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.
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":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake 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":70195030,"text":"sir20185018 - 2018 - Hydrologic and water-quality characteristics of Caño Boquerón, Cabo Rojo, and Puerto Mosquito, Isla de Vieques, Puerto Rico, July 2015–July 2016","interactions":[],"lastModifiedDate":"2018-09-25T06:00:30","indexId":"sir20185018","displayToPublicDate":"2018-05-07T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5018","title":"Hydrologic and water-quality characteristics of Caño Boquerón, Cabo Rojo, and Puerto Mosquito, Isla de Vieques, Puerto Rico, July 2015–July 2016","docAbstract":"Coastal lagoons are common features of the Puerto Rico shoreline that provide habitat for commercial and recreational species and serve important roles in the nutrient cycle of the ecosystems. The U.S. Geological Survey, in cooperation with the Puerto Rico Environmental Quality Board, conducted a limnological study at Caño Boquerón in Cabo Rojo and at Puerto Mosquito on Isla de Vieques, Puerto Rico, to assess the principal mechanisms affecting the hydrology and water-quality characteristics of these coastal lagoons and provide baseline information to the regulatory agencies responsible for the management and conservation of these coastal waters and the preservation of their aquatic life.
Field measurements and water samples were collected and processed during July 2015–July 2016 for analysis of physical, chemical, biological, and bacteriological characteristics. In addition, bathymetric surveys were made and sediment cores were collected in each lagoon to determine water volume and sediment deposition rate. Physicochemical properties assessed at Caño Boquerón indicated values were generally in compliance with Puerto Rico Environmental Quality Board standards; turbidity was occasionally slightly greater than the established standards, and dissolved oxygen concentration at bottom depths was lower than standards limits. Water transparency was evaluated through the Secchi disk method, and the average depth of disappearance was 1.0 meter (m) for Caño Boquerón and 1.9 m for Puerto Mosquito.
Assessment of biological characteristics at both sites included primary productivity calculations as well as carbon production equivalents and monthly water sampling for bacteriological and nutrient analyses. For Caño Boquerón, gross plankton primary productivity averaged 3.38 grams of oxygen per cubic meter per day (gO2/m3-d); this value was computed as the sum of net phytoplankton primary productivity (0.74 gO2/m3-d) and plankton respiration (2.64 gO2/m3-d). Net community primary productivity averaged 1.44 gO2/m3-d, and the community respiration rate averaged 8.10 gO2/m3-d, which indicates that the biological community, aside from phytoplankton, acts as a net consumer rather than a net producer of biomass. In Puerto Mosquito, gross plankton primary productivity averaged 2.07 gO2/m3-d, of which 0.39 gO2/m3-d could be ascribed to net phytoplankton primary productivity, and 1.68 gO2/m3-d could be ascribed to plankton respiration. Diel studies conducted at Puerto Mosquito reflected an average net community primary productivity of 2.43 gO2/m3-d, and the average respiration rate was 6.72 gO2/m3-d.
In a bathymetric survey conducted in August 2015, the water volume for the Caño Boquerón lagoon was calculated as 967,000 cubic meters (m3), and the water volume at Puerto Mosquito was calculated as 1,182,200 m3, with an average depth of 1.5 m for Caño Boquerón and 1.8 m for Puerto Mosquito. The daily seawater exchange for Caño Boquerón and Puerto Mosquito was 13 and 5 percent of their water volumes referenced to mean sea level, respectively. A total of 20 sediment samples were processed and analyzed for cesium-137 (137Cs) and lead-210 (210Pb) radioisotopes. Analyses indicated that the sediment deposition rate at Caño Boquerón ranged from 0.32 to 0.36 centimeter per year, based on age dating analysis of 137Cs and 210Pb data; in Puerto Mosquito, the sediment deposition rate ranged from 0.26 to 0.27 centimeter per year, based on 137Cs and 210Pb data.
Bacteriological analyses at Caño Boquerón and Puerto Mosquito indicated that fecal coliform and enterococci concentrations were below Puerto Rico Environmental Quality Board standards during the study. The highest concentrations of fecal coliform (22 colonies per 100 milliliters) and enterococci (9 colonies per 100 milliliters) at Caño Boquerón occurred in July, which coincided with the busiest season of vacation rentals near the lagoon. Bacteria concentrations were generally lower in Puerto Mosquito than in Caño Boquerón; maximum concentrations of fecal coliform and enterococci bacteria were measured in November 2015. The potential sources of contamination for Puerto Mosquito are limited, because it is within a conservation area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185018","collaboration":"Prepared in cooperation with the Puerto Rico Environmental Quality Board","usgsCitation":"Gómez-Fragoso, J.M., and Santiago-Sáez, J.A., 2018, Hydrologic and water-quality characteristics of Caño Boquerón, Cabo Rojo, and Puerto Mosquito, Isla de Vieques, Puerto Rico, July 2015–July 2016: U.S. Geological Survey Scientific Investigations Report 2018–5018, 34 p., https://doi.org/10.3133/sir20185018.","productDescription":"Report: ix, 34 p.; Data Release","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-078162","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":437920,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WH2P6K","text":"USGS data release","linkHelpText":"Gomez-Fragoso, Julieta, 2017, Data for the Hydrologic and Water-Quality Characterization of Cano Boqueron, Cabo Rojo, and Puerto Mosquito, Isla de Vieques, Puerto Rico, July 2015-July 2016: U.S. Geological Survey data release"},{"id":353903,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7WH2P6K","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for the Hydrologic and Water-Quality Characterization of Puerto Mosquito, Vieques and Caño Boquerón, Cabo Rojo, Puerto Rico, July 2015–July 2016"},{"id":353901,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5018/coverthb2.jpg"},{"id":353902,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5018/sir20185018.pdf","text":"Report","size":"10.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5018"}],"country":"United States","otherGeospatial":"Caño Boquerón, Cabo Rojo, Puerto Mosquito, Isla de Vieques, Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.2167,\n 17.9833\n ],\n [\n -67.1333,\n 17.9833\n ],\n [\n -67.1333,\n 18.04\n ],\n [\n -67.2167,\n 18.04\n ],\n [\n -67.2167,\n 17.9833\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.4833,\n 18.0833\n ],\n [\n -65.4,\n 18.0833\n ],\n [\n -65.4,\n 18.1333\n ],\n [\n -65.4833,\n 18.1333\n ],\n [\n -65.4833,\n 18.0833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
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
Maintaining intraspecific diversity is an important goal for fisheries conservation and recovery actions. While ecomorphological studies have demonstrated intraspecific diversity related to feeding or flow regime, there has been little assessment of such variation in regard to spawning habitat. We evaluated the relationship between individual morphology of Robust Redhorse and Notchlip Redhorse and variables describing the spawning habitat from which they were captured, such as current velocity, depth, and substrate particle size. Robust Redhorse (n = 58) and Notchlip Redhorse (n = 43) were captured from spawning aggregations in the lower Savannah River, South Carolina-Georgia using prepositioned grid electrofishers. They were then measured and photographed before being released. We constructed a truss network using digitized landmarks on each of the photographs. Relationships between the morphological and environmental datasets were assessed using canonical correlation analysis. In both species, these morphological predictors were correlated primarily to depth, though current velocity also contributed to the environmental canonical score for Robust Redhorse. Robust Redhorse captured from the deeper locations with higher current velocities had heads with lower aspect ratio compared to individuals captured from shallower areas. Notchlip Redhorse from shallower areas were deeper-bodied and had shorter trunks than counterparts from deeper areas. These differences suggest that ensuring spawning habitat heterogeneity may be an important component to conserving intraspecific diversity, particularly in systems where such habitat is limiting.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-018-0772-9","usgsCitation":"Grabowski, T.B., Pease, J.E., and Groeschel, J.R., 2018, Intraspecific differences in morphology correspond to differential spawning habitat use in two riverine catostomid species: Environmental Biology of Fishes, v. 101, no. 8, p. 1249-1260, https://doi.org/10.1007/s10641-018-0772-9.","productDescription":"12 p.","startPage":"1249","endPage":"1260","ipdsId":"IP-089415","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":356615,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, South Carolina","otherGeospatial":"Savannah River","volume":"101","issue":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-04","publicationStatus":"PW","scienceBaseUri":"5b98a2c6e4b0702d0e842fe8","contributors":{"authors":[{"text":"Grabowski, Timothy B. 0000-0001-9763-8948 tgrabowski@usgs.gov","orcid":"https://orcid.org/0000-0001-9763-8948","contributorId":4178,"corporation":false,"usgs":true,"family":"Grabowski","given":"Timothy","email":"tgrabowski@usgs.gov","middleInitial":"B.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":742845,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pease, Jessica E.","contributorId":201491,"corporation":false,"usgs":false,"family":"Pease","given":"Jessica","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":743046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Groeschel, Jillian R.","contributorId":172958,"corporation":false,"usgs":false,"family":"Groeschel","given":"Jillian","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":743047,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70198081,"text":"70198081 - 2018 - A tale of two wildfires; testing detection and prediction of invasive species distributions using models fit with topographic and spectral indices","interactions":[],"lastModifiedDate":"2018-07-16T11:25:35","indexId":"70198081","displayToPublicDate":"2018-05-04T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A tale of two wildfires; testing detection and prediction of invasive species distributions using models fit with topographic and spectral indices","docAbstract":"Context
Developing species distribution models (SDMs) to detect invasive species cover and evaluate habitat suitability are high priorities for land managers.
Objectives
We tested SDMs fit with different variable combinations to provide guidelines for future invasive species model development based on transferability between landscapes.
Methods
Generalized linear model, boosted regression trees, multivariate adaptive regression splines, and Random Forests were fit with location data for high cheatgrass (Bromus tectorum) cover in situ for two post-burn sites independently using topographic indices, spectral indices derived from multiple dates of Landsat 8 satellite imagery, or both. Models developed for one site were applied to the other, using independent cheatgrass cover data from the respective ex situ site to test model transferability.
Results
Fitted models were statistically robust and comparable when fit with at least 200 cover plots in situ and transferred to the ex situ site. Only the Random Forests models were robust when fit with a small number of cover plots in situ.
Conclusions
Our study indicated spectral indices can be used in SDMs to estimate species cover across landscapes (e.g., both within the same Landsat scene and in an adjacent Landsat scene). Important considerations for transferability include the model employed, quantity of cover data used to train/test the models, and phenology of the species coupled with the timing of imagery. The results also suggest that when cover data are limited, SDMs fit with topographic indices are sufficient for evaluating cheatgrass habitat suitability in new post-disturbance landscapes; however, spectral indices can provide a more robust estimate for detection based on local phenology.
Many species have distributions that span distinctly different physiographic regions, and effective conservation of such taxa will require a full accounting of all factors that potentially influence populations. Ecologists recognize effects of physiographic differences in topography, geology and climate on local habitat configurations, and thus the relevance of landscape heterogeneity to species distributions and abundances. However, research is lacking that examines how physiography affects the processes underlying metapopulation dynamics. We used data describing occupancy dynamics of stream fishes to evaluate evidence that physiography influences rates at which individual taxa persist in or colonize stream reaches under different flow conditions. Using periodic survey data from a stream fish assemblage in a large river basin that encompasses multiple physiographic regions, we fit multi-species dynamic occupancy models. Our modeling results suggested that stream fish colonization but not persistence was strongly governed by physiography, with estimated colonization rates considerably higher in Coastal Plain streams than in Piedmont and Blue Ridge systems. Like colonization, persistence was positively related to an index of stream flow magnitude, but the relationship between flow and persistence did not depend on physiography. Understanding the relative importance of colonization and persistence, and how one or both processes may change across the landscape, is critical information for the conservation of broadly distributed taxa, and conservation strategies explicitly accounting for spatial variation in these processes are likely to be more successful for such taxa.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2018.04.023","usgsCitation":"Wheeler, K., Wenger, S., Walsh, S.J., Martin, Z.P., Jelks, H.L., and Freeman, M., 2018, Stream fish colonization but not persistence varies regionally across a large North American river basin: Biological Conservation, v. 223, p. 1-10, https://doi.org/10.1016/j.biocon.2018.04.023.","productDescription":"10 p.","startPage":"1","endPage":"10","ipdsId":"IP-091967","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":460780,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2018.04.023","text":"Publisher Index Page"},{"id":353928,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Georgia","otherGeospatial":"Apalachicola-Chattahoochee-Flint River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.792236328125,\n 30.65681556429287\n ],\n [\n -83.38623046875,\n 30.65681556429287\n ],\n [\n -83.38623046875,\n 34.939985151560435\n ],\n [\n -85.792236328125,\n 34.939985151560435\n ],\n [\n -85.792236328125,\n 30.65681556429287\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"223","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6c3e4b0da30c1bfbde4","contributors":{"authors":[{"text":"Wheeler, Kit","contributorId":203872,"corporation":false,"usgs":false,"family":"Wheeler","given":"Kit","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":734573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wenger, Seth J.","contributorId":177838,"corporation":false,"usgs":false,"family":"Wenger","given":"Seth J.","affiliations":[],"preferred":false,"id":734574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, Stephen J. 0000-0002-1009-8537 swalsh@usgs.gov","orcid":"https://orcid.org/0000-0002-1009-8537","contributorId":1456,"corporation":false,"usgs":true,"family":"Walsh","given":"Stephen","email":"swalsh@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":734577,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Zachary P. 0000-0001-5779-3548 zmartin@usgs.gov","orcid":"https://orcid.org/0000-0001-5779-3548","contributorId":204653,"corporation":false,"usgs":false,"family":"Martin","given":"Zachary","email":"zmartin@usgs.gov","middleInitial":"P.","affiliations":[{"id":36970,"text":"Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, USA","active":true,"usgs":false}],"preferred":false,"id":734576,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jelks, Howard L. 0000-0002-0672-6297 hjelks@usgs.gov","orcid":"https://orcid.org/0000-0002-0672-6297","contributorId":168997,"corporation":false,"usgs":true,"family":"Jelks","given":"Howard","email":"hjelks@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":734575,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":734572,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196406,"text":"ds1083 - 2018 - Soil moisture datasets at five sites in the central Sierra Nevada and northern Coast Ranges, California","interactions":[],"lastModifiedDate":"2018-05-04T10:15:17","indexId":"ds1083","displayToPublicDate":"2018-05-03T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1083","title":"Soil moisture datasets at five sites in the central Sierra Nevada and northern Coast Ranges, California","docAbstract":"In situ soil moisture datasets are important inputs used to calibrate and validate watershed, regional, or statewide modeled and satellite-based soil moisture estimates. The soil moisture dataset presented in this report includes hourly time series of the following: soil temperature, volumetric water content, water potential, and total soil water content. Data were collected by the U.S. Geological Survey at five locations in California: three sites in the central Sierra Nevada and two sites in the northern Coast Ranges. This report provides a description of each of the study areas, procedures and equipment used, processing steps, and time series data from each site in the form of comma-separated values (.csv) tables.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1083","collaboration":"Prepared in cooperation with the California Department of Water Resources, National Park Service, and Pepperwood Preserve","usgsCitation":"Stern, M.A., Anderson, F.A., Flint, L.E., and Flint, A.L., 2018, Soil moisture datasets at five sites in the central Sierra Nevada and northern Coast Ranges, California: U.S. Geological Survey Data Series 1083, 23 p., https://doi.org/10.3133/ds1083.","productDescription":"Report: viii, 23 p.; 5 Tables","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080152","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":353696,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1083/coverthb.jpg"},{"id":353697,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1083/ds1083_.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Data Series 1083"},{"id":353698,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/1083/ds1083_tables12-15_17.zip","text":"Tables 12, 13, 14, 15, and 17","size":"5.3 MB","linkFileType":{"id":6,"text":"zip"},"description":"Data Series 1083 table files"}],"country":"United States","state":"California","otherGeospatial":"Central Sierra Nevada Range, Northern Coast Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.83612060546874,\n 37.73379707124429\n ],\n [\n -119.20852661132812,\n 37.73379707124429\n ],\n [\n -119.20852661132812,\n 37.965854128749434\n ],\n [\n -119.83612060546874,\n 37.965854128749434\n ],\n [\n -119.83612060546874,\n 37.73379707124429\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.6963424682617,\n 38.56887107621966\n ],\n [\n -122.68930435180664,\n 38.56887107621966\n ],\n [\n -122.68930435180664,\n 38.57256189519067\n ],\n [\n -122.6963424682617,\n 38.57256189519067\n ],\n [\n -122.6963424682617,\n 38.56887107621966\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
Understanding the spatial and temporal variability in life-history traits among populations is essential for the management of recreational fisheries. However, valuable freshwater recreational fish species often suffer from a lack of catch information. In this study, we demonstrated the use of an approach to estimate the spatial and temporal variability in growth and mortality in the absence of catch data and apply the method to riverine smallmouth bass (Micropterus dolomieu) populations in Pennsylvania, USA. Our approach included a growth analysis and a length-based analysis that estimates mortality. Using a hierarchical Bayesian approach, we examined spatial variability in growth and mortality by assuming parameters vary spatially but remain constant over time and temporal variability by assuming parameters vary spatially and temporally. The estimated growth and mortality of smallmouth bass showed substantial variability over time and across rivers. We explored the relationships of the estimated growth and mortality with spring water temperature and spring flow. Growth rate was likely to be positively correlated with these two factors, while young mortality was likely to be positively correlated with spring flow. The spatially and temporally varying growth and mortality suggest that smallmouth bass populations across rivers may respond differently to management plans and disturbance such as environmental contamination and land-use change. The analytical approach can be extended to other freshwater recreational species that also lack of catch data. The approach could also be useful in developing population assessments with erroneous catch data or be used as a model sensitivity scenario to verify traditional models even when catch data are available.
","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfas-2017-0052","usgsCitation":"Li, Y., Wagner, T., Jiao, Y., Lorantas, R.M., and Murphy, C., 2018, Evaluating spatial and temporal variability in growth and mortality for recreational fisheries with limited catch data: Canadian Journal of Fisheries and Aquatic Sciences, v. 75, no. 9, p. 1436-1452, https://doi.org/10.1139/cjfas-2017-0052.","productDescription":"17 p.","startPage":"1436","endPage":"1452","ipdsId":"IP-084291","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468778,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/99273","text":"External Repository"},{"id":353912,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Yan","contributorId":204633,"corporation":false,"usgs":false,"family":"Jiao","given":"Yan","email":"","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":734529,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lorantas, Robert M.","contributorId":204631,"corporation":false,"usgs":false,"family":"Lorantas","given":"Robert","email":"","middleInitial":"M.","affiliations":[{"id":36966,"text":"Pennsylvania Fish and Boat Commission","active":true,"usgs":false}],"preferred":false,"id":734527,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murphy, Cheryl","contributorId":204632,"corporation":false,"usgs":false,"family":"Murphy","given":"Cheryl","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":734528,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196809,"text":"70196809 - 2018 - Bioactive contaminants of emerging concern in National Park waters of the northern Colorado Plateau, USA","interactions":[],"lastModifiedDate":"2018-05-02T11:32:49","indexId":"70196809","displayToPublicDate":"2018-05-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Bioactive contaminants of emerging concern in National Park waters of the northern Colorado Plateau, USA","docAbstract":"Pharmaceuticals and personal care products (PPCPs), wastewater indicators (WWIs), and pesticides (herein, Contaminants of Emerging Concern [CECs]) have been documented in surface waters throughout the world and have associated risks to aquatic life. While much research has focused on temperate and urbanized watersheds, less is known about CEC presence in semi-arid landscapes, where water availability is limited and populations are low. CEC presence in water and sediment is reported for 21 sites in eight U.S. national parks in the northern Colorado Plateau region. From 2012 to 2016, at least one PPCP and/or WWI was detected at most sites on over half of sampling visits, indicating that CECs are not uncommon even in isolated areas. CEC detections were generally fewer and at lower concentrations than in urbanized or agricultural watersheds. Consistent with studies from other U.S. regions, the most frequently detected CECs in this study include DEET, caffeine, organophosphorus flame retardants, and bisphenol A in water and fecal indicators and polycyclic aromatic hydrocarbons in sediment. Maximum concentrations in this study were generally below available water quality benchmarks, sediment quality guidelines, and risk assessment thresholds associated with vertebrates. Additional work is needed to assess the potential activity of hormones, which had high reporting limits in our study, and potential bioactivity of environmental concentrations for invertebrates, microbial communities, and algae. Potential sources of CEC contamination include upstream wastewater effluent discharges and National Park Service invasive-plant-control herbicide applications. CEC occurrence patterns and similarities between continuous and isolated flow locations suggest that direct contamination from individual visitors may also occur. While our data indicate there is little aquatic health risk associated with CECs at our sites, our results demonstrate the ubiquity of CECs on the landscape and a continued need for public outreach concerning resource-use ethics and the potential effects of upstream development.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2018.04.332","usgsCitation":"Weissinger, R.H., Blackwell, B., Keteles, K., Battaglin, W., and Bradley, P.M., 2018, Bioactive contaminants of emerging concern in National Park waters of the northern Colorado Plateau, USA: Science of the Total Environment, v. 636, p. 910-918, https://doi.org/10.1016/j.scitotenv.2018.04.332.","productDescription":"9 p.","startPage":"910","endPage":"918","ipdsId":"IP-095083","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":460929,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6794149","text":"Publisher Index Page"},{"id":437924,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NP23PC","text":"USGS data release","linkHelpText":"Bioactive Contaminants of Emerging Concern in National Park Waters of the Northern Colorado Plateau, USA, 2012-2016"},{"id":353916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.2965087890625,\n 37.17344871200958\n ],\n [\n -108.48999023437499,\n 37.17344871200958\n ],\n [\n -108.48999023437499,\n 40.63479884404164\n ],\n [\n -113.2965087890625,\n 40.63479884404164\n ],\n [\n -113.2965087890625,\n 37.17344871200958\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"636","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6c3e4b0da30c1bfbdea","contributors":{"authors":[{"text":"Weissinger, Rebecca H","contributorId":204637,"corporation":false,"usgs":false,"family":"Weissinger","given":"Rebecca","email":"","middleInitial":"H","affiliations":[{"id":36968,"text":"US National Parks Service","active":true,"usgs":false}],"preferred":false,"id":734538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blackwell, Brett R.","contributorId":173601,"corporation":false,"usgs":false,"family":"Blackwell","given":"Brett R.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":734539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keteles, Kristen","contributorId":200072,"corporation":false,"usgs":false,"family":"Keteles","given":"Kristen","email":"","affiliations":[],"preferred":false,"id":734540,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Battaglin, William A. 0000-0001-7287-7096","orcid":"https://orcid.org/0000-0001-7287-7096","contributorId":204638,"corporation":false,"usgs":true,"family":"Battaglin","given":"William A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":734541,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bradley, Paul M. 0000-0001-7522-8606 pbradley@usgs.gov","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":204639,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul","email":"pbradley@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":734542,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70201610,"text":"70201610 - 2018 - Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests","interactions":[],"lastModifiedDate":"2018-12-18T14:07:03","indexId":"70201610","displayToPublicDate":"2018-05-01T14:07:08","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1170,"text":"Canadian Journal of Forest Research","active":true,"publicationSubtype":{"id":10}},"title":"Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests","docAbstract":"Timely and accurate knowledge of species-level biomass is essential for forest managers to sustain forest resources and respond to various forest disturbance regimes. In this study, maps of species-level biomass in Chinese boreal forests were generated by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) images with forest inventory data using k nearest neighbor (kNN) methods and evaluated at different scales. The performance of 630 kNN models based on different distance metrics, k values, and temporal MODIS predictor variables were compared. Random Forest (RF) showed the best performance among the six distance metrics: RF, Euclidean distance, Mahalanobis distance, most similar neighbor in canonical correlation space, most similar neighbor computed using projection pursuit, and gradient nearest neighbor. No appreciable improvement was observed using multi-month MODIS data compared with using single-month MODIS data. At the pixel scale, species-level biomass for larch and white birch had relatively good accuracy (root mean square deviation < 62.1%), while the other species had poorer accuracy. The accuracy of most species except for willow and spruce was improved up to the ecoregion scale. The maps of species-level biomass captured the effects of disturbances including fire and harvest and can provide useful information for broad-scale forest monitoring over time.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfr-2017-0346","usgsCitation":"Zhang, Q., He, H.S., Liang, Y., Hawbaker, T., Henne, P., Liu, J., Huang, S., Wu, Z., and Huang, C., 2018, Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests: Canadian Journal of Forest Research, v. 48, no. 5, p. 461-479, https://doi.org/10.1139/cjfr-2017-0346.","productDescription":"19 p.","startPage":"461","endPage":"479","ipdsId":"IP-085665","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":360498,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 121,\n 50\n ],\n [\n 127.5,\n 50\n ],\n [\n 127.5,\n 53.5\n ],\n [\n 121,\n 53.5\n ],\n [\n 121,\n 50\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c1a1533e4b0708288c23538","contributors":{"authors":[{"text":"Zhang, Qinglong","contributorId":211615,"corporation":false,"usgs":false,"family":"Zhang","given":"Qinglong","email":"","affiliations":[{"id":38276,"text":"CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Shenyang 110016, China","active":true,"usgs":false}],"preferred":false,"id":754516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"He, Hong S.","contributorId":211612,"corporation":false,"usgs":true,"family":"He","given":"Hong","email":"","middleInitial":"S.","affiliations":[{"id":38275,"text":"Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; School of Natural Resources, University of Missouri, 203 ABNR Building, Columbia, MO, USA","active":true,"usgs":false}],"preferred":false,"id":754517,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liang, Yu","contributorId":211613,"corporation":false,"usgs":false,"family":"Liang","given":"Yu","email":"","affiliations":[{"id":38274,"text":"Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China","active":true,"usgs":false}],"preferred":false,"id":754518,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":754626,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Henne, Paul D. 0000-0003-1211-5545 phenne@usgs.gov","orcid":"https://orcid.org/0000-0003-1211-5545","contributorId":169166,"corporation":false,"usgs":true,"family":"Henne","given":"Paul D.","email":"phenne@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":754519,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":754520,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huang, Shengli","contributorId":192377,"corporation":false,"usgs":false,"family":"Huang","given":"Shengli","affiliations":[],"preferred":false,"id":754521,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wu, Zhiwei","contributorId":211614,"corporation":false,"usgs":false,"family":"Wu","given":"Zhiwei","email":"","affiliations":[{"id":38274,"text":"Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China","active":true,"usgs":false}],"preferred":false,"id":754522,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Huang, Chao","contributorId":211611,"corporation":false,"usgs":false,"family":"Huang","given":"Chao","email":"","affiliations":[{"id":38274,"text":"Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China","active":true,"usgs":false}],"preferred":true,"id":754523,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70198418,"text":"70198418 - 2018 - Demographic rates of two southeastern populations of Painted Bunting, 2007–2015","interactions":[],"lastModifiedDate":"2018-08-03T14:02:55","indexId":"70198418","displayToPublicDate":"2018-05-01T14:02:42","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Demographic rates of two southeastern populations of Painted Bunting, 2007–2015","docAbstract":"Painted Buntings (Passerina ciris) have been declining in the southeastern United States since the 1970s. A recent demographic assessment highlighted the importance of estimating demographic parameters, which have received little attention to date. The dearth of information is troublesome because attempts to reverse declining trends require a better understanding of the relationship between habitat quality and age- and sex-specific survival and recruitment rates. We used capture–mark–recapture data collected from 2007 to 2015 on Bald Head Island (BHI) and at Hammocks Beach State Park (HBSP) in North Carolina, USA, to estimate local age- and sex-specific annual survival rates and local population size and recruitment rates using programs MARK and LOLASURVIV. Juveniles had lower local survival rates than adults (HBSP: 0.28 ± 0.14 vs. 0.67 ± 0.06; BHI: 0.28 ± 0.04 vs. 0.57 ± 0.02). Local annual survival rates for males on BHI (0.50 ± 0.03) were lower than those for females (0.57 ± 0.02). Age-specific differences were consistent with known differential age-dependent survival skills, and sex-specific differences were consistent with the potential influence of sexual dichromism. Conservative estimates of population size on BHI averaged 101 juveniles and 263 adults annually. Annual in situ reproductive recruitment averaged 28 individuals plus an additional 120 new immigrants, indicating successful reproduction and connectivity with neighboring coastal populations. Local adult survival estimates from our 2 North Carolinian study populations were similar to high-end estimates from across the eastern and western range of the species (∼0.60). Finite observed population growth rate estimates between the BHI population (λ = 1.10) and a South Carolinian population (λ = 0.87) underscore the potential role of differential habitat quality and the importance of information from multiple sites, including nonbreeding grounds, for proper inferences about the status of the species. Reported vital rates provide a stronger foundation on which to base habitat quality as assessed with demographic parameters and to guide Painted Bunting conservation regionally.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-17-74.1","usgsCitation":"Yirka, L.M., Collazo, J., O’Shea, B.J., Gerwin, J., Rotenberg, J.A., and Cobb, D.T., 2018, Demographic rates of two southeastern populations of Painted Bunting, 2007–2015: The Condor, v. 120, no. 2, p. 319-329, https://doi.org/10.1650/CONDOR-17-74.1.","productDescription":"11 p.","startPage":"319","endPage":"329","ipdsId":"IP-083696","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":488790,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-17-74.1","text":"Publisher Index Page"},{"id":356150,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Bald Head Island, Hammocks Beach State Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.5577392578125,\n 33.792843773631844\n ],\n [\n -76.17919921875,\n 33.792843773631844\n ],\n [\n -76.17919921875,\n 34.95574425733423\n ],\n [\n -78.5577392578125,\n 34.95574425733423\n ],\n [\n -78.5577392578125,\n 33.792843773631844\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"120","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc451e4b0f5d57878ea55","contributors":{"authors":[{"text":"Yirka, Liani M.","contributorId":206731,"corporation":false,"usgs":false,"family":"Yirka","given":"Liani","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":741571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collazo, Jaime A. 0000-0002-1816-7744 jaime_collazo@usgs.gov","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":173448,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime A.","email":"jaime_collazo@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":741375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Shea, Brian J.","contributorId":206732,"corporation":false,"usgs":false,"family":"O’Shea","given":"Brian","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":741572,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gerwin, J.A.","contributorId":88149,"corporation":false,"usgs":true,"family":"Gerwin","given":"J.A.","email":"","affiliations":[],"preferred":false,"id":741573,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rotenberg, James A.","contributorId":206733,"corporation":false,"usgs":false,"family":"Rotenberg","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":741574,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cobb, David T.","contributorId":176235,"corporation":false,"usgs":false,"family":"Cobb","given":"David","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":741575,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70194712,"text":"ds1075 - 2018 - Groundwater-level data from an earthen dam site in southern Westchester County, New York","interactions":[],"lastModifiedDate":"2018-05-01T16:08:23","indexId":"ds1075","displayToPublicDate":"2018-05-01T13:45:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1075","title":"Groundwater-level data from an earthen dam site in southern Westchester County, New York","docAbstract":"In 2005, the U.S. Geological Survey began a cooperative study with New York City Department of Environmental Protection to characterize the local groundwater-flow system and identify potential sources of seeps on the southern embankment of the Hillview Reservoir in Westchester County, New York. Groundwater levels were collected at 49 wells at Hillview Reservoir, and 1 well in northern Bronx County, from April 2005 through November 2016. Groundwater levels were measured discretely with a chalked steel or electric tape, or continuously with a digital pressure transducer, or both, in accordance with U.S. Geological Survey groundwatermeasurement standards. These groundwater-level data were plotted as time series and are presented in this report as hydrographs. Twenty-eight of the 50 hydrographs have continuous record and discrete field groundwater-level measurements, 22 of the hydrographs contain only discrete measurements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1075","isbn":"978-1-4113-4200-2","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Noll, M.L., and Chu, Anthony, 2018, Groundwater-level data from an earthen dam site in southern Westchester County, New York: U.S. Geological Survey Data Series 1075, 35 p., https://doi.org/10.3133/ds1075.","productDescription":"Report: vii, 35 p.; Appendix 1","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-084388","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":351582,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1075/ds1075.pdf","text":"Report","size":"15.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1075"},{"id":351583,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1075/ds1075_app1.zip","text":"Appendix 1","size":"8.55 MB","linkHelpText":"- Groundwater-level measurements"},{"id":351581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1075/coverthb.jpg"}],"country":"United States","state":"New York","county":"Westchester County","otherGeospatial":"Hillview Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.87460231781006,\n 40.90598813645525\n ],\n [\n -73.86430263519287,\n 40.90598813645525\n ],\n [\n -73.86430263519287,\n 40.917760911653126\n ],\n [\n -73.87460231781006,\n 40.917760911653126\n ],\n [\n -73.87460231781006,\n 40.90598813645525\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
2045 Route 112, Building 4
Coram, NY 11727
We describe a lightweight, accurate nowcasting model for electron flux levels measured by the Van Allen probes. Largely motivated by Rigler et al. (2004, https://doi.org/10.1029/2003SW000036), we turn to a time‐varying linear filter of previous flux levels and Kp. We train and test this model on data gathered from the 2.10 MeV channel of the Relativistic Electron‐Proton Telescope sensor onboard the Van Allen probes. Dynamic linear models are a specific case of state space models and can be made flexible enough to emulate the nonlinear behavior of particle fluxes within the radiation belts. Real‐time estimation of the parameters of the model is done using a Kalman filter, where the state of the model is exactly the parameters. Nowcast performance is assessed against several baseline interpolation schemes. Our model demonstrates significant improvements in performance over persistence nowcasting. In particular, during times of high geomagnetic activity, our model is able to attain performance substantially better than a persistence model. In addition, residual analysis is conducted in order to assess model fit and to suggest future improvements to the model.
","language":"English","publisher":"AGU","doi":"10.1029/2017SW001788","usgsCitation":"Coleman, T., McCollough, J.P., Young, S.L., and Rigler, E.J., 2018, Operational nowcasting of electron flux levels in the outer zone of Earth's radiation belt: Space Weather, v. 16, no. 5, p. 501-518, https://doi.org/10.1029/2017SW001788.","productDescription":"18 p.","startPage":"501","endPage":"518","ipdsId":"IP-096545","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":361501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Coleman, Tim","contributorId":213545,"corporation":false,"usgs":false,"family":"Coleman","given":"Tim","email":"","affiliations":[],"preferred":false,"id":757979,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCollough, James P.","contributorId":204030,"corporation":false,"usgs":false,"family":"McCollough","given":"James","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":757980,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Shawn L.","contributorId":204031,"corporation":false,"usgs":false,"family":"Young","given":"Shawn","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":757981,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rigler, E. Joshua 0000-0003-4850-3953 erigler@usgs.gov","orcid":"https://orcid.org/0000-0003-4850-3953","contributorId":4367,"corporation":false,"usgs":true,"family":"Rigler","given":"E.","email":"erigler@usgs.gov","middleInitial":"Joshua","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":757982,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201557,"text":"70201557 - 2018 - Multi-year high-frequency hydrothermal monitoring of selected high-threat Cascade Range volcanoes","interactions":[],"lastModifiedDate":"2018-12-18T12:46:20","indexId":"70201557","displayToPublicDate":"2018-05-01T12:46:30","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Multi-year high-frequency hydrothermal monitoring of selected high-threat Cascade Range volcanoes","docAbstract":"From 2009 to 2015 the U.S. Geological Survey (USGS) systematically monitored hydrothermal behavior at selected Cascade Range volcanoes in order to define baseline hydrothermal and geochemical conditions. Gas and water data were collected regularly at 25 sites on 10 of the highest-risk volcanoes in the Cascade Range. These sites include near-summit fumarole groups and springs/streams that show clear evidence of magmatic influence (high 3He/4He ratios and/or large fluxes of magmatic CO2 or heat). Site records consist mainly of hourly temperature and hydrothermal-flux data. Having established baseline conditions during a multiyear quiescent period, the USGS reduced monitoring frequency from 2015 to present. The archived monitoring data are housed at (doi:10.5066/F72N5088). These data (1) are suitable for retrospective comparison with other continuous geophysical monitoring data and (2) will provide context during future episodes of volcanic unrest, such that unrest-related variations at these thoroughly characterized sites will be more clearly recognizable. Relatively high-frequency year-round data are essential to achieve these objectives, because many of the time series reveal significant diurnal, seasonal, and inter-annual variability that would tend to mask unrest signals in the absence of baseline data. Here we characterize normal variability for each site, suggest strategies to detect future volcanic unrest, and explore deviations from background associated with recent unrest.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2018.02.014","usgsCitation":"Crankshaw, I., Archfield, S.A., Newman, A.C., Bergfeld, D., Clor, L., Kelly, P.J., Evans, W.C., Spicer, K.R., and Ingebritsen, S.E., 2018, Multi-year high-frequency hydrothermal monitoring of selected high-threat Cascade Range volcanoes: Journal of Volcanology and Geothermal Research, v. 356, p. 24-35, https://doi.org/10.1016/j.jvolgeores.2018.02.014.","productDescription":"12 p.","startPage":"24","endPage":"35","ipdsId":"IP-091030","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":468784,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2018.02.014","text":"Publisher Index Page"},{"id":360459,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Cascades Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124,\n 40\n ],\n [\n -120,\n 40\n ],\n [\n -120,\n 49\n ],\n [\n -124,\n 49\n ],\n [\n -124,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"356","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c1a1534e4b0708288c2353b","contributors":{"authors":[{"text":"Crankshaw, I.M. 0000-0003-0736-5478","orcid":"https://orcid.org/0000-0003-0736-5478","contributorId":211588,"corporation":false,"usgs":false,"family":"Crankshaw","given":"I.M.","email":"","affiliations":[{"id":17863,"text":"Sonoma County Water Agency","active":true,"usgs":false}],"preferred":false,"id":754436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":754438,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Newman, A. C. 0000-0001-6621-2717","orcid":"https://orcid.org/0000-0001-6621-2717","contributorId":211589,"corporation":false,"usgs":false,"family":"Newman","given":"A.","email":"","middleInitial":"C.","affiliations":[{"id":38269,"text":"Aarhus, Denmark","active":true,"usgs":false}],"preferred":false,"id":754437,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bergfeld, Deborah 0000-0003-4570-7627 dbergfel@usgs.gov","orcid":"https://orcid.org/0000-0003-4570-7627","contributorId":152531,"corporation":false,"usgs":true,"family":"Bergfeld","given":"Deborah","email":"dbergfel@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":754439,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clor, Laura E. 0000-0003-2633-5100","orcid":"https://orcid.org/0000-0003-2633-5100","contributorId":209969,"corporation":false,"usgs":true,"family":"Clor","given":"Laura E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":754440,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kelly, Peter J. 0000-0002-3868-1046 pkelly@usgs.gov","orcid":"https://orcid.org/0000-0002-3868-1046","contributorId":5931,"corporation":false,"usgs":true,"family":"Kelly","given":"Peter","email":"pkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":754540,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Evans, William C. 0000-0001-5942-3102 wcevans@usgs.gov","orcid":"https://orcid.org/0000-0001-5942-3102","contributorId":2353,"corporation":false,"usgs":true,"family":"Evans","given":"William","email":"wcevans@usgs.gov","middleInitial":"C.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":754539,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Spicer, Kurt R. 0000-0001-5030-3198 krspicer@usgs.gov","orcid":"https://orcid.org/0000-0001-5030-3198","contributorId":2684,"corporation":false,"usgs":true,"family":"Spicer","given":"Kurt","email":"krspicer@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":754441,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ingebritsen, Steven E. 0000-0001-6917-9369 seingebr@usgs.gov","orcid":"https://orcid.org/0000-0001-6917-9369","contributorId":818,"corporation":false,"usgs":true,"family":"Ingebritsen","given":"Steven","email":"seingebr@usgs.gov","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":754435,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70197548,"text":"70197548 - 2018 - Estimating fluvial discharges coincident with 21st century coastal storms modeled with CoSMoS","interactions":[],"lastModifiedDate":"2018-09-26T12:40:44","indexId":"70197548","displayToPublicDate":"2018-05-01T12:40:37","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Estimating fluvial discharges coincident with 21st century coastal storms modeled with CoSMoS","docAbstract":"On the open coast, flooding is largely driven by tides, storm surge, waves, and in areas near coastal inlets, the magnitude and co-occurrence of high fluvial discharges. Statistical methods are typically used to estimate the individual probability of coastal storm and fluvial discharge occurrences for use in sophisticated flood hazard models. A challenge arises when considering possible future climate changes and the relation between the intensity of extreme coastal water levels and high fluvial discharges.
In this study, the Coastal Storm Modeling System (CoSMoS) is used to dynamically downscale global climate projections to local-scale storm-driven coastal water levels, including associated fluvial discharges. An efficient approach to derive 21st century projected fluvial discharges for rivers within San Francisco Bay was developed, leveraging a readily-available time-series of projected (2010 – 2100) discharge rates of the predominant river system (the “Delta”). Delta projections were used to estimate flow rates of 8 Bay rivers for the IPCC's CMIP5 RCP4.5 climate scenario. Relationships describing discharge rates, normalized by respective watershed areas, were developed and applied to projected data to generate 21st century fluvial discharge time-series for each river. Results indicate decreasing discharge rates throughout the 21st century with the exception of extreme flows.
","language":"English","publisher":"Coastal Education and Research Foundation","doi":"10.2112/SI85-159.1","usgsCitation":"Erikson, L.H., O'Neill, A., and Barnard, P., 2018, Estimating fluvial discharges coincident with 21st century coastal storms modeled with CoSMoS: Journal of Coastal Research, v. Special Issue No. 85, p. 791-795, https://doi.org/10.2112/SI85-159.1.","productDescription":"5 p.","startPage":"791","endPage":"795","ipdsId":"IP-092829","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":357779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.387451171875,\n 36.94989178681327\n ],\n [\n -121.201171875,\n 36.94989178681327\n ],\n [\n -121.201171875,\n 38.565347844885466\n ],\n [\n -123.387451171875,\n 38.565347844885466\n ],\n [\n -123.387451171875,\n 36.94989178681327\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"Special Issue No. 85","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc02ff9e4b0fc368eb539b6","contributors":{"authors":[{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":737616,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O'Neill, Andrea C. 0000-0003-1656-4372 aoneill@usgs.gov","orcid":"https://orcid.org/0000-0003-1656-4372","contributorId":5351,"corporation":false,"usgs":true,"family":"O'Neill","given":"Andrea C.","email":"aoneill@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":737617,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":746347,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197310,"text":"70197310 - 2018 - Wildlife habitat management on college and university campuses","interactions":[],"lastModifiedDate":"2018-05-29T15:49:53","indexId":"70197310","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5704,"text":"Cities and the Environment","active":true,"publicationSubtype":{"id":10}},"title":"Wildlife habitat management on college and university campuses","docAbstract":"With the increasing involvement of higher education institutions in sustainability movements, it remains unclear to what extent college and university campuses address wildlife habitat. Many campuses encompass significant areas of green space with potential to support diverse wildlife taxa. However, sustainability rating systems generally emphasize efforts like recycling and energy conservation over green landscaping and grounds maintenance. We sought to examine the types of wildlife habitat projects occurring at schools across the United States and whether or not factors like school type (public or private), size (number of students), urban vs. rural setting, and funding played roles in the implementation of such initiatives. Using case studies compiled by the National Wildlife Federation’s Campus Ecology program, we documented wildlife habitat-related projects at 60 campuses. Ten management actions derived from nationwide guidelines were used to describe the projects carried out by these institutions, and we recorded data about cost, funding, and outreach and education methods. We explored potential relationships among management actions and with school characteristics. We extracted themes in project types, along with challenges and responses to those challenges. Native plant species selection and sustainable lawn maintenance and landscaping were the most common management actions among the 60 campuses. According to the case studies we examined, we found that factors like school type, size, and location did not affect the engagement of a campus in wildlife habitat initiatives, nor did they influence the project expenditures or funding received by a campus. Our results suggest that many wildlife habitat initiatives are feasible for higher education institutions and may be successfully implemented at relatively low costs through simple, but deliberate management actions.
","language":"English","publisher":"Loyola Marymount University","usgsCitation":"Bosci, T., Warren, P.S., Harper, R.W., and DeStefano, S., 2018, Wildlife habitat management on college and university campuses: Cities and the Environment, v. 11, no. 1, p. 1-14.","productDescription":"Article 1; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-072274","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":354549,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":354529,"type":{"id":15,"text":"Index Page"},"url":"https://digitalcommons.lmu.edu/cate/vol11/iss1/1"}],"volume":"11","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d84e4b092d9651e1b63","contributors":{"authors":[{"text":"Bosci, Tierney","contributorId":205236,"corporation":false,"usgs":false,"family":"Bosci","given":"Tierney","email":"","affiliations":[{"id":37062,"text":"UMASS","active":true,"usgs":false}],"preferred":false,"id":736614,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warren, Paige S.","contributorId":205237,"corporation":false,"usgs":false,"family":"Warren","given":"Paige","email":"","middleInitial":"S.","affiliations":[{"id":37062,"text":"UMASS","active":true,"usgs":false}],"preferred":false,"id":736615,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harper, Rick W.","contributorId":205262,"corporation":false,"usgs":false,"family":"Harper","given":"Rick","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":736616,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeStefano, Stephen 0000-0003-2472-8373 destef@usgs.gov","orcid":"https://orcid.org/0000-0003-2472-8373","contributorId":166706,"corporation":false,"usgs":true,"family":"DeStefano","given":"Stephen","email":"destef@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":736613,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196771,"text":"70196771 - 2018 - Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches","interactions":[],"lastModifiedDate":"2018-05-01T11:40:01","indexId":"70196771","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches","docAbstract":"Monitoring animal populations is central to wildlife and fisheries management, and the use of N-mixture models toward these efforts has markedly increased in recent years. Nevertheless, relatively little work has evaluated estimator performance when basic assumptions are violated. Moreover, diagnostics to identify when bias in parameter estimates from N-mixture models is likely is largely unexplored. We simulated count data sets using 837 combinations of detection probability, number of sample units, number of survey occasions, and type and extent of heterogeneity in abundance or detectability. We fit Poisson N-mixture models to these data, quantified the bias associated with each combination, and evaluated if the parametric bootstrap goodness-of-fit (GOF) test can be used to indicate bias in parameter estimates. We also explored if assumption violations can be diagnosed prior to fitting N-mixture models. In doing so, we propose a new model diagnostic, which we term the quasi-coefficient of variation (QCV). N-mixture models performed well when assumptions were met and detection probabilities were moderate (i.e., ≥0.3), and the performance of the estimator improved with increasing survey occasions and sample units. However, the magnitude of bias in estimated mean abundance with even slight amounts of unmodeled heterogeneity was substantial. The parametric bootstrap GOF test did not perform well as a diagnostic for bias in parameter estimates when detectability and sample sizes were low. The results indicate the QCV is useful to diagnose potential bias and that potential bias associated with unidirectional trends in abundance or detectability can be diagnosed using Poisson regression. This study represents the most thorough assessment to date of assumption violations and diagnostics when fitting N-mixture models using the most commonly implemented error distribution. Unbiased estimates of population state variables are needed to properly inform management decision making. Therefore, we also discuss alternative approaches to yield unbiased estimates of population state variables using similar data types, and we stress that there is no substitute for an effective sample design that is grounded upon well-defined management objectives.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2018.02.007","usgsCitation":"Duarte, A., Adams, M.J., and Peterson, J., 2018, Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches: Ecological Modelling, v. 374, p. 51-59, https://doi.org/10.1016/j.ecolmodel.2018.02.007.","productDescription":"9 p.","startPage":"51","endPage":"59","ipdsId":"IP-090875","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468791,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2018.02.007","text":"Publisher Index Page"},{"id":353873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"374","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6cce4b0da30c1bfbe16","contributors":{"authors":[{"text":"Duarte, Adam","contributorId":79822,"corporation":false,"usgs":true,"family":"Duarte","given":"Adam","email":"","affiliations":[],"preferred":false,"id":734395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, M. J. 0000-0001-8844-042X mjadams@usgs.gov","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":3133,"corporation":false,"usgs":false,"family":"Adams","given":"M.","email":"mjadams@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":734312,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":734311,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196772,"text":"70196772 - 2018 - Quantifying salinity and season effects on eastern oyster clearance and oxygen consumption rates","interactions":[],"lastModifiedDate":"2018-05-01T11:37:43","indexId":"70196772","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2660,"text":"Marine Biology","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying salinity and season effects on eastern oyster clearance and oxygen consumption rates","docAbstract":"There are few data on Crassostrea virginica physiological rates across the range of salinities and temperatures to which they are regularly exposed, and this limits the applicability of growth and production models using these data. The objectives of this study were to quantify, in winter (17 °C) and summer (27 °C), the clearance and oxygen consumption rates of C. virginica from Louisiana across a range of salinities typical of the region (3, 6, 9, 15 and 25). Salinity and season (temperature and reproduction) affected C. virginica physiology differently; salinity impacted clearance rates with reduced feeding rates at low salinities, while season had a strong effect on respiration rates. Highest clearance rates were found at salinities of 9–25, with reductions ranging from 50 to 80 and 90 to 95% at salinities of 6 and 3, respectively. Oxygen consumption rates in summer were four times higher than in winter. Oxygen consumption rates were within a narrow range and similar among salinities in winter, but varied greatly among individuals and salinities in summer. This likely reflected varying stages of gonad development. Valve movements measured at the five salinities indicated oysters were open 50–60% of the time in the 6–25 salinity range and ~ 30% at a salinity of 3. Reduced opening periods, concomitant with narrower valve gap amplitudes, are in accord with the limited feeding at the lowest salinity (3). These data indicate the need for increased focus on experimental determination of optimal ranges and thresholds to better quantify oyster population responses to environmental changes.
","language":"English","publisher":"Springer","doi":"10.1007/s00227-018-3351-x","usgsCitation":"Casas, S., Lavaud, R., LaPeyre, M.K., Comeau, L., Filgueira, R., and LaPeyre, J.F., 2018, Quantifying salinity and season effects on eastern oyster clearance and oxygen consumption rates: Marine Biology, v. 165, p. 1-13, https://doi.org/10.1007/s00227-018-3351-x.","productDescription":"Article 90; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-092990","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":353872,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"165","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-25","publicationStatus":"PW","scienceBaseUri":"5afee6cce4b0da30c1bfbe14","contributors":{"authors":[{"text":"Casas, S.M.","contributorId":8321,"corporation":false,"usgs":true,"family":"Casas","given":"S.M.","email":"","affiliations":[],"preferred":false,"id":734390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lavaud, Romain","contributorId":200114,"corporation":false,"usgs":false,"family":"Lavaud","given":"Romain","email":"","affiliations":[],"preferred":false,"id":734391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaPeyre, Megan K. 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":585,"corporation":false,"usgs":true,"family":"LaPeyre","given":"Megan","email":"mlapeyre@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":734313,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Comeau, L. A.","contributorId":204577,"corporation":false,"usgs":false,"family":"Comeau","given":"L. A.","affiliations":[],"preferred":false,"id":734392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Filgueira, R.","contributorId":204578,"corporation":false,"usgs":false,"family":"Filgueira","given":"R.","email":"","affiliations":[],"preferred":false,"id":734393,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"LaPeyre, Jerome F.","contributorId":189466,"corporation":false,"usgs":false,"family":"LaPeyre","given":"Jerome","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":734394,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70197428,"text":"70197428 - 2018 - Forecasting an invasive species’ distribution with global distribution data, local data, and physiological information","interactions":[],"lastModifiedDate":"2018-06-04T10:36:56","indexId":"70197428","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting an invasive species’ distribution with global distribution data, local data, and physiological information","docAbstract":"Understanding invasive species distributions and potential invasions often requires broad‐scale information on the environmental tolerances of the species. Further, resource managers are often faced with knowing these broad‐scale relationships as well as nuanced environmental factors related to their landscape that influence where an invasive species occurs and potentially could occur. Using invasive buffelgrass (Cenchrus ciliaris), we developed global models and local models for Saguaro National Park, Arizona, USA, based on location records and literature on physiological tolerances to environmental factors to investigate whether environmental relationships of a species at a global scale are also important at local scales. In addition to correlative models with five commonly used algorithms, we also developed a model using a priori user‐defined relationships between occurrence and environmental characteristics based on a literature review. All correlative models at both scales performed well based on statistical evaluations. The user‐defined curves closely matched those produced by the correlative models, indicating that the correlative models may be capturing mechanisms driving the distribution of buffelgrass. Given climate projections for the region, both global and local models indicate that conditions at Saguaro National Park may become more suitable for buffelgrass. Combining global and local data with correlative models and physiological information provided a holistic approach to forecasting invasive species distributions.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2279","usgsCitation":"Jarnevich, C.S., Young, N.E., Talbert, M., and Talbert, C., 2018, Forecasting an invasive species’ distribution with global distribution data, local data, and physiological information: Ecosphere, v. 9, no. 5, p. 1-12, https://doi.org/10.1002/ecs2.2279.","productDescription":"e02279; 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-097154","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468799,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2279","text":"Publisher Index Page"},{"id":437929,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Y99UFF","text":"USGS data release","linkHelpText":"Data for forecasting buffelgrass distribution with global distribution data, local data, and physiological information"},{"id":354686,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Saguaro National Park","volume":"9","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-29","publicationStatus":"PW","scienceBaseUri":"5b155d84e4b092d9651e1b61","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":737118,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Young, Nicholas E.","contributorId":58572,"corporation":false,"usgs":true,"family":"Young","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":737119,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Talbert, Marian 0000-0003-0588-0265 mtalbert@usgs.gov","orcid":"https://orcid.org/0000-0003-0588-0265","contributorId":196740,"corporation":false,"usgs":true,"family":"Talbert","given":"Marian","email":"mtalbert@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":737120,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Talbert, Colin 0000-0002-9505-1876 talbertc@usgs.gov","orcid":"https://orcid.org/0000-0002-9505-1876","contributorId":181913,"corporation":false,"usgs":true,"family":"Talbert","given":"Colin","email":"talbertc@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":737121,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196457,"text":"sir20185050 - 2018 - Discharge, sediment, and water chemistry in Clear Creek, western Nevada, water years 2013–16","interactions":[],"lastModifiedDate":"2018-05-02T10:35:24","indexId":"sir20185050","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5050","title":"Discharge, sediment, and water chemistry in Clear Creek, western Nevada, water years 2013–16","docAbstract":"Clear Creek is a small stream that drains the eastern Carson Range near Lake Tahoe, flows roughly parallel to the Highway 50 corridor, and discharges to the Carson River near Carson City, Nevada. Historical and ongoing development in the drainage basin is thought to be affecting Clear Creek and its sediment-transport characteristics. Previous studies from water years (WYs) 2004 to 2007 and from 2010 to 2012 evaluated discharge, selected water-quality parameters, and suspended-sediment concentrations, loads, and yields at three Clear Creek sampling sites. This report serves as a continuation of the data collection and analyses of the Clear Creek discharge regime and associated water-chemistry and sediment concentrations and loads during WYs 2013–16.
Total annual sediment loads ranged from 870 to 5,300 tons during WYs 2004–07, from 320 to 1,770 tons during WYs 2010–12, and from 50 to 200 tons during WYs 2013–16. Ranges in annual loads during the three study periods were not significantly different; however, total loads were greater during 2004–07 than they were during 2013–16. Annual suspended-sediment loads in WYs 2013–16 showed no significant change since WYs 2010–12 at sites 1 (U.S. Geological Survey reference site 10310485; Clear Creek above Highway 50, near Spooner Summit, Nevada) or 2 (U.S. Geological Survey streamgage 10310500; Clear Creek above Highway 50, near Spooner Summit, Nevada), but significantly lower loads at site 3 (U.S. Geological Survey site 10310518; Clear Creek at Fuji Park, at Carson City, Nevada), supporting the theory of sediment deposition between sites 2 and 3 where the stream gradient becomes more gradual. Currently, a threshold discharge of about 3.3 cubic feet per second is required to mobilize streambed sediment (bedload) from site 2 in Clear Creek. Mean daily discharge was significantly lower in 2010–12 than in 2004–07 and also significantly lower in 2013–16 than in 2010–12. During this study, lower bedload, and therefore lower total sediment load in Clear Creek was primarily due to significantly lower discharge and cannot be directly attributed to sediment mitigation work in the basin.
Water chemistry in Clear Creek shows that the general water type of the creek under base-flow conditions in autumn is a dilute calcium bicarbonate. During winter and spring, the chemistry shifts toward a slightly more sodium and chloride character. Though the chemical characteristics show seasonal change, the water chemistries examined as part of this investigation remain within ecological criteria as adopted by the Nevada Division of Environmental Protection. There was no evidence of aqueous polynuclear aromatic hydrocarbons (PAHs) present in Clear Creek water during this study. Concentrations of PAHs, as determined in one bed-sediment sample and multiple semi-permeable membrane device extracts, were either less than quantifiable limits of analysis or were found at similar concentrations as blank samples.
In July 2014, a 250–300-acre fire burned in the Clear Creek drainage basin. One day after the fire was extinguished, a thunderstorm washed sediment into the creek. A water chemistry sample collected as part of the post-fire storm event showed that the stormwater entering the creek had increased the concentrations of ammonium and organic nitrogen, phosphorus, manganese, and potassium; a similar finding of many other studies evaluating the effects of fires in small drainage basins. Subsequent chemical analyses of Clear Creek water in August 2014 (one month later) showed that these constituents had returned to pre-fire concentrations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185050","collaboration":"Prepared in cooperation with the Nevada Department of Transportation","usgsCitation":"Huntington, J.M., Riddle, D.J., and Paul, A.P., 2018, Discharge, sediment, and water chemistry in Clear Creek, western Nevada, water years 2013–16: U.S. Geological Survey Scientific-Investigations Report 2018–5050, 55 p., https://doi.org/10.3133/sir20185050.","productDescription":"vii, 55 p.","onlineOnly":"Y","ipdsId":"IP-067971","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":353895,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5050/sir20185050.pdf","text":"Report","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5050"},{"id":353894,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5050/coverthb.jpg"}],"country":"United States","state":"Nevada","city":"Carson City","otherGeospatial":"Clear Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.9,\n 39.19\n ],\n [\n -119.7,\n 39.19\n ],\n [\n -119.7,\n 39.06\n ],\n [\n -119.9,\n 39.06\n ],\n [\n -119.9,\n 39.19\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, Nevada 89701
Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R2 values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2017.1382065","usgsCitation":"Boyte, S.P., Wylie, B.K., Rigge, M.B., and Dahal, D., 2018, Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA: GIScience and Remote Sensing, v. 55, no. 3, p. 376-399, https://doi.org/10.1080/15481603.2017.1382065.","productDescription":"24 p.","startPage":"376","endPage":"399","ipdsId":"IP-087872","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499993,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/d0da5ee1cd9c49fab95dfe363f4d48a7","text":"External Repository"},{"id":437930,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7R20ZVX","text":"USGS data release","linkHelpText":"Downscaled 30 m weekly MODIS NDVI for the Central Great Basin"},{"id":354284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin rangelands","volume":"55","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-28","publicationStatus":"PW","scienceBaseUri":"5afee6c4e4b0da30c1bfbdfc","contributors":{"authors":[{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":734937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","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":734938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","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":734939,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":734940,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198137,"text":"70198137 - 2018 - Taiga bean goose: Harvest assessment for the Central Management Unit: 2018","interactions":[],"lastModifiedDate":"2018-07-24T15:44:17","indexId":"70198137","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Taiga bean goose: Harvest assessment for the Central Management Unit: 2018","docAbstract":"In 2016 the European Goose Management International Working Group (EGM IWG) began development of an adaptive harvest management program for Taiga Bean Geese (TBG). In 2017, the EGM IWG adopted an interim harvest strategy consisting of a constant harvest rate (on adults) of 3% for the Central Management of Taiga Bean Geese. The interim strategy is intended to provide limited hunting opportunity while rebuilding the population. Recent efforts have involved development of a dynamic strategy in which the harvest rate can vary each year with changes in population size, and in which multiple, possibly competing, management objectives can be addressed. This report provides examples of dynamic harvest strategies and compares them with the interim, constant harvest-rate strategy. Until such time that a dynamic strategy is adopted by the EGM IWG, the annual harvest quota and its allocation among Range States is predicated on the interim strategy. Based on a January count of 38,717, the harvest quota for the 2018 hunting season is 1,610 Taiga Bean Geese (compared to 2,335 for the 2017 season). We emphasize that these quotas include both harvest during the regular season and derogation shooting. We acknowledge that the January 2018 count of Taiga Bean Geese in the Central Management Unit was likely biased low, as counts in the autumn and spring in Sweden were higher. Additionally, the size of the harvest during the fall and winter of 2017-18 is unknown, due to an inability to differentiate taiga and Tundra Bean Geese in the harvest, compilation of data too late to be used in this report, and a lack of reporting. Because of problems with both the population and harvest monitoring programs it is difficult to estimate a harvest quota for 2018 with any degree of confidence.
","language":"English","publisher":"AEWA European Goose Management","usgsCitation":"Johnson, F.A., Jensen, G.H., Alhainen, M., Fox, A.D., and Madsen, J., 2018, Taiga bean goose: Harvest assessment for the Central Management Unit: 2018, 15 p.","productDescription":"15 p.","ipdsId":"IP-098380","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":355957,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":355714,"type":{"id":15,"text":"Index Page"},"url":"https://www.unep-aewa.org/sites/default/files/document/AEWA_EGMIWG_3_12_TBG_Harvest_Report.pdf"}],"publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc45de4b0f5d57878ea63","contributors":{"authors":[{"text":"Johnson, Fred A. 0000-0002-5854-3695 fjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":2773,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","email":"fjohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":740183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jensen, Gitte Hoj","contributorId":206363,"corporation":false,"usgs":false,"family":"Jensen","given":"Gitte","email":"","middleInitial":"Hoj","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":740184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alhainen, Mikko","contributorId":141140,"corporation":false,"usgs":false,"family":"Alhainen","given":"Mikko","email":"","affiliations":[{"id":13690,"text":"Finnish Wildlife Agency","active":true,"usgs":false}],"preferred":false,"id":740185,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fox, Anthony D.","contributorId":130960,"corporation":false,"usgs":false,"family":"Fox","given":"Anthony","email":"","middleInitial":"D.","affiliations":[{"id":7177,"text":"Dept of Bioscience, Aahus Univ, Denmark","active":true,"usgs":false}],"preferred":false,"id":740186,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Madsen, Jesper","contributorId":178168,"corporation":false,"usgs":false,"family":"Madsen","given":"Jesper","email":"","affiliations":[],"preferred":false,"id":740187,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70198135,"text":"70198135 - 2018 - Adaptive harvest management for the Svalbard population of pink-footed geese: 2018 progress summary","interactions":[],"lastModifiedDate":"2018-11-20T12:44:35","indexId":"70198135","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Adaptive harvest management for the Svalbard population of pink-footed geese: 2018 progress summary","docAbstract":"This report describes progress on development and implementation of an adaptive harvest management program for maintaining the Svalbard population of Pink-footed Geese (Anser brachyrhynchus) near their target level (60,000) by providing sustainable harvests in Norway and Denmark. Specifically, this report provides an assessment of recent monitoring information and its implications for hunting seasons in 2018.\n\nAn Adaptive Harvest Management (AHM) program requires specification of four elements: (a) A set of alternative population models, which bound the uncertainty about population dynamics; (b) A set of weights describing the relative credibility of the alternative models, which are updated each year based on a comparison of model predictions and monitoring information; (c) A set of alternative harvest quotas from which to choose; and (d) An objective function, by which alternative harvest strategies are evaluated and an optimal strategy chosen.\n\nThe most current set of monitoring information was used to update model weights for the 1991-2017 period. Current model weights suggest little evidence for density-dependent survival and reproduction. These results suggest that the Pink-footed Goose population may have experienced a release from density-dependent mechanisms, corresponding to the period of rapid growth in population size. There is equivocal evidence for the effect of the number of days above freezing in May in Svalbard on survival, but the evidence for an effect on reproduction has been increasing in recent years.\n\nSince the 2016 hunting season, harvest quotas are prescribed on an annual basis rather than every three years because of the potential to better meet management objectives. Based on updated model probabilities, the November 2017 population size (72,000), the proportion of the population comprised of one-year-old birds (0.076), and days above freezing in Svalbard in May 2018 (27), the optimal harvest quota for the 2018 hunting season is approximately 27,000. With the agreed upon harvest allocation of 30% Norway and 70% Denmark, the national quotas are 8,100 and 18,900, respectively, which are higher than the harvests realized in previous years. In 2017 the quota for the two countries combined was 36,000, but only a harvest of about 11,400 was realized. The decrease in harvest quota for 2018 is largely attributable to the apparent decline in population size.\n\nWe also describe the ongoing development of an Integrated Population Model (IPM), which uses all available demographic data for Pink-footed Geese in a single, unified analysis. IPM estimates of harvest rates of adult geese were variable and relatively low prior to the implementation of AHM (2013), and have been relatively high since. The increase in harvest rates has been accompanied by a decline in annual survival. The ratio of young-of-the-year to older birds just prior to the hunting season has been variable over time, and since about 2005 has been highly correlated with the number of days above freezing in May in Svalbard. IPM estimates of population size suggest that abundance of Pink-footed Geese has been relatively stable, or declining slightly, in recent years. Based on the IPM estimate of population size in November 2017 of 68,800 (95% credible interval: 58,200 – 79,400), the optimal harvest quota for the 2018 hunting season is 15,000. This is lower than that derived from the set of nine discrete models because the IPM estimate of November population size is lower than the November count, and because the IPM model does not consider May temperatures in Svalbard, but rather assumes reproductive success varies randomly about the mean.","conferenceTitle":"3rd meeting of the AEWA European Goose Management International Working Group","language":"English","publisher":"AEWA European Goose Management","usgsCitation":"Johnson, F.A., Jensen, G.H., Clausen, K.K., and Madsen, J., 2018, Adaptive harvest management for the Svalbard population of pink-footed geese: 2018 progress summary, 24 p.","productDescription":"24 p.","ipdsId":"IP-098676","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":355958,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":355712,"type":{"id":11,"text":"Document"},"url":"https://www.unep-aewa.org/sites/default/files/document/AEWA_EGM_IWG_3_9_PFG%20AHM%20Report%202018_formatted_final_0.pdf"}],"publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc45de4b0f5d57878ea65","contributors":{"authors":[{"text":"Johnson, Fred A. 0000-0002-5854-3695 fjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":2773,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","email":"fjohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":740176,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jensen, Gitte Hoj","contributorId":206363,"corporation":false,"usgs":false,"family":"Jensen","given":"Gitte","email":"","middleInitial":"Hoj","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":740177,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clausen, Kevin K.","contributorId":174355,"corporation":false,"usgs":false,"family":"Clausen","given":"Kevin","email":"","middleInitial":"K.","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":740178,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Madsen, Jesper","contributorId":178168,"corporation":false,"usgs":false,"family":"Madsen","given":"Jesper","email":"","affiliations":[],"preferred":false,"id":740179,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196206,"text":"sir20185044 - 2018 - Estimation of unregulated monthly, annual, and peak streamflows in Forest City Stream and lake levels in East Grand Lake, United States-Canada border between Maine and New Brunswick","interactions":[],"lastModifiedDate":"2018-05-01T16:07:09","indexId":"sir20185044","displayToPublicDate":"2018-04-30T11:45:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5044","title":"Estimation of unregulated monthly, annual, and peak streamflows in Forest City Stream and lake levels in East Grand Lake, United States-Canada border between Maine and New Brunswick","docAbstract":"The U.S. Geological Survey, in cooperation with the International Joint Commission, compiled historical data on regulated streamflows and lake levels and estimated unregulated streamflows and lake levels on Forest City Stream at Forest City, Maine, and East Grand Lake on the United States-Canada border between Maine and New Brunswick to study the effects on streamflows and lake levels if two or all three dam gates are left open. Historical regulated monthly mean streamflows in Forest City Stream at the outlet of East Grand Lake (referred to as Grand Lake by Environment Canada) fluctuated between 114 cubic feet per second (ft3 /s) (3.23 cubic meters per second [m3 /s]) in November and 318 ft3 /s (9.01 m3 /s) in September from 1975 to 2015 according to Environment Canada streamgaging data. Unregulated monthly mean streamflows at this location estimated from regression equations for unregulated sites range from 59.2 ft3 /s (1.68 m3 /s) in September to 653 ft3 /s (18.5 m3 /s) in April. Historical lake levels in East Grand Lake fluctuated between 431.3 feet (ft) (131.5 meters [m]) in October and 434.0 ft (132.3 m) in May from 1969 to 2016 according to Environment Canada lake level data for East Grand Lake. Average monthly lake levels modeled by using the estimated hydrology for unregulated flows, and an outflow rating built from a hydraulic model with all gates at the dam open, range from 427.7 ft (130.4 m) in September to 431.1 ft (131.4 m) in April. Average monthly lake levels would likely be from 1.8 to 5.4 ft (0.55 to 1.6 m) lower with the gates at the dam opened than they have been historically. The greatest lake level changes would be from June through September.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185044","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Lombard, P.J., 2018, Estimation of unregulated monthly, annual, and peak streamflows in Forest City Stream and lake levels in East Grand Lake, United States-Canada border between Maine and New Brunswick: U.S. Geological Survey Scientific Investigations Report 2018–5044, 8 p., https://doi.org/10.3133/sir20185044.","productDescription":"Report: iv, 8 p.; Data release","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-092951","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":353763,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PN94VN","text":"USGS data release","description":"USGS data release","linkHelpText":"Bathymetric data for St. Croix River at outlet to East Grand Lake and Forest City Dam Survey, United States-Canadian border between Maine and New Brunswick"},{"id":353745,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5044/sir20185044.pdf","text":"Report","size":"873 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5044"},{"id":353744,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5044/coverthb.jpg"}],"country":"Canada, United States","state":"Maine, New Brunswick","otherGeospatial":"East Grand Lake, Forest City Stream","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.884521484375,\n 45.60587170876381\n ],\n [\n -67.68951416015625,\n 45.60587170876381\n ],\n [\n -67.68951416015625,\n 45.82066487514085\n ],\n [\n -67.884521484375,\n 45.82066487514085\n ],\n [\n -67.884521484375,\n 45.60587170876381\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
196 Whitten Road
Augusta, ME 04330