The advent of continuous digital recording of seismograph stations in Alaska did not occur until the fall of 2002. Continuous records of seismic waveforms prior to 2002 were recorded only in analog form. The Alaska Volcano Observatory (AVO) has a substantial archive of continuous analog records made on helicorders in a collection maintained by the University of Alaska Fairbanks Geophysical Institute. As part of the response to the 2006 Augustine Volcano eruption, the AVO scanned analog drum records of the 1986 Augustine eruption to aid in comparing the progression of volcanic seismicity in 2006 with the seismic record of the 1986 eruption. The scanned records proved useful, prompting subsequent efforts to preserve records from other notable episodes of volcanic unrest as readily available scanned images. The data archive accompanying this report contains scanned images of select drum records for the eruptions at Augustine Volcano (1986), Redoubt Volcano (1989–90), Mount Spurr (1992), and Pavlof Volcano (1996), as well as for the 1996 earthquake swarm at Akutan Peak.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1146","usgsCitation":"Dixon, J.P., and Power, J.A., 2022, Alaska Volcano Observatory archive of seismic drum records of eruptions of Augustine Volcano (1986), Redoubt Volcano (1989–90), Mount Spurr (1992), and Pavlof Volcano (1996), and the 1996 earthquake swarm at Akutan Peak: U.S. Geological Survey Data Report 1146, 10 p., https://doi.org/10.3133/dr1146.","productDescription":"Report: v, 10 p.; 5 Databases","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-117992","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":501202,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112152.htm","linkFileType":{"id":5,"text":"html"}},{"id":394675,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_PavlofVolcano.zip","text":"Scanned images of select drum records for the 1996 eruption at Pavlof Volcano","size":"1.35 GB","linkFileType":{"id":6,"text":"zip"}},{"id":394672,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_AkutanVolcano.zip","text":"Scanned images of select drum records for the 1996 earthquake swarm at Akutan Volcano","size":"300 MB","linkFileType":{"id":6,"text":"zip"}},{"id":394674,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_MountSpurr.zip","text":"Scanned images of select drum records for the 1992 eruption at Mount Spurr","size":"620 MB","linkFileType":{"id":6,"text":"zip"}},{"id":394673,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_AugustineVolcano.zip","text":"Scanned images of select drum records for the 1986 eruption at Augustine Volcano","size":"150 MB","linkFileType":{"id":6,"text":"zip"}},{"id":394676,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_RedoubtVolcano.zip","text":"Scanned images of select drum records for the 1989–90 eruption at Redoubt Volcano","size":"2 GB","linkFileType":{"id":6,"text":"zip"}},{"id":394671,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1146/dr1146.pdf","text":"Report","size":"12 MB"},{"id":394670,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1146/covrthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Augustine Volcano, Redoubt Volcano, Mount Spurr, and Pavlof Volcano, Akutan Peak","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.6163330078125,\n 59.30726326510286\n ],\n [\n -153.31008911132812,\n 59.30726326510286\n ],\n [\n -153.31008911132812,\n 59.438791328713094\n ],\n [\n -153.6163330078125,\n 59.438791328713094\n ],\n [\n -153.6163330078125,\n 59.30726326510286\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.0999755859375,\n 60.212533353918424\n ],\n [\n -152.083740234375,\n 60.212533353918424\n ],\n [\n -152.083740234375,\n 60.53026587299222\n ],\n [\n -153.0999755859375,\n 60.53026587299222\n ],\n [\n -153.0999755859375,\n 60.212533353918424\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.8143310546875,\n 61.0649302984405\n ],\n [\n -151.907958984375,\n 61.0649302984405\n ],\n [\n -151.907958984375,\n 61.37435749785111\n ],\n [\n -152.8143310546875,\n 61.37435749785111\n ],\n [\n -152.8143310546875,\n 61.0649302984405\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -166.2286376953125,\n 53.98839506479995\n ],\n [\n -165.465087890625,\n 53.98839506479995\n ],\n [\n -165.465087890625,\n 54.271639968447985\n ],\n [\n -166.2286376953125,\n 54.271639968447985\n ],\n [\n -166.2286376953125,\n 53.98839506479995\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.11013793945312,\n 55.20865504825601\n ],\n [\n -161.57318115234375,\n 55.20865504825601\n ],\n [\n -161.57318115234375,\n 55.531739499542304\n ],\n [\n -162.11013793945312,\n 55.531739499542304\n ],\n [\n -162.11013793945312,\n 55.20865504825601\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory
U.S. Geological Survey
4210 University Drive
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The West Napa Fault has previously been mapped as extending ~45 kilometers (km) from northern Vallejo to southern Saint Helena, California, dominantly running along the western edge of Napa Valley. A zone of fault strands (some previously unmapped) along a ~15-km section of the fault ruptured during the 2014 magnitude 6.0 South Napa earthquake, illustrating the need for further investigation of this little-studied structure. Based on light detection and ranging (lidar) topography and field examination, the fault zone likely extends an additional 10 km or more northward past Saint Helena. In this vicinity, geomorphology suggests two fault strands, one along the range front and another associated with a line of rounded hills that rise 5–10 meters above the middle of the valley. In 2017, we excavated two trenches across an apparent fault scarp on the east side of one elongate hill near Ehlers Lane north of Saint Helena. Examination of the walls revealed three main sedimentary packages. The oldest package, weakly lithified alluvial fan gravels with local sand and silt layers, is tilted 25°–35° to the west. Overlying these tilted strata are two younger sets of strata. On the west side, underlying the crest of the scarp, are alluvial fan gravels with local sand and silt lenses, potentially tilted a few degrees to the west. On the east side, deposited against the scarp, are much finer grained (dominantly fine sand to silt) subhorizontal fluvial strata, likely overbank deposits from the Napa River. We obtained age control on the two younger units through a combination of radiocarbon, infrared-stimulated luminescence, and obsidian hydration dating, establishing that they are latest Pleistocene to modern in age. Although there are no prominent unconformities within the alluvial fan sediments, sample dating indicates there are two generations, one in the 10–20 thousand year (ka) age range and one in the <3 ka age range. Owing to a general lack of well-defined laterally continuous alluvial fan units, it is difficult to distinguish contacts between the two generations except in the immediate proximity of dated samples. The river sediments approximately span the Holocene. No faults were apparent in either trench, indicating that any fault related to the observed surface deformation has not ruptured to the surface at this site during the Holocene and is likely blind.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221002","usgsCitation":"Philibosian, B.E., Sickler, R.R., Prentice, C.S., Pickering, A.J., Gannon, P., Broudy, K.N., Mahan, S.A., Titular, J.N., Turner, E.A., Folmar, C., Patterson, S.F., and Bowman, E.E., 2022, Photomosaics and logs associated with study of West Napa Fault at Ehlers Lane, north of Saint Helena, California: U.S. Geological Survey Open-File Report 2022–1002, 1 sheet, pamphlet 8 p., https://doi.org/10.3133/ofr20221002.","productDescription":"Report: iv, 8 p.; 1 Sheet: 82.00 x 43.00 inches","numberOfPages":"8","additionalOnlineFiles":"Y","ipdsId":"IP-114127","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":501537,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112153.htm","linkFileType":{"id":5,"text":"html"}},{"id":394764,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2022/1002/ofr20221002_sheet.pdf","size":"80 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":394763,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1002/ofr20221002_pamphlet.pdf","text":"Pamphlet","size":"300 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":394762,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1002/covrthb.jpg"}],"country":"United States","state":"California","city":"Saint Helena","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.58407592773438,\n 38.47294404791815\n ],\n [\n -122.38494873046875,\n 38.47294404791815\n ],\n [\n -122.38494873046875,\n 38.586820096127674\n ],\n [\n -122.58407592773438,\n 38.586820096127674\n ],\n [\n -122.58407592773438,\n 38.47294404791815\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Menlo Park, Calif.
Office—Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
The palila (Loxioides bailleui) population on Mauna Kea Volcano, Hawai‘i Island, was estimated from annual surveys in 2019−2021, and a trend analysis was performed on survey data from 1998−2021. The 2019 population was estimated at 1,030−1,899 birds (point estimate: 1,432), the 2020 population was estimated at 964−1,700 birds (point estimate: 1,312), and the 2021 population was estimated at 452−940 birds (point estimate: 678). Since 1998, a visual inspection of the size of the area containing palila detections on the western slope based on the minimum/maximum elevations has not shown a substantial change, indicating that the range of the species has remained stable; although this area represents only about 5% of its historical extent. During 1998−2005, palila numbers fluctuated between 4,000 and 6,000, followed by a steep decline. After 2010, palila estimates stabilized around an abundance of 2,000 with a much slower rate of decline. The decline during 1998−2021 was on average 229 birds per year with very strong statistical support for an overall downward trend in abundance. Over the 23-year monitoring period, the estimated rate of change equated to an 89% decline in the population.
","language":"English","publisher":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","usgsCitation":"Genz, A., Brinck, K., Asing, C.K., Berry, L., Camp, R.J., and Banko, P.C., 2022, 2019-2021 Palila abundance estimates and trend: Hawaii Cooperative Studies Unit Technical Report 101, iii, 17 p.","productDescription":"iii, 17 p.","ipdsId":"IP-134919","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":395157,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":395121,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10790/6858"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Kea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.62477111816406,\n 19.728573985770815\n ],\n [\n -155.35,\n 19.728573985770815\n ],\n [\n -155.35,\n 19.91913050246103\n ],\n [\n -155.62477111816406,\n 19.91913050246103\n ],\n [\n -155.62477111816406,\n 19.728573985770815\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Genz, Ayesha 0000-0002-2916-1436","orcid":"https://orcid.org/0000-0002-2916-1436","contributorId":196671,"corporation":false,"usgs":false,"family":"Genz","given":"Ayesha","email":"","affiliations":[],"preferred":false,"id":832294,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brinck, Kevin W. 0000-0001-7581-2482 kbrinck@usgs.gov","orcid":"https://orcid.org/0000-0001-7581-2482","contributorId":3847,"corporation":false,"usgs":true,"family":"Brinck","given":"Kevin W.","email":"kbrinck@usgs.gov","affiliations":[],"preferred":false,"id":832319,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Asing, Chauncey K.","contributorId":272645,"corporation":false,"usgs":false,"family":"Asing","given":"Chauncey","email":"","middleInitial":"K.","affiliations":[{"id":40951,"text":"University of Hawai‘i - Mānoa","active":true,"usgs":false}],"preferred":false,"id":832296,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berry, Lainie","contributorId":272646,"corporation":false,"usgs":false,"family":"Berry","given":"Lainie","email":"","affiliations":[{"id":56397,"text":"State of Hawai‘i, Division of Forestry and Wildlife","active":true,"usgs":false}],"preferred":false,"id":832297,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":832298,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Banko, Paul C. 0000-0002-6035-9803 pbanko@usgs.gov","orcid":"https://orcid.org/0000-0002-6035-9803","contributorId":3179,"corporation":false,"usgs":true,"family":"Banko","given":"Paul","email":"pbanko@usgs.gov","middleInitial":"C.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":832299,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228233,"text":"70228233 - 2022 - Terrestrial ecosystem modeling with IBIS: Progress and future vision","interactions":[],"lastModifiedDate":"2022-03-30T15:19:39.880176","indexId":"70228233","displayToPublicDate":"2022-01-24T09:21:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5535,"text":"Journal of Resources and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Terrestrial ecosystem modeling with IBIS: Progress and future vision","docAbstract":"Dynamic Global Vegetation Models (DGVM) are powerful tools for studying complicated ecosystem processes and global changes. This review article synthesizes the developments and applications of the Integrated Biosphere Simulator (IBIS), a DGVM, over the past two decades. IBIS has been used to evaluate carbon, nitrogen, and water cycling in terrestrial ecosystems, vegetation changes, land-atmosphere interactions, land-aquatic system integration, and climate change impacts. Here we summarize model development work since IBIS v2.5, covering hydrology (evapotranspiration, groundwater, lateral routing), vegetation dynamics (plant functional type, land cover change), plant physiology (phenology, photosynthesis, carbon allocation, growth), biogeochemistry (soil carbon and nitrogen processes, greenhouse gas emissions), impacts of natural disturbances (drought, insect damage, fire) and human induced land use changes, and computational improvements. We also summarize IBIS model applications around the world in evaluating ecosystem productivity, carbon and water budgets, water use efficiency, natural disturbance effects, and impacts of climate change and land use change on the carbon cycle. Based on this review, visions of future cross-scale, cross-landscape and cross-system model development and applications are discussed.
","language":"English","publisher":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences","doi":"10.5814/j.issn.1674-764x.2022.01.001","usgsCitation":"Liu, J., Lu, X., Zhu, Q., Yuan, W., Yuan, Q., Zhang, Z., Guo, Q., and Deering, C., 2022, Terrestrial ecosystem modeling with IBIS: Progress and future vision: Journal of Resources and Ecology, v. 13, no. 1, p. 2-16, https://doi.org/10.5814/j.issn.1674-764x.2022.01.001.","productDescription":"15 p.","startPage":"2","endPage":"16","ipdsId":"IP-132711","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":395617,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":833489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Xuehe","contributorId":175216,"corporation":false,"usgs":false,"family":"Lu","given":"Xuehe","email":"","affiliations":[{"id":27538,"text":"International Institute for Earth System Science, Nanjing University, Xianlin Avenue 163, Nanjing 210093","active":true,"usgs":false}],"preferred":false,"id":833490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Qiuan","contributorId":197933,"corporation":false,"usgs":false,"family":"Zhu","given":"Qiuan","email":"","affiliations":[{"id":6613,"text":"Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada","active":true,"usgs":false},{"id":6612,"text":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China","active":true,"usgs":false}],"preferred":false,"id":833491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yuan, Wenping","contributorId":274900,"corporation":false,"usgs":false,"family":"Yuan","given":"Wenping","affiliations":[{"id":56683,"text":"Sun Yat-sen University, China","active":true,"usgs":false}],"preferred":false,"id":833492,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yuan, Quanzhi","contributorId":274901,"corporation":false,"usgs":false,"family":"Yuan","given":"Quanzhi","email":"","affiliations":[{"id":56684,"text":"Sichuan Normal University, China","active":true,"usgs":false}],"preferred":false,"id":833493,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Zhen 0000-0003-0899-1139","orcid":"https://orcid.org/0000-0003-0899-1139","contributorId":149173,"corporation":false,"usgs":false,"family":"Zhang","given":"Zhen","email":"","affiliations":[],"preferred":false,"id":833494,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Guo, Qingxi","contributorId":274902,"corporation":false,"usgs":false,"family":"Guo","given":"Qingxi","email":"","affiliations":[{"id":56685,"text":"Northeast Forestry University, China","active":true,"usgs":false}],"preferred":false,"id":833495,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Deering, Carol 0000-0003-3565-6264 cdeering@usgs.gov","orcid":"https://orcid.org/0000-0003-3565-6264","contributorId":3001,"corporation":false,"usgs":true,"family":"Deering","given":"Carol","email":"cdeering@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":833496,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70227638,"text":"70227638 - 2022 - A novel regression method for harmonic analysis of time series","interactions":[],"lastModifiedDate":"2023-11-08T16:37:20.074235","indexId":"70227638","displayToPublicDate":"2022-01-24T08:51:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"A novel regression method for harmonic analysis of time series","docAbstract":"Harmonic analysis of time series is an important technique in remote sensing to reveal seasonal land surface dynamics. However, frequency selection in the harmonic analysis is often difficult because high-frequency components are useful for delineating seasonal dynamics but sensitive to noise and gaps in time series. On the other hand, it is challenging to obtain temporally continuous satellite data with high quality because of atmospheric contamination. We developed a novel regression method named Harmonic Adaptive Penalty Operator (HAPO) for harmonic analysis of unevenly distributed time series. We introduced a new penalty function to minimize unexpected fluctuations in the model, which can substantially reduce the overfitting issue of regression in time series with temporal gaps. Specifically, the new penalty function minimizes the length of the model curve and the value range difference between the model and the time series observations. We compared HAPO with three widely used regression methods (OLS: Ordinary Least Squares; LASSO: Least Absolute Shrinkage and Selection Operator; and Ridge) in different scenarios using Landsat time series data across the United States. First, we evaluated methods using the Landsat surface reflectance time series within a single year. HAPO showed low and consistent monthly Root Mean Square Deviation (RMSD) values, in which most of the time RMSD of predicted reflectance were less than 0.04. More importantly, HAPO showed consistent and less bias given varying density and irregularity of time series. Second, we evaluated methods using multi-year time series. HAPO was a better predictor of relatively short time series (< 4 years) with steady low RMSD values. When a longer time series ( 4 years) was used, all four methods showed similar RMSD values, but HAPO outperformed the other methods if there were temporal gaps. Therefore, for places with large seasonal observation gaps or for time series that are relatively short (less than 4 years), HAPO can provide more consistent and accurate results in harmonic analysis of time series.","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2022.01.006","usgsCitation":"Zhou, Q., Zhu, Z., Xian, G.Z., and Li, C., 2022, A novel regression method for harmonic analysis of time series: ISPRS Journal of Photogrammetry and Remote Sensing, v. 185, p. 48-61, https://doi.org/10.1016/j.isprsjprs.2022.01.006.","productDescription":"14 p.","startPage":"48","endPage":"61","ipdsId":"IP-127335","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":449052,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2022.01.006","text":"Publisher Index Page"},{"id":435991,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VYPPLI","text":"USGS data release","linkHelpText":"Harmonic Adaptive Penalty Operator (HAPO)"},{"id":394758,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"185","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":265886,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":54817,"text":"AFDS, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":831464,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Zhe 0000-0001-8283-6407","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":198887,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[],"preferred":false,"id":831465,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":831466,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Congcong 0000-0002-4311-4169","orcid":"https://orcid.org/0000-0002-4311-4169","contributorId":270142,"corporation":false,"usgs":false,"family":"Li","given":"Congcong","email":"","affiliations":[{"id":52693,"text":"ASRC Federal","active":true,"usgs":false}],"preferred":false,"id":831467,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70233564,"text":"70233564 - 2022 - A model-independent tool for evolutionary constrained multi-objective optimization under uncertainty","interactions":[],"lastModifiedDate":"2022-07-26T11:55:11.529587","indexId":"70233564","displayToPublicDate":"2022-01-24T06:49:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"A model-independent tool for evolutionary constrained multi-objective optimization under uncertainty","docAbstract":"An open-source tool has been developed to facilitate constrained single- and multi-objective optimization under uncertainty (CMOU) analyses. The tool uses the well-known PEST interface protocols to communicate with the underlying forward simulation, making it non-intrusive. The tool contains a built-in parallel run manager to make use of heterogeneous and distributed computing resources. Several popular and well-known evolutionary algorithms are implemented and can be combined with a range of approaches to represent uncertainty in model-derived constraint/objective values. These attributes serve to address the current barrier to adopt advanced CMOU analyses for a wide range of decision-support problems across the environmental modeling spectrum. We demonstrate the capabilities of the CMOU tool on a well-known analytical benchmark problem that we augmented to include uncertainty, as well as on a synthetic density-dependent coastal groundwater management benchmark problem. Both demonstrations highlight the importance of explicitly accounting for uncertainty to convey risk and reliability in pareto-optimal design.
Over the past two decades, extensive monitoring has been conducted in the St. Clair – Detroit River System to describe spatial and temporal patterns of lake sturgeon (Acipenser fulvescens). To characterize spatial patterns in juvenile lake sturgeon (<1000 mm TL) based on survey collections, ‘hot spots’ were identified through optimized hot spot analysis (HSA). This HSA was then interpolated by inverse distance weighted analysis to determine extent of identified ‘hot spots’ and ‘cold spots’. Additionally, habitat variables (i.e., water depth, water velocity, and dominant substrate type) were investigated using a single season occupancy model to determine their influence on juvenile lake sturgeon occupancy probability. In total, 1203 juvenile lake sturgeon were captured across 4197 surveys. Three unique ‘hot spots’ were identified; western Lake Erie, Fighting Island in the Detroit River, and the North Channel in the St. Clair River. Interpolated ‘hot spots’ encompassed 73.1 km² in western Lake Erie, 4.7 km² near Fighting Island, and 6.6 km² in the North Channel. Detection probabilities within ‘hot spots’ ranged from 8.8%–43.4%. No habitat variables significantly predicted juvenile lake sturgeon occupancy. Juvenile lake sturgeon were captured in western Lake Erie where the water depth was >5.1 m and odds of occupancy increased with increased water velocity. Juvenile lake sturgeon in the Detroit and St. Clair River ‘hot spots’ were captured at sites with mean benthic water velocities ranging from 0.20–0.60 m/s and where water depth was >7.3 m. Irrespective of waterbody, 69% of all juveniles were detected over dominant sand and gravel substrates. These results provide valuable insight about juvenile habitat use that can help managers formulate effective conservation and restoration strategies supporting the continued recovery of Great Lakes lake sturgeon.
Improved understanding of angling behavior in recreational fisheries can help managers account for partial controllability in systems in which angling effort is not directly regulated. Relevant aspects of angling behavior include fishing participation, site choice, and catch-and-release actions and can be considered at scales of both aggregate fishing effort and individual-decision-making. Using creel survey data collected across eight fishery seasons, we predicted aggregate angling effort (e.g., daily number of boating trips engaged in fishing) and characterized influences of individual trip-based site choice for recreational fisheries acting on fall-run Chinook salmon and coho salmon near the mouth of the Columbia River, United States of America. We applied an overdispersed Poisson likelihood-based generalized linear model with an autoregressive structure to aggregate boating effort and a multinomial logit model to trip-based site choice decisions among separate ocean and estuary fishing zones. Predictive models explained 71% and 79% of out-of-sample and in-sample variability in aggregate effort, respectively, and included the covariates weekend status, Chinook salmon catch rate, tidal range, and pre-season expectations of fish abundance. In addition to reinforcing the importance of Chinook salmon catch rate and tidal range, site choice model selection revealed influences of weather, fishery restrictions, expected fishery season lengths, and the individual-specific characteristics of boat length and guide status on decision-making. Model results provide predictive models for short-term fishery planning, highlight heterogeneity in individual decision-making, and illustrate the value of standardized creel data for evaluating angling behavior.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2022.106235","usgsCitation":"Jensen, A.J., Dundas, S., and Peterson, J., 2022, Phenomenological and mechanistic modeling of recreational angling behavior using creel data: Fisheries Research, v. 249, 106235, 15 p., https://doi.org/10.1016/j.fishres.2022.106235.","productDescription":"106235, 15 p.","ipdsId":"IP-125527","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Buoy 10 Fishery, Columbia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.02658508278576,\n 46.242723068563095\n ],\n [\n -124.02658508278576,\n 46.14556352523286\n ],\n [\n -123.71717267683536,\n 46.14556352523286\n ],\n [\n -123.71717267683536,\n 46.242723068563095\n ],\n [\n -124.02658508278576,\n 46.242723068563095\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"249","noUsgsAuthors":false,"publicationDate":"2022-01-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Jensen, Alexander J.","contributorId":272039,"corporation":false,"usgs":false,"family":"Jensen","given":"Alexander","email":"","middleInitial":"J.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":934953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dundas, Steven J.","contributorId":354015,"corporation":false,"usgs":false,"family":"Dundas","given":"Steven J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":934954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":934952,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227637,"text":"pp1842C - 2022 - The effects of management practices on grassland birds—Greater Prairie-Chicken (Tympanuchus cupido pinnatus)","interactions":[{"subject":{"id":70227637,"text":"pp1842C - 2022 - The effects of management practices on grassland birds—Greater Prairie-Chicken (Tympanuchus cupido pinnatus)","indexId":"pp1842C","publicationYear":"2022","noYear":false,"chapter":"C","displayTitle":"The Effects of Management Practices on Grassland Birds—Greater Prairie-Chicken (Tympanuchus cupido pinnatus)","title":"The effects of management practices on grassland birds—Greater Prairie-Chicken (Tympanuchus cupido pinnatus)"},"predicate":"IS_PART_OF","object":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"id":1}],"isPartOf":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"lastModifiedDate":"2024-06-26T14:34:28.090181","indexId":"pp1842C","displayToPublicDate":"2022-01-21T18:53:49","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1842","chapter":"C","displayTitle":"The Effects of Management Practices on Grassland Birds—Greater Prairie-Chicken (Tympanuchus cupido pinnatus)","title":"The effects of management practices on grassland birds—Greater Prairie-Chicken (Tympanuchus cupido pinnatus)","docAbstract":"The keys to Greater Prairie-Chicken (Tympanuchus cupido pinnatus) management are maintaining expansive grasslands; preventing populations of Greater Prairie-Chickens from becoming small and isolated; managing grasslands to maintain proper grassland height, density, and vigor; and reducing woody plant invasion and excessive litter buildup. Within these grasslands, areas should contain short herbaceous cover for lek sites; tall residual grasses for nesting; and disturbed habitats for broods with adequate vegetation regrowth that provides insects for food and cover from predators and weather. This account does not address population or harvest management but rather focuses on habitat management. Greater Prairie-Chickens have been reported to use habitats with 5–113 centimeter (cm) average vegetation height, 5–40 cm visual obstruction reading, 18–95 percent grass cover, 1–35 percent forb cover, <45 percent litter cover, <5 percent shrub cover, 3–25 percent bare ground, and <12 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842C","usgsCitation":"Svedarsky, W.D., Toepfer, J.E., Westemeier, R.L., Robel, R.J., Igl, L.D., and Shaffer, J.A., 2022, The effects of management practices on grassland birds—Greater Prairie-Chicken (Tympanuchus cupido pinnatus), chap. C of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 53 p., https://doi.org/10.3133/pp1842C.","productDescription":"v, 53 p.","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-096441","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":394716,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/c/pp1842c.pdf","text":"Report","size":"2.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–C"},{"id":394715,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/c/coverthb.jpg"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
We surveyed for Least Bell’s Vireos (Vireo bellii pusillus; vireo) and Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) at the Mojave River Dam study area near Hesperia, California, in 2021. Four vireo surveys were conducted between April 16 and July 16, 2021, and three flycatcher surveys were conducted between May 27 and July 16, 2021.
We detected four territorial male vireos, including two that were paired and two with undetermined breeding status. No juveniles were observed during surveys. Vireo territories were found in three habitat types: (1) riparian scrub, (2) willow-cottonwood, and (3) willow-sycamore, with willow-cottonwood being the most commonly recorded habitat type. Red or arroyo willow (Salix laevigata or lasiolepis) was the dominant plant species in most vireo territories.
No territorial or transient flycatchers were observed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1149","usgsCitation":"Howell, S.L., and Kus, B.E., 2022, Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the Mojave River Dam, San Bernardino County, California—2021 Data Summary: U.S. Geological Survey Data Report 1149, 7 p., https://doi.org/10.3133/dr1149.","productDescription":"vi, 7 p.","numberOfPages":"7","onlineOnly":"Y","ipdsId":"IP-135057","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":394666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1149/covrthb.jpg"},{"id":394667,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1149/dr1149.pdf","text":"Report","size":"4.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":394668,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1149/dr1149.xml"},{"id":394669,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1149/images"}],"country":"United States","state":"California","county":"San Bernardino County","otherGeospatial":"Mojave River Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.28866577148438,\n 34.32132236979802\n ],\n [\n -117.19802856445311,\n 34.32132236979802\n ],\n [\n -117.19802856445311,\n 34.38821261603411\n ],\n [\n -117.28866577148438,\n 34.38821261603411\n ],\n [\n -117.28866577148438,\n 34.32132236979802\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The native Yellowstone Cutthroat Trout (Oncorhynchus clarkii bouvieri Jordan and Gilbert, 1883) population in Yellowstone Lake, Yellowstone National Park, Wyoming, USA, is in decline because of competition from the introduced, invasive Lake Trout (Salvelinus namaycush Walbaum in Artedi, 1792). Gillnetting is used to suppress adult Lake Trout; however, methods are being developed to suppress embryos, including adding Lake Trout carcasses and carcass-analog pellets to spawning sites. Decomposing carcasses and analog pellets cause decreased dissolved oxygen concentrations thereby leading to Lake Trout embryo mortality, but the effects of these methods on primary producers are unknown. We deployed in-situ nutrient diffusing substrates (NDS) at 3 spawning sites. The 1st site was treated with carcasses, the 2nd site was treated with analog pellets, and a 3rd lacked treatment (control). To estimate how suppression measures may alter nutrient limitation, we measured algal biomass in 6 NDS amendments at each site: nothing (control), N, P, N + P, ground carcasses, or pulverized analog pellets. We deployed 5 replicates of each amendment at each site before and after treating whole sites. N and P co-limited periphyton before carcasses or analog pellets were added to spawning sites (p < 0.01); however, nutrients were not limiting after the treatments were added to spawning sites (p = 0.31–1). Algal biomass was 4× higher after whole-site carcass treatments. In contrast, analog pellets appeared to suppress algal biomass in the amendments (20% of NDS at the control site post-treatment) and in the treatment plot (33% of pre-treatment biomass at analog pellet site). We also measured how individual ingredients in analog pellets altered periphyton biomass, which suggested that vitamin E, estrogen, and soybean oil ingredients reduced the growth of primary producers. Suppression methods may stimulate or reduce algal biomass, depending on the methods used, which could have cascading effects on food webs and potentially reduce the success of the control measures. Estimating how different Lake Trout suppression methods may alter basal resources in the littoral zone of Yellowstone Lake will help natural resource agencies develop effective plans to control invasive predators at early life stages while minimally altering ecosystems.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/718647","usgsCitation":"Lujan, D., Tronstad, L., Briggs, M., Albertson, L., Glassic, H., Guy, C.S., and Koel, T.M., 2022, Response of nutrient limitation to invasive fish suppression: How carcasses and analog pellets alter periphyton: Freshwater Science, v. 41, no. 1, p. 88-99, https://doi.org/10.1086/718647.","productDescription":"12 p.","startPage":"88","endPage":"99","ipdsId":"IP-126589","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":430145,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.61271313918546,\n 44.60032951054663\n ],\n [\n -110.61271313918546,\n 44.26210599708054\n ],\n [\n -110.16635745527641,\n 44.26210599708054\n ],\n [\n -110.16635745527641,\n 44.60032951054663\n ],\n [\n -110.61271313918546,\n 44.60032951054663\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lujan, Dominique R.","contributorId":286901,"corporation":false,"usgs":false,"family":"Lujan","given":"Dominique R.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":903687,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tronstad, Lusha M.","contributorId":338214,"corporation":false,"usgs":false,"family":"Tronstad","given":"Lusha M.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":903688,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Michelle A.","contributorId":286899,"corporation":false,"usgs":false,"family":"Briggs","given":"Michelle A.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":903689,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Albertson, Lindsey K.","contributorId":337789,"corporation":false,"usgs":false,"family":"Albertson","given":"Lindsey K.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":903690,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Glassic, Hayley C.","contributorId":288563,"corporation":false,"usgs":false,"family":"Glassic","given":"Hayley C.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":903691,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903686,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Koel, Todd M","contributorId":276047,"corporation":false,"usgs":false,"family":"Koel","given":"Todd","email":"","middleInitial":"M","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":903692,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256741,"text":"70256741 - 2022 - Critical thermal maximum of stream fishes including distinct populations of Smallmouth Bass","interactions":[],"lastModifiedDate":"2024-09-04T15:05:32.800422","indexId":"70256741","displayToPublicDate":"2022-01-21T09:47:04","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":"Critical thermal maximum of stream fishes including distinct populations of Smallmouth Bass","docAbstract":"Understanding the thermal tolerances of stream fishes, including sport fishes, is important for assessing thermal stressors that are common across the landscape. Our study objectives were to determine the thermal tolerances of 17 stream fishes (15 species and 2 genetically distinct populations of juvenile Smallmouth Bass Micropterus dolomieu: the Neosho subspecies M. dolomieu velox and the Ouachita strain M. sp. cf. dolomieu velox). Fish were collected from the field and acclimated to laboratory conditions at 20°C or 25°C, with dissolved oxygen maintained above 6 mg/L. We determined the critical thermal maximum (CTM) using an incomplete block design with 9–11 replications for each species. During trials, we increased the water temperature at a rate of 2°C per hour until fish experienced loss of equilibrium. The estimated CTM ranged from 32.43°C to 38.26°C among species. The CTM values differed significantly between taxonomic groups and species, including the genetically distinct populations of Smallmouth Bass. The Neosho subspecies of Smallmouth Bass had a significantly lower thermal tolerance than the Ouachita strain at both acclimation temperatures; however, the magnitude of the difference was about 0.5°C greater at the higher acclimation temperature. Closely related species, including the Bigeye Shiner Notropis boops and Kiamichi Shiner N. ortenburgeri, had significantly different thermal tolerances despite occupying similar riverine locations. Our results suggest that our perceptions of a species’ thermal tolerance based on that of closely related species or that of species using similar habitat may be incorrect. Moreover, the differences in thermal tolerances among populations may be an important consideration for conservation and management actions, such as stocking decisions. Laboratory data such as those provided in this study can be integrated with field data to better assess thermal responses of fishes in a changing environment.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10749","usgsCitation":"Brewer, S.K., Mollenhauer, R., Alexander, J., and Moore, D., 2022, Critical thermal maximum of stream fishes including distinct populations of Smallmouth Bass: North American Journal of Fisheries Management, v. 42, no. 2, p. 352-360, https://doi.org/10.1002/nafm.10749.","productDescription":"9 p.","startPage":"352","endPage":"360","ipdsId":"IP-128880","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Ouachita Mountain ecoregion, Ozark Highlands ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.8664012156679,\n 36.99681119417036\n ],\n [\n -95.5111277927191,\n 36.20967738751284\n ],\n [\n -95.222344013415,\n 35.57857852939986\n ],\n [\n -94.48359481054393,\n 35.687747319317126\n ],\n [\n -94.62462874927394,\n 36.45854467258876\n ],\n [\n -94.62462874927394,\n 37.01290083943225\n ],\n [\n -94.8664012156679,\n 36.99681119417036\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.43940916621646,\n 34.76329149945565\n ],\n [\n -96.02893897983913,\n 34.719164102780056\n ],\n [\n -96.6840830246433,\n 34.27659649424358\n ],\n [\n -96.35114096908727,\n 33.96540469177903\n ],\n [\n -94.48236943144916,\n 33.992123122916226\n ],\n [\n -94.43940916621646,\n 34.76329149945565\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":908839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mollenhauer, R.","contributorId":276144,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"R.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, J.","contributorId":305320,"corporation":false,"usgs":false,"family":"Alexander","given":"J.","email":"","affiliations":[],"preferred":false,"id":908841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, D.E.","contributorId":205713,"corporation":false,"usgs":false,"family":"Moore","given":"D.E.","email":"","affiliations":[],"preferred":false,"id":908842,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229821,"text":"70229821 - 2022 - Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie","interactions":[],"lastModifiedDate":"2022-05-13T14:55:00.650722","indexId":"70229821","displayToPublicDate":"2022-01-21T09:13:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie","title":"Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie","docAbstract":"Diet analysis integrates a wide variety of visual, chemical, and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, for example, highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package mvtweedie that extends capabilities of widely used packages in R (mgcv and ggplot2) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted Puffins (Fratercula cirrhata) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980–2000) and then Pacific herring and capelin (2000–2020), while wolves (Canis lupus ligoni) in southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3637","usgsCitation":"Thorson, J.T., Arimitsu, M.L., Levi, T., and Roffler, G., 2022, Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie: Ecology, v. 103, no. 5, e3637, 9 p., https://doi.org/10.1002/ecy.3637.","productDescription":"e3637, 9 p.","ipdsId":"IP-128116","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":449065,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecy.3637","text":"External Repository"},{"id":397303,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Thorson, James T.","contributorId":146580,"corporation":false,"usgs":false,"family":"Thorson","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":838473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":838474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Levi, Taal","contributorId":191295,"corporation":false,"usgs":false,"family":"Levi","given":"Taal","email":"","affiliations":[],"preferred":false,"id":838475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roffler, Gretchen","contributorId":288945,"corporation":false,"usgs":false,"family":"Roffler","given":"Gretchen","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":838476,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227620,"text":"70227620 - 2022 - Using surrogate taxa to inform response methods for invasive Grass Carp in the Laurentian Great Lakes","interactions":[],"lastModifiedDate":"2022-02-15T16:27:30.782829","indexId":"70227620","displayToPublicDate":"2022-01-21T09:10:49","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":"Using surrogate taxa to inform response methods for invasive Grass Carp in the Laurentian Great Lakes","docAbstract":"Sampling method decisions are critical for the effective monitoring and management of fisheries. Deploying the most effective sampling methodologies is particularly important when responding to new invasive species, where early response efforts have the best chances for eradication. In the Laurentian Great Lakes, the invasive Grass Carp Ctenopharyngodon idella is sampled using boat electrofishing and the combination method of boat electrofishing within and around a trammel net enclosure. We conducted a field study to compare the effectiveness of the two methods. We used capture data for surrogate taxa (i.e., Common Carp Cyprinus carpio and buffalo Ictiobus spp.) to compare the two methods because few Grass Carp were collected during the study. The sampling methods were compared within an occupancy modeling framework using an information-criteria model selection approach to evaluate seven alternative models. The base model included sampling method, year, water temperature, and sampling effort as covariates in the detection submodel and assumed that occupancy probability was constant across sites. The other six models built on the base model by including site, water body type (i.e., lentic vs. lotic), and interaction covariates in the detection submodel. The top-performing model, built on the base model, accounted for the influence of water body type and assumed the exchangeability of site effects in the detection submodel. The results indicated that the detection probabilities for both taxa were higher for the combination method than for boat electrofishing, with a median estimated difference in detection probability between the two methods of 0.11 (95% CI: 0.04–0.22) for Common Carp and 0.18 (95% CI: 0.08–0.28) for buffalo. Given that the combination method was more effective for detecting the surrogate taxa, we expect the combination method may be preferable to only boat electrofishing for Grass Carp removal.
Golden eagles (Aquila chrysaetos) are of increasing conservation concern in western North America. Effective conservation measures for this wide-ranging, federally protected raptor species require monitoring frameworks that accommodate strong inference on the status of breeding populations across vast landscapes. We used a broad-scale sampling design to identify relationships between landscape conditions, detection rates, and site occupancy by territorial pairs of golden eagles in coastal southern California, United States. In 2016 and 2017, we surveyed 175 territory-sized sample sites (13.9-km2 randomly selected grid cells) up to four times each year and detected a pair of eagles at least once in 22 (12.6%) sites. The probability of detecting pairs of eagles varied substantially between years and declined with increasing amounts of forest cover at survey sites, which obscured observations of eagles during ground-based surveys. After accounting for variable detection, the mean estimate of expected site occupancy by eagle pairs was 0.156 (SE = 0.081). Site-level estimates of occupancy were greatest (>0.30) at sample sites with more rugged terrain conditions, <20% human development, and lower amounts of scrubland vegetation cover. The proportion of a sample site with open grassland or forest cover was not strongly correlated with occupancy. We estimated that approximately 16% of the 5,338-km2 sampling frame was used by resident pairs of golden eagles, corresponding to a sparsely distributed population of about 60 pairs (95% CI = 19 – 151 pairs). Our study provided baseline data for future surveys of golden eagles along with a widely applicable monitoring framework for identifying spatial conservation priorities in urbanizing landscapes.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2021.665792","usgsCitation":"Wiens, D., Bloom, P., Madden, M., Kolar, P., Tracey, J.A., and Fisher, R.N., 2022, Golden eagle occupancy surveys and monitoring strategy in coastal southern California, United States: Frontiers in Ecology and Environment, v. 9, 665792, 11 p., https://doi.org/10.3389/fevo.2021.665792.","productDescription":"665792, 11 p.","ipdsId":"IP-126757","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":449068,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2021.665792","text":"Publisher Index Page"},{"id":435993,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OIPLHH","text":"USGS data release","linkHelpText":"Detection/non-detection data on territorial pairs of golden eagles in coastal southern California, 2016-2017"},{"id":397934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.45458984375,\n 32.602361666817515\n ],\n [\n -115.97167968750001,\n 32.602361666817515\n ],\n [\n -115.97167968750001,\n 34.21634468843463\n ],\n [\n -118.45458984375,\n 34.21634468843463\n ],\n [\n -118.45458984375,\n 32.602361666817515\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Wiens, David 0000-0002-2020-038X","orcid":"https://orcid.org/0000-0002-2020-038X","contributorId":267230,"corporation":false,"usgs":true,"family":"Wiens","given":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":839325,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bloom, Peter H.","contributorId":289557,"corporation":false,"usgs":false,"family":"Bloom","given":"Peter H.","affiliations":[{"id":38830,"text":"Bloom Research Inc.","active":true,"usgs":false}],"preferred":false,"id":839326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Madden, Melanie C.","contributorId":289559,"corporation":false,"usgs":false,"family":"Madden","given":"Melanie C.","affiliations":[{"id":36522,"text":"U.S. Navy","active":true,"usgs":false}],"preferred":false,"id":839327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kolar, Patrick 0000-0002-0076-7565 pkolar@usgs.gov","orcid":"https://orcid.org/0000-0002-0076-7565","contributorId":189512,"corporation":false,"usgs":true,"family":"Kolar","given":"Patrick","email":"pkolar@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tracey, Jeff A. 0000-0002-1619-1054 jatracey@usgs.gov","orcid":"https://orcid.org/0000-0002-1619-1054","contributorId":5780,"corporation":false,"usgs":true,"family":"Tracey","given":"Jeff","email":"jatracey@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839329,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fisher, Robert N. 0000-0003-2842-422X rdfisher@usgs.gov","orcid":"https://orcid.org/0000-0003-2842-422X","contributorId":289561,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rdfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839330,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227621,"text":"70227621 - 2022 - Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product","interactions":[],"lastModifiedDate":"2022-01-21T15:10:11.763046","indexId":"70227621","displayToPublicDate":"2022-01-21T08:57:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product","docAbstract":"The increasing availability of high-quality remote sensing data and advanced technologies have spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985-2017 at 30-meter resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover and five land surface change related products. The LCMAP land cover products were validated using a collection of ~ 25,000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5%. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and Shared Mesos Cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30m annual land cover and land cover change datasets that span from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future.","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-14-143-2022","usgsCitation":"Xian, G.Z., Smith, K., Wellington, D., Horton, J., Zhou, Q., Li, C., Auch, R.F., Brown, J.F., Zhu, Z., and Reker, R.R., 2022, Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product: Earth System Science Data, v. 14, p. 143-162, https://doi.org/10.5194/essd-14-143-2022.","productDescription":"20 p.","startPage":"143","endPage":"162","ipdsId":"IP-130588","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":449071,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-14-143-2022","text":"Publisher Index Page"},{"id":394657,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Earth","volume":"14","noUsgsAuthors":false,"publicationDate":"2022-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":831379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Kelcy 0000-0001-6811-1485","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":272037,"corporation":false,"usgs":false,"family":"Smith","given":"Kelcy","affiliations":[{"id":56338,"text":"KBR, Inc., Contractor under USGS","active":true,"usgs":false}],"preferred":false,"id":831380,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wellington, Danika F. 0000-0002-2130-0075","orcid":"https://orcid.org/0000-0002-2130-0075","contributorId":237074,"corporation":false,"usgs":false,"family":"Wellington","given":"Danika F.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":831381,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Horton, Josephine 0000-0001-8436-4095","orcid":"https://orcid.org/0000-0001-8436-4095","contributorId":191430,"corporation":false,"usgs":false,"family":"Horton","given":"Josephine","affiliations":[],"preferred":false,"id":831382,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":265886,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":54817,"text":"AFDS, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":831383,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Li, Congcong 0000-0002-4311-4169","orcid":"https://orcid.org/0000-0002-4311-4169","contributorId":270142,"corporation":false,"usgs":false,"family":"Li","given":"Congcong","email":"","affiliations":[{"id":52693,"text":"ASRC Federal","active":true,"usgs":false}],"preferred":false,"id":831384,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":831385,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":831386,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Zhu, Zhe 0000-0003-4716-2309","orcid":"https://orcid.org/0000-0003-4716-2309","contributorId":272038,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":831387,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reker, Ryan R. 0000-0001-7524-0082 rreker@usgs.gov","orcid":"https://orcid.org/0000-0001-7524-0082","contributorId":174136,"corporation":false,"usgs":true,"family":"Reker","given":"Ryan","email":"rreker@usgs.gov","middleInitial":"R.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":831388,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70239092,"text":"70239092 - 2022 - Long-term suspended sediment and particulate organic carbon yields from the Reynolds Creek Experimental Watershed and Critical Zone Observatory","interactions":[],"lastModifiedDate":"2022-12-27T13:21:07.439786","indexId":"70239092","displayToPublicDate":"2022-01-21T07:17:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Long-term suspended sediment and particulate organic carbon yields from the Reynolds Creek Experimental Watershed and Critical Zone Observatory","docAbstract":"Long-term (>20 y) suspended sediment (SS) and particulate organic carbon (POC) records are relatively rare and yet are necessary for understanding linkages between climate, erosion and carbon export. We estimated long-term (>23 y) SS and POC yields from four nested catchments that ranged from <1 to 54 km2 in area across the Reynolds Creek Experimental Watershed and Critical Zone Observatory (RCEW-CZO) in southwestern Idaho, USA. We found strong relationships between log10SS and log10POC (R2 = 0.38–0.86) that varied across catchments but remained robust across years, one dry and one of the wettest water years on record. Mean annual SS yields varied from 18 to 89 g SS m−2 y−1 and POC from 0.6 to 11.0 g C m−2 y−1 across the four catchments. Water yield explained much of the temporal variation (72%–85%) in SS and POC yields except in a small, snow-dominated headwater catchment where it explained 15%–51%. The largest five water years accounted for 69%–84% of the total SS and POC yields in catchments with 24 y records. All catchments had positive slopes (>0) for SS and POC concentration-discharge (C-Q) relationships, with large catchments exhibiting greater slopes (0.66–0.97) than smaller ones (0.14–0.16). In addition, most catchments were dominated (80%) by clockwise hysteretic curves. Lack of seasonal exhaustion in the SS-POC relationships, positive C-Q and clockwise relations indicated that these systems were transport-rather than supply limited, and that sediment and POC appeared to be sourced from channel/bank erosion and remobilization. POC yields represent 1%–10% of mean water year net ecosystem exchange depending on elevation; lower elevation catchments may shift from being carbon sinks to sources after accounting for fluvial POC export associated with changes in climate.
Predator–prey interactions shape ecosystems and can help maintain biodiversity. However, for many of the earth's most biodiverse and abundant organisms, including terrestrial arthropods, these interactions are difficult or impossible to observe directly with traditional approaches. Based on previous theory, it is likely that predator–prey interactions for these organisms are shaped by a combination of predator traits, including body size and species-specific hunting strategies. In this study, we combined diet DNA metabarcoding data of 173 individual invertebrate predators from nine species (a total of 305 individual predator–prey interactions) with an extensive community body size data set of a well-described invertebrate community to explore how predator traits and identity shape interactions. We found that (1) mean size of prey families in the field usually scaled with predator size, with species-specific variation to a general size-scaling relationship (exceptions likely indicating scavenging or feeding on smaller life stages). We also found that (2) although predator hunting traits, including web and venom use, are thought to shape predator–prey interaction outcomes, predator identity more strongly influenced our indirect measure of the relative size of predators and prey (predator:prey size ratios) than either of these hunting traits. Our findings indicate that predator body size and species identity are important in shaping trophic interactions in invertebrate food webs and could help predict how anthropogenic biodiversity change will influence terrestrial invertebrates, the earth's most diverse animal taxonomic group.
The SuperCam instrument on the Perseverance Mars 2020 rover uses a pulsed 1064 nm laser to ablate targets at a distance and conduct laser induced breakdown spectroscopy (LIBS) by analyzing the light from the resulting plasma. SuperCam LIBS spectra are preprocessed to remove ambient light, noise, and the continuum signal present in LIBS observations. Prior to quantification, spectra are masked to remove noisier spectrometer regions and spectra are normalized to minimize signal fluctuations and effects of target distance. In some cases, the spectra are also standardized or binned prior to quantification. To determine quantitative elemental compositions of diverse geologic materials at Jezero crater, Mars, we use a suite of 1198 laboratory spectra of 334 well-characterized reference samples. The samples were selected to span a wide range of compositions and include typical silicate rocks, pure minerals (e.g., silicates, sulfates, carbonates, oxides), more unusual compositions (e.g., Mn ore and sodalite), and replicates of the sintered SuperCam calibration targets (SCCTs) onboard the rover. For each major element (SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O), the database was subdivided into five “folds” with similar distributions of the element of interest. One fold was held out as an independent test set, and the remaining four folds were used to optimize multivariate regression models relating the spectrum to the composition. We considered a variety of models, and selected several for further investigation for each element, based primarily on the root mean squared error of prediction (RMSEP) on the test set, when analyzed at 3 m. In cases with several models of comparable performance at 3 m, we incorporated the SCCT performance at different distances to choose the preferred model. Shortly after landing on Mars and collecting initial spectra of geologic targets, we selected one model per element. Subsequently, with additional data from geologic targets, some models were revised to ensure results that are more consistent with geochemical constraints. The calibration discussed here is a snapshot of an ongoing effort to deliver the most accurate chemical compositions with SuperCam LIBS.
No abstract available.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.2c00052","usgsCitation":"Manceau, A., Brossier, R., Janssen, S., and Poulin, B., 2022, Response to comment on “Mercury isotope fractionation by internal demethylation and biomineralization reactions in seabirds: Implications for environmental mercury science”: Principles and limitations of source tracing and process tracing with stable isotope signatures: Environmental Science and Technology, v. 56, no. 3, p. 2065-2068, https://doi.org/10.1021/acs.est.2c00052.","productDescription":"4 p.","startPage":"2065","endPage":"2068","ipdsId":"IP-136180","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":449086,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hal.science/hal-03688547","text":"External Repository"},{"id":394752,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Manceau, Alain 0000-0003-0845-611X","orcid":"https://orcid.org/0000-0003-0845-611X","contributorId":194255,"corporation":false,"usgs":false,"family":"Manceau","given":"Alain","email":"","affiliations":[],"preferred":false,"id":831520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brossier, Romain 0000-0002-7195-8123","orcid":"https://orcid.org/0000-0002-7195-8123","contributorId":267387,"corporation":false,"usgs":false,"family":"Brossier","given":"Romain","email":"","affiliations":[{"id":55486,"text":"University of Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":831521,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831522,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poulin, Brett 0000-0002-5555-7733","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":260893,"corporation":false,"usgs":false,"family":"Poulin","given":"Brett","affiliations":[{"id":52706,"text":"Department of Environmental Toxicology, University of California Davis, Davis, CA 95616, USA","active":true,"usgs":false}],"preferred":false,"id":831523,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228747,"text":"70228747 - 2022 - Factors affecting spatiotemporal variation in survival of endangered winter-run Chinook Salmon outmigrating from the Sacramento River","interactions":[],"lastModifiedDate":"2022-04-12T13:30:37.906018","indexId":"70228747","displayToPublicDate":"2022-01-21T06:39:28","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":"Factors affecting spatiotemporal variation in survival of endangered winter-run Chinook Salmon outmigrating from the Sacramento River","docAbstract":"Among four extant and declining Chinook salmon (Oncorhynchus tshawytscha) runs in California’s Central Valley, none have declined as precipitously as Sacramento River winter-run Chinook Salmon. In addition to habitat loss, migratory winter-run employ a life history strategy to reside and feed in stopover habitats on their way from freshwaters to the ocean. This life history strategy is widely considered to be a key factor in the continued decline of winter-run. Using acoustic telemetry, we examined conditions that influenced reach-specific movement and survival of outmigrating juveniles during a prolonged, multi-year drought from 2013-2016, followed by one of the wettest years on record in 2017. We modeled how time-varying individual riverine covariates and reach-specific habitat features influenced smolt survival. Model selection favored a model with mean annual flow, intra-annual deviations from the mean flow at the reach scale, reach-specific channel characteristics, and travel time. Mean annual flow had the strongest positive effect on survival. A negative interaction between mean annual flow and intra-annual reach flow indicated that within-year deviations at the reach scale from annual mean flow had larger effects on survival in low flow years. These factors resulted in higher survival in years with pulse flows or high flows. Changes in movement behavior in response to small scale changes in velocity were negatively associated with survival. Covariates of revetment and wooded bank habitat were positively associated with survival but the effect of these fixed habitat features changed depending on whether they were situated in the upper or lower part of the river. Fish exhibited density dependent stopover behavior, with slowed downstream migration in the upper river in the wet years and extending to the lower river in the most critically dry year. This paper contributes two key findings for natural resource managers interested in flow management and targeted habitat restoration. The first is new insight to how the magnitude of pulse flows in dry and wet years affect survival of winter-run. The second is that density dependence influences where stopover habitat is used. Despite this, we identified an area of the river where fish consistently exhibited stopover behavior in all years.
The details and mechanisms for Neogene river reorganization in the U.S. Pacific Northwest and northern Rocky Mountains have been debated for over a century with key implications for how tectonic and volcanic systems modulate topographic development. To evaluate paleo-drainage networks, we produced an expansive data set and provenance analysis of detrital zircon U-Pb ages from Miocene to Pleistocene fluvial strata along proposed proto-Snake and Columbia River pathways. Statistical comparisons of Miocene-Pliocene detrital zircon spectra do not support previously hypothesized drainage routes of the Snake River. We use detrital zircon unmixing models to test prior Snake River routes against a newly hypothesized route, in which the Snake River circumnavigated the northern Rocky Mountains and entered the Columbia Basin from the northeast prior to incision of Hells Canyon. Our proposed ancestral Snake River route best matches detrital zircon age spectra throughout the region. Furthermore, this northerly Snake River route satisfies and provides context for shifts in the sedimentology and fish faunal assemblages of the western Snake River Plain and Columbia Basin through Miocene−Pliocene time. We posit that eastward migration of the Yellowstone Hotspot and its effect on thermally induced buoyancy and topographic uplift, coupled with volcanic densification of the eastern Snake River Plain lithosphere, are the primary mechanisms for drainage reorganization and that the eastern and western Snake River Plain were isolated from one another until the early Pliocene. Following this basin integration, the substantial increase in drainage area to the western Snake River Plain likely overtopped a bedrock threshold that previously contained Lake Idaho, which led to incision of Hells Canyon and establishment of the modern Snake and Columbia River drainage network.
Historically, Cisco Coregonus artedi and deepwater ciscoes Coregonus spp. were the most abundant and ecologically important fish species in the Laurentian Great Lakes, but anthropogenic influences caused nearly all populations to collapse by the 1970s. Fishery managers have begun exploring the feasibility of restoring populations throughout the basin, but questions regarding hatchery propagation and stocking remain. We used historical and contemporary stock-recruit parameters previously estimated for Ciscoes in Wisconsin waters of Lake Superior, with estimates of age-1 Cisco rearing habitat (broadly defined as total ha ≤ 80 m depth) and natural mortality, to estimate how many fry (5.5 months post-hatch), fall fingerling (7.5 months post-hatch), and age-1 (at least 12 months post-hatch) hatchery-reared Ciscoes are needed for stocking in the Great Lakes to mimic recruitment rates in Lake Superior, a lake that has undergone some recovery. Estimated stocking densities suggested that basin-wide stocking would require at least 0.641-billion fry, 0.469-billion fall fingerlings, or 0.343-billion age-1 fish for a simultaneous restoration effort targeting historically important Cisco spawning and rearing areas in Lakes Huron, Michigan, Erie, Ontario, and Saint Clair. Numbers required for basin-wide stocking were considerably greater than current or planned coregonine production capacity, thus simultaneous stocking in the Great Lakes is likely not feasible. Provided current habitat conditions do not preclude Cisco restoration, managers could maximize the effectiveness of available production capacity by concentrating stocking efforts in historically important spawning and rearing areas, similar to the current stocking effort in Saginaw Bay, Lake Huron. Other historically important Cisco spawning and rearing areas within each lake (listed in no particular order) include: (1) Thunder Bay in Lake Huron, (2) Green Bay in Lake Michigan, (3) the islands near Sandusky, Ohio, in western Lake Erie, and (4) the area near Hamilton, Ontario, and Bay of Quinte in Lake Ontario. Our study focused entirely on Ciscoes but may provide a framework for describing future stocking needs for deepwater ciscoes.
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0000-0002-0331-9397","orcid":"https://orcid.org/0000-0002-0331-9397","contributorId":271207,"corporation":false,"usgs":false,"family":"Rook","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":54519,"text":"U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":831190,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hansen, Michael J. 0000-0001-8522-3876 michaelhansen@usgs.gov","orcid":"https://orcid.org/0000-0001-8522-3876","contributorId":5006,"corporation":false,"usgs":true,"family":"Hansen","given":"Michael","email":"michaelhansen@usgs.gov","middleInitial":"J.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":831191,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bronte, Charles R.","contributorId":190727,"corporation":false,"usgs":false,"family":"Bronte","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":831192,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230142,"text":"70230142 - 2022 - Transforming Palmyra Atoll to native-tree dominance will increase net carbon storage and reduce dissolved organic carbon reef runoff","interactions":[],"lastModifiedDate":"2022-03-30T12:26:43.197877","indexId":"70230142","displayToPublicDate":"2022-01-20T07:24:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Transforming Palmyra Atoll to native-tree dominance will increase net carbon storage and reduce dissolved organic carbon reef runoff","docAbstract":"Native forests on tropical islands have been displaced by non-native species, leading to calls for their transformation. Simultaneously, there is increasing recognition that tropical forests can help sequester carbon that would otherwise enter the atmosphere. However, it is unclear if native forests sequester more or less carbon than human-altered landscapes. At Palmyra Atoll, efforts are underway to transform the rainforest composition from coconut palm (Cocos nucifera) dominated to native mixed-species. To better understand how this landscape-level change will alter the atoll’s carbon dynamics, we used field sampling, remote sensing, and parameter estimates from the literature to model the total carbon accumulation potential of Palmyra’s forest before and after transformation. The model predicted that replacing the C. nucifera plantation with native species would reduce aboveground biomass from 692.6 to 433.3 Mg C. However, expansion of the native Pisonia grandis and Heliotropium foertherianum forest community projected an increase in soil carbon to at least 13,590.8 Mg C, thereby increasing the atoll’s overall terrestrial carbon storage potential by 11.6%. Nearshore sites adjacent to C. nucifera canopy had a higher dissolved organic carbon (DOC) concentration (110.0 μMC) than sites adjacent to native forest (81.5 μMC), suggesting that, in conjunction with an increase in terrestrial carbon storage, replacing C. nucifera with native forest will reduce the DOC exported from the forest into in nearshore marine habitats. Lower DOC levels have potential benefits for corals and coral dependent communities. For tropical islands like Palmyra, reverting from C. nucifera dominance to native tree dominance could buffer projected climate change impacts by increasing carbon storage and reducing coral disease.