Two medications (one with florfenicol and one with oxytetracycline) that are approved in the United States to control mortality due to furunculosis associated with Aeromonas salmonicida were assessed to determine their efficacy in medicated feeds to treat A. salmonicida-infected Cisco (also known as Lake Herring) Coregonus artedi. Cisco were subjected to static infection baths containing A. salmonicida or a sham control and then were distributed to replicate test tanks within four treatment groups: (1) fish infected with A. salmonicida and treated with 15 mg florfenicol·kg body weight (BW)−1·d−1, (2) fish infected with A. salmonicida and treated with 83 mg oxytetracycline·kg BW−1·d−1, (3) fish infected with A. salmonicida and treated with a nonmedicated control feed, and (4) uninfected fish treated with a nonmedicated control feed. Medicated and comparative nonmedicated feed rations were administered at 2% BW/d for 10 consecutive days in accordance with the U.S. Food and Drug Administration-approved drug label, followed by a 7-d postdosing observation period using only nonmedicated feed. Cisco that were infected with A. salmonicida and treated with florfenicol (79% survival) and oxytetracycline (85% survival) had significantly higher survival than A. salmonicida-infected fish that received no medicated treatment (3% survival). No statistical difference in Cisco survival between the two medicated feed types was found. Aeromonas salmonicida was not detected in the kidney tissue of any surviving fish treated with medicated feeds at 7 d postdosing using quantitative PCR analysis. Overall, this study demonstrated that florfenicol- and oxytetracycline-medicated feeds were effective A. salmonicida treatments for Cisco. Outcomes may inform ongoing propagation efforts for Cisco restoration within the Great Lakes basin.
Geologic maps are valuable tools in planetary science. Though planetary geologic maps are similar to terrestrial (Earthbased) geologic maps, the nature of planetary exploration introduces unique challenges for geologic mappers. Terrestrial geologic mappers prepare products from field-based observation, often comparing or refining those with aerial and (or) orbital images. Planetary geologic mapping relies almost exclusively on remote observations, which are made by orbiting spacecraft. Therefore, with a few exceptions for locations with rovers, landers, or crewed surface missions, planetary geologic mappers are not able to observe their map area in detail or at smaller scales. As a result, they must interpret and describe their map features differently than those used in terrestrial geologic maps. For example, terrestrial geologic mappers commonly divide units by lithology (rock type) or grain size. However, planetary geologic mappers often do not have detailed enough information to know what type of rock or grain size is present, and instead must divide the planet’s surface into geologic units using differences in tone, color, and surface texture (at multiple scales). Cross-cutting relationships, where apparent, can provide excellent—and often crucial—observations for identifying and subdividing geologic units using orbital datasets. The process of creating planetary maps has evolved over time, from original hand-drawn maps created during the early 1800s through the late 1900s, to the fully digital products created today. Modern-day planetary geologic mapping uses Geographic Information Systems (GIS) software and tools to visualize data, delineate units and landforms, and accurately convey spatial relationships to a map user using cartographic features represented by points, lines, and polygons. This tutorial was written to familiarize both new and experienced planetary geologic mappers with ArcGIS Pro, a commonly used GIS software package developed by Esri. This tutorial introduces new planetary geologic mappers to fundamental concepts and best practices in planetary geologic mapping. For mappers with experience using ArcMap (a previous version of ArcGIS), this tutorial will help to familiarize users with new changes in layout and functionality, so current projects may be migrated into the ArcGIS Pro environment. No prior knowledge is required, although a general familiarity with geology, GIS, and planetary science is recommended. This tutorial includes links to helpful glossaries of common GIS terms and other GIS and planetary science resources in appendix 1. For additional information on planetary geologic mapping, see the U.S Geological Survey (USGS) Astrogeology NASA Planetary Geologic Mapping Program website and the Planetary Mapping Guidelines. Numerous ArcGIS tutorials are also available from Esri’s Tutorials page, through the Esri Academy catalog, and Esri’s ArcGIS Pro Resources page.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11B14","usgsCitation":"Black, S.R., 2023, User’s Guide to planetary image analysis and geologic mapping in ArcGIS Pro: U.S. Geological Survey Techniques and Methods 11–B14, 180 p., https://doi.org/10.3133/tm11B14.","productDescription":"vi, 180 p.","onlineOnly":"Y","ipdsId":"IP-133084","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":413590,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/b14/tm11b14.pdf","text":"Report","size":"88.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 11-B14"},{"id":413589,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11/b14/coverthb.jpg"},{"id":413591,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm11B13","text":"TM 11-B13 —","description":"TM 11-B13","linkHelpText":"Planetary geologic mapping protocol—2022"}],"contact":"Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
River-to-lake transitional areas are biogeochemically active ecosystems that can alter the amount and composition of dissolved organic matter (DOM) as it moves through the aquatic continuum. However, few studies have directly measured carbon processing and assessed the carbon budget of freshwater rivermouths. We compiled measurements of dissolved organic carbon (DOC) and DOM in several water column (light and dark) and sediment incubation experiments conducted in the mouth of the Fox river (Fox rivermouth) upstream from Green Bay, Lake Michigan. Despite variation in the direction of DOC fluxes from sediments, we found that the Fox rivermouth was a net sink of DOC where water column DOC mineralization outweighed the release of DOC from sediments at the rivermouth scale. Although we found DOM composition also changed during our experiments, alterations in DOM optical properties were largely independent of the direction of sediment DOC fluxes. We found a consistent decrease in humic-like and fulvic-like terrestrial DOM and a consistent increase in the overall microbial composition of rivermouth DOM during our incubations. Moreover, greater ambient total dissolved phosphorus concentrations were positively associated with the consumption of terrestrial humic-like, microbial protein-like, and more recently derived DOM but had no effect on bulk DOC in the water column. Unexplained variation indicates that other environmental controls and water column processes affect the processing of DOM in this rivermouth. Nonetheless, the Fox rivermouth appears capable of substantial DOM transformation with implications for the composition of DOM entering Lake Michigan.
Introduction: Climate change is impacting forest-based agricultural systems with implications for producer decision-making and livelihoods. This article presents a case study on the observations, perceptions, knowledge, and adaptation strategies of maple syrup producers in the United States to climate change.
Methods: We carried out two semi-structured surveys with maple producers on: (1) climate change and its impacts on the maple system (n = 106 participants); and (2) responses to climate adaptation scenarios (n = 98 participants). Additionally, we carried out two focus groups and key informant interviews (n = 70+) to understand barriers and opportunities for climate adaptation. One of these focus groups and follow up key informant interviews was with tribally affiliated community members with the intention to acknowledge Indigenous Peoples’ voices, history, and relationships to the land.
Results: Findings highlight that most of the surveyed producers (89%) have experienced the negative impacts of climate on maple syrup production. While 40% of participants feel concerned regarding the future of the maple system, 39% feel hopeful, with significant differences based on the age of the surveyed producers. The majority of producers have adapted their harvesting practices to climate effects. Producers shared knowledge of multiple adaptation strategies in response to climate scenarios comprised of: (1) stand management practices such as diversification of sap species tapped; (2) harvesting practices such as changing the type and number of taps; (3) sap processing practices focused on the integration of technology such as the use of an evaporator and reverse osmosis; and (4) marketing practices such as innovation of products and marketing different maple syrup characteristics. Responses shared by tribally affiliated producers highlight knowledge of multiple adaptation strategies that focus on long-term ecological management of forests rather than technological solutions.
Discussion: Overall, findings emphasize the importance of cooperation and diversification at every level and dimension of the maple system for its long-term resilience.
","language":"English","publisher":"Frontiers","doi":"10.3389/ffgc.2023.1092218","usgsCitation":"Ahmed, S., Lutz, D.A., Rapp, T., Huish, R.H., Dufour, B., Brunelle, A., Morelli, T.L., Stinson, K.A., and Warne, T., 2023, Climate change and maple syrup: Producer observations, perceptions, knowledge, and adaptation strategies: Frontiers in Forests and Global Change, v. 6, 1092218, 21 p., https://doi.org/10.3389/ffgc.2023.1092218.","productDescription":"1092218, 21 p.","ipdsId":"IP-147787","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":444309,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffgc.2023.1092218","text":"Publisher Index Page"},{"id":422654,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","noUsgsAuthors":false,"publicationDate":"2023-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Ahmed, Selena","contributorId":232416,"corporation":false,"usgs":false,"family":"Ahmed","given":"Selena","email":"","affiliations":[],"preferred":false,"id":888249,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lutz, David A.","contributorId":232418,"corporation":false,"usgs":false,"family":"Lutz","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":888250,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rapp, T Joshua","contributorId":331633,"corporation":false,"usgs":false,"family":"Rapp","given":"T Joshua","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":888251,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huish, Ryan H.","contributorId":232414,"corporation":false,"usgs":false,"family":"Huish","given":"Ryan","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":888252,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dufour, Boris","contributorId":232415,"corporation":false,"usgs":false,"family":"Dufour","given":"Boris","email":"","affiliations":[],"preferred":false,"id":888253,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brunelle, Autumn","contributorId":331634,"corporation":false,"usgs":false,"family":"Brunelle","given":"Autumn","affiliations":[{"id":79257,"text":"Monroe Count Government","active":true,"usgs":false}],"preferred":false,"id":888254,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":888255,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stinson, Kristina A.","contributorId":232417,"corporation":false,"usgs":false,"family":"Stinson","given":"Kristina","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":888256,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Warne, Teresa","contributorId":331635,"corporation":false,"usgs":false,"family":"Warne","given":"Teresa","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":888257,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70240996,"text":"70240996 - 2023 - The relative stability of planktic foraminifer thermal preferences over the past 3 million years","interactions":[],"lastModifiedDate":"2023-03-03T12:47:53.890983","indexId":"70240996","displayToPublicDate":"2023-03-02T06:46:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1816,"text":"Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"The relative stability of planktic foraminifer thermal preferences over the past 3 million years","docAbstract":"Stationarity of species’ ecological tolerances is a first-order assumption of paleoenvironmental reconstruction based upon analog methods. To test this and other assumptions used in quantitative analysis of foraminiferal faunas for paleoceanographic reconstruction, we analyzed paired alkenone unsaturation ratio (
Restoration in dryland ecosystems often has poor success due to low and variable water availability, degraded soil conditions, and slow plant community recovery rates. Restoration treatments can mitigate these constraints but, because treatments and subsequent monitoring are typically limited in space and time, our understanding of their applicability across broader environmental gradients remains limited. To address this limitation, we implemented and monitored a standardized set of seeding and soil surface treatments (pits, mulch, and ConMod artificial nurse plants) designed to enhance soil moisture and seedling establishment across RestoreNet, a growing network of 21 diverse dryland restoration sites in the southwestern USA over 3 years. Generally, we found that the timing of precipitation relative to seeding and the use of soil surface treatments were more important in determining seeded species emergence, survival, and growth than site-specific characteristics. Using soil surface treatments in tandem with seeding promoted up to 3× greater seedling emergence densities compared with seeding alone. The positive effect of soil surface treatments became more prominent with increased cumulative precipitation since seeding. The seed mix type with species currently found within or near a site and adapted to the historical climate promoted greater seedling emergence densities compared with the seed mix type with species from warmer, drier conditions expected to perform well under climate change. Seed mix and soil surface treatments had a diminishing effect as plants developed beyond the first season of establishment. However, we found strong effects of the initial period seeded and of the precipitation leading up to each monitoring date on seedling survival over time, especially for annual and perennial forbs. The presence of exotic species exerted a negative influence on seedling survival and growth, but not initial emergence. Our findings suggest that seeded species recruitment across drylands can generally be promoted, regardless of location, by (1) incorporation of soil surface treatments, (2) employment of near-term seasonal climate forecasts, (3) suppression of exotic species, and (4) seeding at multiple times. Taken together, these results point to a multifaceted approach to ameliorate harsh environmental conditions for improved seeding success in drylands, both now and under expected aridification.
In aquifer-dependent regions, balancing aquifer protection, desalination, economic development, agricultural irrigation, and food security can be better managed through discovery and development of sources of sustainable groundwater pumping. Aquifer desalination for irrigation to protect food security can mitigate pressure on local freshwater aquifers. Despite its importance, little peer reviewed work to date has identified the economic capacity to pay for aquifer desalination for irrigation to mitigate freshwater aquifer drawdown. The novel contribution of this work is the development and application of an innovative method to assess the economic capacity to pay for aquifer desalination for irrigation for a recently discovered large saline aquifer. It develops an original framework to assess the capacity to pay for aquifer desalination, the results of which can help guide policymakers on efficient and sustainable pumping approaches across users, aquifers, and time periods. A mathematical programming model is developed to economically analyze the 200 billion cubic meter Lotikipi Aquifer, discovered in 2013 in northern Kenya using modern remote sensing methods. While initial pumping of the Lotikipi Aquifer was halted due to high groundwater salinity levels, interest remains strong in assessing the economic capacity to pay for groundwater desalination because of its potential role in protecting regional food security generated by aquifer pumping for irrigation. The model is formulated by calibrating optimized pumping patterns in two existing freshwater aquifers to replicate observed historical pumping levels. Based on that exercise, a second model is developed to identify a least cost set of pumping restrictions that return each of three regional aquifers to starting conditions over a seven-year time period. A third model extends the second by adding a constraint of a minimum required level of food grain security supported by irrigation pumping from the aquifer system. Results show that the economic capacity to pay for aquifer desalination for irrigated agriculture lies in the range of $0.08 - $0.18 USD per cubic meter under current economic conditions and desalination technologies available. While this economic capacity to pay is lower than its current cost in most places, the future could be more optimistic. Advances in desalination technology, higher crop prices, technical advance in agriculture, and development of drought-resistant crops can all contribute to a future capacity to economically justify the expense of desalination.
Of the emerging contaminant types thought to threaten freshwater biota, per- and polyfluoroalkyl substances appear to be particularly widespread, and limited studies conducted with these compounds thus far indicate insects may be particularly sensitive to them. This study investigated the short- and long-term effects of two commonly detected compounds on the laboratory-reared mayfly Neocloeon triangulifer in water only exposures. In acute tests, the mayfly was approximately 85-fold more sensitive to perfluorooctanesulfonate (PFOS) and 7-fold more sensitive to perfluorooctanoic acid (PFOA) than the next most sensitive species reported in the literature. In 14 day and full-life chronic PFOS toxicity tests, the lowest 10% effect concentration was 0.272 μg of PFOS/L, which is lower than any previous reports to the best of our knowledge, but consistent in demonstrating the sensitivity of insects to this compound. Conversely, N. triangulifer was not particularly chronically sensitive to PFOA. This study demonstrates the risks of environmentally relevant concentrations of PFOS to a freshwater insect and suggests that investigation of the toxicity of more compounds with different carbon-chain lengths and functional groups to freshwater insects is needed.
Introduction: Tree defense characteristics play a crucial role in modulating conifer bark beetle interactions, and there is a growing body of literature investigating factors mediating tree growth and resin-based defenses in conifers. A subset of studies have looked at relationships between tree growth, resin duct morphology and climate; however, these studies are almost exclusively from lower-elevation, moisture-limited systems. The relationship between resin ducts and climate in higher-elevation, energy-limited ecosystems is currently poorly understood.
Methods: In this study, we: (1) evaluated the relationship between biological trends in tree growth, resin duct anatomy, and climatic variability and (2) determined if tree growth and resin duct morphology of whitebark pine, a high-elevation conifer of management concern, is constrained by climate and/or regional drought conditions.
Results: We found that high-elevation whitebark pine trees growing in an energy-limited system experienced increased growth and defense under warmer and regionally drier conditions, with climate variables explaining a substantive proportion of variation (∼20–31%) in tree diameter growth and resin duct anatomy.
Discussion: Our results suggest that whitebark pine growth and defense was historically limited by short growing seasons in high-elevation environments; however, this relationship may change in the future with prolonged warming conditions.
","language":"English","publisher":"Frontiers","doi":"10.3389/ffgc.2023.1089138","usgsCitation":"Kichas, N., Pederson, G.T., Hood, S.M., Everett, R.G., and McWethy, D.B., 2023, Increased whitebark pine (Pinus albicaulis) growth and defense under a warmer and regionally drier climate: Frontiers in Forests and Global Change, v. 6, 1089138, 13 p., https://doi.org/10.3389/ffgc.2023.1089138.","productDescription":"1089138, 13 p.","ipdsId":"IP-136916","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":444315,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffgc.2023.1089138","text":"Publisher Index Page"},{"id":415907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Flathead Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.96443731737476,\n 48.024325063887574\n ],\n [\n -114.96443731737476,\n 47.126084063219224\n ],\n [\n -113.84980448318083,\n 47.126084063219224\n ],\n [\n -113.84980448318083,\n 48.024325063887574\n ],\n [\n -114.96443731737476,\n 48.024325063887574\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2023-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Kichas, Nicholas E.","contributorId":261369,"corporation":false,"usgs":false,"family":"Kichas","given":"Nicholas E.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":869777,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":869778,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hood, Sharon M.","contributorId":221183,"corporation":false,"usgs":false,"family":"Hood","given":"Sharon","email":"","middleInitial":"M.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":869779,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Everett, Richard G.","contributorId":221184,"corporation":false,"usgs":false,"family":"Everett","given":"Richard","email":"","middleInitial":"G.","affiliations":[{"id":37636,"text":"Salish Kootenai College","active":true,"usgs":false}],"preferred":false,"id":869780,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McWethy, David B.","contributorId":207232,"corporation":false,"usgs":false,"family":"McWethy","given":"David","email":"","middleInitial":"B.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":869781,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240870,"text":"sir20225079 - 2023 - Simulation of monthly mean and monthly base flow of streamflow using random forests for the Mississippi River Alluvial Plain, 1901 to 2018","interactions":[],"lastModifiedDate":"2026-02-23T19:17:56.29845","indexId":"sir20225079","displayToPublicDate":"2023-03-01T12:52:03","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5079","displayTitle":"Simulation of Monthly Mean and Monthly Base Flow of Streamflow using Random Forests for the Mississippi River Alluvial Plain, 1901 to 2018","title":"Simulation of monthly mean and monthly base flow of streamflow using random forests for the Mississippi River Alluvial Plain, 1901 to 2018","docAbstract":"Improved simulations of streamflow and base flow for selected sites within and adjacent to the Mississippi River Alluvial Plain area are important for modeling groundwater flow because surface-water flows have a substantial effect on groundwater levels. One method for simulating streamflow and base flow, random forest (RF) models, was developed from the data at gaged sites and, in turn, was used to make monthly mean streamflow and base-flow predictions at 162 ungaged sites in the study area. Daily streamflow observations and computed base flow from 247 streamgages were used as the basis for the development of these RF models. RF models were constructed from basin and climatic characteristics and related to observed monthly mean streamflow values; models were used to compute monthly base-flow estimates from selected streamgages in and adjacent to the Mississippi River Alluvial Plain extent, which includes streamflows from parts of Alabama, Arkansas, Colorado, Florida, Illinois, Indiana, Kansas, Kentucky, Louisiana, Mississippi, Missouri, New Mexico, Tennessee, and Texas. The explanatory variables for the models were selected to represent physical characteristics and climatic time series for the contributing drainage basins to the streamgages and ungaged locations of interest. The Nash-Sutcliffe efficiency between observed and simulated monthly mean streamflow was greater than 0.80 for 155 of the 247 streamgages, with a median Nash-Sutcliffe efficiency value of 0.83. The streamflow and base-flow simulations can be used to improve inflow values and to verify the Mississippi River Alluvial Plain groundwater flow model. The statistical model, input data, and response data (simulated monthly mean streamflows) are available as a U.S. Geological Survey software release and a U.S. Geological Survey data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225079","programNote":"Water Availability and Use Science Program","usgsCitation":"Dietsch, B.J., Asquith, W.H., Breaker, B.K., Westenbroek, S.M., and Kress, W.H., 2023, Simulation of monthly mean and monthly base flow of streamflow using random forests for the Mississippi River Alluvial Plain, 1901 to 2018: U.S. Geological Survey Scientific Investigations Report 2022–5079, 17 p., https://doi.org/10.3133/sir20225079.","productDescription":"Report: v, 17 p.; Tables: 4; Data Release; Dataset; Software Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105480","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":413473,"rank":14,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P92UE6EG","text":"USGS software release","linkHelpText":"—mapRandomForest—Monthly flow estimation in the Mississippi Alluvial Plain by means of random forest modeling"},{"id":413470,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5079/sir20225079_table3.3.csv","text":"Table 3.3","size":"16.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022–5079 Table 3.3","linkHelpText":"—Performance metrics of comparing to the computed monthly base flows with estimated base flows for the model trained with all gaged sites in the Mississippi embayment regional aquifer system, 1901–2018."},{"id":413468,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5079/sir20225079_table3.2.csv","text":"Table 3.2","size":"16.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022–5079 Table 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Because tension infiltrometers apply water through a disk of finite size, the infiltrated water moves laterally as well as downward. Only the vertical component of this flow is indicative of the hydraulic conductivity K, so the algorithm for computing K must include a way of isolating that component from the total flow. Some commonly used formulas correct for the multidimensional effects by subtracting an estimate of the laterally spreading flow. For disks smaller than about 200 mm in diameter, however, lateral spreading constitutes so much of the total flow that these subtractive formulas lose considerable accuracy, and sometimes overcorrect so severely as to produce a negative number for K. Other methods rely on empiricisms that are not completely consistent with unsaturated-flow theory and that require prior knowledge of certain soil properties. We developed a new formula that uses a multiplicative factor instead of a subtracted term to achieve the needed correction. For testing we conducted numerical experiments with synthetic data produced by solving the Richardson-Richards equation using the code VS2DRTI, for diverse media and a range of disk sizes, including the widely used 45-mm diameter. We compared K values calculated from our formula to the actual K used to generate the simulated data, as well as to results from other published formulas. This comparison shows that our method provides an algorithm based in unsaturated-flow theory that produces more reliable values for small disks without requiring prior knowledge of soil properties.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022WR032475","usgsCitation":"Nimmo, J.R., and Voss, P.R., 2023, Improved calculation of hydraulic conductivity for small-disk tension infiltrometers: Water Resources Research, v. 59, no. 3, e2022WR032475, 16 p., https://doi.org/10.1029/2022WR032475.","productDescription":"e2022WR032475, 16 p.","ipdsId":"IP-144414","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":499247,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022wr032475","text":"Publisher Index Page"},{"id":418222,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","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}],"preferred":true,"id":875702,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voss, Paige R.","contributorId":310446,"corporation":false,"usgs":false,"family":"Voss","given":"Paige","email":"","middleInitial":"R.","affiliations":[{"id":67192,"text":"USGS, now at University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":875703,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241614,"text":"70241614 - 2023 - Status and trends in the Lake Superior fish community, 2022","interactions":[],"lastModifiedDate":"2023-03-30T16:36:41.863826","indexId":"70241614","displayToPublicDate":"2023-03-01T11:26:29","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Status and trends in the Lake Superior fish community, 2022","docAbstract":"In 2022, the Lake Superior fish community was sampled with daytime bottom and surface trawls at 71 nearshore locations in May-June and 35 offshore locations in July, and at 51 Coordinated Science and Monitoring Initiative (CSMI) locations in July-October with bottom trawls, surface trawls, mid-water trawls and acoustics that were previously sampled in 2011 and 2016. Nearshore bottom trawls collected 11,603 fish from 25 species or morphotypes. Nearshore mean biomass was 1.6 kg per ha which was one of the lowest biomass estimates over survey’s 45-year history. Offshore bottom trawls collected 13,876 fish from 11 species or morphotypes. Offshore mean biomass was 5.1 kg per ha, which was less than the annual average since 2011 of 6.5 kg per ha. Recruitment, as measured by age-1 densities, was near zero for Bloater (Coregonus hoyi), Cisco (C. artedi), and Kiyi (C. kiyi), 2 age-1 fish per ha for Lake Whitefish (C. clupeaformis) and 77 age-1 fish for Rainbow Smelt (Osmerus mordax). All were less than the long-term averages. Sampling at the CSMI locations collected 26 species and morphotypes. The most abundant species’ lakewide were Deepwater Sculpin (all years), young-of-year ciscoe (Bloater, Cisco, and Kiyi, 2022), and Rainbow Smelt (2011 and 2016). Cisco had the highest estimated lakewide biomass in 2011 and 2022 and siscowet Lake Trout had the highest estimated lakewide biomass in 2016. Native species were more abundant than invasive species by numbers (80, 65, and 92%) and biomass (94, 93, 96%) in 2011, 2016, and 2022, respectively.
Total lakewide benthic fish biomass declined from 47 thousand metric tons in 2011 to 29 thousand metric tons in 2016 and increased to 33 thousand metric tons in 2022. Total lakewide pelagic fish biomass declined from 61 thousand metric tons in 2011 to 25 thousand metric tons in 2016 and increased to 54 thousand metric tons in 2022. The most unexpected result from our sampling in 2022 was the 2 billion age-0 ciscoe estimate from the mid-water trawl and acoustic sampling in August-October. These fish were broadly distributed across the lake, being collected at 53 of the 54 locations, and their population estimates were highest in the depths >100 m. The factors underlying the survival of these ciscoes into late summer in 2022 as compared to previous years have not been identified, but our annual population surveys of larval ciscoes suggests that lake conditions in June and July may have differed from previous years and enhanced survival. In 2022, ciscoe larval densities in May were lower than average (likely due to a cold winter and spring that delayed hatching), June densities were similar to previous years, and July density estimates were more than double that of any previous year’s estimate.
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Persistent declines of northern long-eared bats (Myotis septentrionalis) due to white-nose syndrome have made acquisition of contemporary data difficult. Therefore, use of legacy data may be necessary for creation of species distribution models. We used historical roost and capture records, both individually and in combination, to assess the distribution and availability of northern long-eared bat habitat across the 670,000-ha Monongahela Na- tional Forest (MNF), West Virginia, USA. We created random forest presence/pseudo-absence models to examine influences of various biotic and abi- otic predictors on both roosting and foraging presence locations of northern long-eared bats. Predicted northern long-eared bat habitat was abundant (43.1% of the MNF) and widely dispersed. Generally, all models suggested that northern long-eared bat habitat was characterized by interior forests containing linear edge features. We observed only 3.4% spatial overlap of habitat based on complete model agreement, but 38.5% of all habitat areas resulted from agreement between capture-only and combination models. Our models provide important assessments of habitat availability necessary for addressing state and federal conservation requirements on the MNF and adjacent eastern West Virginia mountains.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"De La Cruz, J., Ford, W., Jones, S.B., Johnson, J., and Silvis, A., 2023, Distribution of northern long-eared bat summer-habitat derived from historical data collected on the Monongahela National Forest, West Virginia, USA: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 10, p. 114-124.","productDescription":"11 p.","startPage":"114","endPage":"124","ipdsId":"IP-144150","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481130,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2023/distribution-northern-long-eared-bat-summer-habitat-monongahela-national-forest-west"},{"id":481153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Monongahela National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.92561978482631,\n 38.52966246222408\n ],\n [\n -80.6422160008137,\n 38.52966246222408\n ],\n [\n -80.6422160008137,\n 38.104444484140316\n ],\n [\n -79.92561978482631,\n 38.104444484140316\n ],\n [\n -79.92561978482631,\n 38.52966246222408\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"De La Cruz, J.L.","contributorId":349847,"corporation":false,"usgs":false,"family":"De La Cruz","given":"J.L.","affiliations":[{"id":81893,"text":"Virginia Polytechnic and State University","active":true,"usgs":false}],"preferred":false,"id":924990,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":924991,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, S. Beaux","contributorId":346278,"corporation":false,"usgs":false,"family":"Jones","given":"S.","email":"","middleInitial":"Beaux","affiliations":[{"id":82811,"text":"The Water Institute, Baton Rouge, Louisiana, USA","active":true,"usgs":false}],"preferred":false,"id":924992,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, J.R.","contributorId":349849,"corporation":false,"usgs":false,"family":"Johnson","given":"J.R.","affiliations":[{"id":32872,"text":"John Hopkins University, Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":924993,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Silvis, A.","contributorId":349851,"corporation":false,"usgs":false,"family":"Silvis","given":"A.","affiliations":[{"id":40299,"text":"West Virginia Division of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":924994,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70257017,"text":"70257017 - 2023 - Increased Population Size of the Federally Endangered Ptychobranchus subtentus in the Wolf River, TN (Fentress and Picket Co.)","interactions":[],"lastModifiedDate":"2024-09-05T16:05:44.799881","indexId":"70257017","displayToPublicDate":"2023-03-01T10:59:12","publicationYear":"2023","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":18518,"text":"Ellipsaria: Newsletter of the Freshwater Mollusk Conservation Society","active":true,"publicationSubtype":{"id":30}},"title":"Increased Population Size of the Federally Endangered Ptychobranchus subtentus in the Wolf River, TN (Fentress and Picket Co.)","docAbstract":"No abstract available.
","language":"English","publisher":"Freshwater Mollusk Conservation Society","usgsCitation":"Fetters, J., Rosenberger, A.E., Irwin Womble, K., Ford, A., and Bajo, B., 2023, Increased Population Size of the Federally Endangered Ptychobranchus subtentus in the Wolf River, TN (Fentress and Picket Co.): Ellipsaria: Newsletter of the Freshwater Mollusk Conservation Society, v. 25, no. 1, p. 11-15.","productDescription":"5 p.","startPage":"11","endPage":"15","ipdsId":"IP-137896","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432310,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://molluskconservation.org/Ellipsaria-archive.html","linkFileType":{"id":5,"text":"html"}},{"id":433510,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fetters, Jack","contributorId":341922,"corporation":false,"usgs":false,"family":"Fetters","given":"Jack","email":"","affiliations":[{"id":81803,"text":"Tennessee Technological University Cooperative Fisheries Research Unit","active":true,"usgs":false}],"preferred":false,"id":909167,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberger, Amanda E. 0000-0002-5520-8349 arosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5520-8349","contributorId":5581,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Amanda","email":"arosenberger@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":909168,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irwin Womble, Kristin","contributorId":341923,"corporation":false,"usgs":false,"family":"Irwin Womble","given":"Kristin","email":"","affiliations":[{"id":81803,"text":"Tennessee Technological University Cooperative Fisheries Research Unit","active":true,"usgs":false}],"preferred":false,"id":909169,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, Anthony","contributorId":341924,"corporation":false,"usgs":false,"family":"Ford","given":"Anthony","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":909170,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bajo, Brittany","contributorId":341925,"corporation":false,"usgs":false,"family":"Bajo","given":"Brittany","email":"","affiliations":[{"id":81803,"text":"Tennessee Technological University Cooperative Fisheries Research Unit","active":true,"usgs":false}],"preferred":false,"id":909171,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268718,"text":"70268718 - 2023 - Central Beaufort Sea Wave and Hydrodynamic Modeling Study; Report 2: Modeled waves, hydrodynamics, and sediment transport within Foggy Island Bay","interactions":[],"lastModifiedDate":"2025-07-07T15:41:04.661133","indexId":"70268718","displayToPublicDate":"2023-03-01T10:37:35","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5709,"text":"OCS Study","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"BOEM 2022-079","title":"Central Beaufort Sea Wave and Hydrodynamic Modeling Study; Report 2: Modeled waves, hydrodynamics, and sediment transport within Foggy Island Bay","docAbstract":"Renewed interest in nearshore oil exploration and production in the shallow waters of the Central Beaufort Sea Shelf has created a need to advance our understanding of the past, current, and future atmospheric and oceanographic conditions that affect existing and planned infrastructure and nearshore ecosystems. At the time of writing this report, Hilcorp Alaska LLC has received BOEM approval for an oil and gas Development and Production Plan (DPP) that includes the construction of the Liberty Drilling Island (LDI) in Foggy Island Bay, situated within Stefansson Sound circa 30 km east of Prudhoe Bay (Figure 1.1). The aim of this study is to investigate how longer periods of open water (defined as < 15% ice cover), decreased sea ice cover, and changes in ocean and atmospheric conditions might affect wave and storm surge conditions, sediment transport patterns, and coastal erosion rates within Foggy Island Bay as well as the modeled influence of the offshore artificial island on sediment transport patterns.","language":"English","publisher":"Bureau of Ocean and Energy Management (BOEM)","usgsCitation":"Erikson, L.H., Nederhoff, C.M., Engelstad, A.C., Kasper, J., and Bieniek, P.A., 2023, Central Beaufort Sea Wave and Hydrodynamic Modeling Study; Report 2: Modeled waves, hydrodynamics, and sediment transport within Foggy Island Bay: OCS Study BOEM 2022-079, 64 p.","productDescription":"64 p.","ipdsId":"IP-147575","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":491591,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://espis.boem.gov/final%20reports/BOEM_2022-079.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":491739,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Foggy Island Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -147.9566685211152,\n 70.37889977965969\n ],\n [\n -147.9566685211152,\n 70.1704960051022\n ],\n [\n -147.22144502523884,\n 70.1704960051022\n ],\n [\n -147.22144502523884,\n 70.37889977965969\n ],\n [\n -147.9566685211152,\n 70.37889977965969\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2023-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nederhoff, Cornelis M. 0000-0003-0552-3428","orcid":"https://orcid.org/0000-0003-0552-3428","contributorId":265889,"corporation":false,"usgs":false,"family":"Nederhoff","given":"Cornelis","email":"","middleInitial":"M.","affiliations":[{"id":33886,"text":"Deltares USA","active":true,"usgs":false}],"preferred":true,"id":941726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engelstad, Anita C 0000-0002-0211-4189","orcid":"https://orcid.org/0000-0002-0211-4189","contributorId":268303,"corporation":false,"usgs":true,"family":"Engelstad","given":"Anita","email":"","middleInitial":"C","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941727,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kasper, Jeremy L. 0000-0003-0975-6114","orcid":"https://orcid.org/0000-0003-0975-6114","contributorId":208630,"corporation":false,"usgs":false,"family":"Kasper","given":"Jeremy L.","affiliations":[{"id":37850,"text":"University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":941728,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bieniek, Peter A.","contributorId":210907,"corporation":false,"usgs":false,"family":"Bieniek","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":941729,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240306,"text":"70240306 - 2023 - Graton Pesticides (GRAPE) Study: Exposure potential from groundwater and air in California wine country, results","interactions":[],"lastModifiedDate":"2024-04-05T16:26:40.087279","indexId":"70240306","displayToPublicDate":"2023-03-01T10:24:31","publicationYear":"2023","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"title":"Graton Pesticides (GRAPE) Study: Exposure potential from groundwater and air in California wine country, results","docAbstract":"No abstract available.
","language":"English","publisher":"California Breast Cancer Research Program","usgsCitation":"Warwick, N., Sellen, J., Reynolds, P., Hladik, M.L., Kolpin, D.W., Von Behren, J., and Burton, J., 2023, Graton Pesticides (GRAPE) Study: Exposure potential from groundwater and air in California wine country, results, 20 p.","productDescription":"20 p.","ipdsId":"IP-140201","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":425730,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.sonomasass.org/grape","linkFileType":{"id":5,"text":"html"}},{"id":425731,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.89461176291844,\n 38.459583811589795\n ],\n [\n -122.89461176291844,\n 38.41918676480765\n ],\n [\n -122.84009216486707,\n 38.41918676480765\n ],\n [\n -122.84009216486707,\n 38.459583811589795\n ],\n [\n -122.89461176291844,\n 38.459583811589795\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Revised March 2023","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Warwick, N.","contributorId":302026,"corporation":false,"usgs":false,"family":"Warwick","given":"N.","email":"","affiliations":[{"id":65399,"text":"SASS","active":true,"usgs":false}],"preferred":false,"id":863325,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sellen, J.","contributorId":302027,"corporation":false,"usgs":false,"family":"Sellen","given":"J.","email":"","affiliations":[{"id":65400,"text":"Pesticide Reform","active":true,"usgs":false}],"preferred":false,"id":863326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reynolds, P.","contributorId":191901,"corporation":false,"usgs":false,"family":"Reynolds","given":"P.","email":"","affiliations":[],"preferred":false,"id":863327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221229,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":863328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kolpin, Dana W. 0000-0002-3629-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3629-6505","contributorId":302028,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":863329,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Von Behren, J.","contributorId":302029,"corporation":false,"usgs":false,"family":"Von Behren","given":"J.","affiliations":[{"id":49956,"text":"University of California San Francisco","active":true,"usgs":false}],"preferred":false,"id":863330,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burton, J.","contributorId":302030,"corporation":false,"usgs":false,"family":"Burton","given":"J.","email":"","affiliations":[{"id":65401,"text":"Breast Cancer Action","active":true,"usgs":false}],"preferred":false,"id":863331,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70265046,"text":"70265046 - 2023 - Using public litigation records to identify priority science needs for managing public lands","interactions":[],"lastModifiedDate":"2025-03-31T15:28:46.250573","indexId":"70265046","displayToPublicDate":"2023-03-01T10:24:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1468,"text":"Ecology and Society","active":true,"publicationSubtype":{"id":10}},"title":"Using public litigation records to identify priority science needs for managing public lands","docAbstract":"Relevant science is essential for effective natural resource decision making, including on public lands managed by the United States Department of the Interior (DOI) Bureau of Land Management (BLM), that cover 1/10th of the United States. Most of the BLM’s management decisions require analyses under the National Environmental Policy Act, and the use of science in these decisions is often challenged. Using coproduction, we assembled an interagency team of scientists and resource managers to develop a method for using public litigation to identify priority science needs for the BLM. We searched publicly available case documents finalized from 2015–2019 in Wyoming, Colorado, Utah, and New Mexico within federal courts and the DOI Office of Hearings and Appeals, and identified 108 case documents that involved challenges to the BLM’s use of science. We retained 48 case documents that contained at least one challenge about the BLM’s use of science for a specific resource. We categorized all challenges in each case document according to the proposed action, affected resource, type of science challenged (data about resources, science relevant to potential impacts, methods for analyzing potential impacts, and mitigation actions), and specific nature of the challenge (e.g., challenging direct effects analysis). We identified priority science needs based on the frequency of challenges, the number of states where similar challenges occurred, whether the BLM lost the challenge, and whether the case was remanded. Top needs related to oil and gas development actions and included science about effects on air quality and climate, water, and socioeconomics; data for air quality and climate; and methods for analyzing potential impacts to cultural resources and air quality and climate. The BLM can use this information to prioritize actions (e.g., funding new research or science syntheses) to strengthen its science foundation for decision-making.
","language":"English","publisher":"Resilience Alliance","doi":"10.5751/ES-13708-280111","usgsCitation":"Foster, A.C., Carter, S.K., Haby, T.S., Espy, L., and Barton, M., 2023, Using public litigation records to identify priority science needs for managing public lands: Ecology and Society, v. 28, no. 1, 11, 27 p., https://doi.org/10.5751/ES-13708-280111.","productDescription":"11, 27 p.","ipdsId":"IP-130700","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":488927,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/es-13708-280111","text":"Publisher Index Page"},{"id":484021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, New Mexico, Utah, 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\"}}]}","volume":"28","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Foster, Alison C. 0000-0002-6659-2120","orcid":"https://orcid.org/0000-0002-6659-2120","contributorId":260599,"corporation":false,"usgs":true,"family":"Foster","given":"Alison","email":"","middleInitial":"C.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":932404,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carter, Sarah K. 0000-0003-3778-8615","orcid":"https://orcid.org/0000-0003-3778-8615","contributorId":192418,"corporation":false,"usgs":true,"family":"Carter","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":932405,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haby, Travis S. 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of evidence of bat maternity activity","interactions":[],"lastModifiedDate":"2025-01-23T16:23:50.220837","indexId":"70262463","displayToPublicDate":"2023-03-01T10:23:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Maximum likelihood estimator and nightly acoustic count values as weight of evidence of bat maternity activity","docAbstract":"Since the spread of white-nose syndrome in North America, several bat species have shown precipitous declines in abundance and distribution. With lower netting detection probabilities for the currently threatened but proposed endangered northern long-eared bat (Myotis septentrionalis) and endangered Indiana bats (Myotis sodalis), determination of presence or absence for regulatory clearance often has shifted to the use of acoustic sur- veys. However, acoustic surveys are unable to differentiate between non-reproductive individuals versus a maternity colony. We used recorded nightly echolocation pass counts of bat species-specific probabilities with maximum likelihood estimator (MLE) scores to determine thresholds by cover type and reproductive period whereby the potential for northern long-eared bat or Indiana bat maternity colonies occurs. Where nightly MLE P-values were
<0.05, mean predicted nightly pass counts were significantly higher in areas of known northern long-eared bat and Indiana bat maternity colonies versus sites that were in either species’ distribution but with no maternity activity known. Nightly pass counts (MLE P < 0.05) were higher for sites with observed maternity activity for both bat species across forest, forest-field edge, and riparian areas versus sites where no maternity activity was known. For northern long-eared bats, nightly pass counts were highest in the juvenile volancy period (after 15 July) whereas, for Indiana bats, nightly pass counts were highest in the lactation period (16 June to 15 July). Except for edge conditions for northern long-eared bats, a MLE P < 0.05 combined with nightly pass counts above thresholds developed from surveys at known maternity colony sites for both species may indicate potential presence of a maternity colony locally and provide a tool to more efficiently use targeted mist-netting for further determination.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Ford, W., Thorne, E., Silvis, A., Barr, E., Armstrong, M., and King, R.A., 2023, Maximum likelihood estimator and nightly acoustic count values as weight of evidence of bat maternity activity: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 100, p. 100-106.","productDescription":"7 p.","startPage":"100","endPage":"106","ipdsId":"IP-141830","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480708,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2023/maximum-likelihood-estimator-and-nightly-acoustic-count-values-weight-evidence-bat"},{"id":481004,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"eastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.52841950928277,\n 45.986380216998896\n ],\n [\n -95.52841950928277,\n 27.24529883757546\n ],\n [\n -72.57107363673734,\n 27.24529883757546\n ],\n [\n -72.57107363673734,\n 45.986380216998896\n ],\n [\n -95.52841950928277,\n 45.986380216998896\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"100","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ford, W. 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Byrd Pool of the Ohio and Kanawha Rivers","docAbstract":"Flathead catfish (Pylodictis olivaris) were sampled in the Robert C. Byrd Pool of the Ohio and Kanawha rivers, West Virginia, to inform management decisions based on population characteristics of size structure, age, growth, and mortality. Sampling was conducted with low-frequency boat electrofishing during late May to early June over a four-year period (2017–2020). We examined size structure using proportional size distribution indi- ces. Growth was evaluated using otolith-derived ages, a von Bertalanffy growth curve, and mean length at age data, including comparisons to published mean length at age data of other populations. Annual mortality was estimated with a weighted catch curve. We documented a high-density population (mean CPUE = 49 fish h–1) with low mortality (A = 11.8%), characterized by slow growing individuals with a maximum recorded age of 36. Our results further demonstrate that this population is characterized by a broad size structure that likely is maintained only through low harvest and high rates of catch and release by anglers.
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Previous studies have demonstrated that freshwater mussels are highly sensitive to K in acute and chronic exposures, and acute toxicity of K decreases with increasing water hardness. However, little is known about the influence of hardness on the chronic toxicity of K. The objective of this study was to evaluate the chronic toxicity of K (tested as KCl) to a commonly tested unionid mussel (fatmucket, Lampsilis siliquoidea) at five hardness levels (25, 50, 100, 200, 300 mg/L as CaCO3) representing most surface waters in the United States. Chronic 28-d K toxicity tests were conducted with 3-week-old juvenile fatmucket in the five hardness waters using an ASTM standard method. The maximum acceptable toxicant concentrations (geometric mean of the no-observed-effect concentration and the lowest-observed-effect concentration) increased from 15.1 to 69.3 mg K/L for survival and from 15.1 to 35.8 mg K/L for growth (length and dry weight) and biomass when water hardness was increased from 25 mg/L (soft) to 300 mg/L (very hard). These results provided evidence to support water hardness influence on chronic K toxicity to juvenile fatmucket. However, the chronic effect concentrations based on the more sensitive endpoint (growth or biomass) increased only 2.4-fold from the soft water to the very hard water, indicating that water hardness had limited influence on the chronic toxicity of K to the mussels. These results can be used to establish chronic toxicity thresholds for K across a broad range of water hardness and to derive environmental guideline values for K to protect freshwater mussels and other organisms.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.5598","usgsCitation":"Wang, N., Dorman, R.A., Kunz, J.L., Cleveland, D.M., Steevens, J.A., Dunn, S., and Martinez, D., 2023, Influences of water hardness on chronic toxicity of potassium chloride to a unionid mussel (Lampsilis siliquoidea): Environmental Toxicology and Chemistry, v. 42, no. 5, p. 1085-1093, https://doi.org/10.1002/etc.5598.","productDescription":"9 p.","startPage":"1085","endPage":"1093","ipdsId":"IP-146694","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":500023,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5598","text":"Publisher Index Page"},{"id":435428,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R8YGM2","text":"USGS data release","linkHelpText":"Survival, growth, and chemical data from a study on influences of water hardness on chronic toxicity of potassium chloride to a Unionid mussel (Lampsilis siliquoidea)"},{"id":413665,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Ning 0000-0002-2846-3352 nwang@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-3352","contributorId":2818,"corporation":false,"usgs":true,"family":"Wang","given":"Ning","email":"nwang@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":865606,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dorman, Rebecca A. 0000-0002-5748-7046","orcid":"https://orcid.org/0000-0002-5748-7046","contributorId":28522,"corporation":false,"usgs":true,"family":"Dorman","given":"Rebecca","email":"","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":865607,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kunz, James L. 0000-0002-1027-158X jkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-1027-158X","contributorId":3309,"corporation":false,"usgs":true,"family":"Kunz","given":"James","email":"jkunz@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":865608,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cleveland, Danielle M. 0000-0003-3880-4584 dcleveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3880-4584","contributorId":187471,"corporation":false,"usgs":true,"family":"Cleveland","given":"Danielle","email":"dcleveland@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":865609,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":865610,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dunn, Suzanne","contributorId":279599,"corporation":false,"usgs":false,"family":"Dunn","given":"Suzanne","email":"","affiliations":[{"id":57309,"text":"US Fish Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":865611,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Martinez, David","contributorId":279598,"corporation":false,"usgs":false,"family":"Martinez","given":"David","email":"","affiliations":[{"id":57309,"text":"US Fish Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":865612,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70245109,"text":"70245109 - 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The approach is extendable to a variety of volcanic settings through systematic tuning of the classifier. Hyperparameters are evaluated by statistical means, yielding a combination of “ideal” SOM parameters for the given data set. Extending from this, we applied a Kernel Density Estimation approach to automatically detect changes within the observed seismicity. We categorize the Whakaari seismic time series into regimes representing distinct volcano-seismic states during recent unrest episodes at Whakaari (2012/2013, 2016, and 2019). There is a clear separation in classification results between background regimes and those representing elevated levels of unrest. Onset of unrest is detected by the classifier 6 weeks before the August 2012 eruption, and ca. 3.5 months before the December 2019 eruption, respectively. Regime changes are corroborated by changes in commonly monitored tremor proxies as well as with reported volcanic activity. The regimes are hypothesized to represent diverse mechanisms including: system pressurization and depressurization, degassing, and elevated surface activity. Labeling these regimes improves visualization of the 2012/2013 and 2019 unrest and eruptive episodes. The pre-eruptive 2016 unrest showed a contrasting shape and nature of seismic regimes, suggesting differing onset and driving processes. The 2016 episode is proposed to result from rapid destabilization of the shallow hydrothermal system, while rising magmatic gases from new injections of magma better explain the 2012/2013 and 2019 episodes.
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The goals of the validation exercise were to determine (a) how well the Marine Benthic Index matches more standard ways of assessing community health and (b) how finely it is possible to distinguish between levels of disturbance. A controlled experiment was devised in which simulated benthic communities were generated to correspond to predetermined levels of disturbance, and experts in benthic ecology determined which communities reflected the more-disturbed conditions. In this way, the index was directly compared to traditional methods of assessing benthic communities.
The results provide strong evidence that the “latent disturbance” model used to derive the Marine Benthic Index is identifying effects that benthic experts recognize as disturbance. Not only did the model agree with the experts overall, but also the probability of agreement strongly increased with increasing difference in disturbance level.
The validation exercise results indicate that the Marine Benthic Index is a reliable method of determining disturbance without the necessity of assuming a priori knowledge of the disturbance. Furthermore, the numerical approach embodied in the Marine Benthic Index has the advantage of being able to find patterns beyond the capability of individual experts to know the effects of human disturbances for all species under all environmental conditions.
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