Coral reefs play vital roles in global marine systems and are currently facing increased threats of bleaching. Coral bleaching is heavily influenced by the host-associated microeukaryote community – most notably the dinoflagellate family Symbiodiniaceae. The apicomplexan family Corallicolidae, is the second most abundant member of the microeukaryote community, yet their role in coral health is largely unknown. To explore the role that this apicomplexan and the greater non-metazoan microeukaryotic community play in coral health, samples of a thermally sensitive scleractinian coral, Acropora hyacinthus, were collected over the course of a severe coral bleaching event and its aftermath. Through 18S rRNA gene sequencing analysis, we found that taxa within the family Corallicolidae were relatively enriched in corals during, and immediately after, the severe bleaching event as compared to before or one year after. Although utilizing 18S rRNA gene sequencing methods is not the standard for Symbiodiniaceae community profiling, we were able to observe symbiont shuffling among the Symbiodiniaceae communities, as the dominant algal symbiont shifted from the genus Cladocopium to the genus Symbiodinium following the bleaching event. Furthermore, the non-metazoan microeukaryote community displayed a general shift towards a state of dysbiosis; evidenced by substantial changes in both microeukaryote community composition and dispersion. These results offer insight into the dynamics of apicomplexans throughout the course of an increasingly common global coral reef stressor.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2025.1626071","usgsCitation":"Peterson, A., Patton, S., Schmeltzer, E.R., Grupstra, C., Howe-Kerr, L., Klinges, J.G., Maher, R., Messyasz, A., Seabrook, S., Thurber, A., Correa, A., and Vega Thurber, R., 2025, Apicomplexan and non-metazoan microeukaryotes in the thermosensitive reef-building coral Acropora hyacinthus shift in abundance throughout an extreme coral bleaching event: Frontiers in Marine Science, v. 12, 1626071, 15 p., https://doi.org/10.3389/fmars.2025.1626071.","productDescription":"1626071, 15 p.","ipdsId":"IP-180885","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":496144,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2025.1626071","text":"Publisher Index Page"},{"id":495897,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"French Polynesia","otherGeospatial":"Mo’orea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -149.94261835394644,\n -17.463455039196617\n ],\n [\n -149.94261835394644,\n -17.612716041327616\n ],\n [\n -149.73838596029304,\n -17.612716041327616\n ],\n [\n -149.73838596029304,\n -17.463455039196617\n ],\n [\n -149.94261835394644,\n -17.463455039196617\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2025-09-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Peterson, Athena","contributorId":361686,"corporation":false,"usgs":false,"family":"Peterson","given":"Athena","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":949231,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patton, Sunni","contributorId":361687,"corporation":false,"usgs":false,"family":"Patton","given":"Sunni","affiliations":[{"id":86323,"text":"Oregon State University; University of California;","active":true,"usgs":false}],"preferred":false,"id":949232,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmeltzer, Emily Rose 0000-0002-5390-4308","orcid":"https://orcid.org/0000-0002-5390-4308","contributorId":361688,"corporation":false,"usgs":true,"family":"Schmeltzer","given":"Emily","middleInitial":"Rose","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":949233,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grupstra, Carsten","contributorId":361689,"corporation":false,"usgs":false,"family":"Grupstra","given":"Carsten","affiliations":[{"id":86324,"text":"Rice University; Boston University; Florida Atlantic University","active":true,"usgs":false}],"preferred":false,"id":949234,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Howe-Kerr, Lauren","contributorId":361690,"corporation":false,"usgs":false,"family":"Howe-Kerr","given":"Lauren","affiliations":[{"id":86325,"text":"Rice University; Minderoo Foundation","active":true,"usgs":false}],"preferred":false,"id":949235,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Klinges, J. 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When singing rates are tied to breeding stage, the rate at which birds are available for detection by surveyors can also show seasonal patterns that may shift with spring phenology. As the timing of peak bird availability changes over years, monitoring programs that do not account for changing availability could incorrectly conclude that there is a change in population size. We used a 20-yr point-count dataset to test for relationships between bird availability and spring vegetation phenology for 27 species in boreal Alaska. Nine of 22 migratory species showed a significant effect of day of spring (DOS) on availability, usually with availability declining over the survey window (late spring and early summer). In contrast, 3 of 5 resident species showed availability increasing over the survey window. We then conducted a simulation study to evaluate how changing spring phenology could affect estimates of population trend under a static survey window. We found that including DOS in the model as a covariate of availability prevented bias in the trend estimates and did not reduce precision. However, when the model ignored the effect of DOS on availability, population trend estimates were often significantly biased when spring phenology was advancing. Our study adds to previous evidence that bird availability is often related to spring phenology, and demonstrates that failing to account for seasonal changes in availability could result in the spurious estimation of a population trend when spring phenology changes over time. In some cases, the bias could be large enough to change species status assessments under IUCN Red List Criteria. Monitoring programs for birds and other taxa with seasonally varying availability could avoid bias by simply measuring and modeling the relationship between DOS and availability.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithapp/duaf052","usgsCitation":"Weiser, E.L., Johnson, J., Matsuoka, S.M., and Handel, C.M., 2025, Accounting for seasonal patterns in bird availability prevents biased population trend estimates with advancing spring phenology: Ornithological Applications, v. 127, no. 4, p. 1-11, https://doi.org/10.1093/ornithapp/duaf052.","productDescription":"11 p.","startPage":"1","endPage":"11","ipdsId":"IP-178417","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":496081,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"127","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Weiser, Emily L. 0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":213770,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":949463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, James","contributorId":173063,"corporation":false,"usgs":false,"family":"Johnson","given":"James","email":"","affiliations":[],"preferred":false,"id":949464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Matsuoka, Steven M. 0000-0001-6415-1885 smatsuoka@usgs.gov","orcid":"https://orcid.org/0000-0001-6415-1885","contributorId":184173,"corporation":false,"usgs":true,"family":"Matsuoka","given":"Steven","email":"smatsuoka@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":949465,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Handel, Colleen M. 0000-0002-0267-7408 cmhandel@usgs.gov","orcid":"https://orcid.org/0000-0002-0267-7408","contributorId":3067,"corporation":false,"usgs":true,"family":"Handel","given":"Colleen","email":"cmhandel@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":949466,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274071,"text":"70274071 - 2025 - Beyond the mangroves: A global synthesis of tidal forested wetland types, drivers and future information opportunities","interactions":[],"lastModifiedDate":"2026-02-23T15:34:30.951807","indexId":"70274071","displayToPublicDate":"2025-09-20T09:23:24","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"title":"Beyond the mangroves: A global synthesis of tidal forested wetland types, drivers and future information opportunities","docAbstract":"There is increasing awareness of the global diversity of tidal forested wetlands (TFWs) and their significance in the provision of ecosystem services. These ecosystems, including mangrove forests, tidal freshwater forested wetlands, supratidal forests and transitional forests together span multiple climatic zones, geomorphic settings, and inundation and salinity regimes. We utilise case studies across five continents to demonstrate the state of knowledge among TFWs. Intertidal mangroves are the best-defined of the TFWs thanks to decades of research on their geomorphology, hydrology and ecology across their broad distribution. Non-mangrove forest settings, however, demonstrate more diverse hydrological, biochemical and vegetation conditions. In many cases, non-mangrove forests are situated at upper intertidal or supratidal elevations, where surface waters and groundwater are subject to interactions between tides freshwater inputs. Salinity datasets show variations ranging from tidal freshwater forested wetlands and ‘low-salinity mangroves’ to mesohaline or marine salinities, often with high temporal variability. While the floristic composition of non-mangrove forests vary among biogeographic regions, locally dominant TFW species are commonly distributed beyond the tidal niche into non-tidal wetland and upland forests. This presents challenges for traditional remote sensing approaches to ecosystem mapping, which are mostly lacking for non-mangrove forests. Geomorphic approaches and developments in machine learning offer opportunities to address this.
","language":"English","publisher":"Earth ArXiv","doi":"10.31223/X5ZF2B","usgsCitation":"Kelleway, J.J., Noe, G.E., Krauss, K., Brophy, L., Conner, W.H., Duberstein, J.A., Friess, D.A., Gedan, K., White, E., Adame, M.F., Adams, J.B., Carvalho, R.C., Freddie, A., Ikenna, I.N., Ocasio, E.R., Owers, C.J., Sasmito, S., Swales, A., Stewart-Sinclair, P., Ward, R.D., Zabarte-Maeztu, I., 2025, Beyond the mangroves: A global synthesis of tidal forested wetland types, drivers and future information opportunities, preprint posted September 20, 2025, https://doi.org/10.31223/X5ZF2B.","productDescription":"85 p.","ipdsId":"IP-185904","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":500404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Kelleway, J. 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Overall, TE water withdrawal and consumption trends declined across CONUS by 14,335 and 278 million liters/day, respectively. Decreasing water withdrawal and consumption trends for TE plants are driven largely by switching from coal-fired plants to other generation technologies. TE plant cooling system technology has also changed, with large declining trends for TE plants using once-through cooling systems and small increasing consumption trends for TE plants using recirculating tower cooling systems. Fifteen hydrologic regions have decreasing trends in withdrawals and consumption. The largest decreases are for coal-fired plants using once-through freshwater cooling systems in the Great Lakes and Ohio hydrologic regions. Natural gas combined cycle plants with recirculating tower cooling systems have increased water consumption trends across most of the CONUS hydrologic regions. Some TE plants with recirculating tower or once-through cooling systems withdraw water volumes that on average are close to or exceed average simulated streamflows. Most of these situations occur in the central and eastern U.S., potentially leading to water availability issues among competing water needs, ecosystem impacts from thermal pollution, and power generation constraints.
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2021","interactions":[],"lastModifiedDate":"2026-02-03T15:29:46.906418","indexId":"sir20255079","displayToPublicDate":"2025-09-19T12:25:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5079","displayTitle":"Microbial Source Tracking in Cedar and Crane Creeks Near Curtice, Ohio, 2021","title":"Microbial source tracking in Cedar and Crane Creeks near Curtice, Ohio, 2021","docAbstract":"Elevated concentrations of Escherichia coli (E. coli) bacteria and signs of sewage lead to impairment of Cedar and Crane Creeks near the town of Curtice, Ohio. In 2021, the U.S. Geological Survey, in cooperation with Ohio Environmental Protection Agency, collected samples and analyzed them for concentrations of E. coli and microbial source tracking (MST) markers to help characterize the locations and sources of fecal contamination and better inform potential remediation strategies. The study included a total of 118 samples collected at 12 sites (6 on Cedar Creek and 6 on Crane Creek) from May to September 2021 during wet and dry weather conditions.
All samples were analyzed for E. coli concentrations, and human and canine-associated MST markers. A subset of samples was analyzed for MST markers associated with swine, ruminant, cattle, horse, waterfowl, and poultry. Human-origin fecal contamination was found at all sites sampled in this study and concentrations of the human-associated MST marker HF183/BacR287 were significantly correlated with E. coli concentrations. The HF183/BacR287 marker was detected in 114 of 118 samples and the detection frequency in samples at each site ranged from 90 to 100 percent. E. coli concentrations exceeded the Ohio Environmental Protection Agency’s regulatory statistical threshold (410 most probable number of E. coli per 100 milliliters) in 91 percent of samples.
These findings verified that Cedar and Crane Creeks are impaired by bacteria, and the HF183/BacR287 marker results support that human-origin fecal contamination is the dominant contributor to that impairment. The canine-associated MST marker BacCan was also prevalent in collected samples (detected in 112 of 118 samples); however, BacCan can also be detected in human waste, so it is not feasible to ascertain whether canine feces is a source of contamination in these watersheds.
Human fecal contamination was nearly uniform among sites, but the Martin Williston Road ditch effluent site along Crane Creek had a significantly higher median HF183/BacR287 concentration than the other Crane Creek sites. Results indicate that the Martin Williston Road ditch is a potential source of human-origin fecal contamination to Crane Creek. There is likely additional human fecal contamination upstream from the study area.
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Emerging infectious diseases and biological invasions pose increasing threats to public and ecosystems health. Proactive measures—such as prevention and surveillance taken before initial detection of the pathogen or species—are essential to ensure minimal spread prior to first detection. We developed an optimization model to determine where, when, and how much effort should be allocated to prevention versus surveillance. The model accounts for imperfect detection, system dynamics, spatial heterogeneity in risk and costs and is scalable to large landscapes. We found that the most cost-effective strategy is to maintain the prevention and surveillance efforts at stable equilibrium for the majority of the time, with deviations occurring only initially to steer the system toward the equilibrium. The equilibrium effort is jointly determined by the introduction risk, management costs, and total budget. Application of this model to chronic wasting disease in New York State suggests that the optimal strategy could reduce the cumulative disease cases before initial detection by an average of 22% compared to current practice. The optimal surveillance strategy could detect the disease on average over 8 mo earlier than the current strategy.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2507202122","usgsCitation":"Wang, J., Hanley, B.J., Thompson, N.E., Gong, Y., Walsh, D.P., Gonzalez-Crespo, C., Huang, Y., Booth, J.G., Caudell, J.N., Miller, L.A., Schuler, K.L., 2025, Strategic planning of prevention and surveillance for emerging diseases and invasive species: PNAS, v. 122, no. 39, e2507202122, 9 p., https://doi.org/10.1073/pnas.2507202122.","productDescription":"e2507202122, 9 p.","ipdsId":"IP-177913","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500578,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12501194","text":"External Repository"},{"id":500349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","issue":"39","noUsgsAuthors":false,"publicationDate":"2025-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Jue","contributorId":211355,"corporation":false,"usgs":false,"family":"Wang","given":"Jue","email":"","affiliations":[],"preferred":false,"id":956087,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanley, Brenda J.","contributorId":366605,"corporation":false,"usgs":false,"family":"Hanley","given":"Brenda","middleInitial":"J.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":956088,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Noelle E.","contributorId":366606,"corporation":false,"usgs":false,"family":"Thompson","given":"Noelle","middleInitial":"E.","affiliations":[{"id":36225,"text":"Western Association of Fish and Wildlife Agencies","active":true,"usgs":false}],"preferred":false,"id":956089,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gong, Yu","contributorId":366607,"corporation":false,"usgs":false,"family":"Gong","given":"Yu","affiliations":[{"id":34006,"text":"Queen’s University","active":true,"usgs":false}],"preferred":false,"id":956090,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":956091,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gonzalez-Crespo, Carlos","contributorId":366611,"corporation":false,"usgs":false,"family":"Gonzalez-Crespo","given":"Carlos","affiliations":[{"id":35327,"text":"University of California – Davis","active":true,"usgs":false}],"preferred":false,"id":956092,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huang, Yitong","contributorId":366612,"corporation":false,"usgs":false,"family":"Huang","given":"Yitong","affiliations":[{"id":35327,"text":"University of California – Davis","active":true,"usgs":false}],"preferred":false,"id":956093,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Booth, James G.","contributorId":366613,"corporation":false,"usgs":false,"family":"Booth","given":"James","middleInitial":"G.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":956094,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Caudell, Joe N.","contributorId":366614,"corporation":false,"usgs":false,"family":"Caudell","given":"Joe","middleInitial":"N.","affiliations":[{"id":55448,"text":"Indiana Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":956095,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Miller, Landon A.","contributorId":366615,"corporation":false,"usgs":false,"family":"Miller","given":"Landon","middleInitial":"A.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":956096,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Schuler, Krysten L.","contributorId":366616,"corporation":false,"usgs":false,"family":"Schuler","given":"Krysten","middleInitial":"L.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":956097,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70274037,"text":"70274037 - 2025 - Ice Age biogeography corresponds with current climate vulnerability of freshwater fishes","interactions":[],"lastModifiedDate":"2026-02-23T17:00:47.816108","indexId":"70274037","displayToPublicDate":"2025-09-19T09:53:05","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Ice Age biogeography corresponds with current climate vulnerability of freshwater fishes","docAbstract":"1. Both local environmental factors and historical biogeography shape ecological communities, but determining which historical biogeographical patterns correspond with contemporary climate vulnerability is an underused conservation method. The historical colonization patterns of freshwater fishes following the Pleistocene (“Ice Age”) glaciations offers an ideal model for comparing historical biogeography and climate-change vulnerability.
2. We used current thermal niches and future stream-temperature projections to estimate the climate vulnerability of 29 Great Plains and Rocky Mountain fishes that we classified as either early or late colonists of the region in the wake of glacial retreat (~19,000 years ago).
3. Ninety-three percent of the most vulnerable species were amongst the earliest colonists of the region and we consider them “postglacial-pioneer species”. Median predicted site loss (number of historically occupied sites predicted to become too warm by end-of-century) was 0% for late colonizing species and 33% for early colonizing species.
4. We provide empirical evidence that postglacial-pioneer fishes are uniquely vulnerable to climate change, and we suggest this may apply to many taxa from formerly glaciated regions. More broadly, we demonstrate that evaluating the relationship between current species-environment patterns and historical biogeography may be a fruitful avenue for future climate change and conservation research.
","language":"English","publisher":"Wiley","doi":"10.1111/fwb.70098","usgsCitation":"Clancy, N.G., Budy, P.E., Walters, A.W., 2025, Ice Age biogeography corresponds with current climate vulnerability of freshwater fishes: Freshwater Biology, v. 70, no. 9, e70098, 11 p., https://doi.org/10.1111/fwb.70098.","productDescription":"e70098, 11 p.","ipdsId":"IP-160661","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.30686503555248,\n 65.96941120658738\n ],\n [\n -124.14315775412638,\n 45.060194959039514\n ],\n [\n -117.50546486911188,\n 33.44662341525755\n ],\n [\n -85.06334833163905,\n 30.623810459514957\n ],\n [\n -64.09085041980521,\n 45.76599507995419\n ],\n [\n -69.37510349475363,\n 64.53352486678543\n ],\n [\n -118.30686503555248,\n 65.96941120658738\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"70","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Clancy, Niall G.","contributorId":366799,"corporation":false,"usgs":false,"family":"Clancy","given":"Niall","middleInitial":"G.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":956244,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budy, Phaedra E. 0000-0002-9918-1678 pbudy@usgs.gov","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":140028,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra","email":"pbudy@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":956245,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956246,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70271479,"text":"sir20255082 - 2025 - Methods for estimating selected low-flow statistics at gaged and ungaged stream sites in Massachusetts","interactions":[],"lastModifiedDate":"2026-02-03T15:29:05.375527","indexId":"sir20255082","displayToPublicDate":"2025-09-19T09:50:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5082","displayTitle":"Methods for Estimating Selected Low-Flow Statistics at Gaged and Ungaged Stream Sites in Massachusetts","title":"Methods for estimating selected low-flow statistics at gaged and ungaged stream sites in Massachusetts","docAbstract":"The U.S. Geological Survey, in cooperation with the Massachusetts Department of Conservation and Recreation, Office of Water Resources, computed selected at-site streamflow statistics at U.S. Geological Survey streamgages in and near Massachusetts and developed regional regression equations for estimating selected streamflows at ungaged stream sites in Massachusetts. Two sets of regional regression equations were developed: (1) the “mainland” equations, for mainland Massachusetts excluding the area covered by the second set, and (2) the “southeastern” equations, for the Plymouth-Carver-Kingston-Duxbury aquifer area in southeastern Massachusetts and for Cape Cod. The regression equations and at-site statistics may be used by Federal, State, and local water managers in addressing water-resources issues relevant in Massachusetts.
Regional regression analyses for the mainland equations were developed to estimate the following 27 streamflow statistics: 99-, 98-, 95-, 90-, 85-, 80-, 75-, 70-, 60-, and 50-percent flow durations; monthly June, July, August, and September 90- and 50-percent flow durations; February, June, and August median of the monthly means; harmonic mean; and medians of the following annual low-flow frequency statistics: 7-day; 7-day, 2-year; 7-day, 10-year; 30-day, 2-year; and 30-day, 10-year. The analyses used 81 streamgages with minimal to no regulations in and near Massachusetts. The regression analyses determined that four basin characteristics—drainage area, combined hydrologic soils A and B, streamflow variability index, and annual mean temperature—were the only significant explanatory variables for the different mainland equations.
Regional regression equations were developed for the Plymouth-Carver-Kingston-Duxbury aquifer area in southeastern Massachusetts and Cape Cod, because surface-water drainage areas and groundwater contributing areas do not always coincide in this area of the State. The regression analyses to estimate 10 flow durations from the 99th to 50th percentiles used 18 streamflow sites with some occasional minor regulations—because there are few unregulated streams in southeastern Massachusetts. The analyses determined that groundwater contributing area and storage (combined water bodies and wetlands) were the only significant explanatory variables in the southeastern equations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255082","collaboration":"Prepared in cooperation with the Massachusetts Department of Conservation and Recreation, Office of Water Resources","usgsCitation":"Bent, G.C., Ahearn, E.A., and Fair, J.H., 2025, Methods for estimating selected low-flow statistics at gaged and ungaged stream sites in Massachusetts: U.S. Geological Survey Scientific Investigations Report 2025–5082, 76 p., https://doi.org/10.3133/sir20255082.","productDescription":"Report: ix, 76 p.; 3 Data Releases","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-164297","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":495617,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P142FWRJ","text":"USGS data release","linkHelpText":"MODPATH6 datasets using MODFLOW and SEAWAT input for development of 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The U.S. Geological Survey’s seismic velocity model for the Cascadia Subduction Zone provides P- and S-wave velocity (VP and VS, respectively) information from 40.2° to 50.0° N. latitude and −129.0° to −121.0° W. longitude, and is used to support a variety of research topics, including three-dimensional (3D) earthquake simulations and seismic hazard assessment in the Pacific Northwest. This report describes an update to the previous version (v) 1.6 of the 3D seismic velocity model for the Cascadia Subduction Zone. This new model (herein referred to as v1.7) contains more detailed near-surface structure for improved earthquake ground motion modeling. Updated features include the addition of a new shallow soil velocity model in the top few hundred meters and the option of adding user-specified topography. Although v1.6 of the Cascadia seismic velocity model has a minimum VS of 600 meters per second (m/s), the new model (v1.7) has a minimum VS of approximately 40 m/s. Overall, this update will allow for more accurate ground motion estimates from 3D simulations of scenario earthquakes in the Cascadia Subduction Zone region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251045","usgsCitation":"Wirth, E.A., Grant, A.R., Stone, I.P., Stephenson, W.J., and Frankel, A.D., 2025, Three-dimensional seismic velocity model for the Cascadia Subduction Zone with shallow soils and topography, version 1.7: U.S. Geological Survey Open-File Report 2025–1045, 18 p., https://doi.org/10.3133/ofr20251045.","productDescription":"Report: vi, 18 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-161899","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":495639,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14HJ3IC","text":"USGS data release","description":"Wirth, E.A., Grant, A.R., Stone, I.P., Stephenson, W.J., and Frankel, A.D., 2025, Data for A 3-D Seismic Velocity Model for Cascadia with Shallow Soils & Topography, Version 1.7: U.S. Geological Survey data release, https://doi.org/10.5066/P14HJ3IC","linkHelpText":"Data for A 3-D Seismic Velocity Model for Cascadia with Shallow Soils & Topography, Version 1.7"},{"id":495638,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1045/images"},{"id":495637,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1045/ofr20251045.XML","description":"OFR 2025-1045 XML"},{"id":495636,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251045/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1045 HTML"},{"id":495635,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1045/ofr20251045.pdf","text":"Report","size":"4.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1045 PDF"},{"id":495634,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1045/coverthb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","otherGeospatial":"Cascadia Subduction Zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121,\n 50\n ],\n [\n -129,\n 50\n ],\n [\n -129,\n 40.2\n ],\n [\n -121,\n 40.2\n ],\n [\n -121,\n 50\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Earthquake Science Center
U.S. Geological Survey
350 N. Akron Rd.
Moffett Field, CA 94035
Earthquakes and their cascading consequences pose a significant threat to the people, environment, infrastructure, and economy of the U.S. Pacific Northwest. The Pacific Northwest is susceptible to three types of earthquakes: deep (intraslab) earthquakes, subduction zone (megathrust) earthquakes, and shallow crustal earthquakes. For each of these earthquake types, earth scientists can use a variety of methods to estimate the probability of occurrence for future events, which constrains seismic hazard and informs building codes. The timing of past earthquakes indicates that there is an 85-percent chance of a magnitude 6.5 or greater deep earthquake in the Puget Sound region; a 10-15-percent chance of an approximately magnitude 9 earthquake on the Cascadia Subduction Zone; and a 17-percent chance of a magnitude 6.5 or greater crustal fault earthquake in the Puget Sound region in the next 50 years. Individuals and communities can take simple steps to prepare for and reduce the impact of future earthquakes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253050","usgsCitation":"Wirth, E., Frankel, A., Sherrod, B., Grant, A., Dunham, A., Stone, I., and Grossman, J., 2025, Earthquake probabilities and hazards in the U.S. Pacific Northwest (ver. 1.1, September 30, 2025): U.S. Geological Survey Fact Sheet 2025–3050, 6 p., https://doi.org/10.3133/fs20253050.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-172377","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":495644,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3050/fs20253050.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3050"},{"id":495647,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253050/full"},{"id":496244,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2025/3050/versionHist.txt","text":"Version History","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":496024,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118878.htm","linkFileType":{"id":5,"text":"html"}},{"id":495646,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3050/images/"},{"id":495645,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3050/fs20253050.XML"},{"id":495643,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3050/coverthb.jpg"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -131.42807526130667,\n 49.386193359384805\n ],\n [\n -131.42807526130667,\n 36.38535423200561\n ],\n [\n -119.71998032318022,\n 36.38535423200561\n ],\n [\n -119.71998032318022,\n 49.386193359384805\n ],\n [\n -131.42807526130667,\n 49.386193359384805\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: September 19, 2025; Version 1.1: September 30, 2025","contact":"Earthquake Science Center, Seattle Field Office
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University of Washington, Department of Earth and Space Sciences
4000 15th Ave NE
Seattle, WA 98195
Earthquakes and their cascading consequences pose a significant threat to the people, environment, infrastructure, and economy of the U.S. Pacific Northwest. The timing of previous earthquakes helps estimate the likelihood of future events.
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This study applied a river catchment-scale approach to identify PFAS source zones and assess the relative importance of industrial PFAS sources in the River Mersey, UK – a post-industrial, densely populated catchment with diverse PFAS sources. Synoptic sampling and PFAS river load analysis identified key sub-catchments and river stretches contributing the majority of PFAS. Notably, the highest PFAS concentrations did not always correspond to the greatest loads. Most PFOS (64 %), PFOA (49 %), 6:2FTS (46 %) and PFHxS (56 %) were exported from the Upper Mersey sub-catchment, despite higher concentrations in northern sub-catchments, emphasising the importance of load-based monitoring. Mass balance analysis of loads highlighted substantial inputs from specific river stretches, notably the Lower Irwell (Bolton to Manchester City Centre), River Tame (Marple Bridge to Stockport), and Upper Mersey (Stockport to Urmston). While PFAS loads generally scaled with catchment area, yield (load per unit area) analysis identified disproportionately high exports from small headwater catchments, notably the upper River Roch (PFOA, PFHpA and PFHxA) and Glaze Brook (PFBS). Industrial sources in these sub-catchments (a waste management facility and landfills, respectively) were confirmed using gadolinium anomaly analysis and consented discharge records. More widely, gadolinium data suggested industrial discharges may contribute to PFAS occurrence at 62 % of our sample sites throughout the catchment. These findings demonstrate that spatial analysis of PFAS loads, rather than concentrations alone, is critical for identifying PFAS source areas. We present a scalable monitoring framework for PFAS source apportionment applied at the river catchment-scale that can be used by environmental managers to target and prioritise PFAS source areas for detailed monitoring and remediation.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.180502","usgsCitation":"Byrne, P., Mayes, W.M., James, A.L., Comber, S., Biles, E., Riley, A.L., Verplanck, P., and Bradley, L., 2025, Spatially resolved source apportionment of per- and polyfluoroalkyl substances (PFAS) within a post-industrial river catchment: Science of the Total Environment, v. 1001, 180502, 12 p., https://doi.org/10.1016/j.scitotenv.2025.180502.","productDescription":"180502, 12 p.","ipdsId":"IP-180073","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":496352,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2025.180502","text":"Publisher Index Page"},{"id":495896,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United Kingdom","otherGeospatial":"River Mersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -2.4656527163469946,\n 53.60530248618926\n ],\n [\n -2.4656527163469946,\n 53.3355442876196\n ],\n [\n -1.966480261260216,\n 53.3355442876196\n ],\n [\n -1.966480261260216,\n 53.60530248618926\n ],\n [\n -2.4656527163469946,\n 53.60530248618926\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"1001","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Byrne, Patrick","contributorId":192845,"corporation":false,"usgs":false,"family":"Byrne","given":"Patrick","affiliations":[],"preferred":false,"id":949261,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mayes, William M.","contributorId":335073,"corporation":false,"usgs":false,"family":"Mayes","given":"William","email":"","middleInitial":"M.","affiliations":[{"id":40174,"text":"University of Hull","active":true,"usgs":false}],"preferred":false,"id":949262,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"James, Alun L.","contributorId":361704,"corporation":false,"usgs":false,"family":"James","given":"Alun","middleInitial":"L.","affiliations":[{"id":86333,"text":"Environment Agency UK","active":true,"usgs":false}],"preferred":false,"id":949263,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Comber, Sean","contributorId":335075,"corporation":false,"usgs":false,"family":"Comber","given":"Sean","email":"","affiliations":[{"id":80302,"text":"University of Plymouth,","active":true,"usgs":false}],"preferred":false,"id":949264,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Biles, Emma","contributorId":335077,"corporation":false,"usgs":false,"family":"Biles","given":"Emma","email":"","affiliations":[{"id":49583,"text":"Liverpool John Moores University","active":true,"usgs":false}],"preferred":false,"id":949265,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Riley, Alex L.","contributorId":361707,"corporation":false,"usgs":false,"family":"Riley","given":"Alex","middleInitial":"L.","affiliations":[{"id":40174,"text":"University of Hull","active":true,"usgs":false}],"preferred":false,"id":949266,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Verplanck, Philip L. 0000-0002-3653-6419","orcid":"https://orcid.org/0000-0002-3653-6419","contributorId":212813,"corporation":false,"usgs":true,"family":"Verplanck","given":"Philip","middleInitial":"L.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":949267,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bradley, Lee","contributorId":361708,"corporation":false,"usgs":false,"family":"Bradley","given":"Lee","affiliations":[{"id":86332,"text":"John Moores University","active":true,"usgs":false}],"preferred":false,"id":949268,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70271720,"text":"70271720 - 2025 - Machine learning generated streamflow drought forecasts for the Conterminous United States (CONUS): Developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations","interactions":[{"subject":{"id":70271720,"text":"70271720 - 2025 - Machine learning generated streamflow drought forecasts for the Conterminous United States (CONUS): Developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations","indexId":"70271720","publicationYear":"2025","noYear":false,"title":"Machine learning generated streamflow drought forecasts for the Conterminous United States (CONUS): Developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations"},"predicate":"SUPERSEDED_BY","object":{"id":70273497,"text":"70273497 - 2026 - Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations","indexId":"70273497","publicationYear":"2026","noYear":false,"title":"Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations"},"id":1}],"supersededBy":{"id":70273497,"text":"70273497 - 2026 - Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations","indexId":"70273497","publicationYear":"2026","noYear":false,"title":"Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations"},"lastModifiedDate":"2026-01-26T16:29:56.651322","indexId":"70271720","displayToPublicDate":"2025-09-19T09:20:12","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18346,"text":"EarthArXiv","active":true,"publicationSubtype":{"id":32}},"title":"Machine learning generated streamflow drought forecasts for the Conterminous United States (CONUS): Developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations","docAbstract":"Forecasts of streamflow drought, when streamflow declines below typical levels, are notably less available than for floods or meteorological drought, despite widespread impacts. To address this gap, we apply machine learning (ML) models to forecast streamflow drought 1-13 weeks into the future at > 3,000 streamgage locations across the conterminous United States (CONUS). We applied two ML methods (Long short-term memory (LSTM) neural networks; Light Gradient-Boosting Machine - LightGBM) and two benchmark model approaches (persistence; Autoregressive Integrated Moving Average - ARIMA) to predict weekly streamflow percentiles with independent models for each forecast horizon. To explore whether a training focus on dry weeks improved performance, both ML models were trained using all percentiles (LSTM-all, LightGBM-all) and only percentiles below 30% (LSTM<30, LightGBM<30). We evaluated model performance regionally and nationally for drought occurrence (the classification performance for a future date) and for drought onset/termination (performance identifying drought starts and ends). ML models generally performed worse than the persistence model for discrete classification (moderate, severe, extreme drought) of drought occurrence but exceeded the benchmark models for onset/termination. ML models outperformed benchmarks in predicting continuous streamflow percentiles below 30%. Occurrence performance was better for less intense droughts and shorter forecast horizons, with the ML models having predictive power at 1-4 week horizons for severe droughts (10th percentile threshold). All models struggled to forecast onset, though the best ML model was the LSTM<30 (sensitivity of 22%). Termination performance was greater, with the drought termination performance greatest for the LightGBM-all model. When estimating model uncertainty, the LSTM<30 model had the narrowest 90% percentile interval with closest to optimal capture. This work highlights the challenges and opportunities to further advance hydrological drought forecasting and supports an experimental operational streamflow drought assessment and forecast tool.
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sampling activities in the U.S. Geological Survey national networks","interactions":[],"lastModifiedDate":"2026-02-03T15:26:53.6234","indexId":"gip260","displayToPublicDate":"2025-09-19T08:39:57","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"260","displayTitle":"PFAS Sampling Activities in the U.S. Geological Survey National Networks","title":"PFAS sampling activities in the U.S. Geological Survey national networks","docAbstract":"Per- and polyfluoroalkyl substances (PFAS), frequently called “forever chemicals,” are used for a wide variety of industrial purposes and are often found in common household and industrial items such as firefighting foams, non-stick cookware, and water-resistant materials. The contamination of water, air, and soil by PFAS is a national and global issue due to their widespread occurrence in multiple applications and resistance to biodegradation and other traditional treatment processes. Research indicates that many PFAS can be emitted to the atmosphere and transported and deposited long distances from the source.
The U.S. Geological Survey (USGS) Water Resources Mission Area received funding to implement a national-scale sampling effort to assess PFAS occurrence. To follow agency directives, the National Water Quality Network (NWQN) added PFAS sample monitoring for both surface water and groundwater, and also added PFAS monitoring to selected sites in the National Atmospheric Deposition Program (NADP).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip260","usgsCitation":"Riskin, M.L., Lindsey, B.D., and McCammon, R.C., 2025, PFAS sampling activities in the U.S. Geological Survey national networks: U.S. Geological Survey General Information Product 260, https://doi.org/10.3133/gip260.","productDescription":"1 p.","onlineOnly":"Y","ipdsId":"IP-169424","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":495819,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/260/gip260.pdf","text":"Report","size":"901 KB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 260"},{"id":495818,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/260/coverthb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": 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Observing Systems Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Aerial photography and satellite imagery can be used to characterize landscape change over time and help to understand how these changes are related to climate and hydrology. Publicly available optical imagery from sources such as the United States National Agricultural Imagery Program (NAIP) is particularly valuable in this context due to its high temporal and spatial resolution. However, the exact time an image was acquired is often unknown, which complicates, if not precludes, linking images with other types of high temporal resolution data, such as streamflow records. In this letter, we propose a ‘sundial method’ to infer image acquisition time from shadow orientation. This approach involves measuring the direction of a shadow on the image and using solar geometry calculated for the known image date and location to infer the former sun position. Time estimates for 16 Worldview satellite and six NAIP aerial images based on 407 independent measurements of shadow orientation demonstrate the sundial method had an error of 2.1 ± 3.4 min, indicating that image acquisition times can be inferred with a high degree of accuracy and precision. Sensitivity analyses confirm the robustness of the method across different object types, shadow lengths, and solar zenith angles, while also providing practical guidelines regarding the number of measurements required and errors associated with uncertainty in the image date.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.70157","usgsCitation":"Bae, I., Legleiter, C.J., and Yager, E., 2025, Sundial: A method for inferring image acquisition time from shadow orientation: Earth Surface Processes and Landforms, v. 50, no. 12, e70157, 10 p., https://doi.org/10.1002/esp.70157.","productDescription":"e70157, 10 p.","ipdsId":"IP-173049","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":495797,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"12","noUsgsAuthors":false,"publicationDate":"2025-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Bae, Inhyeok 0000-0003-3942-4110","orcid":"https://orcid.org/0000-0003-3942-4110","contributorId":347541,"corporation":false,"usgs":false,"family":"Bae","given":"Inhyeok","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":949025,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":949024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":949026,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70271707,"text":"70271707 - 2025 - Scenario projections of COVID-19 burden in the US, 2024-2025","interactions":[],"lastModifiedDate":"2025-09-19T14:41:41.794225","indexId":"70271707","displayToPublicDate":"2025-09-18T09:33:49","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20081,"text":"JAMA Network Open","active":true,"publicationSubtype":{"id":10}},"title":"Scenario projections of COVID-19 burden in the US, 2024-2025","docAbstract":"Importance COVID-19 remains a disease with high burden in the US, prompting continued debate about optimal targets for annual vaccination.
Objective To project COVID-19 burden in the US for April 2024 to April 2025 under 6 scenarios of immune escape (20% and 50% per year) and levels of vaccine recommendation (no recommendation, vaccination for individuals at high risk only, vaccination for all eligible groups) and to assess the potential benefit of vaccine recommendations in reducing disease burden.
Design, Setting, and Participants For this decision analytical model, the US Scenario Modeling Hub, a collaborative modeling effort, convened 9 teams to provide scenario projections of US COVID-19 hospitalizations and deaths for April 2024 to April 2025, under 6 scenarios combining levels of immune escape and possible vaccine recommendations.
Exposure Annually reformulated vaccines were assumed to be 75% effective against hospitalization for variants circulating on June 15, 2024, and available on September 1, 2024. Age- and state-specific coverage was assumed to be as reported in September 2023 to April 2024.
Main Outcomes and Measures Ensemble estimates were made for weekly COVID-19 hospitalizations and deaths. Projections are presented for relative and absolute prevented hospitalizations and deaths averted due to vaccination over the April 2024 to April 2025 period.
Results For the US population (332 million, with an estimated 58 million aged ≥65 years), COVID-19 was expected to cause 814 000 (95% projection interval [PI], 400 000-1.2 million) hospitalizations and 54 000 (95% PI, 17 000-98 000) deaths for April 2024 to April 2025, comparable in magnitude to the prior year. Vaccination of high-risk groups only was projected to reduce hospitalizations (compared to no vaccination recommendation) by 76 000 (95% CI, 34 000-118 000) and deaths by 7000 (95% CI, 3000-11 000) across both immune escape scenarios. Compared with vaccinating high-risk groups only, a universal vaccine recommendation was projected to provide direct and indirect benefits, further preventing 11 000 hospitalizations and 1000 deaths in those aged 65 years and older.
Conclusions and Relevance In this decision analytical modeling study of COVID-19 burden in the US in 2024 to 2025, ensemble projections suggested that although vaccinating high-risk groups had substantial benefits in reducing disease burden, maintaining the vaccine recommendation for all individuals had the potential to save thousands more lives. Despite divergence of projections from observed disease trends in 2024 to 2025—possibly driven by variant emergence patterns and immune escape—averted COVID-19 burden due to vaccination was robust across immune escape scenarios, emphasizing the substantial benefit of broader vaccine availability for all individuals.
","language":"English","publisher":"JAMA","doi":"10.1001/jamanetworkopen.2025.32469","usgsCitation":"Loo, S.L., Jung, S., Contamin, L., Howerton, E., Bents, S., Hochheiser, H., Runge, M., Smith, C.P., Carcelén, E., Yan, K., Lemaitre, J.C., Przykucki, E., McKee, C., Sato, K., Hill, A., Chinazzi, M., Davis, J.T., Bay, C., Vespignani, A., Chen, S., Paul, R., Janies, D., Thill, J., Moore, S., Perkins, T.A., Srivastava, A., Aawar, M.A., Bi, K., Bandekar, S.R., Bouchnita, A., Fox, S., Meyers, L.A., Porebski, P., Venkatramanan, S., Lewis, B., Chen, J., Marathe, M., Ben-Nun, M., Turtle, J., Riley, P., Shea, K., Viboud, C., Lessler, J., and Truelove, S., 2025, Scenario projections of COVID-19 burden in the US, 2024-2025: JAMA Network Open, v. 8, no. 9, e2532469, 12 p., https://doi.org/10.1001/jamanetworkopen.2025.32469.","productDescription":"e2532469, 12 p.","ipdsId":"IP-180247","costCenters":[{"id":50464,"text":"Eastern Ecological Science 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density that inform conservation and management practices for imperiled species. As a result, evaluating the performance of different survey methods across a range of conditions that may be encountered in the field can increase understanding of the time and effort that may be required to ensure that survey results are sufficiently accurate and reliable for conservation goals. We used a spatially explicit agent-based model to simulate four commonly used freshwater mussel field survey methodologies: simple random sampling (SRS), transect random sampling (TRS), adaptive cluster sampling (ACS), and qualitative timed searches (QTS) to investigate the influence of sampling method, spatial distribution, and mussel density on the performance (i.e., accuracy, precision, and detection rate) of survey techniques. Our analysis suggests that mussel density, spatial distribution, and sampling effort influence sampling accuracy, precision, and species detection for all sampling methods. QTS produces highly variable catch-per-unit-effort (CPUE) metrics when mussels are dense and/or clustered, indicating the technique may be unreliable as a proxy for density. Quantitative methods like SRS and TRS may be well-suited for estimating population characteristics, but a high level of effort may be needed to obtain reasonable accuracy when mussels occur at low densities. ACS may be more efficient for mussels at low densities, but it can be challenging to plan for the level of effort required to complete an ACS protocol. Designing an ecological survey requires careful consideration of research objectives and available resources. Future research may consider the performance of qualitative and quantitative surveys in combination as a means of overcoming some of the practical challenges of applying individual survey methods.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2025.114172","usgsCitation":"Foxfoot, I.R., Cushway, K.C., Schwalb, A.N., Smith, D.R., and Swannack, T.M., 2025, Evaluating freshwater mussel sampling methodologies using a simulation model: Ecological Indicators, v. 179, 114172, 14 p., https://doi.org/10.1016/j.ecolind.2025.114172.","productDescription":"114172, 14 p.","ipdsId":"IP-181659","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":499327,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2025.114172","text":"Publisher Index Page"},{"id":499174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"179","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Foxfoot, Iris R.","contributorId":336806,"corporation":false,"usgs":false,"family":"Foxfoot","given":"Iris","middleInitial":"R.","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":954690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cushway, Kiara C.","contributorId":365735,"corporation":false,"usgs":false,"family":"Cushway","given":"Kiara","middleInitial":"C.","affiliations":[{"id":87200,"text":"US Army Engineer Research and Development Center; UIC Government Services LLC","active":true,"usgs":false}],"preferred":false,"id":954691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwalb, Astrid N.","contributorId":333385,"corporation":false,"usgs":false,"family":"Schwalb","given":"Astrid","middleInitial":"N.","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":954692,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":954693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Swannack, Todd M.","contributorId":336813,"corporation":false,"usgs":false,"family":"Swannack","given":"Todd","middleInitial":"M.","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":954694,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70272608,"text":"70272608 - 2025 - Native crayfish shows high desiccation tolerance and potential to outcompete invader","interactions":[],"lastModifiedDate":"2025-11-24T15:46:26.826539","indexId":"70272608","displayToPublicDate":"2025-09-18T08:40:23","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Native crayfish shows high desiccation tolerance and potential to outcompete invader","docAbstract":"Biological invasions threaten global biodiversity, with aquatic systems being particularly susceptible. Invasive crayfish drive native crayfish imperilment in North America and worldwide. Despite the probable increase in extreme hydrological events, the synergistic effects from invasive species and drought on crayfish are understudied. The invasion of Faxonius neglectus chaenodactylus in the Spring River drainage (AR, MO) has likely contributed to native crayfish displacement through mechanisms related to stream drying. F. n. chaenodactylus may further expand its range, posing a threat to other native species like Faxonius marchandi, the Mammoth Spring crayfish, a narrow-ranged endemic. We used stream mesocosms to examine (1) effects of invasive species on F. marchandi growth and survival, (2) responses of both species to simulated stream drying, and (3) additive effects of invasion and drought on F. marchandi. Additionally, we assessed differential desiccation tolerance using environmental chambers. We found no significant interaction between drought and competition nor any significant main effects on crayfish mass change or survival; however, interspecific competition significantly reduced length change in F. n. chaenodactylus. All populations showed differential desiccation tolerance, with survival rates varying significantly (p < 0.05) and carapace length (CL) positively influencing survival (p < 0.01). Understanding the effects of drought, invasion, and their interactions on native crayfish is essential, particularly given the potential expansion of an invader and increasing drought intensity from future climate change.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10530-025-03675-5","usgsCitation":"Bayer, L.M., and Magoulick, D.D., 2025, Native crayfish shows high desiccation tolerance and potential to outcompete invader: Biological Invasions, v. 27, 216, 16 p., https://doi.org/10.1007/s10530-025-03675-5.","productDescription":"216, 16 p.","ipdsId":"IP-170452","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":496826,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri","otherGeospatial":"Spring River drainage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.82880316322702,\n 36.78950866025838\n ],\n [\n -92.82880316322702,\n 35.94152912534426\n ],\n [\n -91.54070609793327,\n 35.94152912534426\n ],\n [\n -91.54070609793327,\n 36.78950866025838\n ],\n [\n -92.82880316322702,\n 36.78950866025838\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"27","noUsgsAuthors":false,"publicationDate":"2025-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Bayer, Leah M.","contributorId":363007,"corporation":false,"usgs":false,"family":"Bayer","given":"Leah","middleInitial":"M.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":950908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":950909,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70271926,"text":"70271926 - 2025 - Reservoir operational strategies for sustainable sand management in the Colorado River","interactions":[],"lastModifiedDate":"2025-09-24T15:24:43.186803","indexId":"70271926","displayToPublicDate":"2025-09-18T08:14:16","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Reservoir operational strategies for sustainable sand management in the Colorado River","docAbstract":"Climate change and increasing societal demands for water pose challenges for the management of dam-regulated rivers. Management decisions impact the environment of these rivers, creating the need to balance societal needs with environmental conservation. Here we present a modeling framework that optimizes resource benefits within imposed water use goals for the Colorado River in Grand Canyon, where sandbars are a valued natural feature. The current sand-management paradigm utilizes controlled dam-release floods to build and maintain sandbars without exhausting the limited sand supplied by tributaries downstream from Glen Canyon Dam, which blocks all sand supplied from upriver. High monthly releases outside of controlled floods erode sandbars and cause net sand export from Grand Canyon, reducing the sand available to build sandbars. Releases are high in some months owing to the need to adjust flows to meet annual delivery targets, which can be updated throughout the year. Here, we present alternative strategies for operations that avoid high releases, while meeting water storage and delivery goals. We test these strategies using a simplified reservoir model which accounts for forecast uncertainty. We show how these strategies affect sand mass balance and sandbar size using previously developed models. Strategies optimal for sustainable sandbar building maintained sufficient reservoir elevations for implementing controlled floods, avoided high monthly releases by relaxing annual release constraints, and implemented controlled floods in fall immediately following tributary sand inputs. Coordinated modeling of reservoir operations and environmental resources is valuable for managers seeking to balance societal and environmental needs in regulated rivers worldwide.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR038315","usgsCitation":"Salter, G.L., Topping, D.J., Wang, J., Schmidt, J.C., Yackulic, C., Bair, L., Mueller, E., and Grams, P.E., 2025, Reservoir operational strategies for sustainable sand management in the Colorado River: Water Resources Research, v. 61, no. 9, e2024WR038315, 27 p., https://doi.org/10.1029/2024WR038315.","productDescription":"e2024WR038315, 27 p.","ipdsId":"IP-167426","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":496155,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr038315","text":"Publisher Index Page"},{"id":496013,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.0504953550317,\n 36.97634003343833\n ],\n [\n -114.0504953550317,\n 35.748902843127084\n ],\n [\n -111.3294352062603,\n 35.748902843127084\n ],\n [\n -111.3294352062603,\n 36.97634003343833\n ],\n [\n -114.0504953550317,\n 36.97634003343833\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"61","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Salter, Gerard Lewis 0000-0001-6426-0133","orcid":"https://orcid.org/0000-0001-6426-0133","contributorId":333645,"corporation":false,"usgs":true,"family":"Salter","given":"Gerard","email":"","middleInitial":"Lewis","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Topping, David J. 0000-0002-2104-4577","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":215068,"corporation":false,"usgs":true,"family":"Topping","given":"David","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Jianghao","contributorId":195004,"corporation":false,"usgs":false,"family":"Wang","given":"Jianghao","email":"","affiliations":[],"preferred":false,"id":949401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmidt, John C.","contributorId":361760,"corporation":false,"usgs":false,"family":"Schmidt","given":"John","middleInitial":"C.","affiliations":[{"id":86346,"text":"Center for Colorado River Studies, Department of Watershed Sciences, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":949402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949403,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bair, Lucas 0000-0002-9911-3624","orcid":"https://orcid.org/0000-0002-9911-3624","contributorId":248714,"corporation":false,"usgs":true,"family":"Bair","given":"Lucas","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949404,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mueller, Erich R. 0000-0001-8202-154X","orcid":"https://orcid.org/0000-0001-8202-154X","contributorId":207750,"corporation":false,"usgs":false,"family":"Mueller","given":"Erich R.","affiliations":[{"id":37626,"text":"Department of Geography, University of Wyoming, Laramie, WY, USA","active":true,"usgs":false}],"preferred":false,"id":949405,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Grams, Paul E. 0000-0002-0873-0708","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":216115,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949406,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70273390,"text":"70273390 - 2025 - Habitat features influencing waterbird use of managed wetlands enrolled in a public-private partnership for land conservation: The California Waterfowl Habitat Program","interactions":[],"lastModifiedDate":"2026-01-12T14:53:19.867404","indexId":"70273390","displayToPublicDate":"2025-09-18T07:47:45","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Habitat features influencing waterbird use of managed wetlands enrolled in a public-private partnership for land conservation: The California Waterfowl Habitat Program","docAbstract":"Draining, water diversion, and development have greatly reduced the availability of freshwater wetland habitat around the world, and many remaining wetlands are on private lands. Public–private partnership programs can be an important means for promoting habitat conservation and management on private lands. We investigated bird use of 117 wetlands enrolled in the California Waterfowl Habitat Program in California's Central Valley, where two-thirds of wetlands are under private ownership and management. Specifically, we quantified the influence of wetland habitat features and surrounding land cover on waterbird density and diversity in late winter and early spring and during the waterfowl breeding season. Dabbling duck and shorebird densities were highest in wetlands that had water depths < 20 cm, and waterbird densities decreased with water depth. Greater amounts of emergent vegetation, especially tall and dense emergent vegetation, had a negative effect on total waterbird density but a positive effect on species richness and secretive marsh bird density. Shorebird and breeding duck densities were lower in wetlands with a large number of trees and other potential perch sites, and waterbird densities decreased with the amount of nearby wetland habitat on the landscape. Overall, we estimated that during late winter and early spring, private properties that were enrolled in the California Waterfowl Habitat Program (8000–8500 ha each year) supported 480,000 birds per day during extreme drought conditions in 2022 and 280,000 birds per day in more normal, non-drought conditions in 2023. Over the 76-day winter and early spring survey period, this amounted to more than 20 million bird use days on wetlands enrolled in the California Waterfowl Habitat Program during late winter and early spring. These results demonstrate the value of public–private wetland conservation partnerships, the influence of wetland habitat features and surrounding land cover on waterbird abundance, and the benefits of habitat features that could be incorporated into management plans and wetland selection criteria for enrollment into public–private conservation programs.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.72032","usgsCitation":"Hartman, C.A., Ackerman, J.T., Peterson, S.H., Fettig, B.L., and Herzog, M.P., 2025, Habitat features influencing waterbird use of managed wetlands enrolled in a public-private partnership for land conservation: The California Waterfowl Habitat Program: Ecology and Evolution, v. 15, no. 9, e72032, 31 p., https://doi.org/10.1002/ece3.72032.","productDescription":"e72032, 31 p.","ipdsId":"IP-177295","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":498681,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.72032","text":"Publisher Index Page"},{"id":498542,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley, San Joaquin Valley, Yolo-Delta and Suisun Marsh area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.95679541485802,\n 39.955731811890246\n ],\n [\n -122.95679541485802,\n 36.78915144435925\n ],\n [\n -120.20809606036002,\n 36.78915144435925\n ],\n [\n -120.20809606036002,\n 39.955731811890246\n ],\n [\n -122.95679541485802,\n 39.955731811890246\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Hartman, C. 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However, the predictive strength of such models depends on how well they resolve various functions of catchment hydrology, which are influenced by gradients in climate, topography, soils, and land use. Most assessments of hydrologic model uncertainty have been limited to traditional statistical methods. Here, we present a proof-of-concept approach that uses interpretable machine learning techniques to provide post hoc assessment of model sensitivity and process deficiency in hydrologic models. We train a random forest model to predict the Kling–Gupta efficiency (KGE) of National Water Model (NWM) and National Hydrologic Model (NHM) streamflow predictions for 4383 stream gauges in the conterminous United States. Thereafter, we explain the local and global controls that 48 catchment attributes exert on KGE prediction using interpretable Shapley values. Overall, we find that soil water content is the most impactful feature controlling successful model performance, suggesting that soil water storage is difficult for hydrologic models to resolve, particularly for arid locations. We identify nonlinear thresholds beyond which predictive performance decreases for NWM and NHM. For example, soil water content less than 210 mm, precipitation less than 900 mm yr−1, road density greater than 5 km km−2, and lake area percent greater than 10 % contributed to lower KGE values. These results suggest that improvements in how these influential processes are represented could result in the largest increases in NWM and NHM predictive performance. This study demonstrates the utility of interrogating process-based models using data-driven techniques, which has broad applicability and potential for improving the next generation of large-scale hydrologic models.
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Joshua","contributorId":352231,"corporation":false,"usgs":false,"family":"Roundy","given":"Joshua","affiliations":[{"id":84135,"text":"Department of Civil, Environmental and Architectural Engineering, University of Kansas","active":true,"usgs":false}],"preferred":false,"id":930393,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273507,"text":"70273507 - 2025 - A glimpse into the future of tectonic tremor monitoring","interactions":[],"lastModifiedDate":"2026-02-10T13:35:45.088926","indexId":"70273507","displayToPublicDate":"2025-09-17T09:10:17","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7501,"text":"JGR Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"A glimpse into the future of tectonic tremor monitoring","docAbstract":"Tectonic tremor is a weak, long-duration seismic signal often observed in subduction zones and on some other plate-bounding faults. Because of tremor's characteristically low amplitude (and low signal-to-noise) and lack of clear phase arrivals, detecting and locating tremor usually requires techniques distinct from those applied to typical earthquakes. Major advances in detection and understanding of tremor have derived in the past from a powerful combination of new data and new analysis techniques. In a recent study, Sagae et al. (2025, https://doi.org/10.1029/2025jb031348) exploit that combination again, developing a new machine-learning based workflow and applying it to the S-net cabled seismic network in the Japan trench offshore northern Honshu. Their approach, although complex, succeeds in detecting several times more tremor activity than earlier studies, resulting in new insights and providing a blueprint for similar approaches that could be applied elsewhere. As real-time earthquake monitoring adopts similar tools, it may present an opportunity to bring tremor monitoring into operational workflows. In turn, this could solidify tremor monitoring as a component of future operational earthquake forecasting.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JB032642","usgsCitation":"Shelly, D.R., 2025, A glimpse into the future of tectonic tremor monitoring: JGR Solid Earth, v. 130, no. 9, e2025JB032642, 5 p., https://doi.org/10.1029/2025JB032642.","productDescription":"e2025JB032642, 5 p.","ipdsId":"IP-181421","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":498930,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025jb032642","text":"Publisher Index Page"},{"id":498798,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"130","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-17","publicationStatus":"PW","contributors":{"authors":[{"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":954083,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70271488,"text":"70271488 - 2025 - Reduced Atlantic reef growth past 2 °C warming amplifies sea-level impacts","interactions":[],"lastModifiedDate":"2025-12-01T16:35:40.162981","indexId":"70271488","displayToPublicDate":"2025-09-17T09:09:26","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2840,"text":"Nature","active":true,"publicationSubtype":{"id":10}},"title":"Reduced Atlantic reef growth past 2 °C warming amplifies sea-level impacts","docAbstract":"Coral reefs form complex physical structures that can help to mitigate coastal flooding risk1,2. This function will be reduced by sea-level rise (SLR) and impaired reef growth caused by climate change and local anthropogenic stressors3. Water depths above reef surfaces are projected to increase as a result, but the magnitudes and timescales of this increase are poorly constrained, which limits modelling of coastal vulnerability4,5. Here we analyse fossil reef deposits to constrain links between reef ecology and growth potential across more than 400 tropical western Atlantic sites, and assess the magnitudes of resultant above-reef increases in water depth through to 2100 under various shared socioeconomic pathway (SSP) emission scenarios. Our analysis predicts that more than 70% of tropical western Atlantic reefs will transition into net erosional states by 2040, but that if warming exceeds 2 °C (SSP2–4.5 and higher), nearly all reefs (at least 99%) will be eroding by 2100. The divergent trajectories of reef growth and SLR will thus magnify the effects of SLR; increases in water depth of around 0.3–0.5 m above the present are projected under all warming scenarios by 2060, but depth increases of 0.7–1.2 m are predicted by 2100 under scenarios in which warming surpasses 2 °C. This would increase the risk of flooding along vulnerable reef-fronted coasts and modify nearshore hydrodynamics and ecosystems. Reef restoration offers one pathway back to higher reef growth6,7, but would dampen the effects of SLR in 2100 only by around 0.3–0.4 m, and only when combined with aggressive climate mitigation.
","language":"English","publisher":"Nature","doi":"10.1038/s41586-025-09439-4","usgsCitation":"Perry, C.T., de Bakker, D., Webb, A., Comeau, S., Harvey, B., Cornwall, C., Alvarez-Filip, L., Perez-Cervantes, E., Morris, J.T., Enochs, I.C., Toth, L., O'Dea, A., Dillon, E.M., Meesters, E.H., and Precht, W., 2025, Reduced Atlantic reef growth past 2 °C warming amplifies sea-level impacts: Nature, v. 646, p. 619-626, https://doi.org/10.1038/s41586-025-09439-4.","productDescription":"8 p.","startPage":"619","endPage":"626","ipdsId":"IP-174363","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":495742,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41586-025-09439-4","text":"Publisher Index Page"},{"id":495706,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bonaire, Mexico, United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.02248566029274,\n 25.530856231957387\n ],\n [\n -82.32796469580698,\n 25.530856231957387\n ],\n [\n -82.32796469580698,\n 24.289944524122234\n ],\n [\n -80.02248566029274,\n 24.289944524122234\n ],\n [\n -80.02248566029274,\n 25.530856231957387\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.83864227388983,\n 21.803130053234568\n ],\n [\n -96.56435479415998,\n 21.803130053234568\n ],\n [\n -96.56435479415998,\n 18.124147478332375\n ],\n [\n -85.83864227388983,\n 18.124147478332375\n ],\n [\n -85.83864227388983,\n 21.803130053234568\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.43913576613566,\n 12.261657501254277\n ],\n [\n -68.43913576613566,\n 12.064862302815328\n ],\n [\n -68.2395272475406,\n 12.064862302815328\n ],\n [\n -68.2395272475406,\n 12.261657501254277\n ],\n [\n -68.43913576613566,\n 12.261657501254277\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"646","noUsgsAuthors":false,"publicationDate":"2025-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Perry, Chris T.","contributorId":361512,"corporation":false,"usgs":false,"family":"Perry","given":"Chris","middleInitial":"T.","affiliations":[{"id":17840,"text":"University of Exeter","active":true,"usgs":false}],"preferred":false,"id":948935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Bakker, Didier","contributorId":361513,"corporation":false,"usgs":false,"family":"de Bakker","given":"Didier","affiliations":[{"id":17840,"text":"University of Exeter","active":true,"usgs":false}],"preferred":false,"id":948936,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Webb, Alice","contributorId":361514,"corporation":false,"usgs":false,"family":"Webb","given":"Alice","affiliations":[{"id":17840,"text":"University of Exeter","active":true,"usgs":false}],"preferred":false,"id":948937,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Comeau, Steeve","contributorId":361519,"corporation":false,"usgs":false,"family":"Comeau","given":"Steeve","affiliations":[{"id":86307,"text":"Sorbonne Universite","active":true,"usgs":false}],"preferred":false,"id":948939,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harvey, Ben","contributorId":361520,"corporation":false,"usgs":false,"family":"Harvey","given":"Ben","affiliations":[{"id":27339,"text":"University of Tsukuba","active":true,"usgs":false}],"preferred":false,"id":948940,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cornwall, Chris","contributorId":361516,"corporation":false,"usgs":false,"family":"Cornwall","given":"Chris","affiliations":[{"id":56217,"text":"Victoria University of Wellington","active":true,"usgs":false}],"preferred":false,"id":948938,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Alvarez-Filip, Lorenzo","contributorId":361523,"corporation":false,"usgs":false,"family":"Alvarez-Filip","given":"Lorenzo","affiliations":[{"id":18923,"text":"Universidad Nacional Autonoma de Mexico","active":true,"usgs":false}],"preferred":false,"id":948941,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Perez-Cervantes, Esmerelda","contributorId":361526,"corporation":false,"usgs":false,"family":"Perez-Cervantes","given":"Esmerelda","affiliations":[{"id":18923,"text":"Universidad Nacional Autonoma de Mexico","active":true,"usgs":false}],"preferred":false,"id":948942,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Morris, John 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Aaron","contributorId":361532,"corporation":false,"usgs":false,"family":"O'Dea","given":"Aaron","affiliations":[{"id":12671,"text":"Smithsonian Tropical Research Institute","active":true,"usgs":false}],"preferred":false,"id":948946,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Dillon, Erin M.","contributorId":221878,"corporation":false,"usgs":false,"family":"Dillon","given":"Erin","email":"","middleInitial":"M.","affiliations":[{"id":34029,"text":"U.C. Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":949002,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Meesters, Erik H,","contributorId":361576,"corporation":false,"usgs":false,"family":"Meesters","given":"Erik","middleInitial":"H,","affiliations":[],"preferred":false,"id":948947,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Precht, William F.","contributorId":119464,"corporation":false,"usgs":true,"family":"Precht","given":"William F.","affiliations":[],"preferred":false,"id":949003,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70269893,"text":"sir20255057 - 2025 - Sources of water and salts for the Zuni Salt Lake in west-central New Mexico","interactions":[],"lastModifiedDate":"2026-02-03T15:26:20.493234","indexId":"sir20255057","displayToPublicDate":"2025-09-17T09:01:13","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5057","displayTitle":"Sources of Water and Salts for the Zuni Salt Lake in West-Central New Mexico","title":"Sources of water and salts for the Zuni Salt Lake in west-central New Mexico","docAbstract":"The Zuni Salt Lake is located in a maar in west-central New Mexico and contains hypersaline water that has long been used by Native Americans for religious purposes and the collection of salt. There have been several investigations suggesting different sources for the water and salt to the lake. Springs, seeps, and ephemeral streamflow have all been observed to contribute freshwater to the lake, and brackish to hypersaline seeps have been documented along the banks of the lake. This report summarizes the findings of a study that characterizes the lake’s hydrology, its water and salinity sources, and the hydrogeologic conceptual model. Regional groundwater levels indicate that each of the aquifers in the area have the potential to discharge groundwater to the lake. There is also evidence of vertical groundwater flow pathways at the maar that were likely created by the igneous intrusion that fractured the intersecting aquifers. A detailed water budget was constructed from continuous lake stage, precipitation, and evaporation data to estimate the groundwater inflow to the Zuni Salt Lake. It was determined that groundwater inflow to the lake is 441 ±94 acre-feet per year, which composes as much as 77 percent of the total inflows. The high sodium and chloride concentrations measured in two hypersaline samples collected near the lake indicate that the majority of the dissolved solids entering the lake are from a hypersaline groundwater source. The geochemical and isotopic compositions measured in the lake and surrounding features support the interpretation that hypersaline groundwater is the primary source of salts to the lake, which is likely sourced from the older (and deeper) Permian units. The hypersaline groundwater samples collected during this investigation have a unique aqueous chemistry relative to each of the mapped aquifers, and variability in groundwater compositions is interpreted to result from differences in minerology and residence time.
Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Contact Us- USGS Publications Warehouse
","tableOfContents":"Arctic cod (Boreogadus saida, also called polar cod) are considered the single most important Arctic forage fish due to their high abundance and nutritional quality. Because Arctic cod are strongly ice associated and prefer colder waters, their frequency in coastal waters has declined with warming, decreasing availability to nearshore predators. To consider the nutritional quality of alternative prey, we measured energy density and estimated whole-body energy of forage-size (39–200 mm) fishes collected during summers 2021–2023 (n = 274). The fishes sampled included 16 potential prey species from Foggy Island Bay (70.3°N, 147.5°W, near Prudhoe Bay) and Lion Bay (70.2°N, 146.4°W, near Flaxman Island), northern Alaska. Dry weight energy densities ranged from 16.2 to 27.5 kJ g-1 (mean ± SD = 22.0 ± 1.73 kJ g-1, n = 274) across individuals. Of common species, Arctic cod had the highest mean energy density (24.3 ± 1.1 kJ g-1, n = 25) and fourhorn sculpin (Myoxocephalus quadricornis) had the lowest (19.7 ± 0.8 kJ g-1, n = 20). To account for size differences among prey species, whole-body energy of typical fish sizes available to predators were modeled using whole-body energy to length relationships and length distributions. Juvenile salmonids (e.g., ciscoes and whitefishes) provided the most energy per individual and were four-fold greater than smaller-bodied Arctic cod. Predators that consume juvenile ciscoes and whitefishes may be more resilient to declines in Arctic cod availability than predators with smaller gapes.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s00227-025-04705-5","usgsCitation":"Stanek, A.E., Uher-Koch, B.D., Dunton, K.H., and von Biela, V.R., 2025, Energetic value of Arctic forage-sized fish with implications for a nearshore seabird predator: Marine Biology, v. 172, 157, 13 p., https://doi.org/10.1007/s00227-025-04705-5.","productDescription":"157, 13 p.","ipdsId":"IP-171231","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":495747,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00227-025-04705-5","text":"Publisher Index Page"},{"id":495713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.2117957640097,\n 71.3543754933907\n ],\n [\n -157.2117957640097,\n 69.83926146873208\n ],\n [\n -145.833226056111,\n 69.83926146873208\n ],\n [\n -145.833226056111,\n 71.3543754933907\n ],\n [\n -157.2117957640097,\n 71.3543754933907\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"172","noUsgsAuthors":false,"publicationDate":"2025-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Stanek, Ashley E. 0000-0001-5184-2126","orcid":"https://orcid.org/0000-0001-5184-2126","contributorId":290682,"corporation":false,"usgs":true,"family":"Stanek","given":"Ashley","email":"","middleInitial":"E.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":948998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uher-Koch, Brian D. 0000-0002-1885-0260 buher-koch@usgs.gov","orcid":"https://orcid.org/0000-0002-1885-0260","contributorId":5117,"corporation":false,"usgs":true,"family":"Uher-Koch","given":"Brian","email":"buher-koch@usgs.gov","middleInitial":"D.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":948999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunton, Kenneth H. 0000-0003-3498-8021","orcid":"https://orcid.org/0000-0003-3498-8021","contributorId":361574,"corporation":false,"usgs":false,"family":"Dunton","given":"Kenneth","middleInitial":"H.","affiliations":[{"id":47685,"text":"Marine Science Institute, University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":949000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"von Biela, Vanessa R. 0000-0002-7139-5981 vvonbiela@usgs.gov","orcid":"https://orcid.org/0000-0002-7139-5981","contributorId":3104,"corporation":false,"usgs":true,"family":"von Biela","given":"Vanessa","email":"vvonbiela@usgs.gov","middleInitial":"R.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":949001,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272173,"text":"70272173 - 2025 - Ecophysiology of two mesophotic octocorals intended for restoration: Effects of light and temperature","interactions":[],"lastModifiedDate":"2025-12-01T16:53:34.273606","indexId":"70272173","displayToPublicDate":"2025-09-17T08:08:58","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Ecophysiology of two mesophotic octocorals intended for restoration: Effects of light and temperature","docAbstract":"Light and temperature are driving forces that shape the evolution and physiology of mesophotic organisms. On the Mississippi-Alabama continental shelf, octocorals dominate the mesophotic seascape and provide habitat for many fish and invertebrate species. Gaps in knowledge regarding the fundamental physiological responses of these species to light and temperature are of particular interest to restoration activities following the Deepwater Horizon oil spill. To address these gaps, the photobiology and thermal tolerance of Swiftia exserta and Muricea pendula were assessed in the field and laboratory. Pulse amplitude modulated fluorometry, histology, light microscopy, and epifluorescence imaging revealed low densities of photosynthetic endobionts in samples of S. exserta and none in samples of M. pendula collected near the determined bottom of the euphotic zone (51.45 m). Response to the recorded monthly mean habitat temperature range (18.5–25.4°C) was assessed using respirometry and polyp activity data from live corals exposed to temperatures between 18°C and 26°C. There was no significant difference in oxygen consumption for either species between 18°C and 26°C, and calculated Q10 values were not significantly different from 1, thus suggesting that both species have a low sensitivity to the local thermal environment. However, a negative correlation between temperature and polyp activity suggests that M. pendula is more sensitive to higher temperatures than S. exserta. This study improves the understanding of the effects of light and temperature on mesophotic octocoral physiology and lays the foundation for future work to explore the thermal thresholds of each species and the endobiont–host relationship in S. exserta.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lno.70214","usgsCitation":"Lange, K., Aquilina-Beck, A., Mccauley, M., Johnstone, J., Demopoulos, A., Greig, T., Beers, J.M., Spalding, H.L., and Etnoyer, P.J., 2025, Ecophysiology of two mesophotic octocorals intended for restoration: Effects of light and temperature: Limnology and Oceanography, v. 70, no. 11, p. 3309-3321, https://doi.org/10.1002/lno.70214.","productDescription":"13 p.","startPage":"3309","endPage":"3321","ipdsId":"IP-170683","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":496730,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.70214","text":"Publisher Index Page"},{"id":496581,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Mississippi, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.45340087314823,\n 30.115510467549512\n ],\n [\n -89.64821880892258,\n 27.852030542053697\n ],\n [\n -77.85588264276542,\n 30.821175006212798\n ],\n [\n -78.19061941496408,\n 33.2102871797333\n ],\n [\n -89.45340087314823,\n 30.115510467549512\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"70","issue":"11","noUsgsAuthors":false,"publicationDate":"2025-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Lange, Kassidy","contributorId":362314,"corporation":false,"usgs":false,"family":"Lange","given":"Kassidy","affiliations":[{"id":86500,"text":"Consolidated Safety Services (CSS) Inc., under contract to National Oceanic and Atmospheric Administration (NOAA), National Ocean Service, National Centers for Coastal Ocean Science, Charleston, SC, United 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