Executive Summary: Under Action Plan I (2010-2014) of the Great Lakes Restoration Initiative (GLRI), Federal and Academic partners began an investigation of the presence and distribution of contaminants of emerging concern (CECs) in the Great Lakes and potential impacts on fish and wildlife. The term CECs is applied to a broad range of chemicals that are currently in use but for which we currently lack good understanding of whether fish, wildlife, or humans are being exposed and/or whether negative health or environmental effects are expected if exposure occurs. Pharmaceuticals, personal care products, flame retardants, many current use pesticides, and poly- and perfluorinated chemicals are some well-known groups of CECs, but there is no definitive or comprehensive list that can be used to support the management of CECs to reduce impacts on the Great Lakes ecosystem.
Four overarching goals were identified for this collaborative investigation:
1. Evaluate the sources, occurrence, and distribution of CECs across the Great Lakes Basin.
2. Examine associations between the distribution of CECs and land-use patterns.
3. Review both scientific literature and field-generated data to determine the potential for CECs to cause adverse effects on Great Lakes fish and wildlife populations.
4. Develop efficient strategies to survey and/or monitor for threats that CECs may pose in order to take effective management actions before those threats evolve into large scale impacts on Great Lakes ecosystems or the services they provide.
Achievement of these goals ensures progress towards Focus Area 1: Toxic Substances and Areas of Concern from GLRI Action Plan I, Goal 5: “The health and integrity of wildlife populations and habitat are protected from adverse chemical and biological effects associated with the presence of toxic substances in the Great Lakes Basin”.
This large-scale research effort was comprised of individual and collaborative projects from multiple federal agencies and academic institutions, involving over 85 investigators, and overseen by the U.S. Environmental Protection Agency (EPA) Region 5, Great Lakes National Program Office. Partners include the United States Geological Survey, the National Oceanic and Atmospheric Administration, U.S. Fish and Wildlife Service, Saint Cloud State University, the U.S. EPA Office of Research and Development, and the U.S. Army Corps of Engineers.
Key findings:
1. Contaminants of emerging concern were found throughout the monitored Great Lakes tributaries, but types and concentrations vary in association with regional land use. CECs were detected in nearly all samples collected. The type and concentration of the specific contaminants detected varied considerably among field sites and in association with land use type, such as urban, agricultural, wetland, 2 or forest. Contaminants were detected in the water column, sediment, and tissues of all species surveyed in the current work (mussels, aquatic insects, fish, and insect-eating birds).
2. There were over 20 contaminants for which CEC concentrations approached or exceeded those reported to cause toxicity in laboratory experiments. This was based on detection in water, sediments and or biota at one or more field sites. These contaminants represent compounds that warrant further investigation and monitoring with respect to potential impacts in certain areas of the Great Lakes basin. Based on the present investigation, compounds of greatest concern include: polycyclic aromatic hydrocarbons, associated with oil-based products and combustion of organic matter; atrazine, an herbicide; dichlorvos, an insecticide; and ibuprofen and venlafaxine, both pharmaceuticals.
3. Results suggest that mixtures of CECs presently found in most Great Lakes tributary locations surveyed may elicit subtle biological effects, but likely are not, alone, causing obvious detriment to current communities of fish and wildlife. CECs detected in the Great Lakes were associated with subtle biological effects like changes in gene expression, altered circulating glucose, etc. in both wild-caught and laboratory-reared organisms. These effects were generally not indicative of reproductive failure or mortality. However, the effects may have more serious implications when combined with other sources of stress like habitat degradation, changing climate conditions, and competition with invasive species. Due to limited historical data, it is unknown whether severe CEC-related impacts have already affected aquatic communities in waterbodies that have received long-term inputs of these contaminants. Likewise, under Action Plan I, biological effects were not necessarily evaluated at the sites where CEC concentrations exceeding laboratory toxicity thresholds were detected. As a result, strategic, ongoing surveillance and monitoring of CECs is warranted.
This collaborative investigation resulted in new tools, approaches, and data that can be used to inform and support the management of CECs to reduce their impacts on Great Lakes natural resources. The following products of this research effort are available through https://communities.geoplatform.gov/glri/ or by contacting the investigators (see technical chapters found in Appendices A-F):
1. Database of CEC occurrence and concentrations in US tributary streams. The database includes CEC detections in water, sediment, and fish and wildlife tissues, and represents the most comprehensive survey of CECs in the Great Lakes Region.
2. Synopses of results and key findings. Integrated summaries of results, conclusions, and management implications of the CEC research are available through reports, topical fact sheets, and presentations.
3. Technical publications: This collaborative research effort has resulted in over 50 peer-reviewed publications, agency reports, and data releases that can be of use to resource managers, the scientific community, and members of the public.
4. Innovative tools. Innovative monitoring devices, sampling equipment, conceptual frameworks, and software applications were developed over the course of this 3 research. These tools are transferable to stakeholders via internet accessibility or via specifications, instructions, and demonstration detailed in technical publications.
Hypotheses to guide CECs research under Action Plan II. Findings from 2010-2014 were used to guide further research in 2015-2018 for basin-wide surveillance of CECs and for sites warranting further study of potential biological impacts of CECs. Additional surveillance included both evaluation of additional classes of contaminants and expanded lists for chemical classes shown to be of greatest concern. Mixtures of some of the most frequently detected contaminants were also tested in laboratory studies to understand whether long term exposures to multiple contaminants may result in effects not evident from uncontrolled, short-term field experiments.
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Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":853001,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, W. Edward","contributorId":296967,"corporation":false,"usgs":false,"family":"Johnson","given":"W.","email":"","middleInitial":"Edward","affiliations":[],"preferred":false,"id":853002,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hummel, Stephanie L.","contributorId":296241,"corporation":false,"usgs":false,"family":"Hummel","given":"Stephanie","email":"","middleInitial":"L.","affiliations":[{"id":16956,"text":"US Fish & Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":853003,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schoenfuss, Heiko L.","contributorId":76409,"corporation":false,"usgs":false,"family":"Schoenfuss","given":"Heiko","email":"","middleInitial":"L.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":853004,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Perkins, Edward J.","contributorId":89063,"corporation":false,"usgs":false,"family":"Perkins","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":26924,"text":"USArmy Engineer Research and Development Center, Vicksburg, MS","active":true,"usgs":false}],"preferred":false,"id":853005,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zack, Sarah A.","contributorId":296968,"corporation":false,"usgs":false,"family":"Zack","given":"Sarah","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":853006,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70232703,"text":"70232703 - 2022 - Rapid diagnostic test to detect and discriminate infectious hematopoietic necrosis virus (IHNV) genogroups U and M to aid management of Pacific Northwest salmonid populations","interactions":[],"lastModifiedDate":"2022-07-12T12:11:26.019734","indexId":"70232703","displayToPublicDate":"2022-07-09T07:06:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5762,"text":"Animals","active":true,"publicationSubtype":{"id":10}},"title":"Rapid diagnostic test to detect and discriminate infectious hematopoietic necrosis virus (IHNV) genogroups U and M to aid management of Pacific Northwest salmonid populations","docAbstract":"Harmful algal blooms produce biotoxins that can injure or kill fish, wildlife, and humans. These blooms occur naturally but have intensified in many locations globally due to recent climatic changes, including ocean warming. Such changes are especially pronounced in northern regions, where the effects of paralytic shellfish toxins (PSTs) on marine wildlife are of growing concern. In Alaska, seabird mortality events have increased in frequency, magnitude, and duration since 2015 alongside anomalously high ocean temperatures. Although starvation has been implicated as the apparent cause of death in many of these die-offs, saxitoxin (STX) and other PSTs have been identified as possible contributing factors. Here, we describe a mortality event at a nesting colony of Arctic Terns (Sterna paradisaea) near Juneau, Alaska in 2019 and report elevated concentrations of PSTs in bird, forage fish, and mussel samples. Concentrations of STX and other PSTs in tern tissues (2.5–51.2 µg 100g−1 STX-equivalents [STX-eq]) were of similar magnitude to those reported from other PST-induced bird die-offs. We documented high PST concentrations in blue mussels (>11,000 µg 100g−1 STX-eq; Mytilus edulis spp.) collected from nearby beaches, as well as in forage fish (up to 494 µg 100g−1 STX-eq) retrieved from Arctic Tern nests, thereby providing direct evidence of PST exposure via the terns’ prey. At maximum concentrations measured in this study, a single 5 g Pacific Sand Lance (Ammodytes personatus) could exceed the median lethal STX dose (LD50) currently estimated for birds, offering strong support for PSTs as a likely source of tern mortality. In addition to describing this localized bird mortality event, we used existing energetics data from adult and nestling Arctic Terns to calculate estimated cumulative daily PST exposure based on ecologically relevant concentrations in forage fish. Our estimates revealed potentially lethal levels of PST exposure even at relatively low (≤30 ug 100g−1 STX-eq) toxin concentrations in prey. These findings suggest that PSTs present a significant hazard to Arctic Terns and other northern seabirds and should be included in future investigations of avian mortality events as well as assessments of population health.
Climate model projections are commonly used to assess potential impacts of global warming on a breadth of social, economic, and environmental topics. Modeling centers throughout the world coordinate to apply a consistent suite of radiative forcing experiments so that all model outputs can be collectively analyzed and compared. Three generations of model outputs have been produced and made available to the scientific community through the Coupled Model Intercomparison Project (CMIP): CMIP3 disseminated during the mid-2000s, CMIP5 during the early-2010s, and CMIP6 during the late-2010s. Twenty-first century sea-ice projections from CMIP3 and CMIP5 models have been used in Bayesian network assessments of how climate change could impact the future persistence of polar bears (Ursus maritimus) throughout their range. In this report, we compare sea-ice projections by CMIP6 models to those of CMIP5 models in each of four polar bear ecoregions over the 21st century. We evaluate differences between the two CMIP generations with respect to other sources of variability that affect uncertainties of the model projections: (1) variability from different models; (2) variability from different greenhouse gas emissions scenarios; and (3) natural (internal) variability in the earth’s climate system. We found that natural variability as well as that attributable to models dominated uncertainties in sea-ice projections in all months and ecoregions during the first half of the 21st century, while emissions scenarios dominated uncertainties during the late 21st century. By comparison, we found only slight differences between the CMIP6 and CMIP5 model projections of sea ice. Applying CMIP6 instead of CMIP5 sea-ice projections to the polar bear Bayesian network model developed in 2016, therefore, would not qualitatively change the population status outcomes published therein.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221062","collaboration":"Prepared in cooperation with the U.S Fish and Wildlife Service","usgsCitation":"Douglas, D.C., and Atwood, T.C., 2022, Comparisons of Coupled Model Intercomparison Project Phase 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) sea-ice projections in polar bear (Ursus maritimus) ecoregions during the 21st century: U.S. Geological Survey Open-File Report 2022–1062, 27 p., https://doi.org/10.3133/ofr20221062.","productDescription":"vii, 27 p.","onlineOnly":"Y","ipdsId":"IP-139269","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":403336,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221062/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1062"},{"id":403335,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1062/ofr20221062.XML"},{"id":403334,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1062/images"},{"id":403333,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1062/ofr20221062.pdf","text":"Report","size":"16.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1062"},{"id":403332,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1062/coverthb.jpg"}],"contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
From the Hindu Kush mountains to the Registan Desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monitor environmental conditions is critical to support decision making for humanitarian assistance. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams provide information on hydrologic states for routine integrated food security analysis. While developed for a specific project, these data are publicly available and useful for other applications that require hydrologic estimates of the water and energy balance. These two data streams are unique because of their suitability for routine monitoring, as well as for being a historical record for computing relative indicators of water availability. The global stream is available at ∼ 1-month latency, and monthly average outputs are on a 10 km grid from 1982–present. The second data stream, Central Asia (21–56∘ N, 30–100∘ E), at ∼ 1 d latency, provides daily average outputs on a 1 km grid from 2000–present. This paper describes the configuration of the two FLDAS data streams, background on the software modeling framework, selected meteorological inputs and parameters, and results from previous evaluation studies. We also provide additional analysis of precipitation and snow cover over Afghanistan. We conclude with an example of how these data are used in integrated food security analysis. For use in new and innovative studies that will improve understanding of this region, these data are hosted by U.S. Geological Survey data portals and the National Aeronautics and Space Administration (NASA). The Central Asia data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/VQ4CD3Y9YC0R (Jacob and Slinski, 2021), and the global data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/5NHC22T9375G (McNally, 2018).
","language":"English","publisher":"Copernicus","doi":"10.5194/essd-14-3115-2022","usgsCitation":"McNally, A., Jacob, J., Arsenault, K., Slinski, K., Sarmiento, D., Hoell, A., Pervez, S., Rowland, J., Budde, M., Kumar, S., Peters-Lidard, C., and Verdin, J., 2022, A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan: Earth System Science Data, v. 14, no. 7, p. 3115-3135, https://doi.org/10.5194/essd-14-3115-2022.","productDescription":"21 p.","startPage":"3115","endPage":"3135","ipdsId":"IP-134002","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":447185,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-14-3115-2022","text":"Publisher Index 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Kimberly","contributorId":337030,"corporation":false,"usgs":false,"family":"Slinski","given":"Kimberly","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":901824,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sarmiento, Daniel","contributorId":337031,"corporation":false,"usgs":false,"family":"Sarmiento","given":"Daniel","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":901825,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hoell, Andrew","contributorId":337032,"corporation":false,"usgs":false,"family":"Hoell","given":"Andrew","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":901826,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pervez, Shahriar 0000-0003-3417-1871","orcid":"https://orcid.org/0000-0003-3417-1871","contributorId":337035,"corporation":false,"usgs":false,"family":"Pervez","given":"Shahriar","affiliations":[{"id":80954,"text":"AFDS contractor to USGS","active":true,"usgs":false}],"preferred":false,"id":901827,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rowland, James 0000-0003-4837-3511 rowland@usgs.gov","orcid":"https://orcid.org/0000-0003-4837-3511","contributorId":145846,"corporation":false,"usgs":true,"family":"Rowland","given":"James","email":"rowland@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":901828,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Budde, Michael 0000-0002-9098-2751 mbudde@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-2751","contributorId":166756,"corporation":false,"usgs":true,"family":"Budde","given":"Michael","email":"mbudde@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":901829,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kumar, Sujay","contributorId":337039,"corporation":false,"usgs":false,"family":"Kumar","given":"Sujay","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":901830,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Peters-Lidard, Christa","contributorId":337041,"corporation":false,"usgs":false,"family":"Peters-Lidard","given":"Christa","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":901831,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Verdin, James","contributorId":337042,"corporation":false,"usgs":false,"family":"Verdin","given":"James","affiliations":[{"id":48664,"text":"USAID","active":true,"usgs":false}],"preferred":false,"id":901832,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70234158,"text":"70234158 - 2022 - Natural and anthropogenic factors influencing nesting ecology of the American crocodile in Florida, United States","interactions":[],"lastModifiedDate":"2022-08-02T12:17:10.197716","indexId":"70234158","displayToPublicDate":"2022-07-08T07:14:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Natural and anthropogenic factors influencing nesting ecology of the American crocodile in Florida, United States","docAbstract":"Nesting ecology of American crocodiles (Crocodylus acutus) in Florida has been both positively and negatively influenced by anthropogenic and natural factors since the species was placed on the federally endangered species list in 1975. This includes a shift in nesting sites and an expansion of nesting to anthropogenic habitat. Using a 50-year record of monitoring data (1970-2020), we assessed factors influencing nesting ecology (number of nests, nest morphology, success rate, and habitat use) from a total of 3,013 nests recorded across South Florida. We detected a change in nesting success rate, increasing from 61% in the 1970’s to near 90% since 2010. Our hot spot analysis illustrates that nesting sites in northeastern Florida Bay and Flamingo/Cape Sable (Everglades National Park) were important for American crocodiles. Anthropogenic habitats, such as canals provided vital habitat nesting in areas such as Flamingo/Cape Sable (Everglades National Park), Turkey Point Power Plant, and Crocodile Lake National Wildlife Refuge for the current Florida population. Environmental parameters suspected to affect nesting success have shown an increasing trend over the past 50 years and minimum temperature and rainfall, during the summer season, are correlated with increased nesting success and temporal variation across South Florida. The adaptive capacity that American crocodiles exhibited in Florida gave the species advantages to face changes in climate and landscape over the last 50 years, however, it does not imply that the adaptive capacity of the species to face these changes (evolutionary potential) cannot reach a limit if changes continue. Here, we document C. acutus nesting ecology population responses to ecosystem restoration efforts in Florida; and further demonstrate the value of protecting and restoring habitat to support recovery of listed species.
Since the last Arctic Monitoring and Assessment Programme (AMAP) effort to review biological effects of mercury (Hg) on Arctic biota in 2011 and 2018, there has been a considerable number of new Arctic bird studies. This review article provides contemporary Hg exposure and potential health risk for 36 Arctic seabird and shorebird species, representing a larger portion of the Arctic than during previous AMAP assessments now also including parts of the Russian Arctic. To assess risk to birds, we used Hg toxicity benchmarks established for blood and converted to egg, liver, and feather tissues. Several Arctic seabird populations showed Hg concentrations that exceeded toxicity benchmarks, with 50 % of individual birds exceeding the “no adverse health effect” level. In particular, 5 % of all studied birds were considered to be at moderate or higher risk to Hg toxicity. However, most seabirds (95 %) were generally at lower risk to Hg toxicity. The highest Hg contamination was observed in seabirds breeding in the western Atlantic and Pacific Oceans. Most Arctic shorebirds exhibited low Hg concentrations, with approximately 45 % of individuals categorized at no risk, 2.5 % at high risk category, and no individual at severe risk. Although the majority Arctic-breeding seabirds and shorebirds appeared at lower risk to Hg toxicity, recent studies have reported deleterious effects of Hg on some pituitary hormones, genotoxicity, and reproductive performance. Adult survival appeared unaffected by Hg exposure, although long-term banding studies incorporating Hg are still limited. Although Hg contamination across the Arctic is considered low for most bird species, Hg in combination with other stressors, including other contaminants, diseases, parasites, and climate change, may still cause adverse effects. Future investigations on the global impact of Hg on Arctic birds should be conducted within a multi-stressor framework. This information helps to address Article 22 (Effectiveness Evaluation) of the Minamata Convention on Mercury as a global pollutant.
The 2012 Little Bear Fire resulted in substantial loss of vegetation in the Eagle Creek Basin, south-central New Mexico, which has been expected to cause a variety of hydrologic responses that could influence geomorphic change to North Fork Eagle Creek. To monitor geomorphic change, surveys of a downstream study reach of North Fork Eagle Creek were conducted in 2017, 2018, and 2019 by the U.S. Geological Survey in cooperation with the Village of Ruidoso, N. Mex. The study included surveys of select cross sections, woody debris accumulations, and pools found in the channel of the study reach. During 2017–19, high-flow events resulting from both monsoonal rainfall and snowmelt runoff occurred in the study reach, and the events appeared to have caused some minor localized geomorphic changes in the study reach, which were evaluated through comparison of the 2017, 2018, and 2019 survey results.
Comparisons of the cross-section survey results indicated that minor geomorphic changes had occurred in 4 of the 14 cross sections surveyed from 2017 to 2019. These geomorphic changes included aggradation or degradation of surface materials by about 1–2 feet in some parts of the affected cross sections. During the 2019 survey, 164 distinct accumulations of woody debris and 228 pools were identified in the study reach. Of the woody debris accumulations identified during the 2019 survey, 67 were certain to have also been present during the 2018 survey, and 21 were certain to have also been present during all three surveys (2017–19), indicating that most of the woody debris accumulations surveyed in 2017 were likely transported during the high-flow events between the 2017 and 2018 surveys. Most woody debris accumulations identified in 2019 did not appear to have substantially influenced geomorphic change in the locations where they were found but may have driven local geomorphic changes.
Because the study began 5 years after the 2012 Little Bear Fire and the geomorphic scope of the study has so far been limited, it cannot be said that the changes observed between the 2017 and 2019 surveys are representative of a pattern of geomorphic change following the Little Bear Fire. Once geomorphic changes identified during the 2017 through 2019 surveys can be compared with results from the remaining planned geomorphic surveys, it may be possible to develop an understanding of the patterns in geomorphic change following the 2012 Little Bear Fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221041","collaboration":"Prepared in cooperation with Village of Ruidoso, New Mexico","usgsCitation":"Graziano, A.P., and Chavarria, S.B., 2022, Geomorphic survey of North Fork Eagle Creek, New Mexico, 2019: U.S. Geological Survey Open-File Report 2022–1041, 36 p., https://doi.org/10.3133/ofr20221041.","productDescription":"Report: v, 36 p.; Data Release; Dataset","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-123645","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":403220,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97ALYNZ","text":"USGS data release","linkHelpText":"Data supporting the 2019 geomorphic survey of North Fork Eagle Creek, New Mexico"},{"id":501773,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113257.htm","linkFileType":{"id":5,"text":"html"}},{"id":403221,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":403219,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1041/images"},{"id":403218,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1041/ofr20221041.XML"},{"id":403215,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1041/coverthb.jpg"},{"id":403216,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1041/ofr20221041.pdf","text":"Report","size":"2.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1041"}],"country":"United States","state":"New Mexico","otherGeospatial":"North Fork Eagle Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.5,\n 33.0\n ],\n [\n -105.1,\n 33.0\n ],\n [\n -105.1,\n 33.4\n ],\n [\n -105.5,\n 33.4\n ],\n [\n -105.5,\n 33.0\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
This report describes the background information related to and the contents of the Lee Acres-Giant Bloomfield Refinery Database (LAGBRD), which is a compilation of monitoring data collected at the Lee Acres Landfill and the Giant Bloomfield Refinery near Farmington, New Mexico. LAGBRD includes monitoring data from as early as 1985, when awareness was increasing regarding contamination from liquid waste lagoons at the landfill and fuel releases at the refinery. Water quality and groundwater elevation data from sampling locations at the landfill and the refinery are included in the database. LAGBRD was compiled by the U.S. Geological Survey in cooperation with the Bureau of Land Management, which operates the Lee Acres Landfill, in order to facilitate future studies into the characteristics of groundwater contamination and background geochemistry at the landfill and refinery sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1154","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Gray, E.L., and Ferguson, C.L., 2022, Database of water quality and groundwater elevation within and surrounding the Lee Acres Landfill, New Mexico, 1985–2020: U.S. Geological Survey Data Report 1154, 80 p., https://doi.org/10.3133/dr1154.","productDescription":"Report: xi, 80 p.; Database","numberOfPages":"96","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-127569","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":501205,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113258.htm","linkFileType":{"id":5,"text":"html"}},{"id":402827,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1154/dr1154_database.zip","size":"14.4 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"—Lee Acres-Giant Bloomfield Refinery Database (LAGBRD)"},{"id":402825,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1154/dr1154.pdf","text":"Report","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1154"},{"id":402824,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1154/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Lee Acres Landfill","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.06564331054688,\n 36.683288049295015\n ],\n [\n -108.00590515136717,\n 36.683288049295015\n ],\n [\n -108.00590515136717,\n 36.72072349483175\n ],\n [\n -108.06564331054688,\n 36.72072349483175\n ],\n [\n -108.06564331054688,\n 36.683288049295015\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Anthropogenic activities, including groundwater withdrawals, return flow from irrigated agriculture, and treated wastewater-effluent disposal have the potential to affect groundwater quality in the Lucerne Valley groundwater basin, located in the southwest Mojave Desert. Questions regarding the current state and potential future of groundwater quality in this basin were addressed by (1) considering groundwater data from and findings of historical water-quality studies, (2) evaluating recent (1990–2021) U.S. Geological Survey water-quality and geochemical-tracer data, and (3) assessing groundwater-quality results from samples collected in 2021 to better understand the transport of applied treated wastewater effluent in the subsurface and associated effects of this practice on water quality. As observed by previous studies, differences in groundwater quality existed among the upper, middle, and lower aquifers of the Lucerne Valley groundwater basin, with the lower aquifer characterized by high dissolved-solid content relative to the middle and upper aquifers. Stable and radioisotope tracers indicate that most of the groundwater sampled in the basin was recharged during cooler, wetter climate conditions than those of the present day (2022). Analyses of the 2021 samples collected to examine the subsurface transport of applied treated wastewater effluent were not conclusive but indicate that water from applied treated wastewater effluent is currently (2022) limited to the upper aquifer and likely to remain so given the extensive confining unit below the upper aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221063","collaboration":"Prepared in cooperation with the Mojave Water Agency","usgsCitation":"Fackrell, J.K., 2022, Groundwater quality of the Lucerne Valley groundwater basin, California: U.S. Geological Survey Open-File Report 2022-1063, 19 p., https://doi.org/10.3133/ofr20221063.","productDescription":"viii, 19 p.","numberOfPages":"19","onlineOnly":"Y","ipdsId":"IP-137528","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":501818,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113259.htm","linkFileType":{"id":5,"text":"html"}},{"id":403158,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1063/ofr20221063.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022–1063"},{"id":403163,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225048","text":"Scientific Investigations Report 2022-5048","description":"Stamos, C.L., Larsen, J.D., Powell, R.E., Matti, J.C., and Martin, P., 2022, Hydrogeology and simulation of groundwater flow in the Lucerne Valley groundwater basin, California: U.S. Geological Survey Scientific Investigations Report 2022-5048, 120 p., https://doi.org/10.3133/sir20225048.","linkHelpText":"- Hydrogeology and Simulation of Groundwater Flow in the Lucerne Valley Groundwater Basin, California"},{"id":403159,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1063/ofr20221063.xml"},{"id":403160,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1063/images"},{"id":403157,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1063/covrthb.jpg"},{"id":403185,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221063/full","text":"Report","description":"Open-File Report 2022-1063"}],"country":"United States","state":"California","otherGeospatial":"Lucerne Valley Groundwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.666667,\n 34.266667\n ],\n [\n -117.083333,\n 34.266667\n ],\n [\n -117.083333,\n 34.666667\n ],\n [\n -116.666667,\n 34.666667\n ],\n [\n -116.666667,\n 34.266667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
In early October of 2020, the U.S. Geological Survey (USGS) held a virtual workshop to discuss Gulf and Atlantic Coastal Plains site-response models. Earthquake researchers came together to assess (1) research related to proposed Coastal Plains amplification models and (2) USGS plans for implementing these models. Presentations spanned a broad range of topics from Atlantic and Gulf Coastal Plains geophysical properties including seismic velocity and attenuation, to ground motion amplification models and their impacts on seismic hazard. Interspersed with these presentations were discussions regarding the definition and extent of the Atlantic and Gulf Coastal Plains, potential complexities of wave propagation in the Atlantic and Gulf Coastal Plains, and problems that need to be overcome to implement various proposed site-response models. Based on feedback from this workshop, the USGS working group on Coastal Plain Amplification is considering applying published models that depend on sediment thickness. The working group is also exploring potential application of models that depend on the length of path traversed across the Coastal Plain, including the Gulf Coastal Plain ground-motion model adjustments from the Next Generation Attenuation Relationships for the Eastern United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221006","usgsCitation":"Boyd, O.S., Pratt, T.L., Chapman, M.C., Shumway, A., Rezaeian, S., Moschetti, M.P., and Petersen, M.D., 2022, U.S. Geological Survey coastal plain amplification virtual workshop: U.S. Geological Survey Open-File Report 2022–1006, 25 p., https://doi.org/10.3133/ofr20221006.","productDescription":"vi, 25 p.","onlineOnly":"Y","ipdsId":"IP-128818","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":402497,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1006/ofr20221006.pdf","text":"Report","size":"849 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1006"},{"id":402498,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1006/ofr20221006.xml"},{"id":402496,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1006/coverthb.jpg"},{"id":405344,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1006/images"},{"id":405560,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221006/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1006"}],"country":"United States","otherGeospatial":"Gulf and Atlantic Coast Plains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.716796875,\n 25.522614647623293\n ],\n [\n -81.03515625,\n 31.541089879585808\n ],\n [\n -75.5419921875,\n 35.35321610123823\n ],\n [\n -73.95996093749999,\n 40.38002840251183\n ],\n [\n -69.5654296875,\n 41.57436130598913\n ],\n [\n -70.48828125,\n 42.19596877629178\n ],\n [\n -70.224609375,\n 43.51668853502906\n ],\n [\n -66.97265625,\n 44.62175409623324\n ],\n [\n -68.2470703125,\n 45.920587344733654\n ],\n [\n -71.630859375,\n 43.45291889355465\n ],\n [\n -76.025390625,\n 40.613952441166596\n ],\n [\n -78.31054687499999,\n 38.54816542304656\n ],\n [\n -81.4306640625,\n 33.578014746143985\n ],\n [\n -83.056640625,\n 30.86451022625836\n ],\n [\n -92.98828125,\n 30.977609093348686\n ],\n [\n -96.328125,\n 30.29701788337205\n ],\n [\n -98.4375,\n 28.57487404744697\n ],\n [\n -98.0859375,\n 26.470573022375085\n ],\n [\n -97.20703125,\n 25.997549919572112\n ],\n [\n -96.9873046875,\n 27.371767300523047\n ],\n [\n -95.5810546875,\n 28.65203063036226\n ],\n [\n -93.8232421875,\n 29.305561325527698\n ],\n [\n -91.40625,\n 29.19053283229458\n ],\n [\n -89.9560546875,\n 28.8831596093235\n ],\n [\n -88.9892578125,\n 29.84064389983441\n ],\n [\n -86.17675781249999,\n 30.031055426540206\n ],\n [\n -84.111328125,\n 29.49698759653577\n ],\n [\n -83.1005859375,\n 28.76765910569123\n ],\n [\n -82.529296875,\n 26.82407078047018\n ],\n [\n -81.123046875,\n 25.045792240303445\n ],\n [\n -80.5078125,\n 24.726874870506972\n ],\n [\n -79.716796875,\n 25.522614647623293\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
P.O. Box 25046, Mail Stop 966
Denver, CO 80225
Revitalization of the Colorado River delta riparian corridor and increasing riparian plant greenness and water use may be accomplished by added water and restoration efforts to offset declines measured since 2000 by Landsat. We use the two-band Enhanced Vegetation Index (EVI2; a proxy for greenness) and evapotranspiration (ET, mm/day) using EVI2 and potential ET(ETo) from Yuma Valley. We assess if restoration with only 7.5% landcover had an impact on the unrestored reach-level landcover by measuring these two metrics, EVI2 and ET(EVI2) by comparing restored and unrestored areas. A key finding is that over 21-years EVI2 in the unrestored corridor decreased by 23.6% and ET(EVI2) decreased by 32% (0.87 mm/day). Since 2011, the unrestored reaches lost EVI2 (11%) and −0.73 mm/day ET(EVI2) (28%), but restored sites increased in EVI2 (36%) and 0.58 mm/day ET(EVI2) (20%). Water delivered to restored sites increased EVI2 by 33.6% and 1.29 mm/day ET(EVI2) (58%). EVI2 and ET(EVI2) as a function of the Standardized Precipitation Evapotranspiration Index for drought periods highlight that restored and unrestored sites respond differently. Unrestored reaches are in decline; restored sites show increases in EVI2 and ET(EVI2). Restored sites do not have a significant impact on unrestored adjacent area, but smaller surface flows, a greater reliance on directed agricultural return flows, and deliveries of water to active restoration sites have revitalized habitat and increased ecosystem services in the delta.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.13036","usgsCitation":"Nagler, P.L., Sall, I., Barreto-Muñoz, A., Gómez-Sapiens, M., Nouri, H., Borujeni, S.C., and Didan, K., 2022, Effect of restoration on plant greenness and water use in relation to drought in the riparian corridor of the Colorado River delta: Journal of the American Water Resource Association (JAWRA), v. 58, no. 5, p. 746-784, https://doi.org/10.1111/1752-1688.13036.","productDescription":"39 p.","startPage":"746","endPage":"784","ipdsId":"IP-133058","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":489192,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.13036","text":"Publisher Index Page"},{"id":403063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","otherGeospatial":"Colorado River delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.90600585937499,\n 31.1140915948987\n ],\n [\n -114.84832763671876,\n 31.015278981711266\n ],\n [\n -114.15069580078125,\n 31.50362930577303\n ],\n [\n -114.29351806640625,\n 31.580875273985466\n ],\n [\n -114.51873779296875,\n 31.69779270531287\n ],\n [\n -114.59564208984374,\n 31.823898240526955\n ],\n [\n -114.76043701171875,\n 31.98012335736804\n ],\n [\n -114.92248535156249,\n 32.115148622612445\n ],\n [\n -114.91424560546875,\n 32.13375715632646\n ],\n [\n -114.98565673828124,\n 32.19188435632485\n ],\n [\n -115.22735595703125,\n 31.982453066235763\n ],\n [\n -115.18615722656249,\n 31.847232251731132\n ],\n [\n -115.10650634765625,\n 31.80289258670676\n ],\n [\n -115.06256103515624,\n 31.704803074739214\n ],\n [\n -115.08453369140625,\n 31.68143311662596\n ],\n [\n -115.07354736328125,\n 31.48020882071693\n ],\n [\n -114.90600585937499,\n 31.1140915948987\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":845784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sall, Ibrahima 0000-0002-7526-636X","orcid":"https://orcid.org/0000-0002-7526-636X","contributorId":251750,"corporation":false,"usgs":false,"family":"Sall","given":"Ibrahima","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":845785,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barreto-Muñoz, Armando","contributorId":239891,"corporation":false,"usgs":false,"family":"Barreto-Muñoz","given":"Armando","affiliations":[{"id":48028,"text":"University of Arizona, Biosystems Engineering, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":845786,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gómez-Sapiens, Martha","contributorId":292779,"corporation":false,"usgs":false,"family":"Gómez-Sapiens","given":"Martha","affiliations":[{"id":62998,"text":"Department of Geosciences, University of Arizona, Tucson, AZ 85721, USA","active":true,"usgs":false}],"preferred":false,"id":845789,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nouri, Hamideh","contributorId":178847,"corporation":false,"usgs":false,"family":"Nouri","given":"Hamideh","affiliations":[],"preferred":false,"id":845787,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Borujeni, Sattar Chavoshi","contributorId":240671,"corporation":false,"usgs":false,"family":"Borujeni","given":"Sattar","email":"","middleInitial":"Chavoshi","affiliations":[],"preferred":false,"id":845788,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Didan, Kamel","contributorId":292780,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":845790,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70232516,"text":"70232516 - 2022 - Depositional controls on detrital zircon provenance: An example from upper Cretaceous strata, southern Patagonia","interactions":[],"lastModifiedDate":"2022-07-06T14:09:16.662089","indexId":"70232516","displayToPublicDate":"2022-07-06T08:53:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Depositional controls on detrital zircon provenance: An example from upper Cretaceous strata, southern Patagonia","docAbstract":"Understanding how depositional environments within a sedimentary system redistribute and sequester sediment is critical for interpreting basin-scale provenance trends. However, sedimentary source-to-sink models commonly examine temporal changes and do not consider how variation in sedimentation processes across a dispersal pathway may result in contrasting provenance signatures. In this paper, we demonstrate a down-paleoslope shift in detrital zircon provenance signatures correlated with shallow-marine lithofacies patterns from the Upper Cretaceous La Anita Formation and underlying continental slope lithofacies of the Alta Vista Formation (Magallanes-Austral Basin, southern Patagonia). New stratigraphic, sedimentologic, and lithofacies analysis results from the La Anita Formation suggest an upward shoaling succession, from a (i) storm-influenced shoreface, (ii) fluvially-dominated, wave-influenced delta, and a (iii) high-energy, gravelly foreshore. Stratigraphic sections are paired with U-Pb detrital zircon sandstone samples (N = 20; n = 5219), which provide both maximum depositional ages and provenance characteristics. While all samples contain abundant zircon derived from the Andean volcanic arc (ca. 145–75 Ma), the amount from both Jurassic distal volcanic massifs (ca. 188–162 Ma) and recycled orogenic sources exhumed during the advance of the Cretaceous fold-and-thrust belt (>200 Ma; 157–142 Ma) vary with changes in depositional environment. We argue that down-paleoslope, systematic enriching of local fold-and-thrust belt material within the La Anita Formation is reflective of progressive mixing of grains transported via shallow-marine processes, while distally enriched fluvio-deltaic transported zircons were sourced from large, regional catchments. This suggests that competition between transport processes across a shallow and marginal marine sequence of rocks affects the resulting provenance signatures recorded within a single stratigraphic succession. These data also detail the degree of sediment pathway connectivity between shallow-marine sources and deep-marine sinks. Detrital zircon results from muddy continental slope facies of the Alta Vista Formation are made up entirely locally derived material, while zircon results from deep-water, sand-rich channel facies of the Formation are indistinguishable from coeval fluvio-deltaic zircon signatures. This implies that continental shelf-to-slope connectivity in a sediment dispersal system, via submarine canyons or shelf-edge delta progradation, is necessary for detrital zircon distributions from the shallow-marine realm to propagate into the deeper marine.","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2022.824930","usgsCitation":"Dobbs, S.C., Malkowski, M.A., Schwartz, T.M., Sickmann, Z.T., and Graham, S.A., 2022, Depositional controls on detrital zircon provenance: An example from upper Cretaceous strata, southern Patagonia: Frontiers in Earth Science, v. 10, 824930, 25 p., https://doi.org/10.3389/feart.2022.824930.","productDescription":"824930, 25 p.","ipdsId":"IP-135536","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":447205,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2022.824930","text":"Publisher Index Page"},{"id":403062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Argentina","otherGeospatial":"Patagonia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.6748046875,\n -46.346927610556754\n ],\n [\n -67.8515625,\n -46.346927610556754\n ],\n [\n -67.8515625,\n -42.90816007196053\n ],\n [\n -71.6748046875,\n -42.90816007196053\n ],\n [\n -71.6748046875,\n -46.346927610556754\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-06-22","publicationStatus":"PW","contributors":{"editors":[{"text":"Galli, Claudia Ines","contributorId":292835,"corporation":false,"usgs":false,"family":"Galli","given":"Claudia","email":"","middleInitial":"Ines","affiliations":[],"preferred":false,"id":845885,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Dobbs, Stephen C.","contributorId":222427,"corporation":false,"usgs":false,"family":"Dobbs","given":"Stephen","email":"","middleInitial":"C.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":845746,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Malkowski, Matthew A.","contributorId":292768,"corporation":false,"usgs":false,"family":"Malkowski","given":"Matthew","email":"","middleInitial":"A.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":845747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwartz, Theresa Maude 0000-0001-6606-4072","orcid":"https://orcid.org/0000-0001-6606-4072","contributorId":245180,"corporation":false,"usgs":true,"family":"Schwartz","given":"Theresa","email":"","middleInitial":"Maude","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":845748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sickmann, Zachary T.","contributorId":292770,"corporation":false,"usgs":false,"family":"Sickmann","given":"Zachary","email":"","middleInitial":"T.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":845749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Graham, Stephan A.","contributorId":45902,"corporation":false,"usgs":true,"family":"Graham","given":"Stephan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":845750,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238710,"text":"70238710 - 2022 - Using mollusks as indicators of restoration in nearshore zones of south Florida's estuaries","interactions":[],"lastModifiedDate":"2022-12-06T12:57:05.199097","indexId":"70238710","displayToPublicDate":"2022-07-06T06:54:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1106,"text":"Bulletin of Marine Science","active":true,"publicationSubtype":{"id":10}},"title":"Using mollusks as indicators of restoration in nearshore zones of south Florida's estuaries","docAbstract":"The timing of autumn migration in ducks is influenced by a range of environmental conditions that may elicit individual experiences and responses from individual birds, yet most studies have investigated relationships at the population level. We used data from individual satellite-tracked mallards (Anas platyrhynchos) to model the timing and environmental drivers of autumn migration movements at a continental scale.
We combined two sets of location records (2004–2007 and 2010–2011) from satellite-tracked mallards during autumn migration in the Mississippi Flyway, and identified records that indicated the start of long-range (≥ 30 km) southward movements during the migration period. We modeled selection of departure date by individual mallards using a discrete choice model accounting for heterogeneity in individual preferences. We developed candidate models to predict the departure date, conditional on daily mean environmental covariates (i.e. temperature, snow and ice cover, wind conditions, precipitation, cloud cover, and pressure) at a 32 × 32 km resolution. We ranked model performance with the Bayesian Information Criterion.
Departure was best predicted (60% accuracy) by a “winter conditions” model containing temperature, and depth and duration of snow cover. Models conditional on wind speed, precipitation, pressure variation, and cloud cover received lower support. Number of days of snow cover, recently experienced snow cover (snow days) and current snow cover had the strongest positive effect on departure likelihood, followed by number of experienced days of freezing temperature (frost days) and current low temperature. Distributions of dominant drivers and of correct vs incorrect prediction along the movement tracks indicate that these responses applied throughout the latitudinal range of migration. Among recorded departures, most were driven by snow days (65%) followed by current temperature (30%).
Our results indicate that among the tested environmental parameters, the dominant environmental driver of departure decision in autumn-migrating mallards was the onset of snow conditions, and secondarily the onset of temperatures close to, or below, the freezing point. Mallards are likely to relocate southwards quickly when faced with snowy conditions, and could use declining temperatures as a more graduated early cue for departure. Our findings provide further insights into the functional response of mallards to weather factors during the migration period that ultimately determine seasonal distributions.
","language":"English","publisher":"Springer","doi":"10.1186/s40462-021-00299-x","usgsCitation":"Weller, F.G., Beatty, W.S., Webb, E.B., Kesler, D.C., Krementz, D.G., Asante, K., and Naylor, L.W., 2022, Environmental drivers of autumn migration departure decisions in midcontinental mallards: Movement Ecology, v. 10, 1, 13 p., https://doi.org/10.1186/s40462-021-00299-x.","productDescription":"1, 13 p.","ipdsId":"IP-132818","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447212,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-021-00299-x","text":"Publisher Index Page"},{"id":432679,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Weller, Florian G.","contributorId":341013,"corporation":false,"usgs":false,"family":"Weller","given":"Florian","email":"","middleInitial":"G.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":909868,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beatty, William S. 0000-0003-0013-3113 wbeatty@usgs.gov","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":173946,"corporation":false,"usgs":true,"family":"Beatty","given":"William","email":"wbeatty@usgs.gov","middleInitial":"S.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":908794,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":908793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kesler, Dylan C.","contributorId":216845,"corporation":false,"usgs":false,"family":"Kesler","given":"Dylan","email":"","middleInitial":"C.","affiliations":[{"id":37290,"text":"The Institute for Bird Populations","active":true,"usgs":false}],"preferred":false,"id":908795,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krementz, David G. 0000-0002-5661-4541 dkrementz@usgs.gov","orcid":"https://orcid.org/0000-0002-5661-4541","contributorId":2827,"corporation":false,"usgs":true,"family":"Krementz","given":"David","email":"dkrementz@usgs.gov","middleInitial":"G.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908796,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Asante, Kwasi","contributorId":59632,"corporation":false,"usgs":true,"family":"Asante","given":"Kwasi","email":"","affiliations":[],"preferred":false,"id":908797,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Naylor, Luke W.","contributorId":145840,"corporation":false,"usgs":false,"family":"Naylor","given":"Luke","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":908798,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70232502,"text":"fs20223045 - 2022 - Connecticut and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:04:51.64174","indexId":"fs20223045","displayToPublicDate":"2022-07-05T13:52:57","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3045","displayTitle":"Connecticut and Landsat","title":"Connecticut and Landsat","docAbstract":"Connecticut, the third-smallest State by land area, is the fourth most densely populated in the United States. Connecticut’s tightly packed cities serve as international hubs for the finance and insurance industries. These same urban enclaves host highly regarded institutions of higher learning, such as Yale, the University of Connecticut, and Quinnipiac and Wesleyan Universities.
Yet Connecticut has surprisingly varied natural features. Its residents are never far from pristine environments that teem with wildlife and convey the sounds, scents, and scenery of the natural world.
The Connecticut River, the longest in New England at 407 miles, cuts through the State’s center and drains into Long Island Sound, as do the Thames and Housatonic Rivers. Together, these rivers form an estuary spawning ground for hundreds of freshwater and saltwater aquatic species. North and east of the coastal lowlands that hug the estuary are the highlands of the Southern Taconic Range and the Berkshires. North and west of the coastline, smaller rivers and valleys weave through densely forested landscapes dotted with State parks and scenic lakes.
Imagery from the U.S. Geological Survey Landsat archive, a trusted public resource with more than 50 years of worldwide Earth observations, provides a powerful tool for analyzing the interplay of human habitation and natural landscapes in Connecticut. Landsat data also offer land managers a critical information source for managing damage from invasive species and monitoring the local landscape effects of a changing global climate.
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12201 Sunrise Valley Drive
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Few studies have evaluated the optimal sampling design for tracking small mammal population trends, especially for rare or difficult to detect species. Spatially explicit capture-recapture (SECR) models present an advancement over non-spatial models by accounting for individual movement when estimating density. The salt marsh harvest mouse (SMHM; Reithrodontomys raviventris) is a federal and California state listed endangered species endemic to the San Francisco Bay-Delta estuary, California, USA; where a population in a subembayment has been continually monitored over an 18-year period using mark-recapture methods. We analyzed capture data within a SECR modeling framework that allowed us to account for differences in detection and movement between sexes. We compared the full dataset to subsampling scenarios to evaluate how the grid size (area) of the trap design, trap density (spacing), and number of consecutive trapping occasions (duration) influenced density estimates. To validate the subsampling methods, we ran Monte Carlo simulations based on the true parameter estimates for each specific year. We found that reducing the area of the trapping design by more than 36% resulted in the inability of the SECR model to replicate density estimates within the SE of the original density estimates. However, when trapping occasions were reduced from 4 to 3-nights the density estimates were indistinguishable from the full dataset. Furthermore, reducing trap density by 50% also resulted in density estimates comparable to the full dataset and was a substantially better model than reducing the trap area by 50%. Overall, our results indicated that moderate reductions in the number of trapping occasions or trap density could yield similar density estimates when using a SECR approach. This approach allows the optimization of field trapping efforts and designs by reducing field efforts while maintaining the same population estimate compared to the full dataset. Using a SECR approach may help other wildlife programs identify sampling efficiencies without sacrificing data integrity for long term monitoring of population densities.
Coral-reef degradation is driving global-scale reductions in reef-building capacity and the ecological, geological, and socioeconomic functions it supports. The persistence of those essential functions will depend on whether coral-reef management is able to rebalance the competing processes of reef accretion and erosion. Here, we reconstructed census-based carbonate budgets of 46 reefs throughout the Florida Keys from 1996 to 2019. We evaluated the environmental and ecological drivers of changing budget states and compared historical trends in reef-accretion potential to millennial-scale baselines of accretion from reef cores and future projections with coral restoration. We found that historically, most reefs had positive carbonate budgets, and many had reef-accretion potential comparable to the ~3 mm year−1 average accretion rate during the peak of regional reef building ~7000 years ago; however, declines in reef-building Acropora palmata and Orbicella spp. corals following a series of thermal stress events and coral disease outbreaks resulted in a shift from positive to negative budgets for most reefs in the region. By 2019, only ~15% of reefs had positive net carbonate production. Most of those reefs were in inshore, Lower Keys patch-reef habitats with low water clarity, supporting the hypothesis that environments with naturally low irradiance may provide a refugia from thermal stress. We caution that our estimated carbonate budgets are likely overly optimistic; comparison of reef-accretion potential to measured accretion from reef cores suggests that, by not accounting for the role of nonbiological physical and chemical erosion, census-based carbonate budgets may underestimate total erosion by ~1 mm year−1 (−1.15 kg CaCO3 m−2 year−1). Although the present state of Florida's reefs is dire, we demonstrate that the restoration of reef-building corals has the potential to help mitigate declines in reef accretion in some locations, which could allow some key ecosystem functions to be maintained until the threat of global climate change is addressed.
Urban forests are recognized as a nature-based solution for stormwater management. This study assessed the underlying processes and extent of runoff reduction due to street trees with a paired-catchment experiment conducted in two sewersheds of Fond du Lac, Wisconsin. Computer models are flexible, fast, and low-cost options to generalize and assess the hydrologic processes determined in field studies. A state-of-the-art, public-domain model, which explicitly simulates urban tree hydrology, i-Tree Hydro, was used to simulate the paired-catchment experiment, and results from field observations and simulation predictions were compared to assess model validity and suitability as per conditions in the broader Great Lakes basin. Model parameters were aligned with observed conditions using automatic and manual calibration. Model performance metrics were used to quantify the weekly performance of calibration and to validate predictions. Those calibration metrics differed substantially between the two periods simulated, but most calibration metrics remained positive, indicating the model was not fitting only the period used for calibration. Predicted avoided runoff for a five-month leaf-on period was 64 L/m2 of canopy, 4 % lower than the field-estimated avoided runoff of 66 L/m2 of canopy. Interception was the most directly comparable process between the model and field observations. Based on 5 storms sampled, field estimation of precipitation intercepted and retained on trees averaged 63 % and ranged from 22 % to 81 %, while model estimation averaged 61 % and ranged from 36 % to 99 %. This model was able to fit predictions to observed catchment discharge but required extensive manual calibration to do so. The i-Tree Hydro model predicted avoided runoff comparable with the field study and earlier assessments. Additional field studies in similar settings are needed to confirm findings and improve transferability to other tree species and environmental settings.
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Many approaches have been used to ensure the regulated community participates in the decision-making process, including public hearings, hunter season-date preference surveys, and hunter task forces or committees. Although hearings, surveys, and task force approaches include portions of the regulated community (typically the most avid) they may not necessarily reflect the opinions and values of all stakeholders. Additionally, these approaches rely heavily on limited anecdotal observations that may be unduly influenced by hunter avidity (e.g., days spent afield), hunter density, species preferences, and property access. To address the challenges caused by engaging only portions of the stakeholder community, we used a structured decision-making framework that included a 2017 duck hunter survey to elucidate values of a representative sample of the regulated community in each waterfowl hunting zone in the state of New York, USA. Rather than asking duck hunters about their specific duck hunting season date preferences, we asked them to rank 6 objectives describing what they value in their hunting experience (e.g., maximizing the opportunity to see mallards [Anas platyrhynchos] and black ducks [Anas rubripes], maximizing the number of weekend days). Four of the 6 objectives described duck species availability (i.e., abundance or immigration) and the remaining 2 described considerations that affect an individual hunter's opportunity or limitation to going duck hunting (i.e., holidays or conflicting hunting seasons). We used spatiotemporal abundance models derived from eBird citizen science data to estimate abundance and immigration rates of ducks in each waterfowl zone. We evaluated up to 9 unique season date alternatives developed by duck hunter task forces to determine which season date alternative best satisfied the competing objectives of duck hunters in each zone. The approach we developed allowed for selection of optimal duck hunting season dates and successfully involved avid duck hunters in the regulation development stages, while ensuring that the values of a representative sample of all stakeholders were directly considered through a clear and transparent decision-making process.
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