Repeated sampling of spatially distributed river chemistry can be used to assess the location, scale, and persistence of carbon and nutrient contributions to watershed exports. Here, we provide a comprehensive set of water chemistry measurements and ecohydrological metrics describing the biogeochemical conditions of permafrost-affected Arctic watersheds. These data were collected in watershed-wide synoptic campaigns in six stream networks across northern Alaska. Three watersheds are associated with the Arctic Long-Term Ecological Research site at Toolik Field Station (TFS), which were sampled seasonally each June and August from 2016 to 2018. Three watersheds were associated with the National Park Service (NPS) of Alaska and the U.S. Geological Survey (USGS) and were sampled annually from 2015 to 2019. Extensive water chemistry characterization included carbon species, dissolved nutrients, and major ions. The objective of the sampling designs and data acquisition was to characterize terrestrial–aquatic linkages and processing of material in stream networks. The data allow estimation of novel ecohydrological metrics that describe the dominant location, scale, and overall persistence of ecosystem processes in continuous permafrost. These metrics are (1) subcatchment leverage, (2) variance collapse, and (3) spatial persistence. Raw data are available at the National Park Service Integrated Resource Management Applications portal (O'Donnell et al., 2021, https://doi.org/10.5066/P9SBK2DZ) and within the Environmental Data Initiative (Abbott, 2021, https://doi.org/10.6073/pasta/258a44fb9055163dd4dd4371b9dce945).
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-14-95-2022","usgsCitation":"Shogren, A., Zarnetske, J.P., Abbott, B., Bratsman, S.P., Brown, B.C., Carey, M.P., Fulweiber, R., Greaves, H., Haines, E., Iannucci, F., Koch, J.C., Medvedeff, A., O’Donnell, J.A., Patch, L., Poulin, B., Williamson, T.J., and Bowden, W.B., 2022, Multi-year, spatially extensive, watershed-scale synoptic stream chemistry and water quality conditions for six permafrost-underlain Arctic watersheds: Earth System Science Data, v. 14, p. 95-116, https://doi.org/10.5194/essd-14-95-2022.","productDescription":"22 p.","startPage":"95","endPage":"116","ipdsId":"IP-127222","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":491327,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SBK2DZ","text":"USGS data release","linkHelpText":"Stream and River Chemistry in Watersheds of Northwestern Alaska, 2015-2019"},{"id":449165,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-14-95-2022","text":"Publisher Index Page"},{"id":408641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -150,\n 69\n ],\n [\n -150,\n 68\n ],\n [\n -148.75,\n 68\n ],\n [\n -148.75,\n 69\n ],\n [\n -150,\n 69\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -163,\n 66.75\n ],\n [\n -157,\n 66.75\n ],\n [\n -157,\n 68\n ],\n [\n -163,\n 68\n ],\n [\n -163,\n 66.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2022-01-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Shogren, Arial","contributorId":298443,"corporation":false,"usgs":false,"family":"Shogren","given":"Arial","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":855623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zarnetske, Jay P.","contributorId":210073,"corporation":false,"usgs":false,"family":"Zarnetske","given":"Jay","email":"","middleInitial":"P.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":855624,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Abbott, Benjamin 0000-0001-5861-3481","orcid":"https://orcid.org/0000-0001-5861-3481","contributorId":215170,"corporation":false,"usgs":false,"family":"Abbott","given":"Benjamin","email":"","affiliations":[{"id":39191,"text":"Bringham Young Unviersity","active":true,"usgs":false}],"preferred":false,"id":855625,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bratsman, Samuel P.","contributorId":247668,"corporation":false,"usgs":false,"family":"Bratsman","given":"Samuel","email":"","middleInitial":"P.","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":855626,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Brian C.","contributorId":257319,"corporation":false,"usgs":false,"family":"Brown","given":"Brian","email":"","middleInitial":"C.","affiliations":[{"id":48387,"text":"BYU","active":true,"usgs":false}],"preferred":false,"id":855627,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carey, Michael P. 0000-0002-3327-8995 mcarey@usgs.gov","orcid":"https://orcid.org/0000-0002-3327-8995","contributorId":5397,"corporation":false,"usgs":true,"family":"Carey","given":"Michael","email":"mcarey@usgs.gov","middleInitial":"P.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":855628,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fulweiber, Randy","contributorId":298445,"corporation":false,"usgs":false,"family":"Fulweiber","given":"Randy","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":855629,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Greaves, Heather","contributorId":298447,"corporation":false,"usgs":false,"family":"Greaves","given":"Heather","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":855630,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Haines, Emma","contributorId":298448,"corporation":false,"usgs":false,"family":"Haines","given":"Emma","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":855631,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Iannucci, Frances","contributorId":298450,"corporation":false,"usgs":false,"family":"Iannucci","given":"Frances","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":855632,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":855633,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Medvedeff, Alex","contributorId":298453,"corporation":false,"usgs":false,"family":"Medvedeff","given":"Alex","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":855634,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"O’Donnell, Jonathan A. 0000-0001-7031-9808","orcid":"https://orcid.org/0000-0001-7031-9808","contributorId":191423,"corporation":false,"usgs":false,"family":"O’Donnell","given":"Jonathan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":855635,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Patch, Leika","contributorId":298455,"corporation":false,"usgs":false,"family":"Patch","given":"Leika","email":"","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":855636,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Poulin, Brett 0000-0002-5555-7733","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":260893,"corporation":false,"usgs":false,"family":"Poulin","given":"Brett","affiliations":[{"id":52706,"text":"Department of Environmental Toxicology, University of California Davis, Davis, CA 95616, USA","active":true,"usgs":false}],"preferred":false,"id":855637,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Williamson, Tanner J.","contributorId":223165,"corporation":false,"usgs":false,"family":"Williamson","given":"Tanner","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":855638,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Bowden, William B.","contributorId":169388,"corporation":false,"usgs":false,"family":"Bowden","given":"William","email":"","middleInitial":"B.","affiliations":[{"id":6735,"text":"University of Vermont, Rubenstein School of Environment and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":855639,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70227459,"text":"70227459 - 2022 - Response to comment on “Evidence of humans in North America during the Last Glacial Maximum”","interactions":[],"lastModifiedDate":"2022-01-18T13:25:51.653562","indexId":"70227459","displayToPublicDate":"2022-01-14T07:24:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Response to comment on “Evidence of humans in North America during the Last Glacial Maximum”","docAbstract":"Population projection models are important tools for conservation and management. They are often used for population status assessments, for threat analyses, and to predict the consequences of conservation actions. Although conservation decisions should be informed by science, critical decisions are often made with very little information to support decision-making. Conversely, postponing decisions until better information is available may reduce the benefit of a conservation decision. When empirical data are limited or lacking, expert elicitation can be used to supplement existing data and inform model parameter estimates. The use of rigorous techniques for expert elicitation that account for uncertainty can improve the quality of the expert elicited values and therefore the accuracy of the projection models. One recurring challenge for summarizing expert elicited values is how to aggregate them. Here, we illustrate a process for population status assessment using a combination of expert elicitation and data from the ecological literature. We discuss the importance of considering various aggregation techniques, and illustrate this process using matrix population models for the wood turtle (Glyptemys insculpta) to assist U.S. Fish and Wildlife Service decision-makers with their Species Status Assessment. We compare estimates of population growth using data from the ecological literature and four alternative aggregation techniques for the expert-elicited values. The estimate of population growth rate based on estimates from the literature (λmean = 0.952, 95% CI: 0.87–1.01) could not be used to unequivocally reject the hypotheses of a rapidly declining population nor the hypothesis of a stable, or even slightly growing population, whereas our results for the expert-elicited estimates supported the hypothesis that the wood turtle population will decline over time. Our results showed that the aggregation techniques used had an impact on model estimates, suggesting that the choice of techniques should be carefully considered. We discuss the benefits and limitations associated with each method and their relevance to the population status assessment. We note a difference in the temporal scope or inference between the literature-based estimates that provided insights about historical changes, whereas the expert-based estimates were forward looking. Therefore, conducting an expert-elicitation in addition to using parameter estimates from the literature improved our understanding of our species of interest.
Models help decision-makers anticipate the consequences of policies for ecosystems and people; for instance, improving our ability to represent interactions between human activities and ecological systems is essential to identify pathways to meet the 2030 Sustainable Development Goals. However, use of modeling outputs in decision-making remains uncommon. We share insights from a multidisciplinary National Socio-Environmental Synthesis Center working group on technical, communication, and process-related factors that facilitate or hamper uptake of model results. We emphasize that it is not simply technical model improvements, but active and iterative stakeholder involvement that can lead to more impactful outcomes. In particular, trust- and relationship-building with decision-makers are key for knowledge-based decision making. In this respect, nurturing knowledge exchange on the interpersonal (e.g., through participatory processes), and institutional level (e.g., through science-policy interfaces across scales), represent promising approaches. To this end, we offer a generalized approach for linking modeling and decision-making.
Dry conditions during 2020 and 2021 affected the water supply within the Souris River Basin and highlighted the need for better understanding of the streamflow dynamics for managing the resource during low-flow conditions. In June 2021, a loss of streamflow was observed on the Souris River between U.S. Geological Survey streamgages on the Souris River near Foxholm, North Dakota (site 1), and near Verendrye, N. Dak. (site 22). The largest loss was upstream from the Souris River above Minot, N. Dak. (site 7). On June 6, 2021, the daily mean streamflow decreased from 33.8 cubic feet per second at site 1 to 16.3 cubic feet per second at site 7, a loss of 17.5 cubic feet per second. To better understand where streamflow losses occurred in the reach from site 1 to site 22, multiple sites were selected for streamflow measurements between the three streamgages (sites 1, 7, and 22). Streamflow measurements made at 22 selected sites on the Souris River on August 31 and September 1, 2021, did not indicate the loss in streamflow that was observed at the three streamgages (sites 1, 7, and 22) in June 2021. Measurements made at the three streamgages (sites 1, 7, and 22) on August 31 had streamflows of 44.2, 45.9, and 46.8 cubic feet per second, respectively. Streamflow measured at all 22 sites on August 31 and September 1 on the Souris River ranged from 38.4 (site 9) to 49.8 cubic feet per second (site 12). In general, the largest change in streamflow was measured among sites on the Souris River in or near the city of Minot, N. Dak.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221005","collaboration":"Prepared in cooperation with the North Dakota Department of Water Resources, the U.S. Fish and Wildlife Service, and the U.S. Army Corps of Engineers, St. Paul District","usgsCitation":"Galloway, J.M., and Hanson, B.R., 2022, Measurements of streamflow gain and loss on the Souris River between Lake Darling and Verendrye, North Dakota, August 31 and September 1, 2021: U.S. Geological Survey Open-File Report 2022–1005, 10 p., https://doi.org/10.3133/ofr20221005.","productDescription":"Report: vi, 10 p.; Dataset","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-135605","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":394315,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1005/coverthb.jpg"},{"id":394317,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":394316,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1005/ofr20221005.pdf","text":"Report","size":"1.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1005"},{"id":501538,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112123.htm","linkFileType":{"id":5,"text":"html"}},{"id":394329,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1005/images","description":"OFR 2022–1005 images"},{"id":394328,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1005/ofr20221005.XML","description":"OFR 2022–1005 XML"}],"country":"United States","state":"North Dakota","otherGeospatial":"Souris River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.6667,\n 48\n ],\n [\n -100.5,\n 48\n ],\n [\n -100.5,\n 48.50\n ],\n [\n -101.6667,\n 48.50\n ],\n [\n -101.6667,\n 48\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
We present a new field measurement and numerical interpretation method (combined termed ‘test’) to parameterize the diffusion of trichloroethene (TCE) and its biodegradation products (DPs) from the matrix of sedimentary rock. The method uses a dual-packer system to interrogate a low-permeability section of the rock matrix adjacent to a previously contaminated borehole and uses the borehole monitoring history to establish the pre-test condition. TCE and its DPs are removed from the groundwater between the packers at the onset of the testing. The parameters estimated by fitting a radial diffusion model to the concentration history and borehole concentration data, also termed back-diffusion, are the tortuosity factor and sorption coefficients of TCE and DPs in the rock matrix and the TCE and DP biodegradation rate coefficients in the borehole. We demonstrate the equipment design and the interpretive method using a borehole accessing the grey mudstone at a TCE contaminated site in the Newark Basin. In this test, both nonreactive (bromide) and reactive (trichlorofluoroethene) tracers are used to constrain the estimated parameters; however, the bromide tracer was not needed to estimate the parameters in this test. The parameters estimated from the field test are consistent with values measured independently in laboratory experiments using field samples of similar lithology. From the interpretation, we compute the TCE and DP concentration distributions in the rock matrix prior to the test to illustrate how the results can be used to enhance understanding of contaminant distribution in the rock matrix.
","language":"English","publisher":"National Ground Water Association","doi":"10.1111/gwmr.12495","usgsCitation":"Allen-King, R.M., Kiekhaefer, R.L., Goode, D.J., Hsieh, P.A., Lorah, M.M., and Imbrigiotta, T.E., 2022, A borehole test for chlorinated solvent diffusion and degradation rates in sedimentary rock: Groundwater Monitoring and Remediation, v. 42, no. 2, p. 23-34, https://doi.org/10.1111/gwmr.12495.","productDescription":"12 p.","startPage":"23","endPage":"34","ipdsId":"IP-122580","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":394652,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":394804,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99I50JE","text":"USGS data release","linkHelpText":"A finite-difference algorithm used to simulate radial diffusion, adsorption, and reactions of chlorinated ethenes in porous media"}],"volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Allen-King, Richelle M. 0000-0001-5559-1213","orcid":"https://orcid.org/0000-0001-5559-1213","contributorId":272047,"corporation":false,"usgs":false,"family":"Allen-King","given":"Richelle","email":"","middleInitial":"M.","affiliations":[{"id":56340,"text":"University at Buffalo, SUNY","active":true,"usgs":false}],"preferred":false,"id":831399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiekhaefer, Rebecca L.","contributorId":272048,"corporation":false,"usgs":false,"family":"Kiekhaefer","given":"Rebecca","email":"","middleInitial":"L.","affiliations":[{"id":56340,"text":"University at Buffalo, SUNY","active":true,"usgs":false}],"preferred":false,"id":831400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goode, Daniel J. 0000-0002-8527-2456","orcid":"https://orcid.org/0000-0002-8527-2456","contributorId":216750,"corporation":false,"usgs":true,"family":"Goode","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hsieh, Paul A. 0000-0003-4873-4874 pahsieh@usgs.gov","orcid":"https://orcid.org/0000-0003-4873-4874","contributorId":1634,"corporation":false,"usgs":true,"family":"Hsieh","given":"Paul","email":"pahsieh@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":39113,"text":"WMA - Office of Quality Assurance","active":true,"usgs":true}],"preferred":true,"id":831402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lorah, Michelle M. 0000-0002-9236-587X","orcid":"https://orcid.org/0000-0002-9236-587X","contributorId":224040,"corporation":false,"usgs":true,"family":"Lorah","given":"Michelle","middleInitial":"M.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831403,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Imbrigiotta, Thomas E. 0000-0003-1716-4768 timbrig@usgs.gov","orcid":"https://orcid.org/0000-0003-1716-4768","contributorId":152114,"corporation":false,"usgs":true,"family":"Imbrigiotta","given":"Thomas","email":"timbrig@usgs.gov","middleInitial":"E.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831404,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259596,"text":"70259596 - 2022 - Microcontinent breakup and links to possible plate boundary reorganization in the northern Gulf of California, México","interactions":[],"lastModifiedDate":"2024-10-16T12:07:00.668741","indexId":"70259596","displayToPublicDate":"2022-01-13T07:05:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Microcontinent breakup and links to possible plate boundary reorganization in the northern Gulf of California, México","docAbstract":"Faults on microcontinents record the dynamic evolution of plate boundaries. However, most microcontinents are submarine and difficult to study. Here, we show that the southern part of the Isla Ángel de la Guarda (IAG) microcontinent, in the northern Gulf of California rift, is densely faulted by a late Quaternary-active normal fault zone. To characterize the onshore kinematics of this Almeja fault zone, we integrated remote fault mapping using high-resolution satellite- and drone-based topography with neotectonic field-mapping. We produced 13 luminescence ages from sediment deposits offset or impounded by faults to constrain the timing of fault offsets. We found that north-striking normal faults in the Almeja fault zone continue offshore to the south and likely into the nascent North Salsipuedes basin southwest of IAG. Late Pleistocene and Holocene luminescence ages indicate that the most recent onshore fault activity occurred in the last ∼50 kyr. These observations suggest that the North Salsipuedes basin is kinematically linked with and continues onshore as the active Almeja fault zone. We suggest that fragmentation of the evolving IAG microcontinent may not yet be complete and that the Pacific-North America plate boundary is either not fully localized onto the Ballenas transform fault and Lower Delfin pull-apart basin or is in the initial stage of a plate boundary reorganization.
Individual variation in behavior, particularly consistent among-individual differences (i.e., personality), has important ecological and evolutionary implications for population and community dynamics, trait divergence, and patterns of speciation. Nevertheless, individual variation in spatial behaviors, such as home range behavior, movement characteristics, or habitat use has yet to be incorporated into the concepts or methodologies of ecology and evolutionary biology. To evaluate evidence for the existence of consistent among-individual differences in spatial behavior – which we refer to as “spatial personality” – we performed a meta-analysis of 200 repeatability estimates of home range size, movement metrics, and habitat use. We found that the existence of spatial personality is a general phenomenon, with consistently high repeatability (r) across classes of spatial behavior (r = 0.67–0.82), taxa (r = 0.31–0.79), and time between repeated measurements (r = 0.54–0.74). These results suggest: 1) repeatable spatial behavior may either be a cause or consequence of the environment experienced and lead to spatial personalities that may limit the ability of individuals to behaviorally adapt to changing landscapes; 2) interactions between spatial phenotypes and environmental conditions could result in differential reproduction, survival, and dispersal, suggesting that among-individual variation may facilitate population-level adaptation; 3) spatial patterns of species' distributions and spatial population dynamics may be better understood by shifting from a mean field analytical approach towards methods that account for spatial personalities and their associated fitness and ecological dynamics.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/beheco/arab147","usgsCitation":"Stuber, E.F., Carlson, B., and Jesmer, B., 2022, Spatial personalities: A meta-analysis of consistent individual differences in spatial behavior: Behavioral Ecology, v. 33, no. 3, p. 477-486, https://doi.org/10.1093/beheco/arab147.","productDescription":"10 p.","startPage":"477","endPage":"486","ipdsId":"IP-130986","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":449182,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/beheco/arab147","text":"Publisher Index Page"},{"id":429855,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Stuber, Erica Francis 0000-0002-2687-6874","orcid":"https://orcid.org/0000-0002-2687-6874","contributorId":298084,"corporation":false,"usgs":true,"family":"Stuber","given":"Erica","email":"","middleInitial":"Francis","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":902619,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlson, Ben","contributorId":337694,"corporation":false,"usgs":false,"family":"Carlson","given":"Ben","affiliations":[{"id":61502,"text":"yu","active":true,"usgs":false}],"preferred":false,"id":902620,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jesmer, Brett","contributorId":337695,"corporation":false,"usgs":false,"family":"Jesmer","given":"Brett","affiliations":[{"id":61502,"text":"yu","active":true,"usgs":false}],"preferred":false,"id":902621,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266212,"text":"70266212 - 2022 - Non-target effects of herbicides on the Zerene silverspot butterfly, a surrogate subspecies for the threatened Oregon silverspot butterfly","interactions":[],"lastModifiedDate":"2025-05-01T13:25:50.118613","indexId":"70266212","displayToPublicDate":"2022-01-13T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2356,"text":"Journal of Insect Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Non-target effects of herbicides on the Zerene silverspot butterfly, a surrogate subspecies for the threatened Oregon silverspot butterfly","docAbstract":"Herbicides are used as management tools to improve habitat for native plants and animals, but their application may also have harmful effects on the native community. The federally threatened Oregon silverspot butterfly (Speyeria = Argynnis zerene hippolyta) resides in remnant native grasslands along the Pacific Northwest coast. However, like many grasslands, many of these areas have high incidences of invasive plants, such as false dandelion (Hypochaeris radicata) and velvet grass (Holcus lanatus). These and other invasive plants severely limit the abundance of the Oregon silverspot’s larval host plant, the early blue violet (Viola adunca). Selective herbicides, such as clopyralid and fluazifop-P-butyl, can reduce invasive plant abundance. However, non-target effects of these herbicides, and of adjuvants applied with these herbicides, on Oregon silverspots are unknown. In our study, we applied herbicides and adjuvants to host plants and Zerene silverspot (S. z. zerene) larvae, a subspecies closely related to Oregon silverspots. Responses in silverspot larvae measured in two experiments included survival, sex ratio, development time, mass, morphology, fecundity, and behavior. Our results suggest that negative effects of herbicides, clopyralid and fluazifop-P-butyl, and adjuvants, Agri-Dex® and Nu-Film®-IR, are limited. However, we detected weak effects from clopyralid and fluazifop-P-butyl with and without Agri-Dex® on larval and pupal development time and pupal mass.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10841-021-00355-2","usgsCitation":"Doll, C., Converse, S.J., and Schultz, C., 2022, Non-target effects of herbicides on the Zerene silverspot butterfly, a surrogate subspecies for the threatened Oregon silverspot butterfly: Journal of Insect Conservation, v. 26, p. 1-15, https://doi.org/10.1007/s10841-021-00355-2.","productDescription":"15 p.","startPage":"1","endPage":"15","ipdsId":"IP-129351","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485212,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","noUsgsAuthors":false,"publicationDate":"2022-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Doll, Cassandra F.","contributorId":354012,"corporation":false,"usgs":false,"family":"Doll","given":"Cassandra F.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":934950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":934949,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schultz, Cheryl B.","contributorId":354013,"corporation":false,"usgs":false,"family":"Schultz","given":"Cheryl B.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":934951,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227867,"text":"70227867 - 2022 - Quantifying regional effects of best management practices on nutrient losses from agricultural lands","interactions":[],"lastModifiedDate":"2022-02-01T18:12:27.328917","indexId":"70227867","displayToPublicDate":"2022-01-12T13:12:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying regional effects of best management practices on nutrient losses from agricultural lands","docAbstract":"Nitrogen (N) and phosphorus (P) losses from agricultural areas have degraded the water quality of downstream rivers, lakes, and oceans. As a result, investment in the adoption of agricultural best management practices (BMPs) has grown, but assessments of their effectiveness at large spatial scales have lagged. This study applies regional Spatially Referenced Regression On Watershed-attributes (SPARROW) models developed for the Midwest, Northeast, and Southeast United States to quantify potential regional effects of BMPs on nutrient losses from agricultural lands. These models were used because they account for specific BMPs in the prediction of instream nutrient loads. The BMPs included in the models were cover crops, no-till, and conservation tillage. Sensitivity testing for the BMPs on agricultural nutrient loads was done using simulations that varied the intensity of BMPs specified in each region. When the BMP intensity was increased 50% relative to the 2012 intensity, the predicted agricultural load of total P decreased across all regions (4% to 14%). The predicted reduction in average P yields in the Midwest, Northeast, and Southeast was 706, 544, and 26 kg km–2, respectively. Increasing BMPs by 50% decreased predicted agricultural total N loads by 3.5% in the Southeast but increased predicted N loads in the Midwest and Northeast by 4.7% and 1.8%, respectively. Model-predicted average N yields increased by 402 kg km–2 and 302 kg km–2 in the Midwest and Northeast, respectively, and decreased in the Southeast by 329 kg km–2. In model simulations, cover crops were more effective at reducing N and P loads than the tillage BMPs despite lower intensity of implementation in 2012. However, at the regional scale of this investigation, implementation of BMPs result in only moderate predicted effects on agricultural nutrient loads
","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.2022.00162","usgsCitation":"Roland, V.L., Garcia, A.M., Saad, D.A., Ator, S., Robertson, D., and Schwarz, G.E., 2022, Quantifying regional effects of best management practices on nutrient losses from agricultural lands: Journal of Soil and Water Conservation, v. 77, no. 1, p. 15-29, https://doi.org/10.2489/jswc.2022.00162.","productDescription":"15 p.","startPage":"15","endPage":"29","ipdsId":"IP-119875","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":449184,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.2022.00162","text":"Publisher Index Page"},{"id":435998,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H2NDWU","text":"USGS data release","linkHelpText":"Nutrient Load Data used to Quantify Regional Effects of Agricultural Best Management Practices: An application of the 2012 SPARROW models for the Midwest, Northeast, and Southeast United States"},{"id":395226,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"77","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Roland, Victor L. II 0000-0002-6260-9351 vroland@usgs.gov","orcid":"https://orcid.org/0000-0002-6260-9351","contributorId":212248,"corporation":false,"usgs":true,"family":"Roland","given":"Victor","suffix":"II","email":"vroland@usgs.gov","middleInitial":"L.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832440,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia, Ana Maria 0000-0002-5388-1281 agarcia@usgs.gov","orcid":"https://orcid.org/0000-0002-5388-1281","contributorId":2035,"corporation":false,"usgs":true,"family":"Garcia","given":"Ana","email":"agarcia@usgs.gov","middleInitial":"Maria","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832441,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saad, David A. 0000-0001-6559-6181 dasaad@usgs.gov","orcid":"https://orcid.org/0000-0001-6559-6181","contributorId":204667,"corporation":false,"usgs":true,"family":"Saad","given":"David","email":"dasaad@usgs.gov","middleInitial":"A.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832442,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ator, Scott W. 0000-0002-9186-4837","orcid":"https://orcid.org/0000-0002-9186-4837","contributorId":220504,"corporation":false,"usgs":true,"family":"Ator","given":"Scott W.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832443,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832444,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":213621,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory","email":"gschwarz@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":832445,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229185,"text":"70229185 - 2022 - The impact of future climate on wetland habitat in a critical migratory waterfowl corridor of the Prairie Pothole Region","interactions":[],"lastModifiedDate":"2022-03-03T15:34:56.28404","indexId":"70229185","displayToPublicDate":"2022-01-12T09:27:17","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":251,"text":"Final Report","active":false,"publicationSubtype":{"id":4}},"title":"The impact of future climate on wetland habitat in a critical migratory waterfowl corridor of the Prairie Pothole Region","docAbstract":"Depressional wetlands are extremely sensitive to changes in temperature and precipitation, so understanding how wetland inundation dynamics respond to changes in climate is essential for describing potential effects on wildlife breeding habitat. Millions of depressional basins make up the largest wetland complex in North America known as the Prairie Pothole Region (PPR). The wetland ecosystems that have formed in these basins provide important migratory-bird breeding habitat. The southeast portion of the U.S. PPR in Minnesota and Iowa has faced some of the greatest challenges in wetland conservation. Many existing prairie-pothole wetlands are small (<1 ha) and shallow (<2 m) and are typically not inundated with surface water year-round. Our goal with this project is to increase the efficacy of mapping tools used by management agencies to predict future changes in water levels in the PPR. We accomplish this goal by improving the link between existing data (about wetland water characteristics) and existing tools (mapping products). Our results successfully validated (2009-2021) the current mapping tool (a wetland hydrology model) used by the U.S. Fish and Wildlife Service (USFWS) to manage 22 wetlands in Minnesota. We were able to hindcast wetland water levels to 1984 and assess the accuracy of a satellite-derived surface water product and forecast water levels through 2099 using a suite of modeled climate data. This newly refined link between monitoring data and remote sensing tools will increase understanding and prediction for other wetlands beyond our study sites and through the Minnesota and Iowa portions of the PPR. Through conference presentations, publications, and development of an interactive climate change dashboard we are now working with managers to determine how we can help incorporate these predicted changes to waterfowl breeding habitat into their future management, acquisition, and restoration strategy.
Ecologists have long sought to understand space use and mechanisms underlying patterns observed in nature. We developed an optimality landscape and mechanistic territory model to understand mechanisms driving space use and compared model predictions to empirical reality. We demonstrate our approach using grey wolves (Canis lupus). In the model, simulated animals selected territories to economically acquire resources by selecting patches with greatest value, accounting for benefits, costs and trade-offs of defending and using space on the optimality landscape. Our approach successfully predicted and explained first- and second-order space use of wolves, including the population's distribution, territories of individual packs, and influences of prey density, competitor density, human-caused mortality risk and seasonality. It accomplished this using simple behavioural rules and limited data to inform the optimality landscape. Results contribute evidence that economical territory selection is a mechanistic bridge between space use and animal distribution on the landscape. This approach and resulting gains in knowledge enable predicting effects of a wide range of environmental conditions, contributing to both basic ecological understanding of natural systems and conservation. We expect this approach will demonstrate applicability across diverse habitats and species, and that its foundation can help continue to advance understanding of spatial behaviour.
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\"}}]}","volume":"289","issue":"1966","noUsgsAuthors":false,"publicationDate":"2022-01-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Sells, Sarah N.","contributorId":171706,"corporation":false,"usgs":false,"family":"Sells","given":"Sarah","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":902988,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mitchell, Michael S.","contributorId":338172,"corporation":false,"usgs":false,"family":"Mitchell","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":902993,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ausband, David Edward 0000-0001-9204-9837","orcid":"https://orcid.org/0000-0001-9204-9837","contributorId":275329,"corporation":false,"usgs":true,"family":"Ausband","given":"David","email":"","middleInitial":"Edward","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902987,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Luis, Angela D.","contributorId":33199,"corporation":false,"usgs":true,"family":"Luis","given":"Angela","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":902989,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Emlen, Douglas J.","contributorId":338162,"corporation":false,"usgs":false,"family":"Emlen","given":"Douglas","email":"","middleInitial":"J.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":902990,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Podruzny, Kevin M.","contributorId":85865,"corporation":false,"usgs":true,"family":"Podruzny","given":"Kevin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":902991,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gude, Justin A.","contributorId":95780,"corporation":false,"usgs":true,"family":"Gude","given":"Justin A.","affiliations":[],"preferred":false,"id":902992,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230367,"text":"70230367 - 2022 - Characterization of bituminite in Kimmeridge Clay by confocal laser scanning and atomic force microscopy","interactions":[],"lastModifiedDate":"2022-04-11T14:00:54.827276","indexId":"70230367","displayToPublicDate":"2022-01-12T08:52:42","publicationYear":"2022","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":"Characterization of bituminite in Kimmeridge Clay by confocal laser scanning and atomic force microscopy","docAbstract":"This work investigates bituminite (amorphous sedimentary organic matter) in Upper Jurassic Kimmeridge Clay source rock via confocal laser scanning microscopy (CLSM) and atomic force microscopy (AFM). These petrographic tools were used to provide better understanding of the nature of bituminite, which has been historically difficult to identify and differentiate from similar organic matter types in source rocks. As part of an International Committee for Coal and Organic Petrology (ICCP) working group, an immature (0.42% vitrinite reflectance), organic-rich (44.1 wt% total organic carbon content) sample of Kimmeridge Clay was distributed to multiple laboratories for CLSM characterization. The primary observations from CLSM imaging and spectroscopy include: 1) the interpreted presence of Botryococcus algae as a contributor to bituminite precursors; 2) color red-shift of sulfide reflectance and bituminite auto-fluorescence from below the sample surface; 3) positive alteration of bituminite from laser-induced photo-oxidation of the sample surface, including fluorescence blue-shift; 4) fluorescence blue-shift associated to higher fluorescence intensity regions in bituminite indicative of compositional (fluorophore) differences; 5) the need for fluorescence spectroscopy standardization as applied via CLSM; and 6) the suitability of CLSM fluorescence spectroscopy to predict solid bitumen reflectance from bituminite spectral emission via calibration to an extant dataset. Secondary CLSM observations include detection of reflected laser light from highly reflective inclusions in bituminite, including sulfides and fusinite, and radiolytic alteration of bituminite caused by substitution of U for Fe in sulfides. Findings from AFM include the observation that surface roughening or surface flattening of bituminite are induced by differential broad ion beam (BIB) milling and are dependent on the location and scale of AFM topology measurement. This result highlights our still limited understanding of the effects of BIB milling on sedimentary organic matter and indicates the need for further research before this technique can be advanced as a standard practice in petrographic sample preparation. Collectively, the results of this study illustrate the general applicability and versatility of AFM and CLSM as tools for organic petrology research, specifically for better understanding of the nature and properties of the bituminite maceral.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2022.103927","usgsCitation":"Hackley, P.C., Kus, J., Mendonca Filho, J.G., Czaja, A.D., Borrego, A., Životić, D., Valentine, B.J., and Hatcherian, J.J., 2022, Characterization of bituminite in Kimmeridge Clay by confocal laser scanning and atomic force microscopy: International Journal of Coal Geology, v. 251, 103927, 17 p., https://doi.org/10.1016/j.coal.2022.103927.","productDescription":"103927, 17 p.","ipdsId":"IP-133126","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":449187,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coal.2022.103927","text":"Publisher Index Page"},{"id":398465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"England","city":"Kimmeridge","otherGeospatial":"Upper Jurassic Kimmeridge Clay Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -2.1928024291992188,\n 50.58607046502884\n ],\n [\n -2.0602798461914062,\n 50.58607046502884\n ],\n [\n -2.0602798461914062,\n 50.629428887865565\n ],\n [\n -2.1928024291992188,\n 50.629428887865565\n ],\n [\n -2.1928024291992188,\n 50.58607046502884\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"251","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":840090,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kus, Jolanta","contributorId":289942,"corporation":false,"usgs":false,"family":"Kus","given":"Jolanta","affiliations":[{"id":62291,"text":"BGR.de","active":true,"usgs":false}],"preferred":false,"id":840091,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mendonca Filho, Joao Graciano","contributorId":289943,"corporation":false,"usgs":false,"family":"Mendonca Filho","given":"Joao","email":"","middleInitial":"Graciano","affiliations":[{"id":62294,"text":"UFRJ","active":true,"usgs":false}],"preferred":false,"id":840092,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Czaja, Andrew D.","contributorId":289944,"corporation":false,"usgs":false,"family":"Czaja","given":"Andrew","email":"","middleInitial":"D.","affiliations":[{"id":62295,"text":"Univ. Cincinnati,","active":true,"usgs":false}],"preferred":false,"id":840093,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Borrego, Angeles G.","contributorId":289945,"corporation":false,"usgs":false,"family":"Borrego","given":"Angeles G.","affiliations":[{"id":27409,"text":"Incar","active":true,"usgs":false}],"preferred":false,"id":840094,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Životić, Dragana","contributorId":289946,"corporation":false,"usgs":false,"family":"Životić","given":"Dragana","affiliations":[{"id":62296,"text":"Univ. Belgrade","active":true,"usgs":false}],"preferred":false,"id":840095,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Valentine, Brett J. 0000-0002-8678-2431 bvalentine@usgs.gov","orcid":"https://orcid.org/0000-0002-8678-2431","contributorId":3846,"corporation":false,"usgs":true,"family":"Valentine","given":"Brett","email":"bvalentine@usgs.gov","middleInitial":"J.","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":840096,"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":840097,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70252840,"text":"70252840 - 2022 - Assessment of native fish passage through Brandon Road Lock and Dam, Des Plaines River, Illinois, using fin ray microchemistry","interactions":[],"lastModifiedDate":"2024-04-09T12:25:01.204236","indexId":"70252840","displayToPublicDate":"2022-01-12T07:22:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of native fish passage through Brandon Road Lock and Dam, Des Plaines River, Illinois, using fin ray microchemistry","docAbstract":"This study examined evidence of native fish passage through Brandon Road Lock and Dam (BRLD) on the Des Plaines River, Illinois, in light of proposed modifications to prevent the upstream passage of invasive carps. Direct evidence of upstream passage by native fishes at BRLD is lacking and could help to inform assessment of the impacts of barrier technology installation. Fin ray microchemistry was used to assess upstream BRLD passage in the native taxa Centrarchidae, Catostomidae, Ictaluridae, and Lepisosteidae. The fin ray edge strontium : calcium ratio (Sr:Ca) of fish sampled from the Des Plaines River upstream of BRLD and in rivers downstream of BRLD was used to characterize ranges of river-specific fin ray Sr:Ca for each taxon. These were applied to Sr:Ca data along a transect from fin ray core to edge to infer the environmental history of individual fish that were captured upstream from BRLD and to estimate the proportion of fish that had passed upstream through BRLD. Depending on the taxon, 6–37% of individuals sampled upstream from BRLD exhibited fin ray Sr:Ca indicating prior residency in rivers downstream of BRLD and therefore upstream passage through BRLD. Upstream passage was indeterminate for 19–91% of individuals in each taxon due to uncertainty in environmental history inferred from fin ray Sr:Ca. These results provide the first definitive evidence of upstream native fish passage at BRLD and suggest that the installation of barrier technology could have an impact on native fish.
Tidal marsh survival in the face of sea level rise (SLR) and declining sediment supply often depends on the ability of marshes to build soil vertically. However, numerical models typically predict survival under rates of SLR that far exceed field-based measurements of vertical accretion. Here, we combine novel measurements from seven U.S. Atlantic Coast marshes and data from 70 additional marshes from around the world to illustrate that—over continental scales—70% of variability in marsh accretion rates can be explained by suspended sediment concentratin (SSC) and spring tidal range (TR). Apparent discrepancies between models and measurements can be explained by differing responses in high marshes and low marshes, the latter of which accretes faster for a given SSC and TR. Together these results help bridge the gap between models and measurements, and reinforce the paradigm that sediment supply is the key determinant of wetland vulnerability at continental scales.
The genetic composition of an individual can markedly affect its survival, reproduction, and ultimately fitness. As some wildlife populations become smaller, conserving genetic diversity will be a conservation challenge. Many imperiled species are already supported through population augmentation efforts and we often do not know if or how genetic diversity is maintained in translocated species. As a case study for understanding the maintenance of genetic diversity in augmented populations, I wanted to know if genetic diversity (i.e., observed heterozygosity) remained high in a population of gray wolves in the Rocky Mountains of the U.S. > 20 years after reintroduction. Additionally, I wanted to know if a potential mechanism for such diversity was individuals with below average genetic diversity choosing mates with above average diversity. I also asked whether there was a preference for mating with unrelated individuals. Finally, I hypothesized that mated pairs with above average heterozygosity would have increased survival of young. Ultimately, I found that females with below average heterozygosity did not choose mates with above average heterozygosity and wolves chose mates randomly with respect to genetic relatedness. Pup survival was not higher for mated pairs with above average heterozygosity in my models. The dominant variables predicting pup survival were harvest rate during their first year of life and years pairs were mated. Ultimately, genetic diversity was relatively unchanged > 20 years after reintroduction. The mechanism for maintaining such diversity does not appear related to individuals preferentially choosing more genetically diverse mates. Inbreeding avoidance, however, appears to be at least one mechanism maintaining genetic diversity in this population.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-021-04449-4","usgsCitation":"Ausband, D.E., 2022, Genetic diversity and mate selection in a reintroduced population of gray wolves: Scientific Reports, v. 12, 535, 7 p., https://doi.org/10.1038/s41598-021-04449-4.","productDescription":"535, 7 p.","ipdsId":"IP-130898","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":449194,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-021-04449-4","text":"Publisher Index Page"},{"id":430019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"12","noUsgsAuthors":false,"publicationDate":"2022-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Ausband, David Edward 0000-0001-9204-9837","orcid":"https://orcid.org/0000-0001-9204-9837","contributorId":275329,"corporation":false,"usgs":true,"family":"Ausband","given":"David","email":"","middleInitial":"Edward","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903384,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70230638,"text":"70230638 - 2022 - Portable optically stimulated luminescence age map of a paleoseismic exposure","interactions":[],"lastModifiedDate":"2022-04-19T14:52:32.370176","indexId":"70230638","displayToPublicDate":"2022-01-11T09:43:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Portable optically stimulated luminescence age map of a paleoseismic exposure","docAbstract":"The quality and quantity of geochronologic data used to constrain the history of major earthquakes in a region exerts a first-order control on the accuracy of seismic hazard assessments that affect millions of people. However, evaluations of geochronological data are limited by uncertainties related to inherently complex depositional processes that may vary spatially and temporally. To improve confidence in models of earthquake timing, we use a high-density suite of radiocarbon and optically stimulated luminescence (OSL) ages with a grid of 342 portable OSL samples to explore spatiotemporal trends in geochronological data across an exemplary normal fault colluvial wedge exposure. The data reveal a two-dimensional age map of the paleoseismic exposure and demonstrate how vertical and horizontal trends in age relate to dominant sedimentary facies and soil characteristics at the site. Portable OSL data provide critical context for the interpretation of 14C and OSL ages, show that geochronologic age boundaries between pre- and post-earthquake deposits do not match stratigraphic contacts, and provide the basis for selecting alternate Bayesian models of earthquake timing. Our results demonstrate the potential to use emergent, portable OSL methods to dramatically improve paleoseismic constraints on earthquake timing.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G49472.1","usgsCitation":"DuRoss, C., Gold, R.D., Gray, H., and Nicovich, S.R., 2022, Portable optically stimulated luminescence age map of a paleoseismic exposure: Geology, v. 50, no. 4, p. 470-475, https://doi.org/10.1130/G49472.1.","productDescription":"6 p.","startPage":"470","endPage":"475","ipdsId":"IP-134256","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":449199,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/g49472.1","text":"Publisher Index Page"},{"id":399085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Deep Creek site, Wasatch fault zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.8639,\n 39.5056\n ],\n [\n -111.8583,\n 39.5056\n ],\n [\n -111.8583,\n 39.5111\n ],\n [\n -111.8639,\n 39.5111\n ],\n [\n -111.8639,\n 39.5056\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"DuRoss, Christopher 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":840958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":840959,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gray, Harrison J. 0000-0002-4555-7473","orcid":"https://orcid.org/0000-0002-4555-7473","contributorId":207019,"corporation":false,"usgs":true,"family":"Gray","given":"Harrison J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":840960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nicovich, Sylvia R.","contributorId":290414,"corporation":false,"usgs":false,"family":"Nicovich","given":"Sylvia","email":"","middleInitial":"R.","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":840961,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255178,"text":"70255178 - 2022 - Site fidelity as a maladaptive behavior in the Anthropocene","interactions":[],"lastModifiedDate":"2024-06-13T14:11:32.632785","indexId":"70255178","displayToPublicDate":"2022-01-11T09:08:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1701,"text":"Frontiers in Ecology and the Environment","active":true,"publicationSubtype":{"id":10}},"title":"Site fidelity as a maladaptive behavior in the Anthropocene","docAbstract":"Site fidelity, or the behavior of returning to previously visited locations, has been observed across taxa and ecosystems. By developing familiarity with a particular location, site fidelity provides a range of benefits and is advantageous in stable or predictable environments. However, the Anthropocene is characterized by rates of environmental change that outpace the evolutionary history of extant taxa, which can result in site fidelity becoming maladaptive. Here we outline the theoretical underpinnings for maladaptive site fidelity and synthesize empirical research supporting its occurrence, and examine it in the context of a related concept, ecological traps, whereby organisms exhibit maladaptive behavior in habitat selection. We then discuss adaptive mechanisms that may enable species with site fidelity to continue to persist in the Anthropocene. With ongoing environmental change, researchers and practitioners should expect fidelity-induced ecological traps to become more common, and initiate projects to identify and understand their origins. Such knowledge will help conserve the widespread and ecologically important behavior of site fidelity.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.2456","usgsCitation":"Merkle, J.A., Abrahms, B., Armstrong, J., Sawyer, H., Costa, D.P., and Chalfoun, A.D., 2022, Site fidelity as a maladaptive behavior in the Anthropocene: Frontiers in Ecology and the Environment, v. 20, no. 3, p. 187-194, https://doi.org/10.1002/fee.2456.","productDescription":"8 p.","startPage":"187","endPage":"194","ipdsId":"IP-125854","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":430131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Merkle, Jerod A.","contributorId":272239,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod","email":"","middleInitial":"A.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":903675,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abrahms, Briana","contributorId":338281,"corporation":false,"usgs":false,"family":"Abrahms","given":"Briana","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":903676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Armstrong, Jonathan B.","contributorId":287296,"corporation":false,"usgs":false,"family":"Armstrong","given":"Jonathan B.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":903677,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sawyer, Hall","contributorId":270423,"corporation":false,"usgs":false,"family":"Sawyer","given":"Hall","email":"","affiliations":[{"id":51998,"text":"Western EcoSystems Technology","active":true,"usgs":false}],"preferred":false,"id":903678,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Costa, Daniel P.","contributorId":141212,"corporation":false,"usgs":false,"family":"Costa","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":903679,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chalfoun, Anna D. 0000-0002-0219-6006 achalfoun@usgs.gov","orcid":"https://orcid.org/0000-0002-0219-6006","contributorId":197589,"corporation":false,"usgs":true,"family":"Chalfoun","given":"Anna","email":"achalfoun@usgs.gov","middleInitial":"D.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903674,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230838,"text":"70230838 - 2022 - Alert optimization of the PLUM earthquake early warning algorithm for the western United States","interactions":[],"lastModifiedDate":"2022-04-26T14:06:19.287356","indexId":"70230838","displayToPublicDate":"2022-01-11T09:01:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Alert optimization of the PLUM earthquake early warning algorithm for the western United States","docAbstract":"We determine an optimal alerting configuration for the propagation of local undamped motion (PLUM) earthquake early warning (EEW) algorithm for use by the U.S. ShakeAlert system covering California, Oregon, and Washington. All EEW systems should balance the primary goal of providing timely alerts for impactful or potentially damaging shaking while limiting alerts for shaking that is too low to be of concern (precautionary alerts). The PLUM EEW algorithm forward predicts observed ground motions to nearby sites within a defined radius without accounting for attenuation, avoiding the earthquake source parameter estimation step of most EEW algorithms. PLUM was originally developed in Japan where the alert regions and ground motions for which alerts are issued differ from those implemented by ShakeAlert. We compare predicted ground motions from PLUM to ShakeMap‐reported ground motions for a set of 22 U.S. West Coast earthquakes of magnitude 4.4–7.2 and evaluate available warning times. We examine a range of prediction radii (20–100 km), thresholds used to issue an alert (alert threshold), and levels of impactful or potentially damaging shaking (target threshold). We find optimal performance when the alert threshold is close to the target threshold, although higher target ground motions benefit from somewhat lower alert thresholds to ensure timely alerts. We also find that performance, measured as the cost reduction that a user can achieve, depends on the user’s tolerance for precautionary alerts. Users with a low target threshold and high tolerance for precautionary alerts achieve optimal performance when larger prediction radii (60–100 km) are used. In contrast, users with high target thresholds and low tolerance for precautionary alerts achieve better performance for smaller prediction radii (30–60 km). Therefore, setting the PLUM prediction radius to 60 km balances the needs of many users and provides warning times of up to ∼20 s.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120210259","usgsCitation":"Cochran, E.S., Saunders, J.K., Minson, S.E., Bunn, J., Baltay Sundstrom, A.S., Kilb, D., O’Rourke, C.T., Hoshiba, M., and Kodera, Y., 2022, Alert optimization of the PLUM earthquake early warning algorithm for the western United States: Bulletin of the Seismological Society of America, v. 112, no. 2, p. 803-819, https://doi.org/10.1785/0120210259.","productDescription":"17 p.","startPage":"803","endPage":"819","ipdsId":"IP-133243","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":399664,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n 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southern Beaufort Sea (SB), where sea ice loss has led to increased use of land-based food resources by bears, and from East Greenland (EG), where persistent sea ice has allowed hunting of ice-associated prey nearly year-round. SB polar bears showed a higher number of total (940 vs. 742) and unique (387 vs. 189) amplicon sequence variants and higher inter-individual variation compared to EG polar bears. Gut microbiome composition differed significantly between the two subpopulations and among sex/age classes, likely driven by diet variation and ontogenetic shifts in the gut microbiome. Dietary tracer analysis using fatty acid signatures for SB polar bears showed that diet explained more intrapopulation variation in gut microbiome composition and diversity than other tested variables, i.e., sex/age class, body condition, and capture year. Substantial differences in the SB gut microbiome relative to EG polar bears, and associations between SB gut microbiome and diet, suggest that the shifting foraging habits of SB polar bears tied to sea ice loss may be altering their gut microbiome, with potential consequences for nutrition and physiology.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41598-021-04340-2","usgsCitation":"Franz, M., White, L., Atwood, T.C., Laidre, K.L., Roy, D., Watson, S., Gongora, E., and McKinney, M., 2022, Distinct gut microbiomes in two polar bear subpopulations inhabiting different sea ice ecoregions: Scientific Reports, v. 12, 522, 15 p., https://doi.org/10.1038/s41598-021-04340-2.","productDescription":"522, 15 p.","ipdsId":"IP-131826","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":449203,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-021-04340-2","text":"Publisher Index Page"},{"id":436001,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97KU2YD","text":"USGS data release","linkHelpText":"Southern Beaufort Sea Polar Bear Diet and Gut Microbiota Data, 2015-2019"},{"id":398307,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Greenland, United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -39.375,\n 61.270232790000634\n ],\n [\n -23.203125,\n 68.78414378041504\n ],\n [\n -35.15625,\n 70.1403642720717\n ],\n [\n -48.1640625,\n 62.59334083012024\n ],\n [\n -39.375,\n 61.270232790000634\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -141.328125,\n 69.53451763078358\n ],\n [\n -142.734375,\n 70.72897946208789\n ],\n [\n -156.796875,\n 71.96538769913127\n ],\n [\n -158.203125,\n 68.78414378041504\n ],\n [\n -140.625,\n 68.00757101804004\n ],\n [\n -141.328125,\n 69.53451763078358\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2022-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Franz, Megan","contributorId":289877,"corporation":false,"usgs":false,"family":"Franz","given":"Megan","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":839971,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Lyle","contributorId":289878,"corporation":false,"usgs":false,"family":"White","given":"Lyle","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":839972,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":839973,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laidre, Kristin L.","contributorId":191798,"corporation":false,"usgs":false,"family":"Laidre","given":"Kristin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":839974,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roy, Denis","contributorId":289880,"corporation":false,"usgs":false,"family":"Roy","given":"Denis","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":839975,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Watson, Sophie","contributorId":222143,"corporation":false,"usgs":false,"family":"Watson","given":"Sophie","email":"","affiliations":[{"id":17940,"text":"Cardiff University","active":true,"usgs":false}],"preferred":false,"id":839976,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gongora, Esteban","contributorId":289882,"corporation":false,"usgs":false,"family":"Gongora","given":"Esteban","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":839977,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McKinney, Melissa","contributorId":222146,"corporation":false,"usgs":false,"family":"McKinney","given":"Melissa","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":839978,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70231511,"text":"70231511 - 2022 - Mine drainage precipitates attenuate and conceal wastewater-derived phosphate pollution in stream water","interactions":[],"lastModifiedDate":"2022-05-12T13:23:18.566652","indexId":"70231511","displayToPublicDate":"2022-01-11T08:14:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Mine drainage precipitates attenuate and conceal wastewater-derived phosphate pollution in stream water","docAbstract":"Hydrous ferric-oxide (HFO) coatings on streambed sediments may attenuate dissolved phosphate (PO4) concentrations at acidic to neutral pH conditions, limiting phosphorus (P) transport and availability in aquatic ecosystems. Mesh-covered tiles on which “natural” HFO from abandoned mine drainage (AMD) had precipitated were exposed to treated municipal wastewater (MWW) effluent or a mixture of stream water and effluent. Between 42 and 99% of the dissolved P in effluent was removed from the water to a thin coating (~2 μm) of HFO on the mesh. Geochemical equilibrium model results predicted the removal of 76 to 99% of PO4 from the water by adsorption to the HFO, depending on the HFO quantity, initial PO4 concentration, and pH. The measurements and model results indicated the capacity for P removal decreased as the concentration of P associated with the HFO increased. Continuing accumulation of HFO from upstream AMD sources replenish the in-stream capacity for P attenuation below the MWW discharge. This indicates AMD pollution may conceal P inputs and limit the amount of dissolved P transported to downstream ecosystems. However, HFO-rich sediments also represent a potential source of “legacy” P that could confound management practices intended to decrease nutrient and metal loadings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.152672","usgsCitation":"Smyntek, P.M., Lamagna, N., Cravotta, C., and Strosnider, W., 2022, Mine drainage precipitates attenuate and conceal wastewater-derived phosphate pollution in stream water: Science of the Total Environment, v. 815, 152672, 8 p., https://doi.org/10.1016/j.scitotenv.2021.152672.","productDescription":"152672, 8 p.","ipdsId":"IP-132530","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":436002,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D8VQDV","text":"USGS data release","linkHelpText":"Interactive PHREEQ-N-Titration-PO4-Adsorption water-quality modeling tools to evaluate potential attenuation of phosphate and associated dissolved constituents by aqueous-solid equilibrium processes (software download)"},{"id":400574,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Four Mile Run","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.44746017456055,\n 40.28279959861071\n ],\n [\n -79.39699172973633,\n 40.28279959861071\n ],\n [\n -79.39699172973633,\n 40.32050383546901\n ],\n [\n -79.44746017456055,\n 40.32050383546901\n ],\n [\n -79.44746017456055,\n 40.28279959861071\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"815","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smyntek, Peter M.","contributorId":291642,"corporation":false,"usgs":false,"family":"Smyntek","given":"Peter","email":"","middleInitial":"M.","affiliations":[{"id":62738,"text":"Saint Vincent College","active":true,"usgs":false}],"preferred":false,"id":842810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamagna, Natalie","contributorId":291643,"corporation":false,"usgs":false,"family":"Lamagna","given":"Natalie","email":"","affiliations":[{"id":62738,"text":"Saint Vincent College","active":true,"usgs":false}],"preferred":false,"id":842811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":842812,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strosnider, William H. J.","contributorId":291644,"corporation":false,"usgs":false,"family":"Strosnider","given":"William H. J.","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":842813,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227380,"text":"70227380 - 2022 - Highly pathogenic avian influenza is an emerging disease threat to wild birds in North America","interactions":[],"lastModifiedDate":"2022-03-15T16:53:15.858946","indexId":"70227380","displayToPublicDate":"2022-01-11T06:52:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Highly pathogenic avian influenza is an emerging disease threat to wild birds in North America","docAbstract":"Prior to the emergence of the A/goose/Guangdong/1/1996 (Gs/GD) H5N1 influenza A virus, the long-held and well-supported paradigm was that highly pathogenic avian influenza (HPAI) outbreaks were restricted to poultry, the result of cross-species transmission of precursor viruses from wild aquatic birds that subsequently gained pathogenicity in domestic birds. Therefore, management agencies typically adopted a prevention, control, and eradication strategy that included strict biosecurity for domestic bird production, isolation of infected and exposed flocks, and prompt depopulation. In most cases, this strategy has proved sufficient for eradicating HPAI. Since 2002, this paradigm has been challenged with many detections of viral descendants of the Gs/GD lineage among wild birds, most of which have been associated with sporadic mortality events. Since the emergence and evolution of the genetically distinct clade 2.3.4.4 Gs/GD lineage HPAI viruses in approximately 2010, there have been further increases in the occurrence of HPAI in wild birds and geographic spread through migratory bird movement. A prominent example is the introduction of clade 2.3.4.4 Gs/GD HPAI viruses from East Asia to North America via migratory birds in autumn 2014 that ultimately led to the largest outbreak of HPAI in the history of the United States. Given the apparent maintenance of Gs/GD lineage HPAI viruses in a global avian reservoir; bidirectional virus exchange between wild and domestic birds facilitating the continued adaptation of Gs/GD HPAI viruses in wild bird hosts; the current frequency of HPAI outbreaks in wild birds globally, and particularly in Eurasia where Gs/GD HPAI viruses may now be enzootic; and ongoing dispersal of AI viruses from East Asia to North America via migratory birds, HPAI now represents an emerging disease threat to North American wildlife. This recent paradigm shift implies that management of HPAI in domestic birds alone may no longer be sufficient to eradicate HPAI viruses from a given country or region. Rather, agencies managing wild birds and their habitats may consider the development or adoption of mitigation strategies to minimize introductions to poultry, to reduce negative impacts on wild bird populations, and to diminish adverse effects to stakeholders using wildlife resources. The main objective of this review is, therefore, to provide information that will assist wildlife managers in developing mitigation strategies or approaches for dealing with outbreaks of Gs/GD HPAI in wild birds in the form of preparedness, surveillance, research, communications, and targeted management actions. Resultant outbreak response plans and actions may represent meaningful steps of wildlife managers toward the use of collaborative and multi-jurisdictional One Health approaches when it comes to the detection, investigation, and mitigation of emerging viruses at the human-domestic animal-wildlife interface.
The COVID-19 pandemic has disrupted field research programs, making conservation and management decision-making more challenging. However, it may be possible to conduct population assessments using integrated models that combine community science data with existing data from structured surveys. We developed a space-time integrated model to characterize spatial and temporal variability in population distribution. We fit our integrated model to 10 years of eBird (2010-2020) and 9 years of aerial survey (2010-2019) mottled duck count data to forecast 2020 population size along the western Gulf Coast of Texas and Louisiana. Estimates of mottled duck abundance were similar in magnitude to estimates calculated using previous methods, but were more precise and showed evidence of a declining population. The spatial distribution for mottled ducks each year was characterized by several concentrations of relatively high abundance, although the location of these abundance ‘hotspots’ varied over time. Expected abundance was higher for areas with a higher proportion of area covered by marsh habitat. By leveraging large-scale community science data, we were able to conduct a population assessment despite the disruption in structured surveys caused by the pandemic. As participation in community science platforms continues to increase, we anticipate modeling frameworks, like the integrated model we developed here, will become increasingly useful for informing conservation and management decision-making.