{"pageNumber":"279","pageRowStart":"6950","pageSize":"25","recordCount":41065,"records":[{"id":70212672,"text":"70212672 - 2020 - A maximum rupture model for the southern San Andreas and San Jacinto Faults California, derived from paleoseismic earthquake ages: Observations and limitations","interactions":[],"lastModifiedDate":"2020-08-25T14:02:24.152487","indexId":"70212672","displayToPublicDate":"2020-07-08T08:58:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"A maximum rupture model for the southern San Andreas and San Jacinto Faults California, derived from paleoseismic earthquake ages: Observations and limitations","docAbstract":"

Paleoseismic rupture histories provide spatiotemporal models of earthquake moment release needed to test numerical models and lengthen the instrumental catalog. We develop a model of the fewest and thus largest magnitude earthquakes permitted by paleoseismic data for the last 1,500 years on the southern San Andreas and San Jacinto Faults, California, USA. The largest geometric complexity appears to regulate the system: Only two ruptures break the San Gorgonio Pass region, followed by episodes of ruptures that could bridge the northern San Jacinto Fault and the San Andreas Fault. When tested against independent data on slip per event, the model produces comparable values indicating the end‐member model does not underpredict rupture rates. Rupture of >85% of the fault length in the historic period between 1800 and 1857 and the subsequent quiescence is similar to epochs of activity in the prehistoric model, suggesting that regional clustering of seismicity could be a trait of the system.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL088532","usgsCitation":"Scharer, K., and Yule, D., 2020, A maximum rupture model for the southern San Andreas and San Jacinto Faults California, derived from paleoseismic earthquake ages: Observations and limitations: Geophysical Research Letters, v. 47, e2020GL088532, 11 p., https://doi.org/10.1029/2020GL088532.","productDescription":"e2020GL088532, 11 p.","ipdsId":"IP-119093","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456089,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl088532","text":"Publisher Index Page"},{"id":377818,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Andreas Fault, San Jacinto Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.39941406249999,\n 36.59788913307022\n ],\n [\n -120.80566406250001,\n 36.13787471840729\n ],\n [\n -116.89453125,\n 32.76880048488168\n ],\n [\n -115.1806640625,\n 32.80574473290688\n ],\n [\n -115.13671875,\n 33.8339199536547\n ],\n [\n -119.39941406249999,\n 36.59788913307022\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","noUsgsAuthors":false,"publicationDate":"2020-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Scharer, Katherine M. 0000-0003-2811-2496","orcid":"https://orcid.org/0000-0003-2811-2496","contributorId":217361,"corporation":false,"usgs":true,"family":"Scharer","given":"Katherine M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797255,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yule, Doug","contributorId":239568,"corporation":false,"usgs":false,"family":"Yule","given":"Doug","email":"","affiliations":[{"id":36305,"text":"CSU Northridge","active":true,"usgs":false}],"preferred":false,"id":797256,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70243728,"text":"70243728 - 2020 - Estimating soil organic carbon redistribution in three major river basins of China based on erosion processes","interactions":[],"lastModifiedDate":"2023-05-18T14:02:55.45746","indexId":"70243728","displayToPublicDate":"2020-07-08T08:55:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9533,"text":"Soil Research","active":true,"publicationSubtype":{"id":10}},"title":"Estimating soil organic carbon redistribution in three major river basins of China based on erosion processes","docAbstract":"

Soil erosion by water affects soil organic carbon (SOC) migration and distribution, which are important processes for defining ecosystem carbon sources and sinks. Little has been done to quantify soil carbon erosion in the three major basins in China, the Yangtze River, Yellow River and Pearl River Basins, which contain the most eroded areas. This research attempts to quantify the lateral movement of SOC based on spatial and temporal patterns of water erosion rates derived from an empirical Unit Stream Power Erosion Deposition Model (USPED) model. The water erosion rates simulated by the USPED model agreed reasonably with observations (R2 = 0.43, P < 0.01). We showed that regional water erosion ranged within 23.3–50 Mg ha–1 year–1 during 1992–2013, inducing the lateral redistribution of SOC caused by erosion in the range of 0.027–0.049 Mg C ha–1 year–1, and that caused by deposition of 0.0079–0.015 Mg C ha–1 year–1, in the three basins. The total eroded SOC was 0.006, 0.002 and 0.001 Pg year–1 in the Yangtze River, Yellow River and Pearl River Basins respectively. The net eroded SOC in the three basins was ~0.0075 Pg C year–1. Overall, the annual average redistributed SOC rate caused by erosion was greater than that caused by deposition, and the SOC loss in the Yangtze River Basin was greatest among the three basins. Our study suggests that considering both processes of erosion and deposition – as well as effects of topography, rainfall, land use types and their interactions – on these processes are important to understand SOC redistribution caused by water erosion.

","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/SR19325","usgsCitation":"Yang, Y., Zhu, Q., Liu, J., Li, M., Yuan, M., Chen, H., Peng, C., and Yang, Z., 2020, Estimating soil organic carbon redistribution in three major river basins of China based on erosion processes: Soil Research, v. 58, no. 6, p. 540-550, https://doi.org/10.1071/SR19325.","productDescription":"11 p.","startPage":"540","endPage":"550","ipdsId":"IP-107196","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":417209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Yangtze River, Yellow River and Pearl River basins","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[110.33919,18.6784],[109.47521,18.1977],[108.65521,18.50768],[108.62622,19.36789],[109.11906,19.82104],[110.2116,20.10125],[110.78655,20.07753],[111.01005,19.69593],[110.57065,19.25588],[110.33919,18.6784]]],[[[127.65741,49.76027],[129.39782,49.4406],[130.58229,48.72969],[130.98728,47.79013],[132.50667,47.78897],[133.3736,48.18344],[135.02631,48.47823],[134.50081,47.57844],[134.11236,47.21247],[133.76964,46.11693],[133.09713,45.14407],[131.88345,45.32116],[131.02521,44.96795],[131.28856,44.11152],[131.14469,42.92999],[130.63387,42.90301],[130.64002,42.39501],[129.99427,42.98539],[129.59667,42.42498],[128.05222,41.99428],[128.20843,41.46677],[127.34378,41.50315],[126.86908,41.81657],[126.18205,41.10734],[125.07994,40.56982],[124.26562,39.92849],[122.86757,39.63779],[122.13139,39.17045],[121.05455,38.89747],[121.58599,39.36085],[121.37676,39.75026],[122.1686,40.42244],[121.64036,40.94639],[120.76863,40.59339],[119.6396,39.89806],[119.02346,39.25233],[118.04275,39.20427],[117.5327,38.73764],[118.0597,38.06148],[118.87815,37.89733],[118.91164,37.44846],[119.7028,37.15639],[120.82346,37.87043],[121.71126,37.48112],[122.35794,37.45448],[122.51999,36.93061],[121.10416,36.65133],[120.63701,36.11144],[119.66456,35.60979],[119.15121,34.90986],[120.22752,34.36033],[120.62037,33.37672],[121.22901,32.46032],[121.90815,31.69217],[121.89192,30.94935],[121.26426,30.67627],[121.50352,30.14291],[122.09211,29.83252],[121.93843,29.01802],[121.68444,28.22551],[121.12566,28.13567],[120.39547,27.05321],[119.5855,25.74078],[118.65687,24.54739],[117.28161,23.6245],[115.89074,22.78287],[114.76383,22.66807],[114.15255,22.22376],[113.80678,22.54834],[113.24108,22.05137],[111.84359,21.55049],[110.78547,21.39714],[110.44404,20.34103],[109.88986,20.28246],[109.62766,21.00823],[109.86449,21.39505],[108.52281,21.71521],[108.05018,21.55238],[107.04342,21.8119],[106.56727,22.2182],[106.7254,22.79427],[105.81125,22.97689],[105.32921,23.35206],[104.47686,22.81915],[103.50451,22.70376],[102.70699,22.7088],[102.17044,22.46475],[101.65202,22.3182],[101.80312,21.17437],[101.27003,21.20165],[101.18001,21.43657],[101.15003,21.84998],[100.41654,21.55884],[99.98349,21.74294],[99.2409,22.11831],[99.53199,22.94904],[98.89875,23.14272],[98.66026,24.06329],[97.60472,23.8974],[97.72461,25.08364],[98.67184,25.9187],[98.71209,26.74354],[98.68269,27.50881],[98.24623,27.74722],[97.91199,28.33595],[97.32711,28.26158],[96.24883,28.41103],[96.58659,28.83098],[96.11768,29.4528],[95.4048,29.03172],[94.56599,29.27744],[93.41335,28.64063],[92.50312,27.89688],[91.69666,27.77174],[91.25885,28.04061],[90.73051,28.06495],[90.01583,28.29644],[89.47581,28.04276],[88.81425,27.29932],[88.73033,28.08686],[88.12044,27.87654],[86.95452,27.97426],[85.82332,28.20358],[85.01164,28.64277],[84.23458,28.83989],[83.89899,29.32023],[83.33712,29.46373],[82.32751,30.11527],[81.5258,30.42272],[81.11126,30.18348],[79.72137,30.88271],[78.73889,31.51591],[78.45845,32.61816],[79.17613,32.48378],[79.20889,32.99439],[78.81109,33.5062],[78.91227,34.32194],[77.83745,35.49401],[76.19285,35.8984],[75.8969,36.66681],[75.15803,37.13303],[74.98,37.41999],[74.82999,37.99001],[74.86482,38.37885],[74.25751,38.60651],[73.92885,38.50582],[73.67538,39.43124],[73.96001,39.66001],[73.82224,39.89397],[74.77686,40.36643],[75.46783,40.56207],[76.52637,40.42795],[76.90448,41.06649],[78.1872,41.18532],[78.54366,41.58224],[80.11943,42.12394],[80.25999,42.35],[80.18015,42.92007],[80.86621,43.18036],[79.96611,44.91752],[81.94707,45.31703],[82.45893,45.53965],[83.18048,47.33003],[85.16429,47.00096],[85.72048,47.45297],[85.76823,48.45575],[86.59878,48.54918],[87.35997,49.21498],[87.75126,49.2972],[88.01383,48.59946],[88.8543,48.06908],[90.28083,47.69355],[90.97081,46.88815],[90.58577,45.71972],[90.94554,45.28607],[92.13389,45.11508],[93.48073,44.97547],[94.68893,44.35233],[95.30688,44.24133],[95.76245,43.31945],[96.3494,42.72564],[97.45176,42.74889],[99.51582,42.52469],[100.84587,42.6638],[101.83304,42.51487],[103.31228,41.90747],[104.52228,41.90835],[104.96499,41.59741],[106.12932,42.13433],[107.74477,42.48152],[109.2436,42.51945],[110.4121,42.87123],[111.12968,43.40683],[111.82959,43.74312],[111.66774,44.07318],[111.34838,44.45744],[111.87331,45.10208],[112.43606,45.01165],[113.46391,44.80889],[114.46033,45.33982],[115.9851,45.72724],[116.71787,46.3882],[117.4217,46.67273],[118.87433,46.80541],[119.66327,46.69268],[119.77282,47.04806],[118.86657,47.74706],[118.06414,48.06673],[117.29551,47.69771],[116.30895,47.85341],[115.74284,47.72654],[115.48528,48.13538],[116.1918,49.1346],[116.6788,49.88853],[117.87924,49.51098],[119.28846,50.14288],[119.27937,50.58291],[120.18205,51.64357],[120.73819,51.96412],[120.72579,52.51623],[120.17709,52.75389],[121.00308,53.2514],[122.24575,53.43173],[123.57151,53.4588],[125.06821,53.16104],[125.94635,52.7928],[126.5644,51.78426],[12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Yan 0000-0003-0858-7603","orcid":"https://orcid.org/0000-0003-0858-7603","contributorId":245232,"corporation":false,"usgs":false,"family":"Yang","given":"Yan","email":"","affiliations":[{"id":6737,"text":"Colorado State University, Department of Ecosystem Science and Sustainability, and Natural Resource Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":873092,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Qiuan","contributorId":197933,"corporation":false,"usgs":false,"family":"Zhu","given":"Qiuan","email":"","affiliations":[{"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},{"id":6613,"text":"Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada","active":true,"usgs":false}],"preferred":false,"id":873093,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":873094,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Mingxu","contributorId":305521,"corporation":false,"usgs":false,"family":"Li","given":"Mingxu","email":"","affiliations":[{"id":66236,"text":"Northwest A&F University, China","active":true,"usgs":false}],"preferred":false,"id":873098,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yuan, Minshu","contributorId":305515,"corporation":false,"usgs":false,"family":"Yuan","given":"Minshu","email":"","affiliations":[{"id":66236,"text":"Northwest A&F University, China","active":true,"usgs":false}],"preferred":false,"id":873095,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chen, Huai","contributorId":172942,"corporation":false,"usgs":false,"family":"Chen","given":"Huai","email":"","affiliations":[{"id":27125,"text":"State Key Lab of Soil Erosion and Dryland Framing, NW A&F Unv, Yangling, China","active":true,"usgs":false}],"preferred":false,"id":873096,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Peng, Changhui","contributorId":197932,"corporation":false,"usgs":false,"family":"Peng","given":"Changhui","email":"","affiliations":[{"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},{"id":6613,"text":"Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada","active":true,"usgs":false}],"preferred":false,"id":873097,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yang, Zhenan","contributorId":305522,"corporation":false,"usgs":false,"family":"Yang","given":"Zhenan","email":"","affiliations":[{"id":66236,"text":"Northwest A&F University, China","active":true,"usgs":false}],"preferred":false,"id":873099,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70210991,"text":"70210991 - 2020 - Segmentation and supercycles: A catalog of earthquake rupture patterns from the Sumatran Sunda Megathrust and other well-studied faults worldwide","interactions":[],"lastModifiedDate":"2020-07-10T13:47:57.191861","indexId":"70210991","displayToPublicDate":"2020-07-08T08:46:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Segmentation and supercycles: A catalog of earthquake rupture patterns from the Sumatran Sunda Megathrust and other well-studied faults worldwide","docAbstract":"After more than 100 years of earthquake research, earthquake forecasting, which relies on knowledge of past fault rupture patterns, has become the foundation for societal defense against seismic natural disasters. A concept that has come into focus more recently is that rupture segmentation and cyclicity can be complex, and that a characteristic earthquake model is too simple to adequately describe much of fault behavior. Nevertheless, recognizable patterns in earthquake recurrence emerge from long, high resolution, spatially distributed chronologies. Researchers now seek to discover the maximum, minimum, and typical rupture areas; the distribution, variability, and spatial applicability of recurrence intervals; and patterns of earthquake clustering in space and time. The term “supercycle” has been used to describe repeating longer periods of elastic strain accumulation and release that involve multiple fault ruptures. However, this term has become very broadly applied, lumping together several distinct phenomena that likely have disparate underlying causes. We divide earthquake cycle behavior into four major classes that have different implications for seismic hazard and fault mechanics: 1) quasi-periodic similar ruptures, 2) clustered similar ruptures, 3) clustered complementary ruptures/rupture cascades, and 4) superimposed cycles. “Segmentation” is likewise an ambiguous term; we identify “master segments” and “asperities” as defined by barriers to fault rupture. These barriers may be persistent (rarely or never traversed), frequent (occasionally traversed), or ephemeral (changing location from cycle to cycle). We compile a catalog of the historical and paleoseismic evidence that currently exists for each of these types of behavior on major well-studied faults worldwide. Due to the unique level of paleoseismic and paleogeodetic detail provided by the coral microatoll technique, the Sumatran Sunda megathrust provides one of the most complete records over multiple earthquake rupture cycles. Long historical records of earthquakes along the South American and Japanese subduction zones are also vital contributors to our catalog, along with additional data compiled from subduction zones in Cascadia, Alaska, and Middle America, as well as the North Anatolian and Dead Sea strike-slip faults in the Middle East. We find that persistent and frequent barriers, rupture cascades, superimposed cycles, and quasi-periodic similar ruptures are common features of most major faults. Clustered similar ruptures do not appear to be common, but broad overlap zones between neighboring segments do occur. Barrier regions accommodate slip through reduced interseismic coupling, slow slip events, and/or smaller more localized ruptures, and are frequently associated with structural features such as subducting seafloor relief or fault trace discontinuities. This catalog of observations provides a basis for exploring and modeling root causes of rupture segmentation and cycle behavior. We expect that researchers will recognize similar behavior styles on other major faults around the world.","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2020.106390","usgsCitation":"Philibosian, B.E., and Meltzner, A.J., 2020, Segmentation and supercycles: A catalog of earthquake rupture patterns from the Sumatran Sunda Megathrust and other well-studied faults worldwide: Quaternary Science Reviews, v. 241, 106390, 43 p., https://doi.org/10.1016/j.quascirev.2020.106390.","productDescription":"106390, 43 p.","ipdsId":"IP-103767","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456092,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2020.106390","text":"Publisher Index Page"},{"id":376257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"241","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Philibosian, Belle E. 0000-0003-3138-4716","orcid":"https://orcid.org/0000-0003-3138-4716","contributorId":206110,"corporation":false,"usgs":true,"family":"Philibosian","given":"Belle","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":792358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meltzner, Aron J.","contributorId":193419,"corporation":false,"usgs":false,"family":"Meltzner","given":"Aron","email":"","middleInitial":"J.","affiliations":[{"id":5110,"text":"Earth Observatory of Singapore, Nanyang Technological University","active":true,"usgs":false},{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":792359,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210949,"text":"ofr20201068 - 2020 - Development of a two-stage life cycle model for Oncorhynchus kisutch (coho salmon) in the upper Cowlitz River Basin, Washington","interactions":[],"lastModifiedDate":"2020-07-09T13:43:08.205679","indexId":"ofr20201068","displayToPublicDate":"2020-07-08T08:31:31","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1068","displayTitle":"Development of a Two-Stage Life Cycle Model for Oncorhynchus kisutch (Coho Salmon) in the Upper Cowlitz River Basin, Washington","title":"Development of a two-stage life cycle model for Oncorhynchus kisutch (coho salmon) in the upper Cowlitz River Basin, Washington","docAbstract":"

Recovery of salmon populations in the upper Cowlitz River Basin depends on trap-and-haul efforts owing to impassable dams. Therefore, successful recovery depends on the collection of out-migrating juvenile salmon at Cowlitz Falls Dam (CFD) for transport below downstream dams, as well as the collection of adults for transport upstream from the dams. Tacoma Power began downstream fish collection efforts at CFD in the mid-1990s and has been working consistently since then to improve collection efficiency to support self-sustaining salmon and steelhead (Onchorhynchus spp.) populations in the upper Cowlitz River Basin. Although much work has focused on estimating fish collection efficiency (FCE), there has been relatively little focus on modeling population dynamics to understand how fish collection efficiency and other factors drive production of both juvenile and adult salmon over their life cycle. As a first step towards understanding the factors affecting population dynamics of Oncorhynchus kisutch (coho salmon) in the upper Cowlitz River Basin, we developed a statistical life cycle model using adult escapement and age structure data, juvenile collection data, and juvenile fish collection efficiency estimates. The goal of the statistical life cycle model is to estimate annual production and survival during two critical life-stage transitions: the freshwater production from escapement of adults upstream from CFD to collection of juveniles at CFD, and the juvenile-to-adult survival from the time of collection at the dam to the return of adults. To structure the life cycle model, we used the Ricker stock-recruitment model to estimate juvenile production from the number of parent spawners. This approach allowed us to account for density dependence at high spawner abundances while estimating annual productivity, defined as the number of juveniles produced per spawner at low spawner abundance. We then expressed productivity as a function two key variables affecting the number of juveniles collected and transported at CFD: (1) annual FCE, and (2) the annual number of days that spill occurred at CFD from September 1 to April 30.

Our key findings were as follows:

  1. FCE was the primary factor affecting productivity of coho salmon upstream from CFD because FCE affects the number of juveniles that survive to continue downstream migration;
  2. Juvenile-to-adult return (JAR) rates were relatively high considering that harvest was included in the estimate, averaging about 3.6 percent and ranging as high as 9.1 percent, suggesting that adult coho salmon may be able to return to CFD at sustainable population sizes; and
  3. Much variation in the estimates of juvenile fish production upriver of CFD was unexplained even after adult escapement and FCE were accounted for, suggesting that the model may be improved by exploring different covariates and model structures for juvenile production as well as JAR rates.

Additionally, by including FCE in the model, we estimated that the median pre-collection productivity, defined as the number of juveniles produced per spawner when FCE=1, was 108.4 juveniles per spawner. Because this two-stage life cycle model partitions factors that affect fish production in river compared to the ocean environment and fish life stages, the model estimates should help inform fishery managers about the overall role that fish collection at CFD may have on the recovery and sustainability of coho salmon populations.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201068","collaboration":"Prepared in cooperation with Tacoma Power","usgsCitation":"Plumb, J.M., and Perry, R.W., 2020, Development of a two-stage life cycle model for Oncorhynchus kisutch (coho salmon) in the upper Cowlitz River Basin, Washington: U.S. Geological Survey Open-File Report 2020–1068, 25 p., https://doi.org/10.3133/ofr20201068.","productDescription":"iv, 25 p.","onlineOnly":"Y","ipdsId":"IP-117483","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":376162,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1068/coverthb.jpg"},{"id":376163,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1068/ofr20201068.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1068"}],"country":"United States","state":"Washington","otherGeospatial":"Cowlitz River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.97821044921875,\n 46.09228143052647\n ],\n [\n -121.8548583984375,\n 46.09228143052647\n ],\n [\n -121.8548583984375,\n 46.70596917928676\n ],\n [\n -122.97821044921875,\n 46.70596917928676\n ],\n [\n -122.97821044921875,\n 46.09228143052647\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016

","tableOfContents":"","publishedDate":"2020-07-08","noUsgsAuthors":false,"publicationDate":"2020-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Plumb, John M. 0000-0003-4255-1612 jplumb@usgs.gov","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":3569,"corporation":false,"usgs":true,"family":"Plumb","given":"John","email":"jplumb@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":792271,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":792272,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70255612,"text":"70255612 - 2020 - Calibrated simulation of the long-term average surficial groundwater system and derived spatial distributions of its characteristics for the contiguous United States","interactions":[],"lastModifiedDate":"2024-06-26T13:27:34.420421","indexId":"70255612","displayToPublicDate":"2020-07-08T08:23:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Calibrated simulation of the long-term average surficial groundwater system and derived spatial distributions of its characteristics for the contiguous United States","docAbstract":"

While the physical processes governing groundwater flow are well understood, and the computational resources now exist for solving the governing equations in three dimensions over continental-scale domains, there remains substantial uncertainty about the subsurface distribution of the properties that control groundwater flow and transport for much of the contiguous United States (CONUS). The transmissivity of the shallow subsurface is a key parameter for the simulation of water table position, shallow groundwater flow, and base-flow discharge, but is not well-characterized at large regional to continental scales. We used a process-based inversion of CONUS-extent groundwater information to generate national data sets of (a) the transmissivity of the shallow groundwater system, (b) the depth to the water table, (c) groundwater discharge as base-flow, and (d) long-term average water content in the unsaturated zone. CONUS-extent coverage was developed in the form of 75 subdomain models, with the spatial distribution of long-term average transmissivity for each subdomain model calibrated against water-levels derived from U.S. Geological Survey (USGS) observation wells, NHDPlusV2 first-order perennial streams, and National Wetlands Inventory (NWI) freshwater wetlands. Estimated transmissivities were lower in the western CONUS than the eastern CONUS, and across the CONUS both transmissivity and depth to water correlate with recharge, elevation, and topographic slope. These generated data sets provide spatially distributed, long-term average estimates of subsurface properties and hydrological states that we anticipate will complement other environmental modeling efforts as explanatory variables, boundary conditions, or transport pathways.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019WR026724","usgsCitation":"Zell, W.O., and Sanford, W.E., 2020, Calibrated simulation of the long-term average surficial groundwater system and derived spatial distributions of its characteristics for the contiguous United States: Water Resources Research, v. 56, no. 8, e2019WR026724, 16 p.; Data Release, https://doi.org/10.1029/2019WR026724.","productDescription":"e2019WR026724, 16 p.; Data Release","ipdsId":"IP-117925","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":436888,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91LFFN1","text":"USGS data release","linkHelpText":"MODFLOW 6 models used to simulate the long-term average surficial groundwater system for the contiguous United 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0000-0002-8782-6627","orcid":"https://orcid.org/0000-0002-8782-6627","contributorId":339721,"corporation":false,"usgs":true,"family":"Zell","given":"Wesley","email":"","middleInitial":"O.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":904935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanford, Ward E. 0000-0002-6624-0280 wsanford@usgs.gov","orcid":"https://orcid.org/0000-0002-6624-0280","contributorId":2268,"corporation":false,"usgs":true,"family":"Sanford","given":"Ward","email":"wsanford@usgs.gov","middleInitial":"E.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":904936,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70212531,"text":"70212531 - 2020 - Deep Learning as a tool to forecast hydrologic response for landslide-prone hillslopes","interactions":[],"lastModifiedDate":"2020-08-19T13:25:09.670826","indexId":"70212531","displayToPublicDate":"2020-07-08T08:19:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Deep Learning as a tool to forecast hydrologic response for landslide-prone hillslopes","docAbstract":"

Empirical thresholds for landslide warning systems have benefitted from the incorporation of soil‐hydrologic monitoring data, but the mechanistic basis for their predictive capabilities is limited. Although physically based hydrologic models can accurately simulate changes in soil moisture and pore pressure that promote landslides, their utility is restricted by high computational costs and nonunique parameterization issues. We construct a deep learning model using soil moisture, pore pressure, and rainfall monitoring data acquired from landslide‐prone hillslopes in Oregon, USA, to predict the timing and magnitude of hydrologic response at multiple soil depths for 36‐hr intervals. We find that observation records as short as 6 months are sufficient for accurate predictions, and our model captures hydrologic response for high‐intensity rainfall events even when those storm types are excluded from model training. We conclude that machine learning can provide an accurate and computationally efficient alternative to empirical methods or physical modeling for landslide hazard warning.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL088731","usgsCitation":"Orland, E., Roering, J., Thomas, M.A., and Mirus, B.B., 2020, Deep Learning as a tool to forecast hydrologic response for landslide-prone hillslopes: Geophysical Research Letters, v. 47, no. 16, e2020GL088731, 9 p., https://doi.org/10.1029/2020GL088731.","productDescription":"e2020GL088731, 9 p.","ipdsId":"IP-119953","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"links":[{"id":456099,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarsbank.uoregon.edu/xmlui/handle/1794/25701","text":"External Repository"},{"id":377642,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.43115234375,\n 42.48830197960227\n ],\n [\n -121.35498046875,\n 42.48830197960227\n ],\n [\n -121.35498046875,\n 44.66865287227321\n ],\n [\n -124.43115234375,\n 44.66865287227321\n ],\n [\n -124.43115234375,\n 42.48830197960227\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"16","noUsgsAuthors":false,"publicationDate":"2020-08-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Orland, Elijah","contributorId":238845,"corporation":false,"usgs":false,"family":"Orland","given":"Elijah","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":796719,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roering, Joshua J.","contributorId":194297,"corporation":false,"usgs":false,"family":"Roering","given":"Joshua J.","affiliations":[],"preferred":false,"id":796720,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thomas, Matthew A. 0000-0002-9828-5539 matthewthomas@usgs.gov","orcid":"https://orcid.org/0000-0002-9828-5539","contributorId":200616,"corporation":false,"usgs":true,"family":"Thomas","given":"Matthew","email":"matthewthomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":796722,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218793,"text":"70218793 - 2020 - Modeling the surface water and groundwater budgets of the US using MODFLOW-OWHM","interactions":[],"lastModifiedDate":"2021-03-12T13:20:11.840585","indexId":"70218793","displayToPublicDate":"2020-07-08T07:17:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":664,"text":"Advances in Water Resources","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the surface water and groundwater budgets of the US using MODFLOW-OWHM","docAbstract":"

Assessments of groundwater and surface water budgets at a large scale, such as the contiguous United States, often separately analyze the complex dynamics linking the surface and subsurface categories of water resources. These dynamics include recharge and groundwater contributions to streamflow. The time-varying simulation of these complex hydrologic dynamics, across large spatial and temporal scales, remains a scientific challenge due to the complexity of the processes and data availability. In this study, groundwater fluxes and surface hydrologic processes are simulated across the contiguous US for 1950-2010. The simulation estimates the monthly water budget components, such as groundwater recharge, surface runoff, and evapotranspiration; streamflow in major rivers is routed while accounting for groundwater exchange. Human impacts are included through groundwater pumping, and climate variability is included, including variability in precipitation, temperature and potential evapotranspiration. The simulated groundwater level and river discharge have strong correlation with USGS observation wells and streamflow gages, with R2 values of 0.992 and 0.946, respectively. The simulated evapotranspiration is compared with three other published estimation methods, showing that it is able to capture the magnitude and seasonality of evapotranspiration over the Mississippi River basin. As such, the model is able to reasonably simulate the surface and groundwater budgets over the US, allowing for questions of the relative importance of climate and human impacts to be explored in the future.

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J","contributorId":255174,"corporation":false,"usgs":false,"family":"Troy","given":"Tara","email":"","middleInitial":"J","affiliations":[{"id":51454,"text":"Department of Civil and Environmental Engineering, Lehigh University, Bethlehem, PA, USA","active":true,"usgs":false}],"preferred":false,"id":811903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Russo, Tess A","contributorId":255175,"corporation":false,"usgs":false,"family":"Russo","given":"Tess","email":"","middleInitial":"A","affiliations":[{"id":51456,"text":"Penn State Univ., Dept. of Mathematics","active":true,"usgs":false}],"preferred":false,"id":811904,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyce, Scott 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811905,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216877,"text":"70216877 - 2020 - Wildfire-initiated talik development exceeds current thaw projections: Observations and models from Alaska's continuous permafrost zone","interactions":[],"lastModifiedDate":"2020-12-11T14:11:17.152266","indexId":"70216877","displayToPublicDate":"2020-07-08T06:48:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Wildfire-initiated talik development exceeds current thaw projections: Observations and models from Alaska's continuous permafrost zone","docAbstract":"

As the Arctic warms and wildfire occurrence increases, talik formation in permafrost regions is projected to expand and affect the cycling of water and carbon. Yet, few unified field and modeling studies have examined this process in detail, particularly in areas of continuous permafrost. We address this gap by presenting multimethod, multiseasonal geophysical measurements of permafrost and liquid‐water content that reveal substantial talik development in response to recent wildfire in continuous permafrost of boreal Alaska. Results from observation‐based cryohydrogeologic model simulations suggest that predisturbance subsurface conditions are key factors influencing thaw response to fire disturbance and air temperature warming. Our high‐resolution integrated study illustrates enhanced vulnerability of boreal continuous permafrost, with observed talik formation that exceeds coarse‐scale model projections by ~100 years even under the most extreme future emissions scenario. Results raise important scaling questions for representing extreme permafrost thaw phenomena of growing widespread importance in large‐scale predictive models.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL087565","usgsCitation":"Rey, D., Walvoord, M.A., Minsley, B.J., Ebel, B., Voss, C., and Singha, K., 2020, Wildfire-initiated talik development exceeds current thaw projections: Observations and models from Alaska's continuous permafrost zone: Geophysical Research Letters, v. 47, no. 15, e2020GL087565, 11 p., https://doi.org/10.1029/2020GL087565.","productDescription":"e2020GL087565, 11 p.","ipdsId":"IP-116894","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":456104,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl087565","text":"Publisher Index Page"},{"id":381213,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Northeast Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -150.82031249999997,\n 64.77412531292873\n ],\n [\n -140.9765625,\n 64.77412531292873\n ],\n [\n -140.9765625,\n 70.37785394109224\n ],\n [\n -150.82031249999997,\n 70.37785394109224\n ],\n [\n -150.82031249999997,\n 64.77412531292873\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"15","noUsgsAuthors":false,"publicationDate":"2020-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806696,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806697,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":806698,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806699,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Voss, Clifford I. 0000-0001-5923-2752","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":211844,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford I.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806700,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Singha, Kamini 0000-0002-0605-3774","orcid":"https://orcid.org/0000-0002-0605-3774","contributorId":191366,"corporation":false,"usgs":false,"family":"Singha","given":"Kamini","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":806701,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219544,"text":"70219544 - 2020 - Do two wrongs make a right? Persistent uncertainties regarding environmental selenium-mercury interactions","interactions":[],"lastModifiedDate":"2021-04-13T12:59:31.232823","indexId":"70219544","displayToPublicDate":"2020-07-07T07:58:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Do two wrongs make a right? Persistent uncertainties regarding environmental selenium-mercury interactions","docAbstract":"

Mercury (Hg) is a pervasive environmental pollutant and contaminant of concern for both people and wildlife that has been a focus of environmental remediation efforts for decades. A growing body of literature has motivated calls for revising Hg consumption advisories to co-consider selenium (Se) levels in seafood and implies that remediating aquatic ecosystems with ecosystem-scale Se additions could be a robust solution to Hg contamination. Provided that elevated Se concentrations are also known toxicological threats to aquatic animals, we performed a literature search to evaluate the strength of evidence supporting three assertions underpinning the ameliorating benefits of Se: (1) dietary Se reduces MeHg toxicity in consumers; (2) environmental Se reduces Hg bioaccumulation and biomagnification in aquatic food webs; and (3) Se inhibits Hg bioavailability to, and/or methylmercury production by, microbial communities. Limited or ambiguous support for each criterion indicates that many scientific uncertainties and gaps remain regarding Se mediation of Hg behavior and toxicity in abiotic and biotic compartments. Significantly more information is needed to provide a strong scientific basis for modifying current fish consumption advisories on the basis of Se:Hg ratios or for applying Se amendments to remediate Hg-contaminated ecosystems.

","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c01894","usgsCitation":"Gerson, J.R., Walters, D., Eagles-Smith, C., Bernhardt, E., and Brandt, J., 2020, Do two wrongs make a right? Persistent uncertainties regarding environmental selenium-mercury interactions: Environmental Science and Technology, v. 54, no. 15, p. 9228-9234, https://doi.org/10.1021/acs.est.0c01894.","productDescription":"7 p.","startPage":"9228","endPage":"9234","ipdsId":"IP-117902","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":385055,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"15","noUsgsAuthors":false,"publicationDate":"2020-07-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Gerson, Jacqueline R.","contributorId":198378,"corporation":false,"usgs":false,"family":"Gerson","given":"Jacqueline","email":"","middleInitial":"R.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false},{"id":27331,"text":"Duke University, Durham, NC","active":true,"usgs":false}],"preferred":false,"id":814109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, David 0000-0002-4237-2158","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":205915,"corporation":false,"usgs":true,"family":"Walters","given":"David","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":814110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":814111,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bernhardt, Emily S.","contributorId":92143,"corporation":false,"usgs":false,"family":"Bernhardt","given":"Emily S.","affiliations":[{"id":27331,"text":"Duke University, Durham, NC","active":true,"usgs":false}],"preferred":false,"id":814112,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brandt, Jessica E","contributorId":257351,"corporation":false,"usgs":false,"family":"Brandt","given":"Jessica E","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":814113,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211284,"text":"70211284 - 2020 - The role of warm, dry summers and variation in snowpack on phytoplankton dynamics in high-elevation lakes","interactions":[],"lastModifiedDate":"2020-10-12T17:06:21.880134","indexId":"70211284","displayToPublicDate":"2020-07-06T10:20:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"The role of warm, dry summers and variation in snowpack on phytoplankton dynamics in high-elevation lakes","docAbstract":"Abstract\nClimate change is altering biogeochemical, metabolic, and ecological functions in lakes across the globe. Historically, mountain lakes in temperate regions have been unproductive due to brief ice-free seasons, a snowmelt-driven hydrograph, cold temperatures, and steep topography with low vegetation and soil cover. We tested the relative importance of winter and summer weather, watershed characteristics, and water chemistry as drivers of phytoplankton dynamics. Using boosted regression tree models for 28 mountain lakes in Colorado we examined regional, intra-seasonal, and inter-annual drivers of variability in chlorophyll a as a proxy for lake phytoplankton. Phytoplankton biomass was inversely related to the maximum snow water equivalent (SWE) of the previous winter, as others have found. However, even in years with average SWE, summer precipitation extremes and warming enhanced phytoplankton biomass. Peak seasonal phytoplankton biomass coincided with the warmest water temperatures and lowest nitrogen-to-phosphorus ratios. While links between snowpack, lake temperature, nutrients, and organic matter dynamics are increasingly recognized as critical drivers of change in high elevation lakes, our results highlight the additional influence of summer conditions on lake productivity in response to ongoing changes in climate. Continued changes in the timing, type, and magnitude of precipitation in combination with other global change drivers (e.g., nutrient deposition) will affect production in mountain lakes, potentially shifting these historically oligotrophic lakes toward new ecosystem states. Ultimately, a deeper understanding of these drivers and pattern at multiple scales will allow us to better anticipate ecological consequences of global change.","language":"English","publisher":"Wiley","doi":"10.1002/ecy.3132","usgsCitation":"Oleksy, I., Beck, W., Lammers, R., Steger, C., Wilson, C., Christensen, K., Vincent, K., Johnson, P., and Baron, J., 2020, The role of warm, dry summers and variation in snowpack on phytoplankton dynamics in high-elevation lakes: Ecology, v. 101, no. 10, e03132, 12 p., https://doi.org/10.1002/ecy.3132.","productDescription":"e03132, 12 p.","ipdsId":"IP-114262","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":456124,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3132","text":"Publisher Index Page"},{"id":376637,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Front Range of the Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.84228515625,\n 39.98132938627215\n ],\n [\n -105.01281738281249,\n 39.98132938627215\n ],\n [\n -105.01281738281249,\n 40.65563874006118\n ],\n [\n -105.84228515625,\n 40.65563874006118\n ],\n [\n -105.84228515625,\n 39.98132938627215\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"101","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Oleksy, Isabella A.","contributorId":229538,"corporation":false,"usgs":false,"family":"Oleksy","given":"Isabella A.","affiliations":[{"id":33412,"text":"Cary Institute for Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":793504,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beck, Whitney","contributorId":229539,"corporation":false,"usgs":false,"family":"Beck","given":"Whitney","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":793505,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lammers, R.","contributorId":229540,"corporation":false,"usgs":false,"family":"Lammers","given":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":793506,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steger, Cara","contributorId":229541,"corporation":false,"usgs":false,"family":"Steger","given":"Cara","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":793507,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Cody","contributorId":229542,"corporation":false,"usgs":false,"family":"Wilson","given":"Cody","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":793508,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Christensen, Kyle","contributorId":229543,"corporation":false,"usgs":false,"family":"Christensen","given":"Kyle","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":793509,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vincent, Kim","contributorId":229544,"corporation":false,"usgs":false,"family":"Vincent","given":"Kim","email":"","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":793510,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Pieter","contributorId":229545,"corporation":false,"usgs":false,"family":"Johnson","given":"Pieter","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":793511,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Baron, Jill 0000-0002-5902-6241 jill_baron@usgs.gov","orcid":"https://orcid.org/0000-0002-5902-6241","contributorId":222907,"corporation":false,"usgs":true,"family":"Baron","given":"Jill","email":"jill_baron@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":793512,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70211293,"text":"70211293 - 2020 - Dating silica sinter (geyserite): A cautionary tale","interactions":[],"lastModifiedDate":"2020-07-22T14:40:18.175529","indexId":"70211293","displayToPublicDate":"2020-07-06T09:37:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Dating silica sinter (geyserite): A cautionary tale","docAbstract":"We describe a new effort to date hydrothermal silica sinter deposits (geyserite) from the Upper Geyser Basin of Yellowstone National Park using 14C of co-deposited organic matter, U-series and cosmogenic 10Be methods. A majority of the samples were collected from stratigraphic sections, mainly at Riverside, Giant, and Castle Geysers. Ages obtained from 41 14C analyses range from modern to 12.1 cal ka BP. Nearly all the 14C ages show inconsistencies with their stratigraphic positions, and several replicate 14C analyses from the same sample result in significantly different ages. The δ13C values of the organic material in the sinter range from -26.6‰ to -12.7‰. The more enriched values are attributed to microbial fixation of dissolved inorganic carbon (DIC), which has heavier δ13C values and is 14C-depleted relative to atmospheric CO2, leading to apparent older ages. U-series analyses on 4 samples yielded ages between 2.2 and 7.4 ka. Large 230Th/U age uncertainties in the sinter, due to low uranium concentrations along with elevated 232Th and associated initial 230Th, make these ages imprecise for use on Holocene deposits. A single cosmogenic 10Be exposure age of 596±18 ka is considerably older than the age of underlying rhyolite and is thus unreliable. This apparent old age results from contamination by meteoric 10Be trapped in the opal that overprints the very small amount of cosmogenic 10Be. By presenting the problems we encountered and discussing their probable cause, this paper highlights the difficulty in obtaining reliable, high-precision geochronological data necessary to use sinter deposits as paleoenvironmental and paleo-hydrothermal archives.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2020.106991","usgsCitation":"Churchill, D.M., Manga, M., Hurwitz, S., Peek, S., Licciardi, J., and Paces, J.B., 2020, Dating silica sinter (geyserite): A cautionary tale: Journal of Volcanology and Geothermal Research, v. 402, 106991, 12 p., https://doi.org/10.1016/j.jvolgeores.2020.106991.","productDescription":"106991, 12 p.","ipdsId":"IP-119376","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":376631,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.060791015625,\n 43.88205730390537\n ],\n [\n -109.3304443359375,\n 43.88205730390537\n ],\n [\n -109.3304443359375,\n 44.999767019181284\n ],\n [\n -111.060791015625,\n 44.999767019181284\n ],\n [\n -111.060791015625,\n 43.88205730390537\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"402","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Churchill, Dakota M.","contributorId":229593,"corporation":false,"usgs":false,"family":"Churchill","given":"Dakota","email":"","middleInitial":"M.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":793593,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manga, Michael","contributorId":229594,"corporation":false,"usgs":false,"family":"Manga","given":"Michael","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":793594,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":793595,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peek, Sara 0000-0002-9770-6557","orcid":"https://orcid.org/0000-0002-9770-6557","contributorId":209971,"corporation":false,"usgs":true,"family":"Peek","given":"Sara","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":793596,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Licciardi, Joseph","contributorId":229595,"corporation":false,"usgs":false,"family":"Licciardi","given":"Joseph","affiliations":[{"id":41689,"text":"U. New Hampshire","active":true,"usgs":false}],"preferred":false,"id":793597,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Paces, James B. 0000-0002-9809-8493","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":215864,"corporation":false,"usgs":true,"family":"Paces","given":"James","email":"","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":793598,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211541,"text":"70211541 - 2020 - Hydrologic modeling to examine the influence of the forestry reclamation approach and climate change on mineland hydrology","interactions":[],"lastModifiedDate":"2020-07-30T15:25:29.367702","indexId":"70211541","displayToPublicDate":"2020-07-05T10:18:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic modeling to examine the influence of the forestry reclamation approach and climate change on mineland hydrology","docAbstract":"

Forests in the Appalachian region of the U.S. are threatened by a variety of short- and long-term pressures, including climate change, invasive species, and resource extraction. Surface mining for coal is one of the most important drivers of land-use change in the region, reducing native forest cover, causing forest fragmentation, eliminating intact soil, and affecting water resources. The Forestry Reclamation Approach (FRA) has been demonstrated as a successful best practice for restoring forests on mine-impacted landscapes, but little information exists on how the practice will affect hydrologic processes. A study was initiated to examine soil-water movement, as in-situ saturated hydraulic conductivity (Ksat), combined with soil porosity to quantify the potential influence on streamflow of reclaimed mines relative to an unmined, forested control site in eastern Kentucky. We compared different reclamation techniques and time since reclamation to determine the extent to which hydrologic function can be restored. We also simulated evapotranspiration at the watershed scale as a function of reclamation technique for both historical and projected (2050) climate. Results indicate that conventional grassland reclamation critically changes how soil water transitions to streamflow, primarily due to Ksat variability that exceeds that measured for intact and FRA soils. Sites reclaimed using FRA exhibited a soil-water environment that was more similar to the unmined control. However, all reclaimed mine soils were thinner, retained and stored less soil water, and thus could provide less plant-available water during the growing season. The plant-available water stored in reclaimed landscapes may not be sufficient to support forest health and this is exacerbated by projected climate conditions. However, soil development under a combination of FRA techniques has the potential to mitigate this limitation.



","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.140605","usgsCitation":"Williamson, T.N., and Barton, C.D., 2020, Hydrologic modeling to examine the influence of the forestry reclamation approach and climate change on mineland hydrology: Science of the Total Environment, v. 743, 140605, 14 p., https://doi.org/10.1016/j.scitotenv.2020.140605.","productDescription":"140605, 14 p.","ipdsId":"IP-118671","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":456126,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.140605","text":"Publisher Index Page"},{"id":436895,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WG0WGQ","text":"USGS data release","linkHelpText":"Hydrologic simulations for Robinson Forest and Reclaimed Mineland Soils"},{"id":436894,"rank":0,"type":{"id":30,"text":"Data 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Tanja N. 0000-0002-7639-8495 tnwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-8495","contributorId":198329,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja","email":"tnwillia@usgs.gov","middleInitial":"N.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barton, Chris D. 0000-0003-0692-3079","orcid":"https://orcid.org/0000-0003-0692-3079","contributorId":236883,"corporation":false,"usgs":false,"family":"Barton","given":"Chris","email":"","middleInitial":"D.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":794575,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215138,"text":"70215138 - 2020 - Methods to quality assure, plot, summarize, interpolate, and extend groundwater-level information—Examples for the Mississippi River Valley alluvial aquifer","interactions":[],"lastModifiedDate":"2020-10-08T12:49:28.043168","indexId":"70215138","displayToPublicDate":"2020-07-03T07:42:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7147,"text":"Journal of Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Methods to quality assure, plot, summarize, interpolate, and extend groundwater-level information—Examples for the Mississippi River Valley alluvial aquifer","docAbstract":"

Large-scale computational investigations of groundwater levels are proposed to accelerate science delivery through a workflow spanning database assembly, statistics, and information synthesis and packaging. A water-availability study of the Mississippi River alluvial plain, and particularly the Mississippi River Valley alluvial aquifer (MRVA), is ongoing. Software (visGWDBmrva) has been released as part of the study that demonstrates groundwater informatics for the aquifer. Considerable water-level data collected by multiple agencies over a seven-state area exist (18,903 wells; 287,272 measurements [April 22, 2019]). Data and metadata quality assurance methods, basic statistics, hydrograph visualization, outlier identification, hypothesis testing, and time-series modeling are described. Two approaches (generalized additive models [GAMs] and support vector machines [SVMs]) are used for data interpolation and extension to monthly water-level estimates. Numerical congruence between GAM and SVM estimates will be useful to limit inclusion of monthly estimates from subsequent science activities.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2020.104758","usgsCitation":"Asquith, W.H., Seanor, R.C., McGuire, V.L., and Kress, W., 2020, Methods to quality assure, plot, summarize, interpolate, and extend groundwater-level information—Examples for the Mississippi River Valley alluvial aquifer: Journal of Environmental Modelling and Software, v. 134, 104758, 19 p., https://doi.org/10.1016/j.envsoft.2020.104758.","productDescription":"104758, 19 p.","ipdsId":"IP-101493","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":456132,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2020.104758","text":"Publisher Index Page"},{"id":436897,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PR7V63","text":"USGS data release","linkHelpText":"Quality Assurance of Water Level Records from Wells in the Mississippi River Valley Alluvial Aquifer in Missouri from the Missouri Department of Natural Resource's Well Information Management System (WIMS)"},{"id":379218,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mississippi River valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.3623046875,\n 42.908160071960566\n ],\n [\n -92.3291015625,\n 39.87601941962116\n ],\n [\n -90.703125,\n 36.63316209558658\n ],\n [\n -91.845703125,\n 34.77771580360469\n ],\n [\n -93.251953125,\n 31.27855085894653\n ],\n [\n -91.0107421875,\n 27.994401411046173\n ],\n [\n -88.59375,\n 28.9600886880068\n ],\n [\n -89.6923828125,\n 30.486550842588485\n ],\n [\n -90.00000000000001,\n 32.39851580247402\n ],\n [\n -88.330078125,\n 36.45663601159623\n ],\n [\n -89.0771484375,\n 38.134556577054134\n ],\n [\n -90.1318359375,\n 39.94343646197423\n ],\n [\n -89.736328125,\n 41.934976500546604\n ],\n [\n -90.17578125,\n 42.74701217318067\n ],\n [\n -91.3623046875,\n 42.908160071960566\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"134","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":800979,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seanor, Ronald C. 0000-0001-5735-5580","orcid":"https://orcid.org/0000-0001-5735-5580","contributorId":218443,"corporation":false,"usgs":true,"family":"Seanor","given":"Ronald","email":"","middleInitial":"C.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":800980,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGuire, Virginia L. 0000-0002-3962-4158 vlmcguir@usgs.gov","orcid":"https://orcid.org/0000-0002-3962-4158","contributorId":404,"corporation":false,"usgs":true,"family":"McGuire","given":"Virginia","email":"vlmcguir@usgs.gov","middleInitial":"L.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":800981,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kress, Wade 0000-0002-6833-028X","orcid":"https://orcid.org/0000-0002-6833-028X","contributorId":203539,"corporation":false,"usgs":true,"family":"Kress","given":"Wade","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":800982,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216415,"text":"70216415 - 2020 - msocc: Fit and analyse computationally efficient multi‐scale occupancy models in R","interactions":[],"lastModifiedDate":"2020-11-17T23:48:25.126332","indexId":"70216415","displayToPublicDate":"2020-07-02T17:45:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"msocc: Fit and analyse computationally efficient multi‐scale occupancy models in R","docAbstract":"
  1. Environmental DNA (eDNA) sampling is a promising tool for the detection of rare and cryptic taxa, such as aquatic pathogens, parasites and invasive species. Environmental DNA sampling workflows commonly rely on multi‐stage hierarchical sampling designs that induce complicated dependencies within the data. This complex dependence structure can be intuitively modelled with Bayesian multi‐scale occupancy models. However, current software for such models are computationally demanding, impeding their use.
  2. We present an r package, msocc, that implements a data augmentation strategy to fit fully Bayesian, computationally efficient multi‐scale occupancy models. The msocc package allows users to fit multi‐scale occupancy models, to estimate and visualize posterior summaries of site, sample and replicate‐level occupancy, and to compare different models using Bayesian information criterion. Additionally, we provide a supplemental web application that allows users to investigate study design for multi‐scale occupancy models and acts as a graphical user interface to the msocc package.
  3. The utility of the msocc package is illustrated on a published dataset and the functions in msocc are compared to the primary Bayesian toolkit for multi‐scale occupancy modelling, eDNAoccupancy, using various computational benchmarks. These benchmarks indicate that msocc is capable of fitting models 50 times faster than eDNAoccupancy.
  4. We hope that access to software that efficiently fits, analyses and conducts study design investigations for multi‐scale occupancy models facilitates their implementation by the research and wildlife management communities.
","language":"English","publisher":"Wiley","doi":"10.1111/2041-210X.13442","usgsCitation":"Stratton, C., Sepulveda, A.J., and Hoegh, A.B., 2020, msocc: Fit and analyse computationally efficient multi‐scale occupancy models in R: Methods in Ecology and Evolution, v. 11, no. 9, p. 1113-1120, https://doi.org/10.1111/2041-210X.13442.","productDescription":"8 p.","startPage":"1113","endPage":"1120","ipdsId":"IP-116614","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":456135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.13442","text":"Publisher Index Page"},{"id":380563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-07-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Stratton, Christian","contributorId":217711,"corporation":false,"usgs":false,"family":"Stratton","given":"Christian","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":804952,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sepulveda, Adam J. 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":150628,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","middleInitial":"J.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":804953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoegh, Andrew B.","contributorId":166684,"corporation":false,"usgs":false,"family":"Hoegh","given":"Andrew","email":"","middleInitial":"B.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":804954,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210917,"text":"70210917 - 2020 - Selective sediment transport during Hurricane Sandy on Fire Island (New York, USA): Inferences from heavy-mineral assemblages","interactions":[],"lastModifiedDate":"2020-07-03T01:53:37.74296","indexId":"70210917","displayToPublicDate":"2020-07-02T15:15:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2451,"text":"Journal of Sedimentary Research","onlineIssn":"1938-3681","printIssn":"1527-1404","active":true,"publicationSubtype":{"id":10}},"title":"Selective sediment transport during Hurricane Sandy on Fire Island (New York, USA): Inferences from heavy-mineral assemblages","docAbstract":"In October 2012, Hurricane Sandy caused severe erosion on beaches and dunes of Fire Island (New York, USA). Major shoreline changes occurred with erosional dominance in the upper shoreline and aggradation in the lowermost section of the beach due to the deposition of eroded upper beach and dune sediment. Sand laminations with a high concentration of heavy minerals (“black sand laminations”) were observed in three excavated trenches located on a washover terrace and fan on the east side of Fire Island. The mineralogical composition of these laminations reveals the presence of high quantities of magnetite, ilmenite (as opaque minerals), and garnet (as main translucent mineral). These heavy mineral enriched laminations were formed as waves eroded and transported sand from the primary dune and smaller relict dunes under specific hydrodynamic conditions that promoted grain sorting according to differences in size and specific gravity. Based on the concentrations of certain heavy minerals, the threshold for primarily density-driven sorting probably lies between the specific gravity of a less dense mineral (ilmenite, 4.7) and Garnet almandine (4.3), the most common transparent heavy mineral. The number of laminations and concentrations of heavy minerals vary between trenches on the overwash terrace and appear to be controlled by their distance from sediment sources. The trench with a greatest number of laminations and higher heavy mineral concentrations is located furthest from the main dune but is just 10 m inland of a relict dune that acts as the primary source of sediment of the washover deposit in this trench. A conceptual model for heavy mineral layer deposition is presented based on geomorphological and sedimentological evidences allowing the definition of a density threshold.","language":"English","publisher":"SEPM","doi":"10.2110/jsr.2020.12","usgsCitation":"Cascalho, J., Costa, P., Gelfenbaum, G.R., La Selle, S., and Jaffe, B.E., 2020, Selective sediment transport during Hurricane Sandy on Fire Island (New York, USA): Inferences from heavy-mineral assemblages: Journal of Sedimentary Research, v. 90, no. 3, p. 269-285, https://doi.org/10.2110/jsr.2020.12.","productDescription":"17 p.","startPage":"269","endPage":"285","ipdsId":"IP-099491","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":376109,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.96295166015625,\n 40.68896903762434\n ],\n [\n -72.8668212890625,\n 40.68896903762434\n ],\n [\n -72.8668212890625,\n 40.73997376331186\n ],\n [\n -72.96295166015625,\n 40.73997376331186\n ],\n [\n -72.96295166015625,\n 40.68896903762434\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"90","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Cascalho, Joao","contributorId":228808,"corporation":false,"usgs":false,"family":"Cascalho","given":"Joao","email":"","affiliations":[{"id":41512,"text":"Instituto D. Luiz and Departamento de Geologia, Faculdade de Ciências da Universidade de Lisboa","active":true,"usgs":false}],"preferred":false,"id":792126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Costa, Pedro","contributorId":228809,"corporation":false,"usgs":false,"family":"Costa","given":"Pedro","affiliations":[{"id":41513,"text":"1Instituto D. Luiz and Departamento de Geologia, Faculdade de Ciências da Universidade de Lisboa","active":true,"usgs":false}],"preferred":false,"id":792127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gelfenbaum, Guy R. 0000-0003-1291-6107 ggelfenbaum@usgs.gov","orcid":"https://orcid.org/0000-0003-1291-6107","contributorId":742,"corporation":false,"usgs":true,"family":"Gelfenbaum","given":"Guy","email":"ggelfenbaum@usgs.gov","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":792128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"La Selle, SeanPaul 0000-0002-4500-7885 slaselle@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-7885","contributorId":181565,"corporation":false,"usgs":true,"family":"La Selle","given":"SeanPaul","email":"slaselle@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":792129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jaffe, Bruce E. 0000-0002-8816-5920 bjaffe@usgs.gov","orcid":"https://orcid.org/0000-0002-8816-5920","contributorId":2049,"corporation":false,"usgs":true,"family":"Jaffe","given":"Bruce","email":"bjaffe@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":792130,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210908,"text":"70210908 - 2020 - Space use and relative habitat selection for immature green turtles within a Caribbean marine protected area","interactions":[],"lastModifiedDate":"2020-07-03T02:06:58.740547","indexId":"70210908","displayToPublicDate":"2020-07-02T13:22:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"Space use and relative habitat selection for immature green turtles within a Caribbean marine protected area","docAbstract":"Background\nA better understanding of sea turtle spatial ecology is critical for the continued conservation of imperiled sea turtles and their habitats. For resource managers to develop the most effective conservation strategies, it is especially important to examine how turtles use and select for habitats within their developmental foraging grounds. Here, we examine the space use and relative habitat selection of immature green turtles (Chelonia mydas) using acoustic telemetry within the marine protected area, Buck Island Reef National Monument (BIRNM), St. Croix, United States Virgin Islands.\n\nResults\nSpace use by turtles was concentrated on the southern side of Buck Island, but also extended to the northeast and northwest areas of the island, as indicated by minimum convex polygons (MCPs) and 99%, 95%, and 50% kernel density estimations (KDEs). On average space use for all categories was<3 km2 with mean KDE area overlap ranging from 41.9 to 67.7%. Cumulative monthly MCPs and their proportions to full MCPs began to stabilize 3 to 6 detection months after release, respectively. Resource selection functions (RSFs) were implemented using a generalized linear mixed effects model with turtle ID as the random effect. After model selection, the accuracy of the top model was 77.3% and showed relative habitat selection values were highest at shallow depths, for areas in close proximity to seagrass, and in reef zones for both day and night, and within lagoon zones at night. The top model was also extended to predict across BIRNM at both day and night.\n\nConclusion\nMore traditional acoustic telemetry analyses in combination with RSFs provide novel insights into animal space use and relative resource selection. Here, we demonstrated immature green turtles within the BIRNM have small, specific home ranges and core use areas with temporally varying relative selection strengths across habitat types. We conclude the BIRNM marine protected area is providing sufficient protection for immature green turtles, however, habitat protection could be focused in both areas of high space use and in locations where high relative selection values were determined. Ultimately, the methodologies and results presented here may help to design strategies to expand habitat protection for immature green turtles across their greater distribution.","language":"English","publisher":"BMC","doi":"10.1186/s40317-020-00209-9","usgsCitation":"Griffin, L., Smith, B., Cherkiss, M., Crowder, A., Pollock, C.G., Hillis-Starr, Z., Danylchuk, A.J., and Hart, K., 2020, Space use and relative habitat selection for immature green turtles within a Caribbean marine protected area: Animal Biotelemetry, v. 8, 22, 13 p., https://doi.org/10.1186/s40317-020-00209-9.","productDescription":"22, 13 p.","ipdsId":"IP-116004","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":456142,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-020-00209-9","text":"Publisher Index Page"},{"id":376103,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States Virgin Islands","state":"St. Croix","otherGeospatial":"Buck Island Reef National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.64750289916992,\n 17.767487103047646\n ],\n [\n -64.59136962890625,\n 17.767487103047646\n ],\n [\n -64.59136962890625,\n 17.80491863487742\n ],\n [\n -64.64750289916992,\n 17.80491863487742\n ],\n [\n -64.64750289916992,\n 17.767487103047646\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-06-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Griffin, Lucas P","contributorId":228796,"corporation":false,"usgs":false,"family":"Griffin","given":"Lucas P","affiliations":[{"id":41510,"text":"Department of Environmental Conservation, University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":792070,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Brian J. 0000-0002-0531-0492","orcid":"https://orcid.org/0000-0002-0531-0492","contributorId":139672,"corporation":false,"usgs":false,"family":"Smith","given":"Brian J.","affiliations":[{"id":12876,"text":"Cherokee Nation Technology Solutions","active":true,"usgs":false}],"preferred":false,"id":792071,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cherkiss, Michael 0000-0002-7802-6791","orcid":"https://orcid.org/0000-0002-7802-6791","contributorId":222174,"corporation":false,"usgs":true,"family":"Cherkiss","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":792072,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crowder, Andrew 0000-0001-6978-6265","orcid":"https://orcid.org/0000-0001-6978-6265","contributorId":218467,"corporation":false,"usgs":true,"family":"Crowder","given":"Andrew","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":792073,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pollock, Clayton G","contributorId":228797,"corporation":false,"usgs":false,"family":"Pollock","given":"Clayton","email":"","middleInitial":"G","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":792074,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hillis-Starr, Zandy","contributorId":179152,"corporation":false,"usgs":false,"family":"Hillis-Starr","given":"Zandy","email":"","affiliations":[],"preferred":false,"id":792075,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Danylchuk, Andy J.","contributorId":138981,"corporation":false,"usgs":false,"family":"Danylchuk","given":"Andy","email":"","middleInitial":"J.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":792076,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":214961,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":792077,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70210903,"text":"70210903 - 2020 - Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey","interactions":[],"lastModifiedDate":"2020-07-03T14:56:34.693731","indexId":"70210903","displayToPublicDate":"2020-07-02T13:07:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1550,"text":"Environmental Modeling & Assessment","onlineIssn":" 1573-296","printIssn":"1420-2026","active":true,"publicationSubtype":{"id":10}},"title":"Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey","docAbstract":"

The US Geological Survey (USGS) is currently (2020) integrating its water science programs to better address the nation’s greatest water resource challenges now and into the future. This integration will rely, in part, on data from 10 or more intensively monitored river basins from across the USA. A team of USGS scientists was convened to develop a systematic, quantitative approach to prioritize candidate basins for this monitoring investment to ensure that, as a group, the 10 basins will support the assessment and forecasting objectives of the major USGS water science programs. Candidate basins were the level-4 hydrologic units (HUC04) with some of the smaller HUC04s being combined; median candidate-basin area is 46,600 km2. Candidate basins for the contiguous United States (CONUS) were grouped into 18 hydrologic regions. Ten geospatial variables representing land use, climate change, water use, water-balance components, streamflow alteration, fire risk, and ecosystem sensitivity were selected to rank candidate basins within each of the 18 hydrologic regions. The two highest ranking candidate basins in each of the 18 regions were identified as finalists for selection as “Integrated Water Science Basins”; final selection will consider input from a variety of stakeholders. The regional framework, with only one basin selected per region, ensures that as a group, the basins represent the range in major drivers of the hydrologic cycle. Ranking within each region, primarily based on anthropogenic stressors of water resources, ensures that settings representing important water-resource challenges for the nation will be studied.

","language":"English","publisher":"Springer","doi":"10.1007/s10661-020-08403-1","usgsCitation":"Van Metre, P.C., Qi, S.L., Deacon, J.R., Dieter, C., Driscoll, J.M., Fienen, M.N., Kenney, T.A., Lambert, P.M., Lesmes, D.P., Mason, C., Mueller-Solger, A., Musgrove, M., Painter, J.A., Rosenberry, D.O., Sprague, L.A., Tesoriero, A.J., Windham-Myers, L., and Wolock, D.M., 2020, Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey: Environmental Modeling & Assessment, v. 192, 458, 17 p., https://doi.org/10.1007/s10661-020-08403-1.","productDescription":"458, 17 p.","ipdsId":"IP-114496","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":456145,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10661-020-08403-1","text":"Publisher Index Page"},{"id":436898,"rank":0,"type":{"id":30,"text":"Data 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Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792056,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Wolock, David M. 0000-0002-6209-938X","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":219213,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":792057,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70228944,"text":"70228944 - 2020 - Wave-like patterns of plant phenology determine ungulate movement tactics","interactions":[],"lastModifiedDate":"2022-02-25T17:47:55.110107","indexId":"70228944","displayToPublicDate":"2020-07-02T11:05:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1352,"text":"Current Biology","active":true,"publicationSubtype":{"id":10}},"title":"Wave-like patterns of plant phenology determine ungulate movement tactics","docAbstract":"Animals exhibit a diversity of movement tactics [1]. Tracking resources that change across space and time is predicted to be a fundamental driver of animal movement [2]. For example, some migratory ungulates (i.e., hooved mammals) closely track the progression of highly nutritious plant green-up, a phenomenon called “green-wave surfing” [3-5]. Yet, general principles describing how the dynamic nature of resources determine movement tactics are lacking [6]. We tested emerging theory that predicts surfing and the existence of migratory behavior will be favored in environments where green-up is fleeting and moves sequentially across large landscapes (i.e., wave-like green-up) [7]. Landscapes exhibiting wave-like patterns of green-up facilitated surfing and explained the existence of migratory behavior across 61 populations of four ungulate species on two continents (n=1,696 individuals). At the species level, foraging benefits were equivalent between tactics, suggesting that each movement tactic is fine tuned to local patterns of plant phenology. For decades, ecologists have sought to understand how animals move to select habitat, commonly defining habitat as a set of static patches [8, 9]. Our findings indicate that animal movement tactics emerge as a function of the flux of resources across space and time, underscoring the need to redefine habitat to include its dynamic attributes. As global habitats continue to be modified by anthropogenic disturbance and climate change [10], our synthesis provides a generalizable framework to understand how animal movement will be influenced by altered patterns of resource phenology.","language":"English","publisher":"Elsevier","doi":"10.1016/j.cub.2020.06.032","usgsCitation":"Aikens, E., Mysterud, A., Merkle, J., Cagnacci, F., Rivrud, I.M., Hebblewhite, M., Hurley, M., Peters, W., Bergen, S., De Groeve, J., Dwinnell, S.P., Gehr, B., Heurich, M., Mark Hewison, A.J., Jarnemo, A., Kjellander, P., Kroschel, M., Licoppe, A., Linnell, J., Merrill, E.H., Middleton, A.D., Morellet, N., Neufeld, L., Ortega, A.C., Parker, K.L., Pedrotti, L., Proffitt, K., Said, S., Sawyer, H., Scurlock, B.M., Signer, J., Stent, P., Sustr, P., Szkorupa, T., Monteith, K., and Kauffman, M., 2020, Wave-like patterns of plant phenology determine ungulate movement tactics: Current Biology, v. 30, no. 17, p. 3444-3449, 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‘New Narratives for Nature: operationalizing the IPBES Nature Futures Scenarios’ was organised by the IPBES task force on scenarios and models and hosted by the Institute for Global Environmental Strategies (IGES), with support from the research team on “Predicting and Assessing Natural Capital and Ecosystem Services through an Integrated Social-Ecological Systems Approach (PANCES)” based at the University of Tokyo, the Research Institute for Humanity and Nature (RIHN), and the United Nations University, with generous financial support from the Ministry of the Environment of Japan. \n\nDue to the COVID-19 virus outbreak, most task force members participated through virtual means, with a subset of task force members meeting in person in Japan.\n\nThe aim of the workshop was to build on the Nature Futures Framework (NFF) and on the ‘nature futures’ participatory scenario-development work initiated by the IPBES expert group on scenarios and models in the first IPBES work programme. This workshop aims to further elaborate the pre-workshop scenario narratives and to enrich discussions on the NFF. The workshop also served to start working on a more detailed task force work plan.\n\nThese aims were achieved through:\n• Task force sessions on the further formulation of the Nature Futures narratives.\n• Task force sessions on the cross-comparison of draft narratives and the further elaboration of the historical-present narrative.\n• Organisational sessions to begin the drafting of sub-deliverable-specific work plans.\n• In parallel to the task force workshop, collaborative sessions between the task force and Japanese researchers took place to discuss the application of the Nature Futures Framework at the national scale, using existing national level scenarios from Japan.\n• A public seminar, in Japan, for a wider audience introducing the scenarios and models task force’s work, the concept of the Nature Futures Framework, and fostered discussions on the concept of transformative change.\n\nSummary of outputs of the workshop in Japan\n• 6 NEW scenario narratives drafts – an evolution of the pre-workshop work using the narrative templates, into a more coherent set of narratives fitting their locations in the Nature Futures Framework, including some illustrative visualisations. \n• A cross-comparison table – to identify the core similarities and differences across the 6 new narratives (including single narrative-between-narrative comparisons).\n• A discussion on how to continue further development, requiring identifying pathways to complete the 6 new narratives.\n• 1 historical-to-present narrative draft – also an evolution of work done prior to the workshop. The task force has yet to synthesize and shorten this draft, ensuring linkages with topics detailed in the 6 new narratives into a more digestible level.\n• Elaboration of a follow-up plan for further development of the narratives, post-workshop, through a “buddy” system of in-depth online discussions per and between narratives. \n• 1 Japan case study – on fitting national level scenarios into the Nature Futures Framework. A summary will be shared by the team who worked closely on this with the PANCES partners, which we expect will give interesting insights to the cross-scale application of the Nature Futures Framework.\n• Detailed work plan implementation drafts (ongoing post workshop in sub-groups).","language":"English","publisher":"PBL Netherlands Environmental Assessment Agency","collaboration":"Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services; And Institute for Global Environmental Strategies (IGES), University of Tokyo, Research Institute for Humanity and Nature (RIHN), United Nations University, Ministry of the Environment of Japan","usgsCitation":"Schoolenberg, M., Okayasu, S., Alkemade, R., Krijgsman, A., Dutra de Aguiar, A.P., Hashimoto, S., Lundquist, C.J., Pereira, L., Peterson, G., Armenteras, D., Cheung, W.W., Diaw, M.C., Duran, A.P., Gasalla, M., Halouani, G., Harrisson, P., Karlsson-Vinkhuyzen, S., Kim, H., Kuiper, J.J., Miller, B., Takahashi, Y., and Pichs, R., 2020, Report on the workshop ‘Next Steps in Developing Nature Futures’, i, 37 p.","productDescription":"i, 37 p.","ipdsId":"IP-118520","costCenters":[{"id":40927,"text":"North Central Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":376289,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":376282,"type":{"id":15,"text":"Index Page"},"url":"https://www.pbl.nl/en/publications/report-on-the-workshop-%E2%80%98new-narratives-for-nature-operationalizing-the-ipbes-nature-futures-scenarios%E2%80%99"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schoolenberg, Machteld","contributorId":228931,"corporation":false,"usgs":false,"family":"Schoolenberg","given":"Machteld","email":"","affiliations":[{"id":41529,"text":"PBL","active":true,"usgs":false}],"preferred":false,"id":792551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Okayasu, Sana","contributorId":228932,"corporation":false,"usgs":false,"family":"Okayasu","given":"Sana","affiliations":[{"id":41529,"text":"PBL","active":true,"usgs":false}],"preferred":false,"id":792552,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alkemade, Rob 0000-0001-8761-1768","orcid":"https://orcid.org/0000-0001-8761-1768","contributorId":202614,"corporation":false,"usgs":false,"family":"Alkemade","given":"Rob","email":"","affiliations":[{"id":36496,"text":"PBL Netherlands Environmental Assessment Agency","active":true,"usgs":false}],"preferred":false,"id":792559,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krijgsman, Amanda","contributorId":228933,"corporation":false,"usgs":false,"family":"Krijgsman","given":"Amanda","email":"","affiliations":[{"id":41529,"text":"PBL","active":true,"usgs":false}],"preferred":false,"id":792553,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dutra de Aguiar, Ana Paula","contributorId":228934,"corporation":false,"usgs":false,"family":"Dutra de Aguiar","given":"Ana","email":"","middleInitial":"Paula","affiliations":[],"preferred":false,"id":792554,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hashimoto, Shizuka","contributorId":228935,"corporation":false,"usgs":false,"family":"Hashimoto","given":"Shizuka","email":"","affiliations":[],"preferred":false,"id":792555,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lundquist, Carolyn J.","contributorId":213140,"corporation":false,"usgs":false,"family":"Lundquist","given":"Carolyn","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":792556,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pereira, Laura M.","contributorId":228936,"corporation":false,"usgs":false,"family":"Pereira","given":"Laura","middleInitial":"M.","affiliations":[],"preferred":false,"id":792557,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Peterson, Garry","contributorId":228937,"corporation":false,"usgs":false,"family":"Peterson","given":"Garry","email":"","affiliations":[],"preferred":false,"id":792558,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Armenteras, Dolors","contributorId":228938,"corporation":false,"usgs":false,"family":"Armenteras","given":"Dolors","email":"","affiliations":[],"preferred":false,"id":792560,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cheung, William W. 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The Biological Condition Gradient (BCG) is a conceptual model that describes changes in aquatic communities under increasing levels of anthropogenic stress. The BCG helps decision-makers connect narrative water quality goals (e.g., maintenance of natural structure and function) to quantitative measures of ecological condition by linking index thresholds based on statistical distributions (e.g., percentiles of reference distributions) to expert descriptions of changes in biological condition along disturbance gradients. As a result, the BCG may be more meaningful to managers and the public than indices alone. To develop a BCG model, biological response to stress is divided into 6 levels of condition, represented as changes in biological structure (abundance and diversity of pollution sensitive versus tolerant taxa) and function. We developed benthic macroinvertebrate (BMI) and algal BCG models for California perennial wadeable streams to support interpretation of percentiles of reference-based thresholds for bioassessment indices (i.e., the California Stream Condition Index [CSCI] for BMI and the Algal Stream Condition Index [ASCI] for diatoms and soft-bodied algae). Two panels (one of BMI ecologists and the other of algal ecologists) each calibrated a general BCG model to California wadeable streams by first assigning taxa to specific tolerance and sensitivity attributes, and then independently assigning test samples (264 BMI and 248 algae samples) to BCG Levels 1–6. Consensus on the assignments was developed within each assemblage panel using a modified Delphi method. Panels then developed detailed narratives of changes in BMI and algal taxa that correspond to the 6 BCG levels. Consensus among experts was high, with 81% and 82% expert agreement within 0.5 units of assigned BCG level for BMIs and algae, respectively. According to both BCG models, the 10th percentiles index scores at reference sites corresponded to a BCG Level 3, suggesting that this type of threshold would protect against moderate changes in structure and function while allowing loss of some sensitive taxa. The BCG provides a framework to interpret changes in aquatic biological condition along a gradient of stress. The resulting relationship between index scores and BCG levels and narratives can help decision-makers select thresholds and communicate how these values protect aquatic life use goals.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2020.106618","usgsCitation":"Paul, M.J., Jessup, B., Brown, L.R., Carter, J., Cantonati, M., Charles, D.F., Gerritsen, J., Herbst, D., Stancheva, R., Howard, J.K., Isham, B., Lowe, R., Mazor, R., Mendez, P.K., Ode, P., O’Dowd, A., Pan, Y., Rehn, A.C., Spaulding, S.A., Sutula, M., and Theroux, S., 2020, Characterizing benthic macroinvertebrate and algal biological condition gradient models for California wadeable Streams, USA: Ecological Indicators, v. 117, 106618, 12 p., https://doi.org/10.1016/j.ecolind.2020.106618.","productDescription":"106618, 12 p.","ipdsId":"IP-112075","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":456163,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2020.106618","text":"Publisher Index 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Center","active":true,"usgs":true}],"preferred":true,"id":804114,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carter, James","contributorId":243300,"corporation":false,"usgs":false,"family":"Carter","given":"James","affiliations":[{"id":48681,"text":"QH Forensic and Scientific Services, Australia","active":true,"usgs":false}],"preferred":false,"id":804115,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cantonati, Marco","contributorId":244528,"corporation":false,"usgs":false,"family":"Cantonati","given":"Marco","email":"","affiliations":[{"id":48928,"text":"Museo delle Scienze, Limnology & Phycology Section, Trento, Italy","active":true,"usgs":false}],"preferred":false,"id":804116,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Charles, Donald F.","contributorId":244530,"corporation":false,"usgs":false,"family":"Charles","given":"Donald","email":"","middleInitial":"F.","affiliations":[{"id":48929,"text":"Academy of Natural Sciences of Drexel University, Patrick Center for Environmental Research, Philadelphia","active":true,"usgs":false}],"preferred":false,"id":804117,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gerritsen, Jeroen 0000-0002-4455-1088","orcid":"https://orcid.org/0000-0002-4455-1088","contributorId":216564,"corporation":false,"usgs":false,"family":"Gerritsen","given":"Jeroen","email":"","affiliations":[],"preferred":false,"id":804118,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Herbst, Dave","contributorId":244532,"corporation":false,"usgs":false,"family":"Herbst","given":"Dave","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":804119,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stancheva, Rosalina","contributorId":244533,"corporation":false,"usgs":false,"family":"Stancheva","given":"Rosalina","email":"","affiliations":[{"id":48930,"text":"University of California, San Marcos","active":true,"usgs":false}],"preferred":false,"id":804120,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Howard, Jeanette K.","contributorId":176714,"corporation":false,"usgs":false,"family":"Howard","given":"Jeanette","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":804121,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Isham, Bill","contributorId":244540,"corporation":false,"usgs":false,"family":"Isham","given":"Bill","email":"","affiliations":[],"preferred":false,"id":804132,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lowe, Rex","contributorId":244541,"corporation":false,"usgs":false,"family":"Lowe","given":"Rex","email":"","affiliations":[],"preferred":false,"id":804133,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Mazor, Raphael D","contributorId":120256,"corporation":false,"usgs":true,"family":"Mazor","given":"Raphael D","affiliations":[],"preferred":false,"id":804134,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Mendez, Patina K.","contributorId":244542,"corporation":false,"usgs":false,"family":"Mendez","given":"Patina","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":804135,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Ode, Peter R","contributorId":121469,"corporation":false,"usgs":true,"family":"Ode","given":"Peter R","affiliations":[],"preferred":false,"id":804136,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"O’Dowd, Alison","contributorId":244543,"corporation":false,"usgs":false,"family":"O’Dowd","given":"Alison","email":"","affiliations":[],"preferred":false,"id":804137,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Pan, Yangdong","contributorId":52865,"corporation":false,"usgs":true,"family":"Pan","given":"Yangdong","email":"","affiliations":[],"preferred":false,"id":804138,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Rehn, Andrew C.","contributorId":47650,"corporation":false,"usgs":true,"family":"Rehn","given":"Andrew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":804139,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Spaulding, Sarah A. 0000-0002-9787-7743 sspaulding@usgs.gov","orcid":"https://orcid.org/0000-0002-9787-7743","contributorId":1157,"corporation":false,"usgs":true,"family":"Spaulding","given":"Sarah","email":"sspaulding@usgs.gov","middleInitial":"A.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":804140,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Sutula, Martha","contributorId":191008,"corporation":false,"usgs":false,"family":"Sutula","given":"Martha","email":"","affiliations":[],"preferred":false,"id":804141,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Theroux, Susanna","contributorId":244544,"corporation":false,"usgs":false,"family":"Theroux","given":"Susanna","affiliations":[],"preferred":false,"id":804142,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70211681,"text":"70211681 - 2020 - The potential of using dynamic strains in earthquake early warning applications","interactions":[],"lastModifiedDate":"2020-09-10T20:25:08.380546","indexId":"70211681","displayToPublicDate":"2020-07-01T17:56:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"The potential of using dynamic strains in earthquake early warning applications","docAbstract":"

We investigate the potential of using borehole strainmeter data from the Network of the Americas (NOTA) and the U.S. Geological Survey networks to estimate earthquake moment magnitudes for earthquake early warning (EEW) applications. We derive an empirical equation relating peak dynamic strain, earthquake moment magnitude, and hypocentral distance, and investigate the effects of different types of instrument calibration on model misfit. We find that raw (uncalibrated) strains fit the model as accurately as calibrated strains. We test the model by estimating moment magnitudes of the largest two earthquakes in the July 2019 Ridgecrest earthquake sequence—the M 6.4 foreshock and the M 7.1 mainshock—using two strainmeters located within ∼50  km of the rupture. In both the cases, the magnitude based on the dynamic strain component is within ∼0.1–0.4 magnitude units of the catalog moment magnitude. We then compare the temporal evolution of our strain‐derived magnitudes for the largest two Ridgecrest events to the real‐time performance of the ShakeAlert EEW System (SAS). The final magnitudes from NOTA borehole strainmeters are close to SAS real‐time estimates for the M 6.4 foreshock, and significantly more accurate for the M 7.1 mainshock.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220190385","usgsCitation":"Farghal, N.S., Barbour, A.J., and Langbein, J., 2020, The potential of using dynamic strains in earthquake early warning applications: Seismological Research Letters, v. 91, no. 5, p. 2817-2827, https://doi.org/10.1785/0220190385.","productDescription":"11 p.","startPage":"2817","endPage":"2827","ipdsId":"IP-112135","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":377141,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.158203125,\n 32.54681317351514\n ],\n [\n -114.345703125,\n 32.76880048488168\n ],\n [\n -114.345703125,\n 34.34343606848294\n ],\n [\n -120.05859375,\n 39.232253141714885\n ],\n [\n -120.14648437499999,\n 41.96765920367816\n ],\n [\n -119.61914062499999,\n 48.83579746243093\n ],\n [\n -123.96972656249999,\n 49.38237278700955\n ],\n [\n -126.3427734375,\n 49.866316729538674\n ],\n [\n -127.08984375000001,\n 48.22467264956519\n ],\n [\n -124.8486328125,\n 47.39834920035926\n ],\n [\n -124.76074218749999,\n 44.74673324024678\n ],\n [\n -125.33203125,\n 41.343824581185686\n ],\n [\n -124.01367187499999,\n 38.238180119798635\n ],\n [\n -121.728515625,\n 35.28150065789119\n ],\n [\n -119.66308593749999,\n 33.7243396617476\n ],\n [\n -117.158203125,\n 32.54681317351514\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"91","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Farghal, Noha Sameh Ahmed 0000-0001-8423-5066","orcid":"https://orcid.org/0000-0001-8423-5066","contributorId":237040,"corporation":false,"usgs":true,"family":"Farghal","given":"Noha","email":"","middleInitial":"Sameh Ahmed","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":795045,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barbour, Andrew J 0000-0001-6473-5493 abarbour@usgs.gov","orcid":"https://orcid.org/0000-0001-6473-5493","contributorId":237041,"corporation":false,"usgs":true,"family":"Barbour","given":"Andrew","email":"abarbour@usgs.gov","middleInitial":"J","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":795046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langbein, John 0000-0002-7821-8101","orcid":"https://orcid.org/0000-0002-7821-8101","contributorId":212735,"corporation":false,"usgs":true,"family":"Langbein","given":"John","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":795047,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228337,"text":"70228337 - 2020 - Living on the edge: Multi-scale analyses of bird habitat use in coastal marshes of Barataria Basin, Louisiana, USA","interactions":[],"lastModifiedDate":"2022-02-09T22:45:53.971165","indexId":"70228337","displayToPublicDate":"2020-07-01T16:38:32","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Living on the edge: Multi-scale analyses of bird habitat use in coastal marshes of Barataria Basin, Louisiana, USA","docAbstract":"

Coastal marsh loss, combined with expected sea-level rise, will cause inundation and extensive shifts to vegetation and salinity regimes that may affect the bird species dependent on coastal ecosystems worldwide. Within coastal marsh habitats, birds provide key targets for coastal management goals. However, limited information on bird-habitat relationships within coastal marshes inhibits the development of restoration projects targeted to bird species. We surveyed birds bi-monthly within Barataria Basin, LA from July 2014 to December 2015 to compare their use between fresh and saline coastal marshes. Additionally, we examined habitat use at finer spatial scales to assess preference for marsh edge microhabitats. Edge habitat supported 1.8 times more bird species (guild) richness than emergent and open water habitat. We concluded that future modelling efforts would be improved if models incorporate edge effects for birds in coastal marshes that extend 20 m from emergent vegetation into open water, with a reduced effect if marsh types convert from fresh to saline. Our data will be useful to simulate the effects of changes in marsh type, area, and edge on habitat quality for birds in coastal Louisiana and will inform habitat restoration and management decisions aimed at optimizing bird use.

","language":"English","publisher":"Springer","doi":"10.1007/s13157-020-01324-2","usgsCitation":"Patton, B., Nyman, J.A., and La Peyre, M., 2020, Living on the edge: Multi-scale analyses of bird habitat use in coastal marshes of Barataria Basin, Louisiana, USA: Wetlands, v. 40, p. 2041-2054, https://doi.org/10.1007/s13157-020-01324-2.","productDescription":"14 p.","startPage":"2041","endPage":"2054","ipdsId":"IP-098169","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":499826,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.lsu.edu/agrnr_pubs/602","text":"External Repository"},{"id":395743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Barataria Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.8349609375,\n 28.714678586705976\n ],\n [\n -89.033203125,\n 28.714678586705976\n ],\n [\n -89.033203125,\n 30.32547125932808\n ],\n [\n -90.8349609375,\n 30.32547125932808\n ],\n [\n -90.8349609375,\n 28.714678586705976\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2020-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Patton, Brett 0000-0002-7396-3452 pattonb@usgs.gov","orcid":"https://orcid.org/0000-0002-7396-3452","contributorId":5458,"corporation":false,"usgs":true,"family":"Patton","given":"Brett","email":"pattonb@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":833827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nyman, J. A.","contributorId":275213,"corporation":false,"usgs":false,"family":"Nyman","given":"J.","email":"","middleInitial":"A.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":833828,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833829,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211872,"text":"70211872 - 2020 - Regionally continuous Miocene rhyolites beneath the eastern Snake River Plain reveal localized flexure at its western margin: Idaho National Laboratory and vicinity","interactions":[],"lastModifiedDate":"2020-12-15T20:23:40.067951","indexId":"70211872","displayToPublicDate":"2020-07-01T16:07:01","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6000,"text":"The Mountain Geologist","active":true,"publicationSubtype":{"id":10}},"title":"Regionally continuous Miocene rhyolites beneath the eastern Snake River Plain reveal localized flexure at its western margin: Idaho National Laboratory and vicinity","docAbstract":"The eastern Snake River Plain (ESRP) is a northeast-trending topographic basin interpreted to be the result of the time-transgressive track of the North American plate above the Yellowstone hotspot. The track is defined by the age progression of silicic volcanic rocks exposed along the margins of the ESRP. However, the bulk of these silicic rocks are buried under 1 to 3 kilometers of younger basalts. Here, silicic volcanic rocks recovered from boreholes that penetrate below the basalts, including INEL-1, WO-2 and new deep borehole USGS-142, are correlated with one another and to surface exposures to assess various models for ESRP subsidence. These correlations are established on U/Pb zircon and 40Ar/39Ar sanidine age determinations, phenocryst assemblages, major and trace element geochemistry, δ18O isotopic data from selected phenocrysts, and initial εHf values of zircon. These data suggest a correlation of: (1) the newly documented 8.1 ± 0.2 Ma rhyolite of Butte Quarry (sample 17KS03), exposed near Arco, Idaho to the upper-most Picabo volcanic field rhyolites found in borehole INEL-1; (2) the 6.73 ± 0.02 Ma East Arco Hills rhyolite (sample 16KS02) to the Blacktail Creek Tuff, which was also encountered at the bottom of borehole WO-2; and (3) the 6.42 ± 0.07 Ma rhyolite of borehole USGS-142 to the Walcott Tuff B encountered in deep borehole WO-2. These results show that rhyolites found along the western margin of the ESRP dip ~20º south-southeast toward the basin axis, and then gradually tilt less steeply in the subsurface as the axis is approached. This subsurface pattern of tilting is consistent with a previously proposed crustal flexural model of subsidence based only on surface exposures, but is inconsistent with subsidence models that require accommodation of ESRP subsidence on either a major normal fault or strike-slip fault.","language":"English","publisher":"Rocky Mountain Association of Geologists","doi":"10.31582/rmag.mg.57.3.241","usgsCitation":"Schusler, K.L., Pearson, D.M., McCurry, M.J., Bartholomay, R.C., and Anders, M.H., 2020, Regionally continuous Miocene rhyolites beneath the eastern Snake River Plain reveal localized flexure at its western margin: Idaho National Laboratory and vicinity: The Mountain Geologist, v. 57, no. 3, p. 241-270, https://doi.org/10.31582/rmag.mg.57.3.241.","productDescription":"30 p.","startPage":"241","endPage":"270","ipdsId":"IP-112371","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":377936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.18002319335938,\n 43.41302868475145\n ],\n [\n -111.93145751953125,\n 43.41302868475145\n ],\n [\n -111.93145751953125,\n 43.55651037504758\n ],\n [\n -112.18002319335938,\n 43.55651037504758\n ],\n [\n -112.18002319335938,\n 43.41302868475145\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Schusler, Kyle L.","contributorId":237858,"corporation":false,"usgs":false,"family":"Schusler","given":"Kyle","email":"","middleInitial":"L.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":795484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearson, David M.","contributorId":237860,"corporation":false,"usgs":false,"family":"Pearson","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":795485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCurry, Michael J.","contributorId":237861,"corporation":false,"usgs":false,"family":"McCurry","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":795486,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bartholomay, Roy C. 0000-0002-4809-9287 rcbarth@usgs.gov","orcid":"https://orcid.org/0000-0002-4809-9287","contributorId":1131,"corporation":false,"usgs":true,"family":"Bartholomay","given":"Roy","email":"rcbarth@usgs.gov","middleInitial":"C.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795487,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anders, Mark H.","contributorId":237862,"corporation":false,"usgs":false,"family":"Anders","given":"Mark","email":"","middleInitial":"H.","affiliations":[{"id":39266,"text":"St. Lawrence University","active":true,"usgs":false}],"preferred":false,"id":795488,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210894,"text":"sir20205064 - 2020 - A summary of water-quality monitoring in San Francisco Bay in water year 2017","interactions":[],"lastModifiedDate":"2020-07-01T21:11:28.152523","indexId":"sir20205064","displayToPublicDate":"2020-07-01T12:33:01","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5064","displayTitle":"A Summary of Water-Quality Monitoring in San Francisco Bay in Water Year 2017","title":"A summary of water-quality monitoring in San Francisco Bay in water year 2017","docAbstract":"

This report summarizes the activities of the U.S. Geological Survey (USGS) San Francisco Bay Water-Quality Monitoring and Sediment Transport Project during water year 2017, including an explanation of methods employed, stations operated, and a graphical summary of data for the period of record for stations operational in water year 2017. In cooperation with partner agencies, the USGS maintains a network of sensors that continuously and autonomously measures water-quality parameters in San Francisco Bay including water temperature, specific conductance, turbidity, and suspended-sediment concentration. Data are collected at several locations in the estuary by a network of water-quality sondes sampled at 15-minute intervals. Methods of data collection are presented along with documentation of the regression models utilized to estimate suspended-sediment concentration from observed turbidity, a commonly utilized surrogate to estimate suspended-sediment concentration. The goals of the data collection effort are to (1) obtain long-term, high-frequency, and high-quality data to describe San Francisco Bay water quality; (2) make the data publicly available on the USGS National Water Information System data portal; and (3) help improve understanding of the spatial and temporal variability of water quality in the estuary, informing management decisions regarding restoration, water supply, navigation, and ecology.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205064","usgsCitation":"Livsey, D., and Downing-Kunz, M., 2020, A summary of water-quality monitoring in San Francisco Bay in water year 2017: U.S. Geological Survey Scientific Investigations Report 2020–5064, 78 p., https://doi.org/10.3133/sir20205064.","productDescription":"Report: vi, 78 p.; Data Release","numberOfPages":"78","onlineOnly":"Y","ipdsId":"IP-104269","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":376068,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","linkHelpText":"National Water Information System"},{"id":376066,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5064/coverthb.jpg"},{"id":376067,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5064/sir20205064.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.62390136718749,\n 37.35050947036205\n ],\n [\n -121.7340087890625,\n 37.35050947036205\n ],\n [\n -121.7340087890625,\n 38.22307753495298\n ],\n [\n -122.62390136718749,\n 38.22307753495298\n ],\n [\n -122.62390136718749,\n 37.35050947036205\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819

","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2020-07-01","noUsgsAuthors":false,"publicationDate":"2020-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Livsey, Daniel N. 0000-0002-2028-6128 dlivsey@usgs.gov","orcid":"https://orcid.org/0000-0002-2028-6128","contributorId":181870,"corporation":false,"usgs":true,"family":"Livsey","given":"Daniel","email":"dlivsey@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Downing-Kunz, Maureen A. 0000-0002-4879-0318 mdowning-kunz@usgs.gov","orcid":"https://orcid.org/0000-0002-4879-0318","contributorId":3690,"corporation":false,"usgs":true,"family":"Downing-Kunz","given":"Maureen","email":"mdowning-kunz@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792012,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210871,"text":"tm11B11 - 2020 - Elevation-derived hydrography acquisition specifications","interactions":[],"lastModifiedDate":"2020-07-01T21:02:43.408557","indexId":"tm11B11","displayToPublicDate":"2020-07-01T11:58:32","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"11-B11","displayTitle":"Elevation-Derived Hydrography Acquisition Specifications","title":"Elevation-derived hydrography acquisition specifications","docAbstract":"

Hydrographic features derived from U.S. Geological Survey (USGS) 3D Elevation Program data, and collected for use by the USGS, must meet the specifications described in this document. The specifications described herein pertain to the final product delivered to the USGS, not to methods used to derive the hydrographic features. The specifications describe the collection area, spatial reference system, attribute table structure, feature codes and values, delineation of hydrographic features, topology, positional assessment, metadata, and delivery formats. A companion document, Elevation-Derived Hydrography—Representation, Extraction, Attribution, and Delineation Rules, defines the fields, domains, and minimum feature collection requirements for hydrography features derived from elevation data. Hydrographic features collected to this specification will be suitable for using as breaklines to hydroflatten digital elevation models, processing for preconflation of features to the National Hydrography Dataset, and using for hydroenforcement of digital elevation models.

","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: U.S. Geological Survey Standards in Book 11 Collection and Delineation of Spatial Data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11B11","usgsCitation":"Terziotti, S., and Archuleta, C.M., 2020, Elevation-Derived Hydrography Acquisition Specifications: U.S. Geological Survey Techniques and Methods, book 11, chap. B11, 74 p., https://doi.org/10.3133/tm11B11.","productDescription":"Report: vii, 74 p.; Companion Report","numberOfPages":"86","onlineOnly":"Y","ipdsId":"IP-111691","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"links":[{"id":376021,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/tm11B12","text":"T&M 11–B12","size":"16.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 11–B12","linkHelpText":"— Elevation-Derived Hydrography—Representation, Extraction, Attribution, and Delineation Rules"},{"id":376020,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/b11/tm11b11.pdf","text":"Report","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 11–B11"},{"id":376019,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11/b11/coverthb.jpg"}],"contact":"

Director, National Geospatial Technical Operations Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-07-01","noUsgsAuthors":false,"publicationDate":"2020-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Terziotti, Silvia 0000-0003-3559-5844 seterzio@usgs.gov","orcid":"https://orcid.org/0000-0003-3559-5844","contributorId":1613,"corporation":false,"usgs":true,"family":"Terziotti","given":"Silvia","email":"seterzio@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Archuleta, Christy-Ann 0000-0002-4522-8573 carchule@usgs.gov","orcid":"https://orcid.org/0000-0002-4522-8573","contributorId":192597,"corporation":false,"usgs":true,"family":"Archuleta","given":"Christy-Ann","email":"carchule@usgs.gov","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":791890,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}