Due to geological complexities and observational gaps, it is challenging to identify the governing physical processes of geothermal field deformation including ground subsidence and earthquakes. In the west and east regions of the Heber Geothermal Field (HGF), decade-long subsidence was occurring despite injection of heat-depleted brines, along with transient reversals between uplift and subsidence. These observed phenomena contradict current knowledge that injection leads to surface uplift. Here we show that high-yield production wells at the HGF center siphon fluid from surrounding regions, which can cause subsidence at low-rate injection locations. Moreover, the thermal contraction effect by cooling increases with time and eventually overwhelms the pressure effects of pressure fluctuation and poroelastic responses, which keep relatively stable during geothermal operations. The observed subsidence anomalies result from the siphoning effect and thermal contraction. We further demonstrate that thermal contraction dominates long-term trends of surface displacement and seismicity growth, while pressure effects drive near-instantaneous changes.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-024-49363-1","usgsCitation":"Jiang, G., Barbour, A.J., Skoumal, R.J., Materna, K.Z., and Crandall-Bear, A., 2024, Relatively stable pressure effects and time-increasing thermal contraction control Heber geothermal field deformation: Nature Communications, v. 15, 5159, 14 p., https://doi.org/10.1038/s41467-024-49363-1.","productDescription":"5159, 14 p.","ipdsId":"IP-152355","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":439387,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-024-49363-1","text":"Publisher Index Page"},{"id":431218,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Heber geothermal field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.6,\n 32.75\n ],\n [\n -115.6,\n 32.68\n ],\n [\n -115.48,\n 32.68\n ],\n [\n -115.48,\n 32.75\n ],\n [\n -115.6,\n 32.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2024-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Jiang, Guoyan 0000-0002-6602-7295","orcid":"https://orcid.org/0000-0002-6602-7295","contributorId":256973,"corporation":false,"usgs":false,"family":"Jiang","given":"Guoyan","email":"","affiliations":[{"id":51926,"text":"CUHK","active":true,"usgs":false}],"preferred":false,"id":906600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barbour, Andrew J. 0000-0002-6890-2452","orcid":"https://orcid.org/0000-0002-6890-2452","contributorId":215339,"corporation":false,"usgs":true,"family":"Barbour","given":"Andrew","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":906601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Skoumal, Robert John 0000-0002-6960-481X rskoumal@usgs.gov","orcid":"https://orcid.org/0000-0002-6960-481X","contributorId":299165,"corporation":false,"usgs":true,"family":"Skoumal","given":"Robert","email":"rskoumal@usgs.gov","middleInitial":"John","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":906602,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Materna, Kathryn Zerbe 0000-0002-6687-980X","orcid":"https://orcid.org/0000-0002-6687-980X","contributorId":261337,"corporation":false,"usgs":true,"family":"Materna","given":"Kathryn","email":"","middleInitial":"Zerbe","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":906603,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crandall-Bear, Aren","contributorId":340209,"corporation":false,"usgs":false,"family":"Crandall-Bear","given":"Aren","affiliations":[{"id":81505,"text":"Univ Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":906604,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255606,"text":"70255606 - 2024 - Visualizing wading bird optimal foraging decisions with aggregation behaviors using individual-based modeling","interactions":[],"lastModifiedDate":"2024-06-26T13:38:24.914144","indexId":"70255606","displayToPublicDate":"2024-06-17T08:36:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Visualizing wading bird optimal foraging decisions with aggregation behaviors using individual-based modeling","docAbstract":"Foragers on patchy landscapes must efficiently balance time between searching for and consuming resources to meet their daily energetic requirements. Spatial aggregation foraging behaviors may improve foraging efficiency by sharing information on locations of resource hotspots. Wading birds are an example of patch foragers that form colonial aggregations during the breeding season to obtain sufficient prey energy to sustain themselves and their offspring each day. Here, we describe a spatially-explicit simulation model of wading bird optimal foraging that represents information sharing through visual cues. The overall purpose of the model is to describe how wading bird daily foraging and reproductive success may change with alternative water control management practices that determine spatial availability of prey for wading birds on the landscape, throughout their breeding seasons. Wading birds are simulated as individuals that operate independently, sampling and selecting among patches based on a prey density tolerance threshold, but also use information from other birds to inform their selection decisions. Foraging success is evaluated against the fundamental objectives of (a) fulfilling daily energetic demands and (b) minimizing predation exposure, by tracking individual daily energetic intake and time spent foraging. In this way, the model approximates population level dynamics of wading bird aggregations that emerge through collective decision making of birds simulated at the lower individual level. Key results of this study suggest that aggregation behaviors may improve population-level foraging success rates, and the optimal settling threshold may modulate when resources become more scarce or difficult to find. Thus, the model addresses ecological theory on the advantages of foraging in groups versus independently. This technique is appropriate for evaluating wading bird populations that forage on patchy landscapes, such as seasonally-pulsed wetlands, wherever sufficient information is available to describe (1) foraging behavior (e.g., feeding rate, flight speeds, patch selection decisions), (2) key landscape characteristics, (3) spatial distributions of prey densities among foraging patches, and (4) changes in prey densities through time. The model was designed to predict qualitative, testable spatial patterns of wading bird foraging movements which can be compared with empirical observations and empirically-derived habitat suitability models. These techniques can also be applied to other bird species, such as shorebirds, or more generally to any species that transits between discrete foraging patches.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2024.110702","usgsCitation":"Yurek, S., DeAngelis, D.L., Lee, H.W., and Tennenbaum, S., 2024, Visualizing wading bird optimal foraging decisions with aggregation behaviors using individual-based modeling: Ecological Modelling, v. 493, 110702, 15 p., https://doi.org/10.1016/j.ecolmodel.2024.110702.","productDescription":"110702, 15 p.","ipdsId":"IP-153166","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488830,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2024.110702","text":"Publisher Index Page"},{"id":430522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"493","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yurek, Simeon 0000-0002-6209-7915","orcid":"https://orcid.org/0000-0002-6209-7915","contributorId":216738,"corporation":false,"usgs":true,"family":"Yurek","given":"Simeon","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":904925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":904926,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, Hyo Won","contributorId":292184,"corporation":false,"usgs":false,"family":"Lee","given":"Hyo","email":"","middleInitial":"Won","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":904927,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tennenbaum, Stephen","contributorId":292180,"corporation":false,"usgs":false,"family":"Tennenbaum","given":"Stephen","email":"","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":904928,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261093,"text":"70261093 - 2024 - Observed and potential range shifts of native and non-native species with climate change","interactions":[],"lastModifiedDate":"2024-11-22T15:36:43.493314","indexId":"70261093","displayToPublicDate":"2024-06-17T08:25:57","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":808,"text":"Annual Review of Ecology, Evolution, and Systematics","active":true,"publicationSubtype":{"id":10}},"title":"Observed and potential range shifts of native and non-native species with climate change","docAbstract":"There is broad concern that the range shifts of global flora and fauna will not keep up with climate change, increasing the likelihood of population declines and extinctions. Many populations of nonnative species already have advantages over native species, including widespread human-aided dispersal and release from natural enemies. But do nonnative species also have an advantage with climate change? Here, we review observed and potential range shifts for native and nonnative species globally. We show that nonnative species are expanding their ranges orders of magnitude faster than native species, reflecting both traits that enable rapid spread and ongoing human-mediated introduction. We further show that nonnative species have large potential ranges and range expansions with climate change, likely due to a combination of widespread introduction and broader climatic tolerances. With faster spread rates and larger potential to persist or expand, nonnative populations have a decided advantage in a changing climate.","language":"English","publisher":"Annual Reviews","doi":"10.1146/annurev-ecolsys-102722-013135","usgsCitation":"Bradley, B., Beaury, E.M., Gallardo, B., Ibáñez, I., Jarnevich, C.S., Morelli, T.L., Sofaer, H., Sorte, C.J., and Vilà, M., 2024, Observed and potential range shifts of native and non-native species with climate change: Annual Review of Ecology, Evolution, and Systematics, v. 55, p. 23-40, https://doi.org/10.1146/annurev-ecolsys-102722-013135.","productDescription":"18 p.","startPage":"23","endPage":"40","ipdsId":"IP-163268","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":466995,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1146/annurev-ecolsys-102722-013135","text":"External Repository"},{"id":464440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bradley, Bethany A. 0000-0003-4912-4971","orcid":"https://orcid.org/0000-0003-4912-4971","contributorId":299998,"corporation":false,"usgs":true,"family":"Bradley","given":"Bethany A.","affiliations":[{"id":64995,"text":"University of Massachusetts, Northeast Climate Adaptation Science Center","active":true,"usgs":false}],"preferred":false,"id":919201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beaury, Evelyn M.","contributorId":236820,"corporation":false,"usgs":false,"family":"Beaury","given":"Evelyn","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":919202,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gallardo, Belinda","contributorId":332551,"corporation":false,"usgs":false,"family":"Gallardo","given":"Belinda","email":"","affiliations":[{"id":79489,"text":"Instituto Pirenaico de Ecología","active":true,"usgs":false}],"preferred":false,"id":919203,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ibáñez, Inés","contributorId":236768,"corporation":false,"usgs":false,"family":"Ibáñez","given":"Inés","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":919204,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":919205,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":919206,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sofaer, Helen 0000-0002-9450-5223","orcid":"https://orcid.org/0000-0002-9450-5223","contributorId":216681,"corporation":false,"usgs":true,"family":"Sofaer","given":"Helen","email":"","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":919207,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sorte, Cascade J.B. 0000-0003-0952-951X","orcid":"https://orcid.org/0000-0003-0952-951X","contributorId":346475,"corporation":false,"usgs":false,"family":"Sorte","given":"Cascade","email":"","middleInitial":"J.B.","affiliations":[{"id":6976,"text":"University of California, Irvine","active":true,"usgs":false}],"preferred":false,"id":919208,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Vilà, Montserrat","contributorId":331419,"corporation":false,"usgs":false,"family":"Vilà","given":"Montserrat","affiliations":[{"id":64996,"text":"University of Sevilla","active":true,"usgs":false}],"preferred":false,"id":919209,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255277,"text":"sir20235064B - 2024 - Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","interactions":[{"subject":{"id":70255277,"text":"sir20235064B - 2024 - Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","indexId":"sir20235064B","publicationYear":"2024","noYear":false,"chapter":"B","displayTitle":"Peak Streamflow Trends in Illinois and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020"},"predicate":"IS_PART_OF","object":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"id":1}],"isPartOf":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"lastModifiedDate":"2024-06-17T22:21:15.873668","indexId":"sir20235064B","displayToPublicDate":"2024-06-17T07:11:12","publicationYear":"2024","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":"2023-5064","chapter":"B","displayTitle":"Peak Streamflow Trends in Illinois and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","docAbstract":"This report characterizes changes in peak streamflow in Illinois and the relation of these changes to climatic variability, and provides a foundation for future studies that can address nonstationarity in peak-flow frequency analysis in Illinois. Records of annual peak and daily streamflow at streamgages and gridded monthly climatic data (observed and modeled) were examined across four trend periods (100 years, water years 1921–2020; 75 years, 1946–2020; 50 years, 1971–2020; 30 years 1991–2020) for trends, change points, and other statistical properties indicative of changing conditions. Median peak streamflows generally exhibit upward trends across the State for the 100- and 75-year trend periods and in northern and southern Illinois for the 50- and 30-year trend periods. The medians of the trend magnitudes (normalized by median peak streamflow) range from a 23-percent increase during the 30-year trend period to a 41-percent increase during the 100-year trend period. Streamgages with trends in peak streamflow often also have change points, or abrupt changes, in streamflow magnitude. More than two-thirds of streamgages at the 100- and 75-year trend periods exhibit a trend and change point in median peak streamflow in the same direction. Temporally, clusters of change points are observed in the late 1960s through early 1980s for the 100- and 75-year trend periods and around 2006 for the 50- and 30-year trend periods. Trends in the 90-percent quantile of peak streamflow, which correspond to the 10-percent exceedance probability often used for the design of drainage structures, increased about the same amount as the 50-percent quantile peak streamflows, except at the 100-year trend period, where the 50-percent quantile peak flow increased more for almost all streamgages. The frequency of high flows has also increased in Illinois, with increases in peaks-over-threshold observed across much of the State for the 100- and 75-year trend periods and in northern and southern Illinois for the 50- and 30-year trend periods.
Upward trends in observed temperature and observed annual precipitation dominate in all trend periods, with clusters of likely upward trends observed in northern and southern Illinois at the 50- and 30-year trend periods. As expected in response to increasing temperature, the modeled proportion of precipitation falling as snow has largely decreased in the study basins across the State, and modeled potential evapotranspiration has increased. Upward trends in modeled annual runoff, which in this report incorporates only the effects of climatic variation, are observed in the same geographic areas where there are increases in observed annual precipitation.
The widespread upward trends in the magnitude of median peak streamflows and the frequency with which high flows occur across the State at the 100- and 75-year trend periods and in northern and southern Illinois at the 50- and 30-year trend periods appear to be driven largely by increases in precipitation based on spatial patterns of these changes and statistical relations between streamflow and climate metrics. Other effects not considered in this report, like urbanization, may be important drivers for certain streamgages in the State.
The prevalence of nonstationarity in peak streamflow in Illinois has important implications for peak-flow frequency analysis. Average annual precipitation and the occurrence of extreme precipitation events are expected to increase across the State. If precipitation continues to increase as expected, peak-flow frequency estimates based on older records may no longer represent the hydrologic regime of today, and methods for nonstationary peak-flow frequency analysis may be needed.
Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Effect concentrations of ammonia, nickel, sodium chloride, and potassium chloride from short-term 7-day tests were compared to those from standard chronic 28-day toxicity tests with juvenile mussels (fatmucket, Lampsilis siliquoidea) to evaluate the sensitivities of the 7-day tests. The effect concentrations for nickel (59 µg Ni/L), chloride (316–519 mg Cl/L, a range from multiple tests), and potassium (15 mg K/L) obtained from the 7-day tests were within a range of effect concentrations for each corresponding chemical in the 28-day tests (41–91 µg Ni/L, 251–>676 mg Cl/L, 15–23 mg K/L), whereas the 7-day ammonia effect concentration (0.40 mg/L total ammonia nitrogen; TAN) was up to 3.3-fold greater than the 28-day effect concentrations (0.12–0.36 mg TAN/L) but with overlapped 95% confidence limits. These results indicate that the 7-day tests produced similar estimates compared to the 28-day tests. Further studies are needed to evaluate the 7-day test sensitivity using additional chemicals with different modes of toxic action. Environ Toxicol Chem 2024;00:1–6. Published 2024. This article is a U.S. Government work and is in the public domain in the USA.
Features of landscape morphology—including slope, curvature, and drainage dissection—are important controls on runoff generation in upland landscapes. Over long timescales, runoff plays an essential role in shaping these same features through surface erosion. This feedback between erosion and runoff generation suggests that modeling long-term landscape evolution together with dynamic runoff generation could provide insight into hydrological function. Here we examine the emergence of variable source area runoff generation in a new coupled hydro-geomorphic model that accounts for water balance partitioning between surface flow, subsurface flow, and evapotranspiration as landscapes evolve over millions of years. We derive a minimal set of dimensionless numbers that provide insight into how hydrologic and geomorphic parameters together affect landscapes. Across the parameter space we investigated, model results collapsed to a single inverse relationship between the dimensionless relief and the ratio of catchment quickflow to discharge. Furthermore, we found an inverse relationship between the Hillslope number, which describes topographic relief relative to aquifer thickness, and the proportion of the landscape that was variably saturated. While the model generally produces fluvial topography visually similar to simpler landscape evolution models, certain parameter combinations produce wide valley bottom wetlands and non-dendritic, trellis-like drainage networks, which may reflect real conditions in some landscapes where aquifer gradients become decoupled from topography. With these results, we demonstrate the power of hydro-geomorphic models for generating new insights into hydrological processes, and also suggest that subsurface hydrology may be integral for modeling aspects of long-term landscape evolution.
The eruption of Hunga volcano on 15 January 2022 produced a higher plume and faster-growing umbrella cloud than has ever been previously recorded. The plume height exceeded 58 km, and the umbrella grew to 450 km in diameter within 50 min. Assuming an umbrella thickness of 10 km, this growth rate implied an average volume injection rate into the umbrella of 330–500 km3 s−1. Conventional relationships between plume height, umbrella-growth rate, and mass eruption rate suggest that this period of activity should have injected a few to several cubic kilometers of rock particles (tephra) into the plume. Yet tephra fall deposits on neighboring islands are only a few centimeters thick and can be reproduced using ash transport simulations with only 0.1–0.2 km3 erupted volume (dense-rock equivalent). How could such a powerful eruption contain so little tephra? Here, we propose that seawater mixing at the vent boosted the plume height and umbrella growth rate. Using the one-dimensional (1-D) steady plume model Plumeria, we find that a plume fed by ~90% water vapor at a temperature of 100 °C (referred to here as steam) could have exceeded 50 km height while keeping the injection rate of solids low enough to be consistent with Hunga’s modest tephra-fall deposit volume. Steam is envisaged to rise from intense phreatomagmatic jets or pyroclastic density currents entering the ocean. Overall, the height and expansion rate of Hunga’s giant plume is consistent with the total mass of fall deposits plus underwater density current deposits, even though most of the erupted mass decoupled from the high plume. This example represents a class of high (> 10 km), ash-poor, steam-driven plumes, that also includes Kīlauea (2020) and Fukutoku-oka-no-ba (2021). Their height is driven by heat flux following well-established relations; however, most of the heat is contained in steam rather than particles. As a result, the heights of these water-rich plumes do not follow well-known relations with the mass eruption rate of tephra.
Nonbreeding helpers can greatly improve the survival of young and the reproductive fitness of breeders in many cooperatively breeding species. Breeder turnover, in turn, can have profound effects on dispersal decisions made by helpers. Despite its importance in explaining group size and predicting the population demography of cooperative breeders, our current understanding of how individual traits influence animal behavior after disruptions to social structure is incomplete particularly for terrestrial mammals. We used 12 yr of genetic sampling and group pedigrees of gray wolves (Canis lupus) in Idaho, USA, to ask questions about how breeder turnover affected the apparent decisions by mature helpers (≥2-yr-old) to stay or leave a group over a 1-yr time interval. We found that helpers showed plasticity in their responses to breeder turnover. Most notably, helpers varied by sex and appeared to base dispersal decisions on the sex of the breeder that was lost as well. Male and female helpers stayed in a group slightly more often when there was breeder turnover of the same sex, although males that stayed were often recent adoptees in the group. Males, however, appeared to remain in a group less often when there was breeding female turnover likely because such vacancies were typically filled by related females from the males’ natal group (i.e. inbreeding avoidance). We show that helpers exploit instability in the breeding pair to secure future breeding opportunities for themselves. The confluence of breeder turnover, helper sex, and dispersal and breeding strategies merge to influence group composition in gray wolves.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/beheco/arae048","usgsCitation":"Ausband, D.E., and Bassing, S., 2024, Helpers show plasticity in their responses to breeder turnover: Behavioral Ecology, v. 35, no. 4, arae048, https://doi.org/10.1093/beheco/arae048.","productDescription":"arae048","ipdsId":"IP-158747","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":496377,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/beheco/arae048","text":"Publisher Index Page"},{"id":486247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"35","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Ausband, David Edward 0000-0001-9204-9837","orcid":"https://orcid.org/0000-0001-9204-9837","contributorId":275329,"corporation":false,"usgs":true,"family":"Ausband","given":"David","email":"","middleInitial":"Edward","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bassing, Sarah B.","contributorId":355572,"corporation":false,"usgs":false,"family":"Bassing","given":"Sarah B.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":937668,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70257499,"text":"70257499 - 2024 - Delayed positive responses of snowshoe hares to prescribed burning in a fire-adapted ecosystem","interactions":[],"lastModifiedDate":"2024-09-09T15:35:23.546173","indexId":"70257499","displayToPublicDate":"2024-06-16T08:29:08","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":774,"text":"Animal Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Delayed positive responses of snowshoe hares to prescribed burning in a fire-adapted ecosystem","docAbstract":"Wildlife populations near the periphery of a species’ range are vulnerable to changes in habitat conditions and climate. However, habitat management and maintenance can help with the persistence of these susceptible populations. Snowshoe hare (Lepus americanus) populations near the southern extent of their range are at risk of extirpation because of changing winter conditions, coupled with reduced early-successional habitat. Prescribed fire has been used to restore and maintain early-successional habitat in the southern range of snowshoe hares, but previous research suggests that burned areas might initially be unsuitable for hares. Therefore, more information is needed on the time required for hares to start using an area post-burning, and the long-term effects of prescribed fire on hare survival. We studied snowshoe hare habitat use and survival in northeastern Pennsylvania, where prescribed fire has been used for scrub oak barrens restoration. We used GPS locations from 71 hares and used resource selection functions to analyze hare selection for burned habitats of varying ages (0–12 years post-burn) and known-fate survival models to evaluate the effects of burning on survival. Hares started using burned areas ≥7 years post-burning but avoided areas burned 0–6 years prior. In addition, hare survival was positively associated with the amount of old burn habitat (≥7 years post-burn) used by an individual. Our results indicate that prescribed burning can be beneficial for hares, but that a time lag of ≥7 years is necessary for positive responses to occur. Planning burns within a mosaic of unburned areas could allow hares to persist during the 0–6 years post-burn when areas are not suitable for hares, which in turn could benefit the persistence of southern snowshoe hare populations. Collectively, our results highlight the importance of long-term demographic monitoring to understand wildlife population responses to management actions.
","language":"English","publisher":"Wiley","doi":"10.1111/acv.12959","usgsCitation":"Gigliotti, L., Boyd, E.S., and Diefenbach, D.R., 2024, Delayed positive responses of snowshoe hares to prescribed burning in a fire-adapted ecosystem: Animal Conservation, 9 p., https://doi.org/10.1111/acv.12959.","productDescription":"9 p.","ipdsId":"IP-154582","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":498269,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/acv.12959","text":"Publisher Index Page"},{"id":433624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"northeastern 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\"}}]}","noUsgsAuthors":false,"publicationDate":"2024-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Gigliotti, Laura C.","contributorId":204828,"corporation":false,"usgs":false,"family":"Gigliotti","given":"Laura C.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":912757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyd, Emily S.","contributorId":342971,"corporation":false,"usgs":false,"family":"Boyd","given":"Emily","email":"","middleInitial":"S.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":910550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diefenbach, Duane R. 0000-0001-5111-1147 drd11@usgs.gov","orcid":"https://orcid.org/0000-0001-5111-1147","contributorId":5235,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Duane","email":"drd11@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910551,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256001,"text":"70256001 - 2024 - Uncertainty in ground-motion-to-intensity conversions significantly affects earthquake early warning alert regions","interactions":[],"lastModifiedDate":"2024-07-12T11:58:11.727858","indexId":"70256001","displayToPublicDate":"2024-06-16T06:56:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty in ground-motion-to-intensity conversions significantly affects earthquake early warning alert regions","docAbstract":"We examine how the choice of ground‐motion‐to‐intensity conversion equations (GMICEs) in earthquake early warning (EEW) systems affects resulting alert regions. We find that existing GMICEs can underestimate observed shaking at short rupture distances or overestimate the extent of low‐intensity shaking. Updated GMICEs that remove these biases would improve the accuracy of alert regions for the ShakeAlert EEW system for the West Coast of the United States. ShakeAlert uses ground‐motion prediction equations (GMPEs), which calculate spatial distributions of peak ground acceleration (PGA) and peak ground velocity (PGV) from earthquake source estimates, combined with GMICEs to translate GMPE output into modified Mercalli intensity (MMI). We find significant epistemic uncertainty in alert distances; near‐source MMI estimates from different GMICEs can differ by over 1 MMI unit, and MMI extents used for public EEW alerts can differ by hundreds of kilometers for larger magnitude earthquakes (M ∼6.5+). We use a catalog of “Did You Feel It?” shaking reports to evaluate how well GMICEs predict observed shaking. Our preferred GMICE is the one that computes MMI using PGV for high intensities and transitions to using PGA for nondamaging intensities. These results motivate updating GMICE relationships more generally, including in ShakeMap applications.
Understanding genetic structure and diversity among remnant populations of rare species can inform conservation and recovery actions. We used a population genetic framework to spatially delineate gene pools and estimate gene flow and effective population sizes for the endangered California Freshwater Shrimp Syncaris pacifica. Tissues of 101 individuals were collected from 11 sites in 5 watersheds, using non-lethal tissue sampling. Single Nucleotide Polymorphism markers were developed de novo using ddRAD-seq methods, resulting in 433 unlinked loci scored with high confidence and low missing data. We found evidence for strong genetic structure across the species range. Two hierarchical levels of significant differentiation were observed: (i) five clusters (regional gene pools, FST = 0.38–0.75) isolated by low gene flow were associated with watershed limits and (ii) modest local structure among tributaries within a watershed that are not connected through direct downstream flow (local gene pools, FST = 0.06–0.10). Sampling sites connected with direct upstream-to-downstream water flow were not differentiated. Our analyses suggest that regional watersheds are isolated from one another, with very limited (possibly no) gene flow over recent generations. This isolation is paired with small effective population sizes across regional gene pools (Ne = 62.4–147.1). Genetic diversity was variable across sites and watersheds (He = 0.09–0.22). Those with the highest diversity may have been refugia and are now potential sources of genetic diversity for other populations. These findings highlight which portions of the species range may be most vulnerable to future habitat fragmentation and provide management consideration for maintaining local effective population sizes and genetic connectivity.
Groundwater contributions to streamflow sustain aquatic ecosystem resilience; streams without significant groundwater inputs often have well-coupled air and water temperatures that degrade cold-water habitat during warm low flow periods. Widespread uncertainty in stream-groundwater connectivity across space and time has created disparate predictions of energy and nutrient fluxes across headwater networks, hindering predictions of cold-water habitat resilience under climate change scenarios. Recently, annual paired air and water temperature signals have been harnessed to indicate stream water thermal sensitivity and the dominance of deep versus shallow groundwater influence, although the utility of diel air–water temperature signal metrics for hydrologic inference has remained unexplored. Here we analyzed two consecutive years of locally paired, air–water temperature data from 47 headwater stream sites in the Catskill Mountains, New York, USA, and discovered characteristic seasonal patterns in diel temperature signal sinusoid metrics (amplitude ratio, phase lag, and mean ratio) driven by shifts in streamflow generation mechanisms and stream network position. Hydrologic interpretations of observed patterns were supported by stream heat budget model scenarios and additional analysis of paired air–water temperature data from two streams in Shenandoah National Park, Virginia, USA, with well characterized stream-groundwater connectivity. We found that within smaller tributaries, streamflow generation transitions from runoff to groundwater dominance were driven by hillslope drying during seasonal periods of lower precipitation. This was evidenced by significant correlations (p < 0.01) between daily water:air temperature signal amplitudes (non-linear decreases of ∼ 50 %) and derived base-flow index at 22 of the 28 sites, indicating enhanced local groundwater influence on streamflow promotes decoupling of diel air–water temperature signals. Additionally, ratios between daily water:air temperature signal means were lower in tributaries (∼0.68) when compared to main-stem (∼0.8) sites, increasing linearly throughout the observational period. In conceptual stream heat budget models, groundwater inflow had minimal effects on daily phase lags (∼0.2 hr), but increases in fractional groundwater discharge (0–50 %) depressed daily amplitude (∼20 % to 50 %) and mean ratios (∼15 %), supporting the sensitivity of daily metrics to interpreted changes in seasonal groundwater contributions to streamflow. During observational periods (i.e., April through October 2021 and 2022), significant differences (p < 0.01) between tributary and main-stem air–water metrics occurred when base-flow contributions were highest (∼0.93 vs. ∼ 0.68), as sites lower in the network had daily temperature metrics dominated by stream channel thermal inertia, rather than local groundwater connectivity, showing enhanced air–water diel signal coupling during warmer, drier periods. Divergent air temperature coupling across the network was interpreted as being driven by distance from local groundwater source zones, additional lateral groundwater inflows do not contribute a meaningful fraction to channel discharge lower in the network. Given the growing footprint of stream temperature observations, diel air–water temperature signals can provide distributed metrics sensitive to upstream groundwater discharge. Consequently, these metrics can support ongoing efforts by resource managers and researchers seeking to forecast the resilience of cold-water habitat to climate warming and changing precipitation regimes in mountain headwater streams.
Preventing the spread of aquatic invasive species is an important management action. Identifying the characteristics of lakes that are susceptible to invasion creates an opportunity for management groups to prioritize limited resources for high-risk areas. In this study, we leveraged big data from a popular fishing app and other publicly available sources of environmental and human-use exposure measurements to develop machine learning models to predict aquatic invasive species presence in 30,375 lakes in the upper Mississippi river basin of the United States. Our results predicted that an additional 665, 771, 544, 703, and 638 lakes in the basin are invaded or at high risk of invasion by Eurasian watermilfoil, curly-leaf pondweed, rusty crayfish, Chinese mystery snail, and dreissenid mussels, respectively. Lake invasions were predicted by a combination of environmental, human-use exposure, and community dynamics variables. Features that made a lake more attractive to recreationists were consistently important across our models including the presence of a boat ramp, larger lake size, and surrounding natural landscape. The importance of co-occurring invasive species in some models could reflect several scenarios including invasional meltdown, facilitation among species, similar pathways for introduction, or similar response to the environment. Our models predicted a higher proportion of invasions in less popular lakes compared to known invasions. The finding underscores the potential importance of less popular lakes in the invasion process and suggests that the detection of invasions may be lower in these lakes. These results serve as a valuable tool for data-driven management decisions and can provide actionable insights for effective aquatic invasive species management.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-024-03367-6","usgsCitation":"Weir, J.L., Daniel, W., Hyder, K., Skov, C., and Venturelli, P.A., 2024, Artificial intelligence applied to big data reveals that lake invasions are predicted by human traffic and co-occurring invasions: Biological Invasions, v. 26, p. 3163-3178, https://doi.org/10.1007/s10530-024-03367-6.","productDescription":"16 p.","startPage":"3163","endPage":"3178","ipdsId":"IP-162115","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":432144,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","noUsgsAuthors":false,"publicationDate":"2024-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Weir, Jessica L.","contributorId":330438,"corporation":false,"usgs":false,"family":"Weir","given":"Jessica","email":"","middleInitial":"L.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":908933,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Daniel, Wesley 0000-0002-7656-8474","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":219312,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":908934,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hyder, Kieran","contributorId":291284,"corporation":false,"usgs":false,"family":"Hyder","given":"Kieran","email":"","affiliations":[{"id":62658,"text":"The Centre for Environment, Fisheries and Aquaculture Science (Cefas) and Collaborative Centre for Sustainable Use of the Seas (CCSUS), School of Environmental Sciences, University of East Anglia, Norwich Research Park","active":true,"usgs":false}],"preferred":false,"id":908935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skov, Christian","contributorId":268055,"corporation":false,"usgs":false,"family":"Skov","given":"Christian","email":"","affiliations":[{"id":50046,"text":"Technical University of Denmark","active":true,"usgs":false}],"preferred":false,"id":908936,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Venturelli, Paul A.","contributorId":171477,"corporation":false,"usgs":false,"family":"Venturelli","given":"Paul","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":908937,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256119,"text":"70256119 - 2024 - Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model","interactions":[],"lastModifiedDate":"2024-07-23T20:23:11.322287","indexId":"70256119","displayToPublicDate":"2024-06-15T09:11:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model","docAbstract":"Invasive grass carp (Ctenopharyngodon idella) are currently reproducing in several tributaries to Lake Erie and threatening the Great Lakes ecosystem and fisheries. Grass carp are pelagic river spawners whose fertilized eggs drift downstream from the spawning site, developing as they drift. Variability in spawning time and location together with nonuniform velocities in natural rivers leads to egg age variability in field samples at downstream sampling sites. In this study, the Fluvial Egg Drift Simulator (FluEgg) model was used to simulate the transport of grass carp eggs collected in 12 samples at 9 sites in the lower Sandusky River (Ohio, USA) on July 12, 2017, to replicate the observed variability in egg-age distributions present in field samples. The variability in egg ages in virtual samples compare well to field samples. The most plausible explanations for differences between virtual and field samples are the existence of multiple spawning locations, including a spawning area approximately 8 kilometers upstream from the river mouth, and idealized flow fields derived from a one-dimensional hydraulic model. Despite multiple sources of uncertainty and the deficiency in prescribing detailed spawning activities in the simulations, the results validate the utility of FluEgg together with ichthyoplankton data to identify plausible spawning areas and interpret age variability in field samples. A comprehensive discussion of model limitations and ichthyoplankton sample interpretation provides guidance for those using drift models to inform management actions for control of invasive carp in North America and to protect and restore carp populations in their native range in Asia.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102376","usgsCitation":"Soong, D., Jackson, P.R., Kocovsky, P.M., Morrison, L., Garcia, T., Santacruz, S., Chen, C., Zhu, Z., and Embke, H.S., 2024, Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model: Journal of Great Lakes Research, v. 50, no. 4, 102376, 14 p., https://doi.org/10.1016/j.jglr.2024.102376.","productDescription":"102376, 14 p.","ipdsId":"IP-157787","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":439399,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102376","text":"Publisher Index Page"},{"id":431354,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Sandusky River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83,\n 41.5\n ],\n [\n -83.25,\n 41.5\n ],\n [\n -83.25,\n 41.25\n ],\n [\n -83,\n 41.25\n ],\n [\n -83,\n 41.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Soong, David 0000-0003-0404-2163","orcid":"https://orcid.org/0000-0003-0404-2163","contributorId":206523,"corporation":false,"usgs":true,"family":"Soong","given":"David","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true}],"preferred":true,"id":906762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morrison, Lori","contributorId":340259,"corporation":false,"usgs":false,"family":"Morrison","given":"Lori","email":"","affiliations":[{"id":81526,"text":"Alaska Water Resources","active":true,"usgs":false}],"preferred":false,"id":906763,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garcia, Tatiana","contributorId":340260,"corporation":false,"usgs":false,"family":"Garcia","given":"Tatiana","affiliations":[{"id":81527,"text":"AquaIntel Inc.","active":true,"usgs":false}],"preferred":false,"id":906764,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Santacruz, Santiago","contributorId":340261,"corporation":false,"usgs":false,"family":"Santacruz","given":"Santiago","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":906765,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chen, Cindy","contributorId":340262,"corporation":false,"usgs":false,"family":"Chen","given":"Cindy","email":"","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":906766,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhu, Zhenduo","contributorId":340263,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhenduo","affiliations":[{"id":81528,"text":"Tsinghua University, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":906767,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Embke, Holly Susan 0000-0002-9897-7068","orcid":"https://orcid.org/0000-0002-9897-7068","contributorId":270754,"corporation":false,"usgs":true,"family":"Embke","given":"Holly","email":"","middleInitial":"Susan","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":906768,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70260439,"text":"70260439 - 2024 - Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations","interactions":[],"lastModifiedDate":"2024-11-01T13:35:38.261218","indexId":"70260439","displayToPublicDate":"2024-06-15T08:26:47","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7359,"text":"Journal of Geophysical Research Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations","docAbstract":"According to classic stomatal optimization theory, plant stomata are regulated to maximize carbon assimilation for a given water loss. A key component of stomatal optimization models is marginal water-use efficiency (mWUE), the ratio of the change of transpiration to the change in carbon assimilation. Although the mWUE is often assumed to be constant, variability of mWUE under changing hydrologic conditions has been reported. However, there has yet to be a consensus on the patterns of mWUE variabilities and their relations with atmospheric aridity. We investigate the dynamics of mWUE in response to vapor pressure deficit (VPD) and aridity index using carbon and water fluxes from 115 eddy covariance towers available from the global database FLUXNET. We demonstrate a non-linear mWUE-VPD relationship at a sub-daily scale in general; mWUE varies substantially at both low and high VPD levels. However, mWUE remains relatively constant within the mid-range of VPD. Despite the highly non-linear relationship between mWUE and VPD, the relationship can be informed by the strong linear relationship between ecosystem-level inherent water-use efficiency (IWUE) and mWUE using the slope, m*. We further identify site-specific m* and its variability with changing site-level aridity across six vegetation types. We suggest accurately representing the relationship between IWUE and VPD using Michaelis–Menten or quadratic functions to ensure precise estimation of mWUE variability for individual sites.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007875","usgsCitation":"Yi, K., Novick, K.A., Zhang, Q., Wang, L., Hwang, T., Yang, X., Mallick, K., Beland, M., Senay, G.B., and Baldocchi, D., 2024, Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations: Journal of Geophysical Research Biogeosciences, v. 129, no. 6, e2023JG007875, 19 p., https://doi.org/10.1029/2023JG007875.","productDescription":"e2023JG007875, 19 p.","ipdsId":"IP-163083","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":466997,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jg007875","text":"Publisher Index Page"},{"id":463530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"129","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Yi, Koong","contributorId":345841,"corporation":false,"usgs":false,"family":"Yi","given":"Koong","email":"","affiliations":[{"id":82725,"text":"Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, CA, U.S.A","active":true,"usgs":false}],"preferred":false,"id":917685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Novick, Kimberly A.","contributorId":196379,"corporation":false,"usgs":false,"family":"Novick","given":"Kimberly","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":917686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Quan","contributorId":345842,"corporation":false,"usgs":false,"family":"Zhang","given":"Quan","email":"","affiliations":[{"id":82726,"text":"State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China.","active":true,"usgs":false}],"preferred":false,"id":917687,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Lixin","contributorId":300466,"corporation":false,"usgs":false,"family":"Wang","given":"Lixin","affiliations":[{"id":65165,"text":"Department of Earth Sciences, Indiana University–Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA.","active":true,"usgs":false}],"preferred":false,"id":917688,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hwang, Taehee","contributorId":345843,"corporation":false,"usgs":false,"family":"Hwang","given":"Taehee","email":"","affiliations":[{"id":82727,"text":"Department of Geography, Indiana University Bloomington, Bloomington, IN, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917689,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Xi","contributorId":245237,"corporation":false,"usgs":false,"family":"Yang","given":"Xi","email":"","affiliations":[],"preferred":false,"id":917690,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mallick, Kanishka","contributorId":345844,"corporation":false,"usgs":false,"family":"Mallick","given":"Kanishka","email":"","affiliations":[{"id":82729,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917691,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Beland, Martin","contributorId":345845,"corporation":false,"usgs":false,"family":"Beland","given":"Martin","email":"","affiliations":[{"id":82729,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917692,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":917693,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Baldocchi, Dennis 0000-0003-3496-4919","orcid":"https://orcid.org/0000-0003-3496-4919","contributorId":167495,"corporation":false,"usgs":false,"family":"Baldocchi","given":"Dennis","affiliations":[{"id":24725,"text":"Ecosystem Science Division, Department of Environmental Science","active":true,"usgs":false}],"preferred":false,"id":917694,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70257874,"text":"70257874 - 2024 - Mast seeding in European beech (Fagus sylvatica L.) is associated with reduced fungal sporocarp production and community diversity","interactions":[],"lastModifiedDate":"2024-08-30T12:09:04.026167","indexId":"70257874","displayToPublicDate":"2024-06-15T07:07:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Mast seeding in European beech (Fagus sylvatica L.) is associated with reduced fungal sporocarp production and community diversity","docAbstract":"Mast seeding is a well-documented phenomenon across diverse forest ecosystems. While its effect on aboveground food webs has been thoroughly studied, how it impacts the soil fungi that drive soil carbon and nutrient cycling has not yet been explored. To evaluate the relationship between mast seeding and fungal resource availability, we paired a Swiss 29-year fungal sporocarp census with contemporaneous seed production for European beech (Fagus sylvatica L.). On average, mast seeding was associated with a 55% reduction in sporocarp production and a compositional community shift towards drought-tolerant taxa across both ectomycorrhizal and saprotrophic guilds. Among ectomycorrhizal fungi, traits associated with carbon cost did not explain species' sensitivity to seed production. Together, our results support a novel hypothesis that mast seeding limits annual resource availability and reproductive investment in soil fungi, creating an ecosystem ‘rhythm’ to forest processes that is synchronized above- and belowground.
We collected Cooper's Hawk (Accipiter cooperii) eggshells from nests in the Tucson, Arizona, USA, area in the 1990s incidental to other activities and compared them to pre-DDT Cooper's Hawk eggshells (119 museum specimens from 14 states, 1894–1939) ranging from 0.284–0.402 mm (x̄= 0.348 mm, SD = 0.0243) and we also compared them to reported thicknesses found in the literature. We found that within-state eggshell thickness varied as did eggshell thickness among states. Of the pre-DDT eggshells measured, those from Arizona, Utah, and Nevada were thinnest and generally eggs from western states (x̄= 0.339 mm, SD = 0.0184) had significantly thinner eggshells than those for eastern states (x̄= 0.359 mm, SD = 0.0256). Other published measurements of pre-DDT Cooper's Hawk eggshells were slightly lower than ours but were generally within the lower range of our measurements, which was expected because of the measuring technique used in earlier studies versus our method. Cooper's Hawk eggshells that were collected from nests in the Tucson area in the 1990s had a mean thickness of 0.309 mm (SD = 0.0191) and the pre-DDT mean thickness of museum eggshells from Arizona was 0.333 mm (SD = 0.018). Although the Tucson eggshells were significantly thinner than pre-DDT eggshells overall (t = 10.8, df = 100.4, P < 0.001), some individual pre-DDT eggshells and even some means from other regions (e.g., New Hampshire, New York, and Nevada) were similarly thin. Measurements of these pre-DDT eggshells show wide variation and demonstrate the importance of comparing eggs from the same geographical area and having an adequate sample size.
","language":"English","publisher":"The Raptor Research Foundation, Inc.","doi":"10.3356/JRR-23-56","usgsCitation":"Santolo, G., and Boal, C.W., 2024, Variation in Cooper's Hawk (Accipiter cooperii) eggshell thickness: DDT, measurement methods, and location: Journal of Raptor Research, v. 58, no. 3, p. 1-9, https://doi.org/10.3356/JRR-23-56.","productDescription":"9 p.","startPage":"1","endPage":"9","ipdsId":"IP-155031","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432230,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","city":"Tuscon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.23084326682839,\n 32.47297048965915\n ],\n [\n -111.23084326682839,\n 31.994860625328343\n ],\n [\n -110.66901597123126,\n 31.994860625328343\n ],\n [\n -110.66901597123126,\n 32.47297048965915\n ],\n [\n -111.23084326682839,\n 32.47297048965915\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"58","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Santolo, G. M.","contributorId":340691,"corporation":false,"usgs":false,"family":"Santolo","given":"G. M.","affiliations":[{"id":80834,"text":"Jacobs","active":true,"usgs":false}],"preferred":false,"id":907460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907461,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70254922,"text":"sir20245027 - 2024 - Accuracy assessment of three-dimensional point cloud data collected with a scanning total station on Shinnecock Nation Tribal lands in Suffolk County, New York","interactions":[],"lastModifiedDate":"2026-02-03T18:16:49.214434","indexId":"sir20245027","displayToPublicDate":"2024-06-14T15:39:00","publicationYear":"2024","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":"2024-5027","displayTitle":"Accuracy Assessment of Three-Dimensional Point Cloud Data Collected With a Scanning Total Station on Shinnecock Nation Tribal Lands in Suffolk County, New York","title":"Accuracy assessment of three-dimensional point cloud data collected with a scanning total station on Shinnecock Nation Tribal lands in Suffolk County, New York","docAbstract":"A combined point cloud of about 85.6 million points was collected during 27 scans of a section of the western shoreline along the Shinnecock Peninsula of Suffolk County, New York, to document baseline geospatial conditions during July and October 2022 using a scanning total station. The three-dimensional accuracy of the combined point cloud is assessed to identify potential systematic error sources associated with the surveying equipment and the novel methodology used to collect and field-register (data are oriented and aligned in real time) point cloud data. The accuracy of the combined point cloud was assessed in terms of relative and absolute reference frames. Relative accuracy provides a measure of error within the local coordinate system and is determined by combining the uncertainty associated with the position of the scan station (the point being occupied by the scanning total station during the scan), the uncertainty associated with the position of the network control points, and the uncertainty associated with the laser of the scanning total station. Assessment of the absolute accuracy includes these three potential error sources combined with the uncertainty associated with the geodetic coordinates to which the local control network is referenced. The combined overall relative horizontal and vertical accuracy of the point cloud is 0.0156 and 0.0241 meter, respectively, at the 95 percent confidence level. The combined overall absolute horizontal and vertical accuracy of the point cloud is 0.0598 and 0.0733 meter, respectively, at the 95 percent confidence level.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245027","collaboration":"Prepared in cooperation with the Shinnecock Nation and the Federal Emergency Management Agency","usgsCitation":"Noll, M.L., Capurso, W.D., and Chu, A., 2024, Accuracy assessment of three-dimensional point cloud data collected with a scanning total station on Shinnecock Nation Tribal lands in Suffolk County, New York: U.S. Geological Survey Scientific Investigations Report 2024–5027, 23 p., https://doi.org/10.3133/sir20245027.","productDescription":"Report: vii, 23 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-153251","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":429787,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5027/sir20245027.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5027 XML"},{"id":429784,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5027/coverthb.jpg"},{"id":429785,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5027/sir20245027.pdf","text":"Report","size":"14.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5027 PDF"},{"id":429786,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245027/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5027 HTML"},{"id":429788,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5027/images/"},{"id":429789,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OG0AAO","text":"USGS data release","linkHelpText":"Three-dimensional point cloud data collected with a scanning total station on the western shoreline of the Shinnecock Nation Tribal lands, Suffolk County, New York, 2022"},{"id":429861,"rank":7,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2024/5027/images/sir20245027_fig02.png","text":"Figure 2","size":"3.20 MB","linkHelpText":"- Map showing the study area where three-dimensional point cloud data were collected with a scanning total station along the western shoreline of the Shinnecock Peninsula in Suffolk County, New York, for a point cloud accuracy assessment"},{"id":429862,"rank":8,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2024/5027/images/sir20245027_fig03.png","text":"Figure 3","size":"3.25 MB","linkHelpText":"- Map showing estimated position of the shoreline after sea-level rise of about 0.46 meter (m) within the study area on the Shinnecock Nation Tribal lands in Suffolk County, New York, using a conservative model projection for 2050"},{"id":429863,"rank":9,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2024/5027/images/sir20245027_fig04.png","text":"Figure 4","size":"1.76 MB","linkHelpText":"- Graphical representation of the point cloud of A, the study area in plan view, B, the coastal spit in plan view, and C, the dune adjacent to the Tribal cemetery on the Shinnecock Nation Tribal lands in Suffolk County, New York, in section view in July 2022"},{"id":499455,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117076.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","county":"Suffolk County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.67660903513142,\n 41.00419828031659\n ],\n [\n -72.67660903513142,\n 40.789306473711775\n ],\n [\n -72.23164172630058,\n 40.789306473711775\n ],\n [\n -72.23164172630058,\n 41.00419828031659\n ],\n [\n -72.67660903513142,\n 41.00419828031659\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The Navajo (N) aquifer is an extensive aquifer and the primary source of groundwater in the 5,400-square-mile Black Mesa area in northeastern Arizona. Water availability is an important issue in the Black Mesa area because of the arid climate, past industrial water use, and continued water requirements for municipal use by a growing population. Precipitation in the area typically ranges from less than 6 to more than 16 inches per year, depending on location.
The U.S. Geological Survey water-monitoring program in the Black Mesa area began in 1971 and provides information about the long-term effects of groundwater withdrawals from the N aquifer for industrial and municipal uses. This report presents the results of data collected as part of the monitoring program in the Black Mesa area from calendar years 2020–2021 and, additionally, uses streamflow statistics from November and December 2019. The monitoring program includes measurements of (1) groundwater withdrawals (pumping), (2) groundwater levels, (3) spring discharge, (4) surface-water discharge, and (5) groundwater chemistry.
In calendar year 2020, total groundwater withdrawals were estimated to be 2,680 acre-feet (acre-ft), and, in 2021, total withdrawals were estimated to be 2,570 acre-ft. Total withdrawals during 2021 were about 65 percent less than total withdrawals in 2005 because the Peabody Western Coal Company discontinued its use of water to transport coal in a coal slurry pipeline after 2005 and ceased mining operations in 2019.
Owing to Navajo Nation and Hopi Reservation access restrictions during the Coronavirus pandemic, water levels were not collected from municipal wells in 2020 or 2021. Water levels measured in 2021 from wells completed in the unconfined areas of the N aquifer within the Black Mesa area showed a decline in 7 of 13 wells when compared with water levels from the prestress period (prior to 1965). The changes in water levels across all 13 wells ranged from +8.4 feet (ft) to −42.4 ft, and the median change was −0.4 ft. Water levels also showed decline in 11 of 12 wells measured in the confined area of the aquifer when compared to the prestress period. The median change for the confined area of the aquifer was −25.9 ft, with changes across all 12 wells ranging from +17.3 ft to −133.7 ft.
Spring flow was measured at four springs between 2020 and 2021. Flow fluctuated during the period of record for Burro Spring and Pasture Canyon Spring, but a decreasing trend was statistically significant (p<0.05) at Moenkopi School Spring and Unnamed Spring near Dennehotso, Arizona. Discharge at Burro Spring has remained relatively constant since it was first measured in the 1980s, and discharge at Pasture Canyon Spring has fluctuated for the period of record.
Continuous records of surface-water discharge in the Black Mesa area were collected from streamflow-gaging stations at the following sites: Moenkopi Wash at Moenkopi 09401260 (1976–2021), Dinnebito Wash near Sand Springs 09401110 (1993–2020), Polacca Wash near Second Mesa 09400568 (1994–2020), and Pasture Canyon Springs 09401265 (2004–2021). Median winter flows (November through February) of each winter were used as an estimate of the amount of groundwater discharge at the above-named sites. For the period of record, the median winter flows have generally remained constant at Polacca Wash and Pasture Canyon Springs, whereas a decreasing trend was observed at Moenkopi Wash and Dinnebito Wash.
In 2020 and 2021, water samples were collected from a total of four springs in the Black Mesa area and analyzed for selected chemical constituents. Results from the four springs were compared with previous analyses from the same springs. Dissolved solids, chloride, and sulfate concentrations increased at Moenkopi School Spring during the more than 30 years of record at that site. Concentrations of dissolved solids and sulfate at Pasture Canyon Spring have not varied significantly (p>0.05) since the early 1980s, and there is no increasing or decreasing trend in those data. However, concentrations of chloride from Pasture Canyon Spring show a diminishing trend. Concentrations of dissolved solids, chloride, and sulfate at Unnamed Spring near Dennehotso have varied for the period of record, but there is no statistical trend in the data. Concentrations of dissolved solids at Burro Spring have varied for the period of record, but there is no statistical trend in the data. However, concentrations of chloride and sulfate from Burro Spring show a trend towards lower concentrations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241019","collaboration":"Prepared in cooperation with the Navajo Nation and Peabody Western Coal Company","usgsCitation":"Mason, J.P., 2024, Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2019–2021: U.S. Geological Survey Open-File Report 2024–1019, 47 p., https://doi.org/10.3133/ofr20241019.","productDescription":"vii, 48 p.","onlineOnly":"Y","ipdsId":"IP-148316","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":430241,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1019/images"},{"id":430240,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1019/ofr20241019.xml"},{"id":430239,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1019/ofr20241019.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":430238,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1019/covrthb.jpg"},{"id":430242,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241019/full"},{"id":499245,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117071.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arizona","otherGeospatial":"Black Mesa Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.50769351138865,\n 36.993810314532595\n ],\n [\n -111.50769351138865,\n 35.29946810356502\n ],\n [\n -109.33240054263857,\n 35.29946810356502\n ],\n [\n -109.33240054263857,\n 36.993810314532595\n ],\n [\n -111.50769351138865,\n 36.993810314532595\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
The Yellowstone Volcano Observatory (YVO) monitors volcanic and hydrothermal activity associated with the Yellowstone magmatic system, carries out research into magmatic processes occurring beneath Yellowstone caldera, and issues timely warnings and guidance related to potential future geologic hazards. YVO is a collaborative consortium that includes the U.S. Geological Survey (USGS), Yellowstone National Park, University of Utah, University of Wyoming, Montana State University, EarthScope Consortium, Wyoming State Geological Survey, Montana Bureau of Mines and Geology, and Idaho Geological Survey. The USGS component of YVO also has the operational responsibility for monitoring volcanic activity in the Intermountain West of the United States, including Arizona, New Mexico, Utah, and Colorado.
Yellowstone Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court, Suite 100
Vancouver, WA 98683
Email: yvowebteam@usgs.gov
","tableOfContents":"Sleeper populations are established populations of introduced species whose population growth is limited by one or more abiotic or biotic conditions. Sleeper populations pose an invasion risk if a change in those limiting conditions, such as climate change, enables population growth and invasion. With thousands of established introduced species, it is critical that we identify and prioritize potential sleepers. Here, we identified introduced plants established in the northeastern United States with high impacts and the potential to expand with climate change. Of 1795 introduced plants established in one or more northeastern states, we focused on 118 taxa regulated by one or more states outside the Northeast plus 61 taxa recorded as invasive globally and under consideration for regulation in the Northeast. We used the environmental impact classification for alien taxa framework to quantify negative ecological and socioeconomic impacts reported in the scientific literature for these 169 taxa. We compared mean minimum winter temperature and annual precipitation where the species were abundant with current and future climate in the Northeast to evaluate whether climate change could increase risk. We identified 49 plants with ecological impacts linked to loss of native diversity and 94 plants with socioeconomic impacts. 81 species showed an increase in climatic suitability for abundant populations with climate change. Using ecological impact, increased climate suitability, and presence in fewer than 20 Northeastern counties, we highlight 18 species as high priorities for potential management in the Northeast. This approach can inform climate-smart, proactive management of sleeper populations before they become invasive.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-024-03351-0","usgsCitation":"O'Uhuru, A., Morelli, T.L., Evans, A.E., Salva, J., and Bradley, B., 2024, Identifying new invasive plants in the face of climate change: A focus on sleeper species: Biological Invasions, v. 26, p. 2989-3001, https://doi.org/10.1007/s10530-024-03351-0.","productDescription":"13 p.","startPage":"2989","endPage":"3001","ipdsId":"IP-159571","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":486085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","noUsgsAuthors":false,"publicationDate":"2024-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"O'Uhuru, A.C.","contributorId":355408,"corporation":false,"usgs":false,"family":"O'Uhuru","given":"A.C.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":937244,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":937245,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evans, Annette E. 0000-0001-6439-4908","orcid":"https://orcid.org/0000-0001-6439-4908","contributorId":328976,"corporation":false,"usgs":false,"family":"Evans","given":"Annette","email":"","middleInitial":"E.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":937246,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Salva, J.D.","contributorId":355409,"corporation":false,"usgs":false,"family":"Salva","given":"J.D.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":937247,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bradley, B.A.","contributorId":355410,"corporation":false,"usgs":false,"family":"Bradley","given":"B.A.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":937248,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256593,"text":"70256593 - 2024 - Designing count-based studies in a world of hierarchical models","interactions":[],"lastModifiedDate":"2024-08-23T15:45:06.312749","indexId":"70256593","displayToPublicDate":"2024-06-14T10:36:52","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Designing count-based studies in a world of hierarchical models","docAbstract":"Advances in hierarchical modeling have improved estimation of ecological parameters from count data, especially those quantifying population abundance, distribution, and dynamics by explicitly accounting for observation processes, particularly incomplete detection. Even hierarchical models that account for incomplete detection, however, cannot compensate for data limitations stemming from poorly planned sampling. Ecologists therefore need guidance for planning count-based studies that follow established sampling theory, collect appropriate data, and apply current modeling approaches to answer their research questions. We synthesize available literature relevant to guiding count-based studies. Considering the central historical and ongoing contributions of avian studies to ecological knowledge, we focus on birds as a case study for this review, but the basic principles apply to all populations whose members are sufficiently observable to be counted. The sequence of our review represents the thought process in which we encourage ecologists to engage 1) the research question(s) and population parameters to measure, 2) sampling design, 3) analytical framework, 4) temporal design, and 5) survey protocol. We also provide 2 hypothetical demonstrations of these study plan components representing different research questions and study systems. Mirroring the structure of hierarchical models, we suggest researchers primarily focus on the ecological processes of interest when designing their approach to sampling, and wait to consider logistical constraints of data collection and observation processes when developing the survey protocol. We offer a broad framework for researchers planning count-based studies, while pointing to relevant literature elaborating on particular tools and concepts.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22622","usgsCitation":"Latif, Q., Valente, J., Johnston, A., Davis, K., Fogarty, F., Green, A.W., Jones, G.M., Leu, M., Michel, N.L., Pavlacky, D.C., Rigby, E., Rushing, C.S., Sanderlin, J., Tingley, M.W., and Zhao, Q., 2024, Designing count-based studies in a world of hierarchical models: Journal of Wildlife Management, v. 88, no. 7, e22622, 31 p., https://doi.org/10.1002/jwmg.22622.","productDescription":"e22622, 31 p.","ipdsId":"IP-153768","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":439401,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/content/qt7h5896rk/qt7h5896rk.pdf","text":"External Repository"},{"id":433103,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"88","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Latif, Quresh S.","contributorId":341286,"corporation":false,"usgs":false,"family":"Latif","given":"Quresh S.","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":908189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Valente, Jonathon Joseph 0000-0002-6519-3523","orcid":"https://orcid.org/0000-0002-6519-3523","contributorId":340615,"corporation":false,"usgs":true,"family":"Valente","given":"Jonathon Joseph","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnston, Alison","contributorId":341287,"corporation":false,"usgs":false,"family":"Johnston","given":"Alison","email":"","affiliations":[{"id":65006,"text":"University of St Andrews","active":true,"usgs":false}],"preferred":false,"id":908191,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Kayla L.","contributorId":341288,"corporation":false,"usgs":false,"family":"Davis","given":"Kayla L.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":908192,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fogarty, Frank A.","contributorId":341289,"corporation":false,"usgs":false,"family":"Fogarty","given":"Frank A.","affiliations":[{"id":37071,"text":"California State Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":908193,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Green, Adam W.","contributorId":341290,"corporation":false,"usgs":false,"family":"Green","given":"Adam","email":"","middleInitial":"W.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":908194,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Gavin M.","contributorId":341291,"corporation":false,"usgs":false,"family":"Jones","given":"Gavin","email":"","middleInitial":"M.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":908195,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Leu, Matthias","contributorId":341292,"corporation":false,"usgs":false,"family":"Leu","given":"Matthias","affiliations":[{"id":57314,"text":"William & Mary","active":true,"usgs":false}],"preferred":false,"id":908196,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Michel, Nicole L.","contributorId":341293,"corporation":false,"usgs":false,"family":"Michel","given":"Nicole","email":"","middleInitial":"L.","affiliations":[{"id":27800,"text":"National Audubon Society","active":true,"usgs":false}],"preferred":false,"id":908197,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pavlacky, David C. 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and associated metal-rich precipitates have recently been proposed as unconventional sources of rare earth elements (REEs). However, the potential occurrence of radium (Ra), a known carcinogen, with the REE-bearing phases has not been investigated. We hypothesized that Ra may occur in solids that are precipitated from CMD as a “radiobarite” solid solution ((Ba,Sr,Ra)SO4) and/or adsorbed with hydrous metal oxides. REEs have been documented to sorb or co-precipitate with iron (Fe), manganese (Mn), and aluminum (Al) oxyhydroxide in CMD solids. Likewise, Ra has been documented to sorb to hydrous Fe and Mn oxides especially where sulfate (SO4) and/or barium (Ba) concentrations are insufficient to precipitate radiobarite. Thus, we conducted the first-ever survey of Ra concentrations in corresponding CMD water and solid samples in the United States. Samples were analyzed from 4 untreated and 9 treated CMD sites in both the bituminous and anthracite coal regions of Pennsylvania across a range of pH and SO4 concentrations. The dissolved Ra in CMD was relatively low (<0.5 Bq/L), consistent with radiobarite solubility; however, CMD solids were largely composed of amorphous Fe, Al, and Mn oxyhydroxide and silicate minerals. Ra was associated with Mn-enriched CMD solids, upwards of 875 Bq/kg. Total REE + yttrium (Y) content in the CMD solids was enriched upwards of 3600 mg/kg and was significantly correlated with Al content. These preliminary results suggest that REE extraction may target Al-rich solids to avoid Ra in Mn-rich solids.
The U.S. Geological Survey Ohio Water Microbiology Laboratory is a part of the Ohio-Kentucky-Indiana Water Science Center. The mission of the laboratory is to provide microbiological data of public health significance from surface waters, groundwaters, and sediments for a variety of study objectives. The laboratory conducts internal projects, works with external cooperators, and assists U.S. Geological Survey offices and National programs. The laboratory offers guidance, study design, and data interpretation expertise to collaborators, all following rigorous quality control and quality assurance procedures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243004","usgsCitation":"Lanier, B.M., Brady, A.M.G., Cicale, J.R., Kephart, C.M., Lynch, L.D., Schroeder, M.W., and Stelzer, E.A., 2024, The U.S. Geological Survey Ohio Water Microbiology Laboratory: U.S. Geological Survey Fact Sheet 2024–3004, 4 p., https://doi.org/10.3133/fs20243004.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-158056","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":429504,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3004/images/"},{"id":429503,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3004/fs20243004.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2024-3004 XML"},{"id":429502,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243004/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3004 HTML"},{"id":429501,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3004/fs20243004.pdf","text":"Report","size":"4.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3004 PDF"},{"id":429500,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3004/coverthb.jpg"}],"contact":"Director, Ohio-Kentucky-Indiana Water Science Center,
Ohio Water Microbiology Laboratory
U.S. Geological Survey
6460 Busch Blvd, Suite 100
Columbus, OH 43229
Laboratory email: gs-w-ohclb_owml@usgs.gov
","tableOfContents":"