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
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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action alternative compared to level 1 scenarios, 1922-2003"},{"id":354080,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix02.pdf","text":"Appendix 2","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 2","linkHelpText":"Simulated daily travel time by year, no action alternative compared to proposed action scenarios, 1922-2003"},{"id":354081,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix03.pdf","text":"Appendix 3","size":"2.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 3","linkHelpText":"Simulated daily routing by year, no action alternative compared to proposed action scenarios, 1922-2003"},{"id":354082,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix04.pdf","text":"Appendix 4","size":"2.4 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1922-2003"},{"id":354088,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix10.pdf","text":"Appendix 10","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 10","linkHelpText":"Simulated route-specific travel time by year, no action alternative compared to level 1 scenarios, 1922-2003"},{"id":354089,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix11.pdf","text":"Appendix 11","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 11","linkHelpText":"North Delta Diversion rule curve optimization"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.33,\n 37.82\n ],\n [\n -121.33,\n 38.5\n ],\n [\n -121.9167,\n 38.5\n ],\n [\n -121.9167,\n 37.82\n ],\n [\n -121.33,\n 37.82\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
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.
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
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
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":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":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":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":70196919,"text":"70196919 - 2018 - Stream permanence is related to crayfish occupancy and abundance in the Ozark Highlands, USA","interactions":[],"lastModifiedDate":"2018-09-12T08:25:57","indexId":"70196919","displayToPublicDate":"2018-05-10T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Stream permanence is related to crayfish occupancy and abundance in the Ozark Highlands, USA","docAbstract":"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":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":734995,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"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":70196909,"text":"fs20183024 - 2018 - Lahar—River of volcanic mud and debris","interactions":[],"lastModifiedDate":"2018-05-14T10:23:05","indexId":"fs20183024","displayToPublicDate":"2018-05-09T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-3024","title":"Lahar—River of volcanic mud and debris","docAbstract":"Lahar, an Indonesian word for volcanic mudflow, is a mixture of water, mud, and volcanic rock flowing swiftly along a channel draining a volcano. Lahars can form during or after eruptions, or even during periods of inactivity. They are among the greatest threats volcanoes pose to people and property. Lahars can occur with little to no warning, and may travel great distances at high speeds, destroying or burying everything in their paths.
Lahars form in many ways. They commonly occur when eruptions melt snow and ice on snow-clad volcanoes; when rains fall on steep slopes covered with fresh volcanic ash; when crater lakes, volcano glaciers or lakes dammed by volcanic debris suddenly release water; and when volcanic landslides evolve into flowing debris. Lahars are especially likely to occur at erupting or recently active volcanoes.
Because lahars are so hazardous, U.S. Geological Survey scientists pay them close attention. They study lahar deposits and limits of inundation, model flow behavior, develop lahar-hazard maps, and work with community leaders and governmental authorities to help them understand and minimize the risks of devastating lahars.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183024","usgsCitation":"Major, J.J., Pierson, T.C., and Vallance, J.W., 2018, Lahar—River of volcanic mud and debris: U.S. Geological Survey Fact Sheet 2018–3024, 6 p., https://doi.org/10.3133/fs20183024.","productDescription":"Report: 6 p.; Video","numberOfPages":"6","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-090719","costCenters":[{"id":157,"text":"Cascades Volcano Observatory","active":false,"usgs":true}],"links":[{"id":354035,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3024/fs20183024.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2018-3024"},{"id":354036,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/fs/2018/3024/fs20183024_laharvideo.mp4","text":"Video","size":"18 MB","description":"Fact Sheet 2018-3024 Video"},{"id":354034,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3024/coverthb_.jpg"}],"contact":"Volcano Science Center
Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA, 98683
Lahars large enough to reach populated areas are a hazard at Mount Adams, a massive volcano in the southern Cascade Range of Washington State (fig. 1). It is considered to be still active and has the potential to erupt again. By definition, lahars are gravity-driven flows of water-saturated mixtures of mud and rock (plus or minus ice, wood, and other debris), which originate from volcanoes and have a variety of potential triggering mechanisms (Vallance, 2000; Vallance and Iverson, 2015). Flowing mixtures can range in fluid consistency from something like a milkshake to something more like wet concrete, and they behave like flash floods, in that they can appear suddenly in river channels with little warning and commonly have boulder- or log-choked flow fronts. Lahars are hazardous because they can flow rapidly in confined valleys (commonly 20–35 miles per hour [mph] or 9–16 meters per second [m/s]), can travel more than 100 miles (mi) (161 kilometers [km]) from a source volcano, and can move with incredible destructive force, carrying multi-ton boulders and logs that can act as battering rams (Pierson, 1998). The biggest threats from lahars to downstream communities are present during eruptive activity, and impacts to communities can be dire. For example, a very large eruption-triggered lahar in Colombia in 1985 surprised and killed more than 20,000 people in a large town located about 45 mi (72 km) downstream and out of sight of the volcano that produced it (Pierson and others, 1990).
Mount Adams, one of the largest volcanoes in the Cascade Range, is a composite stratocone composed primarily of andesite lava flows. It has been the most continuously active volcano within the 480-mi2 Mount Adams volcanic field—a region covering parts of Klickitat, Skamania, Yakima, andLewis Counties and part of the Yakama Nation Reservation in Washington State (Hildreth and Fierstein,1995, 1997). About 500,000 years in age, Mount Adams reached its present size by about 15,000 years ago, primarily through the episodic effusion of lava flows; it has not had a history of major explosive eruptions like Mount St. Helens, its neighbor to the west. Timing of the most recent eruptive activity (recorded by four thin tephra layers) is on the order of 1,000 years ago; the tephras are bracketed by 2,500-year-old and 500-year-old ash layers from Mount St. Helens (Hildreth and Fierstein, 1995, 1997). Mount Adams currently shows no signs of renewed unrest.
Eruptive history does not tell us everything we need to know about hazards at Mount Adams, however, which are fully addressed in the volcano hazard assessment for Mount Adams (W.E. Scott and others, 1995). This volcano has had a long-active hydrothermal system that circulated acidic hydrothermal fluids, formed by the solution of volcanic gases in heated groundwater, through fractures and permeable zones into upper parts of the volcanic cone. Acid sulfate leaching of rocks in the summit area may still be occurring, but chemical and thermal evidence suggests that the main hydrothermal system is no longer active at Mount Adams (Nathenson and Mariner, 2013). However, these rock-weakening chemical reactions have operated long enough to change about 0.4 cubic miles (mi3) (1.7 cubic kilometers [km3]) of the hard lava rock in the volcano’s upper cone to a much weaker clay-rich rock, thus significantly reducing rock strength and thereby slope stability in parts of the cone (Finn and others, 2007). The two largest previous lahars from Mount Adams were triggered by landslides of hydrothermally altered rock from the upper southwestern flank of the cone, and any future large lahars are likely to be triggered by the same mechanism. Mount Rainier also has had extensive hydrothermal alteration of rock in its upper edifice, and it also has a history of large landslides that transform into lahars (K.M. Scott and others, 1995; Vallance and Scott, 1997; Reid and others, 2001).
The spatial depiction of modeled lahar inundation zones accompanying this report, shown in two different map perspectives, is intended to augment (not replace) the existing hazard maps for Mount Adams (W.E. Scott and others, 1995; Vallance, 1999). The maps in this report show potential areas of inundation by lahars of different initial volumes, which are determined by a computer model, LAHARZ (Iverson and others, 1998; Schilling, 1998). One map sheet presents LAHARZ-determined inundation areas on a normal plan-view shaded-relief map of the study area; the other gives an oblique perspective of the landscape with raised topography, as if one were viewing the landscape at an angle from an aircraft (Jenny and Patterson, 2007). LAHARZ was developed after the original hazard maps (based only on mapping of geologic deposits) were made. Predicted inundation zones on these maps provide an alternative approach to estimation of areas that could be inundated as lahars of different volumes pass through the valley. However, there is considerable uncertainty in the exact location of the hazard-zone boundaries shown on these maps, as well as on earlier maps.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181013","usgsCitation":"Griswold, J.P., Pierson, T.C., and Bard J.A., 2018, Modeled inundation limits of potential lahars from Mount Adams in the White Salmon River valley, Washington: U.S. Geological Survey Open-File Report 2018–1013, scale 1:75,000, 14 p., https://doi.org/10.3133/ofr20181013.","productDescription":"Sheet: 42.0 x 42.0 inches; Pamphlet: iii, 14 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078093","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":353953,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1013/ofr20181013_pamphlet.pdf","text":"Pamphlet","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1013 Pamphlet"},{"id":353952,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2018/1013/ofr20181013_sheet_.pdf","size":"41 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1013"},{"id":353951,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1013/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Adams, While Salmon River Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.8333,\n 45.682198608003404\n ],\n [\n -121.25,\n 45.682198608003404\n ],\n [\n -121.25,\n 46.25\n ],\n [\n -121.8333,\n 46.25\n ],\n [\n -121.8333,\n 45.682198608003404\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Science Center
Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA, 98683
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":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"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}]}} ,{"id":70196872,"text":"70196872 - 2018 - Leaf to landscape responses of giant sequoia to hotter drought: An introduction and synthesis for the special section","interactions":[],"lastModifiedDate":"2018-05-08T10:10:22","indexId":"70196872","displayToPublicDate":"2018-05-08T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Leaf to landscape responses of giant sequoia to hotter drought: An introduction and synthesis for the special section","docAbstract":"Hotter droughts are becoming more common as climate change progresses, and they may already have caused instances of forest dieback on all forested continents. Learning from hotter droughts, including where on the landscape forests are more or less vulnerable to these events, is critical to help resource managers proactively prepare for the future. As part of our Leaf to Landscape Project, we measured the response of giant sequoia, the world’s largest tree species, to the extreme 2012–2016 hotter drought in California. The project integrated leaf-level physiology measurements, crown-level foliage dieback surveys, and remotely sensed canopy water content (CWC) to shed light on mechanisms and spatial patterns in drought response. Here we summarize initial findings, present a conceptual model of drought response, and discuss management implications; details are presented in the other four articles of the special section on Giant Sequoias and Drought. Giant sequoias exhibited both leaf- and canopy-level responses that were effective in protecting whole-tree hydraulic integrity for the vast majority of individual sequoias. Very few giant sequoias died during the drought compared to other mixed conifer tree species; however, the magnitude of sequoia drought response varied across the landscape. This variability was partially explained by local site characteristics, including variables related to site water balance. We found that low CWC is an indicator of recent foliage dieback, which occurs when stress levels are high enough that leaf-level adjustments alone are insufficient for giant sequoias to maintain hydraulic integrity. CWC or change in CWC may be useful indicators of drought stress that reveal patterns of vulnerability to future hotter droughts. Future work will measure recovery from the drought and strengthen our ability to interpret CWC maps. Our ultimate goal is to produce giant sequoia vulnerability maps to help target management actions, such as reducing other stressors, increasing resistance to hotter drought through prescribed fire or mechanical thinning, and planting sequoias in projected future suitable habitat, which may occur outside current grove distributions. We suggest that managers compare different types of vulnerability assessments and combine vulnerability maps with other sources of information to inform decisions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2018.03.028","usgsCitation":"Nydick, K.R., Stephenson, N.L., Ambrose, A.R., Asner, G.P., Baxter, W.L., Das, A., Dawson, T.E., Martin, R.E., and Paz-Kagan, T., 2018, Leaf to landscape responses of giant sequoia to hotter drought: An introduction and synthesis for the special section: Forest Ecology and Management, v. 419-420, p. 249-256, https://doi.org/10.1016/j.foreco.2018.03.028.","productDescription":"8 p.","startPage":"249","endPage":"256","ipdsId":"IP-091082","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468772,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2018.03.028","text":"Publisher Index Page"},{"id":353982,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","volume":"419-420","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6c2e4b0da30c1bfbdd0","contributors":{"authors":[{"text":"Nydick, Koren R.","contributorId":196601,"corporation":false,"usgs":false,"family":"Nydick","given":"Koren","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":734830,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stephenson, Nathan L. 0000-0003-0208-7229 nstephenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0208-7229","contributorId":2836,"corporation":false,"usgs":true,"family":"Stephenson","given":"Nathan","email":"nstephenson@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":734829,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ambrose, Anthony R.","contributorId":204732,"corporation":false,"usgs":false,"family":"Ambrose","given":"Anthony","email":"","middleInitial":"R.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":734831,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Asner, Gregory P.","contributorId":25393,"corporation":false,"usgs":false,"family":"Asner","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":734832,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baxter, Wendy L.","contributorId":204733,"corporation":false,"usgs":false,"family":"Baxter","given":"Wendy","email":"","middleInitial":"L.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":734833,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Das, Adrian J. 0000-0002-3937-2616 adas@usgs.gov","orcid":"https://orcid.org/0000-0002-3937-2616","contributorId":3842,"corporation":false,"usgs":true,"family":"Das","given":"Adrian J.","email":"adas@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":734834,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dawson, Todd E.","contributorId":176594,"corporation":false,"usgs":false,"family":"Dawson","given":"Todd","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":734835,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Martin, Roberta E.","contributorId":201234,"corporation":false,"usgs":false,"family":"Martin","given":"Roberta","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":734836,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Paz-Kagan, Tarin","contributorId":196597,"corporation":false,"usgs":false,"family":"Paz-Kagan","given":"Tarin","email":"","affiliations":[],"preferred":false,"id":734837,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70206538,"text":"70206538 - 2018 - A snow density dataset for improving surface boundary conditions in Greenland ice sheet firn modeling","interactions":[],"lastModifiedDate":"2020-06-19T16:12:17.863811","indexId":"70206538","displayToPublicDate":"2018-05-07T10:04:58","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"A snow density dataset for improving surface boundary conditions in Greenland ice sheet firn modeling","docAbstract":"The surface snow density of glaciers and ice sheets is of fundamental importance in converting volume to mass in both altimetry and surface mass balance studies, yet it is often poorly constrained. Site-specific surface snow densities are typically derived from empirical relations based on temperature and wind speed. These parameterizations commonly calculate the average density of the top meter of snow, thereby systematically overestimating snow density at the actual surface. Therefore, constraining surface snow density to the top 0.1 m can improve boundary conditions in high-resolution firn-evolution modeling. We have compiled an extensive dataset of 200 point measurements of surface snow density from firn cores and snow pits on the Greenland ice sheet. We find that surface snow density within 0.1 m of the surface has an average value of 315 kg m−3 with a standard deviation of 44 kg m−3, and has an insignificant annual air temperature dependency. We demonstrate that two widely-used surface snow density parameterizations dependent on temperature systematically overestimate surface snow density over the Greenland ice sheet by 17–19%, and that using a constant density of 315 kg m−3 may give superior results when applied in surface mass budget modeling.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2018.00051","usgsCitation":"Fausto, R., Box, J.E., Baptiste Vandecrux, van As, D., Steffen, K., MacFerrin, M.J., Machguth, H., Colgan, W., Mcgrath, D., Koenig, L.S., Charalampidis, C., and Braithwaite, R.J., 2018, A snow density dataset for improving surface boundary conditions in Greenland ice sheet firn modeling: Frontiers in Earth Science, v. 6, 51, 10 p., https://doi.org/10.3389/feart.2018.00051.","productDescription":"51, 10 p.","ipdsId":"IP-082355","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":468774,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2018.00051","text":"Publisher Index 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PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Fausto, Robert","contributorId":220400,"corporation":false,"usgs":false,"family":"Fausto","given":"Robert","email":"","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":774905,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Box, Jason E.","contributorId":198809,"corporation":false,"usgs":false,"family":"Box","given":"Jason","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":774906,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baptiste Vandecrux","contributorId":220401,"corporation":false,"usgs":false,"family":"Baptiste Vandecrux","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":774907,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van As, Dirk","contributorId":220402,"corporation":false,"usgs":false,"family":"van As","given":"Dirk","email":"","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":774908,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Steffen, Konrad","contributorId":220461,"corporation":false,"usgs":false,"family":"Steffen","given":"Konrad","email":"","affiliations":[],"preferred":false,"id":774970,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"MacFerrin, Michael J.","contributorId":220462,"corporation":false,"usgs":false,"family":"MacFerrin","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":774971,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Machguth, Horst","contributorId":220463,"corporation":false,"usgs":false,"family":"Machguth","given":"Horst","email":"","affiliations":[],"preferred":false,"id":774972,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Colgan, William","contributorId":220464,"corporation":false,"usgs":false,"family":"Colgan","given":"William","affiliations":[],"preferred":false,"id":774973,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mcgrath, Daniel 0000-0002-9462-6842 dmcgrath@usgs.gov","orcid":"https://orcid.org/0000-0002-9462-6842","contributorId":145635,"corporation":false,"usgs":true,"family":"Mcgrath","given":"Daniel","email":"dmcgrath@usgs.gov","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":774904,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Koenig, Lora S.","contributorId":220465,"corporation":false,"usgs":false,"family":"Koenig","given":"Lora","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":774974,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Charalampidis, Charalampos","contributorId":220466,"corporation":false,"usgs":false,"family":"Charalampidis","given":"Charalampos","email":"","affiliations":[],"preferred":false,"id":774975,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Braithwaite, Roger J.","contributorId":220467,"corporation":false,"usgs":false,"family":"Braithwaite","given":"Roger","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":774976,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70195939,"text":"cir1439 - 2018 - Integrating adaptive management and ecosystem services concepts to improve natural resource management: Challenges and opportunities","interactions":[],"lastModifiedDate":"2018-05-07T13:34:22","indexId":"cir1439","displayToPublicDate":"2018-05-07T10:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1439","title":"Integrating adaptive management and ecosystem services concepts to improve natural resource management: Challenges and opportunities","docAbstract":"Natural resource managers must make decisions that affect broad-scale ecosystem processes involving large spatial areas, complex biophysical interactions, numerous competing stakeholder interests, and highly uncertain outcomes. Natural and social science information and analyses are widely recognized as important for informing effective management. Chief among the systematic approaches for improving the integration of science into natural resource management are two emergent science concepts, adaptive management and ecosystem services. Adaptive management (also referred to as “adaptive decision making”) is a deliberate process of learning by doing that focuses on reducing uncertainties about management outcomes and system responses to improve management over time. Ecosystem services is a conceptual framework that refers to the attributes and outputs of ecosystems (and their components and functions) that have value for humans.
This report explores how ecosystem services can be moved from concept into practice through connection to a decision framework—adaptive management—that accounts for inherent uncertainties. Simultaneously, the report examines the value of incorporating ecosystem services framing and concepts into adaptive management efforts.
Adaptive management and ecosystem services analyses have not typically been used jointly in decision making. However, as frameworks, they have a natural—but to date underexplored—affinity. Both are policy and decision oriented in that they attempt to represent the consequences of resource management choices on outcomes of interest to stakeholders. Both adaptive management and ecosystem services analysis take an empirical approach to the analysis of ecological systems. This systems orientation is a byproduct of the fact that natural resource actions affect ecosystems—and corresponding societal outcomes—often across large geographic scales. Moreover, because both frameworks focus on resource systems, both must confront the analytical challenges of systems modeling—in terms of complexity, dynamics, and uncertainty.
Given this affinity, the integration of ecosystem services analysis and adaptive management poses few conceptual hurdles. In this report, we synthesize discussions from two workshops that considered ways in which adaptive management approaches and ecosystem service concepts may be complementary, such that integrating them into a common framework may lead to improved natural resource management outcomes. Although the literature on adaptive management and ecosystem services is vast and growing, the report focuses specifically on the integration of these two concepts rather than aiming to provide new definitions or an indepth review or primer of the concepts individually.
Key issues considered include the bidirectional links between adaptive decision making and ecosystem services, as well as the potential benefits and inevitable challenges arising in the development and use of an integrated framework. Specifically, the workshops addressed the following questions:
Director, Science and Decisions Center
U.S. Geological Survey
913 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
We consider estimation of the covariance matrix of a multivariate normal distribution when the correlation matrix is separable in the sense that it factors as a Kronecker product of two smaller matrices. A computationally convenient coordinate descent-type algorithm is developed for maximum likelihood estimation. Simulations indicate our method often gives smaller estimation error than some common alternatives when correlation is separable, and that correctly sized tests for correlation separability can be obtained using a parametric bootstrap. Using dissolved oxygen data from the Upper Mississippi River, we illustrate how our model can lead to interesting scientific findings that may be missed when using competing models.
Post‐Miocene tectonic uplift along fault‐cored anticlines within central Washington produced the Yakima Fold Province, a region of active NNE‐SSW shortening in the Cascadian backarc. The relative timing and rate of deformation along individual structures is coarsely defined yet imperative for seismic hazard assessment. In this work, we use geomorphic and geophysical mapping, stream profile inversion, and balanced cross‐section methods to constrain fault geometries and slip rates in the Yakima Canyon region. We extract stream profiles from LiDAR data and analytically solve for the rate of relative rock uplift along several active fault‐cored anticlines. To constrain the fault geometries at depth and the long‐term magnitude of deformation, we constructed two line‐balanced cross sections across the folds with forward‐modeled magnetic and gravity anomaly data. Our stream profile results indicate an increase of incision rates in the Pleistocene, and we infer the increase is tectonically controlled. We estimate modern slip rates between 0.4 and 0.6 mm/year accommodated on reverse faults that core the Manastash Ridge, Umtanum Ridge, and Selah Butte anticlines and establish that these faults reactivate and invert older normal faults in basement rocks. Finally, we calculate the time required to accumulate sufficient strain energy for a large magnitude earthquake (M ≥ 7) along individual structures in the Yakima Fold Province. Results show that the Yakima folds likely accommodate large magnitude earthquakes and that it takes several hundred to several thousand years to accumulate sufficient strain energy for an M ≥ 7 earthquake.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2017TC004916","usgsCitation":"Staisch, L.M., Blakely, R.J., Kelsey, H., Styron, R., and Sherrod, B.L., 2018, Crustal structure and quaternary acceleration of deformation rates in central Washington revealed by stream profile inversion, potential field geophysics, and structural geology of the Yakima folds: Tectonics, v. 37, no. 6, p. 1750-1770, https://doi.org/10.1029/2017TC004916.","productDescription":"21 p.","startPage":"1750","endPage":"1770","ipdsId":"IP-092865","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":468777,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017tc004916","text":"Publisher Index Page"},{"id":355670,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Yakima Folds","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.364990234375,\n 45.40616374516014\n ],\n [\n -117.7734375,\n 45.40616374516014\n ],\n [\n -117.7734375,\n 49.0738659012854\n ],\n [\n -125.364990234375,\n 49.0738659012854\n ],\n [\n -125.364990234375,\n 45.40616374516014\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-06","publicationStatus":"PW","scienceBaseUri":"5b6fc450e4b0f5d57878ea4f","contributors":{"authors":[{"text":"Staisch, Lydia M. 0000-0002-1414-5994 lstaisch@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-5994","contributorId":167068,"corporation":false,"usgs":true,"family":"Staisch","given":"Lydia","email":"lstaisch@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":739972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blakely, Richard J. 0000-0003-1701-5236 blakely@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-5236","contributorId":1540,"corporation":false,"usgs":true,"family":"Blakely","given":"Richard","email":"blakely@usgs.gov","middleInitial":"J.","affiliations":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":739973,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelsey, Harvey","contributorId":106978,"corporation":false,"usgs":true,"family":"Kelsey","given":"Harvey","affiliations":[],"preferred":false,"id":739976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Styron, Richard","contributorId":201082,"corporation":false,"usgs":false,"family":"Styron","given":"Richard","email":"","affiliations":[],"preferred":false,"id":739974,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sherrod, Brian L. 0000-0002-4492-8631 bsherrod@usgs.gov","orcid":"https://orcid.org/0000-0002-4492-8631","contributorId":2834,"corporation":false,"usgs":true,"family":"Sherrod","given":"Brian","email":"bsherrod@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":739975,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196859,"text":"70196859 - 2018 - Modeling the fish community population dynamics and forecasting the eradication success of an exotic fish from an alpine stream","interactions":[],"lastModifiedDate":"2018-05-07T11:13:53","indexId":"70196859","displayToPublicDate":"2018-05-07T00:00:00","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":"Modeling the fish community population dynamics and forecasting the eradication success of an exotic fish from an alpine stream","docAbstract":"Management actions aimed at eradicating exotic fish species from riverine ecosystems can be better informed by forecasting abilities of mechanistic models. We illustrate this point with an example of the Logan River, Utah, originally populated with endemic cutthroat trout (Oncorhynchus clarkii utah), which compete with exotic brown trout (Salmo trutta). The coexistence equilibrium was disrupted by a large scale, experimental removal of the exotic species in 2009–2011 (on average, 8.2% of the stock each year), followed by an increase in the density of the native species. We built a spatially-explicit, reaction-diffusion model encompassing four key processes: population growth in heterogeneous habitat, competition, dispersal, and a management action. We calibrated the model with detailed long-term monitoring data (2001–2016) collected along the 35.4-km long river main channel. Our model, although simple, did a remarkable job reproducing the system steady state prior to the management action. Insights gained from the model independent predictions are consistent with available knowledge and indicate that the exotic species is more competitive; however, the native species still occupies more favorable habitat upstream. Dynamic runs of the model also recreated the observed increase of the native species following the management action. The model can simulate two possible distinct long-term outcomes: recovery or eradication of the exotic species. The processing of available knowledge using Bayesian methods allowed us to conclude that the chance for eradication of the invader was low at the beginning of the experimental removal (0.7% in 2009) and increased (20.5% in 2016) by using more recent monitoring data. We show that accessible mathematical and numerical tools can provide highly informative insights for managers (e.g., outcome of their conservation actions), identify knowledge gaps, and provide testable theory for researchers.
The hydrogeologic setting and groundwater flow system in Florida and parts of Georgia, Alabama, and South Carolina is dominated by the highly transmissive Floridan aquifer system. This principal aquifer is a vital source of freshwater for public and domestic supply, as well as for industrial and agricultural uses throughout the southeastern United States. Population growth, increased tourism, and increased agricultural production have led to increased demand on groundwater from the Floridan aquifer system, particularly since 1950. The response of the Floridan aquifer system to these stresses often poses regional challenges for water-resource management that commonly transcend political or jurisdictional boundaries. To help water-resource managers address these regional challenges, the U.S. Geological Survey (USGS) Water Availability and Use Science Program began assessing groundwater availability of the Floridan aquifer system in 2009.
The current conceptual groundwater flow system was developed for the Floridan aquifer system and adjacent systems partly on the basis of previously published USGS Regional Aquifer-System Analysis (RASA) studies, specifically many of the potentiometric maps and the modeling efforts in these studies. The Floridan aquifer system extent was divided into eight hydrogeologically distinct subregional groundwater basins delineated on the basis of the estimated predevelopment (circa 1880s) potentiometric surface: (1) Panhandle, (2) Dougherty Plain-Apalachicola, (3) Thomasville-Tallahassee, (4) Southeast Georgia-Northeast Florida-South South Carolina, (5) Suwannee, (6) West-central Florida, (7) East-central Florida, and (8) South Florida. The use of these subregions allows for a more detailed analysis of the individual basins and the groundwater flow system as a whole.
The hydrologic conditions and associated groundwater budget were updated relative to previous RASA studies to include additional data collected since the 1980s and to reflect the entire groundwater flow system, including the surficial, intermediate, and Floridan aquifer systems for a contemporary period (1995–2010). Inflow to the groundwater flow system of 33,700 million gallons per day (Mgal/d) was assumed to be exclusively from net recharge (precipitation minus evapotranspiration and surface runoff). Outflow from the groundwater flow system included spring discharge (7,700 Mgal/d) and groundwater withdrawals (5,200 Mgal/d). Estimates for all components of the groundwater system were not possible because of large uncertainties associated with internal leakage, coastal discharge, and discharge to streams and lakes. A numerical modeling analysis is required to improve this hydrologic budget calculation and to forecast future changes in groundwater levels and aquifer storage caused by groundwater withdrawals, land-use change, and the effects of climate variability and change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185030","collaboration":"Water Availability and Use Science Program","usgsCitation":"Bellino, J.C., Kuniansky, E.L., O’Reilly, A.M., and Dixon, J.F., 2018, Hydrogeologic setting, conceptual groundwater flow system, and hydrologic conditions 1995–2010 in Florida and parts of Georgia, Alabama, and South Carolina: U.S. Geological Survey Scientific Investigations Report 2018–5030, 103 p., https://doi.org/10.3133/sir20185030.","productDescription":"Report: viii, 103 p.; Plate: 36.0 x 49.0 inches; Data Releases","numberOfPages":"115","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-056534","costCenters":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"links":[{"id":353934,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2018/5030/sir20185030_plate.pdf","text":"Plate 1","size":"3.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5030 Plate 1"},{"id":353936,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CJ8BMS","text":"USGS data release","description":"USGS Data Release","linkHelpText":" Soil-Water-Balance model datasets used to estimate mean groundwater recharge in Florida and parts of Georgia, Alabama, and South Carolina, 1995–2010"},{"id":353933,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5030/sir20185030.pdf","text":"Report","size":"46.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5030"},{"id":353932,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5030/coverthb2.jpg"},{"id":353937,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75Q4TZD","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Potentiometric Surface Contours, Wells, and Groundwater Basin Divides for the Upper Floridan Aquifer in Florida and Parts of Georgia, South Carolina, and Alabama, May–June 2010—Updated"},{"id":353935,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78K7749","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Groundwater Withdrawals in Florida and parts of Georgia, Alabama, and South Carolina, 1995–2010"}],"country":"United States","state":"Alabama, Florida, Georgia, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.17626953125,\n 24.467150664739002\n ],\n [\n -79.6728515625,\n 24.467150664739002\n ],\n [\n -79.6728515625,\n 32.85190345738802\n ],\n [\n -88.17626953125,\n 32.85190345738802\n ],\n [\n -88.17626953125,\n 24.467150664739002\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane
Lutz, FL 33559
Wetlands in the Prairie Pothole Region (PPR) of North America support macroinvertebrate communities that are integral to local food webs and important to breeding waterfowl. Macroinvertebrates in PPR wetlands are primarily generalists and well adapted to within and among year changes in water permanence and salinity. The Williston Basin, a major source of U.S. energy production, underlies the southwest portion of the PPR. Development of oil and gas results in the coproduction of large volumes of highly saline, sodium chloride dominated water (brine) and the introduction of brine can alter wetland salinity. To assess potential effects of brine contamination on macroinvertebrate communities, 155 PPR wetlands spanning a range of hydroperiods and salinities were sampled between 2014 and 2016. Brine contamination was documented in 34 wetlands with contaminated wetlands having significantly higher chloride concentrations, specific conductance and percent dominant taxa, and significantly lower taxonomic richness, Shannon diversity, and Pielou evenness scores compared to uncontaminated wetlands. Non-metric multidimensional scaling found significant correlations between several water quality parameters and macroinvertebrate communities. Chloride concentration and specific conductance, which can be elevated in naturally saline wetlands, but are also associated with brine contamination, had the strongest correlations. Five wetland groups were identified from cluster analysis with many of the highly contaminated wetlands located in a single cluster. Low or moderately contaminated wetlands were distributed among the remaining clusters and had macroinvertebrate communities similar to uncontaminated wetlands. While aggregate changes in macroinvertebrate community structure were observed with brine contamination, systematic changes were not evident, likely due to the strong and potentially confounding influence of hydroperiod and natural salinity. Therefore, despite the observed negative response of macroinvertebrate communities to brine contamination, macroinvertebrate community structure alone is likely not the most sensitive indicator of brine contamination in PPR wetlands.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2018.04.088","usgsCitation":"Preston, T.M., Borgreen, M.J., and Ray, A.M., 2018, Effects of brine contamination from energy development on wetland macroinvertebrate community structure in the Prairie Pothole Region: Environmental Pollution, v. 239, p. 722-732, https://doi.org/10.1016/j.envpol.2018.04.088.","productDescription":"11 p.","startPage":"722","endPage":"732","ipdsId":"IP-093113","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":437922,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DB8141","text":"USGS data release","linkHelpText":"Macroinvertebrate and water quality data from the Prairie Pothole Region of the Williston Basin (2014-2016)"},{"id":353964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","otherGeospatial":"Williston Basin","volume":"239","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6c3e4b0da30c1bfbde2","contributors":{"authors":[{"text":"Preston, Todd M. 0000-0002-8812-9233","orcid":"https://orcid.org/0000-0002-8812-9233","contributorId":204676,"corporation":false,"usgs":true,"family":"Preston","given":"Todd","email":"","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":734648,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Borgreen, Michael J. 0000-0002-5879-6414","orcid":"https://orcid.org/0000-0002-5879-6414","contributorId":204677,"corporation":false,"usgs":false,"family":"Borgreen","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":734649,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ray, Andrew M.","contributorId":167601,"corporation":false,"usgs":false,"family":"Ray","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":5106,"text":"National Park Service, Yellowstone National Park, Mammoth, Wyoming 82190","active":true,"usgs":false}],"preferred":false,"id":734650,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}