentale–Dofen volcanic plumbing system.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/gji/ggag099","usgsCitation":"Deane, C.A., Pesicek, J.D., Bagnardi, M., Shelly, D.R., Prejean, S.G., Yeck, W.L., and Earle, P.S., 2026, Surface-wave relocation and characterization of the 2024–2025 dyking episode along the Fentale–Dofen segment of the Ethiopian rift: Geophysical Journal International, v. 245, no. 3, ggag099, 15 p., https://doi.org/10.1093/gji/ggag099.","productDescription":"ggag099, 15 p.","ipdsId":"IP-183442","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":504062,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1EKD3R7","text":"USGS data release","linkHelpText":"High-Precision Seismicity Catalog for the Ethiopia Dyking Episodes (September 2024 – March 2025)"},{"id":503005,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggag099","text":"Publisher Index Page"},{"id":502779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ethiopia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 40.07693386166687,\n 14.027139942420362\n ],\n [\n 39.37130142715375,\n 10.406777094105706\n ],\n [\n 40.57318165688591,\n 10.098571653063885\n ],\n [\n 41.239047826839084,\n 13.625346480540614\n ],\n [\n 40.07693386166687,\n 14.027139942420362\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"245","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Deane, Chanel A. 0000-0002-7132-0090","orcid":"https://orcid.org/0000-0002-7132-0090","contributorId":369926,"corporation":false,"usgs":false,"family":"Deane","given":"Chanel","middleInitial":"A.","affiliations":[{"id":87886,"text":"former USGS mendenhall","active":true,"usgs":false}],"preferred":false,"id":959370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pesicek, Jeremy D. 0000-0001-7964-5845","orcid":"https://orcid.org/0000-0001-7964-5845","contributorId":202042,"corporation":false,"usgs":true,"family":"Pesicek","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":959372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bagnardi, Marco 0000-0002-4315-0944","orcid":"https://orcid.org/0000-0002-4315-0944","contributorId":335933,"corporation":false,"usgs":true,"family":"Bagnardi","given":"Marco","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":959382,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":959373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prejean, Stephanie G. 0000-0003-0510-1989 sprejean@usgs.gov","orcid":"https://orcid.org/0000-0003-0510-1989","contributorId":172404,"corporation":false,"usgs":true,"family":"Prejean","given":"Stephanie","email":"sprejean@usgs.gov","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":959374,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":959375,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Earle, Paul S. 0000-0002-3500-017X pearle@usgs.gov","orcid":"https://orcid.org/0000-0002-3500-017X","contributorId":173551,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":959376,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70274590,"text":"70274590 - 2026 - The effects of scientific uncertainty and values trade-offs on flow management decisions for an endangered fish","interactions":[],"lastModifiedDate":"2026-04-01T21:22:06.654572","indexId":"70274590","displayToPublicDate":"2026-03-11T14:14:15","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"The effects of scientific uncertainty and values trade-offs on flow management decisions for an endangered fish","docAbstract":"Consumptive use of freshwater is of concern in many estuarine ecosystems, and various frameworks have been used to prescribe environmental flows to benefit native species. However, few of these frameworks explicitly examine the potential trade-offs between socioeconomic and conservation-oriented values. This is exemplified in California, USA, where freshwater management has been an area of focus and controversy. Operations of numerous reservoirs and water diversion facilities distributed throughout the state, while critical for economic and public health benefits, have contributed to the decline of many native species. The endangered delta smelt (Hypomesus transpacificus) is endemic to the Sacramento-San Joaquin Delta, the heart of California's complex water conveyance system. To aid recovery of delta smelt, fall-timed freshwater pulse flows were implemented, which require water to be either released from reservoirs, or made unavailable to export for consumptive uses. Previous research has indicated that the effectiveness of the current pulse flow action could be improved by reconsidering the timing and magnitude; however, uncertainties in the predicted fish response to flow pulses may hinder decision-making about flow management. Using a water resource planning model, different iterations of an individual-based life cycle model, and decision analysis tools, we assessed the importance of sources of uncertainty to hypothetical flow management decisions, including uncertainty surrounding the predicted responses in delta smelt population growth rates, and variability of decision-maker's values. We found both the choice of which (if any) flow action to take for delta smelt, and the expected value of further research, depended on how decision-makers weight the delta smelt and water supply objectives. There was expected value of information (VOI) only if a decision-maker weighted the delta smelt objective ≥0.59, and within this range, research to improve estimates of changes in delta smelt prey items related to flow actions could be prioritized over other sources of uncertainty to improve outcomes of decision-making. Our study demonstrates how uncertainty, even if large, may not be equally relevant to different decision-makers (e.g., with different agency missions), and how VOI analysis can be used to guide management in an overallocated water system such as California.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70558","usgsCitation":"Mahardja, B., Smith, W.E., Healy, B.D., Koizumi, C., Nobriga, M.L., Acuña, S., Crawford, B., Arend, K.K., and Runge, M.C., 2026, The effects of scientific uncertainty and values trade-offs on flow management decisions for an endangered fish: Ecosphere, v. 17, no. 3, e70558, 19 p., https://doi.org/10.1002/ecs2.70558.","productDescription":"e70558, 19 p.","ipdsId":"IP-179082","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":502057,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70558","text":"Publisher Index Page"},{"id":501969,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta, San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.70835642002126,\n 38.292492080303305\n ],\n [\n -122.70835642002126,\n 36.78504888193622\n ],\n [\n -120.74800512414426,\n 36.78504888193622\n ],\n [\n -120.74800512414426,\n 38.292492080303305\n ],\n [\n -122.70835642002126,\n 38.292492080303305\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Mahardja, Brian","contributorId":174645,"corporation":false,"usgs":false,"family":"Mahardja","given":"Brian","email":"","affiliations":[{"id":13461,"text":"U.C. Davis","active":true,"usgs":false}],"preferred":false,"id":958416,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, William E.","contributorId":369083,"corporation":false,"usgs":false,"family":"Smith","given":"William","middleInitial":"E.","affiliations":[{"id":87713,"text":"U.S. Fish and Wildlife Service, San Francisco Bay-Delta Fish and Wildlife Office, Sacramento, CA","active":true,"usgs":false}],"preferred":false,"id":958417,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Healy, Brian D. 0000-0002-4402-638X","orcid":"https://orcid.org/0000-0002-4402-638X","contributorId":304257,"corporation":false,"usgs":true,"family":"Healy","given":"Brian","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":958418,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koizumi, Cameron","contributorId":363551,"corporation":false,"usgs":false,"family":"Koizumi","given":"Cameron","affiliations":[{"id":86721,"text":"US Bureau of Reclamation, Bay-Delta Office, Sacramento, California","active":true,"usgs":false}],"preferred":false,"id":958419,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nobriga, Matthew L.","contributorId":369084,"corporation":false,"usgs":false,"family":"Nobriga","given":"Matthew","middleInitial":"L.","affiliations":[{"id":87713,"text":"U.S. Fish and Wildlife Service, San Francisco Bay-Delta Fish and Wildlife Office, Sacramento, CA","active":true,"usgs":false}],"preferred":false,"id":958420,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Acuña, Shawn","contributorId":293913,"corporation":false,"usgs":false,"family":"Acuña","given":"Shawn","affiliations":[{"id":63555,"text":"Metropolitan Water District Southern California, Sacramento, CA","active":true,"usgs":false}],"preferred":false,"id":958421,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Crawford, Brian A.","contributorId":341519,"corporation":false,"usgs":false,"family":"Crawford","given":"Brian A.","affiliations":[{"id":81748,"text":"Georgia Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":958422,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Arend, Kristin K.","contributorId":369085,"corporation":false,"usgs":false,"family":"Arend","given":"Kristin","middleInitial":"K.","affiliations":[{"id":87713,"text":"U.S. Fish and Wildlife Service, San Francisco Bay-Delta Fish and Wildlife Office, Sacramento, CA","active":true,"usgs":false}],"preferred":false,"id":958423,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":214737,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":958424,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70274193,"text":"fs20263066 - 2026 - Assessment of undiscovered conventional oil and gas resources of South America and the Caribbean, 2025","interactions":[],"lastModifiedDate":"2026-03-12T14:36:49.528121","indexId":"fs20263066","displayToPublicDate":"2026-03-11T11:50:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2026-3066","displayTitle":"Assessment of Undiscovered Conventional Oil and Gas Resources of South America and the Caribbean, 2025","title":"Assessment of undiscovered conventional oil and gas resources of South America and the Caribbean, 2025","docAbstract":"Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean conventional resources of 37.6 billion barrels of oil and 281.6 trillion cubic feet of gas in 31 geologic provinces of South America and the Caribbean.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20263066","programNote":"National and Global Petroleum Assessment","usgsCitation":"Schenk, C.J., Mercier, T.J., Le, P.A., Cicero, A.D., Gelman, S.E., Hearon, J.S., Johnson, B.G., Lagesse, J.H., and Leathers-Miller, H.M., 2026, Assessment of undiscovered conventional oil and gas resources of South America and the Caribbean, 2025: U.S. Geological Survey Fact Sheet 2026–3066, 4 p., https://doi.org/10.3133/fs20263066.","productDescription":"Report: 4 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-183424","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":500988,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20263066/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2026-3066"},{"id":500985,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2026/3066/fs20263066.xml"},{"id":500984,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2026/3066/images"},{"id":500772,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2026/3066/fs20263066.pdf","text":"Report","size":"9.61 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2026-3066"},{"id":500770,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2026/3066/coverthb.jpg"},{"id":500773,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1MI2FYG","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project—South America and Caribbean provinces—Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables"}],"otherGeospatial":"Caribbean, South America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -58.45853817386718,\n 13.43004801084264\n ],\n [\n -61.45333320389646,\n 19.89259212340386\n ],\n [\n -78.2572391598639,\n 26.28452892339132\n ],\n [\n -79.58332927600556,\n 23.742889739729634\n ],\n [\n -84.84696683089616,\n 23.89342153490864\n ],\n [\n -86.89869997108627,\n 19.109914009913254\n ],\n [\n -87.32639646314531,\n 17.087504336902853\n ],\n [\n -81.83228755638481,\n 19.55509737067692\n ],\n [\n -75.72927581835268,\n 16.143834985794\n ],\n [\n -77.6964764087617,\n 6.913580156094852\n ],\n [\n -82.3945350889498,\n -3.0099967310220137\n ],\n [\n -80.62208090735567,\n -9.955564711150814\n ],\n [\n -76.03422687889487,\n -16.134444305261667\n ],\n [\n -71.37414634430803,\n -19.84524857686681\n ],\n [\n -78.43865962428788,\n -49.925990359254726\n ],\n [\n -65.35929186736915,\n -60.50071053060525\n ],\n [\n -54.77968256393444,\n -51.974033013111615\n ],\n [\n -55.79695773401548,\n -39.25031124406797\n ],\n [\n -38.8988403624939,\n -22.50704406545175\n ],\n [\n -31.721830236886944,\n -6.756212684855086\n ],\n [\n -31.84730311368935,\n -1.4371414685820696\n ],\n [\n -58.45853817386718,\n 13.43004801084264\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Grasslands are an imperiled ecosystem, and grassland bird abundance is declining across North America. One of the strongest drivers for these declines is woody plant encroachment of grasslands. In the Great Plains and Sagebrush biomes of North America, spatial covariance—a remote-sensing metric for tracking boundaries between vegetation types—is emerging as a new method to identify and strategize conservation of grassland cores in the face of woody plant encroachment. However, the relationship between spatial covariance and grassland bird community occupancy is unknown. Here, we used Bayesian multispecies occupancy models to understand how occupancy probability of six declining grassland species responded to spatial covariance at three scales (0.81, 7.29, and 65.61 ha) and tree cover in fragmented grasslands of Arkansas, USA. Model selection revealed that the smallest spatial scale (0.81 ha) best explained grassland bird occupancy. Tree cover alone was a poor predictor of grassland bird occupancy compared to models that included spatial covariance at the 0.81- and 7.29-ha scales. Grassland bird occupancy declined at tree-grass boundaries (negative spatial covariance at the 0.81-ha scale) and increased in grassland cores (near-zero or slightly positive spatial covariance at the 0.81-ha scale). At low tree cover, Dickcissel (Spiza americana), Eastern Kingbird (Tyrannus tyrannus), Loggerhead Shrike (Lanius ludovicianus), Northern Bobwhite (Colinus virginianus), and Scissor-tailed Flycatcher (Tyrannus forficatus) occupancy probability more than doubled in grassland cores (where spatial covariance approached zero). Eastern Meadowlark (Sturnella magna) had the weakest relationship with spatial covariance. Our results suggest that spatial covariance can identify grassland cores and serve as a powerful predictor of grassland bird community occupancy, even in highly fragmented grasslands. Identifying grassland cores empowers defending core grasslands from woody plant encroachment and then growing cores via active restoration.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70515","usgsCitation":"Berry, L.L., DeGregorio, B.A., Uden, D.R., and Roberts, C.P., 2026, Finding the (small) cores: Spatial covariance tracks grassland bird community occupancy in fragmented grasslands: Ecosphere, v. 17, no. 3, e70515, 12 p., https://doi.org/10.1002/ecs2.70515.","productDescription":"e70515, 12 p.","ipdsId":"IP-167761","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":502095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70515","text":"Publisher Index Page"},{"id":502026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Bald Knob National Wildlife Refuge, Cache River National Wildlife Refuge, Camp Robinson Special Use Area, Holla Bend National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.55982952388516,\n 35.69384138213361\n ],\n [\n -91.55982952388516,\n 35.08983573060459\n ],\n [\n -90.1524295998061,\n 35.08983573060459\n ],\n [\n -90.1524295998061,\n 35.69384138213361\n ],\n [\n -91.55982952388516,\n 35.69384138213361\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Berry, Lauren L.","contributorId":369145,"corporation":false,"usgs":false,"family":"Berry","given":"Lauren","middleInitial":"L.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":958532,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":958533,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Uden, Daniel R.","contributorId":369146,"corporation":false,"usgs":false,"family":"Uden","given":"Daniel","middleInitial":"R.","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":958534,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roberts, Caleb Powell 0000-0002-8716-0423","orcid":"https://orcid.org/0000-0002-8716-0423","contributorId":288567,"corporation":false,"usgs":true,"family":"Roberts","given":"Caleb","email":"","middleInitial":"Powell","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":958535,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274216,"text":"70274216 - 2026 - Groundwater drought in the United States: Spatial and temporal variability","interactions":[],"lastModifiedDate":"2026-03-13T15:11:23.354627","indexId":"70274216","displayToPublicDate":"2026-03-11T10:03:16","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater drought in the United States: Spatial and temporal variability","docAbstract":"Many communities and ecosystems in the United States that are dependent on groundwater are potentially adversely affected by groundwater drought. We computed yearly groundwater-drought metrics and mean groundwater levels at well locations across the conterminous United States (CONUS), using data from wells and remotely sensed and modeled Gravity Recovery and Climate Experiment Drought Monitor Data Assimilation (GRACE-DADM). We also modeled the probability of low or high human impact at each well location. The spatial distribution of groundwater-drought duration and severity from 2001 to 2020 for 1,510 wells shows longer maximum duration and higher maximum severity events in drier regions like the Southwest than in wetter regions like the Northeast. Based on 613 wells in CONUS from 1981 to 2020, there are many significant decreases in drought duration and severity in the Northeast and many significant increases in annual-mean groundwater levels. In contrast, there are many significant increases in drought metrics and decreases in mean water levels in parts of the Southeast. There are major differences in trends from 2001 to 2020 between well-based and GRACE-DADM-based groundwater metrics in some CONUS regions and a very low correlation between trends at individual locations across CONUS. A potential reason for this disparity is the low GRACE-DADM resolution (∼12 km) and the potential for a large amount of groundwater variation at the local scale. Also, GRACE-DADM represents shallow, unconfined aquifers which may not match the screened interval of the monitoring wells we evaluated. Large spatial gaps in long-term, high frequency, and quality-assured groundwater-well monitoring data present a challenge for understanding groundwater-drought variability across CONUS. Remote sensing tools such as GRACE can help but cannot fully replace well monitoring, as highlighted by our study results. Substantially more long-term monitoring wells would more accurately represent groundwater-drought trends and spatial variability across CONUS, particularly in western regions.
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Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":957077,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dudley, Robert W. 0000-0002-0934-0568","orcid":"https://orcid.org/0000-0002-0934-0568","contributorId":220211,"corporation":false,"usgs":true,"family":"Dudley","given":"Robert W.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":957078,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70274211,"text":"70274211 - 2026 - Small-volume tephra deposits of the May 1924 explosions from Halemaʻumaʻu, Kīlauea volcano, and their origin","interactions":[],"lastModifiedDate":"2026-03-13T14:29:50.599041","indexId":"70274211","displayToPublicDate":"2026-03-11T09:20:41","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Small-volume tephra deposits of the May 1924 explosions from Halemaʻumaʻu, Kīlauea volcano, and their origin","docAbstract":"Mobile device location data offer a low-cost alternative for measuring visitation to outdoor recreation sites and are known to correlate with official visitation counts. Less is known about whether these data can recover recreation demand and consumer surplus comparable to surveybased methods. We compare travel cost models estimated using mobile device and survey data for 17 U.S. National Park Service sites. Results are mixed. We examine the roles of aggregation and sampling bias and use LASSO regression to assess whether sample and site characteristics explain discrepancies.
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Center","active":true,"usgs":true}],"preferred":true,"id":962503,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richardson, Leslie","contributorId":197525,"corporation":false,"usgs":false,"family":"Richardson","given":"Leslie","affiliations":[],"preferred":false,"id":962504,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70276520,"text":"70276520 - 2026 - Relative influence of flow regime, natural and anthropogenic environment on multidimensional stream fish diversity","interactions":[],"lastModifiedDate":"2026-06-09T15:57:36.797681","indexId":"70276520","displayToPublicDate":"2026-03-11T08:53:09","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Relative influence of flow regime, natural and anthropogenic environment on multidimensional stream fish diversity","docAbstract":"The flow regime is considered a ‘master variable’ in riverine ecology because it directly influences stream geomorphology and biological communities. However, other environmental and anthropogenic factors have direct and synergistic effects with flow on fish diversity, complicating estimates of the flow regime's true importance. Moreover, most flow-ecology studies focus only on taxonomic diversity (i.e., species), without considering functional (trait-focused) or phylogenetic (evolution-focused) dimensions of diversity. In this study, we used linear regression with variation partitioning to parse out the independent and shared roles of the flow regime, physical environmental factors (e.g., soil characteristics) and the anthropogenic environment (e.g., developed land cover) for structuring multidimensional diversity of 365 stream fish communities in two biogeographic regions across South Carolina, USA. These variables explained between 8% and 18% of total variation of the local diversity. The flow regime contributed to diversity in all cases, but frequently covaried with physical and/or anthropogenic environmental variables. This covariation indicated that the independent role of flow would have been inflated if other environmental variables were not considered. The three dimensions of stream fish diversity were weakly correlated with one another and were associated with different environmental variables, indicating that each of them represents unique and complimentary facets of fish diversity. Accordingly, only considering the independent effects of instream flow on fish diversity may miss meaningful interactions between the flow regime and the other components of the environment that influence biodiversity patterns, leading to over simplified flow–ecology relationships.
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affect overwintering abundance of monarch butterflies at regional and site scales in California","interactions":[],"lastModifiedDate":"2026-04-20T15:53:44.921013","indexId":"70274538","displayToPublicDate":"2026-03-10T16:01:47","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Density dependence and habitat selection affect overwintering abundance of monarch butterflies at regional and site scales in California","docAbstract":"The monarch butterfly (Danaus plexippus) is a species of iconic cultural interest. Thanks to annual overwintering monarch counts at hundreds of locations in coastal California, we are able to track fluctuations with high temporal and spatial resolution. Between 1997 and 2024, monarch populations at overwintering sites in the western United States experienced severe dips, at times (2018–2020, 2023–2024) giving the appearance of a population collapse. From 2018 to present, the Pismo State Beach Overwintering Monarch Grove has conducted multiple counts during overwintering and geolocated counts of individual monarch clusters to specific trees within the site. This study determined how annual monarch population variability is influenced by both climate and prior year population density at the state, region, and overwintering-site scale. Furthermore, through a machine-learning process, we investigated how overwintering site configuration and structure drive monarch winter space-use dynamics within the Pismo Beach site. Our approach found monarchs exhibit a preference for specific overwintering sites in California, and that 64% of annual variability of counts across sites can be explained by climate and density dependence, with density dependence explaining 50% of total variability. Within the site we found very little regional climate effect, but individual trees, tree size, distance to boundary, and the amount of shade were all strong indicators of monarch presence. Additionally, only 11 out of 320 trees at the Pismo Beach site accounted for 83.6% of all counts over 6 years, highlighting how monarchs use specific trees and how tree structure may create preferred microclimates for clustering.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.70253","usgsCitation":"Ibsen, P.C., Ancona, Z.H., Pelton, E., Little, S., and Diffendorfer, J., 2026, Density dependence and habitat selection affect overwintering abundance of monarch butterflies at regional and site scales in California: Conservation Science and Practice, v. 8, no. 4, e70253, 16 p., https://doi.org/10.1111/csp2.70253.","productDescription":"e70253, 16 p.","ipdsId":"IP-172212","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":501884,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":502079,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.70253","text":"Publisher Index Page"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123,\n 38\n ],\n [\n -123,\n 34\n ],\n [\n -119.5,\n 34\n ],\n [\n -119.5,\n 38\n ],\n [\n -123,\n 38\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","issue":"4","noUsgsAuthors":false,"publicationDate":"2026-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Ibsen, Peter Christian 0000-0002-3436-9100","orcid":"https://orcid.org/0000-0002-3436-9100","contributorId":260735,"corporation":false,"usgs":true,"family":"Ibsen","given":"Peter","email":"","middleInitial":"Christian","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":958161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ancona, Zachary H. 0000-0001-5430-0218 zancona@usgs.gov","orcid":"https://orcid.org/0000-0001-5430-0218","contributorId":5578,"corporation":false,"usgs":true,"family":"Ancona","given":"Zachary","email":"zancona@usgs.gov","middleInitial":"H.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":958162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pelton, Emma","contributorId":357706,"corporation":false,"usgs":false,"family":"Pelton","given":"Emma","affiliations":[{"id":34267,"text":"The Xerces Society for Invertebrate Conservation","active":true,"usgs":false}],"preferred":false,"id":958163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Little, Stephanie","contributorId":368952,"corporation":false,"usgs":false,"family":"Little","given":"Stephanie","affiliations":[{"id":35321,"text":"California Department of Parks and Recreation","active":true,"usgs":false}],"preferred":false,"id":958164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":958165,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70274236,"text":"70274236 - 2026 - Accumulation of per- and polyfluoroalkyl substances (PFAS) and their association with immune parameters in nestling ospreys (Pandion haliaetus) from Chesapeake and Delaware Bays, USA","interactions":[],"lastModifiedDate":"2026-03-23T12:53:32.533122","indexId":"70274236","displayToPublicDate":"2026-03-10T14:19:03","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Accumulation of per- and polyfluoroalkyl substances (PFAS) and their association with immune parameters in nestling ospreys (Pandion haliaetus) from Chesapeake and Delaware Bays, USA","title":"Accumulation of per- and polyfluoroalkyl substances (PFAS) and their association with immune parameters in nestling ospreys (Pandion haliaetus) from Chesapeake and Delaware Bays, USA","docAbstract":"Per- and polyfluoroalkyl substances (PFAS) are a class of widespread, environmentally persistent compounds that pose a potential threat to wildlife and human health. Despite recent efforts to reduce the use of long-chain PFAS in industrial practices and commercial/consumer products, the persistence and solubility of PFAS have led to their detection in wildlife on a global scale. Osprey (Pandion haliaetus) have long been used as a sentinel species with an extensive history of serving as an effective bioindicator of contamination. Here we report on a large-scale evaluation of PFAS and potential health effects in osprey from the Chesapeake and Delaware Bays, USA. In 2011 and 2015, we collected plasma samples from osprey nestlings throughout the Chesapeake and Delaware Bay watersheds. We quantified 40 PFAS congeners in osprey plasma via liquid chromatography-mass spectrometry and analyzed plasma for indicators of immune and thyroid function, and plasma biochemistry. In all birds, perfluorooctanesulfonic acid (PFOS) was the most commonly detected PFAS, followed by perfluoroundecanoic acid, (PFUnA) and perfluorodecanoic acid (PFDA). In nestling plasma from Chesapeake Bay, PFOS tended to be a higher average contributor to PFAS profiles compared to samples from Delaware Bay. In contrast, long-chain perfluoroalkyl carboxylic acids (PFCAs) such as PFUnA and PFDA comprised larger percentages of total PFAS in osprey plasma from Delaware Bay relative to Chesapeake Bay. While some PFAS concentrations were associated with plasma health indicators, the proportion of variation explained was low. Overall, our study provides a more thorough understanding of PFAS presence in the Chesapeake and Delaware Bays and is one of the first to examine whether PFAS exposure is associated with adverse health effects in wildlife.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/etojnl/vgag055","usgsCitation":"Karouna-Renier, N., Haskins, D., Schultz, S.L., Akresh, M., and Rattner, B., 2026, Accumulation of per- and polyfluoroalkyl substances (PFAS) and their association with immune parameters in nestling ospreys (Pandion haliaetus) from Chesapeake and Delaware Bays, USA: Environmental Toxicology and Chemistry, https://doi.org/10.1093/etojnl/vgag055.","ipdsId":"IP-183725","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":501383,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/ja/70274236/images"},{"id":501382,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/ja/70274236/70274236.XML"},{"id":501381,"rank":2,"type":{"id":42,"text":"Open Access USGS Document"},"url":"https://pubs.usgs.gov/publication/70274236/full"},{"id":501230,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake and Delaware Bays","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.61761223029669,\n 39.86211116682409\n ],\n [\n -76.93592404163553,\n 39.86211116682409\n ],\n [\n -76.93592404163553,\n 36.61322897844552\n ],\n [\n -74.61761223029669,\n 36.61322897844552\n ],\n [\n -74.61761223029669,\n 39.86211116682409\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Online First","noUsgsAuthors":false,"publicationDate":"2026-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Karouna-Renier, Natalie 0000-0001-7127-033X nkarouna@usgs.gov","orcid":"https://orcid.org/0000-0001-7127-033X","contributorId":200983,"corporation":false,"usgs":true,"family":"Karouna-Renier","given":"Natalie","email":"nkarouna@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":957120,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haskins, David Lee 0000-0002-6692-3225","orcid":"https://orcid.org/0000-0002-6692-3225","contributorId":357996,"corporation":false,"usgs":true,"family":"Haskins","given":"David Lee","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":957121,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schultz, Sandra L. 0000-0003-3394-2857 sschultz@usgs.gov","orcid":"https://orcid.org/0000-0003-3394-2857","contributorId":5966,"corporation":false,"usgs":true,"family":"Schultz","given":"Sandra","email":"sschultz@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":957122,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Akresh, Michael E.","contributorId":355344,"corporation":false,"usgs":false,"family":"Akresh","given":"Michael E.","affiliations":[{"id":83385,"text":"Antioch University","active":true,"usgs":false}],"preferred":false,"id":957123,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rattner, Barnett 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":221814,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":957124,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70274632,"text":"70274632 - 2026 - Hydrologic variability drives environmental and geospatial relationships in Smallmouth Bass (Micropterus dolomieu) distribution","interactions":[],"lastModifiedDate":"2026-04-02T18:44:26.919721","indexId":"70274632","displayToPublicDate":"2026-03-10T11:32:48","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Hydrologic variability drives environmental and geospatial relationships in Smallmouth Bass (Micropterus dolomieu) distribution","title":"Hydrologic variability drives environmental and geospatial relationships in Smallmouth Bass (Micropterus dolomieu) distribution","docAbstract":"Hydrologic variation is a primary driver of stream ecosystems. Changing hydrology can lead to assemblage shifts and alterations in suitable habitat for freshwater species. As climate change is predicted to alter flow patterns in addition to increasing water temperatures, insight into relationships between species occupancy, hydrology, and temperature is critical for understanding current and future distributions. We examined how hydrologic variability, temperature, and other environmental variables interact to influence Micropterus dolomieu (Smallmouth Bass) occurrence. We used Spatial Stream Network models, allowing for the incorporation of spatial autocorrelation along streams' unique dendritic network, to examine Smallmouth Bass occupancy across a range of hydrologic variation in the Ozark-Ouachita Interior Highlands, USA. Hydrologic variation was the main driver of Smallmouth Bass occurrence, with occurrence more likely in groundwater streams with low hydrologic variation and high flow permanence. For groundwater streams, occurrence was positively associated with summer stream temperature and negatively associated with annual stream temperature. As variation increased, more variables showed significant relationships with occurrence. Distance metrics were important for all models, however as hydrologic disturbance increased, flow connected distance played a lesser role and stream distance played a greater role. Hydrologic variability was the overarching determinant of Smallmouth Bass occurrence and strongly influenced the predictive importance of environmental variables and geospatial relationships. Greater hydrologic variability resulted in stronger statistical relationships between occurrence and environmental variables and an increased importance of system connectivity. As climate change alters hydrologic processes and streams become more variable, understanding and accounting for these shifting relationships is essential.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2026.181562","usgsCitation":"Sorensen, S.F., Fox, J.T., and Magoulick, D.D., 2026, Hydrologic variability drives environmental and geospatial relationships in Smallmouth Bass (Micropterus dolomieu) distribution: Science of the Total Environment, v. 1025, 181562, 9 p., https://doi.org/10.1016/j.scitotenv.2026.181562.","productDescription":"181562, 9 p.","ipdsId":"IP-176491","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":502098,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2026.181562","text":"Publisher Index Page"},{"id":502032,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Kansas, Missouri, Oklahoma","otherGeospatial":"Ozark-Ouachita Interior Highlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.06835076729865,\n 37.54392591075137\n ],\n [\n -95.58753725694291,\n 35.616131979244244\n ],\n [\n -96.86430792379102,\n 34.30485408838467\n ],\n [\n -95.13839254168458,\n 34.13182914589751\n ],\n [\n -93.02844126052034,\n 33.84485206480821\n ],\n [\n -91.20526644252189,\n 35.93662462412837\n ],\n [\n -90.46426221649432,\n 38.03635872039271\n ],\n [\n -95.06835076729865,\n 37.54392591075137\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"1025","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sorensen, Sarah F.","contributorId":369126,"corporation":false,"usgs":false,"family":"Sorensen","given":"Sarah","middleInitial":"F.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":958495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fox, J. Tyler","contributorId":369127,"corporation":false,"usgs":false,"family":"Fox","given":"J.","middleInitial":"Tyler","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":958496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":958497,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70274596,"text":"70274596 - 2026 - Continuous measurements reveal wind and temperature affect orphan well methane emissions on the Kevin-Sunburst Dome, Montana","interactions":[],"lastModifiedDate":"2026-04-01T18:05:24.62617","indexId":"70274596","displayToPublicDate":"2026-03-10T10:59:16","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Continuous measurements reveal wind and temperature affect orphan well methane emissions on the Kevin-Sunburst Dome, Montana","docAbstract":"Fifteen leaking orphan wells on the Kevin-Sunburst Dome in northern Montana had emission rates that were affected by surface winds and diurnal temperature swings based on continuous monitoring data. Some wells showed correlating spikes in emissions when temperatures changed or wind speed increased while others demonstrated independent flow behavior despite being drilled into the same reservoir and located only a few hundred meters apart. Time-weighted mean methane emission rates ranged from non-detectable levels up to 2.7 kg/h in their as-discovered conditions, with leaking wells averaging 211 g/h. Emissions were measured continuously for up to 452 h per well during monitoring, revealing that leak rates can fluctuate by an order of magnitude within hours. Fluctuations in emission rates often synchronized between wells with overlapping emission measurement intervals, suggesting weather conditions, such as temperature and wind, affect emission rates (up to a factor of 4) with the most relevant factor being the effect of wind on wells with open holes. Additionally, this study presents the first methane emissions measured from an orphan well in two distinct conditions: as initially discovered (closed leaking valve, 2.7 kg/h) and again under unrestricted flow conditions (open valve, 11.8 kg/h), illustrating the maximum unobstructed leak rate and quantifying the constraints restricted leaking wells can have on emissions compared to open holes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2026.181577","usgsCitation":"Gianoutsos, N.J., Haase, K.B., Birdwell, J.E., Hofmann, M.H., and Shuck, C.E., 2026, Continuous measurements reveal wind and temperature affect orphan well methane emissions on the Kevin-Sunburst Dome, Montana: Science of the Total Environment, v. 1020, 181577, 12 p., https://doi.org/10.1016/j.scitotenv.2026.181577.","productDescription":"181577, 12 p.","ipdsId":"IP-173165","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":502054,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2026.181577","text":"Publisher Index Page"},{"id":501959,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Kevin-Sunburst Dome","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.08584178503881,\n 48.942193767762035\n ],\n [\n -112.08584178503881,\n 47.38524088668447\n ],\n [\n -109.50509067788585,\n 47.38524088668447\n ],\n [\n -109.50509067788585,\n 48.942193767762035\n ],\n [\n -112.08584178503881,\n 48.942193767762035\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"1020","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gianoutsos, Nicholas J. 0000-0002-6510-6549 ngianoutsos@usgs.gov","orcid":"https://orcid.org/0000-0002-6510-6549","contributorId":3607,"corporation":false,"usgs":true,"family":"Gianoutsos","given":"Nicholas","email":"ngianoutsos@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":958461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haase, Karl B. 0000-0002-6897-6494 khaase@usgs.gov","orcid":"https://orcid.org/0000-0002-6897-6494","contributorId":205943,"corporation":false,"usgs":true,"family":"Haase","given":"Karl","email":"khaase@usgs.gov","middleInitial":"B.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":958462,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":958463,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hofmann, Michael H.","contributorId":369106,"corporation":false,"usgs":false,"family":"Hofmann","given":"Michael","middleInitial":"H.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":958464,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shuck, Curtis E.","contributorId":369107,"corporation":false,"usgs":false,"family":"Shuck","given":"Curtis","middleInitial":"E.","affiliations":[{"id":87723,"text":"Well Done Foundation","active":true,"usgs":false}],"preferred":false,"id":958465,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70274546,"text":"70274546 - 2026 - Scenarios and strategies for future-proofing ecosystem management under climatic novelty","interactions":[],"lastModifiedDate":"2026-06-02T16:04:49.708064","indexId":"70274546","displayToPublicDate":"2026-03-09T15:01:30","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Scenarios and strategies for future-proofing ecosystem management under climatic novelty","docAbstract":"Climate change is driving unprecedented declines in dominant, habitat-forming foundation species across marine and terrestrial ecosystems globally. As climatic novelty becomes the norm, ecosystem reassembly will become increasingly common. Predicting and understanding these transitions, and their implications for future ecosystem functioning, is essential for designing effective forward-looking management strategies. We explored 3 scenarios that describe a range of ecosystem reassembly trajectories following declines in previously dominant habitat-forming taxa: compensation, in which functionally similar subdominant or immigrating taxa maintain ecosystem structure and function; decline, in which no compensation occurs leading to loss of ecosystem structure and function; and transformation, in which the ecosystem present historically can no longer persist and shifts into a fundamentally different ecosystem type with distinct structure and function. This range of potential outcomes highlights the urgent need to assess the ecological feasibility and functional implications of potential management actions. Scientists and managers can work together to quantify local-scale climatic novelty and ecosystem resilience to better predict the most likely reassembly trajectories and identify management interventions that will optimize ecosystem function. This approach would allow for more proactive planning to support persistence of ecosystem structure and function, helping to future-proof ecosystem management in a rapidly changing world.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.70250","usgsCitation":"Toth, L.T., Borer, E.T., Burkepile, D.E., Dudney, J., Lemoine, N.P., Renzi, J.J., Smith, K.E., Courtney, T.A., Goeking, S.A., Hammond, W.M., Hoover, D.L., MacFayden, S., Osland, M.J., Townsend, J.E., and Fidler, R.Y., 2026, Scenarios and strategies for future-proofing ecosystem management under climatic novelty: Conservation Biology, v. 40, no. 3, e70250, 12 p., https://doi.org/10.1111/cobi.70250.","productDescription":"e70250, 12 p.","ipdsId":"IP-176414","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":501973,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":502061,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/cobi.70250","text":"Publisher Index Page"}],"volume":"40","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":958224,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Borer, Elizabeth T.","contributorId":368991,"corporation":false,"usgs":false,"family":"Borer","given":"Elizabeth","middleInitial":"T.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":958225,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burkepile, Deron 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E.","contributorId":369000,"corporation":false,"usgs":false,"family":"Townsend","given":"Joseph","middleInitial":"E.","affiliations":[{"id":34129,"text":"University of Puerto Rico Mayaguez","active":true,"usgs":false}],"preferred":false,"id":958237,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Fidler, Robert Young 0000-0001-8796-9161","orcid":"https://orcid.org/0000-0001-8796-9161","contributorId":369001,"corporation":false,"usgs":true,"family":"Fidler","given":"Robert","middleInitial":"Young","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":958238,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70274195,"text":"sim3545 - 2026 - Water use permits as of July 2024 and reported water use near the North Unit of Theodore Roosevelt National Park, North Dakota, 1980–2023","interactions":[],"lastModifiedDate":"2026-03-13T16:57:02.210512","indexId":"sim3545","displayToPublicDate":"2026-03-09T11:44:43","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3545","displayTitle":"Water Use Permits as of July 2024 and Reported Water Use Near the North Unit of Theodore Roosevelt National Park, North Dakota, 1980–2023","title":"Water use permits as of July 2024 and reported water use near the North Unit of Theodore Roosevelt National Park, North Dakota, 1980–2023","docAbstract":"Starting in the early 2000s, increasing oil and gas development in western North Dakota created a need for additional water resources from surface-water and groundwater sources near the North Unit of Theodore Roosevelt National Park. To summarize the use of water in that area, the U.S. Geological Survey, in cooperation with the National Park Service, developed a map of surface-water and groundwater resources, aquifers, and water-use diversions, and plotted water-use trends from 1980 to 2023. Reported water used from permits in the map area has more than doubled since 2020, increasing from about 750 acre-feet in 2020 to about 2,300 acre-feet in 2022 and 2,000 acre-feet in 2023. Surface water provided the primary source of reported water used for the study period with an average of about 410 acre-feet per year from 1980 through 2017 and about 1,330 acre-feet per year from 2018 through 2023. After 2011, groundwater sourced from the Little Missouri River, Tobacco Garden Creek, Fox Hills, Fort Union, and Dakota aquifers became a larger portion of total annual reported water use from permits in the map area. From 1980 through 2015, water use for irrigation averaged 86 percent of the total annual reported surface-water and groundwater use in the map area. Starting in 2011, however, industrial uses became a proportionally larger total use of water, and in 2015, became the highest reported volume of water use in the map area. From 2011 to 2023, industrial use designated for water depots increased from 50 acre-feet to about 1,370 acre-feet, accounting for about 70 percent of total reported water use in the map area in 2023.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3545","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Anderson, T.M., and Medler, C.J., 2026, Water use permits as of July 2024 and reported water use near the North Unit of Theodore Roosevelt National Park, North Dakota, 1980–2023: U.S. Geological Survey Scientific Investigations Map 3545, 1 p., scale 1:75,000, https://doi.org/10.3133/sim3545.","productDescription":"1 Sheet: 51.96 x 32.00 inches","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-180137","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":501164,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119300.htm","linkFileType":{"id":5,"text":"html"}},{"id":500796,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3545/sim3545.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3545"},{"id":500795,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3545/coverthb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"North Unit of Theodore Roosevelt National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.68187232257335,\n 47.72042516981429\n ],\n [\n -103.68187232257335,\n 47.454023726420644\n ],\n [\n -103.19621849492077,\n 47.454023726420644\n ],\n [\n -103.19621849492077,\n 47.72042516981429\n ],\n [\n -103.68187232257335,\n 47.72042516981429\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
This map shows the location of water permits and graphs of the reported amount of water used from those permits from rivers, streams, and wells as of July 2024, near Theodore Roosevelt National Park in North Dakota. Total water use in the map area more than doubled from 2020 to 2023. From 1980 through 2023, water from rivers and streams was used more than water from wells, but water use from wells began to increase starting in 2011. From 1980 through 2015, most water was used for irrigation, but after 2015, most water was used for industrial purposes.
","publicationDate":"2026-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Todd M. 0000-0001-8971-9502","orcid":"https://orcid.org/0000-0001-8971-9502","contributorId":218978,"corporation":false,"usgs":true,"family":"Anderson","given":"Todd","email":"","middleInitial":"M.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Medler, Colton J. 0000-0001-6119-5065","orcid":"https://orcid.org/0000-0001-6119-5065","contributorId":201463,"corporation":false,"usgs":true,"family":"Medler","given":"Colton","email":"","middleInitial":"J.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":956900,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70274135,"text":"70274135 - 2026 - Alternative future vegetation pathways reveal potential transformations of western US ecosystems","interactions":[],"lastModifiedDate":"2026-03-12T16:41:27.715088","indexId":"70274135","displayToPublicDate":"2026-03-09T11:34:07","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Alternative future vegetation pathways reveal potential transformations of western US ecosystems","docAbstract":"Managing ecosystems in an era of rapid change is inherently challenging not only because of uncertainty in future climate but also due to diverse responses of ecosystems to climate. Projections of ecological transformation alongside information about plausible vegetation trajectories can help land managers explore divergent scenarios and consider how modeled outcomes match their observations. Climate-analog impact models (AIMs) compare environmental information (e.g., vegetation types) between sets of climatically similar locations to infer change and can be used to identify multiple outcomes. We used AIMs to project changes in vegetation across the western United States under a mid-21st century climate scenario, characterize ecological transformation vulnerability based on projection divergence, and demonstrate how AIMs can inform decision-making. We projected high or very high vulnerability to ecological transformation across 29% of the western US, nearly 1 M km2. Vulnerability varied among vegetation groups; 75% of alpine vegetation had high or very high vulnerability vs. 6% of desert scrub. We estimate that 9% of the study area faces a high likelihood of transformation based on combined measures of vulnerability and projection agreement. Transformation at the vegetation type (n = 50) level is projected for 40% (1.4 M km2) of the study area, based on primary projections. As vegetation shifts towards types supported by a more arid climate, forested area is expected to contract by 9% and subalpine forests specifically by 54%. Elsewhere, vulnerability is low or trajectories are uncertain, implying opportunities for managers to intervene. Dry forests, for example, could be stabilized through vegetation management and intentional fire use. Our findings suggest likely ecological transformations with significant downstream consequences for ecosystem services and natural resources. They are best used within decision-making frameworks that draw on multiple lines of evidence including local expertise and complementary knowledge systems.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.70795","usgsCitation":"Hoecker, T.J., Davis, K.T., Littlefield, C.E., Chandler, J.C., Parks, S.A., Maguire, A., Kemp, K., Yegorova, S., and Dobrowski, S., 2026, Alternative future vegetation pathways reveal potential transformations of western US ecosystems: Global Change Biology, v. 32, no. 3, e70795, 15 p., https://doi.org/10.1111/gcb.70795.","productDescription":"e70795, 15 p.","ipdsId":"IP-182529","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":501100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.70795","text":"Publisher Index Page"},{"id":500990,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-104.053249,41.001406],[-102.124972,41.002338],[-102.051292,40.749591],[-102.04192,37.035083],[-102.979613,36.998549],[-103.002247,36.911587],[-103.064423,32.000518],[-106.565142,32.000736],[-106.577244,31.810406],[-106.750547,31.783706],[-108.208394,31.783599],[-108.208573,31.333395],[-111.000643,31.332177],[-114.813613,32.494277],[-114.722746,32.713071],[-117.118868,32.534706],[-117.50565,33.334063],[-118.088896,33.729817],[-118.428407,33.774715],[-118.519514,34.027509],[-119.159554,34.119653],[-119.616862,34.420995],[-120.441975,34.451512],[-120.608355,34.556656],[-120.644311,35.139616],[-120.873046,35.225688],[-120.884757,35.430196],[-121.851967,36.277831],[-121.932508,36.559935],[-121.788278,36.803994],[-121.880167,36.950151],[-122.140578,36.97495],[-122.419113,37.24147],[-122.511983,37.77113],[-122.425942,37.810979],[-122.168449,37.504143],[-122.144396,37.581866],[-122.385908,37.908136],[-122.301804,38.105142],[-122.484411,38.11496],[-122.492474,37.82484],[-122.972378,38.020247],[-123.103706,38.415541],[-123.725367,38.917438],[-123.851714,39.832041],[-124.373599,40.392923],[-124.063076,41.439579],[-124.536073,42.814175],[-124.150267,43.91085],[-123.962887,45.280218],[-123.996766,46.20399],[-123.548194,46.248245],[-124.029924,46.308312],[-124.06842,46.601397],[-123.97083,46.47537],[-123.84621,46.716795],[-124.022413,46.708973],[-124.108078,46.836388],[-123.86018,46.948556],[-124.138035,46.970959],[-124.425195,47.738434],[-124.672427,47.964414],[-124.727022,48.371101],[-123.981032,48.164761],[-122.748911,48.117026],[-122.637425,47.889945],[-123.15598,47.355745],[-122.527593,47.905882],[-122.578211,47.254804],[-122.725738,47.33047],[-122.691771,47.141958],[-122.796646,47.341654],[-122.863732,47.270221],[-122.67813,47.103866],[-122.364168,47.335953],[-122.429841,47.658919],[-122.230046,47.970917],[-122.425572,48.232887],[-122.358375,48.056133],[-122.512031,48.133931],[-122.424102,48.334346],[-122.689121,48.476849],[-122.425271,48.599522],[-122.796887,48.975026],[-104.048736,48.999877],[-104.053249,41.001406]]],[[[-119.789798,34.05726],[-119.5667,34.053452],[-119.795938,33.962929],[-119.916216,34.058351],[-119.789798,34.05726]]],[[[-118.524531,32.895488],[-118.573522,32.969183],[-118.369984,32.839273],[-118.524531,32.895488]]],[[[-118.500212,33.449592],[-118.32446,33.348782],[-118.593969,33.467198],[-118.500212,33.449592]]],[[[-122.519535,48.288314],[-122.66921,48.240614],[-122.400628,48.036563],[-122.419274,47.912125],[-122.744612,48.20965],[-122.664928,48.374823],[-122.519535,48.288314]]],[[[-122.800217,48.60169],[-122.883759,48.418793],[-123.173061,48.579086],[-122.949116,48.693398],[-122.743049,48.661991],[-122.800217,48.60169]]]]},\"properties\":{\"name\":\"Arizona\",\"nation\":\"USA \"}}]}","volume":"32","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Hoecker, Tyler J. 0000-0001-8680-8809","orcid":"https://orcid.org/0000-0001-8680-8809","contributorId":367051,"corporation":false,"usgs":false,"family":"Hoecker","given":"Tyler","middleInitial":"J.","affiliations":[{"id":84304,"text":"Vibrant Planet","active":true,"usgs":false}],"preferred":false,"id":956646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Kimberley T. 0000-0001-9727-374X","orcid":"https://orcid.org/0000-0001-9727-374X","contributorId":355031,"corporation":false,"usgs":false,"family":"Davis","given":"Kimberley","middleInitial":"T.","affiliations":[{"id":84700,"text":"USDA - Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":956647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Littlefield, Caitlin E. 0000-0003-3771-7956","orcid":"https://orcid.org/0000-0003-3771-7956","contributorId":220623,"corporation":false,"usgs":false,"family":"Littlefield","given":"Caitlin","email":"","middleInitial":"E.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":956648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chandler, Jeffrey C","contributorId":223870,"corporation":false,"usgs":false,"family":"Chandler","given":"Jeffrey","email":"","middleInitial":"C","affiliations":[{"id":40781,"text":"USDA/APHIS/WS, National Wildlife Research Center","active":true,"usgs":false}],"preferred":false,"id":956649,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Parks, Sean A. 0000-0002-2982-5255","orcid":"https://orcid.org/0000-0002-2982-5255","contributorId":225035,"corporation":false,"usgs":false,"family":"Parks","given":"Sean","email":"","middleInitial":"A.","affiliations":[{"id":41024,"text":"Aldo Leopold Wilderness Research Institute, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":956650,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Maguire, Andy John 0000-0002-6334-0497","orcid":"https://orcid.org/0000-0002-6334-0497","contributorId":358945,"corporation":false,"usgs":true,"family":"Maguire","given":"Andy John","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":956651,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kemp, Kerry","contributorId":367059,"corporation":false,"usgs":false,"family":"Kemp","given":"Kerry","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":956652,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yegorova, Svetlana 0000-0003-3228-7379","orcid":"https://orcid.org/0000-0003-3228-7379","contributorId":367060,"corporation":false,"usgs":false,"family":"Yegorova","given":"Svetlana","affiliations":[{"id":34255,"text":"Wilfred Laurier University","active":true,"usgs":false}],"preferred":false,"id":956653,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dobrowski, Solomon","contributorId":229621,"corporation":false,"usgs":false,"family":"Dobrowski","given":"Solomon","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":956654,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70274636,"text":"70274636 - 2026 - Seasonal and hydrologic variation influences habitat and functional structure of stream fish assemblages","interactions":[],"lastModifiedDate":"2026-04-02T17:32:18.629216","indexId":"70274636","displayToPublicDate":"2026-03-08T10:25:49","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16456,"text":"Frontiers in Enviornmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal and hydrologic variation influences habitat and functional structure of stream fish assemblages","docAbstract":"Introduction:
Hydrologic variability is a key driver of ecological structure in lotic systems, shaping habitat conditions, taxonomic diversity, and the functional traits that mediate species’ persistence and performance (e.g., reproductive success). While many studies examine taxonomic responses to variation in flows, few evaluate how spatiotemporal hydrologic variation influences the functional organization within stream fish communities.
Methods:
We quantified seasonal habitat structure and functional trait diversity of fish assemblages across six Ozark Plateau headwater streams representing two contrasting flow regimes: Groundwater Flashy and Runoff/Intermittent Flashy. Fish and habitat data were collected seasonally during a dry year (2002) and a wet year (2003). Functional space was constructed using PCoA of morphological, ecological, and life-history traits, and functional diversity was measured using community weighted means (CWMs), functional richness (FRic), functional evenness (FEve), and functional divergence (FDiv).
Results:
We found that habitat structure differed strongly by flow regime and season, with Runoff/Intermittent streams exhibiting pronounced reductions in depth, area, and velocity, while groundwater streams remained structurally stable. Functional identity of assemblages was similar across flow regimes, dominated by benthic, hydrodynamic taxa with opportunistic and periodic life-history strategies. However, functional structure differed significantly: FEve and FDiv were consistently lower in Runoff/Intermittent Flashy streams in both years, indicating assemblage dominance of species with similar trait combinations and reduced trait partitioning under variable flow. FRic and taxonomic richness remained stable across seasons and flow regimes, suggesting high functional redundancy despite species turnover.
Discussion:
Together, results show that flow regime mediates both habitat structural stability and functional organization. As climatic warming and extreme drought increase hydrologic instability in headwaters, functional trait approaches provide a sensitive tool for detecting losses of functional roles that may not be evident by using taxonomic metrics alone.
Rapids are common in steep rivers, often forming where flow transitions from supercritical (Froude number, Fr > 1) to subcritical (Fr < 1) through a hydraulic jump. When upstream Fr is supercritical but close to 1, this transition may occur as an undular hydraulic jump, exhibiting a train of stationary waves downstream of the jump toe. Previous studies proposed a method to estimate discharge using only UHJ wave spacing and channel width combined with a wave dispersion equation for large water depths relative to the UHJ wavelength. This method is based on the hypotheses that, by their presence, UHJs indicate near-critical flow conditions (Fr ≈ 1) and that wave celerity c is equal to and opposite the cross-sectionally averaged flow velocity U. However, these hypotheses have not been thoroughly tested. We used data from published UHJ flume experiments to test the hypotheses that Fr ≈ 1 and c = U, compare the deep-water and general wave dispersion equations, and evaluate the accuracy of discharge estimates. In these experiments, the stationary waves exhibited shallow depths relative to wavelength and flow was subcritical (Fr < 1) when averaged across multiple wavelengths. Additionally, wave celerity more closely approximated the surface flow velocity than U. By using a Fr representative of actual conditions and applying a coefficient to correct for c ≠ U , the accuracy of the discharge estimates improved. This finding suggests that the critical flow-based method is robust and can produce reliable streamflow estimates if the remotely observed wave trains are correctly interpreted as UHJs, without requiring in situ measurements.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025WR040997","usgsCitation":"White, D., Yager, E., Legleiter, C.J., Grant, G., Hempel, L.A., Leonard, C.M., Adler, K., Harlan, M.E., and Fasth, B., 2026, Estimating discharge from undular hydraulic jumps: Feasibility assessment based on flume experiments: Water Resources Research, v. 62, no. 3, e2025WR040997, 19 p., https://doi.org/10.1029/2025WR040997.","productDescription":"e2025WR040997, 19 p.","ipdsId":"IP-172038","costCenters":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":502062,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025wr040997","text":"Publisher Index Page"},{"id":501815,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Daniel C. 0000-0001-8376-8469","orcid":"https://orcid.org/0000-0001-8376-8469","contributorId":347543,"corporation":false,"usgs":false,"family":"White","given":"Daniel C.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":958310,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yager, Elowyn 0000-0002-3382-2356","orcid":"https://orcid.org/0000-0002-3382-2356","contributorId":347542,"corporation":false,"usgs":false,"family":"Yager","given":"Elowyn","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":958311,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":958312,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grant, Gordon","contributorId":349384,"corporation":false,"usgs":false,"family":"Grant","given":"Gordon","affiliations":[{"id":83479,"text":"US Forest Service, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958313,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hempel, Laura A. 0000-0001-5020-6056","orcid":"https://orcid.org/0000-0001-5020-6056","contributorId":224286,"corporation":false,"usgs":true,"family":"Hempel","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958314,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leonard, Christina M. 0000-0002-5096-8103","orcid":"https://orcid.org/0000-0002-5096-8103","contributorId":360578,"corporation":false,"usgs":false,"family":"Leonard","given":"Christina","middleInitial":"M.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":958315,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Adler, Katherine","contributorId":369026,"corporation":false,"usgs":false,"family":"Adler","given":"Katherine","affiliations":[],"preferred":false,"id":958316,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Harlan, Merritt Elizabeth 0000-0002-4019-4888","orcid":"https://orcid.org/0000-0002-4019-4888","contributorId":302672,"corporation":false,"usgs":true,"family":"Harlan","given":"Merritt","email":"","middleInitial":"Elizabeth","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":958317,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fasth, Becky","contributorId":349390,"corporation":false,"usgs":false,"family":"Fasth","given":"Becky","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":958318,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70274666,"text":"70274666 - 2026 - Development and assessment of fluorescent-dyed, preserved invasive grass carp (Ctenopharyngodon idella) eggs as surrogates for live eggs in transport and dispersal control experiments","interactions":[],"lastModifiedDate":"2026-06-02T16:00:31.289319","indexId":"70274666","displayToPublicDate":"2026-03-07T10:24:19","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Development and assessment of fluorescent-dyed, preserved invasive grass carp (Ctenopharyngodon idella) eggs as surrogates for live eggs in transport and dispersal control experiments","title":"Development and assessment of fluorescent-dyed, preserved invasive grass carp (Ctenopharyngodon idella) eggs as surrogates for live eggs in transport and dispersal control experiments","docAbstract":"Invasive species such as grass carp (Ctenopharyngodon idella) pose substantial ecological threats to North American freshwater ecosystems. Understanding their early life stage behavior is critical for management efforts. From spawning to hatching, invasive carp eggs must remain suspended in the water column while drifting downstream for the best chance of survival. This highly vulnerable life stage is a potential target for population control to reduce recruitment. However, studying egg transport and potential dispersal control techniques is challenging, because the availability of live eggs and time period for experimentation are extremely limited. Additionally, accurately replicating the physical characteristics and transport mechanisms of fish eggs using surrogates in laboratory and field studies is not trivial. This study presents a novel method to create fluorescein-dyed, preserved grass carp eggs as surrogates for live eggs in transport and dispersal control experiments. This technique enables year-round studies of grass carp egg transport, offering managers a reliable tool for developing and testing dispersal control and passive sampling methods for invasive carp eggs. In this study, we rehydrate and dye preserved grass carp eggs in varying concentrations of aqueous fluorescein for a range of rehydration times, evaluate dye retention and egg visibility under ultraviolet light (UV-A), and measure diameters and settling velocities for comparison with live eggs. Eggs rehydrated in 0.100 g per liter fluorescein for 30 min maintain adequate brightness for up to 40 min in mixed conditions and exhibit mean settling velocities and densities similar to live eggs, making them ideal for laboratory experiments using quantitative imaging techniques.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.70124","usgsCitation":"Doyle, H.F., Stahlschmidt, B.H., Herndon, A.M., Prasad, V., George, A.E., Fischer, J.R., Jackson, P.R., Cory D. Suski, and Tinoco, R.O., 2026, Development and assessment of fluorescent-dyed, preserved invasive grass carp (Ctenopharyngodon idella) eggs as surrogates for live eggs in transport and dispersal control experiments: River Research and Applications, v. 42, no. 5, p. 976-988, https://doi.org/10.1002/rra.70124.","productDescription":"13 p.","startPage":"976","endPage":"988","ipdsId":"IP-172670","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":502166,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":502461,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.70124","text":"Publisher Index Page"}],"volume":"42","issue":"5","noUsgsAuthors":false,"publicationDate":"2026-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Doyle, Henry F. 0000-0001-9942-8602","orcid":"https://orcid.org/0000-0001-9942-8602","contributorId":369222,"corporation":false,"usgs":false,"family":"Doyle","given":"Henry","middleInitial":"F.","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":958621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stahlschmidt, Benjamin H. 0000-0001-6197-662X","orcid":"https://orcid.org/0000-0001-6197-662X","contributorId":211250,"corporation":false,"usgs":true,"family":"Stahlschmidt","given":"Benjamin","email":"","middleInitial":"H.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":958622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herndon, Anne Marie 0000-0002-7057-0303","orcid":"https://orcid.org/0000-0002-7057-0303","contributorId":332776,"corporation":false,"usgs":true,"family":"Herndon","given":"Anne","email":"","middleInitial":"Marie","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":958623,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prasad, Vindhyawasini 0000-0003-0585-7217","orcid":"https://orcid.org/0000-0003-0585-7217","contributorId":296287,"corporation":false,"usgs":false,"family":"Prasad","given":"Vindhyawasini","email":"","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":958624,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"George, Amy E. 0000-0003-1150-8646 ageorge@usgs.gov","orcid":"https://orcid.org/0000-0003-1150-8646","contributorId":3950,"corporation":false,"usgs":true,"family":"George","given":"Amy","email":"ageorge@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":958625,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fischer, Jesse Robert 0000-0002-9071-7931","orcid":"https://orcid.org/0000-0002-9071-7931","contributorId":329677,"corporation":false,"usgs":true,"family":"Fischer","given":"Jesse","email":"","middleInitial":"Robert","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":958626,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958627,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cory D. Suski","contributorId":369224,"corporation":false,"usgs":false,"family":"Cory D. Suski","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":958628,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tinoco, Rafael O.","contributorId":211779,"corporation":false,"usgs":false,"family":"Tinoco","given":"Rafael","email":"","middleInitial":"O.","affiliations":[{"id":38317,"text":"Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL","active":true,"usgs":false}],"preferred":false,"id":958629,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70274277,"text":"70274277 - 2026 - Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach","interactions":[],"lastModifiedDate":"2026-03-24T17:12:07.583859","indexId":"70274277","displayToPublicDate":"2026-03-07T10:02:44","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach","docAbstract":"Ciénegas are rare wetlands in arid landscapes of the North American Southwest, historically providing critical ecological and hydrological functions but increasingly threatened by changing climate and land use pressures. This study quantifies changes in ciénega condition and floodplain dynamics using a state-and-transition model (STM) informed by expert knowledge and remote sensing. Key factors include woody plant encroachment, water availability, and soil aggradation. We mapped 31 ciénegas with high-resolution imagery and analyzed Landsat data (1985–2023) to assess vegetation health and moisture using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII). Results show substantial interannual variability in phenology, water stress, and soil moisture, with regional drying and elevation strongly influencing ciénega resilience. We classified ciénegas into three functional states—healthy, desiccated, and dormant—and mapped their 2023 condition. Trend analyses indicate most ciénegas exhibit greening despite drought, though localized variability underscores the need for site-specific management. None are in a stable climax (reference) state; rather, they transition among states in response to external drivers. Increasing woody plant cover and surface drying, likely linked to declining regional water tables, favor deep-rooted species over wetland grasses—a pattern mirrored in adjacent control plots. Spatially explicit analysis revealed intra-ciénega variability often masked by aggregated data, highlighting the importance of high-resolution monitoring. Seasonal and long-term trends provide context for understanding ciénega dynamics, including degradation and restoration pathways. This study emphasizes the importance of groundwater conservation and demonstrates how remote sensing supports long-term monitoring. The STM framework offers a practical tool for adaptive management to sustain freshwater resources in arid environments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2026.114741","usgsCitation":"Norman, L., Petrakis, R.E., Wilson, N.R., Middleton, B.R., Villarreal, M.L., Pollock, M., Minckley, T.A., and Hendrickson, D., 2026, Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach: Ecological Indicators, v. 184, 114741, 16 p., https://doi.org/10.1016/j.ecolind.2026.114741.","productDescription":"114741, 16 p.","ipdsId":"IP-179305","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":501684,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2026.114741","text":"Publisher Index Page"},{"id":501477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, New Mexico","otherGeospatial":"Sonora","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.05152972005978,\n 33.0768867725987\n ],\n [\n -112.05152972005978,\n 29.88732922369421\n ],\n [\n -108.36301240182003,\n 29.88732922369421\n ],\n [\n -108.36301240182003,\n 33.0768867725987\n ],\n [\n -112.05152972005978,\n 33.0768867725987\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"184","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":957547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Petrakis, Roy E. 0000-0001-8932-077X rpetrakis@usgs.gov","orcid":"https://orcid.org/0000-0001-8932-077X","contributorId":174623,"corporation":false,"usgs":true,"family":"Petrakis","given":"Roy","email":"rpetrakis@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":957548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, Natalie R. 0000-0001-5145-1221 nrwilson@usgs.gov","orcid":"https://orcid.org/0000-0001-5145-1221","contributorId":214982,"corporation":false,"usgs":true,"family":"Wilson","given":"Natalie","email":"nrwilson@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":957549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Middleton, Barry R.","contributorId":367728,"corporation":false,"usgs":false,"family":"Middleton","given":"Barry","middleInitial":"R.","affiliations":[{"id":36921,"text":"Ret. USGS","active":true,"usgs":false}],"preferred":false,"id":957550,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":214980,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":957551,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pollock, Michael","contributorId":367729,"corporation":false,"usgs":false,"family":"Pollock","given":"Michael","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":957552,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Minckley, Thomas A.","contributorId":367730,"corporation":false,"usgs":false,"family":"Minckley","given":"Thomas","middleInitial":"A.","affiliations":[{"id":87617,"text":"University of Wyoming, Department of Geology and Geophysics, Laramie, WY 82071-2000","active":true,"usgs":false}],"preferred":false,"id":957553,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hendrickson, Dean","contributorId":367731,"corporation":false,"usgs":false,"family":"Hendrickson","given":"Dean","affiliations":[{"id":87618,"text":"University of Texas at Austin, College of Natural Sciences, Austin, TX 78712","active":true,"usgs":false}],"preferred":false,"id":957554,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70274196,"text":"ofr20261063 - 2026 - Evaluation of turbidity corrections for EXO fluorescent dissolved organic matter (fDOM) sensors","interactions":[],"lastModifiedDate":"2026-03-06T21:45:10.353284","indexId":"ofr20261063","displayToPublicDate":"2026-03-06T11:20:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2026-1063","displayTitle":"Evaluation of Turbidity Corrections for EXO Fluorescent Dissolved Organic Matter (fDOM) Sensors","title":"Evaluation of turbidity corrections for EXO fluorescent dissolved organic matter (fDOM) sensors","docAbstract":"The use of field-deployable fluorescence sensors to better understand dissolved organic matter concentrations and composition has grown immensely in recent years. Applications of these sensors to critical monitoring efforts have also grown, encompassing post-fire monitoring, wastewater tracking, and use as a proxy for various contaminants. Despite the growth, it is well known that these sensors require corrections for temperature (Watras and others, 2011) and are subject to many light-field interferences caused by both scattering and absorbance due to dissolved and particulate substances (Downing and others, 2012; Lee and others, 2015; Booth and others, 2023). The most common fluorescence sensors used by the U.S. Geological Survey (USGS) include those targeting fluorescent dissolved organic matter (fDOM) and chlorophylls. Because fDOM sensors primarily measure fluorescence in the dissolved to colloidal phases, corrections to the interferences caused by particulates can be made relatively easily. By the end of 2024, the USGS had 69 fDOM sensors deployed within official water quality monitoring networks included on the USGS National Water Dashboard (https://dashboard.waterdata.usgs.gov/app/nwd/en/) and numerous others used in surveys and research applications across the Nation.
Although temperature corrections are widely applicable across sensor models, interference corrections can be model specific due to differences in design specifications across manufacturers and models (Booth and others, 2023). The corrections are also potentially subject to changes in manufacturing within a specific sensor model. Recently, USGS staff obtained information regarding possible changes in the manufacturing of its most widely-used fDOM sensor model, raising concerns about data consistency and quality in the USGS fDOM sensor networks.
Furthermore, changes in turbidity sensors since the corrections guidance was performed may also affect the performance of the corrections. The turbidity sensor used in the original experiments (Downing and others, 2012) was determined to have a signal output approximately 1.3 times higher than the output of the turbidity sensor currently used in an extensive field comparison study (Messner and others, 2023). With these changes, it is imperative that the corrections be reevaluated to maintain data consistency and continuity across the USGS.
In this study, we evaluated turbidity corrections for fDOM sensors over a range of serial numbers covering manufacturing dates 2015 through 2022 and turbidity serial numbers covering the range 2013 through 2022. The goal was to determine whether reported changes in the manufacturing process of the fDOM and turbidity sensors affected the correction approach developed by Downing and others (2012) such that additional guidance would be required to address this manufacturing change. To evaluate, we repeated a laboratory-based test similar to that performed by Downing and others (2012) in which a series of tank experiments with multiple sensors were deployed in a suspension of Elliot Silt Loam (ESL). High turbidities of the ESL suspension were maintained throughout the tank by turbulent recirculation using submersible pumps. Particulates were removed using a recirculated line equipped with a capsule filter (0.45 micron). Measurements were collected throughout the filtration until turbidities reached approximately 5 formazin nephelometric units (FNU; data available in Baxter and others, 2023). Each experimental run included a mixture of unique sensor combinations to account for variability imposed by the turbidity and temperature sensors. The fDOM correction factor was calculated for each combination of fDOM and turbidity sensors included in the test.
We observed no systematic change in fDOM correction coefficients across serial numbers representing manufacturing years 2015 through 2022. However, the results highlighted questions raised about the corrections for high-turbidity samples, as noted in USGS Techniques and Methods (Booth and others, 2023). Applying the inverse of the commonly-used fDOM ratio with a quadratic fit performed better than the exponential fits when correcting fDOM data for turbidity in the ESL laboratory filtration test and generated a simple scale factor correction equation. This approach also served as a better indicator of data quality than the exponential fit approach. Similar to fDOM, more rigorous quality assurance measures may be necessary to evaluate turbidity sensor calibrations and performance. Sensors exceeding a certain age may need to be replaced despite passing quality assurance checks during calibration. Further testing of the turbidity corrections for different sediment and water types is warranted to better understand the variations in the fits and correctable ranges of turbidity in different systems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20261063","programNote":"Water Resources Mission Area","usgsCitation":"Fleck, J.A., Baxter, T.J., and Hansen, A.M., 2026, Evaluation of turbidity corrections for fluorescent dissolved organic matter (fDOM) sensors: U.S. Geological Survey Open-File Report 2026–1063, 30 p., https://doi.org/10.3133/ofr20261063.","productDescription":"Report: vi, 30 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-171907","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":500842,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2026/1063/coverthb.jpg"},{"id":500843,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2026/1063/ofr20261063.pdf","text":"Report","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2026-1063 PDF"},{"id":500844,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20261063/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2026-1063 HTML"},{"id":500845,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2026/1063/ofr20261063.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2026-1063 XML"},{"id":500846,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2026/1063/images"},{"id":500847,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OB430E","text":"USGS data release","linkHelpText":"Fluorescence sensor measurements in sediment suspensions to evaluate turbidity corrections"}],"contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
We aimed to compare two machine learning approaches—boosted beta regression (BBR) and beta mixed model forest (BMF)—to a Bayesian mixed-effects beta regression (BME) for the prediction of rotary screw trap (RST) efficiency for out-migrating juvenile salmonids from environmental covariates.
We identified two machine learning approaches that shared the ability to model overdispersed probabilities. We compared the BBR and BMF machine learning models to a BME model to evaluate precision in detection probability prediction and model performance on bias in parameter estimation. We tested our three candidate models using a simulation study to understand the specific advantages and disadvantages of each when the data set was increasingly sparse and the capture probabilities were realistically small. We then applied the models to a case study of RST data from the Klamath River in California, United States.
The BME and BMF outperformed BBR in all simulated scenarios, although the BMF displayed poor explanatory power. In the case study, the BME and BMF identified environmental covariates that predicted RST efficiency.
Using the BME as a benchmark for comparing machine learning approaches to trap efficiency modeling, our simulations and case study demonstrated that the BMF performed well and is a viable modeling approach with strong predictive power. The BME model would be the preferred modeling approach when its strong explanatory power is desired.
Restoration of impaired floodplains is an increasingly prevalent strategy for alleviating water quality concerns and reducing downstream flooding at watershed scales. Floodplains temporarily store water and slow flow velocity to promote sedimentation during overbank flooding and remove inorganic nitrogen from floodwater and groundwater via denitrification. Evaluating the impacts of different restoration strategies on denitrification can inform more strategic investments into floodplain modifications that improve water quality outcomes. Our research investigates how denitrification rates in floodplains respond to environmental factors that are actionable from an engineering perspective through design and water resources management. We seasonally measured soil denitrification enzyme activity and various environmental characteristics in 4 floodplains with different restoration design and management approaches at the confluence of the Wabash and Tippecanoe Rivers in Indiana, United States. Our results showed that denitrification rates in an agricultural floodplain were significantly lower than in restored floodplains with native vegetation. Certain soil conditions characteristic of floodplain wetlands were associated with higher denitrification, particularly elevated total nitrogen, moisture, silt, and organic matter contents. Vegetation species composition was correlated with denitrification rates. This link may reflect the direct effects of vegetation on soil conditions, such as supplying labile organic carbon, or indirect effects, such as vegetation acting as an indicator of hydrologic regime and land use. Denitrification seasonally varied, peaking in winter when nitrate supply from rivers draining agricultural watersheds in the region is also high. Substrate limitation of soil denitrification enzyme activity was most significant during the summer when overbank flooding, which replenishes soil nitrogen stocks, rarely occurs. Our findings indicate that denitrification capacity will likely be maximized in riverine floodplains that are restored as wetlands with diverse native vegetation and enhanced hydrologic connectivity. Such restoration activities promote higher denitrification rates via elevated moisture, fine sediment deposition, and soil organic matter.
","language":"English","publisher":"University of Vermont Press","doi":"10.70793/jeed.13","usgsCitation":"Lay, D.W., McMillan, S.W., Hosen, J.D., Dey, S., and Noe, G.E., 2026, Assessing environmental drivers of denitrification in restored riverine floodplains: Journal of Ecological Engineering Design, v. 4, no. 1, 17 p., https://doi.org/10.70793/jeed.13.","productDescription":"17 p.","ipdsId":"IP-179949","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":502474,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.70793/jeed.13","text":"Publisher Index Page"},{"id":502206,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Tippecanoe River, Wabash River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.73421342718163,\n 40.54578001376902\n ],\n [\n -86.77854172394578,\n 40.56893409536539\n ],\n [\n -86.85226822336595,\n 40.486689152088985\n ],\n [\n -86.83272067583472,\n 40.46994703338217\n ],\n [\n -86.73421342718163,\n 40.54578001376902\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","issue":"1","noUsgsAuthors":false,"publicationDate":"2026-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Lay, Danielle Winter","contributorId":369252,"corporation":false,"usgs":false,"family":"Lay","given":"Danielle","middleInitial":"Winter","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":958691,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McMillan, Sara W.","contributorId":369253,"corporation":false,"usgs":false,"family":"McMillan","given":"Sara","middleInitial":"W.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":958692,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hosen, Jacob D.","contributorId":369254,"corporation":false,"usgs":false,"family":"Hosen","given":"Jacob","middleInitial":"D.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":958693,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dey, Sayan","contributorId":369255,"corporation":false,"usgs":false,"family":"Dey","given":"Sayan","affiliations":[{"id":30787,"text":"Saint Louis University","active":true,"usgs":false}],"preferred":false,"id":958694,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":958695,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70275563,"text":"70275563 - 2026 - Carcass size and ground substrate drive detection rates of avian carcasses by human surveyors and a dog team","interactions":[],"lastModifiedDate":"2026-05-04T16:37:19.643763","indexId":"70275563","displayToPublicDate":"2026-03-06T09:32:11","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Carcass size and ground substrate drive detection rates of avian carcasses by human surveyors and a dog team","docAbstract":"Accurate avian mortality estimates are essential for understanding anthropogenic impacts to bird populations and informing conservation strategies. Carcass surveys are commonly conducted by human surveyors or by detection dogs, but the factors influencing surveyor detection abilities have not been fully explored. In this study, we conducted two years of detection trials in the semi-arid high desert of southern New Mexico, USA, testing 27 human surveyors and one conservation detection dog across 1096 trials with 238 carcasses representing 50 avian species. We directly compared detection abilities between surveyor types (human and dog) and identified key factors influencing detection probabilities. The conservation detection dog exhibited a significantly higher detection probability (mean = 0.87) than human surveyors (mean = 0.49, individuals ranged 0.25–0.71), consistent with previous studies. Detection probabilities for both surveyor types were influenced by carcass size and ground substrate; detection probability was higher for larger carcasses and areas with lower vegetative complexity. We discuss our results in the context of common tradeoffs faced by managers in designing carcass surveys and how guidance may vary under different scenarios. Broadly, our study provides valuable insight that can enhance wildlife mortality monitoring, ensuring more accurate mortality estimates to inform management and conservation efforts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2026.e04148","usgsCitation":"Boland, K.C., Lawson, A.J., Osterhaus, D.M., Cutler, P.L., Davidson, G.A., and Desmond, M.J., 2026, Carcass size and ground substrate drive detection rates of avian carcasses by human surveyors and a dog team: Global Ecology and Conservation, v. 67, e04148, 11 p., https://doi.org/10.1016/j.gecco.2026.e04148.","productDescription":"e04148, 11 p.","ipdsId":"IP-183521","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":504181,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2026.e04148","text":"Publisher Index Page"},{"id":503951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"White Sands Missile Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.5450618838406,\n 32.43486825555317\n ],\n [\n -106.5450618838406,\n 32.34937710226822\n ],\n [\n -106.42875020074301,\n 32.34937710226822\n ],\n [\n -106.42875020074301,\n 32.43486825555317\n ],\n [\n -106.5450618838406,\n 32.43486825555317\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"67","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boland, Kelley C.","contributorId":371023,"corporation":false,"usgs":false,"family":"Boland","given":"Kelley","middleInitial":"C.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":960892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lawson, Abigail Jean 0000-0002-2799-8750","orcid":"https://orcid.org/0000-0002-2799-8750","contributorId":276319,"corporation":false,"usgs":true,"family":"Lawson","given":"Abigail","email":"","middleInitial":"Jean","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":960893,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Osterhaus, Dylan M.","contributorId":371024,"corporation":false,"usgs":false,"family":"Osterhaus","given":"Dylan","middleInitial":"M.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":960894,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cutler, Patricia L.","contributorId":371026,"corporation":false,"usgs":false,"family":"Cutler","given":"Patricia","middleInitial":"L.","affiliations":[{"id":88075,"text":"U.S. Army Garrison","active":true,"usgs":false}],"preferred":false,"id":960895,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davidson, Gregory A.","contributorId":371028,"corporation":false,"usgs":false,"family":"Davidson","given":"Gregory","middleInitial":"A.","affiliations":[{"id":88076,"text":"Find It Detection Dogs","active":true,"usgs":false}],"preferred":false,"id":960896,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Desmond, Martha J.","contributorId":371029,"corporation":false,"usgs":false,"family":"Desmond","given":"Martha","middleInitial":"J.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":960897,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70275205,"text":"70275205 - 2026 - Stream macroinvertebrate responses vary with region, land use and management practice type","interactions":[],"lastModifiedDate":"2026-04-22T14:34:11.340833","indexId":"70275205","displayToPublicDate":"2026-03-06T09:21:44","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Stream macroinvertebrate responses vary with region, land use and management practice type","docAbstract":"Intensive land use alters hydrology and water quality, threatening freshwater benthic macroinvertebrates. Over 200,000 management practices (MPs) have been implemented across the Chesapeake Bay watershed since the 1980s, yet biological responses remain inconsistent. We synthesized 29 studies from 4 physiographic provinces covering 8 MP categories and evaluated macroinvertebrate responses along MP gradients using structural (richness), functional (biomass), tolerance, and biotic metrics. We hypothesized that MPs enhancing habitat complexity or restoring flow regimes would benefit taxa sensitive to sediment, hydrologic instability and organic pollution, with outcomes shaped by regional context, land use, and chosen metrics. Four themes emerged. (i) Agricultural Riparian Forest Buffers (RFBs) consistently improved sensitive metrics related to abundance, biomass and richness. (ii) Urban streams with Stream Habitat Improvement and Management (SHIM) showed improved richness and diversity, but biomass and tolerance metrics declined or remained neutral, indicating unresolved hydrologic and pollutant stress. (iii) Structural and functional responses diverged: effect sizes for total and feeding-group biomasses (functional metrics) were negative, whereas genus-level Ephemeroptera-Plecoptera-Trichoptera (EPT) richness (structural metric) was positive, indicating that structural shifts may not track underlying production changes. (iv) Physiographic comparisons showed counterintuitive patterns, as RFBs improved EPT richness in Piedmont streams but had negative effects in the Coastal Plain. Evaluating MP effectiveness requires distinguishing a no-MP pathway (stressors → instream conditions → assemblages → responses) from an MP-mediated pathway (practice regime → modified stressors → instream conditions → assemblages → responses), underscoring the need for region-specific, multi-metric monitoring and improved understanding of MP density thresholds and recovery lags.
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