The Indio subbasin of the Coachella Valley is a desert area of southern California where a growing population depends primarily on groundwater for drinking and agricultural uses. The aquifer system has been supplemented with Colorado River water through managed recharge and widespread irrigation since the mid-20th century. We use a combination of geochemical modeling and trend analysis to identify changes in total dissolved solids through time, elucidate the sources of dissolved solids, and quantify the extent of contributions from those sources throughout the Indio subbasin. We conclude that recharged Colorado River water is the primary source and driver of increasing salinity, particularly in areas immediately downgradient from the recharge locations and in the eastern part of the subbasin away from the recharge ponds due to irrigation using imported water. Other contributions of dissolved solids to groundwater resources include geothermal waters, wastewater effluent, and agricultural return flow, although their effects are more localized. This study presents a broadly applicable framework for identifying sources of dissolved solids in groundwater wells and salinity trends at a regional scale in a large data set.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acsestwater.3c00239","usgsCitation":"Harkness, J.S., McCarthy, P.M., Jurgens, B., and Levy, Z., 2023, Salinity trends in a groundwater system supplemented by 50 years of imported Colorado River water: Environmental Science & Technology Water, v. 3, no. 10, p. 3253-3264, https://doi.org/10.1021/acsestwater.3c00239.","productDescription":"12 p.","startPage":"3253","endPage":"3264","ipdsId":"IP-148329","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":442051,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acsestwater.3c00239","text":"Publisher Index Page"},{"id":435173,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KUBQKM","text":"USGS data release","linkHelpText":"Inverse Model Data for: Salinity trends in a groundwater system supplemented by 50 years of imported Colorado River water"},{"id":421073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Coachella Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.13573439891272,\n 33.44388210065128\n ],\n [\n -115.83959902010358,\n 33.47539980820645\n ],\n [\n -116.10770512437531,\n 33.77373918192059\n ],\n [\n -116.49280298323828,\n 33.977110302145775\n ],\n [\n -116.5817654632921,\n 33.996309353196736\n ],\n [\n -116.645135997029,\n 33.92049840312886\n ],\n [\n -116.51108294489313,\n 33.79298404948595\n ],\n [\n -116.29781672558602,\n 33.66122225831866\n ],\n [\n -116.21372890197347,\n 33.55363612855925\n ],\n [\n -116.13573439891272,\n 33.44388210065128\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Harkness, Jennifer S. 0000-0001-9050-2570 jharkness@usgs.gov","orcid":"https://orcid.org/0000-0001-9050-2570","contributorId":224299,"corporation":false,"usgs":true,"family":"Harkness","given":"Jennifer","email":"jharkness@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883884,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCarthy, Patrick Michael 0000-0002-5492-1409","orcid":"https://orcid.org/0000-0002-5492-1409","contributorId":330033,"corporation":false,"usgs":true,"family":"McCarthy","given":"Patrick","email":"","middleInitial":"Michael","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883885,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203409,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883886,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Levy, Zeno F. 0000-0003-4580-2309","orcid":"https://orcid.org/0000-0003-4580-2309","contributorId":222340,"corporation":false,"usgs":true,"family":"Levy","given":"Zeno","middleInitial":"F.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883887,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249300,"text":"70249300 - 2023 - Future marsh evolution due to tidal changes induced by human adaptation to sea level rise","interactions":[],"lastModifiedDate":"2023-10-04T12:07:02.06102","indexId":"70249300","displayToPublicDate":"2023-09-21T07:00:45","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5053,"text":"Earth's Future","active":true,"publicationSubtype":{"id":10}},"title":"Future marsh evolution due to tidal changes induced by human adaptation to sea level rise","docAbstract":"With sea level rise threatening coastal development, decision-makers are beginning to act by modifying shorelines. Previous research has shown that hardening or softening shorelines may change the tidal range under future sea level rise. Tidal range can also be changed by natural factors. Coastal marshes, which humans increasingly depend on for shoreline protection, are ecologically sensitive to tidal range. Therefore, it is critical to examine how changes in tidal range could influence marsh processes. A marsh accretion model was used to investigate the ecological response of a San Francisco Bay, California, USA marsh to multiple tidal range scenarios and sea level rise from 2010 to 2100. The scenarios include a baseline scenario with no shoreline modifications in the estuary, a shoreline hardening scenario that amplifies the tidal range, and 14 tidal range scenarios as a sensitivity analysis that span tidal amplification and reduction of the baseline scenario. The modeling results expose key tradeoffs to consider when planning for sea level rise. Compared to the baseline, the hardening scenario shows minor differences. However, further tidal amplification prolongs marsh survival but decreases Sarcocornia pacifica cover, an important species for certain threatened wildlife and an effective attenuator of wave energy. Conversely, tidal reduction precipitates marsh drowning but shows gains in Sarcocornia pacifica cover. These mixed impacts of tidal amplification and reduction shown by the model indicate potential tradeoffs in relation to marsh survival, habitat characteristics, and shoreline protection. This study suggests the need for a cross-sectoral, regional approach to sea level rise adaptation.
Tidally influenced coastal wetlands, both saline and fresh, appear where terrestrial and marine environments meet and are considered important ecosystems for identifying the impacts of climate change. Coastal wetlands provide valuable benefits to society and the environment in the form of flood protection, water-quality improvements, and shoreline erosion reduction, making them one of the most important ecosystems in the world. Historically, these ecosystems have vertically adjusted to match rising sea levels through biologic and physical processes, but they are increasingly vulnerable to submergence as sea-level rise accelerates. Measuring vertical change on lands protected from human influence allows scientists to understand how vulnerable coastal wetlands are to submergence. But to fully understand this vulnerability, scientists must identify where vertical change in coastal wetlands is being measured across the lower 48 United States, a task that has not yet been undertaken. In this Fact Sheet, we document the spatial distribution of vertical change measurements in coastal wetlands to inform where gaps may still be in the Surface Elevation Table–Marker Horizon (SET-MH) coverage within protected lands across the lower 48 United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233039","usgsCitation":"Neville, J.A., and Gutenspergen, G.R., 2023, Spatial Distribution of Elevation Change Monitoring in Coastal Wetlands Across Protected Lands of the Lower 48 United States: U.S. Geological Survey Fact Sheet 2023–3029, 4 p., https://doi.org/10.3133/fs20233039.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-155035","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":420847,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3039/fs20233039.XML"},{"id":499693,"rank":6,"type":{"id":36,"text":"NGMDB Index 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U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Introduction: Antimicrobial resistance (AMR) is an increasing public health concern for humans, animals, and the environment. However, the contributions of spatially distributed sources of AMR in the environment are not well defined.
Methods: To identify the sources of environmental AMR, the novel microbial Find, Inform, and Test (FIT) model was applied to a panel of five antibiotic resistance-associated genes (ARGs), namely, erm(B), tet(W), qnrA, sul1, and intI1, quantified from riverbed sediment and surface water from a mixed-use region.
Results: A one standard deviation increase in the modeled contributions of elevated AMR from bovine sources or land-applied waste sources [land application of biosolids, sludge, and industrial wastewater (i.e., food processing) and domestic (i.e., municipal and septage)] was associated with 34–80% and 33–77% increases in the relative abundances of the ARGs in riverbed sediment and surface water, respectively. Sources influenced environmental AMR at overland distances of up to 13 km.
Discussion: Our study corroborates previous evidence of offsite migration of microbial pollution from bovine sources and newly suggests offsite migration from land-applied waste. With FIT, we estimated the distance-based influence range overland and downstream around sources to model the impact these sources may have on AMR at unsampled sites. This modeling supports targeted monitoring of AMR from sources for future exposure and risk mitigation efforts.
","language":"English","publisher":"Frontiers Media S.A.","doi":"10.3389/fmicb.2023.1223876","usgsCitation":"Wiesner-Friedman, C., Beattie, R.E., Stewart, J.R., Hristova, K.R., and Serre, M.L., 2023, Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework: Frontiers in Microbiology, v. 14, 1223876, 14 p., https://doi.org/10.3389/fmicb.2023.1223876.","productDescription":"1223876, 14 p.","ipdsId":"IP-149826","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":442109,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2023.1223876","text":"Publisher Index Page"},{"id":420771,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Kewaunee County","otherGeospatial":"Ahnapee River, East Twin River, Kewaunee River","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":3073,\"properties\":{\"name\":\"Kewaunee\",\"state\":\"WI\"},\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-87.3761,44.6754],[-87.3774,44.674],[-87.381,44.6636],[-87.3858,44.6545],[-87.3911,44.6473],[-87.3944,44.6442],[-87.3966,44.6378],[-87.4045,44.6302],[-87.4085,44.6257],[-87.4137,44.6235],[-87.4223,44.6145],[-87.4263,44.61],[-87.4341,44.6056],[-87.442,44.6011],[-87.4428,44.5934],[-87.4468,44.5893],[-87.4502,44.5816],[-87.4544,44.5721],[-87.4604,44.5622],[-87.4664,44.555],[-87.4738,44.5455],[-87.476,44.5369],[-87.4761,44.5305],[-87.4796,44.5223],[-87.4851,44.5106],[-87.488,44.4974],[-87.4959,44.4706],[-87.5046,44.4575],[-87.5041,44.4534],[-87.5062,44.4457],[-87.5064,44.4375],[-87.5074,44.4279],[-87.5121,44.4188],[-87.5163,44.408],[-87.5191,44.3998],[-87.5212,44.3907],[-87.5209,44.3816],[-87.5218,44.3734],[-87.5232,44.3688],[-87.5279,44.3602],[-87.5351,44.3521],[-87.5386,44.3422],[-87.5368,44.338],[-87.5408,44.3331],[-87.5454,44.3277],[-87.6445,44.3273],[-87.7665,44.3271],[-87.7655,44.4146],[-87.7646,44.5017],[-87.7643,44.5888],[-87.7628,44.6477],[-87.7582,44.6522],[-87.7555,44.6558],[-87.7547,44.6608],[-87.7507,44.6667],[-87.7435,44.673],[-87.7389,44.6775],[-87.6413,44.6757],[-87.5193,44.6753],[-87.4384,44.6754],[-87.3973,44.6753],[-87.3761,44.6754]]]}}]}","volume":"14","noUsgsAuthors":false,"publicationDate":"2023-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Wiesner-Friedman, Corinne","contributorId":329682,"corporation":false,"usgs":false,"family":"Wiesner-Friedman","given":"Corinne","email":"","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":882931,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beattie, Rachelle Elaine 0000-0002-9648-4948","orcid":"https://orcid.org/0000-0002-9648-4948","contributorId":298312,"corporation":false,"usgs":true,"family":"Beattie","given":"Rachelle","email":"","middleInitial":"Elaine","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":882932,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Jill R.","contributorId":329683,"corporation":false,"usgs":false,"family":"Stewart","given":"Jill","email":"","middleInitial":"R.","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":882933,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hristova, Krassimira R.","contributorId":298313,"corporation":false,"usgs":false,"family":"Hristova","given":"Krassimira","email":"","middleInitial":"R.","affiliations":[{"id":64527,"text":"Marquette University","active":true,"usgs":false}],"preferred":false,"id":882934,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Serre, Marc L.","contributorId":329684,"corporation":false,"usgs":false,"family":"Serre","given":"Marc","email":"","middleInitial":"L.","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":882935,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70248428,"text":"ofr20231071 - 2023 - Southern (California) sea otter population status and trends at San Nicolas Island, 2020–2023","interactions":[],"lastModifiedDate":"2023-09-13T13:50:49.576331","indexId":"ofr20231071","displayToPublicDate":"2023-09-12T12:49:56","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1071","displayTitle":"Southern (California) Sea Otter Population Status and Trends at San Nicolas Island, 2020–2023","title":"Southern (California) sea otter population status and trends at San Nicolas Island, 2020–2023","docAbstract":"The population of southern sea otters (Enhydra lutris nereis) at San Nicolas Island, California, has been monitored annually since the translocation of 140 southern sea otters to the island was completed in 1990. Monitoring efforts have varied in frequency and type across years. In 2017, the U.S. Navy and the U.S. Fish and Wildlife Service initiated a southern sea otter monitoring and research plan to determine the effects of military readiness activities on the growth or decline of the southern sea otter population at San Nicolas Island. The southern sea otter is the only subspecies of sea otter in California (hereafter, “sea otter\"). The monitoring program, at its basic level, includes seasonal surveys of population abundance, distribution, and foraging activity. From 2020 to 2023, we measured a 10-percent per annum increase in population abundance (95-percent confidence interval =0–20 percent), with 146 total individuals as of April 2023. Coinciding with the recent population growth, the sea otter distribution, which previously tended to concentrate on the island’s west end during 2003–2006 before shifting toward more use in the north and south sides during 2017–2019, appears to have shifted again during 2020–2023 to concentrate at the island’s east end. Forage data were collected between February 2020 and April 2023. There was a total of 773 forage dives in 60 forage bouts, with most of the identified prey on successful dives (n=401) recorded as sea urchins (66 percent), followed by bivalves (15 percent), snails (12 percent), and crabs (5.2 percent). Two lobsters and three abalone also were identified among the sea otter prey. Estimates of energy intake rates averaged 14.0 kilocalories per minute (95-percent confidence interval =10.8–17.2 kilocalories per minute). Monitoring data from the past two decades indicate that sea otters at San Nicolas Island have maintained a steady pattern of energy intake and population growth characteristic of a robust population, including a sixfold growth between 2000 and 2023. There was no conclusive evidence of density-dependent effects based on these patterns; however, estimates of energy intake rates for 2020–2023 were slightly lower than previous estimates from 2017 to 2019. Additionally, subtidal monitoring results at four sites around San Nicolas Island indicated that counts of purple sea urchins (Strongylocentrotus purpuratus) have increased between 2003 and 2023, whereas sea otter foraging surveys completed during the same period revealed that some sea otters have shifted toward higher consumption of purple sea urchins and bivalves compared to red sea urchins (S. fransicanus), which generally are the preferred larger prey of sea otters. These results contribute to the understanding of population dynamics and to the conservation and planning of future monitoring and research of sea otters at San Nicolas Island.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231071","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the U.S. Navy","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Yee, J.L., Tomoleoni, J.A., Kenner, M.C., Fujii, J.A., Bentall, G.B., Staedler, M.M., and Hatfield, B.B., 2023, Southern (California) sea otter population status and trends at San Nicolas Island, 2020–2023: U.S. Geological Survey Open-File Report 2023–1071, 37 p., https://doi.org/10.3133/ofr20231071.","productDescription":"vii, 37 p.","numberOfPages":"37","onlineOnly":"Y","ipdsId":"IP-155128","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":420734,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231071/full"},{"id":420730,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1071/covrthb.jpg"},{"id":420731,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1071/ofr20231071.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":420732,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1071/ofr20231071.xml"},{"id":420733,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1071/images"}],"country":"United States","state":"California","otherGeospatial":"San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.41914133101146,\n 33.23095571590274\n ],\n [\n -119.5256785251456,\n 33.28967086732948\n ],\n [\n -119.583789721946,\n 33.28157457228808\n ],\n [\n -119.57410452247933,\n 33.24816941739742\n ],\n [\n -119.54504892407897,\n 33.22791765206338\n ],\n [\n -119.47119927814498,\n 33.20867413062352\n ],\n [\n -119.43730108001157,\n 33.21576434144701\n ],\n [\n -119.41914133101146,\n 33.23095571590274\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
This paper applies a catch multiple survey analysis (CMSA) to Atlantic horseshoe crabs Limulus polyphemus in the Delaware Bay to generate robust population estimates for harvest management. Currently, horseshoe crabs along the U.S. Atlantic coast are harvested as bait for other fisheries and collected for their blood, which is used in a biomedical industry. The Delaware Bay is home to the largest population of horseshoe crabs and is a significant stopover for shorebirds to rebuild energy by consuming horseshoe crab eggs prior to completing their northward migration. To address this interrelationship, the Adaptive Resource Management (ARM) Framework has been used since 2013 to ensure that horseshoe crab harvest within the region takes into account the forage needs of migratory birds. Since its inception, the ARM Framework has used a single trawl survey's swept area-based population estimates of horseshoe crab relative abundance and a theoretical population model developed primarily from literature-derived values. With more data collected in the region in recent years and other sources of mortality that can now be quantified, a catch survey model can provide horseshoe crab population estimates going forward.
A CMSA was used to estimate male and female horseshoe crab population size for 2003–2021 using all quantifiable sources of mortality and three fishery-independent indices of abundance.
The CMSA results indicated that adult abundance of male and female horseshoe crabs was stable from 2003 to 2013 and then began to increase through 2017, a result that is consistent with stock rebuilding following a period of harvest restrictions as recommended by the ARM Framework. Population estimates were lower in recent years but remained above the levels estimated before implementation of the ARM Framework. In 2021, the CMSA estimated that there were over 6 million mature females and nearly 16 million mature male horseshoe crabs in the region.
The CMSA provides the best and most comprehensive population estimates of horseshoe crabs in Delaware Bay and will improve modeling efforts within the ARM Framework going forward.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10250","usgsCitation":"Anstead, K.A., Sweka, J., Barry, L., Hallerman, E., Smith, D.R., Ameral, N., Schmidtke, M., and Wong, R.A., 2023, Application of a catch multiple survey analysis for Atlantic horseshoe crab Limulus polyphemus in the Delaware Bay: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 15, no. 5, e10250, 16 p., https://doi.org/10.1002/mcf2.10250.","productDescription":"e10250, 16 p.","ipdsId":"IP-154235","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":442129,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10250","text":"Publisher Index Page"},{"id":420973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, New Jersey","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.0255903899312,\n 40.963933240903344\n ],\n [\n -76.0255903899312,\n 37.040030719320384\n ],\n [\n -73.65356124067296,\n 37.040030719320384\n ],\n [\n -73.65356124067296,\n 40.963933240903344\n ],\n [\n -76.0255903899312,\n 40.963933240903344\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Anstead, Kristen A.","contributorId":329847,"corporation":false,"usgs":false,"family":"Anstead","given":"Kristen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":883459,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sweka, John A.","contributorId":288581,"corporation":false,"usgs":false,"family":"Sweka","given":"John A.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":883460,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barry, Linda","contributorId":329848,"corporation":false,"usgs":false,"family":"Barry","given":"Linda","email":"","affiliations":[],"preferred":false,"id":883461,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hallerman, Eric M.","contributorId":279474,"corporation":false,"usgs":false,"family":"Hallerman","given":"Eric M.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":883462,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, David R. 0000-0001-9560-5210 dvsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-9560-5210","contributorId":329849,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"dvsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":883463,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ameral, Natalie","contributorId":329850,"corporation":false,"usgs":false,"family":"Ameral","given":"Natalie","email":"","affiliations":[],"preferred":false,"id":883464,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmidtke, Michael","contributorId":329851,"corporation":false,"usgs":false,"family":"Schmidtke","given":"Michael","email":"","affiliations":[],"preferred":false,"id":883465,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wong, Richard A.","contributorId":329852,"corporation":false,"usgs":false,"family":"Wong","given":"Richard","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":883466,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70249497,"text":"70249497 - 2023 - Shallow fault slip of the 2020 M5.1 Sparta, North Carolina, earthquake","interactions":[],"lastModifiedDate":"2023-11-07T16:18:22.677501","indexId":"70249497","displayToPublicDate":"2023-09-11T06:36:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Shallow fault slip of the 2020 M5.1 Sparta, North Carolina, earthquake","docAbstract":"The 2020 M 5.1 Sparta, North Carolina, earthquake is the largest in the eastern United States since the 2011 M 5.8 Mineral, Virginia, earthquake and produced a ∼2.5‐km‐long surface rupture, unusual for an event of this magnitude. A geological field study conducted soon after the event indicates oblique slip along a east‐southeast‐trending fault with a consistently observed thrust component. My analysis of regional seismic waveforms, Interferometric Synthetic Aperture Radar, and Global Positioning System survey data yields a compact shallow rupture extending from Earth’s surface down‐dip to the southwest over a ∼3 km fault length. The inferred kinematic rupture is primarily toward the up‐dip and eastward along‐strike directions and has predominantly thrust motion in the west, transitioning to roughly equal thrust and left‐lateral strike‐slip motion in the east. No normal faulting component, as proposed in an earlier geophysical study, is necessary to explain the data. The prevalence of only dip‐slip motions observed at Earth’s surface may demand slip partitioning between dip slip and lateral motions at depth.
Pennsylvania experienced heavy rainfall on September 1 and 2, 2021, as the remnants of Hurricane Ida swept over parts of the State. Much of eastern and south-central Pennsylvania received 5 to 10 inches of rain, and most of the rainfall fell within little more than 6 hours. Southeastern Pennsylvania experienced widespread, substantial flooding, and the city of Philadelphia and surrounding areas were particularly affected by the flooding. U.S. Geological Survey (USGS) streamgages registered peak streamflows of record at 19 locations, and 52 locations experienced top 5 peak streamflows for the period of record and an annual exceedance probability estimate of at least 10 percent. During this September 2021 flood event, USGS personnel made over 60 streamflow measurements at streamgages in Pennsylvania using direct and indirect methods. Many of those streamflow measurements were made to verify or improve the accuracy, extent, or development of new stage-streamflow relations at streamgages operated by the USGS. After the floodwaters receded, USGS personnel identified and documented a total of 338 high-water marks in Pennsylvania, noting such things as their general description, location, height above land surface, and quality. Many of these high-water marks were used to create five flood-documentation maps for selected communities in southeastern Pennsylvania that experienced substantial flooding because of the remnants of Hurricane Ida. Digital datasets of the inundated areas, mapped boundaries, and water depth are available (Stuckey and Conlon, 2023).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235086","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Stuckey, M.H., Conlon, M.D., and Weaver, M.R., 2023, Characterization of peak streamflows and flooding in select areas of Pennsylvania from the remnants of Hurricane Ida, September 1–2, 2021 (ver. 1.1, September 28, 2023): U.S. Geological Survey Scientific Investigations Report 2023–5086, 28 p., https://doi.org/10.3133/sir20235086.","productDescription":"Report: vii, 28 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-145111","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":501049,"rank":8,"type":{"id":36,"text":"NGMDB Index 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\"}}]}","edition":"Version 1.0: September 7, 2023; Version 1.1: September 28, 2023","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 170
Avian influenza viruses pose a threat to wildlife and livestock health. The emergence of highly pathogenic avian influenza (HPAI) in wild birds and poultry in North America in late 2021 was the first such outbreak since 2015 and the largest outbreak in North America to date. Despite its prominence and economic impacts, we know relatively little about how HPAI spreads in wild bird populations. In January 2022, we captured 43 mallards (Anas platyrhynchos) in Tennessee, USA, 11 of which were actively infected with HPAI. These were the first confirmed detections of HPAI H5N1 clade 2.3.4.4b in the Mississippi Flyway. We compared movement patterns of infected and uninfected birds and found no clear differences; infected birds moved just as much during winter, migrated slightly earlier, and migrated similar distances as uninfected birds. Infected mallards also contacted and shared space with uninfected birds while on their wintering grounds, suggesting ongoing transmission of the virus. We found no differences in body condition or survival rates between infected and uninfected birds. Together, these results show that HPAI H5N1 clade 2.3.4.4b infection was unrelated to body condition or movement behavior in mallards infected at this location during winter; if these results are confirmed in other seasons and as HPAI H5N1 continues to evolve, they suggest that these birds could contribute to the maintenance and dispersal of HPAI in North America. Further research on more species across larger geographic areas and multiple seasons would help clarify potential impacts of HPAI on waterfowl and how this emerging disease spreads at continental scales, across species, and potentially between wildlife and domestic animals.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-023-40921-z","usgsCitation":"Teitelbaum, C.S., Masto, N.M., Sullivan, J.D., Keever, A., Poulson, R., Carter, D., Blake-Bradshaw, A., Highway, C., Feddersen, J., Hagy, H.M., Gerhold, R.W., Cohen, B.S., and Prosser, D.J., 2023, North American wintering mallards infected with highly pathogenic avian influenza show few signs of altered local or migratory movements: Scientific Reports, v. 13, 14473, 11 p., https://doi.org/10.1038/s41598-023-40921-z.","productDescription":"14473, 11 p.","ipdsId":"IP-149626","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":442230,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-023-40921-z","text":"Publisher Index Page"},{"id":435195,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AZL1MN","text":"USGS data release","linkHelpText":"Data showing 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0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":882104,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keever, Allison","contributorId":187743,"corporation":false,"usgs":false,"family":"Keever","given":"Allison","email":"","affiliations":[],"preferred":false,"id":882105,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poulson, Rebecca L.","contributorId":198807,"corporation":false,"usgs":false,"family":"Poulson","given":"Rebecca L.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":882106,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carter, Deborah","contributorId":213914,"corporation":false,"usgs":false,"family":"Carter","given":"Deborah","affiliations":[{"id":38928,"text":"University of Georgia Southeastern Cooperative Wildlife Disease Study","active":true,"usgs":false}],"preferred":false,"id":882107,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blake-Bradshaw, Abigail","contributorId":316651,"corporation":false,"usgs":false,"family":"Blake-Bradshaw","given":"Abigail","email":"","affiliations":[{"id":68664,"text":"Tennessee Technical University","active":true,"usgs":false}],"preferred":false,"id":882108,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Highway, Corey","contributorId":329379,"corporation":false,"usgs":false,"family":"Highway","given":"Corey","email":"","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":882109,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Feddersen, Jamie","contributorId":329381,"corporation":false,"usgs":false,"family":"Feddersen","given":"Jamie","email":"","affiliations":[{"id":13408,"text":"Tennessee Wildlife Resources Agency","active":true,"usgs":false}],"preferred":false,"id":882110,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hagy, Heath M.","contributorId":172326,"corporation":false,"usgs":false,"family":"Hagy","given":"Heath","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":882111,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gerhold, Richard W.","contributorId":201770,"corporation":false,"usgs":false,"family":"Gerhold","given":"Richard","email":"","middleInitial":"W.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":882112,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Cohen, Bradley S.","contributorId":171513,"corporation":false,"usgs":false,"family":"Cohen","given":"Bradley","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":882113,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Prosser, Diann J. 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":221167,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":882114,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70248872,"text":"70248872 - 2023 - Ground motion and seismic hazard in the central and eastern United States","interactions":[],"lastModifiedDate":"2026-03-19T15:08:47.759038","indexId":"70248872","displayToPublicDate":"2023-09-01T10:02:25","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":21641,"text":"Technical Letter Report","active":true,"publicationSubtype":{"id":1}},"title":"Ground motion and seismic hazard in the central and eastern United States","docAbstract":"This report describes work carried out under the U.S. Nuclear Regulatory Commission (NRC) Interagency Agreement to the U.S. Geological Survey (USGS) “Research to Support NRC’s Seismic Hazard Analyses” for Task 3, “Seismic Hazard and Ground Motion Models.” The focus of this work has been on evaluation of the Next Generation Attenuation (NGA)-East groundmotion models (GMMs) with available ground-motion data and evaluation of alternative methods for characterizing epistemic uncertainty for probabilistic seismic hazard analysis (PSHA).
When the Interagency Agreement commenced, the USGS’s National Seismic Hazard Model (NSHM) was being updated, and a significant part of the update for the 2018 NSHM included the introduction of new GMMs for the central and eastern United States (CEUS) (Petersen et al., 2020). In addition to the implementation of the then-recently developed NGA-East GMMs (Goulet et al., 2018), the ground-motion characterization for the CEUS in the 2018 NSHM also included a logic-tree branch with weights applied to the updated “adjusted seed” models that were developed as part of the NGA-East process and in updates by the GMM developers.
The Statement of Work for Task 3 of the NRC Interagency Agreement to the USGS included the following parts: (1) Describe technically acceptable approaches in combining GMMs for use in PSHA calculations; (2) Evaluate the effect of using different sets of GMMs (NGA-East SSHAC versus NGA-East USGS) on PSHA calculations; (3) Describe the results of the GMM testing against recorded data, and provide a recommendation on the GMMs application for use in the PSHA; (4) Evaluate the impacts of recently updated individual GMMs on the published NGAEast models; and (5) Submit a final Technical Letter Report documenting results of this task.
We present results from this work in two sections: (Chapter 1) Updated Central and Eastern United States Ground Motions and Ground-Motion Analyses; and (Chapter 2) Approaches to Combining Ground-Motion Models for Probabilistic Seismic Hazard Analysis in the Central and Eastern United States.
","language":"English","publisher":"U.S. Nuclear Regulatory Commission","usgsCitation":"Moschetti, M.P., Thompson, E.M., Boyd, O.S., Engler, D.T., Worden, B., Ferragut, G., Rezaeian, S., and Powers, P.M., 2023, Ground motion and seismic hazard in the central and eastern United States: Technical Letter Report, 73 p.","productDescription":"73 p.","ipdsId":"IP-154468","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":501310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"central and eastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.90807059528223,\n 48.94073422059739\n ],\n [\n -106.90807059528223,\n 20.331161730583176\n ],\n [\n -61.27021618898843,\n 20.331161730583176\n ],\n [\n -61.27021618898843,\n 48.94073422059739\n ],\n [\n -106.90807059528223,\n 48.94073422059739\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":883992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":883993,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, Oliver S. 0000-0001-9457-0407 olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":883994,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Engler, Davis T. 0000-0002-7133-3545","orcid":"https://orcid.org/0000-0002-7133-3545","contributorId":265962,"corporation":false,"usgs":true,"family":"Engler","given":"Davis","email":"","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":883995,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Worden, Bruce","contributorId":64648,"corporation":false,"usgs":true,"family":"Worden","given":"Bruce","affiliations":[],"preferred":false,"id":957156,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ferragut, Gabriel Christian 0000-0002-0776-9176","orcid":"https://orcid.org/0000-0002-0776-9176","contributorId":330101,"corporation":false,"usgs":true,"family":"Ferragut","given":"Gabriel Christian","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":883996,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893 srezaeian@usgs.gov","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":4395,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","email":"srezaeian@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":883997,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Powers, Peter M. 0000-0003-2124-6184 pmpowers@usgs.gov","orcid":"https://orcid.org/0000-0003-2124-6184","contributorId":176814,"corporation":false,"usgs":true,"family":"Powers","given":"Peter","email":"pmpowers@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":883998,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70267362,"text":"70267362 - 2023 - Induced seismicity and its impact on existing seismic hazard analysis","interactions":[],"lastModifiedDate":"2025-05-21T13:52:21.335242","indexId":"70267362","displayToPublicDate":"2023-09-01T08:48:53","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":21641,"text":"Technical Letter Report","active":true,"publicationSubtype":{"id":1}},"title":"Induced seismicity and its impact on existing seismic hazard analysis","docAbstract":"We develop a scheme for mapping changes in earthquake rates within a region in near-real-time. A specific goal of the work is to track recent changes in the rates of induced earthquakes in the central and eastern United States. We map rates in a time window of interest, map rates in a preceding time window, and then ratio the two maps to show changes. A proof-of-concept map is first prepared, comparing rates during the first six months of 2010 with rates from the preceding five years; this demonstration map shows, among other things, the growth of induced seismicity in Oklahoma during 2010. We then present a series of ten ratio maps: the first six months of 2018 compared with the preceding five years, and so on in six-month increments through the end of 2022. These maps show changes in the rates and locations of induced earthquakes, as well as other seismicity trends. Map regions, time windows, and other model parameters are easily adaptable for other applications.","language":"English","publisher":"U.S. Nuclear Regulatory Comission","usgsCitation":"Mueller, C., and Shumway, A., 2023, Induced seismicity and its impact on existing seismic hazard analysis: Technical Letter Report, xv, 17 p.","productDescription":"xv, 17 p.","ipdsId":"IP-152895","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":486279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":486278,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.nrc.gov/docs/ML2325/ML23257A188.pdf"}],"noUsgsAuthors":false,"publicationDate":"2023-09-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Mueller, Charles 0000-0002-1868-9710 cmueller@usgs.gov","orcid":"https://orcid.org/0000-0002-1868-9710","contributorId":140380,"corporation":false,"usgs":true,"family":"Mueller","given":"Charles","email":"cmueller@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shumway, Allison M. 0000-0003-1142-7141 ashumway@usgs.gov","orcid":"https://orcid.org/0000-0003-1142-7141","contributorId":147862,"corporation":false,"usgs":true,"family":"Shumway","given":"Allison","email":"ashumway@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937974,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70252959,"text":"70252959 - 2023 - Interstate 15 wildlife crossing design considerations for focal wildlife species - Santa Ana-Palomar Mountains Linkage southern California","interactions":[],"lastModifiedDate":"2024-04-12T13:52:55.734858","indexId":"70252959","displayToPublicDate":"2023-09-01T08:38:51","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Interstate 15 wildlife crossing design considerations for focal wildlife species - Santa Ana-Palomar Mountains Linkage southern California","docAbstract":"The Nature Conservancy (TNC) and the California Department of Transportation (Caltrans), along with landowners including San Diego State University, California Department of Fish and Wildlife, Western Riverside Regional Conservation Authority and Riverside County Flood Control District are developing wildlife crossing infrastructure projects along a 3-mile stretch of Interstate 15 (I-15) in the Santa Ana-Palomar Mountains Linkage (hereafter ‘Linkage’) in southern California. These crossings will provide a critical missing link that will help reconnect wildlife in the coastal Santa Ana Mountains west of I-15 with those in the interior Palomar and Eastern Peninsular ranges to the east of I-15. The Linkage supports intact and diverse habitats including coastal sage scrub, grasslands, chaparral, and oak and riparian woodlands, and has been a focus of regional conservation efforts for the last 30 years.
The three wildlife crossing infrastructure projects include enhancement of the existing Temecula Creek I-15 Bridge, construction of a new vegetated wildlife overcrossing, and construction of a new stand-alone wildlife culvert.
Given the challenges and level of financial investment required to secure wildlife crossings for I-15 in the Linkage, TNC and Caltrans proposed that planning efforts would benefit from input by taxonomic experts on design concepts that meet the needs of the broadest range of wildlife. While wildlife crossings are becoming more common, optimal designs that meet the needs of a variety of wildlife species are largely unknown and can be site specific. To address this challenge, we held a workshop in February 2022 that brought together over 50 wildlife experts to brainstorm and identify specific design considerations for various focal wildlife species groups (medium/large mammals, small animals, birds, bats, plants, and invertebrates) that might use the identified I-15 wildlife crossings (The Nature Conservancy 2022).
Lead experts for each focal species group worked together to identify specific wildlife crossing features or attributes for each of the three proposed wildlife crossings.
Specific attributes evaluated by experts for each crossing type and species group included, at a minimum:
• Crossing Structure Attributes;
o Habitat features? (cover, habitat structure, substrate, moisture, light and noise mitigation)
• Crossing Approach Area Features;
o Habitat features (cover type/density, substrate, water, light and noise mitigation)
• Barrier design to reduce roadkill and/or to funnel wildlife to the crossing;
• Additional research to resolve uncertainties related to crossing design
Based on the design considerations for each potential crossing type, the experts then weighed in on the suitability of the existing location and probability of use by their focal species or groups of species. With proposed design features, Temecula Creek Bridge has moderate or high probability of use by 27 of the 36 focal wildlife species assessed, while the vegetated overcrossing could meet the needs of 26 of the 36 species. When combined, Temecula Creek Bridge and the vegetated overcrossing have a moderate or high probability of use for 34 of the 36 species. The wildlife culvert has a moderate or high level of expected use by 10 of the 36 focal species and could serve connectivity needs for representative species from all but the bird and plant species groups.
","language":"English","publisher":"The Nature Conservancy","usgsCitation":"Smith, T., Brehme, C.S., Carpenter, J., Frost, N.A., Jennings, M., Kus, B., Quinnell, S., Straham, S., and Vickers, T.W., 2023, Interstate 15 wildlife crossing design considerations for focal wildlife species - Santa Ana-Palomar Mountains Linkage southern California, iv, 46 p.","productDescription":"iv, 46 p.","ipdsId":"IP-156534","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":427729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":427723,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.scienceforconservation.org/products/interstate-15-wildlife-crossing-design"}],"country":"United States","state":"California","otherGeospatial":"Santa Ana-Palomar Mountains Linkage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.96277940038678,\n 33.17061852067067\n ],\n [\n -116.96277940038678,\n 33.583670223835924\n ],\n [\n -117.44162796500501,\n 33.583670223835924\n ],\n [\n -117.44162796500501,\n 33.17061852067067\n ],\n [\n -116.96277940038678,\n 33.17061852067067\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Trish","contributorId":335587,"corporation":false,"usgs":false,"family":"Smith","given":"Trish","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":898763,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brehme, Cheryl S. 0000-0001-8904-3354 cbrehme@usgs.gov","orcid":"https://orcid.org/0000-0001-8904-3354","contributorId":3419,"corporation":false,"usgs":true,"family":"Brehme","given":"Cheryl","email":"cbrehme@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":898764,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carpenter, Jill","contributorId":335588,"corporation":false,"usgs":false,"family":"Carpenter","given":"Jill","email":"","affiliations":[{"id":80443,"text":"LSA Associates","active":true,"usgs":false}],"preferred":false,"id":898765,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frost, Nancy A.","contributorId":200382,"corporation":false,"usgs":false,"family":"Frost","given":"Nancy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":898766,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jennings, Megan","contributorId":298254,"corporation":false,"usgs":false,"family":"Jennings","given":"Megan","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":898767,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kus, Barbara E. 0000-0002-3679-3044 barbara_kus@usgs.gov","orcid":"https://orcid.org/0000-0002-3679-3044","contributorId":3026,"corporation":false,"usgs":true,"family":"Kus","given":"Barbara E.","email":"barbara_kus@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":898768,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Quinnell, Scott","contributorId":335593,"corporation":false,"usgs":false,"family":"Quinnell","given":"Scott","email":"","affiliations":[],"preferred":false,"id":898780,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Straham, Spring","contributorId":335589,"corporation":false,"usgs":false,"family":"Straham","given":"Spring","email":"","affiliations":[{"id":80444,"text":"Wildspring Ecology","active":true,"usgs":false}],"preferred":false,"id":898769,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Vickers, T. Winston","contributorId":198755,"corporation":false,"usgs":false,"family":"Vickers","given":"T.","email":"","middleInitial":"Winston","affiliations":[],"preferred":false,"id":898770,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70264030,"text":"70264030 - 2023 - Eastern Indigo snake (Drymarchon couperi) shelter site use In peninsular Florida, USA, and implicatIons for habItat conservatIon","interactions":[],"lastModifiedDate":"2025-03-05T17:28:54.304812","indexId":"70264030","displayToPublicDate":"2023-08-31T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1894,"text":"Herpetological Conservation and Biology","onlineIssn":"2151-0733","printIssn":"1931-7603","active":true,"publicationSubtype":{"id":10}},"title":"Eastern Indigo snake (Drymarchon couperi) shelter site use In peninsular Florida, USA, and implicatIons for habItat conservatIon","docAbstract":"Shelters are critical for many species as protection from predators and extreme temperatures. Successful conservation of reptiles requires understanding both shelter site requirements and availability. The Eastern Indigo Snake (EIS; Drymarchon couperi) is endemic to the southeastern U.S. and is federally listed. Recovery has focused on maximizing unfragmented landscapes, with less attention on fine-scale features such as shelter sites. In the northern EIS range, Gopher Tortoise (Gopherus polyphemus) burrows are used extensively for shelter. Although EIS in peninsular Florida often shelter in tortoise burrows, they also use other shelters where tortoise burrows are scarce or absent. Solely focusing EIS survey and management efforts where Gopher Tortoises are present may overlook occupied habitats and misallocate resources. We investigated the importance of different shelter sites in central Florida using data from radio-tracked EIS. We modeled the use of shelter categories as a function of sex, season, and habitat using Bayesian multinomial Generalized Linear Models. Results showed that EIS in peninsular Florida used Gopher Tortoise burrows across all seasons and habitats. Tortoise burrow use was highest in xeric habitats and lowest in mesic habitats where burrows are most and least abundant, respectively. There was less variability in shelter site use in disturbed habitats and flatwoods. Tortoise burrow use by EIS in the cool season across sexes and habitats in our study was much lower than in southern Georgia. Our results indicate that EIS are less dependent on Gopher Tortoise burrows in peninsular Florida and that suitable habitats with few or no tortoise burrows could still provide conservation value for EIS.
","language":"English","publisher":"Herpetological Conservation and Biology","usgsCitation":"Bolt, M., Bauder, J.M., Legare, M., Jenkins, C., Rothermel, B., and Breininger, D., 2023, Eastern Indigo snake (Drymarchon couperi) shelter site use In peninsular Florida, USA, and implicatIons for habItat conservatIon: Herpetological Conservation and Biology, v. 18, no. 2, p. 362-373.","productDescription":"12 p.","startPage":"362","endPage":"373","ipdsId":"IP-139825","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":482919,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.herpconbio.org/contents_vol18_issue2.html","linkFileType":{"id":8,"text":"xml"}},{"id":482920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, 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Rebecca","affiliations":[{"id":84051,"text":"NASA Environmental and Medical Contract","active":true,"usgs":false}],"preferred":false,"id":929526,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bauder, Javan Mathias 0000-0002-2055-5324","orcid":"https://orcid.org/0000-0002-2055-5324","contributorId":337814,"corporation":false,"usgs":true,"family":"Bauder","given":"Javan","email":"","middleInitial":"Mathias","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":929527,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Legare, Michael L.","contributorId":351810,"corporation":false,"usgs":false,"family":"Legare","given":"Michael L.","affiliations":[{"id":84053,"text":"Merritt Island National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":929528,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jenkins, Christopher L.","contributorId":351811,"corporation":false,"usgs":false,"family":"Jenkins","given":"Christopher L.","affiliations":[{"id":13223,"text":"The Orianne Society","active":true,"usgs":false}],"preferred":false,"id":929529,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rothermel, Betsie B.","contributorId":351812,"corporation":false,"usgs":false,"family":"Rothermel","given":"Betsie B.","affiliations":[{"id":17991,"text":"Archbold Biological Station","active":true,"usgs":false}],"preferred":false,"id":929530,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Breininger, David R.","contributorId":351813,"corporation":false,"usgs":false,"family":"Breininger","given":"David R.","affiliations":[{"id":84051,"text":"NASA Environmental and Medical Contract","active":true,"usgs":false}],"preferred":false,"id":929531,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70247945,"text":"pp1884 - 2023 - Roles of regional structures and country-rock facies in defining mineral belts in central Idaho mineral province with detail for Yellow Pine and Thunder Mountain mining districts","interactions":[],"lastModifiedDate":"2026-02-19T17:27:07.609639","indexId":"pp1884","displayToPublicDate":"2023-08-29T15:01:01","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1884","displayTitle":"Roles of Regional Structures and Country-Rock Facies in Defining Mineral Belts in Central Idaho Mineral Province with Detail for Yellow Pine and Thunder Mountain Mining Districts","title":"Roles of regional structures and country-rock facies in defining mineral belts in central Idaho mineral province with detail for Yellow Pine and Thunder Mountain mining districts","docAbstract":"The central Idaho metallogenic province hosts numerous mineral deposit types. These include Late Cretaceous precious-polymetallic vein deposits, amagmatic Paleocene–Eocene breccia-hosted gold-tungsten-antimony deposits, and Eocene mercury deposits in metasedimentary roof pendants and in Late Cretaceous granitoids. Hot-springs gold deposits in Eocene volcanic rocks are also included in the central Idaho province. New sensitive high mass-resolution ion microprobe (SHRIMP) uranium-lead (U-Pb) ages for igneous rocks and for detrital zircon analyses of metasedimentary rocks along with geologic mapping clarify the geologic framework of the mineral deposits. This framework includes (1) structural controls for regional distribution of mining districts, (2) progressive structural development of individual districts, (3) regional sedimentary facies and their control of metals associations resulting in regional belts, and (4) influences of the several regional magmatic events.
In central Idaho, 15 mining districts form two clusters that are grouped about a 200-kilometer (km) long system of normal faults. The northwestern cluster is in the regional hanging wall west of large, west-side-down faults, and the mineral deposits are located along smaller faults and fractures that cut the regional hanging wall. The southeastern cluster is in the regional hanging wall east of a linked large east-side-down fault and along and controlled by related hanging wall faults. At the southern extent of the regional fault system, the Yellow Pine-Thunder Mountain districts span a nearly 24-km-wide, east-tilted crustal block of normal-fault dominoes, exposing original crustal depths from 5 to 10 km deep on the west in the Late Cretaceous to shallow-surface depths on the east in the Eocene.
Ore deposition in the northwestern district cluster was primarily Late Cretaceous and related to Idaho batholith plutons with only a single deposit related to a small Eocene intrusion; in the southeastern cluster, most deposits were initiated in the Late Cretaceous but with varying manifestations of overprinted Eocene mineralization activity. In the Yellow Pine-Thunder Mountain districts at the southern extent of the southern cluster, several mineralizing pulses occurred during hanging-wall collapse, such that (1) early deposits were multiply overprinted and (2) deposit depths, ages, and structural characteristics change progressively eastward. Originally deep-seated western Yellow Pine district deposits are Late Cretaceous viscoplastic mesothermal veins overprinted by Paleocene and Eocene breccia-hosted epithermal deposits. Central Yellow Pine district deposits contain early deeper vein systems but are primarily Paleocene and Eocene breccia-hosted epithermal deposits in Late Cretaceous plutonic rocks and Proterozoic–Paleozoic roof pendant rocks. Eastern district deposits are Eocene hot-springs-related deposits in the roof pendant. Thunder Mountain deposits farthest east are near-surface hot-springs deposits in Eocene volcanic and volcaniclastic rocks that overlie buried Cretaceous igneous and older roof pendant rocks.
The mining district clusters are sited across several northwest-striking paleostratigraphic belts that are exposed in roof pendants and are offset by the regional normal fault system. A northeastern belt is Mesoproterozoic strata associated with gold-silver-copper±cobalt deposits. A central belt of Neoproterozoic rocks is not associated with mineral deposits in the central Idaho mineral province. A southwestern belt composed of probable Paleozoic deep-water miogeoclinal slope rocks and late Paleozoic epicratonic basinal rocks is thin and narrowly exposed but associated with gold-silver-antimony-tungsten±mercury deposits. These metasedimentary rocks (and their metal associations) are parts of regional mineral belts in which metal endowments are related to particular sedimentary facies belts and their Cretaceous thrust-fault juxtaposition and where these features have proximity to Late Cretaceous or Eocene igneous rocks. Offset and preservation or erosional stripping of these facies belts, thrust plates, igneous settings, and the associated regional mineral belts were controlled by the sense and magnitude of displacements across the regional normal-fault system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1884","programNote":"Mineral Resources Program","usgsCitation":"Lund, K., Aleinikoff, J.N., and Holm-Denoma, C., 2023, Roles of regional structures and country-rock facies in defining mineral belts in central Idaho mineral province with detail for Yellow Pine and Thunder Mountain mining districts (ver. 1.1, September 2023): U.S. Geological Survey Professional Paper 1884, 53 p., https://doi.org/10.3133/pp1884.","productDescription":"Report: vii, 53 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-108495","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":422432,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/pp/1884/pp1884.xml"},{"id":422431,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/pp/1884/images"},{"id":420574,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/pp/1884/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":500195,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115237.htm","linkFileType":{"id":5,"text":"html"}},{"id":420149,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P931I3A3","text":"USGS data release","linkHelpText":"SHRIMP U-Pb and LA-ICPMS U-Pb geochronologic data for igneous and metasedimentary rocks in central Idaho mineral province, U.S.A., 2023"},{"id":420146,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1884/pp1884.pdf","text":"Report","size":"19.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1884"},{"id":420145,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1884/coverthb2.jpg"},{"id":422433,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/pp1884/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"PP 1884"}],"country":"United States","state":"Idaho","otherGeospatial":"Yellow Pine and Thunder Mountain Mining Districts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.00,\n 47.00\n ],\n [\n -117.00,\n 43.00\n ],\n [\n -112.00,\n 43.00\n ],\n [\n -112.00,\n 47.00\n ],\n [\n -117.00,\n 47.00\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: August 29, 2023; Version 1.1: September 6, 2023","contact":"Center Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, Mail Stop 973
Denver, CO 80225
Acipenser oxyrinchus oxyrinchus (Atlantic sturgeon) were once abundant and supported large-scale fisheries throughout much of the east coast of the United States. However, historic overharvest and habitat loss resulted in dramatic declines in abundance and eventual listing under the Endangered Species Act of the United States. As part of this listing, Atlantic sturgeon populations were divided into five distinct population segments (DPSs), with many management activities occurring at the level of the DPS. However, because subadult and adult Atlantic sturgeon can make large, coast-wide migrations and often mix extensively with individuals from other populations, individuals may be exposed to conservation threats away from their natal river or DPS, ultimately making it difficult to determine the appropriate spatial scale for management activities. To help address this uncertainty, the U.S. Geological Survey performed genetic assignment tests to determine the natal origin of 329 Atlantic sturgeon that were encountered as mortalities or taken during permitted activities in 2021. Overall, most individuals assigned to the Hudson River population, with additional major contributions from the James River Fall and Delaware River populations. However, a sizeable proportion of individuals were assigned to more distantly located populations in the southeastern United States. These results highlight the prevalence of long-distance movements in Atlantic sturgeon and underscore that populations may be vulnerable to threats far from their natal rivers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231054","collaboration":"Prepared in cooperation with the National Marine Fisheries Service","usgsCitation":"White, S.L., Johnson, R.L., Lubinski, B.A., Eackles, M.S., and Kazyak, D.C., 2023, Genetic population assignments of Atlantic sturgeon provided to National Marine Fisheries Service, 2022: U.S. Geological Survey Open-File Report 2023–1054, 10 p., https://doi.org/10.3133/ofr20231054.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-136703","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":419992,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P943B0GT","text":"USGS data release","linkHelpText":"Individual assignments and microsatellite genotypes for Atlantic sturgeon from 2021"},{"id":419987,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1054/coverthb.jpg"},{"id":419988,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1054/ofr20231054.pdf","text":"Report","size":"3.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1054"},{"id":419989,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231054/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1054"},{"id":419990,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1054/images/"},{"id":419991,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1054/ofr20231054.XML"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -66.7831526539585,\n 44.088449979425064\n ],\n [\n -69.15659153864834,\n 48.019075945012474\n ],\n [\n -72.30141815542203,\n 45.45727029656064\n ],\n [\n -75.65820657339519,\n 44.94778904002476\n ],\n [\n -75.28903339967198,\n 40.89558675319671\n ],\n [\n -76.46298604911388,\n 40.19400583735714\n ],\n [\n -78.43915371095366,\n 36.5604218577474\n ],\n [\n -84.16051772711577,\n 32.88156909351903\n ],\n [\n -83.55638083759197,\n 30.468214471664382\n ],\n [\n -80.73133729902378,\n 30.687235465695224\n ],\n [\n -78.61360348508101,\n 32.88392643413653\n ],\n [\n -75.62804928587406,\n 34.56644068656462\n ],\n [\n -74.64749468377897,\n 36.185966080642245\n ],\n [\n -74.65126231991508,\n 38.03204715851999\n ],\n [\n -73.38050438077715,\n 40.0657922005791\n ],\n [\n -69.39263502960705,\n 41.17701014917236\n ],\n [\n -70.1486437939862,\n 43.328483757491426\n ],\n [\n -66.7831526539585,\n 44.088449979425064\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Objective
Sea Lamprey Petromyzon marinus provide important ecological services within their native range, such as nutrient cycling, and can also act as a prey source for other species. Adult Sea Lamprey must access freshwater rivers to spawn, and because of this they are susceptible to changes in river connectivity. Human-made structures, such as dams, can exclude them from usable habitat. Sea Lamprey dam passage has not been extensively studied in Maine, despite Maine being within the native range of this species. The goals of this study were to evaluate upstream passage efficiency at the Milford Dam on the Penobscot River, Maine, and to provide comprehensive information about adult Sea Lamprey passage at five other dams throughout the Penobscot River watershed.
Methods
In 2020–2021 we captured and tagged 150 Sea Lamprey at the Milford Dam, the lowest dam in the Penobscot River, Maine, and displaced them downstream to assess passage efficiency at this dam and five upstream dams. In 2020, 50 Sea Lamprey were released on the east shore of the river downstream of Milford Dam; in 2021, the east shore release was repeated with an additional 50 fish and another 50 fish were released on the west shore.
Result
Between 70–82% of Sea Lamprey were observed passing Milford Dam again after mean delay times of 9–11 days. The release location did not affect dam passage success or the amount of time that was required to locate and use the passage structures. Sea Lampreys from both release groups were equally likely to approach the entrance to the fishway upon returning to Milford Dam, despite the fishway being located against the eastern shore of the river. However, high flows shortly after release may have resulted in higher attraction to the fishway in 2020. Passage success at dams upstream of Milford was highly variable. All Sea Lamprey were able to successfully navigate past West Enfield Dam (100% passage, n = 63), whereas Brownsmill Dam apparently acted as a complete barrier to further migration (0% passage, n = 7). Fish from all years and release groups together had a median upstream migration distance of 38.8 km after fish had passed Milford Dam, and a maximum observed upstream travel distance of approximately 100 km, indicating that most tagged Sea Lamprey ended their migration in the vicinity of a dam.
Conclusion
The results of this study indicate that Sea Lamprey have high passage efficiency at the Milford Dam and highlight areas within the Penobscot River basin—such as the Brownsmill Dam—where passage facilities are currently inadequate for Sea Lamprey.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10919","usgsCitation":"Peterson, E., Thors, R., Frechette, D., and Zydlewski, J.D., 2023, Adult Sea Lamprey approach and passage at the Milford Dam fishway, Penobscot River, Maine, United States: Journal of Fisheries Management, v. 43, no. 4, p. 1052-1065, https://doi.org/10.1002/nafm.10919.","productDescription":"14 p.","startPage":"1052","endPage":"1065","ipdsId":"IP-147299","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467098,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10919","text":"Publisher Index Page"},{"id":466001,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Milford Dam fishway, Penobscot River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.65399945645208,\n 44.94347505924921\n ],\n [\n -68.65399945645208,\n 44.93937668118309\n ],\n [\n -68.64306273135283,\n 44.93937668118309\n ],\n [\n -68.64306273135283,\n 44.94347505924921\n ],\n [\n -68.65399945645208,\n 44.94347505924921\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-08-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Peterson, Erin","contributorId":347938,"corporation":false,"usgs":false,"family":"Peterson","given":"Erin","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":922756,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thors, Rex","contributorId":347941,"corporation":false,"usgs":false,"family":"Thors","given":"Rex","affiliations":[{"id":83269,"text":"Maine Seagrant","active":true,"usgs":false}],"preferred":false,"id":922757,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frechette, Danielle","contributorId":347942,"corporation":false,"usgs":false,"family":"Frechette","given":"Danielle","affiliations":[{"id":68617,"text":"Maine Department of Marine Resources","active":true,"usgs":false}],"preferred":false,"id":922758,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":922759,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70247706,"text":"sir20235067 - 2023 - Geology and assessment of coal resources for the Cherokee coal bed in the Fort Union Formation, south-central Wyoming","interactions":[],"lastModifiedDate":"2026-03-09T16:56:31.872382","indexId":"sir20235067","displayToPublicDate":"2023-08-14T17:00:00","publicationYear":"2023","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":"2023-5067","displayTitle":"Geology and Assessment of Coal Resources for the Cherokee Coal Bed in the Fort Union Formation, South-Central Wyoming","title":"Geology and assessment of coal resources for the Cherokee coal bed in the Fort Union Formation, south-central Wyoming","docAbstract":"The Cherokee coal bed is a locally thick and laterally continuous coal bed in the Overland Member of the Paleocene Fort Union Formation in south-central Wyoming. It represents a significant resource that is easily accessible and may be extractable through both surface and underground mining methods. A database of more than 600 data points, comprising coalbed methane wells, coal exploration drill holes, and measured sections, was compiled from a previously released geologic database and reinterpreted to provide a more detailed geologic model for the Cherokee coal bed. The thickest part of the Cherokee coal bed lies along the crest of the Wamsutter arch, an east-west trending anticlinal feature that separates the Great Divide subbasin to north from the Washakie subbasin to the south. The Cherokee coal bed consists of several laterally persistent benches separated by partings that range in thickness from one inch to greater than 100 feet. A series of detailed geologic cross sections through the study area show both the structural geology and the distribution and areal extent of the individual coal benches of the Cherokee coal bed.
Data generated from the geologic model were used in stochastic geostatistical analyses to estimate the remaining or in-place coal resources. Certain parameters, as described later in the text, were applied to calculate available coal resources for surface and underground mining. This study is part of an ongoing process by the U.S. Geological Survey (USGS) to transition from a distance-based approach to a probabilistic approach for determining uncertainty in coal resource assessment. This probabilistic approach uses quantitative statistical methods to determine the potential range of uncertainty in coal resource estimates, whereas the distance-based approach does not provide any mathematical method to determine the range of uncertainty. Using stochastic geostatistical methods, utilizing 100 realizations or gridding iterations of the data, in-place resources were calculated, with a 90 percent probability, to be 15.261 ± 0.464 billion short tons (bst). Available coal resources tonnages were calculated using separate sets of criteria for surface and underground mining methods, based on probable mining parameters. Tonnage values were calculated based on estimated coal densities determined from available coal quality data. Available coal resources that meet the parameters for surface mining methods were calculated, with a 90 percent probability, to be 0.813 ± 0.038 bst.
Available coal resources that meet the parameters for underground mining methods were calculated, with a 90 percent probability, to be 2.393 ± 0.055 bst. The calculations were based on estimates of the resources that meet the parameters for the optimum mining of the thickest coal benches of the Cherokee coal bed. This is depicted in a series of cross sections through the study area that show projected underground mining horizons in the Cherokee coal bed, based on the thickest combinations of individual coal benches.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20235067","programNote":"Energy Resources Program","usgsCitation":"Shaffer, B.N., and Olea, R.A., 2023, Geology and assessment of coal resources for the Cherokee coal bed in the Fort Union Formation, south-central Wyoming: U.S. Geological Survey Scientific Investigations Report 2023–5067, 29 p., https://doi.org/10.3133/sir20235067.","productDescription":"Report: vii, 30 p.; 6 Figures: 36.00 x 24.00 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-132141","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":419765,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92K1UT6","text":"USGS data release","linkHelpText":"Cherokee coal bed drill hole data from the Fort Union Formation in the Little Snake River coal field and Red Desert area, Wyoming"},{"id":419763,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5067/coverthb2.jpg"},{"id":419764,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067.pdf","text":"Report","size":"6.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067"},{"id":419766,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig07.pdf","text":"Figure 7","size":"108 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 7"},{"id":419768,"rank":6,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig09.pdf","text":"Figure 9","size":"104 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 9"},{"id":419769,"rank":7,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig16.pdf","text":"Figure 16","size":"96 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 16"},{"id":419770,"rank":8,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig17.pdf","text":"Figure 17","size":"104 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 17"},{"id":419771,"rank":9,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig18.pdf","text":"Figure 18","size":"104 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 18"},{"id":419767,"rank":5,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig08.pdf","text":"Figure 8","size":"104 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 8"},{"id":420209,"rank":10,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5067/images"},{"id":420210,"rank":11,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067.xml"},{"id":420217,"rank":12,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235067/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5067"},{"id":500948,"rank":13,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115199.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wyoming","otherGeospatial":"Cherokee Coal Bed, Fort Union Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.3333,\n 42\n ],\n [\n -108.3333,\n 41.4167\n ],\n [\n -107.5,\n 41.4167\n ],\n [\n -107.5,\n 42\n ],\n [\n -108.3333,\n 42\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Lead mining in the Southeast Missouri Lead Mining District began in the 1700s and continued for nearly 300 years; the waste piles associated with smelting, mining, and milling of lead ores have released metal residues that have contaminated soil and water in the region. Previous studies in the district have indicated potential harm to wildlife, including birds, because of elevated lead concentrations associated with mining. Exposure to soil-borne lead was correlated with elevated lead concentrations in tissues, inhibition of δ-aminolevulinic acid dehydratase (δALAD), and renal lesions in birds foraging on ground-dwelling invertebrates at contaminated sites (compared to reference sites) in the Southeast Missouri Lead Mining District.
This study assessed reproductive outcomes for songbirds exposed to soil-borne lead in the district, examined the relation between lead concentrations in soils and in tissues of ground-feeding birds and prey species, and compared the results to literature-based toxicity thresholds for lead that are associated with negative effects in birds. Three lead-contaminated sites and three reference sites (with background concentrations of lead and no known mining inputs) were compared in two ways: individually to all other sites or by site type. Additional effects of lead exposure were evaluated by examining concentrations of biomarkers (oxidative stress, lipid peroxidation, and deoxyribonucleic acid damage) in liver tissues, δALAD inhibition, and renal and hepatic microscopic lesions in birds from lead-contaminated and reference sites.
Lead concentrations in soil were site-dependent and were also generally heterogeneous within the lead-contaminated sites. Between 17 and 74 percent of all soil samples at contaminated sites had lead concentrations that exceeded a threshold (1,000 milligrams per kilogram [mg/kg] lead in soil) previously associated with adverse physiological effects in birds in the Southeast Missouri Lead Mining District. Lead concentrations in mixed invertebrates from lead-contaminated sites (282 to 2,230 mg/kg dry weight [dw]) indicated that consuming soil-dwelling prey species is a potential exposure pathway for adult birds and their broods. At lead-contaminated sites, lead concentrations in 40.5 percent of blood samples (adults and their broods) were within a subclinical effects range (0.9 to 2.3 mg/kg dw), and 18.7 percent of samples had lead concentrations that exceeded clinical effects criteria (greater than 2.3 mg/kg dw). In contrast, only 2.6 percent of blood samples from reference sites were within the subclinical effects range for lead; all other blood samples from the reference sites had lead concentrations representative of background concentrations (less than 0.9 mg/kg dw). Subclinical and clinical threshold exceedances for lead concentrations in livers and kidneys were similarly more prevalent at the contaminated sites compared to the reference sites.
Lead concentrations in blood were positively correlated with lead concentrations in soil, livers, and kidneys. Lead concentrations in blood were negatively correlated with δALAD activity; greater than 50 percent of the birds collected at lead-contaminated sites exhibited injury via greater than 50 percent inhibition of δALAD in blood compared to birds at reference sites. Birds with elevated lead concentrations in tissues also exhibited enhanced oxidative stress. Microscopic lesions in the livers and kidneys of birds had similar rates of occurrence at the contaminated and reference sites, and lesion prevalence could not be directly linked to lead exposure. Reproductive success was monitored at 585 nests, and 3 out of 5 species had reduced nest success associated with elevated lead concentrations in soil; habitat measures did not help explain nest success. Reduced nest success may have resulted from greater nest predation resulting from neurological and behavioral effects of lead exposure. Ultimately, these lines of evidence indicate that bird health and reproduction have been negatively affected by exposure to lead-contaminated soils in the Southeast Missouri Lead Mining District.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235032","usgsCitation":"Brasso, R., Cleveland, D., Thompson, F.R., III, Mosby, D.E., Hixson, K., Roach, M., Rattner, B.A., Karouna-Renier, N.K., and Lankton, J.S., 2023, Effects of lead exposure on birds breeding in the Southeast Missouri Lead Mining District: U.S. Geological Survey Scientific Investigations Report 2023–5032, 127 p., https://doi.org/10.3133/sir20235032.","productDescription":"Report: xi, 127 p.; Data Release","numberOfPages":"144","onlineOnly":"Y","ipdsId":"IP-134142","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":419666,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RV1D60","text":"USGS data release","linkHelpText":"Breeding songbird tissue analyses and metal concentrations in tissues, soil and invertebrates collected near nesting sites within the Southeast Missouri Lead Mining District, 2016–19"},{"id":419662,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5032/coverthb.jpg"},{"id":419663,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5032/sir20235032.pdf","text":"Report","size":"9.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5032"},{"id":419664,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5032/sir20235032.XML"},{"id":500894,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115182.htm","linkFileType":{"id":5,"text":"html"}},{"id":419755,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235032/full"},{"id":419665,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5032/images"}],"country":"United States","state":"Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.66252506041675,\n 39.51237957711933\n ],\n [\n -92.66252506041675,\n 36.74875309799043\n ],\n [\n -89.01662840507522,\n 36.74875309799043\n ],\n [\n -89.01662840507522,\n 39.51237957711933\n ],\n [\n -92.66252506041675,\n 39.51237957711933\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
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Invasive carp (Hypophthalmichthys nobilis [Bighead Carp], Mylopharyngodon piceus [Black Carp], Ctenopharyngodon idella [Grass Carp], and H. molitrix [Silver Carp]) continue to spread in the United States and deterrents at river navigation locks are one emerging control strategy for slowing the spread. High-head navigation dams on large rivers serve as impediments to the upstream spread of these populations. One possible control technique is using a multimodal deterrent that utilizes a combination of lights, sound, and air bubbles to guide fish away from a location. Laboratory tests and small-scale field deployments of similar multi-stimuli deterrents have demonstrated potential for deterring invasive carp. These earlier studies led to the deployment and initiation of a field study of a multimodal deterrent in 2019 at the downstream lock approach at Barkley Lock and Dam on the Cumberland River near Grand Rivers, Kentucky. We are using two types of telemetry systems to evaluate how the multimodal deterrent affects movement and behavior of Silver Carp, Grass Carp, and four native species: Aplodinotus grunniens (Freshwater Drum), Polyodon spathula (Paddlefish), Ictiobus bubalus (Smallmouth Buffalo), and Acipenser fulvescens (Lake Sturgeon). We are evaluating fish movements in response to the multimodal deterrent by using a study design that cycles between 1 week with the multimodal deterrent on and 1 week with it off. This weekly cycling provides baseline comparisons across variable environmental conditions. When the multimodal deterrent is on, the number of Silver Carp completing upstream passage through Barkley Lock is reduced by about one-half. This study that began in 2019 can provide insights into the effectiveness of a multimodal deterrent for limiting upstream movement of invasive carp or effects on native fishes through strategic navigation locks on large rivers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231051","usgsCitation":"Fritts, A., Gibson-Reinemer, D., Stanton, J., Mosel, K., Brey, M., Vallazza, J., Appel, D., Faulkner, J., Tompkins, J., Castro-Santos, T., Sholtis, M., Turnpenny, A., Sorensen, P., and Simmonds, R., 2023, Multimodal invasive carp deterrent study at Barkley Lock and Dam—Status update through 2022: U.S. Geological Survey Open-File Report 2023–1051, 7 p., https://doi.org/10.3133/ofr20231051.","productDescription":"Report: vi, 7 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-152060","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":419719,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231051/full"},{"id":419570,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EKGK94","text":"USGS data release","linkHelpText":"Data release for multimodal invasive carp deterrent study at Barkley Lock and Dam—Status update through 2022"},{"id":419569,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1051/images/"},{"id":419568,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1051/ofr20231051.XML"},{"id":419567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1051/ofr20231051.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1051"},{"id":419566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1051/coverthb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Barkley Lock and Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.06563176427669,\n 36.91369487844129\n ],\n [\n -88.06563176427669,\n 36.769782774646316\n ],\n [\n -87.88100333801793,\n 36.769782774646316\n ],\n [\n -87.88100333801793,\n 36.91369487844129\n ],\n [\n -88.06563176427669,\n 36.91369487844129\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
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In response to the New England Governor and Eastern Canadian Premier 2017 Climate Change Action Plan recommendation to “manage blue carbon resources to preserve and enhance their existing carbon reservoirs,” the U.S. Environmental Protection Agency (EPA) convened a New England Blue Carbon Inventory Workgroup, comprised of a variety of federal, state, academic, and non-profit organizations to develop an inventory of blue carbon stocks from Maine to Long Island, New York. The Workgroup focused its inventory efforts on salt marshes and eelgrass meadows, leveraging existing habitat maps for geographic data. Existing data for soil organic carbon stocks were then used to calculate blue carbon stock estimates. For visual display purposes, sediment carbon heat maps were developed to highlight areas of greatest carbon accumulation. The habitat distribution and sediment carbon heat maps can be accessed on the Northeast Ocean Data Portal (www.northeastoceandata.org/eelgrass) which is a public source of expert-reviewed, interactive maps and data on the ocean ecosystem, economy, and culture of the northeastern United States and can be used to facilitate decision making by government agencies, tribal nations, businesses, non-governmental organizations (NGOs), academic institutions, and individuals. Based on available data and Workgroup calculations, the target geographic area has an estimated 218,222 acres of eelgrass meadows, salt marsh and saline Phragmites, which are estimated to provide a reservoir of 7,523,568 megagrams of blue carbon, or the equivalent to the annual carbon emissions from over 5,944,024 passenger vehicles. Due to data limitations, the carbon stock estimate represents a mere fraction of the actual quantity of accumulated carbon in these habitats. The findings from the Workgroup’s efforts and the resulting map products can help inform land and coastal management policies, fisheries management, and climate change mitigation practices. Further refinements and expansion of data are needed, including more detailed habitat maps, deeper soil core data for soil organic carbon content, and inclusion of more marine flora into calculations.
","language":"English","publisher":"Environmental Protection Agency","usgsCitation":"Colarusso, P., Libohova, Z., Shumchenia, E., Eagle, M.J., Christian, M., Vincent, R., and Johnson, B., 2023, The blue carbon reservoirs from Maine to Long Island, NY, 31 p.","productDescription":"31 p.","ipdsId":"IP-146585","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":421823,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421698,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.northeastoceandata.org/files/metadata/Themes/Habitat/EPABlueCarbonReport.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.61511760727777,\n 45.22754478031186\n ],\n [\n -69.46772958832273,\n 44.45391256542621\n ],\n [\n -70.41808310369574,\n 44.0249641018155\n ],\n [\n -71.18235185260586,\n 43.43128866248264\n ],\n [\n -71.38130070335747,\n 41.926291445574776\n ],\n [\n -72.88727252479508,\n 41.59585766120409\n ],\n [\n -74.23639888538038,\n 41.298612606793824\n ],\n [\n -74.24300744809933,\n 40.29712569967327\n ],\n [\n -72.41592450378879,\n 40.62059607271837\n ],\n [\n -69.64618289257739,\n 41.250157632666884\n ],\n [\n -69.77860402969628,\n 42.24177812265074\n ],\n [\n -70.24692368777268,\n 42.525861412000836\n ],\n [\n -70.24992804020442,\n 43.441149255128096\n ],\n [\n -67.02233592375215,\n 44.65254677637256\n ],\n [\n -67.61511760727777,\n 45.22754478031186\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Colarusso, Philip D.","contributorId":218700,"corporation":false,"usgs":false,"family":"Colarusso","given":"Philip D.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":885521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Libohova, Zamir","contributorId":330648,"corporation":false,"usgs":false,"family":"Libohova","given":"Zamir","email":"","affiliations":[{"id":63834,"text":"United States Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":885522,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shumchenia, Emily","contributorId":330649,"corporation":false,"usgs":false,"family":"Shumchenia","given":"Emily","email":"","affiliations":[{"id":78947,"text":"Northeast Regional Ocean Council","active":true,"usgs":false}],"preferred":false,"id":885523,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eagle, Meagan J. 0000-0001-5072-2755 meagle@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":242890,"corporation":false,"usgs":true,"family":"Eagle","given":"Meagan","email":"meagle@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":885524,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christian, Megan","contributorId":330651,"corporation":false,"usgs":false,"family":"Christian","given":"Megan","email":"","affiliations":[{"id":63834,"text":"United States Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":885525,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vincent, Robert","contributorId":330652,"corporation":false,"usgs":false,"family":"Vincent","given":"Robert","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":885526,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnson, Beverly","contributorId":330653,"corporation":false,"usgs":false,"family":"Johnson","given":"Beverly","email":"","affiliations":[{"id":33413,"text":"Bates College","active":true,"usgs":false}],"preferred":false,"id":885527,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70247452,"text":"70247452 - 2023 - Merging machine learning and geostatistical approaches for spatial modeling of geoenergy resources","interactions":[],"lastModifiedDate":"2023-08-08T11:41:46.30057","indexId":"70247452","displayToPublicDate":"2023-08-06T06:39:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Merging machine learning and geostatistical approaches for spatial modeling of geoenergy resources","docAbstract":"Geostatistics is the most commonly used probabilistic approach for modeling earth systems, including quality parameters of various geoenergy resources. In geostatistics, estimates, either on a point or block support, are generated as a spatially-weighted average of surrounding samples. The optimal weights are determined through the stationary variogram model which accounts for the spatial structure of the samples. Recently, efficient modeling workflows using various machine learning algorithms (MLAs) have been expanded to the spatial context for modeling geological heterogeneity. The flexible use of MLAs as a spatial estimation tool stems mainly from the fact that unlike kriging, they do not require any variogram, nor do they depend strongly on a prior stationarity assumption (i.e., second order stationarity). This study evaluates the performance of two MLAs (ensemble super learner and elliptical radial basis neural network), ordinary kriging, and hybrid spatial modeling approaches using ordinary intrinsic collocated cokriging. The aforementioned modeling techniques are compared for estimating resources for four coal variables (wash yield, ash yield, calorific value and thickness) as an example. The results suggest that MLAs, when implemented alone, do not outperform ordinary kriging, but the estimation accuracy of the final model, measured by the root mean squared error tends to subtly improve (
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2023.104328","usgsCitation":"Erdogan Erten, G., Erten, O., Karacan, C.O., Boisvert, J., and Deutsch, C.V., 2023, Merging machine learning and geostatistical approaches for spatial modeling of geoenergy resources: International Journal of Coal Geology, v. 276, 104328, 16 p., https://doi.org/10.1016/j.coal.2023.104328.","productDescription":"104328, 16 p.","ipdsId":"IP-149602","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":419585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.33693408888138,\n 37.563642070051216\n ],\n [\n -82.33693408888138,\n 37.039521964862686\n ],\n [\n -81.70000033583997,\n 37.039521964862686\n ],\n [\n -81.70000033583997,\n 37.563642070051216\n ],\n [\n -82.33693408888138,\n 37.563642070051216\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"276","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Erdogan Erten, Gamze","contributorId":317909,"corporation":false,"usgs":false,"family":"Erdogan Erten","given":"Gamze","email":"","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erten, Oktay","contributorId":300145,"corporation":false,"usgs":false,"family":"Erten","given":"Oktay","email":"","affiliations":[],"preferred":false,"id":879701,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":879702,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boisvert, Jeff","contributorId":317910,"corporation":false,"usgs":false,"family":"Boisvert","given":"Jeff","email":"","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879703,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Deutsch, Clayton V.","contributorId":317911,"corporation":false,"usgs":false,"family":"Deutsch","given":"Clayton","email":"","middleInitial":"V.","affiliations":[{"id":69186,"text":"U. of Alberta","active":true,"usgs":false}],"preferred":false,"id":879704,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247418,"text":"sir20235093 - 2023 - Analysis of high-resolution single channel seismic data for use in sediment resource evaluation, eastern Texas and western Louisiana Continental Shelf, Gulf of Mexico","interactions":[],"lastModifiedDate":"2026-03-12T21:16:59.60233","indexId":"sir20235093","displayToPublicDate":"2023-08-04T08:46:47","publicationYear":"2023","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":"2023-5093","displayTitle":"Analysis of High-Resolution Single Channel Seismic Data for Use in Sediment Resource Evaluation, Eastern Texas and Western Louisiana Continental Shelf, Gulf of Mexico","title":"Analysis of high-resolution single channel seismic data for use in sediment resource evaluation, eastern Texas and western Louisiana Continental Shelf, Gulf of Mexico","docAbstract":"Shallow subsurface geologic data recorded as high-resolution seismic profiles are used to interpret the geology of coastal and marine systems. These data were originally recorded on paper rolls that are stored in geophysical archives. Data collection has since converted to entirely digital formats, yet the analog data are still useful for geologic interpretation. This report describes the process of recovering analog copies of seismic profiles from physical archives, electronically scanning, and converting them to industry-standard digital format. The recovered data are also reviewed and assessed for potential sediment resources. The data recovered in this study were collected from the Gulf of Mexico continental shelf offshore of East Texas and West Louisiana. The project is a collaborative study between the U.S. Geological Survey and the Bureau of Ocean Energy Management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235093","issn":"2328-0328","collaboration":"Prepared in cooperation with the Bureau of Ocean Energy Management","programNote":"Coastal and Marine Hazards and Resources Program","usgsCitation":"Flocks, J., Forde, A., and Bosse, S., 2023, Analysis of high-resolution single channel seismic data for use in sediment resource evaluation, eastern Texas and western Louisiana Continental Shelf, Gulf of Mexico: U.S. Geological Survey Scientific Investigations Report 2023–5093, 18 p., https://doi.org/10.3133/sir20235093.","productDescription":"Report: viii, 18 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-144157","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":419532,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235093/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5093 HTML"},{"id":419531,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5093/sir20235093.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5093 XML"},{"id":419530,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5093/sir20235093.pdf","size":"4.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5093 pdf"},{"id":419529,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5093/images"},{"id":419528,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5093/coverthb.jpg"},{"id":419533,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EFUAN3","text":"USGS data release—Archive of digitized analog boomer seismic reflection data collected from the northern Gulf of Mexico—Intersea 1980"},{"id":501060,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115126.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Louisiana, Texas","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.86243995309941,\n 29.6\n ],\n [\n -94.1,\n 29.6\n ],\n [\n -94.1,\n 28.4\n ],\n [\n -91.86243995309941,\n 28.4\n ],\n [\n -91.86243995309941,\n 29.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
600 4th Street South
St. Petersburg, FL 33701
Drought and intensive land use can interact as stressors on riparian vegetation, especially along rivers flowing through seasonally dry landscapes. Knowledge of past riparian vegetation response to drought and land use change can provide land managers with a better understanding of changes induced by upstream management actions, climate change, and chronic stressors. To investigate the response of riparian vegetation productivity to drought and land use, we developed a 21-year time series (2000–2020) of growing season vegetation dynamics using near-infrared reflectance of vegetation (NIRV) derived from satellite data across 30 watershed subbasins that drain into the San Francisco Bay Delta in central California, USA. We observed a strong response of riparian vegetation to drought, but rapid recovery and very few long-term declines in productivity. At a local level, vegetation communities' response to drought and post-drought productivity dynamics were highly variable across biophysical settings and land use gradients. Most of the riparian areas with long-term declines in NIRV were located in the lower elevation Coast Range on the western side of the study area where there is little to no water engineering or agricultural irrigation runoff to subsidize riparian vegetation. Riparian areas with the greatest long-term increase were along rivers draining the higher elevation Sierra Nevada range to the east. Our results suggest that river systems with a high proportion of water originating as snowmelt may be more buffered against long-term drought-driven declines in productivity than those dependent exclusively on winter rainfall. The long-term increase in NIRV in the vast majority of riparian areas within our study area may also have been driven in part by increasing atmospheric CO2 concentrations, which have been shown to increase plant water use efficiency.
Invasive annual grasses can promote ecosystem state changes and habitat loss in the American Southwest. Non-native annual grasses such as Bromus spp. and Schismus spp. have invaded the Mojave Desert and degraded habitat through increased fire occurrence, severity, and shifting plant community composition. Thus, it is important to identify and characterize the areas where persistent invasion has occurred, identifying where subsequent habitat degradation has increased. Previous plot and landscape-scale analyses have revealed anthropogenic and biophysical correlates with the establishment and dominance of invasive annual grasses in the Mojave Desert. However, these studies have been limited in spatial and temporal scales. Here we use Landsat imagery validated using an extensive network of plot data to map persistent and productive populations of invasive annual grass, called hot spots, across the entire Mojave Desert ecoregion over 12 years (2009–2020). We also identify important variables for predicting hot spot distribution using the Random Forest algorithm and identifying the most invaded subregions. We identified hot spots in over 5% of the Mojave Desert mostly on the western and eastern edges of the ecoregion, and invasive grasses were detected in over 90% of the Mojave Desert at least once in that time. Across the entire Mojave Desert, our results indicate that soil texture, aspect, winter precipitation, and elevation are the highest-ranking predictive variables of invasive grass hot spots, while anthropogenic variables contributed the least to the accuracy of the predictive model. The total area covered by hot spots varied significantly among subregions of the Mojave Desert. We found that anthropogenic variables became more important in explaining invasive annual establishment and persistence as spatial scale was reduced to the subregional level. Our findings have important implications for informing where land management actions can prioritize reducing invasive annual persistence and promoting restoration efforts.
This pamphlet and accompanying geologic and geophysical maps are the products of cooperative efforts by the California Geological Survey (CGS) and United States Geological Survey (USGS) to compile a comprehensive, digital representation of the bedrock geology, Quaternary surficial deposits, and potential-field anomalies within the boundaries of the Stockton 30’ × 60’ quadrangle. The Stockton 30’ × 60’ quadrangle covers approximately 4,890 km2 of Contra Costa, Alameda, San Joaquin, and Stanislaus Counties, California. From the rugged hillsides of the northern Diablo Range in the west to the San Joaquin Valley in the east, the map extends roughly 88 km across growing suburban communities of the eastern San Francisco Bay Area and Livermore Valley, grass-covered ranchlands along eastern slopes of the Diablo Range, and into the low farmlands of the San Joaquin Valley and Sacramento-San Joaquin River Delta (Figure 1). The elevation ranges from near sea level in the Delta to 1,173 meters on Mt. Diablo, the most prominent peak of the San Francisco Bay region.
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