Soil organic carbon ('SOC') in drylands comprises nearly a third of the global SOC pool and has relatively rapid turnover and thus is a key driver of variability in the global carbon cycle. SOC is also a sensitive indicator of longer-term directional change and disturbance-responses of ecosystem C storage. Biome-scale disruption of the dryland carbon cycle by exotic annual grass invasions (mainly Bromus tectorum, 'Cheatgrass') threatens carbon storage and corresponding benefits to soil hydrology and nutrient retention. Past studies on cheatgrass impacts mainly focused on total C, and of the few that evaluated SOC, none compared the very different fractions of SOC, such as relatively unstable particulate organic carbon (POC) or relatively stable, mineral-associated organic carbon (MAOC). We measured SOC and its POC and MAOC constituents in the surface soils of sites that had sagebrush canopies but differed in whether their understories had been invaded by cheatgrass or not, in both warm and relatively colder ecoregions of the western USA. MAOC stocks were 36.1% less in the 0–10 cm depth and 46.1% less in the 10–20 cm depth in the cheatgrass-invaded stands compared to the uninvaded stands of the warmer Colorado Plateau, but not in the cooler and more carbon-rich Wyoming Basin ecoregion. In plots where cheatgrass increased SOC, it was via unstable POC. These findings indicate that cheatgrass effects on the distribution of soil carbon among POC and MAOC fractions may vary among ecoregions, and that cheatgrass can reduce forms of carbon that are otherwise considered stable and 'secure', i.e. sequestered.
","language":"English","publisher":"IOP Science","doi":"10.1088/2515-7620/adb93f","usgsCitation":"Katz, S., Maxwell, T.M., de Graaff, M., and Germino, M., 2025, Invasion of perennial sagebrush steppe by shallow-rooted exotic cheatgrass reduces stable forms of soil carbon in a warmer but not cooler ecoregion: Environmental Research Communications, v. 7, no. 3, 031001, 9 p., https://doi.org/10.1088/2515-7620/adb93f.","productDescription":"031001, 9 p.","ipdsId":"IP-171618","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":487431,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/2515-7620/adb93f","text":"Publisher Index Page"},{"id":482914,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Katz, Sydney Maya 0000-0003-1682-1885","orcid":"https://orcid.org/0000-0003-1682-1885","contributorId":351089,"corporation":false,"usgs":true,"family":"Katz","given":"Sydney Maya","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":929593,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maxwell, Toby Matthew 0000-0001-5171-0705","orcid":"https://orcid.org/0000-0001-5171-0705","contributorId":334690,"corporation":false,"usgs":true,"family":"Maxwell","given":"Toby","email":"","middleInitial":"Matthew","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":929594,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"de Graaff, Marie-Anne","contributorId":195121,"corporation":false,"usgs":false,"family":"de Graaff","given":"Marie-Anne","email":"","affiliations":[],"preferred":false,"id":929595,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":929596,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70264002,"text":"70264002 - 2025 - Estimating spatially explicit survival and mortality risk from telemetry data with thinned point process models","interactions":[],"lastModifiedDate":"2025-03-27T13:17:10.387768","indexId":"70264002","displayToPublicDate":"2025-03-03T09:34:55","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Estimating spatially explicit survival and mortality risk from telemetry data with thinned point process models","docAbstract":"Mortality risk for animals often varies spatially and can be linked to how animals use landscapes. While numerous studies collect telemetry data on animals, the focus is typically on the period when animals are alive, even though there is important information that could be gleaned about mortality risk. We introduce a thinned spatial point process (SPP) modelling framework that couples relative abundance and space use with a mortality process to formally treat the occurrence of mortality events across the landscape as a spatial process. We show how this model can be embedded in a hierarchical statistical framework and fit to telemetry data to make inferences about how spatial covariates drive both space use and mortality risk. We apply the method to two data sets to study the effects of roads and habitat on spatially explicit mortality risk: (1) VHF telemetry data collected for willow ptarmigan in Alaska, and (2) hourly GPS telemetry data collected for black bears in Colorado. These case studies demonstrate the applicability of this method for different species and data types, making it broadly useful in enabling inferences about the mechanisms influencing animal survival and spatial population processes while formally treating survival as a spatial process, especially as the development and implementation of joint analyses continue to progress.
","language":"English","publisher":"Wiley","doi":"10.1111/ele.70092","usgsCitation":"Eisaguirre, J.M., Lohman, M., Frye, G., Johnson, H.E., Riecke, T., and Williams, P.J., 2025, Estimating spatially explicit survival and mortality risk from telemetry data with thinned point process models: Ecology Letters, v. 28, no. 3, e70092, 11 p., https://doi.org/10.1111/ele.70092.","productDescription":"e70092, 11 p.","ipdsId":"IP-160909","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":496394,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ele.70092","text":"Publisher Index Page"},{"id":492795,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HSN8PV","text":"USGS data release","linkHelpText":"Thinned Point Process Models for Telemetry Data"},{"id":482801,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Eisaguirre, Joseph Michael 0000-0002-0450-8472","orcid":"https://orcid.org/0000-0002-0450-8472","contributorId":301980,"corporation":false,"usgs":true,"family":"Eisaguirre","given":"Joseph","email":"","middleInitial":"Michael","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":929454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lohman, Medeleine G.","contributorId":351785,"corporation":false,"usgs":false,"family":"Lohman","given":"Medeleine G.","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":929455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frye, Graham G.","contributorId":351786,"corporation":false,"usgs":false,"family":"Frye","given":"Graham G.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":929456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Heather E. 0000-0001-5392-7676 hejohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5392-7676","contributorId":205919,"corporation":false,"usgs":true,"family":"Johnson","given":"Heather","email":"hejohnson@usgs.gov","middleInitial":"E.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":929457,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Riecke, Thomas V.","contributorId":171482,"corporation":false,"usgs":false,"family":"Riecke","given":"Thomas V.","affiliations":[],"preferred":false,"id":929458,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Williams, Perry J.","contributorId":169058,"corporation":false,"usgs":false,"family":"Williams","given":"Perry","email":"","middleInitial":"J.","affiliations":[{"id":25400,"text":"U.S. Fish and Wildlife Service, Big Oaks National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":929459,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263874,"text":"70263874 - 2025 - Under the Ice: Lake Whitefish and winter conditions in a Great Lake","interactions":[],"lastModifiedDate":"2025-03-13T14:40:01.411928","indexId":"70263874","displayToPublicDate":"2025-03-03T09:31:41","publicationYear":"2025","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"title":"Under the Ice: Lake Whitefish and winter conditions in a Great Lake","docAbstract":"No abstract available.
","language":"English","publisher":"University of Minnesota","usgsCitation":"Del Fiacco, J., and Hilling, C.D., 2025, Under the Ice: Lake Whitefish and winter conditions in a Great Lake, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-175838","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":483255,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://mwcasc.umn.edu/under-ice","linkFileType":{"id":5,"text":"html"}},{"id":483256,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.90437674926393,\n 42.88573072124271\n ],\n [\n -79.52141499356523,\n 42.87769210959476\n ],\n [\n -80.22678399206747,\n 42.79281581499302\n ],\n [\n -80.51880625151767,\n 42.57814946253396\n ],\n [\n -81.02026663661093,\n 42.67543230196293\n ],\n [\n -81.30128454543694,\n 42.67543535756306\n ],\n [\n -81.53822853578485,\n 42.56596464399709\n ],\n [\n -81.9019334399238,\n 42.33014170165541\n ],\n [\n -81.95153424579222,\n 42.26492779780335\n ],\n [\n -82.12236039987853,\n 42.236375180224144\n ],\n [\n -82.45298532777961,\n 42.08524033767702\n ],\n [\n -82.5191058069868,\n 41.92964634487325\n ],\n [\n -82.61828632462084,\n 42.04024072452731\n ],\n [\n -82.91034362126736,\n 41.987015817617134\n ],\n [\n -83.11972864041921,\n 42.077062926530004\n ],\n [\n -83.10317152070901,\n 42.24453450962994\n ],\n [\n -83.29610139408294,\n 41.9419412108324\n ],\n [\n -83.3622121286252,\n 41.92964573816295\n ],\n [\n -83.51644448225035,\n 41.69555451568095\n ],\n [\n -82.9488535420495,\n 41.41103910261083\n ],\n [\n -82.71193183972241,\n 41.41929329585025\n ],\n [\n -82.47491670465367,\n 41.35733064230888\n ],\n [\n -81.99008606791249,\n 41.477110666960954\n ],\n [\n -81.70899983775189,\n 41.46067864477564\n ],\n [\n -81.25720217469238,\n 41.736707291689584\n ],\n [\n -80.6842102922714,\n 41.88460348122109\n ],\n [\n -79.96258495960994,\n 42.16700280222196\n ],\n [\n -79.42295622097835,\n 42.38302164176207\n ],\n [\n -79.37861904307078,\n 42.4440510296389\n ],\n [\n -79.15795348814065,\n 42.52534601120243\n ],\n [\n -79.00930804606551,\n 42.69563463834368\n ],\n [\n -78.83853732874603,\n 42.76038448982763\n ],\n [\n -78.84931646792415,\n 42.86956831700121\n ],\n [\n -78.90437674926393,\n 42.88573072124271\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Del Fiacco, Jessica","contributorId":351525,"corporation":false,"usgs":false,"family":"Del Fiacco","given":"Jessica","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":928792,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hilling, Corbin David 0000-0003-4040-9516","orcid":"https://orcid.org/0000-0003-4040-9516","contributorId":298946,"corporation":false,"usgs":true,"family":"Hilling","given":"Corbin","email":"","middleInitial":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":928793,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70263872,"text":"dr1208 - 2025 - Bedrock fracture characterization of the New Hampshire State Route 111 bypass, Windham, New Hampshire","interactions":[],"lastModifiedDate":"2025-07-21T18:31:25.600058","indexId":"dr1208","displayToPublicDate":"2025-03-03T09:20:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1208","displayTitle":"Bedrock Fracture Characterization of the New Hampshire State Route 111 Bypass, Windham, New Hampshire","title":"Bedrock fracture characterization of the New Hampshire State Route 111 bypass, Windham, New Hampshire","docAbstract":"Bedrock roadcuts developed with blasting along the New Hampshire State Route 111 bypass in Windham expose the metasedimentary Silurian Berwick Formation and intrusions of multiple phases of foliated to nonfoliated granite to granitic pegmatite of the Devonian New Hampshire Plutonic Suite. Fracture characterization at two roadway rock cuts (roadcuts) included measurement of fractures over a distance of approximately 225 and 85 meters. The Berwick Formation consists of medium-gray biotite-plagioclase-quartz granofels, biotite schist, and lesser calc-silicate rock. The Berwick Formation is locally sulfidic. Fresh, unweathered roadcuts are mostly gray but exhibit locally rusty weathering. The most conspicuous foliation in the region around the studied roadcuts is steeply northwest dipping to subvertical and northeast-southwest striking. Regionally, the strike of the foliation is consistently to the northeast-southwest, but the dip is locally variable to both the southeast and northwest. About 8 percent of the observed foliation surfaces exhibit limited fracture parting. The limited degree of parting agrees with observations for rocks within the garnet zone of metamorphism elsewhere in the Windham 7.5-minute quadrangle. The most prominent fracture trend is subvertical to steeply northeast-dipping and northwest-southeast striking (strike and dip of about 295°, 80°). The peak trend of steeply dipping fractures at the two exposures is 295°±12° and 289°±6°. Veins observed in the granite occur parallel to the peak fracture trend and consist primarily of quartz, tourmaline, and ankerite with minor amounts of sulfides (arsenopyrite, galena, and rare sphalerite), and trace amounts of apatite and rutile. The observed peak fracture trend at these roadcuts closely agrees with the most prominent fracture trend recognized within the Windham quadrangle. Gently south- to southeast-dipping and east- to northeast-striking fractures occur as joints and as parting fractures along a weak S3 cleavage. Water-bearing fractures at one exposure occur along joints and gently dipping contacts between the Berwick Formation and the granite to granitic pegmatite of the New Hampshire Plutonic Suite. About 8 percent of the fractures are water-bearing and most water-bearing fractures are gently dipping to the southeast. Fracture data separated by rock type shows a similar distribution for steeply dipping northwest-striking trends, but with much fewer observed steeply north-dipping fractures in the granitic rocks. Both rock types show a cluster of gently south-dipping fractures. The granites show far fewer steeply dipping northeast-striking fractures, which reflects a greater degree of parting along the foliation in the metasedimentary rocks than in the granites. No foliation-parallel fractures were observed in the granites, but some contacts between granites and the Berwick Formation do exhibit parting. Fracture termination classification yields 3 percent abutting, 76 percent dead end, and 21 percent crossing (or throughgoing) fractures.
Six brittle faults were observed, which strike northeast and most dip steeply to the northwest. Calculated paleostress tensors for the faults show an average stress field that is consistent with Late Triassic to Early Jurassic northwest-southeast extension associated with rifting of the New England crust during the initial opening of the Atlantic Basin. Fault data are consistent with brittle reactivation of the northeast striking and northwest dipping dominant foliation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1208","programNote":"National Cooperative Geologic Mapping Program","usgsCitation":"Walsh, G.J., and Powell, N.E., 2025, Bedrock fracture characterization of the New Hampshire State Route 111 bypass, Windham, New Hampshire: U.S. Geological Survey Data Report 1208, 12 p., https://doi.org/10.3133/dr1208.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-173004","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":492694,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118460.htm","linkFileType":{"id":5,"text":"html"}},{"id":482525,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1208/images/"},{"id":482521,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1208/coverthb.jpg"},{"id":482522,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1208/dr1208.pdf","text":"Report","size":"14.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1208 PDF"},{"id":482524,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1208/dr1208.XML","linkFileType":{"id":8,"text":"xml"},"description":"DR 1208 XML"},{"id":482526,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14A6TJY","text":"USGS data release","linkHelpText":"Fracture data collected at the Route 111 bypass in Windham, New Hampshire"},{"id":482523,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1208/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DR 1208 HTML"}],"country":"United States","state":"New Hampshire","city":"Windham","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.3065034148442,\n 42.818705377139935\n ],\n [\n -71.3065034148442,\n 42.7840391932281\n ],\n [\n -71.26391433086863,\n 42.7840391932281\n ],\n [\n -71.26391433086863,\n 42.818705377139935\n ],\n [\n -71.3065034148442,\n 42.818705377139935\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 926A
Reston, VA 20192
Sea otters are an iconic keystone predator that can maintain kelp forests by preying on grazing invertebrates such as sea urchins. However, the effects of sea otters on kelp forests vary over their geographic range. Here, we analyze two 30-y datasets on kelp forest communities during the reintroduction of sea otters along the west coast of Vancouver Island, BC, Canada, and around San Nicolas Island, CA. We developed a community model to estimate species interactions as dynamic rates, varying with community state. We find evidence of a classic trophic cascade off Vancouver Island; the arrival of otters quickly led to depletion of urchins and recovery of kelp. However, this cascade was muted around San Nicolas Island, with otters, urchins, and kelp all coexisting at intermediate densities for multiple years. Our models show that this difference came from a pulse of strong otter impacts on urchins following recolonization off Vancouver Island, but not off San Nicolas Island. The mean effects of otters on urchins and urchins on kelp were not stronger in the north, indicating that interaction dynamics and not average interaction strength are key to explaining differences in community trajectories. We also find stronger multistep interaction chains in the south, arising from competitive interactions that indirectly buffered otter effects. These findings shed light on long-standing hypotheses about how interspecific interactions can alter the function of keystone species across community contexts. More broadly, we show how community change can be more accurately predicted by considering dynamic interaction strengths.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2413360122","usgsCitation":"Langendorf, R., Estes, J.A., Watson, J.C., Kenner, M.C., Hatfield, B.B., Tinker, M.T., Waddle, E., DeMarche, M., and Doak, D., 2025, Dynamic and context-dependent keystone species effects in kelp forests: Proceedings of the National Academy of Sciences, v. 122, no. 10, e2413360122, 10 p., https://doi.org/10.1073/pnas.2413360122.","productDescription":"e2413360122, 10 p.","ipdsId":"IP-168469","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":486979,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2413360122","text":"Publisher Index Page"},{"id":483132,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Langendorf, Ryan E.","contributorId":352190,"corporation":false,"usgs":false,"family":"Langendorf","given":"Ryan E.","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":930235,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Estes, James A. 0000-0002-3632-4555 jim_estes@usgs.gov","orcid":"https://orcid.org/0000-0002-3632-4555","contributorId":240955,"corporation":false,"usgs":false,"family":"Estes","given":"James","email":"jim_estes@usgs.gov","middleInitial":"A.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":930236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watson, Jane C.","contributorId":201244,"corporation":false,"usgs":false,"family":"Watson","given":"Jane","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":930237,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kenner, Michael C. 0000-0003-4659-461X","orcid":"https://orcid.org/0000-0003-4659-461X","contributorId":208151,"corporation":false,"usgs":true,"family":"Kenner","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":930238,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hatfield, Brian B. 0000-0003-1432-2660 brian_hatfield@usgs.gov","orcid":"https://orcid.org/0000-0003-1432-2660","contributorId":147917,"corporation":false,"usgs":true,"family":"Hatfield","given":"Brian","email":"brian_hatfield@usgs.gov","middleInitial":"B.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":930239,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tinker, M. T. 0000-0002-3314-839X","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":54152,"corporation":false,"usgs":false,"family":"Tinker","given":"M.","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":930240,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Waddle, Ellen","contributorId":352192,"corporation":false,"usgs":false,"family":"Waddle","given":"Ellen","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":930241,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"DeMarche, Megan L.","contributorId":352195,"corporation":false,"usgs":false,"family":"DeMarche","given":"Megan L.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":930242,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Doak, Daniel F.","contributorId":352197,"corporation":false,"usgs":false,"family":"Doak","given":"Daniel F.","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":930243,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70264152,"text":"70264152 - 2025 - METRIC: An interactive framework for integrated visualization and analysis of monitored and expected load reductions for nitrogen, phosphorus, and sediment in the Chesapeake Bay watershed","interactions":[],"lastModifiedDate":"2025-03-07T15:06:33.51307","indexId":"70264152","displayToPublicDate":"2025-03-03T09:03:51","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"METRIC: An interactive framework for integrated visualization and analysis of monitored and expected load reductions for nitrogen, phosphorus, and sediment in the Chesapeake Bay watershed","docAbstract":"Reductions of nitrogen, phosphorus, and sediment loads have been the focus of watershed restoration in many regions for improving water quality, including the Chesapeake Bay. Watershed models and riverine monitoring data can provide important information on the progress of load reductions but do not always generate consistent interpretations. A new framework for integrated visualization and analysis of monitoring and modeling data, named “Monitored and Expected Total Reduction Indicator for the Chesapeake (METRIC),” was developed to provide spatially explicit trends for the subwatersheds of the Chesapeake Bay. METRIC contains up-to-date information on nitrogen, phosphorus, and sediment at 83, 66, and 66 stations, respectively, which can help watershed managers gauge expectations on the trajectory and pace of progress at localized scales. These results were further synthesized to better understand the spatial patterns of the response classes (i.e., agreement between the expected and monitored trends) across the Chesapeake Bay watershed.
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Center","active":true,"usgs":true}],"preferred":true,"id":929955,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bhatt, Gopal","contributorId":331411,"corporation":false,"usgs":false,"family":"Bhatt","given":"Gopal","affiliations":[{"id":6975,"text":"Penn State","active":true,"usgs":false}],"preferred":false,"id":929956,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bertani, Isabella","contributorId":194574,"corporation":false,"usgs":false,"family":"Bertani","given":"Isabella","email":"","affiliations":[{"id":33091,"text":"University of Michigan, Ann Arbor, Michigan","active":true,"usgs":false}],"preferred":false,"id":929957,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70264425,"text":"70264425 - 2025 - Piping Plover home ranges do not appear to be impacted by restoration of barrier islands and headlands","interactions":[],"lastModifiedDate":"2025-03-14T13:56:38.39425","indexId":"70264425","displayToPublicDate":"2025-03-03T08:54:44","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":19846,"text":"BioRxiv","active":true,"publicationSubtype":{"id":32}},"title":"Piping Plover home ranges do not appear to be impacted by restoration of barrier islands and headlands","docAbstract":"Restoration of barrier island and headland habitats can alter existing and create new habitats, which may impact wildlife occupying these areas such as the threatened Piping Plover (Charadrius melodus). We used resight data from banded birds to develop minimum convex polygon (MCP) and kernel density estimates (KDE) of individual Piping Plover home ranges to investigate whether changes in habitat use resulted from restoration activities at Whiskey Island and Caminada Headland, Louisiana. We quantified home range areas for each season and compared changes among pre-restoration, active restoration, and post-restoration phases at each site. We had sufficient sample sizes from Whiskey Island to compare home ranges derived by MCP during the pre-restoration phase to the active restoration phase. However, we did not have enough resight data to analyze post-restoration phase by MCP or any phase by KDE at Whiskey Island. For Caminada Headland, we were able to compare all phases of restoration using MCP, but only had sufficient data to compare pre-restoration and active restoration phases using KDE. Aside from one significant decrease in core (50% isopleth) home range at Caminada Headland when comparing MCPs between post-restoration (∼8 ha) and pre-restoration (∼11 ha) phases, we found no other differences in home range size across phases at either of our study sites. The sum of all evidence generally indicating no change to Piping Plover home range size suggests that barrier island and headland restoration did not have significant positive or negative impacts. The weak response to restoration activities further suggests that birds are using similar or smaller amounts of habitat after restoration is complete and may not need to expand their foraging range following restoration. Further study can help to understand how species of conservation concern respond to coastal restoration efforts, which is critical for establishing comprehensive conservation strategies aimed at species recovery.
","language":"English","publisher":"BioRxiv","doi":"10.1101/2025.03.03.638931","usgsCitation":"Zenzal, T.J., Anderson, A.N., LeBlanc, D., Dobbs, R., Geary, B., and Waddle, H., 2025, Piping Plover home ranges do not appear to be impacted by restoration of barrier islands and headlands: BioRxiv, https://doi.org/10.1101/2025.03.03.638931.","productDescription":"33 p.","ipdsId":"IP-159668","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488296,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1101/2025.03.03.638931","text":"Publisher Index Page"},{"id":483334,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zenzal, Theodore J. Jr. 0000-0001-7342-1373","orcid":"https://orcid.org/0000-0001-7342-1373","contributorId":224399,"corporation":false,"usgs":true,"family":"Zenzal","given":"Theodore","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":930724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Amanda Nicole 0000-0003-3930-3896","orcid":"https://orcid.org/0000-0003-3930-3896","contributorId":224400,"corporation":false,"usgs":true,"family":"Anderson","given":"Amanda","email":"","middleInitial":"Nicole","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":930725,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LeBlanc, Delaina","contributorId":330122,"corporation":false,"usgs":false,"family":"LeBlanc","given":"Delaina","email":"","affiliations":[{"id":78819,"text":"Barataria-Terrebonne National Estuary","active":true,"usgs":false}],"preferred":false,"id":930726,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dobbs, Robert C. 0000-0002-9079-7249 rdobbs@usgs.gov","orcid":"https://orcid.org/0000-0002-9079-7249","contributorId":200300,"corporation":false,"usgs":false,"family":"Dobbs","given":"Robert C.","email":"rdobbs@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":930727,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Geary, Brock","contributorId":352310,"corporation":false,"usgs":false,"family":"Geary","given":"Brock","affiliations":[{"id":16979,"text":"University of Pennsylvania","active":true,"usgs":false}],"preferred":false,"id":930728,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waddle, Hardin 0000-0003-1940-2133","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":201976,"corporation":false,"usgs":true,"family":"Waddle","given":"Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":930729,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263224,"text":"70263224 - 2025 - Documenting, quantifying, and modeling a large glide avalanche in Glacier National Park, Montana, USA","interactions":[],"lastModifiedDate":"2025-02-03T15:39:22.816973","indexId":"70263224","displayToPublicDate":"2025-03-03T08:35:01","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1264,"text":"Cold Regions Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Documenting, quantifying, and modeling a large glide avalanche in Glacier National Park, Montana, USA","docAbstract":"Glide avalanches present a significant and repetitive challenge to many operational forecasting programs, and they are likely to become more frequent. While the spatial location of glide release areas is extremely consistent, the onset of glide avalanche release is notoriously difficult to forecast, and their destructive potential can be immense. Thus, the timing and dynamics of glide avalanches is an important area of study. To better understand these processes, and to improve assessments of risk to transportation corridors and infrastructure, event documentation is key. Here, we survey a large glide avalanche event along the Going-to-the-Sun Road in Glacier National Park, Montana, USA, during road opening operations in the spring of 2022. Using three sets of terrestrial lidar data (pre-event, post-event, and snow-off), we quantified key aspects of the avalanche and created powerful visualizations for analysis. Further, we evaluated meteorological data from automated weather stations between the onset of glide cracking and avalanche release. Last, we synthesized lidar data with a numerical dynamics model to replicate the event in a simulated environment. Using the tuned model, we determined the critical mean snow depth in the release area necessary for an avalanche to reach the road (4.2 m). Our method may be of particular use for glide avalanches, which tend to release in roughly the same place and time each year at a known interface. This could make the calculated critical depths more consistently reliable and preclude the need for additional tuning in dynamics models. As 1) lidar technology continues to improve and reduce in cost, 2) transportation corridors continue to extend into avalanche terrain, and 3) glide avalanches potentially become increasingly frequent, the synthesis outlined here provides a valuable tool for operational forecasters considering infrastructure threatened by glide events.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coldregions.2024.104412","usgsCitation":"Dillon, J.W., Peitzsch, E.H., Miller, Z., Bartelt, P., and Hammonds, K.D., 2025, Documenting, quantifying, and modeling a large glide avalanche in Glacier National Park, Montana, USA: Cold Regions Science and Technology, v. 231, 104412, 10 p., https://doi.org/10.1016/j.coldregions.2024.104412.","productDescription":"104412, 10 p.","ipdsId":"IP-167691","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":489926,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coldregions.2024.104412","text":"Publisher Index Page"},{"id":481608,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.0973168994086,\n 48.755499897415916\n ],\n [\n -114.0973168994086,\n 48.536591733185475\n ],\n [\n -113.50134190625533,\n 48.536591733185475\n ],\n [\n -113.50134190625533,\n 48.755499897415916\n ],\n [\n -114.0973168994086,\n 48.755499897415916\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"231","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dillon, James W.","contributorId":330951,"corporation":false,"usgs":false,"family":"Dillon","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":925961,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":925962,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":925963,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bartelt, Perry","contributorId":336797,"corporation":false,"usgs":false,"family":"Bartelt","given":"Perry","affiliations":[{"id":80867,"text":"SLF","active":true,"usgs":false}],"preferred":false,"id":925964,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hammonds, Kevin D.","contributorId":330952,"corporation":false,"usgs":false,"family":"Hammonds","given":"Kevin","email":"","middleInitial":"D.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":925965,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263659,"text":"70263659 - 2025 - Spatial scale dependence of error in fractional component cover maps","interactions":[],"lastModifiedDate":"2025-02-19T15:38:44.136965","indexId":"70263659","displayToPublicDate":"2025-03-03T08:32:21","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6002,"text":"Rangeland Ecology & Management","active":true,"publicationSubtype":{"id":10}},"title":"Spatial scale dependence of error in fractional component cover maps","docAbstract":"Geospatial products such as fractional vegetation cover maps often report overall, pixel-wise accuracy, but decision-making with these products often occurs at coarser scales. As such, data users often desire guidance on the appropriate spatial scale to apply these data. We worked toward establishing this guidance by assessing RCMAP (Rangeland Condition Monitoring Assessment and Projection) accuracy relative to a series of high-resolution predictions of component cover. We scale the 2-m and RCMAP predictions to various focal window sizes scales ranging from 30 to 1 500 m using focal averaging. We also evaluated variation in scaling effects on error at ecoregion and pasture (mean area of 1 050 ha) scales. Our results demonstrate increased accuracy at broader windows, across all components, and most increases in accuracy level off at ∼200–600 m scales. At the scale with highest accuracy, cross-component average correlation (r) increased by 6.5%, and root mean square error (RMSE) was reduced 46.4% relative to 30-m scale data. Scaling-related improvements to accuracy were greatest in components such as shrub and tree with more spatially heterogeneous cover and in ecoregions with more spatially heterogenous cover. When components were aggregated at the pasture scale, r increased 10% and RMSE decreased 34.3% on average relative to the 30-m scale. Our results provide empirical data on the scale dependence of error, which fractional cover data users may consider alongside their needs when using these data. Although the general principle remains that remotely sensed products are intended to address landscape-scale questions, our analysis indicates that applying data at finer than landscape spatial scales and grouping even a handful of pixels resulted in lowered error compared to pixel-level comparisons. Our results quantify the trade-offs between data granularity and error related to scale for fractional vegetation cover.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2025.01.004","usgsCitation":"Rigge, M.B., Bunde, B., McCord, S.E., Harrison, G., Assal, T.J., and Smith, J.L., 2025, Spatial scale dependence of error in fractional component cover maps: Rangeland Ecology & Management, v. 99, p. 77-87, https://doi.org/10.1016/j.rama.2025.01.004.","productDescription":"11 p.","startPage":"77","endPage":"87","ipdsId":"IP-167033","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":489837,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2025.01.004","text":"Publisher Index Page"},{"id":482211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.10352224242965,\n 48.780109887219425\n ],\n [\n -125.10535497999196,\n 40.261524080431464\n ],\n [\n -120.19844036504566,\n 33.54076957731398\n ],\n [\n -110.34919181669402,\n 31.1726202258846\n ],\n [\n -101.83038001679847,\n 30.617299074802702\n ],\n [\n -99.5482123609311,\n 48.780109887219425\n ],\n [\n -125.10352224242965,\n 48.780109887219425\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"99","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":927714,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunde, Brett 0000-0003-0228-779X","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":288364,"corporation":false,"usgs":false,"family":"Bunde","given":"Brett","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":927715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCord, Sarah E.","contributorId":195931,"corporation":false,"usgs":false,"family":"McCord","given":"Sarah","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":927716,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harrison, Georgia","contributorId":351012,"corporation":false,"usgs":false,"family":"Harrison","given":"Georgia","affiliations":[{"id":51849,"text":"United States Department of Agriculture - Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":927717,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Assal, Timothy J","contributorId":238085,"corporation":false,"usgs":false,"family":"Assal","given":"Timothy","email":"","middleInitial":"J","affiliations":[{"id":18142,"text":"Kent State University","active":true,"usgs":false}],"preferred":false,"id":927718,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, James L.","contributorId":291335,"corporation":false,"usgs":false,"family":"Smith","given":"James","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":927719,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266033,"text":"70266033 - 2025 - Effect of copper mill waste material on benthic invertebrates and zooplankton diversity and abundance","interactions":[],"lastModifiedDate":"2025-04-24T15:34:31.554972","indexId":"70266033","displayToPublicDate":"2025-03-03T08:28:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Effect of copper mill waste material on benthic invertebrates and zooplankton diversity and abundance","docAbstract":"Copper (Cu) stamp mill mining in North America from the early 1900s produced a pulverized ore by-product now known as stamp sands (SS). In a mining operation near the city of Gay (Michigan, USA), SS were originally deposited near a Lake Superior beach, but erosion and wave action have moved many SS into beaches and reefs that are critical spawning and nursery areas for native fish (e.g., Lake Whitefish). Larval and juvenile native fish consume zooplankton and benthic invertebrates during their development, and many of these invertebrate taxa may be sensitive to metal contamination from the SS. Here, we sampled the invertebrate community from beaches with high SS, moderate SS and low SS, as well as a control beach 58 km from the source of the SS. The high SS site was characterized by fewer benthic taxa, and less density of several taxa than the low SS site, especially benthic copepods. All beaches had comparable zooplankton diversity, but the abundance was ~ 2 orders of magnitude lower at the high SS site. Cu and several other metals were elevated at beaches with more SS. We found support for associations between benthic density and diversity with depth (positive effect) and Cu concentration (negative effect). Cu concentration was a better predictor of declines in benthic invertebrate abundance and diversity than SS although sensitivity to Cu varied among taxa. We also observed that the relationship between Cu concentration and SS was non-linear, and highly variable. For example, 149 mg Cu/kg dry weight sediment is a consensus threshold used in the literature to identify Cu toxicity, but the prediction interval for estimating that concentration of Cu from measurements of SS is 26-851 mg Cu/kg dry weight. A better predictive model of this relationship would be beneficial to develop an understanding of what level of SS reduction would prevent Cu impacts on invertebrates.","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0318980","usgsCitation":"Larson, J.H., Lowe, M.R., Bailey, S., Bell, A.H., and Cleveland, D.M., 2025, Effect of copper mill waste material on benthic invertebrates and zooplankton diversity and abundance: PLoS ONE, v. 20, no. 3, e0318980, 27 p., https://doi.org/10.1371/journal.pone.0318980.","productDescription":"e0318980, 27 p.","ipdsId":"IP-159260","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":487903,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0318980","text":"Publisher Index Page"},{"id":484986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Keweenaw Bay, Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.51454370384035,\n 46.89135600920267\n ],\n [\n -88.51454370384035,\n 46.7436589844292\n ],\n [\n -88.37255101673634,\n 46.7436589844292\n ],\n [\n -88.37255101673634,\n 46.89135600920267\n ],\n [\n -88.51454370384035,\n 46.89135600920267\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":934422,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowe, Michael R. 0000-0002-4645-9429","orcid":"https://orcid.org/0000-0002-4645-9429","contributorId":10539,"corporation":false,"usgs":true,"family":"Lowe","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":934423,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bailey, Sean 0000-0003-0361-7914 sbailey@usgs.gov","orcid":"https://orcid.org/0000-0003-0361-7914","contributorId":198515,"corporation":false,"usgs":true,"family":"Bailey","given":"Sean","email":"sbailey@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":934424,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bell, Amanda H. 0000-0002-7199-2145 ahbell@usgs.gov","orcid":"https://orcid.org/0000-0002-7199-2145","contributorId":1752,"corporation":false,"usgs":true,"family":"Bell","given":"Amanda","email":"ahbell@usgs.gov","middleInitial":"H.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934425,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cleveland, Danielle M. 0000-0003-3880-4584 dcleveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3880-4584","contributorId":187471,"corporation":false,"usgs":true,"family":"Cleveland","given":"Danielle","email":"dcleveland@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":934426,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70264764,"text":"70264764 - 2025 - A survey of mammal and fish genetic diversity across the global protected area network","interactions":[],"lastModifiedDate":"2025-03-24T15:26:38.353638","indexId":"70264764","displayToPublicDate":"2025-03-03T08:23:05","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1326,"text":"Conservation Letters","active":true,"publicationSubtype":{"id":10}},"title":"A survey of mammal and fish genetic diversity across the global protected area network","docAbstract":"Global conservation targets aim to expand protected areas and maintain species’ genetic diversity. Whether protected areas capture genetic diversity is unclear. We examined this question using a global sample of nuclear population-level microsatellite data comprising genotypes from 2513 sites, 134,183 individuals, and 176 mammal and marine fish species. The genetic diversity and differentiation of samples inside and outside protected areas were similar, with some evidence for higher diversity in protected areas for small-bodied mammals. Mammal populations, particularly large species, tended to be more genetically diverse when near multiple protected areas, regardless of whether samples were collected in or outside protected areas. Older marine protected areas tended to capture more genetically diverse fish populations. However, limited data availability in many regions hinders the systematic incorporation of genetic diversity into protected area design. Focusing on minimizing population decline and maintaining connectivity between protected areas remain essential proxies for maintaining genetic diversity.
","language":"English","publisher":"The Society for Conservation Biology","doi":"10.1111/conl.13092","usgsCitation":"Schmidt, C., Karachaliou, E., Vandergast, A.G., Crandall, E.D., Falgout, J.T., Hunter, M., Kershaw, F., Leigh, D.M., O'Brien, D., Paz-Vinas, I., Segelbacher, G., and Garroway, C.J., 2025, A survey of mammal and fish genetic diversity across the global protected area network: Conservation Letters, v. 18, no. 2, e13092, 10 p., https://doi.org/10.1111/conl.13092.","productDescription":"e13092, 10 p.","ipdsId":"IP-162653","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488377,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/conl.13092","text":"Publisher Index Page"},{"id":483721,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmidt, Chloe","contributorId":329610,"corporation":false,"usgs":false,"family":"Schmidt","given":"Chloe","affiliations":[{"id":62676,"text":"Department of Ecology and Evolutionary Biology, Yale University","active":true,"usgs":false}],"preferred":false,"id":931568,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karachaliou, Eleana","contributorId":352516,"corporation":false,"usgs":false,"family":"Karachaliou","given":"Eleana","affiliations":[{"id":62682,"text":"Department of Biological Sciences, University of Manitoba","active":true,"usgs":false}],"preferred":false,"id":931569,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":931570,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crandall, Eric D. 0000-0001-8580-3651","orcid":"https://orcid.org/0000-0001-8580-3651","contributorId":337181,"corporation":false,"usgs":false,"family":"Crandall","given":"Eric","email":"","middleInitial":"D.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":931571,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Falgout, Jeff T. 0000-0002-7108-477X jfalgout@usgs.gov","orcid":"https://orcid.org/0000-0002-7108-477X","contributorId":4957,"corporation":false,"usgs":true,"family":"Falgout","given":"Jeff","email":"jfalgout@usgs.gov","middleInitial":"T.","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":931572,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":207589,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":931573,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kershaw, Francine","contributorId":260831,"corporation":false,"usgs":false,"family":"Kershaw","given":"Francine","email":"","affiliations":[{"id":52686,"text":"Natural Resources Defense Council, New York","active":true,"usgs":false}],"preferred":false,"id":931574,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Leigh, Deborah M.","contributorId":291307,"corporation":false,"usgs":false,"family":"Leigh","given":"Deborah","email":"","middleInitial":"M.","affiliations":[{"id":62679,"text":"WSL Swiss Federal Research Institute","active":true,"usgs":false}],"preferred":false,"id":931575,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"O'Brien, David","contributorId":192192,"corporation":false,"usgs":false,"family":"O'Brien","given":"David","affiliations":[],"preferred":false,"id":931576,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Paz-Vinas, Ivan","contributorId":239614,"corporation":false,"usgs":false,"family":"Paz-Vinas","given":"Ivan","email":"","affiliations":[{"id":47934,"text":"Laboratoire Ecologie Fonctionnelle et Environnement, Université de Toulouse","active":true,"usgs":false}],"preferred":false,"id":931577,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Segelbacher, Gernot","contributorId":206584,"corporation":false,"usgs":false,"family":"Segelbacher","given":"Gernot","email":"","affiliations":[{"id":37345,"text":"University of Freiburg, Germany","active":true,"usgs":false}],"preferred":false,"id":931578,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Garroway, Colin J.","contributorId":329611,"corporation":false,"usgs":false,"family":"Garroway","given":"Colin","email":"","middleInitial":"J.","affiliations":[{"id":78674,"text":"Department of Biological Sciences, University of Manitoba, Canada","active":true,"usgs":false}],"preferred":false,"id":931579,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70261952,"text":"70261952 - 2025 - Mercury speciation and stable isotopes in emperor penguins: First evidence for biochemical demethylation of methylmercury to mercury-dithiolate and mercury-tetraselenolate complexes","interactions":[],"lastModifiedDate":"2025-01-06T15:30:53.822942","indexId":"70261952","displayToPublicDate":"2025-03-03T08:17:56","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2331,"text":"Journal of Hazardous Materials","active":true,"publicationSubtype":{"id":10}},"title":"Mercury speciation and stable isotopes in emperor penguins: First evidence for biochemical demethylation of methylmercury to mercury-dithiolate and mercury-tetraselenolate complexes","docAbstract":"Apex marine predators, such as toothed whales and large petrels and albatrosses, ingest mercury (Hg) primarily in the form of methylmercury (MeHg) via prey consumption, which they detoxify as tiemannite (HgSe). One of the most intriguing current questions in Hg research is how more abundant lower trophic level predators detoxify MeHg, particularly in marine environments where tissue Hg burdens can be elevated. To address this need, we used high energy-resolution X-ray absorption near edge structure spectroscopy paired with nitrogen (N) and Hg stable isotopes to identify the chemical forms of Hg, Hg source, and species-specific δ202Hg isotopic values in emperor penguin, a mesopredator feeding primarily on Antarctic silverfish. The penguin liver contains variable proportions of MeHg and two inorganic Hg species (IHg), Hg-dithiolate (Hg(SR)2) and Hg-tetraselenolate (Hg(Sec)4) complexes, each characterized by a specific isotopic value (δ202MeHg = 0.3 ± 0.2‰, δ202Hg(SR)2 = −1.6 ± 0.2‰, δ202Hg(Sec)4 = −2.0 ± 0.1‰). Using δ15N as tracer of food source, we show that Hg(SR)2 is not dietary but a biochemical demethylation product of MeHg metabolism. Penguin females transfer Hg to the egg as MeHg in the egg albumen, 89% MeHg and 11% IHg in the membrane, and 32% MeHg and 68% Hg(Sec)4 in the yolk, on average (n = 15). Despite IHg species in eggs, MeHg is the main species quantitatively transferred by the mother to the chick because of the disproportionate mass of the MeHg-rich albumen compared to the yolk (n = 18). Further research is needed to elucidate the MeHg to Hg(SR)2 demethylation pathway firmly documented here for the first time in multicellular organisms, and to understand why the thiolate ligands are not exchanged for Se ligands to form Hg(Sec)4, as the liver does not suffer from Se deficiency.
","language":"English","publisher":"Elesvier","doi":"10.1016/j.jhazmat.2024.136499","usgsCitation":"Manceau, A., Bustamante, P., Richy, E., Cherel, Y., Janssen, S., Glatzel, P., and Poulin, B., 2025, Mercury speciation and stable isotopes in emperor penguins: First evidence for biochemical demethylation of methylmercury to mercury-dithiolate and mercury-tetraselenolate complexes: Journal of Hazardous Materials, v. 485, 136499, 11 p., https://doi.org/10.1016/j.jhazmat.2024.136499.","productDescription":"136499, 11 p.","ipdsId":"IP-171780","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":489879,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhazmat.2024.136499","text":"Publisher Index Page"},{"id":465672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Antartica","otherGeospatial":"Adelie Land","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 142.82931740033433,\n -66.16249522386957\n ],\n [\n 142.82931740033433,\n -71.51568930876502\n ],\n [\n 172.43330802520745,\n -71.51568930876502\n ],\n [\n 172.43330802520745,\n -66.16249522386957\n ],\n [\n 142.82931740033433,\n -66.16249522386957\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"485","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Manceau, Alain 0000-0003-0845-611X","orcid":"https://orcid.org/0000-0003-0845-611X","contributorId":194255,"corporation":false,"usgs":false,"family":"Manceau","given":"Alain","email":"","affiliations":[],"preferred":false,"id":922389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bustamante, Paco","contributorId":201551,"corporation":false,"usgs":false,"family":"Bustamante","given":"Paco","email":"","affiliations":[{"id":36199,"text":"La Rochelle University","active":true,"usgs":false}],"preferred":false,"id":922390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richy, Etienne","contributorId":347762,"corporation":false,"usgs":false,"family":"Richy","given":"Etienne","affiliations":[{"id":83226,"text":"CNRS-La Rochelle Université","active":true,"usgs":false}],"preferred":false,"id":922391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cherel, Yves 0000-0001-9469-9489","orcid":"https://orcid.org/0000-0001-9469-9489","contributorId":267388,"corporation":false,"usgs":false,"family":"Cherel","given":"Yves","email":"","affiliations":[{"id":55487,"text":"La Rochelle University, Villiers-en-Bois, France","active":true,"usgs":false}],"preferred":false,"id":922392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922393,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Glatzel, Pieter 0000-0001-6532-8144","orcid":"https://orcid.org/0000-0001-6532-8144","contributorId":260892,"corporation":false,"usgs":false,"family":"Glatzel","given":"Pieter","email":"","affiliations":[{"id":52705,"text":"European Synchrotron Radiation Facility (ESRF), Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":922394,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Poulin, Brett A.","contributorId":328488,"corporation":false,"usgs":false,"family":"Poulin","given":"Brett A.","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":922395,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70263177,"text":"70263177 - 2025 - Estimating agricultural irrigation water consumption for the High Plains aquifer region with integrated energy- and water-balance evapotranspiration modeling approaches","interactions":[],"lastModifiedDate":"2025-01-31T15:18:02.303715","indexId":"70263177","displayToPublicDate":"2025-03-03T08:10:09","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":680,"text":"Agricultural Water Management","active":true,"publicationSubtype":{"id":10}},"title":"Estimating agricultural irrigation water consumption for the High Plains aquifer region with integrated energy- and water-balance evapotranspiration modeling approaches","docAbstract":"Estimation of irrigation water use provides essential information for the management and conservation of agricultural water resources. Conventionally, water use data are created based on reports and surveys from water users, whereas manual records may not be complete due to lacking flow meters, measurement gaps, inconsistent methods across regions, and time- and cost-consuming data processing. Alternatively, spatially explicit estimation of irrigation water use can be conducted efficiently using remote sensing evapotranspiration (ET) modeling approaches. In this study, we created a gridded blue water evapotranspiration (BWET) dataset to estimate historical irrigation water consumption (1986 – 2020) in the croplands across the United States High Plains aquifer region. The BWET data were generated by integrating an energy-balance ET model [Operational Simplified Surface Energy Balance model (SSEBop)] and a water-balance ET model [Vegetation ET model (VegET)]. BWET in croplands indicates crop consumptive use of irrigation water extracted from surface water and groundwater resources. The BWET estimates were compared with reported irrigation water use data for all counties within the aquifer region. The results revealed high agreement between growing season (May – September) BWET and annual water withdrawal at county level. Specifically, correlation coefficients of volumetric BWET and water withdrawal were 0.90 and 0.96, respectively, for the entire aquifer region and western Kansas. The timeseries of BWET and water withdrawal showed similar temporal trends and high covariations. The BWET estimates were systematically lower than the water withdrawal measurements, which was primarily attributed to blue water losses in the irrigation system. The irrigation efficiency, calculated as the ratio of BWET to water withdrawal depth, was 0.57 and 0.74 for the entire aquifer region and western Kansas, respectively. This study demonstrates the capability of using satellite-based ET models (e.g., SSEBop and VegET) to efficiently estimate crop water consumption and evaluate irrigation efficiency at landscape, county, and regional scales.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agwat.2025.109308","usgsCitation":"Ji, L., Senay, G.B., Friedrichs, M., and Kagone, S., 2025, Estimating agricultural irrigation water consumption for the High Plains aquifer region with integrated energy- and water-balance evapotranspiration modeling approaches: Agricultural Water Management, v. 309, 109308, 17 p., https://doi.org/10.1016/j.agwat.2025.109308.","productDescription":"109308, 17 p.","ipdsId":"IP-163904","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":489921,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agwat.2025.109308","text":"Publisher Index Page"},{"id":481548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, Wyoming","otherGeospatial":"High Plains","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-104.057698,44.997431],[-104.043814,45.868385],[-103.668479,45.945242],[-96.571871,45.871846],[-96.82616,45.654164],[-96.452315,45.208986],[-96.453049,43.500415],[-96.591213,43.500514],[-96.439335,43.113916],[-96.630311,42.770885],[-96.396107,42.484095],[-96.272901,42.047281],[-96.129186,41.965136],[-96.081843,41.580407],[-95.850188,41.184798],[-95.885349,40.721093],[-95.41932,40.048442],[-94.916918,39.836138],[-95.113077,39.559133],[-94.615834,39.160003],[-94.617919,36.499414],[-94.431822,35.397652],[-94.485528,33.663388],[-94.386086,33.544923],[-94.070395,33.574561],[-94.0427,32.056012],[-93.523248,31.037842],[-93.765822,30.333318],[-93.702436,30.112721],[-93.922744,29.818808],[-93.852868,29.675885],[-94.731047,29.369141],[-94.532348,29.5178],[-94.767246,29.525523],[-94.724616,29.774766],[-94.965963,29.70033],[-94.894234,29.338],[-95.16525,29.113566],[-94.73132,29.338066],[-94.803695,29.279237],[-96.341617,28.417334],[-95.983106,28.641942],[-96.221784,28.580364],[-96.287942,28.683164],[-96.473694,28.57324],[-96.664534,28.696904],[-96.481836,28.407844],[-96.790235,28.383926],[-96.898123,28.152881],[-97.21535,28.076575],[-97.040618,28.028708],[-97.183455,27.833231],[-97.354614,27.849572],[-97.296598,27.613947],[-97.399398,27.344735],[-97.640111,27.270943],[-97.485149,27.250841],[-97.552325,26.867633],[-97.145567,25.971132],[-97.36542,25.849826],[-99.110855,26.426278],[-99.452316,27.062669],[-99.556812,27.614336],[-99.841708,27.766464],[-100.280518,28.267969],[-100.785521,29.228137],[-101.441059,29.753451],[-102.341033,29.869305],[-102.698347,29.695591],[-102.944911,29.18882],[-103.227801,28.991532],[-104.46652,29.609296],[-104.924796,30.604832],[-106.158218,31.438885],[-106.381039,31.73211],[-108.208394,31.783599],[-108.208573,31.333395],[-109.050044,31.332502],[-109.050076,41.000659],[-111.046723,40.997959],[-111.055199,45.001321],[-104.057698,44.997431]]],[[[-97.240849,26.411504],[-97.383531,26.875521],[-97.366771,27.333276],[-96.946988,28.026522],[-96.403206,28.371475],[-96.929053,27.99044],[-97.276091,27.472145],[-97.370731,26.909706],[-97.161471,26.088705],[-97.240849,26.411504]]]]},\"properties\":{\"name\":\"Colorado\",\"nation\":\"USA \"}}]}","volume":"309","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ji, Lei 0000-0002-6133-1036","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":272078,"corporation":false,"usgs":false,"family":"Ji","given":"Lei","affiliations":[{"id":56342,"text":"ASRC Federal Data Solutions, Contractor to USGS Earth Resources Observation and Science Center","active":true,"usgs":false}],"preferred":false,"id":925791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":925792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":199093,"corporation":false,"usgs":false,"family":"Friedrichs","given":"MacKenzie","affiliations":[],"preferred":false,"id":925793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":199091,"corporation":false,"usgs":false,"family":"Kagone","given":"Stefanie","affiliations":[],"preferred":false,"id":925794,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70264065,"text":"70264065 - 2025 - Cytotype and local adaptation drive phenotypic variation in two subspecies of big sagebrush (Artemisia tridentata)","interactions":[],"lastModifiedDate":"2025-03-27T13:18:10.215524","indexId":"70264065","displayToPublicDate":"2025-03-03T07:54:09","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Cytotype and local adaptation drive phenotypic variation in two subspecies of big sagebrush (Artemisia tridentata)","title":"Cytotype and local adaptation drive phenotypic variation in two subspecies of big sagebrush (Artemisia tridentata)","docAbstract":"Big sagebrush (Artemisia tridentata) is a widespread and locally dominant shrub throughout many ecosystems in western North America. A. tridentata ssps. tridentata and wyomingensis are two subspecies whose populations occupy the warm-arid regions of the species range and whose trailing edge is threatened by climate change. Previous studies have presented conflicting results in relation to the genetic control of physiological variation in A. tridentata. Understanding how different genetic factors contribute to physiological variation can provide insight into how these two subspecies may respond to future climate change. To explore possible variation among and within two subspecies of A. tridentata, we measured physiological and morphological traits in A. t. tridentata and A. t. wyomingensis during mid-summer (July), seven years after establishment in a common garden. Contributions to trait variation were quantified for both genetic (subspecies and cytotype) and environmental (climate-of-origin) factors. Measurements revealed an unequal contribution to phenotypic variation by subspecies, cytotype, and climate-of-origin. Ploidy and climate-of-origin were more important than subspecies in driving phenotypic variation in A. tridentata. These findings suggest that A. tridentata has a highly plastic drought response, or that culling (mortality over time due to environmental factors) in the common garden over seven years has led to a lack of genetic diversity within the garden. Understanding what factors drive phenotypic expression in big sagebrush can provide better insight into how climate change may affect migration and extirpation and may aid in the effectiveness of restoration efforts.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70206","usgsCitation":"Roop, S., Reinhardt, K., Aho, K.A., Germino, M., and Richardson, B.A., 2025, Cytotype and local adaptation drive phenotypic variation in two subspecies of big sagebrush (Artemisia tridentata): Ecosphere, v. 16, no. 3, e70206, 16 p., https://doi.org/10.1002/ecs2.70206.","productDescription":"e70206, 16 p.","ipdsId":"IP-160315","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":487453,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70206","text":"Publisher Index Page"},{"id":482918,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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There is now an opportunity to combine these products to construct actionable water quality forecasts. To that end, we couple streamflow forecasts from the NWM to a gradient-boosted decision tree algorithm (LightGBM) trained on 5+ years of high-frequency monitoring data to forecast in-stream turbidity levels in the Catskill Mountains, NY, USA. Results indicate LightGBM models are capable of relatively skillful predictions, which enable robust forecasts for 1–3 days lead times. LightGBM models offer improvements over a simplified linear model across the entire forecast horizon, and more spatially complex models are more resilient to error at shorter lead times (1–3 days). Moreover, interpretation of model features emphasizes high flows as a driver of turbidity in the region. Results suggest that interpretable, flexible, and efficient machine learning algorithms can produce capable water quality forecasts from streamflow forecasts and expand understanding of process dynamics. The use case illustrated here—to our knowledge the first NWM-based water quality forecast—underscores the potential to employ the NWM to expand national water quality forecasting capacity and can overall serve as a guide for similar efforts in basins across the country.
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Drought assessments determine the characteristics, severity, and impacts of a drought. Climate change adds conceptual and quantitative challenges to traditional drought assessments. This paper highlights the challenges of assessing drought in a climate made non-stationary by human activities or natural variability. To address these challenges, we then identify 10 key research priorities for advancing drought science and improving assessments in a changing climate. The priorities focus on improving drought indicators to account for non-stationarity, evaluating drought impacts and their trends, addressing regional differences in non-stationarity, determining the physical drivers of drought and how they are changing, capturing precipitation variability, and understanding the drivers of aridification. Ultimately, improved drought assessments will inform better risk management, adaptation strategies, and planning, especially in areas where climate change significantly alters drought dynamics. This perspective offers a path toward more accurate and effective drought management in a non-stationary climate system.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024EF005276","usgsCitation":"Lisonbee, J., Parker, B., Fleishman, E., Ford, T., Bocinsky, R., Follingstad, G., Frazier, A., Hoylman, Z., Hudson, A., Nielsen-Gammon, J., Umphlett, N., Elliot Wickham, Bamzai-Dodson, A., Fontenot, R., Fuchs, B., Hammond, J., Herrick, J., Hobbins, M., Hoell, A., Jones, J., Lane, E., Leasor, Z., Liu, Y., Otkin, J., Sheffield, A., Todey, D., and Pulwarty, R., 2025, Prioritization of research on drought assessment in a changing climate: Earth's Future, v. 13, no. 3, e2024EF005276, 21 p., https://doi.org/10.1029/2024EF005276.","productDescription":"e2024EF005276, 21 p.","ipdsId":"IP-165309","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":487949,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024ef005276","text":"Publisher Index Page"},{"id":483235,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Lisonbee, Joel","contributorId":347776,"corporation":false,"usgs":false,"family":"Lisonbee","given":"Joel","affiliations":[{"id":83232,"text":"Cooperative Institute for Research in the Environmental Sciences (CIRES), University of Colorado Boulder, and NOAA/National Integrated Drought Information System, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":922436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parker, Britt","contributorId":347777,"corporation":false,"usgs":false,"family":"Parker","given":"Britt","affiliations":[{"id":83233,"text":"NOAA National Integrated Drought Information System, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":922437,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fleishman, Erica","contributorId":347778,"corporation":false,"usgs":false,"family":"Fleishman","given":"Erica","affiliations":[{"id":12961,"text":"College of Earth, Ocean, and Atmospheric Sciences, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":922438,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, Trent","contributorId":347779,"corporation":false,"usgs":false,"family":"Ford","given":"Trent","affiliations":[{"id":83235,"text":"Illinois State Water Survey, Prairie Research Institute, University of Illinois, Urbana-Champaign, Champaign, Illinois","active":true,"usgs":false}],"preferred":false,"id":922439,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bocinsky, R. 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monitoring","interactions":[],"lastModifiedDate":"2025-05-28T14:35:05.649999","indexId":"70267497","displayToPublicDate":"2025-03-02T09:28:53","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"The effects of unpaved roads on instream sediment: Patterns and challenges for monitoring","docAbstract":"Despite > 700,000 km of unpaved roads in the western United States, our knowledge of how roads impact instream sediment is unclear. We combined two studies, including (1) a regional analysis linking stream habitat data from a large-scale monitoring program with road density data to identify generalizable relationships between roads and streambed sediment distributions and (2) a targeted field study to evaluate the responses of streambed and suspended sediment collected at locations above and below road–stream connection points to better understand the consistency of responses. Regional analyses indicated a significant positive relationship between road density and fine sediment in pool tails and a significant negative relationship between road density and median particle size. We also found significant relationships between landscape, climate, and local covariates and streambed sediment metrics, where most of the parameter estimates of the covariates were equal to or stronger than those for road density. Field studies suggested higher suspended sediment levels across the seasonal hydrologic regime where roads were open to travel year-round. However, sediment responses to road–stream connection points varied by metric and site. Together, our results indicated negative relationships between increasing road densities and sediment size distributions, but detecting road effects at site scales will be challenging given the effects of covariates that can overwhelm sediment signals.
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2025 - Genetics of wild, whirling disease resistant rainbow trout populations in Colorado","interactions":[],"lastModifiedDate":"2025-05-20T16:27:40.499928","indexId":"70267296","displayToPublicDate":"2025-03-02T09:21:12","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18328,"text":"Frontiers in Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Genetics of wild, whirling disease resistant rainbow trout populations in Colorado","docAbstract":"Introduction: Myxobolus cerebralis, the parasite responsible for salmonid whirling disease, was unintentionally introduced to and became established in Colorado in the 1990s. Mortality of young-of-year fish due to infection by M. cerebralis resulted in recruitment failure and subsequent significant declines in Rainbow Trout (Oncorhynchus mykiss) populations. The complex multistage lifecycle of M. cerebralis makes it difficult to eradicate and manage, and hatchery control strategies do not work in the wild. A viable method that has been utilized for wild populations is enhancing host resistance. Myxobolus cerebralis resistant Rainbow Trout were discovered at a hatchery in Germany and subsequently incorporated into Colorado's brood stock program. Since 2004, M. cerebralis resistant strains have been stocked into all major Colorado coldwater drainages to re-establish Rainbow Trout populations after whirling disease-related declines, with documented survival and reproduction of stocked disease resistant fish.
Methods and results: Genetic population assignment tests (via putatively neutral microsatellite markers) were used to monitor the stocked populations and indicated that, after only a few years, many of the individuals in these populations unexpectedly assigned to genetic strains that were historically susceptible to M. cerebralis. To further investigate the genetic composition of these fish, a single nucleotide polymorphism (SNP) panel was used to determine the percent genetic composition of resistant strain in these individuals. Microsatellites and SNPs provided similar results, indicating a low percentage of ancestry from the resistant strain in these fish, but they continued to survive exposure to M. cerebralis, suggesting that these individuals possessed genetic loci necessary for resistance. Finally, a quantitative trait locus (QTL) region (termed WDRES-9) was used to identify individuals with alleles associated with disease resistance. Implementation of the WDRES-9 QTL test allowed for more accurate determination of M. cerebralis resistant individuals within wild populations and better described their variability in resistance.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/ffwsc.2025.1500903","usgsCitation":"Avila, B., Fetherman, E., Winkelman, D.L., and Baerwald, M.R., 2025, Genetics of wild, whirling disease resistant rainbow trout populations in Colorado: Frontiers in Freshwater Science, v. 3, 1500903, 13 p., https://doi.org/10.3389/ffwsc.2025.1500903.","productDescription":"1500903, 13 p.","ipdsId":"IP-172851","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489746,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffwsc.2025.1500903","text":"Publisher Index Page"},{"id":486232,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Parshall","otherGeospatial":"upper Colorado River, Windy Gap Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.18116558688932,\n 40.10872906006077\n ],\n [\n -106.18116558688932,\n 40.0474778427253\n ],\n [\n -105.97093255037,\n 40.0474778427253\n ],\n [\n -105.97093255037,\n 40.10872906006077\n ],\n [\n -106.18116558688932,\n 40.10872906006077\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2025-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Avila, Brian W.","contributorId":338191,"corporation":false,"usgs":false,"family":"Avila","given":"Brian W.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":937655,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fetherman, Eric R.","contributorId":288704,"corporation":false,"usgs":false,"family":"Fetherman","given":"Eric R.","affiliations":[{"id":36246,"text":"CPW","active":true,"usgs":false}],"preferred":false,"id":937656,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winkelman, Dana L. 0000-0002-5247-0114 danaw@usgs.gov","orcid":"https://orcid.org/0000-0002-5247-0114","contributorId":4141,"corporation":false,"usgs":true,"family":"Winkelman","given":"Dana","email":"danaw@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937657,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baerwald, Melinda R.","contributorId":171890,"corporation":false,"usgs":false,"family":"Baerwald","given":"Melinda","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":937658,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273978,"text":"70273978 - 2025 - Thamnophis eques megalops (Northern Mexican Gartersnake). Longevity","interactions":[],"lastModifiedDate":"2026-02-23T17:37:38.344922","indexId":"70273978","displayToPublicDate":"2025-03-01T11:31:09","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1898,"text":"Herpetological Review","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Thamnophis eques megalops (Northern Mexican Gartersnake). Longevity","title":"Thamnophis eques megalops (Northern Mexican Gartersnake). Longevity","docAbstract":"No abstract available.
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","usgsCitation":"Ryan, M.J., Goode, M., Pawlicki, A., Bauder, J.M., Foster, C.D., Renner, D., Lawrence, B., 2025, Thamnophis eques megalops (Northern Mexican Gartersnake). Longevity: Herpetological Review, v. 56, no. 1, p. 109-110.","productDescription":"2 p.","startPage":"109","endPage":"110","ipdsId":"IP-173680","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500432,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ryan, Mason J","contributorId":366475,"corporation":false,"usgs":false,"family":"Ryan","given":"Mason","middleInitial":"J","affiliations":[{"id":87489,"text":"Arizona Game & Fish Department","active":true,"usgs":false}],"preferred":false,"id":955971,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goode, Matt","contributorId":360487,"corporation":false,"usgs":false,"family":"Goode","given":"Matt","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":955972,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pawlicki, Anthony","contributorId":360484,"corporation":false,"usgs":false,"family":"Pawlicki","given":"Anthony","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":955973,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bauder, Javan Mathias 0000-0002-2055-5324","orcid":"https://orcid.org/0000-0002-2055-5324","contributorId":337814,"corporation":false,"usgs":true,"family":"Bauder","given":"Javan","email":"","middleInitial":"Mathias","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":955974,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Foster, C. Drew","contributorId":366476,"corporation":false,"usgs":false,"family":"Foster","given":"C.","middleInitial":"Drew","affiliations":[{"id":87491,"text":"Arizona Center for Nature Conservation/ Phoenix Zoo","active":true,"usgs":false}],"preferred":false,"id":955975,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Renner, Damien","contributorId":366477,"corporation":false,"usgs":false,"family":"Renner","given":"Damien","affiliations":[{"id":87491,"text":"Arizona Center for Nature Conservation/ Phoenix Zoo","active":true,"usgs":false}],"preferred":false,"id":955976,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lawrence, Bradley","contributorId":366478,"corporation":false,"usgs":false,"family":"Lawrence","given":"Bradley","affiliations":[{"id":87491,"text":"Arizona Center for Nature Conservation/ Phoenix Zoo","active":true,"usgs":false}],"preferred":false,"id":955977,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70273327,"text":"70273327 - 2025 - Actinemys pallida (Southwestern Pond Turtle). Mechanical injury","interactions":[],"lastModifiedDate":"2026-02-27T16:13:48.759728","indexId":"70273327","displayToPublicDate":"2025-03-01T10:05:41","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1898,"text":"Herpetological Review","active":true,"publicationSubtype":{"id":10}},"title":"Actinemys pallida (Southwestern Pond Turtle). Mechanical injury","docAbstract":"No abstract available.
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","usgsCitation":"Louros, A.J., Williams, S.J., Baumberger, K.L., Heath, J.N., Hollingsworth, G.L., Gallegos, E., Backlin, A.R., and Fisher, R.D., 2025, Actinemys pallida (Southwestern Pond Turtle). Mechanical injury: Herpetological Review, v. 56, no. 1, p. 65-66.","productDescription":"2 p.","startPage":"65","endPage":"66","ipdsId":"IP-174326","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":500650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.47122095135506,\n 33.47331762170087\n ],\n [\n -117.47122095135506,\n 33.47030117171062\n ],\n [\n -117.46617514037251,\n 33.47030117171062\n ],\n [\n -117.46617514037251,\n 33.47331762170087\n ],\n [\n -117.47122095135506,\n 33.47331762170087\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"56","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Louros, Andrew John 0009-0009-1052-5453","orcid":"https://orcid.org/0009-0009-1052-5453","contributorId":364868,"corporation":false,"usgs":true,"family":"Louros","given":"Andrew","middleInitial":"John","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953349,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Spencer James 0009-0006-2062-5114","orcid":"https://orcid.org/0009-0006-2062-5114","contributorId":364869,"corporation":false,"usgs":true,"family":"Williams","given":"Spencer","middleInitial":"James","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953350,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baumberger, Katherine L. 0000-0002-2150-6372 kbaumberger@usgs.gov","orcid":"https://orcid.org/0000-0002-2150-6372","contributorId":225260,"corporation":false,"usgs":true,"family":"Baumberger","given":"Katherine","email":"kbaumberger@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953351,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heath, Jared Nicholas 0009-0002-5901-3562","orcid":"https://orcid.org/0009-0002-5901-3562","contributorId":364870,"corporation":false,"usgs":true,"family":"Heath","given":"Jared","middleInitial":"Nicholas","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953352,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hollingsworth, Gage L.","contributorId":364871,"corporation":false,"usgs":false,"family":"Hollingsworth","given":"Gage","middleInitial":"L.","affiliations":[{"id":86999,"text":"White Mountain Apache Tribe","active":true,"usgs":false}],"preferred":false,"id":953353,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gallegos, Elizabeth 0000-0002-8402-2631 egallegos@usgs.gov","orcid":"https://orcid.org/0000-0002-8402-2631","contributorId":1528,"corporation":false,"usgs":true,"family":"Gallegos","given":"Elizabeth","email":"egallegos@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953354,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Backlin, Adam R. 0000-0001-5618-8426 abacklin@usgs.gov","orcid":"https://orcid.org/0000-0001-5618-8426","contributorId":3802,"corporation":false,"usgs":true,"family":"Backlin","given":"Adam","email":"abacklin@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953355,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fisher, Robert D. 0000-0002-2956-3240 rdfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":3913,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rdfisher@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":953356,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70273360,"text":"70273360 - 2025 - Aspidoscelis tigris (Tiger Whiptail). Diet","interactions":[],"lastModifiedDate":"2026-02-27T16:03:57.382156","indexId":"70273360","displayToPublicDate":"2025-03-01T09:58:31","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1898,"text":"Herpetological Review","active":true,"publicationSubtype":{"id":10}},"title":"Aspidoscelis tigris (Tiger Whiptail). Diet","docAbstract":"No abstract available.
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","usgsCitation":"Williams, S.J., Louros, A.J., and Fisher, R.D., 2025, Aspidoscelis tigris (Tiger Whiptail). Diet: Herpetological Review, v. 56, no. 1, p. 77-77.","productDescription":"1 p.","startPage":"77","endPage":"77","ipdsId":"IP-174343","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":500649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Orange County","otherGeospatial":"Agua Chinon Wash","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.68075608102642,\n 33.7003326722768\n ],\n [\n -117.68757985451833,\n 33.7003326722768\n ],\n [\n -117.68757985451833,\n 33.69356173119685\n ],\n [\n -117.68075608102642,\n 33.69356173119685\n ],\n [\n -117.68075608102642,\n 33.7003326722768\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"56","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Spencer James 0009-0006-2062-5114","orcid":"https://orcid.org/0009-0006-2062-5114","contributorId":364869,"corporation":false,"usgs":true,"family":"Williams","given":"Spencer","middleInitial":"James","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953437,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Louros, Andrew John 0009-0009-1052-5453","orcid":"https://orcid.org/0009-0009-1052-5453","contributorId":364868,"corporation":false,"usgs":true,"family":"Louros","given":"Andrew","middleInitial":"John","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":953438,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisher, Robert D. 0000-0002-2956-3240 rdfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":3913,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rdfisher@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":953439,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70265239,"text":"70265239 - 2025 - Watershed hydrology assessment for the Nueces River basin–Appendix D, RiverWare analyses","interactions":[],"lastModifiedDate":"2025-04-07T15:12:31.587121","indexId":"70265239","displayToPublicDate":"2025-03-01T09:58:03","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Watershed hydrology assessment for the Nueces River basin–Appendix D, RiverWare analyses","docAbstract":"No abstract available.
","language":"English","publisher":"Interagency Flood Risk Management (InFRM)","usgsCitation":"Wallace, D., 2025, Watershed hydrology assessment for the Nueces River basin–Appendix D, RiverWare analyses, 71 p.","productDescription":"71 p.","ipdsId":"IP-151264","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":484249,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":484169,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/"}],"country":"United States","state":"Texas","otherGeospatial":"Nueces River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.48516111849933,\n 28.602627339567448\n ],\n [\n -98.48516111849933,\n 27.684996006363292\n ],\n [\n -97.23485178601354,\n 27.684996006363292\n ],\n [\n -97.23485178601354,\n 28.602627339567448\n ],\n [\n -98.48516111849933,\n 28.602627339567448\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":220786,"corporation":false,"usgs":true,"family":"Wallace","given":"David","email":"","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932583,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70275008,"text":"70275008 - 2025 - Quantifying sea otter abundance, distribution, habitat use, and foraging intake in Cook Inlet, Alaska","interactions":[],"lastModifiedDate":"2026-04-10T15:08:45.046416","indexId":"70275008","displayToPublicDate":"2025-03-01T09:55:18","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5709,"text":"OCS Study","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"BOEM 2025-019","title":"Quantifying sea otter abundance, distribution, habitat use, and foraging intake in Cook Inlet, Alaska","docAbstract":"Following near extirpation from the fur trade, sea otters (Enhydra lutris) have returned to occupy lower Cook Inlet since the 1950s, or earlier, with numbers increasing to ~11,000 and ~9,000 on the west and east side, respectively, by 2017. Northward range expansion on the west side has been negligible for decades with few animals found north of Kamishak Bay, while northward expansion on the east side has been more pronounced in recent decades. The reasons for these contrasting distribution patterns are not certain. Possible explanations for lack of expansion on the west side included 1) poor sea otter habitat north of Kamishak Bay; 2) adequate habitat north of Kamishak Bay but no incentive for sea otters to move north because of abundant food in Kamishak Bay, and/or sea otters discouraged from moving north of Kamishak Bay; 3) seasonal ice formation; or 4) seasonal presence of killer whales. This project was designed to document current sea otter abundance and distribution in lower Cook Inlet, including seasonal variation, and evaluate drivers of habitat use and foraging conditions, including how these overlap with Bureau of Ocean Energy Management (BOEM) Lease Sale blocks.
We found that the probability of sea otter presence in lower Cook Inlet was significantly related to depth and distance from shore with the highest probabilities of sea otter presence in areas ~7–8 kilometers (km) from shore in water ~20–30 meters (m) deep. Historical survey data suggest sea otter distribution has not changed dramatically since 2002, and while we detected significant seasonal changes including due to presence of heavy sea ice, the seasonal changes were modest and primarily related to locations with consistent winter ice formation on the west side of lower Cook Inlet. Overall, throughout the year, sea otters appeared to be utilizing most of the habitat within the 40-m depth contour on the east side of lower Cook Inlet, including Kachemak Bay. Sea otters on the west side reside largely within Kamishak Bay. Because of the shallow bathymetry of lower Cook Inlet, sea otters consistently occurred farther offshore than in many other areas of Alaska. The presence of sea otters, including females with pups, in these offshore waters indicates that sea otters can forage productively in these open water areas. Importantly, we documented that sea otters, including females with pups, occurred in most of the eastern BOEM lease blocks including the original Lease Sale 244 blocks 7064, 7114, 6162, 6310, 6360, 6410, 6458 and 6457. The relinquishments for these 7 leases were effective September 17, 2024: OCS-Y-02434 (block # 7064), OCS-Y-02435 (7114), OCS-Y-02436 (6162), OCS-Y-02438 (6357), OCS-Y-02442 (6407), OCS-Y02446 (6457), OCS-Y-02447 (6458) https://www.boem.gov/sites/default/files/documents/environment/Map%20of%20Active%20Leases%20 Cook%20Inlet%20OCS_0.pdf
Regarding differential northward expansion on the east and west side of lower Cook Inlet, we found that wind and water circulation patterns make winter sea ice more prominent on the west side, but this had minimal effects on sea otter distribution. In addition, although there have been observations of killer whale (Orcinus orca) predation on sea otters in Cook Inlet, we did not see behavioral or distributional evidence that it was prevalent enough to have strong effects on sea otter habitat use. Benthic surveys using a remotely operated vehicle (ROV) indicated that epibenthic substrate and biological community heterogeneity differed between areas with and without otters, suggesting that these factors may explain the current distribution of otters within lower Cook Inlet. However, ROV surveys cannot assess infaunal prey abundance, making assessments of the role of prey availability difficult. The existence of a healthy Pacific razor clam (Siliqua patula) fishery along western lower Cook Inlet along the Lake Clark coast north of Kamishak Bay suggests there may still be a resource base for eventual expansion of sea otters into this area.
Our shore-based forage observations indicated that sea otters exist near carrying capacity densities relative to nearshore prey resources. However, our distribution model suggests the bulk of the population lives offshore beyond our ability to observe their feeding activity. The fact that sea otters are a consistent presence in offshore areas suggests that prey resources in these areas were relatively abundant in comparison to nearshore prey resources, making foraging in offshore areas, with water depths of 0–40 m, energetically profitable. Prey types in these offshore areas of lower Cook Inlet likely included epifauna such as crabs and large urchins, in addition to infaunal clams based on the soft substrate habitat types that characterize lower Cook Inlet.
Collectively, our results suggest that sea otters occupy most areas of lower Cook Inlet with appropriate benthic habitat types and prey resources, which includes areas within BOEM Lease Sale blocks. With the exception of the Lake Clark coast, sea otters may be approaching a food- and habitat-limited distribution and carrying capacity, suggesting that the current status of sea otters in lower Cook Inlet is likely to remain similar in the absence of significant changes to prey, habitat, predation, or anthropogenic disturbance.
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Between 2021 and 2023, The Geysers geothermal field in northern California was monitored with an array of continuous passive seismic sensors and annual repeat magnetotelluric (MT) measurements. Each of these data sets were analyzed and modelled separately to understand the data, sensitivity, and any observable changes. Then, the data were inverted jointly using a crossgradient method to further constrain temporal changes in geophysical properties within the geothermal field. Multiple permutations of annual datasets were used as inputs to the joint inversion. Results demonstrate seismic data constrain smooth inversion of the MT data, and the MT data provide supplementary information about the location of temporal fluid changes. Estimating relative changes in steam saturation for various time intervals of the joint models shows compartmentalized changes in the field, and good spatial correlation with the location of injection wells. These results demonstrate that collecting both passive seismic and MT measurements then modeling them jointly provide complementary information and a relatively inexpensive method for monitoring temporal changes in an active geothermal field that provides results to support operators.
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