The Yellowstone Plateau Volcanic Field (YPVF) contains >10,000 thermal features including hot springs, pools, geysers, mud pots, and fumaroles with diverse chemical compositions. Arsenic (As) concentrations in YPVF thermal waters typically range from 0.005 to 4 mg/L, but an As concentration of 17 mg/L has been reported. Arsenic data from thermal springs, outflow drainages, rivers, and from volcanic rocks and silica sinter were used to identify the sources, characterize geochemical and microbial processes affecting As, and quantify As fluvial transport. Arsenic in YPVF thermal waters is mainly derived from high temperature leaching of rhyolites. Arsenic concentrations in thermal waters primarily depend on water type, which is controlled by boiling, evaporation, mixing, and mineral precipitation and dissolution. Springs with low As concentrations include acid-SO4 (0.1 ± 0.1 mg/L), NH4-SO4 rich (0.003 ± 0.007 mg/L), and dilute thermal waters (0.1 ± 0.1 mg/L); travertine-forming waters have moderate As concentrations (0.4 ± 0.2 mg/L); and neutral- Cl waters (1.2 ± 0.8 mg/L) common in the western portion of the Yellowstone Caldera and Cl-rich waters (1.9 ± 1.2 mg/L) primarily from Basins near the Caldera boundary have elevated As concentrations. Reduced As species (arsenite and thiolated-As species) are most prevalent near the orifice of hot springs, and then As rapidly oxidizes to arsenate along drainages. Previously published cultivation-based studies and metagenomic data from microbial communities inhabiting a variety of hot springs indicate a widespread distribution of arsenite oxidation and arsenate reduction capabilities among the hot springs. Widespread use and transformation of As by thermophilic microorganisms promotes more soluble and toxic forms. Most of the water discharged from thermal springs eventually ends up in a nearby river where As remains soluble and exhibits little attenuation during downstream transport. Since 2010, 183 ± 10 metric tons/year of As were transported from Yellowstone National Park (YNP) via rivers. The discharge from YPVF thermal features impairs river water quality whereby As concentrations exceed 10 μg/L for many rivers reaches within and downstream from YNP.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2022.107709","usgsCitation":"McCleskey, R., Nordstrom, D.K., Hurwitz, S., Colman, D.R., Roth, D.A., Johnson, M.O., and Boyd, E., 2022, The source, fate, and transport of arsenic in the Yellowstone hydrothermal system - An overview: Journal of Volcanology and Geothermal Research, v. 432, 107709, 20 p., https://doi.org/10.1016/j.jvolgeores.2022.107709.","productDescription":"107709, 20 p.","ipdsId":"IP-143378","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":467136,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2022.107709","text":"Publisher Index Page"},{"id":410276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Yellowstone Plateau Volcanic Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.94,\n 45.84\n ],\n [\n -110.94,\n 45.83\n ],\n [\n -110.93,\n 45.83\n ],\n [\n -110.93,\n 45.84\n ],\n [\n -110.94,\n 45.84\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.00219450142964,\n 44.1359427252448\n ],\n [\n -109.9963211835457,\n 44.32742371136524\n ],\n [\n -110.01687779613967,\n 44.32742371136524\n ],\n [\n -110.06973765709618,\n 44.352627456744756\n ],\n [\n -110.06386433921189,\n 44.40719840334816\n ],\n [\n -110.052117703444,\n 44.42397923099992\n ],\n [\n -110.0286244319079,\n 44.43236783899505\n ],\n [\n -110.00513116037179,\n 44.46800599192275\n ],\n [\n -110.00513116037179,\n 44.50781113480417\n ],\n [\n -110.08735761074799,\n 44.491054385481874\n ],\n [\n -110.14021747170415,\n 44.49314924251027\n ],\n [\n -110.15490076641417,\n 44.52037553641682\n ],\n [\n -110.16077408429847,\n 44.54968193879381\n ],\n [\n -110.12259751805233,\n 44.57060605348323\n ],\n [\n -110.07267431603796,\n 44.58733992725041\n ],\n [\n -110.03743440873397,\n 44.60825049691036\n ],\n [\n -110.0198144550818,\n 44.624973534551\n ],\n [\n -109.90234809740163,\n 44.64169175607395\n ],\n [\n -109.83186828279328,\n 44.70225493592861\n ],\n [\n -109.83480494173509,\n 44.721037537133384\n ],\n [\n -109.87004484903945,\n 44.74607152330452\n ],\n [\n -109.94933464047348,\n 44.80860907769713\n ],\n [\n -109.92584136893738,\n 44.83777009077582\n ],\n [\n -109.92290470999559,\n 44.91268782141975\n ],\n [\n -109.95520795835778,\n 44.956344789149\n ],\n [\n -109.96695459412565,\n 44.97504475832312\n ],\n [\n -109.99925784248785,\n 44.972967285003364\n ],\n [\n -110.00513116037179,\n 45.04563407251294\n ],\n [\n -110.01687779613967,\n 45.05393297808422\n ],\n [\n -110.04624438556007,\n 45.03940910312352\n ],\n [\n -110.08148429286405,\n 45.02903264897614\n ],\n [\n -110.7011193296279,\n 45.02903264897614\n ],\n [\n -110.77453580317838,\n 45.064304916740326\n ],\n [\n -110.82152234625059,\n 45.05393297808422\n ],\n [\n -110.8391422999024,\n 45.04148416817836\n ],\n [\n -110.91255877345289,\n 45.02903264897614\n ],\n [\n -110.936052044989,\n 45.049783675815206\n ],\n [\n -110.93311538604685,\n 45.068453165395425\n ],\n [\n -111.01534183642306,\n 45.095409443997795\n ],\n [\n -111.05645506161133,\n 45.066379078696684\n ],\n [\n -111.10931492256749,\n 45.10577385685349\n ],\n [\n -111.14455482987147,\n 45.08711655907109\n ],\n [\n -111.15336480669755,\n 45.04563407251294\n ],\n [\n -111.17979473717546,\n 45.016578420476634\n ],\n [\n -111.20328800871158,\n 45.00204506297516\n ],\n [\n -111.23852791601591,\n 45.00204506297516\n ],\n [\n -111.25614786966773,\n 44.968812112589006\n ],\n [\n -111.25908452860988,\n 44.94387475641295\n ],\n [\n -111.21209798553765,\n 44.910608090355964\n ],\n [\n -111.1827313961176,\n 44.91268782141975\n ],\n [\n -111.1239982172775,\n 44.90644840245457\n ],\n [\n -111.09756828679926,\n 44.88356473806243\n ],\n [\n -111.09756828679926,\n 44.12961955801748\n ],\n [\n -110.00513116037179,\n 44.13383507805938\n ],\n [\n -110.00219450142964,\n 44.1359427252448\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"432","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. Blaine","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":858702,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nordstrom, D. Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","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":false,"id":858703,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":858704,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Colman, Daniel R. 0000-0002-3253-6833","orcid":"https://orcid.org/0000-0002-3253-6833","contributorId":299802,"corporation":false,"usgs":false,"family":"Colman","given":"Daniel","email":"","middleInitial":"R.","affiliations":[{"id":64955,"text":"Department of Microbiology and Cell Biology, Montana State University","active":true,"usgs":false}],"preferred":false,"id":858705,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roth, David A. 0000-0002-7515-3533 daroth@usgs.gov","orcid":"https://orcid.org/0000-0002-7515-3533","contributorId":2340,"corporation":false,"usgs":true,"family":"Roth","given":"David","email":"daroth@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":858706,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Madeline Oxner 0000-0001-7661-9748","orcid":"https://orcid.org/0000-0001-7661-9748","contributorId":299803,"corporation":false,"usgs":true,"family":"Johnson","given":"Madeline","email":"","middleInitial":"Oxner","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":858707,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boyd, Eric S. 0000-0003-4436-5856","orcid":"https://orcid.org/0000-0003-4436-5856","contributorId":299804,"corporation":false,"usgs":false,"family":"Boyd","given":"Eric S.","affiliations":[{"id":64955,"text":"Department of Microbiology and Cell Biology, Montana State University","active":true,"usgs":false}],"preferred":false,"id":858708,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70239344,"text":"70239344 - 2022 - Water and endangered fish in the Klamath River Basin: Do Upper Klamath Lake surface elevation and water quality affect adult Lost River and Shortnose Sucker survival?","interactions":[],"lastModifiedDate":"2023-01-10T13:02:51.552487","indexId":"70239344","displayToPublicDate":"2023-01-06T07:00:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Water and endangered fish in the Klamath River Basin: Do Upper Klamath Lake surface elevation and water quality affect adult Lost River and Shortnose Sucker survival?","docAbstract":"In the western United States, water allocation decisions often incorporate the needs of endangered fish. In the Klamath River basin, an understanding of temporal variation in annual survival rates of Shortnose Suckers Chasmistes brevirostris and Lost River Suckers Deltistes luxatus and their relation to environmental drivers is critical to water management and sucker recovery. Extinction risk is high for these fish because most individuals in the populations are approaching their maximum life span and recruitment of new fish into the adult populations has never exceeded mortality losses in the past 22 years. We used a time series of mark–recapture data from the years 1999–2021 to analyze the relationship between lake level, water quality covariates, and survival of adult Shortnose Suckers and two spawning populations of Lost River Suckers in Upper Klamath Lake, Oregon. We compared competing model hypotheses in a maximum likelihood framework using Akaike's information criterion and then ran the top environmental covariates in a Bayesian framework to estimate how much of the variation in survival was explained by these covariates as compared to random variation. The complementary analyses found almost unequivocal support for our base model without environmental covariates. Estimated adult sucker survival was high across the time series and consistent with sucker life history (mean annual survival = 0.82–0.91). This suggests that adult suckers were generally robust to interannual variation in lake levels as well as consistently poor water quality within the years of our data set. Recovery time is limited, as a declining survival trend for adult suckers in recent years may be due to the onset of senescence. The successful recovery of suckers in Upper Klamath Lake may rely on shifting research from the causes of adult mortality and its relationship with lake surface elevation to the causes of poor recruitment into adult populations.
This chapter provides an overview of machine learning models and their applications to the science of inland waters. Such models serve a wide range of purposes for science and management: predicting water quality, quantity, or ecological dynamics across space, time, or hypothetical scenarios; vetting and distilling raw data for further modeling or analysis; generating and exploring hypotheses; estimating physically or biologically meaningful parameters for use in further modeling; and revealing patterns in complex, multidimensional data or model outputs. An important research frontier is the injection of limnological knowledge into machine-learning models, which has shown great promise for increasing such models’ accuracy, trustworthiness, and interpretability. Here we describe a few of the most powerful machine learning tools, describe best practices for employing these tools and injecting knowledge guidance, and give examples of their applications to advance understanding of inland waters.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of inland waters","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-819166-8.00121-3","usgsCitation":"Appling, A.P., Oliver, S.K., Read, J., Sadler, J.M., and Zwart, J.A., 2022, Machine learning for understanding inland water quantity, quality, and ecology, chap. of Encyclopedia of inland waters, v. 4, p. 585-606, https://doi.org/10.1016/B978-0-12-819166-8.00121-3.","productDescription":"22 p.","startPage":"585","endPage":"606","ipdsId":"IP-122850","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":445607,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.31223/x5964s","text":"External Repository"},{"id":411277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","edition":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Mehner, Thomas","contributorId":272917,"corporation":false,"usgs":false,"family":"Mehner","given":"Thomas","email":"","affiliations":[{"id":38332,"text":"Leibniz-Institute of Freshwater Ecology and Inland Fisheries","active":true,"usgs":false}],"preferred":false,"id":860710,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Tockner, Klement","contributorId":224174,"corporation":false,"usgs":false,"family":"Tockner","given":"Klement","email":"","affiliations":[{"id":40838,"text":"FWF Austrian Science Fund","active":true,"usgs":false}],"preferred":false,"id":860711,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":860690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":860691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":860692,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":860693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":860694,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239174,"text":"70239174 - 2022 - Landslides triggered by the 2002 M 7.9 Denali Fault earthquake, Alaska, USA","interactions":[],"lastModifiedDate":"2023-01-02T19:23:03.108814","indexId":"70239174","displayToPublicDate":"2023-01-02T13:16:33","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Landslides triggered by the 2002 M 7.9 Denali Fault earthquake, Alaska, USA","docAbstract":"The 2002 M 7.9 Denali earthquake in Alaska, USA, was the largest inland earthquake in North America in nearly 150 years. The earthquake involved oblique thrusting but mostly strike-slip motion, and faults ruptured the ground surface over 330 km. Fault rupture occurred in a rugged, mountainous, subarctic environment with extensive permafrost and variable glaciation, geology, and groundwater presence, and many triggered landslides mobilized into avalanches that traversed varied physiographic settings, some moving farther than 11 km. However, only several thousand landslides were triggered, and these occurred in a narrow zone along the fault rupture. These characteristics of the event provide ample opportunities to improve understanding of controls on coseismic landsliding and avalanching mechanisms. The paucity and limited extent of landslides likely resulted from high directivity of seismic energy and relatively low levels of high-amplitude, high-frequency ground motion; glacial damping of seismic energy was likely not a factor. Landslides preferentially occurred on hillslopes steeper and higher than average. Meteorological conditions were near historical averages at the time of the event, although the region has experienced gradual warming historically that appears to have resulted in increased landslide occurrence in other parts of Alaska during recent years. Inherent susceptibility of geological formations to landsliding was not apparent with the available data, although discontinuities created dip-slope conditions for the five largest slides. Historical thinning of glaciers and consequent slope debuttressing may have been a factor in aiding occurrence of some of the earthquake-induced landslides, particularly some of the largest. Mobility of the largest avalanches was above average compared to global data, and mobility of all sizes of avalanches was apparently aided by movement over glacial ice. Avalanche deposits displayed characteristics indicative of turbulent flow on steeper slopes and laminar plug flow in flatter areas, where sliding also likely occurred.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Coseismic landslides: Phenomena, long-term effects and mitigation","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-981-19-6597-5_4","usgsCitation":"Schulz, W.H., 2022, Landslides triggered by the 2002 M 7.9 Denali Fault earthquake, Alaska, USA, chap. of Coseismic landslides: Phenomena, long-term effects and mitigation, p. 83-114, https://doi.org/10.1007/978-981-19-6597-5_4.","productDescription":"32 p.","startPage":"83","endPage":"114","ipdsId":"IP-118114","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":411276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Denali Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159.29368601014386,\n 59.11878225343841\n ],\n [\n -156.0264002993337,\n 58.779813559186465\n ],\n [\n -152.25996460189066,\n 61.06590743096231\n ],\n [\n -149.1643813809494,\n 62.16054388876884\n ],\n [\n -140.9851481053823,\n 61.673553940907254\n ],\n [\n -140.98590984229733,\n 63.075995036970994\n ],\n [\n -148.91306197242972,\n 64.36320333772588\n ],\n [\n -155.3934657440926,\n 62.81575979286217\n ],\n [\n -159.29368601014386,\n 59.11878225343841\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2022-11-04","publicationStatus":"PW","contributors":{"editors":[{"text":"Towhata, Ikuo","contributorId":300539,"corporation":false,"usgs":false,"family":"Towhata","given":"Ikuo","email":"","affiliations":[],"preferred":false,"id":860706,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Wang, Gonghui","contributorId":99452,"corporation":false,"usgs":true,"family":"Wang","given":"Gonghui","affiliations":[],"preferred":false,"id":860707,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Xu, Qiang","contributorId":214818,"corporation":false,"usgs":false,"family":"Xu","given":"Qiang","email":"","affiliations":[{"id":39123,"text":"Key Laboratory of Continental Collision and Plateau Uplift, Institute of Tibetan Plateau Research and Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":860708,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Massey, Chris","contributorId":206127,"corporation":false,"usgs":false,"family":"Massey","given":"Chris","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":860709,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Schulz, William H. 0000-0001-9980-3580 wschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-9980-3580","contributorId":942,"corporation":false,"usgs":true,"family":"Schulz","given":"William","email":"wschulz@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":860684,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70239182,"text":"70239182 - 2022 - Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature","interactions":[],"lastModifiedDate":"2023-01-02T19:15:38.169222","indexId":"70239182","displayToPublicDate":"2023-01-02T13:08:22","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature","docAbstract":"This paper proposes a new data-driven method for predicting water temperature in stream networks with reservoirs. The water flows released from reservoirs greatly affect the water temperature of downstream river segments. However, the information of released water flow is often not available for many reservoirs, which makes it difficult for data-driven models to capture the impact to downstream river segments. In this paper, we first build a state-aware graph model to represent the interactions amongst streams and reservoirs, and then propose a parallel learning structure to extract the reservoir release information and use it to improve the prediction. In particular, for reservoirs with no available release information, we mimic the water managers' release decision process through a pseudo-prospective learning method, which infers the release information from anticipated water temperature dynamics. For reservoirs with the release information, we leverage a physics-based model to simulate the water release temperature and transfer such information to guide the learning process for other reservoirs. The evaluation for the Delaware River Basin shows that the proposed method brings over 10% accuracy improvement over existing data-driven models for stream temperature prediction when the release data is not available for any reservoirs. The performance is further improved after we incorporate the release data and physical simulations for a subset of reservoirs.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2022 SIAM International Conference on Data Mining (SDM)","conferenceDate":"April 28-30, 2022","conferenceLocation":"Alexandria, Virginia, United States","language":"English","publisher":"Society for Industrial and Applied Mathematics","doi":"10.1137/1.9781611977172.11","usgsCitation":"Jia, X., Chen, S., Xie, Y., Yang, H., Appling, A.P., Oliver, S.K., and Jiang, Z., 2022, Modeling reservoir release using pseudo-prospective learning and physical simulations to predict water temperature, in Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), Alexandria, Virginia, United States, April 28-30, 2022, p. 91-99, https://doi.org/10.1137/1.9781611977172.11.","productDescription":"9 p.","startPage":"91","endPage":"99","ipdsId":"IP-134356","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":445610,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://arxiv.org/abs/2202.05714","text":"External Repository"},{"id":411275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-04-20","publicationStatus":"PW","contributors":{"editors":[{"text":"Banerjee, Arindam","contributorId":300535,"corporation":false,"usgs":false,"family":"Banerjee","given":"Arindam","email":"","affiliations":[],"preferred":false,"id":860702,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Zhou, Zhi-Hua","contributorId":300536,"corporation":false,"usgs":false,"family":"Zhou","given":"Zhi-Hua","email":"","affiliations":[],"preferred":false,"id":860703,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Papalexakis, Evangelos E.","contributorId":300537,"corporation":false,"usgs":false,"family":"Papalexakis","given":"Evangelos","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":860704,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Riondato, Matteo","contributorId":300538,"corporation":false,"usgs":false,"family":"Riondato","given":"Matteo","email":"","affiliations":[],"preferred":false,"id":860705,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":860695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Shengyu","contributorId":297452,"corporation":false,"usgs":false,"family":"Chen","given":"Shengyu","email":"","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":860696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xie, Yiqun","contributorId":297447,"corporation":false,"usgs":false,"family":"Xie","given":"Yiqun","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":860697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yang, Haoyu","contributorId":298611,"corporation":false,"usgs":false,"family":"Yang","given":"Haoyu","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":860698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":860699,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":860700,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jiang, Zhe","contributorId":267317,"corporation":false,"usgs":false,"family":"Jiang","given":"Zhe","email":"","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":860701,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236479,"text":"70236479 - 2022 - Assessing the efficacy of oblique bubble screens for control of aquatic invasive species","interactions":[],"lastModifiedDate":"2024-02-22T17:08:42.891975","indexId":"70236479","displayToPublicDate":"2022-12-31T11:06:29","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Assessing the efficacy of oblique bubble screens for control of aquatic invasive species","docAbstract":"Non-physical barriers, such as bubble screens (or curtains), are promising low-impact strategies to deter the spread of Aquatic Invasive Species (AIS) in streams. Bubble screens have been successfully implemented to redirect and/or deter adult fish and to capture plastics in some rivers, but their efficacy on invasive fish at multiple life stages (eggs, larvae, and adult fish) is not yet known. Air bubbles rising from a diffuser placed at the bottom of a stream generate counterrotating eddies that interact with the mean flow. Understanding such interactions allows us to design an Oblique Bubble Screen (OBS), a system able to direct drifting particles (i.e., eggs and larvae) towards the banks of a stream for potential capture, based on the water velocity, river morphology, orientation of the OBS, diffuser material, and air flow rate. We present the results from a series of laboratory experiments at the Ecohydraulics and Ecomorphodynamics Laboratory at the University of Illinois at Urbana-Champaign, using positively buoyant (specific gravity SG=0.9, and diameter d=7.09mm) and negatively buoyant (SG=1.04, d=5.9mm) spherical particles to represent the range of size and density of developing Grass carp eggs, an invasive species in North America (Ctenopharyngodon idella). An air compressor was connected to a porous tube laid at the bottom of a recirculating flume, with a regulator and a flow meter to control air pressure and air flow rate. Acoustic Doppler Velocimeters (ADV) and Surface Particle Image Velocimetry (PIV) were used to measure the effect of the OBS on the velocity field. Our collected data showed that: (1) a single OBS is able to direct drifting particles towards the bank at the downstream end of the OBS, (2) adjusting orientation angle and air flow rate of the diffuser can increase capture efficacy under different flow conditions, reaching up to a 100% of capture for buoyant particles, and (3) the ratio between lateral velocity generated by the OBS (as a function of air flow rate) and the mean longitudinal flow velocities, can be used as an indicator to predict whether the OBS will be able to carry the particles all along the length of the diffuser in wider and deeper streams. The optimal configurations from our study will be tested with live Grass carp eggs and larvae, as well as with upstream-swimming adult carp to assess its potential as a two-way barrier, and to provide design parameters to set the air-flow rate and diffuser type needed for field deployments, according to width-to-depth ratios and stream morphology.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 39th IAHR World Congress, Granada, Spain","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"International Association for Hydro-Environment Engineering and Research","doi":"10.3850/IAHR-39WC2521711920221833","usgsCitation":"Prasad, V., Suski, C., Jackson, P.R., George, A.E., Chapman, D., Fischer, J., and Tinoco, R.O., 2022, Assessing the efficacy of oblique bubble screens for control of aquatic invasive species, in Proceedings of the 39th IAHR World Congress, Granada, Spain, v. 39, p. 1565-1570, https://doi.org/10.3850/IAHR-39WC2521711920221833.","productDescription":"6 p.","startPage":"1565","endPage":"1570","ipdsId":"IP-135122","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":445612,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3850/iahr-39wc2521711920221833","text":"Publisher Index Page"},{"id":425879,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Prasad, Vindhyawasini 0000-0003-0585-7217","orcid":"https://orcid.org/0000-0003-0585-7217","contributorId":296287,"corporation":false,"usgs":false,"family":"Prasad","given":"Vindhyawasini","email":"","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":851177,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Suski, C. D.","contributorId":190151,"corporation":false,"usgs":false,"family":"Suski","given":"C.","middleInitial":"D.","affiliations":[],"preferred":false,"id":851178,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":851179,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"George, Amy E. 0000-0003-1150-8646 ageorge@usgs.gov","orcid":"https://orcid.org/0000-0003-1150-8646","contributorId":3950,"corporation":false,"usgs":true,"family":"George","given":"Amy","email":"ageorge@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":851180,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":851181,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fischer, Jesse Robert","contributorId":296288,"corporation":false,"usgs":true,"family":"Fischer","given":"Jesse Robert","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":851182,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tinoco, Rafael O.","contributorId":211779,"corporation":false,"usgs":false,"family":"Tinoco","given":"Rafael","email":"","middleInitial":"O.","affiliations":[{"id":38317,"text":"Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL","active":true,"usgs":false}],"preferred":false,"id":851183,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70245367,"text":"70245367 - 2022 - Nuclear magnetic resonance logging of a deep test well for estimation of aquifer and confining-unit hydraulic properties, Long Island, New York","interactions":[],"lastModifiedDate":"2024-02-27T17:08:46.461733","indexId":"70245367","displayToPublicDate":"2022-12-31T10:44:22","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Nuclear magnetic resonance logging of a deep test well for estimation of aquifer and confining-unit hydraulic properties, Long Island, New York","docAbstract":"A 1,200-foot deep well in southwestern Nassau County, Long Island, N.Y. was selected to evaluate the application of a nuclear magnetic resonance (NMR) logging tool. Technological advances in NMR borehole systems have allowed for reduced probe length and diameter, and focused measurement at specific diameters beyond the disturbed zone surrounding a well. This 3-inch-diameter NMR tool was specifically developed for use in deep 4-inch-diameter polyvinyl chloride cased wells common to Long Island. Selected intervals of the Magothy and Lloyd aquifers and the Raritan confining unit were logged for the evaluation.
Unlike other petrophysical logs that respond to the rock matrix and fluid properties and are strongly dependent on mineralogy, NMR logs respond to the presence of hydrogen protons in the formation fluid to determine water fraction and pore-size distribution. NMR log analysis provided estimates of the clay-bound, capillary-bound, and mobile water fraction and hydraulic conductivity of aquifers and confining units penetrated by the well. NMR-estimated porosity and mobile water fraction for the Magothy aquifer (0.34 and 0.22 respectively), Magothy/Raritan(?) (0.35 and 0.30), Raritan confining unit (0.30 and 0.13), Raritan clay and silt (0.23 and 0.01), and the Lloyd aquifer (0.27 and 0.19) was determined from the NMR log.
Hydraulic conductivity was estimated from the NMR-log data using the Schlumberger- Doll Research and sum of squared echoes equations with empirically derived constants for unconsolidated aquifers. Average hydraulic conductivity of the Magothy aquifer was 70 ft/d, the Raritan confining unit was 9.0 ft/d overall, the clay-rich lower part 0.24 ft/d, and the Lloyd aquifer was 56 ft/d. The coarse sandy Magothy/Raritan(?) unit between the Magothy aquifer and the top of the Raritan confining unit had the highest hydraulic conductivity of 345 ft/d. The hydraulic-conductivity estimates from the NMR log analysis for the Magothy and Lloyd aquifers were consistent with published values and that estimated for the Lloyd aquifer from a specific-capacity test at the well site.
","conferenceTitle":"29th Conference on Geology of Long Island and Metropolitan New York","conferenceDate":"April 9, 2022","language":"English","publisher":"Long Island Geologists","usgsCitation":"Stumm, F., and Williams, J., 2022, Nuclear magnetic resonance logging of a deep test well for estimation of aquifer and confining-unit hydraulic properties, Long Island, New York, 29th Conference on Geology of Long Island and Metropolitan New York, v. 29, April 9, 2022, 11 p.","productDescription":"11 p.","ipdsId":"IP-138611","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":426032,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":426031,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.stonybrook.edu/commcms/geosciences/about/_LIG-Past-Conferences/2022Conference.php","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","county":"Nassau County","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.695,\n 40.65\n ],\n [\n -73.695,\n 40.64\n ],\n [\n -73.685,\n 40.64\n ],\n [\n -73.685,\n 40.65\n ],\n [\n -73.695,\n 40.65\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stumm, Frederick 0000-0002-5388-8811 fstumm@usgs.gov","orcid":"https://orcid.org/0000-0002-5388-8811","contributorId":1077,"corporation":false,"usgs":true,"family":"Stumm","given":"Frederick","email":"fstumm@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875903,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, John H. 0000-0002-6054-6908 jhwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-6054-6908","contributorId":1553,"corporation":false,"usgs":true,"family":"Williams","given":"John","email":"jhwillia@usgs.gov","middleInitial":"H.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875904,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241106,"text":"70241106 - 2022 - Utilization of genetic data to inform native Brook Trout conservation in North Carolina","interactions":[],"lastModifiedDate":"2023-03-13T10:56:52.942477","indexId":"70241106","displayToPublicDate":"2022-12-31T09:25:06","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Utilization of genetic data to inform native Brook Trout conservation in North Carolina","docAbstract":"As North Carolina’s only native salmonid, Brook Trout Salvelinus fontinalis is a fish of considerable ecological and cultural significance in the state, but anthropogenic alterations to the landscape and introductions of nonnative salmonids have fragmented and reduced its native range. As a result, the North Carolina Wildlife Resources Commission (NCWRC) has enacted numerous efforts to help conserve the species. Annual demographic surveys of self-sustaining Brook Trout populations have been on-going since 1978, which have also included successful efforts to document previously unidentified populations. Beginning in earnest during the 1990s, allozyme testing was used to assess patterns of hatchery introgression, with over 480 collections genotyped at the creatine kinase locus. In 2010, the NCWRC began using microsatellite markers to conduct an extensive survey of Brook Trout genetic diversity and variation. To date, 541 Brook Trout collections representing 11,090 individuals have been genotyped at 12 microsatellite loci. These data have provided insights into evolutionary relationships among populations, spatial patterns of genetic diversity, and the extent of hatchery introgression within populations. Ultimately, increased understanding of genetic diversity and relatedness have been informative for determining that Brook Trout management in North Carolina is likely best enacted at the level of individual populations. Moreover, we have used these data to actively guide stream restoration and population reintroduction activities. Over the last 15 years, NCWRC and its partners have used genetic data to prioritize habitat enhancement activities and guide 17 Brook Trout population reintroduction projects. In the future, we plan to continue expanding the microsatellite genetic baseline while also exploring the utility of phylogenomic analyses to inform Brook Trout conservation activities. Genetic and genomic approaches have great potential to improve the efficacy of conservation actions for Brook Trout in North Carolina and throughout its native range.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of Wild Trout XIII","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Wild Trout XIII","conferenceDate":"September 27-30, 2022","conferenceLocation":"West Yellowstone, MT","language":"English","publisher":"Wild Trout Symposium","usgsCitation":"Rash, J., Kazyak, D., White, S.L., and Lubinski, B.A., 2022, Utilization of genetic data to inform native Brook Trout conservation in North Carolina, in Proceedings of Wild Trout XIII, West Yellowstone, MT, September 27-30, 2022, p. 158-163.","productDescription":"6 p.","startPage":"158","endPage":"163","ipdsId":"IP-143335","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":413954,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.wildtroutsymposium.com/proceedings.php","linkFileType":{"id":5,"text":"html"}},{"id":413955,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.04921892394643,\n 35.16380575406262\n ],\n [\n -80.26699801469118,\n 36.54940722798543\n ],\n [\n -81.72273121506292,\n 36.60331819137433\n ],\n [\n -82.20159275018115,\n 36.10046486828409\n ],\n [\n -82.88139191858609,\n 35.905982228760394\n ],\n [\n -83.58707291840865,\n 35.49691451561709\n ],\n [\n -84.00314930521716,\n 35.43636411766791\n ],\n [\n -84.19291110790027,\n 35.20050466686614\n ],\n [\n -84.40371695037375,\n 34.915348088734206\n ],\n [\n -83.13188407946514,\n 34.95778267269911\n ],\n [\n -82.04921892394643,\n 35.16380575406262\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rash, Jacob","contributorId":202482,"corporation":false,"usgs":false,"family":"Rash","given":"Jacob","affiliations":[{"id":36454,"text":"North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":866100,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":866101,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":866102,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":866103,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70239166,"text":"ofr20221118 - 2022 - Characterization of subsurface conditions and recharge at the irrigated four-plex baseball field, Fort Irwin National Training Center, California, 2018–20","interactions":[],"lastModifiedDate":"2026-02-10T21:20:37.248913","indexId":"ofr20221118","displayToPublicDate":"2022-12-30T13:25:58","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1118","displayTitle":"Characterization of Subsurface Conditions and Recharge at the Irrigated Four-Plex Baseball Field, Fort Irwin National Training Center, California, 2018-20","title":"Characterization of subsurface conditions and recharge at the irrigated four-plex baseball field, Fort Irwin National Training Center, California, 2018–20","docAbstract":"The U.S. Geological Survey performed subsurface and geophysical site characterization of the irrigated four-plex baseball field in the Langford Valley–Irwin Groundwater Subbasin, as part of a research study in cooperation with the U.S. Environmental Protection Agency, the Agricultural Research Service, and the Fort Irwin National Training Center, California. To help meet future demands, the Fort Irwin National Training Center is evaluating the efficacy of gravity-fed drywells to enhance storm-water recharge into the Langford Valley–Irwin Groundwater Subbasin by bypassing fine-grained, less permeable deposits between land surface and the water table. The amount, rate, and location of recharge beneath an irrigated baseball field in the groundwater basin at the Fort Irwin National Training Center is not well understood, so data were collected using physical and geophysical techniques to characterize subsurface materials, geologic controls, and the vertical movement of water through the unsaturated zone to the water table near the drywell at the Fort Irwin National Training Center. Based on the data collected and interpreted from these techniques, several fine-grained deposits were identified. Although these deposits appear to impede the downward movement of water through the unsaturated zone locally, they are not laterally continuous, and water appears to continue to move downward when it reaches the edges of the deposits. These data will help managers evaluate recharge at the site and determine if the use of gravity-fed drywells enhances recharge from surface runoff.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221118","issn":"2331-1258","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","programNote":"U.S. Environmental Protection Agency","usgsCitation":"Densmore, J.N., Dick, M.C., Groover, K.D., Ely, C.P., and Brown, A., 2022, Characterization of subsurface conditions and recharge at the irrigated four-plex baseball field, Fort Irwin National Training Center, California, 2018–20: U.S. Geological Survey Open File Report 2022-1118, 13 p., https://doi.org/10.3133/ofr20221118","productDescription":"13 p.","numberOfPages":"13","onlineOnly":"Y","ipdsId":"IP-129107","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":499727,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114180.htm","linkFileType":{"id":5,"text":"html"}},{"id":411259,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1118/ofr20221118.XML"},{"id":411257,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1118/images"},{"id":411255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1118/coverthb.jpg"},{"id":411256,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1118/ofr20221118.pdf","text":"Report","size":"3.61 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","city":"Fort Irwin","otherGeospatial":"Fort Irwin National Training Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.69261791592291,\n 35.26487666931442\n ],\n [\n -116.69251062756216,\n 35.26470146901906\n ],\n [\n -116.69238188152951,\n 35.26459634865991\n ],\n [\n -116.69212438946424,\n 35.264447427917574\n ],\n [\n -116.69163086300526,\n 35.264359827352905\n ],\n [\n -116.69129826908738,\n 35.26422842632836\n ],\n [\n -116.69092275982544,\n 35.263983143846076\n ],\n [\n -116.68835856800732,\n 35.2661468601287\n ],\n [\n -116.6903755891864,\n 35.26772362102348\n ],\n [\n -116.69260718708664,\n 35.265822744364684\n ],\n [\n -116.69281103497185,\n 35.265752665110384\n ],\n [\n -116.69268228893922,\n 35.26531466839623\n ],\n [\n -116.69261791592291,\n 35.26487666931442\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
https://www.usgs.gov/centers/ca-water/
Contact Pubs Warehouse
https://pubs.er.usgs.gov/contact
Greater sage-grouse (Centrocercus urophasianus) are at the center of state and national land use policies largely because of their unique life-history traits as an ecological indicator for health of sagebrush ecosystems. This updated population trend analysis provides state and federal land and wildlife managers with best-available science to help guide current management and conservation plans aimed at benefitting sage-grouse populations. This analysis relied on previously published population trend modeling methodology from Coates and others (2021) and includes the addition of three analytical updates: (1) identification of population nadirs (lowest points within cycles) at the lek (breeding ground) and neighborhood cluster (group of leks) spatial scales, (2) truncation of prior distributions on rate of change in apparent abundance values to more realistic boundaries for leks with missing data, and (3) addition of 2 years of population lek count data (2020 and 2021) to the current dataset (1953–2021). Bayesian state-space models estimated 2.9 percent average annual decline in sage-grouse populations across their geographical range, which varied among subpopulations at the largest scale of analysis, termed climate clusters (2.2–4.6). Cumulative declines were 42.5, 65.6, and 80.1 percent range-wide across short (19 years), medium (35 years), and long (55 years) temporal periods, respectively. These results indicate that range-wide populations continued to decline during 2020 and 2021, although two climate clusters (eastern area and Bi-State area) have shown growth in population abundance in recent years, indicating they have surpassed a recent population abundance nadir.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1165","issn":"2771-9448","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies and the Bureau of Land Management","programNote":"Species Management Research Program","usgsCitation":"Coates, P.S., Prochazka, B.G., Aldridge, C.L., O'Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2022, Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2021: Data Report 1165, 16 p., https://doi.org/10.3133/dr1165.","productDescription":"Report: viii, 16 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-144163","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":411225,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OQWGIV","text":"U.S. Geological Survey data release","linkHelpText":"Trends and a targeted annual warning system for greater sage-grouse in the western United States (1960–2021)"},{"id":411222,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1165/dr1165.pdf","text":"Report","size":"8.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1165"},{"id":411223,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1165/images"},{"id":411221,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1165/coverthb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.19101705712868,\n 48.89284566335482\n ],\n [\n -122.13131256755497,\n 48.37855671670232\n ],\n [\n -121.63700211579436,\n 47.85380781194411\n ],\n [\n -121.86067071873805,\n 47.51242609246373\n ],\n [\n -121.87668371107034,\n 47.11115078292244\n ],\n [\n -122.4532512741522,\n 46.70567448488106\n ],\n [\n -122.57397075207928,\n 46.22739431053469\n ],\n [\n -122.36606239056442,\n 45.90712886523926\n ],\n [\n -121.91588031540189,\n 45.358384504867246\n ],\n [\n -122.45322729336237,\n 44.953343420279765\n ],\n [\n -122.52983521148596,\n 44.52144457126775\n ],\n [\n -122.54530349941209,\n 44.259263341793\n ],\n [\n -122.85735468588939,\n 43.86827650155766\n ],\n [\n -123.01704364538307,\n 43.46272668287682\n ],\n [\n -123.01026119844099,\n 43.21452795313556\n ],\n [\n -123.10511846238558,\n 42.807780288627384\n ],\n [\n -122.77301525682884,\n 42.07884845828369\n ],\n [\n -122.62454526743153,\n 41.5133850817748\n ],\n [\n -122.14925905063672,\n 40.83036532174481\n ],\n [\n -121.68837858072334,\n 40.49667518946566\n ],\n [\n -121.57750448671058,\n 39.80789795232664\n ],\n [\n -121.16256808423407,\n 39.215880289775015\n ],\n [\n -120.64471039578123,\n 38.57511631320122\n ],\n [\n -119.84212022937817,\n 37.43985090599355\n ],\n [\n -118.83488568782542,\n 36.55782111752346\n ],\n [\n -118.59349323888452,\n 35.83229168807691\n ],\n [\n -117.40450808029976,\n 35.69535862053445\n ],\n [\n -116.43105077565428,\n 35.459239916109155\n ],\n [\n -114.6229823251931,\n 35.413666627683824\n ],\n [\n -96.18756245429773,\n 35.03931170375198\n ],\n [\n -94.57905716272413,\n 37.1225590274522\n ],\n [\n -94.53555587839759,\n 39.07884303955356\n ],\n [\n -95.83917624651616,\n 40.61806593890233\n ],\n [\n -96.37123578579417,\n 42.39205565405334\n ],\n [\n -96.46625839716233,\n 45.31077773932529\n ],\n [\n -97.30155203348903,\n 48.980368873698694\n ],\n [\n -122.19101705712868,\n 48.89284566335482\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
https://www.usgs.gov/centers/werc
Contact Pubs Warehouse
https://pubs.er.usgs.gov/contact
The protection of all sea turtles globally is a high priority, and research projects on these imperiled species are focused on those that are likely to result in improvements in monitoring and management for population recovery. Determining distribution, seasonal movements, vital rates and habitat use for all life-stages of sea turtles has been identified by the US Fish and Wildlife Service (USFWS) and US National Marine Fisheries Service (NMFS) as important for achieving recovery. This study provides information on in-water aggregations of sea turtles in the northern Gulf of Mexico. Data collected includes individual dive profiles, movements, seasonal site fidelity, genetic population structure, and isotopic signatures.
","language":"English","publisher":"Bureau of Ocean Energy Management","usgsCitation":"Hart, K., and Lamont, M., 2022, Discerning behavioral patterns of sea turtles in the Gulf of Mexico to inform management decisions, iv, 76 p.","productDescription":"iv, 76 p.","ipdsId":"IP-133772","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":411563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":411550,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://espis.boem.gov/final%20reports/BOEM_2021-088.pdf"}],"country":"United States","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.65793958565853,\n 25.620964087484467\n ],\n [\n -80.65793958565853,\n 31.33180053527252\n ],\n [\n -99.2828424566061,\n 31.33180053527252\n ],\n [\n -99.2828424566061,\n 25.620964087484467\n ],\n [\n -80.65793958565853,\n 25.620964087484467\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":222407,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":861127,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamont, Margaret 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":222403,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":861128,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241988,"text":"70241988 - 2022 - Red knot stopover population size and migration ecology at Delaware Bay, USA, 2022","interactions":[],"lastModifiedDate":"2023-04-03T12:00:47.057179","indexId":"70241988","displayToPublicDate":"2022-12-30T06:59:14","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Red knot stopover population size and migration ecology at Delaware Bay, USA, 2022","docAbstract":"Red Knots (Calidris canutus rufa) stop at Delaware Bay on the mid-Atlantic coast of North America during northward migration to feed on eggs of horseshoe crabs (Limulus polyphemus). In the late 1990s and early 2000s, the number of Red Knots found at Delaware Bay declined from ~50,000 to ~13,000. Horseshoe crabs have been harvested for use as bait in eel (Anguilla rostrata) and whelk (Busycon) fisheries since at least 1990, and some avian conservation biologists hypothesized that horseshoe crab harvest levels in the 1990s prevented sufficient refueling for successful migration to the breeding grounds, nesting, and survival for the remainder of the annual cycle. Since 2013, the harvest of horseshoe crabs in the Delaware Bay region has been managed using an Adaptive Resource Management (ARM) framework. The objective of the ARM framework is to manage sustainable harvest of Delaware Bay horseshoe crabs while maintaining ecosystem integrity and supporting Red Knot recovery with adequate stopover habitat for Red Knots and other migrating shorebirds. For annual harvest recommendations, the ARM framework requires annual estimates of horseshoe crab population size and the Red Knot stopover population size. We conducted a mark-recapture-resight investigation to estimate the passage population of Red Knots at Delaware Bay in 2022. We used a Bayesian analysis of a Jolly-Seber model, which accounts for turnover in the population and the probability of detection during surveys. The 2022 Red Knot mark-resight dataset","language":"English","publisher":"Delaware Division of Fish and Wildlife","usgsCitation":"Lyons, J.E., 2022, Red knot stopover population size and migration ecology at Delaware Bay, USA, 2022, 23 p.","productDescription":"23 p.","ipdsId":"IP-150372","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":415051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":415047,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://dnrec.alpha.delaware.gov/fish-wildlife/conservation/shorebirds/research/"}],"country":"United States","state":"Delaware, New Jersey","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.89400562504255,\n 39.91434007716924\n ],\n [\n -75.89400562504255,\n 38.41619673661057\n ],\n [\n -74.49548202885819,\n 38.41619673661057\n ],\n [\n -74.49548202885819,\n 39.91434007716924\n ],\n [\n -75.89400562504255,\n 39.91434007716924\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":868432,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70239137,"text":"fs20223087 - 2022 - Continuous water-quality and suspended-sediment transport monitoring in San Francisco Bay, California, water years 2020–21","interactions":[],"lastModifiedDate":"2026-03-25T16:44:46.088797","indexId":"fs20223087","displayToPublicDate":"2022-12-29T12:04:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3087","displayTitle":"Continuous Water-Quality and Suspended-Sediment Transport Monitoring in San Francisco Bay, California, Water Years 2020–21","title":"Continuous water-quality and suspended-sediment transport monitoring in San Francisco Bay, California, water years 2020–21","docAbstract":"The U.S. Geological Survey (USGS) has continuously monitored real-time water quality and suspended-sediment transport in San Francisco Bay (the Bay) since 1989 as part of a multi-agency effort (see “Acknowledgments” section) to address estuary management, water supply, and ecological concerns. The San Francisco Bay area is home to millions of people and biologically diverse marine and terrestrial flora and fauna. Freshwater mixes with saltwater in the Bay and is subject to riverine influences (floods, droughts, managed reservoir releases, and freshwater diversions) and marine influences (tides, waves, and effects of saltwater).
Water temperature, salinity, suspended-sediment concentration (SSC), and turbidity, are used by State and Federal resources managers and are monitored at eight key locations throughout the Bay (fig. 1). Water temperature and salinity affect the density of water, which controls gravity-driven circulation patterns and stratification in the water column. Salinity indicates the relative mixing of fresh and ocean waters in the Bay and is derived from specific conductance measurements. Turbidity is a measure of light scattered from suspended particles in the water that is used to estimate suspended-sediment concentration. Suspended-sediment concentrations also are directly measured through depth-integrated water sampling.
Suspended sediment affects Bay water quality in multiple ways. Suspended sediment affects phytoplankton growth by attenuating sunlight in the water column. Suspended sediment deposition on tidal marshes and intertidal mudflats helps to restore and sustain these habitats as sea level rises. Settling of suspended sediment in ports and shipping channels creates the need for more dredging. In addition, suspended sediment often carries adsorbed contaminants as it is transported in the water column, which affects the distributions and concentrations of adsorbed contaminants in the environment. Excessive concentrations of sediment-adsorbed contaminants in deposits on the bottom of the Bay can affect ecosystem health.
External factors, such as tidal currents, waves, and wind can also affect water quality in the Bay. Tidal currents in the Bay change direction four times daily, and wind direction and intensity typically fluctuate on a daily cycle. Consequently, salinity, water temperature, and suspended-sediment concentration differ spatially and temporally throughout the Bay. Therefore, high-frequency measurements at multiple locations are needed to monitor these changes. Data collected at eight stations throughout the Bay are transmitted in near real-time using cellular telemetry and posted to the USGS National Water Information System (NWIS; https://waterdata.usgs.gov/usa/nwis). The purposes of this fact sheet are to (1) provide information about the USGS San Francisco Bay water-quality monitoring network; (2) highlight various applications in which these data can be used; and (3) provide internet links to access the resulting continuous water-quality data collected by the USGS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223087","usgsCitation":"Palm, D.L., Einhell, D.C., Davila Olivera, S.M., 2022, Continuous water-quality and suspended-sediment transport monitoring in San Francisco Bay, California, water years 2020–21: U.S. Geological Survey Fact Sheet 2022–3087, 4 p., https://doi.org/10.3133/fs20223087.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-138273","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":411162,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3087/coverthb.jpg"},{"id":411163,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3087/fs20223087.pdf","text":"Report","size":"2.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3087"},{"id":411164,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20223087/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3087"},{"id":411165,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3087/images/"},{"id":411166,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3087/fs20223087.XML"},{"id":501514,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114175.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.7032269260365,\n 38.2575437837898\n ],\n [\n -122.64478458511374,\n 38.2575437837898\n ],\n [\n -122.64478458511374,\n 37.373118003359465\n ],\n [\n -121.7032269260365,\n 37.373118003359465\n ],\n [\n -121.7032269260365,\n 38.2575437837898\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
Understanding what drives changes in wildlife demography is fundamental to the conservation and management of depleted or declining populations, though making inference about the intrinsic and extrinsic factors that influence survival and reproduction remains challenging. Here we use mark–resight data from 2000 to 2018 to examine the effects of environmental variability on age-specific survival and natality for the endangered western distinct population segment (wDPS) of Steller sea lions (Eumetopias jubatus) in Alaska, USA. Though this population has been studied extensively over the last four decades, the causes of divergent abundance trends that have been observed across the wDPS range remain unknown. We developed a Bayesian multievent mark–resight model that accounts for female reproductive state uncertainty. Annual survival probabilities for male pups (0.44; 0.36–0.53), female yearlings (0.63; 0.49–0.73), and male yearlings (0.62; 0.51–0.71) born in the western portion of the wDPS range, estimated here for the first time, were lower than those in the eastern portion of the wDPS range, estimated as: male pups (0.69; 0.65–0.74), female yearlings (0.76; 0.71–0.81), and male yearlings (0.71; 0.65–0.78). There was a higher proportion of young female breeders in the western portion of the range, but overall natality was lower (0.69; 0.47–0.96) than in the eastern portion of the range (0.80; 0.74–0.84). Additionally, pup mass had a positive effect on pup survival in the eastern portion of the range and a negative effect in the western portion of the range, potentially due to earlier weaning of heavier pups. Local- and basin-scale oceanographic features such as the Aleutian Low, the Arctic Oscillation Index, the North Pacific Gyre Oscillation, chlorophyll concentration, upwelling, and wind in certain seasons were correlated with vital rates. However, drawing strong inferences from these correlations is challenging given that relationships between ocean conditions and an adaptive top predator in a dynamic ecosystem are exceedingly complex. This study provides the first demographic rate estimates for the western portion of the range where abundance estimates continue to decline. These results will advance efforts to identify factors driving regionally divergent abundance trends, with implications for population-level responses to future climate variability.
Population models often require detailed information on sex-, age-, or size-specific abundances, but population monitoring programs cannot always acquire data at the desired resolution. Thus, state uncertainty in monitoring data can potentially limit the demographic resolution of management decisions, which may be particularly problematic for stage- or size-structured species subject to consumptive use. American alligators (Alligator mississippiensis; hereafter alligator) have a complex life history characterized by delayed maturity and slow somatic growth, which makes the species particularly sensitive to overharvest. Though alligator populations are subject to recreational harvest throughout their range, the most widely used monitoring method (nightlight surveys) is often unable to obtain size class-specific counts, which limits the ability of managers to evaluate the effects of harvest policies. We constructed a Bayesian integrated population model (IPM) for alligators in Georgetown County, SC, USA, using records of mark–recapture–recovery, clutch size, harvest, and nightlight survey counts collected locally, and auxiliary information on fecundity, sex ratio, and somatic growth from other studies. We created a multistate mark–recapture–recovery model with six size classes to estimate survival probability, and we linked it to a state-space count model to derive estimates of size class-specific detection probability and abundance. Because we worked from a count dataset in which 60% of the original observations were of unknown size, we treated size class as a latent property of detections and developed a novel observation model to make use of information where size could be partly observed. Detection probability was positively associated with alligator size and water temperature, and negatively influenced by water level. Survival probability was lowest in the smallest size class but was relatively similar among the other five size classes (>0.90 for each). While the two nightlight survey count sites exhibited relatively stable population trends, we detected substantially different patterns in size class-specific abundance and trends between each site, including 30%–50% declines in the largest size classes at the site with greater harvest pressure. Here, we illustrate the use of IPMs to produce high-resolution output of latent population structure that is partially observed during the monitoring process.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4321","usgsCitation":"Lawson, A.J., Jodice, P.G., Rainwater, T., Dunham, K.D., Hart, M., Butfiloski, J.W., Wilkinson, P., and Moore, C., 2022, Hidden in plain sight: Integrated population models to resolve partially observable latent population structure: Ecosphere, v. 13, e4321, 22 p., https://doi.org/10.1002/ecs2.4321.","productDescription":"e4321, 22 p.","ipdsId":"IP-137983","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":445620,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4321","text":"Publisher Index Page"},{"id":430217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","county":"Georgetown County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-79.2846,33.7178],[-79.2819,33.716],[-79.2718,33.7222],[-79.2697,33.7181],[-79.2753,33.7141],[-79.2754,33.7086],[-79.2721,33.7085],[-79.2693,33.7112],[-79.2649,33.7103],[-79.2649,33.7071],[-79.2678,33.7035],[-79.2639,33.7025],[-79.2584,33.7024],[-79.2423,33.7058],[-79.2402,33.7044],[-79.2408,33.7026],[-79.2463,33.7009],[-79.2458,33.6972],[-79.2349,33.6939],[-79.2315,33.6952],[-79.2304,33.6988],[-79.2248,33.7001],[-79.2104,33.7026],[-79.2071,33.7035],[-79.201,33.702],[-79.1961,33.7019],[-79.196,33.7056],[-79.1948,33.7069],[-79.1905,33.7059],[-79.18,33.7039],[-79.1762,33.6998],[-79.179,33.6976],[-79.1763,33.6962],[-79.1709,33.6929],[-79.1665,33.6915],[-79.1633,33.686],[-79.1573,33.6849],[-79.153,33.6785],[-79.1526,33.6717],[-79.155,33.664],[-79.1518,33.658],[-79.1424,33.6583],[-79.1384,33.6642],[-79.1368,33.6637],[-79.1342,33.6573],[-79.1337,33.6536],[-79.1299,33.6517],[-79.1271,33.6535],[-79.1249,33.6521],[-79.1239,33.6494],[-79.1256,33.6458],[-79.1306,33.6459],[-79.1323,33.6441],[-79.1296,33.6413],[-79.1346,33.6395],[-79.1347,33.6373],[-79.1308,33.6358],[-79.1277,33.6272],[-79.124,33.6221],[-79.1225,33.6171],[-79.1237,33.6121],[-79.1217,33.6048],[-79.1168,33.602],[-79.1091,33.6],[-79.105,33.6104],[-79.1022,33.6113],[-79.0995,33.6094],[-79.0969,33.6021],[-79.0998,33.5958],[-79.0978,33.5894],[-79.1023,33.5826],[-79.1004,33.5717],[-79.0034,33.5718],[-79.0023,33.5727],[-78.9841,33.5929],[-78.9762,33.5991],[-78.9297,33.6488],[-78.919,33.6586],[-78.9001,33.6725],[-78.9005,33.672],[-78.9119,33.6111],[-79,33.5456],[-79.0164,33.5239],[-79.0947,33.4319],[-79.0964,33.43],[-79.1092,33.4144],[-79.1139,33.4089],[-79.1158,33.4069],[-79.1279,33.4166],[-79.1281,33.4167],[-79.1325,33.41],[-79.1325,33.4094],[-79.1333,33.4078],[-79.1339,33.4072],[-79.1339,33.4067],[-79.135,33.4056],[-79.135,33.405],[-79.1356,33.4044],[-79.1355,33.4039],[-79.1367,33.4028],[-79.1367,33.4022],[-79.1408,33.3931],[-79.1408,33.3925],[-79.1414,33.3919],[-79.1544,33.3478],[-79.1547,33.3475],[-79.1558,33.3461],[-79.1558,33.3456],[-79.1567,33.3442],[-79.1572,33.3411],[-79.1575,33.3408],[-79.1581,33.3391],[-79.1586,33.3367],[-79.1589,33.3364],[-79.16,33.3331],[-79.1644,33.3372],[-79.1647,33.3375],[-79.1672,33.3372],[-79.1672,33.3364],[-79.1683,33.3353],[-79.1686,33.3353],[-79.1697,33.3347],[-79.1708,33.3331],[-79.17,33.3331],[-79.1689,33.33],[-79.17,33.3278],[-79.1728,33.3264],[-79.1722,33.325],[-79.1717,33.3222],[-79.1719,33.3219],[-79.1728,33.3175],[-79.1725,33.3172],[-79.1725,33.3156],[-79.1722,33.3153],[-79.1717,33.3139],[-79.1722,33.3133],[-79.1744,33.3119],[-79.1758,33.3111],[-79.1747,33.3103],[-79.1733,33.3106],[-79.1719,33.3114],[-79.1703,33.3147],[-79.17,33.315],[-79.1706,33.3183],[-79.1694,33.3194],[-79.1647,33.3197],[-79.1644,33.3194],[-79.1608,33.3169],[-79.1608,33.3156],[-79.1608,33.315],[-79.1606,33.3142],[-79.1628,33.3128],[-79.1636,33.3103],[-79.1639,33.31],[-79.1683,33.3017],[-79.1683,33.3014],[-79.1689,33.3003],[-79.1692,33.2983],[-79.1694,33.295],[-79.1703,33.2936],[-79.1703,33.2931],[-79.1733,33.2837],[-79.1736,33.2833],[-79.1786,33.2644],[-79.1786,33.2622],[-79.1808,33.2544],[-79.1811,33.2506],[-79.181,33.25],[-79.1819,33.2408],[-79.1814,33.2392],[-79.1817,33.2361],[-79.1808,33.2333],[-79.1808,33.23],[-79.1797,33.2289],[-79.1797,33.2283],[-79.18,33.2256],[-79.1794,33.225],[-79.1794,33.2231],[-79.1786,33.2222],[-79.1786,33.2211],[-79.1783,33.2208],[-79.1761,33.2136],[-79.1758,33.2133],[-79.175,33.2117],[-79.175,33.2106],[-79.1744,33.21],[-79.1742,33.2092],[-79.1678,33.2064],[-79.1619,33.205],[-79.1617,33.2047],[-79.1569,33.2047],[-79.1567,33.205],[-79.1539,33.2047],[-79.1528,33.2047],[-79.1525,33.2044],[-79.1525,33.2031],[-79.1556,33.2033],[-79.1594,33.2033],[-79.1631,33.2028],[-79.1633,33.2031],[-79.1672,33.2042],[-79.1681,33.2042],[-79.1711,33.2053],[-79.1725,33.2058],[-79.1731,33.2064],[-79.1736,33.2064],[-79.1756,33.2078],[-79.1756,33.2081],[-79.1969,33.2606],[-79.1978,33.2614],[-79.1981,33.2631],[-79.1992,33.2647],[-79.2008,33.2717],[-79.2009,33.2723],[-79.1956,33.2872],[-79.195,33.2878],[-79.1953,33.2881],[-79.1939,33.29],[-79.1958,33.2958],[-79.1961,33.2958],[-79.1997,33.2972],[-79.2,33.2975],[-79.2178,33.3022],[-79.2197,33.3025],[-79.22,33.3028],[-79.2211,33.3028],[-79.2214,33.3025],[-79.2231,33.3025],[-79.2233,33.3028],[-79.2247,33.3028],[-79.225,33.3025],[-79.2508,33.3536],[-79.2508,33.3539],[-79.2506,33.3558],[-79.2503,33.3561],[-79.2483,33.3592],[-79.2483,33.3594],[-79.2481,33.3603],[-79.2503,33.3619],[-79.2511,33.3619],[-79.2519,33.3611],[-79.2528,33.3611],[-79.2542,33.3625],[-79.2544,33.3625],[-79.2558,33.365],[-79.2567,33.365],[-79.2592,33.3628],[-79.2594,33.3628],[-79.2633,33.3642],[-79.2636,33.3644],[-79.2642,33.3656],[-79.2658,33.3661],[-79.2667,33.3653],[-79.2686,33.3631],[-79.2697,33.3631],[-79.2703,33.3622],[-79.2708,33.3622],[-79.2728,33.3603],[-79.2728,33.36],[-79.2744,33.3583],[-79.2744,33.3581],[-79.2778,33.3519],[-79.2781,33.3519],[-79.28,33.3547],[-79.2803,33.355],[-79.2803,33.3563],[-79.28,33.3592],[-79.2803,33.3594],[-79.2825,33.3586],[-79.2833,33.3594],[-79.2833,33.36],[-79.2836,33.3619],[-79.2839,33.3622],[-79.285,33.3639],[-79.2853,33.3639],[-79.2878,33.3661],[-79.2886,33.3675],[-79.2894,33.3675],[-79.2911,33.365],[-79.29,33.3639],[-79.2897,33.3639],[-79.2867,33.3614],[-79.2867,33.3611],[-79.2853,33.3575],[-79.2814,33.3469],[-79.2906,33.3316],[-79.2911,33.3281],[-79.2914,33.3278],[-79.2925,33.3233],[-79.2922,33.3231],[-79.2908,33.3211],[-79.2908,33.3186],[-79.2911,33.3173],[-79.2911,33.3147],[-79.2908,33.3144],[-79.2908,33.3106],[-79.2906,33.3103],[-79.2925,33.3039],[-79.2925,33.3036],[-79.2931,33.3],[-79.2928,33.2997],[-79.2928,33.2986],[-79.2931,33.2983],[-79.2883,33.2925],[-79.2883,33.2922],[-79.2797,33.2761],[-79.2797,33.2758],[-79.2764,33.2722],[-79.2761,33.2722],[-79.2736,33.2697],[-79.2733,33.2697],[-79.2714,33.2678],[-79.2711,33.2678],[-79.2656,33.2642],[-79.2592,33.26],[-79.2592,33.2597],[-79.2578,33.2592],[-79.2564,33.2592],[-79.2558,33.2586],[-79.2553,33.2586],[-79.2539,33.2578],[-79.2492,33.2561],[-79.2486,33.2561],[-79.2469,33.2558],[-79.2467,33.2556],[-79.2442,33.2547],[-79.2422,33.2547],[-79.2417,33.2542],[-79.2403,33.2542],[-79.24,33.2539],[-79.2386,33.2539],[-79.2383,33.2536],[-79.2364,33.2536],[-79.2361,33.2533],[-79.2333,33.2522],[-79.2239,33.2489],[-79.2236,33.2486],[-79.2225,33.2486],[-79.2222,33.2483],[-79.2208,33.2478],[-79.22,33.2478],[-79.2186,33.2475],[-79.2183,33.2472],[-79.2108,33.2439],[-79.2106,33.2439],[-79.2078,33.2403],[-79.2078,33.24],[-79.2036,33.235],[-79.2036,33.2344],[-79.2022,33.2281],[-79.2019,33.2278],[-79.2017,33.2214],[-79.2014,33.2211],[-79.2014,33.2194],[-79.2011,33.2192],[-79.2014,33.2169],[-79.2017,33.2167],[-79.2031,33.2108],[-79.2039,33.2053],[-79.1992,33.1978],[-79.1989,33.1981],[-79.1983,33.1975],[-79.1925,33.1972],[-79.1908,33.1928],[-79.1906,33.1925],[-79.1894,33.1908],[-79.1889,33.1914],[-79.1894,33.1919],[-79.1894,33.1925],[-79.1897,33.1961],[-79.19,33.1964],[-79.1925,33.2008],[-79.1928,33.2008],[-79.1936,33.2083],[-79.1917,33.2067],[-79.1917,33.2064],[-79.1892,33.2033],[-79.1892,33.2031],[-79.1867,33.2008],[-79.1864,33.2008],[-79.1841,33.1985],[-79.184,33.1982],[-79.1828,33.1961],[-79.1828,33.1931],[-79.1819,33.1931],[-79.1811,33.1922],[-79.1794,33.1922],[-79.1792,33.1919],[-79.1778,33.1914],[-79.1769,33.1914],[-79.1764,33.1919],[-79.1758,33.1917],[-79.1708,33.1917],[-79.1706,33.1914],[-79.1692,33.1914],[-79.1689,33.1917],[-79.1622,33.1914],[-79.1619,33.1917],[-79.1603,33.1914],[-79.16,33.1917],[-79.1586,33.1917],[-79.1583,33.1919],[-79.1567,33.1919],[-79.1564,33.1917],[-79.1531,33.1908],[-79.1519,33.1908],[-79.1517,33.1911],[-79.1417,33.1908],[-79.1411,33.1903],[-79.1408,33.1906],[-79.1406,33.1894],[-79.1439,33.1892],[-79.1492,33.1889],[-79.1494,33.1892],[-79.1522,33.1897],[-79.1525,33.1894],[-79.1561,33.19],[-79.1564,33.1903],[-79.1575,33.1903],[-79.1578,33.19],[-79.1614,33.19],[-79.1617,33.1897],[-79.1633,33.1897],[-79.1636,33.19],[-79.165,33.19],[-79.1664,33.1892],[-79.1683,33.1897],[-79.1689,33.1897],[-79.1703,33.1897],[-79.1728,33.1897],[-79.1731,33.1894],[-79.1753,33.1897],[-79.1756,33.1894],[-79.18,33.1894],[-79.1831,33.1906],[-79.1856,33.1881],[-79.1886,33.1825],[-79.1878,33.1794],[-79.1881,33.1792],[-79.1906,33.1758],[-79.1983,33.1686],[-79.1992,33.1686],[-79.2014,33.1661],[-79.2014,33.1656],[-79.2036,33.1636],[-79.2055,33.1616],[-79.2075,33.1597],[-79.2078,33.1597],[-79.2128,33.1578],[-79.2139,33.1567],[-79.2147,33.1567],[-79.2161,33.155],[-79.2164,33.155],[-79.2175,33.1539],[-79.2183,33.1539],[-79.2192,33.1531],[-79.2258,33.1481],[-79.226,33.1481],[-79.2292,33.1453],[-79.2294,33.1453],[-79.2306,33.1439],[-79.2319,33.1419],[-79.2322,33.1419],[-79.235,33.1389],[-79.235,33.1386],[-79.2361,33.1367],[-79.2381,33.1347],[-79.2414,33.1353],[-79.2414,33.1361],[-79.2403,33.1372],[-79.2406,33.1375],[-79.2428,33.1392],[-79.2442,33.1392],[-79.2444,33.1389],[-79.2453,33.1389],[-79.2453,33.1383],[-79.2461,33.1369],[-79.2461,33.1339],[-79.2469,33.1331],[-79.2469,33.1322],[-79.2472,33.1294],[-79.2475,33.1292],[-79.2464,33.1272],[-79.2461,33.1272],[-79.2467,33.125],[-79.2506,33.1244],[-79.2511,33.1239],[-79.2517,33.1239],[-79.2521,33.1234],[-79.2706,33.1189],[-79.2714,33.1203],[-79.2708,33.1208],[-79.2719,33.1242],[-79.2731,33.1242],[-79.2747,33.125],[-79.2758,33.1231],[-79.2804,33.1281],[-79.2853,33.1318],[-79.2913,33.1346],[-79.2977,33.1388],[-79.3054,33.1403],[-79.3136,33.1422],[-79.3207,33.1428],[-79.3261,33.1429],[-79.3272,33.1433],[-79.3316,33.1443],[-79.336,33.1458],[-79.3397,33.1481],[-79.3441,33.15],[-79.349,33.1528],[-79.3506,33.1546],[-79.3609,33.1607],[-79.3734,33.1659],[-79.3852,33.1761],[-79.394,33.1775],[-79.4021,33.1831],[-79.4101,33.1923],[-79.4138,33.1978],[-79.4273,33.2094],[-79.4349,33.2122],[-79.4387,33.215],[-79.447,33.2138],[-79.453,33.2157],[-79.4562,33.2184],[-79.4523,33.2225],[-79.4522,33.2266],[-79.4587,33.2317],[-79.4642,33.2327],[-79.4741,33.2296],[-79.4746,33.2319],[-79.469,33.2364],[-79.469,33.2382],[-79.4723,33.2391],[-79.4806,33.2347],[-79.4838,33.237],[-79.4777,33.2442],[-79.4815,33.2465],[-79.4831,33.2457],[-79.492,33.238],[-79.5008,33.2391],[-79.5166,33.2456],[-79.5259,33.2462],[-79.5352,33.2491],[-79.5384,33.2532],[-79.5461,33.2547],[-79.5476,33.2601],[-79.5502,33.2652],[-79.5553,33.2616],[-79.5586,33.2603],[-79.5613,33.2612],[-79.5579,33.2685],[-79.5644,33.2731],[-79.5726,33.2764],[-79.5895,33.2775],[-79.5983,33.279],[-79.6066,33.2791],[-79.6087,33.2814],[-79.6168,33.2874],[-79.6305,33.293],[-79.6452,33.2959],[-79.6507,33.2964],[-79.6573,33.2965],[-79.6672,33.2985],[-79.6769,33.3045],[-79.6758,33.3067],[-79.673,33.3108],[-79.6811,33.3182],[-79.6799,33.3236],[-79.6799,33.3273],[-79.6769,33.3377],[-79.6585,33.3539],[-79.654,33.3606],[-79.6385,33.37],[-79.634,33.3722],[-79.626,33.3899],[-79.612,33.4047],[-79.6058,33.4137],[-79.6019,33.4192],[-79.5963,33.4214],[-79.5916,33.4422],[-79.5865,33.4472],[-79.582,33.4512],[-79.5815,33.4521],[-79.5753,33.4584],[-79.5742,33.4593],[-79.5456,33.4867],[-79.5455,33.4913],[-79.5422,33.4944],[-79.5377,33.4971],[-79.5344,33.4989],[-79.4971,33.517],[-79.4932,33.5202],[-79.4881,33.5292],[-79.4841,33.5364],[-79.4791,33.5409],[-79.4595,33.557],[-79.4477,33.5641],[-79.4354,33.5731],[-79.4337,33.5762],[-79.4348,33.5767],[-79.4369,33.5826],[-79.4417,33.5932],[-79.4427,33.5955],[-79.4369,33.6113],[-79.4351,33.6176],[-79.431,33.6299],[-79.4242,33.6384],[-79.4196,33.6497],[-79.4194,33.6588],[-79.4193,33.6661],[-79.41,33.6892],[-79.3924,33.713],[-79.3722,33.7273],[-79.3586,33.7416],[-79.3479,33.7542],[-79.3456,33.7592],[-79.3386,33.775],[-79.3175,33.7802],[-79.3099,33.7751],[-79.3029,33.7627],[-79.2932,33.7539],[-79.2951,33.7444],[-79.2886,33.7374],[-79.2903,33.7325],[-79.2861,33.7229],[-79.2846,33.7178]]],[[[-79.2361,33.2842],[-79.2358,33.2839],[-79.2356,33.2839],[-79.235,33.2833],[-79.2347,33.2833],[-79.2344,33.2831],[-79.2342,33.2831],[-79.2339,33.2828],[-79.2336,33.2828],[-79.2333,33.2825],[-79.233,33.2825],[-79.2328,33.2825],[-79.2325,33.2822],[-79.2317,33.2822],[-79.2314,33.2819],[-79.2294,33.2814],[-79.2283,33.2806],[-79.2281,33.2806],[-79.2258,33.2797],[-79.2236,33.2778],[-79.2236,33.2775],[-79.2236,33.2772],[-79.2244,33.2764],[-79.2244,33.2758],[-79.2247,33.2756],[-79.2247,33.2733],[-79.2244,33.2731],[-79.2244,33.2728],[-79.2242,33.2725],[-79.2242,33.2722],[-79.2239,33.2719],[-79.2239,33.2714],[-79.2236,33.2711],[-79.2236,33.2706],[-79.2228,33.2697],[-79.2228,33.2694],[-79.2225,33.2692],[-79.2225,33.2686],[-79.2222,33.2683],[-79.2222,33.2678],[-79.2219,33.2675],[-79.2219,33.2669],[-79.2217,33.2667],[-79.2217,33.2664],[-79.2214,33.2661],[-79.2214,33.2658],[-79.2208,33.2653],[-79.2206,33.2653],[-79.2203,33.265],[-79.22,33.265],[-79.2197,33.2647],[-79.2194,33.2647],[-79.2192,33.2644],[-79.2189,33.2644],[-79.2186,33.2642],[-79.2183,33.2642],[-79.2181,33.2639],[-79.2181,33.2636],[-79.2178,33.2633],[-79.2178,33.2631],[-79.2181,33.2628],[-79.2181,33.2625],[-79.2189,33.2617],[-79.219,33.2617],[-79.2192,33.2617],[-79.2194,33.2614],[-79.2217,33.2614],[-79.2219,33.2617],[-79.2225,33.2617],[-79.2228,33.2619],[-79.2231,33.2619],[-79.2247,33.2636],[-79.225,33.2636],[-79.2253,33.2639],[-79.2256,33.2639],[-79.2261,33.2644],[-79.2264,33.2644],[-79.2269,33.265],[-79.2272,33.265],[-79.2275,33.2653],[-79.2278,33.2653],[-79.2281,33.2656],[-79.2289,33.2656],[-79.2353,33.2703],[-79.2352,33.2705],[-79.2381,33.2733],[-79.2381,33.2736],[-79.2386,33.2742],[-79.2386,33.2747],[-79.2389,33.275],[-79.2389,33.2756],[-79.2392,33.2758],[-79.2392,33.2778],[-79.2394,33.2781],[-79.2394,33.2789],[-79.2403,33.2797],[-79.24,33.2819],[-79.2394,33.2822],[-79.2392,33.2825],[-79.2392,33.2828],[-79.2386,33.2833],[-79.2386,33.2836],[-79.2381,33.2842],[-79.2378,33.2842],[-79.2361,33.2842]]],[[[-79.2275,33.2992],[-79.2272,33.2989],[-79.2267,33.2989],[-79.2261,33.2983],[-79.2261,33.2981],[-79.2256,33.2975],[-79.2256,33.2972],[-79.2253,33.2969],[-79.2253,33.2964],[-79.225,33.2961],[-79.225,33.2953],[-79.2247,33.295],[-79.2247,33.2936],[-79.2244,33.2933],[-79.2244,33.2925],[-79.2242,33.2922],[-79.2242,33.2919],[-79.2239,33.2917],[-79.2239,33.2914],[-79.2236,33.2911],[-79.2236,33.2908],[-79.2233,33.2906],[-79.2233,33.29],[-79.2231,33.2897],[-79.2231,33.2892],[-79.2228,33.2889],[-79.2228,33.2875],[-79.2225,33.2872],[-79.2225,33.2864],[-79.2222,33.2861],[-79.2225,33.2858],[-79.2225,33.2853],[-79.2226,33.2851],[-79.2242,33.2836],[-79.2242,33.2831],[-79.2239,33.2828],[-79.2236,33.2828],[-79.2218,33.281],[-79.2211,33.2803],[-79.2211,33.28],[-79.2206,33.2794],[-79.2213,33.2787],[-79.2217,33.2783],[-79.2219,33.2783],[-79.2222,33.2781],[-79.2228,33.2786],[-79.2228,33.2792],[-79.2231,33.2794],[-79.2231,33.2797],[-79.2244,33.2811],[-79.2247,33.2811],[-79.225,33.2814],[-79.2253,33.2814],[-79.2256,33.2811],[-79.2267,33.2822],[-79.2311,33.2836],[-79.2314,33.2836],[-79.2317,33.2839],[-79.2319,33.2839],[-79.2322,33.2842],[-79.2328,33.2842],[-79.2331,33.2844],[-79.2336,33.2844],[-79.2344,33.2853],[-79.2344,33.2864],[-79.2342,33.2867],[-79.2342,33.2872],[-79.2339,33.2875],[-79.2339,33.2878],[-79.2336,33.2881],[-79.2336,33.2886],[-79.2333,33.2889],[-79.2333,33.29],[-79.2332,33.2902],[-79.2331,33.2903],[-79.2331,33.2911],[-79.2328,33.2914],[-79.2328,33.2917],[-79.2325,33.2919],[-79.2325,33.2922],[-79.2322,33.2925],[-79.2322,33.2928],[-79.2319,33.2931],[-79.2319,33.2936],[-79.2317,33.2939],[-79.2317,33.2942],[-79.2314,33.2944],[-79.2314,33.295],[-79.2311,33.2953],[-79.2311,33.2956],[-79.2308,33.2958],[-79.2308,33.2961],[-79.2306,33.2964],[-79.2306,33.2967],[-79.2283,33.2989],[-79.2281,33.2989],[-79.2278,33.2992],[-79.2275,33.2992]]],[[[-79.2717,33.2822],[-79.2717,33.2797],[-79.27,33.2797],[-79.2686,33.2764],[-79.2692,33.2758],[-79.2692,33.2739],[-79.2669,33.2717],[-79.2669,33.2714],[-79.2658,33.2703],[-79.2653,33.2697],[-79.2644,33.2697],[-79.2611,33.2678],[-79.2597,33.2664],[-79.2589,33.2664],[-79.2581,33.2656],[-79.2569,33.2656],[-79.2567,33.2658],[-79.2564,33.2658],[-79.2561,33.2661],[-79.255,33.2661],[-79.2547,33.2658],[-79.2542,33.2658],[-79.2539,33.2656],[-79.2536,33.2656],[-79.2533,33.2653],[-79.2528,33.2653],[-79.2525,33.265],[-79.2522,33.265],[-79.2519,33.2647],[-79.2511,33.2647],[-79.2508,33.2644],[-79.2503,33.2644],[-79.25,33.2642],[-79.2497,33.2642],[-79.2486,33.2631],[-79.2483,33.2631],[-79.2478,33.2625],[-79.2475,33.2624],[-79.2472,33.2622],[-79.2469,33.2622],[-79.2461,33.2614],[-79.2469,33.2606],[-79.2483,33.2606],[-79.2486,33.2603],[-79.2503,33.2603],[-79.2506,33.26],[-79.2508,33.26],[-79.2511,33.2603],[-79.2514,33.2603],[-79.2517,33.2606],[-79.2522,33.2606],[-79.2525,33.2608],[-79.2528,33.2608],[-79.253,33.2612],[-79.2536,33.2611],[-79.2539,33.2614],[-79.2542,33.2614],[-79.2544,33.2617],[-79.2547,33.2617],[-79.255,33.2619],[-79.2553,33.2619],[-79.2556,33.2622],[-79.2558,33.2622],[-79.2561,33.2625],[-79.2566,33.2626],[-79.257,33.2628],[-79.2572,33.2628],[-79.2581,33.2636],[-79.2581,33.2642],[-79.2586,33.2642],[-79.26,33.2656],[-79.2608,33.2656],[-79.2622,33.267],[-79.2656,33.2683],[-79.2669,33.2683],[-79.2683,33.2697],[-79.2683,33.27],[-79.2694,33.2711],[-79.2706,33.2711],[-79.2719,33.2725],[-79.2753,33.275],[-79.2753,33.2753],[-79.2772,33.2772],[-79.2772,33.2792],[-79.2786,33.2806],[-79.275,33.2842],[-79.2736,33.2842],[-79.2717,33.2822]]],[[[-79.2772,33.3061],[-79.2733,33.3022],[-79.2728,33.3022],[-79.27,33.2994],[-79.2697,33.2997],[-79.2689,33.2997],[-79.2658,33.2964],[-79.2614,33.2919],[-79.2611,33.2919],[-79.2608,33.2917],[-79.2608,33.2914],[-79.2611,33.2917],[-79.2642,33.2917],[-79.2667,33.2942],[-79.2692,33.2967],[-79.27,33.2967],[-79.2703,33.2969],[-79.2706,33.2969],[-79.2711,33.2975],[-79.2714,33.2975],[-79.2719,33.2981],[-79.2722,33.2981],[-79.2736,33.2994],[-79.275,33.2994],[-79.2778,33.3022],[-79.2778,33.3031],[-79.2794,33.3047],[-79.2794,33.3061],[-79.2775,33.3081],[-79.2772,33.3061]]],[[[-79.2681,33.3367],[-79.2681,33.3361],[-79.2675,33.3361],[-79.2669,33.3356],[-79.2669,33.3353],[-79.2667,33.335],[-79.2667,33.3347],[-79.2664,33.3344],[-79.2664,33.3339],[-79.2658,33.3333],[-79.2658,33.3308],[-79.266,33.3306],[-79.2661,33.3275],[-79.2661,33.3253],[-79.2664,33.325],[-79.2698,33.3246],[-79.2717,33.3244],[-79.2733,33.3244],[-79.2747,33.3258],[-79.2753,33.327],[-79.2761,33.3287],[-79.2761,33.3336],[-79.2756,33.3342],[-79.2733,33.3361],[-79.2719,33.3361],[-79.2703,33.3378],[-79.2692,33.3378],[-79.2681,33.3367]]],[[[-79.2081,33.2864],[-79.2078,33.2861],[-79.2072,33.2861],[-79.2064,33.2853],[-79.2064,33.2844],[-79.2067,33.2842],[-79.2067,33.2833],[-79.2069,33.2831],[-79.2069,33.2828],[-79.2081,33.2817],[-79.2081,33.2811],[-79.2086,33.2806],[-79.2089,33.2806],[-79.2094,33.2811],[-79.2092,33.2814],[-79.2092,33.2828],[-79.2089,33.2831],[-79.2089,33.2833],[-79.2092,33.2836],[-79.2092,33.2839],[-79.2094,33.2842],[-79.2094,33.2847],[-79.2092,33.285],[-79.2092,33.2856],[-79.2083,33.2864],[-79.2081,33.2864]]],[[[-79.2469,33.2836],[-79.2456,33.2822],[-79.245,33.2822],[-79.2436,33.2808],[-79.2433,33.2808],[-79.2419,33.2794],[-79.2422,33.2792],[-79.2439,33.2792],[-79.2444,33.2797],[-79.245,33.2797],[-79.2453,33.2794],[-79.2456,33.2794],[-79.2458,33.2797],[-79.2472,33.2797],[-79.2483,33.2808],[-79.2483,33.2811],[-79.2489,33.2817],[-79.2489,33.2819],[-79.2494,33.2825],[-79.2494,33.2828],[-79.2497,33.2831],[-79.2497,33.2833],[-79.2506,33.2842],[-79.2503,33.2844],[-79.2486,33.2842],[-79.2469,33.2836]]],[[[-79.2694,33.3203],[-79.2717,33.3181],[-79.2719,33.3183],[-79.2722,33.3183],[-79.2722,33.3186],[-79.2725,33.3189],[-79.2725,33.3192],[-79.2722,33.3194],[-79.2722,33.3197],[-79.2711,33.3208],[-79.2708,33.3208],[-79.2706,33.3211],[-79.2703,33.3211],[-79.27,33.3208],[-79.2694,33.3203]]]]},\"properties\":{\"name\":\"Georgetown\",\"state\":\"SC\"}}]}","volume":"13","noUsgsAuthors":false,"publicationDate":"2022-12-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Lawson, Abigail Jean 0000-0002-2799-8750","orcid":"https://orcid.org/0000-0002-2799-8750","contributorId":276319,"corporation":false,"usgs":true,"family":"Lawson","given":"Abigail","email":"","middleInitial":"Jean","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":903449,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":903450,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rainwater, Thomas R.","contributorId":338672,"corporation":false,"usgs":false,"family":"Rainwater","given":"Thomas R.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":903451,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Kylee Denise 0000-0002-9249-0590","orcid":"https://orcid.org/0000-0002-9249-0590","contributorId":296991,"corporation":false,"usgs":true,"family":"Dunham","given":"Kylee","email":"","middleInitial":"Denise","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":903452,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Morgan","contributorId":338673,"corporation":false,"usgs":false,"family":"Hart","given":"Morgan","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903453,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Butfiloski, Joseph W.","contributorId":338675,"corporation":false,"usgs":false,"family":"Butfiloski","given":"Joseph","email":"","middleInitial":"W.","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903454,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wilkinson, Philip M.","contributorId":338676,"corporation":false,"usgs":false,"family":"Wilkinson","given":"Philip M.","affiliations":[{"id":54598,"text":"Tom Yawkey Wildlife Center","active":true,"usgs":false}],"preferred":false,"id":903455,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moore, Clinton 0000-0001-7782-3994 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0001-7782-3994","contributorId":338679,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903456,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70250707,"text":"70250707 - 2022 - Critical ShakeCast lifeline users and their response protocols","interactions":[],"lastModifiedDate":"2023-12-28T12:59:02.090215","indexId":"70250707","displayToPublicDate":"2022-12-28T06:55:39","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Critical ShakeCast lifeline users and their response protocols","docAbstract":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The Walker River originates in the Sierra Nevada Mountains and flows nearly 160 miles to its terminus at Walker Lake in west-central Nevada. The river provides a source of irrigation water for tens of thousands of acres of agricultural lands in California and Nevada and is the principal source of inflow to Walker Lake. Extraction of groundwater for agricultural use became prevalent in the late 1950s and early 1960s to supplement irrigation demands not met by surface-water diversions during times of drought. There is growing concern that continued groundwater withdrawals within the Walker River Basin are likely contributing to depleted streamflow of the Walker River and the long-term depletion of groundwater storage in the basin. This report documents changes in groundwater storage-volume and trends in Walker River stream efficiency, a measure of change in flow due to gaining or losing conditions, in the two largest agricultural valleys in the Walker River Basin, Smith and Mason Valleys, for a multi-decade period. Groundwater-level maps from previous studies were used for the beginning (1970) and middle (2006) points of this study. Groundwater levels measured from 1991–95 and 2016–20 were used to construct median groundwater-level maps that represented conditions in 1995 and 2020. Valley wide groundwater-level change was calculated by comparing groundwater-level maps for the periods 1970–95, 1996–2006, and 2007–20 and by observing the overall change from 1970 to 2020. Groundwater storage-volume change was calculated using groundwater-level change and previously defined specific yield values. Between 1970 and 2020, groundwater storage-volume declined 287,600 acre-feet in Smith Valley and 269,000 acre-feet in Mason Valley. Using groundwater storage-volume decline and annual groundwater pumpage rates, a maximum groundwater pumpage rate can be computed to support management of water resources. In Smith Valley, groundwater pumping in excess of 22,300 acre-feet per year would likely result in groundwater storage decline. In Mason Valley, groundwater pumping in excess of 75,200 acre-feet per year would likely result in groundwater storage decline. Stream efficiency was calculated using continuous streamflow data and monthly diversion volumes on two reaches: (1) the West Walker River in Smith Valley, from 1948 to 2020 and (2) the Walker River in Mason Valley, from 1958 to 2020. Stream efficiency during non-irrigation season in Smith and Mason Valleys declined at a statistically significant rate of 1.1 and 0.6 percent per year, respectively. Trends in stream efficiency corresponded to occurrence of prolonged drought, deviation from average annual streamflows, and total groundwater pumpage. Long-term declines in groundwater storage-volume and stream efficiency demonstrate that the alluvial aquifer system is becoming increasingly depleted, such that the river can no longer replenish groundwater storage while simultaneously balancing groundwater and surface-water withdrawals. The introduction of supplemental groundwater pumpage was intended to offset surface-water deficits during dry years; however, pumpage occurs even in years when average or above average streamflows meet surface-water demands. Reliance on supplemental groundwater pumpage has resulted in widespread groundwater storage-volume decline and decreased stream efficiency. With each successive drought cycle, the ability of Walker River to sustain streamflows and convey water downstream has diminished. Above average wet periods have a marginal and short-lived effect on rebounding the groundwater levels outside of the river corridor. Moreover, if the trend continues, each future drought cycle may further reduce groundwater supplies and that may further decrease streamflow reliability.
Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road, Suite 3
Carson City, Nevada 89701
The southwestern pond turtle (Actinemys pallida) is a semiaquatic turtle that occasionally spends time on land to bask, oviposit, make intermittent overland movements, and overwinter in terrestrial locations. Use of both aquatic and terrestrial environments exposes semiaquatic turtles to increased risk of injury or mortality from floods, predation attempts, and other environmental hazards (e.g., human activities such as vehicle strikes, etc.). We collected injury and morphological abnormality data from adult turtles at 3 study sites along the length of the Mojave River in San Bernardino County, California: 1 site on the upper half of the Mojave River (hereafter known as UHMRS) and 2 sites each on the lower half of the Mojave River (hereafter known as LHMRS). The studies were conducted when turtles were most active between May and October 1998–1999 and again from April to September 2016–2019. A total of 84 A. pallida were captured among all sites and all years. Seventeen percent (n = 8) of the turtles captured at UHMRS exhibited shell abnormalities (natural variations in shell or bone morphology). Injuries (damage inflicted by force to the shell or body) occurred in 68% (n = 26) of captured turtles at both the LHMRS sites combined and 78% (n = 36) of turtles captured at the UHMRS alone. A total of 74% (n = 62) of turtles had injuries at all sites combined. There was no statistical difference in the proportion of injured and noninjured turtles between the sexes for either the 2 LHMRS sites combined or the UHMRS. Mean carapace length was not significantly different between injured and noninjured turtles for these same sites. Injuries occurred in the majority of captured turtles at all sites and may be an indicator of the extent of threats facing these turtles.
Estimates of riparian vegetation water use are important for hydromorphological assessment, partitioning within human and natural environments, and informing environmental policy decisions. The objectives of this study were to calculate the actual evapotranspiration (ETa) (mm/day and mm/year) and derive riparian vegetation annual consumptive use (CU) in acre-feet (AF) for select riparian areas of the Little Colorado River watershed within the Navajo Nation, in northeastern Arizona, USA. This was accomplished by first estimating the riparian land cover area for trees and shrubs using a 2019 summer scene from National Agricultural Imagery Program (NAIP) (1 m resolution), and then fusing the riparian delineation with Landsat-8 OLI (30-m) to estimate ETa for 2014–2020. We used indirect remote sensing methods based on gridded weather data, Daymet (1 km) and PRISM (4 km), and Landsat measurements of vegetation activity using the two-band Enhanced Vegetation Index (EVI2). Estimates of potential ET were calculated using Blaney-Criddle. Riparian ETa was quantified using the Nagler ET(EVI2) approach. Using both vector and raster estimates of tree, shrub, and total riparian area, we produced the first CU measurements for this region. Our best estimate of annual CU is 36,983 AF with a range between 31,648–41,585 AF and refines earlier projections of 25,387–46,397 AF.
","language":"English","publisher":"MDPI","doi":"10.3390/rs15010052","usgsCitation":"Nagler, P.L., Barreto-Muñoz, A., Sall, I., Lurtz, M.R., and Didan, K., 2022, Riparian plant evapotranspiration and consumptive use for selected areas of the Little Colorado River watershed on the Navajo Nation: Remote Sensing, v. 15, no. 1, 52, 37 p.; Data Release, https://doi.org/10.3390/rs15010052.","productDescription":"52, 37 p.; Data Release","ipdsId":"IP-143742","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":445627,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs15010052","text":"Publisher Index Page"},{"id":435592,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EFZWPP","text":"USGS data release","linkHelpText":"Uncultivated plant water use (riparian evapotranspiration) and consumptive use data for selected areas of the Little Colorado River watershed on the Navajo Nation, Arizona"},{"id":411050,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah","otherGeospatial":"Hopi Reservation, Little Colorado River Watershed, Navajo Nation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.27451835472442,\n 37.936722934098526\n ],\n [\n -112.27451835472442,\n 33.63417184178236\n ],\n [\n -108.7808660109742,\n 33.63417184178236\n ],\n [\n -108.7808660109742,\n 37.936722934098526\n ],\n [\n -112.27451835472442,\n 37.936722934098526\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":859997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barreto-Muñoz, Armando","contributorId":239891,"corporation":false,"usgs":false,"family":"Barreto-Muñoz","given":"Armando","affiliations":[{"id":48028,"text":"University of Arizona, Biosystems Engineering, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":859998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sall, Ibrahima 0000-0002-7526-636X","orcid":"https://orcid.org/0000-0002-7526-636X","contributorId":251750,"corporation":false,"usgs":false,"family":"Sall","given":"Ibrahima","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":859999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lurtz, Matthew R.","contributorId":300337,"corporation":false,"usgs":false,"family":"Lurtz","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":65088,"text":"Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, 80523 USA","active":true,"usgs":false}],"preferred":false,"id":860000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Didan, Kamel","contributorId":292780,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":860001,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70257013,"text":"70257013 - 2022 - Do unpublished data help to redraw distributions? The case of the spectacled bear in Peru","interactions":[],"lastModifiedDate":"2024-09-04T15:45:23.281028","indexId":"70257013","displayToPublicDate":"2022-12-22T08:39:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5278,"text":"Mammal Research","active":true,"publicationSubtype":{"id":10}},"title":"Do unpublished data help to redraw distributions? The case of the spectacled bear in Peru","docAbstract":"Data availability remains a principal factor limiting the use of species distribution models (SDMs) as tools for wildlife conservation and management of rare species. Although data collected in systematic and rigorous fashion are preferable, available data for most species of conservation interest are usually low in both quality and number. Here we show that combining records published in peer-reviewed journals and gray literature sources (e.g., theses, government, and NGO reports) with unpublished records obtained by personal communications from relevant stakeholders affect the predicted distribution of spectacled bears (Tremarctos ornatus) in Peru. We built SDMs using generalized linear models, random forest, and Maxent, first using a dataset that only included published records, and second with a dataset using both published and unpublished records. All models were replicated ten times with random subsets with controlled sample size. Models that combined published and unpublished spectacled bear records had a better performance, irrespective of with SDM method used, increasing the connectivity of the species’ range, and increasing the overall predicted distribution area than models that only included published records. This was because unpublished records added key new localities, reducing spatial sampling biases. Our study shows that the inclusion of commonly disregarded data such as opportunistic records, reports from natural park rangers, student theses, and data-deficient small studies can make an important contribution to the overall ecological knowledge of rare and difficult-to-study species such as the spectacled bear.
","language":"English","publisher":"Springer Link","doi":"10.1007/s13364-022-00664-0","usgsCitation":"Falconi, N., Finn, J.T., Fuller, T., and Organ, J.F., 2022, Do unpublished data help to redraw distributions? The case of the spectacled bear in Peru: Mammal Research, v. 68, p. 143-150, https://doi.org/10.1007/s13364-022-00664-0.","productDescription":"8 p.","startPage":"143","endPage":"150","ipdsId":"IP-119469","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Peru","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-69.59042,-17.58001],[-69.85844,-18.09269],[-70.37257,-18.34798],[-71.37525,-17.7738],[-71.46204,-17.36349],[-73.44453,-16.35936],[-75.23788,-15.26568],[-76.00921,-14.64929],[-76.42347,-13.82319],[-76.25924,-13.53504],[-77.10619,-12.22272],[-78.09215,-10.37771],[-79.03695,-8.38657],[-79.44592,-7.93083],[-79.76058,-7.19434],[-80.53748,-6.54167],[-81.25,-6.13683],[-80.92635,-5.69056],[-81.41094,-4.73676],[-81.09967,-4.03639],[-80.30256,-3.40486],[-80.18401,-3.82116],[-80.46929,-4.05929],[-80.44224,-4.42572],[-80.02891,-4.34609],[-79.62498,-4.4542],[-79.20529,-4.95913],[-78.6399,-4.54778],[-78.45068,-3.8731],[-77.8379,-3.00302],[-76.63539,-2.60868],[-75.545,-1.56161],[-75.23372,-0.91142],[-75.37322,-0.15203],[-75.10662,-0.05721],[-74.4416,-0.53082],[-74.1224,-1.00283],[-73.6595,-1.26049],[-73.07039,-2.30895],[-72.32579,-2.43422],[-71.77476,-2.16979],[-71.41365,-2.3428],[-70.81348,-2.25686],[-70.04771,-2.72516],[-70.69268,-3.74287],[-70.39404,-3.76659],[-69.89364,-4.29819],[-70.79477,-4.25126],[-70.92884,-4.40159],[-71.74841,-4.59398],[-72.89193,-5.27456],[-72.96451,-5.74125],[-73.21971,-6.08919],[-73.12003,-6.62993],[-73.72449,-6.9186],[-73.7234,-7.341],[-73.98724,-7.52383],[-73.57106,-8.42445],[-73.01538,-9.03283],[-73.22671,-9.46221],[-72.56303,-9.52019],[-72.18489,-10.0536],[-71.30241,-10.07944],[-70.48189,-9.49012],[-70.54869,-11.00915],[-70.09375,-11.12397],[-69.52968,-10.95173],[-68.66508,-12.5613],[-68.88008,-12.89973],[-68.92922,-13.60268],[-68.94889,-14.45364],[-69.33953,-14.9532],[-69.16035,-15.32397],[-69.38976,-15.66013],[-68.95964,-16.5007],[-69.59042,-17.58001]]]},\"properties\":{\"name\":\"Peru\"}}]}","volume":"68","noUsgsAuthors":false,"publicationDate":"2022-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Falconi, Nereyda","contributorId":272944,"corporation":false,"usgs":false,"family":"Falconi","given":"Nereyda","email":"","affiliations":[],"preferred":false,"id":909147,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finn, John T.","contributorId":43398,"corporation":false,"usgs":false,"family":"Finn","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":16720,"text":"Department of Environmental Conservation, University of Massachusetts, Amherst, MA 01003-9485, USA","active":true,"usgs":false}],"preferred":false,"id":909148,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuller, Todd K.","contributorId":270781,"corporation":false,"usgs":false,"family":"Fuller","given":"Todd K.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":909149,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Organ, John F. 0000-0002-0959-0639 jorgan@usgs.gov","orcid":"https://orcid.org/0000-0002-0959-0639","contributorId":189047,"corporation":false,"usgs":true,"family":"Organ","given":"John","email":"jorgan@usgs.gov","middleInitial":"F.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":909150,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70239113,"text":"70239113 - 2022 - Models combining multiple scales of inference capture hydrologic and climatic drivers of riparian tree distributions","interactions":[],"lastModifiedDate":"2022-12-28T14:04:34.673006","indexId":"70239113","displayToPublicDate":"2022-12-22T08:00:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Models combining multiple scales of inference capture hydrologic and climatic drivers of riparian tree distributions","docAbstract":"Predicting species geographic distributions is key to managing invasive species, conserving biodiversity, and understanding species' environmental requirements. Species distribution models (SDMs) commonly focus on climatic predictors, but other environmental factors can also be essential, particularly for species with specialized habitats defined by hydrologic, topographic, or edaphic conditions (e.g., riparian, wetland, alpine, coastal, serpentine). Here, we demonstrate a novel approach for capturing strong effects of both hydrologic and climatic predictors in SDMs for riparian plants, by merging analyses targeted at environmental drivers within riparian ecosystems and across the western USA (3.8 × 106 km2). We developed presence-background SDMs from five algorithms for three invasive riparian trees (Tamarix ramossisima/chinensis [saltcedar], Elaeagnus angustifolia [Russian olive], and Ulmus pumila [Siberian elm]) and three native Populus spp. (cottonwoods). We used separate background datasets to develop models with different spatial scales of inference: (1) spatially filtered random points to represent available habitat across the study area and (2) target-group points from Salix (willow) occurrences to represent available riparian habitat. Random-background models captured hydrologic drivers of riparian tree distributions relative to the largely upland western USA, whereas Salix-background models captured climatic drivers within the context of riparian ecosystems. Combining predictions from the two backgrounds identified hydrologically suitable habitats within climatically suitable regions, resulting in fewer false “absences” than either background alone, improving predictions over previous SDMs, and providing more complete information to guide management decisions. Surprisingly, the predicted habitat for U. pumila, a newly recognized riparian invader, was as or more extensive than Populus deltoides/fremontii, T. ramossisima/chinensis, and E. angustifolia, the most common riparian tree complexes in the western USA. Watersheds constituting 20% of U. pumila predicted habitat contained no occurrence records, indicating high risk of future and unrecognized invasions. Combining models from random and ecosystem-specific target-group backgrounds may improve SDMs for species from many specialized habitats, providing a method to link predicted distributions to localized geographic features while capturing broad-scale climatic requirements.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4305","usgsCitation":"Perry, L.G., Jarnevich, C.S., and Shafroth, P., 2022, Models combining multiple scales of inference capture hydrologic and climatic drivers of riparian tree distributions: Ecosphere, v. 13, no. 12, e4305, 22 p., https://doi.org/10.1002/ecs2.4305.","productDescription":"e4305, 22 p.","ipdsId":"IP-133461","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":445636,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4305","text":"Publisher Index Page"},{"id":435593,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LIB2TF","text":"USGS data release","linkHelpText":"Occurrence data and models for woody riparian native and invasive plant species in the conterminous western USA"},{"id":411118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100,\n 49\n ],\n [\n -124,\n 49\n ],\n [\n -124,\n 28\n ],\n [\n -100,\n 28\n ],\n [\n -100,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Perry, Laura G","contributorId":177873,"corporation":false,"usgs":false,"family":"Perry","given":"Laura","email":"","middleInitial":"G","affiliations":[],"preferred":false,"id":860091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":860092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":225182,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":860093,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239072,"text":"70239072 - 2022 - Global ocean wave fields show consistent regional trends between 1980 and 2014 in a multi-product ensemble","interactions":[],"lastModifiedDate":"2022-12-23T12:46:31.884316","indexId":"70239072","displayToPublicDate":"2022-12-21T06:37:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8956,"text":"Communications Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Global ocean wave fields show consistent regional trends between 1980 and 2014 in a multi-product ensemble","docAbstract":"Historical trends in the direction and magnitude of ocean surface wave height, period, or direction are debated due to diverse data, time-periods, or methodologies. Using a consistent community-driven ensemble of global wave products, we quantify and establish regions with robust trends in global multivariate wave fields between 1980 and 2014. We find that about 30–40% of the global ocean experienced robust seasonal trends in mean and extreme wave height, period, and direction. Most of the Southern Hemisphere exhibited strong upward-trending wave heights (1–2 cm per year) and periods during winter and summer. Ocean basins with robust positive trends are far larger than those with negative trends. Historical trends calculated over shorter periods generally agree with satellite records but vary from product to product, with some showing a consistently negative bias. Variability in trends across products and time-periods highlights the importance of considering multiple sources when seeking robust change analyses.
This document is the second of three standard operating procedures providing instructions and considerations for conducting mobile acoustic surveys along road transects to collect bat acoustic data following the North American Bat Monitoring Program (NABat) protocol and sample design. This standard operating procedure focuses specifically on considerations for establishing the field survey season and preparing to conduct mobile acoustic transect surveys. Intended audiences for this document include those in charge of facilitating surveys within their region (for example, state or provincial mangers and NABat regional hub coordinators), project leaders or survey coordinators responsible for setting up and organizing NABat mobile transect monitoring for their organization or area, and field staff preparing to conduct surveys within the field.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2C2","usgsCitation":"Martin, J., Rae, J., Hall, M., Ferrall, E., Li, H., Straw, B., and Reichert, B., 2022, North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 2—Field Season and Survey Preparation: U.S. Geological Survey Techniques and Methods 2–C2, 9 p., https://doi.org/10.3133/tm2C2.","productDescription":"vi, 9 p.","onlineOnly":"Y","ipdsId":"IP-129325","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":411437,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/tm2C2/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"T and M 2-C2"},{"id":410859,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/02/c2/tm2c2.xml"},{"id":410858,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/02/c2/images"},{"id":410374,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm2C3","text":"USGS Techniques and Methods 2-C3—","linkHelpText":"North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 3—Conducting Mobile Transect Surveys"},{"id":410373,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm2C1","text":"USGS Techniques and Methods 2-C1—","linkHelpText":"North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 1—Locating and Establishing Mobile Transect Routes"},{"id":410372,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/c2/tm2c2.pdf","text":"Report","size":"928 kB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 2-C2"},{"id":410371,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/c2/coverthb.jpg"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118