Director,
Alaska Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, Alaska 99508
Diadromous fishes are known biotransport vectors that can move nutrients, energy and contaminants in an upstream direction in lotic ecosystems. This function has been demonstrated repeatedly in anadromous salmonids, but the role of other diadromous species, especially tropical taxa, as biotransport vectors is less studied. Amphidromous fish species exhibit potential to act as upstream vectors of nutrients and contaminants in their postlarval and juvenile stages, but this role is largely unknown because of limited understanding of larval growth habitats. Moreover, because some species are harvested in artisanal fisheries as postlarvae, and postlarvae are consumed by riverine and estuarine predators, heavy contaminant loads may present a human or wildlife health concern. This research incorporates stable isotope and contaminant analyses to infer larval habitats and contaminant accumulation of amphidromous fishes on the Caribbean island of Puerto Rico. The isotopic signatures of postlarval amphidromous fishes indicated marine basal sources and food web components, rather than those from riverine habitats. Additionally, postlarvae did not contain concentrations of anthropogenic pollutants that would be of ecological or human health concern. These findings are the first and strongest evidence that amphidromous fish postlarvae function as biotransport vectors of marine nutrients into and up river ecosystems without posing a health threat to the receiving food web or human consumers.
","language":"English","publisher":"Wiley","doi":"10.1111/eff.12397","usgsCitation":"Engman, A.C., Kwak, T.J., and Cope, W., 2018, Do postlarval amphidromous fishes transport marine‐derived nutrients and pollutants to Caribbean streams?: Ecology of Freshwater Fish, v. 27, no. 3, p. 847-856, https://doi.org/10.1111/eff.12397.","productDescription":"10 p.","startPage":"847","endPage":"856","ipdsId":"IP-091276","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-23","publicationStatus":"PW","scienceBaseUri":"5afee6bfe4b0da30c1bfbd9c","contributors":{"authors":[{"text":"Engman, Augustin C.","contributorId":32145,"corporation":false,"usgs":false,"family":"Engman","given":"Augustin","email":"","middleInitial":"C.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":735225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kwak, Thomas J. 0000-0002-0616-137X tkwak@usgs.gov","orcid":"https://orcid.org/0000-0002-0616-137X","contributorId":834,"corporation":false,"usgs":true,"family":"Kwak","given":"Thomas","email":"tkwak@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cope, W. Gregory","contributorId":70353,"corporation":false,"usgs":true,"family":"Cope","given":"W. Gregory","affiliations":[],"preferred":false,"id":735226,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196888,"text":"70196888 - 2018 - Respiratory disease, behavior, and survival of mountain goat kids","interactions":[],"lastModifiedDate":"2018-07-23T13:00:20","indexId":"70196888","displayToPublicDate":"2018-05-14T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Respiratory disease, behavior, and survival of mountain goat kids","docAbstract":"Bacterial pneumonia is a threat to bighorn sheep (Ovis canadensis) populations. Bighorn sheep in the East Humboldt Mountain Range (EHR), Nevada, USA, experienced a pneumonia epizootic in 2009–2010. Testing of mountain goats (Oreamnos americanus) that were captured or found dead on this range during and after the epizootic detected bacteria commonly associated with bighorn sheep pneumonia die‐offs. Additionally, in years subsequent to the bighorn sheep epizootic, the mountain goat population had low kid:adult ratios, a common outcome for bighorn sheep populations that have experienced a pneumonia epizootic. We hypothesized that pneumonia was present and negatively affecting mountain goat kids in the EHR. From June–August 2013–2015, we attempted to observe mountain goat kids with marked adult females in the EHR at least once per week to document signs of respiratory disease; identify associations between respiratory disease, activity levels, and subsequent disappearance (i.e., death); and estimate weekly survival. Each time we observed a kid with a marked adult female, we recorded any signs of respiratory disease and collected behavior data that we fit to a 3‐state discrete hidden Markov model (HMM) to predict a kid's state (active vs. sedentary) and its probability of disappearing. We first observed clinical signs of respiratory disease in kids in late July–early August each summer. We observed 8 of 31 kids with marked adult females with signs of respiratory disease on 13 occasions. On 11 of these occasions, the HMM predicted that kids were in the sedentary state, which was associated with increased probability of subsequent death. We estimated overall probability of kid survival from June–August to be 0.19 (95% CI = 0.08–0.38), which was lower than has been reported in other mountain goat populations. We concluded that respiratory disease was present in the mountain goat kids in the EHR and negatively affected their activity levels and survival. Our results raise concerns about potential effects of pneumonia to mountain goat populations and the potential for disease transmission between mountain goats and bighorn sheep where the species are sympatric.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21470","usgsCitation":"Blanchong, J.A., Anderson, C.A., Clark, N.J., Klaver, R.W., Plummer, P.J., Cox, M., Mcadoo, C., and Wolff, P.L., 2018, Respiratory disease, behavior, and survival of mountain goat kids: Journal of Wildlife Management, v. 82, no. 6, p. 1243-1251, https://doi.org/10.1002/jwmg.21470.","productDescription":"9 p.","startPage":"1243","endPage":"1251","ipdsId":"IP-094396","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":487211,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/nrem_pubs/276","text":"External Repository"},{"id":354147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-25","publicationStatus":"PW","scienceBaseUri":"5afee6bee4b0da30c1bfbd96","contributors":{"authors":[{"text":"Blanchong, Julie A.","contributorId":6030,"corporation":false,"usgs":false,"family":"Blanchong","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":13018,"text":"Department of Forest and Wildlife Ecology, University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":735235,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Christopher A.","contributorId":204866,"corporation":false,"usgs":false,"family":"Anderson","given":"Christopher","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":735236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clark, Nicholas J.","contributorId":204867,"corporation":false,"usgs":false,"family":"Clark","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":16755,"text":"University of Queensland, Australia","active":true,"usgs":false}],"preferred":false,"id":735237,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":734914,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Plummer, Paul J.","contributorId":204868,"corporation":false,"usgs":false,"family":"Plummer","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":735238,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cox, Mike","contributorId":198457,"corporation":false,"usgs":false,"family":"Cox","given":"Mike","email":"","affiliations":[],"preferred":false,"id":735239,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mcadoo, Caleb","contributorId":204869,"corporation":false,"usgs":false,"family":"Mcadoo","given":"Caleb","email":"","affiliations":[],"preferred":false,"id":735240,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wolff, Peregrine L.","contributorId":69865,"corporation":false,"usgs":true,"family":"Wolff","given":"Peregrine","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":735241,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70196889,"text":"70196889 - 2018 - Hydrologic characteristics of freshwater mussel habitat: novel insights from modeled flows","interactions":[],"lastModifiedDate":"2018-05-21T13:03:19","indexId":"70196889","displayToPublicDate":"2018-05-14T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic characteristics of freshwater mussel habitat: novel insights from modeled flows","docAbstract":"The ability to model freshwater stream habitat and species distributions is limited by the spatially sparse flow data available from long-term gauging stations. Flow data beyond the immediate vicinity of gauging stations would enhance our ability to explore and characterize hydrologic habitat suitability. The southeastern USA supports high aquatic biodiversity, but threats, such as landuse alteration, climate change, conflicting water-resource demands, and pollution, have led to the imperilment and legal protection of many species. The ability to distinguish suitable from unsuitable habitat conditions, including hydrologic suitability, is a key criterion for successful conservation and restoration of aquatic species. We used the example of the critically endangered Tar River Spinymussel (Parvaspina steinstansana) and associated species to demonstrate the value of modeled flow data (WaterFALL™) to generate novel insights into population structure and testable hypotheses regarding hydrologic suitability. With ordination models, we: 1) identified all catchments with potentially suitable hydrology, 2) identified 2 distinct hydrologic environments occupied by the Tar River Spinymussel, and 3) estimated greater hydrological habitat niche breadth of assumed surrogate species associates at the catchment scale. Our findings provide the first demonstrated application of complete, continuous, regional modeled hydrologic data to freshwater mussel distribution and management. This research highlights the utility of modeling and data-mining methods to facilitate further exploration and application of such modeled environmental conditions to inform aquatic species management. We conclude that such an approach can support landscape-scale management decisions that require spatial information at fine resolution (e.g., enhanced National Hydrology Dataset catchments) and broad extent (e.g., multiple river basins).
","language":"English","publisher":"The University of Chicago Press","doi":"10.1086/697947","usgsCitation":"Drew, C.A., Eddy, M., Kwak, T.J., Cope, W., and Augspurger, T., 2018, Hydrologic characteristics of freshwater mussel habitat: novel insights from modeled flows: Freshwater Science, v. 37, no. 2, p. 343-356, https://doi.org/10.1086/697947.","productDescription":"14 p.","startPage":"343","endPage":"356","ipdsId":"IP-095471","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","volume":"37","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6bee4b0da30c1bfbd94","contributors":{"authors":[{"text":"Drew, C. Ashton","contributorId":140953,"corporation":false,"usgs":false,"family":"Drew","given":"C.","email":"","middleInitial":"Ashton","affiliations":[],"preferred":false,"id":735242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eddy, Michele","contributorId":198941,"corporation":false,"usgs":false,"family":"Eddy","given":"Michele","email":"","affiliations":[],"preferred":false,"id":735243,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kwak, Thomas J. 0000-0002-0616-137X tkwak@usgs.gov","orcid":"https://orcid.org/0000-0002-0616-137X","contributorId":834,"corporation":false,"usgs":true,"family":"Kwak","given":"Thomas","email":"tkwak@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734915,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cope, W. Gregory","contributorId":70353,"corporation":false,"usgs":true,"family":"Cope","given":"W. Gregory","affiliations":[],"preferred":false,"id":735244,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Augspurger, Tom","contributorId":189894,"corporation":false,"usgs":false,"family":"Augspurger","given":"Tom","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":735245,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70272980,"text":"70272980 - 2018 - Application and comparison of the MODIS-Derived Enhanced Vegetation Index (EVI) to VIIRS, Landsat 5 TM, and Landsat 8 OLI platforms: A case study in the arid Colorado River Delta, Mexico","interactions":[],"lastModifiedDate":"2025-12-11T16:57:04.855099","indexId":"70272980","displayToPublicDate":"2018-05-13T10:52:11","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3380,"text":"Sensors","active":true,"publicationSubtype":{"id":10}},"title":"Application and comparison of the MODIS-Derived Enhanced Vegetation Index (EVI) to VIIRS, Landsat 5 TM, and Landsat 8 OLI platforms: A case study in the arid Colorado River Delta, Mexico","docAbstract":"The Enhanced Vegetation Index (EVI) is a key Earth science parameter used to assess vegetation, originally developed and calibrated for the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. With the impending decommissioning of the MODIS sensors by the year 2020/2022, alternative platforms will need to be used to estimate EVI. We compared Landsat 5 (2000–2011), 8 (2013–2016) and the Visible Infrared Imaging Radiometer Suite (VIIRS; 2013–2016) to MODIS EVI (2000–2016) over a 420,083-ha area of the arid lower Colorado River Delta in Mexico. Over large areas with mixed land cover or agricultural fields, we found high correspondence between Landsat and MODIS EVI (R2 = 0.93 for the entire area studied and 0.97 for agricultural fields), but the relationship was weak over bare soil (R2 = 0.27) and riparian vegetation (R2 = 0.48). The correlation between MODIS and Landsat EVI was higher over large, homogeneous areas and was generally lower in narrow riparian areas. VIIRS and MODIS EVI were highly similar (R2 = 0.99 for the entire area studied) and did not show the same decrease in performance in smaller, narrower regions as Landsat. Landsat and VIIRS provide EVI estimates of similar quality and characteristics to MODIS, but scale, seasonality and land cover type(s) should be considered before implementing Landsat EVI in a particular area.
","language":"English","publisher":"MDPI","doi":"10.3390/s18051546","usgsCitation":"Jarchow, C., Didan, K., Barreto-Muñoz, A., Nagler, P.L., and Glenn, E., 2018, Application and comparison of the MODIS-Derived Enhanced Vegetation Index (EVI) to VIIRS, Landsat 5 TM, and Landsat 8 OLI platforms: A case study in the arid Colorado River Delta, Mexico: Sensors, v. 18, no. 5, 1546, 17 p., https://doi.org/10.3390/s18051546.","productDescription":"1546, 17 p.","ipdsId":"IP-086846","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":497383,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/s18051546","text":"Publisher Index Page"},{"id":497335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Colorado River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.39760654188758,\n 32.8908327506181\n ],\n [\n -115.33226880384278,\n 32.8908327506181\n ],\n [\n -115.33226880384278,\n 31.59336803534171\n ],\n [\n -114.39760654188758,\n 31.59336803534171\n ],\n [\n -114.39760654188758,\n 32.8908327506181\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2018-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarchow, Christopher 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":196069,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":951980,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Didan, Kamel","contributorId":292780,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":951981,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barreto-Muñoz, Armando","contributorId":239891,"corporation":false,"usgs":false,"family":"Barreto-Muñoz","given":"Armando","affiliations":[{"id":48028,"text":"University of Arizona, Biosystems Engineering, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":951982,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":951983,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Glenn, Edward P.","contributorId":56542,"corporation":false,"usgs":false,"family":"Glenn","given":"Edward P.","affiliations":[{"id":13060,"text":"Department of Soil, Water and Environmental Science, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":951984,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220422,"text":"70220422 - 2018 - Inferring the absence of an incipient population during a rapid response for an invasive species","interactions":[],"lastModifiedDate":"2021-05-13T11:56:12.67446","indexId":"70220422","displayToPublicDate":"2018-05-13T06:51:49","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Inferring the absence of an incipient population during a rapid response for an invasive species","docAbstract":"Successful eradication of invasives is facilitated by early detection and prompt onset of control. However, realizing or verifying that a colonization has occurred is difficult for cryptic species especially at low population densities. Responding to the capture or unconfirmed sighting of a cryptic invasive species, and the associated effort to determine if it indicates an incipient (small, localized) population or merely a lone colonizer, is costly and cannot continue indefinitely. However, insufficient detection effort risks erroneously concluding the species is not present, allowing the population to increase in size and expand its range. Evidence for an incipient population requires detection of ≥1 individual; its absence, on the other hand, must be inferred probabilistically. We use an actual rapid response incident and species-specific detection estimates tied to a known density to calculate the amount of effort (with non-sequential detections) necessary to assert, with a pre-defined confidence, that invasive brown treesnakes are absent from the search area under a wide range of hypothetical population densities. We illustrate that the amount of effort necessary to declare that a species is absent is substantial and increases with decreased individual detection probability, decreased density, and increased level of desired confidence about its absence. Such survey investment would be justified where the cost savings due to early detection are large. Our Poisson-based model application will allow managers to make informed decisions about how long to continue detection efforts, should no additional detections occur, and suggests that effort to do so is significantly higher than previously thought. While our model application informs how long to search to infer absence of an incipient population of brown treesnakes, the approach is sufficiently general to apply to other invasive species if density-dependent detection estimates are known or reliable surrogate estimates are available.
Declines in acidic deposition across Europe and North America have led to decreases in surface water acidity and signs of chemical recovery of soils from acidification. To better understand the link between recovery of soils and surface waters, chemical trends in precipitation, soils, and streamwater were investigated in three watersheds representing a depositional gradient from high to low across the northeastern United States. Significant declines in concentrations of H+ (ranging from −1.2 to −2.74 microequivalents [μeq] L−1 yr−1), NO3− (ranging from −0.6 to −0.84 μeq L−1 yr−1), and SO42− (ranging from −0.95 to −2.13 μeq L−1 yr−1) were detected in precipitation in the three watersheds during the period 1999 to 2013. Soil chemistry in the A horizon of the watershed with the greatest decrease in deposition showed significant decreases in exchangeable Al and increases in exchangeable bases. Soil chemistry did not significantly improve during the study in the other watersheds, and base saturation in the Oa and upper B horizons significantly declined in the watershed with the smallest decrease in deposition. Streamwater SO42−concentrations significantly declined in all three streams (ranging from −2.01 to −2.87 μeq L−1 yr−1) and acid neutralizing capacity increased (ranging from 1.38 to 1.60 μeq L−1 yr−1) in the two streams with the greatest decreases in deposition. Recovery of soils has likely been limited by decades of acid deposition that have leached base cations from soils with base-poor parent material.
This study tested a focused strategy for reducing phosphorus (P) and sediment loads in agricultural streams. The strategy involved selecting small watersheds identified as likely to respond relatively quickly, and then focusing conservation practices on high-contributing fields within those watersheds. Two 5,000 ha (12,360 ac) watersheds in the Driftless Area of south central Wisconsin, previously ranked in the top 6% of similarly sized Wisconsin watersheds for expected responsiveness to conservation efforts to reduce high P and sediment loads, were chosen for the study. The stream outlets from both watersheds were monitored from October of 2006 through September of 2016 for streamflow and concentrations of sediment, total P, and, beginning in October of 2009, total dissolved P. Fields and pastures having the highest potential P delivery to the streams in each watershed were identified using the Wisconsin P Index (Good et al. 2012). After three years of baseline monitoring (2006 to 2009), farmers implemented both field- and farm-based conservation practices in one watershed (treatment) as a means to reduce sediment and P inputs to the stream from the highest contributing areas, whereas there were no out-of-the-ordinary conservation efforts in the second watershed (control). Implementation occurred primarily in 2011 and 2012. In the four years following implementation of conservation practices (2013 through 2016), there was a statistically significant reduction in storm-event suspended sediment loads in the treatment watershed compared to the control watershed when the ground was not frozen (p = 0.047). While there was an apparent reduction in year-round suspended sediment event loads, it was not statistically significant at the 95% confidence level (p = 0.15). Total P loads were significantly reduced for runoff events (p < 0.01) with a median reduction of 50%. Total P and total dissolved P concentrations for low-flow conditions were also significantly reduced (p < 0.01) compared to the control watershed. This study demonstrated that a strategy that first identifies watersheds likely to respond to conservation efforts and then focuses implementation on relatively high-contributing fields within those watersheds can be successful in reducing stream P concentrations and loads.
","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.73.3.298","usgsCitation":"Carvin, R.B., Good, L.W., Fitzpatrick, F.A., Diehl, C., Songer, K., Meyer, K.J., Panuska, J.C., Richter, S., and Whalley, K., 2018, Testing a two-scale focused conservation strategy for reducing phosphorus and sediment loads from agricultural watersheds: Journal of Soil and Water Conservation, v. 73, no. 2, p. 298-309, https://doi.org/10.2489/jswc.73.3.298.","productDescription":"12 p.","startPage":"298","endPage":"309","ipdsId":"IP-077953","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":468766,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.73.3.298","text":"Publisher Index Page"},{"id":354107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","volume":"73","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-03","publicationStatus":"PW","scienceBaseUri":"5afee6bfe4b0da30c1bfbd9e","contributors":{"authors":[{"text":"Carvin, Rebecca B. 0000-0001-7778-4841 rbcarvin@usgs.gov","orcid":"https://orcid.org/0000-0001-7778-4841","contributorId":201609,"corporation":false,"usgs":true,"family":"Carvin","given":"Rebecca","email":"rbcarvin@usgs.gov","middleInitial":"B.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":734941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Good, Laura W.","contributorId":151057,"corporation":false,"usgs":false,"family":"Good","given":"Laura","email":"","middleInitial":"W.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":734942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075 fafitzpa@usgs.gov","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":196543,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith","email":"fafitzpa@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":734943,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diehl, Curt","contributorId":204768,"corporation":false,"usgs":false,"family":"Diehl","given":"Curt","email":"","affiliations":[{"id":36979,"text":"Land Conservation Division, Land and Water Resources Department, Dane County, Wisconsin","active":true,"usgs":false}],"preferred":false,"id":734944,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Songer, Katherine","contributorId":204769,"corporation":false,"usgs":false,"family":"Songer","given":"Katherine","email":"","affiliations":[{"id":36980,"text":"Land Conservation Division, Land and Water Resources Department, Dane County, Wisconsin; graduate student at the University of Wisconsin, Madison, Wisconsin.","active":true,"usgs":false}],"preferred":false,"id":734945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meyer, Kimberly J.","contributorId":204770,"corporation":false,"usgs":false,"family":"Meyer","given":"Kimberly","email":"","middleInitial":"J.","affiliations":[{"id":36981,"text":"Land Conservation Division, Land and Water Resources Department, Dane County, Wisconsin; graduate student at the University of Wisconsin, Madison, Wisconsin; Nutrient management specialist with Madison Metropolitan Sewerage District in Madison, Wisconsin.","active":true,"usgs":false}],"preferred":false,"id":734946,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Panuska, John C.","contributorId":204771,"corporation":false,"usgs":false,"family":"Panuska","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":36982,"text":"Department of Biological Systems Engineering, University of Wisconsin, Madison, Wisconsin","active":true,"usgs":false}],"preferred":false,"id":734947,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Richter, Steve","contributorId":204772,"corporation":false,"usgs":false,"family":"Richter","given":"Steve","email":"","affiliations":[{"id":36983,"text":"The Nature Conservancy, Madison, Wisconsin","active":true,"usgs":false}],"preferred":false,"id":734948,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Whalley, Kyle","contributorId":204773,"corporation":false,"usgs":false,"family":"Whalley","given":"Kyle","email":"","affiliations":[{"id":36979,"text":"Land Conservation Division, Land and Water Resources Department, Dane County, Wisconsin","active":true,"usgs":false}],"preferred":false,"id":734949,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70215292,"text":"70215292 - 2018 - Quantitative evaluation of vitrinite reflectance and atomic O/C in coal using Raman spectroscopy and multivariate analysis","interactions":[],"lastModifiedDate":"2020-10-14T15:21:09.9489","indexId":"70215292","displayToPublicDate":"2018-05-12T10:19:20","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1709,"text":"Fuel","active":true,"publicationSubtype":{"id":10}},"title":"Quantitative evaluation of vitrinite reflectance and atomic O/C in coal using Raman spectroscopy and multivariate analysis","docAbstract":"Vitrinite reflectance (VRo) is a standard petrographic method for assessing thermal maturity (rank) of coal. The vitrinite reflectance technique, however, requires significant petrographic experience, can be time-consuming, and may be biased by analyst subjectivity. Correlations between coal rank and Raman spectral properties are a promising alternative that can supplant some of the limitations inherent in the VRo protocol. The traditional peak-fitting methodologies for quantifying metrics from Raman spectra, however, also suffer from analyst subjectivity that can affect correlations between analyte and spectral properties.
This research combines high-throughput Raman spectroscopy with multivariate analysis (MVA) to create calibration models for the prediction of coal rank though VRo and atomic O/C ratio. MVA techniques eliminate the ambiguous subjectivity prevalent in peak-fitting methods by evaluating the full Raman spectrum, then identifying the integral vibrational modes for constructing accurate models. Partial least squares (PLS) regression models were developed using Raman spectra and VRo values (0.23–5.23%) for 68 geographically diverse coal samples. The calibration set was validated using one-half of the samples to rigorously assess the model’s predictive accuracy. The root mean standard error of prediction was 0.19 for the VRo model and 0.014 for the atomic O/C model. Both models exhibited linear correlations, with coefficients of determination (R2) for the validation set of 0.99 (VRo) and 0.93 (atomic O/C), despite the geographic and rank diversity of the samples. This study demonstrates the applicability and power of using PLS models for the prediction of both the VRo and atomic O/C ratio from Raman spectra. The quantitative MVA protocol contained herein provides a Raman alternative to the VRo industry benchmark for coal rank that is not subject to the limitations and subjectivity of peak-fitting methods.
Codominant marker systems are better suited to analyze population structure and assess the source of an individual in admixture analyses. Currently, there is no codominant marker system using microsatellites developed for the sea sandwort, Honckenya peploides (L.) Ehrh., an early colonizer in island systems. We developed and characterized novel microsatellite loci from H. peploides, using reads collected from whole genome shotgun sequencing on a 454 platform. The combined output from two shotgun runs yielded a total of 62,669 reads, from which 58 loci were screened. We identified 12 polymorphic loci that amplified reliably and exhibited disomic inheritance. Microsatellite data were collected and characterized for the 12 polymorphic loci in two Alaskan populations of H. peploides: Fossil Beach, Kodiak Island (n = 32) and Egg Bay, Atka Island (n = 29). The Atka population exhibited a slightly higher average number of alleles (3.9) and observed heterozygosity (0.483) than the Kodiak population (3.3 and 0.347, respectively). The overall probability of identity values for both populations was PID = 2.892e−6 and PIDsib = 3.361e−3. We also screened the 12 polymorphic loci in Wilhelmsia physodes (Fisch. ex Ser.) McNeill, the most closely related species to H. peploides, and only one locus was polymorphic. These microsatellite markers will allow future investigations into population genetic and colonization patterns of the beach dune ruderal H. peploides on new and recently disturbed islands.
","language":"English","publisher":"Springer","doi":"10.1007/s10265-018-1036-7","usgsCitation":"Gravley, M.C., Sage, G.K., Talbot, S.L., and Carlson, M.L., 2018, Development and characterization of 12 polymorphic microsatellite loci in the sea sandwort, Honckenya peploides: Journal of Plant Research, v. 131, no. 5, p. 879-885, https://doi.org/10.1007/s10265-018-1036-7.","productDescription":"7 p.","startPage":"879","endPage":"885","ipdsId":"IP-092972","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":437893,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BC3XRP","text":"USGS data release","linkHelpText":"Microsatellite Genetic Data for Sea Sandwort (Honckenya peploides) and Merckia (Wilhelmsia physodes), Alaska 2009-2016"},{"id":354106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Atka Island, Kodiak Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.775390625,\n 56.68037378950137\n ],\n [\n -151.9189453125,\n 56.68037378950137\n ],\n [\n -151.9189453125,\n 58.90464570302001\n ],\n [\n -154.775390625,\n 58.90464570302001\n ],\n [\n -154.775390625,\n 56.68037378950137\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -175.3802490234375,\n 51.984880139916626\n ],\n [\n -173.935546875,\n 51.984880139916626\n ],\n [\n -173.935546875,\n 52.466050361889515\n ],\n [\n -175.3802490234375,\n 52.466050361889515\n ],\n [\n -175.3802490234375,\n 51.984880139916626\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"131","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-23","publicationStatus":"PW","scienceBaseUri":"5afee6bfe4b0da30c1bfbda2","contributors":{"authors":[{"text":"Gravley, Megan C. 0000-0002-4947-0236 mgravley@usgs.gov","orcid":"https://orcid.org/0000-0002-4947-0236","contributorId":202812,"corporation":false,"usgs":true,"family":"Gravley","given":"Megan","email":"mgravley@usgs.gov","middleInitial":"C.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":735100,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sage, George K. 0000-0003-1431-2286 ksage@usgs.gov","orcid":"https://orcid.org/0000-0003-1431-2286","contributorId":87833,"corporation":false,"usgs":true,"family":"Sage","given":"George","email":"ksage@usgs.gov","middleInitial":"K.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":735101,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":735102,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carlson, Matthew L.","contributorId":138686,"corporation":false,"usgs":false,"family":"Carlson","given":"Matthew","email":"","middleInitial":"L.","affiliations":[{"id":12492,"text":"UAA Alaska Natural Heritage Program & Biological Sciences Department","active":true,"usgs":false}],"preferred":false,"id":735103,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196826,"text":"sir20185051 - 2018 - Estimates of long-term mean-annual nutrient loads considered for use in SPARROW models of the Midcontinental region of Canada and the United States, 2002 base year","interactions":[],"lastModifiedDate":"2018-05-14T11:09:15","indexId":"sir20185051","displayToPublicDate":"2018-05-11T12:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5051","title":"Estimates of long-term mean-annual nutrient loads considered for use in SPARROW models of the Midcontinental region of Canada and the United States, 2002 base year","docAbstract":"Streamflow and nutrient concentration data needed to compute nitrogen and phosphorus loads were compiled from Federal, State, Provincial, and local agency databases and also from selected university databases. The nitrogen and phosphorus loads are necessary inputs to Spatially Referenced Regressions on Watershed Attributes (SPARROW) models. SPARROW models are a way to estimate the distribution, sources, and transport of nutrients in streams throughout the Midcontinental region of Canada and the United States. After screening the data, approximately 1,500 sites sampled by 34 agencies were identified as having suitable data for calculating the long-term mean-annual nutrient loads required for SPARROW model calibration. These final sites represent a wide range in watershed sizes, types of nutrient sources, and land-use and watershed characteristics in the Midcontinental region of Canada and the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185051","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Saad, D.A., Benoy, G.A., and Robertson, D.M., 2018, Estimates of long-term mean-annual nutrient loads considered for use in SPARROW models of the Midcontinental region of Canada and the United States, 2002 base year: U.S. Geological Survey Scientific Investigations Report 2018–5051, 14 p., https://doi.org/10.3133/sir20185051.","productDescription":"Report: vi, 14 p.; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-084092","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":353945,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VT1R1K","text":"USGS data release","description":"USGS data release","linkHelpText":"Water-quality and streamflow datasets used for estimating loads considered for use in the 2002 Midcontinent nutrient SPARROW models, United States and Canada, 1970-2012"},{"id":353943,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5051/sir20185051.pdf","text":"Report","size":"8.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5051"},{"id":353931,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5051/coverthb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.32617187499999,\n 49.26780455063753\n ],\n [\n -94.3505859375,\n 42.94033923363181\n ],\n [\n -91.7578125,\n 39.16414104768742\n ],\n [\n -88.9013671875,\n 36.63316209558658\n ],\n [\n -86.572265625,\n 35.17380831799959\n ],\n [\n -81.7822265625,\n 37.82280243352756\n ],\n [\n -79.5849609375,\n 40.17887331434696\n ],\n [\n -77.16796875,\n 42.293564192170095\n ],\n [\n -74.794921875,\n 43.739352079154706\n ],\n [\n -75.6298828125,\n 44.933696389694674\n ],\n [\n -78.44238281249999,\n 45.1510532655634\n ],\n [\n -80.2001953125,\n 46.58906908309182\n ],\n [\n -82.2216796875,\n 47.368594345213374\n ],\n [\n -84.287109375,\n 49.781264058178344\n ],\n [\n -87.2314453125,\n 50.233151832472245\n ],\n [\n -90.52734374999999,\n 50.708634400828224\n ],\n [\n -95.1416015625,\n 50.401515322782366\n ],\n [\n -99.66796875,\n 50.064191736659104\n ],\n [\n -100.986328125,\n 51.45400691005982\n ],\n [\n -103.4912109375,\n 51.536085601784755\n ],\n [\n -102.74414062499999,\n 49.97948776108648\n ],\n [\n -104.32617187499999,\n 49.26780455063753\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Three permeable pavement surfaces - asphalt (PA), concrete (PC), and interlocking pavers (PIP) - were evaluated side-by-side to measure changes to the infiltrative capacity and water quality of stormwater runoff originating from a conventional asphalt parking lot in Madison, Wisconsin. During the 24-month monitoring period (2014-16), all three permeable pavements resulted in statistically significant reductions in the cumulative load of solids (total suspended solids and suspended sediment), total phosphorus, Escherichia coli (E. coli), and Enterococci. Most of the removal occurred through capture and retention in the void spaces of each permeable surface and aggregate base. The largest reduction in total suspended solids was for PC at 80 percent, followed by PIP and PA at 69 and 65 percent, respectively. Reductions (generally less than 50 percent) in total phosphorus also were observed, which might have been tempered by increases in the dissolved fraction observed in PIP and PA. Conversely, PC results indicated a slight reduction in dissolved phosphorus but failed to meet statistical significance. E. coli and Enterococci were reduced by about 80 percent for PC, almost twice the amount observed for PIP and PA.
Results for the PIP and PC surfaces initially indicated higher pollutant load reduction than results for the PA surface. The efficiency of PIP and PC surfaces capturing sediment, however, led to a decline in infiltration rates that resulted in more runoff flowing over, not through, the permeable surface. This result led to a decline in treatment until the permeable surface was partially restored through maintenance practices, to which PIP responded more dramatically than PC or PA. Conversely, the PA surface was capable of infiltrating most of the influent runoff volume during the monitoring period and, thus, continued to provide some level of treatment. The combined effect of underdrain and overflow drainage resulted in similar pollutant treatment for all three permeable surfaces.
Temperatures below each permeable surface generally followed changes in air temperature with a more gradual response observed in deeper layers. Therefore, permeable pavement may do little to mitigate heated runoff during summer. During winter, deeper layers remained above freezing even when air temperature was below freezing. Although temperatures were not high enough to melt snow or ice accumulated on the surface, temperatures below each permeable pavement did allow void spaces to remain open, which promoted infiltration of melted ice and snow as air temperatures rose above freezing. These open void spaces could potentially reduce the need for application of deicing agents in winter because melted snow and ice would infiltrate, thereby preventing refreezing of pooled water in what is known as the “black ice” effect.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185037","usgsCitation":"Selbig, W.R., and Buer, Nicolas, 2018, Hydraulic, water-quality, and temperature performance of three types of permeable pavement under high sediment loading conditions: U.S. Geological Survey Scientific Investigations Report 2018–5037, 44 p., https://doi.org/10.3133/sir20185037.","productDescription":"Report: ix, 44 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087882","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":437894,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RX9B09","text":"USGS data release","linkHelpText":"Storm characteristics, concentrations, and loads measured at the Permeable Pavement Research Facility, Madison, Wisconsin (2014 - 2016)"},{"id":354071,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RX9B09 ","text":"USGS data release","description":"USGS data release","linkHelpText":"Storm characteristics, concentrations, and loads measured at the permeable pavement research facility, Madison, Wisconsin (2014 - 2016)"},{"id":354069,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5037/coverthb.jpg"},{"id":354070,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5037/sir20185037.pdf","text":"Report","size":"6.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5037"}],"country":"United States","state":"Wisconsin","city":"Madison","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Groundwater-quality samples and water-level data were collected from 36 wells in the Jerome/Gooding County area of the eastern Snake River Plain aquifer during June 2017. The wells included 30 wells sampled for the U.S. Geological Survey’s National Water-Quality Assessment project, plus an additional 6 wells were selected to increase spatial distribution. The data provide water managers with the ability for an improved understanding of groundwater quality and flow directions in the area. Groundwater-quality samples were analyzed for nutrients, major ions, trace elements, and stable isotopes of water. Quality-assurance and quality-control measures consisted of multiple blank samples and a sequential replicate sample. All data are available online at the USGS National Water Information System.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1085","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources and Idaho Power Company","usgsCitation":"Skinner, K.D., 2018, Groundwater-quality data from the eastern Snake River Plain aquifer, Jerome and Gooding Counties, south-central Idaho, 2017: U.S. Geological Survey Data Series 1085, 20 p., https://doi.org/10.3133/ds1085.","productDescription":"iv, 20 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-093930","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":354092,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1085/ds1085.pdf","text":"Report","size":"1.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1085"},{"id":354091,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1085/coverthb.jpg"}],"country":"United States","state":"Idaho","county":"Gooding County, Jerome County","otherGeospatial":"Snake River Plain Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.33,\n 42.56218435232186\n ],\n [\n -114.33,\n 42.917212160086194\n ],\n [\n -115,\n 42.917212160086194\n ],\n [\n -115,\n 42.56218435232186\n ],\n [\n -114.33,\n 42.56218435232186\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
Context
Landscape resistance is vital to connectivity modeling and frequently derived from resource selection functions (RSFs). RSFs estimate relative probability of use and tend to focus on understanding habitat preferences during slow, routine animal movements (e.g., foraging). Dispersal and migration, however, can produce rarer, faster movements, in which case models of movement speed rather than resource selection may be more realistic for identifying habitats that facilitate connectivity.
Objective
To compare two connectivity modeling approaches applied to resistance estimated from models of movement rate and resource selection.
Methods
Using movement data from migrating elk, we evaluated continuous time Markov chain (CTMC) and movement-based RSF models (i.e., step selection functions [SSFs]). We applied circuit theory and shortest random path (SRP) algorithms to CTMC, SSF and null (i.e., flat) resistance surfaces to predict corridors between elk seasonal ranges. We evaluated prediction accuracy by comparing model predictions to empirical elk movements.
Results
All connectivity models predicted elk movements well, but models applied to CTMC resistance were more accurate than models applied to SSF and null resistance. Circuit theory models were more accurate on average than SRP models.
Conclusions
CTMC can be more realistic than SSFs for estimating resistance for fast movements, though SSFs may demonstrate some predictive ability when animals also move slowly through corridors (e.g., stopover use during migration). High null model accuracy suggests seasonal range data may also be critical for predicting direct migration routes. For animals that migrate or disperse across large landscapes, we recommend incorporating CTMC into the connectivity modeling toolkit.
Efforts to reverse declines in native grasslands benefit from agricultural policies that encourage private land conservation. The U.S. Department of Agriculture’s Conservation Reserve Program (CRP) improved conservation across landscapes but enrollment has declined. We used sequential exploratory mixed methods to compare landowner and conservation practitioners’ perceptions, evaluate perceived benefits, and identify potential improvements to CRP. Focus groups of practitioners informed a quantitative survey of landowners who had properties >160 total acres in Nebraska. Results suggest potential misalignment in perceptions between practitioners and landowners. Practitioners were concerned that conservation, especially of wildlife, was secondary to profit. But the majority of landowners valued CRP-related ecosystem services, including native pollinators. Practitioners posited that younger landowners were primarily profit motivated, but CRP enrollment did not differ by demographics. Practitioners and landowners identified rule complexity as a major challenge and practitioner–landowner relationships as critical to success. Findings suggest that practitioners may underestimate non-economic motivations and illuminate opportunities to encourage private land conservation.
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\"}}]}","volume":"31","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-02","publicationStatus":"PW","scienceBaseUri":"5afee6c0e4b0da30c1bfbdb0","contributors":{"authors":[{"text":"Lute, Michelle L.","contributorId":204819,"corporation":false,"usgs":false,"family":"Lute","given":"Michelle","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":735082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gillespie, Caitlyn R.","contributorId":195835,"corporation":false,"usgs":false,"family":"Gillespie","given":"Caitlyn","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":735083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fontaine, Joseph J. 0000-0002-7639-9156 jfontaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-9156","contributorId":3820,"corporation":false,"usgs":true,"family":"Fontaine","given":"Joseph","email":"jfontaine@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734980,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Dustin R.","contributorId":204239,"corporation":false,"usgs":false,"family":"Martin","given":"Dustin","email":"","middleInitial":"R.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":735084,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196933,"text":"70196933 - 2018 - Mainshock-aftershock clustering in volcanic regions","interactions":[],"lastModifiedDate":"2019-12-21T09:02:02","indexId":"70196933","displayToPublicDate":"2018-05-11T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Mainshock-aftershock clustering in volcanic regions","docAbstract":"Earthquakes break their general Poissonean behavior through two types of seismic bursts:\nswarms and mainshock-aftershock sequences. The former is commonly thought to dominate in volcanic and geothermal regions, but aftershock production, including within swarms, is not well studied in volcanic regions. Here we compare mainshock-aftershock clustering in active volcanic regions in Japan to nearby nonvolcanic regions. We find that aftershock production is similar in both areas by two separate metrics: (1) Both volcanic and nonvolcanic regions have similar proportions of areas that cluster into mainshock-aftershock sequences. (2) Volcanic areas with mainshock-aftershock sequences have aftershock productivity at least as high as nonvolcanic regions. We also find that volcano-tectonic events that are precursors to an eruption are more common at volcanoes without mainshock-aftershock clusters than at volcanoes with well-defined mainshock-aftershock clusters. This last finding hints at a strategy to identify volcanic systems where cataloged earthquakes are good predictors of behavior.","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017GL075738","usgsCitation":"Giron, R.G., Brodsky, E.E., and Prejean, S., 2018, Mainshock-aftershock clustering in volcanic regions: Geophysical Research Letters, v. 45, no. 3, p. 1370-1378, https://doi.org/10.1002/2017GL075738.","productDescription":"9 p.","startPage":"1370","endPage":"1378","ipdsId":"IP-090889","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":499992,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/7bfaa8b5c162471e8cd968a4aefe0937","text":"External Repository"},{"id":354093,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Japan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 129.0234375,\n 33.211116472416855\n ],\n [\n 130.693359375,\n 30.221101852485987\n ],\n [\n 138.955078125,\n 33.063924198120645\n ],\n [\n 143.26171875,\n 37.23032838760387\n ],\n [\n 142.91015625,\n 40.44694705960048\n ],\n [\n 146.689453125,\n 43.96119063892024\n ],\n [\n 141.943359375,\n 46.01222384063236\n ],\n [\n 140.361328125,\n 42.61779143282346\n ],\n [\n 137.98828125,\n 37.64903402157866\n ],\n [\n 129.0234375,\n 33.211116472416855\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-15","publicationStatus":"PW","scienceBaseUri":"5afee6c0e4b0da30c1bfbdae","contributors":{"authors":[{"text":"Giron, Ricardo Garza","contributorId":204807,"corporation":false,"usgs":false,"family":"Giron","given":"Ricardo","email":"","middleInitial":"Garza","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":735054,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brodsky, Emily E.","contributorId":29660,"corporation":false,"usgs":true,"family":"Brodsky","given":"Emily","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":735053,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prejean, Stephanie G. 0000-0003-0510-1989 sprejean@usgs.gov","orcid":"https://orcid.org/0000-0003-0510-1989","contributorId":172404,"corporation":false,"usgs":true,"family":"Prejean","given":"Stephanie","email":"sprejean@usgs.gov","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":735052,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196937,"text":"ofr20181083 - 2018 - A comparison of photograph-interpreted and IfSAR-derived maps of polar bear denning habitat for the 1002 Area of the Arctic National Wildlife Refuge, Alaska","interactions":[],"lastModifiedDate":"2018-05-14T11:31:42","indexId":"ofr20181083","displayToPublicDate":"2018-05-11T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1083","title":"A comparison of photograph-interpreted and IfSAR-derived maps of polar bear denning habitat for the 1002 Area of the Arctic National Wildlife Refuge, Alaska","docAbstract":"Polar bears (Ursus maritimus) in Alaska use the Arctic National Wildlife Refuge (ANWR) for maternal denning. Pregnant bears den in snow banks for more than 3 months in winter during which they give birth to and nurture young. Denning is one of the most vulnerable times in polar bear life history as the family group cannot simply walk away from a disturbance without jeopardizing survival of newly born cubs. The ANWR includes the “1002 Area”, a region recently opened for oil and gas exploration by the U.S. Department of the Interior (DOI). As a part of its mission, the DOI “… protects and manages the Nation's natural resources …” and is therefore responsible for conserving polar bears and encouraging development of energy potential. Because future industrial activities could overlap habitats used by denning polar bears, identifying these habitats can inform the decisions of resource managers tasked to develop resources and protect polar bears. To help inform these efforts, we qualitatively compared the distribution of denning habitat identified by two different methods: previously published habitat from manual interpretation of aerial photographs, and habitat derived by computer interrogation of interferometric synthetic aperture radar (IfSAR) digital terrain models (DTM). Because photograph-interpreted methods depicted denning habitat as a line and IfSAR-derived methods depicted habitat as a polygon, we assessed agreement between the two methods with distance measurements. We found that 77.5 percent of IfSAR-derived denning habitat (79.6 km2 ; 1.2 percent of the 6,837.0 km2 1002 Area) was within 600 m of photograph-interpreted habitat (3,026.9 km), including 53.9 percent within 200 m. This distribution differed from that of randomly distributed points, as only 49.4 percent of these occurred within 600 m of photograph-interpreted habitat, including 18.3 percent within 200 m. Both methods appear to identify the major physiographic features that polar bears might select for denning. IfSAR-derived methods identified habitat at greater frequency beyond major landscape features such as coastal bluffs, river banks and lakeshores, were more likely to identify isolated pockets of putative denning habitat, and were easier to implement than deriving habitat from photograph-interpretive efforts. However, previous research suggests that photograph-interpretation methods may identify denning habitat more correctly than computer interrogation of IfSAR DTMs. Future work should quantify the distribution of IfSAR-derived denning habitat relative to actual landscape features and polar bear maternal dens in the 1002 Area, and investigate the feasibility of habitat identification from finer grained DTMs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181083","usgsCitation":"Durner, G.M., and Atwood, T.C., 2018, A comparison of photograph-interpreted and IfSAR-derived maps of polar bear denning habitat for the 1002 Area of the Arctic National Wildlife Refuge, Alaska: U.S. Geological Survey Open-File Report 2018–1083, 12 p., https://doi.org/10.3133/ofr20181083.","productDescription":"Report: iv, 12 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-095475","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":354103,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DJ5DXT","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data used to compare photo-interpreted and IfSAR-derived maps of polar bear denning habitat for the 1002 Area of the Arctic National Wildlife Refuge, Alaska, 2006-2016"},{"id":354102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1083/ofr20181083.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1083"},{"id":354101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1083/coverthb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -146.5,\n 69.5\n ],\n [\n -142,\n 69.5\n ],\n [\n -142,\n 70.25\n ],\n [\n -146.5,\n 70.25\n ],\n [\n -146.5,\n 69.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, Alaska 99508
The California Department of Water Resources and Bureau of Reclamation propose new water intake facilities on the Sacramento River in northern California that would convey some of the water for export to areas south of the Sacramento-San Joaquin River Delta (hereinafter referred to as the Delta) through tunnels rather than through the Delta. The collection of water intakes, tunnels, pumping facilities, associated structures, and proposed operations are collectively referred to as California WaterFix. The water intake facilities, hereinafter referred to as the North Delta Diversion (NDD), are proposed to be located on the Sacramento River downstream of the city of Sacramento and upstream of the first major river junction where Sutter Slough branches from the Sacramento River. The NDD can divert a maximum discharge of 9,000 cubic feet per second (ft3 /s) from the Sacramento River, which reduces the amount of Sacramento River inflow into the Delta.
In this report, we conduct four analyses to investigate the effect of the NDD and its proposed operation on survival of juvenile Chinook salmon (Oncorhynchus tshawytscha). All analyses used the results of a Bayesian survival model that allowed us to simulate travel time, migration routing, and survival of juvenile Chinook salmon migrating through the Delta in response to NDD operations, which affected both inflows to the Delta and operation of the Delta Cross Channel (DCC).
For the first analysis, we evaluated the effect of the NDD bypass rules on salmon survival. The NDD bypass rules are a set of operational rule curves designed to provide adaptive levels of fish protection by defining allowable diversion rates as a function of (1) Sacramento River discharge as measured at Freeport, and (2) time of year when endangered runs requiring the most protection are present. We determined that all bypass rule curves except constant low-level pumping (maximum diversion of 900 ft3 /s) could cause a sizeable decrease in survival by as much as 6–10 percentage points. The maximum decrease in survival occurred at an intermediate Sacramento River flow of about 20,000–30,000 ft3 /s. Diversion rates increased rapidly as Sacramento River flows increased from 20,000 ft3 /s to 30,000 ft3 /s, until a maximum diversion rate was reached at 9,000 ft3 /s. Because through-Delta survival increases sharply over this range of Sacramento River flow before beginning to level off with further flow increases, increasing diversion rates over this flow range causes a large decrease in survival relative to no diversion.
For the second analysis, we applied the survival model to 82 years of daily simulated flows under the Proposed Action (PA) and No Action Alternative (NAA). The PA includes operation of the Central Valley Project/State Water Project with implementation of the NDD and its operations prescribed by the NDD bypass rules, whereas the NAA assumes system operations without implementation of the NDD. We also evaluated a “Level 1” (L1) scenario, which was similar to the PA scenario but applied the most protective bypass rule known as Level 1 post-pulse operations. We noted a high probability that survival under the PA scenario was lower than under the NAA scenario, and that travel time was longer under PA relative to NAA in most simulation years. However, the largest survival differences between the PA and NAA scenarios occurred during October–November and May–June. Although bypass rules are less restrictive during these periods, we determined that more frequent use of the DCC under PA led to the largest differences in survival between the two scenarios. Additionally, we noted no difference in median survival decreases between the PA and L1 scenarios, although in some years the L1 scenario had a lower survival decrease than the PA scenario.
For the third analysis, we proposed a quantitative approach for developing NDD rule curves (that is, prescribed diversion flows for given inflows) by using the survival model to identify diversion rates that meet a criterion of a having a small probability of exceeding a given decrease in survival. We examined diversion rates that led to a 10% chance of exceeding a given decrease in survival for a range of absolute and relative decreases in survival. To maintain a given constant level of protection across the range of river flows, our analysis indicated that diversions had to increase at a much slower rate with respect to Sacramento River flow relative to the rule curves defined in the NDD bypass table. Additionally, we determined that diversion rates could be higher than under the bypass table rule curves at river flows less than 20,000 ft3 /s, but diversions had to be less than defined by NDD bypass rules at higher flows.
For the fourth analysis, we simulated the effect of “real-time operations” on salmon survival, where bypass flow rates were determined by the presence of juvenile salmon entering the Delta, as indicated by juvenile salmon catch in a rotary screw trap upstream of the Delta. For this analysis, we evaluated NDD operations as defined by the L1 scenario and an additional scenario (Unlimited Pulse Protection [UPP]) that provided protection to an unlimited number of fish pulses. This analysis indicated that the highest catches occurred during flow pulses when daily survival was high, which caused annual survival to be weighted towards periods of high daily survival, resulting in a high annual survival. We determined that the mean annual survival decreased by 1–4 percentage points, and annual survival decreases were more frequently smaller for the UPP scenario. Additionally, because the UPP scenario protected an unlimited number of fish pulses, decreases in daily survival under the UPP scenario were less than under the L1 scenario.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181078","collaboration":"Prepared in cooperation with National Oceanic and Atmospheric Administration, National Marine Fisheries Service","usgsCitation":"Perry, R.W., and Pope, A.C., 2018, Effects of the proposed California WaterFix North Delta Diversion on survival of juvenile Chinook salmon (Oncorhynchus tshawytscha) in the Sacramento-San Joaquin River Delta, northern California: U.S. Geological Survey Open-File Report 2018-1078, 94 p. plus appendixes,\nhttps://doi.org/10.3133/ofr20181078.","productDescription":"Report: x, 94 p.; 11 Appendixes","numberOfPages":"108","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095992","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":354085,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix07.pdf","text":"Appendix 7","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 7","linkHelpText":"Simulated daily travel time by year, no action alternative compared to level 1 scenarios, 1922-2003"},{"id":354087,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix09.pdf","text":"Appendix 9","size":"2.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 9","linkHelpText":"Simulated route-specific survival by year, no action alternative compared to level 1 scenarios, 1922-2003"},{"id":354077,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1078/coverthb.jpg"},{"id":354078,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078.pdf","text":"Report","size":"18.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078"},{"id":354079,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix01.pdf","text":"Appendix 1","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 1","linkHelpText":"Simulated daily survival by year, no action alternative compared to proposed action scenarios, 1922-2003"},{"id":354086,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix08.pdf","text":"Appendix 8","size":"2.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 8","linkHelpText":"Simulated daily routing by year, no action alternative compared to level 1 scenarios, 1922-2003"},{"id":354080,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix02.pdf","text":"Appendix 2","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 2","linkHelpText":"Simulated daily travel time by year, no action alternative compared to proposed action scenarios, 1922-2003"},{"id":354081,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix03.pdf","text":"Appendix 3","size":"2.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 3","linkHelpText":"Simulated daily routing by year, no action alternative compared to proposed action scenarios, 1922-2003"},{"id":354082,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix04.pdf","text":"Appendix 4","size":"2.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 4","linkHelpText":"Simulated route-specific survival by year, no action alternative compared to PA scenarios, 1922-2003"},{"id":354083,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix05.pdf","text":"Appendix 5","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 5","linkHelpText":"Simulated route-specific travel time by year, no action alternative compared to PA scenarios, 1922-2003"},{"id":354084,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix06.pdf","text":"Appendix 6","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 6","linkHelpText":"Simulated daily survival by year, no action alternative compared to level 1 scenarios, 1922-2003"},{"id":354088,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix10.pdf","text":"Appendix 10","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 10","linkHelpText":"Simulated route-specific travel time by year, no action alternative compared to level 1 scenarios, 1922-2003"},{"id":354089,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix11.pdf","text":"Appendix 11","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 11","linkHelpText":"North Delta Diversion rule curve optimization"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.33,\n 37.82\n ],\n [\n -121.33,\n 38.5\n ],\n [\n -121.9167,\n 38.5\n ],\n [\n -121.9167,\n 37.82\n ],\n [\n -121.33,\n 37.82\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
This is the ninth annual report highlighting U.S. Geological Survey (USGS) science and decision-support activities conducted for the Wyoming Landscape Conservation Initiative (WLCI). The activities address specific management needs identified by WLCI partner agencies. In fiscal year (FY) 2016, there were 26 active USGS WLCI science-based projects. Of these 26 projects, one project was new for FY2016, and three were completed by the end of the fiscal year (though final products were still in preparation or review). USGS WLCI projects were grouped under five categories: (1) Baseline Synthesis, (2) Long-Term Monitoring, (3) Effectiveness Monitoring, (4) Mechanistic Studies of Wildlife, and (5) Data and Information Management. Each of these topic areas is designed to address WLCI management needs: identifying key drivers of change, identifying the condition and distribution of key wildlife species and habitats and of species’ habitat requirements, development of an integrated inventory and monitoring strategy, use of emerging technologies and development and testing of innovative methods for maximizing the efficiency and efficacy of monitoring efforts, evaluating the effectiveness of habitat treatment projects, evaluating the responses of wildlife to development, and developing a data clearinghouse and information management framework to support and provide access to results of most USGS WLCI projects.
In FY2016, we assisted with updating the WLCI Conservation Action Plan and associated databases as part of the Comprehensive Assessment, and we also assisted with the Bureau of Land Management 2015 WLCI annual report. By the end of FY2016, we completed or had nearly completed assessments of WLCI energy and mineral resources and had submitted a manuscript on modeled effects of oil and gas development on wildlife to a peer-reviewed journal. We also initiated a study on the effects of wind energy on wildlife in the WLCI region. A USGS circular on WLCI long-term monitoring was in review at the end of the fiscal year, and seven projects monitoring water and vegetation (including changes in sagebrush cover and patterns of sagebrush mortality) continued through the year. USGS scientists continued many projects in FY2016 that evaluate the effectiveness of habitat conservation actions (including sagebrush, cheatgrass, and aspen habitat treatments) and provide tools in support of mechanistic studies of wildlife. In FY2016, USGS scientists, along with university and State partners, continued work on five focal wildlife species/communities (pygmy rabbits [Brachylagus idahoensis], greater sage grouse , mule deer, sagebrush songbirds, and native fish). In FY2016, the USGS Information Management Team presented information to WLCI scientists on how USGS tools and resources can be used to fulfill the requirements of new USGS policies regarding data release, data management, and data visualization.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181048","usgsCitation":"Bowen, Z.H., Aikens, E., Aldridge, C.L., Anderson, P.J., Assal, T.J., Chalfoun, A.D., Chong, G.W., Eddy-Miller, C.A., Garman, S.L., Germaine, S.S., Homer, C.G., Johnston, A., Kauffman, M.J., Manier, D.J., Melcher, C.P., Miller, K.A., Walters, A.W., Wheeler, J.D., Wieferich, D., Wilson, A.B., Wyckoff, T.B., and Zeigenfuss, L.C., 2018, U.S. Geological Survey science for the Wyoming Landscape Conservation Initiative—2016 annual report: U.S. Geological Survey Open-File Report 2018–1048, 49 p., https://doi.org/10.3133/ofr20181048.","productDescription":"vii, 49 p.","onlineOnly":"Y","ipdsId":"IP-091604","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science 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Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
Here, we report the complete mitochondrial genome of the endangered Hine’s emerald dragonfly (HED), Somatochlora hineana Williamson. Data were generated via next generation sequencing (NGS) and assembled using a mitochondrial baiting and iterative mapping approach. The full length circular genome is 15,705 bp with 26.6% GC content. It contains the typical metazoan set of 37 genes: 13 protein-coding genes, 22 transfer RNA (tRNA) and 2 ribosomal RNA (rRNA) genes, and an A + T-rich control region. To our knowledge, this is the first report of the complete HED mitogenome.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/23802359.2018.1463824","usgsCitation":"Jackson, C., McCalla, S.G., Amberg, J., Soluk, D., and Britten, H., 2018, The complete mitochondrial genome of Hine’s emerald dragonfly (Somatochlora hineana Williamson) via NGS sequencing: Mitochondrial DNA Part B, v. 3, no. 2, p. 562-563, https://doi.org/10.1080/23802359.2018.1463824.","productDescription":"2 p.","startPage":"562","endPage":"563","ipdsId":"IP-093895","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":468768,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/23802359.2018.1463824","text":"Publisher Index Page"},{"id":359607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-10","publicationStatus":"PW","scienceBaseUri":"5bf52b6ae4b045bfcae2800e","contributors":{"authors":[{"text":"Jackson, Craig 0000-0003-4023-0276 cjackson@usgs.gov","orcid":"https://orcid.org/0000-0003-4023-0276","contributorId":192276,"corporation":false,"usgs":true,"family":"Jackson","given":"Craig","email":"cjackson@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":733926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCalla, S. Grace 0000-0003-4292-8694 smccalla@usgs.gov","orcid":"https://orcid.org/0000-0003-4292-8694","contributorId":168436,"corporation":false,"usgs":true,"family":"McCalla","given":"S.","email":"smccalla@usgs.gov","middleInitial":"Grace","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":733927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Amberg, Jon 0000-0002-8351-4861 jamberg@usgs.gov","orcid":"https://orcid.org/0000-0002-8351-4861","contributorId":149785,"corporation":false,"usgs":true,"family":"Amberg","given":"Jon","email":"jamberg@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":733928,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Soluk, Daniel","contributorId":204438,"corporation":false,"usgs":false,"family":"Soluk","given":"Daniel","email":"","affiliations":[{"id":36938,"text":"University of South Dakota-Vermillion","active":true,"usgs":false}],"preferred":false,"id":733929,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Britten, Hugh","contributorId":204439,"corporation":false,"usgs":false,"family":"Britten","given":"Hugh","email":"","affiliations":[{"id":36938,"text":"University of South Dakota-Vermillion","active":true,"usgs":false}],"preferred":false,"id":733930,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196459,"text":"sir20185017 - 2018 - Flood-inundation maps for the Wabash River at Lafayette, Indiana","interactions":[],"lastModifiedDate":"2018-05-14T10:39:38","indexId":"sir20185017","displayToPublicDate":"2018-05-10T11:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5017","title":"Flood-inundation maps for the Wabash River at Lafayette, Indiana","docAbstract":"Digital flood-inundation maps for an approximately 4.8-mile reach of the Wabash River at Lafayette, Indiana (Ind.) were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science web site at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at USGS streamgage 03335500, Wabash River at Lafayette, Ind. Current streamflow conditions for estimating near-real-time areas of inundation using USGS streamgage information may be obtained on the internet at https://waterdata.usgs.gov/in/nwis/uv?site_no=03335500. In addition, information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood-warning system (https://water.weather.gov/ahps/). The NWS AHPS forecasts flood hydrographs at many places that are often colocated with USGS streamgages, including the Wabash River at Lafayette, Ind. NWS AHPS-forecast peak-stage information may be used with the maps developed in this study to show predicted areas of flood inundation.
For this study, flood profiles were computed for the Wabash River reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relations at USGS streamgage 03335500, Wabash River at Lafayette, Ind., and high-water marks from the flood of July 2003 (U.S. Army Corps of Engineers [USACE], 2007). The calibrated hydraulic model was then used to determine 23 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a geographic information system digital elevation model derived from light detection and ranging to delineate the area flooded at each water level. The availability of these maps, along with internet information regarding current stage from the USGS streamgage 03335500, Wabash River at Lafayette, Ind., and forecasted high-flow stages from the NWS AHPS, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, and for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185017","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Kim, M.H., 2018, Flood-inundation maps for the Wabash River at Lafayette, Indiana: U.S. Geological Survey Scientific Investigations Report 2018–5017,Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34°C in lake pixels >180 m from land, 4.89°C at the land-water boundary, and 1.11°C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2018.1471545","usgsCitation":"Schaeffer, B.A., Iiames, J., Dwyer, J.L., Urquhart, E., Salls, W., Rover, J., and Seegers, B., 2018, An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries: International Journal of Remote Sensing, v. 39, no. 22, p. 7789-7805, https://doi.org/10.1080/01431161.2018.1471545.","productDescription":"17 p.","startPage":"7789","endPage":"7805","ipdsId":"IP-096965","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468769,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/01431161.2018.1471545","text":"Publisher Index Page"},{"id":362002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"39","issue":"22","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Schaeffer, Blake A.","contributorId":201328,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":759166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iiames, John","contributorId":214110,"corporation":false,"usgs":false,"family":"Iiames","given":"John","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dwyer, John L. 0000-0002-8281-0896 dwyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-0896","contributorId":3481,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","email":"dwyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":759164,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Urquhart, Erin","contributorId":214111,"corporation":false,"usgs":false,"family":"Urquhart","given":"Erin","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Salls, Wilson","contributorId":214112,"corporation":false,"usgs":false,"family":"Salls","given":"Wilson","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759169,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rover, Jennifer 0000-0002-3437-4030","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":211850,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":759165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Seegers, Bridget","contributorId":214113,"corporation":false,"usgs":false,"family":"Seegers","given":"Bridget","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":759170,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196155,"text":"sir20185046 - 2018 - Methods for peak-flow frequency analysis and reporting for streamgages in or near Montana based on data through water year 2015","interactions":[],"lastModifiedDate":"2018-09-25T05:33:19","indexId":"sir20185046","displayToPublicDate":"2018-05-10T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5046","title":"Methods for peak-flow frequency analysis and reporting for streamgages in or near Montana based on data through water year 2015","docAbstract":"This report documents the methods for peak-flow frequency (hereinafter “frequency”) analysis and reporting for streamgages in and near Montana following implementation of the Bulletin 17C guidelines. The methods are used to provide estimates of peak-flow quantiles for 50-, 42.9-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for selected streamgages operated by the U.S. Geological Survey Wyoming-Montana Water Science Center (WY–MT WSC). These annual exceedance probabilities correspond to 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively.
Standard procedures specific to the WY–MT WSC for implementing the Bulletin 17C guidelines include (1) the use of the Expected Moments Algorithm analysis for fitting the log-Pearson Type III distribution, incorporating historical information where applicable; (2) the use of weighted skew coefficients (based on weighting at-site station skew coefficients with generalized skew coefficients from the Bulletin 17B national skew map); and (3) the use of the Multiple Grubbs-Beck Test for identifying potentially influential low flows. For some streamgages, the peak-flow records are not well represented by the standard procedures and require user-specified adjustments informed by hydrologic judgement. The specific characteristics of peak-flow records addressed by the informed-user adjustments include (1) regulated peak-flow records, (2) atypical upper-tail peak-flow records, and (3) atypical lower-tail peak-flow records. In all cases, the informed-user adjustments use the Expected Moments Algorithm fit of the log-Pearson Type III distribution using the at-site station skew coefficient, a manual potentially influential low flow threshold, or both.
Appropriate methods can be applied to at-site frequency estimates to provide improved representation of long-term hydroclimatic conditions. The methods for improving at-site frequency estimates by weighting with regional regression equations and by Maintenance of Variance Extension Type III record extension are described.
Frequency analyses were conducted for 99 example streamgages to indicate various aspects of the frequency-
analysis methods described in this report. The frequency analyses and results for the example streamgages are presented in a separate data release associated with this report consisting of tables and graphical plots that are structured to include information concerning the interpretive decisions involved in the frequency analyses. Further, the separate data release includes the input files to the PeakFQ program, version 7.1, including the peak-flow data file and the analysis specification file that were used in the peak-flow frequency analyses. Peak-flow frequencies are also reported in separate data releases for selected streamgages in the Beaverhead River and Clark Fork Basins and also for selected streamgages in the Ruby, Jefferson, and Madison River Basins.
Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601