The annual Lake Ontario April bottom trawl survey and Alewife, Alosa pseudoharengus, population assessment provide science to inform management decisions related to predator-prey balance and fish community dynamics. The 2022 survey was conducted from March 31 to April 26, included 235 trawls in the main lake and embayments, and sampled depths from 5 to 219 m (16 – 723 ft). The survey captured 311,770 fish from 30 species with a total weight of 7,740 kg (17,028 lbs.). Alewife were 85% of the catch by number while Rainbow Smelt, Osmerus mordax, Round Goby, Neogobius melanostomus, and Deepwater Sculpin, Myoxocephalus thompsonii, comprised 6%, 4%, and 4% of the catch, respectively. The 2022 biomass index for Rainbow Smelt decreased 80% relative to the high values observed in 2021 as did the value for Cisco, Coregonus artedi, (46% decline). Emerald Shiner, Notropis atherinoides, biomass index increased in 2021 and Threespine Stickleback, Gasterosteus aculeatus, biomass remained low. No Bloater, Coregonus hoyi, were captured during the 2022 survey.
In 2022, Alewife biomass in U.S. waters (58.1 kilograms per hectare, kg·ha-1) was substantially higher than Canadian waters (26.3 kg·ha-1). The 2022 Alewife biomass index (41.6 kg·ha-1) decreased 10% from 2021 while the 2022 density index decreased 62% from 2021. Prediction modeling indicated the growth of the abundant 2020 Alewife year class, sampled as age-1 fish in 2021, would cause the adult Alewife biomass to increase in 2022. Although the adult Alewife biomass did increase relative to 2021 (61%), the increase was lower than predicted. The difference between the predictions and observations was because survival of age-1 fish from 2021 to 2022 was lower than had previously been observed. In the three previous years of observations the proportion of age-1 Alewife surviving to age 2 ranged from 0.33 to 0.53; however, that proportion was only 0.21 from 2021 to 2022. Survival estimates of Alewife age-5 through age-8 were higher than previously observed, possibly because salmonid predation focused on the abundant younger Alewife. The catch of age-1 Alewife in 2022, which is a measure of reproductive success in 2021, was below average and similar to the abundances of the 2018 and 2019 year classes. Simulation modeling results indicated the adult Alewife biomass is likely to increase slightly in 2023, whereas predictions for 2024 are less certain.
Hydroacoustic sampling was used to estimate prey fish densities in open-water, pelagic habitats not sampled by the bottom trawl. Bottom trawl-based densities from the lake bottom were at least 25 times greater than densities of prey fish in the water column above the trawl. These results support the idea that, in April, when the warmest, most dense water is on the lake bottom, Alewife and most other pelagic prey fish primarily inhabit deep, near bottom habitats and can be effectively sampled with bottom trawling.
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,{"id":70232214,"text":"70232214 - 2022 - Late Paleozoic flexural extension and overprinting shortening in the southern Ozark dome, Arkansas, USA: Evolving fault kinematics in the foreland of the Ouachita orogen","interactions":[],"lastModifiedDate":"2022-06-14T13:55:55.492663","indexId":"70232214","displayToPublicDate":"2022-06-01T08:52:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Late Paleozoic flexural extension and overprinting shortening in the southern Ozark dome, Arkansas, USA: Evolving fault kinematics in the foreland of the Ouachita orogen","docAbstract":"Faults and folds on the southern flank of the Ozark dome in northern Arkansas, USA, record flexural extension in a foreland area followed by shortening in response to the late Paleozoic Ouachita orogeny. Map-scale structures and an analysis of fault-slip data collected systematically during geologic mapping demonstrate that most deformation in the area accommodated north-south extension as the southern margin of Laurentia was flexed beneath the thrust load of the Ouachita belt, probably during Middle Pennsylvanian. Extension was concentrated in northeast- and west-northwest-trending structural zones having sets of discontinuous, often en echelon normal and strike-slip faults and associated monoclinal folds. Reactivation of basement weaknesses that underlie these zones is indicated by their close match to oblique-rift models in which both the proportions of normal and strike-slip faulting and the internal extension directions vary with orientation of the zones. Subsequent propagation of north-south Ouachita shortening into the foreland formed small-offset strike-slip and sparse reverse faults that overprinted older extensional structures. Strike-slip faults were concentrated in reactivated northeast-trending structural zones. In two areas, reverse faults and local anticlines were developed in the footwalls of older normal faults, both near intersections of northeast- and west-northwest-trending structural zones. These are interpreted as areas of incipient inversion due to compressional stress concentrations at fault-block corners. Spatial overlap of areas of north-south shortening and fluid flux marked by silicification or lead-zinc mineralization indicates that regional fluid flow of brines was coeval with and may have enhanced inversion during Late Pennsylvanian to early Permian.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021TC006706","usgsCitation":"Hudson, M., and Turner, K.J., 2022, Late Paleozoic flexural extension and overprinting shortening in the southern Ozark dome, Arkansas, USA: Evolving fault kinematics in the foreland of the Ouachita orogen: Tectonics, v. 41, e2021TC006706, 27 p., https://doi.org/10.1029/2021TC006706.","productDescription":"e2021TC006706, 27 p.","ipdsId":"IP-125523","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":447580,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021tc006706","text":"Publisher Index Page"},{"id":435830,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92TLPU2","text":"USGS data release","linkHelpText":"Fault data collected between 1996 and 2019 from the Buffalo River watershed area, northern Arkansas"},{"id":402148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Kansas, Missouri, Oklahoma","otherGeospatial":"Ozark dome","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.63623046875,\n 35.51434313431818\n ],\n [\n -92.83447265624999,\n 35.60371874069731\n ],\n [\n -92.30712890625,\n 35.71083783530009\n ],\n [\n -88.22021484375,\n 36.24427318493909\n ],\n [\n -88.3740234375,\n 37.055177106660814\n ],\n [\n -89.14306640625,\n 37.125286284966805\n ],\n [\n -89.5166015625,\n 37.71859032558816\n ],\n [\n -90.28564453124999,\n 38.09998264736481\n ],\n [\n -91.34033203125,\n 38.238180119798635\n ],\n [\n -92.0654296875,\n 38.34165619279595\n ],\n [\n -93.4716796875,\n 38.22091976683121\n ],\n [\n -94.52636718749999,\n 37.47485808497102\n ],\n [\n -94.9658203125,\n 37.23032838760387\n ],\n [\n -95.1416015625,\n 36.58024660149866\n ],\n [\n -95.361328125,\n 35.90684930677121\n ],\n [\n -94.89990234375,\n 35.496456056584165\n ],\n [\n -94.63623046875,\n 35.51434313431818\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","noUsgsAuthors":false,"publicationDate":"2022-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Hudson, Mark R. 0000-0003-0338-6079 mhudson@usgs.gov","orcid":"https://orcid.org/0000-0003-0338-6079","contributorId":1236,"corporation":false,"usgs":true,"family":"Hudson","given":"Mark R.","email":"mhudson@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":844677,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Turner, Kenzie J. 0000-0002-4940-3981 kturner@usgs.gov","orcid":"https://orcid.org/0000-0002-4940-3981","contributorId":496,"corporation":false,"usgs":true,"family":"Turner","given":"Kenzie","email":"kturner@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":844678,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70250193,"text":"70250193 - 2022 - Evolving magma temperature and volatile contents over the 2008–2018 summit eruption of Kīlauea Volcano","interactions":[],"lastModifiedDate":"2023-11-28T13:28:07.883022","indexId":"70250193","displayToPublicDate":"2022-06-01T07:24:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Evolving magma temperature and volatile contents over the 2008–2018 summit eruption of Kīlauea Volcano","docAbstract":"For several decades, induced polarization (IP) effects on transient electromagnetic (TEM) responses have been observed. These effects can manifest as late-time negative transients or as rapidly decaying curves and are usually associated with highly polarizable bodies. If neglected, IP effects can lead to erroneous resistivity models. Recent work allows IP effects to be incorporated into the inversion of TEM data on a more routine basis. In a recent field survey in western Tanzania, strongly IP-affected TEM signals are observed using a towed-transient electromagnetic (tTEM) system. The survey have been carried out to locate drinking water resources in a weathered regolith setting. In these settings, an inversion of tTEM data using a resistivity-only forward model (i.e., IP neglected) cannot fit the data and severely limits the value of the TEM data for hydrogeologic interpretation. To account for IP effects, we have applied a modified version of the Cole-Cole model called the maximum phase angle (MPA) model to invert IP-affected tTEM data. The MPA model incorporates four inversion model parameters: resistivity (
Invasive species threaten island biodiversity globally. For example, the Shiny Cowbird (Molothrus bonariensis) parasitizes many of Puerto Rico’s endemic species, particularly in the open forests in the island’s southwest. Less is known, however, about cowbird parasitism in the agro-ecological highlands, which contain a patchwork of forests, shaded-coffee plantations, and coffee farms without shade. In this paper, we estimated co-occurrence rates, a potential indicator of parasitism rates, between the cowbird and four host species across these three land uses, hypothesizing that cowbirds would most likely co-occur with their hosts in shaded-coffee farms. We also hypothesized that the presence of host species would increase the probability of cowbird occurrence. To investigate these hypotheses, we developed three Bayesian hierarchical occupancy models: one where the hosts and parasite occurred independently, one that used the latent host species richness as a predictor of cowbird occurrence, and one that used each latent host occurrence state as predictors. These methods addressed observation errors and appropriately propagated error to our predictions of co-occurrence rates. We selected the best performing model using WAIC, then used it to predict co-occurrence rates. While there was some evidence that host species richness increased the probability of cowbirds, the parsimonious model assumed no interaction. With this model, we found that cowbirds were more likely to overlap with certain hosts in shaded-coffee plantations. This may suggest increased parasitism at these plantations, potentially presenting challenges for managers who advocate for shade restoration to gain ecological services such as biodiversity conservation.
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Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Ecosystem metabolism quantifies the rate of production, maintenance, and decay of organic matter in terrestrial and aquatic systems. It is a fundamental measure of energy flow associated with biomass production by photosynthesizing organisms and biomass oxidation by respiring plants, animals, algae, and bacteria (Bernhardt et al., 2022) . Ecosystem metabolism also provides an understanding of energy flow to higher trophic levels that supports secondary and tertiary productivity, as well as helping to explain when aquatic ecosystems undergo out-of-balance behaviors such as harmful algal blooms and hypoxia. Recent advances in sensor technology and modeling capabilities have enabled estimation of aquatic system metabolism and gas exchange over long time periods in rivers, streams, ponds, and wetlands where oxygen sensors have been deployed. Here we present RiverMET, a framework for estimation of river metabolism, with workflows to streamline data preparation, run a stream metabolism model, assess the model performance, and flag and censor final output data. The workflows are specifically tailored to use streamMetabolizer, a model for one-station calculations of stream metabolism that calculates gross primary productivity (GPP), ecosystem respiration (ER) and the air-water gas exchange rate constant (K600). We advise potential users of RiverMET to review core publications for the streamMetabolizer model (Appling et al., 2018 a, b, c) to ensure best practices that produce the most useful results. We encourage feedback about our workflows, although issues regarding the streamMetabolizer model itself should be referred to the model authors. We tested RiverMET by calculating GPP, ER, and K600 across 17 river sites in the Illinois River basin (ILRB). Each river had between one and nine years of sensor data appropriate for modeling metabolism. In total, metabolism was modeled on 15,176 days between 2005 and 2020. Overall confidence in the results was rated as high at nine river sites, medium at six river sites, and poor at two river sites. Twenty-nine percent of the total modeled days had performance metrics that triggered flags. Metrics used for daily flagging are provided with the final output, with an option to only retain the censored daily outputs with high confidence (representing 72 %, i.e., 10,938 days, of the total days modeled). This work was completed as part of the U.S. Geological Survey Proxies Project, an effort supported by the Water Mission Area (WMA) Water Quality Processes program to develop estimation methods for harmful algal blooms (HABs), per- and polyfluoroalkyl substances (PFAS), and metals, at multiple spatial and temporal scales.
","language":"English","publisher":"Earth and Space Science Open Archive","doi":"10.1002/essoar.10511255.1","usgsCitation":"Choi, J., Quion, K.M., Reed, A., and Harvey, J., 2022, River Metabolism Estimation Tools (RiverMET) with demo in the Illinois River Basin: ESSOAr, 22 p., https://doi.org/10.1002/essoar.10511255.1.","productDescription":"22 p.","ipdsId":"IP-139945","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":435833,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TEBOUR","text":"USGS data release","linkHelpText":"RiverMET: Workflow and scripts for river metabolism estimation including Illinois River Basin application, 2005 - 2020"},{"id":409056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Illinois River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.901423683579,\n 42.70071815175049\n ],\n [\n -91.86724399607925,\n 42.70071815175049\n ],\n [\n -91.86724399607925,\n 39.14935275277796\n ],\n [\n -86.901423683579,\n 39.14935275277796\n ],\n [\n -86.901423683579,\n 42.70071815175049\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Choi, Jay 0000-0003-1276-481X jchoi@usgs.gov","orcid":"https://orcid.org/0000-0003-1276-481X","contributorId":219096,"corporation":false,"usgs":true,"family":"Choi","given":"Jay","email":"jchoi@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":856403,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quion, Katherine Michelle Bernabe 0000-0003-2388-7508","orcid":"https://orcid.org/0000-0003-2388-7508","contributorId":298787,"corporation":false,"usgs":true,"family":"Quion","given":"Katherine","email":"","middleInitial":"Michelle Bernabe","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856404,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Ariel 0000-0002-0792-5204","orcid":"https://orcid.org/0000-0002-0792-5204","contributorId":298788,"corporation":false,"usgs":false,"family":"Reed","given":"Ariel","affiliations":[],"preferred":false,"id":856405,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856406,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70239791,"text":"70239791 - 2022 - Hydrologic controls on peat permafrost and carbon processes: New insights from past and future modeling","interactions":[],"lastModifiedDate":"2023-01-20T12:46:34.734013","indexId":"70239791","displayToPublicDate":"2022-05-31T06:44:44","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5738,"text":"Frontiers in Environmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic controls on peat permafrost and carbon processes: New insights from past and future modeling","docAbstract":"Soil carbon (C) in permafrost peatlands is vulnerable to decomposition with thaw under a warming climate. The amount and form of C loss likely depends on the site hydrology following permafrost thaw, but antecedent conditions during peat accumulation are also likely important. We test the role of differing hydrologic conditions on rates of peat accumulation, permafrost formation, and response to warming at an Arctic tundra fen using a process-based model of peatland dynamics in wet and dry landscape settings that persist from peat initiation in the mid-Holocene through future simulations to 2100 CE and 2300 CE. Climate conditions for both the wet and dry landscape settings are driven by the same downscaled TraCE-21ka transient paleoclimate simulations and CCSM4 RCP8.5 climate drivers. The landscape setting controlled the rates of peat accumulation, permafrost formation and the response to climatic warming and permafrost thaw. The dry landscape scenario had high rates of initial peat accumulation (11.7 ± 3.4 mm decade−1) and rapid permafrost aggradation but similar total C stocks as the wet landscape scenario. The wet landscape scenario was more resilient to 21st century warming temperatures than the dry landscape scenario and showed 60% smaller C losses and 70% more new net peat C additions by 2100 CE. Differences in the modeled responses indicate the largest effect is related to the landscape setting and basin hydrology due to permafrost controls on decomposition, suggesting an important sensitivity to changing runoff patterns. These subtle hydrological effects will be difficult to capture at circumpolar scales but are important for the carbon balance of permafrost peatlands under future climate warming.
1. Ecological restoration and conservation efforts are increasing worldwide and the management of intraspecific genetic variation in plants and animals, an important component of biodiversity, is increasingly valued. As a result, tailorable, spatially explicit approaches to map genetic variation are needed to support decision-making and management frameworks related to the recovery of threatened and endangered species and the maintenance of genetic resources in species utilized by humans, such as for restoration or agricultural purposes.
2. Here, we describe and demonstrate a workflow to spatially interpolate patterns of genetic differentiation using novel functions in the R package POPMAPS (Population Management using Ancestry Probability Surfaces). Our approach uses empirical genetic data to estimate ancestry coefficients across a user-defined landscape correlated with patterns of differentiation in the focal species. The resulting surface, which we term the ancestry probability surface, includes two components: hard population boundaries and estimations of uncertainty that represent confidence in population assignments (i.e., ancestry probabilities).
3. An ancestry probability surface developed for Hilaria jamesii, an important graminoid utilized in restoration across the western United States, demonstrates the functionality of POPMAPS. Genetic distances among empirical sites correlated better with least-cost distances across suitable habitat than with geographic distances, informing the surface over which the interpolation was conducted (i.e., a model indicating habitat suitability). A jackknifing procedure identified parameter values resulting in robust population assignments across the species’ range, which were utilized in downstream analyses to estimate ancestry coefficients from empirical data. Ancestry coefficients were translated into ancestry probabilities, which tended to be low for cells that were intermediate in distance between empirical sampling locations representing different populations or when influenced by empirical sampling locations with mixed genetic ancestry.
4. POPMAPS allows users to tailor parameter values and analytical approaches and thereby incorporate species-specific biological characteristics and desired levels of uncertainty into maps illustrating patterns of genetic differentiation. Ancestry probability surfaces may be used to guide management or investigate further ecological or evolutionary hypotheses. We discuss how maps produced by POPMAPS can inform multiple management challenges including species recovery planning and the utilization of commonly used species in restoration.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.13902","usgsCitation":"Massatti, R., and Winkler, D.E., 2022, Spatially explicit management of genetic diversity using ancestry probability surfaces: Methods in Ecology and Evolution, v. 13, no. 12, p. 2668-2681, https://doi.org/10.1111/2041-210X.13902.","productDescription":"14 p.","startPage":"2668","endPage":"2681","ipdsId":"IP-133238","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":447618,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.13902","text":"Publisher Index Page"},{"id":435837,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96VLOA5","text":"USGS data release","linkHelpText":"POPMAPS: An R package to estimate ancestry probability surfaces"},{"id":401362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Massatti, Robert 0000-0001-5854-5597","orcid":"https://orcid.org/0000-0001-5854-5597","contributorId":207294,"corporation":false,"usgs":true,"family":"Massatti","given":"Robert","email":"","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":843923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":843924,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256653,"text":"70256653 - 2022 - Integrated animal movement and spatial capture–recapture models: Simulation, implementation, and inference","interactions":[],"lastModifiedDate":"2024-08-29T15:02:58.967417","indexId":"70256653","displayToPublicDate":"2022-05-30T09:59:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Integrated animal movement and spatial capture–recapture models: Simulation, implementation, and inference","docAbstract":"Over the last decade, spatial capture–recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because interesting ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies — individual animals observed at specific locations and times — can provide a rich source of information about these processes and how they relate to demographic rates. We developed SCR models that integrated more complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short-term directional persistence), and habitat-driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using data-augmented Bayesian analysis methods. We evaluated their performance through a simulation study, in which we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T = 25 to T = 15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is particularly appealing because it explicitly links the individual-level movement process with habitat selection and population density. We focused on closed population models, but the movement models developed here can be extended to open SCR models. The movement process models could also be easily extended to accommodate additional “building blocks” of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3771","usgsCitation":"Gardner, B., McClintock, B., Converse, S.J., and Hostetter, N.J., 2022, Integrated animal movement and spatial capture–recapture models: Simulation, implementation, and inference: Ecology, v. 103, e3771, 13 p., https://doi.org/10.1002/ecy.3771.","productDescription":"e3771, 13 p.","ipdsId":"IP-130421","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447622,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"text":"Publisher Index Page"},{"id":433312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","noUsgsAuthors":false,"publicationDate":"2022-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Gardner, B.","contributorId":341497,"corporation":false,"usgs":false,"family":"Gardner","given":"B.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":908507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McClintock, B.T.","contributorId":341498,"corporation":false,"usgs":false,"family":"McClintock","given":"B.T.","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":908508,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908509,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":908510,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236349,"text":"70236349 - 2022 - P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California","interactions":[],"lastModifiedDate":"2022-09-02T14:09:03.829255","indexId":"70236349","displayToPublicDate":"2022-05-30T09:01:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California","docAbstract":"This study uses an ensemble Kalman method for near-surface seismic site characterization of 154 network earthquake monitoring stations in California to improve the resolution of S-wave velocity (VS) and P-wave velocity (VP) profiles—up to the resolution depth—coupled with better quantification of uncertainties compared to previous site characterization studies at this network. These stations were part of the Yong et al. site characterization project, with selected stations based on future recordings of ground motions that are expected to exceed 10 per cent peak ground acceleration in 50 yr. To estimate VS and VP from experimental dispersion data, Yong et al. investigated these stations using linearized (local search and iteration) routines, and Yong et al. later studied a subset of these stations using nonlinear (global search and optimization) routines. In both studies, the selection of model parameters—that is, discretization of the VS and VP profiles with only five fixed thickness layers—was mainly based on trial and error. In contrast, this paper uses an approximate Bayesian method to assimilate experimental dispersion data and sequentially update an ensemble of particle estimates that span the VS and VP parameter spaces. Doing so, we systematically determine the most probable profiles conditioned on the experimental dispersion data, the introduced noise levels, and a priori knowledge in the form of physical constraints. We consider two configurations to discretize the soil depth from the surface to half of the maximum discernible wavelength obtained from the experimental dispersion data, namely refined and coarse models, and two initial models for each configuration to study solution multiplicity. Our results suggest that using the refined model for the top surface layers improves the resolution of near-surface site characteristics and the model’s success rate in capturing dispersion data at high frequencies. All models result in similar VS but distinct VP profiles, with increasing uncertainty at deeper layers, suggesting that the fundamental mode of Rayleigh wave dispersion data is not adequate to constrain the P-wave velocity profile and the S-wave velocity close to the resolution depth.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggac201","usgsCitation":"Bas, E.E., Seylabi, E., Yong, A., Tehrani, H., and Asimaki, D., 2022, P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California: Geophysical Journal International, v. 231, no. 1, p. 536-551, https://doi.org/10.1093/gji/ggac201.","productDescription":"16 p.","startPage":"536","endPage":"551","ipdsId":"IP-132480","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":447625,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://resolver.caltech.edu/CaltechAUTHORS:20220804-250008000","text":"External Repository"},{"id":406134,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.58618164062499,\n 38.75408327579141\n ],\n [\n -122.684326171875,\n 37.900865092570065\n ],\n [\n -122.3876953125,\n 36.98500309285596\n ],\n [\n -121.46484375,\n 35.6126508187567\n ],\n [\n -120.4541015625,\n 34.32529192442733\n ],\n [\n -120.14648437499999,\n 33.60546961227188\n ],\n [\n -119.05883789062501,\n 32.8334428466495\n ],\n [\n -117.18017578125,\n 32.565333160841035\n ],\n [\n -114.47753906249999,\n 32.7503226078097\n ],\n [\n -114.42260742187499,\n 32.99023555965106\n ],\n [\n -114.6533203125,\n 33.073130945006625\n ],\n [\n -114.70825195312501,\n 33.38558626887102\n ],\n [\n -114.47753906249999,\n 33.63291573870479\n ],\n [\n -114.49951171875,\n 33.925129700072\n ],\n [\n -114.071044921875,\n 34.32529192442733\n ],\n [\n -114.444580078125,\n 34.67839374011646\n ],\n [\n -114.664306640625,\n 35.03899204678081\n ],\n [\n -116.46606445312499,\n 36.465471886798134\n ],\n [\n -119.58618164062499,\n 38.75408327579141\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"231","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-05-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Bas, Elif Ecem","contributorId":296121,"corporation":false,"usgs":false,"family":"Bas","given":"Elif","email":"","middleInitial":"Ecem","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":850703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seylabi, Elnaz","contributorId":296122,"corporation":false,"usgs":false,"family":"Seylabi","given":"Elnaz","email":"","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":850704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yong, Alan K. 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":296123,"corporation":false,"usgs":true,"family":"Yong","given":"Alan K.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":850706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tehrani, Hesam","contributorId":296124,"corporation":false,"usgs":false,"family":"Tehrani","given":"Hesam","email":"","affiliations":[],"preferred":false,"id":850707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Asimaki, Domniki","contributorId":146598,"corporation":false,"usgs":false,"family":"Asimaki","given":"Domniki","email":"","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":850705,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238968,"text":"70238968 - 2022 - Stream size, temperature, and density explain body sizes of freshwater salmonids across a range of climate conditions","interactions":[],"lastModifiedDate":"2022-12-19T14:57:53.446229","indexId":"70238968","displayToPublicDate":"2022-05-30T08:57:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Stream size, temperature, and density explain body sizes of freshwater salmonids across a range of climate conditions","docAbstract":"Climate change and anthropogenic activities are altering the body sizes of fishes, yet our understanding of factors influencing body size for many taxa remains incomplete. We evaluated the relationships between climate, environmental, and landscape attributes and the body size of different taxa of freshwater trout (Salmonidae) in the USA. Hierarchical spatial modeling across a gradient of habitats (5221 sites) illustrated the importance of watershed effects, which explained 17%–45% of the of the variation in body size across taxa. Stream size had a strong, positive relationship with body size, yet there was approximately tenfold difference in the strength of the relationship across taxa. Trout body size consistently declined with increasing density across taxa. Despite reliance on cold water, we found positive relationships between summer stream temperature and trout body size across most taxa. Our results highlight how providing trout access to larger, productive rivers for the expression of growth and life-history variation would promote body size diversity within and across populations.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2021-0343","usgsCitation":"Al-Chokhachy, R.K., Letcher, B., Muhlfeld, C.C., Dunham, J., Cline, T.J., Hitt, N.P., Roberts, J., and Schmetterling, D., 2022, Stream size, temperature, and density explain body sizes of freshwater salmonids across a range of climate conditions: Canadian Journal of Fisheries and Aquatic Sciences, v. 79, no. 10, p. 1729-1744, https://doi.org/10.1139/cjfas-2021-0343.","productDescription":"16 p.","startPage":"1729","endPage":"1744","ipdsId":"IP-131094","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447627,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Center","active":true,"usgs":true}],"preferred":true,"id":859448,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":859449,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Jason 0000-0002-6268-0633","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":220078,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":859450,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cline, Timothy Joseph 0000-0002-4955-654X","orcid":"https://orcid.org/0000-0002-4955-654X","contributorId":228871,"corporation":false,"usgs":true,"family":"Cline","given":"Timothy","email":"","middleInitial":"Joseph","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":859451,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568 nhitt@usgs.gov","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":4435,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"nhitt@usgs.gov","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":859452,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Roberts, James J. 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","middleInitial":"J.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859453,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schmetterling, David","contributorId":196555,"corporation":false,"usgs":false,"family":"Schmetterling","given":"David","affiliations":[],"preferred":false,"id":859454,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70255807,"text":"70255807 - 2022 - Comparison of Digital Terrain Models from two photoclinometry methods","interactions":[],"lastModifiedDate":"2024-07-05T12:12:52.867955","indexId":"70255807","displayToPublicDate":"2022-05-30T07:04:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12997,"text":"International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of Digital Terrain Models from two photoclinometry methods","docAbstract":"We evaluate the horizontal resolution and vertical precision for digital topographic models (DTMs) of the Moon derived from image radiance information, a process known as photoclinometry (PC) or shape-from-shading (SfS). We use the implementations in two available planetary image processing software systems, single image PC in the U.S. Geological Survey Integrated Software for Imagers and Spectrometers (ISIS) system, and multi-image SfS in the Ames Stereo Pipeline (ASP), and test results obtained with and without use of a starting solution from stereo, with single and multiple images, and for varying illumination conditions. To obtain the higher quality reference DTMs against which the products can be evaluated, we derived DTMs by stereoanalysis of Lunar Reconnaissance Orbiter Narrow-Angle Camera (LROC NAC) images at their native pixel spacing of ∼0.5 m, then produced a 16-m/post stereo DTM from images downsampled to 4 m/pixel and refined it with images at 16 m/pixel. When used with a single image, both algorithms improved resolution (by a factor of 1.4 for PC and 2.4 for SfS compared to stereo). An albedo map produced in ISIS by ratioing the image to a simulation based on the stereo DTM was well correlated with one output by SfS. The albedo correction was crucial for PC with ∼60° incidence but not at ∼80°. DTMs produced by PC and SfS without a starting stereo DTM had larger errors but good detail, and could be useful for many applications. In SfS, it was necessary to increase smoothing to get a usable DTM when the weighting on an a priori DTM was reduced. Multi-image SfS including modeling of spatially varying albedo reduced vertical errors by factors of 1.5 or more compared to single-image SfS.
Rare earth elements and yttrium (REYs) are critical elements and valuable commodities due to their limited availability and high demand in a wide range of applications and especially in high-technology products. The increased demand and geopolitical pressures motivate the search for alternative sources of REYs, and coal, coal waste, and coal ash are considered as new sources for these critical elements. This research evaluates the REY potential of coals from Indiana (USA). However, although coal data revealed REY potential, it suffered from sparse samples with complete REY measurements. Therefore, we explore the applicability of machine learning (ML) models and data augmentation techniques to demonstrate their applicability to evaluate REY potential in Indiana, and other areas in coal basins, using selected coal parameters (Al2O3, Fe2O3, C, Ash, S, P, Mo, Zn, and As contents) as covariates (indicators). Due to the relatively small sample size with complete REY data in the Indiana Coal Database, two data augmentation techniques (Random Over-Sampling Examples and Synthetic Minority Over-Sampling Technique) were used. Four machine learning algorithms (linear discriminate analysis, support vector machine, random forest, and artificial neural networks) were applied for modeling REY potential as a classification problem. The results show that application of Synthetic Minority Over-Sampling Technique prior to development of the support vector machine (SVM) models generated the best REY classification with an accuracy of 95%. The encouraging results based on Indiana coal data may suggest that a similar approach can be used for other coal basins for screening the locations with REY potential. Those locations then can be targeted for more detailed geochemical surveys to identify most promising areas and evaluate overall REY resources.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2022.104054","usgsCitation":"Chatterjee, S., Mastalerz, M., Drobniak, A., and Karacan, C.O., 2022, Machine learning and data augmentation approach for identification of rare earth element potential in Indiana Coals, USA: International Journal of Coal Geology, v. 259, 104054, 14 p., https://doi.org/10.1016/j.coal.2022.104054.","productDescription":"104054, 14 p.","ipdsId":"IP-138032","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":402804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.56103515625,\n 40.49709237269567\n ],\n [\n -87.5390625,\n 39.35129035526705\n ],\n [\n -87.56103515625,\n 38.839707613545144\n ],\n [\n -87.86865234374999,\n 38.06539235133249\n ],\n [\n -88.11035156249999,\n 37.90953361677018\n ],\n [\n -88.154296875,\n 37.77071473849609\n ],\n [\n -87.451171875,\n 37.92686760148135\n ],\n [\n -87.099609375,\n 37.87485339352928\n ],\n [\n -86.81396484375,\n 38.048091067457236\n ],\n [\n -86.572265625,\n 37.89219554724437\n ],\n [\n -86.396484375,\n 38.11727165830543\n ],\n [\n -86.63818359375,\n 38.95940879245423\n ],\n [\n -86.8359375,\n 40.111688665595956\n ],\n [\n -87.03369140625,\n 40.463666324587685\n ],\n [\n -87.56103515625,\n 40.49709237269567\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"259","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chatterjee, Snahamoy","contributorId":292652,"corporation":false,"usgs":false,"family":"Chatterjee","given":"Snahamoy","email":"","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":845399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastalerz, Maria","contributorId":292654,"corporation":false,"usgs":false,"family":"Mastalerz","given":"Maria","affiliations":[{"id":62959,"text":"IU and Indiana Geological Survey","active":true,"usgs":false}],"preferred":false,"id":845400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drobniak, Agnieszka","contributorId":292655,"corporation":false,"usgs":false,"family":"Drobniak","given":"Agnieszka","email":"","affiliations":[{"id":62959,"text":"IU and Indiana Geological Survey","active":true,"usgs":false}],"preferred":false,"id":845401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":845402,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256676,"text":"70256676 - 2022 - Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model","interactions":[],"lastModifiedDate":"2024-08-30T15:04:17.421329","indexId":"70256676","displayToPublicDate":"2022-05-28T09:39:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model","docAbstract":"Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and therefore omit linkages between individual-level and population-level processes. We describe an integrated spatial capture–recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears (Ursus maritimus) in a 28,125 km2 survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture–recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from <0.75 bears/625 km2 grid cell/day in nearshore cells to 1.6–2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53 to 69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124–250). Abundance estimates were similar to a previous multiyear integrated population model using capture–recapture and telemetry data (2008–2016; Regehr et al., Scientific Reports 8:16780, 2018). Overall, the SCR–movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR–movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3772","usgsCitation":"Hostetter, N.J., Regehr, E., Wilson, R., Royle, A., and Converse, S.J., 2022, Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model: Ecology, v. 103, no. 10, e3772, 13 p., https://doi.org/10.1002/ecy.3772.","productDescription":"e3772, 13 p.","ipdsId":"IP-130471","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447644,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3772","text":"Publisher Index Page"},{"id":433369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","otherGeospatial":"eastern Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -176.16804284685193,\n 70.64271222820804\n ],\n [\n -176.4405798143377,\n 63.26571385602864\n ],\n [\n -160.5704824801017,\n 63.43804844317145\n ],\n [\n -160.5759226643521,\n 70.4273607365736\n ],\n [\n -176.16804284685193,\n 70.64271222820804\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"103","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":908609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Regehr, E.V.","contributorId":341555,"corporation":false,"usgs":false,"family":"Regehr","given":"E.V.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":908610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, R.R.","contributorId":341556,"corporation":false,"usgs":false,"family":"Wilson","given":"R.R.","affiliations":[{"id":40296,"text":"United States Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908611,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":908612,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908613,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70232170,"text":"70232170 - 2022 - N and P constrain C in ecosystems under climate change: Role of nutrient redistribution, accumulation, and stoichiometry","interactions":[],"lastModifiedDate":"2022-12-01T15:55:48.71099","indexId":"70232170","displayToPublicDate":"2022-05-28T07:19:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"N and P constrain C in ecosystems under climate change: Role of nutrient redistribution, accumulation, and stoichiometry","docAbstract":"We use the Multiple Element Limitation (MEL) model to examine responses of twelve ecosystems to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in precipitation. Ecosystems respond synergistically to elevated CO2, warming, and decreased precipitation combined because higher water-use efficiency with elevated CO2 and higher fertility with warming compensate for responses to drought. Response to elevated CO2, warming, and increased precipitation combined is additive. We analyze changes in ecosystem carbon (C) based on four nitrogen (N) and four phosphorus (P) attribution factors: (1) changes in total ecosystem N and P, (2) changes in N and P distribution between vegetation and soil, (3) changes in vegetation C:N and C:P ratios, and (4) changes in soil C:N and C:P ratios. In the combined CO2 and climate change simulations, all ecosystems gain C. The contributions of these four attribution factors to changes in ecosystem C storage varies among ecosystems because of differences in the initial distributions of N and P between vegetation and soil and the openness of the ecosystem N and P cycles. The net transfer of N and P from soil to vegetation dominates the C response of forests. For tundra and grasslands, the C gain is also associated with increased soil C:N and C:P. In ecosystems with symbiotic N fixation, C gains resulted from N accumulation. Because of differences in N versus P cycle openness and the distribution of organic matter between vegetation and soil, changes in the N and P attribution factors do not always parallel one another. Differences among ecosystems in C-nutrient interactions and the amount of woody biomass interact to shape ecosystem C sequestration under simulated global change. We suggest that future studies quantify the openness of the N and P cycles and changes in the distribution of C, N, and P among ecosystem components, which currently limit understanding of nutrient effects on C sequestration and responses to elevated CO2 and climate change.
This report describes the findings of a U.S. Geological Survey study, completed in cooperation with the Bureau of Land Management, focused on better understanding the present-day (1975–2019) hydrogeology and groundwater quality of the San Agustin Basin in west-central New Mexico to support sustainable groundwater resource management. The basin hosts a relatively undeveloped basin-fill and alluvium aquifer system and is topographically divided into east and west subbasins by the McClure Hills. Groundwater chemistry and groundwater elevation data were compiled, collected, and interpreted in the context of groundwater flow and quality. The analyses presented in this report consider groundwater chemistry data collected within the last decade (2010–19) and groundwater elevation data collected from 1975 through 2019 to provide insight into present-day conditions. Groundwater elevations show that groundwater typically moves from the highlands to the lowlands, with a prominent east to west regional trend. Groundwater elevations were lowest in the southwestern portion of the west subbasin, where estimated flow directions suggest underflow through the local highlands into the northern East Fork Gila River watershed, which is further supported by historical groundwater elevation data from the northern East Fork Gila River watershed. Gradual groundwater elevation gradients (about 2 feet per mile) near the east and west subbasin divide suggest that groundwater slowly flows from the east subbasin to the west subbasin.
Quantitative analyses of groundwater chemistry data show that groundwater in both subbasins has similar chemical characteristics. A systematic east to west groundwater evolution in water chemistry was not observed despite evidenced subbasin connectivity. The absence of this pattern suggests that groundwater mixing is regionally prevalent, sediment reactivity is low and variable, and (or) recharge conditions are comparable in both subbasins. Groundwater chemistry was generally independent of aquifer type, suggesting that the aquifers are hydrologically well connected. Corrected carbon-14 groundwater age estimates in the basin ranged from 232 to 13,916 years before present with a median of 5,409 years. A wide range of groundwater ages is therefore present in the basin, with waters commonly being thousands of years old, thereby supporting generally slow regional groundwater movement. A component of relatively young groundwater, for which estimated ages could not be accurately computed, is also present in the basin, and it may commonly mix with older waters. The spatial distribution of categorical and quantitative groundwater ages indicates that most recharge likely occurs in the highlands through mountain-block recharge and as focused recharge within arroyos, although evidence of modern (1953 and after) groundwater was minimal at sampled sites.
Median annual gradients (groundwater elevation change over time) indicate that most groundwater elevations in the lowlands changed little (−0.2 to 0.2 foot per year) from 1975 through 2019. Groundwater elevations in the highlands varied more annually, which is likely due to recharge from precipitation events. These more variable groundwater elevations in the highlands compared with the lowlands, along with groundwater ages, provide further evidence that most groundwater recharge takes place in the highlands, with minimal recharge in the lowlands. Median groundwater elevation change for all sites was −0.05 foot per year. Temporal consistency of lowland groundwater elevations suggests that regional groundwater dynamics have been more or less stable through time under current climate and development conditions, although median annual gradients indicate that groundwater elevations may have slightly declined on average between 1975 and 2019.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225029","collaboration":"Prepared in cooperation with Bureau of Land Management and in collaboration with New Mexico Bureau of Geology and Mineral Resources","usgsCitation":"Pepin, J.D., Travis, R.E., Blake, J.M., Rinehart, A., and Koning, D., 2022, Hydrogeology and groundwater quality in the San Agustin Basin, New Mexico, 1975–2019: U.S. Geological Survey Scientific Investigations Report 2022–5029, 61 p., 4 app., https://doi.org/10.3133/sir20225029.","productDescription":"Report: x, 61 p.; 6 Tables; Dataset","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120066","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":502386,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113080.htm","linkFileType":{"id":5,"text":"html"}},{"id":401145,"rank":11,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":401143,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table3.1.csv","text":"Table 3.1","size":"29.5 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 3.1"},{"id":401142,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table3.1.xlsx","text":"Table 3.1","size":"55.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 3.1"},{"id":401141,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table2.1.csv","text":"Table 2.1","size":"14.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 2.1"},{"id":401140,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table2.1.xlsx","text":"Table 2.1","size":"27.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 2.1"},{"id":401138,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table1.1.xlsx","text":"Table 1.1","size":"116 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 1.1"},{"id":401137,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5029/images"},{"id":401134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5029/coverthb.jpg"},{"id":401135,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029.pdf","text":"Report","size":"8.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5029"},{"id":401139,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table1.1.csv","text":"Table 1.1","size":"146 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 1.1"},{"id":401136,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029.XML"}],"country":"United States","state":"New Mexico","otherGeospatial":"San Agustin Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.666,\n 34.5\n ],\n [\n -107.333,\n 34.5\n ],\n [\n -107.333,\n 33.333\n ],\n [\n -108.666,\n 33.333\n ],\n [\n -108.666,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Nearly half a century ago, two papers postulated the likelihood of lunar lava tube caves using mathematical models. Today, armed with an array of orbiting and fly-by satellites and survey instrumentation, we have now acquired cave data across our solar system—including the identification of potential cave entrances on the Moon, Mars, and at least six other planetary bodies. These discoveries gave rise to the study of planetary caves. To help advance this field, we leveraged the expertise of an interdisciplinary group to identify a strategy to explore caves beyond Earth. Focusing primarily on astrobiology, the cave environment, geology, robotics, instrumentation, and human exploration, our goal was to produce a framework to guide this subdiscipline through at least the next decade. To do this, we first assembled a list of 198 science and engineering questions. Then, through a series of social surveys, 114 scientists and engineers winnowed down the list to the top 53 highest priority questions. This exercise resulted in identifying emerging and crucial research areas that require robust development to ultimately support a robotic mission to a planetary cave—principally the Moon and/or Mars. With the necessary financial investment and institutional support, the research and technological development required to achieve these necessary advancements over the next decade are attainable. Subsequently, we will be positioned to robotically examine lunar caves and search for evidence of life within martian caves; in turn, this will set the stage for human exploration and potential habitation of both the lunar and martian subsurface.
","language":"English","publisher":"Wiley","doi":"10.1029/2022JE007194","usgsCitation":"Wynne, J.J., Titus, T.N., Agha-Mohammadi, A., Azua-Bustos, A., Boston, P.J., de Leon, P., Demirel-Floyd, C., de Waele, J., Jones, H., Malaska, M.J., Miller, A.Z., Sapers, H.M., Sauro, F., Sonderegger, D.L., Uckert, K., Wong, U.Y., Alexander, E.C., Chiao, L., Cushing, G.E., DeDecker, J., Fairen, A.G., Frumkin, A., Harris, G.L., Kearney, M.L., Kerber, L.A., Leveille, R.J., Manyapu, K., Massironi, M., Mylroie, J.E., Onac, B.P., Parazynski, S.E., Phillips-Lander, C.M., Prettyman, T.H., Schulze-Makuch, D., Wagner, R.V., Whittaker, W.L., and Williams, K.E., 2022, Fundamental science and engineering questions in planetary cave exploration: JGR Planets, v. 127, no. 11, e2022JE007194, 32 p., https://doi.org/10.1029/2022JE007194.","productDescription":"e2022JE007194, 32 p.","ipdsId":"IP-131152","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":447653,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2022je007194","text":"External Repository"},{"id":401298,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"127","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Wynne, J. 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Still, many uncertainties and assumptions show an incomplete knowledge of these processes, including the lack of studies in many regions of the world, insufficient sharing of practical methodologies between scientific disciplines, incomplete understanding of processes and parameters specific to the sediment-water interface, and challenges associated with measuring exchange at multiple scales of time and space.
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2022 - #TheSmoreYouKnow and #emergencycute: A conceptual model on the use of humor by science agencies during crisis to create connection, empathy, and compassion","interactions":[],"lastModifiedDate":"2022-06-06T11:51:38.884858","indexId":"70232104","displayToPublicDate":"2022-05-27T06:48:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2036,"text":"International Journal of Disaster Risk Reduction","active":true,"publicationSubtype":{"id":10}},"title":"#TheSmoreYouKnow and #emergencycute: A conceptual model on the use of humor by science agencies during crisis to create connection, empathy, and compassion","docAbstract":"Studies from a variety of disciplines reveal that humor can be a useful method to reduce stress and increase compassion, connection, and empathy between agencies and people they serve during times of crisis. Despite this growing evidence base, humor's use during a geohazard (earthquake, volcanoes, landslides, and tsunami) to aid scientific agencies' crisis communication response has been rarely studied. A broad literature review of humor in crisis and an exploratory examination of several case studies reveal that scientific organizations, specifically those that respond to geohazards, can harness the power of humor to help create connection and empathy with the publics they seek to serve. We find evidence that supports our argument that the use of humor acknowledges a shared human experience, reducing the barriers between public officials, scientists, and the people most impacted by crisis. Public statements made by scientists and public officials during the U.S. Geological Survey (USGS) response to the Kīlauea eruption in 2018 in Hawai'i, United States, and GNS Science/GeoNet (GeoNet) response to the M7.8 Kaikōura/North Hurunui earthquake in 2016 in Aotearoa New Zealand, are used to inform the development of this conceptual model. We then posit a conceptual model which unifies concepts from the literature with our case studies to provide potential guidelines for those crisis communicators working for science agencies on how best to use humor to help people cope during times of crisis. This model can be further tested for future research to determine its effectiveness and utility for scientific agencies responding to geological crises.
Groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit (SacMetro-DSA) was studied from August to November 2017 as part of the second phase of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is in parts of Amador, Placer, Sacramento, and Sutter Counties, and the extent of the study unit was defined by the location of three California Department of Water Resources groundwater subbasins: the North American, the South American, and the Cosumnes. The SacMetro-DSA focused on groundwater resources used for domestic drinking-water supply, which generally correspond to shallower parts of aquifer systems than those of groundwater resources used for public drinking water supply in the same area. The assessments characterized the quality of untreated groundwater, not the quality of drinking water.
This study included two components: (1) a status assessment, which characterized the status of the quality of the groundwater resources used for domestic supply and (2) an understanding assessment, which evaluated the natural and human factors potentially affecting water quality in those resources. The first component of this study—the status assessment—was based on water-quality data collected from 49 sites sampled by the U.S. Geological Survey for the GAMA Priority Basin Project in 2017. The samples were analyzed for volatile organic compounds, pesticides, and naturally present inorganic constituents, such as major ions and trace elements. To provide context, concentrations of constituents measured in groundwater were compared to U.S. Environmental Protection Agency and California State Water Resources Control Board Division of Drinking Water regulatory and non-regulatory benchmarks for drinking-water quality. The status assessment used a grid-based method to estimate the proportion of the groundwater resources that had concentrations of water-quality constituents approaching or above benchmark concentrations. This method provides statistically unbiased results at the study-area scale and permits comparisons to other GAMA Priority Basin Project study areas. The second component of this study—the understanding assessment—identified the natural and human factors that potentially affect groundwater quality by evaluating land-use characteristics, groundwater age, and geochemical and hydrologic conditions of the domestic-supply aquifer and related these data to constituents identified in the status assessment for further evaluation.
In the SacMetro-DSA study unit, arsenic was the only inorganic constituent detected above health-based benchmarks and was detected in 10 percent of the domestic-supply aquifer system. Inorganic constituents were detected above the non-health-based California State Water Resources Control Board—Division of Drinking Water secondary maximum contaminant levels (SMCL-CA) in 16 percent of the system. The inorganic constituents detected above the SMCL-CA were chloride, iron, manganese, and total dissolved solids (TDS). Organic constituents (volatile organic compounds and pesticides) with health-based benchmarks were not detected above health-based benchmarks; however, chloroform was detected at concentrations higher than 10 percent of the health-based benchmark (80 micrograms per liter) in 2 percent of the domestic-supply aquifer system. Of the 310 organic constituents analyzed, 16 constituents were detected; however, only bentazon and chloroform had detection frequencies greater than 10 percent.
Inorganic constituents with health-based benchmarks that were evaluated in the understanding assessment included arsenic and hexavalent chromium. Arsenic and hexavalent chromium are natural constituents of aquifer sediments in the study unit and did not appear to be influenced by anthropogenic processes; rather, the presence of arsenic and hexavalent chromium appeared to be related to geochemical conditions controlled by oxidation–reduction reactions in the aquifer system. Naturally occurring inorganic constituents with SMCL-CAs evaluated in the understanding assessment were the trace elements iron and manganese, the major ion chloride, and TDS. Like arsenic and hexavalent chromium, the presence of iron and manganese was most strongly related to geochemical conditions in the aquifer system, specifically reducing conditions, which were most common near the western edge of the study unit close to the Sacramento River. Concentrations of chloride and TDS are indicators of salinity and were correlated with variables related to well location and included redox, agricultural land use, and elevation. Chloride and TDS were positively correlated to reducing conditions, and agricultural land use was negatively correlated to elevation and well depth. Observed correlations among variables were likely driven by the characteristics of the western part of the study unit, such as its higher proportion of agricultural land use and its relatively low elevation. A large portion of the western edge of the study unit is located in the center of the Sacramento Valley, defined by the location of the Sacramento River. The special-interest constituent perchlorate, also included in the understanding assessment, has natural and anthropogenic sources. Perchlorate was detected frequently and at moderate relative concentrations. In some areas of the study unit, concentrations of perchlorate were higher than what might be expected in nature; therefore, anthropogenic introduction of perchlorate or anthropogenically induced migration of native perchlorate could be occurring.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225021","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","programNote":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","usgsCitation":"Bennett, G.L., V, 2022, Status and understanding of groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2022–5021, 52 p., https://doi.org/10.3133/sir20225021.","productDescription":"Report: xi, 52 p.; Data Release","numberOfPages":"52","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-125530","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":401167,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H4P0XF","text":"Potential explanatory variables for groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project","description":"Bennett, G.L., V, 2022, Potential explanatory variables for groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project: U.S. Geological Survey data release, available at https://doi.org/10.5066/P9H4P0XF."},{"id":401166,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5021/images"},{"id":401165,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5021/sir20225021.xml"},{"id":401164,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5021/sir20225021.pdf","text":"Report","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2022–5021"},{"id":401163,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5021/covrthb.jpg"},{"id":502376,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113075.htm","linkFileType":{"id":5,"text":"html"}},{"id":401191,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225021/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Scientific Investigations Report 2022–5021"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Metropolitan Domestic-Supply Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.51953124999999,\n 37.87485339352928\n ],\n [\n -120.5419921875,\n 37.87485339352928\n ],\n [\n -120.5419921875,\n 39.232253141714885\n ],\n [\n -122.51953124999999,\n 39.232253141714885\n ],\n [\n -122.51953124999999,\n 37.87485339352928\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GAMA Project Chief
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6000 J Street, Placer Hall
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Anthropogenic barriers to main-stem and tributary passage are one of the primary threats associated with declining populations of Pacific Lamprey (Entosphenus tridentatus) in the Columbia River Basin. Juvenile lamprey are of special interest because their downstream migration to the ocean may be affected by barriers such as dams or water diversions. Telemetry studies that describe the movement and passage of juvenile lamprey have not been possible until the recent development of a micro-transmitter specifically for use in juvenile lamprey and eels. Through a collaborative research approach, we used these prototype transmitters and acoustic monitoring arrays installed for a juvenile salmon (Oncorhynchus spp.) migration study to evaluate juvenile lamprey movements in the Yakima River (river kilometer 179 to the river mouth) in 2019 and 2020. We tagged and released 152 juvenile lamprey from April 30 to June 5, 2019, and on June 9, 2020. Lamprey were released 6.9 kilometers (km) upstream from Wapato Dam, 1.2 km upstream from Prosser Dam, and into the canal and tailrace at Prosser Dam. Most tagged lamprey did not initiate downstream movements within the 18 days of tag life, as evidenced by our detections of lamprey in the highest numbers at the first monitoring site downstream from their release site, with limited or no detections at sites farther downstream. There was no evidence of missed detections (lamprey detected at a downstream site without corresponding detections upstream). Overall detections of tagged lamprey were low: 27.0 percent in 2019 and 48.0 percent in 2020. River flows were less than the 10-year average during the monitoring period and water temperatures were variable. Lamprey arrived at detections sites predominantly during periods of darkness (85.3–96.6 percent) following daytime releases. Travel rates through the study area ranged from 0.2 to 45.3 kilometers per day, and lamprey generally remained at each detection station for less than about 20 minutes. Groups of lamprey released together generally had similar travel rates with a small number of fish that moved more quickly or slowly than the remainder of the group. In addition to monitoring the migration and behavior of juvenile lamprey, we also assessed some assumptions of survival models (determining downstream drift of purposely killed fish and empirically measuring transmitter operating life) to benefit future evaluations focused on migration survival of juvenile lamprey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221052","collaboration":"Prepared in cooperation with the Bureau of Reclamation, Yakama Nation Fisheries, McNary Fisheries Compensation Committee, Bonneville Power Administration, and the Pacific Northwest National Laboratory","usgsCitation":"Liedtke, T.L., Lampman, R.T., Monk, P., Hansen, A.C., Kock, T.J., Beals, T.E., Deng, D.Z., and Porter, M.S., 2022, Monitoring the movements of juvenile Pacific Lamprey (Entosphenus tridentatus) in the Yakima River, Washington, using acoustic telemetry, 2019–20: U.S. Geological Survey Open-File Report 2022–1052, 28 p., https://doi.org/10.3133/ofr20221052.","productDescription":"Report: viii, 28 p.; Dataset","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-133893","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":401158,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1052/images"},{"id":401157,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://app.streamnet.org/files/822/","text":"Pacific States Marine Fisheries Commission, StreamNet—Fish Data for the Northwest data files"},{"id":401156,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1052/ofr20221052.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1052"},{"id":401155,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1052/covrthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Yakima River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.684814453125,\n 46.01985337287631\n ],\n [\n -118.94622802734374,\n 46.01985337287631\n ],\n [\n -118.94622802734374,\n 46.71161922789268\n ],\n [\n -120.684814453125,\n 46.71161922789268\n ],\n [\n -120.684814453125,\n 46.01985337287631\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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6505 NE 65th Street
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The U.S. Geological Survey (USGS), in cooperation with the Caddo Nation of Oklahoma and Bureau of Indian Affairs, assessed four groundwater-withdrawal scenarios and their potential effects on the Rush Springs aquifer in and near the Caddo Nation of Oklahoma Tribal jurisdictional area in western Oklahoma. Increases in industrial and public water supply needs have led to increased development of water resources within the Rush Springs aquifer. As new areas within the aquifer are developed, increased water withdrawals may result in decreases in available groundwater resources and conflicts among water users.
For this study, a previously published numerical groundwater-flow model of the Rush Springs aquifer was modified to simulate the potential effects of four groundwater withdrawal scenarios. For the previously published calibrated model, groundwater flow was simulated from 1979 through 2015. In this study, groundwater flow simulations were extended through 2035. The period from 2016 through 2035 is referred to as the “20-year projection.” Four groundwater withdrawal scenarios starting in 2007 and continuing through 2035 were evaluated. Scenario 1 simulated no groundwater withdrawals; scenario 2 simulated no withdrawals allocated for out-of-basin water-use transfers; scenario 3 simulated withdrawals based on reported withdrawals during the 2007–15 simulation period and compounded annual increases in groundwater use during the subsequent 20-year projection; and scenario 4 simulated maximum permitted withdrawals for allocation to out-of-basin water-use transfers. Out-of-basin water transfers were classified as withdrawals that are not returned back to the aquifer.
At the springs of interest, changes in water-level altitudes in response to different groundwater withdrawal scenarios were simulated by comparing the results from different model cells. Between 2007 and 2015, scenarios 2–4 yielded similar simulated water-level altitudes in the model cells containing springs of interest, with water-level altitudes decreasing to below the land surface altitude at 13 of the total 25 springs of interest, whereas under scenario 1 there were only two model cells containing springs of interest where the simulated water-level altitudes of a spring decreased to below land surface altitude. For the 20-year projection, water-level altitudes at springs simulated in model cells in scenarios 2–4 decreased to below land surface altitude for 13 of the total 25 model cells containing springs of interest, whereas under scenario 1 there were only two model cells containing springs of interest where the simulated water-level altitudes of a spring decreased to below land surface altitude.
The potential effects of groundwater withdrawals were evaluated by comparing changes in groundwater storage between the four scenarios. The 2007–15 groundwater withdrawal scenarios were used to simulate the potential effects of groundwater withdrawal rates on groundwater storage of the Rush Springs aquifer. The simulated groundwater storage change in the Rush Springs aquifer ranged from an increase of 2.8 percent for scenario 1 to an increase of 1.0 percent for scenario 4. Projected 20-year groundwater withdrawal scenarios were used to simulate the potential effects of selected groundwater withdrawal rates on groundwater storage of the Rush Springs aquifer. Simulated groundwater storage changes ranged from a decrease of 0.5 percent for scenario 1 to a decrease of 0.7 percent for scenario 4.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225044","collaboration":"Prepared in cooperation with the Caddo Nation of Oklahoma and Bureau of Indian Affairs","usgsCitation":"Labriola, L.G., Russell, C.A., and Ellis, J.H., 2022, Potential effects of out-of-basin groundwater transfers on spring discharge, base flow, and groundwater storage pertaining to the Rush Springs aquifer in and near the Caddo Nation of Oklahoma Tribal jurisdictional area, western Oklahoma: U.S. Geological Survey Scientific Investigations Report 2022–5044, 32 p., https://doi.org/10.3133/sir20225044.","productDescription":"Report: vii, 32 p.; Data Release; Dataset","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-128617","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":400914,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92DYE98","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate the potential effects of out-of-basin transfers for the Rush Springs aquifer in the Caddo Nation of Oklahoma Tribal jurisdictional area, western Oklahoma"},{"id":400911,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5044/sir20225044.pdf","text":"Report","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5044"},{"id":400910,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5044/coverthb.jpg"},{"id":400915,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":502399,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113068.htm","linkFileType":{"id":5,"text":"html"}},{"id":401055,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225044/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":400913,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5044/images"},{"id":400912,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5044/sir20225044.XML"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Rush Springs Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.767578125,\n 34.45221847282654\n ],\n [\n -98.5693359375,\n 36.491973470593685\n ],\n [\n -99.66796875,\n 35.817813158696616\n ],\n [\n -99.0966796875,\n 35.137879119634185\n ],\n [\n -98.61328125,\n 34.488447837809304\n ],\n [\n -97.6904296875,\n 34.34343606848294\n ],\n [\n -96.767578125,\n 34.45221847282654\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501