Groundwater levels have declined since the 1940s in the Wailuku area of central Maui, Hawai‘i, on the eastern flank of West Maui volcano, mainly in response to increased groundwater withdrawals. Available data since the 1980s also indicate a thinning of the freshwater lens and an increase in chloride concentrations of pumped water from production wells. These trends, combined with projected increases in demand for groundwater in central Maui, have led to concerns over groundwater availability and have highlighted a need to improve general understanding of the hydrologic effects of proposed groundwater withdrawals in the Waihe‘e, ‘Īao, and Waikapū areas of central Maui.
A numerical groundwater model was constructed to simulate the flow and salinity of groundwater in central Maui. The model simulates the effects of changes in groundwater withdrawals and recharge on water levels, freshwater-lens thicknesses, and chloride concentrations of pumped water from production wells. The model incorporates updated water-budget estimates of groundwater recharge from infiltration and direct recharge, seepage in stream channels, and inflow from inland areas. Mean annual groundwater recharge from infiltration and direct recharge was estimated using a daily water-budget model and the most current data, including the distributions of monthly rainfall and potential evapotranspiration, for the study area for nine historical periods from 1926 through 2012: 1926–69, 1970–79, 1980–84, 1985–89, 1990–94, 1995–99, 2000–04, 2005–09, and 2010–12. The water-budget model also estimated groundwater recharge based on one hypothetical scenario that used 1980–2010 rainfall and 2017 land cover. For the nine historical periods, estimated recharge from infiltration and direct recharge within the area of the groundwater model ranged from 30.4 million gallons per day (Mgal/d) during 2010–12 to 98.7 Mgal/d during 1926–69. Variability in recharge during these periods mainly reflects changes in rainfall and irrigation over time. Between 2010 and 2014, streamflow restoration in previously diverted streams resulted in an estimated increase in recharge from seepage in stream channels of about 12.5 Mgal/d. Average groundwater inflow of about 39.6 Mgal/d from inland, dike-intruded areas to the main area of interest was estimated from an existing island-wide numerical groundwater-flow model, which is at a larger scale and incorporates a greater number of simplifying assumptions.
The numerical groundwater model developed for this study was calibrated to 1926–2012 transient water levels, vertical salinity profiles, and chloride concentrations of water pumped by production wells in the study area. The model was then used to evaluate one future recharge and six selected withdrawal scenarios, developed in consultation with the Maui Department of Water Supply, in terms of long-term changes in water level and 50-percent ocean-water salinity surface. The groundwater model was also used to simulate the future salinity of water withdrawn by existing and proposed production wells. The simulations were run to steady-state conditions, providing an estimate of the long-term effects of changes in withdrawal and recharge on the groundwater resource. Results of the simulated future withdrawal scenarios indicate that, relative to 2017–18 rates, the scenarios’ long-term effect of increased withdrawals ultimately leads to lower water levels and a higher 50-percent ocean-water salinity surface indicating a thinning of the freshwater lens. Results also indicate that the increased withdrawals produce some groundwater with chloride concentration below 250 milligrams per liter and some groundwater with higher chloride concentration. The amount of drawdown near production wells and the quality of water withdrawn from production wells is dependent on the rate and spatial distribution of the withdrawals.
The model was also used to evaluate how groundwater availability may be affected for a drier recharge scenario based on a published study of future climate. Model results of the future recharge scenario indicate that the rate of groundwater recharge is a controlling factor for (1) water levels, (2) the 50-percent ocean-water salinity surface, and (3) the quality of water withdrawn from production wells in the Wailuku area. Coupled with reduced groundwater recharge (with all other factors remaining equal), the modeled future withdrawals in the scenario would tend to cause lower water levels, a higher 50-percent ocean-water salinity surface, and increased salinity of water withdrawn from production wells.
The three-dimensional numerical groundwater model developed for this study utilizes the latest available hydrologic and geologic information and is a useful tool for understanding the long-term hydrologic effects of additional groundwater withdrawals in central Maui. The model has several limitations, including its non-uniqueness and inability to account for local-scale heterogeneities. Short-term effects of changes in recharge and withdrawals—and optimization of pumping rates to meet increased demand for water with acceptable salinity—are possible conditions for future simulation analyses.
Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Successful migration of Chinook Salmon (Oncorhynchus tshawytscha) smolts seaward in the Sacramento – San Joaquin River Delta (hereafter, Delta) requires navigating a network of numerous branching channels. Within the Delta, several key junctions route smolts either towards more direct paths to the ocean or towards the interior Delta, an area associated with decreased survival. Movements within these junctions that determine route choice can be influenced by numerous behavioral and environmental factors, including the complex interplay between tidal and riverine hydraulics. Here, we apply continuous time multistate Markov models to examine the influence of tidal and riverine hydraulics, behavioral factors, and management actions on smolt movements. These models incorporate more information from acoustic telemetry data compared with previous approaches to modeling smolt movements in the Delta. By decomposing modeled flows into tidal and net flow signals we elucidate how each component influences movements into and out of distributary channels. Increasing net flows generally increased movement rates, while flood tides decreased seaward movement rates. Similarly, ebb tides increased downstream movements as fish go with the flow. We found less support for diel movement behaviors compared to flow metrics. Additionally, we quantify the effects of a large management action, the placement of a physical barrier, which was effective at decreasing entrainment into the interior Delta. Together, these results help inform management of Chinook Salmon and increase our understanding of the major factors driving smolt movements within these key junctions.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-022-01273-1","usgsCitation":"Dodrill, M., Perry, R., Pope, A., and Wang, X., 2022, Quantifying the effects of tides, river flow, and barriers on movements of Chinook Salmon smolts at junctions in the Sacramento–San Joaquin River Delta using multistate models: Environmental Biology of Fishes, v. 105, p. 2065-2082, https://doi.org/10.1007/s10641-022-01273-1.","productDescription":"18 p.","startPage":"2065","endPage":"2082","ipdsId":"IP-133966","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":401641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San-Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.6513671875,\n 37.86618078529668\n ],\n [\n -122.56347656249999,\n 37.779398571318765\n ],\n [\n -122.55249023437501,\n 37.54022177661216\n ],\n [\n -122.0745849609375,\n 37.21720611325497\n ],\n [\n -121.541748046875,\n 37.020098201368114\n ],\n [\n -120.9210205078125,\n 37.24782120155428\n ],\n [\n -120.5804443359375,\n 37.4356124041315\n ],\n [\n -120.62988281249999,\n 37.63163475580643\n ],\n [\n -121.1846923828125,\n 37.896530447543\n ],\n [\n -121.31103515625,\n 38.052416771864834\n ],\n [\n -121.38244628906251,\n 38.268375880204744\n ],\n [\n -121.42639160156249,\n 38.45789034424927\n ],\n [\n -121.56921386718751,\n 38.70265930723801\n ],\n [\n -122.01416015625,\n 38.31149091244452\n ],\n [\n -122.61291503906249,\n 38.238180119798635\n ],\n [\n -122.6513671875,\n 37.86618078529668\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"105","noUsgsAuthors":false,"publicationDate":"2022-05-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Dodrill, Michael J. 0000-0002-7038-7170","orcid":"https://orcid.org/0000-0002-7038-7170","contributorId":206439,"corporation":false,"usgs":true,"family":"Dodrill","given":"Michael","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":844058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220189,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":844059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pope, Adam C. 0000-0002-7253-2247","orcid":"https://orcid.org/0000-0002-7253-2247","contributorId":223237,"corporation":false,"usgs":true,"family":"Pope","given":"Adam","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":844060,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Xiaochun","contributorId":225264,"corporation":false,"usgs":false,"family":"Wang","given":"Xiaochun","email":"","affiliations":[{"id":41085,"text":"California Department of Water Resources, Sacramento, CA, 95819","active":true,"usgs":false}],"preferred":false,"id":844061,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70231905,"text":"70231905 - 2022 - Dynamic sensitivity to resource availability influences population responses to mismatches in a shorebird","interactions":[],"lastModifiedDate":"2022-09-01T14:36:44.26282","indexId":"70231905","displayToPublicDate":"2022-06-02T09:46:27","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":"Dynamic sensitivity to resource availability influences population responses to mismatches in a shorebird","docAbstract":"Climate change has caused shifts in seasonally recurring biological events leading to the temporal decoupling of consumer-resource pairs – i.e., phenological mismatching. Although mismatches often affect individual fitness, they do not invariably scale up to affect populations, making it difficult to assess the risk they pose. Individual variation may contribute to this inconsistency, with changes in resource availability and consumer needs leading mismatches to have different outcomes over time. Nevertheless, most models estimate a consumer’s match from a single timepoint, potentially obscuring when mismatches matter to populations. We analyzed how the effects of mismatches varied over time by studying precocial Hudsonian godwit (Limosa haemastica) chicks and their invertebrate prey from 2009 to 2019. We developed individual and population level models to determine how age-specific variation affect the relationship between godwits and resource availability. We found that periods with abundant resources led to higher growth and survival of godwit chicks, but also that chick survival was increasingly related to the availability of larger prey as chicks aged. At the population level, estimates of mismatches using age-structured consumer demand explained more variation in annual godwit fledging rates than more commonly used alternatives. Our study suggests that modeling the effects of mismatches as the disrupted interaction between dynamic consumer needs and resource availability clarifies when mismatches matter to both individuals and populations.
","language":"English","publisher":"Wiley","doi":"10.1002/ecy.3743","usgsCitation":"Wilde, L.R., Simmons, J.E., Swift, R.J., and Senner, N.R., 2022, Dynamic sensitivity to resource availability influences population responses to mismatches in a shorebird: Ecology, v. 103, no. 9, e3743, 14 p., https://doi.org/10.1002/ecy.3743.","productDescription":"e3743, 14 p.","ipdsId":"IP-125364","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":447562,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecy.3743","text":"External Repository"},{"id":401639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beluga River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151.11968994140625,\n 61.18992667218288\n ],\n [\n -150.91506958007812,\n 61.18992667218288\n ],\n [\n -150.91506958007812,\n 61.264621333832146\n ],\n [\n -151.11968994140625,\n 61.264621333832146\n ],\n [\n -151.11968994140625,\n 61.18992667218288\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"103","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilde, Luke R.","contributorId":291481,"corporation":false,"usgs":false,"family":"Wilde","given":"Luke","email":"","middleInitial":"R.","affiliations":[{"id":62717,"text":"Dept. of Biological Sciences, University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":844068,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simmons, Josiah E.","contributorId":292206,"corporation":false,"usgs":false,"family":"Simmons","given":"Josiah","email":"","middleInitial":"E.","affiliations":[{"id":62841,"text":"Division of Biological Sciences, University of Montana","active":true,"usgs":false}],"preferred":false,"id":844069,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swift, Rose J. 0000-0001-7044-6196","orcid":"https://orcid.org/0000-0001-7044-6196","contributorId":212082,"corporation":false,"usgs":true,"family":"Swift","given":"Rose","email":"","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844070,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Senner, Nathan R.","contributorId":140465,"corporation":false,"usgs":false,"family":"Senner","given":"Nathan","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":844071,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70234201,"text":"70234201 - 2022 - Application of recursive estimation to heat tracing for groundwater/surface-water exchange","interactions":[],"lastModifiedDate":"2022-08-03T11:44:35.813964","indexId":"70234201","displayToPublicDate":"2022-06-02T06:40:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Application of recursive estimation to heat tracing for groundwater/surface-water exchange","docAbstract":"We present and demonstrate a recursive-estimation framework to infer groundwater/surface-water exchange based on temperature time series collected at different vertical depths below the sediment/water interface. We formulate the heat-transport problem as a state-space model (SSM), in which the spatial derivatives in the convection/conduction equation are approximated using finite differences. The SSM is calibrated to estimate time-varying specific discharge using the Extended Kalman Filter (EKF) and Extended Rauch-Tung-Striebel Smoother (ERTSS). Whereas the EKF is suited to real-time (“online”) applications and uses only the past and current measurements for estimation (filtering), the ERTSS is intended for near-real time or batch-processing (“offline”) applications and uses a window of data for batch estimation (smoothing). The two algorithms are demonstrated with synthetic and field-experimental data and are shown to be efficient and rapid for the estimation of time-varying flux over seasonal periods; further, the recursive approaches are effective in the presence of rapidly changing flux and (or) nonperiodic thermal boundary conditions, both of which are problematic for existing approaches to heat tracing of time-varying groundwater/surface-water exchange.
Regional groundwater recharge commonly is estimated using a threshold-type water-budget approach in which groundwater recharge is assumed to occur when water in the plant-root zone exceeds the soil’s moisture storage capacity. A water budget of the plant-soil system accounts for water inputs (rainfall, fog interception, irrigation, septic-system leachate, and other inputs), water outputs (runoff, evaporation, transpiration, and recharge), and changes in stored water during a specified time interval. Water budgets can be computed on any desired interval, including annual, monthly, daily, and subdaily intervals. In general, uncertainty in recharge estimates is expected to be lower using daily or subdaily intervals relative to monthly and annual intervals. Average recharge rates computed over a period of a year or multiple years are commonly determined from water budgets computed using a daily computation interval capable of capturing rainfall and land-cover changes during the period.
This report documents the Water-budget Accounting for Tropical Regions Model, or WATRMod, code that can be used to estimate spatially variable, daily water-budget components in tropical-island and other appropriate settings. The purpose of this report is to provide descriptions of WATRMod’s (1) approach to computing a daily water budget, (2) represented processes, (3) limitations, and (4) execution procedure, input requirements, output files, and example files. The model computes a daily water budget for each hydrologically independent subarea within the overall study area. A subarea is defined by its climatic, soil, land-cover, and human-related (for example, adding irrigation or other water) characteristics. The water-budget model can represent processes including rainfall, fog interception, irrigation, septic-system leachate, direct recharge that bypasses the plant-soil system, runoff, canopy evaporation in forested areas, evapotranspiration, and groundwater recharge. The water-budget model can represent either one of the following different accounting orders: (1) accounting for loss of water by evapotranspiration before accounting for recharge, and (2) accounting for recharge before accounting for evapotranspiration. WATRMod’s limitations include: (1) uncharacterized, subdaily transient changes in water inputs and outputs from the plant-soil system, (2) unrepresented precipitation in the form of snow and sublimation, and (3) routing runoff from one subarea to an adjacent subarea that is not directly represented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221013","usgsCitation":"Oki, D.S., 2022, Water-budget accounting for tropical regions model (WATRMod) documentation: U.S. Geological Survey Open-File Report 2022-1013, 77 p., https://doi.org/10.3133/ofr20221013.","productDescription":"Report: viii, 77 p.; Data Release","numberOfPages":"77","onlineOnly":"Y","ipdsId":"IP-126805","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":501758,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113077.htm","linkFileType":{"id":5,"text":"html"}},{"id":401381,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VPAY41","text":"WATRMod, a Water-budget accounting for tropical regions model—source code, executable file, and example files","description":"Oki, D.S., 2022, WATRMod, a Water-budget accounting for tropical regions model—source code, executable file, and example files: U.S. Geological Survey data release, https://doi.org/10.5066/P9VPAY41."},{"id":401379,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1013/covrthb.jpg"},{"id":401380,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1013/ofr20221013.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1013"}],"country":"United States","state":"Hawaii","otherGeospatial":"Island of Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.3848876953125,\n 20.555652403773365\n ],\n [\n -156.0003662109375,\n 20.6379249854131\n ],\n [\n -155.9454345703125,\n 20.776659051878816\n ],\n [\n -156.26678466796875,\n 20.964004409178308\n ],\n [\n -156.47003173828125,\n 20.925527866647226\n ],\n [\n -156.610107421875,\n 21.056307701901847\n ],\n [\n -156.72271728515625,\n 20.94604992010052\n ],\n [\n -156.67327880859375,\n 20.822875478868443\n ],\n [\n -156.55792236328122,\n 20.761250430919652\n ],\n [\n -156.48651123046875,\n 20.771523019513364\n ],\n [\n -156.4617919921875,\n 20.622502259344817\n ],\n [\n -156.3848876953125,\n 20.555652403773365\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
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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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","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins 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.
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