Vitrinite reflectance (VRo) is a standard petrographic method for assessing thermal maturity (rank) of coal. The vitrinite reflectance technique, however, requires significant petrographic experience, can be time-consuming, and may be biased by analyst subjectivity. Correlations between coal rank and Raman spectral properties are a promising alternative that can supplant some of the limitations inherent in the VRo protocol. The traditional peak-fitting methodologies for quantifying metrics from Raman spectra, however, also suffer from analyst subjectivity that can affect correlations between analyte and spectral properties.
This research combines high-throughput Raman spectroscopy with multivariate analysis (MVA) to create calibration models for the prediction of coal rank though VRo and atomic O/C ratio. MVA techniques eliminate the ambiguous subjectivity prevalent in peak-fitting methods by evaluating the full Raman spectrum, then identifying the integral vibrational modes for constructing accurate models. Partial least squares (PLS) regression models were developed using Raman spectra and VRo values (0.23–5.23%) for 68 geographically diverse coal samples. The calibration set was validated using one-half of the samples to rigorously assess the model’s predictive accuracy. The root mean standard error of prediction was 0.19 for the VRo model and 0.014 for the atomic O/C model. Both models exhibited linear correlations, with coefficients of determination (R2) for the validation set of 0.99 (VRo) and 0.93 (atomic O/C), despite the geographic and rank diversity of the samples. This study demonstrates the applicability and power of using PLS models for the prediction of both the VRo and atomic O/C ratio from Raman spectra. The quantitative MVA protocol contained herein provides a Raman alternative to the VRo industry benchmark for coal rank that is not subject to the limitations and subjectivity of peak-fitting methods.
Codominant marker systems are better suited to analyze population structure and assess the source of an individual in admixture analyses. Currently, there is no codominant marker system using microsatellites developed for the sea sandwort, Honckenya peploides (L.) Ehrh., an early colonizer in island systems. We developed and characterized novel microsatellite loci from H. peploides, using reads collected from whole genome shotgun sequencing on a 454 platform. The combined output from two shotgun runs yielded a total of 62,669 reads, from which 58 loci were screened. We identified 12 polymorphic loci that amplified reliably and exhibited disomic inheritance. Microsatellite data were collected and characterized for the 12 polymorphic loci in two Alaskan populations of H. peploides: Fossil Beach, Kodiak Island (n = 32) and Egg Bay, Atka Island (n = 29). The Atka population exhibited a slightly higher average number of alleles (3.9) and observed heterozygosity (0.483) than the Kodiak population (3.3 and 0.347, respectively). The overall probability of identity values for both populations was PID = 2.892e−6 and PIDsib = 3.361e−3. We also screened the 12 polymorphic loci in Wilhelmsia physodes (Fisch. ex Ser.) McNeill, the most closely related species to H. peploides, and only one locus was polymorphic. These microsatellite markers will allow future investigations into population genetic and colonization patterns of the beach dune ruderal H. peploides on new and recently disturbed islands.
","language":"English","publisher":"Springer","doi":"10.1007/s10265-018-1036-7","usgsCitation":"Gravley, M.C., Sage, G.K., Talbot, S.L., and Carlson, M.L., 2018, Development and characterization of 12 polymorphic microsatellite loci in the sea sandwort, Honckenya peploides: Journal of Plant Research, v. 131, no. 5, p. 879-885, https://doi.org/10.1007/s10265-018-1036-7.","productDescription":"7 p.","startPage":"879","endPage":"885","ipdsId":"IP-092972","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":437893,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BC3XRP","text":"USGS data release","linkHelpText":"Microsatellite Genetic Data for Sea Sandwort (Honckenya peploides) and Merckia (Wilhelmsia physodes), Alaska 2009-2016"},{"id":354106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Atka Island, Kodiak Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.775390625,\n 56.68037378950137\n ],\n [\n -151.9189453125,\n 56.68037378950137\n ],\n [\n -151.9189453125,\n 58.90464570302001\n ],\n [\n -154.775390625,\n 58.90464570302001\n ],\n [\n -154.775390625,\n 56.68037378950137\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -175.3802490234375,\n 51.984880139916626\n ],\n [\n -173.935546875,\n 51.984880139916626\n ],\n [\n -173.935546875,\n 52.466050361889515\n ],\n [\n -175.3802490234375,\n 52.466050361889515\n ],\n [\n -175.3802490234375,\n 51.984880139916626\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"131","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-23","publicationStatus":"PW","scienceBaseUri":"5afee6bfe4b0da30c1bfbda2","contributors":{"authors":[{"text":"Gravley, Megan C. 0000-0002-4947-0236 mgravley@usgs.gov","orcid":"https://orcid.org/0000-0002-4947-0236","contributorId":202812,"corporation":false,"usgs":true,"family":"Gravley","given":"Megan","email":"mgravley@usgs.gov","middleInitial":"C.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":735100,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sage, George K. 0000-0003-1431-2286 ksage@usgs.gov","orcid":"https://orcid.org/0000-0003-1431-2286","contributorId":87833,"corporation":false,"usgs":true,"family":"Sage","given":"George","email":"ksage@usgs.gov","middleInitial":"K.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":735101,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":735102,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carlson, Matthew L.","contributorId":138686,"corporation":false,"usgs":false,"family":"Carlson","given":"Matthew","email":"","middleInitial":"L.","affiliations":[{"id":12492,"text":"UAA Alaska Natural Heritage Program & Biological Sciences Department","active":true,"usgs":false}],"preferred":false,"id":735103,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196932,"text":"sir20185037 - 2018 - Hydraulic, water-quality, and temperature performance of three types of permeable pavement under high sediment loading conditions","interactions":[],"lastModifiedDate":"2018-05-14T11:03:35","indexId":"sir20185037","displayToPublicDate":"2018-05-11T12:30:00","publicationYear":"2018","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":"2018-5037","title":"Hydraulic, water-quality, and temperature performance of three types of permeable pavement under high sediment loading conditions","docAbstract":"Three permeable pavement surfaces - asphalt (PA), concrete (PC), and interlocking pavers (PIP) - were evaluated side-by-side to measure changes to the infiltrative capacity and water quality of stormwater runoff originating from a conventional asphalt parking lot in Madison, Wisconsin. During the 24-month monitoring period (2014-16), all three permeable pavements resulted in statistically significant reductions in the cumulative load of solids (total suspended solids and suspended sediment), total phosphorus, Escherichia coli (E. coli), and Enterococci. Most of the removal occurred through capture and retention in the void spaces of each permeable surface and aggregate base. The largest reduction in total suspended solids was for PC at 80 percent, followed by PIP and PA at 69 and 65 percent, respectively. Reductions (generally less than 50 percent) in total phosphorus also were observed, which might have been tempered by increases in the dissolved fraction observed in PIP and PA. Conversely, PC results indicated a slight reduction in dissolved phosphorus but failed to meet statistical significance. E. coli and Enterococci were reduced by about 80 percent for PC, almost twice the amount observed for PIP and PA.
Results for the PIP and PC surfaces initially indicated higher pollutant load reduction than results for the PA surface. The efficiency of PIP and PC surfaces capturing sediment, however, led to a decline in infiltration rates that resulted in more runoff flowing over, not through, the permeable surface. This result led to a decline in treatment until the permeable surface was partially restored through maintenance practices, to which PIP responded more dramatically than PC or PA. Conversely, the PA surface was capable of infiltrating most of the influent runoff volume during the monitoring period and, thus, continued to provide some level of treatment. The combined effect of underdrain and overflow drainage resulted in similar pollutant treatment for all three permeable surfaces.
Temperatures below each permeable surface generally followed changes in air temperature with a more gradual response observed in deeper layers. Therefore, permeable pavement may do little to mitigate heated runoff during summer. During winter, deeper layers remained above freezing even when air temperature was below freezing. Although temperatures were not high enough to melt snow or ice accumulated on the surface, temperatures below each permeable pavement did allow void spaces to remain open, which promoted infiltration of melted ice and snow as air temperatures rose above freezing. These open void spaces could potentially reduce the need for application of deicing agents in winter because melted snow and ice would infiltrate, thereby preventing refreezing of pooled water in what is known as the “black ice” effect.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185037","usgsCitation":"Selbig, W.R., and Buer, Nicolas, 2018, Hydraulic, water-quality, and temperature performance of three types of permeable pavement under high sediment loading conditions: U.S. Geological Survey Scientific Investigations Report 2018–5037, 44 p., https://doi.org/10.3133/sir20185037.","productDescription":"Report: ix, 44 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087882","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":437894,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RX9B09","text":"USGS data release","linkHelpText":"Storm characteristics, concentrations, and loads measured at the Permeable Pavement Research Facility, Madison, Wisconsin (2014 - 2016)"},{"id":354071,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RX9B09 ","text":"USGS data release","description":"USGS data release","linkHelpText":"Storm characteristics, concentrations, and loads measured at the permeable pavement research facility, Madison, Wisconsin (2014 - 2016)"},{"id":354069,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5037/coverthb.jpg"},{"id":354070,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5037/sir20185037.pdf","text":"Report","size":"6.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5037"}],"country":"United States","state":"Wisconsin","city":"Madison","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Streamflow and nutrient concentration data needed to compute nitrogen and phosphorus loads were compiled from Federal, State, Provincial, and local agency databases and also from selected university databases. The nitrogen and phosphorus loads are necessary inputs to Spatially Referenced Regressions on Watershed Attributes (SPARROW) models. SPARROW models are a way to estimate the distribution, sources, and transport of nutrients in streams throughout the Midcontinental region of Canada and the United States. After screening the data, approximately 1,500 sites sampled by 34 agencies were identified as having suitable data for calculating the long-term mean-annual nutrient loads required for SPARROW model calibration. These final sites represent a wide range in watershed sizes, types of nutrient sources, and land-use and watershed characteristics in the Midcontinental region of Canada and the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185051","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Saad, D.A., Benoy, G.A., and Robertson, D.M., 2018, Estimates of long-term mean-annual nutrient loads considered for use in SPARROW models of the Midcontinental region of Canada and the United States, 2002 base year: U.S. Geological Survey Scientific Investigations Report 2018–5051, 14 p., https://doi.org/10.3133/sir20185051.","productDescription":"Report: vi, 14 p.; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-084092","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":353945,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VT1R1K","text":"USGS data release","description":"USGS data release","linkHelpText":"Water-quality and streamflow datasets used for estimating loads considered for use in the 2002 Midcontinent nutrient SPARROW models, United States and Canada, 1970-2012"},{"id":353943,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5051/sir20185051.pdf","text":"Report","size":"8.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5051"},{"id":353931,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5051/coverthb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.32617187499999,\n 49.26780455063753\n ],\n [\n -94.3505859375,\n 42.94033923363181\n ],\n [\n -91.7578125,\n 39.16414104768742\n ],\n [\n -88.9013671875,\n 36.63316209558658\n ],\n [\n -86.572265625,\n 35.17380831799959\n ],\n [\n -81.7822265625,\n 37.82280243352756\n ],\n [\n -79.5849609375,\n 40.17887331434696\n ],\n [\n -77.16796875,\n 42.293564192170095\n ],\n [\n -74.794921875,\n 43.739352079154706\n ],\n [\n -75.6298828125,\n 44.933696389694674\n ],\n [\n -78.44238281249999,\n 45.1510532655634\n ],\n [\n -80.2001953125,\n 46.58906908309182\n ],\n [\n -82.2216796875,\n 47.368594345213374\n ],\n [\n -84.287109375,\n 49.781264058178344\n ],\n [\n -87.2314453125,\n 50.233151832472245\n ],\n [\n -90.52734374999999,\n 50.708634400828224\n ],\n [\n -95.1416015625,\n 50.401515322782366\n ],\n [\n -99.66796875,\n 50.064191736659104\n ],\n [\n -100.986328125,\n 51.45400691005982\n ],\n [\n -103.4912109375,\n 51.536085601784755\n ],\n [\n -102.74414062499999,\n 49.97948776108648\n ],\n [\n -104.32617187499999,\n 49.26780455063753\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Polar bears (Ursus maritimus) in Alaska use the Arctic National Wildlife Refuge (ANWR) for maternal denning. Pregnant bears den in snow banks for more than 3 months in winter during which they give birth to and nurture young. Denning is one of the most vulnerable times in polar bear life history as the family group cannot simply walk away from a disturbance without jeopardizing survival of newly born cubs. The ANWR includes the “1002 Area”, a region recently opened for oil and gas exploration by the U.S. Department of the Interior (DOI). As a part of its mission, the DOI “… protects and manages the Nation's natural resources …” and is therefore responsible for conserving polar bears and encouraging development of energy potential. Because future industrial activities could overlap habitats used by denning polar bears, identifying these habitats can inform the decisions of resource managers tasked to develop resources and protect polar bears. To help inform these efforts, we qualitatively compared the distribution of denning habitat identified by two different methods: previously published habitat from manual interpretation of aerial photographs, and habitat derived by computer interrogation of interferometric synthetic aperture radar (IfSAR) digital terrain models (DTM). Because photograph-interpreted methods depicted denning habitat as a line and IfSAR-derived methods depicted habitat as a polygon, we assessed agreement between the two methods with distance measurements. We found that 77.5 percent of IfSAR-derived denning habitat (79.6 km2 ; 1.2 percent of the 6,837.0 km2 1002 Area) was within 600 m of photograph-interpreted habitat (3,026.9 km), including 53.9 percent within 200 m. This distribution differed from that of randomly distributed points, as only 49.4 percent of these occurred within 600 m of photograph-interpreted habitat, including 18.3 percent within 200 m. Both methods appear to identify the major physiographic features that polar bears might select for denning. IfSAR-derived methods identified habitat at greater frequency beyond major landscape features such as coastal bluffs, river banks and lakeshores, were more likely to identify isolated pockets of putative denning habitat, and were easier to implement than deriving habitat from photograph-interpretive efforts. However, previous research suggests that photograph-interpretation methods may identify denning habitat more correctly than computer interrogation of IfSAR DTMs. Future work should quantify the distribution of IfSAR-derived denning habitat relative to actual landscape features and polar bear maternal dens in the 1002 Area, and investigate the feasibility of habitat identification from finer grained DTMs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181083","usgsCitation":"Durner, G.M., and Atwood, T.C., 2018, A comparison of photograph-interpreted and IfSAR-derived maps of polar bear denning habitat for the 1002 Area of the Arctic National Wildlife Refuge, Alaska: U.S. Geological Survey Open-File Report 2018–1083, 12 p., https://doi.org/10.3133/ofr20181083.","productDescription":"Report: iv, 12 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-095475","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":354103,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DJ5DXT","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data used to compare photo-interpreted and IfSAR-derived maps of polar bear denning habitat for the 1002 Area of the Arctic National Wildlife Refuge, Alaska, 2006-2016"},{"id":354102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1083/ofr20181083.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1083"},{"id":354101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1083/coverthb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -146.5,\n 69.5\n ],\n [\n -142,\n 69.5\n ],\n [\n -142,\n 70.25\n ],\n [\n -146.5,\n 70.25\n ],\n [\n -146.5,\n 69.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, Alaska 99508
Context
Landscape resistance is vital to connectivity modeling and frequently derived from resource selection functions (RSFs). RSFs estimate relative probability of use and tend to focus on understanding habitat preferences during slow, routine animal movements (e.g., foraging). Dispersal and migration, however, can produce rarer, faster movements, in which case models of movement speed rather than resource selection may be more realistic for identifying habitats that facilitate connectivity.
Objective
To compare two connectivity modeling approaches applied to resistance estimated from models of movement rate and resource selection.
Methods
Using movement data from migrating elk, we evaluated continuous time Markov chain (CTMC) and movement-based RSF models (i.e., step selection functions [SSFs]). We applied circuit theory and shortest random path (SRP) algorithms to CTMC, SSF and null (i.e., flat) resistance surfaces to predict corridors between elk seasonal ranges. We evaluated prediction accuracy by comparing model predictions to empirical elk movements.
Results
All connectivity models predicted elk movements well, but models applied to CTMC resistance were more accurate than models applied to SSF and null resistance. Circuit theory models were more accurate on average than SRP models.
Conclusions
CTMC can be more realistic than SSFs for estimating resistance for fast movements, though SSFs may demonstrate some predictive ability when animals also move slowly through corridors (e.g., stopover use during migration). High null model accuracy suggests seasonal range data may also be critical for predicting direct migration routes. For animals that migrate or disperse across large landscapes, we recommend incorporating CTMC into the connectivity modeling toolkit.
Efforts to reverse declines in native grasslands benefit from agricultural policies that encourage private land conservation. The U.S. Department of Agriculture’s Conservation Reserve Program (CRP) improved conservation across landscapes but enrollment has declined. We used sequential exploratory mixed methods to compare landowner and conservation practitioners’ perceptions, evaluate perceived benefits, and identify potential improvements to CRP. Focus groups of practitioners informed a quantitative survey of landowners who had properties >160 total acres in Nebraska. Results suggest potential misalignment in perceptions between practitioners and landowners. Practitioners were concerned that conservation, especially of wildlife, was secondary to profit. But the majority of landowners valued CRP-related ecosystem services, including native pollinators. Practitioners posited that younger landowners were primarily profit motivated, but CRP enrollment did not differ by demographics. Practitioners and landowners identified rule complexity as a major challenge and practitioner–landowner relationships as critical to success. Findings suggest that practitioners may underestimate non-economic motivations and illuminate opportunities to encourage private land conservation.
","language":"English","publisher":"Informa","doi":"10.1080/08941920.2017.1376139","usgsCitation":"Lute, M.L., Gillespie, C.R., Fontaine, J.J., and Martin, D.R., 2018, Landowner and practitioner perspectives on private land conservation programs: Society & Natural Resources: An International Journal, v. 31, no. 2, p. 218-231, https://doi.org/10.1080/08941920.2017.1376139.","productDescription":"14 p.","startPage":"218","endPage":"231","ipdsId":"IP-074197","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":354094,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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jfontaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-9156","contributorId":3820,"corporation":false,"usgs":true,"family":"Fontaine","given":"Joseph","email":"jfontaine@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734980,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Dustin R.","contributorId":204239,"corporation":false,"usgs":false,"family":"Martin","given":"Dustin","email":"","middleInitial":"R.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":735084,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196638,"text":"ds1085 - 2018 - Groundwater-quality data from the eastern Snake River Plain Aquifer, Jerome and Gooding Counties, south-central Idaho, 2017","interactions":[],"lastModifiedDate":"2018-05-14T11:15:38","indexId":"ds1085","displayToPublicDate":"2018-05-11T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1085","title":"Groundwater-quality data from the eastern Snake River Plain Aquifer, Jerome and Gooding Counties, south-central Idaho, 2017","docAbstract":"Groundwater-quality samples and water-level data were collected from 36 wells in the Jerome/Gooding County area of the eastern Snake River Plain aquifer during June 2017. The wells included 30 wells sampled for the U.S. Geological Survey’s National Water-Quality Assessment project, plus an additional 6 wells were selected to increase spatial distribution. The data provide water managers with the ability for an improved understanding of groundwater quality and flow directions in the area. Groundwater-quality samples were analyzed for nutrients, major ions, trace elements, and stable isotopes of water. Quality-assurance and quality-control measures consisted of multiple blank samples and a sequential replicate sample. All data are available online at the USGS National Water Information System.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1085","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources and Idaho Power Company","usgsCitation":"Skinner, K.D., 2018, Groundwater-quality data from the eastern Snake River Plain aquifer, Jerome and Gooding Counties, south-central Idaho, 2017: U.S. Geological Survey Data Series 1085, 20 p., https://doi.org/10.3133/ds1085.","productDescription":"iv, 20 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-093930","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":354092,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1085/ds1085.pdf","text":"Report","size":"1.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1085"},{"id":354091,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1085/coverthb.jpg"}],"country":"United States","state":"Idaho","county":"Gooding County, Jerome County","otherGeospatial":"Snake River Plain Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.33,\n 42.56218435232186\n ],\n [\n -114.33,\n 42.917212160086194\n ],\n [\n -115,\n 42.917212160086194\n ],\n [\n -115,\n 42.56218435232186\n ],\n [\n -114.33,\n 42.56218435232186\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
The California Department of Water Resources and Bureau of Reclamation propose new water intake facilities on the Sacramento River in northern California that would convey some of the water for export to areas south of the Sacramento-San Joaquin River Delta (hereinafter referred to as the Delta) through tunnels rather than through the Delta. The collection of water intakes, tunnels, pumping facilities, associated structures, and proposed operations are collectively referred to as California WaterFix. The water intake facilities, hereinafter referred to as the North Delta Diversion (NDD), are proposed to be located on the Sacramento River downstream of the city of Sacramento and upstream of the first major river junction where Sutter Slough branches from the Sacramento River. The NDD can divert a maximum discharge of 9,000 cubic feet per second (ft3 /s) from the Sacramento River, which reduces the amount of Sacramento River inflow into the Delta.
In this report, we conduct four analyses to investigate the effect of the NDD and its proposed operation on survival of juvenile Chinook salmon (Oncorhynchus tshawytscha). All analyses used the results of a Bayesian survival model that allowed us to simulate travel time, migration routing, and survival of juvenile Chinook salmon migrating through the Delta in response to NDD operations, which affected both inflows to the Delta and operation of the Delta Cross Channel (DCC).
For the first analysis, we evaluated the effect of the NDD bypass rules on salmon survival. The NDD bypass rules are a set of operational rule curves designed to provide adaptive levels of fish protection by defining allowable diversion rates as a function of (1) Sacramento River discharge as measured at Freeport, and (2) time of year when endangered runs requiring the most protection are present. We determined that all bypass rule curves except constant low-level pumping (maximum diversion of 900 ft3 /s) could cause a sizeable decrease in survival by as much as 6–10 percentage points. The maximum decrease in survival occurred at an intermediate Sacramento River flow of about 20,000–30,000 ft3 /s. Diversion rates increased rapidly as Sacramento River flows increased from 20,000 ft3 /s to 30,000 ft3 /s, until a maximum diversion rate was reached at 9,000 ft3 /s. Because through-Delta survival increases sharply over this range of Sacramento River flow before beginning to level off with further flow increases, increasing diversion rates over this flow range causes a large decrease in survival relative to no diversion.
For the second analysis, we applied the survival model to 82 years of daily simulated flows under the Proposed Action (PA) and No Action Alternative (NAA). The PA includes operation of the Central Valley Project/State Water Project with implementation of the NDD and its operations prescribed by the NDD bypass rules, whereas the NAA assumes system operations without implementation of the NDD. We also evaluated a “Level 1” (L1) scenario, which was similar to the PA scenario but applied the most protective bypass rule known as Level 1 post-pulse operations. We noted a high probability that survival under the PA scenario was lower than under the NAA scenario, and that travel time was longer under PA relative to NAA in most simulation years. However, the largest survival differences between the PA and NAA scenarios occurred during October–November and May–June. Although bypass rules are less restrictive during these periods, we determined that more frequent use of the DCC under PA led to the largest differences in survival between the two scenarios. Additionally, we noted no difference in median survival decreases between the PA and L1 scenarios, although in some years the L1 scenario had a lower survival decrease than the PA scenario.
For the third analysis, we proposed a quantitative approach for developing NDD rule curves (that is, prescribed diversion flows for given inflows) by using the survival model to identify diversion rates that meet a criterion of a having a small probability of exceeding a given decrease in survival. We examined diversion rates that led to a 10% chance of exceeding a given decrease in survival for a range of absolute and relative decreases in survival. To maintain a given constant level of protection across the range of river flows, our analysis indicated that diversions had to increase at a much slower rate with respect to Sacramento River flow relative to the rule curves defined in the NDD bypass table. Additionally, we determined that diversion rates could be higher than under the bypass table rule curves at river flows less than 20,000 ft3 /s, but diversions had to be less than defined by NDD bypass rules at higher flows.
For the fourth analysis, we simulated the effect of “real-time operations” on salmon survival, where bypass flow rates were determined by the presence of juvenile salmon entering the Delta, as indicated by juvenile salmon catch in a rotary screw trap upstream of the Delta. For this analysis, we evaluated NDD operations as defined by the L1 scenario and an additional scenario (Unlimited Pulse Protection [UPP]) that provided protection to an unlimited number of fish pulses. This analysis indicated that the highest catches occurred during flow pulses when daily survival was high, which caused annual survival to be weighted towards periods of high daily survival, resulting in a high annual survival. We determined that the mean annual survival decreased by 1–4 percentage points, and annual survival decreases were more frequently smaller for the UPP scenario. Additionally, because the UPP scenario protected an unlimited number of fish pulses, decreases in daily survival under the UPP scenario were less than under the L1 scenario.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181078","collaboration":"Prepared in cooperation with National Oceanic and Atmospheric Administration, National Marine Fisheries Service","usgsCitation":"Perry, R.W., and Pope, A.C., 2018, Effects of the proposed California WaterFix North Delta Diversion on survival of juvenile Chinook salmon (Oncorhynchus tshawytscha) in the Sacramento-San Joaquin River Delta, northern California: U.S. Geological Survey Open-File Report 2018-1078, 94 p. plus appendixes,\nhttps://doi.org/10.3133/ofr20181078.","productDescription":"Report: x, 94 p.; 11 Appendixes","numberOfPages":"108","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095992","costCenters":[{"id":654,"text":"Western Fisheries Research 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U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
This is the ninth annual report highlighting U.S. Geological Survey (USGS) science and decision-support activities conducted for the Wyoming Landscape Conservation Initiative (WLCI). The activities address specific management needs identified by WLCI partner agencies. In fiscal year (FY) 2016, there were 26 active USGS WLCI science-based projects. Of these 26 projects, one project was new for FY2016, and three were completed by the end of the fiscal year (though final products were still in preparation or review). USGS WLCI projects were grouped under five categories: (1) Baseline Synthesis, (2) Long-Term Monitoring, (3) Effectiveness Monitoring, (4) Mechanistic Studies of Wildlife, and (5) Data and Information Management. Each of these topic areas is designed to address WLCI management needs: identifying key drivers of change, identifying the condition and distribution of key wildlife species and habitats and of species’ habitat requirements, development of an integrated inventory and monitoring strategy, use of emerging technologies and development and testing of innovative methods for maximizing the efficiency and efficacy of monitoring efforts, evaluating the effectiveness of habitat treatment projects, evaluating the responses of wildlife to development, and developing a data clearinghouse and information management framework to support and provide access to results of most USGS WLCI projects.
In FY2016, we assisted with updating the WLCI Conservation Action Plan and associated databases as part of the Comprehensive Assessment, and we also assisted with the Bureau of Land Management 2015 WLCI annual report. By the end of FY2016, we completed or had nearly completed assessments of WLCI energy and mineral resources and had submitted a manuscript on modeled effects of oil and gas development on wildlife to a peer-reviewed journal. We also initiated a study on the effects of wind energy on wildlife in the WLCI region. A USGS circular on WLCI long-term monitoring was in review at the end of the fiscal year, and seven projects monitoring water and vegetation (including changes in sagebrush cover and patterns of sagebrush mortality) continued through the year. USGS scientists continued many projects in FY2016 that evaluate the effectiveness of habitat conservation actions (including sagebrush, cheatgrass, and aspen habitat treatments) and provide tools in support of mechanistic studies of wildlife. In FY2016, USGS scientists, along with university and State partners, continued work on five focal wildlife species/communities (pygmy rabbits [Brachylagus idahoensis], greater sage grouse , mule deer, sagebrush songbirds, and native fish). In FY2016, the USGS Information Management Team presented information to WLCI scientists on how USGS tools and resources can be used to fulfill the requirements of new USGS policies regarding data release, data management, and data visualization.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181048","usgsCitation":"Bowen, Z.H., Aikens, E., Aldridge, C.L., Anderson, P.J., Assal, T.J., Chalfoun, A.D., Chong, G.W., Eddy-Miller, C.A., Garman, S.L., Germaine, S.S., Homer, C.G., Johnston, A., Kauffman, M.J., Manier, D.J., Melcher, C.P., Miller, K.A., Walters, A.W., Wheeler, J.D., Wieferich, D., Wilson, A.B., Wyckoff, T.B., and Zeigenfuss, L.C., 2018, U.S. Geological Survey science for the Wyoming Landscape Conservation Initiative—2016 annual report: U.S. Geological Survey Open-File Report 2018–1048, 49 p., https://doi.org/10.3133/ofr20181048.","productDescription":"vii, 49 p.","onlineOnly":"Y","ipdsId":"IP-091604","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science 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Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
Here, we report the complete mitochondrial genome of the endangered Hine’s emerald dragonfly (HED), Somatochlora hineana Williamson. Data were generated via next generation sequencing (NGS) and assembled using a mitochondrial baiting and iterative mapping approach. The full length circular genome is 15,705 bp with 26.6% GC content. It contains the typical metazoan set of 37 genes: 13 protein-coding genes, 22 transfer RNA (tRNA) and 2 ribosomal RNA (rRNA) genes, and an A + T-rich control region. To our knowledge, this is the first report of the complete HED mitogenome.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/23802359.2018.1463824","usgsCitation":"Jackson, C., McCalla, S.G., Amberg, J., Soluk, D., and Britten, H., 2018, The complete mitochondrial genome of Hine’s emerald dragonfly (Somatochlora hineana Williamson) via NGS sequencing: Mitochondrial DNA Part B, v. 3, no. 2, p. 562-563, https://doi.org/10.1080/23802359.2018.1463824.","productDescription":"2 p.","startPage":"562","endPage":"563","ipdsId":"IP-093895","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":468768,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/23802359.2018.1463824","text":"Publisher Index Page"},{"id":359607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-10","publicationStatus":"PW","scienceBaseUri":"5bf52b6ae4b045bfcae2800e","contributors":{"authors":[{"text":"Jackson, Craig 0000-0003-4023-0276 cjackson@usgs.gov","orcid":"https://orcid.org/0000-0003-4023-0276","contributorId":192276,"corporation":false,"usgs":true,"family":"Jackson","given":"Craig","email":"cjackson@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":733926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCalla, S. Grace 0000-0003-4292-8694 smccalla@usgs.gov","orcid":"https://orcid.org/0000-0003-4292-8694","contributorId":168436,"corporation":false,"usgs":true,"family":"McCalla","given":"S.","email":"smccalla@usgs.gov","middleInitial":"Grace","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":733927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Amberg, Jon 0000-0002-8351-4861 jamberg@usgs.gov","orcid":"https://orcid.org/0000-0002-8351-4861","contributorId":149785,"corporation":false,"usgs":true,"family":"Amberg","given":"Jon","email":"jamberg@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":733928,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Soluk, Daniel","contributorId":204438,"corporation":false,"usgs":false,"family":"Soluk","given":"Daniel","email":"","affiliations":[{"id":36938,"text":"University of South Dakota-Vermillion","active":true,"usgs":false}],"preferred":false,"id":733929,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Britten, Hugh","contributorId":204439,"corporation":false,"usgs":false,"family":"Britten","given":"Hugh","email":"","affiliations":[{"id":36938,"text":"University of South Dakota-Vermillion","active":true,"usgs":false}],"preferred":false,"id":733930,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196459,"text":"sir20185017 - 2018 - Flood-inundation maps for the Wabash River at Lafayette, Indiana","interactions":[],"lastModifiedDate":"2018-05-14T10:39:38","indexId":"sir20185017","displayToPublicDate":"2018-05-10T11:15:00","publicationYear":"2018","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":"2018-5017","title":"Flood-inundation maps for the Wabash River at Lafayette, Indiana","docAbstract":"Digital flood-inundation maps for an approximately 4.8-mile reach of the Wabash River at Lafayette, Indiana (Ind.) were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science web site at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at USGS streamgage 03335500, Wabash River at Lafayette, Ind. Current streamflow conditions for estimating near-real-time areas of inundation using USGS streamgage information may be obtained on the internet at https://waterdata.usgs.gov/in/nwis/uv?site_no=03335500. In addition, information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood-warning system (https://water.weather.gov/ahps/). The NWS AHPS forecasts flood hydrographs at many places that are often colocated with USGS streamgages, including the Wabash River at Lafayette, Ind. NWS AHPS-forecast peak-stage information may be used with the maps developed in this study to show predicted areas of flood inundation.
For this study, flood profiles were computed for the Wabash River reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relations at USGS streamgage 03335500, Wabash River at Lafayette, Ind., and high-water marks from the flood of July 2003 (U.S. Army Corps of Engineers [USACE], 2007). The calibrated hydraulic model was then used to determine 23 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a geographic information system digital elevation model derived from light detection and ranging 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 03335500, Wabash River at Lafayette, Ind., and forecasted high-flow stages from the NWS AHPS, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, and for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185017","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Kim, M.H., 2018, Flood-inundation maps for the Wabash River at Lafayette, Indiana: U.S. Geological Survey Scientific Investigations Report 2018–5017,Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34°C in lake pixels >180 m from land, 4.89°C at the land-water boundary, and 1.11°C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2018.1471545","usgsCitation":"Schaeffer, B.A., Iiames, J., Dwyer, J.L., Urquhart, E., Salls, W., Rover, J., and Seegers, B., 2018, An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries: International Journal of Remote Sensing, v. 39, no. 22, p. 7789-7805, https://doi.org/10.1080/01431161.2018.1471545.","productDescription":"17 p.","startPage":"7789","endPage":"7805","ipdsId":"IP-096965","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468769,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/01431161.2018.1471545","text":"Publisher Index Page"},{"id":362002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"39","issue":"22","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Schaeffer, Blake A.","contributorId":201328,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":759166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iiames, John","contributorId":214110,"corporation":false,"usgs":false,"family":"Iiames","given":"John","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dwyer, John L. 0000-0002-8281-0896 dwyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-0896","contributorId":3481,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","email":"dwyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":759164,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Urquhart, Erin","contributorId":214111,"corporation":false,"usgs":false,"family":"Urquhart","given":"Erin","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Salls, Wilson","contributorId":214112,"corporation":false,"usgs":false,"family":"Salls","given":"Wilson","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759169,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rover, Jennifer 0000-0002-3437-4030","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":211850,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":759165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Seegers, Bridget","contributorId":214113,"corporation":false,"usgs":false,"family":"Seegers","given":"Bridget","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":759170,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196920,"text":"70196920 - 2018 - Integrated analysis for population estimation, management impact evaluation, and decision-making for a declining species","interactions":[],"lastModifiedDate":"2018-05-14T13:07:52","indexId":"70196920","displayToPublicDate":"2018-05-10T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Integrated analysis for population estimation, management impact evaluation, and decision-making for a declining species","docAbstract":"A challenge for making conservation decisions is predicting how wildlife populations respond to multiple, concurrent threats and potential management strategies, usually under substantial uncertainty. Integrated modeling approaches can improve estimation of demographic rates necessary for making predictions, even for rare or cryptic species with sparse data, but their use in management applications is limited. We developed integrated models for a population of diamondback terrapins (Malaclemys terrapin) impacted by road-associated threats to (i) jointly estimate demographic rates from two mark-recapture datasets, while directly estimating road mortality and the impact of management actions deployed during the study; and (ii) project the population using population viability analysis under simulated management strategies to inform decision-making. Without management, population extirpation was nearly certain due to demographic impacts of road mortality, predators, and vegetation. Installation of novel flashing signage increased survival of terrapins that crossed roads by 30%. Signage, along with small roadside barriers installed during the study, increased population persistence probability, but the population was still predicted to decline. Management strategies that included actions targeting multiple threats and demographic rates resulted in the highest persistence probability, and roadside barriers, which increased adult survival, were predicted to increase persistence more than other actions. Our results support earlier findings showing mitigation of multiple threats is likely required to increase the viability of declining populations. Our approach illustrates how integrated models may be adapted to use limited data efficiently, represent system complexity, evaluate impacts of threats and management actions, and provide decision-relevant information for conservation of at-risk populations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2018.03.023","usgsCitation":"Crawford, B.A., Moore, C.T., Norton, T., and Maerz, J.C., 2018, Integrated analysis for population estimation, management impact evaluation, and decision-making for a declining species: Biological Conservation, v. 222, p. 33-43, https://doi.org/10.1016/j.biocon.2018.03.023.","productDescription":"11 p.","startPage":"33","endPage":"43","ipdsId":"IP-083097","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354058,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"222","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6c1e4b0da30c1bfbdc0","contributors":{"authors":[{"text":"Crawford, Brian A.","contributorId":204802,"corporation":false,"usgs":false,"family":"Crawford","given":"Brian","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":735035,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Clinton T. 0000-0002-6053-2880 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-6053-2880","contributorId":3643,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","middleInitial":"T.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734996,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Norton, Terry M.","contributorId":71020,"corporation":false,"usgs":true,"family":"Norton","given":"Terry M.","affiliations":[],"preferred":false,"id":735036,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maerz, John C.","contributorId":171763,"corporation":false,"usgs":false,"family":"Maerz","given":"John","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":735037,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196921,"text":"70196921 - 2018 - Use of non-invasive genetics to generate core-area population estimates of a threatened predator in the Superior National Forest, USA","interactions":[],"lastModifiedDate":"2018-05-10T13:45:44","indexId":"70196921","displayToPublicDate":"2018-05-10T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5015,"text":"Canadian Wildlife Biology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Use of non-invasive genetics to generate core-area population estimates of a threatened predator in the Superior National Forest, USA","docAbstract":"Canada lynx (Lynx canadensis) are found in boreal forests of Canada and Alaska and range southward into the contiguous United States. Much less is understood about lynx in their southern range compared to northern populations. Because lynx are currently listed as threatened under the US Endangered Species Act but have recently been recommended for delisting, information on their southern populations is important for lynx recovery, conservation, and management. We used non-invasive, genetic data collected during lynx snowtracking surveys from 2012-2017 to generate core-area estimates of abundance, trend and density in selected core areas of the Superior National Forest of Minnesota, USA. Lynx abundance estimates averaged 41.8 (SD=14.7, range=24-67) during 2012-2017 in the smaller\ncore areas and averaged 52.3 (SD=8.3, range=43-59) during 2015-2017 in the larger core areas. We found no evidence for a decrease or increase in abundance during either period. Lynx density estimates were approximately 7-10 times lower than densities of lynx in northern populations at the low of the snowshoe hare (Lepus americanus) population cycle. To our knowledge, our results are the first attempt to estimate abundance, trend and density of lynx in Minnesota using non-invasive genetic capture-mark-recapture. Estimates such as ours provide useful benchmarks for future comparisons by providing a context with which to assess 1) potential changes in forest management that may affect lynx recovery and conservation, and 2) possible effects of climate change on the depth, density, and duration of annual snow cover and correspondingly, potential effects on snowshoe hares as well.","language":"English","publisher":"Alpha Wildlife Publications","usgsCitation":"Barber-Meyer, S., Ryan, D., Grosshuesch, D., Catton, T., and Malick-Wahls, S., 2018, Use of non-invasive genetics to generate core-area population estimates of a threatened predator in the Superior National Forest, USA: Canadian Wildlife Biology and Management, v. 7, no. 1, p. 46-55.","productDescription":"10 p.","startPage":"46","endPage":"55","ipdsId":"IP-092810","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research 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Daniel","contributorId":204781,"corporation":false,"usgs":false,"family":"Ryan","given":"Daniel","email":"","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":734998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grosshuesch, David","contributorId":204782,"corporation":false,"usgs":false,"family":"Grosshuesch","given":"David","email":"","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":734999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Catton, Timothy","contributorId":204783,"corporation":false,"usgs":false,"family":"Catton","given":"Timothy","email":"","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":735000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Malick-Wahls, Sarah","contributorId":204784,"corporation":false,"usgs":false,"family":"Malick-Wahls","given":"Sarah","email":"","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":735001,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196155,"text":"sir20185046 - 2018 - Methods for peak-flow frequency analysis and reporting for streamgages in or near Montana based on data through water year 2015","interactions":[],"lastModifiedDate":"2018-09-25T05:33:19","indexId":"sir20185046","displayToPublicDate":"2018-05-10T00:00:00","publicationYear":"2018","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":"2018-5046","title":"Methods for peak-flow frequency analysis and reporting for streamgages in or near Montana based on data through water year 2015","docAbstract":"This report documents the methods for peak-flow frequency (hereinafter “frequency”) analysis and reporting for streamgages in and near Montana following implementation of the Bulletin 17C guidelines. The methods are used to provide estimates of peak-flow quantiles for 50-, 42.9-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for selected streamgages operated by the U.S. Geological Survey Wyoming-Montana Water Science Center (WY–MT WSC). These annual exceedance probabilities correspond to 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively.
Standard procedures specific to the WY–MT WSC for implementing the Bulletin 17C guidelines include (1) the use of the Expected Moments Algorithm analysis for fitting the log-Pearson Type III distribution, incorporating historical information where applicable; (2) the use of weighted skew coefficients (based on weighting at-site station skew coefficients with generalized skew coefficients from the Bulletin 17B national skew map); and (3) the use of the Multiple Grubbs-Beck Test for identifying potentially influential low flows. For some streamgages, the peak-flow records are not well represented by the standard procedures and require user-specified adjustments informed by hydrologic judgement. The specific characteristics of peak-flow records addressed by the informed-user adjustments include (1) regulated peak-flow records, (2) atypical upper-tail peak-flow records, and (3) atypical lower-tail peak-flow records. In all cases, the informed-user adjustments use the Expected Moments Algorithm fit of the log-Pearson Type III distribution using the at-site station skew coefficient, a manual potentially influential low flow threshold, or both.
Appropriate methods can be applied to at-site frequency estimates to provide improved representation of long-term hydroclimatic conditions. The methods for improving at-site frequency estimates by weighting with regional regression equations and by Maintenance of Variance Extension Type III record extension are described.
Frequency analyses were conducted for 99 example streamgages to indicate various aspects of the frequency-
analysis methods described in this report. The frequency analyses and results for the example streamgages are presented in a separate data release associated with this report consisting of tables and graphical plots that are structured to include information concerning the interpretive decisions involved in the frequency analyses. Further, the separate data release includes the input files to the PeakFQ program, version 7.1, including the peak-flow data file and the analysis specification file that were used in the peak-flow frequency analyses. Peak-flow frequencies are also reported in separate data releases for selected streamgages in the Beaverhead River and Clark Fork Basins and also for selected streamgages in the Ruby, Jefferson, and Madison River Basins.
Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Crayfish use of intermittent streams is especially important to understand in the face of global climate change. We examined the influence of stream permanence and local habitat on crayfish occupancy and species densities in the Ozark Highlands, USA. We sampled in June and July 2014 and 2015. We used a quantitative kick–seine method to sample crayfish presence and abundance at 20 stream sites with 32 surveys/site in the Upper White River drainage, and we measured associated local environmental variables each year. We modeled site occupancy and detection probabilities with the software PRESENCE, and we used multiple linear regressions to identify relationships between crayfish species densities and environmental variables. Occupancy of all crayfish species was related to stream permanence. Faxonius meeki was found exclusively in intermittent streams, whereas Faxonius neglectus and Faxonius luteushad higher occupancy and detection probability in permanent than in intermittent streams, and Faxonius williamsi was associated with intermittent streams. Estimates of detection probability ranged from 0.56 to 1, which is high relative to values found by other investigators. With the exception of F. williamsi, species densities were largely related to stream permanence rather than local habitat. Species densities did not differ by year, but total crayfish densities were significantly lower in 2015 than 2014. Increased precipitation and discharge in 2015 probably led to the lower crayfish densities observed during this year. Our study demonstrates that crayfish distribution and abundance is strongly influenced by stream permanence. Some species, including those of conservation concern (i.e., F. williamsi, F. meeki), appear dependent on intermittent streams, and conservation efforts should include consideration of intermittent streams as an important component of freshwater biodiversity.
","language":"English","publisher":"The University of Chicago Press","doi":"10.1086/696020","usgsCitation":"Yarra, A.N., and Magoulick, D.D., 2018, Stream permanence is related to crayfish occupancy and abundance in the Ozark Highlands, USA: Freshwater Science, v. 37, no. 1, p. 54-63, https://doi.org/10.1086/696020.","productDescription":"10 p.","startPage":"54","endPage":"63","ipdsId":"IP-082212","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354059,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri","otherGeospatial":"Upper White River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.37255859375,\n 35.90684930677121\n ],\n [\n -89.9560546875,\n 35.90684930677121\n ],\n [\n -89.9560546875,\n 38.37611542403604\n ],\n [\n -94.37255859375,\n 38.37611542403604\n ],\n [\n -94.37255859375,\n 35.90684930677121\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6c1e4b0da30c1bfbdc2","contributors":{"authors":[{"text":"Yarra, Allyson N.","contributorId":204803,"corporation":false,"usgs":false,"family":"Yarra","given":"Allyson","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":735038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734995,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196913,"text":"70196913 - 2018 - Capture efficiency and injury rates of band-tailed pigeons using whoosh nets","interactions":[],"lastModifiedDate":"2018-05-14T13:10:33","indexId":"70196913","displayToPublicDate":"2018-05-10T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3784,"text":"Wilson Journal of Ornithology","active":true,"publicationSubtype":{"id":10}},"title":"Capture efficiency and injury rates of band-tailed pigeons using whoosh nets","docAbstract":"Catching ground feeding birds has typically been accomplished through small, walk-in funnel-style traps. This approach is limited because it requires a bird to find its way into the trap, is biased toward less wary birds, and does not allow targeted trapping of individual birds. As part of a large study on Band-tailed Pigeons (Patagioenas fasciata) in New Mexico, we needed a trapping method that would allow more control over the number of birds we could trap at one time, when a trap was deployed, and target trapping of specific individuals. We adopted a relatively novel trapping technique used primarily for shorebirds, whoosh nets, to trap Band-tailed Pigeons at 3 different sites where birds were being fed by local landowners. During 2013–2015, whoosh nets were used to trap 702 Band-tailed Pigeons at 3 different locations in New Mexico. We captured 12.54 ± 8.19 pigeons per shot over 56 capture events across 3 locations (range: 2–39). Some superficial injuries occurred using this technique and typically involved damage to the primary and secondary wing coverts. In 2013, 24% of captured birds had an injury of this nature, but after modifying the net speed, injury rates in 2014 and 2015 dropped to 8% and 7%, respectively. Recaptured previously injured birds showed new feather growth within 2 weeks and showed no signs of injury after 4 weeks. Whoosh nets proved to be a highly effective solution for trapping large numbers of pigeons at baited sites. These systems are easily transported, quickly deployed, and easily adapted to a variety of site conditions.
","language":"English","publisher":"The Wilson Ornithological Society","doi":"10.1676/16-069.1","usgsCitation":"Coxen, C.L., Collins, D.P., and Carleton, S.A., 2018, Capture efficiency and injury rates of band-tailed pigeons using whoosh nets: Wilson Journal of Ornithology, v. 130, no. 1, p. 321-326, https://doi.org/10.1676/16-069.1.","productDescription":"6 p.","startPage":"321","endPage":"326","ipdsId":"IP-076977","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":354062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","city":"Los Alamos, Silver City, Weed","volume":"130","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6c1e4b0da30c1bfbdc4","contributors":{"authors":[{"text":"Coxen, Christopher L.","contributorId":198545,"corporation":false,"usgs":false,"family":"Coxen","given":"Christopher","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":735043,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collins, Daniel P.","contributorId":198065,"corporation":false,"usgs":false,"family":"Collins","given":"Daniel","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":735044,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carleton, Scott A. 0000-0001-9609-650X scarleton@usgs.gov","orcid":"https://orcid.org/0000-0001-9609-650X","contributorId":4060,"corporation":false,"usgs":true,"family":"Carleton","given":"Scott","email":"scarleton@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":734983,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196685,"text":"70196685 - 2018 - Seismicity in the Challis, Idaho region, January 2014 - May 2017: Late aftershocks of the 1983 Ms 7.3 Borah Peak earthquake","interactions":[],"lastModifiedDate":"2018-11-02T14:54:58","indexId":"70196685","displayToPublicDate":"2018-05-09T14:54:53","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Seismicity in the Challis, Idaho region, January 2014 - May 2017: Late aftershocks of the 1983 Ms 7.3 Borah Peak earthquake","docAbstract":"In April 2014, after about 20 yrs of relatively low seismicity, an energetic earthquake sequence (maximum
Wildlife–vehicle collisions are a human safety issue and may negatively impact wildlife populations. Most wildlife–vehicle collision studies predict high-risk road segments using only collision data. However, these data lack biologically relevant information such as wildlife population densities and successful road-crossing locations. We overcome this shortcoming with a new method that combines successful road crossings with vehicle collision data, to identify road segments that have both high biological relevance and high risk. We used moose (Alces americanus) road-crossing locations from 20 moose collared with Global Positioning Systems as well as moose–vehicle collision (MVC) data in the state of Massachusetts, USA, to create multi-scale resource selection functions. We predicted the probability of moose road crossings and MVCs across the road network and combined these surfaces to identify road segments that met the dual criteria of having high biological relevance and high risk for MVCs. These road segments occurred mostly on larger roadways in natural areas and were surrounded by forests, wetlands, and a heterogenous mix of land cover types. We found MVCs resulted in the mortality of 3% of the moose population in Massachusetts annually. Although there have been only three human fatalities related to MVCs in Massachusetts since 2003, the human fatality rate was one of the highest reported in the literature. The rate of MVCs relative to the size of the moose population and the risk to human safety suggest a need for road mitigation measures, such as fencing, animal detection systems, and large mammal-crossing structures on roadways in Massachusetts.
","language":"English","publisher":"Springer Link","doi":"10.1007/s00267-018-1058-x","usgsCitation":"Zeller, K., Wattles, D., and Destefano, S., 2018, Incorporating road crossing data into vehicle collision risk models for moose (Alces americanus) in Massachusetts, USA: Environmental Management, v. 62, p. 518-528, https://doi.org/10.1007/s00267-018-1058-x.","productDescription":"11 p.","startPage":"518","endPage":"528","ipdsId":"IP-068512","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":394877,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"62","noUsgsAuthors":false,"publicationDate":"2018-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeller, Katherine 0000-0002-2913-6660","orcid":"https://orcid.org/0000-0002-2913-6660","contributorId":255403,"corporation":false,"usgs":false,"family":"Zeller","given":"Katherine","email":"","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":831702,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wattles, David","contributorId":255402,"corporation":false,"usgs":false,"family":"Wattles","given":"David","affiliations":[{"id":51525,"text":"Massachusetts Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":831703,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Destefano, Stephen 0000-0003-2472-8373","orcid":"https://orcid.org/0000-0003-2472-8373","contributorId":272197,"corporation":false,"usgs":true,"family":"Destefano","given":"Stephen","email":"","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":831701,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70271969,"text":"70271969 - 2018 - Use of imaging spectroscopy and LIDAR to characterize fuels for fire behavior prediction","interactions":[],"lastModifiedDate":"2025-09-29T14:56:13.497511","indexId":"70271969","displayToPublicDate":"2018-05-09T09:50:05","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5098,"text":"Remote Sensing Applications: Society and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Use of imaging spectroscopy and LIDAR to characterize fuels for fire behavior prediction","docAbstract":"To protect ecosystem services and the increasing wildland urban interface in a world with fire, comprehensive maps of wildland fuels are needed to predict fire behavior and effects. Traditionally, fuels have been categorized into a classification scheme whereby a single metric represents vegetation composition and structure, which can then be parameterized based on variable vegetation amount and condition. Remote sensing has been used to extrapolate between known field plots across the landscape, however until recently, those technologies have had limited ability to characterize fuels (e.g., composition, horizontal and vertical connectivity). Using new technologies (imaging spectroscopy and LIDAR), the objectives of this study are to assess: 1) how fuel characteristics observed from remote sensing affect categorical fuel classifications, and 2) how fuel characteristics affect landscape-scale fire behavior (spread rate, areal extent and perimeter). The analysis was conducted over the 2014 California King Fire that burned ~40,000 ha over lands with varying use and history and has unique remote sensing observations from before and after the fire. This analysis compares fuel classifications from a synergistic field, model, and Landsat approach (LANDFIRE) and products derived from the Airborne Visible/Infrared Imaging Spectrometer and LIDAR (MapFUELS). Each classification focuses on different fuel characteristics, which were then used to compare differences in a fire simulation model (CAWFE) and actual fire behavior. The results show that fuel characteristic inputs such as horizontal connectivity or fuel type and vertical structure affect fire spread rate and final fire extent (respectively). These results present the opportunity for future integration of fuel characteristics observed at coarser resolutions (900 m2) into predictions of fire behavior a similar spatial resolutions (as opposed to the current standard based on empirical relationships between fuel and fire behavior at ~12 m2 resolution).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2018.04.010","usgsCitation":"Stavros, E.N., Coen, J., Peterson, B., Singh, H., Kennedy, K., Ramirez, C., and Schimel, D., 2018, Use of imaging spectroscopy and LIDAR to characterize fuels for fire behavior prediction: Remote Sensing Applications: Society and Environment, v. 11, p. 41-50, https://doi.org/10.1016/j.rsase.2018.04.010.","productDescription":"10 p.","startPage":"41","endPage":"50","ipdsId":"IP-097303","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":496224,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stavros, E. Natasha","contributorId":361822,"corporation":false,"usgs":false,"family":"Stavros","given":"E.","middleInitial":"Natasha","affiliations":[{"id":27365,"text":"NASA Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":949522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coen, Janice","contributorId":361823,"corporation":false,"usgs":false,"family":"Coen","given":"Janice","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":949523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Birgit 0000-0002-4356-1540 bpeterson@usgs.gov","orcid":"https://orcid.org/0000-0002-4356-1540","contributorId":192353,"corporation":false,"usgs":true,"family":"Peterson","given":"Birgit","email":"bpeterson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":949524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Singh, Harshvardhan","contributorId":361826,"corporation":false,"usgs":false,"family":"Singh","given":"Harshvardhan","affiliations":[{"id":86363,"text":"Indian Institute of Space Science and Technology","active":true,"usgs":false}],"preferred":false,"id":949525,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kennedy, Kama","contributorId":361827,"corporation":false,"usgs":false,"family":"Kennedy","given":"Kama","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":949526,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ramirez, Carlos","contributorId":177061,"corporation":false,"usgs":false,"family":"Ramirez","given":"Carlos","email":"","affiliations":[],"preferred":false,"id":949527,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schimel, David","contributorId":146637,"corporation":false,"usgs":false,"family":"Schimel","given":"David","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":949528,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70198061,"text":"70198061 - 2018 - Ecological genomics predicts climate vulnerability in an endangered southwestern songbird","interactions":[],"lastModifiedDate":"2018-07-16T11:21:43","indexId":"70198061","displayToPublicDate":"2018-05-09T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Ecological genomics predicts climate vulnerability in an endangered southwestern songbird","docAbstract":"Few regions have been more severely impacted by climate change in the USA than the Desert Southwest. Here, we use ecological genomics to assess the potential for adaptation to rising global temperatures in a widespread songbird, the willow flycatcher (Empidonax traillii), and find the endangered desert southwestern subspecies (E. t. extimus) most vulnerable to future climate change. Highly significant correlations between present abundance and estimates of genomic vulnerability – the mismatch between current and predicted future genotype–environment relationships – indicate small, fragmented populations of the southwestern willow flycatcher will have to adapt most to keep pace with climate change. Links between climate‐associated genotypes and genes important to thermal tolerance in birds provide a potential mechanism for adaptation to temperature extremes. Our results demonstrate that the incorporation of genotype–environment relationships into landscape‐scale models of climate vulnerability can facilitate more precise predictions of climate impacts and help guide conservation in threatened and endangered groups.
","language":"English","publisher":"Wiley","doi":"10.1111/ele.12977","usgsCitation":"Ruegg, K., Bay, R.A., Anderson, E.C., Saracco, J.F., Harrigan, R.J., Whitfield, M.J., Paxton, E., and Smith, T.B., 2018, Ecological genomics predicts climate vulnerability in an endangered southwestern songbird: Ecology Letters, v. 21, p. 1085-1096, https://doi.org/10.1111/ele.12977.","productDescription":"12 p.","startPage":"1085","endPage":"1096","ipdsId":"IP-095047","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":355634,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-09","publicationStatus":"PW","scienceBaseUri":"5b46e58de4b060350a15d1cc","contributors":{"authors":[{"text":"Ruegg, Kristin","contributorId":206224,"corporation":false,"usgs":false,"family":"Ruegg","given":"Kristin","email":"","affiliations":[{"id":33607,"text":"University of California Los Angeles","active":true,"usgs":false}],"preferred":false,"id":739831,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bay, Rachael A.","contributorId":206219,"corporation":false,"usgs":false,"family":"Bay","given":"Rachael","email":"","middleInitial":"A.","affiliations":[{"id":33607,"text":"University of California Los Angeles","active":true,"usgs":false}],"preferred":false,"id":739824,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Eric C.","contributorId":206220,"corporation":false,"usgs":false,"family":"Anderson","given":"Eric","email":"","middleInitial":"C.","affiliations":[{"id":37289,"text":"Southwest Fisheries Science Center, National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":739825,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saracco, James F.","contributorId":206221,"corporation":false,"usgs":false,"family":"Saracco","given":"James","email":"","middleInitial":"F.","affiliations":[{"id":37290,"text":"The Institute for Bird Populations","active":true,"usgs":false}],"preferred":false,"id":739826,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harrigan, Ryan J.","contributorId":206222,"corporation":false,"usgs":false,"family":"Harrigan","given":"Ryan","email":"","middleInitial":"J.","affiliations":[{"id":33607,"text":"University of California Los Angeles","active":true,"usgs":false}],"preferred":false,"id":739827,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whitfield, Mary J.","contributorId":174933,"corporation":false,"usgs":false,"family":"Whitfield","given":"Mary","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":739828,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Paxton, Eben H. 0000-0001-5578-7689 epaxton@usgs.gov","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":438,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben H.","email":"epaxton@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":false,"id":739829,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Smith, Thomas B.","contributorId":206223,"corporation":false,"usgs":false,"family":"Smith","given":"Thomas","email":"","middleInitial":"B.","affiliations":[{"id":33607,"text":"University of California Los Angeles","active":true,"usgs":false}],"preferred":false,"id":739830,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}