VegDischarge v1 is a comprehensive river discharge across Africa (2000–2021), produced by coupling the agro-hydrologic VegET model and the mizuRoute routing framework. Using remote sensing data and hydrological modeling, the 1-km runoff field simulated by VegET, and routed with mizuRoute, covers over 64,000 river segments in Africa. The VegET model simulates runoff based on vegetation and soil moisture dynamics, while mizuRoute processes this runoff through a detailed river network. Performance metrics show strong model reliability, with R² ranging from 0.5 to 0.9, NSE between 0.6 and 0.9, and KGE from 0.5 to 0.8. The total annual average discharge for Africa is quantified at 3238.1 km³.year-1, with contributions to various oceanic basins: 989.9 km³.year-1 to the North Atlantic, primarily from West African rivers like the Senegal, Gambia, Volta, and Niger; 1313.7 km³.year-1 to the South Atlantic, largely from the Congo River; 212.5 km³.year-1 to the Mediterranean Sea, predominantly from the Nile River; and 722.0 km³.year-1 to the Indian Ocean, with substantial inputs from rivers such as the Zambezi. This VegDischarge v1 is valuable for policymakers, stakeholders, and researchers to better understand water availability, its temporal and spatial variations, that impact water-related infrastructure planning, sustainable resource allocation, and the development of climate resilience mitigation strategies.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41597-024-04034-0","usgsCitation":"Akpoti, K., Velpuri, N., Mizukami, N., Kagone, S., Leh, M., Mekonnen, K., Owusu, A., Tinonetsana, P., Phiri, M., Madushanka, L., Perera, T., Prabhath, P.T., Parrish, G.E., Senay, G.B., and Seid, A., 2024, Advancing water security in Africa with new high-resolution discharge data: Scientific Data, v. 11, 1195, 23 p., https://doi.org/10.1038/s41597-024-04034-0.","productDescription":"1195, 23 p.","ipdsId":"IP-163078","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":466784,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-024-04034-0","text":"Publisher Index Page"},{"id":464460,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -32.50283421882472,\n 39.46326270619949\n ],\n [\n -32.50283421882472,\n -37.223842553161056\n ],\n [\n 46.90009232928932,\n -37.223842553161056\n ],\n [\n 46.90009232928932,\n 39.46326270619949\n ],\n [\n -32.50283421882472,\n 39.46326270619949\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2024-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Akpoti, Komlavi","contributorId":333421,"corporation":false,"usgs":false,"family":"Akpoti","given":"Komlavi","email":"","affiliations":[{"id":79870,"text":"International Water Management Institute, Ghana","active":true,"usgs":false}],"preferred":false,"id":919326,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Velpuri, Naga Manohar 0000-0002-6370-1926","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":222983,"corporation":false,"usgs":false,"family":"Velpuri","given":"Naga Manohar","affiliations":[{"id":40633,"text":"CIGAR","active":true,"usgs":false}],"preferred":false,"id":919327,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mizukami, Naoki","contributorId":178120,"corporation":false,"usgs":false,"family":"Mizukami","given":"Naoki","email":"","affiliations":[],"preferred":false,"id":919328,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":210980,"corporation":false,"usgs":true,"family":"Kagone","given":"Stefanie","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":919329,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leh, Mansoor","contributorId":330583,"corporation":false,"usgs":false,"family":"Leh","given":"Mansoor","email":"","affiliations":[{"id":61564,"text":"International Water Management Institute, Colombo, Sri Lanka","active":true,"usgs":false}],"preferred":false,"id":919330,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mekonnen, Kirubel","contributorId":333422,"corporation":false,"usgs":false,"family":"Mekonnen","given":"Kirubel","email":"","affiliations":[{"id":79873,"text":"International Water Management Institute, Ethiopia","active":true,"usgs":false}],"preferred":false,"id":919331,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Owusu, Afua","contributorId":330582,"corporation":false,"usgs":false,"family":"Owusu","given":"Afua","email":"","affiliations":[{"id":78937,"text":"International Water Management Institute, Accra, Ghana","active":true,"usgs":false}],"preferred":false,"id":919332,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tinonetsana, Primrose","contributorId":333423,"corporation":false,"usgs":false,"family":"Tinonetsana","given":"Primrose","email":"","affiliations":[{"id":79871,"text":"International Water Management Institute, Sri Lanka","active":true,"usgs":false}],"preferred":false,"id":919333,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Phiri, Michael","contributorId":335563,"corporation":false,"usgs":false,"family":"Phiri","given":"Michael","email":"","affiliations":[{"id":80437,"text":"IWMI","active":true,"usgs":false}],"preferred":false,"id":919334,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Madushanka, Lahiru","contributorId":335564,"corporation":false,"usgs":false,"family":"Madushanka","given":"Lahiru","email":"","affiliations":[{"id":80437,"text":"IWMI","active":true,"usgs":false}],"preferred":false,"id":919335,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Perera, Tharindu","contributorId":335565,"corporation":false,"usgs":false,"family":"Perera","given":"Tharindu","email":"","affiliations":[{"id":80437,"text":"IWMI","active":true,"usgs":false}],"preferred":false,"id":919336,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Prabhath, Paranamana T.","contributorId":335566,"corporation":false,"usgs":false,"family":"Prabhath","given":"Paranamana","email":"","middleInitial":"T.","affiliations":[{"id":80437,"text":"IWMI","active":true,"usgs":false}],"preferred":false,"id":919337,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Parrish, Gabriel Edwin Lee 0000-0003-4078-3516","orcid":"https://orcid.org/0000-0003-4078-3516","contributorId":267751,"corporation":false,"usgs":false,"family":"Parrish","given":"Gabriel","email":"","middleInitial":"Edwin Lee","affiliations":[{"id":55490,"text":"Innovate! Inc., Contractor to the USGS EROS Center","active":true,"usgs":false}],"preferred":false,"id":919338,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"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":919339,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Seid, Abdulkarim","contributorId":335567,"corporation":false,"usgs":false,"family":"Seid","given":"Abdulkarim","email":"","affiliations":[{"id":80437,"text":"IWMI","active":true,"usgs":false}],"preferred":false,"id":919340,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70260663,"text":"70260663 - 2024 - An evaluation of cyanobacterial occurrence and bloom development in Adirondack lakes","interactions":[],"lastModifiedDate":"2024-12-26T16:54:58.815536","indexId":"70260663","displayToPublicDate":"2024-11-05T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2592,"text":"Lake and Reservoir Management","active":true,"publicationSubtype":{"id":10}},"title":"An evaluation of cyanobacterial occurrence and bloom development in Adirondack lakes","docAbstract":"Cyanobacterial harmful algal blooms (cyanoHABs) have occurred in many low nutrient (oligotrophic) lakes in the northeastern United States. The Adirondack Park in New York is a large, mountainous region with many low nutrient lakes. There is a gap in understanding regarding whether cyanoHAB reporting data are truly reflective of the susceptibility of lakes to develop bloom conditions. We evaluated lakes with and without documented cyanoHABs for cyanotoxin synthetase gene quantification, phytoplankton community composition, and akinete abundance to identify conditions associated with the observation of cyanoHABs. We analyzed: (1) contributions of cyanobacteria to the overall phytoplankton community; (2) differences in cyanobacterial communities and the presence of cyanotoxin synthetase genes; and (3) lake physical and geomorphological attributes as drivers of differences in cyanobacteria occurrence. Two sample types (water and sediment) were collected from two sample locations (nearshore and open water) in five lakes in 2021. We found cyanobacteria in all lakes and sample locations. Phytoplankton biovolume and cyanotoxin synthetase genes differed among lakes and by cyanoHAB history. Samples from lakes with documented blooms were associated with marginally higher total phosphorus. Non-metric multidimensional scaling was used to identify which environmental factors influenced community structure. Our study demonstrates the importance of multifaceted approaches to detect cyanobacteria that may only be apparent during ephemeral bloom events and the similarities among lakes with and without a history of bloom reports. This work contributes to a better understanding of cyanoHAB occurrence in Adirondack lakes, and conditions that may cause low nutrient lakes to be susceptible to cyanoHABs.","language":"English","publisher":"Taylor & Francis Online","doi":"10.1080/10402381.2024.2406283","usgsCitation":"Gorney, R.M., Nystrom, E.A., Stouder, M.D., St. Amand, A.E., Suave, C., Clark, D., Stelzer, E., Givens, C.E., and Graham, J.L., 2024, An evaluation of cyanobacterial occurrence and bloom development in Adirondack lakes: Lake and Reservoir Management, v. 40, no. 4, p. 373-389, https://doi.org/10.1080/10402381.2024.2406283.","productDescription":"17 p.","startPage":"373","endPage":"389","ipdsId":"IP-157540","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":466785,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/10402381.2024.2406283","text":"Publisher Index Page"},{"id":463787,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.85479279895394,\n 44.65472774562582\n ],\n [\n -74.85479279895394,\n 43.22532864552494\n ],\n [\n -73.52390044328924,\n 43.22532864552494\n ],\n [\n -73.52390044328924,\n 44.65472774562582\n ],\n [\n -74.85479279895394,\n 44.65472774562582\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Gorney, Rebecca Michelle 0000-0003-4406-261X","orcid":"https://orcid.org/0000-0003-4406-261X","contributorId":317259,"corporation":false,"usgs":true,"family":"Gorney","given":"Rebecca","email":"","middleInitial":"Michelle","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nystrom, Elizabeth A. 0000-0002-0886-3439 nystrom@usgs.gov","orcid":"https://orcid.org/0000-0002-0886-3439","contributorId":1072,"corporation":false,"usgs":true,"family":"Nystrom","given":"Elizabeth","email":"nystrom@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stouder, Michael D.W. 0000-0002-0446-2574","orcid":"https://orcid.org/0000-0002-0446-2574","contributorId":301805,"corporation":false,"usgs":true,"family":"Stouder","given":"Michael","middleInitial":"D.W.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"St. Amand, Ann E.","contributorId":146962,"corporation":false,"usgs":false,"family":"St. Amand","given":"Ann","email":"","middleInitial":"E.","affiliations":[{"id":16763,"text":"PhycoTech, Inc.","active":true,"usgs":false}],"preferred":false,"id":918129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Suave, Cory","contributorId":346112,"corporation":false,"usgs":false,"family":"Suave","given":"Cory","email":"","affiliations":[{"id":82774,"text":"PhycoTech","active":true,"usgs":false}],"preferred":false,"id":918130,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clark, Denise 0000-0002-9688-2946 drclark@usgs.gov","orcid":"https://orcid.org/0000-0002-9688-2946","contributorId":213957,"corporation":false,"usgs":true,"family":"Clark","given":"Denise","email":"drclark@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":918131,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stelzer, Erin A. 0000-0001-7645-7603","orcid":"https://orcid.org/0000-0001-7645-7603","contributorId":220549,"corporation":false,"usgs":true,"family":"Stelzer","given":"Erin A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918132,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Givens, Carrie E. 0000-0003-2543-9610","orcid":"https://orcid.org/0000-0003-2543-9610","contributorId":247691,"corporation":false,"usgs":true,"family":"Givens","given":"Carrie","middleInitial":"E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918133,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":1769,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918134,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70260934,"text":"70260934 - 2024 - Environmental Flows for Riverine EcoSystem Habitats (E-FRESH) decision support tool user guide","interactions":[],"lastModifiedDate":"2024-12-10T19:08:40.532179","indexId":"70260934","displayToPublicDate":"2024-11-04T13:50:07","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Environmental Flows for Riverine EcoSystem Habitats (E-FRESH) decision support tool user guide","docAbstract":"The E-FRESH decision support tool is intended to facilitate assessment and comparison of different flow management scenarios on available habitat for various aquatic, riparian, and invertebrate species of interest. This tool also allows users to conduct a variety of analyses ranging from large-scale data processing and export to detailed and complex flow scenario manipulation around water rights and alternative climate futures.
","language":"English","publisher":"One Water Solutions Institute","doi":"10.25675/10217/239641","usgsCitation":"Wible, T., Holmquist-Johnson, C., Klingel, H., Morrison, R.R., Merritt, D., and Korsa, M., 2024, Environmental Flows for Riverine EcoSystem Habitats (E-FRESH) decision support tool user guide, 74 p., https://doi.org/10.25675/10217/239641.","productDescription":"74 p.","ipdsId":"IP-169441","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":464207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wible, Tyler","contributorId":346297,"corporation":false,"usgs":false,"family":"Wible","given":"Tyler","email":"","affiliations":[{"id":82824,"text":"CSU One Water Solutions Institute","active":true,"usgs":false}],"preferred":false,"id":918610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holmquist-Johnson, Christopher 0000-0002-2782-7687","orcid":"https://orcid.org/0000-0002-2782-7687","contributorId":210644,"corporation":false,"usgs":true,"family":"Holmquist-Johnson","given":"Christopher","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":918611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klingel, Heidi","contributorId":346298,"corporation":false,"usgs":false,"family":"Klingel","given":"Heidi","email":"","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":918612,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morrison, Ryan R.","contributorId":198245,"corporation":false,"usgs":false,"family":"Morrison","given":"Ryan","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":918613,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Merritt, David","contributorId":189308,"corporation":false,"usgs":false,"family":"Merritt","given":"David","affiliations":[],"preferred":false,"id":918614,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Korsa, Matthew","contributorId":346299,"corporation":false,"usgs":false,"family":"Korsa","given":"Matthew","email":"","affiliations":[{"id":82824,"text":"CSU One Water Solutions Institute","active":true,"usgs":false}],"preferred":false,"id":918615,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70265940,"text":"70265940 - 2024 - Long-term trends in abundance and potential drivers for eight species of coastal birds in the U.S. South Atlantic","interactions":[],"lastModifiedDate":"2025-04-22T17:18:09.018356","indexId":"70265940","displayToPublicDate":"2024-11-04T12:13:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5094,"text":"Regional Studies in Marine Science","onlineIssn":"2352-4855","active":true,"publicationSubtype":{"id":10}},"title":"Long-term trends in abundance and potential drivers for eight species of coastal birds in the U.S. South Atlantic","docAbstract":"The U.S. South Atlantic coastal region is used by many marine birds for foraging, reproduction, and migration. We developed standardized indices of relative abundance from long–term (1980–2016), semi-structured monitoring data (eBird) for eight species: Brown Pelican (Pelecanus occidentalis), Double-Crested Cormorant (Nannopterum auritum), White Ibis (Eudocimus albus), Wood Stork (Mycteria americana), Piping Plover (Charadrius melodus), American Oystercatcher (Haematopus palliatus), Clapper Rail (Rallus crepitans), and Northern Gannet (Morus bassanus). Following a period of stable or declining abundance from the 1980s through the 1990s, most species have shown stable or slightly upward trends through the late 2000s; Brown Pelican and Piping Plover have shown some evidence of recent declines. Species–specific correlations between abundance indices developed from presence/absence data and those developed from count data were positive for all species and ranged from 0.53 to 0.86. Dynamic factor analysis identified common trends in abundance among several species, in particular, Brown Pelican, Double–Crested Cormorant, and White Ibis. Model performance was improved with inclusion of an indicator of sea level rise, but not forage fish abundance or temperature, indicating habitat availability mediated by changing water levels may explain some of the underlying abundance trends. Our results provide baseline information on long–term trends for several important coastal birds that can help inform research, monitoring and conservation efforts in the U.S. South Atlantic region.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsma.2024.103886","usgsCitation":"Craig, J., Siegfried, K., Cheshire, R., Karnauskas, M., and Jodice, P.G., 2024, Long-term trends in abundance and potential drivers for eight species of coastal birds in the U.S. South Atlantic: Regional Studies in Marine Science, v. 80, 103886, 14 p., https://doi.org/10.1016/j.rsma.2024.103886.","productDescription":"103886, 14 p.","ipdsId":"IP-159778","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":490995,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rsma.2024.103886","text":"Publisher Index Page"},{"id":484853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia, North Carolina, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.63812689012559,\n 24.981195366830136\n ],\n [\n -79.74779294326453,\n 26.53588076270711\n ],\n [\n -81.03973835977342,\n 29.95801011198671\n ],\n [\n -81.11631302916135,\n 31.396072044056297\n ],\n [\n -79.30822669011008,\n 33.150807571917255\n ],\n [\n -76.01718491915142,\n 34.68241756177868\n ],\n [\n -75.87971328976948,\n 36.709011056016635\n ],\n [\n -78.23890913908112,\n 36.73325180728209\n ],\n [\n -80.34864182194866,\n 34.55119531258265\n ],\n [\n -83.32101866451728,\n 32.39789594150341\n ],\n [\n -81.81661133200153,\n 28.946064586607065\n ],\n [\n -80.63812689012559,\n 24.981195366830136\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"80","noUsgsAuthors":false,"publicationDate":"2024-11-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Craig, J.K.","contributorId":353621,"corporation":false,"usgs":false,"family":"Craig","given":"J.K.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":934109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siegfried, K.I.","contributorId":353620,"corporation":false,"usgs":false,"family":"Siegfried","given":"K.I.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":934108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cheshire, R.T.","contributorId":353622,"corporation":false,"usgs":false,"family":"Cheshire","given":"R.T.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":934110,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karnauskas, M.","contributorId":353623,"corporation":false,"usgs":false,"family":"Karnauskas","given":"M.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":934111,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":934112,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70260488,"text":"70260488 - 2024 - Individual return patterns of spawning flannelmouth sucker (Catostomus latipinnis) to a desert river tributary","interactions":[],"lastModifiedDate":"2024-11-05T16:05:17.434171","indexId":"70260488","displayToPublicDate":"2024-11-04T09:59:54","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Individual return patterns of spawning flannelmouth sucker (Catostomus latipinnis) to a desert river tributary","title":"Individual return patterns of spawning flannelmouth sucker (Catostomus latipinnis) to a desert river tributary","docAbstract":"Tributaries provide temporal and spatial habitat heterogeneity in river networks that can be critical for parts of the life history of a species. Tributary fidelity can benefit individual fish undergoing spawning migrations by reducing time and energy spent exploring new areas and leveraging previous experience, but anthropogenic activities that fragment or degrade these systems can eliminate those benefits. We used multistate models based on passive integrated transponder (PIT) detection data from 2013 to 2023 to estimate the proportion of flannelmouth suckers (Catostomus latipinnis) migrating to a tributary, McElmo Creek, from the mainstem San Juan River for spawning. Survival varied among years and among states. The top model for migration probability included sex, with males slightly more likely to migrate (0.93 vs 0.90), and the next model identified interannual variation in migration probability ranging from 0.875 to 0.999 across years, indicating high site fidelity. Individuals showed consistency in relative arrival timing across years, with the highest correlation generally during years with greater spring discharge and extended tributary residence time. Successful tributary spawning may be important for the maintenance of the mainstem San Juan River flannelmouth sucker population, but site fidelity may be maladaptive where tributaries are vulnerable to human alterations.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-72273-7","usgsCitation":"Bonjour, S.M., Gido, K., Cathcart, C.N., and McKinstry, M.C., 2024, Individual return patterns of spawning flannelmouth sucker (Catostomus latipinnis) to a desert river tributary: Scientific Reports, v. 14, no. 1, 26690, 12 p., https://doi.org/10.1038/s41598-024-72273-7.","productDescription":"26690, 12 p.","ipdsId":"IP-166408","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":466786,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-72273-7","text":"Publisher Index Page"},{"id":463698,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah","otherGeospatial":"Colorado River, McElmo Creek, San Juan River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112,\n 38\n ],\n [\n -112,\n 36.5\n ],\n [\n -107,\n 36.5\n ],\n [\n -107,\n 38\n ],\n [\n -112,\n 38\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-11-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Bonjour, Sophia Marie 0000-0003-3614-7023","orcid":"https://orcid.org/0000-0003-3614-7023","contributorId":335936,"corporation":false,"usgs":true,"family":"Bonjour","given":"Sophia","email":"","middleInitial":"Marie","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":917859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gido, Keith B.","contributorId":341429,"corporation":false,"usgs":false,"family":"Gido","given":"Keith B.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":917860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cathcart, Charles N.","contributorId":317814,"corporation":false,"usgs":false,"family":"Cathcart","given":"Charles","email":"","middleInitial":"N.","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":917861,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKinstry, Mark C.","contributorId":301155,"corporation":false,"usgs":false,"family":"McKinstry","given":"Mark","email":"","middleInitial":"C.","affiliations":[{"id":65322,"text":"Upper Colorado Regional Office","active":true,"usgs":false}],"preferred":false,"id":917862,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70260973,"text":"70260973 - 2024 - Soil cover heterogeneity associated with biocrusts predicts patch-level plant diversity patterns","interactions":[],"lastModifiedDate":"2024-11-27T16:17:41.334305","indexId":"70260973","displayToPublicDate":"2024-11-01T12:52:03","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Soil cover heterogeneity associated with biocrusts predicts patch-level plant diversity patterns","docAbstract":"Context
Soil resource heterogeneity drives plant species diversity patterns at local and landscape scales. In drylands, biocrusts are patchily distributed and contribute to soil resource heterogeneity important for plant establishment and growth. Yet, we have a limited understanding of how such heterogeneity may relate to patterns of plant diversity and community structure.
Objectives
We explored relationships between biocrust-associated soil cover heterogeneity and plant diversity patterns in a cool desert ecosystem. We asked: (1) does biocrust-associated soil cover heterogeneity predict plant diversity and community composition? and (2) can we use high-resolution remote sensing data to calculate soil cover heterogeneity metrics that could be used to extrapolate these patterns across landscapes?
Methods
We tested associations among field-based measures of plant diversity and soil cover heterogeneity. We then used a Support Vector Machine classification to map soil, plant and biocrust cover from sub-centimeter resolution Unoccupied Aerial System (UAS) imagery and compared the mapped results to field-based measures.
Results
Field-based soil cover heterogeneity and biocrust cover were positively associated with plant diversity and predicted community composition. The accuracy of UAS-mapped soil cover classes varied across sites due to variation in timing and quality of image collections, but the overall results suggest that UAS are a promising data source for generating detailed, spatially explicit soil cover heterogeneity metrics.
Conclusions
Results improve understanding of relationships between biocrust-associated soil cover heterogeneity and plant diversity and highlight the promise of high-resolution UAS data to extrapolate these patterns over larger landscapes which could improve conservation planning and predictions of dryland responses to soil degradation under global change.
Fishery biologists spend considerable effort over multiple years collecting data on fish population and community status using a particular sampling method or set of methods. However, new (and often more effective) sampling methods and technologies are continuously being developed. To incorporate these new sampling techniques, fishery biologists need a means for converting fish sampling data collected using old methods so that they can be compared with data collected using new sampling methods. Similarly, fishery biologists often need a means to compare fish sampling data collected using the same method over time (e.g., from year to year) and space (e.g., between sample sites). If fish abundance, species presence, or richness are estimated using an unbiased statistical estimator (e.g., occupancy estimation, capture-recapture estimation), the estimates can be validly compared even if the fish sample data were collected with different methods. However, if unbiased statistical estimators were not used, biologists need methods for adjusting fish sampling data collected using different methods or using the same method collected under different sampling conditions. In this chapter, we describe and provide examples of statistical techniques for converting nonstandard fish sampling data to American Fisheries Society (AFS) standardized data and for making comparisons of fish sampling data collected at different times or at different locations. We define standard fish sampling data as data collected using the standardized fish sampling methods described throughout this book. Any other sampling methods and associated data are thus defined as nonstandard. Before delving into the details of the techniques that can be used to convert data, we describe the nature of fish sample data, their uses, and their limitations.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Standard methods for sampling North American freshwater fishes","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"American Fisheries Society","doi":"10.47886/9781934874769.ch16","usgsCitation":"Peterson, J.T., de Kerckhove, D.T., Giacomini, H.C., and Paukert, C., 2024, Converting non-standard data to standardized data, chap. 16 of Standard methods for sampling North American freshwater fishes, https://doi.org/10.47886/9781934874769.ch16.","ipdsId":"IP-132428","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":502279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":958909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Kerckhove, Derrick T.","contributorId":369387,"corporation":false,"usgs":false,"family":"de Kerckhove","given":"Derrick","middleInitial":"T.","affiliations":[{"id":16762,"text":"Ontario Ministry of Natural Resources and Forestry","active":true,"usgs":false}],"preferred":false,"id":958910,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Giacomini, Henrique C.","contributorId":369388,"corporation":false,"usgs":false,"family":"Giacomini","given":"Henrique","middleInitial":"C.","affiliations":[{"id":16762,"text":"Ontario Ministry of Natural Resources and Forestry","active":true,"usgs":false}],"preferred":false,"id":958911,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paukert, Craig 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":268045,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":958912,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274726,"text":"70274726 - 2024 - An introduction to standardized sampling","interactions":[],"lastModifiedDate":"2026-04-08T15:23:31.605172","indexId":"70274726","displayToPublicDate":"2024-11-01T10:18:25","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"1","title":"An introduction to standardized sampling","docAbstract":"In 2009, the first edition of Standard Methods for Sampling North American Freshwater Fishes was published. This was the first time in the history of fisheries science that standardization of methods and equipment had taken place on such a large geographic scale. Since its publication, the methods have been used extensively across North America by local, state, and federal agencies, organizations, and universities who have seen the advantages of large-scale data comparison. Authors have been invited to present these methods in other locations around the world to help with standard sampling programs on other continents. Now, with large-scale issues such as human-caused climate change, effects of landscape-scale regulations, and effects of habitat degradation continuing to increase in importance, the ability to compare data across wide regions and political boundaries, compare data over time, and collect data with improved accuracy and precision is more important than ever. This new edition of Standard Methods is sponsored by the American Fisheries Society (AFS), the U.S. Fish and Wildlife Service, and the Association of Fish and Wildlife Agencies (AFWA), with contributions by numerous state, provincial and federal agencies, and numerous academic institutions and nongovernmental organizations (NGOs). It is authored by over 100 experts in fisheries sampling from across Canada, Mexico, and the United States. Most techniques for water body types addressed in the first edition have been kept the same-in the interest of standardization over time; however, many important additions have been made.
Like the first edition, these methods are designed for fish community assessments in North American aquatic systems (e.g., for this edition, lakes, ponds, rivers, and streams containing warmwater and coldwater species; the Great Lakes, wetlands, and cenotes). Although other methods may be available that better target a more specific size-group or species, these techniques were selected as most effective for general surveys of these systems and typically are the most effective for capturing the common fishes found in these waters.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Standard methods for sampling North American freshwater fishes","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"American Fisheries Society","doi":"10.47886/9781934874769.ch1","usgsCitation":"Bonar, S.A., Conroy, J.D., Contreras-Balderas, S., and Iles, A.C., 2024, An introduction to standardized sampling, chap. 1 of Standard methods for sampling North American freshwater fishes, p. 1-22, https://doi.org/10.47886/9781934874769.ch1.","productDescription":"22 p.","startPage":"1","endPage":"22","ipdsId":"IP-152853","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":502275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Second edition","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":958864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conroy, Joseph D.","contributorId":145527,"corporation":false,"usgs":false,"family":"Conroy","given":"Joseph","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":958865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Contreras-Balderas, Salvador","contributorId":35956,"corporation":false,"usgs":true,"family":"Contreras-Balderas","given":"Salvador","email":"","affiliations":[],"preferred":false,"id":958866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iles, Alison C.","contributorId":369326,"corporation":false,"usgs":false,"family":"Iles","given":"Alison","middleInitial":"C.","affiliations":[],"preferred":false,"id":958867,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70262576,"text":"70262576 - 2024 - A strategic and science-based framework for management of invasive annual grasses in the sagebrush biome","interactions":[],"lastModifiedDate":"2025-01-21T16:29:14.018355","indexId":"70262576","displayToPublicDate":"2024-11-01T10:12:36","publicationYear":"2024","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":"A strategic and science-based framework for management of invasive annual grasses in the sagebrush biome","docAbstract":"In the last 20 years, the North American sagebrush biome has lost over 500,000 ha of intact and largely intact sagebrush plant communities on an annual basis. Much of this loss has been associated with expansion and infilling of invasive annual grasses (IAGs). These species are highly competitive against native perennial grasses in disturbed environments, and create fuel conditions that increase both the likelihood of fire ignition and the ease of wildfire spread across large landscapes. Given the current rate of IAG expansion in both burned and unburned rangelands, ameliorating the IAG threat will involve a range-wide paradigm shift from opportunistic and reactive management, to a framework that spatially prioritizes maintenance of largely intact, uninvaded areas and improvement of invaded habitats in strategic locations. We created a framework accompanied by biome-wide priority maps using geospatial overlays that target areas to MAINTAIN large, uninvaded areas as natural resource anchors through activities to prevent IAGs and IMPROVE areas to reduce invasions with the highest potential for management efforts to succeed in restoring large, intact landscapes. We then offer three case studies to illustrate the use of these concepts and map products at multiple scales. Our map products operate at the biome scale using regional data sources but additional data sources may be needed to inform local conservation planning. However, the basic strategic management principles of a) maintaining the intact and uninvaded areas that we can least afford to lose to IAGs and b) improving areas where we have a higher likelihood of restoration success, is timely, relevant, and scalable from the biome to local levels.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2024.08.019","usgsCitation":"Boyd, C.S., Creutzburg, M.K., Kumar, A.V., Smith, J., Doherty, K., Mealor, B.A., Bradford, J., Cahill, M., Copeland, S., Duquette, C., Garner, L., Holdrege, M., Sparklin, B., and Cross, T.B., 2024, A strategic and science-based framework for management of invasive annual grasses in the sagebrush biome: Rangeland Ecology & Management, v. 97, p. 61-72, https://doi.org/10.1016/j.rama.2024.08.019.","productDescription":"12 p.","startPage":"61","endPage":"72","ipdsId":"IP-164191","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":481051,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2024.08.019","text":"Publisher Index Page"},{"id":480832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.35998060853578,\n 48.72039589220637\n ],\n [\n -121.35998060853578,\n 32.36195983901993\n ],\n [\n -99.82677748353605,\n 32.36195983901993\n ],\n [\n -99.82677748353605,\n 48.72039589220637\n ],\n [\n -121.35998060853578,\n 48.72039589220637\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"97","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boyd, Chad S.","contributorId":255106,"corporation":false,"usgs":false,"family":"Boyd","given":"Chad","email":"","middleInitial":"S.","affiliations":[{"id":51433,"text":"Eastern Oregon Agricultural Research Center, USDA Agricultural Research Service, Burns, OR 97720 USA","active":true,"usgs":false}],"preferred":false,"id":924587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Creutzburg, Megan K.","contributorId":296727,"corporation":false,"usgs":false,"family":"Creutzburg","given":"Megan","email":"","middleInitial":"K.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":924588,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kumar, Alexander V. 0000-0003-3831-5924","orcid":"https://orcid.org/0000-0003-3831-5924","contributorId":224038,"corporation":false,"usgs":false,"family":"Kumar","given":"Alexander","email":"","middleInitial":"V.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":924589,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Joseph T.","contributorId":349698,"corporation":false,"usgs":false,"family":"Smith","given":"Joseph T.","affiliations":[{"id":83504,"text":"Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT 59812","active":true,"usgs":false}],"preferred":false,"id":924590,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doherty, Kevin E.","contributorId":177793,"corporation":false,"usgs":false,"family":"Doherty","given":"Kevin E.","affiliations":[],"preferred":false,"id":924591,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mealor, Brian A.","contributorId":152584,"corporation":false,"usgs":false,"family":"Mealor","given":"Brian","email":"","middleInitial":"A.","affiliations":[{"id":6656,"text":"University of Wyoming, Renewable Resources","active":true,"usgs":false}],"preferred":false,"id":924592,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":924593,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cahill, Matthew","contributorId":245219,"corporation":false,"usgs":false,"family":"Cahill","given":"Matthew","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":924666,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Copeland, Stella M.","contributorId":196218,"corporation":false,"usgs":false,"family":"Copeland","given":"Stella M.","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":924595,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Duquette, Cameron A.","contributorId":349699,"corporation":false,"usgs":false,"family":"Duquette","given":"Cameron A.","affiliations":[{"id":83505,"text":"The Nature Conservancy, Eastern Oregon Agricultural Research Center, Burns, OR 97720","active":true,"usgs":false}],"preferred":false,"id":924596,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Garner, Lindy","contributorId":349700,"corporation":false,"usgs":false,"family":"Garner","given":"Lindy","affiliations":[{"id":83506,"text":"US Department of Interior, US Fish and Wildlife Service, Great Falls, MT 59405","active":true,"usgs":false}],"preferred":false,"id":924597,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Holdrege, Martin C. 0000-0003-4078-6012","orcid":"https://orcid.org/0000-0003-4078-6012","contributorId":295782,"corporation":false,"usgs":true,"family":"Holdrege","given":"Martin C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":924598,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sparklin, Bill","contributorId":349701,"corporation":false,"usgs":false,"family":"Sparklin","given":"Bill","affiliations":[{"id":83506,"text":"US Department of Interior, US Fish and Wildlife Service, Great Falls, MT 59405","active":true,"usgs":false}],"preferred":false,"id":924599,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Cross, Todd B.","contributorId":189267,"corporation":false,"usgs":false,"family":"Cross","given":"Todd","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":924600,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70260412,"text":"sim3527 - 2024 - Geomorphic map of the Umatilla River corridor, Oregon","interactions":[],"lastModifiedDate":"2025-12-22T20:27:56.574752","indexId":"sim3527","displayToPublicDate":"2024-11-01T10:05:11","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3527","title":"Geomorphic map of the Umatilla River corridor, Oregon","docAbstract":"This map portrays the distribution of landforms along the Umatilla River in northeastern Oregon and covers a corridor 127 kilometers long from the confluence of the Umatilla River with the Columbia River upstream to Meacham Creek. The map encompasses the valley bottom and extends about 1 kilometer up the adjoining hillslopes. Map data are intended to support water quality and fisheries enhancement efforts pursuant to the First Foods, a resource-management approach that focuses on traditionally gathered foods including water, fish, big game, roots, and berries and calls attention to the reciprocity between people and the foods upon which humans depend.
The Umatilla River drains about 6,300 square kilometers on the northwest slope of the Blue Mountains in northeast Oregon. Most of the drainage basin is underlain by Miocene basalt flows of the Columbia River Basalt Group. Younger, weakly lithified, late Miocene and early Pliocene gravel deposits of local origin (for example, McKay Formation) are mapped in a few places. Upland surfaces are mantled with windborne silt (loess) correlative with deposits elsewhere known as the Palouse Formation. Surfaces below an elevation of about 340 meters were inundated repeatedly by large Pleistocene glacial outburst floods, most emanating from glacial Lake Missoula in western Montana. In backflooded areas such as the lower Umatilla River valley, Missoula floods deposited extensive slack-water silt.
Areas mapped as open water, active channel and tie channel, flood basin, valley bottom, and modified land constitute the geomorphic floodplain: the area subject to occasional inundation by the Umatilla River. Deposits and landforms within the floodplain are inset into Missoula flood deposits and hence postdate the 20–15-kilo-annum Missoula floods. Some floodplain deposits are no more than a few centuries old, as indicated by substantial erosion and deposition during the Umatilla River flood of February 2020, the largest since systematic measurements began in October 1903. Deposits and landforms of the floodplain are transient features within the longer-term incision of the Umatilla River into mid-Miocene flood basalts and younger gravel of the McKay Formation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3527","collaboration":"Prepared in cooperation with the Confederated Tribes of the Umatilla Indian Reservation","usgsCitation":"Yuh, I.P., Haugerud, R.A., O'Connor, J.E., and O'Daniel, S.J., 2024, Geomorphic map of the Umatilla River corridor, Oregon: U.S. Geological Survey Scientific Investigation Map 3527, scale 1:12,000, 6 sheets, https://doi.org/10.3133/sim3527.","productDescription":"6 Sheets: 60.00 x 22.00 inches or smaller; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-158910","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":497889,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117653.htm","linkFileType":{"id":5,"text":"html"}},{"id":463503,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13OOE7Q","description":"Yuh, I.P., Haugerud, R.A., O’Connor, J.E., and O’Daniel, S.J., 2024, Geospatial database for the geomorphic map of the Umatilla River corridor, Oregon: U.S. Geological Survey data release, https://doi.org/10.5066/P13OOE7Q.","linkHelpText":"Geospatial database for the geomorphic map of the Umatilla River corridor, Oregon"},{"id":463502,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3527/sim3527_sheet06.pdf","text":"Sheet 6","size":"14 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":463501,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3527/sim3527_sheet05.pdf","text":"Sheet 5","size":"14 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":463500,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3527/sim3527_sheet04.pdf","text":"Sheet 4","size":"17 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":463499,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3527/sim3527_sheet03.pdf","text":"Sheet 3","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":463498,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3527/sim3527_sheet02.pdf","text":"Sheet 2","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":463497,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3527/sim3527_sheet01.pdf","text":"Sheet 1","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":463496,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3527/covrthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Umatilla River corridor","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.33321833933547,\n 45.68071824192785\n ],\n [\n -118.3561230993523,\n 45.730645505488894\n ],\n [\n -118.68247351832258,\n 45.7153349524672\n ],\n [\n -118.92979975453633,\n 45.69914300966221\n ],\n [\n -119.0766497072885,\n 45.72208021102742\n ],\n [\n -119.23122860492205,\n 45.82720081569687\n ],\n [\n -119.31238252617953,\n 45.9495910938214\n ],\n [\n -119.37228184901241,\n 45.932123278858995\n ],\n [\n -119.33556936082459,\n 45.81912165021458\n ],\n [\n -119.34329830570641,\n 45.7652305840443\n ],\n [\n -119.05732734508436,\n 45.64513599220672\n ],\n [\n -118.76555967580092,\n 45.630274925778025\n ],\n [\n -118.33238843274466,\n 45.66767113997548\n ],\n [\n -118.33321833933547,\n 45.68071824192785\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
350 N. Akron Rd.
Moffett Field, CA 94035
One of the greatest advantages to the standardization of fisheries sampling methods is the comparable data they produce (Bonar et al. 2017). Following American Fisheries Society (AFS) standardized sampling methods, fisheries professionals can more easily compare their data with standardized data collected across North America to address both small- and large-scale fisheries questions. For example, access to standardized data can allow fisheries managers to evaluate if a fish species is within an expected range for weight or length in a particular water body, providing them with valuable information about the baseline health of their fish population. Additionally, given that fish can take years to respond to certain management actions (Meals et al. 2010), access to standardized data over time can be used to assess the effectiveness of these actions. Finally, standardized fisheries data can be analyzed over large geographic regions and provide increased sample sizes to evaluate management actions that cross local or state borders, such as habitat improvements or regulations, as well as the effects of large-scale transformations such as climate change on fish growth or body condition. Ultimately, the use of standardized data enhances the ability of fisheries professionals to address both small- and large-scale threats currently facing freshwater ecosystems and the fishes they support.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Standard methods for sampling North American freshwater fishes","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"American Fisheries Society","doi":"10.47886/9781934874769.ch20","usgsCitation":"Tracy, E.E., Brouder, M.J., Iles, A.C., Teal, C.N., and Bonar, S.A., 2024, Indices for common North American fishes, chap. 20 of Standard methods for sampling North American freshwater fishes, p. 441-786, https://doi.org/10.47886/9781934874769.ch20.","productDescription":"346 p.","startPage":"441","endPage":"786","ipdsId":"IP-157649","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":502271,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Second edition","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tracy, Erin E.","contributorId":369324,"corporation":false,"usgs":false,"family":"Tracy","given":"Erin","middleInitial":"E.","affiliations":[],"preferred":false,"id":958868,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brouder, Mark J.","contributorId":369325,"corporation":false,"usgs":false,"family":"Brouder","given":"Mark","middleInitial":"J.","affiliations":[],"preferred":false,"id":958869,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iles, Alison C.","contributorId":369326,"corporation":false,"usgs":false,"family":"Iles","given":"Alison","middleInitial":"C.","affiliations":[],"preferred":false,"id":958870,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teal, Chad N.","contributorId":337952,"corporation":false,"usgs":false,"family":"Teal","given":"Chad","email":"","middleInitial":"N.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":958962,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":958871,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266578,"text":"70266578 - 2024 - Developing a predictive model to identify Sea Lamprey parasitism on Lake Trout using biologgers","interactions":[],"lastModifiedDate":"2025-05-09T14:36:58.886325","indexId":"70266578","displayToPublicDate":"2024-11-01T09:34:10","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13429,"text":"Transactions of American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Developing a predictive model to identify Sea Lamprey parasitism on Lake Trout using biologgers","docAbstract":"Sea Lamprey Petromyzon marinus remain problematic for Lake Trout Salvelinus namaycush restoration in the Laurentian Great Lakes. Fisheries assessments would benefit from knowledge of spatial–temporal patterns of Sea Lamprey parasitism on Lake Trout; however, such patterns are challenging to estimate from wounding rates on caught Lake Trout. Electronic tags have been used to identify distinct fish behaviors (e.g., foraging or spawning) using measurements of acceleration or heart rate. We hypothesized that Sea Lamprey attachment would elicit changes in the heart rate and swimming behavior of Lake Trout. Here, we determined whether tagging devices could record these changes and whether we could accurately predict lamprey attachment on Lake Trout using these recordings.
Adult Lake Trout (n = 34) were implanted with acceleration and heart rate tags and then were subjected to Sea Lamprey parasitism within a laboratory setting. Approximately 70 different acceleration and heart rate metrics were collected and tried as predictors of lamprey attachment. The top variables were used to train random forest models and then tried on test data sets. The accuracy of these models was then validated using a jackknife approach.
Metrics related to body orientation and heart rate were identified as the best predictors of Sea Lamprey attachment. The best models predicted lamprey attachments with high accuracy; however, individual‐level jackknife tests resulted in less accurate cross‐individual prediction and regularly predicted false negatives. These findings may be related to individual variance in the Lake Trout response to attachment, but there was evidence that the shifting of tags after implantation impacted predictive performance, which could be remedied with adjustments during implantation.
Our study highlights the potential to use tagging devices for quantifying Sea Lamprey attachments on Lake Trout in the wild. Further development appears necessary; however, once improved, these predictive models have the potential to generate field‐based estimates of Sea Lamprey attack rates on Lake Trout.
Sagebrush ecosystems support a suite of unique species such as the emblematic greater sage-grouse (Centrocercus urophasianus; sage-grouse) but are under increasing pressure from anthropogenic stressors such as annual grass invasion, conifer encroachment, altered wildfire regimes, and land use change. We examined the ability of an ecosystem-based framework for sagebrush conservation, the sagebrush conservation design (SCD) strategy, and the associated model of sagebrush ecological integrity (SEI), to identify and rank priority habitats for sage-grouse, a sagebrush indicator species. We compared sage-grouse population trends from 1996–2021 across the three ranked SEI categories. We then modeled those trends directly as a function of the same landcover predictors underlying SEI, used the median trend estimates to recategorize the sage-grouse’s range, and used spatial correlation methods to compare our sage-grouse performance categories with those of SEI. Finally, we compared the sage-grouse condition categories, predicted by our landcover-based model, to empirical trends derived from population count data. We found that the SCD and SEI were effective tools for identifying and ranking priority habitats for sage-grouse. Population trends were stable in the core areas identified by SEI but declining in the lower (i.e., growth and other) condition categories. As a result, core areas encompassed an increasingly larger share of the total sage-grouse population in a disproportionately smaller area. Our model supports the general functional relationships between landcover and sage-grouse performance suggested by SEI. We found strong spatial congruence between our categories of predicted sage-grouse population performance, the condition categories of SEI, and empirical trends derived from population count data. Our analysis demonstrates that proactive ecosystem-based approaches to the conservation of the sagebrush biome can help optimize the return on limited conservation resources and benefits for sagebrush obligate species and help reduce some of the real and perceived conflicts inherent in single-species management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2024.08.021","usgsCitation":"Prochazka, B.G., Lundblad, C.G., Doherty, K., O’Neil, S.T., Tull, J.C., Abele, S., Aldridge, C.L., and Coates, P.S., 2024, Evaluating the sagebrush conservation design through the lens of a sagebrush indicator species: Rangeland Ecology & Management, v. 97, p. 146-159, https://doi.org/10.1016/j.rama.2024.08.021.","productDescription":"14 p.","startPage":"146","endPage":"159","ipdsId":"IP-162640","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":466788,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2024.08.021","text":"Publisher Index Page"},{"id":464350,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, New Mexico, Nevada, Montana, Oregon, Utah, Washington, Wyoming","otherGeospatial":"Great Plains, Intermountain West, Southern Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.46461214797594,\n 47.34790601551299\n ],\n [\n -120.46461214797594,\n 35.22486431641359\n ],\n [\n -104.39395814136043,\n 35.22486431641359\n ],\n [\n -104.39395814136043,\n 47.34790601551299\n ],\n [\n -120.46461214797594,\n 47.34790601551299\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"97","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Prochazka, Brian G. 0000-0001-7270-5550 bprochazka@usgs.gov","orcid":"https://orcid.org/0000-0001-7270-5550","contributorId":174839,"corporation":false,"usgs":true,"family":"Prochazka","given":"Brian","email":"bprochazka@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":918950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lundblad, Carl Gregory 0000-0001-7925-9055","orcid":"https://orcid.org/0000-0001-7925-9055","contributorId":346421,"corporation":false,"usgs":true,"family":"Lundblad","given":"Carl","email":"","middleInitial":"Gregory","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":918951,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doherty, Kevin E.","contributorId":177793,"corporation":false,"usgs":false,"family":"Doherty","given":"Kevin E.","affiliations":[],"preferred":false,"id":918952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":918953,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tull, John C. 0000-0002-0680-008X","orcid":"https://orcid.org/0000-0002-0680-008X","contributorId":201650,"corporation":false,"usgs":false,"family":"Tull","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":918954,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Abele, Steve C.","contributorId":300333,"corporation":false,"usgs":false,"family":"Abele","given":"Steve C.","affiliations":[{"id":65086,"text":"U.S. Fish and Wildlife Service, Reno, Nevada, USA","active":true,"usgs":false}],"preferred":false,"id":918955,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":918956,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":918957,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70261136,"text":"70261136 - 2024 - Machine learning and new-generation spaceborne hyperspectral data advance crop type mapping","interactions":[],"lastModifiedDate":"2024-11-26T15:30:10.726606","indexId":"70261136","displayToPublicDate":"2024-11-01T08:31:26","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning and new-generation spaceborne hyperspectral data advance crop type mapping","docAbstract":"Hyperspectral sensors provide near-continuous spectral data that can facilitate advancements in agricultural crop classification and characterization, which are important for addressing global food and water security issues. We investigated two new-generation hyperspectral sensors, Germany’s Deutsches Zentrum für Luft‐ und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) and Italy’s PRecursore IperSpettrale della Missione Applicativa (PRISMA), within California's Central Valley in August 2021 focusing on five irrigated agricultural crops (alfalfa, almonds, corn, grapes, and pistachios). With reference data from the U.S. Department of Agriculture Cropland Data Layer, we developed a spectral library of the crops and classified them using three machine learning algorithms (support vector machines [SVM], random forest [RF], and spectral angle mapper [SAM]) and two philosophies: 1. Full spectral analysis (FSA) and 2. Optimal hyperspectral narrowband (OHNB) analysis. For FSA, we used 59 DESIS four-bin product bands and 207 of 238 PRISMA bands. For OHNB analysis, 9 DESIS and 16 PRISMA nonredundant OHNBs for studying crops were selected. FSA achieved only 1% to 3% higher accuracies relative to OHNB analysis in most cases. SVM provided the best results, closely followed by RF. Using both DESIS and PRISMA image OHNBs in SVM for classification led to higher accuracy than using either image alone, with an overall accuracy of 99%, producer’s accuracies of 94% to 100%, and user's accuracies of 95% to 100%.
","language":"English","publisher":"Ingenta","doi":"10.14358/PERS.24-00026R2","usgsCitation":"Aneece, I.P., Thenkabail, P., McCormick, R.L., Haireti, A., Foley, D., Oliphant, A., and Teluguntla, P., 2024, Machine learning and new-generation spaceborne hyperspectral data advance crop type mapping: Photogrammetric Engineering and Remote Sensing, v. 90, no. 11, p. 687-698, https://doi.org/10.14358/PERS.24-00026R2.","productDescription":"12 p.","startPage":"687","endPage":"698","ipdsId":"IP-163096","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":498261,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.24-00026r2","text":"Publisher Index Page"},{"id":464465,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.00224368510789,\n 37.29219185284313\n ],\n [\n -120.00224368510789,\n 36.59828829039613\n ],\n [\n -118.67344220549262,\n 36.59828829039613\n ],\n [\n -118.67344220549262,\n 37.29219185284313\n ],\n [\n -120.00224368510789,\n 37.29219185284313\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"90","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Aneece, Itiya P. 0000-0002-1201-5459","orcid":"https://orcid.org/0000-0002-1201-5459","contributorId":208265,"corporation":false,"usgs":true,"family":"Aneece","given":"Itiya","middleInitial":"P.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":919395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad 0000-0002-2182-8822","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":220239,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":919396,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCormick, Richard L. 0009-0002-8208-2136","orcid":"https://orcid.org/0009-0002-8208-2136","contributorId":346504,"corporation":false,"usgs":true,"family":"McCormick","given":"Richard","email":"","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":919400,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haireti, Alifu","contributorId":346506,"corporation":false,"usgs":false,"family":"Haireti","given":"Alifu","email":"","affiliations":[],"preferred":false,"id":919401,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Foley, Daniel 0000-0002-2051-6325","orcid":"https://orcid.org/0000-0002-2051-6325","contributorId":208266,"corporation":false,"usgs":true,"family":"Foley","given":"Daniel","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":919399,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oliphant, Adam 0000-0001-8622-7932 aoliphant@usgs.gov","orcid":"https://orcid.org/0000-0001-8622-7932","contributorId":192325,"corporation":false,"usgs":true,"family":"Oliphant","given":"Adam","email":"aoliphant@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":919398,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Teluguntla, Pardhasaradhi 0000-0001-8060-9841","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":211780,"corporation":false,"usgs":true,"family":"Teluguntla","given":"Pardhasaradhi","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":919397,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266277,"text":"70266277 - 2024 - Droughts reshape apex predator space use and intraguild overlap","interactions":[],"lastModifiedDate":"2025-05-02T17:35:19.0661","indexId":"70266277","displayToPublicDate":"2024-11-01T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Droughts reshape apex predator space use and intraguild overlap","docAbstract":"1. Droughts are increasing in frequency and severity globally due to climate change, leading to changes in resource availability that may have cascading effects on animal ecology. Resource availability is a key driver of animal space use, which in turn influences interspecific interactions like intraguild competition. Understanding how climate-induced changes in resource availability influence animal space use, and how species-specific responses scale up to affect intraguild dynamics, is necessary for predicting broader community-level responses to climatic changes.
2. Although several studies have demonstrated the ecological impacts of drought, the behavioral responses of individuals that scale up to these broader-scale effects are not well known, particularly among animals in top trophic levels, such as large carnivores. Furthermore, we currently lack understanding of how the impacts of climate variability on individual carnivore behavior are linked to intraguild dynamics, in part because multi-species datasets collected at timescales relevant to climatic changes are rare.
3. Using 11 years of GPS data from four sympatric large carnivore species in southern Africa – lions (Panthera leo), leopards (Panthera pardus), African wild dogs (Lycaon pictus), and cheetahs (Acinonyx jubatus) – spanning 4 severe drought events, we test whether drought conditions impact 1) large carnivore space use, 2) broad-scale intraguild spatial overlap, and 3) fine-scale intraguild interactions.
4. Drought conditions expanded space use across species, with carnivores increasing their monthly home range sizes by 35% (wild dogs) to 66% (leopards). Drought conditions increased the amount of spatial overlap between lions and subordinate felids (cheetahs and leopards) by up to 119%, but only lion-cheetah encounter rates were affected by these changes, declining in response to drought.
5. Our findings reveal that drought has a clear signature on the space use of multiple sympatric large carnivore species, which can alter spatiotemporal partitioning between competing species. Our study thereby illuminates the links between environmental change, animal behavior, and intraguild dynamics. While fine-scale avoidance strategies may facilitate intraguild coexistence during periodic droughts, large carnivore conservation may require considerable expansion of protected areas or revised human-carnivore coexistence strategies to accommodate the likely long-term increased space demands of large carnivores under projected increases in drought intensity.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.14192","usgsCitation":"West, L., Rafiq, K., Converse, S.J., Wilson, A., Jordan, N., Golabek, K., McNutt, J., and Abrahms, B., 2024, Droughts reshape apex predator space use and intraguild overlap: Journal of Animal Ecology, v. 93, no. 11, p. 1785-1798, https://doi.org/10.1111/1365-2656.14192.","productDescription":"14 p.","startPage":"1785","endPage":"1798","ipdsId":"IP-166498","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":502514,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":485354,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Botswana","otherGeospatial":"Okavango Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 21.06855004382743,\n -18.33344673603129\n ],\n [\n 21.06855004382743,\n -19.979158912722966\n ],\n [\n 23.959554595120153,\n -19.979158912722966\n ],\n [\n 23.959554595120153,\n -18.33344673603129\n ],\n [\n 21.06855004382743,\n -18.33344673603129\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"93","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"West, Leigh","contributorId":338294,"corporation":false,"usgs":false,"family":"West","given":"Leigh","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":935354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rafiq, Kasim","contributorId":338293,"corporation":false,"usgs":false,"family":"Rafiq","given":"Kasim","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":935355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":935356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Alan M.","contributorId":354290,"corporation":false,"usgs":false,"family":"Wilson","given":"Alan M.","affiliations":[{"id":84607,"text":"Royal Veterinary College","active":true,"usgs":false}],"preferred":false,"id":935357,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jordan, Neil R.","contributorId":354291,"corporation":false,"usgs":false,"family":"Jordan","given":"Neil R.","affiliations":[{"id":84609,"text":"Wild Entrust","active":true,"usgs":false}],"preferred":false,"id":935358,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Golabek, Krystyna A.","contributorId":354292,"corporation":false,"usgs":false,"family":"Golabek","given":"Krystyna A.","affiliations":[{"id":84609,"text":"Wild Entrust","active":true,"usgs":false}],"preferred":false,"id":935359,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McNutt, J. Weldon","contributorId":354293,"corporation":false,"usgs":false,"family":"McNutt","given":"J. Weldon","affiliations":[{"id":84609,"text":"Wild Entrust","active":true,"usgs":false}],"preferred":false,"id":935360,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Abrahms, Briana","contributorId":287294,"corporation":false,"usgs":false,"family":"Abrahms","given":"Briana","affiliations":[{"id":53078,"text":"UWA","active":true,"usgs":false}],"preferred":false,"id":935361,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266842,"text":"70266842 - 2024 - Juvenile coho salmon growth differences track biennial pink salmon spawning patterns","interactions":[],"lastModifiedDate":"2025-05-13T15:33:11.662904","indexId":"70266842","displayToPublicDate":"2024-11-01T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Juvenile coho salmon growth differences track biennial pink salmon spawning patterns","docAbstract":"1. Spawning Pacific salmon (Oncorhynchus spp.) provide marine-derived resources (MDR) to freshwater food webs in the form of eggs, flesh and maggots that consume salmon carcasses, all of which positively impact stream-dwelling fish growth. Pink salmon (O. gorbuscha) are widely distributed throughout coastal catchments along the North Pacific Ocean and display increased spawning abundances in odd years, owing to a fixed 2-year life history. While many studies have found that foraging and growth of stream-dwelling salmonids are improved by increased adult salmon spawning abundance, few studies have investigated the importance of alternating pink salmon spawning abundance between years.
2. Here, we examined how patterns of pink salmon spawning abundance impact the foraging and growth of juvenile coho salmon (O. kisutch). First, we used bioenergetic simulations to generate a hypothesis that coho salmon growth would increase during odd relative to even years. We then collected empirical juvenile coho salmon diet and growth data from a Southeast Alaska catchment in 2021 (pink salmon spawning) and 2022 (no pink salmon spawning). Field data were compared against simulation predictions to understand impacts of biennial pink salmon spawning patterns on juvenile coho salmon growth.
3. Empirical growth data revealed similar patterns to bioenergetic simulations. Age-1 coho salmon grew 16.6 mm longer and 5.5 g heavier on average in 2021 compared to 2022. Age-0 coho salmon displayed minor growth differences between years.
4. These results support bioenergetic model predictions and suggest that patterns of pink salmon spawning abundance can impart interannual growth disparities to juvenile coho salmon. Moreover, we show that distinct spawning characteristics of Pacific salmon species are important when understanding patterns of MDR transfer and growth responses in stream fishes.
","language":"English","publisher":"Wiley","doi":"10.1111/fwb.14328","usgsCitation":"Fitzgerald, K., Bellmore, J., Fellman, J., Cheng, M., Boyles-Muehleck, N., Delbecq, C., and Falke, J.A., 2024, Juvenile coho salmon growth differences track biennial pink salmon spawning patterns: Freshwater Biology, v. 69, no. 11, p. 1583-1595, https://doi.org/10.1111/fwb.14328.","productDescription":"13 p.","startPage":"1583","endPage":"1595","ipdsId":"IP-155328","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485820,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Juneau","otherGeospatial":"Tongass National Forest, upper Montana Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -134.02770952221633,\n 58.39172878863832\n ],\n [\n -134.02770952221633,\n 57.6331041033996\n ],\n [\n -133.0242790552512,\n 57.6331041033996\n ],\n [\n -133.0242790552512,\n 58.39172878863832\n ],\n [\n -134.02770952221633,\n 58.39172878863832\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"69","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Fitzgerald, Kevin A.","contributorId":355111,"corporation":false,"usgs":false,"family":"Fitzgerald","given":"Kevin A.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":936879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bellmore, J. Ryan","contributorId":355112,"corporation":false,"usgs":false,"family":"Bellmore","given":"J. Ryan","affiliations":[{"id":40821,"text":"U. S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":936880,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fellman, Jason B.","contributorId":355113,"corporation":false,"usgs":false,"family":"Fellman","given":"Jason B.","affiliations":[{"id":84706,"text":"University of Alaska Southeast, Forest Service","active":true,"usgs":false}],"preferred":false,"id":936881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cheng, Matthew L.H.","contributorId":355115,"corporation":false,"usgs":false,"family":"Cheng","given":"Matthew L.H.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":936882,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boyles-Muehleck, Naomi","contributorId":355118,"corporation":false,"usgs":false,"family":"Boyles-Muehleck","given":"Naomi","affiliations":[{"id":16298,"text":"University of Alaska Southeast","active":true,"usgs":false}],"preferred":false,"id":936883,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Delbecq, Claire E.","contributorId":355120,"corporation":false,"usgs":false,"family":"Delbecq","given":"Claire E.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":936884,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":936885,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70260921,"text":"70260921 - 2024 - Best practices for incorporating climate change science into Department of the Interior analyses, consultations, and decision making","interactions":[],"lastModifiedDate":"2024-11-15T14:01:02.523252","indexId":"70260921","displayToPublicDate":"2024-10-31T09:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Best practices for incorporating climate change science into Department of the Interior analyses, consultations, and decision making","docAbstract":"The purpose of this document is to provide technical guidance, practical application examples, and resource lists for those who conduct, manage, and/or interpret technical workflows within the Department of the Interior. This document is intended to support implementation of Department of the Interior policy 526 DM 1 and establish best practices for using climate change science to inform analysis, consultation, and decision making.
The Earth’s climate is an interconnected system that distributes energy, heat, and water around the planet. Due to human-driven increases in long-lived greenhouse gases, the Earth’s climate is now changing. For Departmental decision-making purposes, assuming a static, unchanging baseline climate is no longer consistent with current knowledge about the climate system.
There are uncertainties about future climate and how resources or assets (RoAs) will respond to new conditions. To depict the possibilities, the global climate science community develops scenarios and models to explore how future climate may respond to socioeconomic and technological development in the world.
Principles for informing policy development, planning and decisions, and regulatory processes using climate change science must: 1) consider the effects of future climate change, 2) characterize the risks, and 3) characterize the uncertainties.
Best practices include:
Use multiple scenarios to assess risks from a range of plausible societal pathways. When constraints prevent the use of multiple scenarios or if decision makers are risk averse, ensure that the chosen scenario considers higher risk outcomes. This is particularly important for large investments or irreversible decisions and reduces the chances of overconfident decision making.
Use multiple climate models within each scenario to account for the range of outcomes due to model uncertainty. Do not rely solely on a single model or an ensemble average.
Use relevant climate data. Use a time-period for model projections of the future climate change consistent with the relevant timeframe of the policy, action, or decision being considered. Historical observations are useful for understanding past conditions and climate trends for the next several years, but not beyond the next decade. Consult with climate data and modeling experts to assess which data and model resources are most appropriate for any given application.
Clearly describe key analysis uncertainties (including with any climate observations, models, and scenarios used), and how they were addressed in the analysis and/or decision process. This ensures transparency and learning among analysts and decision makers.
","language":"English","publisher":"Department of the Interior","doi":"10.21429/hjgj-j073","usgsCitation":"Terando, A.J., Tucker, A.M., Runyon, A.N., Miller, J., Perkins, J.L., Kimbrel, S.W., Cross, A.S., and Boyles, R.P., 2024, Best practices for incorporating climate change science into Department of the Interior analyses, consultations, and decision making, iv, 72 p., https://doi.org/10.21429/hjgj-j073.","productDescription":"iv, 72 p.","ipdsId":"IP-166512","costCenters":[],"links":[{"id":464070,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/unnumbered/70260921/coverthb.jpg"},{"id":464071,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/unnumbered/70260921/70260921.pdf","linkFileType":{"id":1,"text":"pdf"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Terando, Adam J. 0000-0002-9280-043X aterando@usgs.gov","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":173447,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","email":"aterando@usgs.gov","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":918516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tucker, Anna Maureen 0000-0002-1473-2048 amtucker@usgs.gov","orcid":"https://orcid.org/0000-0002-1473-2048","contributorId":257906,"corporation":false,"usgs":true,"family":"Tucker","given":"Anna","email":"amtucker@usgs.gov","middleInitial":"Maureen","affiliations":[],"preferred":true,"id":918517,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Runyon, Amber N. 0000-0002-7282-1217","orcid":"https://orcid.org/0000-0002-7282-1217","contributorId":346252,"corporation":false,"usgs":false,"family":"Runyon","given":"Amber","email":"","middleInitial":"N.","affiliations":[{"id":36976,"text":"U.S. National Park Service","active":true,"usgs":false}],"preferred":false,"id":918518,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, James A.","contributorId":346253,"corporation":false,"usgs":false,"family":"Miller","given":"James A.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":918519,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Perkins, Judy L.","contributorId":266176,"corporation":false,"usgs":false,"family":"Perkins","given":"Judy","email":"","middleInitial":"L.","affiliations":[{"id":54938,"text":"U.S. Bureau of Land Management, California State Office, 2800 Cottage Way, Sacramento, CA 95825","active":true,"usgs":false}],"preferred":false,"id":918520,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kimbrel, Sean W.","contributorId":346255,"corporation":false,"usgs":false,"family":"Kimbrel","given":"Sean","email":"","middleInitial":"W.","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":918521,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cross, Amanda S.","contributorId":346256,"corporation":false,"usgs":false,"family":"Cross","given":"Amanda","email":"","middleInitial":"S.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":918522,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Boyles, Ryan P. 0000-0001-9272-867X rboyles@usgs.gov","orcid":"https://orcid.org/0000-0001-9272-867X","contributorId":197670,"corporation":false,"usgs":true,"family":"Boyles","given":"Ryan","email":"rboyles@usgs.gov","middleInitial":"P.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":918523,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70260817,"text":"70260817 - 2024 - Early detection of wildlife disease pathogens using CRISPR-Cas system methods","interactions":[],"lastModifiedDate":"2024-12-26T16:53:43.313328","indexId":"70260817","displayToPublicDate":"2024-10-31T06:41:30","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19828,"text":"The CRISPR Journal","active":true,"publicationSubtype":{"id":10}},"title":"Early detection of wildlife disease pathogens using CRISPR-Cas system methods","docAbstract":"Wildlife diseases are a considerable threat to human health, conservation, and the economy. Surveillance is a critical component to mitigate the impact of animal diseases in these sectors. To monitor human diseases, CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated protein) biosensors have proven instrumental as diagnostic tools capable of detecting unique DNA and RNA sequences related to their associated pathogens. However, despite the significant advances in the general development of CRISPR-Cas biosensors, their use to support wildlife disease management is lagging. In some cases, wildlife diseases of concern could be rapidly surveyed using these tools with minimal technical, operational, or cost requirements to end users. This review explores the potential to further leverage this technology to advance wildlife disease monitoring and highlights how concerted standardization of protocols can help to ensure data reliability.","language":"English","publisher":"Mary Ann Liebert, Inc.","doi":"10.1089/crispr.2024.0030","usgsCitation":"Perez, A.A., Vazquez-Meves, G., and Hunter, M., 2024, Early detection of wildlife disease pathogens using CRISPR-Cas system methods: The CRISPR Journal, v. 7, no. 6, p. 327-342, https://doi.org/10.1089/crispr.2024.0030.","productDescription":"16 p.","startPage":"327","endPage":"342","ipdsId":"IP-162277","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":498262,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1089/crispr.2024.0030","text":"Publisher Index Page"},{"id":463845,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Perez, Adam Alberto 0000-0001-5057-1133","orcid":"https://orcid.org/0000-0001-5057-1133","contributorId":332516,"corporation":false,"usgs":true,"family":"Perez","given":"Adam","email":"","middleInitial":"Alberto","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":918205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vazquez-Meves, Guelaguetza 0000-0001-8100-2945","orcid":"https://orcid.org/0000-0001-8100-2945","contributorId":346127,"corporation":false,"usgs":true,"family":"Vazquez-Meves","given":"Guelaguetza","email":"","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":918206,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":207584,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":918207,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259877,"text":"ofr20241055 - 2024 - Sand supply to San Francisco Bay from the Sacramento and San Joaquin Rivers of the Central Valley, California","interactions":[],"lastModifiedDate":"2025-12-22T20:25:53.918489","indexId":"ofr20241055","displayToPublicDate":"2024-10-30T13:10:08","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1055","displayTitle":"Sand Supply to San Francisco Bay from the Sacramento and San Joaquin Rivers of the Central Valley, California","title":"Sand supply to San Francisco Bay from the Sacramento and San Joaquin Rivers of the Central Valley, California","docAbstract":"Sediment from the Central Valley via the Sacramento-San Joaquin Delta (Delta) and Suisun Bay is a primary source of sand to San Francisco Bay, California. Sand is mined from San Francisco Bay for commercial purposes, such as for use in concrete for construction. To better understand the supply of sand to Suisun Bay and San Francisco Bay, the U.S. Geological Survey (USGS), in cooperation with the San Francisco Bay Estuary Institute (SFEI) and the San Francisco Bay Conservation Development Commission (BCDC), initiated this study to compile and synthesize historical data and estimate the total sediment and sand portion of sediment exiting the Delta to Suisun Bay for a 20-year period between water years 2001 and 2020.
Sediment exiting the Delta is a combination of suspended sediment and bedload sediment. Seaward bedload transport was estimated using bedload transport equations and available hydraulic data at the two downstream-most streamgages in the Delta (where velocity is measured). Those two streamgages are about 25 kilometers upstream from the “exit” of the Delta at Mallard Island. The combined average annual net (seaward) bedload at these two streamgages was estimated to be 0.102 million cubic meters per year (Mm3/yr) for the study period. This volume of bedload is equivalent to 0.155 million metric tons per year (Mt/yr), assuming a bulk density of 1.517 metric tons per cubic meter (t/m3). The bedload composition was estimated to be 88 percent sand.
Between the two streamgages and Mallard Island, an annual average of 0.076 Mm3/yr of material was removed through mining during the study period, of which 97.5 percent was sand. In addition, 0.053 Mm3/yr was removed through dredging to support shipping and navigation, of which 76 percent was sand. The total volume of mined and dredged sediment material was approximately 0.128 Mm3/yr, equivalent to 0.194 Mt/yr, assuming a bulk density of 1.517 t/m3.
Assuming the estimated bedload reaching Mallard Island was reduced by mining and dredging, a mean bedload flux of −0.009 Mm3/yr was computed (using a bulk density of 1.517 t/m3), suggesting a deficit or landward transport of bedload. However, the total suspended-sediment and suspended-sand flux was in the seaward direction. The average total suspended flux of sediment to Suisun Bay through the cross section at the Mallard Island streamgage was estimated to be 0.482 million metric tons per year (Mt/yr; 0.015 Mt/yr sand) in the seaward direction. The results indicate a net flux out of the Delta of 0.469 Mt/yr of total sediment and 0.003 Mt/yr of sand.
The primary limitation of the study was the lack of physical bedload measurements to validate the bedload estimates. To better refine the estimates of bedload, physical measurements of bedload or repeat bathymetry would be necessary for a range of flow conditions. Such measurements could be used to calibrate transport equations and quantify the uncertainty in such estimates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241055","collaboration":"Prepared in cooperation with the San Francisco Estuary Institute Aquatic Science Center, the California State Coastal Conservancy, and the San Francisco Bay Conservation and Development Commission","programNote":"Water Availability and Use Science Program","usgsCitation":"Marineau, M.D., Hart, D., Ely, C.P., and McKee, L., 2024, Sand supply to San Francisco Bay from the Sacramento and San Joaquin Rivers of the Central Valley, California: U.S. Geological Survey Open-File Report 2024–1055, 18 p., https://doi.org/10.3133/ofr20241055.","productDescription":"Report: viii, 18 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-157560","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":463205,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1055/images"},{"id":463204,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1055/ofr20241055.xml"},{"id":463203,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1055/ofr20241055.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":463201,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9I18RGG","text":"USGS Data Release","description":"Ely, C.P., and Marineau, M.D., 2023, Estimated bedload transport rates at Rio Vista and Jersey Point, California, 2011–2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9I18RGG.","linkHelpText":"Estimated bedload transport rates at Rio Vista and Jersey Point, California, 2011–2020"},{"id":497888,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117739.htm","linkFileType":{"id":5,"text":"html"}},{"id":463206,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/preview/ofr20241055/full"},{"id":463202,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1055/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.29538442038356,\n 38.56577858557708\n ],\n [\n -122.29538442038356,\n 37.65383277017135\n ],\n [\n -121.19683028697757,\n 37.65383277017135\n ],\n [\n -121.19683028697757,\n 38.56577858557708\n ],\n [\n -122.29538442038356,\n 38.56577858557708\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
In support of Missouri’s Nutrient Loss Reduction Strategy, which was created to reduce the nutrient contamination of Missouri’s waterways from point and nonpoint sources, total phosphorus concentrations and loads were computed for the Missouri River at St. Joseph, Missouri, streamgage (U.S. Geological Survey station 06818000) and the Missouri River at Hermann, Mo., streamgage (U.S. Geological Survey station 06934500) for October 2007 to September 2022 using surrogate models and continuous turbidity sensor data. To obtain a more complete total phosphorus record for the study period, LOAD ESTimator (LOADEST) regression models using flow were used when turbidity sensor data were unavailable to estimate daily total phosphorus loads. This report presents the methods and results for the computed total phosphorus concentrations, loads, and yields for the two study sites on the Missouri River. With continued data collection and ongoing model evaluation and maintenance, the surrogate models may be useful into the future for computing total phosphorus concentrations and loads.
Daily mean total phosphorus concentrations calculated using a surrogate model at the Missouri River at St. Joseph, Mo., streamgage during the 15-year study period (water years 2008 through 2022) ranged from 0.104 to 4.56 milligrams per liter (mg/L; median of 0.272 mg/L), and computed total phosphorus daily loads (with gaps in the daily record filled using the LOADEST regression model) ranged from 5.19 to 1,760 tons per day (tons/d; median of 36.5 tons/d). Annual loads ranged from 9,570 tons in water year 2022 to 50,500 tons in water year 2019. The total load for the study period was 437,000 tons.
For the Missouri River at Hermann, Mo., streamgage during the same 15-year study period, daily mean total phosphorus concentrations, calculated using surrogate models applied to low and high turbidity values, ranged from 0.183 to 1.97 mg/L (median of 0.319 mg/L), and computed total phosphorus daily loads (with gaps in the daily record filled using the LOADEST regression model) ranged from 12.7 to 1,970 tons/d (median of 76.8 tons/d). Annual loads ranged from 22,600 tons in water year 2022 to 101,000 tons in water year 2019. The total load for the study period was 833,000 tons, which is nearly twice that at the Missouri River at St. Joseph, Mo., streamgage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245097","collaboration":"Prepared in cooperation with Missouri Department of Natural Resources","usgsCitation":"Markland, K.M., 2024, Use of continuous water-quality time-series data to compute total phosphorus concentrations and loads for the Missouri River at St. Joseph and Hermann, Missouri, 2007–22: U.S. Geological Survey Scientific Investigations Report 2024–5097, 26 p., https://doi.org/10.3133/sir20245097.","productDescription":"Report: vii, 26 p.; Data Release; Dataset","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-161927","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":463254,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":463253,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245097/full"},{"id":463252,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5097/images/"},{"id":463251,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5097/sir20245097.XML"},{"id":463250,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5097/sir20245097.pdf","text":"Report","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5097"},{"id":463249,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5097/coverthb.jpg"},{"id":497886,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117740.htm","linkFileType":{"id":5,"text":"html"}},{"id":463255,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P17PHYDZ","text":"USGS data release","linkHelpText":"Data and model archive summaries to support use of continuous water-quality time-series data to compute total phosphorus concentrations and loads for the Missouri River at St. Joseph and Hermann, Missouri, 2007–22"}],"country":"United States","state":"Missouri","city":"Hermann, St. Joseph","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.48454431267173,\n 38.7389466373896\n ],\n [\n -91.48454431267173,\n 38.678901791033724\n ],\n [\n -91.40123726792416,\n 38.678901791033724\n ],\n [\n -91.40123726792416,\n 38.7389466373896\n ],\n [\n -91.48454431267173,\n 38.7389466373896\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.002636709889,\n 39.854445017011784\n ],\n [\n -95.002636709889,\n 39.62967769348404\n ],\n [\n -94.65186218929263,\n 39.62967769348404\n ],\n [\n -94.65186218929263,\n 39.854445017011784\n ],\n [\n -95.002636709889,\n 39.854445017011784\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
One of the world’s largest “blue carbon” ecosystems, Louisiana’s tidal wetlands on the US Gulf of Mexico coast, is rapidly being lost. Louisiana’s strong legal, regulatory, and monitoring framework, developed for one of the world’s largest tidal wetland systems, provides an opportunity for a programmatic approach to blue carbon accreditation to support restoration of these ecologically and economically important tidal wetlands. Louisiana’s coastal wetlands span ∼1.4 million ha and accumulate 5.5–7.3 Tg yr−1 of blue carbon (organic carbon), ∼6%–8% of tidal marsh blue carbon accumulation globally. Louisiana has a favorable governance framework to advance blue carbon accreditation, due to centralized restoration planning, long term coastal monitoring, and strong legal and regulatory frameworks around carbon. Additional restoration efforts, planned through Louisiana’s Coastal Master Plan, over 50 years are projected to create, or avoid loss of, up to 81,000 ha of wetland. Current restoration funding, primarily from Deepwater Horizon oil spill settlements, will be fully committed by the early 2030s and additional funding sources are required. Existing accreditation methodologies have not been successfully applied to coastal Louisiana’s ecosystem restoration approaches or herbaceous tidal wetland types. Achieving financial viability for accreditation of these restoration and wetland types will require expanded application of existing blue carbon crediting methodologies. It will also require expanded approaches for predicting the future landscape without restoration, such as numerical modeling, to be validated. Additional methodologies (and/or standards) would have many common elements with those currently available but may be beneficial, depending on the goals and needs of both the state of Louisiana and potential purchasers of Louisiana tidal wetland carbon credits. This study identified twenty targeted needs that will address data and knowledge gaps to maximize financial viability of blue carbon accreditation for Louisiana’s tidal wetlands. Knowledge needs were identified in five categories: legislative and policy, accreditation methodologies and standards, soil carbon flux, methane flux, and lateral carbon flux. Due to the large spatial scale and diversity of tidal wetlands, it is expected that progress in coastal Louisiana has high potential to be generalized to similar wetland ecosystems across the northern Gulf of Mexico and globally.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fenvs.2024.1421850","usgsCitation":"Carruthers, T.J., Jones, S.B., Terrell, M.K., Scheibly, J.F., Player, B.J., Black, V.A., Ehrenwerth, J.R., Biber, P.D., Connolly, R.M., Crooks, S., Curole, J.P., Darnell, K.M., Dausman, A., DeJong, A.L., Doyle, S.M., Esposito, C.R., Friess, D., Fourqurean, J.W., Georgiou, I.Y., Grimsditch, G.D., He, S., Hillmann, E.R., Holm, G.O., Howard, J., Jung, H., Jupiter, S.D., Kiskaddon, E.P., Krauss, K., Lavery, P.S., Liu, B., Lovelock, C.E., Mack, S.K., Macreadie, P.I., McGlathery, K.J., Megonigal, J.P., Roberts, B.J., Settelmyer, S., Staver, L.W., Stevens, H.J., Sutton-Grier, A.E., Villa, J.A., White, J.R., and Waycott, M., 2024, Identifying and filling critical knowledge gaps can optimize financial viability of blue carbon projects in tidal wetlands: Frontiers in Environmental Science, v. 12, 1421850, 16 p., https://doi.org/10.3389/fenvs.2024.1421850.","productDescription":"1421850, 16 p.","ipdsId":"IP-165324","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":466798,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2024.1421850","text":"Publisher Index Page"},{"id":464119,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.5798883414875,\n 30.183721520956823\n ],\n [\n -90.26254794159267,\n 30.659495947506798\n ],\n [\n -93.68519744349099,\n 30.159467726997462\n ],\n [\n -93.87222747091712,\n 29.82740239017822\n ],\n [\n -93.84417296680317,\n 29.62437970908134\n ],\n [\n -91.99257569528429,\n 29.372062504492533\n ],\n [\n -90.7114200074153,\n 28.898303996821014\n ],\n [\n -89.27128879623413,\n 28.832787010801255\n ],\n [\n -88.80371372766903,\n 29.168124063185203\n ],\n [\n -88.71955021532717,\n 29.851737484600278\n ],\n [\n -88.74760471944114,\n 30.127120053251076\n ],\n [\n -89.5798883414875,\n 30.183721520956823\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-10-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Carruthers, Tim J.B.","contributorId":346277,"corporation":false,"usgs":false,"family":"Carruthers","given":"Tim","email":"","middleInitial":"J.B.","affiliations":[{"id":82811,"text":"The Water Institute, Baton Rouge, Louisiana, USA","active":true,"usgs":false}],"preferred":false,"id":918567,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, S. Beaux","contributorId":346278,"corporation":false,"usgs":false,"family":"Jones","given":"S.","email":"","middleInitial":"Beaux","affiliations":[{"id":82811,"text":"The Water Institute, Baton Rouge, Louisiana, USA","active":true,"usgs":false}],"preferred":false,"id":918568,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Terrell, Megan K.","contributorId":346279,"corporation":false,"usgs":false,"family":"Terrell","given":"Megan","email":"","middleInitial":"K.","affiliations":[{"id":82812,"text":"Plauché and Carr, LLP, Baton Rouge, Louisiana, USA","active":true,"usgs":false}],"preferred":false,"id":918569,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scheibly, Jonathan F.","contributorId":346280,"corporation":false,"usgs":false,"family":"Scheibly","given":"Jonathan","email":"","middleInitial":"F.","affiliations":[{"id":82813,"text":"Stantec, Lexington, Kentucky, USA","active":true,"usgs":false}],"preferred":false,"id":918570,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Player, Brendan J.","contributorId":346281,"corporation":false,"usgs":false,"family":"Player","given":"Brendan","email":"","middleInitial":"J.","affiliations":[{"id":82814,"text":"Stantec, Fredericksburg, Virginia, USA","active":true,"usgs":false}],"preferred":false,"id":918571,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Black, Valerie A.","contributorId":346282,"corporation":false,"usgs":false,"family":"Black","given":"Valerie","email":"","middleInitial":"A.","affiliations":[{"id":82811,"text":"The Water Institute, Baton Rouge, Louisiana, USA","active":true,"usgs":false}],"preferred":false,"id":918572,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ehrenwerth, Justin R.","contributorId":346283,"corporation":false,"usgs":false,"family":"Ehrenwerth","given":"Justin","email":"","middleInitial":"R.","affiliations":[{"id":82811,"text":"The Water Institute, Baton Rouge, Louisiana, USA","active":true,"usgs":false}],"preferred":false,"id":918573,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Biber, Patrick D.","contributorId":346284,"corporation":false,"usgs":false,"family":"Biber","given":"Patrick","email":"","middleInitial":"D.","affiliations":[{"id":82815,"text":"School of Ocean Science and Engineering and Gulf Coast Research Laboratory, The University of Southern Mississippi, Ocean Springs, Mississippi, USA","active":true,"usgs":false}],"preferred":false,"id":918574,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Connolly, Rod M.","contributorId":346285,"corporation":false,"usgs":false,"family":"Connolly","given":"Rod","email":"","middleInitial":"M.","affiliations":[{"id":82816,"text":"Coastal and Marine Research Centre, Australian Rivers Institute, School of Environment and Science, Griffith University, Gold Coast, QLD, Australia","active":true,"usgs":false}],"preferred":false,"id":918575,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Crooks, Steve","contributorId":333065,"corporation":false,"usgs":false,"family":"Crooks","given":"Steve","affiliations":[{"id":38182,"text":"Silvestrum Climate Associates","active":true,"usgs":false}],"preferred":false,"id":918576,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Curole, Jason P.","contributorId":346286,"corporation":false,"usgs":false,"family":"Curole","given":"Jason","email":"","middleInitial":"P.","affiliations":[{"id":82811,"text":"The Water Institute, Baton Rouge, Louisiana, USA","active":true,"usgs":false}],"preferred":false,"id":918577,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Darnell, Kelly M.","contributorId":272888,"corporation":false,"usgs":false,"family":"Darnell","given":"Kelly","email":"","middleInitial":"M.","affiliations":[{"id":48626,"text":"The Water Institute of the Gulf, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":918578,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Dausman, Alyssa M.","contributorId":304016,"corporation":false,"usgs":false,"family":"Dausman","given":"Alyssa M.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":918579,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"DeJong, Allison L.","contributorId":346287,"corporation":false,"usgs":false,"family":"DeJong","given":"Allison","email":"","middleInitial":"L.","affiliations":[{"id":82811,"text":"The Water Institute, Baton Rouge, Louisiana, USA","active":true,"usgs":false}],"preferred":false,"id":918580,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Doyle, Shawn M.","contributorId":346288,"corporation":false,"usgs":false,"family":"Doyle","given":"Shawn","email":"","middleInitial":"M.","affiliations":[{"id":82811,"text":"The Water Institute, Baton Rouge, Louisiana, USA","active":true,"usgs":false}],"preferred":false,"id":918581,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Esposito, Christopher R.","contributorId":340205,"corporation":false,"usgs":false,"family":"Esposito","given":"Christopher","email":"","middleInitial":"R.","affiliations":[{"id":81504,"text":"The Water Institute","active":true,"usgs":false}],"preferred":false,"id":918582,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Friess, Daniel A.","contributorId":35454,"corporation":false,"usgs":false,"family":"Friess","given":"Daniel A.","affiliations":[{"id":25407,"text":"Department of Geography, National University of Singapore","active":true,"usgs":false}],"preferred":false,"id":918583,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Fourqurean, James W.","contributorId":211631,"corporation":false,"usgs":false,"family":"Fourqurean","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":38283,"text":"Marine Sciences Program, Florida International University, North Miami, FL 33181","active":true,"usgs":false}],"preferred":false,"id":918584,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Georgiou, Ioannis Y.","contributorId":205361,"corporation":false,"usgs":false,"family":"Georgiou","given":"Ioannis","email":"","middleInitial":"Y.","affiliations":[{"id":37089,"text":"Pontchartrain Institute for Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":918585,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Grimsditch, Gabriel D.","contributorId":346289,"corporation":false,"usgs":false,"family":"Grimsditch","given":"Gabriel","email":"","middleInitial":"D.","affiliations":[{"id":82817,"text":"United Nations Environment Program, UN Avenue, Nairobi, Kenya","active":true,"usgs":false}],"preferred":false,"id":918586,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"He, Songjie","contributorId":329472,"corporation":false,"usgs":false,"family":"He","given":"Songjie","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":918587,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Hillmann, Eva R.","contributorId":200686,"corporation":false,"usgs":false,"family":"Hillmann","given":"Eva","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":918588,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Holm, Guerry O. Jr.","contributorId":204774,"corporation":false,"usgs":false,"family":"Holm","given":"Guerry","suffix":"Jr.","email":"","middleInitial":"O.","affiliations":[{"id":36984,"text":"CH2M","active":true,"usgs":false}],"preferred":false,"id":918589,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Howard, Jennifer","contributorId":149225,"corporation":false,"usgs":false,"family":"Howard","given":"Jennifer","email":"","affiliations":[{"id":17683,"text":"AAAS Science & Technology Policy Fellow/NOAA","active":true,"usgs":false}],"preferred":false,"id":918590,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Jung, Hoonshin","contributorId":305843,"corporation":false,"usgs":false,"family":"Jung","given":"Hoonshin","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":918591,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Jupiter, Stacy D.","contributorId":146440,"corporation":false,"usgs":false,"family":"Jupiter","given":"Stacy","email":"","middleInitial":"D.","affiliations":[{"id":16691,"text":"Research School of Earth Sciences, Australian National University","active":true,"usgs":false}],"preferred":false,"id":918592,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Kiskaddon, Erin P.","contributorId":272886,"corporation":false,"usgs":false,"family":"Kiskaddon","given":"Erin","email":"","middleInitial":"P.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":918593,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":218325,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":918594,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Lavery, Paul S.","contributorId":331977,"corporation":false,"usgs":false,"family":"Lavery","given":"Paul","email":"","middleInitial":"S.","affiliations":[{"id":79331,"text":"School of Sciences Centre for Marine Ecosystems Research, Edith Cowan University, Joondalup, WA, Australia","active":true,"usgs":false}],"preferred":false,"id":918595,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Liu, Bingqing","contributorId":304014,"corporation":false,"usgs":false,"family":"Liu","given":"Bingqing","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":918596,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Lovelock, Catherine E.","contributorId":215562,"corporation":false,"usgs":false,"family":"Lovelock","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":39280,"text":"School of Biological Sciences, The University of Queensland","active":true,"usgs":false}],"preferred":false,"id":918597,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Mack, Sarah K.","contributorId":346290,"corporation":false,"usgs":false,"family":"Mack","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":82818,"text":"Tierra Resources LLC, Lafitte, Louisiana, USA","active":true,"usgs":false}],"preferred":false,"id":918598,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Macreadie, Peter I.","contributorId":331980,"corporation":false,"usgs":false,"family":"Macreadie","given":"Peter","email":"","middleInitial":"I.","affiliations":[{"id":79341,"text":"Deakin University, Centre for Marine Science, School of Life and Environmental Sciences, Burwood, Victoria, Australia","active":true,"usgs":false}],"preferred":false,"id":918599,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"McGlathery, Karen J.","contributorId":346291,"corporation":false,"usgs":false,"family":"McGlathery","given":"Karen","email":"","middleInitial":"J.","affiliations":[{"id":82819,"text":"Department of Environmental Sciences, University of Virginia, Virginia, USA","active":true,"usgs":false}],"preferred":false,"id":918600,"contributorType":{"id":1,"text":"Authors"},"rank":34},{"text":"Megonigal, J. Patrick","contributorId":288317,"corporation":false,"usgs":false,"family":"Megonigal","given":"J.","email":"","middleInitial":"Patrick","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":918601,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Roberts, Brian J.","contributorId":341597,"corporation":false,"usgs":false,"family":"Roberts","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":12699,"text":"Louisiana Universities Marine Consortium","active":true,"usgs":false}],"preferred":false,"id":918602,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Settelmyer, Scott","contributorId":346292,"corporation":false,"usgs":false,"family":"Settelmyer","given":"Scott","email":"","affiliations":[{"id":82820,"text":"TerraCarbon LLC, Peoria, Illinoi, USA","active":true,"usgs":false}],"preferred":false,"id":918603,"contributorType":{"id":1,"text":"Authors"},"rank":37},{"text":"Staver, Lorie W.","contributorId":346293,"corporation":false,"usgs":false,"family":"Staver","given":"Lorie","email":"","middleInitial":"W.","affiliations":[{"id":82821,"text":"University of Maryland Center for Environmental Science, Horn Point Laboratory, Cambridge, Maryland, USA","active":true,"usgs":false}],"preferred":false,"id":918604,"contributorType":{"id":1,"text":"Authors"},"rank":38},{"text":"Stevens, Hilary J.","contributorId":346294,"corporation":false,"usgs":false,"family":"Stevens","given":"Hilary","email":"","middleInitial":"J.","affiliations":[{"id":82822,"text":"Restore America’s Estuaries, Washington, D.C., USA","active":true,"usgs":false}],"preferred":false,"id":918605,"contributorType":{"id":1,"text":"Authors"},"rank":39},{"text":"Sutton-Grier, Ariana Eileen 0000-0002-1242-7728","orcid":"https://orcid.org/0000-0002-1242-7728","contributorId":346295,"corporation":false,"usgs":true,"family":"Sutton-Grier","given":"Ariana","email":"","middleInitial":"Eileen","affiliations":[{"id":24693,"text":"Climate Research and Development","active":true,"usgs":true}],"preferred":true,"id":918606,"contributorType":{"id":1,"text":"Authors"},"rank":40},{"text":"Villa, Jorge A.","contributorId":343780,"corporation":false,"usgs":false,"family":"Villa","given":"Jorge","email":"","middleInitial":"A.","affiliations":[{"id":7155,"text":"University of Louisiana at Lafayette","active":true,"usgs":false}],"preferred":false,"id":918607,"contributorType":{"id":1,"text":"Authors"},"rank":41},{"text":"White, John R.","contributorId":304473,"corporation":false,"usgs":false,"family":"White","given":"John","email":"","middleInitial":"R.","affiliations":[{"id":66084,"text":"Dept. of Oceanography and Coastal Sciences, Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":918608,"contributorType":{"id":1,"text":"Authors"},"rank":42},{"text":"Waycott, Michelle","contributorId":346296,"corporation":false,"usgs":false,"family":"Waycott","given":"Michelle","email":"","affiliations":[{"id":82823,"text":"School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia","active":true,"usgs":false}],"preferred":false,"id":918609,"contributorType":{"id":1,"text":"Authors"},"rank":43}]}} ,{"id":70260403,"text":"70260403 - 2024 - Predictive modeling reveals elevated conductivity relative to background levels in freshwater tributaries within the Chesapeake Bay watershed, USA","interactions":[],"lastModifiedDate":"2024-11-27T15:58:32.944541","indexId":"70260403","displayToPublicDate":"2024-10-30T07:03:12","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19118,"text":"ES&T Water","active":true,"publicationSubtype":{"id":10}},"title":"Predictive modeling reveals elevated conductivity relative to background levels in freshwater tributaries within the Chesapeake Bay watershed, USA","docAbstract":"Elevated conductivity (i.e., specific conductance or SC) causes osmotic stress in freshwater aquatic organisms and may increase the toxicity of some contaminants. Indices of benthic macroinvertebrate integrity have declined in urban areas across the Chesapeake Bay watershed (CBW), and more information is needed about whether these declines may be due to elevated conductivity. A predictive SC model for the CBW was developed using monitoring data from the National Water Quality Portal. Predictor variables representing SC sources were compiled for nontidal reaches across the CBW. Random forests modeling was conducted to predict SC at four time periods (1999–2001, 2004–2006, 2009–2011, and 2014–2016), which were then compared to a national data set of background SC to quantify departures from background SC. Carbonate geology, impervious cover, forest cover, and snow depth were the most important variables for predicting SC. Observations and modeled results showed snow depth amplified the effect of impervious cover on SC. Elevated SC was predicted in two-thirds of reaches in the CBW, and these elevated conditions persisted over time in many areas. These results can be used in stressor identification assessments to prioritize future monitoring and to determine where management activities could be implemented to reduce salinization.
The lack of consolidated information regarding the response of wild bird species to infection with avian influenza virus (AIV) is a challenge to both conservation managers and researchers alike, with related sectors also impacted, such as public health and commercial poultry. Using two independent searches, we reviewed published literature for studies describing wild bird species experimentally infected with avian influenza to assess host species’ relative susceptibility to AIVs. Additionally, we summarize broad-scale parameters for elements such as shedding duration and minimum infectious dose that can be used in transmission modelling efforts. Our synthesis shows that waterfowl (i.e. Anatidae) compose the vast majority of published AIV pathobiology studies, whereas gulls and passerines are less represented in research despite evidence that they also are susceptible and contribute to highly pathogenic avian influenza disease dynamics. This study represents the first comprehensive effort to compile available literature regarding the pathobiology of AIVs in all wild birds in over a decade. This database can now serve as a tool to all researchers, providing generalized estimates of pathobiology parameters for a variety of wild avian families and an opportunity to critically examine and assess what is known and identify where further insight is needed.
Ponds are an important source of greenhouse gases (GHGs) to the atmosphere, yet evaluating their role in global biogeochemical cycling is currently hampered by limitations in quantifying their global distribution. Existing satellite-derived estimates of lake distributions have difficulty identifying small lakes (5–10 ha) and ponds (<5 ha) due to limitations in satellite resolution and challenges extracting individual small waterbodies from low-albedo surfaces, vegetated water, and lotic water systems including rivers and streams. In this study, we developed generalizable pond mapping strategies based on their spatial-temporal-spectral characteristics to fully exploit accessible medium-resolution optical and synthetic aperture radar (SAR) time series to identify ponds. Our novel approach entails: (1) making full use of ponds' characteristics from an object-based perspective; (2) extracting pond objects using seeds of prominent water pixels defined by the SAR VH signal; (3) constructing training samples of ponds with high representativeness; and (4) improving inter-class discrimination by combining features from optical and SAR data. We designed a novel Optical-SAR Pond Object Mapper (OptiSAR-POM) to achieve an improved estimate of pond size distribution by incorporating mapping strategies into the object-based image analysis framework. We generated landscape objects through an elaborate water-focused segmentation approach, which adaptively aligned the segmentation parameters with the size and distribution patterns of ponds to identify small waterbodies and increase inter-class variability. We further introduced an interactive learning process to construct random forests for object-based classification, which incorporated adaptive empirical thresholds to identify potential pond objects and select representative training samples of varying sizes. We tested the OptiSAR-POM framework using Sentinel-1/2 time series at three county-level study sites and three supplementary watershed-level study sites in the United States and China. Our approach yielded high overall accuracy (>95 %) for all sites and highlighted the ability of Sentinel-1/2 imagery to accurately detect small ponds (0.1–1 ha) across diverse landscapes. The average producer's accuracy for small ponds at county-level sites improved by ∼45 % compared to that of all other products with a 10-m or higher spatial resolution, addressing the absence of such information in existing regional and global datasets. The generated county-level pond maps revealed the numerical dominance of ponds in lentic waters, their substantial area contribution in human-impacted regions, and the relevance of studying biogeochemical processes in smaller waterbodies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2024.114484","usgsCitation":"Liu, D., Zhu, X., Holgerson, M., Bansal, S., and Xu, X., 2024, Inventorying ponds through novel size-adaptive object mapping using Sentinel-1/2 time series: Remote Sensing of Environment, v. 315, 114484, 21 p., https://doi.org/10.1016/j.rse.2024.114484.","productDescription":"114484, 21 p.","ipdsId":"IP-167730","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":490911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"315","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Denghong","contributorId":357052,"corporation":false,"usgs":false,"family":"Liu","given":"Denghong","affiliations":[{"id":37969,"text":"Hong Kong Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":940631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Xioalin","contributorId":357055,"corporation":false,"usgs":false,"family":"Zhu","given":"Xioalin","affiliations":[{"id":37969,"text":"Hong Kong Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":940632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holgerson, Meredith","contributorId":218790,"corporation":false,"usgs":false,"family":"Holgerson","given":"Meredith","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":940633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":940634,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xu, Xiangtao","contributorId":348758,"corporation":false,"usgs":false,"family":"Xu","given":"Xiangtao","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":940635,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}