Whitebark pine (Pinus albicaulis Engelm.) has experienced rapid population declines and is listed as threatened under the Endangered Species Act in the United States. Whitebark pine in the Sierra Nevada of California represents the southernmost end of the species' distribution and, like other portions of its range, faces threats from an introduced pathogen, native bark beetles, and a rapidly warming climate. Beyond these chronic stressors, there is also concern about how this species will respond to acute stressors, such as drought. We present patterns of stem growth from 766 large (average diameter at breast height >25 cm), disease-free whitebark pine across the Sierra Nevada before and during a recent period of drought. We contextualize growth patterns using population genomic diversity and structure from a subset of 327 trees. Sampled whitebark pine generally had positive to neutral stem growth trends from 1970 to 2011, which was positively correlated with minimum temperature and precipitation. Indices of stem growth during drought years (2012 to 2015) relative to a predrought interval were mostly positive to neutral at our sampled sites. Individual tree growth response phenotypes appeared to be linked to genotypic variation in climate-associated loci, suggesting that some genotypes can take better advantage of local climatic conditions than others. We speculate that reduced snowpack during the 2012 to 2015 drought years may have lengthened the growing season while retaining sufficient moisture to maintain growth at most study sites. Growth responses may differ under future warming, however, particularly if drought severity increases and modifies interactions with pests and pathogens.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.10072","usgsCitation":"van Mantgem, P., Milano, E.R., Dudney, J., Nesmith, J., Vandergast, A.G., and Zald, H.S., 2023, Growth, drought response, and climate-associated genomic structure in whitebark pine in the Sierra Nevada of California: Ecology and Evolution, v. 13, no. 5, e10072, 20 p., https://doi.org/10.1002/ece3.10072.","productDescription":"e10072, 20 p.","ipdsId":"IP-128278","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":443243,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.10072","text":"Publisher Index Page"},{"id":435298,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EKZR4C","text":"USGS data release","linkHelpText":"Growth, Drought Response, and Genomic Structure Data for Whitebark Pine in the Sierra Nevada of California (ver. 2.0, May 2023)"},{"id":417641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.90787138940058,\n 40.75842259785122\n ],\n [\n -122.22647490502563,\n 40.27400725576794\n ],\n [\n -121.76504912377561,\n 39.786096804298865\n ],\n [\n -121.51236357690067,\n 39.32870262476027\n ],\n [\n -120.86417021752547,\n 38.310125593418576\n ],\n [\n -120.83121123315044,\n 38.08564366930443\n ],\n [\n -119.28213896752547,\n 36.71547588003058\n ],\n [\n -119.01846709252573,\n 36.265018388250496\n ],\n [\n -118.87564482690041,\n 35.70496447203716\n ],\n [\n -118.80972685815075,\n 35.322580152836636\n ],\n [\n -118.79874053002564,\n 35.04422375724022\n ],\n [\n -118.32632842065081,\n 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]\n}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":204320,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":874442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milano, Elizabeth R. 0000-0003-4143-9303","orcid":"https://orcid.org/0000-0003-4143-9303","contributorId":210607,"corporation":false,"usgs":true,"family":"Milano","given":"Elizabeth","email":"","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":874443,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dudney, Joan 0000-0003-3986-065X","orcid":"https://orcid.org/0000-0003-3986-065X","contributorId":305558,"corporation":false,"usgs":false,"family":"Dudney","given":"Joan","email":"","affiliations":[{"id":66253,"text":"Environmental Studies Program, Santa Barbara, California, USA","active":true,"usgs":false}],"preferred":false,"id":874444,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nesmith, Jonathan 0000-0002-8930-9105","orcid":"https://orcid.org/0000-0002-8930-9105","contributorId":306029,"corporation":false,"usgs":false,"family":"Nesmith","given":"Jonathan","email":"","affiliations":[{"id":66353,"text":"USDA FS","active":true,"usgs":false}],"preferred":false,"id":874445,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":97617,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","email":"","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":874446,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zald, Harold S.J. 0000-0002-6505-8233","orcid":"https://orcid.org/0000-0002-6505-8233","contributorId":306030,"corporation":false,"usgs":false,"family":"Zald","given":"Harold","email":"","middleInitial":"S.J.","affiliations":[{"id":66353,"text":"USDA FS","active":true,"usgs":false}],"preferred":false,"id":874447,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70244124,"text":"70244124 - 2023 - Estimating streamflow permanence with the watershed erosion prediction project model: Implications for surface water presence modeling and data collection","interactions":[],"lastModifiedDate":"2023-06-09T15:27:22.153327","indexId":"70244124","displayToPublicDate":"2023-06-01T07:01:39","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating streamflow permanence with the watershed erosion prediction project model: Implications for surface water presence modeling and data collection","docAbstract":"Many data collection efforts and modeling studies have focused on providing accurate estimates of streamflow while fewer efforts have sought to identify when and where surface water is present and the duration of surface water presence in stream channels, hereafter referred to as streamflow permanence. While physically-based hydrological models are frequently used to explore how water quantity may be influenced by various climatic and basin characteristics at local, regional, national, and global extents they are less often used to explore streamflow permanence. Herein, the Watershed Erosion Prediction Project (WEPP) hydrological model is applied to watersheds in the humid H. J. Andrews Experimental Forest (HJA) and watersheds of the arid Willow and Whitehorse creeks (WW), both in Oregon, to simulate daily (WW) and annual (HJA and WW) streamflow permanence. One thousand parameter combinations were tested to calibrate WEPP to observed streamflow in the HJA watersheds and one hundred parameter combinations were tested to calibrate WEPP to observed surface water presence time series data in WW watersheds. When calibrated to observed streamflow, WEPP correctly classified annual streamflow permanence for 83% of HJA stream reaches. In the WW, WEPP simulations correctly classified 63–93% of daily streamflow permanence observations and 59-87% of annual streamflow permanence classifications. Inclusion of a dry-day threshold (the maximum number of days a stream reach could be modeled ‘dry’ but still classified as permanent for the year) improved annual accuracy in three WW watersheds from 2-10%. Parameter sets that produced the best daily accuracies in WW resulted in poor annual accuracies. Results highlight the importance of evaluating physically-based streamflow permanence models on both permanent and nonpermanent streams at daily and annual time scales to ensure evaluation metrics are appropriate for interpretation purposes. Additionally, results suggest that strategic collection of surface water presence observations and streamflow observations may support robust calibration of physically based models to simulate streamflow permanence moving forward.
Two decades of drought in the southwestern USA are spurring concerns about increases in wind erosion, dust emissions, and associated impacts on ecosystems, agriculture, human health, and water supply. Different avenues of investigation into primary drivers of wind erosion and dust have yielded mixed results depending on the spatial and temporal sensitivity of the evidence. We monitored passive aeolian sediment traps from 2017 to 2020 across eighty-one sites near Moab UT to understand patterns of sediment flux. At measurement sites we collated climate, soil, topography and vegetation spatial layers to better understand the context of wind erosion and then combine these data with field observations of land use in models to characterize the influence of cattle grazing, oil and gas well pads, and vehicle/heavy equipment disturbance that potentially drive both exposure of bare soil and increases in erodible sediment supply that increase vulnerability to erosion. Disturbed areas with low soil calcium carbonate content yielded high sediment transport in dry years, but notably areas with little disturbance and low bare soil exposure had much less activity. Cattle grazing had the largest land use association with erosional activity with analyses suggesting that both herbivory and trampling from cattle could be drivers. The amount and distribution of bare soil exposure from new sub-annual fractional cover remote sensing products proved very helpful in mapping erosion, and new predictive maps informed by field data are presented to help depict spatial patterns of wind erosion activity. Our results suggest that despite the magnitude of current droughts, minimizing surface disturbance in vulnerable soils can mitigate a large portion of dust emissions. Results can help managers identify eroding areas where disturbance reduction and soil surface protection measures can be prioritized.
Long-term and accurate wave hindcast databases are often required in different coastal engineering projects. The assessment of the nearshore wave climate is often accomplished by using downscaling techniques to translate offshore waves to coastal areas. However, dynamical downscaling approaches may incur huge computational cost. Additionally, the common use of bulk parameterizations are often not accurate for multidimensional waves. To overcome these limitations, we present a hybrid downscaling approach that combines mathematical algorithms (statistical downscaling) and numerical modeling (dynamical downscaling) over the individual spectral partitions. Every wave partition is downscaled and aggregated afterward by using principles of wave linear theory. By assuming linearity in the propagation of the wave celerity, the application of the method is limited from offshore to intermediate water depths. In addition, the method proposed uses a technique to simplify the spectral boundary conditions in complex domains. The methodology has been applied and validated in the island states of Samoa, American Samoa, Majuro, and Kwajalein, showing good skill at reproducing the spectral hourly time series of significant wave height, peak period, and peak direction. Moreover, an accurate representation of the observed energy spectrum was achieved. This study provides insight into the numerical approximation of the combined sea-swell states while improving the quality of fast spectral forecasting and early warning systems.
1. Conversion of land for settlements and agriculture is increasing globally and can influence wildlife space use. However, there is limited research to identify the thresholds of land-use change that incur wildlife avoidance and how these thresh-olds might vary across levels of selection.
2. We evaluated multi-level avoidance thresholds of elk Cervus canadensis impacted by residential development and irrigated agriculture across the Greater Yellowstone Ecosystem in Idaho, Montana and Wyoming. Using GPS data from765 elk in 21 herds, we estimated habitat selection in relation to development and agriculture at three levels (home range selection, within home range selection and movement path selection). Next, using individual selection covariates and as-sociated measures of land-use availability, we used functional-response models to evaluate how selection varied based on availability, and in turn, to estimate avoidance thresholds.
3. We found individual and level-specific variation in elk responses to environmental factors. Elk exhibited stronger responses (either selection or avoidance) when selecting home range locations (i.e. second-order selection) than when selecting areas within home ranges (i.e. third-order selection) or selecting movement paths (i.e. fourth-order selection). Importantly, elk avoidance of development and agriculture changed as the amount of land in these categories changed. Across all levels of selection elk exhibited neutral selection for human development at low levels of availability (<1.1%–2.2% developed) but avoided areas that were >1.1%–2.2% developed. Conversely, elk selected positively for irrigated agriculture at low to moderate levels of availability (<52.0%–66.2% agriculture) but exhibited neutral selection in areas that were >52.0%–66.2% agriculture.
4. Synthesis and applications. Elk avoidance of low levels of human development suggests conservation efforts such as restrictions on future development or conservation easements could focus on areas that are still below 2% developed. Additionally, because elk selection was strongest at the landscape scale, conservation actions that are based on information about the overall landscape structure may be most impactful. Our results highlight the importance of under-standing variability in wildlife habitat selection at multiple levels, particularly in relation to land-use change, and highlight how functional response modelling can help inform landscape conservation.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.14401","usgsCitation":"Gigliotti, L., Atwood, M., Cole, E.K., Courtemanche, A., Dewey, S., Gude, J., Hurley, M., Kauffman, M., Kroetz, K., Leonard, B., MacNulty, D., Maichak, E., McWhirter, D., Mong, T., Proffitt, K., Scurlock, B., Stahler, D., and Middleton, A., 2023, Multi-level thresholds of residential and agricultural land use for elk avoidance across the Greater Yellowstone Ecosystem: Journal of Applied Ecology, v. 60, no. 6, p. 1089-1099, https://doi.org/10.1111/1365-2664.14401.","productDescription":"11 p.","startPage":"1089","endPage":"1099","ipdsId":"IP-145333","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467109,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.14401","text":"Publisher Index Page"},{"id":466426,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.49834976413001,\n 45.26023090682858\n ],\n [\n -111.49834976413001,\n 43.83379000138524\n ],\n [\n -108.63921958902552,\n 43.83379000138524\n ],\n [\n -108.63921958902552,\n 45.26023090682858\n ],\n [\n -111.49834976413001,\n 45.26023090682858\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Gigliotti, Laura Christine 0000-0002-6390-4133","orcid":"https://orcid.org/0000-0002-6390-4133","contributorId":348259,"corporation":false,"usgs":true,"family":"Gigliotti","given":"Laura Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Atwood, M. Paul","contributorId":348260,"corporation":false,"usgs":false,"family":"Atwood","given":"M. Paul","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":923314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cole, Eric K. 0000-0002-2229-5853 eric_cole@fws.gov","orcid":"https://orcid.org/0000-0002-2229-5853","contributorId":348261,"corporation":false,"usgs":true,"family":"Cole","given":"Eric","email":"eric_cole@fws.gov","middleInitial":"K.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":true,"id":923315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Courtemanche, Alyson","contributorId":348262,"corporation":false,"usgs":false,"family":"Courtemanche","given":"Alyson","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":923316,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dewey, Sarah","contributorId":348263,"corporation":false,"usgs":false,"family":"Dewey","given":"Sarah","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":923317,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gude, Justin A.","contributorId":348264,"corporation":false,"usgs":false,"family":"Gude","given":"Justin A.","affiliations":[{"id":81193,"text":"Montana Department of Fish","active":true,"usgs":false}],"preferred":false,"id":923318,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hurley, Mark","contributorId":348265,"corporation":false,"usgs":false,"family":"Hurley","given":"Mark","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":923319,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":923320,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kroetz, Kailin","contributorId":348267,"corporation":false,"usgs":false,"family":"Kroetz","given":"Kailin","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":923321,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Leonard, Bryan","contributorId":348270,"corporation":false,"usgs":false,"family":"Leonard","given":"Bryan","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":923322,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"MacNulty, Daniel R.","contributorId":179179,"corporation":false,"usgs":false,"family":"MacNulty","given":"Daniel R.","affiliations":[],"preferred":false,"id":923323,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Maichak, Eric","contributorId":348277,"corporation":false,"usgs":false,"family":"Maichak","given":"Eric","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":923324,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"McWhirter, Douglas","contributorId":348281,"corporation":false,"usgs":false,"family":"McWhirter","given":"Douglas","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":923325,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Mong, Tony W.","contributorId":348286,"corporation":false,"usgs":false,"family":"Mong","given":"Tony W.","affiliations":[{"id":83329,"text":"Wyoming Game and Fish Department, Cody, WY 82414","active":true,"usgs":false}],"preferred":false,"id":923326,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Proffitt, Kelly","contributorId":348289,"corporation":false,"usgs":false,"family":"Proffitt","given":"Kelly","affiliations":[{"id":81193,"text":"Montana Department of Fish","active":true,"usgs":false}],"preferred":false,"id":923327,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Scurlock, Brandon","contributorId":348292,"corporation":false,"usgs":false,"family":"Scurlock","given":"Brandon","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":923328,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Stahler, Daniel","contributorId":348295,"corporation":false,"usgs":false,"family":"Stahler","given":"Daniel","affiliations":[{"id":79152,"text":"Yellowstone Center for Resources","active":true,"usgs":false}],"preferred":false,"id":923329,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Middleton, Arthur D.","contributorId":348297,"corporation":false,"usgs":false,"family":"Middleton","given":"Arthur D.","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":923330,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70274657,"text":"70274657 - 2023 - A review of N-mixture models","interactions":[],"lastModifiedDate":"2026-04-02T15:48:25.149913","indexId":"70274657","displayToPublicDate":"2023-06-01T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23780,"text":"WIREs Computational Statistics","active":true,"publicationSubtype":{"id":10}},"title":"A review of N-mixture models","docAbstract":"N-mixture models were born in 2004 of the necessity to model animal population size from point counts with imperfect detection of individuals, where capture-recapture methods are infeasible. Initially developed for applications where population size was assumed constant, N-mixture models were extended in 2011 to include population dynamics, allowing application to populations whose size fluctuates during the study. A further extension in 2014 accommodates populations with multiple “states” such as age class or sex. More recent extensions model spatial movement of animals among habitat patches or the spatial spread of infectious disease in a human population. The core idea underlying this class of models is a hierarchical structure, where the observation model is defined conditional on the model for true abundance. This hierarchy allows researchers to incorporate information about observation and abundance processes, while permitting distinct inferences about elements affecting detection and those affecting abundance. Another benefit of the hierarchical approach is the ability to accommodate many existing sampling protocols such as removal sampling and distance sampling. One drawback to N-mixture models is that since they estimate both abundance and detection from replicated but unmarked counts, model parameters may not be clearly identifiable. A second drawback is that when observed counts are large, calculating the N-mixture likelihood is computationally infeasible. This difficulty motivated an approximate likelihood based on the normal approximation to the binomial. The normal approximation provides a diagnostic of parameter estimability based on the closed-form expression of the Fisher information matrix for a multivariate normal likelihood.
","language":"English","publisher":"Wiley","doi":"10.1002/wics.1625","usgsCitation":"Madsen, L., and Royle, J., 2023, A review of N-mixture models: WIREs Computational Statistics, v. 15, no. 6, e1625, 15 p., https://doi.org/10.1002/wics.1625.","productDescription":"e1625, 15 p.","ipdsId":"IP-147184","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":502082,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wics.1625","text":"Publisher Index Page"},{"id":502005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Madsen, Lisa","contributorId":369194,"corporation":false,"usgs":false,"family":"Madsen","given":"Lisa","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958596,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":958597,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70244057,"text":"sir20235056 - 2023 - Source contributions to suspended sediment and particulate selenium export from the Loutsenhizer Arroyo and Sunflower Drain watersheds in Colorado","interactions":[],"lastModifiedDate":"2026-03-09T16:27:43.171892","indexId":"sir20235056","displayToPublicDate":"2023-05-31T17:25:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5056","displayTitle":"Source Contributions to Suspended Sediment and Particulate Selenium Export from the Loutsenhizer Arroyo and Sunflower Drain Watersheds in Colorado","title":"Source contributions to suspended sediment and particulate selenium export from the Loutsenhizer Arroyo and Sunflower Drain watersheds in Colorado","docAbstract":"Selenium in aquatic ecosystems of the lower Gunnison River Basin in Colorado is affecting the recovery of populations of endangered, native fish species. Dietary exposure is the primary pathway for bioaccumulation of selenium in fish, and particulate selenium can be consumed directly by fish or by the invertebrates on which fish feed. Although selenium can be incorporated into particulate matter via biogeochemical processes, particulate selenium can also enter aquatic ecosystems of the lower Gunnison River Basin from sediments derived from the selenium-rich Mancos Shale. The U.S. Geological Survey, in cooperation with the Colorado Water Conservation Board, conducted this study during 2018–19 to identify sources of selenium-rich suspended sediments from two watersheds underlain by Mancos Shale: Loutsenhizer Arroyo and Sunflower Drain, which is a locally known agricultural drainage near the municipality of Delta, Colorado.
A multipronged approach (fieldwork, laboratory work, and computer modeling) referred to as “sediment fingerprinting” was used to evaluate sources of suspended sediments in the streams flowing out of the two studied watersheds. Four potential source types for suspended sediments were identified and sampled (using soil plugs) within the watersheds: rangelands, agricultural fields, arroyo walls, and streambanks. The sediment fingerprinting approach used elemental concentrations and naturally occurring fallout radionuclides as tracers to apportion percent contributions from the four source types of suspended sediments found in streamflow from both watersheds.
To determine the dominant sources of suspended sediment in streamflow from both watersheds, a mathematical “unmixing” model was used. Unmixing models apportion source percentages to samples of material in which those sources are mixed. These models used elemental and isotopic data in the suspended sediments to unmix them into proportional contributions from source types. The results indicated that arroyo walls and streambanks generally dominated as sources of the suspended sediment. Arroyo walls and streambanks were channel-adjacent sources, with sediments mobilized by water flowing within the stream channel. These sources accounted for greater than 50 percent of suspended sediment in all but one sample and accounted for 100 percent of suspended sediment in 5 of the 11 samples collected. Rangeland and agricultural field sources were located in uplands outside of stream channels and were detected more often during the non-irrigation season. Rangeland and agricultural field sources each were found in 5 of the 11 samples collected. Concentrations of selenium in sediment-source samples were comparatively greater in streambanks and lower in rangelands, with agricultural fields and arroyo walls being intermediate. As a result, source apportionments for particulate selenium skewed towards sources adjacent to stream channels more than for suspended sediments. Water imports for irrigation have changed the hydrology of the watersheds, and a notable fraction of imported water passes through the watersheds rapidly. The rapid flowthrough water during the irrigation season likely contributes heavily to sediment erosion and transport in Loutsenhizer Arroyo and Sunflower Drain, particularly from channel-adjacent sources of sediment. Decreases in irrigation season streamflow, at least in Loutsenhizer Arroyo, may have decreased sediment erosion and transport during the 2018–20 irrigation seasons compared to the 2015–17 seasons.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235056","collaboration":"Prepared in cooperation with the Colorado Water Conservation Board","usgsCitation":"Bern, C.R., Williams, C.A., and Smith, C.G., 2023, Source contributions to suspended sediment and particulate selenium export from the Loutsenhizer Arroyo and Sunflower Drain watersheds in Colorado: U.S. Geological Survey Scientific Investigations Report 2023–5056, 32 p., https://doi.org/10.3133/sir20235056.","productDescription":"Report: vii, 32 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-132717","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":485917,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114745.htm","linkFileType":{"id":5,"text":"html"}},{"id":417883,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235056/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5056"},{"id":417619,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5056/sir20235056.xml"},{"id":417618,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5056/images"},{"id":417612,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99EZZJK","text":"USGS data release","linkHelpText":"Geochemical and fallout radionuclide data for sediment source fingerprinting studies of the Loutsenhizer Arroyo and Sunflower Drain watersheds in western Colorado"},{"id":417611,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5056/sir20235056.pdf","text":"Report","size":"3.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5056"},{"id":417610,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5056/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Loutsenhizer Arroyo Watershed, Sunflower Drain Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.2032088457371,\n 38.871468271392075\n ],\n [\n -108.2032088457371,\n 38.38029358037457\n ],\n [\n -107.27351004163626,\n 38.38029358037457\n ],\n [\n -107.27351004163626,\n 38.871468271392075\n ],\n [\n -108.2032088457371,\n 38.871468271392075\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, Colorado 80225
Planets are expected to conclude their growth through a series of giant impacts: energetic, global events that significantly alter planetary composition and evolution. Computer models and theory have elucidated the diverse outcomes of giant impacts in detail, improving our ability to interpret collision conditions from observations of their remnants. However, many open questions remain, as even the formation of the Moon—a widely suspected giant-impact product for which we have the most information—is still debated. We review giant-impact theory, the diverse nature of giant-impact outcomes, and the governing physical processes. We discuss the importance of computer simulations, informed by experiments, for accurately modeling the impact process. Finally, we outline how the application of probability theory and computational advancements can assist in inferring collision histories from observations, and we identify promising opportunities for advancing giant-impact theory in the future.
River corridors integrate the active channels, geomorphic floodplain and riparian areas, and hyporheic zone while receiving inputs from the uplands and groundwater and exchanging mass and energy with the atmosphere. Here, we trace the development of the contemporary understanding of river corridors from the perspectives of geomorphology, hydrology, ecology, and biogeochemistry. We then summarize contemporary models of the river corridor along multiple axes including dimensions of space and time, disturbance regimes, connectivity, hydrochemical exchange flows, and legacy effects of humans. We explore how river corridor science can be advanced with a critical zone framework by moving beyond a primary focus on discharge-based controls toward multi-factor models that identify dominant processes and thresholds that make predictions that serve society. We then identify opportunities to investigate relationships between large-scale spatial gradients and local-scale processes, embrace that riverine processes are temporally variable and interacting, acknowledge that river corridor processes and services do not respect disciplinary boundaries and increasingly need integrated multidisciplinary investigations, and explicitly integrate humans and their management actions as part of the river corridor. We intend our review to stimulate cross-disciplinary research while recognizing that river corridors occupy a unique position on the Earth's surface.
Both biotic and abiotic factors can influence the survival and growth of age-0 salmonids. Diseases, such as whirling disease, can also affect salmonid demographics and population dynamics. Here, we conducted a supplementary analysis and evaluated specific stream characteristics that may have been responsible for the differences in growth and survival of two whirling disease resistant Rainbow Trout Oncorhynchus mykiss strains observed by Avila et al. (2018). We used regression modeling to analyze the influence of the biotic and abiotic characteristics of nine streams on the short-term apparent survival and growth of two Rainbow Trout strains, 5,000 German Rainbow Trout and 5,000 German Rainbow Trout × Colorado River Rainbow Trout in each stream. Akaike's information criterion (AICc) model selection was used to identify the factors that most affected short-term survival and growth. Average stream temperature had the largest (positive) effect, βtemp = 0.060, on short-term survival. Rainbow Trout strain, average stream temperature (βtemp = 1.55), competitor biomass (βcompetitor biomass = −0.002), and predator number (βpredator number = 0.01) additively affected short-term growth. Our results indicate that both biotic and abiotic factors are important short-term determinants of Rainbow Trout poststocking performance and may account for the differences in survival and growth that we observed among stocking locations.
As scientific understanding of barrier morphodynamics has improved, so has the ability to reproduce observed phenomena and predict future barrier states using mathematical models. To use existing models effectively and improve them, it is important to understand the current state of morphodynamic modeling and the progress that has been made in the field. This manuscript offers a review of the literature regarding advancements in morphodynamic modeling of coastal barrier systems and summarizes current modeling abilities and limitations. Broadly, this review covers both event-scale and long-term morphodynamics. Each of these sections begins with an overview of commonly modeled phenomena and processes, followed by a review of modeling developments. After summarizing the advancements toward the stated modeling goals, we identify research gaps and suggestions for future research under the broad categories of improving our abilities to acquire and access data, furthering our scientific understanding of relevant processes, and advancing our modeling frameworks and approaches.
Missing data are typical yet must be addressed for proper inferences or expanding datasets to guide our limnological understanding and management of aquatic systems. Interpolation methods (i.e., estimating missing values using known values within the dataset) can alleviate data gaps and common problems. We compared seven popular interpolation methods for predicting substantial missingness in a long-term water quality dataset from the Upper Mississippi River, U.S.A. The dataset included 80,000 sampling sites collected over 30 yr that had substantial missingness for total nitrogen (TN), total phosphorus (TP), and water velocity. For all three interpolated water quality variables, random forests had very high prediction accuracy and outperformed the methods of ordinary kriging, polynomial regressions, regression trees, and inverse distance weighting. TP had a mean absolute error (MAE) of 0.03 mg (L-TP)−1, TN had a MAE of 0.39 mg (L-TN)−1, and water velocity had a MAE of 0.10 m s−1. The random forests' error rates were mapped and showed low spatiotemporal variability across the riverscape, indicating high model performance across many habitat types and large spatial scales. In the current era of “big data,” interpolation becomes an imperative step prior to ecological analyses yet remains unfamiliar and underutilized. Our research briefly describes the importance of addressing missingness and provides a roadmap to conduct model intercomparisons of other big datasets. We also share adaptable data analysis scripts, which allows others to readily conduct interpolation comparisons for many limnology applications and contexts.
Satellite-derived shoreline observations combined with dynamic shoreline models enable fine-scale predictions of coastal change across large spatiotemporal scales. Here, we present a satellite-data-assimilated, “littoral-cell”-based, ensemble Kalman-filter shoreline model to predict coastal change and uncertainty due to waves, sea-level rise (SLR), and other natural and anthropogenic processes. We apply the developed ensemble model to the entire California coastline (approximately 1,760 km), much of which is sparsely monitored with traditional survey methods (e.g., Lidar/GPS). Water-level-corrected, satellite-derived shoreline observations (obtained from the CoastSat toolbox) offer a nearly unbiased representation of in situ surveyed shorelines (e.g., mean sea-level elevation contours) at Ocean Beach, San Francisco. We demonstrate that model calibration with satellite observations during a 20-year hindcast period (1995–2015) provides nearly equivalent model forecast accuracy during a validation period (2015–2020) compared to model calibration with monthly in situ observations at Ocean Beach. When comparing model-predicted shoreline positions to satellite-derived observations, the model achieves an accuracy of <10 m RMSE for nearly half of the entire California coastline for the validation period. The calibrated/validated model is then applied for multi-decadal simulations of shoreline change due to projected wave and sea-level conditions, while holding the model parameters fixed. By 2100, the model estimates that 24%–75% of California's beaches may become completely eroded due to SLR scenarios of 1.0–3.0 m, respectively. The satellite-data-assimilated modeling system presented here is generally applicable to a variety of coastal settings around the world owing to the global coverage of satellite imagery.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JF006936","usgsCitation":"Vitousek, S., Vos, K., Splinter, K., Erikson, L.H., and Barnard, P.L., 2023, A model integrating satellite-derived shoreline observations for predicting fine-scale shoreline response to waves and sea-level rise across large coastal regions: JGR Earth Surface, v. 128, no. 7, e2022JF006936, 47 p., https://doi.org/10.1029/2022JF006936.","productDescription":"e2022JF006936, 47 p.","ipdsId":"IP-146968","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443298,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jf006936","text":"Publisher Index Page"},{"id":435306,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95T9188","text":"USGS data release","linkHelpText":"CoSMoS-COAST: The Coastal, One-line, Assimilated, Simulation Tool of the Coastal Storm Modeling System"},{"id":435305,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CJMB2H","text":"USGS data release","linkHelpText":"Projections of shoreline change for California due to 21st century sea-level rise"},{"id":419397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"128","issue":"7","noUsgsAuthors":false,"publicationDate":"2023-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Vitousek, Sean 0000-0002-3369-4673 svitousek@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-4673","contributorId":149065,"corporation":false,"usgs":true,"family":"Vitousek","given":"Sean","email":"svitousek@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":879265,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vos, Kilian","contributorId":317755,"corporation":false,"usgs":false,"family":"Vos","given":"Kilian","affiliations":[{"id":65517,"text":"University of New South Wales - Sydney","active":true,"usgs":false}],"preferred":false,"id":879266,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Splinter, Kristen D.","contributorId":317757,"corporation":false,"usgs":false,"family":"Splinter","given":"Kristen D.","affiliations":[{"id":65517,"text":"University of New South Wales - Sydney","active":true,"usgs":false}],"preferred":false,"id":879267,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":879268,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":879269,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255964,"text":"70255964 - 2023 - Where can managers effectively resist climate-driven ecological transformation in pinyon-juniper woodlands of the US Southwest?","interactions":[],"lastModifiedDate":"2024-07-11T14:40:19.188577","indexId":"70255964","displayToPublicDate":"2023-05-29T09:36:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Where can managers effectively resist climate-driven ecological transformation in pinyon-juniper woodlands of the US Southwest?","docAbstract":"Pinyon–juniper (PJ) woodlands are an important component of dryland ecosystems across the US West and are potentially susceptible to ecological transformation. However, predicting woodland futures is complicated by species-specific strategies for persisting and reproducing under drought conditions, uncertainty in future climate, and limitations to inferring demographic rates from forest inventory data. Here, we leverage new demographic models to quantify how climate change is expected to alter population demographics in five PJ tree species in the US West and place our results in the context of a climate adaptation framework to resist, accept, or direct ecological transformation. Two of five study species, Pinus edulis and Juniperus monosperma, are projected to experience population declines, driven by both rising mortality and decreasing recruitment rates. These declines are reasonably consistent across various climate futures, and the magnitude of uncertainty in population growth due to future climate is less than uncertainty due to how demographic rates will respond to changing climate. We assess the effectiveness of management to reduce tree density and mitigate competition, and use the results to classify southwest woodlands into areas where transformation is (a) unlikely and can be passively resisted, (b) likely but may be resisted by active management, and (c) likely unavoidable, requiring managers to accept or direct the trajectory. Population declines are projected to promote ecological transformation in the warmer and drier PJ communities of the southwest, encompassing 37.1%–81.1% of our sites, depending on future climate scenarios. Less than 20% of sites expected to transform away from PJ have potential to retain existing tree composition by density reduction. Our results inform where this adaptation strategy could successfully resist ecological transformation in coming decades and allow for a portfolio design approach across the geographic range of PJ woodlands.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.16756","usgsCitation":"Noel, A.R., Shriver, R., Crausbay, S.D., and Bradford, J., 2023, Where can managers effectively resist climate-driven ecological transformation in pinyon-juniper woodlands of the US Southwest?: Global Change Biology, v. 29, no. 15, p. 4327-4341, https://doi.org/10.1111/gcb.16756.","productDescription":"15 p.","startPage":"4327","endPage":"4341","ipdsId":"IP-148367","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":443302,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.16756","text":"Publisher Index Page"},{"id":430964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, 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\"}}]}","volume":"29","issue":"15","noUsgsAuthors":false,"publicationDate":"2023-05-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Noel, Adam Roy 0000-0002-0891-4005","orcid":"https://orcid.org/0000-0002-0891-4005","contributorId":294761,"corporation":false,"usgs":true,"family":"Noel","given":"Adam","email":"","middleInitial":"Roy","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":906155,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shriver, Robert K.","contributorId":297511,"corporation":false,"usgs":false,"family":"Shriver","given":"Robert K.","affiliations":[{"id":64419,"text":"Department of Natural Resources and Environmental Science, University of Nevada, Reno; Ecology, Evolution, and Conservation Biology Graduate Program, University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":906156,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crausbay, Shelley D.","contributorId":197220,"corporation":false,"usgs":false,"family":"Crausbay","given":"Shelley","email":"","middleInitial":"D.","affiliations":[{"id":54831,"text":"Conservation Science Partners, Inc","active":true,"usgs":false}],"preferred":false,"id":906157,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":906158,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70244024,"text":"70244024 - 2023 - Integrated water resources trend assessments: State of the science, challenges, and opportunities for advancement","interactions":[],"lastModifiedDate":"2023-12-20T17:45:16.768153","indexId":"70244024","displayToPublicDate":"2023-05-29T08:15:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10956,"text":"Journal of the American Water Resource Association (JAWRA)","active":true,"publicationSubtype":{"id":10}},"title":"Integrated water resources trend assessments: State of the science, challenges, and opportunities for advancement","docAbstract":"Water is vital to human life and healthy ecosystems. Here we outline the current state of national-scale water resources trend assessments, identify key gaps, and suggest advancements to better address critical issues related to changes in water resources that may threaten human development or the environment. Questions like, “Do we have less suitable drinking water now than we had 20 years ago?” or “Are flood events more common now than they were in the past?” prompted improvements in data, trend estimation methods, and modeling frameworks to track changes in, and better understand how land use and climate influence four water resources domains: surface and groundwater quantity and quality. However, continued advancement in trend assessments to better address issues related to changes in water availability is needed. Areas of need include more timely and efficient delivery of water resources trend results and improved capacity to estimate trends at unmonitored locations. Additional integration pieces include increased understanding of groundwater–surface water interactions, incorporation of both quantity and quality trends into water availability estimates, and the refinement of trend metrics to account for the competing needs of society and ecological integrity. Coupled with improved driver attribution studies, these components will better inform current and future water resources management.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.13137","usgsCitation":"Stackpoole, S.M., Oelsner, G.P., Stets, E.G., Hecht, J.S., Johnson, Z., Tesoriero, A.J., Walvoord, M.A., Chanat, J.G., Dunne, K., Goodling, P.J., Lindsey, B.D., Meador, M.R., and Spaulding, S., 2023, Integrated water resources trend assessments: State of the science, challenges, and opportunities for advancement: Journal of the American Water Resource Association (JAWRA), v. 59, no. 6, p. 1181-1197, https://doi.org/10.1111/1752-1688.13137.","productDescription":"17 p.","startPage":"1181","endPage":"1197","ipdsId":"IP-145969","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":443310,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.13137","text":"Publisher Index Page"},{"id":417572,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-05-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Stackpoole, Sarah M. 0000-0002-5876-4922 sstackpoole@usgs.gov","orcid":"https://orcid.org/0000-0002-5876-4922","contributorId":3784,"corporation":false,"usgs":true,"family":"Stackpoole","given":"Sarah","email":"sstackpoole@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":874180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oelsner, Gretchen P. 0000-0001-9329-7357 goelsner@usgs.gov","orcid":"https://orcid.org/0000-0001-9329-7357","contributorId":4440,"corporation":false,"usgs":true,"family":"Oelsner","given":"Gretchen","email":"goelsner@usgs.gov","middleInitial":"P.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stets, Edward G. 0000-0001-5375-0196 estets@usgs.gov","orcid":"https://orcid.org/0000-0001-5375-0196","contributorId":194490,"corporation":false,"usgs":true,"family":"Stets","given":"Edward","email":"estets@usgs.gov","middleInitial":"G.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":874182,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hecht, Jory Seth 0000-0002-9485-3332","orcid":"https://orcid.org/0000-0002-9485-3332","contributorId":257771,"corporation":false,"usgs":true,"family":"Hecht","given":"Jory","email":"","middleInitial":"Seth","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":874183,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874184,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tesoriero, Anthony J. 0000-0003-4674-7364 tesorier@usgs.gov","orcid":"https://orcid.org/0000-0003-4674-7364","contributorId":2693,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony","email":"tesorier@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874185,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":874186,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chanat, Jeffrey G. 0000-0002-3629-7307 jchanat@usgs.gov","orcid":"https://orcid.org/0000-0002-3629-7307","contributorId":5062,"corporation":false,"usgs":true,"family":"Chanat","given":"Jeffrey","email":"jchanat@usgs.gov","middleInitial":"G.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874187,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dunne, Krista A. 0000-0002-1220-6140","orcid":"https://orcid.org/0000-0002-1220-6140","contributorId":305961,"corporation":false,"usgs":false,"family":"Dunne","given":"Krista A.","affiliations":[{"id":12545,"text":"USGS retired","active":true,"usgs":false}],"preferred":false,"id":874188,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Goodling, Phillip J. 0000-0001-5715-8579","orcid":"https://orcid.org/0000-0001-5715-8579","contributorId":239738,"corporation":false,"usgs":true,"family":"Goodling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874189,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lindsey, Bruce D. 0000-0002-7180-4319 blindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-7180-4319","contributorId":175346,"corporation":false,"usgs":true,"family":"Lindsey","given":"Bruce","email":"blindsey@usgs.gov","middleInitial":"D.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874190,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Meador, Michael R. 0000-0001-5956-3340 mrmeador@usgs.gov","orcid":"https://orcid.org/0000-0001-5956-3340","contributorId":219878,"corporation":false,"usgs":true,"family":"Meador","given":"Michael","email":"mrmeador@usgs.gov","middleInitial":"R.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":874191,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Spaulding, Sarah A. 0000-0002-9787-7743","orcid":"https://orcid.org/0000-0002-9787-7743","contributorId":223186,"corporation":false,"usgs":true,"family":"Spaulding","given":"Sarah","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":874192,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70245127,"text":"70245127 - 2023 - Linking ecosystem processes to consumer growth rates: Gross primary productivity as a driver of freshwater fish somatic growth in a resource-limited river","interactions":[],"lastModifiedDate":"2023-09-06T16:13:42.515711","indexId":"70245127","displayToPublicDate":"2023-05-29T06:52:31","publicationYear":"2023","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":"Linking ecosystem processes to consumer growth rates: Gross primary productivity as a driver of freshwater fish somatic growth in a resource-limited river","docAbstract":"Habitat conversion to agriculture and overexploitation of wildlife are the two largest drivers of biodiversity loss globally. Biodiversity loss is especially prevalent in areas undergoing rapid economic development at the expense of natural land cover as is the case across much of South America. Despite expected declines in wildlife populations associated with ongoing large-scale land conversions, for many species we lack sufficient data on key threats and drivers of abundance in order to inform appropriate management and conservation. Collecting data to estimate critical state variables such as abundance can be expensive, logistically challenging, and even implausible on spatial scales relevant to species management, especially for carnivores which are elusive and difficult to monitor. Here, we use detection-non-detection data collected using a structured camera trap survey repeated over two years in the Ecuadorian Andes to produce insights into the habitat associations and potential threats faced by the tayra, a medium-sized carnivore that despite being perceived to be relatively common in South America, remains largely understudied. We use hierarchical modelling to estimate an index of abundance for the tayra while accounting for imperfect detection. We demonstrate the tayra to be a lowland habitat generalist, with conversion of land to agriculture potentially benefitting this species in the short term, with increasing proportion of core agricultural land being associated with higher indices of abundance. However, we highlight that this state could potentially serve as an ecological trap in the long term. We provide evidence for negative impacts of human population density on tayra abundance. We hypothesize this relationship could be underpinned by conflict and subsequent persecution by humans, which is likely to be exacerbated in agricultural landscapes. These findings suggest that like many carnivores, the tayra may be able to adapt and even thrive in human modified landscapes given societal acceptance, thus, conservation strategies for the tayra that focus on fostering co-existence between humans and this medium-sized carnivore may contribute to its future.
The Asian clam, Corbicula fluminea, is an invasive freshwater bivalve that has established populations across the globe and is known to have deleterious effects on natural and human systems. Yet, despite being present in the Columbia River (CR) for nearly a century, little is known about this invader's basic biology and ecology in this large river system. Thus, we undertook a field study to assess its i) broadscale distribution and abundance, and ii) associations with habitat characteristics in the lower CR. During 2019-20, C. fluminea were collected from 27 shore-based stations spanning 481 river kilometers of the lower CR, along with several habitat characteristics (bank slope, temperature, dissolved oxygen, pH, salinity, conductivity, chlorophyll-a concentration, and sediment composition and % organic matter). C. fluminea abundance ranged from 0-430 ind. m-2. Most sites with abundances >100 ind. m-2 were located downstream of Bonneville Dam, while most sites with abundances < 100 ind. m-2 were located upstream. Generalized linear models predicting the abundance of C. fluminea indicated significantly positive correlations with water temperature and % sand, and negative correlations with bank slope and sedimentary % organic matter. We also reviewed the global literature on abundance and habitat associations of C. fluminea and compared this with our own results. Our investigation represents the greatest spatial extent at which C. fluminea has been studied in the CR and our results provide a better understanding of the basic biology and ecology of this global invader, as well as provide natural resource managers with information on habitat conditions favorable for this invasive bivalve within temperate river ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1002/iroh.202202134","usgsCitation":"Robb-Chavez, S.B., Bollens, S.M., Rollwagen-Bollens, G., and Counihan, T., 2023, Broadscale distribution, abundance and habitat associations of the invasive Asian clam (Corbicula fluminea) in the lower Columbia River, USA: International Review of Hydrobiology, v. 107, no. 5-6, p. 179-195, https://doi.org/10.1002/iroh.202202134.","productDescription":"17 p.","startPage":"179","endPage":"195","ipdsId":"IP-144215","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":443323,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1002/iroh.202202134","text":"Publisher Index Page"},{"id":417644,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"lower Columbia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.68681416397084,\n 46.425452015025826\n ],\n [\n -123.89557364633748,\n 46.425452015025826\n ],\n [\n -123.89557364633748,\n 45.394726212253744\n ],\n [\n -118.68681416397084,\n 45.394726212253744\n ],\n [\n -118.68681416397084,\n 46.425452015025826\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"107","issue":"5-6","noUsgsAuthors":false,"publicationDate":"2023-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Robb-Chavez, Salvador B.","contributorId":261391,"corporation":false,"usgs":false,"family":"Robb-Chavez","given":"Salvador","email":"","middleInitial":"B.","affiliations":[{"id":52831,"text":"Washington State University - Vancouver, School of the Environment","active":true,"usgs":false}],"preferred":false,"id":874362,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bollens, Stephen M. 0000-0001-9214-9037","orcid":"https://orcid.org/0000-0001-9214-9037","contributorId":148958,"corporation":false,"usgs":false,"family":"Bollens","given":"Stephen","email":"","middleInitial":"M.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":874363,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rollwagen-Bollens, Gretchen","contributorId":190162,"corporation":false,"usgs":false,"family":"Rollwagen-Bollens","given":"Gretchen","email":"","affiliations":[],"preferred":false,"id":874364,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Counihan, Timothy D. 0000-0003-4967-6514","orcid":"https://orcid.org/0000-0003-4967-6514","contributorId":207532,"corporation":false,"usgs":true,"family":"Counihan","given":"Timothy D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":874365,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70246601,"text":"70246601 - 2023 - Using taxa-based approaches to delineate stream macroinvertebrate assemblage responses to stressor gradients in modified alluvial agroecosystems","interactions":[],"lastModifiedDate":"2023-07-11T11:45:17.44451","indexId":"70246601","displayToPublicDate":"2023-05-27T06:42:46","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Using taxa-based approaches to delineate stream macroinvertebrate assemblage responses to stressor gradients in modified alluvial agroecosystems","docAbstract":"Alluvial plain landscapes are some of the most agriculturally productive lands in the world but often have modified stream ecosystems due to cultivation history. This context requires consideration when establishing water quality management goals. We analyzed state water quality databases to demonstrate that Mississippi Alluvial Plain (MAP) ecoregion streams have elevated specific conductivity (SC) and nutrients and lower macroinvertebrate local and regional taxa pools compared to streams in other ecoregions, potentially reducing the efficacy of traditional biomonitoring approaches within the region. To overcome these challenges, we used threshold indicator taxa analysis (TITAN) to compare macroinvertebrate assemblage responses to water quality gradients among ecoregions in Mississippi. We identified individual taxa and assemblage-level responses to increasing water quality degradation in MAP streams. Observed responses occurred at higher concentrations for SC, total organic carbon (TOC) and total phosphorus (TP), but not total nitrogen (TN) relative to other ecoregions. These responses appeared to be driven by a large proportion of indicator taxa considered tolerant or unresponsive in other ecoregions, responding negatively to increasing water quality stressors in MAP streams. Our observed assemblage-level stressor responses to WQ gradients in MAP streams demonstrate shifting tolerance in highly altered ecosystems may require adjustments to recovery expectations but also provide useful measures for monitoring improvements in regional water quality. For example, our observed macroinvertebrate assemblage response to increasing TP identified a management goal similar to guidance based on distributional analysis of water quality data within the MAP ecoregion (0.11 vs 0.128 mg L−1) and thus provide some biological basis for previously identified nutrient goals for the region. Our approach can guide and monitor success of nutrient reduction efforts in MAP watersheds and other alluvial plain agroecosystems where reference conditions do not exist, and local and regional taxa pools are less diverse and may not support full recovery of ecological assemblages. While our results are promising, they should also be compared with more sensitive and less habitat-limited biological assemblages (e.g., algae or bacteria) to better understand complex ecological responses to best management practices designed to increase sustainability of high production agricultural regions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2023.110377","usgsCitation":"Taylor, J.M., DeVilbiss, S.E., and Hicks, M.B., 2023, Using taxa-based approaches to delineate stream macroinvertebrate assemblage responses to stressor gradients in modified alluvial agroecosystems: Ecological Indicators, v. 153, 110377, 13 p., https://doi.org/10.1016/j.ecolind.2023.110377.","productDescription":"110377, 13 p.","ipdsId":"IP-140788","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":443328,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2023.110377","text":"Publisher Index Page"},{"id":418852,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"153","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Jason M.","contributorId":212809,"corporation":false,"usgs":false,"family":"Taylor","given":"Jason","email":"","middleInitial":"M.","affiliations":[{"id":38685,"text":"USDA, ARS Sedimentation Lab","active":true,"usgs":false}],"preferred":false,"id":877317,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeVilbiss, Stephen E.","contributorId":316291,"corporation":false,"usgs":false,"family":"DeVilbiss","given":"Stephen","email":"","middleInitial":"E.","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":877318,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hicks, Matthew B. 0000-0001-5516-0296 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correction using in situ observations and climatology isohyets for the MENA region","docAbstract":"The availability of reliable gridded precipitation datasets is limited around the world, especially in arid regions. In this study, we utilized observations from satellite-based precipitation data and in situ rain gauge observations to determine a suitable precipitation dataset in the Middle East & North Africa (MENA) region. First, we evaluated seven different precipitation products using rain gauge observations. The validation was conducted at the daily, monthly, and annual time scales. Results indicated a weaker correlation between in situ rain gauge observation and satellite precipitation data at the daily time step (r: 0.02 to 0.44), mainly due to the lack of range in precipitation distribution. However, the agreement between precipitation estimates and in situ gauge observations improved at monthly (r: 0.02 to 0.66) and annual time scales (r: −0.22 to 0.57), indicating greater reliability of satellite-based precipitation at monthly and annual time scales. Based on the results and dataset availability, the Multi-Source Weighted-Ensemble Precipitation (MSWEP) was deemed suitable to create a bias-corrected new precipitation dataset for the MENA region. This study highlights the benefits of an adjusted regional precipitation product for hydrologic applications in the MENA region, such as streamflow or runoff estimation, to improve the reliability of the model outputs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2023.105010","usgsCitation":"Kagone, S., Velpuri, N., Khand, K., Senay, G.B., Van der Valk, M.R., Goode, D.J., Hantash, S.A., Al-Momani, T.M., Momejian, N., and Eggleston, J., 2023, Satellite precipitation bias estimation and correction using in situ observations and climatology isohyets for the MENA region: Journal of Arid Environments, v. 215, 105010, 14 p., https://doi.org/10.1016/j.jaridenv.2023.105010.","productDescription":"105010, 14 p.","ipdsId":"IP-126383","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":443334,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jaridenv.2023.105010","text":"Publisher Index Page"},{"id":417535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Jordan, Lebanon","otherGeospatial":"West Bank","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 39.30649160058408,\n 35.36856869578705\n ],\n [\n 32.084784344491425,\n 35.36856869578705\n ],\n [\n 32.084784344491425,\n 28.71772323486526\n ],\n [\n 39.30649160058408,\n 28.71772323486526\n ],\n [\n 39.30649160058408,\n 35.36856869578705\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"215","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":216913,"corporation":false,"usgs":true,"family":"Kagone","given":"Stefanie","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":873972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Velpuri, Naga Manohar 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Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":873975,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Van der Valk, Michael R.","contributorId":305834,"corporation":false,"usgs":false,"family":"Van der Valk","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":66311,"text":"HYDROLOGY.NL","active":true,"usgs":false}],"preferred":false,"id":873976,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goode, Daniel J. 0000-0002-8527-2456","orcid":"https://orcid.org/0000-0002-8527-2456","contributorId":216750,"corporation":false,"usgs":true,"family":"Goode","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873977,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hantash, Salam Abu","contributorId":305835,"corporation":false,"usgs":false,"family":"Hantash","given":"Salam","email":"","middleInitial":"Abu","affiliations":[{"id":66313,"text":"Palestinian Water Authority, West Bank","active":true,"usgs":false}],"preferred":false,"id":873978,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Al-Momani, Thair M.","contributorId":305836,"corporation":false,"usgs":false,"family":"Al-Momani","given":"Thair","email":"","middleInitial":"M.","affiliations":[{"id":66314,"text":"Ministry of Water and Irrigation, Amman, Jordan","active":true,"usgs":false}],"preferred":false,"id":873979,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Momejian, Nanor","contributorId":305837,"corporation":false,"usgs":false,"family":"Momejian","given":"Nanor","email":"","affiliations":[{"id":66315,"text":"Queens University, Kingston, Canada","active":true,"usgs":false}],"preferred":false,"id":873980,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Eggleston, Jack R. 0000-0001-6633-3041","orcid":"https://orcid.org/0000-0001-6633-3041","contributorId":204628,"corporation":false,"usgs":true,"family":"Eggleston","given":"Jack R.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873981,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70260121,"text":"70260121 - 2023 - Operationalizing crop model data assimilation for improved on-farm situational awareness","interactions":[],"lastModifiedDate":"2024-10-29T12:16:04.993327","indexId":"70260121","displayToPublicDate":"2023-05-26T07:12:10","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"Operationalizing crop model data assimilation for improved on-farm situational awareness","docAbstract":"Modelling and analysis of helicopter electromagnetic data result in resistivity and susceptibility models and derivatives of magnetic data that characterise shallow parts of the Stillwater Complex, critical for aiding exploration and expansion of globally scarce critical and battery mineral resources that include platinum group elements, nickel, copper and chromium. The magnetic susceptibly models derived from the electromagnetic data and the tilt derivative of the magnetic data image layering, mafic dikes, banded iron formation, and serpentinised peridotite. Known areas with contact-type mineralisation are generally characterised by low resistivities and susceptibilities where the volume of mineralised rock is large and/or the depth is shallow. We use iso-cluster and edge detection analysis of both resistivities and susceptibilities to identify potential mineralisation in poorly characterised regions as well as faults. Low resistivity layers beneath large landslides reflect water saturated porous slip surfaces which can interfere with drilling. This uncommon approach of tightly linking the resistivity and susceptibility models and magnetic anomaly data to rock property, surficial geologic, drill hole and soil geochemistry data to image the geology in the upper ∼100 m, aids identification of prospective mineralised regions as well landslides and faults that can impact mineral exploration and local hazards.
Mineral resource assessments performed by the U.S. Geological Survey provide a synthesis of available information about the location of known and suspected mineral deposits. This study focuses on skarn-hosted tungsten resources in the northern Rocky Mountain region of east-central Idaho and western Montana which have seen moderate tungsten trioxide production in the past from a variety of mineralization styles including skarn, vein and replacement, and wolframite-quartz veins. The area’s geology is dominated by large Cretaceous and Tertiary plutons that are emplaced into a belt of Mesoproterozoic to Permian sedimentary rock and affected by tectonism related to the Sevier and later Laramide orogenies. Known tungsten skarn mineral sites are associated with contacts between Cretaceous plutons and calcareous and argillaceous sedimentary or metasedimentary rocks, including two skarn deposits in Montana (Calvert and Browns Lake) that are consistent with an updated grade and tonnage model.
This study (1) delineates permissive tracts where undiscovered tungsten skarn deposits may occur within 1 kilometer of the surface; (2) presents a tungsten mineral site dataset from a variety of public sources; (3) evaluates currently available geochemical, geophysical, and radiometric age data in support of tract delineation; (4) provides probabilistic estimates of the amount of tungsten and tungsten-mineralized rock that could be contained in undiscovered deposits within one major tract; (5) estimates the value of total undiscovered deposits using economic filter analysis; and (6) provides a synthesis of metallogenic controls on regional tungsten skarn and granitoid-related mineral deposits.
Two permissive tracts were delineated: the Great Falls tectonic zone (GFTZ)-Cretaceous tract, for which a quantitative assessment was performed, and the Bitterroot tract, which was assessed in a qualitative manner. The quantitative three-part assessment, conducted in August 2019, indicates that undiscovered tungsten resources might exist in skarn-type deposits within the study area. Using a negative binomial function, a mean of 4 undiscovered deposits was calculated from panel estimates. Simulation results that combine an updated grade and tonnage model with estimates of undiscovered deposits include the amounts of ore and contained tungsten trioxide at different levels of uncertainty. A mean of 250,000 metric tons and median of 200,000 metric tons contained tungsten trioxide was calculated for the undiscovered deposits within the GFTZ-Cretaceous tract. The value of undiscovered deposits was estimated using a new economic filter that considers factors such as mine type, deposit depth, deposit geometry, metallurgical recovery rate, cutoff grade, and tract area.
A review of the regional Archean to Paleogene geology suggests that ore metal (copper, molybdenum, and tungsten) variations in intrusion-related deposits of Montana and Idaho may be controlled by a number of factors including the age and composition of underlying basement terranes, depth of emplacement, pluton chemistry and degree of fractionation, redox conditions, and aqueous fluid-melt partition coefficients.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235012","programNote":"Mineral Resources Program","usgsCitation":"Andersen, A.K., Goldman, M.A., Bennett, M.M., Dicken, C.L., Brown, P.J., and Parks, H.L., 2023, Tungsten resources of the northern Rocky Mountains, Montana and Idaho— A synthesis and quantitative assessment of skarn-hosted resources: U.S. Geological Survey Scientific Investigations Report 2023-5012, 87 p., https://doi.org/10.3133/sir20235012.","productDescription":"Report: viii, 87 p.; Data Release","numberOfPages":"87","onlineOnly":"Y","ipdsId":"IP-122167","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":500706,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114742.htm","linkFileType":{"id":5,"text":"html"}},{"id":417441,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9094RVV","text":"Spatial data associated with tungsten skarn resource assessment of the northern Rocky Mountains, Montana and Idaho","description":"Goldman, M.A., Dicken, C.L., Brown, P.J., Andersen, A.K., Bennett, M.M., and Parks, H.L., 2022, Spatial data associated with tungsten skarn resource assessment of the northern Rocky Mountains, Montana and Idaho: U.S. Geological Survey data release, https://doi.org/10.5066/P9094RVV."},{"id":417443,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5012/sir20235012.pdf","text":"Report","size":"55 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":417442,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5012/covrthb.jpg"}],"country":"United States","state":"Idaho, Montana","otherGeospatial":"Northern Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.93058150555078,\n 48.9704513230287\n ],\n [\n -116.93058150555078,\n 43.38357304109826\n ],\n [\n -111.08762543219147,\n 43.38357304109826\n ],\n [\n -111.08762543219147,\n 48.9704513230287\n ],\n [\n -116.93058150555078,\n 48.9704513230287\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035