Bulletin 17C (B17C) recommends fitting the log-Pearson Type III (LP−III) distribution to a series of annual peak flows at a streamgage by using the method of moments. The third moment, the skewness coefficient (or skew), is important because the magnitudes of annual exceedance probability (AEP) flows estimated by using the LP−III distribution are affected by the skew; interest is focused on the right-hand tail of the distribution, which represents the larger annual peak flows that correspond to small AEPs. For streamgages having modest record lengths, the skew is sensitive to extreme events like large floods, which cause a sample to be highly asymmetrical or “skewed.” For this reason, B17C recommends using a weighted-average skew computed from the station skew for a given streamgage and a regional skew. This report generates an estimate of regional skew for a study area encompassing most of the Great Lakes Basin (hydrologic unit 04) and part of the Ohio River Basin (hydrologic unit 05). A total of 551 candidate streamgages that were unaffected by extensive regulation, diversion, urbanization, or channelization were considered for use in the skew analysis; after screening for redundancy and pseudo record length greater than 36 years, 368 streamgages were selected for use in the study. Flood frequencies for candidate streamgages were analyzed by employing the Expected Moments Algorithm, which extends the method of moments so that it can accommodate interval, censored, and historic/paleo flow data, as well as the Multiple Grubbs-Beck test to identify potentially influential low floods in the data series. Bayesian weighted least squares/Bayesian generalized least squares regression was used to develop a regional skew model for the study area that would incorporate possible variables (basin characteristics) to explain the variation in skew in the study area. Twelve basin characteristics were considered as possible explanatory variables; however, none produced a pseudo coefficient of determination greater than 5 percent; as a result, these characteristics did not help to explain the variation in skew in the study area. Therefore, a constant model having a regional skew coefficient of 0.086 and an average variance of prediction (AVPnew) (which corresponds to the mean square error [MSE]) of 0.13 at a new streamgage was selected. The AVPnew corresponds to an effective record length of 54 years, a marked improvement over the Bulletin 17B national skew map, whose reported MSE of 0.302 indicated a corresponding effective record length of only 17 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195105","usgsCitation":"Veilleux, A.G., and Wagner, D.M., 2019, Methods for estimating regional skewness of annual peak flows in parts of the Great Lakes and Ohio River Basins, based on data through water year 2013: U.S. Geological Survey Scientific Investigations Report 2019–5105, 26 p., https://doi.org/10.3133/sir20195105.","productDescription":"Report: vi, 25 p.; 5 Figures; Table; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101994","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":371689,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_table1.xlsx","text":"Table 1","size":"99.5 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Streamgages in parts of the Great Lakes and Ohio River Basins considered for use in regional skew analysis"},{"id":371684,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig01a.pdf","text":"Figure 1A","size":"5.25 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of study area in the Great Lakes and Ohio River Basins showing 4-digit hydrologic units"},{"id":371685,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig01b.pdf","text":"Figure 1B","size":"2.41 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of study area in the Great Lakes and Ohio River Basins showing locations of streamgages used in skew analysis"},{"id":371682,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N7UAFJ","text":"USGS data release","linkHelpText":"Annual peak-flow data, PeakFQ specification files and PeakFQ output files for 368 selected streamflow gaging stations operated by the U.S. Geological Survey in the Great Lakes and Ohio River basins that were used to estimate regional skewness of annual peak flows"},{"id":371681,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105.pdf","text":"Report","size":"3.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5105"},{"id":371680,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5105/coverthb.jpg"},{"id":399546,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109629.htm"},{"id":371688,"rank":9,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig05.pdf","text":"Figure 5","size":"1.95 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing residuals from constant model of skew for 368 streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"},{"id":371687,"rank":8,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig03.pdf","text":"Figure 3","size":"1.95 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing unbiased station skew of streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"},{"id":371686,"rank":7,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig02.pdf","text":"Figure 2","size":"1.94 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing the pseudo record lengths of streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"}],"country":"United States, Canada","otherGeospatial":"Great Lakes and Ohio River Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.3740234375,\n 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Water Mission Area
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12201 Sunrise Valley Drive
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Bedrock geologic mapping of the Lahore, Va., 7.5-minute quadrangle was completed as part of a broader project, undertaken jointly between the U.S. Geological Survey, the Virginia Division of Geology and Mineral Resources, and other Federal and State agencies to better understand the causative mechanisms of the magnitude-5.8 (M5.8) earthquake that occurred near Mineral, Va., on August 23, 2011. This project involved detailed mapping of eight quadrangles in the epicentral region of the Mineral, Va., earthquake in order to improve our understanding of the geologic framework of the central Virginia seismic zone, which has a long record of historical and prehistoric seismicity.
The Lahore 7.5-minute quadrangle contains the contact between Ordovician to Silurian, dioritic and granodioritic rocks of the Lahore and Ellisville plutons and older metasedimentary and metavolcanic rocks. The Lahore quadrangle is northeast of the Ferncliff and Louisa, Va., quadrangles, where the Shores complex is intruded by the Ellisville pluton along the pluton’s southwestern margin. The new mapping in the Lahore quadrangle shows that the Shores complex continues northeast of the Ellisville pluton. A northeast-trending mafic- and ultramafic-bearing belt within the Shores complex is a fault-bounded accretionary zone (accretionary wedge) between rocks of the Chopawamsic Formation and Laurentian slope-and-rise deposits. In the Lahore quadrangle, this belt contains several mappable, northeast- to southwest-trending mafic bodies and also includes small exposures of gabbro and talc schist.
The Lahore quadrangle contains structures of both early- and late-Paleozoic age that correspond to the Taconic and Alleghanian orogenies. Taconic (Late Ordovician) S1 schistosity in layered rocks is typically fine-grained and parallel to compositional layering, when present. Alleghanian (Pennsylvanian) S2 schistosity is coarser and more micaceous than S1 and is locally accompanied by a lineation that is represented by mineral lineations, micro-crenulations, or mullion fabric, and represents the hinges of F2 folds. A foliation in the plutonic rocks is represented by an equilibrium assemblage of aligned mafic minerals and is early Paleozoic in age.
Metamorphic grade in the non-plutonic rocks of the Lahore quadrangle ranges from lower-greenschist to the northwest to upper-greenschist to the southeast, as represented by mineral assemblages in non-plutonic rocks. The biotite isograd may be, in part, lithologically controlled by the contact between the informally-named Hardware and Byrd Mill formations, and locally affected by contact metamorphism by the Lahore pluton (western part of map). The Taconic garnet isograd is defined by the sparse presence of small (less than 1 millimeter), euhedral garnet crystals. Both the biotite and garnet isograds continue along strike to the southwest into the Ferncliff and Louisa quadrangles, where the isograds have been identified as Ordovician age (Taconic orogeny) based on muscovite, biotite, and amphibole 40Ar/39Ar cooling ages.
Regionally, the most common trend and plunge of joints is northwest and subvertical, respectively, and orthogonal to the regional strike of foliation. Early Mesozoic extension may have reactivated the Harris Creek fault (southeast corner of map), a late Paleozoic (Alleghanian orogeny) transpressional fault that marks the contact between granodiorite of the Ellisville pluton and the Chopawamsic Formation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191110","usgsCitation":"Burton, W.C., 2019, Preliminary bedrock geologic map of the Lahore 7.5-minute quadrangle, Orange, Spotsylvania, and Louisa Counties, Virginia: U.S. Geological Survey Open-File Report 2019–1110, 1 sheet, scale 1:24,000, https://doi.org/10.3133/ofr20191110.","productDescription":"1 Sheet: 37.00 x 34.00 inches; ReadMe; Database; Metadata","numberOfPages":"1","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098254","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":399417,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109624.htm"},{"id":371645,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2019/1110/LahoreVAOFR20191110_database.zip","text":"Geodatabase","linkFileType":{"id":6,"text":"zip"}},{"id":371644,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2019/1110/ofr20191110_openaccess.zip","text":"Open Access","linkFileType":{"id":6,"text":"zip"}},{"id":371643,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2019/1110/LahoreVAOFR20191110Metadata.zip","text":"Metadata","linkFileType":{"id":6,"text":"zip"}},{"id":371642,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2019/1110/ofr20191110_readme.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"}},{"id":371347,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2019/1110/ofr20191110.pdf","text":"Geologic Map","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1110"},{"id":370151,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1110/coverthb.jpg"}],"scale":"24000","country":"United States","state":"Virginia","county":"Louisa County, Orange County, Spotsylvania County","otherGeospatial":"Lahore 7.5-minute quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78,\n 38.125\n ],\n [\n -77.875,\n 38.125\n ],\n [\n -77.875,\n 38.25\n ],\n [\n -78,\n 38.25\n ],\n [\n -78,\n 38.125\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Florence Bascom Geoscience Center
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Published on an annual basis, this report is the earliest Government publication to furnish estimates covering nonfuel mineral industry data and is available at https://minerals.usgs.gov/minerals/pubs/mcs/. Data sheets contain information on the domestic industry structure, Government programs, tariffs, and 5-year salient statistics for more than 90 individual minerals and materials.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/70202434","usgsCitation":"U.S. Geological Survey, 2019, Mineral commodity summaries 2019: U.S. Geological Survey, 200 p., https://doi.org/10.3133/70202434.","productDescription":"204 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-104826","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":361621,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/graphics/minerals-commodity-2019.jpg"},{"id":361618,"type":{"id":15,"text":"Index Page"},"url":"https://minerals.usgs.gov/minerals/pubs/mcs/"}],"contact":"Director, National Minerals Information Center
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12201 Sunrise Valley Drive
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The U.S. Geological Survey National Crustal Model (NCM) is being developed to assist with earthquake hazard and risk assessment by supporting estimates of ground shaking in response to an earthquake. The period-dependent intensity and duration of shaking depend upon the three-dimensional seismic velocity, seismic attenuation, and density distribution of a region, which in turn is governed to a large degree by geology and how that geology behaves under varying temperatures and pressures.
A three-dimensional temperature model is presented here to support the estimation of physical parameters within the U.S. Geological Survey NCM. The crustal model is defined by a geological framework consisting of various lithologies with distinct mineral compositions. A temperature model is needed to calculate mineral density and bulk and shear modulus as a function of position within the crust. These properties control seismic velocity and impedance, which are needed to accurately estimate earthquake travel times and seismic amplitudes in earthquake hazard analyses. The temperature model is constrained by observations of surface temperature, temperature gradient, and conductivity, inferred Moho temperature and depth, and assumed conductivity at the base of the crust. The continental plate is assumed to have heat production that decreases exponentially with depth and thermal conductivity that exponentially changes from a surface value to 3.6 watts per meter-Kelvin at the Moho. The oceanic plate cools as a half-space with a geotherm dependent on plate age. Under these conditions, and the application of observed surface heat production, predicted Moho temperatures match Moho temperatures inferred from seismic P-wave velocities, on average. As has been noted in previous studies, high crustal temperatures are found in the western United States, particularly beneath areas of recent volcanism. In the central and eastern United States, elevated temperatures are found from southeast Texas, into the Mississippi Embayment, and up through Wisconsin. A USGS ScienceBase data release that supports this report is available and consists of grids covering the NCM across the conterminous United States, for example, surface temperature and temperature gradient, that are needed to produce temperature profiles.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191121","usgsCitation":"Boyd, O.S., 2020, Temperature model in support of the U.S. Geological Survey National Crustal Model for seismic hazard studies: U.S. Geological Survey Open-File Report 2019–1121, 15 p., https://doi.org/10.3133/ofr20191121.","productDescription":"Report: iv, 15 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-109788","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":437241,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SL2PVR","text":"USGS data release","linkHelpText":"TherMod"},{"id":399419,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109626.htm"},{"id":371516,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P935DT1G","text":"USGS data release","linkHelpText":"Grids in support 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Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
To address the lack of information on the spatial and temporal variability of recharge to groundwater systems in Maine, a study was initiated in cooperation with the Maine Geological Survey to use the U.S. Geological Survey Soil-Water-Balance model to evaluate annual average potential recharge across the State over a 25-year period from 1991 to 2015. The Maine Soil-Water-Balance model was calibrated using annual observations of recharge, runoff, and evapotranspiration for 32 calibration watersheds in the State during 2001–12 (902 total observations). Observations of recharge, runoff, and evapotranspiration were developed for each watershed to reduce the possibility of nonunique combinations of model parameters during the calibration. The Maine Soil-Water-Balance model was run using an optional evapotranspiration calculation method that provides more control for calibration than the standard method. The model was calibrated using the Parameter ESTimation software suite.
The overall mean model error (average of all annual residuals for recharge, runoff, and precipitation) was 0.39 inch. The mean of the absolute value of the residuals, or the mean absolute error, was 2.32 inches. The root mean squared error for the calibrated model overall was 3.14 inches. Statistical tests indicated that the model residuals are normally distributed. To determine the potential uncertainty in the median annual potential recharge that results from uncertainty in the parameters as they relate to information contained in the observations, 300 alternate model realizations were run, and the standard deviation of the median potential recharge value at every pixel was calculated.
Simulated 25-year median potential recharge across the State is widely variable; this variability closely follows patterns of precipitation, with additional variability contributed by the patchwork nature of the combinations of land-use class and hydrologic soil group inputs, and distribution of available water capacity in the soil across the State. Overall, the 25-year median annual potential recharge across the State is 7.5 inches, ranging from a low of about 5 inches to over 30 inches. The statewide range in the 25-year minimum values is from just over 2 inches to just over 20 inches. The statewide range in the 25-year maximum potential recharge is between 15 and 48 inches per year.
The model areas with the highest simulated median potential recharge include areas underlain by type A soils (sandy and well drained), particularly those that also have land uses with low or little vegetation (blueberry barrens, developed, open space, scrub/shrub, and cropland, for example). The potential recharge values for these areas are similar to previously published values for comparable soil types.
The 25-year average potential recharge grids were compared to recharge evaluated through groundwater-flow models or other methods in four hydrogeologic settings at six study areas in the State. A key factor in the ability of the Soil-Water-Balance model to reproduce the earlier study results was whether the available water-capacity data were an appropriate match for the hydrologic soil groups. The Maine Soil-Water-Balance model does a good job in representing an accurate potential recharge under circumstances where the surficial mapped soils extend below the surface to the water-table aquifer and where the available water-capacity data are in an appropriate range for the hydrologic soil group. One hydrogeologic setting that was challenging for the model was where a silt and clay layer was below a shallow soil unit that did not have available water-capacity data that were appropriate for the hydrologic soil group. In these cases, typically the available water-capacity data were very low, not accounting for the impedance of water flow provided by the underlying soil. The model also does not simulate well areas where bedrock surfaces are above the water table but below the plant rooting zone.
The data products accompanying this report are intended to be used to provide first-cut estimates of recharge for geographic areas no smaller than the smallest watersheds used in the calibration of the model—or about 1.5 square miles. It is recommended that the grids are used to calculate an area-wide average potential recharge for any given area of study, and an uncertainty around the mean should be calculated from the standard deviation grid at the same time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195125","collaboration":"Prepared in cooperation with the Maine Geological Survey","usgsCitation":"Nielsen, M.G., and Westenbroek, S.M., 2019, Groundwater recharge estimates for Maine using a Soil-Water-Balance model—25-year average, range, and uncertainty, 1991 to 2015: U.S. Geological Survey Scientific Investigations Report 2019–5125, 58 p., https://doi.org/10.3133/sir20195125.","productDescription":"Report: vii, 56 p.; Tables; 2 Data Releases","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106360","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":380621,"rank":7,"type":{"id":34,"text":"Image 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
This report is for U.S. Geological Survey (USGS)—and any other—hazard scientists who want to improve the understanding and use of their scientific information, particularly by non-experts. In order for people to use science, they need to understand it. The highly technical, specialized nature of scientific information makes that difficult, particularly when few scientists are trained to communicate with people outside their fields. These issues are of special importance to the USGS because it has many users who are not scientists and because it develops and applies hazard science to help protect the safety, security, and economic well-being of our Nation.
In 2010, the Science Application for Risk Reduction group at the USGS discovered the Center for Research on Environmental Decisions (CRED) guide, “The Psychology of Climate Change Communication.” Ever since, a growing number of USGS staff who need to communicate about hazards have used that guide and have asked CRED for a companion report dedicated to hazard communication to harness knowledge from more than 50 years of social science research.
In 2016, the USGS and CRED launched a collaboration to develop that companion report. Ultimately, a CRED hazard communication guide would be a Columbia University publication with a wide focus and would include many hazards that are outside the USGS purview. This report is a first step and concentrates strictly on hazard communication needs at the USGS.
To identify those needs and tailor this effort to USGS hazard communication priorities, this collaboration began with telephone interviews and an online survey of USGS staff. This report is the result; it summarizes social science research and experience in the areas of hazard communication that USGS participants deemed most important to include.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1449","usgsCitation":"Milch, K.F., Perry, S.C., and Bruce, J.L., 2019, Communicating hazards—A social science review to meet U.S. Geological Survey needs: U.S. Geological Survey Circular 1449, 67 p., https://doi.org/10.3133/cir1449.","productDescription":"vi, 67 p.","onlineOnly":"Y","ipdsId":"IP-095565","costCenters":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"links":[{"id":371014,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1449/cir1449.pdf","text":"Report","size":"25.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1449"},{"id":371013,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1449/coverthb.jpg"}],"contact":"Office of Associate Director, Natural Hazards
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The Bi-State Distinct Population Segment (Bi-State DPS) of greater sage-grouse (Centrocercus urophasianus, hereinafter “sage-grouse”) represents a genetically distinct and geographically isolated population that straddles the border between Nevada and California. The primary threat to these sage-grouse populations is the expansion of single-leaf pinyon (Pinus monophylla) and Utah juniper (Juniperus osteosperma) into sagebrush ecosystems, which fragments and reduces population connectivity and survival. Other important threats include low water availability during brood-rearing, particularly during drought, and increased predation by common ravens (Corvus corax), a generalist predator often associated with anthropogenic resource subsidies. Although the Bi-State DPS occurs at high elevations relative to sage-grouse range-wide, changes in historical wildfire cycles and the conversion of native shrubs to invasive annual grasslands still threaten these populations. The Bi-State DPS has undergone multiple federal status assessments and associated litigation. For example, in October of 2013, the Bi-State DPS was proposed for listing as threatened under the Endangered Species Act of 1973 by the U.S. Fish and Wildlife Service (USFWS), then withdrawn in April 2015. The withdrawal decision was challenged, and in May 2018, a Federal district court ordered the withdrawal decision to be vacated, and USFWS was required to re-open the October 2013 listing evaluation.
In response, the U.S. Geological Survey (USGS), with State and Federal collaborators, embarked on a multipronged analysis to provide current and best available science regarding population status of sage-grouse within the Bi-State DPS. Using data from a long-term monitoring program, we carried out four analytical study objectives, and here, we provide preliminary results of these analyses. First, we used integrated population modeling (IPM) to predict annual population abundance and annual finite rate of population change for the Bi-State DPS, as a whole, and for each subpopulation between 1995 and 2018. Because sage-grouse exhibit population cycles (periodic increases and decreases in abundance across approximately 6- to 10-year wavelengths), we estimated trends across three nested temporal scales that represent one (11 years), two (18 years), and three (24 years) complete population cycles. These estimates of relatively long-term averaged population change account for temporal (that is, interannual) variation. Our model predicted population abundance for the Bi-State DPS during 2018 at 3,305 individuals (2,247–4,683), with the majority occupying Bodie Hills and Long Valley. The model also predicted cyclic dynamics in abundance through time with evidence of 24-year population growth and slight trends of decline over the past 18 years. Specifically, across the Bi-State DPS as a whole, we estimated annual average at 0.99, 0.99, and 1.02 over the one, two, and three population cycles, which equated to a 10.5 percent, 16.6 percent decrease, and 60.0 percent increase in abundance over the 11-, 18-, and 24-year cycles. Estimated abundance in 2018 had not reached numbers lower than those predicted during 1995. However, we found spatial variation in population trends across the three cycles. Bodie Hills subpopulation comprised the greatest (1,521) and exhibited average annual greater than 1.0 across all periods resulting in average annual increases of 7 percent. This relatively large subpopulation has grown 5 times larger than what was predicted in 1995 while experiencing cyclical dynamics within that period.
Conversely, other smaller subpopulations within the Bi-State DPS exhibited average annual equal to or less than 1.0 resulting in estimated 10-year risks of extirpation ranging from 2.0 to 76.1 percent. In general, evidence of decline among smaller subpopulations was greatest for the most recent period (2008–18) compared to a period that encompassed three full population cycles (24-year). This difference coincides with an intense period of drought that began in 2012.
For comparative purposes as part of this first objective, we conducted a similar analysis for populations of sage-grouse within Nevada and California but outside the Bi-State DPS. We developed a region-wide and distance-weighted IPM using lek count from Nevada Department of Wildlife (NDOW) and California Department of Fish and Wildlife (CDFW) databases and with telemetry data collected by USGS across 12 sage-grouse subpopulations. Our models predicted similar patterns in population cycling outside the Bi-State DPS but with much stronger evidence of long-term declines across 24 years. Specifically, median averaged across each year of the 11-, 18-, and 24-year periods resulted in average annual values of 0.94, 0.97, and 0.99, respectively. These values equate to 41.0 percent, 38.5 percent, and 21.3 percent declines over the corresponding periods.
Second, we used lek count data in a state-space modeling framework to compare trends in population abundance across different spatial scales (that is, leks versus Bi-State DPS). This hierarchical framework allowed us to disentangle declines associated with climate conditions as opposed to other local level factors that might signal the need for management intervention. Specifically, we identified 7 leks that were both declining and recently decoupled from larger spatial scale trends, typically governed by climatic conditions (referred to as soft or hard signals). The goal of this analysis was to provide an early warning system that might have implications for conservation actions at local scales.
Third, we developed phenological (spring, summer–fall, and winter) and reproductive life stage (nesting, early brood-rearing, and late-brood rearing) based resource selection functions using various environmental covariates. We report rankings of variable importance for each season and life stage, developed maps of habitat selection indices (HSI), binned categories representing low, moderate, and high classes of quality (where any category greater than or equal to low indicated selected habitat) for each phenological season and life stage, and produced composite maps by selected phenological and reproductive stage to estimate annual habitat.
Fourth, we used for each lek within the Bi-State DPS to carry out a spatial analysis that quantified substantial changes in the distribution of occupied habitat across long- (24-year) and short- (11-year) term periods. Owing to differences among available datasets, the long-term analysis primarily reflected spatial shifts among subpopulations comprising the majority of the Bi-State DPS (that is, Bodie Hills and Long Valley) while the short-term analysis also quantified changes among subpopulations along the periphery. Over long and short-term periods, the overall distribution of occupied habitat (as measured by 99 percent utilization distributions intersecting any quantified habitat) was reduced by 20,573 ha and 55,492 ha, respectively. Occupied core areas (as measured by 50 percent utilization distributions intersecting any quantified habitat) over long-term periods were solely located in Bodie Hills and Long Valley. Although nearly all subpopulations experienced contractions in occupied overall and core distribution, Bodie Hills experienced spatial expansion that occurred with concomitant spatial contraction at Long Valley over both periods. Subpopulations at the northern (Pine Nuts), central (Sagehen) and southern (White-Mountains) extents of the Bi-State DPS also experienced spatial contraction over the short-term period. These findings, coupled with those of population trends, indicate long-term patterns in redistribution of sage-grouse from Long Valley and peripheral subpopulations to Bodie Hills. That is, sage-grouse subpopulations at the periphery are declining while the largest population at the core is increasing, which could have meaningful impacts on overall metapopulation persistence. We provide evidence for loss of occupied habitat (reduced distribution) given local extirpation of subpopulations.
Fifth, we calculated percentages of selected phenological, life stage, and annual habitat that each subpopulation contributed to the Bi-State DPS. We then intersected these maps with a composite estimate of occupied habitat from the fourth objective and calculated percentages of selected habitat likely occupied by sage-grouse that each subpopulation contributed to the Bi-State DPS. These values provide evidence for loss of occupied habitat and subsequent reductions in spatial distribution given reductions in abundance and, in some cases, extirpation of leks within subpopulations.
Lastly, we carried out an initial in-depth analysis of selection for irrigated pastures and wet meadows during the brood-rearing stage for the Long Valley subpopulation. We chose this subpopulation because it represents a population core, representing 26.5 percent of total sage-grouse within the Bi-State DPS, and has exhibited long-term declines in abundance and distribution. This subpopulation is highly sensitive to precipitation and other factors that influence water availability. Models predicted higher use of the interior portions of irrigated pastures and wet meadows during late brood-rearing period, which represented a potentially risky use of habitat that was exacerbated during periods of low moisture (for example, drought, reduced water delivery, or both). Sage-grouse typically used edges of riparian areas and pastures, largely because the interior of these mesic areas consisted of considerably less overhead concealment cover (for example, shrubs) that likely resulted in a higher risk of mortality. We found that a lack of water delivery to pastures in the form of overwinter precipitation or diversion ditches increased the movements of sage-grouse to the interior of pastures. Although further investigation of water delivery impacts on chick survival are warrented, our initial findings regarding resource selection may explain recent declines in population growth at Long Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191149","collaboration":"Prepared in cooperation with the U. S. Fish and Wildlife Service, Bureau of Land Management, California Department of Fish and Wildlife, Nevada Department of Wildlife, and the U.S. Forest Service","usgsCitation":"Coates, P.S., Ricca, M.A., Prochazka, B.G., O’Neil, S,T., Severson, J.P., Mathews, S.R., Espinosa, S., Gardner, S., Lisius, S., and Delehanty, D.J., 2020, Population and habitat analyses for greater sage-grouse (Centrocercus urophasianus) in the bi-state distinct population segment—2018 update: U.S. Geological Survey Open-File Report 2019–1149, 122 p., https://doi.org/10.3133/ofr20191149.","productDescription":"x, 122 p.","onlineOnly":"Y","ipdsId":"IP-113768","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":371329,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1149/ofr20191149.pdf","text":"Report","size":"8.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1149"},{"id":371330,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1149/ofr20191149_hirez.pdf","text":"Report — High resolution graphics","size":"82.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1149 Hi Rez"},{"id":371328,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1149/coverthb.jpg"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.234375,\n 38.90813299596705\n ],\n [\n -119.92675781249999,\n 38.436379603\n ],\n [\n -119.72900390625001,\n 37.50972584293751\n ],\n [\n -118.85009765625,\n 36.90597988519294\n ],\n [\n -117.83935546874999,\n 36.12900165569652\n ],\n [\n -117.586669921875,\n 36.1733569352216\n ],\n [\n -117.31201171875001,\n 35.862343734896484\n ],\n [\n -116.806640625,\n 36.075742215627\n ],\n [\n -115.224609375,\n 35.79108281624994\n ],\n [\n -115.587158203125,\n 37.125286284966805\n ],\n [\n -117.23510742187501,\n 38.62545397209084\n ],\n [\n -119.5751953125,\n 39.16414104768742\n ],\n [\n -120.234375,\n 38.90813299596705\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Approximately 2 million California residents rely on privately owned domestic wells for drinking water. During the California drought of 2012−16 groundwater levels declined in many parts of the state and wells were deepened in response. Most of the wells deepened during this time were domestic wells that were drilled into fractured bedrock throughout the Sierra Nevada foothills region of northern California. To understand the impacts of extreme drought on groundwater supply availability and quality in this setting, the United States Geological Survey completed a geochemical survey of domestic wells throughout the Yuba and Bear River watersheds during 2015–16 as part of the State Water Board’s Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP). This fact sheet highlights key findings from the GAMA-PBP assessment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193077","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Levy, Z.F., Fram, M.S., and Taylor, K.A., 2020, Effects of surface-water use on domestic groundwater availability and quality during drought in the Sierra Nevada foothills, California: U.S. Geological Survey Fact Sheet 2019–3077, 4 p., https://doi.org/10.3133/fs20193077.","productDescription":"4 p.","ipdsId":"IP-109313","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":399144,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109594.htm"},{"id":371195,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3077/coverthb.jpg"},{"id":371196,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3077/fs20193077.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019-3077"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada foothills","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.3314,\n 38.9833\n ],\n [\n -120.3167,\n 38.9833\n ],\n [\n -120.3167,\n 39.7769\n ],\n [\n -121.3314,\n 39.7769\n ],\n [\n -121.3314,\n 38.9833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GAMA Project Chief
U.S. Geological Survey
California Water Science Center
6000 J Street, Placer Hall
Sacramento, CA 95819
Telephone number: (916) 278-3000
GAMA Program Unit Chief
State Water Resources Control Board
Division of Water Quality
PO Box 2231, Sacramento, CA 95812
Telephone number: (916) 341-5855
Multiple aquifer tests were conducted in Lovelock, Nevada, to determine hydraulic conductivity and storage properties to be used with the numerical groundwater flow model of the lower Humboldt River Basin while accounting for the influence of surface features with a modeling component. The numerical model will ultimately provide the Nevada Division of Water Resources (NDWR) with information regarding the impacts of groundwater pumping on the Humboldt River, allowing the Nevada State Engineer to make informed decisions in the conjunctive management of the State’s groundwater and surface water resources. Seven slug tests, one single-well pumping test, and two multi-well pumping tests were conducted to evaluate properties of shallow Lahontan clays and silts; shallow fluvial deposits; and coarser, water-bearing deposits of the younger alluvium. Aquifer tests were conducted between March 2017 and April 2018. Results indicate aquifers in the Lovelock Valley have transmissivity ranging between 0.0001 and 95,000 feet squared per day (ft2/day). The results of these tests provide constraints on hydraulic properties for a numerical groundwater flow model being developed for a capture study in the lower Humboldt River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191133","collaboration":"Prepared in cooperation with the Nevada Division of Water Resources","usgsCitation":"Nadler, C., 2020, Analysis of aquifer framework and hydraulic properties of Lovelock Valley, Lovelock, Nevada: U.S. Geological Survey Open-File Report 2019–1133, 48 p., https://doi.org/10.3133/ofr20191133.","productDescription":"Report: viii, 48 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-098941","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":399425,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109593.htm"},{"id":371146,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1133/ofr20191133.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"}},{"id":371147,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LIL7PZ","linkHelpText":"Supplemental data for analysis of aquifer framework and hydraulic properties of Lovelock Valley, Lovelock, NV"},{"id":371145,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1133/coverthb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Humboldt River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.597412109375,\n 39.14710270770074\n ],\n [\n -114.70825195312501,\n 39.14710270770074\n ],\n [\n -114.70825195312501,\n 41.91045347666418\n ],\n [\n -118.597412109375,\n 41.91045347666418\n ],\n [\n -118.597412109375,\n 39.14710270770074\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819
This geologic map of the Greater Antilles and the Virgin Islands is a compilation of information from the literature, integrated to provide a seamless geologic map of the region. The geology shown on sheet 1 covers Cuba, the island of Hispaniola, which includes Haiti and the Dominican Republic, Jamaica, the Cayman Islands, Puerto Rico, and the U.S. and British Virgin Islands. A second more detailed sheet shows the geology of Puerto Rico and the Virgin Islands. The map units shown here are integrated across the islands of the Greater Antilles and the Virgin Islands.
The Greater Antilles and the Virgin Islands, although they appear to reflect the character of a magmatic arc, actually represent multiple, distinct geologic features. Only in Cuba are there unquestioned Jurassic-age, and perhaps older, rocks present. On the islands of Hispaniola (Haiti and the Dominican Republic) and Puerto Rico, metamorphic assemblages contain rocks that may be of Jurassic age. Ophiolite assemblages that may include rocks of Jurassic age are present in Cuba, the Dominican Republic, Haiti, and Puerto Rico. Metamorphic rocks of Cretaceous age are more widespread, present in Cuba, Hispaniola, and the U.S. and British Virgin Islands. Cretaceous plutonic rocks are present in Cuba and Puerto Rico, as well as in the Dominican Republic (in the Cordillera Central and in the eastern part of the country). Gabbro and trondhjemite of inferred Early Cretaceous age are present in the U.S. Virgin Islands. Cretaceous volcanic rocks are widespread in Cuba, Hispaniola, Puerto Rico, and the Virgin Islands; they are of variable age and do not appear to reflect a single arc system. Cretaceous volcanic rocks are also found in Jamaica, in inliers on the eastern part of the island. Eocene volcanic rocks are prominent in southern Cuba, Haiti, eastern Jamaica, Puerto Rico, and the Virgin Islands. Volcanic rocks possibly as young as early Miocene are present in the southern Dominican Republic; the youngest volcanic rocks in the region are the Low Layton Lavas of Jamaica of late Miocene age and alkali basalt of Quaternary age on Hispaniola.
Carbonate rocks are an important component of the sedimentary section in the Greater Antilles, which is as old as Jurassic in Cuba and as young as Holocene in many areas. In Cuba, Early Cretaceous sedimentary rocks tend to be dominantly carbonates; volcanic clasts and debris are not present until the Late Cretaceous in Cuba, as well as in Jamaica and Puerto Rico. In contrast, Early Cretaceous volcaniclastic sedimentary rocks are common in the Virgin Islands. Olistostrome deposits are commonly described in latest Cretaceous and Eocene rocks; in the Paleocene and the early Eocene, these deposits are commonly associated with mélange units. Volcanic debris and tuff are common in sedimentary rocks of Paleocene and Eocene age, typically associated with carbonate rocks. Sedimentary rocks that postdate the Eocene either are dominantly carbonates or are mixed clastic and carbonate rocks in which the clastic component reflects erosion of earlier units, including older carbonate rocks. Rocks that contain lignite, which are only present in Cuba and on Hispaniola, generally are of Miocene age.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191036","usgsCitation":"Wilson, F.H., Orris, G., and Gray, F., 2019, Preliminary geologic map of the Greater Antilles and the Virgin Islands: U.S. Geological Survey Open-File Report 2019–1036, pamphlet 50 p., 2 sheets, scales 1:2,500,000 and 1:300,000, https://doi.org/10.3133/ofr20191036.","productDescription":"Pamphlet: iv, 50 p.; 2 Sheets: 51.00 x 34.00 inches and 46.10 x 24.93 inches; 2 Tables; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098596","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":399410,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109592.htm"},{"id":399409,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109591.htm"},{"id":371178,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2019/1036/ofr20191036_spatialdata.zip","text":"OFR 2019-1036 spatial data","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2019-1036 Spatial Data"},{"id":371177,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1036/ofr20191036_table2.xlsx","text":"Table 2","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1036 Table 2 XLSX"},{"id":371176,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1036/ofr20191036_table2.pdf","text":"Table 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1036 Table 2 PDF"},{"id":371173,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1036/ofr20191036_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1036"},{"id":371175,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2019/1036/ofr20191036_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1036 Sheet 2"},{"id":371174,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2019/1036/ofr20191036_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1036 Sheet 1"},{"id":371172,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1036/coverthb2.jpg"}],"scale":"2500000","country":"Cuba, Dominican Republic, Great Britain, Haiti, Jamaica, United States","otherGeospatial":"Greater Antilles, Hispaniola, Puerto Rico, Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.517578125,\n 23.725011735951796\n ],\n [\n -85.4296875,\n 22.105998799750566\n ],\n [\n -83.84765625,\n 20.138470312451155\n ],\n [\n -76.2890625,\n 15.623036831528264\n ],\n [\n -69.169921875,\n 15.792253570362446\n ],\n [\n -61.435546875,\n 16.46769474828897\n ],\n [\n -60.64453125000001,\n 17.895114303749143\n ],\n [\n -61.52343749999999,\n 21.04349121680354\n ],\n [\n -72.50976562499999,\n 22.187404991398775\n ],\n [\n -82.265625,\n 24.046463999666567\n ],\n [\n -85.517578125,\n 23.725011735951796\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Science Center
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Dos Palmas Preserve is a Colorado Desert oasis and wetland in Riverside County, California, located near the base of the Orocopia Mountains and northeast of the Salton Sea. The original source of water for the oasis was artesian springs that developed at the base of the Orocopia Mountains, but more abundant water supplies were later provided to Dos Palmas Preserve when the Coachella Canal was built and water seeped from unlined parts of the canal. As a result of this abundant water supply in a desert setting, Dos Palmas Preserve, managed by the Bureau of Land Management, is now a wildlife preserve that became home to multiple plants, fowl, insects, rodents, reptiles, and bats, including some endangered and threatened species. More recently, sections of the Coachella Canal have been lined, resulting in a reduction of water seepage and threatening the sustainability of parts of Dos Palmas Preserve. Faults usually act as barriers to groundwater flow and Dos Palmas Preserve is only a few kilometers from the active trace of the San Andreas Fault, where splays of the fault trend through the area. Additionally, numerous closely spaced faults that have been mapped at the surface northwest of Dos Palmas Preserve are believed to extend southward into the Dos Palmas Preserve, where they are covered by alluvium. Thus, evaluation of the subsurface lithology and structure is needed to determine how the current allocation of water from the Coachella Canal affects various parts of Dos Palmas Preserve.
To better understand the distribution of the shallow lithology, faulting, and groundwater in Dos Palmas Preserve, the U.S. Geological Survey, in collaboration with the Bureau of Land Management, conducted a seismic survey across the northern part of Dos Palmas Preserve. The seismic survey was designed to “map” the upper part of the aquifer system and to more precisely locate faults that may affect groundwater flow in Dos Palmas Preserve. In this report, we present seismic velocity and reflection images of the shallow subsurface and relate those images to interpretative structures and stratigraphy that may affect groundwater at Dos Palmas Preserve.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191130","usgsCitation":"Catchings, R.D., Goldman, M.R., Chan, J.H., Sickler, R.R., Rymer, M.J., and Criley, C.J., 2020, Seismic evaluation of shallow-depth structure, faulting, and groundwater variations across the Dos Palmas Preserve, Riverside County, California: U.S. Geological Survey Open-File Report 2019–1130, 21 p., https://doi.org/ 10.3133/ofr20191130.","productDescription":"Report: vi, 21 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-101151","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":399421,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109590.htm"},{"id":371135,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WQJ5EH","text":"USGS data release","description":"USGS Data Release","linkHelpText":"2015 high resolution seismic acquisition at Dos Palmas Preserve, Mecca, California"},{"id":371134,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1130/ofr20191130.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1130"},{"id":371133,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1130/coverthb.jpg"}],"country":"United States","state":"California","county":"Riverside County","otherGeospatial":"Dos Palmas Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.0925,\n 33.2881\n ],\n [\n -115.4408,\n 33.2881\n ],\n [\n -115.4408,\n 33.6419\n ],\n [\n -116.0925,\n 33.6419\n ],\n [\n -116.0925,\n 33.2881\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Menlo Park, Calif.
Office—Earthquake Science Center
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Repeated sampling and grain-size analysis of surficial sediments along the sandy shorelines of Louisiana is necessary to characterize coastal-zone sediment properties and evaluate sediment transport patterns within the nearshore environments. In 2008, and again in 2015 and 2016, sediment grab samples were collected along the shorelines of the western Chenier plain, the Isles Dernieres (Raccoon, Whiskey, Trinity and East Islands), the Lafourche delta (Timbalier Islands, Caminada Headland, and Grand Isle), the modern delta (Grand Terre Islands from Chaland Headland to Sandy Point), and the Chandeleur Islands (from Curlew Island to Hewes Point). The samples were collected as part of the Louisiana Coastal Protection and Restoration Authority (CPRA) Barrier Island Comprehensive Monitoring (BICM) Program in collaboration with the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS–SPCMSC) and the University of New Orleans Pontchartrain Institute for Environmental Studies (UNO–PIES). Physical properties of the samples (sediment grain size and sorting) were measured and provided in data reports to CPRA. Additional samples collected by the USGS from around Breton Island in 2014 and 2015 supplemented the 2015–2016 BICM data to complete the coastwide dataset. This report compares the results of the 2008 and 2015–2016 sedimentologic analyses and documents changes in composition (percent sand) and mean sediment grain size between the two time periods. At most sample sites, differences in mean grain size varied by less than ±0.25 Φ. The largest changes occurred at sites located near tidal inlets or along rapidly eroding shorelines.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191132","collaboration":"Prepared in cooperation with the University of New Orleans and Louisiana Coastal Protection and Restoration Authority","usgsCitation":"Bosse, S.T., Flocks, J.G., Bernier, J.C., Georgiou, I.Y., Kulp, M.A., and Brown, M., 2019, Louisiana Coastal Zone sediment characterization; comparison of sediment grain sizes for samples collected in 2008 and 2015–2016 from the western Chenier plain to the Chandeleur Islands, Louisiana—Louisiana Barrier Island Comprehensive Monitoring (BICM) Program: U.S. Geological Survey Open-File Report 2019–1132, 17 p., https://doi.org/10.3133/ofr20191132.","productDescription":"vi, 17 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-107806","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":371101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1132/report-thumb.jpg"},{"id":371102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1132/ofr20191132.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1132"},{"id":399424,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109589.htm"},{"id":399423,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109588.htm"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.8333,\n 29.6667\n ],\n [\n -93,\n 29.6667\n ],\n [\n -93,\n 29.7889\n ],\n [\n -93.8333,\n 29.7889\n ],\n [\n -93.8333,\n 29.6667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
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The United States Geological Survey (USGS) provided technical support to the Washington Department of Ecology (Ecology) in their assessment of the role groundwater plays in contributing pollutant loading to the Green-Duwamish River near Seattle, Washington. Ecology is developing watershed hydrology models of the Green-Duwamish watershed, and need to assign realistic contaminant concentrations to the various Hydrologic Response Units represented in their models. The USGS compiled existing groundwater quality data in the Green-Duwamish watershed, and this report summarizes results and interpretation of the dataset, including identifying data gaps and needs for further research and monitoring. The sources of existing data were the USGS’s National Water Information System, Ecology’s Environmental Information Management System, and a compilation of several studies by Leidos, a scientific research company. The water-quality parameters of interest included polychlorinated biphenyl (PCB) Aroclors and congeners, phthalates, carcinogenic polycyclic aromatic hydrocarbons (cPAHs), arsenic, copper, and zinc. Results were grouped into the four subwatersheds delineated in Ecology’s hydrology models: Duwamish, Lower Green, Soos, and Upper Green. Results from the Duwamish subwatershed were further sub-divided by the USGS into the Lower Duwamish, containing land adjacent to the Lower Duwamish Waterway Superfund site, and the Upper Duwamish, containing the remaining area of the Duwamish subwatershed. Groundwater quality data in the Lower Duwamish were treated separately because there is known contamination in this area. The availability of water quality data varied by subwatershed as follows: phthalate data was only available within the Duwamish, PCB data was available within the Duwamish and Lower Green, cPAH data was available within the Duwamish, Lower Green, and Soos, and data for arsenic, copper, and zinc were available within all four subwatersheds. More than 99 percent of the available data was within the Duwamish subwatershed, identifying a need for additional monitoring of groundwater quality in the other subwatersheds.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191131","collaboration":"Prepared in cooperation with the Washington State Department of Ecology","usgsCitation":"Senter, C.A., Conn, K.E., Black, R.W., Welch, W.B., and Fasser, E.T., 2020, Assessment of existing groundwater quality data in the Green-Duwamish watershed, Washington: U.S. Geological Survey Open-File Report 2019-1131, 35 p., https://doi.org/10.3133/ofr20191131.","productDescription":"iv, 35 p.","onlineOnly":"Y","ipdsId":"IP-111911","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":371094,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1131/coverthb2.jpg"},{"id":371095,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1131/ofr20191131.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1131"},{"id":399422,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109587.htm"}],"country":"United States","state":"Washington","otherGeospatial":"Green-Duwamish watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.26684570312499,\n 47.59505101193038\n ],\n [\n -122.420654296875,\n 47.60245929546312\n ],\n [\n -122.43713378906249,\n 47.51349065484327\n ],\n [\n -122.431640625,\n 47.32393057095941\n ],\n [\n -122.420654296875,\n 47.18597932702905\n ],\n [\n -122.3876953125,\n 47.010225655683485\n ],\n [\n -122.288818359375,\n 46.912750956378915\n ],\n [\n -122.2283935546875,\n 46.781254534638606\n ],\n [\n -122.0855712890625,\n 46.558860303117164\n ],\n [\n -121.59667968749999,\n 46.32417161725691\n ],\n [\n -121.39892578125,\n 46.32796494040746\n ],\n [\n -121.30004882812499,\n 46.49839225859763\n ],\n [\n -121.168212890625,\n 46.78501604269254\n ],\n [\n -121.36596679687499,\n 46.991494313050424\n ],\n [\n -121.95922851562501,\n 47.4355191531953\n ],\n [\n -122.26684570312499,\n 47.59505101193038\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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Tacoma, Washington 98402
The Amerasia Basin Province encompasses the Canada Basin and the sediment prisms along the Alaska and Canada margins, outboard from basinward margins (hingelines) of the rift shoulders that formed during extensional opening of the Canada Basin. The province includes the Mackenzie River delta and slope, the outer shelves and marine slopes along the Arctic margins of Alaska and Canada, and the deep Canada Basin.
The province is divided into four assessment units (AUs): (1) The Canning-Mackenzie Deformed Margin AU is that part of the rifted margin where the Brooks Range orogenic belt has overridden the rift shoulder and is deforming the rifted-margin prism of sediment outboard of the hingeline. This is the only part of the Amerasia Basin Province that has been explored and—even though more than 3 billion barrels of oil equivalent (BBOE) of oil, gas, and condensate have been discovered— none has been commercially produced. (2) The Alaska Passive Margin AU is the rifted-margin prism of sediment lying beneath the Beaufort Sea outer shelf and slope that has not been deformed by tectonism. (3) The Canada Passive Margin AU is the rifted-margin prism of sediment lying beneath the Arctic outer shelf and slope (also known as the polar margin) of Canada that has not been deformed by tectonism. (4) The Canada Basin AU includes the sedimentary wedge that lies beneath the deep Canada Basin, north of the marine slope developed along the Alaska and Canada margins. Mean estimates of risked, undiscovered, technically recoverable resources include more than 6 billion barrels of oil (BBO), more than 19 trillion cubic feet (TCF) of associated gas, and more than 16 TCF of nonassociated gas in the Canning-Mackenzie Deformed Margin AU; about 1 BBO, about 3 TCF of associated gas, and about 3 TCF of associated gas in the Alaska Passive Margin AU; and more than 2 BBO, about 7 TCF of associated gas, and about 8 TCF of nonassociated gas in the Canada Passive Margin AU. Quantities of natural gas liquids also are assessed in each AU. The Canada Basin AU was not quantitatively assessed because it is judged to hold less than 10 percent probability of containing at least one accumulation of 50 million barrels of oil equivalent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1824BB","usgsCitation":"Houseknecht, D.W., Bird, K.J., and Garrity, C.P., 2020, Geology and assessment of undiscovered oil and gas resources of the Amerasia Basin Province, 2008, chap. BB of Moore, T.E., and Gautier, D.L., eds., The 2008 Circum-Arctic Resource Appraisal: U.S. Geological Survey Professional Paper 1824, 33 p., https://doi.org/10.3133/pp1824BB. [Supersedes USGS Scientific Investigations Report 2012–5146.","productDescription":"Report: viii, 33 p.; 3 Appendixes","onlineOnly":"Y","ipdsId":"IP-114214","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":371072,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/bb/pp1824bb_appendix3.xlsx","text":"Appendix 3 —","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter BB Appendix 3","linkHelpText":"Input data for the Canada Passive Margin Assessment Unit"},{"id":371071,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/bb/pp1824bb_appendix2.xlsx","text":"Appendix 2 —","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter BB Appendix 2","linkHelpText":"Input data for the Alaska Passive Margin Assessment Unit"},{"id":371070,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/bb/pp1824bb_appendix1.xlsx","text":"Appendix 1 —","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter BB Appendix 1","linkHelpText":"Input data for the Canning-Mackenzie Deformed Margin Assessment Unit"},{"id":371069,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1824/bb/pp1824bb.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1824 Chapter BB"},{"id":371068,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1824/bb/coverthb.jpg"},{"id":399505,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109586.htm"}],"country":"United States, Canada","state":"Alaska","otherGeospatial":"Amerasia Basin Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -165,\n 69.75\n ],\n [\n -85,\n 69.75\n ],\n [\n -85,\n 80\n ],\n [\n -165,\n 80\n ],\n [\n -165,\n 69.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
The key to Horned Lark (Eremophila alpestris) management is maintaining areas with short, sparse vegetation by burning, mowing, or grazing. Horned Larks have been reported to use habitats with less than or equal to (≤) 70 centimeters (cm) average vegetation height, 3–26 cm visual obstruction reading, 15–67 percent grass cover, 3–70 percent forb cover, ≤21 percent shrub cover, 1–44 percent bare ground, ≤63 percent litter cover, and ≤9 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842U","usgsCitation":"Dinkins, M.F., Igl, L.D., Shaffer, J.A., Johnson, D.H., Zimmerman, A.L., Parkin, B.D., Goldade, C.M., and Euliss, B.R., 2019, The effects of management practices on grassland birds—Horned Lark (Eremophila alpestris) (ver. 1.1, July 2022), chap. U of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 24 p., https://doi.org/10.3133/pp1842U.","productDescription":"v, 24 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-097149 ","costCenters":[],"links":[{"id":403248,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/pp/1842/u/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":370702,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/u/pp1842u.pdf","text":"Report","size":"2.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–U"},{"id":370701,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/u/coverthb2.jpg"}],"edition":"Version 1.0: January 8, 2020; Version 1.1: July 8, 2022","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
During 2014, highly pathogenic (HP) influenza A viruses (IAVs) of the A/Goose/Guangdong/1/1996 lineage (GsGD-HP-H5), originating from Asia, were detected in domestic poultry and wild birds in Canada and the US. These clade 2.3.4.4 GsGD-HP-H5 viruses included reassortants possessing North American lineage gene segments; were detected in wild birds in the Pacific, Central, and Mississippi flyways; and caused the largest HP IAV outbreak in poultry in US history. To determine if an antibody response indicative of previous infection with clade 2.3.4.4 GsGD-HP-H5 IAV could be detected in North American wild waterfowl sampled before, during, and after the 2014–15 outbreak, sera from 2,793 geese and 3,715 ducks were tested by blocking enzyme-linked immunosorbent assay and hemagglutination inhibition (HI) tests using both clade 2.3.4.4 GsGD-HPH5 and North American lineage low pathogenic (LP) H5 IAV antigens. We detected an antibody response meeting a comparative titer-based criteria (HI titer observed with 2.3.4.4 GsGD-HP-H5 antigens exceeded the titer observed for LP H5 antigen by two or more dilutions) for previous infection with clade 2.3.4.4 GsGD-HP-H5 IAV in only five birds, one Blue-winged Teal (Spatula discors) sampled during the outbreak and three Mallards (Anas platyrhynchos) and one Canada Goose (Branta canadensis) sampled during the post-outbreak period. These serologic results are consistent with the spatiotemporal extent of the outbreak in wild birds in North America during 2014 and 2015 and limited exposure of waterfowl to GsGD-HP-H5 IAV, particularly in the central and eastern US.
","language":"English","publisher":"BioOne Complete","doi":"10.7589/2019-01-003","usgsCitation":"Stallknecht, D.E., Kienzle-Dean, C., Davis-Fields, N., Jennelle, C.S., Bowman, A.S., Nolting, J.M., Boyce, W., Crum, J., Santos, J., Brown, J.D., Prosser, D., De La Cruz, S.E., Ackerman, J., Casazza, M.L., Krauss, S., Perez, D., Ramey, A.M., and Poulson, R., 2019, Limited detection of antibodies to clade 2.3.4.4 A/Goose/Guangdong/1/1996 lineage highly pathogenic H5 avian influenza virus in North American waterfowl: Journal of Wildlife Diseases, v. 56, no. 1, p. 47-57, https://doi.org/10.7589/2019-01-003.","productDescription":"11 p.","startPage":"47","endPage":"57","ipdsId":"IP-102272","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458853,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8761256","text":"External Repository"},{"id":371090,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, California, Georgia, Maryland, Minnesota, Ohio, Pennsylvania, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.1416015625,\n 49.023461463214126\n ],\n [\n -97.23999023437499,\n 49.009050809382046\n ],\n [\n -97.14111328125,\n 48.63290858589535\n ],\n [\n -97.108154296875,\n 48.06339653776211\n ],\n [\n -96.866455078125,\n 47.56911375866714\n ],\n [\n -96.78955078125,\n 46.53619267489863\n ],\n [\n -96.51489257812499,\n 45.94351068030587\n ],\n [\n 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S.","contributorId":190853,"corporation":false,"usgs":false,"family":"Bowman","given":"Andrew","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":776305,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nolting, Jacqueline M.","contributorId":190855,"corporation":false,"usgs":false,"family":"Nolting","given":"Jacqueline","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":776306,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boyce, Walter","contributorId":179200,"corporation":false,"usgs":false,"family":"Boyce","given":"Walter","affiliations":[],"preferred":false,"id":776307,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Crum, James","contributorId":220952,"corporation":false,"usgs":false,"family":"Crum","given":"James","affiliations":[{"id":40299,"text":"West Virginia Division of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":776308,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Santos, Jefferson","contributorId":220953,"corporation":false,"usgs":false,"family":"Santos","given":"Jefferson","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":776309,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Brown, Justin D.","contributorId":87838,"corporation":false,"usgs":false,"family":"Brown","given":"Justin","email":"","middleInitial":"D.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":776310,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":776311,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"De La Cruz, Susan E.W. 0000-0001-6315-0864","orcid":"https://orcid.org/0000-0001-6315-0864","contributorId":202774,"corporation":false,"usgs":true,"family":"De La Cruz","given":"Susan","email":"","middleInitial":"E.W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":776312,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":776313,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":779159,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Krauss, Scott","contributorId":190854,"corporation":false,"usgs":false,"family":"Krauss","given":"Scott","email":"","affiliations":[],"preferred":false,"id":776314,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Perez, Daniel","contributorId":220954,"corporation":false,"usgs":false,"family":"Perez","given":"Daniel","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":776315,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":776300,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Poulson, Rebecca L.","contributorId":198807,"corporation":false,"usgs":false,"family":"Poulson","given":"Rebecca L.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":776316,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70207534,"text":"ofr20191145 - 2019 - Cross section of the North Carolina coastal plain from Enfield through Cape Hatteras","interactions":[],"lastModifiedDate":"2022-04-21T20:15:36.129511","indexId":"ofr20191145","displayToPublicDate":"2020-01-06T15:40:00","publicationYear":"2019","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":"2019-1145","displayTitle":"Cross Section of the North Carolina Coastal Plain from Enfield through Cape Hatteras","title":"Cross section of the North Carolina coastal plain from Enfield through Cape Hatteras","docAbstract":"The Atlantic Coastal Plain, the southeasternmost physiographic province in the United States, is underlain by strata that regionally dip gently eastward and gradually thicken toward the Atlantic Ocean basin. These strata, ranging in age from Middle Jurassic to Holocene, accumulated along the eastern margin of North America after the break-up of the supercontinent Pangaea during the Early Jurassic. In the east-central United States north of Florida, Cape Hatteras is the point of land that most closely approaches the eastern edge of the Atlantic Continental Shelf of the United States. In 1946, Esso (now part of ExxonMobil) drilled a deep oil exploration well to basement rock near the Cape Hatteras lighthouse. No oil or gas was found there, or in any of the other test wells that were drilled within the onshore North Carolina Coastal Plain. Recent work indicates that the top of the oil window lies at 9,000 feet near the base of the Cape Hatteras Esso #1 test well. Therefore, any mature petroleum source rocks that may be present in the North Carolina Coastal Plain are only likely to be found east of the present coastline.
Although the Cape Hatteras test well did not produce oil or gas, it did produce a wealth of stratigraphic information about the outer portion of the onshore Atlantic Continental Shelf. Advances in global stratigraphic correlation, in tandem with our analyses of calcareous nannofossils and dinoflagellate cysts (dinocysts) from the Cape Hatteras test-well spot samples have produced significant advances beyond earlier interpretations of this well and other deep test wells inshore of Cape Hatteras. These results, when coupled with work done offshore of Cape Hatteras, have allowed us to create a more detailed cross section of the North Carolina Coastal Plain and adjacent continental shelf than previously possible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191145","usgsCitation":"Weems, R.E., Self-Trail, J.M., and Edwards L.E., 2019, Cross section of the North Carolina coastal plain from Enfield through Cape Hatteras: U.S. Geological Survey Open-File Report 2019–1145, 2 sheets, https://doi.org/10.3133/ofr20191145.","productDescription":"2 Sheets: 40.00 x 37.00 inches and 35.00 x 37.00 inches","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-102465","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":399432,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109585.htm"},{"id":370654,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1145/ofr20191145_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1145"},{"id":371011,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1145/ofr20191145_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1145"},{"id":370630,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1145/coverthb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.6513671875,\n 34.63320791137959\n ],\n [\n -75.5419921875,\n 34.63320791137959\n ],\n [\n -75.5419921875,\n 36.1733569352216\n ],\n [\n -77.6513671875,\n 36.1733569352216\n ],\n [\n -77.6513671875,\n 34.63320791137959\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Florence Bascom Geoscience Center
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Harrat Rahat, in the west-central part of the Kingdom of Saudi Arabia, is the largest of 15 Cenozoic harrats (Arabic for “volcanic field”) distributed on the Arabian plate. It extends more than 300 km north-south and 50 to 75 km east-west, and it covers an area of approximately 20,000 km2, has a volume of approximately 2,000 km3, and encompasses more than 900 observable vents. Volcanism commenced around 10 Ma and has continued into historic time, the most recent eruption occurring in 1256 C.E. Volcanic products are dominated by alkali basalt and hawaiite lava flows, with subordinate mugearite lava flows, as well as benmoreite and trachyte lava flows, domes, and pyroclastic flows.
This geologic map distinguishes 239 eruptive units that cover an area of 3,340 km2 in northern Harrat Rahat and the adjacent city of Al-Madinah. Results are presented as a geologic map of the study area at 1:75,000 scale and of smaller regions of particular interest at 1:25,000 scale, along with interpretive text.
Most units are basaltic lava flows that erupted from the broadly north-northwest-trending main vent axis that constructed the topographic crest of the volcanic field. This 300- to 400-m-high vent axis, which has a width of 6 to 10 km, lies in the eastern one-third of northern Harrat Rahat. Basalt and hawaiite lava flows can extend as far as 27 km from their vents, but most are 10 to 15 km long. Evolved products such as mugearites, benmoreites, and trachytes are less extensive; the trachytic pyroclastic flows extend as far as 9 km from their source vents, although most only reach 4 to 6 km. Vents of the evolved products are restricted to the main vent axis or its flanks.
No volcanic rocks older than 1.2 Ma are exposed in the map area, and about 90 percent of the exposed volcanic rocks erupted during the past 570 thousand years. As depicted on the geologic maps, eruption ages and field relations define 12 eruptive stages for northern Harrat Rahat for the past 1.2 million years. Other important geochronological findings include (1) several late Pleistocene lava flows near Al-Madinah, which previously were interpreted as Holocene from archeological evidence; (2) the eruption age of a cluster of cinder cones and small lava flows in the western outskirts of Al-Madinah (previously ascribed to an eruption in 641 C.E.) is actually 13.3±1.9 ka, close to the Pleistocene-Holocene boundary; and (3) only two Holocene eruptions have been identified in the map area, those of the historically described basalt of Al Labah in 1256 C.E. and the dome and pyroclastic flows of the trachyte of Um Rgaibah at 4.2±5.2 ka.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3428","collaboration":"Jointly published with the Saudi Geological Survey [as Saudi Geological Survey Special Report SGS–SP–2019–2]","usgsCitation":"Downs, D.T., Robinson, J.E., Stelten, M.E., Champion, D.E., Dietterich, H.R., Sisson, T.W., Zahran, H., Hassan, K., and Shawali, J., 2019, Geologic map of the northern Harrat Rahat volcanic field, Kingdom of Saudi Arabia: U.S. Geological Survey Scientific Investigations Map 3428 [also released as Saudi Geological Survey Special Report SGS–SP–2019–2], 65 p., 4 sheets, scales 1:75,000, 1:25,000, https://doi.org/10.3133/sim3428.","productDescription":"Report: vi, 65 p.; 4 Sheets; Data Release","numberOfPages":"62","additionalOnlineFiles":"Y","ipdsId":"IP-096080","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":370934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3428/coverthb.jpg"},{"id":370935,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3428/sim3428_pamphlet.pdf","text":"Pamphlet","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3428 Pamphlet"},{"id":370936,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3428/sim3428_sheet1.pdf","text":"Sheet 1","size":"45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3428 Sheet 1","linkHelpText":" - Geologic Map of the Northern Harrat Rahat Volcanic Field, Kingdom of Saudi Arabia"},{"id":370937,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3428/sim3428_sheet2.pdf","text":"Sheet 2","size":"35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3428 Sheet 2","linkHelpText":" - Geologic Map of the Northern, Central, and Southern Fingers Lava Flows, Northern Harrat Rahat Volcanic Field, Kingdom of Saudi Arabia"},{"id":370938,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3428/sim3428_sheet3.pdf","text":"Sheet 3","size":"30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3428 Sheet 3","linkHelpText":" - Geologic Map of the Silicic Volcanic Centers Near Al Efairia, Al Wabarah, and Matan, Northern Harrat Rahat Volcanic Field, Kingdom of Saudi Arabia"},{"id":370939,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3428/sim3428_sheet4.pdf","text":"Sheet 4","size":"18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3428 Sheet 4","linkHelpText":" - Correlation and List of Map Units for the Geologic Map of the Northern Harrat Rahat Volcanic Field, Kingdom of Saudi Arabia"},{"id":370940,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q3WGTN","linkHelpText":"Database for the geologic map of the northern Harrat Rahat volcanic field, Kingdom of Saudi Arabia"}],"country":"Kingdom of Saudi Arabia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[42.77933,16.34789],[42.64957,16.77464],[42.34799,17.07581],[42.27089,17.47472],[41.75438,17.83305],[41.22139,18.6716],[40.93934,19.48649],[40.24765,20.17463],[39.80168,20.33886],[39.1394,21.2919],[39.0237,21.98688],[39.06633,22.57966],[38.49277,23.68845],[38.02386,24.07869],[37.48363,24.28549],[37.15482,24.85848],[37.20949,25.08454],[36.93163,25.60296],[36.6396,25.82623],[36.24914,26.57014],[35.64018,27.37652],[35.13019,28.06335],[34.63234,28.05855],[34.78778,28.60743],[34.83222,28.95748],[34.95604,29.35655],[36.06894,29.19749],[36.50121,29.50525],[36.74053,29.86528],[37.50358,30.00378],[37.66812,30.33867],[37.99885,30.5085],[37.00217,31.50841],[39.00489,32.01022],[39.19547,32.16101],[40.39999,31.88999],[41.88998,31.19001],[44.7095,29.17889],[46.56871,29.09903],[47.45982,29.00252],[47.70885,28.52606],[48.41609,28.552],[48.80759,27.68963],[49.29955,27.46122],[49.47091,27.11],[50.15242,26.68966],[50.21294,26.27703],[50.1133,25.94397],[50.23986,25.60805],[50.52739,25.32781],[50.66056,24.9999],[50.81011,24.75474],[51.11242,24.55633],[51.38961,24.62739],[51.57952,24.2455],[51.61771,24.01422],[52.00073,23.00115],[55.0068,22.49695],[55.20834,22.70833],[55.66666,22],[54.99998,19.99999],[52.00001,19],[49.11667,18.61667],[48.18334,18.16667],[47.46669,17.11668],[47,16.95],[46.74999,17.28334],[46.36666,17.23332],[45.4,17.33334],[45.21665,17.43333],[44.06261,17.41036],[43.79152,17.31998],[43.38079,17.57999],[43.1158,17.08844],[43.21838,16.66689],[42.77933,16.34789]]]},\"properties\":{\"name\":\"Saudi Arabia\"}}]}","contact":"Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
Tidal wetland loss in Jamaica Bay, New York, is well documented. Maintaining wetlands is important from an environmental and ecological perspective and because wetlands buffer coastal communities from storm damage. An estimate of suspended-sediment flux through Rockaway Inlet is needed to improve understanding of sediment dynamics in Jamaica Bay and could be used in salt marsh restoration efforts. To estimate sediment flux, an index-velocity station and turbidity sensor were installed and operated in Rockaway Inlet near the mouth of Jamaica Bay from November 2014 to December 2016 and point and cross-sectional suspended-sediment samples were collected and analyzed. Index-velocity data coupled with cross-sectional acoustic Doppler current profiler measurements were used to develop an index-velocity rating. A simple linear regression rating with a strong coefficient of determination (R2 of 0.981) was developed. Discharge was computed from the stage-area and index-velocity relations, and a low-pass Godin filter was used to remove the tidal aliasing. A second simple linear regression (R2 of 0.75) between fixed-point suspended-sediment concentration (SSC) samples and turbidity allowed for the calculation of SSC through Rockaway Inlet, and then sediment flux was found by multiplying SSC and discharge for continuous (6-minute) data. Turbidity values were low in the near-ocean conditions at Rockaway Inlet, with daily means ranging from 0.6 to 8.2 formazin nephelometric units during the period of November 2014 through December 2016. During this time, computed daily mean suspended-sediment concentrations ranged from 3 to 13 milligrams per liter. High sediment loads generally occurred during incoming tides, during both storm and nonstorm conditions, suggesting a net inward sediment flux into Jamaica Bay. The fate of sediment after it enters Jamaica Bay was not investigated. Trends in sediment flux during major storms could not be evaluated because no major storms occurred during this investigation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195085","usgsCitation":"Cartwright, R.A., and Simonson, A.E., 2019, Estimating sediment flux to Jamaica Bay, New York: U.S. Geological Survey Scientific Investigations Report 2019–5085, 25 p., https://doi.org/10.3133/sir20195085.","productDescription":"vii, 25 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-090709","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":399538,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109566.htm"},{"id":370919,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5085/sir20195085_hi_res.pdf","text":"Report","size":"5.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5085","linkHelpText":"- high resolution, not accessible as defined in Section 508"},{"id":370783,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5085/sir20195085.pdf","text":"Report","size":"3.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5085"},{"id":370634,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5085/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Jamaica Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.94004821777344,\n 40.54980899258771\n ],\n [\n -73.76014709472656,\n 40.603526799885884\n ],\n [\n -73.729248046875,\n 40.645740600821476\n ],\n [\n -73.75053405761719,\n 40.65303410892721\n ],\n [\n -73.77113342285156,\n 40.63844629557923\n ],\n [\n -73.81507873535156,\n 40.66293116628907\n ],\n [\n -73.8720703125,\n 40.65615965408628\n ],\n [\n -73.9215087890625,\n 40.61916465186328\n ],\n [\n -73.93043518066406,\n 40.589449604232975\n ],\n [\n -73.95790100097656,\n 40.57536944461837\n ],\n [\n -73.94004821777344,\n 40.54980899258771\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
This report is a deliverable to the RESTORE Council for Task 7: Document the existing baseline habitat and water quality conditions prior to implementation of the restoration projects; these baseline conditions will serve as a basis for measuring change/progress after restoration. It is the second in a series of CMAP reports. The first report describes the process and development of the CMAP monitoring program inventory, herein the Inventory (NOAA and USGS, 2019). The goals and objectives for the Inventory were to identify and document existing habitat and water quality monitoring, and mapping programs, data, and protocols in the GoM. The Inventory built upon existing databases, such as the Ocean Conservancy (Love, 2015), Global Change Monitoring Portal (GCMP; GCMP, 2017),
and Gulf of Mexico Alliance (GOMA) databases (GOMA, 2013), including habitat and water quality monitoring programs at national, regional, State and local scales. This second report identifies and catalogs existing water quality, habitat and mapping assessments within the Gulf of Mexico. This assessment catalog, herein the Catalog, was intended to supplement the Inventory to identify the best available science necessary for restoration, conservation or management activities. Both the Inventory and the Catalog databases will be accessible to the Council and the greater GoM restoration, monitoring, management, and academic communities through a searchable web-based tool.
Substantial effort has been dedicated to developing reliable monitoring schemes for North American bird populations, but our ability to monitor bird populations in the boreal forest remains limited because of the sparsity of long-term data sets, particularly in northerly regions. Given the importance of the boreal forest for many migratory birds, we set out to (1) summarize the main challenges associated with monitoring avian populations, (2) describe the available statistical tools for population monitoring and their applications, and (3) identify future directions to overcome current challenges in monitoring bird populations in the boreal forest. Defining and delineating populations of interest and identifying the drivers that affect those populations present the greatest current challenges. This is because migratory birds may be affected by many population-limiting processes at different stages of their annual life cycles. These factors are often hierarchically structured and can influence populations at the local, regional, or continental scales. Some of the challenges associated with delineating populations and identifying population drivers can be addressed via the plethora of sampling and analytic methods available to examine population change over time. Choosing the proper analytic methods depends on the goals of the study and the nature of the data such as single or multiple populations, repeated occurrence or count-based surveys, or demographic rates. Recent advances in hierarchical and integrated population models make these analytic approaches some of the most promising avenues for the development of future methods. However, these tools require large data sets, and acquiring sufficient data on bird populations and potential explanatory variables is difficult in the boreal forest. If the current challenges to monitoring birds in the boreal forest are to be overcome, serious effort should be dedicated to integrating existing data and making them accessible. Enhancing survey effort through multispecies surveys will also play an important role. Implementing spatially balanced sampling plans with a rotating panel design could balance the trade-offs between spatial versus temporal replication at an affordable cost. Improving the accessibility of environmental covariates that are spatially and temporally explicit would also enable development of mechanistic population models that improve our understanding of migratory bird population dynamics. Finally, given that long-term monitoring programs can take many decades before delivering reliable population trends and that organizational priorities often change over time, we suggest that collaborative efforts will help ensure the long-term survival of new monitoring programs.
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U.S. Geological Survey
National Center, MS 108
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