Walruses rely on sea-ice to efficiently forage and rest between diving bouts while maintaining proximity to prime foraging habitat. Recent declines in summer sea ice have resulted in walruses hauling out on land where they have to travel farther to access productive benthic habitat while potentially increasing energetic costs. Despite the need to better understand the impact of sea ice loss on energy expenditure, knowledge about metabolic demands of specific behaviours in walruses is scarce. In the present study, 3 adult female Pacific walruses (Odobenus rosmarus divergens) housed in professional care participated in flow-through respirometry trials to measure metabolic rates while floating inactive at the water surface during a minimum of 5 min, during a 180 s stationary dive, and while swimming ∼90 m horizontally underwater. Metabolic rates during stationary dives (3.82±0.56 l O2 min−1) were lower than those measured at the water surface (4.64±1.04 l O2 min−1), which did not differ from rates measured during subsurface swimming (4.91±0.77 l O2 min−1). Thus, neither stationary diving nor subsurface swimming resulted in metabolic rates above those exhibited by walruses at the water surface. These results suggest that walruses minimize their energetic investment during underwater behaviours as reported for other marine mammals. Although environmental factors experienced by free-ranging walruses (e.g. winds or currents) likely affect metabolic rates, our results provide important information for understanding how behavioural changes affect energetic costs and can be used to improve bioenergetics models aimed at predicting the metabolic consequences of climate change on walruses.
In the 1970s and 1980s, the Sahel experienced recurrent drought and famine. Farmers and their development partners reacted to this crisis by developing climate-smart agricultural practices and changes in land use, including water-harvesting techniques to restore degraded land to productivity. In several densely populated parts of the Sahel, farmers began to protect and manage woody species that regenerated naturally on their farmland. Farmer-managed natural regeneration (FMNR) is a foundational practice that produces multiple benefits, such as maintaining or improving soil fertility, which raises crop yields, and increasing the production of tree-based fodder, fruit, and firewood. In Niger’s Maradi and Zinder Regions alone, farmers have applied FMNR practices on 4.2 million hectares. The findings presented in this chapter suggest that the future of agriculture in the Sahel will be largely determined by whether low-income smallholder farmers will manage to improve soil fertility, which will depend on maintaining substantial densities of on-farm trees thus increasing tree cover.
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Thirty public water-supply wells located throughout the Columbia aquifer of the Delaware Coastal Plain were sampled from August through November 2018. Groundwater collected from the wells was analyzed for the occurrence and distribution of 18 per- and polyfluorinated alkyl substances (PFAS) as well as groundwater age. Descriptive statistical analyses were performed to assess PFAS analytical results within the well network and the combined perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) concentrations were compared to the U.S. Environmental Protection Agency’s (EPA) health advisory level (HAL) for informational purposes only and not for evidence of compliance or noncompliance with Federal regulations. The EPA’s HAL is a health-based reference level for public drinking water as supplied to customers and is not applied to source (raw) water. Groundwater-age data were compared for sites sampled in 2000, 2008, and 2018 to document any changes.
All samples were analyzed for 18 PFAS using EPA Method 537 (modified). Forty-four percent of the analyzed PFAS were detected in the study well network. Sixteen of the sampled wells have one or more PFAS detections, and as many as eight different PFAS were found in a single sample. Wells with a higher number of PFAS detected (five or more) were in New Castle and Sussex Counties. The PFAS most frequently detected were PFOA, with 47 percent detection; perfluorohexanoic acid (PFHxA), with 33 percent detection; and PFOS and perfluorohexane sulfonate (PFHxS), with 27 percent detection each. PFAS concentrations were below 1,000 parts per trillion (ppt). Two wells exceeded the EPA’s lifetime-drinking water health advisory level of 70 ppt for combined concentrations of PFOA and PFOS.
The average age of groundwater entering the screens of the supply wells sampled in 2018 ranged from 8.2 to 45.8 years, with a median groundwater age of 25.7 years. Groundwater age was positively correlated with well depth and negatively correlated with dissolved oxygen. Groundwater age and PFAS concentrations were negatively correlated in the Columbia aquifer. Data from the 23 resampled wells indicate a significant positive difference in the average modeled groundwater-sample-age results. The average groundwater age from samples collected in 2018 was generally 5 years older than the average groundwater age from samples collected in 2008. The same pattern was found during cycle two (2008) of this study, where the 2008 groundwater age was on average 7 years older than the samples collected in 2000. The distribution of groundwater sample ages among the 17 trend wells and during the three study cycles (2000, 2008, and 2018) indicates that sample-age medians were statistically different from zero; well-water sample-age data show a slight increase in groundwater sample age.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211109","collaboration":"Prepared in cooperation with the Delaware Geological Survey and Delaware Department of Natural Resources and Environmental Control","usgsCitation":"Reyes, B., 2021, Occurrence and distribution of PFAS in sampled source water of public drinking-water supplies in the surficial aquifer in Delaware, 2018; PFAS and groundwater age-dating results: U.S. Geological Survey Open-File Report 2021–1109, 27 p., https://doi.org/10.3133/ofr20211109.","productDescription":"Report: vii, 27 p.; Data Release; Database","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122437","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":392630,"rank":7,"type":{"id":39,"text":"HTML 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Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
Rangelands have immense inherent spatial and temporal variability, yet land condition and trends are often assessed at a limited number of spatially “representative” points. Spatially comprehensive, and quantitative, Ecological Potential (EP) data provide a baseline for comparison to current rangeland vegetation conditions and trends. Here, we define EP as potential fractional cover (bare ground, herbaceous, litter, shrub, and sagebrush) represented in the least disturbed areas and most productive years of the Landsat satellite archive (1985-present) for each 30-m pixel. We produce EP maps across rangelands in the western United States by training regression tree models using Rangeland Condition Monitoring Assessment and Projection (RCMAP) time-series fractional cover maps in ecologically intact sites (with limited annual herbaceous cover, no recent disturbance or vegetation treatment, and less bare ground cover than expected). As independent predictor variables in these models, we use digital soils and topography data and six bimonthly composites of the 90th percentile of Normalized Difference Vegetation Index (NDVI) and associated spectral bands from the 1985–2020 Landsat archive. EP predictions were successful in capturing biophysical gradients present in the independent variables and depicting potential cover in the absence of disturbance; we found no influence of fires or land treatments in the data. Next, we compared EP to contemporary (2018) cover, to create departure maps that can be used as a screening tool indicating degradation and providing an early warning of vegetation state change. Finally, we used a dichotomous key to convert the 1985 and 2018 RCMAP cover and EP cover into vegetation states important to land management decisions (invaded sagebrush steppe, annual grasslands, etc.). We found that in 1985, 21.2% of the study area had a different vegetation state than EP, and this percentage increased to 24.2% by 2018. More than 50% of the EP native sagebrush steppe was converted to an annual grassland, perennial grassland, or non-sagebrush shrub by 2018, and an additional 7% was classified as invaded sagebrush steppe, at risk of transition to another state. EP products provide a spatio-temporal reference of vegetation conditions from the last three decades across rangelands in the western United States. Use of the EP reference can improve adaptive management practice by providing monitoring and control data, which are often lacking, and assist in differentiating treatment effect from confounding factors.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.108447","usgsCitation":"Rigge, M.B., Meyer, D., and Bunde, B., 2021, Ecological potential fractional component cover based on Long-Term satellite observations across the western United States: Ecological Indicators, v. 133, 108447, 14 p., https://doi.org/10.1016/j.ecolind.2021.108447.","productDescription":"108447, 14 p.","ipdsId":"IP-129599","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":450064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.108447","text":"Publisher Index Page"},{"id":396994,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.755859375,\n 27.839076094777816\n ],\n [\n -103.271484375,\n 33.65120829920497\n ],\n [\n -98.96484375,\n 36.87962060502676\n ],\n [\n -104.853515625,\n 40.17887331434696\n ],\n [\n -105.1171875,\n 41.902277040963696\n ],\n [\n -102.216796875,\n 43.70759350405294\n ],\n [\n -102.3046875,\n 49.03786794532644\n ],\n [\n -122.6953125,\n 49.210420445650286\n ],\n [\n -123.57421875,\n 48.16608541901253\n ],\n [\n -125.595703125,\n 48.22467264956519\n ],\n [\n -124.365234375,\n 44.213709909702054\n ],\n [\n -124.8046875,\n 39.90973623453719\n ],\n [\n -120.58593749999999,\n 34.016241889667015\n ],\n [\n -118.21289062499999,\n 32.84267363195431\n ],\n [\n -116.71874999999999,\n 32.84267363195431\n ],\n [\n -115.09277343749999,\n 32.713355353177555\n ],\n [\n -114.67529296874999,\n 32.76880048488168\n ],\n [\n -114.85107421875,\n 32.52828936482526\n ],\n [\n -111.02783203125,\n 31.297327991404266\n ],\n [\n -108.17138671875,\n 31.353636941500987\n ],\n [\n -108.17138671875,\n 31.765537409484374\n ],\n [\n -106.3916015625,\n 31.74685416292141\n ],\n [\n -104.67773437499999,\n 30.183121842195515\n ],\n [\n -103.11767578124999,\n 28.94086176940557\n ],\n [\n -102.45849609375,\n 29.76437737516313\n ],\n [\n -101.53564453124999,\n 29.76437737516313\n ],\n [\n -99.73388671874999,\n 27.72243591897343\n ],\n [\n -99.755859375,\n 27.839076094777816\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"133","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":837781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyer, Deb 0000-0002-8841-697X","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":288363,"corporation":false,"usgs":false,"family":"Meyer","given":"Deb","affiliations":[{"id":61730,"text":"Retired, KBR","active":true,"usgs":false}],"preferred":false,"id":837782,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bunde, Brett 0000-0003-0228-779X","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":288364,"corporation":false,"usgs":false,"family":"Bunde","given":"Brett","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":837783,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70231634,"text":"70231634 - 2021 - Changing impacts of Alaska-Aleutian subduction zone tsunamis in California under future sea-level rise","interactions":[],"lastModifiedDate":"2022-05-17T12:12:16.00765","indexId":"70231634","displayToPublicDate":"2021-12-08T07:05:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2840,"text":"Nature","active":true,"publicationSubtype":{"id":10}},"title":"Changing impacts of Alaska-Aleutian subduction zone tsunamis in California under future sea-level rise","docAbstract":"The amplification of coastal hazards such as distant-source tsunamis under future relative sea-level rise (RSLR) is poorly constrained. In southern California, the Alaska-Aleutian subduction zone has been identified as an earthquake source region of particular concern for a worst-case scenario distant-source tsunami. Here, we explore how RSLR over the next century will influence future maximum nearshore tsunami heights (MNTH) at the Ports of Los Angeles and Long Beach. Earthquake and tsunami modeling combined with local probabilistic RSLR projections show the increased potential for more frequent, relatively low magnitude earthquakes to produce distant-source tsunamis that exceed historically observed MNTH. By 2100, under RSLR projections for a high-emissions representative concentration pathway (RCP8.5), the earthquake magnitude required to produce >1 m MNTH falls from ~Mw9.1 (required today) to Mw8.0, a magnitude that is ~6.7 times more frequent along the Alaska-Aleutian subduction zone.
Paleoflood studies are an effective means of providing specific information on the recurrence and magnitude of rare and large floods. Such information can be combined with systematic flood measurements to better assess the frequency of large floods. Paleoflood data also provide valuable information about the linkages among climate, land use, flood-hazard assessments, and channel morphology. This document summarizes methods and techniques for the preparation, gathering, evaluation, and interpretation of paleoflood information, including uncertainties, especially with respect to new statistical approaches available to efficiently use such data. We summarize best practices and strategies for assessing and mitigating uncertainties and provide guidelines on appropriate technical review of paleoflood analyses based on project goals and requirements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4B6","collaboration":"Prepared in cooperation with the Nuclear Regulatory Commission","usgsCitation":"Harden, T.M., Ryberg, K.R., O’Connor, J.E., Friedman, J.M., and Kiang, J.E., 2021, Historical and paleoflood analyses for probabilistic flood-hazard assessments—Approaches and review guidelines: U.S. Geological Survey Techniques and Methods, book 4, chap. B6, 91 p., https://doi.org/10.3133/tm4B6.","productDescription":"vii, 91 p.","onlineOnly":"Y","ipdsId":"IP-123028","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":392605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/b06/tm4b6.pdf","text":"Report","size":"18.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-B6"},{"id":392604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/b06/coverthb.jpg"}],"contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 92701
A targeted commercial fishery for Atlantic Sturgeon Acipenser oxyrinchus oxyrinchus once operated in the New York Bight, where it was assumed that most harvested Atlantic Sturgeon were natal to the Hudson River population. However, more recent evidence suggests that the fishery may have been targeting a mixed-stock aggregation, in which case harvested Atlantic Sturgeon could have been comprised of individuals from multiple populations throughout the species’ range. Although there is now a moratorium on Atlantic Sturgeon harvest in the New York Bight, modern molecular approaches provide an opportunity to use archived tissues to perform a retrospective mixed-stock analysis on the fishery. Genomic DNA extracted from archived fin spines from 80 Atlantic Sturgeon collected nearly 30 years ago suggests that the fishery primarily harvested individuals from the Hudson River population. However, based on individual-based assignment tests, our results indicate that the fishery also harvested individuals from at least eight other populations located throughout the species’ range. This study highlights how archival hard parts that were previously used for age and growth analyses can be employed for retrospective genetic analyses. Further, because the New York Bight harbors relatively high concentrations of Atlantic Sturgeon, the study shows how localized management decisions can influence Atlantic Sturgeon conservation at rangewide scales. When integrated with more recent knowledge of species ecology, these analyses can be used to evaluate the efficacy of previous management strategies and understand the effects of historical processes on contemporary demography.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10187","usgsCitation":"White, S.L., Johnson, R.L., Lubinski, B.A., Eackles, M.S., Secor, D.H., and Kazyak, D., 2021, Stock composition of the historical New York Bight Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) intercept fishery revealed through microsatellite analysis of archived spines: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 13, no. 6, p. 720-727, https://doi.org/10.1002/mcf2.10187.","productDescription":"8 p.","startPage":"720","endPage":"727","ipdsId":"IP-126143","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":450069,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10187","text":"Publisher Index Page"},{"id":393866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"New York Bight","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.89978027343749,\n 40.63896734381723\n ],\n [\n -73.9324951171875,\n 40.41976938144622\n ],\n [\n -74.168701171875,\n 39.223742741391305\n ],\n [\n -72.89978027343749,\n 40.63896734381723\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-12-07","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":829934,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Robin L. 0000-0003-4314-3792 rjohnson1@usgs.gov","orcid":"https://orcid.org/0000-0003-4314-3792","contributorId":224717,"corporation":false,"usgs":true,"family":"Johnson","given":"Robin","email":"rjohnson1@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":829935,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":829936,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eackles, Michael S. 0000-0001-5624-5769 meackles@usgs.gov","orcid":"https://orcid.org/0000-0001-5624-5769","contributorId":218936,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":829937,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Secor, David H.","contributorId":179379,"corporation":false,"usgs":false,"family":"Secor","given":"David","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":829938,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":829939,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230603,"text":"70230603 - 2021 - Current distribution and abundance of Kohala forest birds in Hawai‘i","interactions":[],"lastModifiedDate":"2022-04-19T15:05:49.655615","indexId":"70230603","displayToPublicDate":"2021-12-07T09:58:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2284,"text":"Journal of Field Ornithology","active":true,"publicationSubtype":{"id":10}},"title":"Current distribution and abundance of Kohala forest birds in Hawai‘i","docAbstract":"The Kohala volcano is home to the most spatially isolated population of Hawaiian forest birds on Hawai‘i Island and contains one of the few native bird populations in the state that has not been monitored since the original Hawai‘i Forest Bird Survey (HFBS) in 1979. We surveyed 143 stations across 13 transects in Pu‘u ‘O ‘Umi Natural Area Reserve on Kohala from February through April 2017 and compared our results to data from the 1979 HFBS conducted at 80 stations across three transects in the same location as our study site. We detected 2806 individuals of 15 species and measured relative abundance, relative occurrence, and density for seven species. We observed changes in species densities ranging from −8.4% (Hawai‘i ‘Elepaio, Chasiempis sandwichensis) to +714% (‘I‘iwi, Drepanis coccinea). Equivalence testing showed meaningful increases in population densities for all but one species, the Hawai‘i ‘Elepaio. The increases in population densities on Kohala are in stark contrast to the widespread declines in population densities of native species elsewhere in Hawai‘i. Relative occurrence was greater in 2017 than in 1979 for all species except Hawai‘i ‘Elepaios and House Finches (Haemorhous mexicanus), and relative abundance increased for all species except Hawai‘i ‘Elepaios, House Finches, and Melodious Laughing Thrushes (Garrulax canorus). We also documented the range expansion of Japanese Bush Warblers (Cettia diphone) in Kohala. Our results indicate that this spatially isolated avian community remains biologically diverse, and most population densities are increasing in the study area. Our results provide a framework for future surveys and a baseline for understanding possible changes in population and community dynamics as birds respond to climate change and avian disease on Kohala volcano.
","language":"English","publisher":"Wiley","doi":"10.1111/jofo.12386","usgsCitation":"Burnett, K., Camp, R.J., and Hart, P.J., 2021, Current distribution and abundance of Kohala forest birds in Hawai‘i: Journal of Field Ornithology, v. 92, no. 4, p. 377-387, https://doi.org/10.1111/jofo.12386.","productDescription":"11 p.","startPage":"377","endPage":"387","ipdsId":"IP-132811","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":436102,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93Y0MS0","text":"USGS data release","linkHelpText":"Hawaii Island Kohala Mountain complex forest bird survey, 2017"},{"id":399089,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Pu'u 'O 'Umi Natural Area Reserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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],\n [\n -155.6381607055664,\n 20.145561480419286\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"92","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-12-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Burnett, Keith","contributorId":290362,"corporation":false,"usgs":false,"family":"Burnett","given":"Keith","email":"","affiliations":[{"id":34677,"text":"University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":840877,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":840878,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Patrick J.","contributorId":147728,"corporation":false,"usgs":false,"family":"Hart","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":840879,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226746,"text":"70226746 - 2021 - Convergence of undulatory swimming kinematics across a diversity of fishes","interactions":[],"lastModifiedDate":"2021-12-09T12:43:00.474349","indexId":"70226746","displayToPublicDate":"2021-12-07T06:41:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Convergence of undulatory swimming kinematics across a diversity of fishes","docAbstract":"Fishes exhibit an astounding diversity of locomotor behaviors from classic swimming with their body and fins to jumping, flying, walking, and burrowing. Fishes that use their body and caudal fin (BCF) during undulatory swimming have been traditionally divided into modes based on the length of the propulsive body wave and the ratio of head:tail oscillation amplitude: anguilliform, subcarangiform, carangiform, and thunniform. This classification was first proposed based on key morphological traits, such as body stiffness and elongation, to group fishes based on their expected swimming mechanics. Here, we present a comparative study of 44 diverse species quantifying the kinematics and morphology of BCF-swimming fishes. Our results reveal that most species we studied share similar oscillation amplitude during steady locomotion that can be modeled using a second-degree order polynomial. The length of the propulsive body wave was shorter for species classified as anguilliform and longer for those classified as thunniform, although substantial variability existed both within and among species. Moreover, there was no decrease in head:tail amplitude from the anguilliform to thunniform mode of locomotion as we expected from the traditional classification. While the expected swimming modes correlated with morphological traits, they did not accurately represent the kinematics of BCF locomotion. These results indicate that even fish species differing as substantially in morphology as tuna and eel exhibit statistically similar two-dimensional midline kinematics and point toward unifying locomotor hydrodynamic mechanisms that can serve as the basis for understanding aquatic locomotion and controlling biomimetic aquatic robots.
Variation in temperature is known to influence mortality patterns in ectotherms. Even though a few experimental studies on model organisms have reported a positive relationship between temperature and actuarial senescence (i.e., the increase in mortality risk with age), how variation in climate influences the senescence rate across the range of a species is still poorly understood in free-ranging animals. We filled this knowledge gap by investigating the relationships linking senescence rate, adult lifespan, and climatic conditions using long-term capture–recapture data from multiple amphibian populations. We considered two pairs of related anuran species from the Ranidae (Rana luteiventris and Rana temporaria) and Bufonidae (Anaxyrus boreas and Bufo bufo) families, which diverged more than 100 Mya and are broadly distributed in North America and Europe. Senescence rates were positively associated with mean annual temperature in all species. In addition, lifespan was negatively correlated with mean annual temperature in all species except A. boreas. In both R. luteiventris and A. boreas, mean annual precipitation and human environmental footprint both had negligible effects on senescence rates or lifespans. Overall, our findings demonstrate the critical influence of thermal conditions on mortality patterns across anuran species from temperate regions. In the current context of further global temperature increases predicted by Intergovernmental Panel on Climate Change scenarios, a widespread acceleration of aging in amphibians is expected to occur in the decades to come, which might threaten even more seriously the viability of populations and exacerbate global decline.
Hydrologic records used to create previously published maps depicting mean annual runoff are biased to a relatively dry period in Oklahoma history that was dominated by droughts. Therefore, the U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, developed an updated mean annual runoff and annual runoff variability map for Oklahoma and parts of adjacent States. The updated map, which is based on mean-annual-streamflow regression equations developed from available streamgage data through 2007, is assumed to be representative of the long-term mean annual runoff conditions. The map covers all 69 8-digit hydrologic units with at least 1 square mile of area in Oklahoma; those 8-digit hydrologic units contain 2,870 12-digit hydrologic units that provided the geographic framework for the analysis described in this report. Although parts of adjacent States are included in the study area, this report is primarily focused on providing a map of mean annual runoff and annual runoff variability for Oklahoma.
The mean annual runoff increased from less than 0.25 inch per year in the Panhandle of northwestern Oklahoma to more than 30 inches per year in the mountainous terrain of southeastern Oklahoma. The orientation and pattern of mean annual runoff contours in this report were comparable to those of previously published map reports. The annual runoff variability, or the difference between the 80-percent and 20-percent streamflow-duration statistics, increased from less than 0.25 inch per year in the Panhandle of northwestern Oklahoma to more than 40 inches per year in the mountainous terrain of southeastern Oklahoma. The annual runoff variability data were similar in orientation and pattern to the mean annual runoff contours; annual runoff variability generally increased proportionally with increasing mean annual runoff. The annual runoff variability was also greatest, therefore, in the mountainous terrain of southeastern Oklahoma.
The mean annual runoff and annual runoff variability were calculated at sampled points representing the outlets of 12-digit hydrologic units, so the map in this report is most representative of runoff conditions in rural, unregulated drainage basins at the 12-digit hydrologic-unit scale. The map was developed by using regression equations formulated on streamgage data for the entire period of record through 2007, but those equations are biased to the period 1940–2007 when streamgages became more numerous and distributed across Oklahoma. Therefore, the map is likely most representative of runoff conditions during the period 1940–2007. Because runoff is a function of climate variables that can change over time, caution is warranted when using the information in this report to project mean annual runoff and annual runoff variability conditions beyond 2007.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3482","collaboration":"Prepared in cooperation with the Oklahoma Water Resources Board","usgsCitation":"Smith, S.J., and Sherrod, E.M., 2021, Mean annual runoff and annual runoff variability map for Oklahoma, 1940–2007: U.S. Geological Survey Scientific Investigations Map 3482, 1 sheet, scale 1:100,000, 10-p. pamphlet, https://doi.org/10.3133/sim3482.","productDescription":"Pamphlet: vi, 10 p.; Sheet: 34.00 x 24.00 inches; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-127939","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":392378,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SG5ZDO","text":"USGS data release","linkHelpText":"Data release for mean annual runoff and annual runoff variability map for Oklahoma, 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U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
Estuaries, at the nexus of rivers and the ocean, are depositional areas that respond to changes in streamflow, tides, sea level, and inputs of sediment from marine and watershed sources. Understanding changes in bed elevations, deposited and eroded sediment, and water depth throughout estuaries is relevant for understanding their present-day status and long-term evolution, identifying potential hazards to human communities, and informing estuarine conservation. In response to observations of sedimentation in the Nehalem Bay, northwestern Oregon, by the Port of Nehalem, the magnitudes and patterns of bathymetric change in the Bay were documented and described by two approaches. The first approach compared changes in bed elevation with estimated volumes of erosion and deposition from overlapping survey data acquired in 1957 and 2019 for the area of the Nehalem Bay from upstream of the Highway 101 bridge to downstream of Fishery Point. The second approach examined changes in water depth for seven zones from the confluence of the North Fork and Nehalem Rivers to the mouth of the Nehalem River using nautical charts (1891, 1947, 1970, 1990, and 2004). These two approaches were used because the bathymetric surveys from 1957 and 2019 could be tied to a common vertical datum, allowing for a direct comparison of changes in bed elevations, whereas the nautical charts could not be tied to a common vertical datum, which limited the analyses to a comparison of changes in water depths over a broader time frame.
Bed elevation changes from 1957 to 2019 were assessed from upstream of the Highway 101 bridge to downstream of Fishery Point where the two surveys overlapped (2 square kilometers) using thalweg longitudinal profiles, channel cross sections, and digital elevation models (DEMs) showing the elevation differences between the two surveys (or DEMs of difference). The most prominent change between 1957 and 2019 was the migration of the thalweg (or deepest part of the channel) between the downstream end of Lazarus Island and downstream of Fishery Point; this migration resulted in sediment deposition in the former thalweg and sediment erosion in formerly shallow areas to form the new thalweg. Bed elevation changes in the thalweg also varied longitudinally between 1957 and 2019. The bed elevation of the thalweg in both surveys, however, was generally less than 1 meter (m). The thalweg in the area of overlapping surveys shortened from about 7.0 to 6.7 kilometers in length over that same period. The bed elevation changes between the DEMs showed that maximum erosion and deposition was 4.3 and 4.5 m, respectively. In this same time period, the net change in sediment volume was 230,000 cubic meters (m3), indicating net deposition. However, the error estimated for the 95 percent confidence interval analyses is ±315,000 m3, and therefore does not preclude the possibility that net erosion may have occurred.
Historical changes in water depth from soundings depicted on nautical charts from 1891, 1947, 1970, 1990, and 2004 were evaluated by assessing spatial and temporal changes for seven zones of the Nehalem Bay. Across all years and zones, water depths ranged from about 0.2 to 9.4 m, whereas median water depths ranged from 0.3 to 6.4 m. Median depths and the range of water depths did not systematically increase or decrease throughout all zones during the same periods. In all nautical charts, the zone at the mouth of the Nehalem River consistently had the deepest soundings (7.9 to 9.4 m) and the greatest range of water depths (7.3 to 8.8 m). Qualitative evaluation of the nautical charts showed minimal changes in the overall shape of the Nehalem Bay. The exception to this observation was at the mouth of the Bay, where two historical outlets to the Pacific Ocean depicted in the 1891 nautical chart were reduced to one outlet following the construction of jetties (1916 and 1918).
The results of this study emphasize that bed elevations and water depths within the Nehalem Bay have varied between 1891 and 2019, as illustrated by the lateral and vertical changes in the thalweg and changes in water depths over time. Changes in thalweg position and related patterns of sediment erosion and deposition are expected in the future as the Nehalem Bay continues to respond to changes in tides, sea level, streamflow, and sediment inputs from watershed and marine sources. The results of this study and the surveys from 1957 and 2019 provide a foundation for documenting and evaluating future changes in the Nehalem Bay and prioritizing actions to manage and protect natural resources and recreational access to the Nehalem Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215108","collaboration":"Prepared in cooperation with the Port of Nehalem","usgsCitation":"Keith, M.K., Jones, K.L., and Gordon, G.W., 2021, Historical changes in bed elevation and water depth within the Nehalem Bay, Oregon, 1891–2019: U.S. Geological Survey Scientific Investigations Report 2021–5108, 48 p., https://doi.org/10.3133/sir20215108.","productDescription":"Report: x, ; Data Release","onlineOnly":"Y","ipdsId":"IP-115603","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":392536,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VJOGM1","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Digital elevation model of the Nehalem Bay near Wheeler, Oregon 2019"},{"id":392535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5108/sir20215108.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5108"},{"id":392534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5108/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Nehalem Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.95324707031249,\n 45.62172169252446\n ],\n [\n -123.77746582031249,\n 45.62172169252446\n ],\n [\n -123.77746582031249,\n 45.761774855141226\n ],\n [\n -123.95324707031249,\n 45.761774855141226\n ],\n [\n -123.95324707031249,\n 45.62172169252446\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Lake Eucha is a source of water for public supply and recreation for the residents of Tulsa and other municipalities in northeastern Oklahoma. Beaty Creek and Spavinaw Creek flow into Lake Eucha and drain about 388 square miles of agricultural and forested land in northeastern Oklahoma and northwestern Arkansas. Beginning in the 1990s, eutrophication of Lake Eucha characterized by excessive algal blooms resulted in taste and odor problems associated with lake water when it is used for public supply. The predominant sources of phosphorus in the Eucha-Spavinaw drainage area were identified by previous investigators as runoff from fertilized agricultural areas (nonpoint sources) and treated effluent from a wastewater-treatment plant (point source). To further evaluate the transport of nitrogen, phosphorus, and suspended sediment in the Eucha-Spavinaw drainage area, the U.S. Geological Survey (USGS), in collaboration with the City of Tulsa, estimated the loads and computed temporal trends of these constituents from water-quality and streamflow data collected at five USGS streamgages in the Beaty Creek and Spavinaw Creek subbasins.
Estimates and comparisons of total nitrogen, total phosphorus, and suspended-sediment loads from the Beaty Creek and Spavinaw Creek subbasins to Lake Eucha during 2011–18 were made by using different types of regression equations. The first type of regression equation is referred to as “daily mean load regression equations” and was developed from water-quality data obtained from periodic water-quality samples and daily mean streamflow data collected at five USGS streamgages. The second type of regression equation is referred to as “instantaneous continuous load regression equations.” In addition to water-quality data obtained from periodic water-quality samples, continuous real-time (every 15 minutes) measurements of physicochemical properties (specific conductance, water temperature, and turbidity), and continuous streamflow data were used to estimate instantaneous continuous loads of total nitrogen, total phosphorus, and suspended sediment at two of the same five streamgages where daily mean loads were estimated. The use of these two types of regression equations was documented by previous investigators who estimated loads of total nitrogen, total phosphorus, and suspended sediment in the study area by using data collected during 2002–10.
The regression equations used to estimate constituent loads that were based on water-quality data obtained from periodic water-quality samples and continuous water-quality and streamflow data (instantaneous continuous load regression equations) better described the temporal variance in constituent loads compared to the regression equations based only on periodic water-quality data and daily mean streamflows (daily mean load regression equations). Estimates computed using instantaneous continuous load regression equations showed that mean annual loads of 1,844,000 pounds of total nitrogen, 150,300 pounds of total phosphorus, and 78,735,000 pounds of suspended sediment were transported into Lake Eucha from the Beaty Creek and Spavinaw Creek subbasins. Most of the estimated mean annual loads from the Beaty Creek and Spavinaw Creek subbasins entered Lake Eucha during runoff conditions, including about 80 percent of total nitrogen, 95 percent of total phosphorus, and 98 percent of suspended sediment.
Daily, annual, and mean annual load estimates varied substantially, depending on streamflow conditions and the independent variables used to develop the regression equations. Daily and annual loads estimated from instantaneous continuous load regression equations that included specific conductance, water temperature, turbidity, and streamflow described the variability in the field data better than did loads estimated from daily mean load regression equations that included streamflow, seasonality, and time. Loads estimated from the instantaneous continuous load regression equations generally were greater than those estimated from the daily mean load regression equations.
Temporal trends in total nitrogen concentrations showed statistically significant (probability value less than or equal to 0.05) downward trends during both base-flow and runoff conditions at all five USGS streamgages except for the streamgage 07191179 Spavinaw Creek near Cherokee City, Ark. Temporal trends in total phosphorus concentrations were not consistent between streamgages over the study period, showing upward and downward trends throughout the Eucha-Spavinaw drainage area. Total phosphorus concentrations during base-flow and runoff conditions showed statistically significant upward trends at USGS streamgages 07191160 Spavinaw Creek near Maysville, Ark., and 07191222 Beaty Creek near Jay, Okla. Total phosphorus concentrations showed a statistically significant downward trend during base-flow conditions at USGS streamgage 071912213 Spavinaw Creek near Colcord, Okla., and in both base-flow and runoff conditions at USGS streamgage 07191179 Spavinaw Creek near Cherokee City, Ark. Temporal trends in suspended-sediment concentrations were not consistent between streamgages over the study period and were similar to temporal trends in total phosphorus concentrations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215105","collaboration":"Prepared in cooperation with the City of Tulsa, Oklahoma","usgsCitation":"Paizis, N., Becker, C., and Lockmiller, K., 2021, Load estimation and trend analysis for nitrogen, phosphorus, and suspended sediment in the Eucha-Spavinaw drainage area, northeastern Oklahoma and northwestern Arkansas, 2011–18: U.S. Geological Survey Scientific Investigations Report 2021–5105, 57 p., https://doi.org/10.3133/sir20215105.","productDescription":"Report: x, 57 p.; Dataset","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-127112","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":392369,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5105/coverthb.jpg"},{"id":392370,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5105/sir20215105.pdf","size":"2.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5105"},{"id":392371,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5105/images"},{"id":392372,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Arkansas, Oklahoma","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-94.076,36.4991],[-93.9071,36.4983],[-93.8977,36.4983],[-93.8697,36.4982],[-93.8698,36.4324],[-93.8698,36.3871],[-93.8695,36.3758],[-93.8693,36.3467],[-93.8695,36.3073],[-93.8697,36.2918],[-93.8697,36.2705],[-93.8697,36.2669],[-93.87,36.2347],[-93.8883,36.2353],[-93.9893,36.2373],[-94.0024,36.238],[-94.0127,36.2382],[-94.0131,36.2305],[-94.013,36.2083],[-94.021,36.2086],[-94.154,36.2108],[-94.174,36.2114],[-94.1785,36.2113],[-94.2504,36.2127],[-94.279,36.2135],[-94.2819,36.2139],[-94.3349,36.2147],[-94.3352,36.1856],[-94.3367,36.1425],[-94.3561,36.1426],[-94.3891,36.1433],[-94.3889,36.0988],[-94.4071,36.0994],[-94.4242,36.0995],[-94.4447,36.0995],[-94.4624,36.1001],[-94.4801,36.1006],[-94.5274,36.1019],[-94.5433,36.102],[-94.5498,36.1027],[-94.5537,36.1258],[-94.56,36.1623],[-94.583,36.1623],[-94.7959,36.1618],[-95.0114,36.1629],[-95.0119,36.2501],[-95.0039,36.2503],[-95.0072,36.5114],[-95.006,36.6003],[-95.0008,36.6001],[-95.001,36.6723],[-94.6187,36.6694],[-94.6185,36.6004],[-94.6182,36.4984],[-94.4355,36.4997],[-94.3816,36.4996],[-94.1651,36.4996],[-94.076,36.4991]]]},\"properties\":{\"name\":\"Benton\",\"state\":\"AR\"}}]}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
Relatively little research has been conducted to systematically quantify the nationwide earthquake risk of gas pipelines in the US; simultaneously, national guidance is limited for operators across the country to consistently evaluate the earthquake risk of their assets. Furthermore, many challenges and uncertainties exist in a comprehensive seismic risk assessment of gas pipelines. As a first stage in a systematic nationwide assessment, we quantify the earthquake risk of gas transmission pipelines in the conterminous US due to strong ground shaking, including the associated uncertainties. Specifically, we integrate the US Geological Survey 2018 National Seismic Hazard Model, a logic tree–based exposure model, three different vulnerability models, and a consequence model. The results enable comparison against other risk assessment efforts, encourage more transparent deliberation regarding alternative approaches, and facilitate decisions on potentially assessing localized risks due to ground failures that require site-specific data. Based on the uncertainties approximated herein, the resulting sensitivity analyses suggest that the vulnerability model is the most influential source of uncertainty. Finally, we highlight research needs such as (1) developing more vulnerability models for regional seismic risk assessment of gas pipelines; (2) identifying, prioritizing, and measuring input pipeline attributes that are important for estimating seismic damage; and (3) better quantifying seismic hazards with their uncertainties at the national scale, for both ground failures and ground shaking.
Hurricanes are the major flood generating mechanism dominating the upper tail of the peak discharge distribution over the Cape Fear River Basin (CFRB). In 2018, Hurricane Florence swamped CFRB as the ninth-most-destructive hurricane ever hit the United States and set new records of peak discharges over the main river channel and three out of five of its major tributaries. In this study, we examined the hydrometeorology and hydrology of this flood via combined observation and numerical experiment analyses. Our results suggest that the slow-motion in combination to the “L-shaped” path was the most distinctive feature of the hurricane that incurred catastrophic and widespread rainfall and flooding over CFRB. The total rainfall from the storm played a controlling role in the magnitude and spatial distribution of the flood peaks at basin scale. Above that, the spatial heterogeneities of rainfall distribution and hydrologic characteristics was responsible for the distinctive flood responses within the basin. The bi-peak shape of the flood hydrograph for the Deep River was due to the combined effects of rainfall distribution, land cover, and topographic gradient. The exceptional unit peak discharge over the Black River basin was associated with its drainage network structure, topographic gradient and rainfall distribution. The floodplain downstream of the Cape Fear River temporarily stored flood water and attenuated both the riverine floods from upstream and the compound flood over the coastal area. Furthermore, numerical analyses found that re-infiltration accounted for 76% of the total infiltration on average. Re-infiltration was superior to local infiltration over CFRB during Hurricane Florence.
In a recent study, Hough and Martin (2021) considered the extent to which socioeconomic factors influence the numbers and distribution of contributed reports available to characterize the effects of both historical and recent large earthquakes. In this study I explore the question further, focusing on analysis of widely felt earthquakes near major population centers in northern and southern California since 2002. For most of these earthquakes there is a correlation between average household income in a postal ZIP code and the population-normalized rate of responses to the DYFI system. As past studies have demonstrated, there is also a strong correlation between DYFI participation and the severity of shaking. This first-order correlation can obscure correlations with other factors that influence participation. Focusing on five earthquakes between 2011 and 2021 that generated especially uniform shaking across the greater Los Angeles, California, region, response rate varies by two orders of magnitude across the region, with a clear correlation with demographics, and consistent spatial patterns in response rate for earthquakes 10 years apart. While there is no evidence that uneven DYFI participation in California impacts significantly the reliability of intensity data collected, the results reveal that DYFI participation is significantly higher in affluent parts of southern California compared to economically disadvantaged areas.
A reliable chlorophyll–nutrient relationship (CNR) is essential for lake eutrophication management. Although the spatial variability of CNRs has been extensively explored, temporal variations of CNRs at the individual lake scale has rarely been discussed. The paucity of information about temporal dependence in CNRs may in part be due to the lack of a suitable statistical framework that helps guide such investigations. In order to reveal temporal dependence of CNR, this study develop a novel statistical framework. In the framework, we employ quantile regression to generate overall (the entire dataset), annual (subsets for each year), and accumulative (subsets collected before a certain year) CNRs. We aim to 1) show biases of annual relationships by comparing the overall and annual relationships and 2) determine whether or not data accumulation is enough to develop a reliable CNR. We use Lake Champlain and Lake Kasumigaura as case studies to illustrate the necessary steps needed to utilize this novel framework. Results show that large interannual variations exist for CNRs. Accumulative relationships tend to converge to the overall relationship, indicating that overall relationships are reliable for informing lake-specific eutrophication management in the two case study lakes. The novel statistical framework that we propose for a procedure to estimate reliable CNRs is important for informing lake-specific eutrophication control decision-making processes.
Natural reproduction of salmonids occurs in many Lake Michigan tributaries, yet little is known about abundance and the potential contribution of wild fish hatching in Wisconsin tributaries. The objectives of our study were to determine if: 1) abundance of wild juvenile salmonids (primarily adfluvial rainbow trout, Oncorhynchus mykiss, referred to as steelhead) varied among selected Wisconsin streams based on available spawning and age-0 habitat; 2) stream temperature regimes could limit survival of juvenile salmonids, and 3) wild juvenile salmonids outmigrate from Wisconsin tributaries into Lake Michigan or larger tributaries. In 2016 and 2017, juvenile salmonid abundance was estimated in six Wisconsin tributaries to Lake Michigan by multiple-pass depletion sampling using backpack electrofishing. Habitat assessments included steelhead redd surveys, age-0 habitat surveys, and stream temperatures were monitored using in-stream loggers. Passive integrated transponder (PIT) tagging and PIT antennas were used to detect outmigration from three streams (Willow, Stony and Hibbard creeks). Population estimates for individual streams ranged from 75-2,276 for juvenile steelhead and from 0-243 for juvenile coho salmon, Oncorhynchus kisutch. No correlation was detected between juvenile steelhead abundance and quality age-0 habitat. Stream temperatures rarely exceeded the thermal limit for steelhead (27°C). Outmigration rates for three streams ranged from 0.6%-3.1%, but these estimates were considered minimum values. Low abundance of wild juvenile steelhead and coho salmon alone suggest that the contributions of these tributaries to Lake Michigan fisheries are likely small. Furthermore, relying on returns of wild steelhead produced in these streams is probably insufficient to maintain stream fisheries.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.10.005","usgsCitation":"Wegleitner, E., Raabe, J., Dembkowski, D., Legler, N., and Isermann, D.A., 2021, Wild juvenile salmonid abundance in Wisconsin tributaries indicates limited contributions to Lake Michigan fisheries: Journal of Great Lakes Research, v. 47, no. 6, p. 1824-1835, https://doi.org/10.1016/j.jglr.2021.10.005.","productDescription":"12 p.","startPage":"1824","endPage":"1835","ipdsId":"IP-123732","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480824,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Manitowoc County, Ozaukee County, Sheboygan County","otherGeospatial":"Lake 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Eric","contributorId":348814,"corporation":false,"usgs":false,"family":"Wegleitner","given":"Eric","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923799,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Raabe, Joshua","contributorId":348815,"corporation":false,"usgs":false,"family":"Raabe","given":"Joshua","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923800,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dembkowski, Daniel","contributorId":348816,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923801,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Legler, Nicholas","contributorId":348817,"corporation":false,"usgs":false,"family":"Legler","given":"Nicholas","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":923802,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923798,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226713,"text":"70226713 - 2021 - Impact of molecular modifications on the Immunogenicity and efficacy of recombinant raccoon poxvirus-vectored rabies vaccine candidates in mice","interactions":[],"lastModifiedDate":"2021-12-07T14:21:14.009488","indexId":"70226713","displayToPublicDate":"2021-12-04T08:18:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3834,"text":"Vaccines","active":true,"publicationSubtype":{"id":10}},"title":"Impact of molecular modifications on the Immunogenicity and efficacy of recombinant raccoon poxvirus-vectored rabies vaccine candidates in mice","docAbstract":"Rabies is an ancient disease that is responsible for approximately 59,000 human deaths annually. Bats (Order Chiroptera) are thought to be the original hosts of rabies virus (RABV) and currently account for most rabies cases in wildlife in the Americas. Vaccination is being used to manage rabies in other wildlife reservoirs like fox and raccoon, but no rabies vaccine is available for bats. We previously developed a recombinant raccoonpox virus (RCN) vaccine candidate expressing a mosaic glycoprotein (MoG) gene that protected mice and big brown bats when challenged with RABV. In this study, we developed two new recombinant RCN candidates expressing MoG (RCN-tPA-MoG and RCN-SS-TD-MoG) with the aim of improving RCN-MoG. We assessed and compared in vitro expression, in vivo immunogenicity, and protective efficacy in vaccinated mice challenged intracerebrally with RABV. All three candidates induced significant humoral immune responses, and inoculation with RCN-tPA-MoG or RCN-MoG significantly increased survival after RABV challenge. These results demonstrate the importance of considering molecular elements in the design of vaccines, and that vaccination with either RCN-tPA-MoG or RCN-MoG confers adequate protection from rabies infection, and either may be a sufficient vaccine candidate for bats in future work.
","language":"English","publisher":"MDPI","doi":"10.3390/vaccines9121436","usgsCitation":"Malave, C.M., Lopera-Madrid, J., Medina-Magues, L.G., Rocke, T.E., and Osorio, J., 2021, Impact of molecular modifications on the Immunogenicity and efficacy of recombinant raccoon poxvirus-vectored rabies vaccine candidates in mice: Vaccines, v. 9, no. 12, 1436, 12 p., https://doi.org/10.3390/vaccines9121436.","productDescription":"1436, 12 p.","ipdsId":"IP-134515","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":450089,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/vaccines9121436","text":"Publisher Index Page"},{"id":436104,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IERY9D","text":"USGS data release","linkHelpText":"In vitro expression, immunogenicity, and efficacy data from recombinant raccoon poxvirus-vectored rabies vaccine candidates tested in mice"},{"id":392570,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-12-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Malave, Carly Marie 0000-0001-6673-737X","orcid":"https://orcid.org/0000-0001-6673-737X","contributorId":269786,"corporation":false,"usgs":true,"family":"Malave","given":"Carly","email":"","middleInitial":"Marie","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":827916,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lopera-Madrid, Jaime","contributorId":215116,"corporation":false,"usgs":false,"family":"Lopera-Madrid","given":"Jaime","email":"","affiliations":[],"preferred":false,"id":827917,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Medina-Magues, Lex Guillermo","contributorId":269787,"corporation":false,"usgs":false,"family":"Medina-Magues","given":"Lex","email":"","middleInitial":"Guillermo","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":827918,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rocke, Tonie E. 0000-0003-3933-1563 trocke@usgs.gov","orcid":"https://orcid.org/0000-0003-3933-1563","contributorId":2665,"corporation":false,"usgs":true,"family":"Rocke","given":"Tonie","email":"trocke@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":827919,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Osorio, Jorge E.","contributorId":50392,"corporation":false,"usgs":false,"family":"Osorio","given":"Jorge E.","affiliations":[{"id":13052,"text":"Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":827920,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226731,"text":"70226731 - 2021 - Reproductive health and endocrine disruption in smallmouth bass (Micropterus dolomieu) from the Lake Erie drainage, Pennsylvania, USA","interactions":[],"lastModifiedDate":"2021-12-08T12:55:37.263109","indexId":"70226731","displayToPublicDate":"2021-12-04T06:51:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Reproductive health and endocrine disruption in smallmouth bass (Micropterus dolomieu) from the Lake Erie drainage, Pennsylvania, USA","docAbstract":"Smallmouth bass Micropterus dolomieu were sampled from three sites within the Lake Erie drainage (Elk Creek, Twentymile Creek, and Misery Bay, an embayment in Presque Isle Bay). Plasma, tissues for histopathological analyses, and liver and testes preserved in RNALater® were sampled from 30 smallmouth bass (of both sexes) at each site. Liver and testes samples were analyzed for transcript abundance with Nanostring nCounter® technology. Evidence of estrogenic endocrine disruption was assessed by the presence and severity of intersex (testicular oocytes; TO) and concentrations of plasma vitellogenin in male fish. Abundance of 17 liver transcripts associated with reproductive function, endocrine activity, and contaminant detoxification pathways and 40 testes transcripts associated with male and female reproductive function, germ cell development, and steroid biosynthesis were also measured. Males with a high rate of TO (87–100%) and plasma vitellogenin were noted at all sites; however, TO severity was greatest at the site with the highest agricultural land cover. Numerous transcripts were differentially regulated among the sites and patterns of transcript abundance were used to better understand potential risk factors for estrogenic endocrine disruption. The results of this study suggest endocrine disruption is prevalent in this region and further research would benefit to identify the types of contaminants that may be associated with the observed biological effects.
Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean resources of 13.4 billion barrels of oil and 244.4 trillion cubic feet of gas in nine geologic provinces of China.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213051","usgsCitation":"Schenk, C.J., Mercier, T.J., Woodall, C.A., Ellis, G.S., Finn, T.M., Le, P.A., Marra, K.R., Leathers-Miller, H.M., and Drake, R.M., II, 2021, Assessment of undiscovered conventional oil and gas resources of China, 2020: U.S. Geological Survey Fact Sheet 2021–3051, 4 p., https://doi.org/10.3133/fs20213051.","productDescription":"Report: 4 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-124554","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":392447,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SOH5EN","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project-China Assessment Unit Boundaries and Assessment Input Forms"},{"id":392446,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3051/fs20213051.pdf","text":"Report","size":"1.10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021-3051"},{"id":392445,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3051/coverthb.jpg"}],"country":"China","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 69.9609375,\n 41.77131167976407\n ],\n [\n 77.34374999999999,\n 26.745610382199022\n ],\n [\n 101.953125,\n 19.642587534013032\n ],\n [\n 112.8515625,\n 18.646245142670608\n ],\n [\n 121.28906250000001,\n 20.632784250388028\n ],\n [\n 136.40625,\n 42.293564192170095\n ],\n [\n 136.7578125,\n 54.57206165565852\n ],\n [\n 119.17968749999999,\n 54.77534585936447\n ],\n [\n 106.5234375,\n 46.800059446787316\n ],\n [\n 76.640625,\n 47.27922900257082\n ],\n [\n 69.9609375,\n 41.77131167976407\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Saltwater intrusion and declining water levels have been a water-supply problem in Cape May County, New Jersey, for decades. Cape May County is surrounded by saltwater on three sides. Several communities in the county have only one aquifer from which freshwater withdrawals can be made, and that sole source is threatened by saltwater intrusion and (or) substantial declines in water levels caused by groundwater withdrawals. Growth of the year-round and summer tourism populations have caused water demand for some purveyors to approach the full-allocation withdrawal rates set by the New Jersey Department of Environmental Protection, leading these purveyors to request increases in allocations. Simulated water levels resulting from withdrawals including proposed increases in allocations by four purveyors and a shift of some withdrawals from one aquifer to another by a fifth purveyor were compared to simulated baseline water levels with withdrawals at 2012 full-allocation rates.
The Lower Township Scenario simulates proposed full-allocation withdrawals of 1,079 million gallons per year (Mgal/yr) from the Cohansey aquifer, 211 Mgal/yr (24 percent) higher than the 2012 full allocation withdrawals. Lower Township Scenario simulated water levels are between 2 and 4 feet (ft) lower than those of the shallow-aquifer-system Baseline Scenario simulation in much of Lower Township. The simulated 250-milligrams per liter (mg/L) isochlor is a maximum of 750 ft farther eastward than the simulated position in the shallow-aquifer-system Baseline Scenario, and the isochlor is simulated to be 700 ft from the northwestern-most Lower Township Municipal Utility Authority well at the airport in 2050.
The Wildwood Scenario simulates proposed full-allocation withdrawals of 388 Mgal/yr at the Wildwood Water Utility Rio Grande well field in Middle Township from the Rio Grande water-bearing zone (upper Kirkwood Formation) and 776 Mgal/yr from the Atlantic City 800-foot sand (lower Kirkwood Formation). Simulated water levels in the Atlantic City 800-foot sand near the well field are 30–55 ft lower than in the deep-aquifer-system Baseline Scenario, more than 15 ft lower south and west of Cape May Court House, and 5–10 ft lower between Cape May Court House and Woodbine and Upper Township.
The Avalon Scenario simulates proposed full-allocation withdrawals from the Atlantic City 800-foot sand in Avalon Borough of 495 Mgal/yr, which is 141 Mgal/yr (40 percent) higher than the 2012 full-allocation withdrawals. The Cape May Court House Scenario simulates proposed full-allocation withdrawals near Cape May Court House from the Atlantic City 800-foot sand of 495 Mgal/yr, which is 150 Mgal/yr (64 percent) higher than 2012 full-allocation withdrawals. The Strathmere Scenario simulates proposed full-allocation withdrawals in Strathmere from the Atlantic City 800-foot sand of 30 Mgal/yr, which is 11 Mgal/yr (58 percent) higher than 2012 full-allocation withdrawals. All three of these scenarios generally show simulated water levels to be less than 10 ft lower compared to the deep-aquifer-system Baseline Scenario.
The Combined Scenario simulates proposed full-allocation withdrawals, including increased withdrawals from the Atlantic City 800-foot sand in all four locations—the Rio Grande well field, Avalon, Cape May Court House, and Strathmere. Water levels from the Combined Scenario are 40–65 ft lower than those from the deep-aquifer-system Baseline Scenario near the Wildwood Water Utility Rio Grande well field, 15–40 ft lower south of Dennis Township, and 5–15 ft lower in much of the rest of Cape May County.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205052","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Carleton, G.B., 2021, Simulation of potential water allocation changes, Cape May County, New Jersey: U.S. Geological Survey Scientific Investigations Report 2020–5052, 39 p., https://doi.org/10.3133/sir20205052.","productDescription":"Report: vi, 39 p.; Data Release","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-044323","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":392461,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20205052/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":392262,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5052/sir20205052.XML"},{"id":392260,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KC1PGV","text":"USGS data release","linkHelpText":"SEAWAT, MODFLOW-2000, and SHARP models used to simulate potential water-allocation changes, Cape May County, New Jersey"},{"id":392261,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5052/images/"},{"id":392259,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5052/sir20205052.pdf","text":"Report","size":"4.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5052"},{"id":392258,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5052/coverthb.jpg"}],"country":"United States","state":"New Jersey","county":"Cape May County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.99404907226562,\n 38.92843409820933\n ],\n [\n -74.91439819335938,\n 38.91133881927712\n ],\n [\n -74.82376098632812,\n 38.92629741358616\n ],\n [\n -74.77844238281249,\n 38.9807627650163\n ],\n [\n -74.74925994873047,\n 39.041319605445445\n ],\n [\n -74.95010375976561,\n 39.0882354732187\n ],\n [\n -74.99404907226562,\n 38.92843409820933\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
The gut microbiota may modulate the disposition and toxicity of environmental contaminants within a host but, conversely, contaminants may also impact gut bacteria. Such contaminant-gut microbial connections, which could lead to alteration of host health, remain poorly known and are rarely studied in free-ranging wildlife. The polar bear (Ursus maritimus) is a long-lived, wide-ranging apex predator that feeds on a variety of high trophic position seal and cetacean species and, as such, is exposed to among the highest levels of biomagnifying contaminants of all Arctic species. Here, we investigate associations between mercury (THg; a key Arctic contaminant), diet, and the diversity and composition of the gut microbiota of polar bears inhabiting the southern Beaufort Sea, while accounting for host sex, age class and body condition. Bacterial diversity was negatively associated with seal consumption and mercury, a pattern seen for both Shannon and Inverse Simpson alpha diversity indices (adjusted R2 = 0.35, F1,18 = 8.00, P = 0.013 and adjusted R2 = 0.26, F1,18 = 6.04, P = 0.027, respectively). No association was found with sex, age class or body condition of polar bears. Bacteria known to either be involved in THg methylation or considered to be highly contaminant resistant, including Lactobacillales, Bacillales and Aeromonadales, were significantly more abundant in individuals that had higher THg concentrations. Conversely, individuals with higher THg concentrations showed a significantly lower abundance of Bacteroidales, a bacterial order that typically plays an important role in supporting host immune function by stimulating intraepithelial lymphocytes within the epithelial barrier. These associations between diet-acquired mercury and microbiota illustrate a potentially overlooked outcome of mercury accumulation in polar bears.
This article reviews the status of knowledge gaps and co-production process challenges that impede coastal flood hazard resilience planning in communities of northwestern Alaska, where threat levels are high. Discussion focuses on the state of knowledge arising after preparation of the 2019 IPCC Special Report on the Ocean and Cryosphere in a Changing Climate and highlights prospects to address urgent needs. The intent is to identify some key steps necessary to advance the integration of relevant multidisciplinary observations with flood modeling and infrastructure mapping to co-produce new online hazard and risk assessment tools that inform local community planning and improve science collaboration among Federal, state, and regional partners for enhanced pre-storm preparations and post-storm recovery, including partial or complete relocation. By focusing coastal data integration for delivery of priority geospatial hazard map products through a consistent yet customized approach to adaptation planning, the broad collaborative effort in Alaska may yield a path of stakeholder service delivery that can be applied to many Arctic communities and other vulnerable regions of the world.
From October 1, 1999, through September 30, 2009, water-quality samples were collected, and discharge was measured at 13 streamgages within the Catskill and Delaware watersheds of the New York City water supply system. The Catskill and Delaware watersheds supply about 90 percent of the water needed by 9 million customers. On average, 59 water-quality samples were collected at each station during each year of the study and analyzed for major ions and nutrients. At six stations, suspended-sediment samples were collected during 2001–09, and turbidity samples were collected during 2003–09. Surficial geology exerted a strong influence on the water quality of streams in the region. Stations in the Cannonsville Reservoir watershed, which has a high percentage of glacial till, had circumneutral stream water, whereas stations in the Neversink Reservoir watershed, which has a high percentage of sedimentary bedrock outcrops, had acidic stream water. All stations showed significant decreases in stream water sulfate concentrations during the study period; however, only the most acidic watersheds showed decreases in hydrogen-ion concentration. Two of the most acidic stations, East Branch Neversink River northeast of Denning and Rondout Creek above Red Brook at Peekamoose also had significant decreasing trends in inorganic monomeric aluminum concentrations, a form of aluminum that is toxic to some aquatic biota at concentrations greater than 0.05 milligram per liter. Three stations in the Neversink Reservoir watershed had inorganic monomeric aluminum concentrations that commonly exceeded 0.05 milligram per liter during the study period. At the West Branch Neversink River at Winnisook Lake near Frost Valley station concentrations of inorganic monomeric aluminum exceeded 0.3 milligram per liter at the beginning of the study, but never exceeded that level during the last 2 water years of the study. The East Branch Neversink River northeast of Denning and Rondout Creek above Red Brook at Peekamoose stations also showed decreases in inorganic monomeric aluminum concentrations during the study. The reduction in inorganic monomeric aluminum concentrations were the result of reductions in stream acidity. The reductions in stream acidity were driven by reductions in sulfate concentrations in precipitation in response to emission regulations included in title IV of the Clean Air Act Amendments of 1990 (42 USC §7651).
Results indicated increasing trends in sodium and chloride concentrations for all stations with high road density relative to other stations included in the study, which could be a future water-quality concern in the region. The Town Brook watershed southeast of Hobart, the only study watershed that contained dairy farms, had a significant decreasing trend in total dissolved phosphorus concentration that may have been a result of agricultural best management practices implemented on farms by the Watershed Agricultural Program. The watershed with the second highest total phosphorus and total dissolved phosphorus concentrations was a completely forested, but previously agricultural, watershed (Town Brook tributary southeast of Hobart) that had not been actively farmed in about 80 years. The phosphorus concentrations at the Town Brook tributary southeast of Hobart station indicated that previously agricultural watersheds may continue to leach phosphorus to streams for many decades after farming has ceased.
At six of the study watersheds, samples of suspended-sediment and turbidity were also collected. The watersheds with the highest suspended-sediment concentrations and turbidity also had the strongest relations between discharge and suspended-sediment concentrations. In general, the relations between discharge and turbidity were not as strong as the relations between discharge and suspended-sediment concentrations. Results indicated strong relations between suspended-sediment concentrations and turbidity levels at each station; however, relations were less strong in the agricultural watersheds. Suspended-sediment concentrations appeared to decrease at the Stony Clove Creek below Ox Clove at Chichester station following a stream stabilization project completed during the study period. However, we were unable to directly attribute the decrease to the stabilization project; there were many complicating variables that made a direct attribution difficult, such as a series of large storms shortly after the stabilization project was completed and differences in flow conditions before and after the project. However, the results have led to additional monitoring within the watershed specifically designed to determine the effectiveness of stream stabilization projects for reducing suspended-sediment concentrations and turbidity in the upper Esopus Creek watershed, the primary source of water to the Ashokan Reservoir. Water quality in the Catskill and Delaware watersheds is generally improving, and although sodium and chloride concentrations increased at some of the stations from 1999 to 2009, the concentrations in 2009 were still well below U.S. Environmental Protection Agency drinking water standards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205049","collaboration":"Prepared in cooperation with New York City Department of Environmental Protection and the U.S. Environmental Protection Agency","usgsCitation":"McHale, M.R., Siemion, J., and Murdoch, P.S., 2021, The water quality of selected streams in the Catskill and Delaware water-supply watersheds in New York, 1999–2009: U.S. Geological Survey Scientific Investigations Report 2020–5049, 48 p., https://doi.org/10.3133/sir20205049.","productDescription":"viii, 48 p.","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-060224","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":392036,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20205049/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":391750,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5049/sir20205049.XML"},{"id":391749,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5049/images/"},{"id":391748,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5049/sir20205049.pdf","text":"Report","size":"6.70 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5049"},{"id":391747,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5049/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Catskill Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.16845703125001,\n 41.672911819602085\n ],\n [\n -73.93798828125,\n 41.672911819602085\n ],\n [\n -73.93798828125,\n 42.43156587257916\n ],\n [\n -75.16845703125001,\n 42.43156587257916\n ],\n [\n -75.16845703125001,\n 41.672911819602085\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349