Throughout the western United States, efforts are underway to better understand and preserve migration and movement corridors for mule deer and other big game and to minimize the impacts of development and other land-use change on populations. San Diego County is home to a unique non-migratory subspecies of mule deer, the Southern mule deer (Odocoileus hemionus fuliginatus; herein referred to as “mule deer”). Because it is the only large herbivorous mammal in San Diego, connectivity among mule deer groups is an important indicator of functional connectivity throughout San Diego County urban preserves and has therefore been monitored within central and eastern San Diego County using DNA fingerprinting since 2005. To continue this effort and to assess genetic connectivity in north San Diego County (herein “North County”), we genotyped scat samples from preserves in the area and tissue samples from Marine Corps Base Camp Pendleton (MCBCP). We used non-invasive capture/recapture analyses and pedigree analyses for assessing short-term movement and population clustering analyses to assess gene flow in North County. Additionally, we performed similar analyses on the combined San Diego County dataset, which was composed of the North County dataset collected for this study and a previously collected dataset from central and eastern San Diego County. Using recapture data, we found multiple instances of mule deer crossing roads in urban North County preserves, with several of these events occurring in areas where there are underpasses and culverts known to be used by mule deer. Corroborating previous studies in the region and statewide, pedigree and population structure analyses support the presence of two genetic clusters for mule deer in San Diego County—the “Coastal” and “Inland/Mountain” clusters. Low estimates of effective population size, especially in the Coastal cluster, suggest that to further understand potential vulnerabilities of mule deer in this region, it is important to continue to monitor connectivity, in particular, at the boundary between these two clusters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191138","usgsCitation":"Mitelberg, A., Smith, J.G., and Vandergast, A.G., 2019, DNA Fingerprinting of Southern mule deer (Odocoileus hemionus fuliginatus) in north San Diego County, California (2018–19): U.S. Geological Survey Open-File Report 2019–1138, 25 p., https://doi.org/10.3133/ofr20191138.","productDescription":"vi, 25 p.","numberOfPages":"25","onlineOnly":"Y","ipdsId":"IP-112707","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":437245,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YXWXA9","text":"USGS data release","linkHelpText":"Microsatellite Genetic Marker Genotypes from Southern Mule Deer (Odocoileus hemionus fuliginatus) Sampled in San Diego County, California"},{"id":370869,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1138/ofr20191138.pdf","text":"Report","size":"31 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":370868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1138/coverthb.jpg"}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.31201171875001,\n 32.713355353177555\n ],\n [\n -116.05957031249999,\n 32.713355353177555\n ],\n [\n -116.05957031249999,\n 33.25706340236547\n ],\n [\n -117.31201171875001,\n 33.25706340236547\n ],\n [\n -117.31201171875001,\n 32.713355353177555\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
Investments in landscape-scale restoration and fuels management projects can protect publicly managed trusts, enhance public health and safety, and help to preserve the many environmental goods and services enjoyed by the public. These investments can also support jobs and generate business sales activities within nearby local economies. This report investigates how investments made by the Rio Grande Water Fund (RGWF) on wildfire risk reduction and source water protection projects in northern New Mexico and southern Colorado affect local economic activity. To implement these projects, the RGWF spent a total of $855,000 in 2018 on contractors located in the Western States regional economy. Including direct and secondary effects, these expenditures supported an estimated 22 jobs, $1,089,000 in labor income, $1,324,000 in value added, and $1,907,000 in economic output in the 17 Western States economy. The majority (73 percent or $623,000) of these expenditures were made by hiring local businesses operating within a 13-county region in northern New Mexico and southern Colorado that comprises the RGWF project area. Including direct and secondary effects, local expenditures support an estimate 15 jobs, $676,000 in labor income, $791,000 in value added, and $1,120,000 in economic output within the 13-county RGWF project area. These results demonstrate how investments in wildfire risk reduction and source water protection projects can support jobs and livelihoods, small businesses, and rural economies in the Mountain West.
","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191108","collaboration":"Prepared in cooperation with The Nature Conservancy","usgsCitation":"Huber, C., Cullinane Thomas, C., Meldrum, J.R., Meier, R., and Bassett, S., 2019, Economic effects of wildfire risk reduction and source water protection projects in the Rio Grande River Basin in northern New Mexico and southern Colorado: U.S. Geological Survey Open-File Report 2019–1108, 8 p., https://doi.org/10.3133/ofr20191108.","productDescription":"iv, 8 p.","onlineOnly":"Y","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":399416,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109581.htm"},{"id":370215,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1108/ofr20191108.pdf","text":"Report","size":"7.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1108"},{"id":370214,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1108/coverthb.jpg"}],"country":"United States","state":"Colorado, New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.6408,\n 34.2403\n ],\n [\n -103.6667,\n 34.2403\n ],\n [\n -103.6667,\n 37.3417\n ],\n [\n -107.6408,\n 37.3417\n ],\n [\n -107.6408,\n 34.2403\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
Chief, Analysis and Prediction Branch
Integrated Modeling and Prediction Division
Water Resources Mission Area
U.S. Geological Survey, Mail Stop 415
12201 Sunrise Valley Drive
Reston, VA 20192
Otolith microanalysis is often used to assess population age structure and growth of fishes during their early stages. Shoal Bass Micropterus cataractae is a recently described species of conservation concern and little is known regarding factors affecting their recruitment. In 2004, Georgia Department of Natural Resources (GADNR) and the US National Park Service (NPS) stocked Shoal Bass marked with oxytetracycline (OTC) in the Chattahoochee River near Atlanta, Georgia in an effort to restore this population, creating known-age fish to examine the effect of environment on daily age accuracy. We obtained samples of stocked juvenile (<150 mm) Shoal Bass from standard monitoring that occurred approximately 30–60 days after stocking in the Chattahoochee River to 1) validate daily rings for estimating age, hatch dates, and growth rates for stocked age-0 Shoal Bass, and 2) evaluate the effect of habitat (location) on age bias. Shoal Bass otoliths were examined for OTC marks and daily rings were counted in reference to OTC marks to assess age estimation accuracy. Age estimation accuracy ranged from -2 to -25 days and was influenced by the environment where Shoal Bass were captured, with greater inaccuracy in colder water temperatures. Fish collected from locations with colder temperatures displayed closer spacing of daily rings, potentially leading to greater underestimation of age. Growth rates of stocked Shoal Bass, corrected for age estimation error, ranged from 0.5 mm/day to 0.8 mm/day. This study demonstrates the effect of environmental variability on age inaccuracy and subsequent interpretation of results. Incorporating methods to assess age estimation accuracy is needed to understand interspecific differences in recruitment among black bass species in the variety of natural and human-modified environments they inhabit.
Reliable estimates of life history parameters and their functional role in animal population trajectories are critical, yet often missing, components in conservation and management. We developed seasonal matrix population models of the Red-tailed Hawk Buteo jamaicensis jamaicensis in the upper and lower forests of the Luquillo Mountains, Puerto Rico, to describe the influence of early life stages (nestling and clutch survival) on population growth. Modelled populations exhibited positive discrete rates of growth in forests above 400 m (λ highlands = 1.05) and in forests below 400 m (λ lowlands = 1.27) of the Luquillo Mountains. Further, adult survival was the parameter with the highest proportional effect and direct contribution to growth of the population. Besides survival of adults, our results identified that nestling survival had the second greatest influence on λ, stressing the importance of this life stage for the population growth rate of Red-tailed Hawks in our study area. Seasonal matrices are not commonly used to describe population dynamics of birds. However, these may be a useful tool to analyse the influence of life stages in the annual cycle to better address conservation and management needs, especially for species inhabiting oceanic islands.
This report summarizes a national assessment of flowing waters conducted by the U.S. Geological Survey’s (USGS) National Water-Quality Assessment (NAWQA) Project and addresses several pressing questions about the modification of natural flows in streams and rivers. The assessment is based on the integration, modeling, and synthesis of monitoring data collected by the USGS and the U.S. Environmental Protection Agency at more than 7,000 streams and rivers across the conterminous United States from 1980 to 2014. Key findings include the following. First, flow in many of the Nation’s streams and rivers is different from what it would be under natural conditions. In particular, low flows are more frequent, are of shorter duration, and vary less from one year to the next than they would naturally. In addition, high flows have been reduced in magnitude, are of shorter duration, are less frequent, and vary less from one year to the next than they would naturally. Other characteristics of natural flows also have been modified. Second, over the last 60 years (1955–2014), climatic trends have caused a change of 50 percent or more in one or more streamflow attributes at two-thirds of climate-sensitive streamgaging sites. However, these climate-induced changes have been less influential on streamflow modification than have land and water-management practices. Third, in every region assessed, streamflow modification was associated with reduced ecological health, as indicated by two biological communities—invertebrates and fish. Biological communities were increasingly likely to be impaired (defined as having lost a statistically significant number of species) in streams with flows most different from natural conditions. Finally, several case studies are presented that illustrate viable management strategies for balancing the water needs of people and ecosystems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1461","collaboration":"National Water-Quality ProgramNational Water-Quality Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Autumn ungulate hunting in the Greater Yellowstone Ecosystem carries the risk of hunter–grizzly bear (Ursus arctos) conflict and creates a substantial challenge for managers. For Grand Teton National Park, Wyoming, USA, a key information need is whether increased availability of elk (Cervus canadensis) carcasses during a late autumn (Nov–Dec) harvest within the national park attracts grizzly bears and increases the potential for conflict with hunters. Using a robust design analysis with 6 primary sampling periods during 2014–2015, we tested the hypothesis that the elk harvest resulted in temporary movements of grizzly bears into the hunt areas, thus increasing bear numbers. We detected 31 unique individuals (6 F, 25 M) through genetic sampling and retained 26 encounter histories for analysis. Markovian movement models had more support than a null model of no temporary movement. Contrary to our research hypothesis, temporary movements into the study area occurred between the July–August (no hunt; N̄2014–2015 = 5) and September–October (no hunt; N̄2014–2015 = 24) primary periods each year, rather than during the transition from September–October (no hunt) to November–December (hunt; N̄2014–2015 = 15). A post hoc analysis indicated that September–October population estimates were biased high by detections of transient bears. Grizzly bear presence during the elk hunt was limited to approximately 15 resident bears that specialized in accessing elk carcasses. The late timing of the elk hunt likely moderated the effect of carcasses as a food attractant because it coincides with the onset of hibernation. From a population response perspective, the current timing of the elk harvest likely represents a scenario of low relative risk of hunter–bear conflicts. The risk of hunter–grizzly bear encounters remains, but may be more a function of factors that operate at the level of individual bears and hunters, such as hunter movements and bear responses to olfactory cues.
","language":"English","publisher":"International Association for Bear Research and Management","doi":"10.2192/URSUS-D-18-00018R2","usgsCitation":"van Manen, F.T., Ebinger, M.R., Gustine, D.D., Haroldson, M.A., Wilmot, K.R., and Whitman, C., 2019, Primarily resident grizzly bears respond to late-season elk harvest: Ursus, v. 30, no. e1, 15 p., https://doi.org/10.2192/URSUS-D-18-00018R2.","productDescription":"15 p.","ipdsId":"IP-099097","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":458896,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2192/ursus-d-18-00018r2","text":"Publisher Index Page"},{"id":437249,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IWSJUX","text":"USGS data release","linkHelpText":"Detection histories of grizzly bears in Grand Teton National Park, 2014-2015"},{"id":380007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Grand Teton National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.85205078124999,\n 43.6599240747891\n ],\n [\n -110.49224853515625,\n 43.6599240747891\n ],\n [\n -110.49224853515625,\n 43.91372326852401\n ],\n [\n -110.85205078124999,\n 43.91372326852401\n ],\n [\n -110.85205078124999,\n 43.6599240747891\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"e1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":803625,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ebinger, Michael R. 0000-0002-2586-7829 mebinger@usgs.gov","orcid":"https://orcid.org/0000-0002-2586-7829","contributorId":244264,"corporation":false,"usgs":true,"family":"Ebinger","given":"Michael","email":"mebinger@usgs.gov","middleInitial":"R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":803626,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gustine, David D. 0000-0003-1087-1937","orcid":"https://orcid.org/0000-0003-1087-1937","contributorId":201734,"corporation":false,"usgs":false,"family":"Gustine","given":"David","email":"","middleInitial":"D.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":803627,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haroldson, Mark A. 0000-0002-7457-7676 mharoldson@usgs.gov","orcid":"https://orcid.org/0000-0002-7457-7676","contributorId":1773,"corporation":false,"usgs":true,"family":"Haroldson","given":"Mark","email":"mharoldson@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":803628,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilmot, Katharine R.","contributorId":244265,"corporation":false,"usgs":false,"family":"Wilmot","given":"Katharine","email":"","middleInitial":"R.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":803629,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whitman, Craig 0000-0002-1187-4649 cwhitman@usgs.gov","orcid":"https://orcid.org/0000-0002-1187-4649","contributorId":206044,"corporation":false,"usgs":true,"family":"Whitman","given":"Craig","email":"cwhitman@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":803630,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70205417,"text":"sir20195099 - 2019 - Flood-inundation maps for the North Platte River at Scottsbluff and Gering, Nebraska, 2018","interactions":[],"lastModifiedDate":"2022-04-22T21:43:56.878264","indexId":"sir20195099","displayToPublicDate":"2019-12-23T20:34:51","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5099","displayTitle":"Flood-Inundation Maps for the North Platte River at Scottsbluff and Gering, Nebraska, 2018","title":"Flood-inundation maps for the North Platte River at Scottsbluff and Gering, Nebraska, 2018","docAbstract":"Digital flood-inundation maps for an 8.8-mile reach of the North Platte River, from 1.5 miles upstream from the Highway 92 bridge to 3 miles downstream from the Highway 71 bridge in Scottsbluff County, were created by the U.S. Geological Survey (USGS) in cooperation with the Cities of Scottsbluff and Gering, Nebraska. The flood-inundation maps, which can be accessed through the Flood Inundation Mapping (FIM) Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program?qt-science_center_objects=0#qt-science_center_objects, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the North Platte River at Scottsbluff, Nebr. (station number 06680500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or from the National Weather Service Advanced Hydrologic Prediction Service (site SBRN1) at https://water.weather.gov/ahps2/hydrograph.php?wfo=cys&gage=sbrn1.
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated by using the current (2018) stage-discharge relation at the North Platte River at Scottsbluff, Nebr., streamgage.
The hydraulic model was then used to compute 10 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 9 ft, or near bankfull, to 18 ft, which exceeds the stage that corresponds to the estimated 1-percent annual exceedance probability flood (100-year recurrence interval flood). The simulated water-surface profiles were then combined with a geographic information system digital elevation model derived from light detection and ranging data having a 0.6-ft root mean square error and 2-ft horizontal resolution resampled to a 6-ft grid to delineate the area flooded at each water level. The availability of these maps, along with internet information regarding current stage from the USGS streamgage, may provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195099","collaboration":"Prepared in cooperation with the City of Scottsbluff and the City of Gering","usgsCitation":"Strauch, K.R., 2019, Flood-inundation maps for the North Platte River at Scottsbluff and Gering, Nebraska, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5099, 9 p., https://doi.org/10.3133/sir20195099.","productDescription":"Report: vi, 9 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-102434","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":399544,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109564.htm"},{"id":370451,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5099/sir20195099.pdf","text":"Report","size":"25.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5099"},{"id":370452,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NCAIKN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood-inundation geospatial datasets for the North Platte River at Scottsbluff and Gering, Nebraska"},{"id":370450,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5099/coverthb.jpg"}],"country":"United States","state":"Nebraska","city":"Scottsbluff, Gering","otherGeospatial":"North Platte River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.05426025390625,\n 41.74467659677642\n ],\n [\n -103.33740234375,\n 41.74467659677642\n ],\n [\n -103.33740234375,\n 42.05948945192712\n ],\n [\n -104.05426025390625,\n 42.05948945192712\n ],\n [\n -104.05426025390625,\n 41.74467659677642\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Beaver Lake was constructed in 1966 on the White River in the northwest corner of Arkansas for flood control, hydroelectric power, public water supply, and recreation. The surface area of Beaver Lake is about 27,900 acres and approximately 449 miles of shoreline are at the conservation pool level (1,120 feet above the North American Vertical Datum of 1988). Sedimentation in reservoirs can result in reduced water storage capacity and a reduction in usable aquatic habitat. Therefore, accurate and up-to-date estimates of reservoir water capacity are important for managing pool levels, power generation, recreation, and downstream aquatic habitat. Many of the lakes operated by the U.S. Army Corps of Engineers are periodically surveyed to monitor bathymetric changes that affect water capacity. In October 2018, the U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, completed one such survey of Beaver Lake using a multibeam echosounder. The echosounder data were combined with light detection and ranging (lidar) data to prepare a bathymetric map and a surface area and capacity table.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3445","collaboration":"Prepared in cooperation with the U.S. Army Corp of Engineers, Southwestern Division, Little Rock District","usgsCitation":"Huizinga, R.J., Ellis, J.T., Sharpe, J.B., LeRoy, J.Z., and Richards, J.M., 2019, Bathymetric map and surface area and capacity table for Beaver Lake near Rogers, Arkansas, 2018: U.S. Geological Survey Scientific Investigations Map 3445,\n2 sheets, https://doi.org/10.3133/sim3445.","productDescription":"2 Sheets: 44 x 36 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-113370","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":370609,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3445/coverthb.jpg"},{"id":399518,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109565.htm"},{"id":370612,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91PLLGV","text":"USGS data release","linkHelpText":"Bathymetric and supporting data for Beaver Lake near Rogers, Arkansas, 2018"},{"id":370610,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3445/sim3445_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3445 Sheet 1"},{"id":370611,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3445/sim3445_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3445 Sheet 2"}],"scale":"24000","country":"United States","state":"Arkansas","city":"Rogers","otherGeospatial":"Beaver Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.11575317382812,\n 36.17446549576358\n ],\n [\n -93.79440307617188,\n 36.17446549576358\n ],\n [\n -93.79440307617188,\n 36.45829281489\n ],\n [\n -94.11575317382812,\n 36.45829281489\n ],\n [\n -94.11575317382812,\n 36.17446549576358\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
The U.S. Geological Survey (USGS), in cooperation with the Oklahoma Department of Transportation, updated peak-streamflow regression equations for estimating flows with annual exceedance probabilities from 50 to 0.2 percent for the State of Oklahoma. These regression equations incorporate basin characteristics to estimate peak-streamflow magnitude and frequency throughout the State by use of a generalized least-squares regression analysis. The most statistically significant independent variables required to estimate peak-streamflow magnitude and frequency for unregulated streams in Oklahoma are contributing drainage area, mean-annual precipitation, and main-channel slope. The regression equations are applicable for stream basins with drainage areas less than 2,510 square miles that are not affected by regulation. The standard model error ranged from 31.28 to 49.32 percent for the different annual exceedance probabilities that were computed.
Annual-maximum peak flows observed at 212 USGS streamgages through water year 2017 were used for the regression analysis, excluding the Oklahoma Panhandle region. The USGS StreamStats web application was used to obtain the independent variables required for the peak-streamflow regression equations. Limitations on the use of the regression equations and the reliability of regression estimates for natural unregulated streams are described. Log-Pearson Type III analysis information, basin and climate characteristics, and the peak-streamflow frequency estimates for the 212 streamgages in and near Oklahoma are provided in this report.
This report contains descriptions of the methods that can be used to estimate peak streamflows at ungaged sites by using estimates from streamgages on unregulated streams. For ungaged sites on urban streams and streams regulated by small floodwater-retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow magnitude and frequency.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195143","collaboration":"Prepared in cooperation with the Oklahoma Department of Transportation","usgsCitation":"Lewis, J.M., Hunter, S.L., and Labriola, L.G., 2019, Methods for estimating the magnitude and frequency of peak streamflows for unregulated streams in Oklahoma developed by using streamflow data through 2017 (ver. 1.1, March 2020): U.S. Geological Survey Scientific Investigations Report 2019–5143, 39 p., https://doi.org/10.3133/sir20195143.","productDescription":"Report: v, 39 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-111975","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":373219,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5143/sir20195143_v1.1.pdf","text":"Report","size":"5.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5143"},{"id":370619,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B99TQZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data release of basin characteristics, generalized skew map and peak-streamflow frequency estimates in Oklahoma, 2017"},{"id":373218,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5143/coverthb2.jpg"},{"id":373266,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5143/versionHist.txt","text":"Version History","description":"Version History"},{"id":399618,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109563.htm"}],"country":"United States","state":"Arkansas, Kansas, Missouri, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.919921875,\n 36.87962060502676\n ],\n [\n -102.83203125,\n 34.415973384481866\n ],\n [\n -97.91015624999999,\n 33.97980872872457\n ],\n [\n -94.5703125,\n 33.17434155100208\n ],\n [\n -93.515625,\n 33.97980872872457\n ],\n [\n -93.251953125,\n 37.125286284966805\n ],\n [\n -93.7353515625,\n 38.09998264736481\n ],\n [\n -99.8876953125,\n 38.09998264736481\n ],\n [\n -101.953125,\n 37.71859032558816\n ],\n [\n -102.919921875,\n 36.87962060502676\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: March 2020; Version 1.0: December 2019","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
River connectivity is defined as the water-mediated exchange of matter, energy, and biota between different elements of the riverine landscape. Connectivity is an especially important concept in large-river corridors (channel plus floodplain ) because large rivers integrate fluxes of water, sediment, nutrients, contaminants, and other transported constituents emanating from large contributing drainage basins, and thereby contribute to the complexity of large-river ecosystems. Large rivers are also highly valued for socioeconomic goods and services, which has led to historical fragmentation, lack of connectivity, and contentiousness about best policies for managing large-river corridors. The classification is intended to serve as a template for understanding geographic variation in large rivers within the Midwest, to aid in designing scientific studies of large river ecological processes, and to match specific river-management and restoration objectives to specific river reaches. The focus of the classification is on measuring river connectivity from available hydrological and geomorphic data.
We provide a multiscale assessment and classification for segments of 15 rivers that meet various criteria for largeness. All rivers are tributaries to the Mississippi River system. The 11,600 kilometers (km) that qualified as large were classified by major alterations (unimpounded, navigation pools, storage reservoir) and additionally assessed for their network continuity as a function of numbers and heights of dams. Among the 15 rivers, 55 percent of segment length was unimpounded, 30 percent was in navigation pools, and 15 percent was under storage reservoirs. Assessment of network longitudinal connectivity among river segments documented the contrast between river segments with low-head navigation dams (Upper Mississippi, Illinois, Ohio, Green, and Cumberland Rivers) and those segments with high-head dams (mostly in the Upper Missouri River). The longest unimpounded river pathways exist in the Lower Missouri River and connected tributaries where nearly 1,300 km of the Missouri River connect to an additional 1,800 km of the Middle and Lower Mississippi Rivers.
At our finest scale, we present a statistically based, component classification based on 10-km segments. Cluster analysis of hydrologic variables from 66 streamflow-gaging stations yielded 5 clusters calculated from 5 ecohydrological metrics related to lateral connectivity with the floodplain. A separate cluster analysis of 5 geomorphologic variables associated with each of the 1,172 river segments also yielded 5 clusters. When the hydrologic variables were associated with corresponding segments, the cluster analysis yielded 8 hydrogeomorphic clusters that could be explained in terms of their contribution to floodplain connectivity. Although the clusters overlap considerably in principal component space, the resulting hydrogeomorphic classification leads to a physically reasonable distribution of classes. The resulting classification is intended to increase geographic awareness of the range of variation of connectivity potential among large rivers of the Upper Midwest, to increase understanding of the extent of alteration of these rivers, and potentially to serve as a template for stratifying study designs of large-river corridor ecological processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195132","usgsCitation":"Jacobson, R.B., Rohweder, J.J., and DeJager, N.R., 2019, A hydrogeomorphic classification of connectivity of large rivers of the Upper Midwest, United States: U.S. Geological Survey Scientific Investigations Report 2019–5132, 55 p., https://doi.org/10.3133/sir20195132.","productDescription":"Report: vi, 55 p.; 2 Data Releases","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-104678","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":399611,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109562.htm"},{"id":370623,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5132/sir20195132.pdf","text":"Report","size":"9.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5132"},{"id":370625,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HFYOLO","text":"USGS data release","linkHelpText":"River valley boundaries and transects generated for select large rivers of the Upper Midwest, United States"},{"id":370624,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YGOKWZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Segment-scale classification, large rivers of the Upper Midwest United States"},{"id":370622,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5132/coverthb.jpg"}],"country":"United States","state":"Colorado, Illinois, Indiana, Iowa, Kansas, Kentucky, Minnesota, Missouri, Montana, Nebraska, New York, North Carolilna,North Dakota, Ohio, Pennsylvania, South Dakota, Tennessee, Virginia, West Virginia, Wisconsin, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.8251953125,\n 35.460669951495305\n ],\n [\n -80.5078125,\n 37.020098201368114\n ],\n [\n -80.15625,\n 35.60371874069731\n ],\n [\n -79.541015625,\n 37.43997405227057\n ],\n [\n -79.365234375,\n 39.87601941962116\n ],\n [\n -77.7392578125,\n 42.74701217318067\n ],\n [\n -82.265625,\n 40.91351257612758\n ],\n [\n -86.8359375,\n 41.73852846935917\n ],\n [\n -87.5830078125,\n 41.60722821271717\n ],\n [\n -88.9453125,\n 43.61221676817573\n ],\n [\n -89.4287109375,\n 43.45291889355465\n ],\n [\n -89.7802734375,\n 46.10370875598026\n ],\n [\n -90.439453125,\n 46.37725420510028\n ],\n [\n -93.42773437499999,\n 46.86019101567027\n ],\n [\n -94.9658203125,\n 47.57652571374621\n ],\n [\n -96.85546875,\n 45.55252525134013\n ],\n [\n -100.94238281249999,\n 48.42920055556841\n ],\n [\n -104.150390625,\n 49.210420445650286\n ],\n [\n -112.0166015625,\n 49.03786794532644\n ],\n [\n -113.99414062499999,\n 45.79816953017265\n ],\n [\n -112.9833984375,\n 44.5278427984555\n ],\n [\n -111.09374999999999,\n 44.77793589631623\n ],\n [\n -106.3916015625,\n 41.47566020027821\n ],\n [\n -105.6884765625,\n 39.67337039176558\n ],\n [\n -104.150390625,\n 38.75408327579141\n ],\n [\n -94.921875,\n 38.16911413556086\n ],\n [\n -91.93359375,\n 36.98500309285596\n ],\n [\n -89.736328125,\n 36.80928470205937\n ],\n [\n -85.8251953125,\n 35.460669951495305\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
Earth has a stunning variety of landscapes. The colors, patterns, textures, and shapes all make for intriguing artwork as seen from the perspective of space.
Earth As Art shows not only what satellites capture in the visible wavelengths of light you and I can see, but also what’s hiding in the invisible wavelengths that Landsat sensors can detect in the infrared part of the electromagnetic spectrum. Those combinations can bring out much more scientific value, but also can produce imagery of breathtaking beauty.
Earth As Art 6 even includes images from U.S. Geological Survey (USGS) Unmanned Aircraft Systems (UAS), commonly known as drones. Sensors attached to a UAS also capture visible and infrared light and have proven their value at monitoring change over time alongside their spaceborne partners. Besides, their images look great, too. Enjoy the latest from Earth As Art!
https://eros.usgs.gov/image-gallery/earth-art-6
Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198–0001
North American sea ducks generally breed in mid- to northern-latitude regions and nearly all rely upon marine habitats for much of their annual cycle. Most sea duck species remained poorly studied until the 1990s when declines were noted in several species and populations. Subsequent research, much of which was funded by the Sea Duck Joint Venture, began in the late 1990s with an emphasis on defining use areas throughout the annual cycle, migration patterns, and determining if there were distinct populations, within species, across North America. These studies relied largely upon satellite telemetry information to identify winter, breeding, and molting areas of sea ducks. New information from band recovery and genetic markers was added, contributing to hypotheses and initial conclusions about population delineation. Information on population units across North America is critical for identifying appropriate scales for evaluating population status and trends through annual monitoring surveys, harvest assessments, habitat protection and measuring effectiveness of management applications. Previous descriptions of population segments were for single species or smaller groups of similar species. Here, we summarize current knowledge on the general distribution and population segments of 13 species of sea ducks in North America by comparing range maps to long-term band recovery, genetic, and satellite telemetry data to inform population delineation assessments and future research. These comparisons show a high degree of consistency in population patterns for most species across the independent data types. These maps provide a foundation for developing new hypothesis-driven research to address remaining knowledge gaps and questions about population differentiation, annual cycle distribution, habitat use, and harvest assessment.
","largerWorkTitle":"USGS Open File Report","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191142","collaboration":"Prepared in Cooperation with the Sea Duck Joint Venture Continental Technical Team","usgsCitation":"Pearce, J.M., Flint, P.L., Whalen, M.E., Sonsthagen, S.A., Stiller, J., Patil, V.P., Bowman, T., Boyd, S., Badzinski, S.S., Gilchrist, H.G., Gilliland, S.G., Lepage, C., Loring, P., McAuley, D., McLellan, N.R., Osenkowski, J., Reed, E.T., Roberts, A.J., Robertson, M.O., Rothe, T., Safine, D.E., Silverman, E.D., and Spragens, D., 2019, Visualizing populations of North American Sea Ducks—Maps to guide research and management planning: U.S. Geological Survey Open-File Report 2019-1142, 50 p., plus appendixes, https://doi.org/10.3133/ofr20191142.","productDescription":"vi, 50 p.","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-109661","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":437250,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93EF3TH","text":"USGS data release","linkHelpText":"Tracking Data for Black Scoter (Melanitta americana)"},{"id":370662,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1142/ofr20191142.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1142"},{"id":370661,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1142/coverthb.jpg"}],"country":"Canada, Russia, United States","otherGeospatial":"North America","contact":"Director,
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
In East Africa, accurate grain yield predictions can help save lives and protect livelihoods. Regional grain yield forecasts can inform decisions regarding the availability and prices of key staples, food aid, and large humanitarian responses. Here, we use earth observation (EO) products to develop and evaluate subnational grain yield forecasts for 56 regions located in two severely food insecure countries: Kenya and Somalia. We identify, for a given region and time of year, which, if any, product is the best indicator for end-of-season maize yields. Our analysis seeks to inform a real-world situation in which analysts have access to multiple regularly updated EO data products, but predictive skill corresponding to each may vary across these regions and throughout the season. We find that the most accurate predictions can be made for high-producing areas, but that the relationship between production and forecast accuracy diminishes in areas with yields averaging greater than one metric ton per hectare. However, while forecast accuracy is highest in high production areas, in many of these regions, the forecast accuracy of models using EO products is not better than a set of baseline models that do not use EO products. Overall, we find that rainfall is the best indicator in low-producing regions and that other EO products work best in areas where yields are relatively consistent, but production is still limited by environmental factors.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ab5ccd","usgsCitation":"Davenport, F., Harrison, L., Shukla, S., Husak, G., Funk, C., and McNally, A., 2019, Using out-of-sample yield forecast experiments to evaluate which earth observation products best indicate end of season maize yields: Environmental Research Letters, v. 14, no. 2, 124095, 13 p., https://doi.org/10.1088/1748-9326/ab5ccd.","productDescription":"124095, 13 p.","ipdsId":"IP-101895","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458900,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab5ccd","text":"Publisher Index Page"},{"id":372947,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Kenya, Somalia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 31.728515624999996,\n -5.615985819155327\n ],\n [\n 51.50390625,\n -5.615985819155327\n ],\n [\n 51.50390625,\n 10.833305983642491\n ],\n [\n 31.728515624999996,\n 10.833305983642491\n ],\n [\n 31.728515624999996,\n -5.615985819155327\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Davenport, Frank","contributorId":145816,"corporation":false,"usgs":false,"family":"Davenport","given":"Frank","email":"","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":783964,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, Laura","contributorId":192382,"corporation":false,"usgs":false,"family":"Harrison","given":"Laura","email":"","affiliations":[],"preferred":false,"id":784025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shukla, Shraddhanand","contributorId":145841,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16255,"text":"Climate Hazards Group University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":783965,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Husak, Gregory","contributorId":145811,"corporation":false,"usgs":false,"family":"Husak","given":"Gregory","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":783966,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":783963,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McNally, Amy","contributorId":53225,"corporation":false,"usgs":true,"family":"McNally","given":"Amy","affiliations":[],"preferred":false,"id":784026,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208826,"text":"70208826 - 2019 - The influence of layout on Appalachian Trail soil loss, widening, and muddiness: Implications for sustainable trail design and management","interactions":[],"lastModifiedDate":"2020-03-03T09:09:25","indexId":"70208826","displayToPublicDate":"2019-12-23T09:06:10","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"The influence of layout on Appalachian Trail soil loss, widening, and muddiness: Implications for sustainable trail design and management","docAbstract":"This research investigates the influence of layout and design on the severity of trail degradation. Previous trail studies have been restricted by relatively small study areas which provide a limited range of environmental conditions and therefore produce findings with limited applicability; this research improves on this limitation by analyzing a representative sample of the Appalachian Trail with significant topographical, ecological, use-related, and managerial diversity. Many trail science studies have also focused on a singular form of trail degradation, whereas this study investigates all three core types of trail impact: trail soil loss, widening and muddiness. Relational analyses with all three indicators provide a more cohesive understanding of trail impact and reveal interrelationships between trail degradation processes. ANOVA testing of the mean values for these trail impact indicators across categories of influential independent factors confirms and refines the relevance of core trail design principles, specifically the sustainability advantages of trails with low grades and side-hill alignments. Findings also reveal and clarify the importance of landform grade in determining the susceptibility of trails to degradation and the influence of routing decisions; these relationships have received relatively little attention in the literature. The results also reveal several methodological considerations for trail alignment metrics and trail impact indicators","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2019.109986","usgsCitation":"Meadema, F., Marion, J.L., Arredondo, J., and Wimpey, J., 2019, The influence of layout on Appalachian Trail soil loss, widening, and muddiness: Implications for sustainable trail design and management: Journal of Environmental Management, v. 257, 109986, 10 p., https://doi.org/10.1016/j.jenvman.2019.109986.","productDescription":"109986, 10 p.","ipdsId":"IP-105613","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":458902,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1016/j.jenvman.2019.109986","text":"External Repository"},{"id":372839,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Georgia, Maine, Massachusetts, Maryland, New Hampshire, New Jersey, New York, North Carolina, Pennsylvania, Tennessee, Vermont, Virginia","otherGeospatial":"Appalachian Trail","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.671875,\n 32.509761735919426\n ],\n [\n -82.08984375,\n 32.02670629333614\n ],\n [\n -79.62890625,\n 33.02708758002874\n ],\n [\n -76.9921875,\n 35.67514743608467\n ],\n [\n -76.5966796875,\n 37.61423141542417\n ],\n [\n -76.552734375,\n 38.89103282648846\n ],\n [\n -75.2783203125,\n 40.413496049701955\n ],\n [\n -71.7626953125,\n 42.52069952914966\n ],\n [\n -70.3564453125,\n 43.644025847699496\n ],\n [\n -69.521484375,\n 44.465151013519616\n ],\n [\n -68.15917968749999,\n 45.058001435398275\n ],\n [\n -68.02734375,\n 46.164614496897094\n ],\n [\n -68.291015625,\n 46.6795944656402\n ],\n [\n -69.345703125,\n 46.46813299215554\n ],\n [\n -70.5322265625,\n 45.213003555993964\n ],\n [\n -72.158203125,\n 44.653024159812\n ],\n [\n -74.8388671875,\n 43.389081939117496\n ],\n [\n -75.76171875,\n 42.00032514831621\n ],\n [\n -78.22265625,\n 40.68063802521456\n ],\n [\n -79.013671875,\n 39.87601941962116\n ],\n [\n -80.244140625,\n 38.37611542403604\n ],\n [\n -81.650390625,\n 35.28150065789119\n ],\n [\n -83.8037109375,\n 34.08906131584994\n ],\n [\n -84.111328125,\n 33.50475906922609\n ],\n [\n -83.671875,\n 32.509761735919426\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"257","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Meadema, Fletcher","contributorId":207912,"corporation":false,"usgs":false,"family":"Meadema","given":"Fletcher","affiliations":[{"id":37662,"text":"Virginia Tech Master's student","active":true,"usgs":false}],"preferred":false,"id":783507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marion, Jeffrey L. 0000-0003-2226-689X jeff_marion@usgs.gov","orcid":"https://orcid.org/0000-0003-2226-689X","contributorId":3614,"corporation":false,"usgs":true,"family":"Marion","given":"Jeffrey","email":"jeff_marion@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":783506,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arredondo, Johanna","contributorId":192143,"corporation":false,"usgs":false,"family":"Arredondo","given":"Johanna","affiliations":[],"preferred":false,"id":783508,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wimpey, Jeremy","contributorId":207952,"corporation":false,"usgs":false,"family":"Wimpey","given":"Jeremy","affiliations":[{"id":32905,"text":"Applied Trails Research","active":true,"usgs":false}],"preferred":false,"id":783509,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211054,"text":"70211054 - 2019 - Functional characterization and osmoregulatory role of the Na+/K+/2Cl--cotransporter (NKCC1) in the gill of sea lamprey (Petromyzon marinus), a basal vertebrate","interactions":[],"lastModifiedDate":"2020-07-13T13:51:22.896616","indexId":"70211054","displayToPublicDate":"2019-12-23T08:50:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":730,"text":"American Journal of Physiology - Regulatory, Integrative and Comparative Physiology","onlineIssn":"1522-1490","printIssn":"0363-6119","active":true,"publicationSubtype":{"id":10}},"title":"Functional characterization and osmoregulatory role of the Na+/K+/2Cl--cotransporter (NKCC1) in the gill of sea lamprey (Petromyzon marinus), a basal vertebrate","docAbstract":"The present study provides molecular and functional characterization of Na+/K+/2Cl- cotransporter (nkcc1/NKCC1) in the gills of sea lamprey, the most basal extant vertebrate with an osmoregulatory strategy. We report the full-length peptide sequence for the lamprey NKCC1, which we show to group strongly with and occupy a basal position among other vertebrate NKCC1 sequences. Lamprey nkcc1 mRNA were present in many tissues but was 5-fold higher in the gill than any other tissue. NKCC1 protein was only detected in the gill. Gill mRNA and protein abundances of NKCC1 and Na+/K+-ATPase (NKA) were significantly upregulated (20- to 200-fold) in late metamorphosis in freshwater, coinciding with the development of salinity tolerance, and were upregulated an additional 2-fold after acclimation to seawater. Immunohistochemistry revealed that NKCC1 in the gill is found in filamental ionocytes that develop during metamorphosis. Lamprey treated with bumetanide, a widely used pharmacological inhibitor of NKCC1, exhibited higher plasma Cl- and osmolality and reduced muscle water content after 24 h in seawater, but had no effect in FW. This work provides the first functional characterization of NKCC1 as having a functional role mechanism for branchial salt secretion in lampreys, providing evidence that this mode of Cl- secretion has been present among vertebrates for ~550 million years.","language":"English","publisher":"American Physiological Society","doi":"10.1152/ajpregu.00125.2019","usgsCitation":"Shaughnessy, C.A., and McCormick, S.D., 2019, Functional characterization and osmoregulatory role of the Na+/K+/2Cl--cotransporter (NKCC1) in the gill of sea lamprey (Petromyzon marinus), a basal vertebrate: American Journal of Physiology - Regulatory, Integrative and Comparative Physiology, v. 318, no. 1, p. R17-R29, https://doi.org/10.1152/ajpregu.00125.2019.","productDescription":"13 p.","startPage":"R17","endPage":"R29","ipdsId":"IP-107838","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":458904,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1152/ajpregu.00125.2019","text":"Publisher Index Page"},{"id":376301,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"318","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shaughnessy, Ciaran Alvar Seeland 0000-0003-2146-9126","orcid":"https://orcid.org/0000-0003-2146-9126","contributorId":228962,"corporation":false,"usgs":true,"family":"Shaughnessy","given":"Ciaran","email":"","middleInitial":"Alvar Seeland","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":792612,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCormick, Stephen D. 0000-0003-0621-6200 smccormick@usgs.gov","orcid":"https://orcid.org/0000-0003-0621-6200","contributorId":139214,"corporation":false,"usgs":true,"family":"McCormick","given":"Stephen","email":"smccormick@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":792613,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210434,"text":"70210434 - 2019 - Aridity drives spatiotemporal patterns of masting across the latitudinal range of a dryland conifer","interactions":[],"lastModifiedDate":"2020-06-03T12:59:07.49479","indexId":"70210434","displayToPublicDate":"2019-12-23T07:56:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Aridity drives spatiotemporal patterns of masting across the latitudinal range of a dryland conifer","docAbstract":"Masting, or the synchronous and irregular production of seed crops, is controlled by environmental conditions and resource budgets. Increasing temperatures and shifting precipitation regimes may alter the frequency and magnitude of masting, especially in species that experience chronic resource stress. Yet the effects of a changing climate on seed production are unlikely to be uniform across populations, particularly those that span broad abiotic gradients. In this study, we assessed the spatiotemporal patterns of masting across the latitudinal distribution of a widely distributed dryland conifer species, piñon pine Pinus edulis . We quantified seed cone production from 2004 to 2017 using cone abscission scars in 187 trees from 28 sites along an 1100 km latitudinal gradient to investigate the spatiotemporal drivers of seed cone production and synchrony across populations. Populations from chronically hot and dry areas (greater climatic water deficits and less monsoonal precipitation) tended to have greater interannual variability in seed cone production and smaller crop sizes. Mast years generally followed years with low vapor pressure deficits and high precipitation during key periods of the reproductive process, but the strength of these relationships varied across the region. Populations that received greater monsoonal precipitation were less sensitive to late summer vapor pressure deficits during seed cone initiation yet more sensitive to spring vapor pressure deficits during pollination. Spatially correlated patterns of vapor pressure deficit better predicted synchrony in seed cone production than geographic distance, and these patterns were conserved at distances up to 500 km. These results demonstrate that aridity drives spatiotemporal variability in seed cone production. As a result, projected increases in aridity are likely to decrease the frequency and magnitude of masting in these dry forests and woodlands. Declines in seed production may compound climatic limitations to recruitment and impede tree regeneration, with cascading effects for numerous wildlife species.