Haskell Lake is a shallow, 89-acre drainage lake in the headwaters of the Squirrel River, on the Lac du Flambeau Reservation in northern Wisconsin. Historically, this lake was an important producer of wild rice for the Lac du Flambeau Band of Lake Superior Chippewa Indians (LDF Tribe); but, beginning in the late 1970s, the rice began to diminish and by the late 1990s, the lake no longer had harvestable stands. Restoring wild rice to Haskell Lake is a long-term priority for the LDF Tribe. A first step towards that effort is the cleanup of a petroleum-contamination plume in the shallow aquifer upgradient of the northern end of the lake. Knowledge of the downgradient extent of the plume and the locations where contaminated water is discharging to the lake is needed to inform cleanup efforts.
A cooperative study between the U.S. Geological Survey and the LDF Tribe was initiated to characterize the distribution of groundwater discharge to Haskell Lake in the areas downgradient of the contamination plume. A fiber optic distributed temperature sensing system was used to monitor temperatures at the sediment-water interface for a 7-day period in July and August 2016. Challenges during the investigation included data storage and power supply limitations, maintenance of calibration baths, accurate location of the cable in space, cable placement in weeds and soft sediment, the confounding effects of solar radiation, and contamination of the data by multiple sources of instrument noise. The problem of instrument noise was overcome by solving the fiber optic distributed temperature sensing calibration equation for two parameters that describe temporal variation in the source laser and the photon detectors that observe the backscatter. Early morning temperatures, when the influence of solar radiation via direct warming of the sediment-water interface is minimized, were used to evaluate groundwater discharge, similar to other studies. The results indicate a persistent, horizontal variation in temperature of as much as 5.5 degrees Celsius across the study area, with cooler temperatures interpreted to indicate spatially discrete preferential groundwater discharge. Results of the study can be used to determine locations for collecting lakebed pore water samples to better define the extent of contamination discharging to the lake.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205005","collaboration":"Prepared in cooperation with the Lac du Flambeau Band of Lake Superior Chippewa Indians","usgsCitation":"Leaf, A.T., 2020, A distributed temperature sensing investigation of groundwater discharge to Haskell Lake, Lac du Flambeau Reservation, Wisconsin, July 27–August 1, 2016: U.S. Geological Survey Scientific Investigations Report 2020–5005, 17 p., https://doi.org/10.3133/sir20205005.","productDescription":"Report: vi, 17 p.; Data Release; Companion Report","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-100793","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":376503,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5005/coverthb.jpg"},{"id":376504,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5005/sir20205005.pdf","text":"Report","size":"3.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5005"},{"id":376505,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X2OHNX","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Distributed lakebed temperature data, Haskell Lake, Lac du Flambeau Reservation, Wisconsin, July 27–August 1, 2016"},{"id":377597,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20205024","text":"SIR 2020–5024","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5024","linkHelpText":"— Hydrology of Haskell Lake and investigation of a groundwater contamination plume, Lac du Flambeau Reservation, Wisconsin"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Haskell Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.93322372436523,\n 45.89717666670996\n ],\n [\n -89.89992141723633,\n 45.89717666670996\n ],\n [\n -89.89992141723633,\n 45.920467927558576\n ],\n [\n -89.93322372436523,\n 45.920467927558576\n ],\n [\n -89.93322372436523,\n 45.89717666670996\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Long‐distance migration presents complex conservation challenges, and migratory species often experience shortfalls in conservation due to the difficulty of identifying important locations and resources throughout the annual cycle. In order to prioritize habitats for conservation of migratory wildlife, it is necessary to understand how habitat needs change throughout the annual cycle, as well as to identify key habitat sites and features that concentrate large numbers of individuals and species. Among long‐distance migrants, sea ducks have particularly complex migratory patterns, which often include distinct post‐breeding molt sites as well as breeding, staging and wintering locations. Using a large set of individual tracking data (n = 476 individuals) from five species of sea ducks in eastern North America, we evaluated multi‐species habitat suitability and partitioning across the breeding, post‐breeding migration and molt, wintering and pre‐breeding migration seasons. During breeding, species generally occupied distinct habitat areas, with the highest levels of multi‐species overlap occurring in the Barrenlands west of Hudson Bay. Species generally preferred flatter areas closer to lakes with lower maximum temperatures relative to average conditions, but varied in distance to shore, elevation and precipitation. During non‐breeding, species overlapped extensively during winter but diverged during migration. All species preferred shallow‐water, nearshore habitats with high productivity, but varied in their relationships to salinity, temperature and bottom slope. Sea ducks selected most strongly for preferred habitats during post‐breeding migration, with high partitioning among species; however, both selection and partitioning were weaker during pre‐breeding migration. The addition of tidal current velocity, aquatic vegetation presence and bottom substrate improved non‐breeding habitat models where available. Our results highlight the utility of multi‐species, annual‐cycle habitat assessments in identifying key habitat features and periods of vulnerability in order to optimize conservation strategies for migratory wildlife.
","language":"English","publisher":"Wiley","doi":"10.1111/ecog.05003","usgsCitation":"Lamb, J.S., Paton, P.W., Osenkowski, J.E., Badzinski, S.S., Berlin, A., Bowman, T.D., Dwyer, C., Fara, L., Gilliland, S.G., Kenow, K.P., Lepage, C., Mallory, M.L., Olsen, G., Perry, M., Petrie, S.A., Savard, J.L., Savoy, L., Schummer, M.L., Spiegel, C.S., and McWilliams, S.R., 2020, Assessing year‐round habitat use by migratory sea ducks in a multi‐species context reveals seasonal variation in habitat selection and partitioning: Ecography, v. 43, no. 12, p. 1842-1858, https://doi.org/10.1111/ecog.05003.","productDescription":"17 p.","startPage":"1842","endPage":"1858","onlineOnly":"Y","ipdsId":"IP-115137","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455607,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.05003","text":"Publisher Index 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0000-0002-7188-6203","orcid":"https://orcid.org/0000-0002-7188-6203","contributorId":239542,"corporation":false,"usgs":true,"family":"Olsen","given":"Glenn H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":797139,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Perry, Matthew 0000-0001-6452-9534 mperry@usgs.gov","orcid":"https://orcid.org/0000-0001-6452-9534","contributorId":179173,"corporation":false,"usgs":true,"family":"Perry","given":"Matthew","email":"mperry@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":797140,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Petrie, Scott A.","contributorId":141223,"corporation":false,"usgs":false,"family":"Petrie","given":"Scott","email":"","middleInitial":"A.","affiliations":[{"id":13717,"text":"Long Point Waterfowl","active":true,"usgs":false}],"preferred":false,"id":797141,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Savard, Jean-Pierre L.","contributorId":101776,"corporation":false,"usgs":false,"family":"Savard","given":"Jean-Pierre","email":"","middleInitial":"L.","affiliations":[{"id":6962,"text":"Science and Technology Branch, Environment Canada","active":true,"usgs":false}],"preferred":false,"id":797142,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Savoy, Lucas","contributorId":171896,"corporation":false,"usgs":false,"family":"Savoy","given":"Lucas","affiliations":[{"id":6928,"text":"BioDiversity Research Institute, Gorham, ME 04038","active":true,"usgs":false}],"preferred":false,"id":797143,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Schummer, Michael L.","contributorId":176347,"corporation":false,"usgs":false,"family":"Schummer","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":797144,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Spiegel, Caleb S.","contributorId":216938,"corporation":false,"usgs":false,"family":"Spiegel","given":"Caleb","email":"","middleInitial":"S.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":797145,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"McWilliams, Scott R.","contributorId":172328,"corporation":false,"usgs":false,"family":"McWilliams","given":"Scott","email":"","middleInitial":"R.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":797146,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70212606,"text":"70212606 - 2020 - Bioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates","interactions":[],"lastModifiedDate":"2020-08-24T13:27:00.53768","indexId":"70212606","displayToPublicDate":"2020-08-18T08:21:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Bioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates","docAbstract":"Land managers need new tools for planning novel futures due to climate change. Species distribution modeling (SDM) has been used extensively to predict future distributions of species under different climates, but their map products are often too coarse for fine-scale operational use. In this study we developed a flexible, efficient, and robust method for mapping current and future distributions and abundances of vegetation species and communities at the fine spatial resolutions that are germane to land management. First, we mapped Potential Vegetation Types (PVTs) using conventional statistical modeling techniques (Random Forests) that used bioclimatic ecosystem process and climate variables as predictors. We obtained over 50% accuracy across 13 mapped PVTs for our study area. We then applied future climate projections as climate input to the Random Forest model to generate future PVT maps, and used field data describing the occurrence of tree and non-tree species in each PVT category to model and map species distribution for current and future climate. These maps were then compared to two previous SDM mapping efforts with over 80% agreement and equivalent accuracy. Because PVTs represent the biophysical potential of the landscape to support vegetation communities as opposed to the vegetation that currently exists, they can be readily linked to climate forecasts and correlated with other, climate-sensitive ecological processes significant in land management, such as fire regimes and site productivity.
As acidic deposition has decreased across Eastern North America, forest soils at some sites are beginning to show reversal of soil acidification. However, the degree of recovery appears to vary and is not fully explained by deposition declines alone. To assess if other site and soil factors can help to explain degree of recovery from acid deposition, soil resampling chemistry data (8- to 24-year time interval) from 23 sites in the United States and Canada, located across 25° longitude from Eastern Maine to Western Ontario, were explored. Site and soil factors included recovery years, sulfate (SO42−) deposition history, SO42− reduction rate, C horizon pH and exchangeable calcium (Ca), O and B horizon pH, base saturation, and exchangeable Ca and aluminum (Al) at the time of the initial sampling. We found that O and B horizons that were initially acidified to a greater degree showed greater recovery and B horizon recovery was further associated with an increase in recovery years and lower initial SO42− deposition. Forest soils that seemingly have low buffering capacity and a reduced potential for recovery have the resilience to recover from the effects of previous high levels of acidic deposition. This suggests, that predictions of where forest soils acidification reversal will occur across the landscape should be refined to acknowledge the importance of upper soil profile horizon chemistry rather than the more traditional approach using only parent material characteristics.
","language":"English","publisher":"MDPI","doi":"10.3390/soilsystems4030054","issn":"2571-8789","usgsCitation":"Hazlett, P., Emilson, C., Lawrence, G.B., Fernandez, I.J., Ouimet, R., and Bailey, S., 2020, Reversal of forest soil acidification in the northeastern United States and eastern Canada: Site and soil factors contributing to recovery: Soil Systems, v. 4, no. 3, 54, 22 p., https://doi.org/10.3390/soilsystems4030054.","productDescription":"54, 22 p.","ipdsId":"IP-120230","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":455610,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/soilsystems4030054","text":"Publisher Index Page"},{"id":377978,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.615234375,\n 48.719961222646276\n ],\n [\n -94.39453125,\n 45.30580259943578\n ],\n [\n -93.55957031249999,\n 41.31082388091818\n ],\n [\n -91.97753906249999,\n 37.16031654673677\n ],\n [\n -81.650390625,\n 38.92522904714054\n ],\n [\n -75.9814453125,\n 39.9434364619742\n ],\n [\n -70.3564453125,\n 41.541477666790286\n ],\n [\n -63.984375,\n 46.13417004624326\n ],\n [\n -64.599609375,\n 49.15296965617042\n ],\n [\n -79.365234375,\n 47.754097979680026\n ],\n [\n -90.615234375,\n 48.719961222646276\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Hazlett, P.W.","contributorId":239646,"corporation":false,"usgs":false,"family":"Hazlett","given":"P.W.","email":"","affiliations":[{"id":13540,"text":"Canadian Forest Service","active":true,"usgs":false}],"preferred":false,"id":797473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Emilson, C.E. 0000-0002-4770-1117","orcid":"https://orcid.org/0000-0002-4770-1117","contributorId":239647,"corporation":false,"usgs":false,"family":"Emilson","given":"C.E.","email":"","affiliations":[{"id":13540,"text":"Canadian Forest Service","active":true,"usgs":false}],"preferred":false,"id":797474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fernandez, I. 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Introduced in the 1930s, the benthivorous Longnose Sucker (Catostomus catostomus) became established in Yellowstone Lake, Wyoming, USA, and used tributary streams for spawning. With this introduction, concerns were raised regarding their possible competition for food resources with native adfluvial Yellowstone Cutthroat Trout (Oncorhynchus clarkii bouvieri). Additionally, insufficient literature exists on Longnose Sucker feeding habits throughout their range, and there has been no comprehensive study of Longnose Sucker diet in Yellowstone Lake. The need exists for understanding the community ecology and food web dynamics in Yellowstone Lake, especially as non-native Lake Trout (Salvelinus namaycush) have caused declines in Yellowstone Cutthroat Trout through predation. The objectives of this study were to examine possible size-specific shifts in feeding habits, evaluate feeding strategy, and compare historical and contemporary diet data of Longnose Suckers in Yellowstone Lake. Diet data collected during summer of 2018 were analyzed by length-class to test for size-specific diet shifts. As Longnose Sucker length increased, copepods (Diacyclops bicuspidatus thomasi, Leptodiaptomus ashlandi or Hesperodiaptomus shoshone) decreased in proportion by weight. In contrast, dipterans (Chironomidae) and amphipods (Hyalella spp. or Gammarus spp.) varied in proportion by weight in the diet across length classes. We assessed the feeding strategy by evaluating the relationship between prey-specific abundance and percent frequency of occurrence. This assessment indicates that Longnose Suckers have a heterogeneous diet and generalized feeding strategy as all prey items had a prey-specific abundance value of <50%. Diet composition differed significantly between historical and contemporary samples, likely related to the differences in sampling locations and possibly due to a Lake Trout-induced trophic cascade. This study established the diet composition and feeding habits of Longnose Suckers residing in Yellowstone Lake, thus, expanding our knowledge of Longnose Sucker feeding patterns and ecology.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02705060.2020.1807421","usgsCitation":"Furey, K.M., Glassic, H., Guy, C.S., Koel, T., Arnold, J.L., Doepke, P., and Bigelow, P., 2020, Diets of Longnose Sucker in Yellowstone Lake, Yellowstone National Park, U.S.A.: Journal of Freshwater Ecology, v. 35, no. 1, p. 291-303, https://doi.org/10.1080/02705060.2020.1807421.","productDescription":"13 p.","startPage":"291","endPage":"303","ipdsId":"IP-118728","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":455620,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02705060.2020.1807421","text":"Publisher Index Page"},{"id":395973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.5938720703125,\n 44.23929609118664\n ],\n [\n -110.2,\n 44.23929609118664\n ],\n [\n -110.2,\n 44.59633476144439\n ],\n [\n -110.5938720703125,\n 44.59633476144439\n ],\n [\n -110.5938720703125,\n 44.23929609118664\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-08-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Furey, Kaitlyn M.","contributorId":278612,"corporation":false,"usgs":false,"family":"Furey","given":"Kaitlyn","email":"","middleInitial":"M.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":834857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glassic, Hayley C.","contributorId":278613,"corporation":false,"usgs":false,"family":"Glassic","given":"Hayley C.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":834858,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":834856,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koel, Todd M.","contributorId":278608,"corporation":false,"usgs":false,"family":"Koel","given":"Todd M.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":834852,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arnold, Jeffrey L.","contributorId":278609,"corporation":false,"usgs":false,"family":"Arnold","given":"Jeffrey","email":"","middleInitial":"L.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":834853,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Doepke, Philip D.","contributorId":278610,"corporation":false,"usgs":false,"family":"Doepke","given":"Philip D.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":834854,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bigelow, Patricia E.","contributorId":278611,"corporation":false,"usgs":false,"family":"Bigelow","given":"Patricia E.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":834855,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70212589,"text":"70212589 - 2020 - A multi-state occupancy modelling framework for robust estimation of disease prevalence in multi-tissue disease systems","interactions":[],"lastModifiedDate":"2020-12-14T16:00:22.571788","indexId":"70212589","displayToPublicDate":"2020-08-16T09:01:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A multi-state occupancy modelling framework for robust estimation of disease prevalence in multi-tissue disease systems","docAbstract":"North American waterfowl harvest regulations are largely guided by the status of breeding populations. Nonetheless, understanding the demographics of wintering waterfowl populations can elucidate the effects of hunting pressure on population dynamics. The ring‐necked duck (Aythya collaris) breeds and winters in all North American administrative flyways and is one of the most abundant and most harvested diving ducks in the Atlantic Flyway. But few studies have investigated the winter ecology of ring‐necked ducks. We used a known‐fate analysis to estimate period survival probability using data from 87 female ring‐necked ducks marked with satellite transmitters in 2 regions of the southern Atlantic Flyway during winters of 2017–2018 and 2018–2019. Winter (128‐day) survival probability was higher for individuals in the Red Hills region of southern Georgia and northern Florida (0.875, 95% CI = 0.691–0.952) than individuals in central South Carolina (0.288, 95% CI = 0.082–0.514). We attribute the regional disparity in winter survival probabilities to differences in hunting pressure, which are reflected in the number of harvests we observed in each region. Our findings warrant further investigation into regional variation in winter survival of southern Atlantic Flyway ring‐necked ducks, and, specifically, the relationship between variable harvest pressure and winter survival and its influence on ring‐necked duck population dynamics and adaptive harvest management decisions.
Declines among species of insect pollinators, especially butterflies, has garnered attention from scientists and managers. Often these declines have spurred governments to declare some species as threatened or endangered. We used existing presence–absence data from surveys for the threatened Dakota skipper Hesperia dacotae (Skinner) to build statistical maps of species presence that could be used to inform future monitoring designs. We developed a hierarchical Bayesian modeling approach to estimate the spatial distribution and temporal trend in Dakota skipper probability of presence. Our model included a spatial random effect and fixed effects for the proportion of two grassland habitat types: those on well-drained soils and those on poorly drained soils; as well as the topographic slope. The results from this model were then used to assess sampling strategies with two different monitoring objectives: locating new Dakota skipper colonies or monitoring the proportion of historically (pre-2000) extant colonies. Our modeling results suggested that the distribution of Dakota skippers followed the distribution of remnant grasslands and that probabilities of presence tended to be higher in topographically diverse grasslands with well-drained soils. Our analysis also showed that the probability of presence declined throughout the northern Great Plains range. Our simulations of the different sampling designs suggested that new detections were expected when sampling where Dakota skippers likely occurred historically, but this may lead to a tradeoff with monitoring existing sites. Prior information about the extant sites may help to ameliorate this tradeoff.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ee/nvaa081","usgsCitation":"Post van der Burg, M., Austin, J.E., Wiltermuth, M.T., Newton, W.E., and MacDonald, G.J., 2020, Capturing spatiotemporal patterns in presence-absence data to inform monitoring and sampling designs for the threatened Dakota skipper (Lepidoptera: Hesperiidae) in the Great Plains of the United States: Environmental Entomology, v. 49, no. 5, p. 1252-1261, https://doi.org/10.1093/ee/nvaa081.","productDescription":"10 p.","startPage":"1252","endPage":"1261","ipdsId":"IP-113665","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455635,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ee/nvaa081","text":"Publisher Index Page"},{"id":377598,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Minnesota, North Dakota, South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.72412109375,\n 42.68243539838623\n ],\n [\n -91.91162109375,\n 42.89206418807337\n ],\n [\n -91.318359375,\n 43.30919109985686\n ],\n [\n -91.51611328125,\n 43.83452678223682\n ],\n [\n -92.900390625,\n 44.85586880735725\n ],\n [\n -93.0322265625,\n 45.78284835197676\n ],\n [\n -94.68017578125,\n 48.647427805533546\n ],\n [\n -95.77880859375,\n 48.951366470947725\n ],\n [\n -103.90869140625,\n 48.951366470947725\n ],\n [\n -100.39306640625,\n 43.35713822211053\n ],\n [\n -94.72412109375,\n 42.68243539838623\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Post van der Burg, Max 0000-0002-3943-4194 maxpostvanderburg@usgs.gov","orcid":"https://orcid.org/0000-0002-3943-4194","contributorId":4947,"corporation":false,"usgs":true,"family":"Post van der Burg","given":"Max","email":"maxpostvanderburg@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":796623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Austin, Jane E. 0000-0001-8775-2210 jaustin@usgs.gov","orcid":"https://orcid.org/0000-0001-8775-2210","contributorId":146411,"corporation":false,"usgs":true,"family":"Austin","given":"Jane","email":"jaustin@usgs.gov","middleInitial":"E.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":796624,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiltermuth, Mark T. 0000-0002-8871-2816 mwiltermuth@usgs.gov","orcid":"https://orcid.org/0000-0002-8871-2816","contributorId":708,"corporation":false,"usgs":true,"family":"Wiltermuth","given":"Mark","email":"mwiltermuth@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":796625,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Newton, Wesley E. 0000-0002-1377-043X wnewton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-043X","contributorId":3661,"corporation":false,"usgs":true,"family":"Newton","given":"Wesley","email":"wnewton@usgs.gov","middleInitial":"E.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":796626,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"MacDonald, Garrett J. 0000-0002-9487-7721","orcid":"https://orcid.org/0000-0002-9487-7721","contributorId":238820,"corporation":false,"usgs":true,"family":"MacDonald","given":"Garrett","email":"","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":796627,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211972,"text":"sir20205089 - 2020 - Status of groundwater-level altitudes and long-term groundwater-level changes in the Chicot, Evangeline, and Jasper aquifers, Houston-Galveston region, Texas, 2020","interactions":[],"lastModifiedDate":"2020-08-14T14:22:37.455961","indexId":"sir20205089","displayToPublicDate":"2020-08-13T12:39:14","publicationYear":"2020","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":"2020-5089","displayTitle":"Status of Groundwater-Level Altitudes and Long-Term Groundwater-Level Changes in the Chicot, Evangeline, and Jasper Aquifers, Houston-Galveston Region, Texas, 2020","title":"Status of groundwater-level altitudes and long-term groundwater-level changes in the Chicot, Evangeline, and Jasper aquifers, Houston-Galveston region, Texas, 2020","docAbstract":"Since the early 1900s, most of the groundwater withdrawals in the Houston-Galveston region, Texas, have been from the three primary aquifers that compose the Gulf Coast aquifer system—the Chicot, Evangeline, and Jasper aquifers. Withdrawals from these aquifers are used for municipal supply, commercial and industrial use, and irrigation. This report, prepared by the U.S. Geological Survey in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District, is one in an annual series of reports depicting the status of groundwater-level altitudes and long-term groundwater-level changes in the Chicot, Evangeline, and Jasper aquifers in the Houston-Galveston region. This report contains regional-scale maps depicting approximate 2020 groundwater-level altitudes (represented by measurements made during December 2019 through March 2020) and long-term groundwater-level changes in the Chicot, Evangeline, and Jasper aquifers.
In 2020, groundwater-level-altitude contours for the Chicot aquifer ranged from 150 feet (ft) below the North American Vertical Datum of 1988 (hereinafter referred to as “datum”) to 200 ft above datum. The 1977–2020 groundwater-level-change contours for the Chicot aquifer depict a large area of decline in groundwater-level altitudes (120 ft) in northwestern Harris County. The largest rise in groundwater-level altitudes in the Chicot aquifer from 1977 to 2020 (200 ft) was in southeastern Harris County.
In 2020, groundwater-level-altitude contours for the Evangeline aquifer ranged from 250 ft below datum to 200 ft above datum. The 1977–2020 groundwater-level-change contours for the Evangeline aquifer depict broad areas where groundwater-level altitudes either declined or rose. The largest decline in groundwater-level altitudes (280 ft) was in southern Montgomery and northern Harris Counties. The largest rise in groundwater-level altitudes in the Evangeline aquifer from 1977 to 2020 (240 ft) was in southeastern Harris County.
In 2020, groundwater-level-altitude contours for the Jasper aquifer ranged from 200 ft below datum to 250 ft above datum. The 2000–20 groundwater-level-change contours for the Jasper aquifer depict groundwater-level declines throughout most of the study area where groundwater-level-altitude data from the Jasper aquifer were collected, with the largest decline (220 ft) in southern Montgomery County.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205089","collaboration":"Prepared in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District","usgsCitation":"Braun, C.L., and Ramage, J.K., 2020, Status of groundwater-level altitudes and long-term groundwater-level changes in the Chicot, Evangeline, and Jasper aquifers, Houston-Galveston region, Texas, 2020: U.S. Geological Survey Scientific Investigations Report 2020–5089, 18 p., https://doi.org/10.3133/sir20205089.","productDescription":"Report: v, 18 p.; 2 Data Releases","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-118353","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":377449,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5089/coverthb.jpg"},{"id":377450,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5089/sir20205089.pdf","text":"Report","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5089"},{"id":377451,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90EJL2E","text":"USGS data release","description":"USGS data release","linkHelpText":"Depth to groundwater measured from wells completed in the Chicot, Evangeline, and Jasper aquifers, Houston-Galveston region, Texas, 2020"},{"id":377452,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98IX48O","text":"USGS data release","description":"USGS data release","linkHelpText":"Groundwater-level altitudes and long-term groundwater-level changes in the Chicot, Evangeline, and Jasper aquifers, Houston-Galveston region, Texas, 2020"}],"country":"United States","state":"Texas","city":"Galveston, Houston","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.35882568359375,\n 29.556734316910855\n ],\n [\n -94.79827880859375,\n 30.107117887092357\n ],\n [\n -95.39154052734374,\n 30.401306519203583\n ],\n [\n -95.635986328125,\n 30.61191363386011\n ],\n [\n -95.86395263671875,\n 30.774878871959746\n ],\n [\n -96.6412353515625,\n 30.09286062952815\n ],\n [\n -95.70465087890625,\n 28.72190478475891\n ],\n [\n -94.35882568359375,\n 29.556734316910855\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
The North American Breeding Bird Survey (BBS) has been the cornerstone of continental bird conservation and management for hundreds of North American bird species in the United States and Canada for more than 50 years. This strategic plan was developed in collaboration with key partners and stakeholders and charts the ambitious course for the BBS over the next decade (2020–30). Using this plan as a guide, the BBS program will set out to improve the breadth and depth of standardized data collection and analytical products; ensure its products are widely used and recognized as the authoritative source for long-term population change information for most birds; and secure adequate resources, internally and through partnerships, to realize the expanded vision of the BBS intended to support avian management needs through 2030.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1466","usgsCitation":"U.S. Geological Survey and Canadian Wildlife Service, 2020, Strategic Plan for the North American Breeding Bird Survey, 2020–30: U.S. Geological Survey Circular 1466, 10 p., https://doi.org/10.3133/cir1466. [Supersedes USGS Circular 1307.]","productDescription":"vi, 10 p.","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-118858","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":377479,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1466/cir1466.pdf","text":"Report","size":"24.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1466"},{"id":377478,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1466/coverthb.jpg"}],"country":"Canada, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.2412109375,\n 14.647368383896632\n ],\n [\n -92.2412109375,\n 15.623036831528264\n ],\n [\n -91.2744140625,\n 16.04581345375217\n ],\n [\n -90.2197265625,\n 16.25686733062344\n ],\n [\n -91.0986328125,\n 17.22475820662464\n ],\n [\n -90.9228515625,\n 17.811456088564483\n ],\n [\n -88.681640625,\n 17.97873309555617\n ],\n [\n -88.11035156249999,\n 18.521283325496277\n ],\n [\n -87.4951171875,\n 18.22935133838668\n ],\n [\n -84.9462890625,\n 23.36242859340884\n ],\n [\n -80.068359375,\n 25.20494115356912\n ],\n [\n -79.62890625,\n 26.902476886279832\n ],\n [\n -80.68359375,\n 31.12819929911196\n ],\n [\n -73.8720703125,\n 36.24427318493909\n ],\n [\n -73.30078125,\n 39.9434364619742\n ],\n [\n -62.666015625,\n 44.24519901522129\n ],\n [\n -51.85546874999999,\n 46.76996843356982\n ],\n [\n -55.9423828125,\n 53.9560855309879\n ],\n [\n -63.720703125,\n 60.994423108456154\n ],\n [\n -60.90820312499999,\n 67.1016555307692\n ],\n [\n -77.16796875,\n 74.83343569581844\n ],\n [\n -73.4326171875,\n 78.98818668348777\n ],\n [\n -64.1162109375,\n 81.38765019536312\n ],\n [\n -60.42480468749999,\n 82.40823147725087\n ],\n [\n -65.4345703125,\n 83.06346888871603\n ],\n [\n -78.486328125,\n 83.22606748157787\n ],\n [\n -91.845703125,\n 82.08819592233118\n ],\n [\n -108.28125,\n 79.16307540424508\n ],\n [\n -114.12597656249999,\n 78.62133875728088\n ],\n [\n -128.49609375,\n 74.98218270428184\n ],\n [\n -130.078125,\n 71.32895017791999\n ],\n [\n -134.208984375,\n 70.46620742226558\n ],\n [\n -137.724609375,\n 69.41124235697256\n ],\n [\n -155.7421875,\n 71.93815765811694\n ],\n [\n -167.431640625,\n 69.00567519658819\n ],\n [\n -168.22265625,\n 65.14611484756372\n ],\n [\n -172.705078125,\n 63.54855223203644\n ],\n [\n -168.48632812499997,\n 52.908902047770255\n ],\n [\n -160.927734375,\n 54.57206165565852\n ],\n [\n -145.546875,\n 59.80063426102869\n ],\n [\n -138.076171875,\n 57.844750992891\n ],\n [\n -133.2421875,\n 52.16045455774706\n ],\n [\n -125.595703125,\n 47.931066347509784\n ],\n [\n -124.27734374999999,\n 44.902577996288876\n ],\n [\n -124.892578125,\n 42.5530802889558\n ],\n [\n -125.33203125,\n 40.64730356252251\n ],\n [\n -123.48632812499999,\n 36.66841891894786\n ],\n [\n -117.861328125,\n 31.57853542647338\n ],\n [\n -115.31249999999999,\n 27.137368359795584\n ],\n [\n -108.10546875,\n 21.207458730482642\n ],\n [\n -100.986328125,\n 16.04581345375217\n ],\n [\n -92.2412109375,\n 14.647368383896632\n ]\n ]\n ]\n }\n }\n ]\n}","publicComments":"Circular 1466 supersedes Circular 1307.","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708-4039
Digital flood-inundation maps for a 9.9-mile reach of Dardenne Creek, St. Charles County, Missouri, were created by the U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Transportation, St. Charles County, and the Cities of O’Fallon and St. Peters, Mo. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgages 05514860 Dardenne Creek at Old Town St. Peters, Mo., and 05587450 Mississippi River at Grafton, Illinois. Near-real-time stages at these streamgages may be obtained from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ ahps2/ hydrograph.php? wfo= lsx&gage= drcm7 and https://water.weather.gov/ ahps2/ hydrograph.php? wfo= lsx&gage= grfi2, which also forecasts flood hydrographs at these sites (sites DRCM7 and GRFI2).
Flood profiles were computed for the Dardenne Creek stream reach by means of a one-dimensional model for simulating water-surface profiles with steady-state flow computations. The model was calibrated by using the current stage-streamflow relation at the USGS streamgages 05514840 Dardenne Creek at O’Fallon, Mo., and 05514860 Dardenne Creek at Old Town St. Peters, Mo., and the documented high-water marks from the flood of December 2015.
The hydraulic model was then used to compute 17 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 16 ft, or near bankfull, to 32 ft at the reference streamgage 05514860. Stages in the lower Dardenne Creek can be affected by backwater from the Mississippi River; therefore, several sets of water-surface profiles were developed representing the extent of varying levels of backwater as referenced to the USGS streamgage 05587450 on the Mississippi River at Grafton, Ill. The upper stage for each map library exceeds the stage corresponding to the estimated 0.2-percent annual exceedance probability flood (500-year recurrence interval flood) at the streamgage location. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.26-ft vertical accuracy and 0.71-ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps, along with real-time information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood mitigation, preparedness and planning, flood-response activities such as evacuations and road closures, and postflood recovery efforts.
Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
As part of CRMS, Digital Orthophoto Quarter Quadrangles (DOQQs) for the coastal region of Louisiana are created for years when coastwide land-water classifications are required. A DOQQ is a raster image in which displacement in the image caused by sensor orientation and terrain relief has been corrected. These images combine the image characteristics of a photo with the geometric qualities of a map. The DOQQs generated for this project consist of four components or spectral bands of information: blue, green, red and very-near infrared (VNIR). These images are referred to as Color Infrared (CIR) digital imagery.
CRMS site-level assessments of land-water coverage will be based off of color-infrared photography acquired for coastal Louisiana and clipped to each 1-km2 CRMS-Wetlands site. Unless otherwise noted as a specific preliminary condition, all vegetation such as scrub-shrub, emergent vegetation, and forested areas will fall under the land classification, while open water, non-vegetated, regularly flooded mud flats, and aquatic vegetation beds will be characterized as water.
CRMS imagery contracts are managed and issued by the United States Geological Survey (USGS) Wetland and Aquatic Research Center.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"A standard operating procedures manual for the Coastwide Reference Monitoring System-Wetlands and the System-Wide Assessment and Monitoring Program: Methods for site establishment, data collection, and quality assurance/quality control","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"Louisiana Coastal Protection and Restoration Authority","collaboration":"Coastal Protection and Restoration Authority of Louisiana","usgsCitation":"Folse, T.M., McGinnis, T., Sharp, L.A., West, J.L., Hymel, M.K., Troutman, J.P., Weifenbach, D., Boshart, W.M., Rodrigue, L.B., Richardi, D.C., Wood, W.B., Miller, C.M., Robinson, E.M., Freeman, A.M., Stagg, C., Couvillion, B., and Beck, H., 2020, Imagery, 5 p.","productDescription":"5 p.","startPage":"10-1","endPage":"10-5","ipdsId":"IP-120085","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research 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Imagine a single month of earthquake data delivery, serving 3.6 billion total data requests, including 29 million pageviews by 7.1 million users, 606 million automated data feeds, and 45 million catalog downloads. Yet, some challenges and lapses in delivery have happened at critical times, including during the Ridgecrest earthquakes in 2019. We delve into the evolving demand for real‐time information as well as the technologies put in place to support the ever‐growing number of users in an increasingly mobile world.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220200130","usgsCitation":"Leith, W.S., Fee, J., Martinez, E.M., and Lastowka, L.A., 2020, Now trending … Earthquake information: Seismological Research Letters, v. 91, no. 5, p. 2900-2903, https://doi.org/10.1785/0220200130.","productDescription":"4 p.","startPage":"2900","endPage":"2903","ipdsId":"IP-119902","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":387630,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"91","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Leith, William S. 0000-0002-3463-3119","orcid":"https://orcid.org/0000-0002-3463-3119","contributorId":261659,"corporation":false,"usgs":true,"family":"Leith","given":"William","email":"","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820347,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fee, Jeremy 0000-0002-6851-2796 jmfee@usgs.gov","orcid":"https://orcid.org/0000-0002-6851-2796","contributorId":194758,"corporation":false,"usgs":true,"family":"Fee","given":"Jeremy","email":"jmfee@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820348,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martinez, Eric M. 0000-0002-5697-5654","orcid":"https://orcid.org/0000-0002-5697-5654","contributorId":261660,"corporation":false,"usgs":true,"family":"Martinez","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820349,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lastowka, Lynda A. 0000-0001-5469-7577 llastowka@usgs.gov","orcid":"https://orcid.org/0000-0001-5469-7577","contributorId":261661,"corporation":false,"usgs":true,"family":"Lastowka","given":"Lynda","email":"llastowka@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820350,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227268,"text":"70227268 - 2020 - Ultraviolet-assisted oiling assessment improves detection of oiled birds experiencing clinical signs of hemolytic anemia after exposure to the Deepwater Horizon oil spill","interactions":[],"lastModifiedDate":"2022-01-06T15:02:36.641948","indexId":"70227268","displayToPublicDate":"2020-08-12T08:54:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1479,"text":"Ecotoxicology","active":true,"publicationSubtype":{"id":10}},"title":"Ultraviolet-assisted oiling assessment improves detection of oiled birds experiencing clinical signs of hemolytic anemia after exposure to the Deepwater Horizon oil spill","docAbstract":"While large-scale oil spills can cause acute mortality events in birds, there is increasing evidence that sublethal oil exposure can trigger physiological changes that have implications for individual performance and survival. Therefore, improved methods for identifying small amounts of oil on birds are needed. Because ultraviolet (UV) light can be used to identify thin crude oil films in water and on substrate that are not visually apparent under normal lighting conditions, we hypothesized that UV light could be useful for detecting small amounts of oil present on the plumage of birds. We evaluated black skimmers (Rynchops niger), brown pelicans (Pelecanus occidentalis), clapper rails (Rallus crepitans), great egrets (Ardea alba), and seaside sparrows (Ammodramus maritimus) exposed to areas affected by the Deepwater Horizon oil spill in the Gulf of Mexico as well as from reference areas from 20 June, 2010 to 23 February, 2011. When visually assessed without UV light, 19.6% of birds evaluated from areas affected by the spill were determined to be oiled (previously published data), whereas when examined under UV light, 56.3% of the same birds were determined to have oil exposure. Of 705 individuals examined in areas potentially impacted by the spill, we found that fluorescence under UV light assessment identified 259 oiled birds that appeared to be oil-free on visual exam, supporting its utility as a simple tool for improving detection of modestly oiled birds in the field. Further, UV assessment revealed an increase in qualitative severity of oiling (approximate % of body surface oiled) in 40% of birds compared to what was determined on visual exam. Additionally, black skimmers, brown pelicans, and great egrets exposed to oil as determined using UV light experienced oxidative injury to erythrocytes, had decreased numbers of circulating erythrocytes, and showed evidence of a regenerative hematological response in the form of increased reticulocytes. This evidence of adverse effects was similar to changes identified in birds with oil exposure as determined by visual examination without UV light, and is consistent with hemolytic anemia likely caused by oil exposure. Thus, UV assessment proved useful for enhancing detection of birds exposed to oil, but did not increase detection of birds experiencing clinical signs of anemia compared to standard visual oiling assessment. We conclude that UV light evaluation can help identify oil exposure in many birds that would otherwise be identified visually as unexposed during oil spill events.
","language":"English","publisher":"Springer","doi":"10.1007/s10646-020-02255-8","usgsCitation":"Fallon, J.A., Smith, E.P., Shoch, N., Paruk, J., Adams, E., Evers, D., Jodice, P.G., Perkins, M., Meatty, D.E., and Hopkins, W., 2020, Ultraviolet-assisted oiling assessment improves detection of oiled birds experiencing clinical signs of hemolytic anemia after exposure to the Deepwater Horizon oil spill: Ecotoxicology, v. 29, p. 1399-1408, https://doi.org/10.1007/s10646-020-02255-8.","productDescription":"10 p.","startPage":"1399","endPage":"1408","ipdsId":"IP-107889","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":467280,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s10646-020-02255-8","text":"External Repository"},{"id":393957,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.2509765625,\n 26.15543796871355\n ],\n [\n -82.705078125,\n 26.15543796871355\n ],\n [\n -82.705078125,\n 30.600093873550072\n ],\n [\n -97.2509765625,\n 30.600093873550072\n ],\n [\n -97.2509765625,\n 26.15543796871355\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationDate":"2020-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Fallon, J. A.","contributorId":270956,"corporation":false,"usgs":false,"family":"Fallon","given":"J.","email":"","middleInitial":"A.","affiliations":[{"id":56231,"text":"Virginia Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":830209,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, E. P.","contributorId":270957,"corporation":false,"usgs":false,"family":"Smith","given":"E.","email":"","middleInitial":"P.","affiliations":[{"id":56231,"text":"Virginia Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":830210,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shoch, N.","contributorId":270958,"corporation":false,"usgs":false,"family":"Shoch","given":"N.","email":"","affiliations":[{"id":56232,"text":"Adirondack Center for Loon Conservation","active":true,"usgs":false}],"preferred":false,"id":830211,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paruk, J. D.","contributorId":270959,"corporation":false,"usgs":false,"family":"Paruk","given":"J. D.","affiliations":[{"id":56233,"text":"Saint Joseph's College of Maine","active":true,"usgs":false}],"preferred":false,"id":830212,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, E. A.","contributorId":270960,"corporation":false,"usgs":false,"family":"Adams","given":"E. A.","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":830213,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Evers, D. C.","contributorId":270961,"corporation":false,"usgs":false,"family":"Evers","given":"D. C.","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":830214,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":830215,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Perkins, M.","contributorId":270962,"corporation":false,"usgs":false,"family":"Perkins","given":"M.","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":830216,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Meatty, D. E.","contributorId":270963,"corporation":false,"usgs":false,"family":"Meatty","given":"D.","email":"","middleInitial":"E.","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":830217,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hopkins, W. A.","contributorId":270964,"corporation":false,"usgs":false,"family":"Hopkins","given":"W. A.","affiliations":[{"id":56231,"text":"Virginia Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":830218,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70215193,"text":"70215193 - 2020 - Assessing the potential for spectrally based remote sensing of salmon spawning locations","interactions":[],"lastModifiedDate":"2020-10-10T13:13:47.748404","indexId":"70215193","displayToPublicDate":"2020-08-12T08:10:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the potential for spectrally based remote sensing of salmon spawning locations","docAbstract":"Remote sensing tools are increasingly used for quantitative mapping of fluvial habitats, yet few techniques exist for continuous sampling of aquatic organisms, such as spawning salmonids. This study assessed the potential for spectrally based remote sensing of salmon spawning locations (i.e., redds) using data acquired from unmanned aircraft systems (UAS) along a large, gravel‐bed river. We developed a novel, semi‐automated approach for detecting salmon redds by applying machine learning classification and object detection techniques to UAS‐based imagery. We found that both true colour (RGB) and hyperspectral imagery could be used to identify salmon redds, though with varying degrees of accuracy. Redds were mapped with accuracies of ~0.75 from RGB imagery using logistic regression and support vector machines (SVM) classification algorithms, but this type of data could not be used to identify redds using Object‐based Image Analysis (OBIA). The hyperspectral imagery was more useful for mapping salmon redds, with accuracies greater than 0.9 for both logistic regression and SVM classifiers; OBIA of the hyperspectral data resulted in redd detection accuracies up to 0.86. The hyperspectral imagery also yielded complementary physical habitat information including water depth and substrate composition, which we quantified on the basis of a spectrally based chlorophyll absorption ratio. Overall, the hyperspectral imagery more effectively identified salmon spawning locations than RGB images and was more conducive to the classification approaches we evaluated. Each type of remotely sensed data had advantages and limitations, which are important for potential users to understand when incorporating UAS‐based data collection into river ecosystem studies.
The Taiya River flows through the Chilkoot Trail Unit of Klondike Gold Rush National Historical Park in southeast Alaska, which was founded to preserve cultural and historical resources and further understanding of natural processes active in the surrounding coastal-to-subarctic basin. Riverine processes exert an important influence on ecologically important boreal toad (Anaxryus boreas boreas), salmon [chum salmon (Oncorhynchus keta), pink salmon (O. gorbushca), and coho salmon (O. kisutch)], and eulachon (Thaleichthys pacificus) habitats, erosion of the historic ghost town of Dyea and other cultural and historical artifacts, and recreational opportunities in the lower 7.5 kilometers (km) of the Taiya River valley bottom. Recurrent consideration of hydroelectric development in West Creek upstream of the park since the 1980s has included proposals for damming and diverting West Creek, which could alter the delivery of water and sediment to this section of the Taiya River. To improve understanding of the hydrologic dependence of park resources for the purposes of guiding effective monitoring and conservation, this study, conducted by the U.S. Geological Survey in cooperation with the National Park Service, used a review of hydrologic data, collection of discrete suspended sediment data, geomorphic mapping, and analysis of historical aerial and ground photographs in a reconnaissance of formative geomorphic processes and hydrologic conditions in the lower 7.5 km of the Taiya River valley bottom.
Streamflow and suspended sediment data collected at the U.S. Geological Survey streamgages on the Taiya River and West Creek, combined with historical data, document conditions consistent with streams draining strongly glacierized basins in Alaska. Suspended sediment concentrations from samples collected concurrently over six varying flow levels during 2017–18 ranged from 6 to 284 milligrams per liter (mg/L) for the Taiya River and 13 to 162 mg/L for West Creek, which are similar to or slightly higher than historical values. For the common period of record (1970–77), correlation of daily mean discharge between the two streams was strongest (Pearson’s r = 0.97) during the prolonged May–October high-flow season and weakest (r = 0.90) during the November–April low-flow season, when West Creek daily mean discharge was proportionally higher. For the Taiya River, streamflow data compared between the available periods of record (1970–77 and 2004–17) showed no decadal-scale patterns in mean annual discharge but did show a shift toward an earlier spring snowmelt pulse. Notable flooding in the Taiya River Basin includes glacial lake outburst floods from the Nourse River valley prior to and during the 1897–98 Gold Rush, a 2002 glacial lake outburst flood from the West Creek valley, and a 1967 rainfall-generated flood.
Geomorphic mapping identified four categories of surfaces in the valley bottom—active main stem, abandoned main stem, alluvial fans, and emergent tidal surfaces. Using the maps, main-stem surfaces were subdivided into age categories to identify channel migration patterns from prior to 1940s to 2018. The valley bottom is dominated by active or abandoned channels of the Taiya River except at the extensive low-angle West Creek fan. The active main stem presently supports a mostly single-thread channel with bars and a few sloughs, but the channel actively moved and sometimes was braided within multiple, wider unvegetated corridors in 1894 and earlier. An inventory of 29 off-main-stem channels identified for the study indicates that abandoned main stem channels provide local topographic lows that can intercept groundwater or sustain tributary flow, facilitating the formation of most nonestuarine wetlands in the valley and sustaining important boreal toad breeding habitat.
Within the active main stem corridor, the channel has episodically built and reworked meanders and bars, eroding more than one-half of the historic Dyea townsite, in response to glacially controlled delivery of water and sediment, flooding, inputs from West Creek, local features including large woody debris and beaver dams, and rapid uplift from isostatic rebound. West Creek has constructed a large, persistent fan, provoked kilometer-scale Taiya River channel change near the confluence, constructively added to high-season streamflow that affects Taiya River channel migration capacity, disproportionately contributed early-season streamflow, and possibly contributed to groundwater levels in the valley bottom. The progressive narrowing and stability of the main stem corridor, possibly a result of reduction in the magnitude or frequency of glacial lake outburst floods or glacial sediment delivery to streams, indicates less active future reworking of abandoned main-stem surfaces or regeneration of wetland features. The fluvial history of the Taiya River valley bottom collectively indicates continued channel change within a limited corridor, relative stability in wetland locations but uncertainty in stability of groundwater supply to them, and channel incision and extension in response to uplift.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205059","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Curran, J.H., 2020, Hydrology and geomorphology of the Taiya River near the West Creek Tributary, southeast Alaska: U.S. Geological Survey Scientific Investigations Report 2020–5059, 57 p., https://doi.org/10.3133/sir20205059.","productDescription":"Report: viii, 57 p.; Data Release","numberOfPages":"57","onlineOnly":"Y","ipdsId":"IP-102183","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":376975,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5059/covrthb.jpg"},{"id":376976,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5059/sir20205059.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376977,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XP1SE7","linkHelpText":"Geomorphic surface and channel boundaries for the lower 7.5 kilometers of the Taiya River Valley, southeast Alaska, 2018"}],"country":"United States","state":"Alaska","otherGeospatial":"Taiya River Near the West Creek Tributary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -137.5927734375,\n 57.71588512774503\n ],\n [\n -135,\n 57.657157596582984\n ],\n [\n -132.64892578125,\n 57.621875380195455\n ],\n [\n -132.64892578125,\n 59.877911874831156\n ],\n [\n -137.61474609375,\n 59.877911874831156\n ],\n [\n -137.5927734375,\n 57.71588512774503\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Modeling of ecosystems is a part of the U.S. Environmental Protection Agency’s protocol for developing site-specific selenium guidelines for protection of aquatic life. Selenium as an environmental contaminant is known to bioaccumulate and cause reproductive effects in fish and wildlife. Here we apply a modeling methodology—ecosystem-scale selenium modeling—to understand and document the scientific basis for predicting and validating ecological protection for Lake Koocanusa, a transboundary reservoir between Montana and British Columbia. A comprehensive set of site-specific data compiled from public databases (Federal, State, and Provincial) and reports by Teck Coal Ltd., is available in a companion U.S. Geological Survey data release. The tissue guideline used within modeling here to assess protection is the U.S. Environmental Protection Agency’s national selenium guideline for whole-body fish (dry weight); however, other numeric values for a whole-body guideline or other tissue types may be assumed if applicable tissue-to-tissue conversion factors are available.
We consider the report assembled here as a working document that presents a model that can effectively address and structure the needs of (1) scientific understanding in representing the lake’s ecosystem and selenium biodynamics and (2) policy and management development during a decision-making process, but it is open to modification and updating as more ecologically detailed data become available. The approach brings together the main concerns involved in selenium toxicity: likelihood of high exposure, inherent species sensitivity, and close connectivity of ecosystem characteristics and behavioral ecology of predators. Detailed site-specific modeling equations are provided to document the linked factors that determine the responses of ecosystems to selenium. A series of scenarios quantifies the implications of choices of site-specific variables including food-web species, bioavailability of particulate material, and partitioning between the dissolved and particulate phases at the base of food webs. A gradient mapping tool applied to Lake Koocanusa provides a precedent for ecosystem-scale modeling of lakes by recognizing the importance of lake strata and hydrodynamics as components of modeling.
Data requirements for ecosystem modeling, including ecological and hydrological process information fundamental to the dietary biodynamics of selenium in site-specific food webs, were assessed as a precursor to model validation for Lake Koocanusa. Understanding these relationships is necessary to connect modeling outcomes to reproductive effects and establish boundaries, in the case of Lake Koocanusa, for the influences of dam operation, fish-community viability, and its Clean Water Act impaired 303(d)-listing status on ecosystem function.
We find that an assemblage of conditions affects the representation of Lake Koocanusa’s ecosystem within modeling scenarios but that the constructed gradient maps, mechanistic model, and associated bioaccumulation potentials portray and quantify the variables that are determinative to protection of predator species. Ecological and hydrological sorting of compiled individual data points on a site- and species-specific basis helps identify and address model uncertainties. Sources of uncertainty include (1) the scarcity of data for some environmental media compartments across time and locations, (2) the complexity of hydrodynamic conditions that can lead to seasonal ecological disconnects such as in selenium partitioning from water into particulates, and (3) the functional status of Lake Koocanusa’s ecosystem because of cumulative effects of various environmental stresses (for example, fish-community changes, flow regime changes, parasites, gonadal dysfunction, and increasing mining input-selenium concentrations since 1984). To this last point, it is important to determine where Lake Koocanusa is in an impairment-restoration cycle so as not to base protection on survivor bias, the maintenance of a currently degraded ecosystem, or normalized toxicity. In a broader context, one of the overall consequences of revised selenium regulations is that their derivation is now dependent on being able to define and understand the status of the ecosystem on which protection is based.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201098","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality","usgsCitation":"Presser, T.S., and Naftz, D.L., 2020, Understanding and documenting the scientific basis of selenium ecological protection in support of site-specific guidelines development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada: U.S. Geological Survey Open-File Report 2020–1098, 40 p., https://doi.org/10.3133/ofr20201098.","productDescription":"Report: viii, 40 p.; 3 Tables; Data Releases","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120031","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":436823,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99LM27E","text":"USGS data release","linkHelpText":"Results of Ecosystem Scale Selenium Modeling in Support of Site-Specific Guidelines Development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada, 2020"},{"id":377297,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HB5S5F","text":"USGS data release","description":"USGS Data Release","linkHelpText":"USGS measurements of dissolved and suspended particulate material selenium in Lake Koocanusa in the vicinity of Libby Dam (MT), 2015–2017 (update)"},{"id":377296,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VXYSNZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Selenium concentrations in food webs of Lake Koocanusa in the vicinity of Libby Dam (MT) and the Elk River (BC) as the basis for applying ecosystem-scale modeling, 2008–2018"},{"id":377295,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1098/ofr20201098.pdf","text":"Report","size":"19.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1098"},{"id":377294,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1098/coverthb.jpg"},{"id":377363,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1098/ofr20201098_tables_1_and_3_to_10.xlsx","text":"Tables 1 and 3–10","size":"91.5 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2020–1098 Tables"}],"country":"United States, Canada","state":"Montana, British Columbia","otherGeospatial":"Lake Koocanusa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.72998046875,\n 48.33251726168281\n ],\n [\n -114.90600585937499,\n 48.33251726168281\n ],\n [\n -114.90600585937499,\n 49.457413352792216\n ],\n [\n -115.72998046875,\n 49.457413352792216\n ],\n [\n -115.72998046875,\n 48.33251726168281\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Water Mission Area
U.S. Geological Survey
345 Middlefield Rd.
Menlo Park, CA 94025
Tallgrass prairie is a disturbance‐dependent ecosystem that has suffered steep declines in the midwestern United States. The necessity of disturbance, typically fire or grazing, presents challenges to managers who must apply them on increasingly small and fragmented parcels. The goal of this study was to compare effects of management using cattle grazing or fire on vegetation and soil characteristics to aid managers in making decisions regarding the kind of disturbance to apply. We selected 73 sites, of which 27 were managed solely by cattle grazing and 46 solely by fire, for at least 11 yr leading up to the study. We stratified the sites by prairie type (dry, mesic, and wet) and sampled frequency of plant species on randomly placed transects, supplemented with botanist‐directed walks, and collected and composited five soil cores on a randomly selected transect within each prairie type at each site. We calculated rarefied richness and Shannon evenness from the transect data and mean coefficient of conservatism (CofC) from the total list of species. Soil samples were analyzed for texture, bulk density, total N and C, and potential net N nitrification and mineralization. A nonmetric multidimensional scaling analysis of the plant community data revealed differences in species associated with mesic and wet prairies, but no separation by management type. Similarly, none of the vegetation variables we calculated varied by management type, as determined by mixed‐effects models, but soil bulk density was 17.5% higher and total N was 22% higher on grazed sites than burned sites. Sites burned more recently had higher species richness and mean CofC, but fire was not associated with any soil variables. Sites grazed more recently had higher bulk density, total N and C, and faster N cycling rates. Overall, 28% of plant species were found exclusively in one management type or the other, but these species did not vary in mean CofC. We conclude that, at the levels of burning and grazing intensity we studied, both management approaches produce similar C storage and vegetation responses. To maintain maximum diversity across the landscape, however, both approaches are necessary.
Kelp forests and rocky reefs are among the most recognized marine ecosystems and provide the primary habitat for several species of fishes, invertebrates, and algal assemblages (Stephens and others, 2006). In addition, kelp forests have been shown to be important carbon dioxide sinks (Wilmers and others, 2012) and are an important source of nearshore marine primary production (Duggins and others, 1989). These highly dynamic ecosystems are extremely variable, and both top-down and bottom-up ecological controls drive this rich trophic environment. Giant kelp (Macrocystis pyrifera) forests and the species that inhabit these ecosystems are influenced by several environmental conditions, such as wave exposure, water temperature, water clarity, bottom depth and composition, species composition, and the density of kelp and other algal assemblages (Schiel and Foster, 2015). However, in addition to “normal” variability, kelp forests can undergo extreme regime shifts from kelp canopy forested areas to barrens characterized by high densities of urchins and encrusting coralline algae (Harrold and Reed, 1985).
San Nicolas Island (SNI), outermost of the California Channel Islands, is home to a diverse group of terrestrial and marine organisms and includes kelp bed and rocky reef habitats (fig. 1). The SNI kelp forests not only provide food and shelter for fishes and invertebrates within the habitat, but also they support higher trophic level consumers such as marine birds and several marine mammal species including the southern sea otter (Enhydra lutris nereis), a major predator on sea urchins and other marine invertebrates.
Owing to concern about the vulnerability of the California population, the U.S. Fish and Wildlife Service (USFWS) translocated 140 southern sea otters from the central California coast to SNI between 1987 and 1990. Although only approximately 14 translocated otters are thought to have remained at SNI (U.S. Fish and Wildlife Service, 2012), their population at the island has increased and is currently greater than 120 individuals (Hatfield and others, 2019). Sea otters are a natural part of the kelp forest ecosystem, but their presence has implications for community dynamics as they repopulate a region from which they were extirpated in the 19th century. At SNI, sea otters have been concentrated mostly around the west end of the island, with some use of the south side and very little, but expanding, use of the northeast side. An ecosystem shift from urchin dominated to kelp dominated, that occurred at a site at the west end of the island in the early 2000s, though initiated by sea urchin disease, was likely facilitated to some degree by sea otter foraging (Kenner and Tinker, 2018).
These ecosystems also are the target of many fisheries, including urchin and lobster. Urchin fisheries, which target the larger red sea urchin, may release the smaller but more mobile purple sea urchin from competitive control (Dayton and others, 1998). Lobster fisheries may release purple sea urchins from predatory control (Lafferty, 2004). Owing to the distance from the mainland, however, SNI kelp forests and reefs have been somewhat protected from the degree of harvest and other anthropogenic impacts experienced by the southern California mainland. Invasive species are another issue, and there are a few invasive subtidal macroalgae of concern in southern California waters. Although the brown alga Sargassum muticum has been established at the island for decades, S. horneri has only recently been seen at SNI and, so far, the invasive kelp Undaria pinnatifida and the green alga Caulerpa taxifolia have not been observed there. Sargassum horneri, in particular, has demonstrated a capability to outcompete native kelps at some of the other Channel Islands but it is unclear what indirect effects it may have on community structure (Marks and others, 2015).
Because the surrounding kelp forests fall within the management boundary of the SNI Integrated Natural Resources Management Plan (INRMP; U.S. Navy, 2015), USGS works with the Navy to provide surveys of this ecologically important ecosystem that inform natural resource managers of trends in the population abundance of particular species. In addition, long-term surveys allow for an understanding of potential changes in species diversity and community composition as a result of trophic or other interactions.
The U.S. Geological Survey (USGS) implemented a kelp forest monitoring program for the U.S. Navy at San Nicolas Island in 2014, building on sites and methods established by USFWS scientists in 1980 (appendix 1). This report focuses on data collected during sampling expeditions to these sites in fall 2018 (October 2–5) and spring 2019 (April 3–6). Together they will be herein referred to as year 5 because, although the trips were made in different calendar years, they were approximately 6 months apart and were conducted under the fifth year of this contract. The previous sampling year (fall 2017 and spring 2018) is referred to as year 4. The year 5 data are compared with data collected during eight trips from fall 2014 through spring 2018. Differences in counts between these expeditions can result from seasonal factors, stochastic variation, or sampling error, but temporal comparison can reveal population trends. Where appropriate, long-term data collected during the 33 years prior to the implementation of these slightly revised protocols will be presented in order to lend some context to the observations reported here.
Genus and species names used in this report are those currently recognized as valid in the Integrated Taxonomic Information System (ITIS.gov). Upon first use, the name recognized as valid by the World Register of Marine Species (WoRMS; marinespecies.org) is shown in brackets if different. The exception is Sargassum horneri which does not show up in any discernable form in ITIS.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201091","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Kenner, M.C., and Tomoleoni, J.A., 2020, Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2018 and Spring 2019, fifth annual report: U.S. Geological Survey Open-File Report 2020–1091, 93 p., https://doi.org/10.3133/ofr20201091.","productDescription":"ix, 93 p.","onlineOnly":"Y","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":377300,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1091/coverthb.jpg"},{"id":377301,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1091/ofr20201091.pdf","text":"Report","size":"6.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1091"}],"country":"United States","state":"California","county":"Ventura County","otherGeospatial":"Naval Facility San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.60197448730467,\n 33.19675310661128\n ],\n [\n -119.41383361816405,\n 33.19675310661128\n ],\n [\n -119.41383361816405,\n 33.290359825563534\n ],\n [\n -119.60197448730467,\n 33.290359825563534\n ],\n [\n -119.60197448730467,\n 33.19675310661128\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
As much as 22 inches of rain fell in Oklahoma in May 2019, resulting in historic flooding along the Arkansas River and its tributaries in eastern and northeastern Oklahoma. The flooding along the Arkansas River and its tributaries that began in May continued into June 2019. Peaks of record were measured at nine U.S. Geological Survey (USGS) and U.S. Army Corps of Engineers (USACE) streamgages on various streams in eastern and northeastern Oklahoma. This report documents the peak streamflows and stages for 38 selected streamgages in eastern and northeastern Oklahoma and is a followup to a previous report by the USGS that documented flood peaks associated with the May 2019 flood event. Most of the flood peaks occurred from May 26 to June 4, 2019. This report includes data from streamgages on tributaries to the Arkansas River and uses modeling methods to extend the period of record for Arkansas River streamgages. The historic flooding caused homes to fall into the river as a result of bank erosion, forced some towns to be evacuated, and resulted in the highest flood depths in Tulsa, Oklahoma, since 1986. Several USGS and USACE streamgages along the Arkansas River and its tributaries recorded new peaks of record.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201090","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency and the U.S. Army Corps of Engineers","usgsCitation":"Lewis, J.M., Williams, D.J., Harris, S.J., and Trevisan, A.R., 2020, Characterization of peak streamflow and stages at selected streamgages in eastern and northeastern Oklahoma from the May to June 2019 flood event—With an emphasis on flood peaks downstream from dams and on tributaries to the Arkansas River: U.S. Geological Survey Open-File Report 2020–1090, 18 p., https://doi.org/10.3133/ofr20201090.","productDescription":"Report: iv, 18 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-118379","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":377112,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T3Q6MB","text":"USGS data release","description":"USGS Data Release","linkHelpText":"RiverWare model outputs for flood calculations along the Arkansas River for a flood event in eastern and northeastern Oklahoma during May–June 2019"},{"id":377111,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1090/ofr20201090.pdf","text":"Report","size":"4.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1090"},{"id":377110,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1090/coverthb.jpg"}],"country":"United States","state":"Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.61328125,\n 34.59704151614417\n ],\n [\n -94.1748046875,\n 34.59704151614417\n ],\n [\n -94.1748046875,\n 37.125286284966805\n ],\n [\n -98.61328125,\n 37.125286284966805\n ],\n [\n -98.61328125,\n 34.59704151614417\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Hydrodynamic model hindcasts of the surface water and groundwater of the Everglades and the greater Miami, Florida, area were used to simulate hydrology using estimated storm surge height, wind field, and rainfall for the Great Miami Hurricane (GMH), which struck on September 18, 1926. Ranked estimates of losses from hurricanes in inflation-adjusted dollars indicate that the GMH was one of the most damaging tropical cyclones to make landfall in the United States, but little hydrologic data were collected because many types of field stations did not exist at the time. Several techniques were used to estimate previously unknown critical storm variables for model input, demonstrating the value of reanalyzing historical storm events using modern hydrodynamic modeling. This representation of the 1926 GMH was then used to develop a hypothetical simulation of the hydrologic effects of a similar hurricane occurring in contemporary (1996) times. Results indicate that the 18-centimeter sea-level rise between 1926 and 1996 had a greater effect on salinity intrusion than climatic differences or the development of modern canal-based infrastructure. Moreover, the post-1926 canal infrastructure does not seem to substantially mitigate the deleterious effects of sea-level rise.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201010","usgsCitation":"Krohn, M.D., Swain, E.D., Langtimm, C.A., and Obeysekera, J., 2020, Repurposing a hindcast simulation of the 1926 Great Miami Hurricane, south Florida: U.S. Geological Survey Open-File Report 2020–1010, 9 p., https://doi.org/10.3133/ofr20201010.","productDescription":"Report: iv, 9 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-073595","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":375607,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C681IV","text":"USGS data release","linkHelpText":"FTLOADDS (combined SWIFT2D surface-water model and SEAWAT groundwater model) simulator used to repurpose a hindcast simulation of the 1926 Great Miami Hurricane using the south Florida peninsula for the Biscayne and Southern Everglades Coastal Transport (BISECT) model"},{"id":375605,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1010/coverthb.jpg"},{"id":375606,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1010/ofr20201010.pdf","text":"Report","size":"2.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1010"}],"country":"United States","state":"Florida","city":"Miami","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.55999755859375,\n 25.209911213827688\n ],\n [\n -80.28533935546875,\n 25.199970890386023\n ],\n [\n -80.04638671875,\n 25.403584973186703\n ],\n [\n -80.04638671875,\n 26.23430203240673\n ],\n [\n -80.52978515625,\n 26.23430203240673\n ],\n [\n -80.55999755859375,\n 25.209911213827688\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, Florida 33559
A comprehensive study to evaluate water-quality trends, while considering natural hydroclimatic variability, in the Red River of the North Basin and assess water-quality conditions for the Red River of the North crossing the international boundary near Emerson, Manitoba, Canada (the binational site), was completed by the U.S. Geological Survey in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality, and Minnesota Pollution Control Agency and in collaboration with Manitoba Sustainable Development and Environment and Climate Change Canada. The international Red River of the North Basin encompasses 3 U.S. States (South Dakota, North Dakota, and Minnesota) and 1 Canadian Province (Manitoba). Water quality in the Red River of the North Basin is of concern for both Federal governments and State and Provincial governments. Water-quality objectives have been previously established for selected dissolved ions and recently (2019) proposed for selected nutrients for the binational site.
In the current (2020) study, water-quality data from State, Provincial, and Federal agencies in the United States and Canada for sites in the Red River of the North Basin from 1970 to 2017 were compiled and used for trend analysis. Trend analysis using a water-quality dataset from multiple agencies that collect water-quality data for various objectives presented multiple challenges. The trend-analysis approach was able to accommodate differences in water-quality data caused by field-collection and laboratory-analytical method differences, disparities in sampling frequencies, and spatial and temporal gaps in data. Most of these challenges were overcome by the statistical tool, R–QWTREND, which identifies trends in concentration unrelated to variability in streamflow.
The integrated basin approach used in the current study, combined with comparing current data trends with historical trends, provided valuable insights into understanding how water quality is changing spatially (34 sites analyzed for a recent period, 2000–15) and temporally (5 sites analyzed for a 45-year historical period, 1970–2015) within the Red River of the North Basin. One of the most consistent spatial and temporal changes observed in the current study was increasing concentrations of sulfate among tributary and main-stem sites since 2000. For some sites, increases were detected starting as early as 1985. Total dissolved solids and chloride concentrations had spatial and temporal patterns like sulfate. Although R–QWTREND removes the variability in constituent concentration caused by natural streamflow variability, all variability in sulfate caused by hydroclimatic variability may not be captured because of changes in hydrologic pathways and changes in the contributions of sulfate from various natural sources.
Nutrient concentrations demonstrated less consistent spatial and temporal changes than sulfate, and changes in nutrient concentrations were assumed to be more closely tied to human-induced rather than natural changes. Nitrate-plus-nitrite concentrations were mostly increasing in the upper Red River of the North subbasin, and for nitrate plus nitrite and total nitrogen, the Sheyenne River subbasin had consistent decreasing concentrations. Since 2000, total phosphorus has decreased in the upper Red River of the North subbasin, but total phosphorus concentration has increased for sites in the lower Red River of the North subbasin, and for some main-stem sites, concentrations have been increasing since 1985. Unlike sulfate, the pattern in historical trends for total phosphorus for the main-stem sites differed from tributary sites, indicating that human-induced changes affected tributaries and main-stem sites differently.
The more detailed evaluation of flow-averaged water-quality conditions for the binational site provided an understanding of how loads have changed over time and what proportion of the year and season concentrations are expected to exceed water-quality objectives. In a basin with highly variable streamflow like the Red River of the North, the trend in flow-averaged load (assuming streamflow conditions are the same year after year) provided a robust measure of change over time. Increasing concentrations of sulfate, chloride, total dissolved solids, and total phosphorus since 1985 for the binational site resulted in longer periods of exceedance of water-quality objectives per year occurring over time. For total nitrogen, decreasing concentrations resulted in shorter periods of exceedance per year during 1980 to 2015, but concentrations were still expected to exceed the water-quality objective about half the year. Periods of when exceedances were likely to occur during the year were affected by the source and transport mechanisms of the constituent.
Trend results from this effort identified how water quality has changed across the basin, and further investigation would help to identify causes for the trends observed here. Information from the current study provides a basis for future trend attribution studies, evaluation of water-quality objectives, and development of comprehensive strategies for reducing nutrients to desired targets and establishes a baseline for tracking future progress in the Red River of the North Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205079","collaboration":"Prepared in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality, and Minnesota Pollution Control Agency and in collaboration with Manitoba Sustainable Development and Environment and Climate Change Canada","usgsCitation":"Nustad, R.A., and Vecchia, A.V., 2020, Water-quality trends for selected sites and constituents in the international Red River of the North Basin, Minnesota and North Dakota, United States, and Manitoba, Canada, 1970–2017: U.S. Geological Survey Scientific Investigations Report 2020–5079, 75 p., https://doi.org/10.3133/sir20205079.","productDescription":"Report: ix, 75 p.; 2 Tables; Data Release; Dataset","numberOfPages":"90","onlineOnly":"Y","ipdsId":"IP-113881","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":377257,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5079/coverthb.jpg"},{"id":377260,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C9JAMY","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-quality and streamflow data for United States and Canadian sites in the Red River Basin and scripts for trend analysis—Data supporting water-quality trend analysis in the Red River of the North basin, 1970–2017"},{"id":377258,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5079/sir20205079.pdf","text":"Report","size":"11.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5079"},{"id":377259,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5079/sir20205079_tables_2_and_3.xlsx","text":"Tables 2 and 3","size":"60.3 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5079 Tables 2 and 3"},{"id":377261,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation","description":"USGS Data Release","linkHelpText":"— U.S. Geological Survey National Water Information System database"}],"country":"United States, Canada","state":"Minnesota, North Dakota, South Dakota, Manitoba","otherGeospatial":"Red River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.27294921875,\n 50.14874640066278\n ],\n [\n -98.85498046875,\n 49.710272582105695\n ],\n [\n -100.81054687499999,\n 49.38237278700955\n ],\n [\n -100.7666015625,\n 48.58932584966975\n ],\n [\n -99.86572265625,\n 47.040182144806664\n ],\n [\n -98.525390625,\n 46.7248003746672\n ],\n [\n -98.76708984374999,\n 46.37725420510028\n ],\n [\n -98.63525390624999,\n 45.96642454131025\n ],\n [\n -97.91015624999999,\n 45.55252525134013\n ],\n [\n -97.14111328125,\n 45.321254361171476\n ],\n [\n -95.77880859375,\n 45.89000815866184\n ],\n [\n -95.2294921875,\n 46.28622391806706\n ],\n [\n -95.1416015625,\n 46.73986059969267\n ],\n [\n -95.0537109375,\n 47.68018294648414\n ],\n [\n -94.59228515625,\n 47.79839667295524\n ],\n [\n -94.306640625,\n 48.07807894349862\n ],\n [\n -94.54833984375,\n 48.29781249243716\n ],\n [\n -95.1416015625,\n 48.23930899024907\n ],\n [\n -95.2734375,\n 48.850258199721495\n ],\n [\n -95.42724609375,\n 49.1242192485914\n ],\n [\n -96.7236328125,\n 50.02185841773444\n ],\n [\n -97.27294921875,\n 50.14874640066278\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503 1608
Mountain View Road,
Rapid City, SD 57702