Background: The geographic expansion and evolution of A/Goose/Guangdong/1/1996(H5N1) (Gs/GD) lineage H5Nx highly pathogenic avian influenza (HPAI) viruses since 1996 have raised awareness of enzootic circulation among migratory birds and the potential for intercontinental transport and spread. Recent Pacific- and Atlantic-route introductions of HPAI to North America were facilitated by avian migration through subarctic zones, specifically Alaska and Iceland. This study aimed to identify recent historical patterns of exposure to HPAI viruses among birds within and migrating through both regions and evaluate how geographic, demographic, and taxonomic differences contribute to exposure risk at two intercontinental staging locations.
Methods: During 2010-2019, blood samples were obtained from captured wild migratory seabirds and waterfowl in Alaska and Iceland. All live birds were released following completion of sampling. Sampling date, species, sampling location, and age class was documented for each bird, and sex was documented when possible. Lentiviral pseudoviruses that express the influenza hemagglutinin surface glycoprotein for H5Nx HPAI and H5 low-pathogenicity avian influenza (LPAI) were constructed for use in serological assays to screen for and quantify titers of antibodies against the latter viruses. Data were analyzed to compare (a) categorical baseline ecological traits between Iceland and Alaska, and (b) ecological traits between birds identified to be seropositive and suggestive/seronegative/fully cross-reactive birds to H5Nx HPAI in Iceland and Alaska. Factors associated with seroreactivity to H5Nx HPAI and H5 LPAI were assessed.
Results:The seroprevalence of HPAI among birds in both locations was 7.3% (112/1526). Findings reveal variability in seroprevalence by year, higher rates of exposure to H5 LPAI than H5Nx HPAI overall, and significantly more seropositive and suggestive exposure of birds to H5Nx HPAI in Alaska as compared to Iceland. Geographic, demographic, and taxonomic differences contribute to exposure risk between Alaska and Iceland. Most tested birds were immuno-naïve to HPAI in both locations, which indicates many migratory birds in the subarctic are susceptible to HPAI infection, demonstrating substantial risk for intercontinental transmission between Asia, Europe, and North America.
Conclusions: Our findings provide further justification for increased viral and serosurveillance in Alaska and Iceland to monitor subarctic movements of migratory birds and intercontinental transmission dynamics of currently circulating and new strains of HPAI globally.
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U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Under climate change, ecosystems are experiencing novel drought regimes, often in combination with stressors that reduce resilience and amplify drought’s impacts. Consequently, drought appears increasingly likely to push systems beyond important physiological and ecological thresholds, resulting in substantial changes in ecosystem characteristics persisting long after drought ends (i.e., ecological transformation). In the present article, we clarify how drought can lead to transformation across a wide variety of ecosystems including forests, woodlands, and grasslands. Specifically, we describe how climate change alters drought regimes and how this translates to impacts on plant population growth, either directly or through drought's interactions with factors such as land management, biotic interactions, and other disturbances. We emphasize how interactions among mechanisms can inhibit postdrought recovery and can shift trajectories toward alternate states. Providing a holistic picture of how drought initiates long-term change supports the development of risk assessments, predictive models, and management strategies, enhancing preparedness for a complex and growing challenge.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/biosci/biae050","usgsCitation":"Moss, W.E., Crausbay, S., Rangwala, I., Wason, J., Trauernicht, C., Stevens-Rumann, C.S., Sala, A., Rottler, C.M., Pederson, G.T., Miller, B.W., Magness, D., Littell, J., Frelich, L., Frazier, A.G., Davis, K., Coop, J., Cartwright, J.M., and Booth, R.K., 2024, Drought as an emergent driver of ecological transformation in the twenty-first century: BioScience, v. 74, no. 8, p. 524-538, https://doi.org/10.1093/biosci/biae050.","productDescription":"15 p.","startPage":"524","endPage":"538","ipdsId":"IP-153234","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":490028,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biae050","text":"Publisher Index Page"},{"id":430975,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"74","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Moss, Wynne Emily 0000-0002-2813-1710","orcid":"https://orcid.org/0000-0002-2813-1710","contributorId":338331,"corporation":false,"usgs":true,"family":"Moss","given":"Wynne","email":"","middleInitial":"Emily","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":906093,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crausbay, Shelley","contributorId":217758,"corporation":false,"usgs":false,"family":"Crausbay","given":"Shelley","affiliations":[{"id":13470,"text":"Conservation Science Partners","active":true,"usgs":false}],"preferred":false,"id":906094,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rangwala, Imtiaz","contributorId":259891,"corporation":false,"usgs":false,"family":"Rangwala","given":"Imtiaz","affiliations":[{"id":52460,"text":"North Central Climate Adaptation Science Center","active":true,"usgs":false}],"preferred":false,"id":906095,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wason, Jay","contributorId":300108,"corporation":false,"usgs":false,"family":"Wason","given":"Jay","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":906096,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Trauernicht, Clay","contributorId":221125,"corporation":false,"usgs":false,"family":"Trauernicht","given":"Clay","email":"","affiliations":[{"id":40329,"text":"University of Hawai‘i at Mānoa, Department of Natural Resources and Environmental Management","active":true,"usgs":false}],"preferred":false,"id":906097,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stevens-Rumann, Camille S.","contributorId":274486,"corporation":false,"usgs":false,"family":"Stevens-Rumann","given":"Camille","email":"","middleInitial":"S.","affiliations":[{"id":56622,"text":"Forest Restoration Institute, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":906098,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sala, Anna","contributorId":147094,"corporation":false,"usgs":false,"family":"Sala","given":"Anna","email":"","affiliations":[{"id":5103,"text":"The University of Montana, Division of Biological Sciences, Missoula, Montana 59812","active":true,"usgs":false}],"preferred":false,"id":906099,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rottler, Caitlin M.","contributorId":138853,"corporation":false,"usgs":false,"family":"Rottler","given":"Caitlin","email":"","middleInitial":"M.","affiliations":[{"id":12546,"text":"Univ of Wyoming, Department of Botany, 1000 E. University Ave., Laramie, WY 82071; Univ of WY, Program in Ecology, 1000 E. 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This study considered whether vegetation-generated turbulence had a similar influence on natural cohesive sediment. Cores were collected from a black mangrove forest with aboveground biomass and exposed to stepwise increases in velocity. Erosion was recorded through suspended sediment concentration. For the same velocity, cores with pneumatophores had elevated turbulent kinetic energy compared to bare cores without pneumatophores. However, the vegetation-generated turbulence did not increase bed stress or the rate of resuspension, relative to bare cores. It was hypothesized that the short time-scale fluctuations associated with vegetation-generated turbulence were not of sufficient duration to break cohesion between grains, explaining why elevated levels of turbulence associated with the pneumatophores had no impact on the erosion threshold or rate.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GL109730","usgsCitation":"Deitrick, A.R., Ralston, D.K., Esposito, C.R., Baustian, M.M., Beltran Burgos, M., Courtois, A.J., and Nepf, H.M., 2024, Vegetation-generated turbulence does not impact the erosion of natural cohesive sediment: Geophysical Research Letters, v. 51, no. 14, e2024GL109730, 10 p., https://doi.org/10.1029/2024GL109730.","productDescription":"e2024GL109730, 10 p.","ipdsId":"IP-157158","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":439285,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl109730","text":"Publisher Index Page"},{"id":431220,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","city":"Port Fourchon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.21841177935528,\n 29.15\n ],\n [\n -90.27,\n 29.15\n ],\n [\n -90.27,\n 29.09003621081129\n ],\n [\n -90.21841177935528,\n 29.09003621081129\n ],\n [\n -90.21841177935528,\n 29.15\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"14","noUsgsAuthors":false,"publicationDate":"2024-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Deitrick, Autumn R.","contributorId":340204,"corporation":false,"usgs":false,"family":"Deitrick","given":"Autumn","email":"","middleInitial":"R.","affiliations":[{"id":81503,"text":"Department of Civil and Environmental Engineering, MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering, Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":906565,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ralston, David K. 0000-0002-0774-3101","orcid":"https://orcid.org/0000-0002-0774-3101","contributorId":195909,"corporation":false,"usgs":false,"family":"Ralston","given":"David","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":906566,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":906567,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baustian, Melissa Millman 0000-0003-2467-2533","orcid":"https://orcid.org/0000-0003-2467-2533","contributorId":304015,"corporation":false,"usgs":true,"family":"Baustian","given":"Melissa","email":"","middleInitial":"Millman","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":906568,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beltran Burgos, Maricel","contributorId":340206,"corporation":false,"usgs":false,"family":"Beltran Burgos","given":"Maricel","email":"","affiliations":[{"id":81504,"text":"The Water Institute","active":true,"usgs":false}],"preferred":false,"id":906569,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Courtois, Andrew J.","contributorId":340207,"corporation":false,"usgs":false,"family":"Courtois","given":"Andrew","email":"","middleInitial":"J.","affiliations":[{"id":81504,"text":"The Water Institute","active":true,"usgs":false}],"preferred":false,"id":906570,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nepf, Heidi M.","contributorId":173323,"corporation":false,"usgs":false,"family":"Nepf","given":"Heidi","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":906571,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256416,"text":"70256416 - 2024 - A conceptual framework to assess post-wildfire water quality: State of the science and knowledge gaps","interactions":[],"lastModifiedDate":"2024-08-01T14:20:43.087876","indexId":"70256416","displayToPublicDate":"2024-07-10T09:19:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"A conceptual framework to assess post-wildfire water quality: State of the science and knowledge gaps","docAbstract":"Wildfire substantially alters aquatic ecosystems by inducing moderate to catastrophic physical and chemical changes. However, the relations of environmental and watershed variables that drive those effects are complex. We present a Driver-Factor-Stressor-Effect (DFSE) conceptual framework to assess the current state of the science related to post-wildfire water-quality. We reviewed 64 peer-reviewed papers using the DFSE framework to identify drivers, factors, stressors, and effects associated with each study. A total of five drivers were identified and ranked according to their frequency of occurrence in the literature: atmospheric processes > fire characteristics > ecologic processes and characteristics > land surface characteristics > soil characteristics. Commonly reported stressors include increased nutrients, runoff, and sediment transport. Furthermore, although several different factors have been used at least once to explain water-quality effects, relatively few factors outside of precipitation and fire characteristics are frequently studied. We identified several gaps indicating the need for long-term monitoring, multi-factor studies, consideration of organic contaminants, consideration of groundwater, and inclusion of soil characteristics. This assessment expands on other reviews and meta-analyses by exploring causal linkages between influential variables and overall effects in post-wildfire watersheds. Information gathered from our assessment and the framework itself can be used to inform future monitoring plans and as a guide for modeling efforts focused on better understanding specific processes or to mitigate potential risks of post-wildfire water quality.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023WR036260","usgsCitation":"Elliott, S.M., Hornberger, M.I., Rosenberry, D.O., Frus, R., and Webb, R.M., 2024, A conceptual framework to assess post-wildfire water quality: State of the science and knowledge gaps: Water Resources Research, v. 60, no. 7, e2023WR036260, 20 p.; Data Release, https://doi.org/10.1029/2023WR036260.","productDescription":"e2023WR036260, 20 p.; Data Release","ipdsId":"IP-156361","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":439286,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023wr036260","text":"Publisher Index Page"},{"id":434931,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97JZOVY","text":"USGS data release","linkHelpText":"Annotated bibliography of 64 papers reviewed and summarized in a conceptual framework to assess post-wildfire water quality"},{"id":432026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Elliott, Sarah M. 0000-0002-1414-3024 selliott@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-3024","contributorId":1472,"corporation":false,"usgs":true,"family":"Elliott","given":"Sarah","email":"selliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":907311,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hornberger, Michelle I. 0000-0002-7787-3446 mhornber@usgs.gov","orcid":"https://orcid.org/0000-0002-7787-3446","contributorId":1037,"corporation":false,"usgs":true,"family":"Hornberger","given":"Michelle","email":"mhornber@usgs.gov","middleInitial":"I.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":907312,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":907313,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frus, Rebecca J. 0000-0002-2435-7202","orcid":"https://orcid.org/0000-0002-2435-7202","contributorId":340187,"corporation":false,"usgs":false,"family":"Frus","given":"Rebecca J.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":907314,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Webb, Richard M. 0000-0001-9531-2207 rmwebb@usgs.gov","orcid":"https://orcid.org/0000-0001-9531-2207","contributorId":1570,"corporation":false,"usgs":true,"family":"Webb","given":"Richard","email":"rmwebb@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":907315,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70257502,"text":"70257502 - 2024 - Same streams in a different forest? Investigations of forest harvest legacies and future trajectories across 30 years of stream habitat monitoring on the Tongass National Forest, Alaska","interactions":[],"lastModifiedDate":"2024-09-09T16:16:56.834676","indexId":"70257502","displayToPublicDate":"2024-07-10T09:07:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Same streams in a different forest? Investigations of forest harvest legacies and future trajectories across 30 years of stream habitat monitoring on the Tongass National Forest, Alaska","docAbstract":"The effects of timber harvest practices and climate change have altered forest ecosystems in southeast Alaska. However, quantification of patterns and trends in stream habitats associated with these forests is limited owing to a paucity of data available in remote watersheds. Here, we analyzed a 30-year dataset from southeast Alaska's Tongass National Forest to understand how these factors shape stream habitats. First, we examined differences between broad management classes (i.e., harvested and non-harvested) that have been used to guide stream channel restoration goals. Second, we assessed associations between intrinsic landscape characteristics, watershed management, and timber harvest legacies on aquatic habitat metrics. And third, we examined trends in stream habitat metrics over the duration of the dataset to anticipate future management challenges for these systems. Small effect sizes for some harvest-related predictors suggest that some stream habitat metrics, such as pool densities, are less responsive than others, and management practices such as protecting riparian buffers as well as post-harvest restoration may help conserve fish habitats. Large wood densities increased with time since harvest at sites harvested >50 years ago, indicating that multiple decades of post-harvest forest regrowth may contribute large wood to streams (possibly alder), but that it is not enough time for old-growth trees (e.g., spruce, Picea, or hemlock, Tsuga,), classified as key wood, to develop and be delivered to streams. The declining trend in key wood (i.e., the largest size class of wood) regardless of management history may reflect that pre-harvest legacy old-growth trees are declining along streams, with low replacement. The introduction of wood to maintain complex stream habitats may fill this gap until riparian stands again contribute structural key wood to streams. Trend analyses indicate an increasing spatial extent of undercut banks that may also be influenced by shifting hydrologic regimes under climate change.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0301723","usgsCitation":"Moore, M.J., Flitcroft, R., Tucker, E., Prussian, K.K., and Claeson, S.M., 2024, Same streams in a different forest? Investigations of forest harvest legacies and future trajectories across 30 years of stream habitat monitoring on the Tongass National Forest, Alaska: PLoS ONE, v. 19, no. 7, e0301723, 28 p., https://doi.org/10.1371/journal.pone.0301723.","productDescription":"e0301723, 28 p.","ipdsId":"IP-146418","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":439287,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1371/journal.pone.0301723","text":"Publisher Index Page"},{"id":433632,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Tongass National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.79598611853271,\n 35.785590501974994\n ],\n [\n -120.79598611853271,\n 35.758103908925094\n ],\n [\n -120.7544854283608,\n 35.758103908925094\n ],\n [\n -120.7544854283608,\n 35.785590501974994\n ],\n [\n -120.79598611853271,\n 35.785590501974994\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -136.02998750758678,\n 57.464783614185876\n ],\n [\n -136.02998750758678,\n 55.846134928392786\n ],\n [\n -133.47293766914498,\n 55.846134928392786\n ],\n [\n -133.47293766914498,\n 57.464783614185876\n ],\n [\n -136.02998750758678,\n 57.464783614185876\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Michael J. 0000-0002-5495-7049","orcid":"https://orcid.org/0000-0002-5495-7049","contributorId":304258,"corporation":false,"usgs":true,"family":"Moore","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910552,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flitcroft, R.","contributorId":342974,"corporation":false,"usgs":false,"family":"Flitcroft","given":"R.","email":"","affiliations":[{"id":81962,"text":"Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":910553,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tucker, E.","contributorId":342975,"corporation":false,"usgs":false,"family":"Tucker","given":"E.","email":"","affiliations":[{"id":81965,"text":"Tongass National Forest","active":true,"usgs":false}],"preferred":false,"id":910554,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prussian, K. 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Myotis ciliolabrum (western small-footed bat) is a species of insectivorous bat distributed from the Great Plains to the Pacific Coast and from British Columbia to Central Mexico. The species is spottily distributed within this wider range but is locally abundant in areas containing appropriate rocky habitat. Despite M. ciliolabrum being relatively common, little information is available about its feeding ecology. Earlier studies have suggested that skull morphology can be used to determine whether insectivorous bats select for soft- or hard-bodied prey, but we hypothesized that M. ciliolabrum may feed opportunistically on a variety of insect types. To test this hypothesis, we examined the feeding habits of western small-footed bats captured during 2000–2022 at 6 national parks and 1 national conservation area across the Southwest. Visual analysis of prey remains in guano revealed that western small-footed bats within our study area consumed insects belonging to 7 orders (Lepidoptera, Coleoptera, Hymenoptera, Hemiptera, Neuroptera, Diptera, and Odonata) and 20 families. Diet varied over time and across geographic space, though we observed a general trend in which the orders Lepidoptera, Coleoptera, and Hymenoptera were consumed at the highest volumes. This varied diet indicates opportunistic feeding in which M. ciliolabrum makes use of locally abundant resources.
","language":"English","publisher":"Brigham Young University","doi":"10.3398/064.084.0205","usgsCitation":"Loehn, A., Sparks, D.W., and Valdez, E.W., 2024, Diet of Myotis ciliolabrum from six sites in the southwestern United States: Western North American Naturalist, v. 84, no. 2, p. 210-218, https://doi.org/10.3398/064.084.0205.","productDescription":"9 p.","startPage":"210","endPage":"218","ipdsId":"IP-145936","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":465666,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, New Mexico, Texas","otherGeospatial":"Capulin Volcano National Monument, Chaco Culture National Historical Park, El Malpais National Monument and El Malpais National Conservation Area, Guadalupe Mountains National Park, Mesa Verde National Park, Pecos National Historical Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.74441287180503,\n 37.54721127749278\n ],\n [\n -108.74441287180503,\n 31.40819797728362\n ],\n [\n -102.61675201918152,\n 31.40819797728362\n ],\n [\n -102.61675201918152,\n 37.54721127749278\n ],\n [\n -108.74441287180503,\n 37.54721127749278\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"84","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Loehn, Ashley E","contributorId":347763,"corporation":false,"usgs":false,"family":"Loehn","given":"Ashley E","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":922399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sparks, Dale W.","contributorId":171601,"corporation":false,"usgs":false,"family":"Sparks","given":"Dale","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":922400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Valdez, Ernest W. 0000-0002-7262-3069 ernie@usgs.gov","orcid":"https://orcid.org/0000-0002-7262-3069","contributorId":3600,"corporation":false,"usgs":true,"family":"Valdez","given":"Ernest","email":"ernie@usgs.gov","middleInitial":"W.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":922401,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70255973,"text":"70255973 - 2024 - Decoding paleomire conditions of Paleogene superhigh-organic-sulfur coals","interactions":[],"lastModifiedDate":"2024-07-11T13:45:51.459538","indexId":"70255973","displayToPublicDate":"2024-07-10T08:38:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Decoding paleomire conditions of Paleogene superhigh-organic-sulfur coals","docAbstract":"Superhigh-organic‑sulfur (SHOS) coals (coals with organic sulfur content >4 wt%) are unique coal deposits found at a few notable locations in the world. Specific peat accumulation and preservation conditions must be met to form SHOS coals. Organic sulfur is a major constituent of such coals, and it may have various sources depending on the prevailing paleomire conditions. Understanding such paleomire conditions sheds light on the formation mechanisms of SHOS coals. This investigation decodes the paleomire conditions of the Paleogene SHOS coals from Meghalaya, India, using sulfur isotopic compositions (δ34S) of organic sulfur (δ34SOS) and pyritic sulfur (δ34SPy) along with organic petrography, pyrite morphology and trace element ratios. Thirty coal samples were collected from the Jaintia Hills in the east, Khasi Hills in the middle, and Garo Hills in the west of Meghalaya. The organic sulfur content in the Garo, Khasi, and Jaintia coals varies from 1.0 to 3.3 wt%, 1.4 to 13.8 wt%, and 1.0 to 7.2 wt%, respectively. Further, after separation from pyritic sulfur and sulfate sulfur phases, the organic sulfur content ranges from 54.4 to 69.2%, 63.8 to 79.9%, and 59.3 to 73.8%, in the Garo, Khasi, and Jaintia Hills, respectively, suggesting the SHOS nature of these coal samples. The δ34SPy varies from −29.3 ‰ to +5.7 ‰, −21.3 ‰ to +27.3 ‰, and −12.1 ‰ to −4.3 ‰, in the Jaintia, Khasi, and Garo Hills, respectively, while the δ34SOS fluctuates from −4.6 ‰ to +3.7 ‰, −9.3 ‰ to +7.8 ‰, and − 9.0 ‰ to −5.0 ‰, respectively. The δ34S values of pyrite and organic sulfur (OS) in Jaintia coals are 34S depleted compared to seawater sulfate (+22 ‰), leading to fractionations in the range of −51.3 ‰ to −16.3 ‰ (mean − 31.6 ‰) and − 26.6 ‰ to −18.3 ‰ (mean − 23.1 ‰) for pyritic and organic sulfur (OS), respectively. Pyrite in Khasi coals show a relatively heavier δ34S composition averaging at −20.5 ‰, whereas organic sulfur (OS) isotope compositions range from −31.3 ‰ to −14.2 ‰ with a mean of −22.6 ‰. Pyrite and OS in the Garo coals are depleted compared to seawater sulfate. Isotope variations in the Jaintia, Khasi, and Garo coals indicate microbial sulfate reduction (MSR) of seawater sulfate. Large isotopic fractionations between Eocene seawater sulfate and pyritic sulfur (Δ34SSO4Eocene – pyrite = up to −51.3 ‰; mean − 31.6 ‰) in Jaintia coals indicate their possible formation in the water column/near the sediment-seawater interface (open system) and also hint toward dissimilatory sulfate reduction pathways that prevailed under anoxic redox conditions. However, mean values of Δ34SSO4Eocene – pyrite (−20.5 ‰) in the Khasi coals imply pyrite formation deeper in the sediments (more closed system) under dysoxic conditions. The dominance of OS over pyritic sulfur, framboidal pyrite, and its microcrystal size distributions in Jaintia coals may suggest syngenetic pyrite formation in open water reducing/anoxic conditions under paralic environments. Elevated Sr/Ba and U/Th values in these coals further confirm the anoxic conditions. Nevertheless, the presence of euhedral pyrite with the alleviated pyrite framboids in the Khasi coals and their complete absence in the Garo coals may suggest dysoxic-suboxic and suboxic-oxic depositional conditions, respectively. The isotopic signatures of the Garo coals suggest sulfur contribution from the parent paleobiota and MSR under a freshwater-oxic environment. Insignificant fractionations between δ34SPy and δ34SOS indicate limited iron and sulfate availability for additional sulfur cycling and disproportionation reactions, typical of oxic conditions. The absence of framboidal pyrite, elevated sulfate concentration, and mean Sr/Ba and U/Th values of 0.5 and 0.3, respectively, further suggest the freshwater peat deposition in the Garo Hills under limnotelmatic to telmatic freshwater conditions. Moreover, high inertinite content (Immf = 9.77–33.16 vol%), possibly induced by atmospheric peat exposure, supports the interpretation of suboxic-oxic paleomire conditions in Garo Hills. Gradually decreasing mineral matter content from Jaintia (mean 13.6 vol%) to Garo coals (mean 7.4 vol%) additionally projects a transition from mesotrophic brackish to freshwater limnotelmatic environment, complementing the shift in the paleomire condition from eastern (Jaintia) to western (Garo) Meghalayan Hills.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2024.104559","usgsCitation":"Adsul, T., O’Beirne, M.D., Fike, D., Ghosh, S., Werne, J.P., Gilhooly, W., Hackley, P.C., Hatcherian, J.J., Philip, B., Hazra, B., Bhattachryya, S., Konar, R., and Varma, A.K., 2024, Decoding paleomire conditions of Paleogene superhigh-organic-sulfur coals: International Journal of Coal Geology, v. 290, 104559, 19 p., https://doi.org/10.1016/j.coal.2024.104559.","productDescription":"104559, 19 p.","ipdsId":"IP-158499","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":500066,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coal.2024.104559","text":"Publisher Index Page"},{"id":430956,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 90,\n 26\n ],\n [\n 90,\n 25.25\n ],\n [\n 92.55,\n 25.25\n ],\n [\n 92.55,\n 26\n ],\n [\n 90,\n 26\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"290","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Adsul, Tushar","contributorId":330815,"corporation":false,"usgs":false,"family":"Adsul","given":"Tushar","email":"","affiliations":[{"id":79028,"text":"Indian Institute of Technology (Indian School of Mines), India","active":true,"usgs":false}],"preferred":false,"id":906187,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Beirne, Molly D.","contributorId":340100,"corporation":false,"usgs":false,"family":"O’Beirne","given":"Molly","email":"","middleInitial":"D.","affiliations":[{"id":81466,"text":"Organic and Stable Isotope Biogeochemistry Laboratory, Department of Geology and Environmental Science, University of Pittsburgh, 200 Space Research Coordination Center, 4107 O'Hara Street, Pittsburgh, PA 15260, USA","active":true,"usgs":false}],"preferred":false,"id":906188,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fike, David","contributorId":340101,"corporation":false,"usgs":false,"family":"Fike","given":"David","email":"","affiliations":[{"id":81467,"text":"Department of Earth and Planetary Sciences, Washington University in St. Louis, 1 Brookings Drive, St. Louis, MO 63130-4899","active":true,"usgs":false}],"preferred":false,"id":906189,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ghosh, Santanu","contributorId":330824,"corporation":false,"usgs":false,"family":"Ghosh","given":"Santanu","email":"","affiliations":[{"id":79037,"text":"Mizoram University, India","active":true,"usgs":false}],"preferred":false,"id":906190,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Werne, Josef P.","contributorId":340102,"corporation":false,"usgs":false,"family":"Werne","given":"Josef","email":"","middleInitial":"P.","affiliations":[{"id":81466,"text":"Organic and Stable Isotope Biogeochemistry Laboratory, Department of Geology and Environmental Science, University of Pittsburgh, 200 Space Research Coordination Center, 4107 O'Hara Street, Pittsburgh, PA 15260, USA","active":true,"usgs":false}],"preferred":false,"id":906191,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gilhooly, William P. III","contributorId":340103,"corporation":false,"usgs":false,"family":"Gilhooly","given":"William P.","suffix":"III","affiliations":[{"id":81470,"text":"Department of Earth Sciences, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA","active":true,"usgs":false}],"preferred":false,"id":906192,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":906193,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hatcherian, Javin J. 0000-0001-9151-6798 jhatcherian@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-6798","contributorId":195770,"corporation":false,"usgs":true,"family":"Hatcherian","given":"Javin","email":"jhatcherian@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":906194,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Philip, Bright","contributorId":340104,"corporation":false,"usgs":false,"family":"Philip","given":"Bright","email":"","affiliations":[{"id":81471,"text":"Department of Chemistry, K J Somaiya College of Science and Commerce, Vidya Nagar, Vidyavihar, Mumbai-400077, Maharashtra, India","active":true,"usgs":false}],"preferred":false,"id":906195,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hazra, Bodhisastwa","contributorId":340105,"corporation":false,"usgs":false,"family":"Hazra","given":"Bodhisastwa","email":"","affiliations":[{"id":81472,"text":"Central Mining and Fuel Research Institute, Dhanbad, Jharkhand-826001, India","active":true,"usgs":false}],"preferred":false,"id":906196,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Bhattachryya, Sudip","contributorId":340106,"corporation":false,"usgs":false,"family":"Bhattachryya","given":"Sudip","email":"","affiliations":[{"id":81473,"text":"Specialized Coal Petrography Laboratory, Natural Energy Resources, Mission II-B, Geological Survey of India, DK-6, Sector-II, Salt Lake City, Kolkata 700091, West Bengal, India","active":true,"usgs":false}],"preferred":false,"id":906197,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Konar, Ritam","contributorId":340107,"corporation":false,"usgs":false,"family":"Konar","given":"Ritam","email":"","affiliations":[{"id":81473,"text":"Specialized Coal Petrography Laboratory, Natural Energy Resources, Mission II-B, Geological Survey of India, DK-6, Sector-II, Salt Lake City, Kolkata 700091, West Bengal, India","active":true,"usgs":false}],"preferred":false,"id":906198,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Varma, Atul Kumar","contributorId":261448,"corporation":false,"usgs":false,"family":"Varma","given":"Atul","email":"","middleInitial":"Kumar","affiliations":[],"preferred":false,"id":906199,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70256048,"text":"70256048 - 2024 - Insufficient and biased representation of species geographic responses to climate change","interactions":[],"lastModifiedDate":"2024-07-17T11:56:56.822408","indexId":"70256048","displayToPublicDate":"2024-07-10T06:53:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Insufficient and biased representation of species geographic responses to climate change","docAbstract":"The geographic redistributions of species due to a rapidly changing climate are poised to perturb ecological communities and significantly impact ecosystems and human livelihoods. Effectively managing these biological impacts requires a thorough understanding of the patterns and processes of species geographic range shifts. While substantial recent redistributions have been identified and recognized to vary by taxon, region, and range geometry, there are large gaps and biases in the available evidence. Here, we use the largest compilation of geographic range change observations to date, comprised of 33,016 potential redistributions across 12,009 species, to formally assess within- and cross-species coverage and biases and to motivate future data collection. We find that species coverage varies strongly by taxon and underrepresents species at high and low latitudes. Within species, assessments of potential redistributions came from parts of their geographic range that were highly uneven and non-representative. For most species and taxa, studies were strongly biased toward the colder parts of species' distributions and thus significantly underrepresented populations that might get pushed beyond their maximum temperature limits. Coverage of potential leading and trailing geographic range edges under a changing climate was similarly uneven. Only 8% of studied species were assessed at both high and low latitude and elevation range edges, with most only covered at one edge. This suggests that substantial within-species biases exacerbate the considerable geographic and taxonomic among-species unevenness in evidence. Our results open the door for a more quantitative accounting for existing knowledge biases in climate change ecology and a more informed management and conservation. Our findings offer guidance for future data collection that better addresses information gaps and provides a more effective foundation for managing the biological impacts of climate change.
Wave-driven erosion of marsh boundaries is a major cause of marsh loss, but little research has captured the effect of seasonal differences on marsh-edge retreat rates to illuminate temporal patterns of when the majority of this erosion is occurring. Using five surface models captured over a study year of a marsh with a steep escarped boundary in South San Francisco Bay, we find a pronounced seasonal signal, where rapid marsh retreat in the spring and summer is driven by a strong sea breeze but little change is found in the marsh-edge position in the fall and winter. We found accretion in the mudflat transition region close to the marsh boundary in the calmer seasons however, suggesting intertwined morphodynamics of mudflats and the eroding marsh-scarp. We observed large spatial heterogeneity in retreat rates within seasons, but less on longer (annual and decadal) timescales. The relationship between marsh-edge retreat rates and properties of the wave field nearby is explored and contextualized against extant relationships, but our results speak to the difficulty in addressing spatial erosion/accretion variability on short (seasonal) timescales with simple models.
In the field of natural hazards, communicating science with the public and stakeholders (i.e., interested parties) involves entering the challenging and complex world of hazard and risk communication, the ultimate purpose of which is to reduce the impact of impending hazards on people and property at risk. Hazard and risk communication are adequate if they reach people with the information that they need, at the right time, and in a form that they can use. This task appears to be particularly difficult when decisions by the public and stakeholders have to be made in the presence of uncertainty about what could happen, as is often the case with geohazards. Moreover, decision-making is complex when there are time pressures, human and economic resources are limited, and multiple sources of information need to be considered. This poses several challenges for the development of two-way and people-centered risk communication for geohazards.
The “Enabling People-Centered Risk Communication for Geohazards” Research Topic analyses these challenges and identifies innovative pathways to address them. More precisely, it draws together 13 state-of-the-art articles from around the world on improving communication practices, strategies, and understandings relating to a range of various geohazards and weather-related hazards.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2024.1444551","usgsCitation":"Amato, A., Potter, S., Scolobig, A., and Thompson, E.M., 2024, Editorial: Enabling people-centered risk communication for geohazards: Frontiers in Earth Science, v. 12, 1444551, 3 pp., https://doi.org/10.3389/feart.2024.1444551.","productDescription":"1444551, 3 pp.","ipdsId":"IP-166434","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":488704,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2024.1444551","text":"Publisher Index Page"},{"id":483951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2024-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Amato, Alessandro","contributorId":352841,"corporation":false,"usgs":false,"family":"Amato","given":"Alessandro","affiliations":[{"id":84295,"text":"National Institute of Geophysics and Volcanology","active":true,"usgs":false}],"preferred":false,"id":932201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Potter, Sally H.","contributorId":352842,"corporation":false,"usgs":false,"family":"Potter","given":"Sally H.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":932202,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scolobig, Anna","contributorId":352843,"corporation":false,"usgs":false,"family":"Scolobig","given":"Anna","affiliations":[{"id":25472,"text":"University of Geneva","active":true,"usgs":false}],"preferred":false,"id":932203,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":932204,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70258330,"text":"70258330 - 2024 - Next generation public supply water withdrawal estimation for the conterminous United States using machine learning and operational frameworks","interactions":[],"lastModifiedDate":"2024-09-11T14:29:26.678447","indexId":"70258330","displayToPublicDate":"2024-07-09T09:25:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Next generation public supply water withdrawal estimation for the conterminous United States using machine learning and operational frameworks","docAbstract":"Estimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation, and thermoelectric water withdrawal. Stewards of water resources in the USA require estimates of water withdrawals to manage and plan for future demands and sustainable water supplies. This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. The modeling approach provides service area resolution estimates to allow for annual and monthly water withdrawal estimation while incorporating a complex array of driving factors that include hydroclimatic, demographic, socioeconomic, geographic, and land use factors. Model results reveal highly variable and lognormally distributed per-capita water withdrawal, spanning from 30 to 650 gallons per capita per day (GPCD), across community, regional, and national scales, with pronounced seasonal variations. Analysis of estimated withdrawal trends indicates that the national annual average withdrawal experienced a decline at a rate of 0.58 GPCD/year during the period from 2000 to 2020. Model interpretation reveals a complex interplay between public supply withdrawal and key predictors, including population size, warm-season precipitation, counts of large buildings and houses, and areas of urban and commercial land use. The developed models can forecast future public supply driven by various climate, demographic, and socioeconomic scenarios.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023WR036632","usgsCitation":"Alzraiee, A.H., Niswonger, R.G., Luukkonen, C., Larsen, J., Martin, D., Herbert, D.M., Buchwald, C.A., Dieter, C., Miller, L.D., Stewart, J.S., Houston, N., Paulinski, S., and Kristen Valseth, 2024, Next generation public supply water withdrawal estimation for the conterminous United States using machine learning and operational frameworks: Water Resources Research, v. 60, no. 7, e2023WR036632, 25 p., https://doi.org/10.1029/2023WR036632.","productDescription":"e2023WR036632, 25 p.","ipdsId":"IP-154316","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science 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This paper describes the characteristics, habitat, population status, distribution, life history, threats, and restoration of endangered plant species of the Lower Rio Grande Valley, Texas, which includes Cameron, Willacy, Hildago, and Starr County. Seven federally listed species are considered including Ambrosia cheiranthifolia, Asclepias prostrata, Astrophytum asterias, Ayenia limitaris, Manihot walkerae, Lesquerella thamnophila (syn. Physaria thamnophila), and Thymophylla tephroleuca. An eighth species, Physostegia correllii is under consideration for federal listing by the U.S. Fish and Wildlife Service. 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IL","active":true,"usgs":false}],"preferred":false,"id":906879,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"de la Garza, Laura M.","contributorId":340316,"corporation":false,"usgs":false,"family":"de la Garza","given":"Laura","email":"","middleInitial":"M.","affiliations":[{"id":81575,"text":"U.S. Fish and Wildlife Service, Alamo, Texas","active":true,"usgs":false}],"preferred":false,"id":906880,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Feria-Arroyo, Teresa P.","contributorId":340317,"corporation":false,"usgs":false,"family":"Feria-Arroyo","given":"Teresa","email":"","middleInitial":"P.","affiliations":[{"id":81576,"text":"Department of Biology, University of Texas Rio Grande Valley, Edinburg, Texas","active":true,"usgs":false}],"preferred":false,"id":906881,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70255702,"text":"dr1195 - 2024 - Pesticide concentrations of surface water and suspended sediment in Yolo By-Pass and Cache Slough Complex, California, 2019–2021","interactions":[],"lastModifiedDate":"2026-01-27T17:31:25.509382","indexId":"dr1195","displayToPublicDate":"2024-07-09T07:40:19","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1195","displayTitle":"Pesticide Concentrations of Surface Water and Suspended Sediment in Yolo By-Pass and Cache Slough Complex, California, 2019–2021","title":"Pesticide concentrations of surface water and suspended sediment in Yolo By-Pass and Cache Slough Complex, California, 2019–2021","docAbstract":"Managed flow pulses in the north Sacramento-San Joaquin Delta are an adaptive management tool used in efforts to enhance food availability in delta smelt (Hypomesus transpacificus) habitat as part of the North Delta Food Subsidies Action. The California Department of Water Resources (DWR) monitors non-managed seasonal and local flow pulses and managed flow pulses from agricultural drainage or main stem Sacramento River water redirected through Yolo By-Pass. Augmented flow pulses are hypothesized to improve net positive flow during summer and fall in Yolo By-Pass and enhance plankton availability in delta smelt habitat in Cache Slough complex. However, flow pulses may also result in unintended negative effects of increased pesticides that are transported through Yolo By-Pass. Here, we evaluate pesticides in surface water and suspended sediment correlated with flow pulses in Yolo By-Pass during the 2019–21 calendar years.
Surface-water and suspended-sediment samples were collected by DWR personnel. Water samples were analyzed at the U.S. Geological Survey Organic Chemistry Research Laboratory in Sacramento, California, for a suite of as many as 178 current-use pesticides and pesticide degradates using gas chromatography with mass spectrometry (GC/MS), gas chromatography with tandem mass spectrometry, and liquid chromatography with tandem mass spectrometry. Suspended sediments filtered from water samples were analyzed for a suite of as many as 173 current-use pesticides and pesticide degradates.
There were 52 different current-use pesticides and pesticide degradates detected in water samples collected throughout the study. Concentrations ranged from below method detection limits to 4,070 nanograms per liter. Five different compounds in water samples were detected with concentrations above U.S. Environmental Protection Agency aquatic life benchmarks. In suspended-sediment samples collected throughout the study, eight different current-use pesticides and pesticide degradates were detected.
Total pesticide concentrations were highest at surface-water sites in the northern end of Yolo By-Pass and decreased farther downstream during the same sampling events. Total pesticide concentrations generally were higher for most surface-water sites immediately before or during the managed flow pulse in 2019 versus after the flow pulse. Finally, mean total pesticide concentrations for each surface-water site generally were higher during all of 2019 than 2021, regardless of sampling period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1195","collaboration":"Prepared in cooperation with the California Department of Water Resources","programNote":"Water Availability and Use Science Program","usgsCitation":"Uychutin, M., Orlando, J.L., Hladik, M.L., Sanders, C.J., Gross, M.S., De Parsia, M.D., LaBarbera, E.M., Twardochleb, L., and Davis, B.E., 2024, Pesticide concentrations of surface water and suspended sediment in Yolo By-Pass and Cache Slough Complex, California, 2019–2021: U.S. Geological Survey Data Report 1195, 24 p., https://doi.org/10.3133/dr1195.","productDescription":"v, 24 p.","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-139194","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":499108,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117119.htm","linkFileType":{"id":5,"text":"html"}},{"id":430682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1195/covrthb.jpg"},{"id":430683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1195/dr1195.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":430684,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1195/dr1195.xml"},{"id":430686,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1195/full"},{"id":430685,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1195/images/"}],"country":"United States","state":"California","otherGeospatial":"Yolo By-Pass and Cache Slough Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.916667,\n 38.8333\n ],\n [\n -121.916667,\n 38.1667\n ],\n [\n -121.33,\n 38.1667\n ],\n [\n -121.333,\n 38.8333\n ],\n [\n -121.916667,\n 38.8333\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
An important symbolic and subsistence animal for many Native American Tribes, the moose (Alces alces; mos in Algonquin, Penobscot language) has been under consistent threat in the northeastern United States because of winter tick (Dermacentor albipictus) parasitism over the past several decades, causing declines in moose populations throughout the region. This decline has raised concern for Tribes and agencies that are invested in moose. Given this concern, it is increasingly important to effectively monitor and develop strategies to manage winter ticks to address consistent population declines of moose due to winter ticks. The Penobscot Nation developed a novel strategy to sample questing winter ticks (i.e., ticks that are actively seeking hosts) using a plot-based sampling protocol that may be suitable for heterogeneous habitats. We deployed this protocol in the northeastern United States in 2022 during the tick questing period (Sep–Dec) on Penobscot Nation sovereign trust lands, the White Mountain National Forest and Umbagog National Wildlife Refuge, and western-central Massachusetts, USA. We analyzed the data using occupancy and N-mixture models. Detection probability peaked during mid-October and tick occupancy and abundance were greatest at sites with intermediate understory vegetation height. The sampling protocol was successful at sampling ticks in Massachusetts, where abundances were expected to be low, indicating that it may be useful for studies planning to monitor winter tick distribution and abundance in areas with sub-optimal moose habitat and where winter tick abundance is expected to be low. This approach may also benefit managers or researchers intending to monitor many species of hard ticks, and where imperfect detection is expected.
This cooperative study between the City of Durham Public Works Department, Stormwater Division and U.S. Geological Survey evaluated whether alternate monitoring strategies that incorporated samples collected across an increased range of streamflows would improve nutrient load estimates for Ellerbe and Sandy Creeks, two small, highly urbanized streams in the City of Durham, North Carolina. Water-quality and streamflow data collected between January 2009 and December 2020 were used to develop instream nutrient-load models using the U.S. Geological Survey R-LOADEST program. This study compared model results from two sampling scenarios: routine monthly (fixed frequency) sampling combined with targeted high-streamflow sampling (scenario A), and fixed frequency sampling only (scenario B).
Calibration diagnostic results were used to select the final, or most optimal, models. Most final models included seasonality terms to compensate for intra-annual variability in the data. Storm-runoff samples provided better definition at higher streamflows and improved the overall concentration versus flow relations for all constituents, except nitrate + nitrite. Uncertainties in the nutrient load estimates were lower and less variable for the scenario A tests compared to the scenario B tests.
Five time steps representing 12-, 9-, 7-, 6-, and 5-year subsets of the overall dataset were used to examine the effect of prediction period length on the computed loads and uncertainties. In focusing on the scenario A results, nutrient loads tended to be higher for the shorter time steps. These shorter time steps also produced higher errors, or uncertainty, in the load estimates compared to longer time steps. Evaluations of annual nutrient loads during 2016–20 indicated that the most consistent load estimates and tightest confidence intervals were obtained for longer 12- and 9-year time steps. Estimated loads were more variable and uncertain when based on the shorter 6- and 5-year time steps. The degree of uncertainty (standard error of prediction) in the nutrient load estimation results was influenced by sampling approach, calibration time step, and hydrologic characteristics during the model period of interest.
Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, suite 500
Norcross, GA 30093
Contact Us- USGS Publications Warehouse
","tableOfContents":"In central Virginia, the Pamunkey Indian Tribe and Reservation are facing increasingly complex water resource issues related to quantity and quality. Documentation of surface-water, groundwater, water quality, land subsidence, sea-level rise, and river ecology issues in the Pamunkey River watershed and incorporation of traditional ecological knowledge into these research topics may improve understanding of the water resources broadly. This report summarizes the relevant traditional ecological knowledge and scientific literature to elucidate gaps in the total combined knowledge of a suite of water science topics concerning the Pamunkey River watershed. This suite of water science topics includes some of the issues that the Pamunkey Indian Tribe and Reservation are facing within the watershed: fragmentation of streams and management of streamflow, flooding, degrading water quality, groundwater extraction, relative sea-level rise, rapid changes in ecosystems, loss of ecological and biological diversity, and climate change. Gaps in total combined knowledge are pervasive throughout each of these water resource topics. Identifying these knowledge gaps can help inform future research and management strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245024","collaboration":"Prepared in cooperation with The Pamunkey Indian Tribe","usgsCitation":"Foster, B.M., Lopez, R., Crawford, E.R., Cook, W., Krigsvold, J., Langston, J.H., Langston, T., Miles, G., Moore, K., Garman, G.C., Rice, K.C., and Jastram, J.D., 2024, Characterization of the water resources of the Pamunkey River watershed in Virginia—A review of water science, management, and traditional ecological knowledge: U.S. Geological Survey Scientific Investigations Report 2024–5024, 64 p., https://doi.org/10.3133/sir20245024.","productDescription":"ix, 64 p.","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-150381","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":430658,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5024/images/"},{"id":430656,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245024/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5024 HTML"},{"id":430654,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5024/coverthb.jpg"},{"id":430655,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5024/sir20245024.pdf","text":"Report","size":"32.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5024 PDF"},{"id":430657,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5024/sir20245024.XML","description":"SIR 2024-5024 XML"}],"country":"United States","state":"Virginia","otherGeospatial":"Pamunkey River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.37200097289723,\n 38.37682664861029\n ],\n [\n -78.37200097289723,\n 37.038722790200765\n ],\n [\n -76.04924547820508,\n 37.038722790200765\n ],\n [\n -76.04924547820508,\n 38.37682664861029\n ],\n [\n -78.37200097289723,\n 38.37682664861029\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Virgnia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Machine learning (ML) models are increasingly popular in environmental and hydrologic modeling, but they typically contain uncertainties resulting from noisy data (erroneous or outlier data). This paper presents a novel probabilistic approach that combines ML and Markov Chain Monte Carlo simulation to (1) detect and underweight likely noisy data, (2) develop an approach capable of detecting noisy data during model deployment, and (3) interpret the reasons why a data point is deemed noisy to help heuristically distinguish between outliers and erroneous data. The new algorithm recognizes that there is no unique way to split the training data into noisy and clean data, and thus produces an ensemble of plausible splits. The algorithm successfully detected noisy data in synthetic benchmark problems with varying complexity and a real-world public supply water withdrawal dataset. The algorithm is generic and flexible, making it suitable for application across a broad range of hydrologic and environmental disciplines.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2024.106133","usgsCitation":"Alzraiee, A.H., and Niswonger, R.G., 2024, A probabilistic approach to training machine learning models using noisy data: Environmental Modelling & Software, v. 179, 106133, 15 p., https://doi.org/10.1016/j.envsoft.2024.106133.","productDescription":"106133, 15 p.","ipdsId":"IP-151600","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":439292,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2024.106133","text":"Publisher Index Page"},{"id":433694,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"179","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Alzraiee, Ayman H. 0000-0001-7576-3449","orcid":"https://orcid.org/0000-0001-7576-3449","contributorId":272120,"corporation":false,"usgs":true,"family":"Alzraiee","given":"Ayman","email":"","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912927,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70259332,"text":"70259332 - 2024 - Summer 2024","interactions":[],"lastModifiedDate":"2024-10-04T14:40:26.857377","indexId":"70259332","displayToPublicDate":"2024-07-08T09:39:29","publicationYear":"2024","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":10554,"text":"Watermarks New England Water Science Center Newsletter","active":true,"publicationSubtype":{"id":30}},"title":"Summer 2024","docAbstract":"No abstract available.
","language":"English","publisher":"New England Water Science Center","usgsCitation":"Rossos, K., 2024, Summer 2024: Watermarks New England Water Science Center Newsletter, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-167734","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":462600,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/watermarks-new-england-wsc-newsletters/watermarks-newsletter-summer-2024","linkFileType":{"id":5,"text":"html"}},{"id":462601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rossos, Katrina 0000-0002-3819-4344","orcid":"https://orcid.org/0000-0002-3819-4344","contributorId":331723,"corporation":false,"usgs":true,"family":"Rossos","given":"Katrina","email":"","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914963,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70255904,"text":"70255904 - 2024 - Perfluorooctane sulfonamide induced autotoxic effects on the zebrafish immune system","interactions":[],"lastModifiedDate":"2024-08-13T14:37:34.726179","indexId":"70255904","displayToPublicDate":"2024-07-08T06:58:03","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Perfluorooctane sulfonamide induced autotoxic effects on the zebrafish immune system","docAbstract":"Perfluorooctane sulfonamide (PFOSA) is an immediate perfluorooctanesulfonate (PFOS) precursor (PreFOS). Previous studies have shown PFOSA to induce stronger toxic responses compared to other perfluorinated compounds (PFCs). However, the specific nature of PFOSA-induced toxicity, whether autonomous or mediated by its metabolite PFOS, has not been fully elucidated. This study systematically investigates the immunomodulatory effects of PFOSA and PFOS in zebrafish (Danio rerio). Exposure to PFOSA compromised the zebrafish’s ability to defend against pathogenic infections, as evidenced by increased bacterial adhesion to their skin and reduced levels of the biocidal protein lysozyme (LYSO). Moreover, PFOSA exposure was associated with disruptions in inflammatory markers and immune indicators, along with a decrease in immune cell counts. The findings from this study suggest that the immunotoxicity effects of PFOSA are primarily due to its own toxicity rather than its metabolite PFOS. This conclusion was supported by dose-dependent responses, the severity of observed effects, and multivariate analysis. In addition, our experiments using NF-κB-morpholino knock-down techniques further confirmed the role of the Nuclear factor-κappa B pathway in mediating PFOSA-induced immunotoxicity. In conclusion, this study reveals that PFOSA impairs the immune system in zebrafish through an autotoxic mechanism, providing valuable insights for assessing the ecological risks of PFOSA.
The low-carbon energy transition requires significant increases in production for many mineral commodities. Understanding demand, technological requirements, and prices associated with this production increase requires understanding the supply chain dynamics of many minerals simultaneously, and via a consistent framework. A generalized economics-informed material flow method, global materials modeling using Bayesian optimization, captures the market dynamics of key mineral commodities. The method relies only on a limited set of widely available historical data as input, enabling quantification of economic relationships (elasticities) for supply chain components where data are sparse, and relationships cannot be obtained via traditional statistical approaches. Building upon established material flow analysis (MFA) and economic modeling techniques, Bayesian optimization was applied to fit an economics-informed MFA model to global historical demand, supply, and price for aluminum, copper, gold, lead, nickel, silver, iron, tin, and zinc. This approach enables estimates for the evolution of ore grades, mine costs, refining charges, sector-specific demand, and scrap collection for each commodity. Economic relationships were quantified and compared with a database compiled from the literature, including 1333 values from 213 analyses across 65 publications. Discrepancies in methods and limited coverage make use of these parameters in modeling efforts difficult. This work provides a single, homogeneous, probabilistic approach to identifying economic relationships across mineral supply chains, with uncertainty quantification, a literature database for comparison, and a modeling framework in which to use them. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.
","language":"English","publisher":"Wiley","doi":"10.1111/jiec.13517","usgsCitation":"Ryter, J.W., Bhuwalka, K., O’Rourke, M., Montanelli, L., Cohen-Tanugi, D., Roth, R., and Olivetti, E., 2024, Understanding key mineral supply chain dynamics using economics-informed material flow analysis and Bayesian optimization: Journal of Industrial Ecology, v. 28, no. 4, p. 709-726, https://doi.org/10.1111/jiec.13517.","productDescription":"18 p.","startPage":"709","endPage":"726","ipdsId":"IP-157688","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":466986,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jiec.13517","text":"Publisher Index Page"},{"id":463085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Ryter, John W. 0000-0002-0343-7553","orcid":"https://orcid.org/0000-0002-0343-7553","contributorId":345416,"corporation":false,"usgs":true,"family":"Ryter","given":"John","middleInitial":"W.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":916487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bhuwalka, Karan 0000-0002-1963-6717","orcid":"https://orcid.org/0000-0002-1963-6717","contributorId":345417,"corporation":false,"usgs":false,"family":"Bhuwalka","given":"Karan","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Rourke, Michelena","contributorId":345418,"corporation":false,"usgs":false,"family":"O’Rourke","given":"Michelena","email":"","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":916489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Montanelli, Luca 0000-0002-7784-7627","orcid":"https://orcid.org/0000-0002-7784-7627","contributorId":345419,"corporation":false,"usgs":false,"family":"Montanelli","given":"Luca","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cohen-Tanugi, David 0000-0003-2488-4819","orcid":"https://orcid.org/0000-0003-2488-4819","contributorId":345420,"corporation":false,"usgs":false,"family":"Cohen-Tanugi","given":"David","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916491,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roth, Richard","contributorId":257597,"corporation":false,"usgs":false,"family":"Roth","given":"Richard","affiliations":[{"id":52064,"text":"Materials Systems Lab, MIT","active":true,"usgs":false}],"preferred":false,"id":916492,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Olivetti, Elsa 0009-0005-4190-8319","orcid":"https://orcid.org/0009-0005-4190-8319","contributorId":345421,"corporation":false,"usgs":false,"family":"Olivetti","given":"Elsa","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916493,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70257506,"text":"70257506 - 2024 - Integrating ecological value and charismatic species habitats to prioritize habitats for conservation: A case study from Greater Yellowstone","interactions":[],"lastModifiedDate":"2024-09-10T11:27:55.620233","indexId":"70257506","displayToPublicDate":"2024-07-08T06:24:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Integrating ecological value and charismatic species habitats to prioritize habitats for conservation: A case study from Greater Yellowstone","docAbstract":"Expanding human pressure has reduced natural habitats globally and motivated strategies to conserve remaining natural habitats. Decisions about conservation on private lands, however, are typically made by local stakeholders who are motivated by the elements of nature they most highly value. Thus, national prioritization for conservation should be complemented by local analysis of species or habitats that most influence local landowner decisions. We demonstrate within the Greater Yellowstone Ecosystem how quantitative mapping of wildlife species that are highly valued by local residents can be integrated with indices of ecosystem integrity to prioritize private lands for conservation. We found that natural vegetation cover (NVC) comprised 81% of the private lands. Some watersheds have lost 6% of NVC since 2001 and developed lands now cover >40% of their areas. Locations high in ecological value, elk habitat, and grizzly habitat occurred in different biophysical settings. Consequently, only 2% of the NVC supports high levels of all three biodiversity measures and 26% of this area was within conservation easements. The remaining areas of high biodiversity value that are unprotected are priorities for conservation. We suggest that national-scale conservation planning will be most effective on private lands if additional within-ecoregion analyses are done on the elements of biodiversity that are most valued by local people.
Northern peatlands are a globally significant source of methane (CH4), and emissions are projected to increase due to warming and permafrost loss. Understanding the microbial mechanisms behind patterns in CH4 production in these systems will be key to predicting annual emissions changes, with stable carbon isotopes (δ13C-CH4) being a powerful tool for characterizing these drivers. Given that δ13C signatures of CH4 are used in top-down atmospheric inversion models to partition sources, our ability to model CH4 production pathways and associated δ13C-CH4 signatures in peatland types impacted by a changing climate is critical. We sought to characterize the role of environmental conditions, including both hydrologic and vegetation patterns associated with permafrost thaw, on δ13C-CH4 signatures from a diverse set of high-latitude peatlands. We measured porewater and emitted CH4 stable isotopes, pH, and vegetation composition from five boreal-Arctic peatlands. Porewater δ13C-CH4 was strongly associated with peatland type, with δ13C enriched values obtained from more minerotrophic fens (-61.2 ± 9.1‰) compared to permafrost-free bogs (-74.1 ± 9.4‰) and raised permafrost bogs (-81.6 ± 11.5‰). Variation in porewater δ13C-CH4 was best explained by sedge cover, CH4 concentration, and the interactive effect of peatland type and pH (r2 = 0.50, p < 0.001). Emitted δ13C-CH4 varied greatly but was positively correlated with porewater δ13C-CH4, suggesting that porewater data can be used to predict changing emissions signatures from these systems. We calculated a weighted mean mixed atmospheric CH4 signature for northern peatlands of -65.3 ± 7‰ and show that this signature is more sensitive to landscape drying (4 to 10 % depletion in δ13C) than wetting (1.5 to 5% enrichment in δ13C) under permafrost thaw scenarios. Our results suggest northern peatland δ13C-CH4 signatures are likely to shift in the future which has important implications for source partitioning in atmospheric inversion models.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007837","usgsCitation":"Kuhn, M.A., Varner, R.K., McCalley, C.K., Perryman, C.R., Aurela, M., Burke, S.A., Chanton, J., Crill, P., DelGreco, J., Deng, J., Heffernan, L., Herrick, C., Hodgkins, S.B., Jones, C.P., Juutinen, S., Kane, E., Lamit, L.J., Larmola, T., Lilleskov, E., Olefeldt, D., Palace, M.W., Rich, V.I., Schulze, C., Shorter, J.H., Sullivan, F., Sonnentag, O., Turetsky, M., and Waldrop, M., 2024, Controls on stable methane isotope signatures in northern peatlands and potential shifts in signatures under permafrost thaw scenarios: Journal of Geophysical Research: Biogeosciences, v. 129, no. 7, e2023JG007837, 17 p., https://doi.org/10.1029/2023JG007837.","productDescription":"e2023JG007837, 17 p.","ipdsId":"IP-162425","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":466987,"rank":2,"type":{"id":40,"text":"Open 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One potential source is oil and gas wastewater (i.e., produced water or brine), which naturally has high total dissolved solids (TDS) concentrations, that can also be enriched in Li (>100 mg/L). Understanding the sources and mechanisms responsible for high naturally-occurring Li concentrations can aid in efficient targeting of these brines. The isotopic composition (δ7Li, δ11B, δ138Ba) of produced water and core samples from the Utica Shale and Point Pleasant Formation (UPP) in the Appalachian Basin, USA indicates that depth-dependent thermal maturity and water-rock interaction, including diagenetic clay mineral transformations, likely control Li concentrations. A survey of Li content in produced waters throughout the USA indicates that Appalachian Basin brines from the Marcellus Shale to the UPP have the potential for economic resource recovery.